Metagenomic Systems Biology: Integrative Analysis of the Microbiome [1st ed.] 9789811585616, 9789811585623

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Shailza Singh  Editor

Metagenomic Systems Biology Integrative Analysis of the Microbiome

Metagenomic Systems Biology

Shailza Singh Editor

Metagenomic Systems Biology Integrative Analysis of the Microbiome

Editor Shailza Singh Computational and Systems Biology Lab National Centre for Cell Science Pune, Maharashtra, India

ISBN 978-981-15-8561-6 ISBN 978-981-15-8562-3 https://doi.org/10.1007/978-981-15-8562-3

(eBook)

# Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

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Gut Microbiome in Microbial Pathogenicity . . . . . . . . . . . . . . . . . . . Pragya Misra and Shailza Singh

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding of Microbiome Association . . . . . . . . . . . . . . Rashmi Dahiya, Taj Mohammad, and Md. Imtaiyaz Hassan

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Understanding Microbiome Science Through Big Data Analysis . . . . Aditya Narayan, Ajeet Singh, and Shailesh Kumar

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Comparative Genomics Facilitates Drug Target Selection and Develops Intervention Strategies Against Leishmania Infections . . . . Anindita Paul and Sushma Singh

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Culture-Independent Omics-Techniques for Microbiome-Based Molecular Therapeutics Against Infectious Diseases . . . . . . . . . . . . . Santanu Sasidharan, K. Divakar, and Prakash Saudagar

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The Earth’s Microbiome: Significance in Sustainable Development and Impact of Climate Changes . . . . . . . . . . . . . . . . . . 115 Deepika Goyal, Manali Vaijanapurkar, Eden Jacques, Janmejay Pandey, and Om Prakash

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Microbiome for Personalized Medicine . . . . . . . . . . . . . . . . . . . . . . . 141 Preeti Rathi, Deepanshu Verma, Ashutosh Singh, and Neha Garg

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Metagenomic Insights of Yarrowia lipolytica in Food Industry . . . . . . 159 Ashok Bankar, Laxmi Jadhav, and Vrushali Phalke

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Foodomics: The What, Why and How of It . . . . . . . . . . . . . . . . . . . . 185 Malathi Srinivasan

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About the Editor

Dr. Shailza Singh is Scientist E and In-Charge of Bioinformatics and High Performance Computing Facility at National Centre for Cell Science, Pune. Her lab focuses on systems and synthetic biology of infectious disease leishmaniasis, wherein she is trying to integrate the action of regulatory circuits, cross talk between pathways and non-linear kinetics of biochemical processes through mathematical modeling. The hypotheses laid down through theoretical approaches are validated in in vitro and in vivo systems. Currently, she has started working in the area of Precision Oncology. She is the recipient of several awards such as RGYI, DST-Young Scientist, INSA Bilateral Exchange, SAKURA Exchange Program etc. She is the reviewer of various international journals of repute like Scientific Reports, PlosOne, Molecular Biosystems, Oncotarget, BMC Systems Biology, Parasites and Vectors, ACS Journal of Natural products etc. She also serves as reviewers for various national and international grants like DST, DBT, CSIR, RCUK etc. She has guided a number of MTech, MSc. and PhD students.

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Gut Microbiome in Microbial Pathogenicity Pragya Misra and Shailza Singh

Abstract

Gut microbiota with diverse population has gained a lot of attention in the last decade. This microbial population along with maintaining homeostasis in gut also shapes the immune system in gut by interacting with various arms of the immune system. It has an important role in development of gut-associated lymphoid tissues (GALTs), regulating the Th17 cells development by intrinsic mechanism along with their importance in differentiation of Th17 cells in lamina propria. It has also been observed that host-intestinal T cell mutualism is also maintained by Treg-cell response generated due to microbial colonization. Along with this, in this chapter, we have focussed on mechanism by which these commensal confer resistance to various infections. Deciphering all this knowledge about gut-host interaction, the present chapter aims at understanding how to modulate gut microbiome for personalized medicine. It is well proven that microbiome acts as an important player in drug response variability or toxicity for various diseases. This provides a lead to understand ways by which microbiome could be used as a target for improving the efficacy of drugs along with safety by manipulating its composition. Various strategies have been adapted for this such as pathogenspecific approach, faecal microbiota transplantation (FMT), removing specific strain or species of a bacteria, inhibiting metabolic function of gut microbiota enzymes, introducing engineered strains in the gut and genetically modifying bacterial cells present in the gut. Thorough evaluation of all the available literature made us give our insight which suggests that developing microbial therapeutics which could be well adapted in the specific body environment is necessary for its efficient activity. Based on this observation, we suggest that construction of

P. Misra · S. Singh (*) National Centre for Cell Science, NCCS Complex, Ganeshkhind, SPPU Campus, Pune, India e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_1

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synthetic circuits which could be functionally active in in vivo conditions along with establishing regulatory frameworks for safety could be fruitful approach. Keywords

Gut microbiota · Gut immune system · Drug metabolism · Drug toxicity · Faecal microbiota · Personalized medicine

Abbreviations GALTs LTi ILFs AID EHEC FXR CDDL CDI FMT

1.1

Gut-associated lymphoid tissues Lymphoid tissue inducer Isolated lymphoid follicles Activation-induced cytidine deaminase Enterohaemorrhagic E. coli Farnesoid X receptor Cytidine deaminase C. difficile infection Faecal microbiota transplantation

Introduction

Gut microbiota comprises of diverse microbial population that is present in the intestine. This microbial population is of immense benefits to the host; most importantly, it shapes the gut immune system (Round and Mazmanian 2009) by helping in its development and maturation. These bacteria which reside in the intestine are well adapted to the environment there and have strong interactions with various other bacterial species. They fulfil their nutritional requirement by acquiring the nutrients from the host. It has been found that neonatal mice have less microbiota when compared with that of adult. However, as the diet of mice changes from mother’s milk to the diet which is rich in fibres, these mice show dramatic change in their microbiota by acquiring Clostridiales and Bacteroidales which are the dominant taxa present in the adult intestine (Marcobal et al. 2010). It has been shown in a study that microbiota of neonatal mice was unable to prevent the colonization of two bacterial pathogens, leading to their mortality; however when above-mentioned two bacterial species were administered in these mice, neonatal mice were protected from pathogenic infection. When Clostridiales was abolished from adult mice, they also lost the resistance to colonization (Kim et al. 2017). Many available sugar components present in the large intestine are not digested by host, these two enzymes help in breakdown of complex polysaccharides as an energy source, and this is the reason that bacteria of order Bacteroidales and Clostridiales are dominantly present in intestine. This type of co-evolution has been developed between the host and microbes through millions of years, wherein microbes help in various physiological

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processes of host and in turn they are benefited for their nutritional requirements. Along with these metabolic advantages, host microbiota helps in various other functions as discussed above to shape the immune system. In short, microbiota contributes towards digestion of various nutrients, for producing vitamins, helps in development of lymphoid tissues associated with the gut (GALTs), along with polarizing immune response in the gut, and enables resistance towards colonization by pathogenic organisms (Renz et al. 2012; Hooper and MacPherson 2010). However, most of the gut microorganism has a mutual advantageous relationship with host; it’s not always that the intestinal microbial community has a beneficial role towards the host. There exists many microbial species which can exist on different scale between mutual relationship and pathogenic function. For example, Bacteroides fragilis is a bacterium which is predominant microbial species in close association with mucosal surface; however it has capability to opportunistically invade intestinal tissues (Kuwahara et al. 2004). This type of bacterial species is in controlled state in healthy individuals; however, these can pose serious threats in immunodeficient individuals. Another important advantage of intestinal microbiome is that it protects host intestine from colonization by pathogenic microbial species, termed as “colonization resistance” (Buffie and Pamer 2013). This fundamental concept was studied many decades ago, and it was believed that microorganisms directly mediate the inhibition which has been extended by recent work that the commensal bacteria in the intestine can control the pathogens by modulating the immune responses in the intestinal region also termed as “immune-mediated colonization resistance”. In this chapter, we would discuss various aspects of microbiota affecting the immune response and how microbiota affects pathogen microorganism and vice versa. We would also focus on clinical implication of gut microbiome for treatment against infection.

1.2

Gut Microbiota and Gut Immunology

In this section, we would focus on how gut microbiota interacts and affects various arms of immune system in the intestine. It has been well established that gut microbiota has an important role in development of gut-associated lymphoid tissues (GALTs). These GALTs are the structures of immune system wherein antigen uptake and presentation by APCs occur; therefore, these are very important for functions of lymphocytes. It has been found that development of secondary lymphoid tissues, e.g. lymph nodes and Peyer’s patches, in foetus is induced by lymphoid tissue inducer (LTi) cells (Mebius 2003). Many small clusters of LTi known as cryopatches are formed between the crypts in the lamina propria of the intestine after birth, and there are evidences suggesting that these cryopatches recruit B cells during the colonization of bacteria and further develop into isolated lymphoid follicles (ILFs) (Pabst et al. 2005; Ebertl and Litman 2004). It further recapitulates the foetal development of lymph nodes and Peyer’s patches (Eberl 2005). It has been observed that ILFs are abnormally large in the mice which have a vast microflora due

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to absence of IgA (Bouskra et al. 2008). It has been observed that mice which are deficient in pattern recognition receptors such as TLRs, NOD2 and their adapter molecule such as MyD88 which are activated by bacterial stimuli have ileal and colon ILFs not completely mature (Bouskra et al. 2008). It was shown in a study that genesis of ILFs is induced by peptidoglycan from Gram-negative bacteria mediated by recognition by NOD1 (nucleotide-binding oligomerization domain containing 1) innate receptor present in epithelial cells and b-defensin 3 (Bouskra et al. 2008). Various subsets of lymphocytes and their development are regulated by gut microbiota. One such cell subset is Th17 cells which are present selectively and constitutively in the intestinal lamina propria, giving a notion that their development might be regulated by intrinsic mechanism of gut. It was studied that ATP whose source can be commensal bacteria can activate a special subset of cells in lamina propria, viz. CD70high CD11clow cells, which further cause differentiation of Th17 cells. Using germ-free mice, it was shown that since these mice have low concentration of ATP in lumen, there is less frequency of Th17 cells in lamina propria; however, administration of ATP in these mice showed marked increase in Th17 cells in lamina propria (Atarashi et al. 2008). Another study showed that specific commensal bacteria are essential for the differentiation of Th17 cells in lamina propria, and it was correlated specially to the presence of Cytophaga-Flavobacterium-Bacteroides bacteria in the intestine. This differentiation was found to be independent of various other important factors such as TLR, IL-21 or IL-23 signalling; however, activation of TGF-β was required. It was found that if this specific bacterium was not present in lamina propria, increased frequency of Foxp3+ regulatory T cells was observed. This study again supported the fact that bacterial composition regulates balance of Th17 and Treg cells in lamina propria and thereby affects the immunity inside the intestine (Ivanov et al. 2008). Ivanov et al. showed induction of CD4+ T helper cells that produce IL-17 and IL-22 (Th17 cells) in the lamina propria, could be induced by colonization of small intestine of mice by a single commensal microbe, segmented filamentous bacterium (SFB). It was also observed that in mice which have IL-17, SFB adheres strongly to the surface of epithelial cells in the ileum of those mice; on the contrary, it is absent in mice with few Th17 cells (Ivanov et al. 2009). Innate lymphoid cells (ILC) are recognized as innate immune cells which have functions similar to T cells. These cells produce many cytokines associated with Th cells, but these do not express cell surface markers which are associated with the other immune cells. These ILC have been categorized in three groups: group 1 ILC comprising cells such as natural killer (NK) cells and group 2 ILC which expresses IL-5, IL-13 and GATA-binding protein 3 (GATA3) and retinoic acid receptorrelated orphan receptor-α (RORα) which are required for their development. Other group is ILC3 which are intestinal lymphoid cells expressing receptor NKp46 which is NK cell-activating receptor and have some functional activity similar to NK cells, and transcription factor RORγ is required for their development (Walker et al. 2013). Role of microflora in development and function of ILCs is a controversial subject. A study confirmed that intestinal NKp46 + IL-22+ cells can be generated by a local

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process which requires commensal bacteria, RORγt and mice which did not have IL-22 producing NKp46+ cells and showed increased susceptibility to the pathogen Citrobacter rodentium (Satoh-Takayama et al. 2008). Evidences was provided by another study that commensal microbiota helps in differentiation of lymphocytic population which co-express stimulatory natural killer cell receptors and the transcription factor RORγt that produced interleukin 22 (IL-22) (Sanos et al. 2009). Contrary to these findings, another study showed that RORγt+ ILCs produce intestinal IL-22 constitutively and the symbiont microbiota which is present repress this function of ILCs by epithelial expression of IL-25 (Sawa et al. 2011). Treg cells constitute an important subset of T cells which play a role in gut homeostasis. These Foxp3+ Treg cells present in intestinal mucosa and GALT have a critical role in controlling inflammatory responses (Izcue et al. 2009). Moreover, various data have shown that Treg cells in mucosa are generated continuously by the activity of specialized dendritic cell (DC) subsets via retinoic acid (Benson et al. 2007; Coombes et al. 2007; Jaensson et al. 2008). On the contrary, CD4+ T cell compartment of immune system is formed in the presence of specific microbes or components of bacteria. It is difficult to decipher if these effects are a mutual immune adaptation to the colonization in the intestine or an individual immune responses. To define this, a study was conducted using altered Schaedler flora (ASF). This intestinal colonization resulted in activation and de novo generation of Treg cells in the colon. Failed activation of Treg cells induced Th17 and Th1 cells responses, these responses were reversed by wild-type Treg cells. To maintain the intestinal homeostasis after damage induced by dextran sulphate sodium in the colon, efficient induction of Treg cells was required. Data suggested that Treg-cell responses generated due to microbial colonization are basic intrinsic mechanism for inducing and maintaining the host-intestinal microbial T cell mutualism (Geuking et al. 2011). One of the indigenous spore-forming bacteria of murine gastrointestinal tract is Clostridia, and clusters IV and XIVa also known as Clostridium leptum and coccoides groups, respectively, have a role in maintaining homeostasis in the intestine and prevent inflammatory bowel disease. It was shown in a study that Treg cells are present in mucosa of the colon in a large number and clusters IV and XIVa of the genus Clostridium helped in accumulating Treg cells. It was also observed that when a mouse is colonized with defined mix of strains of Clostridium, it provided an environment rich in TGF-β and also affected the number and function of Treg cells. Moreover, when conventionally reared mice were orally inoculated with Clostridium, they were found to be resistant to colitis and showed systemic immunoglobulin E responses in adult mice (Atarashi et al. 2011). Role of Toll-like receptors (TLRs) has also been demonstrated in establishment of symbiotic relationship of host and microbe with focus on gut commensal, Bacteroides fragilis which activates TLR pathway on T cells. It was observed that deletion of TLR2 on CD4+ T cells induced antimicrobial immune response which reduced colonization of B. fragilis. It was found that TLR2 directly on Foxp3+ regulatory T cells are activated by a symbiosis factor (PSA) of B. fragilis via novel mechanism to maintain mucosal tolerance. The bacteria which did not have PSA were not able to confine the immune response of host and were found to be defective

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in niche-specific colonization in mucosa. This data suggested that TLR ligands not only activate inflammatory reactions, but some bacteria have the capability to exploit the TLR pathway for suppressing immune responses proposing that not only host mechanism but some special molecules of symbiotic pathogen have the ability to mediate immunologic distinction between pathogens and the microbiota (Round et al. 2011). B cell response specific to gut and intestinal microflora is also strongly co-related. 80% of plasma cells are located in the gut, and these produce IgA more than any other Ig isotype. It is secreted as dimer or polymer once it associates with transmembrane epithelial protein known as polymeric Ig receptor (pIgR) and incorporates joining (J) chain (Brandtzaeg et al. 1999; Jung et al. 2006; Mostov 1994). Since IgA is the most abundant immunoglobulin in mucosal secretions, role of IgA at mucosal surface was deciphered a lot. It’s interesting to understand the physiological importance of IgA in the gut as it has been observed that IgA deficiency is the most common deficiency present in humans which suggests that somehow secretory IgA might not be essential for survival and there must be some alternative protective mechanism at gut (Macpherson 2006). Interestingly, it has been reported that patients who have low levels of IgA and IgG and reduced SHM have frequent episodes of respiratory and gastrointestinal infections and sometimes develop nodular follicular hyperplasia which is a lympho-proliferative disorder of the small intestine (Shussman and Wexner 2014; Burt 1999). Due to heterogeneity of clinical manifestation in IgA patients, biological and physiological functions of IgA are very poorly understood; moreover, animal models generated to study IgA functions are also associated with problem (Harriman et al. 1999; Johansen et al. 1999). It has been noted in neonates that secretory IgA has the ability to limit the penetration of commensal bacterium via epithelium of intestine, but it does not need the diversification of natural antibody repertoire (Harris et al. 2006). It was seen that in young quasi-monoclonal mice as well as adult mice, the non-mutated nitrophenyl-specific IgAs limited the translocation of aerobic bacteria from lumen to mesenteric lymph node (Macpherson and Uhr 2004). However pIgR
/
mice showed increased translocation of bacteria in mesenteric lymph node (Sait et al. 2007), and these mice which lack secretory antibodies like IgA and IgM had higher susceptibility to infection with Salmonella typhimurium than are wild-type mice and shed more bacteria serving as an infection reservoir (Wijburg et al. 2006). This suggests that IgA protects individuals by preventing the transmission of microbial pathogens and it regulates the composition and function of gut microbiota. Activation-induced cytidine deaminase (AID)-deficient mice model reiterates the pathology of CVID in humans which in turn causes deficiency or dysfunction of IgA, and it was found that these mice have protruding follicle like structures which indicates hypertrophia of isolated lymphoid follicles (ILFs). It was also seen that these AID
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mice had an abnormal expansion of Gram (+) anaerobes in the small intestine and segmented filamentous bacteria was prominent among them which is considered as a potent inducer of IgA synthesis (Ivanov et al. 2009; Fagarasan and Honjo 2003; Suzuki et al. 2004; Davis and Savage 1974; Ohashi et al. 2006).

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To ascertain that when germ-free mice is colonized with normal gut microflora, it induces bacteria-specific IgA antibody responses, a gnotobiotic mice was developed which has just one bacterial species and the antibody repertoire to a single, monoclonal IgA against the bacterium’s capsular polysaccharide. The group introduced Bacteroides thetaiotaomicron into germ-free wild-type, immunodeficient Rag1
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or Rag1
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mice harbouring IgA-producing hybridoma cells. It was observed that without IgA, this bacteria induced a potent innate immune response and reacted to this immune response by inducing genes that metabolize host oxidative products; however, IgA reduced the pro-inflammatory response signalling in the intestine along with bacterial epitope expression which balanced the suppression of the oxidative burst with the antibody’s negative impact on bacterial fitness (Peterson et al. 2007). Germinal centres (GCs) present in Peyer’s patches (PP) are induced by bacteria, and herein very high amount of IgA is being generated. It was shown by Suzuki that direct stimulation of follicular dendritic cells by bacterial products and retinoic acid have a synergistic effect on enhanced expression of CXCL13, BAFF and molecules that facilitate the secretion and activation of the cytokine TGF-b1. It was observed that reduced production of above-said molecules by PP follicular dendritic cells (FDCs) is associated with a deficiency in TLR pathway along with the fact that reduced vitamin A also resulted in less number of GC B cells and defective generation of IgA+ B cells within PP GCs (Suzuki et al. 2010). Interestingly, it was studied that flagellin derived from commensal bacteria for a specific subset of lamina propria DCs helped in production of retinoic acid which is an essential molecule for differentiation of IgA-producing B cells (Mora et al. 2006; Uematsu et al. 2008). In addition to this, it was also found that upon microbial exposure, intestinal plasma cells express TNF and iNOS which promote B cells for secretion of IgA (Fritz et al. 2012). Overall discussion suggests that microbiota instructs the intestinal cells for differentiation of IgA-producing cells and, in turn, IgA regulates the function and composition of the gut microbiota to maintain mutualism between the host and the microbiota.

1.3

How Gut Microbiota Helps in Pathogen Resistance

Various studies have shown that germ-free mice are more susceptible to various pathogens when compared with conventional mice which suggest the role of residing microflora in resistance to the disease (Osawa and Mitsuhashi 1964). The focus of present ongoing studies is to decipher the mechanism by which these commensal confer resistance to various infections. These gut symbionts establish a steady community in the gut which helps in resistance to invasion by non-native bacteria and also resists expansion of pathobionts which is termed as “colonization resistance”. This colonization resistance includes various aspects such as resistance to initial infection, improved tolerance of an established infection and clearance of the infection. These all arise from the constant competition between normal gut residents (mutualists,

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commensals and pathobionts). The mechanisms bacteria use to compete in the gut can be divided into two broad categories: direct and indirect.

1.4

Direct Mechanism by Gut Microflora for Colonization Resistance

Inhibition of pathogens in the intestine is regulated by bacteria present by either competition for the limited supply of nutrients or these may also produce inhibitory molecules for this. Various examples of phenomenon wherein bacteria compete with pathogen for nutrients are present. It has been seen that Escherichia coli can have competition for organic acids, amino acids and various other nutrients with enterohaemorrhagic E. coli (EHEC; an enteric pathogen that causes substantial morbidity and mortality worldwide) (Momose et al. 2008a, b; Fabich et al. 2008; Leatham et al. 2009). Study by Kamada et al. showed that germ-free animals were not able to eliminate Citrobacter rodentium, which is a model for infections in human with attaching/ effacing bacteria. It was observed that during early infection expression of virulent genes was seen and these were required for growth of pathogen in conventional mice but not in germ-free mice. The expression of these genes was down-regulated during late infection, and the pathogen was relocated to lumen of the intestine where it faced competition with commensal microorganisms. This capability of commensals to outcompete C. rodentium was attributed to a structurally similar carbohydrate required for growth of both. This study suggested that colonization of pathogen was controlled by bacterial virulence and through competition with metabolically related commensals (Kamada et al. 2012). The reason for reduced invasion of epithelial cells of the intestine by Salmonella enterica was studied by comparative transcriptomic analysis of Salmonella enterica serovar Enteritidis and Salmonella enterica serovar Typhimurium grown in medium with butyrate. It was found that it down-regulated expression of 19 genes by twofold or more (Gantois et al. 2006). Enterococcus faecalis colonizes gastrointestinal tract, and thereby systemic infections with multi-drug enterococci occur subsequently, and preventing this colonization could be a preventive approach. Presence of pheromone-responsive conjugative plasmids encoding bacteriocins in enterococcal strains enables the ability to modulate the competition for niche among enterococci and also between enterococci and commensal microbiota. Role of pPD1 expressing bacteriocin 21 was evaluated in enterococcal colonization. It was observed that the E. faecalis which have pPD1 replaced indigenous enterococci and they outcompeted E. faecalis which lack pPD1.Vancomycin-resistant enterococci were cleared after colonization by a conjugation-defective pPD1 mutant carrying strain of E. faecalis. These studies suggest that bacteriocins which are delivered by commensals could be an effective strategy for eliminating multi-drug-resistant bacterial colonization (Kommineni et al. 2015).

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Bacillus thuringiensis DPC 6431 is a bacterial strain which was isolated from human faeces, and thuricin CD is produced from them which contains two peptides. These two peptides, namely, thuricin Trn-alpha and Trn-beta, have a synergistic role to kill a wide range of clinical C. difficile isolates, including ribotypes commonly associated with CDAD (e.g. ribotype 027) (Rea et al. 2010). Bile acids also help intestinal microbiota in inhibiting pathogens. Bile acids are amphipathic, cholesterol-derived molecules which are prepared in the liver and transported to duodenum and are modified into various secondary bile acids by gut microbiota which have antibacterial role. It has been well established that microbiota in gastrointestinal tract is reduced significantly by antibiotics which in turn reduces the colonization resistance against pathogens including Clostridium difficile. It was shown in a study that treatment with antibiotics not only changed the microbial community in the gut of mice susceptible to C. difficile infection but also affects its metabolome. Noticeably, the levels of secondary bile acids decreased and primary bile acids along with sugar alcohol increased; data suggested that the diminution of microbial members which were involved in conversion of primary bile acids into secondary bile acids helped in colonization of the gastrointestinal tract by C. difficile. C. difficile exploited the specific metabolites present in the gut after antibiotic treatment, for growth. Overall observations suggest that alteration in gut microbiota by antibiotics can convert the metabolic profile in gut favourable for C. difficile germination and growth (Britton and Young 2014). Theriot et al. studied the physiologically relevant concentrations of primary and secondary bile acids present in the murine small and large intestinal tracts and how these impact C. difficile dynamics using the target bile acid metabolomics. Mice were treated with a range of antibiotics for creating different microbial and metabolic environment, and these were tested for their ability to support growth or inhibit the spore germination and outgrowth of C. difficile ex vivo. It was recorded that large intestine was susceptible to C. difficile under specific broad-spectrum antibiotic treatment and significantly reduced the loss of secondary bile acids (deoxycholate, lithocholate, ursodeoxycholate, hyodeoxycholate and ω-muricholate). Loss of specific microbial community members of Lachnospiraceae and Ruminococcaceae families was associated with these changes. It was also found that secondary bile acids present in physiological concentrations during C. difficile resistance had the ability for inhibition of germination of spores and outgrowth under in vitro conditions (Theriot et al. 2016). Proliferation of bacteria and mucosal injury in the small intestine are results of obstructed bile flow and can be inhibited by administering bile acids. A nuclear receptor for bile acids, farnesoid X receptor (FXR), was shown to induce genes which were involved in enteroprotection along with inhibition of bacterial growth and ileum injury. This study suggested that FXR plays a major role in protection of the small intestine from bacterial invasion, and it was concluded that agonists of FXR could help in preventing deterioration of epithelium and translocation of bacteria in patients which have impaired flow of bile (Inagaki et al. 2006).

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Another study used different antibiotics to treat mice which changed the microbial species and as a result susceptibility to C. difficile. Mathematical modelling supported by microbiota analysis of patient identified that bacteria associated with resistance are common in mice and humans. Clostridium scindens was identified as a bile acid 7α-dehydroxylating intestinal bacterium and was confirmed to be associated with resistance to C. difficile infection, and when it was administered, it was found that it increased the resistance to infection dependent on secondary bile acid (Buffie et al. 2015). An interesting treatment protocol for recurrent infection of Clostridium difficile infection developed due to antibiotic treatment is faecal microbiota transplantation. It is believed that microbiota present in the intestine has an effect on metabolism of bile acids in the colon which further affects C. difficile life cycle. With a hypothesis that faecal bile acid composition gets altered during C. difficile infection, FMT was used for this normalization. To determine the bacterial composition, pre- and postFMT analysis was done using 16S rRNA gene sequencing. It was found that bacterial composition of faecal samples was found to change rapidly after FMT and was found to be same as donor. High concentration of primary bile acids and bile salts was found in pre-FMT samples, whereas secondary bile acids were non-detectable. The levels were found in contrast in post-FMT samples. This study suggested that FMT can result in normalizing the faecal bacterial community and its metabolic composition (Weingarden et al. 2014).

1.5

The Intestine Serves as Barrier

Intestinal pathogens also help in protecting the intestine from pathogens by strengthening intestinal barrier such as affecting the composition and thickness of mucous layer and maintaining tight junctions of the intestine. Locations wherein intestinal bacteria are located also affect the activation of innate immune response. Most of the bacterial species are located in the outer mucosal layer with inner layer being sterile. It was inferred that altered localization of microbiota could also be a feature of driving lower inflammation and metabolic syndrome. This was observed microscopically by visualizing host-bacteria-mucus juxtaposition. It was seen that in wild-type mice very few bacteria were observed within 10 microns of the epithelium. Bacteria found to be closest was 25 microns apart from epithelium, whereas in mice which had low-grade inflammation and features of metabolic syndrome, bacteria were found to be apparently in direct contact with the epithelium, where the average distance of closest bacteria was less than 10 microns (Chassaing et al. 2014, 2015; Chassaing and Gewirtz 2016). As discussed above, antibiotics affect the microbial composition. Study conducted by Wlodarska et al. used Citrobacter rodentium as pathogenic model to see how perturbation in microbial community affects the intestinal barriers and causes increased susceptibility to colitis. Two antibiotics, viz. streptomycin and metronidazole were used for pre-treatment to disrupt the microbial community. It was observed that pre-treatment with metronidazole prior to infection with

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C. rodentium not only increased the susceptibility to colitis but also resulted in increased intestinal inflammatory tone as suggested by increase in epithelial stimulation by bacteria along with alterations in functions of goblet cells, thin inner mucus layer which indicates weakened mucosal barrier. Increased attachment of C. rodentium to intestinal epithelium was attributed to reduced thickness of mucus which further contributed to severity of colitis in mice which were pre-treated with metronidazole. Data suggested that intestinal homeostasis is affected by antibiotic treatment affecting microbiota and overall effect on intestinal defence mechanism which helps in protection against pathogens (Wlodarska et al. 2011). Intestinal barrier is maintained by production of short-chain fatty acids which serve as primary nutrient for epithelium and help in controlled production of mucin specially mucin 2 which protects gastrointestinal mucosa from various physical, chemical and microbial challenges (Willemsen et al. 2003; Shimotoyodome et al. 2000). This suggests that short-chain fatty acids decreased production which could lead to reduced thickness of mucus thereby affecting mucus barrier.

1.6

Role of Gut Microbiota in Virulence of Pathogen

We have discussed various mechanisms which help in protecting the intestine from various bacterial and other pathogenic infections by microbiota; however, on the other side of the picture, this microbiota can also help in triggering the virulence of these pathogenic bacteria. In the saturated environment of gastrointestinal tract, the incoming pathogens have a capability to exploit the nutritional web for their proliferation and establish their virulence regimen. Many bacterial species present in gut microbiome, especially of phylum Bacteroidetes, have the ability to degrade the carbohydrates, and other species which lack such ability use the sugars released during the breakdown as a source of nutrition. Enteric pathogens also utilize these sugars for their establishment of infection. It was shown in a study that ability of Clostridium difficile and Salmonella typhimurium in a mouse treated with antibiotic to catabolize sialic acid helped in increased colonization of these bacteria in spite of the fact that these species lack sialidase enzyme to liberate this sugar. These two pathogenic bacteria used the sialidase activity of microbiota, and commensal microbial species Bacteroides thetaiotaomicron helped in restoring sialic acid catabolism in germ-free mice. In a similar fashion, vancomycin-resistant Enterococcus which cannot survive on the purified mucin usually grow on pre-digested mucin with human stool extracts which indicates that it takes benefit from mucosal sugar liberated from microbiota in gut (El Kaoutari et al. 2013; Ng et al. 2013; Xu et al. 2003; Martens et al. 2008; Pultz et al. 2006). These examples suggest how the pathogenic bacteria with different requirements have evolved strategies for exploiting glycosidic activities of microbiota present in the gut for their expansion. Along with sugars, commensal microbial species present in the gut also produce various other metabolites such as gases, short-chain fatty acids and organic acids which are utilized by enteric pathogens as nutrients. A by-product of anaerobic fermentation succinate is utilized by C. difficile for growth (Reichardt et al. 2014). It

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converts succinate into butyrate for regeneration of NAD+ (Ferreyra et al. 2014). This process helps in fermenting dietary sugars such as sorbitol whose levels increase after antibiotic treatment and is deleterious to C. difficile (Theriot et al. 2014). This succinate-to-butyrate pathway helps in growth of C. difficile in vivo (Ferreyra et al. 2014). Another by-product of anaerobic fermentation of microbiota is molecular hydrogen. S. typhimurium utilizes this hydrogen as energy source with the help of Hyb hydrogenase enhancing its growth during initial stage of invasion (Maier et al. 2013). Diarrhoea is caused by enteric pathogen enterohaemorrhagic Escherichia coli (EHEC); role of gut microbiota was assessed in this infection. It was found that predominantly Bacteroides thetaiotaomicron (Bt) was present at the attachment site of EHEC, and a study group showed that Bt enhanced the virulent gene expression of EHEC by transcription factor Cra whose function is sensitive to concentration of sugar. Attaching and effacing lesions which are required for EHEC attachment site were also increased by enhanced virulence. Studies were conducted using Citrobacter rodentium infection model, which is a homologous mouse pathogen for EHEC. Bacteroides thetaiotaomicron (Bt), is resident at EHEC attachment sites. Lietrature reveals that infection with Citrobacter rodentium in Bt-reconstituted mice results in increased gut permeability along with inflamed pathology of the host and mortality compared to mice deplete of microflora. Bacteroides thetaiotaomicron (Bt) was found to modify the metabolic environment near the infection site with increased metabolites which were involved in gluconeogenesis and increased succinate which can be recognized by Cra. Overall observations suggested that disease outcome is affected by microbial composition (Curtis et al. 2014). Another example is of Clostridium difficile which utilizes microbial by-products for their growth. It was observed by examining intestinal and faecal extract from untreated and antibiotic-treated mice that extracts from antibiotic-treated mice stimulated colony formation from spores at a higher level. When these extracts were treated with cholestyramine which is resin for binding bile salts, ability of extracts to stimulate the formation of colony from spores decreased noticeably. This suggested that bile salts helped in stimulating the germination of C. difficile spores in vivo. Proportion of primary to secondary bile salts was higher in extracts which stimulated colony formation in comparison to extracts which were not able to. Clostridium difficile, a spore-forming bacterium, causes antibiotic-associated diarrhoea. In order to produce toxins and cause disease, C. difficile spores must germinate and grow out as vegetative cells in the host. Although a few compounds capable of germinating C. difficile spores in vitro have been identified, the in vivo signal(s) to which the spores respond were not previously known. Examination of intestinal and caecal extracts from untreated and antibiotic-treated mice revealed that extracts from the antibiotic-treated mice can stimulate colony formation from spores to greater levels. Treatment of these extracts with cholestyramine, a bile salt-binding resin, severely decreased the ability of the extracts to stimulate colony formation from spores. This result, along with the facts that the germination factor is small, heat-stable and water-soluble, supports the idea that bile salts stimulate germination

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of C. difficile spores in vivo. All extracts able to stimulate high level of colony formation from spores had a higher proportion of primary to secondary bile salts than extracts that could not. In addition, caecal flora from antibiotic-treated mice was less able to modify the germinant taurocholate relative to flora from untreated mice, indicating that the population of bile salt-modifying bacteria differed between the two groups. Taken together, these data suggest that an in vivo-produced compound, likely bile salts, stimulates colony formation from C. difficile spores and that levels of this compound are influenced by the commensal gastrointestinal flora (Giel et al. 2010). Gastrointestinal tract of bovines is a major pool of EHEC which causes various food-borne infections. Therefore, nutrients which promote the carriage of this pathogen were studied to develop strategies to reduce their survival in bovine gastrointestinal tract. It was shown that free ethanolamine served as a source of nitrogen for O157:H7 EHEC strain EDL933 in bovine intestine due to induction of ethanolamine utilization (eut) gene cluster. This eut gene cluster is not present genome of most of the species which are present in gut microbiota of mammals. This capacity of E.coli to utilize EA as a source of nitrogen serves as a growth advantage for the bacteria suggesting that EA represents an ecological niche for persistence of EHEC in the bovine intestine (Bertin et al. 2011). Another studied showed that EA not only helps in metabolism of nitrogen but also acts as a signalling molecule during cell-cell signalling for activation of virulence gene in EHEC. Interestingly this study shows that bacterial pathogens not only utilize the metabolite but also use it as a signalling molecule for promoting expression of virulence gene (Kendall et al. 2012). A mucin-degrading bacterium Akkermansia muciniphila was isolated which is present in the mucus layer. This bacterium has an inverse correlation with body weight in rodents and humans. It also helps in improvement of metabolic profile, and it was demonstrated that treatment with A. muciniphila helped in reversal of metabolic disorders induced by high-fat diet along with fat-mass gain, inflammation of adipose tissue and insulin resistance (Everard et al. 2013). However, excessive degradation of mucin by these bacteria could also cause inflammatory bowel diseases because this facilitates access to luminal antigens by intestinal immune system. It was studied how A. muciniphila affected the severity of Salmonella typhimurium-induced gut inflammation. It was observed that presence of A. muciniphila in gnotobiotic mice having microbiota of eight bacterial species had an impact on inflammation as well as infection caused by S. typhimurium in these mice. Significant increase in histopathology scores and elevated mRNA levels of IFN-γ, IP-10, TNF-α, IL-12, IL-17 and IL-6 in caecal and colonic tissue was observed which was also accompanied with many folds higher number of S. typhimurium in mice with A. muciniphila when compared with the mice with S. typhimurium alone. It suggests that A. muciniphila exacerbates the inflammation (Ganesh et al. 2013).

