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Members of the human gut microbiota involved in recovery from Vibrio cholerae infection



Given the global burden of diarrhoeal diseases1, it is important to understand how members of the gut microbiota affect the risk for, course of, and recovery from disease in children and adults. The acute, voluminous diarrhoea caused by Vibrio cholerae represents a dramatic example of enteropathogen invasion and gut microbial community disruption. Here we conduct a detailed time-series metagenomic study of faecal microbiota collected during the acute diarrhoeal and recovery phases of cholera in a cohort of Bangladeshi adults living in an area with a high burden of disease2. We find that recovery is characterized by a pattern of accumulation of bacterial taxa that shows similarities to the pattern of assembly/maturation of the gut microbiota in healthy Bangladeshi children3. To define the underlying mechanisms, we introduce into gnotobiotic mice an artificial community composed of human gut bacterial species that directly correlate with recovery from cholera in adults and are indicative of normal microbiota maturation in healthy Bangladeshi children3. One of the species, Ruminococcus obeum, exhibits consistent increases in its relative abundance upon V. cholerae infection of the mice. Follow-up analyses, including mono- and co-colonization studies, establish that R. obeum restricts V. cholerae colonization, that R. obeum luxS (autoinducer-2 (AI-2) synthase) expression and AI-2 production increase significantly with V. cholerae invasion, and that R. obeum AI-2 causes quorum-sensing-mediated repression of several V. cholerae colonization factors. Co-colonization with V. cholerae mutants discloses that R. obeum AI-2 reduces Vibrio colonization/pathogenicity through a novel pathway that does not depend on the V. cholerae AI-2 sensor, LuxP. The approach described can be used to mine the gut microbiota of Bangladeshi or other populations for members that use autoinducers and/or other mechanisms to limit colonization with V. cholerae, or conceivably other enteropathogens.

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Figure 1: R. obeum restricts V. cholerae colonization in adult gnotobiotic mice.
Figure 2: R. obeum AI-2 reduces V. cholerae colonization and virulence gene expression.

Accession codes

Primary accessions

European Nucleotide Archive

Data deposits

All 16S rRNA, shotgun sequencing, and RNA-seq data sets generated from faecal samples have been deposited in the European Nucleotide Archive in raw format before post-processing and data analysis under accession number PRJEB6358.


  1. World Health Organization Cholera, 2013. Wkly Epidemiol. Rec. 89, 345–356 (2014)

    Google Scholar 

  2. Chowdhury, F. et al. Impact of rapid urbanization on the rates of infection by Vibrio cholerae O1 and enterotoxigenic Escherichia coli in Dhaka, Bangladesh. PLoS Negl. Trop. Dis. 5, e999 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  3. Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997)

    Google Scholar 

  5. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Martens, E. C. et al. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol. 9, e1001221 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. McNulty, N. P. et al. Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome. PLoS Biol. 11, e1001637 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. McNulty, N. P. et al. The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Sci. Translat. Med. 3, 106ra106 (2011)

    Article  Google Scholar 

  9. Taylor, R. K., Miller, V. L., Furlong, D. B. & Mekalanos, J. J. Use of phoA gene fusions to identify a pilus colonization factor coordinately regulated with cholera toxin. Proc. Natl Acad. Sci. USA 84, 2833–2837 (1987)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Herrington, D. A. et al. Toxin, toxin-coregulated pili, and the toxR regulon are essential for Vibrio cholerae pathogenesis in humans. J. Exp. Med. 168, 1487–1492 (1988)

    Article  CAS  PubMed  Google Scholar 

  11. Olivier, V., Salzman, N. H. & Satchell, K. J. Prolonged colonization of mice by Vibrio cholerae El Tor O1 depends on accessory toxins. Infect. Immun. 75, 5043–5051 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Olivier, V., Haines, G. K., III, Tan, Y. & Satchell, K. J. Hemolysin and the multifunctional autoprocessing RTX toxin are virulence factors during intestinal infection of mice with Vibrio cholerae El Tor O1 strains. Infect. Immun. 75, 5035–5042 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yang, M. et al. Bile salt-induced intermolecular disulfide bond formation activates Vibrio cholerae virulence. Proc. Natl Acad. Sci. USA 110, 2348–2353 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Miller, M. B., Skorupski, K., Lenz, D. H., Taylor, R. K. & Bassler, B. L. Parallel quorum sensing systems converge to regulate virulence in Vibrio cholerae . Cell 110, 303–314 (2002)

