Polysaccharide Degrading Biocatalysts 0323999867, 9780323999861

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POLYSACCHARIDE-DEGRADING BIOCATALYSTS

Foundations and Frontiers in Enzymology Series Series Editor: Munishwar Nath Gupta

Series Website: https://www.elsevier.com/books-and-journals/book-series/books-serieslanding-page-request-foundations-and-frontiers-in-enzymology

FOUNDATIONS AND FRONTIERS IN ENZYMOLOGY

POLYSACCHARIDEDEGRADING BIOCATALYSTS Edited by

ROSANA GOLDBECK Associate Professor, Bioprocess and Metabolic Engineering Laboratory, School of Food Engineering, Department of Food Engineering and Technology, University of Campinas, Brazil

PATRI´CIA POLETTO Associate Professor, Laboratory of Biological Engineering, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Brazil

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN 978-0-323-99986-1 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

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Contents

5. Final considerations 50 References 51

Contributors ix 1. Plant cell wall polysaccharides: Methodologies for compositional, structural, and physicochemical characterization

3. The diversity of plant carbohydrate hydrolysis in nature and technology

Ingrid Santos Miguez, Fernanda Thimoteo Azevedo Jorge, Roberta Pereira Espinheira, Ronaldo Rodrigues de Sousa, Viridiana Santana Ferreira Leita˜o, Ricardo Sposina Sobral Teixeira, Carmen Lucia de Oliveira Petkowicz, and Ayla Sant’Ana da Silva

Marcos S. Buckeridge

1. Introduction 55 2. Types of hydrolysis 56 3. Plant sugars 58 4. Concluding remarks 69 Acknowledgments 71 References 71

1. Introduction to the analysis of plant cell wall polysaccharides 1 2. Sample preparation for the polysaccharides’ analysis 4 3. Chemical analysis of plant cell wall polysaccharides—Glycosyl residues composition 7 4. Structural analysis of plant cell wall polysaccharides 12 5. Complementary methods for the analysis of plant cell wall polysaccharides 20 6. Concluding remarks 27 References 27

4. State-of-the-art experimental and computational approaches to investigate structure, substrate recognition, and catalytic mechanism of enzymes Camila Ramos Santos, Clelton Aparecido dos Santos, Evandro Ares de Araujo, Mariana Abraha˜o Bueno Morais, Maxuel de Oliveira Andrade, Tatiani Brenelli de Lima, Wesley Cardoso Generoso, and Mario Tyago Murakami

2. Genetic modification of plants to increase the saccharification of lignocellulose

1. Sample preparation for structural and biophysical analyses 75 2. Methods to analyze enzyme stability and structural homogeneity 78 3. Methods to analyze protein conformation and oligomerization 82 4. Methods to analyze enzyme-substrate interactions 83 5. Experimental approaches for structure elucidation 85 6. In silico methods for structural analysis 93 References 96

Joa˜o Vitor Furtado da Silva, Breno Miguel Joia, Wagner Mansano Cavalini, Rodrigo Polimeni Constantin, Marco Aur
elio Sch€ uler de Oliveira, Rog
erio Marchiosi, Osvaldo Ferrarese-Filho, and Wanderley Dantas dos Santos

1. 2. 3. 4.

Introduction 39 Lignin biosynthesis 42 Molecular approaches to cell wall modification 44 Technologies for genetic modification of the cell wall 46

v

vi

Contents

5. Pretreatments as a key for enzymatic hydrolysis of lignocellulosic biomass Sarita C^andida Rabelo, Lı´via Beatriz Brenelli, Thaynara Coradini Pin, Eupı´dio Scopel, and Aline Carvalho da Costa

1. Introduction 109 2. Factors affecting enzymatic hydrolysis and their relationship with the pretreatment 110 3. Pretreatment of lignocellulosic biomass 116 4. Conclusions 130 References 131

6. Importance of accessory enzymes in hemicellulose degradation Vladimı´r Puchart, Katarı´na Sˇuchova´, and Peter Biely

1. Introduction 139 2. Xylanolytic enzymes 141 3. Mannanolytic enzymes 158 4. Conclusions 166 Funding 167 References 167

7. How ligninolytic enzymes can help in the degradation of biomass polysaccharides, cleavage, and catalytic mechanisms? Willian Daniel Hahn Schneider, Marli Camassola, and Roselei Claudete Fontana

1. Lignocellulosic biomass 177 2. Lignin 180 3. Ligninases 180 4. Ligninolytic enzymes and prospects 187 References 188

8. Biochemical and biotechnological aspects of microbial amylases Jinu John

1. Introduction 191 2. Amylase: The starch-digesting enzyme 192 3. Commercial production of α-amylases 196 4. Applications of α-amylase 197 5. Conclusion and future perspectives 201 References 201

9. Hydrolysis of complex pectin structures: Biocatalysis and bioproducts Kanchan Yadav, Sangeeta Yadav, Gautam Anand, Pramod K. Yadav, and Dinesh Yadav

1. 2. 3. 4. 5. 6. 7.

Introduction 205 Pectin complex structure 206 Types of pectins 207 Sources of pectin 208 Pectin: Diverse uses 208 Pectinases 209 Structural aspects of protein families related to pectin degradation 212 8. Conclusion 219 Acknowledgment 219 References 219

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production Yasmin Khambhaty and R. Reena

1. 2. 3. 4. 5.

Introduction 227 The bio-refinery concept 228 Algae and its classification 229 Extraction of macroalgal polysaccharides 234 Biocatalysts in bio-refinery and biofuel production 236 6. Conclusions and future prospects 262 Acknowledgments 264 References 264

11. Mathematical modeling of the enzymatic hydrolysis of polysaccharides: A primer David Alexander Mitchell and Nadia Krieger

1. Aims and scope of this chapter 275 2. Scales at which the enzymatic hydrolysis of polysaccharides can be modeled 275 3. Features of substrates, enzymes, and models 277 4. The appropriate level of complexity for representing the system 281 5. General approaches to using deterministic models based on differential equations 284

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Contents

6. General approaches to using stochastic models 291 7. “Fingerprinting models” as tools for estimating specificity constants 296 8. Conclusion 302 References 303

14. Carbohydrate-active enzymes in the production of lactose-derived tagatose Ravena Casemiro Oliveira, Laiza Brito Ribeiro, Ticiane Cavalcante de Souza, Lucas Almeida de Freitas, Ana Carolina Pinto de Almeida, and Luciana Rocha Barros Gonc¸alves

1.

12. Polysaccharide deconstruction products: Production of bio-based building blocks Jaciane Lutz Ienczak, Aline Carvalho da Costa, Karen Cristina Collograi, Aline Soares Bretas, and Isabela de Oliveira Pereira

1. Introduction 305 2. Succinic acid as a promising bio-based building block 308 3. Bio-based lactic acid: An important building block in biorefinery concept 315 4. Microbial propionic acid production 322 5. Conclusions 326 Acknowledgments 326 Conflicts of interest 327 References 327

13. Polysaccharide degradation for oligosaccharide production with nutraceutical potential for the food industry ´ vila, Patrı´cia Poletto, and Manoela Martins, Patrı´cia F. A Rosana Goldbeck

1. Introduction 335 2. Functional oligosaccharides 337 3. Sucrose-related oligosaccharides 342 4. Lactose-related oligosaccharides 344 5. Starch-related oligosaccharides 346 6. Nonstarch oligosaccharides 348 7. Algal-oligosaccharides 353 8. Concluding remarks 356 Acknowledgment 356 References 357

D-Tagatose

and production strategies of a rare sugar 365 2. β-Galactosidase and its applications 370 3. L-Arabinose isomerase 374 4. Integrated production of tagatose using immobilized enzymes 377 References 378

15. Immobilized biocatalysts for hydrolysis of polysaccharides Martina C.C. Pinto, Luciana Dutra, Luana X.S.G.M. F
e, Denise Maria Guimara˜es Freire, Evelin A. Manoel, and Eliane P. Cipolatti

1. Introduction 385 2. Enzyme immobilization 386 3. Materials and techniques applied to hydrolysis of polysaccharides 389 4. Industrial applications 395 5. Final considerations 402 Acknowledgments 403 References 403

16. Carbohydrate-based economy: Perspectives and challenges Caroline Dalastra, Natalia Klanovicz, Simone Kubeneck, Fa´bio Spitza Stefanski, Debora Fretes Argenta, Gabriela Schneider Rauber, Thiago Caon, Rafael Dorighello Cadamuro, Gislaine Fongaro, and Helen Treichel

1. Introduction 409 2. Market opportunities for carbohydrate-based products 410 3. What about the operational and environmental point of view? 424 References 426

Index 435

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Contributors of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil

Gautam Anand Department of Plant Pathology and Weed Research, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel

Eliane P. Cipolatti Pharmaceutical Biotechnology Department, Pharmacy College, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro; Chemical Engineering Department, Institute of Technology, Rural Federal University of Rio de Janeiro (UFRRJ), Serop
edica, RJ, Brazil

Debora Fretes Argenta Department of Pharmaceutical Sciences, Federal University of Santa Catarina, Floriano´polis, Brazil ´ vila Laboratory of Bioprocess and Patrı´cia F. A Metabolic Engineering, School of Food Engineering, State University of Campinas (UNICAMP), Campinas, SP, Brazil

Karen Cristina Collograi School of Chemical Engineering, State University of Campinas (UNICAMP), Campinas, Sa˜o Paulo, Brazil

Peter Biely Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovak Republic

Rodrigo Polimeni Constantin Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil

Lı´via Beatriz Brenelli Interdisciplinary Center of Energy Planning (NIPE), State University of Campinas (Unicamp), Campinas, Sa˜o Paulo, Brazil

Aline Carvalho da Costa School of Chemical Engineering, State University of Campinas (UNICAMP), Campinas, Sa˜o Paulo, Brazil

Aline Soares Bretas School of Chemical Engineering, State University of Campinas (UNICAMP), Campinas, Sa˜o Paulo, Brazil

Joa˜o Vitor Furtado da Silva Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil

Marcos S. Buckeridge Institute of Biosciences, Department of Botany, University of Sa˜o Paulo, Sa˜o Paulo, Brazil Rafael Dorighello Cadamuro Laboratory of Applied Virology, Department of Microbiology, Immunology, and Parasitology, Federal University of Santa Catarina, Floriano´polis, Brazil

Caroline Dalastra Laboratory of Microbiology and Bioprocesses, Federal University of Fronteira Sul, Erechim, Brazil Ana Carolina Pinto de Almeida Department of Chemical Engineering, Federal University of Ceara´, Fortaleza, CE, Brazil

Marli Camassola Enzymes and Biomass Laboratory, Institute of Biotechnology, University of Caxias do Sul, Caxias do Sul, RS, Brazil Thiago Caon Department of Pharmaceutical Sciences, Federal University of Santa Catarina, Floriano´polis, Brazil

Evandro Ares de Araujo Brazilian Synchrotron Light Laboratory (LNLS-Sirius), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil

Wagner Mansano Cavalini Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center

Lucas Almeida de Freitas Department of Chemical Engineering, Federal University of Ceara´, Fortaleza, CE, Brazil

ix

x

Contributors

Tatiani Brenelli de Lima Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil

Gislaine Fongaro Laboratory of Applied Virology, Department of Microbiology, Immunology, and Parasitology, Federal University of Santa Catarina, Floriano´polis, Brazil

Marco Aur
elio Sch€ uler de Oliveira Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil

Roselei Claudete Fontana Enzymes and Biomass Laboratory, Institute of Biotechnology, University of Caxias do Sul, Caxias do Sul, RS, Brazil

Maxuel de Oliveira Andrade Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil Ticiane Cavalcante de Souza Department of Chemical Engineering, Federal University of Ceara´, Fortaleza, CE, Brazil Clelton Aparecido dos Santos Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil Wanderley Dantas dos Santos Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil Luciana Dutra Chemical Engineering Program (PEQ), Alberto Luiz Coimbra Institute of Postgraduation and Research of Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil Roberta Pereira Espinheira Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia; Department of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil Luana X.S.G.M. F
e Pharmaceutical Biotechnology Department, Pharmacy College, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil Osvaldo Ferrarese-Filho Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil

Denise Maria Guimara˜es Freire Biochemistry Department, Chemistry Institute, Technology Centre (CT), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil Wesley Cardoso Generoso Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil Rosana Goldbeck Laboratory of Bioprocess and Metabolic Engineering, School of Food Engineering, State University of Campinas (UNICAMP), Campinas, SP, Brazil Luciana Rocha Barros Gonc¸ alves Department of Chemical Engineering, Federal University of Ceara´, Fortaleza, CE, Brazil Jaciane Lutz Ienczak Department of Chemical and Food Engineering, Federal University of Santa Catarina, Floriano´polis, SC, Brazil Jinu John Department of Biotechnology, CMS College, Kottayam, Kerala, India Breno Miguel Joia Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil Fernanda Thimoteo Azevedo Jorge Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia, Rio de Janeiro, RJ, Brazil Yasmin Khambhaty Microbiology Department, CSIR-Central Leather Research Institute, Adyar, Chennai, India Natalia Klanovicz Laboratory of Microbiology and Bioprocesses, Federal University of Fronteira Sul, Erechim; Research Group in Advanced Oxidation Processes (AdOx), Department of Chemical Engineering, Escola

Contributors

Polit
ecnica, University of Sa˜o Paulo, Sa˜o Paulo, Brazil Nadia Krieger Department of Chemistry, Federal University of Parana´, Curitiba, Parana´, Brazil Simone Kubeneck Laboratory of Microbiology and Bioprocesses, Federal University of Fronteira Sul, Erechim, Brazil Viridiana Santana Ferreira Leita˜o Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia; Department of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil Evelin A. Manoel Biochemistry Department, Chemistry Institute, Technology Centre (CT); Pharmaceutical Biotechnology Department, Pharmacy College, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil Rog
erio Marchiosi Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil Manoela Martins Laboratory of Bioprocess and Metabolic Engineering, School of Food Engineering, State University of Campinas (UNICAMP), Campinas, SP, Brazil Ingrid Santos Miguez Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia; Department of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil David Alexander Mitchell Department of Biochemistry and Molecular Biology, Federal University of Parana´, Curitiba, Parana´, Brazil Mariana Abraha˜o Bueno Morais Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil Mario Tyago Murakami Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil

xi

Ravena Casemiro Oliveira Department of Chemical Engineering, Federal University of Ceara´, Fortaleza, CE, Brazil Isabela de Oliveira Pereira Department of Chemical and Food Engineering, Federal University of Santa Catarina, Floriano´polis, SC, Brazil Carmen Lucia de Oliveira Petkowicz Department of Biochemistry and Molecular Biology, Universidade Federal do Parana´, Curitiba, PR, Brazil Thaynara Coradini Pin School of Chemical Engineering, State University of Campinas (UNICAMP), Campinas, Sa˜o Paulo, Brazil Martina C.C. Pinto Chemical Engineering Program (PEQ), Alberto Luiz Coimbra Institute of Post-graduation and Research of Engineering (COPPE); Biochemistry Department, Chemistry Institute, Technology Centre (CT), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil Patrı´cia Poletto Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Floriano´polis, SC, Brazil Vladimı´r Puchart Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovak Republic Sarita C^ andida Rabelo School of Agriculture, Sa˜o Paulo State University (Unesp), Botucatu, Sa˜o Paulo, Brazil Gabriela Schneider Rauber Department of Pharmaceutical Sciences, Federal University of Santa Catarina, Floriano´polis, Brazil R. Reena Microbiology Department, CSIR-Central Leather Research Institute, Adyar, Chennai, India Laiza Brito Ribeiro Department of Chemical Engineering, Federal University of Ceara´, Fortaleza, CE, Brazil Camila Ramos Santos Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil Willian Daniel Hahn Schneider Enzymes and Biomass Laboratory, Institute of Biotechnology, University of Caxias do Sul, Caxias do Sul, RS, Brazil

xii

Contributors

Eupı´dio Scopel Institute of Chemistry, State University of Campinas (Unicamp), Campinas, Sa˜o Paulo, Brazil

Ricardo Sposina Sobral Teixeira Department of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

Ayla Sant’Ana da Silva Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia; Department of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

Helen Treichel Laboratory of Microbiology and Bioprocesses, Federal University of Fronteira Sul, Erechim, Brazil

Ronaldo Rodrigues de Sousa Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia; Department of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil Fa´bio Spitza Stefanski Laboratory of Microbiology and Bioprocesses, Federal University of Fronteira Sul, Erechim, Brazil Katarı´na Sˇuchova´ Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovak Republic

Dinesh Yadav Department of Biotechnology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India Kanchan Yadav Department of Biotechnology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India Pramod K. Yadav Department of Life Sciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University (formerly Kanpur University), Kanpur, Uttar Pradesh, India Sangeeta Yadav Department of Biotechnology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India

C H A P T E R

1 Plant cell wall polysaccharides: Methodologies for compositional, structural, and physicochemical characterization Ingrid Santos Migueza,b, Fernanda Thimoteo Azevedo Jorgea, Roberta Pereira Espinheiraa,b, Ronaldo Rodrigues de Sousaa,b, Viridiana Santana Ferreira Leita˜oa,b, Ricardo Sposina Sobral Teixeirab, Carmen Lucia de Oliveira Petkowiczc, and Ayla Sant’Ana da Silvaa,b a

Division of Catalysis, Biocatalysis and Chemical Processes, Ministry of Science, Technology, and Innovations, Instituto Nacional de Tecnologia, Rio de Janeiro, RJ, Brazil bDepartment of Biochemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil cDepartment of Biochemistry and Molecular Biology, Universidade Federal do Parana´, Curitiba, PR, Brazil

1. Introduction to the analysis of plant cell wall polysaccharides The plant cell wall (PCW) has mechanical properties related to its components and their interactions. As the main PCW components, polysaccharides assume a structural role in providing mechanical stability for the cell [1]. Arranged in the primary and secondary walls, the wall layers of this extracellular matrix consist of polymer networks based on covalent and noncovalent bonds [2]. In the primary cell wall, 90% of the dry weight corresponds to polysaccharides, among which cellulose microfibrils and hemicelluloses are embedded in a matrix of pectin. Proteins and phenolics are also typical components of primary PCWs of dicots and grass, respectively.

Polysaccharide-Degrading Biocatalysts https://doi.org/10.1016/B978-0-323-99986-1.00002-8

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Copyright # 2023 Elsevier Inc. All rights reserved.

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1. Plant cell wall polysaccharides

On the other hand, in the secondary cell wall, pectin and proteins are low or absent, and 60% of the dry weight is constituted of cellulose and hemicelluloses, enclosed by lignin, composing a recalcitrant structure whose polymers are harder to isolate compared with the primary wall components [3,4]. Fig. 1 illustrates a general representation of the layered and heterogeneous PCW. Despite the similarities, the composition and organization of the PCWs vary depending on the biomass species and other biological characteristics, such as the cell type, function of the tissue, and growth conditions [1]. Moreover, botanical studies indicate that polysaccharides change their structure during plant cell development, impacting the overall composition [5]. The cell wall polysaccharides are highly heterogeneous due to the diversity of glycosyl residues with different configurations (D/L and α/β), glycosidic linkages, degree and pattern of branching, and presence of substituents such as methyl and acetyl groups [6], which render them as challenging analytical targets. Increased complexity of cell wall polymers arises from inter- and intrachain interactions between polysaccharides and the cross-linking between structural carbohydrates by lignin. Consequently, these carbohydrates can exhibit many physicochemical properties, biological functions, and potential industrial applications that significantly depend on their structural and molecular features [7]. Nowadays, lignocellulosic biomass (i.e., PCWs) is the primary choice of feedstock for producing alternative fuels and chemicals to meet the needs of the transition to a biobased economy as an action to address the climate change [8]. Given that PCWs are primarily

FIG. 1 Representation of the major components from the primary and secondary plant cell walls.

1. Introduction to the analysis of plant cell wall polysaccharides

3

composed of polysaccharides, methods for characterizing the PCW carbohydrates become fundamental for determining target applications well-matched with each biomass source’s properties to reach economic and reproducible processes. For this, the accurate determination of glycosyl residues composition, structures of polysaccharides, and properties is essential for understanding the structure-activity relationship and calculating conversion yields and process economics [9]. Due to the complexity of those macromolecules, a complete characterization of PCW polysaccharides requires time-consuming and often complex and expensive analytical methods. Thus, depending on the scientific question behind the characterization effort, different strategies can be traced and combined as analytical approaches based on the sample’s conditions and biological characteristics. In this chapter, we will discuss techniques to analyze different aspects of the chemical and physical nature of polysaccharides from the PCWs (Fig. 2), aiming to aid the reader in identifying the most appropriate methods for different biomass samples. Still, we do not intend to deliver an exhaustive review of each technique. The chapter initiates by presenting the most common carbohydrates’ isolation and purification methods that can be necessary before multiple characterization techniques. Then, the discussion of consolidated methodologies was grouped into three categories: (i) glycosyl residues composition analysis, (ii) structural analysis, and (iii) complementary methodologies. Nevertheless, it is necessary to emphasize that we do not intend to be comprehensive but to give an overview of the most commonly reported techniques, in

FIG. 2 Analytical tools to characterize structural polysaccharides from the plant cell wall.

4

1. Plant cell wall polysaccharides

view of the vast amount of work related to this topic and other numerous valuable characterizing methods not included here. Considering the importance of PCW characterization to the economic competitiveness of the production of sugar syrups derived from lignocellulosic biomass, this chapter has gathered relevant information on this subject, aiming to provide a critical discussion on the limitations and advantages of the primary methodologies applied today by most research groups in this area.

2. Sample preparation for the polysaccharides’ analysis The analysis of PCW polysaccharides can range from total sugar composition identification techniques to advanced methods for detailed structural features. Depending on the chosen methodology, the purpose of the analysis, and the samples’ nature, sample preparation steps may be required. The main objective of sample preparation before polysaccharide analysis is to fractionate (partially or totally) the PCW components to eliminate or reduce as much as possible interferences in the analysis, which can occur by the presence of proteins, lipids, phenols, pigments, free sugars, or other polysaccharides [10,11]. However, separating polysaccharides from PCW can be laborious due to the heterogeneous nature of the samples [7]. Thus, critical analysis regarding the stability and representativeness of the obtained samples after preparation should always be carried out for a more accurate determination of carbohydrates in the PCWs [12]. Typically, before polysaccharides’ analysis, several steps can be applied to PCW samples, starting from routine procedures for size reduction and drying, passing through solvent extraction, and, depending on the study’s objective, going through a series of fractionation steps for isolating an enriched fraction of specific polysaccharides (Fig. 3).

FIG. 3

Fractionation steps for isolating enriched fractions of specific polysaccharides from plant cell walls. AIR, alcohol-insoluble residue.

2. Sample preparation for the polysaccharides’ analysis

5

As starting points for preparing the samples, a myriad of processes may include cut operations, drying by air, heating or lyophilization, freezing, grinding, and sieving. Grinding operations are of utmost importance to obtain relatively homogeneous powder suitable for subsequent chemical treatment steps and can be applied to different tissues [13]. In some cases, it is recommended to freeze the sample by using liquid nitrogen as quickly as possible to suppress enzymatic activities in vegetal tissues, which could result in partial degradation and/or modification of polymers [14]. After grinding, the most widely adopted step is the samples’ solvent extraction to prepare an alcohol-insoluble residue (AIR). The AIR preparation method consists of alcohol-based solid-liquid extractions, in which nonstructural materials are separated from plant biomass, such as inorganic salts, low-molecular-weight metabolites, and free saccharides. At the same time, the alcohol treatment provides the inactivation of endogenous enzymes. For this process, methanol, ethanol, or isopropanol can be used in concentrations that vary from 10% to 96% in aqueous solution in combination or not with other additional organic compounds and heating, although ethanol is ubiquitously adopted [12,15]. Lipids, chlorophyll, and some pigments can also be removed, depending on the solvent used [12]. Considering that there is a significant variation in AIR preparation protocols, one must evaluate the methodology most suitable for the samples’ characteristics, as different AIR preparation parameters might impact on extraction, detection, and degradation of certain PCW components [12,13]. For more detailed information, refer to the study of Fangel et al. [12], which compared 10 AIR preparation protocols. The obtained AIR by those procedures enables further analysis of PCW polysaccharides by many methodologies, such as the ones aiming at the total glycosyl residues composition; however, AIR is not effective in fractionating polymeric carbohydrates, which requires several additional steps for a more comprehensive analysis of individual polysaccharides [16] (Fig. 3). Also, other macromolecules that are not related to structural polysaccharides may remain insoluble in alcohol, such as starch, RNA, lipids, polyphenolic compounds, and proteins from the cytoplasm, demanding additional extraction steps of the obtained AIR, depending on the accuracy required for polysaccharide characterization and the type of vegetal tissue [16,17]. For example, if necessary, AIR may be defatted by a proper organic solvent (as petroleum ether) and deproteinated by using Sevag reagent, a mixture of chloroform and butanol (4:1 v/v) able to denature the free proteins present in PCW [18]. A detailed fractionation protocol usually starts with removing starch from the AIR. The presence of starch (a nonstructural carbohydrate) may lead to an inaccurate interpretation of the glycosyl compositional analysis of structural polysaccharides and complicate the interpretation of linkage data [13]. Starch can be removed by enzymatic extractions using α-amylases in a neutral phosphate buffer or by extraction with 90% dimethyl sulfoxide (DMSO). Mammalian pancreatic amylases are preferable, as microbial amylases might contain hydrolytic enzymes that attack other structural polysaccharides. In contrast, the use of DMSO needs to be carefully evaluated as it may lead to the removal of other cell wall components [13]. The destarched AIR samples then undergo a series of sequential extraction with increasingly harsh reagents, aiming to obtain fractions enriched in different polysaccharides. Hot water, ammonium oxalate, and diluted sodium carbonate are respectively used to remove pectins that are weakly bounded, calcium-associated, and covalently attached.

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1. Plant cell wall polysaccharides

Ethylenediaminetetraacetic acid (EDTA) and 1,2-cyclohexylenedinitrilotetraacetic acid (CDTA) are alternative chelators used in this step that can affect the calcium bridges in which pectins are held. Still, their use has the drawback of difficult removal by further dialysis steps [19]. The sequential fractioning steps involve using aqueous alkaline solutions (NaOH or KOH) in different concentrations and treatment conditions. Alkaline solutions enable the removal of hemicelluloses, including lignin-bounded polysaccharides, by affecting the hydrogen bonds and promoting hydrolysis of ester linkages between polysaccharides and lignin. However, in the alkaline environment, terminal reducing units of polysaccharides may be attacked in a chain-reaction mechanism leading to an intense carbohydrates degradation. Thus, borohydride salts (as NaBH4) are used in association with alkaline solutions to promote a reduction of end units and avoid the depolymerization of carbohydrates [20]. After alkaline extraction, lignin can be removed from the recovered solid by sodium chlorite [21]. For samples undergoing sodium chlorite treatment, after removing chlorite gas, the washed pellet obtained after lignin extraction can undergo a postchlorite extraction with an alkaline solution to extract lignin-bound polysaccharides. However, this step can be omitted for samples that are not lignified or do not contain significant levels of lignin [16]. Finally, the residue obtained will contain the typically insoluble polysaccharides, such as cellulose and linear mannan. In addition, all the extracts obtained in each of the mentioned steps can be dialyzed and lyophilized for storage until undergoing detailed characterization analysis [16]. Alternatively, the polysaccharides can be recovered from the dialyzed extract by precipitation using ethanol or isopropanol (2–3 volumes), followed by drying under mild temperatures. After AIR preparation or for polysaccharides derived from each fractionation step, the determination of the monosaccharide composition requires breaking the polysaccharides into their monomeric units [22,23]. In these cases, total acid hydrolysis should be applied to conciliate effective depolymerization of polysaccharides without noticeable degradation of resultant monosaccharides, which means that the concentration of reagents, time, and temperature should be carefully adjusted [15]. The use of trifluoroacetic acid (TFA), proposed by Albersheim in 1967 [24], is wide disseminated due to the easy separation by evaporation posthydrolysis. However, TFA hydrolysis cannot depolymerize all the cellulose content in crystalline form, being more suitable for analyzing other components of PCW [13]. A typical alternative is carried out with mineral acids (HCl or H2SO4), as the method proposed by Saeman et al. and optimized by Sluiter and coworkers [25,26]. While TFA is volatile and easily removed from the sample (which is a requirement for some analysis), it will only hydrolyze the noncellulosic component of PCW. Thus, for some samples, the use of sulfuric acid will be necessary, and its removal (if needed) will require precipitation with barium hydroxide or neutralization with N,N-dioctylamine in chloroform followed by successive washes [13]. However, constraints related to toxicity and safety issues, reaction time, and separation posthydrolysis led researchers to find alternatives, such as protocols based on enzymatic hydrolysis [27]. It should be noted that, in this section, we intended to bring some standard preparation methods before general procedures of PCW polysaccharide analysis. Still, specific studies may require additional preparation steps not included here.

3. Chemical analysis of plant cell wall polysaccharides—Glycosyl residues composition

7

3. Chemical analysis of plant cell wall polysaccharides—Glycosyl residues composition Usually, an initial assessment of the polysaccharide content of biomass samples is performed by analyzing the glycosyl residue composition. The qualitative and quantitative information obtained with these analyses is essential to have clues on polysaccharides’ content, which will help select other overlapping and complementary characterizing methodologies aiming to reconstitute the original macromolecular structure that they are derived from. After preparing the AIR from the target sample or isolating the carbohydrate fraction, it is possible to go on with the glycosyl residues composition determination and quantification [28]. In these analyses, the polysaccharides are hydrolyzed into monosaccharides (as commented in the Sample Preparation section) that are usually detected by chromatographic methods that can further require an additional derivatization step. The accuracy of the quantitative data obtained by these approaches will depend on the efficiency of the depolymerizing method for the cleavage of glycosidic linkages, the stability of released monosaccharides, and the effectiveness of derivatizing methods if required [28,29]. Despite the constraints of chromatographic glycosyl residues analyses aiming to build up the PCW polysaccharide content quantitatively, those techniques are preferable and more specific than the routine detergent fiber gravimetric methods used to predict total cellulose and hemicellulose content in forage crops, such as the Van Soest method [30,31]. However, it is important to mention that the glycosidic linkages between acidic monosaccharides and acidic or neutral monosaccharides are notoriously stable [32]. Thus, irrespective of the monosaccharide analysis method, it is difficult to determine the quantitative monosaccharide composition of polysaccharides rich in uronic acids based on the monomers released by acid hydrolysis. In this section, three common quantitative methods used for monosaccharide composition analysis, high-performance liquid chromatography, high-performance anion-exchange chromatography, and gas chromatography, will be discussed. These analytical tools are summarized in Fig. 4.

3.1 High-performance liquid chromatography (HPLC) HPLC is a common tool available in many research laboratories for quantitative analysis of sugars derived from the hydrolysis of PCW polysaccharides. Combining the appropriate stationary phase and detection system can give rapid and specific responses with versatility [28]. The choice of the stationary and mobile phase type varies according to the sugar’s nature that will be separated. The separation mechanisms include ion-exchange, ion-exclusion, ion-pair, hydrophilic interaction, or reverse-phase [33]. The detection systems for sugar analysis by HPLC include the refractive index detector (RID), ultraviolet and visible light absorption (UV-Vis), photodiode array detector (PDA), fluorescence, evaporative light scattering detector (ELSD), charged aerosol detector (CAD), or mass spectrometry (MS), for example [34]. Among the detectors, HPLC-RID has become one of the most common methods for a direct quantitative determination of five neutral monosaccharides derived from structural polysaccharides (glucose, xylose, galactose, mannose, and arabinose) since intact carbohydrates do

8

1. Plant cell wall polysaccharides

FIG. 4 Analytical methods for identifying and quantifying glycosyl residues constituents of PCW polysaccharides; (A) HPLC-RID/UV and HPAEC-PAD; (B) alditol acetate (AA) and trimethylsilyl (TMS) derivatives analysis by GC-FID/MS.

not have UV chromophores. RID is a universal detector that responds to all solutes as long as the refractive index of analytes is distinguished from the eluent [35]. Much of the preference for this methodology comes from the simplicity of the technique, mainly concerning the sample preparation (no derivatization required) [36]. In fact, one of the most well-established and cited modern protocols for biomass composition analysis, developed by the National Renewable Energy Laboratory (NREL), United States, uses HPLC-RID as a default method to quantify monosaccharides in biomass hydrolysates [37]. This applicability of HPLC-RID for reliable routine quantification of glycosyl residues from PCW was reported decades ago [38] and has been used to characterize a variety of woody and herbaceous biomasses. For monosaccharide determination, the protocol recommends using lead cation (Pb2+) exchange columns and a simple isocratic HPLC separation with water as a mobile phase, which gives a near-baseline resolution for the common biomass neutral sugars [39]. However, the NREL methodology does not detect deoxy (rhamnose and fucose) and acidic sugars (glucuronic and galacturonic acids), preventing the correct analysis of pectins and some hemicelluloses such as xyloglucans and glucuronoarabinoxylans [25]. Another inconvenience of the method is that RID detection is based on differential measurements; therefore, it is influenced by temperature, composition, and the flow rate variation of the mobile phase, which must be expertly controlled, without using gradient elution. Also, this method is not sensitive enough for trace sugar analysis and has poor selectivity [40]. In fact, the quantification of glucose and xylose, the major biomass sugars, by HPLC-RID using a Pb2+ column has

3. Chemical analysis of plant cell wall polysaccharides—Glycosyl residues composition

9

been demonstrated to be more accurate when compared to the analysis of less abundant sugars, such as mannose, galactose, and arabinose [41]. As an alternative to RID, photometers detectors based on UV-Vis or PDA are widely used in reversed-phase (RP) HPLC analysis of carbohydrates [42]. UV-Vis detectors quantify the analyte concentration according to the absorption light between 190 and 600 nm wavelengths, have relatively high sensitivity, but are nonselective, and usually depend on the previous derivatization of the sugars since most carbohydrates absorb light at a UV range below 200 nm [33,35]. There are precolumn and postcolumn reported methods to derivatize the samples, where the most popular and facile method applied is the precolumn derivatization using 3-methyl-1-phenyl-2-pyrazoline-5-one (PMP), which reacts with reducing carbohydrates under mild conditions [43]. This method has been successfully applied in various studies employing carbohydrate analysis to determine monosaccharides, including its validation using an HPLC-DAD with precolumn PMP derivatization for simultaneous estimation of seven neutral (mannose, rhamnose, glucose, galactose, xylose, arabinose, and fucose) and two acidic sugars (glucuronic and galacturonic acids) with baseline separation aiming to analyze cell wall polysaccharides from agro-industrial wastes [42,44]. Other precolumn methods applied to RP-HPLC-UV-Vis detection of PCW sugars report the derivatization by reductive amination using 2-aminobenzamide or 3-amino-9-ethylcarbazole [45,46]. Despite the advantageous increase in detection levels with the UV-Vis detectors, the derivatization techniques are a laborious step that makes the method less attractive for routine analysis than HPLC-RID. Some studies evaluated the UV-based identification of unlabeled sugars using both isocratic and gradient methods, providing comparable results to RID in terms of the limits of detection and quantification, but its application to the variety of PCW sugars needs to be demonstrated [35]. Many other HPLC-based methods have also been proposed to analyze PCW sugars. For example, to improve the selectivity and sensitivity of the technique, online derivatization through a postcolumn reaction of reducing sugars with Cu(II)-neocuproine reagent has been reported [47]. Moreover, to better detect hemicellulose and cellulose monosaccharides, different detectors have been successfully applied, such as HPLC-CAD, which increased the specificity of the analysis compared with the NREL method [48]. HPLC-based analyses can range from simple and rapid assays to more complex procedures to accurately determine PCW sugar composition. The HPLC-RID has excellent versatility as a universal detector in routine analysis of PCW sugars. However, it has low stability and low sensibility, and the isocratic elution does not enable the separation of all monomers from PCW polysaccharides. The UV-Vis solves the problem with sensibility; nevertheless, it requires laborious derivatization steps. Therefore, the appropriate technique can be chosen by balancing the importance of accuracy versus the time consumed for the analysis and the available resources [34].

3.2 High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) Among liquid chromatography techniques, HPAEC-PAD is suitable for the simultaneous identification of neutral and acidic sugars from PCW polysaccharides with high selectivity

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and sensitivity (low-picomole quantities), also enabling the detection of oligosaccharides derived from partial hydrolysis of PCW, having the benefit of not requiring derivatization steps [49,50]. HPAEC-PAD analysis for mono- and oligosaccharides was developed in the 1980s due to the development and combination of two analytical technologies (HPAEC and PAD). The methodology was based on the fortuitous observation of borate exclusion from a NaOHborate eluent, which led to the separation of reducing sugars as oxyanions by anion-exchange chromatography at high pH values [51,52]. In parallel, the development of a cyclic, pulsed amperometric technique with cleaning potentials applied to an electrode that is regenerated in every cycle allowed the stable and reproducible detection of carbohydrates, which circumvented the problem of electrode fouling when using constant-potential amperometric detection [52]. The HPAEC carbohydrate separation lies in the weakly acidic properties of sugar molecules and their dissociation constants, in high pH (>12), sugars show different net acidity due to differences in their hydroxyl groups [53], resulting in different pKa values and retention capacities. For example, mannose, glucose, and galactose, three hexoses, have pKa values of 12.08, 12.28, and 12.39, respectively [54]. The eluents used for HPAEC analysis are generally composed of NaOH or other hydroxides, and “pusher” anions with higher affinity to the stationary phase than hydroxide (such as acetate) and gradients can be used to increase the quality of the results. The standard stationary phase used for HPAEC carbohydrate analysis is the quaternary-ammonium-bonded pellicular anion-exchange resins that, in alkaline solutions, interact with neutral sugars. The alkaline environment is crucial for the sugars binding to the column and the detection method, as the electrode detects the products of the sugars oxyanions oxidation [55,56]. Specific methods and columns able to handle sulfate from H2SO4 hydrolysis of plant materials have been developed to give better resolution of the typical monosaccharides derived from PCW [50,56,57] and have been applied in various studies, including the analysis of the cell wall carbohydrates of multiple plants, such as broccoli, carrot, tomato, sisal waste, cotton, sesame, and grass, to name a few [58–61]. Thus, HPAEC-PAD is becoming a well-established analytical technique for the PCW glycosyl residue composition characterization. However, few drawbacks are observed during PCW sugar analysis. For the characterization of sugars from PCW, satisfactory separation of rhamnose from arabinose and xylose from mannose has been one of the main challenges of this technique [62]. The almost coelution of xylose and mannose, especially for samples that are rich in xylan and/or xyloglucan, was observed by Biswal and coauthors [61], although this can be overcome by method optimization [50,62]. Also, a baseline draft could occur when using acetate anions as “pushers,” which is mainly observed when analyzing oligosaccharides [63]. Furthermore, the lack of ready compatibility with mass spectrometry to further investigate the peaks can be considered another disadvantage of this technique when dealing with plant samples of unknown composition or unknown peaks derived from plant biomass acid hydrolysates [50,61]. Nonetheless, advantages related to the needlessness derivatization, the selectivity, and sensitivity of the detection method, almost 200 times higher than HPLC-RID analysis [64], place HPAEC-PAD as one of the best methods of choice for the direct analysis of PCW glycosyl residues composition [65].

3. Chemical analysis of plant cell wall polysaccharides—Glycosyl residues composition

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3.3 Gas chromatography—The alditol acetate (AA) and trimethylsilyl (TMS) derivatives The quantitative analysis of sugars in PCW polysaccharides by GC has been employed since the 1960s [24], with the flame ionization detector (FID) being the most common detector. Nowadays, this tool is, in most cases, coupled with a mass spectrometry detection (GC-MS) [66] to enhance the specificity for an accurate analysis of the glycosyl residues. As the method relies on evaluating volatile compounds and sugars have finite volatility, it is necessary to derivatize the monosaccharides in a volatile form previously. A specific and detailed review of the main derivatization techniques of carbohydrates for GC and GC-MS analyses can be found in the report of Ruiz-Matute and collaborators [67]. It is important to note that the chosen methodology must be adequate to the type of biomass sample and its expected carbohydrate content. Currently, the common derivatives used are methyl ethers, acetates, trifluoroacetate, and trimethylsilyl ethers. Here, we will present the most common sugar composition analysis methods for PCW samples: the alditol acetate (AA) and trimethylsilyl (TMS) methods [68]. Before the derivatization for GC analysis, samples must be hydrolyzed, and any trace of acids used should be removed. Various techniques to acetylate the PCW monosaccharides are variances of the original AA method [24,69,70]. In the most straightforward protocol detailed by Pettolino et al. [13], the monosaccharides are reduced to alditols using NaBH4 or NaBD4 and sequentially acetylated using acetic anhydride. Prior to acetylation, it is important to entirely remove boron since borate can form complexes with the hydroxyl groups resulting in underestimated levels of monosaccharides. This is usually done by treatment with methanol followed by evaporation [71]. The hydroxyl groups from alditols readily react with acetic anhydride in the presence of a catalyst, being the most common sodium acetate, pyridine, and methylimidazole [72]. The produced volatile compounds can be analyzed by GC, whose results are single peaks for each sugar identified by their retention time relative to the internal standard, usually myo-inositol or scyllo-inositol. However, it has been reported that glucose content derived from the hydrolysis of various lignocellulosic biomass was slightly underestimated by AA-GC analysis compared to HPLCRID, while it gave a better response to hemicellulose-derived sugars [41]. Also, the AA classic method is unsuitable for the acidic sugars, impairing the characterization of structural polysaccharides constituted by these sugars, such as pectin and glucuronoxylan [61]. The entire composition can be achieved after carbodiimide activation and reduction of the carboxyl groups using NaBD4, which yields the 6,6-dideuterio neutral sugars of galacturonic and glucuronic acids, to be analyzed by monosaccharide analysis by AA/GC-MS method [13,73]. Additionally, the reduction of carboxyl groups eliminates hydrolysis’s difficulties, enabling a more reliable quantification. Alternatively, a spectrophotometric method [74] can be used to provide the total uronic acid content but with no identification of the uronic acid kind. In contrast, the TMS method has mainly been applied to PCW neutral sugars and acidic sugars analysis [75]. The methodology, in the majority, follows the procedure description of Sweeley et al. [76] with some adaptations and new silylating reagents. With a simpler sample preparation compared with the AA method, hydrolyzed glycosyl residues can be submitted to sequential methanolysis and trimethylsilylation, resulting in TMS-methyl

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glycosides with favorable volatility and stability [61], and GC is used to separate TMS derivatives. Nevertheless, the interpretation of the GC profiles is difficult due to the presence of multiple peaks derived from each monosaccharide derivative, of both the α- and β-anomeric configurations and the pyranose and furanose ring forms [77]. Furthermore, the TMS method is not indicated for insoluble polysaccharides’ analysis, such as cellulose and linear mannan, since using a harsh hydrolysis step can result in the loss of less stable portions of the sugars [78]. The comparison of carbodiimide method/AA and TMS approaches efficiency in analyzing the glycosyl residues composition from dicot and grass species has shown that the acid conditions regularly employed are not enough to hydrolyze the cellulose; therefore, quantification is usually restricted to noncellulosic sugar content [61]. Both methods provided comparable results for neutral and acidic sugars present in the PCWs, but the TMS method gave a slightly higher yield for most sugars. In summary, the GC-MS technique provides further refinement in identifying PCW carbohydrates with a good sensitivity, resolution, and robustness with simple instrumentation [28]. However, the laborious derivatization procedures are subject to significant experimental errors, are time-consuming, and are not very practical for routine analysis in an industrial environment, such as biorefineries for biomass processing. Despite these constraints, AA and TMS methods have been recommended by the American Society for Testing and Materials [68] to analyze sugars in plant biomass. It has been demonstrated that the carbodiimide method/AA, TMS, and HPAEC approaches provided highly comparable results and are suitable for investigating the PCW polysaccharides [61]. Therefore, technicians should critically evaluate the available toolkit for PCW glycosyl composition analysis to select the most suitable technique (or combinations of methods) by considering the factors.

4. Structural analysis of plant cell wall polysaccharides The analysis of the monomeric sugar composition of PCW samples can provide a lot of helpful information to support studies and applications. However, some scientific endeavors and industrial uses require more detailed molecular knowledge to understand structureproperty relationships [6]. Therefore, elucidating anomeric configuration, linkage pattern, and side chains distribution will often be necessary. The specific nature of carbohydrates allows innumerable structural possibilities, from multiple branching and substitution, furanose or pyranose ring sizes, and different configurations (D or L, and the anomeric α- or β-glycosidic linkages). Hence, it is challenging to contemplate structural polysaccharides’ analysis to cover a broad range of structural aspects [7]. However, there are currently some analytical tools for analyzing these complex PCW polysaccharides, although not every method is suitable for all polysaccharides in the PCW, which leads to the use of different complementary techniques for a complete characterization [6,79]. This section will overview the use of nuclear magnetic resonance, methylation analysis, and immunological approaches, as those methodologies have been commonly used for detailed structural characterization of PCW polysaccharides (Fig. 5).

4. Structural analysis of plant cell wall polysaccharides

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FIG. 5 Analytical methods for the identification of glycosyl residues constituents of PCW polysaccharides; (A) NMR spectroscopy; (B) methylation analysis by gas chromatography; (C) glycome profile/immunolocalization.

4.1 Nuclear magnetic resonance spectroscopy (NMR) NMR is one of the main techniques applied for the structural characterization of complex PCW polysaccharides, giving information at an atomic and molecular level to elucidate the structure and conformation. It enables confirmation of glycosyl residues composition, the identification of α- or β-anomeric configurations, the position of substituent groups, linkage patterns, and conformation [80,81]. Structural analysis of PCW polysaccharides by NMR is typically performed to various degrees by choosing different 1D and 2D NMR experimental strategies that usually rely on NMR nuclei 1H and 13C. This section will briefly comment on the solution-state NMR, the most used in carbohydrate analysis, but it will also encompass solid-state NMR. Advances in NMR technology have made the analysis of purified polysaccharides’ fractions and whole PCW samples possible. The study of the whole PCW by NMR can improve the understanding of polysaccharides’ interactions with each other and with other

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biomolecules (such as lignin), but the increased complexity in spectra interpretation limits the complete characterization of PCW polysaccharides [82,83]. Solid-state NMR can be applied to evaluate intact cell walls with minimal perturbance of the physical state and molecular interactions of polysaccharides [84,85] but with limited resolution. As an alternative to improve spectral resolution, solution-state NMR can be applied to analyze full dissolved PCW without prior polysaccharide purification. For this purpose, the PCW material is derivatized through acetylation or methylation and then dissolved in a common perdeuterated organic solvent or an appropriate solvent system, such as DMSO-d6/NMI-d6, 6 wt% LiCl-DMSO, or an ionic liquid, which are used to directly dissolve the ball-milled plant cell wall [83,86–88]. In general, NMR spectra of carbohydrates have several crowded and overlapped signals, particularly in the nonanomeric region. In addition, the ionic liquids or extensive ball-milling used in the analyses of whole PCW can cause modification on the cell wall components, which could lead to inaccurate interpretation [83,88]. Thus, despite the great importance of whole PCW studies by NMR, this section will focus on characterization studies of isolated PCW polysaccharides (after fractionation of the AIR), as this strategy reduces the number of signals and overlapping to make the interpretation less difficult, resulting in rich information for atomic-level structure determination [89]. In this context, the characterization of PCW soluble polysaccharides by NMR in solutionstate is commonly used. The sample preparation requires exchanging the water for a solvent whose proton does not have NMR absorptions detected under the same conditions as the sample. For this, the solvent most used for carbohydrate analysis is deuterium oxide (D2O), but others, such as DMSO-d6, have also been used [90,91]. In some cases, for less soluble polysaccharides in pure D2O as linear mannan, 50% urea D2O can improve solubility [92]. The sample should be prepared with minimal or no content of residual water, which requires three or more steps of solubilization/lyophilization, also ensuring that hydroxyl groups of carbohydrate are fully deuterium exchanged. It is desirable to analyze the sample in the highest concentration possible, as it directly affects the time required for data acquisition and the signal-to-noise ratio [81]. However, the high viscosity of the sample can be a constraint for getting a high concentration solution. In this case, partial hydrolysis has been used to obtain less viscous solutions [93]. In addition, partial hydrolysis, mainly by enzymatic processing, is sometimes used to produce representative oligosaccharides whose spectra are easier to elucidate the structure than the native PCW polysaccharides [91]. The determination of the primary structure of isolated and soluble PCW polysaccharides usually starts with identifying each glycosyl residue in the polymer. The structural analyses by 1D 1H NMR and 13C NMR can detect α- or β-configuration of the anomeric proton and carbon in a sugar residue, respectively [94]. It is noteworthy that, except for mannose and rhamnose, most PCW monosaccharides in the pyranose form have α-configuration of the anomeric proton in 5.1–5.8 ppm and β-configuration among 4.3–4.8 ppm, while α- and β-configurations of the anomeric carbon are in 98–103 ppm and 103–106 ppm, respectively [95,96]. However, in both 1H NMR and 13C NMR, common functional groups in PCW polysaccharides, such as the O-acetyl group and O-alkyl group, can change the chemical shifts of adjacent protons and carbon [96]. This can be an issue in the case of rhamnogalacturonan II, which is only a minor component of PCW [19]. On the other hand, one bond 13C-1H coupling constants are an option to determine the anomeric configuration unequivocally [97].

4. Structural analysis of plant cell wall polysaccharides

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Apart from the anomeric protons, acetyl (δ 2.0–2.1), and methyl (δ 1.2) groups, other signals are not well resolved in the 1H spectra of carbohydrates. Thus, 1D 1H NMR is not appropriate to fully characterize the structure of PCW polysaccharides. Nevertheless, it has been used to determine the degree of methyl-esterification and acetylation of pectins [98,99], and it can be used to determine the molar ratios of monomers by integrating intensities of anomeric protons signals [81,100]. The 13C resonances of carbohydrates are spread out over a broader range than 1H, and for simpler structures, 1D 13C NMR spectra can provide structural characterization. In many studies, identifying typical signals in an uncharacterized sample can be enough to determine structural features by comparing the identified signals with available literature. For example, the structure of a linear [1,4]-linked-D-mannan in the sample from a Brazilian plant was confirmed, based on six characteristic signals in the 13C NMR spectra for this polysaccharide, at 101.8 (C-1), 78.1 (C-4), 76.5 (C-5), 73.0 (C-3), 71.6 (C-2), and 62.0 (C-6) [101]. However, due to the limitations of 1D NMR, 2D NMR technology plays an essential role in the structural analysis of more complex polysaccharides by establishing correlations between different nuclei of the molecule. For example, the 1H NMR spectra of xylooligosaccharides obtained by enzymatic hydrolysis of xylans from various monocots allowed the diagnosis of glucuronic acid and 4-O-methylated glucuronic acid residues bearing a substituent at O-2, while 2D NMR experiments established that the substituted glucuronic acid was attached to O-2 of a 4-linked xylopyranose [91]. Thus, the complexity of the target PCW polysaccharide will dictate the NMR experimental strategy, where a series of 2D experiments (or even 3D) can be combined to help elucidate structural aspects. Here, to exemplify a strategy for the structural analysis of a complex PCW soluble polysaccharide, we will take as an example the study of a highly branched arabinoxylan from dicot seeds [93]. Firstly, the highly viscous polysaccharide was subjected to partial acid hydrolysis to be analyzed by NMR. 1D 1H and 13C NMR spectra were acquired, and the 1H NMR spectrum allowed identifying regions of α-linked Araf and β-linked Xylp, while the 13C NMR spectrum enabled the identification of a methyl carbon of rhamnose and nine anomeric carbon signals relative to different residues. Then, by associating these data with the 2D experiment HSQC (heteronuclear single quantum coherence), it was possible to identify the signals of the anomeric protons directly bound to the anomeric carbons, as HSQC allows establishing the correlation between hydrogens directly bonded to carbons [81]. After that, each of the nine residues had its protons and carbons chemicals shifts completely assigned by combining information from HSQC, TOCSY (total correlation spectroscopy), and COSY (correlation spectroscopy), thus allowing assured identification of the sugar residues. In this strategy, TOCSY and COSY were used to assign all other protons besides the anomeric protons that had already been identified, and carbons’ chemical shifts were then assigned from HSQC. The rationale behind this strategy is based on the fact that TOCSY provides correlations among all the hydrogens of the same monosaccharide; therefore, by using the anomeric hydrogen as a starting point, it is possible to assign the other hydrogens. COSY is used for the same purpose, but its analysis together with TOCSY helps the assignment of the hydrogens, as COSY allows establishing a correlation between a hydrogen and the adjacent hydrogen (up to three bonds) in the same sugar residue [81]. Then, by having information regarding all protons from the sugars’ residues, the assignment of the carbons from HSQC spectra was facilitated. Lastly, information regarding the sequence of glycosyl residues

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in the arabinoxylan structure was determined by the HMBC spectrum (heteronuclear multiple bond correlation), which makes possible the identification of bonds between adjacent sugar residues through the coupling between hydrogens and carbons two or three bonds apart. Thus, by following this experimental strategy, combined with methylation data analysis (see Section 4.2), it was possible to propose the structure of a highly branched arabinoxylan with a β-1,4-linked Xylp backbone, with short side chains attached to its O-2 (β-1,2,4-linked Xylp) or O-3 (β-1,3,4-linked Xylp) positions, containing α-T-linked Araf, β-T-linked Xylp, and α-T-linked GlcAp as main terminal residues [93]. However, it is important to emphasize that the ideal strategy will depend on the complexity of the soluble PCW polysaccharide and that NMR information can be combined with other techniques when not all data can be resolved solely by this methodology. Nevertheless, the evaluation of cellulose by solution-state NMR methodologies is more complex due to its insolubility in D2O. Other solvent systems have been evaluated to overcome solubility limitations, such as DMSO-d6/pyridine-d5, allowing the assignment of chemical shifts for nonderivatized amorphous cellulose [92,102]. In addition, techniques to derivatize the polysaccharides to facilitate the dissolution of both soluble and insoluble polysaccharide fractions of PCW are also available [87]. On the other hand, solid-state 13C NMR is an alternative to investigate structural features of cellulose in an intact state, providing not only information on crystallinity index but also about the ultrastructure of cellulose [103]. The evaluation of the cellulose ultrastructure by 13 C solid-state NMR allows characterizing other forms of cellulose, such as paracrystalline cellulose and amorphous cellulose fibril surfaces, as peaks corresponding to the crystalline structure (δ 86–92 ppm) differ from the one of the amorphous region (δ 80–86 ppm). With all the advances in the NMR spectroscopy technique, the method has been an easy and accurate choice with high reproducibility and structure-sensitivity to determine the anomeric configuration, location and degree of substitution, and the monosaccharide linkage sequence of polysaccharides from PCW [79]. Nowadays, carbohydrate identification can be facilitated by bioinformatic tools and a collection of NMR data of polysaccharides from PCW available in the literature and online databases [104,105]. Despite the advantages, the complete elucidation of most structurally complex polysaccharides will require combined data for specific characterization, such as the complementary methylation analysis by GC-MS for the linkage patterns confirmation.

4.2 Gas chromatography—Methylation analysis Methylation analysis coupled with detection by GC-MS is a widely used method for determining the linkage structure of PCW polysaccharides based on the identification of partially methylated sugar derivatives. Since the first preparation of O-methylated sugars in the early 1900s [106], several methods of polysaccharide methylation have been developed with different solvents and basic agents, which are reviewed elsewhere [107,108]. Here, two of the most commonly used methylation methods applied to characterize PCW samples will be discussed (the methods of Hakomori and Ciucanu and Kerek) [109,110]. Independently of the methodology used, in this procedure, the reaction of a methylating agent with sugars residues in the presence of a strong base converts all free hydroxyl groups

4. Structural analysis of plant cell wall polysaccharides

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of a polysaccharide (i.e., hydroxyl groups which are not involved in the glycosidic linkage) into methyl ethers, producing complete methylation (per-O-methylation). The base is required to produce the alkoxide that makes a nucleophilic attack on the methylating reagent. Methyl iodide is the usual methylating agent, but dimethyl sulfate has also been used in the Haworth method [108,111]. The methylation analysis encompasses the methylation procedure, total acid hydrolysis of permethylated polysaccharides to release partially methylated sugars that are subsequently reduced to alditols, which are further acetylated, resulting in partially methylated alditol acetates (PMAAs). For the reduction, NaBD4 is used to tag the anomeric carbon with deuterium and differentiate symmetrical derivatives that are unresolved chromatographically [112]. The PMAAs are analyzed by GC-MS using a combination of their retention time and mass spectra, and the linkage analysis in the polysaccharide is deduced from the identification of these derivatives [13,113]. The method of Hakomori [109] and Ciucanu and Kerek [110] will differ in the initial methylation step by the use of a different base. The choice of one method over the other will be dictated by the samples’ characteristics, as methylation conditions are not universally applicable to all carbohydrate types [114]. In the Hakomori method, the base is the methylsulfinyl carbanion (CH3-SO-CH
2 ), also known as dimsyl anion, which is prepared by dissolving sodium hydride or potassium hydride in DMSO (NaH/DMSO) under a nitrogen stream at room temperature [109,112]. The dimsyl anion is sensitive to oxygen, carbon dioxide, and water, thus requiring an inert and anhydrous atmosphere during reaction and storage, and it suffers exothermic decomposition at relatively low temperatures, generating noncondensable gases that can cause explosions, posing a significant safety concern [115]. This method also presents the disadvantage of producing many side products, such as ethyl methyl sulfoxide, disulfur derivatives, and further condensation and methylation derivatives, which can hamper the separation and identification of PMMAs [13,108]. Due to these concerns about using dimsyl anion as the basic agent in methylation reactions, the simpler and safer method of Ciucanu and Kerek (1984) has been extensively used for linkage analysis. In this procedure, finely powdered sodium hydroxide replaces the dimsyl anion [110]. Regardless of the method used, undermethylation is a typical problem associated with methylation analysis. The total methylation of a PCW polysaccharide usually requires more than one methylation step. The disappearance of the characteristic broad band from hydroxyl groups in FTIR spectra (3200–3400 cm
1) can be used to check the success of methylation. One of the leading causes of undermethylation is the poor solubility in DMSO. PCW polysaccharides exhibit different solubilities in DMSO, but it is usually accepted that lyophilized samples are more easily dispersed in DMSO [116]. Ultrasonic treatment, heating (up to 70°C), and preacetylation using acetic anhydride/pyridine can be used to assist the solubilization. In the last case, the O-acetyl groups are split off during the subsequent base treatment for methylation [117]. Another possible approach for polysaccharides insoluble in DMSO is the Haworth procedure, which uses an aqueous solution of NaOH as solvent [111,118]. A first step using the Haworth method can also be combined with a second one using the Ciucanu and Kerek method, as the resulting partially methylated polymer will be more easily dispersed in DMSO.

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1. Plant cell wall polysaccharides

Methylation protocols also require some modifications to properly analyze PCW polysaccharides containing uronic acids (such as pectins). Namely, for samples containing uronic acids, a carboxyl reduction step is necessary before methylation to convert the uronic acids into their neutral sugars counterparts. The use of deuterated sodium borohydride is recommended in this step to enable the quantitative determination of uronic acids. Methylation is often combined with other methods to confirm the structural characterization of PCW polysaccharides, especially monosaccharide composition and NMR analyses [119–121]. NMR spectra are typically interpreted alongside methylation analysis data to confirm the glycosidic linkages and determine their anomeric configuration. Also, methylation data should be consistent with the monosaccharide composition of the PCW. However, it is notable that quantitative composition analysis data may be inconsistent with the linkage analysis data. Alca´ntara and co-workers [121] reported that the methylation results of recalcitrant carbohydrates from lignocellulosic biomasses indicated higher amounts of 4-linked glucopyranosyl residues, while composition analysis suggested a lesser amount of glucose. This difference is due to the fact that permethylation is efficient in solubilizing cellulose, while monosaccharide analysis methods such as TMS and AA are not [78,121]. In fact, permethylation is being used as a strategy to dissolve both soluble and insoluble polysaccharide fractions of PCWs to improve existing methods or implement new analytical tools for carbohydrate analysis. Notably, a recently developed method has utilized permethylation for solution-state NMR spectroscopy of insoluble polysaccharide materials, achieving high spectral resolution and allowing facile relative quantitative analysis of the polysaccharide composition of switchgrass and poplar samples [87]. In addition, a modified methylation method has been developed to accomplish the monosaccharide composition analysis of insoluble polysaccharides [78]. In this procedure, the permethylation enables solubilizing equally all polysaccharides and achieving complete depolymerization by acid hydrolysis. After hydrolysis and reduction, a second methylation step is performed, giving rise to methyl alditol derivatives. Also, methylation analysis can be helpful in investigations to elucidate how different polysaccharides are structurally associated with each other and even with noncarbohydrate components of the PCW. For instance, methylation has assisted the elucidation of a tight association of cellulose to xylans and mannans in the wheat endosperm [120] and has been used to determine the locations and frequency of binding sites of hemicelluloses to lignin in softwood and hardwood [122]. This method has also been used to identify recalcitrant glucan and xylan in corn stover and sugarcane straw biomasses after pretreatment and enzymatic hydrolysis, providing insight into the lack of accessibility of enzymes to the pretreated materials [121]. In conclusion, methylation analysis is an important tool for the elucidation of carbohydrate structure, being applicable for a wide range of samples containing either neutral or acidic glycosyl residues [107], and it can be used to dissolve both soluble and insoluble polysaccharide fractions of PCW in appropriate solvents [78]. Furthermore, it can provide an estimate of the relative proportion of different polysaccharides in the whole cell wall from a single analysis using prior knowledge of the type of cell wall and its potential classes of polysaccharides. However, for a mixture of polysaccharides as well as more complex structures, this method is more qualitative than quantitative because standards for each PMAA are hardly available. Besides, methylation analysis has other limitations as it cannot provide information on the

4. Structural analysis of plant cell wall polysaccharides

19

anomeric configuration or the position of the sugar residues in the polymer. Also, it can only determine acidic monosaccharide linkage if a previous carboxyl reduction step is performed to convert uronic acids into their neutral sugar counterparts [13].

4.3 Immunological approaches for structural polysaccharides’ characterization Among the diversity of analytical tools, it is possible to apply immunological approaches to characterize polysaccharides from PCW. In this characterization strategy, molecular probes can be used to comprehensively monitor the overall presence and distribution patterns of most major PCW glycan-binding sites using two complementary methodologies: glycome profiling, an in vitro approach, and immunolocalization, an in situ technique. The molecular probes most reported for PCW analysis used in both methods include carbohydrate-binding modules (CBMs) or glycan-directed monoclonal antibodies (mAbs) [123,124]. The in situ analysis of a specific carbohydrate structure by immunolocalization will depend on the correct choice of the corresponding probe. Thus, the selection of CBM or mAb for these analyses can be facilitated if a screening is previously carried out with semipurified carbohydrate fractions of the same sample to trace the relevant probes for the in situ characterization of the PCW sample. Two techniques have been described for this screening: glycomic profiling and comprehensive microarray polymer profiling (CoMPP) [16]. Glycome profiling is an ELISA-based method with a wide-range coverage of multiple structural features on most PCW glycans, being considered a moderate to a high-throughput method [16]. CoMPP is a dot blotting-based method typically employed for screening carbohydrates with about 20 glycan-direct probes [125,126]. For both methods, the fractionation of the PCW by sequential extraction (as mentioned in the section on sample preparation) is required to avoid masking CBM binding sites or mAb epitopes [127]. After that, the extracted polysaccharide must be immobilized to a solid support for the in vitro analysis. For this step, the immobilization of samples with less than 20 kDa requires additional strategies to adhere to the solid support, and it may be necessary to establish a covalent bond with the support or use a protein carrier [124]. The evaluation of the carbohydrates’ structure can be complemented by in situ analysis directly in the PCW through immunolocalization [125]. This methodology is also helpful in monitoring changes in wall composition and structure through development [128]. For that, CBMs and mAbs, the primary probes, are applied to the sectioned, fixed, and embedded PCWs. Then, in situ glycan localization/distribution in the PCW can be detected by fluorescence microscopy after incubating the samples with fluorescently tagged secondary antibodies [127,129]. Glycan-directed mAbs became the most common probe used for PCW analysis [125], and the actual cataloged collection of mAbs is over 200 mAbs, which are described in antiglycan antibodies studies [130] and online databases [131,132]. The advantages are related to their sensitivity for binding to epitopes with high affinity (Kd  10
6 M) [123,124]. The collection is wide enough to cover the major noncellulosic PCW glycans, including arabinogalactans, xyloglucans, xylans, galactomannans, homogalacturonans, rhamnogalacturonan I, and rhamnogalacturonan II [129,133]. However, there is no glycan-directed mAb available with binding specificity to cellulose. Another drawback is the presence of

20

1. Plant cell wall polysaccharides

pectic homogalacturonan polysaccharides and heteroxylan in the PCW, which usually mask other polysaccharides in primary cell walls [134]. In contrast, CBMs are substrate recognition moieties of carbohydrate-active enzymes which are widely used for cellulose-binding, besides diverse PCW polysaccharides than cellulose, including xylans [135], mannans [136], or galactan side chains of rhamnogalacturonan I [137]. Many works demonstrate that the use of CBM as probes is a powerful tool for the direct analysis of carbohydrates in PCW. Nevertheless, CBMs’ specificity and sensitivity are still a limitation to the spread use of this technique [123,125] since the same CBM family can recognize more than one structure (some CBM families can bind to crystalline cellulose and also to xyloglucan, for example) [138]. Accordingly, the immunological approaches do not elucidate the distribution of sugars across different cell layers or within a particular wall [13]. Meanwhile, this nonquantitative methodology still has a gap in the information regarding many molecular probes’ binding specificities and the limited immunogenicity of plant carbohydrates [139]. No binding to glycans does not indicate the absence of the glycan, but maybe only the absence of the epitope or a chemical modification in the binding site [129]. It should be noted that the probe is epitope-specific and not polymer-specific; thus, it can bind not only to the polysaccharide that is under evaluation but also to other cell wall components that have glycans in the backbone [129,140]. Nevertheless, immunocytochemistry studies are relevant tools to understand the specific structure of polysaccharides in PCW architectures in a rapid method that provides a minimal or no modification of the carbohydrate analyzed in situ [129,141].

5. Complementary methods for the analysis of plant cell wall polysaccharides Several techniques have been used extensively to reveal the architecture of PCW, mainly regarding structural polysaccharides’ organization, type, properties, localization, and biological function. In this sense, advanced cell wall imaging can help understand how carbohydrates are organized into the PCW matrix at macro-, micro-, and nanoscale levels. Likewise, some parameters of the polysaccharides, such as the shape, size, and crystallinity, influence the mechanical properties of PCW. Thus, monitoring these features can positively affect the biomass bioconversion processing into valuable bioproducts. Hence, this session describes analytical tools based on diffraction, spectroscopy, microscopy, and physicochemical assays (Fig. 6), emphasizing their principles, advantages, and disadvantages.

5.1 Atomic force microscopy (AFM) AFM is a high-resolution nonoptical imaging technique that offers remarkable insights into the surface structure and properties of the PCWs on a nanometer scale [142–145]. Because of that, AFM has been widely used for structural polysaccharides’ analysis, as this technique allows the determination of their morphology (in situ or after isolation), as well as the determination of modifications in their arrangement after physical/chemical or enzymatic treatments [143,146].

5. Complementary methods for the analysis of plant cell wall polysaccharides

21

FIG. 6

Analytical methods for physicochemical properties analysis of polysaccharides from PCW; (A) microscopic tools; (B) X-ray diffraction analysis; (C) FTIR.

AFM image is acquired by scanning the sample surface, which is done by controlling forces between the sample surface and a tiny and sharp probe or tip (3–6 μm) integrated into a thin and flexible cantilever (100–200 μm). The tip moves through the sample surface, and the cantilever deflection changes are tracked by a laser reflected off from the cantilever into a photodetector. AFM is usually carried out in two typical modes based on the force interaction between the sample and the equipment cantilever, known as contact and dynamic modes. More information on the most common AFM modes’ principles, advantages, and limitations can be found elsewhere [147–149]. Different approaches stand out in the AFM study of structural polysaccharides. One common method is to isolate and analyze individual polysaccharides to get information at the molecular level about their heterogeneity and structural properties, such as the height and length of molecules, linear chains, branching points of the networks, aggregates, helix, stiff chains, and networks [146,150–152]. The preparation process of the sample and its deposition method onto a flat material (usually mica) are the main factors that might influence polysaccharide imaging. Polysaccharides should be isolated using specific extraction methods such as, the consecutive extraction of pectin with different solvents at controlled pH and temperature [58,144], allied or not to a biomass physical/chemical pretreatment.

22

1. Plant cell wall polysaccharides

Independent of the isolation strategy, proper conditions are optimized to maintain the natural integrity and avoid changes in size and conformation, and mainly to prevent aggregation of the separated polysaccharides. Therefore, polysaccharide solutions are frequently diluted, heated, or sonicated before deposition onto the support, to minimize the strong intermolecular interaction of some polysaccharides, such as pectin [150]. For example, better resolution images of individual pectin polymers are obtained by diluting the sample with distilled water before heating at 80°C for 10 min [150]. In addition, surfactants such as dodecyl sulfate and Tween could be used for obtaining unaggregated polysaccharides’ solutions [153]. Ultracentrifugation, adsorption, and drop deposition followed by evaporation are the most common polysaccharides’ deposition methods on mica reported in AFM studies [151,154,155]. Many factors should be considered when depositing polysaccharides onto a surface, such as surface charge and polysaccharide interactions with the support, aiming to immobilize them for better resolution images or to avoid aggregation [142,151,156]. For more details, the use of AFM technology in polysaccharide research has been reviewed, and the factors that might influence polysaccharide imaging were discussed [151]. In another approach, polysaccharides can be visualized directly on a biomass surface or solid fractions obtained after chemical, physical, and enzymatic treatments, which allows the analysis of their natural association and localization on the cell wall architecture [143,157–159]. For instance, it is possible to visualize and study cellulose microfibrils or cellulose nanocrystals obtained via mechanical, chemical, or enzymatic treatments [143,157–161]. Reports have shown the use of AFM for monitoring cellulose microfibrils orientation and movements in the PCW due to forced mechanical extension, enzymatic treatment with cellulases [158], or delignification [143]. Those studies enabled revealing the spatial scale of molecular connections between microfibrils, demonstrating the orientation of microfibrils, such as the parallel and helicoidally arranged microfibril aggregates in S1 layers, and understanding the contribution of lignin to microfibril aggregates in S2 layers, providing new information to cell wall models. Although AFM has the disadvantage of being restricted to surfaces, its application for PCW polysaccharides’ characterization can be handy due to the direct imaging of microand nanoscale structural features of PCW or their carbohydrate fractions with high spatial resolution, the capability to probe living native structures, and minimal sample preparation, which reduces the introduction of potential artifacts. AFM and its modes of operation continue to be improved at an extraordinary speed, helping understand polysaccharides’ roles in the PCW architecture.

5.2 Electron microscopy Electron microscopy has been widely employed in PCW studies to evaluate the effects of pretreatment methods on biomass morphology and structure, giving information about structural polysaccharides. Two types of electron microscopy are commonly used for high-resolution imaging of PCWs: scanning electron microscopy (SEM) and transmission electron microscopy (TEM). SEM scans a bulk sample’s surface with a focused beam of electrons, which releases secondary electrons, backscattered electrons, and X-rays that are

5. Complementary methods for the analysis of plant cell wall polysaccharides

23

detected to create a high-resolution pseudo-3D image up to 15 nm, thus providing information on the PWC’s surface topography and morphology [149,162]. In the case of TEM, the electrons are transmitted through a thin sample and deflected to various degrees depending on the mass of the cellular components within the sample, hence providing information on the internal structure of PCWs with image resolutions up to 0.1 nm [145,149]. Both SEM and TEM share some basic features and principles, such as using accelerated electrons and electromagnetic lenses to generate images, besides typically operating under a high vacuum to avoid the scattering of electrons by air molecules. Due to the high vacuum, biological samples usually need to be dehydrated and coated with a conductive material before imaging, and this procedure can distort cellular features, creating artifacts [149]. In addition, samples for TEM require sectioning to create a thin specimen. Therefore, sample preparation is an important limitation in SEM and TEM. Nonetheless, the coating is not necessary for an environmental pressure or a variable-pressure SEM because the imaging gas in the specimen chamber helps to reduce charging artifacts at the sample surface. In addition, in the case of environmental-SEM where water is the imaging gas, PCWs can be analyzed hydrated with no processing [163,164]. For example, a protocol for imaging uncoated plant tissues in the variable-pressure SEM was developed, focusing on collecting images for quantification of cell size and shape [163]. SEM allows the observation of changes in the PCW roughness, particle size, cellulose fibers length and diameter, and surface structures of native and pretreated PCWs [165], while TEM allows the evaluation of delamination, pore formation, and disruption of cellulose microfibrils [149]. Those analyses are useful as they provide important information to monitor the PCW structure before and after physical/chemical disruption. SEM has also been applied to investigate whether the particle size, surface structure, and inner pore size affect the cellulose enzymatic hydrolysis efficiency [166,167]. In addition, SEM and TEM imaging have played an important role in the production and characterization of cellulose micro- and nanofibers. SEM allows the visualization of defibrillation and formation of microfibrils after different physicochemical treatments of lignocellulosic materials, while TEM can provide the dimensions of cellulose nanofibers [168,169]. In this sense, SEM micrographs have been used to elucidate the overall morphology of cellulose microfibrils isolated from different PCW materials and to identify the presence or absence of a layer of covering components (pectin, waxes, hemicellulose, and lignin) on the cellulose microfibrils [170–172]. In addition, TEM images can provide information on the nanoscale structure of cellulose nanofibers, such as determining their aspect ratio (length to diameter ratio), an important parameter that affects the reinforcing capability of nanofibers [171]. Electron microscopy has also been used to analyze the microstructure and morphology of pectin extracted from PCWs. By the analysis of SEM images, the morphology of pectins from different sources can be compared, revealing differential aspects such as one being more layered, undulating, or smoother than the other, which can be correlated to different molecular weights and monosaccharide compositions of pectins [173]. Also, the investigation of purified pectin fractions can reveal aspects of their three-dimensional structure, such as irregular sheet-like and partly porous structures [174]. Lastly, many studies have reported the distribution of polysaccharides and other components in PCWs using stains or immunochemical probes in combination with TEM [175]. For instance, pectins are well contrasted with the ruthenium red stain, while potassium

24

1. Plant cell wall polysaccharides

permanganate is commonly used to distinguish lignin in PCW [176]. Immunogold labeling has been widely used to determine hemicelluloses deposition in PWC materials, such as xylan deposition [177,178] and O-acetyl-galactoglucomannans and mannan spatial distribution [178,179]. Therefore, electron microscopy is an important technique for characterizing PCWs that can provide information on the localization of cell wall polysaccharides and their accessibility to carbohydrate-active enzymes [145,180]. SEM and TEM observations should ideally be supported by other PCW characterization methods, such as other microscopic and spectroscopic analyses. Their results should be correlated to the glycosyl residue composition analysis of the biomass and enzymatic hydrolysis performance [181]. Nevertheless, sample preparation for both techniques is laborious and may create artifacts, requiring an experienced, knowledgeable researcher and good sample preparation skills to identify or avoid all potential artifacts [162].

