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The human gut microbiota: environment and composition
The GIT is a heterogeneous environment which can be divided into several distinct regions showing important differences due to their physiological functions. Along with the human evolution, the miscalled “gut flora”, relic of a static Linnaeus Scala naturæ view of life, has also evolved to adapt to these particular environmental constraints, and some species developed specific features, to allow them to colonize efficiently these environments. One of the most important differences between the different areas in the GIT is pH, which progressively increases along the GIT, starting with a very acidic pH of 1 to 2 in the stomach, to a value comprised between 5 to 7 in the intestine (Prakash et al. 2011). After the stomach, the GIT is composed of two distinct elements, the small intestine (duodenum, jejunum and ileum) where most of the host intake process takes part, and the colon (or large intestine) (fig. 1a). Together with this progressive pH increase, the number of micro-organisms increases along the GIT, starting with 101 to 102 cells per gram of wet weight of stomach content to no less than 1012 cells per gram of intestinal content in the colon (fig. 1a). Different elements may explain such a high longitudinal variation in the amount of micro-organisms. First, the very acidic pH of the stomach inhibits the growing of most of micro-organisms, and only very specialized and extremophile ones can grow there. In the duodenum, bile and pancreatic juice which contains digestive enzymes (mainly proteases, but also pancreatic lipases and amylases), form a harsh environment for micro-organisms, preventing their growth. In the small intestine, even if pH is much more compatible with bacterial growth (between 6 and 7), the host digestive process involves mechanical movements called ‘peristalsis’, which are periodic contractions of smooth muscles surrounding the gut, which negatively impact micro-organism growth (Dukowicz et al. 2007). Finally, the best conditions for microbial growth are found in the distal part of the GIT, the colon. There, the pH is suitable for life, and there are no digestive enzymes which would inhibit bacterial growth. In addition, this organ is dedicated to water absorption, and to nutrient absorption. For that purpose, peristaltic movements are much slower, allowing optimal microbial growth, mainly by fermenting dietary constituents that were not digested in the upper parts of the GIT.
Most of the gut microbiota is composed of strict anaerobes, as their environment is not in contact with the exterior medium. However, presence in lower amounts from two to three orders of magnitude of facultative aerobes and aerobes has been reported (Sekirov et al. 2010). Historically, advances in microbial composition studies can be divided into two majors eras, related to technological developments. Culture based and microscopic descriptions were the first methods used to describe microbial content in ecosystems. After 1995, development of next generation sequencing (NGS) technologies allowed to enter the metagenomic era, providing new data and expanding our knowledge in microbial diversity. Even if most of changes in bacterial diversity and amounts are correlated to diet, pH, local oxygenation levels and balance with the immune system, microbial diversity and amounts can be described according to three main parameters. This three dimensional diversity can be described first longitudinally, as the stomach mainly contains Helicobacter, Streptoccoci or Lactobacilli genus, the small intestine mainly contains Streptococaceae, Actinobacteria (high G+C organisms), while Firmicutes and Bacteroidetes are forming the main part of the colon microbiota (fig. 1a) (Lagier et al. 2012; Zoetendal et al. 2012). Secondly, we can note that diversity and specialization are found transversally, as oxygenation, pH and kind of nutrient are varying from the lumen to the epithelium. Phyla such as Clostridium, Lactobacillus and Enterococcus are the most common ones found in the mucus layer and epithelial crypts in the small intestine, while Bacteroides or Firmicutes are mainly found in feces (Lagier et al. 2012) (fig. 1b). Finally, temporal differences are found from birth to death with changes