Bile metabolism

Bile metabolism DEFAULT


The human gut microbiota impacts host metabolism and has been implicated in the pathophysiology of obesity and metabolic syndromes. However, defining the roles of specific microbial activities and metabolites on host phenotypes has proven challenging due to the complexity of the microbiome-host ecosystem. Here, we identify strains from the abundant gut bacterial phylum Bacteroidetes that display selective bile salt hydrolase (BSH) activity. Using isogenic strains of wild-type and BSH-deleted Bacteroides thetaiotaomicron, we selectively modulated the levels of the bile acid tauro-β-muricholic acid in monocolonized gnotobiotic mice. B. thetaiotaomicron BSH mutant-colonized mice displayed altered metabolism, including reduced weight gain and respiratory exchange ratios, as well as transcriptional changes in metabolic, circadian rhythm, and immune pathways in the gut and liver. Our results demonstrate that metabolites generated by a single microbial gene and enzymatic activity can profoundly alter host metabolism and gene expression at local and organism-level scales.

eLife digest

The microbiome, the collection of bacteria that live in and on human bodies, has a strong influence on how well the body works. However, the diversity of the microbiome makes it difficult to untangle exactly how it has these effects. For example, it is poorly understood how the hundreds of species of bacteria that live in the gut affect metabolism – the chemical processes that make life possible. But they are known to influence how metabolic diseases like diabetes and obesity develop.

When we eat a meal, the body releases compounds called bile acids to help to digest the food. Once the bile acids reach the colon, the bacteria residing there use enzymes to chemically modify the compounds. Imbalances in the resulting pool of over 50 different bile acids may accelerate how quickly people develop metabolic disorders. It is not clear, however, which bile acids have helpful or harmful effects on metabolism.

Yao et al. first identified a selective version of a prevalent gut bacterial enzyme called a bile salt hydrolase. This enzyme was then deleted from a common gut bacterium using genetic tools. Finally, Yao et al. colonized mice lacking any bacteria (i.e., germ-free mice) with either the original bacterium or the hydrolase-deleted bacterium. Mice colonized with the hydrolase-deleted bacteria gained less weight on a high fat diet and had lower levels of fat in their blood and liver. These mice also shifted to burning fats instead of carbohydrates for energy.

The changes in the bile acid pool produced in mice colonized with hydrolase-deleted bacteria did not only affect metabolism. Yao et al. found differences in the activity of genes important for other biological processes as well, such as those that control circadian rhythms and immune responses.

Further research is needed to investigate whether limiting the activity of the bile salt hydrolase enzyme has similar effects in humans. If so, developing drugs or probiotics that target the enzyme could lead to new treatments for people with metabolic diseases like obesity and fatty liver disease. Investigating the biological effects of other bacterially modified bile acids may identify other possible treatments as well.


The human gut microbiome is known to play a crucial role in human energy harvest and homeostasis (BäckhedBackhed et al., 2004; Turnbaugh et al., 2006). Lean and obese people harbor different gut bacterial communities, suggesting that developing gut bacterial imbalances may contribute to obesity (Ley et al., 2006; Turnbaugh et al., 2006; Turnbaugh et al., 2008). Importantly, transplantation of the fecal microbiota from obese humans to germ-free (GF) mice has been shown to result in the development of obesity-associated metabolic phenotypes in recipient mice (Ridaura et al., 2013). These studies establish a causal relationship between gut bacteria and host metabolic status. The molecular mechanisms by which gut microbes regulate host metabolism, however, remain largely unknown. This lack of mechanistic understanding regarding the functions of microbial species and their metabolic capabilities has limited the effectiveness of both dietary and therapeutic approaches to improving host physiology (Jia et al., 2008; Wallace et al., 2010).

The investigation of microbial metabolite production represents both an important opportunity and a challenge in the search to uncover the causal underpinnings of the effects of gut bacteria on host metabolism. One of the most concrete effects that human-associated bacteria have on the host is the production of small molecule metabolites, some of which accumulate to levels in the body higher than that of a typical drug (Donia and Fischbach, 2015). Recent research suggests that bacterial metabolites play important roles in host metabolism by regulating host glucose and energy homeostasis (De Vadder et al., 2014; Gao et al., 2009; Todesco et al., 1991). The complexity of gut microbial ecosystems and associated microbial and host-derived microbial metabolites, however, presents significant obstacles on the path to defining how individual compounds elicit specific in vivo effects. Means to control specific metabolites is critical to understanding how these molecules affect host physiology. In this work, we selectively modulate the in vivo levels of bile acids and demonstrate that this controlled alteration of the metabolite pool exerts distinct effects on host physiology.

Bile acids are steroidal natural products that are synthesized from cholesterol in the liver and constitute an important part of the molecular environment of a healthy human gut (Ridlon et al., 2006). Upon ingestion of a meal, bile acids are secreted from the liver and gallbladder into the duodenum where, with the activities of pancreatic enzymes, they form micelles that solubilize lipids and fat-soluble vitamins that are otherwise poorly absorbed. Remaining free bile acids are efficiently reabsorbed from the ileum via the action of bile acid transporters and recirculated back to the liver. Approximately 3–5% of bile acids escape enterohepatic recirculation and enter the colon at a rate of 400–800 mg/day, forming a concentrated pool of metabolites (200 to 1000 μM) (Hamilton et al., 2007). In the colon, these molecules are modified by the resident bacteria in near-quantitative fashion, forming a class of on the order of 50 different metabolites called secondary bile acids (Figure 1A). In addition to their role in digestion, many primary and secondary bile acids act as ligands for host nuclear receptors, including the farnesoid X receptor (FXR), the pregnane X receptor (PXR), the vitamin D receptor (VDR), the liver X receptor (LXR) and the G-protein-coupled receptor TGR5 (Fiorucci and Distrutti, 2015; Katsuma et al., 2005; Makishima et al., 2002; Song et al., 2000; Staudinger et al., 2001). By acting as agonists or antagonists for these receptors, bile acids further impact the regulation of glucose tolerance and homeostasis, insulin sensitivity, lipid metabolism, triglyceride and cholesterol levels, and energy expenditure by the host (Fiorucci and Distrutti, 2015; Modica et al., 2010). Additionally, bile acids regulate their own biosynthesis via an FXR-mediated negative feedback mechanism, which affects downstream nutrient availability for the host (Modica et al., 2010). As a result of these interactions, bile acid imbalance has been implicated as having a causal effect in the development of diet-induced obesity (Fiorucci and Distrutti, 2015). Conversely, modification of the bile acid pool by commensal bacteria has been suggested to induce beneficial changes in host metabolism (Joyce et al., 2014).

The mechanisms underlying these effects, however, remain largely undefined. Due to the large number of compounds and receptors involved as well as the additional role of bile acids as biological detergents, the in vivo roles of specific bile acids have been difficult to untangle. Our novel approach to deconvoluting the physiological role of structurally distinct bile acids is to control the in vivo activity of selective bacterial bile salt hydrolases (BSH). BSH hydrolyze conjugated bile acids that have been linked to either taurine or glycine by host liver enzymes, revealing unconjugated bile acids (Figure 1A). This deconjugation step occurs prior to subsequent bacterial conversion of primary bile acids (e.g. cholic acid and chenodeoxycholic acid) to secondary bile acids (e.g. deoxycholic acid and lithocholic acid) (Ridlon et al., 2006).

Prior work suggests that BSH play a critical role in regulating host metabolism. However, these studies have not yet uncovered how specific bile acid metabolites exert their in vivo effects on host metabolism, and conflicting results have been reported regarding whether BSH activity should be increased or decreased to achieve host metabolic benefits (Joyce et al., 2014; Li et al., 2013). Research efforts to date have either examined correlative relationships between BSH activities, bile acid levels, and metabolic indications (Li et al., 2013) or investigated the metabolic effects of ‘unconjugated’ versus ‘conjugated’ groups of bile acids (Joyce et al., 2014). It is imperative to be able to differentiate bile acids in vivo based on their structure in order to understand their effects on host metabolism. As an important example, taurocholic acid (TCA) and tauro-β-muricholic acid (TβMCA) are both conjugated bile acids but exert different physiological effects: TCA is an FXR agonist, while TβMCA is an FXR antagonist (Figure 1B) (Li et al., 2013; Sayin et al., 2013).

Herein, we uncover a group of bacteria within the abundant human gut commensal genus Bacteroides that possess selective BSH activity. We then identify the gene responsible for this activity in Bacteroides thetaiotaomicron and construct a knockout strain. By monocolonizing germ-free (GF) mice with the wild-type or BSH-deleted strain, we demonstrate that we can predictably alter the in vivo bile acid pool using this selective enzyme and that this change has significant effects on host metabolic status. Our results demonstrate that the deletion of a single bacterial gene can exert significant effects on host metabolism in a gnotobiotic environment and highlight the importance of modulating specific compounds when seeking to understand the effects of bacterial metabolites on host physiology.


Selected species of Bacteroides accept distinct bile acid cores as BSH substrates

BSH (EC are found across a wide range of bacterial genera from the two dominant gut phyla, Bacteroidetes and Firmicutes (Jones et al., 2008). However, the structural and activity characterization of these enzymes has been largely limited to Gram-positive species (i.e. Clostridia, Lactobacillus, Bifidobacterium, Listeria) (Begley et al., 2006; Rossocha et al., 2005). These enzymes largely demonstrate non-selective activities, cleaving all conjugated bile acids independent of either the bile acid core or amino acid conjugate (taurine or glycine) (Ridlon et al., 2006). While differential reactivity toward conjugated substrates has been observed in some Gram-positive strains, in these cases, the selectivity has been based on a preference for one amino acid over the other, not on the structure of the steroidal core (De Boever P and Verstraete, 1999; Grill et al., 1995; Kim et al., 2004; Ridlon et al., 2006). In contrast, the activity of Gram-negative bacteria has been largely underexplored. While Bacteroides fragilis ATCC 25285 was reported to exhibit non-selective BSH activity (Stellwag and Hylemon, 1976), some Bacteroides vulgatus strains were observed to cleave taurochenodeoxycholic acid (TCDCA) and TβMCA but minimally cleaved TCA (Chikai et al., 1987; Kawamoto et al., 1989), thus exhibiting a degree of selectivity based on the hydroxylation pattern of the steroid. These results suggested to us that perhaps other strains within the phylum Bacteroidetes might display steroidal core-based selectivity. To investigate this question, we performed a screen of the BSH activity of twenty Bacteroidetes strains found in the human gut (Figure 2A and Figure 2—figure supplement 1 and 2) (Kraal et al., 2014). We also tested Clostridium perfringens and Lactobacillus plantarum, two Gram-positive species with known non-selective BSH activities, for comparison. We incubated pre-log phase cultures of individual strains with a group of either the most abundant tauro- or the most abundant glyco-conjugated bile acids found in the human and murine GI tracts. We monitored deconjugation over time by UPLC-MS and determined that all hydrolysis reactions had reached steady state by 48 hr (Figure 2B, Figure 2—figure supplement 3). We then quenched the cultures and profiled bacterial bile acid metabolism. As expected, C. perfringens ATCC 13124, L. plantarum WCFS1, and B. fragilis ATCC 25285 deconjugated all conjugated bile acid substrates tested. Strikingly, the majority of Bacteroidetes strains tested displayed some degree of selectivity for conjugated bile acid substrates, with a preference for deconjugating tauro- over glyco-conjugated substrates. A subset of these strains (B. thetaiotaomicron VPI-5482, B. caccae ATCC 43185, B. fragilis 638R, Bacteroides sp. D2, and Bacteroides sp. 2_1_16; Group I – red, Figure 2A) exhibited selectivity exclusively based on the steroidal core structure, deconjugating C12 = H primary bile acids (i.e. TCDCA, GCDCA, and TβMCA) but not C12 = OH primary bile acids (i.e. TCA and GCA).

To our knowledge, this study represents the first systematic evaluation of BSH activity in the common gut-bacterial phylum Bacteroidetes. Given that specific conjugated and unconjugated bile acids bind to different host receptors and have the potential to exert different downstream effects, the selectivity uncovered here may have important physiological consequences depending on which Bacteroides species colonize the host. To further explore this possibility and define the effects of selective BSH on host physiology, we monocolonized GF mice with isogenic strains of wild-type and BSH-deleted B. thetaiotaomicron as described below.

BT2086 is responsible for BSH activity in B. thetaiotaomicron

We recognized that deletion of the BSH enzyme from one of the Group I Bacteroides species would provide us with a paired set of isogenic strains (wild-type and knockout) that would allow us to rationally manipulate the in vivo bile acid pool in a highly specific manner. In mice, the two most abundant primary bile acids are TCA and TβMCA (Sayin et al., 2013). Based on the observed selectivity for deconjugating C12 = H but not C12 = OH core primary bile acids, we predicted that colonization with a BSH wild-type strain would result in lower levels of TβMCA (C12 = H) relative to knockout colonized mice, while the levels of TCA (C12 = OH) in both groups would remain constant. All the five Group I strains displayed weak-to-moderate deconjugation of TβMCA in vitro (Figure 2A). Importantly, we did not detect any products of TCA deconjugation from any of these strains. This result suggested that the levels of deconjugated CA in mice colonized with these bacteria would remain low to undetectable, while the levels of deconjugated βMCA could build up due to enterohepatic recirculation. We decided to focus our efforts on generating paired isogenic strains in one of these species, B. thetaiotaomicron (Bt). Although this strain displayed relatively weak TβMCA-deconjugating activity, Bt had been previously shown to be amenable to genetic manipulation, allowing knockout of putative BSH genes (Cullen et al., 2015; Koropatkin et al., 2008).

We performed a BLASTP search of the characterized BSH from C. perfringens (Ridlon et al., 2006) against the Bt genome and identified two genes, BT2086 and BT1259, as putative BSH. We constructed unmarked deletions of these genes using allelic exchange and then tested the resultant mutants for their ability to deconjugate bile acids in whole cell culture using UPLC-MS. The BtΔ2086 mutant (henceforth referred to as Bt KO) had lost the ability to cleave conjugated bile acid substrates. In contrast, the BtΔ1259 mutant displayed no loss-of-function phenotype (Figure 2C). Complementation of the Bt KO strain with BT2086 restored BSH activity (Figure 2C), confirming that BT2086 is necessary for bile acid deconjugation in Bt. Since bile salt hydrolases and penicillin V amidases (PVA) both belong to the cholylglycine hydrolase (CGH) family and share a high degree of sequence homology, it is possible that BT1259 is a PVA, although additional experiments would be needed to definitively establish this activity (Jones et al., 2008; Panigrahi et al., 2014). Finally, we verified that when incubated with both TβMCA and TCA, Bt wild-type (Bt WT) deconjugated TβMCA but not TCA, whereas the Bt KO strain did not deconjugate either bile acid (Figure 2D).

Bacteroidetes BSH exhibit evolutionary diversity

A phylogenetic grouping of the 20 Bacteroidetes strains assayed revealed that while the species that deconjugate bile acids based on the amino acid conjugate (Group II – gray, Figure 2A) form a partial clade (Figure 3A), the strains that exhibit selectivity based on the steroid core (Group I – red) and those that display no selectivity (Group III – blue) are not separated into distinct clades. A BLAST-P search using BT2086 as a query gene identified candidate BSH genes in 19 of the 20 Bacteroidetes strains tested. Bacteroides finegoldii DSM 17565 did not display BSH activity and also lacked a putative BSH. A phylogenetic tree resulting from the multiple sequence alignment of these 19 candidate BSH genes revealed a lack of homology among enzymes within a given activity group (Figure 3B). Group II enzymes, which had formed a clade at the strain level, are now separated into two groups, and steroid core-selective strains (Group I) do not cluster significantly. Taken together, these findings suggest that preference for C12 = H over C12 = OH primary bile acid cores is an activity that may have evolved multiple times independently from related members of the BSH superfamily.

Genetic removal of Bt BSH results in specific changes to murine bile acid pools in vivo

To test our hypothesis that deleting a single bacterial gene, the bile salt hydrolase BT2086, would result in a predictable and selective alteration of the in vivo bile acid pools, GF mice were monocolonized with Bt WT or Bt KO (monocolonization experiment, Figure 4A). To further assess effects of this single microbial gene on overall host metabolism and energy utilization, we also performed an experiment in CLAMS (Comprehensive Lab Animal Monitoring System) cages using three groups of animals: (1) mice monoassociated with Bt WT, (2) mice monoassociated with Bt KO or (3) GF control mice which remained sterile (CLAMS experiment, Figure 4A). For both studies, over a 4-week period, mice were fed a high-fat, high-sugar diet designed to mimic a Western-style human diet (60% kcal% fat). For the last week of the CLAMS experiment, mice were transferred from gnotobiotic isolators to pre-sterilized metabolic cages with continuous monitoring in the CLAMS system in order to carefully monitor metabolic status.

We first confirmed that BT2086 was expressed in vivo by performing qRT-PCR on cecal contents from Bt WT-colonized mice (Figure 4—figure supplement 1). As expected, no BT2086 transcripts were detected in the cecal contents of BT KO-colonized mice. We then performed bile acid analyses on tissues and blood from mice in both experiments. As we predicted, Bt KO-colonized mice displayed higher levels of TβMCA in cecal contents than Bt WT-colonized mice in the monocolonization experiment (Figure 4B). Bt KO-colonized mice also exhibited significantly lower levels of βMCA (p<0.0001), the product of TβMCA hydrolysis, than Bt WT-colonized mice. Importantly, the levels of TCA remained unchanged between the two groups, and no CA was detected in either group. These results are consistent with our in vitro data showing that the Bt BSH can deconjugate C12 = H but not C12 = OH primary bile acids. We observed the same significant difference in βMCA levels in feces (Figure 4C, red highlight boxes). In the CLAMS experiment, in agreement with previous reports (Sayin et al., 2013), GF mice had significantly higher overall bile acid levels than colonized mice (p=0.0012 Bt WT vs GF, p=0.0071 BT KO vs GF). Consistent with the monocolonization experiment, cecal contents of Bt KO-colonized mice displayed significantly lower levels of βMCA (p<0.0001, Bt WT vs GF and BT KO vs GF) than cecal contents of Bt WT-colonized mice, while CA remained undetectable in both groups (Figure 4B).

We also profiled the bile acid composition in the distal ileum, the site of active bile acid reuptake from the small intestine, in the CLAMS experiment. As expected, the bile acid concentrations were approximately fivefold higher in this compartment than in cecal and fecal contents (Figure 4D) (Sayin et al., 2013). We observed the same trend as we had noted in cecal contents, with higher TβMCA levels in Bt KO-colonized mice, although the differences were not statistically significant (p=0.9343). During sacrifice, we noted that the distribution of this food debris was not uniform along the length of the small intestine. This heterogeneity of contents in the distal ileum may help explain the large range of bile acid measurements observed in this compartment.

In contrast to the cecum, feces, and distal ileum, the liver and circulating plasma (Figure 4—figure supplement 2) of Bt WT- and Bt KO-colonized mice contained similar bile acid compositions, with no significant differences noted. These data are consistent with previous observations that the greatest differences between GF and conventionally raised mice were in the cecum and colon, not in the liver or the blood (Sayin et al., 2013). We also observed a significant upregulation of bile acid synthesis genes in the liver (vide infra), suggesting that de novo bile acid synthesis may lessen the observed differences between the two groups.

Taken together, our data show that we can rationally manipulate the in vivo bile acid pool in the cecum and to a lesser extent in the small intestine and distal colon (i.e. feces) using a Bacteroides BSH enzyme that selectively cleaves C12 = H but not C12 = OH conjugated primary bile acids. Importantly, this selective hydrolysis allows us to modulate the levels of TβMCA, a known FXR antagonist, while leaving the levels of TCA, an FXR agonist unchanged.

Bt BSH status affects host metabolic indications

Having shown that Bt BSH status selectively determines composition of the bile acid pool in monocolonized GF mice, we next sought to explore how these specific changes in bile acid levels affected host metabolism. Strikingly, Bt KO-colonized mice gained less weight on the high-fat diet than Bt WT-colonized mice in the monocolonization experiment (Figure 5A). This result is notable because it has been shown that GF mice are more resistant to weight gain when fed a high-fat diet (Bäckhed et al., 2007). In addition, we performed a relatively short diet intervention compared to other studies that have used HFD to study metabolic changes (Jiang et al., 2015; Joyce et al., 2014; Rao et al., 2016; Serino et al., 2012), and we did not expect to observe significant changes in body weight over the course of a shorter experiment. Importantly, the host effects observed are not due to differences in colonization efficiency. In both experiments, Bt WT and Bt KO efficiently colonized the GI tract and remained the only bacterial species in the mono-associated animals (Figure 5B and Figure 4—figure supplement 1). These data suggest that the observed metabolic changes are rather due to alterations in the bile acid pool driven by the presence or absence of the Bt BSH.

Consistent with the reduced weight gain phenotype, we observed lower levels of triglycerides, cholesterol, and free fatty acids in plasma (Figure 5C) as well as lower triglyceride levels in liver (Figure 5D) of Bt KO-colonized compared to Bt WT-colonized mice in the monocolonization experiment. Bt KO-colonized mice also exhibited less liver steatosis than Bt WT-colonized mice, consistent with the lower liver triglyceride levels in the former group (Figure 5E).

In order to further investigate the effects of Bt BSH status on host metabolism, we transferred Bt KO- or Bt WT-colonized or GF mice to metabolic cages (CLAMS experiment). After a 24 hr acclimation period, we monitored metabolic inputs and outputs for 6 days. We observed significant metabolic differences between the three groups of mice. Both Bt KO-colonized mice and GF mice displayed a lower respiratory exchange ratio (RER) than Bt WT-colonized mice (Figure 6A). RER is calculated as the ratio of carbon dioxide produced to oxygen consumed and is used as a measurement of the relative utilization of carbohydrates versus lipids as an energy source (carbohydrate utilization RER = 1, lipid RER = 0.7). Thus, our data indicate that both the Bt KO-colonized and GF mice are utilizing more lipids for energy than carbohydrates relative to the Bt WT-colonized mice. While Bt KO-colonized mice consumed more oxygen than Bt WT-colonized mice (Figure 6B), there were no significant differences in carbon dioxide production between groups (full day, Bt WT vs Bt KO p=0.4041; Bt WT vs GF p=0.3239; Bt KO vs GF p=0.0606) (Figure 6C). These data are consistent with the lower RER observed in Bt KO-colonized mice. No statistically significant differences in locomotor activity were noted between the three groups (Figure 6—figure supplement 1). We then used linear regression to investigate the relationship between metabolic rate and body weight in the three groups of mice. Conventionally raised mice as well as humans display a positive linear correlation between energy expenditure and body mass (Fricker et al., 1989; Moruppa, 1990). While Bt WT-colonized mice displayed this linear relationship (p=0.0168, R2 = 0.7134), strikingly, both Bt KO-colonized (p=0.6806, R2 = 0.03017) and GF (p=0.6930, R2 = 0.02782) mice did not (Figure 6D). These data suggest that the deletion of a single bacterial gene, a selective bile salt hydrolase, results in loss of the relationship between metabolic rate and body weight in the host. Taken together, our data from both the monocolonized experiment and the CLAMS experiment suggest that the Bt KO-colonized mice exhibit a metabolic phenotype distinct from Bt WT-colonized mice.

Distal ileum bile acid pools exhibit similar detergent properties

The reduced respiratory exchange ratio and weight gain of Bt KO-colonized mice suggest a reduced energy availability profile that is consistent with either reduced food consumption or less efficient caloric extraction from food. In the monocolonization experiment, Bt KO-colonized mice consumed less food during HFD feeding than Bt WT-colonized mice (−2.28 g ± 1.36 g vs. +3.39 g±1.61 g per cage per week, respectively, compared to weekly average for all cages, p=0.0165). This result indicates that decreased caloric intake may be a contributing factor in the former group’s decreased weight gain. Since bile acids act as biological detergents that aid in digestion, it is conceivable that the differences in bile acid pools between the groups could alter caloric extraction efficiency. To test this hypothesis, we performed a detergent assay in which we determined the ability of the bile acid pools to solubilize a mixture of fats representative of lipolysis products in the small intestine (Hofmann, 1963). Bile acid pools for Bt KO- and Bt WT-colonized mice were reconstituted using the mean values for individual compounds measured in the distal ileum and incubated with a 1:1:1 mixture of oleic acid, sodium oleate and 1-oleoyl-rac-glycerol under conditions representative of those in the small intestine (150 mM NaCl, pH 6.3, 37˚C) (Hofmann, 1963). Sodium dodecyl sulfate (SDS) was used as a positive control at its critical micelle concentration (8.2 mM). At both 5 hr and 24 hr time points, we did not detect any differences in solubilization at four different fat concentrations as measured by the turbidity of the resulting mixtures (Figure 7A). Taken together, these data suggest that the metabolic differences observed between the Bt WT- and Bt KO-colonized mice are not due to different detergent abilities of the bile acid pools. In further support of this conclusion, fecal bomb calorimetry did not reveal any differences in energy remaining in fecal pellets from Bt WT-colonized, Bt KO-colonized, or GF mice, indicating that there were no notable differences in caloric energy extraction from food between these groups (Figure 7B). These data suggest that the observed metabolic differences between Bt WT- and Bt KO-colonized mice may be due to differences in bile acids acting as signaling molecules in the host.

Bt BSH status affects host global transcriptional response

In order to investigate the gene regulatory mechanisms underlying the metabolic changes observed in Bt KO- compared to Bt WT-colonized mice, we performed RNA-sequencing (RNA-Seq) on distal ileum from the monocolonization experiment (Figure 8—figure supplement 1). We decided to focus our analysis on the distal ileum for three reasons. First, while known bile acid receptors are highly expressed in both liver and intestinal tissue, we observed larger differences in bile acid pool composition in the GI tract (i.e. small intestine, cecum, feces) than the liver and blood, suggesting that differences in bile-acid-mediated signaling effects will likely be greater in the small intestine than in the liver. Second, bile acid concentrations are significantly higher in the small intestine than the cecum and colon, the other sites at which we observed differences in bile acid pool composition (approximately 5-fold and 100-fold higher, respectively) (Sayin et al., 2013). Third, following passage through the small intestine, bile acids are absorbed and recirculated back to the liver primarily in the distal ileum (Dawson et al., 2009), making this site the nexus for bile acid sensing and transport in the GI tract.

Global transcriptional analysis of the distal ileum identified 12,432 genes, of which 428 genes were differentially expressed (adjusted FDR ≤ 0.05, fold-change ≥±1.5) between the Bt KO- and Bt WT-colonized mice. Of those genes, the majority (314 genes) were increased in the Bt KO-colonized mice (Figure 8A). Multidimensional scaling analysis (MDS) revealed that the two monocolonized groups segregate based on their transcriptional profiles (Figure 8B). Gene Ontology (GO) and KEGG pathway analyses of RNA-Seq expression data revealed coordinated changes in gene expression related to metabolism, circadian rhythm, immune response, and histone modifications (Figure 8C).

