Synthetic gutomics: Deciphering the microbial code for futuristic diagnosis and personalized medicine

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Synthetic gutomics: Deciphering the microbial code for futuristic diagnosis and personalized medicine

9

Pankaj Satapathya, Kounaina Khanb, Aishwarya Tripurasundari Devic, Anirudh Gururaj Patila, Avinash Mirle Govindarajud, Shubha Gopald, Mysore Nagalingaswamy Nagendra Prasadc, Veena Sunil Moree, Raghava Reddy Kakarlaf, Anjanapura V. Raghug, Shivaprasad Hudedab, Sunil Shivaji Morea, Farhan Zameera,* a

School of Basic and Applied Sciences, Department of Biological Sciences, Dayananda Sagar University, Bengaluru, Karnataka, India b Department of Dravyaguna, JSS Ayurvedic Medical College, Mysuru, Karnataka, India c Department of Biotechnology, JSS Science and Technology University, SJCE Campus, Mysore, Karnataka, India d Department of Studies in Microbiology, University of Mysuru, Mysuru, Karnataka, India e Department of Biotechnology, Saptagiri Engineering College, Bangalore, Karnataka, India f School of Chemical and Biomolecular Engineering, The University of Sydney, Sydney, NSW, Australia g Department of Chemistry, School of Engineering and Technology and Center for Emerging Technology, Jain Global Campus, Jain University, Bengaluru, Karnataka, India *Corresponding author: e-mail address: [email protected]

1 Introduction to microbes Microbes are organisms which are not visible to the naked eye but have a role to play in the diversity of life. These single celled organisms can only be visualized under the microscope. Microbes are the oldest known life on earth and some have existed for billions of years. Microbes are omnipresent present in the food we eat, the air we breathe and the water we drink. They may exist in clusters or as individuals; over 95% of these microbes have a beneficial effect on humans. Microorganisms are diverse in nature and represent a great kingdom of life.

Methods in Microbiology, Volume 46, ISSN 0580-9517, https://doi.org/10.1016/bs.mim.2019.02.001 © 2019 Elsevier Ltd. All rights reserved.

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The human gut which is also known as the second brain plays an important role in human health maintenance. The human gut is made up of a plethora of microorganisms which regulate disease condition and maintenance. The gut microflora is unique to each individual and changes based on the disease condition. The gut also plays a crucial role in the immune modulation which is responsible for various disease conditions. This collection of microrganisms in the human gut has led to the introduction of the term gutomics. Synthetic biology helps in producing the next generation of treatment. As bacteria are easy to manipulate and monitor their effects, this tool has been used to engineer bacteria genetically according to the commercial need. This technology is further explored on the human gut and its effect on human health. Some of them have shown successful results and other studies are in their naive stage. The combination of synthetic biology and gut microbes led to the word synthetic gutomics. This study will help in profiling for disease condition and come up with the cause or cure or alteration in disease mechanism. In this chapter, we have discussed the advances in synthetic gutomics which can promote the concept of personalized and futuristic medicine.

2 Host gut and microbe interaction Microbes play an important role in the life of every human individual. Around 40% of the human body is made up of microbes, with the health and wellbeing of an individual influenced by the microbes present. Most studies on microbes have been carried out with bacteria, but a plethora of studies have also been carried out on fungi, parasites, helminths and viruses. With the advancement in technology our understanding of the complexity of microbiota has also increased. Though the microflora of other parts of the body have been sequenced, the gut microflora have been most widely studied (Turnbaugh et al., 2007). Due to their presence and functional influence on the gut microbiota, bacteria have gained importance. For their characterization, two major projects initiated by The National Institutes of Health (NIH), the Human Microbiome Project (HMP) and Metagenomics of the Human Intestinal Tract (MetaHIT) have recently been completed (Group et al., 2009; Qin et al., 2010). The various types of gut microbes present in the gut are depicted in Fig. 1.

2.1 Fungi Apart from bacteria, the study of other microbes such as fungi have gained momentum in research and in 2009 the term “Mycobiome” was coined (Gillevet, Sikaroodi, & Torzilli, 2009). Fungi that have a commensal relationship with the human gut are less studied as most of them are unculturable (Huffnagle & Noverr, 2013). Fungal microbiome studies are important in characterizing the fungi into pathobionts and non-pathobionts which are in a commensal relationship with the human gut. Some fungi cause disease in individuals lacking immunity rather than a normal individual as the fungus forms a commensal relationship with the human gut,

2 Host gut and microbe interaction

FIG. 1 The various type of gut microbes present in the human gut and microbe constitution.

for example, Candida albicans (Cohen, Roth, Delgado, Ahearn, & Kalser, 1969). Many questions are yet to be answered like what makes fungi pathogenic, why they generally affect immunocompromised patients in which we see a mortality of 35–45% (Drgona et al., 2014). Next genome sequencing has aided the discovery of diverse fungal Phyla in the human gut, made up of Ascomycota, Basidiomycota and Zygomycota (Hoffmann et al., 2013; Mar Rodrı´guez et al., 2015; Suhr & Hallen-Adams, 2015). In one of the studies 16 fungal species were found in stool samples among which Galactomyces geotrichum was the most frequent (Hamad, Sokhna, Raoult, & Bittar, 2012). Many other studies revealed Saccharomyces as the most frequent followed by Candida and Cladosporium (Hoffmann et al., 2013). This was corroborated in another study by the occurrence of Penicillium, Candida and Saccharomyces as the most frequent (Hamad et al., 2012). In the mucosa of colon species like Dothideomycete, G. geotrichum and Ustilago were prevalent (Ott et al., 2008). The ecology of the mycobiome is diverse in the human GI tract and further studies are required. To characterize the mycobiota by the sequencing method, the variable portions of the fungal genome were targeted, thereby identifying the genus of fungus. This was achieved by using the ITS (internal transcribed spacer) region of the rRNA locus. The sequences were then categorized into OTUs (Operational Taxonomical Units) and compared to those available in databases (Di Bella, Bao, Gloor, Burton, & Reid, 2013; Kuczynski et al., 2012; Weinstock, 2012).

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Hoffmann et al., (Hoffmann et al., 2013) helped in identifying a strong relationship between food and fungi in the gut. They found an influence of diet on bacteria and fungi. The bacteria were influenced by a high protein diet, whereas fungi were influenced by a high carbohydrate content. This suggested that gut microbes have an association with the immune system. In the innate immune system a cell facilitates pattern recognition receptors (PRRs) on its surface to identify pathogen associated molecular patterns (PAMPs) which are the conserved motifs present in pathogens (Moyes & Naglik, 2011).

2.2 Protozoa A vast range of protozoa both intra- and extra-cellular as well as invasive and noninvasive are localized in the human gastrointestinal tract. A thorough study using various classical parasitological diagnostic methods, PCR amplification, combined with sequencing of the small subunit of the rRNA gene was used to discover the diversity of eukaryotes in the human gut (Hamad, 2016). This study discovered as many as 15 different protozoan genera known to have a commensal or parasitic influence with the human intestinal tract (Hamad, 2016). Colonization of the protozoan Tritrichomonas musculis (a parabasalid commensal of the rodent microbiome) in the intestine leads to inflammasome activation and cytokine IL-18 release helping the prevention of diseases such as enterocolitis (Chudnovskiy, 2016). Studies also emerged on protozoan diversity and gut microbiota composition. This led to the discovery of linking the presence of Giardia intestinalis to the gut bacterial community using animal and ex-vivo models (Barash, 2017; Beatty, 2017). Changes in Giardia specific microbiome composition have been reported (Barash, 2017; Beatty, 2017; Iebba, 2016; Slapeta, 2015). In one study, patients from northern India with Entamoeba histolytica showed a significant increase in Bifidobacterium species and a decrease in healthy bacteria (Verma, 2012). Further studies on protozoa have yet to be performed to further define their role.

2.3 Helminths It is quite possible that the gut microbiome and helminths have co-evolved. Previous research has only been conducted on pigs with respect to Campylobacteriosis (Mansfield et al., 2003). Next generation sequencing (NGS) showed an increase in the number of the Mucispirillum bacteria in infected individuals (Li et al., 2012). Shot gun sequencing results showed a decrease in carbohydrate metabolism by Trichuris suis; this coincides with a decrease in Ruminococcus bacteria which are cellulolytic (Li et al., 2012). A study conducted in order to compare the association of mucosal gut immunity and worms in pigs revealed an increased number of campylobacter in infected pigs. This study reported an increased expression of inflammatory genes like arg1, Cxcr2, c3ar1, il6, muc5ac and ptg2 (Wu et al., 2012) in infected animals. However the functional relationship between the bacterial burden, worm burden and immune response can be better determined in mice.

2 Host gut and microbe interaction

When studies were executed in mice they revealed the role of Heligmosomoides polygyrus in altering the microbiota of healthy mice. This microbe was prominent in the large intestine in spite of the fact that H. polygyrus is also found in the small intestine. Various independent studies revealed an increase in bacteria from the family Lactobacillaceae and Enterobacteria (Rausch et al., 2013; Reynolds et al., 2014; Walk, Blum, Ewing, Weinstock, & Young, 2010) in H. polygyrus-infected animals. Further experiments in anoxogenic mice demonstrated an association of fewer adult worms with eosinophilia, granulomas and thickening of the small intestine. The abundance of Lactobacillus showed a positive correlation with H. polygyrus which resulted in worm infection (Reynolds et al., 2014). T. muris utilizes the caecal microbiota to provide an environment for hatching of larvae (Hayes et al., 2010). Increased numbers of Lactobacillus are not just limited to the GI tract of the nematodes but other worms like liver fluke Opisthorchis viverrini, Lachnospiraceae and Ruminococcaceae are limited to the GI tract of nematodes (Plieskatt et al., 2013).

2.4 Viruses Viruses are small components but their influence on human health is significant. The load of eukaryotic viruses increases in diseased individuals, compared to healthy individuals. Metagenomics data on viruses from the gut of diarrhoea and non-polio acute flaccid paralysis patients revealed the existence of enteroviruses, adenoviruses, caliciviruses, and parvoviruses (Finkbeiner et al., 2008). Other studies have identified a link between diarrhoea and astrovirus, cosavirus (Finkbeiner et al., 2008) and bocavirus (Victoria, Kapoor, & Li, 2009). Further studies examining unique viruses is required in order to define their role in human diseases (Finkbeiner et al., 2008). Many other diseases are caused by undiagnosed pathogens but they are speculated to be viral. Sequencing has discovered many viruses in the following groups: arenaviruses (Palacios, Druce, & Du, 2008), astroviruses (Finkbeiner, Le, et al., 2009; Finkbeiner, Li, & Ruone, 2009; Kapoor, Li, & Victoria, 2009), rhinoviruses (McErlean et al., 2007), polyomaviruses (Allander, Andreasson, & Gupta, 2007; Allander et al., 2005), bocaviruses (Morgan, Segata, & Huttenhower, 2013) among others. Many viruses remain undiscovered with further investigations required to identify them.

