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Multi-omic profiling a defined bacterial consortium for treatment of recurrent Clostridioides difficile infection

Nature Medicine volume 31, pages 223–234 (2025)Cite this article

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Abstract

Donor-derived fecal microbiota treatments are efficacious in preventing recurrent Clostridioides difficile infection (rCDI), but they have inherently variable quality attributes, are difficult to scale and harbor the risk of pathogen transfer. In contrast, VE303 is a defined consortium of eight purified, clonal bacterial strains developed for prevention of rCDI. In the phase 2 CONSORTIUM study, high-dose VE303 was well tolerated and reduced the odds of rCDI by more than 80% compared to placebo. VE303 organisms robustly colonized the gut in the high-dose group and were among the top taxa associated with non-recurrence. Multi-omic modeling identified antibiotic history, baseline stool metabolites and serum cytokines as predictors of both on-study CDI recurrence and VE303 colonization. VE303 potentiated early recovery of the host microbiome and metabolites with increases in short-chain fatty acids, secondary bile acids and bile salt hydrolase genes after antibiotic treatment for CDI, which is considered important to prevent CDI recurrences. These results support the idea that VE303 promotes efficacy in rCDI through multiple mechanisms.

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Fig. 1: Colonization of VE303 strains over time across treatment groups and association with clinical efficacy.
Fig. 2: Predictors of VE303 strain colonization at screening.
Fig. 3: Effect of antibiotic pre-treatment and residual vancomycin on VE303 colonization.
Fig. 4: Microbial, metabolite and immune profiles at screening are linked with development of on-study CDI recurrence.
Fig. 5: Recovery of endogenous microbiota and associations with CDI recurrence.
Fig. 6: SCFA and BA associations with endogenous bacteria and clinical outcome in VE303 recipients.

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Data availability

VE303 strain genomes are available at the National Center for Biotechnology Information (NCBI) GenBank under study ID PRJNA755324, and the unique markers used to track the strains were previously published10. VE303 strain assignments were assessed using the Genome Taxonomy Database version 220 (2024). Raw stool metagenomics datasets and disaggregated clinical metadata have been uploaded to the NCBI Sequence Read Archive under study ID PRJNA1121316. Other unique biological materials generated in this study will be made available under a material transfer agreement with Vedanta Biosciences. Correspondence should be addressed to legal@vedantabio.com and the corresponding authors and will be addressed within 2 weeks. Source data are provided with this paper.

Code availability

Processed datasets and code associated with this manuscript can be accessed on GitHub: https://github.com/vedantabio/VE303-Ph2-paper. All statistical analyses were carried out using R software (version 4.3.1 or higher). Linear mixed effects models were fit using R packages ‘lme4’ (version 1.1-35.1) and ‘lmertest’ (version 3.1-3) and the Maaslin2 package; recurrence-free survival analysis was conducted using the R package ‘survival’ (version 3.2-12); random forest analysis and feature selection were performed using the R packages ‘randomForest’ (version 4.7-1.1) and ‘Boruta’ (version 8.0.0); and bile acid genes in participant metagenomes were profiled using ShortBRED software (version 0.9.5). Further details on statistical and modeling approaches are described in the Methods section.

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Acknowledgements

We thank the Multiplex Core at the Forsyth Institute, which provided all multiplex protein analysis services. This project was supported in whole or in part with federal funds from the US Department of Health and Human Services, Administration for Strategic Preparedness and Response, Biomedical Advanced Research and Development Authority (BARDA), under contract number 75A50120C00177. As the trial sponsor, Vedanta Biosciences designed the study, collected and analyzed data and wrote the manuscript. BARDA provided partial reimbursement for the costs of the VE303 program and reviewed the manuscript before submission. V.B. and S.K.B. are supported by a sponsored research agreement with Vedanta Biosciences.

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  1. Vedanta Biosciences, Inc., Cambridge, MA, USA

    Rajita Menon, Emily Crossette, Amanda L. Prince, Bernat Olle, Jeffrey L. Silber & Jason M. Norman

  2. Program in Microbiome Dynamics, Department of Microbiology, University of Massachusetts Chan Medical School, Worcester, MA, USA

    Shakti K. Bhattarai & Vanni Bucci

  3. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jeremiah Faith

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Contributions

R.M., E.C., A.L.P., B.O., J.L.S., J.F. and J.M.N. conceptualized and designed the study. R.M., S.K.B. and V.B. developed the methodology for statistical and computational analysis. R.M., S.K.B. and E.C. curated and analyzed the data. R.M., S.K.B., E.C., A.L.P., V.B. and J.M.N. contributed to investigation and interpretation of the data. R.M. and J.M.N. drafted the manuscript. All authors contributed to substantial revisions of the manuscript. All authors critically reviewed the manuscript for intellectual content. All authors approved the final version of the submitted manuscript, agree to be accountable for all aspects and vouch for data accuracy and completeness.

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Correspondence to Rajita Menon or Jason M. Norman.

