The Autotrophic Biorefinery: Raw Materials from Biotechnology 3110549883, 9783110549881

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Robert Kourist, Sandy Schmidt (Eds.) The Autotrophic Biorefinery

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The Autotrophic Biorefinery Raw Materials from Biotechnology Edited by Robert Kourist and Sandy Schmidt

Editors Univ.-Prof. Dr. Robert Kourist Institute of Molecular Biotechnology Graz University of Technology Petersgasse 14 8010 Graz Austria [email protected] Dr. Sandy Schmidt Department of Chemical and Pharmaceutical Biology Groningen Research Institute of Pharmacy University of Groningen Antonius Deusinglaan 1 9713AV Groningen The Netherlands [email protected]

ISBN 978-3-11-054988-1 e-ISBN (PDF) 978-3-11-055060-3 e-ISBN (EPUB) 978-3-11-054995-9 Library of Congress Control Number: 2021937071 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2021 Walter de Gruyter GmbH, Berlin/Boston Cover image: with kind approval of ecoduna AG | eparella GmbH Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Contents List of authors

VII

Leen Assil-Companioni, Giovanni Davide Barone, Marc M. Nowaczyk, Robert Kourist Chapter 1 A short recapitulation of the autotrophic metabolism 1 Birthe Halmschlag, Lars M. Blank Chapter 2 Metabolic engineering of microbes

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Thomas Bayer, Kathleen Balke, Emil Hamnevik, Uwe T. Bornscheuer Chapter 3 Protein engineering 47 Lydia Rachbauer, Günther Bochmann, Werner Fuchs Chapter 4 Gas fermentation 85 Philipp Doppler, Oliver Spadiut Chapter 5 Introduction to autotrophic cultivation of microalgae in photobioreactors Catarina C. Pacheco, Eunice A. Ferreira, Paulo Oliveira, Paula Tamagnini Chapter 6 Synthetic biology of cyanobacteria 131 Sara B. Pereira Chapter 7 Algal biotechnology

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Hanna C. Grimm, Elif Erdem, Robert Kourist Chapter 8 Biocatalytic applications of autotrophic organisms

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Contents

Georg Höfler, Frank Hollmann, Caroline E. Paul, Marine Rauch, Morten van Schie, Sebastien Willot Chapter 9 Photocatalysis to promote cell-free biocatalytic reactions 247 Laura Rago, Falk Harnisch Chapter 10 Electroautotrophs: feeding microbes with current for CO2 fixation

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Hannah Wohlers, Leen Assil-Companioni, Dirk Holtmann Chapter 11 Cupriavidus necator – a broadly applicable aerobic hydrogen-oxidizing bacterium 297 Katharina Meixner, Jacqueline Jerney, Adriana Kovalcik, Ines Fritz, Bernhard Drosg Chapter 12 Poly(3-hydroxybutyrate) as renewable resource 319 Sandy Schmidt Chapter 13 Applications of mixed microbial cultures in industrial biotechnology Sarah Refai, Jonathan Z. Bloh, Markus Müller Chapter 14 Economic framework of autotrophic processes Index

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List of authors Leen Assil-Companioni Institute of Molecular Biotechnology Graz University of Technology ACIB GmbH Petersgasse 14/1 8010 Graz Austria

Thomas Bayer Institute of Molecular Biotechnology Graz University of Technology Petersgasse 14 8010 Graz Austria [email protected]

Giovanni Davide Barone Institute of Molecular Biotechnology Graz University of Technology Petersgasse 14 8010 Graz Austria

Kathleen Balke Department of Biotechnology and Enzyme Catalysis Institute of Biochemistry University of Greifswald Felix-Hausdorff-Straße 4 17487 Greifswald Germany

Marc M. Nowaczyk Plant Biochemistry Ruhr-University Bochum Universitätsstr. 150 44801 Bochum Germany Robert Kourist Institute of Molecular Biotechnology Graz University of Technology Petersgasse 14 8010 Graz Austria [email protected] Birthe Halmschlag ABBt – Aachen Biology and Biotechnology iAMB – Institute of Applied Microbiology RWTH Aachen University Worringer Weg 1 52074 Aachen Germany Lars M. Blank ABBt – Aachen Biology and Biotechnology iAMB – Institute of Applied Microbiology RWTH Aachen University Worringer Weg 1 52074 Aachen Germany [email protected]

https://doi.org/10.1515/9783110550603-203

Emil Hamnevik Department of Chemistry – BMC Uppsala University Husargatan 3 752 37 Uppsala Sweden Uwe T. Bornscheuer Department of Biotechnology and Enzyme Catalysis Institute of Biochemistry University of Greifswald Felix-Hausdorff-Straße 4 17487 Greifswald Germany [email protected] Lydia Rachbauer BEST – Bioenergy and Sustainable Technologies GmbH Research Site Tulln Konrad Lorenz-Straße 20 3430 Tulln Austria [email protected]

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Günther Bochmann Department of Agrobiotechnology IFA-Tulln Institute of Environmental Biotechnology University of Natural Resources and Life Sciences Vienna Konrad Lorenz-Straße 20 3430 Tulln Austria [email protected] Werner Fuchs Department of Agrobiotechnology IFA-Tulln Institute of Environmental Biotechnology University of Natural Resources and Life Sciences Vienna Konrad Lorenz-Straße 20 3430 Tulln Austria [email protected] Philipp Doppler Research Division Biochemical Engineering Institute of Chemical, Environmental and Bioscience Engineering Technische Universität Wien Gumpendorfer Strasse 1a 1060 Vienna Austria [email protected] Oliver Spadiut Research Division Biochemical Engineering Institute of Chemical, Environmental and Bioscience Engineering Technische Universität Wien Gumpendorfer Strasse 1a 1060 Vienna Austria [email protected] Catarina C. Pacheco i3S – Instituto de Investigação e Inovação em Saúde IBMC- Instituto de Biologia Molecular e Celular Universidade do Porto Rua Alfredo Allen 208 4200-135 Porto Portugal [email protected]

Eunice A. Ferreira i3S- Instituto de Investigação e Inovação em Saúde IBMC- Instituto de Biologia Molecular e Celular Universidade do Porto Rua Alfredo Allen 208 ICBAS - Instituto de Ciências Biomédicas Abel Salazar Universidade do Porto Rua Jorge de Viterbo Ferreira 228 4050-313 Porto Portugal [email protected] Paulo Oliveira i3S- Instituto de Investigação e Inovação em Saúde IBMC- Instituto de Biologia Molecular e Celular Universidade do Porto Rua Alfredo Allen 208 4200-135 Porto Departamento de Biologia, Faculdade de Ciências Universidade do Porto Rua do Campo Alegre FC4 4200-135 Porto Portugal [email protected] Paula Tamagnini i3S- Instituto de Investigação e Inovação em Saúde IBMC- Instituto de Biologia Molecular e Celular Universidade do Porto Rua Alfredo Allen 208 4200-135 Porto Departamento de Biologia, Faculdade de Ciências Universidade do Porto Rua do Campo Alegre FC4 4169-007 Porto Portugal [email protected]

List of authors

Sara B. Pereira i3S – Instituto de Investigação e Inovação em Saúde IBMC - Instituto de Biologia Molecular e Celular Universidade do Porto Rua Alfredo Allen 208 4200-135 Porto Portugal [email protected] Hanna C. Grimm Institute of Molecular Biotechnology Graz University of Technology Petersgasse 14 8010 Graz Austria Elif Erdem Centrale Marseille Aix Marseille Universite CNRS iSm2 UMR 7313 13013 Marseille France Georg Höfler Department of Biotechnology Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands Frank Hollmann Department of Biotechnology Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands [email protected] Caroline E. Paul Department of Biotechnology Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands [email protected]

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Marine Rauch Department of Biotechnology Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands Morten van Schie Department of Biotechnology Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands Sébastien Willot Department of Biotechnology Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands Laura Rago Department of Environmental Microbiology Helmholtz Centre for Environmental Research GmbH – UFZ Permoserstraße 15 04318 Leipzig Germany [email protected] Falk Harnisch Department of Environmental Microbiology Helmholtz Centre for Environmental Research GmbH – UFZ Permoserstraße 15 04318 Leipzig Germany [email protected] Hannah Wohlers Industrial Biotechnology DECHEMA Research Institute Theodor-Heuss-Allee 25 60486 Frankfurt am Main Germany

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Dirk Holtmann Institute of Bioprocess Engineering and Pharmaceutical Technology University of Applied Sciences Mittelhessen Wiesenstraße 14 35390 Giessen Germany [email protected] Katharina Meixner Department of Agrobiotechnology IFA-Tulln Institute of Environmental Biotechnology University of Natural Resources and Life Sciences Vienna Konrad Lorenz-Straße 20 3430 Tulln Austria [email protected] [email protected] Jacqueline Jerney Marine Research Centre Finnish Environment Institute Agnes Sjöbergin katu 2 00790 Helsinki Finland Adriana Kovalcik Department of Food Chemistry and Biotechnology Faculty of Chemistry Brno University of Technology Purkynova 118 612 00 Brno Czech Republic Ines Fritz Department of Agrobiotechnology IFA-Tulln Institute of Environmental Biotechnology University of Natural Resources and Life Sciences Vienna Konrad Lorenz-Straße 20 3430 Tulln Austria

Bernhard Drosg BEST – Bioenergy and Sustainable Technologies GmbH Tulln Research Site Konrad Lorenz-Straße 20 3430 Tulln Austria Sandy Schmidt Chemical and Pharmaceutical Biology Groningen Research Institute of Pharmacy Antonius Deusinglaan 1 9713AV Groningen The Netherlands [email protected] Jonathan Z. Bloh DECHEMA-Forschungsinstitut Theodor-Heuss-Allee 25 60486 Frankfurt am Main Germany [email protected] Sarah Refai CLIB – Cluster Industrial Biotechnology Völklinger Straße 4 40219 Düsseldorf Germany [email protected] Markus Müller CLIB – Cluster Industrial Biotechnology Völklinger Straße 4 40219 Düsseldorf Germany [email protected]

Leen Assil-Companioni, Giovanni Davide Barone, Marc M. Nowaczyk, Robert Kourist

Chapter 1 A short recapitulation of the autotrophic metabolism Abstract: As the consequences of climate change and global warming become ever more palpable, society as a whole urgently needs to reevaluate current practices. To this end, the implementation of autotrophic microorganisms in biotechnology may hold the key to cleaner and greener alternatives. In order to fully appreciate the complexity and potentiality of autotrophic microorganisms, an in-depth understanding of their metabolism and the pathways that govern it is paramount. In this chapter, autotrophy and its key subtypes (primarily photoautotrophy and chemolithoautotrophy) are defined, and their nuanced promise and current bottlenecks in biotechnology are introduced. Additionally, a short overview of key metabolic pathways is presented. Keywords: autotrophy, metabolism, biotechnology, photoautotrophy, chemolithoautotrophy

The Earth is home to a wide array of nutritional diversity exhibited by different biological organisms. This diversity provides them with different mechanisms for growth, metabolism and survival and has long been exploited in biotechnology. Each metabolic approach offers its own host of advantages and disadvantages. Defining these mechanisms of nutrition and growth is, therefore, paramount to their understanding. The first term we consider is heterotrophy. The word heterotrophy originates from Greek with hetero meaning other and trophy meaning nutrition. The term first appeared in literature in 1946 as a part of an effort to classify microorganisms by their mode of nutrition. By definition, a heterotrophic organism requires an external supply of various essential metabolites due to its incapability of self-synthesizing; thus, it depends on sources other than itself for nutrition and energy [1]. Heterotrophic organisms represent, by far, the largest diversity in our ecosystem, comprising approximately 95% of all known and described living organisms. The bacterium Escherichia coli (E. coli) and the yeast Saccharomyces cerevisiae (S. cerevisiae), two widely utilized biotechnological production organisms, are prominent examples of heterotrophs. Most heterotrophs are considered to be organoheterotrophs that derive their carbon from an organic material. The oxidation of these compounds leads to adenosine triphosphate (ATP) synthesis, which provides energy and generates the necessary precursors for various synthetic processes in the microorganisms [2]. https://doi.org/10.1515/9783110550603-001

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The second term, autotrophy, translates to self-feeding –from the Greek word for self – autos-. In contrast to heterotrophy, it is a unique form of metabolism that is found throughout the tree of life and is, in fact, the basis for life as we know it as the main producers in ecological pyramids are autotrophs. Despite the lesser known diversity of autotrophic organisms, the biomass distribution of plants alone on our planet is estimated to be ~ 450 gigatons of carbon (Gt C) out of a total ~ 550 Gt C in the biosphere [3]. Autotrophs are capable of synthesizing complex organic material by employing only inorganic compounds that are present in their natural surroundings. By virtue of this ability, such organisms are considered to be the “producers” in any food chain, implying the reliance of life in its entirety on them. Autotrophs are generally categorized into photoautotrophs and chemoautotrophs. Both categories assimilate inorganic carbon (CO2) while relying on light and inorganic compounds for electrons, respectively. Cyanobacteria, which are oxygenic photoautotrophic bacteria, are a key example that emphasizes the importance of autotrophy on a global scale. In reality, we can greatly credit cyanobacteria with the rise of oxygen on the Earth, thanks to observations made from 3.5-billion-year-old microscopic fossils [4]. These microorganisms single-handedly resulted in the culmination of what is termed the great oxidation event, which gave way for the development of aerobic respiration and led to the formation of complex, multicellular life-forms (Figure 1.1). Throughout this introductory chapter, we provide a general overview of key examples of autotrophs that can be applied for biotechnological applications. We begin by elucidating their potential by highlighting advantages that can arise through their implementation in industrial settings. We then proceed to provide an overview of photoautotrophy and chemolithoautotrophy in aerobic organisms along with different autotrophic pathways used to assimilate carbon dioxide.

1.1 The perspective of autotrophic microorganisms The utilization of autotrophic microorganisms industrially pales in comparison to the widespread use of heterotrophs. Nevertheless, there is a growing movement within the context of sustainable biotechnology which encourages their study and further development. Autotrophic organisms can prove to be valuable as they expand the realm of sustainable processes that can be applied industrially. Their large-scale implementation can, for example, provide an avenue that avoids the pitfall of land-use conflicts (Box 1.1). This becomes even more realizable when one accounts for the plethora of data stemming from genome sequencing, transcriptomics, physiological studies, microbiological chemistry, enzymatic assays and 13C-based metabolomics which are being applied to exponentially expand the existing knowledge of central carbon metabolism and typical enzymes and pathways that operate in autotrophs.

Chapter 1 A short recapitulation of the autotrophic metabolism

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Figure 1.1: The great oxidation event, estimated to have started 3.2 billion years ago, coincides with the estimated appearance of the first oxygen evolving cyanobacteria. This signaled the introduction of free oxygen in the atmosphere and marked the beginning of the evolution of more complex forms of life such as algae, plants and mammals. Image adapted from Govindjee & Shevela [5] and Holland [6].

One cannot turn a blind eye to the formidable consequences of climate change that are becoming ever more tangible. As of 2021, many countries are formulating ambitious goals to achieve substantial cuts in greenhouse gas emissions. Carbon dioxide, being the primary greenhouse gas, is emitted by human activity and its reduction is paramount for the future health of all species and the planet. If we are able to realize the wide-scale application of autotrophic microorganisms as biotechnological chassis, harmful carbon dioxide–rich emissions could be valorized and converted into harmless – perhaps even commercially valuable – chemicals and products in a carbon-neutral manner [7, 8]. Extensive know-how of autotrophs and their metabolism will be the key that unlocks their potential for carbon capture and utilization/storage (CCU/CCS, Box 1.2). This would go a long way toward reaching carbon dioxide reduction goals, and although a full evaluation of the approach’s sustainability is required, it will undoubtedly be an improvement over current methodology. However,

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despite the aforementioned data boom and foreseeable advantages, molecular tools available remain relatively limited and the handling of naturally occurring autotrophs is often time-consuming due to their slow growth and propensity for contamination and overgrowth by other microorganisms [9]. The optimization and development of natural and engineered microorganisms that can act as autotrophic biorefineries can take on many forms. The most obvious approach comprises the exploitation of an autotroph’s innate ability to thrive in the absence of fermentable carbon sources; a prime example of this approach would be the utilization of cyanobacteria (see Chapter 7) and microalgae (Chapter 5) [10]. Alternatively, metabolic engineering (Chapter 2), synthetic biology (Chapter 8) and mixed culture (Chapter 13) approaches can be pursued. Box 1.1: The land-use debate Reduced carbon sources, which are typically required to culture heterotrophs, present a rising dilemma for the biotechnological industry – namely the land-use debate. Although this debate is more hotly discussed within the scope of biofuel production from crops, it nevertheless presents itself in the broader field of biotechnology. Furthermore, using heterotrophic microorganisms does not fare well from an economical point of view because utilized carbon sources such as sugars experience price fluctuations that can further contribute to costs. Hitherto, the search for alternative carbon sources and implementation of autotrophic microorganisms become paramount.

Box 1.2: Carbon capture and utilization/storage Carbon dioxide is a very large contributor to greenhouse gas emissions; its rise since the industrial revolution has brought about global warming which is a large component of the grander climate change problem. As a result, many legislator bodies, such as the European Commission, have set goals to reduce carbon dioxide emissions. As an answer to these problems, CCU or CCS technologies have evolved. Conceptually, CCU technologies are aimed toward the conversion or transformation of harmful carbon emissions into useful commodities and products. In contrast, CCS aims to simply capture carbon emissions and store them. Due to the sheer abundance of carbon, the development and sophistication of these technologies can play a significant role in future, where CO2 is a primary, raw and sustainable starting material.

1.2 Phototrophy Photosynthesis is the only major natural solar energy storage mechanism on the Earth, and arguably the most important biological process in existence. Phototrophs are organisms that can carry out photosynthesis, in particular those that use light to assimilate carbon dioxide are photoautotrophs (e.g., green plants and cyanobacteria); others that use organic carbon are photoheterotrophs and will not be discussed further in this book.

Chapter 1 A short recapitulation of the autotrophic metabolism

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Photoautotrophy is driven by two sets of reactions: (1) ATP and NADPH-generating light reactions and (2) CO2-reducing light-independent (or dark) reactions. In green plants and cyanobacteria, the reducing equivalents (e.g., NADPH) and energy (ATP) required for CO2 fixation are produced from light-induced electron transport and the resultant proton gradient, respectively. In order for these photosynthetic processes to occur, water acts as an electron donor; it is the oxidation of this water that causes the formation of oxygen as a by-product which is characteristic in oxygenic photosynthesis (Box 1.3). Box 1.3: Naturally occurring oxygenic photosynthesis requires two sets of reactions in order to assimilate carbon dioxide and biomass. These reactions are termed (I) light-dependent reactions that necessitate the presence of light and, thus, naturally occur during the day; and (II) light-independent or, dark, reactions which occur naturally during the night in the absence of light. These can be summarized as follows: CO2 + H2 O + hv ðphotosÞ ! CH2 OðbiomassÞ + O2 where in (I) light-dependent reactions, photons are taken up by photosystems to generate reducing equivalents of biomass and energy resulting in the formation of oxygen as a by-product due to the splitting of water: 2H2 O + hv ! O2 + 4H + + 4e − +

4H +

4e −

+ 2NADP + + 3ADP ! 2ðNADPH + H + Þ + 3ATP

The light-independent, or dark, reactions (II) comprise the Calvin–Benson–Bassham (CBB) cycle, which is discussed in further detail later in this chapter.

In oxygenic photoautotrophic microorganisms, light-driven electron and proton transport is mediated by three transmembrane protein complexes – photosystem II (PSII), the cytochrome b6f complex (cyt b6f) and photosystem I (PSI) – which are located in the so-called thylakoid membrane of cyanobacteria and chloroplasts (Figure 1.3). This specialized membrane system forms an intracellular compartment – the thylakoid lumen – that is used to generate a proton gradient for ATP synthesis. The basic principle for light-induced electron transfer is similar in both photosystems: PSI and PSII. Light energy is harvested by chlorophyll containing antennae proteins and the excitations (excitons) are funneled to specialized reaction center of chlorophylls at the core, where charge separation is induced. The electron is transferred very rapidly to the primary acceptor (within picoseconds) and subsequent redox centers to avoid recombination of the electron with the electron hole. This allows diffusible mediators to pick up electrons at the photosystems and to transfer them to the next protein (complex) along the chain. The reaction center will be closed (photochemically incompetent) until it becomes ready for the next charge separation, which results in excess excitation energy being dissipated as fluorescence or heat (Box 1.4). In the case of PSII, electrons are transferred from the reaction center P680 via pheophytin and plastoquinone A to mobile plastoquinone B, which is protonated

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and released as plastoquinole from the complex at the cytoplasmic side after accepting two electrons. Each electron hole is filled by subsequent release of four electrons from the Mn4CaO5 cluster, which catalyzes the unique water oxidation reaction that liberates O2 and four protons from two water molecules per cycle. Plastoquinole is oxidized by the cyt b6f complex and the protons are released to the thylakoid lumen, thus generating a directional proton transfer from the cytoplasm to the lumen. This electrochemical gradient is used by the F1–Fo ATPase for the generation of ATP. However, the free energy of the electrons at this point of the chain is not sufficient to reduce NADP+. Therefore, electrons are elevated to a more negative potential by PSI, which acts as an electron pump. Electrons are transferred from the reaction center P700 via phylloquinone and FeS clusters to the mobile electron carrier ferredoxin (Fd), which reduces ferredoxin–NADP+ reductase to generate NADPH. Re-reduction of P700+ is mediated by lumen-localized proteins (plastocyanine or cytochrome c) that shuttle electrons from the cyt b6f complex to PSI. This linear electron transport leads to the generation of both energy carriers: ATP and NADPH. However, the actual electron transfer network is much more complex because alternative pathways exist (e.g., cyclic electron transfer), which enable the cell to react to changing light conditions or to adjust the ATP/NADPH ratio. Box 1.4: Chlorophyll fluorescence can be used as a noninvasive probe to monitor the physiological state of photosynthesis in a photosynthetic organism. One of the most important parameters is conversion efficiency (quantum yield, ΦPSII) for the conversion of light energy into chemical energy in PSII, which can be determined by pulse amplitude modulation (PAM) fluorescence spectroscopy. Chlorophyll fluorescence is measured based on PAM, which means that chlorophyll fluorescence is induced by a light source that emits modulated light in regular intervals. Consequently, only the variable fluorescence is monitored and provides information about conversion efficiency. This variable fluorescence (FV = FM – F0) is a unique feature of photosynthetic systems. In contrast, isolated chlorophyll shows a constant fluorescence intensity, which is proportional to chlorophyll concentration at constant illumination, and no variable fluorescence. F0 reflects the minimal fluorescence level that is probed with pulsed measuring light under conditions when all reaction centers are open (no actinic light), whereas FM is reached when all reaction centers in a dark-adapted sample are closed due to a short light pulse (SP) of strong actinic light. The FV/FM parameter reflects the potential quantum efficiency of PSII, with a maximal value of 0.83 for intact PSII. Thus, ~ 80% of the absorbed light energy is converted into photochemical energy under optimal conditions. The actual quantum yield parameter (ΦPSII = (F′M – F)/F′M) can be calculated by measuring the maximal variable fluorescence in light (F′M) and the stationary fluorescence (F) under constant illumination. This parameter provides information about the effective photochemical quantum yield of PSII.

The resulting ATP and NADPH produced through these processes are then used to assimilate carbon dioxide via the autotrophic CBB cycle. This cycle is, perhaps, the most dominant carbon dioxide pathway; in addition to photoautotrophs, it can also be found in most other chemolithoautotrophs. This light-independent cycle requires carbon dioxide, NADPH, ATP and some key enzymes, namely, ribulose-1,5-bisphosphate

Chapter 1 A short recapitulation of the autotrophic metabolism

Figure 1.2: Chlorophyll fluorescence transient measured with a DUAL-PAM-100 instrument (Walz, Germany). Unpublished data provided by Marc M. Nowaczyk.

Figure 1.3: A simplified, schematic overview of the photosynthetic machinery of a model oxygenic cyanobacteria. Light energy, in the form of photons (hv), is captured by photosystem II (PSII), where it is utilized to split water to generate oxygen, electrons and protons. The electrons travel down the photosynthetic electron transfer (PET) chain to reach photosystem I (PSI), which uses light energy in a series of reactions to eventually derive reducing equivalents that are key for various metabolic processes including carbon fixation. Other abbreviations: PQH2, plastoquinol; Cyt b6f, cytochrome b6f complex; Fd, ferredoxin; FNR, ferredoxin–NADP(+) reductase. Image created on BioRender.

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carboxylase/oxygenase (RuBisCO). Organisms that grow in aqueous environments, such as cyanobacteria and microalgae, face a formidable challenge with regard to CO2 capture; this is because CO2 diffusion is approximately 10,000 slower in water when compared to air [11]. In order to overcome this hurdle, these organisms developed sophisticated carbon concentrating machinery (CCM) to aid in the process. The CBB cycle in addition to CCM is discussed in further detail in Section 1.5. Cyanobacteria, which use the CBB cycle, are good candidates for photoautotrophic biorefineries as they can carry out the complex photosynthesis detailed above while remaining relatively simple to handle. The elucidation of their basic biology and other advances in synthetic biology have catapulted the investigation and production of genetic toolkits to enable nonnative functionalities in engineered cyanobacteria [12–15]. The engineering of the metabolism of cyanobacteria is discussed in Chapter 8.

1.3 Chemolithoautotrophy Chemolithotrophs are microbes that harness energy through the oxidation of environmentally available organic or inorganic electron donors. Most of these organisms are actually autotrophs; however, some can be purely heterotrophic while others can thrive mixotrophically through the use of inorganic electrons to assimilate organic carbon in addition to CO2. Chemoautotrophic organisms, first described by microbiologist Sergei Winogradsky [16], are capable of oxidizing an array of inorganic compounds that include hydrogen, carbon monoxide and reduced nitrogen compounds. The aforementioned processes proceed successfully only upon the availability of appropriate electron acceptors – mainly oxygen or nitrate and nitrite by facultative anaerobic organisms [17]. In order to elucidate the great industrial potential of chemolithoautotrophs, one must note that a great genetic diversity exists within them. This provides a vast array of valuable enzymes and endogenous pathways that can be studied and exploited. For example, specialized marine bacteria can potentially be used for nitrogen cycling in oceans [18], while hydrogen-oxidizing bacteria, such as Cupriavidus necator (C. necator), hold the potential for the production of biofuels and biomaterials [19]. Due to the large variety of microorganisms, we have chosen to discuss the simplest of these autotrophs, C. necator, to gain an understanding of basic mechanisms of growth. A detailed account of C. necator and its applications can be found in Chapter 9. C. necator is classified as a gram-negative soil and water-dwelling proteo-β-bacterium which was first isolated from soil in Göttingen (Germany) and is, perhaps, the most widely investigated hydrogen-oxidizing Knallgas bacteria. Notably, it exhibits a very flexible metabolism – whereby research has shown that it is naturally capable of using a wide array of “substrates” for growth, including toxic carbon monoxide [20], to a certain degree. This is evolutionarily sensible when one accounts for its natural extremely variable habitat where the ability to utilize alternate

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substrates can prove to be beneficial. As inferred, C. necator can grow both hetero- and autotrophically (concurrently or concomitantly), thereby making it a possible transitional chassis that couples the ease of handling, commonly associated with E. coli, and the valuable attribute of autotrophy. Hence, knowledge of the different proteins that govern its metabolic pathways is vital to its efficient utilization as a chassis strain. The tantalizing goal of widespread C. necator use for the production of various value-added products such as fuels [21], polymers [22] and fine chemicals [23] is further supported by the availability of its genome sequence and capacity to grow to high cell densities with minimal inclusion body formation [24, 25]. When growing under strictly lithoautotrophic as well as mixotrophic conditions, carbon dioxide is assimilated through the CBB cycle. The energy and reductants required to fuel the process under these conditions are supplied by the oxidation of hydrogen. The relatively robust mechanisms and enzymes that allow for the oxidation of hydrogen evolved in C. necator due to its existence at the interface of oxic and anoxic environments. Specialized enzymes, hydrogenases, that drive hydrogen oxidation are typically extremely sensitive to oxygen; however, in order to persist in its ever-changing environment, C. necator evolved to possess oxygen-tolerant hydrogenases. This aspect makes its use in laboratories far more straightforward as eliminates the need of constantly maintaining anaerobic environments. In addition to this trait, it expresses sensitive regulatory elements that aid in the rapid detection of hydrogen – a major component of which is a cytoplasmic hydrogenase-like protein termed the regulatory hydrogenase (RH). When RH senses the presence of hydrogen, it drives the transcription of two other hydrogenases: the membrane-bound hydrogenase (MBH) and the soluble hydrogenase (SH) [26]. The MBH that is tethered to the membrane is heavily involved in the formation of a proton gradient and is part of a hydrogen-dependent electron transport chain which is responsible for energy conservation [27]. The SH, present within the cytoplasm, contains an FMN-accommodating NADH dehydrogenase moiety which allows the reduction of NAD+ to NADH [28], supplying the reductants required for carbon dioxide assimilation during lithoautotrophic growth (Figure 1.4). Research also suggests that these hydrogenases, particularly SH, can accept NADPH to a certain degree as well [29]. This attribute, in addition to the availability of some transhydrogenases that convert NADH to NADPH, provides us with a larger repertoire of enzymes that can be used in C. necator and, thus, asserts its potential as a biotechnological powerhouse [30]. Examining the available molecular tools for the engineering of this organism helps identify what can be achieved with the current state of the art and what remains to be developed. An important proponent and driving force in the rising interest in C. necator is, in fact, the expansion and significant improvement of such tools. Plasmidderived expression systems that currently exist can provide technical simplicity and high expression levels; in addition, these recently developed systems boast several advantages over previous ones, which include plasmid stability over time [31].

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Figure 1.4: A simplified schematic representation of the chemolithoautotrophic metabolism in the model Knallgas organism, Cupriavidus necator. In the presence of hydrogen, sensed by the regulatory hydrogenase (not shown), it is taken up and oxidized by the oxygen-tolerant membranebound hydrogenase (MBH) to form the proton gradient necessary for the conservation of energy. The cytoplasmic soluble hydrogenase (SH) reduces NAD+ to NADH. All these different steps provide the necessary materials for the fixation of carbon dioxide via the CBB cycle. This image was adapted from the textbook Brock Biology of Microorganisms and created using BioRender.

1.4 Autotrophic growth under anaerobic conditions An anaerobic organism is one that does not require any oxygen in order to grow; in fact, the presence of oxygen is often detrimental to strict anaerobes since they lack protection systems against reactive oxygen species. Autotrophy also spans an array of anaerobic microorganisms with a typical example being anoxygenic photosynthetic sulfur bacteria, which use light as an energy source to fix inorganic carbon [32]. This photosynthetic process is referred to as anoxygenic, which means it does not generate oxygen, thereby, oxygen cannot be used as an electron donor; instead, sulfide or other reduced sulfur compounds act as electron donors in this case for photosynthetic carbon fixation [33]. Anoxygenic photosynthesis produces oxidized sulfur compounds, such as elemental sulfur (S0) or sulfate (SO42−); this process can be as follows, for example: 2H2S + light + CO2 → CH2O + H2O + 2S0 [34] OR H2S + 2CO2 + 2H2O → 2CH2O + H2SO4 [35] Photosynthetic sulfur bacteria are usually abundant in anoxic sulfide-containing water; their existence in lakes is mainly related to the availability of light, sulfide and

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inorganic nutrients. Sulfide-dependent anoxygenic photosynthesis is performed by two main groups of sulfur bacteria: the purple and the green sulfur bacteria [35]. Green sulfur bacteria absorb longer wavelengths of light compared to the purple variety. These types of microorganisms can also arise in anaerobic, sulfate‐containing environments including wetlands and overloaded facultative lagoons, where they reside between the algae and anaerobic zone [34]. Purple sulfur bacteria have bacteriochlorophyll a as their main photopigment, which shows strong absorption near infrared wavelengths; similar organisms also possess bacteriochlorophyll b. In both cases, these include two families of γ-proteobacteria: the Chromatiaceae and the Ectothiorhodospiraceae. The chromatiales are anoxygenic phototrophs that mainly grow photolithoautotrophically using sulfide or elemental sulfur as an electron donor for photosynthetic carbon fixation through the CBB cycle. Many of these species are strictly anaerobic and obligate phototrophs while others can also grow chemolithoautotrophically or chemoorganoheterotrophically. This family includes species from freshwater and salt-rich environments in marine or saline inland waters. In these places, such bacteria live in anoxic stagnant water bodies and/or sediments, where enough light arrives to allow for phototrophic growth. As previously mentioned, the second phylogenetic group of purple sulfur bacteria is in the family Ectothiorhodospiraceae. These usually include halophilic and/or alkaliphilic purple sulfur bacteria that grow under anaerobic conditions in light with reduced sulfur compounds as photosynthetic electron donors [35]. Although its main metabolic way of life is photoautotrophic and linked to the placement of the inner elemental sulfur outside the cell through globules, some species can also grow photoheterotrophically, or under microaerobic or aerobic conditions in darkness. Anoxygenic photosynthesis with sulfide as an electron donor has also been demonstrated for some cyanobacterial strains, despite cyanobacteria being known for plant-like oxygenic photosynthesis as mentioned previously [36].

1.5 Metabolic pathways for CO2 fixation in microorganisms One of the main challenges for the scientific community is the utilization of carbon CO2 as one carbon (C1) building block. C1 compounds, including CO2, formate and methanol, are considered as alternative feedstocks for biotechnologically interesting microbes [35, 36]. Mechanisms for carbon fixation and its utilization in biological systems have evolved over the last 4 billion years. CO2 can be utilized to assemble C–O (carbon–oxygen), C–H (carbon–hydrogen), C–C (carbon–carbon) and C–N (carbon– nitrogen) bonds via enzymatic reactions. Carbon fixation is a fundamental biochemical process in the biosphere that occurs via either (i) carboxylation where CO2 is

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attached to an existing metabolite or (ii) reduction where CO2 is converted to formate or carbon monoxide before further assimilation [37]. Carboxylation reactions are generally more common since inorganic carbon enters via a carboxylase in several carbon fixation pathways such as the CBB cycle. Six metabolic routes for CO2 fixation in prokaryotic cells have been reported: (i) the CBB cycle, (ii) 3-hydroxypropionate cycle, (iii) reductive acetyl-CoA–rAcCoA pathway or Wood–Ljungdahl pathway, (iv) tricarboxylic acid reductive cycle, (v) dicarboxylate/ 4-hydroxybutyrate cycle and (vi) 3-hydroxypropionate/4-hydroxybutyrate cycle. These cycles differ by the diverse enzymatic sets that govern them and their range of oxygen sensitivity.

1.5.1 The Calvin–Benson–Bassham cycle The CBB cycle is the principal pathway for atmospheric CO2 fixation and energy storage in carbon bonds, shaping the precursors of most primary and secondary metabolites necessary for cellular life across various species. It, in fact, is said to be responsible for over 99% of CO2 fixation globally [38]. It can be subdivided into three main stages: (1) carbon fixation, (2) reduction and (3) regeneration – a general overview of the cycle is shown in Figure 1.5. The first step involves the enzyme ribulose biphosphate carboxylase (RubisCo) catalyzing the formation of two 3-phosphoglyceric acid (3-PGA) molecules from a carbon dioxide and five carbon acceptor molecular (ribulose-1,5-biphosphate (RuBP). The 3-PGA released in this first step is then used to carry out the second reduction stage where both ATP and NADPH work unison to help convert the two 3-PGA molecules into glyceraldehyde-3-phosphate (G3P). Some of this G3P can then exit the cycle and go toward glucose synthesis; the rest is recycled in the third stage to restore the RuBP acceptor. In total, a total of three CBB cycles make one G3P molecule that can actually exit and progress to glucose synthesis. For every glucose molecule, 6 molecules of carbon dioxide need to be assimilated, resulting in the exhaustion of 12 NADPH and 18 ATP molecules. Despite the unequivocal importance of the CBB cycle, its key enzyme RuBisCo has a slow turnover (kcat ~ 3 s, determined from spinach) [37] and carries out a competitive and wasteful oxygenation side reaction as well. In microorganisms, such as cyanobacteria, the so-called CO2 concentrating mechanism (CCM) (Figure 1.6) effectively improves the activity of RuBisCO by elevating surrounding CO2 levels [39]. Considering the composition of the atmosphere on the Earth during its history, the CO2:O2 ratio has diminished. This variation resulted in an important adaptation in eukaryotic algae and cyanobacteria. Several components of CCM in green algae are dissimilar from those in cyanobacteria, especially regarding the central regulators and functional genes [40]. Differently from green algae, five distinct carriers mediate five uptake systems for inorganic carbons in most cyanobacteria; these include

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Figure 1.5: A schematic overview of the CBB cycle for the assimilation of CO2. The figure depicts the three distinct stages of CO2 assimilation via the CBB cycle: (I) CO2 fixation, (II) reduction of 3-phosphoglycerate to G3P and (III) the regeneration of ribulose 1,5-biphosphate from G3P.

two Na+-dependent HCO3–, one traffic ATPase (BCT1) and two CO2 uptake transporters [41]. The main intracellular factors of this mechanism are: (i) active bicarbonate [HCO3−) uptake transporters, (ii) section for carbonic anhydrases (CAs) and (iii) microcompartments for most of the RuBisCO. CAs quickly come in contact with HCO3−, which is converted into CO2, and RuBisCo successively makes use of it [41]. There are three distinct inorganic carbon uptake strategies: (i) the conversion of bicarbonates into CO2, using extracellular CAs that can freely diffuse into the cells; (ii) direct assimilation of CO2, through the plasmatic membrane; and (iii) the direct uptake of bicarbonates via active membrane transporters [42].

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The most important similarities of the CCM between green algae and cyanobacteria refer to the active accumulation of HCO3–, the microcompartmentation for RuBisCO and HCO3– dehydration toward CO2 in the chloroplast [43]. In cyanobacteria, HCO3– can be accumulated and then transported at the proximity of RuBisCO inside the carboxysome. It is then converted into CO2 by CA, thereby creating a microenvironment with a significant CO2 concentration [43]. Five distinct components mediate CO2 uptake in most cyanobacteria; these include three bicarbonate transporters and two uptake systems present on thylakoids [44].

Figure 1.6: The CCM in cyanobacteria (e.g., Synechocystis sp. PCC 6803; left) and in eukaryotic green algae (e.g., Chlamydomonas reinhardtii; right). The major intracellular components are (i) active HCO3− uptake transporters, (ii) section for CAs, (iii) microcompartment for most of the RuBisCO (e.g., carboxysome for cyanobacteria, and pyrenoids for eukaryotic green alga). CA, carbonic anhydrase; CAH3, thylakoid lumen carbonic anhydrase; RuBP, ribulose-1,5bisphosphate; 3PGA, 3-phosphoglycerate. Image adapted from Moroney et al. [43].

1.6 Conclusion Ongoing climatic change requires a fundamental transformation of society and industry by transitioning toward renewable and sustainable technologies. Industry sectors, like energy and mobility, are examples of these tremendous changes. Comparing the current situation of biotechnology to these sectors, one cannot fail to notice that here the implementation of renewable technologies is severely lacking. While biotechnological production does offer mild reaction conditions and is itself considered sustainable, the potential use of side streams and the application of autotrophy in biotechnology that has yet to be sufficiently realized influence global carbon dioxide emissions.

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While the metabolic capacities of autotrophic organisms could revolutionize biotechnology, their application in large scales has, as of yet, not been successful leaving the biotechnological potential of autotrophy untapped. This can be explained, on the one hand, with difficulties to provide autotrophs with either light or gaseous substrates to achieve yields that are comparable to those obtained with heterotrophs. On the other hand, many autotrophs are more difficult to manipulate than their well-explored heterotrophic counterparts. Progress in different technologies such as molecular biology, gas fermentation and illumination provide tools that have the potential to overcome these difficulties. Throughout this book, different applications of autotrophic microorganisms and processes are explored. In addition, their strengths and limitations in view of future widespread applications are also discussed.

References [1]

Lwoff A, Niel CBV, Ryan PJ, Tatum EL. Nomenclature of Nutritional types of Microorganisms. In: Cold Spring Harbor Symposia on Quantitative Biology. 5th ed, New York, The Biological Laboratory, Cold Spring Harbor, 1946, 302–303. [2] Raven JA. Photosynthetic and non-photosynthetic roles of carbonic anhydrase in algae and cyanobacteria. Phycologia. 1995, 34(2), 93–101. [3] Bar-On YM, Phillips R, Milo R. The biomass distribution on Earth. Proc Natl Acad Sci. 2018, 115 (25), 6506–6511. [4] Schirrmeister BE, Gugger M, Donoghue PCJ. Cyanobacteria and the Great Oxidation Event: evidence from genes and fossils. Palaeontology. 2015, 58(5), 769–785. [5] Govindjee SD. Adventures with cyanobacteria: a personal perspective. Front Plant Sci. 2011, 2(8). [6] Holland HD. The oxygenation of the atmosphere and oceans. Philos Trans R Soc B Biol Sci. 2006, 361(1470), 903–915. [7] Garcia-Gonzalez L, De Wever H. Valorisation of CO2-rich off-gases to biopolymers through biotechnological process. FEMS Microbiol Lett. 2017, 364, 20. [8] Majumdar P, Pant D, Patra S. Integrated photobioelectrochemical systems: a paradigm shift in artificial photosynthesis. Trends Biotechnol. 2017, 35(4), 285–287. [9] Lau N-S, Matsui M, Abdullah -A-A-A. Cyanobacteria: photoautotrophic microbial factories for the sustainable synthesis of industrial products. BioMed Res Int. 2015, 2015, 1–9. [10] Knoot CJ, Ungerer J, Wangikar PP, Pakrasi HB. Cyanobacteria: promising biocatalysts for sustainable chemical production. J Biol Chem. 2018, 293(14), 5044–5052. [11] Hwangbo K, Lim J-M, Jeong S-W, Vikramathithan J, Park Y-I, Jeong W-J. Elevated inorganic carbon concentrating mechanism confers tolerance to high light in an arctic chlorella sp. ArM0029B. Front Plant Sci. 2018, 9, 590. [12] Angermayr SA, Gorchs Rovira A, Hellingwerf KJ. Metabolic engineering of cyanobacteria for the synthesis of commodity products. Trends Biotechnol. 2015, 33(6), 352–361. [13] Lai M, Lan E. Advances in metabolic engineering of cyanobacteria for photosynthetic biochemical production. Metabolites. 2015, 5(4), 636–658.

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[14] Liu X, Miao R, Lindberg P, Lindblad P. Modular engineering for efficient photosynthetic biosynthesis of 1-butanol from CO2 in cyanobacteria. Energy Environ Sci. 2019, 12(9), 2765–2777. [15] Xia P, Ling H, Foo JL, Chang MW. Synthetic biology toolkits for metabolic engineering of cyanobacteria. Biotechnol J. 2019, 14(6), 1800496. [16] Dworkin M, Gutnick D. Sergei Winogradsky: a founder of modern microbiology and the first microbial ecologist. FEMS Microbiol Rev. 2012, 36(2), 364–379. [17] Kelly DP, Wood AP. The Chemolithotrophic Prokaryotes. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors, The Prokaryotes. Berlin, Heidelberg, Springer Berlin Heidelberg, 2013, 275–287. [18] Shah V, Chang BX, Morris RM. Cultivation of a chemoautotroph from the SUP05 clade of marine bacteria that produces nitrite and consumes ammonium. ISME J. 2017, 11(1), 263–271. [19] Thakur IS, Kumar M, Varjani SJ, Wu Y, Gnansounou E, Ravindran S. Sequestration and utilization of carbon dioxide by chemical and biological methods for biofuels and biomaterials by chemoautotrophs: opportunities and challenges. Bioresour Technol. 2018, 256, 478–490. [20] Heinrich D, Raberg M, Steinbüchel A. Studies on the aerobic utilization of synthesis gas (syngas) by wild type and recombinant strains of Ralstonia eutropha H16. Microb Biotechnol. 2018, 11(4), 647–656. [21] Bi C, Su P, Müller J, Yeh Y-C, Chhabra SR, Beller HR, et al. Development of a broad-host synthetic biology toolbox for Ralstonia eutropha and its application to engineering hydrocarbon biofuel production. Microb Cell Factories, 2013, 12(1), 107. [22] Przybylski D, Rohwerder T, Dilßner C, Maskow T, Harms H, Müller RH. Exploiting mixtures of H2, CO2, and O2 for improved production of methacrylate precursor 2-hydroxyisobutyric acid by engineered Cupriavidus necator strains. Appl Microbiol Biotechnol. 2015, 99(5), 2131–2145. [23] Nybo SE, Khan NE, Woolston BM, Curtis WR. Metabolic engineering in chemolithoautotrophic hosts for the production of fuels and chemicals. Metab Eng. 2015, 30, 105–120. [24] Park JM, Jang Y-S, Kim TY, Lee SY. Development of a gene knockout system for Ralstonia eutropha H16 based on the broad-host-range vector expressing a mobile group II intron: a mobile group II intron gene knockout system for Ralstonia. FEMS Microbiol Lett. 2010, Jul 1; no-no. [25] Pohlmann A, Fricke WF, Reinecke F, Kusian B, Liesegang H, Cramm R, et al. Genome sequence of the bioplastic-producing “Knallgas” bacterium Ralstonia eutropha H16. Nat Biotechnol, 2006, 24(10), 1257–1262. [26] Kleihues L, Lenz O, Bernhard M, Buhrke T, Friedrich B. The H(2) sensor of Ralstonia eutropha is a member of the subclass of regulatory [NiFe] hydrogenases. J Bacteriol. 2000, 182(10), 2716–2724. [27] Schwartz E, Voigt B, Zã¼hlke D, Pohlmann A, Lenz O, Albrecht D, et al. A proteomic view of the facultatively chemolithoautotrophic lifestyle of Ralstonia eutropha H16. Proteomics, 2009, 9(22), 5132–5142. [28] Jugder B-E, Chen Z, Ping DTT, Lebhar H, Welch J, Marquis CP. An analysis of the changes in soluble hydrogenase and global gene expression in Cupriavidus necator (Ralstonia eutropha) H16 grown in heterotrophic diauxic batch culture. Microb Cell Factories. 2015, 14(1), 42. [29] Burgdorf T, Van Der Linden E, Bernhard M, Yin QY, Back JW, Hartog AF, et al. The Soluble NAD+-Reducing [NiFe]-Hydrogenase from Ralstonia eutropha H16 Consists of Six Subunits and Can Be Specifically Activated by NADPH. J Bacteriol, 2005, 187(9), 3122–3132.

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[30] Assil‐Companioni L, Schmidt S, Heidinger P, Schwab H, Kourist R. Hydrogen‐driven cofactor regeneration for stereoselective whole‐Cell C=C bond reduction in Cupriavidus necator. ChemSusChem. 2019, 12(11), 2361–2365. [31] Gruber S, Schwendenwein D, Magomedova Z, Thaler E, Hagen J, Schwab H, et al.Design of inducible expression vectors for improved protein production in Ralstonia eutropha H16 derived host strains. J Biotechnol. 2016, 235, 92–99. [32] Imhoff JF. Taxonomy and Physiology of Phototrophic Purple Bacteria and Green Sulfur Bacteria. Blankenship RE, Madigan MT, Bauer CE, In: Anoxygenic Photosynthetic Bacteria, Dordrecht, Kluwer Academic Publishers, 2004, 1–15. (Advances in Photosynthesis and Respiration; vol. 2). [33] Madigan MT, Jung DO. An Overview of Purple Bacteria: Systematics, Physiology, and Habitats. Hunter CN, Daldal F, Thurnauer MC, Beatty JT, In: The Purple Phototrophic Bacteria, Dordrecht, Springer Netherlands, 2009, 1–15. (Advances in Photosynthesis and Respiration; vol. 28). [34] Gerardi MH, Lytle B. The Biology and Troubleshooting of Facultative Lagoons. First, John Wiley & Sons, Ltd, 2015, 244. [35] Camacho A. Sulfur Bacteria. In: Encyclopedia of Inland Waters, Elsevier, 2009, 261–278. [36] Klatt JM, Maa A-N, Yilmaz P, Lavik G, De Beer D, Polerecky L. Anoxygenic photosynthesis controls oxygenic photosynthesis in a cyanobacterium from a sulfidic spring. Kostka JE, editor. Appl Environ Microbiol. 2015, 81(6), 2025–2031. [37] McNevin D, Von Caemmerer S, Farquhar G. Determining RuBisCO activation kinetics and other rate and equilibrium constants by simultaneous multiple non-linear regression of a kinetic model. J Exp Bot. 2006, 57(14), 3883–3900. [38] Raven J. Contributions of anoxygenic and oxygenic phototrophy and chemolithotrophy to carbon and oxygen fluxes in aquatic environments. Aquat Microb Ecol. 2009, 56, 177–192. [39] Badger MR. CO2 concentrating mechanisms in cyanobacteria: molecular components, their diversity and evolution. J Exp Bot. 2003, 54(383), 609–622. [40] Zhu B, Chen G, Cao X, Wei D. Molecular characterization of CO2 sequestration and assimilation in microalgae and its biotechnological applications. SIAlgal Biorefinery. 2017, 244, 1207–1215. [41] Thakur IS, Kumar M, Varjani SJ, Wu Y, Gnansounou E, Ravindran S. Sequestration and utilization of carbon dioxide by chemical and biological methods for biofuels and biomaterials by chemoautotrophs: opportunities and challenges. Bioresour Technol. 2018, 256, 478–490. [42] Vuppaladadiyam AK, Yao JG, Florin N, George A, Wang X, Labeeuw L, et al.Impact of flue gas compounds on microalgae and mechanisms for carbon assimilation and utilization. ChemSusChem, 2018, 11(2), 334–355. [43] Moroney JV, Jungnick N, DiMario RJ, Longstreth DJ. Photorespiration and carbon concentrating mechanisms: two adaptations to high O2, low CO2 conditions. Photosynth Res. 2013, 117(1–3), 121–131. [44] Price GD, Badger MR, Woodger FJ, Long BM. Advances in understanding the cyanobacterial CO2-concentrating-mechanism (CCM): functional components, Ci transporters, diversity, genetic regulation and prospects for engineering into plants. J Exp Bot. 2008, 59(7), 1441–1461.

Birthe Halmschlag, Lars M. Blank

Chapter 2 Metabolic engineering of microbes Abstract: Microbes have developed a multitude of possibilities to utilize CO2 as carbon source. While the CO2 fixation pathways are optimized through evolution, the production of valuable products thereof is rarely a natural trait. To efficiently use autotrophic microbes in industrial biotechnology, metabolic engineering can contribute to further yield and to rate improvement of CO2 fixation as well as to the exploitation of microbial synthesis capacities. The optimized metabolic engineering workflow includes consecutive design–built–test cycles. The computational design of metabolic networks built on genome sequencing data can direct strain engineering and thereby reduce experimental work. With growing numbers of available gene editing methods and expanding molecular toolkits, the construction of genetically engineered organisms greatly accelerated. The development of high-throughput screening systems for clone identification and phenotyping is a prerequisite to keep pace with the progress in cell factory design. The herein described metabolic engineering techniques will greatly expand the potential of autotrophic microbes in industrial biotechnology processes. Keywords: synthetic biology, metabolic engineering, quantitative physiology, GEM, design-built-test cycle

2.1 Metabolic engineering We are living in a time of great challenges that were highlighted by the United Nations in the 17 sustainable development goals (see United Nations – sustainable development knowledge platform) [1]. With the now active discussion in our societies, the “Friday for Future” movement and the increasingly louder call for drastic reductions in CO2 emissions, the previously postulated circular (bio)economy is or at least should become a reality. Utilizing not only carbon fixation by plants, for example, firstand second-generation carbon feedstocks like starch, sucrose, molasses and biomass hydrolyzates but also CO2 directly for the microbial synthesis of our chemicals and materials of daily life would significantly contribute to this envisaged circular economy. Microbes have developed a multitude of possibilities to utilize CO2 as carbon source, from anaerobes utilizing, for example, hydrogen as electron donor to photosynthetic cyanobacteria. While the CO2 fixation pathways are optimized through evolution, although not at all in the ever-important performance parameters such as titer, rate and yield (TRY, see Box 2.1), the production of valuable products thereof is rarely a natural trait. Hence, researchers aiming to exploit the synthesis capacities of https://doi.org/10.1515/9783110550603-002

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autotrophic microbes require genetic access to these organisms and tools to engineer the metabolic network at will. Box 2.1: Performance parameters for metabolic engineering The parameters TRY are utilized to describe the performance of microbes in biotechnological processes. Key performance indicators are frequently used in many industries. – Titer: This represents the concentration of a substrate or a product. The titer determines (partially) the cost of purification. A rule of thumb indicates a minimum target of 100 g/L for fine chemicals production or cheaper. Common units are g/L or mmol/L. – Rate: This represents the time the production or consumption takes. In metabolic engineering often the specific rate, that is, the rate per biomass is of interest, as we want to optimize the cellular activity. In industry, the volumetric rate is of key interest, as it indicates the required volume of the reactor(s), and hence operation, invest and capital cost. Common units for the specific rate are gproduct/gCDW/h and mmolproduct/gCDW/h, while for the volumetric rate gproduct/L/h and mmolproduct/L/h are used. – Yield: This denotes the amount of product produced per amount of a given substrate (biomass) consumed. Yields can be freely defined; however, in industry the product yield on substrate is central, as this value indicates the price of substrate. In literature, especially the recombinant protein production literature yield is often synonymously used for titer. Common yields are gproduct/ gsubstrate and gproduct/gcdw. In metabolic engineering we like to make mass balances and a Cmol definition is helpful, in which glucose is not written as C6H12O6, but rather per Cmol as CH2O. Units are Cmolproduct/Cmolsubstrate and Cmolproduct/gCDW.

Researchers can employ two alternative strategies (Figure 2.1), either using a microbial isolate (complex organism) that has several of the desired phenotypical properties from the beginning or a well-known microbe such as Escherichia coli, Bacillus subtilis or Saccharomyces cerevisiae as chassis that is easily amenable for genetic engineering [2]. In the former, the tasks are manifold from side-product elimination to high celldensity fermentations, while the latter approach can be immediately challenging when trying to implement the required enzymatic and transport activities [3]. For both strategies, the toolbox of metabolic engineering offers a lot (Figure 2.1). Metabolic engineering can contribute to our ongoing race to understand the origin of life all the way to yield and to rate efficient CO2 fixation in industrial biotechnology. Indeed, CO2 fixation of microbes can be truly rapid [4], opening again novel possibilities for engineered microbes. In this chapter, we briefly introduce a possible workflow of a metabolic engineering campaign, starting with an overview of computational approaches for strain design, followed by molecular tools for their implementation, and ending with the possibilities of quantitative physiology for strain characterization (see Box 2.2). Box 2.2: Design–built–test cycles for metabolic engineering Metabolic engineering for the performance improvement of microbes in biotechnological processes includes multiple design–built–test cycles:

Chapter 2 Metabolic engineering of microbes

– –

–

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Design: Microbial strains are designed in silico using computational methods. Based on genomescale metabolic models (GEMs), a multitude of genome editing targets can be identified. Built: Genomic modifications are introduced into microbial strains to alter their phenotype. Various genome editing methods have been developed that enable the genomic modification of many different microorganisms. Test: The identification of strain improvement requires high-throughput test systems. Automated, parallelized and miniaturized test processes are developed to test the multitude of created strains.

The lessons learned from a metabolic engineering cycle are used for the refinement of the metabolic model resulting in an iterative strain optimization process.

Figure 2.1: Metabolic engineering approaches for the construction of tailor-made organisms modified from Reuss et al. [2]. Wiley; https://onlinelibrary.wiley.com/page/journal/17517915/ homepage/permissions.html.

2.2 Computational-based design of metabolic networks Beginning in the mid-1990s, technological advances rapidly transformed the biological research. The evolving high-throughput DNA sequencing technologies dramatically increased the data availability. The genome sequencing technology was followed by further omics technologies broadening the data spectrum. With the fast increasing

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number of genomes being sequenced, also the number of genome-scale metabolic reconstructions is growing [5]. GEMs are making use of the acquired genome data. The models represent the whole metabolism of one organism, for which an ever-increasing range of applications can be computed [6]. In recent years, GEMs for more than 6,239 single- and multicellular, pro- and eukaryotic organisms have been built [6]. The organisms for which GEMs are available include pathogens like Haemophilus influenzae [7] and Helicobacter pylori [8] or industrially important microorganisms like E. coli [9], S. cerevisiae [10–12] and B. subtilis [13]. More recently, genome-scale models for autotrophic microbes like the acetogene Clostridium ljungdahlii [14], photosynthetic cyanobacterium Synechocystis sp. [15] and the microalgae Chlamydomonas reinhardtii [16] have been constructed.

2.2.1 Metabolic model development All metabolic models are set up using genomic information and knowledge about enzymes and their encoded reactions. With increasing biological information, also the metabolic models built thereon become more accurate. In order to deal with the everincreasing amount of data, advanced tools as well as databases are needed, which allow the analysis and management of data and the development of new models (for algorithms, see Figure 2.2). To speed up the model generation process, the SEED framework, a web-based resource for high-throughput generation, optimization and analysis of GEMs has been developed [5]. The available models can be accessed by databases like BiGG [17]. Already 85 metabolic models are included in the database. As the number of metabolic models is vastly increasing, the list of metabolic model reconstructions must be updated continuously. However, the number of metabolic model reconstruction does not come close to the number of genomes that have been sequenced (>300,000 sequencing projects [18]). To speed up the model reconstruction process, automated tools have been developed that minimize the demand for manual model curation [19]. The reuse and comparison of constructed models are only possible if all models rely on some general standards. These standards include the coding in the Systems Biology Markup Language (SBML) [20], which represents models in an XML-based exchange format. Using the SBML Toolbox for MATLAB®, the model can be easily edited and reused [21]. The reliability of a model can be improved by testing against experimental datasets that cover such different information as gene essentiality and protein–protein interactions. Therefore, the model creation is an iterative process including many cycles of experimental work and model updating. The never-ending iteration is arguably most advanced for E. coli [22, 23] and S. cerevisiae [24].

PROM

Flux minimization

Thermodynamic realizability TMFA NET analysis Thermodynamic parameter-based analysis

Regulatory mechanisms

0

0

0

1

th o d s

1

II-COBRA

BOSS

GrowMatch

OptKnock Objective tilting Reaction perturbation design

Reaction addition design

OptStrain

Gene GDLS deletion design CiED OptGene SA. SEAs

Gap-filling

Algorithmic gap-filling SCAR GapFind, GapFill

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Figure 2.2: “Phylogeny” of constraint-based metabolic model analysis [25]. Reprinted with permission from Springer Nature Customer Service Centre GmbH: Springer Nature Reviews Microbiology (Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods, Lewis et al. [25].

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2.2.2 “Omics” technologies for metabolic model construction The experimental datasets applied to the model building process include all kinds of omics data. All studies on genomes are accessible through the online database of genomes [18]. Completed genome projects included in this database increased from 400 to 35,000 within 10 years. The dynamics of gene expression are investigated in the field of transcriptomics. The development of microarrays [26], quantitative realtime PCR (polymerase chain reaction), serial analysis of gene expression [27] and RNA-seq approaches [28] enable an ever-more comprehensive and quantitative detection of transcripts. As the first link between genomic information and phenotypes, the transcriptome indicates active pathways and their dynamics in the cells. However, all kinds of posttranscriptional control mechanisms are not reflected by transcriptomic analyses. Posttranscriptional control is better indicated by the quantification of all proteins of a cell, represented by the proteome. Proteomics is not only the study of all proteins but also focusses on interactions between proteins, protein isoforms and modifications [29]. Also proteome research came a long way from simple electrophoresis to two-dimensional gel electrophoresis and nowadays gel-free mass spectrometry methods [30]. The final investigation stage of cellular components is represented by the identification and quantification of all cellular metabolites by metabolomics [31]. The metabolome is determined by the output of the earlier mentioned omics data, thereby the metabolome most directly reflects the phenotype. Hence, a metabolome analysis is a useful tool to study the influence of environmental or genetic perturbations on the cellular system. Since the metabolome represents a large set of diverse molecules, advanced analytical methods are a prerequisite for metabolome analysis. Methods typically used for metabolomics include mass spectrometry or NMR (nuclear magnetic resonance) spectroscopy [32, 33]. Advances in these analytical techniques turned metabolomics into a favorable tool in guiding metabolic engineering [34]. Despite the mentioned analyses of cellular components, further efforts aim at molecular interactions inside cells [35]. Interaction datasets may include information about protein–protein or protein–DNA interaction. The cellular phenotype can further be described by the total metabolic fluxes (fluxomics) within the cell and across the cell membrane [36, 37]. The analysis of metabolic fluxes is mainly performed to analyze engineered strains, which is described in detail in Section 2.4.2.

2.2.3 Model-driven metabolic engineering GEMs are of growing interest in biology since they offer the opportunity to direct metabolic engineering to special targets. The models allow the prediction of phenotypes during growth on alternative substrates. Furthermore, models are used to predict targets for gene manipulations like overexpression or knockouts [34]. Hence, the in silico modeling prior to experimental work is useful to direct strain engineering in terms of

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production improvement for yield or stability [38]. To analyze models, numerous tools and algorithms have been developed (Figure 2.2). Among them, one widely used tool is flux balance analysis (FBA) [39]. A stoichiometric metabolic network reconstruction is used as basis for FBA. The output depends on the defined biological objective that can be grown, that is, biomass formation or product synthesis. Depending on the objective, FBA is applied to predict phenotypes for genetic or environmental modifications. Based on the analysis results, strategies for bioprocess design and metabolic engineering can be deduced [40]. To minimize efforts in strain engineering, routines that search for optimal genetic perturbations have been described [41]. For example, bilevel optimization problems can take into account the engineering objective (e.g., high product yield) and the microbial phenotype (e.g., a minimal required growth rate). Microbial strain design using genetic optimization algorithms with multiple engineering objectives was recently demonstrated [42]. Among autotrophic organisms, cyanobacteria and C. reinhardtii are well studied. Metabolic models and applications of these have been reported for the two mentioned organisms. C. reinhardtii has arisen as the hallmark model organism among eukaryotic microalgae. It has been used to study photosynthesis, cell motility and phototaxis, cell wall biogenesis and other fundamental cellular processes [43]. The metabolic model of Chlamydomonas has been expanded to the first network that accounts for detailed photon absorption permitting growth simulations under different light regimes [44]. The network offers insights into possible engineering targets to increase the C. reinhardtii photosynthetic activity. As a second example, FBA applied to an extended network of cyanobacterium Synechocystis sp. PCC6803 has given new insights into the cyanobacterial TCA cycle, putative glyoxylate shunt and the role of photorespiration in cyanobacterial growth [45]. Recently, computational analysis has been applied to carbon dioxide fixation pathways to study the potential of native and synthetic CO2 fixation routes (Figure 2.3). The results demonstrate both native and synthetic CO2 fixation pathways as promising alternatives to sugar-based fermentations and may support future metabolic engineering attempts for enhanced CO2 fixation with microorganisms [46].

2.3 Molecular construction of production strains 2.3.1 Molecular biology tools To implement the genomic targets identified for production strain engineering, a range of genetic engineering tools are available. Regardless of the type of modification, either genomic integrations and deletions or plasmid-based overexpression, the genetic construction necessitates methods for DNA assembly and transformation. To meet these requirements, a vast number of assembly methods has been developed (for review, see [47]). Before the development of new technologies, classical

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Figure 2.3: Investigation of carbon dioxide assimilation pathways for computational analysis of the performance potential of cyanobacteria, acetogens, methylotrophs and synthetic pathways in S. cerevisiae in terms of production rates, production yields and optimization potential [46] (CC BY-NC-ND 4.0).