2.5 Bacteria Bacteria play an important role in symbiosis and dysbiosis in the human gut. Symbiotic bacteria are required to maintain a healthy condition in the human gut. Next generation sequencing of 16S rRNA and a combination of metagenomics, proteomics and metabolomics has aided the determination of bacterial physiology (Di Bella et al., 2013; Morgan et al., 2013; Segata et al., 2013). The microbiota varies in the gut depending on the pH, nutrient availability and competition (Momozawa, Deffontaine, Louis, & Medrano, 2011); low pH and sterile conditions of the stomach aids in the development of Helicobacter pylori (Smyk et al., 2014) leading to peptic

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Table 1 Methods for identification of microbes. Omics

Genomics

Constitutes

DNA: the complete set of instructions for building an organism

Represents

Genes for building proteins and other molecules

Technology used

Next generation sequencing (NGS)

Transcriptomics

Proteomics

Metabolomics

RNA: proteinbuilding instructions

Protein: structural and metabolic/ enzymatic activities

Reflects which genes are turned on and off and their activities levels RNA-seq. (NGS): microarray; nanostring

How the genetic instructions are actually being applied Mass spectometry; protein array

small molecules (metabolite) substrates/ inhibitors/ products of metabolism Chemical reactions happening inside an organism Mass spectometry; nuclear magnetic resonance (NMR)

ulcers in the stomach. The bacterial human gut colonizers include Firmicutes and Bacteroidetes, present in the colon, together with Proteobacteria, Actinobacteria, Cyanobacteria (Johnson, Gaudreau, Al-Gadban, Gudi, & Vasu, 2015), Fusobacteria and Verrucomicrobia (Aguirre et al., 2015) in the remaining part of the gut. The gut microbiota generates short chain fatty acids as their metabolic products using dietary fibres (Demehri et al., 2016; MacFabe, 2015; Nastasi et al., 2015; Park, Goergen, HogenEsch, & Kim, 2016). These signal the G protein coupled receptors which in turn regulate energy metabolism. The various methods that are used to identify the gut microbiome are summarized in Table 1. The immune response generated by the gut is shown in Fig. 2.

3 Microbiota and nutrition Gut microbiota will be the next major advance in medical research with respect to health and diseases. The advancement of technologies like metabolomics and metagenomics has helped in depicting microbial activity and production in the gut. This has revolutionized the novel therapeutic production of gut microbiota. However the question of the gut microbiota composition remains largely unsolved; a lack of strategy and design has, to date hindered the translation of laboratory trials to clinical settings (Quigley, 2017). As complex, commensal relationships between the gut microflora and the human host are now widely postulated we can begin deducing how the host health and pathological conditions are related (Quigley, 2013). The best studied disease for gut dysbiosys is Clostridium difficile infection (CDI)— C. difficile—associated disease (CDAD). There appears to be a juncture in the

3 Microbiota and nutrition

Induce cytokine expression Mucosal

Innate Neutrophil killing effects Non-mucosal Macrophage accumulation and M1 polarisation Influence the No. of IgAproducing B cells

Mucosal

Controls the functions of B cells Balance between Th17 and Th1 cells

Adaptive Affect B cell to form germinal centre in the spleen Non-mucosal

Activate retina-specific T cells and contribute to uveitis Induce the generation of Th17

FIG. 2 Gut microbiota and immune response.

elucidation of CDAD epidemics and the effect of antibiotics on the host effect of CDI (Britton & Young, 2012). It can be inferred that antibiotics affect the commensal host bacteria which causes vulnerability to diarrhoea and CDAD (Britton & Young, 2012). How the gut microbiota provides protection against CDI infection is deduced by various outcomes. For example, the stimulation of secondary bile acids inhibits bacteria (Ridlon, Kang, & Hylemon, 2006). Studies have shown exclusion competition through nutrients (Merrigan, Sambol, Johnson, & Gerding, 2003; Sambol, Merrigan, Tang, Johnson, & Gerding, 2002) help in the bacterial inhibition of C. difficile. Bacteriocins (a detailed review is given by Ramith et al., 2015) also affect C. difficile (Britton & Young, 2012; Rea, Sit, & Clayton, 2010); bacteriocins regulate the Toll-like receptor signalling (Britton & Young, 2012; Lawley, Clare, & Walker, 2009). Gut microbiota dysfunction is associated with metabolic disorders such as diabetes and the prevalence of gastroparesis in affected individuals (Qin, Li, & Cai, 2012). Based on the faecal metagenomics data of women before and after glucose dysbiosis a mathematical model has been developed (Karlsson, Tremaroli, & Nookaew, 2013). This model gives predictive data for the progression of type 2 diabetes (Karlsson et al., 2013). Some bacterial taxa are responsible for the blood sugar level and respond to everyday meals (Zeevi, Korem, & Zmora, 2015). A relationship has been deduced from these observations between glycaemia, gastric emptying and gastric

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neurophysiology (Hayashi, Toyomasu, & Saravanaperumal, 2017). Furthermore the influence of metformin on diabetes and prediabetes on gut microbiota has been deciphered (Bauer, Duca, & Waise, 2018; Wu, Esteve, & Tremaroli, 2017). These concepts on microbiota can be extended to other diseases for their early prediction (Aziz, Dor
e, Emmanuel, Guarner, & Quigley, 2013). However, to date these studies have many limitations (Quigley, 2017) including the heterogeneity of the bacterial population, the reporting of single point studies, and unknown impacts of therapy, diet influence and the relationship between the gut microbiome and those reported in faecal samples.

3.1 Microbiota modulation and clinical impacts The previous sections have outlined how changes in dietary habits can modify the gut bacterial diversity and function. Food plays an important role in the appearance of symptoms in functional gastrointestinal disorders (FGIDs) and these are incorporated in their therapies. FGID patients are recommended to implement exclusions from a diet or to modify their diet, which is likely to modulate the composition of microbiota. For example, when a person was subjected to a gluten free diet for a month the commensal (Bifidobacterium and Lactobacillus) gut microbiota reduced but pathogenic (Enterobacteriaceae) bacteria increased (De Palma, Nadal, Collado, & Sanz, 2009). These observations strengthened the link between polysaccharides and an energy source for commensal bacteria. Low fermented oligo-, di-, or monosaccharide and polyol (FODMAPs) diets have become common among patients suffering from irritable bowel syndrome patients (IBS) (Halmos, Power, Shepherd, Gibson, & Muir, 2014; Staudacher, Whelan, Irving, & Lomer, 2011). This has been corroborated with clinical trials but the exclusion of FODMAPs in diets has raised questions regarding the impact on the commensal bacterial population. For example, oligosaccharides have been found to act as prebiotics which harm the commensal bacterial population resulting in a decrease in the dietary intake components (Bouhnik, Flouri
e, & Riottot, 1996; Davis, Martinez, Walter, & Hutkins, 2010). Also FODMAPs acts as a substrate for the formation of short chain fatty acids (SCFAs) which provides energy for colonic epithelial cells (Cook & Sellin, 1998). In order to study the effect of probiotics and the consequences of low FODMAP diet on the gut microbiome, a placebo control involving IBS symptoms and faecal microbiota was investigated. The results showed the positive effect of probiotics on the gut microbiome (Staudacher, Lomer, & Farquharson, 2017). In an independent study conducted on children who responded to low FODMAPs diets, a difference in the fermentation of carbohydrates was demonstrated (Chumpitazi, Cope, & Hollister, 2015). The responders showed greater saccharolytic activity due to Bacteroides, Ruminococcus, and Dorea. The non-responders showed reduced saccharolytic activity primarily through an increase in Turiciorsrud (2018) evaluted low FODMAP diets and bacter. Bennet, B€ ohn, and St€ traditional IBS and concluded that low FODMAP shows gut dysbiosys.

4 Emergence of probiotics

Various prebiotics studied in humans include inulin galacto-oligosaccharides, fructo-oligosaccharides, and xylo-oligosaccharides (Gibson, Hutkins, & Sanders, 2017). These have been shown to influence the gut bacterial composition and related immunity (Holscher, 2017). Some are selective while others show general effects on bacteria (Rastall & Gibson, 2015). Most of the studies are focussed on only prebiotics or probiotics but the effect of a combination of both has yet to be explored.

4 Emergence of probiotics Probiotics has captured the market by conferring perceived health benefit. However, little clinical evidence has been provided. Technological advancements has made it possible to characterize probiotics at the genomic level; this approach has been implemented on Bifidobacterium longum subsp. 35,624, (Altmann, Kosma, & O’Callaghan, 2016; Schiavi, Gleinser, & Molloy, 2016). Bifidobacterium longum prevents IBS effects; now that the complete genome of Bifidobacterium longum is available investigators have identified the gene required for synthesis of lush coat polysaccharides (EPS) which has an anti-inflammatory effect (Charbonneau, Gibb, & Quigley, 2013). Two characters that define probiotics are (1) it should be live (Dunne, O’Mahony, Murphy, et al., 2001) and (2) it can transit the GI tract (Charbonneau et al., 2013). Hence it should be continuously administered (Charbonneau et al., 2013) in order to make it effective. Before implementation on humans the probiotic should be characterized on the basis of biological activity including anti-bacterial, anti-inflammatory, immune modulatory etc. Currently a number of models are being studied (Collins, Dunne, & Murphy, 2002; Dunne et al., 2001; O’Mahony, Feeney, & O’Halloran, 2001; Sibartie, O’Hara, & Ryan, 2009) in terms of these effects. It is proven that probiotics effect immune cells, enhancing IgA production, downregulating pro-inflammatory cytokines via engagement of dendritic cells (DCs) and inducing Treg cells. It is evident that immune cells can differentiate between commensals and pathogens. Instead of activating the nuclear factor-kappa B (NF-kB) commensal bacteria connect with DCs which activates the Treg pathway leading to organism tolerance (Konieczna, Akdis, Quigley, Shanahan, & O’Mahony, 2012; Konieczna, Groeger, & Ziegler, 2012; Konieczna, Ferstl, & Ziegler, 2013; O’Mahony, O’Callaghan, & McCarthy, 2006; Scully, Macsharry, & O’Mahony, 2013; Sibartie et al., 2009; Symonds, O’Mahony, & Lapthorne, 2012). This phenomenon has been observed in healthy human volunteers (Konieczna, Akdis, et al., 2012; Konieczna, Groeger, & Ziegler, 2012). The probiotic B. longum shows a synergistic effect by increasing secretion of Cytokine IL-10 from human DCs (Konieczna, Akdis, et al., 2012; Konieczna, Groeger, & Ziegler, 2012). This effect was further proven by the effect of the probiotics strain on the reduction of circulating levels of C-reactive proteins (Groeger, O’Mahony, & Murphy, 2013). The pharmacobiotic concept has gone beyond live organisms, dead animals and bacterial constituents (Shanahan & Collins, 2010) to antibacterial components.

