Balance between bile acid conjugation and hydrolysis activity can alter outcomes of gut inflammation

doi:10.21203/rs.3.rs-5005563/v1

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Balance between bile acid conjugation and hydrolysis activity can alter outcomes of gut inflammation

https://doi.org/10.21203/rs.3.rs-5005563/v1

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Conjugated bile acids (BAs) are multi-functional detergents produced in the gastrointestinal (GI) tract by the liver enzyme bile acid:amino acid N-acyltransferase (BAAT) and by the microbiome from acyltransferase activity of the ubiquitous enzyme bile salt hydrolase (BSH). Humans with inflammatory bowel disease (IBD) have an enrichment in both host and microbially conjugated BAs (MCBAs), but their impacts on GI inflammation are not well understood. We investigated the role of host-conjugated BAs in the dextran sodium sulfate (DSS) model of colitis using a BAAT knockout background. Baat^-/- KO mice have severe phenotypes in the DSS model that were rescued by supplementation with taurocholate (TCA). Gene expression and histological analysis showed that this rescue was likely due to an improved epithelial barrier and goblet cell function. TCA supplementation also increased microbiome diversity, particularly the BA metabolizing Lachnospiraceae. Metabolomics showed that TCA supplementation in the DSS model increased all known forms of conjugated BAs including both host and microbial sources, but also their hydrolysis and metabolism to secondary BAs. The ability of TCA to improve pathology under DSS inflammation despite its ready hydrolysis led us to investigate the BSH activity of diverse gut bacteria on a panel of conjugated BAs in vitro and in vivo. Exposure of 17 gut bacterial isolates to a panel of 10 amino acid conjugated BAs showed broad hydrolytic capacity depending on the bacterium. Host-produced TCA and the MCBAs SerCA and AlaCA were readily hydrolyzed, whereas GluCA, AspCA and ThrCA were more resistant. This hydrolytic variability translated in vivo where mice fed the recalcitrant GluCA had less secondary BAs production in the DSS model compared to TCA. The complexity of microbial dysbiosis and conjugated BA metabolism in an inflamed murine gut led to the exploration of BSHs genes in metagenomic data from human IBD patients. Certain bsh sequences were enriched in the diseased states including that from Ruminococcus gnavus and Enterocloster clostridioformis in people with Crohn’s disease. Collectively, this study shows that the conjugated BAs may provide benefits to those with IBD, but this is dictated by a delicate balance between BA conjugation/deconjugation based on the BSH sequences present.

Biological sciences/Microbiology/Microbial communities/Microbiome

Health sciences/Gastroenterology/Gastrointestinal diseases/Inflammatory bowel disease/Crohn's disease

Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gastrointestinal (GI) tract, including Crohn’s disease (CD) and ulcerative colitis (UC) [1]. IBD has become a global disease with a rising prevalence, especially in the Western world, affecting more than 2.39 million individuals in North America [2]. Although myriad therapies are available, including aminosalicylates, corticosteroids, immunomodulators and biologics, these existing treatments only provide temporary remission for most patients and mostly target symptoms of the disease not its underlying causes [3]. The etiology of IBD is still unclear, but evidence suggests that a multitude of factors are responsible for its pathogenesis, including genetic predispositions, environmental triggers, mucosal immune dysregulation, and intestinal microbiota dysbiosis [4, 5]. Mounting evidence shows that the trillions of microorganisms that inhabit the human gut play an important role in the occurrence and development of IBD, but the mechanisms of their contribution to disease development remains mostly unknown [6].

Previous studies have highlighted microbially-derived metabolites as bioactive in IBD, particularly bile acids (BAs) produced by the host liver and then modified by the microbiome [7–9]. BAs are steroidal natural products that are synthesized in the liver from cholesterol [10]. The two primary BAs, cholic acid (CA) and chenodeoxycholic acid (CDCA), are conjugated with either taurine or glycine in a reaction catalyzed by bile acid CoA:amino acid N-acyltransferase (BAAT) [11]. These human conjugated primary BAs are then released into the gallbladder before postprandial secretion into the small intestine where they facilitate fat digestion and absorption of cholesterol and vitamins [12]. About 95% of secreted BAs are reabsorbed in the terminal ileum and returned to the liver in a mechanism known as enterohepatic circulation [13, 14]. The remaining 5% of BAs are transformed by the gut microbiome to form a diverse pool of secondary BAs in the gastrointestinal tract [13]. The critical first step in the metabolism of BAs by the gut microbiome is catalyzed by bile salt hydrolase (BSH, EC 3.5.1.24, also known as choloylglycine hydrolase), a homotetrameric enzyme that deconjugates acyl- bound glycine and/or taurine from the sterol core of the primary BAs [15]. This enzymatic step is considered a "gateway reaction" to subsequent microbial BAs modifications, which are diverse and control the host’s overall BA pool [11, 16]. BSHs are identified across all major bacterial and archaeal phyla in the gut, but their catalytic efficiencies and substrate preferences vary [15, 17]. Two recent studies revealed that BSH also has aminoacyltransferase activity that conjugates diverse amino acids to cholic acid, to generate the microbial conjugated bile acids (MCBAs), which further increase the complexity and diversity of the human BAs pool [18, 19]. Some of these novel MCBAs were found to activate the transcription factors Pregnane X Receptor and Aryl Hydrocarbon Receptor, indicating that they may have a role in regulating metabolic homeostasis [7, 20]. The recent expansion of conjugated BA diversity from the microbiome brings into question the role these compounds may have in gut inflammation and IBD.

Some studies have demonstrated that dysregulation of BA metabolism and BSH activity was directly related to IBD [7, 21, 22]. BA profiling of GI samples from patients have consistently shown increased levels of primary BAs and decreased secondary BAs [8, 23, 24]. More recent studies have shown that MCBAs are also elevated in the inflamed gut [7, 20], which is somewhat of an enzymatic contradiction, because BSH is responsible for hydrolyzing the abundant primary BAs while simultaneously producing MCBAs from its acyl transfer activity [18]. Complicating the matter further, patients with active IBD had been shown to have decreased levels of BSH activity in their fecal samples [22]. Clearly, the balance between conjugation and deconjugation of host and microbial enzymes is complex and there is a knowledge gap surrounding the role of BSHs and conjugated BAs in IBD. Filling that gap may lead to the development of novel therapeutics using conjugated BAs or specific BSHs for treatment of this chronic inflammatory disease.

This study investigated the implications of BA conjugation in a mouse model of GI inflammation. Our initial experiments utilized a novel BAAT knockout mouse model with highly reduced conjugated BAs to determine if the absence of these molecules would alter disease pathology. It was found that conjugated BAs are important for limiting pathology in this mouse model, with indications of a mechanism via improved epithelial barrier function. In light of these findings and knew knowledge of the diversity of conjugated BAs [7, 20], we investigated the hydrolytic capacity of 17 bacteria on diverse BAs and explored the BSH sequence space in publicly available metagenomic datasets from subjects with IBD. Our results show that conjugated BAs are important for reducing pathology in an inflamed mammalian gut, but that the balance between hydrolysis and conjugation is complex and dependent on the BSH sequences present in the microbiome.