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Competing interests

R.M., E.C., A.L.P., B.O., J.L.S. and J.M.N. are employees of Vedanta Biosciences and have an equity interest in the company. Vedanta Biosciences holds patents related to this work. J.F. and V.B. are members of the scientific advisory board of Vedanta Biosciences; J.F. has an equity interest in the company. The other authors declare no competing interests. This content is solely the responsibility of the authors and does not necessarily represent the official views of the US Department of Health and Human Services, Administration for Strategic Preparedness and Response.

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Extended data

Extended Data Fig. 1 Clinical Study Design and Summary of Data Collection.

A, Clinical study design. Participants were treated with VE303 or placebo after being randomized 1:1:1 into treatment groups. CDI recurrence was evaluated at Week 8, with additional efficacy and safety follow-up through Week 24. B, Metagenomic, metabolomic, and immune datasets examined in the study. C, Sample collection summary (N = total samples, R = samples from recurrent individuals, NR = samples from non-recurrent individuals) for VE303-dosed groups up to Day 14.

Source data

Extended Data Fig. 2 Detection of VE303 Strains in All Participants Over Time.

Stacked-bar charts showing detection of individual VE303 strains over time, presented for each participant by treatment group: A, VE303 high-dose, B, VE303 low-dose, and C, placebo. Data are shown for all analyzed samples. D, Exposure to individual VE303 strains after screening and through Day 28. The mean relative abundance per participant over the indicated period is shown across the three treatment groups; VE303 high-dose n = 26; VE303 low-dose n = 26; placebo n = 20. The box-and-whisker plots depict the median, inter-quartile range [IQR] (at the top and bottom of the boxes), and reasonable extreme values at 1.5x IQR in the dataset.

Source data

Extended Data Fig. 3 Random Forest Classification Model Schematic and Performance.

A, Schematic of random forest model used to predict features of CDI recurrence and VE303 colonization. Participants were classified into recurrent/non-recurrent and VE303 colonized/non-colonized using a combination of continuous and categorical independent variables including metagenomics, immunological and metabolite datasets, and patient-level metadata such as demographics and medical history. Random forest classification across different data modalities is used to predict B, the colonization status of VE303 strains across all dosed participants; diamonds and error bars represent the mean area under the receiver operating characteristic curve [AUC] ± standard error (se) over n = 10 model iterations C, on-study CDI recurrence status including all VE303 recipients; diamonds and error bars represent the mean AUC ± standard error (se) over n = 100 model iterations and D, on-study CDI recurrence status excluding subjects who recurred later than Day 14; diamonds and error bars represent the mean AUC ± standard error (se) over n = 100 model iterations. Leave-one-out cross-validation was performed to assess the model performance. AUC was computed for all models; an AUC of ≤0.5 (dashed red line) indicates random classification or unreliable model performance. Number labels on each graph indicate the total number of samples available per timepoint and dataset; brackets convey class sizes for (B) colonized/non-colonized and (C, D) recurrent/non-recurrent classes. Missing values indicate lack of sample availability with fewer than 3 recurrent participants available for analysis and unreliable results in the corresponding model.

Source data

Extended Data Fig. 4 Random Forest Model Summary for Features Linked to CDI Recurrence.

Random forest model results for microbial, metabolic, and immune features predicting CDI recurrence (at screening) or associated with CDI recurrence (post-screening) in VE303 recipients A, including all participants and B, excluding participants who recurred later than Day 14. The heatmaps represent the frequency with which the feature was selected to be important across 100 model iterations. Features are plotted if permutation importance < 0.1 and frequency > 0.25. Boxes show feature associations with recurrence or non-recurrence identified by the model at the corresponding timepoint.

Source data

Extended Data Fig. 5 Increased Age and Inflammation at Screening and CDI Recurrence Prediction.

A, Concentration of cytokines predictive of on-study CDI recurrence at Screening in VE303 recipients with recurrence (n = 12) and without recurrence (n = 32); permutation importance < 0.1, frequency > 0.25 over 100 iterations of a random forest regression model. B, Box-and-violin plots showing age of participants in the VE303-dosed groups; with recurrence (n = 14) and without recurrence (n = 42). C, Correlations between MIP-1β concentrations at baseline and participant age. The blue line indicates the fit from a linear model, while the shaded region indicates the 95% confidence interval for predictions from a linear model. The box-and-whisker plots depict the median, inter-quartile range [IQR] (at the top and bottom of the boxes), and reasonable extreme values at 1.5x IQR.

Source data

Extended Data Fig. 6 Associations of Endogenous Taxa at Species, Genus, Family, Order and Class Level with CDI Recurrence and VE303 Abundance.