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Evolving Microbiota and Drug Interaction for Personalized Medicine

The sections till now have addressed the importance of gut microbiota in refraining from as well as establishment of pathogens. Now our focus would be how the microbiome acts as an important player in drug response variability or toxicity for various diseases followed by how the microbiome could be used as a target for improving the efficacy of drugs along with safety by manipulating its composition. Termed as “pharmacomicrobiomics”, it is involved in investigation of correlating the microbial variation with drug response and disposition. It has been well established since the twentieth century that intestinal microbiome possesses metabolic activities which can modulate the metabolism, action of more than 30 approved drugs along with immune checkpoint inhibitors by transforming these drugs to bioactive, inactive or toxic metabolites by microbial action alone or its interaction with host.

1.8

Effect of Gut Microbiota on Drug Efficacy, Metabolism and Toxicity

Drugs which are ingested orally usually have the route of passage from the upper gastrointestinal tract to the small intestine and large intestine, which serve as a reservoir for enormous population of microbial species which are present in human gut. Interaction between drug and microbes usually occurs in the colon. Intestinal microenvironment along with microbial metabolism and growth can be affected by drugs which can ultimately alter the microbial community composition along with function. On the contrary, gut microbiota also have the ability to transform the drug chemically. Usually drug is metabolized in the liver with two-step reaction; one is modification and other conjugation. Modification in drug molecule due to gut microbiome is very different as it usually causes hydrolytic and reductive reactions, whereas in the liver usually oxidation and reduction reactions occur (Koppel et al. 2017). In usual circumstances metabolized drug is usually transported to the target organ or excretory organ; in spite of this, in gut, the metabolized drug can undergo bacterial metabolism of its metabolites again (Stein et al. 2010).

1.9

Few Studies of Drug Metabolism Influencing Their Activity and Toxicity by Microbes

Presence of enzyme family in microbial community for metabolizing various products has been well proven (Turnbaugh et al. 2009, 2010; Bayer et al. 2008). As discussed above, gut microbes metabolize drugs in two steps, reduction and hydrolysis, which indicates that energy is required by gut microbiota. Since the gut is anaerobic, these gut microbes cannot depend on oxygen as electron acceptor for respiratory process; therefore, reductive xenobiotic metabolism might help in

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respiratory process, and hydrolysis could provide substrate for growth of microbes (Arkhipova and Akimenko 2005; Novel et al. 1974). Most of the drugs, whether it undergoes structure reduction and/or hydrolysis, suggests that the intestinal microbiota affects drug absorption causing change in the pharmacological effects of drugs and apparently their bioavailability (Haiser et al. 2014). Identifying these major players would help in predicting the fate of a novel drug when exposed to gut microbiota. This would help in revolutionizing the area of drug development and precision medicine (Spanogiannopoulos et al. 2016). β-glucuronidase (GUS) enzymes encoded by microbes have a major role in metabolism of drugs in the intestinal tract. A study identified total 3013 GUS enzymes, of which 279 were unique microbiome-encoded GUS proteins (Pollet et al. 2017). These 279 unique proteins were grouped into 6 structural categories which were different in ability to accommodate small molecules in comparison to macromolecules. These GUS enzymes have been recently studied for their involvement in toxicity associated with a common drug CPT-11/irinotecan used for colon cancer chemotherapy. Irinotecan is metabolized in the liver by human carboxylesterases which activate it to its cytotoxic metabolite SN-38 having potential to inhibit nuclear topoisomerase 1 enzyme, important for replication of DNA. For the elimination of this drug, it is glucuronidated to an inactive form SN-38G by an enzyme of the liver, UDP-glucuronosyltransferase, which is excreted by biliary track into the gut. Herein, GUS can cause its reactivation by conversion of SN-38G again to SN-38 which is toxic to the epithelial cells of the intestine causing diarrhoea (Stein et al. 2010). GUS can also induce toxicity of non-steroidal anti-inflammatory drugs (NSAIDs) causing gastroduodenal mucosal lesions in more than 50% people using it (Higuchi et al. 2009). How NSAIDs induce toxicity to the intestinal wall? This mechanism is mediated by bacterial GUS in a manner that when glucuronidated NSAIDs reach distal small intestinal lumen, these bacterial GUS produce aglycones, taken up by enterocytes, and cytochrome P450 of the intestine, which further metabolizes these aglycones to reactive intermediates and causes severe stress in endoplasmic reticulum/mitochondria, both of which lead to cell death (Boelsterli et al. 2013). Since a wide range of these β-glucuronidases are present in dominant gut bacteria, it poses a challenge to design bacterium-specific targets to reduce drug toxicity. To understand the drug metabolism by microbes, inactivation of digoxin, which is a drug for heart failure and arrhythmias for years, has been a promising point. Its inactivation by gut microbiota was first reported in the 1980s (Lindenbaum et al. 1981), and it was also observed that treatment with antibiotics increased the serum concentration of digoxin. Mechanism underlying this was deciphered, when an enzyme, cardiac glycoside reductase 2 [Cgr2], was discovered and characterized. It was found that this enzyme is sufficient for reductive inactivation of digoxin by bacterial strain actinobacterium Eggerthella lenta present in the gut (Haiser et al. 2013). Comparative genomics data revealed a single genomic locus-an 8-gene cgr associated gene cluster which was predictive for reduction of digoxin. Further transcriptomics confirmed that two of these candidate genes were highly up-regulated in the presence of digoxin. Further these genes were narrowed down

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to one gene encoding a single enzyme (Cgr2) by heterologous expression and biochemical characterization for reduction of digoxin. It was also found that this enzyme was inhibited by arginine and was not present in non-metabolizing strains of E. lenta. Gnotobiotic mice were used for pharmacokinetic studies which revealed that in vivo metabolism of digoxin was reduced by dietary proteins and serum and urine showed remarkable changes in drug concentration. Study emphasized the need for viewing pharmacology both in terms of human and microbial genome (Haiser et al. 2013). Tacrolimus is a drug used in patients of organ transplant, and establishing the therapeutic dose of this drug is a great challenge due to variability in its absorption, metabolism and disposition both interpatient and intrapatient. Effect of gut microbiota on metabolism of this drug was studied in a pilot study of kidney transplant for which faecal specimens were collected during first month of transplantation from 19 transplant recipients which were grouped into recipients and showed requirement for nearly 50% increased dose of tacrolimus during this duration or dose escalation group. Those which did not require any further increase in doses can further be classified as dose stable group. Deep sequencing of PCR amplified 16S rRNA V4-V5 region was carried out to characterize the bacterial species. It was found by bacterial population characterization that percentage of faecal Faecalibacterium prausnitzii was significantly higher during first week of transplantation in dose escalation group as compared to dose stable group. Abundance of these bacteria was positively correlated with future dosing of tacrolimus at 1 month. Such studies could be helpful in explaining the differences in doses of tacrolimus between different individuals for therapeutic efficacy (Lee et al. 2015). This study was further extended to test if F. prausnitzii and other gut bacterial species have ability to metabolize tacrolimus. It was observed that incubating tacrolimus with F. prausnitzii resulted in production of two compounds (the major one named M1) which were not produced when tacrolimus was incubated with hepatic microsomes. M1 was identified as C-9 keto-reduction product of tacrolimus which was 15-fold less efficient as an immunosuppressant than tacrolimus. This conversion was also verified in stool of tow healthy adults, and M1 was also found in stool samples of kidney transplant recipients on tacrolimus (Guo et al. 2019). These studies suggest that gut bacterial species can have conflicting effects relevant to disease. Another study using colon cancer model suggested that bacteria can metabolize chemotherapy drug gemcitabine (20 ,20 -difluorodeoxycytidine) into its inactive form, 20 ,20 -difluorodeoxyuridine. This metabolism was mediated by expression of a bacterial enzyme cytidine deaminase (CDDL), seen primarily in Gammaproteobacteria. Mouse model of colon cancer demonstrated that Gammaproteobacteria present in tumour induced resistance to gemcitabine which was dependent on expression of CDDL by bacteria, and this resistance was abrogated on treatment with antibiotic ciprofloxacin. We can say that resident commensal bacteria can play a significant role in mediating drug resistance in cancer. If deciphered, then the drug response could be potentiated by co-administering antibiotics indicating that effect of intra-tumour

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bacteria on tumour immunity also needs to be explored for effective treatment strategy (Geller et al. 2017). Inconsistent results have been shown by antiretroviral-based strategies for prevention of HIV in females. A trial was conducted in Africa to assess if microbiota present in the vagina modulates the efficacy of tenofovir gel microbicide. It was observed that female vaginal bacterial community was dominated by two major bacteria – one with Lactobacillus and other with Gardnerella vaginalis. It was recorded that tenofovir reduced cases of HIV in lactobacillus group in higher percentage compared to G. vaginalis group. It was around threefold difference in efficacy. Mucosal tenofovir was very low when detected in group where females did not have lactobacillus suggesting that tenofovir was depleted by metabolism more rapidly than target cells convert it to pharmacologically active drug. This study again confirms that metabolism of drug by commensal bacteria reduced its availability for its efficacy (Klatt et al. 2017).

1.10

How Microbes Manipulate the Response of Immunomodulatory Drugs?

As discussed in section above, microbiome has strong and intimate link with host immune system. We have discussed in detail how aetiology to disease is being affected by this interaction; this section focuses on how this host-microbiome interaction has significance in response to immunomodulatory drugs. Various landmark studies have been conducted to understand the effect of gut microbiome on efficacy of immunotherapy based on PD-1 blocking. Oral and gut microbiome of melanoma patients undergoing anti-PD1 therapy was examined, and it was observed that significant differences exist in diversity and composition of the patient gut microbiome of responders and non-responders. Higher alpha diversity and abundance of bacteria of the Ruminococcaceae family were seen in faecal samples of responders. Functional differences were also found in gut bacteria of responders by metagenomics studies which included enrichment of metabolic pathway. Enhanced systemic and antitumour immunity was observed in responding patients which had a favourable gut microbiome. Faecal microbiota transplantation experiments were performed in germ-free recipient mice wherein mice were transplanted with stool from responders of anti-PD1 therapy and these mice showed significantly reduced tumour size. These data suggest the therapeutic strategy to modulate the gut microbiome in patients who receive checkpoint blocking therapy (Gopalakrishnan et al. 2018). Routy et al. conducted study in non-small cell lung cancer and kidney cancer. They also showed that resistance to immune checkpoint inhibitors was attributed to the composition of gut microbiome. Antibiotics repressed the effect of this therapy in patients with advanced cancer. It was observed that when faecal transplant was done in germ-free/antibiotic-treated mice from responders, it ameliorated the antitumour effects of PD-1 blockade, whereas FMT from non-responding patients failed to do so. Faecal stool of responders when studied by metagenomics revealed the

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abundance of Akkermansia muciniphila. Moreover, oral supplementation with A. muciniphila after faecal transplantation in non-responders refurbished the efficacy of anti-PD1 therapy in an IL-12 dependent manner which increased the recruitment of CCR9 + CXCR3 + CD4+ T lymphocytes into mouse tumour beds (Routy et al. 2018). Another study in metastatic melanoma patients analysed the stool samples by integration of 16S ribosomal RNA gene sequencing, metagenomic shotgun sequencing and quantitative polymerase chain reaction for selected bacteria before initiation of immunotherapy. Responders had abundance of Bifidobacterium longum, Collinsella aerofaciens and Enterococcus faecium suggesting that commensal microbiota has a mechanistic impact on antitumour immunity (Matson et al. 2018). Most of the studies identified different biomarker bacterial species in studies related to checkpoint inhibitors, it is an essential urge to identify that this mechanism of host-microbiome interaction is specific to this immune checkpoint blocking or this might implicate to broader perspective. We found that not only checkpoint inhibitor-based studies but also various other studies have shown such interaction. A study was conducted to evaluate the mucosa associated microbial composition during surgical resection in Crohn disease (CD) patients. After a period of 6 months, they identified that reduced bacterial population of Faecalibacterium prausnitzii is found to be associated with recurrence of ileal CD post-operation. To evaluate whether it has immunomodulatory effect, both in vitro and in vivo studies were done in colitis mouse model. It was found that F. prausnitzii was orally administered, and it not only reduced the severity of colitis but also corrected the dysbiosis which was generated due to colitis as was evident by real-time PCR data. It exhibited anti-inflammatory effect by blocking NF-kappaB activation and IL-8 production due to the metabolites secreted. F. prausnitzii was therefore identified as an anti-inflammatory bacterial candidate (Sokol et al. 2008). Another study associated with inflammatory bowel disease (IBD) compared two strains of same subspecies Bifidobacterium longum ssp. longum strains for their ability to prevent the experimental colitis. They found that Bl 7952, but not Bl 372, was able to protect mice from developing experimental colitis. This data suggested that immunomodulatory potential of many bacterial species is strain specific; therefore, a careful selection of probiotic bacteria is needed for such inflammatory disorders (Srutkova et al. 2015). We have also discussed in the above section how the microbial metabolism affects NSAIDs, and these are metabolized to acyl glucuronides (AGs) and/or ether glucuronides. They are further exported to biliary system. These in gut are cleaved by GUS and release aglycone which is potentially harmful. As discussed previously, gut microbiome has a central role in IBDs; a study was conducted to see if gut microbiome can predict the response to therapy for IBD using anti-integrin therapy (vedolizumab). Stool metagenomics and disease activity were assessed at baseline and various time points. It was found that α-diversity of community was very high with Roseburia inulinivorans and a Burkholderiales species in abundance in Crohn’s disease patient at baseline achieving week 14 remissions. They also elucidated significant association with various microbial

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functions among CD patients, achieving remission such as 13 pathways which included branched chain amino acid synthesis were significantly enriched in baseline samples for CD patients. This study suggests that early clinical remission could be anticipated by microbial functional composition at the baseline (Ananthakrishnan et al. 2017). In spite of so many studies, still the mechanism which is responsible for the microbial biomarkers of drug response is not completely elucidated. These effects appear to be a crosstalk which is mediated by manipulation of immune response, metabolism of drugs by microbes and changes in microbial population due to drug exposure. There is need to evolve more advanced approaches to study these complex interactions. For example, Yissachar et al. devised a microfabricated organ culture system which has the ability to preserve the normal multicellular composition of the intestine of mouse viably along with luminal flow to control perturbations. They demonstrated using this system that colonization of the intestine by symbiotic bacteria triggered a quick transcriptional response from the local intestinal tissue which precedes the immunological responses generated by the microbes present (Yissachar et al. 2017).

1.11

Modifying Gut Microbiota for Précised Medicine

We have done a detailed discussion focused on major aspects of host-microbiome interaction during the diseased state and drug-microbiome interaction leading to various effects on host. The present era in microbiome study is therefore focused on translating and applying this fundamental knowledge for the improvement of treatment for the disease. Various strategies have been adapted to manipulate the microbiome in favour of the host. One such strategy is pathogen-specific approach as antibiotics with broad spectrum devastates the microbiota of gut leading to various adverse effects of health of the individual. A study was conducted using Debio-1452 which is a staphylococcusselective enoyl-acyl carrier protein reductase (FabI) inhibitor, to assess if selective pathogenic inhibitors could cause fewer disturbances in the microbiome in mice model. It was found that gut bacterial load and composition in mice treated with Debio-1452 was similar to that of untreated mice, whereas profound decreased gut bacteria and altered composition was observed in mice treated with antibiotics suggesting that this pathogen-selective approach could help in minimizing the disturbance caused to gut microbiome (Yao et al. 2016). Similar results were obtained in case of ridinilazole which is targeted for treating C. difficile infection (CDI) and was found to be superior to vancomycin in Phase 2 study, and a recent nested cohort study compared the effect of both on faecal microbiota during and after treatment in Phase 2 study using qPCR and highthroughput sequencing on participants’ stools collected at multiple time points. It was shown that Bacteroides, C. coccoides, C. leptum and Prevotella groups were significantly lost in group treated with vancomycin at the end of the treatment with ridinilazole having minimal effect on C. leptum which recovered after day 25.

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Enterobacteriaceae group increased profoundly in vancomycin-treated group. Ridinilazole was found to have a low impact with small reduction in relative abundance of Firmicutes taxa. On the contrary vancomycin reduced Firmicutes, Bacteroidetes and Actinobacteria significantly and increased Proteobacteria. These findings again supported the fact that specific inhibitors have less disruptive effect on microbiome along with better efficacy and reduced recurrence of the disease (Thorpe et al. 2018). Another approach being used is faecal microbiota transplantation (FMT) wherein large changes are being introduced by transferring sample from a healthy donor to the individual with depleted gut microbiota. Faecal bacteriotherapy or MFT as discussed could be an effective strategy, but keeping in mind that infection could also be transmitted from the donor, a stool substitute was prepared from purified intestinal bacterial culture (33 isolates) which were derived from single donor and was used to treat recurrent C. difficile infection (hypervirulent C. difficile strain, ribotype 078) which had failed antibiotic therapy. After treatment patient returned to normal bowel movement, and this proof-ofconcept study demonstrated that stool substitute mixture having varied bacterial species could cure antibiotic-resistant C. difficile colitis (Petrof et al. 2013). Munoz et al. have shown using Salmonella model of colitis that mice which received a defined community of gut microbiota had reduced systemic inflammation and splenic S. typhimurium infection along with reduced infiltration of neutrophils in caecum as compared to vehicle control (Munoz et al. 2016). Recently many other approaches such as removing specific strain or species of a bacteria, inhibiting metabolic function of gut microbiota enzymes, introducing engineered strains in the gut and genetically modifying bacterial cells present in gut have been adopted which we would discuss herein with few examples. Selective depletion of strains which have undesirable activity such as those acting on drugs to produce toxic metabolites has been on the approach studied widely. Phage therapy to combat the increased incidences of multi-drug-resistant bacterial infections with a view point of specificity of phages for their bacterial host has interested many researchers in this league. However, phage-resistant bacteria are a barrier to such therapies. A novel lytic phage was identified from municipal sewage which had the ability to kill Enterococcus faecalis which is Gram-positive opportunistic pathogen in the human intestine. A predicted integral membrane protein named as PIPEF was identified to be required for phage infection of E. faecalis, which was found to be conserved in E. faecalis and has a 160-amino-acid hypervariable region that regulates the tropism of phage for distinct strain of enterococcus. It was shown in gnotobiotic mice that sewage phage reduced the colonization of E. faecalis in the intestine but E. faecalis acquired resistance to phage by mutation in PIPEF suggesting the molecular mechanism for evolutionary arms race between E. faecalis and the lytic phages. This also suggested that approaches should be used to engineer E. faecalis phages which have an altered host specificity and can circumvent the problem of phage resistance in bacteria (Duerkop et al. 2016). To evaluate the role of phage in shaping gut microbiota, 15 sequenced human bacteria were colonized in germ-free mice. These were then subjected to a staged

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phage-like particles attack which was purified from faecal microbiota of five phages. It was subsequently found that the population of targeted bacteria decreased and the abundance of phage increased. It was found that the population of targeted bacteria decreased and the abundance of phage increased. Data indicates that phages can reduce the bacterial target in in vivo (Reyes et al. 2013). These data suggests that phages could be exploited to modify the gut microbiome under specific conditions. CRISPR-Cas technology was applied using phage as a transfer system for sequence-specific killing of pathogens. In a study RNA-guided nucleases which targeted specific DNA sequences were delivered to microbes using bacteriophages. The target of these genes included few undesirable genes such as genes causing antibiotic resistance or responsible for virulence of the pathogenic carbapenemresistant Enterobacteriaceae and enterohaemorrhagic Escherichia coli. It was shown that this delivery based on RNA-guided nucleases improved survival in Galleria mellonella infection model wherein the group used a ΦRGN to target intimin, which is a virulence factor of enterohaemorrhagic E. coli O157:H7 (EHEC) and important for colonization of bacteria in the intestine (Citorik et al. 2014). This strategy if applicable for selective removal of genes which are specific for strains in gut microbiota for a specific function such as drug metabolism could be a hallmark finding. Another strategy exploited a lot is to treat microbial enzymes which catalyse undesirable reactions as “targets” against which selective inhibitors could be designed (Wallace and Redinbo 2013). We have discussed in detail the role of GUS in drug toxicity; various studies have been performed to design inhibitors against these gut bacterial b-glucuronidases as crystal structure of these glucuronidases has been deciphered for members of Firmicutes and Bacteroidetes. Several inhibitors for GUS have been identified which have strong efficiency for enzyme inhibition without affecting bacterial growth or any harm to host epithelial tissue (Wallace et al. 2015). As discussed in detail, irinotecan (CPT-11) is used for various forms of cancer as a potential drug, and it has been explored that its active metabolite form is processed to form inactivated glucuronide conjugate SN-38-Gby UDP-glucuronosyltransferases and eliminated via gastrointestinal tract. In the tract, the bacterial GUS removes the glucuronic acid, and it reactivates SN-38 which is DNA topoisomerase poison causing damage in GI along with diarrhoea (Stein et al. 2010). However the inhibitor designed for b-glucuronidases showed reduced druginduced cytotoxicity in mice, but it was effective only for b-glucuronidases which had a loop structure. This suggested that complete understanding of molecular mechanism of microbial bio-transformations is required for selective inhibition. Many new microbial genes which are involved in drug metabolism which affect drug behaviour and enhance side effects have been discovered, and studies have been focussed to exploit them. A gut-associated bacterium was found to decarboxylate drug levodopa to dopamine with the help of bacterial tyrosine decarboxylases, even in presence of tyrosine. This compromises the levels of levodopa in patients with Parkinson’s disease. It was thought that inhibitors of human DOPA decarboxylases could be administered along with levodopa, but these were found to be inactive against bacterial decarboxylases (van Kessel et al. 2019).

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Table 1.1 Gut microbiome and their relationship with few arms of the immune system S. No. 1.

Immunology arm affected Isolated lymphoid follicles (ILFs)

2.

Th17 cells in lamina propria

Differentiation of Th17 cells

CD4+ T helper cells that produce IL-17 and IL-22 (Th17 cells) in the lamina propria 3.

Treg cells are present in mucosa of colon in a large number

4.

Toll-like receptors (TLRs)

Deletion of TLR2 on CD4+ T cells TLR2 directly on Foxp3+ regulatory T cells 5.

Role of IgA at mucosal surface

Activation-induced cytidine deaminase (AID)-deficient mice model (mimic pathology of CVID) AID
/
mice

Mechanism/effect due to microbial community Genesis of ILFs is induced by peptidoglycan from Gram-negative bacteria mediated by recognition by NOD1 (nucleotide-binding oligomerization domain containing 1) innate receptor present in epithelial cells and b-defensin 3 (Bouskra et al. 2008) ATP whose source can be commensal bacteria can activate a special subset of cells in lamina propria, viz. CD70highCD11clow cells, which further cause differentiation of Th17 cells (Atarashi et al. 2008) Presence of Cytophaga-FlavobacteriumBacteroides bacteria in the intestine (Ivanov et al. 2008) Induced by colonization of the small intestine of mice by a single commensal microbe, segmented filamentous bacterium (SFB) (Ivanov et al. 2009) Clusters IV and XIVa of the genus Clostridium help in accumulating Treg cells (Atarashi et al. 2011) Plays a role in establishment of symbiotic relationship of host and microbe with focus on gut commensal Induced antimicrobial immune response which reduced colonization of B. fragilis Activated by a symbiosis factor (PSA) of B. fragilis via novel mechanism to maintain mucosal tolerance (Round et al. 2011) IgA protects individuals by preventing the transmission of microbial pathogens, and it regulates the composition and function of gut microbiota (Macpherson and Uhr 2004; Sait et al. 2007; Wijburg et al. 2006) Leads to dysfunction of IgA and these mice showed protruding follicle-like structures which indicate hypertrophia of isolated lymphoid follicles (ILFs) Showed abnormal expansion of Gram (+) anaerobes in the small intestine and segmented filamentous bacteria were dominant, which are potent inducer of IgA synthesis (Ivanov et al. 2009; Fagarasan and Honjo 2003; Suzuki et al. 2004; Davis and Savage 1974; Ohashi et al. 2006) (continued)

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Table 1.1 (continued) S. No.

Immunology arm affected A gnotobiotic mouse model with microbiota reduced to one bacterial species and the antibody repertoire to a single, monoclonal IgA against the bacterium’s capsular polysaccharide was generated Bacteroides thetaiotaomicron was introduced into germ-free wild-type, immunodeficient Rag1
/
or Rag1
/
mice harbouring IgA-producing hybridoma cells Differentiation of IgA-producing B cells B cells are promoted for secretion of IgA

6.

Intestinal NKp46 + IL-22+ cells

Mechanism/effect due to microbial community IgA reduces intestinal pro-inflammatory signalling and bacterial epitope expression, thereby balancing suppression of the oxidative burst with the antibody’s negative impact on bacterial fitness (Peterson et al. 2007) Flagellin derived from commensal bacteria for a specific subset of lamina propria DCs helped in production of retinoic acid for differentiation (Mora et al. 2006; Uematsu et al. 2008) Upon microbial exposure intestinal plasma cells express TNF and iNOS which help in this process (Fritz et al. 2012) Requires commensal bacteria and RORγt and mice which did not have IL-22producing NKp46+ cells showed increased susceptibility to the pathogen Citrobacter rodentium (Satoh-Takayama et al. 2008)

An alternative approach is recently been discussed which involves interference with essential co-factors of a class of enzymes. Zhu et al. showed that expansion of Enterobacteriaceae during inflammation of gut can be prevented by tungstate treatment which inhibits molybdenum-cofactordependent microbial respiratory pathways selectively which are present only during inflammation; on the contrary, tungstate caused very minor changes to the microbiota during normal conditions (Zhu et al. 2018). Recently efforts have been taken to introduce engineered strains instead of removing strain as live bacterial therapeutics. Engineered bacteria have been used to express genes which are capable of complimenting the absent function of host in diseases caused by genetic mutations. Study was addressed to compensate the host enzyme defect which leads to hyperammonemia which causes increased blood ammonia levels. Orally deliverable probiotic Escherichia coli Nissle 1917 was engineered to develop strain SYNB1020 that converts NH3 to l-arginine (l-arg). This strain was able to reduce systemic hyperammonemia and improved survival in ornithine transcarbamylase-deficient splash mice (Kurtz et al. 2019). Other than these, efforts are being made to genetically edit bacteria which are actively colonizing in the gastrointestinal tract known as in vivo or in situ engineering which refers to modification in genes of bacteria within host (Ronda et al. 2019). This is a fascinating approach and this along with above-said approach of strain engineering will provide vast avenues for manipulating diverse gut microbiota.

Digoxin

Tacrolimus

2.

3.

S. No Drugs/Immunomodulators Drugs 1. CPT-11/irinotecan used for colon cancer chemotherapy Non-steroidal antiinflammatory drugs (NSAIDs)

Faecalibacterium prausnitzii

Drug used in patients of organ transplant Establishing the therapeutic dose of this drug is a great challenge due to variability in its absorption, metabolism and disposition

Drug for heart failure and arrhythmias for years

For elimination it is glucuronidated to an inactive form SN-38G by an enzyme of the liver, UDP-glucuronosyltransferase Glucuronidated NSAIDs

β-glucuronidase (GUS) enzymes encoded by microbes

Cardiac glycoside reductase 2 [Cgr2]

General outcome/effect/action

Microbial effect

Table 1.2 Few drugs/immunomodulators affected by microbial population with final outcome

GUS can cause its reactivation by conversion of SN-38G again to SN-38 which is toxic to the epithelial cells of the intestine causing diarrhoea (Stein et al. 2010) GUS produces aglycones from these glucuronidated NSAIDs, taken up by enterocytes and cytochrome P450, metabolized to reactive the intermediates causing severe stress in endoplasmic reticulum or cause mitochondrial stress leading to cell death (Boelsterli et al. 2013) Cgr2 is sufficient for reductive inactivation of digoxin by bacterial strain actinobacterium Eggerthella lenta present in the gut (Haiser et al. 2013) This enzyme was inhibited by arginine and was not present in non-metabolizing strains of E. lenta Abundance of this bacteria was positively correlated with future dosing of tacrolimus at 1 month (Lee et al. 2015) Tacrolimus+ F. prausnitzii resulted in production of two compounds (the

Modified outcome due to microbial population

24 P. Misra and S. Singh

Prontosil

Metronidazole

Omeprazole

Gemcitabine (20 ,20 -difluorodeoxycytidine)

Trail conducted with tenofovir gel

4.

5.

6.

7.

8.

Two major bacteria – one with lactobacillus and other with Gardnerella vaginalis in female vagina bacterial community

Anaerobic bacteria such as Bacteroides strains Bacterial enzyme cytidine deaminase (CDDL), seen primarily in Gammaproteobacteria

Clostridium perfringens

Bacterial azoreductases

Anti-retroviral drug

Used to treat patients with pancreatic, lung, breast or bladder cancers

Less efficacious

(continued)

major one named M1) M1 was 15-fold less efficient as an immunosuppressant than tacrolimus (Guo et al. 2019) Cleaves azo-bond to sulphanilamide with more efficacy Ring is cleaved to N-(2-hydroxyethyl)-oxamic acid and acetamide by enzymatic cleavage Enzymatic reduction to corresponding sulphide metabolites Gammaproteobacteria present in tumour induced resistance to gemcitabine which was dependent on expression of CDDL by bacteria (Geller et al. 2017) Tenofovir reduced cases of HIV in lactobacillus group in higher percentage compared to G. vaginalis group In study group without lactobacillus, tenofovir was detected in low amount suggesting rapid metabolism and hence less activity (Klatt et al. 2017)

1 Gut Microbiome in Microbial Pathogenicity 25

10.

CTLA-4 inhibitors Ipilimumab

S. No Drugs/Immunomodulators Immunomodulators 9. Anti-PD1 therapy

Table 1.2 (continued)

MCA205 sarcomas in mice

Treatment for melanoma patients Non-small cell lung cancer, renal cell carcinoma and urothelial carcinoma Metastatic melanoma patients

Higher alpha diversity and abundance of bacteria of the Ruminococcaceae family

Bacteroides species

General outcome/effect/action

Microbial effect

Different oral and gut microbiome in responders and non-responders FMT from responders to non-responders reduced tumour size in non-responders Modulation of gut microbiome effects efficacy (Gopalakrishnan et al. 2018) Faecal stool of responders had abundant Akkermansia muciniphila FMT+ A. muciniphila refurbished the efficacy of anti-PD1 therapy in non-responders in an IL-12-dependent manner, increased recruitment of CCR9 + CXCR3 + CD4+ T lymphocytes into mouse tumour beds (Routy et al. 2018) Responders had abundance of Bifidobacterium longum, Collinsella aerofaciens and Enterococcus faecium T cell responses specific for B. thetaiotaomicron or B. fragilis were associated with the efficacy of CTLA-4 blockade

Modified outcome due to microbial population

26 P. Misra and S. Singh

Faecalibacterium prausnitzii as immunomodulator/antiinflammatory

Bifidobacterium longum ssp. longum strain Bl 7952 as immunomodulator

11.

12.

Inflammatory bowel disease (IBD)

Crohn disease (CD) patients

In mouse model of colitis, F. prausnitzii orally administered reduced the severity of colitis Corrected dysbiosis due to the disease Showed anti-inflammatory effect by blocking NF-kappaB activation and IL-8 production due to the metabolites secreted (Sokol et al. 2008) Protected mice during severe colitis (Srutkova et al. 2015)

1 Gut Microbiome in Microbial Pathogenicity 27

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P. Misra and S. Singh

Table 1.3 Approaches to modify gut microbiome with few examples Sno 1.

Approach used Pathogenspecific approach

Few examples Debio-1452 Ridinilazole

Objective A staphylococcusselective enoyl-acyl carrier protein reductase (FabI) inhibitor C. difficile infection (CDI)

2.

Faecal microbiota transplantation (FMT)

A stool substitute was prepared from purified intestinal bacterial culture (33 isolates) Defined community of gut microbiota

For treating C. difficile infection (hypervirulent C. difficile strain, ribotype 078) with failed antibiotic therapy Salmonella model of colitis

3.

Selective depletion of strains

15 sequenced human bacteria were colonized in germfree mice ΦRGN to target intimin, which is a virulence factor of enterohaemorrhagic E. coli O157:H7 (EHEC)

4.

Microbial enzyme/cofactor inhibitors

Phage therapy Staged phage-like particles attack which was purified from faeces microbiota of five phages RNA-guided nucleases delivered using bacteriophages targeted specific DNA sequence Inhibitors for GUS Molybdenum cofactor

Piperazinecontaining GUS inhibitors Three old drugs (Ndesmethylclozapine, aspartame and gemifloxacin) Tungstate treatment

Outcome No disturbance to normal gut microbiome on the contrary to disturbed gut microbiome in antibiotic treated group (Yao et al. 2016) Had less disruptive effect on microbiome along with better efficacy and reduced recurrence of the disease (Thorpe et al. 2018) Patient returned to normal bowel movement (Petrof et al. 2013) Reduced systemic inflammation and splenic S. typhimurium infection (Munoz et al. 2016) Population of targeted bacteria decreased (Reyes et al. 2013) Improved survival in Galleria mellonella infection model

Inhibited microbiome GUS enzymes by intercepting the glycosyl-enzyme catalytic intermediate Identified as selective bacterial β-GUS inhibitors Inhibits (continued)

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Table 1.3 (continued) Sno

5.

1.12

Approach used

Engineered strains

Few examples

Engineered E. coli strain

Objective

A strain called SYNB1618, derived from E. coli Nissle, constructed to express proteins for phenylalanine degradation, inducible both ( for Disease phenylketonuria (PKU) Derivative of E. coli Nissle, SYNB1020, was engineered for high expression of enzymes for the biosynthesis of arginine from ammonia

Outcome molybdenumcofactor-dependent microbial respiratory pathways selectively which are present only during inflammation Mice injected with phenylalanine and orally dosed with SYNB1618 had decreased blood phenylalanine levels The modifications enabled higher ammonia consumption and arginine production than E.coli Nissle, and SYNB1020 showed low levels of ammonia in mouse models of hyperammonemia

New Insights

Above-detailed overview of how therapeutically targeting human microbiome is being exploited for potential health benefits indicates that strategies to manipulate human microbiome including engineered bacteria, designing inhibitors and manipulating host microbiome in in vivo have shown positive results. However, the challenge remains in creating these microbial therapeutics in such a way that they are well adapted to specific body environment, and are able to achieve colonization, etc. Constructing effective gene synthetic circuits which could be functionally active in in vivo conditions along with establishing regulatory frameworks for safety could be fruitful approach. These circuits could help in engineering bacteria and viruses which can act as sensors to record the changes in the cellular environment. Synthetic biology could also be used to improve the efficacy of therapeutic probiotics, and logic-gating along with biocontainment mechanism needs to be implicated in synthetic probiotics. Such approaches can be of help once deep interactions between host and microbe are being dissected (Tables 1.1, 1.2 and 1.3).