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For example, Lactobacillus salivarius UCC118 produces the bacteriocin Abp118 which protects against the pathogen Listeria monocytogenes; this was shown using the mutant strain (Corr et al., 2007) lacking bacteriocin production. This was further elucidated by the use of thruicin from Bacillus thuringiensis DPC 6431 which was effective against the pathogen C. difficile while showing little effect on commensal microbes (Rea et al., 2010). This effect was further demonstrated for the bacteriophage (Łusiak-Szelachowska, Weber-Da˛browska, Jo
nczyk-Matysiak, Wojciechowska, & Go´rski, 2017) by proving the bacteriophage effect against E. coli in Krohn’s disease patients (Galtier, De Sordi, & Sivignon, 2017).

4.1 Synthetic biology The gut microbiome plays an important role in human health which makes it an attractive target for therapeutics using synthetic biology. Major studies have been performed on microbes by using metagenomics and meta-transcriptomic sequencing techniques to provide an overview of the composition of microbial species and their expressed genes to correlate health and disease states. However, experimental approaches that extend beyond the sequencing platform are necessary in order to completely understand the mechanistic relationships between the individual microbial species and their host. The latest advances in synthetic biology offer a means not only to determine structure-function relationships but also to engineer the microbiota to have a positive influence on its host. However, the tools that are needed to engineer complex phenotypes have only been developed for use in model laboratory microbes like E. coli, and there is a requirement to extend such studies to host-associated microbial species to advance mechanistic understanding and engineering of hostmicrobiota interactions. The richness of human-associated bacteria such as Lactic acid bacteria (LABs), Bacteroides and Bifidobacteria which provides health benefits makes them a desirable engineering target for therapeutic applications. Probiotics have a dedicated industry for their manufacture and they may have a valuable effect on humans with disorders ranging from IBD (Lee et al., 2017), eczema (Li, Wang, Wang, Hu, & Chen, 2016) to anxiety disorders (Li et al., 2016). These studies will help in manipulating the microbes in the human gut. For example, one report concluded that commensal microbiome metabolites like SCFAs or quorum ones influence human metabolism, immune response and mood (Lin & Zhang, 2017). Prebiotics and probiotics, manufactured in genetically engineered bacteria have been shown to be capable of treating obesity, diabetes and colitis in animal models (Davies et al., 2014; Duan, Curtis, & March, 2008; Steidler et al., 2000). One of these studies involved the production of cytokines (Braat et al., 2006) by Lactococcus lactis; this study has been translated to clinical trials. Future major challenges lie in the multi-functionality of probiotics. These probiotics must mimic natural microbes present in the gut and show equal efficacy. To achieve this, a technique is needed which targets multiple parts of a pathway rather than a single point of it.

4 Emergence of probiotics

Synthetic biology may equip us with tools to produce the next generation of probiotics. In order to detect the gut environment researchers have to develop a nitric oxide-sensing switch (Archer, Robinson, & S€uel, 2012) or an integrase-based memory array (Mimee, Tucker, Voigt, & Lu, 2015). Engineered bacteria have extended their use in infectious disease eradication (Saeidi et al., 2011) and cancer diagnostics (Danino, Prindle, Kwong, Skalak, & Li, 2015). Before going into the complexity of the therapeutic system we need to address the safety issues. Synthetic biology may help to overcome these issues. Lastly, we are going to discuss how synthetic biology may be used in the design of improved therapeutics and in managing future challenges.

4.2 Species used in synthetic biology The influence of gut microbiotas on human health and its dysregulation is related to many diseases. Based on this microbes are used to treat many diseases by rebalancing the gut microbiome using various probiotic species that are beneficial. Some success has been observed in treating various disorders, including virus -induced diarrhoea (Aoki-Yoshida et al., 2016), colitis (Westendorf et al., 2005), eczema and dermatitis (Martı´n et al., 2013), anxiety disorders (Desbonnet, Garrett, Clarke, Bienenstock, & Dinan, 2008; Savignac, Tramullas, Kiely, Dinan, & Cryan, 2015) and depression (Wallace & Milev, 2017). Human clinical trials results have to date only shown a positive result for the treatment of C. difficile infection (Spinler, Ross, & Savidge, 2016). This may be because of a lack of understanding of species physiology. Probiotic species are complex and there is a need to streamline bioengineering with targeted systems.

4.3 Therapies by engineered probiotics For now probiotics are engineered to generate therapeutic molecules which fight infection, reduce inflammation and treat obesity induced by diet (Mimee, Citorik, & Lu, 2016). Now this is being extended to treat simian HIV (SHIV) (Lagenaur et al., 2011) and Vibrio cholera (Duan & March, 2010). Other methods have used N-acylethanolamides (NAEs) which are naturally produced in humans to treat pathogenesis. In a mouse polygenic obesity model, when bacteria producing NAE precursors (NAPEs) were administered, metabolic rate increased and food intake decreased (Davies et al., 2014). Some studies have used IL-10 (Steidler et al., 2000), TGF-β1 (Hamady et al., 2011) KGF-2 (Hamady, 2013; Hamady et al., 2010) serine protease inhibitors (Bermu´dez-Humara´n et al., 2015) and elafin (Motta et al., 2012) as markers on colitis mouse models that were ameliorated by L. lactis; another study focussed on oral mucositis (Caluwaerts et al., 2010). The role of engendered probiotics in cell longevity has been shown by Duan et al. in which they treated a diabetic rat using E. coli/ Nissle strain expressing the peptide GLP-1 (Duan, Liu, & March, 2015), which plays a role in insulin production (Duan et al., 2008). This showed distinctive markers (Hamady et al., 2011) which were similar to normal β-cells (non-diabetic) (Duan et al., 2015). The traditional intraperitoneal

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Table 2 A brief account of the molecular therapies derived from bacterial strains with respective response. Molecular therapeutics

Strain producing

AI-2

E. coli strain Nissle 1917(EcN)

Pyocin S5

E. coli (EcN)

IL-10

KGF-2

Lactococcus lactis L. lactis, Bacteroides ovatus B. ovatus

SPLI[82]

L. lactis

Growth factor involved in homeostasis and repair Serine protease inhibitor

Elafin

L. lactis, L. casei

Serine protease inhibitor

TGF-B1

Response

References

Circumvention of Vibrio cholera quorum sensing mechanism Toxin targeting Pseudomonas aeruginosa Anti-inflammatory cytokine

Duan and March (2010)

Anti-inflammatory cytokine

Saeidi et al. (2011) Steidler et al. (2000) and Braat et al. (2006) Bermu´dez-Humara´n et al. (2015) Hamady et al. (2010) Bermu´dez-Humara´n et al. (2015) Bermu´dez-Humara´n et al. (2015) and Motta et al. (2012)

method of GLP-1 (Suzuki, Nakauchi, & Taniguchi, 2003) was replaced by commensal bacterial delivery (Duan et al., 2015) which shows better therapeutic retention. Furthermore it was elucidated that a cocktail of IL-10 with antioxidant producing probiotics treated colorectal cancer (Del Carmen et al., 2017) and IL-10 with antibody therapies treated diabetes (Robert et al., 2014; Takiishi et al., 2012). It was shown that a combination of therapeutics were better in treatment than an individual treatment. Below is a brief account of the molecular therapies mentioned in Table 2.

5 Tools and techniques for synthetic probiotics Synthetic biology is growing at a rapid pace to produce programmed cells using natural and synthetic biological components. This kind of forward engineering has produced many biotechnological advances from chemical product biosynthesis to complex therapeutics (Cameron, Bashor, & Collins, 2014; Ullah, Khattak, Ul-Islam, Khan, & Park, 2016). Next genome sequencing has helped to accelerate this research by analysing large libraries of genetic components. The main developments include synthetic cell to cell communication (Marchand & Collins, 2016), complex and large scale genetic circuits (Brophy & Voigt, 2014) and cluster regularly interspaced short palindromic repeats (CRISPR) based regulation (Hsu, Lander, & Zhang, 2014). For the microbiota engineering field, synthetic biology provides a platform to study structure-function relationships among microbiota and engineer novel biotic therapeutics. Synthetic genetic component-incorporation

5 Tools and techniques for synthetic probiotics

broadens the abilities of the engineered microbe to sense, record and respond to its local environment (Park, Tsai, & Chen, 2013; Rong, Corrie, & Clark, 2017). This success of design is limited but recent advances in expanding genetic tools have opened the door for new and emerging applications.

5.1 Present state of genetic engineering Sequencing techniques such as metagenomics and 16S ribosomal RNA (rRNA) sequencing have been used for analysing human faecal samples, giving rise to genomic data which has helped to identify the composition of the gut microbe (Huttenhower, Gevers, Knight, Abubucker, & Badger, 2012). Comparative analysis of samples from healthy and diseased individuals has generated hypotheses that the compositional makeup of the gut microbiota might play a major role in determining the physiological state of the host (Huttenhower et al., 2012). For example, reduction in bacterial diversity in the gut has been related to obesity (Tsai, Cheng, & Pan, 2014), whereas an increase in species abundance in the vagina has been associated with bacterial vaginosis (Cribby, Taylor, & Reid, 2008). Unfortunately most of the species present in the gut have not been isolated and remain unculturable using traditional methods. Identification of conditions for the proper isolation and culturing of specific species represents the first steps in genetic tool development. Recently, Lagier, Khelaifia, Alou, Ndongo, and Dione (2016) combined high-throughput culture condition testing with MALDI-TOF (matrix-assisted laser desorption/ ionization–time of flight) or 16S rRNA amplification and sequencing to identify unknown species within metagenomics sequence databases. The next steps to create genetically tractable strains will be (a) deliver and maintain exogenous DNA, (b) enable predictable expression of heterologous genes, and (c) edit or regulate endogenous genes. Of the identified bacterial species in the human gut microbiota only a handful at present possess the tools needed for genetic manipulation. Many non-model gut isolates of the genera Bacteroides, Bifidobacterium, and Lactobacillus, and LABs (such as pediococci, lactococci, and streptococci) have been genetically modified like E. coli (Waller, Bober, Nair, & Beisel, 2017).

