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Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity

Nature Microbiology volume 7, pages 262–276 (2022)Cite this article

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Abstract

Ulcerative colitis (UC) is driven by disruptions in host–microbiota homoeostasis, but current treatments exclusively target host inflammatory pathways. To understand how host–microbiota interactions become disrupted in UC, we collected and analysed six faecal- or serum-based omic datasets (metaproteomic, metabolomic, metagenomic, metapeptidomic and amplicon sequencing profiles of faecal samples and proteomic profiles of serum samples) from 40 UC patients at a single inflammatory bowel disease centre, as well as various clinical, endoscopic and histologic measures of disease activity. A validation cohort of 210 samples (73 UC, 117 Crohn’s disease, 20 healthy controls) was collected and analysed separately and independently. Data integration across both cohorts showed that a subset of the clinically active UC patients had an overabundance of proteases that originated from the bacterium Bacteroides vulgatus. To test whether B. vulgatus proteases contribute to UC disease activity, we first profiled B. vulgatus proteases found in patients and bacterial cultures. Use of a broad-spectrum protease inhibitor improved B. vulgatus-induced barrier dysfunction in vitro, and prevented colitis in B. vulgatus monocolonized, IL10-deficient mice. Furthermore, transplantation of faeces from UC patients with a high abundance of B. vulgatus proteases into germfree mice induced colitis dependent on protease activity. These results, stemming from a multi-omics approach, improve understanding of functional microbiota alterations that drive UC and provide a resource for identifying other pathways that could be inhibited as a strategy to treat this disease.

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Fig. 1: Multi-omic diversity correlates with IBD disease activity.
Fig. 2: Multi-omic analysis of IBD disease activity.
Fig. 3: Integrated metagenomic–metaproteomic analyses reveal Bacteroides proteases distinguishing a subset of active UC patients.
Fig. 4: Assessing proteolysis in UC patients and Bacteroides supernatant.
Fig. 5: Protease inhibition protects from B. vulgatus and faecal transplant induced pathology in vitro and in vivo.

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Data availability

Metabolomic data, proteomic data and additional supplementary files for reanalyzing the data collected here are available online at https://massive.ucsd.edu (Cohort 1 proteomics and metabolomics study ID MSV000082094, Cohort 2 study ID MSV000086509, Cohort 2 metabolomics study ID MSV000084908). Proteomic data and supplementary files for reanalyzing data collected from the faecal transplant study and Bacteroides supernatant are under MassIVE identifiers MSV000086510 and MSV000086511, respectively. Genomic data has been uploaded through EBI https://www.ebi.ac.uk/ena under the study identifiers PRJEB42151 for Cohort 1 and PRJEB42155 for Cohort 2. Comparisons with data generated in this study were also made with proteomics data downloaded from the IBD multi-omics database (https://ibdmdb.org/tunnel/public/HMP2/Proteomics/1633/rawfiles). Databases used in this study include UniRef50 (https://www.uniprot.org/downloads), the human proteome (https://www.uniprot.org/proteomes/UP000005640), mouse proteome (https://www.uniprot.org/proteomes/UP000000589), B. vulgatus proteome (https://www.uniprot.org/proteomes/UP000002861), B. theta proteome (https://www.uniprot.org/proteomes/UP000001414), B. dorei proteome (https://www.uniprot.org/proteomes/UP000005974), a microbial genome database (https://biocore.github.io/wol/) and a human gut microbiome database (https://db.cngb.org/microbiome/genecatalog/genecatalog_human/). Source data are available for in vitro and in vivo experiments. Source data are provided with this paper.

Code availability

The code used in the analysis and visualization of data is available at https://github.com/knightlab-analyses/uc-severity-multiomics.

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Acknowledgements

P.S.D., R.H.M. and C.S. were supported through a UCSD training grant from the NIH/NIDDK Gastroenterology Training Program (T32 DK007202). P.S.D. was also supported by an American Gastroenterology Association Research Scholar Award. We thank E. Griffis, D. Bindels and the Nikon Imaging Center at UCSD for help with confocal microscopy, and the UCSD Neuroscience Microscopy Shared Facility (NS047101). This study was supported in part by NIDDK-funded San Diego Digestive Diseases Research Center (P30 DK120515, D.J.G., P.S.D.) and the UCSD Collaborative Center of Multiplexed Proteomics.