DNA cloning techniques employing restriction and ligation enzymes were commonly used. The classical DNA cloning is limited by several factors. The use of restriction enzymes requires specific recognition sites that are naturally available or specifically introduced into the organism. Further, the number of assembled fragments is limited by these specific sites and the efficiency of ligation. The success of the assembly strongly depends on the ratio of inserted DNA fragments to the vector backbone. Therefore, larger amounts of DNA were needed when compared to modern methods. To overcome these limitations, new assembly techniques including Golden Gate cloning [48], Gibson assembly [49] and ligase cycling reaction [50, 51] were developed, and are here briefly summarized. The requirement for newly developed methods is to allow seamless assembly without any constraints on the DNA sequences and a workflow that can be adapted to automatization. The benefit of Golden Gate cloning is based on the use of type IIs restriction enzymes [48]. In contrast to the majority of restriction enzymes, these enzymes cleave DNA outside of their recognition site. The resulting four nucleotide overhangs are independent from the restriction site, resulting in DNA fragments with up to 256 possible nucleotide combinations. Hence, the method allows the seamless assembly of multiple fragments in a one-pot reaction. For efficient and easy assembly of DNA blocks, several systems based on the Golden Gate method were developed, such as the MoClo system [52]. Those modular cloning systems allow the fast assembly of

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DNA fragments such as promoters, signal peptides, coding sequences and terminators. The use of restriction sites with optimized overhangs ensures high efficiency of the modular cloning. The choice of the overhangs is crucial since the nucleotide sequence was observed to strongly influence the accuracy of the assembly. By examination of all possible overhangs and subsequent data-driven selection of high fidelity overhangs, the assembly of up to 24 fragments in one single reaction was achieved [53]. The Gibson assembly method is an isothermal DNA assembly method that is completely independent from restriction enzymes [49]. This method takes advantage of three enzymes: a 5ʹ-exonuclease, a polymerase and a ligase. DNA fragments with complementary ends are assembled with these enzymes in a single isothermal reaction. The exonuclease removes nucleotides from the 5ʹ-end of double-stranded DNA fragments. Due to its heat lability at the reaction temperature of 50 °C, the enzyme is inactivated during the assembly. After annealing of the DNA fragments via their complementary, single-stranded overhangs, the polymerase fills the gaps that resulted from the exonuclease activity. The ligase seals nicks in the DNA backbone. The Gibson assembly method was successfully used to seamlessly assemble up to 900 kb DNA molecules from multiple fragments providing an efficient synthetic biology tool [49]. The ligase cycling reaction, initially developed for the highly specific detection of single-nucleotide polymorphisms in DNA templates, can be used for the assembly of double-stranded DNA fragments with single-stranded bridging oligonucleotides [51]. The bridging oligonucleotides are complementary to the ends of two DNA fragments to be assembled. After denaturation of the double-stranded DNA fragments, the bridging oligonucleotides anneal to the fragments that enable the ligation of fragments. In a second cycle of denaturation, annealing and ligation, the ligated DNA fragments serve as a template for the annealing and ligation of the lower strands. The ligase cycling reaction is reported to be superior to other assembly methods in terms of the assembly of multiple fragments [50]. The method enabled the assembly of up to 12 DNA fragments with a high percentage of correct colonies obtained. Comparing this method to DNA assembly via homologous recombination in yeast cells, the assembly time is greatly reduced. Further, the possibility of automatization of ligase cycling reaction for multifragment DNA assembly of biochemical pathways has been demonstrated [54]. The techniques discussed here create a convenient workflow and enable the automatization of DNA assembly for high-throughput construction of DNA constructs. Making use of the described methods, the development of a modular cloning toolkit for the microalga C. reinhardtii greatly expands the possibilities for autotrophic organisms in biotechnology [55]. The MoClo Toolkit with 119 genetic parts includes promoters, UTRs, terminators, tags, reporters, antibiotic resistance genes and introns for engineering microalgae. Recently, not only DNA assembly methods but also the genome editing methods have been improved to simplify strain development for biotechnological approaches. Along with zinc-finger nucleases and transcription activator-like effector nucleases, especially the clustered regularly short palindromic repeats (CRISPR)/Cas (CRISPR-

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Figure 2.4: Genome editing strategies including homologous recombination and recently developed nuclease-based methods [56] (CC BY 4.0).

associated protein) method has extensively been used for genome editing in all types of organisms (Figure 2.4). In 2012, the CRISPR/Cas9 method was first introduced as a genome editing method for bacteria [57]. Thereafter, the adaption of the CRISPR/Cas method for various organisms including other bacteria [58], mice [59] and plants [60] was reported. The method makes use of the bacterial adaptive immune system consisting of CRISPR and Cas proteins. The CRISPR-associated protein Cas9 from Streptococcus pyogenes is a DNA endonuclease. Forming a complex with a guiding RNA originally encoded in the CRISPR locus, Cas9 is triggered to generate double-strand DNA breaks. The cleavage site is determined by a 20 base pair sequence of the guiding RNA complementary to the DNA target site. For genome editing purposes, the choice of this 20 base pair RNA sequence enables targeting specific genes. The sequence is only restricted by the requirement of the protospacer adjacent motif (PAM), a threebase-pair site of NGG located directly downstream of the DNA target site. The actual genome editing functions either by nonhomologous end joining (NHEJ) or by homology-directed repair (HDR) after the double-strand break. The two mentioned mechanisms are both alternatives of homologous recombination of which the frequency is greatly increased in response to the double-strand break [61]. The NHEJ happens in the absence of a DNA repair template, causing the introduction of random mutations. Alternatively, the provision of an exogenous DNA repair template facilitates precise genome editing by HDR. The DNA repair template must be designed with regions homologous

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to the genomic DNA up- and downstream of the introduced double-strand break. Any desired DNA sequence to be introduced to the genome can be located between those homologous regions. The CRISPR/Cas method is very site specific with only little offtarget effects that were even reduced by engineering the Cas protein and the guide RNA [62–64]. Since the only requirement for this method is the presence of a PAM at the target site, it is universally applicable. Further, multiple mutations can be introduced simultaneously using several guide RNAs, which makes CRISPR/Cas superior to previous genome editing methods [65–67]. To create higher genomic variety, CRISPR/Cas was successfully combined with methods for the generation of sequence diversity such as error-prone PCR [68]. With the development of CRISPR/Cas9 for genome editing, new possibilities for high-throughput functional genomics arise [69]. Several examples with libraries of guide RNAs have been reported. Library sizes of more than 200,000 sgRNAs were used for genome-wide modifications and gain-of-function screenings [70]. To address not only single genome engineering targets, but produce a large genomic diversity, research also focusses on automated genome editing methods. Multiplex automated genome engineering (MAGE) is the prototype of such an automated method [71]. For this process, numerous synthetic single-stranded oligonucleotides are introduced to a cell population by electroporation. The oligonucleotides are used as starting points for DNA replication, that is, as Okazaki fragments. Since several oligonucleotides may serve as Okazaki fragments in one cell, multiple genomic targets can be addressed simultaneously. The uptake of different oligonucleotides in each cell produces a heterogeneous population with high genomic diversity. The generation of 4.3 billion genomic variants within 3 days editing 24 genomic targets and thereby increasing the lycopene production in E. coli 5-fold impressively demonstrates the potential of automated genome editing [71]. The workflow of MAGE was automated on a dedicated device that facilitates the ever occurring steps of growth, cell harvest and transformation. Standard genome editing protocols relying on Gibson assembly or yeast homologues recombination are semiautomated on liquid handling platforms, referred to as biofoundries [72]. The newest generation of biofoundries miniaturize the DNA assembly and, where possible, other steps on tip-free acoustic liquid handling platforms [73]. For sure, the molecular biologist in a genome editing project already in the near future will be little involved in liquid handling, while spend most of the time on the design, as seen in the most modern labs already today.

2.3.2 Genetic engineering of model organism Bacillus subtilis The success of genome editing methods depends on several features which are independent of the type of organism to be engineered. A DNA manipulation method, a method for introduction of DNA into the cells, the integration of the target DNA by homologous recombination and a method for strong selection must exist. For wellinvestigated organisms frequently used in laboratories for several decades such as

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B. subtilis, methods to meet these requirements were developed. As the first nonpathogenic microorganism to be transformed [74], B. subtilis evolved into a model system for the genetics of Gram-positive bacteria. The natural development of competence in B. subtilis toward the end of the exponential phase can be easily used for transformation. Specific growth conditions may be used to experimentally induce natural competence [75]. Besides natural competence, protoplast transformation [76] and the transformation via electroporation [77] have also been described for B. subtilis. Subsequent to the uptake of exogenous DNA, B. subtilis integrates DNA into the genome via homologous recombination [78]. B. subtilis is an industrially relevant production host for the production of enzymes such as amylases and proteases, antibiotics, vitamins and poly-γ-glutamic acid [79–81]. To optimize production strains regarding adverse properties such as extracellular proteases, sporulation and competing metabolic pathways, genetic engineering tools for multiple genetic modifications are required. Due to its ability for homologous recombination, chromosomal integrations and deletions can be obtained by the construction of expression/deletion cassettes with flanking regions that are homologous to the genome and an antibiotic resistance marker. Advanced methods including recognition sites for site-specific recombinases have been applied for the reuse of the same marker gene and cassettes in multiple genome editing steps [82, 83]. However, the recognition sites of the recombinase will remain as scars in the genome and an additional transformation step of a recombinase containing plasmid is required. These drawbacks have been overcome with the development of a markerless genome editing system that is based on the phosphoenolpyruvate-dependent phosphotransferase system for mannose [84]. The manP gene encodes the mannosespecific permease of the phosphotransferase system (PTS). Intracellular mannose-6phosphate is further converted to fructose-6-phosphate by the mannose-6-phosphate isomerase encoded by manA. The growth of manA deletion mutants is inhibited by the presence of mannose. Hence, manP can be used as a counter-selectable marker in a manPA deletion strain. Plasmids used for genome editing in manPA-negative B. subtilis strains contain an origin of replication that prevents replication in B. subtilis but enables cloning in E. coli, a deletion or integration cassette with flanks for genomic integration via homologous recombination, an antibiotic resistance gene for selection of transformants and manP as counter-selectable marker for cells that spontaneously lost the vector after integration. The vector loss theoretically results in the desired gene deletion or integration in half the transformants, whereas the other half will resemble the original genotype. The markerless system has been applied to reduce the B. subtilis genome by approximately 36% [85].

2.3.3 Molecular manipulation of autotrophic organisms To make use of autotrophic chassis such as the photosynthetic microalgae C. reinhardtii, molecular biology techniques were adapted (see Chapter 6). Enabling chloroplast

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transformation for microalgae in 1990 [86], those organisms were made accessible for biotechnological approaches. Further enhancements in the spectrum of genetic elements including promoters, selectable marker genes and expression vectors simplified the biotechnological applications [87]. However, the unusual codon usage frequently occurring in algae often necessitates further adaptions [88]. Despite existing challenges in genetic engineering of algae, the progress is steep. The trophic conversion of the obligate photoautotrophic diatom Phaeodactylum through heterologous expression of a functional glucose transporter represents a landmark in genetic engineering of microalgae [89]. With emerging automated technologies and simplified workflows, the biotechnological applications in terms of biofuel and heterologous protein production will further be enhanced (for review, see [90]). Recently, the biotechnological application of acetogenic bacteria including C. ljungdahlii attracts interest. The ability of CO2 fixation via the Wood–Ljungdahl pathway makes C. ljungdahlii a promising biotechnological production host [91]. The company Lanzatech is exploiting engineered acetogens for the valorization of syngas (mixture of H2/CO/CO2, see Chapter 04) originating in steel plants, for example, for ethanol production [92]. To efficiently engineer C. ljungdahlii, the molecular toolkit has been expanded in recent years including transformation protocols [93] and genome editing methods such as CRISPR/Cas9 [94].

2.4 Performance testing Developing genetic engineering tools enables the creation of a vast number of mutants in short periods of time. The number of potentially beneficial candidates out of the large library must be narrowed down for deeper analysis of improved mutants. The identification of beneficial mutations within the large number of developed strains requires sophisticated screening technologies to test the performance of the created mutants. Furthermore, methods for the detailed characterization of the reduced number of strains will be discussed.

2.4.1 Miniaturization and parallelization The rapid increase in our understanding of microbes in general and metabolic network operation specifically is fostered by the seemingly ever rapid development of omics technologies, becoming increasingly quantitative [95]. The progress in genomics, transcriptomics, proteomics and metabolomics coupled with the aforedescribed genome editing methods speed up the construction of cell factories. Time and cost reductions enable the development of large microbial libraries. With increasing mutant library sizes, the demand for high-throughput screening systems rises (see Chapter 3). For satisfactory screening, the ability to measure relevant process parameters is

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critical, often requiring a trade-off between measurable parameters and screening throughput [96]. Indeed, screening for valuable mutants or phenotypes often represents the bottleneck in cell factory engineering limiting the size of the mutant library investigated. Microtiter plate-based assays are labor intensive but can be enriched to high content phenotyping [97] and possibly even to automation [98, 99], and microfluidics allow further reduction in size and thereby parallelization [100], while dropletbased formats facilitate ultimate throughput [101]. The information content gained during miniaturized cultivation in microtiter plates has been strongly improved through the readout of culturing parameters such as cell density, pH and dissolved oxygen [102]. Multi-well format cultivation was applied to optimize the growth conditions for microalgae [103]. The rapid development of liquid handling systems, from simple one at a time, to high-throughput pipetting tip utilizing systems, to acoustic liquid handling [73], has greatly increased the throughput of microtiter plate-based assays. The possibilities in liquid handling are for now exploited for molecular biology, while equivalent applications in phenotyping are mostly missing. Parallelization and miniaturization by microfluidics is one possible approach to increase the throughput by several orders of magnitude (microtiter plate volumes are in the 100 µL range and higher, while microfluidic allows batch fermentation at volumes as low as 10 pL) [104]. These miniaturized reactors were used, for example, for single algal cell characterizations [105]. Besides testing mutant libraries, microfluidics have been shown to be useful for each step of the cell factory development allowing, for example, combinatorial DNA synthesis in droplets, volume-scaled MAGE [106] and droplet-based mutant identification [101]. Indeed, efforts to automate and miniaturize the entire workflow are published [107, 108]. A microfluidic high-throughput selection system was used to identify microalgal mutants with improved photosynthetic activity [109]. Engineering strategies to improve the rate of photosynthesis are demanding due to the complexity of photosynthesis. In these microalgae, photosynthetic productivity correlates positively with their phototaxis ability. Indeed, single mutants with 1.9-fold and 8.1-fold increased photoautotrophic cell growth and lipid production, respectively, were selected. The mutations were traced back to candidate genes that are targets for further strain engineering demonstrating the use of a microfluidic screening system for the selection of autotrophic cells. While cultivation is miniaturized down to volumes of single cells, the analytics ideally enabling cell performance quantifications are becoming increasingly challenging [110]. Fluorescence-activated cell sorting (FACS [111]) allows single cell discrimination, using cells from all different growth formats. Miniaturized FACS, that is, microfluidic versions [112], so-called µFACS [113], can be directly linked to microfluidic cultivation systems. For the screening of the productivity of engineered cell factories, biosensors that link the productivity to a measurable output signal (mostly fluorescent protein based) are applied [114]. The detection of beneficial genotypes is conventionally based on three different kinds of biosensors [115] (Figure 2.5).

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Figure 2.5: Schematic representation of different types of biosensors. The binding of the analyte (red dot) leads to a conformational change of the biosensor turning it from its OFF state (left) to its ON state (right). (a) In its OFF state, the RNA biosensor that is mRNA encoding green fluorescent protein (GFP) is rapidly degraded by a cis-acting ribozyme (purple star). Binding of the analyte leads to misfolding and thereby inactivity of the ribozyme, resulting in more stable mRNA and ultimately higher fluorescence. (b) RNA mimics of GFP promote proper folding of an additional fluorophorebinding aptamer in the same RNA molecule when they are in their ON state after binding the analyte. (c) The transcription factor that acts as a biosensor binds to a promoter and activates the expression of a fluorescence protein in its ON state (analyte bound). Without binding of the analyte and thereby activation of the transcription factor, the expression of the fluorescence protein is low. (d) Förster resonance energy transfer (FRET) biosensors consist of an autofluorescent protein pair, where one fluorescence protein acts as a FRET donor and the other one as an acceptor. A sensory domain separates the fluorescence proteins when no analyte is bound. Binding of the analyte leads to a conformational change that enables the energy transfer from acceptor to donor, thereby modulating the fluorescence signal (e.g., from CFP to YFP) [115].

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RNA-based sensors (Figure 2.5a and b) couple a ligand-binding-induced structural RNA change to a change in translation efficiency. The range of detectable molecules has been widened by the development of ligand-binding RNA aptamers [116]. Meyer et al. demonstrated the use of RNA-based sensors for the optimization of vitamin B2-producing B. subtilis whole-cell biocatalysts [117]. They encapsulated B. subtilis cells from a genetic library with E. coli sensor cells in gel capsules that served as nanoliter reactors. The B. subtilis catalysts produced vitamin B2 from cellobiose as a substrate. The product was sensed by the E. coli cells by a sequence of reactions, giving higher fluorescence intensity for higher vitamin B2 concentrations. The sensor cells converted vitamin B2 to flavin mononucleotide (FMN). The formed FMN was bound by an RNA riboswitch causing the self-cleavage of the RNA and subsequently resulting in the expression of green fluorescent protein as a measurable signal. Gel particles that exhibited high fluorescence were sorted by a particle sorter and used for the recovery of high vitamin B2-producing B. subtilis cells. With the only requirement of a suitable RNA-based sensor, the presented method is generally applicable for the rapid optimization of whole-cell biocatalysts. Recently, the application of RNA aptamers in droplets has been presented as a general approach to convert the production of extracellular molecules and proteins into a measurable fluorescence signal, thereby enabling the screening for microbes with evolved productivity [118]. Another type of biosensors makes use of transcription factors (Figure 2.5c). Many transcription factors bind to their corresponding promoter in response to an interaction with a specific small molecule. The interaction of a transcription factor with the corresponding promoter for controlling the expression of a reporter gene can be used to estimate intracellular metabolite concentrations [114]. In biotechnology, one of the first examples was the detection of benzoate and 2-hydroxybenzoate in E. coli by expression of the transcriptional activator NahR from Pseudomonas putida [119]. In this example, the transcription of the tetracycline resistance gene tetA was controlled by NahR allowing for the selection of 107 mutants by antibiotic selection. In later examples, fluorescence reporter genes were employed, which enabled FACS-based high throughput screening with transcription factor based biosensors for the selection of mevalonate producing E. coli [120] or amino acid production in C. glutamicum [114]. Förster resonance energy transfer (FRET)-based sensors are used to determine the concentration of intracellular small molecules [121]. FRET is a process involving the radiationless transfer of energy from a “donor” fluorophore to an “acceptor” fluorophore. A FRET-based biosensor includes these two fluorophores and a region for binding of the analyte. In the absence of the detectable molecule, the gap between the fluorophores prevents the transfer of energy emitted by the donor to the acceptor. Hence, the sensor exhibits low fluorescence. The binding of the small molecule induces a conformational shift in the sensor protein leading to proximity of the two

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fluorophores resulting in increasing fluorescence. Ideally, the fluorescence intensity correlates with the concentration of the investigated molecule. A toolbox of FRET-based biosensors for L-lysine analysis in C. glutamicum was reported [122]. The technology can be extended to cyanobacteria. In one example, the TCA cycle intermediate 2-oxoglutarate was targeted [123]. In summary, genetic construction nowadays can result in very high numbers of mutants and engineered strains that phenotyping becomes limiting. The miniaturization and parallelization of cultivations are possibilities to cope with these high numbers of candidate strains. Then, the analytics are becoming challenging and must be adapted ideally to the single cell. In the future, we will see integration of cell engineering with cell phenotyping connected by computational approaches, including machine learning, that allow the optimization toward the desired cell factory performances.

2.4.2 High content characterization of improved strains by metabolic flux analysis For a reduced number of mutants, a more detailed characterization is required and with the ever-improving analytical tools possible. Not only whole genome sequencing but also further omics technologies are applied to characterize the engineered strains in detail. This systems metabolic engineering approach was coined by Sang Yup Lee [124] and is now commonly used in strain optimization campaigns [125]. The biotechnological synthesis of high value products requires a high flux from a substrate to a product. Hence, the characterization of engineered strains concerning their flux distribution is of special interest in the field of metabolic engineering. Several metabolic flux analyses (MFA) methods have been developed to elucidate intracellular fluxes in a large variety of biological systems. The determination of metabolic fluxes provides insights in the altered performance of engineered strains and thus can be used to evaluate genetic modifications and to find further engineering targets. The basis for the calculation of metabolic fluxes is the representation of intracellular reactions as a stoichiometric metabolic network model [126]. For linear pathways, as for the lactate production in Lactococcus lactis, intracellular fluxes can be easily determined from the carbon source uptake rate (Figure 2.6a). The determination of flux distributions in metabolic networks with a higher degree of freedom requires additional data to calculate the flux distribution (Figure 2.6b). MFA methods are classified depending on the use of tracers and the metabolic state of the cells [127]. Stationary MFA methods assume a metabolic steady state of the cells where the metabolic flux is constant, whereas dynamic methods are used to determine time-dependent flux profiles. Further, MFA methods can be discriminated on whether they use stable isotope tracers or not. The stoichiometric analysis without the use of tracers is used to

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calculate intracellular rates in small-scale models based on measurable exchange rates and biological constraints on upper and lower bounds of reactions [126]. The integration of measured isotope tracer data widens the applicability to larger metabolic models as shown for genome-scale metabolic networks with more than 2,000 reactions [128, 129]. First examples of MFA using isotopomer analysis have been reported in the 1980s with 14C labeling experiments [130] and 13C NMR data [131]. In the 1990s, advances in computational analysis of the labeling data was going hand in hand with simplification of experimental procedures, like the use of GC-MS for analyzing 13C-labeled amino acids [132–135]. Zupke and Stephanopoulos were the first to perform MFA in eukaryotic hybridomas [136]. Since then, the results of MFA have been used to identify targets for genetic modifications and to analyze the engineered strains in detail. One of the first examples for the detailed characterization of microbial metabolism with MFA was the investigation of metabolic fluxes in wild-type and riboflavin-producing B. subtilis [137]. The detailed analysis of riboflavin formation under glucose-limited conditions revealed the growth coupling of the production process and gave insights how to improve the design of the production process. Building up on this, a detailed isotopomer model for riboflavin production with B. subtilis was developed [138, 139]. The metabolic model has been applied for the characterization of the production process with substrate mixtures emphasizing cofeeding strategies for process improvement [140]. The applicability of MFA to identify targets for genetic modifications has been demonstrated with a detailed stoichiometric model for growth and penicillin-G production with Penicillium chrysogenum [141]. MFA performed in this study for the use of various substrates demonstrated that the penicillin-G production is rather limited by cofactor regeneration than by carbon precursor supply. The importance of cofactors and the cellular energy metabolism for biotechnological processes have also been demonstrated for riboflavin production with B. subtilis. The application of 13C MFA has elucidated the high impact of energy metabolism on the production process [142]. The demand for a metabolic and isotopic stationary state limits the application of MFA. For example, cyclic fluxes for carbon storage [143], futile cycles and the assimilation of CO2 during photoautotrophic growth cannot be resolved using stationary 13C MFA. Especially in photoautotrophic organisms, the 13C labeling pattern is insensitive to variations in fluxes due to the assimilation of CO2 [144]. The use of fast sampling technologies and computational analyses [145] has widened the applicability of MFA to the time-dependent analysis of the isotopomer pattern, enabling instationary 13C MFA [146]. Instationary 13C MFA has been applied to determine the metabolic fluxes in the cyanobacterium Synechocystis sp. PCC6803 [147]. Isotope labeling dynamics were obtained by varying the carbon source from 12C to 13C CO2 and by rapid sampling techniques. The instationary labeling data, combined with metabolite analysis to estimate intracellular metabolite pools, were then used to computationally estimate the intracellular fluxes (Figure 2.7). The analysis identified several side reactions that limited the photoautotrophic growth of Synechocystis. While the experimental effort is considerable, the lessons learned are a few in numbers and

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Figure 2.6: Representation of the central carbon metabolism of Lactococcus lactis (a) and E. coli (b). The linear pathway in L. lactis exhibits a degree of freedom that is zero, allowing the flux determination from the substrate uptake rate. The E. coli network with a degree of freedom that is eight requires additional data to calculate the flux distribution [37]. Reprinted with permission from Springer Nature Customer Service Centre GmbH: Springer Nature Applied Microbiology and Biotechnology (Metabolic flux distributions: genetic information, computational predictions, and experimental validation, Blank and Kuepfer [37]).

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every researcher should make a qualified decision if the resources committed are worth the expected outcome (see Chapter 6). Anyway, MFA of photoautotrophic organisms is also feasible and might guide future metabolic engineering developments of these organisms as biotechnological chasses.

Figure 2.7: Overview of instationary 13C-CO2 flux analysis in photoautotrophic organisms [147]. Reprinted with permission from Elsevier: Metabolic Engineering (Mapping photoautotrophic metabolism with isotopically nonstationary 13C flux analysis [147]).

In each attempt of improving the host organism by rerouting metabolic fluxes, the physiology will be altered. The introduced genetic modifications and the thereby altered performance can be analyzed by the above-mentioned methods. The presented procedure may subsequently be applied to the engineered strains to address further targets, thereby resulting in a design–built–test cycle for the ongoing production strain improvement.

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Thomas Bayer, Kathleen Balke, Emil Hamnevik, Uwe T. Bornscheuer

Chapter 3 Protein engineering Abstract: The ever-expanding repertoire of biocatalysts and advancements in genetic manipulations has led to the customization of natural enzymes and the generation of “new-to-nature” catalytic functions. Computational tools greatly assisted (semi-)rational protein engineering approaches by more reliable predictions of mutations, ultimately, catering for desired traits. Their combination with in vitro compartmentalization and microfluidic techniques elevated the number of protein variants being efficiently screened and selected from a few hundreds to libraries containing hundreds of millions of mutants. This chapter highlights the engineering of proteins from autotrophic organisms including the famous RuBisCO and the adaption of enzymes from heterotrophs to function in autotrophs and vice versa. Together, these examples demonstrate the capability of modern protein engineering techniques to transform autotrophic microorganisms into competitors to wellestablished heterotrophic hosts to tackle today’s challenges like unprecedented enzymatic circuits for the fixation of CO2, the stewardship of resources and the sustainable production of value-added chemicals. Keywords: directed evolution, high-throughput screening, library design, cofactor balancing, whole-cell biocatalysis

3.1 Introduction In the last decades, an increasing number of enzymes have been identified and utilized as sustainable biocatalysts, improving – even replacing – synthetic routes employed by the chemical and pharmaceutical industry. Advances in DNA technologies and bioinformatics have enabled the identification and prediction of new enzymatic functions from (meta)genome data [1]. Improved genetic tools allow both the tailoring of natural and the assembly of de novo metabolic pathways [2]. Essential for the transfer of the rapidly expanding repertoire of biocatalysts from their natural contexts to non-native environments – be it the reaction vessels of industrial processes or a heterologous host cell – has been the ability to customize native enzymes by protein engineering. Protein engineering approaches have led to enzyme variants exhibiting

Acknowledgments: Thomas Bayer was funded through the Erwin Schrödinger Fellowship (project no.: J4231-B21) granted by the Austrian Science Fund (FWF). https://doi.org/10.1515/9783110550603-003

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improved catalytic activities, broadened or altered substrate specificities and the desired stereoselectivity. Enzymes have been tailored to operate under non-physiological conditions including elevated temperatures, the presence of organic solvents and drastic pH values (Box 3.1) [1]. Recently, autotrophic microorganisms have emerged for biotechnological applications. In contrast to heterotrophic organisms, which utilize glucose and other complex compounds as carbon and energy source, autotrophs depend on inorganic molecules like CO2 and convert them into organic compounds. Photoautotrophs use sunlight as the sole source of energy, whereas some chemolithoautotrophs including Cupriavidus necator (C. necator) - formerly known as Ralstonia eutropha - utilize H2 as the electron donor to ensure cellular survival, growth and reproduction [3]. This striking difference is realized by distinct sets of proteins and enzymes that act in highly specialized organelles, cellular complexes or metabolic pathways. It is no surprise that scientists have aimed at exploiting these features and tailoring autotrophic organisms to produce value-added chemicals [3–5]. Protein engineering in this regard has been employed to alter the properties of native enzymes such as ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Furthermore, the metabolic background in autotrophs differs from established heterotrophic hosts like Escherichia coli (E. coli) [5]. Differences do not only affect the availability of redox cofactors such as NAD(P)H; light as energy source can influence the chemical properties of prosthetic groups including flavins, may produce reactive oxygen species (ROS), and induce photodamage [6, 7]. In general, protein engineering can provide solutions to generate variants of (heterologous) enzymes to adapt to these new cellular environments. Therefore, this book chapter will provide an overview of established (semi-)rational design and directed evolution approaches, enabling the efficient engineering of proteins. The importance of both in silico tools and adequate (high-throughput; HT) selection and screening protocols to predict and identify enzyme variants with desired properties, respectively, are highlighted. Selected examples from literature will demonstrate the successful application of the described protein engineering techniques and will cover the alteration of cofactor specificity as well as the implementation of artificial cofactors. Whereas stability improvements against increased temperatures and organic solvents were successful, the issues of ROS and light-induced protein damage still need to be addressed in detail. Finally, examples of protein engineering in plants will be discussed, pointing toward new opportunities for the autotrophic biorefinery. BOX 3.1: Protein engineering in a nutshell Rational design. One of two general strategies in protein engineering. Rational design aims at the prediction of the fold and/or the catalytic function of an amino acid sequence of interest and relies on an in-depth knowledge of both the protein structure and catalytic mechanism; specific changes are introduced by site-directed mutagenesis (Figure 3.1). Directed evolution. This second general strategy mimics Darwinian evolution in the controlled environment of a laboratory. The process is used to customize peptides, proteins, or nucleic acids

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toward a user-defined trait and usually involves iterative rounds of mutagenesis (i.e., library creation of variants), selection (i.e., expression and isolation of variants with the desired property) and amplification before resubmitting to the next round (Figure 3.1). Directed evolution can be employed without detailed knowledge about the engineering target but greatly depends on HT assays to determine the effects of different random mutations. Hence, appropriate screening or selection conditions are crucial to engineering success. Screening. Refers to the assaying of individual variants, which allows a quantitative threshold to be set to sort a variant or cohort of variants exhibiting the desired property (Table 3.1). Selection. In selection systems, the survival of the gene is directly coupled to the evolved trait or function. Both selection and screening can be performed in living cells (in vivo) or in cell-free solutions (in vitro); see also Table 3.1. Semi-rational approaches. Employed to address the limitations of both rational design and directed evolution. The creation of (focused) libraries of variants requires some knowledge of which residues to mutate (e.g., active site residues).

3.2 None without the other: protein engineering, selection and screening strategies In the past, biocatalysts regularly failed to meet (industrial) process criteria including pH and thermostability or organic solvent tolerance [1, 8, 9]. These shortcomings of enzymes in the context of non-native environments were overcome by Arnold and Stemmer, who pioneered enzyme engineering methods and efficiently modified the amino acid sequence of biocatalysts by mimicking Darwinian evolution in a test tube at a fast pace [1, 8, 10]. Since then, the evolution of proteins undeniably has been a story of success and peaked in 2018 with Frances H. Arnold being awarded the Nobel Prize in Chemistry for the evolution of enzymes [11]. Various research groups have impressively applied two strategies, directed evolution [12, 13] and rational design [14–16], to improve the properties of biocatalysts and even equip them with completely new functions (Figure 3.1) [1, 9, 17, 18]. Enzyme engineering by directed evolution involves iterative cycles of random gene mutagenesis (e.g., error-prone polymerase chain reaction (epPCR) and deoxyribonucleic acid (DNA) shuffling, but also using mutator strains [23], chemical mutagens or irradiation), thus, creating large libraries of mutants which have to be expressed and screened to identify mutant enzymes exhibiting the desired property (Figure 3.1A and C). In contrast, a rational design largely depends on the availability of knowledge about structurefunction relationships of target enzymes, and only a few residues are specifically targeted by site-directed mutagenesis (Figure 3.1B) [21, 24]. Initial approaches were based on the comparison of sequence homology and aimed at generating enzyme mutants with improved solubility, thermostability or organic solvent tolerance [25].

Figure 3.1: Biocatalyst evolution. The engineering of proteins involves iterative rounds of (1) mutagenesis (A–D), (2) the screening of libraries of variants (E–G) and (3) the selection of biocatalysts with improved traits or their resubmission. Exemplary mutagenesis methods: (A) epPCR employs conditions to enhance the intrinsic error rate of PCRs and randomly targets the whole amplicon. (B) Directed mutagenesis by designed primers carrying the desired mutation (e.g., QuikChange® protocol) targeting specific base pairs. (C) DNA shuffling recombines portions of different mutant genes to generate chimeric genes. (D) CASTing: amino acids (A, B, C, . . . ) of the enzyme binding pocket are identified by X-ray structure or homology models and systematically targeted by saturation mutagenesis; (C) and (D) were reproduced and adapted from Reetz [9, 19, 20]. Screening methods: Low-throughput methods involve (E) the inspection of visual signals of individual colonies on solid media or (F) the cultivation of liquid cultures and subsequent transformations converting a suitable substrate into a detectable product. The product can be quantified by chromatographic methods, for example. (G) Current high-throughput screening and selection methods combine in vitro compartmentalization (IVC) with microfluidics. For example, droplets (water-in-oil emulsions) containing the desired biocatalyst variant (droplet in green) can be separated from droplets containing inactive variants (grey); see also Table 3.1 [21, 22].

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A combinatorial strategy is the semi-rational design, in which sequence alignments and structural information are used to create “small but smart” libraries by saturation mutagenesis methods [26]. The screening of smaller libraries can drastically reduce workload, time and costs to identify enzyme variants. Library sizes were reduced, for example, by the usage of a reduced amino-acid alphabet (e.g., NDT encoding only 12 of the possible 20 proteinogenic amino acids) [24, 26–30]. A commonly used saturation mutagenesis technique is the iterative saturation mutagenesis (ISM) [31–33] in combination with combinatorial active-site saturation testing (CASTing) [9, 19] in order to limit the location of amino acid substitutions to the catalytic center (Figure 3.1D). Other approaches rely on statistical and bioinformatic methods such as the protein sequence-activity relationship (ProSAR) algorithm [34] or the utilization of 3DM databases [35, 36]. Today, a variety of in silico tools assist (semi-)rational designs, some of which are given below. The employment of (HT) selection and screening methods became an integral part of protein engineering endeavors to identify desired protein variants (Figure 3.1E–G) [18, 22].

3.2.1 Site-specific, to be precise: rational and semi-rational design (Semi-)rational design is a highly knowledge-based engineering approach used to not only tailor a protein to operate under (industrial) process conditions but to improve natural activities, change stereo- and regio-specificities, expand substrate scopes to non-natural compounds, or even create entirely new functions not known from natural biocatalysts. Ideally, detailed structural information of the protein of interest is available to select relevant amino acid residues for mutation. To ease the decision-making process, a variety of computational tools exist and will be described in the following. In classical (semi-)rational engineering approaches, the selection of interesting mutation sites is done by visual inspection of the structure. Chemical and mechanistical knowledge of the engineering target help to rationally decide on the amino acids to be changed. The more structural and mechanistic information about the protein of interest is available, the more likely will the identification of interesting positions be. Nonetheless, the identified variants must be tested for the improved trait using suitable assays as described later in the following subchapter. For the visualization of protein structures and manual inspection, PyMOL1 and UCSF Chimera2 are frequently used. Both programs visualize protein structures, create density

1 Schrödinger, LLC. The PyMOL Molecular Graphics System, Version 1.8, Schrödinger, LLC. New York, NY, USA; date of publication: 2010-08-19 [updated: 2015-12-22; cited: 2021-02-23]. Available from: https://pymol.org/ 2 UCSF Resource for Biocomputing, Visualization, and Informatics. UCSF Chimera – an Extensible Molecular Modeling System. University of California, San Francisco, CA, USA; [updated: 2019-11-12; cited: 2021-02-23]. Available from: https://www.rbvi.ucsf.edu/chimera/

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maps, measure relevant distances and angles, and can produce high-quality figures and movies. Although the number of protein sequences (i.e., future engineering targets) constantly increases, crystal structures are, by far, not available for all of them. In February 2021, the UniProtKB database3 contained >208,365,000 entries, of which >564,000 were manually annotated and >20,780,000 were annotated automatically [37]. Complementary, the RCSB Protein Data Bank (PDB)4 contained about 175,000 entries in total [38]. For the latter, almost 90% contain structural information obtained by X-ray analysis. Alternatively, if no crystal structure exists, homology models can be built on the basis of the primary protein sequence of interest. Therefore, an experimentally solved three-dimensional (3D) structure of a homologous protein serves as a template [39–41]. Several ways to produce 3D homology models exist, for example, comparative modeling [42] and threading are commonly used [43]. Comparative modeling builds on the observation that evolutionary-related sequences have similar structural folds. Thus, a structure can be built from using a template with an adequately high sequence similarity, where the root mean square deviation can be 1–2 Å for sequence identities >30% [42]. Threading or fold recognition is used to identify a suitable template and fit the target protein to that fold if the sequence identity is 200-fold improvement of kcat/Km. Similarly, enzymes for retro-aldol reactions [77] and Diels-Alder reactions were successfully designed [72, 78]. Since then, new enzyme structures have been computationally designed and created and find now applications in therapeutics, biosensing and nanomaterials [79]. (Semi-)rational protein engineering approaches have greatly benefited from the development of computational programs and algorithms. To verify the positive effect of predicted mutations, scientists can choose from a variety of advanced site-directed mutation strategies of which a more recent discovery enables the simultaneous manipulation of several genes (i.e., engineering targets) with very high precision. The discovery of clustered regularly interspaced short palindromic repeats (CRISPRs) in the genome of E. coli had not been conclusive when first discovered [80] but could later be attributed to be part of the defense against bacteriophages [81–84]. In nature, CRISPRs direct the sequence-specific restriction of viral DNA to prevent virus replication [85]. As a genomic engineering tool, Charpentier and Doudna utilized the system to efficiently cleave target DNA sequences with less off-site effects [86]. As briefly described in Chapter 2 in this book, the CRISPR-associated protein 9 (Cas9) endonuclease is recruited to the site of action by a guide RNA (gRNA), where it induces double-strand breaks (DSBs). The gRNA is a short (synthetic) RNA composed of a conserved loop recognized by Cas9 and a customized spacer sequence of 20 nucleotides, defining the genomic target to be modified. The only requirement here is the presence of a protospacer adjacent motif (PAM = NGG) in proximity to the target sequence (Figure 3.2). Conveniently, by altering the target sequence in the gRNA, the

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genomic target of Cas9 can be changed. Co-expression of both, Cas9 and the gRNA, is sufficient to knock out chromosomal DNA (see also Figure 2.4) [86]. The CRISPR/Cas9 gene-editing tool has been rapidly adopted to engineer eukaryotic cells [87–93] but is still underrepresented as a manipulation method in (autotrophic) microorganisms [94–96]. For biotechnological purposes, CRISPR/Cas9 has been mainly employed for targeted gene knock-out (KO) to reorganize the metabolic background of microbial host cells, [97] as also highlighted in the Chapter 2. Pioneering studies successfully integrated linear DNA fragments with homologous ends into the yeast chromosome, taking advantage of the intrinsic and efficient homologous recombination and DNA repair mechanisms in yeast [98–100]. Since then, numerous genomic engineering endeavors have been carried out in yeast including, for example, the HT creation and functional profiling of DNA sequence libraries [101], (multiplex) genome and metabolic pathway engineering, [102–105] or the engineering of transcriptional activators as biosensors for metabolites [106]. Furthermore, improved Cas9 endonuclease variants not only exhibit minimized off-target nuclease activities; engineering studies converted the nuclease into a DNA nickase [86, 102, 107]. Nicked (genomic) DNA is typically repaired either seamlessly or through high-fidelity homology-directed repair. Combined engineering efforts facilitated the adoption of CRISPR/Cas9 for gene editing in bacteria like E. coli [108, 109] and C. necator [94], in which DNA repair mechanisms otherwise fail to prevent the deleterious accumulation of DSBs resulting from Cas9 endonuclease activity [95, 96, 110], and offer alternatives to combining CRISPR/Cas9 with the λ Red system to artificially increase homologous recombination (Figure 3.2B) [8, 111]. With the advance of the CRISPR/Cas9 technology, precise substitutions and the insertion of DNA sequences with variable lengths enable the efficient engineering of proteins. Systems like CasPER represent directed evolution platforms that combine the established mutagenesis techniques described above with CRISPR/Cas9 to robustly diversify large sequence spaces in cognate genomic contexts [87]. Today, specific gRNA design and selection are assisted by online web tools like CRISPy [112]. Applying CasPER, the group of Keasling engineered two essential enzymes in the mevalonate pathway of Saccharomyces cerevisiae by epPCR, improving the production of isoprenoids (e.g., β-carotene) 11-fold [87]. Recently, the group of Dueber introduced EvolvR, a system that allows diversification of all nucleotides within a tunable window length at genomic loci defined by the user [107]. Mutations are directly introduced by an engineered DNA polymerase, which is targeted to the loci by a fused CRISPR-guided nickase. Engineered nickase and polymerase variants exhibited up to almost 8-million-fold enhanced mutagenesis rates compared to naturally occurring mutagenesis events and were employed to identify new ribosomal mutations conferring resistance to spectinomycin. The proof-of-principle study demonstrated multiplexed and continuous diversification of the genomic loci encoding the ribosomal protein subunit 5 gene of E. coli (rpsE) [113] and holds promise for broad biotechnological and protein engineering applications, also suitable for the autotrophic biorefinery.

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Figure 3.2: CRISPR/Cas9 protein engineering. (A) Genome editing depends on a ribonucleoprotein composed of the Cas9 endonuclease and a gRNA, which directs the ribonucleoprotein to the target sequence in the genome. Cas9 introduces a double-strand break (DSB) upstream of the PAM. (B) In the presence of a homologous DNA molecule containing the desired sequence variant to be integrated, λ Red can be employed, for example, to facilitate homologous recombination to prevent the deleterious effects from DSBs introduced previously by Cas9 [20].

3.2.2 You get what you select for: library selection and screening Library diversity is typically introduced through alterations on the genetic level (i.e., mutations) that will translate into changes in the amino acid sequence of the target protein (Figure 3.1) [8]. Hence, crucial to all selection and screening efforts is the tight linkage between genotype and phenotype [22, 114, 115]. With advances in protein engineering techniques and assisting prediction tools, the tailoring of biocatalysts resulted in steadily increasing numbers of amino acid substitutions. Whereas one to five mutations were typical in the early 1990s, 35 amino acid exchanges were made on average during protein engineering around 2010 [1]. The directed evolution of a halohydrin dehalogenase for the precursor synthesis of atorvastatin, a statin medication used to prevent cardiovascular disease and to treat high blood pressure, changed ≥35 of the 254 amino acids [34]. In the transaminase variant accepting the precursor of sitagliptin, an important antidiabetic drug, 27 of 330 residues were

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substituted [1, 116]. This creates vast genetic diversity, which is depicted in the exponential size increase of protein variant libraries. Despite the above-mentioned advances in reducing library size, major challenges remain since changing 30–40 amino acids and screening tens of thousands of candidate mutants certainly is laborious, expensive and time-consuming [1]. An immediate solution to this problem has been an area of vivid research and resulted in the development of highly efficient library analysis tools [117]. Established strategies include library analysis by auxotrophic complementation or cellular viability and can be performed on solid media or liquid cultures in (micro-) plate-based screenings (Table 3.1). Usually, isolated host colonies with a defined genotype are grown, the protein variant expressed and the phenotype evaluated individually [21]. Recently, Wilson and co-workers introduced an improved RuBisCO directed evolution (RDE) assay in E. coli. RDE assays rely on the toxicity of ribulose-1,5-bis-phosphate (RuBP) a foreign metabolite produced from the pentose phosphate pathway (PPP) intermediate ribulose-5-phosphate (Ru-5-P) by recombinant phosphoribulokinase (PRK) expression (Figure 3.3A). Cell viability is restored either through functional RuBisCO expression, where the growth rate is related to RuBisCO activity, or through transposon silencing of PRK expression that produces false positives at a high frequency [124]. Wilson et al. modified the RDE assay by employing a PRK/neomycin phosphotransferase II (NPTII) fusion; NPTII is tethered in frame to the C terminus of PRK conveying kanamycin resistance to E. coli unless PRK/NPTII expression is transposon silenced (Figure 3.3B) [120]. Apart from auxotrophic selection and cell viability screening, the direct transformation of specific substrates in liquid cultures offers flexibility regarding the type of library analysis assay and can be assisted by robotic equipment. However, the throughput of such screening methods still remains rather low (Table 3.1). Importantly, functional assays of the engineered protein with substrates can be problematic since they must cross the host cell membrane, either by passive diffusion or active transport. Once inside the cell, endogenous enzymes can potently interfere with functional assays. Whereas the necessity to transport substrates across the membrane can be circumvented by cell lysis, an elegant solution to both overcoming transport limitations and minimizing interference by the enzymatic host background has been the introduction of cell surface display systems [125–128]. Using host membrane proteins as fusion partners, engineered proteins have been efficiently exported to the extracellular surface of viruses, bacteria and yeast cells. Since they remain covalently bound to the host cell, the linkage to the genotype remains intact [21]. Noteworthy in this regard, George Smith and Sir Gregory Winter were jointly awarded the other half of the Nobel Prize in Chemistry for the phage display of peptides and antibodies in 2018 besides Frances Arnold [11]. However, cell surface display systems are not suitable for proteins that cannot be functionally presented, for example, if prosthetic groups like

Spatial separation of variants

Spatial separation of variants

Compartmentalization (cellular membranes)

Emulsions [g]

Colonies on solid media

Isolated liquid cultures

Cell surface display

IVC [f]

FACS, FADS, AADS [h]

ELISA, FACS [b, d]

Visible signals, biochemical assays [a, b, c]

Manual inspection of visible signals [a, b]

Detection

– (high)

 (high)

– (low)

– (low)

Library size (throughput)

Quantitative

Yeast display: eukaryotic gene expression, posttranslational modification

Straightforward implementation; flexibility in reporter/detection method; quantitative

Straightforward implementation

Advantages

IVC and microfluidic techniques require expertise and optimization

Limited application to certain biocatalysts [e]

Laborious

Laborious; qualitative and semi-quantitative

Disadvantages

[a] For example, fluorescence or colorimetric assays of (surrogate) substrates [118, 119]. [b] Selection might also be coupled to organismal fitness/survival or auxotrophy [120]. [c] Gas chromatography, high-performance liquid chromatography, nuclear magnetic resonance, mass spectrometry, fluorescent or colorimetric (microplate) assays. [d] Enzyme-linked immunosorbent assay (ELISA), fluorescence-activated cell sorting (FACS). [e] For example, esterases or proteases [1, 117]. [f] In vitro compartmentalization (IVC) [121, 122]. [g] For example, water-in-oil emulsions or (self-assembling) polyelectrolyte shells [117]. [h] Apart from fluorescence-activated droplet sorting (FADS; e.g., activation of a fluorophore or release of a quencher), absorbance-activated droplet sorting (AADS) can be employed [22, 123]. The table was adapted from Packer and Liu [117] and updated [21, 22].

Genotype/ phenotype linkage

Method

Table 3.1: Screening strategies.

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Figure 3.3: RuBisCO directed evolution (RDE) assay. (A) The cytotoxic ribulose-1,5-bis-phosphate (RuBP) is produced from the pentose phosphate pathway (PPP) intermediate ribulose-5-phosphate (Ru-5-P) by recombinant PRK. Functional co-expression of RuBisCO (variants) leads to the fixation of CO2 and the production of the glycolytic intermediates 3-phosphoglyceraldehyde (3-PGA) and pyruvate (Pyr) restores cell growth. Alternatively, transposon silencing of PRK yields false positives at high frequencies. (B) In a modified RDE screen, PRK is fused to NPTII. Transposon silencing leads to sensitivity to kanamycin, thereby, eliminating false positives. The figure was reproduced and adapted from Wilson et al. [120].

flavin adenine dinucleotide (FAD) are required for proper folding but proteins cannot pass the membrane in their folded state. Although the methods described above are still relevant, recent developments in robotics and microfluidics have introduced powerful new strategies for the functional assessment of enzyme variants [22, 129]. A real change in the analysis of large protein libraries was the development of in vitro compartmentalization (IVC) by Tawfik and Griffiths [121]. The utilization of water-oil-emulsions offered a simple yet effective method to establish an artificial genotype-phenotype linkage. To date, numerous studies demonstrated the versatility of IVC to screen large protein libraries and isolate variants with the desired properties [130, 131]. More importantly, IVC combined with microfluidics has now become one of the major technologies for HT screening of engineered libraries (Table 3.1) [22, 122, 132–134]. Droplet-based microfluidics enable the screening of large libraries at high rates by continuous flow on chip [122]. Since the advent of this technology, important

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technological improvements including droplet fusion, incubation and splitting allow more sophisticated sorting procedures of engineered protein variants in micro-, nano-, even picoliter volumes. This further reduces costs and the amount of waste produced compared to other screening procedures (e.g., automated microtiter plate screenings) [21, 22, 116]. Recently, absorbance-based microfluidics screen has extended the range of assays for droplet sorting, making IVC approaches more flexible for current and future screening endeavors [123].

3.3 Protein engineering examples for the autotrophic biorefinery Protein engineering provides solutions to generate variants of enzymes with exquisitely improved and altered properties. With the harnessing of autotrophic organisms for biotechnological applications, the same tools and protocols have been used to both engineer “autotrophic” protein systems and transplant enzymes from autotrophs into heterotrophs and vice versa. The challenges scientists had to tackle will be discussed in the following paragraphs.

3.3.1 Engineering “autotrophic” protein systems Whereas heterotrophs depend on rather complex organic compounds, photoautotrophic (micro)organisms can ensure cellular functions including growth and reproduction by – essentially – using sunlight as the sole source of energy and directly converting (atmospheric) CO2 to organic compounds, releasing O2 as a byproduct. What sounds simple is actually highly complex with independent metabolic pathways for energy utilization and carbon metabolism that are not only regulated differently; they are separated spatially and also in time. This is achieved in specialized cellular compartments including the chloroplasts in plants and the carboxysomes in cyanobacteria [5, 135] or pyrenoids in algae, only to mention some [136–139]. These compartments harbor a variety of unique enzymes and accessory proteins that have been targeted by protein engineering efforts to improve photosynthetic efficiency and redirecting – even redesigning – endogenous carbon fluxes to, ultimately, harness the unique metabolic features of autotrophs to synthesize value-added compounds [5, 140]. One of the early engineering targets has been ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), the enzyme responsible for the first step of CO2 fixation. RuBisCO is a slow enzyme with a turnover rate of 1–5 s−1 (compared to >100 s−1 for many enzymes and ≈10 s−1 for the “average enzyme”) and two divergent functions: the fixation of O2 instead of CO2 occurs in up to one-third of reaction cycles [141–143].

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The side reaction with O2 leads to photorespiration at the expense of previously fixed CO2 and a loss of chemical energy [144, 145]. In plants, these shortcomings are compensated by large amounts of RuBisCO, constituting up to 60% of the total protein amount in leaves, therefore, making it the earth’s most abundant protein [142, 146]. Great efforts have been made to improve the carboxylation efficiency and/or decrease its oxygenation activity but only a few successes have been achieved [147–152]. This is also due to the structural diversity of RuBisCO enzymes in nature, resulting in structure-function relationships that are difficult to predict and hard to beneficially modify. Type I enzymes are composed of eight large and eight small subunits and are predominantly found in most autotrophic organisms dependent on the Calvin-Benson cycle (CBC) [153, 154]. Type II are composed solely of two large subunits and can be found in some photoautotrophs and chemoautotrophic bacteria [154], whereas archaeal type III RuBisCOs contain only large subunits in different quaternary arrangements [155–157]. Not surprisingly, RuBisCO biogenesis requires assembly chaperones, accumulation factors and repair enzymes [147, 158–160]. The need for RuBisCO to maintain complementarity with these accessory proteins not only complicates the engineering but limits introduction and functional assembly in foreign hosts like E. coli [120, 159]. Hence, variants have to be carefully characterized through appropriate selection systems to distinguish between mutants with truly increased activity and those that are simply better expressed [148, 161, 162]. Furthermore, engineering of the catalytical properties of RuBisCO is often a trade-off between the specificity for CO2 over O2 and the catalytic speed of the enzyme [120, 163]. In this regard, RuBisCO enzymes from organisms that have undergone different selection pressures than plants are currently explored [144]. Microalgae might be promising sources for RuBisCO variants, exhibiting favorable affinities for CO2 without compromised catalytic rates [164–166]. Furthermore, alternative strategies to enhance RuBisCO efficiency are the implementation and co-engineering of carbon concentrating mechanisms (CCMs). Cyanobacterial RuBisCO enzymes have a low affinity for CO2 (Km >200 µM or approximately 8- to 13-fold that of RuBisCO from C3 plants) [5]. To counter this evolutionary shortcoming, cyanobacteria have evolved carboxysomes in which the CCM is working through the combined actions of a bicarbonate transporter (transporting CO2 as bicarbonate in water to the cyanobacterial membrane) [167], a carbonic anhydrase (converting bicarbonate into CO2) and RuBisCO (carboxylating RuBP more efficiently by the local increase in CO2 concentration) [5, 137, 168, 169]. Other strategies to increase the photosynthetic efficiency by targeting “autotrophic” proteins include the optimization of enzymes active in the CBC and the photorespiration pathway [170–173]. To optimize the light-use efficiency, changes in the antenna size of the photosystems in plants have also been suggested as a route offering potential to enhance solar conversion efficiency while reducing non-photochemical quenching (NPQ). NPQ is the process by which plants deal with an excess of light that cannot be utilized anymore, converting it into heat [174]. Engineered (i.e., truncated) light-harvesting antenna have been successfully applied in cyanobacteria [175] and microalgae [176, 177]. Although this approach is challenging due to the complexity of

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factors influencing the determination of both the optimal antenna size and chlorophyll concentration, for example, it represents a new engineering target and certainly complements the approaches described above [176]. Despite today’s repertoire of mutagenesis techniques, computational tools and a good general understanding of the reaction mechanism of RuBisCO, its efficient engineering remains one of the Holy Grails in protein engineering. Early (rational) engineering efforts targeted the active site (e.g., loop 6 harboring the active site residue lysine and stabilizing the reaction intermediate in type I to type III RuBisCO enzymes) [157, 163] but came with a trade-off between specificity for CO2 and the catalytic rate [5, 120, 144]. Due to different quaternary structures and the complex biogenesis of natural RuBisCO enzymes, random mutagenesis studies have not yielded mutation hot spots. Instead, mutations often occur close to subunit interfaces that are far away from the active site [5, 120]. Excitingly, synthetic photosynthesis has made progress recently by the implementation of CBC-like assimilation in non-autotrophic organisms like E. coli, equipping host cells with the production capability of sugars from atmospheric CO2 [178, 179]. Along such lines, Schwander et al. designed a synthetic pathway for the fixation of CO2 in vitro [180]. Based on metabolic retrosynthesis [181, 182], their optimized pathway consisted of 17 enzymes from nine different organisms from all three domains of life. Key enzymes are not RuBisCO variants but coenzyme A (CoA)-dependent carboxylases and enoyl-CoA carboxylases/reductases. They operate in metabolic pathways in Streptomyces strains, for example, but not in any autotrophic CO2 fixation pathways [183]. Importantly, these enzymes do not accept O2 as a substrate, omitting the oxygenation problem of RuBisCO. Several rounds of enzyme engineering included the structure-guided (i.e., rational) evolution of a FAD-dependent acyl-CoA dehydrogenase (DH) into a functional oxidase, directly using O2 as the electron acceptor. Most recently, the group of Erb presented a glycolyl-CoA carboxylase (GCC), a new-to-nature enzyme. Effective development of their GCC combined rational protein design, HT microfluidics, and microplate-based assays, improving the catalytic efficiency of GCC by three orders of magnitude to match the activity of natural enzymes for CO2 fixation. Ultimately, two more enzymes were engineered and – together with GCC – yielded a carboxylation module for the conversion of glycolate (C2) to glycerate (C3). The functionality of the module was demonstrated by interfacing with natural photorespiration, ethylene glycol conversion and artificial CO2 fixation [184]. The successful reconstruction of a de novo network of enzymes to convert CO2 into organic molecules opens the door for future applications such as the in vivo transplantation into heterotrophic organisms [185], the development of synthetic photosynthesis processes (e.g., in combination with photovoltaics or artificial leaves) [186] and, generally, the utilization of CO2 as an economic carbon feedstock in the future [5, 169, 180, 184, 187]. Along these lines – pointing toward future applications already today – is the transformation of obligate heterotroph E. coli strains into fully autotrophs that use CO2 as the sole source of carbon through a non-native CBC module [188].

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3.3.2 The adaption of “heterotrophic” enzymes to autotrophic environments The construction of synthetic CO2 fixation pathways and other examples above already showcased the engineering of enzymes from heterotrophic organisms and their use in autotrophic contexts. The following section focuses on cofactor engineering, reflecting differences in prevalence and usage in metabolic pathways of auto- and heterotrophic organisms.

3.3.2.1 Cofactor engineering Oxidoreductases are versatile enzymes present in all organisms. As redox enzymes, they are able to facilitate regio-, enantio- and stereoselective conversions, hence, are valuable biocatalysts for industrial applications. Most of these enzymes are dependent on nicotinamide cofactors for the storage and transfer of electrons. However, in many oxidoreductase-catalyzed reactions, nicotinamide dinucleotide (phosphate) [NAD(P)] only serves as a mediator and is not directly involved in the catalytic mechanism. Prosthetic groups such as flavins and heme-groups facilitate the reduction or oxidation of the substrate. Due to the different availability of cofactors in autotrophs and heterotrophic host organisms, protein engineering of heterologously expressed enzymes from autotrophic sources might be desirable. But also, autotrophic systems are targeted to resolve cofactor imbalances. Different approaches aiming at changing the cofactor usage from NADPH to NADH have been employed in cyanobacteria. These included the fueling of intracellular NADH pools or changing the cofactor specificity of NADH-dependent enzymes from NADH to NADPH [189]. Complementary, from an application-oriented point of view of heterotrophic enzymes, changing the cofactor specificity, introducing cofactor analogues or allowing for an alternative cofactor recycling are reasonable engineering targets. Nicotinamides have been of special interest from an industrial point of view since NADH is cheaper and more stable than NADPH. Since 1990, when the first reversal of cofactor specificity was reported, there have been quite a few examples demonstrating the switch of cofactor specificity [190]. For this, different methods ranging from directed evolution to (semi-)rational design have been employed. Even though NADPH and NADH only differ in an additional phosphate group at the 2ʹ-position of the ribose moiety, most enzymes show a high preference for either of these cofactors. This preference is mostly dictated by the charge and size of the nicotinamide cofactor binding pocket [190]. Whereas NADPH-dependent enzymes usually have a larger binding pocket lined with positively charged residues or residues which can function as hydrogen donors for the phosphate group [191], in NADH-preferring biocatalysts, negatively charged residues are able to form hydrogen bonds with the 2ʹ- and 3ʹ-OH groups of the ribose moiety. The negative charge

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also leads to a repulsion of the negatively charged phosphate group of NADPH, thus, increasing the enzyme’s specificity for NADH [191]. Over the years, different computational tools have been designed to predict and even switch cofactor preference and certainly complement the in silico tools described earlier. The prediction of Rossmann folds, which represent the classical structural motif for the binding of nucleotide-based cofactors, can be accomplished using hidden Markov models [192, 193]. Other examples are NADbinder, FADpred and Nucleos, which aim at predicting residues involved in the binding of NAD(P), FAD and/or other nucleotide-based cofactors, respectively [194–196]. Engineering the cofactor specificity can be aided by applying the rational iterative protein redesign and optimization (IPRO) algorithm developed by Khoury and coworkers [197]. Assisted by IPRO, in silico mutations of a xylose reductase from Candida boidinii were created. Mutations leading to improved interaction energies between NADH and the enzyme were incorporated in the protein sequence. Seven out of ten variants displayed a switch of cofactor specificity, while another two variants were able to use both NADH and NADPH. Another computational approach was used by Cui et al. and aimed at enhancing the hydrogen-bond interaction between enzyme and cofactor. This method depends on the availability of the protein structure and was used to switch the preference of short-chain DH Gox2181 from Gluconobacter oxydans from NADH to NADPH [198]. However, since minor changes to the cofactor and its binding site can have a drastic effect on activity, enzyme kinetics and substrate specificity [199], computational and rational approaches often lead to enzyme variants not displaying the desired switch in cofactor binding properties – despite the major advances in the last two decades. In this regard, random mutagenesis is not an ideal engineering strategy either since crucial residues for switching the cofactor specificity are mostly positioned in the cofactor binding pocket close to the 2ʹ-moiety of NAD(P)H, and simultaneous mutations are needed to reverse cofactor specificity. A structure-guided, semi-rational strategy for changing the enzymatic cofactor preference called CSR-SALAD (Coenzyme Specificity Reversal-Structural Analysis and Library Design) was developed in the group of Frances Arnold [200]. This strategy also depends on a protein structure, from which the specificity-determining residues are identified and both hot spots for switching the cofactor and “rescuing” of the biocatalytic activity are suggested. Importantly, CSR-SALAD only considers residues that interact with the 2ʹ-position of the cofactor – either directly or through water-mediated hydrogen bonds. Based on this information, a small library is designed by using sub-saturation degenerate codon libraries, in which combinations of amino acids at the targeted positions are generated with specified mixtures of nucleotides. The experimental validation of the CSR-SALAD algorithm was performed with four different oxidoreductases including a glyoxylate reductase from Arabidopsis thaliana. Additionally, previously reported results of studies aiming at switching the cofactor specificity were recapitulated with CSR-SALAD and the published mutations were also predicted by CSR-SALAD [200]. The only enzymes for which cofactor specificity reversal and recovery of enzyme activity

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could not be predicted by CSR-SALAD were mono- and dioxygenases due to the more complex multistep electron transfer reactions catalyzed by these enzymes. However, Beier et al. were able to achieve this task for the cyclohexanone monooxygenase from Acinetobacter sp. by employing a rational approach including structure analysis, sequence alignments and careful evaluation of literature data [201]. Other examples of success include imine reductases [202, 203], reductoisomerases [204] and DHs [205, 206], among many others [190, 197, 207]. All these examples demonstrate that reversing the cofactor specificity of enzymes for NAD(P)H is not a straightforward task, which is also due to the different structural cofactor binding motifs and the diverse electron transfer chains present in NAD(P)H-dependent enzymes. As mentioned above, many oxidoreductases employ flavins or heme as prosthetic groups. Most known enzymes use either flavin mononucleotide (FMN) or FAD as cofactors, but there have been examples of natural enzymes using modified flavins [208–215] or alternative mechanisms involving covalently bound adducts [216]. Incorporation of flavin analogues in enzymes typically using FAD/FMN usually leads to inactivation of the enzyme and is mostly used to study mechanistic details and interaction of cofactor and enzyme [217, 218]. There are, however, few examples of catalytically active synthetic enzymes with modified flavins. Artificial flavo-enzymes were created by incorporating flavin analogues in riboflavin-binding protein from chicken, which does not exhibit catalytic activity as an apo-enzyme. The synthetic flavoproteins were tested for their ability to catalyze enantioselective sulfoxidations, which led to the formation of different enantiomers when employing different flavin analogues [219]. In contrast to monooxygenases, which are able to catalyze sulfoxidation reactions, these artificial flavo-enzymes do not depend on NADPH for their catalytic activity, which is actually peroxide-driven. Another example shows the potential of cofactor engineering for altering the substrate specificity of enzymes: a dehalogenase (mammalian iodotyrosine deiodinase) was converted into a nitroreductase. In a first study, this was achieved by inhibiting single-electron transfer and the formation of an intermediate semiquinone radical FMNH, which is essential for dehalogenation reactions, by a threonine to alanine mutation [220]. In subsequent studies, FMN was substituted with the corresponding 5ʹ-deazaflavin analogue in the native iodotyrosine deiodinase, which generated a nitroreductase able to continuously convert a nitroaromatic substrate to the corresponding amine in the presence of NaBH4 as a reducing agent [221]. These examples demonstrate that enzymes are not restricted to FAD/ FMN as flavin cofactors and it should be considered that a newly discovered enzyme from an autotrophic organism might be dependent on a modified flavin cofactor that cannot be provided by the host organism. Hemoproteins typically depend on iron protoporphyrin IX for their biological functions including oxygen transport, electron transfer and the catalysis of a variety of metabolic reactions. The specific catalytic properties of hemoproteins are hereby controlled by structural aspects, such as the conformation of the cofactor and the

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topology of the active site and by the physicochemical properties of the ligands and the residues lining the active site. Thus, replacing the heme group, exchanging the metal ion and/or mutating active site residues can have a tremendous effect on the activity, selectivity and stability of the enzyme. This can even lead to new types of reactions catalyzed by these artificial metalloenzymes and will be discussed in the next section [218].

3.3.2.2 Metals at heart Coelho et al. investigated whether a set of heme proteins can catalyze the non-natural cyclopropanation of styrene with ethyl diazoacetate since free iron protoporphyrins have been reported to display this activity [222]. The low detected carbene transfer activities of these enzymes caused the scientists to screen their available P450-BM3 monooxygenase library for elevated activities. Variants were successfully identified and the influence of the individual mutations on this new enzymatic function was investigated by further protein engineering studies. This newly identified functionalization catalyzed by engineered P450 variants was further explored in subsequent studies conducted by the Arnold group, also showing carbene-insertion into N-H bonds [223–225]. In the Fasan group, a rational protein design approach was chosen to successfully create carbene transfer activity in myoglobin. Besides olefin cyclopropanation, intermolecular N-H and S-H carbene insertions have been reported for myoglobin variants [226–229]. Since it has been shown in several studies that free second- and third-row transition metal protoporphyrins with Ru, Rh and Ir are less dioxygen-sensitive than ferroushemes and capable of efficiently catalyzing carbene transfer reactions, it was attempted to insert these unusual metalloporphyrins into natural hemoproteins [230, 231]. Key et al. combined protein engineering with cofactor replacement by reconstituting apomyoglobin variants from Physeter macrocephalus with a mutation of the axial histidine ligand with porphyrin cofactors containing Co(Cl), Cu, Mn(Cl), Rh, Ir(Cl), Ir(Me), Ru (CO) and Ag sites. They identified several Ir(Me)-containing variants as the most promising candidates for carbene insertion reactions of different diazoesters and the cyclopropanation of β-methyl styrene and 1-octene with ethyl diazoacetate. In a following semi-rational protein design approach, it was possible to create Ir(Me)-myoglobin variants forming both enantiomers from the seven targeted diazoesters with enantioselectivities ranging from 75% to 92% ee [230]. Wolf et al. focused on Ru mesoporphyrin IX-myoglobin catalysts for studying carbene transfer reactions, cyclopropanations and N-H insertions. The diastereoselectivity of the cyclopropane products can be increased when using a rationally designed protein variant [231]. In other studies, myoglobin was reconstituted with cofactors of entirely different structure. The semisynthetic myoglobin derivative with chromium(III)salophen catalyzed the H2O2-dependent sulfoxidation of thioanisole [232]. The rate

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and enantioselectivity of this sulfoxidation reaction could later be increased by changing the size of the salophen substituents; by inserting Mn(III), Fe(III) and Cr(III) Schiff-base complexes into a designed apo-myoglobin [233] and by enabling a covalent binding of a manganese-salen complex to introduced cysteine residues [234].