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Shimotoyodome A, Meguro S, Hase T, Tokimitsu I, Sakata T (2000) Short chain fatty acids but not lactate or succinate stimulate mucus release in the rat colon. Comp Biochem Physiol - A Mol Integr Physiol. https://doi.org/10.1016/S1095-6433(00)00183-5 Shussman N, Wexner SD (2014) Colorectal polyps and polyposis syndromes. Gastroenterol Rep. https://doi.org/10.1093/gastro/got041 Sokol H, Pigneur B, Watterlot L, Lakhdari O, Bermúdez-Humarán LG, Gratadoux JJ et al (2008) Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci U S A. https://doi.org/10. 1073/pnas.0804812105 Spanogiannopoulos P, Bess EN, Carmody RN, Turnbaugh PJ (2016) The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat Rev Microbiol. https://doi.org/ 10.1038/nrmicro.2016.17 Srutkova D, Schwarzer M, Hudcovic T, Zakostelska Z, Drab V, Spanova A et al (2015) Bifidobacterium longum CCM 7952 promotes epithelial barrier function and prevents acute dss-induced colitis in strictly strain-Specific manner. PLoS One. https://doi.org/10.1371/journal. pone.0134050 Stein A, Voigt W, Jordan K (2010) Review: chemotherapy-induced diarrhea: pathophysiology, frequency and guideline-based management. Ther Adv Med Oncol. https://doi.org/10.1177/ 1758834009355164 Suzuki K, Meek B, Doi Y, Muramatsu M, Chiba T, Honjo T et al (2004) Aberrant expansion of segmented filamentous bacteria in IgA-deficient gut. Proc Natl Acad Sci U S A. https://doi.org/ 10.1073/pnas.0307317101 Suzuki K, Maruya M, Kawamoto S, Sitnik K, Kitamura H, Agace WW et al (2010) The sensing of environmental stimuli by follicular dendritic cells promotes immunoglobulin A generation in the gut. Immunity. https://doi.org/10.1016/j.immuni.2010.07.003 Theriot CM, Koenigsknecht MJ, Carlson PE, Hatton GE, Nelson AM, Li B et al (2014) Antibioticinduced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection. Nat Commun. https://doi.org/10.1038/ncomms4114 Theriot CM, Bowman AA, Young VB (2016) Antibiotic-induced alterations of the gut microbiota alter secondary bile acid production and allow for Clostridium difficile spore germination and outgrowth in the large intestine. MSphere. https://doi.org/10.1128/msphere.00045-15 Thorpe CM, Kane AV, Chang J, Tai A, Vickers RJ, Snydman DR (2018) Enhanced preservation of the human intestinal microbiota by ridinilazole, a novel Clostridium difficile-targeting antibacterial, compared to vancomycin. PLoS One. https://doi.org/10.1371/journal.pone. 0199810 Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE et al (2009) A core gut microbiome in obese and lean twins. Nature. https://doi.org/10.1038/nature07540 Turnbaugh PJ, Henrissat B, Gordon JI (2010) Viewing the human microbiome through threedimensional glasses: Integrating structural and functional studies to better define the properties of myriad carbohydrate-active enzymes. Acta Crystallogr Sect F Struct Biol Cryst Commun. https://doi.org/10.1107/S1744309110029088 Uematsu S, Fujimoto K, Jang MH, Yang BG, Jung YJ, Nishiyama M et al (2008) Regulation of humoral and cellular gut immunity by lamina propria dendritic cells expressing Toll-like receptor 5. Nat Immunol. https://doi.org/10.1038/ni.1622 van Kessel SP, Frye AK, El-Gendy AO, Castejon M, Keshavarzian A, van Dijk G et al (2019) Gut bacterial tyrosine decarboxylases restrict levels of levodopa in the treatment of Parkinson’s disease. Nat Commun. https://doi.org/10.1038/s41467-019-08294-y Walker JA, Barlow JL, McKenzie ANJ (2013) Innate lymphoid cells-how did we miss them? Nat Rev Immunol. https://doi.org/10.1038/nri3349 Wallace BD, Redinbo MR (2013) The human microbiome is a source of therapeutic drug targets. Curr Opin Chem Biol. https://doi.org/10.1016/j.cbpa.2013.04.011

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding of Microbiome Association Rashmi Dahiya, Taj Mohammad, and Md. Imtaiyaz Hassan

Abstract

The participation of the microbiome is well-established in the modulation of health and diseases in humans. The microbiome-human interface has a tremendous impact on metabolic functions, immune system, and the overall well-being. The emergence of microbiome genome-wide association studies has extensively facilitated our knowledge of interactions that exist between the microbiome and genetic variants. Many additional discoveries were possible because of the advent of highly sophisticated and sensitive next-generation sequencing technologies which enabled our understanding of the complex interaction between distinct microbial taxa’s and host genetics. In this chapter, we highlight the significance of microbiome in human health, its understanding through large-scale microbiome genome-wide association studies, and its implication as a therapeutic target. Keywords

Genome-wide association · Microbiome-GWAS · Host microbiota · Hostpathogen interaction · Next-generation sequencing · Microbial genome

2.1

Introduction

The microbiome consists of trillions of microbial cells residing in the organism which are involved in overall modulation of health and metabolism (Ursell et al. 2012). Microbiome has evolved parallelly with the organism birth and is tremendously affected by the environmental influences and diets (Turnbaugh et al. 2007). The emergence of new microbial communities pose a serious challenge R. Dahiya · T. Mohammad · M. I. Hassan (*) Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_2

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toward human wellness and disease status. Moreover, dysbiosis (microbiome imbalance) is associated with numerous disease which include inflammatory, immunological, neurological, skin, and metabolic disorders (Kho and Lal 2018). Many health consequences due to the presence of microbiome in host have led to the development of newer therapeutics focusing on manipulating the host microbiome. This can be achieved either through the removal of detrimental or re-establishing the advantageous microbial taxa to rewire the functional roles they execute. However, these approaches have proven challenging due to limited essential knowledge. A thorough understanding of outsized microbial taxa by their culturing in laboratories is highly cumbersome and problematic. Comprehensive cataloging of the individual microbiome community pose difficulty in the understanding of their collective functions and host-pathogen interactions (Kurilshikov et al. 2017). Recent technological advancements in the field of next-generation sequencing have enabled the exploration of the complex interaction between microbial communities and how the host genomes facilitated their development and functions through extensive metagenomic studies (Abrahamsson et al.). Moreover, these newer approaches along with several computational tools have enabled us to understand the roles of genetic variants of host in microbiome development. The incorporation of microbiome in scientific studies is important to understand their quantitative interactions between genotypes and environment to predict disease susceptibility. The specific attention toward the development of high-throughput microbiome genome-wide association studies (mGWAS) has extensively helped in the survey of host-microbe interactions (Goodrich et al. 2017). In addition, it addresses the correlation between disease states and the microbiome composition. For instance, mGWAS led to the understanding of the positive association of increased bifidobacteria within the human gut and its benefits toward human lactase nonpersistent genotype which is known to confer lactose intolerance in different human populations (Goodrich et al. 2016; Bonder et al. 2016; Rothschild et al. 2018). Many of these studies have provided strategic insights into the human microbiota and various other hosts of microbiomes in typically extreme conditions. Indeed, a detailed understanding of the relationships between the host and its associated microbiome is essentially required for the determination of ailments diagnosis and their potential treatments. In this chapter, we review the recent highlights of mGWAS, its critical roles in the discovery of the human microbiome, advancements to investigate complex hostmicrobiome interactions and the underlying therapeutic targets. This chapter attempts to provide an outline of the diseases that are significantly associated with microbiome and the currently available high-throughput sequencing technologies software used for the study of host-microbe interactions with mGWAS. We also discussed the recent advances and applications of the state-of-the-art computations and bioinformatics software, pipeline, and workflows for the analysis of metagenomics data. Finally, a comprehensive discussion is provided for the presently available challenges in the field of metagenomic studies.

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding. . . 39

2.2

Diversity of Microbiomes

Microbes are ubiquitous and can inhabit any imaginable environment. Microbes, although generally invisible, play an essential role in human life and ecosystem (Fierer 2017; Bardgett and Van Der Putten 2014), through various processes such as plant growth, nutrient cycling within soil, biogeochemical cycling, etc. (Fuhrman 2009; Van Der Heijden et al. 2008; Hamady and Knight 2009). A countless number of microbes (commensal, symbiotic, and pathogenic) populate the human body and mutually organizes to generate the human microbiome. The complex interactive network between the human body and microbiome is broadly recognized to influence health (Qin et al. 2010). A well-functioning microbiome is a requisite for the host and contributes in important physiological processes. Microbiomes co-evolve with their host either as symbionts or pathobionts (Kahn et al. 2002; Vieira et al. 2014). For instance, certain commensals of the human gut drive proper functioning of the immune system and maturation of immune cells. Indeed, microbial communities constitute specific structures depending on their physical surroundings and host types (Weinstock 2012). Accordingly, microbial identification and characterization within their divergent hosts and the biological paths they regulate to confer distinct phenotypes are the major focus of host-microbiome research. Due to the extreme diversity within microbial communities and their propensity to form convoluted networks, individual organisms have never been cultured in laboratories (Kallmeyer et al. 2012; Fuhrman 2009). Moreover, mechanistic contacts between microbiome consortia and its functioning make it more challenging for characterization. Despite their critical roles in human metabolism, the structurefunction dynamics and characterization of their interactions have been problematic. The core analysis of host-microbe interaction can disclose their mechanistic insights. Standard approaches to study host-microbiome associations relied on culturedependent approaches which despite producing interesting data also suffered from a false view of the microbiome. On the other hand, sensitive PCR-based, cultureindependent approaches have extensively changed our perception of the human microbiome and evolved for the qualitative and quantitative establishment of metagenomics. Host-pathogenic interactions through metagenomics have paved the way for identification of genetic factors that permit microbiomes to impact their hosts in many unexpected ways. Metagenomics has indeed proven useful to discern the structure of microbial populations, evolutionary aspects of host-microbe interactions, pathogen surveillance, and functional dysbiosis (Rosario and Breitbart 2011). For instance, metagenomics has identified associations of the gut microbiome with diabetes (Devaraj et al. 2013), rheumatoid arthritis (Scher and Abramson 2011), and depression (Foster and Neufeld 2013). Any material from the environment can be used for metagenomic analysis, provided the nucleic acids are extracted from it. Indeed, historical traces revealed several changes from the metagenome of coprolites (Tito et al. 2012), teeth (Adler et al. 2013), and other tissues (D’Costa et al. 2011). Global Ocean Sampling represents one of the largest metagenomic studies conducted to date.

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The Human Microbiome: Functional Link with Health and Diseases

The complex network of microbiome has gathered massive interest within scientific community due to its intimate association with various life-threatening diseases. Microbiome has been considered as human’s “second genome” owing to its coevolution for million years. Decoding the composition and functional aspects of the human microbiome can potentially enhance human health and counteract diseases. Through the advent of metagenomics analysis, a considerate insight into microbiome structure and functions has been achieved. Moreover, the metagenome exploration to understand human microbiota has been represented as a leading edge in human genetics (Fig. 2.1). Interestingly, the human gut has grabbed major attention in the area of microbiome research due to the extensive microbial colonization of the human digestive system and their influence on health. The gut microbiome is enormously diverse along with substantial variations in individuals across the human population (Huttenhower et al. 2012).

Environmental Factors

Host Genetics

Microbiome

Gene Regulation

Host Phenotype Fig. 2.1 The multidirectional interactions of host-microbes. Many environmental factors could potentially govern the host genetics and either directly or indirectly affect the host phenotype. The multidirectional interactions occurring between environmental factors and host genome could potentially modulate the microbiome which eventually governs the host phenotype

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding. . . 41

The gut microbiota is preferentially ruled by four phyla: Bacteroidetes, Actinobacteria, Firmicutes, and Proteobacteria with small colonization by Verrucomicrobia and Fusobacteria (Belenguer et al. 2011). The changes in the ratios of bacterial colonization are established as a potential index for clinical diagnosis. Lactic acid bacteria (LAB) from phyla Firmicutes and Actinobacteria mainly Lactobacillus and Bifidobacterium, respectively, along with the butyrateproducing bacteria from Firmicutes mainly Clostridium and Roseburia are considered to provide benefits to host through anti-inflammatory, tumor-suppressive along with the pathogen elimination properties (Marteau 2013; Fischbach et al. 2008). Moreover, the interaction between the LAB and butyrate-producing bacteria provides the former the capability to feed on lactate (Belenguer et al. 2011). Various perturbations or dysbiosis can elevate the disease risk by disturbing the delicate balance that exists between host and microbiome. Gut microbiota offer widespread benefits to the host and perform many vital functions, including (i) detoxification of lethal xenobiotics; (ii) synthesis of nutritional products from food ingredients; (iii) developing the tough immunity; (iv) signal transduction for renewal and maintenance of gut integrity; and (v) resistance of colonization of pathogenic microbes through antimicrobial secretion (Cipriani et al. 2010; Makishima et al. 2002). Interestingly, gut microbiota can efficiently adjust to physiological changes occurring on a daily basis; however, its absence in a diseased state, environmental triggers, or through antibiotics leads to serious health consequences. Pro-inflammatory metabolites generated by pathogenic microbes in the gut can negatively affect the GIT (gastrointestinal tract) and many other vital organs. Gut microbes produce short-chain fatty acids (SCFA) including butyrate, propionate, and acetate as major fermentation products which provide energy source as well as other benefits for intestinal colonization and homeostasis. Butyrate has grabbed the researcher’s attention due to its role in health and resistance for diseases like IBD (inflammatory bowel disease) (Machiels et al. 2014). Despite its production in the colon, it can also modulate other organs, and slight shifts in its cellular levels can have serious clinical implications. The gut microbiome produce vitamins (folate, riboflavin), linoleic acids, bile acids, as well as many unidentified metabolites with putative local and systemic effects (Delzenne and Cani 2011). Bile acids (~5% of secretion) could enter the colon through the escape of reabsorption in the ileum and could suppress the growth of commensals bile-intolerant microbiota in the colon. Microbes, through 7-alpha dihydroxylation, convert the primary bile acids into secondary bile acids (SBA) which then suppress muscle fat deposition through the activation of the farnesoid X receptor in the nucleus (Cipriani et al. 2010). Moreover, SBA lithocholic acid (LCA) promotes detoxification and protects cells from injury and inflammation through its binding to the vitamin D receptor (Jurutka et al. 2005; Makishima et al. 2002). Conversely, higher SBA levels could lead to the development of inflammation and cancer. Hence, gut microbiome function extends beyond the intestine and affects systemic and metabolic processes. Indeed, many microbial compounds have been identified in blood using metabolomics studies which further impacted our knowledge of their broad effects on physiological regulation throughout the body.

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Mechanistic Connection of Human Health and Dysbiosis: mGWAS Studies

As discussed, the GIT harbors an enormous microbial diversity, comprising of complex intrinsic networks existing among distinct microbes as well as host-genome (Sung et al. 2017). To potentially uncover new and unidentified host-microbiome interactive networks, human genes with distinct alleles need to be searched across a given population having microbial traits. Interestingly, emerging results from mGWAS showed a consistent association between bifidobacteria, host-genome, and milk consumption (Bonder et al. 2016; Goodrich et al. 2016). Bifidobacteria represent a highly abundant gut microbe in the human host which lacks the lactasepersister genotype and ingest milk post-weaning (Fig. 2.2). The synthesis of oligosaccharide moiety H antigen on the intestinal mucosa and body fluids is performed by an enzyme fucosyltransferase 2 (FUT2) which functions as a carbon source as well as an attachment site for intestinal microbiota. Homozygous LOF alleles of FUT2 were found to be associated with increased predisposition to Crohn’s disease (CD) (Tong et al. 2014). Moreover, mice bearing the FUT2
/
genotype showed enhanced metabolism of lipid, carbohydrate, glycans, cofactor, and vitamins and reduction of amino acid metabolism. A significant association between nucleotide oligomerization domain 2 (NOD2) and abundance of Enterobacteriaceae with increased risk of IBDs has been previously reported (Li et al. 2012; Zhang and Li 2014; Ogura et al. 2001). MEFE gene (Mediterranean

LCT Lactose tolerance

LCT Lactose intolerance

Lactose consumption

Lactase

Bifidobaterium abundance

Lactose tolerance

Glucose Galactose Lactase Bifidobateria Fermentation products

Fig. 2.2 Interaction between bifidobacteria and lactose-rich diet in host organism. Strong genetic associations are observed between the variants of the LCT gene and lactose persistence. The relative abundance of Bifidobacterium in fecal samples is associated with LCT in mGWAS studies. Lactose is broken down into glucose and galactose in the small intestine of the host with lactase persister genotype; however, in lactase nonpersister, lactose travels to the large intestine and fermented by lactose-utilizing Bifidobacterium. The presence of lactose promotes the abundance of bifidobacteria through a positive feedback loop. Figure regenerated from Goodrich et al. 2016

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding. . . 43

Fever), encodes a protein known as pyrin/marenostrin, which regulates innate immunity. Single-nucleotide polymorphisms (SNPs) in the MEFV gene were found to be associated with changes within the gut microbiome in humans (Ogura et al. 2001). Many additional patterns of association between human genetic variation and the variation in the microbiome are starting to emerge (Table 2.1 and Fig. 2.1). For instance, many IBD-associated SNPs have been conserved across cohorts in their association with distinct microbial taxas, implicating gene enrichments underlying the regulation of innate immunity, JAK-STAT cascade, and other metabolic pathways. It may be possible that human alleles responsible for the maintenance of essential microbial functions are permanently established in the gene pool. Indeed, there is a complete absence of genetic variations within human genes required to preserve the microbial territory and functions of gut microbiome across populations. Conversely, microbes or their functions required only in a setting might represent complex genetic variations and respective associations with the microbiome. For instance, the robust indication of human genomic selection was reported in geographically limited areas which offered specific environmental encounters, such as high toxicity, high altitude, and high pathogenic load (Ranciaro et al. 2014). Moreover, it is also likely that microbiome selection in recent evolution may be attributed to challenges that humans have tackled. Indeed, lactose persistence represents the strongest genetic selection in human evolution.

2.5

Genesis of Diseases with Dysbiosis

Before the advent of high-throughput metagenomic studies with sophisticated computational tools, the linkage of the human microbiome with diseases remained largely unexplored. mGWAS studies linked the disease genesis and evolution with dysbiosis (microbial imbalance). The use of peripheral factors for the modulation of microbiome has been reported as a potential therapeutic approach to address many health-related problems (Larsen and Claassen 2018). At normal physiological conditions, microbes maintain a commensal relationship with the host and facilitate several functions like strengthening of the immune system and digestion, preventing the invasions of pathogens; however, numerous epidemiological studies identified a strong link of diseases with the imbalances in the host microbiota. Intestinal colonization of microbes at a very early age could support sufficient immune stimulation and maturation of immune cells and reduced diversity of microbiota in infants (18 months) leading to atopic eczema (Wang et al. 2008). Moreover, the low diversity of gut microbiota during infancy is further associated with allergic diseases in school age like asthma and several inflammatory diseases (Abrahamsson et al. 2014; Bisgaard et al. 2011). In line with this, numerous studies featuring young and aged humans have highlighted the crucial roles of the gut microbiome in the modulation of diseases like diabetes and obesity (Karlsson et al. 2013), GIT disorders such as IBD and IBS (Ferreira et al. 2014; Kennedy et al. 2014), colitis (Kau et al. 2011), chronic fatigue syndrome (Lakhan and

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Table 2.1 Summary of mGWAS studies carried out to understand host-genome interactions Sample size and host sampling site n ¼ 93, from multiple sites (15)

mGWAS study Blekhman et al. (2015), USA

Sequencing and analysis approach Shotgun Metagenomic combined with PLINK

Davenport et al. (2015) Hutterites (USA) Hua et al. (2016), Italy

Sequencing 16S rRNA with GEMMA

n ¼ 127, gut

16S rRNA coupled with mGWAS

N ¼ 147, lung

Goodrich et al. (2016), UK

16S rRNA coupled with mGWAS and GEMMA

n ¼ 1126 twin pairs, gut

Bonder et al. (2016), Dutch

Shotgun Metagenomic combined with R

n ¼ 1514, gut

Turpin et al. (2016), Canada and USA

16S rRNA sequencing

n ¼ 1098, gut

Summary of associations studied Alpha and beta diversity, along with bacterial taxa were phenotyped. A total of 83 associations were identified. Correlation of host genetic variants with microbiome composition was found. LCT gene variants correlated with Bifidobacterium abundance (P ¼ 1.16  10
5). Leptin signalling pathway genes were significantly associated with microbiome composition Bacterial taxa were phenotyped, associations of host genome SNPs, PLD1 gene SNPs associated with Akkermansia abundance Alpha and beta diversity were phenotyped, SNPs association with beta diversity; however, no SNPs were found significantly associated with beta diversity distributions after correcting for skewness and kurtosis Bacterial taxa as well as beta diversity were phenotyped. 31 associated host loci, LCT gene association with Bifidobacterium abundance. Strong association of R3HDM1 gene SNPs with Bifidobacterium (P ¼ 4.38  10
8). Association of ALDH1L1gene with SHA-98 bacteria. SNPs associations with beta diversity metrics. Bifidobacterium, Turicibacter, and Blautia were identified as heritable taxa Bacterial taxa and pathways, total 42 host loci associations: 9 with bacterial taxa and 33 with bacterial pathways (P < 5  10
8). LCT SNPs associated with Bifidobacterium abundance (P (¼3.45  10
8) Bacterial taxa and alpha diversity, significant association of six host loci, Rikenellaceae, Faecalibacterium, Lachnospira, and Eubacterium were identified to be highly associated. Heritable taxa identified. No significant association with alpha diversity (continued)

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding. . . 45

Table 2.1 (continued) mGWAS study Wang et al. (2016), Germany

Sequencing and analysis approach 16S rRNA sequencing coupled with R

Igartua et al. (2017), Hutteries USA Rühlemann et al. (2018), Germany Kolde et al. (2018), USA

16S rRNA coupled with GEMMA

Rothschild et al. (2018), Israel

16S rRNA with R

Sample size and host sampling site n ¼ 1812, gut

n ¼ 144, vestibule and nasopharynx sites n ¼ 1767 Gut

Shotgun Metagenomic coupled with matrix eQTL

n ¼ 298, multiple sites (6)

16S rRNA and shotgun Metagenomic with R and FaSTLMM

n ¼ 1046, gut

Summary of associations studied Bacterial taxa, beta diversity, significant associations of 54 loci, vitamin D receptor association with beta diversity (P < 5  10
8). Association of host loci with Firmicutes, Proteobacteria and Bacteroidetes Alpha and beta diversity as well as relative abundance of bacterial taxa, 37 significant associations were identified Four significant associations identified with beta diversity Relative abundance of bacterial taxa and pathways, five significant associations were identified. In stool samples, Lachnospiraceae bacterium, Roseburia intestinalis, Subdoligranulum, and Sutterella were significantly associated RA of bacterial taxa, along with alpha and beta diversity, no significant associations were identified after multiple testing corrections

Kirchgessner 2010), anxiety and depression (Lach et al. 2018), bacterial vaginosis (Africa et al. 2014), and cancer (Castellarin et al. 2012). These observations encouraged the development and advent of mGWAS. The gut microbiome maintains the harmony between the host and its trillions of resident bacteria though the important immune sentinel: Tregs (regulatory T cells) which were shown to be regulated by specific bacterial strains in mouse models. Indeed, SCFAs produced by bacterial fermentation of dietary fibers restore Tregs in mice devoid of gut microbiota. The regulatory effects of SCFAs on Tregs were mediated through SCFAs receptor GPCR43 expressed on Tregs. SCFAs feeding in mice leads to protection against experimentally induced colitis (Smith et al. 2013). Indeed, microbial-derived butyric acid, acetic acid, propionic acid, and SCFAs lead to cTregs homeostasis, immune maturation, and tolerance in humans (Geuking et al. 2013; Smith et al. 2013). Humans and microbes consume carbohydrates as an important energy source; however, many complex carbohydrates (xylans, cellulose, resistant starch, and inulin) cannot be degraded by human enzymes and rather are fermented by its gut microbiome to yield energy and SCFAs, butyrate, propionate, and acetate as end

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products. These end products are immensely important as an immune modulator, energy sources, disease resistance, vasodilation, wound healing, and gut mobility (Bergman 1990). Indeed, the consumption of carbohydrates by gut microbiota determines the amount, types, and patterns of fermentation and consequently its end products. For instance, the co-colonization of Bacteroides thetaiotaomicron with Methanobrevibacter smithii in the gnotobiotic mice model promotes the dietary fermentation of fructans (Samuel and Gordon 2006). Gut microbes, therefore, promote the efficient breakdown of complex carbohydrates along with enhanced absorption (Tremaroli and Bäckhed 2012). The composition and metabolic interactions of gut microbiota also affect digestion and utilization of polysaccharides (Gill et al. 2006) and provide defense against diseases via xenobiotic toxicity, improved metabolism, pharmacokinetics (Spanogiannopoulos et al. 2016), immune modulation (Medina et al. 2007), and transplantation of fecal microbiome (Gupta et al. 2016).

2.6

mGWAS: Approaches and Tools

Genome-wide association study (GWAS) represents the association of diseases or traits with millions of SNPs present in a genome using chip-based approaches. Microbiome-GWAS (mGWAS) is a promising field to study the association between host genetic information with microbiome (16S rRNA or metagenome sequencing) (Hall et al. 2017). Microbiota QTLs or mbQTL in mGWAS represent microbial abundance-associated genetic variants or quantitative trait loci (Imhann et al. 2018). Recent mGWAS studies identified genetic variations in the nucleotide-binding oligomerization domain-containing protein 2 gene (NOD2) and lactase gene (LCT) and their significant associations with Enterobacteriaceae and Bifidobacterium abundance (Knights et al. 2014; Bonder et al. 2016). Moreover, vitamin D receptor (VDR) gene alleles influence the metabolism of bile acid through bacterial taxa (Wang et al. 2016). Variation in the gut microbiome is reported in in-bred strains of mice with multiple genetic backgrounds (Org et al. 2015). Therefore, host genetics plays a robust role in defining the composition and function of the microbiome. Microbiomes exert their functions as a community, and therefore the analysis of beta diversity in GWAS studies is more robust (Hua et al. 2016). The comparative abundance of each microbial taxon may be used to perform analysis as a trait or phenotype to assess the SNP association with the respective taxon. Using the host’s genotype data as the explanatory variable and microbiome attributes such as a relative abundance of bacterial taxa, as well as alpha and beta diversity as the response variable, mGWAS identify genetic factors that adjust the microbiome composition and functional diversity. Studies published till date using mGWAS have provided numerous insights regarding the intricate host-microbe interactions (Blekhman et al. 2015; Davenport 2016; Hua et al. 2016; Goodrich et al. 2016; Bonder et al. 2016; Wang et al. 2016; Igartua et al. 2017; Rothschild et al. 2018; Rühlemann et al. 2018).

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Role of Genome-Wide Association Studies in Host Genetics: Toward Understanding. . . 47

2005

454 (Rohe) Pyrosequencing (SBS)

2006

Illumina (SBS)

2007

Helicos (SMS), ABI SOLiD (SBL)

2010

Ion Torrent (SBS), Pacific BioSciences SMRT (SMRT)

2014

Oxford Nanopore (SMS)

2015

Qiagen Gene Reader (SBS)

Fig. 2.3 Timeline of advances in next-generation DNA sequencing technologies and platforms which provide a sensitive, cost-effective, and fast approach for highly accurate data analysis. NGS provides a cloning-free approach and involve minimal technical expertise to achieve scientifically and statistically relevant data. SBL, sequencing by synthesis and SMRT, single-molecule real-time sequencing

The first generation of DNA sequencing technology (Sanger sequencing) was widely used for more than three decades and later replaced due to its limitations of low throughput and high cost (Sanger and Coulson 1975). The advent of sensitive NGS technologies has significantly helped in the identification and characterization of host-microbe interaction in both healthy and diseased state. Moreover, NGS techniques are valuable in the investigation of composition and functions of microbial communities as well as their genetic and metabolic properties. Progress in NGS represents a major paradigm shift in offering a high-speed, high-throughput, sensitive, and cost-effective approach for the analysis of large data with high accuracy (Hutchison III 2007; Sanger 1988). Many second- and third-generation NGS platforms including the Illumina (MiSeq and HiSeq), Roche 454 Genome Sequencer FLX, Ion Torrent/IonProton/Ion Proton, Qiagen gene reader, Oxford Nanopore, and ABI SOLiD 5500 series have been developed and are generally in use. Presently, microbiome studies have focused on high-throughput sequencing using either amplicon sequencing (Caporaso et al. 2012) or functional metagenomics (Pester et al. 2012). Despite many advantages, the analysis of millions of sequences poses a major challenge with NGS data to achieve scientifically and statistically meaningful conclusions (Fig. 2.3).

2.7

Current Challenges Underpinning mGWAS

mGWAS suffers many serious challenges with small cohort sizes which acts as a major limiting factor. As such, it has been challenging for associations of specific alleles with microbiome traits to reach study-wide significance due to the burden of

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multiple testing in these small studies. Indeed, the study-wide significance is a high bar when the number of tests is based on the total number of SNPs (hundreds of thousands to millions) combined with the total number of traits (typically in the high hundreds to thousands). Indeed, major mGWAS studies were conducted with very small cohorts. For example, Blekhman et al. (2015) reported considerable association of host genes and microbial pathways through mGWAS performed in a small cohort of only 93 individuals using data from the Human Microbiome Project (Gilbert et al. 2018). Davenport et al. (2015) reported the associations of host SNPs with an abundance of specific bacterial taxon using the gut microbiome of a cohort of 127 people from North America. Later, Yu et al. (2016) reported data from 147 tissue samples using 16S rRNA sequencing to ascertain the association between microbial composition and lung cancer risk SNPs. mGWAS studies published later have reported significant data from large sample sizes (Bonder et al. 2016; Goodrich et al. 2016; Turpin et al. 2016; Wang et al. 2016). Surprisingly Kolde et al. (2018) reported very few individual microbepolymorphism associations with a high genome-wide significance, and only the NOD2 and LCT genes were significantly found to be associated with IBD and lactose tolerance, respectively, as previously reported by other groups. The main reason for this disparity could be the use of significance thresholds. For instance, Belkhman et al. used false discovery rate (FDR), while Kolde et al. (Rothschild et al. 2018) used Bonferroni correction multiple hypothesis test corrections (Kolde et al. 2018; Blekhman et al. 2015). Recently, Rothschild et al. also failed to report substantial associations within 1046 Israelis sharing a common environment with different ancestral origins. These results indicated that despite the heritability of some bacterial taxa, all the mGWAS studies could not be replicated except for the significant association of LCT loci with Bifidobacterium. Table 2.1 summarizes the association studies uncovered by mGWAS. mGWAS studies face serious statistical challenges due to relatively small sample sizes. Indeed, to date, a relatively small number of studies (~1800) have been included for a successful GWAS of any microbial trait. Besides, for the appropriate clinical associations, cohorts with particular diseases are required to have microbial dysbiosis accompanied by genetic complexity (Zhang et al. 2015; Gevers et al. 2014).

2.8

Concluding Remark and Prospects

Understanding the microbiome and the discovery that it plays substantial roles in human metabolism and immunity in both normal and diseased states has been a large step forward in the area of genetics. Large-scale population cohorts and twins studies have spurred major attention toward the elucidation of the host-genetic interactions. Moreover, the substantial role of genetic variations and their associations with the microbiome led to the development and advancement of mGWAS. Genetic variants within the host genome contributing toward the

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compositional and functional diversity of the microbiome are being continuously identified using mGWAS. Indeed, several mGWAS studies indicate a bidirectional communication between microbiome and the host immune system. Moreover, microbiome implications as a therapeutic agent in human health have been extensively reported previously (Bashiardes et al. 2017; Sartor and Wu 2017). The advents of second- and third-generation sequencing technologies have resulted in the generation of enormous datasets that require sophisticated bioinformatics and computational resources for their in-depth analysis. Indeed, the understanding of metagenomics and further mining of valid and relevant information from the mGWAS depend on the sensitive computational programs. Indeed, continuous development of specialized analytical tools and software programs is required for the translation of unprocessed sequences into meaningful data to provide the taxonomic and functional characterization of diverse metagenomes. The major aim is to construct an analytic pipeline which provides ease of access, graphical representations of datasets, open-source availability, etc. In line with this, software like Mothur, EBI, MG-RAST, and QIIME have been described. mGWAS identified that human genes could directly influence beneficial or pathogenic microbiome. Indeed, the abundance of subsets of microbes from the gut microbiome could be determined genetically by the host. mGWAS has proven challenging due to small cohorts in GWAS standards. Moreover, the choices of different analytical approaches have severely hampered the cross-study comparisons. Even so, few associations between human genes and microbes have consistently emerged from mGWAS. Most notable examples are the association between human lactase nonpersister genotype and abundance of gut microbe bifidobacteria conferring lactose intolerance in different human populations. Regardless of the remarkable understandings of mGWAS, it is time to incorporate the associations of human genetics with lifestyle, environment, and food with the microbiome to foresee disease susceptibility and further development of personalized medication/microbiota or lifestyle changes for the targeted treatment and disease prevention. mGWAS studies revealed an unprecedented rise in our understanding of hostmicrobe interactions. However, it also presents some key challenges which need to be addressed to tackle the complexity within host-microbiome data. Due to the high sensitivity of the human microbiome toward various factors like demography (Yatsunenko et al. 2012), age (Mäkivuokko et al. 2010), and gender (Foster et al. 2017), the statistical power of the mGWAS data could reduce because of artifacts and biases. To address these problems, a statistical model needs to be established. Additionally, the discrepancy in the sampling population of mGWAS caught interest toward the crucial need for incorporating diverse populations which further pose a challenge due to the microbiome dimension consisting of thousands of taxa. This leads to the multidimensional mGWAS data in terms of phenotype and their correlations using stringent association networks. This represents a critical issue due to the intricate interaction network generated by multiple taxa with the host genes. The solution is to avoid multiple testing and target a subset of taxa.

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In the future, mGWAS studies with increased sample size and environmentally controlled experiments are essential steps toward uncovering the role of the human microbiome in health and diseases. Therefore, a critical understanding of hostmicrobe interaction will help to unravel the disease pathogenesis and causality. Conflict of Interest Authors declare no conflict of interest.

