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Environmentally dependent interactions shape patterns in gene content across natural microbiomes

Nature Microbiology volume 9, pages 2022–2037 (2024)Cite this article

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Abstract

Sequencing surveys of microbial communities in hosts, oceans and soils have revealed ubiquitous patterns linking community composition to environmental conditions. While metabolic capabilities restrict the environments suitable for growth, the influence of ecological interactions on patterns observed in natural microbiomes remains uncertain. Here we use denitrification as a model system to demonstrate how metagenomic patterns in soil microbiomes can emerge from pH-dependent interactions. In an analysis of a global soil sequencing survey, we find that the abundances of two genotypes trade off with pH; nar gene abundances increase while nap abundances decrease with declining pH. We then show that in acidic conditions strains possessing nar fail to grow in isolation but are enriched in the community due to an ecological interaction with nap genotypes. Our study provides a road map for dissecting how associations between environmental variables and gene abundances arise from environmentally modulated community interactions.

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Fig. 1: pH is associated with covariation in denitrification pathway composition in the global topsoil microbiome.
Fig. 2: Enrichment cultures reproduce patterns in denitrification gene content in the topsoil microbiome.
Fig. 3: Individual traits do not explain the outcome of acidic enrichments.
Fig. 4: Nitrite toxicity impacts denitrification activity of isolates at low pH.
Fig. 5: Co-culture alleviates nitrite toxicity under acidic conditions.
Fig. 6: Nar+ and Nap+ phenotypes are conserved across diverse taxa.

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Data availability

Data associated with this manuscript are publicly available at https://doi.org/10.17605/OSF.IO/N4J6F and by request. Raw sequence reads for soil enrichment experiments are deposited under National Center for Biotechnology Information BioProject ID PRJNA976277, and raw sequence reads for co-culture experiments are deposited under National Center for Biotechnology Information BioProject ID PRJNA1109838. Bacterial isolates are available by request. Gene abundance tables and environmental variables for the global topsoil microbiome dataset were provided by Bahram et al.4. The SILVA rRNA database (refs. 82,83,84; https://www.arb-silva.de) was used for taxonomic classification.

Code availability

Code associated with this manuscript is publicly available at https://doi.org/10.17605/OSF.IO/N4J6F and by request.

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Acknowledgements

We acknowledge L. M. de Jesús Astacio and M. Yousef for collecting soils, R. A. Oliveira for help with colony counting technique, M. Bahram for previously published global topsoil microbiome data and K. Husain for useful discussions. Soil sampling done in the CAF was, in part, a contribution from the LTAR network. LTAR is supported by the United States Department of Agriculture. This work was supported by the National Science Foundation Division of Emerging Frontiers EF 2025293 (S.K.) and EF 2025521 (M.M.), as well as the National Science Foundation Graduate Research Fellowship Program under grant number DGE 1746045 (M.C-W). S.K. acknowledges the Center for the Physics of Evolving Systems at the University of Chicago, National Institute of General Medical Sciences R01GM151538, and support from the National Science Foundation through the Center for Living Systems (grant number 2317138). K.G. acknowledges a James S. McDonnell Foundation Postdoctoral Fellowship Award 220020499. M.T. acknowledges support from NSF grant PHY-2310746. M.C-W. acknowledges a Fannie and John Hertz Fellowship Award. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Ecology and Evolution, The University of Chicago, Chicago, IL, USA

    Kyle Crocker, Kiseok Keith Lee, Zeqian Li & Seppe Kuehn

  2. Center for the Physics of Evolving Systems, The University of Chicago, Chicago, IL, USA

    Kyle Crocker, Kiseok Keith Lee, Milena Chakraverti-Wuerthwein, Zeqian Li & Seppe Kuehn

  3. Center for Living Systems, The University of Chicago, Chicago, IL, USA

    Kyle Crocker, Kiseok Keith Lee, Milena Chakraverti-Wuerthwein & Seppe Kuehn

  4. Biophysical Sciences Graduate Program, The University of Chicago, Chicago, IL, USA

    Milena Chakraverti-Wuerthwein

  5. Department of Physics, The University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Zeqian Li

  6. Department of Physics, Washington University in St. Louis, St. Louis, MO, USA

    Mikhail Tikhonov

  7. Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA

    Madhav Mani

  8. Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA

    Madhav Mani

  9. NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL, USA

    Madhav Mani

  10. National Institute for Theory and Mathematics in Biology, Northwestern University and The University of Chicago, Chicago, IL, USA

    Madhav Mani & Seppe Kuehn

  11. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    Karna Gowda

  12. Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA

    Karna Gowda

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Contributions

K.C., K.G., M.M. and S.K. conceptualized the research. K.C., K.G. and S.K. designed the experiments. K.C., K.G., K.K.L. and M.C.-W. performed the experiments. K.G. performed statistical analysis of the topsoil microbiome data and performed and analysed soil enrichment experiments. M.C.-W. performed mortality rate experiments, advised by K.C. and S.K. Z.L. performed co-evolution analysis, advised by K.G. and S.K. K.C. performed all other experiments and analyses, advised by K.G. and S.K. K.C. performed consumer-resource model fits, predictions and simulations, adapting code written by K.G. K.K.L. sampled topsoils and performed the soil microcosm experiments. M.T. performed the analysis of sequencing data for the soil microcosm experiment in Supplementary Fig. 3. The topsoil microcosm experiment was, in part, a contribution from the LTAR network. LTAR is supported by the United States Department of Agriculture. K.C., K.G., Z.L., K.K.L., M.T. and S.K. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Karna Gowda or Seppe Kuehn.