3.3.2.3 Relief stress by protein engineering As mentioned earlier in this chapter, the metabolic activities of heterotrophic and autotrophic organisms reflect their capabilities to access different nutrient and energy sources. Light as an energy source can change the chemical properties of prosthetic groups and can be linked to the production of ROS and photodamage [6, 7]. Whereas these issues remain targets for future protein engineering efforts, other traits to be engineered in autotrophs, especially plants, are the introduction of resistances to different pests and the improvement of herbicides [235]. Protein engineering and directed evolution have been used to engineer protein-based toxins to increase toxicity against insect pests. The toxin from Bacillus thuringiensis has been modified through truncations, domain swapping, and site-directed mutagenesis [236]. For the toxins Cry1Ab and Cry1Ac, strong binders were identified from randomized peptide libraries by phage display and then introduced into the Cry1Ab protein [237]. Furthermore, the toxicity of Cry1Ac was improved by phage-assisted continuous evolution (PACE) [238]. Another example is the engineering of the resistance to glyphosate [N-(phosphonomethyl)glycine], one of the best-selling herbicides (Box 3.3). It inhibits the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which is involved in the shikimate pathway. There have been two different strategies for increasing plant resistance to glyphosate: through engineering of EPSPS or through enhanced removal of the glyphosate from plant cells. Engineering of the EPSPS has been achieved by DNA shuffling [239] and epPCR [240]. The best variants were introduced into rice [239] and Arabidopsis thaliana [240] and high tolerances toward glyphosate could be verified. As to the removal of glyphosate by degradation, Zhan et al. engineered a glycine oxidase from Bacillus cereus by epPCR, site-directed mutagenesis and DNA shuffling [241]. This resulted in a variant with 160-fold improved affinity and 326-fold enhances catalytic efficiency. Nicolia et al. engineered a glycine oxidase from Bacillus subtilis with the same purpose, applying site saturation and site-directed mutagenesis to improve the glyphosate degradation activity [242]. BOX 3.3: The glyphosate controversy Glyphosate is an organophosphorus compound – a phosphonate – and used as a broad-spectrum herbicide. As such, it was discovered as early as 1970. Farmers and agricultural plants have since adopted glyphosate to control weed growth, especially after the Monsanto Company introduced genetically modified, glyphosate-resistant crops (e.g., soy, maize and cotton). Whereas the hazard to

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humans is still debated, the excessive use of glyphosate has led to the rapid emergence of resistant weeds. Consequently, the negative impact on the environment and crop yields calls for response from farmers, businesses, scientists and politicians.

3.4 Future directions The engineering of proteins has undeniably been a success and led to the customization of many natural proteins to efficiently function under industrial process conditions, accept non-natural substrates and yield the desired product configurations; engineering efforts even created biocatalysts with functions not known in nature and hold potential to create new enzymes from scratch. The many different engineering strategies discussed in this chapter – directed evolution, semi-rational and rational designs – depend on increasing levels of (structural) information about the engineering target. Their success rates to create protein variants with the desired trait are not only depicted by the exponential gain of knowledge about protein structure, amino acid chemistry and enzyme function; the technological improvements also presented in this chapter range from tools to visualize (e.g., Chimera and PyMOL) and manipulate protein structures in silico, analyze structural elements with tools like CAVER, identify and even predict mutational hot spots that (rationally) reduce the numbers of mutants to be generated (e.g., 3DM, HotSpot Wizard, FRESCO) and, ultimately, screened. Although the creation of “small but smart” libraries by saturation mutagenesis methods can significantly reduce screening efforts, the screening of large (combinatorial) libraries of proteins bearing ≥30 mutations still requires the experimental examination of tens of thousands of variants. With the advent of HT screenings including IVC in combination with microfluidics, this limiting factor has been overcome and will accelerate the identification of hit candidates. Complemented by techniques like CRISPR/Cas9, which enables the engineering of target proteins in multiplex on genomic scales, the future of protein engineering endeavors has never looked brighter and will certainly help to move the emerging hosts of the autotrophic biorefinery into the spotlight. The selected protein engineering examples in this chapter covered both wellestablished customizations (e.g., thermostability, organic solvent tolerance, stereoselectivity and substrate specificity) and novel engineering approaches including the alteration of cofactor specificities and introduction of artificial cofactors and prosthetic groups. This also pays tribute to the distinct cofactor preferences in heteroand autotrophic (micro)organisms and project their different metabolic contexts and capabilities. In this regard, photoautotrophs are of special interest since they can utilize inorganic CO2 and sunlight as the sole source of energy to produce complex compounds through photosynthesis. Aiming at harnessing these exquisite synthetic powers for the production of chemicals or as a food and feedstock for humans and animals,

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the involved metabolic enzymes are current and future engineering targets. RuBisCO and de novo CO2 fixation pathways have probably been amongst both the most exciting and challenging targets, not only to increase crop yields to feed an ever-increasing population. Protein engineering can accelerate the transfer of “heterotrophic” proteins into autotrophs (and vice versa) and adapt them to their new environments, which might lead to the evolvement of other traits currently underrepresented such as the stability against irradiation (i.e., photodamage). Modern protein engineering certainly will be an integral part in transforming autotrophic microorganisms into serious competitors to established heterotrophic biotechnological hosts to tackle today’s environmental and social challenges including the fixation of CO2 and the stewardship of resources and to sustainably produce the chemicals of the future.

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Lydia Rachbauer, Günther Bochmann, Werner Fuchs

Chapter 4 Gas fermentation Abstract: Gas fermentation is an upcoming technology utilizing autotrophic microorganisms to convert gaseous feedstock into products of higher value. As a key element, the envisioned “autotrophic biorefinery” incorporates CO2/CO as an alternative carbon source aiming to establish decarbonization of the energy and material sector. However, besides its high potential, gas fermentation comes up with several new challenges. Gas fermentations are characterized by limited substrate solubility, and thus, low mass transfer. This chapter summarizes potential feedstocks and products as well as different types of microbial cultures employed for bioconversion. It discusses applicable reactor configurations to enhance gas transfer from gas to liquid phase and, finally, presents various approaches and upcoming techniques, for example, electrofermentation, to overcome specific barriers of this highly promising bioconversion concept. Keywords: gas fermentation, carbon/nitrogen fixation, autotrophic biorefinery, acetogens, diazotrophs

4.1 Introduction Gas fermentation is an upcoming technology that employs microorganisms to convert gaseous feedstocks such as carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2) or gas mixtures (syngas, product gas) into products of higher value. This approach contrasts with classical fermentation processes, where the major substrates are supplied with the liquid phase. Gas conversion strategies have been in the focus of research for several years now and still raise increasing interest. Biological transformation of gaseous substrates comprises a wide range of different routes with huge potential in the context of biorefinery concepts. Many of the envisioned processes aim to incorporate CO2/CO as an alternative carbon source to establish decarbonization of the energy and material sector. Gas fermentation employing microorganisms capable to fix C1 compounds can decouple the dependency on fossil resources through conversion of gaseous carbon to fuels and chemicals. However, the idea of a circular economy (Box 4.1) based on gas fermentation comes up with new challenges, for example, regarding microbial catalysts or appropriate bioreactor design. Generally, the employed microorganisms and metabolic pathways may be quite diverse and, thus, result in a wide range of process-specific requirements including adequate strategies for gas supply as well as for product withdrawal and recovery.

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Box 4.1: Circular economy and the biorefinery concept Although the concept of circular economy has been discussed earlier, its importance on the agenda of European policy makers was underlined by a comprehensive European Circular Economy package [1]. In general, a circular economy aims at minimizing its waste and emissions, as well as the material and energy input required for the provision of our everyday needs by closing material and energy loops [2]. Biorefinery concepts strongly contribute to a circular economy as such strategies exploit the biomass in a cascadic manner. According to the product value chain, it starts with high value products from highquality biomass and the residues are then further converted to lower value products, and ultimately energy. Thus, the input of raw materials is reduced while fully exploiting the biomass for a broad product spectrum.

4.2 Gaseous feedstock 4.2.1 Carbon dioxide (CO2) It is obvious that the use of CO2-rich gases as a sole carbon source for microbial conversion is a highly attractive option. Exhaust gases with high CO2 content are available in abundant volume deriving from numerous human activities such as electricity generation in coal or gas power stations, waste combustion in incineration plants and many other processes driven by fossil resources. Unfortunately, CO2 by itself does not provide any metabolic energy to sustain microbial life. Moreover, the carbon atom in CO2 is in its highest oxidized redox state (+4), whereas in biomass carbon is at a redox level of around zero. Consequently, the supplementation with extra redox equivalents is necessary. Nevertheless, such strategies have been developed utilizing phototrophic organisms, which exploit sunlight for energy provision and reduction of CO2. Involved microorganisms comprise eukaryotic higher algae or microalgae as well as prokaryotic cyanobacteria. A few phototrophic fermentation systems are under investigation and have, at least in part, reached already full-scale application. This includes production of pigments, oily components for biodiesel and whole cells as food additives. Algal/cyanobacterial growth systems have been extensively reviewed elsewhere (see Chapter 5) [3]. Although they are typically not addressed when talking about gas fermentation, numerous of the topics discussed in the current paper, for example, gas transfer reactor systems, are applicable. Nevertheless, the unique extra requirement for exposure to sufficient light makes this application a class of its own.

4.2.2 Carbon monoxide (CO)/syngas Another gaseous C1 compound is carbon monoxide. It derives mainly from incomplete combustion processes of organic matter or fossil carbon. In contrast to CO2,

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autotrophic growth on CO as sole energy and carbon source has been demonstrated for a diverse group of microorganisms [4]. However, growth rates on plain CO are generally very low, whereas the addition of H2 significantly enhances metabolic turnover [5]. A variety of industrial processes generate off-gas that can be utilized. Examples are emission gases from steel mills, metal industry, refineries and chemical plants producing gases with variable compositions of CO, H2 and CO2. Frequently, these gases are flared or, more preferably, burned for energy generation within the production facility. Syngas (see Chapter 13) generated through pyrolysis or gasification of biomass is another gas source of high potential. This approach is increasingly recognized as an alternative to direct fermentation of the frequently poorly degradable residues that may need extensive pretreatment [6]. It has been emphasized that virtually any organic waste product can be recycled by turning it into syngas [7]. Thus, the technology may utilize a wide range of feedstocks including gasified organic matter from municipal solid waste, industrial waste, biomass and agricultural residues.

4.2.3 Methane (CH4) Methane is considered to be one of the most potent feedstocks of gaseous C1 compounds. This includes huge amounts of currently flared or vented natural gas but also renewable sources from anaerobic digestion in biogas plants and landfill sites. Despite the frequently small scale of individual-site methane generation, the large number of sites results in substantial total resources [8, 9]. Regarding its oxidation state, CH4 is at the other end of the spectrum when compared to CO2, being the most reduced C compound. Consequently, microbial conversion is either through a respiratory mechanism using oxygen or – at lower efficiency – other electron acceptors such as nitrate or sulfate. The group of aerobic methanotrophs can use methane as its sole source of energy and carbon to produce a wide variety of products, including biomass protein, bioplastics and biodiesel. However, some product-specific characteristics like limited end-product tolerance or low growth rates might require the incorporation of a synthetic methanotrophic metabolism in another production organism like E. coli or yeasts for biotechnological deployment [10]. A recent review on various biological approaches proposed on the bioconversion of methane into chemicals and fuels using metabolically engineered microorganisms, including engineered nonnative hosts with synthetic methanotrophy/methylotrophy, aerobic methanotrophs with synthetic pathways for valuable products and engineered anaerobic methanogen with reverse methanogenesis capable of anaerobic methane oxidation [11].

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4.2.4 Nitrogen (N2) Another essential nutrient that can be supplied via the gas phase is nitrogen. In fact, in such a manner the two most essential elements for microbial growth, C and N, may be delivered through gaseous feedstocks. Nitrogen is fundamental for the formation of amino acids and nucleobases or the corresponding polymers, proteins and nucleic acids. Without these biopolymers, life on the Earth is not conceivable. In the first decade of the twentieth century, the invention of the ammonia synthesis of Haber and Bosch changed crop cultivation. In 2013, worldwide 140 million tons (Mio t) of ammonia were produced, which means 1.4% of the global energy demand or 3–5% of CO2 emissions [12]. Apart from that, biological nitrogen fixation plays an important role in nature. About 140 Mio t of nitrogen is fixed in the ocean by microorganisms, about 60 Mio t by nitrogen-fixing bacteria in soils in the rhizosphere and rhizobia in root nodules fix about 60–90 Mio t. Nonmicrobiological nitrogen fixation from lightning or combustion is assumed with about 100 Mio t of nitrogen [13]. Thus, the fixation of molecular nitrogen from air is one of the most important fermentations by metabolizing gas-to-liquids or solids and is essential for life on the Earth.

4.3 Microbial catalysts The organisms utilized for gas fermentation are typically of autotrophic nature, that is, they are not dependent on the provision of organic substrates and are capable to form organic cell matter from inorganic materials. This style of life is widely distributed among microbes. There are a variety of options to gain energy from chemical interactions without the necessity of organic substrates. However, autotrophic metabolism typically generates less energy than aerobic processes using oxygen as the terminal electron acceptor. Moreover, fixation of oxidized C1 compounds requires the provision of sufficient redox equivalents (e.g., from splitting of H2O or H2S) at the expense of additional metabolic energy. Alternatively, H2 as a reducing agent may be delivered with the gas itself. Generally, growth rates of autotrophic microorganisms relying on alternative electron acceptors other than oxygen are considerably lower compared to heterotrophs. This imposes a significant challenge on biotechnological process design and the provision of sufficient biomass to obtain economically feasible product generation rates.

4.3.1 Metabolism of gas conversion Frequently, gas fermentation refers to microbiological syngas conversion only. In this context, acetogenic bacteria and some archaea are most relevant [14]. Acetogens

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(Box 4.2) do not belong to a distinct phylogenetic group but have been found in more than 20 different genera. These anaerobic microorganisms are ubiquitous, for example, in soils, sediments, sludge and the intestinal tract of animals [15]. Also, the microbial flora present in anaerobic digester can serve as a rich source for specific enrichment of acetogens as numerous microorganisms involved in anaerobic digestion metabolize gaseous intermediates. Depending on how broad the term of gas fermentation is defined, many more microbial groups can be involved. This includes phototrophs for CO2 fixation, aerobic organisms that oxidatively convert CO or CH4, and N2-fixing microbes.

4.3.1.1 Carbon fixation CO2 fixation is the conversion of inorganic carbon into organic compounds by living organisms. It is one of the major biochemical processes in the biosphere and CO2 represents the most essential source of carbon building blocks for all living organisms [16]. Until now, six metabolic pathways for carbon fixation are known: Calvin–Benson– Bassham (CBB) cycle, reductive citric acid cycle, Wood–Ljungdahl and three variations of the 3-hydroxypropionate bicycle [17]. The CBB cycle is the predominant mechanism utilized by many prokaryotes and all plants fix CO2 into biomass [18]. In the 1960s, the second autotrophic CO2 fixation cycle, the reductive citric acid cycle, was discovered [19]. More recently, some additional options were followed. With one exception, all these CO2 fixation pathways require the provision of extra energy (e.g., from lithotrophic processes or light energy) for fixation of CO2 to the form of biomass. Only the Wood–Ljungdahl pathway, used by acetogenic bacteria, complies with both requirements to sustain life: (a) provision of energy and (b) production of biomass [20]. This pathway comprises two branches, the methyl branch and the carbonyl branch, each of them using a different manner to fix CO2 with H2 as the reductant, and to ultimately form acetate (Figure 4.1). It conserves energy via the generation of a proton gradient which can be exploited by an ATP synthase. Being part of the microbial catabolism, which is the metabolic pathway responsible for energy production, it does not necessarily aim at incorporating CO2 into biomass. Acetate or other metabolites are generated as end products and excreted into the media. The amount of energy recovered is very low with less than an estimated 0.5 mol ATP per 1 mol acetate formed, or 4 mol H2 consumed [20]. In consequence, substrate throughput must be high to sustain bacterial growth or standing in turn for high CO2 fixation rates. Another interesting aspect of the Wood–Ljungdahl pathway is that one of its branches, the carbonyl branch, allows CO utilization as an alternative C1 source. The fixation of CO plays an essential role during the metabolism of syngas with its main components: H2, CO2 and CO [21]. In its simplest version, fermentative CO oxidation is coupled to proton reduction resulting in the formation of H2 and CO2, a biochemical reaction also known as the water–gas shift reaction:

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CO + H2 O ! CO2 + H2

(4:1)

Only three enzymes are required: carbon monoxide dehydrogenase, an electron transfer protein and an energy-converting hydrogenase [5]. Besides the so-called hydrogenogenic carboxydotrophs, (Box 4.2) two other types of CO metabolizing microorganisms can be distinguished: methanogenic and acetogenic organisms generating methane and acetate, respectively, as the metabolic end products.

Figure 4.1: Wood–Ljungdahl pathway – methyl or carbonyl branch from CO2 to acetate [20].

Also, at industrial scale, companies like INEOS Bio, Coskata and LanzaTech already apply the Wood–Ljungdahl pathway to produce ethanol from CO2 or CO and H2. The companies use obligate anaerobic clostridia as production strains mostly, including Clostridium ljungdahlii, Clostridium carboxidivorans, Clostridium ragsdalei, Clostridium

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coskatii or Clostridium autoethanogenum [22]. In several studies, further acetogens like Acetobacterium woodii, Thermoanaerobacter kivui, Eubacterium aggregans, Moorella thermoacetica and Oxobacter pfennigii were identified and are currently in the focus of research to expand the already applied industrial system to a broader product spectrum [23]. Box 4.2: Acetogens and carboxydotrophs Acetogens are a group of organisms, mostly clostridia, which are growing under anaerobic conditions and convert CO2 and H2 to organic acids. Homoacetogenic conversion via the Wood–Ljungdahl pathway as a main metabolic route would result in solely acetic acid as a final product, while some organisms yield a broader spectrum of organic acids. When CO is used as a carbon source, the term carboxydotrophic bacteria is commonly used. These organisms have the rare ability to use CO aerobically as sole carbon and energy source. However, when the required reducing equivalents are provided by H2 under anaerobic conditions, such conversion is done by hydrogenogenic carboxydotrophs.

4.3.1.2 Nitrogen fixation Only prokaryotes can fix molecular nitrogen in the form of ammonia or amino acids and they are divided into free living and associative organisms. The free living group is represented by, for example, Cyanobacteria, Azotobacter or Clostridium, and the associative group is represented by Rhizobium, which lives in symbiosis with roots of leguminous plants, or Frankia, which lives in symbiosis with dikotyles [24]. The majority of prokaryotes live in the rhizosphere. Symbiotic effects are specific between both partners and are not only limited to the exchange of energy vectors and the nitrogen source like ammonia or amino acids [25]. To fixate nitrogen, microorganisms require the enzyme nitrogenase. This specific group of enzymes is divided into three different categories based on the prosthetic group: molybdenum–iron (MoFe), vanadium–iron (VaFe) and iron–iron (FeFe). Highest activity was reported for MoFe nitrogenase, whereas FeFe nitrogenases show lowest activities [26]. For the reduction of N2 to ammonia as given in the following equation, these enzymes require a low redox potential: N2 + 8e − + 8H + + 16ATP ! 2NH3 + H2 + 16ADP + 16Pi

(4:2)

Nitrogenases are easily inhibited by oxygen and nitrate. Still several of nitrogen fixating bacteria are aerobic organisms due to the high amount of energy required for the nitrogen fixation. Thus, the aerobic nitrogen fixating microorganisms require a strategy to avoid this inhibition by free oxygen. Up to date, four different strategies are known to avoid this inhibition. One strategy originates from eukaryotes as some plants provide leghemoglobin to buffer the oxygen concentration in the cytoplasma. Leghemoglobin shows an affinity to bind oxygen 10 times higher than human hemoglobin.

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Filamentous cyanobacteria show another strategy to protect their nitrogenase by the formation of heterocysts. Diazotroph (Box 4.3) In these cyanobacteria, some vegetative cells switch off their photosynthesis – fixation of CO2 – and perform nitrogen fixation instead. Vegetative cells provide heterocysts with carbohydrates like sucrose to conduct the energy-intensive process of nitrogen fixation. The third strategy is the spatial separation of nitrogen fixation and cytochrome to avoid contact between oxygen and nitrogenase. The most prominent strategy to cover the nitrogenase from oxygen is the implementation in the rhizosphere [25]. Box 4.3: Diazotrophs and heterocysts Diazotrophs are bacteria that fix molecular nitrogen with the enzyme nitrogenase. Some nitrogenfixing bacteria form symbiosis with plants. The location where this symbiosis takes place is the nodules found in the plant roots of legumes. The plants provide the energy in form of sugar to the bacteria which in return provide a nitrogen source in the form of ammonia. Free-living filamentous cyanobacteria form heterocysts during nitrogen starvation to ensure their survival. Heterocysts then change their metabolism irreversibly from photosynthesis to nitrogen fixation to provide the cells in the filament with nitrogen for their biosynthesis.

4.3.2 Mixed microbial cultures versus pure strains or defined consortia Frequently, environmental biotechnology relies on mixed microbial cultures (see Chapter 13). Hereby, process development aims at providing appropriate conditions to sustain microorganisms fulfilling the aspired conversion process and to suppress unwanted side reactions. In contrast, pure culture fermentations utilize only a single microbial species. However, sterilization of the substrate as well as maintaining sterile conditions inside the reactor resemble a high technical and financial effort. Accordingly, pure cultures are mainly applied in a high value product segment. It is interesting to note that even under the so-called sterile conditions, frequently small levels of contamination occur: at full scale, pure culture systems are typically operated in discontinuous (batch and fed batch) mode. Continuous operation under sterile conditions is still a major challenge, and reactor operation over long periods typically employs mixed microbial culture due to their higher robustness. Each approach – mixed or pure culture – has its merits and the choice depends on a range of boundary conditions. On the one hand, mixed microbial cultures exhibit faster adaptation to varying gas composition and quality as, for example, required for syngas fermentation. On the other hand, they probably exhibit a wider product spectrum and undesired side reactions may occur. Depending on the prevailing conditions, products might serve as a substrate for syntrophic communities and are thus re-consumed (e.g., volatile fatty acids, hydrogen and alcohols). Also, antagonists thriving on the delivered substrates or parasites may arise. Consequently, close

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monitoring and control of gas fermentation process is required to establish and sustain a highly specified microbial production system delivering the targeted products. In contrast, the utilization of high-performance strains usually involves pure culture fermentation. Such strains typically exhibit low robustness and have limited assertiveness against other microbes. A certain compromise between the two approaches is the utilization of autoselective conditions that suppresses the growth of unwanted species. Examples are conditions of low pH, high temperature or a combination of both [27]. Coculture engineering (Box 4.4) using multiple specific microbes to gradually complete bioconversion of a substrate into a final product is another aspect that has recently gained increasing attention. This approach enables metabolic conversion pathways not feasible for single microbes or improves biosynthesis efficiency through optimization of the individual metabolic steps [28]. For some products, the approach of sequential mixed culture cultivation might also be the way of choice. Such cultivations usually rely on a set of different mixed microbial fermentations where the product of the first fermentation step, often acetate, is used as feed for a sequential fermentation. Thus, energetic limitations to produce longer chain products of higher commercial value – for example, malic acid – can be overcome [29]. Box 4.4: Cocultivation Each organism has a defined substrate spectrum and metabolic pathway. Thus, in some cases, it might be beneficial to use a variety of different organisms to sequentially convert the provided substrate into the final product of interest. One option is to use a mixed microbial culture from a natural habitat which already perform such sequential conversions. However, such consortia often act as a black box with numerous unknown interactions. An alternative is the cocultivation approach. Here, selected pure strains with known metabolism are cultivated together in the same media. The provided substrate is specifically converted into an intermediate, which then is taken up by another specified microbe. In such manner, interferences with alternative pathways can be excluded and a tailor-made route is established.

4.4 Gas fermentation processes First, gas fermentations targeted gas and exhaust gas treatment as an environmental biotechnological application with the aim to remove toxic or harmful contaminants from the gas streams. One well-established example for such biotechnological gas treatment procedure is biogas desulfurization to avoid odor and corrosion. Such processes must be simple and cost-efficient as they are set up as gas cleanup step only without producing a value-added product to justify costly reactor configurations and demanding operation conditions. Desulfurization as an example is usually done by simple adsorption of hydrogen sulfide on activated carbon and so on, or by microbiological oxidation in a packed bed with oxygen/air injection [30–36]. Depending on the

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amount of available oxygen, hydrogen sulfide is either partly oxidized to elemental sulfur under limited oxygen conditions (Equation (4.3)), or sulfuric acid is formed as the terminal product when sufficient oxygen is available (Equation (4.4)) by Thiobacillus species: HS − + 0.5O2 ! S0 + OH −

(4:3)

HS − + 2O2 ! SO4 2 − + H +

(4:4)

Another prominent and well-known applications of gas fermentations are phototrophic systems for algae cultivation. For this application, the algal biomass itself is usually targeted as a product. Depending on the final application of the biomass as a food additive or feed, for further extraction of interesting compound like pigments, antioxidants and lipids, higher costs for the production systems might be justified. Within the last century, biorefinery concepts have given rise to another field – microbial gas fermentation as a production system – providing both carbon and energy source within the gaseous substrate at once. Microbial fermentation is an important production technique for a variety of chemical substances. They play an essential role in the generation of bioactive compounds in pharmaceutical industry. The picture is somehow different when it comes to the production of chemicals and precursors of lower commercial value but high consumption. In this context, only a few processes have reached mass production, and the most prominent being ethanol fermentation from sugarcane. However, undoubtedly this situation will change in a future low carbon society in which biorefineries (e.g., based on synthesis gas) will play an essential role in the formation of chemical products and materials [37, 38]. Biogenic CO2, CO from industrial emissions, biomass-based syngas and furnace gases can readily serve as abundant carbon source for chemical production [39]. The gaseous substrates can be converted into a variety of fuels and chemical precursors, such as methane, acetic acid, butyric acid, ethanol and butanol. Biotechnical production of such precursors may offer several advantages over catalytic techniques. On the one hand, biological conversion occurs under relatively mild temperatures and pressures, whereas catalytic reactors are typically operated at high temperatures and pressures. On the other hand, the product specificity of enzymes is much higher than that of inorganic catalysts. Moreover, the susceptibility to some impurities within the substrate gas is potentially lower because microorganisms have a certain flexibility to adapt to the changing environmental conditions. Examples are sulfur components that are frequently poisonous to the chemical catalysts. Higher specificity improves product yields, simplifies product recovery and reduces the formation of toxic by-products. In cases where cells have enzymatic pathways for multiple products, fermentation conditions can often be adjusted to favor formation of one product over another.

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4.4.1 Products from gaseous carbon sources Depending on the targeted product, some critical criteria must be considered. Effort and, thus, costs of downstream processing as one prominent example strongly depend on the type or product targeted. Three main product categories can be distinguished: (a) a gaseous or liquid product, (b) intracellular or extracellular and (c) biomass as a product. In the case of methane as a gaseous product, the operational conditions of gas fermentation define if the gas stream at reactor outlet consists of relatively pure product (slow but complete conversion of substrate gases as to limited substrate solubility at low substrate concentrations) or a mixture of substrate and product gases (at high conversion rates). Liquid products often comprise alcohols and acids and might also exhibit an inhibitory or even toxic effect, especially at elevated product concentrations. Thus, a production system with continuous product recovery is preferred. Such systems include membrane techniques or fractional distillation to separate the product from the liquid fermentation fraction. When the biomass itself is targeted as a final product, a batch system for cultivation is commonly used. Biomass is then harvested via solid–liquid separation by disk stack centrifugation and usually freeze-dried (as applied for algal biomass). However, latest research also targets high protein biomass as an innovative alternative in the form of microbial biomass-based food, feed and fertilizer [40]. A list of main products that can potentially be produced from various gas sources is provided in Figure 4.2. The spectrum of compounds that are directly obtained via the natural metabolic pathways of microbes applied in gas fermentation is limited to simple and short-chain metabolic intermediates, storage compounds or biomass itself. This is obvious when considering the little energy amount obtained via the Wood– Ljungdahl pathway. However, the list can be further extended when recombinant production pathways are designed and inserted into the organisms used [41]. Another way to extend the product spectrum is the catalytic upgrading of microbial produced precursors and the use of such simple precursors as substrates for a secondary or cofermentation. This concept makes use of organisms that can take up and further convert these simple natural compounds into more complex higher value products.

4.4.1.1 Biomethanation The process of biomethanation is commonly reported on as a biological tool for upgrading of biogas (Box 4.5) to biomethane quality within the framework of the powerto-gas concept. In general, power-to-gas aims at the storage of renewable electricity, mainly from fluctuating production via wind or solar power in a gaseous form [43]. The electric power is converted into hydrogen, and oxygen as a side product by

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Figure 4.2: Main products of C1 feedstocks [14, 41, 42].

electrolytic water splitting. A subsequent conversion of this renewable hydrogen together with CO2 from, for example, biogas in the form of methane allows storage within the natural gas grid, a vast network with significant storage capacity when compared to the electric grid. Thus, upgrading of biogas and injection into the natural gas grid does not only allow the use of available infrastructure to deliver energy to the place of demand but also makes beneficial use of the storage capacity of the gas grid [44–46]. Power-to-gas provides efficient means to store electric energy to compensate the fluctuations in electricity generation from wind and solar energy. It facilitates sustained storage of electricity produced in renewable manner, which cannot be immediately delivered to the electric grid in times of excessive energy generation [47, 48]. Consequently, this technology has the capacity to store H2, for example, produced by electrolysis from photovoltaics at higher energy density per unit volume. Although this methanation process can be carried out also catalytically, this chapter deals with biomethanation only. Biological methanation of CO2 by pure cultures is a well-studied process [49–54] but was proved also for adapted hydrogenotrophic communities as present in anaerobic digesters for biogas production [55, 56]. The microorganisms responsible for this hydrogenotrophic methane formation from CO2 and H2, also reported on as CO2-type hydrogenotrophs, belong to the domain Archaea. They closely interact with syntrophic

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bacteria that convert organic acids and alcohols into acetate, CO2 and H2. CO2-type hydrogenotrophs utilize the previously produced H2 via anaerobic respiration, using the following reaction [57, 58]: CO2 + 4H2 ! CH4 + 2H2 O

(4:5)

Biological biogas upgrading by biomethanation of CO2 is one option to produce fuel quality biomethane and thus create an added value. Although biogas still meets the requirements, on the one hand, for demand-driven provision of heat and power, storage with high capacity when upgraded biogas is injected as biomethane in the natural gas grid enables a sustainable substitution of natural gas. Box 4.5: Biological methane formation under anaerobic conditions When organic matter is anaerobically degraded, methane is formed as an end product. Several different organisms with their specific enzymes syntrophically metabolize the material and enable energy formation by this specialized microbial interaction (Figure 4.3).

Figure 4.3: Overview of syntrophic steps during anaerobic degradation of organic matter for methane production. The final step, methane formation, is performed by a specific group of microorganism termed Archaea. They convert a very limited substrate spectrum only and thus rely on the previous anaerobic oxidation of acids and alcohols. In contrast, bacteria involved in acidification and anaerobic oxidation of acids and alcohols produce H2 as a side product. These previous steps are thermodynamically feasible only when H2 is further converted and rely on hydrogenotrophic methane formation by Archaea.

4.4.1.2 Acids and alcohols from CO2 Various anaerobic organisms, mostly Clostridium species, are using the reductive CoA pathway to fixate CO2 and CO in the form of liquid products. Homoacetogenic

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clostridia such as the thermophilic strain Moorella thermoacetica or the mesophilic Acetobacterium woodii are established model organisms to produce acetic acid [59, 60]. Solventogenic Clostridium including C. ljungdahlii, C. autoethanogenum, C. ragsdalei, Alkalibaculum bacchi, C. carboxidivorans and Butyribacterium methylotrophicum, moreover, expand the product spectrum to longer chain acids and alcohols. However, the natural product spectrum of such CO2-fixating organisms is limited due to the very minor energy generation that is possible via this pathway. Although recent studies also report on the formation of more complex compounds of higher market value, for example, 2,3-butanediol, the productivities related to such conversion are low [22, 61]. Nevertheless, simple compounds like acetic acid can readily serve as a substrate for subsequent secondary fermentation. This yields more complex value-added products of longer chain length as alternative nonbiomass-based feedstock to substitute sugars as the main carbon source used within the conventional fermentation processes. Another prominent product of interest is formate. Current studies on CO2-based microbial formate formation are either linked to enzymatic approaches that apply highly specified formate dehydrogenases or reported on using microbial electrosynthesis (MES) [62, 63]. The MES concept aims at using a direct electron transfer for the reduction of the relatively inert CO2 molecule instead of H2 as a reducing agent. The basic principles of such electrofermentations are briefly discussed in the following chapter.

4.4.1.3 Other products Besides gaseous and liquid products from CO2 or CO, also nitrogen-based products and intracellular storage compounds shall be briefly touched upon. Moreover, also the biomass formation can be of interest as such biomasses might as well serve as food or feed. 4.4.1.3.1 Ammonia and amino acids from molecular nitrogen In agriculture, the microbial group of diazotrophs (Box 4.3) is used to increase plant health, resistance and growth performance. In South America, the most prominent diazotrophic bacterium Azospirillum is in commercial use on 3.5 million ha agricultural land mainly for cereal cultivation [64]. In general, these microorganisms are fermented in continuously stirred tank reactors and use sucrose or glucose as carbon and energy source in general. During fermentation, nitrogen is fixed from molecular nitrogen to form ammonia or amino acids as products of interest. Additionally, several Azospirillum species form phytohormones which, besides nitrogen as fertilizer, also support the plant growth [65, 66].

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4.4.1.3.2 Intracellular storage compounds, such as polyhydroxybutyrate (PHB) Some organisms rely on the intracellular storage of carbon, usually in the form of inclusion bodies. Such storage compounds are formed during stressed conditions, for example, by limited nutrient availability. One very prominent storage compound is polyhydroxyalkanoates with polyhydroxybutyrate (PHB) being the commonly best known. Such PHB is reported as a green alternative to conventional rigid plastic products and can be produced by several algal strains. 4.4.1.3.3 Biomass as product Normally, biomass in gas fermentations is used as a catalyst for the conversion of substrate gases only. Still, autotrophic growth of some microorganisms allows a significant buildup of biomass that can serve as food or feed. When thinking of such autotrophic biomass formation, usually algal biomass such as Limonspira is targeted and has been produced as a food additive since several years. Details on algae cultivation are discussed in a separate chapter in thorough detail. However, the tailored formation of a specific kind of organisms is another relatively new option to provide an alternative protein source [40].

4.4.2 Electrofermentation The novel technology of electrofermentation merges traditional fermentation with electrochemistry to overcome several limitations regarding formation of organic molecules with higher chain length from simple compounds (see Chapter 10). Electrofermentation has been recently developed to enhance the potential of conventional fermentation methods [67, 68] and deals with the electrochemical control over microbial metabolism. The electrodes used during the process can work as either electron source or sink, thus allowing a fermentation process under unbalanced conditions. This effect has a significant impact on microbial metabolism or on the selection of dominating population within a mixed culture [69, 70]. Various compounds have been produced employing electrofermentative approaches including organic acids (C1–C6), and alcohols such as ethanol, (iso)butanol and hexanol [71]. The production of these compounds is generally associated with much lower production rates than those observed in acetic acid production regardless of the variations in reactors, organisms and working potentials. In addition, some of these compounds, such as formic acid and ethanol, are only present as intermediate species rather than as final products. Interesting development was made recently to use sequential reactions of acetogenesis, solventogenesis and reverse β-oxidation to produce alcohols and medium-chain fatty acids [72, 73]. In the past decade, electrofermentation-based strategies have advanced in several fields such that the compactness and efficiency of the whole setup could become cost-effective and sustainable. Although this technology is still considered basic

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research, it has the potential to expand its application in various research areas of materials science, engineering, waste management and applied microbiology through an integrative approach [74]. Due to its newness, electrofermentation still faces significant challenges. Above all, the lack of detailed know-how on how microbial metabolism is affected by electrical potential and current, specific fermenter design incorporating electrodes, stability of complex surface-based systems and technoeconomic validity are currently the main bottlenecks.

4.4.3 Problems related to gas fermentation Gas fermentation in general is related to several problems. Firstly, cells need to be retained in the digester to prevent washout and increase biomass density. This can be established also by applying suitable reactor design (e.g., fixed-bed or membrane bioreactors) that combine cell retention with enhanced gas-to-liquid mass transfer and thus better substrate availability [75, 76]. Secondly, CO can negatively affect the microbiology involved in the bioconversion processes due to its high toxicity [77]. However, CO can also serve as a renewable carbon source and is metabolized within the reductive acetyl–CoA pathway (Wood–Ljungdahl pathway) to produce ethanol, acetic acid and other by-products such as clostridia species. By applying a mixed anaerobic consortium adapted to CO utilization for biomethanation, it was demonstrated that the microbial inhibition by CO can be overcome and the provided CO was fully converted [78]. Biological water–gas shift reaction by carboxydotrophic organisms – conversion of CO and water into CO2 and H2 (Equation (4.1)) – was reported under anaerobic conditions for various strains [79]. Methane production from synthesis gas has been reported using acetate and CO2/H2 as intermediates [80]. Involved organisms comprise carboxydotrophic bacteria (Box 4.2) and methanogenic archaea. Experimental results on real syngas fermentation for biomethane production, though, are still scarce. To establish an efficient conversion process based on real syngas microbial adaption needs to be combined with optimized reactor configuration to enhance substrate availability as mass transfer of gas to liquid is typically the rate-limiting step in syngas fermentation [81]. Mass transfer limitation needs to be overcome within a gas fermentation process. Most studies on syngas fermentation were carried out in stirred-tank reactor systems and use mechanical mixing, an energy-intensive method, to counteract this limitation.

4.5 Reactor design Gas fermentations are generally characterized by limited substrate solubility, and thus, frequently operated under mass transfer–limited conditions. Moreover, they can be kinetically limited when only little biomass is present because of the typically low

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growth rates of the involved microorganism [82]. These two aspects are of prime concern when searching for suitable reactor designs. At best, they should sustain optimum gas transfer in combination with efficient retention of biomass through immobilization to allow accumulation of high biomass densities.

4.5.1 Gas transfer and solubility Common to all gases is their low solubility in comparison to typical organic substrates like sugars. Additionally, as is well known, their solubility decreases with increasing temperature. Therefore, efforts to achieve enhanced biochemical conversion through elevated temperature are thwarted by lower substrate concentration. On a mass basis, the solubilities of CO and H2 are only 60% and 4%, respectively, of the solubility of oxygen [75]. However, looking at the molar values (Table 4.1), the solubility of most gases is in a similar range. An exception is CO2 due to its interaction and chemical reaction with water. Table 4.1: Solubility of selected gases in water at 20 and 35 °C at a partial pressure of gas of 101.325 kPa (data given as the mole fraction solubility of the gas in solution) [83]. Gas

Formula

Hydrogen Oxygen Nitrogen Carbon monoxide Carbon dioxide* Hydrogen sulfide* Methane

H O N CO CO HS CH

Solubility at  °C (mol/mol)

Solubility at  °C (mol/mol)

. × − . × − . × − . × − . × − . × − . × −

. × − . × − . × − . × − . × − . × − . × −

*Solubilities given for those gases that react with water, namely carbon dioxide and hydrogen sulfide, are recorded as bulk solubilities; all chemical species of the gas and its reaction products with water are included.

A consequence of low substrate solubility (Box 4.6) is a low specific mass transfer
rate kLa. Basically, mass transfer flux NA of component A is given as NA = kL a c* − c ,
where c* − c is the concentration gradient of component A between the concentration in liquid phase c and the equilibrium concentration c*. This concentration c* depends on Henry’s law according to which the amount of dissolved gas in liquid is proportional to its partial pressure in the gaseous phase above the liquid. The proportionality factor is called Henry’s law constant which has a pronounced dependency on temperature and certain other parameters such as salinity [84]. Gas–liquid mass transfer can be accelerated basically by two means: firstly, increasing the volume-specific power input into bioreactors with dispersed gas phase.

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The reduced size of gas bubbles as to the fine dispersion results in a significantly increased exchange surface area, and consequently, enhanced gas–liquid transfer. Secondly, an increase of the partial pressure of input gases in the gas phase also positively impacts gas–liquid mass transfer. Increasing the power input may be economically challenging specifically when the targeted products are of low or medium market value. Thus, frequently, it is preferable to increase the partial pressures of the gaseous substrates. This can be either achieved by pressurizing the reactor vessel or through the hydrostatic pressure at the bottom of the liquid phase where the gas is dispersed. Within immobilized biofilm systems, besides mass transfer from gaseous into liquid phase, the transfer of soluble substrate by diffusion within the biofilm itself needs to be considered. This phenomenon describes the prevailing mass transfer conditions and was reported on as a three-phase system [82, 85]. An illustration of mass transfer in such systems is shown in Figure 4.4. This kind of system is characterized by two mass transfer interfaces: a. Gas–liquid transfer b. Transfer in the biofilm

Figure 4.4: Mass transfer in a three-phase system comprising gas, liquid and biofilm, as present in trickle-bed reactors applied for gas fermentation (at quasi-steady state).

Although mass transfer coefficients for gas phase, liquid phase and biofilm are functions of physicochemical properties such as the gas solubility in liquid, reactor characteristics and operation conditions, the overall mass transfer coefficient kLa is directly related to the effective exchange area (m2/m3) [75]. In contrast to many other gases (such as O2 or H2), CO2 can react with water to form carbonic acid (H2CO3). In subsequence, the carbonic acid may lose up to two protons according to the acid equilibria, depending on the pH and other dissolved compounds. At neutral pH, most of the CO2 is present in the form of bicarbonate (HCO3–). Total CO2 concentration of all species in the aqueous phase is therefore much higher than the specific concentration of dissolved CO2 alone. Consequently, CO2 can be accumulated in the aqueous phase to a much higher level than other gases that do not interact with water. However, the rate-limiting step of CO2 transfer is the adsorption and the subsequent hydration ultimately yielding bicarbonate. Unfortunately, this reaction is very slow [86]. It has therefore been suggested to employ

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carbonic anhydrase, an enzyme capable to significantly accelerate the named chemical reaction, to enhance industrial CO2 capture and sequestration processes [87]. Box 4.6: Mass transfer limitations For all substrates, be it organic compounds or gases, the availability to the microorganism must be secured. Similar to sugars and salts, also gases need to be in aqueous solution in order to be taken up by the organisms; they have to be present in a so-called bioavailable form. Thus, the solubility of each nutrient compound plays an essential role and depends on the equilibrium between dissolved and undissolved form of the specific compound. For gases, the amount of dissolved gas is defined by Henry’s law and is influenced by the concentration of a compound in the gas phase and the pressure in the system, as well as by the temperature.

4.5.2 Cell retention/immobilization Despite the obvious benefits of microbial syngas conversion such as the mild conditions at ambient pressure and moderate temperatures, still several problems are related to biotechnological processes; for example, low product concentrations, low productivity and difficult product recovery. For large-scale application of biochemical conversion routes, these processes must be improved, for example, by retaining the catalyst; in this case, the cells themselves, in the system and hence, establish a continuous process with improved product recovery [37, 88–90]. One prominent option for cell retention is immobilization techniques. In principle, immobilization approaches can be divided into (a) carrier binding, or (b) entrapment techniques. In literature, adsorption onto a vast number of synthetic and natural materials, covalent bonding, entrapment within polymeric matrices or membranes and microencapsulation in cryogels are mentioned [91–95]. Bioreactor types exhibiting high production rates often use immobilized cells for biochemical production. Cell densities for such biofilm reactors are increased because of biofilm formation and result in superior productivities compared to any other reactor types. The assembly of such a biofilm reactor can be manifold and includes configurations like continuous stirred tank reactors, packed bed, trickling bed, fluidized bed, airlift reactors, upflow granular sludge blanket and expanded bed reactors [96, 97]. Another example to increase biocatalyst concentration, and thus, productivity is cell recycling. This technique is using filtration membranes to combine the recycling of cells with product recovery and was reported with a strong focus on ABE (acetone, butanol and ethanol) fermentation [98–100].

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4.5.3 Reactor types Current research and development in gas fermentation has a strong focus on commercial scale-up [42, 101, 102]. Thus, a cost-effective strategy to provide reactor systems featuring high kLa values as well as efficient biomass retention is required. Frequently, reactor designs with high internal surface areas enhancing gas–liquid mass transfer are applied to increase kLa and to allow enhanced biofilm formation. On the other hand, improved gas dispersion technologies such as microbubble sparging are employed mainly for lab-scale applications [75, 76, 103, 104]. Optimum gas–liquid transfer has been a traditional challenge in reactor technology. In biotechnology, considerable analogous experience exists from optimizing oxygen transfer to accelerate oxidative degradation or conversion of a substrate. Also, in chemical reaction technology, gas–liquid transfer has been studied intensively, for example, for gas scrubbing. For industrial-scale applications, the volumetric masstransfer coefficient per unit power input (kLa P−1) is the critical parameter [101]. This is of specific importance, as systems with highest kLa do not necessarily exhibit highest kLa P−1 as power consumption varies for different stirring or mixing devices [105]. Systems utilizing turbulent gas mixing (e.g., stirred tanks) employ power-intensive stirring to disperse gas bubbles, and thus, to increase the exchange area. A different approach is laminar contactors such as trickle-bed reactors that provide high contact surface to facilitate gas transfer. Generally, the choice of reactor design is an optimization problem balancing maximum gas transfer versus energy input, moreover, attaching importance on simplicity and sturdiness to minimize investments and operational costs. The most prominent reactor types for large-scale gas fermentation are as follows (Figure 4.5).

4.5.3.1 Bubble column Basically, a bubble column reactor is a cylindrical vessel with a gas distributor at the bottom. The gas is sparged in the form of bubbles into either the liquid phase or a liquid–solid suspension. Bubble columns are intensively used as multiphase contactors and reactors in chemical and biotechnological industries. They provide several advantages such as excellent mass transfer rates, compactness and low operating and maintenance costs. Three-phase bubble column reactors, that is, bubble columns with floating carriers, are widely employed in reaction engineering in the presence of a catalyst and in biotechnological applications, where microorganisms are utilized in suspension to manufacture industrially valuable bioproducts [106].

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4.5.3.2 Gas lift reactor A gas lift reactor is a specific type of bubble reactor with a draft tube that promotes mixing and gas–liquid mass transfer. It comprises three regions: gas riser, down-comer and disengagement zone. Gas is sparged into the riser, and the resulting decreased fluid density causes the liquid to move upward. As gas bubbles disengage from the liquid at the top, heavier bubble-free liquid recirculates through the down-comer. Also, air lift reactors are widely used in biotechnological process industry as efficient contactors for gas transfer. Moreover, air lift reactors have been largely used for algae cultures [107]. Their main advantages are low shear rate, high capacity, good mixing and absence of mechanical agitators.

4.5.3.3 Trickle-bed reactor Trickle-bed reactors are well renowned for their simple design and low energy requirement. This type of reactor has been extensively employed in wastewater treatment technology for low cost oxygen transfer to sustain efficient degradation of the contaminants. However, other environmental processes like gas scrubbing rely on this well-proven technology. Such reactors employ bedding material that is rinsed to provide high internal surface area ensuring intensive contact of the solid and liquid phases [75]. Moreover, the carrier material may act as a support for immobilization of microorganisms in form of a biofilm. In such manner, efficient biomass retention and efficient mass transfer from the gaseous phase can be combined. While certain lowtech applications of trickle-bed reactors rely on natural gas convection, in gas fermentation compressors are employed for enhanced gas throughput through the reactor.

4.5.3.4 Jet loop reactor Primary element of the jet loop reactor is a tank with a draft tube recirculating liquid phase in a loop back to reactor. In the end of the tube, a nozzle is installed. The high velocity flow through throat of the nozzle generates an underpressure which is used for gas suction. By such means, a fine gas–liquid dispersion is generated and injected into the reactor [108]. The jet loop reactor offers an excellent mixing performance at moderate energy consumption, making it particularly interesting for the application in mass transfer–limited multiphase reaction systems. Based on its mixing principle, large specific internal surfaces can be achieved resulting in a distinguished convective and diffusive mass transfer. The fine dispersion of reactants, moreover, promotes an evenly distributed heat transfer which is beneficial in regard to selectivity [109, 110].

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Figure 4.5: Typical reactor designs for gas fermentation: (a) bubble column, (b) gas lift, (c) trickle bed and (d) jet loop.

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Chapter 5 Introduction to autotrophic cultivation of microalgae in photobioreactors Abstract: Microalgae assimilate the green-house gas CO2 exclusively by utilizing the power of light allowing autotrophic growth. This makes them a sustainable, literally green alternative for the production of various value-added compounds. Different reactor types have evolved to provide optimum conditions for microalgal biomass and product formation. This book chapter provides an introduction to autotrophic growth of microalgae and presents an overview of currently used photobioreactor designs. Keywords: microalgae, photobioreactor, light, mass transfer, autotrophy

5.1 Introduction An alga, as defined by applied phycology, refers to any organism containing chlorophyll a and a thallus not differentiated into roots, stem and leaves [1]. This definition is also true for oxygenic photosynthetic bacteria, the cyanobacteria. Therefore, the term “microalgae” refers to both, microscopic eukaryotic green algae and prokaryotic cyanobacteria [2]. The latter were one of the first living organisms on Earth. Up to this day, cyanobacteria and green algae are responsible for most oxygen (O2) in the atmosphere as a by-product of converting inorganic carbon dioxide (CO2) into organic carbon in form of carbohydrates and subsequently biomass [3]. This famous process is called “photosynthesis” and is relentlessly powered by the sun [4]. The potential of the sun as an energy source is immense. In total 1.28 · 1017 W of photon energy constantly reaches Earth’s surface. Within one hour, this is more energy than humans are currently consuming a year, which is mostly generated by fossil fuel sources. For fulfilling humanity’s ever-growing energy demand, vast amounts of CO2 are released into the atmosphere causing global warming. This has led to increased average global temperatures and thus, rapid climate changes in recent years [5]. Since the Paris Climate Agreement [6], the use of microalgae for the sequestration of atmospheric CO2 is receiving great attention [7–9]. Microalgae utilizing photosynthesis do not require other carbon sources than the green-house gas CO2 making them a sustainable, literally green alternative for the production of several industrially Acknowledgments: The authors thank the Technische Universität Wien for funding the doctoral college “Bioactive”. https://doi.org/10.1515/9783110550603-005

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interesting products, like biofuels [10], bio-based materials [11], unsaturated fatty acids and proteins for nutrition [12], bioactive compounds [13, 14] and high-value pharmaceuticals [15, 16]. Moreover, CRISPR-Cas9 systems are already developed and applied to engineer the composition of strains for nutrition and biofuel production and even phototrophic heterologous expression of proteins of pharmaceutical interest [17]. The remarkable ability of survival and reproduction of microalgae is mainly based on their long history of evolution [1, 2]. Different species have perfectly adopted to alternating habitat conditions. Their major energy source, the sun’s radiation, is strongly depending on the time of the day, the four seasons, local weather and latitude. Microalgae mastered these fluctuations in their natural environments and therefore, can be found all over our planet, in oceans, covering soils and even in more extreme habitats, like hot springs and on glaciers [18]. Today, the major challenge for biochemical engineers and biotechnologists is to simulate these natural conditions in an artificial surrounding, called a photobioreactor (PBR). In a PBR microalgal cells are cultivated under controlled process parameters which reflect their optimum conditions for growth or induced product formation. The most significant factors microalgae are depending on are i) light, surely the key parameter, as well as ii) the mass transfer of CO2 gas into liquid phase and across the cell membrane (and vice versa for O2). Autotrophic cultures experiencing limitations in any form of these two factors often show reduced biomass concentrations and therefore, lowered overall productivity and space-time yields during cultivations. Many different PBR systems for cultivation of green algae and cyanobacteria are available and used in industry and all of these systems are facing limitations leading to inhibited biomass growth. In this book chapter we introduce the basics of autotrophic growth of microalgae and briefly describe the most common PBRs. More details on PBR designs and advantages and drawbacks in the perspective of cultivation can be found in [19–24].

5.2 Factors influencing biomass growth 5.2.1 Light Photosynthesis-active organisms, like plants or microalgae, harvest light in the range of visible light between 400 (“blue”) and 700 nm (“red”). This includes photons with energy states from 171 kJ · mol−1 for red to 217 kJ · mol−1 for blue (Box 5.1). BOX 5.1: The energy of light Light is electromagnetic radiation and includes the visible portion of the electromagnetic spectrum with wavelengths from 380 to 750 nm between ultraviolet (UV) < 380 nm and infrared (IR) radiation > 750 nm. The energy of one single photon is inverse proportional to its wavelength, described by Equation (5.1) [25]:

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E = h*f = h*ðc=λÞ Equation 5.1: Einstein equation on energy content of a single photon at specific frequency or wavelength. E: Energy of single photon [J]; h: Planck constant [6.626 · 10–34 J · s] [26]; f: frequency of photon; c: speed of light in vacuum [299,792,458 m · s−1]; λ: wavelength of photon [nm]). The amount of light energy in this range per square meter and second is called the “photosynthetic active radiation (PAR) [J · m−2 · s−1]”. For measuring the number of photons, the unit Einstein [1 E] or micro-Einstein [1 µE ≡ 10–6 E] was defined, which is equivalent to number of photons in mole. This definition leads to photosynthetic photon flux (PPF) [µE · s−1] and photosynthetic photon flux density (PPFD) [µE · m−2 · s−1]. The latter providing the best measure for comparison of illumination strategies of PBRs in terms of photosynthetic efficiency [27].

5.2.1.1 Photosynthesis: light-dependent reaction Photosynthesis in Greek is literally translated to “putting together with light”. This means, light provides required energy for conversion of inorganic carbon dioxide (CO2) into chemical energy in form of organic carbon. This photoreaction can be seen as a redox reaction where electrons (e-) are transferred from water (H2O) to CO2, while H2O is oxidized to O2 and CO2 reduced to a (CH2O)-block, one carbon equivalent, for subsequent carbohydrate buildup. The overall reaction from CO2 to (CH2O) is split into two separate reactions, i) the light-dependent reaction of photophosphorylation for providing chemically reduced nicotinamide adenine dinucleotide phosphate (NADPH) and adenosine triphosphate (ATP) (represented in Equation (5.2)) and ii) the light-independent or dark reaction, in which CO2 is reduced to carbohydrates (see Equation (5.3)) [28]. 2 H2 O + 8 photons + 2 NADP + + 3 ADP + 3 Pi ! 2 H + + O2 + 2 NADPH + 3 ATP Equation 5.2: Light-dependent redox reaction of photosynthesis.

Prokaryotic cyanobacteria and eukaryotic green algae largely differ in cell organization, since prokaryotes lack membrane-bound organelles. Therefore, their photosynthetic active center in so-called “thylakoid membranes” is located in the cytoplasm in parallel stacked layers close to the cell surface. In eukaryotic algal cells, organelles called “chloroplasts” are filled with flattened thylakoid membranes harboring the photo-active machinery. The thylakoid membrane consists of a lipid bilayer membrane, which separates the inner, more acidic side “lumen” and outer side “stroma”. It anchors five major protein-protein or protein-pigment complexes, the light-harvesting antennae, the two photoreaction centers photosystem II (PS II) and photosystem I (PS I), cytochrome b6/f and ATP-synthase. Microalgae, as all other phototrophic organisms, utilize the outer light-harvesting antenna to harvest photons and channel this energy through the membrane to the

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photoreaction center of PS II, respectively PS I, by multi-protein complexes with associated pigments, like chlorophylls, carotenoids or phycobilins. The biochemical reaction has the main purpose to generate the strong reducing agent NADPH and high-energy compound ATP for the second stage of photosynthesis, the dark reaction for assimilation of CO2. The overall photoreaction carrying two electrons from two H2O molecules to NADP+ is connected via a series of e- carriers and is illustrated by the “Z-scheme” [29]. The first photon excites chlorophyll P680 in PS II. The resulting e- hole is highly reactive and oxidizes H2O molecules to molecular O2 in the catalytic manganese-center of PS II. The excited e- (P680*) is shuttled via several intermediates and cytochrome b6/f to chlorophyll P700 in the reaction center of PS I. By this, protons (H+) are channeled across the membrane, generating a gradient, which is needed for ATP synthesis. In PS I another light quantum excites the e- (P700*) and it is brought downhill by ferredoxin protein complexes via one-electron transfers. As final reaction, the enzyme ferredoxin-NADP+ reductase (FNR) converts NADP+ to NADPH. To sum up, two photons are needed to transport one e- from H2O to NADP+. For the reduction of NADP+ two e- and for reducing CO2 two equivalents NADPH are required. In total eight mole photons are used for one mole CO2 giving a maximum theoretical quantum yield of 12.5%. For a more detailed view, this cascade is thoroughly explained in [28].

5.2.1.2 Photosynthesis: light-independent reaction The light-independent or dark reaction of photosynthesis where CO2 is actually bound, famously known as the Calvin-Benson-Bassham cycle or in short Calvin cycle is summed up in Equation (5.3) and explained in more detail in [30, 31]. CO2 + 2 NADPH + 3 ATP + 2 H +

RuBisCO

! ðCH2 OÞ + 2 NADP + + 3 ADP + 3 Pi + H2 O

Equation 5.3: Light-independent chemical reaction of photosynthesis catalyzed by RuBisCO.

Ribulose 1,5-biophosphate carboxylase/oxygenase (RuBisCO; see Chapter 3) catalyzes the first step of the cycle, where ribulose 1,5-biphosphate (C5, means 5 carbon atoms in the molecule) reacts with or fixes CO2 (C1) in the Mg2+ catalytic center. The formed C6 molecule is cleaved into two phosphoglycerate (C3). After reduction by NADPH and ATP in step two, two molecules of glyceraldehyde 3-phosphate are formed, which are represented as (CH2O) equivalents. During step three, 5 out of 6 produced glyceraldehyde 3-phosphate molecules are recycled in a biochemically complex reaction to ribulose 1,5-biphosphate, the initial substrate for step one, and the only remaining glyceraldehyde 3-phosphate molecule is converted to carbohydrates. Additionally, RuBisCO’s oxygenase activity is responsible for photorespiration, an unwanted competing reaction to photosynthesis in PBRs. In this process it oxidizes

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ribulose-bisphosphate into hydroxyacetic acid for energy generation. The rate of this reaction depends on the ratio of accumulated O2 to available CO2 in the broth.

5.2.1.3 Illumination in PBRs For autotrophic cultivation of microalgae in PBRs the rate of photosynthesis is of greatest interest. It describes the efficiency of cultures to absorb photons provided by natural or artificial light sources and to convert this energy into biomass. The photosynthetic rate, usually calculated as oxygen evolution rate (OER) [mmolO2 · L−1 · h−1] or carbon dioxide uptake rate (CUR) [mmolCO2 · L−1 · h−1], is depending on the photosynthetic photon flux density (PPFD) [µE · m−2 · s−1] and depicted in a light response or P/I curve (Figure 5.1).

Figure 5.1: Light response (P/I) curve in four phases (I–IV), photosynthetic rate P in relation to PPFD. I: Photorespiration; II: photolimitation; III: photosaturation; IV: photoinhibition; Pmax: maximum photosynthetic rate; α: initial slope of light response; Ic: light compensation point; Ik: boarder between photolimitation and -saturation; Ii: start of photoinhibition.

The P/I curve can be divided in four different zones: photorespiration, photolimitation, photosaturation and photoinhibition. During dark phases, the algal metabolism relies on photorespiration, also called “dark respiration” (phase I). The CO2-fixing enzyme RuBisCO can use its oxygenase activity to convert organic carbon into energy. Additionally, microalgae are metabolizing carbon storage compounds, as glycogen or starch via glycolysis, the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. By this process O2 is taken up and consumed, which gives a negative OER. This process is similar to heterotrophic organisms [32]. With rising PPFD the photosynthetic machinery begins to turn, until the light compensation point (Ic) is reached, where O2

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uptake by dark-respiration and O2 evolution by photosynthesis are equal. Upon further increase of light intensity, the photosynthetic rate rises linearly. For low irradiation of dense cultures resulting in fully absorption of photons, this rise is linear with an initial slope of the curve (α). This initial slope is used as measure of the maximum quantum yield of photosynthesis [33]. For higher illumination intensity this relationship is not linear anymore. Once a certain value of photosynthetic rate (Pmax) is reached, further increase in irradiance cannot elevate the turnover number of photons anymore. This level is the absolute maximum light energy which can be utilized by the microalgal culture. The PPFD value (Ik) at the intersect between α and Pmax is the defined border between photolimited and photosaturated conditions (phase III). In case irradiation gets even higher, exceeding the photosaturation (Ii), photoinhibition starts (phase IV). By high PPFD cultures get harmed and therefore, the photosynthetic rate drops again due to the formation of radical species. The dynamic process when cultures adapt to high irradiation by changing their structural and chemical composition during a time span which may last several days is called “photoadaptation” and leads to “photoacclimation”. These adjustments involve optical, biophysical, biochemical, ultrastructural, physiological and molecular levels [33]. In PBRs, PPFD should be ideally kept somewhere between photolimitation and photoinhibition depending on the setup and illumination strategy. With higher biomass concentration cells closer to the light source experience a much higher photon flux than cells in further distance. This exponential drop is due to the law of LambertBeer and called mutual cell shading [34]. However, by raising irradiation to provide proper photon intensity to more distant harvesting antennae, cells close to the light source could experience photoinhibition and in worst case get irreversibly injured. In addition, photobleaching might occur, what can be observed in a lighter, brown to yellowish color of the cell broth [35]. Inducing turbulences by mixing would increase the cell’s frequency of shifting between high and low irradiation zones. By this, long exposure times and hence, photoinhibition could be avoided. This also means, rigorous mixing in stirred tank PBRs could provide a great advantage [36]. Summed up, long exposure times in inhibiting zones should be omitted in controlled PBR environments. Furthermore, the impact of optimized day and night cycles and flashing light for cultivations is investigated [37, 38].

5.2.2 Mass transfer of gases The fundamental physical principles for mass transfer between gas and liquid phase are based on the processes of convection and diffusion (Box 5.2). The way of one single gas molecule to the catalytic center of the cell proceeds in several steps; (i) diffusion from gas phase to gas-liquid interface, (ii) transport across the gas-liquid interface, (iii) diffusion through the layer of liquid around the gas bubble to the

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well-mixed zone, (iv) transport to the cell, (v) diffusion through the liquid film surrounding the cell, (vi) transport or diffusion across the cell membrane and (vii) transport within the cell to the intracellular reaction site. In general, for cultivations with freely suspended cells step (iii) is the rate-limiting step of the overall gas transport process [39]. Since microalgae assimilate CO2 from the liquid phase, the CO2 supply in form of air or CO2-enriched air is of great importance. However, O2 released by microalgal cells has to be transported the inverse way, since accumulated O2 is inhibiting photosynthesis.

Box 5.2: The volumetric mass transfer rate For a mathematical description of mass transport phenomena between liquid and gas phase, Henry’s law and the two-film theory (illustrated in Figure 5.2, C) of gas absorption are applied [40]. After some rearranging and substitutions, a linear relation can be obtained, shown in Equation (5.4) [39]. Factor kla is the volumetric mass transfer coefficient and term (c*A – cA) is referred to as driving force of mass transport [41]. qtA = kl a* c*A − cA




Equation 5.4: Calculation of volumetric mass transfer rate of compound A, qtA [mol · L−1 · s−1]. kla: volumetric mass transfer coefficient [s−1]; c*A: saturation concentration of compound A [mol · L−1]; cA: concentration of compound A [mol · L−1].

In principle, the chemical equilibrium of inorganic carbon as CO2, HCO3– or CO32– in aqueous solution is depending on pH. For most microalgae cultivations it is between 6 and 9.5 and thus, mostly CO2 and HCO3– are present with pKA approx. 6.5 [39]. Since the substrate CO2 is generally low in concentration and the reaction is inhibited by O2, microalgae have developed a “carbon dioxide concentrating mechanism (CCM)” for raising CO2 levels close to the catalytic center of the key enzyme RuBisCO. Because of the CO2 to HCO3– equilibrium, the overall mechanism is highly pH-dependent [42]. CCM is slightly different for prokaryotic cyanobacteria and eukaryotic green algae because of their differences in cellular structure and organization [43]. However, species of both kingdoms utilize several different active inorganic carbon transporters (CO2 or HCO3–) in addition to passive diffusion of CO2 through their membrane. Active transporters are contributing most to CCM and overexpression leads to higher growth and photosynthetic rates [44]. Because of higher pH in the cell, HCO3– is the predominant species. In cyanobacteria HCO3– is further shuttled into carboxysomes, a specialized compartment, full of RuBisCO in which HCO3– gets turned into CO2 by carbonic anhydrases (CA). In green algae HCO3– is transported across the chloroplast’s thylakoid membrane into the lumen, the RuBisCO-rich and acidified (by light reaction) compartment, which moves the equilibrium to CO2. In both cases, this last conversion step rises CO2 concentration close to the site of catalytic reaction in favor of the dark

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Figure 5.2: Schematic photobioreactor with mass transfer. A: air-lift column PBR; B: detail of one bubble in the culture; C: zoom into gas liquid interface of gas bubble and liquid; πA: partial pressure of compound A in gas phase; πA,i: concentration of compound A at the interface; cA: concentration of compound A in liquid phase; cA,i: concentration of compound A at the interface; green: microalgal culture; grey: (CO2-enriched) air.

reaction of photosynthesis. For a more detailed view on these complex mechanisms, please confer to [45].