The largest group of differentially expressed genes were those related to host metabolism. We observed significant changes in genes related to carbohydrate and lipid metabolism, amino acid degradation and nitrogen metabolism, and xenobiotic metabolism. In particular, genes involved in the transport (Slc2a1) and breakdown (Hk1/2, Pfkl/m) of glucose were upregulated, whereas G6pc (glucose-6-phosphatase), the final enzyme in the gluconeogenesis pathway, was significantly downregulated (8.8-fold), indicating a shift away from gluconeogenesis and toward glycolysis in the distal ileum of Bt KO-colonized mice. We confirmed the transcriptional change of G6pc in distal ileum using qPCR (Figure 8D). Consistent with these findings, we observed significantly higher blood glucose levels in Bt KO-colonized mice compared to Bt WT-colonized mice in the CLAMS experiment (p=0.0228), indicating an increase in glucose available for glycolysis in the distal ileum (Figure 9A).

The expression pattern for genes related to lipid metabolism was more complex, with pathways related to both lipogenesis and lipid breakdown upregulated in Bt KO-colonized animals. Two key genes in the ketogenesis pathway, Bdh1 (3-hydroxybutyrate dehydrogenase 1) and Hmgcs2 (3-hydroxy-3-methylglutaryl-CoA synthase 2), were significantly upregulated (Figure 8C), indicating an increase in the use of lipid and ketogenic amino acid degradation for energy production in the host. Additional genes related to amino acid degradation (Hao2, Nos1, Pcca, Tat) were also significantly upregulated. Expression of genes involved in the biosynthesis of both glycerophospholipids, in particular phosphatidic acid (Dgkg, Dgkh, Gpam, Mboat1, Mboat2), and sphingolipids, in particular cerebrosides and gangliosides (Glb1, St3gal5, St6galnac6, Ugt8a), was also higher in KO-colonized mice. Complex fats synthesized via de novo lipogenesis serve as ligands for PPAR type II nuclear receptors (Lodhi et al., 2011). RNA-Seq data revealed that Pparg expression was significantly up-regulated in Bt KO-colonized mice (Figure 8C). Activation of Pparg has been shown to both enhance glucose metabolism and increase lipid uptake (Martin et al., 1998), consistent with our broader transcriptional analysis. We confirmed that Pparg expression was significantly upregulated in KO-colonized animals by qPCR (p=0.0207) (Figure 8D). Collectively, these data suggest that ileal cells in KO-colonized mice have shifted toward a regime of enhanced glycolysis and increased lipid uptake for the purposes of both the synthesis of complex fats and the breakdown of lipids for energy.

Transcriptional analysis also revealed changes in genes regulating circadian rhythm. The observed inverse relationship between expression of the transcriptional activators (Npas2 and Arntl, decreased in Bt KO-colonized mice) and circadian repressors (Per1, Per2, Per3, Cry2, increased in Bt KO-colonized mice) is consistent with the transcription-translation negative feedback loop that establishes diurnal rhythms (King and Takahashi, 2000). The relative changes in circadian rhythm regulation genes were validated using qPCR (Figure 8E). These data indicate that tissues in the distal ileum of Bt KO-colonized mice exist in an altered circadian synchronization state compared to those of Bt WT-colonized mice. Genes involved in immune homeostasis and histone modifications were also differentially expressed. Of particular note, Toll-like receptors (Tlr1, Tlr2, Tlr4), innate immune receptors that play key roles in recognizing microbially produced molecules (Akira et al., 2001), were significantly upregulated in our Bt KO-colonized mice. Taken together, these data suggest that bile acid pool alteration elicited a broader scope of changes in the host beyond those directly related to energy production and lipid synthesis.

Bile acid pools alter the expression of FXR-dependent and FXR-independent genes in the liver and distal ileum

We next sought to investigate the hypothesis that the two bile acid pools would differentially and predictably affect FXR signaling in the small intestine and the liver. Prior work has shown that the gut microbiome mainly affects FXR targets in the ileum but not the liver (Sayin et al., 2013). Specifically, activation of ileal FXR leads to production of fibroblast growth factor 15/19 (FGF15 in mice and FGF19 in humans). FGF15 then translocates to the liver where it binds to the FGFR4/β-Klotho complex and represses the expression of Cyp7a1, which encodes an enzyme catalyzing the rate-limiting step in bile acid synthesis from cholesterol (Ding et al., 2015). In this way, activation of FXR in the ileum downregulates bile acid synthesis in the liver. In our system, the levels of the FXR antagonist TβMCA were higher in the cecal contents of Bt KO- versus Bt WT-colonized mice, while the levels of the FXR agonist TCA remain constant between these two groups. Based on these results, we predicted that we would observe inhibition of FXR-dependent pathways in the distal ileum and perhaps the liver in Bt KO-colonized mice. We measured expression of FXR-dependent genes in these tissues using qPCR. Contrary to our expectations, we did not observe a significant difference in genes downstream of FXR, including Nr0b2/Shp (p=0.2018), Fgf15 (p=0.6213), and Fabp6/Ibabp (p=0.6425), in the distal ileum (Figure 9B). We did observe a downregulation of Nr0b2/Shp and upregulation of Cyp7a1 in the liver of Bt KO-colonized mice, results that are consistent with increased TβMCA-mediated FXR antagonism in Bt KO-colonized mice (Figure 9C). The total bile acid pool concentration in cecal contents was higher in Bt KO-colonized mice (Figure 4A), consistent with an increase in Cyp7a1 transcription resulting in an increase in bile acid synthesis. We also observed decreases in the expression of other genes in the liver that are regulated by FXR, including Apoc2, which encodes a protein that is secreted into plasma and activates lipoprotein lipase, as well as increases in genes that are negatively regulated by the FXR target gene Nr0b2/Shp, including sterol regulatory element-binding protein 2 (Srebf2/Srebp2) and glucose-6-phosphatase (G6pc) (Figure 9C). While the former gene regulates cholesterol biosynthesis in the liver, the later gene catalyzes the final step in gluconeogenesis. The increase in G6pc in the liver of Bt KO-colonized mice is notable because this gene is significantly downregulated in the distal ilea of these mice (Figure 8D). Taken together, our data are consistent with a scenario in which bile-acid-mediated FXR antagonism is affecting pathways in the liver but not the ileum of Bt KO-colonized mice.

While some patterns of gene expression in the liver may be explained by FXR signaling, changes in the expression levels of certain notable pathways are not consistent with FXR-controlled regulation. We would expect to see an increase in the expression of the gene encoding sterol regulatory element binding protein 1 c (Srebf1/Srebp1c) as well as the downstream genes Fas and Acc, which are involved in de novo fatty acid synthesis, in the liver of Bt KO-colonized animals. No significant differences in expression of these genes, however, were observed between Bt KO- and WT-colonized mice (Srebf1/Srebp1c, p=0.5018; Fas, p=0.3292; Acaca, p=0.1302) (Figure 9C). In addition, we observed significant decreases in genes not known to be under the control of FXR, including Cd36 (p=0.0015), a gene encoding a fatty acid transporter, the immune-related genes tumor necrosis factor alpha (Tnf/Tnfα, p=0.0225) and EGF-like module-containing mucin-like hormone receptor-like 1 (Adgre1/Emr1, p=0.0011), and the G-protein-coupled receptor S1pr2 target gene sphingosine kinase 2 (Sphk2, p=0.0274) (Nagahashi et al., 2015), in the liver of Bt KO-colonized mice (Figure 9C). These results indicate that other host receptors may be involved in the transcriptional changes and metabolic differences observed. Taken together, our data suggest that changing the in vivo bile acid pool using selective expression of a bacterial bile salt hydrolase results in significant alterations in host gene expression, and that these changes are due not to the detergent properties of bile acids but rather to their activities as signaling molecules.


In this work, we identified a group of gut strains from the bacterial phylum Bacteroidetes that exhibit selective bile salt hydrolase activity. These bacteria selectively hydrolyze conjugated bile acid substrates based on the hydroxylation pattern of the steroidal core as opposed to the amino acid conjugate. Since the majority of BSH characterized to date from Bacteroidetes and Firmicutes are promiscuous and do not display selective deconjugation activity based on the bile acid substrate, it is possible that selective BSH activity may be an evolved trait. The lack of distinct clustering of Group I (i.e. steroidal core-selective) BSH at both the strain and protein levels suggests this activity that may have arisen multiple times in evolutionary history from different bacterial hydrolase precursors. Structural comparisons of closely related BSH with different selectivity profiles may reveal individual amino acids that could be responsible for the activities observed. It is also possible that differential trafficking of either the bile acid substrate or product or of the BSH protein itself (Begley et al., 2006) in these Bacteroidetes strains may be responsible for some of the differences in reactivity. Additional microbiological, biochemical, and structural studies will be needed to answer these questions.

After identifying the gene responsible for BSH activity in B. thetaiotaomicron and generating a mutant (Bt KO), we leveraged these isogenic strains in order to manipulate the in vivo bile acid pool in a highly specific manner in monocolonized GF mice. Bt KO-colonized mice, which contained significantly higher cecal TβMCA levels than Bt wild type (WT)-colonized mice, gained less weight on a HFD, had lower liver and plasma lipid levels, and displayed a respiratory exchange ratio that was shifted toward lipid utilization. These changes in host metabolism are particularly striking in light of the fact that the only difference between these two groups of mice was the presence or absence of a single bacterial gene. Remarkably, the presence of this BSH gene in BT WT-colonized mice was able to recover the positive linear correlation between energy expenditure and lean body mass normally observed in both conventional mice and humans (Fricker et al., 1989; Moruppa, 1990). This result suggests that specific genes in the gut microbiome may contribute to the establishment of host phenotypes not previously considered to be affected by the resident microbiota.

At a transcriptional level, genes related to metabolic pathways, circadian rhythm, immune modulation, and histone modifications were significantly altered in Bt KO- compared to Bt WT-colonized mice. Since TβMCA is a known FXR antagonist, we expected to observe changes in host gene expression that were consistent with downregulation of FXR-mediated pathways in Bt KO-colonized mice. The decreased expression of FXR target genes Nr0b2/Shp and Apoc2 as well as the increased expression of Cyp7a1, the rate-limiting enzyme in bile acid biosynthesis, are consistent with a regime of FXR antagonism in the livers of Bt KO- compared to Bt WT-colonized mice. These transcriptional changes suggest that the observed increase in total bile acids in the cecal contents of Bt KO-colonized mice is due to FXR-dependent bile acid biosynthesis in the liver.

Other phenotypic and transcriptional differences observed between Bt KO- and Bt WT-colonized mice are not readily explained by FXR antagonism, however. Conventionally colonized FXR knockout (Nr1h4-/-) mice display less weight gain on a high-fat diet than wild-type mice (Prawitt et al., 2011) and also have decreased liver expression of Nr0b2/Shp and increased expression of Cyp7a1 (Sayin et al., 2013), consistent with our results in Bt KO-colonized mice. Nr1h4-/- mice, however, exhibit increased triglyceride and cholesterol levels in plasma (Cariou et al., 2006; Lambert et al., 2003; Sinal et al., 2000) and low blood glucose and delayed intestinal glucose absorption when fasted (Cariou et al., 2006; van Dijk et al., 2009). Bt KO-colonized mice displayed the opposite phenotypes, including increased glucose and decreased triglyceride and cholesterol levels in plasma and a shift toward increased expression of glucose uptake and utilization genes in the distal ileum when fasted. While the expression of the FXR target genes Nr0b2/Shp, Fgf15, and Fabp6/Ibabp in the ileum are decreased in Nr1h4-/- mice (Sayin et al., 2013), we observed no transcriptional differences in these genes in Bt KO- and Bt WT-colonized mouse ilea. In addition, while the expression of hepatic gluconeogenesis genes is decreased in FXR-deficient mice (Cariou et al., 2005; Duran-Sandoval et al., 2005; Ma et al., 2006), we observed an increase in the expression of glucose-6-phosphatase (G6pc) in the liver of Bt KO-colonized mice. Taken together, these comparisons may indicate that many of the phenotypic and transcriptional differences noted in BT KO-colonized mice are either FXR-independent or not directly dependent on FXR-mediated signaling.

These results raise the possibility, then, that bile acid signaling through other host receptors may be in part responsible for the observed differences in host metabolism. Returning to the RNA-Seq data, we noted that there were significant differences in the expression of ileal genes involved in xenobiotic metabolism in Bt KO-colonized compared to BT WT-colonized mice. The pregnane X receptor (PXR) has been shown to play a central role in the response to xenobiotics, and in particular, in the transcriptional regulation of cytochrome P450 3A (Cyp3A) genes (Bertilsson et al., 1998). The expression of Cyp3a11, a mouse gene known to be regulated by PXR (Kliewer et al., 1998), was significantly decreased (3.7-fold) in Bt KO-colonized animals. This result indicates that PXR-dependent pathways may be suppressed in these mice compared to Bt WT-colonized mice.

PXR also plays an important role in glucose and lipid homeostasis and energy metabolism (Gao and Xie, 2010; Kodama et al., 2004; Kodama et al., 2007; Nakamura et al., 2007). PXR knockout (Nr1i2-/-) mice gain less weight on a high-fat diet than wild-type mice and also display decreased liver steatosis and hepatic triglyeride levels (Spruiell et al., 2014). Importantly, in contrast to FXR knockout (Nr1h4-/-) mice, Nr1i2-/- mice fed a high-fat diet exhibit increased fasting blood glucose levels and unchanged fasting insulin levels compared to wild-type mice (He et al., 2013; Spruiell et al., 2014). These metabolic phenotypes are consistent with those observed in Bt KO-colonized mice. Finally, PXR has been shown to be necessary and sufficient for the activation of the fatty acid transport gene Cd36 in the liver (Zhou et al., 2006), and we observed a decrease in hepatic Cd36 expression in BT KO-colonized mice. Taken together, our data are consistent with a regime of reduced PXR activation in Bt KO-colonized mice and perhaps suggest that PXR signaling may be involved in some of the metabolic phenotypes observed.

We cannot rule out the possibility that host receptors beyond FXR and PXR may be involved in the differences noted between Bt KO- and Bt WT-colonized mice. Exploration of bile acids as modulators of host metabolic, circadian rhythm, and immune response via binding to nuclear receptors and GPCRs is an experimental trajectory that warrants further investigation. Moreover, although our results support the conclusion that the observed changes in host metabolism are mediated by signaling properties of bile acids and not by their detergent activities, it is not yet clear which gene-level changes are driving the organism-level metabolic effects. Although our biochemical, transcriptional, and CLAMS data are consistent with a regime of decreased food intake in Bt KO- compared to Bt WT-colonized mice, we did not observe significant differences in plasma levels of leptin (p=0.4648, Bt WT vs Bt KO) and ghrelin (p=0.7783, Bt WT vs Bt KO), hormones regulating satiety and hunger (Figure 8C). Additional studies are needed to explore the contributions of bile acid signaling to host energy expenditure and feeding behavior.

Finally, because mice and humans possess different primary bile acids, there is the question of whether the observed changes in both bile acid composition and host metabolism are relevant for humans. While the major primary bile acids in mice are TβMCA and TCA (Sayin et al., 2013), humans produce glyco- and tauro-conjugated CDCA and CA (Russell, 2003). Our in vitro results show that selective Bacteroides strains cleave conjugated C12 = H (e.g. βMCA, CDCA) but not C12 = OH (e.g. CA) primary bile acids. Based on our in vivo results, one would predict that these strains would cleave conjugated CDCA while leaving conjugated CA untouched in the human gut. Furthermore, analysis of data from the first and second phases of the Human Microbiome Project has revealed that the composition of the human gut community, specifically species of Bacteroidetes, is highly personalized. While Firmicutes were more temporally variable within individuals, Bacteroidetes species, and in particular the Bacteroides genus, displayed mainly inter-individual variation (Kraal et al., 2014; Lloyd-Price et al., 2017). Our results suggest that Bacteroides species status in individuals may in part determine downstream bile acid pool composition in these people. Finally, the FXR pathway as well as other host receptor pathways that may act as bile acid targets are highly conserved in mammals (Reschly et al., 2008), suggesting that discoveries about fundamental host signaling in mice are also likely to be operable in humans. Future studies in mice with humanized bile acid pools may reveal how selective Bacteroides BSH activity is likely to affect metabolism in the human host.

Materials and methods

Reagent type (species)
or resource
DesignationSource or referenceIdentifiersAdditional information
Strain, strain background
(Bacteroides thetaiotaomicron)
VPI 5482 [CIP 104206T,
E50, NCTC 10582]
Strain, strain background
(Bacteroides caccae)
VPI 3452A
[CIP 104201T, JCM 9498]
Strain, strain background
(Bacteroides ovatus)
[NCTC 11153]ATCCATCC 8483
Strain, strain background
(Bacteroides vulgatus)
[NCTC 11154]ATCCATCC 8482
Strain, strain background
(Bacteroides uniformis)
Not applicableATCCATCC 8492
Strain, strain background
(Parabacteroides distasonis)
[NCTC 11152]ATCCATCC 8503
Strain, strain background
(Bacteroides fragilis)
VPI 2553
[EN-2; NCTC 9343]
Strain, strain background
(Parabacteroides merdae)
VPI T4-1
[CIP 104202T, JCM 9497]
Strain, strain background
(Bacteroides eggerthii)
Not applicableDSMZDSM-20697
Strain, strain background
(Bacteroides finegoldii)
Strain, strain background
(Bacteroides dorei)
Strain, strain background
(Bacteroides dorei)
Strain, strain background
(Bacteroides sp.)
Strain, strain background
(Bacteroides sp.)
Strain, strain background
(Bacteroides sp.)
Strain, strain background
(Bacteroides sp.)
Strain, strain background
(Bacteroides sp.)
Strain, strain background
(Parabacteroides sp.)
20_3 (Deposited as
Bacteroides sp., Strain 20_3)
Strain, strain background
(Bacteroides sp.)
Strain, strain background
(Bacteroides fragilis)
638ROtherGift from Seth
Rakoff-Nahoum, Boston
Children's Hospital
Strain, strain background
(Lactobacillus plantarum)
NCIMB 8826
[Hayward 3A, WCFS1]
Strain, strain background
(Clostridium perfringens)
NCTC 8237 [ATCC 19408,
CIP 103 409, CN 1491,
NCIB 6125, NCTC 6125, S 107]
Strain, strain background
(Escherichia coli S17-1 λ pir)
E. coli S17-1 λ pirOtherGift from Michael Fischbach,
Stanford University
Strain, strain background
(B. thetaiotaomicron
VPI-5482 Δtdk)
Bt WTOtherGift from Michael Fischbach,
Stanford University
Strain, strain background
(B. thetaiotaomicron
VPI-5482 ΔtdkΔ2086)
BtΔ2086This paperSee Materials and methods,
‘Construction of Bacteroides
knockout mutants’
Strain, strain background
(B. thetaiotaomicron
VPI-5482 ΔtdkΔ1259)
BtΔ1259This paperSee Materials and methods,
‘Construction of Bacteroides
knockout mutants’
Strain, strain background
(B. thetaiotaomicron
VPI-5482 Δtdk Δ2086
BtΔ2086,2086+This paperSee Materials and methods,
‘Construction of Bacteroides
complementation strains’
Strain, strain background
(B. thetaiotaomicron
VPI-5482 Δtdk Δ2086
BtΔ2086,CTRL+This paperSee Materials and methods, ‘
Construction of Bacteroides
complementation strains’
Recombinant DNA reagentpExchange-tdk
PMID: 18611383
Recombinant DNA reagentpNBU2_erm_us1311
PMID: 25574022
reagent (knockout primer pairs)
reagent (knockout primer pairs)
reagent (knockout primer pairs)
reagent (knockout primer pairs)
BT2086_DREurofins GenomicsCCA CCG CGG TGG CGG CCG
(knockout primer pairs)
(knockout primer pairs)
(knockout primer pairs)
(knockout primer pairs)
BT1259_DREurofins GenomicsCCA CCG CGG TGG CGG CCG
reagent (complementation
primer pairs)
us1311-BT-For-NdeIEurofins GenomicsGGG TCC ATA TGA AGA AAA AAC
Sequence-based reagent
primer pairs)
reagent (diagnostic primer)
pExchange_seq_UFEurofins GenomicsCGG TGA TCT GGC ATC TTT CT
reagent (diagnostic primer)
pExchange_seq_DREurofins GenomicsAAC GCA CTG AGA AGC CCT TA
reagent (diagnostic primer)
BT2086_seq_F1Eurofins GenomicsCAA CTG TCC GGG TGA ATA TAA AG
reagent (diagnostic primer)
BT2086_seq_F2Eurofins GenomicsGAA GTT TTC GTT GGG TGA ATG
reagent (diagnostic primer)
BT1259_seq_F1Eurofins GenomicsAGA AGG TAC ATC GCC TGT AC
reagent (diagnostic primer)
BT1259_seq_F2Eurofins GenomicsTAC TAT TCA CGC ACC ACA CC
reagent (diagnostic primer)
reagent (diagnostic primer)
reagent (qRT-PCR primer)
reagent (qRT-PCR primer)
reagent (qRT-PCR primer)
reagent (qRT-PCR primer)


Conjugated and unconjugated bile acids were purchased from Steraloids Inc. (Newport, RI). Oleic acid, sodium oleate and 1-oleoyl-rac-glycerol were purchased from Sigma Aldrich.

Bacterial culturing

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All Bacteroidetes strains were cultured at 37°C in brain heart infusion agar (Bacto) supplemented with with 5 mg/L hemin, 2.5 uL/L Vitamin K, and 500 mg/L cysteine HCl (BHI+). All strains were cultured under anaerobic conditions using an anaerobic chamber (Coy Laboratory Products) with a gas mix of 5% hydrogen and 20% carbon dioxide (balance nitrogen). Escherichia coli strains were grown aerobically at 37˚C in LB medium supplemented with ampicillin to select for the pExchange-tdk plasmid.

Bioinformatic search for candidate BSH in B. thetaiotaomicron

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A BLASTP search was performed on Integrated Microbial Genomes, the US Department of Energy’s Joint Genome Institute (IMG JGI) using the bile salt hydrolase from C. perfringens (NCBI Protein accession code WP_003461725) as the query sequence, with a cutoff expectation value of 1 × 10−5.

Construction of B. thetaiotaomicron knockout mutants

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Plasmids and primers are listed in the Key Resources Table. All mutants were created in the B. thetaiotaomicron VPI-5482 Δtdk background. The BtΔ2086 and BtΔ1259 mutants were constructed using a previously described counterselectable allelic exchange method (Koropatkin et al., 2008). Briefly, ~1 kb fragments upstream and downstream of the BT2086 and BT1259 genes were cloned and fused using primer pairs (BT2086KO UF/UR and DF/DR; BT1259KO UF/UR and DF/DR) and ligated into the suicide vector pExchange-tdk. The resulting vectors were electroporated into Escherichia coli S17-1 λ pir and then conjugated into B. thetaiotaomicron. Single-crossover integrants were selected on BHI-blood agar plates containing 200 μg/ml gentamicin and 25 μg/mL erythromycin, cultured in TYG medium overnight, and then plated onto BHI-blood agar plates containing 200 μg/ml 5-fluoro-2-deoxyuridine (FUdR). Candidate BT2086 and BT1259 deletions were screened by PCR using the diagnostic primers listed in the Key Resources Table and confirmed by DNA sequencing to identify isolates that had lost the gene.

Construction of B. thetaiotaomicron complementation strains

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The B. thetaiotaomicron complementation strains were constructed using a previously described method with slight modifications. Assembled construct designs were based on the mobilizable Bacteroides element NBU2 (Wang et al., 2000). Briefly, BT2086 was PCR-amplified, cloned as an NdeI/XbaI fragment into the constitutive expression vector pNBU2_erm_us1311, which contains the 300 bp region upstream of BT1311 (σ70), and transformed into E. coli S17-1 λ pir chemically competent cells (Cullen et al., 2015; Degnan et al., 2014). E. coli S17 lambda pir containing the desired plasmid or the pNBU2_erm_us1311 control vector were cultured aerobically in 5 mL of LB media at 37°C, and the Bacteroides recipient strain (BtΔ2086) was cultured anaerobically in 5 mL BHI+ media at 37°C. The E. coli S17 donor strains and B. theta recipient strain were then subcultured in 5 mL of fresh media. At mid to late log growth, the transformed S17-1 cells were spun down, resuspended with Bacteroides strain (BtΔ2086) culture in 1 mL BHI+ media, spreaded on to a BHI+ 10% horse blood agar plate, and incubated aerobically at 37°C agar side down. After 16–24 hr, bacterial biomass from the conjugation plates was scraped and resuspended in 5 mL of BHI+ media and spread on to a BHI-blood agar plate containing 200 μg/mL gentamycin and 25 μg/mL erythromycin. Colonies were confirmed via PCR and sequencing using the diagnostic primers listed in the Key Resources Table. Recovery of function of the complementation strain was confirmed via UPLC-MS with 100 μM TUDCA as substrate.

Phylogenetic analysis of candidate BSHs and Bacteroidetes strains

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BLASTP searches were performed on Integrated Microbial Genomes (IMG JGI) using BT2086 as the query sequence, with a cutoff expectation value of 1 × 10−5. Putative Bacteroidetes BSHs were identified from 19 of the 20 Bacteroidetes strains tested. A multiple sequence alignment was calculated using MUSCLE (Edgar, 2004). A phylogenetic tree was then computed from this alignment using PhyML (Guindon et al., 2010), choosing the LG substitution model, the SPR and NII (best) tree improvement method, 10 random starting trees, and bootstrap with 1000 replicas. The phylogenetic tree was visualized using iTOL (Letunic and Bork, 2011). A phylogenetic analysis of 20 Bacteroidetes strains was performed using PhyloPhlAn (Segata et al., 2013). All 20 fully or partial sequenced microbial genomes were retrieved from IMG and the National Center for Biotechnology Information (NCBI).

In vitro assays for bile acid deconjugation by Bacteroidetes

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All strains were cultured in 4 mL of BHI+ medium overnight. The following day they were diluted to pre-log phase (OD600 = 0.1) in fresh BHI+ to a final volume of 4 mL. Stock solution of taurine conjugated bile acids (TCA, TDCA, TCDCA, TUDCA, TLCA and TβMCA) or glycine conjugated bile acids (GCA, GDCA, GCDCA, GUDCA and GLCA) were added to each culture to obtain a final concentration of 50 µM of each bile acid. Cultures were then incubated in the anaerobic chamber at 37°C for 48 hr. At the 24 hr and 48 hr time points, 2 mL of each culture was extracted via the method described under ‘Bile Acid Analysis - Sample Preparation for Bacterial Culture’.