5.3 X-ray diffraction (XRD) analysis X-ray diffraction (XRD) for PCW analysis helps reveal some characteristics of polysaccharides’ crystal structures, such as crystallinity, unit lattice parameters, and the microfibrils organization [145]. XRD is a nondestructive accurate method with minimal sample preparation, based on the diffraction resultant after the incidence of monochromatic X-rays on the sample [182]. The emitted radiation of each atom is specific for each element, providing a “fingerprint” for each material [183]. The PCW analysis using XRD is vastly used during their compositional characterization. Among the crystalline PCW polysaccharides, cellulose was the first one well defined by XRD patterns. The methodology developed in Nishikawa and Ono’s report became a standard technique to identify cellulose’s allomorphs, determine its degree of crystallinity, and characterize its microfibrils until nowadays [184]. Those approaches have already been employed to investigate the cellulose’s pyrolysis [185], the cellulose rate of conversion during enzymatic hydrolysis [186,187], to characterize cellulose nanocrystals [188], and to characterize the microfibrils after wood thermal modification [189]. The crystalline nature of the mannan-rich PCW has also been determined with XRD, and the crystalline polymorphs of linear mannans, mannan I and II, were identified [190]. The crystallinity index is estimated according to the mass fraction of crystalline polysaccharides within the total biomass. Well-established techniques are applied to get these measures: peak height or Segal method, peak deconvolution of crystalline and amorphous peaks, or the amorphous subtraction or Ruland-Vonk method [191]. Concerning the PCWs, XRD is also used to analyze the interaction of carbohydrates with the other PCWs components, such as cellulose and xyloglucans, and pectin [145], mainly to evaluate the effect of the removal of those carbohydrates during biomass pretreatment [192–194]. Also, the XRD technique could be used to solely analyze the other PCW carbohydrates, e.g., xyloglucans structures [195]. Few drawbacks are observed with the technique application, such as the lack of dynamic observation of the PCW and the challenges in measuring the degree of order in PCWs, leading to variations of 30%–40% in crystallinity values measured depending on the XRD analysis

5. Complementary methods for the analysis of plant cell wall polysaccharides

25

approach used [145]. Nevertheless, the advantages, e.g., easy and nondestructive sample preparation, a fast acquisition time, and the potential to identify unknown PCW materials [182], are the reason why the XRD technique is commonly used during biomass characterization.

5.4 Fourier-transform infrared spectroscopy (FTIR) Fourier-transform infrared spectroscopy (FTIR) is commonly reported as a complementary method for PCW characterization, being especially useful for the high-throughput identification of the major compositional differences across a large set of samples [196,197]. This method is based on the absorption of the infrared radiation by interatomic bonds. This infrared incidence in the sample provokes the bond’s vibration and bending, and the frequency of vibration that corresponds to absorption can be detected and converted into a spectrum using Fourier transform algorithms [198]. Additionally, attenuated total reflectance (ATR) is a special accessory unit that can be coupled with FTIR spectrometers, enhancing the surface sensitivity and allowing PCW samples to be analyzed in their native state without any sample preparation. Thus, ATR-FTIR consists of a nondestructive technique that requires small amounts of samples and provides information about cell wall polymers and functional groups. ATR, in general, has a penetration depth of around 1 or 2 μm, depending on sample features [199]. Many recent reports exemplify the applicability of ATR-FTIR as a powerful tool to investigate PCW polymer composition. Former studies of model pectin and hemicelluloses demonstrated that 1200–800 cm
1 region can be used to identify PCW polysaccharides; for example, typical bands of β-1,4-mannan were found at 1066–1064 cm
1 while xyloglucans bands were at 1041 cm
1 [200]. According to Liu and co-workers [201], the region 1800–800 cm
1 was applied to analyze PCW polysaccharides. The main wave numbers for each cell wall component are described as follows: 1035 cm
1 was related to xylose-containing hemicelluloses, 1065 and 807 cm
1 to mannose-containing hemicelluloses, 988 cm
1 to cellulose, 1740 and 1600 cm
1 to homogalacturonans according to the degree of methylation. It is important to mention that the macromolecular organization affects some band positions, mainly observed by hemicellulose-cellulose interactions. The authors also reported that absorption bands are not well distinguishable in the case of arabinan and galactan. Badhan and collaborators [198] reported that esterified xylan and pectin were monitored by C]O peaks at 1714 cm
1 and 1738–1747 cm
1, respectively. The pectin FTIR spectra contain bands at approximately 1740 cm
1 corresponding to methyl-esterified carboxyl groups and around 1630 cm
1 from carboxylate anion. Integration of these peak areas can be used to determine the degree of methyl-esterification (DM) of pectins. The peak area ratio from esterified carboxyl to the sum of areas of the peaks from esterified and unesterified is related to the DM of pectin. A calibration curve, constructed using pectins of known DM, is obtained by plotting the DM values against the FTIR spectral band area ratios [202–204]. For samples with high protein content, peak deconvolution is recommended for a more accurate determination. Nevertheless, most plant materials contain low protein levels, and FTIR without deconvolution has been considered a reliable method for DM determination [204]. ATR-FTIR spectroscopy has also been highlighted as useful for studying biomass process changes during biotechnological processing [198,205]. For example, ATR-FTIR was

26

1. Plant cell wall polysaccharides

successfully used to study complex enzyme saccharification processes, such as to identify recalcitrant aspects in the forage that are resistant to rumen digestion [206]. In another example, ATR-FTIR was also applied to screen the most suitable corn stover samples for enzymatic conversion derived from different recombinant maize cultivars. The technique allowed the authors to obtain a rapid and broad vision of cell wall compositional features and correlate these results to the saccharification performance, helping establish new targets for research regarding lignocellulosic bioconversion [197]. Additionally, ATR-FTIR is a versatile analytical tool to study plant growth, enabling monitoring of cell wall alterations due to several factors, such as growth and development processes, genetic modifications, inhibitors to cellulose biosynthesis, and stress responses to abiotic or biotic factors. In this sense, Abidi and co-workers [207] used universal attenuated total reflectance FTIR (UATR-FTIR) in the study of changes in cellulose during different stages of cotton fiber development (Gossypium hirsutum L.), being able to identify the transition between primary and secondary cell wall synthesis. Photoacoustic spectroscopy (FTIR-PAS) is another approach that can overcome some common problems of conventional FTIR, such as low sample homogeneity, complex matrix, light dispersion, and reflection. PAS measures the fraction of the intensity-modulated IR radiation absorbed by the sample that is transformed into heat [208]. As pointed out by McClelland and co-workers [209], the main interest in PAS is that the spectrum of opaque samples can be directly measured based on the material’s thermal and optical properties rather than by physical thickness, as those two properties define the sample depth in PAS. It has been shown that this method can be used for screening biomass samples and provides a rapid estimation of their suitability for sugar release for bioethanol production. By analyzing 1122 wheat straw samples, the developers could correlate the samples’ original chemical structures that contributed to or inhibited/retarded the sugars released after pretreatment and enzymatic hydrolysis, demonstrating that FTIR-PAS is also a powerful technique for high-throughput structural polysaccharides’ analysis [196]. It is worth mentioning that near-infrared (NIR) spectroscopy combined with multivariate calibration methods has been developed as a valuable alternative to identify, classify, and quantify carbohydrates in plant tissues. NIR differs from FTIR by using near-infrared wavelengths (2.5 to 0.8 μm), while FTIR uses mid-infrared wavelengths of light (25 to 2.5 μm). Although the determination of a suitable calibration cannot be trivial at the beginning, the technique is well-established to predict qualitative and quantitative PCW polysaccharide composition with few or no sample preparation in nondestructive procedures that scan a large variety of samples simultaneously and relatively quickly [210,211]. It is clear that FTIR brings advantages to profile samples’ structural polysaccharides’ composition compared with the traditional analyses of the PCWs, especially when dealing with numerous samples. This technique’s main benefits consist of the rapid analysis of small amounts of samples in their original states and environments. However, FTIR analysis of structural polysaccharides in PCW encounters limitations derived from the complexity of spectra with overlapping peaks and the vibrational coupling of chemical bonds from different cell wall polymers [198,201,205,207]. Thus, for a more detailed characterization analysis, FTIR may not be as specific as necessary to provide all the information regarding the sample’s composition. Thus, its use will be more valuable for a high-throughput overview or to confirm the data obtained with other chemical analyses of entire or fractionated cell walls.

References

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6. Concluding remarks Substantial progress has been made toward the PCW analysis to develop more precise methods to enlarge our fundamental understanding of structural polysaccharides’ compositional and structural characteristics. Also, studies on PCW polysaccharides have been frequently motivated by their industrial applications rather than by only basic science studies. Thus, the development of new, more accurate, and high-throughput methods has been, in many cases, derived from the urge to find the most suitable applications for those substances or to correlate their characteristics with inefficiencies encountered in their industrial processing. However, the characterization of PCW polysaccharides, especially in their native state, remains an interesting scientific challenge. The complexity, heterogeneity of PCWs, and their complex network of physically interacting components result in the impossibility of using a universal method for the complete characterization of structural polysaccharides from different plant sources. It is clear that the available methodologies for polysaccharides’ characterization in most cases result in ambiguous data, and therefore, complementary approaches are required for a complete elucidation. In most cases, the accurate analysis requires experienced technicians for laborious and multistep experimental phases, besides it requires the interpretation of large data sets obtained from complementary chemical, structural, and physicochemical characterizations. Each analytical tool has its own strengths, weaknesses, and scope of application. The choice of an approach should also consider the features and the properties of the PCWs, besides the experimental conditions and the available infrastructure. Even with a determined method, there may be differences in equipment and calibration that should be considered. Considering the importance of plant biomass in a growing biobased economy, the development of new methods with simpler sample processing giving accurate and quantitative results for PCW polysaccharides will continue to be a relevant research topic. Thus, we can look forward to an enhanced toolkit for structural polysaccharides’ characterization in the future.

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C H A P T E R

2 Genetic modification of plants to increase the saccharification of lignocellulose Joa˜o Vitor Furtado da Silva, Breno Miguel Joia, Wagner Mansano Cavalini, Rodrigo Polimeni Constantin, Marco Aur
elio Sch€ uler de Oliveira, Rog
erio Marchiosi, Osvaldo Ferrarese-Filho, and Wanderley Dantas dos Santos Laboratory of Plant Biochemistry and Laboratory of Molecular Biochemistry, Department of Biochemistry, Center of Biological Sciences (CCB), State University of Maringa, Maringa´, PR, Brazil

1. Introduction The continued exploitation of fossil fuels has resulted in the accumulation of polluting leftovers that not only have an influence on the environment but also pose a threat to our civilization. As a result, promoting a transition to a sustainable economy is one of the century’s most critical issues. The utilization of green carbon sources for the production of fuels and other bioproducts is a major goal of modern research and industry [1]. Lignocellulosic biomass has emerged as the only sustainable source capable of replacing oil on the scale we use today [2]. Following saccharification, lignocellulose monomers and wastes can produce high-value-added biomaterials that are readily available, inexpensive in cost, and ecologically friendly. Lignocellulose accounts for more than 60% of all biomasses generated globally, although it is mostly utilized as a solid fuel with little additional value [3]. Lignocellulosic biomass, which is made up of cellulose, lignin, hemicellulose, and pectins, offers stiffness to limit cell expansion and assist plant growth. The greatest impediment to its biotechnological usage is its recalcitrance to saccharification, which is provided by

Polysaccharide-Degrading Biocatalysts https://doi.org/10.1016/B978-0-323-99986-1.00006-5

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complex polysaccharides such as hemicellulose and pectin, as well as by lignin. Each cell wall component, held together primarily by covalent and hydrogen bonds, plays a role in cell wall structure and metabolism. They are synthesized in a different anabolic pathway, forming the hydrophilic walls of parenchymatic and meristematic cells (filling and growing tissues) and the hydrophobic cell walls of lignin-coated tissues such as fibers, vessels, sclerenchyma, and cortex, and also in mature-filling tissues. This increasing buildup of lignin impedes biomass saccharification and, as a result, its industrial utilization [4]. Cellulose is a linear polymer composed of anhydro-β-D-glucopyranose units, often referred to as glucose units, linked together by β(1 ! 4) glycosidic bonds assembled in repeated groups of two Glu-forming cellobiose units (Fig. 1). The cellulose chains forming cellulose microfibrils are kept together through hydrogen bonding. In turn, the microfibrils are held together and prevented from collapsing into large macrofibrils by hemicelluloses, also known as crosslinking glycans. Hemicelluloses are branching heteropolysaccharides containing hexoses

FIG. 1 Simplified scheme of the cell wall composition and architecture. Two microfibril units made up of 36 molecules of cellulose interlinked by hydrogen bonds. Hemicelluloses cross-link the microfibril [5].

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such as β-D-glucose, β-D-mannose, and α-D-galactose, and/or pentoses such as β-D-xylose and α-L-arabinose connected by glycosidic bonds. They are categorized as xylans, mannans, and glucans based on the kind of sugar that makes up their central chain. In general, they present branches linked in the α-configuration [6]. Branches can be found as prefixes in hemicellulose names such as arabinoxylans (xylan branched with arabinose), glucomannans, and xyloglucans. In grasses and other commelinid monocots, hemicellulose may include phenolic acids such as ferulic and p-coumaric acid ester linked to its hydroxyls. Known as noncore lignin, they can dimerize with vicinal phenolic compounds cross-linking hemicelluloses and anchoring lignin to the cell wall’s polysaccharide fraction. The highly hydrated pectin fraction fills the gaps and keeps the apoplast wet, allowing enzyme activity and transport of chemical compounds. Pectin includes acidic monosaccharides (uronic acids) that form complexations with cations such as boron and calcium. In dicots, pectin may also present phenolic esters, which act as anchors for lignin polymerization after cell growth has ceased. These pectic structures aid in cell adhesion in the middle lamella. All these components can be found in the cell walls of all plants [7], albeit the amounts and particular composition may differ. Pectin contains a wide range of polysaccharides. The pectic matrix serves as the primary adhesive substance between the cells, preserves moisture, and provides electrostatic charges. It is found in all terrestrial plants and is composed of uronic acids such as glucuronic acid and galacturonic acid, although its structure can be quite complicated. The most common pectins are homogalacturonan and rhamnogalacturonan I. The first is entirely composed of galacturonic acid and complex ions of calcium, a structure known as an egg box. The second is composed of a central chain that alternates glucuronic acid and rhamnose residues and is frequently branched with neutral sugar oligomers such as arabinans and galactans. Other pectins like rhamnogalacturonan II (RGII) present extraordinary monomeric complexity that forms complex boron ions between two vicinal RGII [8]. Lignin is a phenolic polymer that provides hydrophobicity, rigidity, and resistance against a pathogen attack. Its core monomers are synthesized in the phenylpropanoid pathway as coniferyl alcohol, sinapyl alcohol, and p-coumaryl alcohol. They polymerize through radical reactions catalyzed by peroxidases and laccases. For historical reasons, after polymerization, the phenolic alcohol residues receive new names: guaiacyl (G), syringyl (S), and p-hydroxyphenyl (H) (Fig. 2). Because of the ether bonds produced throughout the polymerization, lignin is quite resistant to hydrolysis. The proportion of each monolignol and the amount of lignin in plants affect the mechanical and chemical characteristics of lignin, particularly how it inhibits saccharification. Angiosperms have lignins with equivalent G and S unit composition, which, although producing a mechanically resistant lignin, presents a reduced number of ramifications. The S monomer has methoxy groups protecting the 3 and 5 positions, preventing the production of branches (units forming more than two covalent ether links). Gymnosperms, on the other hand, possess G lignin, which makes their woods softer but chemically less vulnerable to breakdown by pathogen saccharases [9,10]. We have used approaches such as genetic regulation of lignin production and administration of chemical substances that can assist delignification to boost the bioavailability of cellulose for industrial processes. The identification of elements that can influence lignin biosynthesis in plants without influencing biomass production is critical for increased profitability of the process with minimal ecological impact [11].

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FIG. 2

Chemical structure of lignin and its monomers: guaiacyl (G) and syringyl (S) and p-hydroxyphenyl (H) compounds. Adapted from Marchiosi R, dos Santos WD, Constantin RP, de Lima RB, Soares AR, … Ferrarese-Filho O. Biosynthesis and metabolic actions of simple phenolic acids in plants. Phytochem Rev 2020;19(4):865–906. https://doi. org/10.1007/s11101-020-09689-2.

2. Lignin biosynthesis Disturbances in genes involved in the phenylpropanoid pathway can affect the structural composition of the macromolecule and enhance lignocellulosic biomass saccharification. The mere adjustment of lignin content, on the other hand, is not always favorable since it can impair plant growth as well as negatively influence other phenotypes. Thus, to create plants with high lignocellulosic productivity and susceptible to saccharification, it is necessary to understand how to manage the flow of phenylpropanoid intermediates to the core and noncore monolignols in order to get events of interest [12]. All plants synthesize lignin components via the phenylpropanoid pathway, which uses phenylalanine (Phe) as a substrate, whereas grasses use tyrosine (Tyr) as an extra substrate (Fig. 3). The enzyme phenylalanine ammonia-lyase (PAL) first deaminates the Phe molecule before converting it to t-cinnamic acid. The enzyme cinnamate-4-hydroxylase (C4H) adds an OH to form p-coumarate. If the precursor is Tyr, the enzyme tyrosine ammonia-lyase (TAL) deaminates the molecule to form p-coumarate [13]. Coumarate-CoA ligase (4CL) transforms p-coumarate to p-coumaryl-CoA, which is then converted to p-coumaraldehyde by cinnamoyl-CoA reductase (CCR). Cinnamyl alcohol dehydrogenase (CAD) then generates p-coumaryl alcohol, the precursor of p-hydroxyphenyl monolignol (H), by radical polymerization catalyzed by peroxidase (PRX) and laccase (LAC) [14]. Alternatively, hydroxycinnamoyl-CoA:shikimate/quinate hydroxycinnamoyl-CoA (HCT) converts p-coumaroyl-CoA to p-coumaroyl-shikimate by transesterification. An additional

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FIG. 3

Simplified scheme for lignin biosynthesis. Phenylalanine ammonia-lyase (PAL); cinnamate-4-hydroxylase (C4H); Tyrosine ammonia-lyase (PTAL); coumarate-3-hydroxylase (C3H); coumarate-CoA-Ligase (4CL); cinnamoylCoA Reductase (CCR); cinnamyl alcohol dehydrogenase (CAD); peroxidase (PRX); and laccase (LAC); hydroxycinnamoyl-CoA: shikimate/hydroxycinnamoyl-CoA (HCT); p-coumaroyl shikimate/quinate 3-hydroxylase (C30 H); caffeoyl shikimate esterase (CSE); caffeoyl-CoA 3-O-methyltransferase (CCoAOMT); coniferaldehyde dehydrogenase (CALDH); Ferulate-5-hydroxylase (F5H); caffeate/5-hydroxyferular 3-O-methyltransferase (COMT); H, p-hydroxyphenyl; G, guaiacyl; S, syringyl. Initial pathway reactions appear in dark gray. Reactions that lead to the biosynthesis of p-hydroxyphenyl (light gray), caffeoyl-CoA (yellow), guaiacyl (green), 5-hydroxyconiferyl alcohol (blue), syringyl (purple), and feruloyl esters of lignin and arabinose (pink).

hydroxylation of the aromatic ring catalyzed by p-coumaroyl-shikimate/quinate 3-hydroxylase (C3H) forms caffeoyl-shikimate, which is again transesterified by HCT to form caffeoyl-CoA. Caffeoyl shikimate esterase (CSE) can alternatively cleave caffeoyl shikimate to form caffeate. Caffeoyl-CoA methylated by caffeoyl-CoA 3-O-methyltransferase (CCoAOMT) forms feruloyl-CoA. Feruloyl transferase can ester-link the activated ferulate

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to arabinose to form FA-GAX in commelinids. The enzyme CCR can alternatively reduce feruloyl-CoA to coniferaldehyde. Coniferaldehyde dehydrogenase then further reduces coniferaldehyde to coniferyl alcohol, the precursor of G lignin [15]. Coniferaldehyde dehydrogenase (CALDH) can alternatively oxidate coniferaldehyde to ferulic acid, but it can also be hydroxylated by ferulate-5-hydroxylase (F5H) to generate 5-hydroxyconiferaldehyde, which forms alcohol-5-hydroxyconiferyl via CAD. These last two metabolites can undergo methylation by caffeate/5-hydroxyferule 3-O-methyltransferase (COMT) and form sinapaldehyde and sinapyl alcohol, respectively. Finally, coniferyl and sinapyl alcohols are oxidatively polymerized by PRX and LAC, generating guaiacyl (G) and syringyl (S), respectively (Fig. 3) [16].

3. Molecular approaches to cell wall modification To control plant development, the cell wall organizes a complex network of polysaccharides, phenolic compounds, and proteins. Identifying the genes involved in the biosynthesis pathways of cell wall components is the first step toward developing genetic engineering technologies for generating genetically modified plants for industrial uses such as biofuel generation. Researchers have found over a 1000 genes involved in cell wall production pathways, which may allow for cell wall structural alteration.

3.1 Genetic modification in cellulose biosynthesis Cellulose must be converted into high-value products by enzymatic hydrolysis in a biorefinery. Enzyme saccharification, on the other hand, necessitates the use of costly procedures in order to produce high yields. The sorts of linkages that make up the cellulose microfibril make the process challenging, with glucan chains orientated in parallel generating crystalline sections that are very stable, while amorphous portions of the chain are more susceptible to them. The state of crystallinity depends on the bonds formed in the construction of cellulose, which can be Iα and Iβ; they differ in linking the chain. While Iα bonds form a specific three-dimensional conformation, Iβ bonds form a more stable linear chain and are more difficult to hydrolyze. Normally, higher plants contain a higher proportion of Iβ bonds [17]. Cellulose biosynthesis involves genes from the CesA family that are expressed during the development of the primary or secondary cell wall and function in rosette assemblies to create cellulose microfibrils. Genetic changes in the genes of this family can affect cellulose crystallization. A groundbreaking study looked at an Arabidopsis mutant rsw1 with a point mutation in a base pair that replaced valine with alanine in the AtCesA1 protein. The mutation disrupted the cellulose synthase complexes and enhanced the formation of noncrystalline cellulose in the plant tissues; nonetheless, they produced plants with swelling in the roots at 21° C and stunted growth with malformed cells in all tissues when the temperature was raised to 31°C [18]. A study in Arabidopsis ixr1–2 mutant carrying a point mutation in the gene region that codes for a highly conserved domain of the C-terminal transmembrane motif of the CesA3 protein resulted in possible alteration in the obligatory subunit in the cellulose synthesis

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machinery. Plants had a crystallization index of 34%, reduced biomass, and an increase in cellulose digestibility in raw biomass of 151% [19]. Huang et al. [20] evaluated cell wall features and biomass saccharification in transgenic rice plants overexpressing two genes of the plant glycoside hydrolase 9 family (OsGH9B1 and OsGH9B3 genes). As a result, the researchers observed a rise in enzyme activity when compared to wild plants, as well as a modest alteration in cell wall composition, indicating a structural shift in the lignin, cellulose, and hemicellulose chains and a high yield of sugar converted to bioethanol. Overproduction of the enzyme increased lignocellulosic biomass saccharification, demonstrating the potential of genes in this family to manipulate structural changes in the cell wall and increase biomass saccharification.

3.2 Genetic modification in hemicellulose biosynthesis Hemicelluloses are a heterogeneous, amorphous, noncellulosic group of cell wall polysaccharides. Its structural intricacy and close association with cellulose microfibrils make it difficult for cellulolytic enzymes to reach cellulose, delaying the enzymatic saccharification process. Interfering the hemicellulose biosynthesis may enhance biomass saccharification. Xylan is a component of hemicellulose that, in addition to linking with cellulose via hydrogen bonds, may also cross-link via diferulic bonds. Xylan is composed of a linear chain of β(1 ! 4)D-xylosyl residues that may branch with α-arabinosyl residues to generate arabinoxylans and glucuronic acid to form glucuronoarabinoxylans (GAX). Arabinosyl residues can generate FA-GAX or p-CA-GAX by ester linking to ferulic or p-coumaric acids. Xylans and FA-GAX account for 20% to 30% of the total cell wall in grasses and other commelinids with type II cell walls [21]. Xylosyltransferases catalyze the reaction between UDP-D-xylose and β-D-xylosyl. Mutant rice with a xax1 gene mutation has decreased the production of ferulic and p-coumaric acid, which promotes sugar release. The mutants had a larger xylan extraction capacity and enhanced saccharification, with a 62% increase in total sugars released, but they had a dwarf phenotype. The findings indicate a better knowledge of xylan biosynthesis in relation to ferulic acid binding processes and highlight the necessity of studying the xax1 gene function for improving saccharification [22]. A study by Petersen et al. [23] generated a knockout of Arabidopsis thaliana mutants perturbing the expression of GAX proteins. Such mutations reduced the amount of xylem in the plants and caused the xylem vessels to weaken and collapse inward, causing the plant’s morphology and fertility to suffer greatly. After hot water treatment, xylose concentration was 23% lower than in wild plants, and cellulose content was likewise lower, while saccharification rose to 42%. To lower biomass recalcitrance while not interfering with plant growth, relevant genes must be identified. Li et al. overexpressed rice genes involved in the production of the hemicellulose main chain (OSIRX9 and OSIRX14), and genes particularly engaged in the elongation of the hemicellulose side chain (OSXAT2 and OSXAT3). The results suggested that they were associated with a lower rate of cellulose crystallization. Furthermore, the same study discovered that rice mutants overexpressing the GH9B1 gene, which is involved in cellulose biosynthesis, help reduce cellulose crystallinity, which directly interferes with biomass

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saccharification, implying that GH9B1 is a point of interest for further understanding of hemicellulose biosynthesis and controlling saccharification [24].

3.3 Genetic modification in lignin biosynthesis Modification of genes involved in lignin production is one option for improving plant biomass saccharification. The heterogeneity of lignin, which has a high degree of structural variety, plays crucial physiological and defensive roles in plants. One of them is that it makes pathogens and predators’ digestion harder, reducing its availability for biotechnological activities. Proceeding from the shikimate pathway and providing phenylalanine and tyrosine as initial substrates for the formation of phenolic compounds, the genes of these pathways are key pieces to manage genetic engineering studies whose negative regulation induces specific modifications in the cell wall features. The initial step in the biosynthesis of lignin and other phenolic chemicals involved in plantdefensive responses is performed by phenylalanine ammonia-lyase (PAL). By transforming tobacco with a heterologous bean PAL2 gen, Elkind et al. [25] found a significant reduction in PAL through the co-suppression process. Such regulation resulted in a considerable drop in PAL activity. The drop in lignin content resulted in stunted growth and alterations in morphology, pigmentation, and pollen viability as a result of this regulation. These results indicate that regulating the PAL gene affects the formation of lignin and also the generation of phenolic chemicals implicated in many pathways critical to the normal functioning of the plant. The enzyme CAD performs the last step in the formation of monolignols during the final phase of the phenylpropanoid pathway. Pilate et al. [26] developed poplar plants with antisense CAD gene (ASCAD21 and ASCAD52) downregulation. The plants were healthy, with normal growth and appearance, and lesser lignin synthesis, indicating that the amount of lignin monomers in the wood composition had an impact on the gene’s negative regulation. Removal of the cellulosic pulp uses a smaller content of chemical compounds. In the same study, researchers used antisense downregulation of the COMT genes (ASOMT2B and ASOMT10B) in poplar to produce wood with improved cellulosic pulping properties, demonstrating the potential of genetic engineering to contribute to the improvement of wood for polysaccharide extraction. The downregulation of the Pt4CL1 gene, which encodes the enzyme 4CL from the Populus tremuloides, reduced lignin by around 45% while boosting cellulose levels by 15%. The plant developed thicker stems, longer internodes, and larger leaves, indicating that control of the 4CL family gene is an alternative target for the creation of trees ideal for cellulose extraction while having little effect on growth and structural integrity [27].

4. Technologies for genetic modification of the cell wall 4.1 Antisense oligonucleotide An antisense oligonucleotide (ASO) is a short, single-stranded deoxyribonucleotide (13–25 nucleotides) that is complementary to a target mRNA. In general, the approach’s goal is to

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negatively regulate a molecular target. Based on their modes of action, we may split them into two classes (a) RNase H-dependent oligonucleotides and (b) steric-blocking oligonucleotides, which include splicing inhibition and 50 capping inhibition. The transfer of information from DNA to protein appears to be controlled by ASO, but the mechanisms by which an oligonucleotide might cause a biological impact are nuanced and complicated [28]. The RNase H-dependent mechanism can be extremely effective, downregulating mRNA stability and protein translation by 80% to 95%. This endonuclease cleaves the RNA-DNA heteroduplex, reaching such a level of efficiency. Inhibition by splicing occurs in the maturation stage, as well as the inhibition of the 50 capping, thus causing the interruption of the polypeptide chain synthesis [29]. Paul Miller of Johns Hopkins University (Baltimore), a pioneer in oligonucleotide research, employed rabbit globin messenger RNAs as targets and rabbit reticulocyte lysates/wheat germ extracts as expression systems. After adding antisense oligonucleotides to the incubation medium, he discovered a clear suppression in the production of these proteins in wheat germ lysate [30]. Because native oligonucleotides have a limited affinity for proteins, he modified their backbone to improve their stability and dispersion in tissues. The first generation of ASO has modified phosphodiester bonds in one of the hydrogen atoms, with phosphorothioate oligonucleotides being the most extensively studied. The second generation has sugar moieties that have been modified, while the third generation has various phosphate groups. The study of such biotransformations is beneficial since the products of their metabolism might be hazardous to the organism of interest [31]. Antisense oligonucleotide technology is a legitimate molecular strategy for controlling gene expression in many species, with the notable benefit of being able to synthesize them swiftly and efficiently, making them available today. Studies enable the identification of new targets and chemicals that have particular linkages to RNA or DNA, proteins, and other molecules [32].

4.2 RNAi In addition to the oligonucleotides just discussed, interfering RNAs are another family of noncoding RNAs that can affect gene expression. We can classify RNA interference according to their origin and function in at least three categories: micro RNAs (miRNAs), short interfering RNAs (siRNAs), and short hairpin RNAs (shRNAs). Small double-stranded RNA molecules can effectively initiate RNAi silencing of certain genes, inhibiting gene expression during translation or hindering gene transcription. By actively incorporating itself into an intracytoplasmic complex, dsRNA binds to the complementary nucleotides of a sequence located on the target mRNA. This mechanism is responsible for posttranscriptional gene silencing (PTGS) by inhibiting translation and/or mRNA degradation. It was originally described in plants in the early 1990s. By studying transgenic petunias present in white flowers, even overexpressing genes for pigment production, scientists discovered that the inhibition of pigment synthesis occurred by silencing the transgene and the endogenous gene, a phenomenon known as co-suppression [33]. In the early 21st century, short RNA sequences were discovered to be a large class of tiny RNA regulators with dozens of species of animals and plants having representatives. miRNA

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are posttranscriptional silencers that hinder the translation of target mRNA. These are basic regulators of gene expression that include around 22 nucleotides. Its biogenesis begins with the transcription of its gene by RNA polymerase II, which results in the production of a transcript known as primary miRNA (pri-miRNA). Still in the nucleus, the RNase III cleaves the pri-miRNA to form the mature miRNA precursor molecule (pre-miRNA) with around 70 nucleotides that will enter the cytoplasm and undergo a series of pre-miRNA processing stages, resulting in a double strand of RNA with 22 nucleotides, the miRNA. This product will form a multimeric complex known as RISC (RNA-induced silence complex), with just one guide strand remaining to control the posttranscriptional expression of target genes by sequence complementarity. siRNAs are double-stranded molecules of 19 to 30 bp that act on specific gene degradation, similarly, to miRNAs, guiding endonucleolytic cleavage as a way of defending genome integrity in responses to foreign nucleic acids, such as transgenes, transposons, and viruses [34]. Short hairpin RNAs are part of a silencing mechanism encoded in a DNA vector, introduced into cells through plasmid transfection or viral transduction. Designed shRNAs are pre-miRNA cloned into viral vectors, having single-stranded molecules of 50 to 70 nucleotides forming loop structures. They also contain a rod-shaped part, which contains 19 to 29 pairs of double-stranded RNA. When transcribed, shRNA leaves the nucleus, is cleaved, and enters the RISC complex, where it directs mRNA degradation. One advantage of shRNA is that we can deliver them into difficult-to-transfect primary cells [35].

4.3 Transcription activator-like effector nucleases (TALEN) Transcription activator-like effector nucleases (TALENs) are nucleases that may introduce mutations, insertions, substitutions, or chromosomal rearrangements at particular genomic sites. The capacity of TALE-like proteins to bind DNA, revealed in 2007, is part of a vast family of effector proteins in Xanthomonas bacteria, which are recognized as agricultural plant diseases. These bacteria directly inject effector proteins into the cytoplasm of plant cells, which influence the biological processes and make plants more susceptible to pathogens. TALE proteins, which are similar to eukaryotic transcription factors, may bind to the plant DNA [36]. TALENs are plasmid-derived artificial chimeric restriction enzymes with a DNA-binding domain (DBD), a restriction endonuclease catalytic domain FokI (TALE family endonuclease), a C-terminal region with a nuclear localization signal (NLS), and an activation domain (AD) that will activate transcription in the host. Composed of 7 to 34 highly conserved modular repeats and 2 polymorphic amino acid residues located at positions 12 and 13, called variable repeat di-residues (RVD) responsible for recognizing a specific nucleotide, DBD is responsible for the precision of binding to the target DNA and the specificity of the host. Most investigations employ particular monomers for project-specific interactions with nucleotides of interest, such as Asn and Ile for adenine, Asn and Gly for thymine, two Asn residues for guanine, and His and Asp for cytosine. This makes it a “one-to-one” type of correspondence that constitutes a unique and more accurate code [37]. In plants, gene editing with TALENs has become a more viable application; studies in model plants such as tobacco and

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Arabidopsis, as well as barley, corn, rice, soybean, tomato, and wheat show that the genes submitted for gene editing induced a variety of heritable mutations and production improvements such as improved oil quality, disease resistance, storage tolerance, temperature adaptability, and so on, demonstrating the effectiveness of the tool [38]. The development of the TALEN technology is undeniably remarkable; the capacity to manipulate the genome in a simple and low-cost manner is one of the tool’s main draws, and its application in many domains and cellular models leads researchers to investigate its processes in more depth. Biosafety barriers continue to be a difficulty, and there are still unknowns regarding the optimum way of application and the precision of the mechanism to the target, which results in undesired mutations. However, genome editing technology using effector nucleases has the potential to broaden the range of science and applications by creating a new area for studying metabolic pathways and developing novel products in various cellular models [39].

4.4 CRISPR/Cas9 In 2013, emerged is the gene editing technology CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPER-associated proteins) system. It has gained popularity due to its ease of manipulation and excellent efficiency. It was discovered as a component of bacterial adaptive immunity, and it consists of a Cas nuclease that cleaves the DNA of an invading phage with a programmable CRISPR RNA (crRNA) and a fixed transactivating crRNA (tracrRNA) before allocating it to a specific region of the bacterium genome known as the CRISPR locus. When infected again by the same phage, the bacteria will create an RNA from the stored sequence, bind it with the Cas enzyme, target the viral DNA, cleave it in the corresponding region, and therefore inactivate it precisely after the invading DNA’s target sequence (Fig. 4) [40]. There are three types of CRISPR/Cas systems (I, II, and III). Types I and II require multiple proteins to compose all Cas mechanisms, whereas type II only requires one, the Cas9 protein, with a single-guide RNA (sgRNA) that acts as a fusion of crRNA and tracrRNA, making it useful for genetic engineering applications [41]. CRISPR/Cas9-mediated sequence-specific cleavage requires a protospacer of DNA sequence corresponding to sgRNA, dubbed PAM (protospacer adjacent motif) containing an adjacent guide sequence of three nucleotides NGG (N meaning any nucleotide and G for guanine) which guides Cas9 to base pairing, differentiating the viral DNA from its own and preventing the CRISPR locus from being targeted and destroyed by the nuclease [42]. In recent years, the CRISPR/Cas9 system has evolved into an effective technique for straightforward gene editing, with broad applications. CRISPR makes it easy to edit target genes to investigate biosynthetic pathways and their regulatory mechanisms, and a wide spectrum of RD&I has employed CRISPR/Cas9 to increase our understanding of molecular biology as well as modify animals to achieve desirable phenotypes through genome alteration. Such unique properties make CRISPR/Cas9 a game-changing technology, elevating gene modification to a new level. Nonetheless, there remain obstacles to overcome, such as a better understanding of the entire process and how to improve its accuracy [43].

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FIG. 4 The CRISPR/Cas system. Adapted from Horvath P, Barrangou R. CRISPR/Cas, the immune system of bacteria and archaea. Science 2010;327(5962):167–70. https://doi.org/10.1126/science.1179555. PMID: 20056882.

5. Final considerations CRISPR, TALEN, RNAi, and antisense oligonucleotides are examples of molecular biology methods that have been used to advance genetic engineering. Such methods enabled a better understanding of the functions of each component in the construction of the cell wall architecture when applied to cell wall biosynthetic pathways. These new technologies also allow us to alter cell wall characteristics to optimize polysaccharide enzymatic saccharification and, as a result, biofuel generation. The rapid increase in planetary repercussions of oil misuse, such as microplastic buildup in seas and human bodies, as well as the severe climate changes and extreme occurrences caused by excessive CO2 emissions from fossil fuels, need immediate adjustments in our energy sources. The sun’s energy is stored in photosynthates, which

References

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highlight plant lignocellulose, and accumulates 10 times quicker than our present energy demands, making it the only renewable source capable of replacing oil in the massive proportions that we require today. Molecular methods to examine how plant lignocellulose organizes have yielded vital knowledge on cell wall organization and promise to deliver revolutionary agroenergetic consequences, notwithstanding their early stages. These accomplishments demonstrate genetic engineering’s incredible potential for civilization and underline the significance of investing in the field.

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[18] Arioli T, Peng L, Betzner AS, Burn J, Wittke W, Herth W, Williamson RE. Molecular analysis of cellulose biosynthesis in Arabidopsis. Science 1998;279(5351):717–20. https://doi.org/10.1126/science.279.5351.717. DOI:10.1126/science.279.5351.71. [19] Harris D, Stork J, Debolt SETH. Genetic modification in cellulose-synthase reduces crystallinity and improves biochemical conversion to fermentable sugar. GCB Bioenergy 2009;1(1):51–61. https://doi.org/10.1111/j.17571707.2009.01000.x. [20] Huang J, Xia T, Li G, Li X, Li Y, Wang L. Overproduction of native endo-β-1,4-glucanases leads to largely enhanced biomass saccharification and bioethanol production by specific modification of cellulose features in transgenic rice. Biotechnol Biofuels 2019;12(1):1–15. https://doi.org/10.1186/s13068-018-1351-1. [21] Vermerris W, Abril A. Enhancing cellulose utilization for fuels and chemicals by genetic modification of plant cell wall architecture. Curr Opin Biotechnol 2015;32:104–12. https://doi.org/10.1016/j.copbio.2014.11.024. [22] Chiniquy D, Sharma V, Schultink A, Baidoo EE, Rautengarten C, Cheng K, Ronald PC. XAX1 from glycosyltransferase family 61 mediates xylosyltransfer to rice xylan. Proc Natl Acad Sci U S A 2012;109(42):17117–22. https://doi.org/10.1073/pnas.1202079109. [23] Petersen PD, Lau J, Ebert B, Yang F, Verhertbruggen Y, Kim JS, Scheller HV. Engineering of plants with improved properties as biofuels feedstocks by vessel-specific complementation of xylan biosynthesis mutants. Biotechnol Biofuels 2012;5(1):1–19. https://doi.org/10.1186/1754-6834-5-84. [24] Li F, Zhang M, Guo K, Hu Z, Zhang R, Feng Y, Peng L. High-level hemicellulosic arabinose predominately affects lignocellulose crystallinity for genetically enhancing both plant lodging resistance and biomass enzymatic digestibility in rice mutants. Plant Biotechnol J 2015;13(4):514–25. https://onlinelibrary.wiley.com/doi/10. 1111/pbi.12276. [25] Elkind Y, Edwards R, Mavandad M, Hedrick SA, Ribak O, Dixon RA, Lamb CJ. Abnormal plant development and down-regulation of phenylpropanoid biosynthesis in transgenic tobacco containing a heterologous phenylalanine ammonia-lyase gene. Proc Natl Acad Sci 1990;87(22):9057–61. https://www.pnas.org/doi/abs/10. 1073/pnas.87.22.9057. [26] Pilate G, Guiney E, Holt K, Petit-Conil M, Lapierre C, Lepl
e JC, Halpin C. Field and pulping performances of transgenic trees with altered lignification. Nat Biotechnol 2002;20(6):607–12. https://doi.org/10.1038/ nbt0602-607. [27] Hu WJ, Harding SA, Lung J, Popko JL, Ralph J, Chiang VL. Repression of lignin biosynthesis promotes cellulose accumulation and growth in transgenic trees. Nat Biotechnol 1999;17(8):808–12. https://doi.org/10.1038/11758. [28] Crooke ST. Molecular mechanisms of antisense oligonucleotides. Nucleic Acid Ther 2017;27(2):70–7. https:// www.liebertpub.com/doi/10.1089/nat.2016.0656. [29] Dias N, Stein CA. Antisense oligonucleotides: basic concepts and mechanisms. Mol Cancer Ther 2002;1 (5):347–55. [30] Cravador A. Antisense synthetic DNA. New Chem 1998;21(4):441–52. https://doi.org/10.1590/S010040421998000400013. [31] Gaynor JW, Cosstick R. Dinucleotides containing 30 -S-phosphorothiolate linkages. In: Therapeutic Oligonucleotides. New Jersey: Humana Press; 2011. [32] Kilanowska A, Studzi
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C H A P T E R

3 The diversity of plant carbohydrate hydrolysis in nature and technology Marcos S. Buckeridge Institute of Biosciences, Department of Botany, University of Sa˜o Paulo, Sa˜o Paulo, Brazil

1. Introduction Hydrolysis is the cleavage of the glycosidic chemical bonds between sugars (monosaccharides), the basic elements that form carbohydrate polymers and oligomers. They are sucrose/ raffinose, starch, and the plant cell wall polymers such as cellulose, hemicelluloses, and pectins. These compounds’ hydrolysis may occur internally, i.e., within plant cells (including the extracellular matrix), here named endogenous hydrolysis, and externally by microorganisms and insects, called exogenous hydrolysis. This chapter examines the degradation of oligo- and polysaccharides that occur in plants. The endogenous hydrolysis consists of mechanisms that plants display to degrade their walls. They involve molecular and biochemical systems that express genes and produce specific enzymes deployed precisely and timely to degrade a single polymer (e.g., starch and storage cell wall degradation) or part of the wall. This process will produce compounds that perform physiological functions, as in the case of the formation of aerenchyma in several plant tissues. The exogenous form of polysaccharide degradation occurs in organs or tissues discarded by the plant (leaves in a decidual forest, for instance), and the degradation of the polymers is performed by microorganisms such as fungi or bacteria. Alternatively, predation of plant organs (e.g., fruits and seeds) and tissues can also occur, leading to the degradation of the polymers within the digestive systems of other organisms. This is the case with oligosaccharides (sucrose) and polysaccharides (starch and cell wall polymers). Another exogenous degradation can occur when humans use plant materials (tissues, organs, or the entire plant body) for biotechnological purposes. One example is the use of enzymes to degrade sucrose, starch, or cell wall polysaccharides hydrolysis, followed by yeast fermentation and bioethanol production. Another is the direct use of cell wall polysaccharides to produce biomaterials.

Polysaccharide-Degrading Biocatalysts https://doi.org/10.1016/B978-0-323-99986-1.00015-6

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Copyright # 2023 Elsevier Inc. All rights reserved.

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3. Diversity of carbohydrate hydrolysis

Acid hydrolysis is an option to obtain a first approach to the composition of the plant’s oligo- and polysaccharides. In this case, precision and accuracy are of high relevance. Both internal and external hydrolyses are involved in critical processes in nature and industry, and understanding hydrolysis mechanisms add valuable knowledge to humanity. This chapter discusses the hydrolysis of oligo and polysaccharides from plants using a few examples. The intent is not to produce an exhaustive revision of publications in the area but to explain some of the basic principles of hydrolysis and its applications, guiding the reader to further search for deeper information.

2. Types of hydrolysis Oligosaccharides and polymers can be hydrolyzed by two main routes: acid and enzymatic.

2.1 Acid hydrolysis The acid route involves the protonation of the glycosidic oxygen via the acid catalyst action, with water functioning as a nucleophile. For hydrolysis of the glycosidic linkage and the subsequent formation of two free monosaccharides (if a disaccharide is dissolved in aqueous acid, for instance), the reaction must be performed at a high temperature. The hydrolysis reaction involves the incorporation of one molecule of water for each glycosidic linkage, therefore the name hydrolysis. Hydrolysis will occur in different ways depending on the temperature, the incubation time, and the acid type. Usually, adequate conditions for acid hydrolysis are associated with using sulfuric or trifluoroacetic acid (TFA). For efficient hydrolysis of stronger linkages (beta-1,4 glucosyl linkages in cellulose, for instance), sulfuric acid can be used with a “prehydrolysis” step with higher acid concentration (3%) followed by dilution to 1% and hydrolysis for 1 h at 100°C or in an autoclave for 1 h at 121°C. This method is generally named Saeman hydrolysis [1]. Other acids (e.g., HCl) can be used. They may be efficient, but TFA and H2SO4 have been widely adopted for practical purposes. They afford adequate preservation of the hydrolysis products (monosaccharides) and a more accurate interpretation of the composition of polymers, especially heteropolysaccharides. For acid hydrolysis of plant polysaccharides containing weaker glycosidic linkages (alpha linkages) such as starch or cell wall polymers branched with the monosaccharide’s galactose and arabinose, TFA is the adequate acid [2]. Typically, a previous study must be made varying acid concentration and time of hydrolysis at 100°C (or different temperatures). The rates of recovery of monosaccharides biomass after hydrolysis (% of biomass hydrolyzed) must be as high as possible. The most common combination of monosaccharides, as the ones in polymers such as galactomannans, xyloglucans, and arabinoxylans, can be efficiently hydrolyzed with TFA incubation at 100°C for 3 h. Even under relatively mild acid hydrolysis conditions, free sugars are released at different speeds into the water solution. The acid usually attacks other linkages within the monosaccharide’s molecules, degrading them and forming other compounds. Furfurals and hydroxymethyl-furfurals are the main ones, giving rise to a yellow color in the solution.

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2. Types of hydrolysis

Under mild controlled conditions, even heteropolysaccharides (polysaccharides with different monosaccharides like the ones cited above) can be hydrolyzed to produce a water-soluble mixture rich in monosaccharides. Acid hydrolysis followed by monosaccharide analysis has been a way to discover which monosaccharides form a given polymer. In the cases of homopolymers such as cellulose and starch, glucose alone will be produced after acid hydrolysis, for instance. Care must be taken with alpha linkages since they are broken more easily under acid hydrolysis than beta linkages. The rapid release of monosaccharides to the acid solution leads to the degradation of these compounds, causing loss of monosaccharides and leading to loss of sugar and bias in the results.

2.2 Enzymatic hydrolysis Enzymatic hydrolysis consists of the attack of proteins named hydrolases onto the glycosidic linkages. The mechanism also includes incorporating water (hydrolysis) and the release of monosaccharides (or/and oligosaccharides) in a water solution. Many glycosyl hydrolases (GH) have been characterized and cataloged [3] (http://www.cazy.org/GlycosideHydrolases.html). To date, 173 GH families have been registered in the Cazy database. GHs are proteins that originate from the expression of genes belonging to the genome of microorganisms (bacteria, viruses, fungi), insects and other animals, and plants. The Cazy database is biased toward microorganisms’ enzymes due to the facility to detect and characterize genes and enzymes from these organisms. The diversity of cleavage modes of the glycosidic linkages of sugars is documented in Cazypedia [3]. Relevant to this chapter are the modes of action named exo and endo (Fig. 1). The exo-acting GHs usually cleave the linkages of the extremes of the polymer. Exo-hydrolases can attack small molecules containing sugars (e.g., phenolic compounds esterified with mono- or oligosaccharides),

ENDO- & EXO- ACTIONS OF HYDROLASES Non Reducing End

Glc

Glc

Glc

Reducing End

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Endo ac vity

Exo ac vity

FIG. 1

Illustration, using cellulose of exo and endo action on polysaccharides. Exo-enzymes can attack the nonreducing (more frequent) and the reducing end of polysaccharides. Endo-activity varies depending on the specificity. Generally, endo-enzymes attack the glycosidic linkages within the main chain of polysaccharides. However, depending on the branching (galactose residues in galactomannan, xylosyl, galactosyl, and fucosyl residues in xyloglucan, for instance), endo-enzymes may break specific linkages. Two examples are the endo-beta-glucanases that degrade xyloglucan (attacks only unbranched regions of the polymer) and lychenase (attacks the 1,4-linkages of a glucose only besides a beta-1,3 linkage in the main chain of the polymer).

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3. Diversity of carbohydrate hydrolysis

oligosaccharides (e.g., sucrose and raffinose), and large polymers (e.g., starch and cellulose). Regarding polymers, the most frequently found exo-hydrolases cleave the nonreducing end of the polymers, producing a single monosaccharide (but some exo-enzymes can produce disaccharides as well). Some GHs can also hydrolyze the reducing end, but they are less frequent among the known GHs. Endo-hydrolases can cleave the glycosidic linkages in the middle of a long carbohydrate chain (starch and cellulose, for instance), forming smaller but still relatively long polymers (Fig. 1). These fragments can be further attacked by endo- and exo-hydrolases so that the polymer is completely dismantled to its monosaccharides. Under natural conditions, microorganisms use this strategy to acquire mono- or/and oligosaccharides that they can use as a source of energy for growth [4]. Plants can use the same strategy in seeds and subterranean organs to feed plant growth and development [5].

3. Plant sugars In plants, two are the general sugar chain types: oligosaccharides (containing 2–10 monosaccharides) and polysaccharides (containing more than 10 monosaccharides). Among the oligosaccharides, sucrose and raffinose are the most widely found, whereas the main polysaccharides are starch and cell wall polymers. Sucrose and raffinose are transport sugars but can also be stored in seeds and subterranean organs. These sugars may be part of the diet of microorganisms and animals, playing an important role in ecological relations in nature.

3.1 Sucrose, raffinose, and fructans In most plants, sucrose (beta 2,1 linked glucose + fructose) is the disaccharide formed after glucose production from photosynthesis. The galactinol synthase may transform sucrose into raffinose by adding a galactosyl residue to the carbon 6 of the glucose residue (linked 1,6 to the glucosyl moiety). A series of galactosyl residues may be added sequentially, all alpha-1,6 linked, named raffinose series (or family) oligosaccharides. In these cases, invertase and alpha-galactosidase are the hydrolytic enzymes that break the two oligosaccharides into their monosaccharides (Fig. 2). Sucrose is well-known as a sweetener in the human diet. However, the disaccharide cannot be absorbed by human cells, and its hydrolysis by intestinal invertase leads to the production of glucose and fructose that serve as sources of energy for the functioning of mitochondria and therefore are directly related to cellular respiration and life maintenance [6]. Alternatively, to the raffinose family oligosaccharides, sucrose molecules can be “elongated” with fructose moieties, giving rise to a class of polymers collectively named fructans. They are formed by the elongation of chains of fructose units that, when displaying up to 10 monosaccharides, are called fructan oligosaccharides (FOs). When there are more than 10 monosaccharides, it is named a fructan polysaccharide. The most well-known fructan polymer is inulin, from chicory, which is a commercial product. Fructan polymers are also relevant in Agave plants, where they serve as the raw product for tequila production [7]. Fructans function as storage carbohydrate polymers in plants, and transferases rather than

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3. Plant sugars

A – hydrolysis of sucrose

B – hydrolysis of raffinose Fru

Fru Glc

Acid or enzyma c hydrolysis by INVERTASE

sucrose

Glc

Fru

Fru

Fru Glc Gal

Glc

Enzyma c hydrolysis by INVERTASE

sucrose

Enzyma c hydrolysis by galactosidase

Glc

raffinose

Gal

FIG. 2 Hydrolysis of sucrose and raffinose. Sucrose is broken in glucose and fructose by invertase. Raffinose requires action of an alpha-galactosidase that produces sucrose that is then degraded by invertase.

hydrolases mainly perform their metabolism. The exception is the long polymers that fructan hydrolases can hydrolyze. Thus, because this chapter deals with hydrolysis, fructans will not be discussed further. For a recent review of fructan’s structure and metabolism, refer to Yoshida [8].

3.2 Starch With ongoing photosynthesis, most plants and algae accumulate starch in the chloroplasts. Overnight, the stored starch is degraded, and its products are transported to the growing cells and plant tissues. While photosynthesis produces glucose, part of it is transformed into fructose, ending up in sucrose or/and the raffinose series oligosaccharides, which are transported to the plant’s growing regions, where they are consumed basically for three purposes: (1) respiration; (2) forming the plant body (leading to cell walls, proteins, and lipids); and (3) long-term storage. Starch is a polymer formed basically by a linear polymer of glucose molecules attached by alpha-1,4-glycosidic linkages. Other amylose chains may branch these linear chains (amylose) through alpha-1,6 linkages (amylopectins). A network of biochemical and cellular processes leads to the formation of starch granules whose diversity is enormous [9]. The physiological conditions of the plant also influence such diversity. One example is the findings by Zhao et al. [10] that, in maize, different levels of N affect the size and morphology of the granules. When naturally assembled in the granules, the amylose chains interact, forming crystalline structures interspaced with amorphous structures [11]. Together, these complexes emerge as partly crystalline, partly amorphous lamellae [12]. Such a diversity of structures interferes with the hydrolysis of starches since the polymers associate in different ways. Starch hydrolysis occurs in different ways in nature by the action of different enzymes (Fig. 3). Starch degradation differs according to the organ (see reviews by Smith et al. [13,14] and Smith [15]). In leaves, starch granules located in the chloroplast stroma are thought to be firstly attacked by Glucan, Water Dikinase (GWD), and Phosphoglucan, Water Dikinase (PWD), which phosphorylate—with consumption of ATP—the surface of the granule. Through a cycle of phosphorylation-dephosphorylation (the later performed by the enzymes

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3. Diversity of carbohydrate hydrolysis

Starch granule

STARCH HYDROLYSIS

Glucan Water Dikinase GWD

glucosidase

Glc

Non Reducing End

Glc

Glucoamylase

Phosphoglucan Water Dikinase PWD

Amylose chain

Glc

maltose

Glc

E-amylase (leaves) (successive exo)

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Glc

Debranching Enzyme ISA3 Limit Dextrinase Glucan Phosphorilase PHS1

Glc

Glc

Glc

Glc

Glc-1P

amylase (endo) D-enzyme (DPE1)

Glc

Glc

Glc

Glc

Glc

Amylopecn

Reducing End

Glc

Glc

leaves seeds leaves and seeds microorganisms

maltotriose

FIG. 3 Routes of degradation of starch. The alpha linked polymer is degraded for several enzymes, whose identity varies according to the organism. As starch is a short- (in leaves) as well as a long-term storage (in seeds and subterranean organs) in plants, different enzymes evolved to efficiently degrade the polymer. Microorganisms use analogous enzymes but evolved independently (to our knowledge) from the plant enzymes. Starch needs a prior attack of dikinases that disrupt the crystal structure of the granule and opens the way for hydrolases such as debranching enzymes, alpha- and beta-amylases, alpha-glucosidases, amyloglucosidases, and phosphorilases. The products of starch hydrolysis are free glucose (via maltose) or glucose-1-phosphate which will be used by the plant or other organisms (microorganisms and animals).

Starch Excess 4 (SEX4) and Like Sex 4 (LSF)), starch molecules (amylose and amylopectin) become susceptible to hydrolysis. Maltose and maltotriose will be released through the action of isoamylase (ISA) and beta-amylases. Maltose is then transported to the cytosol, where it is degraded by one of the disproportionating enzymes (DPE2), which transfers one glucose to a cytosolic heteroglycan (probably an arabinogalactan) and produces one free glucose that is phosphorylated. Another disproportionating enzyme (DPE1) is present in the chloroplast and acts on maltotriose. In the cytosol, together with the phosphorylation of the glucose attached to the heteroglycan by cytosolic glucan phosphorylase (PHS2), hexose phosphate molecules will go forward within the metabolism to the formation of sucrose. Since starch is a long-term storage polysaccharide in seeds, its hydrolysis in cotyledons of eudicots and endosperms of grasses has been studied and can be compared to the degradation in leaves [13]. In seeds, starch is stored in amyloplasts, which evolutionarily derive from the chloroplasts [16]. The degradation in cotyledons, which resemble leaves, starts in the amyloplast, where GDW attacks granules, followed by limit dextrinases and alpha-amylase. Regarding the endosperms, no amyloplasts are present since these tissues are usually nonliving, and granules are degraded directly by limit dextrinase and alpha-amylase. This process is followed by beta-amylase, generating maltose. In this case, free glucose is produced by alpha-glucosidases, an exo-enzyme.

3. Plant sugars

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A glucan phosphorylase will produce glucose-1-phosphate in the cytosol, which leads to sucrose metabolism. Therefore, the general hydrolysis process appears to require an initial phosphorylation step in leaves and long-term starch-storing organs and tissues. The phosphorylation appears to be a step necessary to “break” the crystallinity of the starch granule so that hydrolases can gain access to the polysaccharides. In the human body, starch is initially hydrolyzed by a salivary alpha-amylase. As the digestion progresses, the more quantitatively significant degradation of starch occurs in the small intestine, made by the pancreatic amylase. The reaction products are maltose, maltotriose, and maltodextrins. These products will be further hydrolyzed by maltase (alpha-glucosidase) in the brash border, where glucose is produced and absorbed by the human digestive system [17]. The association with microorganisms is highly flexible, with different groups of species acting depending on the type of starch under degradation. Differently from humans and other animals, in ruminants, starch is firstly attacked by amylases from the salivary gland and further degraded by mechanisms present in the microorganisms of the rumen but still complemented by the enzymes present in the digestive systems [18,19]. Birds can also digest starch efficiently [20] through the production of alpha-amylase, which occurs in the jejunum and is thought to degrade some types of starch completely. The hydrolysis mechanism used when starch needs to be degraded in vitro [21] to determine its content in different plant tissues is like the latter. In these cases, first, it is necessary to swell the granules so that alpha-amylase gains access to the surface without needing phosphorylation. An assay follows this with alpha-glucosidase, and the release of free glucose is used to estimate how much starch is present.

3.3 Cell wall polysaccharides Plant cell walls are among the most complex assemble of polysaccharides on Earth. Its complexity is associated with a Glycomic Code, which reflects the molecular structures and interactions derived from biological evolution [22]. The Glycomic Code limits hydrolysis [23] making the cell walls hard to hydrolyze, even by plants themselves. However, natural walls are not impenetrable, and degradation mechanisms, most involving hydrolysis, occur both in endogenous and exogenous ways (Fig. 4). Analogously to starch, cell walls can be degraded by plants, animals, and microorganisms. However, similar to starch, sophisticated mechanisms are necessary for complete degradation. There are no known mechanisms in a single organism that display “complete efficiency,” i.e., that would be able to completely degrade all the polymers of a given cell wall into their basic components. Plants hardly do it except for unique walls (the storage walls) that have been specially shaped during evolution for complete hydrolysis (see below). 3.3.1 Exogenous degradation of cell walls: Hydrolysis by microorganisms and animals Exogenous degradation of the cell walls occurs in a series of situations. Cell walls are completely degraded in the field since the biomass disappears entirely after a period [24]. However, the degradation of cell walls in nature is poorly known [25], with the diversity

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3. Diversity of carbohydrate hydrolysis

HYDROLYSIS DIVERSITY ON EARTH Galacto(gluco)mannans

Xyloglucan

Galactan

Cosme cs Paper

Beta-glucan

Food Eudicots

Grasses

Sucrose Raffinose Fructan

Natural (?) ENZYMATIC

Cell Wall Storage Polysaccharides Starch/Fructan

Industry

ACID

BIOTECHNOLOGY Aerenchynma

hindguts

Fruit ripening Abscission Short term

Compo on analysis

Bioenergy

Ruminants

Viruses

Long term Fungy

STRUCTURAL

Protozoa

Insects Bacteria

ANIMALS MICROORGANISMS

STORAGE

HYDROLYSIS

FIG. 4 Several pathways of carbohydrate hydrolysis evolved in the biosphere. Plants can endogenously degrade sucrose, starch, and storage cell wall polysaccharides efficiently. However, cell walls (the main source of polysaccharides on earth) can be degraded only partially, these mechanisms being related to plant developmental processes. Exogenous hydrolysis is crucial for the carbon cycle in the atmosphere (see Fig. 7). Animals and microorganisms degrade carbohydrates using different mechanisms. In several cases, these mechanisms are used through mutualistic interactions, what increases the efficiency of hydrolysis considerably. Another exogenous pathway of hydrolysis is under development by biotechnology. Using acids or enzymes (recombinant or native enzymes), industrial applications have been developing rapidly mainly in the XX and XXI Centuries.

of microorganisms and animals capable of degrading cell wall polymers being quite limited. According to Wilson [25], most knowledge about cell wall degradation by microorganisms comes from the mechanisms found in ruminants and composts. The author points out that microorganisms display high specificity for different cell wall polymers (cellulose, mannans, xyloglucan, xylans, and lignin). Thus, the range of cell wall degrading mechanisms in nature is still under construction, with only a few examples being known [26]. Figs. 5 and 6 illustrate some plant polysaccharides and respective hydrolases involved in processes of exogenous (by microorganisms and animals) and endogenous (by plants) hydrolysis. Such specificity has been illustrated by comparing the cell wall-related proteins produced by two fungal species during the degradation of the same biomass (sugarcane) [27]. In this study, the authors demonstrated that Trichoderma resei and Aspergillus niger display different strategies to degrade the sugarcane cell wall. Whereas the former attacks mainly cellulose, the second exerts a more integral attack on the cell walls, with proteins such as pectinases being produced along with cellulases and hemicellulases.

FIG. 5

Examples of modes of action of hydrolases on cellulose and hemicelluloses of plants. The number of enzymes discovered to date is quite high (see comments in text about Cazy [3]), and the hydrolysis points shown in this figure is merely illustrative.

Non Reducing End

Endo-D-polygalacturonase

GalA

Me D-methyl esterase

– 1,4

GalA

GalA

Rham

GalA

GalA

GalA

Rham

GalA

GalA

Rhamnogalacuronan I

Ara

Gal

GalA

Reducing End

PECTIN HYDROLYSIS

E – 1,3 or 1,4

Ara

Gal

Gal

D-arabinofuranosidase

Ara -arabinanase

Gal

Ara

Ara Ara

Gal

Ara

Gal

D-arabinofuranosidase

E-galactosidase

Ara

Gal arabinogalacan

arabinan

Non Reducing End

FIG. 6 Examples of modes of action of hydrolases on pectin. Here, only rhamnogalacturonan I is shown. The pectins display very high complexity and little is known about several of the glycosidic linkages of, for example, rhamnogalacturonan II. The number of enzymes discovered to date is quite high (see comments in text about Cazy [3]), and the hydrolysis points shown in this figure is merely illustrative.

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3. Diversity of carbohydrate hydrolysis

The degree of difficulty for cell wall hydrolysis is, on the one hand, related to crystallinity and, on the other hand, to the heterogeneity of its polymer composition. Among animals, ruminants are the most studied, but hindgut [28] and insects [29] have been among other studies subjects. In all cases, microorganisms (bacteria, fungi, and protozoa) play a role in cell wall degradation through a symbiotic relationship with their hosts. The variety of mechanisms is enormous, reflecting the diverse ways evolution has produced hydrolysis mechanisms in plants, animals, and microorganisms. It is reasonable to suppose, at least based on the knowledge available that multiple organisms perform cell wall hydrolysis in nature in different ways. Although further studies on the evolutionary aspect of cell wall hydrolysis are necessary, it is possible to suppose that different mechanisms evolved independently in distinct groups of organisms. Similarly to starch, cellulose crystallinity is a significant barrier to hydrolysis. Cellulose is a linear polymer formed by chains of beta-1,4-glucans that bind to each other, forming a crystal structure. In general, 36 glucan chains are aggregated into structures named microfibrils [30,31]. In some cases, such as grasses, macro-, instead of microfibrils, are formed in which the latter is associated in “packages” of seven, leading to a structure containing hundreds of molecules [32]. The nature of the crystals prevents interaction with water so that in natural assemblies of cellulose, hydrolysis is not possible. A relatively recent discovery was an enzyme named lytic polysaccharide monooxygenases (LPMOs), or auxiliary activity (AA) which uses a redox mechanism thought to disrupt the crystal structure of cellulose microfibrils and, with this, opening the way for the action of cellulases and other glycosidases (for reviews see Wang et al. [33] and Karnaouri et al. [34]). LPMOs act on substrates such as cellulose, hemicellulose, chitin, xylans, and starch (as reviewed by Wang et al. [33]). The auxiliary character of LPMOs is related to the fact that when this enzyme is present with hydrolases, it accelerates the degradation of several polymers (e.g., [35]). In a sense, LPMOs seem to be analogous to the Glucan Water Dikinase (GWD) mentioned above as a gate opener for starch hydrolysis. It is, therefore, reasonable to assume that for the hydrolysis of polysaccharides—molecules that are quite interactive—to occur, the action of enzymes capable of phosphorylation and oxidation is necessary. In the case of LPMOs, the glycosidic linkage is broken, causing the production of a lactone (after oxidation of carbon 1) or a ketolactone (after oxidation of carbon 4), both leading at the same time to the liberation of free sugars. After polysaccharides, such as cellulose, xylan, and xyloglucan, become accessible to hydrolases, they will be attacked by groups of endo- and exo-hydrolases that will reduce the polymers to their corresponding monosaccharides. In the case of cellulose, endo-beta-glucanases, cellobiohydrolases, cellodextrinases, betaglucosidases, and cellodextrin phosphorylases have been detected [29]. Acting synergistically, these enzymes can disassemble cellulose to produce free glucose, oxidized glucose (aldonic acid, for instance), and phosphorylated glucose. The other two classes of cell wall polysaccharides are hemicelluloses and pectins. Plant cell walls have been classified in two ways: developmental and structural. One type of classification (based on development) is the division between primary and secondary walls. The primary wall is defined as the first wall deposited when cells divide. For instance, this wall can be modified by the accretion of polymers that will change cell

3. Plant sugars

65

function. A typical example is the secondary deposition of cellulose on top or primary walls that form the xylem of plant vascular systems. The phloem also receives a secondary deposition of callose—a beta-1,3-glucan—which can also be classified as secondary. Another type of secondary wall that is not usually considered so but indeed represents a secondary deposition is the storage wall. Seeds and subterranean organs are usually the sites where this type of secondary wall is present. Another classification, based on structure, was proposed by Carpita and Gibeaut [36]. They divided cell walls into two types (I and II), which differ by the proportion of hemicelluloses and pectins and, simultaneously, by the type of hemicellulose. In type I walls (mostly eudicotyledons species), the main hemicellulose is xyloglucan, a polymer formed by a glucan chain like cellulose but with branching units of xylose, galactose, arabinose, and fucose. In contradistinction, Type II walls (characteristic of the grasses) display glucuronoarabinoxylan as the main hemicelluloses. Xylans are beta-1,4-linked polymers with decorations of arabinosyl and glucuronosyl residues. Also, Type II walls contain significantly less pectin than Type I. In 2013, Silva et al. [37] discovered that a large group of ferns display Type III walls containing mannan as the main hemicellulose subclass. Another independent classification referring to the secondary walls but comparing gymnosperms with angiosperms is the occurrence of glucomannan in the wood of the former and xylan in the wood of the latter. 3.3.2 Endogenous degradation of cell walls Hydrolysis of the cell walls described above within the plants is limited. Grandis et al. [38] and Tavares et al. [23] reviewed some of the primary endogenous hydrolytic systems studied. The processes in which cell wall hydrolysis has been more evidently characterized in plants are (1) abscission, (2) fruit ripening, (3) aerenchyma formation, and (4) long-term storage heteropolysaccharides mobilization (Fig. 4). 3.3.2.1 Abscission

Abscission is the detachment of plant organs such as leaves, fruits, and flowers [39]. During abscission, the main targets are the polysaccharides of the middle lamellae (pectins) since the process consists of cell separation and the attack to other wall domains is limited. Several hydrolases are involved in the abscission of flowers and floral parts [40,41]. Most of the studies concluded that flower and fruit abscission result from middle lamella solubilization followed by cell wall degradation in some cases (see [40] for a review of the species studied for flowering). The general view to date is that in the abscission zone, cell separation is performed by pectinases, and some actions of cellulases are critical in the abscission processes. 3.3.2.2 Fruit ripening

Fruit softening (in fleshy fruits) is thought to occur due to loss of cell adhesion and loosening of the cell walls. The modifications in the cell walls include depolymerization and solubilization of wall polymers, in some instances, including a decrease in molecular weight of detectable polymers (e.g., [42]). In fruit ripening, the cell wall changes have been studied in several species (see [43], for a review). The main targets are the pectins, especially the ones in the middle lamella and hemicelluloses. The principal model for fruit ripening is tomato [43,44], a climacteric fruit (ripening induced by ethylene) that undertakes depolymerization and solubilization of pectic and

66

3. Diversity of carbohydrate hydrolysis

hemicellulosic (xyloglucan) polysaccharides. Although model species help understand the basic mechanisms of cell wall degradation in fruits, the diversity of mechanisms is becoming accessible slowly [43]. These authors compiled the literature on cell wall changes, hydrolases, and gene expression in fruits of many species. During ripening, several enzymes have been detected in fruits [42,43,45,46]. Many of these enzymes are related to pectin degradation (endo- and exo-polygalacturonases, arabinanases, and galactanases), which are thought to be responsible for the loss of cell adhesion within fruit tissue. At the same time, hemicellulases, mainly related to xyloglucan hydrolysis (endoglucanases, alpha-xylanases, beta-galactosidases, and beta-glucosidases), are thought to be responsible for wall loosening along with expansins. The latter are not enzymes but proteins that can disrupt hydrogen bonds responsible for polymer interactions, such as the binding of xyloglucan to cellulose. Cellulases sometimes are present, but cellulose hydrolysis is hardly detected during fruit ripening. The modifications of pectins with the dissolution of the middle lamella during fruit ripening have been structurally characterized by Pos
e et al. [47] confirming that this biological process can be characterized by an essential step of cell adhesion disruption, which is the first step that leads to softening during ripening. Biologically, the modifications in the fleshy fruit tissue are responsible for the efficiency of seed dispersion in nature. From a human viewpoint, this process’s commercial and nutritional value is enormous. If, on the one hand, understanding what happens in the cell walls of fleshy fruits is ecologically important, the comprehension of the hydrolysis processes involved in ripening is vital for modern biotechnology. 3.3.2.3 Aerenchyma

Plants adapt to the stresses of drought and flooding by forming gas spaces (aerenchyma) that provide oxygen to submersed plant tissues, mainly in the roots [48]. Many other adaptations, such as opening spaces in leaves (in several species of Araceae), are thought to increase structural efficiency in the environment. Furthermore, aerenchyma appears in fleshy fruits during ripening and within the parenchyma of most eudicots leaves, where spaces allow higher efficiency to capture CO2 for photosynthesis. The formation of spaces, even if not for the conduction of gasses, seems to share the same developmental basis [49] (with the formation of the xylem and fiber cells, which undertake programmed cell death, forming tubes that allow nutrient conduction in all vascular plants). As in the two processes described above (abscission and fruit ripening), pectins appear to be the main target during aerenchyma formation [50–53]. The cell wall changes related to this process have been intensely studied in Araceae, the grasses, and Fabaceae species. Gunawardena et al. [50] pointed out that an attack on pectins was probably the first stage of aerenchyma formation in the roots of maize after flooding. Flores-Borges et al. [51] used monoclonal antibodies and highlighted pectin methyl de-methyl-esterification during the formation of aerenchyma in Pistia stratiotes (Araceae). In 2017, Leite et al. [52] confirmed that pectins are the first target of modification in sugarcane roots during aerenchyma formation (constitutive and does not depend on stress). They expanded the study to show further changes in the cell walls during aerenchyma formation, such as degradation of the mixed linkage beta-glucan and attacks on xyloglucan and arabinoxylan. Pegg et al. [53] used three legume species to demonstrate the role of de-methyl-esterification as a probable first step in

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the formation of aerenchyma in flooded roots of Cicer arietinum (chickpea), Pisum sativum (pea), and Phaseolus coccineus (scarlet runner bean). In these tree plant systems, it has been shown that pectins in the middle lamella are the first polymers to be attacked by enzymes. The sugarcane roots are one of the most profoundly studied systems regarding cell walls. They display a Type II wall containing relatively lower proportions of pectins. The hemicelluloses are composed mainly of beta-glucan and arabinoxylan, with some xyloglucan. During sugarcane aerenchyma formation, the hydrolysis of beta-glucan as well as galactans has been observed [52]. The same system has been further studied by Grandis et al. [38], who used a mixture of gene expression, proteomics, and hydrolase activities. The authors demonstrated that several hydrolases are active during the whole process, helping in the modifications of the wall that lead to the formation of the gas spaces in the root of sugarcane. Later in 2021, Tavares et al. found that the expression of genes encoding pectic enzymes, xyloglucan endo-transglycosylase, and expansins controlled microRNAs, characterizing an epigenetic level of control in the sugarcane aerenchyma formation system. 3.3.2.4 Long-term storage heteropolysaccharides mobilization

Seeds, pseudobulbs, and subterranean organs of plants can store carbon in heteropolysaccharides in storage cell walls [5,54–58] or in the special cells named idioblasts (e.g., konjac corms—Chua et al. [58] pseudobulbs of orchids—He et al. [59]). In idioblasts-storing systems, the polymer found is glucomannan. One of the first consistent observations of glucomannan degradation—and consequently storage mobilization—in pseudobulbs of orchids was made by Stancato et al. [57]. The authors found that polysaccharide degradation is induced by drought in leaves and pseudobulbs of the epiphytic orchid hybrid Cattleya forbesi X Laelia tenebrosa. Glucomannans from cell walls are usually highly self-interactive and insoluble. However, in the case of the orchid glucomannan, the polymers have been subjected to FTIR and treatment with NaOH, revealing that molecules are heavily acetylated (unpublished results). This is important because, depending on how wide this feature is spread within soluble glucomannan in idioblasts, endo-beta-mannanase (usually thought as the enzyme responsible for glucomannan hydrolysis—see [58,59]—could not be the single enzyme involved in the hydrolysis of the polymer. An acetyl-esterase would likely be necessary to open the glucomannan chain for endo-hydrolysis. Furthermore, the oligosaccharides produced by endo-beta-mannanase would probably require one or more exo-enzymes (beta-glucosidase or/and beta-mannosidase) to produce the free sugars that would then be available for subsequent catabolism. The cell wall storage polysaccharides are secondary walls that serve as long-term storage to support plant development. They occur in seeds of several species where single polymers are stored within the wall. The polysaccharides found as seed storage polymers are mannans (pure mannans, galacto-, and glucomannans), xyloglucans, galactans, and beta-glucans. Cellulose has not been characterized as storage polymers in seeds or other plant organs. For a review on the storage cell wall polysaccharides in seeds, refer to Ref. [5]. Mannans are formed by beta-1,4-linked main chains, which may be pure or branched with galactose residues (galactomannans). The glucomannans display the main chains of beta-1,4linked mannose interspaced with glucose residues. Glucomannans can also be branched with galactose, as happens in galactomannans.