Animals

All animal procedures were approved by the Michigan State University Institutional Animal Care and Use Committee and conformed to NIH guidelines. Mice were housed in a temperature and humidity-controlled room under a 12-h cycle of light and darkness. Cage changes were performed weekly in a laminar flow hood. In our initial experiment, 9 wild-type (WT) and 9 Baat^−/− KO C57BL/6 mice (6–8 weeks of age) were bred in-house, and WT and Baat^−/− KO mice were randomly assigned to four groups (n = 4 WT-healthy control (HC), 5 WT-DSS, 4 KO-HC and 5 KO-DSS groups). The mice in the DSS group were administered 2.5% dextran sodium sulfate (DSS, molecular weight range: 35,000–50,000, Thermo Scientific, USA) daily in drinking water for 7 days to induce colitis. Mice were weighed daily and on day 7 all mice were euthanized through anesthesia using isoflurane followed by cervical dislocation. At necropsy, colon lengths were measured, and spleens were weighed for comparisons across treatment groups.

In a follow-up taurocholic acid (TCA) supplementation experiment, Baat^−/− KO mice were subjected to a treatment with TCA or mock control. For this study, 27 Baat^−/− KO mice (6–8 weeks of age) were randomly assigned to four treatment groups (n = 6 HC, 6 TCA, 7 DSS, and 8 DSS-TCA). For DSS treatment groups, 2.5% DSS was administered in drinking water for 6 days, followed by 7 days of normal, non-DSS-treated water. HC and TCA group mice were given regular water throughout the experiment. Mice in TCA treatment groups were fed with food containing 0.3% TCA (ENVIGO, USA), while mice in the HC and DSS groups were given the same food but without TCA. Body weight was recorded daily, and fecal samples were collected by temporarily placing the mice in clean plastic cups on day 13 prior to sacrifice. On day 13 all mice were euthanized through anesthesia using isoflurane followed by cervical dislocation. Colon, liver, spleen, and cecal content were then harvested after mice were euthanized. Colon lengths were measured, and spleens were weighed.

In a third experiment, to explore the degree of hydrolysis of an MCBA (glutaminocholic acid, Glu-CA) compared to TCA in the inflamed gut, C57BL/6J wildtype mice 6–8 weeks of age were purchased from Jackson Laboratories and acclimated in the new facility for 1 week (note: Baat^−/− animals were not used in this study to better reproduce the native condition). Then, mice were singly housed in cages and acclimated to the singly housed environment for 3 days. After this acclimation period, mice were fasted for 12 h and then given a plain peanut butter pellet for training (fasted training). The peanut-butter training is part of the peanut butter feeding method a procedure developed to efficiently and accurately administer BAs to mice [18, 25]. All mice that passed the training were selected to the next stage of training (non-fasted training), where they were given a plain pure peanut butter pellet under normal chow feed supply for three consecutive days. The mice that successfully completed training were included in subsequent study. Forty male mice were randomly assigned to four groups (HC, DSS, DSS-TCA, and DSS-GluCA groups) with 10 mice per group. Mice in HC and DSS groups were treated with two 10 mg peanut butter pellets every day for 17 days, and mice in DSS-TCA and DSS-GluCA groups were weighed and treated with two 10 mg peanut butter pellets with 25 mg/kg TCA or GluCA every day for 17 days. For the DSS-treated groups, 2.5% DSS was added to the drinking water for 7 days after the 10 days normal water. Mice were weighed every day and fecal samples were collected before sacrifice. On day 17 all mice were euthanized through anesthesia using isoflurane followed by cervical dislocation. Spleen and colon were then harvested after mice were euthanized. Colon lengths were measured, and spleen were weighed.

Tissues collected included serum, liver, cecum, and feces and were frozen immediately by liquid nitrogen and stored at -80°C until they were thawed for use. Assessment of spleen inflammation index was performed by the spleen weight (g) normalized by its body weight (g). Disease activity index (DAI) was assessed by measuring the following: Body weight, rectal bleeding, and stool status were scored daily. Rectal bleeding: 0 = no bleeding, 2 = slight bleeding, and 4 = gross bleeding; diarrhea: 0 = well-formed stools, 2 = soft and pasty stools, and 4 = watery stools.

Histological assessment of colon inflammation

Colon specimens were fixed overnight in 10% formalin at room temperature, sectioned at 5 µm, paraffin-embedded, and stained with hematoxylin and eosin (H&E). A subset was stained with Alcian blue-periodic acid-Schiff (AB-PAS) reaction to highlight mucin-containing goblet cells. An independent board-certified veterinary anatomic pathologist who was blinded to the treatment evaluated the slides. A modified scoring system was used to determine a histologic score, including degree of inflammatory cell infiltration (normal = 0 dense inflammatory infiltrate = 3) and changes to crypt architecture (normal = 0, severe crypt distortion with loss of entire crypts = 3). In addition, epithelial injury was determined by % of surface erosions, mucosal depth was measured, goblet cell depletion was determined (depleted = 0, normal = 1), and goblet cell counts were obtained for the subset stained with AB-PAS.

RNA extraction and quantitative real-time PCR

RNA from colon samples of mice was extracted by the Quick-RNA Midiprep Kit (Zymo, USA) according to the manufacturer’s instructions. Total RNA was reverse transcribed using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, USA). RT-qPCR was performed using SYBR Green Master Mix (Applied Biosystems, USA) in CFX96 System (BIO-RAD, USA) using cDNA under following conditions: 50°C for 2 min, 95°C for 10 min, then 40 cycles of 95°C for 15 s and 60°C for 1 min. The mRNA expression of mouse genes was analyzed with the specific oligo nucleotide primers information is listed in the Table S1. The relative mRNA expression was calculated using the comparative cycle method (2^−ΔΔCt). β-actin served as an internal reference gene.

Microbial 16S rRNA gene sequencing

DNA from cecum and feces was extracted using the Quick-DNA Fecal/Soil Microbe Miniprep kit (Zymo, USA) according to the manufacturer’s instructions. To test extraction efficacy, the full length 16S rRNA genes were amplified using primers 27f (5’-AGAGTTTGATCMTGGCTCAG-3’) and 1492r (5’-TACGGYTACCTTGTTACGACTT-3’) and analyzed via agarose gel electrophoresis. Subsequent microbiome sequencing was performed on amplifiable samples using Illumina compatible primers 515f and 806r to amplify the V4 hypervariable region of the 16S rRNA gene. Sequencing was performed on the MiSeq platform at the Michigan State University RTSF Genomics Core following the protocol previously described by [26].

Raw sequences data were analyzed using QIIME2 (version 2022.08) [27]. Sequences were filtered based on quality to generate amplicon sequence variants (ASVs) via the DADA2 method [28]. Taxonomy was assigned using the q2-feature-classifier against the Greengene2 database (version 2022.10) [29]. Sequences were rarefied to 9,000 reads per sample and core diversity metrics, including Shannon index, Observed ASVs, and Bray-Curtis dissimilarity, were calculated by QIIME2.

Liver, serum, cecum and fecal metabolite extraction

Phosphate buffered saline (PBS) was added to liver, cecum and feces samples (3:1, v:w) and homogenized via bead bashing at 20 Hz for 30 s with 1 min of rest three times using a Bead Ruptor (Omni International, GA). Then 50 µL of liver, cecal or fecal homogenate and 50 µL of serum was added to 150 µL ice cold methanol at a final concentration of 60% methanol followed by overnight incubation at 4℃. Extracted metabolites were stored at -80℃ prior to mass spectrometry analysis.

