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Gut microbial β-glucuronidases regulate host luminal proteases and are depleted in irritable bowel syndrome

Nature Microbiology volume 7, pages 680–694 (2022)Cite this article

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Abstract

Intestinal proteases mediate digestion and immune signalling, while increased gut proteolytic activity disrupts the intestinal barrier and generates visceral hypersensitivity, which is common in irritable bowel syndrome (IBS). However, the mechanisms controlling protease function are unclear. Here we show that members of the gut microbiota suppress intestinal proteolytic activity through production of unconjugated bilirubin. This occurs via microbial β-glucuronidase-mediated conversion of bilirubin conjugates. Metagenomic analysis of faecal samples from patients with post-infection IBS (n = 52) revealed an altered gut microbiota composition, in particular a reduction in Alistipes taxa, and high gut proteolytic activity driven by specific host serine proteases compared with controls. Germ-free mice showed 10-fold higher proteolytic activity compared with conventional mice. Colonization with microbiota samples from high proteolytic activity IBS patients failed to suppress proteolytic activity in germ-free mice, but suppression of proteolytic activity was achieved with colonization using microbiota from healthy donors. High proteolytic activity mice had higher intestinal permeability, a higher relative abundance of Bacteroides and a reduction in Alistipes taxa compared with low proteolytic activity mice. High proteolytic activity IBS patients had lower fecal β-glucuronidase activity and end-products of bilirubin deconjugation. Mice treated with unconjugated bilirubin and β-glucuronidase-overexpressing E. coli significantly reduced proteolytic activity, while inhibitors of microbial β-glucuronidases increased proteolytic activity. Together, these data define a disease-relevant mechanism of host–microbial interaction that maintains protease homoeostasis in the gut.

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Fig. 1: High PA following C. jejuni infection is characterized by reduced microbiota diversity and taxa loss.
Fig. 2: Faecal and tissue proteomics demonstrate that serine proteases of human pancreatic origin drive high PA in PI-IBS.
Fig. 3: Gnotobiotic mice demonstrate that healthy commensal microbiota suppresses host-derived PA while high-PA PI-IBS microbiota does not.
Fig. 4: Microbial diversity and composition in humanized mice identify specific microbial taxa predictive of PA status and metabolic pathway differences.
Fig. 5: Faecal microbiome transfer of low-PA microbial communities lowers PA of high-PA humanized mice in an Alistipes-dependent manner.
Fig. 6: Microbiota mediates PA suppression through microbial GUS enzymatic activity and production of unconjugated bilirubin.

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Data availability

Raw data for human and mouse metagenomics are publicly available via sequence read archive (SRA) under the BioProject accession IDs PRJNA705217 and PRJNA705695, respectively. Human RNA-seq data are deposited in the Gene Expression Omnibus (GEO) under accession number GSE168759. Faecal metaproteomic data are available at PRIDE PXD025127 and descriptors are provided in Supplementary File 1. Human colonic proteomics dataset is available as Fig. 2 source data. Human and mice metabolomic raw reads are available at Metabolomics Workbench via ST002094 and ST002090, respectively (https://www.metabolomicsworkbench.org/). Source data are provided with this paper.

Code availability

Proteomics codes are available at https://github.com/galaxyproteomics/metaquantome, https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/unipept and https://bioconductor.org/packages/release/bioc/html/PECA.html. Microbiome codes are available at https://github.com/chloelulu/PIIBSpaper.

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Acknowledgements

We thank L. Anderson and K. Zodrow for administrative support; the Center for Mass Spectrometry and Proteomics at the University of Minnesota for resources used to generate and analyse faecal metaproteomics data. Funds for data collection, analysis and personnel support were provided to M.G. by: the National Institutes of Health (DK 103911, DK 120745, DK 127998); the Mayo Clinic Research Pipeline K2R Program Award; the Pilot & Feasibility Award from Mayo Clinic Cell Signalling in Gastroenterology via P30DK084567 through the National Institutes of Health.