5.1.1 Foreign DNA incorporation and maintenance After isolation of the culturable bacterial strain which is being considered, exogenous DNA needs to be incorporated stably into the cell. Firstly the acceptance of foreign DNA depends on its transport across the cell wall and membranes. Then rejection by the host’s defence systems must be avoided and followed by active DNA replication within the host. Transport across the cell can be achieved by disruption of the cellular wall (chemical disturbance, electroporation), injecting the DNA (transduction, conjugation) or using already present machinery within the host cell (natural competence). The conditions for transformation need to be optimized for different strains within the same species. Inside the cell the DNA is scrutinized by the host defence system, which is present to eliminate foreign DNA. Examples of these systems are restriction modification, CRISPR/Cas systems and abortive infection systems. Restriction-modification systems pose a barrier to DNA transformation

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as they are present in all bacterial species (Waller et al., 2017). To overcome this barrier three methods have been reported: (a) Use of a readily available intermediate transformable host with a methylation pattern, (b) Use of an engineered intermediate host which expresses the methyltransferases which are predicted to be present in the targeted microbe, and (c) In vitro incubation of DNA with commercially available methyltransferase to match the host’s DNA methylation patterns. A new technique has been used to elevate the transformation efficiency of gut associated Lactobacillus plantarum as compared to E. coli (Spath, Hein, & Grabherr, 2012). Once recognized by the host, the foreign DNA is maintained as a plasmid episomally with the help of selectable markers and a compatible origin of replication or genome integration site using recombination. The first broad range plasmid replicons eligible for expression in LABs and E. coli are pWV01, pSH71 and pAMβ-1 (De Vos, 1987; Fang & O’Toole, 2009; Kok, Van Der Vossen, & Venema, 1984; Landete, 2017; Leenhouts, Kok, & Venema, 1991). E. coli–Bifidobacterium and E. coli–Bacteroides shuttle vectors have been also constructed as genetic tools (Alvarez-Martin, Florez, Margolles, Del Solar, & Mayo, 2008; Garrigues-Jeanjean, Wittmer, Ouriet, & Duval-Iflah, 1999; Matsumura, Takeuchi, & Kano, 1997; Missich, Sgorbati, & LeBlanc, 1994).

5.1.2 Getting predictable gene expression Introducing DNA into the cell allows expression of genes or pathways, changes in cell metabolism and cellular regulation. Though unregulated constitutive expression of genes may be a suitable solution for some applications, it’s often needed to specifically tune expression levels. One of the methods to achieve a range of expression levels is to tune constitutive expression through modification of the ribosomal binding site (RBS) or the Shine–Dalgarno sequence (Chan, Simons, & Maranas, 2017) in the form of a promoter library. This method was used to create a library of promoters with an approximately 104-fold range of expression in L. plantarum and Bacteroides thetaiotaomicron (Mimee et al., 2015; Tauer, Heinl, Egger, Heiss, & Grabherr, 2014). Another method is to utilize a one or two component signal transduction regulatory system for inducible gene expression. Here two quorum sensing systems have been adapted for use as inducible promoter systems in LABs, a nisin lantibiotic dependant system from L. lactis (NICE) and a sakacin-P pheromone controller system from Lactobacillus sakei—both are used widely as a part of LAB engineering (Kaswurm, Nguyen, Maischberger, Kulbe, & Michlmayr, 2013; Mathiesen et al., 2004; Mierau & Kleerebezem, 2005). Induction systems which are chemical based from E coli [isopropyl β-D-1-thiogalactopyranoside (IPTG), D-xylose] have also been established with some success in L. plantarum (Heiss, Hormann, Tauer, Sonnleitner, & Egger, 2016) and B. thetaiotaomicron (Horn et al., 2016). Compatibility will be one of the issues with non-native induction systems which depend on the components across species. For example, the

5 Tools and techniques for synthetic probiotics

sakacin P induction system is incompatible with L. lactis but the NICE system is compatible with various L. lactis strains (Bosma, Forster, & Nielsen, 2017).

5.1.3 Gene and genome scale tools for engineering Heterologous DNA can be transferred into microbial cells to precisely alter the genomic constituent of a cell in order to alter the metabolic flux or to analyse gene function and regulation. Traditional methods for site specific genome editing are based on homologous recombination events, which are enhanced through the use of phage-based recombinases. One more advance came from multiplexed genome editing, which allowed multiple loci to be altered in a precise manner (Dalia, McDonough, & Camilli, 2014). However, recombination was observed only in a few strains of commensal organisms (Lambert, Bongers, & Kleerebezem, 2007; Van Pijkeren & Britton, 2012). CRISPR/Cas9, a genome editing tool which uses RNA-guided DNA cleavage to select for cells that have undergone recombination, has been revolutionary for genome editing (Jiang, Bikard, Cox, Zhang, & Marraffin, 2013). This method can be used to produce large mutant libraries in species having high transformation efficiencies and recombinase activity, which restricts the broader application of CRISPR/Cas9 based genome editing. For instance Van Pijkeren and Britton (2014) and Oh and van Pijkeren (2014) have pioneered the expansion of oligonucleotide-mediated recombination technology in LABs, which was enhanced by using CRISPR/Cas9. These pioneering tools in heterologous expression and site specific genomic mutations will provide many new ventures in the synthetic biology of LABs. A catalytically inactive form of Cas9, d Cas9, has been constructed for use in guiding promoter or coding regions to interfere with transcription rather than DNA cleavage. This tool is termed CRISPRi (CRISPR interference) (Larson et al., 2013). This can be used for controlling gene expression in both native and heterologous DNA in B. thetaiotaomicron, significantly increasing the ability to engineer this commensal bacteria present in the gut (Mimee et al., 2015).

5.1.4 Programming cellular behaviour using genetic circuits Cellular behaviour can be monitored by synthetic genetic regulatory circuits comprised of sensors, processors and actuators. These components can be amalgamated to act as more complex genetic devices, like switches, logic gates, oscillators and biosensors, which facilitate more precise cellular programing for smart applications (Brophy & Voigt, 2014). At present the use of these tools has been established only in a select set of bacteria namely E .coli. Danino, Prindle, Kwong, Skalak, and Li (2015) developed a programmable EcN platform in E. coli (termed PROP-Z) appropriate for in vivo diagnostics. PROP-Z EcN has a luminescence expression system for ex vivo imaging, a toxin- antioxidant system for long term plasmid maintenance and an inducible lacZ reporter. These techniques are being used in detecting tumours in mice but the ability to colonize the human GUT is in question (Ou, Yang, Tham, Chen, & Guo, 2016; Stritzker et al., 2007). This tool can be helpful in engineering the probiotic host and in the treatment of diseases.

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6 Application of synthetic probiotics to human health The application of synthetic probiotics to human health is summarized in Fig. 3 and discussed further below.

6.1 Metabolic modelling of human gut microbiota In spite of our increased knowledge about the microbiome, many aspects remain undiscovered; for example, how microbes metabolically interact among themselves and with the host. This can be well understood with computational methods by postulating hypotheses and followed by testing in the laboratory. One such method is the constrain-based reconstruction and analysis (COBRA) approach (Stritzker et al., 2007), which is used to study metabolic pathways at the intra and inter species levels (Heinken & Thiele, 2015; Oberhardt, Palsson, & Papin, 2009). In Table 3 the advancement of COBRA from 2010 to 2017 is represented.

FIG. 3 Application of synthetic biology to human health.

Table 3 The advancement of COBRA from 2010 to 2017. Year

Advancement

References

2010 2011 2012

Environmental effects on microbial interactions Competition and cooperation within microbial communities Opt com multiple objective microbe-microbe models

2013

First host gut microbe model First human gut microbe-microbe model COMETS spatio-temporal microbe modelling Removing oxygen increases mutualism in gut Dietary changes affect gut microbe metabolism Gut microbes affect host amino acid metabolism Microbe—microbe cooperation-reduces essential metabolites AGORA human gut microbe GENRE resources Steadycom stable microbial composition prediction BacArena individual based spatio-temporal modelling

Klitgord and Segre (2010) Freilich et al. (2011) Zomorrodi and Maranas (2012) Heinken et al. (2013) Shoaie et al. (2013) Harcombe et al. (2014) Heinken and Thiele (2015) Shoaie et al. (2015) Mardinoglu et al. (2015) Zelezniak et al. (2015)

2014 2015

2017

Magnusdottir et al. (2017) Chan et al. (2017) Bauer et al. (2017)

8 Future prospective and challenges

7 Limitations Apart from advancements there are also many current limitations in probiotic systems which need to be evaluated further before further development. Safety is one of the major issues which needs to be addressed apart from the persistence of bacteria. The existing systems available are limited to either L. lactis or E. coli which are not native to the human system. In time these need to be replaced by better adaptive microbes having high efficacy. Many antibiotic treatment approaches for the gut have been explored. Many have tried but failed using B. thetaiotaomicron which is present in the major part of the gut. Researchers have tried to stabilize the gut with a synthetic microbiome but Gibson, Pesesky, and Dantas (2014) screened bacterial genes and suggested the genes may give unexpected products. Single species colonization may lead to further gut dysbiosis. Many further studies are required.

8 Future prospective and challenges There is a need to design a complete model for decision states. The question of how an ideal synthetic probiotic will look like arises? What capabilities and behaviours would it exhibit? Some of the properties which may answer these questions are: (a) A probiotic should be able to colonize the gut during the duration of treatment. (b) A probiotic should be able to diagnose the disease of interest with a series of inputs. (c) Based on the disease a probiotic should be able to initiate outputs by producing therapeutic effects. (d) A probiotic should be able to adjust to the disease state and environmental changes like stress, diet etc. (e) A probiotic may have its output adjusted in an individual in a non-invasive manner. (f ) Lastly when the host reaches a stable state a probiotic should be able to selfdestruct. However, a significant amount of work has to be done in order to achieve these goals. The bacterial systems that are currently available are limited to a set of simple systems. The set of biosensors that are verified are limited and it is urgently required to develop sensors of biologically relevant compounds, improving biosynthesis techniques and continuing to integrate technologies like CRISPRi (Mimee et al., 2015) and siRNA (Xiang, Fruehauf, & Li, 2006). We further need to make the technologies move forward to the next level of clinical trials. The task ahead is daunting but the future for synthetic biology is great with respect to probiotics. The microbial profiling of an individual will help in understanding the dysbiosis of various microorganisms. These results will help in providing the required microorganism to reach homeostasis in the gut which will further treat the disease condition. The use of technology to manipulate bacteria and understanding mechanisms will promote the view of personalized medicine.