correlated to origin, environment, nutrition habits, prebiotics and antibiotics consumption or diseases (the last one will be further detailed). Initial establishment of gut microbiota begins at birth and its composition is first affected by the way babies are delivered. Babies born by cesarean show different microbial composition compared to those born by vaginal delivery, the later showing similarities with the mother’s vaginal composition (Donovan et al. 2012). During the first year of life, microbial composition and diversity are rapidly increasing, only stabilizing after teenage, the gut microbiota being so far not similar to the ones of adults (Agans et al. 2011) (fig. 1c). Contradictory results tend to show that gut composition may differ depending on the origin of the individuals (Lay et al. 2005; Grześkowiak et al. 2012), but these differences seems to be mostly artefactual, and primarily due to the wealth of the studied population, largely impacting diet habits which seems to be the most impacting factor. In the same way, prebiotics, a category of functional foods composed by oligosaccharidic and polysaccharidic fibers, can affect bacterial composition, as they feed specific bacterial genus (Quigley 2010). Surprisingly, probiotics (ingested living microorganisms which beneficially affects human health and digestive comfort) consumption has been shown to weakly impact bacterial composition, while significantly changing the functional gene content (Gerritsen et al. 2011; McNulty et al. 2011). Antibiotic consumption is also an important factor shaping the bacterial composition and diversity, as antibiotic treatments often used in western countries induce important changes in bacterial composition (Sommer et al. 2010). These changes occur rapidly, and bacterial equilibrium recovery is dependent on the treatment duration (Lagier et al. 2012; David et al. 2014). However, it has been shown that most of the commensal bacteria possess one or more genes responsible for resistance to antibiotics, and antibiotic treatments affect only some phyla, depending on the antibiotic used. But considering the very high number of microorganisms living in the gut, it can be considered as a reservoir of bacterial species sharing and exchanging antibiotic resistance genes. Actually, since the beginning of antibiotic use at a large scale, some commensal bacteria have developed strong antibiotic resistance capacity, which may be correlated to antibiotic resistance gene spreading from pathogens.
Inflammatory Bowel diseases
Among the intestinal diseases, one major concern are the inflammatory bowel diseases (IBD). They form a group of diseases characterized by a thinner and discontinuous mucus layer leading to chronic inflammatory conditions in the small intestine and the colon (Strugala et al. 2008). They are represented by two main diseases, the Crohn’s disease (CD) and the ulcerative colitis (UC) manifested by symptoms such as diarrhea, severe abdominal pains or weight loss. Crohn’s disease manifests all along the GIT, while UC is restrained to the distal part of the colon. In all cases, patients bear reduced microbiota diversity, but, as for the chicken or the egg, whether this altered microbiota is causal or secondary is still in debate (Walker et al. 2011). There is currently no etiologic agent identified for IBD, but theories have emerged to explain these affections. A first hypothesis for IBD would be that they would be consequent to an inadequate genetic predisposed host’s immune response to the microbiota, in which the host does not tolerate its own microbiota any longer. This would lead to a disturbance of the signals that maintain barrier function, associated with an inflammation that can favor the selection of aggressive symbionts (Juste et al. 2014). Indeed, opportunistic pathogens may trigger this pre-inflammatory state. Even if no single species has been found constantly associated with IBD, adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Chlamydia trachomatis, Pseudomonas maltophilia, Bacteroides fragilis, Mycobacterium kansasii, and Mycobacterium avium paratuberculosis appear to be regularly associated with IBD, while levels of butyrate producers such as Roseburia and Feacalibacterium appear to be lowered (Strober 2011; Ouwerkerk et al. 2013a; Naser et al. 2014).