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Authors and Affiliations

  1. Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA

    Adam L. Edwinson, Stephanie Peters, Nikita Hanning, Margaret Breen-Lyles, Lakshmikanth Chikkamenahalli, Purna Kashyap, Gianrico Farrugia & Madhusudan Grover

  2. Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA

    Lu Yang, Asha Nair, Surendra Dasari & Jun Chen

  3. Laboratory of Experimental Medicine and Pediatrics and Infla-Med, research center of excellence, University of Antwerp, Antwerp, Belgium

    Nikita Hanning & Benedicte De Winter

  4. Department of Surgery, Mayo Clinic, Rochester, MN, USA

    Patricio Jeraldo

  5. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA

    Pratik Jagtap, Tzu-Yi Yang, Praveen Kumar, Subina Mehta & Timothy Griffin

  6. Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA

    Joshua B. Simpson & Matthew R. Redinbo

  7. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    Rondell P. Graham

  8. Division of Gastroenterology and Hepatology, Antwerp University Hospital, Edegem, Belgium

    Benedicte De Winter

  9. Division of Clinical Microbiology, Mayo Clinic, Rochester, MN, USA

    Robin Patel

  10. Departments of Biochemistry and Biophysics, and Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA

    Matthew R. Redinbo

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  1. Adam L. Edwinson
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Contributions

A.L.E., L.Y., S.P., N.H. S.D., P. Kashyap, T.G., J.C, M.R.R. and M.G. conceived and designed the experiments. A.L.E., L.Y., S.P., N.H., P. Jeraldo, P. Jagtap, T.-Y.Y., P. Kumar, S.M., A.N., L.C., R.P.G., T.G., J.C., S.D. and M.G. performed the experiments. A.L.E., L.Y., P. Jeraldo, P. Jagtap, P. Kumar, S.M., A.N., S.D., T.G., J.C. and M.G. analysed the data. L.Y., P. Jeraldo, P. Jagtap, P. Kumar, S.M., A.N., S.D., T.G., J.C., P. Kashyap, G.F., M.R.R. and M.G. contributed materials/analysis tools. A.L.E., L.Y., S.P., N.H., P. Jeraldo, P. Jagtap, J.B.S., T.-Y.Y., P. Kumar, S.M., A.N., M.B.-L., R.P.G., B.D.W., R.P., S.D., P. Kashyap, T.G., J.C., G.F., M.R.R. and M.G. wrote the paper.

Corresponding author

Correspondence to Madhusudan Grover.

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Competing interests

M.G. has disclosure to Mayo Clinic Ventures on microbes described in this manuscript. The other authors declare no competing interests.

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Nature Microbiology thanks Nathalie Vergnolle, James Versalovic and Michael Zimmermann for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Taxa differentially abundant between low and high PA status and correlation with fecal PA.

a, The microbiota of high and low PA PI-IBS patients (n = 12 high PA PI-IBS, 17 low PA PI-IBS) were compared at the phyla, class, and family levels. At both phyla and class levels of taxonomy, no significant differences were observed between the two groups of patients. However, one taxon, Rikenellaceae, was differential at the family level (FDR < 0.05). Rikenellaceae, the family of A. putredinis and was in higher abundance in low PA PI-IBS subjects with an average abundance of 5.3%, compared to 1.1% in high PA PI-IBS individuals. b, Correlation scatterplots for differentially abundant taxa negatively correlating with PA. In addition to the top 3, an additional 8 differentially abundant (11/14) taxa correlated with low fecal PA. They exhibited weak negative correlation (0.31 ≤ |r | ≤ 0.38). High PA (orange) and healthy volunteers (green) are plotted. Correlation coefficients (r) and q-values are generated from comparisons within the entire cohort to assess the relationship between PA and taxa abundance (n = 21 HV, 12 high PA PI-IBS, 17 low PA PI-IBS). Grey area shows 95% confidence level for linear smooths. c, Correlation scatterplots for differentially abundant taxa that positively correlate with PA. Of the 14 differential taxa, only 3 positively correlated with high fecal PA. Clostridium clostridioforme, Pseudomonas and Ruminococcus gnavus all had a weak, positive correlation (0.32 ≤ |r | ≤ 0.35). Plots were generated as stated above. Grey area shows 95% confidence level for linear smooths.