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Pure mannans are found in hard seeds such as palms. In these seeds, mannan is degraded by endo-beta-mannanase, and the mannose residues serve as an energy source for embryo development. The process is prolonged and synchronized with the embryo’s development, which occurs during germination [60]. Mannans in the cell walls of endosperms are degraded by endo-beta-mannanase after germination when it serves as a source of carbon and energy for seedling growth (e.g., [61,62]). Another classical mannan storing seed of high commercial importance is coffee. Lettuce and tomato seeds contain glucomannan in their endosperm. These degradation systems have been thoroughly studied, with the characterization of enzymes and genes related to the storage and structural mobilization processes (e.g., [63–65]). Galactomannan is among the most studied hydrolysis system for cell wall polysaccharides. It occurs in many species’ seeds and correlates with plant species’ evolution [54,66,67]. Galactomannans are typically in the endosperms, where different degradation systems are known. In some species (e.g., Trigonella foenum-graecum), the endosperm is nonliving [68]. During seed maturation, programmed cell death occurs, and the entire cells become filled with the cell wall. In such systems, an aleurone layer exists, where alpha-galactosidase, endo-beta-mannanase, and exo-mannosidase are produced during germination and released into the endosperm so that complete degradation occurs [69] (Fig. 5). The products (mannose and galactose) are absorbed by the embryo, where they serve as a source of carbon and energy for seedling growth. There are cases in which cells live and store reserve proteins whose degradation is synchronized with cell wall hydrolysis in the same fashion mentioned above. This is the case of legume species such as Cyamopsis tetragonolobus (guar) [70], Ceratonia siliqua (carob) [71], and Sesbania virgata [72], which are among the most profoundly studied systems of this kind (see [5] for a review). The xyloglucan storing systems are composed of cotyledon cells containing thick cell walls and protein bodies in the cytoplasm. In an analogous mechanism to galactomannans of living endosperms, xyloglucan is completely hydrolyzed, producing monosaccharides used by the growing seedling. It is not known how these monosaccharides are metabolized in the cotyledons. Still, Santos and Buckeridge [73] demonstrated, using radioactive sucrose, that a flow of sucrose occurs from cotyledons to growing organs during the mobilization of xyloglucan in seedlings of the legume tree Hymenaea courbaril. Storage xyloglucans are structurally unique, with the typical cellulosic main chain of beta1,4-linked glucan regularly branched with alpha-1,6-xylose units, which can be further branched with galactosyl residues at regular positions [74]. A group of specific hydrolases precisely disassembles storage xyloglucan molecules (see Buckeridge [5] for a review) (Fig. 5). The most studied species are Tropaeolum majus and Hymenaea courbaril. A xyloglucan endo-transglycosylase attacks the main chain of the polymer, producing oligosaccharides that become substrates for exo-enzymes (alpha-xylosidase, betagalactosidase, and beta-glucosidase) that attack the oligosaccharides’ reducing end, taking one monosaccharide at a time until only free glucose, xylose, and galactose are produced [55,75,76]. The disassembly mechanisms of xyloglucan seem to have evolved toward a precise hydrolytic system that depends on a structural Glycomic Code, which is read by the enzymes with great efficiency [22].

4. Concluding remarks

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A unique feature of xyloglucan as a storage polymer is that they are deposited between two primary walls, first observed by Reis et al. [77] in tamarind seeds. The same feature has been observed for other legume species, Hymenaea courbaril and Copaifera langsdorffii, using monoclonal antibodies that bind specifically to the fucosylated xyloglucan, which is exclusive of the primary walls (M.A.S. Tin
e, M.R. Braga, G. Freshour, M. Hahn, and M.S. Buckeridge, unpublished data, 2003). Another hemicellulosic polymer that serves as storage is the mixed-linkage-beta-glucan (or beta-glucan). The polymer is unbranched, and the chains are composed of beta-1,4-linked glucan with kinks in the chain given by beta-1,3-linkages (see [78] for a review). This mixed-linkage chain confers water solubility and decreases self-interaction, avoiding forming microfibrils. These properties make beta-glucan an ideal storage polymer since it is readily accessible to hydrolases such as endo-beta glucanase (also called lichenase) and betaglucosidase. Furthermore, the hydrolysis product is glucose, which can be quickly metabolized for energy production. It is well established that beta-glucans are storage polymers in seeds. However, it was also a storage polymer in leaves under extreme cases (starving). Roulin and Feller [79] demonstrated such a function in wheat leaves, and later in 2002, Roulin et al. [80] found similar behavior in barley leaves. Attempts to confirm these observations in sugarcane failed (AP De Souza and M.S. Buckeridge, unpublished results). Besides hemicelluloses (mannans and xyloglucans), pectins also figure as storage cell wall polysaccharides in plants [81]. In lupins, long chains of linear beta-1,4-galactans branches rhamnogalacturonan, forming thick cell walls that, after germination, function as storage and are degraded by a specific endo-beta-galactanase [82] (Fig. 6). The enzyme is released into the inner side of the wall, and hydrolysis progresses toward the outer side. After complete hydrolysis, two primary walls (inner and outer walls) collapse [83]. This observation demonstrated that the storage wall in lupin is deposited within the primary wall opening a storage space that is later, after germination, retrieved back to monosaccharides used for early seedling growth. It is not yet known whether galactomannans are also deposited between primary walls, but some empirical observations are conducive to this conclusion. We cannot assume that this would be a pattern for all seed cell wall storage polysaccharides because, in seeds of Copaifera langsdorffii, only the outer wall is present. However, it is clear that, at least for xyloglucan and galactan, storage cell wall polysaccharides are secondary depositions with unique structural features adapted for quick and efficient hydrolysis. Seed cell wall storage polysaccharides are notable as polymers applicable in several industry sectors. Food, medical, textile, petroleum, mining, and cosmetic additives exist with galactomannans [54,84,85].

4. Concluding remarks The carbon cycle in the atmosphere has been unveiled only recently [86,87], highlighting the importance of understanding this cycle due to human interference leading to climate change.

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3. Diversity of carbohydrate hydrolysis

Malhi [86] calculated the times of carbon residence in different parts of the Earth, which I used to construct Fig. 7. Taking Malhi’s perspective together with the perspective put forward in this chapter, carbohydrate hydrolysis can be considered one of the main propellers associated with the mechanisms that recycle the carbon fixed during photosynthesis. Therefore, the diversity of mechanisms of degradation of carbohydrates discussed above—the most abundant compounds holding carbon atoms on Earth—is vital to maintain the equilibrium of life in the biosphere. Hydrolysis of the many kinds of polymeric carbohydrates (particularly the most abundant of them, the cell walls) can be made by microorganisms and animals which feed on plant materials transforming polymeric sugars (cellulose, hemicelluloses, pectins, and starches) into monosaccharides, which are the raw material for the obtention of energy through respiration and for construction of the bodies of the organisms that absorb them. A panoramic view of the diversity of hydrolysis mechanisms in nature (Fig. 4) leads to the assumption that different hydrolytic processes have evolved independently in distinct groups of organisms. Microorganisms, animals, and plants use their system and several mutualistic cooperations that improve hydrolysis mechanisms resulting in the efficient recycling of C in the atmosphere. Plants themselves possess complex control mechanisms to use carbon internally (carbon transport and storage) as well as use hydrolysis to control vital physiological processes that

CARBOHYDRATE HYDROLYSIS AND THE CARBON CYCLE ON EARTH photosynthesis

CO2

Plant respira on

animal respira on

sea respira on photosynthesis Soil respira on Hydrolysis by microorganisms and animals C recycling in about 17 years

pho c zone Hydrolysis by microorganisms and animals C recycling in about 1 year

Time of residence of ocean C = ca. 400 years

Time of residence of terrestrial C = ca. 15 years

FIG. 7

Time of residence of geological C = ca. millions of years

The carbon cycle on Earth is pictured highlighting issues related to hydrolysis. Plant biomass is by far the largest storage of terrestrial carbon (and carbohydrates) on Earth. This is the result of photosynthesis, which end up producing plant biomass with—on average—more than 60% of carbohydrate polymers. These polymers are constantly hydrolyzed, closing the carbon cycle through food chains functioning in terrestrial and sea ecosystems. Without hydrolysis, the carbon cycle on Earth would probably collapse in a few decades. Thus, carbohydrate hydrolysis, individually and collectively (as shown in Fig. 4), is vital for biosphere functioning. The numbers calculated by Malhi [86] were used to build this figure.

References

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afford efficiency in reproduction (flower senescence and fruit ripening) and substitution of organs (leaf abscission). These processes are at the heart of the forest functioning because forests and soils are where most of the terrestrial carbon is processed in the biosphere. As discussed in the chapter, hydrolysis is vital for biotechnology, with many of these currently being used by modern civilization. Understanding exogenous and endogenous hydrolysis mechanisms can lead to a better understanding of natural mechanisms that would afford better ecological management and improve technologies to deal with crucial agronomic strategies associated with food, medicine, and materials produced in the industry. Finally, the deconstruction of polymeric carbohydrates is as crucial to life on our planet as the mechanisms that lead to their construction. Although research performed in the XX Century has produced a significant amount of knowledge about hydrolysis, the biodiversity of these mechanisms produced during evolution is far from being known well enough.

Acknowledgments The author acknowledges financial support from the National Institute of Science and Technology of Bioethanol (Sa˜o Paulo Research Foundation FAPESP 2014/50884-5 and National Council for Scientific and Technological Development CNPq 465319/2014-9), the Research Center of Green House Gas Innovation RCGI (FAPESP/Shell 2020/15230-5). These projects have been funded by FAPESP and CNPq as well as many previous grants that made it possible to construct the set of ideas presented in this chapter. The author thanks these agencies’ support for more than 30 years.

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e S, Paniagua C, Matas AJ, Gunning AP, Morris VJ, Quesada MA, et al. A nanostructural view of the cell wall disassembly process during fruit ripening and postharvest storage by atomic force microscopy. Trends Food Sci Technol 2018;87:47–58. [48] Evans DE. Aerenchyma formation. New Phytol 2004;161:35–49. [49] Kozela C, Regan S. How plants make tubes. Trends Plant Sci 2003;8(4):159–64. [50] Gunawardena AHLAN, Pearce DME, Jackson MB, Hawes CR, Evans DE. Rapid changes in cell wall pectic polysaccharides are closely associated with early stages of aerenchyma formation, a spatially localized form of programmed cell death in roots of maize (Zea mays L.) promoted by ethylene. Plant Cell Environ 2001;24 (12):1369–75. [51] Flores-Borges DNA, Mastroberti AA, Bona C. Ontogeny and distribution of cell-wall glycans during aerenchyma formation in roots of Pistia stratiotes (Araceae). Bot J Linn Soc 2016;180(3):401–12. [52] Leite DCC, Grandis A, Tavares EQP, Piovezani AR, Pattathil S, Avci U, et al. Cell wall changes during the formation of aerenchyma in sugarcane roots. Ann Bot 2017;120(5):693–708. [53] Pegg TJ, Edelmann RE, Gladish DK. Progression of cell wall matrix alterations during aerenchyma formation in Pisum sativum root cortical cells. Microsc Microanal 2018;24(S1):1378–9. [54] Buckeridge MS, Dietrich SMC, de Lima DU. Galactomannans as the reserve carbohydrate in legume seeds. Dev Crop Sci 2000;26:283–316. [55] Buckeridge MS, dos Santos HP, Tin
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e MAS, Buckeridge MS. Endo-beta-mannanase from the endosperm of seeds of Sesbania virgata (Cav.) Pers. (Leguminosae): purification, characterisation and importance for germination and early seedling growth. Braz J Plant Physiol 2006;18(2):269–80. [62] Tonini PP, Lisboa CGS, Freschi L, Mercier H, Mazzoni-Viveiros SC, Buckeridge MS. Effect of abscisic acid on galactomannan degradation and endo-beta-mannanase activity in seeds of Sesbania virgata (Cav.) Pers. (Leguminosae). Trees Struct Funct 2006;20:669–78. [63] Bewley JD, Burton RA, Morohashi Y, Fincher GB. Molecular cloning of a cDNA encoding a (1!4)-β-mannan endohydrolase from the seeds of germinated tomato (Lycopersicon esculentum). Planta 1997;203(4):454–9. [64] Nonogaki H, Gee OH, Bradford KJ. A germination-specific endo-β-mannanase gene is expressed in the micropylar endosperm cap of tomato seeds. Plant Physiol 2000;123(4):1235–46. [65] Dutta S, Bradford KJ, Nevins DJ. Endo-beta-mannanase activity presente in cell wall extracts of lettuce endosperm prior to radicle emergence. Plant Physiol 1997;113:155–61. [66] Buckeridge MS, Dietrich SMC. Galactomannans from Brazilian legume seeds. Rev Br Bot 1990;13:109–12.

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[67] Buckeridge MS, Panegassi VR, Rocha DC, Dietrich SMC. Seed galactomannan in the classification and evolution of the leguminosae. Phytochemistry 1995;38(4):871–5. [68] Reid JSG. Reserve carbohydrate metabolism in germinating seeds of trigonella foenum-graecum L. (Leguminosae). Planta 1971;100(2):131–42. [69] Reid JSG, Meier H. The function of the aleurone layer during galactomannan mobilisation in germinating seeds of fenugreek (Trigonella foenum-graecum L.), crimson clover (Trifolium incarnatum L.) and lucerne (Medicago sativa L.): a correlative biochemical and ultrastructural study. Planta 1972;106(1):44–60. [70] Mcclendon JH, Nolan WG, Wenzler HF. The role of the endosperm in the germination of legumes: galactomannan, nitrogen, and phosphorus changes in the germination of guar (Cyamopsis tetrabonoloba; Leguminosae). Am J Bot 1976;63(6):790–7. [71] Seiler A. Galaktomannanabbau in keimenden Johannisbrotsamen (Ceratonia siliqua L.). Planta 1977;134 (3):209–21. [72] Buckeridge MS, Dietrich SMC. Mobilisation of the raffinose family oligosaccharides and galactomannan in germinating seeds of Sesbania marginata Benth. (Leguminosae-Faboideae). Plant Sci 1996;117(1–2):33–43. [73] Santos HP, Buckeridge MS. The role of the storage carbon of cotyledons in the establishment of seedlings of Hymenaea courbaril under different light conditions. Ann Bot 2004;94(6):819–30. [74] Buckeridge MS, Rocha DC, Reid JSG, Dietrich SMC. Xyloglucan structure and post-germinative metabolism in seeds of Copaifera langsdorfii from savanna and forest populations. Physiol Plant 1992;86:145–51. [75] Edwards M, Dea ICM, Bulpin PV, Reid JSG. Xyloglucan (amyloid) mobilisation in the cotyledons of Tropaeolum majus L. seeds following germination. Planta 1985;163(1):133–40. [76] Tin
e MAS, Cortelazzo AL, Buckeridge MS. Xyloglucan mobilisation in cotyledons of developing plantlets of Hymenaea courbaril L. (Leguminosae-Caesalpinoideae). Plant Sci 2000;154(2):117–26. [77] Reis D, Vian B, Darzens D, Roland JC. Sequential patterns of intramural digestion of galactoxyloglucan in tamarind seedlings. Planta 1987;170(1):60–73. [78] Buckeridge MS, Rayon C, Urbanowicz B, Tin
e MAS, Carpita NC. Mixed linkage (1!3),(1!4)-β-d-glucans of grasses. Cereal Chem 2004;81(1):115–27. [79] Roulin S, Feller U. Reversible accumulation of (1!3,1!4)-β-glucan endohydrolase in wheat leaves under sugar depletion. J Exp Bot 2001;52(365):2323–32. [80] Roulin S, Buchala AJ, Fincher GB. Induction of (1!3,1!4)-β-D-glucan hydrolases in leaves of dark-incubated barley seedlings. Planta 2002;215(1):51–9. [81] Crawshaw LA, Reid JSG. Changes in cell-wall polysaccharides in relation to seedling development and the mobilisation of reserves in the cotyledons of Lupinus angustifolius cv. Unicrop. Planta 1984;160(5):449–54. [82] Buckeridge MS, Reid JSG. Purification and properties of a novel β-galactosidase or exo-(1 ! 4)-β-d-galactanase from the cotyledons of germinated Lupinus angustifolius L. seeds. Planta 1994;192(4):502–11. [83] Buckeridge MS, Hutcheon IS, Reid JSG. The role of exo-(1!4)-β-galactanase in the mobilization of polysaccharides from the cotyledon cell walls of Lupinus angustifolius following germination. Ann Bot 2005;96(3):435–44. [84] Mishra A, Malhotra AV. Tamarind xyloglucan: a polysaccharide with versatile application potential. J Mater Chem 2009;19(45):8528–36. [85] Prajapati VD, Jani GK, Moradiya NG, Randeria NP, Nagar BJ, Naikwadi NN, et al. Galactomannan: a versatile biodegradable seed polysaccharide. Int J Biol Macromol 2013;60:83–92. [86] Malhi Y. Carbon in the atmosphere and terrestrial biosphere in the 21st century. Philos Trans R Soc Lond Ser A 1801;2002(360):2925–45. [87] Falkowski P, Scholes RJ, Boyle E, Canadell J, Canfield D, Elser J, et al. The global carbon cycle: a test of our knowledge of earth as a system. Science 2000;290(5490):291–6.

C H A P T E R

4 State-of-the-art experimental and computational approaches to investigate structure, substrate recognition, and catalytic mechanism of enzymes Camila Ramos Santosa, Clelton Aparecido dos Santosa, Evandro Ares de Araujob, Mariana Abraha˜o Bueno Moraisa, Maxuel de Oliveira Andradea, Tatiani Brenelli de Limaa, Wesley Cardoso Generosoa, and Mario Tyago Murakamia a

Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil bBrazilian Synchrotron Light Laboratory (LNLS-Sirius), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sa˜o Paulo, Brazil

1. Sample preparation for structural and biophysical analyses Sample preparation is undoubtedly the most critical step in the structural and conformational characterization of macromolecules. The sample quality will define, for instance, the crystallizability of your enzyme, the resolution of your diffraction or electron microscopy data, and the accuracy of your biophysical analyses. Therefore, the first section of this chapter is devoted to describe successful strategies to obtain a stable, pure, and homogenous sample for structural and biophysical studies.

Polysaccharide-Degrading Biocatalysts https://doi.org/10.1016/B978-0-323-99986-1.00023-5

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Copyright # 2023 Elsevier Inc. All rights reserved.

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FIG. 1 Schematic workflow of processes for heterologous protein expression and purification at laboratorial scale. The scheme does not discriminate between bacterial or eukaryotic host utilization. Dashed arrow demonstrates the shortcut between extracellular protein expression and protein purification.

1.1 Molecular cloning strategies for enzyme expression Production of recombinant enzymes in heterologous expression systems has been extensively exploited for research and industrial applications following a canonical workflow simplified in the Fig. 1. Typical molecular cloning methods employed for creating protein expression constructions may employ common molecular methods with type II restriction endonucleases and DNA ligase [1] or more recent molecular cloning that are listed in the Table 1. Although the detailed description of molecular cloning methods is out of scope of this chapter, it is important to highlight that some strategies may introduce additional nucleotides into the coding sequence, which leads to incorporation of nonnative amino acid residues into the expressed protein that can be very harmful for some enzymes such as lytic polysaccharide monosaccharides (LPMOs) [2,3]. These enzymes strictly require a native N-terminus for metal coordination to be functional and seamless cloning methods is mandatory to avoid undesired nucleotides or scar sequences. The cloning strategy is also affected by the type of (small or macromolecular) tag to be added into your coding sequence, which typically is employed to facilitate purification, increase solubility, assist protein folding, or improve crystallizability [4]. In addition, the effective vector design depends on the host since some of them exhibit specific attributes such as an efficient system for disulfide bound formation, posttranslational modifications for eukaryotic proteins, and the presence of tRNAs that can recognize “rare” codons encoded by a gene of interest sequence and avoid ribosome

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TABLE 1

Ligase-independent molecular cloning techniques.

Cloning method

Enzymes involved

Overlapping sequence between vector and insert

LIC (LigationIndependent Cloning)

T4 DNA polymerase exonuclease activity is used to treat the linearized vector and insert

Single-strand DNA annealing (>12 bp)

[9]

SLIC (Sequence and Ligation Independent Cloning)

T4 DNA polymerase exonuclease activity and RecA

Single-strand DNA annealing (20–40 bp)

[10]

TEDA (T5 exonuclease dependent assembly)

T5 exonuclease activity

Single-strand DNA annealing (
15 bp)

[11]

Gibson assembly

NEB—T5 exonuclease, Phusion DNA polymerase, Taq ligase

Single-strand DNA annealing (15–80 bp)

[12]

In-Fusion

Takara—based on exonuclease activity of optimized VVpol (Vaccinia virus DNA polymerase)

Single-strand DNA annealing (15 bp)

[13]

SENAX (Stellar exonuclease assembly mix)

XthA E. coli exonuclease III

Single-strand DNA annealing (15–18 bp)

[14]

Gateway

Thermo Fisher—mix of Int (Integrase) and IHF (Integration Host Factor) proteins that catalyze the in vitro recombination

In vitro recombinationdependent cloning (Att sites—25 bp)

[15]

MAGIC (Matingassisted genetically integrated cloning)

I-Sce endonuclease and λRed-αβγ proteins

In vivo recombinationdependent cloning (50 bp)

[16]

SLICE (Seamless Ligation Cloning Extract)

E. coli cell extract produced from cells expressing λRed-αβγ proteins

In vitro recombinationdependent cloning (15–52 bp)

[17]

PIPE (Polymerase incomplete primer extension)

DNA polymerase, DpnI

PCR-based cloning— single-strand DNA annealing (14–17 bp)

[18]

RF (Restriction-Free cloning)

DNA polymerase, DpnI

PCR-based cloning— single-strand DNA annealing (24–30 bp)

[19]

References

stalling during protein synthesis [5]. As a rule of thumb, the fusion of the target sequence with polyhistidine (His-Tag), glutathione S-transferase (GST), or small ubiquitin-like modifier (SUMO) is typically the first choice for enzyme expression [6]. Regarding the hosts, Escherichia coli by far is the most utilized followed by Pichia pastoris and distinct filamentous fungi from genera Aspergillus and Trichoderma [7,8]. An effective cloning strategy requires a deep-seated understanding of the structural and biochemical requirements of the target enzyme, which will therefore define the most suitable fusion tag, host, and expression conditions.

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1.2 Enzyme purification for chemical and structural homogeneity Once obtained your enzyme in the active and soluble form, the purification is a fundamental step in order to achieve a structural biology-grade sample, i.e., a sample chemically and structurally homogenous and stable. The concept of structural homogeneity surpasses the fact of having a single band in the polyacrylamide gel considering that aggregation and conformational heterogeneities cannot be detected by gel electrophoresis. Due to the importance of this concept, a section in this chapter will be dedicated to methods to assess stability and structural homogeneity. However, it is important to note that your purification strategy will be determinant for your sample quality. Three chromatographic techniques are widely employed for enzyme purification, which are based on tag affinity, ion exchange, and size exclusion. Probably, the most employed and successful combination is the tag-affinity chromatography followed by size-exclusion chromatography (SEC). It is relatively common to observe a chemically pure protein, according to gel electrophoresis to be eluted in different peaks in an affinity chromatography such as immobilized metal affinity chromatography (IMAC) for His-tagged proteins. It is only possible to observe when IMAC is monitored with a fast protein liquid chromatography (FPLC) system in which a nonlinear gradient of the elution buffer is employed. Distinct peaks from affinity chromatography can never be pooled because it looks identical in a polyacrylamide gel. Each peak must then be submitted to a SEC separately in order to guarantee maximum structural homogeneity. Each fraction of the expected single peak from the SEC needs also to be analyzed independently as discussed in Section 2. Ultimately, only chemically and structurally homogenous fractions will be pooled and concentrated for structural and biophysical studies. The understanding of this concept, structural homogeneity, and means to achieve it by far is the most important in this chapter and will definitely determine your success in a structural biology investigation.

2. Methods to analyze enzyme stability and structural homogeneity Enzymatic activity can be influenced by many variables. Thus, both accurate and reproducible results are highly dependent on enzyme stability [20,21]. For example, a few degrees of temperature change can lead to high enzyme activity variation. In this way, for consistent and reproducible analysis, an enzymatic assay must be performed in a well-defined condition that can be replicated in another laboratory. Variables such as pH dependence, buffer type, ionic strength, and temperature must be strictly controlled. These variables might affect enzyme conformation and stability so that the substrate cannot properly bind to the active site or cannot be converted into the final product [22,23]. Most of the changes in enzyme conformation induced by external variables (i.e., pH dependence, temperature, and ionic strength) can be assessed by spectroscopic methods, with the two dominant types being absorption and fluorescence [24]. In Fig. 2 the principal techniques routinely employed to analyze enzyme stability and structural homogeneity are presented.

2. Methods to analyze enzyme stability and structural homogeneity

79

FIG. 2 Principal techniques routinely employed for the characterization of protein stability, homogeneity and oligomeric states in solution. Structure represented in the center is from the PDB entry 6UAS [25]. AUC, analytical ultracentrifugation; CD, circular dichroism; DLS, dynamic light scattering; DSC, differential scanning calorimetry; SAXS, small-angle X-ray scattering; SEC-MALS, size-exclusion chromatography with multiangle light scattering.

2.1 Dynamic light scattering Dynamic light scattering (DLS), also called photon correlation spectroscopy, (PCS) is a biophysical technique used to determine the size distribution profile of macromolecules in solution [26–28]. DLS primarily measures the Brownian motion of macromolecules in a solution [29] and relates this motion to the particle size, being the macromolecule motion dependent on its particle size, temperature, and solvent viscosity. The measured diffusion coefficient (Dτ) is then used to calculate the hydrodynamic radius through the Stokes-Einstein equation [30]. The size determined by DLS relates to the macromolecule itself and other molecules and solvent around the macromolecule that move together as a single entity or particle. If the system is monodisperse, i.e., structurally homogeneous, there should only be a single population of nearly identical particles (in size and shape) in solution, whereas a polydisperse system would show multiple particle-sized populations, generally represented by asymmetry in the distribution curve [31]. As a rule of thumb, any protein going to be submitted to biophysical analysis would be assessed by DLS to check monodispersity, in other words,

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4. State-of-the-art experimental and computational approaches

structural homogeneity, and only samples with polydispersity index (PDI) below 20% are the most suitable for further characterization. It is often that fractions from the same peak in a SEC exhibit very distinct PDI, which justifies the need to treat each individually. This judicious treatment can determine the success of a structural biology study, mainly when you are facing a challenging sample. DLS technique has a number of advantages over other methods, including conducting experiments with a wide range of sample buffers, temperatures as well as sample concentrations. Also, DLS requires comparatively low amounts of samples and provides reliable estimates of the quality, and the experimental data acquisition can be obtained in a few minutes. DLS is highly sensitive to the presence of protein aggregates, being largely used in structural biology, to identify solutions in which macromolecules remain monodisperse during crystal nucleation and growth. Other application of DLS include detecting aggregation of recombinant proteins, protein-protein interaction studies, protein-small molecules interactions, and homogeneity of biomolecules among others [27,31–33]. Enzyme stability studies are also conveniently performed using DLS. Routine DLS experiments can link a decrease in the catalytic rate of an enzyme and protein aggregate over time by seeing whether the hydrodynamic radius of the particle increases [31]. If particles aggregate, there will be a larger population of particles with a larger radius, and the protein oligomers can lose its biochemical properties, necessary for the enzymatic activity, for example. In conclusion, DLS is a user-friendly fast and precise technique that provides reproducible study of sample quality and aggregation in biomolecular preparation, which has been increasingly used in molecular biology laboratories. It is a first choice to assess structural homogeneity, a key parameter to succeed in any biophysical analysis.

2.2 Circular dichroism spectroscopy Another powerful technique to assess protein stability is the circular dichroism (CD) spectroscopy, which exploits the differential absorption of circularly polarized light by optically active chiral molecules such as the peptide bonds in proteins [34,35]. CD spectroscopy or spectropolarimetry of proteins is performed in the far-ultraviolet range (170–250 nm), and because of the development of synchrotron-radiation circular dichroism (SRCD), which can operate at lower wavelengths is now being rediscovered with a wide range of applications in many different fields [36–38]. Most notably, CD is used to investigate the secondary structure content of proteins [37]. The technique allows us to investigate whether an expressed protein was correctly folded or whether a mutation can affect its conformation or stability. Also, protein modulation by interactions with substrate and/or ligands can be assessed. CD is a routine method to determine the secondary content of proteins prior to functional/biochemical analysis. In addition, this technique can be employed to estimate the secondary structure of unknown proteins, as well as monitor conformational changes due to binding interactions, denaturant binding, and thermal stability [34,35]. Thus, CD spectroscopy can be used to screen a large number of solvent conditions, varying temperature, pH, salinity, and the presence of various cofactors aiming best protein stability and structural homogeneity conditions. The method has the advantage of not requiring large amounts of proteins, 20 μg or less of pure protein in buffer solution, and the data can be collected and analyzed in a few hours without the need for extensive data processing. Complementary to DLS, thermal denaturation using

2. Methods to analyze enzyme stability and structural homogeneity

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CD spectroscopy can provide the most stabilizing condition for the sample, which often reflects the condition in which the protein is more stable and structurally homogenous.

2.3 Intrinsic and extrinsic fluorescence Fluorescence spectroscopy has also become an important tool for studying proteins/enzymes by virtue of its high sensitivity, fast, and nonlaborious experimental settings [24]. The intrinsic fluorescence is one of the most widely used methods for studying the structure and function of biomolecules, particularly proteins [39–41]. The most frequent application of intrinsic fluorescence is as a probe for conformational transitions of proteins, including protein unfolding transitions (equilibrium and kinetics), ligand binding, and protein-protein interaction. Exploring aromatic amino acid residues in proteins, especially tryptophan, tyrosine, and phenylalanine residues, intrinsic fluorescence can reveal a variety of information related to folding/unfolding transitions, such those induced by temperature, pH changes, chemical denaturants, and pressure [42]. Tryptophan, in particular, is the typical probe since it is much more sensitive to solvent polarity and its microenvironment than is tyrosine and phenylalanine residues [39]. However, it is not considered a straightforward method for assessing protein stability and screening of best conditions for structural homogeneity. On the other hand, extrinsic fluorescence based on hydrophobic probes has been demonstrated very valuable in assessing best conditions for protein stability, particularly thermostability [43]. Differential scanning fluorimetry (DSF) is a rapid and inexpensive screening methodology to identify ligands that bind and stabilize purified proteins [43,44]. DSF measures protein unfolding, monitoring changes in fluorescence as a function of temperature, by using a hydrophobic fluorescent dye such as SYPRO Orange or 1-anilinonaphthalene-8-sulfonate (ANS) that binds to proteins as they unfold [44]. The dye undergoes a change in fluorescence properties upon binding to the hydrophobic parts of the unfolded protein. Data processing is based on a simple fitting procedure that allows quick calculation of the melting temperature (Tm) under different conditions tested [45]. A major advantage of this technique is that very low protein concentration is required (typically 30% of sequence identity with the target sequence) must be available. Many programs and web-based applications for structural determination through modeling are currently available, such as MODELLER [213], SWISS-MODEL [214], I-TASSER [215], and ROSETTACM [216]. They constitute easy and fast tools to obtain a good model whenever suitable templates can be searched and used. The accuracy of several molecular modeling tools, based on different methodologies, can be assessed in the Continuous Automated Model EvaluatiOn website [217,218]. Recent years have been marked by the outstanding results in structural prediction leveraged by deep learning-based modeling methods, led by the AlphaFold2 development [135], followed by the RosettaFold initiative [219]. Both pipelines bring an end-to-end solution to model protein structures from the amino acid sequence, demonstrating a high accuracy over the prediction of backbone and side-chain atoms, achieving a very close approximation to experimental data. The fact that no knowledge of prior homologous structure is required, it has opened unprecedented possibilities to obtain a three-dimensional model of proteins (difficult cases). Differently from comparative methods that rely on similar protein resembling the structure of its close homologous, AlphaFold2 architecture performs what can be called ab initio (or template-free) structural prediction. It first establishes evolutionary relationships based on protein sequences and applies physical and geometric constraints into each peptide, followed by a structure relaxation with the use of force fields

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[135]. The use of models generated by AlphaFold2 for phase determination by molecular replacement (MR) method in X-ray crystallography has been reported [220], adding another remarkable alternative for structural biologists. Other promising feature is the capability of AI-based algorithms to include the prediction of oligomeric conformations [221,222]. In order to help to overcome the existing gap between the increasing number of known protein sequences (boosted by the advance of omics and sequencing methods) and available three-dimensional structures, the AlphaFold Protein Structure Database was created from a partnership between DeepMind and the EMBL-European Bioinformatics Institute (EMBLEBI) [223,224]. With this database, scientists can freely have access to the structure predictions already available. Another powerful and easy-to-use tool is the Robetta server (robetta. bakerlab.org), in which one can perform molecular modeling through several Rosetta codes, including structural predictions with RosettaFold [219].

6.2 Molecular docking: Modeling of enzyme-ligand binding Molecular docking is a computational method that enables the exploration of enzyme binding sites, substrate interactions, and their preferred orientations when an experimental enzyme-substrate complex is not available. It can also be used to infer the location of the enzyme active site without a priori knowledge. Depending on the system to be studied, different poses of the ligand resulting from molecular docking can be selected for subsequent studies by molecular dynamics (MD) simulations. As a starting point for molecular docking, one should have an initial three-dimensional structure of the protein (receptor) and of the substrate molecule (ligand). The initial macromolecular structure can be obtained directly from the experiments (X-ray crystallography, NMR, or cryo-EM), retrieved from PDB database [123,132] or from modeling programs, either based on homology modeling or artificial intelligence (AI). Besides having a reliable structural model and a correct description of the ligand geometry, several docking parameters, such as the box size, coordinates of the grid box center, exhaustiveness, and energy range can be optimized to achieve better results. In general protocols, the ligand is treated as flexible, while the receptor is considered rigid, however, some programs allow the user to consider the flexibility of atoms of the receptor, which might be an interesting feature, especially when dealing with challenging large and complex glycans. Several programs and/or online tools for molecular docking have been developed over the last decades, such as the widely used AutoDock [225], AutoDock Vina [226], SwissDock [227], DockThor [228], and many others. Moreover, some codes were later developed to cope specifically with glycans, including GlycanDock [229] and Vina-Carb [230]. Molecular docking programs estimate the ligand binding affinity and score the best poses found as result. Nowadays, the codes run with relatively low computational cost and can be useful for ligands screening and to obtain initial complexed structures for molecular dynamics studies, for example. However, since docking protocols present several limitations, such as missing the solvent contribution, an accurate and detailed description of the enzyme-substrate interactions and energy values require quantitative measurements, obtained either experimentally or by computer simulations that can be performed with different levels of theory. Enhanced sampling approaches have contributed to free-energy calculations from docking [231] and complete descriptions of binding/unbinding processes [232] with accurate energy estimations.

6. In silico methods for structural analysis

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6.3 QM/MM-based simulations to analyze biocatalytic reactions Computer simulations can provide insightful information regarding the enzyme structural organization and dynamics, which relate to its function. A sort of in silico methods (ranging from coarse-grained to first principles methods) can be applied using different levels of approximations/accuracy, which directly relates to the computational cost. The Newtonian dynamics based on force fields can describe relevant molecular events in large and solvated systems achieving up to μs-ms timescale. The main force fields currently used to model enzymatic systems are available at highly distributed packages, such as AMBER [233], CHARMM [234], GROMOS [235], GROMACS [236], and NAMD [237]. For dealing with carbohydrates, the all-atom force-field GLYCAM06 [238] offers the parameters for most sugars. Despite the constant improvements in the description of the parameters sets, molecular mechanics (MM) descriptions have limitations, such as not allowing the modeling of chemical reactions, since each atom is treated as point particle. On the other hand, quantum mechanics (QM)-based atomic simulations (ab initio) can provide an accurate description of the electronic structure, allowing a precise modeling of conformations and chemical reactions, as the ones catalyzed by enzymes. However, due to high computational costs, QM simulations are limited to small systems and shorter time scales. To overcome these limitations, hybrid methods (quantum mechanics/molecular mechanics, QM/MM) have been used to obtain a good description of carbohydrates conformations and to model enzymatic reactions. For QM/MM simulations, in general, the ligand (or substrate) molecule and/or the main residues of the active site are treated by QM, whereas the remaining protein and solvent atoms are treated classically by MM. For the treatment of the atoms in the QM region, involved in biochemical processes, the density functional theory (DFT) has been used, providing good results in terms of molecular arrangements and energy estimative, frequently compatible to experimental results. Depending on the level of accuracy desired, semiempirical methods, such as DFTB can also be employed, offering a lower computational cost alternative in comparison with first principles-based methods [239,240]. In order to address the time scale problem, frequently present in conformational exploration and catalytic reactions where one or more energy minima need to be overcome, enhanced sampling techniques, such as umbrella sampling [241], transition path sampling [242], and metadynamics [243], can be employed. It is important to highlight that, whereas the two first techniques require that both reactants and product states are previously known, metadynamics allow to follow the evolution of the reactions even when the product is unknown, as long as the collective variables are adequately chosen. Metadynamics has been successfully employed to describe the molecular mechanisms of glycoside hydrolases (GH) and transferases in QM/MM simulations [244]. QM/MM MD DFT-based simulations routines are implemented in HPC efficient codes, such as CPMD (IBM Corp. and Max Planck Institute), and CP2K [245], which can be interfaced with Plumed [246] to perform metadynamics, for example. An overall view of the steps that can be adopted for obtaining catalytic itinerary and an accurate QM/MM-based free-energy landscape starting from a PDB (ideally from an enzyme-substrate complex obtained experimentally) are summarized in Fig. 4 and can be very briefly described as: (1) preparation of the system (this includes to revert possible

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4. State-of-the-art experimental and computational approaches

FIG. 4

Simplified workflow for determining the enzyme molecular mechanisms for biocatalytic reactions though QM/MM simulations. Structures represented (blue cartoon and surface) are from the PDB entry 6UAS [25]. The free energy landscape consists in an illustrative example of the GH43 enzyme [248].

mutations, add possible missing loops in the protein structure, solvate of the system, set protonation states, neutralize the charges of the system); (2) classical molecular dynamics simulations (based on force fields) to equilibrate the system at a given temperature (this includes several steps from minimization and heating to achieving stability before production); (3) QM/MM preparation and system optimization (this includes defining the QM region, QM-MM boundaries, the treatment of electrostatic interactions, geometry optimization, equilibration though QM/MM MD at a given temperature); (4) activation of the chemical reaction/conformational changes by an enhanced sampling method (here we use metadynamics as an example) (this step include the definition of collective variables and gaussian parameters, which are very critical points). Of course, this is a very brief description of one among a sort of possible protocols to be adopted and a review of this methodology can be found in the literature [247].

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[223] Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 2022;50:D439–44. https://doi.org/10.1093/nar/gkab1061. [224] https://alphafold.ebi.ac.uk [Accessed 3 December 2022] n.d. € [225] Osterberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS. Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins: Struct Funct Bioinf 2002;46:34–40. https://doi.org/10.1002/prot.10028. [226] Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455–61. https://doi.org/10.1002/ jcc.21334. [227] Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 2011;39:W270–7. https://doi.org/10.1093/nar/gkr366. [228] Santos KB, Guedes IA, Karl ALM, Dardenne LE. Highly flexible ligand docking: benchmarking of the DockThor program on the LEADS-PEP protein-peptide data set. J Chem Inf Model 2020;60:667–83. https://doi.org/ 10.1021/acs.jcim.9b00905. [229] Nance ML, Labonte JW, Adolf-Bryfogle J, Gray JJ. Development and evaluation of GlycanDock: a proteinglycoligand docking refinement algorithm in Rosetta. J Phys Chem B 2021. https://doi.org/10.1021/acs. jpcb.1c00910. [230] Nivedha AK, Thieker DF, Makeneni S, Hu H, Woods RJ. Vina-Carb: improving glycosidic angles during carbohydrate docking. J Chem Theory Comput 2016;12:892–901. https://doi.org/10.1021/acs.jctc.5b00834. [231] Gervasio FL, Laio A, Parrinello M. Flexible docking in solution using metadynamics. J Am Chem Soc 2005;127:2600–7. https://doi.org/10.1021/ja0445950. [232] Limongelli V, Bonomi M, Parrinello M. Funnel metadynamics as accurate binding free-energy method. Proc Natl Acad Sci U S A 2013;110:6358–63. https://doi.org/10.1073/pnas.1303186110. [233] Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, et al. The Amber biomolecular simulation programs. J Comput Chem 2005;26:1668–88. https://doi.org/10.1002/jcc.20290. [234] Brooks BR, Brooks CL, Mackerell AD, Nilsson L, Petrella RJ, Roux B, et al. CHARMM: the biomolecular simulation program. J Comput Chem 2009;30:1545–614. https://doi.org/10.1002/jcc.21287. [235] Christen M, H€ unenberger PH, Bakowies D, Baron R, B€ urgi R, Geerke DP, et al. The GROMOS software for biomolecular simulation: GROMOS05. J Comput Chem 2005;26:1719–51. https://doi.org/10.1002/jcc.20303. [236] Abraham MJ, Murtola T, Schulz R, Pa´ll S, Smith JC, Hess B, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015;1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001. [237] Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 2020;153, 044130. https://doi.org/10.1063/5.0014475. [238] Kirschner KN, Yongye AB, Tschampel SM, Gonza´lez-Outeirin˜o J, Daniels CR, Foley BL, et al. GLYCAM06: a generalizable biomolecular force field. Carbohydrates. J Comput Chem 2008;29:622–55. https://doi.org/ 10.1002/jcc.20820. [239] Elstner M, Porezag D, Jungnickel G, Elsner J, Haugk M, Frauenheim T, et al. Self-consistent-charge densityfunctional tight-binding method for simulations of complex materials properties. Phys Rev B 1998;58:7260–8. https://doi.org/10.1103/PhysRevB.58.7260. [240] Govender K, Gao J, Naidoo KJ. AM1/d-CB1: a semiempirical model for QM/MM simulations of chemical glycobiology systems. J Chem Theory Comput 2014;10:4694–707. https://doi.org/10.1021/ct500372s. [241] Torrie GM, Valleau JP. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 1977;23:187–99. https://doi.org/10.1016/0021-9991(77)90121-8. [242] Dellago C, Bolhuis PG, Csajka FS, Chandler D. Transition path sampling and the calculation of rate constants. J Chem Phys 1998;108:1964–77. https://doi.org/10.1063/1.475562. [243] Laio A, Parrinello M. Escaping free-energy minima. Proc Natl Acad Sci U S A 2002;99:12562–6. https://doi.org/ 10.1073/pnas.202427399. [244] Arde`vol A, Rovira C. Reaction mechanisms in carbohydrate-active enzymes: glycoside hydrolases and glycosyltransferases. Insights from ab initio quantum mechanics/molecular mechanics dynamic simulations. J Am Chem Soc 2015;137:7528–47. https://doi.org/10.1021/jacs.5b01156.

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C H A P T E R

5 Pretreatments as a key for enzymatic hydrolysis of lignocellulosic biomass Sarita C^ andida Rabeloa, Lı´via Beatriz Brenellib, Thaynara Coradini Pinc, Eupı´dio Scopeld, and Aline Carvalho da Costac a

School of Agriculture, Sa˜o Paulo State University (Unesp), Botucatu, Sa˜o Paulo, Brazil Interdisciplinary Center of Energy Planning (NIPE), State University of Campinas (Unicamp), Campinas, Sa˜o Paulo, Brazil cSchool of Chemical Engineering, State University of Campinas (UNICAMP), Campinas, Sa˜o Paulo, Brazil dInstitute of Chemistry, State University of Campinas (Unicamp), Campinas, Sa˜o Paulo, Brazil b

1. Introduction Lignocellulose is the most abundant carbon renewable resource on Earth, and its use to produce biofuels and other products has attracted much interest in recent years. It has three main components: cellulose, a glucose homopolymer; hemicellulose, a heteropolymer of pentoses and hexoses; and lignin, a macromolecule formed by phenylpropane units [1]. One of the ways to use lignocellulose is through the enzymatic hydrolysis process, where the main interest is the hydrolysis of cellulose to obtain glucose monomers. In order to increase hydrolysis yields, a pretreatment step is essential as it increases the access of the cellulolytic complex enzymes to cellulose. The pretreatment step also fractionates different components of lignocellulose, and the choice of the type of pretreatment plays a very important role in the feasibility (technical and economic) of the process as a whole [2,3]. Some important questions when choosing a pretreatment step are as follows: how are the macrocomponents (cellulose, hemicellulose, and lignin) fractionated? What are the economic and environmental impacts caused by the use of a given solvent? Does the chosen pretreatment produce inhibitors? Will removing these inhibitors increase the cost of the final process?

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The choice of a pretreatment step will have a direct impact on the enzymatic hydrolysis performance as it strongly influences the maximum glucose concentration that can be achieved, impacting the hydrolysis yield and productivity. It also determines the enzyme and solid loads that must be used to attain a given final glucose concentration [4]. Furthermore, the solid load affects mass and heat transfer in the reactor, as well as the energy used while stirring [5]. Thus, an efficient pretreatment is the key factor to an efficient hydrolysis. However, pretreatments that lead to the best results are, in general, the most expensive, and an analysis of the whole process must be made to determine the optimal choice. The purpose of this chapter is to discuss the impact of pretreatment on the biomass factors that influence enzymatic hydrolysis, as well as present a review of the main pretreatments considered by the scientific and industrial communities and their influence on enzymatic hydrolysis performance.

2. Factors affecting enzymatic hydrolysis and their relationship with the pretreatment In practice, the enzymatic hydrolysis process using commercial enzymes and different types of plant biomass is affected by several factors that compromise industrial yields and the feasibility of producing bioproducts at competitive costs (Fig. 1) [6]. These factors are related to the characteristics of the substrate, enzymes, and the surrounding environment, but so far, the understanding of how they interact, their relative contribution to hydrolysis, and the mechanisms by which they influence hydrolysis are still in progress. The following sections will outline the key factors related to the pretreatment step that affects the enzymatic hydrolysis of lignocellulosic biomass. The idea is to understand aspects that can improve biomass-based technologies, as well as the design of better biocatalytic processes.

2.1 Biomass physical and chemical factors The recalcitrance of different renewable feedstocks is mainly linked to the plant cell wall structure. As stated before, cellulose, hemicellulose, and lignin are the main components of the plant cell wall fibers, and their proportions differ among plant species, although the basic structure is generally the same [7]. In the absence of water, fibers have a smooth and not very porous surface, which is thus not very accessible to hydrolytic enzymes [8]. Substrate-related factors that can impact enzymatic hydrolysis can be classified as direct and indirect. The accessible surface area is a direct factor, while porosity, particle size, specific surface area, chemical composition, cellulose crystallinity, and degree of polymerization are indirect factors of the plant biomass structure [9]. 2.1.1 Accessible, specific, and internal surface area The accessible surface area of lignocellulose is a critical factor influencing enzymatic hydrolysis and is inextricably linked to the pore volume and specific surface area. The latter

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FIG. 1 Schematic representation of the pretreatment effects on lignocellulosic biomass to enhance the enzymatic action. Credit: Authors’ own elaboration.

is often used to calculate the actual available surface, as the accessible surface area is often misestimated [10]. Milling and crushing are examples of pretreatments that reduce particle size and thus increase the accessible surface area [11]. Indeed, pretreatments disturb cell wall matrices to different extents, but commonly, they contribute to increase the pore volume and allowing free penetration of the wall by hydrolytic enzymes [12]. Some studies showed a strong positive correlation between the specific surface area, adsorption of high amounts of enzymes, and better conversion yields, while others observed only weak correlations between the initial specific surface area and enzymatic conversion yield of cellulose-rich substrates [13,14]. Ideally, a greater accessible surface area should ensure more adsorbed enzymes, thus resulting in higher product yield, but, in reality, there are more factors influencing at the same time during the reaction. Concerning the internal surface area, that is, pore volume, only pores larger in size and shape than hydrolytic enzymes are expected to be accessible. The pore size must be at least in the range of 50–100 nm to allow significant diffusion of enzymes [15]. However, the literature suggests that the positive correlations between porosity and hydrolysis yields depend on the type of biomass, the pretreatment applied, and chemical composition, that is, hemicellulose and lignin contents. Herbaut et al. [16] demonstrated that the increase in porosity displayed overall positive correlations with saccharification efficiency of wheat straw, poplar, and miscanthus submitted to different pretreatments, although pore sizes were only strongly correlated with hydrolysis efficiency when pore size was below specific values that were dependent on the kind of biomass.

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2.1.2 Cellulose crystallinity and degree of polymerization The crystallinity of lignocellulosic biomass has been extensively studied and discussed once it is a supramolecular property of cellulose that influences recalcitrance, thus affecting enzymatic hydrolysis. The crystalline region is the portion of cellulose where the fibers are closely intertwined by noncovalent hydrogen bonds, and for this reason, hydrolysis rates in this region are much lower than those in the amorphous regions. Nonetheless, the influence of the crystallinity of the pretreated biomass in enzymatic hydrolysis is not completely understood [17,18]. Cellulose crystallinity is affected when lignocellulosic biomass is subjected to a pretreatment step. In general, the relative crystallinity index of cellulose increases after diluted acid, hydrothermal, steam explosion, and alkaline pretreatments but decreases after ammonia fiber explosion pretreatment, for example. Accordingly, despite the changes in the relative crystallinity index throughout different pretreatments, the removal of hemicellulose and lignin greatly improves enzymatic hydrolysis [1]. Initially, it was proposed by some authors that hydrolysis rates depended on cellulose crystallinity and that the deceleration or termination of enzymatic hydrolysis could happen after the degradation of the entire amorphous region of cellulose, which resulted in only crystalline regions available for hydrolysis [19,20]. Thus, it was expected that the crystallinity degree should increase after enzymatic hydrolysis as a result of amorphous cellulose removal. However, some authors reported no significant change in crystallinity during the hydrolysis of pure cellulose. Besides, it was observed that the initial enzymatic rate continued to increase with decreasing crystallinity, although the concentration of adsorbed enzymes remained constant [17]. The role of crystallinity was reported to be important only at the initial stage of enzymatic hydrolysis of pretreated corn stover [13]. Later, in another study using pure cellulose and other lignocellulosic substrates, it was observed that the degree of crystallinity of cellulose did not change after enzymatic hydrolysis [14]. Nevertheless, further studies are needed to elucidate the influence of cellulose crystallinity on the effectiveness of enzyme hydrolysis and enzyme adsorption, particularly with complex lignocellulosic substrates. The degree of polymerization (DP) of cellulose is the number of glucose units in the polymer chain. As well as crystallinity, DP plays an important role in substrate accessibility. Low-DP cellulose offers a greater number of binding sites for cellulases, whereas high-DP cellulose hampers enzymatic action [21]. Sinitsyn et al. [22] altered the DP of pure cellulose and bagasse by γ-irradiation pretreatment, keeping other properties constant, including the crystallinity index. The synergy between some endoglucanases and cellobiohydrolases at lower cellulose DP was observed, but it had little effect on the overall enzymatic hydrolysis efficiency. Cateto et al. [23] observed a decrease in the DP in the first 2 h of enzymatic hydrolysis of pretreated switchgrass, which then remained approximately constant for the next 8 h. It was also reported that the DP of lignin-free model biomass samples did not correlate with hydrolysis rates [24]. Later, Lu et al. [13] reported that the enzymatic hydrolysis yield of milled cellulose had a negative correlation with the DP and crystallinity. The understanding of the individual impact of biomass DP on hydrolysis is still limited because alterations in the DP are accomplished by changes in crystallinity, porosity, particle size, and/or surface area. Even more, the effects of cellulose DP following pretreatment are investigated as changes in biomass properties happen simultaneously.

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Similar to cellulose crystallinity, the DP of cellulose is also impacted during biomass pretreatment, although the differences observed are controversial when comparing different substrates and pretreatments, as described in the literature. Diluted acid, steam explosion, and alkaline pretreatments could lead to a reduction in the DP of cellulose to different extents, whereas ammonia fiber explosion (AFEX) pretreatment impacts the DP minimally [1]. 2.1.3 Chemical composition The chemical composition of the biomass directly influences enzymatic hydrolysis, and pretreated biomasses are more effectively hydrolyzed than raw substrates. In this section, we will discuss the influence of the chemical composition of the pretreated biomass in enzymatic action. The biomass fractionation depends on the type of pretreatment, which generates two fractions: a solid fraction (pretreated biomass), which goes on to enzymatic hydrolysis to obtain fermentable sugars, and a liquid fraction (pretreatment liquor), which can be directed together or separately with the solid fraction for applications in subsequent process steps. In this way, the choice of pretreatment defines the composition of the pretreated biomass and brings advantages or disadvantages to the enzymatic hydrolysis step, as discussed later. 2.1.3.1 Pretreatments promoting hemicellulose removal

Nondelignified pretreated biomass is obtained by pretreatments that hydrolyze most of the hemicellulose and extract their fragments to the liquid fraction, producing a pretreated solid that contains mainly cellulose and lignin (Fig. 2A). In general, this type of fractionation is obtained in acid pretreatments [5] or neutral/near neutral pretreatments [25]. pH exerts a great influence on the result of the pretreatment process, and low pH values promote the hydrolysis of hemicellulose, producing monomeric sugars as the main product in the pretreatment liquor. Depending on other operating parameters, this type of process, in general, leads to low formation of inhibitors. In addition, processes carried out at acidic pH, if well controlled, tend to keep the cellulose well preserved [26]. Neutral or close to neutral pHs also favor the hydrolysis of hemicellulose from lignocellulosic biomass mainly because of the presence of organic acids, such as uronic acids and acetyl groups, in the polysaccharide structure. These acids are released during hydrolysis and contribute to autohydrolytic processes. Since the conditions are less severe, hemicellulose is hydrolyzed at minor levels and is recovered mostly in the form of oligosaccharides. In most grasses, which have a high concentration of acids in the hemicellulose structure, the recovery of oligosaccharides is favored in neutral conditions. In biomasses in which the presence of hemicellulose acids is not significant, such as in softwoods, only the solvolysis effect of the water prevails, and, therefore, low quantities of hemicellulose are solubilized [27,28]. The disadvantage of the enzymatic hydrolysis step in substrates treated in acidic or neutral conditions is the presence of lignin in the pretreated solid [4] and, thus, the low percentage of cellulose available for conversion. This has a direct influence on enzymatic hydrolysis due to several factors. The low percentage of cellulose requires a high solid concentration to reach a high final glucose concentration, thus impacting the heat and mass transfer in the reactor, as well as the energy input required for stirring. Moreover, lignin acts as a physical barrier that hampers the enzyme access to hydrolyze cellulose. Finally, lignin adsorbs the enzymes

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FIG. 2 Flow diagram for different biomass fractionation. Pretreatments promoting (A) mainly hemicellulose removal, (B) hemicellulose and lignin solubilization, and (C) delignification. Credit: Authors’ own elaboration.

nonproductively, further declining the performance of the hydrolysis process, as will be discussed in more detail in Section 2.1.3.3 [4]. 2.1.3.2 Delignified pretreated biomass

In pretreatments carried out at alkaline pH, lignin is preferentially solubilized in the liquid fraction. In some cases, hemicellulose is also hydrolyzed, resulting in a solid fraction rich in cellulose, as can be seen in Fig. 1B. The performance of enzymatic hydrolysis is usually outstanding as the pretreated biomass has a high percentage of cellulose and is highly susceptible to enzymatic hydrolysis. Examples of pretreatments that provide this type of fractionation are pretreatment with alkaline hydrogen peroxide [29], organosolv [4], and some pretreatments with ionic liquids [25]. The conditions that preferentially achieve lignin solubilization (Fig. 1C) result in solid fractions that are richer in both cellulose and hemicellulose. This type of fractionation can be carried out at high pHs using different types of alkaline catalysts that promote the dissolution of lignin and, depending on the conditions, preserve a good part of the hemicellulose in the solid fraction [30]. It is worth mentioning that organic and inorganic solvents, such as specially designed ionic liquids and eutectic solvents, despite not operating at high pH values, can also promote quite similar results, producing a solid fraction rich in polysaccharides—cellulose and hemicellulose—while most of the lignin is solubilized. As the pretreated solid is mainly composed of both cellulose and hemicellulose, a liquor rich in pentoses and hexoses is obtained

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after enzymatic hydrolysis. The advantage is that hemicellulose sugars are recovered in the hydrolysis liquor, which has fewer inhibitors than the pretreatment liquor, eliminating the detoxification step usually necessary for the use of these sugars. Despite the great advantage of obtaining pretreated biomasses with low lignin content, the cost of the solvents used in delignifying pretreatments is high, and the recovery and reuse of the solvent are mandatory for the economic viability of the process. 2.1.3.3 Effect of residual lignin on enzymatic hydrolysis

It is important to highlight that the fractionation is not complete in any pretreatment, and practically all of them produce solid and liquid fractions rich in some specific components but with the presence of small amounts of all other components. Of these, the component present in the pretreated biomass that has the greatest influence on enzymatic hydrolysis is lignin [31]. Ideally, a good one-step or combined pretreatment would decrease lignin content to less than 10% of the whole material without interfering with degrading enzyme accessibility to polysaccharides [32]. In fact, thorough removal of lignin can cause cellulose aggregation and decrease cellulose accessibility and digestibility [33]. Pretreatment can induce lignin depolymerization and solubilization, followed by some lignin fragment redeposition onto the fibers, blocking pores that were accessible to enzymes before. Besides, lignin fragments can undergo condensation events (with or without carbohydrates) during pretreatment and result in “pseudolignin,” thereby hindering enzymatic hydrolysis. It is likely that this material is originated from the cleavage of native lignin ether linkages, elimination of aliphatic hydroxyls, variations of the S/G ratio, and formation of new functional groups (phenolic hydroxyls, carboxylic acids) and carbon-carbon bonds [34,35]. For example, the increase in free phenolic hydroxyls and condensed aromatic structures in lignin residues increase hydrogen bonding and hydrophobic interactions between lignin and cellulases [36]. The extent of the residual lignin inhibition on enzymatic hydrolysis is highly correlated to the structure of the original lignin and is significantly affected by the pretreatment to which the biomass is subjected [37]. A positive impact on enzymatic hydrolysis has been reported in organosolv pretreatment of bamboo, which decreased β-O-4 linkages and increased carboxylic groups [38]. Solubilized lignin fragments generated during pretreatment have a minimal impact on enzymatic hydrolysis when they are not deposited back onto the surface of the biomass. Actually, lignin-derived compounds released from pretreated biomass through oxidative enzymes, that is, laccases, may have a beneficial effect when cellulase cocktails contain lytic polysaccharide monooxygenases (LPMOs). These are copper-dependent enzymes that cleave polysaccharides through an oxidative mechanism. It was observed that laccasederived lignin compounds were able to donate electrons to LPMO enzymes, boosting the overall enzymatic hydrolysis of cellulose [39]. The lignin inhibition topic is so relevant that many studies have focused on pretreatment development and optimization aiming to minimize the negative impact of lignin on enzymatic hydrolysis [37]. It is believed that the nonproductive binding of lignin to cellulases significantly decreases the efficiency of the enzymatic hydrolysis process and may result in higher enzyme loading requirements [19,20]. Hydrophobic, hydrogen bonding, and electrostatic interactions have been identified as the driving forces that govern the interaction

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between lignin and enzyme. However, the fundamental understanding of the mechanism behind the lignin chemistry after pretreatment and how it affects these interactions remains unclear. Hydrophobicity has been proposed as one of the major driving forces contributing to the nonproductive adsorption between residual lignin and cellulases. Lignin has stronger hydrophobicity than cellulose because it contains lots of methoxy groups, so cellulase is more likely to bind to lignin because it contains hydrophobic amino acid residues from tryptophan, phenylalanine, and tyrosine. The lignin surface is dominated by hydroxyl and carboxylic acid groups, and hydrogens are responsible to interact with enzymes through hydrogen bonding at different extents, but it is not the dominant force. The potential electrostatic interactions between lignin and cellulases come from the fact that in an aqueous solution and specific pH, there are the association and dissociation of functional groups present in residual lignin and enzymes and changes in their electrical properties [21,22].

2.2 Enzyme inhibitors produced in the pretreatment step Although pretreatments offer many advantages from the lignocellulosic biomass structure perspective, these processes commonly form inhibitory substances. The so-called inhibitors are generated during pretreatment from polysaccharides and lignin degradation, including soluble sugars, furan derivatives, organic acids, and phenolic compounds. Formic, acetic, and levulinic acids, furfural, 5-hydroxymethyl furfural (5-HMF), syringaldehyde, 4-hydroxybenzaldehyde, and vanillin are examples of inhibitors that can inactivate the enzymes or partially affect their activity [40]. According to Mhlongo et al. [41], the inhibition of cellulases largely depends on the specific relationship between the enzyme and the inhibitor. Their studies showed that weak acids such as formic acid resulted in severe inhibition of cellobiohydrolase 1 (CBH1), β-glucosidase 1 (BGL1), and endoglucanase 2 (EG2). The inhibition effect provoked by acetic acid and other monomeric phenols depended on the concentration of these compounds. Moreover, the study emphasized that the hydrophobicity of both enzymes and inhibitors, along with the functional groups present on the surface of the inhibitors, could facilitate the nonproductive adsorption of the enzymes to phenolic inhibitor compounds. Later, three classes of phenolic compounds have been identified as inhibitors (quercetin, kaempferol, transcinnamic acid, luteolin, ellagic acid), noninhibitors (p-coumaric acid, rutin, caffeic acid), and activators (ferulic acid, syringic acid, sinapic acid, vanillic acid), based on their effect on the hydrolysis of cellulose. Apparently, a methoxy group (dOCH3) is necessary for the activating effect [42].

3. Pretreatment of lignocellulosic biomass As already discussed, the lignocellulosic biomass resistance to enzymatic hydrolysis is attributed to many factors, such as crystallinity, accessible surface area, particle size, degree of polymerization (DP), and the arrangement and proportion of macromolecular components [43]. Thus, a single pretreatment can be enough to achieve the desired modifications in the biomass, but a combination of different pretreatments may be required, which can lead to high equipment and operating costs [3].

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Pretreatment processes are traditionally classified as physical, chemical, physicochemical, or biological, in addition to a possible combination between them [44]. The choice of pretreatment needs to consider the technological chain to be developed, mainly based on the main product of interest and the technical-economic feasibility. Some important and intrinsic factors to this choice are the possibility of scaling the process; obtaining high recovery yields of the fractions of interest; minimizing the amount of waste and toxic compounds generated in the process; compatibility of the pretreatment type with postprocessing; efficient lignin recovery to add value to the chain; and reducing the cost of equipment and energy demand [45].

3.1 Physical pretreatments Physical pretreatments are mainly carried out to reduce the size of the particles, which results in an increase in the surface area and a decrease in the degree of polymerization and crystallinity of the material [46]. Therefore, it is often used together with other pretreatment methods, especially chemical methods, making the process more effective in obtaining the fractions of interest [47]. Physical pretreatments are considered sustainable processes as they rarely produce any residue that is difficult to treat. However, one of the major disadvantages is the high energy consumption required in most processes, which can vary according to the biomass used and the required size of the material [48]. Examples of physical pretreatment methods are mechanical, microwave, and ultrasound. Their main advantages and disadvantages can be seen in Table 1. TABLE 1 Process

Advantages and disadvantages of physical pretreatment processes. Techniques and tools

Advantages

Disadvantages

References

Mechanical

Millers Grinders Screws

Particle size reduction Increase in surface area Reduction in chemical residues

High energy requirement and cost

[44,49]

Ultrasound

Cavitation

Short time Low operation temperature

Effectiveness is dependent on the lignocellulosic material and its connection with another pretreatment

[50,51]

Short time Low energy requirements High temperature achieved

Sugar degradation in case of high temperature

[52]

Microwave

Credit: Authors’ own elaboration.

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3.1.1 Mechanical pretreatment Mechanical pretreatments have been shown to be important processes aiming to reduce the particle size, which often can provide a higher concentration of sugars after the hydrolytic processes due to the greater accessibility of the enzymes to the polysaccharide structure. In addition, these processes decrease the limitations of heat and mass transfer during the reaction, helping to reduce the formation of degradation products [49]. In mechanical processes, biomass particle size reduction is achieved using a combination of different mechanical stresses such as impact, compression, friction, and/or shear stress, as can be seen in Fig. 3, where all effects can coexist in a single commercial equipment [50]. Different grinding equipment can be used to fragment and dissociate the macromolecular fractions of lignocellulosic biomass, such as knife mills, hammer mills, centrifuges, and refining equipment. These types of equipment have adjustable speeds and destructure the material by impact and shear stress. In ball mills, the lignocellulosic biomass undergoes impacts and compression when collisions occur between the balls and the walls of the equipment. On the other hand, in an extruder, the main mechanical stress is the shear stress that occurs between the screw and the walls of the equipment. It is a continuous process, easy to adjust online and be used for large-scale applications, providing high yields of sugars after the enzymatic hydrolysis step [51]. The extruder has different screw configurations, such as single-screw extruders and twin-screw extruders. The double screw has advantages as it can perform several functions in a single step, such as material transport, heating, mixing, shearing, grinding, chemical reaction, drying, and liquid-solid separation [52], being considered a very complete and versatile equipment. The choice of the type of equipment will depend on several factors, such as the physical and chemical properties of the biomass, moisture content, final size required for the particle, the particle size distribution, energy consumption, and objectives for the application of the biomass/fractions. Extruders, for example, are able to pretreat biomasses with moisture contents above 15%–25%, while hammer and knife mills are suitable for pretreating dry

FIG. 3 Schematic representation of the different mechanical stresses generated. Credit: Authors’ own elaboration.

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biomasses with moisture contents up to 10%–15% [53]. Regarding the size of the particles, in general, they must be smaller than 3 mm in order to lead to gains in the enzymatic hydrolysis step [49], which ends up resulting in high energy consumption and costs. As mentioned before, a trend in the use of mechanical pretreatments is to later couple them to chemical or physicochemical pretreatments in order to reduce the energy consumption of this stage while still providing gains in particle size, increased surface area, decreased cellulose crystallinity, and increased biomass digestibility and bioconversion. Disc refiners, widely used in the pulp and paper sector, are considered innovative and interesting technologies to increase sugar yields in the enzymatic hydrolysis stage, leading to relative increases of more than 10% in yield due to the reduction in the size of the fiber, internal delamination, increase in pores, and swelling of the biomass [54]. 3.1.2 Ultrasound pretreatment Ultrasound pretreatment is based on the principle of cavitation, described by the spontaneous formation, growth, and subsequent collapse of microsized bubbles caused by the propagation of ultrasonic waves in a liquid medium. These waves oscillate at frequencies above 16 kHz—and induce physical phenomena in the medium, such as heating, acoustic transmission, acoustic cavitation, and nebulization, in addition to chemical phenomena, such as radical formation (•H and/or •OH) [55]. At low frequency (20 kHz), cavitation bubbles are less numerous but have larger diameters, which provides a more intense collapse, with greater mechanical energy delivered. On the other hand, frequencies around 300 kHz lead to a more intense formation of radicals, thus favoring chemical phenomena [56]. However, these effects are not frequently observed in the pretreatment of lignocellulosic biomasses, mainly due to the reduced permeation potential of cavitation bubbles formed at low frequencies. In this case, the energy released by the collapse of the bubbles is retained on the surface of the lignocellulosic material, thus hindering the effective separation of the biomass fractions [55]. In this sense, as well as in mechanical processes, ultrasound pretreatment has been used more in combination with other chemical pretreatments or even used during the enzymatic hydrolysis process, thus increasing the efficiency in obtaining fermentable sugars [55,57]. The ultrasound pretreatment used together with chemical catalysts has helped to obtain a more hydrolyzable cellulose fraction since it favors the weakening of the intermolecular hydrogen bonds of the cellulose, reducing the organization of the chains, which favors the accessibility and hydrophobicity of this fraction [58]. In addition, the integrated process promotes the reduction of energy requirements and costs [59]. When coupled with the enzymatic hydrolysis processes, it has great advantages because the ideal temperature for enzymatic hydrolysis of lignocellulosic biomass is around 50°C, where the maximum effects of cavitation are observed in the ultrasound processes [60]. 3.1.3 Microwave pretreatment Microwaves are a type of nonionizing electromagnetic radiation, with a frequency between infrared and radio waves, which act quickly and safely in heating systems [61]. The mechanism of microwave heating is the inverse of conventional heating, in which heat is transferred to the surface of a material via an external heat source by conduction/convection or radiation and is then transferred to cooler interior regions by thermal conduction.

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Microwave heating is therefore described as a form of energy conversion rather than a form of heating since electromagnetic energy is converted into heat [62]. This heating profile offers many benefits, such as an increase in energy transfer efficiency and a reduction in the heating time of the processes. Several mechanistic theories about the effect of microwave radiation on lignocellulosic biomass have been reported over the last few years. Shi et al. [63] suggested that the presence of polar groups in the cellulosic fraction of the biomass leads to the formation of hot spots in the structure due to the heat generated by microwaves, thus favoring its fractionation. Another theory presented is that microwaves promote a continuous explosion inside biomass particles, accelerating the relocation of the cellulose crystalline structure [64], which would promote the sugar increment after enzymatic processes. The main advantages of microwave heating in relation to conventional heating are lower energy consumption and a reduction of pretreatment time by up to 10 times [65], which avoids the degradation of polysaccharides [44]. On the other hand, the poor energy distribution is reported due to the heterogeneity of the biomasses, in addition to the low penetration of radiation, as mentioned earlier. Thus, like other physical pretreatments, microwave pretreatment has been applied together with chemical pretreatments, thus promoting a considerable increase in enzymatic hydrolysis yields [64,65].

3.2 Chemical and physicochemical pretreatments 3.2.1 Acid pretreatment Acid pretreatment is a widely used and very effective process for improving the enzymatic action in lignocellulosic biomasses due to the high susceptibility of carbohydrate glycosidic bonds to be hydrolyzed at low pH. In this process, the hydronium ions from acid solutions act as catalysts and break down the polysaccharides into sugar monomers, such as glucose, xylose, arabinose, and mannose, which are solubilized in the liquid fraction [66]. Hemicellulose is the main fraction hydrolyzed in acid treatments, even at diluted acid concentrations (0.1%–10%), but cellulose is also hydrolyzed at more concentrated conditions (up to 70%) [46]. Although acid treatment is focused on carbohydrates, less expressively, some lignin is hydrolyzed to form smaller fragments (acid-soluble lignin) and phenolic molecules, which are also solubilized in the liquid fraction [66,67]. Typically, mineral acids are the most used, including sulfuric, hydrochloric, phosphoric, and nitric acids [46]. Sulfuric acid is the most reported source of hydronium ions due to its high catalytic activity, low volatility, and higher efficiency in biomass modification [66,68]. Besides, organic acids are also reported, although less frequently, including formic, maleic, and oxalic acids [46]. The temperature range of acid treatments depends on the acid concentration: diluted conditions are performed at higher temperatures (100–250°C), while concentrated treatments are carried out at lower temperatures ( MnPs > Lacs [23]. While LiPs act by directly oxidizing both phenolic and nonphenolic compounds, MnPs and Lacs can act only on phenolic compounds but can oxidize nonphenolic compounds indirectly through the action of a mediator (Fig. 3). The mechanism of VPs is similar to LiPs and MnPs, since this enzyme is not specific for the MnP substrate, and can also oxidize typical LiP compounds, such as veratryl alcohol, in the absence of manganese, in addition to simple phenols, which are substrates for other peroxidases. One explanation for this is that there are amino acid residues typical of MnPs at the Mn(II) binding site in the molecular structure of VPs, as well as residues typical of LiPs, which involve interaction with veratryl alcohol and aromatic compounds, thus generating this characteristic ambiguity of MnPs and LiPs in VPs [24]. Fungal ligninolytic enzymes are the best known and are present in ascomycetes, basidiomycetes, and deuteromycetes. Among these groups, the most studied are Trametes versicolor, Phanerochaete chrysosporium, Pleurotus ostreatus, Dicomitus squalens, Lentinula edodes, Irpex lacteus, and Cerrena maxima [11].

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FIG. 3 Direct and indirect action of ligninolytic enzymes on phenolic and nonphenolic compounds. L, lignin.

3.1 Laccases (Lacs) Lacs are oxidoreductases that belong to the group of multicopper proteins and have been described for years in plants, fungi, insects, bacteria, and archaea [25–27]. They have four copper atoms in their active site that participate in the reduction of oxygen, with the production of water. Lacs have a lower redox potential when compared to other ligninolytic enzymes; however, fungal lacs continue to show a higher redox potential than bacterial lacs [11,28]. The Lacs active site is well conserved and contains three copper sites: type 1 (T1, one Cu atom), type 2 (T2, one Cu atom), and type 3 (T3, two Cu atoms) per molecule of copper. In laccase, the O2 molecule binds to the Type 2 and Type 3 Cu active sites consisting of a trinuclear cluster for asymmetric activation. Molecular oxygen acts as an electron acceptor, representing a catalytic cycle of substrate oxidation. Electrons are transferred internally from the T1 Cu site to a trinuclear cluster formed by the Type 2 and Type 3 Cu sites, whose O2 is involved in the enzyme’s catalytic mechanism [11]. Lacs are mostly extracellular enzymes that oxidize various aromatic and nonaromatic compounds, including phenols, anilines, some inorganic ions, and a variety of nonphenolic compounds. They act by abstracting an electron from phenols, due to the reduction of Cu2+ to Cu1+, which in turn reduces oxygen to water, allowing the enzyme to act in a cyclic way. Reactions catalyzed by lac are of two types: direct (the substrate is oxidized) and indirect oxidation (the substrate is oxidized in the presence of a mediator) [11,29].

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Due to the characteristic of requiring only molecular oxygen to oxidize a wide variety of substrates, they are considered a “green tool/green catalyst” in biotechnology. The oxidation of nonphenolic aromatic structures by lacs occurs through the oxidation of some mediators, which can be synthetic, such as 2,20 -azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 1-hydroxybenzotriazole (HBT), 2,6-dimethoxyphenol (DMP), or natural, such as methylsyringate (MeS), syringaldehyde, p-coumaric acid, and vanillin [30,31] (Fig. 4). The low redox potential of lacs only allows the direct oxidation of phenolic units of lignin, which represents a small percentage of the polymer. Studies of a laccase-mediator (SLM) system have gradually increased in recent years, mainly due to its wide industrial applicability, in paper bleaching processes, polymer modification, degradation and detoxification of environmental effluents, and wood delignification [32]. In the case of SLM applied to lignin degradation, the initial substrate of the enzyme is a low molar mass compound (mediator) that is in turn oxidized, forming radicals that act as a diffusing agent, oxidizing lignin. This system is of great value, since lac is a large enzyme that is not able to pass through the fiber of the secondary wall to come into direct contact with lignin [33]. Although most of the mediators studied are synthetic molecules, there is an effort to search for natural mediators, such as 3-hydroxy-anthranilic acid, produced by the fungus Pycnoporus cinnabarinus [34,35]. The search for natural mediators is interesting, since they are safer, less polluting, and possibly, more economical than synthetic mediators [36,37]. Regarding the ability of lacs to degrade the lignin present in lignocellulosic biomass, the change in lignin structure is initiated when this recalcitrant structure is oxidized by the abstraction of a single electron from a phenylpropane subunit. This electron abstraction activates the lignin, creating an active radical and making lignin more reactive at a given location. Consequently, this lignin activation can induce different reactive events, such as cleavages, modifications, and/or couplings (Fig. 5) [39].