5.2.2.1 Mass transfer in PBRs During biomass growth, microalgal cultures gather all available inorganic carbon in the cultivation medium by their CCM. Since this is the only carbon source in autotrophic cultivation, the supply through air or CO2-enriched air must be maintained. Further, the generated O2 of photosynthesis, which accumulates in the medium must be efficiently stripped-out. To understand how the mass transfer rate can be controlled in PBRs, one has to mention qtA (confer Equation (5.3)). As described, the transfer of molecules between gas and liquid is quantified by the kla value. This simplified factor is technically a combination of the specific volumetric mass transfer coefficient kl [s−1 · m−2] and the surface area of air bubbles in liquid phase a [m2] [41]. However, the multiplied factor kla is more suitable to determine in experiments, for comparison of reactor setups and for scaling up and down [39]. Gas bubbles in the liquid phase (Figures 5.2, A and 5.2, B) are highly important for mass transfer and increasing bubble surface leads to an increased transfer rate. By raising the aeration rate in volume gas flow per volume medium and minute, known as “vvm” [min−1], the total gas-liquid-interface area gets higher, assuming constant bubble sizes. Elevating vvm cannot be exploited to an infinite value since it may cause “flooding”, where little or no dispersion of gas bubbles takes place and so

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mass transport decreases drastically. Additionally, foam generation could induce cell floatation or injure cells by bursting bubbles. Furthermore, the high gas-liquid interface area reflects light and thus makes it harder for efficient illumination. Because of geometrical reasons smaller bubbles have a higher surface to volume ratio and hydrodynamics makes them rise slower, leading to longer residence times and advanced transfer efficiency. Smaller bubbles can be generated by special aeration instrumentation, like micro-porous spargers, or by higher power input in actively stirred PBRs due to mixing-induced turbulences. However, smaller bursting bubbles release relatively more energy and higher power input requires higher tip speed and therefore shear forces in stirred PBRs, both damaging microalgae [46]. For more exact calculation about fluid dynamics and resulting shear forces CFD simulations could also be done [47]. Aeration strategies for large-scale cultivations are reviewed by [48]. Another screw to adjust in mass transport is the saturation concentration term cA*, which is dependent on physical and the chemical environment in PBRs. To raise cCO2*, CO2-enriched air or pure CO2 could be sparged into the medium. By CO2 absorption, this strategy changes the pH of the medium and hence, requires active pH setpoint control. Moreover, cell physiology is affected, since CCM is less important in this surrounding. A comprehensive review on the effects of CO2 is provided by [49]. Concerning pH, one could also take advantage of the pH dependent chemical CO2 solubility in aqueous solution. This means, at higher pH the equilibrium is in favor of HCO3– or even CO32– leading to a higher absolute amount of inorganic carbon dissolved. An additional physiological parameter to tune is temperature. In general, lowering the temperature boosts gas solubility, in this case cCO2* but requires temperature setpoint control. Obviously, pH and temperature are approaches which are limited by the tolerance of specific microalgal species. Applying pressure to closed PBRs increases partial pressure of CO2 in the gas phase (πA), leading in turn to a higher cCO2* (Henry’s law). As a convenient side effect, pressurized vessels make it a lot easier to maintain axenic cultures. Nevertheless, heating or cooling and providing overpressure are consuming valuable energy. Altogether, the interplay of aeration, agitation, and all other varying process parameters for microalgae cultures in PBRs need exact reconcilement for any given setup and cultivated strain. Providing excess CO2 for maintaining a maximum photosynthetic rate even in dense cultures is of utmost importance. This means, for economic efficient microalgae biomass production cultures and photosynthesis must always be lightlimited and not limited by CO2 transport and availability. Further, O2 must be efficiently stripped-out to avoid the effect of photorespiration.

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5.3 Commonly used photobioreactor designs In principal, there are two major strategies to culture microalgae, namely in either open pond or closed photobioreactor (PBR) systems (Box 5.3). Box 5.3: Design principles of closed photobioreactors Closed PBR systems can be classified as i) flat plate, ii) tubular, iii) column, iv) soft-frame and v) stirred tank PBR designs (see Figure 5.3). All more complex designs can be assigned to one or more of these categories and are called “hybrid” systems.

For the sake of completeness, open pond systems are shortly noted here since they are still extensively used in large-scale. Their major advantage is the low energy consumption. However, open ponds are unstable ecosystems, which are hard to maintain, and contaminations of other organisms can hardly be avoided. In addition, genetically modified organisms (GMOs) are not allowed to be cultured in open systems. For more information on the evolution of PBRs confer to [50] and for recent reviews [19–24]. See Chapter 12 for an industrially used open pond cultivation system.

Figure 5.3: Schematic photobioreactor types. A: flat plate PBR in vertical (A.1) and horizontal (A.2) orientation; B: tubular PBR in serpentine design; C: column PBR as air-lift manifold system; D: stirred tank PBR with internal illumination; black boxes: devices for mixing; yellow: natural or artificial light sources; green: microalgal cultures; grey: (CO2-enriched) air.

5.3.1 Flat plate Flat plate reactors consist of two flat, parallel and transparent layers of glass or plastic with a liquid layer of algal culture in between. Light is irradiating these layers and their distance defines the volume of the PBR. For thin panels showing high surface to

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volume ratios, high biomass values can be obtained since light is penetrating the whole culture even at high biomass concentrations and thus light-limitation is usually not an issue [51]. However, this advantage is disappearing with bigger distance between the layers for raising the cultivation volume. The consistent light supply leads to high photosynthetic rates. For optimal growth appropriate amounts of CO2 by (CO2-enriched) air must be provided. O2 must be stripped-out to avoid accumulation in the medium. By sparging gas through vertical-orientated reactors (Figure 5.3, A.1), where separate gaseous and liquidous phases are present, the transport is limited due to low mixing forces, respectively turbulences. For horizontal flat plates (Figure 5.3, A.2) with solely liquid phase, gas exchange must be done in an external unit. However, the pumping rate for mixing is still limited by the sensitive cells and their morphology. Using external devices also brings the option of external pH control and cooling the system for optimal growth temperature. The only way for scaling up and retaining the same surface to volume ratio is to increase the surface area of the cover plates, since increasing the distance between the layers would lower the efficient light penetration. Optionally, more than one of these PBRs could be connected and share an external device for gas exchange and general process control. Slab waveguides with embedded light scatters for stacking up ten layers of flat plates were developed [52]. This would mean a numbering up instead of scaling up and would make efficient cleaning-in-place (CIP) and sanitization-in-place (SIP), and consequently maintaining axenic cultures an even bigger challenge. Additional issues to mention are sedimentation and cell adhesion on the big surface areas due to low turbulences via sparging. Figure 5.4 illustrates an industrial large-scale flat plate PBR production system with. It comprises 700 PBRs with a total volume of 130,000 liter for astaxanthin production.

Figure 5.4: Flat plate PBR in production scale (provided by Subitec, Germany).

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5.3.2 Tubular Tubular PBRs are constructed in various shapes and forms. They can be of vertical or horizontal orientation and are constructed in parallel tubes, serpentines (Figure 5.3, B) or helical arrangements. Light is irradiating the culture through the surface of the transparent tubes made, for example, of glass or plastics. The lower the diameter of the tubes, the better is the light penetration efficiency and therefore photosynthetic rate and maximum biomass concentration [51]. For a constant PBR process volume this would imply long tubes, which brings along some bioprocess issues. Besides stability of materials, maintaining CO2 levels for pH and growth and removing O2 are most challenging. For vertical-oriented tubular PBRs mixing is normally done as air-lift system. This system was developed to protect shear-sensitive species but provide enough gas exchange at the same time. Air or CO2-enriched air bubbles rise by density differences in the “riser”. This motion drives a mixing cycle where in a second, physically separated tube the culture flows from top to bottom, the “down comer”. Due to the smaller diameters and length of the tubes, the contact time is long enough for adequate mass transfer. To enhance mass transfer wall turbulence promoters could be installed [53]. Horizontal or serpentine reactor tubes require additional pumps in a separate unit for gas exchange, pH, or temperature control, lowering the overall energy conversion efficiency. Altogether, a compromise must be found for economic construction and cultivation in terms of illumination, tube diameter, length and mixing strategy. Long tubes worsen the controllability and mass transfer cannot be assured anymore. Hence, a series of parallel tubes can be connected on both sides and these single modules linked for larger scales. This means again numbering up and not scaling up. Culturing axenic microalgal species in large-scale tubular PBRs is also challenging since CIP and effective SIP are hard to accomplish in long reactor tubes.

5.3.3 Column In principal, column PBRs (Figure 5.3, C) are vertical tubular PBRs with higher diameters but shorter lengths. By increasing the diameter, the volume increases quadratically and thus, worsens the surface to volume ratio for proper irradiation. Column PBRs can be made of transparent materials as glass or plastics with external illumination. Compared to flat plate and tubular PBRs which are both gas transport limited, microalgae cultivations in column PBRs are usually light limited. Aeration is done by compressed, optionally CO2-enriched air which is also mixing the culture by rising air bubbles. The major challenge for scaling up column reactors is providing enough light for the whole culture. In transparent reactors the outer layer is experiencing high illumination whereas the inner zones are limited in light availability. These light intensity gradients from the surface to the axis of the column is due to cell-shading, being an issue even in low density cultures [51]. Additionally, it leads to boundary values of maximum

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diameters. In large-scale several column PBRs can be connected at the bottom and/or at the top, called a manifold system, for a more homogeneous and better mixing of the whole system. Figure 5.5 shows a large-scale plant in air-lift column PBR design. A total of 44,000 glass columns are connected for algal biomass production.

Figure 5.5: Column PBR in air-lift manifold design in production scale (provided by Ecoduna, Austria).

5.3.4 Soft-frame Soft-frame PBRs are made of soft materials, like foils. The bags filled with culture are either hanging on racks, floating on water, or just lying on the ground. Thus, they are extremely flexible and mobile. The transparent bags are directly illuminated by light. The dimensions of the bags, that is, the surface to volume ratio, determines the photosynthetic efficiency. Gas exchange is done in separate devices where optionally pH and temperature can be adjusted. Circulation by mixing is done with connected pumps, but it is quite ineffective leading to sedimentation and inhomogeneous culture conditions. By increasing the number of bags connected to a central gas exchange or process control unit the system’s volumetric scale can be increased. A major drawback is that the bags are hard to clean and almost impossible to sterilize, therefore lifetime becomes short. This creates tons of additional plastic waste, diminishing the sustainable argument of microalgal cultivation processes.

5.3.5 Stirred tank PBRs in stirred tank design could also be classified as very short but wide column PBRs (Figure 5.3, D). They are designed similar to stirred tank reactors which are well known from cultivations of heterotrophic organisms, but with the need of illumination.

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As their name suggests, stirred tank PBRs are actively stirred by various forms of different impellers for enhanced energy input. The induced turbulences improve CO2 and O2 mass transfer rates, guarantee homogeneous distribution and exact control of process parameters, as pH and temperature. However, impellers induce shear forces which might harm microalgae [46]. Sparging of air or a mixture with CO2 can be done by ring or microporous spargers for even smaller bubbles and better gaseous mass transfers. Because of their geometry, stirred tank PBRs have very high volume to surface ratio, meaning they need less space for a given culture volume compared to other discussed designs. However, their geometry is also the biggest challenge for engineers in terms of efficient and homogenous illumination and light distribution. Smaller, lab-scale stirred PBRs which are made out of glass can be illuminated from the outside; for example, by light bulbs, LED panels or strips (see Figure 5.6, left). For bigger, industrial scales the vessels are made of stainless steel which implies the necessity of internal illumination (see Figure 5.6, right). Here internally installed light sources or light guides are used to bring light into the dark, inner zones [22, 38]. Wireless internal illumination by light sources freely dispersed in the

Figure 5.6: Stirred tank PBR. Left: 5.7-liter glass vessel with external illumination used for cultivations in [57]; right: 20-liter stainless steel tank with illumination by light guides; yellow arrows: indicate light sources (pictures taken at Technische Universität Wien, Austria).

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cultivation media was also introduced recently [54]. The zone or the algal cells around these light sources are experiencing excess of photon radiation [51, 55]. Here rigorous mixing in a stirred tank PBR shortens the residence times and distributes the photons more evenly to the cell population for a more efficient photosynthesis [56]. Further, stainless steel column PBRs have the possibility to get properly CIP and SIP which would make them a good system for axenic microalgal cultivations.

5.4 Outlook In the future, sustainable production of microalgae will get economically feasible by large-scale cultivations. Over the time more so-called hybrid systems utilizing the advantages of basic designs will get developed for research purposes. For industrial applications, the PBR of choice strongly depends on the product of interest. For biomass generation, for example, for biofuel production, open ponds will be used because of their cheap running costs. Closed PBR types with higher production and operation costs can only be used for more complex products, as secondary metabolites or bioactive substances. Additionally, more sophisticated processes, where microalgae are used as whole-cell catalysts for the production of bio-based products require closed systems providing axenic cultures over long time periods. In the future more research has to be done to exactly understand how the process conditions influence the microalgal cultures and how this can be used to provide a more energy-efficient growth. Along with the development of novel molecular biology tools, microalgae could get the production host of choice of various chemical building blocks, bio-based products or even pharmaceuticals in the future, and this could all be produced just by the power of light and fixation of the green-house gas CO2. In our mind, large-scale cultivation of microalgae will be done in stainless steel stirred photobioreactors. Since the materials allows efficient cleaning and sanitization possibilities, axenic cultures can be cultivated over long time. For providing sufficient irradiation, artificial LED light at the optimum mix of wavelengths will be used. The light will be channeled to the cells by novel light guides. The growth of cyanobacteria or green algae will always be controlled in light-limitation phase by increasing light intensity depending on optical density of biomass. This guarantees optimum energy utilization, photosynthetic yield and reproducible batches. Inorganic carbon will be provided by (optionally flue gas CO2-enrichted) air and due to actively stirred cultivation, the availability of CO2 is always in excess for accelerated biomass growth.

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Catarina C. Pacheco, Eunice A. Ferreira, Paulo Oliveira, Paula Tamagnini

Chapter 6 Synthetic biology of cyanobacteria Abstract: Synthetic biology has revolutionized the engineering and manipulation of microorganisms with valuable contributions to fundamental and applied science. In this context, cyanobacteria emerge as potential contenders as chassis, enabling the harnessing of solar energy and CO2 fixation for the production of a variety of compounds. This chapter reviews key aspects of cyanobacterial synthetic biology, focusing on the strains that meet the characteristics to be used as chassis, namely Synechocystis and the fast-growing Synechococcus strains, the development of genome-scale metabolic models as tools to predict chassis behavior and design metabolic engineering strategies and; the significant efforts that are being made to expand and refine the available tools for the rational design of synthetic devices and for the genetic manipulation of this group of organisms. Furthermore, a selection of works focusing on the production of compounds using a synthetic biology-based approach is summarized and, the current limitations and future perspectives for cyanobacterial synthetic biology are discussed. Keywords: cyanobacterial chassis, synthetic biology, metabolic flux models, genetic engineering, molecular toolbox

6.1 Principles of synthetic biology Synthetic biology (SB) has emerged due to developments in computational and molecular biology, protein engineering, systems biology [1] and, most importantly, the cost-effective DNA sequencing and synthesis. This interdisciplinary field applies “science, technology and engineering to facilitate and accelerate the design, manufacture and/or modification of genetic materials in living organisms” [2]. The SB approach applies the engineering concepts of abstraction, decoupling and standardization to Acknowledgements: This work was supported by FEDER – Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020 – Operacional Programme for Competitiveness and Internationalisation (POCI), Portugal 2020, and by Portuguese funds through FCT – Fundação para a Ciência e a Tecnologia, I.P./Ministério da Ciência, Tecnologia e Ensino Superior, under the projects UIDB/04293/2020 and UIDP/04293/2020; and in the framework of the project POCI-01-0145FEDER-029540 (PTDC/BIA-OUT/29540/2017). The authors also greatly acknowledge FCT for the scholarship SFRH/BD/117508/2016 (EAF), the Assistant Researcher contract CEECIND/00259/2017 (CCP) and FCT investigator grant IF/00256/2015 (PO). https://doi.org/10.1515/9783110550603-006

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biology, simplifying complex living systems through the establishment of different hierarchic levels [3]. Box 6.1: Synthetic biology Synthetic biology – aims at designing and engineering biologically based parts, novel devices and systems as well as redesigning existing, natural biological systems. Standardized biological part/BioBrick – oligonucleotide sequence with a specific biological function that cannot be subdivided into smaller functional units, such as a promoter or a coding sequence. Composite part – is a DNA unit consisting of two or more basic parts assembled together. Device – is a type of composite part that produces an output or operation in the cell. Circuit – is a complex composite part composed of two or more synthetic devices, designed to perform logical functions. Chassis – biological entity in which the devices or circuits are implemented, it can be a living organism (host) or an in vitro system for transcription and translation. Scar – region of DNA formed where two separated parts are joined together, which cannot be cut by the enzymes that originally cut the two joined sequences.

Figure 6.1: Schematic representation of the synthetic biology principles applied to DNA. Biological systems can be simplified by the establishment of biological parts with specific functions (e.g., promoters and RBS), built according to specifications that make them standardized. This enables the assembly of parts following methods, such as the “standard assembly”, that resorts to simple molecular biology techniques (cut and paste = restriction and ligation) to generate composite parts. These devices and circuits are designed for specific purposes and must eventually be introduced into the host organism – the chassis.

At the bottom of the hierarchy are the biological parts or building blocks – DNA, RNA, proteins and metabolites – that are used for the next level, the assembly of devices that will function to regulate the flow of information and/or lead to biochemical reactions. In the third hierarchic level, devices are used to generate synthetic circuits that form functional complex pathways designed for specific purposes [3, 4]

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(Box: 6.1, Figure 6.1). This rational approach is extended to the genetic building blocks – the biological parts or BioBricks that are oligonucleotide sequences with specific functions [e.g., promoters, ribosome binding sites (RBS), open reading frames (ORFs)], built according to specifications that make them standardized and interchangeable [5, 6]. Most biological parts available are compatible with the RFC[10] standard [7] that describes the structural features of BioBricks, in which the DNA sequence (part) must be flanked by a prefix (containing restriction sites for the enzymes EcoRI and XbaI) and by a suffix (containing restriction sites for the enzymes SpeI and PstI). These specifications allow the use of the standard assembly method, resorting to simple molecular biology techniques for the generation of composite structures assembled from multiple parts that, in the end, maintain the prefix and suffix sequences thus increasing the speed and tractability of the cloning process (Figure 6.2). Between the assembled parts remains a six-nucleotide scar that is not recognized by any restriction enzyme, making it impossible to disassemble the composite. Several modifications and extensions of the RFC[10] standard have been proposed, mainly to alter or eliminate the stop codon present in the scar that is an undesirable feature when constructing devices with protein fusions or protein complexes with multiple domains [8, 9]. Moreover, the standard assembly method is iterative requiring steps of restriction enzyme digestion and ligation to assemble parts, which becomes inefficient for the generation of large devices/circuits and/or variant gene libraries. To overcome these hurdles, the Gibson Assembly, the Golden Gate, the MoClo or the Loop systems have been developed based on isothermal, single-reaction enzymatic assembly or in the use of type IIS restriction enzymes. These methods allow the assembly of multiple parts in one reaction, rapidly generating large DNA molecules [10–14]. Currently, type II assembly standards based on MoClo and Loop are accepted and the technical specifications of iGEM Type IIS standard – RFC[1000] – are already available [15]. Nevertheless, the RFC[10] standard is still widely used, much due to the iGEM – International Genetically Engineered Machine Competition – an annual, worldwide, SB event meant for undergraduate university students, as well as high school and graduate students. The iGEM teams build genetically engineered systems using BioBricks aiming at creating a positive contribution to their communities and the world [16]. Furthermore, iGEM is a program dedicated to the advancement of SB and is responsible for the Registry of Standard Biological Parts [17]. This registry is a database that gathers information on the design and characterization of the existing BioBricks as well as other tools and host organisms, which in the SB jargon are termed chassis. In addition, the iGEM Foundation also maintains a repository of all DNA parts that are in the registry providing a source of genetic parts to iGEM teams and academic laboratories all over the world. Therefore, the iGEM Foundation has played a key role in the dissemination of SB leveraging the development and characterization of numerous parts, devices and circuits with tremendous impact in fundamental and applied science.

Figure 6.2: Schematic representation of the RFC[10] standard assembly (left panel) and sequence technical details (right panel). Biological parts compatible with the RFC[10] standard are flanked by a BB prefix harboring the EcoRI (E) and XbaI (X) restriction sites, and a BB suffix harboring the SpeI (S) and PstI (P) restriction sites. For the assembly of two parts (A and B), part A is digested with EcoRI and SpeI restriction enzymes while part B must be digested with EcoRI and XbaI; the products of these restriction reactions are subsequently mixed and ligated. The XbaI and SpeI enzymes produce compatible overhangs that ligate, generating a scar sequence of 6 bp containing the TAG stop codon. In the end, a composite comprised of parts A and B is generated, maintaining the BB prefix and BB suffix.

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6.2 Chassis For an organism to be used as a chassis for SB applications, it must be well studied with a vast knowledge base, amenable to genetic manipulation, and have a relatively minimalist set of genes that allow retaining viability, avoiding possible cross talk between the synthetic devices/circuits and the genomic context [4, 18]. In the framework of the minimal genomes, the Mycoplasma genus has been the focus of attention. In 1995, the genome of Mycoplasma genitalium (0.58 Mb) was sequenced [19], at the time, it was the smallest known self-replicating organism harboring less than 500 genes, of which, 100 revealed to be non-essential [20]. Later on, the 1.08Mb genome of Mycoplasma mycoides was chemically synthesized, assembled and subsequently, transferred to M. capricolum thus creating the first bacterium controlled by a synthetic genome – M. mycoides JCVI-syn1.0 [21]. The pursuit for a chassis with a minimal genome continued, and the JCVI-syn1.0 synthetic genome was streamlined down to 0.53 Mb – the JCVI-syn3.0 – harboring 473 genes, 149 with unknown function [22]. Apart from the minimal chassis, the model organisms established in molecular biology and genetic engineering, Escherichia coli and Saccharomyces cerevisiae emerged as primordial contenders to become universal chassis for SB [23]. These organisms, together with Bacillus subtilis, currently possess the largest SB toolboxes and efficient manipulation techniques that have allowed the successful implementation/optimization of complex pathways [24–30]. The increasing complexity and sophistication of synthetic devices and circuits has unveiled the need to have several chassis with robust performances in different scenarios/conditions. In this quest for new chassis, cyanobacteria are increasingly attractive biotechnology platforms due to their simple nutritional requirements, the ability to perform oxygenic photosynthesis and metabolic plasticity [31, 32]. In addition, the autotrophic metabolism enables the sustainable production of compounds through carbon sequestration. The metabolic network of these organisms is among the most complicated in nature, displaying redundant central carbon pathways that allow the mitigation of energy demands under multiple physiological conditions contributing to the ecological ubiquity of these organisms [31]. Compared to other photosynthetic organisms (namely plants), cyanobacteria are more amenable to genetic manipulation, have faster growth, culture scale-up is easier and still maintain some degree of subcellular compartmentalization [33, 34]. Within the Cyanobacteria phylum, the unicellular freshwater strains Synechocystis sp. PCC 6803 (Synechocystis 6803) and Synechococcus elongatus PCC 7942 (S. elongatus 7942), and the filamentous, heterocyst-forming strain Nostoc sp. PCC 7120 (Nostoc 7120) are among the most studied organisms. Synechocystis 6803 is a non-nitrogen-fixing bacterium capable of sustaining growth under autotrophic, mixotrophic (using glucose as carbon source) or heterotrophic (with a daily short light pulse) conditions [35], with doubling time around 6.6 h [36, 37] under fast growth conditions. This cyanobacterium has a medium-size genome of 3.6 Mb and harbors seven plasmids with sizes ranging from 2.3 to 120 kb, whose copy numbers

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vary depending on growth phase and conditions [34, 38–40]. Synechocystis 6803 was the first photosynthetic cyanobacterium to have its genome sequenced and annotated [41], becoming the model organism in numerous proteomic, transcriptomic and metabolomics studies [42–47]. Furthermore, it can be genetically manipulated through different methods including natural transformation, conjugation or electroporation [48, 49]. More recently, genome-editing platforms based on clustered regularly interspaced short palindromic repeats (CRISPR) have been developed for this cyanobacterium (for more details on the CRISPR system, see Section 6.3.8 and Chapter 2) [50, 51]. The unicellular cyanobacterium S. elongatus 7942 has been extensively used as a model organism for the study of prokaryotic circadian clocks [52, 53]. S. elongatus 7942 is unable to fix nitrogen (similarly to Synechocystis 6803) and it is able to grow only under autotrophic conditions with doubling times down to 4.9 h [36]. This cyanobacterium harbors two plasmids (pANS with 7.8 kb and pANL with 46.2 kb) and a small genome of 2.7 Mb [34]. Genetic manipulation through natural DNA uptake was first reported for this cyanobacterium [54], and it is also amenable to transformation through conjugation and electroporation [48, 55]; and genome editing using CRISPR was also reported [51]. The generation of segregated mutants in S. elongatus 7942 is significantly less time consuming than in Synechocystis 6803, since it maintains 3–4 copies of the genome instead of the 12 reported and generally accepted for Synechocystis [39, 56]. One strategy envisaging the easier engineering of chassis is related to the simplification of biological complexity by genome streamlining and, in this context, Delaye et al. [57] generated a blueprint for a minimal S. elongatus 7942 genome by setting a guide to non-essential genes as targets for deletion. However, there have been no reports regarding the implementation of such plan. In addition to the unicellular cyanobacteria mentioned above, the filamentous Nostoc 7120 is also considered a model organism, as it has been the subject of numerous studies focusing in nitrogen fixation and cellular differentiation [58, 59]. For many decades, this freshwater strain was considered an obligate photoautotroph, with doubling time of approximately 14 h [60], but recently its ability to sustain growth under mixotrophic and heterotrophic conditions was reported [61]. Conjugation and electroporation protocols for its genetic manipulation have long been established [62, 63], and since this is a filamentous strain, disruption of the filaments may be required to facilitate manipulation. This cyanobacterium has a large size genome of 6.4 Mb and six plasmids ranging from 5.6 to 408 kb [64]. Despite being the best-studied cyanobacteria within the phylum, the growth rates of these model organisms are modest and the robustness under high light and temperature fluctuations is limited [34]. In an effort to find a cyanobacterium more suitable for production purposes, the filamentous non-heterocystous Leptolyngbya sp. strain BL0902 (Leptolyngbya BL0902) was isolated based on its growth traits under outdoor cultivation conditions. The doubling times registered for this cyanobacterium are comparable to those of the Arthrospira genus, which are grown at industrial scale to be used as nutritional supplements [65]. Unlike the latter, this Leptolyngbya is amenable

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to genetic transformation through conjugation [65]. Although this strain held promising capabilities and some tools were developed [66, 67], not much progress has been made using it. Nevertheless, several fast growing unicellular strains have emerged as candidates to become microbial cell factories: Synechococcus sp. PCC 7002, S. elongatus UTEX 2973 and S. elongatus PCC 11801 [36, 68, 69]. Synechococcus sp. PCC 7002 (Synechococcus 7002) is a unicellular marine strain, capable of growth under autotrophic and mixotrophic conditions, and is extremely tolerant to high light intensity and salinity conditions [68, 70, 71]. So far, this cyanobacterium was reported as one of the fastest growing within the group with a doubling time of 2.6 h under optimal conditions [68]. In addition, Synechococcus 7002 is amenable to genetic manipulation by natural transformation and conjugation [72–74], and advanced techniques for genetic manipulation have also been developed [75]. The sequence of the 3 Mb genome and of the six plasmids, with sizes ranging from 4.8 to 186 kb, have been publicly available since 2008 [76]. Other fast-growing strains were reported, including S. elongatus UTEX 2973 (S. elongatus 2973) and S. elongatus PCC 11801 (S. elongatus 11801), with doubling times of 1.9 and 2.3 h under optimal conditions, respectively [36, 69]. Genomic and proteomic analyzes were performed for both organisms to identify the determinants associated to the fast-growth phenotype [36, 69, 77–80]. S. elongatus 2973 has a genome of 2.7 Mb and its sequence is 99.8% similar to that of S. elongatus 7942, with a total of 55 differences (single-nucleotide polymorphisms (SNPs) and insertions–deletions) between the two genomes [36]. In addition, the photophysiology of these organisms was investigated revealing that S. elongatus 2973 has increased photosynthetic rates compared to 7942, due to differences in the photosynthetic apparatus such as increased content in plastocyanin, cytochrome b6f and photosystem I (PSI) and decreased phycobilisomes [77]. A systematic mutational analysis using CRISPR was performed in S. elongatus 2973 associating the fast-growth phenotype to SNPs in the genes atpA (encoding the alpha subunit of the ATP synthase), ppnK (encoding a NAD+ kinase) and rpaA (encoding the master regulator of the circadian clock) [78]. The SNPs in the atpA and ppnK resulted in enzymes with improved performance leading to an increase in the ATP and NADP+ pools. However, the interactions between the energy balance and the circadian clock are complex and require further analysis to elucidate the fast-growth phenotype. In addition, the authors demonstrated that the insertion of these modifications in S. elongatus 7942 drastically reduces the doubling time from 6.8 to 2.3 h [78]. In terms of genetic manipulation, S. elongatus 2973 is not naturally competent, but can be transformed by conjugation through triparental mating and, as abovementioned, a CRISPR platform was also developed for this organism [36, 56]. S. elongatus 11801 shows the highest growth rate under atmospheric CO2, high temperatures and light intensities, and tolerates sea salt concentrations [69]. Whole genome sequencing and annotation revealed that S. elongatus 11801 shares approximately 83% genome identity with S. elongatus 7942 and 2973. Compared to these two organisms, most of the proteins identified as unique in S. elongatus 11801 are involved in adaptation to abiotic stresses thus implying its robust phenotype [69]. This cyanobacterium

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can be genetically manipulated since it was shown to be naturally competent, further demonstrating the potential of this strain to be used as a chassis [69]. The proteome of S. elongatus 11801 under different CO2 concentrations was investigated envisaging future strategies for biofuel production [80]. Recently, S. elongatus PCC 11802 (S. elongatus 11802) was isolated, being closely related to S. elongatus 11801 with ~97% genome identity [81]. S. elongatus 11802 has a doubling time of 2.8 h at 1% CO2, 1,000 µE m−2 s−1 and shows faster growth at high CO2 and temperature conditions compared to 11801. Similar to the latter, S. elongatus 11802 can be manipulated by natural transformation. Analysis of the metabolic profile under high CO2 shows that the accumulation of key intermediate metabolites of the Calvin–Benson–Bassham (CBB) cycle allied to the fast growth phenotype makes this cyanobacterium an ideal chassis for the synthesis of products primarily derived from the CBB cycle [81].

6.2.1 Development of genome-scale metabolic flux models for cyanobacteria In addition to finding suitable chassis that can meet the requirements to fulfil the envisaged goals, it is equally important to develop accurate in silico models that are essential tools to predict chassis behavior, production rates, and aid in the design of more complex devices and circuits. In this context, genome-scale metabolic models based on flux balance analysis (FBA) have been developed (see Chapter 2). The FBA models represent the organism’s metabolic network and consider all genes encoding proteins with associated reactions. Moreover, models are based on the premise that growth is in steady state as in the exponential growth phase, in which the rate of production of a given metabolite is equal to the rate of consumption [82]. The vast knowledge available for Synechocystis 6803 has enabled the construction of the first cyanobacterial genomescale metabolic models [83–85] using FBA. The model by Fu et al. [83] considers 633 genes in a metabolic network of 831 reactions, enabling the simulation of cell growth and metabolite production. Two years later, a more comprehensive model was reported – the iSyn669 [84], considering 669 genes and 882 reactions including a more detailed representation of photosynthesis. Furthermore, the iSyn669 was used as a scaffold for the integration of transcriptomic data that allowed the identification of metabolic hotspots. This model was later upgraded to the iSyn811, consisting of 956 reactions accounting for 811 genes; flux-coupling analysis was also performed for this metabolic network leading to the identification of potential bottlenecks for compound production, namely hydrogen and ethanol [85]. Additional models for Synechocystis 6803 have been generated, updating pathway topologies and analyzing light/dark cycles of metabolism [86]; describing the photosynthetic process in mechanistic detail and analyzing it under different light, inorganic carbon conditions and genetic perturbations [87]. More recently, two updated models – the iSynCJ816 and the imSyn716 – were generated based on metabolic networks or metabolic models available [88, 89]. The iSynCJ816

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model was used to determine the possibility of engineering Synechocystis 6803 to increase carbon fixation [88]. The imSyn716 includes 18 novel carbon pathways and 190 additional metabolites; using the updated network and performing instationary 13Cmetabolic flux analysis, it was possible to determine that 98% of the fixed carbon is directed to biomass generation with only small amounts channeled to organic acids and glycogen storage [89]. Building on the experience of the Synechocystis 6803 models, Triana et al. [90] reported the construction of the S. elongatus 7942 metabolic model – the iSyf715 that includes 851 reactions and 838 metabolites. Similarly, to the iSyn models, the iSyf allows simulations of S. elongatus 7942 behavior under photoautotrophic conditions, constituting an important tool for the use of this organism as a cell factory for industrially relevant products. Two years later, Broddrick et al. [91] generated the iJB785 model, a metabolic network of 850 reactions that highlighted areas of the cyanobacterial metabolism where knowledge is scarce, like the nucleotide salvage system and also unveiled the importance of metabolic features such as an abridged TCA cycle. S. elongatus 7942 harbors only the TCA enzymes required for the synthesis of oxaloacetate and 2-oxoglutarate that are necessary for many biomass components, and the latter is also the gateway for nitrogen assimilation [91]. Therefore, the iJB785 model constitutes not only an important tool for designing strategies in S. elongatus 7942 but it is also a source for biological discovery. The vast knowledge base available for Nostoc 7120 also enabled the development of a genome-scale model, in this case with the particular aim of shedding light on the putative metabolic exchange reactions between vegetative cells and heterocysts [92]. This model led to the establishment of a more accurate metabolic network for this filamentous, heterocyst-forming cyanobacterium, and even though the need for model improvement is acknowledged, it constitutes an important tool for the use of Nostoc 7120 as chassis. The first genome-scale metabolic model of Synechococcus 7002 – the iSyp611 – followed a rather different construction scheme. The iSyp611 was generated using the high-throughput genome-scale metabolic reconstruction pipeline (in the SEED framework), the organism’s genome sequence, and the metabolic network of another cyanobacterium, Cyanothece sp. ATCC 54112 (Cyanothece 54112) [93–95]. This pipeline introduces automation in the generation of the network, bypassing time-consuming manual steps such as the annotation of the genome and the reaction network [93]. The iSyp611 was later updated to a version that accounts 708 reactions, the iSyp708; it was used together with other computational methods to predict the theoretical yields of several chemicals under dark and photoautotrophic conditions, examining also the effect light and CO2 limitations in production [96]. In addition, this work also identifies gene knockouts with potential to increase production of specific compounds thus demonstrating the usefulness of the model as a tool for engineering Synechococcus 7002 [96]. A third version of the model was reported in 2016, in which the cyanobacterium’s essential genes and metabolic robustness were reassessed, and strategies for improved

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production of ethanol and glycerol were analyzed [97]. Qian et al. [98] reported the generation of the iSyp821 metabolic model that incorporates a light-dependent algorithm, enabling the consistent simulation of autotrophic growth under different light intensities. In addition, this model is able to predict the redistribution pattern of CO2 uptake, amino acid, glycogen and lipid synthesis under nitrogen depletion. A semi-automated method previously established [99] was used to generate a composite genome-scale metabolic model of S. elongatus 2973 and 7942; the iSyu683 was based on the genome sequences of both organisms and on the genome-scale metabolic models proposed for Cyanothece 51142, Synechocystis 6803 and Synechococcus 7002. Together with experimental data, the model was used to pinpoint the carbon uptake rate as the main factor contributing to the differences between the growth rates of S. elongatus 2973 and 7942 [79]. This compilation further highlights the relevance of constructing/refining reliable genome-scale metabolic models of cyanobacterial chassis for the identification of key steps in metabolic networks, as well as to unveil potential tweaking points for improved compound production.

6.3 Tools The establishment of an organism as a cell factory for SB applications requires a customized toolbox comprising well-characterized parts such as regulatory elements (promoters, RBS, terminators), reporter genes, degradation tags and plasmids. The rational design of synthetic devices and circuits is based on standardized parts with a predictable behavior. In the advent of cyanobacterial SB, researchers have realized that the existing parts developed for the heterotrophic organisms E. coli, B. subtilis and S. cerevisiae work poorly or not at all in photoautotrophic bacteria [100]. In the last years, a tremendous effort has been made to develop tools for different cyanobacterial chassis. An overview of the existing SB tools for cyanobacteria is given here, focusing in the regulation of transcription by promoters, the fine tuning of translation by RBS and other RNAbased tools (riboswitches and riboregulators), and other tools such as reporter genes, degradation tags and terminators. In addition, some attention is dedicated to replicative plasmids, integrative plasmids and neutral sites as integration platforms. Finally, the generation of markerless mutants is addressed focusing on counter selection systems and the recent techniques for genome editing based on the CRISPR system. Box 6.2: Genetics Gene – DNA region that can be transcribed into a functional RNA molecule; most of the times into a messenger RNA (mRNA), that encodes the amino acid sequence of a polypeptide chain. Promoter – DNA sequence located in the 5ʹ end of the gene, to which the RNA polymerase and necessary transcription factors (proteins) bind to initiate transcription. Promoters are the major elements used to control gene expression (at transcription level).

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RBS – sequence located approximately six nucleotides upstream the initiation codon of the mRNA, to which ribosomes bind to initiate translation. ORF – DNA region that contains the information for the sequence and structure of the gene product (usually a protein). Terminator – DNA sequence present in the end of the gene that dictates the end of transcription. There are two types of terminators: the Rho-dependent, requiring the presence of the Rho protein for the termination of transcription to occur; and the Rho-independent, relying solely on the formation of a hairpin or stem-loop structure for the release of the RNA polymerase and the mRNA. Transcription – process in which the information in a DNA sequence is converted into RNA by the enzyme RNA polymerase. Translation – in this process, the information encoded in ribonucleotide sequences is converted into amino acid sequences. Protein synthesis is carried out by complexes composed of RNA and proteins – the ribosomes – that translate the information in the codons (groups of three nucleotides in mRNA) into specific amino acids to be incorporated into the nascent peptide.

Figure 6.3: Schematic representation of the modulation of device regulation using standard biological parts. (A) Transcription can be regulated using promoters with different strengths (e.g. weak, medium or strong) leading to differential transcript (mRNA) levels that ultimately result in different protein levels. (B) At the translation level, the use of RBS with different strengths will not affect the mRNA levels, instead it will influence the recruitment of ribosomes that will carry out protein transcription. Translation can also be modulated using other types of biological parts like riboswitches or riboregulators. In the end, modulation of synthetic device regulation either at transcription or at translation will have an impact in the device output, e.g. chassis harboring GFP generators regulated by different elements can display different fluorescence levels.

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6.3.1 Promoters Promoters are crucial biological parts for the transcriptional regulation of genes in a synthetic circuit (Box: 6.2, Figure 6.3A). Cyanobacterial promoters can be categorized in three types (I, II and III), differing on the arrangement of the binding motifs. Type I promoters include both the −35 and −10 consensus elements spaced by 17 bp. Type II promoters have only the −10 box and can harbor enhancer-motif sequences or operators that bind activator proteins. Type III promoters are distinct from the first two, having −32 and −12 boxes spaced by 15 bp [101]. The transcription process is driven by the RNA polymerase holoenzyme, comprising a core enzyme and a sigma factor. The recognition and binding of the sigma factor to the operators is important for the efficiency of transcription initiation, and thus can define the strength of a promoter [102]. These regulatory elements can be organized in different categories depending on their origin and the host organism and are termed as native, heterologous, redesigned or synthetic promoters. When used in the native context, these regulatory parts have the advantage of responding to environmental stimuli in a dose-dependent manner, leading to the modulation of transcription [103]. On the other hand, these promoters are not insulated from the intrinsic regulation, which can lead to cross talk and compromise the predictability of the system. To overcome this limitation, promoters from other organisms or heterologous promoters have been commonly used. However, their characterization has shown that these elements can behave in a completely unexpected manner when in the genetic context of another organism [100, 104]. Consequently, the rational redesign of heterologous promoters and the generation of synthetic ones have been used as strategies to improve promoter insulation and efficiency [105, 106]. Another alternative to eliminate cross talk and ensure proper functionality/performance is the use of orthogonal parts. A successful example is the use of the bacteriophage T7 RNA polymerase to promote transcription that has been implemented in several prokaryotes, including cyanobacteria [107, 108]. Regarding the type of expression, constitutive promoters can be useful when an “always ON” gene expression is desired. However, for applications with a high metabolic burden, the use of this type of expression leads to toxicity/exhaustion of resources, hindering or impairing growth. In these situations, or when a more refined regulation is required, inducible promoters can be used having the obvious advantage that transcription is only “turned ON” when needed. Regulated promoters responding to many different stimuli have been reported, including light, metals, chemicals and metabolites, among others [105]. Despite the successful use of these regulatory elements in numerous applications, there are some disadvantages associated to their use. In the case of light-inducible promoters, there is the drawback of expression being dependent on specific light wavelength and intensity [109]. When metal- or chemical-inducible promoters are used, the inducer can be pumped out of the cells, degraded or cannot be removed from the culture resulting in lack of, or continuous expression [110, 111].

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Overall, promoter characterization in cyanobacteria has been performed mainly for the model cyanobacterium Synechocystis 6803 (see Table 6.1, for a review see Till et al. [112]). Nevertheless, exciting progress is being made in characterizing and developing regulatory parts for Synechococcus 7002, S. elongatus 7942 (see Table 6.1) and also for the fast-growing strains S. elongatus 2973 and S. elongatus 11801 and 11802 [81, 113, 222]. The relentless search for promoters that are easy to use, have low leakiness and a dynamic range of induction and strength, is increasing the array of regulatory parts available in the cyanobacterial SB toolbox (Box: 6.2, Figure 6.3); which can be used in the construction of synthetic devices/circuits envisaging biotechnological applications using photoautotrophic organisms as chassis. Table 6.1: Characteristics of promoters characterized in Synechocystis 6803, Synechococcus 7002 and S. elongatus 7942. The PrnpB is considered the reference promoter in cyanobacteria, with strength equal to 1. Therefore, the strength of constitutive promoters is presented relative to PrnpB whereas the strength of regulated promoters is represented as the ratio between induced versus non-induced expression. Promoter

Source

Type of expression

Repressor (R)/ Strength – output inducer (I) amount

Reference

Synechocystis  PrnpB

Native

Constitutive

NA

-fold – GFPmutB (Bba_E)

[]

PrbcL

Native

Constitutive

NA

~, fluorescence Abs− [AU] – EYFP

[]

Pcpc

Native

Constitutive

NA

Up to % of total soluble proteins – Ter and DldhE

[]

PnirA

Native

Constitutive

NA

~, fluorescence Abs− [AU] – EYFP

[]

PcpcB

Native

Constitutive

NA

~, fluorescence Abs− [AU] – EYFP

[]

PrbcL variants

Redesigned from Constitutive Synechocystis 

NA

–-fold – GFPmutB (Bba_E)

[]

PpsbA*

Synthetic from Synechocystis 

NA

-fold – GFP

[]

Constitutive

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Table 6.1 (continued ) Source

PJ### library

Synthetic from E. Constitutive coli

NA

~–, fluorescence Abs− [AU] – EYFP

[]

PtrcO

Heterologous from E. coli

Constitutive

NA

-fold – GFPmutB, .-fold – EFE, ~, fluorescence Abs− [AU] – EYFP

[, , ]

Ptrc.x. lacO

Redesigned from Constitutive E. coli

NA

-fold – GFP

[]

Ptrc.x. tetO

Redesigned from Constitutive E. coli

NA

-fold – GFP

[]

PλcI

Heterologous from bacteriophage λ

Constitutive

NA

-fold – GFP

[]

PTpol

Orthogonal from Constitutive bacteriophage T

NA

.-fold – GFP

[]

PpsbA

Native

Light inducible

 µE m− s− (I)

~ fluorescence Abs− [AU] – EYFP

[]

PcpcG

Native

Green light inducible (CcaS/CcaR)

 µE m− s− -fold – GFPuv (I)

[]

PcpcG

Native

Red light inducible (CcaS#/ CcaR)

 µE m− s− -fold – GFPuv (I)

[]

PompC

Heterologous from E. coli

Dark inducible

NA

PnrsB

Native

Nickel inducible .– µM Ni+ × RT-PCR – nrsB, (I) -fold – EYFP

PnrsD

Native

Nickel inducible  µM Ni+ (I)

PnrsS PcoaT

Native Native

Type of expression

Repressor (R)/ Strength – output inducer (I) amount

Promoter

>-fold – EYFP

+

Nickel inducible  µM Ni Cobalt inducible

(I)

– µM Co (I)

+

Reference

[] [, ]

× – EYFP

[]

× – EYFP

[]

× RT-PCR – coaT, ~ -fold – EYFP

[, ]

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Table 6.1 (continued ) Promoter

Source

Type of expression

Repressor (R)/ Strength – output inducer (I) amount

PpetE

Native

Copper inducible

 µM Cu+ (I)

~, fluorescence Abs− [AU] – EYFP

PziaA

Native

Zinc inducible

– µM Zn+ (I)

× RT-PCR – ziaA, ~ -fold – EYFP

Ptac

Synthetic, hybrid LacI from E. coli repressible, ITPG inducible

 mM IPTG (I)

× – GFP

[]

PtrcO

Heterologous from E. coli

 mM IPTG (I)

.-fold – GFPmutB (Bba_E)

[]

PtrcO

Redesigned from LacI repressible,  mM IPTG (I) E. coli IPTG inducible

-fold – GFPmutB (Bba_E)

[]

Ptrc.x. lacO

Redesigned from LacI E. coli repressible, IPTG inducible

.-fold – GFP

[]

PL

Redesigned from TetR  µg mL− E. coli repressible, aTc aTc (I) inducible

-fold – EYFP

[]

Ptrc.x. tetO

Redesigned from TetR  µg mL− aTc .-fold – GFP E. coli repressible, aTc (I) inducible

PBAD variant

Redesigned from Arabinose E. coli inducible

 mM Larabinose (I)

~ fluorescence– [AU] – EYFP

[]

PvanCC

Redesigned from VanR Caulobacter repressible, crescentus vanillate inducible

 mM vanillate

-fold – mVenus

[]

PrhaBAD

Heterologous from E. coli

RhaS repressible, rhamnose inducible

 µg mL− Lrhamnose (I)

~, fluorescence intensity [AU] – YFP

[]

PnirA

Heterologous from S. elongatus 

NH+ repressible, NO− inducible

 µM NH+ (R),  µM NO (I)

~, RLU Abs− – LuxAB

[]

LacI repressible, IPTG inducible

 mM IPTG (I)

Reference

[]

[, ]

[]

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Table 6.1 (continued ) Promoter

Source

Type of expression

Repressor (R)/ Strength – output inducer (I) amount

High light repressible, CO inducible

 µE m− s− (R), .% CO (I)

~, fluorescence Abs− [AU] – eYFP -fold – GUS, -fold – eYFP

Reference

S. elongatus  []

PcpcB

Native

Ptrc

Synthetic from E. LacI coli repressible, ITPG inducible

 mM IPTG (I)

PT

Orthogonal from bacteriophage T

LacI repressible, ITPG inducible

. mM IPTG -fold – eYFP (I)

[]

PtetA

Heterologous from E. coli

TetR  nM aTc (I) -fold – GFP repressible, aTc inducible

[]

PBAD

Heterologous from E. coli

Arabinose inducible

 g L− L-arabinose (I)

~ , fluorescence Abs− [AU] – mtGFP

[]

Prbc

Native

CO repressible .% CO (R)

~, fluorescence Abs− [AU] – eYFP

[]

[, ]

Synechococcus  PA#### library

Native

Constitutive

NA

–-fold – Ypet

[]

PisiAB

Native

Iron inducible

 nM Fe (I)

-fold – LuxAB

[]

PcpcB library

Redesigned from LacI Synechocystis repressible,  IPTG inducible

 mM IPTG (I)

-fold – YFP

[]

PEZtet

Synthetic from Synechocystis 

TetR  µg mL− aTc -fold – GFP repressible, aTc (I) inducible

[]

 µM Zn+ (I) -fold – YFP

[]

PsmtA Heterologous from S. elongatus  NA – not applicable.

smtB repressible, zinc inducible

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6.3.2 Ribosome binding sites RBS are key features in the initiation of translation (Box: 6.2, Figure 6.3B), a process that starts with the binding of the ribosome to the Shine–Dalgarno (SD) sequence, present in the RBS at the 5ʹ end of a mRNA. The efficiency of the RBS to drive translation depends on the sequence-embedded context that dictates the distance between the SD sequence and the start codon (AUG), and the possible formation of secondary structures or interaction of RNA-binding proteins. The vast majority of studies performed in cyanobacteria involving the heterologous expression of genes, or the overexpression of native ones, used the RBS originally present in the sequence. The knowledge on RBS efficiency is scarce, making it difficult to anticipate the rate of protein synthesis when designing a synthetic device. A genomic comparison of SD sequences in 30 prokaryotic genomes was performed to unveil the role of these sequences in translation initiation and protein expression levels [130]. Based on this work, Heidorn et al. [37] designed an RBS sequence that is complementary to the anti-SD sequence (present in the ribosomes) of Synechocystis 6803, and termed it RBS*. The RBS* together with three BioBrick RBS – BBa_B0030, BBa_B0032 and BBa_B0034 – were characterized in this cyanobacterium and in E. coli. The RBS efficiency in Synechocystis was determined to be RBS* > B0030 > B0032 ~ B0034 while for E. coli the relative efficiency was found to be B0034 > B0030 > RBS* > B0032, thus reinforcing that the performance of parts is dependent on the organism’s genomic context [37]. These RBS were included in a set of 11, together with other BioBrick RBS and native RBS sequences from psbA2, rbcL and rnpB genes, for further characterization [111]. RBS efficiencies were determined using two different constructs varying the promoter (PpetE and PpsbA2S) and the reporter (enhanced yellow fluorescent protein (EYFP) and mTagBFP) used; the 11 RBS showed a dynamic range in terms of expression strength with similar results for the two reporters tested, with the exception of BBa_B0035 and RBS* [111]. In addition, 20 native RBS were characterized in Synechocystis 6803 revealing that sequence embedded context and the RBS sequence length are determinant for protein expression, since no EYFP fluorescence was detected when using nine of the RBS tested [114]. An effort to develop synthetic RBS for different cyanobacteria using in silico modeling methods has been made in the last years [67, 106, 116, 131]. A synthetic RBS sequence was designed, considering S. elongatus 7942 as reference organism, and the expression of two different reporters was found to increase when the synthetic RBS was tested in four different cyanobacteria (S. elongatus 7942, Synechocystis 6803, Nostoc 7120 and Leptolyngbya LB0902) [67]. A set of 10 synthetic RBS was developed for Synechococcus 7002 using the RBS library calculator, having a predicted 213-fold range of translation initiation rate. However, the translation rates obtained empirically correlated poorly with the predictions providing only a 30-fold translation range [106]. Synthetic RBS libraries were also developed for Synechocystis 6803. Wang et al. [116] designed and constructed a library of 13 RBS based on the

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previously described synthetic RBSv4. A complementary approach to the RBS prediction software was used by using design principles established to optimize RBS sequences in E.coli [132]. Therefore, using these principles, the RBSv4 sequence and length was modified as well as the spacer between the SD sequence and the ATG codon. The resulting 13 RBS were tested in Synechocystis 6803 and, a 254% increase in expression of the target protein was obtained using RBSv33 [116]. In addition, Thiel et al. [131] performed the systematic characterization of additional RBS in Synechocystis 6803, including cyanobacterial native and synthetic elements previously developed for E. coli. The RBS tested displayed a wide dynamic range of translation efficiencies that were shown to be dependent on downstream gene sequence. Different online prediction tools were used to identify factors underlying the reporter-protein-dependent RBS efficiency. However, the RNAfold [133] and mfold [134] algorithms did not identify any unfavorable interactions at the sequence level, while prediction by the RBS calculator [135] and untranslated region (UTR) designer [136] were significantly different from the empirical results [131]. The usefulness of these tools may be limited in cyanobacteria, as the software was primarily developed for E. coli, however, they may still be useful tools when designing libraries from scratch [116]. Alternative strategies have been developed to prevent interactions between the UTR and coding sequences that undermine the translation process. The bicistronic design involves two cistrons or genes of interest (GOI), the first of which consists of a short coding sequence (up to 20 amino acids) that harbors the RBS for the second GOI embedded in its 3ʹ end. The sequence of the first GOI is optimized for transcription/ translation initiation and ensures the high expression of the second GOI that is the target gene [137]. This design architecture was applied to a toxin/antitoxin system meant for Synechocystis 6803 showing promising results, and was successfully used for the expression of two heterologous hydrogenases in this cyanobacterium [138].

6.3.3 RNA-based regulation Numerous RNA-based regulation mechanisms have been described including riboregulators, riboswitches and small RNAs, the last two classes have been extensively studied due to its preponderant roles. Nevertheless, in the frame of SB, riboswitches and riboregulators have been the focus of attention and are reviewed below.

6.3.3.1 Riboswitches Bacterial riboswitches are usually located in the 5ʹ UTR of an mRNA; these structures are comprised of an aptamer or ligand-binding domain and a regulatory domain or expression platform. The ligand can be a metabolite or a metal that binds to the aptamer and stabilizes this structure causing the conformational change and

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further activation of the expression platform, thus mediating gene expression (Figure 6.4) [139, 140].

Figure 6.4: Schematic representation of the mechanism of regulation by an ON-riboswitch. A hairpin structure is formed in the 5’ end of the mRNA, sequestering the RBS. Upon addition of the ligand (e.g. metabolite or metal) the riboswitch conformation is changed, exposing the RBS thus enabling translation initiation.

More than 20 riboswitch–ligand pairs have been identified including thiamin, flavin mononucleotide (FMN), S-adenosylmethionine, lysine, guanine/adenine and glycine. However, many of these molecules are not suitable to be used in genetic tools since they are important metabolites or can be cytotoxic [140]. The use of the theophylline-dependent riboswitch has been explored in cyanobacteria; in this system, the aptamer domain forms a hairpin sequestering the RBS and, upon addition of theophylline, the RBS is released and transcript translation is enabled. Nakahira et al. [141] tested three synthetic theophylline-dependent riboswitches (previously developed for E. coli) in S. elongatus 7942. The expression of luciferase was shown to increase between 33- and 190-fold in presence of theophylline, and dose-dependent expression could be observed between 0 and 2 mM, allowing the fine-tuning of expression [141]. The use of synthetic theophylline-dependent riboswitches was validated in Synechocystis sp. strain WHSyn, Nostoc 7120 and Leptolyngbya LB0902 using the YFP reporter [66]. The riboswitch showing the highest fold increase in luciferase expression in S. elongatus 7942, riboswitch E*, was also tested in Synechocystis 6803 demonstrating that GFP expression could be detected with a 6-fold increase between 0.1 and 1 mM of theophylline [142]. In addition, this riboswitch was also validated as a tool for the control of translation in S. elongatus 2973 showing low leakage levels and a linear response to increasing concentrations of theophylline with a protein increase up to 100-fold [143]. In this work, two other riboswitches responding to theophylline (theo/yitJ) and adenine (xpt(C74U)/metE) were also shown to be functional in S. elongatus 2973. In the presence of ligands, these riboswitches work as transcription inhibitors and the response was demonstrated to be dose-dependent, with a 50% repression of transcription in the presence of 2 mM of theophylline or adenine (increasing up to 80% upon addition of 5 mM of the ligands). Additionally, a cobalamin

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transcriptional OFF-riboswitch was identified in Synechococcus sp. strain PCC 73109 and validated in Synechococcus 7002, showing a significant translation inhibition in the presence of 4 µg L−1 of cobalamin [144].

6.3.3.2 Riboregulators Riboregulators can be used in the regulation of translation. These tools are composed by two independent RNA fragments: the trans-activating RNA (taRNA) and cis-repressed RNA (crRNA). Upon transcription, the crRNA sequence forms a loop structure in the 5ʹ UTR of the mRNA of interest, interfering with the respective RBS and leading to translation repression. When the taRNA is expressed, it targets the crRNA causing an alteration in the loop structure that activates translation (Figure 6.5) [145].

Figure 6.5: Schematic representation of the mechanism of regulation by a riboregulator. The cisrepressed RNA (crRNA) sequence forms a loop structure in the 5ʹ UTR of the mRNA of interest, sequestering the respective RBS. The trans-activating RNA (taRNA) binds to the crRNA leaving the RBS exposed, which activates translation.

The first riboregulators designed for Synechocystis 6803 were based on the riboregulator crR12/trR12 previously developed for E. coli [146, 147]. The RBS present in the crR12 was replaced by the RBS* and additional mismatches were introduced in the stem region of the crRNA to allow high activation in presence of the taRNA and low background transcription in its absence. Following this strategy, two riboregulators were generated – crR*1/trR*1 and crR*2/trR*2 – and tested in E. coli. The crR*2/ trR*2 riboregulator showed the highest ON/OFF ratio in E. coli, and was subsequently tested in Synechocystis 6803 also showing to be an effective tool in the regulation of translation in this cyanobacterium [147]. This riboregulator was further improved using a rational design approach based on RNA secondary structure and prediction of hybridization using free-energy values [148]. Using this approach, the crR*4/trR*4 riboregulator was developed and a 51-fold induction in translation was detected in Synechocystis 6803, a significant improvement compared to the 19-fold induction observed with crR*2/trR*2. The addition of two AA nucleotides in the 3ʹ end of the crR*4 sequence led to an increase in RBS accessibility upon crR4*/trR*4 hybridization, which resulted in an improvement of the fold induction to 78 [148].

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A tool for the attenuation of translation was developed for Synechococcus 7002; this system is based on the E. coli IS10 RNA-IN/OUT regulator. When expressed, the small RNA or RNA-OUT will target the RNA-IN present in 5ʹ UTR of the mRNA reducing translation of the target gene by 70% [128].

6.3.4 Degradation tags Degradation tags are used as post-transcriptional tools to control protein levels by reducing their stability. These tags are based on the small stable RNA A (ssrA) from E. coli that works a rescue system: when problems occur in protein translation, the ssRA adds a 11 codon degradation tag and a stop codon to the mRNA, enabling the detachment of the ribosome and targeting the incomplete protein to degradation by proteases. Keiler and Sauer [149] reported that alterations in the final three amino acids of the C-terminal peptide to AAV, ASV, LVA and LAA affect protein stability differently, resulting in different protein half-life. The degradation tags available in the BioBricks – ASV (BBa_E0436), AAV (BBa_E0434) and LVA (BBa_E0432) – were characterized in Synechocystis 6803. The stability of the EYFP was evaluated after 48 h revealing that all tags are functional, with the LVA tag having the highest impact in reduction of protein stability followed by the AAV and ASV [100]. Landry et al. [150] generated a collection of C-terminal protein degradation tags based on the consensus for ssrA tag sequences determined using the information from 71 cyanobacterial genomes. The collection is comprised of native cyanobacterial and E. coli tags and its variants, and was tested in Synechocystis 6803 using EYFP as a reporter. The assessment revealed that EYFP fusion with the developed tags resulted in a fluorescence reduction ranging from 50% to 99% [150].

6.3.5 Reporter proteins Characterization of synthetic parts, devices and circuits often requires a reporter system that enables the detection of an output signal correlated with expression, interaction or localization of a protein. Ideally, the system should not implicate destruction of the biological sample or require additional inputs and it should be easy to detect, with high sensitivity allowing the detection of low signal levels. Bioluminescent assays have been extensively used in cyanobacteria, namely, the luciferasebased systems that use luciferin as substrate generating light as a product [151]. These reporter systems generate dim signals that make them highly quantitative and extremely useful for real time reporting due to the short half-life of its components [152]. In comparison, fluorescent proteins generate signals with higher brightness making them more suitable for subcellular localization or cell-sorting purposes. The use of these reporters in cyanobacteria can be difficult due to autofluorescence of the

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photosynthetic pigments that limits the use of red fluorophores [153]. Nevertheless, fluorescent proteins emitting light in other wavelengths are available and, for example, the cerulean (blue), GFPmut3b mutant of the green fluorescent protein and EYFP have been used in cyanobacteria [100]. New variants and fluorophores have been developed by increasing solubility (super folder variants), brightness or photostability [34, 154]. In addition, the use of degradation tags to reduce the half-life of the reporter proteins can increase the range of applications [155]. Bacterial luciferases and traditional fluorescent proteins require oxygen to work properly, thus their use is not recommended under oxygen-depleted conditions as happens during dark cycles [82]. FMN-based fluorescent protein that works under anaerobic conditions was already used for the development of an oxygen sensor in Synechocystis 6803 [156].

6.3.6 Terminators Transcriptional terminators can be used to insulate a synthetic device or circuit preventing cross talk between this device/circuit and the genetic elements in the vicinity. There are two main types of terminators: (i) the Rho-dependent, which require the Rho protein for unwinding the RNA–DNA hybrid and thus preventing RNA elongation and (ii) the intrinsic or Rho-independent, which it relies on the formation of a hairpin loop secondary structure in the nascent RNA strand that once formed leads to the dissociation of the elongation complex. In the latter, termination is intrinsic to the nucleotide sequence of the RNA composed by an adenosine-rich tract (A-tract) located upstream the loop that includes a 4–18 bp GC-rich stem and 3–5 bp loop nucleotides followed by a 6–8 bp highly conserved uracil-rich tract (U-tract) [157]. So far, no Rho homologues have been found in cyanobacterial genomes. An analysis of the RNA-folding energetics near stop codons in Synechocystis 6803, did not indicate the formation of hairpin loop structures at these sites, suggesting that this is not a predominant mechanism in this cyanobacterium [158, 159]. The same observation was not confirmed in S. elongatus 7942 with the analysis of the RNA free energies suggesting the formation of discrete stem loop structures closer to the 3ʹ end of genes [159]. A few terminator sequences have been used in cyanobacteria, including the native RuBisCO terminator, the rrnB terminator (E. coli), the T7 terminator and the BioBrick double terminators rrnBT1-T7TE (BBa_B0015) [160]. Four other terminators retrieved from BioBrick registry, including the artificial terminator BBa_B1006, the rrnC terminator (E. coli, BBa_B0052) and its reverse sequence (BBa_B0062), and the reverse sequence from terminator rrnBT1 (BBa_B0010) were used to insulate a synthetic multiple cloning site included in vectors targeting five Synechocystis 6803 genome loci [161]. In this work, the successful insulation of GFP generator device integrated into five chromosome loci was demonstrated. More recently, the systematic evaluation of two libraries of terminators was performed in Synechocystis 6803 [114, 162]. One of the sets included the rrnB terminator and seven

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native elements, and the results showed that none of the terminators were able to completely abolish transcription of the reporter, not even the strong E. coli rrnB terminator. The performance of these elements showed a 10-fold variation, with the psbC terminator showing the highest efficiency [114]. Kelly et al. [162] characterized a set of 19 Rho-independent terminators including synthetic, heterologous (E. coli and bacteriophage) and one native element. From this set, 11 revealed to be strong transcriptional terminators, as negligible fluorescence levels were registered during the course of the experiments (96 h). Four of these terminators were further validated showing that they can be used for the insulation of a synthetic device (YFP generator) from its genomic context [162].

