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.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
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.
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.
Turk, B. Targeting proteases: successes, failures and future prospects. Nat. Rev. Drug Discov. 5, 785–799 (2006).
Edogawa, S. et al. Serine proteases as luminal mediators of intestinal barrier dysfunction and symptom severity in IBS. Gut 69, 62–73 (2020).
Cenac, N. et al. Role for protease activity in visceral pain in irritable bowel syndrome. J. Clin. Invest. 117, 636–647 (2007).
Annahazi, A. et al. Luminal cysteine-proteases degrade colonic tight junction structure and are responsible for abdominal pain in constipation-predominant IBS. Am. J. Gastroenterol. 108, 1322–1331 (2013).
Denadai-Souza, A. et al. Functional proteomic profiling of secreted serine proteases in health and inflammatory bowel disease. Sci. Rep. 8, 7834 (2018).
Galipeau, H. J. et al. Novel fecal biomarkers that precede clinical diagnosis of ulcerative colitis. Gastroenterology https://doi.org/10.1053/j.gastro.2020.12.004 (2020).
Klem, F. et al. Prevalence, risk factors, and outcomes of irritable bowel syndrome after infectious enteritis: a systematic review and meta-analysis. Gastroenterology 152, 1042–1054.e1 (2017).
Barbara, G. et al. Rome Foundation Working Team report on post-infection irritable bowel syndrome. Gastroenterology 156, 46–58.e7 (2019).
Wouters, M. M. et al. Psychological comorbidity increases the risk for postinfectious IBS partly by enhanced susceptibility to develop infectious gastroenteritis. Gut 65, 1279–1288 (2016).
Sundin, J. et al. Altered faecal and mucosal microbial composition in post-infectious irritable bowel syndrome patients correlates with mucosal lymphocyte phenotypes and psychological distress. Aliment. Pharmacol. Ther. 41, 342–351 (2015).
Jalanka-Tuovinen, J. et al. Faecal microbiota composition and host-microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome. Gut 63, 1737–1745 (2014).
Xu, D. et al. Efficacy of fecal microbiota transplantation in irritable bowel syndrome: a systematic review and meta-analysis. Am. J. Gastroenterol. 114, 1043–1050 (2019).
El-Salhy, M., Hatlebakk, J. G., Gilja, O. H., Brathen Kristoffersen, A. & Hausken, T. Efficacy of faecal microbiota transplantation for patients with irritable bowel syndrome in a randomised, double-blind, placebo-controlled study. Gut 69, 859–867 (2020).
Ford, A. C., Harris, L. A., Lacy, B. E., Quigley, E. M. M. & Moayyedi, P. Systematic review with meta-analysis: the efficacy of prebiotics, probiotics, synbiotics and antibiotics in irritable bowel syndrome. Aliment. Pharmacol. Ther. 48, 1044–1060 (2018).
Bohe, M., Borgstrom, A., Genell, S. & Ohlsson, K. Determination of immunoreactive trypsin, pancreatic elastase and chymotrypsin in extracts of human feces and ileostomy drainage. Digestion 27, 8–15 (1983).
Genell, S. & Gustafsson, B. E. Impaired enteric degradation of pancreatic endopeptidases in antibiotic-treated rats. Scand. J. Gastroenterol. 12, 801–809 (1977).
Vergnolle, N. Protease inhibition as new therapeutic strategy for GI diseases. Gut 65, 1215–1224 (2016).
Galipeau, H. J. et al. Novel role of the serine protease inhibitor elafin in gluten-related disorders. Am. J. Gastroenterol. 109, 748–756 (2014).
Motta, J. P. et al. Food-grade bacteria expressing elafin protect against inflammation and restore colon homeostasis. Sci. Transl. Med. 4, 158ra144 (2012).
Qin, X. Inactivation of digestive proteases by deconjugated bilirubin: the possible evolutionary driving force for bilirubin or biliverdin predominance in animals. Gut 56, 1641–1642 (2007).
Pollet, R. M. et al. An atlas of beta-glucuronidases in the human intestinal microbiome. Structure 25, 967–977.e5 (2017).
He, Y. et al. Bacterial beta-glucuronidase alleviates dextran sulfate sodium-induced colitis in mice: a possible crucial new diagnostic and therapeutic target for inflammatory bowel disease. Biochem. Biophys. Res. Commun. 513, 426–433 (2019).
Berumen, A. et al. Characteristics and risk factors of post-infection irritable bowel syndrome after Campylobacter enteritis. Clin. Gastroenterol. Hepatol. https://doi.org/10.1016/j.cgh.2020.07.033 (2020).
Yoon, H. et al. Increased pancreatic protease activity in response to antibiotics impairs gut barrier and triggers colitis. Cell. Mol. Gastroenterol. Hepatol. 6, 370–388 e373 (2018).
Blank, C. et al. Disseminating metaproteomic informatics capabilities and knowledge using the Galaxy-P framework. Proteomes https://doi.org/10.3390/proteomes6010007 (2018).