    Article  CAS  PubMed  Google Scholar 

  15. Zhu, J. et al. Quorum-sensing regulators control virulence gene expression in Vibrio cholerae . Proc. Natl Acad. Sci. USA 99, 3129–3134 (2002)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kovacikova, G. & Skorupski, K. Regulation of virulence gene expression in Vibrio cholerae by quorum sensing: HapR functions at the aphA promoter. Mol. Microbiol. 46, 1135–1147 (2002)

    Article  CAS  PubMed  Google Scholar 

  17. Higgins, D. A. et al. The major Vibrio cholerae autoinducer and its role in virulence factor production. Nature 450, 883–886 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  18. Pereira, C. S., Thompson, J. A. & Xavier, K. B. AI-2-mediated signalling in bacteria. FEMS Microbiol. Rev. 37, 156–181 (2013)

    Article  CAS  PubMed  Google Scholar 

  19. Sun, J., Daniel, R., Wagner-Dobler, I. & Zeng, A. P. Is autoinducer-2 a universal signal for interspecies communication: a comparative genomic and phylogenetic analysis of the synthesis and signal transduction pathways. BMC Evol. Biol. 4, 36 (2004)

    Article  PubMed  PubMed Central  Google Scholar 

  20. Duan, F. & March, J. C. Engineered bacterial communication prevents Vibrio cholerae virulence in an infant mouse model. Proc. Natl Acad. Sci. USA 107, 11260–11264 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu, Z. et al. Mucosal penetration primes Vibrio cholerae for host colonization by repressing quorum sensing. Proc. Natl Acad. Sci. USA 105, 9769–9774 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu, Z., Stirling, F. R. & Zhu, J. Temporal quorum-sensing induction regulates Vibrio cholerae biofilm architecture. Infect. Immun. 75, 122–126 (2007)

    Article  CAS  PubMed  Google Scholar 

  23. Taga, M. E., Semmelhack, J. L. & Bassler, B. L. The LuxS-dependent autoinducer AI-2 controls the expression of an ABC transporter that functions in AI-2 uptake in Salmonella typhimurium . Mol. Microbiol. 42, 777–793 (2001)

    Article  CAS  PubMed  Google Scholar 

  24. Bassler, B. L., Wright, M. & Silverman, M. R. Multiple signalling systems controlling expression of luminescence in Vibrio harveyi: sequence and function of genes encoding a second sensory pathway. Mol. Microbiol. 13, 273–286 (1994)

    Article  CAS  PubMed  Google Scholar 

  25. Surette, M. G., Miller, M. B. & Bassler, B. L. Quorum sensing in Escherichia coli, Salmonella typhimurium, and Vibrio harveyi: a new family of genes responsible for autoinducer production. Proc. Natl Acad. Sci. USA 96, 1639–1644 (1999)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Iwanaga, M. et al. Culture conditions for stimulating cholera toxin production by Vibrio cholerae O1 El Tor. Microbiol. Immunol. 30, 1075–1083 (1986)

    Article  CAS  PubMed  Google Scholar 

  27. Liu, Z., Hsiao, A., Joelsson, A. & Zhu, J. The transcriptional regulator VqmA increases expression of the quorum-sensing activator HapR in Vibrio cholerae . J. Bacteriol. 188, 2446–2453 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Faiz, M. A. & Basher, A. Antimicrobial resistance: Bangladesh experience. Reg. Health Forum 15, 1–18 (2011)

    Google Scholar 

  29. Morgan, D. J., Okeke, I. N., Laxminarayan, R., Perencevich, E. N. & Weisenberg, S. Non-prescription antimicrobial use worldwide: a systematic review. Lancet Infect. Dis. 11, 692–701 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  30. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cole, J. R. et al. The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res. 35, D169–D172 (2007)