6.3.7 Replicative plasmids, integrative plasmids and neutral sites These tools are essential for the genetic manipulation of microorganisms, serving as vehicles for the introduction of exogenous DNA into cells. For this purpose, replicative or integrative plasmids can be used depending on stability or type of application required. In general, replicative plasmids are used for fast generation of transformants. These shuttle vectors can harbor broad-host range replicons that allow its replication in different organism or can have specific replicons, usually isolated from endogenous plasmids that function in a restricted number of organisms. The replicative plasmids available for cyanobacteria are limited, in particular the broad-host range type, most of which are derived from the RSF1010 [37, 160]. In the last decade, an effort has been made in the development of new plasmids with a modular organization to facilitate standardization and envisaging the cloning and characterization of parts and devices. The pPMQAK1 was the first BioBrick compatible replicative plasmid developed for cyanobacteria, which was validated for Synechocystis 6803, Nostoc 7120 and Nostoc punctiforme [100]. The development of additional self-replicating vectors based on native cyanobacterial plasmids such as pANS from S. elongatus 7942 and, pCA2.4, pCB2.4 and pCC5.2 from Synechocystis 6803 was also reported [114, 163, 164]. The new plasmids exhibit interesting new features such as the ability to replicate in other hosts (e.g., E.coli or Nostoc 7120), long-term maintenance in absence of selective pressure, high-copy number or stable co-existence with RSF1010-based plasmids. In addition, the use of replicative vectors from the modular SEVA system based on the RSF1010 and RK2 origins of replication was also validated in Synechocystis 6803 [108, 113, 165]. For long-term stable integration of exogenous DNA, integrative or suicide plasmids are used, which are unable to replicate in the host and so the DNA they carry must integrate before the plasmid is eliminated. Integration vectors can be used for the targeted insertion of the DNA and thus must harbor recombination arms homologous to the DNA sequences flanking the site targeted for integration. If a single

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recombination event occurs, it leads to the integration of the whole plasmid whereas a double recombination event will result in the replacement of the target locus by the DNA construct present between homologous regions. The efficiency of DNA integration varies with the size of the flanking regions and it was also shown to be strain dependent [37]. Since cyanobacteria usually harbor multiple copies of the chromosome or of the native plasmids, the generation of mutants is a time-consuming task since the introduction of the mutation in all genome copies will require several segregation steps under increasing selective pressure. The increasing complexity of synthetic designs will eventually require several integration sites to accommodate different devices in a given chassis. The disruption of these loci should not have any effect on the viability or phenotypic traits of the chassis, and thus are termed neutral sites [166]. In the last decades, a few integration sites have been used in cyanobacteria, presuming their neutrality without any extensive characterization [167–170]. The identification of neutral sites, located either in the chromosome or in the native plasmids, was recently performed in Synechocystis, S. elongatus 7942 and Synechococcus 7002 [107, 161, 171–173]. Ng et al. [171] report the characterization of three neutral sites in Synechocystis 6803, one targeting the native plasmid pCC5.2 and two in the chromosome. The integration of EYFP in these loci revealed that expression from the plasmid was 14-fold higher compared to the chromosome, and in the latter expression levels obtained for both sites was equivalent [171]. Five additional chromosomal neutral sites were identified and validated in Synechocystis 6803; the characterization included an evaluation of the sites neutrality and showed that the sites are equivalent in terms of expression [161]. Moreover, the plasmids targeting these neutral sites include a multiple cloning site compatible with the BioBrick system and includes double terminators that guarantee the insulation of the synthetic devices. Three chromosomal neutral sites were validated in Synechococcus 7002; the integration of a kanamycin resistance cassette in these loci had a minor impact in the chassis fitness and, in addition, integrative vectors compatible with the BioBrick system were developed [172]. Envisaging the expansion of the neutral sites set available for this cyanobacterium, the identification of 51 putative genome neutral sites was performed based on genomic and transcriptomics data [173]. This work also reported the validation of a site located in the intergenic sequence between two neutral sites as a suitable region to be used for the efficient integration of synthetic devices. Modular systems for the construction of plasmids dedicated to cyanobacteria have been developed. Similar to the pSEVA system [165], the CYANO-VECTOR web server (http://golden.ucsd.edu/CyanoVECTOR) can be used for the in silico design of replicative (narrow or broad range) or integrative plasmids and, in addition, several selection markers and expression or reporter cassettes can be selected [67]. This system was tested in several cyanobacterial strains, such as Nostoc 7120, Leptolyngbya BL0902, Synechocystis 6803, Synechocystis WHSyn, S. elongatus 7942, Synechococcus 7002. An additional SB platform for expression in S. elongatus 7942 was developed, simplifying the

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construction of SyneBrick vectors that include replicative and integrative vectors harboring different antibiotic resistance cassettes and expression systems [107]. More recently, a cloning suite for cyanobacteria – the CyanoGate – was developed based on the standardized modular cloning system – MoClo, previously developed for plants [113]. Besides the construction of different vectors for the expression and integration of DNA, this cloning suite envisages the design and construction of multigene expression system based on the MoClo syntax. The system was tested and validated using the model Synechocystis 6803 and the fast-growing S. elongatus 2973.

6.3.8 Generation of markerless knockout mutants For decades, the generation of knockout mutants in cyanobacteria was mainly carried out by homologous recombination. The generation of markerless mutants through this process requires two rounds of transformation, in the first round a double selection cassette usually harboring an antibiotic resistance and a counter-selection marker replaces the gene to be deleted and, transformants are selected screening for the antibiotic resistance phenotype. In the second round, the selection cassette is replaced by the flanking regions of the gene previously deleted based on the counter-selection method [174]. The counter-selection method more commonly used in cyanobacteria involves the sacB gene (conferring sensitivity to sucrose), even though other systems based on the mazF toxin, rps12 mutation, and aaS, acsA or nblA knockouts have been described for Synechocystis 6803, S. elongatus 7942 and 2973 and Synechococcus 7002 [56, 175–178]. Recent techniques for genome editing such as, zinc fingers (ZFNs), transcription activator-like effector nucleases (TALENs) and CRISPR/CRISPR-associated protein (Cas) rely on native or engineered nucleases for the introduction of strand breaks at specific sites in the genome [179]. However, the use of ZFNs and TALENs in bacteria was limited due to the restricted target specificity and customized engineering required by these techniques. On the other hand, CRISPR/Cas (see Chapter 2) has rapidly become popular for cyanobacterial engineering by drastically reducing the time needed to obtain mutants and allowing the manipulation of several genes at the same time [51]. Briefly, the CRISPR/Cas method is able to target a nuclease to a specific site in a very precise manner determined by the single-guide RNA (sgRNA) that has a complementary sequence to the target DNA site. The sgRNA assembles with the Cas9 or Cas12a (Cpf1) to form an effector complex and, depending on the CRISPR system, this complex has different requirements to be active. The CRISPR/Cas9 requires two separate RNA sequences, the crRNA that encodes the guide sequence and a trans-activating crRNA (tacrRNA) whereas the CRISPR/Cas12a (Cpf1) system

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requires a single crRNA [34]. After the double-stranded cleavage of the genome, the DNA repair system known as homology-directed repair is induced and, if an appropriate repair template is provided, specific mutations or deletions can be introduced in the target site. The CRISPR/Cas system can also be used as tool for regulation of gene expression, the CRISPR interference (CRISPRi) methodology uses a variant of the Cas9 – the deficient or dead Cas9 (dCas9) – that maintains the ability to bind the target DNA with the sgRNA but is unable to cleave DNA. Yao et al. [50] used CRISPRi to repress gfp expression, to inhibit the production of polyhydroxybutyrate and glycogen, and inhibit simultaneously four aldehyde reductases/dehydrogenase in Synechocystis 6803. CRISPRi was used again for gene repression in this cyanobacterium leading to an increase in the production of fatty alcohols and n-butanol [180, 181]. This methodology has also been extended to other cyanobacterial strains, including Synechococcus 7002, S. elongatus 7942 and Nostoc 7120 [75, 182, 183]. More recently, an inducible CRISPRi library including 10,498 clones for Synechocystis 6803 was generated, enabling system-wide analysis of gene essentiality and function [184]. This pooled library was used to explore increased L-lactate production and tolerance, demonstrating the potential for screening mutants with improved industrial phenotypes. The use of the CRISPR/Cas9 system for gene knockout and knock-in was first demonstrated in S. elongatus 7942, deleting the glgC and introducing the gltA and ppc genes that resulted in an increased succinate production [185]. Moreover, a Cas9based tool was used for the deletion of 3 native plasmids of Synechocystis 6803 [186]. The Cas9 protein revealed to be toxic in S. elongatus 2973 but this hurdle was overcome by the conditional expression of this protein [56]. An alternative system using the Cas12a (Cpf1) was successfully used for genome editing in Nostoc 7120, Synechocystis 6803, S. elongatus 7942, displaying no signs of toxicity [187, 188]. Recently, the CRISPRi methodology using the deficient or dead Cas12a – dCas12a was also applied to S. elongatus 7942, S. elongatus 2973 [189, 190]. In addition, the development of reversibly induced CRISPRi system aiming at knocking down and recovering the expression of target genes was reported for Synechocystis 6803 [191]. For more detailed reviews on genome editing technologies, see for example, Lin et al. [192], Ng et al. [193] and Zhang et al. [194].

6.4 Using a SB approach for industrial biotechnology Cyanobacteria are continuously being engineered to produce compounds with varied chemical properties and with potential uses as biofuels, pharmaceuticals, feed and food supplements, plastic precursors, among others. Several studies have shown

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that it is possible not only to optimize native cyanobacterial production pathways by redirecting metabolic fluxes towards the product of interest, but also to use cyanobacteria as hosts of heterologous biosynthetic pathways. In this context, multiple metabolic engineering approaches have been described but those using a SB-based design are more limited. In Table 6.2, we present a selection of compounds that have been produced by various cyanobacterial strains using such principles, as to illustrate the potential of combining the metabolic plasticity of cyanobacteria with this consolidated discipline.

Table 6.2: Selection of works describing engineering of cyanobacteria with a SB-based design for the production of various types of compounds. Strain

Product

Production rate or capacity

Reference

Cyanothece 

Limonene

.–. mg L−

[]

Nostoc 

Ammonium

 µM in  days

[]

Nostoc 

Cryptomaldamide

. mg g− (DCW) in  weeks

[]

Synechococcus 

Mannitol

. g L−

[]

S. elongatus 

Lytic polysaccharide monooxygenase

 µg L−

[] −

S. elongatus 

Indole–isonitrile biosynthetic .– mg L intermediates and hapalindoles H and -epi-hapalindole U

[]

S. elongatus  (in a consortium with E. coli)

-Hydroxypropionic acid

. mg L−

[]

S. elongatus 

Sucrose

. mg L− h−

[]

S. elongatus  (in a consortium with Halomonas boliviensis)

Polyhydroxybutyrate

. mg L− day−

[]

S. elongatus  (in a consortium with E. coli)

Polyhydroxybutyrate

. mg L− in  days

[]

S. elongatus  (in a consortium with Pseudomonas putida)

Polyhydroxyalkanoates

. mg L− d−

[]

S. elongatus 

-Hydroxybutyrate

. g L− in  days

[]

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Table 6.2 (continued ) Strain

Product

Production rate or capacity

S. elongatus 

Limonene

. mg L− OD− day−

[]

S. elongatus 

-Butanol

. mg L− in  days

[]

S. elongatus 

Isobutyraldehyde

 mg L− in  days

[]

S. elongatus 

,-Butanediol

 mg L− in  h

[]

Synechocystis 

Lactate

. mmol g− (DCW) h−

[]

Synechocystis 

-Hydroxybutyrate

. mg L− in  days

[]

Synechocystis 

Squalene

. mg OD− L−

[]

Synechocystis 

Manoyl oxide

. mg g− (DCW)

[]

Synechocystis 

Isoprene

. mg g− (DCW)

[]

Synechocystis 

Ethanol

 mg L− in  days

[]

Synechocystis 

Ethanol

 mg L− in  days

[]

Synechocystis 

Isobutanol

 mg L− OD− in  days

[]

Synechocystis 

Alkanes

. mg g− (DCW)

[]

Synechocystis 

-Octanol and -decanol

 mg g− (DCW)

[]

Synechocystis 

,-Butanediol

. mmol L− in  days

[]

Synechocystis 

Shinorine

. mg g− (DCW)

[]

Synechocystis 

Mannitol

> µM for  days

[]

DCW – dry cell weight.

Reference

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6.5 Current limitations and future perspectives In the last years, significant developments in cyanobacterial SB have been reported demonstrating the potential to use these photoautotrophic bacteria as chassis. Research has focused in the use of the model cyanobacteria but recently the array of prospective chassis has increased. The isolation and characterization of more robust and fast-growing strains embraces the new model of chassis with optimal performances in different scenarios thus leaving behind the paradigm of the universal chassis. The continuous survey of novel cyanobacterial biodiversity will certainly contribute to the discovery of new strains with attractive characteristics. In addition, the exponential increase in the number of sequenced cyanobacterial genomes available and the developments in the generation of genome-scale metabolic models has enabled the rapid establishment of these tools for the fast-growing strains. Nevertheless, the use of these models and the FBA is difficult due to the lack of user-friendly tools. The development of such tools will allow researcher to perform the constraint-based genome-scale analysis with significant contributions for metabolic engineering by the identification of regulatory hotspots, potential bottlenecks, gene knockouts and knock-ins. Future developments in genome streamlining and synthesis will also contribute to improve or generate new chassis with customized performances for specific purposes. A significant number of SB tools have been developed and characterized already enabling the implementation of successful metabolic engineering strategies. Still there are important gaps that need to be filled namely the development of efficient inducible systems for the control of gene expression. For some of the currently available inducible systems, like the IPTG- and the aTc-regulated, high levels of repression have been achieved but, in most cases, the levels of derepression are very modest. The rational design of regulatory parts has been significantly hampered by the lack of predictive tools, which aid in the design of parts such as promoters and RBS. Moreover, these strategies can generate a high number of variants, therefore requiring high-throughput methodologies for their evaluation. Single-cell strategies have been developed using polyhydroxybutyrate, L-lactate or resorufin as reporters. Further refinements in these methodologies may decrease costs and thus increase their applicability in the future. The development and evaluation of libraries of parts and its variants might facilitate a better understanding of complex phenotypes related to the sequence-embedded context that has a deep impact on the performance of RBS and promoter/RBS preventing behavior predictability. The issues related to rational design of parts is significantly delaying the development of logic gate-based regulation that provides higher-level functions required by complex circuits. Polyploidy is a significant aspect affecting the genetic manipulation of cyanobacteria that turns transformation into a time-consuming process. The recent developments in genome editing techniques based on the CRISPR/Cas system promise to revolutionize the generation of mutants in cyanobacteria. Nevertheless, polyploidy

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goes hand in hand with gene dosage, a criterion that must be considered in the design of strategies. For this reason, it is important to further study the polyploidy phenomenon in cyanobacteria since it will affect the performance of synthetic devices and circuits that are integrated in the chromosome or the native plasmids. Future advances regarding the abovementioned aspect and others, such as optimization of large-scale cultivation and downstream processing, and the development of efficient cost-effective photobioreactors will be determinant for the success of cyanobacteria as effective industrial chassis.

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[203] Hays SG, Yan LLW, Silver PA, Ducat DC. Synthetic photosynthetic consortia define interactions leading to robustness and photoproduction. J Biol Eng. 2017, 11(1), 4. [204] Löwe H, Hobmeier K, Moos M, Kremling A, Pflüger-Grau K. Photoautotrophic production of polyhydroxyalkanoates in a synthetic mixed culture of Synechococcus elongatus cscB and Pseudomonas putida cscAB. Biotechnol Biofuels. 2017, 10(1), 190. [205] Ku JT, Lan EI. A balanced ATP driving force module for enhancing photosynthetic biosynthesis of 3-hydroxybutyrate from CO2. Metab Eng. 2018, 46, 35–42. [206] Wang X, Liu W, Xin C, Zheng Y, Cheng Y, Sun S, et al. Enhanced limonene production in cyanobacteria reveals photosynthesis limitations. Proc Natl Acad Sci U.S.A. 2016, 113(50), 14225–14230. [207] Lan EI, Liao JC. Metabolic engineering of cyanobacteria for 1-butanol production from carbon dioxide. Metab Eng. 2011, 13(4), 353–363. [208] Atsumi S, Higashide W, Liao JC. Direct photosynthetic recycling of carbon dioxide to isobutyraldehyde. Nat Biotechnol. 2009, 27, 1177. [209] Kanno M, Atsumi S. Engineering an obligate photoautotrophic cyanobacterium to utilize glycerol for growth and chemical production. ACS Synth Biol. 2017, 6(1), 69–75. [210] Angermayr SA, Paszota M, Hellingwerf KJ. Engineering a cyanobacterial cell factory for production of lactic acid. Appl Environ Microbiol. 2012, 78(19), 7098–7106. [211] Wang B, Pugh S, Nielsen DR, Zhang W, Meldrum DR. Engineering cyanobacteria for photosynthetic production of 3-hydroxybutyrate directly from CO2. Metab Eng. 2013, 16, 68–77. [212] Englund E, Pattanaik B, Ubhayasekera SJK, Stensjö K, Bergquist J, Lindberg P. Production of squalene in Synechocystis sp. PCC 6803. PLoS ONE. 2014, 9(3), e90270. [213] Englund E, Andersen-Ranberg J, Miao R, Hamberger B, Lindberg P. Metabolic engineering of Synechocystis sp. PCC 6803 for production of the plant diterpenoid manoyl oxide. ACS Synth Biol. 2015, 4(12), 1270–1278. [214] Englund E, Shabestary K, Hudson EP, Lindberg P. Systematic overexpression study to find target enzymes enhancing production of terpenes in Synechocystis PCC 6803, using isoprene as a model compound. Metab Eng. 2018, 49, 164–177. [215] Liang F, Englund E, Lindberg P, Lindblad P. Engineered cyanobacteria with enhanced growth show increased ethanol production and higher biofuel to biomass ratio. Metab Eng. 2018, 46, 51–59. [216] Miao R, Liu X, Englund E, Lindberg P, Lindblad P. Isobutanol production in Synechocystis PCC 6803 using heterologous and endogenous alcohol dehydrogenases. Metab Eng Commun. 2017, 5, 45–53. [217] Yunus IS, Wichmann J, Wördenweber R, Lauersen KJ, Kruse O, Jones PR. Synthetic metabolic pathways for photobiological conversion of CO2 into hydrocarbon fuel. Metab Eng. 2018, 49, 201–211. [218] Yunus IS, Jones PR. Photosynthesis-dependent biosynthesis of medium chain-length fatty acids and alcohols. Metab Eng. 2018, 49, 59–68. [219] Savakis PE, Angermayr SA, Hellingwerf KJ. Synthesis of 2,3-butanediol by Synechocystis sp. PCC 6803 via heterologous expression of a catabolic pathway from lactic acid- and enterobacteria. Metabolic Eng. 2013, 20, 121–130. [220] Yang G, Cozad MA, Holland DA, Zhang Y, Luesch H, Ding Y. Photosynthetic production of sunscreen shinorine using an engineered cyanobacterium. ACS Synth Biol. 2018, 7(2), 664–671. [221] Wu W, Du W, Gallego RP, Hellingwerf KJ, Van Der Woude AD, Branco Dos Santos F. Using osmotic stress to stabilize mannitol production in Synechocystis sp. PCC6803. Biotechnol Biofuels. 2020, 13(1), 117. [222] Sengupta A, Madhu S, Wangikar PP. A library of tunable, portable, and inducer-free promoters derived from cyanobacteria. ACS Synth Biol. 2020, 9(7), 1790–1801.

Sara B. Pereira

Chapter 7 Algal biotechnology Abstract: Algae are an ancient group of photosynthetic organisms with ubiquitous distribution and high morphological diversity, including the unicellular microalgae and the complex multicellular macroalgae (commonly known as seaweeds). The extensive biological diversity of algae provides a good opportunity to explore these organisms for the production of a wide range of valuable products. In addition, algae-derived products are produced at the expense of CO2, contributing to decrease the levels of this greenhouse gas in the atmosphere and to alleviate the problems associated with the depletion of fossil fuels. As a result, the field of algal biotechnology has significantly developed and expanded towards novel fields of applications. This chapter provides an outline of the recent developments in algal biotechnology, focusing on three high-value products successfully produced at industrial scale, namely, omega-3 fatty acids and astaxanthin from microalgae and polysaccharides produced by macroalgae. Current challenges and strategies to overcome limitations and promote a sustainable, “green” and efficient algae-based bio-economy are also discussed. Keywords: algae, astaxanthin, biotechnology, omega-3, phycocolloids, polysaccharide-based polymers

7.1 Introduction Algae are an ancient and diversified group of photosynthetic organisms found in almost every environment, including seas, rivers, lakes, soils and in symbiotic associations with animal and plants [1, 2]. These organisms can be divided into the small unicellular microalgae and the complex multicellular macroalgae [1, 3, 4]. Although cyanobacteria have been traditionally identified as “blue-green algae,” they are prokaryotes and, thus, will not be discussed here [5]. Algae have been a subject of interest for centuries, first as food and medicine and more recently as source of high-value added compounds [3]. Thus, while some wellestablished industrial sectors already use algae as feedstock, their full potential is still emerging [6]. The development of a sustainable and efficient algae-based bio-economy has been prompted by the possibility to explore their extensive biological diversity for Acknowledgments: This work is funded by FCT – Fundação para a Ciência e a Tecnologia under the contract DL57/2016/CP1327/CT0007. The author would like to thank Robert Kourist for the important comments and suggestions. https://doi.org/10.1515/9783110550603-007

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the production of a wide range of valuable products at the expense of CO2, eliminating the need to supply carbon feedstocks while contributing to reduce the levels of this greenhouse gas in the atmosphere [6–8]. CO2 biofixation by algae is classified as a direct CO2 mitigation technology, with potential to alleviate the impacts of the booming world population, climate change, depletion of fossil fuels and ever-increasing demand for food and energy [7, 9]. In theory, microalgae alone are capable of generating about 280 tons of dry biomass/ha/year, sequestering approximately 513 tons of CO2 in this process [10]. However, CO2 utilization by algae still requires scientific and technological advances to be able to provide a significant contribution to solve the global problem of CO2 accumulation [9]. Consequently, algal CO2 biofixation has become a focal point of research initiatives and business ventures. Nowadays, algae are being used in different branches of industry and the global production of algal biomass is increasing [3]. Algal biotechnology can be divided into two branches: microalgal and macroalgal, with unique specificities [3].

7.2 Microalgal biotechnology Microalgae are particularly rich in protein and polyunsaturated fatty acids (PUFAs), being currently exploited for the production of proteins, fatty acids, carotenoids (e.g., β-carotene, astaxanthin), phycocyanin and vitamins, that find applications in the food, feed, cosmetic, pharmaceutical and biofuel industries [3, 11, 12]. Microalgae have a higher photosynthetic efficiency compared to crop plants (up to 10%, compared to 1–4% for other phototrophs) and can be cultivated on non-arable land with minimal use of freshwater or grow in seawater or wastewater [6]. Therefore, they have been often proposed as third-generation feedstock for biofuel production that does not compete for freshwater or land resources [8]. In addition, compared to other microbial platforms, microalgae biofactories may be more suitable to produce certain plant-derived products [6]. For all of the above reasons, microalgae-based biotechnology has gained considerable importance in the last decades, with an estimated global market size for products extracted from microalgae of about US$1,143 million by 2024 [13]. Large-scale cultivation of microalgae in autotrophic conditions is usually performed in one of the two main systems available: (i) open ponds or (ii) photobioreactors (PBRs). Open ponds have the advantages of being cheaper to construct and operate (3 to 10 times less expensive than PBRs), as well as easier to clean and operate at large scales (Figure 7.1) [6, 14–17]. Thus, is not surprising that these ponds, in particular the raceway ponds, are the most extensively used open algal cultivation system worldwide [15]. However, open ponds are usually less efficient than PBRs, have higher chances of irregular growth due to light and temperature fluctuations, and higher vulnerability to biological contamination [8, 15].

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Figure 7.1: A) Light micrograph and B) large-scale cultivation of the microalgae Microchloropsis salina. Scale bar: 5 µm. Pictures were kindly provided by Daniel Garbe from the Technical University of Munich.

Contamination is a big concern, since it greatly reduces the biomass yield and may affect its quality, reducing the economic benefits [18]. The most common contaminants are grazers, fungi, bacteria, viruses and other photosynthetic organisms (e.g., cyanobacteria) and, thus, several strategies have been developed to control these predators [18, 19]. The selection of the best strategy has to be considered case by case, since it has to be aligned with the economics of the production process and be suitable to the target algae species [19]. Some of the strategies rely on treatments with chemicals (e.g., pesticides) that are differentially toxic to the contaminants yet harmless to the algae at the concentrations used [20]. In the case of grazers, filtration of the incoming water can be used to remove particles larger than 10 μm, thus retaining the grazers that are usually larger than 16 μm [18, 21]. However, the most effective approach consists in maintaining an extreme culture environment, such as high salinity, high alkalinity or high nutritional status [19, 22]. PBRs have the major advantages of enabling the microalgae growth in a controlled environment and be less prone to contamination [15]. However, light exposure is not homogeneous inside PBRs, strongly depending on the geometry of the PBR and the hydrodynamic properties of the culture. In addition, the cells auto-shading and the formation of biofilms on the PBRs walls may also have a strong impact on light penetration [23]. Regardless of the cultivation system used, several issues still need to be tackled. Therefore, significant efforts have been dedicated to establish the best balance between light availability and biomass concentrations, increase microalgae photosynthetic efficiency, reduce the energy input, operational and harvesting costs, utilize inexpensive CO2 resources (such as a flue gas) and saline water resources [15, 24]. Likewise, since downstream processing also accounts for a large part of the total production cost, it is also necessary to identify the optimal process for each application/ product to be isolated [15]. These issues can be partially alleviated by exploiting several components of the biomass through a biorefinery approach [25]. At the moment, high-value compounds such as PUFAs and carotenoids, particularly astaxanthin,

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justify the high cost of microalgae cultivation and processing technologies, being successfully produced at industrial scale [3, 26].

7.2.1 Omega-3 fatty acids Omega-3 fatty acids are PUFAs (i.e., fatty acids with two or more double bonds) in which a carbon double bond is at the third position from the methyl group [15, 27, 28]. The consumption of omega-3 is associated with several health benefits, including the prevention of cardiovascular diseases and liver injuries, anti-inflammatory and antitumor activities, reduction of triglycerides level and blood pressure, normal fetal brain development, growth and development of infants/children, and alleviation of depression and post-natal depression symptoms [28–40]. As a result, omega3 enjoy great commercial popularity, with a projected global demand of more than 135,500 tons by 2025, according to the regulatory agencies and the Food and Agriculture Organization/World Health Organization [41, 42]. This represents an estimated annual growth rate of 16% from 2015 to 2025 and a global omega-3 market size of US$3.77 billion by 2025 [43]. The three main biologically active omega-3 are the α-linolenic acid (ALA), composed of 18 carbons and 3 double bonds (18:3:ω3), and the long-chain (≥ 20 carbon atoms) eicosapentaenoic (EPA; 20:5:ω3) and docosahexaenoic acids (DHA; 22:6:ω3) (Figure 7.2) [28, 44]. A H 3C

COOH

B H3C

COOH

C H3C COOH Figure 7.2: Schematic representation of the chemical structure of the omega-3 fatty acids A) α-linolenic acid (ALA; 18:3:ω3); B) eicosapentanoic acid (EPA; 20:5:ω3) and C) docosahexanoic acid (DHA; 22:6:ω3).

ALA is present in the green leaves of plants, but is mostly obtained from a number of nuts, seeds and their oils, including walnuts, linseeds, etc. This fatty acid is the

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precursor of the more biologically active EPA and DHA through a series of denaturation and elongation reactions. Mammals, including humans, cannot synthesize ALA due to the absence of an enzyme (Δ15-desaturase) involved in its biosynthetic pathway [45]. Moreover, even though they can metabolize it into the longer chain omega-3 [45–47], this conversion is limited, being insufficient to fulfill the daily intake requirements [45, 46, 48]. Consequently, EPA and DHA are often determined as essential dietary nutrients [32], being mainly obtained from aquatic organisms that feed on primary producers, such as salmon, mackerel and herring [27, 32, 49, 50]. However, the growing demand for omega-3 cannot be satisfied by the current global fish stocks [32], especially considering the rapid depletion of marine resources due to overfishing and the increase of ocean pollution [44]. Indiscriminate fishing may also lead to unpredictable population imbalances and has a severe impact on biodiversity [42]. Other drawbacks associated with the consumption of fish-derived omega-3 are related to the presence of contaminants in fishes, the unattractive taste and odor of the oils, and the incompatibility with vegetarian diets [25, 40, 42, 48]. These disadvantages motivated the search for more sustainable sources of EPA and DHA [27]. In this context, microalgae emerge as a green and sustainable source of omega-3. In addition, since plants are unable to produce EPA and DHA unless genetically modified, microalgae are the only vegetarian source of these long-chain omega-3 [40]. Microalgae can also produce significantly higher levels of EPA and DHA compared to other sources, including bacteria and fungi [51]. For all of these reasons, microalgae are an important commercial source of omega-3, with the current wholesale market price for algaederived omega-3 being approximately US$140 per kilogram [14, 52]. BOX 1: Fatty acids Fatty acids are organic compounds formed by a long hydrocarbon chain with a terminal carboxyl group (―COOH). Depending on the nature of the hydrocarbon chain, fatty acids may be saturated (no double bonds) or unsaturated (with double bonds). Monounsaturated fatty acids, known as MUFAs, contain a single double bond, whereas polyunsaturated fatty acids or PUFAs contain two or more double bonds. Owing to their health promoting properties, the most commonly known PUFAs are omega-3 and omega-6, which can be distinguished by the first double bond position counted from the methyl end at carbon 3 and 6, respectively.

7.2.1.1 Omega-3 production by microalgae The efficiency of omega-3 production by microalgae depends on the producing species, culture characteristics and downstream processing. These parameters are interdependent and need to be carefully evaluated to make this process economically viable [28, 48]. One of the most important factors to consider is lipid productivity. The average lipid content of microalgae ranges from 20% to 70% of the dry weight

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[14, 16, 53]. However, this value can increase to 90% under certain conditions [16, 53]. The lipid content of different species of microalgae is listed in Table 7.1. Table 7.1: Lipid content reported for different species of microalgae. Species

% Lipids (w/w)

References

Auxenochlorella protothecoides

–

[]

Botryococcus braunii

–

[, ]

Chaetoceros calcitrans

–

[, , ]

–

[, ]



[]

Chlamydomonas reinhardtii

–

[, ]

Chlorella sp.

–

[]

–

[, ]

Chlorella protothecoides



[]

Chlorella zofingiensis



[]

Crypthecodinium cohnii



[]

–

[, ]

–

[, ]

Dunaliella tertiolecta



[]

Isochrysis sp.



[]

Isochrysis galbana



[]

Nannochloropsis sp.

–

[, ]

Nannochloropsis granulata

–

[]

Nannochloropsis oculata

–

[, ]

Neochloris oleabundans UTEX #

–

[]



[]

–

[, , ]



[]

Phaeodactylum tricornutum

–

[, , , ]

Porphyridium cruentum

–

[, , ]

–

[, ]

Chaetoceros gracilis Chaetoceros muelleri

Chlorella vulgaris

Dunaliella sp. Dunaliella salina

Nitzschia closterium Pavlova sp. Pavlova salina

Scenedesmus sp.

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Table 7.1 (continued ) Species

% Lipids (w/w)

References

Scenedesmus obliquus

–

[, ]

Schizochytrium sp.

–

[]

Skeletonema costatum Tetraselmis sp.

 –

[] [, ]



[, ]

–

[, ]



[]

Tetraselmis chuii Tetraselmis suecica Thalassiosira pseudonana

The lipids produced by microalgae are usually classified into (i) storage or neutral lipids, including acylglycerols (e.g., triacylglycerol), free fatty acids and carotenoids, and (ii) structural or polar lipids, such as phospholipids and galactolipids, that are mainly present in the membranes [14, 85]. Triacylglycerols are predominantly composed of saturated (SFAs) and monounsaturated (MUFAs) fatty acids, whereas PUFAs are mostly present in the structural lipids, where they help to maintain membrane fluidity [14, 24, 86–88]. Therefore, maximal levels of PUFAs, including omega-3, are usually observed in the early stationary phase of growth, decreasing throughout the stationary phase with the concomitant increase of SFAs and MUFAs [27]. Nevertheless, some microalgal species can accumulate higher concentrations of PUFAs as triacylglycerols [85]. Importantly, the total amount and relative proportion of lipids vary considerably between species and culture conditions, including the growth phase, nutrient availability, salinity, light intensity, temperature and pH [28, 53, 85, 86, 89]. Nannochloropsis species are considered one of the most promising EPA and DHA natural producers. Other species also currently used for the production of omega-3 include Chaetoceros calcitrans, Isochrysis galbana, Pavlova lutheri, Pseudoisochrysis paradoxa, Tetraselmis suecica, Skeletonema costatum and members of the genera Dunaliella, Chlorella and Thalassiosira [15, 28, 32, 90, 91]. In general, marine microalgae have a higher content of omega-3 compared to freshwater species since they require more PUFAs to survive in the marine environment [92]. Thus, the exploitation of marine microalgae may be of higher economic interest [48]. The conditions in which microalgae are cultivated strongly influence their lipid content and composition [16]. The cultivation of omega-3-producing microalgae in heterotrophic conditions is well established. However, the interest in autotrophic systems has greatly increased, since it avoids the price tag of the organic compounds that need to be supplied to heterotrophic algae [15, 51]. Two of the most popular strategies used to increase the levels of PUFAs in microalgae consist in promoting a sudden change in the growth conditions or exposing the cells to stress conditions, including UV radiation, low temperature, salinity or nutrient deprivation [27, 28, 51]. However, these conditions

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usually have a negative impact in growth, decreasing biomass production and its profitability, and thus it is necessary to achieve the right balance for each species [16, 28, 51, 93]. One way to overcome this bottleneck is to use a two-phase approach, in which microalgae are cultivated under optimal conditions to stimulate growth/biomass production and then exposed to more severe conditions for accumulation of the lipids [93].

7.2.1.2 Extraction and purification of PUFAs Sustainable production of PUFAs also depends on efficient cell harvesting and lipid extraction from the microalgae. No standard protocols exist for these processes, as their efficiency varies considerably according to the species characteristics (e.g., cell size and cell wall properties) and the envisaged use of the end product [48, 51]. Usually, cells are separated from culture media by gravity sedimentation, centrifugation, filtration, flotation or flocculation [15, 23, 48, 51]. After cell harvesting, it is usually necessary to dry the biomass. This step is particularly important when algae have to be stored or transported, as otherwise they will rapidly decay [15]. Subsequently, cell disruption is performed using either mechanical (oil or expeller press, bead beating, etc.), physical (e.g., ultrasounds, osmotic shock), chemical (e.g., solvents) and/or enzymatic methods [14, 33, 48]. Traditionally, lipid extraction from microalgae has been performed using non-polar organic solvents or solvent mixtures, such as chloroform–methanol and hexane–isopropanol [14]. However, the use of large amounts of solvents for the extraction process has raised serious health, safety and environmental concerns. Thus, efforts are being made to replace the commonly used solvents with greener ones, recycle the solvents and/or implement more sustainable processes, including supercritical fluid extraction [14, 15, 48]. Following lipid extraction, PUFAs are separated from other lipids by winterization (reduction of the oil temperature to precipitate the more saturated lipids), molecular distillation and/or urea complexation [14, 48, 51]. The fraction obtained still needs further purification to remove impurities, odor and taste and be suitable for human consumption, which is achieved by filtration, bleaching, deodorization or polishing, along with antioxidant addition to enhance the quality and shelf-life of PUFAs [14, 51].

7.2.1.3 Genetic engineering for improved production of PUFAs Despite the enormous potential of microalgae as natural producers of PUFAs and the advances in photobiotechnology, some challenges still need be addressed, namely, the low product titers and productivity (e.g., 14.4 mg/L day of EPA in N. oceanica), which are not sufficient to meet the market demands [44, 89, 91]. The recent progresses in microalgal genetic transformation and molecular engineering opened the way to increase the efficiency of omega-3 production by enhancing growth performance and

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increasing or modifying lipid content and fatty acid composition [24, 94]. Genetic engineering also allows the generation of strains with improved capacity to capture light in dense cultures and resist to contamination [24]. The first step to enhance the production of PUFAs by microalgae is to understand the biochemical and metabolic mechanisms related to their biosynthesis and accumulation [48]. In microalgae, the production of PUFAs begins with the biosynthesis of fatty acids in the chloroplast, via the fatty acid synthetase (FAS) pathway. Briefly, this pathway involves the carboxylation and condensation of acetyl-CoA to malonyl-CoA, which is then transferred to an acyl carrier protein (ACP) forming malonyl-ACP. This compound serves as substrate for further elongation reactions to create long-chain saturated fatty acids [44, 51, 89]. The newly synthesized fatty acids are either transferred to glycerol-3-phosphate to form triacylglycerol via the triacylglycerols (TAG) biosynthesis pathway or are further desaturated and elongated to generate PUFAs. Notably, in addition to this desaturase/elongase pathway, microalgae possess an alternative polyketide synthase (PKS) pathway for the de novo synthesis of PUFAs [51, 89, 95]. Therefore, it is necessary to identify which biosynthetic pathways are responsible for the synthesis of PUFAs in the selected microalgal species [89]. Several enzymes involved in the FAS, TAG or PUFAs biosynthesis pathways were previously identified and targeted for genetic engineering (for recent reviews see [44, 89]). For example, the gene encoding the acetyl-CoA carboxylase that catalyzes the conversion acetyl-CoA into malonyl-CoA in the first step of the FAS pathway was overexpressed [48, 89]. However, this strategy had little success in microalgae such as Cyclotella cryptica and Navicula saprophila, suggesting that the transcript and enzyme levels fulfill the needs for the biosynthesis of fatty acids [89, 96]. On the other hand, overexpression of the gene encoding the malonyl CoA-acyl carrier protein transacylase, involved in the formation malonyl-ACP, was effective in several microalgae [44]. In Nannochloropsis oceanica, this strategy led to a 31% increase in the total lipid content and an 8% increase in the fraction of EPA [97]. The manipulation of several other enzymes of the FAS pathway led to different phenotypes and, thus, these enzymes are now recognized as challenging targets for genetic engineering seeking enhanced levels of fatty acids [89]. Overexpression of enzymes involved in TAG biosynthesis also contributed to improve PUFA content in microalgae by depositing some proportion of newly synthetized PUFAs into triacylglycerols, as previously observed for Phaeodactylum tricornutum [44, 98]. However, the identification of the majority of the microalgal genes encoding fatty acid desaturases (FADs) and elongases (ELOs) opened the way to increase PUFAs’ levels by targeting these enzymes for metabolic engineering approaches [89]. Overall, the overexpression of a single gene encoding a FAD or an ELO resulted in an increase in PUFAs levels [44, 89, 99–105], whereas the concomitant overexpression of a FAD and/or an ELO led to different outcomes [44]. Considerably less is known about the PKS pathway for the de novo synthesis of PUFAs. Therefore, it is important to elucidate the functional domains involved

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in this pathway to achieve an optimized, controllable and economically viable system of PUFAs’ production [89]. Noteworthy, the lipid metabolism can be markedly affected by enzymes that are not directly involved in lipid biosynthesis but regulate the flux and accumulation of essential metabolites for this process [89]. Therefore, other strategies implemented to increase lipid accumulation focused on enhancing photosynthetic efficiency and/or increasing the supply of the precursors and cofactors (acetyl-CoA and NADPH) by engineering the expressions of key genes related to glycolysis, tricarboxylic acid cycle, and the pentose phosphate pathway or blocking competing pathways [44, 89]. The suppression or knockout of genes related to lipid oxidation, degradation and inhibition of their synthesis is another commonly used approach [48]. It has also been proposed that transporting the lipids to the extracellular environment can improve PUFAs production process by simplifying the downstream processing [48]. Genes encoding transcription factors are other targets for the enhanced production of PUFAs, allowing the simultaneous regulation of multiple genes related to lipid accumulation [44, 89]. Despite the recent achievements in the engineering of microalgae for optimized production of PUFAs, there is still room for improvements, including (i) obtaining a deeper knowledge of microalgal metabolic processes, (ii) increasing the number and efficiency of microalgal genome editing toolboxes and (iii) adopting alternative engineering strategies that allow the concomitant modification of multiple components in a metabolic pathway, such as CRISPR/Cas9 technology [44, 89, 106]. This last strategy has been successfully used in Chlorella vulgaris FSP-E, leading to a higher fatty acid accumulation in this species [107].

7.2.2 Carotenoids Carotenoids are among the most valuable microalgae-derived metabolites [108]. These yellow/orange lipophilic compounds are major components of algae chloroplasts and chromoplasts, where they play an important role in the protection against photooxidative damages [108–110]. All carotenoids contain a 40-carbon unsaturated backbone derived from isoprene molecules [111]. They are divided into two groups: carotenes, which consist of only hydrocarbons, and xanthophylls, which contain one or more oxygen-bearing functional groups such as hydroxyl and ketone [110]. As liposoluble and hydrophobic pigments, carotenoids can be found in their free form or esterified with fatty acids [108, 112]. Carotenoids are not essential but are beneficial for animal and human health, as they decrease the risk of certain disorders by preventing oxidative damages [113]. Humans and animals cannot synthesize carotenoids and, thus, have to obtain it from diet [114]. These compounds are mainly used in feed, food and cosmetic applications [115–118]. For instance, they are vastly used in aquaculture to induce broodstock producing offspring with good quality [108, 119]. In terms of human health, they show

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positive effects in alleviating certain disorders such as cancer, age-related macular degeneration and cardiovascular diseases [11, 108, 120–122]. Such effects can also be potentiated by synergistic interactions with other antioxidant molecules present in the algal biomass [108]. Owing to the increased industrial and commercial use of carotenoids in the last decade, the market value of these compounds is expected to reach around US$1.53 billion in 2021 [11, 108]. Nowadays, synthetic carotenoids still dominate the market. However, their importance has started to decline due to their potential toxic effects, lower efficiency in terms of health-promoting properties and the increased demand of “greener” solutions and natural products [11, 110, 123]. Following this tendency, algae-derived carotenoids have gained commercial recognition in the global market, and are now one of the most popular growing fields for bio-based economy. However, the production of carotenoids from algae is still not sufficiently cost-effective to compete with their synthetic counterparts [11].

7.2.2.1 Astaxanthin production by microalgae Microalgae produce a large array of carotenoids, including α-carotene, β-carotene, astaxanthin, violaxanthin, lutein, alloxanthin, neoxanthin, zeaxanthin, cryptoxanthin and fucoxanthin [113]. From these, astaxanthin, β-carotene and lutein have the highest commercial interest, being used in nutraceuticals, pharmaceuticals, food, dietary supplements, feed and cosmetics [11, 52, 108]. Astaxanthin (3,3ʹ-dihydroxyβ,βʹ-carotene-4,4ʹ-dione) is member of the xanthophyll family of carotenoids and is the highest-value product derived from microalgae [17]. It contains both a hydroxyl and a ketone group at each of its molecular ends [111, 124] (Figure 7.3). The two additional oxygenated groups on each ring make astaxanthin one of the most powerful antioxidants in nature, displaying an antioxidative activity 65, 10 and 100 times higher than those of vitamin C, β-carotene and α-tocopherol, respectively [26, 124, 125]. Also, due to the presence of the hydrophilic groups and lipophilic backbone, astaxanthin can be incorporated into the cell membranes, with the polar end groups oriented toward the polar region of the lipid membranes, scavenging free radicals generated at

CH3

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Figure 7.3: Schematic representation of the chemical structure of astaxanthin. Astaxanthin is a xanthophyll containing both a hydroxyl and a ketone group in each of its terminus.

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the surface of membranes or even inside [126, 127]. It can also cross the cell membranes by passive transport, exerting antioxidant activity inside and outside of the cells [126, 128]. Astaxanthin is still mainly used as feed in aquaculture [124, 129]. However, it has also been approved as a dietary supplement in European countries and United States of America [11, 108] and is currently marketed as a food supplement [130]. In 2017, the reported market value of astaxanthin was over US$550 million and is expected to reach US$800 million by 2022 and US$2.57 billion by 2025 [130, 131]. Currently, over 95% of the astaxanthin available in the market is produced synthetically, whereas microalgae-derived astaxanthin corresponds to less than 1% [26]. This is justified by the higher production costs of microalgal-derived astaxanthin compared to the synthetic one [17]. However, with the increased demand for astaxanthin with the food-label “natural”, the cultivation of astaxanthin-producing microalgae at industrial scale has great potential and is an attractive business opportunity [26]. The unicellular freshwater microalga Haematococcus pluvialis is considered the best natural source of astaxanthin (up to 4% of the dry weight) and is the main producer of this commercial product, even if other genera including Chlorella sp. Chlorococcum sp. and Scenedesmus sp. are also important producers of astaxanthin [108]. Whereas synthetic astaxanthin is a mixture of three stereoisomers, H. pluvialis produces only one, in both free and esterified forms [52]. Astaxanthin from H. pluvialis is a preferred choice for high-end markets and was granted the “GRAS” (Generally Recognized As Safe) status by the Food and Drug Administration (FDA) [26]. H. pluvialis can grow in photoautotrophic, heterotrophic or mixotrophic conditions; in indoors, open raceway ponds or closed photobioreactors; and in batch or continuous modes [132–137]. However, the accumulation of astaxanthin strongly depends on the growth conditions, being affected by factors such as light, temperature, pH, salt concentration and nutritional stress [26, 111]. Regarding the last factor, it has been reported that nutrient deficiency results in higher accumulation of carotenoids in algae [138, 139]. Based on the life cycle of H. pluvialis, maximal astaxanthin productivity is usually obtained by cultivating this microalga in a two-step process [140–142]. The cycle consists of a green stage, in which the cells can reproduce and accumulate biomass and a red stage, where the cells lose the ability of reproduction and mobility but are capable of accumulating astaxanthin [143, 144]. Thus, H. pluvialis is usually cultivated in photobioreactors, in normal phototrophically or mixotrophically conditions, for cell proliferation and biomass accumulation, before being transferred into either larger-scale photobioreactors or raceway ponds where astaxanthin accumulation is promoted under stress and nutrient-deficient conditions [111]. Importantly, nutrient starvation induces not only astaxanthin formation in H. pluvialis, but also the deposition of TAGs, important for the biofuel industry. Therefore, microalgae emerge as a promising platform for the development of a dedicated microalgal biorefinery [26].

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7.2.2.2 Extraction and purification of astaxanthin The extraction and processing of carotenoids from microalgae is challenging. Therefore, various strategies are currently available, all involving the harvesting and processing of the cells followed by the extraction and purification of the carotenoids [110]. Regarding astaxanthin extraction form H. pluvialis, the cells are usually harvested by gravitational settling and centrifugation [111, 145]. Subsequently, the biomass is usually dried by spray or freeze drying to eliminate moisture and prevent the degradation of the pigments [17, 145]. Cell breakage can be performed using several methods, including mortar and pestle, two-phase extraction, milling, ultra-sonication, microwave, thawing and freezing [11, 111]. Importantly, to avoid the degradation of the light-dependent astaxanthin, the lysed cells are protected from light and the pigment is rapidly recovered (within few hours) [145]. Usually, this is achieved by conventional solvent extraction techniques using aliphatic alcohols (e.g., methanol, ethanol) or concentrated acids and alkalis or edible oils [11, 145]. To avoid the use of large quantities of expensive and/or harmful solvents, most companies use supercritical fluid extraction with CO2 as the only solvent [11, 111, 146, 147]. This technique provides a short extraction time and better quality of bioproduct. In addition, compared to the conventional solvents, supercritical CO2 is a more sustainable and greener alternative [145, 148, 149] and the extract obtained is highly concentrated and free of residues [150]. Despite the efficient and highly selective extraction of astaxanthin, supercritical CO2 extraction requires a high-pressure equipment which is highly expensive compared to other techniques [145].

7.2.2.3 Genetic engineering for improved production of astaxanthin Isopentenyl pyrophosphate (IPP) is the key intermediate for the synthesis of all carotenoids, including astaxanthin [26]. IPP can be originated from glyceraldehyde-3phosphate and pyruvate by two different pathways: the cytosolic mevalonate (MVA) and the 2-C-methyl-D-erythritol 4-phosphate (MEP), also known as the non-mevalonate or 1-deoxy-D-xylulose 5-phosphate (DXP) pathway, located in the chloroplast. [26, 130 151–154] In H. pluvialis, IPP is most likely synthesized using the DXP pathway, as this microalga lacks three key enzymes of the MVA pathway [26, 155]. IPP undergoes isomerization to dimethylallyl pyrophosphate (DMAPP) and, subsequently, IPP and DMAPP are condensed into geranylgeranyl pyrophosphate (GGPP) by farnesyl pyrophosphate (FPP) and GGPP synthases [26, 130]. GGPP is a shared precursor with other isoprenoids [26]. The first committed step to the synthesis of carotenoids is the conversion of GGPP into phytoene by phytoene synthase, with is then converted into lycopene by desaturases [156]. Lycopene is then transformed into β-carotene by lycopene β-cyclase. Until this point, the biosynthetic pathway is linear and essentially the same in different organisms. However, after formation of β-carotene, the biosynthetic

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routes differ among different organisms [130]. In algae, β-carotene can be oxidized into 4-keto intermediates or 3-hydroxyl intermediates by β-carotene ketolases and hydroxylases respectively, in a total of two hydroxylation and two ketolation reactions that can occur sequentially or non-sequentially (Figure 7.4) [130]. Consequently, there are seven intermediates between β-carotene and astaxanthin. In algae, the β-carotene ketolases and hydrolyases are encoded by crtO (or bkt) and chyb (or crtR-b), respec-

Figure 7.4: Schematic diagram of the biosynthetic pathway of astaxanthin in algae. IPP, isopentenyl pyrophosphate; DMAPP, dimethylallyl pyrophosphate; GGPP, geranylgeranyl pyrophosphate; FPPS, farnesyl pyrophosphate synthase; GGPPS, GGPP synthase; PSY, phytoene synthase; PDS, phytoene desaturase; ZDS, ζ-carotene desaturase; LCY-b, lycopene β-cyclase; Bkt, β-carotene ketolase; CrtR-b, β-carotene hydroxylase. The grey area highlights the reactions that are shared by different organisms (e.g., algae, bacteria, fungi, plants). Adapted from [26, 130].

tively [130]. The final two oxygenation steps catalyzed by Bkt and CrtR-b are rate limiting steps of astaxanthin biosynthesis [26, 157]. Several efforts were made to genetically enhance the production of astaxanthin by H. pluvialis using classical mutagenesis and genetic engineering [158, 159]. Due to the limited amount of genetic engineering tools available, traditional approaches such as random mutagenesis (e.g., by UV radiation, treatment with ethyl methanesulphonate, diethyl sulfate or herbicides) followed by phenotype screening have been preferred [130]. These strategies led to the generation of different mutants with increased astaxanthin production capacity [160–164]. However, the more recent developments in H. pluvialis and astaxanthin biology allow transformations of H. pluvialis chloroplast and nuclear genomes [26, 165, 166]. As an example, H. pluvialis was transformed with the endogenous gene encoding the β-carotene ketolase, bkt, in a self-cloning approach, leading to a significant increase in the transcript levels of carotenogenic genes and amount of carotenoids in the transformants compared to the wild type [167].

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As new developments in the genetic engineering of H. pluvialis are made, the role of this organism as astaxanthin producer and model for carotenoid synthesis and accumulation studies is consolidated [26].

7.3 Macroalgal biotechnology Macroalgae, commonly referred to as seaweeds, are particularly rich in polysaccharides (from 4% to 76% of the dry weight), usually possessing lower protein (7–15% of the dry weight) and lipid contents (1–5% of the dry weight) compared to microalgae [12, 168–170]. These organisms have long been used as food ingredients and traditional remedies in East Asia countries [171]. Currently, macroalgae are also one of the main sources of polysaccharides, particularly those with high molecular weight known as phycocolloids. The global sector of macroalgae is worth over US$6 billion per year, with main contributions from the hydrocolloids, food, feed, fertilizer/crop protection products markets [3, 171, 172]. Several factors make macroalgae attractive candidates for supplying sustainable photosynthetic-derived feedstocks, such as their higher growth rates and photosynthetic efficiency compared to terrestrial plants, minimal nutrient requirements and year-round production of biomass [15, 170, 172]. Although only a dozen of macroalgae are commercially cultivated, the amount of macroalgal mass-cultivated has increased over the last 10 years at an average of 10% [170]. It is estimated that marine agronomy is able to produce 30 million dry tones of macroalgae per year, from which 29 are cultivated in marine agriculture settings and 1 are harvested in the wild [172]. The cultivation of macroalgae can take place in natural waters, either off shore or near shore, or in open ponds. Due to the increasing demand of near shore space by other markets and activities (e.g., shipping, oil exploration, recreational use, etc.) offshore and open ponds are currently receiving the most attention [15, 172]. Between offshore cultivation and open ponds, the latter presents the advantages of being easier to manage the cultures and add nutrients to the medium, does not require the use of holdfast structures to withstand weather conditions and keep algae intact, and avoids problems associated with weather, disease and predators. On the other hand, offshore cultivation does not require land and avoids the costs of nutrient supply [15]. Therefore, a comprehensive analysis of the different cultivations systems needs to be performed for each case in order to select the most advantageous alternative.

7.3.1 Polysaccharide-based biopolymers Polysaccharide-based biopolymers are important ingredients in different commercial areas such as food, pharmaceuticals, biomedicine, electronics and bioremediation

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[173]. Due to their biocompatibility, non-toxicity, flexibility, functionality and biodegradability, these polysaccharides embody a greener and safer alternative to the petrochemical-derived polymers and promote sustainable biotechnology and bioeconomy [168, 174]. They also present a wide range of physicochemical properties such as solubility, viscosity, gelling capacity, chain length and linkage types, which provided them the versatility required for industrial exploitation [173]. Due to their high content in polysaccharides (from 4% to 76% of the dry weight), marine macroalgae are one of the main sources of natural polysaccharides. The polysaccharides produced by macroalgae often contain rare sugars (sugar alcohols, deoxy sugars, uronic acids) and sulfate groups, and present high solubility in water and unique rheological properties [3, 168, 169]. All together, these features provide macroalgal polymers with water-binding, gelation and/or emulsifying capacities that make them valuable additives as stabilizers, thickening agents and texture modifiers in food and cosmetic industries [175, 176]. Others are important ingredients in nutraceutical and pharmaceutical preparations due to their antioxidant, anti-melanogenic, skin anti-aging, anticoagulant, antioxidant, antitumor and immunomodulatory bioactivities [14, 176, 177]. Consequently, polysaccharide-based biopolymers produced from by macroalgae have gained much attention as a means to expand the global industry [178]. Box 2: Hydrocolloids Hydrocolloids, commonly known as gums, are high molecular weight hydrophilic polymers that contain polar or charged functional groups, rendering them soluble in water. Hydrocolloids comprise a number of polysaccharides and are vastly used in the food and pharmaceutical industries as texturizing agents, thickeners, stabilizers and emulsifiers. The hydrocolloids derived from seaweeds are designated as phycocolloids. The three phycocolloids with higher commercial importance are alginates, agars and carrageenans.

7.3.1.1 Commercially relevant macroalgal polysaccharides Macroalgae are commonly subdivided into three major groups based on their pigmentation and chemical composition, namely: (i) red algae (Rhodophyta), (ii) brown algae (Heterocontophyta) and (iii) green algae (Chlorophyta) [179]. Members from all three groups produce industrially relevant polysaccharides [180], the most successful being the alginates and fucoidans from by brown algae and agars/agaroses and carrageenans produced by red algae [178]. Alginates, agars and carrageenans are the main commercial phycocolloids [181], with a worldwide annual production of approx. 100,000 tons and a value above US$1.1 billion [182].

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7.3.1.1.1 Alginates Alginate is the generic term of alginic acids and their salts, mainly sodium and calcium [15, 183, 184]. These polysaccharides are the major structural component of the cell walls of brown algae, with the salt forms comprising up to 40–47% of the total biomass [168, 185]. Alginates are high molecular weight, linear anionic polysaccharides composed of two types of uronic acids, namely, β-D-mannuronic acid (M) and α -Lguluronic acid (G), arranged randomly (Figure 7.5) [15, 168, 180]. Variation in the molar ratios of β-D-mannuronic acid to α-L-guluronic acid residues controls the molecular weight and material properties of alginates and, thus, commercial alginate is often characterized by its “M:G” ratio [168, 185].

Figure 7.5: Schematic representation of the alginic acids’ repeating units. These compounds are linear polysaccharides composed by β-D-mannuronic acid (M) and α-L-guluronic acid (G), arranged randomly. Adapted from [15].

The most exploited sources of alginate are species from the genera Laminaria (Laminaria hyperborea, Laminaria digitata, Laminaria japonica), Ascophyllum nodosum and Macrocystis pyrifera [168, 178]. Globally, the demand for alginates was approximately US$624 million in 2016 and is projected to reach US$923.8 million by 2025 with a consumption volume of 21,516 tons [185]. Alginates are used as emulsifiers, gelling agents, coating and thickening agents in the food and cosmeceutical industries, in medicine (e.g., impression-making material in dentistry, prosthetics, lifecasting, wound healing) and biotechnology (e.g., gel-like pigment preparations, aqueous printing inks, production of sculptures in plastic arts) [15, 186]. 7.3.1.1.2 Fucoidans Fucans are sulfated polysaccharides widely distributed in the cell wall of brown algae with heterogeneous chemical structure. These polymers are generally classified into fucoidans, glycuronogalactofucans and xylofucoglycuronans [180, 187]. Fucoidans are the most abundant sulfated polysaccharides extracted from brown algae, constituting

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up to 25–30% of the algae dry weight [180]. These polymers are primarily composed of α-L-fucose-4-sulfate, with branching or a sulfate ester group on C3, and small amounts of D-xylose, D-galactose, D-mannose and uronic acids [187]. The molecular weight of fucoidans varies from 13 to 950 kDa depending on the source and extraction method [168]. Fucoidans are mainly obtained not only from species of the genera Fucus and Laminaria, but also from Sargassum, Ascophyllum, Pelvetia, Cladosiphon, Nemacystus and Padina [182, 188]. The market value of fucoidans is around US$33 million and is expected to grow up to US$38 million in 2025. Fucoidans do not form viscous solutions and, thus, are not used as thickening or gelling agents [180]. However, they possess several important biological properties such as antiviral, anticoagulant, antibacterial and anticancer activities, being an important ingredient in the food, cosmetic and pharmaceutical industries [178, 186]. 7.3.1.1.3 Agar and agarose Agar is a mixture of approximately 70% of the non-ionic, gelatinizing agarose, and 30% of the ionic, non-gelatinizing agaropectin [15]. Agarose is a linear polysaccharide composed of repeating units of D-galactose and 3,6-anhydro-L-galactopyranose [15]. Agaropectin also consists of a D-galactose/3,6-anhydro-L-galactopyranose backbone, but contains partially sulfated galactose moieties at the six-position, a methoxyl group, a pyruvic acid residue, and D-glucuronic acid in various proportions [15, 180]. Several species from the genera Gelidium, Gracilaria, Gracilariopsis, Gelidiella and Pterocladia are rich in agar. However, the major part of the world’s agar production (53%) is obtained from Gracilaria and Gracilariopsis [180, 189]. Agar was the first phycocolloid to be used and one of the first food ingredients approved as GRAS (Generally Recognized as Safe) by the Food and Drug Administration [189]. It is also the most expensive colloid (about US$18/kg) [180], with the price varying according to its composition and brand [186]. The global production of agar is approximately 9,600 tons, with a gross market value of US$173 million per year [190]. Agar is colorless, tasteless, water-soluble once heated and has a strong thickening effect [15]. Other physical properties such as gel strength, gelling and melting temperature are variable, depending on its chemical structure [180]. Agar and agarose are vastly used in the pharmaceutical and in the food industries as alternative to gelatin [15, 184], as well as for scientific purposes, including gel electrophoresis in the case of agarose (due to its non-ionic nature, high melting temperature – above 70 °C – and low gelling temperature – about 37 °C) and growth media formulations [15, 168, 184, 191] (Figure 7.6). 7.3.1.1.4 Carrageenans Carrageenans are a family of hydrophilic polysaccharides that form one of the major constituents of red algae cell walls, representing 30–75% of their dry weight [168].

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Figure 7.6: Examples of applications of agar and agarose in scientific research. A) Petri dish with bacterial growth medium solidified using agar. B) Agarose gel for nucleic acid electrophoresis.

These polysaccharides are composed of repeating galactose units and 3,6-anhydrogalactose, linked by alternating α-1,3 and β-1,4 glycosidic bonds [15] (Figure 7.7). Carrageenans have a high molecular weight (100 to 1,000 kDa) and are highly sulfated, with the sulfate content varying between 15% and 40% [15]. The number and position of the sulfate groups in the repeating galactose units allow the classification of carrageenans in three main commercially relevant families, namely, kappa (κ), iota (ι) and lambda (λ) [168, 180]. Carrageenans are mainly isolated from the red algae Kappaphycus alvarezii, but also from Chondrus crispus (Irish moss), Gigartina, Eucheuma cottonii and Spinosum [15, 180, 185, 186]. Global carrageenans production is approximately 50,500 tons, with a value of US$525 million annually [190]. Carrageenans are able to dissolve in water and form highly viscous solutions that remain stable over a wide pH range [168]. Thus, these polysaccharides are primarily used as gelling agents and thickeners in the food and cosmetic industries [15, 186]. They also have antimicrobial, antitumor and anticoagulant properties, which have been associated with their sulfate groups [180].

7.3.1.2 Extraction and purification of polysaccharides The preparation of the macroalgal biomass usually involves a first step of washing to eliminate impurities, followed by drying to preserve the macroalgae in optimum conditions until further processing [192]. Subsequently, polysaccharides can be extracted using different methodologies, according to the species and the envisage use of the polymer [3]. Most commonly, the extraction is performed using hot water [14, 177, 186]. This method is popular due to its simplicity and ease of scalability, but it is also time-consuming, requires high temperatures and has a low extraction efficiency. As a result, novel extraction techniques, including supercritical fluid, microwave-, ultrasonic- and enzyme-assisted extractions, are gaining increasing popularity

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Figure 7.7: Schematic representation of the repeating units of the three main commercially relevant carrageenans. A) κ-carrageenan is constituted by a D-galactose sulfated in C4 linked to anhydrogalactose; B) λ- carrageenan is formed by a D-galactose sulfated in C2 linked to a D-galactose sulfated in C2 and C6; C) ι-carrageenan is constituted by a galactose sulfated in C4 linked to an anhydrogalactose sulfated in C2. Adapted from [168].

[14, 177]. These methods are faster and require less energy and solvents, and therefore are considered greener alternatives [177]. Regardless of the method used, a pre-treatment is often required for maximum extraction efficiency. In the case of agar, an alkali treatment is performed before extraction from Gracilaria to increase the gel strength, whereas for Gelidium a mild alkaline solution is used to eliminate phycoerythrine and prepare the algae for a more efficient extraction [180]. Alginates are also extracted after a pre-treatment with HCl, followed by neutralization with sodium carbonate or sodium hydroxide and precipitation with calcium as Ca-alginate [180, 190]. In the case of fucoidans, it is necessary to promote the precipitation of alginate with calcium to obtain isolated fucoidans [168]. The ever-growing demand for macroalgal polysaccharides will

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continue to promote the exploration of more convenient/efficient extraction methods [177]. Following the extraction of the polysaccharides, purification steps are usually performed to remove interfering substances like proteins, lipids and low molecular weight compounds [14, 177]. This can be achieved by precipitation with organic solvents (e.g., ethanol), membrane separation (e.g., diafiltration, ultrafiltration, reverse osmosis, nanofiltration), ion-exchange, size-exclusion or affinity chromatographic methods, depending on the purity requirements and end-use of the polysaccharides [177]. For the food industry, macroalgal polysaccharides are usually obtained by ethanol precipitation followed by membrane separation. On the other hand, the isolation of highly pure and active fractions of polysaccharides meant for the pharmaceutical industry are mainly obtained by size-exclusion chromatography and affinity chromatography [177]. As a final step, the polysaccharides are dried, usually by hot air drying or freeze-drying. However, it is necessary to take into consideration that these methods can negatively affect the quality of the polymers and of the gels that can be formed [192].

7.4 Current limitations and future perspectives In the last decade, algal biotechnology has grown into a global industry, with an increasing number of efforts being made to explore the biochemical diversity of these organisms to solve different societal issues [51]. Although most of the interest was primarily directed toward the use of algae (mainly microalgae) for the production of biofuels, this process is not yet able to economically compete with the use of petroleum-based fuels. This led many companies to shift their focus and invest in the production of other algae-based compounds, with high demand and high commercialization price, such as omega-3 and carotenoids [150]. Nevertheless, algae are still and underexplored resource, mostly due to the hurdles involved in the production of algae-based products [8, 12]. For instance, the use of PBRs equipped with artificial light increases the cultivation costs of microalgae compared to those of bacterial fermentation. The light dependency associated with autotrophic growth also imposes a lower limit on the biomass concentration (around 3 g per liter) compared to that of heterotrophic bacteria (about 30 to 100 g per liter) [8]. Other drawbacks are the high downstream processing costs, which can reach up to 40% of the total cost, and the complex rules and regulations of the regulatory authorities on novel food products [8, 193]. Regarding macroalgae, although the potential of their polysaccharides for biotechnological and biomedical applications has been vastly demonstrated at the research level, commercial products based on these polymers are still scarce on the market, with most being used as additives or excipients. One of the main factors hindering the transition of new polymers to the market are the low yields obtained with the extraction processes currently used [169]. Thus, despite the

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increasing industrial demand for phycocolloids, the methods used for their extraction and purification have been scarcely modified over the years. To overcome these limitations, innovative technologies, based on more sustainable and “green” procedures, leading to higher yields and better purity of the polymers are necessary [192]. The increased availability of algal genome sequences enabled a rapid evolution of genetic engineering and synthetic biology tools that can be used to redirect the metabolism into the production of desired products and enhance product titers. However, more and better tools are still required to make the production processes cost effective [11]. In addition, it is also necessary to evaluate the potential environmental risks associated with the use of genetically modified organisms (GMOs), especially in outdoors settings, as well as the public perception toward the use of products derived from modified organisms [6, 15]. Even though the products extracted from GMOs are DNA- and toxin-free and, thus, are not labeled as GMO materials, those derived from natural sources still have a greater regulatory acceptance and better consumer reception, particularly when targeting the food and feed markets [6, 130]. The implementation of a biorefinery approach based on the sequential extraction of multiple high-value compounds while simultaneously reducing the waste will also be critical to further increase algae competitiveness [8, 192]. For that, further in-depth research in algal biorefinery is necessary to develop more sustainable and cost-effective downstream methodologies for the extraction of co-products [8]. Addressing the issues discussed above will significantly contribute for the progress of algal biotechnology and to create a sustainable, “green” and efficient algae-based bio-economy.