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References Aguirre, M., Eck, A., Koenen, M. E., Savelkoul, P. H., Budding, A. E., & Venema, K. (2015). Evaluation of an optimal preparation of human standardized fecal inocula for in vitro fermentation studies. Journal of Microbiological Methods, 117, 78–84. Allander, T., Andreasson, K., & Gupta, S. (2007). Identification of a third human polyomavirus. Journal of Virology, 81, 4130–4136. Allander, T., Tammi, M. T., Eriksson, M., Bjerkner, A., Tiveljung-Lindell, A., & Andersson, B. (2005). Cloning of a human parvovirus by molecular screening of respiratory tract samples. Proceedings of the National Academy of Sciences of the United States of America, 102, 12891–12896. Altmann, F., Kosma, P., & O’Callaghan, A. (2016). Genome analysis and characterisation of the exopolysaccharide produced by Bifidobacterium longum subsp. longum 35624. PLoS One, 11, e0162983. Alvarez-Martin, P., Florez, A. B., Margolles, A., Del Solar, G., & Mayo, B. (2008). Improved cloning vectors for bifidobacteria, based on the Bifidobacterium catenulatum pBC1 replicon. Applied and Environmental Microbiology, 74, 4656–4665. Aoki-Yoshida, A., Saito, S., Fukiya, S., Aoki, R., Takayama, Y., Suzuki, C., et al. (2016). Lactobacillus rhamnosus GG increases toll-like receptor 3 gene expression in murine small intestine ex vivo and in vivo. Beneficial Microbes, 7, 421–429. Archer, E. J., Robinson, A. B., & S€uel, G. M. (2012). Engineered E. coli that detect and respond to gut inflammation through nitric oxide sensing. ACS Synthetic Biology, 1, 451–457. Aziz, Q., Dor
e, J., Emmanuel, A., Guarner, F., & Quigley, E. M. (2013). Gut microbiota and gastrointestinal health: Current concepts and future directions. Neurogastroenterology and Motility, 25, 4–15. Barash, N. R. (2017). Giardia alters commensal microbial diversity throughout the urine gut. Infection and Immunity, 85, e00948-16. Bauer, P. V., Duca, F. A., & Waise, T. M. Z. (2018). Metformin alters upper small intestinal microbiota that impact a glucose-SGLT1-sensing glucoregulatory pathway. Cell Metabolism, 27, 101–117.e5. Bauer, E., Zimmermann, J., Baldini, F., Thiele, I., & Kaleta, C. (2017). BacArena: Individualbased metabolic modeling of heterogeneous microbes in complex communities. PLoS Computational Biology, 13, 1–22. Beatty, J. K. (2017). Giardia duodenalis induces pathogenic dysbiosis of human intestinal microbiota biofilms. International Journal for Parasitology, 47, 311–326. Bennet, S. M. P., B€ohn, L., & St€orsrud, S. (2018). Multivariate modelling of faecal bacterial profiles of patients with IBS predicts responsiveness to a diet low in FODMAPs. Gut, 67, 872–881. Bermu´dez-Humara´n, L. G., Motta, J. P., Aubry, C., Kharrat, P., Rous-Martin, L., Sallenave, J. M., et al. (2015). Serine protease inhibitors protect better than IL-10 and TGF-β anti-inflammatory cytokines against mouse colitis when delivered by recombinant lactococci. Microbial Cell Factories, 14, 26. Bosma, E. F., Forster, J., & Nielsen, A. T. (2017). Lactobacilli and pediococci as versatile cell factories—Evaluation of strain properties and genetic tools. Biotechnology Advances, 35, 419–442. Bouhnik, Y., Flouri
e, B., & Riottot, M. (1996). Effects of fructo-oligosaccharides ingestion on fecal bifidobacteria and selected metabolic indexes of colon carcinogenesis in healthy humans. Nutrition and Cancer, 26, 21–29.

References

Braat, H., Rottiers, P., Hommes, D. W., Huyghebaert, N., Remaut, E., Remon, J., et al. (2006). A phase I trial with transgenic bacteria expressing interleukin-10 in Crohn’s disease. Clinical Gastroenterology and Hepatology, 4, 754–759. Britton, R. A., & Young, V. B. (2012). Interaction between the intestinal microbiota and host in Clostridium difficile colonization resistance. Trends in Microbiology, 20, 313–319. Brophy, J. A. N., & Voigt, C. A. (2014). Principles of genetic circuit design. Nature Methods, 11, 508–520. Caluwaerts, S., Vandenbroucke, K., Steidler, L., Neirynck, S., Vanhoenacker, P., Corveleyn, S., et al. (2010). AG013, a mouth rinse formulation of Lactococcus lactis secreting human trefoil factor 1, provides a safe and efficacious therapeutic tool for treating oral mucositis. Oral Oncology, 46, 564–570. Cameron, D. E., Bashor, C. J., & Collins, J. J. (2014). A brief history of synthetic biology. Nature Reviews. Microbiology, 12, 381–390. Chan, S. H. J., Simons, M. N., & Maranas, C. D. (2017). SteadyCom: Predicting microbial abundances while ensuring community stability. PLoS Computational Biology, 13, e1005539. Charbonneau, D., Gibb, R. D., & Quigley, E. M. (2013). Fecal excretion of Bifidobacterium infantis 35624 and changes in fecal microbiota after eight weeks of oral supplementation with encapsulated probiotic. Gut Microbes, 4, 201–211. Chudnovskiy, A. (2016). Host–protozoan interactions protect from mucosal infections through activation of the inflammasome. Cell, 167, 444–456.e14. Chumpitazi, B. P., Cope, J. L., & Hollister, E. B. (2015). Randomised clinical trial: Gut microbiome biomarkers are associated with clinical response to a low FODMAP diet in children with the irritable bowel syndrome. Alimentary Pharmacology & Therapeutics, 42, 418–427. Cohen, R., Roth, F. J., Delgado, E., Ahearn, D. G., & Kalser, M. H. (1969). Fungal flora of the normal human small and large intestine. The New England Journal of Medicine, 280, 638–641. Collins, J. K., Dunne, C., & Murphy, L. (2002). A randomised controlled trial of a probiotic Lactobacillus strain in healthy adults: Assessment of its delivery, transit and influence on microbial flora and enteric immunity. Microbial Ecology in Health and Disease, 14, 81–89. Cook, S. I., & Sellin, J. H. (1998). Review article: Short chain fatty acids in health and disease. Alimentary Pharmacology & Therapeutics, 12, 499–507. Corr, S. C., Li, Y., Riedel, C. U., O’Toole, P. W., Hill, C., & Gahan, C. G. (2007). Bacteriocin production as a mechanism for the anti infective activity of Lactobacillus salivarius UCC118. Proceedings of the National Academy of Sciences of the United States of America, 104, 7617–7621. Cribby, S., Taylor, M., & Reid, G. (2008). Vaginal microbiota and the use of probiotics. Interdisciplinary Perspectives on Infectious Diseases, 2008, 256490. Dalia, A. B., McDonough, E., & Camilli, A. (2014). Multiplex genome editing by natural transformation. Proceedings of the National Academy of Sciences of the United States of America, 111, 8937–8942. Danino, T., Prindle, A., Kwong, G. A., Skalak, M., & Li, H. (2015). Programmable probiotics for detection of cancer in urine. Science Translational Medicine, 7, 289ra84. Davies, S. S., Chen, Z., Guo, L., Zhang, Y., Stien, X., & Coulon, D. (2014). Incorporation of therapeutically modified bacteria into gut microbiota inhibits obesity. Free Radical Biology & Medicine, 124, 3381–3406.

215

216

CHAPTER 9 Deciphering the microbial code for futuristic diagnosis

Davis, L. M., Martinez, I., Walter, J., & Hutkins, R. (2010). Aldose dependent impact of prebiotic galacto oligosaccharides on the intestinal microbiota of healthy adults. International Journal of Food Microbiology, 144, 285–292. De Palma, G., Nadal, I., Collado, M. C., & Sanz, Y. (2009). Effects of a gluten-free diet on gut microbiota and immune function in healthy adult human subjects. The British Journal of Nutrition, 102, 1154–1160. De Vos, W. M. (1987). Gene cloning and expression in lactic streptococci. FEMS Microbiology Letters, 46, 281–295. Del Carmen, S., Moreno, d., de LeBlanc, A., Levit, R., Azevedo, V., Langella, P., et al. (2017). Anti-cancer effect of lactic acid bacteria expressing antioxidant enzymes or IL-10 in a colorectal cancer mouse model. International Immunopharmacology, 42, 122–129. Demehri, F. R., Frykman, P. K., Cheng, Z., Ruan, C., Wester, T., Nordenskjold, A., et al. (2016). Altered fecal short chain fatty acid composition in children with a history of Hirschsprung-associated enterocolitis. Journal of Pediatric Surgery, 51, 81–86. Desbonnet, L., Garrett, L., Clarke, G., Bienenstock, J., & Dinan, T. G. (2008). The probiotic Bifidobacteria infantis: An assessment of potential antidepressant properties in the rat. Journal of Psychiatric Research, 43, 164–174. Di Bella, J. M., Bao, Y., Gloor, G. B., Burton, J. P., & Reid, G. (2013). High throughput sequencing methods and analysis for microbiome research. Journal of Microbiological Methods, 95, 401–414. Drgona, L., Khachatryan, A., Stephens, J., Charbonneau, C., Kantecki, M., Haider, S., et al. (2014). Clinical and economic burden of invasive fungal diseases in Europe: Focus on preemptive and empirical treatment of aspergillus and candida species. European Journal of Clinical Microbiology & Infectious Diseases, 33, 7–21. Duan, F., Curtis, K. L., & March, J. C. (2008). Secretion of insulinotropic proteins by commensal bacteria: Rewiring the gut to treat diabetes. Applied and Environmental Microbiology, 74, 7437–7438. Duan, F. F., Liu, J. H., & March, J. C. (2015). Engineered commensal bacteria reprogram intestinal cells into glucose-responsive insulin-secreting cells for the treatment of diabetes. Diabetes, 64, 1794–1803. Duan, F., & March, J. C. (2010). Engineered bacterial communication prevents Vibrio cholera virulence in an infant mouse model. Proceedings of the National Academy of Sciences of the United States of America, 107, 11260–11264. Dunne, C., O’Mahony, L., Murphy, L., et al. (2001). In vitro selection criteria for probiotic bacteria of human origin: Correlation with in vivo findings. The American Journal of Clinical Nutrition, 73, 386s–392s. Fang, F., & O’Toole, P. W. (2009). Genetic tools for investigating the biology of commensal lactobacilli. Frontiers in Bioscience, 14, 3111–3127. Finkbeiner, S. R., Allred, A. F., Tarr, P. I., Klein, E. J., Kirkwood, C. D., & Wang, D. (2008). Metagenomic analysis of human diarrhea: Viral detection and discovery. PLoS Pathogens, 4, e1000011. Finkbeiner, S. R., Le, B. M., Holtz, L. R., Storch, G. A., & Wang, D. (2009). Detection of newly described astrovirus MLB1 in stool samples from children. Emerging Infectious Diseases, 15, 441–444. Finkbeiner, S. R., Li, Y., & Ruone, S. (2009). Identification of a novel astrovirus (astrovirus VA1) associated with an outbreak of acute gastroenteritis. Journal of Virology, 83, 10836–10839.

