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Bacterial hydrogen sulfide drives cryptic redox chemistry in gut microbial communities

Nature Metabolism volume 4, pages 1260–1270 (2022)Cite this article

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Abstract

Microbial biochemistry contributes to a dynamic environment in the gut. Yet, how bacterial metabolites such as hydrogen sulfide (H2S) mechanistically alter the gut chemical landscape is poorly understood. Here we show that microbially generated H2S drives the abiotic reduction of azo (R–N = N–R’) xenobiotics, which are commonly found in Western food dyes and drugs. This nonenzymatic reduction of azo compounds is demonstrated in Escherichia coli cultures, in human faecal microbial communities and in vivo in male mice. Changing dietary levels of the H2S xenobiotic redox partner Red 40 transiently decreases mouse faecal sulfide levels, demonstrating that a xenobiotic can attenuate sulfide concentration and alleviate H2S accumulation in vivo. Cryptic H2S redox chemistry thus can modulate sulfur homeostasis, alter the chemical landscape in the gut and contribute to azo food dye and drug metabolism. Interactions between chemicals derived from microbial communities may be a key feature shaping metabolism in the gut.

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Fig. 1: Sulfide reduces diverse azo compounds.
Fig. 2: Anaerobic E. coli undergoes azoreduction via hydrogen sulfide production.
Fig. 3: Human faecal microbiomes reduce Red 40 in proportion to available sulfur sources.
Fig. 4: Diet alters faecal sulfide in mice.

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Data availability

Data supporting the findings of this study are available within the paper and Supplementary Information. No custom computer code was used to generate results reported in the paper. Correspondence and requests for materials may be addressed to L.K. Source data are provided with this paper.

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Acknowledgements

The authors thank A. Ryan (Northumbria University) for sharing knowledge of azoreductase enzymes, and the laboratories of S. Almo and T. Grove (Albert Einstein College of Medicine) for experimental and intellectual support in the related work. L.K. was supported in part by a US Department of Defense Cancer Research Program Career Development Award (CA171019) and the National Institutes of Health NHLBI (R01HL069438-21). R.H. was supported by the Albert Einstein College of Medicine PhD in Clinical Investigation training grant (TL1TR001072). S.K. was supported by the Einstein Medical Scientist Training Program (2T32GM007288) and a National Institutes of Health T32 Fellowship in Geographic Medicine and Emerging Infectious Diseases (2T32AI070117). L.A. was supported by National Cancer Institute Department of Cancer Prevention Nutrition grants (1R01CA214625 and 1R01CA229216). Support was also received by the Albert Einstein Cancer Center core support grant (P30CA013330).

Author information

Author notes

  1. These authors contributed equally: Sarah J. Wolfson, Reese Hitchings.

Authors and Affiliations

  1. Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA

    Sarah J. Wolfson, Reese Hitchings, Ziv Cohen, Saad Khan, Marcel Malena & Libusha Kelly

  2. Departments of Medicine and Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA

    Karina Peregrina & Leonard Augenlicht

  3. Department of Chemical Engineering, Northeastern University, Boston, MA, USA

    Tugba Yilmaz & Edgar D. Goluch

  4. Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA

    Libusha Kelly

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Contributions

S.J.W., R.H., L.K. and L.A. drafted the manuscript. S.J.W. performed E. coli experiments, inactivated faecal experiments, metagenomic analysis and mouse faecal sulfide determination. R.H. performed abiotic and faecal slurry experiments, as well as MS. K.P. performed all mouse experimentation and maintained the mouse colony. L.A. guided mouse experiments and diet formulation. T.Y. performed cyclic voltammetry of azo compounds. E.D.G. guided electrochemistry experiments. M.M. and Z.C. assisted with faecal slurry experiments. S.K. assisted with metagenomic analysis. All authors edited the manuscript and contributed intellectually.

Corresponding author

Correspondence to Libusha Kelly.

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Extended data

Extended Data Fig. 1 Azoreduction with and without FMN.

Normalized MS intensities of azo compounds and their proposed azoreduced metabolites (mean ± sd, n = 3 independent incubations per compound). Dashed lines indicate conditions without FMN as an electron shuttle, solid lines indicate conditions supplemented with FMN as an electron shuttle.

Source data

Extended Data Fig. 2 Dietary cysteine and faecal sulfide.

Mice fed 8 g/kg cysteine (High Cys) could not be distinguished from mice fed 4 g/kg cysteine (Control Cys) after 1 week. After 2 weeks, fecal sulfide is lower in 0 Cysteine diet than either Control or High Cysteine diets. (n = 30, 10/diet). Boxes show the upper and lower quartiles, and whiskers depict range excluding outliers. Outliers are defined as points > 1.5 times the inter-quartile range. Statistical difference was determined by one-way ANOVA, Tukey’s test.