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Chapter 8 Biocatalytic applications of autotrophic organisms Abstract: The autotrophic metabolism enables the fixation of inorganic carbon using energy sources such as light or reduced molecules from the environment. Whole-cell biotransformations in autotrophic cells use these energy modules for the sustainable regeneration of redox cofactors to drive enzymatic redox reactions. While reactions in wild-type hosts target oxidoreductases from the natural enzyme pool, genetic modification of cells allows the production of desired enzymes from different origins. The strategy circumvents disadvantages of traditional recycling systems such as a low atom efficiency and the requirement of sacrificial cosubstrates and paves the way for sustainable future biocatalysis to produce fine chemicals. Keywords: whole-cell biotransformation, cofactor recycling, autotrophic bacteria, oxidoreductases, photocatalysis

8.1 Introduction Biocatalysis has emerged as widely used, environmentally friendly catalytic technology. Chemical conversions catalyzed by enzymes and whole cells offer several advantages over established chemical approaches. First, biocatalysts derive from a renewable source, are biodegradable and non-toxic. Furthermore, they exhibit high selectivity and allow a broad range of reactions under ambient temperature, atmospheric pressure and neutral pH. In particular, the high selectivity of enzymes often allows shortening of chemical synthesis routes, which leads to considerable savings in terms of costs, energy and waste formation [1, 2]. In general, biotechnological production can be divided into two categories: fermentative systems and biotransformations. Fermentative processes use the cellular metabolism to convert a carbon source into an, often highly complex, final product. Carbon, nitrogen and phosphate sources are fed into the cellular metabolism and used for the synthesis of products by multi-enzymatic pathways in living microorganisms (Figure 8.1). Thereby, the enzymes mostly fulfil their natural activity within the metabolism and, thus, convert their natural substrates. Fermentations have been used for thousands of years in human history and have accompanied the development of the human civilization. The understanding of the microbial background widened the applications for fermentative processes. Nowadays, fermentations offer a useful strategy to access broad-range chemicals as solvents or secondary metabolites, e.g., antibiotics https://doi.org/10.1515/9783110550603-008

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and vitamins, that cannot be produced by classical chemical synthesis. Recently, the substitution of organic (and usually agricultural) carbon sources by carbon dioxide as starting material has been considered as an approach to bind carbon dioxide and to avoid conflicts associated with the use of agricultural products for the production of chemicals and fuels. This approach is discussed in Chapters 2, 4, 7 and 12.

Figure 8.1: Schematic representation of A) a fermentative process and B) a biotransformation with resting cells or isolated enzymes. Fermentations are performed are supplemented with a carbon, phosphate and nitrate source and are coupled to biomass formation. In contrast, a specific substrate is added to the biotransformation and enzymatic reactions.

While fermentations include the feeding of the starting material into the central catabolism of the cell, a biotransformation is the direct conversion of a substrates into a final product via one or several enzymatic steps (Figure 8.1). In industrial biotechnology, the term “biotransformation” often refers to enzymatic conversions that are separated from their natural environment or function. This means that the involved enzymes often act on compounds that are not their natural substrates. In many cases, biotransformation processes employ cells that express the genes of heterologous enzymes, i.e., enzymes that stem from a different origin. Biotransformations can be conducted either with growing cells or with resting cells. In the latter case, cells are cultivated and harvested before the substrate is added to initiate the biotransformation (Figure 8.1). This option is usually preferred since the desired reaction can be performed in conditions that might not support cellular growth. Many biocatalytic processes use cell-free enzyme extracts or even isolated enzymes, which themselves are usually produced in recombinant producer organisms. The choice of the most suitable biocatalytic system for a desired reaction is often a case-to-case decision as both cellular and cell-free systems have their advantages

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and drawbacks. For instance, living cells retain a protective, physiological environment for the enzymes which is beneficial for its stability [2, 3]. The enzyme can be replenished during the reaction, which is often an advantage for unstable enzymes. Furthermore, the reaction can involve natural metabolic pathways, for instance by using cosubstrates from the cellular metabolism. On the other hand, side reactions catalyzed by enzymes from the host [4], toxic effects of substrates and products or transport problems of compounds across the cell borders can limit the productivity of a whole-cell catalyst [1, 5]. Here, cell-free systems constitute an alternative. In the last few years, the interest in autotrophic organisms as hosts for biotransformations has received increasing attention. The metabolism of autotrophic organisms possesses a unique feature: the possibility to provide reduction equivalents in the form of NAD(P)H or reduced ferredoxins without the need to oxidize organic molecules, which offers considerable savings in terms of petrol chemicals or agricultural products.

8.2 Cofactor recycling Oxidoreductases are enzymes that catalyze the transfer of electrons from one molecule to another. They represent the largest class of enzymes and find wide application in the manufacture of fine chemicals, food additives and building blocks for pharmaceutical ingredients. Oxidoreductases require either an electron donor or acceptor to reduce or oxidize a substrate, respectively. Nature employs very few molecules as mediators in redox processes. These are often organic molecules – so-called external cofactors – such as the nicotinamide adenine dinucleotides NAD and NADP [6] or protein-based mediators such as ferredoxin (Figure 8.2). These small proteins contain iron and sulfur as internal cofactors and mediate single-electron transfers [7]. In contrast, nicotinamide cofactors transfer two electrons. Chapter 9 deals with natural and unnatural electron mediators in more detail, and Chapter 3 shows how the acceptance of redox cofactors by oxidoreductases can be modified by enzyme engineering. This chapter focuses on redox processes that employ nicotinamide cofactors. Generally, enzymes that require phosphorylated NADPH or NADP+ are often involved in anabolic reactions, while the non-phosphorylated cofactors NADH and NAD+ are used for catabolic reactions. For instance, glycolytic pathways and the citric acid cycle reduce NAD+ to NADH. In respiration, oxidation of NADH (with oxygen as terminal oxidant) leads to a proton gradient at the biomembrane, which is used to fuel ATP synthesis. Each step in an enzymatic redox reaction requires equimolar amounts of cofactor and substrate. As redox cofactors are very expensive, addition of equal amounts of NADH or NADPH to a biocatalytic reaction is economically prohibitive as the cost for the cofactor exceeds the added value of the reaction by far [6, 8, 9]. Instead,

Figure 8.2: Natural electron mediators for redox-reactions: A) Nicotinamide adenine dinucleotides NAD(P)H and it oxidized form NAD(P)+ and B) example for a cyanobacterial ferredoxin with a 2Fe-2S-cluster. The depicted ferredoxin is the natural electron donor for the glutamate synthase from Synechocystis sp. PCC 6803 (PDB accession code: 1OFF) [7].

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several recycling strategies ranging from chemical, electrochemical, photochemical, microbial to enzymatic reactions exist today [6]. A suitable cofactor recycling (Box 8.1) system should be inexpensive and easy to apply. If required, the cosubstrates should be cheap and optimally, the regeneration system results in volatile coproducts. Thereby, the equilibrium is shifted, downstream processes are facilitated and waste production is minimized. In the best case, all electrons from the cosubstrate are used for the cofactor recycling; thus, the system should be highly atom efficient. Catalysts suitable for cofactor recycling should be stable and highly selective. Furthermore, their reusability and sustainability shall be considered. Figure 8.3 summarizes the most common strategies.

Figure 8.3: Schematic overview of recycling strategies for the nicotinamide cofactor NAD(P)H. Dashed lines indicate a cell for cellular regeneration systems.

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Box 8.1: Cofactor regeneration Oxidoreductases (EC 1) are enzymes that catalyze reactions involving the transfer of electrons between different atoms or molecules. Such redox reactions always involve the oxidation of one compound and the reduction of another one. Therefore, oxidoreductases require an additional electron donor or acceptor in stoichiometric amounts beside the actual substrate. Examples for electron donors are the nicotinamides NADH and NADPH. Cost-expensive as these are, an addition in equimolar amounts to the reaction is not economical. Therefore, an appropriate and sustainable recycling system during the biotransformations is indispensable.

8.2.1 Cofactor recycling using sacrificial electron donor molecules Nicotinamide cofactors have several reactive functional groups. This makes a selective chemical reduction of nicotinamide group by chemical reactions very challenging. In contrast, enzymes have the capacity to reduce NAD+ and NADP+ without detrimental effects on stability of the cofactors. Therefore, processes for enzymatic cofactor-recycling are widely used in industrial biotechnology. The simplest possibility for enzymatic cofactor recycling is the use of the same enzyme for both, the bioconversion of a substrate and the cofactor recycling. Therefore, a cosubstrate is converted by the enzyme with the expense of the opposite nicotinamide cofactor. Alcohol dehydrogenases are often used for this kind of recycling, and isopropanol is a commonly used cosubstrate (Figure 8.4) [10, 11]. As the same enzyme is used to catalyze two separate reactions simultaneously, the adjustment of the reaction conditions might be difficult to favor both reactions [6, 12]. Often, the addition of a second enzyme for cofactor recycling offers broader options for cosubstrates and reaction conditions. Well-established systems for the regeneration of NAD(P)H involve, for example, a formate dehydrogenase (FDH) or a glucose dehydrogenase (GDH) with formate or glucose as second substrates (Figure 8.4) [13–16]. NAD(P)+ could be recycled by using a glutamate dehydrogenase, a lactate dehydrogenase or a NADH oxidase [16]. For whole-cell biotransformations in heterotrophic hosts such as Escherichia coli, the addition of glucose or another rich carbon source is used to regenerate NADPH from the pentose phosphate pathway and NADH from the glycolysis (Figure 8.4). Glucose is cheap, highly soluble in water and innocuous but always competes with food demands. Additionally, only few electrons from glucose are channeled toward the desired reaction [17]. It should be noted that the cells use a considerable part of the electrons for respiration and thus the formation of biomass; this can be considered as an unwanted side-product and greatly reduces the atom economy. Thus, cofactor recycling systems using glucose as cosubstrate have a reduced atom economy (Box 8.2), an important parameter for environmentally friendly reactions that considers how many atoms of the used substrates do reappear in the desired product.

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Figure 8.4: Frequently used enzymatic reactions for the recycling of NAD(P)H and NAD(P)+. The respective enzymes are framed in blue. The host cell is indicated by a dashed line for recycling systems that only work in cellular. All the other can be applied in cellular and in cell-free reactions.

Box 8.2: Atom economy Atom economy or atom efficiency is one of the twelve principles of green chemistry, which strives for environmentally friendly and sustainable processes. The use of raw material should be optimized so that the final product contains the maximum number of atoms from the reactants. In the ideal reaction, all atoms from the reactants are incorporated into the product [18] which minimizes the production of waste products that need to be separated from the reaction solution. An example for an industrial applied enzymatic process is the synthesis of the cholesterol-lowering drug atorvastatin [2]. Several enzymatic routes exist and one of them starts with the reduction of ethyl-

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4-chloroacetoacetate catalyzed by an NADPH-dependent ketoreductase (KRED) (Figure 8.5). The cofactor is regenerated using the GDH catalyzed oxidation of glucose [2]. Only 48% of the used atom mass per mol does reappear in the product, if the molecular weight of the product (Mw = 166.60 g mol−1) is compared to the molecular weight of the substrates (Mw = 164.59 g mol−1 + 180.70 g mol−1). Fiftytwo percent is found in the side product of the cofactor recycling, underlining the low atom economy of the process.

Figure 8.5: First step of one possible route for the synthesis of atorvastatin. The required cofactor is regenerated by the oxidation of the sacrificial cosubstrate glucose catalyzed by the GDH [2].

Coproducts with no further use are side products that not only need to be separated from the reaction solution but are considered as waste [16]. Gaseous coproducts such as CO2 are more beneficial because they can evaporate from the reaction mixture. This easy coproduct removal not only facilitates downstream processing but also pulls the equilibrium of the recycling reaction into the desired direction [13]. However, the corresponding substrate needs to be compatible to process specifications especially in terms of toxicity. An elegant way to avoid valueless coproducts is to use them as starting material for a second reaction [16]. One example of such a linear one pot multi-step reaction is the conversion of cyclohexanol to ε-caprolactone (Figure 8.6). First, cyclohexanol is oxidized in a NADP+-dependent reaction catalyzed by an alcohol dehydrogenase (ADH). Subsequently, the product cyclohexanone is converted to ε-caprolactone by an NADPH- and oxygen-dependent Baeyer-Villiger-Monooxygenase (BVMO) [19–22]. The cascade is completed by the polymerization of ε-caprolactone to poly-ε-caprolactone catalysed by the lipase CAL-A. This creates a process that is, in principle, very sustainable [20]. Despite their elegances, redox-neutral enzyme cascades are applicable only for very few cases. One of the cheapest reducing agents that can be used for cofactor regeneration is molecular hydrogen (H2). Bidirectional hydrogenases, another class of oxidoreductases, can reversibly cleave molecular hydrogen into electron and protons, H2 ! H − + H + , which make them particularly attractive for biotechnological applications [23]. Cofactor recycling systems based on hydrogenases show optimal atom economy, as all electrons of the substrate are used. The fact that protons are the sole side product greatly

Figure 8.6: Linear cascade to produce poly-ε-caprolactone from cyclohexanol. The cofactor NADPH required for the monooxygenation in the second step is recycled by the preceding oxidation of cyclohexanol. Thus, no cosubstrates for cofactor recycling are required [20].

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facilitates the downstream processing [23]. Already in 1981, Wong and coworkers used hydrogenase-containing extract from Methanothermobacter thermoautotrophicus for the regeneration of NADPH [24]. However, a drawback of all hydrogenases is their typically low tolerance toward O2 which necessitates strictly anaerobic workflows and exclude their combination with oxidoreductases that require oxygen. Therefore, the remarkably O2-tolerant hydrogenase SH from Cupriavidus necator is in the focus for research. Several examples for cofactor recycling systems using purified SH exist. One example is the coupling of SH to a carbonyl reductase that catalyzes the enantioselective reduction of acetophenone [23]. In comparison with the generally employed FDH, the hydrogenase exhibited much higher specific activity and turnover numbers. The study also reveals the main disadvantages of SH being its high sensitivity toward changes in temperature, pH and mechanical stress [23]. A strategy to improve SH stability is immobilization; however, reported immobilization yields were low and difficult to reproduce [12]. Furthermore, stabilization strategies always require a cost–benefit analysis to determine if the increased stability justifies the spent time, costs and effort [12]. Thus, the low space time yields of hydrogenase in cell-free systems have been an obstacle for their use so far. In summary, numerous enzymatic cofactor recycling systems exist today but advantages and disadvantages of every strategy need careful assessment. Stable recycling systems that work under diverse reaction conditions such as using GDH often use few electrons of the substrate and result in waste products. On the other hand, systems with a better atom economy are often slower or not applicable because of the low stability of the catalyst or the demanding process requirements. All this fuels the investigation of alternative cofactor recycling strategies till today.

8.2.2 Alternative approaches Chemical, photochemical and electrochemical cofactor recycling strategies use organic or inorganic electron mediators instead of enzymatic reactions [25]. For instance, iridium, ruthenium and rhodium complexes can be used for metal-complex based cofactor recycling with the expense of H2 or formate [26]. Both cosubstrates are beneficial: all electrons of H2 are used for the recycling and the reduction of formate results in gaseous CO2 that leaves the reaction. One of the main advantages of electrochemical cofactor regeneration is that only electrons are transferred or adsorbed by an electrode [8, 9]. NAD(P)+ can directly react with the electrode in a two-step reaction. In the first step, a radical species is formed that is protonated and takes up a second electron in the second step. However, this direct regeneration bears two major risks: two radicals can dimerize to an enzymatically inactive side product and the protonation can occur at the wrong C-atom resulting in the formation of inactive 1,6-NAD(P)H [9, 27]. The use of electron mediators such as rhodium complexes or methyl viologen (MV2+) can

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circumvent these drawbacks and selectively carry electrons from the electrode to NAD(P)+ [9]. However, the potential toxicity of electron mediators such as MV2+ needs to be considered [8]. Electrochemistry can be also used for the recycling of NAD(P)+. The direct oxidation of NAD(P)+ at the electrode requires a high overpotential (at least 900 mV vs. the saturated calomel electrode (SCE) as reference electrode) and is suitable only for reactions with oxidation stable substrates to avoid the formation of side products [9]. Out of this reason, many systems for NAD(P)+ recycling also involve electron mediators [9]. Photochemical recycling systems use light-active molecules, so-called photosensitizer. These trigger changes in redox states of the adjacent molecules, e.g., NAD(P)+, when exposed to light [28]. This works similar to dyes in natural photosystems: delocalized electrons in the photosensitizer are excited by light to a higher energy level and are passed on to the target molecule or enzyme [28]. Electron donors that can spontaneously release electrons are used to refill the photoexcited electrons. Examples for such electron donors are ethylenediaminetetraacetate (EDTA) or the buffer substances 3-(N-morpholino) propanesulfonic acid (MOPS) and 2-(N-morpholino) ethanesulfonic acid (MES) [17]. Photochemical approaches exist for both cell-free [28] and cellular [17, 29] systems. Light intensity is an important parameter, as photosensitizers might bleach. In conclusion, several approaches for chemical, electrochemical and photochemical cofactor recycling exist. All of them have in common that they aim to circumvent the use of a second enzyme as this is often accompanied by time-expensive preparation and downstream processing. However, and despite all efforts, these strategies are till today less efficient and selective than enzymatic regeneration systems [9].

8.3 Biotransformations in autotrophic bacteria beside cyanobacteria In the last years, the autotrophic bacteria have been receiving increasing attention as hosts for redox biotransformations. As explained in Chapter 1, autotrophs assimilate inorganic CO2 and use energy sources such as light or reduced molecules in the environment. The energy modules of the autotrophic metabolism can be used for the sustainable regeneration of redox cofactors for whole-cell biotransformation. The following paragraphs will discuss the potential of hydrogen-utilizing Cupriavidus necator and photoautotrophic Rhodobacter sphaeroides as host for biotransformations.

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8.3.1 H2-driven cofactor-recycling by Cupriavidus necator H16 Cupriavidus necator (formerly Ralstonia eutropha H16) is a gram-negative, facultative chemolithotrophic bacterium and ubiquitously found in soil and freshwater biotopes [30]. It is a versatile, non-pathogenic organism and capable to adapt to different environmental situation. Thus, it can shift between heterotrophic and autotrophic growth with the use of both organic compounds and molecular H2 as source of energy. In the absence of O2, C. necator can switch to anaerobic respiration and uses alternative electron acceptors as NO3– and NO2– [30]. Furthermore, C. necator can fix CO2 via the Calvin-Benson-Bassham (CBB) cycle and stockpile organic carbon in the form of polyhydroxybutyrate (PHB). The supplied gas mixture of H2, O2 and CO2 is within the gasexplosion range which necessitates special care [31]. Furthermore, the low solubility of H2 and O2 in water lowers the efficiency of gas utilization and should be optimized [31]. Chapter 4 gives an overview on challenges and strategies of fermentations using gaseous substrates. The growing interest in C. necator fueled the investigation of the genetic tools for engineering. Several self-replicating plasmids were designed and applied in C. necator [32–37]. The plasmids and variety of regulatory elements such as promoters are described in detail in Chapter 11, while this chapter will focus on a few examples. One of the main problems is the significant loss of plasmids during cultivations in the absence of high antibiotic pressure [32, 35, 36], which is a particular problem for redox biotransformation reactions that require high intracellular concentration of the heterologously produced enzymes. To improve plasmid stability, different maintenance strategies are available. For example, a partitioning system can help to improve the physical separation of plasmid molecules during cell division. In a toxin/antitoxin system, cells are cultured in the presence of a toxin and the plasmid encodes a neutralization system. Thus, only cells harboring the plasmid survive [38]. Another strategy are metabolic-deficient strains, where essential genes are deleted and complemented by the designed plasmid. Budde et al. deleted the essential gene proC for the pyrroline-5-carboxylate reductases from the strain’s genome. Hence, proC was expressed from the plasmid making it indispensable for growth [34]. Sato et al. successfully used the partitioning system of the endogenous megaplasmid pMOL28 from Cupriavidus metallidurans CH34, a bacterium from the same genus as C. necator, to increase plasmid maintenance [32]. Gruber et al. expanded the genetic toolbox by two plasmids pKRL-Pj5 and pKRC-Pj5 that are stabilized by the RP4 partitioning system [36, 39]. pKRL-Pj5 bases on lacI and can be induced by β-D-1-thiogalactopyranoside (IPTG). As the wild type cannot uptake IPTG the gene lacY for the lactose permease was integrated into the genome [36]. The second plasmid pKRC-Pj5 is induced by p-cumate and this inductor can easily bypass the cell membrane [5, 36]. The set was complemented by an L-rhamnose inducible promoter rhaPBAD by Sydow et al. which is especially beneficial as L-rhamnose is comparably cheap [37]. All plasmids are

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self-replicating, broad-range plasmids and are transferred into C. necator by conjugation using E. coli S17 as donor strain [5, 36, 37, 40]. The genetic accessibility of C. necator has broadened the scope for its application and for biotransformations. Especially, bioreductions catalyzed by NAD(P)Hdependent oxidoreductases are advantageous, since the nicotinamide cofactors can be efficiently regenerated by natural hydrogenases like SH in C. necator. Oda et al. [41] demonstrated the coupling of H2-driven cofactor recycling to a recombinantly produced oxidoreductase (Figure 8.7a). The authors expressed the alcohol dehydrogenase KlADH from Kluyveromyces lactis in recombinant C. necator and investigated the asymmetric reduction of hydroxyacetone to (R)-1,2-propanediol [41]. The KlADH gene was introduced on a plasmid and controlled by the native promoter for the SH hydrogenase of C. necator. A high product concentration of 67.7 g/L was obtained by periodical addition of hydroxyacetone [41]. Assil Companioni et al. expressed the gene of the flavin-dependent ene-reductase YqjM from Bacillus subtilis in C. necator (Figure 8.7b) [5] using the above-mentioned vector systems of Gruber et al. [36]. Protein production could be verified by SDS-PAGE analysis for the p-cumate induced system whereas the IPTG induced system led to production at the detection limit. Nevertheless, both recombinant strains successfully converted the model substrates cyclopentenone and cyclohexenone to their corresponding saturated cyclic ketones [5]. Thereby, the activity of heterotrophically grown cells with fructose as carbon source and the activity of lithoautotrophically grown cells with CO2 and H2 as carbon and energy source were compared. The fact that the fructose-driven system exhibited higher activity and conversions than the H2-driven biotransformations showed that in the latter, supply of reduced cofactors was limiting. Overcoming this limitation by metabolic engineering could be a strategy for future improvement. Generally, known limitations of whole-cell biocatalysts as side reactions catalyzed by native enzymes and transport limitations also apply for C. necator. In case the of ene-reduction, native alcohol dehydrogenases reduces the product cyclohexanone to the corresponding alcohol [5], a problem that is also observed in cyanobacteria [4] and will be discussed below. C. necator with its capability to grow lithoautotrophic with CO2 as sole carbon source and H2 as energy donor is an attractive host for hydrogen-driven biotechnological processes and particularly bioreductions with hydrogen as sole cosubstrate. The possibility of high-density cultivation, the effectiveness of H2-driven cofactor recycling by the SH hydrogenase, its genetic accessibility and its versatile metabolism underline the usefulness of this autotroph as host for whole-cell biotransformations.

8.3.2 Light-driven cofactor recycling in Rhodobacter sphaeroides Among all phototrophic bacteria, cyanobacteria are the only phylum performing oxygenic photosynthesis with water as electron donor and oxygen as side product.

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Figure 8.7: Whole-cell biotransformation in other autotrophic bacteria beside cyanobacteria. A) Lithoautotrophic whole-cell biotransformation in recombinant C.necator harboring an alcohol dehydrogenase from K.lactis (KlADH) [41] and B) the ene-reductase YqjM from B. subtilis [5]. In both cases, the required cofactors are regenerated from H2 using endogenous hydrogenases in C. necator. (C) Light-driven whole-cell biotransformation in recombinant R. sphaeroides harboring an LsADH from Leifsonia sp. The cofactor required for the reduction of 3ʹ-chloroacetophenone is regenerated via anoxygenic photosynthesis. Sodium acetate and sodium thiosulfate were used as electron donor and hydrogen donor, respectively [42].

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Other bacteria such as purple bacteria and green sulfur bacteria carry out anoxygenic photosynthesis; a process using sulfides, hydrogen or organic substrates as electron-donors [43]. From an evolutionary point of view, anoxygenic photosynthesis occurred before oxygenic photosynthesis and led to a much greater phylogenetic distribution. Therefore, the composition of the photosynthetic apparatus varies broadly among anoxygenic phototrophs. In contrast to oxygenic phototrophic organisms, anoxygenic phototrophs possess only one photosystem and use bacteriochlorophyll(s) as essential photo-pigment(s). Differences in the absorption band of the light-harvesting antennas enable several anoxygenic phototrophs to coexist in the same environment as they do not compete for light with the same wavelength [43]. Furthermore, not all anoxygenic phototrophs are autotrophs: the group of aerobic anoxygenic phototrophic bacteria require organic substrates and perform respiration beside photosynthesis [43]. Rhodobacter sphaeroides is a gram-negative, non-sulfur phototrophic bacterium that belongs to the phylum of the proteobacteria [44]. Like other representatives of the purple bacteria, R. sphaeroides has a highly versatile metabolism and can adapt to different environmental conditions. Thus, it can switch between aerobic and anaerobic respiration, fermentation and anoxygenic photosynthesis [44, 45] which makes it an excellent model system to study photosynthesis-associated membrane proteins, bioenergetics and bacterial metabolism [42, 44]. Ma et al. investigated the potential of R. sphaeroides as host for recombinant oxidoreductases (Figure 8.7c). The authors expressed an NADPH-dependent alcohol dehydrogenase (ADH) from Leifsonia sp. using the self-replicating plasmid pIND4 [42, 46]. Among other features, this plasmid harbors an isopropyl-β-D-thiogalactopyranoside (IPTG)-inducible expression cassette and the pMG160 origin of replication [46]. To increase plasmid maintenance in R. sphaeroides, the origin of replication from an endogenous plasmid pMG160 of a related purple non-sulfur bacterium was used [47]. Hence, the above-mentioned plasmid pIND4 delivers a copy number of 18 to 23 and could be retained over 85 generation without antibiotic selection [46]. Indeed, Ma et al. successfully proved ADHs expression in presence of IPTG in, R. sphaeroides [42]. The recombinant strain reduced the model substrate, 3ʹ-chloroacetophenone, to the corresponding (R)-alcohol with excellent enantioselectivity and moderate conversion in aqueous solution (around 70% of 20 mM substrate in 10 h). Activity was only observed in presence of light, as only illumination allowed NADPH recycling. Therefore, sodium acetate and sodium thiosulfate were added as electron donor and hydrogen donor, respectively [42]. Furthermore, the use of a biphasic system with n-hexane as organic phase alleviated substrate inhibition of the enzyme and increased the optimal substrate concentrations from 20 mM to 70 mM. However, conversion did not exceed 35% under these conditions [42].

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8.4 Light-driven biotransformation in cyanobacteria Like algae and higher plant but in contrast to other phototrophic bacteria, cyanobacteria carry out oxygenic photosynthesis [48]. As cyanobacteria grow faster than plants and require a much simpler methodology for genetic manipulation, they became model organisms for the investigation of oxygenic photosynthesis [48]. Figure 8.8 shows a schematic overview of the photosynthetic machinery anchored in the thylakoid membrane. The first complex involved in photosynthesis is the photosystem II (PS II), a highly conserved, transmembrane protein complex [49]. It is connected to the phycobilisomes which are light-harvesting antenna proteins on the stroma side of the membrane. These proteins enable the cyanobacteria to use a broader range of the visible light spectrum. The absorbed light is channeled to the reaction center P680 of PSII. Here, two chlorophyll a molecules form the primary electron donor P680 which is exited to P680* [50]. An electron is released and transported along the electron transport chain across the thylakoid membrane. The electrons for this reduction are generated from the stepwise oxidation of water that takes place at the Mn4Ca cluster located at the luminal site of PS II. Products of this oxidation are molecular oxygen and protons that generate a proton gradient across the membrane. This gradient is the driving force enabling ATP formation catalyzed by ATPase Synthetase. The electrons derived from P680 are transported across the membrane toward the second photosystem, the photosystem I (PS I). One electron transporter on this way is the membrane-bound chemical molecule plastoquinone (PQ) which can accept two electrons and is thus reduced to plastoquinol (PQH2). PQH2 shuttles the electrons across the thylakoid membrane to the next protein complex, the cytochrome b6f c cytochrome b6f (cyt b6f). Cyt b6f transfers the electrons from PQH2 to the soluble plastocyanin (PC), a copper-containing protein that passes the electrons to PS I. Like PS II, PS I is a transmembrane multi-subunit protein complex. The reaction center of PSI, P700, is excited by light energy and one electron is released which is passed onto a ferredoxin (Fdx). The missing electron in the P700 reaction center is replaced by the electrons from the electron transport chain. Ferredoxin is an ironsulfur protein that mediates the electron transfer to the ferredoxin NADP+ reductase (FNR). Finally, the FNR reduces a NADP+ molecule to NADPH. The light-driven regeneration of NADPH can potentially be exploited as electron-source for whole-cell biotransformations. This radical approach substitutes organic electron donors by the light-driven oxidation of water as electron source. The sum formulae of a light-driven biotransformation in cyanobacterial underline the great advantages for the atom economy of the reaction: h*ν + 2H2 O + 2 substrateox ! 2 productred + O2

(8:1)

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Figure 8.8: Simplified overview of the photosynthetic machinery anchored in the thylakoid membrane. PS II = photosystem II, PQ = plastoquinone, PQH2 = plastoquinol, Cyt b6f = cytochrome b6f, PC = plastocyanin, PS I = photosystem I, Fdx = ferredoxin, FNR = ferredoxin NADP+ reductase, FDP = flavodiiron proteins, NDH1 = NADH dehydrogenase complex 1, ATP-S = ATP-Synthase.

8.4.1 Biotransformations in non-modified cyanobacterial wild-type strains Nature seems to provide an almost infinitive pool of enzymatic reactions. Microorganisms often exhibit surprising capabilities for bioconversions with their endogenous arsenal of biocatalysts. Whole-cell biotransformations in so-called “wild-type” cells, i.e., without any genetical modifications (Figure 8.9), can catalyze an impressive series of redox reactions. A great advantage of the approach is its simplicity. The wild-type strain is cultivated under standard conditions before a substrate of interest is added to start the biotransformation. After a defined time, the reaction suspension is analyzed, and products are isolated. Figure 8.9 shows the principles of biotransformations with wild-type and genetically modified cyanobacteria. In the wild-type strain, which and how many enzymes are responsible for the observed activity often remain unknown. This constitutes one of the main drawbacks compared to recombinant biotransformations where the reaction is catalyzed by a specific enzyme whose gene has been integrated into the host organism (Figure 8.9). Reaction mechanism, selectivity and preferred reaction conditions of this so-called recombinant enzyme are often well understood which is a great advantage for the application of such biotransformations. Most studies about cyanobacterial biotransformations in wild-type strains describe highly stereospecific asymmetric reductions of prochiral ketones. Such reactions are usually NAD(P)H-dependent and catalyzed by alcohol dehydrogenases

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Figure 8.9: Comparison of whole-cell biotransformations in wild-type and recombinant cyanobacterial cells. A) Wild-type cells are not genetically modified. The conversion of an exogenously added substrate is catalyzed by the native enzyme pool. Thereby, one or several enzymes can be involved in the reaction. If the involved enzymes exhibit opposite selectivities, asymmetric reductions result in a racemic mixture of the desired product. B) Nowadays, tools for genetic manipulation allow the integration of a gene of interest (GOI) into the genome of the respective strain, for example via homologous recombination. The GOI can encode for a highly selective enzyme from another organism that is consequently produced in the recombinant cyanobacterial strain and can catalyze the target reaction.

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(ADHs), an enzyme class that is ubiquitously found in all organisms and catalyze several reactions within the metabolism. In contrast to ADHs in E. coli that prefer NAD+/NADH, ADHs in cyanobacteria are often NADP+/NADPH dependent [51–53]. One of the first examples for biotransformations in wild-type cyanobacteria was the study of Jüttner and Hans in 1986. Authors investigated three unicellular and two filamentous cyanobacterial strains for their potential to reduce a wide range of aldehydes and ketones [54]. Since the early twenty-first century, the reduction of acetophenone and related aryl methyl ketones with different electron pulling moieties such as halogens or methoxide groups was investigated (Figure 8.10) [55–61]. Strains like Synechococcus sp. PCC 7942 and Anabaena variabilis reduced around 5 mM of substrate such as 2ʹ-3ʹ-4ʹ-5ʹ-6ʹ-pentafluoroacetophenone in 48 h (Figure 8.10) [58]. Furthermore, terpenes, [54, 62–66] enones [64], hydrocortisone [67], oxophosphonates [68] and chalcones [69–71] were successfully reduced in cyanobacterial wholecell biotransformations. In all cases, the conversion of a few milligrams of substrate took several hours to days pointing out that low space time yields are still a considerable limitation of whole-cell biotransformations with wild-type strains. A few examples and main conclusions will be discussed in more detail in the following as well as in Figure 8.10 and Table 8.1. Oxophosphonates belong to so-called xenobiotics which are not or rarely found in nature and constitute interesting target molecules for synthetic applications. The cyanobacterium Nodularia sphaerocarpa completely reduced the carbonyl moiety in direct vicinity to the aromatic ring of the substrate 2-oxo-2-phenylethylphosphonate (1 mM in 7 days) (Figure 8.10) [68]. This activity was never observed before and underlines that cyanobacteria represent a completely different enzymatic profile in comparison to known biocatalysts as E. coli and baker’s yeast [68]. Furthermore, N. sphaerocarpa exhibited a higher tolerance toward the substrate than previously described hosts for the reduction of oxophosphonates [68]. The toxicity of substrates and products is a crucial parameter for cyanobacterial whole-cell biotransformation because cofactor recycling via photosynthesis requires viable cells [72]. Chalcones are precursors for flavonoids and known for their pharmacological activity. Furthermore, they might find application in food industry because of their sweet taste [69, 70]. Chalcones are usually isolated from plants, but their concentrations in plant tissues are low and vary depending on environmental, seasonal and regional influences [69]. Therefore, Zyszka et al. investigated eight cyanobacterial wild-type strains for their ability to reduce chalcone (1,3-diphenyl-2-propen-1-one) [69] (Figure 8.10) and several substituted chalcones [70, 71]. They showed the potential of all strains to produce a large variety of reduced and hydroxylated chalcones in a preparative scale (250 mL reaction volume). Despite the high conversion, the production rates of the reactions were low, considering the long reaction time (14 days) and the low substrate concentration (20 mg L−1) that was limited due to toxicity effects (Figure 8.10) [69–71].

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Like cyanobacteria, microalgae are also possible hosts for whole-cell biotransformations. Böhmer et al. characterized several members of the Old Yellow Enzyme in the eukaryotic microalgae Chlamydomonas reinhardtii. Biotransformations in C. reinhardtii allowed the C=C-double bond reduction of several substrates with reaction rates that were comparable to those obtained in wild-type cyanobacteria [73].

Figure 8.10: Examples for reductions in cyanobacteria. A) The reduction of 2ʹ-3ʹ-4ʹ-5ʹpentafluoroacetophenone was investigated for three cyanobacterial strains [55, 58] and B) Nodularia sphaerocarpa is the first organism known to reduce xenobiotics like oxophosphonates [68]. C) Seven cyanobacterial wild-type strains reduce chalcone with high yields in preparative scale [69].

Control experiments in darkness exhibited less activity and lower product concentrations which confirms that the bioreductions are indeed light-driven [56, 60]. In general, cyanobacteria can provide enough cofactor for full conversions of low substrate concentration ( 

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      

. . . . . . .

[] [] [] [] [] [] []

C (%)

[a] converted to mM from molecular weight of the substrate, the given substrate concentration in mg L−1 and the used volume given in the respective study [b] calculated from the values for the initial substrate concentration c(S) and the conversion (C) in the defined time interval given in the respective study [c] formation of two other products, cinnamic acid (3%) and hydrocinnamic acid (10%), was observed [d] formation of two other products, diphenylpropenol (7%) and diphenylpropanol (6%), was observed S = substrate, P = product, c(S) = initial substrate concentration, LI = light intensity, C = conversion.

for example is a known inhibitor for photosynthesis. If a respective biotransformation is light-driven, an addition of DCMU would reduce the obtained yields [56, 60]. Furthermore, a bioreductions competes with other metabolic reactions inside the cell. For example, the Calvin-Benson-Bassham (CBB) cycle requires 6 NADPH equivalents per cycle and can be inhibited by iodoacetamide, iodoacetic acid, D,L-glyceraldehyde or 5ʹ-AMP [60]. The addition of the inhibitors might enhance ketone reduction, because blocking the CBB increases NADPH concentrations available for the reduction [60]. Wild-type biotransformations have the disadvantage that the observed activity cannot necessarily be attributed to a single enzyme, but is more likely a result of multiple enzyme activities. Besides, the involved enzymes, their expression control mechanisms, cofactor preferences and selectivities often remain unknown. Shimoda et al. investigated the reduction of small cyclic enone substrates and observed overreduction of saturated ketone product to the corresponding alcohols [64]. This observation indicates the involvement of two enzyme classes for the conversion. First, the C=C double bond of the enone is reduced by ene-reductases and subsequently, alcohol dehydrogenases hydroxylate the ketone group of the saturated product.

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Moreover, altering cultivation conditions can influence the expression patterns of the involved enzymes which can have unpredictable effects on the outcome of the biotransformation. Several studies reported decreased enantioselectivity in darkness [56, 58, 66]. For example, the light-driven bioreduction of 4-chloroacetophenone by A. variabilis exhibited excellent enantioselectivity (ee > 99%); however, observed enantiomeric purities in darkness were considerably lower (ee = 50–80%) [58]. This result can be explained by a difference in the involved enzymes under both light and dark conditions. Furthermore, the present redox state of the cells can favor enzymatic activities depending on their cofactor preference. Tamoi et al. investigated the NADP(H) to NAD(H) ratio in Synechococcus sp. PCC 7942. The NADPH concentration was considerably higher than the NADH concentration. This difference was more distinct when cells were illuminated (6.5-fold compared to 3.8-fold higher in darkness) [75]. Therefore, light conditions of the cultivation and the reaction need to be clearly defined. Light intensity and wavelength composition of the light source can as well influence the reaction as both are directly connected to the photosynthetic efficiency. With today’s access to genome sequences, genetic engineering becomes an attractive solution for process optimization. However, oxidoreductases are widely distributed in the genomes. Therefore, the identification and characterization of the involved enzymes is often a time- and cost-expensive search of the needle in a haystack [51]. Takemura et al. tried to further increase the enantioselectivity of light-driven biotransformation by knocking out oxidoreductases in the genome of Synechocystis sp. PCC 6803 [59]. A knock-out describes the targeted deactivation of a gene which is a common strategy for tailoring host organism to process requirements. In the case of Takemura and coworkers, the knockouts slightly influenced the enantioselectivity for the asymmetric reduction of the model reaction [59]. So far, the discussed examples directly used cyanobacteria as production host exploiting natural enzyme activities. An interesting approach to couple an autotrophic organism as energy module to extracellular enzymatic reactions was presented by Löwe et al [76]. Chlamydomonas reinhardtii produces formate from starch under darkness and anaerobic conditions which is excreted and thus accumulated in the supernatant. Löwe et al. have exploited this property by coupling the formate formation in C. reinhardtii to the in situ formation of bulk amines (Figure 8.11). An externally added FDH recycled the required cofactor NADH by oxidizing the accumulated formate [76]. Thus, the microalgae recycles the electron mediator but does not act as production host. In conclusion, most examples for whole-cell biotransformations in non-modified cyanobacteria wild-type strains used endogenous ADHs to catalyze the stereoselective reduction of prochiral ketones with high stereoselectivity [55–61]. Yet, the space-timeyields were limited to the conversion in the lower millimolar range after several hours. Without knowledge about the involved enzymes, an improvement of the reaction is a case to case scenario. Here, recombinant biotransformations constitute an attractive

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Figure 8.11: The production of formate by the microalgae C. reinhardtii is coupled to the NADH recycling catalyzed by an externally added formate dehydrogenase (FDH). The regenerated cofactor is used to produce bulk amines, which is catalyzed by an amine dehydrogenase (EsLeuDH-DM) [76].

alternative. Genetic tools enable the insertion of desired genes into the cyanobacterial strain and their targeted expression (see Chapter 6). Examples for biotransformations in genetically modified cyanobacteria from the last years demonstrate the broad applicability of the approach. They will be intensively discussed in the next paragraph.

8.4.2 Biotransformation reactions using genetically modified cyanobacteria Genetic engineering allows a target-orientated application of cyanobacteria for biotechnological purposes. In all examples discussed below, the gene of an oxidoreductase from a different origin was incorporated into the respective cyanobacterium either via a self-replicating plasmid or via a suicide vector that enables gene integration into the genome by homologous recombination. The controlled expression of the gene of an oxidoreductase by different native or synthetic promoters [77, 78]. allows to achieve higher intracellular enzyme concentrations and hence higher reaction rates (Table 8.2). Chapter 6 discusses regulatory elements for cyanobacteria in more detail; therefore, this chapter will only name a few important examples. The strongest native promoter in cyanobacteria is called Pcpc [77, 79] and regulates the expression of the cpc-operon encoding for proteins of the phycobilisoms [80]. Furthermore, the light-inducible psbA-promoters PpsbA1 and PpsbA2 are often used for recombinant gene expression [4, 72, 74, 81, 82]. Naturally, they each control the expression of one isoform of the D1 subunit of photosystem II [81, 83]. Other promoters such as Pcoa [84, 85] or Pzia [82, 86, 87] are metal inducible and known for weaker gene expression. An example for a synthetic promoter is Ptrc1O [88, 89] that combines the trc-promotor and the lac operator from E. coli [89]. Thus, the promotor is repressed by the lac repressor LacI and gene expression can be induced by isopropyl-β-D-thiogalactopyranoside (IPTG) – a well-working system in E. coli, however, less efficient in cyanobacteria [89]. The examples for light-driven biotransformation in recombinant cyanobacteria concentrate on the two unicellular strains Synechocystis sp. PCC 6803 and Synechococcus

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elongatus PCC 7942 and can be separated into two types of reactions – reductive biotransformations and oxyfunctionalizations.

Figure 8.12: Examples for reductive biotransformation in genetically modified Synechocystis sp. PCC 6803 and Synechococcus elongatus sp. PCC 7942. The required cofactor NADPH is recycled via photosynthesis. A) C = C double bond reduction catalyzed by the ene reductase YqjM from Bacillus subtilis [74, 82]. B) Heterologous expression of imine reductases (IREDs) in Synechocystis sp. PCC 6803 for the selective reduction of imines [72]. C) Conversion of acetophenone to (R)-1phenylethanol in whole-cell biotransformation with cells expressing the gene of Lactobacillus kefir alcohol dehydrogenase (LkADH) [81].

Reductive biotransformations, such as the asymmetric conversion of C=C double bonds and keto groups, are widely used in biocatalysis for the synthesis of optically pure compounds, including large processes such as the synthesis of the unnatural amino acid tert-leucine [90]. Light-driven whole-cell biotransformations using the cyanobacterium Synechocystis sp. PCC 6803 with an integrated heterologous gene of the ene-reductase YqjM from Bacillus subtilis demonstrated the advantages of genetically modified cells (Figure 8.12). The whole-cell biocatalyst showed initial reaction rates exceeding 100 U per gram cell dry weight (CDW) and completely converted 20 mM of the prochiral substrate into the optically pure product within a few hours [74]. The study underlines that the application of recombinant cyanobacteria outstands reaction rates that can be reached by using non-modified strains and their native enzyme pool. This is mainly attributed to the higher concentration of the recombinant protein within the cells, showing the importance of the catalyst’s concentration for whole-cell biotransformations.

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Imine reductases (IREDs) are highly interesting biocatalysts for the stereoselective synthesis of secondary amines [72]. IREDs are strictly NADPH-dependent enzymes which make them excellent candidates for bioreductions in cyanobacteria. Thus, the genes of three different IREDs were expressed in Synechocystis sp. PCC 6803 [72]. The resulting whole-cell biocatalysts showed activity and selectivity in the light-driven reduction of several cyclic imine substrates. First, the promoter PpsbA2 was utilized for the expression which led to the highest conversion of small cyclic imines catalyzed by the IRED from Streptomyces sp. GF3587 (83% within 12 h) with the specific activity of 2.7 U gCDW−1 (Figure 8.12). After switching the promoter from Ppsba2 to the stronger Pcpc [77, 79], specific activities increased up to 21 U gCDW−1. However, toxicity of the imine substrates was observed. While host such as E. coli could fully reduce up to 100 mM of substrate [91], conversion in Synechocystis was limited to few millimolar [72]. Here, increasing the cell density proved to be a viable strategy to alleviate this. Toxic effects exerted by substrates and products are a common problem of whole-cell biotransformation, especially if vital cells are required for cofactor recycling. To overcome this problem, more robust cyanobacteria need to be identified. Another example for a reductive biotransformation in recombinant cyanobacteria was described by Sengupta et al [81]. The gene of an NADPH-dependent alcohol dehydrogenase from Lactobacillus kefir (LkADH) was integrated into the genome of Synechococcus elongatus PCC 7942 and expressed under the control of the PpsbA1 promoter. Subsequently, the asymmetric reduction of acetophenone that is only poorly accepted by the native alcohol dehydrogenases in Synechococcus elongatus PCC 7942 [56] was investigated and optimized. Under the best conditions (light intensity: 150 µE m−2 s−1, CO2-supply: 0.5%) 0.66 gCDW L−1 of the recombinant cells successfully converted 20 mM acetophenone to (R)-1-phenylethanol (ee > 99%) within 6 h and with a rate of 3.1 mM h−1 (Figure 8.12) [81]. Carboxylic acid reductases (CARs) are ATP- and NADPH-dependent oxidoreductases that catalyze the reduction of carboxylic acids into the corresponding aldehydes. Their cofactor requirement makes them interesting enzymes for the application in cyanobacteria that regenerate both, ATP and NADPH, via photosynthesis. Yunus et al. expressed the CAR from Mycobacterium marinum together with a phosphopantetheinyl transferase (Sfp) and a fatty acyl-ACP thioesterase (Tes3) in Synechocystis sp. PCC 6803 for the synthesis of intermediate chain-length fatty acids and alcohols [85]. The genes were introduced on a self-replicative plasmid and besides others, their expression was controlled by the cobalt inducible promoter Pcoa, resulting in the production of C8-fatty acid and corresponding alcohol 1-octanol. The first engineered strain showed low productivity and signs of reduced viability (pale green color after 10 days), indicating that the presence of CAR-derived aldehydes and corresponding alcohols were toxic to the cells and that the level of enzyme production affected the cellular health. Optimization of the growth conditions, exchange of promotor and ribosome binding site as well as a solvent overlay (isopropyl myristate) could mitigate the limiting factors and titers of more than 100 mg L−1 of 1-octanol and 1-decanol were achieved [85]. However, after

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several subculturing, the strain lost activity, although the antibiotic resistance was maintained. Sequencing results revealed a genetic instability of the introduced plasmid [85]. Box 8.3: Self-shading Light availability is a crucial factor for efficient photosynthetic electron transport and thus for lightdriven biotransformations. In cultures with > 10 gCDW L−1 cells obscure each other, which is referred to as self-shading. In photobioreactors with increasing diameter, insufficient mixing and high cell densities result in inhomogeneous light distribution with bright and dark zones inside the culture. Outside cells are exposed high light intensities that overtax metabolic energy requirements and lead to photoinhibition. On the other side, inside cells suffer from light-limiting conditions and inefficient photosynthesis. In both cases, biotransformations coupled to the light-driven regeneration of NADPH experience a drop of cell activity. Increasing catalyst concentration to improve spacetime yields, which is a common strategy for heterotrophic production processes, is therefore only possible to a limited extent for photoautotrophic systems.

Oxyfunctionalizations are reactions that involve the incorporation of at least one oxygen atom into the final product. In organic chemistry, they still pose a great challenge as a delicate balance between reactivity and selectivity is required [92]. Reactions with oxygen as reactant require harsh conditions and therefore highly regulated process control regimes [93]. Enzymes such as Baeyer-Villiger monooxygenases and P450 monooxygenases selectively catalyze oxyfunctionalizations under milder reaction conditions and are mainly used in whole-cell biotransformations as both require NAD(P)H as electron donor. However, their application in heterotrophs is not only limited by the requirement of a cofactor recycling system. Additionally, the low solubility of oxygen in aqueous media results in a poor gas–liquid oxygen transfer rate (OTR) [94] and in aerobic hosts, the constant O2 consumption by the respiratory chain competes with the oxygen supply for the reaction [93]. Photosynthetic water oxidation in phototrophs like cyanobacteria generates O2 and, thus, could be used to homogenously drive oxygenase catalyzed reactions in the cell [93]. However, the oxygen evolution rate depends on the light availability and correlates with the concentration of the photo-biocatalyst if the light is not limited. On the other site, increased cell densities lead to self-shading (Box 8.3), a phenomenon in which the cells cover each other and thus reduce the photosynthetic efficiency. Böhmer et al. demonstrated the feasibility of enzymatic Baeyer-Villiger oxidations in Synechocystis sp. PCC 6803 [4]. The gene of the cyclohexanone monooxygenase (CHMO) from Acinetobacter calcoaceticus NCIMB 9871 (Figure 8.13), was inserted into the genome of the cyanobacteria under control of the light-inducible promoter PpsbA2 [4]. Conversion of cyclohexanone to ε-caprolactone, a precursor molecule of synthetic polymers, was observed within 24 h [4]. However, the reaction efficiency was decreased because part of the substrate was converted cyclohexanol as side product rather than to the targeted lactone. In literature, this so-called ketoreduction was often observed in wild-type cyanobacteria due to the presence of native alcohol

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dehydrogenases.[54–57, 64] Only cyclopentanone was not accepted by the native ADHs in Synechocystis [4]. Consequently, the Baeyer-Villiger oxidation of cyclopentanone proceeded in the cells without side-product formation. The cells producing CHMO showed specific activity toward different substrates in the range of 2–5 U gCDW−1 [4], which is much lower compared to the activity of the ene-reductase YqjM (100 U gCDW−1) in the same strain [74]. As both enzymes are NADPH-dependent, cofactor availability in Synechocystis is not the bottleneck for the BVMO reaction. Due the low stability of these enzymes [20, 95] reaction conditions must be optimized accordingly with the enzyme’s needs to operate with high efficiency. Furthermore, the amount of enzyme in the cyanobacterial cell might be a limiting factor. This study, however, revealed the opportunity to use cyanobacteria for oxyfunctionalizations [4]. Thus, besides CHMO other monooxygenases have been investigated for this approach. Hoschek et al. showed that the cyanobacterial biocatalysts utilize indeed photosynthetic oxygen as cosubstrate [93, 96]. They introduced the alkane hydroxylase system AlkBGT from Pseudomonas putida GPo1 into Synechocystis sp. PCC 6803. The system consists out of three enzymes, the membrane-bound monooxygenase AlkB, the rubredoxin AlkG, and rubredoxin reductase AlkT, respectively [96]. AlkT oxidizes NADH and the electron transfer to AlkB is mediated by AlkG. NADH is not a primary electron shuttle in the phototrophic metabolism. However, authors observed that the electron transfer to AlkB in Synechocystis sp. PCC 6803 depends on the presence of AlkG but not AlkT. This indicates that AlkG can be endogenously reduced and thus NADH availability is not crucial for cell activity [96]. The system was used for the light-driven oxyfunctionalization of nonanoic acid methyl ester (NAME) to ω-hydroxynonanoic acid methyl ester (H-NAME) (Figure 8.13) [93, 96]. Most important, under anaerobic conditions, conversion occurred only when the reaction was irradiated, i.e., when photosynthetic water oxidation produces O2. Separate measurements of the oxygen evolution rate revealed that 25% of the produced O2 is captured by the monooxygenase [93]. Authors further optimized the reaction and showed that the amount of CO2 and light is critical for the cellular growth of cyanobacteria. When the CO2 concentration and light intensity were increased, the growth rate was improved but the hydroxylation activity of AlkBGT containing cells dropped from 2.1 to 1.1 U gCDW−1 [96]. On the other hand, the specific activity increased by 25% when NaHCO3 is supplied. Under dark conditions, cells still showed high specific activities indicating that the electron supply can be covered by the carbohydrate metabolism of the cell. Another limitation of the system is the toxicity of NAME leading to a decrease of cell activity and reaction yield after 26 h [96]. To overcome this limitation, Hoschek et al. applied a two-phase liquid system with diisononyl phthalate (DINP) as organic carrier solvent. When provided in optimal amounts within the two-phase system (50% (v/v)), NAME is taken up by the cells and its toxic effect on the cells is mitigated. Authors managed to set up a continuous system for the hydroxylation of NAME by recombinant Synechocystis sp. PCC 6803 resulting in increased specific activity of 5.2 U gCDW−1 and higher product yield (Figure 8.13) [96]. Upscaling

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to 0.8 L negatively influenced the activity [96]. Probably, the two-phase system diminishes toxicity effects in small scale but rotation of more culture volume in a reactor increases the interaction of NAME with the cells and thus affects their viability. Nevertheless, by using the two-phase system in stirred tank photobioreactor, Hoschek et al. achieved a productivity of 0.086 mmoltotal product Laq−1 h−1 [96]. Compared to E. coli, the reaction rate in cyanobacteria is still much lower. Therefore, the system still needs further optimization, for example, by increasing the production level of AlkBGT in the cells. However, the studies [93, 96] showed that photosynthetic O2 can be used by O2-dependent oxidation reactions within the engineered cyanobacteria cells and, thereby, provide a much safer platform for oxidation reactions in future.

Figure 8.13: Examples for oxyfunctionalizations in genetically modified Synechocystis PCC 6803. A) Baeyer -Villiger oxidation performed by the cyclohexanone monooxygenase (CHMO) from Acinetobacter calcoaceticus NCIMB 9871, which use NADPH as a cofactor. The reaction is hampered by a side reaction. Native alcohol dehydrogenases (ADHs) reduce the ketones to corresponding alcohols [4]. B) Hydroxylation of Nonanoic acid methyl ester (NAME) catalyzed by the monooxygenase AlkB from Pseudomonas putida GPo1 that is supplied with electrons by its native rubredoxin AlkG [93, 96]. C) Bioconversion of cyclohexane to cyclohexanone with the P450 monooxygenase from Acidovorax sp. CHX100. Electrons are transferred by the Acidovorax ferredoxin (Fd). Substrate was supplied to the system via biphasic system to prevent substrate toxicity [88].

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The hydroxylation of cyclohexane using the three-enzyme system from Acidovorax sp. CHX100 represents another example for light-driven oxyfunctionalizations in engineered Synechocystis sp. PCC 6803. The system includes the P450 monooxygenase (CYP450) and its native electron shuttle system, a FAD-Ferredoxin reductase (FnR) and a Ferredoxin (Fn) (Figure 8.13) [88]. The low solubility of cyclohexane in water, its high volatility and toxic effect to the organisms make it a challenging substrate for the catalysis studies. To prevent the toxicity and to increase substrate-mass transfer, the same biphasic system then for the AlkBGT system was employed during the study [88, 96]. Compared to the reactions performed in darkness (6.2 U gCDW−1), the whole-cell biotransformations under light (150 µmolphotonsm−2 s−1) resulted in higher conversion with the increased specific activity of 26.3 U gCDW−1 [88]. This clearly underlines the lightdependency of the reaction, although the CYP450 system in principle depends on NADH. A detailed investigation of the electron transfer to the monooxygenase is still pending but the results indicate that electrons from the photosynthetic electron transport chain are captured either directly or indirectly by the Acidovorax ferredoxin [88]. To prevent a drop of the specific activity after two hours, the substrate was fed to the reaction via the organic carrier solvent, DINP, which further increased the specific activity to 39 U gCDW−1. The whole-cell catalysis reaction was then performed in 3 L stirred-tank photobioreactors [88]. However, after several hours, the biocatalyst activity decreased. Due to aeration, the CO2 and the substrate were constantly removed. After omitting the aeration, the reaction became constant and within 52 h, 2.6 g of the nylon precursor cyclohexanol was produced [88]. In most catalytic processes, increase of the catalyst concentration results in a faster overall reaction. For photoautotrophic organisms, however, it should be noted that the increase of the cell density above a few grams per liter limits their photosynthetic activity and thus their productivity. Chapter 5 discusses photobioreactor concepts to overcome this limitation. So far, among the column, flat and tubular photobioreactors, the tubular photobioreactors showed promising results and are currently being used at an industrial level. Tubular photobioreactors have a surface area/volume ratio of over 100 m2 m−3 [97]. However, this system requires a high amount of energy to degas O2, and to supply CO2. Capillary reactors are a favorable alternative and convince by a surface area/volume ratio of 1,333–4,000 m2 m−3 as well as a low light penetration depth [97]. When applied for cultivation and whole-cell biotransformations with recombinant Synechocystis sp. PCC 6803, O2 accumulation prevented the formation of a constant system [97]. Therefore, Hoschek et al., succeeded in cultivating high cell density photoautotrophic biofilms in a capillary reactor (48 gBDWL−1) by combining photoautotrophic Synechocystis sp. PCC 6803, and chemoheterotrophic, O2-respiring Pseudomonas taiwanensis VLB120. Both strains, carrying cyclohexane monooxygenase encoding genes, were introduced to the capillary reactor and gene expression was induced by IPTG [97]. The system was remarkable stable and efficiently run for a month, resulting in 98.9% cyclohexane conversion with 84.5% cyclohexanol production (3.76 g m−2 day−1), and 15.5% cyclohexanone as the only side-product [97].

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The few examples for whole-cell biotransformations in recombinant cyanobacteria could exceed production rates and yields of reactions in wild-type cyanobacteria by at least one order of magnitude but are still low compared to heterotrophic production hosts [97, 98] or existing chemical processes [99]. The space time yield to produce cyclohexanol in heterotrophic Pseudomonas taiwanensis (0.4 g L−1 h−1) [100] is 10-fold higher compared to the light-driven production in Synechocystis sp. PCC 6803 (0.04 g L−1 h−1) [88, 98]. Although cocultivation of both strains increased space time yields to 0.2 g L−1 h−1 the reaction can still not compete with the existing chemical process (ca. 25 g L−1 h−1) [99, 101]. Generally, heterotrophic hosts reach constant product titers in the order of 100 g L−1 and surpass productivities of 1 g L−1 h−1 [102]. This illustrates that the productivity of light-driven biotransformations needs to be significantly increased to meet industrial standards. Future research must concentrate on the optimization of (i) the genetic toolbox, (ii) the design of photobioreactors and (iii) cultivation and reaction conditions to solve the process limitations associated with light-driven whole-cell biotransformations. With space time yields up to 1.1 g L−1 h−1, the light-driven bioreductions by the ene-reductase YqjM from Bacillus subtilis is among the fastest ever reported reactions in recombinant Synechocystis sp. PCC 6803 [74, 82]. For further acceleration, Assil Companioni and Büchsenschütz et al. first increased the YqjM concentration inside the cell by changing previously reported PpsbA2 to PcpcB controlled gene expression [82]. Authors observed a 1.3-fold improved cell activity that was not in line with the activity of the crude cell extract (1.7-fold improvement). In contrast to the reaction inside the cyanobacteria, experiments with crude cell extract are supplemented with external NADPH in excess and thus only depend on the enzyme’s concentration. In conclusion, the results strongly indicated that the YqjM catalyzed reaction in Synechocystis was limited by the electron-donor NADPH. The assumption was further proved by pulse amplitude modulated (PAM) fluorescence spectroscopy that measured the intracellular NADPH concentrations [82]. The measurements showed that the introduced YqjM reaction constitutes a strong electron sink that directly utilizes photosynthetically produced NADPH [82]. Increasing the productivity by raising the cell density is possible only to a certain extent, as self-shading would reduce the light availability and thus NADPH regeneration. Therefore, recent studies aimed to improve the NAPDH channeling toward the reaction by engineering of the photosynthetic electron transport chain (PETC) and the deletion of competing electron sinks [82, 103]. In nature, cyanobacteria constantly adjust their metabolism to changes in the environment and the introduction of an additional electron sink results in a physiological response of the cyanobacteria [104]. Grund et al. investigated this by varying culture conditions of Synechocystis sp. PCC 6803 in the presence of ammonium or nitrate. Both N sources constitute electron sinks as their assimilation requires electrons. Selected conditions with different light and carbon accessibility forced the cells into three different sink–source balanced metabolic states that were analyzed

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[104]. Surprisingly, the addition of nitrate as strong electron sink increased photosynthetic efficiency [104]. The results of the study indicate that cells might not fully exploit the capacity of the light reaction under light-limiting conditions and demonstrate the potential of Synechocystis sp. PCC 6803 to adjust to additional electron withdrawal [104]. Especially, the increased photosynthetic activity under high light intensities can result in the over-reduction of the PETC [105] and, thus, in the generation of reactive oxygen species and photoinhibition [103]. Therefore, photosynthetic organisms have developed several regulatory mechanisms to protect the photosynthetic machinery from changing environmental conditions like fluctuating light intensities [104]. The NAD(P)H dehydrogenase-like complex 1 (NDH 1) catalyzes the cyclic electron transfer from ferredoxin (Fd) back to plastoquinone (PQ) accompanied with the pumping of protons into the thylakoid lumen (Figure 8.8) [103, 105]. Flavodiiron proteins (FDPs) can redirect the surplus of photosynthetically produced electrons back to oxygen using Fd as electron donor (Figure 8.8) [105]. Since the light-induced water splitting at photosystem II leads in the formation of oxygen and the FDP catalyzed reaction reduces oxygen back to water, a water-water cycle is created in which no net electrons are gained. Both, NDH 1 and FDPs, are essential to maintain the redox balance between the PETC and the cytosolic carbon assimilation under natural conditions but might be dispensable under controlled laboratory conditions [105, 106]. Additionally, both constitute strong photoprotective electron sinks that constantly redirect electrons and therefore compete with the introduced reaction. Removing alternative electron transport pathway could improve the electron flux to a heterologous enzyme [82, 103]. For this purpose, Berepiki et al. disrupted the NDH-1 complex by deleting its D2 subunit in the cyanobacterium Synechococcus sp. PCC 7022. Subsequently, the P450 monooxygenase CYP1A1 from Rattus norvegicus [107] was recombinantly expressed in the obtained mutant ΔndhD2 [103]. The conversion of the non-fluorescent substrate ethoxyresorufin to a fluorescent was monitored using a fluorescence assay. Authors observed a twofold increase of the CYP1A1 activity accompanied with an increased photosynthetic capacity of the strain [103]. The activity of Synechocystis cells producing YqjM was increased by effective engineering of the PETC [82]. Synechocystis possess four isoforms of FDPs, Flv1-4, which mainly function as the hetero-oligomers Flv1/3 or Flv2/4, respectively [105]. In their study, Assil Companioni and Büchsenschütz et al. integrated the expression cassette for YqjM in knock-out mutants of Synechocystis (ΔFlv1 and ΔFlv3) [106] with disrupted Flv1/3 hetero-oligomer. Indeed, the deletion of this competitive electron sink enhanced the electron channeling toward the reaction and the production rate of the cells was increased from 7.2 mM h−1 to 18.3 mM h−1 for the model substrate 2methylmaleimide. Furthermore, the mutant ΔFlv1::PcpcBYqjM fully converted 60 mM of the substrate within 4 h at moderate cell densities (2.4 gCDW L−1) [82]. However, the enhancing effect was observed only for fast reactions with cell activities above 50 U gCDW−1, indicating that the photosynthetic supply of NADPH is sufficient for



ω-hydroxynonanoic acid  methyl ester [d]

  

. n.a.   

. > 

. .

. .[c] .

.

.

. . .

n.a.

time Spec. [h] activity (U gCDW−)

[a] two phase system, 25% (v/v) diisononyl phthalate containing 20% (v/v) substrate [b] Upscaling, 3 L photobioreactor, two phase system 25% (v/v) diisononyl phthalate containing 5% (v/v) substrate [c] in the knock-out mutant Synechocystis sp. PCC 6803::ΔFlv1 PcpcYqjM [d] two phase system, 1:3 Org:Aq phase ratio with diisononyl phthalate containing 50% (v/v) substrate LI = light intensity, c(S) = substrate concentration, CDW = cell dry weight, C = conversion, P = product, n.a. = not analyzed.

nonanoic acid methyl ester

Synechocystis sp. PCC 

AlkBGT







  

-methylmaleimide -methylsuccinimid -methyl-N-methyl- -methyl-N-methylmaleimide succinimide

Synechocystis sp. PCC 

YqjM



ε-caprolactone

cyclohexanone

CHMOacineto Synechocystis sp. PCC 



-methylpyrrolidine



 [a] [b]

-methylpyrroline

Synechocystis sp. PCC 

IRED-A





c (S) LI C Cell (mM) (µE m− s−) density (%) [gCDW L−]

cyclohexanol

Synechocystis sp. PCC 

CYP

(R)--phenylethanol

Product (P)

cyclohexane

Synechococcus sp. acetophenone PCC 

LkADH

Substrate (S)

Host

Enzyme

Table 8.2: Reaction parameters for the selected examples for biotransformation performed in recombinant cyanobacteria.

[]

Ref.

[]

[]

. mmol gCDW− . mmol gCDW−

[]

. mM h− [] . mM h− [] . mM h− []

n.a.

. mM h−

[] n.a. . mmol gCDW− . g gCDW−

n.a.

Product formation

238 Hanna C. Grimm, Elif Erdem, Robert Kourist

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slower reactions. Both studies represent a significant step toward engineering of photosynthesis for biotechnological purposes. In conclusion, recombinant cyanobacteria are beneficial hosts for reductive biotransformations and oxyfunctionalizations due to the light-driven cofactor regeneration and oxygen formation, respectively. However, the studies revealed some limitations of the concept such as toxicity effects or the low amount of recombinant enzyme inside the cyanobacterial cell [72, 82, 88, 96]. Using a two-phase system [88, 96] or a stronger promoter [72] were both successful strategies to improve the outcome of the reaction. However, further improvement or identification of suitable regulatory tools for recombinant gene expression in cyanobacteria are necessary to completely overcome the problem of low enzyme concentration, especially as the cell density cannot exceed a few grams per liter due to self-shading [82]. The effect occurs when cells conceal each other which results in a heterologous availability of light within the culture and thus a decrease in photosynthetic activity [108]. In the worst case, outside cells exposed to high light intensities are photo-inhibited while cells in the middle of the culture experience zones of darkness. Therefore, the design of suitable photobioreactors and the engineering of the photosynthetic electron transfer chain are important steps into the right direction. However, high specific activities of up to 170 U gCDW L−1 [82] and first production of products in gram-scale [88] pave the way for more sustainable biocatalysis in future.