    Article  CAS  PubMed  Google Scholar 

  33. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rodrigue, S. et al. Unlocking short read sequencing for metagenomics. PLoS ONE 5, e11840 (2010)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  35. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010)

    Article  CAS  PubMed  Google Scholar 

  37. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013)

    Article  PubMed  Google Scholar 

  38. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

    PubMed  PubMed Central  Google Scholar 

  39. Kristiansson, E., Hugenholtz, P. & Dalevi, D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics 25, 2737–2738 (2009)

    Article  CAS  PubMed  Google Scholar 

  40. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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We thank S. Wagoner, J. Hoisington-López, M. Meier, J. Cheng, D. O’Donnell, and M. Karlsson for technical support, J. Zhu for providing strains of V. cholerae and Vibrio harveyi, and W.-L. Ng for providing ΔluxP V. cholerae. This work was supported in part by a grant from the Bill & Melinda Gates Foundation. The singleton birth cohort of Bangladeshi children was supported by a grant from the National Institutes of Health (AI 43596). The post-doctoral fellowship stipend of A.H. was funded in part by NIH training grants (T32DK077653, T32AI007172) and by the Crohn’s and Colitis Foundation of America. The International Centre for Diarrhoeal Disease Research, Bangladesh, acknowledges the following donors, which provided unrestricted support: the Australian Agency for International Development, the Government of Bangladesh, the Canadian International Development Agency, the Swedish International Development Cooperation Agency, and the Department for International Development, UK.

Author information

Authors and Affiliations



A.H. and J.I.G. designed the metagenomic and gnotobiotic mouse study; A.M.S.A., R.H., and T.A. designed and implemented the clinical study, participated in patient recruitment, sample collection, sample preservation and clinical evaluations; R.H. and W.A.P. participated in recruitment of and sample collection from healthy Bangladeshi controls; A.H. generated the 16S rRNA, AI-2, RNA-seq, shotgun microbial community DNA sequencing, and V. cholerae colonization data. S.S. generated 16S rRNA data from extended sampling of the Bangladeshi singleton birth cohort. L.L.D. performed 16S rRNA sequencing of the additional samples from patients C and E and helped generate the colonization data in in vivo competition experiments involving isogenic wild-type, ΔvqmA and ΔluxP strains of V. cholerae C6706; A.H., S.S., N.W.G., and J.I.G. analysed the data; A.H. and J.I.G. wrote the paper.

Corresponding author

Correspondence to Jeffrey I. Gordon.

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Competing interests

J.I.G. is co-founder of Matatu Inc., a company that characterizes the role of diet-by-microbiota interactions in defining health.

Extended data figures and tables

Extended Data Figure 1 Experimental designs for clinical study and gnotobiotic mouse experiments.

a, Sampling schedule for human cholera study. b, Frequency of diarrhoeal episodes over time for a representative participant (patient A). Initial time (black circle) represents beginning of diarrhoea. The long vertical line marks enrollment into the study. Colours and short vertical lines denote boundaries of study phases defined in a. ce, Gnotobiotic mouse experimental design. The number (n) of animals in each treatment group is shown.

Extended Data Figure 2 Bacterial taxa associated with diarrhoeal and recovery phase.