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  1. These authors contributed equally: Robert H. Mills, Parambir S. Dulai.

Authors and Affiliations

  1. Department of Pharmacology, University of California, San Diego, CA, USA

    Robert H. Mills, Consuelo Sauceda, Mario Malfavon, Marvic Carrillo-Terrazas & David J. Gonzalez

  2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA

    Robert H. Mills, Consuelo Sauceda, Marvic Carrillo-Terrazas, Pieter C. Dorrestein & David J. Gonzalez

  3. Department of Pediatrics, University of California, San Diego, CA, USA

    Robert H. Mills, Yoshiki Vázquez-Baeza, Romana R. Gerner, Qiyun Zhu, Greg Humphrey, Lindsay DeRight Goldasich, MacKenzie Bryant, Pieter C. Dorrestein & Rob Knight

  4. Division of Gastroenterology, University of California, San Diego, CA, USA

    Parambir S. Dulai & William J. Sandborn

  5. Department of Computer Science and Engineering, University of California, San Diego, CA, USA

    Yoshiki Vázquez-Baeza & Rob Knight

  6. Center for Microbiome Innovation, University of California, San Diego, CA, USA

    Yoshiki Vázquez-Baeza, Kelly Weldon, Manuela Raffatellu, Pieter C. Dorrestein, Rob Knight & David J. Gonzalez

  7. INSERM U1016, team Mucosal microbiota in chronic inflammatory diseases, CNRS UMR 8104, Université de Paris, Paris, France

    Noëmie Daniel & Benoit Chassaing

  8. Division of Host–Microbe Systems and Therapeutics, University of California, San Diego, CA, USA

    Romana R. Gerner & Manuela Raffatellu

  9. Department of Pathology, University of California, San Diego, CA, USA

    Lakshmi E. Batachari, Marvic Carrillo-Terrazas & Hiutung Chu

  10. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Qiyun Zhu

  11. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA

    Robert A. Quinn

  12. Center for Inflammation, Immunity and Infection, Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA

    Andrew T. Gewirtz

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Contributions

R.H.M., D.J.G., R.K., P.S.D., H.C., A.T.G., B.C. and P.C.D. conceived and designed the study. R.H.M., P.S.D., R.A.Q., H.C., A.T.G., B.C., Y.V.-B., Q.Z. and R.K. developed the methodology. R.H.M., M.M.O., K.W., M.C.-T., R.A.Q., G.H., L.D.G. and M.B. acquired multi-omics data. R.H.M. and Y.V.-B. analysed multi-omics data. B.C., N.D., R.R.G., L.E.B. and H.C. conducted animal studies. R.H.M. and C.S. conducted mammalian and bacterial culture studies. R.H.M., P.S.D., H.C., Y.V.-B., Q.Z., R.K., D.J.G., R.A.Q. and P.C.D. interpreted the data. R.H.M., P.S.D. and D.J.G. wrote the manuscript. R.H.M., P.S.D., C.S., R.A.Q., Y.V.-B., R.K., M.R., W.J.S., D.J.G., Q.Z.,Y.Z., A.T.G., B.C. and H.C. reviewed and revised the manuscript.

Corresponding authors

Correspondence to Rob Knight or David J. Gonzalez.

Ethics declarations

Competing interests

R.H.M., P.S.D. and D.J.G. have jointly filed for a patent based on this work (International Application No. PCT/US2020/057784). Over the course of the publication process, R.H.M. started employment at Precidiag Inc., a company that has licensed the patent based on this work. All other authors declare no competing interests.

Peer review information

Nature Microbiology thanks Daniel Figeys and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Study Design and Database Generation.

Paired faecal and serum samples were collected from 40 patients with varying severity of Ulcerative Colitis. A separately analyzed cohort of faecal samples was also collected on 210 samples with 73 UC, 117 CD and 20 healthy controls. Samples were processed for proteomics using a Tandem Mass Tag multiplexing workflow. Faecal samples were also subjected to both 16S and shotgun metagenomic analyses for microbial composition and gene quantification respectively. In parallel, a metabolomics workflow was performed on faecal samples where collected MS2 spectra were analyzed for both metabolites and peptides in two separate computational pipelines. A custom database was compiled from the metagenome of faecal samples to mediate a comparative analysis between shotgun metagenomic and metaproteomic data sets. This eliminated database dependent bias and the shared reference was used for estimating copy number.