LC-MS/MS assay

Extract samples were spun down at 12,000 g for 5 min, and the supernatant was transferred to 96-well plates followed by dilution 1:1 (v:v) in 50% methanol containing 2.5 µg/mL phenolsulfonphthalein (as an internal standard) for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Liquid chromatography was performed using a Vanquish Autosampler (Thermo Scientific, USA) and an Acquity UPLC BEH C-18 column, 2.1 mm × 100 mm (Waters, USA). Mass spectrometry was performed using a Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Scientific, USA). All analyses used a 10 µL injection volume, 0.4 mL/min flow rate, and 60°C column temperature. Samples were eluted using a linear solvent gradient of water (A) and acetonitrile (B), each containing 0.1% formic acid, across a 12-minute chromatographic run as follows: 0–1 min, 2% B; 1–8 min, 2-100% B; 8–12 min, 100% B; 10–12 min, 2% B. Data was acquired using electrospray ionization in positive mode. MS¹ data were collected using a 35,000 resolution, AGC target of 1e⁶, maximum iteration time of 100 ms, and a scan range set from 100 to 1500 m/z during (min 1–10). Data-dependent MS² spectra were collected for the top 5 most abundant peaks identified in MS¹ survey scans. Files were converted to mzXML format and submitted to the Global Natural Products Social Molecular Networking Database (GNPS, gnps.ucsd.edu) for molecular networking and spectral library matching [30].

Bacterial and mouse fecal community culturing exposed to diverse BAs

All strains used in this study are listed in Table S2 in the supplementary material. All bacterial strains were cultured in Reinforced Clostridial Medium (RCM), which contained 10 g of proteose peptone, 10 g of beef extract, 5 g of yeast extract, 10 g of lactose, 5 g of sodium chloride, 3 g of sodium acetate, 2 g of K₂HPO₄, 0.25 g of calcium chloride, 0.2% Tween 80, and 0.5 g of L-cysteine in 1 L distilled water. The medium was adjusted to a final pH of 7.0 and autoclaved at 121°C for 15 min.

All strains and the mouse fecal community were grown at 37°C under anaerobic conditions. Starting from − 80°C freezer stocks, bacterial strains were first grown to an optical density at 600 nm (OD₆₀₀) of at least 1.0 in 15 mL polypropylene tubes containing RCM medium. These 50 µL cultures were then used to inoculate 450 µL of RCM medium with supplemented BAs in 1-mL 96-deep-well plates (Thermo Scientific, USA). The medium contained one of each of the 10 BAs, TCA, glycocholic acid (GCA), alanocholic acid (AlaCA), serocholic acid (SerCA), phenylalanocholic acid (PheCA), threonocholic acid (ThrCA), tryptophanocholic acid (TyrCA), glutaminocholic acid (GluCA), leucocholic acid (LeuCA), and aspartocholic acid (AspCA), at 1 mM each. Fresh fecal samples from 5 WT C57BL/6 mice in different cages were collected. Mice fecal samples were homogenized in the 15 mL tube with 4 mL anaerobic Brain Heart Infusion (BHI, Sigma-Aldrich USA) medium, and cultured for 24 h. Subsequently passaging was then performed in 15 mL tubes with 4 mL BHI medium at a dilution of 1:4 every 24 h for a total of 2 passages. 50 µL passaged mice fecal bacteria community (OD₆₀₀ = 0.6) was inoculated to 450 µL BHI medium with the 10 BA conjugations mentioned above, at 1 mM each in 1-mL 96-deep-well plates (Thermo Scientific, USA), and cultured for 48 h. Plates were sealed with a rubber mat and incubated at 37°C for 48 h under anaerobic conditions. Metabolites were extracted as previously described for UPLC-MS/MS analysis [26]. Briefly, 200 µL of culture was added to 300 µL ice cold methanol at a final concentration of 60% methanol followed by overnight incubation at 4°C. Extracted metabolites were stored at -80°C prior to mass spectrometry analysis.

Reference BSH sequences collection

The query BSH protein sequences were collected from the National Center for Biotechnology Information (NCBI) using the keywords "bile salt hydrolase" and "choloylglycine hydrolase". The source was limited to bacteria and archaea, and the amino acid sequence length was 300 to 400. A total 3,058 BSH sequences were obtained and then filtered for non-redundancy using CD-HIT (version 4.8.1, default parameter) at a 95% sequence identity threshold, and the final output 778 BSHs sequences were used as a representative set of BSHs for further analysis. The taxonomy information of BSHs was obtained from the NCBI taxonomy browser.

IBD metagenomic sequence data collection

Metagenomic sequence data was downloaded from NCBI Sequence Read Archive (SRA) database under the accession number PRJNA00072 (PRISM and LLDeep) [9], PRJEB15371 [31], PRJNA429990 [32], PRJEB1220 [33], and PRJNA389280 (HMP2) [34]. The metadata information was obtained from the materials provided in corresponding papers. For the longitudinal dataset, only the first time point for each individual was selected.

A total of 774 samples were included in the analysis. Data from PRJNA0072 were acquired from two different cohorts, which were PRISM and LLDeep. From PRISM cohort, 34, 68, and 51 fecal samples were analyzed from Healthy subjects (HC), those with Crohn’s disease (CD), or ulcerative colitis (UC), respectively. From LLDeep cohort, 22, 20, and 23 samples of HC, CD, and UC, respectively. From PRJEB15371, 53, and 26 samples of HC, and CD, respectively. From PRJNA429990, 15, 41, and 25 samples of HC, CD, and UC, respectively. From PRJEB1220, 236, 12, and 67 of HC, CD, and UC, respectively. From HMP2 cohort, 21, 40, and 20 of HC, CD, and UC, respectively.

Metagenomic data processing and BSHs abundance analysis

The raw sequencing data from the publicly available IBD cohorts was quality controlled using Metawrap (version 1.3.2) with default parameters [35]. The clean reads from each sample were individually assembled to contiguous sequences (contigs) using Megahit (version 1.1.3). After assembly, open reading frames (ORFs) in the contigs were predicted with Prodigal (version 2.6.3). The non-redundant gene set was clustered by CD-HIT (version 4.8.1) at a 95% sequence identity threshold. The clean reads from each sample were compared to the non-redundant gene set by BWA (version 0.7.17). The relative abundance of each gene in the sample was calculated based on the number of reads mapped to each gene by BBtools [36]. The BSHs were identified by taking the referenced 778 BSHs as query sequences and using DIAMOND (version 0.9.36) BLASTP with an e-value = 1e^-5 and 40% sequence identity as the cutoff values. To ensure the accuracy and reliability of the statistical analysis, the putative BSHs present in less than 10% of all samples were excluded, and a pseudo-count of 1×10^–10 was added to avoid nonfinite values. The cumulative BSHs relative abundance was calculated by summing the relative abundance of all BSHs in each sample.

BSHs diversity characterization

Alpha-diversity metrics, including the Shannon index and observed BSHs calculated by vegan package (version 2.6-4) in R (version 4.3.0), were used to evaluate the diversity of BSHs in IBD patients and healthy controls. Beta-diversity was evaluated by calculating the Bray-Curtis distance using vegan package in R, to quantify the differences of BSHs among samples. Moreover, the permutational multivariate analysis of variance (PERMANOVA) was used to investigate the differences in BSHs composition between IBD patients and healthy controls by vegan package in R. All identified BSHs were aligned by their amino acid sequences with the multiple sequence alignment MUSCLE and a phylogenetic tree was built using the maximum likelihood method in the software MEGA (version 11.0). The phylogenetic tree was visualized and decorated by iTOL [37].