Gnotobiotic mouse experiments

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Germ-free C57BL/6 mice were maintained in gnotobiotic isolators at the Massachusetts Host-Microbiome Center under a strict 12 hr light cycle and a constant temperature (21 ± 1°C) and humidity (55–65%). All experiments were conducted on 8–9 week old male mice. GF status prior to the gavage was confirmed on a bi-weekly basis microbiologically through culturing mouse stool on broad-spectrum plates in both aerobic and anaerobic conditions, as well as Gram staining homogenized mouse stool in 1xPBS. TSA Blood Agar plates were used for aerobic conditions, while Brucella Blood Agar plates were used for anaerobic conditions. Monocolonization or GF status following gavage was confirmed by plating of fecal pellets (described below) and by 16S rRNA gene PCR and 16S sequencing. All experiments involving mice were performed using IACUC approved protocols under Brigham and Women’s Hospital Center for Comparative Medicine.

For the monocolonization experiment, 12 weight-matched mice per group were colonized with either B. thetaiotaomicron wild type (Bt WT) or the Δ2086 mutant (Bt KO) by oral gavage of overnight cultures as previously described (Marcobal et al., 2011). Frozen feces (day 2 post-colonization) were plated to determine CFU/g. The mice were co-housed in their respective groups in gnotobiotic isolators for the entire duration of the experiment. The mice were fed a standard diet containing 24% of calories from fat, 23% from protein, and 53% from carbohydrates (Autoclavable Mouse Breeder Diet 5021; LabDiet) for the first 4 days after gavage. After a 4-day acclimation period post-gavage, the mice were switched to a high-fat diet (Research Diets D12492) with 60 kcal% of fat sterilized by 10–20 kGy of gamma-irradiation. Mouse feces were collected 2 days after colonization to check bacterial colonization efficiency. This was achieved by homogenizing 1–2 fecal pellets in 1 mL PBS and then plating out 1:10 serial dilutions of the homogenate on BHI+ agar plates in the anaerobic chamber. BSH enzyme activities in different experimental groups were also checked via UPLC-MS for validation purposes. Fecal samples were collected on days 2, 4, 11, 18, 25 and 32 post-colonization and frozen at −80˚C prior to analysis. Mice were fasted for 4 hr prior to sacrifice, at which point tissues and blood were collected.

For the CLAMS experiment, GF mice were colonized as above with a third group of age- and weight-matched GF mice used as an additional control group (eight mice per group). Fresh feces (day 2 post-colonization) were plated to determine CFU/g. On day 24 and day 25 post-colonization, mice were transported in pre-sterilized CLAMS cages to Brigham and Women’s Hospital (BWH) Metabolic Core facility to conduct metabolic studies. Animals were housed individually in metabolic chambers maintained at 22˚C under a 12 hr light/dark cycle with a constant access to food and water and maintained on a high-fat diet (Research Diets D12492). One mouse from the Bt WT-colonized group was excluded from the study because this animal refused food, lost 35% of its initial body weight in the CLAMS, and displayed GI tract abnormalities during sacrifice. Whole body metabolic rate was measured using the Oxymax open-circuit indirect calorimeter, Comprehensive Lab Animal Monitoring System (CLAMS, Columbus Instruments). Body composition was examined with Echo MRI (Echo Medical Systems, Houston, Texas) using the 3-in-1 Echo MRI Composition Analyzer (Kazak et al., 2017; Mina et al., 2017), and respiratory exchange ratio (RER), calorific value (CV), and energy expenditure (EE) are calculated by the equations below:

During sacrifice, whole blood was collected into commercially available EDTA-coated tubes (Milian Dutscher group). Cells were removed from plasma by centrifugation for 15 min at 2,000 g at 4°C. Plasma was transferred to a new eppendorf tube from the supernatant and stored in −80°C for further investigation. Cecal contents were collected at sacrifice (day 32) using sterile tools on a sterile field and plated to confirm maintenance of monocolonized or GF status throughout the experiment. No CFU were detected in the GF group. RNA extracted from these cecal contents was used for qRT-PCR using 16 s rRNA and Bt BSH primers (Key Resources Table, Figure 4—figure supplement 1).

Plasma insulin, glucose and ghrelin levels were analyzed by the Vanderbilt Mouse Metabolic Phenotyping Center. Plasma glucose was measured by a glucose oxidase method using an Analox Instruments GM9 glucose analyzer (Stourbridge, UK). Plasma insulin was measured by radioimmunoassay (Millipore). Total ghrelin was measured by radioimmunoassay (Millipore). Plasma leptin, glucagon levels were analyzed by ELISA kits (Crystal Chem). Total plasma cholesterol, triglyceride and free fatty acids (FFA) were measured by standard enzymatic assays, and liver tissues were extracted (Folch et al., 1957) and analyzed by the Vanderbilt University Metabolic Phenotyping Center (VUMC) using GC.

Bile acid analysis


Stock solutions of all bile acids were prepared by dissolving compounds in molecular biology grade DMSO (Sigma Aldrich). These solutions were also used to establish standard curves. GCA and βMCA or GCDCA were used as the internal standard for GF mouse experiments and in vitro bacterial culture (for glyco-conjugated or tauro-conjugated bile acid), respectively. Solvents used for preparing UPLC samples were HPLC grade.

Sample Preparation for Bacterial Culture

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For extraction of bile acids, 2 mL of a bacterial culture was acidified to pH = 1 using 6N HCl. The culture was then extracted twice using 2 mL of ethyl acetate. In case of an emulsion, the biphasic solution was centrifuged at 2,500 g for 3 min to obtain a clear separation. The combined organic extracts were then dried over a Na2SO4 cotton plug, air dried, and reconstituted in 50% MeOH in dH2O for UPLC-MS analysis.

Sample Preparation for Serum and Tissues

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Bile acids from serum and tissues that were collected from GF mouse experiments were extracted using the method of Sayin et. al with the following modifications (Sayin et al., 2013). Serum was transferred to a 1.5 mL eppendorf tube. 100 µL of bile acid standard with 1 µM GCA (internal standard) dissolved in MeOH was added to the tube. After vortexing for 1 min, the sample was cooled to −20°C for 20 min. The sample was then centrifuged for 10 min at 15,000 g, and the supernatant (~100 µL) was transferred to another 1.5 mL eppendorf tube containing 50 µL of 50% MeOH in dH2O. The sample was then centrifuged for another 10 min at 15,000 g, and 50 µL of the supernatant was transferred to a mass spec vial and injected onto the UPLC-MS. Dilutions were applied if the concentrations of certain bile acids were out of the maximum detection range of the standard curve.

Tissue samples (approximately 100 mg) were pre-weighed in homogenizing tubes (Precellys lysing kit tough micro-organism lysing VK05 tubes) with ceramic beads. 400 uL MeOH containing 10 uM internal std (GCA) was added and thereafter homogenized (5000 speed for 90 s*2, 6500 speed for 60 s, sample kept on ice between two runs) and spun down for 20 min at 15,000 g. Of the supernatant, 200 µL was then transferred to a tube containing 200 µL of 50% Methanol in dH2O followed by centrifugation for an additional 5 min at 15,000 g. Of the supernatant, 50 µL was used transferred to a mass spec vial and injected onto the UPLC-MS. Dilutions were applied if the concentrations of certain bile acids were out of the maximum detection range of the standard curve. For quantifying bile acids, a mixture of bile acid standard pool was always carried out along with the experiment.

UPLC-MS analysis

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UPLC-MS was performed using a published method (Swann et al., 2011) with modifications outlined as follows. 1 µL of a 200 µM solution of extracted bile acids was injected onto a Phenomenex 1.7 µm, C18 100 Å, 100 × 21 mm LC colum at room temperature and was eluted using a 30 min gradient of 75% A to 100% B (A = water + 0.05% formic acid; B = acetone + 0.05% formic acid) at a flow rate of 0.350 mL/min. Samples were analyzed using an Agilent Technologies 1290 Infinity II UPLC system coupled online to an Agilent Technologies 6120 Quadrupole LC/MS spectrometer in negative electrospray mode with a scan range of 350–550 m/z (other mass spec settings: fragmentor - 250, gain - 3.00, threshold - 150, Step size - 0.10, speed (u/sec) - 743). Capillary voltage was 4500 V, drying gas temperature was 300°C, and drying gas flow was 3 L/min. Analytes were identified according to their mass and retention time. For quantification of the analytes, standard curves were obtained using known bile acids, and then each analyte was quantified based on the standard curve and normalized based on the internal standards. The limits of detection for individual bile acids are as follows: βMCA, 0.03 picomol/μL in serum or 0.1 picomol/mg wet mass in tissues; TβMCA, 0.01 picomol/μL, 0.04 picomol/mg wet mass; CA, 0.04 picomol/μL, 0.17 picomol/mg wet mass; TCA, 0.01 picomol/μL, 0.04 picomol/mg wet mass; UDCA, 0.04 picomol/μL, 0.16 picmol/mg wet mass; TUDCA, 0.01 picmol/μL, 0.04 picmol/mg wet mass; CDCA, 0.04 picmol/μL, 0.14 picomol/mg wet mass; TCDCA, 0.01 picomol/μL, 0.03 picomol/mg wet mass.

Liver histological analysis

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Histology of the liver samples for steatosis was performed using a reported method (Rao et al., 2016) in the Harvard Rodent Histopathology Core. Briefly, a portion of liver sample was cut and formalin-fixed, trimmed, cassetted and embedded in paraffin and stained with hematoxylin and eosin. Liver histology was assessed for steatosis on blinded sections.

Fecal bomb calorimetry

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To determine the remaining caloric content in the mouse feces, bomb calorimetry was carried out using the Parr Oxygen Bomb equipped with a Parr 6725 Semimicro Calorimeter module and aParr 6772 Calorimetric Thermometer module at the Brigham and Women’s Hospital (BWH) Metabolic Core facility. Briefly, 30–100 mg of pooled fecal samples from the sample mice were dehydrated at 60˚C for 48 hr in a micro centrifuge tube. Calculated heats (cal/g) take into account diurnal variations in fecal output as well as any contaminants that had entered into the sample.

Detergent assay

Synthesis of the sodium salt of β-muricholic acid

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To a solution of β-muricholic acid (6.0 mg, 0.0147 mmol) in 1.47 mL of methanol:toluene (1:1), 1M sodium hydroxide solution (0.15 μL, 0.016) was added and the resulting mixture was heated to 60°C for 18 hr. The reaction mixture was then cooled to room temperature and concentrated using a rotovap. The mixture was azeotroped thrice using toluene to ensure removal of water and the residue was dried thoroughly before being used in the assay.

The detergent ability of bile acid pools was investigated using a modified fat solubility assay described by Hofmann and coworkers (Hofmann, 1963; Lillienau et al., 1992). Briefly, stock solutions of respective fats and bile acids of known concentrations were made by dissolving them in methanol. The fats were then mixed in 1:1:1 ratio in a 96-well plate to obtain the required final concentrations. Respective amounts of different bile acid stock solutions were then added to each of the wells to reconstitute the bile acid pool concentrations observed in vivo (Bt WT bile acid pool: 8.7 mM total, including 7.1 mM TβMCA; Bt KO bile acid pool: 14.0 mM total, including 11.9 mM TβMCA). For comparison, the detergent sodium dodecyl sulfate (SDS) was used as a positive control at its critical micellar concentration (8.2 mM). SDS was added as a solution in methanol to obtain the required final concentration. The plate was then dried overnight. The dried residue was then suspended in freshly prepared 0.15 M sodium phosphate at pH 6.3. In order to account for the slight difference in the concentration of Na+ ions arising from the difference in concentrations of the bile acids in the two pools, exogenous sodium chloride was added to maintain similar concentrations. The plate was then sealed and incubated at 37°C for 24 hr after which the absorbance was measured at 400 nm using a SpectraMax Plus 384 Microplate Reader spectrophotometer. At the lowest fat concentration (2 mM), this assay was performed in 1 mL of 0.15 M sodium phosphate in a 1 mL cuvette in order to obtain accurate OD400 measurements. The solutions of fats and detergents were prepared in a similar manner as described above.

Gene expression analysis

RNA-Seq analysis

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Total RNA was extracted from frozen mouse distal ileum obtained from the monocolonization experiment using RNeasy Mini Kit (Qiagen) and DNA was removed by on-column DNase digestion using RNAse-free DNAse Set (Qiagen) (n = 6). RNA quality was checked on Bioanalyzer before proceeding to library preparation and RNA-sequencing (paired-end, 100 bp read length) on Illumina HiSeq 2500 platform by the Biopolymers Facility at Harvard Medical School. Illumina's Ribo Zero H/M/R kit was used to perform ribosomal reduction. Agilent Tapestation 4200 was used for post-prep QC with the High Sensitivity D1000 assay. The data from the Tapestation assay was combined with the data from KAPA library quantification qPCR on the Applied Biosystems QuantStudio seven instrument. The libraries were pooled equimolar and were sequenced on the HiSeq 2500 at 8.0pM with 5% PhiX.

Reads were assessed for quality using FastQC and aggregated in MultiQC (Cambridge, UK: Babraham Institute, 2011, n.d.; Ewels et al., 2016). STAR aligner was used against mouse geneome GRCm38 revision 91 (Dobin et al., 2013). The edgeR package was performed for the differential expression analysis, using the exacTest calculation and Benjamini-Hochberg correction (FDR) (McCarthy et al., 2012; Robinson et al., 2010). FDR ≤ 0.05, fold-change (FC) ≥±1.5 were set as the threshold. The goseq package was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, with significant differentially expressed genes subjected to a probability weighting function and gene length bias accounted (Young et al., 2010). RNA-Seq data are deposited in the Gene Expression Omnibus (GEO) database (accession GSE112571).


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Total RNA was extracted from liver and distal ileum tissues obtained from mice from the monocolonization experiment as previously described and reverse transcribed using a High-Capacity cDNA Reverse Transcription kit (Applied Biosystems). The resultant cDNA was diluted and analyzed by qRT–PCR using LightCycler 480 SYBR Green I Master (Roche). Reactions were performed in a 384-well format using a LightCycler 480 System (Roche) at Harvard Medical School's ICCB-Longwood Screening Facility. The 2-ΔΔCt method (Livak and Schmittgen, 2001) was used to calculate relative changes in gene expression and all results were normalized to the mouse ribosomal protein L32 mRNA.

Statistical analysis

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Unless otherwise indicated in the figure legends, differences between experimental groups or conditions were evaluated using unpaired two-tailed Welch’s t test for pairwise comparison, one-way ANOVA for multiple comparisons. Significance was determined as p value < 0.05. Statistical analysis and plotting for metabolic studies was performed in the R programming language with CalR, a custom package for analysis of indirect calorimetry using analysis of covariance with a graphical user interface (Mina et al., 2017).

Data availability

RNA-Seq data are deposited in the Gene Expression Omnibus (GEO) database (accession GSE112571). All other data generated or analyzed during this study are included in the manuscript and supporting files.

The following data sets were generated



    Bile Acid Synthesis: From Nature to the Chemical Modification and Synthesis and Their Applications as Drugs and Nutrients


    Bile acids are classified into a heterogeneous group of amphiphilic steroidal molecules. They consist of a saturated tetracyclic hydrocarbon cyclopentanoperhydrophenanthrene ring, containing three six-membered rings (A, B, and C) and a five-membered ring (D). Also, BAs have a rigid steroid nucleus and a short aliphatic side chain. There are angular methyl groups at positions C-18 and C-19 (Kuhajda et al., 2006). In higher vertebrates, the bile acid nucleus is curved because the A and the B rings are in a cis-fused configuration. In addition, there are angular methyl groups at positions C-18 and C-19. The BAs in lower vertebrates are known as allo-BAs. In this case, A i B rings are trans linked (5α-stereochemistry). There are four different types of BAs (C27, C26, C25 and C24), and they occur in the less -developed forms of life. There are two major classes of BAs depending on the length of the side chain: C27 and C24 BAs (Kuhajda et al., 2006). In higher vertebrates, C24 BAs constitute a major part of the bile. The BAs are facially amphipathic, i.e. they contain both hydrophobic (lipid soluble) and hydrophilic (polar) faces. Iida and coworkers explained the chemical synthesis of several rare BAs. An unusual bile acid, 16α-chenodeoxycholic acid (CDCA), has recently been isolated from certain species of storks and herons (Pellicciari et al., 2016). This bile acid was named avicholic acid. It was formed from CDCA. This rare bile acid is prepared from readily available CDCA using Breslow's biomimetic remote functionalization in a key step. The BAs are conjugated to glycine or taurine to yield the conjugated form of BAs. They are synthesized in the hepatocytes as the main product of cholesterol catabolism (Björkhem et al., 2010). Primary BAs are produced in the hepatocytes (Mukhopadhyay and Maitra, 2004a). By modifying primary BAs in the intestinal ileum, secondary BAs are formed. Due to their amphiphilic structure, BAs allow the emulsification, digestion, and absorption of liphophilic xenobiotics after a meal. Also, solubilization of cholesterol in the bile and biliary secretion of phospholipids take place, which have been the main roles considered over decades. Mikov et al. (2007) The BAs also have significant antibacterial properties, influencing the composition of the intestinal microflora and maintaining the sterility of the biliary tract. They exhibit a cytotoxic and membranolytic effect due to their detergent activity at the cell membrane level in concentrations above the critical micelle concentration (CMC). Bile acids are surface active molecules, which are characterized by a tendency for the spontaneous formation of aggregates on the boundary surface of the hydrophilic and lipophilic faces, as long as they are present in concentrations above the CMC. They have the ability to aggregate into micelles. Hydroxyl groups at positions C3, C7, and C12, which are evolutionarily highly conserved in higher vertebrates, represent an optimal configuration for the establishment of hydrogen bonds (Natalini et al., 2014). Bile acids with hydroxyl groups localized on both sides of the hydrophobic steroid core (α- and β- orientation) are more hydrophilic than the molecules with the same number of hydroxyl groups only in the α-orientation. Thus, they have less ability of aggregat, i.e., possess a higher CMC value, due to their hydrophilicity. The significance of the contact of hydrophobic surfaces in bile acid aggregation is evident in the differences between the CMC values of the two epimers of BAs (chenodeoxycholic acid, 3α,7α-dihydroxy−5β-cholan−24–oic acid, CDCA) and ursodeoxycholic acid (3α,7β-dihydroxy−5β-cholan−24–oic acid, UDCA). Hydrophilicity of free and conjugated BAs decreases in the following order–UDCA >CA > CDCA > DCA > LCA; taurine salts of BAs > glycine salts of BAs > free bile acid (Mikov et al., 2007). The structure of the side chain influences the CMC value by reducing the number of carbon atoms, thus increasing the ability to associate as micelles (Natalini et al., 2014). The formation of micelles is primarily due to the hydrophobic interactions of nonpolar convex β surfaces of the nucleus and hydrogen bonds established through polar structures. Bile salt mixed micelles are promising systems for drug delivery, and they can solubilize cholesterol, lecithin, and monoglycerides, which are intrinsically water insoluble. (Natalini et al., 2014) The aqueous solubility of cholesterol (~1 nM) can increase more than a million fold in the presence of bile-salt micelles. Hydrosolubility is primarily determined by substituents on the steroid core, especially when the hydroxyl groups are localized from the same, concave, α-surface of a steroid nucleus, the hydrosolubility increases due to the cooperative formation of hydrogen bonds of the hydroxyl groups with the solvent. In contrast, the β-stereoisomerization of hydroxyl groups reduces the potential of amphiphilic hydrophobic associations. In concentrations above the CMC, BAs form the small so-called primary micelles (2–10 aggregation units), while at higher concentrations, secondary micelles are formed by the aggregation of primary ones (Poša et al., 2014). The size and shape of the aggregates, in addition to the structure of BAs, are influenced by the conditions of the environmental factors such as pH, temperature, and ionic strength of the solution. Addition of electrolytes reduces the repulsive electrostatic interactions between charged groups, reducing CMC and promoting the formation of micelles. Lowering the pH to the value close to the pKa of BAs leads to the partial protonation of bile anhydride by increasing the CMC. The pKa values of BAs are significantly higher in micellar aggregates compared with the monomer form due to the electrostatic effect (Natalini et al., 2014). They are characterized by antimicrobial activity and prevent excessive development of bacteria in the small intestine. They act as signaling molecules that regulate their own synthesis through activation of nuclear receptors (FXR) and membrane TGR5 receptors and modulate metabolic pathways involving lipoproteins, glucose and drugs. Bile acids have the ability to facilitate the transport of molecules through biomembranes and improve the pharmacokinetic properties of drugs, whose absorption being incomplete and if they have low bioavailability after oral administration (Mikov et al., 2007; Stojančević et al., 2013). The number and position of the hydroxyl groups of BAs directly determine the physico–chemical properties and emulsification potential and the formation of micelles as described by the value of CMC (a higher value of CMC indicates a lower potential for the formation of micelles). In this context, the determination of the CMC value and the resulting cytotoxic potential are essential steps in the characterization of the molecules. Numerous studies have contributed to the understanding of how specific chemical modifications of the steroid core and the side chain of BAs induce different conformational changes that then alter the physico–chemical properties, metabolic properties, distribution in different body compartments and tissues and the cyto(toxic) profile of new analogs. The BAs have more recently been found to act as signaling molecules, notably through the farnesoid X receptor (FXR), a nuclear receptor expressed in the liver, intestine, adrenal glands, and kidneys that has a central role in the synthesis and enterohepatic circulation of BAs. Understanding the relationship between the structure and physico–chemical and physiological properties is a key factor that can contribute to the identification of new bile acid derivatives with favorable pharmacodynamic and pharmacokinetic characteristics as the candidate molecules for testing in preclinical and clinical studies (Chiang, 2009).

    Synthesis of BAs represents the dominant metabolic pathway of the catabolism of cholesterol (Björkhem et al., 2010). The conversion of cholesterol to BAs involves a multiplicative enzymatic process, wherein the hepatocytes contain an entire set of 17 enzymes necessary for modifying the cholesterol steroid core, removing the side chain, and conjugation with glycine (~75%) and taurine (25%), resulting in primary BAs–(3α, 7α, 12α-trihydroxy-5β-cholan-24-oic acid (cholic acid, CA) and 3α, 7α-dihydroxy-5β-cholan-24-oic acid (CDCA) (Li et al., 2013). The conversion of cholesterol to BAs involves the processes of hydroxylation, saturation of the double bond between the C5–C6, the epimerization of the C3 hydroxyl group, and the oxidative removal of the three carbon units from the side chain. Biosynthetic reactions take place in the endoplasmic reticulum, mitochondria, cytoplasm, and peroxisomes. Biosynthesis of BAs takes place through four different pathways: classic, alternative, Yamasaki, and 25–hydroxylation pathways. The exact sequence of the biosynthetic step has still not been defined since many intermediates are substituents for the same enzymes. Also, the transport of BAs and intermediates between different subcellular compartments and the course of biosynthesis are still not well known (Li et al., 2013).

    The Classic Pathway of Biosynthesis of BAs

    The classic or neutral pathway of the bile acid synthesis cascade is the most important biosynthetic mechanism responsible for the production of 90% of the total amount of BAs. This way, cholic acid (CA) and CDCA are synthesized in almost equal amounts. The 7α-hydroxylase cholesterol (CYP 7A1) is a key enzyme of this catabolic pathway that determines the size of the bile acid pool, catalyzing the hydroxylation of cholesterol to 7α-hydroxycholesterol (Björkhem et al., 2010). Modification of the steroid ring precedes the oxidative shortening of the aliphatic side chain. The 3β-hydroxy–Δ5-C27-oxidoreductase (HSD3B7) converts 7α-hydroxycholesterol to the 3–oxo–Δ4-form after which Δ4-3–oxosteroid−5β-reductase (AKR1D1) reduces the Δ4 double bond leading to the formation of 5β-hydrogen configuration (Russell, 2003).

    The final step in modifying the ring structure is the reduction of the 3–oxo group into the 3α-hydroxyl group by the 3α-hydroxysteroid dehydrogenase (AKR1C4) enzyme. When the hydroxylation reaction takes place in the C12 position with sterol, 12α-hydroxylase (CYP 8B1) produces a CA; however, if there is no hydroxylation in this position, it produces a CDCA (Li et al., 2013). Activity of the CYP8B1 enzyme determines the overall hydrophobicity of the pool of BAs, since CA is more hydrophilic compared with CDC. After modification of a ring structure, the carboxyl group is formed at the C27 position by the mitochondrial sterol 27–hydroxylase (CYP 27A1), forming C27 bile intermediates: 3α, 7α-dihydroxy−5β-cholestanoic acid (DHCA) and 3α, 7α, 12α-trihydroxy−5β-cholestanoic acid (THCA). The C27 bile intermediates are then activated into the corresponding coenzyme A (CoA) ester using two enzymes located in the endoplasmic reticulum: bile acid–CoA synthetase (BACS) and a very long chain acyl–CoA synthetase. Then, activated CoA C27 bile esters are transported in peroxisome membrane protein using peroxisomal membrane protein 70 (PMP 70, ABCD3), where prior to trimming the side chain, chiral carbon C25 racemizes from the R– to S– configuration using α-methylacyl CoA racemase (AMACR), after which the side chain can be shortened by peroxisomal β-oxidation. In the peroxisomes, (D / T) HC–CoA is oxidized by acyl CoA oxidase 2 (ACOX2), forming a double bond at position C24 (Chiang, 2015). This double bond is hydroxylated into 24–hydroxy–(D / T) HC–CoA followed by the dehydrogenation into the 24–keto (D / T) HC–CoA, and D–bifunctional protein (DBP) catalyzes both reactions (DBP). Propionyl CoA, CDCA–CoA, or CA–CoA are produced by the thiolytic separation of the resulting ketone using a sterol carrier protein X (SCPx) (Li et al., 2013).

    The Alternative Pathway of Biosynthesis of BAs

    An alternative pathway (also known as the acidic pathway due to the synthesis of acidic intermediates) involves the conversion of C27 BAs as well as oxysterols formed in different cell types, which are then transported to the liver and metabolized to BAs. Mitochondrial CYP 27A1 and microsomal oxysterol 7α-hydroxylase are key enzymes that make it possible to shorten the side chain in C24 BAs and 7α-hydroxylation in the hepatocytes with the highest percentage of CDCA (Kevresan et al., 2007). Alternatively, less than 10% of the total amount of BAs is synthesized. The alternative pathway is thought to be significantly more active in childhood, while later on in the course of life, the classical pathway becomes significant in the contribution to the composition of bile acid pool (Scheme 1; Sarenac and Mikov, 2017).

    The Yamasaki Pathway of Biosynthesis of BAs

    In the Yamasaki pathway, the first reaction of the biosynthesis of BAs takes place similar to the alternative pathway resulting in the formation of C24 BAs and 3β-hydroxy−5–cholenoic, followed by structural modifications of the steroid ring. In humans, 7α-hydroxylation before or after peroxisomal β-oxidation results in the formation of 3β, 7α-dihydroxy−5–cholanoic acid, a CDCA precursor as the most important product in the biosynthetic pathway. The presence of monohydroxy BAs in the fetal bile and relatively high concentrations of these BAs in meconium and amniotic fluid suggest the importance of this pathway during development (Scheme 1; Kevresan et al., 2007).