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Georg Höfler, Frank Hollmann, Caroline E. Paul, Marine Rauch, Morten van Schie, Sebastien Willot

Chapter 9 Photocatalysis to promote cell-free biocatalytic reactions Abstract: Cofactors assist enzymes to catalyze reactions and are indispensable and ubiquitous in nature, playing a central role in metabolic pathways. In biocatalysis, common redox cofactors such as nicotinamide, flavin and heme can be activated by light or synthetized to vary redox potentials, leading to different types of reactions for the formation of interesting chiral products, unattainable through classical chemical methods. This chapter will focus on light-driven cell-free biocatalytic reactions activated via their redox cofactors. Keywords: redox reactions, nicotinamide cofactor, flavin, oxidoreductases, photobiocatalysis

9.1 Introduction Organic synthesis using enzymes is usually called biocatalysis. During the past decades, biocatalysis has been enjoying an ever-increasing popularity among synthetic organic chemists. Especially, the mild reaction conditions and the usually high selectivity of enzyme-catalyzed reactions are valued on lab and industrial scale [1–5]. While industrial biocatalysis mostly relies on one-step transformations the trend in academic research more and more is shifting toward multistep syntheses, transforming simple starting materials into significantly more complex (and value-added) products [6, 7]. Such cascade reactions are particularly attractive if intermediate product isolation and purification can be omitted leading to significant savings in solvent use and reduced environmental footprints [8]. Cascades comprising several enzymatic steps or combining transition metal catalysis, organo catalysis or heterogeneous catalysis are frequently reported nowadays [6, 7]. Following them, autotrophic organisms such as cyanobacteria are starting to be increasingly used for applications in biocatalysis (see Chapters 5–8), and cell-free photoenzymatic reactions (combining photocatalytic reactions with biocatalytic ones) are catching up [9–12]. Photobiocatalysis using isolated enzymes can be divided into (1) photocatalytic regeneration cascades, (2) “true” photoenzymatic cascades and (3) photoenzymatic reactions. In photoenzymatic cascades, redox enzymes are supplied with redox equivalents needed for their catalytic cycles, that is, photocatalytic regeneration of redox enzymes. “True” photoenzymatic cascades combine a biocatalytic transformation with https://doi.org/10.1515/9783110550603-009

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a photocatalytic generation of the enzyme’s starting material or a follow-up step of the enzymatic product. “Photoenzymes” need light to perform their catalytic reaction. In this contribution we critically review the current state-of-the-art of all types of photoenzymatic cascades.

9.2 Photocatalysis to regenerate redox enzymes Cofactors (Box 9.1) can refer to inorganic metal ions such as Zn or Fe, or organic molecules called coenzymes, that assist enzymes to catalyze reactions (see info Box 9.1). In particular, nicotinamide adenine dinucleotide (NAD(P))- and flavin-dependent oxidoreductases (see info Box 9.2) play a central role in the energy metabolism of heterotrophic and autotrophic organisms, which place their redox cofactors at the center of metabolic pathways. Understanding the role these cofactors play and how to use them is necessary for the development of photobiocatalysis processes via the regeneration of their cofactor. Box 9.1: Cofactors and coenzymes in biotechnology Cofactors are non-protein organic molecules (also known as coenzymes) or inorganic metal ions required by an enzyme to assist during a biocatalytic reaction. Typical cofactors for oxidoreductases are redox coenzymes: nicotinamide adenine dinucleotide NAD(P), flavin adenine dinucleotide (FAD), flavin mononucleotide (FMN), and heme. Coenzymes can be found to be covalently or tightly bound as a prosthetic group to a protein, or only transiently bound and used as co-substrates. Inorganic cofactors, such as Mg, Zn, Co, Mo and iron-sulfur (Fe-S) clusters, can play both functional and structural roles. A cofactor-bound enzyme is a holoenzyme, whereas an enzyme without its cofactor is an inactive apoenzyme. As enzymes can display high cofactor specificity, coenzymes require an appropriate and efficient regeneration system in whole-cells and cell-free biocatalytic systems. Protein engineering can be used to switch cofactor preference. New synthetic (biomimetics) and natural coenzyme derivatives (F420) are continuously being discovered for improved and diverse types of reactions.

9.2.1 Reductive regeneration A broad range of biocatalytic redox reactions require reductive regeneration, that is, provision of the production enzyme with reducing equivalents. First, reduction reactions catalyzed by reductases obviously require reducing equivalents. However, a wide variety of oxidation reactions involve reduction of the production enzymes (monooxygenases). This seeming contradiction can be explained by the catalytic mechanism of monooxygenases: molecular oxygen is reductively activated at the enzymes’ active sites to be incorporated into the substrates. Principally, reductive regeneration of redox enzymes (Box 9.2) can be achieved either directly, that is, by direct reduction of the enzymes’ active sites or indirectly,

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that is, involving the nicotinamide cofactors. Both approaches will be outlined in the following sections.

9.2.1.1 Via regeneration of reduced nicotinamide cofactors The reduced nicotinamide cofactors NADH and NADPH play a pivotal role as electron donors in many biocatalytic redox reactions (Scheme 9.1). The basic electrochemical features of the nicotinamide cofactors are shown in Scheme 9.2. In essence, NAD(P)H serves as biological hydride donor while its oxidized pendants (NAD(P)+) serve as hydride acceptors. Box 9.2: Oxidoreductases and cofactors Oxidoreductases are one of the seven classes of enzymes (EC 1), and these enzymes catalyze reduction (gain of electrons), such as carbonyl reduction, and oxidation (loss of electrons) reactions, such as hydroxylation, via their redox cofactor. Approximately 80% of known oxidoreductases require the nicotinamide adenine dinucleotide cofactor, which plays a central role in metabolic pathways (see info Box 9.1). Several oxidoreductases, such as alcohol dehydrogenases (ADHs) and medium chain reductases, can also depend on Zn for structural and catalytic activity. Coenzymes needed in cell-free biocatalytic reactions, for example, NAD(P)H, tend to be expensive and thus are used in catalytic amounts coupled to a regeneration system.

The central role of NAD(P) as electron donor and acceptor in biocatalytic redox reactions has motivated researchers to develop in situ regeneration systems to allow for the use of these costly cofactors in catalytic amounts and thereby reduce their cost contribution to the desired product [13]. Today, enzymatic regeneration systems prevail in preparative application, mostly due to their inherent compatibility with the enzymatic production systems but also due to the ease of application. The most common systems are shown in Table 9.1. Another reason for the dominance of enzymatic regeneration systems lies in their intrinsic regioselectivity. The reduction of NAD(P)+ to NAD(P)H can principally lead to three different regioisomers of NAD(P)H while only the 1,4-NAD(P)H can be used by the production enzyme. Hence, a successful NAD(P)H regeneration system must be highly selective otherwise, losses in the costly nicotinamide cofactor due to formation of inactive regioisomers will make the approach economically unattractive [14]. Unfortunately, the majority of photocatalysts follow a so-called ECE (electron transfer – chemical – electron transfer) mechanism resulting in two major issues for the selective formation of 1,4-NAD(P)H. First, the intermediate NAD-radical can dimerize (comprising yet another pathway to inactivate the nicotinamide cofactor). Second, the chemical protonation step seldom is regioselective leading to the formation of the undesired NAD(P)H isomers (Scheme 9.3) [15].

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Scheme 9.1: Selection of preparatively relevant NAD(P)H-dependent redox reactions. ADH: alcohol dehydrogenase, IRED: imine reductase, CAR: carboxylic acid reductase, ER: ene reductase, BVMO: Baeyer-Villiger monooxygenase; MO: monooxygenase (general).

To circumvent (or at least alleviate) the loss of enzyme-active 1,4-NAD(P)H due to direct single electron reduction by the reduced photocatalyst, generally a relay system is applied to convert the ECE-steps into a regioselective hydride transfer step. The organometallic complex [Cp*Rh(bpy)(H2O)]2+ proposed by Steckhan[[16–19]] or NAD (P)H:flavin oxidoreductases[[20–23]] are the most frequently used for this purpose.

Chapter 9 Photocatalysis to promote cell-free biocatalytic reactions

Scheme 9.2: Structure and basic electrochemistry of the nicotinamide cofactors. Table 9.1: Selection of common enzymatic NAD(P)H regeneration systems.

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Scheme 9.3: ECE mechanism of NAD(P)+ reduction and its consequences for the formation of NAD(P)-dimers and NAD(P)H isomers.

Chapter 9 Photocatalysis to promote cell-free biocatalytic reactions

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A selection of photochemical NAD(P)H regeneration systems used to promote biocatalytic reduction reactions are summarized in Table 9.2. Although various photocatalysts and relay systems have been reported in the past ten years, the overall NAD(P) turnover numbers and the product concentrations achieved so far are disillusioning. Compared to the multiple thousands (even millions) reported for enzymatic regeneration systems the current performance falls back by orders of magnitude. Significant improvements will be necessary in the nearer future to make photochemical NAD(P)H regeneration systems a viable alternative (rather than a lab curiosity) to existing enzymatic systems. Table 9.2: Selection of indirect photochemical NAD(P)H regeneration systems.

Cosubstrate Photocatalyst

Production enzyme / Product (final con. [mM])

TN TN (NAD(P) (Catalysts)

Ref

[Cp*Rh(bpy)(HO)]+ as a relay system  Rh:  CNR: n.d. GluDH: n.d.

[]

 Rh:  mCNS: n.d. LacDH: n.d.

[]

 Rh:  Eosin Y:  GluDH: n.d.

[, ]

TEOA

CNR

GluDH / Glutamate ()

TEOA

mCNS

LacDH / Lactate ()

TEOA

Eosin Y

GluDH / Glutamate ()

TEOA

[Ru(bpy)]+

GluDH / Glutamate ()



[]

HO

[Co(HO)(PWO)]-

GluDH / Glutamate ()

.

[]

TEOA

Chemically converted graphene

LbADH / Various alcohols (,

>,

various

various

[]

[]

[]

[]

[]

[]

,

-phenyl ethanol

[]

[, ]

%

Acetate and small amounts of -oxobutyrate

Acetate and ethanol

Acetate

Acetate, small concentrations of ethanol and propionic, n-butanoic and iso-pantanoic acids

Acetate -Oxobutyrate Formate

Sporomusa ovata

Sporomusa ovata

Sporomusa ovata

Sporomusa ovata

Sporomusa silvacetica Sporomusa sphaeroides Clostridium ljungdahlii Clostridium aceticum Moorella thermoacetica

(continued )

[]

[]

[]

[]

From −. to −.

up to %

Formic acid

Sporomusa fumaroxidans

Reference

Cathode V versus SHE

CE

Product

Microorganism

Table 10.3: Electroautrophic CO2 fixation: studies using different pure cultures, the gained products, coulombic efficiencies (CE) and applied potentials.

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285

−. V

−. V

–

% –%

Formate

Formate

Methane without -bromoethane sulfonate (BES) hydrogen or formate with BES

Biomass and fuel alcohols

Wildtype and engineered Methylobacterium extorquens AM

Methylobacterium extorquens

Methanococcus maripaludis

Wild-type and engineered Cupriavidus necator

Solar-to-biomass conversion efficiencies .–.%

−. V

–

Formate

Methylobacterium suomiense Methylobacterium platani Methylobacterium adhaesivum Methylobacterium soli Methylobacterium chloromethanicum Methylobacterium extorquens AM

Electrolysis was carried out at . V

−. V −. V

Cathode V versus SHE

CE

Product

Microorganism

Table 10.3 (continued )

[]

[]

[]

[]

[]

Reference

286 Laura Rago, Falk Harnisch

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experimental conditions. This microorganism was demonstrated to produce ethanol, but also propionic, n-butanoic and iso-pentanoic acids [36, 37]. Thereafter, several other acetogenic bacteria were studied in pure culture under electroautotrophic conditions to test their capability of current consumption while producing CO2 [38]. Clostridium aceticum, Sporomusa sphaeroides, Clostridium ljungdahlii and Moorella thermoacetica were demonstrated to produce mainly acetate, but also 2-oxobutyrate and formate, while Acetobacterium woodii was unable to consume electric current [38]. Several wild type but also genetically modified strains of the genus Methylobacterium were demonstrated to produce formate, if exposed to a cathode polarized at −0.55 V versus SHE [39–41]. Other pure culture studies involved the well-known methanogen Methanococcus maripaludis [42] with the aim of producing methane, but also wild-type and engineered Cupriavidis necator for biomass and fuel alcohols [43]. Exploiting pure cultures for MES for the conversion of CO2 to more complex molecules is highly promising. However, the currently reported performances of the MES are still far too low for industrial scale application (see also Box 10.3) [44]. Moreover, the desire to explore new electroautotrophs and the weak knowledge on the EET related to the electroautotrophic metabolisms in pure cultures pushed the research on the study of reactor microbiome-based MES [45]. Box 10.3: Reactors for cultivation of electrotrophs The cultivation of electroactive microorganisms needs reactors that provide a process environment for biological conversions and allows interfacing these to electrochemical reactions at the same time. Therefore, no cultivation vessels and methods common to microbiology or conventional reactors for bioprocess engineering can be used. This has led to a high diversity of reactors in terms of, among others, volume, geometry, design and materials, that are used in microbial electrochemistry and technology, recently reviewed in [46, 47]. Reactors range from devices with a few microliter volume via the widely used H-type and four-necked round bottom flasks bioelectrochemical cells to electrobiorectors tailored for process engineering, e.g. based on continuous stirred tanks reactors (CSTR) or flat panel reactors. The level of process control or even characterization of these reactors is highly diverse and the suitability of a specific reactor system strongly depends on the target process. For electroautotrophs the extra-challenge for reactor design is the supply of CO2 from a gas phase. In summary, the complex interplay of reactor design as well as design of other materials like membranes and electrodes provides a mostly untapped field for future engineering for MES. Thus, also the comparison or benchmarking of electrobiotechnological processes to “conventional” bioproduction is not straightforward. Electrobiotechnology needs to interface reactions meaning that a solid electrode is needed to transport electrons as reactants to (or from) the liquid. Thus, the size of this interphase that is expressed in surface per volume – for instance, in geometric cathode area per reactor volume, cm2 L−1 – is of interest and can be limiting. For increasing the surface area per reactor volume at similar size of the electrode, the engineering of materials, topographies, needs to be performed.

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10.5 Electroautotrophic synthesis using reactor microbiomes The use of electroautotrophic reactor microbiomes, that is, mixed cultures (see Chapter 13) enriched for the MES from CO2, was largely explored in the last years (Table 10.4). Electroautotrophic reactor microbiomes were enriched with the aim of CO2 conversion to acetate in two different studies [48, 49] which demonstrated the same CE of 53% at similar applied cathode potentials of ~0.7 V versus SHE. Min et al. [48] also demonstrated that at more negative potential a higher CE was obtained, probably due to the higher abiotic electrochemical hydrogen evolution reaction (HER). Strictly speaking, this involvement of hydrogen as electron carriers can be referred to as a chemolithoautotrophic metabolism of the microbial community performing MES. Several studies aimed to explore the methanogenesis by MES at different potentials and with different efficiencies, using different anaerobic sludge [50–55]. Also in these cases the trend indicates that more negative potentials – where the HER is more likely – are related to a higher CE. Two other studies demonstrated that the cathode can support the growth of selected reactor microbiomes that are able to fix CO2 in multicarbon organic molecules (up to C4-compounds) and specifically butyrate [56], but also ethanol and butanol [57]. Research on MES from CO2 up to now focused on pure culture (vide supra) or undefined mixed cultures (mainly from sewage and anaerobic sludge inocula). The research conducted till now suggested that, due to the low CE and the likely accumulation of undesired products, the undefined reactor microbiomes can be considered only as a small step forward. According to Rosenbaum et al. [45], the main focus of the study of undefined mixed cultures should be limited to the selection of species for pure or defined mixed culture applications, from different inocula, environmental and experimental conditions. Recently, defined co-cultures were also used for MES from CO2. This approach possesses the advantage that certain process steps can be performed by different members of the consortium, while metabolic as well as possible further interactions can be engineered [45]. This may allow targeting more complex products with high yield and titer by MES. Moreover, the defined mixed culture communities need a lesser effort to prevent contamination with other species compared with pure culture MES, thus the need for working in extreme sterile conditions is decreased [58]. In 2017, Deutzmann and Spormann [59] used a pure culture of Strain IS4 to generate two different defined co-cultures with Methanococcus maripaludis and Acetobacterium woodii, respectively, yielding methane and acetate. This demonstrated that defined cathodic microbial co-cultures based on interspecies hydrogen transfer, can be considered as a new frontier of MES from CO2.

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Table 10.4: Electroautrophic CO2 fixation: microbial electrosynthesis from CO2 using reactor microbiomes, the gained products, coulombic efficiencies (CE) and applied potentials. Microorganism

Product

CE

Cathode V versus SHE

Activated sludge from the municipal wastewater treatment

Acetate

.% .%

−. V −. V

[]

Biological sludge from a treatment plant

Acetate

%

−. V −. V

[]

Mixed culture dominated Methanobacterium palustre

Methane

%

−. V

[]

Mixed culture

Methane

Exceeding Reactor potential % range from −. to −. V

[]

Upflow anaerobic sludge blanket reactor treating distillery wastewater

Methane

% −. V Up to %

[]

Anaerobic sludge obtained from an Methane anaerobic digester treating municipal wastewater

%

−. V

[]

Bioelectrocatalytic application of microorganisms for carbon dioxide reduction to methane Anaerobic mixed culture from wastewater treatment of paper industry (%) + anaerobic sludge from the municipal wastewater treatment (%)

Methane

%

−. V

[]

Methane

–%

−. to −. V with graphite granules (GG) .–. V granular activated carbon (GAC)

[]

Mixed culture from a syngas (% CO, % H, % CO and % N) fermenting lab-scale reactor

Butyrate

–%

−. V

[]

%

−. V

[]

Anaerobic selectively enriched mixed C-organics, culture especially ethanol and butanol

Reference

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10.6 Microbial synthesis from CO2 using abiotic electrochemistry Figure 10.3 shows an approach for exploiting CO2 in secondary MES. Here, first an abiotic electrochemical CO2 reduction takes place, for example, to formate. The product of this abiotic electrochemical reaction is subsequently used for microbial synthesis, for example, by formatotrophic microorganisms. This concept was first reported by the group of James Liao [60]. In their study, electrochemical CO2 reduction to formate was coupled to its microbial conversion to higher alcohols by an engineered Cupriavidus necator strain in the same process medium. The advantage of this approach is that the limited product spectrum of the abiotic electrochemical CO2 reduction can be circumvented. Mainly C1 compounds (often also called C1building blocks) like formate, formaldehyde, carbon monoxide, methanol or methane can be gained with high yield [24, 61, 62] and further reduced for the production of higher value chemicals [62]. At the same time, the concept of coupling electrochemical CO2 reduction to formate with subsequent biosynthesis in situ creates challenges for electrochemical engineering. Most important is the need to design an efficient and robust electrochemical process at biocompatible conditions characterized by near neutral pH, ambient temperature and pressure as well as physiologic salinity and the presence of components of microbial media like amino acids, vitamins and trace metals. We have shown in the recent years that a systematic engineering allows a continuous progress, now allowing with high reproducibility a CE of 94.5 ± 2%, formate production rates 0.136 ± 0.016 mmol h−1 cm−2 (at −2.2 vs. Ag/AgCl), space-time yields of formate of up to 0.254 ± 0.031 mmol h−1 cm−2 in 50 mL scale [27, 63]. At the same time, the engineering of reactors advanced significantly, making this approach highly promising [46, 64], albeit several questions on materials and process engineering need to be addressed as well as on the benchmarking to “conventional” bioprocesses (see also Box 10.3).

Figure 10.3: CO2 reduction in secondary MES. (A) Abiotic electrochemical CO2 reduction to C1-building blocks, for example, formate, and (B) subsequent biosynthesis for the production of higher value chemicals. The C1-building blocks conversion to Cx compounds can be performed by microbial cells (in suspension or in biofilm) with or without the use of the cathode as electron donor.

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10.7 The prospects of electroautotrophs in biorefineries Process lines that convert biogenic feedstock by different steps into, for example, chemicals and fuels, are referred to as biorefineries [61]. An advancement of this concept is electrobiorefineries. Electrobiorefineries can be defined as facilities that integrate biomass conversion processes and equipment to produce fuels, power and chemicals from biomass and that also exploit the combination of microbial and electrochemical conversions [65]. Figure 10.4 illustrates the general concept of an electrobiorefinery and highlights the two main options for integrating MES from CO2 in the process line. Microbial electrochemical synthesis from CO2 using pure cultures and reactor microbiomes as discussed in Sections 10.4 and 10.5 do allow to gain already complex products but do require EET that takes place. Here we see especially the need for further microbial resource mining in order to select microbial pure cultures as well as microbiomes allowing to gain high-value products with highest CE, carbon yield and

Renewable electric energy

B Electrosynthesis of feedstock

CO2

A

Org an was ic te

Pretreatment

Bioconversion

Separation

Product (liquid)

Renewable substrate Microbial electrosynthesis

Products (gaseous)

Products (solid)

Figure 10.4: Schematic illustration of the electrobiorefinery concept adapted from [65], with highlighted paths for integrating CO2 fixation. (A) Microbial electrochemical synthesis from CO2 (see Sections 10.4 and 10.5), (B) providing chemical feedstock from CO2 by electrochemical reduction for subsequent microbial conversions upgrading of microbial products (see Section 10.6).

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productivity as well as titer. Alternatively, as discussed in Section 10.6, chemical feedstock from CO2 by electrochemical reduction for subsequent microbial conversions upgrading of microbial products can be provided. In this regard, the process environment for the abiotic electrochemical reduction is highly challenging and especially reactor development and process integration will be of outmost importance. Both directions of research and development are rewarding.

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[38] Nevin KP, Hensley SA, Franks AE, Summers ZM, Ou J, Woodard TL, et al. Electrosynthesis of organic compounds from carbon dioxide is catalyzed by a diversity of acetogenic microorganisms. Appl Environ Microbiol. 2011 May 1, 77(9), 2882–2886. [39] Hwang H, Yeon YJ, Lee S, Choe H, Jang MG, Cho DH, et al. Electro-biocatalytic production of formate from carbon dioxide using an oxygen-stable whole cell biocatalyst. Bioresour Technol. 2015 Jun, 185, 35–39. [40] Jang J, Jeon BW, Kim YH. Bioelectrochemical conversion of CO2 to value added product formate using engineered Methylobacterium extorquens. Sci Rep. 2018, 8(1), 7211. [41] Seelajaroen H, Haberbauer M, Hemmelmair C, Aljabour A, Dumitru LM, Hassel AW, et al. Enhanced bio‐electrochemical reduction of carbon dioxide by using neutral red as a redox mediator. ChemBioChem. 2019, 20(9), 1196–1205. [42] Lohner ST, Deutzmann JS, Logan BE, Leigh J, Spormann AM. Hydrogenase-independent uptake and metabolism of electrons by the archaeon Methanococcus maripaludis. ISME J. 2014, 8(8), 1673–1681. [43] Torella JP, Gagliardi CJ, Chen JS, Bediako DK, Colón B, Way JC, et al. Efficient solar-to-fuels production from a hybrid microbial–water-splitting catalyst system. Proc Natl Acad Sci. 2015, 112(8), 2337–2342. [44] Harnisch F, Rosa LFM, Kracke F, Virdis B, Krömer JO. Electrifying white biotechnology: engineering and economic potential of electricity-driven bio-production. ChemSusChem. 2015, 8(5), 758–766. [45] Rosenbaum MA, Berger C, Schmitz S, Uhlig R. Microbial Electrosynthesis I: Pure and Defined Mixed Culture Engineering. In: Harnisch F, Holtmann D editors, Bioelectrosynthesis Advances in Biochemical Engineering/ Biotechnology. 1st editio, Springer, Cham, 2017, 181–202. [46] Krieg T, Madjarov J, Rosa LFM, Enzmann F, Harnisch F, Holtmann D, et al. Reactors for Microbial Electrobiotechnology. In: Harnisch F, Holtmann D, editor, Bioelectrosynthesis Advances in Biochemical Engineering/ Biotechnology. 1st editio, Springer Nature, 2018, 2 31–271. [47] Krieg T, Sydow A, Schröder U, Schrader J, Holtmann D. Reactor concepts for bioelectrochemical syntheses and energy conversion. Trends Biotechnol. 2014, 32(12), 645–655. [48] Su M. Production of acetate from carbon dioxide in bioelectrochemical systems based on autotrophic mixed culture. J Microbiol Biotechnol. 2013, 23(8), 1140–1146. [49] Blanchet E, Duquenne F, Rafrafi Y, Etcheverry L, Erable B, Bergel A. Importance of the hydrogen route in up-scaling electrosynthesis for microbial CO 2 reduction. Energy Environ Sci. 2015, 8(12), 3731–3744. [50] Cheng S, Xing D, Call DF, Logan BE. Direct biological conversion of electrical current into methane by electromethanogenesis. Environ Sci Technol. 2009, 43(10), 3953–3958. [51] Van Eerten-Jansen MCAA, Ter Heijne A, Grootscholten TIM, Steinbusch KJJ, Sleutels THJA, Hamelers HVM, et al. Bioelectrochemical production of caproate and caprylate from acetate by mixed cultures. ACS Sustain Chem Eng. 2013, 1(5), 513–518. [52] van Eerten-jansen MCAA, Jansen NC, Plugge CM, De Wilde V, Buisman CJN, Ter Heijne A. Analysis of the mechanisms of bioelectrochemical methane production by mixed cultures. J Chem Technol Biotechnol. 2015, 90(5), 963–970. [53] Schlager S, Haberbauer M, Fuchsbauer A, Hemmelmair C, Dumitru LM, Hinterberger G, et al. Bio-electrocatalytic application of microorganisms for carbon dioxide reduction to methane. ChemSusChem. 2017, 10(1), 226–233. [54] Liu D, Roca-Puigros M, Geppert F, Caizán-Juanarena L, Na Ayudthaya SP, Buisman C, et al. Granular carbon-based electrodes as cathodes in methane-producing bioelectrochemical systems. Front Bioeng Biotechnol. 2018, 6(JUN), 1–10.

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[55] Villano M, Aulenta F, Ciucci C, Ferri T, Giuliano A, Majone M. Bioelectrochemical reduction of CO2 to CH4 via direct and indirect extracellular electron transfer by a hydrogenophilic methanogenic culture. Bioresour Technol. 2010, 101(9), 3085–3090. [56] Ganigué R, Puig S, Batlle-Vilanova P, Balaguer MD, Colprim J. Microbial electrosynthesis of butyrate from carbon dioxide. Chem Commun. 2015, 51(15), 3235–3238. [57] Srikanth S, Singh D, Vanbroekhoven K, Pant D, Kumar M, Puri SK, et al. Electro-biocatalytic conversion of carbon dioxide to alcohols using gas diffusion electrode. Bioresour Technol. 2018, 265(2017), 45–51. [58] Rago L, Pant D, Schievano A. Electro-Fermentation – Microbial Electrochemistry as New Frontier in Biomass Refineries and Industrial Fermentations. In: Hosseini M, editor, Advanced Bioprocessing for Alternative Fuels, Biobased Chemicals, and Bioproducts. 1st editio, Elsevier, 2019, 265–287. [59] Deutzmann JS, Spormann AM. Enhanced microbial electrosynthesis by using defined cocultures. ISME J. 2017, Mar 1, 11(3), 704–714. [60] Li H, Opgenorth PH, Wernick DG, Rogers S, Wu T-Y, Higashide W, et al. Integrated electromicrobial conversion of CO2 to higher alcohols. Science (80-). 2012, 335(6076), 1596–1596. [61] Shavan A, Aigner I. Roadmap Bioraffinerien. Berlin Rostock, Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutzt (BMELV), 2012, 1–108. [62] Francke R, Schille B, Roemelt M. Homogeneously catalyzed electroreduction of carbon dioxide – methods, mechanisms, and catalysts. Chem Rev. 2018, 118(9), 4631–4701. [63] Gimkiewicz C, Hegner R, Gutensohn MF, Koch C, Harnisch F. Study of electrochemical reduction of CO 2 for future use in secondary microbial electrochemical technologies. ChemSusChem. 2017, 10(5), 958–967. [64] Rosa LFM, Hunger S, Gimkiewicz C, Zehnsdorf A, Harnisch F. Paving the way for bioelectrotechnology: integrating electrochemistry into bioreactors. Eng Life Sci. 2017, 17(1), 77–85. [65] Harnisch F, Urban C. Electrobiorefineries unlocking the synergy of electrochemical and microbial conversions. Angew Chemie Int Ed. 2018, 57(32), 10016–10023.

Hannah Wohlers, Leen Assil-Companioni, Dirk Holtmann

Chapter 11 Cupriavidus necator – a broadly applicable aerobic hydrogen-oxidizing bacterium Abstract: Cupriavidus necator (previously Ralstonia eutropha) is a highly flexible chemolithoautotrophic bacterium capable of oxidizing molecular hydrogen to serve as the sole electron source and assimilate inorganic carbon. Furthermore, this aerobic Knallgas microorganism can also rely on carbon sources to drive its metabolism in a heterotrophic fashion. This metabolic flexibility makes C. necator a potentially invaluable tool for a plethora of applications including the production of chemicals and bioplastic precursors as well as hydrogen-driven cofactor regeneration and biocatalysis. In this chapter, the complex metabolism of C. necator and key players of autotrophic growth (hydrogenases) are introduced and summarized. Furthermore, state-of-the-art cultivation techniques and methodologies for the biotechnological utilization of C. necator are highlighted. Keywords: Cupriavidus necator, Knallgas bacteria, hydrogenase, metabolism, PHB

11.1 Introduction The oxidation of molecular hydrogen for energy has been realized by various hydrogenase-expressing microorganisms that are taxonomically widespread. These hydrogenoxidizing microorganisms discussed here have several defining characteristics, with the most important being their ability to employ molecular hydrogen and oxygen as an electron donor and acceptor, respectively. This is further paired with carbon dioxide fixing properties – a combination that is thought to be biologically unique. Additionally, they are also able to grow heterotrophically and mixotrophically on a variety of organic substances, such as D-fructose, and amino acids like histidine and tryptophan [1]. This mixotrophic survival capacity has equipped these organisms with an evolutionary advantage that allowed them to populate special niches and explains their spread across taxonomies. Hydrogen-oxidizing (also referred to as Knallgas (Box 11.1)) bacteria can mostly be isolated from upper layers of soil or mud in aquatic environments where they thrive due to the abundance of both hydrogen and carbon. This richness is due to anaerobic bacteria that decompose organic matter releasing hydrogen and carbon dioxide into the soil [2]. Since a relatively small amount of these gases are released into the atmosphere, plenty remains for hydrogen oxidizers to utilize and flourish as was elucidated by Schlegel in 1974 [3].

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Cupriavidus necator H16 is, arguably, the most extensively studied and industrially exploited Knallgas bacterium to date and is considered to be a model organism for many physiological and biochemical investigations since its initial isolation from sludge collected in central Germany in 1952 by Albert Schatz and Carlton Bovell [4]. It is described as a gram-negative facultative chemolithotroph and boasts the incredibly flexible metabolism that is characteristic of Knallgas bacteria. It also lends itself to genetic manipulation, which accompanied with the expansion of available genetic tools [5–7] and its sequenced genome [8, 9], further facilitates its use for research and other novel applications. It is worth noting that the name C. necator is, in and of itself, a culmination of the many years of investigation into hydrogen-oxidizing bacteria. It was originally named Hydrogenomonas facilis by Schatz and Bovell [4] due to the ease of experimental manipulation it allowed; however, around the same time, Erika Wilde in Europe was working on the characterization of the same organism and named it Hydrogenomonas eutropha [10] due to its robust growth on various carbon sources – including carbon dioxide. It was this name that was originally adopted and persisted for almost a decade and, in fact, it is this strain which we now consider to be the wildtype strain H16. As the years rolled by, H. eutropha underwent extensive taxonomic rearrangement leading to the infamous series of name changes that led to C. necator. Initially, after the complete rejection and disbanding of the Hydrogenomonas genus, it was reclassified in 1969 [11] and named Alcaligenes eutropha due to the nature of its flagella. Later, further studies into its phenotype resulted in its reallocation to the genus Ralstonia in 1995 [12] where it remained until 2004 when it was briefly reclassified into Wautersia [13] before in depth DNA–DNA hybridization studies in the same year led to its allocation into the genus Cupriavidus that was, actually, originally described in 1987 [14, 15]. Box 11.1: Knallgas (oxyhydrogen) Knallgas is a mixture of hydrogen and oxygen gas. At standard temperature and pressure, it can burn when at least 4% and at maximum 18% hydrogen by volume is present and is explosive between 18% and 76% hydrogen by volume. When ignited, 241.8 kJ of energy for every mole of hydrogen burned is released, and the gas mixture is converted to water vapor. The mode of combustion has no influence on the amount of heat energy released, but the temperature of the flame varies.

11.2 Metabolism 11.2.1 Heterotrophic growth C. necator possesses a great metabolic versatility as it can grow heterotrophically and autotrophically, which makes it an attractive host for biotechnological applications. During heterotrophic growth, C. necator is capable of metabolizing different

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organic acids (e.g., acetic acid, succinic acid), sugar acids (gluconic acid) as well as amino acids, fatty acids and fructose [10, 16, 17]. In addition, there are also genetically engineered strains of C. necator which are capable of using glucose or sucrose as a carbon source without any growth impairment [18, 19]. The maximum growth rate is dependent upon the chosen carbon source (Table 11.1). Table 11.1: Maximum growth rates of C. necator under heterotrophic growth with different carbon sources [20]. Carbon source

Maximum growth rate µmax (h−)

Formate

.

Glycerol

.

Fructose

.

Gluconic acid

.

Citric acid

.

Pyruvate

.

Succinate

.

Acetic acid

.

Hereby fructose is the most common carbon and energy source used for biotechnological applications of C. necator. It is imported by an ABC-type transporter and subsequently phosphorylated by a fructokinase as part of the Calvin–Benson–Bassham (CBB) cycle (Figure 11.1). A glucose-6-phosphate isomerase then catalyzes the conversion of fructose-6-phosphate to glucose-6-phosphate which can be metabolized via the Entner–Doudoroff pathway to two equivalents of pyruvate [21]. While glycolysis leads to the synthesis of two ATP molecules per sugar, the degradation of fructose only yields one molecule ATP and two molecules of redox equivalents (NAD(P)H). Concerning the Embden–Meyerhoff–Parnas (glycolysis) and the oxidative pentose phosphate pathways, only genes for anabolically operating gluconeogenesis are present in C. necator, while the genes for phosphofructokinase and 6-phosphogluconate dehydrogenase were not identified in its genome [21]. Biochemical analysis conducted by Gottschalk et al. [22] supports the hypothesis that C. necator is not able to use glycolysis for energy generation as it cannot metabolize sugars like glucose or saccharose [22].

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Figure 11.1: Main carbon degradation pathway from fructose under heterotrophic conditions (based on [23, 24]). This figure highlights the main pathways. Three CO2 molecules need to be fixed for the generation of 1 GAP in the Calvin cycle. During each fixation step, two glycerate-3-P are synthesized and since three CO2 molecules produce six glycerate-3-P molecules, five are used to regenerate three ribulose-1,5-BP via the reductive pentose phosphate pathway. KDPG, 2-keto-3-desoxy-6phosphogluconate; GAP, glyceraldehyde 3-phosphate; DHAP, dihydroxyacetone phosphate; PEP, phosphoenolpyruvate.

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11.2.2 Autotrophic growth Like most autotrophic aerobic bacteria, C. necator uses the CBB cycle, also called reductive pentose phosphate pathway, for CO2 fixation in which ribulose-bisphosphate carboxylase (RuBisCO) and the ribulose-5-phosphate-kinase are the key enzymes. Ribulose-1,5-bisphosphate serves as a starting compound for the cycle, which consists of three stages: (1) CO2 fixation, (2) reduction and (3) regeneration (Figure 11.1). For every three cycle rounds, three molecules of CO2 are fixed and one molecule of triosephosphate is generated, which can be branched off for catabolic metabolism. This process is very energy intense as it consumes nine molecules of ATP and six molecules of reduction equivalents. To provide that amount of energy, C. necator utilizes two substrates for energy conservation – hydrogen and formate [25]. At least one soluble and three membrane-bound formate dehydrogenases catalyze the oxidation of formate which generates one equivalent NADH per formate (Equation (11.1)) [21]: Formate + NAD + , CO2 + NADH + H +

(11:1)

For the exploitation of hydrogen as energy source, C. necator employs two [NiFe]-hydrogenases. The membrane-bound one oxidizes H2 to protons while reducing ubiquinone and is coupled to the generation of a proton gradient which drives the synthesis of ATP via an ATPase. The soluble, cytoplasmatic hydrogenase catalyzes the oxidation of H2 associated with the reduction of NAD+ to NADH + H+. In the upcoming section, the structures and mechanisms of the hydrogenases will be further discussed. C. necator’s efficient chemolithoautotrophic lifestyle offers a great opportunity to recycle CO2 from waste- or industrial exhaust gases for the production of various value-added compounds (e.g., fuels and fine chemicals). One approach to this has been the use of syngas (see Chapter 13) as gaseous feedstock. It consists mainly of CO and H2 as well as CO2 and can be produced by gasification of organic or fossilbased carbonaceous materials. Steinbüchel et al. (2018) genetically engineered C. necator to express a gene coding for the aerobic carbon monoxide dehydrogenase (CODH) from the carboxydotrophic Oligotropha carboxidovorans OM5 as well as the genes necessary for the CODH maturation. The mutant strain could grow at rates of 0.33 day−1 on CO as sole carbon source as it was able to oxidize CO to CO2 [26].

11.2.3 Anaerobic respiration Even though C. necator is cultivated aerobically in nearly all of its biotechnological applications, it is also capable of using nitrate as terminal electron acceptor during anaerobic respiration. This denitrification process can be divided in two phases (Figure 11.2).

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Figure 11.2: Denitrification process. NAP, periplasmatic nitrate reductase; NAR, membrane-bound nitrate reductase; NIR, nitrite oxide reductase; NOR, nitric oxide reductase; NOS, nitrous oxide reductase; UQ/H, ubiquinone oxidized/reduced form; Cyt bc1, coenzyme Q-cytochrome c reductase; Cyt c, cytochrome c.

At first, nitrate is converted to nitrite via a periplasmatic (NAP) and a membranebound nitrate reductase (NAR). Subsequently, nitrite is reduced to nitric oxide by a nitrite oxide reductase (NIR) which receives the necessary electrons for this reaction indirectly from the respiratory chain via a cytochrome c. Nitric oxide is further reduced to nitrous oxide by a nitric oxide reductase (NOR) which, similarly to the NAR, uses ubiquinone as an electron donor. Finally, nitrous oxide is converted to atmospheric nitrogen by a nitrous oxide reductase (NOS) [21, 27]. The coupling of nitrate reduction to proton gradient generation drives the synthesis of ATP via an ATPase. However, growth rates with nitrate as the sole electron acceptor during autotrophic growth are significantly lower with only 0.021 h−1 compared to 0.42 h−1 with oxygen [28, 29].

11.2.4 Polyhydroxybutyrate synthesis Polyhydroxybutyrate (PHB) is a polyhydroxyalkanoate which is used for bio-derived and biodegradable plastics and is produced as intracellular storage compounds by various bacteria in response to physiological stress (see Chapter 12) [30]. C. necator produces PHB under growth-impaired conditions, such as a lack of essential macroelements (S, O, N, P or Fe) occurring with an excess of carbon source. The accumulated PHB can make up over 80% of C. necator’s cell dry weight [29, 31]. For PHB synthesis (Figure 11.3), two molecules of acetyl-CoA are condensed by a β-ketothiolase to form acetoacetyl-CoA which then is reduced to hydroxybutyryl-CoA by a NADPH-dependent acetoacetyl-CoA reductase. Following this step, a PHB synthase

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catalyzes the polymerization of hydroxybutyryl-CoA resulting in poly[3-(‒)-3-hydroxybutyrate] which is transported into intracellular storage granules. To mobilize the stored PHB during carbon starvation, PHB depolymerases degrade it to hydroxybutyrate trimers which are then further split up to hydroxybutyrate by PHB-oligomer hydrolases [21]. OH

O

O

2x OH

CoA Acetyl-CoA

3-hydroxybutanoic acid

β-Ketothiolase O

OH

O

CoA Acetoacetyl-CoA

OH O

O

OH O

Acetoacetyl-CoA reductase OH

O

O OH

Degradation

Synthesis

O

PHB-oligomer hydrolases

PHB depolymerase

CoA Hydroxybutyryl-CoA OH

O

•

O

PHB synthase

• n

Poly [R-(–)-3-hydroxybutyrate]

Storgae granule Figure 11.3: PHB synthesis and degradation in C. necator.

11.3 Hydrogenases 11.3.1 General The tremendous potential of H2 as an energy source has been exploited for billions of years by some of the oldest life forms on earth by employing enzymes called hydrogenases. These are metal containing proteins which catalyze the reversible oxidation of molecular hydrogen and can be found in methanogenic archaea, sulfur-reducing bacteria, anoxygenic phototrophic bacteria as well as obligate and facultative chemolithoautotrophic organisms [32]. The direction of the catalyzed reaction is dependent upon the redox potential of the components the enzyme uses as substrate. If a compound is

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present that serves as electron acceptor, the hydrogenase acts as a H2 uptake enzyme. Otherwise, it can transfer electrons from an electron donor to protons from water and release H2. Hydrogenases can be divided into three classes based on their active site, the [NiFe] hydrogenases, the [FeFe] hydrogenases and the [Fe] hydrogenases. The first class possesses a heterodinuclear Ni–Fe-active site and is mainly involved in H2 uptake, while the second one harbors a di-iron, H2-activating site and is primarily engaged in H2 evolution. The third class is restricted to a small group of methanogenic bacteria and contains two irons per homodimer but no iron–sulfur cluster contrary to the other classes. These enzymes function as H2-forming methylene tetrahydromethanopterine dehydrogenases (Hmd) [33, 34].

11.3.2 H2 oxidation in C. necator C. necator harbors two energy-generating hydrogenases and one regulatory hydrogenase (RH) which all belong to the [NiFe] hydrogenase class. They consist of a large subunit containing the Ni–Fe active site and a small subunit including three Fe–S clusters (Figure 11.5) [33, 35]. The main parts of the H2 oxidation are shown in Figure 11.4. One of the energy-generating hydrogenases is the membrane-bound (MBH) which uses hydrogen oxidation to reduce ubiquinone while establishing a proton gradient across the membrane. The other energy-generating hydrogenase is the soluble hydrogenase (SH) which is located in the cytoplasm and catalyzes the electron transfer from H2 to NAD+ [32, 36]. A membrane-bound transhydrogenase is able to couple the reduction of NADP+ by NADH, a hydride transfer reaction, to the inward translocation of protons across the cytoplasmatic membrane [23, 37, 38]. The RH is tightly bound to a histidine protein kinase and forms a hydrogenase-specific two-component regulatory system which controls the gene transcription of the MBH and the SH. The RH exhibits poor hydrogenase activity which is presumably due to its regulatory function [36, 39, 40]. All of the hydrogenases mentioned above are designed to operate in mixtures of H2 and O2 and are therefore oxygen tolerant contrary to most other known hydrogenases. [FeFe]-hydrogenases are highly productive regarding H2 evolution, but are irreversibly inactivated by only trace amounts of O2 [41]. Even though regular [NiFe] hydrogenases are less sensitive, O2 still can react with the active site leading to different partly reversible inactive states, depending on the nature of the oxygen ligand bridging the Ni and Fe atoms [42]. A deeper look at the crystal structure of the MBH of C. necator (Figure 11.5) reveals two features which could explain why this enzyme is capable of oxidizing H2 at atmospheric pO2 in contrast to its relatives. Instead of the prevalent [4Fe4S] cluster at the position proximal to the active site, a Fe–S cluster containing four iron atoms and only three sulfides was found. Besides the four cysteines, which are conserved in [NiFe] hydrogenases, two additional cysteines could be found that coordinate this cluster, which seem to be exclusive for

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Figure 11.4: Schematic figure of the H2 oxidation in C. necator. MBH, membrane-bound hydrogenase; SH, soluble hydrogenase; TH, transhydrogenase; UQ/H, ubiquinone oxidized/ reduced form; COX, cytochrome c oxidase; RH, regulatory hydrogenase; K, histidine protein kinase.

oxygen-tolerant enzymes [43–45]. They ligate three of the four iron atoms leading to a conformation where the regular distance of 2.7 Å between the iron atoms is enlarged to a distance of 3.5 and 4.0 Å. Due to this conformation, the Fe–S clusters possess comparatively high redox potentials which seem to be linked to the reduction of oxygen approaching the catalytic center. To deal with the resulting continuous production of water the enzyme possesses water-filled cavities that connect the active site with the solvent, which could not be found in O2-sensitive [NiFe]-hydrogenases [35]. Many industrially used redox enzymes, for example, dehydrogenases and oxygenases, need expensive cofactors like NADH. During the catalyzed reaction, it serves as electron donor, and oxidized NAD+ is built up. To avoid further addition of NADH, an effective and stable regeneration system is necessary for the reduction of NAD+. Oxygen-tolerant hydrogenases fulfil all the requirements to serve as efficient regeneration systems as they just need H2 as reducing agent and have only protons as a side product [47, 48]. Furthermore, hydrogenases have been shown to be applicable as hydrogen sensors [49] as well as in biological fuel cells [50].

11.4 Genetic tools To exploit the biotechnological potential of C. necator, suitable genetic tools are necessary; and to establish such tools, a solid genetic basis of the organism is required.

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Figure 11.5: Crystal structure of the membrane-bound hydrogenase of C. necator (PDB: 3RGW) [35, 46]. The blue ribbon depicts the large subunit containing the catalytic center, shown as spheres. The turquoise ribbon depicts the small subunit with three Fe–S clusters symbolized as spheres.

The genome of the wildtype strain H16 has been sequenced and annotated which was the first step to enable metabolic engineering. The genome includes two chromosomes and one megaplasmid pHG1 [51]. Almost all essential genes regarding metabolism and cell function are located on chromosome 1 (4,052,032 bp) [21]. Genes that enable C. necator to use a broader range of substrates and terminal electron acceptors are located on chromosome 2 (2,912,490 bp) and on the megaplasmid (452,156 bp). To adjust gene expression or to introduce heterologous genes, functional genetic elements like promoters (constitutive and inducible), ribosome binding sites and other regulatory mRNA elements (e.g., palindromic or/and A/U-rich sequences) have to be utilized. Constitutive native promoters from C. necator that are derived from operons involved in PHB biosynthesis (PphaC), acetoin catabolism (PacoD, PacoX, PacoE) or pyruvate metabolism (PpdhE) were tested in expression studies but showed only weak activities compared to common heterologous promoters used for overexpression. Nevertheless, different inducible promoters (Table 11.2) have been shown to be suitable for C. necator as well as some constitutive promoters, derived from bacteriophage T5 [52, 53]. The well-known promoters Ptac and Plac act as strong promoters in C. necator under constitutive expression conditions but due to its inability to take up lactose or isopropyl-β-D-thiogalactopyranoside (IPTG) they cannot be induced [54]. Therefore Gruber et al. integrated the lactose permease gene (lacY) under the control of the constitutive H16_B1772 promoter derived from C. necator into the phaC locus on chromosome 1 which led to sufficient IPTG transport across the cell’s membranes [6]. Based on origins of replication derived from broad-host-range plasmids RP4, RSF1010, pBBR1 and the megaplasmid pMOL28 from Cupriavidus metallidurans, different autonomously replicating expression vectors were designed for the use in

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Table 11.2: Examples of inducible promoters used in C. necator. Promoter

Induced by

Reference

ParaBAD

L-Arabinose

[]

PxylS

m-Toluic acid

[]

Plac

Lactose

[]

PhpdH

-Hydroxypropionic acid

[]

PrhaBAD

Rhamnose

PrrsC-tetO

Anhydrotetracycline

Pj

Cumate, lactose

[] [] []

C. necator. Unfortunately, all of these expression vectors suffered from high segregational instability and significant plasmid loss during fermentation without any antibiotic pressure applied [58]. To solve this problem, Gruber et al. investigated the influence of the integration of RP4, pBBR and RSF1010 based mobilization sequences into the previously mentioned expression vectors. The 2.3 kb par region of RP4 encoding a site-specific recombination system and a toxin/antitoxin system nearly completely prevented plasmid loss without antibiotic selection [53]. This toxin/antitoxin plasmid addiction system leads to the expression of a stable toxin and an unstable antitoxin which form a non-toxic complex as long as the plasmid is not lost [59]. Another possibility to prevent plasmid loss is the use of auxotrophic strains with plasmid addiction systems which complement the corresponding essential metabolic function. One example is the KDPG-aldolase gene (eda)-dependent addiction system which combined features of a multicopy plasmid with stabilized expression of the cyanophycin synthetase gene (cphA) under heterotrophic conditions [60]. Lütte et al. developed an addiction system based on the hydrogenase transcription factor HoxA, which is also suitable for autotrophic cultivation of C. necator [61]. For chromosomal insertion, modification or deletion of a gene of interest homologous recombination (HR) together with the sacB counter-selection is a suitable tool. The expression of sacB which occurs in the presence of 5% sucrose is lethal for a wide range of gram-negative bacteria [62]. A CRISPR/Cas9 (Box 11.2, see also Chapter 2) method has also been established for C. necator. It requires expressing guide RNAs directly onto a plasmid, which also encodes Cas9 from Streptomyces pyogenes, followed by electroporation and 5 days of expression; resulting colonies are then screened for desired mutations. Neither the native non-homologous end joining (NHEJ) mechanism nor the inserted heterologous NHEJ mechanisms from Mycobacterium tuberculosis were able to repair the CRISPR–Cas9-induced double-strand breaks. On the other hand, the HR system of

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C. necator enabled the CRISPR–Cas9-based genome editing with editing efficiencies ranging from 78.3% to 100% depending on the targeted locus [63]. For the transformation of C. necator two techniques are available so far [58]. These are electroporation and conjugative plasmid transformation which uses specialized E. coli donor strains and functions via bi- or triparental mating. For the latter, broad-host-range vectors harboring an origin of transfer (oriT) are used which can be mobilized by the RK2 mobilization machinery. This machinery is either integrated into the chromosome (diparental mating) or on the pRK2013 plasmid (triparental mating) [64, 65]. Box 11.2: CRISPR/Cas The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas method is based on an adaptive antiviral defense mechanism of bacteria. The DNA-cutting enzyme Cas binds to a sgRNA (single guide RNA) which guides the enzyme to the target DNA. CRISPR/Cas9 belongs to the Class 2 CRISPR–Cas system which recognizes unique sequences and generates double-strand breaks (DSB) at the target locus, which then are repaired either through NHEJ or HR. Cas9-mediated genome editing is programmable through the design of single guide RNAs.

11.5 Biotechnological applications Not only does C. necator have remarkable metabolic versatility, but it also served as a model organism for many years to study the mechanisms of H2- and CO2-based chemolithoautotrophy and PHB synthesis – meaning its physiology and metabolism are well known. In addition, genetic tools that have been established enable its metabolic engineering. This and its fast growth up to high biomass densities exceeding 100 g/L within 2 days [66] qualify C. necator as a production platform strain for a large spectrum of products. Its industrial career started when Imperial Industries Ltd. recognized that C. necator synthesizes large amounts of PHB and proceeded to establish fermentation processes for the production of thermoplastic polyesters (PHB). Since then, many different approaches have been studied to couple the production of PHB to the utilization of different materials (Table 11.3). The main focus of this research was to find a way to recycle the tremendous amounts of carbon containing waste produced mostly by food, wood-processing and agricultural industries. Another well-known application of C. necator started in the 1970s with its use as single cell protein (SCP) for animal feed, but did not receive much attention due to the competition from soy-based protein [78]. Another obstacle was the PHB accumulation inside the cells as PHB is poorly digestible and therefore gets excreted by the animals. Nevertheless, up to 74% of the cellular content of C. necator’s biomass consists of amino acids with a composition resembling casein protein with limitation in

Chapter 11 Cupriavidus necator

309

Table 11.3: PHB production from different base materials. n.r., not reported. Base material

PHB titer (g L−)

Cultivation and working volume

Reference

CO

.

Airlift bioreactor  L

[]

Waste glycerol + CO

.

Fed-batch bioreactor . L

[]

Molasses

.

Bioreactor  L

[]

Bagasse hydrolyzates

.

Shake flasks n.r.

[]

Mixtures of plant oils

.

Shake flasks n.r.

[]

Waste glycerol

.

Fed-batch bioreactor . L

[]

Palm oil

.

Shake flasks . L

[]

Cyclone bioreactor  L

[]

.

Fed-batch bioreactor  L

[]

.

Fed-batch bioreactor n.r.

[]

Fed-batch bioreactor . L

[]

Potato starch waste Fermented kitchen waste Wheat-derived media Food waste

.

.

sulfur-containing amino acids [78]. Even though bacterial SCP cannot compete with products of conventional agriculture for economic and other reasons thus far [79], a study could show an eco-friendly method to recover PHB from bacterial cells using a biological process [80]. For that, they fed rats with freeze-dried C. necator H16 cells containing a moderate PHB content, which resulted in the release of PHB granules during the excretion process as PHB granules cannot be digested due to a missing PHB depolymerase in the rat’s digestive system. In a subsequent study, they scaled up the process exchanging the rats with insect larvae as recovery agent. Even though a higher protein content was found in larvae that were fed freeze-dried C. necator H16 cells compared to those getting oats as food, the recovered PHB granules still contained a significant amount of protein [81]. C. necator, additionally, is also suitable for the production of other valuable chemicals besides PHB (Table 11.4). While fructose still is the most used carbon source for biotechnological applications, attempts to replace it with CO2 for reasons of sustainability are being investigated.

11.6 Electrotrophic cultivation The so-called electroactive bacteria (EAB) are capable of transporting electrons over biological membranes to link intra and extracellular electron acceptor/donor systems (see Chapter 10). Those EAB can either be used in microbial fuel cells (MFCs)

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Hannah Wohlers, Leen Assil-Companioni, Dirk Holtmann

Table 11.4: Further products produced by C. necator. Product

Base material

Product titer

Cultivation

Isopropanol

Fructose CO

. g L− . g L− . g L−

Fed-batch bioreactor

−

Reference [] []

Isobutanol -Methyl--butanol

Fructose

 mg L  mg L−

Semicontinuous flask cultivation

[]

Alka(e)nes

Fructose CO

 mg L− . mg L−

Fed-batch culture

[]

α-Humulene

CO

. mg L− . mg L−

Septum flasks Fed-batch bioelectrochemical reactor

[]

-Hydroxyisobutyric acid

CO

. g L−

Batch bioreactor

[]

Methylketones

Fructose CO

– mg L− – mg L−

Batch culture Fed-batch bioreactor

[]

Cyanophycin

CO

.–. g L−

Fed-batch bioreactor

[]

or microbial electrosynthesis (MES). Inside the MFCs, the bacteria oxidate organic fuels, like lactate, as well as complex mixtures of organic matter, and transfer the metabolically generated electrons onto the anode. The best-known application of MFCs thus far is for wastewater treatment [89]. The MES works in the opposite way as the bacteria consume the electrons delivered by extracellular sources, such as electrodes, and use them for reductive carbon fixation [90]. There are three different ways of extracellular electron transfer described, direct electron transfer (DET), mediated electron transfer (MET) and indirect electron transfer (IET). For DET, a physical contact with the electrode is needed, which can be achieved via pili, nanowires or membrane-bound cytochromes. It is often used by biofilm forming bacteria like Geobacter sulfurreducens. For the MET no physical contact is needed, instead bacteria, for example, Shewanella oneidensis, use redox-active mediator molecules like flavins to transfer the electrons between the cell and the electrode. These mediator molecules can be regenerated while in IET the electron-shuttling compounds, like hydrogen or formic acid, are consumed [91]. As mentioned before, C. necator is able to use hydrogen as electron source and carbon dioxide as carbon substrate to build up biomass and natural products. Therefore, it exhibits great potential for the application in MES. This has already been tested in the 1960s by Schlegel and Lafferty which could show chemolithotrophic growth of C. necator from H2 and O2 generated by electrolysis [92].

Chapter 11 Cupriavidus necator

H2O

O2

CO2

H2

A

NADH

egas inlet

NAD

O2 H+ H2O

H+

Cathode

Anode

central carbon metabolism

311

CO2

H2

Figure 11.6: Electrotrophic cultivation of C. necator inside a bioelectrochemical system. With the help of applied current, water is oxidized at the anode which leads to the formation of molecular oxygen and protons. The protons are reduced at the cathode and hydrogen is formed. C. necator uses the oxygen as terminal electron acceptor and reduces reduction equivalents like NAD+ via its hydrogenases. The supplied carbon dioxide is used for the central carbon metabolism.

So far, isopropanol [93], isobutanol [94], 2-methyl-1-butanol [94], PHB [95] and α-humulene [86] could be produced via electrotrophic cultivation of C. necator (see Table 11.4). By optimizing the process, CO2 reduction energy efficiencies of ca. 10% could be achieved, which surpasses the efficiency of natural photosynthetic systems [95].

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[75] Farah NO, Norrsquo Aini AR, Halimatun SH, Tabassum M, Phang LY, Mohd AH. Utilization of kitchen waste for the production of green thermoplastic polyhydroxybutyrate (PHB) by Cupriavidus necator CCGUG 52238. African J Microbiol Res. 2011, 5(19), 2873–2879. [76] Xu Y, Wang RH, Koutinas AA, Webb C. Microbial biodegradable plastic production from a wheat-based biorefining strategy. Process Biochem. 2010 Feb, 45(2), 153–163. [77] Du G, Yu J. Green technology for conversion of food scraps to biodegradable thermoplastic polyhydroxyalkanoates. Environ Sci Technol [Internet]. 2002 Dec cited 2019 Dec 17, 36(24), 5511–5516. [78] Calloway DH, Kumar AM. Protein quality of the bacterium Hydrogenomonas eutropha. Appl Microbiol. 1969, 17(1), 176–178. [79] Israelidis C.J. Nutrition-Single cell protein, twenty years later. Proceedings from First Biointernational Conference. 2003. [80] Kunasundari B, Murugaiyah V, Kaur G, Maurer FHJJ, Sudesh K. Revisiting the single cell protein application of Cupriavidus necator H16 and recovering bioplastic granules simultaneously. Appanna VD, editor. PLoS One [Internet]. 2013 Oct 24 cited 2020 Jan 31, 8 (10), 1–15. [81] Murugan P, Han L, Gan CY, Maurer FHJ, Sudesh K. A new biological recovery approach for PHA using mealworm, Tenebrio molitor. J Biotechnol. 2016 Dec 10, 239, 98–105. [82] Marc J, Grousseau E, Lombard E, Sinskey AJ, Gorret N, Guillouet SE. Over expression of GroESL in Cupriavidus necator for heterotrophic and autotrophic isopropanol production. Metab Eng. 2017, 42(May), 74–84. [83] Garrigues L, Maignien L, Lombard E, Singh J, Guillouet SE. Isopropanol production from carbon dioxide in Cupriavidus necator in a pressurized bioreactor. N Biotechnol. 2020 May 25, 56, 16–20. [84] Lu J, Brigham CJ, Gai CS, Sinskey AJ. Studies on the production of branched-chain alcohols in engineered Ralstonia eutropha. Appl Microbiol Biotechnol. 2012, 96(1), 283–297. [85] Crépin L, Lombard E, Guillouet SE. Metabolic engineering of Cupriavidus necator for heterotrophic and autotrophic alka(e)ne production. Metab Eng [Internet]. 2016, 37, 92–101. [86] Krieg T, Sydow A, Faust S, Huth I, Holtmann D. CO2 to terpenes: autotrophic and electroautotrophic α-humulene production with Cupriavidus necator. Angew Chemie – Int Ed. 2018, 57(7), 1879–1882. [87] Przybylski D, Rohwerder T, Dilßner C, Maskow T, Harms H, Müller RH. Exploiting mixtures of H2, CO2, and O2 for improved production of methacrylate precursor 2-hydroxyisobutyric acid by engineered Cupriavidus necator strains. Appl Microbiol Biotechnol. 2015, 99(5), 2131–2145. [88] Müller J, MacEachran D, Burd H, Sathitsuksanoh N, Bi C, Yeh YC, et al. Engineering of Ralstonia eutropha H16 for autotrophic and heterotrophic production of methyl ketones. Appl Environ Microbiol. 2013, 79(14), 4433–4439. [89] Logan BE. Exoelectrogenic bacteria that power microbial fuel cells. Nat Rev Microbiol. 2009, 7(5), 375–381. [90] Rabaey K, Angenent L, Schröder U, Keller J. Bioelectrochemical Systems: From Extracellular Electron Transfer to Biotechnological Application. IWA Publishing. 2009. [91] Sydow A, Krieg T, Mayer F, Schrader J, Holtmann D. Electroactive bacteria—molecular mechanisms and genetic tools. Appl Microbiol Biotechnol. 2014, 98(20), 8481–8495. [92] Schuster E, Schlegel HG. Chemolithotrophes Wachstum von Hydrogenomonas H16 im Chemostaten mit elektrolytischer Knallgaserzeugung. Arch Mikrobiol. 1967 Dec, 58(4), 380–409. [93] Torella JP, Gagliardi CJ, Chen JS, Bediako DK, Colón B, Way JC, et al. Efficient solar-to-fuels production from a hybrid microbial-water-splitting catalyst system. Proc Natl Acad Sci U S A. 2015 Feb 24, 112(8), 2337–2342.

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[94] Li H, Opgenorth PH, Wernick DG, Rogers S, Wu TY, Higashide W, et al. Integrated electromicrobial conversion of CO2 to higher alcohols. Science. 2012, 335, American Association for the Advancement of Science, 1596. [95] Liu C, Colón BC, Ziesack M, Silver PA, Nocera DG. Water splitting-biosynthetic system with CO2 reduction efficiencies exceeding photosynthesis. Science (80-). 2016 Jun 3, 352(6290), 1210–1213.

Katharina Meixner, Jacqueline Jerney, Adriána Kovalcik, Ines Fritz, Bernhard Drosg

Chapter 12 Poly(3-hydroxybutyrate) as renewable resource Abstract: This chapter deals with the production, processing and application of biobased and biodegradable polyhydroxyalkanoates (PHAs), especially poly(3-hydroxybutyrate) (PHB) with a focus on photoautotrophic cultivation of cyanobacteria. PHB is accumulated as a storage compound by a wide variety of taxonomically different bacteria including cyanobacteria, aerobic anoxygenic phototrophic, purple non-sulfur as well as heterotrophic bacteria. Cyanobacteria are photoautotrophic organisms and need to be provided with light, CO2 and mineral nutrients. They can be cultivated in open or closed systems (so-called photobioreactors). After cultivation, biomass is harvested and PHB extracted, where two main methods can be distinguished: polymer solubilization and solubilization of non-PHB biomass. PHB can be processed via extrusion or injection molding and used for various applications, ranging from agriculture/ fishery/gardening and food packaging to applications in medicine or pharmacy. These applications are mainly based on the biodegradability of the polymer. PHB is degraded in soil and liquid environments and even at unfavorable conditions (e.g., low temperature). Some companies produce PHA at a large scale, but quantities are too low to substitute conventional polymers, mainly due to rather high PHA market prices. This is, among others, a reason why research focused on the utilization of cheap nutrient sources and thereby on the integration of PHA production into biorefinery. Keywords: polyhydroxyalkanoates, biodegradable polymer, cyanobacteria

12.1 Poly(3-hydroxybutyrate) in brief Polyhydroxyalkanoates (PHAs) are semicrystalline polyesters (Figure 12.1a), including poly(3-hydroxypropionate) (PHP), (-butyrate, PHB or P(3HB)), (-valerate, PHV) and (-hexanoate, PHH), Figure 12.1b) [1], which are metabolized by a wide range of taxonomically different groups of microorganisms (as outlined in Section 12.2) [2]. These organisms accumulate PHAs under nutrient-starved or stress conditions [3] and excess of carbon as carbon and energy storage [4] in the form of intracellular granules (Figure 12.1c) [2]. Two types of poly(R-3-hydroxyalkanoates) have been described: a Acknowledgments: Adriana Kovalcik is grateful for the support by the project GA-19-29651L of the Czech Science Foundation (GACR). Additionally, the authors thank Eva Sykacek and Clemens Troschl of University of Natural Resources and Life Sciences, Vienna as well as Jiří Kopecky of Centre Algatech for kindly providing pictures for this chapter. https://doi.org/10.1515/9783110550603-012

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high-molecular-weight microbial storage material (PHA), and a short-chain variety, composed of butyrate and valerate residues, complexed with other biomacromolecules (PHBscl) [5]. PHB is not just an inert storage polymer, restricted to certain bacteria, but a ubiquitous, interactive, biopolymer involved in important physiological functions [6]. PHAs have thermoplastic and elastomeric properties [7], making it suitable for thermal processing as extrusion, injection molding [8], melt spinning and 3D printing [9] and therefore for a wide range of applications. The most investigated and commercially available PHA is PHB [2]. According to Mozumder et al. PHB has the potential to substitute fossil-based plastics (Box 12.1) in the future [10]. (a)

(b)

(c)

Figure 12.1: Polyhydroxyalkanoates (PHAs) (a) granulate (© Sykacek, BOKU), (b) structure (based on [12], (c) granules in Synechocystis salina, stained with Nile red (© Troschl, BOKU).