Source data

Extended Data Fig. 3 Initial rate of azoreduction in active faecal microcosm.

Rate of initial azoreduction in faecal microcosms (as in Fig. 3a, n = 3 biologically independent incubations per condition) (mean ± SEM). Conditions azoreduce at different rates (one-way ANOVA, p = 0.0004), Mucin azoreduces faster than the Enzymatic Control (Dunnett’s multiple comparisons test, p = 0.047).

Source data

Extended Data Fig. 4 Active faecal microcosm azoreduction with and without sulfate amendment.

Red 40 azoreduction does not differ between Enzymatic Control and cultures amended with sulfate (unpaired, two-sided Wilcoxon rank-sum test, p > 0.05 all timepoints) (mean ± sd; Cx/C0, ratio of remaining Red 40 to 500 µM starting amendment; n = 3 biological replicates per condition). Minimal hydrogen sulfide accumulates in the 6.5 hour experimental window regardless of Red 40 presence.

Source data

Extended Data Fig. 5 Dissimilatory sulfate reduction in active faecal microcosm during extended incubation.

The healthy human faecal microcosm was incubated for ~3.5 days (90.5 hours) to allow the sulfate reducing bacterial community to acclimate. No additional thiol or sulfur sources were provided in the chemically defined media. Hydrogen sulfide accumulation, beginning after 18.5 hours, indicated dissimilatory sulfate reduction activity, and cultures were autoclaved following a 90.5 hour incubation for the heat inactivated fecal SRB experiment (Fig. 3c). H2S does not differ between the two conditions incubated with sulfate allocated for the heat inactivated faecal SRB experiment (mean ± sd; sulfate/sulfate control, unpaired, two-sided Wilcoxon rank-sum test, p > 0.05 all timepoints; n = 3 biological replicates per condition).

Source data

Extended Data Table 1 Elution schemes for each drug and metabolite
Full size table
Extended Data Table 2 Components for E. coli sulfide azoreduction and enzymatic azoreductase
Full size table
Extended Data Table 3 Components for faecal microcosms
Full size table
Extended Data Table 4 Mouse diet composition
Full size table

Supplementary information

Reporting Summary

Supplementary Tables 1 and 2

Supplementary Table 1 Profile of sulfidogenic cysteine-degrading genes in human gut symbiont genomes (Fig. 3d). Presence (1) or absence (0) of nine genes (CBS (K01697), CSE (K01758), cysK (K01738), cysM (K12339), cyuA (COG3681), malY (K14155), metC (K01760), sseA (K01011) and tnaA (K01667)) encoding sulfidogenic cysteine-degrading proteins in genomes of common, non-pathogenic human gut microorganisms. The subset of gut symbionts was identified from 8,548 metagenomic samples of participants from 51 studies using the R package curatedMetagenomicData48 (March 2021). To identify each gene in a genome, a reference database for each gene was aligned and HMMs were constructed. The nine HMMs were searched against bacterial genomes using hmmsearch with a cutoff of 1 × 10−10. One hit in a genome indicated the presence of the gene in a particular genome; multiple hits were ignored. Supplementary Table 2 Profile of sulfidogenic cysteine-degrading genes in bacteria with completely sequenced genomes. Presence (1) or absence (0) of nine genes ((CBS (K01697), CSE (K01758), cysK (K01738), cysM (K12339), cyuA (COG3681), malY (K14155); metC (K01760); sseA (K01011) and tnaA (K01667)) encoding sulfidogenic cysteine-degrading proteins in 24,758 NCBI bacterial genomes (GenBank, April 2021)42. To identify each gene in a genome, a reference database for each gene was aligned and HMMs were constructed. The nine HMMs were searched against bacterial genomes using hmmsearch with a cutoff of 1 × 10−10. One hit in a genome indicated the presence of the gene in a particular genome; multiple hits were ignored.

Source data

Source Data Fig. 1

MS and half-life data plotted in Fig. 1.

Source Data Fig. 2

Red 40 and sulfide concentrations plotted in Fig. 2.

Source Data Fig. 3

Red 40 and sulfide concentrations plotted in Fig. 3.

Source Data Fig. 4

Mouse cohort and sulfide concentrations plotted in Fig. 4.

Source Data Extended Data Fig. 1

MS data plotted in Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Mouse cohort and sulfide concentrations plotted in Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Red 40 loss rates as plotted in Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Red 40 loss ratio as plotted in Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Sulfide concentrations as plotted in Extended Data Fig. 5.

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Wolfson, S.J., Hitchings, R., Peregrina, K. et al. Bacterial hydrogen sulfide drives cryptic redox chemistry in gut microbial communities. Nat Metab 4, 1260–1270 (2022). https://doi.org/10.1038/s42255-022-00656-z

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