    25–Hydroxylation Pathway of Bile Acid Biosynthesis

    Synthesis of C24 BAs after structural modification of the ring, without the need for 27–hydroxylation and consequent β-oxidation implies a 25–hydroxylation pathway of biosynthesis. Microsomal 25–hydroxylase (CH25H) catalyzes for formation of 3α, 7α, 12α-trihydroxy−5β-cholestane−25–tetrol, which is further transferred to 24S–pentol by hydroxylation, followed by dehydroxylation, which produces the 24–oxo–tetrol, which is degraded to CA and acetone (Scheme 1; Kevresan et al., 2007).

    The BAs are not excreted in the bile ducts as free carboxylic acids, but, previously, the carboxyl group is activated with acetyl CoA, and then the resulting ester is linked by an amide linkage with the amino acids, glycine or taurine. This process is catalyzed by the enzyme (BACS) and bile acid–CoA: amino acid N acetyl transferase. The amide link increases the ionization constant of BAs (pKa~ 5), so that the conjugates of glycine have a pKa value of about 3 (at concentrations below CMC), while the presence of the sulphuric group of taurine provides pKa < 2(Sarenac and Mikov, 2017). By the conjugation process, there is a possibility of reducing the precipitation of bile salts, as well as increasing hydrophilicity of molecules with consequently reduced potential for passing through the cell membrane and reduced cytotoxic and membranolytic properties (Kevresan et al., 2007). Free BAs can diffuse through the cell membrane, while conjugates with glycine or taurine and bile salts are transmitted by active transport using ATP–binding cassette type protein (ATP-binding cassette, ABC). The secretion of bile salts from the hepatocytes into the lumen of the biliary canaliculus is mediated by two ABC transporters: the pump for the export of bile salts, (Bile Salts Export Pump (BSEP), the ATP–binding cassette of subfamily B article 11 (ABCB11), encoding ABCB11 gene) and the multi associated protein–2, MRP2 (Multidrug Resistance–Associated Protein–2 and ATP–linking cassette of subfamily C article 2 (ABCC2), encoding the ABCC2 gene), which are the basic mechanisms for the secretion of the bile. Mutation of BSEP encoding genes are responsible for the development of progressive familiar intrahepatic type 2 cholestasis and accumulation of toxic BAs in hepatocytes, while mutations in MRP2 are basis of Dubin–Johnson syndrome (Mikov et al., 2007).

    Intestinal Phase of Biotransformation of BAs

    After ingestion of a fat–containing meal, BAs released from the gall bladder into the duodenum and the intestinal lumen participate in the formation of mixed micelles containing cholesterol, phospholipids, and bile salts intercalated between the polar heads of phospholipids.

    Formation of micelles facilitates the digestion and absorption of lipids and liposoluble vitamins from food and aids in the action of pancreatic enzymes. Intestinal microflora has an extremely significant effect on the metabolism of BAs with the primary aim of reducing their bacterial activity. In the lumen of ileum and colon, conjugated BAs are subjected to the deconjugation process under the influence of bacterial hydrolases. From the reaction of 7α-hydroxylation is formed a 3α,12α-dihydroxy−5β-cholan−24–oic acid, while 7α-dehydroxylation of CDCA produces 3α-hydroxy−5β-cholan−24–oic acid, i.e. lithocholic acid (LCA). Other changes in the structure of BAs include oxidation of hydroxyl groups into oxo groups, epimerization of C3, C7 or C12 hydroxyl groups and isomerization of the compound between rings A / B (Sarenac and Mikov, 2017). It results in the epimerization of the 7α-hydroxyl group of CDCA to the 7β-epimer of UDCA with properties favorable to both the host organ and the intestinal bacteria (Mikov et al., 2007) (Scheme 1).

    Enterohepatic Recirculation and Excretion of BAs

    Homeostasis of BAs in enterohepatic circulation is controlled by genes of nuclear receptors (NRs). Apart from the nuclear receptors as intracellular bile acid sensors, some cells also contain bile acid receptors at the cell surface including a G–protein coupled receptor (TGR 5 / M–BAR / GPBAR1). These regulatory networks under physiological conditions preserve the enterohepatic circulation of BAs and limit the intracellular levels of potentially toxic BAs. Deconjugated BAs are reabsorbed passively (Roberts et al., 2002).

    In total, about 95% is effectively resorbed at the level of the distal ileum by the apical Na–dependent bile acid transporter, (ASBT) encoding the SLC 10A2 gene), localized in the apical membrane of the enterocyte (Roberts et al., 2002). The intestinal Bile Acid–Binding Protein (IBAB–P), gastrotropin, encoded by the FABP6 gene provides the trans–enterocytic transport of BAs, while on the basolateral pole of ileal enterocytes, heterodimeric Organic Solute Transporter α / β (OSTα / β) provides their efflux in the portal circulation. Bile salts linked to albumin are transported to hepatocytes, which are taken over by means of Na–dependent cotransporter of BAs, (Sodium Dependent Bile Acid Transporter (NTCP), which encodes the SLC10A1 gene), while unconjugated BAs are transmitted using Na+ independent multispecific transporter of organic anions (Organic Anion Transporter (OATPs) and SLC 21A transport proteins) localized in the membrane of the sinusoidal hepatocyte pole. The composition of the pool of BAs (conjugated after release from cholecysts and deconjugated under the action of intestinal microflora in intestinal lumen depends on the availability of nutrients, i.e. the state of satiety and hunger and consists approximately 30% CA 40% CDCA, 20–30% deoxycholic acid (DCA), and less of 5% LCA (Roberts et al., 2002). The LCA as a high toxic bile acid is mostly excreted by feces. A small amount of LCA, which is recycled back into the liver, before repeated rebilayer secretion is subjected to sulfoconjugation at the 3–hydroxy position of sulfotransferase 2A1 (SULT2A1). Sulfoconjugated BAs are almost not reabsorbed by the most important transport proteins, and they are excreted from the body. In kidney glomerulus, BAs are filtered into the primary urine after which they are almost completely reabsorbed with ASBT transporter in the membrane of proximal renal tubulocytes. The pool of BAs (total amount of about 1.5–4 g) is recycled from 4 to 14 times during the day. The fraction that is excreted by the feces per cycle (about 5%, i.e., 0.2–0.6 g / day) is compensated by the synthesis from cholesterol–a mechanism that significantly regulates the plasma cholesterol concentration. Daily amount of newly synthesized BAs in the organism of an adult is about 500 mg, which represented about 50% of cholesterol turnover (Figure 1; Mikov et al., 2007).

    Figure 1. Enterohepatic recirculation of bile salts. Bile salts entering the intestinal tract may be absorbed into the portal circulation where they can be removed from systemic circulation by hepatic uptake. The compound may then be excreted into the bile and pass back into the intestinal tract and become available for enterohepatic cycling. Biotransformation in enterocytes, hepatocytes, and the intestinal tract and throughout the body can convert the drug into metabolites, which may undergo enterohepatic cycling or escape into the urine and feces. Some very lipophilic solutes may bypass the portal circulation and be absorbed into the systemic circulation via the lymphatic system (Mikov et al., 2007).

    Oxidation Reaction of BAs

    Regioselective oxidation of hydroxyl group at C7 of CA was carried out with an aqueous solution of potassium dichromate in acetic acid in the presence of sodium acetate to 3α,12α-dihydroxy−7–keto−5β-cholanic acid (Scheme 2; Kuhajda et al., 2007).

    Scheme 2. Regioselective oxidation of the CA to 3α,12α-dihydroxy−7–keto−5β-cholanic acid (Kuhajda et al., 2007).

    The selective oxidation of the OH group at C3 carbon of the methyl cholate is carried out on Celite, which is impregnated with Ag2CO3 in boiling Toluene, whereby methyl 7α,12α-dihydroxy−3–keto−5β-cholanate is formed (Scheme 3; Kuhajda et al., 2007).

    Scheme 3. The selective oxidation of methyl cholate to the methyl 7α,12α-dihydroxy−3–keto−5β-cholanate (Kuhajda et al., 2007).

    The selective oxidation of C6 OH group of hiodeoxycholic acid (3α,6α-dihydroxy-5β-cholanic acid) is carried out using chromium (VI) oxide in acetic acid. The selective oxidation of the C6 OH group of the molecule of 3α,6α-dihydroxy–5β-cholanic acid (hyodeoxycholic acid) gives the 3α-hydroxy–6-keto–5β-cholanic acid and 3,6-diketo–5β-cholanic acid (Scheme 4; Kuhajda et al., 2007).

    Scheme 4. The selective oxidation of the C6 OH group of 3α,6α-dihydroxy−5β-cholanic acid (hiodeoxycholic acid), where are obtained 3α-hydroxy–6–keto–5β-cholanic acid and 3,6–diketo–5β-cholanic acid (Kuhajda et al., 2007).

    When the 3,6–diketo−5β-cholanic acid is heated in acetic acid with a catalytic amount of hydrochloric acid, then it leads to the isomerization of 5β-hydrogen into 5α-hydrogen. The reaction is carried out via the enol intermediate (6β-hydroxy−3–keto−5–cholenic acid), thereby, forming the 3,6–diketo−5α-cholanic acid (Scheme 5; Kuhajda et al., 2007).

    Scheme 5. Heating reaction of 3,6–diketo−5β-cholanic acid in acetic acid with a catalytic amount of hydrochloric acid via the enol intermediate resulting in isomerization of 5β-hydrogen into 5α-hydrogen (Kuhajda et al., 2007).

    This transformation is possible for each steroid compound, which has a keto group in the C6 position. Isomerization takes place by the keto–enol tautomeric mechanism (Kuhajda et al., 2007).

    If the steroid skeleton of BAs has a double bond, then allyl oxidation is possible, that is, the regioselective introduction of the oxo group into the allyl position. In the oxidation reaction of methyl 3α-acetoxy−5–cholenate with tert-butylhydroxyperoxide pyridinium dichromate (t-BHPO-PDC), the methyl 3α-acetoxy−12α-hydroxy−7–keto−5–cholenate (Scheme 6; Kuhajda et al., 2007) is obtained.

    Scheme 6. Oxidation reaction of methyl 3α-acetoxy−5–cholenate with tert–butylhydroxyperoxide whereby the methyl 3α-acetoxy−12α-hydroxy−7–keto−5–cholenate is obtained (Kuhajda et al., 2007).

    Regioselective oxidation of CA is carried out with an aqueous solution of potassium dichromate in acetic acid in the presence of sodium acetate, whereby the 3α,12α-dihydroxy−7–keto−5β-cholanic acid is formed (Scheme 7; Kuhajda et al., 2007).

    Scheme 7. Regioselective oxidation of CA with an aqueous solution of potassium dichromate in acetic acid in presence of sodium acetate gives the 3α,12α-dihydroxy−7–keto−5β-cholanic acid (Kuhajda et al., 2007).

    Both reactions, selective oxidation of ethyl 3α,7α,12α-trihydroxy−5β-cholanate with chromium trioxide in acetic acid at −7 to 0°C and reaction of hydrolysis involve forming the 3α,12α-dihydroxy−7–keto−5β-cholanic acid (Scheme 8; Kuhajda et al., 2007).

    Scheme 8. Both reactions, selective oxidation of ethyl 3α,7α,12α-trihydroxy−5β-cholanate with chromium trioxide in acetic acid and reaction of hydrolysis give the corresponding product (3α,12α-dihydroxy−7–keto−5β-cholanic acid) (Kuhajda et al., 2007).

    The nitrate groups of 3,12–dinitroester of 3α,12α-dihydroxy−7–keto−5β-cholanic acid were removed with zinc in glacial acetic acid, yielding the 3α,12α-dihydroxy−7–keto−5β-cholanic acid (Scheme 9; Kuhajda et al., 2007).

    Scheme 9. Transformation reaction of 3,12–Dinitroester of 3α,12α-dihydroxy−7–keto−5β-cholanic acid to the 3α,12α-dihydroxy−7–keto−5β-cholanic acid (Kuhajda et al., 2007).

    The methyl ester of CA is converted into the amide of CA, which is then oxidized with an equivalent amount of bromine in alkaline methanol. The regioselectivity of the reaction is determined by the N–bromoamide function from the side chain. In the reaction, the 3α, 7α-dihydroxy−12–keto−5β-cholic acid is obtained (Scheme 10; Kuhajda et al., 2007).

    Scheme 10. Regioselective oxidation of C12 OH group of the methyl ester of cholic acid to obtaines the 3α,7α-dihydroxy−12–keto−5β-cholanic acid (Kuhajda et al., 2007).

    We have also considered the synthesis of 3α,7α, 16α-trihydroxy-5β-cholan-24-oic acid (avicholic acid) and natural BA first isolated from avian species (Shoebill stork and herons) and its derivatives 5−7 as TGR5 ligands (Mukhopadhyay and Maitra, 2004b). 6alfa-Ethyl-Avicholic acid sodium salt, 3alfa,7alfa,16beta-trihydroxy-5beta-cholan-24-oic acid sodium salt and 3alfa,7alfa,16beta-trihydroxy-6alfa-ethyl-5beta-cholan-24-oic acid sodium salt.

    Development of ligands for BA-activated receptors, starts from cholic acid (CA) as a lead compound and INT-777 as a potent and selective TGR5 agonist in vivo. INT-777 is able to stimulate type 2 iodothyronine deiodinase (D2) activity in brown adipose tissue (BAT) and muscle, as well as induce the release of glucagon-like protein 1 (GLP-1) in enteroendocrine cells.

    Avicholic acid derivatives were prepared according to the synthetic approach reported previously by Mukhopadhyay. Chenodeoxycholic acid and 6α–ethyl chenodeoxycholic acid were treated with p-toluensulfonic acid (pTSA) and MeOH at room temperature to get the corresponding methyl ester analogs. They were selectively protected at the C3 position by reaction with acetic anhydride in the presence of NaHCO3 in refluxing THF.

    The methyl 3α-acetoxy–6α–alkyl–7α–hydroxy–5β–cholan–24–ate was functionalized at the C7 position with a 3-iodo-benzoyl moiety by reaction with 3-iodo-benzoylchloride in the presence of CaH2 and BnEt3N+Cl in toluene to afford methyl–3α–acetoxy–6α–alkyl–7α–m–iodobenzoyloxy–5β–cholan–24–ate in good yield (Mukhopadhyay and Maitra, 2004b). Breslow's photolysis of PhICl2 in the presence of methyl 3α–acetoxy–6α–alkyl–7α–m–iodobenzoyloxy–5β–cholan–24–ate was achieved by 0.3 mM tBuOH / CH2Cl2 solution to yield the 17Cl–derivative of methyl–3α–acetoxy–6α–alkyl–7α–m–iodobenzoyloxy–5β–cholan–24–ate in quantitative yield. C16−17 olefinic intermediate was obtained by refluxing 17Cl–derivative of methyl–3α–acetoxy–6α–alkyl–7α–m–iodobenzoyloxy–5β–cholan–24–ate in pyridine. The 6α–alkyl–5β–cholan–3α,7α,16α,24–tetrol was obtained by hydroboration-oxidation of C16−17 olefinic intermediate by borane-THF complex and alkaline hydrogen peroxide, followed by base hydrolysis (NaOH/MeOH). It is carried out a selective oxidation of the C24 position was achieved by using a (2,2,6,6–tetramethylpiperidin–1–yl) oxyl (TEMPO) in CH2Cl2 / H2O and Aliquat as a phase transfer catalyst to afford the lactone. Alkaline hydrolysis (NaOH / MeOH) was afforded avicholic acid sodium salt and 6α-ethyl-avicholic acid sodium salt (Mukhopadhyay and Maitra, 2004b) (Scheme 11).

    Scheme 11. Synthesis of Avicholic Acid Derivatives. Reagents and conditions: (i) (1) MeOH, pTSA; (2) Ac2O, NaHCO3, THF. (ii) 3-I-benzoylchloride, CaH2, BnEt3N+Cl. (iii) PhICl2, t-BuOH 0.3 M, hυ. (iv) Py, reflux. (v) (1) BH3·THF; (2) NaOH, H2O2; (3) KOH, MeOH. (vi) NCS, TEMPO, Aliquat, CH2Cl2/H2O. (vii) NaOH, MeOH.

    The solution of K2CO3 in CH3OH at room temperature is used, when the C16–C17 olefinic derivative of methyl–3α–acetoxy–6α-alkyl–7α–m–iodobenzoyloxy–5β–cholan–24–ate was submitted to hydroboration–oxidation reaction and selectively deprotected at C3 position. The 6α–alkyl–7α-m-iodobenzoyloxy-5β-cholan-3α,16α,24–triol was reacted with Jones reagent and esterified at the terminal carboxylic group by treatment with pTSA in MeOH at room temperature to give methyl 3,16–diketo–6α-alkyl–7α–m–iodobenzoyloxy –5β–cholan–24–ate in nearly quantitative yield. By reduction of the keto groups with tert–butylamine–borane complex in CH2Cl2 at reflux, followed by hydrolysis with NaOH in CH3OH and purification by medium pressure liquid chromatography are formed 3α,7α,16β-trihydroxy-5β-cholan-24-oic acid sodium salt and 3α,7α,16β-trihydroxy-6α-ethyl-5β-cholan-24-oic acid sodium salt. Compounds 4–7 were unstable in the free acid form, spontaneously reacting to give the corresponding lactone derivatives (Mukhopadhyay and Maitra, 2004b) (Scheme 12). Avicholic acid sodium salt, 6alfa-Ethyl-Avicholic acid sodium salt, 3alfa,7alfa,16beta-trihydroxy-5beta-cholan-24-oic acid sodium salt and 3alfa, 7alfa,16beta-trihydroxy-6alfa-ethyl-5beta-cholan-24-oic acid sodium salt.

    Scheme 12. Synthesis of 16-epi-Avicholic Acid derivatives. Reagents and conditions: [(i) (1) BH3·THF; (2) NaOH, H2O2; (3) K2CO3, MeOH. (ii) (1) Jones reagent; (2) MeOH, pTSA. (iii) (1) t-BuNH2BH3, CH2Cl2, reflux; (2) NaOH, MeOH].

    The (3α,12α,16β-trihydroxy-5β-cholan-24-oic acid, EPCA) is an epimer of pythocholic acid at snakes (3α,12α,16α-trihydroxy-5β-cholan-24-oic acid, PCA) and involves a series of simple and selective chemical transformations of CA. The CMC of 16-epi-pythocholate in aqueous media was determined using pyrene as a fluorescent probe. In vitro cholesterol solubilization ability was evaluated using anhydrous cholesterol, and results were compared with those of other natural di- and trihydroxy BAs. The 16-epi-pythocholic acid (16β-hydroxy-deoxycholic acid) behaves similar to CA and avicholic acid (3α,7α,16α-trihydroxy-5β-cholan-24-oic acid, ACA) in its aggregation behavior and cholesterol dissolution properties (Nonappa and Maitra, 2010). Pythocholic acid formed a methyl ester, which was oxidized by chromic oxide to the triketone methyl dehydropythocholate. The obtained compound was reduced by the Wolff–Kishner method to cholanoic acid (Nonappa and Uday, 2007).

    Pythocholic acid contains three secondary hydroxyl groups attached to the cholanic acid nucleus.

    Pythocholic lactone formed a diketone, dehydropythocholic lactone, by chromic oxidation. Then, the dehydropythocholic lactone reacts with NaOH and in methylation reaction occurs the methyl ester of 3,12–diketo−16α-hydroxy–cholanic acid (Scheme 13; Nonappa and Uday, 2007).

    Scheme 13. Synthesis of methyl ester of 3,12–diketo−16α-hydroxy–cholanic acid from the pythocholic acid.

    The Role of Membrane Transport Proteins of BAs in the Distribution of Drugs

    Many metabolic enzymes and transport proteins bind to corresponding substrates, such as different drugs. They are responsible for the metabolism and transport of BAs through cell membranes in both directions. It is important to note that nuclear receptors and other ligand–dependent transcription factors have the role of sensor that detects the presence of drugs or BAs, and regulates the expression of metabolic enzymes and transport proteins in order to maintain homeostasis (Staudinger et al., 2013). Molecular mechanisms regulate the inducible expression of the gene by drugs and BAs, where key mediators in these processes are nuclear subfamily 1 receptors [pregnane X receptor (PXR), constitutive androstane receptor (CAR), FXR, and vitamin D receptor (VDR)]. Membrane transport proteins regulate the transport of substrates and, for this reason, they represent the essential regulators of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of any substrate in body, both natural ligands and metabolites and medicaments (Kramer, 2011). Also, many xenobiotics are involved in the transport roads common to physiological intermediates. Many physiological transporters are not monospecific, but have a wide range of specific substrate. Interactions of substrate (endogenous metabolite or drug / xenobiotics) with transport proteins have a very significant effect on both the efficacy and safety profile of the drug. The most important bile acid transport proteins with a wide range of substrate specificities of great importance for drug action and disposition are the influx transporters from the group of organic anion transporters that belong to the solute carrier (SLCO) gene family as well as an efflux ABC transporter protein. For some transporter proteins from the above–metioned groups, BAs represent physiological substrates with proven effects on absorption, distribution, metabolism, excretion, and toxicity features of many medicaments (substrates for said membrane transporters) (Kramer, 2011). Transport proteins for the substrate acquisition have developed evolutionarily for the purpose of facilitating not only the takeover of cell nutrients and vitamins, but also resorption of endogenous products such as glucose and other carbohydrates, amino acids, and small peptide or BAs. Many of these transporters use an electrochemical gradient of ions such as Na+ for transport versus a concentration gradient (Staudinger et al., 2013).

    Among the transport proteins for the transmission of BAs is OATP1A2, responsible for transporting various endogenous substrates (BAs, steroid conjugates, prostaglandins, thyroid hormones T3, T4, and rT3), and exogenous substrates (rocuronium, fexofenadine, MRI contrast, etc.) from the intestinal lumen and port of circulation. The expression of this transporter is controlled by PXR and VDR (Claro da Silva et al., 2013). The transport protein OATP1B1 is used to carry endogenous substrates (cholate, thyroxine, bilirubin, leukotrien, C4 and E4, estradiol 17β-glucuronide, etc) and clinically relevant drugs (statins, rifapmicin, enalapril, methotrexate, olmesatran) (Claro da Silva et al., 2013). This transporter is located on the basolateral membrane of the hepatocytes, and it is transcriptionally controlled by the PXR and FXR receptors. The OATP1B3 (SLCO1B3) is a hepatic–specific transporter from the OATP family, which mediates transfer of Na+–independent xenobiotics (docetaxel, enalapril, erythromycin, fexofenadine, fluvastatin, methotrexate, rifampin, and pacitaxel) and plays a key role in transmission of BAs and bilirubin. The OATP1B1 and OATP1B3 are transporters in the embryonal kidney cells, HEK293, and they are responsible for transport of CA, CDCA, and DCA (Claro da Silva et al., 2013). It has been proven that conjugated glycin and taurine derivatives of these BAs (glycochenodeoxycholic acid, taurochenodeoxycholic acid, glycodeoxycholic acid, taurodeoxycholic acid, glycolithocholic acid, and taurolithocholic acid) are natural substrates for OATP1B1 and OATP1B3. At physiological pH = 7.4, unconjugated BAs pass through the cell membrane by passive diffusion, while the transmission of conjugated BAs to be mediated by transporters is necessary. Chenodeoxycholic acid is the most potential endogenous FXR agonist, which induces the promoter activity of transport protein in HepG2 and Huh 7 cell lines. Rifampicin, a potent PXR activator, reduces the expression of the transport protein to the basolateral hepatocyte membrane. Transporter NTCP (SLC10A1) localized to the basolateral hepatocyte membrane and transporter ASBT (SLC10A2) localized to the apical enterocyte membrane are Na+–dependent transporters, whose ligands are conjugated and unconjugated BAs. Both transporters are under the negative transcriptional control of FXR (Claro da Silva et al., 2013).

    Application of BAs in the Development of New Drug Formulation

    The BAs are used as drug delivery systems. They can play a role of drug carrier in the form of mixed micelles, liposomes stabilized by BAs (bilosomes), and chemical conjugates with different drugs (Faustino et al., 2016). Organotropicity of BAs and affinity for protein transport systems within the enterohepatic system have been used for the selective therapeutic targeting of liver tissue to improve intestinal absorption and the metabolic stability of drug. The BAs can represent carrier linkers of various structures, lengths, charges, functions, and stereochemistry. The BAs allow addition of different drugs to one of the functional groups, including the hydroxyl groups at positions C3, C7, C12 or the side chain carboxyl group at C24 position. These conjugates get involved in the interactions with bile acid transporters (Stojančević et al., 2013). The BAs may alter the pharmacokinetic and pharmacodynamic properties of the medicinal substance, which they carry. In accordance with the predictions of the quantitative relationship of structure and activity, QSAR, on transporter models, the most effective molecular recognition by the membrane transporter of hepatocyte and ileal enterocyte is achieved linking the drug at the C3 position of BAs (Faustino et al., 2016). The hydroxymethylglutaryl (HMG)–CoA reductase conjugate associated with an amide linkage to the C3 position of the CA derivative (S−3554) with a free C24 carboxyl group has led to a specific inhibition of cholesterol biosynthesis in hepatocytes. The corresponding taurine conjugate released through in the portal vein is transported unchanged through hepatocytes and excreted into the bile without the intracellular drug release and inhibition of HMG–CoA reductase. The intracellular transfer of the drug and the availability of organelles can be coordinated by the conjugation status of bile acid side chain. Chlorambucil–taurocholate conjugate, S2776, exhibited all the pharmacokinetics properties of the bile acid conjugate, including specific interactions with the ileal transporter, ileal bile acid-binding protein (IBAB–P) (Stojančević et al., 2013).

    Uptake transporters may facilitate drug transfer from blood to liver for further processing by metabolizing enzymes and/or excretory transporters. There is a change in the pathway of drug secretion from the renal to the biliary pathway and in transcellular transport of drug from the blood to the bile in competition with natural BAs. NTCP is the main transporter accounting for the liver uptake of conjugated bile acids (e.g., taurocholate, tauroursodeoxycholate and taurochenodeoxycolate), but it is also able to transport, although with less efficiency, non conjugated bile acids. OATPs are membrane influx transporters that regulate cellular uptake of a number of endogenous compounds and clinically important drugs. The ASBT transport system has the ability to recognize the drugs and peptides as substrates of bile acid conjugates, which potentially can result in the increase of the intestinal resorption of the drug. It is possible to add the peptide to the C24 position of the bile acid side chain, but the resorption of such conjugates in the ileum is small, since the negative charge of the bile acid side chain is necessary for molecular recognition by the transporter (Faustino et al., 2016).