An increasing number of studies also tend to show that IBD would be initiated by an over-controlled auto-immune reaction, in which a pathogenic bacterial epitope would mimic a commensal’s one (Sartor 2008; Principato and Qian 2014). One could think that such an event appears to be highly probable, considering the quasi unlimited number of epitopes presented by the commensal microbiota that would lead to CD4+ memory T cells number increase. In fact, under normal conditions, the immune system is finely balanced to discriminate between commensals and pathogens, avoiding an uncontrolled growing of these particular adaptive immune memory cells. First, to avoid a chronic stimulation of the immune system, a physical separation between the microbiota and immune cells is strongly established, through a mucosal firewall combining the mucus layers, antimicrobial peptides and secreted immunoglobulin A (IgA) (fig. 3). Two mucus layers are actually found at the surface of the GIT epithelium. The inner one, firmly adherent, contains a lot of antimicrobial peptides and secreted IgA associated with a dense network of mucins and is assumed to be devoid of bacteria. On the contrary, the outer layer only contains diluted antimicrobial peptides and IgA which allow bacteria to penetrate it. While the inner mucus layer thickness remain constant along the GIT (100µm), the outer one is varying upon the location in the GIT (150-70 µm), providing it with a variable viscoelasticity, giving the host a way to control the bacterial charge associated to its epithelium upon the location, and fatalistically saying, finely tune the sensitization of immune cells (Ouwerkerk et al. 2013b). Colonization of the inner mucus layer by commensals has been associated with respectively 94% and 98% of case of UC and CD, 78% of cases of colitis, while only 11% in healthy patients, showing the importance of inner mucus bacterial colonization in gastro-intestinal malignancies (Dejea et al. 2013). In normal cases, if bacteria penetrate this defense line, immune system is activated, involving Th1 lymphocytes. After clearance of intruders and restoration of the mucosal firewall, regulation of this adaptive response normally occurs, involving regulatory T lymphocytes (Treg), a subgroup of T cells involved in T lymphocyte regulation, especially in regard with self-specific and commensal-specific ones (Belkaid et al. 2013). The key point in IBD appears to be in the incorrect commensal-specific T cell shut down by Treg lymphocytes, which leads to their quasi permanent activation. As a consequence, a massive amount of pro-inflammatory cytokines are produced such as HIP/PAP (homolog of the antimicrobial peptide RegIII-γ produced by TLR activation) which has been shown to be specifically increased in inflamed parts of the colon and in enteropancreatic tumors (Ogawa et al. 2003). Genetic susceptibility has been shown to be also involved in the pathology. For instance, mutations in the DLG5 gene, involved in epithelial protein scaffolding, or MDR1, an efflux transporter for drugs, have been associated respectively with CD and UC, underlining the fact that these diseases remains largely multifactorial (Sartor 2006). Finally, diet-related causes may be involved in IBD development. Indeed, existing data suggest that decreased dietary plant polysaccharides shifts microbiota’s nutritional support to host mucus glycans, leading to an increased stress over this barrier (Mahowald et al. 2009). Logically, the idea of using pre- or probiotics for IBD treatment emerged, but to date, studies are mostly restricted to in vitro and animal model analyses, with the exception of very few analyses on small patient cohorts (Scaldaferri et al. 2013). The results remain preliminary and partially unconsistant even if these studies tend to show a decrease in inflammation markers when using prebiotics (inulin in combination with fructo-oligosaccharides) or some probiotics such as Lactobacillus GG for UC.
The third most common cancer worldwide in 2012, colorectal cancer, is of particular interest in public health (World Health Organization 2003). Even if carcinogenesis remains primarily due to human DNA damage, environmental factors are the main source of elements leading to DNA instability. Also, in colorectal cancers, enrichment in specific gut bacteria have been observed, particularly for Clostridium or Bacteroides spp, but again, no evidence tend to show that this dysbiosis is the cause or the consequence of the disease (Scanlan et al. 2008). Statistics show that diet is a major cause of colorectal cancers and it has been proposed that microbiota metabolic by-products could be involved in the tumorigenesis process (O’Keefe 2008). Of particular interest is hydrogen sulfide, a major toxic compound, which is produced by sulfate reducing bacteria through amino acids metabolism, and hence proteins degradation (Kim et al. 2013). Epidemiological surveys suggests that the western high-meat diet is positively correlated with an increase in colorectal cancer risk, but a clear link between high-protein diet and increased amounts in sulfate reducing bacteria remains to be established. However, even without involving diet specific compounds, it has been demonstrated that metabolization of common nutrients by commensals such as Enterococcus faecalis ATCC 29212 may produce reactive oxygen species (ROS), which are well known to induce DNA damage, hence promoting tumorigenesis (Wink et al. 2011).