FIG. 4

Catalyzed cycle of direct oxidation (A) and indirect oxidation by laccases (B) [18]. Illustration adapted from Kumar A, Chandra R. Ligninolytic enzymes and its mechanisms for degradation of lignocellulosic waste in environment. Heliyon 2020;6(2):e03170.

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FIG. 5 Proposal for plausible changes that may occur during the action of laccase on lignin. Each box signifies events that should be attributed to a specific type of event. The arrows indicate how one event may induce the next. Specific reactions are illustrated using a phenolic β-1 model dimer [38]. From Munk L, Sitarz AK, Kalyani DC, Mikkelsen JD, Meyer AS. Can laccases catalyze bond cleavage in lignin? Biotechnol Adv 2015;33(1):13–24.

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Lac is known to act by catalyzing polymerizations, while SLM catalyzes depolymerizations [38]. However, recent studies have demonstrated its potential to both polymerize and depolymerize lignin, even in the absence of mediators. Li et al. [40] suggested that a competition between polymerization and depolymerization occurs when the β-O-40 bond of poplar wood is broken during a steam explosion pretreatment. The enzyme’s ability to perform one of these actions is influenced by the surrounding conditions and the presence of mediators. A similar competition between reaction pathways is likely to occur after the activation of lignin subunits (Fig. 5). It also appears that the type of radical created by the SLM can influence the course of action for depolymerization or polymerization [11]. The existence of competition between reactions has already been reported by Moldes et al. [41] and Barneto et al. [42] who investigated the effect of various SLMs on the removal of lignin residues in cellulose pulp. The results showed that the choice of mediator is an important factor, but how the reaction conditions favor one of the two specific actions is still unclear [39].

3.2 Lignin peroxidase (LiP) LiPs have a high redox potential and can remove electrons from phenolic and nonphenolic aromatic structures; in the case of lignins, this gives rise to cation radicals. The enzyme is activated by oxidation with H2O2, leading to the formation of compound I (CI), which is a twoelectron deficient complex. The reduction of CI to the native enzyme occurs through two steps, with the abstraction of one electron at a time. The reduction of CI to CII, and also of CII to C0, can occur through the oxidation of phenolic and nonphenolic substrates, leading to the formation of cation radicals (Fig. 6A). Veratryl alcohol (3,4-dimethoxy-benzyl alcohol), produced by P. chrysosporium, was one of the first enzymatic mediators described and, when oxidized by LiP, generates an extremely unstable cation radical that can oxidize lignin (Fig. 6B). In all LiPs, there is invariably a tryptophan residue in the protein chain. It is assumed that this tryptophan acts as an electron transfer link with aromatic substrates that cannot have direct contact with the heme group of the enzyme [43]. LiP also plays an important role in the degradation of lignin, phenolic and nonphenolic compounds, including dyes and hydrocarbons. Fig. 6C shows the catalytic cycle of an iron-dependent LiP, with its reaction comprising the oxidative catalysis of veratryl alcohol and the oxidative catalysis of lignin monomer and dimer compounds.

3.3 Manganese peroxidases (MnPs) MnPs are Mn2+-dependent enzymes for the reduction of a compound, being able to abstract electrons only from phenolic structures (Fig. 7). They contain a binding site for manganese, making them different from LiPs, which have a tryptophan residue. The enzyme is also activated by its oxidation by H2O2, leading to the formation of compound I (CI), which is a two-electron deficient complex. The reduction of CI to the native enzyme also occurs in two steps, with the abstraction of one electron at a time. MnPs depend on Mn2+ for the reduction of CII to C0, while compound I can be reduced to compound II by means of the oxidation of a phenolic structure or a Mn2+ atom. The Mn3+ formed is quite reactive, can act as a mediator of MnP, and is normally stabilized by chelators produced by the fungus itself, such as oxalate.

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(A) Catalytic cycle of lignin peroxidases. S, phenolic or nonphenolic aromatic substrate. (B) Oxidation of veratryl alcohol by lignin peroxidase. AV, veratryl alcohol (adapted from Goodell B, Jellison J, Liu J, Daniel G, Paszczynski A, Fekete F, Krishnamurthy S, Jun L, Xu G. Low molecular weight chelators and phenolic compounds isolated from wood decay fungi and their role in the fungal biodegradation of wood. J Biotechnol 1997;53 (2–3):133–162). (C) Scheme of the catalytic action of lignin peroxidase (LiP) on lignin (from Singh AK, Bilal M, Iqbal HMN, Raj A. Lignin peroxidase in focus for catalytic elimination of contaminants—a critical review on recent progress and perspectives. Int J Biol Macromol 2021;177:58–82).

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FIG. 7 Catalytic cycle of manganese peroxidase (MnP). (A) Catalytic cycle of manganese peroxidases. FOH, phe-

nolic substrate. (B) Production of the Mn3+ complex during the oxidation of a phenolic structure or a Mn2+ atom. Adapted from Goodell B, Jellison J, Liu J, Daniel G, Paszczynski A, Fekete F, Krishnamurthy S, Jun L, Xu G. Low molecular weight chelators and phenolic compounds isolated from wood decay fungi and their role in the fungal biodegradation of wood. J Biotechnol 1997;53(2–3):133–162.

The Mn3+-oxalate complex, in turn, can be reduced at the expense of the oxidation of another phenolic structure. The chelates formed can even lead to the formation of superoxide, which becomes a source of peroxides in the absence of H2O2 [43].

3.4 Versatile peroxidase (VP) VP is an oxidoreductase, also known as hybrid peroxidase, which is capable of carrying out the catalytic activities of MnP and LiP, meaning that they can oxidize Mn2+ and nonphenolic compounds with high redox potential [44]. These groups of enzymes are capable of oxidizing phenolic, nonphenolic, and lignin derivatives in the absence of manganese and do not require any mediator for oxidation. Molecular characterization of these VP enzymes is seen as LiP and MnP isoenzymes that contain four amino acids (methionines) at positions 152, 247, 262, and 265 [45]. Fig. 8 shows a scheme of the joint action of ligninases in the deconstruction of lignin [46].

4. Ligninolytic enzymes and prospects The presence of lignin is a challenge in studies on the use of the fraction of polysaccharides present in lignocellulosic biomass, and the efficiency of the delignification process regulates the yield of products/compounds obtained. The pretreatment of lignocellulosic biomass using different species of bacteria and fungi and the direct use of ligninases are promising resources to transform abundantly available natural resources into products with high biotechnological potential. Although this process is possible, the problems of low productivity, long duration, and low yield must be solved for industrial application. Furthermore, the optimization of process parameters and appropriate selection of microorganisms must be undertaken in order to carry out the biological conversion of lignocellulosic biomass.

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FIG. 8 Representative scheme of the joint action of ligninases. From Bilal M, Qamar AS, Yadav V, Cheng H, Khan M, Adil SF, Taherzadeh MJ, Hafiz MN, Iqbal HMN. Exploring the potential of ligninolytic armory for lignin valorization – a way forward for sustainable and cleaner production. J Clean Prod 2021;326:129420.

Enzyme performance and stability must also be improved by manipulating genetic and metabolic engineering pathways. This combination of conservative and modern technologies will pave the way for the fungal integrated lignocellulosic biorefinery, producing environmentally acceptable energy and other value-added products [14].

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C H A P T E R

8 Biochemical and biotechnological aspects of microbial amylases Jinu John Department of Biotechnology, CMS College, Kottayam, Kerala, India

1. Introduction Enzymes are biocatalysts that alter the reaction rate and produce specific products in biological reactions. Their applications in the manufacturing processes make it economical and eco-friendly with minimum energy utilization along with better substrate specificity. Amylases are the class of enzymes that catalyze the hydrolysis of glycosidic bonds in starch and convert them into smaller carbohydrate molecules such as glucose and maltose. Three categories of amylases have been reported, alpha, beta, and gamma, which differ in the way they attack the bonds of the starch molecules. α-Amylase is present in animals including humans, plants, and microbes and is essential for metabolism. Microorganisms are major sources of industrially important enzymes, among which α-amylase for commercial applications is mainly derived from the genus Bacillus. β-Amylase is found in microbes and plants, while γ-amylase is found in both plants and animals. α-Amylases are one among the widely used industrial enzymes that find applications in food, beverages, detergent, textile, and pharmaceutical industries. Microbial α-amylases randomly cleave α-1,4-glycosidic linkages in starch leading to the formation of limited dextrins. α-Amylases from different microbial sources vary in their properties, thus, suit specific applications; moreover, they can be engineered for specific properties by modern genetic engineering. The pervasive nature, easy production, and wide range of applications make amylase an industrially pivotal enzyme [1]. In the past decades, there is a noticeable shift from the acid hydrolysis of starch to the use of starchconverting enzymes in the production of maltodextrin, modified starches, or glucose and fructose syrups. Increasing demand for more environmentally friendly processes will lead to the increasing use of hydrolytic and other enzymes instead of chemicals. The conditions of temperature and pH used in biotechnical industrial processes may be different from the physiological

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conditions in which enzymes operate optimally. With the aid of genetic engineering, it is possible to alter or engineer protein properties in a controlled manner. Cloning, expression, structural improvements, and protein engineering of α-amylases from different bacterial and archaeal sources have provided enzymes with the desired characteristics for specific industrial applications [2]. Starch is the major storage and most abundant form of carbohydrates, available for modern dietary energy intake. It is a biopolymer of glucose composed of straight-chain α-1,4-linked glucose polymer (amylose) and branched chains of glucose (amylopectin) with molecular masses ranging from 105 to more than 106 Da. Amylopectin is branched starch with a backbone of α-1,4-linked glucose and α-1,6-linked glucose branches about every 20–25 residues. Amylose is water-insoluble, while amylopectin is a branched water-soluble polysaccharide with short α-1,4-linked linear chains of 10–60 glucose units, and α-1,6-linked side chains with 15–45 glucose units (Fig. 1). The ratio of the two polysaccharides depends on the botanical origin of the starch, but the representative levels of amylose to amylopectin are 25–28 and 72%–75%, respectively. The ratio of amylose and amylopectin affects the starch structure in terms of crystallinity, size of the granules, and chemical nature of polymers within the granule [3]. The major natural sources of industrial starch are wheat, rice, tapioca, potato, and maize. They find applications in the food industry such as bakery products, canned jams, confectionaries, and commercial production of caramel and monosodium glutamate (MSG). It is also used in nonfood industries such as paper, textile, building materials, mining, and consumer products. The emerging trend of biofuels and biomaterials and the consumption of starch in this industry are expected to drive the growth of the industrial starch market and amylases. Glucose syrup is extensively used in a large number of confectionery items and in other bakery products owing to its ability to add sweetness and bulk. Moreover, modified starches are already being used in several bio-fuel, bio-plastic, and food industry applications.

2. Amylase: The starch-digesting enzyme Amylase is considered one of the first enzymes in history to be scientifically investigated and was first described in the early 1800s. It was initially termed diastase and later renamed amylase in the early 20th century [4]. The general classification of amylases is based on how the glycosidic bonds are attacked by the enzyme. Ohlsson classified the starch digestive enzymes in malt as α- and β-amylases according to the anomeric type of sugars produced by the enzyme reaction. α-Amylase (1,4-α-D-glucan-glucanhydrolase, EC. 3.2.1.1) is a widely distributed secretary enzyme [5]. In the digestive systems of humans and many other mammals, an α-amylase called ptyalin is produced by the salivary glands, and pancreatic amylase is secreted into the small intestine. The bacterial α-amylases are found to be quite distinct from the eukaryotic enzymes evolutionarily. Among all the bacterial enzymes, α-amylases are the most diverse [6]. α-Amylases from different organisms share about 30% amino acid sequence identity and all belong to the same glycosyl hydrolase family 13 [7]. The amylases enzymes produced by the filamentous fungi such as Aspergillus oryzae and Aspergillus niger are extensively used in the industry. The products of amylase action on starch are referred to as

2. Amylase: The starch-digesting enzyme

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FIG. 1 Chemical structure of amylose and amylopectin.

dextrins. Chemically, dextrins are α-1,4-linked glucose dimers (maltose), α-1,4-linked glucose trimers (maltotriose), and branched oligosaccharides of 6 to 8 glucose units that contain both α-1,6- and α-1,4-linkages (limit dextrins).

2.1 α-Amylase: Structure and mechanism of action α-Amylases are endoenzymes, which preferentially cleave interior α-1,4-linkages and have very low activity against the bonds of terminal glucose units. Further, it cannot hydrolyze the α-1,6-linkages in amylopectin. The three-dimensional (3D) structures of α-amylases have revealed monomeric, calcium-containing enzymes, with a single polypeptide chain folded into three domains (A–C). The most conserved domain in α-amylase family enzymes, the A-domain, consists of a highly symmetrical fold of eight parallel β-strands arranged in a barrel encircled by eight α-helices. The highly conserved amino acid residues of the α-amylase family involved in catalysis and substrate binding are located in loops at the C-termini of

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β-strands in this domain. This is typical of all enzymes belonging to the α/β-barrel protein family [8]. The B-domain of α-amylases protrudes between β-sheet no 3 and α-helix no. 3. It ranges from 44 to 133 amino acid residues and plays a role in the substrate or Ca2+ binding [9]. All known α-amylases, with a few exceptions, contain a conserved Ca2+ binding site, which is located at the interface between domains A and B [10]. The domain C of α-amylases is relatively conserved and folds into an antiparallel β-barrel. The orientation of domain C relative to domain A varies depending on the type and source of amylase [11]. Structural studies have confirmed that the active sites of glycosyl hydrolases are composed of multiple binding sites, or subsites, for the sugar units of polymeric substrates. The open active site cleft is formed between domains A and B, so that residues from domain B participate in substrate binding. The substrate-binding sites are commonly lined with aromatic residues, which make hydrophobic stacking interactions with the sugar rings. In addition, the active sites contain many residues which form hydrogen bonds to the substrate either directly or via water molecules [12,13]. X-ray crystallographic study of Taka-amylase A revealed the presence of three acidic residues, i.e., one glutamic and two aspartic acids at the center of the active site [14], and subsequent mutational studies have shown that these residues are essential for catalysis [15]. The glutamic acid residue is believed to be the proton donor, while the first of the two conserved aspartic acids appearing in the amino acid sequence of an α-amylase family member is thought to act as the nucleophile [16]. The optimum pH of α-amylase is 6.7–7.0. There are reports of α-amylase with acidic pH optima of 1 and 3 from Bacillus sp. and Alicyclobacillus acidocaldarius, respectively [17]. While some other alkaliphilic Bacillus spp. are reported to have pH optima of 9–10.5 [18]. An extremely alkaline α-amylase with pH optima of 11.5 was reported from Bacillus sp. GM8901 [19]. The optimum temperature for bacterial α-amylases is range from 25°C to around 100°C [20,21]. Archaeal α-amylases, in general, are thermostable and acidic (pH 5–6) in nature. For example, the highest temperature optimum has been reported as 100°C and 130°C from archaea, Pyrococcus furiosus, and P. woesei, respectively [22,23]. Many factors contribute to the stability of thermostable proteins including the presence of hydrogen bonds, electrostatic interactions, salt bridges, hydrophobic interactions, disulfide bonds, reduced entropy of unfolding, oligomerization, and increased occurrence of proline residues, and others [3]. For several decades, a wide array of techniques have been developed allowing the engineering of the enzyme properties. Directed evolution and site-directed mutagenesis are powerful tools for engineering enzymes, to improve their functions and alter their properties like activity, selectivity, substrate specificity, stability, and solubility. Thermostability, activity in the acidic range, and Ca2+-independence of α-amylases are desirable for their use in the starch saccharification process, and activity in the alkaline range and oxidative stability are a prerequisite for its applicability in the detergent industry. These and several other properties of α-amylases have been improved by various methods such as site-directed mutagenesis or the directed evolution approach [24]. According to the degree of hydrolysis, α-amylases are divided into two categories. Saccharifying α-amylases produce free sugars and reduce the viscosity less rapidly in comparison with the amount of reducing sugars released. While liquefying α-amylases, on the other hand, break down the starch polymer but does not produce free sugars and cause a more rapid reduction in the viscosity of starch pastes. Saccharifying α-amylases hydrolyze 50%–60% and liquefying α-amylases cleave about 30%–40% of the glycosidic linkages of

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starch [25]. α-Amylase has an absolute requirement for calcium ions and is activated by anions such as chloride, bromide, iodide, or fluoride. Heavy metals inhibit the enzyme [26]. Hg2+ ions are found to inhibit α-amylase activity. Inhibition of α-amylase by Hg2+ ions indicates the presence of carboxyl groups in enzyme molecules [27]. Hg2+ is also known to oxidize indole rings and interact with aromatic rings present in tryptophan [28]. There are three steps involved in the catalytic mechanism for retaining glycosyl hydrolases [29]. First, the glycosidic oxygen is protonated by the proton donor (Glu261). This is followed by a nucleophilic attack on the C1 of the sugar residue in subsite-1 by Asp231. Once the glycon part of the substrate leaves, a water molecule is activated presumably by the deprotonated Glu261. This water molecule hydrolyses the covalent bond between the nucleophilic oxygen and the Cl of the sugar residue in subsite-1, thereby completing the catalytic cycle [30]. Termamyl (1,4-α-D-glucan glucano-hydrolase (EC 3.2.1.1)) is a commercial liquid enzyme preparation, from Novozymes, containing an outstandingly heat-stable α-amylase expressed in and produced by a genetically modified strain of Bacillus licheniformis. The enzyme is an endoamylase that hydrolyzes 1,4-α-glucosidic linkages in amylose and amylopectin. Termamyl helps in the rapid conversion of starch into soluble dextrins and oligosaccharides. Considering the advantage of extreme heat stability of this enzyme, Termamyl is used for continuous liquefaction of starch in steam jet cookers or similar equipment operating at temperatures up to 105–110°C. In the alcohol industry, Termamyl is used for the thinning of starch in distilling mashes. Here too, the advantage is taken of the heat stability of the enzyme. Because of the relatively broad pH tolerance and low Ca requirements of the enzyme, it is possible to work without pH adjustment and Ca addition despite conditions not being optimal. This minimizes the risk of Ca scaling in the distillation column. Termamyl is used for adjunct liquefaction in the brewing industry. The advantage is that here the cooking procedure can be simplified due to the extreme heat stability of the enzyme. Termamyl is used in the sugar industry to break down the starch present in cane juice. It facilitates to reduce of the starch content in the raw sugar and simplifies the filtration procedure [31].

2.2 β-Amylase β-Amylase (E.C.3.2.1.2) is an exoenzyme that hydrolyzes every alternate α-1,4-linkage from the nonreducing end, causing an inversion of the anomeric configuration of the liberated maltose to its β-form, and hence, they are called β-amylases. This enzyme bypasses α-1,6linkages of branched substrates producing maltose and high molecular weight β-limit dextrins [32]. In comparison, glucoamylase (E.C.3.2.1.3), also known as amyloglucosidase or γ-amylase, slowly acts on α-1,4-linkages of α-glucans from the nonreducing ends and also α-1,6-linkages. It preferentially degrades polysaccharides with a high molecular weight. These enzymes hydrolyze starch to yield glucose in theoretically 100% yield [33].

2.3 γ-Amylase (EC 3.2.1.3) γ-Amylase cleaves α(1–6) glycosidic linkages, in addition to cleaving the last α(1–4) glycosidic linkages at the nonreducing end of amylose and amylopectin, yielding glucose. γ-amylase is most efficient in acidic environments compared to other amylases and has an optimum pH of 3.

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3. Commercial production of α-amylases α-Amylase isolated from fungal and bacterial sources has dominated applications in industrial sectors due to their economy, consistency, less time and space requirement for production, and ease of process optimization and modification. Amylases from plant and microbial sources are employed for centuries for food preparation. Barley amylases are used in the brewing industry. Fungal amylases are widely used in the preparation of oriental foods [34]. Among bacteria, Bacillus sp. is widely used for the production of amylases. Species like B. subtilis, B. stearothermophilus, B. licheniformis, and B. amyloliquefaciens are known to be good producers of α-amylase. Similarly, filamentous fungi have also been widely used for the production of amylases. Among fungi, the genus Aspergillus has been most commonly employed for the production of α-amylase [35]. Industrially important enzymes have traditionally been produced in submerged fermentation, but these enzymes are also produced by solid-state fermentation. Solid-state fermentation using these molds turned out to be a cost-effective production technique for enzymes. The utilization of wheat bran for the production of α-amylase is found to be an efficient medium for the production of α-amylases [36]. Among the physical parameters, the temperature and pH of the medium play an important role in α-amylase production. The influence of temperature on α-amylase production is related to biomass production. α-Amylase production has been reported from thermophilic and hyperthermophilic bacteria and archaea like Pyrococcus, Thermococcus, and Sulfolobus species at comparatively high temperatures [37]. High enzyme production was found to be supported by different carbon sources such as starch, fructose, glucose, and rice flour [38]. Another major factor that influences α-amylase production is nitrogen source. Saxena et al. have reported a maximum yield of α-amylase with organic nitrogen sources [39]. Soybean meal and peptone were found as the best nitrogen source for α-amylase by Bacillus sp. [40]. Strains of Bacillus stearothermophilus and B. amylolyticus secreted maximum α-amylase in a medium supplemented with 1% peptone, 0.5% yeast extract, and 0.5% maltose under vigorous shaking conditions [41]. In general, α-amylase production is found to be inducible [42], but in a few cases, α-amylase production is also constitutive [43]. Like most other inducible enzymes, α-amylase production is subjected to catabolite repression by maltose and glucose, starch hydrolytic products [44] except for some Bacillus strains [45]. The combination of low molecular weight dextran with Tween-80 increased 27-fold higher α-amylase production [46]. Various metal ions like Ca2+, Fe2+, Mg2+, and K+ are added to the α-amylase production medium [28]. In recent years, the potential of using microorganisms as biotechnological sources of industrially relevant enzymes has stimulated interest in the exploration of extracellular enzymatic activity in several microorganisms. Genetic engineers have made several attempts to increase the yield and optimization of physical and chemical characteristics of amylases, by cloning α-amylase encoding genes from several bacteria and archaea in heterologous hosts such as E. coli. A thermostable α-amylase gene of 1203 bp encoding a 401-amino acid protein of Thermococcus profundus was cloned and expressed in E. coli. Recombinant α-amylase production was 155.5-fold higher than that in the wild strain [47]. Another α-amylase gene (1383 bp encoding 461 amino acid residues) from a hyperthermophilic archaeon, Pyrococcus sp. KODl was cloned and expressed in E. coli. The optimum temperature and pH for the enzyme activity are 90°C and pH 6.5. Ca2+ (2.0 mM) enhanced the thermostability of this enzyme [48]. A gene-encoding acidic, thermostable, and raw starch-hydrolyzing α-amylase was

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cloned from an extreme thermophile G. thermoleovorans and expressed [49]. Recombinant fungal amylases have been isolated from mesophilic hosts such as Aspergillus oryzae and are of particular interest to the food industry as they match the temperature and pH range used in typical applications in the baking process, where they are active in the dough but inactivated during baking. Fungal enzymes used for the production of food and food ingredients are considered generally recognized as safe (GRAS) by organizations including the USFDA [50]. Increased demands for α-amylases with desired properties for industrial applications also encouraged the exploration of metagenomes from different habitats. The screening of clones from a soil metagenomic library constructed in a pUC 19 vector led to finding a putative amylase gene (amyM), which was overexpressed and purified. This enzyme was optimally active at 42°C and pH 9.0 with transglycosylation activity and the requirement for Ca2+ for its stability [51]. The downstream processing of commercially produced enzymes depends on the nature of the application or its end-use. Downstream processing of enzyme production generally constitutes a major percentage of overall production cost especially if end purity requirements are stringent. The purification process in downstream processing after fermentation strongly depends on the market, processing cost, final quality, and available technology. Crude enzymes isolated by precipitation and membrane separations are finally purified by chromatographic techniques [38]. The industrial use of α-amylase generally does not require purification of the enzyme, but enzyme applications in pharmaceutical and clinical sectors require high-purity amylases. Different strategies for the purification of enzymes have been investigated, exploiting specific characteristics of the target biomolecule. Laboratory-scale purification for α-amylase includes various combinations of ion exchange, gel filtration, hydrophobicity interactions, and reverse-phase chromatography. Alternatively, α-amylase extraction protocols using organic solvents such as ethanol, acetone, and ammonium sulfate precipitation and ultrafiltration have been proposed [52]. Liquidliquid extraction, an alternative simple purification procedure, involves the transfer of certain components from one phase to another when immiscible or partially soluble liquid phases are brought into contact with each other. Purification of biomolecules using liquid-liquid extraction has been successfully carried out on a large scale for more than a decade. Advantages of using this system are lower viscosity, lower cost of chemicals, and shorter phase separation time [53].

4. Applications of α-amylase The shift toward eco-friendly approaches and the introduction of novel biotechnological tools resulted in the application of enzymes in various industrial sectors. The specificity for substrates, economic viability, and availability made enzymes a popular tool for the biotransformation of various organic molecules. The amylolytic enzymes that produce specific malto-oligosaccharides in high yields from starch have gained significant attention, and these enzymes find application in the food, chemical, and pharmaceutical industries. It is extensively used in pharmaceutical industries in digestive tonics, for hydrolysis of starch to produce different sugars like glucose and maltose, which have several applications (Table 1). The amylase enzymes from the Bacillus species are of special interest for large-scale

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TABLE 1 Source and applications of various amylases used in industries. Applications Enzyme activity

Production organism

Donor

Food

Amylase (alpha)

Aspergillus niger*

None

Yes

Amylase (alpha)

Aspergillus oryzae

None

Yes

Amylase (alpha)

Bacillus amyloliquefaciens or subtilis

Bacillus sp.

Yes

Yes

Yes

Amylase (alpha)

Bacillus amyloliquefaciens or subtilis

None

Yes

Yes

Yes

Amylase (alpha)

Bacillus amyloliquefaciens or subtilis

Thermoactinomyces sp.

Yes

Yes

Amylase (alpha)

Bacillus licheniformis

Bacillus sp.

Yes

Amylase (alpha)

Bacillus licheniformis

None

Yes

Amylase (alpha)

Bacillus stearothermophilus

None

Yes

Amylase (alpha)

Microbacterium imperiale

None

Yes

Amylase (alpha)

Streptomyces violaceoruber

Streptomyces sp.

Yes

Amylase (alpha)

Trichoderma reesei or longibrachiatum

Aspergillus sp.

Yes

Amylase (beta)

Bacillus flexus

None

Yes

Amylase (beta)

Barley

None

Yes

Amylase (beta)

Soybean

None

Yes

Amylase (beta)

Sweet potato

None

Yes

Feed

Technical

Yes

Yes Yes

Yes Yes

Yes

biotechnological processes due to their remarkable thermostability and availability of efficient expression systems for these enzymes [10].

4.1 Food industry Amylases are extensively employed in various food industries such as baking, brewing, preparation of digestive aids, production of cakes, fruit juices, and starch syrups. The major applications of α-amylases are in the starch-based food industries are its use for starch hydrolysis in the starch liquefaction process, which converts starch into fructose and glucose syrups. Among these, the foremost applicability of α-amylases in the industry is in the formation of high fructose corn syrups, which are used in huge quantities in the beverage industry as sweeteners for soft drinks. Amylase enzymes can be added to the dough of bread to degrade the starch in the flour into smaller dextrins, which are subsequently fermented by the yeast. The addition of α-amylase to the dough results in enhancing the rate of fermentation and the reduction of the viscosity of the dough, resulting in improvements in the volume and texture of the product. Moreover, it generates additional sugar in the dough, which improves the taste, aroma, crust color, and toasting qualities of the bread. Besides generating fermentable compounds, α-amylases also have an antistaling effect in bread baking and enhance the

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softness retention of baked goods, thereby increasing the shelf life of these products. Supplementation of α-amylases to the dough improves the crumb grain, volume, texture, flavor, and shelf-life of the bread [54]. Currently, thermostable maltogenic amylase of Bacillus stearothermophilus is used commercially in the bakery industry. Amylases are also used for the clarification of beer or fruit juices or for the pretreatment of animal feed to improve the digestibility of fiber [55]. Sharma and Satyanarayana (2010) report that the bread prepared by supplementing the dough with α-amylase of B. acidicola had a higher moisture content, reducing sugars, and soluble protein than the bread made by using other enzymes. It was also found to have a better shelf-life of 3 days at room temperature and showed amelioration in the texture and softness [56]. Amylases play an important role in the industries of chocolates and confectionery. Amylases are treated with cocoa slurries to produce chocolate syrup, in which chocolate starch is dextrinizing and thus syrup does not become thick. Cocoa-flavored syrups having a high cocoa content and excellent stability and flow properties at room temperature are produced by using amylolytic enzymes and a sufficient proportion of Dutch-process cocoa to provide a syrup pH of 5.5 to 7.5. The stabilized cocoa-flavored syrups are used in the production of quiescently frozen chocolate flavored confectionery [57].

4.2 Textile industry Amylases are used in the textile industry for the desizing process. To prevent breakage of threads during the weaving process, the threads are usually reinforced by coating (sizing) with a gelatinous substance like starch. As a consequence of the sizing, the warp threads of the fabric are not able to absorb water or finishing agents to a sufficient degree. For that, the size must be removed (desizing) before finishing. The complete removal of starchcontaining size without fiber damage is best obtained by using enzymatic desizing agents. An amylase enzyme for desizing must be active at 70°C or higher and optimum pH 5.5–6.5, although efficient desizing has been reported at lower temperatures as well. Aquazym 120 L, Aquazym Ultra 250 L, and Termamyl 60 L, enzymes from Novo Nordisk are commercially for used desizing. The α-amylases remove selectively the size and do not attack the fibers. Amylase from Bacillus strain was employed in textile industries for quite a long time [58,59].

4.3 Bio-fuel production Ethanol is a renewable biofuel because it is made from biomass. Ethanol-fueled vehicles produce lower carbon dioxide emissions, and the same or lower levels of hydrocarbon and oxides of nitrogen emissions. For ethanol production, starches from grain, potatoes, and so on, are the most used substrate due to their low price and easily available raw material in most regions of the world. The bioconversion of starch into alcohol involves a natural process and saccharification, where starch is converted into sugar using an amylolytic microorganism or enzymes like α-amylase, followed by fermentation, where sugar is converted into ethanol by fermenting microorganisms such as yeast Saccharomyces cerevisiae. Starch from grain, potatoes, and so on, is used as raw material that helps to manufacture ethyl alcohol. Among bacteria, α-amylase obtained from thermoresistant bacteria like Bacillus licheniformis

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or engineered strains of Escherichia coli or Bacillus subtilis is used during the first step of hydrolysis of starch suspensions [60]. Chi et al. (2009) report a new yeast strain, which is developed by protoplast fusion was between the amylolytic yeast Saccharomyces fibuligera and S. cerevisiae can directly produce ethanol from starch without the need for a separate saccharifying process [61].

4.4 Detergent industry The use of enzymes in detergents formulations enhances the detergent’s ability to remove tough stains and makes the detergent environmentally safe. The amylases are specifically supplemented to the detergent to digest starchy stains. Most of the solid and liquid detergents commercially available today contain alkaline enzymes. The advantages of using alkaline enzymes in the detergent formulation are that they can effectively and environmentally friendly remove stains as they reduce the use of synthetic chemical ingredients. Amylases active at low temperatures are preferred as the energy consumption gets reduced, and the whole process becomes cost-effective [62]. These enzymes are used in detergents for laundry and automatic dishwashing to remove the residues of starchy foods such as gravies, chocolate, custard, and so on. Amylases have activity at lower temperatures and alkaline pH, maintaining the necessary stability under detergent conditions and the oxidative stability of amylases is one of the most important criteria for their use in detergents where the washing environment is very oxidizing [63]. Removal of starch from surfaces is also important in providing a whiteness benefit, since starch can attractant dirt or particulate soils. Amylase derived from Bacillus or Aspergillus is used in the detergent industry [64].

4.5 Paper industry In the paper industry, starch is used to improve the stiffness and bonding within a sheet of paper. It is also widely used for the preparation of sizing agents such as alkenyl succinic anhydride. The use of α-amylases within the pulp and paper business is for the modification of starch of coated paper, that is for the assembly of low-viscosity, high molecular weight starch. The sizing or coating treatment serves to make the surface of paper sufficiently smooth and strong to improve the writing quality of the paper. Commercial microbial amylases such as Amizyme (PMP Fermentation Products, Peoria, United States), Termamyl, Fungamyl, BAN (Novozymes, Denmark), and α-amylase G9995 (Enzyme Biosystems, United States) used in the paper industry [65].

4.6 Other promising applications Enzyme-modified starch is being used for several industrial purposes. Amylases are used to improve the nutritional value of starch materials in animal feed. Modified starch is used in the manufacture of gypsum board for drywall construction. αAmylases are currently used for the biotechnological treatment of food processing wastewater, which can produce valuable products such as microbial biomass protein and also purifies the effluent [66]. There are also reports on the effective utilization of α-amylases biodegradation of n-alkanes and the synthesis of nanoparticles. The efficiency of biodegradation of α-amylase from Bacillus subtilis TB1 was found to be better in the presence of starch and the obtained residual hydrocarbons in the

References

201

system were 53% less than the samples without starch. These observations were found to be supportive of the data obtained from in silico docking of α-amylase with different molecular weight n-alkanes. These findings confirmed the catalytic effect of α-amylase on n-alkanes degradation [67]. Arunkumar et al. (2013) report the utilization of extracellular α-amylase of M. luteus for the synthesis of gold nanoparticles [68].

4.7 Biomedical significances α-Amylases with suitable properties would be potentially useful in pharmaceutical and fine chemicals. It was used as a pharmaceutical aid for the treatment of digestive disorders. Synthetic and natural biodegradable polymers have been a major focus of interest in pharmaceutical research. The biodegradable polymers are used to control the drug release rate from parenteral controlled delivery systems. Biodegradable polysaccharide matrices are of interest since the degradation of a natural product like starch occurs naturally in the human body. Alpha-starch (pregelatinized starch) and cross-linked starch have been used as hydrogels [69]. Amylase is primarily used in diagnosing pancreatic diseases. Amylase inhibitors such as acarbose have been used in the treatment of type 2 diabetes and have been shown to reduce hemoglobin A1C and peak postprandial glucose [70].

5. Conclusion and future perspectives Enzymes are considered a useful alternative to conventional process technology in the industrial fields because of their advantages over chemical catalysts such as they provide efficient, eco-friendly, and economical procedures. α-Amylases are being used in several industries for a variety of applications ranging from the food industry to the paper and textile industries. The enzyme has crucial applications including the production of fructose syrup, environmentally safe detergents, and baked products. It is used for biofuel production with starch as a raw material, which provides an alternative to fossil fuel, thus this enzyme is a ray of hope. Being an industrially important enzyme that helps to keep the environment clean, more research has to be focused on microbial amylases. Exploration of α-amylases from novel sources by using newer technologies and approaches must be continued. Cloning, expression, structural studies, and protein engineering of α-amylases from different bacterial and archaeal sources have been carried out for developing enzymes with the desired characteristics of specific industrial applications. It is also essential to bring down enzyme production costs and meet consumer demands in all applicable sectors.

References [1] Suriya JS, Bharathiraja M, Krishnan P, Manivasagan S, Kim K. Marine microbial amylases: properties and applications. In: Kim S-K, Toldra´ F, editors. Advances in Food and Nutrition Research, 79. Academic Press; 2016. p. 161–77 [chapter eight]. [2] Anu K. Structure-function studies of two polysaccharide-degrading enzymes: Bacillus stearothermophilus α-amylase and Trichoderma reesei cellobiohydrolase II. 277. Espoo Technical Research Centre of Finland, VTT Publications; 1996. p. 97.

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[3] Mehta D, Satyanarayana T. Bacterial and archaeal α-amylases. Diversity and amelioration of the desirable characteristics for industrial applications. Front Microbiol 2016;7:1129. [4] Akinfemiwa O, Muniraj T. Amylase. In: StatPearls. Treasure Island (FL): StatPearls; 2021. [5] John D, McCarter G, Withers S. Mechanisms of enzymatic glycoside hydrolysis. Curr Opin Struct Biol 1994;4 (6):885–92. [6] Pandey A, Nigam P, Soccol CR, Soccol VT, Singh D, Mohan R. Advances in microbial amylases. Appl Biochem Biotechnol 2000;31:135–52. [7] Henrissat B, Bairoch A. New families in the classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J 1993;293(3):781–8. [8] Farber G, Petsko G. The evolution of α/ß–barrel enzymes. Trends Biochem Sci 1990;15(6):228–34. [9] Van Der Maarel M, Uitdehaag J, Leemhuis H, Dijkhuizen. Properties and applications of starch-converting enzymes of the α-amylase family. J Biotechnol 2002;94(2):137–55. [10] Prakash O, Jaiswal N. Alpha-amylase: an ideal representative of thermostable enzymes. Appl Biochem Biotechnol 2009;160(8):2401–14. https://doi.org/10.1007/s12010-009-8735-4. [11] Bayer E, Morag E, Wilcheck M, Lamed R, Yaron S, Shoham Y. Cellulosome domains for novel biotechnological application. In: Petersen B, Srensson B, Pedersen S, editors. Carbohydrate bioengineering. Progress in biotechnology. Elsevier; 1995. p. 251–9. [12] Aleshin A, Firsov L, Honzatko R. Refined structure for the complex of acarbose with glucoamylase from Aspergillus awamori Var. X100 to 2.4-A resolution. J Biol Chem 1994;269(22):15631–9. [13] Svensson B, Sogaard M. Mutational analysis of glycosylase function. J Biotechnol 1993;29(1):1–37. [14] Matsuura Y, Kusunoki M, Harada W, Kakudo M. Structure and possible catalytic residues of Taka-amylase A. J Biochem 1984;95:697–702. [15] Janecek S. Alpha-amylase family: molecular biology and evolution. Prog Biophys Mol Biol 1997;67:67–97. [16] Uitdehaag J, Mosi R, Kalk K, van der Veen B, Dijkhuizen L, Withers S, Dijkstra B. X-ray structures along the reaction pathway of cyclodextrin glycosyltransferase elucidate catalysis in the α-amylase family. Nat Struct Biol 1999;6(5):432–6. [17] Schwermann B, Pfau K, Liliensiek B, Schleyer M, Fischer T, Bakker EP. Purification, properties and structural aspects of a thermoacidophilic α-amylase from Alicyclobacillus acidocaldarius ATCC 27009: insight into acidostability of proteins. Eur J Biochem 1994;226:981–91. [18] Shinke R, Aoki K, Murokam S, Inoue T, Babat T. Alkaline and thermophilic amylases of industrial use. In: Dordick JS, Russell AJ, editors. Enzyme engineering XIII, vol. 799. New York, NY: Annals of the New York Academy of Sciences; 1996. p. 332–40. [19] Kim TU, Gu BG, Jeong JY, Byun SM, Shin YC. Purification and characterization of a maltotetraose forming alkaline (alpha)-amylase from an alkalophilic Bacillus sp. GM8901. Appl Environ Microbiol 1995;61:3105–12. [20] Feller G, Bussy O, Houssier C, Gerday C. Structural and functional aspects of chloride binding to Alteromonas haloplanctis alpha-amylase. J Biol Chem 1996;271:23836–41. [21] Rao JLUM, Satyanarayana T. Purification and characterization of a hyperthermostable and high maltogenic α-amylase of an extreme thermophile Geobacillus thermoleovorans. Appl Biochem Biotechnol 2007;142:179–93. [22] Koch R, Spreinat A, Lemke K, Antranikian G. Purification and properties of a hyperthermoactive α-amylase from the archaeo-bacterium Pyrococcus woesei. Arch Microbiol 1991;155:572–8. [23] Laderman KA, Davis BR, Krutzsch HC, Lewis MS, Griko YV, Privalov PL, et al. The purification and characterization of an extremely thermostable α-amylase from hypothermophilic archaebacterium Pyrococcus furiosus. J Biol Chem 1993;268:24394–401. [24] Rubin-Pitel SB, Zhao H. Recent advances in biocatalysis by directed enzyme evolution. Comb Chem High Throughput Screen 2006;9:247–57. [25] El-Fallal A, Dobara MA, El-Sayed A, Omar N. Starch and microbial α-amylases: from concepts to biotechnological applications. In: Carbohydrates—Comprehensive studies on glycobiology and glycotechnology. Intech Open; 2012. [26] Lowe ME. Amylase in encyclopedia of gastroenterology; 2004. [27] Dey G, Palit S, Banerjee R, Maiti BR. Purification and characterization of maltooligosaccharide-forming amylase from Bacillus circulans GRS 313. J Ind Microbiol Biotechnol 2002;28:193–200.

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C H A P T E R

9 Hydrolysis of complex pectin structures: Biocatalysis and bioproducts Kanchan Yadava, Sangeeta Yadava, Gautam Anandb, Pramod K. Yadavc, and Dinesh Yadava a

Department of Biotechnology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India bDepartment of Plant Pathology and Weed Research, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel cDepartment of Life Sciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University (formerly Kanpur University), Kanpur, Uttar Pradesh, India

1. Introduction Pectin is a predominant polysaccharide in the cell wall and middle lamella of plants, especially cereals, vegetables, and fruits and assists cell cohesion [1–4]. Pectins are most commonly found in commercially produced apple pomace and orange peel. It is ideal for low-sugar, low-fat recipes and acts as a carrier for medication administration to the gastrointestinal tract in either in matrix tablets, gel beads, or film-coated dosage forms. Pectin is also known to enhance the flavor qualities of foods [5]. Pectin constitutes around 0.5% to 4% of the plant’s dry mass and is a nontoxic anionic heteropolysaccharide [6–8]. Pectin comprises of four subclasses, i.e., rhamnogalacturonan (RG-I and RG-II), homogalacturonan (HG), and xylogalacturonan (XGA) [9]. The α-1,4-linked galacturonic acid (GalA) backbone of RG-II, HG, and XGA residues can be methyl-esterified at the C6 carboxyl group and/or acetylated at O2 or O3 position. Subclass RG-I possesses an alternate rhamnose and GalA backbone and has a structurally varied functional group predominately arabinose and galactose. RG-II has a complex side chain with at least 12 different sugars, and XGA is HG with an additional β-1,3-xylosyl functional group [9]. Pectic polysaccharide production is considered to be the outcome of different enzymes action like glycosyltransferases, methyltransferases, and acetyltransferases on their substrate [9,10].

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Pectinase has a complex enzymatic system that breaks down pectic compounds [11]. Pectinase comprises various enzymes known as polygalacturonase, pectinesterase, pectate lyases, and pectin lyases [12]. In the pectin structure, main chain comprises methyl-esterified 1,4-D-galacturonan. It also has demethylated pectin called pectic acid (pectate) or polygalacturonic acid. Pectinases assist in breaking glycosidic bonds, which causes generation of monogalacturonic acid [3]. Plants and microorganisms have been identified as critical sources of the pectinase enzyme. Pectinase production from microbial sources, on the other hand, is becoming a significant element of scientific interest due to its technological and commercial potential [13,14]. Usually, this enzyme is associated with the metabolic activities of nearly all life forms, from the most sophisticated to the most basic, including plants, animals, fungi, bacteria, and viruses [14]. Scientists are presently concentrating on improving efficiency in different aspects by incorporating various biotechnological technologies. Additionally, researchers are focusing on customizing pectinase to be used in a variety of bioprocessing industries [15,16]. Pectinases are used in various industries, although they are most commonly utilized in the food industry for activities such as the clarification of fruit juices and wines. Several other applications have also been reported like retting of fibers, wastewater treatment, protoplast isolation, paper bleaching, in the pharmaceutical industry, etc. [3,6,17–19].

2. Pectin complex structure Pectin is a natural biopolymer that is a vital part of the cell walls of all higher plants and plays an important role in several processes like cell expansion, morphogenesis, permeability, seed hydration, leaf senescence, fruit ripening, and defense mechanism [9,20]. In terms of dry weight, pectin constitutes 30%–35% of plant cell walls of dicots and nongraminaceous monocots, 2%–10% grasses, and 5% of wood [9,21]. Depending on the source, various stages of fruit and plant, different extraction conditions, and extraction procedures, the molecular weight of pectin varies from 60,000 to 318,000 g/mol [22,23]. Apples and citrus fruits are the most typical sources of commercial pectin. However, the search for alternating sources mainly from diverse industrial by-products in compliance with the cyclic green economy concept is recently being investigated [23–27]. It is impossible to determine pectin’s structure precisely, and its structure is still a topic of dispute. Multiple models have been proposed to reveal the complex structure of pectins like the “smooth and hairy region” model and the “RG-I backbone” model [8]. In pectin polysaccharides, D-galacturonic acid is the most important component which is held together by α-1,4-glycosidic bonds and the cell wall contains roughly 70% galacturonic acids [7,8,10,28]. Further, it has been observed that pectin’s structure and molecular mass are not constant. It shows variability based on cell origin, cell type, developmental stages, methods of extraction, and thickness of cell wall [10,29]. Further, the pectin molecules of equal mass may show varying degree of hydrokinetic properties owing to differences in methylation, branching, and neutral sugar content. Pectin comprises approximately 65% HG; 20%–35% XGA; and 10% RG-I and RG-II [9].

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2.1 Homogalacturonan (HG) In pectin, the HG moiety represents the backbone chain and consists linear polymers of α-D-galacturonic acid (GalA) residues which are linked by α-1,4-glycosidic bonds. HG represents approximately 75%–100% of the pectin molecule. HG is estimated to be formed of 100 GalA residues which could be either acetylated or methyl-esterified. The functional attributes of pectin are influenced by the degree of acetylation or methylation [8,30,31].

2.2 Xylogalacturonans Xylogalacturonans are homogalacturonans with a β-linked D-xylose-(1–3) at the O-3 position. A β-linked D-xylose often follows it at the O-4 position [30,32,33]. A portion of the galacturonan backbone is methyl-esterified in the xylogalacturonan region which is generally independent of the xylose substitutions [34,35]. Fruits and seeds are the primary tissues containing xylogalacturonans, and they also serve as storage and reproductive organs of plants [32,34].

2.3 Rhamnogalacturonan-I (RG-I) Rhamnogalacturonan-I (RG-I) comprises neutral sugars like arabinose, galactose, and mannose occupying the side chains of L-rhamnopyranose α-1,2-linked residues. Repeated units of L-rhamnose and galacturonic acid in XGA serve as a backbone of rhamnogalacturonan-I [36]. In RG-I, which is a highly branched structure commonly known as the “hairy region” and comprises approximately 300 rhamnosyl and galactosyluronic acid residues [21].

2.4 Rhamnogalacturonan-II (RG-II) In rhamnogalacturonan-II (RG-II) structure, galacturonic acid units are replaced primarily with L-rhamnose and D-galactose. Several uncommon sugars like apiose, aceric acid, 3-O-methyl-L-fucose, 2-O-methyl-D-xylose, 3-carboxy-5-deoxy-L-xylose, 3-deoxy-D-mannooctulosonic acid, and 3-deoxy-D-ly lyxo-heptulosaric acid are also substituted [21,30,37]. RG-II is considered a conserved and complex domain in plant species, and the amount of RG-II in pectin is about 10% [30]. Small changes in the structure of RG-II cause plant growth abnormalities [38].

3. Types of pectins Based on the degree of esterification, pectins are of two types, namely high methoxyl pectin (HMP) and low methoxyl pectin (LMP). The number of carboxyl groups esterified in the structure of pectin often referred to as degrees of esterification shows different amalgamating, texturizing, and gel-forming properties. HMP has degree of esterification value of more than 50%. It is predominately utilized in the food industry owing to its thickening and gel-forming attributes. In HMP, large amount of sugar is needed for gel formation and it is said to be acid-sensitive.

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HMP forms a gel at low pH ranging from 2.5 to 3.5 and also in the presence of soluble solids predominantly sucrose. The other co-solutes such as sorbitol or ethylene glycol are also preferred for gel-forming [30,39,40]. During gel formation, sugar minimizes water activity and promotes hydrophobic interactions, which helps to stabilize junction sites. The function of sugars is determined by their molecular arrangement and interactions with adjacent water molecules [41,42]. The 2D matrix of the pectin molecule is formed because of immobilization of solvent, co-solutes sugars, and acids, and this structure can withstand deformation. Further, the 3D network structure of pectin is formed due to the construction of junction zones. It is mainly stabilized by hydrogen bonds formed between the carboxyl and secondary alcohol groups and the hydrophobic interactions among the methyl esters [43]. In general, HMP gels are recyclable, and to avoid lumping, dispersion agent like dextrose is added [30]. Low methoxyl pectin (LMP) has less than 50% degree of esterification and is typically generated via the de-esterification of HMP. They form gel independent of sugar concentration and have comparatively higher chemical stability to moisture and heat than HMP. It is generally more pH-resistant, and gels can be formed at broad pH range [39,44]. Further, gel formation is preferred when divalent cations like Ca2+ are present, which can be reversed by incorporating monovalent cations such as Na+ or K+ [45].

4. Sources of pectin Several conventional and nonconventional sources of pectin have been reported. These are ubiquitously present citrus fruits like oranges, lemons, grapefruit, and apples [30,46,47]. Fruit pulps have high pectic substances and can be obtained from juice-processing waste, though the color depends on the source [48]. The most commonly used pectin is extracted from citrus fruits like oranges, lemons, grapefruit, and apples. Pectin dry weight in apple pulp is around 15% to 20%, while citrus peel has 30% to 35% pectin. Other fruits like quince, plums, apricots, cherries, oranges, carrots, gooseberries, strawberries, etc., also have an appreciable percentage of pectin [30]. Search for new sources of pectin is underway, and efforts are being made to use various by-products of industries. Pectin of the good gelling property has been extracted from sunflower head residues [49–51]. Similarly, sugar beet has around 23% pectin containing ferulic acid residues which enable cross-linking in the presence of a chemical or enzymatic oxidant has been reported [52–54]. By-products from different industries such as potato pulp [55], pumpkin pulp [56], peach pulp [57], and linseed seeds [58] are also reported to have a good yield of pectin.

5. Pectin: Diverse uses Pectin is a nontoxic, safe, and low-cost product with high accessibility and low production costs [27]. Its structure affects its functionalities as well [59]. Several applications of pectin have been documented in the literature as discussed below. Pectin is extensively utilized in the food industry owing to attributes like gelling, thickening, stabilizing, and emulsifying properties [27,60–63]. The gelling properties of pectin are

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extensively utilized in jams, fruit juices, desserts, dairy products, and jellies [64,65]. It is also preferred as a stabilizer in colloidal dispersions, such as emulsions, fruit drinks, and antioxidant-rich foods [27,59,66]. Pectin is also used in food packaging as pectin coatings are renewable, biodegradable, and biocompatible, allowing food products to be shelf-stable for longer periods of time [60,67,68]. It is a polysaccharide having bioactive properties and thus is renowned for its health benefits. Pectin is a biopolymer that could be used to deliver drugs, to transfer drugs, to deliver genes, to heal wounds, to reduce cholesterol, to fabricate contact lenses, in artificial corneas, in catheters, and to exhibit anticancer activity, and act as mucosal and gastrointestinal vehicles to deliver drugs and deliver bone tissue cells [24,27,59,69,70]. Further, pectin is also known to possess probiotic and antimutagenic activity. It can also enhance the immune and inhibit tumor growth [27,66]. It has also been reported to cure obesity and eye treatment due to its ability to gelate in an acidic medium [27]. Pectin also has cholesterol-lowering properties and thus helps reduce the risk of cardiovascular disease [71]. Pectin has potential for wound healing and is thus preferred as biocomposite dressings [72]. It is estimated that global cancer cases will increase by 47% by 2040, with 19.3 million new cases registered worldwide in 2020, resulting in 10 million deaths. While chemotherapy, radiotherapy, immunotherapy, and gene therapy have made significant advances, deaths generally result from metastases [73]. The traditional approach for cancer treatments suffers from several drawbacks as they may target the normal healthy cells along with cancer cells [74]. Curcumin, an anticancer agent of plant origin, is emerging as an alternative to chemotherapy. These molecules are nontoxic and natural and have fewer side effects [75,76]. Preclinical and clinical studies on curcumin oral administration suggest that it has low systemic bioavailability and is highly susceptible to metabolic activity. Pectin is a promising curcumin nanocarrier that crosses the gastrointestinal epithelium effectively and, as a result, circumvents the metabolic restrictions within the intestinal tract [77,78]. Several other applications are being explored besides the potential application of pectin in the food, pharmaceutical, and biomedical sectors. Research on biodegradable pectin from different biomass sources is being investigated [79,80]. A hybrid pectin and starch nanoparticle is being employed in the textile industry, especially as adsorbents for methylene blue dyes. Pectin with alginate is used in fluoride removal. Recently, pectin from citrus peel and tomato peels was reported as an inhibitor of corrosion for mild steel in hydrochloric acid solutions and tin corrosion inhibitor, respectively [81,82].

6. Pectinases These enzymes belong to the polysaccharide family, which is involved in the catalysis of pectic polymers and also called as pectolytic or pectic enzymes [83]. It is widely found in bacteria, fungi, and plants and primarily comprises protopectinase, esterase, and depolymerase. These are mainly classified based on the mechanism and type of substrates. Protopectinases hydrolyze protopectin and convert it into soluble pectin. Esterases assist the generation of polygalacturonic acid by degrading methoxyl and acetyl esters from pectin. Depolymerases

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break down pectic substances either by hydrolyzing or trans-eliminating α-(1,4)-glycosidic bonds in D-GalA units [6,84].

6.1 Protopectinase Protopectinases (PPases) are enzymes that convert insoluble protopectin into soluble pectin in the presence of water. Pectinosinase is an alternative term used for protopectinase [85]. The term “protopectin” has been used since 1927 and is becoming increasingly popular due to its importance in many areas like single-cell protein, pectin production, and protoplast isolation [86,87]. The target of PPases is the glycosidic bond of protopectin. They act on sites having three or more nonmethylated GalA molecules. Based on their mode of action, they are divided into type A and type B enzymes. Type A acts at the internal region of insoluble protopectin or polygalacturonic acid, whereas type B acts at the outer region of protopectin or polysaccharide chains. Fungi like Kluyveromyces fragilis IFO 0288, Galactomyces reessi, and Trichosporon penicillatum SNO3 and bacteria Bacillus subtilis IFO 3134 are some reported sources of type A [88,89]. Similarly, type B has been reported from B. subtilis IFO 3134 and Trametes sunginea [3,90].

6.2 Esterase This enzyme is carboxylic acid esterase that helps the de-esterification of methyl ester bonds of galacturonan backbone present in pectin. Pectic acid and methanol are the end products’ esterase reaction [6,14]. Plant and fungal pectin esterase show single-chain and multiple-chain mechanisms, respectively. During single-chain mechanism, the enzyme acts on either the nonreducing end or the terminal next to a free carboxyl group followed by linear progressing. While in the case of the multichain mechanism, methyl groups are eliminated in an absolute manner [3,85,91]. Further, pectin esterases are divided into two classes according to the target functional groups, namely pectin esterase or pectin methylesterase (PME) and pectin acetyl esterase (PAE). PMEs use double displacement mechanism to catalyze reactions. Based on the presence or absence of a PRO domain, PMEs are further classified into type I and type II. Type I PMEs have one to three PRO domains, while it is not observed in Type II PMEs. Bacterial and plant PMEs generally show single- and multiple-chain mechanisms, while fungal PMEs work predominately on single-chain mechanisms [92]. Pectin acetyl esterase (PAE) acts on the acetyl ester group of the homogalacturonan region of pectin by hydrolysis mechanism and results in the formation of pectic acid and acetate [93]. It has been reported from microbes, plants, and animals as well [3,94]. The divalent ions such as Ca2+, Ba2+, and Sr2+ have greatly affected the enzymatic activity of pectinesterase. Moreover, Ca2+ ions are reported to be more efficient than other divalent ions in large-scale preparations [95]. It possesses molecular weight that varies from 22 to 90 kDa revealing diversity in protein conformations. The activity of the PE enzyme is reported to have pH range 4.0–8.0.

6. Pectinases

211

Bacterial PE generally has high pH optima as compared to fungal PE. Temperature optima range from 40°C to 50°C for most PEs [3,6].

6.3 Depolymerases Depolymerases break pectic substances through two mechanisms namely hydrolysis and transelimination lysis. It comprises two classes of enzymes namely hydrolases and transeliminases. Hydrolases include polygalacturonase (PG) and polymethylgalacturonase (PMG). Pectic hydrolases are depolymerases that hydrolytically break the α-(1,4)-glycosidic bonds between galacturonic monomers, releasing mono-, di-, and oligogalacturonates. Further, based on the site of action which may be either random or terminal, they are referred to as endo or exo enzyme, respectively [6,14]. Transeliminase, on the other hand, comprises polygalacturonate lyase (PGL) and polymethyl galacturonate lyase (PMGL). These mainly catalyze α-(1 ! 4)glycosidic bonds by transamination reaction and form unsaturated galacturonates [96].

6.4 Polygalacturonase PGs are pectinases that catalyze the hydrolysis of the polygalacturonic acid chain by introducing water across the oxygen bridge. This family of pectinolytic enzymes has been studied extensively. Upon reaction with pectin, the structural conformation of PG is lost, possibly because the target molecules of PG contain free carboxylic groups. The viscosity of the solution decreases when the enzyme meets substrates that have reducing end groups [89]. Depending on the mode of action, PGs are classified into exo-polygalacturonase and endo-polygalacturonase. Exo-polygalacturonase attacks the terminal ends of pectin molecules resulting in a substantial decrease in chain length, while endo-polygalacturonase targets entire chain links at once, resulting in quicker and more acute outcomes [97,98]. PGs are extensively distributed among microbes like fungi, bacteria and yeasts, higher plants, and some plantparasitic nematodes. They have been found in various fungi like Aspergillus fumigatus MTCC 2584 [99], A. flavus MTCC 7589 [100], A. niger MTCC 478 [101], Rhizopus oryzae [102] Aspergillus nomius MR103, B. paralicheniformi, A. fumigatus R6, P. oxalicum, A. flavus MTCC 7589, Fusarium graminearum and bacteria like Bacillus licheniformis [103]. PGs from various microbial sources possess several interesting biochemical properties. Most of the microbial PGs exhibit optimal enzymatic activity at the optimal range from 3.5 to 5.5 with the optimum temperature that ranges from 30°C to 50°C. In general, almost all microbial PGs have been found to work efficiently in acidic pH ranges. However, several alkaline PGs have also been identified from bacteria including Bacillus licheniformis and fungi like Aspergillus fumigatus MTCC 2584 [99] Rhizopus oryzae MTCC 1987 [102]. PG I, PG II, PG III, and PG IV are four isoenzymes having the same molecular weight but different isoelectric points are also reported [104]. PGs from Bacillus licheniformis, Bacillus sp. KSM-P443, and Fusarium oxysporum f. sp. lycopersci can catalyze the hydrolysis of pectic substances at higher temperatures. However, most of them work in a temperature range of 30–40°C [3]. Polygalacturonases have molecular weight

212

9. Hydrolysis of complex pectin structures

ranging from 38 to 65 kDa. Nevertheless, a polygalacturonase with molecular weight of 496 kDa has been reported from Kluyveromyces marxianus [104] and 124 kDa from Aspergillus niger MTCC 478 [101].

6.5 Lyases Lyase enzymes are also known as transeliminases, which break down pectate or pectinate polymers using a transeliminative mechanism. Typically, lyases break the glycosidic linkages at the fourth carbon and then remove a hydrogen atom from the fifth carbon to form unsaturated products. Lyases are divided into two categories based on the substrates they work on: polygalacturonate lyase and polymethylgalacturonate lyase. Based on mechanism, lyases are divided into five subtypes, namely endo-polygalacturonate lyases, exo-polygalacturonate lyases, endo-polymethylgalacturonate lyases, exo-polymethylgalacturonate, and oligo-Dgalactosiduronate lyases [85]. Polygalacturonate lyase or pectate lyase and polymethylgalacturonate lyase or pectin lyase are further classified into exo and endo depending on their mode of action. Endo-polygalacturonate lyases act on the substrate irregularly, whereas exo-polygalacturonate lyases cleave the substrates from the nonreducing end of pectic acid. Endo-polymethylgalacturonate lyases catalyze pectin by cleaving α-1,4-glycosidic bond arbitrarily resulting in the formation of unsaturated methyloligogalacturonates, whereas exopolymethylgalacturonate lyases catalyze transeliminative cleavage of pectin in a stepwise manner to generate unsaturated methylmonogalacturonates [3,105,106]. Microbes like Fusarium lateritum MTCC 8794 [107], F. decemcellulare MTCC 2079 [108], Oidiodendron echinulatum MTCC 1356 [109], and Penicillium citrinum MTCC8897 [110] are great sources for isolating pectin lyases, although each isolated lyase has varied biochemical characteristics. Divalent ions play an important role in the catalytic properties of lyases. Ca2+ ions are required for the activation of polygalacturonate lyases or pectate lyases. However, divalent ions such as Co2+, Mn2+, and Ni2+ are required for activating some cytoplasmic or intracellular lyases [101]. There is no metal ion requirement for activating the polymethylgalacturonate lyases or pectin lyases. However, arginines are found at the Ca2+ ion position in the case of pectate lyases [111]. In general, both classes of enzymes work efficiently in an alkaline pH range of 7.5–10.0 and at temperatures between 40°C and 50°C. The molecular weights of lyases range between 22 and 90 kDa. Two polymethylgalacturonate lyases with molecular weight 89 and 90 kDa from Aureobasidium pullulans LV-10 and Pichia pinus have been reported, respectively. The molecular weight of polygalacturonate lyases has been reported to be 55 kDa and 74 kDa in Yersinia enterocolitica and Bacteroides thetaiotaomicron, respectively. Several lyases have isoelectric points ranging from 5.2 to 10.7 [3,6].

7. Structural aspects of protein families related to pectin degradation The carbohydrate-active enzymes database (http://www.cazy.org/) is created exclusively for enzymes catalyze the modification and breakdown of carbohydrates. It includes families of structurally related catalytic and carbohydrate-binding domains or functional domains of enzymes that are responsible for degrading, modifying, or creating glycosidic bonds.

7. Structural aspects of protein families related to pectin degradation

213

Structural aspects of the different classes of protein families related to pectin degradation and their mechanism are discussed below and also in Table 1. Right-handed β-helix: The right-handed β-helix is the most common fold among the PL families, and it was initially identified in pectate lyase C (PelC) from Erwinia chrysanthemi [112] (Fig. 1A). The four families, PL1, PL3, PL6, and PL9, have this protein fold in their 3D structures. PL1, with more than 35 known 3D structures and 400 assigned sequences, includes proteins from bacteria, plants, and fungi. The helical feature consists of three β-strands connected by turns 1, T, and T3 (Fig. 1B). Proteins of this class have 8 to 14 helical turns, forming 3 parallel β-sheets that extend along the helical axis [113]. PL1 family of pectate lyases upon sequence alignment reveals that PB2, PB3, and T2 are highly conserved. The substratebinding site is composed of the PB1 strands and T3 turns, which also contribute to the formation of the active site and show more significant variability [114]. Members of the PL3 family exhibit β-helix, which is different from other PL families. The members of the PL3 family form the shortest β-helix, which comprises eight turns and has no N-terminal capping α-helix (Fig. 1C). β-Jelly roll class: The enzymes representing β-jelly roll PL class shared by five families of PLs, namely PL7, PL13, PL14, PL18, and PL20, are associated with the degradation of various polysaccharide. Members of families PL7, PL14, and PL18 are involved with alginate degradation, while PL13 and PL20 degrade glucuronate and heparin, respectively. The PL7, PL13, and PL18 comprise bacterial enzymes, whereas PL14 contains viral enzymes, and PL20 represents eukaryotic enzymes. The regular fold is formed by two antiparallel seven-stranded β-sheets bent nearly 90° in the middle of the fold [115], making a curvature where the substrate binds and indicating the position of the active site (Fig. 2A and B). (α/α)n Toroid class: The toroid, also known as the barrel class of protein, is composed of two antiparallel α-helices, with each helix comprises of 10–20 amino acids and hairpins (n) varies from three to seven. There are two major subclasses of the class, namely single-domain (α/α)n toroids that have three, four, or seven α-helical hairpins, and multidomain proteins that have an (α/α)5,6 toroid domain, a C-terminal domain formed of a β-sandwich of four antiparallel β-sheet and an extra β-sheet N-terminal domain. PL2, PL5, and PL10 families contain (α/α)n single-domain toroid subclasses. A short N-terminal β-sandwich domain is seen in several lyases, such as ChonABC and HL [116–118]. The C-terminal domain helps to constitute the substrate-binding site and adds one amino acid to the catalytic tetrad in such subclass. The PL8 family is part of this subclass and includes lyases that can break down several substrates: chondroitin sulfate CS-A, CS-B, or CS-H, hyaluronic acid (HA), and xanthan. PL8, the multidomain (α/α)5,6 toroid subclass, is shared by two other minor families, PL15 and PL21. Bacterial oligoalginate or exotype alginate lyases belong to the PL15 family. The structure of the first enzyme from this family has already been revealed (PBD code 3AFL) [119]. PL15 lyase exhibits structural similarities with family PL21 rather than with family PL8 lyase (Fig. 3). β-Propeller class: The PL11 and PL22 proteins display the β-propeller fold. N-terminal domain is composed of two tri-stranded antiparallel β-sheets, accompanied by an eight-bladed β-propeller domain. Each blade consists of four antiparallel β-strands, except for blade D. The N-terminal domain occludes one end of the propeller, and the C-terminal α-helix plugs the other end. The seven-bladed β-propellers do not contain any extra domains (Fig. 4).

TABLE 1 List of PL families with structural and mechanism details. PL family

Enzyme

EC No.

Primary structure

Mechanism

1

Pectate lyase Exo-pectate lyase Pectin lyase

4.2.2.2 4.2.2.9 4.2.2.10

Parallel β-helix

β-Elimination

2

Pectate lyase Exo-polygalacturonate lyase

4.2.2.2 4.2.2.9

(α/α)7 barrel

β-Elimination

3

Pectate lyase

4.2.2.2

Parallel β-helix

β-Elimination

4

Rhamnogalacturonan endolyase

4.2.2.23

β-Sandwich + β-sheet

β-Elimination

5

Alginate lyase Endo-β-1,4-glucuronan lyase

4.2.2.3 4.2.2.14

(α/α)6 barrel

β-Elimination

6

Alginate lyase Chondroitinase B MG-specific alginate lyase Poly(α-L-guluronate) lyase/G-specific alginate lyase Oligoalginate lyase/exo-alginate lyase

4.2.2.3 4.2.2.19 4.2.2 4.2.2.11 4.2.2.26

Parallel β-helix

β-Elimination

7

Poly(β-mannuronate) lyase/M-specific alginate lyase α-L-guluronate lyase/G-specific alginate lyase Poly-(MG)-lyase/MG-specific alginate lyase Endo-β-1,4-glucuronan lyase Oligoalginate lyase/exoalginate lyase

4.2.2.3 4.2.2.11 4.2.2 4.2.2.14 4.2.2.26

β-Jelly roll

β-Elimination

8

Hyaluronate lyase Chondroitin AC lyase Xanthan lyase Chondroitin ABC lyase Heparin lyase/heparin lyase I Poly(β-mannuronate) lyase/M-specific alginate lyase

4.2.2.1 4.2.2.5 4.2.2.12 4.2.2.20 4.2.2.7 4.2.2.3

(α/α)6 toroid + antiparallel β-sheet

β-Elimination

9

Pectate lyase Exo-polygalacturonate lyase Thiopeptidoglycan lyase Rhamnogalacturonan endolyase

4.2.2.2 4.2.2.9 4.2.2 4.2.2.23

Parallel β-helix

β-Elimination

10

Pectate lyase

4.2.2.2

(α/α)3 barrel

β-Elimination

11

Rhamnogalacturonan endolyase Rhamnogalacturonan exolyase

4.2.2.23 4.2.2.24

β-Propeller

β-Elimination

12

Heparin-sulfate lyase Heparin lyase/heparin lyase I

4.2.2.8 4.2.2.7

(α/α)5 toroid + antiparallel β-sheet

β-Elimination

13

Heparin lyase

4.2.2.7

β-Jelly roll

β-Elimination

14

Poly(β-mannuronate) lyase/M-specific alginate lyase Exo-oligoalginate lyase β-1,4-Glucuronan lyase Alginate lyase

4.2.2.3 4.2.2.26 4.2.2.14 4.2.2

β-Jelly roll

β-Elimination

15

Alginate lyase Oligoalginate lyase/exo-alginate lyase Heparin lyase/heparin lyase I Heparin-sulfate lyase/heparin lyase III

4.2.2.3 4.2.2.26 4.2.2.7 4.2.2.8

(α/α)6 toroid + antiparallel β-sheet

β-Elimination

16

Hyaluronan lyase

4.2.2.1

Triple-strand β-helix

β-Elimination

17

Alginate lyase Oligoalginate lyase

4.2.2.3 4.2.2.26

(α/α)6 toroid + antiparallel β-sheet

β-Elimination

18

Alginate lyase Poly(α-L-guluronate) lyase/G-specific alginate lyase MG-specific alginate lyase

4.2.2.3 4.2.2.11 4.2.2

β-Jelly roll

β-Elimination

19

Deleted and now family GH91

20

Endo-β-1,4-glucuronan lyase

4.2.2.14

β-Jelly roll

β-Elimination

21

Heparin lyase Heparin-sulfate lyase Acharan-sulfate lyase

4.2.2.7 4.2.2.8 4.2.2

(α/α)6 toroid + antiparallel β-sheet

β-Elimination

22

Oligogalacturonate lyase/oligogalacturonide lyase

4.2.2.6

β-Propeller

β-Elimination

23

Chondroitin lyase

4.2.2

(α/α)5 toroid + antiparallel β-sheet

β-Elimination

24

Ulvan lyase

4.2.2

β-Propeller

β-Elimination

25

Ulvan lyase

4.2.2

β-Propeller

β-Elimination

26

Rhamnogalacturonan exolyase

4.2.2.24

(α/α)6 barrel

β-Elimination

27

L-Rhamnose-α-1,4-D-glucuronate

4.2.2

(α/α)6 barrel

β-Elimination

28

Ulvan lyase

4.2.2

β-Jelly roll

β-Elimination

lyase

Continued

TABLE 1 List of PL families with structural and mechanism details—cont’d PL family

Enzyme

EC No.

Primary structure

Mechanism

29

Chondroitin-sulfate ABC endolyase

4.2.2.20

–

β-Elimination

30

Hyaluronate lyase

4.2.2.1

31

Endo-β-1,4-glucuronan lyase Poly(β-mannuronate) lyase/M-specific alginate lyase

4.2.2.14 4.2.2.3

32

Poly(β-mannuronate) lyase/M-specific alginate lyase

4.2.2.3

β-Elimination

33

Hyaluronate lyase Gellan lyase Chondroitin sulfate lyase

4.2.2.1 4.2.2.25 4.2.2.20

β-Elimination

34

Alginate lyase

4.2.2

β-Elimination

35

Chondroitin lyase/chondroitinase

4.2.2

β-Elimination

36

Poly(β-mannuronate) lyase/M-specific alginate lyase

4.2.2.3

37

Chondroitin-sulfate ABC endolyase Heparin-sulfate lyase/heparin lyase III Ulvan lyase

4.2.2.20 4.2.2.8 4.2.2

38

Endo-β-1,4-glucuronan lyase

4.2.2.14

(α/α)7 barrel

β-Elimination

39

Alginate lyase

4.2.2

(α/α)6 toroid + antiparallel β-sheet

β-Elimination

40

Ulvan lyase

4.2.2

41

Alginate lyase

4.2.2.3

–

–

42

L-Rhamnose-α-1,4-D-glucuronate

4.2.2 3.2.1

Sevenfold β-propeller

β-Elimination

L-Rhα-α-1,4-GlcA

lyase α-L-rhamnohydrolase

β-Elimination Parallel β-helix

β-Jelly roll

β-Elimination

β-Elimination β-Elimination

β-Elimination

7. Structural aspects of protein families related to pectin degradation

217

FIG. 1 (A) The right-handed β-helix fold in pectate lyase (PDB code 2EWE). (B) Organization of β-helix axis with strands and turn. (C) The right-handed β-helix fold in pectate lyase from Bacillus sp. (PDB code 1EE6).

FIG. 2 (A) Typical β-jelly roll fold for enzymes belongs to PL7, PL13, and PL18 families (PDB code 2ZAA); (B) structure of the β-jelly roll fold with small domains inserted into the canonical fold (PDB code 3INA). https:// doi.org/10.1016/j.procbio.2008.09.012.

β-Sandwich class: N-terminal β-sandwich is found in the PL4 family, represented by rhamnogalacturonan lyases (RLs). The N-terminal β-sandwich domain is composed of two antiparallel eight-stranded β-sheets [120]. RL from A. aculeatus is the single structural representative enzyme with this fold in PL4 family [120]. A. aculeatus RL has three domains (Fig. 5), and the N-terminal domain is suggested to have the catalytic site. On the other hand, the

FIG. 3 The (α/α)n domain fold showing (α/α)7 toroid (PDB code 2V8K).

FIG. 4 Organization of β-propeller fold in crystal structure of rhamnogalacturonan lyase (PDB code 2Z8S).

FIG. 5

Organization of β-sandwich fold in rhamnogalacturonan lyase from Aspergillus aculeatus (PDB code 1NKG).

References

219

C-terminal and central domains are suggested to have role in substrate recognition and binding. Triple-stranded β-helix: Bacteriophage tail spike proteins (TSPs) contain triple-stranded β-helix. It consists of three interwoven polypeptides that form a triple-stranded β-helix [121,122]. The strands either fold independently into three β-helices, or they are interwoven to form a single β-helix. Few TSPs exhibit enzymatic activity and catalyze the degradation of bacterial envelope polysaccharides. This fold is also found in PL16 family polysaccharide lyase like, as hyaluronidase (HL) [114].

8. Conclusion Microbial research in the current era of innovation and up-to-date research has created a permanent rebirth for finding novel benefits from microorganisms and their products. It is concluded that pectinases and pectins play an essential role in diverse industrial processes, prompting results. The extensive research suggests that pectinases play a crucial role in innovation strategies for substantial developments or improvements of enzymes relevant to industrial products. Despite this, application of pectinase and deduced pectic substances to pharmaceutical products is the least explored. As a result, strengthening and expanding the use of such valuable enzymes in the pharmaceutical sector is critical. In addition, biotechnological intervention is essential for developing a broad-spectrum pectinase with high catalytic affinities. As a result, in-depth insights into the expression mechanism at the biochemical and molecular levels are required.