Box 12.1: Classification of polymers Polymers can be categorized according to their origin and their degradability. There are fossil based versus biobased and non-degradable versus biodegradable polymers. Fossil and biobased polymers can be biodegradable or non-biodegradable. PHAs and hence PHB are biobased as well as biodegradable [11].

PA, polyamides; PBAT, polybutylene adipate terephthalate; PBS, polybutylene succinate; PCL, polycaprolactone; PE, polyethylene; PET, polyethylene terephthalate; PHA, polyhydroxyalkanoates; PLA, polylactic acid; PP, polypropylene; PTT, polytrimethylene terephthalate (adapted from [11]).

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12.2 PHB-producing microorganisms PHB is produced by a wide variety of taxonomically different bacteria (Table 12.1) [13, 14], and has also been found in extremely halophilic archaea [15, 16]. Based on their metabolism, bacteria can be grouped into heterotrophic and autotrophic bacteria. Heterotrophic bacteria rely on metabolic compounds of other organisms as an external energy source. In contrast, autotrophic bacteria can synthesize organic substances from inorganic compounds and utilize energy from oxidation (chemoautotrophs) or light energy (photoautotrophs). The accumulation of PHB in heterotrophic bacteria has been intensively studied on a wide range of bacteria and various carbon sources [17]. High accumulation capacity was reported e.g., for Pseudomonas fluorescence, grown in sugar cane liquor medium as carbon source, which accumulated up to 70% PHB per dry weight [18]. The use of PHB from bacterial fermentation as a commercial product is limited by its high production costs, compared to some widely used petroleum derived plastics [19]. To make the production process of PHB more cost effectives, different carbon sources have been tested [17]. Most of these carbon sources are food crop-based, meaning that an increased PHB production would probably result in competition for arable land, which is not desired. To avoid ethical conflicts and reduce production costs, wastewater streams can be utilized as cultivation medium for bacteria [20]. Photoautotrophic bacteria could provide further advantages, because they can utilize sunlight as energy source. Anoxygenic phototrophic bacteria (purple bacteria; grow without oxygen production) for example have been studied substantially with respect to PHA production and were found to accumulate up to 60–70% PHB per dry weight [21]. They can utilize various types of organic compounds as carbon sources and have been tested for use in a variety of applications [22]. Additionally, marine bacteria were studied more intensively because they have a number of potential advantages, like use of sterilized sea water as culture medium and reduced contamination potential [23]. In natural oxic environments, another important group of PHB producing, phototrophic bacteria, namely aerobic anoxygenic phototrophic bacteria (AAPB) play a significant role in carbon cycling in the ocean [24]. AAPB are alpha- and gammaproteobacteria, which are obligate aerobe, capture energy from light by anoxygenic photosynthesis and therefore do not produce oxygen [24]. Furthermore, phototrophic oxygenic bacteria (cyanobacteria) were suggested to be a promising alternative, because in addition to utilizing sunlight as energy source they can capture CO2 and can be cultivated on non-arable land (Section 12.3) [25]. With regards to the culture conditions required for PHA production, bacteria can be divided into two groups [26]: The first group requires limitation of an essential nutrient, such as phosphorus, nitrogen, sulfur or magnesium to synthesize PHA from an excess carbon source and PHAs are not accumulated during the growth phase (e.g., Alcaligenes eutrophus, Protomonas extorquens and Protomonas oleovorans). The second

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group accumulates PHAs during the growth phase, without the requirement of nutrient limitation (e.g. Alcaligenes latus, a mutant strain of Azotobacter vinelandii, and recombinant E. coli). These characteristics need to be taken into account for successful PHA production [26]. The choice of bacteria for PHA production should be based on several factors including the cell’s ability to utilize an inexpensive carbon source, growth rate, polymer synthesis rate and the maximum extent of polymer accumulation [26]. It is generally proposed that PHAs serve primarily as a carbon and energy storage material when exogenous carbon sources are depleted [13]. Furthermore, intracellular PHA accumulation and degradation enhance the resistance of bacterial cells to various stress conditions like pH fluctuations, low temperatures, freezing, UV radiation, desiccation, osmotic pressure and microbial inhibitors [2, 27, 28]. The protective role of PHA is based on a combination of factors, including the biophysical and morphological properties of native PHA granules, the diverse biological functions of PHA granule-associated proteins, and the cyclic nature of PHA metabolism as well as its connection to other metabolic pathways [27]. The accumulation of PHB is subject to extensive regulation by biosynthesis genes, which can be manipulated for producing high amounts of polyesters through recombinant bacteria [29]. An interesting approach is the potential use of PHA granules, formed inside recombinant bacterial cells, as tailor-made functionalized micro- or nano-beads [29]. Specific proteins attached to the PHA core (Figure 12.2) have been engineered to display various protein functions [29]. The utilization of recombinant bacteria with heterologous PHA biosynthetic genes allows a high level of subcellular PHA accumulation, but elevated costs of this process encouraged many studies to focus on utilization of non-recombinant photosynthetic and heterotrophic organisms [25].

Figure 12.2: Structure of polyhydroxyalkanoate (PHA) granules of Cupriavidus necator (referred to as Ralstonia eutropha, modified from [29]).

. .

CO CO

Chroococcus sp.

Gloeocapsa gelatinosa

[] []

Nitrogen/phosphorus limitation/ NADPH (. mM) Nitrogen/phosphorus limitation

. . .

PHB waste CO CO CO CO

Limnospira sp. LEB*

Lynbgya sp.

Nostoc muscorum

Nostoc muscorum

Phormidium sp.

.

.

.–.

(continued )

[]

[]

CO/acetate

Limnospira sp.*

Nitrogen limitation

CO/pyruvate

Limnospira sp.*

.–.

CO

Limnospira sp.*

[]

CO/acetate .–

[] []

Phosphorus



.

Nitrogen limitation

Limnospira platensis LB/ *

Limnospira maxima*

[]

. ± .

CO

Calothrix scytonemicola

[]

[]

.

CO

Aulosira fertilissma



Nitrogen/phosphorus limitation

Acetate

.

Anabaena cylindrica C

[]

Reference

CO

Nitrogen limitation

Induction of PHB accumulation

Anabaena cylindrica C

15 Mio. tons/year) contains (R)-(+)-limonene, which is industrially isolated from orange peel waste by energy intensive steam distillation or cold expression [92]. The great market potential of limonene and its oxygenated derivatives (menthol, perillyl alcohol, carveol and carvone) as solvents, fine chemicals, flavors, fragrances or even fuels increased the research on the valorization of limonene from orange peels. Since the regio- and stereospecific hydroxylation of limonene is difficult to achieve by chemical means, increasing research efforts are made in the field of biocatalytic transformations of this compound [93]. The hydroxylation of limonene to carveol can be catalyzed by non-heme iron-dependent dioxygenases. Cumene dioxygenase (CumDO) from Pseudomonas sp. PWD32 was recently cloned for expression in Pseudomonas putida S12 [94]. This selective hydroxylation by CumDO expressed in P. putida S12 was connected to synthetic mini-pathway in E. coli that uses carveol as a starting substrate (Scheme 13.2) [95]. This artificial “mini” metabolic pathway in E. coli comprises an alcohol dehydrogenase (ADH), an ene-reductase (ERED) and a cyclohexanone monooxygenase (AcCHMO) for the synthesis of chiral lactones starting from allylic secondary cyclic alcohols. Both recombinant organisms have been used in a mixed culture approach for the conversion of limonene extracted from orange peels to chiral carvolactone via carveol, carvone and dihydrocarvone. Chiral carvolactones represent interesting building blocks for the syntheses of bioactive and natural products, and can further serve as starting material for polymers [96, 97]. Oberleitner et al. investigated several routes for the direct conversion of limonene present in orange peel to chiral carvolactones, including liquid biphasic systems, the direct utilization of the orange peels with the resting cells in aqueous buffer,

Scheme 13.2: Mixed culture approach for the conversion of limonene to chiral carvolactone. The first hydroxylation step is catalyzed by a P. putida whole-cell catalyst overexpressing a dioxygenase. This reaction step is followed by alcohol oxidation (ADH), ene reduction (ERED) and finally the AcCHMO-catalyzed BV oxidation mediated by recombinant E. coli cells. Adapted from Oberleitner et al. 2017 [95].

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hydrophilic ionic liquids as additives and the partial or complete dissolution of biomass in pure ionic liquids should enable enhanced extraction efficiency of limonene from orange peel [95]. The most facile and economic approach was found to be the direct use of orange peels as the substrate reservoir in a substrate feed product removal approach in aqueous buffer, leading to the production of up to 6.3 mg carvolactone per g orange peel (29% yield over four steps) in a one-pot sequential biocatalyst addition approach.

13.3.8.3 1,3-Propanediol Crude glycerol which derives from biodiesel production plants normally contains impurities such as methanol, fatty acids, salts or heavy metals and may need to be purified for fermentation processes employing pure cultures [98]. In conventional processes, 1,3-propanediol (1,3-PDO) is produced either by a natural strain utilizing glycerol or a genetically engineered one utilizing glucose as substrate [16]. However, also mixed culture-based processes have been developed to convert crude glycerol into 1,3-PDO, which is an important building block for the polymer poly-trimethyleneterephthalate [99, 100]. As mentioned above, crude glycerol can be obtained as a by-product with impurities from such a plant. However, the microbial conversion of crude glycerol into 1,3-PDO is always associated with the formation of organic acids as by-products. These acids can limit the cell growth due to the toxic effects and thus lower the productivity of the process. On the other hand, only about 50% of the glycerol are converted into 1,3-PDO, which lowers the overall sustainability of the fermentation process [101]. Thus, mixed cultures represent an effective solution to these problems. It was shown that 1,3-PDO can be produced in a fed-batch approach with a product concentration of up to 70 g/L when using crude glycerol as the carbon source [102]. The overall yield of 0.57–0.72 mol 1,3-PDO per mol glycerol is close to the theoretical maximal yield of anaerobic glycerol conversion [102]. In comparison to 1,3-PDO production in typical pure cultures, this process achieved the same levels of product titer, yield and productivity, but with the advantage of operating under non-sterile conditions and overall reduction of investment and production costs [1, 102].

13.3.8.4 Polyhydroxyalkanoates (PHAs) Polyhydroxyalkanoates (PHAs, see Chapter 12) are used for the production of biodegradable plastics. The majority of PHAs produced by bacteria is polyhydroxybutyrate (PHB), which has similar properties to polypropylene. Industrially, PHB is currently produced by genetically modified Escherichia coli and Alkaligenes species. However, the pure culture production of PHB offers several disadvantages. On the one hand,

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the costs for the pure substrates as well as the sterile pre-cultivation of the cells are quite high, and on the other hand the full process has to be operated under sterile conditions. The potential use of mixed microbial cultures for the production of PHAs from waste streams that are rich in organic compounds has been investigated [103, 104]. The most successful method for the natural selection of microorganisms with the capacity to store PHAs is based on cultivation in an aerobic sequencing batch reactor. In this batch-wise feeding reactors, periods with substrate and without substrate are applied. Microorganisms that produce PHA and subsequently use them for growth will have a more equilibrated growth rate compared to microorganisms that are only capable to growth in the presence of substrate [105]. Moreover, the metabolic route to convert the substrate (e.g., acetate) into PHA is much shorter and less energy-demanding than the production of biomass. This allows for an effective selection of PHA-storing microorganisms from a mixed population [103, 106, 107]. Marang et al. have shown that the continuous supply of acetate in an enrichment culture in SFBR50 was dominated by P. acidivorans and accumulated up to 85 wt% PHB [108]. The possibility to apply less strict famine conditions has strong advantages for scale-up of the sequencing batch reactor process itself. Feeding substrate throughout the cycle reduces the need for buffer volume and alkalinity, as the dosage of the acidic substrate no longer outruns the consumption. In this way, 70–85% of the dry matter formed in the process can be recovered as PHA. This is comparable to the PHA production process established for E. coli, and potentially allows to establish an economically feasible process [103]. In regard to that, cyanobacterial carbohydrates represent an emerging and renewable feedstock in industrial biotechnology, in contrast to crop-based feedstocks. Due to the intrinsic limitations of cyanobacteria to achieve high yields in the production of biotechnology-relevant products, one approach to circumvent these limitations is the application of a synthetic coculture for the carbon-neutral production of PHA from CO2 [5]. Therein, the cyanobacterial strain Synechococcus elongatus cscB fixes CO2, converts it to sucrose and exports it into the culture supernatant. Subsequently, this sugar serves as carbon source for Pseudomonas putida cscAB that is converting it to PHAs and accumulating it in the cytoplasm. Overall, a PHA production rate of 23.8 mg/(L day) and a maximal titer of 156 mg/L has been achieved with this mixed culture.

13.3.9 Natural products (isoprenoids) A two-strain coculture approach between Escherichia coli and Saccharomyces cerevisiae has been utilized for the production of oxygenated isoprenoids by Zhou et al. (Figure 13.6) [109]. One of the key advantages of using a coculture-based approach is the ability to tailor the genetic optimization of each module to the needs of the specific biosynthetic steps. This advantage can be realized in a couple of different ways. In this study, the engineered upstream E. coli strain was used for the de novo

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biosynthesis of the intermediate taxadiene. However, E. coli is not an optimal host to express cytochrome P450 (CYP) for subsequent monooxygenase-catalyzed bioconversions. To overcome this limitation, the authors constructed a S. cerevisiae module capable of efficient P450 expression. Coupling the E. coli and S. cerevisiae modules enabled the first example of microbe-based production of oxygenated taxanes from a simple carbon source (i.e., xylose). Additionally, to control population densities of the two strains, a mutualistic carbon-source method was employed where the xylose was consumed solely by E. coli, subsequently producing acetate that was metabolized by S. cerevisiae.

13.4 Conclusion Given the importance of microbial communication in the production of secondary metabolites, microorganisms represent a treasure chest for natural product discovery. This is illustrated by the tremendous momentum that cocultivation studies are currently gaining, with the first reported study on “mixed cultures” dating back to 1918. One century later, researchers in the fields of microbiology, biotechnology and natural product discovery still explore cocultivation experiments to pursue potentials and limitations of mixed microbial fermentations. Moreover, mixed cultures are not only considered as unexplored source of natural products, but also offer major possibilities for other novel applications, aiming to overcome the limitations posed by single-culture approaches. In fact, they represent renewable and abundant resources with great future potential for bioconversion to value-added products. Coculture fermentations offer the possibility to implement all necessary enzymatic conversions in one bioreactor. Interest has recently emerged in engineering microbial consortia, but there is still major progress necessary to implement synthetic engineered consortia in industrial processes [39, 110]. In particular, functional genomic studies and systems biology of microbial consortia are just in their infancy and scientists are facing important challenges in constructing sophisticated multicellular systems. Nevertheless, in a short- to middle-term perspective, the use of defined or minimal microbial consortia involving a few species seems to be promising. They can well serve as model system(s) for method and technology development. Tools such as bioaugmentation could aid to produce additional fermentation end products or to enhance an existing bioprocess [111]. Finally, Industrial biotechnology using mixed culture is already making definite inroads into the chemical industry as an enabling technology, and its role will continue to grow during the coming years. To sum it up, we need to have a deeper understanding and description of the different phenomena and processes from molecular to process levels in a multiscale and interdisciplinary approach.

Figure 13.6: A mutualistic E. coli–S. cerevisiae consortium for production of oxygenated taxanes. (A) The recombinant E. coli strain grows on xylose and produces acetate that serves as the sole carbon source for the yeast. (B) The taxadiene that is produced by E. coli is further oxygenated in the recombinant yeast strain. Stable coculture in the same bioreactor was achieved by designing a mutualistic relationship between the two species in which a metabolic intermediate produced by E. coli was used and functionalized by yeast [109].

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[20] Fu N, Peiris P, Markham J, Bavor J. A novel co-culture process with Zymomonas mobilis and Pichia stipitis for efficient ethanol production on glucose/xylose mixtures. Enzyme Microb Technol. 2009, 45(3), 210–217. [21] Maki M, Leung KT, Qin W. The prospects of cellulase-producing bacteria for the bioconversion of lignocellulosic biomass. Int J Biol Sci. 2009, 5(5), 500–516. [22] Li C, Fang HHP. Fermentative hydrogen production from wastewater and solid wastes by mixed cultures. Crit Rev Environ Sci Technol. 2007, 37(1), 1–39. [23] Wang S, Zhang T, Bao M, Su H, Xu P. Microbial Production of Hydrogen by Mixed Culture Technologies: A Review. Biotechnol J. 2020, 15(1), 1–8. [24] Mishra P, Krishnan S, Rana S, Singh L, Sakinah M, Ab Wahid Z. Outlook of fermentative hydrogen production techniques: An overview of dark, photo and integrated dark-photo fermentative approach to biomass. Energy Strategy Rev. 2019, 2016, 24, 27–37. [25] Nagarajan D, Lee DJ, Kondo A, Chang JS. Recent insights into biohydrogen production by microalgae – From biophotolysis to dark fermentation. Bioresour Technol. 2017, 227, 373–387. [26] Rai PK, Singh SP. Integrated dark- and photo-fermentation: Recent advances and provisions for improvement. Int J Hydrogen Energy. 2016, 41(44), 19957–19971. [27] Yang H, Zhang J, Wang X, Jiangtao F, Yan W, Liejin G. A newly isolated Rhodobacter sphaeroides HY01 with high hydrogen production performance. Int J Hydrogen Energy. 2014, 39(19), 10051–10060. [28] Cai J, Wang G. Hydrogen production from glucose by a mutant strain of Rhodovulum sulfidophilum P5 in single-stage photofermentation. Int J Hydrogen Energy. 2014, 39(36), 20979–20986. [29] Abo-Hashesh M, Wang R, Hallenbeck PC. Metabolic engineering in dark fermentative hydrogen production; theory and practice. Bioresour Technol. 2011, 102(18), 8414–8422. [30] Cai G, Jin B, Monis P, Saint C. Metabolic flux network and analysis of fermentative hydrogen production. Biotechnol Adv. 2011, 29(4), 375–387. [31] Kleerebezem R, Van Loosdrecht MC. Mixed culture biotechnology for bioenergy production. Curr Opin Biotechnol. 2007, 18(3), 207–212. [32] Sivagurunathan P, Kumar G, Bakonyi P, Kim SH, Kobayashi T, Xu KQ, et al. A critical review on issues and overcoming strategies for the enhancement of dark fermentative hydrogen production in continuous systems. Int J Hydrogen Energy. 2016, 41(6), 3820–3836. [33] Saady NMC. Homoacetogenesis during hydrogen production by mixed cultures dark fermentation: Unresolved challenge. Int J Hydrogen Energy. 2013, 38(30), 13172–13191. [34] Wong YM, Wu TY, Juan JC. A review of sustainable hydrogen production using seed sludge via dark fermentation. Renewable Sustainable Energy Rev. 2014, 34, 471–482. [35] Faloye FD, Gueguim Kana EB, Schmidt S. Optimization of hybrid inoculum development techniques for biohydrogen production and preliminary scale up. Int J Hydrogen Energy. 2013, 38(27), 11765–11773. [36] Li Q, Liu CZ. Co-culture of Clostridium thermocellum and Clostridium thermosaccharolyticum for enhancing hydrogen production via thermophilic fermentation of cornstalk waste. Int J Hydrogen Energy. 2012, 37(14), 10648–10654. [37] Sgobba E, Wendisch VF. Synthetic microbial consortia for small molecule production. Curr Opin Biotechnol. 2020, 62, 72–79. [38] Kenny DJ, Balskus EP. Engineering chemical interactions in microbial communities. Chem Soc Rev. 2018, 47(5), 1705–1729. [39] Brenner K, You L, Arnold FH. Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 2008, 26(9), 483–489. [40] Maeda T, Sanchez-Torres V, Wood TK. Hydrogen production by recombinant Escherichia coli strains. Microbial Biotechnol. 2012, 5(2), 214–225.

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Chapter 14 Economic framework of autotrophic processes Abstract: In this chapter, the economic framework and action points for the upscaling and commercialization of biotechnological processes as well as product designs will be examined with a special focus on autotrophic processes. The most important factors influencing the evaluation of processes and the investment readiness will be summarized. Besides other factors, the regulatory framework, sustainability, the unique selling point, chances and barriers will be described with a special emphasis on autotrophic processes. The need and benefit of using defined technology readiness levels will be introduced. The technological challenges of autotrophic processes in particular will be described including required implementation steps, such as devices and downstream processing technologies. Keywords: unique selling point (USP), life cycle analysis (LCA), techno-economic evaluation (TEE), technology readiness level (TRL), downstream processing (DSP), carbon footprint, greenhouse gas emissions

14.1 Introduction Multiple aspects must be considered during the development, scale-up and market entry of a new biotechnological process, or a novel biobased or biotechnological product. Not all these aspects are directly related to the development of the new technology or the production process itself. Rather, ecological and economic impacts, carbon-neutrality, global developments and political decisions must also be taken into account and be related to the new biotechnological processes or products under investigation. One of the most important aspects is that a new production process, an innovative technology or a novel product design shows an added value compared to existing solutions. It is important to identify all added values the proposed design offers and to describe what makes these benefits valuable. Bioeconomy and biotechnological processes create a huge potential for creating an added value in new value chains of products and technologies that are currently still based on fossil oil, natural gas and coal. Thus, these processes contribute to the raw material transition from fossil to regenerative carbon sources. However, the existing planetary boundaries make it almost impossible to completely replace all fossil-based value chains with biobased alternatives, as the global economy currently consumes twice as much fossil carbon as biogenic carbon [1]. Consequently, markets and sectors need to be identified for which the utilization of all carbon sources (carbon from biomass, CO2 or recycled https://doi.org/10.1515/9783110550603-014

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carbon) represent a new value proposition. This is especially the case for many products from the chemical, food and feed, textile, pharmaceutical and polymer industries, but also in the market segments of personal and home care, as well as alternative fuels, renewable carbons play a decisive role, already now but also in the future. A process can have a value proposition in one or many aspects compared to what already exists. Nevertheless, it is crucial to define the unique selling proposition or the unique selling point (USP) of a process or product. In other words, to work out the outstanding performance feature that clearly sets an innovation apart from the competition. In summary, especially in the case of new biobased or biotechnological production processes or new technologies for the utilization of renewable carbon and valuable resource streams (raw materials, side streams, waste or waste gas streams), it is important to take a closer look and evaluate the entire life cycle of all raw materials and in- and output streams of the process. For this purpose, a life cycle analysis (LCA) is carried out (Box 14.1). An LCA may include all environmental impacts of a whole value chain: the used substrate, the production process and end-of-life treatment of the generated products and byproducts, as well as the associated upstream and downstream processes. Environmental impacts include all relevant extractions from the environment as well as emissions which might be generated. Conducting an LCA is particularly important from a sustainability point of view with regard to ecological aspects like climate change impacting the economic efficiency of a process or product.

Box 14.1: Life cycle analysis (LCA) An LCA is conducted to evaluate the entire life cycle of all raw materials and in- and output streams of the process carried out. LCA includes all environmental impacts during raw material extraction, production, processing, transport, utilization, sale, and disposal or recycling. All technologies, as well as energy and material streams that go into and out of the process in the individual phases are taken into account in an LCA. In order to be able to carry out an LCA, the goal and scope must be defined, an evaluation of the current status must be carried out, as well as an impact assessment and, of course, a cumulation and interpretation of the data.

Various strategic regulations at national and international level define and control the taxation of CO2 emissions or the pricing of corresponding certificates. The use of renewable energies and the production of hydrogen to generate these renewable energies also play a decisive role. However, not only the carbon footprint and the avoided greenhouse gas emissions are important, but also the recyclability and degradability of products, byproducts and all side streams that can arise in a biotechnological process have to be considered. All these aspects have an influence on whether a process is economically feasible in a new or already existing value chain. In addition to an LCA, technical and economic aspects of a new biotechnological production process must also be analyzed. This can be done through a comprehensive techno-economic

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evaluation (TEE) (Box 14.2). A structured economic analysis for an innovative production process comprises the evaluation of opportunities and risks, important stakeholders, target markets and finally the development potential of the process itself.

Box 14.2: Techno-economic evaluation (TEE) A TEE is a method to analyze the technical and economic performance of a process, product, or service. In a TEE process modeling, engineering design and economic criteria are investigated. Many factors strongly influence the economic potential and a possible market entry of a process or a product being analyzed and evaluated in a TEE.

When analyzing opportunities and risks, it is important to start at a global level. Entrepreneurs should be aware of global developments, perspectives and trends, impacting the world economy, e.g., strongly fluctuating crude oil prices, increasing global population and food shortage, climate change, biodiversity, or a global pandemic. Thus, each individual industrial sector and global economic structures are influenced by these developments, which naturally affect the economic viability of a new biotechnological process or product. An example of such regulatory influence on products is the use of microplastics. Microplastics are added to a range of products, including fertilizers, pesticides, cosmetics, household and industrial cleaners, detergents, paints, and products used in the oil and gas industry. Microplastics are also used as a soft infill material in artificial turf. In consumer products, microplastics are mainly known as abrasives, or to affect the thickness, texture and stability of a product. The European Chemicals Agency’s Risk Assessment Committee (RAC) issued its opinion in June 2020 [2]. The RAC supported the proposal and recommended stricter criteria for biodegradable polymers and a ban on microplastics used as infill material in artificial turf pitches after a transition period of six years. Projecting the process development or a potential new product in a future global context could help to identify crucial effects influencing the potential demand of brand owners and consumers. Beside global developments, political decision makers are constantly shaping the economic framework of tomorrow. Indicators and political contracts can be used to optimize market entry strategies. These political agreements should be evaluated on global (e.g., Paris Agreement on Climate Change), continental (e.g., EU Sustainability Development Goals), national (e.g., bioeconomy strategy of the German government) and regional level. In addition to the opportunities and risks, the analysis of suitable stakeholders is also of great importance. Stakeholders can be individual persons, groups of people or organizations like companies. It should be targeted to know which stakeholders are in favor or against the market entry of the new technology or innovative product. A detailed TEE for a new product or a new production process implies the identification and classification of the most important stakeholders. By comparing the power and influence of all stakeholders will enable the identification of promoters, competitors or opponents for a certain new development.

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Besides this, it is essential to have extensive knowledge of the target market to ensure a successful market entry. First, it has to be examined whether the developed sustainable biotechnological process replaces an already existing technology or product as a drop-in solution that has been produced by fossil-based materials, so far, or whether an innovative, new product is to be introduced to the market. Independent of whether a technology or product generates something new or replaces something existing, it is essential that a significant added value can be identified for a market. If it is a drop-in solution, it must be evaluated compared to an already existing product or process. Some important aspects that must be compatible are price, functionalities and properties, production conditions, amount of energy and resources needed for production, and the CO2 saving potential. In addition, the size of the existing sales market must be closely examined, and the new production capacities put in relation to it. Furthermore, it can be very interesting to examine whether the new product can offer added value and additional functionalities through the biotechnological production method compared to the fossil-based alternative product. If it is a new process or a new product that will be launched, among others, possible markets, the national and international patent situation, availability of raw materials, sales markets and important requirements for process development must be analyzed in more detail. Knowledge of the desired production volume and costs as well as the geographical target markets and industry sectors is a prerequisite for estimating the time to market. Basically, however, the transition phase of the raw material from fossil to renewable energy and carbon sources is very critical and difficult, since bioeconomic value chains must be established, while at the same time they have to compete with a still dominant economy based on fossil raw materials. How this process is shaped depends largely on the political framework conditions, including climate targets, the potential and strategy of each individual company to introduce and establish new innovative and sustainable value chains, the availability of renewable energies and finally the acceptance of consumers.

14.2 Economic framework of biotechnological and autotrophic processes With the demand for a sustainable economy, the resources available in nature have increasingly become the focus of societal attention since the 1980s. For politics and business, this was linked to the realization that the protection of natural resources for future generations cannot be guaranteed in the long term with existing industrial processes: Above all, the protection of the limited availability of fossil carbon and also the avoidance of releasing carbon emissions into the atmosphere to save our climate contributed to the rethinking and intensified the search for alternatives. So,

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the use of renewable raw materials and the application of biotechnological processes are becoming increasingly important for industrial applications. Using enzymes and microorganisms, many production processes can be designed environmentally friendly and cost-effective. Industrial biotechnology is an important pillar for an innovative bioeconomy. Strategic alliances of industry and academia along value chains lay the foundations for efficient, innovative and sustainable processes that have been built on limited fossil resources, so far. Microbes as customized cell factories as well as enzymes or enzymatic cascades make it possible to utilize renewable raw materials and to transform them into highly specific target molecules. Biotechnological production is often the more sustainable strategy compared to chemical synthesis, especially in the production of fine chemicals. Fine chemicals are substances that have a high degree of functionalization and are produced in volumes of less than 10,000 t/a worldwide with a price higher than 100 €/kg of product. Especially, highly complex molecules with a high degree of functionalization are often very difficult to synthesize chemically. Beside long synthesis routes with constantly reducing overall yields after each reaction step, high temperatures, high pressures and the use of ecologically harmful chemicals are often required. Biotechnological processes offer the advantage over chemical processes that they can often take place under mild, environmentally friendly conditions. Microorganisms achieve complex multi-step substance transformations with high yields at room temperature and ambient pressure. In addition to the use of renewable raw materials, the exclusion of aggressive media and the reduced energy requirements strongly reduce the environmental impact of biotechnological processes. Multi-stage processes in chemistry can be shortened by a fermentation process with low by-product formation. Industrial biotechnology and a sustainable bioeconomy are already well established in many sectors, such as parts of the food industry, detergent or textile production. In many research and development projects the basis for efficient biotechnological production methods in the production of bioplastics or the generation of energy from renewable raw materials was established. For many, especially very complex molecules, such as rhamnolipids, industrial biotechnology is even the only possible production method. However, ensuring the constant availability of large quantities and invariant qualities of biomass also pose a challenge for biotechnological processes. Certain microorganisms show a high potential to overcome these limitations. These microorganisms are able to convert a carbon-containing raw material that is available in almost unlimited quantities, CO2. Autotrophic metabolism could thus be the basis for a further field of application in industrial biotechnology. A special feature of many autotrophic microorganisms is that they can use not only pure CO2 streams, but also gas streams with impurities, such as process gases from steel or cement production. Nevertheless, various reasons have prevented a broad industrial application of autotrophic biotechnology. The achievable cell densities under chemolithoautotrophic or photoautotrophic conditions are limited to a few grams per liter.

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These limits are given by the low gas–liquid transfer of the substrate, resulting in low substrate availability for growing cells. The low solubility of carbon-containing gases in an aqueous cultivation medium results in a substrate limitation of the organisms, already at a low cell density. In addition, many chemolithoautotrophic organisms are not yet genetically accessible and thus only genetically modifiable to a limited extent. There are only a few exceptions within the autotrophic microorganisms, which show genetic accessibility, including Cupriavidus necator and Rhodopseudomonas palustris. In photoautotrophic bacteria, the dependence of cell metabolism on light is particularly difficult. Light-dependent processes are very difficult in upscaling because a large surface area and sufficient light in the form of solar irradiation or LED, as well as an effective system to combat reactive oxygen species, which are produced within the metabolism and can cause great damage within the cells are required. In addition, the cell densities in photoautotrophic cultures are also very low, as the organisms are often unable to operate an optimal cell metabolism due to self-shading. All these aspects mean that, regardless of whether chemoautotrophic or photoautotrophic, autotrophic systems are often not competitive compared to heterotrophic microbes in industrial applications. For an industrial process it is very important to create a highly efficient production system to design an economically viable process. Accordingly, not only the need for a technology, the available framework conditions, or sufficiently quantities of raw materials and energy are necessary for industrial economic processes, but also scalability of the technology to be used.

14.3 Technology readiness level (TRL) A process or technology development faces many challenges. Just because a system works on a laboratory scale does not mean it will work in a large-scale reactor. For this reason, it is important to start an economic analysis by looking at the process development level and the required steps to generate an economical feasible process design. Therefore, several different technological dimensions need to be evaluated. For this purpose, the technology readiness level (TRL) was defined (Box 14.3). The TRL is a specific value between 0 and 9 categorizing the development state of a newly designed processes or technology. This indicator is important to easily describe the current development state towards important stakeholders like investors, policy makers or authorities. Table 14.1 is giving a rough categorization of the individual TRLs.

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Table 14.1: Overview on technology readiness levels (TRLs). Based on the EU Horizon 2020 Work Program 2016–2017 [3]. TRL Title

Workplace

Description



Idea

Sheet of paper

Basic idea formulated, concept not proven, no tests performed



Basic research

Sheet of paper

Principles postulated and observed but no experimental proof



Technology formulation

Sheet of paper

Concept and application formulated, laboratory tests planned



Applied research

Laboratory

First laboratory tests completed, proof-of-concept successful



Small-scale prototype

Laboratory

First process optimizations successful, target values met and redefined



Large-scale prototype

Pilot plant

Scale-up to pilot scale successful, target values for final process defined



Prototype system

Pilot plant

Target values for final process met, finalization of product properties



Demonstration system

Demonstration Scale-up to pre-commercial scale successful, plant production in industrially relevant environment



First of a kind commercial system

Production site Scale-up to commercial scale successful, final customer acceptance of product



Full commercial application

Production site Target values for full-scale process met, process optimized and finalized

Box 14.3: Technology readiness level (TRL) The definition of a TRL is a method originally used in aviation and space technology. A TRL describes the technical development status of a technology or process. For categorizing technologies and processes being developed in the framework of the EU Horizon 2020 program, the TRL descriptions were adapted by the EU for a broader applicability. Usually, the TRL ranges from TRL 1 (basic principles observed) to TRL 9 (system functions in operational competitive environment). For a better understanding, TRL 0 can be added to describe the first documentation of an idea on a new technology or process. The TRL helps to estimate the risk of investment for funding agencies and investors, to classify the technology status in a quantifiable way, and to make decisions about the transition of a technology to the next level.

Usually, the development of a novel technology starts with an idea and a first theoretical concept (TRL 0). This concept is then elaborated further, current research and patents are screened for comparable technologies and the state of the art is defined (TRL 1). Fields of application and potential process routes for producing the

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newly designed product are specified further leading to the concept for first experimental tests (TRL 2). Laboratory testing on a scientific level is conducted at TRL 3–4, leading to a process that is optimized at lab scale to a certain extend. Key parameters of the process and/or the final product are defined and met. The following development phase is commonly known as the “valley of death.” Even though the production costs per unit product often decrease with scale, the overall costs and complexity multiply at a post-lab scale. Therefore, most development projects lack the required financial support and engineering expertise to be transferred to a pilot plant. Specified scale-up facilities help start-ups and SME to take those processes to the next TRLs 5–7. Having defined and met certain target values for the process and the final commercial product at pre-commercial (demonstration) scale, the produced product can be tested by customers for their further applications. Once the process has been proven to run reproducible and reliable at demonstration scale (TRL 7), the next TRLs 8–9 describe the transfer into a fully commercial plant. The TRL helps to classify the current state of a process development project and to demonstrate the route and key milestones to be achieved on the road toward commercialization. Beyond the information given in Table 14.1, the TRL can also help to estimate the performance of a specific process in earlier development stages. Key performance indicators (KPI) describe characteristic properties of a technology or process. In biotechnological processes, common KPIs are, for example, production rate, final product concentration, concentration of by-products, purity or space-time-yield. Usually, production processes show better KPI values compared to lab-scale or pilotscale processes. Therefore, the performance of a new, alternative process for producing a given product should rather be compared to the conventional process at the same TRL than to corresponding conventional process at production scale. Toward investors, policy makers and funding agencies, a proper classification of the process TRL helps to receive funding and support that is essential for successful commercialization and market entry. The number of biotechnological process developments that are still in their infancy is high, which results in a huge number of processes with low TRL. The idea for a novel production process or an innovative product can be generated fast and with low efforts in time or money. During the elaboration of concepts and process development more and more technologies are not pursued further because they are not developing as expected. Thus, the total number of developments in the pipeline toward commercialization is being reduced constantly. Until TRL 4–5 (pilot scale), the technologies are often developed at research institutes and therefore predominantly public funded. This leads to a high number of technologies being available at lab scale. Consequently, only a few of these technologies are ready to be used at commercial scale making it difficult to estimate their economic viability. Many of the technologies presented in this book did not yet reach the level of commercialization. Consequently, specific equipment required for their implementation at larger scales may not be readily available as it is at a low TRL as well. This means that

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judging the economic viability of these processes comes with a significant uncertainty. Also, it will take a considerable time before they can be implemented at industrial scale as the development, testing and production of the required equipment needs to be accounted for. Contrary to many classical biotechnological processes which often yield medium or high-value products, autotrophic fermentation typically produces commodity chemicals of only low chemical complexity and therefore low value. For instance, the bulk price of products such as acetate, ethanol or butanol on the world market is well below 1 €/kg. This puts severe constrains on the employed processes and equipment to maintain economic feasibility, as complex and energy-intensive techniques may simply be too expensive even accounting for economy-of-scale effects. The following gives a brief overview of the current state-of-the-art of equipment needed to implement the technologies discussed in this book at larger scales. For gas fermentation, it is particularly important to achieve a high mass transfer of the gas into the medium in order to achieve a high production rate and cell density. While continuously stirred tank reactors (CSTR) are very popular at lab- or even miniplant scale, their high specific energy demand renders them unsuitable for large-scale industrial implementation. Suitable designs which are less energy intensive are, for example, bubble-column or airlift reactors [4]. Although many companies have been or are exploring syngas fermentation, LanzaTech is currently (as of 2021) the only company that is running syngas fermentation plants at a commercial scale [4]. So, while this could mostly be considered a mature technology, the lack of widespread application means that the availability of expertise and equipment at industrial scale needs to be carefully evaluated. Some of the processes discussed in this book require the illumination of the reactors, for instance, by sunlight. This is a considerable challenge as at large scale the surface-to-volume ratio of traditional reactor types decreases while at the same time, the light penetration depth stays constant, resulting in poorer illumination efficiency. This challenge is addressed by specific photobioreactors (PBRs), which are typically modular designs of smaller dimensioned subunits such as vertical column, horizontal tubular or flat panel PBRs [5]. The modular nature of these systems means that scaleup is mostly achieved through numbering-up so cost reductions of industrial-scale systems are limited to the effects of mass-production. As a low-cost option, also openpond cultivation may be considered but this is very difficult to control due to the constant exchange with the environment and comes with a significant contamination risk. One of the main cost drivers can also be the CO2 supply, which may be counterintuitive since CO2 is usually regarded as having a negative price (e.g., due to credits in emission certificates and CO2 taxation). However, this is only the case if a waste gas stream from other nearby processes (e.g., power plants or steel mills) is used, which is therefore highly recommended [5]. If instead, CO2 is generated via direct air capture or transported from other sites, the associated costs will put a significant strain on the economic feasibility of the whole process.

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Instead of sunlight, these photo-driven processes may also be powered by artificial light sources. However, even though recent advances in LED technology have significantly lowered both the investment and operating costs, the generation of photons still comes with a significant price tag [6], thus this is only an option for processes generating higher-value products. Even though there are commercial-scale photoreactors available, these were mostly designed for radical chain reactions such as photochlorination. Due to the chain propagation these reactions have an extremely high quantum yield, so illumination and energy efficiency were never one of the main design criteria here. However, for the processes discussed herein with quantum yields well below unity, illumination efficiency is very important both to achieve high space time yield and to keep the overall costs in check. At lab scale, there exist numerous, easy-to-implement options for efficiently illuminated photoreactors such as photomicroreactors made from coiled tubing, which unfortunately are typically not readily scalable into larger dimensions [7]. This leaves the option of numbering-up these small systems, but this is a very cost-intensive option only feasible for the production of very highly valued products, for example, pharmaceutically active compounds [7, 8]. The use of wirelessly powered internal illumination also appears to be a promising technique both for the cultivation of phototrophic microorganisms and powering photoenzymatic processes, which theoretically enables a traditional scale-up, but is not yet available at industrial scale [9, 10]. For electroautotrophic cultivation, adequate reactor designs currently only exist at lab scale, so considerable effort needs to be invested before these processes can readily be scaled into pilot and industrial scale [11]. Here, the major constrain for upscaling is the electrical resistance of the reaction medium, so both electrodes need to be very close to each other. Apart from numbering up smaller dimensioned subunits as with PBRs, rectangular tanks with flat plate electrodes also present a directly scalable design with one study demonstrating this at 1 m3 scale [12]. Alternatively, electrochemistry and biotechnology may also be coupled ex-situ via a mediator, for example, electrolysis coupled with subsequent gas fermentation [13]. As both of these separate technologies are already state of the art, this presents an approach which can readily be implemented at industrial scale with current technology [14]. Downstream processing, that is, the recovery and purification of the final product, is also an important aspect to consider in the economic framework. Contrary to the production of high-value products such as enzymes or biopharmaceuticals where the downstream processing costs often make up more than half of the total (high) production costs [5], many of the products of the processes discussed in this book are low-value commodities such as acetate or PHB. The low value of these products only allows for few and inexpensive purification steps. Particularly interesting are methods which can achieve already a high purity after a facile initial process step such as crystallization or distillation. However, this typically requires already a fairly high concentration of the product and minimal contaminants with similar properties. Pre-concentration steps such as thermal evaporation,

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nanofiltration or electrodialysis are energy and thereby cost intensive and tend to also concentrate some of the impurities[15]. It is therefore important that the fermentation itself already facilitates the product recovery by producing fairly high concentrations of the target product (product titers of >10 g/L) with a high selectivity. This is of course only of minor importance if only the production of biomass is desired or the primarily generated product is used directly as feedstock for a subsequent fermentation process. In these cases, a very simple and inexpensive treatment such as microfiltration to separate the cells is usually sufficient. If the desired product is not secreted and located inside the cells, its extraction requires to break down the cells. Subsequently, the main challenge is the separation of the product from all other biomass constituents, which is usually achieved through extraction (e.g., using a solvent-antisolvent or aqueous two-phase system approach) [16]. In this case also, a high concentration of the desired product in the biomass, for example, >50% for PHB, is necessary to achieve a high purity while keeping the processing costs in check.

References [1]

Umweltbundesamt. Sustainable Use of Global Land and Biomass Ressources [Internet]. 2013 [cited 2021 Mar 23]. Available from: https://www.umweltbundesamt.de/sites/default/files/ medien/419/publikationen/130617_englisch_lang_web.pdf [2] ECHA. Microplastics - Registry of restriction intentions until outcome [Internet]. 2018 [cited 2021 Mar 23]. Available from: https://echa.europa.eu/de/registry-of-restriction-intentions/-/ dislist/details/0b0236e18244cd73 [3] European Commission. Horizon 2020 Work Programme 2016-2017, 20. General Annexes [Internet]. 2017 [cited 2021 Mar 23]. Available from: https://ec.europa.eu/research/partici pants/data/ref/h2020/other/wp/2016-2017/annexes/h2020-wp1617-annex-ga_en.pdf [4] Stoll IK, Boukis N, Sauer J. Syngas fermentation to alcohols: reactor technology and application perspective. Chem Ing Tech. 2020, 92(1–2), 125–136. [5] Gupta PL, Lee SM, Choi HJ. A mini review: photobioreactors for large scale algal cultivation. World J Microbiol Biotechnol. 2015, 31(9), 1409–1417. [6] Sender M, Ziegenbalg D. Light sources for photochemical processes – estimation of technological potentials. Chem Ing Tech. 2017, 89(9), 1159–1173. [7] Noël T. A personal perspective on the future of flow photochemistry. J Flow Chem. 2017, 7 (3–4), 87–93. [8] Su Y, Kuijpers K, Hessel V, Noël T. A convenient numbering-up strategy for the scale-up of gas-liquid photoredox catalysis in flow. React Chem Eng. 2016, 1(1), 73–81. [9] Heining M, Sutor A, Stute SC, Lindenberger CP, Buchholz R. Internal illumination of photobioreactors via wireless light emitters: a proof of concept. J Appl Phycol. 2015, 27(1), 59–66. [10] Duong HT, Wu Y, Sutor A, Burek BO, Hollmann F, Bloh JZ. Intensification of photobiocatalytic decarboxylation of fatty acids for the production of biodiesel. ChemSusChem. 2021, 14(4), 1053–1056. [11] Enzmann F, Stöckl M, Zeng AP, Holtmann D. Same but different–scale up and numbering up in electrobiotechnology and photobiotechnology. Eng Life Sci. 2019, 19(2), 121–132.

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[12] Cusick RD, Bryan B, Parker DS, Merrill MD, Mehanna M, Kiely PD, Liu G, Logan BE. Performance of a pilot-scale continuous flow microbial electrolysis cell fed winery wastewater. Appl Microbiol Biotechnol. 2011, 89(6), 2053–2063. [13] Stöckl M, Harms S, Dinges I, Dimitrova S, Holtmann D. From CO2 to bioplastic – coupling the electrochemical CO2 reduction with a microbial product generation by drop-in electrolysis. ChemSusChem. 2020, 4086–4093. [14] Haas T, Krause R, Weber R, Demler M, Schmid G. Technical photosynthesis involving CO2 electrolysis and fermentation. Nat Catal. 2018, 1(1), 32–39.6 [15] Han IS, Cheryan M. Downstream processing of acetate fermentation broths by nanofiltration. Seventeenth Symp Biotechnol Fuels Chem. 1996, 57, 19–28. [16] Tripathi AD, Joshi T, Khosravi-Darani K, Koller M, Singh SP, Shrivastava A, Mishra S. Recovery and Characterization of Polyhydroxyalkanoates. In: Koller M. Recent Advances in Biotechnology Microbial Biopolyester Production, Performance and Processing Volume 2: Bioengineering, Characterization, and Sustainability. Bentham Science Publishers. 2016, 266–302.

Index (meta)genome 47 1,3-propanediol 365, 375 3DM 51, 53, 70 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) 69 AADS 60 absorbance-activated droplet sorting 60 accumulation 321–322, 326–327, 333, 343–344 acetate 323, 325–327 acetogenic bacteria 88 Acinetobacter 67 active site 56, 64, 68 acyl-CoA dehydrogenase 64 ADH-catalyzed oxidation reactions 259 adsorption 102 alcohol dehydrogenase 212, 214, 219–221, 223, 227, 230–231, 233–234, 371, 373 algae 193 alginate 189 anaerobic digestion 87 anoxygenic photosynthesis 220–221 antagonism 355 antagonistic 355 antagonists 92 antenna size 63 antibiotics 370 antimicrobial substances 370 applications 320–321, 339–340 Arabidopsis thaliana 66, 69 archaea 88, 321 astaxanthin 175, 183–186 asymmetric reduction 219, 223–224, 226, 228, 231 atom economy 212, 214, 216, 222 atorvastatin 58 ATP synthesis 116 autotrophic 48, 57, 62–65, 67, 69–71, 301–302, 307, 354 autotrophic biotechnology 389 autotrophy 2 auxotrophic 59 Bacillus 69 bacteria 320–326, 328, 332, 341, 343 bacterial cocultures 353 https://doi.org/10.1515/9783110550603-015

bacterial consortia 353 Baeyer−Villiger monooxygenase 371 basic local alignment search tool (BLAST) 52 B-factors 53 bicarbonate transporter 63 bioactive compounds 94 bioavailable 103 BioBricks. see biological parts biocatalysts 47, 49–51, 58, 60, 65, 70, 247 biodegradability 341 biodegradable plastics 375 biodiesel 375 bioeconomy 385 bioelectricity 369 Bioethanol 365 biofilm 102 biogas 342 biological parts 132–133, 151 – prefix 133 – registry 133 – suffix 133 biomass 327, 330–335, 337, 343 biomethane 95 bioreduction 219, 226–228, 231, 236 biorefinery 85, 342–343 biosynthesis 322, 327, 329 biotechnological processes 385 biotechnology 174, 188–189, 193 biotransformation 207, 371 biotransformations 360 BLAST 52–53 blends 341 bubble-column or airlift reactors 393 C. necator 48, 57, 298–299, 301–311 Calvin-Benson cycle. see CBC Calvin-Benson-Bassham cycle 12, 116 Calvin–Benson–Bassham 299, 301 CamSol 55 Candida boidinii 66 carbene transfer 68 carbon 319, 321–322, 327, 330, 333, 335, 341–343 carbon capture 3 carbon concentrating machinery 8 carbon concentrating mechanisms (CCMs) 63

398

Index

carbon dioxide concentrating mechanism (CCM) 119 carbon dioxide fixation 354 carbon dioxide uptake rate (CUR) 117 carbon transporters 119 carbonic anhydrase 63 carboxydotrophic 91 carboxylation 63–64 carboxysome 119 carboxysomes 62–63 carvolactone 375 carvolactones 373 cascadic 86 CasPER 57 CASTing 50–51 catabolism 89 catalysts 85 cathode 280 CAVER 53, 70 CBC 63–64 cell shading 118 cell surface display 59 cell viability screening 59 chain 320, 327, 329, 334, 337, 339 chassis 9, 135, 137, 159 chemolithoautotrophic 354, 389 chemolithoautotrophs 48 chemolithoautotrophy 2, 8 Chimera 51, 70 chlorophyll 5, 64 chloroplasts 62, 115 circular economy 85 climate change 3 Clostridia 364 clustered regularly interspaced short palindromic repeats. see CRISPRs CO2 fixation 55, 62, 64–65, 71 coculture 93 coenzyme A (CoA) 64 cofactor engineering 65, 67 cofactor recycling 207, 209, 211–212, 214–217, 219, 225–226, 231–232 cofactor specificity 48, 65–67 cofactors 48, 65–68, 70, 248 column photobioreactor 124 commercial 321, 330 commercialization 392 companies 341

compartmentalization 356–357 continuously stirred tank reactors (CSTR) 393 corrosion 93 covalent bonding 103 CRISPR/Cas 27, 155, 159 – Cas12a/Cpf1 155 – Cas9 155–156 – toxicity 156 CRISPR/Cas9 28, 57–58, 70, 307 CRISPR-associated protein 9 (Cas9) 56 CRISPRi 156 – dCas12a 156 – dCas9 156 – library 156 CRISPRs 56 Cry1Ab 69 Cry1Ac 69 cryogels 103 crystallinity 334, 337–338, 341 CSR-SALAD (Coenzyme Specificity ReversalStructural Analysis and Library Design) 66 cultivation 321, 328, 330–333, 338, 343–344 cumene dioxygenase 373 Cupriavidus necator. see C. necator Cupriavidus necator 48 Cupriavidus necator H16 216–219 Cupriavidus necator H16 220 cyanobacteria 2, 62–63, 65, 86, 319, 321–324, 327–328, 330, 332–334, 342–343, 357 cyclohexanone monooxygenase 67, 373 cyclopropanation 68 cytochrome P450 377 dark fermentation 362 dark reaction 116 dark respiration 117 Darwinian evolution 49 decarbonization 85 degradation 322, 327, 334, 337, 339–340 degradation tags 151 dehalogenase 58, 67 desulfurization 93 Diels-Alder reactions 56 direct electron transfer 98 direct regeneration 255 directed evolution 47–49, 56–59, 61, 65, 69–70 DNA shuffling 50, 69

Index

domain swapping 69 double-strand breaks (DSBs) 56 downstream processes 386 downstream processing 95, 394

399

ferredoxin protein complexe 116 fine chemicals 389 Fischer–Tropsch synthesis 367 flat plate photobioreactor 122 flavin 247–248 flavin adenine dinucleotide. see FAD flavin mononucleotide. see FMN/FMNH flavin-dependent reductases 255 flavins 48, 65, 67, 255 flavo-enzymes 67 flavoenzymes 255 flooding 120 fluorescence-activated cell sorting (FACS) 60 fluorescence-activated droplet sorting 60 fluxomics 24 FMN 67 FMNH 67 FoldX 54 force field 54 formate dehydrogenase 257 fractional distillation 95 Framework for Rapid Enzyme Stabilization by Computational Libraries (FRESCO) 54 FRESCO 54, 70 furnace gases 94

E. coli 48, 55–57, 59, 63–64 economic framework 388 Einstein (unit) 115 Einstein equation 115 electroautotrophic cultivation 394 electroautotrophic synthesis 370 electrobiorectors 287 electrochemistry 278 electrolysis 96 electrolytic water splitting 96 electromagnetic spectrum 114 electron acceptors 88 electron donor 278 electron transport 5 electrotrophic 309, 311 electrotrophs 278 ELISA 60 Embden–Meyerhoff–Parnas 299 ene reductases 255 ene-reductase 219–220, 227, 230, 233, 236, 373 energy 319, 321–322, 327, 333–335, 342 enoyl-CoA carboxylases/reductases 64 Entner–Doudoroff 299 environments 321, 330, 339–340 enzymatic conversions 371 EnzymeMiner 55 epPCR 49–50, 57, 69 equilibrium 101 Escherichia coli. see E. coli eukaryotic 356 European Chemicals Agency’s Risk Assessment Committee (RAC) 387 EvolvR 57 extracellular electron transfer 310 extraction 332–336, 338, 343 extrusion 320, 337–339

gas fermentation 85, 393 gas scrubbing 104 gasification 87 gas-liquid interface 120 genome editing 27 Geobacter 282 Gibbs free energy 369 Gluconobacter oxydans 66 glyceraldehyde 3-phosphate 116 glycogen 327, 333 glycolyl-CoA carboxylase (GCC) 64 glyphosate 69 granules 319–320, 322, 327, 337 great oxidation event 2 gRNA 57–58 guide RNA (gRNA) 56

FACS 60 FAD 61, 64, 66–67 FADS 60 fatty acids 367 fermentation 207–208, 218, 221, 321, 327, 333, 354

heme 65, 67–68 hemoproteins 67–68 Henry’s law 119 heterocysts 92 heterotrophic 48, 55, 64–65, 69, 71, 298–300, 307, 354

400

Index

heterotrophic bacteria 321, 327–328, 332, 341–343 heterotrophs 354 heterotrophy 1 high-fidelity homology directed repair 57 homoacetogenic 91 homoacetogens 363 homologous recombination 57–58, 153, 155, 224, 229 homology models 50, 52 hot spots 53, 64, 70 HotSpot Wizard 53, 70 humidity 337 hybrid photobioreactor 122 hydrogen 8 hydrogen production 361 hydrogenase 9, 297, 301, 304–307 – membrane-bound hydrogenase 9 – soluble hydrogenase 9 hydrogenases 301, 303–305, 311 hydrogenogenic carboxydotrophs 90 hydrogenotrophic 96 hydrogen-producing bacteria 363 hydrostatic pressure 102 in silico tools 48, 51, 53, 56, 66 in vitro compartmentalization. see IVC inclusion bodies 99 injection molding 320, 337, 339 ISM 51, 53 isolation 334, 336 isoprenoids 376 iterative protein redesign and optimization (IPRO) 66 IVC 50, 60–62, 70 Japanese Sake 366 Kemp eliminases 56 Key performance indicators (KPI) 392 kinetically limited 100 Knallgas 8, 297–298 knockout mutant 155 knock-out mutant 237 laminar 104 land use 2 leghemoglobin 91 Leptolyngbya 136

– plasmid modular system 154 – RBS 147 – riboswitch 149 life cycle analysis (LCA) 386 light 2, 10, 114 light guide 126 light response curve 117 light-dependent reaction 115 light-harvesting 115 light-independent reaction 116 lignocellulosic biomass 366 limitation 321, 323–324, 326 liquefaction 365 lithotrophic 89 lumen 115 macroalgae 187–188, 191, 193 manganese-center 116 mass transfer 100 mealworms 343 membrane 95 MES 310 mesophilic 98 metabolic engineering 19–20, 306, 308 metabolic fluxes 24 metabolic models 138, 159 – composite GSM 140 – flux balance analysis 138 – high-throughput GSM 139 – SEED framework 139 metabolic pathways 47–48, 62, 64–65 metabolic retrosynthesis 64 metabolism 297–298, 301, 306, 308, 311 methanogens 363, 368 methanotrophy 87 methylotrophy 87 MFC 309 microalgae 174, 177, 181, 183, 193 microbial communities 355 microbial consortia 353 microbial electrochemical technologies 283 microbial electrosynthesis 98, 282 microbial fuel cells 284, 369 microfluidics 50, 61, 64, 70 microorganisms – electroactive 278 minimal genome 135 – Mycoplasma 135 mixed cultures 353

Index

401

molecular dynamics (MD) simulations 54 molecular weight 334, 337–338, 341, 343 monooxygenase 232–235, 237 municipal solid waste 87 mutation sites 51–53 mutator strains 49 myoglobin 68

oxygen 2 oxygen dilemma 254 oxygen evolution rate (OER) 117 oxygen transmission 336, 338 oxygenation 63–64 oxygenic photosynthesis 219, 221–222 OYEs 255

NAD(P) 248 NADH 65–66 NADPH 65–67 natural products 376 neomycin phosphotransferase II (NPTII) 59 neutral 355 neutral sites 154 – chromosome 154 – native plasmid 154 nickase 57 nicotinamide cofactor 247 nicotinamide dinucleotide (phosphate). see NADH/NADPH nisin 370 nitrogen 323–324, 326, 342 nitroreductase 67 Nobel Prize 49, 59 non-photochemical quenching (NPQ) 63 Nostoc 7120 135–136 – CRISPR/Cas12a 156 – CRISPRi 156 – genome 136 – metabolic model 139 – plasmid modular system 154 – RBS 147 – replicative plasmid 153 – riboswitch 149 – transformation 136 NPTII 61

P/I curve 117 P450 monooxygenases 258 P450-BM3 monooxygenase 68 packaging 339–340 PAM 58 partial pressure 102 parts assembly – MoClo 133, 155 – standard assembly 133 pathways 322, 327, 329 PDB 52–53 pentose phosphate pathway 59, 61 peroxygenases 261 phage-assisted continuous evolution (PACE) 69 PHB 297, 302–303, 306, 308–309, 311 phosphoribulokinase. see PRK phosphorus 321, 323–324, 326, 328, 342 photo fermentation 362 photoacclimation 118 photoactivated enzymes 266 photoadaptation 118 photoautotrophic 354, 389 photoautotrophs 63, 70 photoautotrophy 2 photobiocatalysis 247 photobioreactor (PBR) 122 photobioreactor 331–332 photobioreactors (PBRs) 393 photobleaching 118 photocatalytic regeneration cascades 247 photochemical NAD(P)H regeneration systems 253 photodamage 48, 69, 71 photodecarboxylases 266 photoenzymatic cascades 247 photoenzymatic reactions 247 photoinhibition 118 photolimitation 118 photolyases 266 photomicroreactors 394 photon 114

omega-3 176–177, 179, 193 omega-3 production 177 one-electron transfer 116 open pond 122 operational stability 360 orange peel waste 373 organic solvent tolerance 49, 70 oxidoreductase 207, 209, 214, 216, 219, 221, 228–229, 231 oxidoreductases 66–67, 247 oxyfunctionalization 230, 232–235, 239

402

Index

photorespiration 63–64, 116–117 photosaturation 118 photosensitizers 258 photosynthesis 4, 64, 70, 114 – anoxygenic photosynthesis 10 – oxygenic photosynthesis 5 photosynthetic active radiation (PAR) 115 photosynthetic electron transport chain 235–237 photosynthetic photon flux (PPF) 115 photosynthetic photon flux density (PPFD) 115 photosynthetic rate 117 photosystem I 116 photoystem II 116 phycocolloids 187–188, 194 phytohormones 98 pigments 332, 334, 343 plantaricin 370 plasmid 153 – counter-selection 155 – integrative 153 – modular system 154 – CYANO-VECTOR 154 – CyanoGate 155 – pSEVA 154 – SyneBrick 155 – replicative 153 – based on cyano native plasmids 153 – RK2 153 – RSF1010 153 polycaprolactone 371 polyhydroxyalkanoates 375 polyhydroxybutyrate 375 polymer 319–320, 322, 329, 334–336, 338–340, 375 polymer building blocks 371 polymers 371 polypropylene 375 polysaccharides 188–189, 191, 193 poly-trimethylene-terephthalate 375 porphyrin 68 position-specific substitution matrix (PSSM) 55 power-to-gas 95 PRK 59, 61 process 321–322, 333–334, 337, 339, 341, 343 production 321, 327–328, 330–334, 338, 341–342 products 333, 337, 339–340, 343 prokaryotic 356

promoter 306 promoters 142–143, 159, 306–307 – constitutive 142 – heterologous 142 – native 142 – orthogonal 142 – regulated 142 – strength 142 – type I 142 properties 320, 322, 334, 336, 338–341 Propionibacterium 366 propionic acid 366 PROSS 55 prosthetic group 91 Protein Data Bank. see PDB protein motifs 56 Protein Repair One-Stop Shop (PROSS) 55 protein sequence-activity relationship (ProSAR) 51 protochlorophyllide-reductases 266 protoporphyrins 68 protospacer adjacent motif 56 pure cultures 355 PyMOL 51, 70 pyrolysis 87 pyruvate 61 pyruvate-ferredoxin oxidoreductase (PFOR) pathway 362 pyruvate-formate lyase (PFL) pathway 362 quantum mechanics (QM) 56 quantum yield 116, 118 QuikChange® 50 Ralstonia eutropha 48 rational design 48–49, 51, 55, 65 RBS 159 – bicistronic design 148 RDE assay 59 reactive oxygen species. see ROS reactor microbiomes 288 recombinant 95 redox cofactors. see cofactors redox reactions 247 redox state 86 reduced amino-acid alphabet 51 reductive biotransformation 230–231, 239 reductive CoA pathway 97 reductive regeneration 248

Index

regeneration systems 249 reporter 151, 159 – FbFP 152 – fluorescent protein 151 – luciferase 151 retro-aldol reactions 56 reverse β-oxidation 99 rhizosphere 88 Rhodobacter sphaeroides 217, 219, 221 Rhodobacter sphaeroides 220 riboregulators 150 – RNA-IN/OUT 151 – taRNA/crRNA 150 ribosome binding sites. see RBS riboswitch 148 – adenine 149 – cobalamin 150 – theophylline 149 ribulose biphosphate carboxylase 12 ribulose-1,5-bis-phosphate 59, 61 ribulose-1,5-bisphosphate carboxylase/ oxygenase. see RuBisCO ribulose-5-phosphate 59, 61 robustness of mixed cultures 360 ROS 48, 69 Rosetta 54, 56 Rossmann folds 66 RuBisCO 48, 55, 59, 61–62, 64, 71, 116, 119 RuBisCO biogenesis 63 S. elongatus 11801 137 – promoters 143 – transformation 138 S. elongatus 11802 138 – promoters 143 – transformation 138 S. elongatus 2973 137 – CRISPRi 156 – genome 137 – growth phenotype 137 – metabolic models 140 – plasmid modular system 155 – promoters 143 – riboswitch 149 – transformation 137 S. elongatus 7942 135–136 – CRISPR/Cas12a 156 – CRISPR/Cas9 156 – CRISPRi 156

– genome 136 – metabolic models 139 – neutral site 154 – plasmid modular system 154 – promoters 146 – RBS 147 – riboswitch 149 – terminator 152 – transformation 136 S. elongatus 7942 – metabolic model 140 Saccharomyces cerevisiae 57 saturation mutagenesis 50–51, 70 see C. necator 48 self-replicating plasmid 218–219, 221, 229 self-shading 232, 236, 239 sequence alignments 51, 67 sequence homology 49, 52 sequence identity 52–53 sequence similarity 52 shear forces 121 shear rate 105 silent pathways 371 single cell protein 308 single cultures 355 sitagliptin 58 site-directed mutagenesis 49, 69 soft-frame photobioreactor 125 solubility 49, 55, 334–335, 338 solubilization 334–337 solventogenesis 365 solvents 334–335, 343 stability 337–339 stakeholders 387 sterilization 92 stirred tank photobioreactor 125 storage 319, 322, 327 stress 319, 322, 328, 337 stroma 115 structure 320, 337 structure-function relationships 49, 63 suicide vector 229 sulfoxidation 67–68 surface display 59–60 sustainable bioeconomy 389 symbiotic 91 Synechococcus 7002 137 – CRISPRi 156 – genome 137

403

404

Index

– metabolic model 139 – neutral sites 154 – plasmid modular system 154 – promoters 146 – RBS 147 – riboregulators 151 – riboswitch 150 – transformation 137 Synechococcus elongatus 376 Synechococcus elongatus PCC 7942 225, 228, 230–231, 238 Synechocystis 6803 135 – CRISPR/Cas12a 156 – CRISPRi 156 – degradation tags 151 – genome 135 – metabolic models 138 – neutral sites 154 – plasmid modular system 154 – promoters 143 – RBS 147–148 – replicative plasmids 153 – riboregulators 150 – riboswitch 149 – terminator 152 – transformation 136 Synechocystis sp. PCC 6803 210, 230 Synechocystis sp. PCC 6803 228–233, 235–238 synergism 355 synergistic 355 syngas 367 syngas fermentation 393 synthesis 322, 327–328 synthetic biology 27, 131, 140 – applications 156–157 – circuits 132–133 – devices 132–133 – iGEM 133 – tools 140, 159 synthetic genome 135 synthetic pathways 354 target market 388

taxadiene 377 techno-economic evaluation (TEE) 387 technology readiness level (TRL) 390 tensile strength 336, 338 terminator 152 – Rho-dependent 152 – Rho-independent 152 Thermal stability 339 thermophilic 98 thermostability 49, 70 thylakoid membranes 115 transposon 59, 61 TRL 0 391 TRL 1 391 TRL 2 392 TRL 3-4 392 TRL 7 392 TRLs 5-7 392 TRLs 8-9 392 tubular photobioreactor 124 turbulent 104 two-film theory 119 UniProtKB database 52 unique selling point (USP) 386 unsterile fermentation 358 unsterile fermentation conditions 358 volatile fatty acids 368 volumetric mass transfer coefficient (kla) 119 water vapor 336, 338 whole-cell biotransformation 207, 212, 219, 222–226, 228, 230, 232, 235–236 YASARA 52, 54 yeast 57, 59 Young’s modulus 336 Z-scheme 116 ε-caprolactone 371 λ Red 57–58