Statistical analysis

Data was expressed as means ± standard errors of the means (SEM) and boxplots show the median, interquartile ranges, minimum and maximum with individual data points visualized. For the murine experiments, statistical significance between groups for weight changes, colon lengths, spleen index, DAI, mRNA expression and microbiome diversity metrics among groups were analyzed by one-way ANOVA followed by Tukey’s post-hoc test. The statistical significance differences of the metabolome data were calculated using Kruskal-Wallis followed by Dunn’s post-hoc test. Statistically significant differences of BSHs between IBD patients and healthy control were determined by Wilcox rank-sum test followed by Benjamini-Hochberg (BH) correction. Beta-diversity of the microbiome, metabolome and BSHs data was quantified using principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity. PERMANOVA testing was used to determine if there were significant differences among the groups. All analyses were performed in R (version 4.3.0), and p values less than 0.05 (p < 0.05) was considered statically significant, as specified in the text and in the figure legends.

Baat ^−/− KO mice have heightened pathology compared to wild-type mice under DSS treatment

As an approach to investigate the roles of BA conjugation in gut inflammation, we employed a recently developed Baat^−/− KO mouse model that has highly reduced conjugated BAs, particularly reduced TCA (full characterization of Baat^−/− KO model available in [26, 38]). The animal experiment was carried out according to the procedure shown in Fig. 1a. Mice in the DSS groups were treated with 2.5% DSS for 7 days and were then humanely euthanized on day 7. Daily measurement of body weight showed that Baat^−/− KO mice lost more weight compared with WT animals during the course of the experiment (Fig. 1b). The DAI score (Fig. 1c) in the KO-DSS group was significantly higher after 7 days compared to the WT-DSS group (p < 0.05), indicating greater impact of DSS on Baat^−/− KO mice. Additionally, the measurement of colon lengths demonstrated that Baat^−/− KO group mice displayed significantly (p < 0.05) shorter colons compared to the WT-DSS group (Fig. 1d, e). There was no significant difference in histologic score, epithelial damage, nor mucosal depth in Baat^−/− KO mice compared with WT mice under DSS treatment (Fig. 1f, g). Taken together, the results indicated that Baat^−/− KO mice showed enhanced signs of DSS-induced colitis compared to the wild-type mice, without a notable difference in histopathology.

TCA supplementation rescues severe phenotypes of Baat^−/− KO mice under DSS-induced colitis

Because our previous study found that the Baat^−/− KO mice had 27-fold lower taurine-conjugated BAs than WT in their liver [26], we hypothesized that TCA plays a vital role in the heightened host response to DSS in these animals. To test this, we supplemented Baat^−/− KO mice with chow containing 0.3% TCA with and without DSS treatment (Fig. 2a). After 6 days of treatment with 2.5% DSS, mice in the DSS-TCA treatment group lost less weight compared to those only exposed to DSS (Fig. 2b). Moreover, TCA supplementation resulted in a more rapid recovery of body weight during the 7-day recovery period post-DSS treatment than for mice only exposed to DSS (p < 0.05, Fig. 2b). Additionally, TCA supplementation had a positive effect on the length of the mouse colon (Fig. 2c, d, p < 0.05) and reduced the impact of DSS treatment on the spleen index (spleen weight: body weight ratio) (Fig. 2e, p < 0.05). The DAI score on day 13 in the DSS-TCA group was also significantly lower compared to the DSS group (Fig. 2f, p < 0.05). While the overall histologic scores were not significantly different (Fig. 2g, h), the number of goblet cells counted by AB staining was higher in DSS-TCA group compared to the DSS group (p = 0.062), with visual detection of goblet cells and mucus staining returning in mice receiving TCA supplementation (Fig. 2i, j).

TCA treatment increased the expression of mucosal integrity and BA receptors markers in Baat^−/− KO mice on DSS

In light of the histological analysis indicating improved goblet cell function and mucus production we explored the gene expression changes related to cell barrier integrity and mucus physiology in the colons in DSS mice supplemented with TCA by RT-qPCR. The gene expression of MUC-2 was increased in the DSS-TCA group compared to the DSS group (Fig. 2k, p < 0.05), which is consistent with the AB-PAS goblet cell counts. Additionally, we found that the expression of intercellular tight junction genes, including Claudin-1, Occludin-1, and ZO-1 was higher in the TCA-supplemented groups compared to the non-supplemented groups, though only ZO-1 reached significance at an alpha-level below 0.05 (Fig. 2i and Figure S3a, b, Claudin-1 p = 0.09).

To gain further insight into the mechanism by which TCA affects intestinal inflammation, we also measured mRNA expression levels of pro-inflammatory factors and bile acid-related receptors (Figure S3c, d). Tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β) gene expressions were higher in DSS treated animals as expected, however, the DSS-TCA and DSS groups were not significantly different from each other (p > 0.05). Expression of BA receptors FXR and TGR5 were significantly increased in the TCA treatment groups compared to untreated groups (p < 0.05) and were significantly higher in the TCA group than in the TCA-DSS group (Fig. 2m, n, p < 0.05).

Microbiome changes from TCA treatment of Baat^−/− KO mice on DSS

To further investigate the effects of TCA supplementation on Baat^−/− KO mice under DSS-induced colitis, the cecum and feces were collected for 16S rRNA gene amplicon sequencing. Microbiome community changes among the treatment groups were similar in the cecum and fecal data (Figure S1), we therefore focused more detailed analysis on the cecum profiles. TCA treatment slightly decreased the microbiome alpha-diversity and species richness compared to untreated animals (p > 0.05) and DSS treatment decreased these measures further (p < 0.05, Fig. 3a). TCA supplementation during DSS treatment prevented the DSS-induced loss in microbiome diversity, making its measure more similar to the TCA only treated group (Fig. 3a, p < 0.05). The PCoA analysis results showed that the experimental groups had significantly different microbiome profiles from each other with the DSS and non-DSS groups being distinctly different. Interestingly, TCA supplementation during DSS treatment shifted the microbiome to become more similar to the non-DSS animals, indicating some recovery of the microbiome structure (Fig. 3b, as indicated by PERMANOVA testing p < 0.05). Analysis of taxa changes at the phylum level between DSS and DSS-TCA treatments showed a reduction in the relative abundance of Firmicutes_A and Bacteroidota, and an increase in the relative abundance of Proteobacteria in the DSS treatment (Figure S2a). At the genus level, the relative abundance of Akkermansia was increased in the DSS-TCA treatment compared to DSS treatment in both the cecum and feces, but this did not reach statistical significance (p = 0.28, Figure S2b). TCA supplementation significantly diminished the DSS-induced loss in the relative abundance of Lachnospiraceae family (p < 0.05, Fig. 3c), a group known to metabolize BAs and produce secondary BAs such as deoxycholate (DCA).