Extended Data Fig. 2 A multiplexing approach improves the depth and sparsity of metaproteomics data.

a, Multiplexed metaproteomic methods increase the total number of proteins quantified. Shown is a bar graph showing the total number of proteins identified when using identical database methodology between the 102 UC samples from the IBD multiomics database, the 40 UC samples from cohort 1 of this study, and the 205 samples from cohort 2 of this study. b, Multiplexed metaproteomic methods improve the number of proteins quantified per sample. Displayed are the mean + /- SD of the proteins identified per sample from studies shown in (a). Data derived from n = 102, 40, 205 biologically independent samples as described for (a). One-way ANOVA p-values adjusted for multiple comparisons are shown (P < 0.0001). c, Multiplexed metaproteomic methods decrease the sparsity of metaproteomic studies. The percentage of missing quantification values for proteins in each data set is shown.

Source data

Extended Data Fig. 3 Characterizing uneven samples.

a, Alpha diversity (using Pielou’s evenness metric) by disease activity as shown in Fig. 1b, but highlighting classification of samples as uneven when below Pielou Evenness of 0.5. Best-fit linear regression lines with 95% confidence intervals are shown and an R2 statistic is reported from an ordinary least-squares regression using the formula (Disease Activity + Diagnosis + Disease Activity:Diagnosis). b, 16 S beta-diversity is strongly influenced by community evenness. The weighted UniFrac distance metric was used and each sample was classified by community evenness, diagnosis and whether the most abundant 16 S feature was from the family Enterobacteriaceae. c, Characterizing the most abundant 16 S features. Each sample was classified as either “Uneven” (Pielou Evenness < 0.5) or “Other” as shown in (a). Abundances of each amplicon sequence variant were summed by their highest resolution taxonomic annotation and the most abundant feature of samples are represented in a donut plot. The inside ring represents the fractional composition of each patient subgroup and the outside rings represents the number of patients within each subgroup whom share a similar most abundant feature. Less common features for each patient subgroup are counted as “Other”.

Extended Data Fig. 4 Comparison of genera annotations from genes and proteins correlated to disease severity.

The genus composition of genes and proteins correlated to disease activity were compared with different levels of sparsity as a requirement for being deemed “correlated”. Stacked bar charts summarize the number of genes or proteins from the 10 most common genus assignments when correlated to either partial Mayo severity in UC cohorts or CDAI in CD patients. Only genes or proteins with |r | > 0.3 from linear regression were included. a, Genus composition of significant positively and negatively correlated genes from the MG with no sparsity requirement. b, Genus composition of significantly positively and negatively correlated proteins from the MP with no sparsity requirement. c, Genus composition of associated proteins as in Fig. 3a, but without removing host proteins (genus Homo). d, Genes correlated to disease activity from the MG when filtering out genes appearing in less than 40% of patients within each category. e, Summary of comparing the portions of positively and negatively correlated genes and proteins from each patient cohort when examining the top 10 genera identified in the MG. This analysis is analogous to Fig. 3b, but displaying the top MG genera.

Extended Data Fig. 5 Comparison of genera and functional annotations from genes and proteins correlated to disease severity in CD subtypes.

a, Genus level barcharts of significantly correlated genes or proteins stratified by CD subtype. The genus composition of genes and proteins from either the MG or MP were correlated to CDAI and shown in stacked bar charts. Only genes or proteins with |r | > 0.3 from linear regression were included, and the top 10 genera are displayed with other genera compiled into an “Others” category. b, CD subtypes genus level association comparison. The portion of genes or proteins correlated with disease activity from (a) are plotted by a Log10 comparison between the proportion of positive to negative correlations. Genes correlated to disease activity from the MG when filtering out genes appearing in less than 40% of patients within each category. c, CD subtypes functional association comparison. This analysis is analogous to (b) but summarizing the associations to KEGG functional category annotations in the MP.

Extended Data Fig. 6 Patients with overproduction of Bacteroides vulgatus proteases have increased endoscopic and histological severity.

a, Bacteroides protease production corresponds to increased endoscopic severity. The disease activity of overproducers, underproducers, and other patients are individually plotted over boxplots. Two-tailed, t-test p-values are displayed above the boxplots. Sample sizes include n = 16, 14 and 71 for overproducers, underproducers and others respectively. Boxplots are defined by the median, quartiles and 1.5x inter-quartile range. b, Bacteroides protease production corresponds to a patient population with a decreased proportion of patients in histological remission. Each UC patient sample was categorized by Bacteroides vulgatus protease production category and the percent of patients in histological remission is shown in a bargraph with the number of samples in each category displayed above each bar. Histological remission is defined here as Geboes Grade 3 = 0.