References

Freilich, S., Zarecki, R., Eilam, O., Segal, E. S., Henry, C. S., Kupiec, M., et al. (2011). Competitive and cooperative metabolic interactions in bacterial communities. Nature Communications, 2, 589. Galtier, M., De Sordi, L., & Sivignon, A. (2017). Bacteriophages targeting adherent invasive Escherichia coli strains as a promising new treatment for Crohn’s disease. Journal of Crohn’s & Colitis, 11, 840–847. Garrigues-Jeanjean, N., Wittmer, A., Ouriet, M., & Duval-Iflah, Y. (1999). Transfer of the shuttle vector pRRI207 between Escherichia coli and Bacteroides spp. in vitro and in vivo in the digestive tract of axenic mice and in gnotoxenic mice inoculated with a human microflora. FEMS Microbiology Ecology, 29, 33–43. Gibson, G. R., Hutkins, R., & Sanders, M. E. (2017). Expert consensus document: The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nature Reviews. Gastroenterology & Hepatology, 14, 491–502. Gibson, M. K., Pesesky, M. W., & Dantas, G. (2014). The yin and yang of bacterial resilience in the human gut microbiota. Journal of Molecular Biology, 426, 3866–3876. Gillevet, P. M., Sikaroodi, M., & Torzilli, A. P. (2009). Analyzing salt-marsh fungal diversity: Comparing arisa fingerprinting with clone sequencing and pyrosequencing. Fungal Ecology, 2, 160–167. Groeger, D., O’Mahony, L., & Murphy, E. F. (2013). Bifidobacterium infantis 35624 modulates host inflammatory processes beyond the gut. Gut Microbes, 4, 325–339. Group TNHW, Peterson, J., Garges, S., Giovanni, M., McInnes, P., Wang, L., et al. (2009). The NIH human microbiome project. Genome Research, 19, 2317–2323. Halmos, E. P., Power, V. A., Shepherd, S. J., Gibson, P. R., & Muir, J. G. (2014). A diet low in FODMAPs reduces symptoms of irritable bowel syndrome. Gastroenterology, 146, 67–75.e5. Hamad, I. (2016). Repertory of eukaryotes (eukaryome) in the human gastro intestinal tract: Taxonomy and detection methods. Parasite Immunology, 38, 12–36. Hamad, I., Sokhna, C., Raoult, D., & Bittar, F. (2012). Molecular detection of eukaryotes in a single human stool sample from Senegal. PLoS One, 7, e40888. Hamady, Z. (2013). Novel xylan-controlled delivery of therapeutic proteins to inflamed colon by the human anaerobic commensal bacterium. Annals of the Royal College of Surgeons of England, 95, 235–240. Hamady, Z. Z. R., Scott, N., Farrar, M. D., Lodge, J. P. A., Holland, K. T., Whitehead, T., et al. (2010). Xylan-regulated delivery of human keratinocyte growth factor-2 to the inflamed colon by the human anaerobic commensal bacterium Bacteroides ovatus. Gut, 59, 461–469. Hamady, Z. Z. R., Scott, N., Farrar, M. D., Wadhwa, M., Dilger, P., Whitehead, T. R., et al. (2011). Treatment of colitis with a commensal gut bacterium engineered to secrete human TGF-β1 under the control of dietary xylan. Inflammatory Bowel Diseases, 17, 1925–1935. Harcombe, W. R., Riehl, W. J., Dukovski, I., Granger, B. R., Betts, A., Lang, A. H., et al. (2014). Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Reports, 7, 1104–1115. Hayashi, Y., Toyomasu, Y., & Saravanaperumal, S. A. (2017). Hyperglycemia increases interstitial cells of Cajal via MAPK1 and MAPK3 signaling to ETV1 and KIT, leading to rapid gastric emptying. Gastroenterology, 153, 521–535.e20.

217

218

CHAPTER 9 Deciphering the microbial code for futuristic diagnosis

Hayes, K. S., Bancroft, A. J., Goldrick, M., Portsmouth, C., Roberts, I. S., & Grencis, R. K. (2010). Exploitation of the intestinal microflora by the parasitic nematode Trichuris muris. Science, 328(5984), 1391–1394. Heinken, A., Sahoo, S., Fleming, R. M., & Thiele, I. (2013). Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes, 4, 28–40. Heinken, A., & Thiele, I. (2015). Systems biology of host–microbe metabolomics. Wiley Interdisciplinary Reviews. Systems Biology and Medicine, 7, 195–219. Heiss, S., Hormann, A., Tauer, C., Sonnleitner, M., & Egger, E. (2016). Evaluation of novel inducible promoter/repressor systems for recombinant protein expression in Lactobacillus plantarum. Microbial Cell Factories, 15, 50. Hoffmann, C., Dollive, S., Grunberg, S., Chen, J., Li, H., Wu, G. D., et al. (2013). Archaea and fungi of the human gut microbiome: Correlations with diet and bacterial residents. PLoS One, 8, e66019. Holscher, H. D. (2017). Dietary fiber and prebiotics and the gastrointestinal microbiota. Gut Microbes, 8, 172–184. Horn, N., Carvalho, A. L., Overweg, K., Wegmann, U., Carding, S. R., & Stentz, R. (2016). A novel tightly regulated gene expression system for the human intestinal symbiont Bacteroides thetaiotaomicron. Frontiers in Microbiology, 7, 1080. Hsu, P. D., Lander, E. S., & Zhang, F. (2014). Development and applications of CRISPR-Cas9 for genome engineering. Cell, 157, 1262–1278. Huffnagle, G. B., & Noverr, M. C. (2013). The emerging world of the fungal microbiome. Trends in Microbiology, 21, 334–341. Huttenhower, C., Gevers, D., Knight, R., Abubucker, S., & Badger, J. H. (2012). Structure, function and diversity of the healthy human microbiome. Nature, 486, 207–214. Iebba, V. (2016). Gut microbiota related to Giardia duodenalis, Entamoeba spp. and Blastocystis hominis infections in humans from Coted’Ivoire. Journal of Infection in Developing Countries, 10, 1035–1041. Jiang, W., Bikard, D., Cox, D., Zhang, F., & Marraffin, L. A. (2013). CRISPR-assisted editing of bacterial genomes. Nature Biotechnology, 31, 233–239. Johnson, B. M., Gaudreau, M. C., Al-Gadban, M. M., Gudi, R., & Vasu, C. (2015). Impact of dietary deviation on disease progression and gut microbiome composition in lupus-prone SNF1 mice. Clinical and Experimental Immunology, 181, 323–337. Kapoor, A., Li, L., & Victoria, J. (2009). Multiple novel astrovirus species in human stool. The Journal of General Virology, 90, 2965–2972. Karlsson, F. H., Tremaroli, V., & Nookaew, I. (2013). Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature, 498, 99–103. Kaswurm, V., Nguyen, T. T., Maischberger, T., Kulbe, K. D., & Michlmayr, H. (2013). Evaluation of the food grade expression systems NICE and pSIP for the production of 2,5-diketo-D-gluconic acid reductase from Corynebacterium glutamicum. AMB Express, 3, 7. Klitgord, N., & Segre, D. (2010). Environments that induce synthetic microbial ecosystems. PLoS Computational Biology, 6, e1001002. Kok, J., Van Der Vossen, J. M. B. M., & Venema, G. (1984). Construction of plasmid cloning vectors for lactic streptococci which also replicate in Bacillus subtilis and Escherichia coli. Applied and Environmental Microbiology, 48, 726–731. Konieczna, P., Akdis, C. A., Quigley, E. M., Shanahan, F., & O’Mahony, L. (2012). Portrait of an immune regulatory bifidobacterium. Gut Microbes, 3, 261–266.