DSS and TCA treatment alter conjugated and secondary BA profiles in Baat^−/− KO mice

We used untargeted metabolomics to profile changes in the metabolome under TCA supplementation in the DSS Baat^−/− KO mouse experiment. Although TCA supplementation had no significant effect on overall metabolite structure of the liver or serum (Fig. 3d, e, as indicated by PERMANOVA analysis p > 0.05), it had a pronounced impact on cecum and fecal metabolomes (Fig. 3e, f, as indicated by PERMANOVA analysis p < 0.05). Interestingly, the beta diversity results of the metabolomes showed that the TCA supplemented groups were more similar to each other regardless of DSS treatment, though the effect of DSS was also strong (Fig. 3f, g).

As expected, supplementation of the control and DSS treated animal’s diet with TCA significantly increased its abundance in liver, serum, and cecum (p < 0.05), but not in the fecal samples (p > 0.05, Fig. 3h). Similar to reports in human IBD literature [8], the induction of gut inflammation by DSS alone increased TCA abundance in feces (Fig. 3h, p > 0.05). The other primary conjugated BA glycocholic acid (GCA), only detected in cecum and feces, was also higher in samples from the DSS treatment group compared to the combined DSS-TCA treatment or the non-DSS treatments (Fig. 3h, p < 0.05). Accordingly, the abundance of CA was markedly increased by TCA treatment in the liver, serum, cecum, and feces of both control and DSS treated animals (Fig. 3h, p < 0.05). TCA treatment also increased the abundance of secondary BAs produced by the microbiome including DCA in serum, cecum, and feces (Fig. 3h) and the oxidized secondary BAs (oxoCAs) in cecum and feces (Fig. 3h, p < 0.05). This indicated that TCA was being hydrolyzed by the intestinal microbiome in the colon to generate CA, DCA and oxoCAs, though other aspects of BA metabolism and signaling may contribute to increased CA. This finding contrasted that from the human IBD literature, where there is abundant TCA in those with IBD but secondary BAs, such as DCA, are not increased [8].

Due to the growing diversity of BA conjugation biochemistry from the host and its microbiome [18, 26], we investigated differences in the abundances of some of the novel conjugated BAs in response to DSS and TCA treatments. The abundance of the host produced CA-methylcysteamine (CA-MCY), CA-methylcysteamine sulfoxide (CA-MCYO) and CA-methylcysteamine sulfodioxide (CA-MCYO₂) was generally increased in the inflamed gut of mice on DSS, with statistical significance found in the fecal samples (HC group compared to DSS group p < 0.05, Fig. 3h). TCA supplementation subsequently reduced the levels of these unique cysteamine-conjugated BAs particularly CA-MCYO and CA-MCYO₂ in the feces (p < 0.05, Fig. 3h). MCBAs, including CysCA, GluCA and LysCA, were also significantly higher in the feces of mice on DSS treatment (p < 0.05, Fig. 3h), another trend matching that observed in people with IBD [7, 20]. TCA decreased MCBAs under DSS-induced colitis, though this did not reach statistical significance between the DSS and DSS-TCA groups.

Highly variable hydrolytic activity of gut bacteria on conjugated BAs in vitro and in vivo

Collectively, our multi-omics profiling of Baat^−/− KO mice pointed to the importance of diverse conjugated BAs in promoting health in the inflamed mammalian gut; however, there was also evidence for extensive hydrolysis of the supplemented TCA and production of various secondary BAs by the microbiome, which could compromise the beneficial effects. Therefore, to better understand the relationships between altered conjugated and secondary BA profiles and microbiome structure in a model of GI inflammation, we explored the BSH hydrolysis activity among diverse gut bacteria on diverse conjugated BAs. A total of 17 gut bacterial strains belonging to 9 different genera (strain list in supplementary Table S2) and a fresh community of gut microbes from mouse fecal samples were evaluated for their ability to hydrolyze TCA, GCA and varied MCBAs in vitro. Genome sequence searching determined that 14 of these strains contained an annotated bsh gene and 3 (Clostridium hylemonae, Clostridium scindens and Peptostreptococcus anaerobius) did not. The strains and fecal community were separately cultured for 48 h with each of the 10 conjugated BAs at 1 mM concentration then analyzed for hydrolysis by UPLC-MS/MS. We observed an overall low level of conjugate hydrolysis, which expectedly, included those strains without a known bsh gene (Fig. 4a). However, C. scindens was able to hydrolyze diverse conjugated BAs (particularly those with small amino acid conjugates GCA, SerCA and AlaCA), even though it does not have a known bsh enzyme in its genome. This is similar to its MCBA production activity as reported in [18]. Among bacteria containing the bsh gene, we observed variable levels of hydrolysis that depended on the amino acid conjugate. Enterococcus faecalis, Lactiplantibacillus plantarum, Clostridium perfringens and Enterocloster bolteae showed robust hydrolysis of the conjugates, but with some limition in their ability to hydrolyze GluCA and ThrCA. Bacteroides fragilis exhibited a strong propensity for the host-produced TCA, GCA, with some ability to hydrolyze the MCBAs SerCA and AlaCA. In addition, in vitro culture of a mouse fecal community also showed strong hydrolysis of TCA, SerCA, TyrCA, and AlaCA, but GluCA, ThrCA, and LeuCA were more recalcitrant (Figure S4). Ruminococcus gnavus (newly Mediterraneibacter gnavus) and Enterocloster clostridioformis, which were enriched in CD patients (see below), exhibited a weak ability to hydrolyze conjugated BAs. Overall, these in vitro experiments showed that that TCA, GCA, SerCA and AlaCA were more prone to hydrolysis whereas GluCA, and ThrCA were more resistant. These varied hydrolytic capacities of different gut bacteria may explain both observations in our murine experiments, that linked an altered gut microbiome and secondary BA production in the inflamed murine gut, and evidence in the literature, showing altered conjugated BA profiles from the host and its microbiome in patients with IBD [7].

Given that GluCA was more resistant to hydrolysis than TCA by bacteria in vitro, as well as its markedly enriched in CD patients [7], we further compared the differences in the effects of TCA and GluCA supplementation on BA hydrolysis in DSS induced colitis model. Wild type C57BL/6J mice in DSS-TCA and DSS-GluCA groups mice were fed peanut butter pellets infused with 50 mg/kg of TCA or GluCA every day for 17 days. On day 10, mice in the DSS groups were treated with 2.5% DSS for 7 days and then all animals were humanely euthanized on day 17 (Fig. 4b). In this C57BL/6J wild type background, there was no significant difference among the DSS, DSS-TCA and DSS-GluCA groups in terms of disease signs including weight loss, colon length or spleen index (Figure S5a-c, p > 0.05). Metabolomics analysis showed that TCA significantly increased the abundance of CA and other secondary BAs (oxo-BAs), but GluCA treatment did not (Fig. 4c-d, p < 0.05). DCA showed a similar trend, but this did not reach statistical significance. This suggests that TCA was more readily hydrolyzed by the gut microbiome to produce secondary BAs than GluCA treatment, mirroring the effects shown from bacterial cultures in vitro.