Easterly, C. W. et al. metaQuantome: an integrated, quantitative metaproteomics approach reveals connections between taxonomy and protein function in complex microbiomes. Mol. Cell. Proteom. 18, S82–S91 (2019).
Rawlings, N. D. et al. The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res. 46, D624–D632 (2018).
Rolland-Fourcade, C. et al. Epithelial expression and function of trypsin-3 in irritable bowel syndrome. Gut 66, 1767–1778 (2017).
Jacobs, J. P. et al. A disease-associated microbial and metabolomics state in relatives of pediatric inflammatory bowel disease patients. Cell. Mol. Gastroenterol. Hepatol. 2, 750–766 (2016).
Flores, R. et al. Association of fecal microbial diversity and taxonomy with selected enzymatic functions. PLoS ONE 7, e39745 (2012).
Creekmore, B. C. et al. Mouse gut microbiome-encoded beta-glucuronidases identified using metagenome analysis guided by protein structure. mSystems https://doi.org/10.1128/mSystems.00452-19 (2019).
Macfadyen, A. & Ho, K. J. d-glucaro-1,4-lactone: its excretion in the bile and urine and effect on the biliary secretion of beta-glucuronidase after oral administration in rats. Hepatology 9, 552–556 (1989).
Pellock, S. J. et al. Gut microbial beta-glucuronidase inhibition via catalytic cycle interception. ACS Cent. Sci. 4, 868–879 (2018).
Gecse, K. et al. Increased faecal serine protease activity in diarrhoeic IBS patients: a colonic lumenal factor impairing colonic permeability and sensitivity. Gut 57, 591–599 (2008).
Tooth, D. et al. Characterisation of faecal protease activity in irritable bowel syndrome with diarrhoea: origin and effect of gut transit. Gut 63, 753–760 (2014).
Carroll, I. M. & Maharshak, N. Enteric bacterial proteases in inflammatory bowel disease – pathophysiology and clinical implications. World J. Gastroenterol. 19, 7531–7543 (2013).
Jablaoui, A. et al. Fecal serine protease profiling in inflammatory bowel diseases. Front. Cell. Infect. Microbiol. 10, 21 (2020).
Caminero, A. et al. Duodenal bacterial proteolytic activity determines sensitivity to dietary antigen through protease-activated receptor-2. Nat. Commun. 10, 1198 (2019).
McCarville, J. L. et al. A commensal Bifidobacterium longum strain prevents gluten-related immunopathology in mice through expression of a serine protease inhibitor. Appl. Environ. Microbiol. 83, e01323-17 (2017).
Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus Alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).
Dziarski, R., Park, S. Y., Kashyap, D. R., Dowd, S. E. & Gupta, D. Pglyrp-regulated gut microflora Prevotella falsenii, Parabacteroides distasonis and Bacteroides eggerthii enhance and Alistipes finegoldii attenuates colitis in mice. PLoS ONE 11, e0146162 (2016).
Butera, A. et al. Nod2 deficiency in mice is associated with microbiota variation favouring the expansion of mucosal CD4+ LAP+ regulatory cells. Sci. Rep. 8, 14241 (2018).
Rodriguez-Palacios, A. et al. The artificial sweetener Splenda promotes gut proteobacteria, dysbiosis, and myeloperoxidase reactivity in Crohn’s disease-like ileitis. Inflamm. Bowel Dis. 24, 1005–1020 (2018).
Genell, S., Gustafsson, B. E. & Ohlsson, K. Quantitation of active pancreatic endopeptidases in the intestinal contents of germfree and conventional rats. Scand. J. Gastroenterol. 11, 757–762 (1976).
Carroll, I. M. et al. Fecal protease activity is associated with compositional alterations in the intestinal microbiota. PLoS ONE 8, e78017 (2013).
Roka, R. et al. Colonic luminal proteases activate colonocyte proteinase-activated receptor-2 and regulate paracellular permeability in mice. Neurogastroenterol. Motil. 19, 57–65 (2007).
Aroniadis, O. C. et al. Faecal microbiota transplantation for diarrhoea-predominant irritable bowel syndrome: a double-blind, randomised, placebo-controlled trial. Lancet Gastroenterol. Hepatol. 4, 675–685 (2019).
Halkjaer, S. I. et al. Faecal microbiota transplantation alters gut microbiota in patients with irritable bowel syndrome: results from a randomised, double-blind placebo-controlled study. Gut 67, 2107–2115 (2018).
Holster, S. et al. The effect of allogenic versus autologous fecal microbiota transfer on symptoms, visceral perception and fecal and mucosal microbiota in irritable bowel syndrome: a randomized controlled study. Clin. Transl. Gastroenterol. 10, e00034 (2019).
Goll, R. et al. Effects of fecal microbiota transplantation in subjects with irritable bowel syndrome are mirrored by changes in gut microbiome. Gut Microbes 12, 1794263 (2020).
Smillie, C. S. et al. Strain tracking reveals the determinants of bacterial engraftment in the human gut following fecal microbiota transplantation. Cell Host Microbe 23, 229–240.e5 (2018).
Mizuno, S. et al. Bifidobacterium-rich fecal donor may be a positive predictor for successful fecal microbiota transplantation in patients with irritable bowel syndrome. Digestion 96, 29–38 (2017).