Extended Data Fig. 7 Peptide fragments are increased in active UC patients and Bacteroides protease enriched patients.

a, Comparison of peptide fragments identified in patients with varying abundance of Bacteroides proteases. Overproducers from UC cohort 1 had increased peptide fragments in comparison to other patients (Two-tailed t-test P = 3.5E-2). Data was derived from n = 8, 9, 23 UC cohort 1 samples and n = 6, 6, 49 UC cohort 2 samples from patients classified as underproducer, overproducer and other respectively. b, Peptide termini indicate unique proteolysis of human and microbial proteins. The frequency of each amino acid within the N and C terminus of human and de-novo peptides was compared to either the human proteome or the total amino acid content of de novo peptides. The Y-axis represents the percent difference of each residue and the letter indicates the amino acid associated with the difference. The N and C terminus are shown separately and each residue is colored by chemical property (Green = polar, Black = Hydrophobic, Red = Acidic, Blue = Basic, Purple = Neutral). c, Peptide fragment identification comparison by disease activity in UC cohort 1. Boxplots with a two-tailed t-test p-value is shown (P = 4.7E-3). Data was derived from n = 18, 12, 10 patient samples with low moderate or high disease activity respectively. d, Peptide fragment identification comparison by disease and disease activity state for cohort 2 samples. Boxplots are shown with overlaid two-tailed t-test p-values. Data was derived from n = 19 healthy controls, n = 39, 30, 12 UC samples, and n = 64, 30, 8 CD samples from patients of low, moderate and high activity respectively. Boxplots in (a,c,d) are defined by the median, quartiles and 1.5x inter-quartile range.

Extended Data Fig. 8 Determining the impact of Bacteroides species on TEER using co-culture, supernatants and protease inhibitors.

a, Bacteroides vulgatus and Bacteroides dorei, but not other Bacteroides species disrupt Caco-2 epithelial barriers. Barplots are showing the mean and standard deviation of the change in TEER at different time points. Data was derived from n = 3 independent cultures collected over n = 2 independent experiments. b, Growth curves of Bacteroides vulgatus with protease inhibitors under different growth conditions. OD600 was measured at indicated time points and a non-linear fit is shown. Data was derived from n = 3 independent cultures collected over n = 1 independent experiments. c, Supernatants from Bacteroides in mid-log phase growth do not significantly impact TEER. B. vulgatus and B. theta were grown to mid-log phase, and their supernatants were concentrated and added to Caco-2 monolayers. TEER was measured at the initial time-point and compared to TEER measured after 1, 4, and 8 h of incubation. Plotted are the mean and SEM from n = 3 independent experiments each representing the mean of n = 3 independent wells/experiment (n = 4 wells/experiment for B. vulgatus group). No significant differences were found at any timepoint.

Source data

Extended Data Fig. 9 Additional measurements from faecal transplant experiments.

a-f Barplots showing the mean + /- SD of macroscopic organ measurements from fecal transplant of UC patients samples in IL10-/- mice with or without administration of a protease inhibitor. Dots represent one mouse, with each group representing results from 3 UC patient fecal samples with each sample given to 3 co-housed mice. Measurements include final weight of the mice (a), colon weight (b), ratios of the colon weight to length (c), caecum weight (d), fat pad weight (e), liver weight (f). g-h Barplots showing the mean + /- SEM for the concentration of an intestinal inflammatory marker, fecal lipocalin2 (g), and amount of 16 S rRNA in the spleen of mice for an estimate of the splenic bacterial load (h). Each dot in g-h represents the mean of n = 3 mice transplanted with the same UC fecal sample (with the exception of a mean from n = 2 mice for one patient sample in the Abundant Proteases + Inhibitor Cocktail group) from n = 2 independent experiments. i, Metaproteome genera composition of mice transplanted with UC fecal samples. Fecal samples taken at 8-weeks from mice transplanted with one high protease containing sample (H19) and one control patient sample (L3) were analyzed by mass spectrometry based metaproteomics. Stacked barplots are shown for each mouse displaying the proportion of protein signal derived from the most common genera. j, Molecular function of B. vulgatus proteases identified in mice receiving UC fecal samples. The relative abundance of each B. vulgatus protease is shown in stacked barplots grouped by the Gene Ontology molecular function associated with each protein. k, Top B. vulgatus or B. dorei proteases associated with the fecal samples of mice receiving the H19 sample. Each protein is ranked by pi-score, which combines two-sided t-test p-values and the fold-change difference between all H19 and L3 samples. l, Cumulative protease comparisons. A venndiagram is shown comparing the protein names of B. vulgatus or B. dorei proteases from four independent proteomics experiments performed in this study. A full list of the Bacteroides proteases identified in this analysis can be found in Supplementary Table 4.