Acknowledgment The corresponding author would like to acknowledge the Head, Department of Biotechnology, D.D.U Gorakhpur University, for infrastructural support. The financial support by the Department of Science and Technology, New Delhi in the form of DST-INSPIRE Fellowship (DST/INSPIRE Fellowship/2019/IF190080) to Kanchan Yadav and the Financial support to PramodK.Yadav in the form of DBT Ramalingaswami Re-entry fellowship (Grant Number D.O.NO.BT/HRD/35/02/2006) from Department of Biotechnology (DBT), New Delhi is duly acknowledged. The contribution of Vinita Yadav, Ph.D., in editing a part of this chapter is also acknowledged.

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C H A P T E R

10 Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production Yasmin Khambhaty and R. Reena Microbiology Department, CSIR-Central Leather Research Institute, Adyar, Chennai, India

1. Introduction During primeval times, earth could not sustain any forms of life since it was filled with atmospheric CO2; nevertheless, by photosynthetic process, algae and cyanobacteria sequestered the CO2 and released molecular oxygen, thus paving way for the evolution of life on earth. Algae are found in terrestrial as well as marine environments; need CO2, water, and sunlight for their growth; and convert solar energy into carbohydrates, oils, and proteins [1]. Currently, the emission of greenhouse gas (GHG) is majorly due to anthropogenic sources such as transportation (20%) and energy (60%) sectors [2]. In order to reduce this GHG emission, implementation of bio-refinery is the need of the hour. On the other hand, high demand of energy has led to a rapid depletion in fossil fuel sources and has surged the search on alternative, renewable, and sustainable energy resources. Particularly, great attention is being given to the use of biomass as feedstock, owing to the fact that it is the most abundant renewable carbon resource on earth [3]. Besides, it is the second largest source after fossil fuels (oil, coal, and natural gas), which provide
80% of the energy derived from renewable sources [4]. Through biological and thermo-chemical conversions, all forms of energy, i.e., biofuels (liquid, gas, or solid), heat, and electricity, can be generated from biomass, thus marking it as the most versatile renewable energy resource and a potential alternative feedstock to fossil fuels [5]. In addition to the increase in energy efficiencies on supply as well as demand sides, the amount of renewable energy must be increased in all sectors, while securing the sustainability and affordability of biomass production considering the growing world population.

Polysaccharide-Degrading Biocatalysts https://doi.org/10.1016/B978-0-323-99986-1.00009-0

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2. The bio-refinery concept Among the several definitions of bio-refinery, one of the most meticulous was coined by the IEA Bioenergy Task 42 “Biorefineries,” which states that “bio-refining is the sustainable processing of biomass into a spectrum of marketable products and energy” [6]. The biorefinery concept encompasses a wide range of technologies that translate biomass into their building blocks (e.g., proteins, lipids, carbohydrates), which in turn can be converted to value-added products, biofuels/bioenergy, and chemicals. Based upon the raw materials, bio-refineries are classified into first, second, and third generation. As already known, the use of edible crops, viz., corn, sorghum, sugarcane, etc., as raw materials are categorized as first-generation bio-refinery; conversely, the second generation makes use of lignocellulosic materials (forestry residues, wood wastes, etc.), and finally the exploitation of aquatic biomass such as microalgae and/or macroalgae is the third-generation bio-refinery [7]. The first-generation feedstocks are under serious controversy considering the feud between food and fuel. On the other hand, the use of second-generation biomasses is limited owing to the high cost involved in lignin removal [8]. From an environmental perspective, terrestrial biomass-based bio-refinery can rather aggravate climate change when the life cycle of its final products is considered. Reports also mention that the change in direct and indirect land usage pattern for energy crop cultivation significantly induces carbon debt and water consumption [9,10]. Thus, terrestrial biomass-based bio-refinery seems not to be sustainable due to its environmental and economic impacts. Presently, the algal bio-refinery concept is gaining momentum, which is an integrated process whereby the algal biomass is converted into fuel and other value-added products [11]. The aim of this process is to derive utmost value out of algal biomass and release minimum wastes to the environment, thereby making it sustainable [12]. Microalgae and macroalgae have both been explored as potential fuel sources; however, productivity, scalability, and incessant supply of biomass are critical factors in selecting the feedstock. Processes such as fermentation, anaerobic digestion, transesterification, liquefaction, and pyrolysis can convert algal biomass into bioenergy, such as biogas, bioethanol, biodiesel, and bio-oil [13]. A general observation indicates that the research on microalgae-a lipid-based bio-refinery has dominated over the past few years owing to its high per hectare yield [14] and easy degradability due to low carbohydrate content [15], while the poor returns in biofuel production and energy balance partly limits it [16]. Conversely, the marine macroalgae, the so-called seaweeds, have emerged as potential candidates for renewable energy production since they can fix the greenhouse gas (CO2) by photosynthesis, the average efficiency of which is
6%–8% and is much higher than that of terrestrial biomass (
1.8%–2.2%) [17]. Macroalgae do not need land and freshwater for their cultivation and are quite efficient in utilizing the nutrients like nitrogen and phosphorus from wastewater due to its rapid growth rate where the nutrients can be recycled back [18]. Additionally, the water content in macroalgae is quite high compared to terrestrial biomass (80%–85%), making them more suited for microbial conversion than for direct combustion or thermo-chemical processes [19]. Macroalgae are multicellular and possess plant-like characteristics, making their harvest easier and their high carbohydrate content makes them suitable candidates for the production of biofuels. Recently, polysaccharides from marine macroalgae are receiving global attention and have risen as promising biomass resources for third-generation bio-refineries—an alternative to

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FIG. 1 A holistic view on the macroalgae bio-refinery concept.

petrochemical-derived fuels [20]. Macroalgae have already been commercially exploited as food and for obtaining polysaccharides (viz., carrageenan, alginate, agar, etc.), accounting for about 83%–90% of their global market value [21]. They are also known to produce diverse primary or secondary metabolites with proven applications in pharmaceutical, nutraceutical, and cosmetic industries. Recently, sulfated oligosaccharides have attracted huge attention due to their biological activities against pathogenic bacteria. Macroalgae are great source of rare sugars-polysaccharides with unique chemical structure. Also, the proportion of high-value proteins containing essential amino acids in macroalgae is relatively high (38%–40%) [22]. Hence, identifying these compounds and their potential markets is necessary for the development of macroalgal-based bio-refinery. Besides, the implementation of enzyme-based technology to convert complex macroalgal polysaccharides into various products can efficiently utilize the raw material, minimize production cost, and reduce environmental impacts [23]. Fig. 1 represents a holistic view on the macroalgal bio-refinery concept.

3. Algae and its classification Algae is an informal term used for a huge and diverse group of photosynthetic organisms with chlorophyll-a as their photosynthetic pigment. It is a polyphyletic group that includes

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species ranging from unicellular to multicellular forms. Most of them are aquatic and autotrophic and lack many of the distinct cell and tissue types, found in land plants. These are majorly divided into two categories: microalgae and macroalgae.

3.1 Microalgae About 50,000 microalgal species are estimated to exist, of which only
30,000 have been explored [24]. The term “microalgae” is generally used for both prokaryotic blue green algae (cyanobacteria) and eukaryotic microalgae, including green algae, red algae, and diatoms. Microalgae are composed of lipids (7%–23%), carbohydrates (5%–23%), and proteins (6%– 52%) and are being sought as attractive bio-factories for the sequestration of CO2 and simultaneous production of renewable biofuel, food, animal, and/or aquaculture feed and other value-added products [25,26]; their conversion technologies and life cycle analyses have also been extensively reviewed [27]. The microalgal pigments (chlorophyll a and b, lutein, astaxanthin, β-carotene, phycobilin, phycocyanin, etc.) have found wide applications as dyes, in cosmetics, as food and feed additives, in nutraceuticals and pharmaceuticals, as bioactive components, antioxidants, nutritive, and neuroprotective agents [28]. They are also rich source of amino acids, carbohydrates, vitamins, and minerals and widely used as health supplements and medicines. Other benefits include their use as omega fatty acids, which have gained popularity as essential for human brain development, production of extracellular polymeric substances (EPSs) and polyhydroxyalkanoates (PHAs) [29].

3.2 Macroalgae Macroalgae/seaweeds are assemblages found in groups like Chlorophyceae (green), Rhodophyceae (red), and Phaeophyceae (brown) and are important photosynthetic primary producers of the sea [30]. Most algal cells are surrounded by a polysaccharide-rich cell wall (comprising at least 50% of the algal dry weight) representing a major deposit of photosynthetically fixed carbon [31]. Among macroalgae, cell wall differentiation is observed, with differences in polysaccharides depending on the species, the part of the algae, the developmental and life cycle stage, the season, and habitat. Even within the algal lineages, polysaccharides appear highly diverse in terms of their degree of sulfation, esterification, molecular weight, and sugar residue conformation [32]. Fig. 2 gives a brief description about the types of macroalgae and the structures of major polysaccharides contained within their cell walls. 3.2.1 Green algal polysaccharides The green algae (Chlorophyta) consists of three large and diverse classes, viz., Ulvophyceae, Trebouxiophyceae, and Chlorophyceae [33]. Significantly less is known about the polysaccharides of green algae compared to other counterparts. This is possibly attributed to their complexity and the variety of sulfated polysaccharides, but is also because there are fewer industrial applications [34]. The cell wall of these algae can be abridged as containing fibrillar material ranging from cellulose-pectin complexes to hydroxyproline-rich glycoproteins. In addition to cellulose, xylans also form microfibrils, whereas mannans form short rods during different life cycle phases. They also produce complex matrix sulfated

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FIG. 2 Macroalgae and its major cell wall polysaccharides.

polysaccharides as well as extensin [32]. According to a study, the polysaccharides are divided into two major groups, viz., uronic acid rich or uronic acid limited [35]. The uronic acid rich (sulfated) are present in various species of Ulva, Gayralia, and Monostroma. The uronic acid-limited polysaccharides are seen in species of Codium and are reported as sulfated galactans, arabinopyranans, and mannans [36,37]. 3.2.2 Red algal polysaccharides Red algae (Rhodophyta) are multicellular, photosynthetic, eukaryotic organisms, of which about 7000 species are known. They grow in marine habitats, freshwater environments, and warmer areas. Phycocyanin and phycoerythrin are present in these algae and give the characteristic red color [38]. The cell wall of these algae includes polymeric materials originating from the metabolic activities of the algae. Various polysaccharides, proteoglycans, proteins, lipids, and associated inorganic constituents are identified to be the components of the red algal cell wall. The polysaccharides may be grouped with the rigid structural (β-linked) glycans such as cellulose, mannans, and xylans, as well as with the flexible and frequently sulfated glycans that comprise the matrix in which the skeletal fibers are embedded [39].

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3.2.2.1 Cellulose

Evidently, cellulose is the most intensively studied polysaccharide. At the molecular level, the polymer consists solely of β-(1!4)-linked glucopyranosyl units in a ribbon-like structure. These chains are arranged in sheets and further stabilized by hydrogen bonds and by intermolecular forces to form a highly rigid structure [40]. 3.2.2.2 Mannan

There is unquestionable evidence for an insoluble structural mannan that can be substituted for cellulose in members of the order Bangiales [41]. The chemical linkage of the mannan from Porphyra has long been established. More than 70% of the mannose occurs as an insoluble granular mannan, and the observed chemical properties are consistent with those of a β-(1!4) mannan [42]. 3.2.2.3 Xylan D-Xylose is observed as a common and possibly universal acid hydrolysis product of red algal cell walls. It is a water- and alkali-soluble compound that is obtained with mucilage fractions, where it frequently occurs as branches on a complex heteroglycan. It can function as a vital bridging molecule in peptide-oligosaccharide conjugates. In the case of pure xylan, both β-(1!3) and β-(1!4) glycosidic linkages have been reported [43].

3.2.2.4 Sulfated galactans

The most common and abundant cell wall constituents so far encountered in the Rhodophyta are galactans informally referred to as the agar and carrageenan. The backbone structure of these is based on repeating galactose and 3,6-anhydrogalactose residues with β-(1!4) and α-(1!3) linkages, respectively. 3.2.2.4.1 Agar Agar is composed of agarose and agaropectin. It is extracted from species of Gelidium and Gracilaria [44]. The strong gelling agarose constitutes 70% of the polysaccharide and is made up of a high-molecular-weight polymer of strictly alternating 3-O-linked β-Dgalactopyranose and 4-O-linked 3,6-anhydro-α-L-galactose [39]. Agaropectins have higher amount of sulfate ester groups than agarose [45]. 3.2.2.4.2 Carrageenan Carrageenan is a sulfated polysaccharide, composed of alternating α-1,3-linked D-galactopyranosyl and β-1,4-linked D-galactopyranosyl groups and 3,6anhydrogalactose residues. The three major types are kappa (κ), iota (ι), and lambda (λ). κ-Carrageenan is composed of 25%–30% sulfate ester groups and 28%–35% anhydrogalactose. ι-Carrageenan has 28%–30% sulfate ester and 25%–30% of anhydrogalactose units, while λ-carrageenan has 32%–39% sulfate ester and no anhydrogalactose residues [46]. Kappaphycus alvarezii and Eucheuma denticulatum are made of κ-carrageenan and ι-carrageenan, respectively. Both κ- and λ-carrageenan are seen in Chondrus crispus and Sarcothalia crispata. The presence of sulfate ester groups determines the water solubility and gelation of carrageenan [47].

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3.2.3 Brown algal polysaccharides Brown seaweeds (Phaeophyceae) constitute a large group of multicellular organisms with about 2060 known species; they grow in marine, fresh, and brackish waters with a size ranging from few millimeters to 70 meters [48,49]. They play an important role in marine environment, both as food and for the habitats they form. The brown color is due the presence of a pigment called fucoxanthin. Polysaccharides such as alginate, cellulose, fucoidan, and laminarin are present in the cell wall of these algae. Fucose-containing sulfated polysaccharides (FCSPs) act as major cross-linking glycan, which interlocks the cellulose microfibrils [50]. Hemicellulose acts as link between cellulose and the cross-linking FCSPs. The cell wall matrix mainly consists of alginates, iodine, proteins, polyphenols, phlorotannin (sulfated phenolic compounds), and minerals ( calcium, magnesium, potassium, and sodium), the content of which may differ between species due to environmental factors [51]. 3.2.3.1 Alginate

Alginates are linear unbranched copolymers comprising β-1,4-D-mannuronic acid and α-1,4-L-guluronic acid, which are analogues to pectin in terrestrial plants [52]. The M/G ratio of alginates differs between algal species. Interaction of alginic acid with metal ions such as Ca2+, Mg2+, and Na2+ leads to the formation of alginate, the stability of which is affected by free radical generated [53]. Alginate exhibits properties such as emulsifying, stabilizing, and thickening and is used in pharmaceutical, agricultural, cosmetic, textile, food, and paper industries [54]; it is also used as biomaterial for tissue engineering and bio-printing due to its biocompatible nature [55]. 3.2.3.2 Fucoidan

Discovered by Kylin [56], fucoidans are sulfated polysaccharides produced in brown algae, initially, named as “fucoidin.” They are fucose-containing sulfated polysaccharides that are also composed of galactose, mannose, xylose, and residues of glucuronic acid [57]. These are divided into two: one is composed of α-1,3-L-fucopyranose residues and the other alternating 1,3- and 1,4-linked α-L-fucopyranose residues [58]. 3.2.3.3 Laminarin

Laminarin is the most important storage polysaccharide contained in Laminariales and to a lesser extent in Fucaceae and can represent from 2% to 34% dry weight of the algae [30]. It is an analogue to starch in terrestrial plants and is a carbohydrate reserve for many brown seaweeds [49]. It is a β-1,3-glucan, consisting of β-1,3-D-glucopyranose units interspersed with β-1,6-linked D-glucopyranose units. Two different types are G-type and M-type, where the former contains glucopyranose residues and latter is terminated with 1-O-linked D-mannitol [51]. Laminarin exhibits probiotic, antioxidant, anticoagulant, hypocholesterolemic, antimutagenic, anti-inflammatory, and anticancer activities [59]. 3.2.3.4 Mannitol

Mannitol is a six-carbon sugar alcohol and is one of the most abundant polyols occurring in nature. In photosynthetic organisms, mannitol is synthesized as a major primary

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photosynthetic product and is used as an important translocatory and storage compound [60]. Found in brown algae, it is accumulated especially in Laminaria and Saccharina species, where it can make up 20%–30% dry weight of the algae.

4. Extraction of macroalgal polysaccharides The continuous cultivation of macroalgae is increasing production owing to an elevated global demand. Despite the proven potential of macroalgal polysaccharides for next-generation applications, commercial products are scarce on the market. Normally, the extraction of polysaccharides from seaweed generates low yields, but novel methods are being developed to facilitate and improve the yields. The processes for pre-treatment and extraction are not distinctly different when obtaining polysaccharides from seaweed [20]. Various extraction methods are commonly combined with a view to increase yields. The pre-treatment of macroalgae for the extraction of polysaccharides consists of mainly mechanical, thermal, chemical, and biological methods [61], where the first three are very superficially discussed in the subsequent section following a major emphasis on biological treatment.

4.1 Mechanical treatment A range of mechanical pre-treatments to improve access of polysaccharides by hydrolyzing agents are reported; nevertheless, a direct comparison between each treatment among different seaweeds in terms of percentage yields is difficult as various researchers treat the biomass in a diverse manner [61]. 4.1.1 Size reduction In order to increase the surface area-to-volume ratio, chopping or milling of the biomass is commonly adopted, which also helps improve the hydrolysis of complex carbohydrates to sugars for fermentation or anaerobic digestion [62]. The differences in the cell wall ultrastructure can determine the beneficial value of mechanical treatment, where those with more fibrous cell walls would benefit from size reduction [63]. 4.1.2 Beating This technique involves pounding the seaweed against a plate, enabling the production of seaweed pulp with varying consistencies depending on the machine setting [64]. 4.1.3 Washing Washing in freshwater is normally done to remove impurities like gravel and sand, limiting their build-up in reactors, and is a pre-treatment step regularly used in a research involving seaweed biofuel [65]. Washing also removes salts, which can be inhibitory at high concentrations to both methane (CH4) production and enzymatic hydrolysis for bioethanol production [66].

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4.1.4 Ultrasound-assisted extraction (UAE) Among the novel techniques, UAE has emerged as the most practical at industrial level because of its simplicity, faster extraction rate, increased yield as well as reduced cost and processing time [67]. It can also be combined with other non-conventional techniques, such as enzymatic processing or microwave-assisted extraction (MAE) [68] for better yields.

4.2 Thermal treatment The thermal pre-treatment extracts polysaccharides and enables the release of sugars from seaweed. However, certain structures such as the cortex of the seaweed were observed to be unaffected even after autoclave treatment, signifying the importance of the structural makeup of seaweed and the appropriate pre-treatment method required to hydrolyze these components effectively [61]. 4.2.1 Microwave Microwave-assisted pre-treatment is quite appropriate due to the high moisture content of seaweed, which facilitates a quick rise in temperature and pressure inside the cells, leading to cell wall rupturing, thereby increasing the surface area for subsequent bioethanol or biogas production. Yet another advantage is its rapid heating time that could stabilize and minimize sugar degradation at high temperatures, resulting in lower concentrations of inhibitory product, often formed during conventional inductive heating [69]. 4.2.2 Steam explosion The steam explosion technique has not been highly investigated possibly because it is less recalcitrant. It involves both thermal and mechanical means to hydrolyze the seaweed; however, due to its high energy costs, it may not be an appropriate choice for seaweeds. 4.2.3 Pressurized liquid extraction (PLE) PLE is a novel extraction technique based on elevated temperatures and pressures in oxygen and light-free environment within a short time period and using less solvent. Elevated temperature allows the sample to become more soluble and achieves a higher diffusion rate, while elevated pressure keeps the solvent below its boiling point [70]. 4.2.4 Other thermal pre-treatments Other thermal pre-treatments include wet oxidation and plasma-assisted methods. An increase in glucan content of the pre-treated biomass compared to the untreated one was reported by wet oxidation [71]. Plasma-assisted pre-treatment involves the generation of ozone in the reactor, which is believed to react and degrade unsaturated organic compounds within the biomass [72]. Even though the combined levels of glucan, xylan, and arabinan in the pre-treated biomass was lower than untreated biomass, the pre-treatment was believed to allow better enzymatic hydrolysis, leading to higher biofuel production than untreated seaweed.

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4.3 Chemical treatment Improvement in hydrolysis and solubilization of seaweed using acid and alkali pretreatments makes them highly investigated methods prior to fermentation or anaerobic digestion [73]. 4.3.1 Alkali or acid treatment The addition of alkali (often NaOH) is said to cause swelling of fibers and increase the pore size, enabling the release of sugars from cell walls facilitating efficient enzymatic hydrolysis or fermentation. In the case of acidic pre-treatment, H2SO4, HCl, and flue gas condensates with low pH values have been investigated. Acids are believed to hydrolyze cellulose, hemicellulose, and other storage carbohydrates such as laminarin. However, the use of acids has not been well received due to high acid recycling costs and requirement of acid-resistant process equipment [73]. 4.3.2 Peroxide treatment This method uses thermal treatment to enhance the disruption of seaweed crystalline structure and hydrogen bonds by hydroxyl radicals. A higher conversion rate was achieved using hot water pre-treatment (100°C, 30 min) followed by a hydroxyl radical reaction step (0.018% H2O2 and 11.9 mM FeSO4). The risk of inhibitory furfural production was also reduced and polysaccharides, such as laminarin and alginates, remained intact with this pre-treatment [74,75].

4.4 Biological treatment The biological pre-treatment involves the use of enzymes or the direct use of microorganisms for the hydrolysis of seaweed. These may be used in combination or subsequent to other pre-treatment methods. It is normally performed for dissociating the polysaccharides from other compounds of the cell wall for their eventually saccharification/hydrolysis by enzymes. This method will be discussed in detail in the subsequent sections.

5. Biocatalysts in bio-refinery and biofuel production In spite of several advantages, the third-generation bio-refinery platform is not gaining impetus due to various drawbacks. In light of this, efficient biological tools and processes in order to advance this platform toward a more mature one is the need of the hour. Bacteria, actinomycete, and fungi are well-known producers of enzymes required for the bio-refinery concept; however, their use is restricted mainly due to high cost. Various methods are being explored to reduce the enzyme cost, the use of cocktail enzymes being one of them [76]. The main objective of the integrated bio-refinery concept is to produce a wide variety of valueadded products, where pre-treatment plays a critical role in enabling the raw material more accessible for hydrolysis leading to a suitable substrate to be used as a carbon source for further process. Enzymes can be used at the pre-processing stage for enzyme-aided fractionation, purification of specific components, for creating added value by downstream

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refining of primary components, for producing bioactive oligosaccharides that can be further enzymatically modified reducing the processing costs, etc. [77,78]. Enzymes can also be used to convert polysaccharides to monosaccharides—these partially or fully degraded polysaccharides can be further converted by anaerobic/aerobic fermentation to products of industrial interest [79]. In order to commercialize macroalgae-based bio-refinery, priority needs to be placed on identifying microorganism that can metabolize major but unique or specific macroalgal carbohydrates with the help of their enzymes [80]. These enzymes play a vital role in the hydrolysis of the biomass and generation of different precursor chemicals [81]. Algae mostly constitutes lipids and proteins with cell wall consisting of polysaccharides such as hemicelluloses and celluloses. The group of enzymes that can facilitate the hydrolysis of these components are cellulases, hemicellulases, laccases, peroxidases, lytic polysaccharide monooxygenases (LPMOs), pectinases, lipases, and proteases [82]. Different microorganisms possess the ability to produce cellulose-degrading enzymes, which are classified under the glycosyl hydrolase type of enzymes according to the CAZy database. On the other hand, the depolymerization of hemicelluloses occurs by the action of a broad group of enzymes involving xylanases, galactanases, glucanases, arabinases, and mannases, which also have glycosyl hydrolase activity [83,84]. The efficiency of enzymatic methods can be attributed to a reduction in extraction time and energy consumption, minimization of solvent use, increase in yield and preservation of biological activity [85]. Enzyme-assisted techniques coupled with additional treatments have been widely investigated to enhance the extraction of cell wall components and metabolites of interest. The major advantage of third-generation bio-refineries using 3G feedstocks for biofuel production is the absence or low amount of lignin and the homogeneous structure such as the lack of root and leaf-like parts. Microbes in the marine environment often associated with the seaweeds are capable to degrade and utilize algal carbohydrates as a carbon source, which naturally implies that these organisms possess the necessary enzymes for the same. However, compounds such as ethanol and butanol are produced by aerobic/anaerobic fermentation that requires the presence of specific metabolic pathways generating these compounds as end products, e.g., yeast for ethanol production and Clostridia for butanol production. The production of bioethanol, biobutanol, and biogas by different conversion methods, including fermentation and anaerobic digestion, are outlined in Fig. 3. In the following sections, the major role of enzymes in biofuel/bioenergy production has been overviewed, from pre-treatment processes aimed at increasing yields of substrate, to saccharification and fermentative processes for the generation of intermediate and end products.

5.1 Bioethanol production Four stages are majorly involved in bioethanol production, viz., biomass pre-treatment, hydrolysis/saccharification, fermentation, and recovery. 5.1.1 Pre-treatment The complex composition of seaweeds makes pre-treatment one of the decisive steps in macroalgae bio-refinery. Pre-treatment separates the biomass and releases the polysaccharides, which enhances the hydrolysate’s accessibility to enzymes [86]. The hydrolysis of

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FIG. 3 Production of various biofuels by different conversion methods.

biomass breaks down polysaccharide by the addition of water, catalyzed by either acid or enzyme where the latter is supposed to be a better option since it requires less maintenance cost and can work in mild environmental conditions. Cellulase, lipase, and immobilized cellulase are used to disrupt the cell walls of different macroalgae. Fleurence et al. [87] used agarase, carrageenase, cellulase, and xylanase to extract protein from the rhodophytes C. crispus, Gracilaria verrucosa, and Palmaria palmata. The cell wall of macroalgae Grateloupia turuturu was disrupted using cellulase alone and cellulase-κ-carrageenase combination [88]. Cellulase-macerozyme mixture along with an extract of gut from abalone was used to digest cell wall of green and brown seaweed respectively [89]. 5.1.2 Hydrolysis Hydrolysis helps in breakdown of complex sugar molecules of seaweeds (laminarin, cellulose, mannitol, alginate, ulvan, carrageenan, and agar) to simple sugars (glucose, galactose, mannose, fucose, xylose, and arabinose) [90]. Chemical, mechanical, thermal, and enzymatic hydrolysis are often employed where a combination of these treatments gave higher yield in some studies [91]. Hydrolytic enzymes such as cellulases, hemicellulases, lytic polysaccharide monooxygenases (LPMOs), amylases, and pectinases play an

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important role in the conversion of polysaccharide to the respective monomeric fermentable sugars during saccharification/hydrolysis. Lipases and proteases are important for the utilization of lipid and protein-rich algal biomass [92]. Use of commercial enzymes such as Cellulase, Celluclast 1.5L, Viscozyme L, Novozyme 188, Termamyl 120L, β-glucosidase, Multifect, Meicelase, and Amyloglucosidase are also reported for the said purpose [93,94]. Cellulase is normally obtained from thermophilic and psychrophilic microbes [95] and based on their specific activity, they are classified into three types, viz., endoglucanases, exoglucanases, and β-glucosidases [81]. The endoglucanase (E.C. 3.2.1.4) randomly cleaves internal amorphous sites in cellulose-generating oligosaccharides through a substitution reaction with a water molecule in the 1,4-β bond, leading to the formation of new reducing and non-reducing ends, which are subsequently catalyzed by exoglucanase (E.C. 3.2.1.91), resulting in the formation of cellobiose units [96]. On the other hand, β-glucosidase (E.C. 3.2.1.21) hydrolyzes soluble cellobiose and other cellodextrins in the aqueous phase to release glucose as final product [97]. Amylase is used in the hydrolysis of brown seaweed, since it contains glucans made up of α-(1,3), α-(1,3)–(1,4), and α-(1,3)–(1,2) glycosidic linkages [98]. Pentose-specific enzymes are endo-1,4-β-D-xylanases, exo-1,4-β-Dxylocuronidases, α-L-arabinofuranosidases, endo-1,4-β-D-mannanases, β-mannosidases, acetyl xylan esterases, α-glucuronidases, and α-galactosidases. Ulvan is a complex polysaccharide found in the green algal cell wall and composed of 3-sulfated rhamnose (Rha3S) linked to either D-glucuronic acid, L-iduronic acid, or D-xylose. The ulvan lyase isolated from Pseudoalteromonas sp. endolytically cleaved the glycoside bond between Rha3S and uronic acid via a β-elimination mechanism [99]. On hydrolysis of polysaccharides, these enzymes release both pentoses and hexoses. The combined effect of dilute acid and enzyme is astounding but the use of both methods faces technical difficulty due to its cost and inhibitor formation. Long reaction time and difficulty in enzyme recovery are other disadvantages [100]. 5.1.3 Fermentation Most of the researches have utilized yeast Saccharomyces cerevisiae for fermentation reaction. Nevertheless, single or combination of strains is being attempted for the utilization of sugars. For example, laminaran and mannitol obtained from Laminaria hyperborea were subjected to fermentation using one bacterium (Zymobacter palmae T109) and three yeast strains (Pichia angophorae, Pachysolen tannophilus, and Kluyveromyces marxianus) [19]. Similarly, Kim et al. [101] demonstrated the possibility of ethanogenic Escherichia coli KO11 to catalyze the fermentation of four algal species consisting of Ulva lactuca, Gelidium amansii, Laminaria japonica, and Sargassum fulvellum. Moving on to the methods, separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF) are the currently used methods for bioethanol production from seaweed. In the case of the former, seaweed biomass is first hydrolyzed and then subjected to bacterial or yeast fermentation in separate units; however, in the case of latter, both the hydrolysis and fermentation are carried out simultaneously [102]. 5.1.4 Recovery processes After fermentation, bioethanol is produced through distillation and dehydration processes [100]. Distillation is a major and energy-intensive step in the entire ethanol production [103]. For analytical and research work, simple distillation units and rotary evaporators are used for

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bioethanol recovery; however, for commercial purpose, distillation columns and ethanol dewatering technique such as the use of molecular sieves (Zeolite) to recover ethanol are used [104]. Distillation columns can distil fermentation broths to 95.6% ethanol concentration. This is followed by dehydration processes such as vacuum distillation, pressure swing, membranes, or molecular sieves to obtain >99% grade ethanol [105]. 5.1.5 Researches related to the use of enzymes in ethanol production from different macroalgae Since macroalgae have diverse carbohydrates, the choice of appropriate microorganisms is pivotal for successful bioethanol production. The potential of ethanol production from seaweeds can be calculated on the following assumptions: say if the carbohydrate content is 60% of dry weight and a conversion ratio of 90%, 1 g of sugar can yield 0.4 g ethanol through fermentation. This will yield 0.22 kg or 0.27 L ethanol from 1 kg dry seaweed biomass, corresponding to approximately 0.05 L ethanol per kg wet weight [106]. Various enzymes play an important role in the conversion of polysaccharides to the respective monomeric fermentable sugars during saccharification or hydrolysis [107], whereas the lipases and proteases are important for the utilization of lipid and protein-rich algal biomass. Cellulases are produced on an industrial scale largely by fungi and bacteria. Several mesophilic fungal strains of Aspergillus genera and aerobic bacteria such as Cellulomonas fimi, Cellvibrio japonicus, and Pseudomonas fluorescens are source of cellulases [108]. Similarly, hemicellulases are produced by several strains of bacteria (Arthrobacter, Bacillus, Actinomycetes, and Clostridium), fungus (Aspergillus, Chaetomium, and Trichoderma), and algae [109]. Some studies have also reported macroalgae-specific enzymes for saccharification; however, those enzymes exhibited low hydrolysis efficiency. Hence, additional pre-treatment or multi-enzyme complexes or a combination of chemical and enzymatic hydrolysis was sought [110] to obtain monosugars from macroalgae [111]. Yanagisawa et al. [112] have suggested the use of tailor-made saccharification methods to increase bioethanol yields for green, red, and brown algae. Prospecting of brown macroalgae as feedstocks for bioconversion into biofuels is limited primarily by the availability of tractable microbes that can metabolize alginates. Bacteria can metabolize uronic acids to pyruvate and glyceraldehyde-3-phosphate, which may then be fermented to ethanol by the glycolytic pathway [113]. In anaerobic fermentation, as ethanol and butanol are produced, oxygen is not available for the removal of excess hydrogen generated, implying that the conversion reaction from substrate to product must be redoxbalanced. In many bacteria but not in yeast, the enzyme transhydrogenase solves this problem. Yeasts can avoid the problem by receiving a small, controlled supply of oxygen. However, oxygen leads to complete oxidation of the substrate to CO2 and water and reduced ethanol yields. Another strategy is the introduction of transhydrogenase into strains that lack this, through genetic engineering [114]. Enquist-Newman et al. [115] produced ethanol from brown macroalgal sugars by a synthetic yeast platform. They discovered an alginate monomer transporter from the alginolytic Asteromyces cruciatus. The genomic integration and overexpression of the gene encoding this transporter, together with the necessary bacterial alginate and de-regulated native mannitol catabolism genes, conferred the ability to S. cerevisiae to efficiently metabolize DEHU and mannitol. When this was grown on mannitol and DEHU under anaerobic conditions, it fermented ethanol, achieving titers of 4.6% (v/v) and yields up to 83% of the maximum theoretical yield from consumed sugars. Basically, the

241

5. Biocatalysts in bio-refinery and biofuel production

hydrolysis of laminarin requires four enzymes, namely, endo-β (1–6) glucanases (EC 3.2.1.75) that hydrolyzed the branched chains, endo β (1–3) glucanases (EC 3.2.1.39) and exo-β (1–3) glucanases (EC 3.2.1.58) that degraded linear laminarin into laminaritriose and laminaribiose and β-glucosidases (EC 3.2.1.21) that lysed laminarin oligosaccharides into glucose [116]. Ethanol production from several green, red, and brown algae has been reported with the aid of enzymes either for pre-treatment or for hydrolysis. A detailed list of these alga, types of enzymes used, and the overall ethanol production is given in Tables 1–3.

TABLE 1 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using green algal biomass. Seaweed

Hydrolysis and fermentation conditions

Yield (sugar and/or ethanol)

References

M. nitidum

Hydrothermal pre-treatment at 423 K for 30 min prior to enzymatic hydrolysis (Cellulosin T2) at 37°C for 48 h

Glucose yield was 0.107  0.010 g/g of biomass

[117]

Spirogyra sp.

Pre-treated with 1% NaOH for 2 h followed by treatment with enzymes from A. niger for 6 d at 30°C followed by fermentation for 6 d with S. cerevisiae

Ethanol yield was 8.0 g/100 g in untreated substrate and 3.86 g/100 g in enzyme-pre-treated substrate

[118]

Spirogyra sp.

Thermal pre-treatment at 100°C for 2 h followed by the addition of α-amylase for 1 h, followed by fermentation with Z. mobilis and S. crerviceae

Z. mobilis and S. cerevisiae produced 9.7% and 4.42% ethanol, respectively, after 96 h

[119]

U. lactuca

Acid pre-treatment with 0.1 N HCl followed by hydrolysis using Celluclast 1.5 L and Viscozyme L. Fermentation with S. cerevisiae (ATCC 24853, recombinant E. coli)

Reducing sugar yield of 19.4%

[101]

U. lactuca

Simultaneous saccharification and fermentation using crude enzymes (cellulase and amylase) from mid-gut gland of Scallops together with S. cerevisiae at 35°C for 72 h

Ethanol yield was 7.2 g/L

[112]

U. pertusa

Hydrolysis of glucans with Meicelase at 50°C for 120 h and subsequent fermentation of the resulting glucose by S. cerevisiae IAM 4178

Ethanol yield after 24 h was 30 g/L

[120]

U. pertusa

High-temperature liquefaction at 150°C for 15 min followed by treatment with cellulase (Cellubrix L, 96 IU FPA/mL; Novozyme, A/S, USA) and amyloglucosidase (400 units/mL)

Overall yield of sugar 3.1%–12.6% and approximately 90% of the maximum theoretical ethanol yield

[121]

Continued

242

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 1 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using green algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (sugar and/or ethanol)

References

U. lactuca

Hot water treatment followed by hydrolysis using commercial cellulase cocktail (GC220, Genencor) and fermentation with C. beijerinckii

Glucose (52.2 g/L), xylose (3.9 g/L), rhamnose (5.6 g/L), ethanol yield of 0.5 g/L

[122]

U. fasciata

Pre-treatment with hot sodium acetate buffer at 120°C for 1 h followed by treatment with cellulase 22119, incubation for 36 h at 45°C, fermentation with S. cerevisiae (MTCC No. 180)

Reducing sugar yield of 0.205 g/g, ethanol yield 0.45 g/g, reducing sugar accounting for 88.2% efficiency

[123]

U. reticulata

Pre-treatment by H2O2 followed by enzymatic saccharification using T. reesei cellulase. This resulted in 20% recovery of glucose by 24 h at 25°C and subjected to fermentation using S. cerevisiae

Ethanol yield corresponded to approximately 90 L/ton of dried macroalgae

[124]

E. intestinalis

Pre-treatment using 75 mM H2SO4 and 13% (w/v) slurry at 121°C for 60 min followed by treatment with Celluclast 1.5 L and Viscozyme L followed by fermentation with S. cerevisiae KCTC 1126

Ethanol yield by separate hydrolysis and fermentation-SHF was 8.6 g/L, ethanol yield by simultaneous hydrolysis and fermentation-SSF was 7.6 g/L

[125]

C. linum

Pre-hydrolysis was performed for 24 h at 50°C followed by commercial enzymes (Celluclast 1.5 L and Novozyme 188, Novozymes A/S, Denmark) treatment (15 FPU/g) to dry pre-treated substrate. This was followed by SSF at 32°C for 200 h

Ethanol yield of 18 g/100 g DM

[71]

C. fragile

H2SO4 hydrolysis of dried seaweed at 150°C for 30–90 min followed by neutralization using NaOH, followed by enzymatic saccharification using Lactozym (2.0 mL of enzyme per 10 g seaweed), Spirizyme, Viscozyme, and AMG (glucoamylase)

Ethanol yield was
7%

[93]

E. intestinalis

Hydrothermal treatment at 170°C for 60 min followed by enzymatic hydrolysis (Viscozyme L and Cellic CTec2 (1:1, v/v) [Novozymes]

Reducing sugar yield was 20.1 g/L

[126]

U. pertusa

Pre-treatment at 50 W of microwave irradiation and 150°C of hydrothermal temperature, followed by treatment with cellulase, α-amylase, and Novozyme 188 having β-glucosidase activity

96 wt% of total carbohydrate converted within 3 h, while it took 24 h for the enzymatic hydrolysis of untreated alga

[127]

243

5. Biocatalysts in bio-refinery and biofuel production

TABLE 1 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using green algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (sugar and/or ethanol)

References

U. rigida

Hydrolysis of algae under mild sonication carried out in a bath sonicator (3 h at 37°C, pH 5), with enzyme (amyloglucosidase, α-amylase, cellulase) for SSF process at
25–35°C using S. cerevisiae

Ethanol yield was 4.3  0.26 wt% by 30 min, which increased steadily and reached 6.2  0.13 wt% at 180 min

[128]

C. linum

Thermal treatment at 120°C for 20 min followed by saccharification using enzyme from A. avamori followed by fermentation with S. cerevisiae

Reducing sugar yield was 0.41 g/g, ethanol yield was 0.26 L/g VS

[129]

C. linum

Hydrothermal pre-treatment followed by treatment with cellulase (from A. niger) at various ratios (50–100 mL/g), enzyme concentrations (10–60 U/g), incubation times (4–44 h) and fermentation with S. cerevisiae

Sugar yield was 30.2 g/100 g DM, ethanol yield was 8.6 g/100 g DM

[130]

Enteromorpha sp.

Air drying and milling followed by enzymatic hydrolysis (Cellulase from A. niger (0.8 U/mg biomass) [FLUKA:22178, Germany])

Reducing sugar yield was 70.48%

[131]

U. lactuca

Different concentrations of H2SO4 and NaOH (0.4–2 N), autoclaved at 121°C for 30 min followed by treatment with enzyme from P. piscicida and fermented at 35°C for 48 h using normal and immobilized S. cerevisiae

Normal yeast produced ethanol of 12  0.5 g/g, while immobilized yeast gave ethanol production of 13.3 g/g of sugar/L

[132]

U. prolifera

Pre-treatment with H2O2 0.2% at 50°C, pH 4 for 12 h, followed by hydrolysis with commercial cellulase (Jienuo Enzyme Co., China. 45 FPU/g) and cellobiase (Sigma Chemical Co., USA 250 U/mL), loading corresponding to 7.5 FPU and 4.5 U/g biomass at pH 4.8, 50°C for 48 h and fermentation with S. cerevisiae

Yield of reducing sugar was 0.42 g/g Rate of conversion of reducing sugar to bioethanol reached 31.4%

[75]

U. fasciata

Hydrolysis of feedstock with cellulase from marine fungus C. sphaerospermum through solid state fermentation (10 U/g) for 24 h at 40°C and pH 4 followed by fermentation

Sugar yield was 112  10 mg/g DW, ethanol yield of 0.47 g/g reducing sugar, corresponding to 93.81% conversion efficiency

[95]

Continued

244

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 1 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using green algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (sugar and/or ethanol)

References

U. fasciata

Saccharification of extracted cellulose or residual biomass after ulvan extraction was carried out with commercial cellulase 22086 (Novozyme, Denmark) (2%, v/v) at 45°C for 36 h. This was followed by fermentation with S. cerevisiae (MTCC No. 180) for 12 h at 28  2°C at 120 rpm

Ethanol yield was 0.45 g/g reducing sugar

[133]

U. lactuca

Thermal hydrolysis (121°C, 45 min) followed by xylanase treatment from Bacillus sp. BT21 (50 IU/mg dw)

Reducing sugar obtained was 100  6.1 μg/mg of seaweed

[134]

U. lactuca

Effective pre-treatment for sugar conversion was LWH with a yield of 97.5%, followed by saccharification with Celluclast 1.5 L (cellulase activity of 10 U/g of biomass) using 10% (w/v) of native or pre-treated algae prepared in 0.1 M sodium acetate buffer pH 4.8. The mix was shaken in a thermomixer at 900 rpm and 50°C

Theoretical ethanol production was 22 g/100 g DM; however, by orgnosolv, it was 34 g/100 g DM

[135]

U. lactuca

Two types of milling modes were performed followed by Haliatase (30 g/L) treatment (KURA BIOTECH SPA, Chile) followed by fermentation with S. cerevisiae at 37°C, pH 5.5, 500 rpm for 72 h

Total sugar released was 13.1 g/100 g TS, ethanol yield was 6 g/100 g TS

[136]

U. intestinalis and U. lactuca

Dilute acid-pre-treated biomass was subjected to enzyme hydrolysis (cellulase from V. parahaemolyticus) at 55°C pH 6.8 for 36 h

Reducing sugar yield of 107.6 and 135.9 mg/g from U. lactuca and U. intestinalis, respectively

[137]

U. lactuca

Pre-treatment at 100°C with 2N H2SO4 for 60 min, enzymatic hydrolysis with cellulase and α-amylase for 24 h at 45°C at 150 rpm where enzyme loadings were 2 mg/g algae dry matter. Fermentation was carried out using S. cerevisiae at 150 rpm, 40°C for 48 h

Ethanol yield of 24.48% was obtained

[138]

Ulva sp.

Pre-treatment with 3% NaOH at 120°C for 20 min at pH 5, followed by enzyme hydrolysis (cellulase from A. fumigatus)

An increase of 36% in saccharification yield under optimized conditions

[139]

U. fasciata, U. rigida, U. ohnoi

Treatment of dried algal biomass with cellulase, amyloglucosidase, α-amylase

Ethanol yield was 0.3  0.06 g/g sugar

[140]

.

245

5. Biocatalysts in bio-refinery and biofuel production

TABLE 1 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using green algal biomass—cont’d Seaweed U. lactuca

Hydrolysis and fermentation conditions Pre-treatment at 121°C, 24 min at 10% solids loading (w/v) followed by hydrolysis with Novozyme Cellic CTec2 dosed at 50 FPU/g biomass, 50°C at 8% solids loading (w/v), further fermented with. S. cerevisiae NCYC2592

Yield (sugar and/or ethanol)

References

Glucose yield of
400 mg/g Ethanol yield of 7.8 g/L

[141]

Note: The names of algae and microbes have been used as per following abbreviations in the order of appearance in table. Monostroma nitidum (M. nitidum), U. lactuca (U. lactuca), U. pertusa (U. pertusa), Ulva fasciata (U. fasciata), Ulva reticulata (U. reticulata), E. intestinalis (E. intestinalis), C. linum (C. linum), Codium fragile (C. fragile), Ulva rigida (U. rigida), Ulva prolifera (U. prolfera), U. intestinalis (U. intestinalis), Ulva ohnoi (U. ohnoi). Aspergillus niger (A. niger), S. cerevisiae (S. cerevisiae), Zymomonas mobilis (Z. mobilis), E. coli (E. coli), Clostridium beijerinckii (C. beijerinckii), Trichoderma reesei (T. reesei), Aspergillus avamori (A. avamori), Pfiesteria piscicida (P. poscicida), Cladosporium sphaerospermum (C. sphaerospermum), Vibrio parahaemolyticus (V. parahaemolyticus), Aspergillus fumigatus (A. fumigatus).

TABLE 2 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using red algal biomass. Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

G. amansii

Pre-treatment with NaClO2 (1 g/g biomass) and CH3COOH (0.2 mL/g biomass) at 70°C for 0.5–4 h followed by treatment with r-cellulase (200 U/g dw), r-beta-glucosidase (100 U/g dw), and r-xylanase (20 U/g dw)

Reducing sugar yield was 661.8 g/kg biomass

[142]

G. turuturu

Hydrolysis using four polysaccharidases: Onozuka R-10 cellulase (Yakult Pharmaceutical, Japan), agarase from P. atlantica (Sigma A-6306, USA), κ-carrageenase from P. carrageenovora, and i-carrageenase from recombinant E. coli BL21 (DE3)

Combination of cellulase (0.48) agarase (1.1), and κ-carrageenase (0.01) yielded 41.9 mg glucose/g dw

[88]

S. pacifica

Hydrothermal pre-treatment at 473 K for 30 min prior to hydrolysis with Cellulosin T2 at 37°C for 48 h

Glucose yield was 0.036  0.005 g/g

[117]

G. amansii

Pre-treatment with 0.1 N HCl at 121°C for 15 min followed by enzymatic hydrolysis with Celluclast 1.5 L and Viscozyme L (0.01 g/g DM) at 50°C for 24 h

Initial reducing sugar yield was 11.6% DM and 56.6% DM after enzyme treatment, increased reducing sugar yield of 79%

[143]

Continued

246

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 2 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using red algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

G. salicornia

Pre-treatment with dilute H2SO4 at 120°C for 30 min and two-stage hydrolysis (combination of dilute acid hydrolysis with enzyme hydrolysis), fermentation with E. coli KO11 at 30°C, pH 7.2 for 50 h

Glucose yield was 4.3 g/kg fresh biomass, glucose yield of 13.8 g/kg fresh biomass, produced 79.1 g ethanol/kg dried alga

[144]

G. elegans

Treatment of 1.5 g biomass with 2% H2SO4 at 121°C for 30 min, pH 5.5 followed by treatment with Meicelase (5 g/L) (CEPB5394, Meiji Seika Kaisya Ltd., Japan) at 50°C for 120 h and subsequent fermentation by S. cerevisiae IAM 4178

Glucose yield of 70.9 g/L, galactose yield of 53.2 g/L, ethanol yield of 25 g/L

[112]

Marine macroalgal processing waste-FR

Floating residue (FR) were pre-treated with H2SO4 at 121°C for 1 h, followed by cellulase (45 FPU/g) and Cellobiase (55 CBU/g) treatment and fermentation with S. cerevisiae at 30°C, pH 6.5 for 48 h

Reducing sugar yield was 277.5 mg/g DW, ethanol yield was 23.3 g/L

[111]

G. amansii

Fermentation by B. custersii KCTC18154P, 30°C, pH 5.5, 150 rpm for 39 h

Produced 11.8 g/L ethanol from 90 g/L sugar in a batch reactor and 27.6 g/L ethanol from 72.2 g/L sugar in a continuous reactor

[145]

Agarose

Liquefaction in mild conditions (dilute acetic acid at 80°C for 1–6 h) followed by treatment with Aga16B, Aga50D and DagA and NABH

Converted 79.1% of agarose to monosugars, chemical liquefaction and SSF of 30 g/L agarose resulted in 4.4 g/L ethanol

[146]

K. alvarezii

The SSF process began with enzymatic pre-hydrolysis of cellulose residue at 50°C at 150 rpm for 24 h, fermentation with S. cerevisiae CBS1782, at 30°C, 150 rpm, 144 h

Ethanol yield was 64.30 g/L

[91]

G. verrucosa

Residual algal pulp suspended in buffer (pH 5.0) at 50°C, incubated on rotatory shaker for 2 h. Further supplemented with cellulase (20 FPU/g dry substrate) from T. reesei and β-glucosidase (60 U/g dry substrate) from A. niger at 50°C and 150 rpm, fermentation with S. cerevisiae

Sugar yield was 38.93 g/L Ethanol yield was 0.43 g/g sugars

[147]

G. amansii

Pre-treatment with 94 mM H2SO4, 10% (w/v) seaweed slurry at 121°C for 60 min followed by enzymatic hydrolysis using 0.024 U/mL of Viscozyme L (1.2 FBG/mL, beta-glucanase, Novozymes, Bagsvaerd, Denmark) and 0.168 U/mL Celluclast 1.5 L (8.4 EGU/mL, endo-glucanase, Novozymes), fermentation was carried out using yeast S. stipitis KCTC 7228

Sugar yield was 43.5 g/L Ethanol concentration was 20.5 g/L with yield of 0.47 at 96 h

[148]

247

5. Biocatalysts in bio-refinery and biofuel production

TABLE 2 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using red algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

G. amansii

Pre-treated dried seaweed with 2.5–12.5 wt% H2SO4 at 150°C for 30–90 min, followed by neutralized with NaOH. Enzymatic saccharification using Lactozym, Spirizyme, Viscozyme and AMG


10% ethanol yield using enzyme Lactozym

[93]

Gracilaria sp.

Sequential acid and enzymatic hydrolysis (2% (w/v) biomass, 0.1 N H2SO4, 121°C, 60 min, pH 4.8), followed by Cellulase (0.01 g/g biomass) treatment and fermentation with S. cerevisiae

Reducing sugar yield was 0.48 g/g Ethanol concentration was 4.72 g/L

[149]

K. alvarezii

Hydrolysis with 0.2 M H2SO4 (110°C for 90 min) followed by hydrolysis with Celluclast at 50°C for 48 h

Reducing sugar yield was 34.3 g/L Reducing sugar yield was 49.9 g/L

[150]

K. alvarezii

Soaking in water for 2 h and size reduction followed by cellulase treatment (1 g/mL), Endsany Jaya Engineering, Surakarta, at 50°C, 100 rpm for 12 h

Reducing sugar yield of 8.04 g/L

[151]

G. acerosa, G. pusillum, G. dura

Cellulose from alga mixed with cellulase 22086 (Novozyme, Denmark) in 30 mL of sodium acetate buffer (pH 4.8) and incubated for 36 h at 45°C and fermentation with S. cerevisiae (MTCC 180) for 12 h

Reducing sugar from G. acerosa, G. pusillum, and G. dura were 920  5, 930  5 and 910  3 mg/g of cellulose respectively. Ethanol of 418  3, 416  4.5, and 411  5 mg per g reducing sugar respectively

[152]

E. cottonii

E. cottonii was boiled at 90°C for 1 h; the macroalgae cellulosic residue (MCR) was dried at 50°C, pulverized, and used for subsequent experiments. Enzymatic hydrolysis was done using cellulase (Celluclast 1.5 L, Novozyme, Denmark) and β-glucosidase (Novozyme 188, Novozyme, Denmark), at a loading of 45 FPU/g of cellulase and 52 CBU/g of β-glucosidase at 43°C for 8 h to allow for SSF after inoculation with S. cerevisiae

Ethanol concentration of 14.1 g/L was obtained from MCR

[153]

G. verrucosa

Citric acid pre-treatment under 1:10 solidto-liquid ratio, 0.1 M citric acid, 150°C, 60 min, followed by subsequent enzymatic hydrolysis using enzyme mixture (20 FBG/g-biomass and 28 FPU/g-biomass)

Citric acid treatment gave total reducing sugar of 50.9%, followed by enzymatic treatment, which gave 57.8% total reducing sugar

[154]

Continued

248

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 2 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using red algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

K. alvarezii

Pre-treatment with 6% KOH (w/v) at 25°C for 24 h and Cellic CTec II (10 FPU/g dw) (Novozymes)

Reducing sugar yield was 13.7 g/L

[155]

A. plicata

Thermal hydrolysis (121°C, 45 min) followed by xylanase treatment from Bacillus sp. BT21 (50 IU/mg dw)

Reducing sugar yield was 233  5.3 μg/mg biomass

[134]

G. verrucosa

Thermal acid hydrolysis followed by saccharification using Celluclast 1.5 L and Viscozyme L and fermentation with S. cerevisiae KCTC 1126

Sugar yield of 0.48 g/L, final ethanol productivity of 0.16 g/L with YEtOH of 0.43 was obtained

[156]

G.sesquipedale

Two types of milling modes were performed followed by Haliatase (30 g/L) treatment (KURA BIOTECH SPA, Chile) followed by fermentation with S. cerevisiae at 37°C, pH 5.5, 500 rpm for 72 h

Total sugars released 10.8 g/100 g TS, ethanol-4 g/100 g TS

[136]

G. verrucosa

Pre-treated biomass followed by treatment with agarase, carrageenase, and neoagarobiose hydrolase

Sugar release of 679 mg/L with 67.9% saccharification

[157]

Gracilaria sp.

Two-step hydrolysis with 4% H2SO4 at 121°C for 30 min, pH 6.5–7 followed by treatment with Cellulase (53 FPU/g dry substrate) and β-glucosidase (30 U/g dry substrate) and fermentation with S. cerevisiae MTCC174 at 30°C, pH 5, 125 rpm for 96 h

Reducing sugar yield of 140.6  1.7 mg/g biomass, ethanol yield was 28.7  0.4 g/L

[158]

G. verrucosa

Pre-treatment with 100 mM H2NSO3H at 130°C for 90 min, pH 4.8 followed by treatment with Viscozyme L, Cellic CTec2, and Cellic HTec2 (1:1:0.1, v/v/v ratio per dried biomass) [Novozymes]

Reducing sugar yield was 69.1%

[159]

G. verrucosa

Pre-treatment with HCl at 121°C for 15 min, pH 7 followed by recombinant agarases (aga50D 0.5 U/mg plus NABH 0.5 U/mg)

Reducing sugar yield was 47.4%

[160]

G. verrucosa, E. cottonni

Pre-treatment with 4% NaOH for 24 h. Residues were bleached with 3% H2O2 solution, washed with distilled water until neutral, and dried in an oven at 105°C. This was followed by treatment with cellulase from C. acetobutylicum at 30°C, pH 5.0 for 14 d. Fermentation using C. acetobutylicum for 14 d at 29°C

At day 10, the ethanol levels increased to 5.7% for G. verrucosa and 6.1% for E. cottonii

[161]

249

5. Biocatalysts in bio-refinery and biofuel production

TABLE 2 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using red algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

D. carnosa

Auto-hydrolytic treatment for 30 min at 121°C followed by hydrolysis using enzyme cocktail predominantly containing cellulase followed by fermentation with S. cerevisiae NCYC2592

125.0 mg of glucose/g of pre-treated seaweed, ethanol yield was 5.4 g/L

[141]

G. verrucosa

Dried biomass was treated with 100 mM of H2SO4 and 60% ultrasonic amplitude at unregulated temperature for 60 min, followed by treatment with Cellic CTec2, Cellic HTec2, and Viscozyme L (Novozymes A/S, Denmark)

Pre-treatment and subsequent enzymatic hydrolysis resulted in enhanced yields of reducing sugar of 60.4% and 76.3%

[162]

Note: The names of algae and microbes have been used as per following abbreviations in the order of appearance in table. G. amansii (G. amansii), Grateloupia turuturu (G. turuturu), Solieria pacifica (S. pacifica), Gracilaria salicornia (G. Salicornia), Gelidium elegans (G. elegans), Kappaphycus alvarezii (K. alvarezii), Gracilaria verrucosa (G. verrucose), Gelidiella acerosa (G. acerosa), Gracilaria dura (G. dura), Geranium pusillum (G. pusillum), Eucheuma cottonni (E. cottonni), Gracilaria sesquipedale (G. sesquipedale), Ahnfeltia plicata (A. plicata), Dilsea carnosa (D. carnosa), Kappaphycus alvarezii (K. alvarezii), Eucheuma cottonii (E. cottonii), A. plicata (A. plicata), G. sesquipedale (G. sesquipedale), D. carnosa (D. carnosa). E. coli (E. coli), Pseudoalteromonas atlantica (P. atlantica), Pseudoalteromonas carrageenovora (P. carrageenovora), S. cerevisiae (S. cerevisiae), Brettanomyces custersii (B. custersii), Trichoderma reesei (T. reesei), Aspergillus niger (A. niger), Scheffersomyces stipitis (S. stipites), C. acetobutylicum (C. acetobutylicum).

TABLE 3 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using brown algal biomass. Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

L. digitata and U. pinnatifida

Various chemicals, viz., ascorbic acid, HCl, and NaOH, were used to investigate the effect on apparent viscosity of raw seaweed. Sterilization was carried out for 1–3 h at 121°C, 1.0 kgf/cm2 of pressure. The fed-batch experiment for saccharification was carried out in reactor for 16 h

Sugar concentration obtained was 27.2 g/L, which was about 80.6% of the saccharification yield

[107]

S. latisimma

Pre-treatment with 2 M HCl, pH 2 and 6, incubated at 70°C for 30 min followed by the addition of 0.1 U laminarinase (Sigma) and yeast (ethanol red yeast) preparation at a 0.5% (v/v), 32°C, pH 6 for 60 h

Ethanol yield of 0.45% (v/v)

[163]

Continued

250

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 3 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using brown algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

L. digitata

Dry biomass was mixed with deionized water while stirring to pH 4 with 2 M and 0.2 M HCl to produce a slurry, followed by addition of yeast solution with and without 0.5 U Trichoderma laminarinase and fermentation with P. angophorae

Ethanol yield was 167 mL/kg of L. digitata after 69 h

[110]

Saccharina and Laminaria sp.

Acid hydrolysis and enzymatic hydrolysis using cellulase and cellobiase were employed for the production of simple fermentable sugars from floating residues (byproduct from alginate extraction process)

Maximum glucose yield was 277.5 mg/g FR, conversion of cellulose reached 92.5% and ethanol yield was 0.143 L/ kg FR

Ge et al. [111]

L. japonica

Pre-treatment with 0.1 N HCl at 121° C for 15 min followed by hydrolysis with Celluclast 1.5 L, Novoprime B959, Novoprime B969, and Viscozyme L (0.01 g/g DM) at 50°C for 24 h and fermentation with S. cerevisiae (ATCC #24858), recombinant E. coli KO11 (ATCC #55124)

Reducing sugar yield of 37.6% (w/ w), ethanol yield of 0.4 g/g of sugar

[101]

S. fulvellum

Pre-treatment with 0.1 N HCl at 121° C for 15 min followed by hydrolysis with Celluclast 1.5 L and Viscozyme L (1:1 ratio)

Reducing sugar yield was 9.6% (w/ w)

[101]

A. crassifolia

Treatment with 2% H2SO4 at 121°C for 30 min, pH 5.5 followed by treatment with Meicelase (5 g/L) (CEPB5394, Meiji Seika Kaisya Ltd., Japan) at 50°C for 120 h and fermentation by S. cerevisiae IAM 4178

Glucose yield 123 g/L Ethanol yield 34.4 g/L

[112]

Alginate

Saccharification using metabolically engineered Sphingomonas sp. A1 by introducing the pyruvate decarboxylase (pdc) and alcohol dehydrogenase B (adhB) genes from Z. mobilis under control of a strong constitutive promoter and deleted lactate dehydrogenase gene (ldh)

Ethanol yield of 13 g/L

[164]

S. japonica

Thermal acid hydrolysis with 40 mM H2SO4 followed by hydrolysis using 1.5 KNU/mL of Termamyl 120 L

Reducing sugar yield by acid hydrolysis 14.5  2.1 g/L, combined treatment gave 20.6  1.9 g/L of sugar, ethanol yield of 7.7 g/L with a theoretical yield of 33.3%

[80]

251

5. Biocatalysts in bio-refinery and biofuel production

TABLE 3 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using brown algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

S. japonica

36-kb DNA fragment from V. splendidus which encode enzymes for alginate transport and metabolism. They engineered a secretable Aly (alginate lyase) system to degrade alginate by microbial platform

Ethanol yield of 0.281/weight dry macroalgae

[165]

Sargassum sp.

Pre-treatment with 4% H2SO4 at 115°C for 90 min followed by enzymatic hydrolysis (50 FPU cellulase/g DM) followed by fermentation with S. cerevisiae at 40° C, pH 4.5 for 48 h

Reducing sugar yield of 17.7 g/L with a glucose concentration of 3.5 g/L, ethanol yield of 2.79  0.09 g/L

[166]

S. japonica

Pre-treatment with 0.06% (w/w) H2SO4 at 70°C for 15 min. Subsequent simultaneous saccharification and fermentation (SSF) was conducted using S. cerevisiae DK 410362 and cellulase and β-glucosidase

Ethanol yield in untreated alga was 38.47% based on its glucan content. In SSF, ethanol yield of 67.41% based on the total available glucan of the pre-treated biomass

[167]

S. japonica

Pre-treatment with H2O2 at 121°C for 60 min followed by enzymatic saccharification using Viscozyme L (1.2 FBG/mL, Beta-glucanase, Novozymes, Bagsvaerd, Denmark) and Celluclast 1.5L (8.4 EGU/mL, Endo-glucanase, Novozymes, Bagsvaerd, Denmark) at 45°C at 150 rpm, fermentation with S. cerevisiae KCCM 1129 and mannitol-acclimated P. angophorae KCTC 17574

The two-stage fermentation resulted in the production of 9.9 g ethanol/L

[168]

U. pinnatifida

Pre-treatment was carried out with 75 mM H2SO4, at 121°C for 60 min. Termamyl 120 L was used during this treatment. The treatments with the other commercial enzymes, AMG 300 L, Viscozyme L, and Celluclast 1.5 L, were carried out at 45°C and 150 rpm for 24 h, following acid hydrolysis. S. cerevisiae KCCM 1129 was used for fermentation by separate hydrolysis and fermentation

Ethanol concentration at bench scale was 8.5 g and YEtOH of 0.44, while the ethanol concentration and yield using the PDU-scale 500-L fermenter was 7.9 g/L and a YEtOH of 0.41 at 18 h

[98]

Continued

252

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 3 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using brown algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

L. digitata

Acid hydrolysis (H2SO4) of dried seaweed at 150°C for 30–90 min. Then seaweed was neutralized using NaOH. For saccharification, pre-treated biomass and enzymatic saccharification using Lactozym, Spirizyme, Viscozyme, and AMG (glucoamylase) was conducted


9% ethanol yield using Spirizym from L. digitata

[93]

Brown algae

The hydrolysis was done using dilute H2SO4 and steam heating at 120°C for 0.5 h. Subsequently, the slurry was mixed with cellulase enzyme at 20 mg/g laminaran and cellulose at 48°C for 64 h

Total conversion of 91.7% for laminaran and cellulose and 95% for mannitol was obtained by combined saccharification

[169]

S. latissima

Hydrolysis of S. latissima using laminarinase and cellulase alone and in combination was done

The combination of a laminarinase and cellulase resulted in a 53% increase of glucose release from S. latissima than laminarinase alone

[170]

L. digitata

Commercial enzymes, Celluclast 1.5 L (Novozymes A/S) and Alginate Lyase (EC4.2.2.3) from S. spiritivorum (Sigma Aldrich), were tested. Fermentation was carried out by SHF as well as SSF using S. cerevisiae at 250 rpm at 50°C for 2 h

Glucose recovery by hydrolysis in SHF was 84.1% and 64.5% by SSF on 5% DM. Ethanol yields (% theoretical) from SHF and SSF were 77.7% and 56.4% respectively

[171]

M. pyrifera

Pre-treatment condition was 100°C, 30 min with 5.3 mM H2O2 (determined FeSO4 concentration of 11.9 mM). The pre-treated biomass was subjected to enzymatic hydrolysis with cellulase from T. reesei ATCC 26921 at 50°C for 72 h (Sigma–Aldrich Co.) at substrate concentration of 5% (w/v) and enzyme loading of 5 FPU/g substrate

Overall increase in glucose yield was obtained

[74]

N. zanardini

Pre-treatments were performed at 121°C and different solid loadings (5% and 10%, w/v), retention times (30, 45, and 60 min), and sulfuric acid concentration (7.0%, w/w). Enzymatic hydrolysis using cellulase (15 FPU/g of dried biomass) and β-glucosidase (30 IU/g of dried biomass), at 45°C and 100 rpm for 48 h. After cooling inoculated with S. cerevisiae and fermentations performed at 32°C and 100 rpm for 48 h

Ethanol yield of 0.42 g/g glucose was obtained

[172]

253

5. Biocatalysts in bio-refinery and biofuel production

TABLE 3 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using brown algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

A. nodosum and L. digitata

Pre-treatment with 0.2 M H2SO4 at 121°C for 20 min with 10% (w/v) biomass, final pH 5.5, followed by enzymatic hydrolysis with commercial enzymes (Novozymes Biomass Kit) and then fermentation using S. stipitis and K. marxianus

Sugar yield was 29.30 g/L from Laminaria and ethanol yield was 6 g/L using K. marxianus

[173]

S. crassifolium

Pre-treatment with 0.2 M H2SO4 at 121°C for 15 min followed by Cellulase treatment (30 U/g) (Sigma) and fermentation with S. cerevisiae JCM3012 at 30°C, pH 5.5 at 200 rpm for 12 h

Sugar yield was 68.32 g/L, ethanol yield was 43.92 g/L

[174]

E. radiata

Dried seaweed powder was dispersed in buffer solutions or water at a ratio of 1:100 (w/v) and incubated for 10 min under continuous shaking. The enzyme preparation (10%, v/w of alga) was then added and incubated at 50°C for 24 h using six commercial enzyme mixtures and individual enzymes including three cell wall carbohydrate hydrolytic preparations, namely, Viscozyme L, Celluclast 1.5L, Ultraflo L and the three proteases Alacalase 2.4L FG, Neutrase 0.8L, and Flavourzyme 1000L

The most significant effect on the total sugar yield was observed when comparing the use of salt-containing buffers (7.9–23.9 g/100 g DW) to pH-adjusted water (23.6–27.9 g/100 g DW)

[175]

S. japonica

Saccharification using D. phaphyphila-known to possess extracellular polymer-degrading enzymes such as alginase and laminarinase

Ethanol yields of 0.44, 0.47, and 0.3 g/g of sugars were obtained by fermenting glucose, mannitol, and alginate, respectively

[176]

Waste seaweed

Pre-treatment with H2SO4 for 90 min at 121°C. Enzymatic saccharification with 16 units/mL Celluclast 1.5 L and Viscozyme L mixture at 45°C for 48 h, then fermented with S. cerevisiae KCTC 1126 andP. angophorae KCTC 17574 at 30° C, 150 rpm for 24 h

Monosaccharide content was 30.2 g/L, ethanol yield was 13.5 g/L

[177]

P. tetrastromatica

Thermal hydrolysis (121°C, 45 min) followed by xylanase treatment from Bacillus sp. BT21 (50 IU/mg dw)

Reducing sugar yield was 73.3  4.1 μg/mg of seaweed biomass

[134]

Continued

254

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 3 Studies on various pre-treatment and fermentation conditions in combination with enzymatic hydrolysis for bioethanol production using brown algal biomass—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

Sargassum sp.

Laminarinase produced from Bacillus sp.

Hydrolysis using both cellulase and laminarinase produced 0.15% ethanol

[178]

Sargassum sp.

Two-step hydrolysis with 4% H2SO4 at 121°C for 30 min, pH 6.5–7 followed by treatment with Cellulase (53 FPU/g dry substrate) and β-glucosidase (30 U/g dry substrate) and fermentation with S. cerevisiae MTCC174 at 30°C, pH 5, 125 rpm for 96 h

Reducing sugar yield was 110  1.6 mg/g biomass, ethanol yield was 19.9  0.3 g/L

[158]

S. latifolium

Pre-treatment using 1% HCl at 121° C for 60 min followed by treatment with T. asperellum RM1, S. cerevisiae ATCC76621, and S. cerevisiae RM2 at 30°C, pH 6, 150 rpm for 96 h

Reducing sugar was 510 mg/g algal biomass, ethanol yield was 0.29 g/g reducing sugar

[179]

E. kurome

Treatment with engineered S. cerevisiae strains; alginate- and mannitol-assimilating yeast (AM1), and cellulase-displaying yeast (CDY) at 37°C, pH 6, 60 rpm for 120 h

2.1 g/L of ethanol

[180]

L. digitata

Pre-treatment with 0.75 M H2SO4 for 24 min at 121°C followed by cellulase treatment and then, fermented with S. cerevisiae

Ethanol yield of 3.2 g/L

[141]

S. muticum

Autohydrolysis pre-treatment followed by enzyme hydrolysis using Celluclast 1.5 L, cellulases from T. reesei, Novozyme 188, b-glucosidase from A. niger, and Viscozyme 1.5L00 carbohydrases and pectinases from A. aculeatus. Fermentation was carried out with three different S. cerevisiae strains (two industrial strains: Ethanol Red and PE2 and a laboratory strain CEN.PK 113-7D)

Industrial strain PE2 worked well with the alga reaching high ethanol yields at high speed. Ethanol yields from 68% to 100% were achieved in less than 10 h and maximum ethanol concentrations of 10.72–14.10 g of ethanol/L

[181]

Note: The names of algae and microbes have been used as per following abbreviations in the order of appearance in table. Laminaria digitata (L. digitata), Undaria pinnatifida (U. pinnatifida), Saccharina latisimma (S. latisimma), Laminaria japonica (L. japonica), Sargassum fulvellum (S. fulvellum), Alaria crassifolia (A. crassifolia), Sargassum japonica (S. japonica), Macrocystis pyrifera (M. pyrifera), Nizimuddinia zanardini (N. zanardini), Ascophylum nodosum (A. nodosum), S. crassifolium (S. crassifolium), Ecklonia radiata (E. radiata), Padina tetrastrmatica (P. tetrastrmatica), Sargassum latifolium (S. latifolium), Ecklonia kurome (E. kurome), Sargassum muticum (S. muticum). Pichia angophorae (P. angophorae), Sacharomyces cerevisiae (S. cerevisiae), E. coli (E. coli), Z. mobilis (Z. mobilis), Vibrio splendidus (V. splendidus), Sphingobacterium spiritivorum (S. spiritivorum), Trichoderma reesei (T. reesei), Scheffersomyces stipitis (S. stipitis), Kluyveromyces marxianus (K. marxianus), Defluviitalea phaphyphila (D. phaphyphila), Trichoderma asperellum (T. asperellium), Aspergillus niger (A. niger), Aspergillus aculeatus (A. aculeatus).

5. Biocatalysts in bio-refinery and biofuel production

255

5.2 Biobutanol production Biobutanol can be produced from carbohydrate-rich macroalgae through the acetonebutanol (AB) fermentation using anaerobic bacteria such as Clostridium sp. Under appropriate operating conditions, Clostridium strains are saccharolytic, butyric acid-producing bacteria that ferment a variety of substrates (pentoses, hexoses, mono-, di-, and polysaccharides), cellulosic materials, and other biomass. They have the ability to secrete copious enzymes that assist in the metabolism of polysaccharides into monosaccharides. Few species that produce butane effectively include Clostridium acetobutylicum, C. beijerinckii, C. saccaroperbutylacetonicum, C. saccharoacetobutylicum, C. aurantibutyricum, C. pasteurianum, C. sporogenes, C. cadaveris, and C. tetanomorphum. Out of these, the first four have already shown a significant butanol production with high yields. Nevertheless, the choice of the strains for butanol production depends on (i) type of substrate, (ii) nutrient requirement, (iii) tolerance of butanol, (iv) yield and concentration, and (v) resistance to bacteriophage and antibiotics [182]. One-step aceto-butylic fermentation is a traditional method, where Clostridium sp. converts sugars to acetone, butanol, and ethanol. However, in a two-step method, bacteria convert sugars to butyric acid and then to butanol. This method is said to have potential for enhanced butanol yields than ABE fermentation; however, higher amounts of butanol accumulation inhibit the fermentation process [183]. It was also observed that this bacterium did not effectively utilize some glucose-based polysaccharides (such as mannitol from brown algae), which caused slow reaction rate and low productivity of organic acids and total solvents [122,184]. The ABE fermentation process involves two major phases, namely, (i) acidogenesis, where acids are produced parallel to cell growth and (ii) solventogenesis, where the growth is ceased and the acids are converted into solvents, which is always carried out by aldehyde dehydrogenase (ALDH), butanol dehydrogenase (BDH), and/or alcohol dehydrogenase (ADH). The ALDH catalyzes the production of the respective aldehydes from butyryl-CoA and acetyl CoA, which is then converted to butanol by BDH or ADH [185]. As in ethanol production, butanol production can also be divided into steps like pre-treatment, hydrolysis, fermentation, and recovery. Although ethanol fermentation is the most established process for obtaining liquid biofuels, butanol production is well recognized due to attributes like better blending to gasoline, low vapor pressure, or higher specific energy content, even though yields are lower as compared to ethanol [171]. Additionally, ABE fermentation has also been carried out at both pilot and industrial scales [184]. These authors have subjected mannitol, either as a sole carbon source or in combination with glucose and aqueous extracts of Saccharina sp. containing mannitol and laminarin, to acetone-butanol fermentation by C. acetobutylicum (ATCC 824). The fermentation exhibited triauxic growth, with glucose being utilized first, mannitol second, and “polysaccharide-bound sugars” last. This was most likely to happen since it contained laminarin and C. acetobutylicum appeared to secrete specific saccharolytic enzymes such as laminarinase, to break down this complex polysaccharide into fermentable sugars [186]. Some species of green seaweeds, including U. lactuca, which are rich in the deoxysugar rhamnose [187], can also be fermented anaerobically into 1,2-propanediol by a number of microbial species, including some Clostridial strains [188] in a pathway analogous to that in E. coli, Salmonella typhimurium, and Caldicellulosiruptor saccharolyticus [189]. Yet another study was aimed

256

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

at the fractionation of U. lactuca using aqueous pre-treatment followed by enzymatic hydrolysis, to evaluate the potential of the liquid fraction for fermentative production of chemicals and fuels by C. beijerinckii [190]. Enzymatic hydrolysis of Laminaria digitata was carried out by cellulase mixtures, which contained different β-glucanases and β-glucosidases and C. beijerinckii DSM-6422 was used for the fermentation of glucose to butanol. The increased butanol yield was attributed partly to a significantly higher butanol/ABE molar ratio (0.85) than the typical ratio (0.6), which indicated the favored regulation of fermentation pathway to butanol production [191]. Two galactose-utilizing Clostridial strains, viz. C. acetobutylicum strain WA and C. beijerinckii strain WB, were isolated with a view to utilize galactose—the main component of red algal biomass. The metabolic pathways involved in the generation of biofuels and other potential products were reconstructed based on the utilization of the biomass. Finally, a batch fermentation process was carried out to verify the fermentative products using 60 g/L of galactose—the main hydrolysate from algal biomass. It was observed that strains WA and WB could produce up to 16.98 and 12.47 g/L of butanol, together with 21,560 and 10,140 mL/L of biohydrogen, respectively [192]. In yet another study, the fermentation parameters like temperature, pH, and glucose concentrations were optimized for biobutanol production using pre-treated Gracilaria edulis biomass with C. acetobutylicum MTCC 11274, which was found to generate
8.56 g/L biobutanol via optimization using RSM [193]. Enzymatic hydrolysis of E. intestinalis yielded glucose, which on further, fermentation with C. acetobutylicum under controlled pH produced ABE contents of 8.5 g/L with a YABE of 0.36 g/g [194]. Following fermentation, butanol can be recovered by techniques such as liquid-liquid extraction, adsorption, pervaporation, and gas stripping [195]. Pervaporation is a membrane process for liquid separation, in which a polymeric or inorganic membrane usually serves the separating barrier. Its application for butanol recovery from fermentation broth is based on the selective permeation of organic compounds through the membrane when the nature of the membrane is hydrophobic [196]. Biobutanol can be recovered from the ABE mixture by the separation units. Three classic distillation columns are combined in one azeotropic dividing-wall column that is effectively coupled with a compressor for vapor recompression and a decanter that is used for the liquid-liquid split of the heterogeneous azeotrope butanol-water [197]. A detailed list of the different algae, types of enzymes used, and the overall butanol production is given in Table 4.