BSHs characterization in IBD

Our in vivo and in vitro data show that conjugate BAs, particularly TCA, are important for reducing pathology in the inflamed murine gut, but the abundance of both conjugate BAs and secondary BAs are dictated by the activity of BSH enzymes from the gut microbiome, which have highly varied activity depending on the gene sequence and BA conjugate encountered. Therefore, we mined the bsh gene sequences and abundance from 774 samples of 6 publicly available metagenomics datasets (Table S3) for comparison among disease types. Subjects with either Crohn’s or ulcerative colitis had lower Shannon diversity indices and observed sequences of BSHs than healthy individuals (Fig. 5a, b, p < 0.05). The alpha-diversity of BSHs was significantly lower in CD and UC patients than in healthy controls (Figure. 5a, b), but there was some variation depending on the cohort analyzed (Figure S6a, b). Beta-diversity of BSH profiles was also different between IBD patients and healthy controls (as indicated by PERMANOVA analysis of the collective cohorts, p < 0.05), with some variation within cohorts (Figure S7). Furthermore, the healthy controls exhibited the highest cumulative relative abundance of BSHs, while the CD patients exhibited the lowest (Fig. 5d; Figure S8). Our results suggest reduced BSHs diversity and abundance in IBD patients, especially those with CD, and the collective sequence space of BSH genes in the microbiome are unique among IBD subtypes.

A BSH phylogenetic tree was built using the amino sequences of the 244 identified BSHs (Figure S9) analyzed for enrichment in particular bacterial BSH sequences. The BSHs were assigned to 76 genera from 4 phyla, including Actinomycetota, Bacillota, Euryarchaeota, and Pseudomonadota (Fig. 5e). The majority of BSHs belonged to the Lachnospiraceae, Oscillospiraceae, and Clostridiaceae families (Fig. 5f), and the Blautia, Clostridium, Ruminococcus, Gemmiger, Roseburia, and Eubacterium genera (Fig. 5g). The abundance changes of BSHs in patients with CD (n = 207) and UC (n = 186) compared to healthy controls (n = 381) were tested via Wilcox rank-sum test followed by BH correction. The results showed that there were 218 and 159 BSHs significantly (p < 0.05) changed in CD and UC patients, respectively, compared to healthy controls. Among these, the proportion of BSHs with increased relative abundance in patients with CD and UC was 14.68% (32/218) and 29.56% (47/159), respectively. This reflected that the relative abundance of BSHs was mostly decreased in the patients with IBD. The BSHs from Lachnospiraceae were significantly decreased in patients with CD compared to healthy controls (Fig. 5h, p < 0.05). Additionally, the BSHs from unclassified Clostridium, unclassified Bacillota and Boudabousia were significantly decreased in the CD patients compared to the healthy controls across the 6 IBD cohorts (p < 0.05). Notably, the relative abundance of the BSH gene of an unknown Ruminococcus spp. was significantly reduced in CD patients (Fig. 5i), however, Ruminococcus gnavus BSH (found in all cohorts except for HMP and PRJNA42990) was significantly increased (Fig. 5j, p < 0.05). The gene from the Lachnospiraceae member Enterocloster clostridioformis was significantly also increased in LLdeep, PRISM and PRJEB1220 cohorts compared to the compared to healthy controls (Fig. 5k, p < 0.05). Collectively, this analysis of data from subjects with IBD shows the varied BSH sequence space between diseased and healthy subjects maybe responsible for the altered conjugated BA profiles which have been reported in the IBD literature [8, 23, 24].

BAs are hepatically synthesized cholesterol derivatives with profound effects on systemic metabolism, such as lipid digestion, vitamin absorption and immune regulation [39]. Recent research has shown that BA chemical diversity was greatly underappreciated, particularly that of conjugated BAs [7, 20, 40]. Thus, our understanding of the role of BAs in GI disease is incomplete, making it challenging to establish clear causation between altered BA profiles and a diseased state, which is a characteristic of virtually all GI disorders under study [7, 20, 41]. For example, clinical studies of IBD have shown a dysregulation of BAs metabolism, with a consistently observed increase in primary conjugated BAs, including TCA and GCA, and a decrease in secondary BAs, such as DCA [8, 23]. However, the causal relationships between BA dysregulation and IBD, the role of the ever-expanding diversity of these molecules, and the mechanisms by which they impact disease are poorly understood.

To begin to fill this knowledge gap, we employed the novel Baat^−/− KO mouse model that presents with highly reduced primary BAs to assess their effects on DSS-induced colitis. Our first experiments showed that Baat^−/− KO mice exhibited more severe colitis signs compared to WT mice under 2.5% DSS induced colitis, with shorter colon length and more weight loss, indicating the host-conjugated TCA plays an important role in this inflammatory condition. To verify this, we fed Baat^−/− KO mice TCA supplemented food which largely decreased signs of colitis from developing. Though further work on cytokine profiling and immune cell responses are needed in this KO mouse model, there was preliminary evidence that this improvement was due to the preservation of intestinal epithelial integrity, not necessarily a reduction in inflammatory signaling. Colitis-induced damage to the epithelial barrier was limited by TCA supplementation, as seen by the thicker mucus layer of the colon compared to mice only subjected to DSS, higher staining of mucus filled goblet cells, maintenance of mucin expression levels, and rescued expression of tight junction markers, particularly ZO-1. The mucus layer of the intestines plays a crucial role in maintaining gut health and protecting the epithelium from harmful factors [42, 43]. The MUC-2 glycoprotein, which is synthesized in goblet cells and a major component of the gut mucus layer [44], is altered in IBD, with UC patients excreting less during the active phase [42]. Similarly, tight junctions genes, which play a central role in epithelial barrier regulation [45], are also down-regulated in IBD patients [46]. The pathological and multi-omics analysis of Baat^−/− KO mice in the DSS model demonstrates that conjugated BAs are important for reducing pathology from inflammation and the likely mechanism is through their maintenance of the mucus barrier.

Several previous studies have demonstrated that intestinal microbiota plays a critical role in IBD where there is a dysbiosis and overall loss microbial diversity compared to healthy individuals, particularly during flares of disease symptoms [8]. Our study in mice replicated this microbial diversity loss, which was then rescued by TCA supplementation. Furthermore, we found a higher relative abundance of Lachnospiraceae in response to TCA supplementation, irrespective of DSS treatment. The bacteria belonging to the Lachnospiraceae family, whose abundance is also reduced in patient with IBD [47], are known to manipulate BAs including the 7α-dehydroxylating bacteria, which are responsible for converting primary BAs into secondary BAs [48]. This is an intriguing finding, as it indicates that this conjugated BA may facilitate microbial niche diversification and expansion of microbes manipulating these compounds. The mechanism of TCA induced microbial diversity in these experiments remains unknown but could be due to antimicrobial properties of BAs reshaping microbial interactions or simply a manifestation of the improved GI epithelial barrier health. Due to the varied antimicrobial activity of these molecules [18], the effect of conjugated BAs on gut microbial diversity in an inflamed environment is an important area for further research.