a, Proportion of bacterial species-level taxa that were observed in both diarrhoeal and recovery phases, in D-Ph1 to D-Ph4 only, and in R-Ph1 to R-Ph3 only. Mean values ± s.e.m. are plotted. *P < 0.05, ***P < 0.001 (unpaired Mann–Whitney U-test). b, Phylum-level analysis. Mean values are plotted. c, Proportion of study participants having bacterial taxa associated by indicator species analysis with the diarrhoeal or recovery phase. The x axis shows species associated with each phase, ranked by proportion of subjects harbouring that species. For each species, ‘representation in study participants’ is the average presence/absence of all 97%-identity OTUs with that species taxonomic assignment. The OTU table was rarefied to 49,000 reads per sample. d, Bacterial species identified by indicator analysis as indicative of diarrhoea or recovery phases in adult patients with cholera, and species identified by Random Forests analysis as discriminatory for different stages in the maturation of the gut microbiota of healthy Bangladeshi infants/children aged 1–24 months (denoted by the symbol †). The heat map in the left-hand portion of the panel shows mean relative abundances of species across all individuals during D-Ph1 to D-Ph4, with each phase subdivided into four equal time bins. For recovery time points, columns represent the mean relative abundances for each sampling time point during R-Ph1 to R-Ph3. Mean relative abundance values are also presented for these same species in the faecal microbiota of 50 healthy Bangladeshi children sampled from 1 to 2 years of age at monthly intervals. Unsupervised hierarchical clustering used relative abundances of species in the faecal microbiota of the patients with cholera. The green portion of the tree encompasses species that are more abundant during recovery whereas the red portion encompasses species that are more abundant during diarrhoea. Indicator scores are presented in the right-hand portion of the panel, with ‘score’ for a given taxon defined as its indicator value for recovery minus its indicator value for diarrhoea (−1, highly diarrhoea-associated; +1, highly recovery-associated). Spearman’s rank correlation coefficients of mean relative abundances of species by sample in the cholera study versus the mean sample-weighted UniFrac distance to healthy adult faecal microbiota are shown at the extreme right together with the statistical significance of correlations after Benjamini–Hochberg false discovery rate correction for multiple hypothesis testing (NS, not significant; *P < 0.05; **P < 0.01; ***P < 0.001). Higher coefficients indicate increasing divergence from a healthy configuration with higher relative abundance of a given species. Species shown satisfied two or more of the following criteria: (1) presence among the list of the top 40 age-discriminatory species in the Random-Forests-based model of gut microbiota maturation in healthy infants and children; (2) indicator value score greater than 0.7; (3) significant correlation (Spearman’s r) between relative abundance in the faecal microbiota of patients with cholera and UniFrac distance to healthy adult faecal microbiota; and (4) inclusion in the artificial 14-member human gut community (species name highlighted in blue).

Extended Data Figure 3 The 97%-identity OTUs observed in both diarrhoeal and recovery phases.

The proportion of 97%-identity OTUs with a given species-level taxonomic assignment that were present in both diarrhoeal and recovery phases is shown for each individual in the study. The number of 97%-identity OTUs with a given species assignment is shown in parentheses. Species are ordered based on their ‘indicator scores’ (defined as indicator valuerecovery minus indicator valuediarrhoea). Age-discriminatory bacterial species incorporated into a Random-Forests-based model for defining relative microbiota maturity and microbiota-for-age z-scores3 in healthy Bangladeshi infants and children are marked with a ‘+’ symbol. The 97%-identity OTUs were derived from data sets generated from all samples from adult patents with cholera; the OTU table was rarefied to 49,000 reads per sample.

Extended Data Figure 4 Pattern of appearance of age-discriminatory 97%-identity OTUs in the faecal microbiota of patients with cholera mirrors the normal age-dependent pattern in the faecal microbiota of healthy Bangladeshi infants and children.

a, Left portion of the panel shows hierarchical clustering of relative abundance values for each of the top 60 most age-discriminatory 97%-identity OTUs in a Random-Forests-based model of normal maturation of the microbiota in healthy Bangladeshi infants/children (importance scores for the age-discriminatory taxa defined by Random Forests analysis are reported in ref. 3; these 60 97%-identity OTUs can be grouped into 40 species-level taxa). Right portion of the panel presents the mean relative abundances of these OTUs in samples obtained from patients with cholera during D-Ph1 to D-Ph4, and R-Ph1 to R-Ph3. The 97%-identity OTUs corresponding to species included in the artificial community that was introduced into gnotobiotic mice are highlighted in blue. b, Relative abundance of R. obeum strains in the faecal microbiota of healthy Bangladeshi children sampled monthly through the first 3 years of life. Mean values ± s.e.m. are plotted.