Extended Data Fig. 10 Working hypothesis.

The results of our study may indicate that certain species from the genus Bacteroides, particularly those recently reclassified under the genus Phocaeicola (for example Bacteroides vulgatus & Bacteroides dorei), may be implicated in the transition from remission to active disease in UC. We hypothesize that a stressor in the UC gut such as nutrient deprivation or cell-to-cell competition may increase protease production, and a switch in the utilization of carbohydrates to proteins as a nutrient source. Some of these proteases may be involved in the disruption of the epithelial barrier, allowing an influx of innate immune cells which further exacerbate disease.

Supplementary information

Supplementary Information

Supplementary Table 1 and Figs. 1–6.

Reporting Summary

41564_2021_1050_MOESM3_ESM.xlsx

Supplementary Table 2 Association of UC clinical variables to alpha and beta diversity. For alpha diversity, Kruskal–Wallis tests were performed on each of the listed categorical variables, and linear regression was applied for quantitative variables. P values are reported for all tests, and r values are reported for quantitative variables. When more than two categories were present in categorical variables, the P value between the two largest categories was reported. For beta diversity, categorical variables were tested using PERMANOVA, continuous variables were tested using Adonis. P values followed by pseudo-F values for each category are reported. For continuous variables, R2 values are reported after pseudo-F values. Testing was based on Bray–Curtis distance matrices, unless otherwise specified. Significance level is indicated according to P value (* <0.05, ** <0.01, *** <0.001).

41564_2021_1050_MOESM4_ESM.xlsx

Supplementary Table 3 Features of importance to predicting UC disease activity. Feature importance values from the 100 random forest iterations predicting UC disease activity are reported for each omic data type from both UC cohorts. The top-100 features from each data type are provided, ranked by the summed importance scores from both cohorts. From the combined datasets, the top-100 features and annotation information related to each feature’s data type are also provided. These data correspond to the random forest results shown in Fig. 2c.

41564_2021_1050_MOESM5_ESM.xlsx

Supplementary Table 4 Bacteroides proteases identified in UC patients, bacterial supernatants and faecal material from humanized mice. Protein names of peptidases or proteases identified throughout the multiple proteomic experiments from this study are listed. Lists are provided for proteases from B. vulgatus or B. dorei that were positively associated (r > 0.3) with UC patient disease activity from either cohort. Additionally, proteases or peptidases identified in the supernatant from different species of Bacteroides are listed. Finally, from a metaproteomic analysis of the faecal material from mice humanized by UC patient faecal samples, B. vulgatus or B. dorei proteases increased (π > 1) in mice transplanted with a sample overabundant in proteases compared with mice transplanted without overabundant proteases.

Source data

Source Data Fig. 4

Tables containing de novo peptide identifications and data from experiments of B. vulgatus supernatant protease activity with different inhibitors.

Source Data Fig. 5

Multiple files related to in vitro and in vivo studies displayed in Fig. 5. This includes the raw data from Caco-2 co-cultures with B. vulgatus and protease inhibitors (Fig. 5b,c), the original image files from Fig. 5d, the quantification of cell morphology (Fig. 5e), original data from the monocolonization experiments (Fig. 5f–i) and the original data from faecal transplant studies (Fig. 5j–n).

Source Data Extended Data Fig. 2

Tables containing the underlying data from Extended Data Fig. 2.

Source Data Extended Data Fig. 8

Tables containing the underlying data from Extended Data Figure 8.

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Mills, R.H., Dulai, P.S., Vázquez-Baeza, Y. et al. Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity. Nat Microbiol 7, 262–276 (2022). https://doi.org/10.1038/s41564-021-01050-3

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