    The BAs as Therapeutic Agents (Drugs)

    The BAs and their derivatives may possess therapeutic effects in the treatment of the human type of immunodeficiency virus 1 (HIV 1). Difficulties in the delivery of BAs in the blood explain why BAs are not fully used as therapeutic agents against wrapped viruses. Taurolithocholic acid 3–sulfate is very effective against Herpes simplex virus 1 and 2, HIV, Neisseria gonorrhoeae and Chlamydia trachomatis with low and non citotoxicity to human cervical epithelial cells (Mikov et al., 2007). Ursodeoxycholic acid plays a role in improving hepatic histology and reduces serum bilirubin level as an important analytic marker in primary biliary cirrhosis (PBC). Long–term liver survival after transplantation was established in patients who received 13–15 mg / kg / day of UDCA for a period of four years. The UDCA acid was used in patients with primary sclerosing cholangitis in several doses over a period of one year (Mikov et al., 2007). Sclerosing cholangitis is characterized by chronic inflammation of intrahepatic and extrahepatic gallbladder, leading to fibrosis and possible damage of bile ducts. In addition to cholecystectomy, and laparoscopic cholecystectomy, BAs are used for the removal of gallstones. Chenodeoxycholic acid reduces the activity of HMG–CoA reductase, an enzyme that limits the rate of cholesterol formation. Size of stones in the bile is an important factor in the treatment with BAs. In patients, who were treated with UDCA, it has been found that gallstones were completely destroyed after two years of treatment. The UDCA Ursodeoxycholic acid is effective in the treatment of choledocholithiasis and hepatolithiasis associated with Caroli syndrome. The UDCA and CDCA have shown antiproliferative effect and they are able to induce apoptosis in carcinogenic cells (Mikov et al., 2007). Obeticholic acid (OCA) is used for the treatment of PBC. Primary biliary cholangitis, previously known as primary biliary cirrhosis, is a chronic cholestatic liver disease with an autoimmune basis, affecting mostly middle-aged women (Momah and Lindor, 2014). In 2017, the FDA approved FXR agonist and, currently, numerous FXR agonists are under clinical trials for nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) (Le et al., 2012). The NAFLD refers to a spectrum ranging from noninflammatory isolated steatosis to NASH, which is characterized by steatosis, necroinflammatory changes, and varying degrees of liver fibrosis (Le et al., 2012). The OCA is a derivative of CDCA, the primary human BA, and it is the natural ligand for FXR. By activating FXR in the ileum, OCA decreases BA reabsorption through downregulation of the apical sodium-dependent BA transporter and increased expression of fibroblast growth factor (FGF) 19, which, in the liver, also decreases BA synthesis through CYP7A1. The OCA may have antifibrotic properties and the potential to improve portal hypertension (Hirschfield et al., 2009). Ligand-activated nuclear receptors control key steps in lipid metabolism as well as inflammation and fibrogenesis and thus, are potentially crucial players in NAFLD/NASH pathogenesis (Le et al., 2012). Such a receptor is the FXR. The FXR is a key regulator of hepatic lipid metabolism. The role of FXR has been emphasized on by the development of hepatosteatosis and hyperlipidemia in FXR−/− mice (Ali et al., 2015). Farnesoid X receptors are considered nuclear hormone receptors, due to their actions in the nucleus of the hepatic and intestinal cells. In the enterocytes, the activity of Farnesoid X receptor is reflected in the regulation of bile acid synthesis by releasing FGF−19 into the portal circulation (Ali et al., 2015). Farnesoid X receptors are involved in the modulation of hepatic inflammation, fibrosis, metabolic pathways, and regeneration. The FXRs facilitate bile salt secretion through the downregulation of the bile salt export pump (Ali et al., 2015). The FXR agonists are classified into important regulators of bile acid metabolism pathways, which are involved in regulation of the production and flow of BAs in the liver. The OCA is a first class agonist, which selectively binds to FXR. The OCD increases insulin sensitivity and reduces markers of liver inflammation and fibrosis. The OCA has significant beneficial effects on NASH related liver health (Hirschfield et al., 2015). It is also indicated for the treatment of PBC in combination with UDCA in adults with an “inadequate” response to UDCA or as monotherapy in adults unable to tolerate UDCA. The BA transporters modulators or BA sequestrants could have beneficial therapeutic effects in (NAFLD / NASH) (Trivedi et al., 2016; Arab et al., 2017). Treatment with OCA led to a significant reduction of liver fibrosis as compared with patients treated with placebo (Kim et al., 2000). The OCA has a role in the regulation of lipid, glucose, and energy homeostasis, and it is a potential target for the treatment of obesity and NAFLD. Also, intestine-specific FXR agonist, Fexaramine, has been shown to increase energy expenditure to reduce body weight gain in obese animal model (Neuschwander-Tetri et al., 2015).

    Secondary BAs produced in the intestine (colon) by gut bacteria activate TGR5 signaling, which induces cAMP/PKA signaling to stimulate energy metabolism in BAT, relax and refill the gallbladder, and secrete GLP-1 from the intestinal endocrine L cells (Reich et al., 2016).

    Hypoglycemic Effect of BAs and Their Application in Treatment of Diabetes Mellitus

    The BAs have the ability to act as permeation enhancers for antidiabetic drugs over the ileal mucosa and through the blood–brain barrier. They also showed potential health benefits in the treatment of diabetes by their endocrine, metabolic, energy, and other effects. The strongest hypoglycemic effect in type 1 diabetes (T1D) is exhibited by 3α,7α-dihydroxy−12–keto−5β-cholanic acid, while still better effects are achieved when applying the mentioned 12–keto derivative of CA in combination with hypoglycemic agent gliclazide or a composition of stevioside (Mikov et al., 2008). It has been proven that the best effects of glycemic control has been achieved in the case when rats with T1D were prebiotically pretreated and then using 3α,7α-dihydroxy−12–keto−5β-cholanic acid and gliclazide simultaneously (Mikov et al., 2008). It has been known that the transporter function is disrupted or suppressed in diabetes. Contrary to in vivo results, in vitro studies have produced the opposite results. It has been found that the 3α,7α-dihydroxy−12–keto−5β-cholanic acid acts as an inhibitor of the efflux transporter and the transfer of substances in the direction from mucosa to the serosa via the inhibition of MrP3 transporter. It is believed that this is a disagreement with the results of in vivo studies because in in vivo biotransformations (metabolic transformations), mono keto derivatives enhance in vivo absorption of gliclazide in ileum (Al-Salami et al., 2008).

    In intravenous administration (independent of the use of gliclazide or probiotic pretreatment), the pharmacokinetic properties of 3α,7α-dihydroxy−12–keto−5β-cholanic acid remain unchanged, but they are significantly changed in case of oral administration. It has been proven that 12–ketocholic acid enhances the nasal absorption of insulin in rats (Al-Salami et al., 2008).

    Gliclazide is used in the treatment of type 2 diabetes (T2D) to help stimulate the insulin production. It also has beneficial extrapancreatic effects, which makes it potentially useful in T1D. Some patients with T2D continue to use gliclazide even after their diabetes progresses to T1D, since it provides better glycemic control than insulin alone (Dutta et al., 2016).

    The administration of gliclazide and 3α,7α-dihydroxy−12–keto−5β-cholanic acid allows the most significant reduction in blood glucose level in probiotic–pretreated diabetic rats (from 12.6 ± 2.0 to 10 ± 2.0 mmol / l, p < 0.01). Pretreatment with probiotics and subsequent oral administration of gliclazide + 12–ketocholic acid resulted in the greatest effect in the treatment of T1D, as well as in improved signs and symptoms in the animals. (Al-Salami et al., 2009)

    The effect of oral 3α,7α-dihydroxy−12–keto−5β-cholanic acid was not significant in probiotic–pretreated diabetic rats that had lower blood glucose levels at the time of administration of the 12–keto derivative of CA, possibly due to an interaction in the gut (Al-Salami et al., 2009).

    The combination of gliclazide + 3α,7α-dihydroxy−12–keto−5β-cholanic acid produced a greater effect in diabetic rats than 3α,7α-dihydroxy−12–keto−5β-cholanic acid alone (Mikov et al., 2008).

    In the regulation of glycemic response in patients with T2D, an important effect of bile acid sequestrants in bariatric surgery has been demonstrated, which greatly affect the level of glucose and profile of BAs. Bile acid sequestrants have beneficial effects on the glycemic control and insulin sensitivity in patients with T2D and diabetic rodents. One bile acid sequestrant, colesevelam is an approved drug, used to treat diabetes (Brufau et al., 2010). Patients with T2D, who are treated with colesevelam, showed significant reduction in HbA1c and postprandial glucose levels, even when colesevelam was given in combination with other antidiabetic drugs. The healthy insulin–sensitive subjects remained unaffected by colesevelam treatment (Brufau et al., 2010). Patients with T2D and treated with colesevelam exhibited an increase in plasma triglyceride levels, which precludes colesevelam treatment in T2D subjects with hyperglyceridemia (Brufau et al., 2010).

    Obesity and diabetic mice treated with colesevelam exhibited an improved glycemic response mediated by a double mechanism:

    • The TGR5 mediated by GLP−1 secretion in L cells and

    • intestinal expression of proglucagon

    Bile acid sequestrants affect glucose absorption. The first dose of colesevelam with the standard meal had no effect on postprandial concentrations of glucose compared with baseline and placebo. Colesevelam did not appear to affect hepatic or peripheral insulin sensitivity as measured by the hyperinsulinemic–euglycemic clamp technique (Mari et al., 2011).

    Neither acute nor chronic treatment with colesevelam seems to affect post–OGGT glucose concentrations (Li et al., 2012). Interventions by BAs and probiotics exert a direct and significantly positive effect on glycemic control and the progression of diabetic complications (Brufau et al., 2010).

    The contribution of BAs, gliclazide, and probiotics in the regulation of glycemic responses in T1D is demonstrated in Figure 2 (Mikov et al., 2017).

    Figure 2. Potential synergistic effects due to concomitant administration of gliclazide, BAs and probiotics in treating T1D (Mikov et al., 2017).

    Bile acid metabolism has been altered in patients with T2D (Zammitt and Frier, 2005). Modification of the bile acid pool leads to an improvement in glycemic control in such patients. The expression level of FXR in the liver has been reduced in diabetic animals. Activation of FXR through the small heterodimer partner (SHP) leads to a reduction in the expression of the gene for phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6–phosphatase (G6Pase) involved in the process of gluconeogenesis (Zammitt and Frier, 2005).

    The Role of BAs in Digestion of Nutrients

    The BAs have important roles in lipid metabolism. They are essential for the formation of mixed micelles in the small intestine that facilitates solubilization, digestion, and absorption of dietary lipids and fat-soluble vitamins. The BAs function as nutrient–signaling hormones by activating specific nuclear receptors (FXR, PXR, vitamin D) and G-protein coupled receptors [TGR5, sphingosine-1 phosphate receptor 2 (S1PR2), muscarinic receptors].

    The BAs and insulin appear to collaborate in regulating the metabolism of nutrients in the liver. They both activate the AKT and ERK1/2 signaling pathways. The disruption of these signaling pathways may increase the risk of fatty liver and NAFLD. Bile acid induction of the FXR-α target gene, small heterodimer partner (SHP), is highly dependent on the activation of the PKCζ, a branch of the insulin signaling pathway. The SHP is an important regulator of glucose and lipid metabolism in the liver. The BAs promote intestinal absorption of biliary and dietary lipids, prior to their return to the liver through the enterohepatic circulation or their excretion in the feces. The BAs are present in micellar concentrations and form mixed micelles with dietary lipids and their digestion products, such as monoacylglycerols and fatty acids (Russell, 2009).

    The BAs also solubilize nonpolar lipids such as cholesterol and fat–soluble vitamin, increasing their water solubility and promoting their diffusion across the unstirred water layer for delivery to the intestinal epithelium. The BAs are powerful regulators of metabolism. Mice, when treated orally with CA, are protected from diet–induced obesity, hepatic lipid accumulation, and increased plasma triacylglycerol and glucose levels. The bile acid receptor (FXR) is involved in regulation of lipid and carbohydrate metabolism. Interruption of the enterohepatic recirculation of BAs, using bile acid sequestrants in patients with hypercholesterolemia or after ileal reaction, results in an increased level of triglycerides in the plasma. The FXR links the metabolism of BAs and triglycerides, such as via SHP protein, and reduces the expression of the transcription factor sterol regulatory element–binding protein 1c and its target genes for acetyl CoA synthetase, malic enzyme, and stearoyl–CoA desaturase 1, which are involved in biosynthesis of fatty acids and triglycerides. Activation of FXR stimulates β-oxidation of fatty acids and contributes to the reduction of lipid levels in the liver (Kalaany and Mangelsdorf, 2006).

    Stimulation by FXR steroidal and nonsteroidal agonists leads to an increase in LDL levels and a decrease in HDL cholesterol levels. In humans, it has been confirmed that long–term administration of CDCA leads to a mild increase in serum LDL cholesterol concentrations. The effect of FXR on the expression of CYP 7A1 enzyme results in the inhibition of the conversion of cholesterol into BAs. Application of agonist FXR has a positive effect on the metabolism of glucose because it reduces insulin resistance and the concentration of glucose in the blood of animals (Kalaany and Mangelsdorf, 2006).

    In the intestine, BAs are taken up into the enterocyte by the ASBT and SLC10A2, bound by the cytosolic ileal bile acid binding protein [IBABP; fatty acid binding protein 6 (FABP6)], and then exported across the basolateral membrane by the heteromeric, SLC51A; OSTβ, SLC51B) (Dawson et al., 2010).

    In the distal ileum, bile acid absorption from the lumen occurs via ASBT and bile acid efflux out of the cell via OSTα / OSTβ. The FXR target genes are SHP, FGF 15, IBABP, OSTα, and OSTβ, and secretion of FGF15 takes place into the portal blood.

    The BAs activate FXR in the liver inducing SHP, which inhibits the transcription of the CYP7A1 and CYP8B1 gene under physiological conditions. In the intestine, activated FXR induces FGF15/19, an intestinal hormone that induces FGFR4 on the hepatocytes and via cJUN inhibits CYP7A1. The FXR suppresses synthesis of BAs and also regulates its enterohepatic circulation. In the liver, FXR induces biliary bile acid excretion by BSEP and MRP2 transporters, which are located at the apical membrane of hepatocytes. The FXR induces hepatocyte basolateral transporters Ostα/β and MRP3/4, providing an alternative excretion route for BAs into the systemic circulation. In the ileum, FXR decreases reabsorption of conjugated BAs via ASBT, while inducing IBAP and Ostα/β promotes enterohepatic bile acid circulation. Face I (CYP3A4) and face II (SULT2A1 and UGT2B4) of bile acid detoxification are also positively regulated by FXR, rendering BAs more hydrophilic and less toxic (Figure 3; Stanimirov et al., 2012).

    Figure 3. Bile Acid Metabolism in Liver and Intestine.


    The amphiphilic nature of BAs is used to test the improvement of drug transport through biological membranes, allowing the design of new pharmaceutical formulations. Mainly, the mechanistic role of BAs is extended to the pleated regulatory functions, including cellular homeostasis, metabolic processes, regulation of cell proliferation, cell death, and the process of carcinogenesis. The BAs are recognized as paracrine and endocrine signaling molecules with the ability to activate different nuclear receptors such as the FXR, PXR, CAR, and VDR and membrane receptors–protein–coupled bile acid receptor (TGR5, GPBAR1) as well as various kinase signaling pathways that regulate the phosphorylation of histone and histone–regulatory proteins, thereby, affecting the regulation of gene expression involved in integrative metabolism. In addition to regulating the gene expression through nucleic receptors, the activation of the TGR5 receptor of bile acid exhibits genetic-independent effects. By activating the TGR5 receptor in enteroendocrine L cells, BAs improve glucose–induced insulin secretion and postprandial glycemia via glucagon like peptide−1 (GLP−1). Also, TGR5 is expressed in several regions of the central nervous system, where it has a role of neurosteroid receptor indicating that BAs have a far more important role than initially assumed. The alterations of bile acid homeostasis and bile acid–mediated signaling pathways contribute to the pathogenesis of hepato–biliary and intestinal diseases and disordes of metabolism of glucose and lipoproteins with the development of T2D, atherosclerosis with cardiovascular and cerebrovascular sequelae, NAFLD, inflammatory intestine disease, and neoplasms of gastrointestinal and hepatobiliary tract. The field of testing BAs has become extremely attractive to the scientific community in several research centers around the world with the aim of understanding the enigma of a complex network of signaling pathways mediated by BAs, improving the properties of existing drugs by developing new pharmaceutical formulations with BAs, and the development of new semisynthetics, which are analogs of BAs with the selective effect on nuclear and membrane receptors in order to prevent and treat various metabolic and nonmetabolic diseases. Three natural BAs are registered by regulatory agencies as medicines for human application. The UDCA has been used for about 30 years in the treatment of cholelithiasis and PBC. The Food and Drug Administration (FDA) has issued an authorization for the use of CA in the treatment of bile acid synthesis induced by enzymatic defect and as an additional treatment for peroxisomal disorders including Zellweger's cerebro-hepato-renal syndrome, as well as the injection of DCA in order to suppress submental fat tissue.

    Author Contributions

    All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

    Conflict of Interest Statement

    The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


    Great thanks to MM for his assistance in preparing the research. This work has been supported by the Ministry of Education, Science and Technology of Republic of Serbia (Project No. III 41012).


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    Keywords: BAs, chemical modification, drugs, biosynthesis of BAs, chemical synthesis

    Citation: Šarenac TM and Mikov M (2018) Bile Acid Synthesis: From Nature to the Chemical Modification and Synthesis and Their Applications as Drugs and Nutrients. Front. Pharmacol. 9:939. doi: 10.3389/fphar.2018.00939

    Received: 20 April 2018; Accepted: 30 July 2018;
    Published: 25 September 2018.

    Reviewed by:

    Sungsoon Fang, Yonsei University College of Medicine, South Korea
    Nonappa, School of Science, Aalto University, Finland

    Copyright © 2018 Šarenac and Mikov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    *Correspondence: Tanja M. Šarenac, [email protected]


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    Bilirubin Metabolism

    Intestinal Bacteria Interplay With Bile and Cholesterol Metabolism: Implications on Host Physiology


    The human gastrointestinal tract (GIT) is colonized by a vast array of microbes which dynamically interact with dietary and host-derived molecules in the intestinal lumen, significantly contributing to host physiology. Indeed, several animal and human studies have demonstrated that specific gut microbiota configurations contribute to inflammatory and metabolic diseases (Wu et al., 2015), although the precise molecular mechanisms behind the microbiota-host interactions impacting host health remain largely unknown. Cholesterol and bile acids (BAs) are important signaling molecules that, apart from exerting digestive functions, regulate multiple physiological processes in the host (Hegyi et al., 2018). Besides, the interaction of cholesterol and BAs with gut bacteria has been known for decades, although the role of these interactions in host health, and the possibility to modulate them through targeting the gut microbiota composition to improve human health, have only started to be recently explored.

    Bile acids are synthesized in hepatocytes from cholesterol and conjugated to glycine and taurine before being secreted into the small intestine with the bile flow, which plays a major role in fat emulsification and absorption. Bile composition depends on the diet and intrinsic characteristics of the individuals, but usually contains over 50% BAs, over 20% fatty acids and cholesterol, and lower amounts of other molecules such as bilirubin or phospholipids (Farina et al., 2009). During its gastrointestinal transit, most BAs and cholesterol are reabsorbed in the distal small intestine, though a significant proportion evades this process, being excreted with feces (Islam et al., 2011).

    Bile acids and cholesterol reaching the large intestine dynamically interact with our gut microbes. Indeed, BAs strongly compromise bacterial survival in the GIT, thus gut microbes must have developed mechanisms to counteract bile toxicity (Ruiz et al., 2013). Besides, gut microbial communities are capable of chemically modifying cholesterol and BAs, transformations that impact the gut microbiota and the BAs pool and, consequently, the signaling mechanisms they mediate. Accordingly, changes in this gut microbiota-bile axis are now acknowledged to have decisive implications in human health (Long et al., 2017).

    The present minireview examines the current knowledge on the enzymatic activities of intestinal bacteria over BAs and cholesterol, and their implications in human physiology, with a particular emphasis on their impact on gastrointestinal disorders and aging-associated decline. Opportunities and limitations to translate this body of knowledge into novel microbiome-based applications for some of these diseases are also discussed.

    Cholesterol Metabolism by Intestinal Bacteria

    Cholesterol is a terpenoid lipid with a carbon skeleton formed by four fused alicyclic rings. It is an essential component of the mammalian cell membranes and precursor of steroid hormones, vitamin D, and primary BAs (García et al., 2012). Following its GIT passage, most cholesterol is absorbed in the duodenum and proximal jejunum by a passive diffusion process. Reabsorbed cholesterol is incorporated with triglycerides and lipoproteins into transportable complexes called chylomicrons, which return to the liver through the enterohepatic circulation. The cholesterol escaping this re-absorption reaches the colon, where it can be metabolized by the intestinal microbiota and/or excreted with feces (Gérard, 2013).

    The metabolism of cholesterol by gut microbes has been described since the 30s (Schoenheimer, 1931) and has been supported by studies on germ-free animal models (Gérard et al., 2007). The microbial activities on cholesterol are based on its enzymatic reduction to produce coprostanone and coprostanol (Figure 1), which is poorly absorbable in the intestine. Thus, coprostanol production leads to increased cholesterol excretion into feces, contributing to reduce blood cholesterol level (Lye et al., 2010). Two different pathways have been proposed for this microbial reduction of cholesterol. The first pathway involves the direct reduction of the double bond 5–6 to give coprostanol, by cholesterol reductases (Gérard et al., 2004). The second pathway involves the oxidation of the 3β-hydroxy group and the isomerization of the double bond to produce 4-cholesten-3-one by cholesterol oxidases (ChOx) or 3β-hydroxysteroid dehydrogenases/isomerases (HSD) (García et al., 2012), followed by two reductions to form coprostanone and finally coprostanol (Gérard, 2013). Very limited information is available on the occurrence and distribution of the latter enzymes, although sequences belonging to ChOx are frequently found in the genomes of intestinal bacteria and gut/fecal metagenomes, indicating that cholesterol oxidation is a common activity in the gut microbiota. Remarkably, ChOx-encoding genes are found in the phyla Bacteroidetes, Proteobacteria and Actinobacteria, displaying a lower degree of conservation in Actinobacteria, but are absent in Firmicutes, one of the dominant phyla in the human gut microbiota (Figure 2 and Supplementary Figure 1).

    Figure 1. Bacterial cholesterol and bile metabolism in the gut, including microbiota-mediated transformations. (A) Metabolism of cholesterol in the hepatocyte. The conversion of cholesterol to primary BAs and their subsequent conjugation is carried out in the hepatocyte. (1) The primary BAs, cholic and chenodeoxycholic acids, are synthesized through the cytochrome P450 pathway. First, 7α-hydroxycholesterol is produced by the action of cholesterol 7α-hydroxylase. (2) Subsequently, several steps mediated by 12α-hydroxylase and 27α-hydroxylase generate the primary BAs. (3) The conjugation with glycine or taurine is mediated by the enzymes bile acid CoA synthetase and bile acid-CoA: amino acid N-acyltransferase. These conjugated BAs are excreted into bile by a BA export pump (BSEP) and stored in the gallbladder. (B) Bile composition. Conjugated primary BAs (glycocholic, taurocholic, glycochenodeoxycholic and taurochenodeoxycholic acids) are the main components of bile. Cholesterol, fatty acids, bilirubin and phospholipids are present in lower amounts. (C) Metabolism of BAs and cholesterol by intestinal bacteria. (4) The first reaction in the metabolism of BAs is the deconjugation or hydrolysis of conjugated BAs, catalyzed by bile salt hydrolases (BSHs). (5) Then, a bile salt 7α-dehydroxylase carries out the conversion of primary BAs to secondary BAs, deoxycholic and lithocholic acids. A part of the cholesterol is absorbed in the duodenum and proximal jejunum, returning to the liver. Remaining cholesterol reaches the large intestine, where it can be further metabolized by the intestinal microbiota or excreted with the feces. (6) Regarding cholesterol metabolism, the main gut microbial activity reaction involves the direct reduction of cholesterol to produce coprostanol, a reaction carried out by cholesterol reductases. (7) The indirect pathway begins with the oxidation of the 3β-hydroxy group by cholesterol oxidases (ChOx) or 3β-hydroxysteroid dehydrogenases/isomerases (HSD) to form 4-cholesten-3-one, and then cholesterol dehydrogenases produce coprostanone. Finally, cholesterol reductases form coprostanol. (D) BAs and sterols in feces. The main BAs in feces are secondary BAs, deoxycholic acid and lithocholic acid, with a lower concentration of primary BAs. Feces do also contain products of cholesterol metabolism such as coprostanol and coprostanone, that represent more than 50% of the total fecal sterols.

    Figure 2. Phylogenetic analysis of bile salt hydrolases (BSH) (A) and cholesterol oxidases (ChOx) (B). The construction of the phylogenetic trees and the clustering methods are described in detail in Supplementary Figure 1. The edition of the phylogenetic trees was performed with FigTree v1.3.1 ( The trees were divided into groups, depending on the grouping at phylum level.

    Several factors throughout life, including changes in diet or antibiotics consumption (Korpela and Adlercreutz, 1985; Norin, 1997), have been suggested to affect the gut microbiota’s ability to reduce cholesterol to coprostanol, which exhibits higher rates of conversion in elderly individuals (Benno et al., 2009). Indeed, these factors are known to affect the gut microbiota composition in humans, although the real impact of lifestyle and other clinical factors in the microbial reduction of cholesterol, and the particular gut bacteria/activities implicated warrant further investigation.

    Several cholesterol-reducing strains have been isolated from the intestine and feces of mammals (Eyssen et al., 1973; Brinkley et al., 1980, 1982). The first described cholesterol-reducing isolate of human origin was the Bacteroides sp. strain D8 (Gérard et al., 2007). Otherwise, only a few cholesterol-reducing intestinal bacteria have been identified, most of them belonging to the genus Eubacterium, although the genes or enzymes involved in this metabolism have not been well characterized yet.

    Some other gut bacterial inhabitants, including lactobacilli and bifidobacteria species usually used as probiotics, have been long studied for their possible cholesterol-lowering activities. Although different mechanisms of action (involving removal, co-precipitation or assimilation) have been proposed (Pereira and Gibson, 2002; Liong and Shah, 2005; Tomaro-Duchesneau et al., 2014; Zanotti et al., 2015), to date, the real contribution of these microbial groups toward cholesterol-lowering and the molecular activities involved remain mostly unknown.

    Bacterial Bile Metabolism: Implications on Health and Disease

    The metabolism of BAs by the gut microbiota has been known for decades, although its consequences on human health have only started to be considered (Farina et al., 2009; Islam et al., 2011; Gérard, 2013; Jia et al., 2017; Long et al., 2017), opening a new area of research in the microbiome-host interactions field. Key findings on this microbiota-BA signaling and host health are presented below.