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3

Understanding Microbiome Science Through Big Data Analysis Aditya Narayan, Ajeet Singh, and Shailesh Kumar

Abstract

The emerging disciplines of microbiome research and data science have offered countless promising innovations in human genetics. Alongside clinical information, the microbiome may be considered another dimension of patient clinical status. Over the past 15 years, the microbiome has become increasingly recognized as a moderator of interactions between both the external environment and our body, as well as between body systems themselves. These insights have led to an explosion in sequencing data, the key source of information upon which much of this research rests. To match this growing supply of information, there has been a corresponding increase in the number of tools that allow even nonprofessional individual access to data science methods. Such tools streamline the sequencing analysis process to make critical observations readily accessible. This chapter first aims to describe the historical significance of microbiome data science and the methods used to generate sequencing data. Following this, we offer a comprehensive guide to several commonly applied tools in microbiome data science. Keywords

Microbiome · Data science · High throughput sequencing

A. Narayan University of Virginia, Charlottesville, VA, USA e-mail: [email protected] A. Singh · S. Kumar (*) National Institute of Plant Genome Research (NIPGR), New Delhi, India e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_3

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3.1

Introduction

3.1.1

The Origin of the Term “Microbiome”

We as scientists are currently operating in a microbiome revolution. It is an era in which many of the most novel insights generated through research are associated with understanding the increasingly recognized “second genome” existing within our body - one that is acquired, rather than inherited. The term “microbiota” itself is largely attributed to Joshua Lederberg, microbiologist and Nobel Prize laureate who explained it to signify the community of symbiotic, commensal, and pathogenic organisms which shared our body and were understudied determinants of health. While the microbiota is the community of microorganisms itself, the microbiome may then be considered the entirety of genetic elements contained in the microbiota. The term “microbiota” even predates Lederberg, however, and was defined previously by Alan Logan in 1998. The biological link between microbiota and brain chemistry was demonstrated as early as 1986, with Linda Hegstrand and Roberta Hine exploring histamine levels in exposed vs germ-free animals (Prescott 2017). It is therefore evident that the research community has been familiar with the existence of this critical field of study for many years, though recent technological advances have fueled rapid advancements. In this chapter, we will discuss what insights we can glean about human health from the microbiome as well as how we approach studying it through the lens of data science.

3.1.2

The Study of the Microbiome

The human body is uniquely complex due to being inhabited with a diverse and poorly characterized microbial community. This community is comprised of various bacteria, fungi, viruses, and archaea which interact with each other and the host in unique ways to ultimately shape human health alongside host genetics. These organisms rarely exist outside of a host organism and are capable of influencing the environment they live in through metabolic and energetic functions. Microbial colonization begins immediately at birth and is influenced by various stimuli including but not limited to diet, activity, travel, diseases, therapies, antibiotics, and so on while reaching equilibrium in status in healthy adults. In the human body, these microbes exist in nearly every organ system though certain areas contain a larger concentration. In particular, the gastrointestinal tract contains over 1000 species of microbes and varies incredibly between individuals (D’Argenio and Salvatore 2015). However, there are a number of other locations microbes occupy both on and inside of the human body including the eyes, mouth, vagina, and skin (Flint et al. 2012; Qin et al. 2010). The influence of these organisms on our health is not surprising, given that the number of microbial cells has been estimated at over tenfold greater than the number of human cells, ultimately comprising roughly 1–2 kg of body weight (Walker et al. 2014). The microbiome, in turn, may ultimately be considered an extension of the

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human genome, given that an enormous number of functions are housed in the microbiome but not the human body itself. This insight merely serves to illustrate the potential breadth and unique character of potential interactions. One of the most widely recognized and understood influences of the microbiome on health manifests in our digestion and nutrition processing. In particular, polysaccharides such as plant cellulose are indigestible without the influence of gut microbiota which is capable of breaking down these complex molecules into digestible short-chain fatty acids. This may be extended further as the gut microbiota composition has been linked to key differences in metabolism and weight gain. Studies in mice, for example, have found that higher short-chain fatty acid production is associated with lower obesity risk (Lin et al. 2012). Another field of study which has proven fruitful on exploration is the role of microbiota in mediating immune system development and host response to pathogens (Sekirov et al. 2010). Immune system dysregulation serves as a contributing factor to a number of conditions ranging from allergic responses to cancer. The gut microbiota, in particular, has been linked to this dysregulation, with key microbes acting to prevent inflammatory bowel disease, among other non-infectious disease states. Therefore, the presence or absence of particular organisms may be linked causatively to the aforementioned dysregulation and, in turn, may be shaped by the immune response in a cyclic fashion (Round and Mazmanian 2009). These insights are further supported by studies performed on mice in which those raised germ-free showed increased inflammation, indicating asthma and inflammatory bowel disease (IBD), a state corrected by subsequent exposure to microbes (Olszak et al. 2012). Perhaps even further removed from consideration was any relationship between the microbiome and behavior. However, one of the most rapidly expanding areas of scientific study is now the gut-brain axis, a relationship between the gut microbiota and the central nervous system through the vagus nerve (Cryan and O’Mahony 2011). Gut microbes have been shown to produce neurotransmitters such as serotonin which stimulates the vagus nerve and, in turn, influences the brain. Evidence has been found from mice possessing different strains of microbiomes in which the vagus nerve was destroyed. With this change, the mice no longer showed any significant difference in emotional behavior, thereby highlighting the bidirectional relationship. Further, evidence has been found showing a high correlation between intestinal issues and autism, further supporting the potential role of gut microbes in neurocognitive development (Bravo et al. 2011). The above studies represent merely a surface-level display of the potential clinical implications associated with the gut microbiome, with various relationships spanning the entirety of the human body and representing a critical new field in genomics in seeking to understand disease processes.

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Microbiome Big Data

It is worth considering the various methods currently in use for the generation of large quantities of metagenomic data from RNA and DNA information, each of which offers different benefits. The comparison of the pros and cons of these methods is explained in Table 3.1. 16S rRNA 16S rRNA sequencing is likely the least expensive and most efficient method for studying the microbiome. The method offers a wide range of uses, including the characterization of bacterial populations, species identification, and taxonomic analysis. If eukaryotic species are present, it becomes necessary to apply 18S RNA sequencing. The core of the technique lies in the fact that bacteria possess the 16S rRNA gene, which consists of a unique structure containing 9 hypervariable regions, which are capable of identifying particular microbes and are interspersed with highly conserved regions. Amplification via polymerase chain reaction (PCR) is therefore possible with primers targeting the flanking conserved regions. In a single PCR reaction, it becomes possible to amplify the sequences of a variety of bacteria present in a target environment, as well as map each sequence to its origin bacteria based on the unique hypervariable regions. However, despite its ease of use, no single hypervariable region may differentiate among all species present and readlength limitations from next-generation sequencing technology further limit accuracy. Further, DNA extraction and PCR application may introduce confounding errors. Finally, this method is only an introductory method that fails to provide information on bacterial function (Janda and Abbott 2007).

3.2.1

Shotgun Sequencing

Shotgun sequencing is widely applied in the sequencing of an entire microbial community. The process begins with the isolation of DNA from the sample of interest and functions by shearing large DNA pieces into smaller fragments that are sequenced at random. These fragments must then be computationally reassembled using various bioinformatics tools to construct a composition Table 3.1 Overview of each method’s advantages and disadvantages Method Culturing Amplicon sequencing Shotgun sequencing Metabolomics

Pros Highly sensitive, phenotypic analysis, cheap High throughput, fast, cheap, wider array of species detection All organisms will be represented in final data, high throughput Analyze all small molecules present in a sample, capable of functional analysis, high throughput

Cons Limited to known taxa, low throughput, fast Largely limited to bacteria, eukaryote require 18S sequencing More expensive than amplicon sequencing Expensive

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representative of the original sequence. A large number of sequences are produced due to redundancy resulting from the random shearing and reassembly of fragments as well as the need to increase the probability of fragments overlapping to be able to produce the sequence in its entirety. The method has enormous advantages over PCR or other wet-lab techniques due to the direct analysis of the DNA at scale without the need for physical methods such as cultivation. However, the programs used for analysis must account for the possibility of errors in the extraction or sequencing processes and may misattribute reads to the wrong species (Parla et al. 2011).

3.2.2

Metatranscriptomics

Metatranscriptomics offers an extension of metagenomics; and rather than simply analyzing sequence, through RNA-Seq profiling of a microbial community, it offers information on functional activity of genes in that environment. Thus, it offers a complete understanding of the transcriptome as a means of gathering functional insights. However, it is limited due to extraction, storage, and sequencing issues as well as rRNA contamination (Shakya et al. 2019). There are various sequencing technologies, and till now they are developing to get high accuracy and low cost. The brief explanation of sequencing technologies is as follows.

3.3

Roche 454 Genome Sequencing

454 sequencing allows for the production of large quantities of short sequences ( 4.5). Other lactic acid–producing bacterias like Bifidobacterium, Atopobium vaginae, Megasphaera sp., and Leptotrichia sp. also thrive in this ecosystem. In some ethnic groups, where the vaginal pH was higher, bacteria like Gardnerella vaginalis, Prevotella sp., Pseudomonas sp., and Streptococcus sp. have been recorded. Several other studies have shown different microbiota profiles with the domination by a single or less than two bacterial taxons. The transition from one taxon to another might also be interpreted as a change in healthy and disease states. In the skin microbiome analysis, the metagenomics approach identified the human polyomavirus virus on healthy skin and in most of the Merkel cell carcinomas (Wieland et al. 2009; Schowalter et al. 2010). The skin metagenomics study will also allow the detection of microbiome patterns that develop during skin conditions (Foulongne et al. 2012). In the human respiratory system, the microbiota decreases from the upper tract to the lower tract and the respiratory pathogens can be detected in the upper respiratory tract (García-Rodríguez and Fresnadillo Martínez 2002; Charlson et al. 2011). This colonization results in secretions that cause spreading. A metagenomics study also found that the microbiota in the nasopharyngeal region differs in young children depending upon the seasons (Bogaert et al. 2011). The metagenomics approach also helps in the diagnosis of viruses present in the nasopharyngeal region at around 30% which otherwise goes undetected in diagnostic kits (Heikkinen and Järvinen 2003; Lysholm et al. 2012). As for the lower respiratory tract, very little is known of the microbiota. The region is responsible for most of the respiratory conditions like cystic fibrosis and respiratory diseases like obstructive pulmonary disorder and asthma in kids and adults (Beck et al. 2012). The characterization of the lung microbiome can help in the diagnosis and therapy of several respiratory diseases as well as conditions like smoking (Kiley 2011). Integrating the metagenomics and system biology approach will serve to look at the chosen microbiome as a single super-organism. For complete understanding of microbiome and their interactions among the complex microbial communities, on system-level, and restructuring the genetic/metabolic network by assimilating metagenomics data with genome-scale metabolic modelling would be more favorable.

5.9

Meta-transcriptomics and Human Health

Several studies have been conducted to understand the meta-transcriptome analysis and correlate it to the metagenomics data. The characterization of mRNA of active bacteria in the gastrointestinal tract transcript of the healthy and diseased individual has provided information on pathways involved in carbohydrate metabolism, synthesis of cell components, and production of energy (Gosalbes et al. 2011). The

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metagenomics study of the human gut also showed a predominance of Lachnospiraceaei, Ruminococcaceae, Bacteroidaceae, Prevotellaceae, and Rickenellaceae in the active microbiota of healthy individuals. The metatranscriptome analysis is also used to understand the effect of xenobiotics on commensal bacteria of the gut microbiome. There are over 40 xenobiotics, whose effect on the gut microbiome is still unexplored (Haiser and Turnbaugh 2012). One case of toxicity was the prescription of sorivudine and 5-fluorouracil which led to the death of 18 patients. It was found after, that the metabolism of sorivudine into a compound, inhibited the activity of 5-fluorouracil leading to toxicity due to accumulation (Sousa et al. 2008). Maurice et al. showed that the effect of xenobiotics on the gut bacteria is not only on the gut microbiome structure but also on their gene expression profiles by meta-transcriptomic analysis (Maurice et al. 2013). Understanding the gut microbiome and their effect on xenobiotic administration will help in avoiding repercussions and other toxicity-related effects. Another study decoded the expression profiles of about 1,60,000 genes between healthy and periodontal disease patients. The study found that despite high inter-variability between the patients, the metabolic profiles are conserved in disease patients (Jorth et al. 2014). The relation between metagenomics and meta-transcriptomics has been further supported in gut microbiome studies too. There were several gene families that were available in abundance in the gut microbiome at the metagenomics level but down-regulated in the meta-transcriptomics level. At such conditions, the function of the cohort is overestimated (Franzosa et al. 2014). Fecal microbial community analysis in 372 human fecal transcriptomes and 929 metagenomes found a metatranscriptomic core that is transcribed in time and across participants. Other than housekeeping functions, the microorganisms exhibited a variable metatranscriptome which includes specialized pathways (Abu-Ali et al. 2018). Metatranscriptomic analysis of commensal bacteria in TLR-5 knockout (KO) bacteria and in wild-type mice displayed no change in the presence of functional genes, but the meta-transcriptomics analysis showed up-regulation of flagellar motility-associated genes of gut microbiota in TLR5-KO mice. The absence of TLR5 reduced the production of anti-flagellin antibody, thereby causing dysbiosis in TLR5-KO mice (Cullender et al. 2013).

5.10

Conclusion

Humans have entered an era where microbiome plays a crucial role in the determination of well-being and diseased state. This, in a way, is now modifiable with a healthy diet and hygienic habits. The microbiota at various regions of the human host participates actively in the dysbiosis and in the establishment of diseases. From the studies discussed in this chapter, it is evident that there exists a host-microbiota relationship that helps in identifying the conditions of a person and the therapies that can be tailor-made to treat the condition. Current technology advancement allows us to determine the structure of the microbiome and the different functions related to the composition of the microbiome. The culture-independent techniques in

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metagenomics and meta-transcriptomics that use the technologically advanced tools like NGS and mass spectroscopy allow us to identify different infectious diseases and the change in microbiota with respect to it. The immune response modulations by the microbiome in disease states are also possible and studied widely. A key challenge of applying metagenomics to study the association of microbiome with disease state is the reconstruction of metabolic network from the obtained complete metagenomics data. With the advent of newer techniques and availability of deeper information, the diagnosis and treatment of infectious diseases will be personalized and available to the common at economical costs.

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The Earth’s Microbiome: Significance in Sustainable Development and Impact of Climate Changes Deepika Goyal, Manali Vaijanapurkar, Eden Jacques, Janmejay Pandey, and Om Prakash

Abstract

Microbiome, the unique combination of microorganisms present at any specific ecological niche, contributes to a plethora of vital functions that determines the functionality of the concerned niche. Noticeably, the significance of microbiomes is not only limited to certain model ecological niches such as human body, wherein human microbiome in general and gut-associated microbiome in particular have been reported to be one of the most critical components involved in efficient functioning of various human organ systems including digestive system, immune system and even the nervous system. Several other environmentally relevant ecological niches such as lakes, rivers, oceans, agriculture fields, mountains, glaciers as well as sites of industrial activities harbour microbiomes which impact the entire ecosystems. The vast microbiomes present in different ecological niches help directly or indirectly in their functioning by regulating many functions including the biogeochemical cycles of major elements such as carbon, nitrogen and phosphorus. Without an optimally functional microbiome, the rate of geochemical cycling would decrease immensely, leading to potential collapse of the entire environment. Microbiome structure also supports a range of functions amongst eukaryotic species; however, the majority of the microbiome– ecosystem interactions and their underlying mechanisms are not yet well understood. The diversity of the microbiome itself and the dynamics that they exhibit to the environmental challenges make it extremely challenging to delineate their explicit role in the Earth’s functionality. With recent advancements in the field of

D. Goyal · J. Pandey (*) Department of Biotechnology, Central University of Rajasthan, Ajmer, Rajasthan, India e-mail: [email protected] M. Vaijanapurkar · E. Jacques · O. Prakash (*) National Centre for Microbial Resource, Pune, Maharashtra, India e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_6

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microbiome, DNA sequencing and post sequencing – computational data analyses, the researchers are only beginning to understand and appreciate the complexities of environmental microbiomes but also the potential role that they play in fostering the functionality of the Earth’s environment. An idea that is relatively nascent, at present, is that climate changes and processes related to ecological imbalance are threatening to disrupt the microbiomes as well as the key functions they provide. Climate change has been suggested to be the dominant reason for decrease in population of certain types of organisms within the microbiome; thus, climate changes could modify the composition, spatiotemporal distribution of species and their interaction within and amongst different microbiomes. Therefore, it is critical to assess various microbiomes for their structure, function and dynamics in the light of climate change. Such assessments are also critical for ensuring the collective environmental sustainability. The present book chapter attempts to provide an updated comprehension of studies/ reports and understanding of the impacts of climate changes on the Earth’s microbiome and its potential consequence on environmental sustainability. Keywords

Environmental microbiome · Climate change · Biodiversity

6.1

Introduction

The global collective composition of microorganisms, which is also referred to as the Earth’s microbiome, supports the functionality and survival of all the other life forms. Therefore, understanding how microorganisms contribute to the lives of human being and other life forms is of great significance (Bahram et al. 2018; Gilbert et al. 2018a). It is equally important to determine how these microorganisms withstand harsh environmental conditions are either induced by natural cosmic events or through anthropogenic activities. The global effect of human activities on the Earth’s microbiota has not yet been addressed in detail (Pointing et al. 2016). In order to address these pertinent questions, it is critical to integrate all the information available about microorganisms, including the uncultivated ‘unseen majority’. Although the importance of studying microorganisms was realized a long time ago, characterization of the uncultivable majority was not appreciated till the late twentieth century, when it was realized that (1) the vast majority of microorganisms are recalcitrant to traditional laboratory-based cultivation approaches and (2) microorganisms are the major determinants of almost everything related to human health and well-being, ranging from digestive functions to mood disorders, development, immune system and its dysfunction (Whitman et al. 1998). It is widely acknowledged that microorganisms play similar vital roles across other ecological niches throughout the world and form the basis of microbiomes being the major drivers of global food web, such that all life forms are directly or indirectly dependent on functions being performed by soil microbes (Delgado-Baquerizo et al.

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2016). One important revelation in this regard was presented in a recent report wherein experimental evidences were presented for showing colonization of emerging continental landmass by microbial life which was an important evolutionary step in evolutionary history of the Earth (Homann et al. 2018). It was reported that life on land was present 3220 million years ago in the form of terrestrial microbial mats draping fluvial conglomerates and gravelly sandstones of the Moodies Group, South Africa (Homann et al. 2018). During the subsequent evolution of the Earth, the growth and survival of other forms of life including plants and animals have been governed by the Earth’s microbiome. Therefore, in order to understand life on Earth, it is critical to develop a sound understanding about the Earth’s microbiome. Also, it is important to understand how the composition and the dynamics of microbiome may get influenced by the alteration in the physicochemical characteristics of the environment. In the pursuit of characterizing microbiomes, there are certain inherent limitations. Some of them are more evident than others. For example, due to their extremely small sizes, the microorganisms are invisible to the unaided human eye; therefore, most of the human beings do not appreciate microorganisms or consider them an important, tangible component of the global environment (Cavicchioli et al. 2019; Timmis et al. 2019). Similarly, the adverse effects of anthropogenic activities on microbiomes are viewed to be rather insignificant. As a consequence, majority of scientific endeavours in the direction of characterizing the adverse effects of climate change have not cared to include microbial communities in the analyses. Contrary to the general belief, the huge abundance of microbial diversity is the single most important component responsible for ensuring the good health of the global environment. The biochemical and physiological activities carried by microorganisms exert important life-sustaining effects on all other life (Bar-On et al. 2018). Similarly activities carried out by other life forms including human beings also wield diverse effects on structure, composition and dynamics of microbial communities and microbiomes. Thus one of the key challenges emerging from climate changes is to determine how it affects the microbiomes and in turn how it affects the capabilities of all other life forms to respond to climate (Pointing et al. 2016). The ability of human being to withstand and survive the adverse impacts of climate changes may hugely depend how the Earth’s microbiome responds to it (Jansson and Hofmockel 2020; Dubey et al. 2019). In other words, a sustainable positive response of the Earth’s microbiome will be essential for survival of human beings and other life forms in times of climate change and towards attaining the Goals of Sustainable Development (DESA 2018). Lack of scientific understanding regarding how the Earth’s microbiome would respond to climate changes may threaten all other efforts that are being put forward to create a sustainable environment. Recent past has seen several efforts being made to understand the harmful effects of human activities on global climate and environment. For example, in year 1992, a warning document was generated and signed by ~1700 environmentalists and scientists to spread awareness that anthropogenic activities are threatening to put the whole of the living world at a serious risk in the near future. The second version of this warning document was signed by ~15,000

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scientists in 2017. These warning documents primarily endorse for making policies to stop environmental destruction for ensuring a sustainable future. The other major point emphasized in these documents is the requirement of scientific intervention to ensure conservation of the global biodiversity consisting of macroscopic as well as microscopic diversity (Scientists 1993). The second version of this warning document was signed by ~15,000 scientists in 2017 (Ripple et al. 2017). These warning documents primarily endorse for making policies to stop environmental destruction towards ensuring a sustainable future. One of the other major points emphasized in these documents is requirement of scientific intervention to ensure conservation of global biodiversity consisting of macroscopic as well as microscopic diversity (Scientists 1993; Ripple et al. 2017). To achieve these goals, the policies for future economic developments and expansions must be framed such that they minimize further climate change, they mitigate the adverse effects of climate change that have happened in past and they must address environmental sustainability. Ensuring a healthy and an optimally functioning microbiome in context of the climate changes will be necessary for achieving the overarching goals of environmental sustainability as highlighted in a number of reports and policy documents (Griggs et al. 2013; Sachs 2012; Robert et al. 2005).

6.2

Goals of Environmental Sustainability

Ensuring sustainable development across the globe has emerged as the most important goal of the present century. The same was evident in 2015, when the Sustainable Development Goal (SDG) was defined; 195 nations agreed that they can change the world for the better (Colglazier 2015). It is proposed that the Goals of Sustainable Development will be achieved by bringing together the respective governments, businesses, media, institutions of higher education and local NGOs to improve the lives of the people in their country by the year 2030. The overall SDG consists of a set of 17 main objectives that are to be reached, by developed and developing countries, by 2030 (Sachs 2015). These goals are designed to help, improve and better the future while tackling climate change and global warming. The list of 17 Goals of Sustainable Developments that are globally agreed, accepted and listed by the United Nations Development Program are presented in Table 6.1. Noticeably, the Earth’s microbiome in general and microbial biotechnology in particular have been proposed for extremely important roles in achieving these goals through enabling technologies for each of the 17 goals and most specifically by ensuring sustainable agriculture, efficient environmental clean-up, affordable and clean energy, etc. (O’Toole and Paoli 2017). Further, a large amount of existing literature from independent discreet studies have shown how microorganisms can contribute in realizing some of the key Goal (e.g. sustainable agriculture, clean water and sanitation, affordable and clean energy, etc.) of the Sustainable Development.

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Table 6.1 List of 17 integrated Goals of Sustainable Development as adopted by all United Nations Member States in 2015; adapted from United Nations Development Program

Sl. no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

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Goals of sustainable development End poverty Sustainable agriculture Good health and well-being Equitable education Gender equality Clean water and sanitation Affordable and clean energy Sustainable economic growth Innovation and infrastructure Reduced inequality Sustainable cities and communities Responsible consumption and production Climate action Life below water – clean aquatic ecosystems Life on land – clean terrestrial ecosystems Peace, justice and strong governance Partnerships for the goal

Adapted from Vako (2015) and https://sustainabledevelopment.un. org/sdgs

6.2.1

Role of Microbiome in Sustainable Agriculture

Microbiomes in general and those associated with plant rhizospheres in particular have well-defined capabilities to stimulate plant growth and increase agronomic productivity. Rhizosphere microbial communities provide several services for enhancing plant growth; the underlying mechanism of plant growth promotion may vary, such as nitrogen fixation, phosphate solubilization, siderophore and phytohormone production, ammonia production, HCN production and ACC deaminase production; however, most of these mechanisms fall under the following three categories: (1) enhancement of nutrient availability, (2) mitigating the biotic and abiotic stress encountered by the crops and (3) control of harmful phytopathogens (Olanrewaju et al. 2017; Gamalero and Glick 2011). Thus, plant–microbe interactions constitute the major determinant of soil fertility, plant health and plant productivity. Several instances of harnessing these useful characteristics of rhizospheric microorganisms have been reported for enhancing agricultural yield as well as enhancing the nutritional quality of high-value commercial crops, e.g. tomato (Kwak et al. 2018), wheat (Bharti et al. 2016), cotton (Yao et al. 2010), etc. On the basis of this understanding, it is widely acknowledged and practised that soil inoculation/seed pretreatment with plant growth-promoting bacteria can contribute significantly in increasing the agronomic efficiency and productivity towards ensuring the sustainability of agriculture. In the scenario of global climate change and ever-increasing practices of monocropping, there is an observed increase in pest and disease incidences in crop fields

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(Elad and Pertot 2014; Chakraborty et al. 2000). For managing diseases effectively, synthetic chemical-based pest control measures have been widely implemented; however, these chemicals are increasingly being reported for harmful and potentially toxic effects on nearly all life forms including human beings. Additionally, there has been a steady decline in soil health because of heavy use of agrochemicals, deforestation and unregulated release of pollutants to the soil. Some of the predictions suggest that effects of climate change are much more complex than just increase in temperature. There are changes in water and nutrient availability within soil; these can influence decomposition (Powlson 2005). Thus, there exists a ‘double edged sword’ situation, wherein not using a pest control measure leads to substantial destruction and loss of crop and yield, while using it leads to persistent contamination of terrestrial and aquatic niches with toxic chemical pollutants. Another critical issue with regard to sustainability of agriculture is that anthropogenic activities have grossly degraded ecosystem function by altering microbial species number (richness) as well as skewing the relative abundance of species (evenness). Further, agricultural pest-management practices have also resulted in altered food web structure and communities dominated by a few common species. Together these two situations contribute to pest outbreaks (Crowder et al. 2010). Some efforts have been made in restoring or maintaining species number, whereas the adverse ecological impacts of altered microbial species abundance have remained overlooked while developing agriculture practices and approaches (Benayas et al. 2009; Hillebrand et al. 2008). Therefore, there is an urgent need to develop economical and environmentally benign approaches for sustainable agriculture that would not only take care of pest and disease control but also ensure conservation of microbial diversity in terms of both community richness and evenness. This requirement could be addressed by using rhizospheric and/or endophytic microorganism having symbiotic association with plants. Microbes capable of positive interactions with plant roots can improve soil quality and could be extremely useful for reclaiming degraded, marginal and unfertile lands. These microorganisms could also be useful in mitigating the harmful effects of emission of trace gases (CO2, CH4, N2O and fluoride gases) which are reported as major global challenges of the twenty-first century (Olanrewaju et al. 2017; Gamalero and Glick 2011). Plant growth-promoting bacteria can also mitigate the ecological damage by promoting evenness amongst within the plant rhizosphere microbiome (Crowder et al. 2010). Thus, implementing plant growth-promoting microorganisms has a dual positive impact on Goals of Sustainable Development: (1) it is an ecofriendly and efficient method that could help in ensuring food availability for all; and (2) it could lead to reduction of environmental pollutants in the future. In order for plant growthpromoting bacteria to be successful at field sites, a number of factors need to be optimal. Some of these factors include (1) optimal density and quality of inoculum of plant root-colonizing bacteria, (2) types and relative quantities of plant root exudate and (3) overall soil health. A large number of studies have reported successful application of plant growth-promoting microorganisms using approaches such as ‘soil pretreatment’, ‘seed bio-priming’, etc. In these cases, success was achieved

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through screening of multiple iterations and determination of optimal performance parameter. Once success is achieved with this approach, the use of chemical fertilizers can be significantly reduced or completely eliminated. From the economics point of view, this approach is extremely important in cost cutting of the production costs, mandatory for production of synthetic chemical fertilizers. Together, the profits of using microorganisms as bio-fertilizer, viz. ensuring food availability, reducing environmental pollution and production cost cutting, are some of the most important Goals of Sustainable Development. Therefore, it could be suggested that plant rhizosphere-associated microbiomes are an important driving force that can help in achieving Goals of Sustainable Development.

6.2.2

Role of Microbiome in Environment Clean-Up

As the global human population continues to increase at a rapid rate, the intensive anthropogenic activities (agricultural and industrial activities) also continue to escalate. Unfortunately, with escalation of the anthropogenic activities, there is accompanying generation of toxic waste and pollutants that are getting accumulated world over. According to a recently published report, >1.3 billion tons of urban municipal waste is generated every year because of anthropogenic activities (Akinwumi et al. 2018). Majority of this waste is stored indefinitely in landfill sites or dumped into the water bodies including rivers and oceans. These pollutants are extremely harmful and threatening to the concept and Goals of Sustainable Development. As per the collective estimates carried out in 2006–2007, ~62 million deaths per year occur across the world due to environmental pollution. This accounts for nearly 40% of the total global deaths per year (Pimentel et al. 2007). In common men’s language, pollution is probably the single largest cause of human deaths all over the world. Apart from threatening human health and well-being, environmental pollution is also a direct menace to (1) survival of all other life forms, (2) capability of soil to grow food and (3) sustainability of planet Earth. It is one of the biggest obstacles in achieving Goals of Sustainable Development. Therefore, development and implementation of technologies, for decontamination of pollutants and recovery of contaminated ecological niches, are critical for ensuring Sustainable Development (Sachs 2012). It is important to mention that degradation of toxic pollutants alone may not be sufficient to mitigate the adverse effects caused by environmental pollution; it is equally and absolutely necessary to reclaim the contaminated niches, especially the contaminated terrestrial niches. Soil and inland water bodies (terrestrial niches) have got polluted by a variety of sources including petroleum spillage, chemical spillages, industrial emissions and effluent discharge and use of pesticides and other agricultural products (Boyce et al. 2005). This has resulted in progressively reduced availability of terrestrial niche for agricultural activities. Thus there is a potential threat for nonavailability of sufficient land to grow food for future generations. Therefore, in order to ensure successful accomplishment of Sustainable Development, it is critical to develop and implement efficient yet economical clean-up

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technologies that can be used for degradation of toxic pollutants and recovery of contaminated ecological niches. Decontamination of polluted soil and water from contaminated niches can be carried out by either physicochemical approaches (e.g. chemical oxidation, ionization, sonication, incineration, landfilling, etc.) or biological approaches (microbial biostimulation, microbial bioaugmentation, rhizoremediation, phytoremediation, etc.) (Marican and Durán-Lara 2018; Ye et al. 2017). In comparison to the physicochemical approaches, the biological approaches are widely acknowledged to be economical and environment-friendly (Ye et al. 2017). Amongst the different biological approaches, microorganism-based approaches, referred to as ‘bioremediation’, are more effective due to a number of technical advantages associated with handling microorganisms in comparison to handling plants. The most important advantage of using microorganism is ‘easy scale-up’. Any clean-up technology developed with microorganisms can be scaled up and could be used for decontamination of large polluted niches (Ye et al. 2017; Adams et al. 2015). In one recent and evident example, London’s Olympic Park which was heavily contaminated with industrial waste generated during hundreds of years of industrial activity was successfully decontaminated with the use of combination of chemical treatments and microbial bioremediation (Mead et al. 2013; Hou et al. 2015). This site was polluted with large quantities of radioactive material and ammonia due to long history of industrial activities at this site. During bioremediation treatment, archaeal microbes were used for breaking down ammonia into nitrogen gas. A total of 1.7 million cubic metre of heavily polluted soil was decontaminated to create worldclass sports facilities that were used for 2012 London Olympic Games. In subsequent appreciation, London Olympic and Paralympic Games were acknowledged as the ‘greenest’ and most sustainable games ever held. There are many examples of efficient, economic and on-site decontamination of polluted soil, water as well as air with uses of microbial bioremediation approaches. Cleaning up oil-polluted soil is another prominent example, wherein biostimulation has been used to enhance growth of native microorganism; it results in decontamination of oil-associated pollutants. Many reports in this regard have shown that providing growth-limiting nutrient, e.g. nitrogen and phosphorus to the native microbiome, stimulates the natural growth rate of oil-degrading bacteria (Ebuehi et al. 2005; Wang et al. 2019; Wu et al. 2017). These examples clearly establish the role of microorganisms in clean-up towards achieving an important Goal of Sustainable Development in the future. Application of microbial metabolic potential for pollutant clean-up is not a new technique; however, with increasing understanding of microbial metabolic processes as well as composition of microbiomes associated with polluted ecological niches, the technical capabilities of microbial remediation have also increased significantly. With present ‘state-of-the-art’ microbial bioremediation approach, treatments could be specifically designed as per the physicochemical characteristics and pollutant degradation requirements of the polluted site. It is speculated that combining the understanding of microbiomes from contaminated niches and implementation of microbial potential promises will be a unique way to harness biotechnology as

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ecofriendly methods for remediation and restoration polluted ecological niches (Tripathi et al. 2017).

6.2.3

Role of Microbiome in Clean Energy

The sustainable future of planet Earth depends upon several key factors; one of these factors is continuous availability of energy for fuelling various processes of development (Rasul 2016; Franco et al. 2020). According to a conservative estimate, the global energy requirement is projected to increase at least three-fold by the end of the present century. The ever-increasing energy requirement is likely to pose severe pressure on fossil fuel combustion based on traditional modes of energy generation. This represents a unique challenge to global environmental sustainability and economic stability. Additionally, fossil fuel combustion is also implicated in numerous problems for human health and environment pollution because of generation of ‘greenhouse gases’ and ‘hazardous chemical pollutants’ during fossil fuel combustion (Sanchez and Stern 2016). Therefore, discovery and development of alternative, renewable source of clean energy is one of the greatest challenges in achieving the Goals of Sustainable Development. The answer to this overriding challenge may be provided by microbiomes; as a few studies from recent past have reported the use of microorganisms for generation of electrical current which is generated during the metabolic processes (Zhao et al. 2018; Santoro et al. 2017). A large pool of electrons is generated within the microbial cells during metabolic processes; however, majority of these electrons remain within the microbial cells. It is technically challenging to extract or retrieve these electrons efficiently across the thick microbial cell wall (Zhou et al. 2016; Shahane et al. 2019). Accordingly, till now, the technology for generation of electrical current using microorganism is restricted to extracellular electrons that are transferred by microbial cells outside of their own cell. Several studies are being carried out with the objective of isolating and characterizing exoelectrogenic microorganisms (Zhou et al. 2016; Jiang et al. 2016, 2018). Interestingly, in one of the studies, isolation and characterization of petroleum-degrading species were reported for production of electric current in bioelectrochemical system (Zhou et al. 2016). This report suggests for a dual positive role (pollutant removal and clean energy generation) of such microorganism in achieving Goals of Sustainable Development. Alternatively, photosynthetic microorganisms could be employed for providing solutions to the global energy challenge (Aro 2016). Photosynthetic microorganisms can capture solar energy, which is a free, abundant and underutilized source of energy (Aro 2016). Harnessing even a minute fraction of total solar energy with implementation photosynthetic microorganisms is estimated to make a substantial impact to global energy needs. Studies carried out with model photosynthetic microorganism, viz. Thermosynechococcus elongatus, have provided remarkable insight into the processes that could be used for developing solar power-driven microbial fuel cells (Zilliges and Dau 2016). It is also anticipated that the unique metabolic capabilities of photosynthetic microorganisms could be utilized for

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generation of high-energy and multiple carbon atom containing chemicals (such as alcohols, alkanes, polyhydroxyalkanoates, fats, etc.) to be used as precursors or feedstocks for the chemical industries (Higuchi-Takeuchi et al. 2016). Another widely acknowledged source of clean energy for sustainable future is microbial conversion of lignocellulose to biofuels (Prasad et al. 2019; Gaurav et al. 2017). Lignocellulose is the polymer that constitutes the structural building block for plant biomass. Plant biomass in general and lignocellulose in particular are the most abundant sources of renewable energy on Earth. However, due to its chemical and structural complexity, lignocellulose remains extremely recalcitrant to regular microbial decomposition process (Soccol et al. 2019; Yousuf et al. 2020). Technically, the first step and the bottleneck step in generation of biofuels from plant biomass is deconstruction of lignin, because lignin is a strong inhibitor that blocks the action of cellulases. Fortunately, some of the microorganisms/microbiomes (e.g. bacterial communities within the tropical forests) are much better adapted at metabolizing lignocellulose (Wilhelm et al. 2019). It is indicated that microbiomes within tropical forest and associated water bodies (e.g. Amazon Forest and Amazon River) may have dominance of microorganisms capable of performing efficient biochemical reactions for lignin degradation (Santos et al. 2019). Therefore, detailed characterization of microbiomes from tropical forest soils could provide insight for developing and optimizing the technology for microbial degradation of lignocellulose and generation of biofuel. This technology will be advantageous because of lignocellulose production being independent of food-/cropgrowing agriculture processes. In one of the sample studies, a lignin-degrading anaerobic bacterium identified as Enterobacter lignolyticus SCF1 was characterized with the help of transcriptomics and proteomics approaches. It was found that strain SCF1 is capable of degrading lignin via dissimilatory as well as assimilatory mechanisms (Orellana et al. 2017). It is expected that many microorganisms with similar lignin-degrading capabilities might be present within the microbiomes of tropic forest soils. These microbiomes must be explored with both culture-dependent and culture-independent approaches to harness the lignin degradation capabilities in order to realize the potential of microbial biofuel technology and achieving this extremely important Goal of Sustainable Development.