Mkaouar, H. et al. Siropins, novel serine protease inhibitors from gut microbiota acting on human proteases involved in inflammatory bowel diseases. Microb. Cell Fact. 15, 201 (2016).
Awolade, P. et al. Therapeutic significance of beta-glucuronidase activity and its inhibitors: a review. Eur. J. Med. Chem. 187, 111921 (2020).
Hamoud, A. R., Weaver, L., Stec, D. E. & Hinds, T. D. Jr. Bilirubin in the liver-gut signaling axis. Trends Endocrinol. Metab. 29, 140–150 (2018).
Ervin, S. M. et al. Gut microbial beta-glucuronidases reactivate estrogens as components of the estrobolome that reactivate estrogens. J. Biol. Chem. 294, 18586–18599 (2019).
Bhatt, A. P. et al. Targeted inhibition of gut bacterial beta-glucuronidase activity enhances anticancer drug efficacy. Proc. Natl Acad. Sci. USA 117, 7374–7381 (2020).
Wallace, B. D. et al. Structure and inhibition of microbiome beta-glucuronidases essential to the alleviation of cancer drug toxicity. Chem. Biol. 22, 1238–1249 (2015).
Koren, O. et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150, 470–480 (2012).
Arrieta, M. C. et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci. Transl. Med. 7, 307ra152 (2015).
De Palma, G. et al. Transplantation of fecal microbiota from patients with irritable bowel syndrome alters gut function and behavior in recipient mice. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aaf6397 (2017).
Longstreth, G. F. et al. Functional bowel disorders. Gastroenterology 130, 1480–1491 (2006).
Francis, C. Y., Morris, J. & Whorwell, P. J. The irritable bowel severity scoring system: a simple method of monitoring irritable bowel syndrome and its progress. Aliment. Pharmacol. Ther. 11, 395–402 (1997).
Drossman, D. A. et al. Further validation of the IBS-QOL: a disease-specific quality-of-life questionnaire. Am. J. Gastroenterol. 95, 999–1007 (2000).
Blake, M. R., Raker, J. M. & Whelan, K. Validity and reliability of the Bristol Stool Form Scale in healthy adults and patients with diarrhoea-predominant irritable bowel syndrome. Aliment. Pharmacol. Ther. 44, 693–703 (2016).
Zigmond, A. S. & Snaith, R. P. The hospital anxiety and depression scale. Acta Psychiatr. Scand. 67, 361–370 (1983).
BBMap v. 38.73 (https://sourceforge.net/projects/bbmap/, 2019).
Hillmann, B. et al. SHOGUN: a modular, accurate and scalable framework for microbiome quantification. Bioinformatics 36, 4088–4090 (2020).
Al-Ghalith, G. & Knights, D. BURST enables mathematically optimal short-read alignment for big data. Preprint at bioRxiv https://doi.org/10.1101/2020.09.08.287128 (2020).
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).
Flores, R., Shi, J., Gail, M. H., Ravel, J. & Goedert, J. J. Assessment of the human faecal microbiota: I. Measurement and reproducibility of selected enzymatic activities. Eur. J. Clin. Invest. 42, 848–854 (2012).
Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).
Fitzpatrick, L. R. et al. Bacillus coagulans GBI-30, 6086 limits the recurrence of Clostridium difficile-induced colitis following vancomycin withdrawal in mice. Gut Pathog. 4, 13 (2012).
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Ripley, B. et al. Package ‘MASS’. Cran R 538, 113–120 (2013).
Chen, L. et al. GMPR: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. Peerj https://doi.org/10.7717/peerj.4600 (2018).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Robin, X. et al. pROC: an open-source package for R and S plus to analyze and compare ROC curves. BMC Bioinformatics https://doi.org/10.1186/1471-2105-12-77 (2011).
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.
M.G. has disclosure to Mayo Clinic Ventures on microbes described in this manuscript. The other authors declare no competing interests.
Peer review information
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
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).
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.
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).
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.
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.
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.
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).
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).
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).
Tables containing the underlying data in Fig. 4.
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).
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).
Tables containing the underlying volunteer data from Extended Data Table 1.
Table containing the underlying raw data used to calculate enzyme kinetics in the presence of bilirubin metabolites in Extended Data Table 2.
Tables containing taxonomic characterization of low PA and high PA PI-IBS and correlation scatterplots.
Raw data for proteolytic profiles of humanized mice and the human volunteers that were used for humanization.
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.
Tables containing taxonomic characterization of healthy and both low-PA and high-PA PI-IBS human faecal samples used for humanization.
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.
Tables containing taxonomic characterization of high-PA control and high-PA FMT-treated animals and correlation scatterplots.
Tables with the raw data for in vitro inhibition of trypsin activity and in vivo permeability data in GUS inhibitor treated mice.
About this article
Cite this article
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
This article is cited by
Nature Reviews Gastroenterology & Hepatology (2023)
Nature Reviews Chemistry (2023)
Nature Reviews Gastroenterology & Hepatology (2022)