5.3 Biogas production Biogas production is an age-old technology with a number of active installations, from largescale to small ones fed with various feedstocks—the conventional ones being agricultural crops, animal wastes, sewage sludge, domestic rejects, and so on [201]. Technically, biogas production from macroalgae seems more viable than other fuels since all the organic components contained in macroalgae can be converted into biogas by anaerobic digestion (AD) and low lignin—an attribute that makes their degradation easier [13]. Biogas produced by AD consists mainly of methane and CO2 with minor amounts of hydrogen sulfide (H2S) and ammonia (NH3) [202]. The digestion process generally consists of the stages, viz., hydrolysis, acetogenesis, acidogenesis, and methanogenesis [203]. Initially, the hydrolytic and fermentative bacteria

257

5. Biocatalysts in bio-refinery and biofuel production

TABLE 4 Studies on various pre-treatment conditions in combination with enzymatic hydrolysis for various macroalgae for biobutanol production. Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

L. japonica, P. elliptica, C. ocellatus, and E. compressa

Acid and enzymatic treatment followed by fermentation with C. beijerinckii NCIMB 8052

L. japonica and P. elliptica produced reducing sugar of 6.27  0.46 g/L and 6.22  0.12 g/L, resp. Butanol yield of 0.27 g/L in untreated L digitata, while 0.34 g/L in combined treatment for P. elliptica

[198]

Wastewater algae

Pre-treatment using 1 M H2SO4 at 90°C for 30 min. Followed by treatment using 5 M NaOH at 90°C for 30 min. ABE production using 10% pre-treated algae supplemented with 10 U of endo1,4-b xylanase from T. longibrachiatum and 100 U of endo-1,4-b-cellulase from A. niger

Fermentation of acid/base-pre-treated algae gave 2.74 g/L of ABE, as compared to 7.27 g/L from pre-treated algae supplemented with 1% glucose. Additionally, 9.74 g/L of ABE was produced when xylanase and cellulase enzymes were added to the pre-treated algae

[199]

Aqueous extracts of Saccharina

Fermentation by C. cetobutylicum (ATCC 824), where it secreted specific saccharolytic enzymes like laminarinase, to break down this complex polysaccharide into fermentable sugars

Butanol yield was 0.12 g/g of seaweed

[184]

U. lactuca

Mild pre-treatment with NaOH and incubation at 85°C for 4 h. H2SO4 pretreatment at pH 2 and incubation at 150°C for 10 min, followed by enzymatic hydrolysis by a commercial cellulase cocktail (GC220, Genencor) at 50°C for 96 h, and fermentation with C. acetobutylicum ATCC 824 and C. beijerinckii NCIMB 8052

Butanol production of 8.4 g/L for C. beijerinckii cultures in hydrolysate supplemented with glucose, xylose, and nutrients and 11.4 g/L for C. acetobutylicum in hydrolysate supplemented with glucose and xylose

[122]

U. lactuca

Pre-treatment at 150°C for 10 min, followed by enzymatic hydrolysis with cellulase cocktail at a loading of 0.3 mL/g DM for 24 h at 50°C and fermentation with C. beijerinckii NCIMB 8052

Monosaccharides obtained was 38.8 g/L. ABE production up to 7.5 g/L, butanol production of 5 g/L

[190]

L. digitata

Biomass was added with water with continuous stirring, pH 5.04 by adjusting with H3PO4 at 45°C and added with Cellulases mixture (NS81016; Novozymes A/S), containing β-glucanases and β-glucosidases, at loading of 10% (v/ w) of seaweed dry weight. This was followed by fermentation with C. beijerinckii DSM-6422

Butanol yield from seaweed hydrolysate (0.42 g/g-consumed substrates or 0.27 g/g-total-substrates

[191]

Continued

258

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 4 Studies on various pre-treatment conditions in combination with enzymatic hydrolysis for various macroalgae for biobutanol production—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

E. intestinalis

Pre-treatment with 270 mM H2SO4 at 121°C for 60 min followed by saccharification using Celluclast 1.5 L (854 endoglucanase units/mL; Novozymes, Bagsværd, Denmark) and Viscozyme L (121 fungal β-glucanase units/mL; Novozymes) at a 1:1 ratio (4–32 U/mL) were added to 100 g/L of slurry after thermal acid hydrolysis at pH 5.0, 45°C, and 150 rpm for 0–60 h. Followed by fermentation with C. acetobutylycum

When mixtures of Viscozyme L and Celluclast 1.5 L were applied at ratios of 2:1, 1:1, and 1:2, the monosaccharide concentration increased to 20.9, 24.2, and 22.13 g/L, respectively. Fermentation without and with pH control gave ABE concentration of 8.5 and 6.0 g/L respectively

[194]

S. latissima

Cellic CTec2 (Novozymes A/S) and two alginate lyases, Alg3 and Alg4, were tested in five different combinations (Alg3, Alg4, Cellic CTec2 by themselves as well as sequential treatments of Alg3 followed by Cellic CTec2 and Alg4 followed by Cellic CTec2). This was followed by fermentation with C. acetobutylicum ATCC 824

Glucose release of
80%, using a combination of Cellic CTec2 with novel alginate lyase enzymes ABE yield was 0.23  0.02 g/g sugar similar to control fermentation in control medium (0.19 g ABE/g sugar)

[200]

Note: The names of algae and microbes have been used as per following abbreviations in the order of appearance in table. Lamanaria japonica (L. japonica), Pachymeniopsis elliptica (P. elliptica), Chondrus ocellatus (C. ocellatus), Enteromorpha compressa (E. compressa), U. lactuca (U. lactuca), Laminaria digitata (L. digitate), E. intestinalis (E. intestinalis), Saccharina latissima (S. latissima). C. beijerinckii (C. beijerinckii), Trichoderma longibrachiatum (T. longibrachiatum), Aspergillus niger (A. niger), Clostridium cetobutylicum (C. cetobutylicum).

hydrolyze polymers, then ferment the resulting monosaccharide to carboxylic acids and alcohols, which are converted to acetate, hydrogen, and CO2 by acetogenic bacteria. As a last step, the methanogenic bacteria convert the end products of acetogenic reactions mainly to methane and CO2. As already mentioned, the purpose of enzyme addition is to break down polymers into substrate, which can be applied in three different ways: by direct addition to the vessel for single-stage AD; by addition to the hydrolysis and acidification vessel (first stage) of a two-stage system; or by addition to a dedicated enzymatic pre-treatment vessel [204]. During hydrolysis, microorganism such as Clostridium sp. secrete extracellular enzymes like endo-1,4-β-gluconase, exo-1,4-β-gluconase, and β-galactosidase involved in the hydrolysis of cellulose to glucose [205]. One of the challenges that biogas production from macroalgae may face is the presence of significant amounts of salts (sodium, potassium, calcium, and magnesium), halogens, and sulfur, which may restrict the growth and productivity of the

5. Biocatalysts in bio-refinery and biofuel production

259

anaerobic microorganisms and are likely to cause fouling issues [206]. Another challenge could be the inhibition of methanogens by sulfide toxicity even at low dissolved sulfide level. The methanogenesis inhibition could be linked to considerable acetate accumulation [207]. The biomethane thus obtained can be directly used in gas stoves as a substitute for liquefied petroleum gas; however, its use in commercial engine needs upgradation. Quite few studies related to the enzymatic hydrolysis followed by AD for biogas production are available. Sangeetha et al. [208] reported the green seaweed Chaetomorpha litorea to contain 25 μg/g of auxin, 45 μg/g of cytokinins, and 12μg/g gibberellins and under AD, it generated 80.5L of total biogas per kg of dry biomass. Grala et al. [209] reported that during the fermentation of algal biomass, the utilization of organic compounds was higher in the case of substrates subjected to additional enzymatic hydrolysis than in the case of substrates subjected to thermal depolymerization alone. As per a lone study on the use of Rhizoclonium biomass for CH4 production appeared disadvantageous due to low CH4 yield; however, investigating other pre-treatment methods that could potentially improve the biomass-to-CH4 conversions could be explored [210]. Vanegas and Bartlett [211] compared the suitability of five Irish seaweed species to produce biogas and CH4 via co-digestion with bovine slurry. Low biogas from Fucus serratus was attributed to inhibitory or recalcitrant compounds and the inaccessibility of microbial enzymes to the cellular components of the seaweed. Yet another study reported pre-treatments with organic acids and enzymes for biogas production from L. digitata; despite the enhanced reducing sugar obtained by pre-treatment, no considerable improvement in biogas production was observed [212]. Conversely, enhanced biogas production using Ulva rigida hydrolysate, hydrolyzed using Aspergillus niger filtrate containing enzymes like β-glucosidase and CMCase endoglucanase, is also available [213]. The authors opined that low difference in two batch experiments of enzymatic pre-treatment could be due to the synergic effect of hydrolytic enzymes in crude broth of A. niger. Yet another study demonstrated the feasibility of co-producing biogas and bioethanol from C. linum where the biomass was pre-treated using thermal hydrolysis followed by enzyme treatment produced from Aspergillus awamori, possessing cellulolytic, hemicellulolytic, and proteolytic activities [129]. An improvement in the rate of biogas production was observed due to enzyme hydrolysis step from S. latissima [214]. In a similar study, a combined action of hydrolytic enzyme mixture secreted by species of Trichoderma, Penicillium, Fusarium, Chaetomium, and Myrothecium was used, thus facilitating access of anaerobic fermentation bacteria to cellulosic fibers. The results showed enhancement in cumulative biogas volume for the enzymatically pre-treated biomass sample compared to the untreated one [215]. K. alvarezii biomass was subjected to autoclave (120°C and 1 atm for 6 h) treatment and/or enzymatic (Celluclast® and/or a recombinant β-glucosidase) hydrolysis, to break down complex carbohydrates into sugars that were used to produce hydrogen by fermentation. Macroalgae biomass treated with Celluclast® + β-glucosidase and with combined thermal and enzymatic hydrolysis reached very similar total reducing sugar productivities of 0.24 and 0.22 g of TRS/L, respectively. The enzymatically treated biomass was employed as feedstock to produce H2 by C. beijerinckii Br21, which gave high yield: 21.3 mmol of H2/g of dry algae biomass. A detailed list on the different algae used for the production of biogas using enzymatic pre-treatment is given in Table 5.

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TABLE 5 Studies on various pre-treatment conditions in combination with enzymatic hydrolysis for various macroalgae for biogas production. Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

C. litorea

Cow dung mixed with water @ 1:1 (w/v) and fermented for obtaining seed culture of methanogenic bacteria for 30 d. This was added with algal slurry (1.0 kg dry powdered seaweed), pH 7.0. Possibility of activities like amylase and protease of the microbes during the fermentation process on the substrate

Biogas obtained was 80.5 L/kg, which had 65% of CH4

[208]

Pilayella (90%), Ectocarpus (8%), and Enteromorpha

Hydrothermal depolymerization of algal substrate at 200°C under a pressure of 1.7 MPa for 120 min in a muffle furnace, followed by treatment with multicomplex of Celluclast 1.5 L, Novozym 188, and Hemicellulase, incubated at 20°C for 24 h

CH4 content in biogas ranged from 63.0% to 73.0%. Enzymatic hydrolysis yielded better results in comparison with variants in which the enzyme was not applied

[209]

F. vesiculosus, F. serratus, Z. marina, F. lumbricalis, Polysiphonia sp., Ceramium sp.

Digestion of these macro-algae through continuous anaerobic digestion at 35 and 55°C temperatures. Enzymatic pretreatment was carried out using Cellic CTec2 at 50°C for 5 d with three different doses of enzyme 15, 30, and 45 FPU/g TS

Digestion of enzyme-pre-treated algae in continuous digestion showed relatively acceptable CH4 yields (about 400 NmL CH4/g VS fed) for both thermophilic and mesophilic reactors

[207]

Rhizoclonium sp.

The enzyme pre-treatment using a recommended dosage of 1% (w/w biomass) for all the investigated enzymes

Action of cellulase resulted in CH4 yield of 130 mL/g TS of biomass

[210]

F. vesiculosus, F. lumbricalis and filamentous red algae

The enzymatic pre-treatment with individual and combination of protease, cellulase, pectinase, hemicellulase with algae at 50°C for 6.5 h, which increased the CH4 production for most samples, however to a much smaller extent than mechanical pre-treatment

The final CH4 potential (after 50 d of digestion) could not be increased by thermal or enzymatic pretreatment for filamentous red algae but could be increased by 100% for F. vesiculosus

[216]

U. rigida

Hydrolysis of biomass using an enzyme preparation obtained from the fermentation of wheat bran by A. niger with high b-glucosidase activity (137  15 IU/mL) and low carboxymethyl activity (1.50  0.5 IU/mL) and subsequently subjected to anaerobic digestion

Reducing sugar yields of 3.62, 2.88, 2.53, and 7.3 g/L obtained in acid catalysis, thermoalkaline, ultrasonication, and enzymatic pre-treatments, respectively. The enzymatic hydrolysis gave the best biogas yield of 626.5 mL/g CODint with 62.65% of biodegradability

[213]

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5. Biocatalysts in bio-refinery and biofuel production

TABLE 5 Studies on various pre-treatment conditions in combination with enzymatic hydrolysis for various macroalgae for biogas production—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

L. digitata and S. latissima

Seaweed were mixed with 5 mL of dH2O and subjected to enzymatic hydrolysis using Cellulase from T. longibrachiatum (Sigma C9748), alginate lyase from S. multivorum (Sigma A1603), and Celluclast R 1.5 L from T. reesei (University of Reading/Novo Nordisk). Hydrolysis of the seaweed with the range of enzymes was carried out for a period of 24 h in a rotary incubator at 300 rpm at the corresponding temperature

Reducing sugar yield with Celluclast: 4 and 6 mg/mL for L. digitata and S. latissima, respectively, with alginate lyase (10 and 11 mg/mL, respectively) Cellulase improved the biodegradability of the seaweed to some extent (17% in terms of VSD). However, only a 1.7% increase in total biogas was observed (232 mL biogas g/VS)

[212]

U. rigida

Enzymatic cocktail was isolated from A. niger, the cocktail was rich in β-glucosidase, pectinase, and carboxy-methyl-cellulase (CMCase) activities

Enzyme cocktail showed high rate of reduced sugar release, and the biogas production reached 1175 mL/g CODint

[217]

C. linum

Hydrolysis for 20 min at 120°C without catalyst for neutral pretreatment and treatment in the presence of 3% NaOH and 0.6% H2SO4. This was followed by enzymatic saccharification of alkali-pre-treated biomass, at pH 5. This was followed by anaerobic digestion by mixing with a suitable inoculum of methanogenic bacteria

Biomethane yield was 0.26 L/g VS

[129]

Macroalgae consortium (MC)

Dry MC was suspended in water (10%, w/v), pH 5 and inoculated with mycelial suspension of T. hirsuta, incubated at 35°C, 150 rpm in an orbital shaker for 6 d

MC + fungi produced the highest CH4 yield as compared to nonpretreated MC, biogas production reached 104 L CH4/kg VS

[218]

S. latissima

Dry macroalgae pulp (10%, w/v) was suspended in 0.15 M Na2CO3, pH 9.0, for 2 h at 50°C. For enzyme hydrolysis, commercial β-glucanase (G4423) from T. longibrachiatum (Sigma Aldrich, Germany) was used (mixture of β-1-3/1-4-glucanase, xylanase, cellulase, β-glucosidase, β-xylosidase, α-L-arabinofuranosidase, and amylase) at a loading of 5 mg/g of seaweed, pH 6 for 48 h at room temperature. The microbial inoculum for the

Reducing sugar yield was 30.11  2.30 g/kg of seaweed Biogas yield was 459  30 mL/g VS, resulting in 257  17 mL/g VS of biomethane

[214]

Continued

262

10. Macroalgal polysaccharides: Biocatalysts in biofuel/bioenergy production

TABLE 5 Studies on various pre-treatment conditions in combination with enzymatic hydrolysis for various macroalgae for biogas production—cont’d Seaweed

Hydrolysis and fermentation conditions

Yield (glucose and/or ethanol)

References

Biogas volume obtained was 1242 mL for the control sample and 1356 mL for the enzymatic pretreated sample representing an improvement in the total biogas of 9.2%

[215]

biomethane was obtained from Biokraft biogas plant (Skogn, Norway) U. intestinalis

Biomass was inoculated with a native mixed fermenting bacterium from cattle manure followed by the addition of mixed enzymes from fungi and conditioned at 28  1°C for 24 h (T. reesei, T. versicolor, P. chrysosporium, F. solani, C. thermophile and M. verrucaria)

Note: The names of algae and microbes have been used as per following abbreviations in the order of appearance in table. Chaetomorpha litorea (C. litorea), F. vesiculosus (F. vesiculosus), Fucus serratus (F. serratus), Zostera marina (Z. marina), Furcellaria lumbricalis (F. lumbricalis), Ulva rigida (U. rigida), Laminaria digitata (L. digitata), Saccharina latissima (S. latissimi), U. rigida (U. rigida), C. linum (C. linum), U. intestinalis (U. intestinalis). Aspergillus niger (A. niger), Trichoderma longibbrachiatum (T. longibbrachiatum), Sphingobacterium multivorum (S., ultivorum), Trichoderma reesei (T. reesei), Trichoderma hirsuta (T. hirsuta), Trichoderma versicolor (T. versicolor), Penicillium chrysosporium (P. chrysosporium), Fusarium solani (F. solani), Chaetomium thermophile (C. thermophile), Myrothecium verrucaria (M. verrucaria).

6. Conclusions and future prospects The implementation of bio-refinery is essential to reduce greenhouse gas and safeguard our future. The economic feasibility of seaweed cultivation makes them potential feedstocks, promising an algae-based bio-refinery platform; nevertheless, it should meet the demands of the market and pave way for commercial-scale production. Different biofuel/bioenergy is obtained from algae; the production of which depends on the algal strain, their lipid and/ or carbohydrate content and various growth parameters. The macroalgae-based bio-refinery is expected to dramatically develop in the near future due to many environmental and economic gains. However, a successful algal bio-refinery should focus on enhancing the yield, identification of new microbes/enzymes capable of utilizing an array of macroalgal carbohydrates, technology and process development and reducing the overall operating cost. Enzyme technology is a sure shot means of moving toward cleaner production over conventional chemical processes and may result in benefits that cannot be achieved with traditional processes. Enzymes are preferred since they are known to efficiently utilize raw materials, minimize production cost, reduce impacts to the environment, and lower contamination of the end product with toxic substances. In addition to apt pre-treatment/hydrolysis of substrate, the development of efficient and cost-effective fermentation and post-fermentation markets also requires research. The fermentation stage is limited in proficiency, mainly, by the fermenting organisms capable of converting sugars to ethanol and not by the choice of method. In the bio-refinery concept, enzymes play a crucial role and directly influence the yield by processes such as enzymatic saccharification, enzyme-assisted extraction of

6. Conclusions and future prospects

263

compounds, and enzyme-catalyzed transformations. Additionally, the utilization of only the carbohydrate fraction during hydrolysis and fermentation processes from the entire seaweed biomass results in the generation of large amounts of organic wastes, which can be resolved using enzyme engineering. Moreover, areas like enzyme immobilization, extraction methods, and separation of the products also need focus. Development of effective enzymes and efficient hydrolysis is today’s demand and to ameliorate the process of bio-refinery, focus on other methods in combination with enzymatic ones should be explored/perfected. In addition, the mechanism and synergistic interaction of enzymes during the extraction process require extensive investigation. Above all, the cost of enzymes is relatively high and hence, focus also needs to be directed toward the use of cheap biomass waste as a substrate for producing economic and efficient enzymes. The development of novel and efficient enzymes via either metagenomics or metabolic engineering will pave the way for creating innovative and high-value products in the bio-economy. Another strategy to decrease the cost of enzyme could be to recover and recycle them from the hydrolysis process. More efficient enzyme preparation can also be achieved by approaches such as selective screening of candidate enzymes, enzyme engineering, and purification of enzyme cocktails, which need to be extensively researched and developed. In the case of butanol production, numerous efforts are being made to increase its production from Clostridium sp. Nevertheless, co-culturing of butanol-producing microbe or using genetically engineered microbes able to degrade substrates can also be attempted. Focused research toward exploring new heterologous hosts rather than achieving high yield through Clostridium sp. alone is also required. Even the heterologous hosts faces the problem of butanol toxicity resulting in low butanol yield, thereby increasing the need for further studies to improve its tolerance against butanol. Apart from developing tolerance in heterologous hosts, research on naturally tolerant hosts can also emerge as promising candidates for butanol production. As far as biogas production is concerned, one of the limiting factors is the poor conversion rate of substrates into biogas. This is mainly due to the difficult and restricted metabolism of the biomass by the microbial consortium present in the digestor mainly due to the complexity of cellulose, hemicellulose, and other components. Additional research in order to elucidate the mechanisms by which enzymatic pre-treatment technique acts at the cellular level, is needed. This review highlights the importance and potential of macroalgal biomass through a biorefinery concept. The abundant availability and diversity of biomass provides enormous scope for the production of biofuels and other valuable products that can be of use in various industries. The performance of any pre-treatment method is quantified based on the economic feasibility and environmental impacts, which includes the cost of pre-treatment vs the value of biofuel yield. Biological disintegration is devoid of chemical contamination and energy inputs and the use of an enzyme-secreting bacterial consortium for biomass is beneficial, but restricts its use due to high reaction times and cost. Moreover, most studies in the literature are conducted as laboratory scale and do not represent the same output that could be achieved at larger facilities. Hence, there is a continuous need for newer and cleaner methods of biomass processing with less energy demand and lower waste generation. We also demonstrated that enzymatic technology is an important upstream process that acts as a driver for the efficient development of algal bio-refinery. However, thorough investigations in greater detail are required to make these competitive with petroleum-based ones.

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Acknowledgments The authors thank the organization for providing the facility to compile this work. R.R. also thanks CSIR for granting the CSIR-JRF fellowship.

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[189] Van de Werken HJG, Verhaart MRA, Van Fossen AL, Willquist K, Lewis DL, Nichols JD, et al. Hydrogenomics of the extremely thermophilic bacterium Caldicellulosiruptor saccharolyticus. Appl Environ Microbiol 2008;74:6720–9. [190] Bikker P, Van Krimpen MM, Wikselaar PV, Houweling-Tan B, Scaccia N, Van Hal JW, et al. Biorefinery of the green seaweed Ulva lactuca to produce animal feed, chemicals and biofuels. J Appl Phycol 2016;28:3511–25. [191] Hou X, From N, Angelidaki I, Huijgen WJJ, Bjerre AB. Butanol fermentation of the brown seaweed Laminaria digitata by Clostridium beijerinckii DSM-6422. Bioresour Technol 2017;238:16–21. [192] Sun C, Zhang S, Xin F, Shanmugam S, Wu YR. Genomic comparison of Clostridium species with the potential of utilizing red algal biomass for biobutanol production. Biotechnol Biofuels 2018;11:42. [193] Rana S, Kashyap D, Dhingra A. Enhanced biobutanol production by optimization of the medium from Clostridium acetobutylicum MTCC 11274 using macroalgae Gracilaria edulis. J Appl Biol Biotechnol 2019;7(06):48–55. [194] Nguyen TH, Sunwoo IY, Ra CH, Jeong GT, Kim SK. Acetone, butanol, and ethanol production from the green seaweed Enteromorpha intestinalis via the separate hydrolysis and fermentation. Bioprocess Biosyst Eng 2019;42:415–24. [195] Quiroz-Ramirez JJ, Sa´nchez-Ramı´rez E, Segovia-Herna´ndez JG. Planning of biobutanol production considering raw material availability: economic and environmental optimization. Comput Aided Chem Eng 2017;40:757–62. [196] Liu G, Wei W, Jin W. Pervaporation membranes for biobutanol production. ACS Sustain Chem Eng 2014;2 (4):546–60. [197] Patras¸ cu I, Bıˆldea CS, Kiss AA. Eco-efficient downstream processing of biobutanol by enhanced process intensification and integration. ACS Sustain Chem Eng 2018;6(4):5452–61. [198] Pan L, Byungwoo K, Jieun L. Butanol fermentation with the hydrolysates of macroalgae by Clostridium beijerinckii NCIMB 8052. KSBB J 2011;04:144. [199] Ellis JT, Hengge NN, Sims RC, Miller CD. Acetone, butanol, and ethanol production from wastewater algae. Bioresour Technol 2012;111:491–5. [200] Schultze-Jena A, Vroon RC, Macleod AKA, Hreggvidsson G, Adalsteinsson BT, Engelen-Smit NPE, et al. Production of acetone, butanol, and ethanol by fermentation of Saccharina latissima: cultivation, enzymatic hydrolysis, inhibitor removal, and fermentation. Algal Res 2022;62, 102628. [201] Demirbas A. Use of algae as biofuel sources. Energy Convers Manage 2010;51:2738–49. [202] Vergara-Fernandez A, Vargas G, Alarco´n N, Velasco A. Evaluation of marine algae as a source of biogas in a two-stage anaerobic reactor system. Biomass Bioenergy 2008;32(4):338–44. [203] Roesijadi G, Jones SB, Snowden-Swan LJ, Zhu Y. Macroalgae as a biomass feedstock: a preliminary analysis (No. PNNL-19944). Richland, WA: Pacific Northwest National Lab. (PNNL); 2010. [204] Bochmann G, Montgomery LFR. Storage and pre-treatment of substrates for biogas production. In: The biogas handbook science. Production and applications woodhead publishing series in energy. Woodhead Publishing; 2013. p. 85–103. [205] Marquez GPB, Santian˜ez WJE, Trono Jr GC, Montan˜ob MNE, Araki H, Takeuchi H. Seaweed biomass of the Philippines: sustainable feedstock for biogas production. Renew Sustain Energy Rev 2014;38:1056–68. [206] Ross AB, Jones JM, Kubacki ML, Bridgeman T. Classification of macroalgae as fuel and its thermochemical behaviour. Bioresour Technol 2008;99:6494–504. [207] Haghighatafshar S, Kjerstadius H, Jansen JLC, Davidsson A. Management of hydrogen sulfide in anaerobic digestion of enzyme pre-treated marine macro-algae. Water Resour Res 2012;68:265–73. [208] Sangeetha P, Babu S, Rengasamy R. Potential of green alga Chaetomorpha litorea (Harvey) for biogas production. Int J Curr Sci 2011;1:24–9. [209] Grala A, Zieli nski M, Dębowski M, Dudek M. Effects of hydrothermal depolymerization and enzymatic hydrolysis of algae biomass on yield of methane fermentation process. Pol J Environ Stud 2012;21:363–8. [210] Ehimen E, Holm-Nielsen J, Poulsen M, Boelsmand JE. Influence of different pre-treatment routes on the anaerobic digestion of a filamentous algae. Renew Energy 2013;50:476–80. [211] Vanegas CH, Bartlett J. Green energy from marine algae: biogas production and composition from the anaerobic digestion of Irish seaweed species. Environ Technol 2013;34(15):2277–83. [212] Vanegas CH, Hernon A, Bartlett J. Enzymatic and organic acid pre-treatment of seaweed: effect on reducing sugars production and on biogas inhibition. Int J Ambient Energy 2015;1:36.

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C H A P T E R

11 Mathematical modeling of the enzymatic hydrolysis of polysaccharides: A primer David Alexander Mitchella and Nadia Kriegerb a

Department of Biochemistry and Molecular Biology, Federal University of Parana´, Curitiba, Parana´, Brazil bDepartment of Chemistry, Federal University of Parana´, Curitiba, Parana´, Brazil

1. Aims and scope of this chapter This chapter is written for readers who are familiar with experimental aspects of the enzymatic hydrolysis of polysaccharides and who have not yet ventured into modeling their systems, but are interested in doing so. It does not aim to teach readers how to develop a model, nor to review previously published models of the enzymatic hydrolysis of polysaccharides. Rather, this chapter discusses various issues that arise when one wishes to model the enzymatic hydrolysis of polysaccharides. Even if one does not intend to develop the model oneself, but rather will collaborate with a colleague who has the appropriate modeling skills, one needs to understand these issues in order to make informed decisions. However, this chapter does assume that all readers have a basic grounding in the fundamentals of classical enzyme kinetics, such as the Michaelis-Menten equation and the estimation of its parameters in initial rate assays, the mechanisms and kinetics of reversible inhibition, and the mechanisms of two-substrate two-product reactions.

2. Scales at which the enzymatic hydrolysis of polysaccharides can be modeled Models that describe the enzymatic hydrolysis of polysaccharides can represent the system and describe the phenomena that occur at different scales. These different models have different uses.

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2.1 Atomic-scale models of interaction between enzymes and polysaccharides In models of atomic-scale interaction between enzymes and polysaccharides, the threedimensional structures of the enzyme (or a part of it, usually the active site) and the polysaccharide (or a part of it, normally a part that fits into the active site) are represented at the atomic level. There are two basic approaches to modeling atomic-scale interactions, molecular docking, and molecular dynamics. In molecular docking simulations, the fit between the enzyme (or its active site) and the polysaccharide (or part of it) is tested [1,2]. Various fits are tested, with the polysaccharide in various orientations and conformations (i.e., rotatable bonds are tested at different rotational angles), with the aim of finding the fit that minimizes the free energy of the complex. Flexibility can also be incorporated into key parts of the active site itself. In molecular dynamics simulations, the movements of the molecules (enzyme and polysaccharide) and the solvent are simulated over time by applying Newton’s laws of motion and taking into account the forces between these interacting molecules [2,3]. These simulations are computationally demanding and are often done on supercomputers. Molecular docking and molecular dynamics simulations are useful for understanding atomic-scale aspects of the enzyme-polysaccharide interaction and the catalytic mechanism. The understanding obtained can be used to guide protein engineering, in attempts to improve catalysis. This chapter does not address these types of models.

2.2 Molecular-scale models of interaction between enzymes and polysaccharides In molecular-scale models of interaction between enzymes and polysaccharides, the structures of the substrate and the enzyme are physically represented, but the positions of individual atoms are not described [4]. Rather, the model describes the general positions and volumes of the enzymes and of the monosaccharide (or disaccharide) units within the polymeric substrate. Such models cannot give insights into the catalytic mechanism at the atomic level, but do give insights into the degree to which different parts of the substrate are accessible to the enzyme and how this accessibility changes during the hydrolysis. They can also give insights into steric impediments between enzyme molecules. Such models can be used to identify the degree to which these phenomena limit the efficiency of hydrolysis. However, these molecular-scale models describe the process at such a level of detail that it would be too computationally demanding to try to describe a whole bioreactor; therefore, they typically focus on describing relatively small volumes. This chapter does not address these types of models.

2.3 Dynamic macroscale models of enzymatic hydrolysis processes Macroscale models represent enzymes and polymeric substrates as populations of molecules, without attempting to describe the molecules as space-filling structures. Some models of this type recognize individual molecules, while many simply represent different species in terms of concentrations. Macroscale models of enzymatic hydrolysis are suitable for incorporation into bioreactor models. In other words, the expressions that describe the hydrolysis itself can be incorporated into mass or mole balance equations. These balance equations can then be used to predict how the concentrations of various species in the system vary over

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time and, if appropriate, how they vary with position in the bioreactor. These species include not only substrates, intermediates, and final products, but also active enzymes. The balance equations can be adapted to describe different modes of bioreactor operation, varying from well-mixed batch processes, through fed-batch processes, to continuous-flow bioreactors. As such, these macroscale models are useful tools for guiding the design of enzymatic hydrolysis bioreactors and the optimization of the operation of these bioreactors. These models will be referred to as dynamic process models, with the word “dynamic” indicating that they describe the change of the variables of the system over time. Such dynamic process models are required for calculating volumetric productivities, which must be known in order to determine the size of bioreactor that is required to give the desired rate of production of the final hydrolysis product. In processes involving enzyme cocktails, dynamic process models can also be used to identify the optimum ratio of the enzymes, for example, the ratio between an exo-acting enzyme and an endo-acting enzyme. Dynamic process models are the focus of this chapter.

2.4 Models used specifically as tools in parameter estimation It is also possible to develop models that do not aim to describe the evolution of the process over time, but which are useful in parameter estimation. This chapter addresses a modeling approach to determining specificity constants, which are important parameters in models describing the enzymatic hydrolysis of polysaccharides.

3. Features of substrates, enzymes, and models 3.1 Features of polysaccharide substrates Polysaccharide substrates of interest for enzymatic hydrolysis processes include starch, cellulose, pectin, hemicellulose, and xylan. The structural and physicochemical features of these polysaccharides need to be considered when representing them within the model. The simpler the polysaccharide, the easier it is to represent it in a model. Linear polysaccharides are simpler than branched polysaccharides. Homopolysaccharides (built from a single monosaccharide) are simpler than heteropolysaccharides (built from different monosaccharides). Polysaccharides containing unsubstituted monosaccharide units are simpler than polysaccharides containing monosaccharide units that are esterified with alcohols or acids. Other complicating factors are that a natural polysaccharide will contain molecules that are of different lengths (i.e., there is a distribution of chain lengths) and also might contain different polymers (e.g., starch is a mixture of amylose and amylopectin). Hydrolysis processes will typically be carried out in systems with high water contents. In such systems, polysaccharides may form colloidal suspensions (e.g., starch). In this case, the model will often treat the system as being well mixed and the substrate will be treated as though it were fully soluble. The various molecules in the system (i.e., polymers and oligomers of different chain lengths) may simply be described by their concentrations, with no attempt being made to describe the physical dimensions of the molecule or the spatial conformation of the chain.

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At the other extreme, the polysaccharide may be insoluble, which is often the case for cellulosic substrates. Many models of cellulose hydrolysis represent the substrate as insoluble particles. These particles may be treated as consisting of bundles of parallel cellulose chains. In some models, these bundles are represented within computer programs using threedimensional arrays, where each position in the array represents a monosaccharide or a disaccharide [4]. In this manner, the array can be used to represent the physical dimensions of a cellulose fiber. The physicochemical properties of the substrate will depend on the source of the polymer. Starches from different sources have different amylose-to-amylopectin ratios. Lignocellulosic substrates from different sources have different lignin contents and contain cellulose with different degrees of crystallinity. These properties will then be affected by the types and severity of preprocessing and pretreatment processes. Native starch consists of insoluble starch granules, with the colloidal suspension used in hydrolysis processes being produced only after gelatinization of the starch. In natural sources of cellulose, the cellulose fibers are part of a supramolecular structure that also involves lignocellulose and hemicellulose. The lignocellulose and hemicellulose are often removed by pretreatments. The lengths and the degree of crystallinity of the remaining cellulose fibers will depend on the type and severity of these pretreatments and subsequent processing. The consequence for modeling is that the structure of the substrate fed to the enzymatic hydrolysis process will be situation-specific: even if different hydrolysis processes involve the same type of polysaccharide, the particular combination of the source of the polysaccharide and the pretreatment that it received may lead to a substrate with a unique set of properties that need to be determined experimentally.

3.2 Features of enzymes used to hydrolyze polymeric substrates The enzymatic systems that are used to hydrolyze polysaccharides range from single enzymes to complex mixtures. The kinetics of these systems need to be incorporated into hydrolysis models. Types of enzymes typically involved in the hydrolysis of polysaccharides include: • endo-acting enzymes—enzymes that attack the glycosidic bonds within the chain. The attack is usually random, but the likelihood of attack may be different near the extremities and attack may be impeded near branch points. Each enzyme will be specific for a particular type of glycosidic bond (e.g., α1 ! 4 or β1 ! 3, etc.) between particular monosaccharide units, which is important in the hydrolysis of heteropolysaccharides. • exo-acting enzymes—enzymes that attack glycosidic bonds at one of the extremities of the polysaccharide chain, removing either monosaccharide or disaccharide units. Exo-acting enzymes are usually specific for either the nonreducing end or the reducing end of the chain. Exo-acting enzymes may not be able to pass branch points. • debranching enzymes—enzymes that attack at the branch point, removing the branch from the main chain. • auxiliary enzymes—such as enzymes that remove ester groups from esterified monosaccharide units.

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Various types of these enzymes follow a “single-attack” mechanism. In this case, after hydrolyzing a bond, the enzyme dissociates from the polysaccharide chain. The next attack by this enzyme will be carried out at a randomly selected position or chain. However, in the hydrolysis of polysaccharides, it is common for enzymes to follow a “multiple-attack” mechanism [5]. In this case, after hydrolyzing a bond, the enzyme liberates a hydrolysis product, but does not dissociate from the residual polysaccharide chain. Rather, it slides along it, repositioning itself for a subsequent attack on the same chain. Such enzymes are often called processive enzymes. In systems for the hydrolysis of polysaccharides, it is common to find processive exo-acting enzymes, which liberate monosaccharides or disaccharides from one of the extremities of the chain. The processivity can be quite strong; in this case, once the enzyme binds to the polymer, it hydrolyzes the whole chain without dissociating from it. The processivity can also be weaker; in this case, variable numbers of units are removed, with the enzyme having a certain probability of dissociating from the residual polymer chain after each hydrolysis reaction, instead of sliding along it. There are also some processive endoacting enzymes. These are enzymes that, after an initial endo-attack, carry out a limited processive attack before dissociating from the polysaccharide chain [6]. Processivity has also been described for esterases; after removing one ester group, they slide along the chain until they find another [7]. The importance of processive action is that it affects the description of the hydrolysis kinetics. In addition to the enzymes listed earlier, the efficient saccharification of a polysaccharide may require the action of enzymes that hydrolyze oligomers (especially disaccharides). Processes for the enzymatic hydrolysis of polysaccharides require the action of one or more of these enzymes. Complete hydrolysis might be achieved with a single enzyme, or the process may involve several sequential processing steps, each involving a single enzyme. For example, the production of high-fructose corn syrup from starch involves three successive steps: (i) liquefaction with an α-amylase, an endo-acting enzyme, to produce oligomers; (ii) saccharification of the oligomers with glucoamylase, an exo-acting enzyme that removes glucose units from the nonreducing end; and (iii) isomerization of the glucose to fructose with glucose isomerase [8]. Alternatively, a mixture of enzymes may act simultaneously. Such a mixture can be carefully controlled, in other words, formulated from pure enzymes that have known actions and kinetics. However, it can also be a natural mixture, such as a crude extract obtained from a microorganism. In this case, in order to represent the hydrolysis process correctly in the model, it is necessary to characterize the activities present in this crude extract. Models for the enzymatic hydrolysis of polysaccharides may also need to describe particular enzyme-related phenomena, such as denaturation, sorption onto insoluble substrate particles (which is common in the hydrolysis of cellulose), sorption onto nonsubstrate compounds (e.g., cellulases can sorb onto lignin), steric hindrance between enzymes (which can occur between exo-acting cellulases attacking insoluble cellulose substrates) and product inhibition (which is common in the enzymatic hydrolysis of polysaccharides).

3.3 Types of models Models of polysaccharide hydrolysis can be classified according to a criterion that is different from that mentioned in Section 2: they can be classified as being either deterministic

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models or stochastic models. Both types of models can be used to predict how concentrations of the initial polysaccharides, oligosaccharide intermediates, and final products change as a function of either time or percentage hydrolysis. If enzyme denaturation or other forms of irreversible inactivation occur, then these models can also be used to predict changes in the concentration of one or more enzymes. 3.3.1 Stochastic models Stochastic models typically progress in iterations. To understand the principle of stochastic models, it is useful to consider a simple stochastic model in which a linear homopolysaccharide is attacked by an endo-acting enzyme and random numbers are used to decide what happens in each iteration. Such a model tracks how many molecules of each chain length are present in the system. At the beginning of each iteration, the various actions available to the enzyme are identified and assigned a probability. In other words, the probabilities of each different glycosidic bond in the system (i.e., each bond at a particular position within a poly- or oligosaccharide of a particular length) being attacked in that iteration are mapped onto a 0 to 1 number line. Then, a random number between 0 and 1 is generated. The position of this random number on the number line determines which type of bond is attacked in that iteration. This attack eliminates one molecule of the chain length chosen for attack and generates two smaller molecules. The number of molecules of each chain length is updated, and a new iteration is started with the new mixture. Due to the different composition of the reaction mixture at the start of the new iteration, the probabilities of attack on the various bonds in the system will be slightly different from what they were in the previous iteration. An important feature of stochastic models is that, since the actions that are executed in each iteration depend on randomly generated numbers, even if the model parameters are identical and the model is started with the same initial conditions, the reaction profiles of repeated simulations will be different. 3.3.2 Deterministic models Deterministic models most commonly consist of sets of differential equations. These differential equations typically have time as the independent variable; however, in some cases, the degree of hydrolysis is the independent variable. A differential equation may be written for each different chain length of polysaccharide or oligosaccharide. Alternatively, a range of chain lengths may be grouped together into a single class, such that there are several classes, each representing a different interval of chain lengths. If several enzymes are involved in the hydrolysis and can suffer denaturation, then a differential equation may be written for each enzyme. The defining feature of deterministic models is that they will predict identical progress curves if they are solved with the same initial conditions and the same parameter values. It is important to note that it is possible to have a model that works in iterations in a manner similar to that described above for stochastic models, but in which the rules for action are totally deterministic; in other words, probabilities and random numbers are not used in deciding what happens in each iteration. Such a model is deterministic, despite its apparent similarities with stochastic models.

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3.3.3 Choosing between deterministic and stochastic models Deterministic models are probably most appropriate for simple systems, for example, systems involving linear homopolymers. This is due to the ease of use: many programming languages have ready-to-use functions for integrating differential equations numerically. The main task of the researcher is to provide a function containing the set of differential equations to be solved. For the hydrolysis of linear polysaccharides, there may be many equations because the system involves species of many different chain lengths. However, the equations for many of the intermediates usually have the same mathematical structure and appropriate loops can be used in the algorithm. Such models typically describe the overall hydrolysis reaction. They usually do not explicitly describe the elementary steps of the hydrolysis, such as substrate binding, catalysis, and product liberation, although these steps may have been considered in deriving the kinetic expressions. When deterministic models are applied to more complicated systems, it is often necessary to represent the system in a simplified manner. As the system to be modeled becomes more complex, there will be a greater advantage in using a stochastic model. The complexity of the polysaccharide itself is especially important: the presence of different types of monosaccharide units, the branching of polysaccharides, and the random esterification of monosaccharide units all complicate the representation of the substrates and intermediates and make it difficult to describe the hydrolysis process with differential equations unless significant simplifications are made. The disadvantage of stochastic models is that user-friendly stochastic modeling software with the flexibility to represent a wide range of different polysaccharide-enzyme systems is not available. As a result, researchers will need to write a computer program using a general programming language, and such programs typically require hundreds or even thousands of lines of code.

4. The appropriate level of complexity for representing the system Whether one decides to use a deterministic model or a stochastic model, it is necessary to choose an appropriate level of complexity for representing the system of interest and to consider how much effort one is willing to dedicate to estimating the parameters of the model. To make these decisions, one needs to have a clear idea of how robust and accurate the model needs to be to fulfill its intended purpose. In this context, a robust model is one that is not limited to describing a particular set of experimental data, but rather can make good predictions about process performance under conditions different from those that were used in building the model. In reading the possible simplifications below, one needs to keep in mind that the more the model is simplified, the less robust it is likely to be and the less accurate its predictions are likely to be.

4.1 Simplifications regarding the representation of the system Simplifications can be made with respect to the representation of the substrate. For example, a simple model of cellulose hydrolysis may ignore questions of steric impediment, which can restrict access of enzymes to the cellulose chains, and may ignore the fact that some of the regions of the substrate are amorphous, while others are crystalline. As another example,

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molecules can be grouped into molecular weight ranges (rather than individual chain lengths being recognized); in a deterministic model, this will reduce the number of differential equations that need to be written. Simplifications can also be made with respect to the representation of the enzyme or enzymes present in the system. For example, one can subsume the activities of several different enzymes, such as exo-acting enzymes and endo-acting enzymes, into a single “pseudo-enzyme” and determine the parameters of the pseudo-enzyme in a totally empirical manner. In such a case, the model may not try to describe the production and consumption of intermediates. Rather, it might simply describe the production of the final disaccharide or monosaccharide product. Simplifications can also be made with respect to the mechanism of hydrolysis. For example, in a simple deterministic model, all steps might be treated as being first order in the substrate of the step, without taking into account the phenomena of saturation and inhibition. Even if one decides to describe the enzyme action in a more mechanistic manner, hydrolysis models typically do not use bi-bi (i.e., two-substrate two-product) kinetics, even though hydrolysis involves two substrates, the polysaccharide and water. Bi-bi kinetic equations are much more complicated than the Michaelis-Menten equation. They are unnecessary if the water content is high, since the water concentration can be treated as constant and the bi-bi kinetic equations then simplify to Michaelis-Menten-type equations, with the water concentration being subsumed into the constants. However, this simplification may not be appropriate for systems involving very high polysaccharide concentrations. Simplifications can also be made with respect to the breadth of conditions under which the model will be used. If the model will be used under a specific combination of temperature and pH, then it will be sufficient to determine a single value for each parameter, valid for that particular condition. On the other hand, if the model will be used under a range of conditions, parameters are unlikely to have a single value. Rather, theoretical or empirical equations will be used to express the parameter values as functions of these conditions. Obtaining these equations may require a significant amount of experimental work.

4.2 Advantages and disadvantages of using simple or complicated models If one chooses a simple representation of the system, one is likely to have relatively few parameters. The model itself is likely to be “fast-solving,” with the computational solution of the model taking a few minutes at most. The downside is that the model is likely to be less accurate. For example, if certain phenomena are ignored in order to simplify the model, this may limit the ability of the model to fit well to the data. Further, a simple model is unlikely to be robust. If one chooses a more complex representation of the system, then the model is likely to be more robust than a simple model. However, there is a price to be paid for this robustness. A complex model will typically have more parameters than a simple model. Determination of these parameters may be time-consuming and may require sophisticated experiments. Further, depending on the complexity of the model, the computational solution may require hours, days, or even weeks.

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4.3 The amount of effort that one is willing to put into parameter estimation It is possible to simplify the obtaining of parameters by using them as fitting parameters. In other words, the values of the parameters may be chosen simply to ensure that the model fits as closely as possible to experimental hydrolysis profiles. However, this approach has dangers. The first danger is that the model may be fundamentally incorrect. For example, the mathematical expressions used may omit important phenomena that occur in the system. As one example, the product may inhibit the hydrolysis process, but this inhibition might not be incorporated into the equations. It may be possible to fit such a model to hydrolysis profiles. However, the values obtained for the parameters will not be the correct values; rather, they will be distorted by having to describe the product inhibition that is present in the experimental data but has no dedicated parameter describing it in the equations. Alternatively, a model may include all important phenomena, but may describe some of them incorrectly. For example, product inhibition might be incorporated into the equations, but in a manner that does not describe the phenomenon correctly. Even though it might be possible to fit the incorrect model to hydrolysis profiles, one cannot be confident that the values obtained for the parameters reflect their true values. In both of the cases described above, if the model fits the data set used for parameter estimation, one might not even suspect that the model is incorrect. It is highly likely that such a model will make incorrect predictions if the conditions are changed from those that were used to generate the data set used for parameter estimation. The second danger is that even if the model is fundamentally correct (i.e., it describes all important phenomena and does so with correct mathematical formulations), parameters may be correlated with one another when the model is adjusted to hydrolysis profiles. In other words, during the fitting process, an incorrect value for one parameter might be compensated by an incorrect value for another parameter, such that a pair of incorrect parameters might give a fit that is as good as the fit given by the correct values of these parameters. In this case, there is the danger that the researcher will obtain a set of incorrect parameters that give a good fit and simply assume that the good fit means that they are reliable estimates of the real values. Although it is typically more demanding, in terms of time and resources, one should try to avoid estimating parameters by simply fitting the model to hydrolysis profiles. If possible, it is preferable to design experiments that focus on parameters that characterize specific phenomena, without interference from other phenomena in the system. One example of this in classical enzyme kinetics is the use of initial rate studies to determine the catalytic constant kcat and the saturation constant KM. Product inhibition may occur in the system, but if one takes care to obtain true estimates of the initial rate, the product inhibition does not interfere with the estimation of these two parameters. These initial rate studies can also allow one to determine whether the enzyme follows Michaelis-Menten kinetics or some other form of kinetics (such as substrate inhibition kinetics). Variations of these initial rate experiments can then be used to determine whether product inhibition occurs and, if it does, to determine the value of the appropriate parameter (typically a “KMP,” representing the dissociation constant of the enzyme-product complex). As models become more complex, describing the processes that occur during hydrolysis in greater detail, it is likely that more sophisticated experimental techniques will be necessary to

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determine the values of key parameters. For example, some models of cellulose hydrolysis use the parameters “kon” and “koff” to describe the processes of binding and dissociation of the enzyme and the polymeric substrate. In classical initial rate studies, these two parameters are subsumed, together with kcat, into KM, and cannot be estimated individually from the data obtained in these studies. The experiments necessary to determine “kon” and “koff” are more sophisticated. There is an additional complication when crude extracts are used. It is often difficult to characterize individual activities using the crude extract itself. Ideally, the individual enzymes should be purified and the kinetics of each determined separately. However, this creates a large demand in terms of time and resources.

5. General approaches to using deterministic models based on differential equations Deterministic models of polysaccharide hydrolysis can be quite complicated. This section limits itself to outlining some general principles, in the context of processes involving the hydrolysis of linear homopolysaccharides.

5.1 Kinetics of the hydrolysis of linear homopolysaccharides For simple systems, involving the enzymatic hydrolysis of unbranched homopolysaccharides by an enzyme that is not capable of processive action, each chain length can be represented as a separate population. A general balance equation would have the form: d½Si X ¼ rate terms for each reaction generating Si dt X  rate terms for each reaction consuming Si

(1)

where Si represents a substrate with i units of a monosaccharide S. One such equation would be written for each Si. In the equations below, rx!y,z represents a rate term, namely, a term that describes the rate of the hydrolysis reaction that, starting with molecule Sx, generates both (i) a product Sy that contains the nonreducing end of the original Sx and a new reducing end and (ii) a product Sz that contains the reducing end of the original Sx and a new nonreducing end. The letter m represents the number of monosaccharide units in the longest molecule in the system. If the model is used to describe hydrolysis by an exo-acting enzyme, there will be only one reaction that produces Si and only one reaction that consumes Si. For example, for the hydrolysis of a polysaccharide by an exo-acting enzyme that removes a monosaccharide from the nonreducing end, a balance on molecules of chain length Si gives:

d½Si ¼ dt

generation of Si from Si + 1 zfflfflffl}|fflfflffl{ ri+1!1,i 

generation of Si  1 from Si zfflfflffl}|fflfflffl{ ri!1,i1

(2)

5. General approaches to using deterministic models based on differential equations

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If the enzyme under consideration removes a disaccharide, then each time “1” appears as a subscript in Eq. (2), it would be replaced with “2.” On the other hand, if the model is used to describe hydrolysis by an endo-acting enzyme, then typically several different reactions can generate an intermediate Si (i.e., from longer molecules of different lengths) and several different reactions can consume Si (producing different pairs of shorter molecules). For an exo-acting enzyme that can attack anywhere in the chain, the rate of variation of a particular intermediate Si (containing i units) is given by: terms describing all reactions with molecules longer than Si that generate a molecule of Si as a product

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{ d½Si rm!i,mi + rm!mi,i + rm1!i,m1i + rm1!m1i,i + ⋯ ¼ ⋯+ri!1,i1 + ri!2,i2 + ⋯+ri!i2,2 + ri!i1,1 dt |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

(3)

terms describing all the different reactions with Si that consume Si, generating molecules shorter than Si

In most cases, there will be two reactions from the same starting substrate that can generate the same combination of products, a reaction “Sx ! Sy + Sz” and a reaction “Sx ! Sz + Sy.” Exceptions are (i) there is only one hydrolysis reaction possible in the middle of a substrate with an even number of units, namely, “Sx ! Sy + Sy,” and (ii) in practice, some of the bonds near the ends may not be attackable, such that the jth bond from one end may be attackable, whereas the jth bond from the other end may not. An equation similar to Eq. (3) can be written for the longest molecule present in the system, Sm, but it will only contain terms for the consumption of Sm. An important consideration is that all reactions compete for catalysis by the enzyme. For an exo-acting enzyme, this involves molecules of different lengths (i.e., different Si) competing for the active site. For an endo-acting enzyme, this competition goes beyond the different Si molecules competing for the active site: If a molecule Si can suffer j different reactions, this means that it can form j different enzyme-substrate complexes, with the molecule Si being positioned differently in the active site. Each of these possible reactions is characterized by its own reaction parameters. For bonds in the middle of a large polymer, these reaction parameters are likely to be the same (if the ends of the polymer are sufficiently distant, then they do not influence binding and catalysis in the region under consideration). For bonds near the ends of a large polymer, or bonds within oligosaccharides, these parameters may be different. During a reaction, there will normally be many molecules of different lengths present (i.e., many different Si), each capable of being attacked at different bond positions. If both exoacting enzymes and endo-acting enzymes are present, then there will be terms describing the actions of both in the balance equations for the various Si. In other words, there will be many equations, and each equation will have many different terms. For an oligosaccharide, it is typically not too onerous to determine the network of possible reactions and write terms for them. However, this can become challenging for a polysaccharide, unless it is a welldispersed linear homopolysaccharide. Even if it is linear, it can be tedious. To understand the tedium, one can consider a mixture of oligomers and polymers that contains all chain lengths from S2 to S999 and an endo-acting enzyme that can attack at any position along the chain. The mid-sized molecule S500 can be generated directly from all molecules S501 to S999, in two manners for each Si in this range (one with the hydrolysis occurring nearer to the nonreducing end and one with the hydrolysis occurring nearer to the reducing end). It can also be attacked in 499 different ways. In other words, there could be almost 1500 rate terms

286

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

in the differential equation describing the balance on S500. Similar arguments could be made for Si of other chain lengths. There is an additional consequence of the fact that an enzyme can catalyze various reactions with various substrates: if a mechanistic approach is used to derive expressions for each rate term, then each rate term will have a denominator that represents (i) free enzyme; (ii) every reaction that can occur in the system with the particular substrate mixture that is present at a particular instant; and (iii) reversible inhibition by other molecules, such as final products. Another way of saying this is that the rate of the particular reaction described by a particular rate term is affected by all other reactions that are occurring, since all reactions are competing for the enzyme. The above argument has shown that a mechanistic model for the hydrolysis of a linear homopolymer involves many equations, each equation involves many rate terms, and the denominators of these terms themselves contain many terms. Of course, the complexity is not insurmountable. In the equations themselves, one can use sums (i.e., the “Σ” function) to represent repetitive terms. In the program used to solve the model, one can generate repetitive terms in loops. However, great care is necessary to avoid errors. It is worthwhile to explore the form of the individual rate terms that appear in the above equations. As argued above, the high water content means that each hydrolysis reaction can typically be treated using a Michaelis-Menten-type scheme, namely, “Si + E Ð ESi ! E + Sy + Sz.” For the sake of simplification, one can assume that a final product of the hydrolysis process, such as a mono- or disaccharide, causes competitive inhibition but that no other inhibitors are present. This product is denoted as P. Using well-known techniques for deriving the equations (not covered here) and expressing the equation in the form in which the Michaelis-Menten equation is most commonly written, namely, in terms of kcat and KM, the rate term for the jth reaction with substrate i (denoted as ri, j) is: ri,j ¼ 0 @1 +

kcatði, jÞ ½Si½ET X ½Sx x6¼i

KMx

+

X m6¼j

½Si KMði, mÞ

+

½P KMP

1

(4)

AKMði, jÞ + ½Si

where [E]T is the total concentration of the enzyme in the system. Within the parentheses of the denominator of Eq. (4), “1” is related to the free enzyme. The other three terms within these parentheses represent competing phenomena that deviate the kinetics from simple Michaelis-Menten kinetics (if these three terms did not appear, one would have the Michaelis-Menten equation for the jth reaction with Si): P ½Sx • KMx represents competition by reactions with substrates that have different lengths from x6¼i

the of interest. P substrate ½Si • represents competition by reactions other than the reaction of interest, with the KMði, mÞ m6¼j

particular substrate under consideration, Si. •

½P KMP

represents enzyme that is bound to the inhibiting product.

In this equation, both “reaction-specific” and “global” KM values appear. A reactionspecific KM has two subscripts (e.g., KM(i,m)), the first subscript indicates the substrate

5. General approaches to using deterministic models based on differential equations

287

molecule, and the second subscript indicates the particular reaction under consideration. The global KM for a substrate has only one subscript (e.g., KMx) that indicates the substrate molecule. It can be expressed in terms of the reaction-specific KM values as follows: X 1 1 ¼ (5) j KMi KMði, jÞ where j different reactions are possible with substrate Si. However, if the rate terms are written as in Eq. (4), then the denominator of each rate term will be slightly different. This is not convenient. The situation can be remedied by dividing both the numerator and denominator of each rate term (i.e., of each ri,j) by “KM(i, j).” This gives: kcatði, KMði,

ri,j ¼ 0 @1 +

X ½Sx x6¼i

KMx

k In the numerator of this equation, KcatMðði,i,

+

jÞ jÞ

X m6¼j

½Si½ET ½Si KMði, mÞ

+

1 ½ P KMP

A+

(6) ½Si KMði, jÞ

jÞ

represents the specificity constant of the enzyme for jÞ P ½Si ½Si the jth reaction with substrate Si, written as ki, j. In the denominator, KMði, mÞ and KMði, jÞ can be m6¼j

summed. With these modifications, the equation can be rewritten as: ri,j ¼ P ½Sx

ki,j ½Si½ET X ½Sx 1+ KMx + x

!

(7)

½P KMP

The term KMx in the denominator represents all reactions with all substrates, the substrate x of interest, and all other substrates. The advantage of doing this is that all the rate terms now have the same denominator, which simplifies automatic generation of the balance equations. However, expressing the equations in this manner also gives a useful conceptual insight. When the reactions catalyzed by a particular enzyme are considered, all rate terms have the same denominator and contain [E]T in their numerators. This leads to the conclusion that the relative rates of the various reactions catalyzed by a particular enzyme depend directly on the relative values of the factors “ki, j[Si]” in the numerators of the corresponding rate terms. In other words, the relative rates of the different reactions catalyzed by a particular enzyme depend both on the innate preferences of the enzyme for catalyzing these reactions (reflected by the specificity constants) and on the relative concentrations of their substrates.

5.2 Is it reasonable to treat reactions as pseudo-first order in substrate concentration? Some authors have proposed models for the enzymatic hydrolysis of polysaccharides that are not derived by assuming the mechanism of formation of an enzyme-substrate complex followed by a catalytic hydrolysis step (i.e., a Michaelis-Menten-type reaction scheme); rather,

288

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

the reactions are treated as simply being first order in the substrate concentration. Such models will be referred to as pseudo-first-order models. Such a model will have the following basic form, considering endo-attack: d½Si ðcm!i,mi ½Sm + cm!mi, i ½Sm + cm1!i, m1i ½Sm  1 + cm1!m1i, i ½Sm  1 + ⋯ ¼ … + ci!1,i1 ½Si + ci!2,i2 ½Si + ⋯ + ci!i2,2 ½Si + ci!i1,1 ½SiÞ½ET dt

(8)

where the letter c is used to denote a constant. Such an approach says that the rate of a particular reaction with a particular substrate depends on the concentration of that substrate but is independent of the concentrations of other substrates. However, this is not true: as shown in Section 5.1, other substrates of an enzyme act as competitive inhibitors of the substrate of interest. In other words, the rate at which a particular substrate is consumed depends on how many molecules of other substrates are present. Additionally, a pseudo-first-order model does not describe the phenomenon of saturation, in which the reaction rate reaches a maximum value (Vmax ¼ kcat[E]T) at high substrate concentrations, in other words, becoming zero order in substrate concentration. In fact, enzyme-catalyzed reactions are only first order in substrate concentration when the substrate is present at concentrations well below KM. Since pseudo-first-order models have no theoretical basis, their parameters are determined by using them as fitting constants to adjust the model to hydrolysis profiles. Even though reasonable fits may be obtained, the parameter values obtained have no mechanistic significance. In fact, each constant in Eq. (8) effectively represents a mixture of mechanistic parameters and concentrations of substrates and products, as can be seen by comparing a typical term of a pseudo-first-order equation with a typical term of a mechanistic equation: ci,j ½Si≡

ki,j ½ET X ½Sx 1+ KMx + x

! ½Si

(9)

½P KMP

In other words, the first-order constants in Eq. (8) cannot really be expected to be constant, unless, by a mathematical coincidence, the value of the denominator on the right-hand side of Eq. (9) varies negligibly as the reaction proceeds. This analysis does not mean that first-order models cannot be used. However, they must be used with great care, as they are empirical models that might be able to be adjusted to a particular dataset, but will likely have difficulty in describing data sets obtained in different conditions (for example, with different starting concentrations of substrate).

5.3 How to describe processivity in deterministic models? In systems involving the enzymatic hydrolysis of polysaccharides, the phenomenon of processivity is relatively common. The basic kinetics of processivity therefore deserve attention. The example in this subsection deals with a processive exo-acting enzyme that removes disaccharide units from the nonreducing end of a linear homopolysaccharide [9]. One way of representing such systems is shown in Fig. 1. Parts (A) and (B) of this figure represent the

5. General approaches to using deterministic models based on differential equations

S2

(A) slide

ESn*

S2

ESn-2

kcat(n)

ESn-2°

slide Pn-2 1-Pn-2

koff(n-2)

kon(n) E + Sn

289

ESn-2*

kcat(n-2)

kon(n-2)

E + Sn-2

(B) slide kon(n) ESn* kcat(n)

koff(n-2) ESn-2°

1-Pn-2

Sn-2

ESn-2

+ slide

Pn-2

E kon(n-2)

ESn-2*

kcat(n-2)

FIG. 1 Two representations of the action of a processive exo-acting enzyme that removes disaccharide units from the nonreducing end of a linear homopolysaccharide. (A) Schematic representation. (B) Pictorial representation of the same scheme, in which the shaded hexagon represents the reducing end of the original polysaccharide chain and the solid triangle ( ) marks the location within the active site of the amino acid residues responsible for catalyzing the hydrolysis of the glycosidic bond. The dashed rectangle indicates that in the kinetic equations, the complexes ESn  2° and ESn  2* are not distinguished; rather, they are treated as a single pool, represented by ESn  2.

same scheme: Part (A) is schematic and oriented horizontally, while part (B) is pictorial and oriented vertically. This scheme recognizes two different complexes between the enzyme and a polysaccharide molecule of a particular length. In complexes with an asterisk, the polysaccharide is correctly placed within the active site for hydrolysis of the glycosidic bond. Complexes with the degree symbol represent the complex that is produced immediately after liberation of the disaccharide; this complex cannot catalyze bond hydrolysis because the polysaccharide is incorrectly positioned. On the left of Fig. 1A, Sn is bound correctly for hydrolysis in complex ESn*. This complex may have been formed by processive action (represented by the leftmost horizontal arrow) or

290

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

it may have been formed by free enzyme (E) binding to free Sn (represented by the leftmost angled arrow). Catalytic action removes a disaccharide (S2), producing complex ESn  2°. Two actions are then possible: (i) The enzyme can slide along the polysaccharide Sn  2 to position it correctly for another round of hydrolysis. This represents processive action and occurs with a probability Pn2, called the probability of processive action. (ii) The enzyme can liberate polysaccharide Sn  2 into the reaction medium. This has a probability of 1  Pn2 of occurring. Fig. 1A can be extended to both the left and the right, allowing for a long sequence of processive steps. In the kinetic equations, the complexes ESxo and ESx* are not distinguished; rather, they are treated as a single pool, represented by ESx. The pseudo-steady-state approximation (which says that the concentrations of complexes change relatively slowly during the reactions such that the balances on these complexes can be equaled to zero) can be used to deduce the following equation that gives the balance on free Sn, a polysaccharide chain of intermediate length [9]: 0 1 0 0 1 1 m m 2 X Y @ @ki ½Si@ Pj Að1  Pn ÞA  kn ½SnA½ET i¼n + 2 j¼n + 2 d½Sn ¼ (10) dt D In this equation, m represents the length of the longest polysaccharide molecule present. The numerator of this equation contains two terms within the outermost set of parentheses on the right-hand side. The second of these terms, “kn[Sn],” where kn is the specificity constant of the enzyme for Sn, represents the consumption of Sn from the reaction medium due to free enzyme binding to it and catalyzing the removal of a disaccharide (S2). This term is simple because the first attack by the enzyme on a molecule Sn taken from the reaction medium cannot be processive. There may be subsequent processive attacks on the bound Sn  2 and the shorter bound molecules that are subsequently produced, but these attacks do not directly influence the concentration of Sn in the reaction medium. The term with the sum (i.e., Σ(…)) in the numerator of Eq. (10) reflects the fact that an intermediate chain Sn can be generated by processive actions that started with many different longer chains. The structure of this term can be understood by considering that, in order for a longer molecule, Si, to generate a free Sn, a combination of several events is required: • Si must bind to the free enzyme and suffer catalysis, which is reflected by ki[Si]. • The enzyme must slide along Si  2 (rather than dissociate) and must slide along subsequent shorter molecules (rather than dissociate) until it reaches ESn. The probability of all these sliding events occurring in sequence is given by the product of all the probabilities of processive action along the sequence. This corresponds to the product, “Π Pj.” • The enzyme must liberate Sn rather than slide along it. Since Pn is the probability that the enzyme will slide along Sn after generating it in the reaction ESn + 2 ! ESn + S2, the probability that it will dissociate from Sn is 1  Pn. For longer molecules, the probabilities of processive action would likely be quite similar; in other words, independent of chain length, as the fit of the nonreducing end of the polysaccharide into the active site would be the same. However, smaller oligosaccharides may not fit equally well into the active site and the probabilities of processive action associated with

6. General approaches to using stochastic models

291

them would likely be smaller than those for longer chains; also, they might change significantly with the length of the oligosaccharide. The denominator “D” of the above equation is quite complex. Q  11 0 0 i2 m m i2 X X X g¼j + 2 Pg ½S2 ½ Sf  ½Si ½ Si  @ @kcatðiÞ   AA D¼1+ + + + KM2 KMf i¼f + 2 KMðiÞ i¼f + 2 KMðiÞ k + koffð jÞ j¼f + 2 catð jÞ Q 1 0 i2 m P X h h¼f + 2 @ ½Si kcatðiÞ   A + (11) K koffð f Þ MðiÞ i¼f + 2 In this denominator, f represents the chain length of the final residual oligomer, namely, an oligomer Sf that contains the reducing end of the initial chain, but which cannot be further attacked by the exo-acting enzyme. Together, the first four terms of the denominator represent the denominator that would be obtained for totally nonprocessive action with substrates Si of various chain lengths and product inhibition by both the hydrolysis product S2 and the final residual oligomer Sf. “1” is related to free enzyme. [S2]/KM2 represents product inhibition by S2, and [Sf]/KMf represents product inhibition by the final residual oligomer Sf. Each term [Si]/KM(i ) is related to the formation of complex ESi from the direct binding of free Si to the free enzyme E. The last two terms of Eq. (11) appear as a consequence of the processive action. Each of these terms involves a sum and therefore can be expanded into several subterms:   1 0 i2 Q Pg 2 B iP C g¼j + 2  B C is related to the • Within the second-last term, a subterm K½Si k cat ð i Þ @ MðiÞ ðk + koffðjÞ Þ A j¼f + 2 catðjÞ generation, starting with a longer moleculeSi, of various shorter ESj complexes through 0 i2 1 Q processive action. B h¼f + 2 Ph C  B C is related to the generation, starting • Within the last term, a subterm K½Si k catðiÞ @ MðiÞ ðkoffðf Þ Þ A with a longer molecule Si, of a complex between the enzyme and the final residual oligomer Sf. It should be noted that when rate terms are proposed for a nonprocessive exo-acting enzyme, they can be written fully in terms of specificity constants and global KM values, such that it is not necessary to obtain estimates of kcat and koff. In the case in which there is processive action, in addition to specificity constants and global KM values, kcat and koff appear explicitly in the equation, and therefore also need to be estimated.

6. General approaches to using stochastic models Various approaches can be taken to describing enzymatic reactions using stochastic models. However, it is beyond the scope of this chapter to review these approaches. This section focuses on the type of stochastic model described in Section 3.3.1, namely, stochastic

292

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

models that operate in iterations, with an enzyme choosing one action per iteration. This action may be to break a bond, meaning that the iteration was “productive,” but some models allow the possibility of “nonproductive” iterations, in which a bond is not broken. This might occur, for example, if the enzyme chooses to bind to an inhibitor in an iteration. Some stochastic models of the enzymatic hydrolysis of polysaccharides progress in terms of degree of hydrolysis, with each iteration corresponding to the hydrolysis of one glycosidic bond. However, for a stochastic model to be useful in guiding the design and optimization of the process, it needs to progress in terms of time. Therefore, a key consideration that will be addressed in this section is to how to translate iterations into time. Although stochastic models that work in terms of degree of hydrolysis are not the focus of this section, it should be noted that they can be quite useful in giving insights into the phenomena that limit the hydrolysis process.

6.1 How many molecules should a stochastic simulation involve? Many stochastic models represent individual molecules of the various species involved in the reaction, namely, enzymes, the initial polysaccharide, intermediates that are formed and consumed during hydrolysis and final products. In such models, it is impractical to represent the real number of molecules in the system. To see why, one can imagine a polysaccharide of 200 kDa present at a concentration of 20 g L1. This gives a concentration of 100 μM, which represents approximately 6  1019 molecules per liter. If simulations were to be done to represent the complete hydrolysis of the polysaccharide in 1 L of this suspension, with one glycosidic bond being broken per iteration, the number of iterations required would be far greater than 1020. Most computers would take an unacceptably long time to execute these iterations. Stochastic models use a much smaller number of molecules to represent the system, typically within the range of 103 to 106 initial substrate molecules. As mentioned in Section 3.3.1, the use of random numbers means that repeated simulations with stochastic models give different predicted profiles, even when the parameters and initial conditions are identical. The smaller the number of molecules used to represent the system, then the greater is the variation between these repeated simulations. It is, therefore, advisable to represent the system with as high a number of molecules as possible, while keeping the time required for the simulation within acceptable limits.

6.2 How to translate between numbers of molecules and concentrations? As pointed out in Section 6.1, a stochastic simulation will typically involve a limited number of molecules. However, if the stochastic model is to be used as a tool for optimizing the design and operation of hydrolysis processes, it is not sufficient for the model to output its results in terms of numbers of molecules. The results must be translated from numbers of molecules to concentrations. In a similar manner, the initial concentrations of substrates and enzymes used in the real system must be translated into the initial numbers of molecules to be used in the simulation. In other words, it is necessary to use a “scale factor” [10]. If the initial concentration of a substrate molecule of interest is [S]o and one decides to use NSo molecules to represent this initial concentration, then the scale factor must be NSo/[S]o:

293

6. General approaches to using stochastic models

 N So ¼ ½So  scale factor ¼ ½So

NSo ½So

 (12)

This scale factor can then be used to convert the concentrations of any other species present in the initial reaction mixture into a corresponding number of molecules. For example, for the enzyme, one can write:   N So N Eo ¼ ½Eo  scale factor ¼ ½Eo (13) ½So As noted earlier, the substrate for a hydrolysis process is typically a mixture of different chain lengths. In this case, the scale factor can take into account all molecules that are initially present, being defined as: X N Sio i Scale factor ¼ X

(14)

½Sio

i

During the iterations, the stochastic model tracks how many molecules there are of each species. At the end of the simulation, these numbers of molecules must be translated back to concentrations, so that the predicted reaction profiles can be plotted. The numbers of the various species in the reaction mixture in the nth iteration can be converted into concentrations by dividing them by the scale factor:   N Sin ½So ¼ NSin ½Sin ¼ (15a) scale factor N So   N En ½So ¼ N En (15b) ½En ¼ scale factor NSo   N Pn ½So ¼ N Pn (15c) ½Pn ¼ scale factor NSo

6.3 How to model systems in which there is only one type of enzyme? When a single enzyme is present, the key question is which of the available reactions it will choose to catalyze in a given iteration. The probability of the jth reaction occurring with a polysaccharide or oligosaccharide Si, denoted Pi, j, can be calculated as: ri,j ki,j ½Si ki,j N Si Pi,j ¼ X ¼ X ¼ X ðra,x Þ ðka,x ½SaÞ ðka,x NSa Þ a,x

a,x

(16)

a,x

The numerator of this equation is related to the rate of the reaction of interest, whereas the denominator is related to the sum of the rates of all the reactions occurring in the system. The k-values are the specificity constants of the enzyme for specific reactions. The equation has

294

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

this simple form because when the rate terms (ri, j) are expressed with specificity constants in the numerator, the denominators are identical (see Eq. (7) in Section 5.1) and cancel out when the ratio of rate terms is taken in the probability calculation. For example, for a system in which a single endo-acting enzyme is used to hydrolyze molecules containing up to 17 monosaccharide units, the above equation can be expressed as [10]: Pi,j ¼

a¼17 X a¼5

N Sa

a3 X

! kSa,x

kSi,j N Si

(17)

+ N S4 ðkS4,1 + kS4,2 Þ + kS3,1 N S3

x¼1

The probabilities are calculated in this manner for all possible reactions and arranged on a 0 to 1 number line. A random number is then generated, and its position on the number line determines the particular reaction that will occur in the iteration. If the selected reaction is Sx ! Sy + Sz, then NSx is decreased by one and NSy and NSz are each increased by one. The simulation then moves on to the next iteration. Stochastic models can simply progress in terms of iterations, without any attempt being made to translate the model predictions into time-based curves. However, as mentioned above, for a model to be useful in guiding the design and operation of an enzymatic hydrolysis process, it must make predictions in terms of time. In some stochastic models, the time per iteration is estimated after the simulation, by scaling the abscissa of the model results, plotted in terms of the number of iterations, to the abscissa of an experimental hydrolysis profile, plotted in terms of time, such that the predicted curve is superimposed onto the experimental curve. However, although this may allow one to describe experimental results that one has already obtained, it is unlikely to result in a robust model that can be used to explore model predictions over a range of operating conditions. Recently, a strategy was proposed in which one glycosidic bond is broken per iteration, and the length of time to be attributed to the iteration (Δtiteration) is calculated [10]. This Δtiteration is the reciprocal of the average rate of breakage of glycosidic bonds. If one were working in terms of concentrations, then the average rate of breakage of glycosidic bonds would be given by: d½bonds X ¼ r i,j i,j dt

(18)

where ri, j represents the rate of the jth reaction with substrate Si and the sum on the righthand side therefore represents the sum of the rates of all the reactions in the system. The time for one iteration, which represents the breakage of a single glycosidic bond, is then: 1 Δtiteration ¼ X ri,j

(19)

i,j

The time attributed to the iterations will typically increase during the process, as substrate concentrations fall and inhibitory products accumulate. During simulations with a stochastic model, one does not work with concentrations, but rather with numbers of molecules. The rates of the various reactions (i.e., the ri, j terms) therefore need to be expressed in terms of numbers of molecules. For the system involving the

295

6. General approaches to using stochastic models

hydrolysis of oligosaccharides up to 17 units long by an endo-acting enzyme, Eq. (19) can be expressed as [10]: 1

Δtiteration ¼ 0

1

(20)

C B B2 1 3 C C B a¼17 0 a3 C B X X B4 @NSa kSa, j A + NS4 ðkS4,1 + kS4,2 Þ + kS3,1 NS3 5NE C C B C B a¼5 j¼1 C B 1 0 17 C B C B X C B NSio C B C B 17 C X NSx B i¼1 C B N S1 C+ B C B + C BX C B 17 A j¼3 KMSx KMS1 @ A @ ½Sio i¼1

The denominator of this equation is a fraction. The numerator of this fraction represents the sum of the numerators of the rate terms of all the reactions occurring in the system; the k-values represent the specificity constants of the various reactions. The denominator of this fraction represents the common denominator that one obtains when one writes the numerators of the individual rate terms using specificity constants (see Section 5.1); the first term in this denominator is the scale factor for the simulation. An advantage of this approach is that the model describes phenomena that can slow the reaction, such as enzyme denaturation (which affects NE) and the accumulation of inhibitory end products (which affects NS1/KMS1), while still ensuring that one glycosidic bond is broken per iteration. In other words, all iterations are productive and computational time is therefore not wasted with nonproductive iterations.