References

Konieczna, P., Ferstl, R., & Ziegler, M. (2013). Immunomodulation by Bifidobacterium infantis 35624 in the murine lamina propria requires retinoic acid-dependent and independent mechanisms. PLoS One, 8, e62617. Konieczna, P., Groeger, D., & Ziegler, M. (2012). Bifidobacterium infantis 35624 administration induces Foxp3 T regulatory cells in human peripheral blood: Potential role for myeloid and plasmacytoid dendritic cells. Gut, 61, 354–366. Kuczynski, J., Lauber, C. L., Walters, W. A., Parfrey, L. W., Clemente, J. C., Gevers, D., et al. (2012). Experimental and analytical tools for studying the human microbiome. Nature Reviews. Genetics, 13, 47–58. Lagenaur, L. A., Sanders-Beer, B. E., Brichacek, B., Pal, R., Liu, X., Liu, Y., et al. (2011). Prevention of vaginal SHIV transmission in macaques by a live recombinant Lactobacillus. Mucosal Immunology, 4, 648–657. Lagier, J. C., Khelaifia, S., Alou, M. T., Ndongo, S., & Dione, N. (2016). Culture of previously uncultured members of the human gut microbiota by culturomics. Nature Microbiology, 1, 1–8. Lambert, J. M., Bongers, R. S., & Kleerebezem, M. (2007). Cre-lox-based system for multiple gene deletions and selectable-marker removal in Lactobacillus plantarum. Applied and Environmental Microbiology, 73, 1126–1135. Landete, J. M. (2017). A review of food-grade vectors in lactic acid bacteria: From the laboratory to their application. Critical Reviews in Biotechnology, 37, 296–308. Larson, M. H., Gilbert, L. A., Wang, X., Lim, W. A., Weissman, J. S., & Qi, L. S. (2013). CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nature Protocols, 8, 2180–2196. Lawley, T. D., Clare, S., & Walker, A. W. (2009). Antibiotic treatment of Clostridium difficile carrier mice triggers a supershedder state, spore-mediated transmission, and severe disease in immunocompromised hosts. Infection and Immunity, 77, 3661–3669. Lee, H. J., Choi, J. K., Ryu, H. S., Choi, C. H., Kang, E. H., Park, K. S., et al. (2017). Therapeutic modulation of gut microbiota in functional bowel disorders. Journal of Neurogastroenterology and Motility, 23, 9–19. Leenhouts, K. J., Kok, J., & Venema, G. (1991). Lactococcal plasmid pWVO1 as an integration vector for lactococci. Applied and Environmental Microbiology, 57, 2562–2567. Li, D., Wang, P., Wang, P., Hu, X., & Chen, F. (2016). The gut microbiota: A treasure for human health. Biotechnology Advances, 34, 1210–1224. Li, R. W., Wu, S., Li, W., Navarro, K., Couch, R. D., & Hill, D. (2012). Alterations in the porcine colon microbiota induced by the gastrointestinal nematode Trichuris suis. Infection and Immunity, 80(6), 2150–2157. Lin, L., & Zhang, J. (2017). Role of intestinal microbiota and metabolites on gut homeostasis and human diseases. BMC Immunology, 18, 2. Łusiak-Szelachowska, M., Weber-Da˛browska, B., Jo
nczyk-Matysiak, E., Wojciechowska, R., & Go´rski, A. (2017). Bacteriophages in the gastrointestinal tract and their implications. Gut Pathogens, 9, 44. MacFabe, D. F. (2015). Enteric short-chain fatty acids: Microbial messengers of metabolism, mitochondria, and mind: Implications in autism spectrum disorders. Microbial Ecology in Health and Disease, 26, 28177. Magnusdottir, S., Heinken, A., Kutt, L., Ravcheev, D. A., Bauer, E., Noronha, A., et al. (2017). Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nature Biotechnology, 35, 81–89.

219

220

CHAPTER 9 Deciphering the microbial code for futuristic diagnosis

Mansfield, L. S., Gauthier, D. T., Abner, S. R., Jones, K. M., Wilder, S. R., & Urban, J. F. (2003). Enhancement of disease and pathology by synergy of Trichuris suis and Campylobacter jejuni in the colon of immunologically naive swine. The American Journal of Tropical Medicine and Hygiene, 68(3), 70–80. Mar Rodrı´guez, M., P
erez, D., Javier Chaves, F., Esteve, E., Marin-Garcia, P., Xifra, G., et al. (2015). Obesity changes the human gut mycobiome. Scientific Reports, 5, 14600. Marchand, N., & Collins, C. H. (2016). Synthetic quorum sensing and cell–cell communication in gram-positive Bacillus megaterium. ACS Synthetic Biology, 5, 597–606. Mardinoglu, A., Shoaie, S., Bergentall, M., Ghaffari, P., Zhang, C., Larsson, E., et al. (2015). The gut microbiota modulates host amino acid and glutathione metabolism in mice. Molecular Systems Biology, 11, 834. Martı´n, R., Miquel, S., Ulmer, J., Kechaou, N., Langella, P., & Bermu´dez-Humara´n, L. G. (2013). Role of commensal and probiotic bacteria in human health: A focus on inflammatory bowel disease. Microbial Cell Factories, 12, 71. Mathiesen, G., Sørvig, E., Blatny, J., Naterstad, K., Axelsson, L., & Eijsink, V. G. H. (2004). High-level gene expression in Lactobacillus plantarum using a pheromone-regulated bacteriocin promoter. Letters in Applied Microbiology, 39, 137–143. Matsumura, H., Takeuchi, A., & Kano, Y. (1997). Construction of Escherichia coli–Bifidobacterium longum shuttle vector transforming B. longum 105-A and 108-A. Bioscience, Biotechnology, and Biochemistry, 61, 1211–1212. McErlean, P., Shackelton, L. A., Lambert, S. B., Nissen, M. D., Sloots, T. P., & Mackay, I. M. (2007). Characterisation of a newly identified human rhinovirus, HRV-QPM, discovered in infants with bronchiolitis. Journal of Clinical Virology, 39, 67–75. Merrigan, M. M., Sambol, S. P., Johnson, S., & Gerding, D. N. (2003). Prevention of fatal Clostridium difficile associated disease during continuous administration of clindamycin in hamsters. The Journal of Infectious Diseases, 188, 1922–1927. Mierau, I., & Kleerebezem, M. (2005). 10 years of the nisin-controlled gene expression system (NICE) in Lactococcus lactis. Applied Microbiology and Biotechnology, 68, 705–717. Mimee, M., Citorik, R. J., & Lu, T. K. (2016). Microbiome therapeutics—Advances and challenges. Advanced Drug Delivery Reviews, 105, 44–54. Mimee, M., Tucker, A. C., Voigt, C. A., & Lu, T. K. (2015). Programming a human commensal bacterium, Bacteroides thetaiotaomicron, to sense and respond to stimuli in the murine gut microbiota. Cell Systems, 1, 62–71. Missich, R., Sgorbati, B., & LeBlanc, D. J. (1994). Transformation of Bifidobacterium longum with pRM2, a constructed Escherichia coli–B. longum shuttle vector. Plasmid, 32, 208–211. Momozawa, Y., Deffontaine, V., Louis, E., & Medrano, J. F. (2011). Characterization of bacteria in biopsies of colon and stools by high throughput sequencing of the V2 region of bacterial 16S rRNA gene in human. PLoS One, 6, e16952. Morgan, X. C., Segata, N., & Huttenhower, C. (2013). Biodiversity and functional genomics in the human microbiome. Trends in Genetics, 29, 51–58. Motta, J. P., Bermu´dez-Humara´n, L. G., Deraison, C., Martin, L., Rolland, C., Rousset, P., et al. (2012). Food-grade bacteria expressing elafin protect against inflammation and restore colon homeostasis. Science Translational Medicine, 4, 158ra144. Moyes, D. L., & Naglik, J. R. (2011). Mucosal immunity and Candida albicans infection. Clinical & Developmental Immunology, 2011, 346307.

References

Nastasi, C., Candela, M., Bonefeld, C. M., Geisler, C., Hansen, M., Krejsgaard, T., et al. (2015). The effect of short-chain fatty acids on human monocyte-derived dendritic cells. Scientific Reports, 5, 16148. Oberhardt, M. A., Palsson, B. O., & Papin, J. A. (2009). Applications of genome scale metabolic reconstructions. Molecular Systems Biology, 5, 320. Oh, J. H., & van Pijkeren, J. P. (2014). CRISPR-Cas9-assisted recombineering in Lactobacillus reuteri. Nucleic Acids Research, 42, e131. O’Mahony, L., Feeney, M., & O’Halloran, S. (2001). Probiotic impact on microbial flora, inflammation and tumour development in IL-10 knockout mice. Alimentary Pharmacology & Therapeutics, 15, 1219–1225. O’Mahony, L., O’Callaghan, L., & McCarthy, J. (2006). Differential cytokine response from dendritic cells to commensal and pathogenic bacteria in different lymphoid compartments in humans. American Journal of Physiology. Gastrointestinal and Liver Physiology, 290, G839–G845. Ott, S. J., K€ uhbacher, T., Musfeldt, M., Rosenstiel, P., Hellmig, S., Rehman, A., et al. (2008). Fungi and inflammatory bowel diseases: Alterations of composition and diversity. Scandinavian Journal of Gastroenterology, 43, 831–841. Ou, B., Yang, Y., Tham, W. L., Chen, L., & Guo, J. (2016). Genetic engineering of probiotic Escherichia coli Nissle1917 for clinical application. Applied Microbiology and Biotechnology, 100, 8693–8699. Palacios, G., Druce, J., & Du, L. (2008). A new arenavirus in a cluster of fatal transplantassociated diseases. The New England Journal of Medicine, 358, 991–998. Park, J., Goergen, C. J., HogenEsch, H., & Kim, C. H. (2016). Chronically elevated levels of short-chain fatty acids induce T cell-mediated ureteritis and hydronephrosis. Journal of Immunology, 196, 2388–2400. Park, M., Tsai, S. L., & Chen, W. (2013). Microbial biosensors: Engineered microorganisms as the sensing machinery. Sensors, 3, 5777–5795. Plieskatt, J. L., Deenonpoe, R., Mulvenna, J. P., Krause, L., Sripa, B., & Bethony, J. M. (2013). Infection with the carcinogenic liver fluke Opisthorchis viverrini modifies intestinal and biliary microbiome. FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 27(11), 4572–4584. Qin, J., Li, Y., & Cai, Z. (2012). A metagenome-wide association study of gut microbiota in type2 diabetes. Nature, 490, 55–60. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., et al. (2010). A human gut microbial gene catalog established by metagenomic sequencing. Nature, 464, 59–65. Quigley, E. M. (2013). Gut bacteria in health and disease. Gastroenterology & Hepatology, 9, 560–569. Quigley, E. M. M. (2017). Gut microbiome as a clinical tool in gastrointestinal disease management: Are we there yet? Nature Reviews. Gastroenterology & Hepatology, 14, 315–320. Ramith, R., Prithvi, S. S., Aishwarya, T. D., Ashwini, P., Kumuda, J., Lochana, M. S., et al. (2015). Bacteriocins and their applications in food preservation. Critical Reviews in Food Science and Nutrition, 20, 0. Rastall, R. A., & Gibson, G. R. (2015). Recent developments in prebiotics to selectively impact beneficial microbes and promote intestinal health. Current Opinion in Biotechnology, 32, 42–46.