Extended Data Figure 5 Pattern of recovery of the gut microbiota in patients with cholera.

a, b, Mean unweighted (a) and weighted (b) UniFrac distances to healthy adult controls at each of the defined phases of diarrhoea and recovery. c, d, Principal coordinates analysis of UniFrac distances between gut microbiota samples. Location along the principal axis of variation (PC1) shows how acute diarrhoeal communities first resemble those of healthy Bangladeshi children sampled during the first 2 years of life, then evolve their phylogenetic configurations during the recovery phase towards those of healthy Bangladeshi adults. PC1 accounts for 34.3% variation for weighted and 17.7% variation for unweighted UniFrac values. e, Alpha diversity (whole-tree phylogenetic diversity) measurements of faecal microbial communities through all study phases. Mean values ± s.e.m. are plotted. *P < 0.05, **P < 0.01, ****P < 0.0001 (Kruskal–Wallis analysis of variance followed by multiple comparisons test).

Extended Data Figure 6 Proportional representation of genes encoding enzymes (classified according to Enzyme Commission number identifiers) in faecal microbiomes sampled during the diarrhoeal and recovery phases of cholera.

Shotgun sequencing of faecal community DNA was performed (MiSeq 2000 instrument; 2 × 250bp paired-end reads; 341,701 ± 145,681 reads (mean ± s.d. per sample)). Read pairs were assembled (SHERA software package34). Read counts were collapsed based on their assignment to Enzyme Commission (EC) number identifiers. The significance of differences in EC abundances compared with faecal microbiomes in healthy adult Bangladeshi controls was defined using ShotgunFunctionalizeR39. Unsupervised hierarchical clustering identifies groups of ECs that characterize the faecal microbiomes of patients with cholera at varying diarrhoeal and recovery phases. The heat map on the left shows the results of EC-based clustering by phase (diarrhoea/recovery). An asterisk on the extreme right of the figure indicates that differences in EC abundance observed across the specified study phases were statistically significant (adjusted P < 0.00001, ShotgunFunctionalizeR). The heat map on the right presents the results of a global clustering of all time-points and study phases. Genes encoding 102 ECs were identified with (1) at least 0.1% average relative abundance across the study and (2) significant differences in their representation relative to healthy microbiomes in at least one comparison (adjusted P < 0.00001 based on ShotgunFunctionalizeR). In each of the heat maps, z-scores for each EC across all samples are plotted. ECs are grouped by KEGG level 1 assignment and further annotated based on their KEGG Pathway assignments. A ‘+’ symbol indicates that the EC has additional KEGG level 2 annotations (see Supplementary Table 8 for a list of all assignable functional annotations). Note that the majority of the 46 ECs that were more prominently represented in faecal microbiomes during diarrhoeal phases in study participants are related to carbohydrate metabolism. The faecal microbiomes of patients during recovery are enriched for genes involved in vitamin and cofactor metabolism (Supplementary Table 8).

Extended Data Figure 7 R. obeum encodes a functional AI-2 system, and R. obeum AI-2 production is stimulated by the presence of V. cholerae.