6.2.4

Role of Microbiome in Human Health and Resilience

Human health and well-being are at the core of all the Goals of Sustainable Development (Gupta and Vegelin 2016). Therefore, ensuring the well-being of each human being whether in a healthy or a diseased stage throughout his/her lifespan must be one of the most important aspects of any developmental program to be implemented in the future. In this regard, the role of microorganisms in general and the role of human microbiome in particular are of immense significance (Gilbert et al. 2018b). There is a substantial amount of literature available that drives the understanding about the role of microorganisms in human health including the social and population health (Herd et al. 2018). Majority of this understanding about the

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role of microorganisms on human health has emerged through studies focusing on host–pathogen interactions and carried out with the reductionist approach, wherein interaction of human beings/experimental model systems was studied with microorganisms in a ‘one at a time’ manner. Results obtained with such approaches have been the benchmark of this specific domain of microbiology for almost two centuries. However, with recent advancements in next-generation sequencing-based metagenomic characterization of human microbiome, the paradigm for the role of microorganisms in human health is rapidly shifting. According to the present understanding, human well-being and health is an outcome of human–human microbiome interactions (Luca et al. 2018). An improved understanding of human–human microbiome interactions has the potential for achieving the status of being the single most important determinant of human health and well-being. There is already a wealth of literature available which provides support for this notion. In the context of human health and disease, many disease conditions including aging, autism, autoimmune disorders, inflammatory bowel disease, obesity, type 2 diabetes, hypertension, malnutrition, chronic kidney disease, inflammatory arthritis and human rheumatic diseases and other metabolic syndromes have been reported to be manifested because of gut microbiome dysbiosis (Vuong and Hsiao 2017; Carter et al. 2019; Gereige and Maglione 2019; Sharma and Tripathi 2019; Li et al. 2017; Yang et al. 2018; Scher et al. 2016). In terms of beneficial effects, the role of specific nutrients, e.g. vitamins in promoting the selective growth and development of healthy gut microbiome, is also well documented. Beyond the gut microbiome, the remaining components of human microbiome have been suggested to affect all organs through the immune, circulatory and nervous systems, including complex cognitive function, e.g. mood swings and general behaviour (Sarkar et al. 2018). Another emerging concept with regard to the role of human microbiome in health and well-being is that a portion of human microbiome is heritable and potentially it is passed from generation to generation through vertical transmission (Duranti et al. 2017). This concept is seen as a natural extension to the observation that microbiome plays essential roles across the entire lifespan of a human being including early age development, maturation as well as reproduction. Also, it has been experimentally demonstrated that selective maternal seeding and environment shape the human gut microbiome (Korpela et al. 2018). A growing body of experimental evidence indicates that any perturbation of microbiome during the early stages of development may result in increased sensitivity to type 1 diabetes; allergic as well as autoimmune conditions, such as food allergies; and inflammatory bowel disease; asthma; and atopic dermatitis (Livanos et al. 2016; Lee et al. 2018; Franzosa et al. 2019; Stokholm et al. 2018). These indications put forward the recommendations that it is critical to sustain and preserve a healthy human microbiome for ensuring human well-being as the pivotal Goal of Sustainable Development in the future. In the same vein, it is critical to recognize that non-judicious implementation of antibiotics in recent past has resulted in progressive loss of microbial diversity within the human microbiome, some of which may be essential. All possible efforts must be made to ensure that the healthy human microbiome is established and preserved

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throughout the global human population. One of the simple and easy ways to implement this approach for the same is the use of dietary prebiotics that are often defined as selectively fermented ingredients which cause specific changes in composition of gut microbiome and confer beneficial effects. The positive effects of prebiotics, on modulation of composition and diversity of gut microbiome, have been shown in a number of studies (Foo et al. 2017; Azcarate-Peril 2019). One of the common and most significant beneficial effects with prebiotic use was found to be the significantly increased prevalence of beneficial bacteria Faecalibacterium prausnitzii, Bifidobacterium adolescentis species and Bifidobacterium bifidum. Other reports on human microbiome have collectively indicated its vital role and importance in human health and overall well-being. These reports provide strong evidence that healthy human microbiome in general and healthy gut microbiome in particular are indispensable in overall health and well-being of human species. A detailed account of impacts of loss of human microbiome is presented in a comprehensive review (Blaser and Falkow 2009).

6.3

Microbiomes and Climate Change

Microorganisms are often defined as the single most critical biotic component responsible for evolution of the Earth’s environments. The presence of microorganisms on Earth dates back to ~3.8 billion years ago and also marks the origin of life on Earth (Homann et al. 2018). During the time of their existence, microorganisms have survived geological time scale as well as the catastrophic events which led to the extinction of several other life forms. They are likely to still exist beyond any future catastrophic extinction events. Apart from their ability to survive geological time scale and extinction events, microorganisms are also unique because of their ability to not only thrive in all environments that are occupied by any other form of life but also within environments that are characterized by extremes of physicochemical parameters, e.g. deep subsurface hydrothermal vents, acid mine drainage sites, etc. (Bar-On et al. 2018; Dudhagara et al. 2017). Not only do microorganisms occupy the Earth’s environment, but they also carry out extremely important functions (e.g. carbon and nutrient cycling, maintenance of animal and plant health and upkeep of global food web) that are essential for maintaining a healthy and sustainable global environment in both aquatic and terrestrial ecosystems (Delgado-Baquerizo et al. 2016). To put it in simple words, the global microbiome constitutes the life support system of the biosphere (Whitman et al. 1998). Despite the central position that microorganisms occupy in the Earth’s environment and its sustainability, the global microbiome remains grossly overlooked in the context of climate change studies. Climate change is widely acknowledged for causing the overwhelming deterioration of flora and fauna diversity (Hoffmann et al. 2019). This deterioration is suggested to be mediated mainly through changes in the nature and extent of ecological interactions between ecosystems and their respective abiotic and biotic components (Jordano 2016). The ecological interactions

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are fundamental for sustainability of biodiversity as well as ecosystem functioning (Jordano 2016). Unfortunately, a similar understanding is not yet well established with regard to climate change-mediated loss of microbial biodiversity. It is technically challenging to perform experiments that could mimic the various effects of climate change; therefore, in turn it is difficult to model the changes in outcome that may transpire in the future due to climate changes including any measurable change on microbial community composition, diversity and functions (Hutchins et al. 2019). The massive diversity and varied responses of microorganisms to environmental change also make it challenging to accurately determine the impact of climate change on global microbiome. Likewise, it is also difficult to experimentally determine or predict how global microbiome would affect climate change in the future (Hutchins et al. 2019). Nonetheless, it is imperative that the scientific community as well as the law makers must appreciate the importance of global microbiome and its response to ongoing climate changes (Jansson and Hofmockel 2020). The lack of this understanding may jeopardize efforts being put forward towards achieving the Goals of Sustainable Development. Recent past has seen some development in experimental procedures for addressing or modelling: (1) the effects of climate change on microorganisms, including microbial-related ecosystem processes and their drivers, and (2) the effects of climate change on microbiomes composition and function, physiological responses and evolutionary adaptation. A number of studies have been carried out for assessing the changes in microbiome composition in response to changes in the concentrations of atmospheric CO2 and/or carbon and nitrogen cycling (UsyskinTonne et al. 2020; Minich et al. 2018). In contrast, significantly fewer studies have focused on the impact of climate change on microbiome structure and dynamics. This situation could be ascribed to availability of limited scientific approaches to study the impact of climate change on microbiome.

6.4

Scientific Approaches to Study Impact of Climate Change on Microbiome

Scientific community trying to address the unique problems of assessing the impact of climate change on microbiomes and vice versa desperately needs to make efforts towards establishing scientific approaches. The major technical challenge in this pursuit is that microorganisms are so tiny and so diverse that even a soil sample of few grams may contain thousands of species and nearly billion microbial cells (Jansson and Hofmockel 2020; Hutchins et al. 2019). The other major challenge in investing the Earth’s microbiome is the massive size of samples that must be collected for determining the microbiomes from thousands of ecosystems around the world that constitute the Earth’s microbiome (Gilbert et al. 2018a). Apart from above challenges, another factor that contributes to the complexity of the global microbiome characterization is that microbiomes orchestrate enormously complex functions that are difficult to dissect with the presently available conventional approaches. With conventional approaches of studying microbial communities,

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researchers have gained a reasonable understanding about the complexities of the microbiome of a few model ecosystems and also about the role microbiome plays in general in fostering a healthy ecosystem. One of the remarkable study in this direction showed that with the use of metagenomics and meta-barcoding of global topsoil samples (collected at 189 sites, 7560 subsamples) bacterial genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance (Bahram et al. 2018). It is noteworthy that this study and most of similar studies have followed experimental procedure limited to a two-analysis process: (1) physicochemical characterization of collected samples and (2) microbiome structure composition of the collected samples. A few more elaborate studies as mentioned above have focused on presence/abundance and activation of gene diversity and metabolic functions. Studies with conventional approaches of addressing the Earth’s microbiome and its response to global climate have provided preliminary insight, yet they are not sufficient for addressing more pertinent questions. With the use of conventional approaches, it would be extremely difficult to address more relevant questions such as (1) how climate changes are threatening the Earth’s microbiomes and (2) whether the Earth’s microbiome is imparting a protecting or damaging effect on the global climate change scenario. It is extremely important to appreciate that just because of the miscellany of impacts caused by climate changes, it would be technically very challenging to determine the precise impact on microbiomes. It would be further difficult to model or experimentally demonstrate whether there exists any links within the tripartite system consisting of three components, viz. (1) climate change, (2) macroscopic organisms and (3) microbiomes. The fundamental limitation in understanding the responses of microbiome to climate change is the scarcity of appropriate sample material (e.g. soil samples of the Earth’s biosphere before and after the climate change). Therefore, there is an urgent and unique requirement for developing innovative experimentations to address how climate change affects microbiomes and vice versa. A few notable studies have attempted to experimentally determine the impact of climate changes on microbiome composition, activity and adaptations. In one such study, researchers from the Pacific Northwest National Laboratory shifted bulk soil from a cold, moist, high-altitude site to warm, dry and low-altitude site and vice versa. After nearly 17 years of initial intervention, the shifted bulk soil samples were examined for many parameters including microbiome adaptation to the new residence site. It was observed that functional adaptation of shifted microbiome to the new residence site was extremely poor (Bond-Lamberty et al. 2016). This study suggested the possible bad sign for microbiomes in the world faced with adverse climate changes (Bond-Lamberty et al. 2016). Prof. Vanessa Bailey, the corresponding author of the above publication, said ‘These microbes have somehow lost the capacity to adapt to the new conditions’. This statement raises serious concerns about the resilience and plasticity of the Earth’s microbiomes to rapidly adapt to ongoing environmental and climate changes. Their inability to adapt to climate change may result is overarching loss of environment functionality. Some

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researchers are suggesting that with climate changes, especially global warming, the essential diversity and function in the microbial world might be lost (Cavicchioli et al. 2019). Along with the loss of microbial diversity, there would also be a significant carbon loss with global warming (Walker et al. 2018). Researchers also realize that with climate change, if the diversity and function within the microbial world get lost to a great extent, then the nature and pattern of crop growth would change drastically (Cavicchioli et al. 2019; Dehghan et al. 2019). With the loss of microbiome diversity, many areas will not be able to grow the same crops. Therefore, it is critical to develop more robust and technically sound experimental approaches to investigate the response of the Earth’s microbiome to global climate change. The major pioneering studies being carried out in the direction of the Earth’s microbiome are (1) Global Soil Biodiversity Initiative; (2) TerraGenome Project; and (3) Microbiomes in Transitions (Gilbert et al. 2018a; Blackall 2018; Gomez et al. 2017). Each of these studies is addressing different issues associated with the Earth’s microbiome and its response to global climate change. The Global Soil Biodiversity Initiative is focused on preserving the services (e.g. places and support for plants to grow, breakdown of natural and anthropogenic waste and natural filtration of water) provided by healthy soil ecosystems. The TerraGenome Project aims to sequence the metagenome of soil microbes, whereas the Microbiome in Transition project is trying to address how environmental ‘perturbations’ – disruptions including climate change and pollution – affect both the microbiomes around human beings as well as within the human beings. The most urgent question being addressed within this project is centred on defining the role of soil and soil microbiome in modulation of ‘greenhouse gases’ at present and in the future. The impact of climate change on microbiomes can be seen along different ecosystems including extremophilic ecosystems, e.g. Arctic permafrost. It has been projected that with increasing global temperature, the Arctic permafrost regions would also warm up rapidly leading to thawing of frozen vegetation leading to sequestered organic material becoming available to microorganisms for degradation (Du Toit 2018). This in turn will contribute to the global flux of greenhouse gases. While this projection seems scientifically logical and a few studies have provided some insights into the microbial lineages involved in carbon processing in thawing permafrost. In one of the representative studies, a metagenomic analysis of microbial communities was carried out using permafrost soil sample collected from an Arctic site in Sweden. Metagenomic data was examined to address how specific microbial lineages contribute in transformation of organic matter during permafrost thaw (Woodcroft et al. 2018). In such a situation, microorganisms will consume newly available vegetation and release greenhouse gases (e.g. CO2, NO2 and CH4). This may lead further to a cascade or a positive feedback loop eventually leading to more warming and thawing. Based on the above examples, it could be put forward that scientific fraternity is trying hard to understand how climate change impacts the dynamics of greenhouse gases and role of microbiome therein. However, experimental approaches for developing an accurate comprehension of such complex environmental phenomenon are

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still very limited. There is an urgent need for development of experimental approaches for evaluating long-term effects of climate change events. Such approaches are necessary so that effects of climate changes can be modelled effectively. It is pertinent to mention that some studies have been carried out on modelling the impact of climate change; however, they have focused either on (1) flora/fauna biodiversity or (2) abiotic components of environment, e.g. freshwater, clean air, carbon dynamics, etc. (Faramarzi et al. 2013; Songer et al. 2012; Kang et al. 2011). Similar models for the impact of climate change on microbiomes are almost nonexisting.

6.5

Possible Extinction of the Earth’s Microbiomes Due to Ongoing Climate Change

The Earth’s microbiome is undergoing potentially threatening alterations in response to the ongoing global climate change. The most intimidating of these changes is inability of microbiomes to adapt and continue their essential function as soil temperature increases (Bond-Lamberty et al. 2016). While it is projected that there exists a great degree of functional redundancy within the microbial communities, therefore in most of the cases, microorganisms being adversely affected by climate change could be potentially replaced by other microorganisms capable of replenishing the lost functions (Onen et al. 2020). The other ‘school of thoughts’ argues that within each ecosystem, there are a few key microbes. If these microbes become extinct due to climate change, then the fundamental characteristics of concerned microbiome, and in turn the concerned ecosystem, would also get extinct (Cavicchioli et al. 2019). On similar lines, it is proposed that some microbes may become extremely active upon exposure to climate change. For example, a slowgrowing oligotrophic cyanobacterium called Trichodesmium has been experimentally demonstrated to achieve a very high metabolic state and rapidly exhaust growth-limiting nutrients, when subjected to growth at increased incubation temperatures (Boatman et al. 2017). In another experimental demonstration, longterm artificial warming of Harvard Forest was reported to result in significant reductions in microbiome diversity as well as functions (Frey et al. 2008; DeAngelis et al. 2015). Interestingly, immigration of new bacteria to those communities was unable to rescue them from these effects, indicating that replenishing the site with other forms of microorganisms may not be enough to prevent negative impacts of climate change (DeAngelis et al. 2015). Observations reported in these studies and presented above strongly suggest that climate changes may cause nutrient imbalance, eventually leading to overgrowth of some microorganisms while extinction of the others. Therefore, it could be put forward that the impact of climate change on the global microbiome diversity and functionality may be more devastating than ever imagined. Furthermore, considering the direct significance of the global microbiomes in achieving the Goals of Sustainable Development, the scientific fraternity must continue to make efforts for developing experimental and modelling approach for clearly understanding how

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microbiomes will respond changing climate. This pursuit may take more time than available for preserving the composition and functions of global microbiome. Therefore, alternative measures, e.g. ‘Approaches for preserving intact microbiome’, must be developed, optimized and implemented on a priority basis to those ecosystems that are feared to be most adversely affected. This idea of preserving intact microbiomes is presently nascent; however, it is projected to emerge as one of the most important approaches towards ensuring the conservation of biodiversity in general and microbiome diversity in particular.

6.6

Significance of Intact Microbiome Preservation Studies in Climate Change

Ever since it has been realized that just like the other forms of life, the microorganisms are also facing the crisis of extinction due to the ongoing climate change; there has been a pressing need for development of approaches for preserving the microbial diversity of the planet. Amongst the presently available approaches, the preservation of intact microbiome sample with all viable microbial components for future OMICS and cultivation-based studies seems to be the most promising approach (Prakash et al. 2013a; Bello et al. 2018; Prakash et al. 2013b). Microbial community shift is a continuous process of the nature because nature in itself is highly dynamic in terms of physics and chemistry that affects its biological components. The microbial community of any habitat present today will not be the same after a few decades due to several ecological and environmental perturbations. Global warming, intensity of precipitation, carbon flux, fertilization, increasing load of pollutants and human intervention all affect the microbial community structure and functions. To perform the long-term comparative study on effects of the abovementioned factors on structure and functions of microbial community of any specific habitat and to decide the role of specific microbial components in ecosystem functioning, isolation and preservation of every component as pure culture is imperative (Prakash et al. 2013a; Bello et al. 2018). The culture-independent metagenomics studies have revealed immense microbial diversity in any habitat, but due to technical and physiological limitations, it is not yet feasible to cultivate each and every microbial component of any ecosystem. The current pace of cultivating new microorganisms is extremely sluggish; at this pace, it would not be possible to cultivate all of the microorganisms in coming 1000 years (Prakash et al. 2013a, b). Therefore, there is requirement of implementing culture-independent OMICS approach as good alternative option to perform comparative studies on structural as well as functional aspects of microbiome diversity without carrying out cultivation (Bodelier 2011). Metagenomics, transcriptomics, meta-proteomics and meta-metabolomics are the emerging approach in environmental studies and for understanding, managing and protecting microbial ecosystems (Bodelier 2011). It is speculated that with improved understanding of microbiomes via culture-independent approaches, it would be feasible in the future to cultivate many more microbes (those which are not yet cultured today for industrial,

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agricultural and clinical applications) with implementation of approaches termed as culturomics (Lagier et al. 2016). While approaches for improved microbial cultivation get optimized and become available for implementation at any ecosystem of choice, there is a need for developing parallel approaches for preserving the microbial diversity as intact microbiomes. Preservation of intact sample with viable microbial components for future OMICS and cultivation is the need of the time to save the valuable microbial diversity for extinction and endangerment (Prakash and Jangid 2015). For the preservation of intact microbiomes, the critical step is to carry out appropriate sampling and long-term preservation of the samples for comparative study using similar tools and laboratory conditions (Tzeneva et al. 2009). In this attempt, the improper sampling, transportation and storage of samples often lead to compromised result and mislead the findings. Preservation of intact DNA, proteins and metabolites from degradation and damage during long-term storage of sample is essential for OMICS-related studies, while preservation of intact microbial cells with viability and functionality is imperative for future cultivation (Prakash and Jangid 2015; Tzeneva et al. 2009). Efforts have been started in the direction of intact sample, mixed microbiome and consortium preservation. Several groups have studied the effect of sampling methods, sample transportation method and preservation of samples on community structure and functions (Morono et al. 2015). As discussed above, often it is difficult to cultivate all the organisms of the community in pure culture; yet there are certain areas where instead of pure culture enriched microbial consortium is very much essential. Anaerobic digestion of waste in biogas plant and wastewater treatment plants requires seed inoculum to quicken operation of the process. After getting the stable population in reactor, it can be stored for future use in the form of active seed inoculum to fasten the reactor operation without starting it from scratch. Such approach for preservation of intact microbiome can save huge cost and time and have a huge positive effect towards ensuring the successful achievement of the Goals of Sustainable Development. In a similar manner, preservation of intact microbiome is equally important for ensuring good human health (Gilbert et al. 2018b; Herd et al. 2018). With increasing interest in human gut ecology, it has been proven that, as discussed above, human gut microbiota has the critical role in human health and diseases. Further it is also acknowledged that human gut microbiota cannot be completely explored using cultivation approaches due to anoxic nature of gut and difficulty in cultivation of each and every microbial component therein. Recently the concept of intact faecal microbiota preservation and transplantation is emerging as a treatment approach to treat the gut dysbiosis in several physiological disorders (Smits et al. 2013). Several promising results have been obtained with faecal transplantation in case of treatment of Clostridium difficile infection (Bakken et al. 2011). The concept of bio-banking and preservation of total intact microbiota from human faecal materials are coming in light for faecal material transplant (FMT) purposes (Khoruts et al. 2015). In addition to study the role of organisms in human health and disease, availability of live pure culture is also essential from the point of view of future studies. In this context also, preservation of intact faecal microbiota is an important approach for

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microbiology, microbial ecology, systems biology as well as human health and wellbeing. Having mentioned about the significance of preserving the intact microbiome, it is important to indicate that at present there are no specific protocols that can be used exclusively for preservation of intact environmental samples, microbiome, faecal materials and mixed microbiome. Heterogeneity in microbial composition of different type of samples and different texture, matrix and physiochemical features of different samples gives variable result with different preservation protocols. Several experimental iterations are currently being evaluated for their potential impact on collected microbiome samples (Tap et al. 2019; Tatangelo et al. 2014; Yarberry et al. 2019; Bircher et al. 2018a, b; Lee et al. 2019; Vekeman and Heylen 2015; Kerckhof et al. 2014). These iterations are being carried out with rationale that inclusion of different environmental samples, their multiple replicates and post-preservation evaluation of the viability and functionality of the total microbiota using different combinations of cryoprotectants and storage condition is necessary to determine the correct preservation conditions towards development of right protocols. Further development of technical capabilities with regard to preservation of intact microbiome is necessary for appropriately complementing the ongoing efforts in the direction of understanding the spatial and temporal dynamics of the Earth’s microbiome in response to climate changes. Findings from these efforts are expected to lead to a set of ‘standard protocols’ that could be used for microbiome preservation.

6.7

Conclusions

As elaborated throughout this document, nearly all of the ecological and environmental processes including cycling of materials, degradation of pollutants, methanogenesis, methanotrophy, nitrogen fixation, denitrification, sulphate reduction, anoxic ammonia oxidation, plant growth promotion, generation of greenhouse gases and global climate change and release of breathable oxygen are operated by different groups of microbes. In other words, it could be said that microbes are the key providers of the ecosystem services and play a pivotal role in ensuring the sustainability of the Earth and its environment. Therefore, to understand the role of microbes in ecological and environmental processes, understanding of microbes and their functionality at cellular and community levels is critical for ensuring sustainable development. Spatial and temporal variation in microbial community structure due to variations in external environmental factors and geochemical composition is a very common observation of microbial ecologists. Initially, it was believed that microbes are universally distributed and do not face the problem of extinction and endangerment due to their fast generation time and quick resilience in perturbed ecosystem. Anthropogenic activities pertaining to agriculture and industries have been the single most important cause of environmental perturbation and global climate change. The adverse impacts of this phenomenon would be observed in terms of loss of total biological diversity. Amongst the total biodiversity, the

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microscopic diversity that is seemingly intangible is the target that is expected to undergo very rapid and dynamic alteration as a consequence of the ongoing climate changes. With recent developments in experimental approaches to study microorganisms at the natural sites and at the community level, it has been observed that microbes are facing the problems of extinction and endangerment due to global climate change. Extinction of host plant and animals, changing environmental conditions, destruction of habitats, excess use of antibiotics and sudden release of pollutants are some of the factors leading to the extinction of certain microbial species. The rapid dynamics of microbial diversity is expected to have far more devastating impact on the Earth’s environment and future sustainable development. In such a scenario, it is indispensable to study and determine the impact of global climate change on the Earth’s microbiome. It is also equally important to preserve the microbiomes with viable microbial components for future OMICS and cultivation, and the need of the time to save the valuable microbial diversity from extinction and endangerment is necessary for ensuring the preservation of global microbial diversity towards achieving the Goals of Sustainable Development.

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Microbiome for Personalized Medicine Preeti Rathi, Deepanshu Verma, Ashutosh Singh, and Neha Garg

Abstract

In recent years, microbiome has attracted much attention due to its role in host homeostasis and disease. Based on the microbiota of an individual, the microbiome can act as the biomarker as well as aid to personalized medicine, therefore reducing the adverse effects and cost of the medications associated. Comparing the microbiome profile of the diseased and the healthy individual and correlating with the bacteria causing the disease can provide clues regarding the associated biomarkers. Re-establishing the microbiome via microbiota transfer, diet, or medicine can act as personalized medicine for the individual. Due to the dearth of culturable properties of various microbial communities, advancement in the sequencing technologies has opened new doors in the microbiome world. In this chapter, we have emphasized the role of microbiome/microbiota with personalized medicine in various diseases like cancer, liver disorders, diabetes, and obesity. Keywords

Microbiome · Personalized medicine · Biomarkers · Diet · Microbiota transfer

Joint first author: Deepanshu Verma and Ashutosh Singh. P. Rathi · D. Verma · A. Singh School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Mandi, Himachal Pradesh, India N. Garg (*) Department of Medicinal Chemistry, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_7

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Introduction

A large population of the microbial world lives in an ecological relationship (mutualism/commensalism) with the human body forming the ecosystem. These microbial communities living in different parts of the human body lead to the formation of the human microbiota. The human microbiota is highly diverse, and an estimated number of bacterial species residing within the human body at a time is around 500–1000 (Turnbaugh et al. 2007). The collective genome of all these microbial communities present at different sites in our body is known as the human microbiome (Marchesi and Ravel 2015). Bacteroidetes and Firmicutes are predominant phyla present in the human body to which represents most of the bacterial species (Lozupone et al. 2012). The unified genome of these communities is approximately ten times more than the human genome. Though the term microbiome is in use for many years, from the last 15 years, it has been substantially linked with human health (The Human Microbiome Handbook - Google Books n.d.). The human microbiota dwells at different anatomical sites, including the gastrointestinal tract (including buccal cavity), respiratory tract, genitourinary system, skin, and ocular surfaces. The number and diversity of species differ a lot among these body parts. Along with bacterial communities, other microorganisms such as Archaea, fungi, and viruses are part of the human microbiome. Bacterial species from phyla Bacteroidetes and Firmicutes are primarily discovered to be present in the human host. Besides these two Proteobacteria, Verrucomicrobia, Actinobacteria, Fusobacteria, and Cyanobacteria also play their respective roles in framing human bacterial microbiome (Sommer and Bäckhed 2013). Among Archaea, methanogens are primarily present; however, colonization is variable among humans (Duncan et al. 2007). The collective species of fungi residing in the human body, also called mycobiota, is predominated by the yeast species (Cui et al. 2013; Martins et al. 2014; Wang et al. 2014; Erdogan and Rao 2015). Candida species are primarily studied because of its pathogenic capabilities in immunocompromised models as well as sometimes in healthy subjects (Martins et al. 2014; Wang et al. 2014; Erdogan and Rao 2015). Viruses, most importantly, bacterial viruses, result in the formation of human virome at different sites like the skin, gut, lungs, and oral cavity (Hannigan et al. 2015; Minot et al. 2011; Young et al. 2015; Abeles et al. 2014). As the microbiome plays a vital role in maintaining health and upregulating the immunity of the individual, it can also act as a remedy for various disorders. In the current regime, generalized medicines are extensively used for the treatment of various diseases but have side effects like allergic reactions. These medicines also react with the active components of co-administered medicines (Medicines and side effects - Better Health Channel n.d.) and can alter the microbiome present in the body, leading to parallel side reactions. In order to minimize the side effects, personalized medicines are required to be designed according to the individual microbiota profile.

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Microbiome and Health

The nutritional composition and surroundings of each microbiome constitute a welldefined environment that maintains the symbiosis of microbiota with the human host (Khajuria and Metgud 2015). Microbes help in maintaining the health of the human by regulating the metabolic activities of the host and providing protection against pathogens, leading to the enhancement of the immune system of an individual (Shreiner et al. 2015; Rajpoot et al. 2018). The gut microbiome holds a majority of the bacterial population, particularly in two divisions, namely, Bacteroidetes and Firmicutes, as mentioned above, along with a well-known archaeon, that is, Methanobrevibacter smithii (Turnbaugh et al. 2007; Eckburg et al. 2005a) (Fig. 7.1). Gut microbiome reinforces the human host by playing its role in immune regulation and maintaining gut structure. Gut microbiome disturbance, i.e., in the condition of dysbiosis, results in gastrointestinal and extragastrointestinal diseases such as inflammatory bowel disease (IBD) (Shroff et al. 1995; Round and Mazmanian 2009; McCarville et al. 2016). The human gut harbors a highly dense population of microbes that plays a significant role in host homeostasis. It is the largest and most complex microbiota among all the microbiomes present in the human body and thus performs many biological functions, including conversion of complex food into simpler ones, toxic

Fig. 7.1 Different kinds of microbiome at different sites of the human body (Schoenmakers et al. 2019)

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Fig. 7.2 Microbial communities comprising the human microbiome (Abeles and Pride 2014; Forbes et al. 2019)

compound degradation, and production of essential vitamins that directly affect the human health (Rajpoot et al. 2018; Gerritsen et al. 2011). Microbes of this microbiota balance their commensal relationship by carrying out regulation of glycans and fat metabolism, fostering host immunity, providing security from pathogens, and helping in the development of the enteric nervous system of the small intestine in mid-distal portion (Gill et al. 2006; Ley et al. 2006; Collins et al. 2014). Though the microbial population was thought to be in mutualism with the human host, the microbiome of the oral cavity has been responsible for two prevalent diseases in the oral cavity, namely, dental caries and periodontal diseases (Wade 2013). The environment of the oral cavity is heterogeneous as the mouth offers different habitats to microbial communities, which includes the teeth, tongue, gingiva, hard and soft palate, and many more. Streptococcus, Veillonella, Actinomyces, and Neisseria are a few species that cover the central part of the microbiome in the oral cavity (Avila et al. 2009; Kumar et al. 2013) (Fig. 7.2). Not all but a few bacterial species live in mutualism with the human host and do not allow the pathogens to enter into the mucosal surface and adhere so that they could not harm the host (Avila et al. 2009; Jenkinson and Lamont 2005).

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Microbiome as a Biomarker for Disease

For a long time, microorganisms are being used in the treatment procedures of various diseases. Microbial composition of the human microbiome varies from person to person, and even microbial variations may be found at different anatomical sites such as the oral cavity, ocular surface, GI tract, and skin. These variations may arise because every part has a different biogeography and physiological makeup, and the microbiota shows specificity toward this. Apart from body sites, some other factors also contribute to microbial diversity among individuals, including dietary habits, age, health, variations in pH, atmospheric conditions, and personal hygiene (Rajpoot et al. 2018; Gilbert et al. 2018). So, the microbiome can be considered as a personalized option for each individual. The vast microbial diversity can be realized from the fact that the individuals show a 99.5% similarity between their genomes and show only 0.5% uniqueness in the microbiome genome (Gilbert et al. 2018). Additionally, the microbial composition is different for the healthy and unhealthy subject. Furthermore, studies have shown that the personalized microbiome has been used like the fingerprints of individuals because of dynamic interactions within the environment and transfer from the host’s surface to other surfaces (Fierer et al. 2010). Presently, the researchers are heading in the direction of limiting the treatment of a disease to a personal level, i.e., personalization of medicament. The concept built behind this is in the different responses of individuals for the same treatment. It may be because the genetic makeup of everyone is divergent. In the last few years, it has been found that this variable response for the same drug is associated with age, nutritional profile, health status, environmental exposure, microbial composition, and epigenetic factors (Vogenberg et al. 2010). Personalized medicine refers to the idea that a patient’s health should be managed according to the specific characteristics, as mentioned above. Personalized therapeutics would help in precise and safer treatment for a patient. Customizing the health care to the distinctive genetic makeup of each person is also known as individualized medicine, personalized medicine, or genomic medicine. Personalized medicines will help in safer use of drugs and avoid adverse reactions. It can reduce the time and cost of clinical trials. Variable responses for drugs with respect to the genetic makeup of an organism have given emergence to a new discipline, namely, pharmacogenetics and a merge of three different fields, i.e., genetics, biochemistry, and pharmacology (Vogenberg et al. 2010).

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As microbiome is specific and variable for every individual, it can lead us to a way toward personalized medicine. Exposing manipulation of the host genome is a difficult task in both technical and ethical respects, but manipulating the microbiome to cure a disease can be easy and can be materialized (Manipulation of the human genome: ethics and law n.d.). Current research has revealed that a person-specific

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Fig. 7.3 Flow chart of the process followed for using microbiome as personalized medicine

microbiome can help to bring out precise and personalized diagnostic techniques and treatments (Zmora et al. 2016). With the advancement in sequencing technologies, it is possible to investigate the diversity of microbiome without traditional culturing techniques. After extracting the microbiota sample from the patient’s body, generally, 16 s rRNA is sequenced, and shotgun sequencing is carried out to obtain the metagenome (Fig. 7.3). Later, it is compared within the healthy and diseased microbial landscape, and the alterations are used as fingerprints which are targeted for personalized medicine. There are several studies where microbiome is used as personalized medicine in diseases like obesity, liver disorders, cancer, and neurodegenerative disorders.