Carbohydrate foraging by Bacteroides species
Bacteroides thetaiotaomicron (BT), first identified from human feces in 1912 by A. Distaso, is an important member of the Bacteroides genus, accounting for up to 12% and 6% of the Bacteroides and total gut microbes (in terms of bacterial cells), respectively. BT VPI-5482 genome, which was the first Bacteroides genome to be sequenced, consists in one circular chromosome sizing 6.26 Mbp, and of the 33 kbp p5482 plasmid. They code for 4864 and 38 predicted proteins, respectively (Xu et al. 2003). Among them, an impressive proportion is dedicated to polysaccharide sensing, adhesion, synthesis or degradation processes, which is only exceeded by Bacteroides cellulosilyticus WH2 (McNulty et al. 2013) in term of CAZyme content. B. thetaiotaomicron is able to cleave most of the major glycosidic linkages found in Nature (Xu et al. 2004). No less than 261 catalytic CAZyme modules were found to be encoded by the BT VPI-5482 genome, together with 208 sugar transporters and sensors (Martens et al. 2009b). As we saw above, among the overall carbohydrate content supplied to the gut through diet, only a small fraction is made of simple sugars, while the rest is composed of dietary fibers. The success displayed by Bacteroides species in their colonization of the colon is therefore associated to an efficient breakdown of a wide array of carbohydrates whatever they come from plant, mucus glycans and even meat, milk or bacterial glycans, which is a very useful feature when living in an omnivorous host’s intestines (Martens et al. 2011)(fig. 4).
In addition to their extraordinary capability to breakdown plant polysaccharides, gut Bacteroides are indeed also able to efficiently metabolize host glycans, as highlighted by the pioneer transcriptomic studies of Martens et al, targeting Bacteroides thetaiotaomicron VPI-5482 and Bacteroides ovatus ATCC 8483 (Martens et al. 2008a; Martens et al. 2011). The Bacteroides capability to grow on host glycans is a great advantageous in the ecosystem, because it gives to them the access to a permanently renewed carbon source. For instance, it has been shown that BT VPI-5482 grows on host glycan only when dietary fibers are absent in the gut, for example in infants before weaning (Sonnenburg et al. 2005). Prioritization in host glycan use is observed, glucosaminoglycans being used first, followed by O-glycans and finally N-glycans (Martens et al. 2008b). In the same way, the other well-characterized Bacteroides species, Bacteroides fragilis, is able to efficiently use mucins as carbon source, particularly in case of infections, when it escapes from the gut and no more accesses to any other carbohydrate source of dietary origin (Cao et al. 2014).
As seen in this chapter, gut bacteria play a critical and sometimes dual role in maintaining or negatively affecting human health. To maintain in this competitive environment, they have developed a highly complex enzymatic machinery to harvest carbohydrates, their main carbon source, from both dietary and host glycans. The massive sequencing of gut bacterial genomes and metagenomes highlighted these last years the vast diversity of the CAZymes produced by gut bacteria, notably from the Bacteroides genus. To understand how these enzymes act, alone and synergistically, to breakdown complex carbohydrates, Chapter 2 will focus on their classification, mode of action, and genomic organization.
Bacterial genomic organisation of CAZy encoding genes: the Bacteroidetes Polysaccharide Utilisation Loci (PULs)
To efficiently perform polysaccharide sequestration, depolymerisation and metabolization, many bacteria evolved a particular strategy, involving specific carbohydrate transporters and the synergetic action of different CAZymes, encoded by specifically regulated multigenic clusters, named polysaccharide utilization loci (PULs). As these loci have been particularly studied in Bacteroidetes species (Bjursell et al. 2006), the PUL denomination is specific to these bacteria (Terrapon and Henrissat 2014a). However, other PUL-like systems have been predicted in other genomes, in particular from gut bacteria, and were also evidenced in few metagenomic studies which generated sufficiently large contigs to identify these kinds of multigenic clusters (Tasse et al. 2010a; Cecchini et al. 2013). However, these metagenomic CAZy gene clusters still remain putative PULs, which are not experimentally validated.