    Metabolism of BAs by Intestinal Bacteria

    The composition of the BAs pool in humans is determined by the enterohepatic cycle and the microbial metabolism of intestinal BAs. Briefly, the liver synthesizes two primary BAs from cholesterol, cholic acid and chenodeoxycholic acid, which are conjugated to either taurine or glycine before being poured into the bile flow. Conjugated BAs are the primary components of bile, which is stored in the gallbladder before being excreted into the small intestine during digestion. Over 95% of the BAs secreted in bile are reabsorbed in the terminal ileum, returning to the liver through the enterohepatic circulation, and only 5% reach the large intestine, being excreted in feces. In the large intestine, BAs can suffer several microbial-mediated transformations including deconjugation, carried out by bile salt hydrolases (BSHs) that hydrolyze the amide bond, and transformation of primary deconjugated BAs into secondary BAs mainly by a 7α-dehydroxylation (Figure 1). Whereas deconjugation reactions are carried out by a broad spectrum of colonic bacteria (Figure 2 and Supplementary Figure 1), 7α-dehydroxylation appears to be restricted to a limited number of intestinal bacteria (Ridlon et al., 2006). Thus, the BAs profile excreted in feces, mainly composed of secondary BAs, largely depends on the gut microbiota metabolism (Perwaiz et al., 2002).

    Deconjugation of BAs

    Bile salt hydrolases encoding genes have been detected and characterized in diverse gut microbes including species belonging to the genera Bacteroides, Clostridium, Lactobacillus, and Bifidobacterium, among others, being more diverse in members of the phylum Firmicutes (Figure 2 and Supplementary Figure 1) (Jones et al., 2008). BSH activity has been suggested as a BA detoxification mechanism for bacteria, although they may also obtain carbon, nitrogen and even sulfur from BA deconjugation. This latter element has relevance in the production of hydrogen sulfide that may have lasting health consequences as it increases colonocyte turnover and has been associated with inflammation and cancer (Carbonero et al., 2012). Through regulation of key genes involved in cholesterol metabolism and gastrointestinal homeostasis, BSH activity was proposed as a gut microbial activity with capacity to profoundly alter local (gastrointestinal) and systemic (hepatic) host functions as revealed by different studies in mice (Joyce et al., 2014).

    7-Dehydroxylation of BAs

    The conversion of primary BAs to secondary BAs by 7α-dehydroxylases is probably one of the most physiologically relevant microbial transformations of BAs in humans (Duboc et al., 2013). Through 7α-dehydroxylation, the primary cholic acid is transformed into the secondary deoxycholic acid, and the primary chenodeoxycholic acid is transformed into the secondary lithocholic acid. To date, 7α-dehydroxylation activities have been characterized only in species belonging to the genera Eubacterium and Clostridium, including the species Clostridium scindens and Clostridium hylemonae (Ridlon et al., 2010). C. scindens is also capable of performing a 7β-dehydroxylation on ursodeoxycholic acid (the 7β epimer of chenodeoxycholic acid), yielding lithocholic acid (Ridlon et al., 2006, 2016).

    Other Microbial Enzymatic Activities Acting on BAs

    Other BA modifications such as amidation, oxidation-reduction, epimerization, esterification and desulfatation, can be carried out by intestinal microbes. Among them, oxidation-reduction and epimerization have received particular attention as some intestinal microbes synthesize HSD capable of performing reversible oxidation/reduction reactions and hydroxyl groups epimerization (Ridlon et al., 2016). Indeed, BA epimerization reactions have been largely overlooked due to the lack of appropriate analytical methods, although some iso-BAs have been suggested to represent the most abundant BAs in human feces (Hamilton et al., 2007). HSD activities are present in the four major phyla of the intestinal microbiota Actinobacteria, Proteobacteria, Firmicutes, and Bacteroidetes (Wahlströ et al., 2016), and the capability to carry out epimerization reactions has been characterized in several intestinal bacteria, including Clostridium, Collinsella, Ruminococcus or Eubacterium species (White et al., 1982; Lepercq et al., 2004; Liu et al., 2011; Lee et al., 2013). However, the physiological and functional significance of this metabolic activity remains largely unclear.

    Host Health Implications of Microbial Bile Metabolism

    The microbial-mediated transformations of BAs at the intestinal level have been shown to be essential for intestinal and systemic health maintenance as the intestinal BAs and the gut microbiota mutually influence each other and, accordingly, BA-microbiota crosstalk disruption has been associated with several gastrointestinal, metabolic and inflammatory disorders, including those associated with aging-related decline (Jia et al., 2017), as summarized below.

    BAs Metabolism and Inflammation

    The gut microbiota-mediated biotransformation of the BA pool regulates BAs signaling by affecting the activation of host BA receptors such as the nuclear receptor farnesoid X receptor (FXR), which governs bile, glucose and lipid metabolism (Gadaleta et al., 2011). Indeed, a disrupted gut microbiota including reduced bile metabolizing bacteria significantly impairs BA metabolism and, consequently, the host metabolic pathways regulated by BA signaling, affecting glucose and cholesterol homeostasis, as well as immune states. Indeed, disorders associated with chronic low-grade inflammation have been linked to gut dysbiosis and altered BA profiles in humans (Chavez-Talavera et al., 2017), although few works have established a connection among specific activities of the microbiota on bile and cholesterol and the physiological alterations observed. As an example, analysis of existing gut metagenomic datasets evidenced that the abundance of the BSH gene bsh was significantly reduced in inflammatory bowel disease (IBD) and type-2 diabetes patients (Labbé et al., 2014). Accordingly, IBD patients evidenced increased fecal conjugated and sulphated BAs, and reduced fecal secondary BAs, suggesting the existence of characteristic alterations of bile metabolism associated with gut microbial shifts in IBD (Duboc et al., 2012, 2013). Indeed, some of these changes might be linked to dietary factors such as a diet high in saturated fat and increased sulfur-rich taurine conjugate BAs, which in turn promoted the expansion of the sulphite-reducing pathobiont Bilophila wadsworthia in mice. The resulting dysbiosis lead to an associated pro-inflammatory Th1 response and acute colitis in a mouse model, further demonstrating how microbial activity on a particular BA can impact inflammatory states and host health (Devkota et al., 2012).

    BAs Metabolism and Colorectal Cancer

    The relation between diet, microbial metabolism of BAs and human disorders, including colorectal cancer risk (CRC), is further supported by the fact that dietary fat increases biliary hepatic synthesis and, thus, the quantity of BAs that reach the colon, providing substrate for the synthesis of secondary BAs. These have been described as proinflammatory (Bernstein et al., 2011) and their increase may contribute to the pathogenesis of several gastrointestinal diseases, having been associated with colon polyps (de Kok et al., 1999) and CRC (Bernstein et al., 2005; O’Keefe et al., 2015). Indeed, fecal secondary BAs and microbial genes encoding for 7α-dehydroxylases were more common in African Americans who had a high risk of suffering CRC as compared with rural native Africans (Ou et al., 2013).

    BAs Metabolism and Liver Diseases

    Several chronic liver-related disorders, including non-alcoholic fatty liver disease (NAFLD), primary sclerosing cholangitis, steatosis and hepatic cancer – frequently associated with obesity – have been related to different intestinal microbial patterns (Adolph et al., 2018). In some of these diseases, an altered liver-microbiota-BAs crosstalk has also been defined. For instance, the ratio between primary and secondary BAs in feces and the levels of conjugated and unconjugated BAs in serum are higher in NAFLD patients (Kakiyama et al., 2013; Mouzaki et al., 2016; Jiao et al., 2018). Interestingly, an increase in taurine metabolizing activities has been evidenced in the gut microbiota of these patients, associated with increased representation of Bilophila species, and increased secondary BAs production (Jiao et al., 2018). Additionally, NAFLD is frequently associated with obese patients, for whom specific dysbiosis signatures have been defined (Gao et al., 2018). Consequently, in addition to affecting bile metabolism within the gut, the microbiota might also contribute to NAFLD pathogenesis through other mechanisms including increased energy intake, intestinal permeability and contribution to chronic pro-inflammatory states (Han et al., 2018), which go beyond the scope of this mini review.

    Gut Microbiota Shifts in Aging Impact BAs Metabolism and Signaling

    Gut microbiota changes throughout life, including loss of diversity, are associated with lifestyle and dietary changes in the elderly population, though they may also modulate elements of aging frailty such as innate immunity or cognitive function. Indeed, recent studies have evidenced that alterations in BAs metabolism accompany these aging-associated microbiota shifts and health decline. For instance, increased fecal excretion of deconjugated BAs has been observed in old mice in association with a shift toward pro-inflammatory states in the gut (Becker et al., 2019). In addition, a reduction in cholic acid and an increase in secondary BAs have been noticed in the serum of patients with Alzheimer disease (AD) (MahmoudianDehkordi et al., 2019), presumably reflecting augmented 7α-dehydroxylase activity in the gut microbiota. In fact, a mice model of AD has evidenced changes in the gut microbiota, including an increase in members of the Clostridium group, among which 7α-dehydroxylase activity is frequent (Brandscheid et al., 2017). Nevertheless, comprehensive studies of the gut microbiota and concomitant BAs metabolic changes in AD human cohorts are still lacking.

    Microbiota Modulation of Bile and Cholesterol Metabolism: Influence on Host Physiology and Signaling Mechanisms Involved

    Several studies on germ-free animal models have evidenced the microbiota’s involvement in cholesterol and bile metabolism. For instance, the lack of gut microbiota in mice deficient in ApoE (a protein involved in the metabolism of fats) increased the plasma and liver cholesterol levels and reduced hepatic BAs synthesis (Kasahara et al., 2017). Also, the reverse cholesterol transport from peripheral tissues to the liver is augmented in germ-free mice (Mistry et al., 2017). These observations suggest that specific targeting of the intestinal microbiota could significantly impact cholesterol metabolism and cardiovascular diseases. Furthermore, germ-free animals lack secondary BAs production, and their microbial colonization modifies intestinal and serum BA fingerprinting, increasing total BAs concentrations (Joyce et al., 2014).

    Since BAs are ligands of bile-responsive receptors involved in host metabolism, changes in BAs composition orchestrated by the intestinal microbiota activity, may affect their interaction with specific receptors, such as pregnane-activated receptor, vitamin D receptor, sphingosine-1-phosphate receptor, muscarinic receptor (Ridlon et al., 2016). Additionally, FXR, a nuclear transcription factor that regulates a wide range of genes (Teodoro et al., 2011), as well as the plasma membrane-bound G-protein coupled receptor TGR5 (Kawamata et al., 2003), have been remarkably characterized in relation to bile signaling. Both receptors are ubiquitously distributed in several tissues and have different affinity for individual BAs. TGR5 is mainly activated by the secondary BAs litocholic and deoxycholic acids, and recognizes both conjugated and deconjugated forms (Long et al., 2017). The most potent ligand for FXR is chenodeoxycholic acid, with cholic acid, deoxycholic acid and litocholic acid having a lower effect (Wahlströ et al., 2016). FXR activation can induce innate immune genes, promote the synthesis of antimicrobial agents acting on the gut microbiota (Inagaki et al., 2006), and regulate BA synthesis (Sinal et al., 2000). On the other hand, TGR5 plays a role in the regulation of BA and energy homeostasis (Wahlströ et al., 2016). Therefore, through these receptors, BAs act as signaling factors beyond the GIT. Further, considering that the gut microbiota deeply influences the BAs signature, different microbial communities can differentially impact bile signaling and determine the degree of activation of these receptors, with a concomitant impact on host metabolism. Indeed, BA receptors are currently considered therapeutic targets for several gastrointestinal and hepatic diseases (Firoucci et al., 2007); thus, microbiota-based approaches to modulate their activation may represent novel alternatives for certain disorders and warrant further investigation.

    Future Perspectives: Potential of Microbiota-Based Approaches to Modulate Bile Metabolism and Associated Conditions

    In light of the recently unearthed gut microbiota-BA-host signaling interactions, microbiota-based approaches, from probiotics to dietary interventions, may become novel strategies to manage specific diseases linked to BAs metabolism dysregulation, as suggested by some in vivo studies (Devkota and Chang, 2015; Fukui, 2017). Most studies to date have focused on the potential of probiotics administration to reduce serum cholesterol levels. In this context, administration of probiotic strains to healthy mice increased deconjugation of BAs and fecal excretion (Jeun et al., 2010; Degirolamo et al., 2014) in association with increased BSH activity in the gut and overall modification of the microbiota composition (Degirolamo et al., 2014; Joyce et al., 2014; Tsai et al., 2014; Lye et al., 2017), changes that may have implications for host lipid metabolism. Indeed, a cholesterol-lowering effect was also observed following supplementation of a BSH-positive Lactobacillus strain to mice fed high-fat diets (Michael et al., 2017). However, limited studies have been conducted in human subjects in this regard. Remarkably, consumption of a BSH-positive Lactobacillus strain significantly reduced cholesterol in hypercholesterolemic subjects (Jones et al., 2012), although the observed effect might be the result of a complex metabolic re-arrangement, rather than solely a consequence of an increase in bile excretion.

    Some probiotic interventions have also demonstrated their efficacy to ameliorate liver and inflammatory markers in models of NAFLD and IBD, although results are not yet conclusive (Han et al., 2018; Kobyliak et al., 2018). Besides, the strains tested in most studies were not specifically selected for their activities over bile metabolism, and the impact of the intervention on the fecal or serum BAs profiles, on the fecal microbiota composition or on their metabolic capability over bile and cholesterol, was not always evaluated. This strongly hampers establishing causal relationships between the metabolic activities of the microbiota over these compounds and the physiological effects observed.

    Diet is another factor known to affect the gut microbiota and the BAs host signature. For instance, in a dietary intervention study in humans, a diet rich in animal-based fats was associated with increased excretion of secondary BAs, in accordance with an increased overall expression of bsh encoding genes in the gut microbiota, and an increase in the representation of potential pathobiont species such as B. wadsworthia (David et al., 2014). Thus, dietary strategies aimed at modulating BA metabolism through balancing the microbiota may represent alternative approaches to manage diseases linked to BA dysmetabolism (Ghaffarzadegan et al., 2018). Though these have been scarcely studied in humans, studies in mice models have showed the potential of certain dietary ingredients to modulate gut microbiota and BAs profile. For instance, Akkermansia muciniphila enrichment through administration of epigallocatechin-3-gallate prevented diet-induced obesity and regulated bile signaling (Sheng et al., 2018), although the contribution of changes in specific microbial metabolic activities over bile and cholesterol in this model has not been determined.


    In summary, it has become increasingly clear that BAs exert a much wider range of biological activities than initially recognized and that BAs, gut microbiota and health status are closely linked and hold a yet- underexplored valuable potential to design novel diagnostic and therapeutic approaches based on specific gut microbiota activities. Elucidating the molecular mechanisms underlying the gut microbiota-BA-host health interplay will establish the basis to fully understand the gut microbiota potential to modulate bile metabolism and host health. Further studies using specifically designed in vivo models or human trials, and exploiting microorganisms or activities with demonstrated capacity to specifically act on selected BAs, are necessary for aiding the development of novel microbiome-based approaches for disorders associated with BAs dysregulation.

    Author Contributions

    AM, SD, and BS conceived and organized the manuscript. NM designed the figures. NM, LR, BS, AM, and SD contributed to the writing, critically reviewed the manuscript, and approved the final version of the manuscript.


    This study was supported by MINECO under grant number AGL2013-44761-P. LR is a postdoctoral researcher supported by the Juan de la Cierva Postdoctoral Trainee Program (MINECO, JCI-2015-23196) and NM is the recipient of an FPI Predoctoral Grant (BES-2014-068736).

    Conflict of Interest Statement

    The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

    Supplementary Material

    The Supplementary Material for this article can be found online at:


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    Keywords: gut microbiota, bile acids, cholesterol, gut microbiota-host interplay, bile signaling

    Citation: Molinero N, Ruiz L, Sánchez B, Margolles A and Delgado S (2019) Intestinal Bacteria Interplay With Bile and Cholesterol Metabolism: Implications on Host Physiology. Front. Physiol. 10:185. doi: 10.3389/fphys.2019.00185

    Received: 28 November 2018; Accepted: 14 February 2019;
    Published: 14 March 2019.

    Edited by:

    Yuheng Luo, Sichuan Agricultural University, China

    Copyright © 2019 Molinero, Ruiz, Sánchez, Margolles and Delgado. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    *Correspondence: Lorena Ruiz, [email protected]


    Metabolism bile

    Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

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    The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities.


    Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model.


    This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.


    The human gut microbiome performs essential functions for human health and is directly implicated in the pathogenesis of complex diseases, such as inflammatory bowel disease, obesity, and type II diabetes [1]. Since the etiology of these diseases is multifactorial, they can be seen as having a malfunctioning network rather than a single cause [1]. To understand the interplay between the factors underlying the disease network, such as genome, microbiome, and diet, computational systems biology approaches are necessary to integrate the different -omes, such as metagenome and metabolome, and to identify key interactions in an unbiased manner [1]. Such data-driven systems biology approaches could also identify drug-network interactions [1] and predict individual treatment responses in patients [1, 2].

    One important function carried out by the human gut microbiome is the deconjugation of human primary bile acids and their subsequent biotransformation to secondary bile acids with implications for human health [3]. Briefly, the human liver synthesizes the primary bile acids cholate (CA) and chenodeoxycholate (CDCA), which are each conjugated with either taurine or glycine [4]. Conjugated bile acids are stored in the gall bladder and released into the small intestine after a meal [4]. In the intestine, they are subject to extensive metabolism by gut microbes, namely deconjugation of glycine or taurine, and biotransformation of the unconjugated primary bile acids to secondary bile acids [4]. Primary and secondary bile acids have endocrine functions and modulate host metabolism [3]; thus, their composition has important implications for human health. A link between microbial bile acid metabolism and inflammatory bowel disease (IBD), i.e., ulcerative colitis and Crohn’s Disease, has been repeatedly demonstrated [5]. In IBD patients, fecal conjugated bile acid levels are higher while secondary bile acid levels are lower and the deconjugation and transformation abilities of IBD-associated microbiomes are impaired [5]. Other diseases that have been associated with alterations of the intestinal bile acids pool include liver cirrhosis, liver cancer, irritable bowel syndrome, short bowel syndrome, and obesity [3, 6]; however, a mechanistic understanding of these bile acid-microbiome-disease associations is lacking. Thus, the role of bile acid composition and its relationship with the gut microbiome in these diseases needs to be elucidated and to be considered for therapeutic options [6].

    A well-established computational approach for modeling human and microbial metabolism is Constraint-based Reconstruction and Analysis (COBRA) [7]. The COBRA approach relies on having genome-scale reconstruction of a target organism, which assembled based on the organism’s genome sequence and manually curated against the available genomic data and literature following established protocols [8]. A genome-scale reconstruction can readily be converted into a mathematical model, in which reactions and metabolites are represented as a stochiometric matrix, and interrogated using established methods such as flux balance analysis (FBA) [9]. Briefly, FBA relies on physicochemical (e.g., mass-charge balance) and environmental (e.g., nutrient uptake) constraints that limit the flow of metabolites through the network resulting in a solution space of feasible flux distributions [9]. Generally, FBA relies on the definition of an objective function, such as the biomass reaction, which sums all known precursors required to form a new cell. The objective function is then minimized or maximized, and the optimal solution, aka flux distribution, under the given condition-specific constraints is computed [9]. FBA operates under the steady-state assumption and as such does not require kinetic parameters to compute an optimal solution [9]. Through implementation of condition-specific constraints, e.g., a certain dietary regime, COBRA simulations have provided further insight into the metabolic capabilities of, e.g., human intestinal microbes [10,11,12,13,14], for which a comprehensive collection of reconstructions (AGORA) has been published [15, 16]. An advantage of the COBRA approach for microbial community modeling is that the underlying genome-scale metabolic networks enable mechanistic predictions of metabolic fluxes in each individual species while taking into account biological features, such as substrate availability or species-species boundaries [17, 18]. Previous studies have already demonstrated the use of constraint-based multi-species models for the prediction of host-microbe interactions [12, 19] and gut microbial community interactions [13, 20]. COBRA models can also be contextualized through omics data, e.g., metagenomic data [2, 14]. More importantly, by mapping metagenomic data of an individual, the metabolic microbial community model is personalized to this individual enabling the prediction of personalized metabolic profiles, which can be used to ultimately stratify disease and control groups [2, 14].


    To investigate the microbiome-level bile-acid production potential of healthy individuals and IBD patients, we derived a systematic, reproducible workflow (Fig. 1). First, we expanded bile acid metabolism pathways captured in 232 gut microbial reconstructions using state-of-the-art comparative genomics methods. We then joined these reconstructions into pairwise microbial models and predicted their potential to cooperatively produce secondary bile acids. While each microbe could only produce up to six of the 13 secondary bile acids in silico, microbial pairs could produce up to 12 of the 13 bile acids, highlighting bile acid biotransformation as a microbial community task. Subsequently, we constructed functional and personalized gut microbiome models using metagenomics data from healthy and IBD individuals to predict an individual’s bile acid biosynthesis potential. We found inter-individual variation in the production capability of bile acids in healthy individuals as well as significant differences between healthy and IBD microbiomes. Moreover, we were able to compute the contribution of each strain to bile acid deconjugation and transformation while taking the metabolic network of the whole microbiome community and the applied constraints (e.g., dietary uptake) into account. Finally, we identified bottlenecks limiting the biotransformation potential into secondary bile acids. This mechanistic, microbiome-wide modeling approach can be readily applied to the personalized computation of other health-relevant human-microbial co-metabolites.

    Schematic overview of the workflow in this study. a Comparative genomic and metabolic reconstruction approach used to expand the AGORA [15] resource with a bile acid (BA) deconjugation and biotransformation subsystem. The comparative genomic approach was performed in the PubSEED [26, 27] platform. Quality controland quality assurance (QC/QA) during reaction and metabolite formulation and addition to the AGORA reconstructions were ensured by using the reconstruction tool rBioNet [74]. b Computational pipeline used to predict the sample-specific bile acid deconjugation and biotransformation by human gut microbiomes. First, publicly available metagenomic data was retrieved from HMP [35], and the COMBO/PLEASE [36, 37] cohort. Next, the strain-level abundances were mapped onto the reference set of AGORA genomes. Microbial community models were constructed using the illustrated workflow, as implemented in the Microbiome Modeling Toolbox [33], and they account for the strain-level composition of each individual microbiome. Finally, each personalized community model was constrained with an “Average European” diet supplemented with conjugated primary bile acids and its individual-specific, primary bile acid deconjugation and biotransformation potential was computed using flux balance analysis [9, 76].

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    Distribution of microbial bile acid deconjugation and biotransformation pathways across taxa

    To determine how widely genes encoding for bile acid pathways are spread in human gut microbes, we performed a systematic comparative genomic analysis of the bile acid deconjugation and transformation pathway (Fig. 2), starting with previously characterized enzymes for primary bile acid deconjugation [21] and transformation into secondary bile acids [22,23,24,25]. Of the currently 818 microbial AGORA reconstructions, which include 46 newly reconstructed gut microbes (see the “Materials and methods” for details), only 670 genomes were available at the PubSEED database [26, 27]. We additionally analyzed 23 further microbial genomes, yielding a total of 693 considered genomes (Fig. 1a). We found the bile salt hydrolase (bsh) gene, which encodes the deconjugation of conjugated primary bile acids, in 204 of the 693 (29%) genomes, including two archaeal genomes, Methanobrevibacter smithii ATCC 35061 and Methanosphaera stadtmanae DSM 3091 (Additional file 1: Table S1). The distribution of the bsh gene in Actinobacteria, Bacteroidetes, Firmicutes, as well as the two archaea (Fig. 2, Additional file 2: Figure S1) was in line with previously reported results [21]. Additionally, the bsh gene was found in 22 Proteobacteria genomes (Additional file 1: Table S1). Among all analyzed hydroxysteroid dehydrogenases (HSDHs), 7α-HSDH was the most widespread enzyme as it was found in 46 of the 693 (7%) genomes (Additional file 1: Table S1 and Additional file 2: Figure S2). Additionally, 3α-, 3β-, and 7β-HSDHs were found in 17, 12, and 3 genomes, respectively (Additional file 1: Table S1 and Additional file 2: Figure S2). Using results from a recent work [24], we found the 12α-HSDH in 39 genomes, which belonged mostly to Firmicutes representatives (Additional file 1: Table S1). We could not find the 12α-HSDH in the Clostridium leptum genome, although the enzymatic activity has been demonstrated [28].

    Illustration of bile acid pathways in human gut microbes reconstructed for AGORA. a Deconjugation of Tauro-CA/Glyco-CA and subsequent conversion to 12-dehydro-CA, UCA, and Iso-CA. b Deconjugation of Tauro-CDCA/Glyco-CDCA and subsequent conversion to UDCA. c Conversion of CA to DCA via the bai pathway. d Conversion of CDCA to LCA via the bai pathway. e Conversion of UDCA to LCA via the bai pathway. CoA Coenzyme A. For metabolite abbreviations, see Table 1.

    Full size image

    The bile acid-inducible (bai) gene cluster for the multistep 7α/β-dehydroxylation pathway, which has been reported for Clostridiaceae and Eggerthella spp. [4, 23], was found in seven analyzed genomes belonging to Clostridioides sp., Lachnoclostridium sp., and Eggerthella sp. (Additional file 2: Figure S2). Remarkably, all these genomes also have genes for 12α-HSDH as well as either 7α-HSDH or genes for both 3α- and 3β-HSDHs (Additional file 1: Table S1). Thus, these microbes could play a crucial role in biotransformation of bile acids in the human intestine. The genes encoding for the last two steps of the 7α/β-dehydroxylation pathway, i.e., the NADH-dependent reduction and the export of secondary bile acids (Fig. 2c–e), have not been identified. Recently, the baiN gene was shown to encode a bi-functional enzyme NADH-dependent ∆6/∆4-hydroxysteroid reductase (Fig. 2) [29]. We analyzed the genomic context of this gene and found it in the C. scindens genome to be chromosomally co-localized with the gene for a probable NAD(FAD)-utilizing dehydrogenase (CLOSCI_00522). This chromosomal clustering was conserved in all Clostridiales genomes having the bai pathway, except for Clostridium hiranonis DSM 13275 (Additional file 2: Figure S3). It has been previously shown that genes encoding enzymes for the same metabolic pathway are often clustered on chromosome and such a clustering is conserved in genomes of related organisms [30]. Thus, the genes baiN and CLOSCI_00522 can possibly belong to the same metabolic pathway, \namely bai pathway. The enzyme for the last step of the bai pathway, NADH-dependent 3α-hydroxysteroid reductase, is unknown. Because the product of CLOSCI_00522 is a NADH-depended reductase, we propose that product is an enzyme catalyzing the final reaction of the bai pathway (Fig. 2) and rename the gene to baiO. The bai pathway has also been found in Eggerthella lenta [23]; consequently, we searched for orthologs of the baiNO genes in the E. lenta genome. Because C. scindens and E. lenta belong to different phyla, we defined orthologs of the analyzed genes as best/symmetrical bidirectional hits (see the “Materials and methods” section). An ortholog of the BaiN in E. lenta is likely to be encoded by the gene Elen_1017 (protein identity = 32%, e-value for the protein alignment = 3e−44), whereas the BaiO ortholog was encoded by the gene Elen_1018 (identity = 45%, e-value = e−126). These genes were co-localized in E. lenta’s genome as well in genomes of Eggerthella sp. 1_3_56FAA and Eggerthella sp. HGA1 (Additional file 2: Figure S3). Additionally, these genes were co-localized with a gene encoding for a probable transporter (Elen_1016) in the genomes of E. lenta and Eggerthella sp. HGA1. Hence, the gene Elen_1016 was assumed to encode a transporter for the products of the bai pathway and was named here baiP. An ortholog of this gene (CLOSCI_01264, identity = 59%, e-value = e−180) was found in C. scindens genome as well in other genomes of Clostridiaceae, having the bai pathway (Additional file 2: Figure S3), whereas in the C. hiranonis genome, this gene was co-localized with the baiO gene. Phylogenetic analysis of the BaiNOP proteins and their homologs revealed that BaiN and BaiO proteins of Eggerthella spp. and Clostridiales are phylogenetically distant from each other (Additional file 2: Figure S4 and S5), whereas BaiP proteins from these groups of genomes are phylogenetically close (Additional file 2: Figure S6).