One of the more interesting findings in this study was the increase in conjugated BAs of diverse sources in tandem with the increase in secondary BAs. It has long been established that patients with IBD have elevated liver-conjugated BAs (TCA and GCA) in their gut and recent evidence suggests microbial conjugates are also increased [7, 8, 20]. Accordingly, diverse conjugated BAs, including MCBAs and those made by a yet to be determined host mechanism (MCYs), were also elevated in the inflamed animals in this study. This demonstrates that GI inflammation in humans and mice selects for the production of diverse forms of conjugated BAs beyond just TCA and GCA. However, the IBD literature shows that this is at the expense of lowered secondary BAs, likely due to reduced BSH activity from the microbiome [22]. In part to better understand this discrepancy, we explored the hydrolytic capacity of BSH enzymes from diverse gut bacteria exposed to chemically synthesized MCBAs compared to TCA and GCA. We found generally lower hydrolytic capability than might be expected, even when including bacteria with a bsh gene annotated in their genome. Furthermore, acyl-conjugate chemical diversity altered the hydrolytic efficiency, demonstrating BSH substrate specificity at the acyl-site also varies. Enterococcus faecalis, Lactiplantibacillus plantarum, Clostridium perfringens and Enterocloster bolteae showed robust hydrolysis of the conjugated BAs, and our previous study found that these bacteria were also robust MCBAs producers [18]. Surprisingly, C. scindens, without a clear bsh gene, could hydrolyze GCA, SerCA and AlaCA, which indicates there may be other enzymes capable of performing hydrolase functions on conjugated BAs yet to be identified. We also found that TCA, GCA, SerCA and AlaCA were more readily hydrolyzed by bacteria than other conjugated BAs. Foley et al. found that the deconjugation of AlaCA and SerCA from Lactobacillaceae BSH was robust, matching that of GCA and TCA [49]. They suggested that because alanine and serine are small amino acids, akin to glycine, that steric constraints may limit deconjugation of larger ones [49]. GluCA and ThrCA were less susceptible to hydrolysis by the bacteria tested here, implicating side chain hydrophilicity may also limit hydrolysis. These in vitro results translated in vivo, where mice self-administered peanut butter pellets infused with TCA and GluCA under DSS-induced colitis had varied production of secondary BAs, with GluCA limiting their production. Further work on the hydrolytic susceptibility of the diverse BAs now found in humans [7] and their implications for GI health are needed, especially considering this study shows that conjugated BAs can alter outcomes of GI inflammation.

The findings here that TCA improves pathology in DSS induced colitis, but its presence in the gut can be dictated by a balance between host conjugation and the varied hydrolytic capacities of the gut microbiota, led us to investigate the BSH sequence space in metagenomic data from patients with IBD. It was found that BSH diversity and abundance is significantly decreased in patients with IBD compared to the healthy controls, particularly those from the Lachnospiraceae; however, BSHs from Ruminococcus gnavus and Enterocloster clostridioformis were significantly increased. Comparing this finding to our hydrolytic measurements and MCBA conjugation from previous studies [18] revealed these two bacteria were robust producers of MCBAs but had limited hydrolysis of conjugated BAs. This in vivo to in vitro comparison demonstrates the importance of understanding the hydrolytic capacity of diverse conjugated BAs and the role of a dysbiotic microbiome in shaping the BA profile in an inflamed human gut. It may also help explain why patients with IBD have high amounts of host-conjugated BAs and MCBAs, which is somewhat of an enzymatic paradox, because the former are eliminated and the later produced by activity of the same BSH enzyme [18, 19]. Collectively, our study points to an intricate balance between BA conjugation by the host, deconjugation and reconjugation by the microbiota and a healthy mucus barrier in IBD. The implications of which may drive disease outcomes creating the potential to manipulate the conjugated BA profile and gut bacteria that shape it to benefit patients with this chronic disease.

As the understanding of conjugated BA diversity grows, including the fact that some are produced by the same enzyme that hydrolyzes them, it is becoming clear that we have an overall poor understanding of the mechanisms behind their production in the human gut, which can involve host and microbial enzymes performing molecular crosstalk with BA receptors in the epithelium [16]. The need to parse out these mechanisms is heightened by the principal finding of this study, that at least one prevalent conjugated BA is important for improving pathology in an inflamed GI tract. It is possible that other forms of conjugated BAs, including the MCYs or MCBAs, may have even stronger effects, but the novelty and lack of commercial availability of these molecules limits current ability to experiment. TCA, GCA and the diverse other forms of conjugated BAs could be promising therapeutics for subjects with gut inflammation, particularly those with lower levels of these molecules natively, which can be readily assayed by mass spectrometry. To translate new knowledge of these molecules to medical use for any disease state, the balance between their conjugation/deconjugation in the gut must be better understood.

Acknowledgements

The authors would like to thank MSU mass spectrometry and metabolomics core for their help.

Funding

This work was funded by the Biochemistry and Molecular Biology Department Team-Up Grant and the Tetrad Grant Program of Michigan State University and the National Institutes of Diabetes and Digestive Diseases grant #1R01DK140854 awarded to PI Quinn.

Disclosure statement

The authors declare that there is no conflict of interest.

Author contributions

YF performed the experiments, collected the data, data analysis, and wrote the paper; SLR, KH, CTG, MO, DVG, CM, CB and VEW reviewed & edited the paper; VEW performed the H&E analysis; RAQ conceived the designed, analysis, and revised the paper.

Data Availability

Mass spectrometry data are publicly available within the MassIVE database (gnps.ucsd.edu) under MassIVE ID MSV000095705. 16S rRNA gene amplicon data were deposited in the NCBI under project number PRJNA1152983.