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Extended Data Fig. 2 Specific serine protease activity assays in both high PA human and high PA humanized mouse fecal supernatants.

a, Specific serine protease activity of human fecal samples. Using an enzyme preferential substrate assay, fecal trypsin (*p = 0.049, **p = 0.004), chymotrypsin (**p = 0.004) and pancreatic elastase (*p = 0.03, **p = 0.007) activities were increased in fecal supernatants generated from high PA individuals compared to both low PA PI-IBS and healthy volunteers (One-way ANOVA, multiple comparisons Kruskal-Wallis, n = 6 FSNs/group). b, Specific activity of serine proteases in humanized mice and germ-free mice. Mice humanized with high PA microbiota had increased chymotrypsin (*p = 0.049, **p = 0.004), pancreatic elastase (healthy **p = 0.008, low PA PI-IBS **p = 0.002) and neutrophil elastase activity (*p = 0.01, **p = 0.002) in fecal supernatants compared to either healthy volunteer or low PA PI-IBS humanized mice. Decreased trypsin (healthy **p = 0.008, low PA PI-IBS **p = 0.002), chymotrypsin (**p = 0.002), pancreatic elastase (**p = 0.002) and neutrophil elastase (*p = 0.02, **p = 0.004) activity were seen in healthy and low PA PI-IBS humanized mice compared to the germ-free (GF) state. (One-way ANOVA, multiple comparisons Kruskal-Wallis, n = 3 mice tested/humanization, 6 humanizations/phenotype, n = 6 germ-free mice). Boxplots: lower, middle and upper hinges correspond to 25th, 50th and 75th percentiles. Upper and lower whiskers extend to the largest and smallest value no further than 1.5 * IQR from the respective hinge.

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Extended Data Fig. 3 Tissue protease activity, gross morphology or histopathology of the high PA, low PA and healthy volunteer humanized mice.

a, In situ zymography for trypsin-like activity in mouse colonic tissue. No differences were observed between high PA and healthy humanized mice (n = 6 mice/group, data presented as mean ± SD). b, Representative in situ zymography image of high PA and healthy mouse tissue. SYTOX Green Nuclear Stain (ThermoFisher, S7020) pseudocolored blue, N-p-Tosyl_Gly-Pro-Arg 7-amido-4-methylcoumarin hydrochloride cleavage pseudocolored green (Scale bar 50 µm), c, Mouse weight, cecal weight and colonic length of humanized mice. Post-mortem weight was collected on humanized mice after which the gastrointestinal tract was removed and cecal weight was recorded. Colonic length measurements were done from proximal cecum to the distal rectum (n = 6 human feces/phenotype, dots represent average). Boxplots: lower, middle and upper hinges correspond to 25th, 50th and 75th percentiles. Upper and lower whiskers extend to the largest and smallest value no further than 1.5 * IQR from the respective hinge. d, Histological examination of the gastrointestinal tract of humanized mice. Distal small bowel and distal colon tissue sections were evaluated by a pathologist (RG) in blinded manner. No observed differences in inflammation and presence of immune cells between humanized mice (n = 6 mice scored/phenotype, Scale bar 50 µm).

Source data

Extended Data Fig. 4 Higher level taxonomic evaluation of human fecal samples used for humanization.

Comparisons were made between the microbiota of healthy, low PA and high PA PI-IBS fecal slurries used for mouse humanization (n = 6 human feces/phenotype) which showed no significant differences at phylum, class and family levels.