    In summary, our comparative genomics results expanded substantially our knowledge about bile acid deconjugation and transformation in gut microbes, while being consistent with previous studies [21,22,23,24,25]. Consequently, we propose that 253 of the 693 analyzed intestinal microbes (37%) can deconjugate and/or transform bile acids, including 232 reconstructed AGORA organisms (Additional file 1: Table S1, Fig. 1a).

    Expansion of the gut microbial genome-scale reconstructions by a species-specific bile acid subsystem.

    The manual curation and refinement of genome-scale reconstructions is an iterative process [8]. Species-specific pathways are typically absent in draft reconstructions [31]. Since we did not explicitly account for bile acid pathways in the curation of AGORA [15] prior to the present paper, this subsystem was absent. The 232 metabolic reconstructions found to carry bile acid enzymes (Additional file 1: Table S1) were expanded with the corresponding metabolites and reactions, while ensuring functionality of the included pathways, following established procedures [8, 32] (see “Materials and methods” section). The complete reconstructed bile acid biotransformation subsystem contained 39 bile acid metabolites and 82 reactions (Fig. 2, Table 1, Additional file 1: Table S2a, b). For CA, CDCA, and the 13 secondary bile acids (Table 1), transport and exchange reactions enabling the uptake and secretion of these metabolites were added to the corresponding reconstructions. Taken together, we expanded the AGORA reconstructions with a bile acid module thus further improving their predictive potential and enabling their use for large-scale simulations of bile acid deconjugation and transformation.

    Full size table

    Investigating the complementary capabilities of human gut microbes in silico

    The majority of primary bile acids, released by the human gallbladder into the intestine, where the gut microbiome encounters them, are conjugated to glycine or taurine [3]. However, many strains capable of synthesizing secondary bile acids do not possess the bile salt hydrolase (Additional file 1: Table S1) and thus, rely on bile salt hydrolase-encoding strains to access the deconjugated primary bile acids.

    To determine the capability of each strain alone to convert the deconjugated primary bile acids into secondary bile acids, the 232 corresponding AGORA reconstructions were converted into condition-specific models by applying an Average European diet supplemented with taurocholate (Tauro-CA), glycocholate (Glyco-CA), taurochenodeoxycholate (Tauro-CDCA), and glycochenodeoxycholate (Glyco-CDCA) (Additional file 1: Table S3) as modeling constraints. The maximally possible production flux for the 13 secondary bile acids was predicted for each strain using flux balance analysis [9] while setting the corresponding exchange reactions as the objective function (see the “Materials and methods” section). A total of nine strains could synthesize 7-ketodeoxycholate (7-keto-DCA) and 7-dehydrochenodeoxycholate (7-dehydro-CDCA) from the conjugated primary bile acids as they possessed both the bile salt hydrolase and the 7α-HSDH (Table 1). In contrast, no single strain was capable of synthesizing 12-dehydrocholate (12-dehydro-CA), ursocholate (UCA), and UDCA from the conjugated primary bile acids. Of the five strains carrying the bai gene cluster, only Clostridium hiranonis TO-931 could synthesize LCA and DCA from the conjugated primary bile acids (Table 1) as it also possessed the bile salt hydrolase enzyme in contrast to the other four strains. Taken together, only few strains could both deconjugate and biotransform primary acids in isolation.

    To investigate whether pairwise combinations of certain strains could complement each other’s bile acid pathways, the 232 bile acid-producing AGORA models were joined in every possible combination resulting in 26,796 pairwise models using the Microbiome Modeling Toolbox [33], a COBRA Toolbox [34] extension. When comparing the bile acid production capabilities of the pairwise models with the respective single-strain models on the bile acid-supplemented “Average European” diet, we identified 7673 microbe pairs (29%) that could synthesize at least one secondary bile acid whereas the respective two individual strains were incapable to do so, resulting in 19,883 cooperative bile acid syntheses (Fig. 3a, Additional file 1: Table S5). For example, 3135/7673 pairs (40.9%) could synthesize 12-dehydro-CA from Glyco-CA or Tauro-CA (Additional file 1: Table S5). Further, 736 pairs (2.7%) and 100 pairs (0.4%) could synthesize DCA/LCA and UCA/UDCA, respectively, from the conjugated primary bile acids (Additional file 1: Table S5), demonstrating distinct bile acid synthesis capabilities of microbial pairs. There was no pairwise combination enabling synthesis of all secondary bile acids as the maximal number of secondary bile acids to be synthesized by any pair was 12 out of 13 (Fig. 3a). Taken together, while only few strains were capable of both bile acid deconjugation and biotransformation, many of the microbial pairs are predicted to synthesize secondary bile acids from the conjugated bile acids. This example demonstrates that constraint-based modeling is an efficient approach to elucidate the combined capabilities present in thousands of microbe pairs compared with the single microbes. The presented computational workflow could readily be applied to other microbial pathways of interest, in which enzymes are distributed across multiple taxa in an ecosystem.

    The predicted bile acid metabolic profiles of microbe-microbe pairs and individual gut microbiomes. a Complementary bile acid biosynthesis capabilities of the 232 gut microbial models with bile acid pathways joined in all possible combinations. The numbers of secondary bile acids (out of 13), which can be produced by each pair, are shown. b, c Total secretion potential in the healthy adults (Healthy_HMP), IBD patients (IBD_pediatric), and healthy pediatric controls (Healthy_pediatric) (flux values are given in mmol × person-1 × day-1): b deconjugated cholate, c 12-dehydro-CA. Significant difference (p value < 0.001) is indicated by stars. d Principal Coordinates Analysis of the strain-level contributions to two deconjugated primary and 13 secondary bile acids for the healthy adults, IBD patients, and healthy pediatric controls. Details on the strain to metabolite contributions are shown in Additional file 1: Table S8, and in Additional file 2: Figure S7.

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    Large-scale modeling of the interpersonal variation in the bile acid deconjugation and transformation of gut microbiomes.

    We next aimed to predict the bile acid deconjugation and biotransformation potential of individual-specific gut microbiomes. It is well known that bile acid metabolism is altered in individuals with IBD [3]. We were interested whether personalized modeling could provide novel insight into the differences in bile acid deconjugation and biotransformation potential between the microbiomes of IBD patients and controls as well as elucidate species contributing to the pathways. For comparison, we also evaluated the range in bile acid metabolic capabilities in healthy adults. We used metagenomic data from two sources: (1) 149 healthy American donors aged 18–40 years provided by the Human Microbiome Project Consortium [35] and (2) 20 children with newly diagnosed Crohn’s disease and microbial dysbiosis and 25 healthy controls (COMBO/PLEASE cohort [36, 37]). Using strain-level abundances, after mapping the reads onto the reference set of AGORA genomes [38], we generated personalized microbiome community models for each of the 194 sample by joining the corresponding metabolic reconstructions (Fig. 1b, the “Materials and methods” section ). Each microbiome model was constrained with the “Average European” diet supplemented with conjugated primary bile acids (Additional file 1: Table S3). A typical personalized microbiome model contained 127 AGORA models and 142,000 reactions (Table 2) making this work one of the largest constraint-based modeling efforts to date.

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    The bile acid deconjugation and biotransformation potential is variable in healthy individuals and depleted in Crohn’s Disease patients

    To predict the maximally possible bile acid deconjugation and biotransformation potential of the 194 microbiome models, we performed flux balance analysis while maximizing for the fecal secretion reaction flux (mmol × person-1 × day-1) of the primary bile acids, CA and CDCA, and 13 secondary bile acids (Fig. 3b, c and Additional file 1: Table S6). The quantitative production potential varied significantly between the models, with the quantitative production potential of LCA and DCA varying by a factor of 100 (Additional file 1: Table S6). A statistical analysis (Wilcoxon rank sum test, with p values adjusted for false discovery rate (FDR) by Benjamini-Hochberg method) was performed on the total community production potential of the 20 IBD patients (IBD_pediatric) and the 25 control microbiomes (Healthy_pediatric) (Fig. 3b, c, Additional file 1: Table S7). Compared with the microbiomes of healthy children, the IBD patient microbiomes were significantly depleted in 12-dehydrocholate production potential (p value < 0.001). Primary bile acid deconjugation potential was lower in IBD patients but only borderline significant after adjustment for FDR (adjusted p value = 0.0551); however, the abundance of the bile salt hydrolase reaction was significantly reduced in IBD microbiomes (p value 0.0235, Additional file 1: Table S7) and also differed based on phylum-and genus-level reaction abundances for many taxa (Additional file 1: Table S7). Microbiomes with low CA/CDCA liberation potential from the conjugated bile acids also had a low secondary bile acid potential (Additional file 1: Table S6) in agreement with the fact that the bile salt hydrolase is the gateway reaction in the pathway [39]. Taken together, we predicted the inter-person variability in the bile acid biosynthesis potential with microbiomes from IBD patients being significantly depleted in bile acid deconjugation and biotransformation potential, consistent with reports that IBD patients have higher levels of fecal conjugated and lower levels of secondary bile acids [5].

    Functional analysis of strain-level contributions in each microbiome.

    What is the contribution of individual strains to the overall bile acid deconjugation and biotransformation potential? While previous studies have correlated certain taxa to measured metabolite levels [40], we determined here exactly which strains were producing the bile acids in the individual microbiome models using the aforementioned simulation results. Overall, 198 strains contributed to total production flux of at least one bile acid in at least one microbiome model (Additional file 1: Table S8). Of those, 15 strains contributed in > 90% of communities across both cohorts and thus play a significant role in bile acid metabolism. These strains included known commensals, such as Ruminococcus gnavus ATCC 29149, Coprococcus comes ATCC 27758, Faecalibacterium prausnitzii L2_6, Clostridium sp. M62_1, Eubacterium ventriosum ATCC 27560, Bacteroides pectinophilus ATCC 43243, and Dorea formicigenerans ATCC 27755. A variety of Bacteroides strains performed bile acid deconjugation and 7-keto-DCA/7-dehydro-CDCA biosynthesis, and their contribution was significantly depleted in the IBD microbiomes (p values for all < 0.01, Additional file 1: Table S7). Consistently, a positive correlation between Bacteroides spp. and secondary bile acid biosynthesis was found [36]. Using a Principal Coordinates Analysis on the strain-level contributions, we observed a clear separation between the IBD patients and controls (Fig. 3d), as well as between the HMP individuals and the pediatric individuals, due to the difference in strains (Fig. 3d, Additional file 2: Figure S7). The strain difference is most likely due to differences in age, location, and ethnicity of the two cohorts. On the phylum level, the contributions in both the healthy adult and healthy pediatric microbiomes were mostly driven by Actinobacteria, Bacteroidetes, and Firmicutes representatives, as expected, while Proteobacteria contributed significantly in the IBD microbiomes (p values for all < 0.05, Additional file 1: Table S7 and Additional file 2: Figure S7). A Wilcoxon rank sum test adjusted for FDR on strain-level contributions revealed that 303 strain-level contributions differed significantly between dysbiotic IBD and healthy pediatric microbiomes (p value < 0.05, Additional file 1: Table S7). Strain contributions mostly depleted in the IBD microbiomes included those of Bacteroides vulgatus ATCC 8482, Ruminococcus (Blautia) torques L2-14, Faecalibacterium prausnitzii strains, Eubacterium rectale strains, Ruminococcus sp. SR1-5, and Clostridium sp. M62-1 (p values for strains were < 0.001, Additional file 1: Table S7). The IBD microbiomes had significantly lower deconjugated CA and CDCA contributions by Actinobacteria and Bacteroidetes representatives (p values < 0.05, Additional file 1: Table S7 and Additional file 2: Figure S7). The contribution of 12-dehydro-CA production flux, which was significantly reduced in the IBD microbiomes (p value < 0.001, Additional file 1: Table S7) was attributed mostly to representatives of the Lachnospiraceae and Ruminococcaceae families, which are considered to be beneficial due to containing many butyrate producers [41]. These representatives included two Faecalibacterium prausnitzii strains (p value < 0.001), a species well known to be depleted in IBD [42]. In contrast, the overall 7-dehydro-CA production potential was comparable between the IBD and the healthy pediatric microbiomes (Additional file 1: Table S7). However, the strains contributing were different, with reduced contributions by commensal bacteria to the production of 7-keto-DCA/7-dehydro-CDCA, and increased contributions of the pathogenic Escherichia coli strains O157-H7 Sakai and UTI89 UPEC (Additional file 1: Table S7). These two strains contributed significantly higher to deconjugation and 7-keto-DCA/7-dehydro-CDCA production in the IBD microbiomes (p value < 0.001, Additional file 1: Table S7). Taken together, the dysbiotic IBD microbiomes, compared to the healthy control microbiomes, were depleted in contributions of a variety of commensal microbes to bile salt hydrolase and to bile acid biotransformation but enriched in contributions of pathogenic Escherichia sp. (Additional file 1: Table S7). Thus, the IBD microbiomes had distinct bile acid deconjugation and transformation potential, consistent with reports that bile acid composition in IBD patients is abnormal [5]. In total, 488 analyzed features, which encompasses total production, strain contributions, and reaction abundances, were significantly different (p-value <0.05), of which 375 were highly significant (p-value <0.001) (Additional file 1: Table S7).

    Shadow price analysis identifies individual-specific bottlenecks in bile acid biotransformation potential.

    To test whether the bile acid production potential could be directly predicted from the abundance of the metagenomics data mapped onto the AGORA reconstruction (i.e., from the encoding gene abundance), we calculated the Spearman correlation between the individual production potential for the two deconjugated primary and 13 secondary bile acids (Table 1) and the total community abundance for all reactions in the bile acid pathway in the 194 community models. Consistently, for 13 of the 15 bile acids, the correlation between production potential and the abundance of reaction directly synthesizing the respective bile acid was 0.96 or higher (Additional file 1: Table S9). The secondary bile acid Iso-CA is synthesized from 3-dehydro-CA by 3β-HSDH. Surprisingly, the Spearman correlation between production potential for Iso-CA/Iso-CDCA and the abundance of the 3β-HSDH reaction (VMH ID: ICA3bHSDHe/ ICDCA3bHSDHe) calculated for all 194 microbiome models was only 0.18 indicating that the reaction abundance of the producing reaction did not correlate with production (Additional file 1: Table S9). In fact, only a minority of the 194 microbiome models with a high abundance of the 3β-HSDH reaction (VMH ID: ICA3bHSDHe), all of which were IBD microbiomes, also had a high Iso-CA production flux (Fig. 4a). Thus, factors other than 3β-HSDH abundance limited the production flux. To identify these factors, the metabolic fluxes needed to be analyzed in the context of the pathway and the microbial community. Constraint-based modeling is ideal for such analyses of metabolic dependencies since it is mechanistic on the molecule level and takes species-species metabolic exchanges and boundaries into account [18].

    Metabolic bottlenecks and shadow price profiles computed when optimizing for Iso-CA production in 194 microbiome community models. a Abundance of the 3β-HSDH reaction yielding Iso-CA (VMH ID: ICA3bHSDHe) plotted against Iso-CA production potential (flux values are given in mmol gDW−1 h−1) for the 194 microbiomes. b Heat map of the shadow prices retrieved from the 194 microbiomes when the production of two deconjugated primary and 13 secondary bile acids was optimized. Blue and white data points show nonzero and zero shadow prices, respectively. The columns show the 194 microbiomes annotated by group. The rows show all metabolites that had a nonzero shadow price in at least one community model. The metabolites are annotated by taxonomy. Entries annotated with “Diet_metabolite” represent bile acid metabolites present in the dietary, luminal, or fecal compartment of the microbiome model. The “bile acid optimized” color bar shows the bile acid for which production was optimized. c Number of metabolites with nonzero shadow prices in the microbiome models of healthy adults, IBD patients, and healthy pediatric controls. Significant difference (p value < 0.001) is indicated by stars. d Shadow prices for the biomass metabolite of Holdemania filiformis DSM 12042, a strain carrying 3α-HSDH but not 3β-HSDH, plotted against Iso-CA production and abundance of the 3β-HSDH reaction. Blue dots indicate models belonging to Scenario 2 (see main text). e Shadow prices for the biomass metabolite of Collinsella aerofaciens ATCC 25986, a strain carrying 3β-HSDH but not 3α-HSDH, plotted against Iso-CA production and abundance of the 3β-HSDH reaction. Blue dots indicate models belonging to Scenario 3 (see main text). f Shadow prices for the biomass metabolite of Ruminococcus gnavus ATCC 29149, a strain carrying both 3α-HSDH and 3β-HSDH, plotted against Iso-CA production and abundance of the 3β-HSDH reaction. g Pathway for Iso-CA biosynthesis from Glyco-or Tauro-CA, and depiction of the steps representing bottlenecks in Scenarios 2 and 3 (see main text). For metabolite abbreviations see Table 1. For simplicity, sections of the y axis without any data are omitted in a and df (indicated by the two gray lines).

    Full size image

    To identify the factors limiting the production potential for secondary bile acids, the shadow prices associated with the flux solutions of each microbiome model were analyzed. Shadow prices are a standard feature of constraint-based modeling that are routinely calculated with each feasible flux balance analysis solution. Briefly, the shadow price is a measurement for the value of a metabolite towards the optimized objective function, which indicates whether the flux through the objective function would increase or decrease when the availability of this metabolite would increase by one unit [7]. A positive or negative shadow price indicates that increased availability of the metabolite would either increase or decrease the flux through the objective function (note that this definition varies by solver), respectively. In contrast, the availability of a metabolite with a shadow price of zero has no influence on the flux through the objective function. To identify limiting factors for secondary bile acid production, we investigated the shadow prices in the flux balance analysis solutions (see the “Materials and methods” section) when optimizing the production of the secondary bile acids in the 194 microbiome models (Fig. 4b, Additional file 1: Table S10). Nonzero shadow prices with an absolute value higher than 10−6 indicating importance for bile acid production flux were found for biomass metabolites of 129 strains carrying bile acid enzymes, for strain-specific metabolites in the bile acid pathway, and for dietary exchange metabolites of the bile acids. Overall, 1138 microbial and dietary metabolites were found to be relevant for bile-acid synthesis in the entire set of microbiome models (Additional file 1: Table S10). When comparing the shadow prices in the three groups, the number of metabolites with nonzero shadow prices was significantly lower in the IBD microbiomes than either in the healthy pediatric or healthy adult microbiomes (Fig. 4c). Hence, the pediatric IBD patients were depleted in strains with bile acid biosynthesis capabilities. This result highlights that an increase in secondary bile acid biosynthesis in these individual communities could only be achieved by introducing additional microbial strains.

    Next, we aimed to identify the factors limiting Iso-CA biosynthesis potential. Of the reconstructed strains, 16 and 11 strains, respectively, carried the 3α- and 3β-HSDH enzyme (Additional file 1: Table S1). Five strains (Eggerthella sp. 1_3_56FAA, Eggerthella lenta DSM 2243, Gordonibacter pamelaeae 7-10-1-bT, Mycobacterium avium subsp. avium ATCC 25291, and Ruminococcus gnavus ATCC 29149) possessed both enzymes. Of the strains possessing either or both enzymes, 18 were present in at least one of the 194 microbiome models. The shadow prices corresponding to Iso-CA production were inspected (Additional file 1: Table S10). Note that shadow prices for Iso-CDCA were analogous.

    Four scenarios could be distinguished based on the shadow prices. Seven microbiomes belonging to the first scenario were unable to synthesize Iso-CA (Additional file 1: Table S6). Consequently, in these microbiomes, shadow prices were only nonzero for dietary Iso-CA (Additional file 1: Table S10) indicating that Iso-CA levels could only be increased by directly providing it. In the second scenario, which was found in 19 microbiomes, shadow prices for the six strains carrying 3β-HSDH but not 3α-HSDH were nonzero for at least one of the six strains’ biomass metabolites (Additional file 1: Table S10). In the same 19 microbiomes, shadow prices for all eight strains carrying 3α-HSDH but not 3β-HSDH were zero. This result showed that 3α-HSDH abundance was not a bottleneck and Iso-CA production could be increased by increasing the abundance of strains carrying 3β-HSDH. In these microbiomes the 3β-HSDH abundance directly correlated with Iso-CA production flux, as illustrated with the example of Holdemania filiformis DSM 12042 in Fig. 4d. In the third scenario, which contained the majority of microbiomes (145 cases), the shadow prices for all six strains carrying 3β-HSDH but no 3α-HSDH were zero. Instead, the shadow prices for at least one of the eight strains carrying 3α-HSDH but not 3β-HSDH were nonzero. Consequently, in these microbiomes, the availability of the precursor 3-dehydro-CA was flux-limiting and Iso-CA production could not be increased by increasing the abundance of strains carrying only 3β-HSDH. These 145 microbiomes had the lower than expected Iso-CA production potential (Fig. 4e). As expected, in all 145 microbiomes, the shadow price for dietary 3-dehydro-CA, the precursor of Iso-CA, was also nonzero (Additional file 1: Table S10). Finally, in the fourth scenario, which consisted of 22 microbiomes, shadow prices were nonzero only for the biomass metabolite of Ruminococcus gnavus ATCC 29149 and in some cases Eggerthella lenta DSM 2243 (Fig. 4f). These two strains possess both 3α-HSDH and 3β-HSDH and are present in most microbiomes in this study. Thus, they played a central role for all microbiomes’ capabilities to synthesize Iso-CA.

    In summary, by analyzing the shadow prices associated with each flux balance analysis solution when optimizing for secondary bile acid production, strain-specific contributions to their biosynthesis were determined for each personalized community model. Four scenarios with different bottlenecks for the biosynthesis of Iso-CA were identified. This analysis highlights once more that the metabolic potential of an individual microbiome, and strategies to manipulate this metabolic potential, cannot be inferred solely from the abundance of single genes and depends on the community-wide metabolic network as well as metabolic constraints (e.g., substrate availability). We demonstrated that constraint-based modeling allows for the generation of mechanistic, testable hypotheses.


    In this work, we used a systematic computational modeling workflow to investigate the bile acid production capabilities of gut microbes and gut microbial communities. After annotating and reconstructing the bile acid deconjugation and transformation pathways (Fig. 1a, Fig. 2) in 693 human gut microbe genomes, we first built pairwise microbial models providing novel insight into strain-specific bile-acid production capabilities. We then assembled gut microbiome models for each metagenomics sample of either healthy individuals or pediatric IDB patients. The three key results of our analysis are as follows: (1) microbes can complement each other’s bile acid pathway to achieve the broader bile acid production repertoire observed in fecal samples, (2) bile acid production profiles of 194 microbiome models were individual-specific and distinguished healthy controls from pediatric IBD patients, and (3) the bile acid production profiles could not be predicted by reaction (gene) abundance alone, as illustrated for Iso-CA illustrating the added value of computational modeling of metabolite production capabilities of microbial communities.

    While it can be intuitively understood that bile acid biosynthesis is a cooperative task in the gut microbiome from the known fact that no strain possesses the complete pathway [23], these microbe-microbe metabolic dependencies could be exactly predicted through constraint-based modeling yielding more than 7000 pairs of microbes (Fig. 3a, Additional file 1: Table S5). The capabilities of most strains to generate secondary bile acids were shown to be very limited. For example, no strain alone but 100 pairs could convert tauro-or glyco-CDCA into UDCA (Additional file 1: Table S5). This analysis demonstrated that strain-specific microbe-microbe interactions need to be considered when studying the metabolic crosstalk between the gut microbiome and the mammalian host. Similar microbial corporations through cross-feeding of metabolic products have been suggested, e.g., for intestinal microbial metabolism of b-vitamins [43], of host-derived mucins [44], of dietary glycans [45], of flavonoids [46], for short-chain fatty acid production [41], and for microbial respiratory capabilities [47].

    The personalized bile acid metabolism profile of 194 microbiomes, which included the total production potential and the strain-level contributions to overall production was individual-specific and distinct from healthy controls in pediatric IBD patients (Fig. 3b–d, Additional file 2: Figure S7). Our finding that the bile acid profiles of IBD patients differ from healthy controls agrees with experimental reports. For instance, a recent study has investigated the microbiomes and fecal metabolomes of pediatric IBD patients and their relatives and could distinguish two metabotypes both in patients and relatives [48]. The IBD-associated metabotype has been characterized by an altered bile acid profile, with increased levels of cholate and sulfated and taurine-conjugated primary bile acids. The altered bile acid profile suggests a reduced bile acid deconjugation and conversion potential of the gut microbiota [48], which we could demonstrate being the case with our in silico results (Fig. 3b–c).

    In most analyzed microbiome models, the production potential for Iso-CA was found to be lower than expected from the abundance of the 3β-HSDH. Analyzing the shadow prices [7] revealed that the presence of strains capable of synthesizing the precursor 3-dehydro-CA was a bottleneck in many microbiomes. In fact, we identified four scenarios, for which different strategies could be used to increase overall Iso-CA production capabilities in a given microbiome. In these four scenarios, Iso-CA production flux could be increased (1) only by directly providing it, (2) by increasing the abundance of strains carrying 3β-HSDH, (3) either by providing 3-dehydro-CA or by increasing the abundance of strains carrying 3α-HSDH, and (4) by increasing the abundance of Ruminococcus gnavus ATCC 29149 and in some cases Eggerthella lenta DSM 2243. To complete the systems biology cycle, these predictions require experimental validation, e.g., by measuring the amount of Iso-CA levels in in vitro cultures from fecal samples. A shadow price analysis has the advantage of being an unbiased indicator for metabolites in a pathway that are of key importance for the end product of the pathway. It could be readily applied to other health-relevant metabolites produced by the gut microbiome (e.g., short-chain fatty acids) and key synthesis-limiting steps in the relevant pathways.