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Microbiome in Cancer

Cancer is still one of the threatening complex diseases in the world, causing millions of deaths worldwide. Several studies have reported an alteration in the microbiome in several acute and chronic diseases, including cancer. The microbiome has found to regulate the inflammatory response in the host after the anticancer treatment (Iida et al. 2013; Viaud et al. 2013). With the help of NGS and gene expression microarray technologies, the genetic alteration can be identified at the time of tumor formation (Vogelstein et al. 2013). Therefore, increasing the abundance of microbial communities which shows antitumor activity can be used as therapeutics. There are examples where microbiome was used as a therapeutic option in cancer and also used to enhance anticancer therapies. In one study in the mouse model of melanoma, it was found that Bifidobacterium increases the tumor control during treatment with anti-programmed death-ligand 1 (Sivan et al. 2015). In another example, Bacteroides were found to show the antitumor effect by blocking the functioning of cytotoxic T-lymphocyte-associated protein 4 (Vétizou et al. 2015). Contradictorily, the gut microbiota can also show harmful effects by interacting with anticancer drugs. For example, a chemotherapeutic drug irinotecan that is used for the treatment of colorectal cancer (CRC) can be glucuronidated by gut microbes, thereby converting the drug into an active metabolite which results in severe diarrhea (Wallace et al. 2010) (Fig. 7.4). Therefore, microbial communities can play an active role in personalized therapeutics in cancer.

7.4.2

Role of the Microbiome in Treating Clostridium difficile Infection (CDI)

Clostridium difficile is a gram-positive bacillus that produces toxins and forms spore. CDI can lead to symptoms like diarrhea (antibiotic-associated diarrhea – AAD) and can be the cause of severe diseases like pseudomembranous colitis (PMC) and toxic megacolon (Bartlett 1984; McCollum and Rodriguez 2012; Stanley et al. 2013). It is mainly caused by the use of broad-spectrum antibiotic medication, for example, clindamycin and ampicillin (Bartlett 2002). Antibiotics show a negative impact on microbiota, decreasing the diversity of the microbial population. Therefore, studies were conducted on animal models, which show that bacterial diversity was degraded (reduction in Bacteroidetes and Firmicutes and increase in Proteobacteria) after antibiotic doses (Peterfreund et al. 2012). Restoring the species from the healthy individual shows positive effects. The fecal microbiota transplant can be helpful in curing Clostridium difficile infection, as the composition of the microbiome in the stool is different when compared among the diseased and healthy conditions. Fecal microbiota transplant has proved to be more potent than the standard antibiotics in the case of recurrent Clostridium difficile infection (Weingarden et al. 2015). When the microbiota from the healthy fecal sample is transferred to the patients with CDI, there was a positive change in microbial communities of patients. However, the

Fig. 7.4 A representation of microbiome playing a role in the modulation of chemotherapy. Chemotherapeutic drugs translocate the commensals to secondary lymphoid organs through the damaged epithelial barrier, and then microbiota helps in apoptosis by inflammatory responses. Microbiota also increases ROS production efficiency of chemotherapeutics and thus apoptosis. Enzymes secreted by gut microbiome reactivate the inactivated form of irinotecan accumulation which causes toxicity and severe diarrhea. Certain anaerobes hinder the detoxifications of drugs like 5-FU, which results in mucositis and other damages to the GI tract. Enzyme pyrimidine nucleoside phosphorylase and enzyme cytidine deaminase secreted by M. hyorhinis catalyze phosphorolysis and deamination, respectively, of gemcitabine to a less cytotoxic metabolite 20 ,20 -difluoro-20 -deoxyuridine

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treatment may vary from individual to individual due to the various factors involved in shaping the microbiome described above.

7.4.3

Role of Prevotella copri in Glucose Tolerance and Insulin Sensitivity

Prevotella copri was reported to show beneficial function in the human body. De Vadder et al. showed that P. copri helps in the production of succinate by fermentation of dietary fibers, which helped in the improvement of glucose metabolism and insulin sensitivity. Further, when they fed mice with dietary fibers, the concentration of succinate in cecal content was high when compared to the portal vein in mice. This result also suggested that produced succinate acted as a substrate for intestinal cells. As in the process of intestinal gluconeogenesis, which improves glucose homeostasis, succinate acts as a substrate. Therefore, feeding fibers and transplanting P. copri improve glucose tolerance and insulin sensitivity (De Vadder et al. 2016; De Vadder et al. 2014). P. copri was also reported to show some non-beneficial effects, Pedersen et al. found the existence of high levels of branched-chain amino acids in the metabolic profile of insulin-resistant persons. They identified P. copri and Bacteroides vulgatus as a pathogenic strain that helps in the biosynthetic pathways of branched-chain amino acids. When they fed the high-fat diet and P. copri to the mice, they found that the presence of P. copri worsens the glucose intolerance and also decreases insulin sensitivity, which resulted in an overall increase of branchedchain amino acid in serum (Pedersen et al. 2016). These two studies of P. copri, in one it is beneficial, and in another, it showed harmful effects, are due to the differential dietary intake of the subjects. P. copri can act as a biomarker to the disease as well as personalized medicine in combination with diet to get rid of glucose tolerance and insulin sensitivity.

7.4.4

Gut Microbiome in Rheumatoid Arthritis (RA)

Gut microbiota tends to be helpful in the pathogenesis of rheumatoid arthritis (Cénit et al. 2014). RA is an autoimmune disease, responsible for inflammation and pain in joints. When the fecal bacteria of healthy individuals and patients of RA were compared using 16 s rRNA gene sequencing and shotgun sequencing, the composition of gut microbiota was found to be altered (Fecal microbiota in early rheumatoid arthritis n.d.). There was an abundance of Prevotella copri, which dominated on Bifidobacterium, Bacteroides-Porphyromomas-Prevotella, Bacteroides fragilis, and Eubacterium rectale-Clostridium coccoides in the gut of an individual suffering from RA (Scher et al. 2013). Therefore, controlling the abundance of Prevotella copri can be used to treat RA. Dietary intervention, i.e., use of probiotics and fecal microbiota transplant, can be used for altering the bacterial diversity toward the healthy state in RA.

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Akkermansia muciniphila in Metabolic Disorders

A. muciniphila is known to show beneficiary effect in humans and help in the improvement of many cardiometabolic diseases. When A. muciniphila was administered and supplemented, it helps in the protection from different metabolic disorders by reducing hepatic steatosis, obesity, inflammation, cholesterol levels, and atherosclerosis and improving insulin resistance. It also helps in recovering the gut barrier function by playing a decisive role in mucus layer thickness, tight junction proteins, antimicrobial peptides, and immunity (Everard et al. 2013; Grander et al. 2018; Plovier et al. 2017; Li et al. 2016; Hänninen et al. 2018; Shen et al. 2016). Therefore A. muciniphila can be supplemented to improve the metabolic condition, and there are studies that show that feeding prebiotics increases the abundance of A. muciniphila (Everard et al. 2011; Anhê et al. 2015; Greer et al. 2016).

7.4.6

Role of the Microbiome in Inflammatory Bowel Disease (IBD)

The comparison of the microbiome from patients suffering from inflammatory bowel disease with healthy individuals showed an altered microbiome of the gastrointestinal tract. IBD is divided into Crohn’s disease (CD) and ulcerative colitis (UC). UC is generally restricted to the colon, whereas CD can affect any part of the digestive tract from the mouth to the anus (Cosnes et al. 2011). Using the advanced DNA sequencing technologies, it was observed that there was a dominance of Firmicutes, Bacteroidetes, and comparatively less Proteobacteria and Actinobacteria in the gastrointestinal (GI) tract of a healthy human (Eckburg et al. 2005b; Huttenhower et al. 2012). In the case of IBD (both UC and CD), bacterial diversity (α diversity) was found to be decreased (Manichanh et al. 2006). In the diseased state, the population of Firmicutes and Enterobacteriaceae was elevated along with some altered metabolic pathways, like oxidative stress pathway, decreased metabolism of carbohydrate, and decrease amino acid biosynthesis (Morgan et al. 2012). This altered interaction leads to show a negative effect on the mucosal immune system (Kostic et al. 2014). Using antibiotics in the case of IBD is not considered beneficial as it could disturb the prevalent bacterial community, which leads to a decrease in diversity. Several studies show that specific microbial communities show protection in the case of IBD. These microbial communities prevent the colonization of unhealthy bacteria and help in the removal of pathogenic bacteria (Callaway et al. 2008; Kamada et al. 2012). Therefore these beneficial species can be administered to restore microbial diversity. Prebiotics, in this case, have given some beneficial results (Whelan and Quigley 2013). Other than this, the fecal microbiota transfer can be a potential therapeutic in restoring bacterial diversity; there are cases where it has been beneficial in UC (Damman et al. 2012).

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Food as Personalized Medicine

Our diet contributes a lot in maintaining gut microbiota and can also result in the dysbiosis of the microbiota. This dysbiosis is prevalent in some autoimmune diseases and cardiometabolic diseases and can lead to early aging. Diet can help in harvesting the beneficial bacteria and can help in positive alteration of microbiome instead of removing the harmful bacteria with antibiotics. Microbiota also helps in fermenting the dietary fibers to short-chain fatty acids and provides nutrients like vitamins and antioxidant that helps in improving the immune system. Additives like prebiotic and probiotics have beneficial effects on human health. Ingestion of bacterial preparations in the form of probiotics has been a popular treatment for the problems related to the gastrointestinal tract (Preidis and Versalovic 2009). Probiotic food generally contains Bifidobacterium and Lactobacillus acidophilus (Pallister and Spector 2016). Prebiotic food generally contains dietary fibers that are indigestible by the human body. These supplements help in the nourishment of microbes, and these dietary fibers are fermented to short-chain fatty acids that protect from obesity and insulin resistance induced by diet (Lin et al. 2012). Polyphenols present in foods are metabolized by the gut microbiome and help in protection from obesity (Possemiers et al. 2011). Polyphenols from pomegranate are reported to show the desired effects (Neyrinck et al. 2013). The gut microbiota can be changed via diet within a few days. So, we can help in the enrichment of beneficial microbes with the help of diet according to the need of the individual.

7.4.8

Microbiome and Neurodegenerative Disorders

Neurodegenerative disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis have been a challenge to the scientific community for ages. In recent years, studies have shown the relation between the microbiome and neurodegenerative disorders. It has been shown that gut microbiota activates the immune system due to defective gut barrier function which results in inflammatory response impairing the blood-brain barrier which promotes neuroinflammation, neural injury, and degeneration resulting in deposition of β-amyloid in Alzheimer’s disease and neuropathological characteristics of Parkinson’s disease (Pistollato et al. 2016; Houser and Tansey 2017; Westfall et al. 2017; Itzhaki et al. 2016; Felice et al. 2016). It has been reported that in AD, anti-inflammatory species like Eubacterium rectale were reduced and pro-inflammatory species like Escherichia and Shigella were increased (Cattaneo et al. 2017). Whereas in PD, there was a reduction in Prevotellaceae and some anti-inflammatory microbes like Blautia, Coprococcus, Roseburia, and Faecalibacterium and abundance in pro-inflammatory microbes like Proteobacteria, Enterococcaceae, and Enterobacteriaceae (Keshavarzian et al. 2015; Scheperjans et al. 2015). Therefore restoring the altered microbiome according to the individual can show improved results in the medication. The restoring methods can be FMT or by dietary interventions. For example, in AD,

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polyphenols from pomegranates and seeds of grapes show beneficial effects (Wang et al. 2015; Yuan et al. 2016).

7.4.9

Microbiome in Autoimmune Diseases

While studying autoimmune diseases, it has been found that the microbiome plays a crucial role and can be the reason behind its cause. Alteration in the microbiome of the individual can result in the loss of immune tolerance which can lead to the diseased state like rheumatoid arthritis, systemic lupus erythematosus (SLE), antiphospholipid syndrome (APS), Sjögren’s syndrome (SS), and systemic sclerosis (SSc) (Belkaid and Hand 2014; Shamriz et al. 2016). It has been observed from scientific reports that in SLE, the microbiota is altered. It has been observed that the SLE patients have lower Firmicutes-to-Bacteroidetes ratio and deficiency of Dialister and Pseudobutyrivibrio, while Rhodococcus, Eggerthella, Klebsiella, Prevotella, Eubacterium, and Flavonifractor were found in abundance (Hevia et al. 2014; He et al. 2016). In SS, it was found that the oral microbiota is altered with an increasing population of Firmicutes (Streptococcus and Veillonella) and decreased population of Synergistetes and Spirochaetes. Also, when fecal samples were tested, it was found that there was a reduction in Faecalibacterium (F. prausnitzii) (Siddiqui et al. 2016; De Paiva et al. 2016). In the microbiome profile of SSc patients, it was observed that Faecalibacterium and Clostridium were decreased and Fusobacterium and γ-Proteobacteria were increased. Microbial communities like Bifidobacterium and Lactobacillus, which were decreased during inflammation state, were found to be increased in this case (Volkmann et al. 2016). In all the above cases, the microbiome profile of the patients was compared with the healthy individuals, and the dysbiosis was tried to be omitted. In animal models, when the altered microbiota was restored after inducing the diseased conditions, there was a significant improvement in the diseased state. Therefore, after microbiome profiling, the restoration methods could be different for different individuals based on the microbial species acting as personalization of medication.

7.5

Conclusion and Future Perspectives

Every individual harbors a diverse range of microbiota among them, which is responsible for the healthy and diseased condition. Microbial diversity acts as a fingerprint of the individual while treating against any disease. Generalized medicine acts against numerous factors and has both beneficial and harmful effects. So, the concept of personalized medicine arises, keeping the microbiota in mind to counteract the adverse effects on the body and reducing the cost of medication. Many microbial communities inside our body cannot be cultured via traditional culturing techniques, which created difficulties in studying the microbiome. The recent advancement and ease in sequencing technologies have allowed us to study the role of the microbiome in the healthy and diseased state. It was reported in several

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studies that during the diseased condition, the microbiome of the individual is altered and restoring the microbiome within an individual leads to a healthy state. This restoration is generally done by targeting the unhealthy bacteria or by supplementing the beneficial bacteria to give a desirable result in an individual. It is well known that the microbial composition at different anatomical sites is different and therefore, the microbial restoration is mainly governed by the specific interaction between the microbes and its niche. Currently, fecal microbiota transfer (FMT), dietary interventions, and medicines target a deleterious bacterial community and precision prebiotics being used depending on the individual microbiome. Using the microbiome as personalized medicine is still in its early days. Several factors like microbial diversity, host living style, and interacting components need to be thoroughly considered while using microbiome as personalized therapeutics.

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Viaud S, Saccheri F, Mignot G et al (2013) The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342(6161):971–976 Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW (2013) Cancer genome landscapes. Science 339(6127):1546–1558. science.sciencemag.org Vogenberg FR, Barash CI, Pursel M (2010) Personalized medicine - part 1: evolution and development into theranostics. P T 35:560–576 Volkmann ER, Chang YL, Barroso N et al (2016) Association of systemic sclerosis with a unique colonic microbial consortium. Arthritis Rheumatol 68:1483–1492. https://doi.org/10.1002/art. 39572 Wade WG (2013) The oral microbiome in health and disease. Pharmacol Res 69:137–143 Wallace BD, Wang H, Lane KT et al (2010) Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science (80- ) 330:831–835. https://doi.org/10.1126/science.1191175 Wang ZK, Yang YS, Stefka AT et al (2014) Review article: fungal microbiota and digestive diseases. Aliment Pharmacol Ther 39:751–766. https://doi.org/10.1111/apt.12665 Wang D, Ho L, Faith J et al (2015) Role of intestinal microbiota in the generation of polyphenolderived phenolic acid mediated attenuation of Alzheimer’s disease β-amyloid oligomerization. Mol Nutr Food Res 59:1025–1040. https://doi.org/10.1002/mnfr.201400544 Weingarden A, González A, Vázquez-Baeza Y et al (2015) Dynamic changes in short- and longterm bacterial composition following fecal microbiota transplantation for recurrent Clostridium difficile infection. Microbiome 3:10. https://doi.org/10.1186/s40168-015-0070-0 Westfall S, Lomis N, Kahouli I et al (2017) Microbiome, probiotics and neurodegenerative diseases: deciphering the gut brain axis. Cell Mol Life Sci 74:3769–3787 Whelan K, Quigley EMM (2013) Probiotics in the management of irritable bowel syndrome and inflammatory bowel disease. Curr Opin Gastroenterol 29(2):184–189 Young JC, Chehoud C, Bittinger K et al (2015) Viral metagenomics reveal blooms of anelloviruses in the respiratory tract of lung transplant recipients. Am J Transplant 15:200–209. https://doi. org/10.1111/ajt.13031 Yuan T, Ma H, Liu W et al (2016) Pomegranate’s neuroprotective effects against Alzheimer’s disease are mediated by Urolithins, its Ellagitannin-gut microbial derived metabolites. ACS Publ 7:26–33. https://doi.org/10.1021/acschemneuro.5b00260 Zmora N, Zeevi D, Korem T et al (2016) Taking it personally: personalized utilization of the human microbiome in health and disease. Cell Host Microbe 19:12–20

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Metagenomic Insights of Yarrowia lipolytica in Food Industry Ashok Bankar, Laxmi Jadhav, and Vrushali Phalke

Abstract

Yarrowia lipolytica is non-conventional dimorphic yeast that has been considered as generally regarded as safe (GRAS). This yeast has several biotechnological applications. Some special physiological and biochemical characteristics of this yeast make them eligible for food-related applications. This chapter focused on several food-related applications of Y. lipolytica. Applications of Y. lipolytica in meat, dairy products and cheeses manufacturing has been discussed in detail. The role of this yeast in the improvement of textures and flavours of several food products has been explained. Further Y. lipolytica has potential to produce valued-added products such as organic acids, aroma compounds, emulsifiers and biosurfactants which have food-related applications. A significant value of Y. lipolytica as a food supplement, single-cell protein (SCP) and single-cell oil (SCO) has been dealt in this chapter. At the end, the role of Y. lipolytica in bioremediation of food waste has been highlighted. Keywords

Bioremediation · Cheese · GRAS · Organic acids · SCO · SCP · Y. lipolytica

8.1

Introduction

Yarrowia lipolytica is a strictly aerobic, dimorphic and ascomycetous yeast species that has been studied well (Ruiz-Herrera and Sentandreu 2002; Coelho et al. 2010). This yeast has been used as a model organism to study dimorphism, i.e. yeastmycelium transition under different environmental parameters (Bankar et al. 2018a; A. Bankar (*) · L. Jadhav · V. Phalke MES Abasaheb Garware College Affiliated to Savitribai Phule Pune University, Pune, Maharashtra, India # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_8

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Kawasse et al. 2003). This yeast was named as Candida lipolytica previously. Further, it was seen that this yeast has the phylogenetic difference with Candida. Therefore, the yeast was again renamed as Y. lipolytica in honour of David Yarrow. The species name lipolytica has been given due to its ability of lipid hydrolysis (Nicaud 2012). Y. lipolytica has the ability to tolerate some physical parameters like low temperatures, high salt concentrations, heavy metals, acidic and alkaline conditions, etc. (Coelho et al. 2010; Bankar et al. 2018b). Y. lipolytica has many environmental and industrial applications reviewed previously. This yeast has been employed in bioremediation of environments contaminated with aliphatic and aromatic compounds, 2,4,6-trinitrotoluene, organic pollutants and heavy metals (Bankar et al. 2009). Recently, Y. lipolytica has received some special attention due to its inherent ability to produce some value-added products like lipases, proteases, phosphatases, inulinases, α-mannosidases, esterases, citric acid, aroma compounds, β-hydroxy butyrate, L-dopa, emulsifiers and polyalcohols. This yeast has been reported for high metabolites secretion efficiency, high productivity and reproducibility. These metabolites have been broadly used in food, environment, detergent and pharmaceutical industries (Zinjarde 2014; Coelho et al. 2010; Bankar et al. 2009). Y. lipolytica has been considered as a non-pathogenic fungus. This fungus is also considered as GRAS (generally regarded as safe) by Food and Drug Administration, USA (Groenewald et al. 2014). Thus, Y. lipolytica is considered a safe organism for its applications in the food industry. This yeast has been inheritably associated with various poultry, dairy and meat products. The yeast biomass has been utilized as a safe nutritional supplement and consumed as food and feed reviewed earlier (Zinjarde 2014). In this chapter, different food-related applications of Y. lipolytica have been discussed in detail.

8.2

Applications of Y. lipolytica in Dairy and Meat Products

Y. lipolytica strains have been employed in a variety of food products due to their high nutritional values. They have a significant value in dairy, meat and poultry products (Zinjarde 2014). Y. lipolytica has the ability to utilize different substrates such as galactose, lactose, citric acid and lactate. They have also potential to survive in extreme conditions such as low temperatures and high salt concentrations. The strong lipolytic and proteolytic activities of Y. lipolytica contributing texture and aroma development during the cheese-making process have been reviewed previously (Zinjarde 2014). Y. lipolytica has the ability to produce different types of lipases and esterases. Thus, lipolytic activities of this yeast have been considered to be a major contributor for cheese-ripening processes. A huge quantity of free fatty acids such as butyric, propionic, myristic, palmitic, strearic, oleic and palmitoleic acids are released due to lipolytic activities of Y. lipolytica. These free fatty acids are considered to contribute to the sensory characteristics of cheese. Conversion of tributyrin to butanoic acid flavours the variety of cheeses like Cheddar and Camembert cheese. Casein can be easily hydrolyzed by protease enzymes of Y. lipolytica.

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Hydrolysis of ⍺s1-casein and β-casein results in free amino acids and small peptides that have high significance in the blue cheese–making process (De Wit et al. 2005; Curioni and Bosset 2002). The cheese aroma is developed due to different types of volatile compounds formed after the action of microbial enzymes on lipids, proteins and lactose in the curd reviewed previously (Zinjarde 2014). Y. lipolytica strains are well known to produce some valuable volatile sulphur compounds (VSCs) that provide flavour to the cheese (Hébert et al. 2013). Debaryomyces hansenii and Y. lipolytica have been reported for their strong proteolytic and lipolytic activities. These two yeasts have been used for enhancing flavour to Cheddar cheese during maturation (Ferreira and Viljoen 2003). Lanciotti et al. (2005a) have evaluated four strains of Y. lipolytica as cheese-ripening adjuncts with milk culture. Fourier transform infrared (FTIR) analysis revealed that free fatty acids are released during the cheese maturation process due to their lipase activities (Lanciotti et al. 2005a). Y. lipolytica JII1c has been used in the manufacturing of a Dutch-type cheese. The lipolytic activity of this yeast generated a high amount of free fatty acids such as butyric, palmitic, myristic, oleic acids and stearic. Thus, Y. lipolytica JII1c strain successfully grew well in the cheese and enhanced the cheese-ripening process (Szoltysik et al. 2013). The potential of microbial adjunct cultures of Kocuria varians and Y. lipolytica strains has been evaluated for the manufacturing of the Tetilla cheese. The volatile compounds such as 3-methylbutanol, dimethyl disulfide and dimethyl trisulfide have been produced in a large quantity when the cheeses are ripened with only the K. varians adjunct. The amount of hexanoic and octanoic acids are found highest in the cheeses ripened with Y. lipolytica adjunct. The cheeses manufactured with both K. varians and Y. lipolytica adjunct cultures have developed the highest flavours (Centeno et al. 2017). It has been reported that Y. lipolytica and K. lactis showed strong proteolytic activities on skim milk agar. Y. lipolytica also showed the highest lipase activity on Tween 80 and on tributyrin. Free fatty acid, butanoic acid esters, and sulphur compounds produced in pasteurized milk were responsible for rancid and cheesy flavours. Thus, Y. lipolytica strains have been utilized as adjunct cultures in the manufacture of Arzúa-Ulloa and Tetilla cheeses (Atanassova et al. 2016). The utilization of lactate and amino acids is of high importance for aroma production during cheese ripening. A strain of Y. lipolytica has been isolated from cheese and evaluated for their ability to utilize amino acids and lactate. It was observed that this yeast has the ability to utilize high concentrations of amino acids and limited amounts of lactate (Mansour et al. 2008). The diversity of proteolytic and lipolytic activities has been studied in Y. lipolytica strains isolated from dairy products (Suzzi et al. 2001). Y. lipolytica has been isolated from Robiola di Roccaverano cheese as natural starter culture (Biolcati et al. 2020). Several yeast species have been isolated that were present throughout the ripening process of Pecorino Crotonese. All the strains of Y. lipolytica isolated have the ability to utilize citrate and lactate in the presence of 7.5% NaCl. These isolates displayed the highest proteolytic and lipolytic activities as shown in Fig. 8.1. They also have capability to catabolize tyrosine-producing brown pigment, phenylalanine, decarboxylating ornithine, lysine and tyrosine (Gardini et al. 2006).

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Fig. 8.1 Application of Y. lipolytica in meat and dairy products Table 8.1 Role of Y. lipolytica in the manufacturing of different cheeses Sr No. 1. 2. 3. 4. 5.

Cheese variety Robiola di Roccaverano cheese Tetilla cheese

Role of Y. lipolytica Detected as natural culture during cheese ripening Cheese ripening and flavouring

Arzúa-Ulloa and Tetilla cheeses Dutch-type cheese

Proteolytic and lipolytic activities Lipolytic activity

6.

Serbian and Croatian fresh soft artisanal Cantalet

Lipolytic activity Lipolytic activity

7.

Pecorino Crotonese

Dominant during cheese ripening

8.

Cheddar cheese

Proteolytic and lipolytic activities

References Biolcati et al. (2020) Centeno et al. (2017) Atanassova et al. (2016) Szoltysik et al. (2013) Golić et al. (2013) De Freitas et al. (2009) Gardini et al. (2006) Ferreira and Viljoen (2003)

Y. lipolytica CBS 2075 has been used for the cheese-ripening process. Cheese flavouring compounds such as sulphides, furans and short chains ketones were produced during the cheese ripening (Sørensen et al. 2011). Thus, Y. lipolytica has a significant role in the production of different cheese shown in Table 8.1. Lipases are monomeric proteins having molecular weight in the range of 19–60 kDa. The physical properties of the lipases mostly depend on the fatty acid chain length, degree of fatty acid unsaturation and the position of fatty acid in the backbone of glycerol. These characteristics contribute to the nutritional and sensory value of a triglyceride (Jaeger and Reetz 1998). Several lipases are involved in catalysis of a variety of valuable reactions including esterification (Sharma et al.

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2001). Lipases have been utilized in esters synthesis. These esters have been synthesized from short-chain fatty acids used as flavouring agents in the food industry. Immobilized lipases on silica and microemulsion have been widely utilized to produce esters (Sharma et al. 2001; Ghosh et al. 1996). Optimum lipase concentration, temperature, pH and emulsion content are of high importance to enhance the flavour and fragrance (Pandey et al. 1999). Butter fat treated with lipase enzyme has a wide range of applications in various food processes. Many lipase-modified food products such as bread, low-calorie health oils, EMC, nutraceuticals and chocolate with cocoa-butter substitutes have been produced (Uhlig 1998). Lipases are commonly utilized in the dairy industry for milk fat hydrolysis. Lipases are also used in the modification of the fatty acid chain lengths in order to maximize the cheese flavours and accelerate the cheese ripening with the lipolysis of fat, butter and cream (Sharma et al. 2001). Y. lipolytica strains have been used in a variety of foods due to their high nutritional significance. The flavours added to the dry sausages are because of organic acids secreted by microbes during the fermentation process. Several yeasts have been used for the ripening of dried fermented sausages (Andrade et al. 2006). Y. lipolytica strains have been isolated from the meat products that have the ability to secrete biotechnological value-added products including citric acid and lipase enzyme (Mirbagheri et al. 2012; Sanz et al. 2005). Yeasts have a significant role in the maturation process of dry cured meats in fermentations reported previously (Andrade et al. 2010; Patrignani et al. 2007). The predominant yeast species isolated from sausages were identified genera as Rhodotorula, Debaryomyces, Torulopsis, Hansenula and Yarrowia (Gardini et al. 2001; Grazia et al. 1986; Gianni et al. 2008). Due to their proteolytic and lipolytic activities, these yeasts displayed several applications in the food industry. Y. lipolytica strains isolated from chilled meat products displayed positive impact on sensory characteristics of fermented sausages (Iucci et al. 2007; Patrignani et al. 2007). Y. lipolytica strains have the ability to produce free fatty acids and further produce desirable flavouring compounds. Thus, they change the sensory characteristics of ripened products. The sensory impact is due to the production of some biomolecules such as aldehydes, ketones and lactones that are responsible for flavouring the final quality products (Fickers et al. 2005). Y. lipolytica Y16A has been evaluated for their lipase activity, free fatty acid and volatile compound production in pork fat–based medium. Thus, the release of free fatty acids and volatile compounds enhanced the sensory characteristics of pork fat. Y. lipolytica Y16A has been evaluated for their lipase activity, free fatty acid and volatile compound production in pork fat–based medium. Thus, the release of free fatty acids and volatile compounds enhanced the sensory characteristics of pork fat (Patrignani et al. 2011). Patrignani et al. (2011) have inoculated 35 different strains of Y. lipolytica in pork fat. After 7 and 21 days of storage at 15
C, free fatty acids were released and developed flavour. Thus, the study suggested that Y. lipolytica strains may be potential yeast in sausage making to improve the overall aroma.

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Use of Y. lipolytica for Organic Acids Production

Different types of organic acids such as citric acid (CA), isocitric acid (ICA), α-ketoglutaric acid (α-KGA), succinic acid (SA) and pyruvic acid have been produced by the Y. lipolytica as shown in Fig. 8.2. The overproduction of these organic acids is dependent on growth factors such as carbon source, mineral salts, thiamine and nitrogen (Otto et al. 2013). It has been studied that different combinations of carbon sources and limiting factors can enhance the organic acid production. For example, the excess carbon source combined with growth limitation by nitrogen triggers citric and isocitric acid production reported previously (Treton et al. 1978; Fickers et al. 2005). Citric acid (CA) has received much more attention due to its several applications in the food, pharmaceutical and chemical industries. Thus, demand for CA has increased worldwide. Every year more than 2 million tons of CA has been produced around the word for industrial applications. Y. lipolytica has been investigated as a promising candidate for CA production. The CA production by Y. lipolytica depends on some parameters such as medium components, type of the strain and cultivation conditions (Cavallo et al. 2017; Carsanba et al. 2019). Citric acid is an intermediate of the tricarboxylic acid (TCA) cycle found useful as a flavouring agent in a variety of foods including jams, jellies and candies, etc. It has also been employed as an antioxidant agent in the production of different beverages reported previously (Cavallo et al. 2017). For the production of CA, the mycelial fungus Aspergillus niger is most commonly employed. However, A. niger is investigated as an opportunistic pathogen which may cause allergic diseases and aspergillosis (Finogenova et al. 2005; Anastassiadis et al. 2008). Therefore, the non-conventional yeast, Y. lipolytica, has been investigated as an alternative source for the CA production.

Fig. 8.2 Different organic acid productions by Y. lipolytica

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This yeast has the ability to grow on a variety of cheaper substrates including glucose containing wood hydrolysates, glycerol containing waste from biodiesel industry, olive mill wastewater (OMW) reported previously (Morgunov and Kamzolova 2015; Papanikolaou et al. 2008), etc. Also, the other renewable raw material ethanol can be useful for CA production which needs high levels of zinc and iron ions. Maximum CA production by Y. lipolytica was 14.2–19.2 g/l when ethanol concentrations were used in the range of 0.01–1.0 g/l (Finogenova et al. 2002). Y. lipolytica SWJ-1b was also grown on a medium containing corn steep liquor (CLS) instead of yeast extract. It was observed that a medium containing 1.0 g/L of CSL showed a maximum 27.5 g/L of CA production by Y. lipolytica SWJ-1b. This yield was a 1.24-fold more than the control medium containing yeast extract. Thus, the addition of CSL in a medium showed an influence on the metabolism of Y. lipolytica SWJ-1b. The increasing activity of the key intracellular enzymes revealed that the pyruvate carboxylation pathway has been enhanced (Liu et al. 2015). Papanikolaou et al. (2008) cultivated Y. lipolytica ACA-DC 50109 on an OMW-based medium containing high glucose concentration (65 g/L) and CA was produced up to 28.9 g/L. Further, Rymowicz et al. (2010) revealed that raw glycerol is a low-cost waste product of biodiesel production. Thus, Y. lipolytica A-101-1.22 has been cultivated on raw glycerol and produced 112 g/L of CA. Y. lipolytica has been used to utilize the raw agro-industrial fat for the commercial production of citric acid. Thirty strains of Y. lipolytica have been tested for the production of CA on media containing rapeseed oil or animal fat as a sole energy and carbon source. Amongst the other strains, Y. lipolytica 187/1 showed highest CA production about 135 g/L on rapeseed oil under optimal conditions of cultivation (Kamzolova et al. 2005). Agricultural residues like Jerusalem artichoke tuber extract have also been utilized as a sole carbon source and produced about 68.3 g/L of CA studied previously (Wang et al. 2013). The mutant strain of Y. lipolytica NG40/UV5 has been used for CA production by utilizing renewable carbon substrates such as glucose, glycerol, ethanol, rapeseed oil, glycerol-containing waste of biodiesel industry and glucose-containing aspen waste reported previously. A high CA production was 100–140 g/L achieved when ethanol, raw glycerol and rapeseed oil have been used as substrates (Morgunov et al. 2018). Recombinant strains of Y. lipolytica have also been used to increase CA production. The strain of Y. lipolytica [H222- S4(p67ICL1) T5] possesses ScSUC2 gene obtained from S. cereviciae encoding invertase which produced 127–140 g/L CA at pH 6.0–6.8. Similarly, another recombinant (SUC+) strain of Y. lipolytica (A-101-B56-5) has been prepared for CA production (Förster et al. 2007; Lazar et al. 2011). The mutant strain of Y. lipolytica NG40/UV5 has been used for CA production by utilizing renewable carbon substrates such as glucose, glycerol, ethanol, rapeseed oil, glycerol-containing waste of biodiesel industry and glucosecontaining aspen waste reported previously. A high CA production was 100–140 g/L achieved when ethanol, raw glycerol and rapeseed oil have been used as substrates (Morgunov et al. 2018). Isocitric acid (ICA) is another most important intermediate of the TCA cycle produced by Y. lipolytica along with CA. ICA is widely utilized in the agriculture,

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medicine and food industries (Finogenova et al. 2005; Aurich et al. 2012). The ratio of ICA with CA is mostly dependent on the carbon sources such as n-alkane, ethanol, and oils. When these carbon sources are added in the growth medium, the equal amount of ICA and CA have been produced (Stottmeister et al. 1982; Barth and Gaillardin 1996; Fickers et al. 2005). The proportion of ICA has been increased when optimum pH value has been adjusted in between 5.5 and 6.0 with an increasing amount of ferrum ions and oxygen of 60–95% studied previously (Finogenova et al. 1991, 2002, 2005). A cheap crude glycerol has been used as a substrate for the production of ICA by using genetically modified strains of Y. lipolytica overexpressing Gut1 and Gut2. The modified strain of Y. lipolytica has been grown well on crude glycerol, and the highest amount of isocitric acid was 42.5  2.4 g/L (Rzechonek et al. 2019). The wild-type strain Y. lipolytica VKM Y-2373 and its mutant Y. lipolytica 704-UV4-A/NG50 were cultivated on rapeseed oil under optimal conditions for the production of ICA (Kamzolova et al. 2013). Wild-type strains of Y. lipolytica have been mutated with the treatment of UV irradiation and N-methyl-N0 -nitro-Nnitrosoguanidine (NG). The cultivation of the Y. lipolytica UV/NG mutant on a medium containing canola oil produced 88.7 g/L of isocitric acid with a yield of 90% (Kamzolova et al. 2015). A strain of Y. lipolytica VKM Y-2373 has been grown on biodiesel waste for ICA production. Under optimum conditions, the ICA was produced in a range of 58.32–90.2 g/L, the product yield (Y) by 40% reported previously (Morgunov et al. 2020). Succinic acid (SA) is well known as an antimicrobial, pH regulator and flavouring agent used in the food industry (Carlson et al. 2016). It has also been utilized for the production of some amino acids, antibiotics, vitamins and pharmaceutical products (Zeikus et al. 1999). Different strains of Y. lipolytica have been screened for SA production on ethanol-containing media with nitrogen-limiting conditions (Kamzolova et al. 2009). Further, the media containing glycerol (5.1 g/L) and media containing glucose (1.4 g/L) have been used for SA production (Kretzschmar 2010; Yuzbashev et al. 2010). Recently, two different genetically modified strains of Y. lipolytica (H222-AZ1 and H222-AZ2) have been created. SA production was enhanced significantly when the recombinant strain of Y. lipolytica H222-AZ1 was used for SA production under limited oxygen conditions (Jost et al. 2015). Similarly, another strain of Y. lipolytica PGC01003 has been metabolically modified to enhance the SA production (Gao et al. 2016). However, this strain has a low growth rate on the glycerol. Therefore, the glycerol kinase gene (gene GUT1) has been overexpressed in order to increase the glycerol uptake by PGC01003. Thus, the new strain has been named as RIY420 and the positive impact of the gene overexpression on SA production has been noticed (Ong et al. 2020).