Polysaccharide utilization by Bacteroidetes species has mostly been studied through BT starch utilization system (Sus), which is the prototype of all other PULs (also called Sus-like systems). The Sus-like systems are multiple cell-envelope associated protein complexes used for sensing, binding, and subsequent depolymerization of complex carbohydrates (fig. 9). For instance, the BT Sus system is composed of eight proteins (SusR, A, B, C, D, E, F, and G) expressed when growing on starch. Only several of them are necessary, since some have redundant or complementary functions. Indeed, recognition of starch is mainly performed by the membrane associated SusC and SusD proteins, providing 60% of the total starch binding affinity, the rest being provided by additional recognition mediated by SusE and SusF (Shipman et al. 2000a). After binding to the membrane, the substrate is roughly deconstructed by action of SusG, an endo α-amylase, yielding malto-oligosaccharides that are translocated to the periplasmic space via SusC. Final deconstruction of the malto-oligosaccharides is performed in the periplasm by the action of SusA and SusB to yield glucose units that finally are imported in the cytoplasm. The correct expression of the proteins of the Sus system is finely controlled by SusR, a regulatory protein, which senses the periplasmic space for malto-oligosaccharides, leading to the transcription of the required genes only when starch is present. It has been shown that Bacteroidetes species, and BT in particular, possess multiple PULs dedicated to the assimilation of various oligo- and polysaccharides (Martens et al. 2009b). Hence, the BT CAZyme repertoire is grouped in 88 PULs, encompassing 18% of its genome (Martens et al. 2008b).
GP classification and substrate specificity
As described above, it appears that GPs are no more different from GHs than GTs from a mechanical as well as structural point of view, which explains that no specific sequence signature allows for GP prediction from their sequence, neither allows to group them into a specific family. Logically, identifying a GP appears to be a matter of luck, which may explain why so few data on these particularly interesting enzymes have been gathered so far. Therefore, GPs are listed in both GHs and GTs families, namely GH3, GH13, GH65, GH94, GH112, GT4, GT35 and GH130 (Table 1).
All characterised GPs belonging to GT families are acting with a retaining catalytic mechanism and are part of the GT35 and GT4 families. Representatives of both families are acting on-glucosides, namely glycogen/starch or trehalose, respectively. It is to note that only two GH families contains retaining GPs. GH13 GPs also act on-glucans and sucrose, yielding glucose--1-phosphate. The sole characterized GP of the GH3 family, the N-acetylglucosamine phosphorylase Nag3 from Cellulomonas fimi, was recently identified as a phosphorylase because it is able to perform both phosphorolysis and hydrolysis (Macdonald et al. 2014). This enzyme is involved in peptidoglycan recycling, degrading the core-D-GlcNAc-1,4-MurNAc disaccharide to yield-D-Glcp-1-phosphate. This enzyme, part of a subset of GH3 sequences, is specific in that it is using as conserved His-Asp catalytic diad as acid-base residue which favours anionic phosphate binding rather than water.
All the other GPs belonging to GH families harbour a catalytic mechanism yielding an inversion of configuration on the released glycosyl-phosphate. Those acting on glucosides,-and-linked, share a common GH-L fold but are separated in the two GH65 and GH94 families, respectively. All are disaccharide phosphorylases, the GH65 acting on maltose, trehalose and other isomers of-linked di-glucosyl units, while those belonging to the GH94 family act on-linked di-glucosyl units, namely cellobiose, laminaribiose and cellodextrins. A few exceptions concern the enzymes acting on modified glycosyl units, namely the GH94 chitobiose phosphorylase (Hidaka et al. 2004) and the cellobionic acid phosphorylases (Nihira et al. 2013a), the β-1,2-oligo-D-glucan phosphorylase which is the sole GH94 acting on a polymer (Nakajima et al. 2014), and finally the GH65 glucopyranosyl glycerol phosphorylase which is the unique example of natural phosphorylase which does not process a disaccharide (Nihira et al. 2014). Recently, two other GPs have been characterized, the 1,3-β-D-glucan phosphorylase from Ochromonas danica and the GH94 laminaribiose phosphorylase, two enzymes acting on 1,3-linked β-D-glucosyl units which differ only on their ability to degrade long substrates or not, respectively (Nihira et al. 2012; Yamamoto et al. 2013). The other characterized inverting GPs are the GH112 phosphorylases acting on-galactosides and the GH130-mannoside phosphorylases. This thesis focuses on this latter family of enzymes involved in metabolization of-1,4-Manno-oligosaccharides,-1,4 mannan, β-D-Mannopyranosyl-1,4-D-glucose, β-D-Mannopyranosyl-1,4-N-acetyl-D-glucosamine or β-D-Mannopyranosyl-1,4-N,N’-diacetylchitobiose, other associated-linked mannosides, and of-linked galactosides. More about this specific topic will be discussed further in this bibliographic introduction and in the ‘Results’ section.