    Compared with commonly used computational and multivariate statistical approaches, the constraint-based modeling approach applied in this study has several key advantages. First of all, unlike quantifications of total gene abundance (e.g., [49]) and correlation-based approaches (e.g., [50]), our approach is mechanistic and obeys physicochemical and environmental constraints (e.g., mass-charge conservation, laws of thermodynamics, substrate uptake). This property enabled us to predict the metabolic capabilities of a given microbial community, as defined by metagenomics data. Importantly, the predicted capabilities are physiologically, physicochemically, and thermodynamically feasible under the given medium conditions (i.e., diet). Second, the metabolic reconstructions used in our approach are strain-resolved, and the capabilities included in each metabolic network are based on the microbes’ genome, detailed comparative genomic analyses as well as an extensive review of the literature for biochemical and physiological data [15]. As a consequence, the metabolic contribution of each strain in each individual microbiome can be exactly predicted with high confidence. Another advantage of our approach is the incorporation of species-species boundaries and transport capabilities. As stated above, species-species cross-feeding plays a key role for the metabolic potential of a microbial community and thus needs to be considered. Finally, it is challenging to link typical metagenomics-based approaches to a particular host function. Microbial species or functions can be correlated with certain host metabolites through top-down multivariate statistical analyses [50]. However, mechanisms explaining these correlations are often lacking. As more omics data become available for microbiome samples, the generated microbiome models can be further constrained and personalized through the integration of meta-transcriptomic [51], meta-metabolomic [52], meta-proteomic data [53], or nutritional information via the Virtual Metabolic Human database [16]. The microbiome models can also be integrated with the global human reconstruction, Recon3D, which includes a secondary bile detoxification subsystem [54], or with the whole-body organ-resolved reconstruction of human metabolism [19] thanks to the use of a consistent namespace [15]. The integrated analysis can predict organ-specific metabolic changes due to differences in microbial community composition and yield novel hypotheses about host-microbiome co-metabolism [19].

    One limitation of the method is the steady-state assumption of flux balance analysis and the resulting computation of fluxes rather than concentrations. Moreover, the AGORA reconstructions and our modeling framework do not include regulatory constraints and kinetic parameters. As a result, the modeling framework does not account for substrate specificity and transporter capacity, although the latter could be incorporated as reaction constraints dependent on data availability. This limitation could be overcome using hybrid modeling techniques that integrate the dynamics and the regulation of biochemical processes through with differential equations [55,56,57,58]. Furthermore, our method does not allow predicting microbial composition or organismal abundances in the microbiome, again due to the steady-state assumption. The method relies on parameterizing the personalized models with the relative microbial abundances calculated from the metagenomic data. For predicting microbial abundances, dynamic community flux balance analysis methods [58, 59] are more appropriate. Consequently, we focus the application of our framework on exploring the metabolic profile of a given gut microbiome with known microbial composition. Finally, it is well known that the gut microbiome fluctuates over time [60], however, each simulation performed with the personalized models only represents the fecal microbiome at a single time point. This is expected as the fecal metagenomic sample that serves as the input data also only captures the gut microbiome at a single time point. Fecal metagenomic samples from the same individuals at multiple time points are, for example, available in [61]. Such data could be used to model a time series of metabolic states and elucidate how the gut microbial metabolic profiles fluctuate over time.

    Flux profiles predicted by the framework can be readily compared with qualitative increases or decreases in metabolites in disease conditions to validate simulation results, which would require metagenomic or 16S rRNA data as well as fecal metabolomics from the same subjects. Metagenomic and fecal metabolomic measurements of bile acids have been performed in [62] and such data could be linked through modeling in future efforts. Such comparisons have valuable applications for mechanistically linking metagenomic and metabolomic measurements from the same sample. Moreover, qualitative and quantitative metabolomic data could be used as input data to contextualize the models further. A COBRA Toolbox module for the implementation of metabolomic data with constraint-based models has been developed [52].

    While the scope of the present work is the prediction of bile acid metabolism, in future efforts, other health-relevant microbial metabolic subsystems may be considered. For instance, Lewis et al. found that several pathways, e.g., glycerophospholipid metabolism, amino benzoate degradation, sulfur relay system, and glutathione metabolism, separated healthy and dysbiotic microbiomes [36]. In a follow-up work, fecal amino acid levels have been found to be altered in IBD patients and to positively correlate with Proteobacteria [40]. Applying the computational workflow presented in this study to predict the gut microbial metabolome beyond bile acid metabolism would allow us to mechanistically link altered metabolites with strain-specific capabilities. Ultimately, such analysis could lead to novel insights into the mechanisms behind altered metabolomes in disease states and allow pinpointing disease-relevant species and/or enzymes that may serve as novel drug targets.


    We demonstrated that an in silico metabolic modeling workflow could elucidate the metabolic potential of an individual’s microbiome, which cannot be done based on gene count and reaction abundance alone. We illustrated this workflow using metagenomics data of healthy individuals and IBD patients while focusing on bile acid metabolism. Importantly, we were able to demonstrate that this mechanistic, strain- and metabolite-resolved, unbiased, and inexpensive approach allows for the systematic interrogation of the metabolic potential of an individual’s microbiome and can yield testable novel hypotheses. Integrative systems biology approaches are urgently needed to gain novel insight into complex, multifactorial diseases, such as IBD [1]. In future efforts, personalized modeling could also be applied to predicting individual-specific dietary or therapeutic interventions [63]. The AGORA resource, the COBRA Toolbox, and the Microbiome Modeling Toolbox are freely available to the scientific community. We have also created extensive tutorials (available at the COBRA Toolbox GitHub) aiding users interested in applying our framework. We expect that the metabolic modeling approach presented will have valuable applications in unraveling the role of human-gut microbiome metabolic interactions in human health and disease.

    Materials and methods

    Comparative genomic approach

    All 773 strains of the AGORA resource [15], 46 strains reconstructed in this study, and 23 currently not reconstructed strains were analyzed for the presence of their genomes at the PubSEED resource [26, 27], resulting in 690 bacterial and three archaeal genomes to be considered in this study (Fig. 1a). Note that only 670 of the reconstructed microbes had their genomes available in PubSEED and were consequently used for the comparative genomic approach. All 693 human gut microbe genomes were analyzed for the presence of orthologs of bile acid deconjugation and biotransformation genes (Additional file 1: Table S1). Orthologs are defined as genes that satisfy the following conditions: (1) Orthologs should be closely homologous proteins (e-value cutoff = e−50). (2) Orthologs should be found in the same genomic context, i.e., the structure of gene locus should be conserved in related genomes. (3) Orthologs should form a monophyletic branch of a phylogenetic tree.

    For the search of homologs and analysis of genomic context, the PubSEED platform was used along with phylogenetic trees for protein domains in MicrobesOnline [64]. Multiple protein alignments were performed using the MUSCLE v. 3.8.31 tool [65, 66]. Phylogenetic trees were constructed using the maximum-likelihood method with the default parameters implemented in PhyML-3.0 [67]. The obtained trees were visualized and midpoint-rooted using the interactive viewer Dendroscope, version 3.2.10, build 19 [68].

    The following previously analyzed genes were used as a starting point: (1) genes for bile salt BSH from multiple genomes [21], (2) 7α–HSDH) from Bacteroides fragilis [22], (3) 3α- and 3β-HSDHs genes from Eggerthella lenta DSM 2243 and Ruminococcus gnavus ATCC 29149 [23], (4) 7α-HSDH and baiABCDEFGHI genes for a multistep 7α/β-dehydroxylation pathway, (5) bai genes from Eggerthella lenta DSM [23], (6) 7β-HSDH gene from Clostridium absonum [25], and (7) 12α-HSDH from Clostridium hylemonae DSM 15053, Clostridium scindens ATCC 35704, and Clostridium hiranonis DSM 13275 [24]. Note that Clostridium leptum has been experimentally shown to have 12α-HSDH activity [28]; however, we were unable to identify the 12α-HSDH gene in its genome. BSH proteins are closely related to the penicillin V amidase (PVA) proteins [21]. To avoid mis-annotations, a phylogenetic tree for BSH proteins and their homologs in the analyzed genomes was constructed (Additional file 2: Figure S1), and orthologs of the known BSH genes were identified. All HSDH proteins listed above demonstrated similarity to each other and with BaiA proteins. Thus, orthologs for HSDH/BaiA proteins were resolved through the construction of a phylogenetic tree (Additional file 2: Figure S2). Finally, two new genes in the 7α/β-dehydroxylation pathway (BaiO and BaiP, Fig. 2) were predicted in this work. All of the annotated genes are represented as a subsystem at the PubSEED website [69] and can be found in Additional file 1: Table S1.

    Formulation and addition of reactions

    Reaction mechanisms were retrieved from the KEGG database [70] as well as published literature (e.g., [71]). For all genomes having genes for BSH, HSDHs, or the complete 7α/β-dehydroxylation pathway (Fig. 2), metabolic mass- and charge-balanced reactions were formulated. Exchange reactions were added for all extracellular metabolites. Most reactions were associated with genes and proteins annotated in the analyzed genomes. Reactions not-associated with genes and proteins were only added if the gene was unknown but the reaction was required to eliminate dead-ends in a metabolic pathway. Thus, the following gap-filling reactions were added without associations with genes or proteins: (1) A transport reaction for LCA, the final product of 7α/β-dehydroxylation pathway which was added as the transporter is unknown. (2) Pathways that yield allolithocholate (allo-LCA) and allodeoxycholate (allo-DCA) were included for strains possessing the bai gene cluster as these compounds are known to be side-products of the 7α/β-dehydroxylation pathway [72] and found in human adults under certain circumstances [3].

    Pathways for cholesterol reduction to coprostanol were also reconstructed. These enzymatic activities, both cytoplasmic and extracellular, have been shown in Lactobacillus acidophilus, Lactobacillus bulgaricus, and Lactobacillus casei [73]. The precise mechanisms of these reactions as well as the enzyme-encoding genes are unknown, but the biotransformation has been shown to be associated with the oxidation of NADH to NAD+ [73]. Consequently, reactions for extracellular and cytoplasmic NADH-dependent reduction of cholesterol to coprostanol were added to six Lactobacillus sp. models, together with exchange reactions for cholesterol and coprostanol as well as a predicted transport reaction for cholesterol uptake.

    All metabolites and reactions were formulated following an established reconstruction protocol [8]. Metabolites and reaction abbreviations in the bile acid subsystem were created in accordance with the Virtual Metabolic Human (VMH) [16] nomenclature to ensure compatibility with the human metabolic reconstruction. The MATLAB-based reconstruction tool rBioNet [74], which ensures quality control and quality assurance, such as mass- and charge-balance, was used to add the metabolites and reactions to the appropriate reconstructions. All reactions and metabolites in the reconstructed bile acid subsystem are described in Additional file 1: Table S2a, b.

    Expansion of AGORA

    A total of 46 gut microbial strains were newly reconstructed. The reconstructions were generated by semi-automatically expanding and curating KBase [75] draft reconstructions following the established AGORA pipeline used in [15] (Additional file 1: Table S11). Of the 773 AGORA strains and 46 newly reconstructed strains, 232 strains total carried at least one gene in the bile acid pathway (Additional file 1: Table S1) and six produced coprostanol. The corresponding reconstructions were expanded by the appropriate metabolites and reactions using rBioNet [74] and subjected to extensive quality-assurance and control measures [8, 32] (Fig. 1a). The expanded resource, accounting for 818 strains, is available on the Virtual Metabolic Human website [16].

    Construction of pairwise models

    Bilirubin Metabolism

    Bile Acid Metabolism and Signaling

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    You will also be interested:

    Bile acid

    steroid acid found predominantly in the bile of mammals and other vertebrates

    Bile acids are steroidacids found predominantly in the bile of mammals and other vertebrates. Diverse bile acids are synthesized in the liver.[1] Bile acids are conjugated with taurine or glycine residues to give anions called bile salts.[2][3][4]

    Primary bile acids are those synthesized by the liver. Secondary bile acids result from bacterial actions in the colon. In humans, taurocholic acid and glycocholic acid (derivatives of cholic acid) and taurochenodeoxycholic acid and glycochenodeoxycholic acid (derivatives of chenodeoxycholic acid) are the major bile salts. They are roughly equal in concentration.[5] The salts of their 7-alpha-dehydroxylated derivatives, deoxycholic acid and lithocholic acid, are also found, with derivatives of cholic, chenodeoxycholic and deoxycholic acids accounting for over 90% of human biliary bile acids.[5]

    Bile acids comprise about 80% of the organic compounds in bile (others are phospholipids and cholesterol).[5] An increased secretion of bile acids produces an increase in bile flow. Bile acids facilitate digestion of dietary fats and oils. They serve as micelle-forming surfactants, which encapsulate nutrients, facilitating their absorption.[6] These micelles are suspended in the chyme before further processing. Bile acids also have hormonal actions throughout the body, particularly through the farnesoid X receptor and GPBAR1 (also known as TGR5).[7]

    Structure of cholic acidshowing relationship to other bile acids


    Primary bile acids[edit]

    Bile acid synthesis occurs in liver cells, which synthesize primary bile acids (cholic acid and chenodeoxycholic acid in humans) via cytochrome P450-mediated oxidation of cholesterol in a multi-step process. Approximately 600 mg of bile salts are synthesized daily to replace bile acids lost in the feces, although, as described below, much larger amounts are secreted, reabsorbed in the gut and recycled.

    The rate-limiting step in synthesis is the addition of a hydroxyl group of the 7th position of the steroid nucleus by the enzyme cholesterol 7 alpha-hydroxylase. This enzyme is down-regulated by cholic acid, up-regulated by cholesterol and is inhibited by the actions of the ilealhormoneFGF15/19.[2][3]

    Prior to secreting any of the bile acids (primary or secondary, see below), liver cells conjugate them with either glycine or taurine, to form a total of 8 possible conjugated bile acids. These conjugated bile acids are often referred to as bile salts. The pKa of the unconjugated bile acids are between 5 and 6.5,[4] and the pH of the duodenum ranges between 3 and 5, so when unconjugated bile acids are in the duodenum, they are almost always protonated (HA form), which makes them relatively insoluble in water. Conjugating bile acids with amino acids lowers the pKa of the bile-acid/amino-acid conjugate to between 1 and 4. Thus conjugated bile acids are almost always in their deprotonated (A-) form in the duodenum, which makes them much more water-soluble and much more able to fulfil their physiologic function of emulsifying fats.[8][9]

    Secondary bile acids[edit]

    Once secreted into the lumen of the intestine, bile salts are modified by gut bacteria. They are partially dehydroxylated. Their glycine and taurine groups are removed to give the secondary bile acids, deoxycholic acid and lithocholic acid. Cholic acid is converted into deoxycholic acid and chenodeoxycholic acid into lithocholic acid. All four of these bile acids recycled, in a process known as enterohepatic circulation.[2][3]


    Lipids digestion[edit]

    As amphipathic molecules with hydrophobic and hydrophilic regions, conjugated bile salts sit at the lipid/water interface and, above the right concentration, form micelles.[9] The added solubility of conjugated bile salts aids in their function by preventing passive re-absorption in the small intestine. As a result, the concentration of bile acids/salts in the small intestine is high enough to form micelles and solubilize lipids. "Critical micellar concentration" refers to both an intrinsic property of the bile acid itself and amount of bile acid necessary to function in the spontaneous and dynamic formation of micelles.[9] Bile acid-containing micelles aid lipases to digest lipids and bring them near the intestinal brush border membrane, which results in fat absorption.[6]

    Synthesis of bile acids is a major route of cholesterol metabolism in most species other than humans. The body produces about 800 mg of cholesterol per day and about half of that is used for bile acid synthesis producing 400–600 mg daily. Human adults secrete between 12-18 g of bile acids into the intestine each day, mostly after meals. The bile acid pool size is between 4–6 g, which means that bile acids are recycled several times each day. About 95% of bile acids are reabsorbed by active transport in the ileum and recycled back to the liver for further secretion into the biliary system and gallbladder. This enterohepatic circulation of bile acids allows a low rate of synthesis, only about 0.3g/day, but with large amounts being secreted into the intestine.[5]

    Bile acids have other functions, including eliminating cholesterol from the body, driving the flow of bile to eliminate certain catabolites (including bilirubin), emulsifying fat-soluble vitamins to enable their absorption, and aiding in motility and the reduction of the bacteria flora found in the small intestine and biliary tract.[5]

    Cell signalling[edit]

    Bile acids have metabolic actions in the body resembling those of hormones, acting through two specific receptors, the farnesoid X receptor and G protein-coupled bile acid receptor/TGR5.[7][10] They bind less specifically to some other receptors and have been reported to regulate the activity of certain enzymes [11] and ion channels [12] and the synthesis of diverse substances including endogenous fatty acid ethanolamides.[13][14]

    Structure and synthesis[edit]

    Bile salts constitute a large family of molecules, composed of a steroid structure with four rings, a five- or eight-carbon side-chain terminating in a carboxylic acid, and several hydroxyl groups, the number and orientation of which is different among the specific bile salts.[1] The four rings are labeled A, B, C, and D, from the farthest to the closest to the side chain with the carboxyl group. The D-ring is smaller by one carbon than the other three. The structure is commonly drawn with A at the left and D at the right. The hydroxyl groups can be in either of two configurations: either up (or out), termed beta (β; often drawn by convention as a solid line), or down, termed alpha (α; displayed as a dashed line). All bile acids have a 3-hydroxyl group, derived from the parent molecule, cholesterol, in which the 3-hydroxyl is beta.[1]

    IUPACrecommended ring lettering (left) and atom numbering (right) of the steroid skeleton. The four rings A-D form a steranecore.

    The initial step in the classical pathway of hepatic synthesis of bile acids is the enzymatic addition of a 7α hydroxyl group by cholesterol 7α-hydroxylase (CYP7A1) forming 7α-hydroxycholesterol. This is then metabolised to 7α-hydroxy-4-cholesten-3-one. There are multiple steps in bile acid synthesis requiring 14 enzymes in all.[3] These result in the junction between the first two steroid rings (A and B) being altered, making the molecule bent; in this process, the 3-hydroxyl is converted to the α orientation. The simplest 24-carbon bile acid has two hydroxyl groups at positions 3α and 7α. This is 3α,7α-dihydroxy-5β-cholan-24-oic acid, or, as more usually known, chenodeoxycholic acid. This bile acid was first isolated from the domestic goose, from which the "cheno" portion of the name was derived (Greek: χήν = goose). The 5β in the name denotes the orientation of the junction between rings A and B of the steroid nucleus (in this case, they are bent). The term "cholan" denotes a particular steroid structure of 24 carbons, and the "24-oic acid" indicates that the carboxylic acid is found at position 24, at the end of the side-chain. Chenodeoxycholic acid is made by many species, and is the prototypic functional bile acid.[2][3]

    An alternative (acidic) pathway of bile acid synthesis is initiated by mitochondrial sterol 27-hydroxylase (CYP27A1), expressed in liver, and also in macrophages and other tissues. CYP27A1 contributes significantly to total bile acid synthesis by catalyzing sterol side chain oxidation, after which cleavage of a three-carbon unit in the peroxisomes leads to formation of a C24 bile acid. Minor pathways initiated by 25-hydroxylase in the liver and 24-hydroxylase in the brain also may contribute to bile acid synthesis. 7α-hydroxylase (CYP7B1) generates oxysterols, which may be further converted in the liver to CDCA.[2][3]

    Cholic acid, 3α,7α,12α-trihydroxy-5β-cholan-24-oic acid, the most abundant bile acid in humans and many other species, was discovered before chenodeoxycholic acid. It is a tri-hydroxy-bile acid with 3 hydroxyl groups (3α, 7α and 12α). In its synthesis in the liver, 12α hydroxylation is performed by the additional action of CYP8B1. As this had already been described, the discovery of chenodeoxcholic acid (with 2 hydroxyl groups) made this new bile acid a "deoxycholic acid" in that it had one fewer hydroxyl group than cholic acid.[2][3]

    Deoxycholic acid is formed from cholic acid by 7-dehydroxylation, resulting in 2 hydroxyl groups (3α and 12α). This process with chenodeoxycholic acid results in a bile acid with only a 3α hydroxyl group, termed lithocholic acid (litho = stone) having been identified first in a gallstone from a calf. It is poorly water-soluble and rather toxic to cells.[2][3]

    Different vertebrate families have evolved to use modifications of most positions on the steroid nucleus and side-chain of the bile acid structure. To avoid the problems associated with the production of lithocholic acid, most species add a third hydroxyl group to chenodeoxycholic acid. The subsequent removal of the 7α hydroxyl group by intestinal bacteria will then result in a less toxic but still-functional dihydroxy bile acid. Over the course of vertebrate evolution, a number of positions have been chosen for placement of the third hydroxyl group. Initially, the 16α position was favored, in particular in birds. Later, this position was superseded in a large number of species selecting the 12α position. Primates (including humans) utilize 12α for their third hydroxyl group position, producing cholic acid. In mice and other rodents, 6β hydroxylation forms muricholic acids (α or β depending on the 7 hydroxyl position). Pigs have 6α hydroxylation in hyocholic acid (3α,6α,7α-trihydroxy-5β-cholanoic acid), and other species have a hydroxyl group on position 23 of the side-chain.

    Ursodeoxycholic acid was first isolated from bear bile, which has been used medicinally for centuries. Its structure resembles chenodeoxycholic acid but with the 7-hydroxyl group in the β position.[1]

    Obeticholic acid, 6α-ethyl-chenodeoxycholic acid, is a semi-synthetic bile acid with greater activity as FXR agonist which is undergoing investigation as a pharmaceutical agent.

    Hormonal actions[edit]

    Bile acids also act as steroid hormones, secreted from the liver, absorbed from the intestine and having various direct metabolic actions in the body through the nuclear receptor Farnesoid X receptor (FXR), also known by its gene name NR1H4.[15][16][17] Another bile acid receptor is the cell membrane receptor known as G protein-coupled bile acid receptor 1 or TGR5. Many of their functions as signaling molecules in the liver and the intestines are by activating FXR, whereas TGR5 may be involved in metabolic, endocrine and neurological functions.[7][18]

    Regulation of synthesis[edit]

    As surfactants or detergents, bile acids are potentially toxic to cells, and so their concentrations are tightly regulated. Activation of FXR in the liver inhibits synthesis of bile acids, and is one mechanism of feedback control when bile acid levels are too high. Secondly, FXR activation by bile acids during absorption in the intestine increases transcription and synthesis of FGF19, which then inhibits bile acid synthesis in the liver.[19]

    Metabolic functions[edit]

    Emerging evidence associates FXR activation with alterations in triglyceridemetabolism, glucose metabolism, and liver growth.[7][20][18]

    Other interactions[edit]

    Bile acids bind to some other proteins in addition to their hormone receptors (FXR and TGR5) and their transporters. Among these protein targets, the enzyme N-acyl phosphatidylethanolamine-specific phospholipase D (NAPE-PLD) generates bioactive lipid amides (e.g. the endogenous cannabinoidanandamide) that play important roles in several physiological pathways including stress and pain responses, appetite, and lifespan. NAPE-PLD orchestrates a direct cross-talk between lipid amide signals and bile acid physiology.[13]

    Clinical significance[edit]


    As bile acids are made from endogenous cholesterol, disruption of the enterohepatic circulation of bile acids will lower cholesterol. Bile acid sequestrants bind bile acids in the gut, preventing reabsorption. In so doing, more endogenous cholesterol is shunted into the production of bile acids, thereby lowering cholesterol levels. The sequestered bile acids are then excreted in the feces.[21]


    Tests for bile acids are useful in both human and veterinary medicine, as they aid in the diagnosis of a number of conditions, including types of cholestasis such as intrahepatic cholestasis of pregnancy, portosystemic shunt, and hepatic microvascular dysplasia in dogs.[22] Structural or functional abnormalities of the biliary system result in an increase in bilirubin (jaundice) and in bile acids in the blood. Bile acids are related to the itching (pruritus) which is common in cholestatic conditions such as primary biliary cirrhosis (PBC), primary sclerosing cholangitis or intrahepatic cholestasis of pregnancy.[23] Treatment with ursodeoxycholic acid has been used for many years in these cholestatic disorders.[24][25]


    Main article: Gallstones

    The relationship of bile acids to cholesterol saturation in bile and cholesterol precipitation to produce gallstones has been studied extensively. Gallstones may result from increased saturation of cholesterol or bilirubin, or from bile stasis. Lower concentrations of bile acids or phospholipids in bile reduce cholesterol solubility and lead to microcrystal formation. Oral therapy with chenodeoxycholic acid and/or ursodeoxycholic acid has been used to dissolve cholesterol gallstones.[26][27][28] Stones may recur when treatment is stopped. Bile acid therapy may be of value to prevent stones in certain circumstances such as following bariatric surgery.[29]

    Bile acid diarrhea[edit]

    Excess concentrations of bile acids in the colon are a cause of chronic diarrhea. It is commonly found when the ileum is abnormal or has been surgically removed, as in Crohn's disease, or cause a condition that resembles diarrhea-predominant irritable bowel syndrome (IBS-D). This condition of bile acid diarrhea/bile acid malabsorption can be diagnosed by the SeHCAT test and treated with bile acid sequestrants.[30]

    Bile acids and colon cancer[edit]

    Bile acids may have some importance in the development of colorectal cancer.[31]Deoxycholic acid (DCA) is increased in the colonic contents of humans in response to a high fat diet.[32] In populations with a high incidence of colorectal cancer, fecal concentrations of bile acids are higher,[33][34] and this association suggests that increased colonic exposure to bile acids could play a role in the development of cancer. In one particular comparison, the fecal DCA concentrations in Native Africans in South Africa (who eat a low fat diet) compared to African Americans (who eat a higher fat diet) was 7.30 vs. 37.51 nmol/g wet weight stool.[35] Native Africans in South Africa have a low incidence rate of colon cancer of less than 1:100,000,[36] compared to the high incidence rate for male African Americans of 72:100,000.[37]

    Experimental studies also suggest mechanisms for bile acids in colon cancer. Exposure of colonic cells to high DCA concentrations increase formation of reactive oxygen species, causing oxidative stress, and also increase DNA damage.[38] Mice fed a diet with added DCA mimicking colonic DCA levels in humans on a high fat diet developed colonic neoplasia, including adenomas and adenocarcinomas (cancers), unlike mice fed a control diet producing one-tenth the level of colonic DCA who had no colonic neoplasia.[39][40]

    The effects of ursodeoxycholic acid (UDCA) in modifying the risk of colorectal cancer has been looked at in several studies, particularly in primary sclerosing cholangitis and inflammatory bowel disease, with varying results partly related to dosage.[41][42] Genetic variation in the key bile acid synthesis enzyme, CYP7A1, influenced the effectiveness of UDCA in colorectal adenoma prevention in a large trial.[43]


    Bile acids may be used in subcutaneous injections to remove unwanted fat (see Mesotherapy). Deoxycholic acid as an injectable has received FDA approval to dissolve submental fat.[44] Phase III trials showed significant responses although many subjects had mild adverse reactions of bruising, swelling, pain, numbness, erythema, and firmness around the treated area.[45][46]


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