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167

Production of Biosurfactants (BS) or Bioemulsifiers (BE) by Y. lipolytica

Y. lipolytica has the ability to synthesize biosurfactants and bioemulsifiers which are amphiphilic molecules. These molecules have surface activity and emulsifying properties and thus have several applications in the agricultural, food and beverage industries (Banat et al. 2000). They also have high importance in the stabilization of flavour oils as well as in bakery and dairy formulations (Kosaric and Sukan 2014; Kosaric 2001). There are several food products such as dairy-based products, fermented products, mayonnaise, bakery products, desserts and salads in which BS or BE has promising applications (Satpute et al. 2018). There are several strains of Y. lipolytica reported for their ability to produce different types of BS/BE reviewed previously (Bankar et al. 2009). The production of BS/BE by Y. lipolytica using different medium components and their applications are summarized in Table 8.2.

Table 8.2 Biosurfactant/bioemulsifier production by Y. lipolytica Sr no 1.

Strain of Y. lipolytica IMUFRJ 50682

2.

UCP 0988

3.

Y. lipolytica VEMS 51

4.

Y. lipolytica CTN-08, CTN-10, CTN-14, CTN-90, CTN-136

Residual glycerol as a carbon source

5.

Y. lipolytica MTCC 9520

Use of lipid waste, chicken tallow from slaughterhouses

Media or substrates Medium with CCAJ and raw glycerol

Emulsifier production in the presence of seawater with nitrogen and phosphate sources, diesel oil as a substrate Isolated from mangrove sediment samples, Yeast Malt (YM) agar used

Application Emulsification index: (68.0% and 70.2%). Surfactant tension of 18.0–22.0 Mn. m1 High emulsification activities (>5,4 UEA) after 168 h. Emulsification index: 51.7  1.1%, Cell surface hudrophobicity: 65  3.1%. Surface tension averaged 41.7 Mn.m1, Emulsification index reached to 56%. Emulsification activity: 55%, Surface tension decreased to 37 Mn.m1 at the end of 96 h.

References Fontes et al. (2012)

Souza et al. (2012)

Thenmozhi et al. (2018)

da Silva et al. (2020)

Radha et al. (2020)

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Use of Y. lipolytica as a Food Supplement

The production of β-carotene has received special attention due to an increasing demand for safer and more natural colorants, nutritional supplements, and antioxidants. It has several health benefits like preventing cancer, reducing macular degeneration and maintaining cognitive function. A recent study revealed that Y. lipolytica has a potential to produce a β-carotene. Y. lipolytica strain has been tested in micro-fermentations partly to perform media optimization study. The medium optimization increased the 50% yield of β-carotene (Jacobsen et al. 2020). Βeta (β) carotene is a red/orange pigment that gives bright colours to a variety of fruits and vegetables. It is also a well-known precursor of vitamin A (Grune et al. 2010). Natural food sources of β carotene including carrots, oranges, tomatoes, dark green leafy vegetables, pumpkins, spinach, etc., are well known. Daily requirement of β carotene is 4.0 mg/day for men and 4.4 mg/day for women to reduce the lung cancer risk. This daily requirement of β carotene is not fulfilled through diet, and thus, supplements are needed (Grenfell-Lee et al. 2014). It has been reported that genetically modified Y. lipolytica is an alternative source of β carotene food supplement (Grenfell-Lee et al. 2014). A recombinant strain of Y. lipolytica has been constructed in order to change their metabolic pathway, and thus, the resultant recombinant strain has been used to produce large quantities of carotenoids (Bailey et al. 2010). Genotoxic and sub-chronic toxicity tests have been carried out in order to prove that β carotene is a safe food supplement (Grenfell-Lee et al. 2014). Further, Y. lipolytica has been used as a vitamin B12 supplement. They are grown on biofuel waste materials in order to obtain vitamin B12-enriched yeast biomass. This yeast biomass can be a source of vitamin B12 for the people who do not consume meat or meat products. Yeast biomass can be also used as a feed supplement for livestock animals (Jach et al. 2020). The selenium-enriched biomass of Y. lipolytica has been considered as a novel food (NF). It is the dried and heat-killed selenium-enriched biomass that has been proposed as a food supplement. The selenium present in biomass is as safe as selenium from other dietary sources (EFSA 2020).

8.6

Application of Y. lipolytica as a Single-Cell Protein (SCP)

Single-cell protein (SCP) is a biomass or protein extracted from pure or mixed microbial cultures. The SCP includes dried cells of microorganisms such as yeast, fungi and algae that have been used as protein supplements for humans and animals. Due to increasing human population, protein shortage in diet has been seen, and therefore, SCP is considered as an alternative source of protein (Zinjarde 2014). Y. lipolytica has been used as SCP for many days. Many new strains of Y. lipolytica have been isolated which have the ability to produce a high yield of SCP when grown on a variety of substrates. The yeast cultures of Y. lipolytica have been grown on biofuel wastes in order to obtain protein-rich biomass. This protein-rich biomass has been reported as a safe food and feed supplement. Y. lipolytica A-101- strain has been used as SCP for the people who do not eat meat and meat products. Thus, SCP

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can be highly beneficial as a food supplement (Jach et al. 2017). It has been noted that 100 g powder of Y. lipolytica contains 50 g of proteins. The daily requirement of protein for human beings is 50 g that has been recommended by the European parliament and the council of the European Union (European Commission 2011). The SCP obtained from Y. lipolytica is also composed of balanced amino acids and safe levels of nucleic acid. Thus, due to high nutritional values, Yarrowia powder has been acknowledged as ‘complete protein’ (Jach et al. 2017). Y. lipolytica biomass has been produced after yeast cells grown on raw glycerol. Raw glycerol has a low value because of their high levels of impurities. This raw glycerol is the main by-product formed during biodiesel production. Newly isolated strains of Y. lipolytica that grow on raw glycerol and obtain biomass have enough nutritional content (Juszczyk et al. 2013). A recombinant antibacterial peptide gene has been expressed in Y. lipolytica and is successful in yielding simultaneous production of SCP. This recombinant yeast obtained displayed high levels of protein content (45–49 g% w/w) (Zhao et al. 2013). Y. lipolytica SWJ-1b strain obtained from marine fish gut has been used as SCP (Cui et al. 2011). In order to obtain SCP from the Y. lipolytica strain, the exo-inulinase gene from K. marxianus has been expressed in Y. lipolytica. Thus, obtained engineered Y. lipolytica has utilized inulincontaining substrates and produced 47.5% crude protein and 20.1 gL1 cell mass (Cui et al. 2011). Three different types of agro-industrial wastes such as sugarcane molasses, waste cooking oil and crude glycerol have been used to obtain SCP from Y. lipolytica. It was seen that sugarcane molasses was the most cost-effective medium and produced 151.2 g/L of SCP at 10-litre fermentation scale. In vivo oral feeding tests on fish showed that SCP is an excellent feed additive (Yan et al. 2018). Thus, several researchers revealed that the use of Y. lipolytica biomass is an alternative protein source/supplement for animal feeds (Patsios et al. 2020).

8.7

Use of Y. lipolytica as Single-Cell Oil (SCOs)

Several microorganisms have been reported for their capability to accumulate 20–80% more lipids than their biomass. These microbes including yeast, fungi, moulds, bacteria and algae are known as oleaginous microorganisms, and the oil obtained from these microbes is referred to as single-cell oils (SCOs) (Madani et al. 2017; Ochsenreither et al. 2016). These SCOs are intracellular storage lipids of cells containing triacylglycerol (Ochsenreither et al. 2016). SCOs are valuable sources of polyunsaturated fatty acids which are essential nutrients for human beings (Ochsenreither et al. 2016; Economou et al. 2011). Humans do not have the ability to produce essential polyunsaturated fatty acids, and thus, they are dependent on other food sources such as porcine liver and fish oil. Microbial SCOs obtained from oleaginous microorganisms are now being considered as a promising source for these essential fatty acids (Ochsenreither et al. 2016). Y. lipolytica is a non-conventional yeast, extensively studied and utilized for the synthesis of (Katre et al. 2012). It has been used as a model organism for bio-oil production and to study the detailed mechanisms of lipid accumulation. The reasons for selecting this yeast

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as a model organism are its capability of lipid accumulation and unique ability to utilize hydrophobic substrates more efficiently as a sole carbon (Beopoulos et al. 2009a, b). Although SCOs have received much more attention, their high fermentation costs prevent their further uses. To solve this problem, SCOs are produced by using low-cost substrates such as lignocellulose biomass, and sewage water reported previously (Huang et al. 2013). Other low-cost substrates were also investigated. Fontanille et al. (2012) valorized volatile fatty acids (VFAs) into microbial lipids by using Y. lipolytica. These VFAs have been produced by using agro-industrial wastewater, sludge and biodegradable wastes as an alternative carbon source in order to reduce the fermentation cost. Initially, Y. lipolytica was grown on glucose or glycerol and carbon sources (glucose/glycerol). After the utilization of these carbon sources, acetic acid was further added under nitrogen-limiting conditions. The study revealed that 40 g/L initial concentration of glucose gave 3 g/L biomass and 12.4 g/L lipid yield. Y. lipolytica is an attractive microbial source that has the ability to convert glycerol into oils via lipogenesis process. This process is a highly complex phenomenon and needs to be understood in detail (Dobrowolski and Mirończuk 2020). Decanter effluent oil from palm oil mills and glycerol from a biodiesel plant have been utilized as substrates (Louhasakul and Cheirsilp 2013). Further, food and municipal wastewater have been used as effective substrates for the production of SCOs (Chi et al. 2011). Y. lipolytica has been grown in hydrolyzed food waste. Y. lipolytica showed satisfactory growth on food wastewater. Further, five different strains of Y. lipolytica (NCIM 3229, NCIM 3450, NCIM 3472. NCIM 3589, NCIM 3590) were reported for more than 20% (w/w) of lipid accumulation in their dry cell mass (Katre et al. 2012). Few agricultural by-products have been utilized as substrates for the production of SCOs. Sugarcane bagasse is a useful substrate containing high levels of carbohydrates and low lignin content. It is low-cost lignocellulosic material and easily available (Pandey and Soccol 2000). Thus, sugarcane bagasse is an inexpensive substrate for the production of bio-oil. Sugarcane bagasse hydrolysate (DSCBH) has been utilized as an alternative carbon source by Y. lipolytica Po1g strain for the production of microbial oil and biodiesel (Tsigie et al. 2011). Wheat straw hydrolysate has been used for microbial oil production reported previously (Yu et al. 2011). Agricultural wastes like wheat straw and sugarcane bagasse can thus be easily available and inexpensive substrates for the production of SCOs. Papanikolaou and Aggelis (2010) have used Y. lipolytica as a model organism for the production of tailor-made lipids. In their study, Y. lipolytica ACA-DC 50109 strain has been used for the accumulation of fat. This strain accumulates high levels of stearic (18:0) acid inside the cells and is further used to produce cocoa butter substitutes (Papanikolaou et al. 2003). This strain of Y. lipolytica was further grown on a mixture of animal fat, glucose and raw glycerol to orient the cellular metabolism towards lipid accumulation. When glycerol and stearin were utilized as co-substrates, the lipid production was increased (Papanikolaou et al. 2002). The microbial SCOs have been used as dietary supplements which are enriched in ɣ-linolenic acid, arachidonic acid and docosahexaenoic acid (Ratledge 2010). Conjugated linoleic acid has anti-

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carcinogenic, anti-diabetic, anti-atherogenic, anti-obesity and anti-inflammatory properties in humans reported previously (Zhang et al. 2012). Thus, Y. lipolytica can be the best source of SCOs in future. Further study is of high importance in order to understand the novel applications of SCOs. Lipid biosynthetic pathway modification has the potential to enhance the lipid accumulation by Y. lipolytica. Three diacylglycerol acyltransferases in Y. lipolytica are encoded by LRO1, DGA1 and DGA2 genes involved in lipid synthesis. A strain of Y. lipolytica JMY3580 has been created by overexpressing the DGA2 gene in Q4 strain (dga1 Δ dga2 Δ lro1 Δ are1 Δ). Thus, the reconstruction of triacylglycerol synthesis pathway has enhanced lipid accumulation significantly. A strain of Y. lipolytica JMY3580 showed accumulation of over 40% of lipids in biomass when cultivated on a glycerol-based medium. A wild type of strain could produce only 20% of lipids. Thus, the genetically engineered strain of Y. lipolytica has the higher lipid-producing ability (Gajdoš et al. 2017). The gene YALI0E16797g (LRO1) encoding a major triacylglycerol synthase of Y. lipolytica has an importance in the acylation process. Overexpression of LRO1 gene in Y. lipolytica produced higher lipid content as well as lipid yield (Amalia et al. 2020). The gene EeDAcT encoding the diacylglycerol acetyltransferase has been overexpressed in strains of Y. lipolytica. It was seen that the strain YL33 accumulated 20% of intracellular lipids. The fatty acid profile revealed that lipids were rich in oleic (45%) and palmitic (25%) acids (Gajdoš et al. 2020).

8.8

Production of Aroma Compounds by Y. lipolytica

Lactone flavours with milky, coconut, fruity and other aromas are extensively used in the food and fragrance industries (Marella et al. 2019). Human beings have been using the flavours and fragrances that are extracted from plants and essential oils (Moradi et al. 2013). Lactones are flavouring and aroma constituents found in several plant volatiles reported previously. Several microorganisms have the ability to synthesize such flavour compounds (Schrader 2007). ɣ- Decalactone, a lactone of 4-hydroxydecanoic acid, is of industrially important flavour compound having a peachy aroma. Such flavouring agents have been approved by FDA as a food additive (Zinjarde 2014). Y. lipolytica is reported for the production of ɣdecalactone (Pagot et al. 1997). First study on ɣ-decalactone accumulation in Candida species is reported by Okui et al. (1963). Further, it has been reviewed the production and biological pathway of Y. lipolytica required for the production of ɣ- decalactone. They reviewed the importance of β-oxidation genes, size of substrate droplets and other parameters involved in lactone production (Waché et al. 2003). In most of the biochemical processes, ricinoleic fatty acid are converted into ɣ-decalactone via β -oxidation studied previously (Moradi et al. 2013). Castor oil possesses 85% of ricinoleic fatty acid. Therefore, castor oil and castor oil hydrolysates have been utilized as cheap substrates for ɣ-decalactone production at the industrial level (Gomes et al. 2010; Maume and Cheetham 1991). Fed batch and batch cultivation of Y. lipolytica DSM 3286 on castor oil has been carried out for

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ɣ-decalactone production. It is noticed that the initial yields of ɣ-decalactone were found to be 65 and 70 mg L1 and further increased to 220 mg L1 when fed-batch fermentations were employed (Moradi et al. 2013). Similarly, constant medium feeding rate and intermittent fed batch fermentation strategies have been employed in order to increase the production of ɣ-decalactone by process parameter optimization reported previously (Gomes et al. 2010). It has been reported that lactones have a toxic effect on the growing cells resulting in low yield (Waché et al. 2003). To solve this issue, immobilization of cells has been investigated as a simple and cost-effective alternative (Braga and Belo 2013). It was seen that oxygen is one of the limiting factors for the production of ɣ-decalactone. Oxygen transfer rate is mostly dependent on temperature, agitation, pressure, fluid physical properties and volumetric mass transfer coefficient (kLa) (Zinjarde 2014). Therefore, the impact of kLa on ɣ-decalactone production has been studied. At kLa of 70 h1, the maximum aroma compound was 141  21 mg L1 reported in previous study (Gomes et al. 2007). Further, it has been noticed that along with process optimization, genetic modification of cells can also be beneficial in order to enhance the productivity of aroma compounds. Guo et al. (2012) studied that the enzyme Aox3 (product of POX 3 gene) degrades lactone in Y. lipolytica CGMCC 2.1405 strain. Thus, the other study also revealed the involvement of POX3 gene product in reducing the ɣ-decalactone productivity reported previously. Therefore, the disruption of short-chain-specific acyl CoA oxidase (Aox-3) encoding by gene POX3 has reduced the lactone degradation (Waché et al. 2003). In addition to ɣ-decalactone, Y. lipolytica has been reported for the synthesis of another flavouring compound, i.e. 2-phenylethanol (2-PE) that possess a rose-like odour. Y. lipolytica NCYC 3825 strain has been used for 2-PE production (Celińska et al. 2013). The 2-PE has high importance in cosmetics and food production. Thus, a study revealed that Y. lipolytica is a novel source of 2-PE. Green-note compounds like C6 aldehydes with distinctive grassy and green aroma have been produced by Y. lipolytica (Chen et al. 2015). Further, hexanal and trans-2-hexenal (C6 compounds) are produced by genetically modified Y. lipolytica when grown on an olive oil substrate (Bourel et al. 2004). A genetically modified strain of Y. lipolytica has been used for γ-dodecalactone production from oleic acid. Similarly, δ-decalactone production has been produced from linoleic acid. It was seen that metabolically modified strain has improved the titer of γ-dodecalactone fourfold, and thus, 282 mg/L γ-dodecalactone was produced in a fed-batch bioreactor (Marella et al. 2019). β-ionone is an apocarotenoid derived from carotenoids (C40 terpenoids) and has a woody, warm and violet-like aroma. These aroma compounds are extensively utilized in food and cosmetic industries (González-Verdejo et al. 2015). Thus, the demand of β-ionone production is increasing continuously (Beekwilder et al. 2014). Chemical methods are used for the commercial production of β-ionone. However, chemical methods are not eco-friendly and generate undesirable by-products (Nacke et al. 2012). Therefore, microorganisms are an alternative natural source for β-ionone production. The use of ‘generally recognized as safe’ (GRAS) microbes have a significant role in transforming the raw materials into aroma compounds

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(Berger 2007). The GRAS, non-conventional yeast, Y. lipolytica has been employed for the terpenoids production because of their own endogenous mevalonate pathway (MVA) and the ability of high lipid production (Abdel-Mawgoud et al. 2018; Ma et al. 2019). Recently, this yeast has been genetically modified for monoterpenoids production such as limonene (Cao et al. 2016) and linalool (Cao et al. 2017). They have also been used for the production of sesquiterpenoids (Guo et al. 2018) and tetraterpenoids, such as lycopene (Nambou et al. 2015), β-carotene (Larroude et al. 2018) and astaxanthin (Kildegaard et al. 2017). The carotenogenic genes such as carB and bi-functional carRP have been obtained from Mucor circinelloides, and carotenoid cleavage dioxygenase 1 (CCD1) from Petunia hybrida have been expressed in Y. lipolytica. Further, a modular engineering strategy has been used for the overproduction of β-ionone in Y. lipolytica. In this strategy the cytosolic acetyl-CoA supply and MVA pathway flux were enhanced. A β-ionone yield obtained was 358 mg/L in shake-flask fermentation and approximately 1 g/L in fed-batch fermentation processes reported earlier (Lu et al. 2020). A push-pull strategy has been used in order to derive a genetically modified strain of Y. lipolytica PO1f. The β-ionone production in shaking flasks was 68 mg/L while 380 mg/L was observed in a 2 L fermenter (Czajka et al. 2018). Thus, Y. lipolytica has the ability to produce different types of aroma compounds as shown in Fig. 8.3.

Fig. 8.3 Different aroma compounds produced by Y. lipolytica

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Food Industry Waste Degradation by Y. lipolytica

With an increasing world population, high demand for food production has also risen. This consequently results in food wastage in large quantity (Ravindran and Jaiswal 2016). Effluents of the food industry have become a serious environmental issue. Therefore, bioremediation is an attractive biological method that has been preferred for the treatment of contaminated environments (Dunoyer et al. 2020). Several oily wastes from olive mills, palm oil mills and other food wastes have been treated by using Y. lipolytica (Bankar et al. 2009). The main purpose of the food industry waste treatment was to complete waste degradation and reduce the COD with obtaining some value-added products (Zinjarde 2014). Oily wastewater pollution may cause serious environmental problems due to its high COD levels. Oil mill wastewater has been utilised as a sole carbon source by Y. lipolytica to secrete citric acid as a value-added product (Papanikolaou et al. 2008). Thus, after treatment the COD and BOD values of palm oil mill effluent have been reduced significantly (Oswal et al. 2002). Genetically modified Y. lipolytica displaying lipase activity has been used for the treatment of oily wastewater. This genetically modified yeast has removed about 96.9% of oil and 97.6% of COD within 72 h (Song et al. 2011). Similarly, other strains of Y. lipolytica have been employed for the treatment of oil mill wastewater (Lanciotti et al. 2005b). Agro-industrial wastes are also a major serious issue for several industries. Many yeasts have been isolated from agroindustrial wastes employed to remove environmental pollutants and also to obtain some value-added products. It was reported that Y. lipolytica M1 and M2 strains have produced 11 and 8.30 U/ml of industrially important lipase enzymes, respectively. They also produced about 27 and 8 g/L citric acid (Mafakher et al. 2010). Other food wastes such as pineapple, vegetable oil refinery and industrial fats are alternative substrates to obtain some value-added products (Imandi et al. 2008; Rufino et al. 2007; Papanikolaou et al. 2002). Enzymatic extract of Y. lipolytica ATCC 9773 has been used as a potential agent for bioremediation of fat in dairy waste. Different concentrations of enzymatic extracts have been employed in a fermentative medium. It was seen that 82.88% of fat was removed. Further, it was noticed that BOD, COD and total solids were reduced by 43.32, 44.3 and 13.58%, respectively, at pH 5.0 for 32 h of fermentation. Thus, Y. lipolytica has been found to be an effective potential candidate for removal of fat from the dairy waste (Dunoyer et al. 2020). The biodegradation of cooking fats such as butter and olive oil has been carried out by a strain of Y. lipolytica LFMB 20 and Bacillus spp. and Pseudomonas putida CP1 strain. These microbes were cultivated on an enriched medium added with ca 0.85% w/v of waste fat. At the end of the fermentation process, it was observed that Y. lipolytica removed ca 90% of the fat and the bacteria removed ca 95% of both fats reported previously (Tzirita et al. 2018). A strain of Y. lipolytica has been used to obtain the fatty acid ethyl esters from dextrose and vegetable cooking oil as a model food waste. The modified strain of Y. lipolytica has revealed 24-fold more fatty acid ethyl esters by utilizing vegetable cooking oil. Thus, Y. lipolytica is found to be an effective agent for the degradation of food waste (Ng et al. 2020) as shown in Table 8.3.

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Table 8.3 Degradation of different food waste by Y. lipolytica Sr No. 1. 2.

3. 4.

5. 6.

Food waste Oily wastewater Oil mill wastewater (OMW) Oil mill wastewater Palm oil mill effluent (POME) Crude coconut fat Agro-industrial waste

7.

Pineapple waste

8.

Vegetable oil refinery waste

9.

Industrial fats

8.10

Comments Lipase displaying arming yeast was created. It removed 96.9% oil 97.6% COD. Y. lipolytica strains Y17, PO1, B16, C11, Y9 have been used for the treatment of OMW.

References Song et al. (2011) Lanciotti et al. (2005a, b)

Y. lipolytica ACA-DC-50109 has been used to treat OMW and produce citric acid Y. lipolytica NCIM 3589 reduced the COD and BOD values of POME.

Papanikolaou et al. (2008) Oswal et al. (2002)

Y. lipolytica R013 cultivated on crude coconut fats and produced Lauric acid. Y. lipolytica M1, M2 strains isolated from agroindustrial wastes removed pollutants and also produced citric acid and lipase as value-added products. Y. lipolytica NCIM 3589 has been used in SSF with pineapple waste as a sole substrate and produced 202.35 g of citric acid per kg of pineapple waste at optimized conditions. Candida lipolytica UCP 0988 cultivated on vegetable oil refinery waste and produced 8 g/L biosurfactant. Y. lipolytica ACA-DC 50109 cultivated on industrial fats to yield SCO.

Parfene et al. (2013) Mafakher et al. (2010)

Imandi et al. (2008)

Rufino et al. (2007) Papanikolaou et al. (2002)

Conclusions

Y. lipolytica has received a GRAS status governed by the Food and Drug Administration, USA. Therefore, this yeast has significance in food-related applications. Y. lipolytica is inheritably associated with poultry, dairy and meat products. Thus, this yeast has a variety of uses in dairy, meat and poultry products. Different strains of Y. lipolytica strains have been used in various food products due to its nutritional values. This yeast has been used as a safe nutritional supplement, SCP, SCOs. The strong lipolytic and proteolytic activities of Y. lipolytica contribute texture and aroma development during the cheese manufacturing. It is also concluded that the lipolytic activities of Y. lipolytica have been considered to be a major contributor for cheese-ripening processes. Several free fatty acids such as butyric, propionic, myristic, palmitic, strearic, oleic and palmitoleic acids have been released during lipolytic activities of Y. lipolytica. These free fatty acids are responsible for the sensory characteristics of cheese. Further, the flavours added to the several food products are due to the ability of Y. lipolytica to produce organic acids such citric acid (CA), isocitric acid (ICA) and

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succinic acid (SA). These organic acids have several applications in the food industry. Y. lipolytica also has the ability to produce biosurfactants and bioemulsifiers that are extensively used in food and beverage industries. Y. lipolytica has the ability to produce aroma compounds such as γ-dodecalactone that have milky, coconut, fruity, and other aromas. These aroma compounds are extensively used in the food and fragrance industries. Effluents generated from different food industries are a major environmental issue. Y. lipolytica has been used for the treatment of several oily wastes from olive mills, palm oil mills and other food wastes. Thus, it is concluded that Y. lipolytica has several food-related applications and also plays a significant role in food waste management. Acknowledgement All authors are thankful to DST, New Delhi, India for financial assistance in the form of a major project (DST-SERB file no. EEQ/2018/001202).

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Foodomics: The What, Why and How of It Malathi Srinivasan

Abstract

Foodomics is a science of this millennium and encompasses the advanced technologies of omics in the field of food science. With change in food habits and food culture, increased transportation of food, processing and storage, the quality of food is always a cause of concern. While traditional food testing laboratories depended on chemical analysis of the food samples, recent trends in omics allow to understand and comprehend the changes in the food at the genomic, transcriptomic and metabolomic level. These techniques, apart from assuring quality of the food, also contribute towards food safety methods in terms of checking for adulteration and identification of food spoilage. These are very important to ensure prevention of food borne pathogens and diseases. Foodomics also helps in profiling a food sample for all its macro and micro constituents, in ascertaining the role of nutraceuticals and functional foods in human health, in the functionality and metabolism of food, in safety and traceability of foods. While there are many research publications on the various aspects of food where foodomics can be applied, this chapter is an effort to provide, in a concise manner, the advantages of this new field and its applications in the area of food science and technology. Keywords

Food · Safety · Quality · -omics · Chromatography · Capillary electrophoresis

M. Srinivasan (*) Central Food Technological Research Institute, Mysore, India e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2020 S. Singh (ed.), Metagenomic Systems Biology, https://doi.org/10.1007/978-981-15-8562-3_9

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Introduction

This millennium has seen the emergence of several modern technologies in science, almost in rapid succession. This has further led to the christening of many new areas that evolved in science, which adopted these technologies. The catchiest term that resonates in every modern biologist’s laboratory is “omics”. Starting with the study of the genome, the proteome, the metabolome, the transcriptome and the microbiome up to the study of food, omics has invaded every possible phase of the living system. One would be surprised to note that the omics called metagenomics had evolved much earlier than most of these omics in the late 1990s. It dealt with the collection and sequencing of the genes from an environment instead of the genes from a single genome, and the advantage of this kind of study was the lack of the necessity to isolate and culture individual species in a laboratory setup, which is both timeconsuming and labour-intensive. In the food arena, this omics finds extensive application in the areas of microbiology and food toxicology, where it will help to expedite the studies on a bigger sample than individual samples. Metagenomics uses the shotgun approach, where the entire sample matrix gets sequenced (Ferrocino and Cocolin 2017). This could lead to certain nonspecific and redundant data regarding the sample, as the data pertains to all the material in the sample – living, active and dead. Hence, application of this technique should be done with caution, as it could give spurious results when accuracy regarding the sample is desired. However, despite the drawback, this field is gaining importance, especially when coupled with other sophisticated approaches like bioinformatics and big data analyses. Systems biology is another upcoming field, which provides an integrated approach to understand holistically and in an interdisciplinary manner the sample that is being studied, which could be a cell, tissue, organ or even food! This approach is inclusive of the molecules, cells, organs, individuals and ecosystems and provides data pertaining to various levels of gene expression – viz., mRNA, protein and the metabolite. Instead of dealing with the individual results from these techniques, it takes the global data sets into consideration to derive conclusions (Garcia-Canas et al. 2012a). However, this aspect has both pros and cons – while the huge amount of data can help provide the researcher with even the minor details in each level of expression (i.e., the transcriptomics, genomics, proteomics and metabolomics), it could also provide unwanted noise, masking the relevant and more important data sets. Nevertheless, such unwanted noise generated at each level in each platform can be filtered by focusing only on those endpoints that are common between the various platforms, and this can be achieved by using appropriate statistical models (Mutch et al. 2005). Amongst the omics, foodomics is a fairly recent term. Coined in 2009 and getting popular at an international conference in Cesena, Italy, this discipline deals with the integrated application of various omics technologies in the realm of food and nutrition, in such a way that it confers better health, improved knowledge, and overall well-being of its consumers. This discipline integrates several basic fields like food chemistry, biology, toxicology, epidemiology and data analyses and the

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modern fields like genomics, transcriptomics, proteomics and metabolomics. It finds application in newer fields such as epigenetics and customised nutrition, as it encompasses the recent branches of food science like nutriomics, nutrigenetics, nutrimetabolomics, nutritranscriptomics, nutriproteomics and metagenomics. The use of systems biology in foodomics is still quite scarce, although insights from other similar disciplines where it has been applied will provide cues for its effective application in foodomics in the future. In recent times, foodomics is being seen as a potential harbinger in the prevention of many non-communicable diseases. This is because every individual is metabolically unique and one size won’t fit all! Each individual’s metabolic phenotype is but a complex interplay of his diet, his gut microbiome, his genetic composition and the environment he is exposed to. Hence, tackling an individual’s health condition at some point needs to be personalised or custom-made, which is where foodomics comes to play. With its multiple branches, foodomics has made possible the tracking and identification of an individual’s gene metabolite, diet metabolite and other genediet interactions (Vimaleswaran et al. 2015). With the individual’s complete data, including the genetic information in hand, a complete understanding of the human gut microbiome, its diverse gene sets, its microbial flora and their cause-effect with respect to disease aetiology sourced from metagenomics data helps in providing a strategy for disease diagnosis and a potential preventive care for diseases like obesity, cardiovascular disease and diabetes. Thus, the use of metagenomics, in combination with other omics approaches like metatranscriptomics, metaproteomics and metabolomics (Wang et al. 2015), could revolutionise modern strategies in disease alleviation. A lot has been researched and reported on foodomics, despite it being a juvenile area in food science. Several books and book chapters apart from review articles in reputed journals have dealt with various omics approaches in food science and nutrition over the last decade. Multiple articles dealing with specific scientific studies on the genomics or proteomics or the metabolomics of food are scattered throughout the literature. Several special journal issues and foodomics-themed books are available to provide the nitty-gritties of each and every omics approach. This chapter is only an attempt to compile these reports, review articles and book chapters and provide the quintessence of foodomics – the what, why and how of it.

9.2

Foodomics: The What of It

As mentioned earlier, foodomics was first defined in 2009 by a group in Spain at the Instituto de investigación en Ciencias de la Alimentación (CIAL) and found its first mention in the Journal of Chromatography (Cifuentes 2009) as a discipline that studies the domains of food and nutrition through the application and integration of advanced omics technologies in order to improve the wellness and health of its consumers. Soon, the use of mass analysis techniques to study various aspects of food and nutrition like bioactivity, safety and quality at the molecular level became possible. Well, given that food is the basic succour of our very existence and has

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been around for aeons since the very beginning of life on this planet, as we all know from the biblical reference of the “forbidden apple”, what is the sudden hype about the science around it in this millennium? Wasn’t the food consumed all these years nutritional? Wasn’t the food healthy? Was the food causing morbidities? Were our ancestors actually not eating “good food”? The answers to these queries are simple – times have changed and so should the concept of food and the science of food. Today, due to the exploding global population, reduction in arable lands where each can cultivate their own food, increased industrialisation, improved food processing methods and change in our lifestyles and eating habits, it is no longer a mandate to prepare every day’s meal within the confines of one’s home. Food can be “bought” according to one’s choice. Processed foods, fast foods, restaurant foods and takeaways are growing trends these days, where quality could be compromised. Hence, food is looked at as the main causative for all our health issues; food also serves as the vehicle for many pathogens that find their way into the human gut; food has failed to abide by the nutritional requirements; food doesn’t satisfy the norms of safety and quality; food can cause as much harm as it can cause good. This is why a whole new field was born to address the multifarious issues that surround food, its quality, nutrition and safety. Today, food safety regulators, nutritionists, dietitians, microbiologists, gastroenterologists, general physicians, scientists and even epidemiologists depend on foodomics data. It is being considered important in overcoming public health problems, wherein it can help in designing novel strategies to manipulate phenotypic changes through diets (Mahmoodpoor et al. 2020), as in the case of the various diets targeted to alleviate obesity/promote weight loss. For example, Atkin’s diet and keto diet are gaining popularity amongst the health conscious and celebrities, while the Mediterranean diet and the DASH diet (Dietary Approaches to Stop Hypertension) have been tested on several populations in randomised controlled trials as a public health solution to control hypertension and cardiovascular disease risks. Such studies over the years and their metadata analyses have provided important insights into the cause and effect of food on health. Food is both the reason and the remedy for health problems. Foodomics is the holistic study of the foodome using modern approaches. Foodome is the food sample under investigation and the microcosm around it that interacts with the food. Simple as it may sound, the new findings and developments that have come out of this research area actually show the complexity of this discipline. It encompasses a wide array of subjects and specialisations that makes it extremely complex to comprehend and conclude. Food by itself includes quality, nutrition, safety (from chemical contaminants, adulterants and/microbes), toxicology (allergies), bioavailability, metabolism, effect of excess or deficiencies of nutrients on health, etc., while the microcosm around it includes much more. The collective interplay of these factors impacts our health and well-being, and it is an arduous task to compile, comprehend and conclude from all these data. Appreciation of the importance of food in health requires an elementary and alimentary approach to understand its role in the cause and cure of diseases. The fact that food can be medicine is seen from the market space for bioactives, functional foods and nutraceuticals in recent times. In fact, a 2017 book entitled Awakening from

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Alzheimer’s claims that even Alzheimer’s is reversible by proper and careful use of food and nutraceuticals (Sarlin 2016). Several such health claims are rampant with nutraceuticals, and this is possible from the foodomics data. Since foodomics involves high-throughput studies, mostly at the molecular level with sophisticated precision instruments for analysis, like mass spectrometry and capillary electrophoresis, data acquisition is huge and requires careful analyses and interpretations. This branch of food science has immense potential in better understanding several areas like the bioactives in food; food microbiology; the mycobolome, in situations where mycotoxins are involved; chemical contamination and illegal additives in food; food safety; quality and traceability; impact of food on human nutrition and physiology; food allergens; novel components in functional foods and alternate medicine; and food nanotechnology for increased bioavailability – to name a few.