6.4 How to model systems in which there is more than one type of enzyme? Many processes for the enzymatic hydrolysis of polysaccharides involve the simultaneous action of more than one type of enzyme. Simulating such systems with stochastic models raises the issue as to how to choose which type of enzyme will act in a particular iteration. This question was addressed in a model developed for the combined action of two enzymes, denoted E1 and E2 [10]. The probability of E1 being chosen to act in an iteration is calculated as: X ri,j,E1 PE1 ¼ X i,j

P

i,j

ri,j,E1 +

X

ri,j,E2

(21)

i,j

ri,j,E1 represents the rates of all the reactions that E1 can catalyze within the P reaction mixture, and the term ri,j,E2 represents the rates of all the reactions that E2 can catwhere the term

i,j

i,j

alyze within the reaction mixture. The probability of E2 being chosen to act in an iteration is then calculated as: PE2 ¼ 1  PE1

(22)

296

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

FIG. 2 Illustration of how a particular reaction is chosen in an iteration when two enzymes are present in the system. In this hypothetical example, with the particular reaction mixture that is present, E1 can calculate 7 reactions and E2 can calculate 3 reactions. The probabilities of the various reactions being chosen are calculated using the equations shown in the figure. Pa,x,E1 is the probability of E1 being chosen to execute the xth possible reaction with substrate Sa. Pb,z,E2 is the probability of E2 being chosen to execute the zth possible reaction with substrate Sb. If the random number that is generated falls at the position marked by “ ” on the number line, then E1 will execute the fifth possible reaction available to it in that iteration.

A random number between 0 and 1 is generated, and its value determines whether E1 or E2 is chosen to act in the iteration. This random number also determines the particular reaction that the chosen enzyme will execute in the iteration, based on exactly where it falls in the interval for that enzyme (see Fig. 2). The time to be attributed to the iteration is then calculated. It is given by the reciprocal of the sum of the rates of all reactions occurring in the system: Δtiteration ¼ X

1 ri,j,E1 +

i,j

X

ri,j,E2

(23)

i,j

In a case study in which esterified oligomers were attacked by both an endo-acting enzyme and a methylesterase, Δtiteration was given by [10]: 1

Δtiteration ¼

kB N E1 N B NUo ½U o

+

NB KMB

+

NQ1 KMQ1

+

kM N E2 N M N Uo N +K M ½U o MM

(24)

where NE1 is the number of molecules of the endo-acting enzyme and NB is the number of glycosidic bonds that it can attack; NE2 is the number of molecules of the methylesterase and NM is the number of methyl-ester bonds that it can attack. NUo/[U]o is the scale factor. kB is the specificity constant of the endo-acting enzyme for glycosidic bonds, assumed to be the same for all glycosidic bonds. kM is the specificity constant of the methylesterase for methyl-ester bonds, assumed to be the same for all methyl-ester bonds.

7. “Fingerprinting models” as tools for estimating specificity constants This section shows how fingerprinting models can be used to estimate specificity constants. These specificity constants are useful parameters in both deterministic and stochastic models. When the rate terms of deterministic models are expressed in terms of specificity

7. “Fingerprinting models” as tools for estimating specificity constants

297

constants rather than in terms of catalytic constants, the expressions for the rates of different reactions catalyzed by the same enzyme have the same denominator (see Eq. (7) in Section 5.1). In the case of stochastic models, the probabilities of an enzyme catalyzing the various reactions that are available to it in the reaction mixture can be expressed in terms of specificity constants (see Eq. (16) in Section 6.3). In the hydrolysis of a polysaccharide by an endo-acting enzyme, the value of the specificity constant will likely be similar for different internal bonds. In a similar manner, in the hydrolysis of a polysaccharide by an exo-acting enzyme, the specificity constant for attacking the extremity will likely be independent of the length of the polysaccharide chain. However, as the hydrolysis progresses, oligosaccharides will be produced. Each of these oligosaccharides will interact with the subsites of the active site differently, meaning that exo-acting enzymes will have different specificity constants for different oligomers and endo-acting enzymes will have different specificity constants for the different internal bonds of the oligomers. It is, therefore, of particular interest to determine the specificity constants that an enzyme has for the different reactions that it can catalyze with oligosaccharides. The so-called fingerprinting method can be used to do this.

7.1 General description of the fingerprinting method Application of the fingerprinting method involves experimental and modeling parts [11]. In the experimental part, an enzyme is used to hydrolyze an oligosaccharide. It is important to note that the method can only be used with experimental results involving a single enzyme; it cannot be used with enzyme mixtures. In other words, if the hydrolysis process involves an enzyme mixture, the enzymes must be purified from the mixture and analyzed individually. To obtain the set of specificity constants of an enzyme, a single hydrolysis experiment can be done with a longer oligosaccharide, although it is preferable to do several experiments, each starting with a different oligosaccharide. The analysis is more convenient if the initial oligosaccharide in each experiment is pure, although this is not essential, as long as the composition of the initial mixture is known. During the hydrolysis experiments, progress curves are obtained. Samples are removed at various times, and all reaction species are quantified in each sample: the initial substrate, any intermediates, and final products. It is important that the sampling times be chosen so that there is a good distribution of sampling points in terms of degree of hydrolysis, which may mean that the time intervals between samples are not constant. Typically, a hydrolysis process will proceed more quickly at the beginning, so the time between samples should be shorter at the beginning and longer near the end. The concentrations of the reaction species in a sample are then used to calculate the fractional degree of hydrolysis, which can be defined as the number of glycosidic bonds that have been hydrolyzed divided by the number of glycosidic bonds that were present in the initial reaction mixture. For each reaction species, a concentration profile is plotted against the fractional degree of reaction. In the modeling part of the analysis, a model for the hydrolysis of the oligosaccharide is written with fractional degree of hydrolysis as the independent variable. This involves

298

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

writing a classical dynamic model with time as the independent variable and then transforming the independent variable to the fractional degree of hydrolysis. If the classical dynamic model is written with the numerators of the rate expressions expressed in terms of specificity constants, then this transformation of the independent variable eliminates both the total enzyme concentration ([E]T) and the denominators of the original rate expressions. The final set of differential equations then has specificity constants as its only parameters. The relative values of these specificity constants are estimated by using them as fitting parameters to adjust the model to the experimental data.

7.2 Case study to demonstrate the principles of the fingerprinting method This subsection presents a simple case study to demonstrate the basic logic of the fingerprinting method. This case study considers hydrolysis of a trisaccharide (Fig. 3). The enzyme can hydrolyze either of the glycosidic bonds of the trisaccharide, but the preferences for attacking the two bonds are not the same. The enzyme can also attack the disaccharides that it generates. The final monosaccharide product inhibits the reaction. In this case study, the monosaccharide unit at the reducing end of the original trisaccharide is assumed to be marked with 13C. This marked unit can therefore be traced in the various reaction species, but its presence does not interfere with the interaction of these species with the active site, such that it does not influence the values of the specificity constants of the enzyme for the various species. This allows one to distinguish between the reaction routes 3a and 3b in Fig. 3. Route 3a will generate a marked disaccharide, represented by S2, while route 3b will generate an unmarked disaccharide, represented by S2°. S1

S1

+ k2

+ k3a S3

S2×

S1 S2o

k3b

S1

+ k2

+ S1

S1

FIG. 3 The system of the case study. A trisaccharide, denoted S3, is hydrolyzed by an enzyme that can attack either of the two glycosidic bonds, but does so with different specificities. The monosaccharide unit at the reducing end of the original trisaccharide is marked with 13C. This allows the two disaccharides generated by hydrolysis of the trisaccharide to be distinguished from one another: The marked disaccharide is denoted S2, while the unmarked disaccharide is denoted S2°. Although it would be possible also to distinguish marked and unmarked monosaccharides, this is not done; rather, a single pool S1 is recognized. The value of k2 is the same for S2 and S2° because it is assumed that the marking does not affect the specificity constant.

7. “Fingerprinting models” as tools for estimating specificity constants

299

The following equation set describes the kinetics of hydrolysis with time as the independent variable: d½S3 ¼ r3a  r3b dt

(25a)

d½S2  ¼ r3a  r2 dt

(25b)

d½S2° ¼ r3b  r2o dt

(25c)

d½S1 ¼ r3a + r3b + 2r2 + 2r2o dt

(25d)

where r3a and r3b are the rates of hydrolysis of S3 by routes 3a and 3b, respectively, r2 is the rate of hydrolysis of S2 and r2o is the rate of hydrolysis of S2°. If the hydrolysis reaction is irreversible, then the rate terms in these equations can be written as: r3a ¼ r3b ¼ r2 ¼ r2o ¼

1+

k3a ½S3½ET ½S3 ½S2 ½S1 KM3 + KM2 + KM1

(26a)

1+

k3b ½S3½ET ½S3 ½S2 ½S1 KM3 + KM2 + KM1

(26b)

k2 ½S2 ½ET

1+

½S3 KM3

1+

k2 ½S2°½ET ½S3 ½S2 ½S1 KM3 + KM2 + KM1

+

½S2 KM2

+

½S1 KM1

(26c)

(26d)

where [S2] ¼ [S2] + [S2°]. The fractional degree of hydrolysis is calculated as: F¼

2½S3o  ð2½S3 + ½S2  + ½S2°Þ 2½S3 + ½S2  + ½S2° ¼1 2½S3o 2½S3o

(27)

The time-based derivative of the fractional degree of hydrolysis (dF/dt) is given by:   dF 1 d½S3 d½S2  d½S2° ¼ + + 2 (28) dt 2½S3o dt dt dt Substituting Eqs. (25a)–(25d) into Eq. (28) gives: dF 1 ¼ ðr3a + r3b + r2 + r2o Þ dt 2½S3o

(29)

The transformation of the independent variable from time to the fractional degree of hydrolysis is achieved by noting that, for any reaction species X,

300

11. Mathematical modeling of enzymatic polysaccharide hydrolysis



d½X dt



dF ¼ dt

d½ X  dF

(30)

It is therefore possible to write the following equation set: d½S3 r3a  r3b ¼ 2½S3o dF ðr3a + r3b + r2 + r2o Þ

(31a)

d½S2  r3a  r2 ¼ 2½S3o dF ðr3a + r3b + r2 + r2o Þ

(31b)

d½S2° r3b  r2o ¼ 2½S3o dF ðr3a + r3b + r2 + r2o Þ

(31c)

d½S1 r3a + r3b + 2r2 + 2r2o ¼ 2½S3o dF ðr3a + r3b + r2 + r2o Þ

(31d)

Each term in the numerator and the denominator on the right-hand side of these equations is a rate term. As can be seen in Eqs. (26a)–(26d), each of these rate terms has [E]T in its numerator and each has the same denominator. Both [E]T and the denominators of the rate terms then cancel out, such that Eqs. (31a)–(31d) can be written as: d½S3 k3a ½S3  k3b ½S3 ¼ 2½S3o dF ðk3a ½S3 + k3b ½S3 + k2 ½S2  + k2 ½S2°Þ

(32a)

d½S2  k3a ½S3  k2 ½S2  ¼ 2½S3o dF ðk3a ½S3 + k3b ½S3 + k2 ½S2  + k2 ½S2°Þ

(32b)

d½S2° k3b ½S3  k2 ½S2° ¼ 2½S3o dF ðk3a ½S3 + k3b ½S3 + k2 ½S2  + k2 ½S2°Þ

(32c)

d½S1 k3a ½S3 + k3b ½S3 + 2k2 ½S2  + 2k2 ½S2° ¼ 2½S3o dF ðk3a ½S3 + k3b ½S3 + k2 ½S2  + k2 ½S2°Þ

(32d)

Each of the terms in each of the numerators and the denominators of Eqs. (32a)–(32d) contains a specificity constant. This means that the absolute values of the specificity constants cannot be estimated by fitting the above equations to the experimental profiles for the various reaction species. It is only possible to estimate the relative values of the specificity constants. One of the specificity constants is therefore used to divide both the numerator and the denominator of all equations. For example, if the numerator and the denominator are divided by k2, the above equation set becomes: d½S3 R3a ½S3  R3b ½S3 ¼ 2½S3o dF ðR3a ½S3 + R3b ½S3 + ½S2  + ½S2°Þ

(33a)

d½S2  R3a ½S3  ½S2  ¼ 2½S3o dF ðR3a ½S3 + R3b ½S3 + ½S2  + ½S2°Þ

(33b)

d½S2° R3b ½S3  ½S2° ¼ 2½S3o dF ðR3a ½S3 + R3b ½S3 + ½S2  + ½S2°Þ

(33c)

7. “Fingerprinting models” as tools for estimating specificity constants

301

100

Concentration (mM)

(A) 80 60 40 20 0

(B)

Concentration (mM)

300

200

100

0 0

0.2

0.4

0.6

0.8

1

Fractional degree of hydrolysis FIG. 4 Example of how reaction profiles plotted as a function of the fractional degree of hydrolysis can be used to estimate parameters. The example is related to the case study presented in Section 7.2. The symbols represent experimental data for ( ) S3, ( ) S2, ( ) S2°, and ( ) S1. The lines represent the fitted curves. The fit was obtained with Eqs. (33a)–(33d) and gives R3a ¼ 12 and R3b ¼ 3, using initial concentrations of 100 mM for S3 and 0 mM for S2, S2°, and S1.

d½S1 R3a ½S3 + R3b ½S3 + 2½S2  + 2½S2° ¼ 2½S3o dF ðR3a ½S3 + R3b ½S3 + ½S2  + ½S2°Þ

(33d)

where R3a ¼ k3a/k2 and R3b ¼ k3b/k2. These “relative specificity constants” are the only two parameters of the model. They are estimated by using a fitting program to fit the model represented by Eqs. (33a)–(33d) to the experimental profiles for the reaction species, plotted as functions of the fractional degree of reaction (Fig. 4). After this fitting, k2 can be determined experimentally based on initial velocity experiments with S2, allowing one to calculate k3a and k3b. These initial velocity experiments are relatively simple to perform and analyze, since there is only one possible reaction with S2.

7.3 Considerations about the fingerprinting method The case study presented in Section 7.2 is a simple example. The method has been applied to more complicated systems, including branched reaction schemes involving the hydrolysis of oligosaccharides of up to 7 monosaccharide units by an endopolygalacturonase, with the

302

11. Mathematical modeling of enzymatic polysaccharide hydrolysis

estimation of 11 relative specificity constants [11], and also the hydrolysis of undecamers by a processive exo-acting enzyme that removes disaccharide units from the nonreducing end of the oligosaccharides [9]. In the latter case, the analysis was used to determine not only the specificity constants of the enzyme for the various oligosaccharides, but also the probability of processive action, which can vary as a function of oligosaccharide chain length. In the fingerprinting method, the concentrations of the various reaction species must be determined with good accuracy so that the fractional degree of reaction can be calculated correctly. In good-quality data, the sum of monosaccharide units within the system, whether free or contained in longer molecules, must always remain equal to the number in the original oligosaccharide substrate. Due to limitations of detection and quantification methods, it becomes more and more difficult to ensure this as longer and longer oligosaccharides are used. The strength of the fingerprinting method is that it isolates the phenomenon of substrate selection, such that the relative values of the specificity constants (and probabilities of processivity, in the case of processive enzymes) are the only parameters of the equation. This eliminates the effects of phenomena that influence the rate of the reaction but do not affect substrate selection. Since [E]T cancels out of the rate expressions during the deduction of the equations, the analysis is not affected by enzyme denaturation. Likewise, since the denominator of the time-based rate expressions cancels out, the analysis is not affected by the presence of inhibitors. This is an advantage because the final product causes competitive inhibition in many polysaccharide hydrolysis processes. In isolating the phenomenon of substrate selection, the fingerprinting method limits itself to estimating the relative values of the specificity constants. As mentioned above, the absolute values can be calculated by experimentally determining the absolute value of the specificity constant that was used as a reference. It should be noted that, in the case of attack by an endoacting enzyme, the analysis does not give a single specificity constant for each substrate; rather, it gives a different specificity constant for each reaction. In other words, if the enzyme can catalyze n different reactions with the same substrate, the analysis will give n different specificity constants for that substrate. However, for a model to be able to make predictions in time, in addition to the values of the specificity constants, one needs estimates of KM values and inhibition parameters. These values will need to be obtained in classical initial rate assays. For each oligosaccharide, assays will need to be done at several different initial concentrations and the initial rate of disappearance of the oligosaccharide determined. The advantage is that it is sufficient to obtain a single global KM for each substrate; even when the enzyme can catalyze several different reactions with it, it is not necessary to determine a reaction-specific KM for each of these reactions. Consequently, one does not need to determine the rates of production of the various reaction products. In a similar manner, in order to obtain KMP values characterizing inhibition by the final product, initial rate experiments would be done with the final product being added at zero time.

8. Conclusion Modeling the enzymatic hydrolysis of polysaccharides is complex, and this chapter has given only a very basic introduction, with the modeling examples being limited to linear homopolysaccharides. It is significantly more challenging to develop dynamic models that

References

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are useful tools for guiding the design and optimization of bioreactors for the hydrolysis of more complex polysaccharides. Such models will not be fully mechanistic. In deciding upon appropriate simplifications, one needs to consider very carefully how much time and other resources one is willing to put into the development of the model and must balance this against one’s expectations of the model in terms of its ability to make accurate predictions over a wide range of operating conditions.

References [1] Selvam K, Senbagam D, Selvankumar T, Sudhakar C, Kamala-Kannan S, Senthilkumar B, Govarthanan M. Cellulase enzyme: homology modeling, binding site identification and molecular docking. J Mol Struct 2017;1150:61–7. https://doi.org/10.1016/j.molstruc.2017.08.067. [2] Bernardi RC, Cann I, Schulten K. Molecular dynamics study of enhanced Man5B enzymatic activity. Biotechnol Biofuels 2014;7:83. https://doi.org/10.1186/1754-6834-7-83. [3] Sonoda MT, Godoy AS, Pellegrini VOA, Kadowaki MAS, Nascimento AS, Polikarpov I. Structure and dynamics of Trichoderma harzianum Cel7B suggest molecular architecture adaptations required for a wide spectrum of activities on plant cell wall polysaccharides. Biochim Biophys Acta Gen Subj 2019;1863:1015–26. https://doi.org/ 10.1016/j.bbagen.2019.03.013. [4] Eibinger M, Zahel T, Ganner T, Plank H, Nidetzky B. Cellular automata modeling depicts degradation of cellulosic material by a cellulase system with single-molecule resolution. Biotechnol Biofuels 2016;9:56. https:// doi.org/10.1186/s13068-016-0463-8. [5] Knott BC, Crowley MF, Himmel ME, Sta˚hlberg J, Beckham GT. Carbohydrate–protein interactions that drive processive polysaccharide translocation in enzymes revealed from a computational study of cellobiohydrolase processivity. J Am Chem Soc 2014;136:8810–9. https://doi.org/10.1021/ja504074g. [6] Bijttebier A, Goesaert H, Delcour JA. Amylase action pattern on starch polymers. Biologia 2008;63:989–99. https://doi.org/10.2478/s11756-008-0169-x. [7] Mercadante D, Melton LD, Jameson GB, Williams MAK. Processive pectin methylesterases: the role of electrostatic potential, breathing motions and bond cleavage in the rectification of Brownian motions. PLoS ONE 2014;9, e87581. https://doi.org/10.1371/journal.pone.0087581. [8] Helstad S. Corn sweeteners. In: Serna-Saldivar SO, editor. Corn. 3rd ed. Oxford: AACC International Press; 2019. p. 551–91. https://doi.org/10.1016/B978-0-12-811971-6.00020-6. [9] Mello GR, Pereira AB, Voll FAP, Krieger N, Mitchell DA. Fingerprinting processive β-amylases. Biochem Eng J 2018;137:334–43. https://doi.org/10.1016/j.bej.2018.05.025. [10] Moreira I, Krieger N, Mitchell DA. Time is of the essence: a new strategy for time-stepping in stochastic models describing the enzymatic hydrolysis of colloidal suspensions of polysaccharides. Chem Eng J 2021;405, 126672. https://doi.org/10.1016/j.cej.2020.126672. [11] Pereira AB, Krieger N, Mitchell DA. Fingerprinting of oligosaccharide-hydrolyzing enzymes that catalyze branched reaction schemes. Biochem Eng J 2016;113:93–101. https://doi.org/10.1016/j.bej.2016.05.012.

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C H A P T E R

12 Polysaccharide deconstruction products: Production of bio-based building blocks Jaciane Lutz Ienczaka, Aline Carvalho da Costab, Karen Cristina Collograib, Aline Soares Bretasb, and Isabela de Oliveira Pereiraa a

Department of Chemical and Food Engineering, Federal University of Santa Catarina, Floriano´polis, SC, Brazil bSchool of Chemical Engineering, State University of Campinas (UNICAMP), Campinas, Sa˜o Paulo, Brazil

1. Introduction The most abundant and renewable material in the world is the lignocellulosic biomass, and its fractionation is crucial to the development of economically viable biorefineries [1]. The biorefinery concept promotes the integration of facilities and processes where the lignocellulosic biomass is converted into fuel and/or high value-added products (Fig. 1), with lower greenhouse gas (GHG) and waste emission [2,3]. Lignocellulosic biomasses are mainly composed of two polysaccharides (cellulose and hemicelluloses) and an aromatic macromolecule (lignin), in addition to low contents of structural proteins, lipids, and ashes [4]. These materials should be previously fractionated in a process called pretreatment to be used in the biorefinery concept. According to Ienczak et al. [5], there is still no consensus on the best pretreatment (ammonia fiber expansion [6], organosolv [7], steam explosion [8], diluted sulfuric acid [9], alkaline [10], among others), since pretreatment must be chosen according to the desired end product and raw material. However, it is worth mentioning that the pretreatments by steam explosion and diluted sulfuric acid are the most used for bioethanol production from lignocellulosic biomass (called second generation ethanol, 2G), as they can recover up to 90% of hemicellulose [8,11]. These two pretreatments produce a liquid fraction rich in pentose (C5) sugars and a solid fraction Polysaccharide-Degrading Biocatalysts https://doi.org/10.1016/B978-0-323-99986-1.00001-6

305

Copyright # 2023 Elsevier Inc. All rights reserved.

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12. Polysaccharide deconstruction products

FIG. 1 Lactic acid, propionic acid, and succinic acid bio-based chemical building blocks. Created in BioRender.

called cellulignin, which can be subjected to a subsequent enzymatic hydrolysis to release glucose (C6) [11]. The C6 and C5 hydrolysates can be fermented (separately or together) to produce fuels or high value-added products in the biorefinery concept. Lignin and the remaining cellulose are the main residues obtained after these processes and they can be burnt with the aim to generate steam and energy, as proposed by Klein and coworkers [12]. The catalytic activity of living organisms such as fungi, yeasts, microalgae, or bacteria that convert carbohydrates into a variety of high value-added molecules is defined as a bio-based process. The main advantages of bio-based conversion in relation to chemical routes are the ability to operate at pH close to neutral, room temperatures, and atmospheric pressures [13]. It is important to highlight that the deconstruction of the lignocellulosic biomass by pretreatment processes generates some inhibitors that can impair carbohydrate bio-based conversion. Organic acids (acetic, formic, and levulinic acids), furanic compounds (furfural and 5-hydroxymethylfurfural (5-HMF)), and lignin derivatives (p-coumaric, ferulic, vanillic acids, among others) are the main representatives of the inhibitors (Fig. 1). Some strategies can be applied to overcome the inhibitory effect of lignocellulosic hydrolysates on

1. Introduction

307

microorganisms in bio-based processes, such as the use of activated charcoal [14], ion exchange resins [15], overliming [15], and liquid-liquid extraction [9]. However, detoxification methods are difficult to use in large-scale applications and can make final bio-based production expensive [16]. The bio-based conversion of carbohydrates into fuels and high value-added molecules has traditionally been limited by the robustness of the fermentative organism in the presence of inhibitors and by competitive organisms [17]. The discovery of new detoxification methods and more robust fermentative organisms is addressing this problem. More robust fermentation organisms that can tolerate inhibitors will enable more severe pretreatment conditions and better fermentation performance [18]. The cost of bio-based high value-added molecules (organic acids, alcohols, solvents, etc.) largely depends on the cost of the biomass, and lignocellulosic residues from forests and agriculture can constitute a potential source of carbohydrates. Therefore, the fractionation of the polysaccharides present in the lignocellulosic biomass into fermentable carbohydrates has application to obtain bio-based molecules of high value-addition, such as molecules of the compounds called building blocks (Fig. 1). Building blocks are molecules that can be converted into various secondary chemicals and intermediates and, therefore, can give rise to a broad range of different downstream uses. When this molecule is derived from biomass and is converted into high value-added molecules by microorganisms, it can be called a bio-based chemical building block (BCBB) [19]. Propionic acid (PA), succinic acid (SA), and lactic acid (LA) are classified by the US Department of Energy (DOE) as representatives of the “Top 10 target structures” list to be produced as bio-based chemical building blocks [20]. These organic acids are also part of the list of the 30 most promising chemical platforms for industrial use [21]. SA stands out as one of the most important chemical building blocks, given its variety of applications, being able to replace more than 250 products derived from benzene, proven to be carcinogenic to humans [22]. Fig. 1 shows some molecules derived from SA, such as dimethyl succinate (DBE), which can be used as a flavoring agent, functional fluids, paint and coating additives, pigments, solvents, and viscosity adjustors [23]. Indeed, SA can also be converted into succinonitrile, 1,4-butanediol, or N-methylpyrrolidone (NMP) [24]. The demand from medical, pharmaceutical, and food industries for polymers and esters derived from LA has been increasing, due to their biodegradable and biocompatible properties [25]. LA can be used as a building block to obtain acrylic acid, which can be used to produce resins, coatings, adhesives, oil treatment chemicals, detergent intermediates, water treatment chemicals, and water absorbent polyacrylic acid polymers [26]. Another important molecule produced from LA in a building block concept is 2,3-pentanedione, which can be converted into solvent for cellulose acetate, paints, inks, and lacquers, starting material for dyes, pesticides, and drugs [27]. Bio-based PA has an enormous versatility in applications in different industrial segments, as it is classified as a GRAS (Generally Recognized as Safe) compound that can be used as a food additive by the FDA (Food and Drug Administration). Due to this characteristic, it has aroused interest in the food sector, mainly when it is used as sodium propionate (Fig. 1), due to its antimicrobial and antioxidant ability [28] in controlling pathogens and spoilage. It is possible to notice in Fig. 1 that the bio-based PA can be converted into propylene and later applied to obtain polypropylene. In fact, the PA building block can be converted to

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12. Polysaccharide deconstruction products

propionic anhydride and used as a raw material for dyes, pharmaceuticals, agrochemicals, fragrance chemicals, and for the production of cellulose acetate propionate. This chapter reviews the state-of-the-art regarding bio-based carbohydrate conversion into the building blocks succinic acid, lactic acid, and propionic acid. We focus on describing the bio-based production system, market size, main polysaccharide sources (with focus on lignocellulosic biomasses) and fermentation strategies, in addition to the applications of SA, LA, and PA in a building block context.

2. Succinic acid as a promising bio-based building block Succinic acid (SA) is a dicarboxylic acid with the molecular formula C4H6O4, and because it is a carboxylic acid with a relatively high molar mass, it appears in the solid state at room temperature, being colorless and odorless. In addition to its traditional application in the chemical and pharmaceutical industry, SA is used for the production of bio-based plasticizers, poly(butylene succinate), polyester polyols, coatings, lubricants, resins, and personal care products. It can also be used as a building block to produce 1,4-butanediol, tetrahydrofuran and γ-butyrolactone, among other chemicals, as presented in Fig. 1. In the food industry, it is used as a pH modifier and as an antimicrobial agent. Sodium succinate derived from SA is added to food to enhance the flavor and can be used in the place of sodium glutamate [29]. The global SA market size was valued at USD 222.9 million in 2021 and is expected to expand at a compound annual growth rate of 9.7% from 2022 to 2030. The growth is attributed to the increasing usage of SA in the pharmaceutical industry. The increasing preference for SA over butane-based maleic anhydride in the production of chemicals, such as succinic anhydride, plastics, diethyl maleate, polymers, fumaric, and glyoxylic acid, which are conventionally manufactured from butane, is expected to positively influence the market growth [30]. Some prominent players in the global succinic acid market include BASF SE, Myriant Corporation, BioAmber, Parchem, Dow Chemicals, Ernesto Ventos S.A., The Chemical Company, Kawasaki Kasei Chemicals Ltd., Mitsubishi Chemical Corporation [30,31]. SA can be produced from petrochemical or bio-based processes. It is chemically produced from the catalytic hydrogenation of maleic anhydride or by the oxidation of paraffin, both obtained by the petrochemical route [32]. In the paraffin oxidation process, a catalyst (calcium or manganese) is used, resulting in a mixture of dicarboxylic acids. The SA is then recovered and purified by distillation, crystallization, and drying. However, this technology has low yield and results in low purity acid. The hydrogenation process is a mature technology for the chemical production of SA, being applied on an industrial scale since the 1930s. The reaction can be carried out in a homogeneous or heterogeneous way by the action of catalyzing agents. Although this process presents high yields and high purity of SA, the operation of this process is complex, expensive, and can cause environmental implications [24,32]. Even so the petroleum-based process dominated the global market with a revenue share of more than 50% in 2021. The bio-based segment is the second-largest revenue contributor for SA production at commercial scale. Increasing health consciousness among consumers and the rising focus of the

2. Succinic acid as a promising bio-based building block

309

governments on environmental concerns have created a large barrier to the market growth of petrochemical products. In the bio-based production of organic acids, about 60% of the total production costs are related to the steps of purification and recovery of the product from the fermented broth (downstream processing). Thus, in order to obtain competitive prices with the petrochemical route, it is necessary to minimize production costs, either by improving the fermentation step or reducing the costs of the separation step. To make a bio-based process for SA production an attractive business for large-scale production it is necessary that the technology meets several criteria, such as high yields and high titer, in addition to using cheap raw materials and nutrients for the composition of the fermentation medium [33]. It is also worth noting the use of lignocellulosic biomasses to produce SA, as they are included in the biorefinery concept. SA is a common intermediate in the metabolic pathway of different anaerobic and facultative anaerobic microorganisms. It can be produced by propionate-producing bacteria (such as Propionibacterium species), gastrointestinal bacteria (Escherichia coli, Pectinatus and Bacteroides species), rumen bacteria (Ruminococcus flavefaciens, Actinobacillus succinogenes, Bacteroides amylophilus, Prevotella ruminicola, Succinimonas amylolytica, Succinivibrio dextrinisolvens, Wolinella succinogenes, and Cytophaga succinicans), and some strains of Lactobacillus [28]. Most microorganisms were isolated from the rumen, where succinate is an important precursor of propionate, that is absorbed by the rumen walls, providing energy and biosynthetic precursors to the animal [28]. Actinobacillus succinogenes was recognized as the most promising producer, not only because of its broad spectrum of substrates consumption but also because of its relatively strong anaerobic growth capacity [34,35]. This strain can consume a wide range of C5 and C6 sugars, as well as various disaccharides and other carbon sources, such as glucose, xylose, arabinose, mannose, galactose, fructose, sucrose, lactose, cellobiose, mannitol, maltose, and glycerol. Biologically, it is possible to obtain bio-based SA by three routes: reductive pathway of the TCA cycle under anaerobic conditions, oxidative pathway of the TCA cycle, and glyoxylate pathway. In the reductive TCA cycle, in the presence of high levels of carbon dioxide and under anaerobic conditions, there is accumulation of succinate from phosphoenolpyruvate (PEP). In this pathway, phosphoenolpyruvate (PEP), derived from glucose metabolism, is converted into oxalate (OAA) from CO2 fixation. The enzyme malate dehydrogenase, using the cofactor NADH, oxidizes OAA to malate (MA), which is converted into fumarate (FA), which, by the action of the enzyme fumarate reductase, is reduced to succinate (SA). In this pathway, 2 mol of NADH are consumed per mole of succinic acid produced. Stoichiometrically, each mole of glucose generates 2 mol of NADH and 2 mol of PEP. Each mole of PEP can be converted into 1 mol of SA. Thus, during glucose metabolism via the reductive TCA cycle, a limitation of NADH is observed, becoming an obstacle in obtaining high product yields. Stoichiometrically, each mole of glucose generates 2 mol of NADH during the formation of each PEP; however, each mole of glucose can be metabolized to 2 mol of PEP. Each mole of PEP can be converted into 1 mol of SA. Thus, during glucose metabolism via the reductive TCA cycle, a limitation of NADH is observed, becoming an obstacle in obtaining high product yields [36–38]. The maximum theoretical yield of glucose conversion to SA is 1.12 g SA/g glucose (or 1.71 mol SA/mol glucose). However, this value is hardly reached due to the formation of by-products, usually in response to the imbalance of cofactors in the biochemical pathway [39]. Cheng et al. [37] concluded that, due to this NADH limitation, assuming that the flow is fully directed toward anaerobic fermentation, the molar yield is limited to 1 mol

310

12. Polysaccharide deconstruction products

SA/mol glucose. Therefore, the production of SA is directly affected by the redox imbalance, causing the accumulation of intermediate by-products of the metabolic pathway. This unwanted formation, in addition to reducing fermentation yield, promotes microbial inhibition. Therefore, one of the key factors to obtain good yields in succinic fermentation is to reduce the formation of by-products. In the oxidative pathway of TCA, under aerobic conditions, acetyl-CoA is converted into citrate, isocitrate, and succinate, which is further converted into fumarate by the enzyme succinate dehydrogenase. Aerobic production of succinic acid is not naturally possible, as SA is an intermediate product of the cycle. Therefore, to promote the accumulation of SA, it is necessary to inactivate a gene in the pathway (sdhA), blocking the conversion of succinate to fumarate. In this pathway, the theoretical yield is 1 mol SA/mol glucose, generating 3 mol of NADH [37,38]. The glyoxylate pathway, essentially active under aerobic conditions, is an anaplerotic reaction, that is, a filling reaction to balance the lack of molecules in the TCA cycle. This pathway operates to convert 2 mol of acetyl-CoA into 1 mol of SA, generating extra NADH. This additional production of the cofactor NADH helps to reduce the imbalance of the anaerobic fermentation pathway, increasing the succinate yield [37,38]. An important parameter of the succinic fermentation process is the presence of CO2. Glassner and Datta [33] tested fermentations with and without CO2 injection for A. succiniciproducens and observed that, after 40 h of process, the yield for fermentation with gas injection was 87.8%, against 2.6% with no injection. In the absence of carbon dioxide, metabolism was mostly diverted to lactate formation (60% yield), with no records of concentrations of this by-product in fermentation with gas addition. Thus, CO2 is one of the key factors to maintain optimal yields and minimize the formation of by-products [39]. The need for CO2 feeding during fermentation to produce SA allows the possibility of integration with the bioethanol unit, for example, where the CO2 produced in the alcoholic fermentation can be reused. Therefore, integration brings mutual benefits to both processes, especially in terms of the environment (carbon sequestration and CO2 recycling) [39–41]. In this way, an integrated biorefinery of co-products becomes a strategy to provide the technical-economic feasibility of the processes, in addition to promoting the circular economy in the sector. Table 1 presents different kinds of lignocellulosic biomass studied for SA production. In the works cited in this table, the most applied fractionation process to release carbohydrates from biomass is based on diluted acid pretreatment followed by enzymatic hydrolysis. It is also important to highlight that almost all the studies presented in Table 1 describe fermentation culture media using yeast extract (YE) or urea as nitrogen sources, as SA production requires a significant amount of nitrogen sources. In this sense, reducing the cost of bio-based SA production requires the use of low-cost nitrogen sources, such as corn steep liquor (CSL), or another nitrogen-rich biomass [39]. In order to evaluate CSL as nitrogen source, Lee et al. [51] evaluated the substitution of yeast extract, using whey as carbon source. The results showed an SA yield of 0.72 g SA/g lactose in the presence of YE, which was nearly the same (0.71 g SA/g lactose) in the presence of CSL. Chen and coworkers [63] also tested an alternative nitrogen source for bio-based SA production by A. succinogenes NJ113. They used spent yeast cell hydrolysate and corn fiber hydrolysate as nitrogen and carbon source, respectively, for the replacement of YE and

TABLE 1 Fermentation parameters obtained in literature for SA production by different strains, biomass and fermentation process with the respective results of SA titer, productivity and yield. AS titer (g L21)

Yield (g g21)

Productivity (g L21 h21)

Reference

Batch SSF (simultaneous saccharification and fermentation)

47.4

0.72

0.99

[42]

A. succinogenes F26 (mutant)

Batch

70.6

0.88

0.70

[43]

Spent yeast cell hydrolysate

A. succinogenes NJ113

Batch

35.4

0.72

0.63

[44]

Yeast extract

A. succinogenes CICC 11014

Batch

23.6

0.58

0.49

[45]

Yeast extract

Thermoanaerobacterium thermosaccharolyticum M5 and A. succinogenes 130Z

Co-culture for simultaneous hydrolysis and fermentation (SHF)

32.5

0.39

ND

[46]

Corn straw

Yeast extract

A. succinogenes CGMCC1593

Fed batch

53.2

0.82

1.21

[47]

Corn stalk

Yeast extract and urea

A. succinogenes CGMCC 2650

Batch

17.8

0.66

0.56

[36]

Yeast extract, tryptone, and ammonium sulfate

Escherichia coli SD121

Dual phase fed batch

57.8

0.87

0.96

[48]

Wheat

None enrichment

A. succinogenes 130Z

Batch

64.2

0.81

1.19

[49]

Whey

CLS and tryptophane

Actinobacillus succiniciproducens

Continuous

19.8

0.64

3.0

[50]

CSL

Mannheimia succiniciproducens MBEL55E

Continuous

13.4

0.71

1.18

[51]

Nitrogen source

Microorganism

Fermentation process

Corn stover

Corn steep liquor (CSL)

Actinobacillus succinogenes

Corn fiber

Yeast extract and biotin

Biomass

Corncob

Continued

TABLE 1 Fermentation parameters obtained in literature for SA production by different strains, biomass and fermentation process with the respective results of SA titer, productivity and yield—cont’d

Fermentation process

AS titer (g L21)

Yield (g g21)

Productivity (g L21 h21)

Reference

A. succinogenes 130Z

Batch

36.8

0.92

3.9

[52]

None enrichment

A. succinogenes ATCC 55618

Batch

21.81

0.45

0.30

[12]

Yeast extract and CSL

A. succinogenes NJ113

Batch

20

0.61

0.65

[53]

CSL

Yarrowia lipolytica PSA02004 (mutant)

Batch

33.2

0.58

0.33

[54]

Cassava roots

None enrichment

A. succinogenes ATCC55618

Fed batch

1.27

2.4

3.22

[55]

Sweet sorghum bagasse

Yeast extract

A. succinogenes NJ113

Batch

0.15

0.93

0.98

[56]

Wood chips and soybean hulls

None enrichment

Aspergillus niger, Trichoderma reesei, and Phanerochaete chrysosporium

Co-culture. Dual phase fed batch (solid-state prefermentation followed by a two-stage fermentation stage)

26

0.24

ND

[57]

Pretreated wood hydrolysatea

Yeast extract

M. succiniciproducens MBEL55E

Continuous

7.98

0.55

3.19

[58]

Macroalgae Laminaria digitata

Yeast extract

A. succinogenes 130Z

Batch

24.39

0.86

0.50

[59]

Detoxified softwood dilute sulfuric acid hydrolysates

CSL and ammonium sulfate

E. coli AFP 184 metabolically engineered

Batch

42.2

0.72

ND

[60]

Wheat straw hydrolysate

Ammonium nitrate and biotin

A. niger metabolically engineered

Batch

9.0

0.22

ND

[61]

Soybean meal

None enrichment

E. coli engineered

Batchb

312 mM

0.82

ND

[62]

Nitrogen source

Microorganism

Microalgae hydrolysate

Yeast extract

Sugarcane bagasse

Biomass

a

NaOH-treated wood hydrolysate. The wood hydrolysate-based medium was treated with NaOH before sterilization to reduce the formation of inhibitory compound. Aerobic process. ND, not determined.

b

2. Succinic acid as a promising bio-based building block

313

glucose. The results indicated that spent yeast cell hydrolysate could be an alternative nitrogen source for the economic production of SA from renewable resources. Fermentations to produce SA are conventionally monocultures, but an alternative to optimize the production of this acid from biomass is the use of mixed cultures. Interspecies interactions are models observed in nature, such as in the rumen ecosystem, from where the main strains producing these acids were isolated, enabling replication in the laboratory [64]. To produce SA, there are already some studies described in the literature using co-culture. Some authors used the mixed culture approach to promote the bioconversion of lignocellulosic biomass into SA (Table 1). For this, the co-culture system needs to contain bacteria that hydrolyze cellulose and hemicellulose and strains that produce SA from the carbohydrates released from this hydrolysis. Alcantara et al. [57] developed a consortium of mixed fungi designed to directly produce SA from minimally pretreated lignocellulosic biomass. This process included a solid-state prefermentation using Aspergillus niger and Trichoderma reesei, followed by a two-stage fermentation stage using Phanerochaete chrysosporium. A. niger and T. reesei were the main responsible for the secretion of cellulases and hemicellulases, while P. chrysosporium induced ligninolytic enzymes, making the substrate more susceptible to cellulase hydrolysis. About 10 g L 1 of SA was obtained from soybean hulls and wood chips (lignocellulosic biomasses used in this process). When the process was carried out in fed batch mode, almost 26 g L 1 of SA was produced with a yield of 0.24 g g 1. According to the authors, the consortium of fungi in mixed cultures can effectively reduce production costs, representing a promising alternative to produce SA from lignocellulosic material. However, the production of hydrolases and the production of SA were still separated into two different steps, which would prolong the duration of the fermentation process. Recently, Lu and coworkers [65] also designed a microbial consortium to produce SA directly from lignocellulosic biomass. This work presented the strategy of consolidated bioprocessing (CBP) to achieve cost-effective and efficient bioconversion of lignocellulosic biomass into valuable chemicals. In this study, Thermoanaerobacterium thermosaccharolyticum M5 and A. succinogenes 130Z were combined in a microbial consortium, and were responsible for hemicellulase secretion and SA production, respectively. After process optimization, SA concentrations of 32.50 g L 1 were reached from xylan, with a yield of 0.39 g g 1. Furthermore, xylanase and β-xylosidase activities remained at 0.38–0.43 and 6.02–7.34 U mL 1, respectively, within this microbial co-culture system. When 80 g L 1 of untreated corn cob was used as substrate, 12.51 g L 1 of SA was obtained. However, the hemicellulose hydrolysis efficiency of the M5 strain still cannot meet the 130Z strain requirement for high xylose utilization, resulting in the prolonged fermentation time. Future studies are still needed to build more efficient microbial consortia to achieve efficiency in the bioconversion of lignocellulose to succinic acid. Kim et al. [66] investigated SA production using Escherichia coli and employed adaptive evolution and co-culture strategy to improve acid yield. E. coli hardly grows anaerobically on glycerol without an exogenous electron acceptor. In this sense, they used the strategy of adding a second microorganism to the culture, Methanobacterium formicicum, which could act as a live electron acceptor in glycerol fermentation when cultured with E. coli. After 39 rounds (273 days) of adaptation in glycerol, the co-culture system of wild E. coli and M. formicicum produced 2.35 g L 1 of SA, a result twice as high as the nonadapted organisms (1.14 g L 1). In addition, by-products such as formic acid and ethanol were significantly

314

12. Polysaccharide deconstruction products

reduced. The authors point out that the results demonstrate that the co-culture system is more useful than the use of genetically modified strains (mutant E. coli described by Wang et al. [67]). However, the titers were still low, requiring further studies to increase this acid production. Other alternatives to improve SA production besides the use of co-culture or the improvement of the fermentation process and the medium composition are the improvements of the strain. In this sense, genetic engineering can manipulate the metabolic pathways of the microorganism; however, deletion of byproduct-producing genes will not always reflect improvements in the formation of the product of interest, due to rearrangement promoted in flux distribution and alteration of the original intracellular redox balance [37]. All of the reported genetic modifications of A. succinogenes that led to higher SA production have been achieved by adaptation and metabolic evolution [34]. The genome shuffling by protoplast fusion strategy was also reported for A. succinogenes, with mutants derived for nitrosoguanidine and UV treatment [68]. Although genetic engineering strategies to enhance SA production in this strain have not yet been reported, shuttle vectors have been successfully constructed and tested for their functionality. Lee et al. [69] suggested genetic engineering strategies to improve product yield, titer, and productivity. One possible solution for A. succinogenes genetic modification would be the enhancement of SA titer and yield by gene amplification of the pathways that lead to the desired product or deletion of genes that control pathways leading to the formation of other products. Genes that take part in the reductive pathway of the TCA cycle (PEPCK, MDH, Fm, and Fr) could be amplified or genes responsible for by-product formation (AK, PFL) could be knocked out to favor the fluxes to the C4 pathway (Fig. 2). Other hosts like E. coli and some yeasts and molds were considered for genetic modification and heterologous genes prospection for SA production [62,70–72]. Hodge and coworkers [60], for example, used an E. coli metabolically engineered to ferment lignocellulosic biomass sugars into SA, and tested detoxified softwood dilute sulfuric acid hydrolysates for growth and fermentation. The minimum detoxification requirements were investigated with activated carbon and/or overliming treatments. Fermentation resulted in 42.2 g L 1 of SA and acid yield of 0.72 (g g 1). It was also found that any HMF remaining after detoxification was completely metabolized during aerobic cell growth in the hydrolysates that could support growth. In another study, Yang et al. [61] performed consecutive genetic manipulations in the citric acid-producing strain A. niger ATCC 1015 with a ribonucleoprotein (RNP)-based CRISPR-Cas9 system. Two genes involved in the production of two by-products, gluconic acid and oxalic acid, were disrupted. In addition, an efficient C4-dicarboxylate transporter and a soluble NADH-dependent fumarate reductase were overexpressed. The resulting strain, SAP-3, produced 17g L 1 succinic acid while there was no succinic acid detected at a measurable level in the wild-type strain using a synthetic substrate. Two types of biomasses were explored as substrates for succinic acid production. After 6 days, the SAP-3 strain produced 23 g L 1 and 9 g L 1 SA from sugar beet molasses and wheat straw hydrolysate, respectively. The company Succinity (Spain) uses a native strain for bio-based SA production. Typically, industrial fermentation with native strains is simpler and cheaper than fermentation with genetically modified strains. The use of genetically modified organisms (GMOs), in some cases, can make the process more expensive, as they require complex culture media, increasing costs on a large scale. In addition, some genetic modifications require containment, that is,

3. Bio-based lactic acid: An important building block in biorefinery concept

315

FIG. 2

Metabolic pathways involved in SA production and possible genetic modifications to improve succinate production flux according to references [39] and [61]. Bacteria A. succinogenes had an incomplete TCA cycle and lactic acid production occurs in other succinic acid producing strains like Basfia succiniciproducens. For anaerobic processes, the knockout of the gene Asuc 1564-5 that encodes succinyl-CoA synthetase will induce succinate accumulation. In aerobic processes, it is necessary to knock out fumarase gene (Asuc 0956) to avoid fumarate formation. The super expression is indicated in genes for sugars transporters (like GP1 for glucose for example) and also for the genes that encode enzymes in order to favor the fluxes to the C4 pathway.

conditions are necessary that do not allow the escape or release of the microorganism into the environment, and this process requires greater care in operations, such as closed vats and decontamination of all material generated in the process. These additional precautions lead to higher operating and installation design costs [73,74].

3. Bio-based lactic acid: An important building block in biorefinery concept Lactic acid (LA) (HOCH3CHCOOH), or 2-hydroxypropionic acid, is the most widely occurring hydroxycarboxylic acid [75]. This organic acid is a simple chiral molecule that exists as two enantiomers, L- and D-lactic acid [76]. LA is considered one of the 12 most promising building blocks that can be produced from carbohydrates [77]. It has been used by the food industry in the acid and salt forms as an acidulant (as a preservative and flavoring agent) and as a chemical intermediate in the production of polymers (e.g., polylactic acid (PLA)) and esters (e.g., ethyl lactate) [25].

316

12. Polysaccharide deconstruction products

The global market size of LA was estimated at $2.90 billion in 2021 [78] and can reach $5.02 billion in 2028. LA consumption can increase with a compound annual growth rate (CAGR) of 8.0% from 2021 to 2028 [79]. The demand from medical, pharmaceutical, and food industries for polymers and esters derived from LA has been increasing, due to their biodegradable and biocompatible properties [25]. An example is the expansion of the demand for LA to produce PLA (polylactide), a biodegradable and biocompatible polymer, which is used to produce biomedical devices and packaging and fibers for foams [75]. In addition, PLA-based bioplastic is one of the most commercially used and successful rigid bioplastics due to its good processability and mechanical properties. This bioplastic has higher durability, mechanical strength, and transparency than other biodegradable products [80]. The commercial production of LA is carried out by fermentation (bio-based) or by the petrochemical route [76] (Fig. 3). LA production by the petrochemical route is carried out by the lactonitrile route, a by-product of acrylonitrile technology, developed in 1863 by Wislicenus. In this technology, lactonitrile is obtained by reacting hydrogen cyanide (HCN) with liquid acetaldehyde (CH3CHO) in the presence of a basic catalyst under high pressure. Lactonitrile is recovered, purified by distillation, and hydrolyzed with sulfuric acid (H2SO4), producing lactic acid and ammonium salt ((NH4)2SO4). LA is esterified with methanol to obtain methyl lactate (CH3CHOHCOOCH3), which is recovered and purified by distillation. Methyl lactate is hydrolyzed with acidified water to obtain LA and methanol. The recovery of methanol is performed by distillation. The LA obtained by the petrochemical route consists of a racemic mixture of D- and L-LA [75].

FIG. 3 Lactic acid production by the petrochemical route (lactronitrile route) and by fermentation (bio-based) using corn starch and lignocellulosic biomass as carbon sources.

3. Bio-based lactic acid: An important building block in biorefinery concept

317

The petrochemical route was mostly used until about 1990, when production by fermentation became more economically viable than the petrochemical route [76]. Nowadays, bio-based LA production is predominant by using first-generation carbohydrates (1G), such as corn starch or sugarcane juice. The main raw material used to produce bio-based LA is corn starch. The production of LA from corn starch is mostly carried out by submerged fermentation [75] (Fig. 3). In this process, corn starch is gelatinized and enzymatically liquefied by α-amylase at high temperatures (90–130°C for 15 min), resulting in the production of maltose and dextrin. These sugars are enzymatically hydrolyzed to glucose by glucoamylase. The saccharification can be carried out simultaneously (SSF) or separately from the fermentation. Microorganisms, mostly belonging to the Lactobacillus genus, are used to convert glucose into LA [81,82]. Calcium hydroxide is added to neutralize LA in the fermentation broth. In the next step, cells are removed by filtration and the fermentation broth is concentrated by evaporation. The calcium lactate produced at the end of the fermentation is converted into LA and insoluble gypsum by acidification using H2SO4. Methanol is used to esterify the LA into methyl lactate, which is recovered by distillation and hydrolyzed [83]. An advantage of bio-based LA production compared with the petrochemical route is the production of pure isomers of LA, L-, or D-lactic acid [75]. Although L- and D-LA enantiomers have the same physical and chemical properties in their pure form [80], the proportion of each enantiomer confers different physical properties to the final product [83]. L-LA is the enantiomer preferred to produce PLA. Normally, the commercial PLA is produced with 98%–99% of L-LA and less than 1%–2% of D-LA [80]. L-LA is also preferred by the pharmaceutical and food industries, because D-lactic acid in high doses can be harmful to human health and can cause acidosis or decalcification [83]. D-LA is required to produce polylactic blends with a high melting point [84]. The carbohydrate and nutritional requirements of the microorganism represent a considerable part of the cost of bio-based LA production. As mentioned in Section 1, lignocellulosic biomass has been considered a promising polysaccharide source for bio-based LA production. An important advantage of these materials is that they do not compete with the food supply [83]. A wide variety of lignocellulosic biomasses can be converted to monosaccharides and used in fermentation to produce PA (Table 2), such as pulp mill residue [92], corn-stover [85,86,90,95,98], beechwood [87], orange peel waste [88], pine [87], sugarcane bagasse [89,95], soybean hull [91], corncob molasses [93], wheat straw slurry [97], coffee pulp [99], corncob (XOS) [100], waste office paper [104], tobacco waste water-extract [102], and jerusalem artichoke tuber powder [103]. The cellulosic biosludge generated in Kraft pulp mill is the residue from the water treatment in the pulping and washing steps. This residue is mainly composed by cellulose (44%) and protein (22%). Cellulosic biosludge is an advantageous feedstock due to its high enzymatic digestibility associated with low lignin content and small particle size, high protein content, low cost, and the reduction of waste volume. The production of LA using cellulosic sludge hydrolysate without nutrient supplementation resulted in a final LA titer of 39.4 g L 1 with a volumetric productivity of 0.82 g L 1 h 1 and a product yield of 35.5 g of L-LA per 100 g of biosludge [104].

TABLE 2 Fermentative kinetic parameters of the conversion of lignocellulosic biomass into lactic acid (LA). Microorganism

Lignocellulosic biomass

Fermentation system

Final titer LA (g L21)

Yield (g g21)

Productivity (g L21 h21)

Reference

Lactobacillus rhamnosus CECT-288

Cellulosic biosludges from Kraft pulp mill

SSF

39.4

0.355

0.82

[84]

Lactobacillus brevis

Corn-stover

Batch

18.2

0.740

0.76

[85]

L. brevis ATCC 367

Corn-stover

Batch

16.71

0.540

0.45

[86]

L. rhamnosus

Corn-stover

Batch

17.70

0.590

0.49

[86]

L. brevis ATCC 367 + L. rhamnosus

Corn-stover

Batch

20.95

0.700

0.58

[86]

Lactobacillus delbrueckii subsp. bulgaricus ATCC 11842

Beechwood

SSF

62.0

0.690

0.86

[87]

L. delbrueckii ssp. bulgaricus CECT 5037

Orange peel waste

Batch

ND

0.840

0.55

[88]

L. delbrueckii ssp. bulgaricus ATCC11842

Pine

SSF

36.4

0.400

0.51

[87]

L. delbrueckii ssp. delbrueckii CECT 286

Orange peel waste

Batch

ND

0.860

0.63

[88]

Lactobacillus pentosus ATCC 8041

Sugarcane bagasse

Batch

42.4

0.710

1.02

[89]

L. pentosus FL0421

Corn-stover

Fed-batch SSF

92.3

0.66

1.92

[90]

Lactobacillus plantarum BL011

Soybean hull

Fed-batch

58.59

ND

1.22

[91]

Lactobacillus coryniformis subsp. torquens

Pulp mill residue

Batch

57.18

0.97

2.80

[92]

Bacillus sp. strain

Corncob molasses

Fed-batch

74.7

0.500

0.38

[93]

Bacillus coagulans

Corn stover

Batch

18.2

0.734

0.38

[94]

B. coagulans

Sugarcane bagasse

Fed-batch

185

0.990

1.93

[95]

B. coagulans

Corn stover

Fed-batch SFF

97.59

0.680

1.63

[96]

B. coagulans M-13

Wheat straw slurry

Batch

33.71

0.920

1.18

[97]

B. coagulans NBRC 12714

Corn stover

Membrane integrated continuous fermentation

92.00

0.910

13.8

[98]

B. coagulans

Coffee pulp

Fed-batch

ND

0.540

3.57

[99]

Rhizopus oryzae NLX-1-M

Corncob (XOS)

Batch SFF

60.3

0.60

1.00

[100]

R. oryzae NRRL 395

Waste office paper

Batch

49.1

0.59

ND

[101]

R. oryzae As 3.819

Tobacco waste waterextract

Fed-batch

173.5

0.860

1.45

[102]

Kluyveromyces marxianus (metabolic engineered)

Jerusalem artichoke tuber powder

Batch

130.0

0.98

2

[103]

SSF, simultaneously hydrolysis and fermentation; XOS, xylooligosacharides; ND, not determined.

3. Bio-based lactic acid: An important building block in biorefinery concept

319

Corn stover is a residue generated after grain harvesting [90] and can be used to produce biofuels and high value-added products [105]. The world corn production for 2020/2021 was approximately 1.125 billion metric tons. Approximately, 50 pounds of corn residue (cobs, leaves, stalks, and husks) are generated for each bushel of shelled corn [84]. This lignocellulosic biomass is composed of around 37.5% of cellulose, 22.4% of hemicellulose, and 17.6% of lignin [90]. As aforementioned, the deconstruction of this biomass results in the release of hexoses and pentoses [90]. The fermentation of corn stover hydrolysate using the Lactobacillus genus resulted in LA final titers that varied from 16 to 21 g L 1, with productivities that varied from 0.45 to 0.76 g L 1 h 1 and yields from 0.54 to 0.74 g g 1 [85,86]. The fermentations carried out in the fed-batch mode with the Lactobacillus genus have a higher LA final titer (92 g L 1), productivity (1.92 g L 1 h 1), and yield (0.66 g g 1) [90]. The fed-batch fermentation strategy decreases product inhibition and increases the cell concentration [84]. Corncobs have been used to produce xylo-oligosaccharides (XOS). This industry generates a large amount of waste, which can be fermented to produce biofuels and high value-added products. The residue from XOS production (XOS waste hydrolysate) is a lignocellulosic residue that has low cost, high enzymatic digestibility, low lignin concentration, and small particles. It has glucose as the main sugar, as xylan is removed in the production process. The fermentation of XOS waste hydrolysate with Rhizopus oryzae NLX-1-M had a LA final titer of 60 g L 1 with a productivity of 1 g L 1 h 1 and a yield of 0.6 g g 1 [100]. Corncobs can also be hydrolyzed to produce xylitol. The residue of this process is corncob molasses, a substrate with high mixed sugar content (approximately 60%), including xylose, glucose, and arabinose [93]. Annually, 50 million metric tons of oranges are consumed, which generates a great amount of waste. Approximately 45%–60% of the total fruit weight represents waste, a lignocellulosic biomass. This residue is used as an ingredient for cattle feed or as pelletized dry solid fuel, but these applications are associated with water pollution. Orange waste has low levels of lignin and a large amount of sugars, being a potential substrate for fermentation after the pretreatment and enzymatic hydrolysis stages. Lactobacillus delbrueckii ssp. delbrueckii CECT 286 produced D-LA with a productivity of 0.65 g L 1 h 1 and 99.5% of purity in batch fermentations with orange peel waste [88]. Sugarcane is a perennial grass mainly used for sugar production. A production of 1.89 billion tons of sugarcane per year is estimated, with a generation of 250–270 kg of bagasse per ton produced [84]. Sugarcane bagasse is composed of around 43.6% of cellulose, 33.8% of hemicellulose, and 18.1% of lignin [95]. Batch fermentations using sugarcane bagasse hydrolysate and Lactobacillus pentosus ATCC 8041 produced a LA final titer of 42.4 g L 1 with a productivity of 1.02 g L 1 h 1 and yield of 0.71 g g 1 [89]. The fermentation parameters were intensified using a fed-batch fermentation strategy. The fed-batch fermentation using Bacillus coagulans produced a LA final titer of 185 g L 1 with a productivity of 1.93 g L 1 h 1 and 0.99 g g 1 of yield [95]. Soybean is among the most cultivated crops in the world, with a global production of approximately 337 million tons. Soybean hull hydrolysate is a potential substrate to produce biofuels and BCBB due to its high sugar and low inhibitor concentrations. Fed-batch fermentation using this carbon source and Lactobacillus plantarum BL011 produced an LA final titer of 58.59 g L 1 with a productivity of 1.22 g L 1 h 1 [91].

320

12. Polysaccharide deconstruction products

Coffee pulp is a complex biomass that surrounds the coffee bean and is a waste of coffee production with an estimated annual production of 9.4 million tons. Coffee pulp is composed by (w/w): proteins (9%–11%), lipids (2%–17%), cellulose (13%–27%), tannins (4.5%), pectic matter (6.5%), reducing sugar (12.4%), and nonnitrogen extracts (57%–63%). In fed-batch fermentations performed with coffee pulp and Bacillus coagulans, this strain was able to produce LA with a productivity of 3.57 g L 1 h 1 and a yield of 0.54 g g 1 [99]. Jerusalem artichoke (JA) (Helianthus tuberosus) is a low-requirement crop, tolerant to biotic and abiotic stress. JA tuber has 20% (w/w) of carbohydrates, inulin being the main sugar. Inulin is a mixture of two linear fructan oligosaccharides, one with a terminal sucrose and the other with fructopyranose. Kluyveromyces marxianus modified by genetic engineering to convert inulin into LA was able to produce LA with a final titer of 130 g L 1, a productivity of 2 g L 1 h 1, and a yield of 0.98 g g 1 [91]. Bio-based production of LA can be carried out by homofermentative and heterofermentative lactic acid bacteria (LAB), Bacillus strains, filamentous fungi of the Rhizopus genus, and engineered strains (Corynebacterium glutamicum, Escherichia coli, Saccharomyces cerevisiae, Candida ssp., Kluyveromyces lactis, Kluyveromyces marxianus, Zygosaccharomyces, and Pichia stipitis) [106]. Lactic acid bacteria (LAB) are Gram-positive microorganisms [106] predominantly facultative anaerobic, catalase-negative, nonmotile, and nonspore-forming. Bacteria belonging to this group have an optimal growth temperature varying from 20°C to 75°C. LAB are acidtolerant strains and can develop at pH 5 and lower. LAB synthesizes LA in the glycolysis pathway under anaerobic condition using hexoses and/or pentoses as carbon source, the main sugars in lignocellulosic biomass. LAB are classified according to fermentative end products. Homofermentative LAB produces exclusively LA during the glucose metabolism and heterofermentative LAB converts glucose into ethanol, CO2, and LA. Heterofermentative LAB can be divided into obligatory and facultative strains [75]. Fig. 4 shows the metabolic pathways to produce LA using different carbon sources. Homofermentative LAB usually metabolizes hexose and pentoses sugars via the EmbdenMeyerhof. The glycolysis pathway and phosphate pathway are used to convert hexoses and pentoses, respectively. In these microorganisms, the glycolysis pathway produces two lactic acid molecules per glucose molecule with a theoretical yield higher than 0.9 g of LA per g of glucose. Homofermentative LAB includes Lactobacillus acidophilus, Lactobacillus amylophilus, Lactobacillus bulgaricus, Lactobacillus helveticus, Lactobacillus salivarius, Lactobacillus delbrueckii subsp. bulgaricus, Streptococcus salivarius subsp. thermophilus [75,106]. Heterofermentative LAB metabolizes glucose by the phosphogluconate pathway with a theoretical yield of 0.5 g g 1 and xylose by the phosphoketolase pathway with a theoretical yield of 0.6 g g 1. LA, acetic acid (AA), formate, ethanol, diacetyl, acetoin, and carbon dioxide are the end products of the glucose fermentation with these strains [106]. Facultative heterofermentative LAB can use both homofermentative or heterofermentative pathways [75]. Heterofermentative LAB include Lactobacillus alimentarius, Lactobacillus plantarum, Lactobacillus casei, Lactobacillus rhamnosus, Lactococcus lactis, Lactobacillus pentosus, and Lactobacillus xylosus [75]. Bacillus coagulans, Bacillus stearothermophilus, and Bacillus licheniformis have been studied as options to produce LA by fermentation. This genus has the advantages of simple nutritional requirements, nonsterilization fermentation, simple maintenance of stock cultures [93], in

3. Bio-based lactic acid: An important building block in biorefinery concept

321

FIG. 4 Metabolic pathways of LA production from biomass. 1. Exo-β1,4-glucanase; 2. Amylase, 3. β-Glucosidase, 4. Phosphoenolpyruvate carboxylase, 5. D-Lactic acid dehydrogenase, 6. Pyruvate dehydrogenase complex, 7. Pta, 8. Aldehyde dehydrogenase, 9. Acetate kinase, 10. Acetaldehyde dehydrogenase, 11. Alcohol dehydrogenase, 12. Ribulokinase, 13. Arabinose isomerase, 14. Ribulose 5-phosphate 3-epimerase, 15. Xylose reductase and xylitol dehydrogenase, 16. Lactate dehydrogenase.

addition to being able to secrete various hydrolytic enzymes, such as proteases, amylases, and cellulases [97]. Bacillus sp. metabolizes xylose through the pentose phosphate pathway, which maximizes the conversion of pentose into LA. Five moles of LA are produced from 3 mol of xylose in this pathway [94,107]. Bacillus genus converts hexoses efficiently into LA using the Embden-Meyerhof-Parnas pathway [108]. The Rhizopus genus can produce LA using a variety of renewable materials. These microorganisms, when compared with LAB, have advantages such as amylolytic characteristics, low nutrient requirements, consumption of complex carbohydrates and pentoses sugars, and production of high titer of pure L-lactic acid. This optical isomer is preferred for polylactide manufacture [82,106]. Ten strains belonging to the Rhizopus genus evaluated in fermentations with 30 g L 1 of xylose enabled between 56% and 82% of carbon recovery with a yield of 0.42 g of LA per g of xylose [109]. Metabolic engineering is used as a tool to obtain strains able to overcome some bottlenecks in LA production from lignocellulosic biomass, such as low yield, substrate specificity, optical purity, acid tolerance, etc. [84]. Corynebacterium glutamicum is an aerobic Gram-positive strain that can excrete amino acids (L-lysine and L-glutamate) and a small amount of the organic acids (LA, SA, and AA). This strain can consume D-glucose, L-arabinose, D-xylose, and D-cellobiose. Genetic modification that have been performed in C. glutamicum to improve the production of LA includes (a) simultaneously knocking out of the L-LHD gene and overexpression of the D-LHD encoding gene to produce D-LA with higher productivity

322

12. Polysaccharide deconstruction products

and purity; (b) insertion of the genes xylA (xylose isomerase) and xylB (xylulokinase) to improve xylose consumption; (c) expression of the genes araA (arabinose isomerase), araB (ribulokinase), and araD (ribulose-5-phosphate 4-epimerase) to improve arabinose consumption [106]. E. coli can consume hexoses and pentoses sugars of a mixture of organic acids (AA, SA, and formic acid) and ethanol. Genetic modification performed in E. coli to improve the production of LA includes (a) replacement of D-LDH by L-LDH from LAB, bovine, and other; (b) omission of methylglyoxal bypass route to synthesize pure isomers of LA; (c) blocking the aerobic LLHD to avoid the utilization of L-lactate [106]. S. cerevisiae is a yeast with high tolerance to low pH values that can grow aerobically on glucose and in anaerobic growth required oleic acid, nicotinic acid, and ergosterol. The production of LA can be performed by elimination of the coding section of pyruvate decarboxylase 1 (PDC1) and the insertion of the D-lactate dehydrogenase (D-LDH) [106]. A recombinant strain which is able to convert cellobiose and xylose into LA was obtained by the addition of the genes cdt-1, gh1-1, XYL1, XYL2, XYL3, and ldhA, with coding cellobiose transporter, β-glucosidase, xylose reductase, xylitol dehydrogenase, xylulokinase, and lactate dehydrogenase, respectively. In this strain, pyruvate decarboxylase (pcd) and alcohol dehydrogenase were deleted [84]. Candida boidinii and Candida utilis naturally produce LA and ethanol. C. boidinii was genetically modified to decrease the production of ethanol, with elimination of the PDC1 gene that encodes pyruvate carboxylase. C. utilis is a promising strain to produce LA because this strain requires inexpensive substrates for growing, such as pulping waste liquors. High amounts of L-LA were obtained by knocking out the gene encoding pyruvate decarboxylase (CuPDC1) and adding two copies of the bovine L-lactate dehydrogenase. Candida sonorensis consumes hexoses and pentoses, but these sugars are not converted into LA. A mutant strain which is able to produce LA was obtained by the expression of the LDH gene as well as elimination of the two pyruvate decarboxylase genes (PCD) 1 and 2 [106]. Kluyveromyces lactis, Kluyveromyces marxianus, Pichia stipitis, and Zygosaccharomyces are other yeasts used as hosts in metabolic engineering to improve the production of LA. K. lactis synthesizes ethanol and LA from carbohydrate sources. This strain has a single gene (KlPDC1) that encodes pyruvate carboxylase, while S. cerevisiae has two active structural genes (PDC1 and PDC5). The omission of KlPDC1 results in a strain without PDC activity and increases LA production without the production of ethanol. The insertion of the L-lactate dehydrogenase gene (LDH) in K. lactis, K. marxianus, and Zygosaccharomyces bailii has increased the production of LA. K. marxianus is an advantageous strain because it can grow at high temperature (52°C), which reduces bacterial contamination. Pichia stipitis metabolizes hexoses and pentoses from lignocellulosic biomass into ethanol. P. stipitis can produce LA by the deletion of alcohol dehydrogenase 1 (ADH1) and insertion of L-LDH [106].

4. Microbial propionic acid production Propionic acid (PA) (CH3CH2COOH) is an organic acid with three carbons authorized by the US Food and Drug Administration (FDA) as GRAS (generally regarded as safe) [110]. This

4. Microbial propionic acid production

323

saturated short-chain fatty acid consists of ethane bound to the carbon of a carboxyl group (Fig. 1) [111]. PA is a colorless, corrosive, and water-miscible liquid with a sharp and pungent odor [112]. In the industry, PA has been used as a preservative in animal feed and human food to inhibit the development of mold, as well as a chemical intermediate in the production of cellulose fiber, herbicides, perfumes, and pharmaceuticals. In the food industry, PA is mainly added as a salt (sodium propionate) in the production of bread, cakes, cheese, and animal feed [110,113]. Inhibition of the spoilage microorganisms by this additive occurs by the diffusion of the undissociated acid through the bacterial membrane into the cytoplasm. This diffusion occurs because of the hydrophobic nature of both the PA and the microorganism cell membrane. The PA dissociates into a proton and a propionate anion inside the cell, creating an inward “leak” of protons. In this way, extra adenosine triphosphate (ATP) is consumed by H+-ATPase to expel the proton and maintain the functional proton gradient across the membrane. Thus, the ATP available for cell metabolism decreases, resulting in cell death [114]. In the food industry, this organic acid is also used as a flavor enhancer in the form of citronellyl or geranyl propionate [110]. PA is also used in the chemical, pharmaceutical, and cosmetic industries. In the chemical industry, this organic acid is an intermediate in the production of cellulose-derived plastics (such as textiles, membranes for reverse osmosis, air filter), or as a component of lacquer and molding plastics. In the pharmaceutical industry, PA is used in animal therapy for the treatment of wound infections and in the production of conjunctivitis and antiarthritic drugs. In the cosmetic industry, PA is applied in the manufacture of the perfumes to improve the consistency and shelf life of products [110]. The global PA market size of was estimated at 470,000 tons in 2019 and can reach 550,000 tons in 2026 [115]. Currently, the commercial production of PA is carried out through the petrochemical route, in which three different processes can be used: Reppe, Larson, and FischerTropsch. The Reppe process synthesizes propionate by the conversion of ethylene, carbon monoxide, and steam. The Larson process converts ethanol and carbon monoxide to propionate using boron trifluoride as a catalyst. The Fischer-Tropsch process obtains propionate as a by-product of the oxidation of propionaldehyde [116]. PA can also be produced using LA and carbohydrates from lignocellulosic biomass by chemical reaction, using Zn2+ and Co2+ as reducing agent and catalyst, respectively [117]. The production of PA can also be carried by fermentation using bacteria, such as Propionibacterium ssp., Clostridium homopropionicum, Veillonella parvula, Pelobacter propionicus, Selenomonas ruminatium, Veillonella criceti, Clostridium neopropiocum, Megasphaera elsdenii, Prevotella ruminicola, or Roseuria inulinivorans [115]. Propionibacterium ssp. is the most promising genus to produce PA by fermentation. This genus uses the Wood-Werkman cycle (Fig. 5), the most energy-efficient metabolic pathway, and can use a wide variety of carbon sources (such as glycerol, glucose, lactate, and xylose) [110,113]. Propionibacterium ssp. are Gram-positive bacilli, nonmobile, catalase-positive, nonspore-forming, and facultative anaerobes. The Propionibacterium genus is classified into two groups: skin and classical. The skin group is composed of pathogenic strains isolated from the skin, oral, and gastrointestinal mucosa. The classical group is composed of species able to produce high value-added products, such as PA, B12 vitamin, bacteriocin, trehalose, and n-propanol [113], a BCBB. Propionibacterium

324

12. Polysaccharide deconstruction products

FIG. 5 Metabolic pathways of propionic acid production.

acidipropionici, Propionibacterium jensenii (subsp. shermanii, subsp. freudenreichii), and Propionibacterium thoenii are examples of bacteria belonging to the classical group. Propionibacterium ssp. converts glycerol, glucose, and lactic acid in PA with theoretical yields of 0.80, 0.548, and 0.577 g g 1, respectively [118,119]. The commercial bio-based production of PA using the genus Propionibacterium is still a challenge, due to the product inhibition that leads to low PA final titer (20 g L 1) and productivity (0.3 g L 1 h 1). In addition, the purification of PA has a high cost, because the genus Propionibacterium has a heterofermentative metabolism [110,120]. As mentioned, lignocellulosic biomasses can reduce bio-based production cost, and for this reason, they have been widely studied for bio-based PA production. P. acidipropionici can grow and convert both glucose (obtained from cellulose deconstruction) and xylose (released from the hemicellulose fraction) from lignocellulosic hydrolysates into PA (Table 3) [131]. Hemicellulose is a renewable carbon source widely used in biorefineries. The main sugars found in hemicellulose are glucose, xylose, and arabinose, with xylose being the predominant sugar. The comparison among the production of PA from pure xylose or from corn cob molasses (by-product of xylitol production) was investigated using P. acidipropionici ATCC 4875, and the results obtained for concentration (71.8 g L 1 vs 53.2 g L 1) and yield (0.28 g L 1 h 1 vs 0.23 g L 1 h 1) were better when using corn cob molasses [123], which shows that lignocellulosic hydrolysates have great potential for PA production. Other authors also investigated the production of PA using cheaper carbon and nitrogen sources. The use of crude glycerol, hydrolyzed cassava bagasse, and macerated corn liquor led to kinetics similar to the use of a solution 2:1 glycerol/glucose [119]. However, the yield

325

4. Microbial propionic acid production

TABLE 3 acid.