Flynn S, Eisenstein S. Inflammatory Bowel Disease Presentation and Diagnosis. Surgical Clinics of North America 2019;99:1051–1062.
Lewis JD, Parlett LE, Jonsson Funk ML, Brensinger C, Pate V, et al. Incidence, Prevalence, and Racial and Ethnic Distribution of Inflammatory Bowel Disease in the United States. Gastroenterology 2023;165:1197-1205.e2.
Burisch J, Zhao M, Odes S, De Cruz P, Vermeire S, et al. The cost of inflammatory bowel disease in high-income settings: a Lancet Gastroenterology & Hepatology Commission. The Lancet Gastroenterology & Hepatology 2023;S2468125323000031.
Caruso R, Lo BC, Núñez G. Host–microbiota interactions in inflammatory bowel disease. Nat Rev Immunol. Epub ahead of print 31 January 2020. DOI: 10.1038/s41577-019-0268-7.
Neurath MF. Host–microbiota interactions in inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. Epub ahead of print 17 December 2019. DOI: 10.1038/s41575-019-0248-1.
Li M, Yang L, Mu C, Sun Y, Gu Y, et al. Gut microbial metabolome in inflammatory bowel disease: From association to therapeutic perspectives. Computational and Structural Biotechnology Journal 2022;20:2402–2414.
Gentry EC, Collins SL, Panitchpakdi M, Belda-Ferre P, Stewart AK, et al. Reverse metabolomics for the discovery of chemical structures from humans. Nature 2024;626:419–426.
IBDMDB Investigators, Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 2019;569:655–662.
Franzosa EA, Sirota-Madi A, Avila-Pacheco J, Fornelos N, Haiser HJ, et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol 2019;4:293–305.
Hsu CL, Schnabl B. The gut–liver axis and gut microbiota in health and liver disease. Nat Rev Microbiol. Epub ahead of print 14 June 2023. DOI: 10.1038/s41579-023-00904-3.
Guzior DV, Quinn RA. Review: microbial transformations of human bile acids. Microbiome 2021;9:140.
Collins SL, Stine JG, Bisanz JE, Okafor CD, Patterson AD. Bile acids and the gut microbiota: metabolic interactions and impacts on disease. Nat Rev Microbiol. Epub ahead of print 17 October 2022. DOI: 10.1038/s41579-022-00805-x.
Ridlon JM, Gaskins HR. Another renaissance for bile acid gastrointestinal microbiology. Nat Rev Gastroenterol Hepatol. Epub ahead of print 21 February 2024. DOI: 10.1038/s41575-024-00896-2.
Fleishman JS, Kumar S. Bile acid metabolism and signaling in health and disease: molecular mechanisms and therapeutic targets. Sig Transduct Target Ther 2024;9:97.
Song Z, Cai Y, Lao X, Wang X, Lin X, et al. Taxonomic profiling and populational patterns of bacterial bile salt hydrolase (BSH) genes based on worldwide human gut microbiome. Microbiome 2019;7:9.
Mohanty I, Allaband C, Mannochio-Russo H, El Abiead Y, Hagey LR, et al. The changing metabolic landscape of bile acids – keys to metabolism and immune regulation. Nat Rev Gastroenterol Hepatol. Epub ahead of print 4 April 2024. DOI: 10.1038/s41575-024-00914-3.
Yao L, Seaton SC, Ndousse-Fetter S, Adhikari AA, DiBenedetto N, et al. A selective gut bacterial bile salt hydrolase alters host metabolism. eLife 2018;7:e37182.
Guzior DV, Okros M, Shivel M, Armwald B, Bridges C, et al. Bile salt hydrolase acyltransferase activity expands bile acid diversity. Nature. Epub ahead of print 7 February 2024. DOI: 10.1038/s41586-024-07017-8.
Rimal B, Collins SL, Tanes CE, Rocha ER, Granda MA, et al. Bile salt hydrolase catalyses formation of amine-conjugated bile acids. Nature. Epub ahead of print 7 February 2024. DOI: 10.1038/s41586-023-06990-w.
Quinn RA, Melnik AV, Vrbanac A, Fu T, Patras KA, et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 2020;579:123–129.
Pan Y, Zhang H, Li M, He T, Guo S, et al. Novel approaches in IBD therapy: targeting the gut microbiota-bile acid axis. Gut Microbes 2024;16:2356284.
Khodakivskyi PV, Lauber CL, Yevtodiyenko A, Bazhin AA, Bruce S, et al. Noninvasive imaging and quantification of bile salt hydrolase activity: From bacteria to humans. Sci Adv 2021;7:eaaz9857.
Sinha SR, Haileselassie Y, Nguyen LP, Tropini C, Wang M, et al. Dysbiosis-Induced Secondary Bile Acid Deficiency Promotes Intestinal Inflammation. Cell Host & Microbe 2020;27:659-670.e5.
Yang Z-H, Liu F, Zhu X-R, Suo F-Y, Jia Z, et al. Altered profiles of fecal bile acids correlate with gut microbiota and inflammatory responses in patients with ulcerative colitis. WJG 2021;27:3609–3629.
Zapata RC, Zhang D, Chaudry B, Osborn O. Self-Administration of Drugs in Mouse Models of Feeding and Obesity. JoVE 2021;62775.
Neugebauer KA, Okros M, Guzior DV, Feiner J, Chargo NJ, et al. Baat Gene Knockout Alters Post-Natal Development, the Gut Microbiome, and Reveals Unusual Bile Acids in Mice. Journal of Lipid Research 2022;63:100297.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37:852–857.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 2016;13:581–583.
McDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, et al. Greengenes2 enables a shared data universe for microbiome studies. Epub ahead of print 20 December 2022. DOI: 10.1101/2022.12.19.520774.
Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 2016;34:828–837.
He Q, Gao Y, Jie Z, Yu X, Laursen JM, et al. Two distinct metacommunities characterize the gut microbiota in Crohn’s disease patients. GigaScience;6. Epub ahead of print 1 July 2017. DOI: 10.1093/gigascience/gix050.
Weng YJ, Gan HY, Li X, Huang Y, Li ZC, et al. Correlation of diet, microbiota and metabolite networks in inflammatory bowel disease. J of Digest Diseases 2019;20:447–459.
MetaHIT Consortium, Nielsen HB, Almeida M, Juncker AS, Rasmussen S, et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol 2014;32:822–828.
Schirmer M, Franzosa EA, Lloyd-Price J, McIver LJ, Schwager R, et al. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome. Nat Microbiol 2018;3:337–346.
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018;6:158.
Bushnell B, Rood J, Singer E. BBMerge – Accurate paired shotgun read merging via overlap. PLoS ONE 2017;12:e0185056.
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Research 2021;49:W293–W296.
Alrehaili BD, Lee M, Takahashi S, Novak R, Rimal B, et al. Bile acid conjugation deficiency causes hypercholanemia, hyperphagia, islet dysfunction, and gut dysbiosis in mice. Hepatology Communications 2022;hep4.2041.
Fogelson KA, Dorrestein PC, Zarrinpar A, Knight R. The Gut Microbial Bile Acid Modulation and Its Relevance to Digestive Health and Diseases. Gastroenterology 2023;S0016508523001610.
Mohanty I, Mannochio-Russo H, Schweer JV, El Abiead Y, Bittremieux W, et al. The underappreciated diversity of bile acid modifications. Cell 2024;187:1801-1818.e20.
Mohanty I, Mannochio-Russo H, Schweer JV, El Abiead Y, Bittremieux W, et al. The underappreciated diversity of bile acid modifications. Cell 2024;187:1801-1818.e20.
Kang Y, Park H, Choe B-H, Kang B. The Role and Function of Mucins and Its Relationship to Inflammatory Bowel Disease. Front Med 2022;9:848344.
Stange EF, Schroeder BO. Microbiota and mucosal defense in IBD: an update. Expert Review of Gastroenterology & Hepatology 2019;13:963–976.
Luis AS, Hansson GC. Intestinal mucus and their glycans: A habitat for thriving microbiota. Cell Host & Microbe 2023;31:1087–1100.
Kuo W-T, Zuo L, Odenwald MA, Madha S, Singh G, et al. The Tight Junction Protein ZO-1 Is Dispensable for Barrier Function but Critical for Effective Mucosal Repair. Gastroenterology 2021;161:1924–1939.
Keita ÅV, Lindqvist CM, Öst Å, Magana CDL, Schoultz I, et al. Gut Barrier Dysfunction—A Primary Defect in Twins with Crohn’s Disease Predominantly Caused by Genetic Predisposition. Journal of Crohn’s and Colitis 2018;12:1200–1209.
Berry D, Reinisch W. Intestinal microbiota: A source of novel biomarkers in inflammatory bowel diseases? Best Practice & Research Clinical Gastroenterology 2013;27:47–58.
Hu J, Wang C, Huang X, Yi S, Pan S, et al. Gut microbiota-mediated secondary bile acids regulate dendritic cells to attenuate autoimmune uveitis through TGR5 signaling. Cell Reports 2021;36:109726.
Foley MH, Walker ME, Stewart AK, O’Flaherty S, Gentry EC, et al. Bile salt hydrolases shape the bile acid landscape and restrict Clostridioides difficile growth in the murine gut. Nat Microbiol. Epub ahead of print 13 March 2023. DOI: 10.1038/s41564-023-01337-7.

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Balance between bile acid conjugation and hydrolysis activity can alter outcomes of gut inflammation

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Abstract

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Introduction

Materials and methods

Animals

Histological assessment of colon inflammation

RNA extraction and quantitative real-time PCR

Microbial 16S rRNA gene sequencing

Liver, serum, cecum and fecal metabolite extraction

LC-MS/MS assay

Bacterial and mouse fecal community culturing exposed to diverse BAs

IBD metagenomic sequence data collection

Metagenomic data processing and BSHs abundance analysis

BSHs diversity characterization

Statistical analysis

Results

BSHs characterization in IBD

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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