Focus onto the Glycoside Hydrolase 130 family
The GH130 family was created in 2011, consecutively to the functional characterization of its first member, the β-D-Mannopyranosyl-1,4-D-glucose (mannosyl-glucose) phosphorylase from Bacteroides fragilis NCTC 9343 (BfMGP) (Senoura et al. 2011). This is the first example of CAZy family creation based on the characterisation of a phosphorylase, which are not as frequent as real GHs or GTs. In addition, surprisingly, this family contained more 3D structures available than functionally characterized representatives when created. Indeed, four 3D structures of enzymes belonging to this family were deposited in the protein data bank (PDB) before its creation. These 3D structures were all determined by a structural genomic consortium, the Joint Center for Structural Genomics (JCSG) as early as 2004. These are the 3D structure of the Tm1225 protein from Thermotoga maritima MSB8 (PDB code 1VKD), the BACOVA_03624 protein from Bacteroides ovatus ATCC 8483 (3QC2), the BT4094 protein from B. thetaiotaomicron VPI-5482 (3R67), and the BDI_3141 protein from Parabacteroides distasonis ATCC 8503 (3TAW). All these proteins crystallized as monomers except for Tm1225 which crystallized as a dimer. However, none of these structures was joined to either a publication or any functional characterization. That was the picture of the family when this thesis started, in october 2011: only one functionally characterized member and four 3D structures with no associated function.
In 2012, a Japanese group characterized two new GH130 enzymes (Kawahara et al. 2012a). These two enzymes, RaMP1 and RaMP2, originate from a ruminal bacterium, Ruminococcus albus 7. Similarly to BfMGP, RaMP1 catalyses the conversion of β-D-Manp-1,4-D-Glcp and inorganic phosphate (Pi) into α-D-Manp-1-phosphate and D-Glucose. Reverse phosphorolysis reaction attempts also showed that both BfMGP and RaMP1 share a very narrow flexibility since only-D-mannose-1-phosphate is tolerated in the -1 subsite, while only glucose is accepted in the +1 (a very low activity on laminaribiose was however observed for RaMP1 exclusively, without any activity on cellobiose). The authors deduced from the genomic context of RaMP1 and RaMP2 encoding genes that they are involved in a mannan/glucomannan degradation pathway, as further described in chapter 3.
In 2013, the GH130 family was already containing more than 300 sequences (369 late January), and two additional members were concomitantly functionally characterized: the β-D-mannopyranosyl-1,4-N-acetyl-D-glucosamine phosphorylases Bt1033 from B. thetaiotaomicron VPI-5482 (Nihira et al. 2013b), published during the 2nd review of the first paper presented in the ‘Results’ section of this manuscript), and the β-D-mannopyranosyl-1,4-N-glycan phosphorylase Uhgb_MP from an uncultured gut bacterium (Ladevèze et al. 2013), which constitutes the main target of this thesis project. The functional characterization of Uhgb_MP is detailed in the first chapter of the ‘results’ section of this manuscript. Both enzymes appear to be very similar to one another. Indeed, as the other GH130 characterized enzymes, the -1 subsite is highly specific for-D-mannose-linked mannosyl residue. Phosphorolysis reaction assays showed that the preferred substrate of Bt1033 and Uhgb_MP was not β-D-Manp-1,4-D-Glcp but β-D-Manp-1,4-GlcNAc. Together with the analysis of Bt1033 and Uhgb_MP genomic environments, Nihira et al. and we hypothesized a functional role in vivo different to the one of the others previously characterized GH130 enzymes, namely BfMGP, RaMP1 and RaMP2. Taking also into account the fact that the micro-organism producing this intracellular enzyme originates from the human gut, collectively with Nihira et al, we deduced that such a disaccharide is likely to be derived from the degradation of host N-glycans lining the intestinal epithelium. This N-glycan degradation pathway will be described in details in Chapter 3.