Fermentative kinetic parameters of the conversion of lignocellulosic hydrolysates into propionic

Microorganism

Lignocellulosic biomass

Fermentation system

Titer (g L21)

Yield (g g21)

Productivity (g L21 h21)

Reference

P. acidipropionici ATCC 4875

Populus tremuloides hydrolysate

Batch

18.3

0.75

0.28

[121]

Corn meal hydrolysate

FBB

ND

0.58

2.12

[122]

Corncob molasses

Fed-batch

71.8

ND

0.28

[123]

Jerusalem artichoke

FBB

68.9

0.43

1.55

[124]

Corn stover hydrolysate

Fed-batch with high cell concentration

64.7

ND

0.77

[125]

P. acidipropionici CGMCC 1.2230 (adapted)

Sugarcane bagasse hydrolysate

FBB

22.9

0.51

0.96

[126]

P. acidipropionici NRRL B 3569

Wheat flour hydrolysate

Batch

37.70

ND

0.33

[127]

P. acidipropionici DSM 4902

Sweet sorghum bagasse hydrolysate

Batch

22

0.45

0.168

[128]

P. acidipropionici CIP 53164

Sorghum bagasse hydrolysate

Sequential batch (cell immobilized in sorghum bagasse)

35.3

0.67

1.17

[129]

P. jensenii DSN 20274

Cocoa pod husk hydrolysate supplemented with glycerol

Batch

10.28

ND

0.08

[130]

ND, not determined; FBB, cell immobilized in fibrous-bed bioreactor.

and productivity results using these cheaper raw materials were superior to the use of pure raw materials (such as glycerol, glucose, yeast extract, and trypticase) [119]. These studies point the way to economic viability of the fermentation process and competition with other methods of production of PA. P. freudenreichii and P. jensenii are promising strains for PA production, but they do not consume xylose naturally, and this condition impairs the utilization of C5 sugars from hemicellulose fractionation. The xylose utilization pathway of P. acidipropionici is composed of xylose isomerase (xyLA, PACID_RS01700), major facilitator transporters (xyT, PACID_RS01695), and xylulokinase (xyLB, PACID_RS01690). P. freudenreichii and P. jensenii have been genetically modified to express the abovementioned genes for the production of PA from xylose, and these strains are more easily engineered than P. acidipropionici [131]. P. freudenreichii ssp. shermanii has only one gene required for xylose metabolism, xylulokinase (GENE ID: 9283358). The expression of xyLA,

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12. Polysaccharide deconstruction products

xyT and xyLB from P. acidipropionici ATCC 4875 in P. shermanii DSM 4902 resulted in a strain able to consume xylose and produce PA with 0.438 g g 1 of yield and productivity of 0.132 g L 1 h 1 [131]. The production of PA by fermentation is influenced by very important steps, such as the choices of strain, carbon source and system used in the fermentation, as well as the separation and purification process. The process of separation and purification of PA is quite complex due to some factors. The fermentation broth has a small concentration of PA in a large volume of water, and the volatility of these two compounds is very close, in addition to PA being highly hydrophilic. The optimal pH range in fermentation is 6.0–7.0, and in this range, PA is in the ionized form, and thus nonvolatile. Another point is that during the production of PA by fermentation, acetic acid (AA) and succinic acid (AS) are also produced. The pka value of these acids are very close—PA (4.87), AA (4.76) [132], and SA (4.21 and 5.64) [133]—a factor that makes the process of separating these acids difficult. Some improvement factors can contribute to making production economically attractive and competitive, such as the use of genetically or metabolically modified strains, the use of cheaper carbon sources, and the use of the medium produced without the separation of the three carboxylic acids (AA, SA, PA). A review of fermentation strategies to improve propionic acid production with Propionibacterium ssp., characterization and nutritional requirements of this genus, as well as the Wood-Werkman cycle can be found in the work of Collograi, Costa and Ienczak (2022).

5. Conclusions It is well founded that the bio-based production of building blocks such as SA, LA, and PA offers an interesting alternative to their production by the petrochemical route, mainly due to the lower GHG emissions. However, some challenges need to be overcome to increase BCBB production. In order to reduce production costs and achieve competitive prices, the use of low-cost substrates such as lignocellulosic biomasses is an alternative. The use of these raw materials can also include the production of these acids in the biorefinery concept and circular economy. For the challenges inherent to the fermentation process, higher productivity can be obtained in processes with immobilized cells, in continuous systems or in fed batch mode [134]. The inhibition of the microorganisms by the products (SA, LA, or PA) can be solved by using adapted or genetically improved strains, or by in situ extraction of the acids during fermentation. The inhibition of the microorganisms by inhibitors present in the lignocellulosic hydrolysates can also be overcome with evolutionary adaptation processes when it is not possible to obtain hydrolysates with low inhibitor content. Still in this sense, the use of co-cultures also presents an alternative for increasing process yields and specially thinking in processes that integrate biomass hydrolysis and fermentation process.

Acknowledgments This work was financed by Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel Superior—Brasil (CAPES)— Finance Code 1 (process number 88887.463971/2019-00, 88887.495360/2020-00 and 88887.619536/2021-00) and National Council for Scientific and Technological Development (process number 141130/2019-9).

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Conflicts of interest The authors declare no conflict of interest.

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C H A P T E R

13 Polysaccharide degradation for oligosaccharide production with nutraceutical potential for the food industry ´ vilaa, Patrı´cia Polettob, Manoela Martinsa, Patrı´cia F. A and Rosana Goldbecka a

Laboratory of Bioprocess and Metabolic Engineering, School of Food Engineering, State University of Campinas (UNICAMP), Campinas, SP, Brazil bDepartment of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Floriano´polis, SC, Brazil

1. Introduction Oligosaccharides (OS) are considered functional fibers or prebiotics containing mainly carbohydrates in their composition. These molecules stimulate the growth of beneficial bacteria in the gut microbiota showing a beneficial effect on body weight control, improving blood glucose and lipid levels [1], and reducing the symptoms of colon cancer [2]. In fact, prebiotics are substrates for probiotic microorganisms that produce important metabolites, recently called postbiotics, which are associated with most health-promoting effects [3]. Prebiotics are also cited among the “Top Ten Functional Food Trends,” and foods containing these compounds will continue to find a welcome market [4]. As life expectancy rises, population aging demands health-promoting factors to enhance wellness and prevent diseases through lifestyle changes, practice of physical exercises, and adoption of a healthy diet. This philosophy, powered by research efforts to identify properties and potential applications of natural substances, accrues from public consciousness in healthrelated information and the growing demand for products containing fewer chemicals and

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synthetic additives in the food and pharmaceutical market. Prevention is the most desired feature, a health guarantee path from nutraceuticals and functional foods consumption, with minimal side effects that, if consumed daily, increase the health promotion [5,6]. The neologism “Nutraceutical” (a contraction of nutrition and pharmaceutical) is simply a scientific definition to describe any product derived from natural sources that benefits health. In contrast to pharmaceutical products, nutraceutical substances usually do not have patent protection. It does not mean that nutraceuticals can be compared to medicines. Instead, it generates a new field of research on the mechanisms of action and benefits that can be derived from consuming evidence-based dietary patterns, discerning the contribution of specific compounds. Proof-of-concept studies, based on clinical trials and population-based prospective, establish safe and efficacious quantities of the bioactive compound without unbalancing the overall macronutrient and calorie intake. Regarding the potential applications, genetic engineering and biotechnology often present novel technologies for feasible large-scale production of any new bioactive compound, which can come even from agro-industrial residues such as peels, husks, and seeds [5–8]. According to the Grand View Research market report, the nutraceutical global market size was valued at USD 412.7 billion in 2020. A Compound Annual Growth Rate (CAGR) of 8.3% was expected until 2027. Increasing application for the treatment of cardiovascular disorders, obesity, diabetes, malnutrition, and the rising healthcare costs and aging stimulates the growth of this market. The categories of the nutraceutical market are dietary supplements, functional beverages, and functional foods. The beverages segment led the overall commerce for nutraceuticals in 2019, followed by functional foods and supplements [9,10]. Nutraceuticals can be classified based on their natural source (plant, animal, and microbial), chemical structure (amino acid-based, fatty acids, carbohydrates derivatives, minerals, phenolic compounds, and isoprenoids), or mechanism of action (antioxidant, antibacterial, antihypertensive, antihypercholesterolemic, antiinflammatory, anticarcinogenic, osteoprotective, and so on). The food sources used as nutraceutical can be categorized as dietary fiber, probiotic, prebiotic, fatty acids, antioxidants, and polyphenols (Fig. 1) [5,8]. Some sources have clear reasons to be established as a nutraceutical, such as vitamins or minerals. However, some nonobvious sources have gained outstanding popularity for their benefits. Dietary carbohydrates showed to be more than just an energy basis for calorie intake balance. They also consist of edible plants remains, polysaccharides, lignin, and associated substances resistant to digestion by endogenous human enzymes. Carbohydrate fibers include cellulose, hemicelluloses, lignin, gums, mucilage, oligosaccharides, pectin, and other associated minor substances. More than that, several insoluble and nonfermentable fibers can by hydrolyzed by enzymatic or physicochemical process to become soluble, fermentable, and functional. Oligosaccharides (OS), especially the nondigestible, obtained from bacteria, algae, fungi, and higher plants, have been used as dietary fiber, sweetener, and humectant. Notably, these functional OS have also been effective in gastrointestinal normal flora proliferation and pathogen suppression, enhancement of immunity, mineral absorption facilitation, antioxidant, and antibiotic activities [11,12]. In addition to providing modifications to food and applications of flavor and physicochemical characteristics, many of these sugars possess beneficial properties for the health of the consumer. Thus, this chapter discusses the types of food-grade OS studied on a laboratory scale or already commercially available, their properties, applications, recent developments, and manufacturing trends.

2. Functional oligosaccharides

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FIG. 1 Categories and examples of nutraceutical compounds.

2. Functional oligosaccharides 2.1 Properties and food industrial application Functional oligosaccharides (OS) are nondigestible carbohydrates containing up to 10 monosaccharides joined by glycosidic bonds. These oligosaccharides are found in small quantities in milk, honey, and structural plant fibers, such as garlic, onions, asparagus, bananas, and wheat bran. Their large-scale production is via hydrolysis of polysaccharides, such as xylan, chitosan, pectin, and inulin, and transglycosylation mechanisms when sucrose and lactose are the substrates. Fructooligosaccharides (FOS) and galactooligosaccharides (GOS) are currently the most utilized functional OS in the industry. However, agro-residuederived OS gained attention due to their relatively low production costs [13–15]. As nutraceuticals, oligosaccharides are used in beverages (fruit drinks, coffee, cocoa, tea, soda, soya milk, soft drinks, health drinks, and alcoholic beverages) and milk products (fermented milk, instant powders, powdered milk, yoghurt, and ice cream). Also, the functional oligosaccharides are amended in desserts (puddings and sherbets), table spreads (jellies, jams, marmalades, honey products), confectionary (candy, cookies, biscuits, chocolate, sweets), bakery products (breads and pastries), breakfast cereals, meat products (fish paste and tofu), and food for infants, elders, and diabetics [16]. Their trade began in the 1980s as low-calorie bulking agents. Currently, they arouse a lot of interest in the food and pharmaceutical sector due to the consumer preference for healthier food [11]. Japan first introduced the concept of functional food, where several oligosaccharides received the classification of foods for specific health use (FOSHU) [12].

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The interest in functional OS study is growing in the food and pharmaceutical industries mainly due to their activity as prebiotics. Their prebiotic definition comes from the selective growth stimulation of microbiota, impairing the growth of pathogenic organisms. Besides, they enhance minerals absorption, improve the ratio of HDL/LDL, decrease serum lipids and blood cholesterol, improve blood pressure, decrease fecal pH, remove toxins from the body, and decrease the glycemic index by slowing down the carbohydrates absorption rate. Their metabolization produces short-chain fatty acids related to beneficial effects on the colon against several diseases such as cardiovascular disease, cancer, obesity, and type-2 diabetes [17–21]. A recent study reported the antimicrobial, antiadhesive, and antiinflammatory properties of functional oligosaccharides such as alginate oligosaccharides (AOS) and chitosan oligosaccharides (CHOS). These oligosaccharides might decrease antibiotic concentration against pathogenic bacteria [7]. On the other hand, several studies have published the association of increased consumption of OS with the resurgence of symptoms of irritable bowel syndrome [22–24]. Hence, quality control of foods containing OS is necessary for ensuring food safety and quality [25]. Functional oligosaccharides have about 0.3–0.6 times the sweetness of sucrose and a low caloric density. The β-glycosidic bonds are generally stronger than α-linkages, and hexoses stronger linked than pentoses [12]. Technological properties of OS, such as gel-forming ability and water-holding characteristic, can improve the sensory and physicochemical characteristics of food products, leading to the increased OS application in the food industry [26–28]. They also present good stability over a broad range of temperature and pH, proving their suitability for food incorporation [12].

2.2 Polysaccharides sources and production process Plants and algae are the richest sources of functional oligosaccharides [12,21] (Table 1), which can be obtained via extraction from a natural source. However, they generally are produced through the enzymatic hydrolysis of polysaccharides and other substrates at the industrial level [25]. Commercial FOS are either obtained from enzymatic hydrolysis of oligofructose (DP 10–12) from chicory roots [29], yacon root (Smallanthus sonchifolius) [30], and Jerusalem artichoke [31], or enzymatic transfructosylation of sucrose by fructosyltransferase enzymes [19,32]. Similarly, commercial GOS are produced from transglycosylation reactions by β-galactosidase enzymes using lactose as substrate [33,34]. However, these processes account for high production costs due to the high concentration requirement and procurement costs of starting materials. As a result, recent methodologies have sought low-cost alternatives to produce functional oligosaccharides, such as agricultural wastes [11,25]. Lignocellulose sources are considered attractive raw materials to produce nonstarch oligosaccharides. Three main classes of polymers constitute the lignocellulosic biomass, cellulose, hemicellulose, and lignin, which can be cleaved by enzymatic hydrolysis or autocatalysis, releasing a wide range of compounds with different properties, such as glucose, xylose, furfural, xylo-oligosaccharides (XOS), and cello-oligosaccharides (COS) [35]. The lignocellulose hydrolysate composition depends on the type of raw material, pretreatment, and hydrolysis conditions. Depolymerization of suitable raw materials or partial enzymatic hydrolysis of

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TABLE 1 Oligosaccharides obtained from natural sources by enzymatic processes based on work of Patel and Goyal [4] and Ibrahim [3].

Oligosaccharide and molecular structure

Polysaccharide source and production mechanism

Isomalto-oligosaccharides • Starch/maltose: Wheat, barley, potato, rice, cassava, honey • Hydrolysis by α-amylase, pullanase and β-amylase and transglycosylation by α-D-glucosidase

Pectin-oligosaccharides • Pectin: Apple pulp, sugar beet, onion skin, potato pulp, soy hull, olive pomace, citrus waste • Hydrolysis by polygalacturonase, pectin lyase and pectin esterase Chitosan-oligosaccharides • Chitin or chitosan: Crab and shrimp shell wastes • Hydrolysis by chitosanase Cello-oligosaccharides • Cellulose: Sugarcane straw, birch, spruce, wheat straw, maize bran, corncob • Hydrolysis by cellulases: (cellobiohydrolase, endoglucanases); lytic polysaccharide • Monooxygenases Continued

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TABLE 1 Oligosaccharides obtained from natural sources by enzymatic processes based on work of Patel and Goyal [4] and Ibrahim [3]—cont’d

Oligosaccharide and molecular structure

Polysaccharide source and production mechanism

Cyclodextrins oligosaccharides • Starch: Transformation of starch by certain bacteria such as Bacillus macerans • Transglycosylation: Cyclodextrin glucanotransferase and partly α-amylases

Xylo-oligosaccharides • Xylan: Hardwood, corncob, wheat straw, rice hull, barley straw, sugarcane biomass • Hydrolysis by 1,4-βendoxylanases and arabinofuranosidases Fructo-oligosaccharides • Sucrose/inulin: Artichoke, garlic, onion, asparagus, chicory • Hydrolysis by fructosyltransferase/βfructo-furanosidase

2. Functional oligosaccharides

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TABLE 1 Oligosaccharides obtained from natural sources by enzymatic processes based on work of Patel and Goyal [4] and Ibrahim [3]—cont’d

Oligosaccharide and molecular structure

Polysaccharide source and production mechanism

Galacto-oligosaccharides • Lactose: Milk • Galactosylation by β-galactosidase Algal-oligosaccharides • Agarose: Polysaccharide extracts of agarose • Hydrolysis by β-agarases, α-agarases

purified pectin produces pectic oligosaccharides (POS) [36]. Pectin depolymerization by acid treatment is scarce due to the autohydrolysis and enzymatic hydrolysis advantages. Depending on the nature and concentration of the acid used, a variety of POS can be obtained, as pectin is composed of neutral and acidic sugars linked in different ways [37]. Generally, enzymatic processes are preferred due to mild operational conditions and low release of monosaccharides and inhibitory compounds (e.g., furfural and hydroxymethylfurfural). Moreover, it is eco-friendly and facilitates the downstream process. The enzymatic reaction stops by changes in pH or an increase in temperature, and the downstream process feasibility is dependent on the final product. Several microorganisms can produce specific enzymes to fit industrial needs, but usually, recombinant enzymes are the most applied due to safety and productivity. Genetically modified GRAS microorganisms, such as Saccharomyces cerevisiae, Pichia pastoris, and Aspergillus niger, can express enzymes with enhanced thermotolerance and catalytic activity [38]. The large-scale purification process of oligosaccharides is usually by ion-exchange chromatographic, nanofiltration, precipitation with ethanol, or activated charcoal. The cationexchange resins are the most used due to the highest affinity for monosaccharides; thus oligosaccharides are the first to elute from the column, and activated charcoal presents a higher affinity for oligo than mono- and disaccharides, increasing the advantage at the industrial level with low substrate losses [39,40]. Given the growing interest in these functional oligosaccharides, researchers have developed enzymatic processes that contribute to reducing production costs (up and downstream), facilitating purification and increasing yields, replacing the costly natural extraction in plant tissue [41]. This chapter presents details from each functional oligosaccharide class obtained from polysaccharides, classified in (1) sucrose-related oligosaccharides; (2) lactose-related oligosaccharides; (3) starch-related oligosaccharides; (4) nonstarch oligosaccharides; (5) algae-oligosaccharides.

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3. Sucrose-related oligosaccharides 3.1 Fructooligosaccharides FOS are water-soluble, thermostable, stable in pH from 4.0 to 7.0, and 0.3 to 0.6 times the sweetness of sucrose, depending on the ratio of the three chemical structures that constitute FOS, which are 1-kestose, nystose, and 1-β-fructofuranosyl nystose (Table 1). These three FOS chemical structures have higher molecular weight than sucrose, thus higher viscosity at the same concentration as sucrose. Additionally, FOS are recognized as a low-calorie sweetener ranging from 1.5 to 2.0 kcal/g, about half the caloric values of sucrose [42]. The configuration of the glycosidic bond resists hydrolysis by saliva and intestinal enzymes reducing the consumer’s blood sugar, making them useful for dietetic formulations [43]. These characteristics are acceptable for applications in foods, beverages, pharmaceutical drugs, and cosmetics products as bulk agents and humectants, in addition to sweetener or prebiotic fiber for constipation and to enhance the growth of probiotic microorganisms. Besides, FOS demonstrated to improve mineral absorption, decrease the levels of cholesterol and triglycerides in blood [44], and treat the symptoms of irritable bowel syndrome [45] and functional abdominal pain [46]. The daily intake of FOS that is acceptable to avoid side effects is up to 20 g/day for adults and not more than 4.2 g/day for children. Exceeding these limits may cause bloating, stomach cramps, and diarrhea [47]. In addition, excess FOS intake could negatively affect patients suffering from irritable bowel syndrome and small intestinal bacterial overgrowth syndrome, and might also lead to other chronic health conditions [23–25]. FOS emerged in the 1980s, and since then, its demand continues rising. In 2015, the global market reached $ 1.2 billion, with an annual growth rate of 10.4%. FOS market may reach $ 3.9 billion by the year 2027. Major key players in global FOS manufacturing and marketing include Cargill Incorporation (United States), Ingredion Incorporated (United States), BeneoOrafti SA (Belgium), Cosucra-Groupe Warcoing SA (Belgium), and Beghin Meiji (Japan) [48]. The FOS structure is fructose units linked by β-(2 ! 1) glycosidic bonds with terminal glucose, joined to fructose by α-(1 ! 2) glycosidic bonds. These oligosaccharides present higher concentrations in chicory roots and Jerusalem artichoke and lower concentrations in grains like wheat and barley. For commercial purposes, their manufacturing utilizes disaccharide sucrose or the polysaccharide inulin as substrates [48]. 3.1.1 Sucrose as substrate The enzymes that convert sucrose into FOS, on a large scale, are fructosyl-transferase and β-fructofuranosidase. The reaction also releases free glucose units, which are enzyme inhibitors, decreasing the bioconversion efficiency and lowering the yield of FOS. There are two approaches to circumvent this limitation: (1) the incorporation of the enzyme glucose oxidase (EC 1.1.3.4), which converts β-D-glucose into D-glucono-1,5-lactone and then hydrolyzes it into gluconic acid. Glucose oxidase requires flavin adenine dinucleotide (FAD), a common cofactor for biological oxidation-reduction; (2) cross-flow ultrafiltration membrane to remove the glucose units [48,49]. Fructosyl-transferase (EC 2.4.1.9) occurs in higher plants, such as asparagus, chicory roots, and onions, and also in microorganisms such as Aspergillus niger and Saccharomyces

3. Sucrose-related oligosaccharides

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FIG. 2 Fructo-oligosaccharides synthesis.

cerevisiae. This hydrolase catalyzes the transfer of fructosyl group (fructose) from the disaccharide sucrose as a donor to the second disaccharide or a short-chain FOS as an acceptor (Fig. 2). The fructose released from the sucrose plays a dual role as donor and receptor. The reaction equilibrium shifts from the hydrolysis of sucrose toward the synthesis of fructooligosaccharides (FOS). β-Fructofuranosidase (EC 3.2.1.26) found in similar plants and microorganisms as fructosyl-transferase is an invertase that hydrolyzes sucrose into glucose and fructose [50]. In short, sucrose is hydrolyzed into glucose and fructose by β-fructofuranosidase. Glucose is released, and fructose is bound to the enzyme fructosyl-transferase. The fructose bound to this enzyme is released and becomes a donor to another sucrose molecule to form 1-kestose, a shorter chain of FOS; the continuous enzymatic reaction produces longer chains of FOS molecules. 3.1.2 Inulin as substrate High yields of FOS from inulin as substrate derive from the use of the microbial enzyme endo-inulinase (β-fructan-fructanohydrolase, EC 3.2.1.7). This enzyme randomly hydrolyzes long chains of fructose polymers (inulin) into short chains of FOS by cleaving β-(2 ! l) glycosidic bonds yielding a mixture of FOS of 2–9 fructose units joint with α-(1 ! 2) glycosidic bonds, and the terminal fructose is linked to a glucose unit by β-(1 ! 2) glycosidic bond [48]. Higher plants, such as chicory roots [29] and industrial wastes, such as Stevia rebaudiana stems [51], are sources rich in inulin, extracted by hot water and precipitated with ethanol or

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ion exchange methodologies. The refined inulin is concentrated by evaporation and dried using a spray dryer or lyophilization to produce a powder form acceptable as a substrate for FOS production [48,51] using endonucleases in batch or immobilized process [29].

4. Lactose-related oligosaccharides 4.1 Galactooligosaccharides, lactulose, and lactosucrose The general mechanism for lactose enzymatic hydrolysis involves sequential reactions with other disaccharides and higher saccharides, named galactooligosaccharides (GOS), as intermediate products. GOS are a mixture of lactose derivatives containing two to eight saccharide units, with one of these units being terminal glucose and the remaining units being galactose (Table 1) [39]. GOS are currently produced in a reaction of lactose transgalactosylation catalyzed by β-galactosidases (E.C. 3.2.1.23) from different microbial strains [40]. Among all types of functional oligosaccharides, it is the only nonsynthetic oligosaccharide derived from animal milk [52]. The use of GOS emerged in Japan in the 1970s as a substitute for human milk-oligosaccharides for infant formulas to promote a healthy gut microflora of not breast-fed newborns [40]. In 2013, GOS with different degrees of purity and formats (concentrated syrup or solid powder) were supplied by four major companies: Friesland Foods Domo, Yakult Honsha, Nisin Sugar Manufacturing Co, and Ingredion. In 2020, the global GOS market reached 175.7 kilotons worth USD 1.01 billion, driven by the increasing attention on the unique properties of GOS among the prebiotics on food nutrition. The most recent studies demonstrated the potential of GOS to improve colonic healing and surgical recovery [53], inhibit the attachment of pathogenic bacteria to the colonic epithelium, reduce serum cholesterol and blood pressure, prevent colon cancer, and enhance immunity [52,54]. As food ingredients/sweeteners, GOS present a low-glycemic index, low-calorie (1–2 kcal/g), and resistance to acidic pH values and high temperatures (pasteurization and baking) [55], besides the well-known prebiotic effect, preserving probiotic strains during freeze-drying processes [52]. Most of the GOS prophylactic health effects arise from their selective consumption by Bifidobacteria and Lactobacilli. Supplemented infant formulas usually contain 6.0–7.2 g/L GOS mixed with 0.6–0.8 g/L FOS. To avoid side effects, like diarrhea, the daily dose should not exceed 0.3–0.4 g/kg body weight, or 20 g per person [39]. GOS production involves the kinetically controlled two-step mechanism: first, a molecule of lactose binds to the β-galactosidase active site to form the galactosyl-enzyme complex while liberating one glucose; second, the transition intermediate reacts with another lactose molecule forming a gal-gal-glu trisaccharide (GOS-3), which in turn acts as an acceptor of the galactosylenzyme complex to generate a gal-gal-gal-glu tetrasaccharide (GOS-4), and so on, to produce longer chain GOS (Fig. 3). In this way, lactose acts both as donor and acceptor of the galactose moiety. Water is the nucleophile and can also act as an acceptor of the galactosyl-enzyme complex, releasing galactose, resulting in the hydrolysis of lactose into its monosaccharide components glucose and galactose [40]. β-(1 ! 2), β-(1 ! 3), β-(1 ! 4), β-(1 ! 6) GOS vary in oligosaccharide content depending on β-galactosidase specificities for building glycosidic linkages [56]. Other hydroxyl-containing compounds, like alcohols, can act as acceptors of the galactosyl group as well, producing lactulose, lactosucrose, and lactitol (Fig. 4) [40,57].

4. Lactose-related oligosaccharides

FIG. 3 Galacto-oligosaccharides structure and synthesis.

FIG. 4 Molecular structure of lactulose, lactosucrose, and lactitol.

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The synthetic disaccharide lactulose is a well-established prebiotic produced by transgalactosylation of fructose with lactose, and it has been demonstrated that the reaction can be driven to the synthesis of GOS or lactulose by controlling the proportions of lactose and fructose in the medium [57,58]; the mixture of GOS and lactulose is often interesting for prebiotic supplements [40]. Lactosucrose is a potential prebiotic that can also be produced with GOS by transgalactosylation of sucrose but is mostly produced via lactose transfructosylation with β-fructofuranosidase. Lactitol is a polyhydroxyalcohol with prebiotic potential obtained by catalytic hydrogenation of the glucose moiety in lactose [40]. At the onset of reaction, the high concentration of lactose favors transgalactosylation, making GOS reach maximum concentration when the hydrolysis and transgalactosylation rates become equal, and then the reaction must stop; if not, hydrolysis will prevail and reduce GOS concentration [40]. Generally, the synthesis yield of GOS from lactose can increase using a high starting concentration of lactose, decreasing water thermodynamic activity (e.g., micro-aqueous environment), removing the final products or inhibitors from the medium, and modifying the enzyme [39]. Although β-galactosidase can originate from various microorganisms, Aspergillus oryzae is the most promising source for commercial application in terms of food safety and expenditure [59], fundamentally because of its ability to catalyze the transgalactosylation and its affinity for the GOS production [39].

5. Starch-related oligosaccharides 5.1 Malto-oligosaccharides Malto-oligosaccharides (MOS) consist of 3–10 α-D-glucose units linked by α-1,4-glycosidic linkages and are mainly applied in food products, as syrup or powder to improve hygroscopicity, viscosity, permeability, stability, and gelling. These characteristics make them suitable for sugar and fat substitution, artificial preservatives, and encapsulation material. Regarding their health effects, MOS showed to fight fatigue, improve intestinal function, regulate blood sugar and cholesterol levels, and prevent constipation. The large-scale production of MOS comes from starch hydrolysis by the action of amylases (EC 3.2.1.1). A second enzyme, pullulanase (EC 3.2.1.9), added to break down α-(1 ! 6) glycosidic bonds in the branched amylopectin, improves production yield [11,60].

5.2 Trehalose Trehalose is a nonreducing disaccharide composed of two glucose residues linked by α-(1 ! 1) glycosidic bond. It is naturally present in mushrooms and commercially produced from yeast fermented dough for baked goods and also from MOS enzymatic hydrolysis. Two mutases are involved in this process: MOS synthase (EC 5.4.99.15) that shift the α-(1 ! 4) glycosidic bond from reducing end of the two glucose units into α-(1 ! 1) glycosidic bond; maltooligosyltrehalose trehalohydrolase (EC 3.2.1.141) that cleave the α (1 ! 4) glycosidic bond next to the α-(1 ! 1) glycosidic bond in the terminal disaccharide of malto-oligosyltrehalose to produce the end product of trehalose and a shorter chain maltooligosaccharide [61].

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As opposed to the other oligosaccharides, trehalose can be digested by the intestinal enzyme trehalase (EC 3.2.1.28) into two glucose molecules, providing 4 kcal/g, similar to calorie generated from sucrose. It has 45% sweetness of sucrose and heat stability, which helps to cryo-preserve cell structure, biological materials, and foods after heating or freezing [11]. Trehalose triggers a small increase of blood insulin levels, which may improve glucose tolerance and reduce the progress of insulin resistance. Some researchers suggested that trehalose can potentially reduce the development of metabolic syndrome and reduce other associated lifestyle-related diseases such as type 2 diabetes [62].

5.3 Isomalto-oligosaccharides Isomalto-oligosaccharides (ISMO) are composed of three to six glucose molecules linked by α-(1 ! 6) glycosidic bonds (Table 1). They are present naturally at low concentrations in honey, and some fermented foods such as in soy sauce are manufactured on large scale by enzymatic hydrolysis of starch. This process involves first the enzymes amylase (EC 3.2.1.1 and 3.2.1.2), endodextranase (E.C. 3.2.1.11), α-glucosidases (E.C. 3.2.1.20), pullulanase (E.C. 3.2.1.41), and maltogenic α-amylase (E.C. 3.2.1.133) for starch liquefaction and saccharification, followed by transglucosidase (EC 3.2.1.20) to convert the glucose glycosidic bonds α-(1 ! 4) into indigestible glycosidic bonds of α-(1 ! 6) in isomalto-oligosaccharides. This process generates some impurities of glucose, maltose, and maltotriose that can be removed by yeast fermentation. The final products are isomaltose, panose, isomaltotriose, and other higher oligosaccharides. Simultaneous saccharification, engineered recombinant enzymes, fusion of enzymes, and enzyme immobilization are new technologies developed to enhance ISMO yields [11,63]. Isomalto-oligosaccharides are low caloric and 60% as sweet as sucrose, applied in food as bulking agent, dietary fibers, and to mask unpleasant taste of artificial sweeteners such as saccharine, aspartame, stevia, sucralose, and acesulfame-k. ISMO are partially digested by isomaltase in the jejunum and residual oligosaccharides are fermented by gut microbes. Hence, they are considered slowly digestible prebiotic oligosaccharides [64]. ISMO have high stability, high-moisture-retaining properties, lower water activity, and are not digested by yeasts. Their health benefits include prebiotic effect, improving colonic microbial profile, intestinal function and blood cholesterol, and help mineral absorption. Worldwide regulatory agencies approve ISMO as safe (GRAS) with maximum daily intake of 30 g/day [63,65].

5.4 Cyclodextrins Cyclodextrins (CDs) are cyclic oligosaccharides consisting of glucose units creating a cone shape of α-cyclodextrin containing six glucose units, β-cyclodextrin containing seven glucose units, and γ-cyclodextrin containing eight glucose units in a ring-forming shape of glucopyranose molecules (Table 1). They are a result of intramolecular transglycosylation reaction from the degradation of starch by cyclodextrin glucanotransferase (CGTase) enzyme (EC 2.4.1.19) [11]. Unlike γ-cyclodextrin, both α- and β-cyclodextrin cannot be hydrolyzed by human saliva and pancreatic amylases [11,66]. They are described as nondigestible prebiotic carbohydrates,

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which support the beneficial intestinal microflora in the colon. Overall, the metabolic fate of α-cyclodextrin ingested is similar to that of other nondigestible but fermentable carbohydrates such as resistant starch or inulin. The carbohydrate which is slowly but still fully digested in the small intestine could have important applications in the dietary management of diabetes (avoidance of night-time hypoglycemia), sports nutrition (sustained release of glucose for endurance events), and management of glycogen storage disorders (reduction in fasting hypoglycemia) [67]. In addition, they are reported as useful for controlling the body weight and blood lipid profile [67,68]. Cyclodextrins, α, β, and γ, are generally recognized as safe by FDA in the United States and approved by other worldwide organizations for the application in both food and pharmaceutical formulations. In the pharmaceutical industry, they are used as complexing agents to increase the aqueous solubility of poorly water-soluble drugs, increase both drugs bioavailability and stability, reduce or prevent gastrointestinal irritation, reduce or eliminate unpleasant smells or tastes, prevent drugs interactions, and convert oils and liquid drugs into microcrystalline or amorphous powders [11,69]. In the food industry, CDs are used for stabilization of flavors, flavor delivery, elimination of undesired tastes and microbiological contaminations, and developing browning reactions in foods [11]. In Japan, CDs have been approved as “modified starch” for food applications for more than two decades, serving to mask odors in fresh food and stabilize fish oils. One or two European countries, for example, Hungary, have approved γ-cyclodextrin for use in particular applications due to its low toxicity [67]. The complexation of CDs with sweetening agents such as aspartame stabilizes and improves the taste, and also eliminates the bitter aftertaste of other sweeteners such as stevioside, glycyrrhizin, and rubusoside. Enhancement of flavor has also been claimed for alcoholic beverages such as whisky and beer [68]. The bitterness of citrus fruit juices is a dominant problem in the industry caused by limonoids (essentially limonin) and flavonoids (mainly naringin). Cross-linked cyclodextrin polymers are useful to remove these bitter components by inclusion complexes [69]. In the cosmetic industry, cyclodextrins control the release of fragrances in products such as detergents, perfumes, and room fresheners [68]. In the agriculture industry, cyclodextrins are used in manufacturing complex formulas of pesticides, herbicides, and insect repellents for safe and environmental-friendly products. In general, the three types of CDs have a wide range of applications, and β-cyclodextrin seems to be the more used especially in the pharmaceutical industry due to its efficacy, and its lower production cost [11,70].

6. Nonstarch oligosaccharides 6.1 Xylo-oligosaccharides The amorphous polymer xylan is the most abundant component in hemicellulose, composed of a backbone of xylose units linked via β-(1 ! 4) glycosidic bonds. The depolymerization of xylan by xylanolytic enzymes (mainly endoxylanases) releases short-chain xylooligosaccharides (XOS) (Table 1). The structure of XOS can vary greatly in terms of side groups (acetyl, arabinofuranosyl residues, 4-O-methyl, and uronic acids) and degree of polymerization (2–10), as well as the conditions for their production (pretreatment of lignocellulosic biomass and enzymatic cocktails) [15,71].

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XOS present higher bifidogenic activity compared to other prebiotics, and many studies reported their potential to inhibit carcinogenesis [72], antiinflammatory, and antioxidant properties [73]. The antioxidant activity of XOS is associated with the presence of acetyl and uronyl groups and also ester-linked hydroxycinnamic acid derivatives as caffeic, ferulic, syringic, and coumaric acid residues and methyl glucuronic acid from xylan side chains [74,75]. As a food ingredient, XOS are stable over a wide range of temperatures (up to 100°C) and pH (from 2.5 to 8), and have acceptable odor, flavor, and relative sweetness. They are commonly used as a gelling agent to modify viscosity and stabilize foam [25]. Although a disaccharide is not an oligosaccharide, the disaccharide xylobiose is considered an XOS for food purpose [76]. Xylobiose has 30% of the sweetness of sucrose and low energycontent (1.5 kcal/g). The possibility of having a sweetener with antioxidant and prebiotic activity and low calorie implies several advantages for food enrichment. The recommended daily dose is 8–12 g/day for healthy adults [15,77]. Recent research applied XOS as a fat replacer in dairy products, improving elasticity and firmness of low-fat cream cheese [78], and increasing their storage stability in dulce de leche [79] and cookies [80]. XOS also showed potential application in microencapsulation systems to protect probiotic microorganisms [81].

6.2 Cello-oligosaccharides Cello-oligosaccharides (COS), a new class of nondigestible oligosaccharides, have been the subject of study in recent years for their production from lignocellulosic biomass. From a biochemical point of view, COS are defined as important biomolecules constituted by glucose units interconnected by β-1,4-type bonds [82]. They are differentiated according to their degree of polymerization and can be classified and/or characterized as cellobiose (two glucose monomers), cellotriosis (three glucose monomers), cellotetraose (four glucose monomers), cellopentose (five glucose monomers), and cellohexose (six glucose monomers) (Table 1). COS are intermediates from the hydrolysis of cellulose to glucose, using two main strategies: acid-based and enzyme-based cellulose hydrolysis. The latter has been considered more attractive due to milder reaction conditions and lower production of monomers. Cellobiohydrolases (CBHs) and some endoglucanases (EGs) have been reported as fundamental enzymes for COS production, especially cellobiose. Both belong to a group called processive enzymes, whose main characteristic is the release of soluble products from the terminal of the cellulose chains, carrying out several hydrolysis cycles moving in the same chain [83]. Lately, new approaches for optimizing COS production have been used regular EGs, processive EGs, and auxiliary enzymes such as lytic polysaccharide monooxygenases (LMPOs) [83–85]. Lignocellulosic biomass such as forest residues pretreated with organosolv (birch and spruce) have been tested in the presence of processive EGs of bacterial and fungal origin, belonging to the GH9, GH6, and GH48 families and LPMOs for their ability to release COS [85]. Barbosa et al. [84,86] also have optimized COS production, using different pretreated sugarcane straw biomass using a combination of heterologous expressed processive EGs of bacterial and fungal origin, belonging to GH9 and GH45, LPMOs, and cellobiose dehydrogenase enzyme. However, there is a lack of information regarding the large-scale production, which could be associated with the recalcitrant nature of cellulose compared to other polysaccharides, which hinders the hydrolysis of its dense network [37,82].

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Regarding the physical-chemical, rheological, and functional properties, COS became important compounds with high potential application in the most diverse segments of the food and pharmaceutical industry. One of the most important properties currently studied is their prebiotic functional activity. They may be able to resist the initial stages of the digestion process, serving as a substrate for beneficial microorganisms present in the intestinal microbiota [87]. Furthermore, they play an important role in inhibiting pathogenic microorganisms, in addition to other properties that make them biomolecules of great interest and target of research in the medical, pharmaceutical, and cosmetic fields. By oral ingestion, COS are also responsible for lowering total cholesterol and neutral fat concentration in the liver, preventing or helping to cure diseases and, consequently, promoting a better lifestyle [88]. All these mechanisms triggered by their prebiotic effect favor cello-oligosaccharides, considering that, like other prebiotic substances, COS have considerable potential to be incorporated into food formulations, such as cereal bars, bakery products, sweets, ice creams, dairy desserts, juices, nectars, sauces, symbiotic foods, etc. They can also be incorporated into dietary supplements or even into medicines that assist in the replacement of the intestinal microbiota [71]. In the field of cosmetics, COS has shown potential to improve the effectiveness of moisturizing creams. When combined with fatty acids, they can still be used as new bio-based amphiphilic compounds [89]. Hydroxyl groups in COS can be derived by a variety of methods, adjusting their properties and opening up other fields of application [88].

6.3 Pectin-oligosaccharides Pectin comprises a family of acidic polymers, with several neutral sugars/polymers such as arabinans, galactans, and arabinogalactans (attached as side chains) [90]. Four main pectic components have been identified, namely, homogalacturonan (HG), rhamnogalacturonan-I (RG-I), rhamnogalacturonan-II (RG-II), apiogalacturonan (AG), and xylogalacturonan (XG) (Fig. 5). All these pectic components are connected by either covalent or ionic cross-links [36]. The extraction of these neutral and acidic polymers in the form of pectic oligosaccharide (POS) is a promising step toward the manufacture of prebiotics from agricultural by-products such as apple pulp, sugar beet, onion skin, potato pulp, soy hull, olive pomace, and citrus waste, which contain significant amounts of pectin [13]. The most common and well-known POS are arabinogalacto-oligosaccharides, arabinoxylo-oligosaccharides, arabino-oligosaccharides, galacto-oligosaccharides, oligo-galactouronides, and rhamnogalacturonan-oligosaccharides [13,91,92]. The advantage of POS over pectin (a fermentable fiber too) is the higher prebiotic potential associated with the reduction in the size of the molecule chain, a less complex structure, and a low steric hindrance. These characteristics favor their fermentation yielding larger amounts of short-chain fatty acids [93]. POS have been obtained by either enzymatic or chemical-physical methods. Various enzymes have been widely used for the production of POS because of their specificity and selectivity. In addition, the use of enzymes over other pretreatment methods is regarded as safe due to minimum adverse chemical modifications of products. The fermentation properties of these oligosaccharide have been investigated using pH-controlled fecal batch cultures [94]. POS were more selective for Bifidobacteria and Lactobacilli than pectin, and also more selective for Bifidobacteria than FOS [95]. The colonic

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FIG. 5 Schematic structure of pectin and the main enzymes responsible for its hydrolysis.

fermentation of prebiotic POS results in the generation of short-chain fatty acids (SCFA), which exerts a number of health effects such inhibition of pathogenic bacteria, relief of constipation, reduction in blood glucose levels, improvement in mineral absorption, decreased incidence of colonic cancer, and modulation of the immune system [36]. The literature also suggests that POS can act as phytoalexin elicitor, flowering inducer, and antibacterial agent in plants [96]. However, to further substantiate the claim of the prebiotic efficacy and other health benefits of POS, more rigorous in vitro investigations are required and in vivo studies will validate the claim [13].

6.4 Soy-oligosaccharides Soy-oligosaccharides refer to oligosaccharides found in soybeans, which account for approximately 40–50 g/kg of dry weight of soybean meal [97]. The principal oligosaccharides in mature soybeans are stachyose (14–41 g/kg), raffinose (1–9 g/kg), and verbascose (2–3 g/kg). Raffinose is a trisaccharide containing galactose linked by α-(1 ! 6) bond to the glucose unit of sucrose. Stachyose is a tetrasaccharide containing extra galactose unit linked to raffinose by α-(1 ! 6) bond. Verbascose is pentasaccharide containing extra galactose unit linked to stachyose by α-(1 ! 6) bond. Ajugose is a hexasaccharide, that is, verbascose that has an additional unit of α-D-galactopyranose attached by α-(1 ! 6) bond (Fig. 6) [11,97]. Soy-oligosaccharides are low-calorie sweeteners and soluble fiber that can make the stools softer. The recommended daily intake of these compounds is 4 g/day to improve absorption of calcium and other minerals, increase microbial metabolites of short-chain fatty acid that help reducing the risk of colon cancer, and minimize toxic metabolites production that effect liver. However, it is important to highlight that overconsumption of soy-oligosaccharides may trigger abdominal bloating, excessive gas, and diarrhea [11,98].

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FIG. 6 Soy-oligosaccharides structures.

The characteristic of α-(1 ! 6)-linked bonds in soy-oligosaccharides chemical structures is unbreakable due to the lack of α-galactosidase enzyme in human digestive system. The undigested soy-oligosaccharides reached the colon intact, where they are hydrolyzed by α-galactosidase of intestinal microflora into fermentable sugars that enhance the growth of enteric bacteria in the colon [11]. However, some scientists did not consider soyoligosaccharides as prebiotic due to conflict in published research reports. Some researchers have observed a significant increase only in beneficial bacteria of Bifidobacterium species [99], while others observed significant increase in both Bifidobacterium species and the pathogenic Clostridium species [100]. These disputed research reports on the prebiotic property of soyoligosaccharides still need further investigation.

6.5 Chitosan-oligosaccharides Chitosan-oligosaccharides (CHOS) are the degraded products of chitin/chitosan by acid hydrolysis, enzymatic degradation, or both (Table 1) [101]. They are characterized by a degree of deacetylation (DD) >90% [102,103], a degree of polymerization (DP) 100 μM), Trypanosoma brucei (IC50 > 100 μM), and Leishmania major (IC50 > 100 μM) [135]. Trypanosoma brucei was affected when investigated the activity of dihydroisocoumarins (trans and cis 4,8-dihydroxy-3-methylisochroman-1-one, 5-hydroxymellein, and -mellein or 8-hydroxy-3-methylisochroman-1-one) and naphthoquinones (anhydrofusarubin, javanicin, dihydrojavanicin, and solaniol), produced by fungi Fusarium sp. and Lasiodiplodia theobromae. The IC50 value of the compounds ranged between 0.32 and 12.5 μM [136]. Trypanosoma cruzi is the etiologic agent that causes Chagas disease. Notwithstanding diagnoses still insufficient, it is estimated that only 10% of patients infected are diagnosed [137], being a neglected tropical disease, considering the lack of interest by the pharmaceutical industry. Chagas is responsible for more than 400,000 deaths worldwide, being a niche for bioprospecting new compounds to control it. In this way, fungi extracts were evaluated against this pathogen, showing promising results [138]. Ferreira et al. [139] evaluated extracts obtained from fungi Diaporthe cf. mayteni and Endomelanconiopsis endophytica, showing efficacy against amastigote form of T. cruzi. The same group published the isolation of the compounds ophiobolin K and 6-epi-ophiobolin K from Aspergillus calidoustus with positive results against trypanosomatid. The IC50 values of the compounds were 13 and 9.62 μM, four and two times higher than the benznidazole drug control (3.84 μM) [140]. There is still a lack in the literature of bioactive compounds obtained from fungi against T. cruzi or others protozoan pathogenic, resulting in few isolations and researches demonstrating activity of these metabolites. Cytotoxicity is an essential parameter to classify new bioactive compounds as safe to human cells—the compounds need to present low toxicity and be effective against pathogens.

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Rosa et al. [141] identified 34 active fungal extracts with activity against amoeba Naegleria fowleri. Two compounds showed high toxicity against J774 macrophage cells (IC50 < 5 μg mL 1) and others with activity against Leishmania donovani. In this way, a criterion was established to determine antiparasitic activity (IC50 < 1.0 μg mL 1) and the lowest cytotoxicity (J774 IC 50 > 5 μg mL 1), remaining 116 from a total of 562 extracts. Antagonistically, the highest cytotoxicity can be helpful in research aiming to combat cancer and tumor cells [137,140]. 2.4.2 Antibacterial Livestock is essential for food production in our society, demanding the insurance of animal’s health. Several pathogens can affect animals, causing diseases and death, such as bacteria, particularly enterobacteria, due to their resistance to pH and temperature variations, contaminating animals, and thus food in industrial processes [142,143]. Evaluating the activity of metabolites against Gram-negative/enterobacteria and Grampositive is useful considering animal and human health. A mycotoxin produced by Aspergillus flavus cultivated in carbohydrate sources had activity against Bacillus subtilis, Escherichia coli, and Enterobacter aerogenes, with IC50 values of 1.7, 22.5, and 1.1 μM [144]. A sesquiterpenoid was isolated from Aspergillus spp., which presented moderate activity against Staphylococcus aureus—IC50 values between 31.5 and 41.9 μM [145]. The fungi Pestalotiopsis sp. was isolated from the leaves of Rhizophora mucronata in the Dong Zhai Gang-Mangrove Garden region. The compounds obtained have a novel hybrid sesquiterpene-cyclopaldic acid metabolite being named pestalotiopisorin A. They were evaluated against Enterococcus faecalis pathogenic, showing intermediate results (MIC 1.6 μg mL 1) [67]. Another compound was isolated from the fungi Pestalotiopsis sp.—a new derivative pestalotiopisorin B—tested against Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, B. subtilis, and Escherichia coli. Pestalotiopisorin B showed activity against Pseudomonas aeruginosa and Escherichia coli of 50 and 12.5 μg mL 1, respectively [146]. Ascomycota sp. is a fungus isolated from Pluchea indica and several compounds can be isolated from it. Four compounds present antibacterial activity against Gram-negative Escherichia coli, Klebsiella pneumoniae and Acinetobacter calcoaceticus, and Gram-positive Staphylococcus aureus and B. subtilis. The compounds were characterized as dichloroisocoumarinsdichlorodiaportintone, desmethyldichlorodiaportin, and dichlorodiaportin. The compounds presented antiinflammatory and antibacterial activity against Acinetobacter calcoaceticus, B. subtilis, Escherichia coli, and K. pneumoniae with minimum inhibitory concentration (MIC) values of 25–50 μg mL 1 [147]. 2.4.3 Antiviral Viruses can be divided into enveloped and nonenveloped: viruses that cause flu or COVID are enveloped. Nonenveloped viruses are characterized by their resistance to pH and temperature variations, which elect them as waterborne pathogens, causing dysentery [148,149]. Viruses are obligatory intracellular parasites requiring the host cell machinery for replication, being intracellular-dependent. Considering this, the prospection of biocompounds to affect receptors used by viruses for replication is necessary to viral inhibition [150]. Paclitaxel is a compound isolated from different sources, such as western yew or Fusarium oxysporum, which is an endophytic fungus isolated from Rhizophora annamalayana. This compound

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presented antitumor activities, anticancer, and anti-HIV at a concentration of 20 μg mL 1, inhibiting HIV-integrase and protease, essential proteins for HIV replication [151]. The fungi Neosartorya udagawae can be isolated from the roots of the plant Aricennia marina and in carbohydrate sources produce Neosartoryadins A and B (secondary metabolites). These compounds are characterized as fumiquinazoline alkaloids, with IC50 66 and 58 μM, respectively, effective against H1N1. Other compounds showing activity against H1N1 are emerimidine A and B isodolines, pestalotiopsone F, pestalotiopsone B,3,8dihydroxy-6-methyl-9-oxo-9H-xanthene-1-carboxylate, and 5-chloroisothiorin polyketides. These compounds can be obtained from Emericella sp. and Pestalotiopsis spp., and isolated, respectively, from Aegiceras corniculatum and Rhizophora stylosa [152].

3. What about the operational and environmental point of view? The creation and insertion of new products and processes in the market must follow industrial trends. The exponential advances of the global economy can be observed in the transition period between industrial revolutions (IR), with the 1st, 2nd, and 3rd lasting centuries and the most recent 4th and 5th with only decades to transition [153]. While the industry is still adapting to the 4th IR, we can already start thinking about carbohydrate-based technologies for the 5th IR. The document scenario in the Scopus database about the last two industrial revolutions, as shown in Fig. 3, indicates that the subject entered the R&D in 2012. From 2016 to the present, it has been growing exponentially. Subject publications are predominantly articles (44.7%) in the Engineering and Information Technology fields, concentrated in South Korea, Malaysia, and Germany. When looking at more specific subject areas in carbohydrate-based

FIG. 3 Compiled publications by Scopus database that refer to the 4th and 5th industrial revolution. Note: The author’s search selected documents with one of the following expressions in title, abstract, or keywords: “4th industrial revolution” or “5th industrial revolution.”

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products, e.g., Materials Science, Environmental Science, Energy, Biochemistry, Agricultural, and Biological Sciences, an inexpressive number of publications are addressing aspects of recent industrial revolutions. But what is the relationship between the industrial revolution and the operational and environmental aspects of the carbohydrate-based market? Let us understand. The world moved slowly from mechanization (1st IR) and mass production using assembly lines (2nd IR) to automation using computers (3rd IR). Now, we are fast entering the cyber-physical systems of the 4th IR, governed by globalization and digitalization. The upcoming 5th IR will introduce the cyber-physical cognitive concept, where personalization will be valorized, and the multilevel cooperation between man and machine will emerge [154]. Beyond the operational and technological aspects, the circular economy has become substantially relevant in the current and future IR. The 4th IR disrupts business models and can be seen as an environmental, societal, and economic revolution. With products and processes developed by technology, machine learning, and extensive data integration, the 4th IR provides an ecosystem where things are “made on the internet,” and the physical and virtual boundaries are lost. This revolutionary environment introduces the concept of Smart Lifecycle Management, a comprehensive approach to manage products and processes in the 4th IR by connecting physical and virtual processes. However, this system should overcome the challenge of maintaining balance between users, customers and companies desires, and sustainable production and consumption to become truly eco [155–157]. Suppose all the aspects mentioned above go well. In that case, the 4th and 5th IR tools can positively and exponentially affect the global economy through productivity, life quality, and market share improvements [158]. In addition to the manufacturing environment changes, the COVID-19 pandemic taught a great lesson about how the 4th and 5th industrial revolution tools can improve global life quality in real-time. To Neto et al. [159], the virus has been a catalyst to the 4th IR, marking the integration between the physical, digital, and biological aspects during the social isolation. The first aspect was marked with telemedicine, healthcare automation, and robot doctor ascension. The SARS-CoV-2 digital component is the big data and cloud, supercomputers and their algorithms, and the 5G technology. And in the biological component, we can identify the monitoring technologies (cell phone location, government apps to record symptoms) and the fast growth of virology science. All the tools mentioned above from 4th IR have been essential to understand and fight against the virus. Countries that understood its importance and implemented such tools to fight the pandemic in the health and economy fields and adopted public policies accepting scientific advice were prosperous in reducing the damages. The example of the pandemic scenario can be extended to solve other problems facing the world and shows that the current and future industrial revolution can exponentially improve the global population life-quality. Nahavandi [153] defined the 5th industrial revolution as a human-centric solution, in which robots will be coupled with the human mind and work as a collaborator. Through brainmachine interface and advances in artificial intelligence (AI), the next industrial revolution is required because the current one has been ignoring the human cost from manufacturing workplaces and increasing efficiency and automatization in production lines. Furthermore, existing AI algorithms for environmental management does not cover all related problems in factories, and the 5th IR promises to have a strong focus and action in the environmental field. It intends to create an ecosystem getting humans, machines, and the environment closer.

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Carbohydrate-based manufacturing has already successfully implemented tools from industry 4.0. Cheminformatics was a valuable information technology in Bardant et al. [160] study, contributing to the operational optimization in bioethanol production when water hyacinth was considered biomass. This tool shares many similarities with bioinformatics and biochemistry and focuses on collecting, storing, analyzing, and manipulating data [161]. In association with Response Surface Methodology, Bardant et al. [160] explored the relationships between several explanatory variables and one or more responses. Their mathematical model predicted the sugar and ethanol yield from NaOH concentration in pretreatment and substrate and enzyme loading in saccharification and fermentation with the commercial strain S. cerevisiae. Besides the mathematical models, a worldly connected database about biochemistry is very relevant to industrial processes since the traditional trial and error protocol is no longer acceptable when resources are scarce. Continuous flow chemistry has been growing fast in pharmaceuticals manufacture, blurring the lines between chemistry and engineering. According to Neyt and Riley [162], modern chemical manufacturing is entering the advanced computer technology era, integrating software for multistep synthesis under flow conditions. Some examples of continuous flow chemistry practices are algorithmic chemistry, flow automation allowing several reactions on the same system, production of new and known target compounds efficiently, digital cameras connected to computer vision algorithms, purification/downstream processing technologies in flow, among others. Another lesson from the chemistry industry is the advances in reactor and equipment design, mainly applied in active pharmaceutical ingredients synthesis. The commercially available flow photochemical reactors and modules are increasing, and some companies have been expanding their application to large-scale production and several chemical transformations. The next step in reactor design is 3D printing, a layer-by-layer manufacturing method that can accelerate mathematical modeling and phenomena prediction [162]. The market, particularly the carbohydrate-based, can have considerable gains in adopting industry 4.0 and the incoming 5th industrial revolution concepts.

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Index Note: Page numbers followed by f indicate figures and t indicate tables.

A AI-based modeling (Alpha-fold/RosettaFold), 93–94 Alcohol-insoluble residue (AIR), 5 Algae biobutanol production, 255–256, 257–258t bioethanol production ethanol production, 240–254 fermentation, 239 hydrolysis, 238–239 pre-treatment, 237–238 recovery process, 239–240 biogas production, 256–261, 260–262t bio-refinery platform, 262–263 macroalgae biological treatment, 236 brown algal polysaccharides, 233–234 chemical treatment, 236 green algal polysaccharides, 230–231 mechanical treatment, 234–235 red algal polysaccharides, 231–232 thermal treatment, 235 microalgae, 230 seaweed biomass, 262–263 Algal-oligosaccharide (AOS) agaro and neoagaro-oligosaccharides, 353 alginate-oligosaccharides, 354 carrageenan-oligosaccharides, 354–355 fucoidan-oligosaccharides, 355 laminarin-oligosaccharides, 355 porphyran-oligosaccharides, 356 α-amylases applications of, 197–201 bio-fuel production, 199–200 biomedical significances, 201 commercial production, 196–197 detergent industry, 200 food industry, 198–199 paper industry, 200 promising applications, 200–201 structure and mechanism of, 193–195 textile industry, 199

α–glucuronidase reaction catalyzed and classification, 141–143 substrate specificity, 143 α–L-arabinofuranosidases classification, 144–145 reaction catalyzed, 144 substrate specificity and reaction mechanism, 145 Amylase α-amylases applications of, 197–201 bio-fuel production, 199–200 biomedical significances, 201 commercial production, 196–197 detergent industry, 200 food industry, 198–199 paper industry, 200 promising applications, 200–201 structure and mechanism of, 193–195 textile industry, 199 β-amylases, 195 chemical structure of amylose and amylopectin, 193f γ-amylases, 195 overview, 191–192 starch-digesting enzyme, 192–195 Analytical ultracentrifugation (AUC), 82 Antisense oligonucleotide (ASO), 46–47 Atomic force microscopy (AFM), 20–22

B

β-galactosidase (β-gal), 370–374 Bioactive fungi compounds antibacterial activity, 423 antiprotozoal activity, 422–423 antiviral activity, 423–424 endophytic fungi, 422 multidrug-resistant bacteria, 421 Bio-based lactic acid advantage of, 317 C. boidinii, 322 cellulosic biosludge, 317 commercial production, 316 corncobs, 319

435

436 Bio-based lactic acid (Continued) corn stover, 319 C. utilis, 322 E. coli, 322 fermentative kinetic parameters, 318t global market size, 316 gram-positive microorganisms, 320 heterofermentative LAB metabolizes, 320 jerusalem artichoke, 320 Kluyveromyces lactis, 322 metabolic pathways, 320, 321f S. cerevisiae, 322 soybean, 319 sugarcane, 319 Bioethanol production ethanol production, 240–254 fermentation, 239 hydrolysis, 238–239 pre-treatment, 237–238 recovery process, 239–240 Bio-refinery, 228–229 holistic view, 229f β-jelly roll PL class, 213 β-propeller class, 213 Bragg’s diffraction, 87–88 Brown algal polysaccharides alginate, 233 fucoidans, 233 laminarin, 233 mannitol, 233–234 pre-treatment and fermentation condition, 249–254t β-sandwich class, 217–219

C Carbodiimide method, 12 Carbohydrate-based products bioactive fungi compounds antibacterial activity, 423 antiprotozoal activity, 422–423 antiviral activity, 423–424 endophytic fungi, 422 multidrug-resistant bacteria, 421 chemical modification of, 413–415 circular bioeconomy, 410–413 component of, 410f fiber-reinforced composites, 415 industrial enzymatic biocatalysis biocatalysis application, 419–420t enzyme immobilization, 418 food and beverage industries, 418–420 textile industries, 420 upstream process, 418 market opportunities for, 410–424, 411f

Index

nanoproducts, 415 operational and environmental point of view cheminformatics, 426 circular economy, 425 Scopus database, 424–425, 424f Carbohydrate esterases acetylxylan esterase, 146 feruloyl esterase, 146–149 glucuronoyl esterase, 150–151 Cellulose, 40–41 Cellulose biosynthesis, 44–45 Cell wall modification, 44–46 Cell wall polysaccharides abscission, 65 aerenchyma, 66–67 endogenous degradation, 65–69 exogenous degradation, 61–65 fruit ripening, 65–66 heteropolysaccharides mobilization, 67–69 sucrose, starch, and storage cell wall polysaccharides, 62f Chemical cross-linking and native mass spectrometry (XL-MS), 84 Circular dichroism (CD) spectroscopy, 80–81 CRISPR/Cas system, 49, 50f

D D-Tagatose, 365–370 Dynamic light scattering (DLS), 79–80

E Enzymatic hydrolysis of polysaccharides atomic-scale interaction, 276 deterministic models, 280 hydrolysis of linear homopolysaccharides, 284–287 processivity in, 288–291 pseudo-first order in substrate concentration, 287–288 dynamic macroscale models, 276–277 features of enzymes, 278–279 features of polysaccharide substrates, 277–278 fingerprinting models concentrations of, 302 description of, 297–298 principles of, 298–301 strength of, 302 substrate selection, 302 molecular-scale interaction, 276 parameter estimation, 277 ready-to-use functions, 281 representing the system advantages and disadvantages, 282 parameter estimation, 283–284

Index

simplifications, 281–282 stochastic models, 280 model systems in only one type of enzyme, 293–295 model systems is more than one type of enzyme, 295–296 molecules and concentrations, 292–293 simulation involvement, 292 Enzyme immobilization animal nutrition, 399 binding enzyme, 388–389 energy demand, 401–402 enzyme cross-linking, 387–388 enzyme encapsulation/entrapment, 388 food industry, 395–396 fundamentals of, 386–389 human nutrition lactose intolerance, 398–399 prebiotics, 398–399 hydrolysis of polysaccharides, 389–394, 392t, 403 pharmaceutical industry monoclonal antibodies, 399–400 oral drug delivery, 400 textile industry, 396–398 water treatment removal of pathogens, 401 removal of pollutants, 401 Enzyme-ligand binding, 94 Enzyme stability and structural homogeneity circular dichroism (CD) spectroscopy, 80–81 dynamic light scattering (DLS), 79–80 enzyme purification, 78 intrinsic and extrinsic fluorescence, 81 principal techniques, 79f Enzyme-substrate interactions chemical cross-linking and native mass spectrometry (XL-MS), 84 hydrogen-deuterium exchange (HDX), 85 isothermal titration calorimetry, 83–84 microscale thermophoresis, 84 Eutectic solvents (ESs) degree of polymerization, 129 fractionation of lignocellulosic biomass, 129 hydrogen bond acceptor (HBA), 128 hydrogen bond donor (HBD), 128

F Fast protein liquid chromatography (FPLC) system, 78 Fed-batch fermentation strategy, 319 Fingerprinting models concentrations of, 302 description of, 297–298

437

principles of, 298–301 strength of, 302 substrate selection, 302 Fourier-transform infrared spectroscopy (FTIR), 25–26 Fructooligosaccharides inulin as substrate, 343–344 sucrose as substrate, 342–343 Fungal pretreatment process, 129–130

G Galactomannan, 68–69 Galactooligosaccharides (GOS), 344–346 Gas chromatography alditol acetate (AA), 11–12 methylation analysis, 16–19 trimethylsilyl (TMS) methods, 11–12 Generally recognized as safe (GRAS), 365–366 Genetic modification antisense oligonucleotide (ASO), 46–47 biofuel generation, 50–51 cellulose biosynthesis, 44–45 CRISPR/Cas systems, 49 hemicellulose biosynthesis, 45–46 lignin biosynthesis, 46 RNA interference, 47–48 TALENs, 48–49 Glucose-free syrup, 377 Glucuronoarabinoxylans (GAX), 45–46 Glycoside hydrolases (GH), 95 Grand View Research market report, 336 GRAS (Generally Recognized as Safe) compound, 307 Green algal polysaccharides, 230–231 pre-treatment and fermentation condition, 241–245t Greenhouse gas (GHG), 205, 305 Gymnosperms, 41

H Hemicellulose biosynthesis, 45–46 Hemicelluloses, 139–140 High-performance liquid chromatography (HPLC), 7–9 Human nutrition lactose intolerance, 398–399 prebiotics, 398–399 Hydrogen bond acceptor (HBA), 128 Hydrogen bond donor (HBD), 128 Hydrolysis, 389–394, 392t, 403 acid hydrolysis, 56–57 description, 55 endogenous hydrolysis, 55 enzymatic hydrolysis, 57–58 exogenous degradation, 55

438

Index

I Immobilized metal affinity chromatography (IMAC), 78 Industrial enzymatic biocatalysis biocatalysis application, 419–420t enzyme immobilization, 418 food and beverage industries, 418–420 textile industries, 420 upstream process, 418 In silico methods AI-based modeling (Alpha-fold/RosettaFold), 93–94 molecular docking, 94 QM/MM-based simulations, 95–96 Ionic liquids (ILs), 126–128 Isothermal titration calorimetry, 83–84

L L-Arabinose isomerase (L-AI), 374–377 biochemical characteristics, 376t Escherichia coli, 374f isomerization reactions, 375f reversible isomerization, 374 Ligase-independent molecular cloning techniques, 77t Lignin, 41 degradation, 180 occurrence of, 179f removal of, 180 Ligninases delignification process, 181 direct and indirect action, 183f enzyme performance and stability, 187–188 fungal ligninolytic enzymes, 181 industrial applications, 180t laccases (lacs), 182–185 lignin peroxidase (LiP), 185 manganese peroxidases (MnPs), 185–187, 187f representative scheme of, 188f versatile peroxidase (VP), 187 Lignin biosynthesis, 42–44 Lignocellulose biological pretreatments, 129–130 biomass physical and chemical factors, 109 accessible, specific, and internal surface area, 110–111 cellulose crystallinity, 112 degree of polymerization (DP), 112 delignified pretreated biomass, 114–115 pretreatments promoting hemicellulose removal, 113–114 residual lignin on enzymatic hydrolysis, 115–116 chemical and physicochemical pretreatments acid pretreatment, 120–121 alkaline pretreatments, 123–124

eutectic solvents (ESs), 128–129 hydrothermal treatments, 121–123 ionic liquids (ILs), 126–128 organosolv pretreatment, 124–126 steam explosion, 121–123 physical pretreatment process advantages and disadvantages of, 117t mechanical pretreatments, 118–119 microwave pretreatment, 119–120 ultrasound pretreatment, 119 pretreatment step, 109, 116 Lignocellulosic biomass, 39–40, 305 enzymatic hydrolysis of, 110–116 fungi and bacteria, 178 plant cell wall, 178f polysaccharides, 177–178 structure of, 178 Lytic polysaccharide monooxygenase (LPMO), 77t, 402

M Macroalgae biological treatment, 236 brown algal polysaccharides, 233–234 chemical treatment, 236 green algal polysaccharides, 230–231 mechanical treatment, 234–235 red algal polysaccharides, 231–232 thermal treatment, 235 Mannanolytic enzymes enzymes acting on the side chains acetylmannan esterase, 160–161 α-glucuronidase, 158–160 exo-acting mannanolytic enzymes attack main chain β-glucosidases, 163 β-manno-based substrates, 163–165 β-mannosidases, 161–162 mannobiohydrolases, 162–163 Mannans, 67 Michaelis-Menten equation, 275, 286 Microalgae, 207 Molecular docking, 94

N NMR spectroscopy, 92–93 Nonstarch oligosaccharides cello-oligosaccharides (COS), 349–350 chitosan-oligosaccharides (CHOS), 352–353 pectin-oligosaccharides, 350–351 soy-oligosaccharides, 351–352 xylo-oligosaccharides, 348–349 Nuclear magnetic resonance spectroscopy (NMR), 13–16 Nutraceuticals, 336, 337f

Index

O Oligosaccharides (OS) algal-oligosaccharide (AOS) agaro and neoagaro-oligosaccharides, 353 alginate-oligosaccharides, 354 carrageenan-oligosaccharides, 354–355 fucoidan-oligosaccharides, 355 laminarin-oligosaccharides, 355 porphyran-oligosaccharides, 356 dietary carbohydrates, 336 functional oligosaccharides large-scale purification process, 341 natural sources by enzymatic process, 339–341t pectic oligosaccharides (POS), 338–341 polysaccharides sources and production process, 338–341 properties and food industrial application, 337–338 lactose-related oligosaccharides, 344–346 nonstarch oligosaccharides cello-oligosaccharides (COS), 349–350 chitosan-oligosaccharides (CHOS), 352–353 pectin-oligosaccharides, 350–351 soy-oligosaccharides, 351–352 xylo-oligosaccharides, 348–349 starch-related oligosaccharides cyclodextrins (CDs), 347–348 isomalto-oligosaccharides (ISMO), 347 malto-oligosaccharides (MOS), 346 trehalose, 346–347 sucrose-related oligosaccharides, 342–344

P Pectin, 41 β-jelly roll PL class, 213 β-propeller class, 213 β-sandwich class, 217–219 carbohydrate-active enzymes database, 212–213 complex structure, 206–207 homogalacturonan (HG), 207 rhamnogalacturonan-I (RG-I), 207 rhamnogalacturonan-II (RG-II), 207 xylogalacturonans, 207 overview, 205–206 pectinases depolymerases, 211 esterase, 210–211 lyase enzymes, 212 polygalacturonase, 211–212 protopectinases (PPases), 210 right-handed β-helix, 213 sources of, 208 structural and mechanism details, 214–216t toroid class, 213 triple stranded β-helix, 219 types of, 207–208

439

Pharmaceutical industry monoclonal antibodies, 399–400 oral drug delivery, 400 Phenylalanine ammonia-lyase (PAL), 46 Phosphorylases acting on β-manno-based substrates β-1,4-mannooligosaccharide phosphorylases, 162 mannosylglucose phosphorylases, 164–165 Photoacoustic spectroscopy (FTIR-PAS), 26 Pichia pastoris cells, 377 Plant cell wall (PCW) AA/GC-MS method, 11 alcohol-insoluble residue (AIR), 5 atomic force microscopy (AFM), 20–22 characterization of, 3, 27 chemical analysis of, 7–12 composition and organization of, 2 dicots and grass, 1–2 electron microscopy, 22–24 fourier-transform infrared spectroscopy (FTIR), 25–26 gas chromatography, 11–12, 16–19 glycan-directed mAbs, 19–20 high-performance liquid chromatography (HPLC), 7–9 HPAEC-PAD analysis, 9–10 immunological approach, 19–20 lignocellulosic biomass, 2–3 nuclear magnetic resonance spectroscopy (NMR), 13–16 polysaccharide analysis, 4–6 representation of, 2f sequential extraction, 5–6 structural analysis of, 12–20 structural polysaccharides, 3f TOCSY (total correlation spectroscopy), 15–16 X-ray diffraction (XRD) analysis, 24–25 Plant sugars, 58–69 Propionic acid (PA) (CH3CH2COOH) fermentation, 326 global market size, 323 hemicellulose, 324 lignocellulosic hydrolysates, 325t metabolic pathways of, 324f P. freudenreichii, 325–326 P. jensenii, 325–326 production of, 323 Propionibacterium ssp, 323

Q QM/MM-based simulations, 95–96

R Red algal polysaccharides cellulose, 232 mannan, 232

440

Index

Red algal polysaccharides (Continued) pre-treatment and fermentation condition, 245–249t sulfated galactans, 232 xylan, 232 RNAinterference, 47–48

T

S

Universal attenuated total reflectance FTIR (UATR-FTIR), 25–26

Scopus database, 424–425, 424f Separate hydrolysis and biotransformation (SHB), 377 Simultaneous saccharification and biotransformation (SSB), 377 Single-particle cryogenic electron microscopy (cryo-EM), 89–92 Size-exclusion chromatography (SEC), 78 Size-exclusion chromatography with multiangle light scattering (SEC-MALS), 82 Small-angle X-ray scattering (SAXS), 83 Starch, 59–61, 192 Starch-related oligosaccharides cyclodextrins (CDs), 347–348 isomalto-oligosaccharides (ISMO), 347 malto-oligosaccharides (MOS), 346 trehalose, 346–347 Steam explosion, 305–306 Structural and biophysical analyses enzyme purification, 78 ligase-independent molecular cloning techniques, 77t molecular cloning strategies for enzyme expression, 76–77 sample preparation, 75–78 size-exclusion chromatography (SEC), 78 Succinic acid (SA) bio-based building block, 308–315 Escherichia coli, 313–314 fermentation parameters, 311–312t glyoxylate pathway, 310 lignocellulosic biomass, 313 metabolic pathway, 309, 315f oxidative pathway, 310 Sucrose, raffinose, and fructans, 58–59

Textile industry, 396–398 Transcription activator-like effector nucleases (TALENs), 48–49

U

W Water treatment removal of pathogens, 401 removal of pollutants, 401

X X-ray crystallography crystallization, 86–87 3D-structure prediction, 87–88 free-electron laser, 88–89 ligand diffusion timescale, 88–89 phase problem, 87–88 time-resolved serial crystallography, 88–89 X-ray sources, 87–88 Xylan and mannans, 140 Xylanolytic enzymes carbohydrate esterases acetylxylan esterase, 146 feruloyl esterase, 146–149 glucuronoyl esterase, 150–151 debranching enzymes α-L-galactosidases, 152–153 α-xylosidase, 151–152 β–1,3–xylosidases, 153 enzymes acting on the side chains α–L-arabinofuranosidases, 144–145 α-glucuronidase, 141–143 exo-acting xylanolytic enzymes β–xylosidases, 153–156 exo-β-1, 4-xylanase, 156 reducing-end xylose-releasing enzyme, 158 xylobiohydrolase, 157 Xyloglucan storing systems, 68