FOODOMICS

Mycobolome

Genomics

Proteomics

Toxicology

Nutriomics

9.3

Metabolomics

Foodomics: The Why of It

Food is the very breath of human existence. It is required for nutrition and good health. However, it can also be the cause for several problems, if handled/consumed carelessly. Food is known to cause health issues ranging from the common food allergy, food poisoning, nausea and indigestion to inflammatory bowel disease (IBD) and the gluten-sensitive Crohn’s disease to non-communicable morbidities like diabetes, CVD and obesity to even more serious issues like cancer and Alzheimer’s disease. In short, food is the major cause for most health conditions, the other causes being genetic inheritance, environment and accidents. Strangely, food was not seen so critically as a science of medical importance, until recently.

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Hence, recent advances in food research opt for more stringent analytical and testing methods.

9.3.1

How Are the Various Omics Possible with Food?

Since food could be plant-based/animal-based and raw/cooked, there is always some DNA present in food samples to perform genomics studies. Barring some highly processed and refined foods like sugars, oils, purified extracts like flavours and other inorganic constituents like salt, water, etc., all foods contain DNA as they contain cells. While raw diets have more of the nucleic acid material, cooking could denature some part of the DNA. It is reported that the plum fruit has the maximum DNA followed by lettuce. Apple has a bigger genome size than humans! But once these are consumed, our body breaks down the foreign DNA and ensures it does not affect our genetic make-up. Cooking and processing denatures most of the DNA present or fragments them. However, there is enough DNA present in the food samples to conduct the genomics and transcriptomics research. Chemical constituents like flavonoids and polyphenols help to perform the metabolomics, which involves high-throughput assays, nuclear magnetic resonance (NMR) and LC/MS, GC/MS and HRMS techniques in targeted and/untargeted shotgun approaches. The large amounts of data thus acquired are analysed using sophisticated statistical tools and interpreted. A group at the Ohio State University headed by Dr. Jessica Cooperstone is using metabolomics to understand the health-promoting compounds in fruits and vegetables. Unlike studies in the last millennium which depended on isolation, purification and identification of compounds present in an extract, like, say, curcumin in turmeric or piperine in pepper, the current trend is to employ a bottom-top approach. All the compounds in the extract are first identified based on their masses, and then the interesting compounds are separated and purified for further characterisation. Metabolomics is also used to detect changes postprocessing of food, adulterations, changes in nutrition profiles, changes in flavour compounds, etc. and also of the impact of the dietary compounds on the human metabolome, which will take into consideration the analyses of blood, urine and faeces by these techniques. One such study was on the effect of certain phytochemicals/bioactives from tomatoes in a non-melanoma skin cancer model, showing the medical application of metabolomics in food. On the other hand, these data have also helped plant breeders to try new plant varieties with increased bioactives that are of health benefit. Amino acid analyses help in proteomics studies. While the cooking temperatures decide the denaturing of amino acids, most of it is generally retained. Apart from the proteomics of the food per se, the proteomics post-food consumption of the consumer is critical. This helps in understanding the role played by the bioactives as signals in the transcription and translation processes resulting in the gene and protein expressions and their functions, which in turn decide the disease and health condition of the individual. Proteomics helps in deciphering the signalling pathways and cellular processes targeted by the bioactives in the food and in providing clarity on

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the functional mechanisms and intermediate molecules involved (Valdes et al. 2017). Thus, food samples – raw or cooked or processed – and their ingredients can be subjected to most of these omics studies.

9.3.2

Application of Foodomics

9.3.2.1 Food Contamination and Foodomics Contamination of food has been an issue from the time food has been consumed by humans. It was more during the cave man times, as the food eaten was mostly raw, which allowed for the incidence and growth of microorganisms. However, with the advent of fire and cooking practices, the microbial load on food decreased to a significant extent, as most microbes were killed at the cooking temperatures. With changing times, there is a trend to link food and health, and food is also seen as a cause and cure for diseases. This has resulted in the consumers and the food regulators taking cognisance of the various aspects of food safety, traceability and food quality. Food laboratories which were relying on traditional and classical detection methods have upgraded themselves to use more sophisticated instrumentation and modern analytical techniques and appropriate protocols that provide data with more sensitivity, precision, speed and specificity in order to meet the requirements of the new European regulations in the EU countries (Regulation EC 258/97; EN 29000 and subsequent issues), the Nutrition Labeling and Education Act in the United States, the Montreal Protocol of Canada and the FSSAI Regulations in India. Despite these advancements in food analyses, there are still issues that are unresolved. We still have hundreds of food-borne infections globally, arising from the rapid globalisation and the resulting movement of food and raw materials around the world. Most processed foods incorporate ingredients shipped from different parts of the world, making food safety, quality and traceability more complicated. This has also led to transhipment of pathogenic strains from one port to another. In recent years, there have been several episodes of food contamination across the globe, e.g. generic E.coli and Salmonella contamination in spinach. Microbiologists, in an attempt to detect pathogens in foods, have developed robust and reliable culture-based techniques, but although they are the “gold standards” in detection, they are laborious and time-consuming (Garcia-Canas et al. 2012a, b). Gene-based omics techniques have now made it possible to give more specific and sensitive tests for the detection of microbial pathogens. Today, it is possible to identify the microbe present in the food matrix to the precision of its species level based on the genetic sequence information. Apart from the identification of the organism, omics studies of the pathogens and of the food matrix that is contaminated with the pathogens provide information on the microbial activities following infection (Giacometti and Josic 2013). There are several microbial markers that indicate contamination of food. PCR-based detection of microbial contamination actually paved the way for these advanced omics technologies. Later, it was possible to understand the genome, proteome, lipidome and

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metabolome of the pathogens and their metabolites in the contaminated food matrices that helped in comprehending the activities during infection and possible outbreak of diseases (Giacometti and Josic 2013). DNA microarray technology, wholegenome sequencing (WGS) and RNA-Seq-based genomics and transcriptomics, GC-MS-based metabolomics, LC-MS/MS and HRMS-based proteomics and lipidomics have been used to detect microbial contamination in food and to detect changes in food during processing (Gajdosik et al. 2013). Foodomics has also allowed the possibility of having model microorganisms and their simulated data in stress conditions to compare and correlate with real-time food samples. This allows the rapid identification of the microbial contaminants and their further characterisations. It can be appreciated that these advanced rapid sensitive methods are very essential, since it has been observed that the food-borne pathogens like bacteria and fungi can contaminate the food at any stage from production to consumption, besides being resilient and resistant to harsh treatments during processing, making food safety from microbes a very complex problem (Martinovic et al. 2016). Food safety is also closely aligned to quality control and traceability and authenticity. Metabolomics approaches are useful in these studies to identify specific metabolites and biochemicals (Xu et al. 2015; Cubero-Leon et al. 2014). Food toxicology is another aspect under this section, where it deals with the bacterial and fungal toxins formed extracellularly due to the contaminating microflora, and it forms an important public health issue (Kaferstein and Abdussalam 1999). Aflatoxins due to the inhabiting Aspergillus flavus in peanuts are a wellknown example. It is also possible that these mycoflora come from the soil, water and air that the food is exposed to and secrete poisonous mycotoxins in the food. Extensive studies on the mycobolome (fungal microcosm) and characterisation and fingerprinting of these toxins using the advanced methods have made detection easier and rapid. Rychlik et al. (2017), besides coining the term “mycobolome”, have defined it as the whole set of fungal metabolites including all the “modifications”, as they classify the mycotoxins into new, emerging, modified and masked mycotoxins. Apart from the contaminating pathogenic microbial flora in terms of food safety, foodomics in food microbiology also focusses on the gut microbiome from the perspective of health and nutrition. It is now well known that the nutrition derived from the food consumed depends to a great extent on the individual’s gut microbiome. There are several interesting studies in this area using transcriptomics, metabolomics and proteomics approaches as succinctly reviewed by Y.-J Xu (2017). These studies have opened new arenas in nutrition and health, leading to another new concept called nutraceuticals, which is based on using food/food molecules as substitutes to pharmaceuticals.

9.3.2.2 Food Adulteration and Foodomics With soaring prices of commodities in the last few decades, adulteration is ubiquitous. Advancement in technology and machinery has in fact helped in seamless adulteration, that it is practically impossible to identify the adulterant with naked eyes. However, adulteration is a serious aspect of food quality and safety. Earlier

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methods of detection of food adulteration and food analyses were mostly chemistrybased laboratory tests – a series of test tube experiments which depended mostly on solution chemistry, conjugate chemistry and colour chemistry, but they are now evolving towards those depending on modern instrumental platforms (Gallo and Ferranti 2016). Food safety and quality testing laboratories were equipped with trained analytical and inorganic chemists. But today, much of these detections are done by sophisticated instruments and omics approaches. Oil is one commodity that can be easily adulterated. Since it is adulterated with other cheap edible oils, it is extremely difficult to detect the adulteration, as all parameters will respond for oil. Given the exorbitant pricing of extra virgin olive oil, it is often adulterated with different vegetable oils like soya, sunflower and corn oils. Detection of this is very difficult and time-consuming and needs to be highly sensitive. Today, it is possible to detect adulteration in olive oil, even at very low amounts, using foodomics, which include vibrational spectroscopic techniques like visible Fourier-transformed IR, mid- and near-IR methods (Cozzolino 2015), NMR and mass spectroscopic techniques (Klampfl 2018). Like chemistry-based detection kits, omics-based detection models are possible today. Christy et al. (2004) have used NIR and chemometric analyses on pure and adulterated olive oil and by employing Savitzky-Golay smoothing have created models that were able to detect olive oil adulteration with 100% certainty and error less than 1.5% (w/w). Partial least squares (PLS) algorithms have also been used to make these calibration models (Kasemsumran et al. 2005). A recent report by Pasias et al. (2020) clearly outlines the application of recent foodomics techniques in the detection of olive oil adulteration and also in the identification of geographical and varietal differences that help in traceability. Like oil, all other food commodities get adulterated, and foodomics is being used extensively in their detection. Other examples are the use of proteomics to find marker proteins. This helps in the detection of adulterants especially of proteinaceous nature. For example, the adulteration of meat products with soybean proteins can be detected by multidimensional liquid chromatography-tandem mass spectrometry (Leitner A 2006), while whey proteins have been used as markers to detect adulteration in mozzarella and ewe cheese using matrix-assisted laser desorption/ ionisation mass spectrometry (Cozzolino et al. 2002). According to Zhu et al. (2010), food nutrient analysis techniques, combining chemometrics with GC-MS, can also be used to detect adulterants in edible oils and foods.

9.3.2.3 Food and Diet Claims and Foodomics Recent health trends have seen a drastic paradigm shift from pharmaceuticals to phytoceuticals to nutraceuticals. There is also a rising trend in following certain diet patterns for certain health claims. Personalised nutrition and customised nutrition are in vogue, following the fact that every individual has a unique composition and metabolism and hence what fits one may not fit the other. Today, the human trend is towards food as the panacea for all ills. This is because food has been used by man for aeons and is time tested as safe. It is not known to have any side effect, provided it is clean, hygienic, of good quality and safe. Hence, they are ready to take food

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molecules or follow diet regimes to alleviate diseases. In the last decade or so, the market space is rampant with these nutraceuticals in the name of health supplements. The flip side to this though is the increasing health claims that are made on these food molecules or diet patterns. Some of the controversial molecules include coconut oil, omega-3 fatty acids, Coenzyme Q, alpha-lipoic acid, curcumin, etc. While all these molecules are edible and healthy, some of the claims made on these molecules are not scientifically validated. Validation needs to be carried out in animal models (for toxicity and efficiency) and has to be followed up with human trials in randomised controlled trials (RCTs) before being commercialised with a health claim. Lack of scientific validation in human trials can jeopardise the claim made. Several controversial claims exist. For example, a study reported in The Lancet (Dehghan et al. 2017) has highlighted the existing controversy between macronutrients (carbohydrates and fat), cardiovascular disease (CVD) and mortality. In a cohort study of 135,335 individuals spanning over 7.4 years, the authors concluded that high carbohydrate intake was directly related to high risk of mortality, while the total fat or the types of fat (saturated or not) were related to lower mortality. Contrary to the common belief, the study also showed that the total fat or the types of fat were not associated to CVD or CVD mortality and that saturated fats had an inverse association with stroke. Such studies cannot be done without a foodomics approach. Several parameters in this study would have liberated huge data sets that must have been analysed using sophisticated bioinformatics, algorithms and statistical tools. In a similar approach to validate health claims, we, in our laboratory in collaboration with the National Institute of Mental Health and Neuro-Sciences, Bangalore, India, tried to correlate brain health with the amount of omega-3 fatty acids in red blood corpuscles (RBCs) of the subjects. Using GC/MS, more than 100 RBC samples were profiled for their fatty acid content, and this data was correlated with the structural and functional MRIs of the brain using algorithms (data unpublished). These kinds of human intervention studies are critical in making health claims on any food molecule or diet regime, and foodomics is critical for such studies. Since food molecules and diet may not show immediate side effects but can produce longterm effects on our health, these studies are very important. They also become important for the policy-makers who set the global dietary guidelines, and hence the precision in these studies, together with bigger sample sizes, is very critical. They are also important for procuring the regulatory clearance which is dependent on both the product and the health claims that are made on the labels; these regulations vary with jurisdictions. There are also several categories of health claims that are permitted: disease risk reduction claim, therapeutic claim, function claim, general health claim, natural food products, authorised health claim and qualified health claim. Clinical trials are a must to support regulatory clearance or substantiate health claims for new and existing products. The level of scientific substantiation needed to support a particular health claim for a food product in turn decides that regulatory clearance is required to bring the food product to market. Foodomics plays a very significant role in getting a product through this complex regulatory pathway.

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9.3.2.4 Food in Health and Foodomics “We are what we eat”. A 2016 research from Oxford University using parasites has shown that the DNA sequence of the organism changed depending on the food it was given (Seward and Kelly 2016). This means that the food we consume can decide the genetic make-up, hence the protein expression, in turn the metabolism and thus the health and well-being of an individual. Food, which was meant to just be a source of energy and nutrition, is today being looked at as a substitute for medicine, which has brought about the term “diet”. Changing lifestyles and eating practices have resulted in several health conditions that were not known until the twentieth century. These could be the effect of complex diets on human health, since food is a complex matrix where multiple components bring in their effects in a synergistic or antagonistic way. The use of omics in diet studies can help clarify some of these complexities by providing an understanding of the molecular mechanisms of the diet, their interactions within themselves and with the human system and the nutritional data of the diet – its bioavailability and its positive (or negative) effect on the human health. These could pave way for identifying possible biomarkers that are related to the diet, the nutrition and the health condition (Fito et al. 2016). 1. Food in Wellness: The current global situation is the best example for food in wellness. With the spread of the pandemic and with no cure available in the immediate future, the only resort is to ensure that our body’s immunity is high and will be able to fight the virus. It is also clear that it is the better immune system that is making about 40–50% of the infected COVID-positive patients to recover and get discharged compared to those who are succumbing to the infection in India. The patients in the Indian hospitals are treated with just immune-boosting “diet” and herbal extracts along with exercise. In India, the use of “kabasura kudineer” or the alternative therapy suggested by Patanjali or the Siddha medicines practised by Siddha practitioners as a prophylactic only proves that some spices and herbs (that mostly are ingredients in an Asian/Indian cuisine) are immune boosters. These were based on the several studies that focused on the fingerprinting of these formulations and the effect of those molecules on immunity. Another aspect of foodomics in wellness is the nutrigenomics and personalised nutrition. This takes into consideration the genome, the metabolome and the gut microbiome of the individual to suggest a diet regime to ensure sustained good health. There is a growing body of evidence on the role of foodomics in studying the diet-induced changes at the molecular level on the gut microbiota, thereby providing scope to improve health and wellness through manipulation of diet (Putignani and Dallapiccola 2016). A well-balanced, hygienic diet eaten regularly is sure to provide good health. One has to recall Hippocrates’ quote: “Let food be thy medicine and medicine be thy food”. 2. Food in Illness: Food can also be the cause for illness. Food poisoning, food allergens, contaminated food, overeating, oily diet, high-protein diet and high carb diet are all known to cause illnesses. One of the main objectives of food/ nutrition is to improve human health. But changing lifestyles have changed the diet patterns worldwide, resulting in several health problems. Uncontrolled use of

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more processed foods, precooked foods and frozen foods can cause health issues, while the packaging, transport and storage of the foods also add to the issues. Food-borne infections are very common, affecting about one-third the population in industrialised countries, annually (Garcia-Canas et al. 2012a, b). In order to address these situations, more robust methods of pathogen detection and highthroughput screenings should be made available. Microarray-based technologies allow for small sample volumes, bigger sample sizes and ease of automation to detect the microbial pathogens (Lancova et al. 2011) that help in species-level identification. Omics technology can also help in profiling and fingerprinting several food components, (e.g. fatty acid profiles to detect the presence of saturated fats vs polyunsaturated fats (PUFAs) in a food matrix) and the effect of food composition on the genome, transcriptome, proteome and metabolome. Today, food and nutrition is being dealt at the molecular level, and their metabolism in the human system is also well studied. Putting together these complex data, it is possible to optimise and design dietary compositions to suit the human physiology and recommend them for disease prevention (Corella et al. 2011). While illnesses caused by food poisoning and allergens could be mild and short -lived, more seriously, food can be the cause for many non-communicable diseases like diabetes mellitus, cholesterol, obesity, cardiovascular diseases and more recently Alzheimer’s. The composition of the food, the way it is prepared, the time it is consumed, the combination in which it is consumed and the quantity and quality consumed are also major reasons for ill health. For example, cooking carbohydrates at high temperatures can lead to the formation of advanced glycation end products (AGEs) that have deleterious effects on heart health and could clog the arteries. It is in this context several human studies must be conducted spanning over few years and the findings must be integrated with metagenomics to provide clarity and guidance on these subtle but important aspects to the layman.

9.3.2.5 Food Safety and Foodomics While safety of the food can be compromised by microbial contamination and adulteration (which has been dealt earlier in this chapter), there are other aspects that contribute/compromise food safety. This is a very serious global issue, drawing the attention of policy-makers worldwide to keep issuing guidelines and framing regulations. Foodomics plays an important role in this compliance, albeit, with one analytical challenge – of providing reliable data in the shortest time (LeDoux 2011). According to Andjelkovic et al. (2017), food safety primarily deals with the elimination of pathogenic microbes and toxins that can cause food-borne infections, removal of food allergens and elimination of other contaminants that can cause teratogenic, immunotoxic, nephrotoxic or estrogenic effects. For example, the compound bisphenol A that leaches out of substandard plastic bottles causes estrogenic effect that results in the polycystic ovary syndrome (PCOS) in adolescent girls. Hence, it is crucial to detect these levels in water and beverages that are packed and sold in plastic containers. It may be required to have more stringent laws and more sensitive analytical methods to deal with food allergens. Allergy is caused by even

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trace amounts of the allergen and can lead to critical conditions in sensitive people. Traditional methods have depended on immunological detection kits, but today most allergenic proteins can be detected by LC/MS methods to high precision (Faeste et al. 2011). Piras et al. (2016) have extensively reviewed the application of proteomics in food safety, especially of animal origin, as they are more proteinaceous and more prone to contamination. Furthermore, safety of food needs to be ascertained all through the journey from the source to the consumer, i.e. from production to consumption. This includes quality and safety from the time the plant or animal source is produced and harvested to its collection, processing, packing, transport, storage and marketing. All preharvest and postharvest steps have to be monitored for quality and safety assurance. Foodomics has led to the successful identification of pathogens, adulterants, allergens and proteins. These proteins also serve as markers along with other markers like the polynucleotides (from genomics), peptides (from proteomics) and metabolites (from metabolomics). Food safety regulations also deal with pesticide residues, cross-contamination of commodities and processing changes like formation of trans fats in the products, apart from the most important regulation on genetically modified crops. MS-based omics approaches are used to investigate unintended modifications in transgenic foods, while PCR and sequencing methods are used to detect genetically modified commodities in countries where they are not allowed for consumption. Newer omics methods and analytical methods are being evolved, especially in a high-throughput mode with fast data analyses by integrating with bioinformatics and statistics in order to meet the increasing demand and increasing standards of food safety globally. In vivo foodomics can also help in food quality control and quality assurance (Havelaar et al. 2010).

9.3.2.6 Food Functionality and Foodomics We know that food has several major functions, apart from just providing energy. The functions range from helping in growth and development, repair and maintenance of cells, fighting against disease and maintenance of healthy organs. This is possible because each component of food contributes towards these functions, for example: 1. 2. 3. 4. 5.

Proteins help in building muscles Fats and carbohydrates are burned to provide energy. Fats serve as energy source Calcium helps in bone and teeth development. Vitamins help in maintaining healthy organs – vitamin A for eyes, vitamin D for skin and bones and vitamin C for immunity and gum health 6. Minerals like zinc help in immunity; potassium in maintenance of fluid balance, muscle contractions and nerve signals; sodium in control of blood pressure and blood volume 7. So on and so forth. While these are the roles played by the primary food components, minor components – present in microgram or even nanogram quantities – are also known

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to contribute to certain critical functions. For example, polyphenols present in most foods are good antioxidants and protect the body from cellular damage and senescence. While in the past the food that was consumed regularly was filled with this goodness, current-day foods are bereft of these health components and hence the lack of the corresponding functions in the body. This is why deficiencies and malnutrition have become so common. This has necessitated the analyses and labelling of the constitution of food, called nutrition facts. Interestingly, the history of labelling dates back to the 1850s when there were serious food-borne illness outbreaks, a notable death being that of the 12th US President, Zachary Taylor, who died of stomach flu. This resulted in the creation of the United States Department of Agriculture (USDA) in 1862 by President Abraham Lincoln, which created strict guidelines for food handling and processing. However, not until 1966 the USDA made it mandatory for listing the ingredients on all products. Today, this labelling is mandatory across the globe. Given that the nutrition facts of every food is now available using sophisticated analytical instruments like HPLC, GC, mass spectrometry and atomic absorption spectrometry, the consumer is able to understand the probable health benefits and functions from the food consumed. Many researchers are putting into practice the combination of multiple omics and integrating their data to comprehensively understand the functionality of food components (Kato et al. 2011). A nutrigenomics database is also available, which is a compilation of two different sets of data – (1) the publication data of nutrigenomics research and (2) DNA microarray data (Nutrigenomics Database, http://www.nutrigenomics.jp, Saito et al. 2005). These depositories help in accelerating the development of omics analyses in food science, which otherwise is a herculean task, with handling of huge data sets.

9.3.2.7 Food Traceability and Foodomics Creydt and Fischer (2018) have reviewed in detail the various omics approaches in food authentication and traceability. This flows from the adulteration aspect of food that was dealt with earlier. Spink and Moyer have coined a term called “food fraud” in 2011, to include all “deliberate and intentional substitutions, addition, tampering or misrepresentation of food, food ingredients or food packing; or false or misleading statements about a product for economic gain” (Spink and Moyer 2011). Rapid globalisation and technological advancement have increased the incidence of such food frauds. Some of the foods that are often counterfeited are oils like extra virgin olive oil, dairy products like milk and cheese, honey and spices. Food fraud can be at the level of false declaration of the geographical origin, especially with commodities that are GI tags of a place (e.g. Assam tea), or could be misleading statements about the processing (e.g. cold pressed oil, virgin coconut/olive oil, organic produce, filtered/refined, etc.) or false statements about the biological identity (e.g. rice variety IR-8 or Basmati). This is always done deliberately, keeping in mind the consumerism. These food frauds are more prevalent these days after the purchasing power of the common man has increased and he is ready to pay more for the extraordinary tags that are found on the label. A striking example is the increasingly growing demand for organic products in the last decade, which has

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become a status symbol amongst the urban populace. Taking advantage of this consumer psychology, companies are lured to false certify their products as organic and sell their commodities at a prohibitively expensive price. Countries around the globe have tried to circumvent these malpractices by implementing certain monitoring protocols; however, they are not fool proof. According to Creydt and Fischer (2018), authenticity can be ensured in two ways – (1) tracking and tracing methods and (2) targeted and nontargeted analyses of the samples. By the late 1900s, technology had advanced a big way in enabling tracing of food products, using bar codes. Barcoding was a unique identification card for plant varieties and to a great extent forgery proof. This was made by including the details of the plant variety in several one-dimensional vertical lines of different widths, separated by different distance. Barcoding in products incorporated several critical information like expiration dates, lot number, serial number, etc. that can be read under a barcode reader. Since one-dimensional barcodes could not accommodate more data, 2D barcodes were made, as shown below. Of these, the QR (quick response) codes that can be read using QR scanner (an app that can be downloaded onto a smartphone) have more space and accommodate more information and are mostly used on all products, while the Aztec code is mostly used in logistics and healthcare.

One-dimensional barcode

Two-dimensional barcodes (PDF417; QR Code; AZTEC code)

More recent and sophisticated and forgery-proof methods to monitor the goods at the processing factories include radio frequency-based identification (RFIDs) tags. While these are tamper-proof and forgery-proof, compliance to these methods is often not strict. The other traditional method of tracking is by testing, using analytical methods. This is the age-old, reliable method, but earlier methods suffered from the time it consumed and the medium-throughput nature. However, current advancement in instrumentation and analytical methods has resulted in faster, more accurate and high-throughput testing protocols. These methods depend upon the genomics, proteomics and metabolomics of the sample. They mostly seek the fingerprint (complete profile) of the sample through mass spectrometric and NMR methods. Both methods work on different principles, yet they allow information of all the compounds/ metabolites present in the sample. These are nontargeted approaches, where the complete information of the sample is obtained; but these methods rely upon some reference standards for accuracy. In cases where the samples have not been studied earlier and standards do not exist, de novo studies are done with the data acquired. Foodomics plays a very significant role in these studies, as it brings together the data

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from different omics platforms and provides more comprehensive idea of the sample. Analysis is easier when the approach is targeted. Here, the sample is screened for definitive and known markers, and these methods are generally in accordance and in compliance with ISO standards and industry standards. These are generally costeffective, rapid screening protocols that do not require highly skilled personnel. This advantage makes these targeted approaches more sought-after by the producers. Moreover, the omics technologies provide data that are mostly ambiguous, due to the lack of reference databases and integration of the data from various platforms. Future tracking and tracing protocols should integrate seamlessly the data from the various omics platforms and provide more reference databases for reliable and rapid referencing.

9.4

Foodomics: The How of It

There are several research articles, reviews and special issues on foodomics ever since the concept was introduced in 2009 by the father of foodomics, Alejandro Cifuentes, who runs the laboratory of foodomics at Madrid, Spain. Since foodomics is the integration of all omics platforms that are relevant to food and food matrices, it depends mostly on data analyses, statistics and bioinformatics of the data acquired from these platforms, seamlessly amalgamating with the metagenomics data acquired from the immediate microcosm of the food matrix. From what started off as laboratory test tube experiments for detection of food pathogens, food science has come a long way in evolving as a separate omics platform. This platform deals with genomics, transcriptomics, proteomics, lipidomics, metabolomics, mycobolomics, nutrigenomics and some yet to be coined omics, and hence the methods involved are plenty. Before the method, sampling and sample preparation are very critical when it comes to foodomics. For this, we require both high-throughput and high-performance instrumentation that are high in precision and sensitivity, coupled with skilled analysts, from analytical chemistry and biological sciences backgrounds to critically analyse and interpret the data. Mass spectrometry has revolutionised chemical and biological analyses. Although it suffers from the drawback that it requires highly skilled professionals unlike the Petri dish culture techniques that were followed traditionally and that it requires expensive reagents, this instrument can identify compounds/bioactives/molecules/ peptides to the nanogram quantities. It can provide direct qualitative and quantitative information. The triple quadrupole machines allow shotgun approaches, where the samples undergo nontargeted analyses. In case of targeted approaches also, the timeof-flight machines and the trap machines can precisely give the data based on the masses of the constituents present in the sample. The sample needed is minimum, and there are not too many sample preparation steps. In case of very crude samples, it may be advisable to pass the samples through an HPLC or UPLC prior to loading into the MS/MS. The demand for precision and speed has resulted in newer versions of these sophisticated spectrometers, the latest being called the HRMS

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(high-resolution mass spectrometer). Besides the complex hardware of these machines, the reliability in their data comes from the interfacing with some reliable software that have set gold standards for identification and analyses. Mass spectrometry is by far the best analytical method for proteins and peptides (proteomics) and for identification of metabolites in the samples (metabolomics). Mass spectrometry also helps in the fingerprinting or nutrition profiling of samples especially for small molecules like vitamins and other secondary metabolites like polyphenols and flavonoids. Samples like oils are subjected to gas chromatography interfaced with mass spectrometer that can run several hundreds of samples rapidly using an autosampler. Andjelkovic et al. (2017) have reviewed in detail the various omics methods used in foodomics, while Rychlik et al. (2017) have reviewed the use of targeted metabolomics for mycotoxin quantitation. Accordingly, LC coupled with triple quadrupole or TOF mass spectrometers have provided accurate results They have also discussed the use of Fourier-transform spectroscopy like FT-ICR-MS (Fouriertransform ion cyclotron resonance mass spectrometry) techniques for mycotoxin analyses. FT-ICR-MS is also a great tool for fatty acid analyses, although care must be taken with sample preparation. In fact, unlike chemical compounds that are most often pure, food matrices are extremely complex, and hence the most critical step is the sample preparation step. It is mandatory for improving the precision and accuracy since it allows better extraction of the target analytes which is a priority in analytical chemistry (Maciel et al. 2018). This is possible with the advent of new materials that are substituting the traditional organic solvents like ionic liquids, graphene-based materials, polymers, nanoparticles, etc., either singly or in combinations. In terms of proteomics, the traditional methods used two-dimensional polyacrylamide gel electrophoresis, which is a tedious process. Today, these are substituted by sophisticated mass spectrometry methods that are extremely versatile and suit all kinds of proteins – low or high molecular weight, alkaline or hydrophobic proteins (Zheng and Chen 2014). Here, the proteins are isolated using a multidimensional liquid chromatography followed by mass spectrometry, which could either be a bottom -up strategy wherein the enzymolysis precedes the separation or a top-down strategy where the chromatographic separation is followed by the enzymolysis. Amongst the techniques in metabolomics, capillary electrophoresis (CE) is a very popular method for food samples. This is usually used in combination with mass spectrometry for analysis of the various components in the food matrix, namely, amino acids, amines, proteins and peptides, nucleic acids, polyphenols, sugars, pigments, additives, toxins, pesticides, etc. Thus, CE is very useful in quality control, in food safety and in nutritional evaluation (Ibanez et al. 2013). Andjelkovic et al. (2017) have provided some additional methods that can be used as complementary tools to MS for validation and confirmation. They have also listed some of the drawbacks of these methods. For example, the affinity-based methods used in food analyses tend to result in cross-reactivity of similar molecules and can result in spurious data; nevertheless, they can be used as a pre-screening step when samples are large in number. Advancement in bioinformatics tools, software

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and in silico information due to the increase in number of databases available in public domain, modelling and artificial intelligence and big data analyses are together providing impetus to the application of foodomics in food science and nutrition.

9.5

Green Foodomics

A cursory search for foodomics will result in this hit – green foodomics. This is basically the adaptation of green chemistry in the foodomics protocols. As we have discussed in detail, most methods in the omics platforms (transcriptome, proteome and metabolome), especially those using mass spectrometry and capillary electrophoresis methods, require the use of chemicals that can sometimes harm the environment. Hence, it is suggested that such methods are modified taking care to not impact the environment. Mendiola et al. (2013) has listed out possibilities of improving the analytical methods under the umbrella of green analytical chemistry, with a word of caution to implement them with care, so as to not compromise on the main purpose of the analysis. For instance, capillary electrophoresis can be modified with greener methods to consume fewer solvents; HPLC can be substituted by UPLC to consume less acetonitrile which is a carcinogen; sample preparation can be modified to be more sustainable and less hazardous; analytical methods and steps can be minimised to save power. This does not take into consideration all the methods that are used in the transcriptomics, proteomics and metabolomics steps as yet, but in the future, keeping in mind the global warming, increasing carbon footprint and the catastrophe that could follow these natural events, it will be best to conscientiously modify these steps to be eco-friendlier, providing scope for green foodomics.

9.6

Foodomics in Times of Pandemics: Lessons from COVID-19

The year 2020 has seen a huge setback globally, in all sectors, due to the COVID-19 pandemic. However, the coronavirus SARS-CoV2 is not a food-borne pathogen, and there is no evidence that food could be a source or transmission route for the COVID-19 illness. The possibility of the presence of viable virus particles in food is low, and it has not been reported to multiply in foods. Also, heating and other treatments of food kill or inactivate the virus. Again, upon ingestion, food enters the gastrointestinal tract where the stomach acids would inactivate the virus. Although it is not a food-borne illness, COVID-19 is a threat to public health which in turn affects the food businesses and food security. Keeping this in mind, Leon Gorris (2020) has provided guidelines towards ensuring food safety during the pandemic. Another aspect where food comes into play during this pandemic is in the need to have increased innate immunity to win the battle with the virus. And immunity comes from the food we eat. This is where foodomics like nutrigenomics becomes essential. Keeping in mind the gut microbiota which is unique to each individual,

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doctors and nutritionists are able to draw dietary plans for COVID patients. With no specific treatment available for the pandemic, reports from all COVID hospitals that are discharging patients owe it to the diet regime given to their patients which includes several food-based immunity boosters like ginger and lemon. Focusing on deciphering the function and roles of these nutrients and metabolites in the pathophysiology of the COVID-19 disease can help in formulating an optimal diet for managing the disease (Mahmoodpoor et al. 2020). Taking cues from COVID-19, future preparedness should focus on food-based treatments and prophylactic practices based on foodomics data drawn from various regions, ethnicities and dietary practice for such unprecedented eventualities. In the past, there have been food-borne disease outbreaks too, when food safety was of paramount importance. In 2008, the World Health Organization issued guidelines for investigation and control of such outbreaks. It has clearly recorded that it is a multidisciplinary task and requires skills in areas of clinical medicine, epidemiology, food microbiology, food chemistry, food safety and food control, besides others. But taking lessons from the COVID-19 pandemic, it is recommended that we are prepared for any adverse situation. Foodomics can play a very important role, to analyse huge numbers of samples, to provide capabilities to do highthroughput analysis, to analyse huge data sets statistically and to help in the control of the spread of the disease. It may be important to develop and optimise methods for rapid detection of pathogens, in huge populations.

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