221

222

CHAPTER 9 Deciphering the microbial code for futuristic diagnosis

Rausch, S., Held, J., Fischer, A., Heimesaat, M. M., Kuhl, A. A., & Bereswill, S. (2013). Small intestinal nematode infection of mice is associated with increased enterobacterial loads alongside the intestinal tract. PLoS One, 8(9), e74026. Rea, M. C., Sit, C. S., & Clayton, E. (2010). Thuricin CD. A post translationally modified bacteriocin with a narrow spectrum of activity against Clostridium difficile. Proceedings of the National Academy of Sciences of the United States of America, 107, 9352–9357. Reynolds, L. A., Smith, K. A., Filbey, K. J., Harcus, Y., Hewitson, J. P., & Redpath, S. A. (2014). Commensal-pathogen interactions in the intestinal tract: Lactobacilli promote infection with, and are promoted by, helminth parasites. Gut Microbes, 5(4), 522–532. Ridlon, J. M., Kang, D. J., & Hylemon, P. B. (2006). Bile salt biotransformations by human intestinal bacteria. Journal of Lipid Research, 47, 241–259. Robert, S., Gysemans, C., Takiishi, T., Korf, H., Spagnuolo, I., Sebastiani, G., et al. (2014). Oral delivery of glutamic acid decarboxylase (GAD)-65 and IL10 by Lactococcus lactis reverses diabetes in recent-onset NOD mice. Diabetes, 63, 2876–2887. Rong, G., Corrie, S. R., & Clark, H. A. (2017). In vivo biosensing: Progress and perspectives. ACS Sensors, 2, 327–338. Saeidi, N., Wong, C. K., Lo, T. M., Nguyen, H. X., Ling, H., Leong, S. S. J., et al. (2011). Engineering microbes to sense and eradicate Pseudomonas aeruginosa, a human pathogen. Molecular Systems Biology, 7, 521. Sambol, S. P., Merrigan, M. M., Tang, J. K., Johnson, S., & Gerding, D. N. (2002). Colonization for the prevention of Clostridium difficile disease in hamsters. The Journal of Infectious Diseases, 186, 1781–1789. Savignac, H. M., Tramullas, M., Kiely, B., Dinan, T. G., & Cryan, J. F. (2015). Bifidobacteria modulate cognitive processes in an anxious mouse strain. Behavioural Brain Research, 287, 59–72. Schiavi, E., Gleinser, M., & Molloy, E. (2016). The surface-associated exopolysaccharide of Bifidobacterium longum 35624 plays an essential role in dampening host proinflammatory responses and repressing local TH17 responses. Applied and Environmental Microbiology, 82, 7185–7196. Scully, P., Macsharry, J., & O’Mahony, D. (2013). Bifidobacterium infantis suppression of Peyer’s patch MIP-1alpha and MIP-1beta secretion during Salmonella infection correlates with increased local CD4+ CD25+ T cell numbers. Cellular Immunology, 281, 134–140. Segata, N., Boernigen, D., Tickle, T. L., Morgan, X. C., Garrett, W. S., & Huttenhower, C. (2013). Computational meta’omics for microbial community studies. Molecular Systems Biology, 9, 666. Shanahan, F., & Collins, S. M. (2010). Pharmabiotic manipulation of the microbiota in gastrointestinal disorders, from rationale to reality. Gastroenterology Clinics of North America, 39, 721–726. Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., Mardinoglu, A., Sen, P., Pujos-Guillot, E., et al. (2015). Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metabolism, 22, 320–331. Shoaie, S., Karlsson, F., Mardinoglu, A., Nookaew, I., Bordel, S., & Nielsen, J. (2013). Understanding the interactions between bacteria in the human gut through metabolic modeling. Scientific Reports, 3, 2532. Sibartie, S., O’Hara, A. M., & Ryan, J. (2009). Modulation of pathogen-induced CCL20 secretion from HT-29 human intestinal epithelial cells by commensal bacteria. BMC Immunology, 10, 54.

References

Slapeta, J. (2015). Differences in the faecal microbiome of non-diarrhoeic clinically healthy dogs and cats associated with Giardia duodenalis infection: Impact of hookworms and coccidia. International Journal for Parasitology, 45, 585–594. Smyk, D. S., Koutsoumpas, A. L., Mytilinaiou, M. G., Rigopoulou, E. I., Sakkas, L. I., & Bogdanos, D. P. (2014). Helicobacter pylori and autoimmune disease: Cause or bystander. World Journal of Gastroenterology, 20, 613–629. Spath, K., Hein, S., & Grabherr, R. (2012). Direct cloning in Lactobacillus plantarum: Electroporation with non-methylated plasmid DNA enhances transformation efficiency and makes shuttle vectors obsolete. Microbial Cell Factories, 11, 141. Spinler, J. K., Ross, C. L., & Savidge, T. C. (2016). Probiotics as adjunctive therapy for preventing Clostridium difficile infection—What are we waiting for? Anaerobe, 41, 51–57. Staudacher, H. M., Lomer, M. C. E., & Farquharson, F. M. (2017). A diet low in FODMAPs reduces symptoms in patients with irritable bowel syndrome and a probiotic restores Bifidobacterium species: A randomized controlled trial. Gastroenterology, 153, 936–947. Staudacher, H. M., Whelan, K., Irving, P. M., & Lomer, M. C. (2011). Comparison of symptom response following advice for a diet low in fermentable carbohydrates (FODMAPs) versus standard dietary advice in patients with irritable bowel syndrome. Journal of Human Nutrition and Dietetics, 24, 487–495. Steidler, L., Hans, W., Schotte, L., Neirynck, S., Obermeier, F., Falk, W., et al. (2000). Treatment of murine colitis by Lactococcus lactis secreting interleukin-10. Science, 289, 1352–1355. Stritzker, J., Weibel, S., Hill, P. J., Oelschlaeger, T. A., Goebel, W., & Szalay, A. A. (2007). Tumor-specific colonization, tissue distribution, and gene induction by probiotic Escherichia coli Nissle 1917 in live mice. International Journal of Medical Microbiology, 297, 151–162. Suhr, M. J., & Hallen-Adams, H. E. (2015). The human gut mycobiome: Pitfalls and potentials—A mycologist’s perspective. Mycologia, 107, 1057–1073. Suzuki, A., Nakauchi, H., & Taniguchi, H. (2003). Glucagon-like peptide 1 (1-37) converts intestinal epithelial cells into insulin-producing cells. Proceedings of the National Academy of Sciences of the United States of America, 100, 5034–5039. Symonds, E. L., O’Mahony, C., & Lapthorne, S. (2012). Bifidobacterium infantis 35624 protects against salmonella-induced reductions in digestive enzyme activity in mice by attenuation of the host inflammatory response. Clinical and Translational Gastroenterology, 3, e15. Takiishi, T., Korf, H., Van Belle, T. L., Robert, S., Grieco, F. A., Caluwaerts, S., et al. (2012). Reversal of autoimmune diabetes by restoration of antigen-specific tolerance using genetically modified Lactococcus lactis in mice. The Journal of Clinical Investigation, 122, 1717–1725. Tauer, C., Heinl, S., Egger, E., Heiss, S., & Grabherr, R. (2014). Tuning constitutive recombinant gene expression in Lactobacillus plantarum. Microbial Cell Factories, 13, 150. Tsai, Y. T., Cheng, P. C., & Pan, T. M. (2014). Anti-obesity effects of gut microbiota are associated with lactic acid bacteria. Applied Microbiology and Biotechnology, 98, 1–10. Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C., Knight, R., & Gordon, J. I. (2007). The human microbiome project: Exploring the microbial part of ourselves in a changing world. Nature, 449, 804–810. Ullah, M. W., Khattak, W. A., Ul-Islam, M., Khan, S., & Park, J. K. (2016). Metabolic engineering of synthetic cell-free systems: Strategies and applications. Biochemical Engineering Journal, 105, 391–405.

223

224

CHAPTER 9 Deciphering the microbial code for futuristic diagnosis

Van Pijkeren, J. P., & Britton, R. A. (2012). High efficiency recombineering in lactic acid bacteria. Nucleic Acids Research, 40, e76. Van Pijkeren, J. P., & Britton, R. A. (2014). Precision genome engineering in lactic acid bacteria. Microbial Cell Factories, 13, S10. Verma, A. K. (2012). Real-time analysis of gut flora in Entamoeba histolytica infected patients of Northern India. BMC Microbiology, 12, 183. Victoria, J. G., Kapoor, A., & Li, L. (2009). Metagenomic analyses of viruses in stool samples from children with acute flaccid paralysis. Journal of Virology, 83, 4642–4651. Walk, S. T., Blum, A. M., Ewing, S. A., Weinstock, J. V., & Young, V. B. (2010). Alteration of the murine gut microbiota during infection with the parasitic helminth Heligmosomoides polygyrus. Inflammatory Bowel Diseases, 16(11), 1841–1849. Wallace, C. J. K., & Milev, R. (2017). The effects of probiotics on depressive symptoms in humans: A systematic review. Annals of General Psychiatry, 16, 14. Waller, M. C., Bober, J. R., Nair, N. U., & Beisel, C. L. (2017). Toward a genetic tool development pipeline for host-associated bacteria. Current Opinion in Microbiology, 38, 156–164. Weinstock, G. M. (2012). Genomic approaches to studying the human microbiota. Nature, 489, 250–256. Westendorf, A. M., Gunzer, F., Deppenmeier, S., Tapadar, D., Hunger, J. K., Schmidt, M. A., et al. (2005). Intestinal immunity of Escherichia coli Nissle 1917: A safe carrier for therapeutic molecules. FEMS Immunology and Medical Microbiology, 4, 373–384. Wu, H., Esteve, E., & Tremaroli, V. (2017). Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nature Medicine, 23, 850–858. Wu, S., Li, R. W., Li, W., Beshah, E., Dawson, H. D., & Urban, J. F., Jr. (2012). Worm burdendependent disruption of the porcine colon microbiota by Trichuris suis infection. PLoS One, 7(4), e35470. Xiang, S., Fruehauf, J., & Li, C. J. (2006). Short hairpin RNA-expressing bacteria elicit RNA interference in mammals. Nature Biotechnology, 24, 697–702. Zeevi, D., Korem, T., & Zmora, N. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163, 1079–1094. Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D. R., Bork, P., & Patil, K. R. (2015). Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proceedings of the National Academy of Sciences of the United States of America, 112, 6449–6454. Zomorrodi, A. R., & Maranas, C. D. (2012). OptCom: A multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Computational Biology, 8, e1002363.

Further reading Duan, F., & March, J. C. (2008). Interrupting Vibrio cholerae infection of human epithelial cells with engineered commensal bacterial signaling. Biotechnology and Bioengineering, 101, 128–134. Halmos, E. P., Christophersen, C. T., Bird, A. R., Shepherd, S. J., Gibson, P. R., & Muir, J. G. (2015). Diets that differ in their FODMAP content alter the colonic luminal microenvironment. Gut, 64, 93–100.

Further reading

Kumamoto, C. A. (2011). Inflammation and gastrointestinal candida colonization. Current Opinion in Microbiology, 14, 386–391. Palsson, B. (2015). Systems biology: Constraint-based reconstruction and analysis. Cambridge University Press. Wescott, R. B. (1968). Experimental Nematospiroides dubius infection in germfree and conventional mice. Experimental Parasitology, 22(2), 245–249.

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