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Extended Data Fig. 5 Differentially abundant taxa and KEGG pathways between healthy, low PA and high PA PI-IBS humanized mice.

a, Comparison between healthy and high PA PI-IBS humanized mice engrafted microbiota. 32 differentially abundant taxa were identified between high PA with healthy humanized mice. Of the identified taxa, 13 were in greater fold abundance in healthy humanized mice compared to high PA PI-IBS mice. Colors denote greater abundance in the respective humanized group (green: healthy, orange: high PA PI-IBS, n = 6 feces/phenotype). Numbers labeling the taxa correspond to the labels presented in the main manuscript, Fig. 4f. b, Differences in observed taxa between engrafted microbiota of low PA and high PA PI-IBS humanized mice. Microbiome analysis showed 25 differential taxa between low PA and high PA PI-IBS humanized groups, with 13 in greater abundance in low PA, and 12 in high PA. Colors denote greater abundance in the respective humanized group (blue: low PA PI-IBS, orange: high PA PI-IBS n = 6 feces/phenotype). Numbers adjacent to taxa correspond to labels provided in main manuscript Fig. 4g. c, Receiver operating curve assessing random forest ability to predict PA status based on taxa in humanized mice. The ability of random forest modelling algorithm to predict PA status based on selected taxa was assessed in humanized mice with an area under curve (AUC) of 0.914, (95% CI 0.848-0.981). Grey area shows confidence shape. d, Heatmap of predicted KEGG pathway differences between high PA and healthy humanized mice (n = 6 feces/phenotype).

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Extended Data Fig. 6 Fecal microbiome transfer in high PA humanized mice results in a compositional changes.

a, Microbiome profiles of high PA humanized mice receiving either a control or an FMT with healthy microbiota (n = 9 mice/group) were compared at the phyla, class, and family levels. At both phyla and class levels of taxonomy, no significant differences were observed between the two groups of mice. However, at the family level, 9 differential taxa were observed. With increased abundance of Prevotellaceae, Eubacteriaceae, Enterobacteriaceae, Bacteroidaceae, and Clostridiaceae in controls while Odoribacteraceae, Rikenellaceae, Barnesiellaceae and Sutterellaceae were more abundant in mice receiving FMT (FDR < 0.1).b, Correlation scatterplots for differentially abundant taxa that negatively correlate with PA post-FMT treatment. After FMT, three bacterial species were found to negatively correlate with fecal PA, all at a q < 0.1. Taxa identified were A. putredinis, Barnesiella intestinihominis, and A. obesi. All of these taxa had strong negative correlations with PA status in mice post-FMT (0.6 ≤ |r | ≤ 0.79). Correlation coefficients (r) and q-values are generated from comparisons within FMT and control animals to assess the relationship between PA and differentially abundant taxa (n = 9 mice/group). Grey area shows 95% confidence level for linear smooths.

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Extended Data Fig. 7 In vitro trypsin activity is suppressed by unconjugated bilirubin, and inhibition of GUS enzymes results in increased intestinal permeability.

a, Trypsin activity in the presence of metabolites within the bilirubin deconjugation pathway. Compared to the other metabolites used for experimentation, unconjugated bilirubin was the only metabolite that significantly inhibited trypsin activity across all concentrations examined. Data presented as ∆fluorescence/time, normalized to a trypsin-only control (2-Way ANOVA, Tukey’s multiple comparison test, n = 3 *p = 0.001, data presented as mean ± SD). b, Time course inhibition of trypsin activity in the presence of bilirubin metabolites (n = 3 biologically independent replicates, data presented as mean ± SD). c, Measurement of intestinal permeability in D-Glucaro-1,4-lactone treated humanized mice. Serum 4-kDa FITC-dextran levels were greater in healthy humanized mice treated with D-Glucaro-1,4-lactone indicating inhibition of GUS enzymes causes an increase in leak pathway permeability (2-sided Mann-Whitney, n = 4/group *p = 0.03). Boxplots: lower, middle and upper hinges correspond to 25th, 50th and 75th percentiles. Upper and lower whiskers extend to the largest and smallest value no further than 1.5 * IQR from the respective hinge. d, Proposed mechanism of microbial based inhibition of host proteases via the production of GUS enzymes.

Source data

Extended Data Table 1 Demographic and clinical characteristics of study volunteers
Full size table
Extended Data Table 2 Enzymatic kinetics of trypsin inhibition by metabolites in bilirubin deconjugation pathway
Full size table

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table

Supplementary Table 1 File identification for faecal metaproteomic analysis uploaded to PRIDE. Supplementary Table 2 Comparisons of the RNA-seq profiles between high-PA and low-PA PI-IBS patients reveal no changes in protease and protease inhibitor expression.