Effect-size bar plots for all organisms associated with CDI recurrence or non-recurrence (linear mixed effects [LME] model, Results are shown for taxa with un-adjusted p-value < 0.05 computed using the t-statistic.) at the level of A, species, B, genus, C, family, D, order, and E, class. Taxa in the right half of each plot are associated with non-recurrence; those in the left half are associated with recurrence. Bars depict the model effect size estimates for taxa-outcome correlations. Vertical annotations on the right indicate whether taxa were correlated with total VE303 abundance up to Day 14 (LME model); boxes indicate the model estimates for positive or negative correlation with VE303; stars show the level of significance level of correlation with p-values computed using the t-statistic and adjusted for multiple hypotheses by the Benjamini-Hochberg procedure.

Source data

Extended Data Fig. 7 Concentration of Primary Bile Acids, Secondary Bile Acids, and Short-chain Fatty Acids Over Time and Differences by Recurrence Status.

Log-transformed total stool concentrations (ng/mg) in individuals with or without a CDI recurrence of A, primary bile acids [BA], B, secondary BA, and C, short-chain fatty acids over time; n samples represented (recurrent/non-recurrent) from Screening to Day 56 are as follows: placebo [9/11, 10/12, 9/11, 8/12, 2/8]; VE303 low-dose [8/16, 10/17, 9/17, 7/14, 1/13]; VE303 high-dose [4/22, 4/25, 3/23, 0/22, 0/22]. The primary BAs include CA, CDCA, TCA, TCDCA, GCA, GCDCA. The secondary BAs include DCA, LCA, DHLCA, TLCA, TDCA, GLCA, GDCA, UDCA, TUDCA, GUDCA, Iso-DCA, and Isoallo- + IsoLCA. The box-and-whisker plots depict the median, inter-quartile range [IQR] (at the top and bottom of the boxes), and reasonable extreme values at 1.5x IQR of the log-transformed metabolite concentrations at each timepoint. D, the heatmap shows the mean log-transformed metabolite concentration per participant during Day 1 through Day 14. Participants are grouped to illustrate differences by recurrence outcomes.

Source data

Extended Data Fig. 8 Bile Acid Pathways and Correlation with Bacterial Abundance in Study Participants.

Bile acid genes with differential detection in A, VE303 HD and VE303 LD groups versus placebo group and B, recurrent versus non-recurrent individuals. Only genes with significant differences are shown (false discovery rate [FDR] < 0.1, Linear mixed effects [LME] model). C, Associations between BA genes and the endogenous microbiota (FDR < 0.05, LME), boxes indicate positive or negative correlations between the indicate pairs. VE303 organisms are highlighted on the x-axis; endogenous bacteria with a previously validated role in BA modification are indicated with an asterisk49.

Source data

Extended Data Fig. 9 Detection of VE303 Strains in VE303 Recipients at Day 14.

Brick plot showing detection of individual VE303 strains at Day 14 in participants dosed with VE303.

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Extended Data Table 1 In vitro and in vivo characterization of VE303 organisms
Full size table

Supplementary information

Supplementary Tables 1 and 3–5 and Fig. 1.

Reporting Summary

Supplementary Table 2

Summary of VE303 strain detection in study participants at scheduled timepoints

Source data

Source Data Fig. 1

Source data for plotting VE303 colonization.

Source Data Fig. 2

Statistical source data for plotting features predictive of VE303 colonization.

Source Data Fig. 3

Source data for vancomycin concentrations and VE303 colonization.

Source Data Fig. 4

Statistical source data for plotting features predictive of clinical outcome.

Source Data Fig. 5

Source data for plotting diversity indices and statistical source data for plotting bacterial species associated with clinical outcome.

Source Data Fig. 6

Source data for plotting BAs and SCFAs and statistical source data for plotting host bacteria associations with gut metabolites.

Source Data Extended Data Fig./Table 1

Source data for plotting sample collection schedules for data types collected in study.

Source Data Extended Data Fig./Table 2

Source data for plotting VE303 strain detection in individual subjects.

Source Data Extended Data Fig./Table 3

Statistical source data for RF model performance for predicting VE303 colonization, recurrence and early (by day 14) recurrence.

Source Data Extended Data Fig./Table 4

Statistical source data for RF model importance summaries of features predicting recurrence and early (by day 14) recurrence.

Source Data Extended Data Fig./Table 5

Source data for plotting serum cytokines.

Source Data Extended Data Fig./Table 6

Statistical source data for plotting bacterial taxa at all levels associated with clinical outcome.

Source Data Extended Data Fig./Table 7

Source data for plotting BAs and SCFAs.

Source Data Extended Data Fig./Table 8

Statistical source data for plotting BA genes associated with dose, recurrence and host bacterial taxa.

Source Data Extended Data Fig./Table 9

Source data for plotting VE303 strain detection in individual subjects.

Source Data Extended Data Fig./Table 10

Source data for plotting VE303 strain detection in individual subjects.

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Menon, R., Bhattarai, S.K., Crossette, E. et al. Multi-omic profiling a defined bacterial consortium for treatment of recurrent Clostridioides difficile infection. Nat Med 31, 223–234 (2025). https://doi.org/10.1038/s41591-024-03337-4

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