a, Relative abundances of R. obeum and V. cholerae in the faecal microbiota after introduction of V. cholerae into mice harbouring the artificial 14-member human gut community (D14invasion group, see Extended Data Figure 1c). ‘Days post V. cholerae gavage’ refers to the second of two daily gavages of 109 c.f.u. V. cholerae into animals that had been colonized 14 days earlier with the 14-member community. Mean values ± s.e.m. are shown (n = 4 or 5 mice, *P < 0.05, unpaired Student’s t-test). b, Left panel shows AI-2 signalling pathway components represented in the R. obeum genome. Right panel plots changes in expression of these components as defined by microbial RNA-seq of faecal samples obtained (1) 4 days after colonization of mice with the 14-member community and (2) 4 days after gavage of mice with the 14-member community together with 109 c.f.u. of V. cholerae (n = 4–6 animals per group; one faecal sample analysed per animal). Mean values ± s.e.m. are shown. *P < 0.05 (Mann–Whitney U-test). c, RNA-seq of faecal samples collected at the time points and treatment groups indicated reveals that R. obeum luxS transcription is directly correlated to V. cholerae abundance in the context of the 14-member community. **P < 0.01 (F test). d, R. obeum luxS expression. Mice were colonized first with R. obeum for 7 day. Faecal samples were collected for microbial RNA-seq analysis 1 day before gavage of 109 c.f.u. of a V. cholerae ΔluxS mutant, and then 2 days post-gavage (d2pg). Mean values for relative R. obeum luxS transcript levels ( ± s.e.m.) are shown (n = 5 or 6 animals per group per experiment, n = 3 independent experiments; **P < 0.01 unpaired Mann–Whitney U-test). e, AI-2 levels in faecal samples, taken 1 day before and 3 days after gavage of the V. cholerae ΔluxS strain, from the same mice as those analysed in a. AI-2 levels were measured based on induction of bioluminescence in V. harveyi BB170 using the same mass of input faecal sample for all assays. Mean values ± s.e.m. are shown; ****P < 0.0001 (unpaired Mann–Whitney U-test). f, R. obeum produces AI-2 when co-cultured with V. cholerae in vitro. Aliquots of the supernatant from cultures containing R. obeum alone, or R. obeum plus the V. cholerae ΔluxS mutant, were assayed for their ability to induce V. harveyi bioluminescence. Mean values ± s.e.m. are presented (n = 4 independent experiments). LU, light units; RPKM, reads per kilobase per million reads. ****P < 0.0001 (unpaired Mann–Whitney U-test). Note that (1) the number of R. obeum c.f.u. present in the samples obtained from mono-cultures of the organism was similar to the number in co-culture, as measured by selective plating, and (2) the V. cholerae ΔluxS mutant cultured alone produced levels of AI-2 signal that were not significantly different from that of R. obeum in mono-culture (data not shown).

Extended Data Figure 8 UPLC–MS analysis of faecal bile acid profiles in gnotobiotic mice.

Targeted UPLC–MS used methanol extracts of faecal pellets obtained from age- and gender-matched germ-free C57BL/6J mice and gnotobiotic mice colonized for 3 days with R. obeum alone, for 7 days with the 14-member community (‘D1invasion group’), and for 3 days with the 13-member community that lacked R. obeum (n = 4–6 mice per treatment group; one faecal sample analysed per animal). a, Faecal levels of taurocholic acid. Mean values ± s.e.m. are plotted. *P < 0.05, **P < 0.01, Mann–Whitney U-test. b, Mean relative abundance of ten bile acid species in faecal samples obtained from the mice shown in a.

Extended Data Figure 9 Phylogenetic tree of luxS genes present in human gut bacterial symbionts and enteropathogens.

The tree was constructed from amino-acid sequence alignments using Clustal X. Red type indicates that the homologue is represented in the genomes of members of the 14-member artificial human gut bacterial community.

Extended Data Figure 10 In vivo tests of the effects of known quorum-sensing components on R. obeum-mediated reductions in V. cholerae colonization.

a, Competitive index of ΔluxP versus wild-type C6706 V. cholerae when colonized with or without R. obeum (n = 4–6 animals per group). Horizontal bars, mean values. Data from individual animals are shown using the indicated symbols. b, Transcript abundance (reads per kilobase per million reads) for selected quorum-sensing and virulence gene regulators in V. cholerae. Microbial RNA-seq was performed on faecal samples collected 2 days after mono-colonization of germ-free mice with V. cholerae (circles), or 2 days after V. cholerae was introduced into mice that had been mono-colonized for 7 days with R. obeum (squares) (n = 5 animals per group; NS, not significant (P ≥ 0.05); **P < 0.01, ***P < 0.001, ****P < 0.0001 (unpaired two-tailed Student’s t-test)).

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Hsiao, A., Ahmed, A., Subramanian, S. et al. Members of the human gut microbiota involved in recovery from Vibrio cholerae infection. Nature 515, 423–426 (2014).

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