Source data

Source Data Fig. 1

Tables containing the underlying data from Fig. 1, including measured proteolytic activity in humans (a), beta and alpha diversity measures (b,c), higher-level taxonomy (phylum, class, family) of high-PA PI-IBS and healthy volunteers (d), species-level resolution of the correlation matrix between PA and taxa for high PA and healthy volunteers (e). Also included are the data for PA–taxa correlations (f) and the data for taxa that were differentially abundant when comparing high PA and healthy alone (g).

Source Data Fig. 2

Included are the identified peptides from the metaproteomic analysis of human faecal samples, identifying greater abundance of identified proteases in high-PA samples (b), the SOMAscan tissue proteomic data used to identify tissue-expressed proteases and protease inhibitors (c,d) and the raw data files of protease inhibitor treatment of high-PA FSNs (e).

Source Data Fig. 3

Raw and original PA data for conventional, germ-free and humanized mice (a,b). Data used to generate 6 week PA and % of baseline PA figures (d,e), faecal pellet frequency (f) and faecal pellet consistency (g). Raw and original data for in vivo permeability tracers and measurements (h–j).

Source Data Fig. 4

Tables containing the underlying data in Fig. 4.

Source Data Fig. 5

Raw and original data for experiments involving faecal microbiota transplantation (FMT) in high-PA humanized mice (a) and the proteolytic profile of high-PA mice receiving FMT (b). Included are the data, in tables, used to generate the plots found, which include alpha and beta diversity, taxonomy, species-level difference after FMT and changes in predicted KEGG pathways (c–g). Raw and original data for PA after FMT with microbial communities that have or lack A. putredinis (h) and both raw and original data for A. putredinis-spiked experiments in high-PA faecal slurry (i).

Source Data Fig. 6

Raw and original data for GUS activity in human faecal samples (a) and the faecal metabolomic data in humans (b). Experimental data and original data for overall faecal PA (d) and the preferred substrate assay for trypsin activity in GUS colonization and unconjugated bilirubin experiments in mice, respectively. Tables containing the raw data used to create the heat maps of faecal metabolomics done on mice that were used for GUS experiments (f). Raw and original data from d-glucaro-1-4-lactone and UNC10201652-based GUS inhibition experiments in healthy humanized mice (h,i).

Source Data Extended Data Table 1

Tables containing the underlying volunteer data from Extended Data Table 1.

Source Data Extended Data Table 2

Table containing the underlying raw data used to calculate enzyme kinetics in the presence of bilirubin metabolites in Extended Data Table 2.

Source Data Extended Data Fig. 1

Tables containing taxonomic characterization of low PA and high PA PI-IBS and correlation scatterplots.

Source Data Extended Data Fig. 2

Raw data for proteolytic profiles of humanized mice and the human volunteers that were used for humanization.

Source Data Extended Data Fig. 3

Multiple files related to tissue-expressed proteases, humanized mouse characteristics and histology of mouse tissue. This includes the raw data for in situ zymography data, original image files for both zymography and hematoxylin and eosin staining, and the weight, colon and caecal weight of humanized mice.

Source Data Extended Data Fig. 4

Tables containing taxonomic characterization of healthy and both low-PA and high-PA PI-IBS human faecal samples used for humanization.

Source Data Extended Data Fig. 5

Tables containing the species level differences between healthy and high-PA PI-IBS and between low-PA and high-PA PI-IBS mouse faecal samples and predicted KEGG pathway differences between healthy and high-PA PI-IBS humanized mice.

Source Data Extended Data Fig. 6

Tables containing taxonomic characterization of high-PA control and high-PA FMT-treated animals and correlation scatterplots.

Source Data Extended Data Fig. 7

Tables with the raw data for in vitro inhibition of trypsin activity and in vivo permeability data in GUS inhibitor treated mice.

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Edwinson, A.L., Yang, L., Peters, S. et al. Gut microbial β-glucuronidases regulate host luminal proteases and are depleted in irritable bowel syndrome. Nat Microbiol 7, 680–694 (2022). https://doi.org/10.1038/s41564-022-01103-1

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