Late 2013, the crystal structure of BfMGP was determined in the apo form (4KMI) and in complex with its different substrates (3WAT, 3WAS, 3WAU) (Nakae et al. 2013). An unusual hexameric structure was shown, and the analysis of the-D-mannose-1-phosphate binding mode, as well as the direct in cristallo observation of the reverse phosphorolysis reaction allowed the authors to propose the existence of a non-canonical catalytic mechanism for inverting GPs (fig. 15). Indeed, the structure revealed that neither the distance between the catalytic proton donor and the interosidic oxygen was suitable for direct proton transfer, nor a water molecule that could relay it was observed. Consequently, the authors proposed an alternative mechanism, similar to the canonical mechanism for inverting GPs, but in which the proton is relayed through the mannose C3 hydroxyl, which is at H-bond distance of both the catalytic aspartic acid and the interosidic oxygen atom (fig. 15). Such a carbohydrate hydroxyl mediated proton transfer has been proposed for other enzymes, including ribozyme (Filling et al. 2002; Schmeing et al. 2005), and a closely related mechanism involving a C2-OH neighbouring group participation has been proposed for the GH99 endo--mannosidase from B. thetaiotaomicron VPI-5482, yielding a 1,2 anhydro-sugar as transition state (Thompson et al. 2012). However, the mechanism harboured by the latter case is not suitable for BfMGP because it is an exo-acting inverting-mannoside phosphorylase, for which the reaction occurs in one step, which means a direct attack of the nucleophile onto the anomeric carbon. On the contrary, the involvement of the C3 hydroxyl for proton relay is supported by the specific position of the mannose hydroxyls in the adopted boat B2,5 transition state conformation. It is less unstable in mannosides compared to other monosaccharides, due to the pseudo-equatorial position of the C2-OH, in an anti configuration to the ring oxygen (Speciale et al. 2014), thus bending the C3-OH towards the -glycosidic oxygen, in a syn-axial position. This work, published concomitantly with our firststudy regarding Uhgb_MP and the identification of its most probable catalytic residue (Ladevèze et al. 2013), allowed one to answer the crucial question of the mechanism of the GH130 mannoside-phosphorylases.
Table of contents :
1. THE HUMAN GUT MICROBIOTA
1.1. The human gut microbiota: environment and composition
1.2. Physiological roles
1.2.1. Food – gut microbiota interactions
1.2.2. Host – gut microbiota interactions
1.3. Health issues
1.3.1. Inflammatory Bowel diseases
1.3.2. Colorectal cancer
1.3.3. Metabolic diseases
1.4. Bacteroides: colleagues or traitors?
1.4.1. Beneficial effects
1.4.2. Deleterious effects
1.4.3. Carbohydrate foraging by Bacteroides species
2. GLYCOSIDE-PHOSPHORYLASES, THESE FASCINATING CARBOHYDRATE ACTIVE ENZYMES
2.1. The CAZy classification
2.2. Bacterial genomic organisation of CAZy encoding genes: the Bacteroidetes Polysaccharide Utilisation Loci (PULs)
2.3. Glycoside Phosphorylases
2.3.1. GP catalytic mechanisms
2.3.2. GP classification and substrate specificity
2.3.3. GP structures
2.3.4. Focus onto the Glycoside Hydrolase 130 family
2.3.5. Biotechnological applications of glycoside phosphorylases
3. MANNOSIDE RECOGNITION AND DEGRADATION BY BACTERIA
3.2. Diversity of mannosides structures
3.2.1. Eukaryotic mannosides
126.96.36.199. MAMMALIAN MANNOSIDES
188.8.131.52. PLANT MANNOSIDES
184.108.40.206. SPECIFICITIES OF YEAST AND FUNGAL MANNOSIDES
220.127.116.11. PROTOZOAN MANNOSIDES
3.2.2. Prokaryotic mannosides
3.3. Eukaryotic Mannosides Recognition by bacteria
3.4. Mannosides Degradation by micro-organisms
3.4.1. Soil and spring bacteria
3.4.2. Plant associated bacteria
3.4.3. Mammal gut bacteria