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The authors declare no competing interests.

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Nature Microbiology thanks Nicholas Bouskill, Eoin Brodie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Principal components resulting from the uiSVD decomposition of the global topsoil microbiome.

Bars show the loadings of each gene in the six principal components (PCs) resulting from the uiSVD decomposition of denitrification reductase (narG, napA, nirS, nirK, norB, nosZ) relative abundances from the global topsoil microbiome.

Extended Data Fig. 2 C/N ratio correlates negatively with denitrification pathway magnitude.

Unit-invariant singular value decomposition (uiSVD) was used to decompose denitrification reductase (narG, napA, nirS, nirK, norB, nosZ) relative abundances from the global topsoil microbiome into contributions due to pathway magnitude and composition (Fig. 1a–d). Pathway magnitude (d) most strongly correlated with C/N ratio (ρ = − 0.59, p < 10−6 via one-tailed randomization test; Fig. 1f).

Extended Data Fig. 3 Enrichment across a broader range of pH values.

Additional enrichments were performed at pH 5.0, 5.5, 6.0, and 7.3 and the endpoint cultures were shotgun sequenced to infer taxonomic composition and genotypes. (A) Endpoint community compositions of the enrichments inferred via 16S miTAGs are shown. Taxa with Nar+ genotypes are indicated in shades of blue, while taxa that possess Nap and not Nar are indicated in shades of red. Compositions are shown at the level of taxonomic order, and taxa present at a level of less than 1% are omitted. (B) Median denitrification reductase genotypes inferred via annotation of metagenome assembled genomes are shown.

Extended Data Fig. 4 pH 7.3 monoculture data are explained by individual fitness effects.

(A)-(H) Consumer resource model (Supplementary Information Eq. S1) fit to monoculture metabolite data at pH 7.3. Dots indicate nitrate concentrations, stars indicate nitrite concentrations, solid lines show fits to nitrate dynamics, and dash-dot lines show fits to nitrite dynamics. All concentrations are averaged over biological replicates (n = 3). Panels (A, C, E, G) are fits to PD Nar+ data, while panels (B, D, F, H) are fits to RH Nap+ data. To infer nitrate and nitrite reduction rates independently fits were performed for a number of different initial conditions ([NO−3], [NO−2]). Panels (A) and (B) correspond to (1.75, 0), (C) and (D) to (0.875, 0), (E) and (F) to (0.4375, 1.3125), (G) and (H) to (0.875, 0.4375). All concentrations are reported in units of mM. (I) Co-culture relative abundance prediction based on monoculture phenotypes. RH Nap+ is predicted to approach a relative abundance of 1 for all initial conditions at pH 7.3. This is consistent with what was observed in enrichment experiments (Fig. 5f).

Extended Data Fig. 5 Co-culture enrichment biomass and metabolite dynamics.

Details of the PD Nar+ and RH Nap+ co-culture experiment shown in Fig. 5. (A) PD Nar+ relative abundance dynamics at pH 6.0 are shown. \({f}_{0,Na{r}^{+}}=0,0.03,0.5,0.97\) and 1 are highlighted. Due to small levels of cross-contamination between pure and mixed cultures, \({f}_{Na{r}^{+}}\) increases from 0 and decreases from 1. Although this was unintentional, it indicates that each of these strains is invasible by the other in this condition, providing more evidence that they coexist. (B) PD Nar+ relative abundance dynamics at pH 7.3 are shown. In panels A and B, data points are means of biological replicates (n = 4 for PD Nar+ relative abundance > = 0.5 at pH 6 and relative abundance < = 0.03 and = 1 in both pH conditions; n = 3 for all other conditions) of inferred relative abundances (Methods) and errorbars are calculated as described in the Fig. 5 caption and Methods. (C) Endpoint biomass dynamics, measured via absorbance at 600 nm, are shown for each cycle at pH 6.0. The \({f}_{0,Na{r}^{+}}=0\) condition produces much less biomass than the other conditions, as expected. (D) Endpoint biomass dynamics are shown for each cycle at pH 7.3. (E, G) NO−3 and NO−2 dynamics are measured using a Griess assay (117) and shown at pH 6.0. Aside from the \({f}_{0,Na{r}^{+}}=0\) condition, for which biomass is very low (panel C), increasing \({f}_{Na{r}^{+}}\) (panel A) corresponds to increasing nitrite accumulation. (F, H) Metabolite dynamics are shown at pH 7.3. Decreasing \({f}_{Na{r}^{+}}\) (B) corresponds to decreasing nitrite accumulation. Points and error bars in panels C-H show means and standard deviations over biological replicates (n = 4 for PD Nar+ relative abundance > = 0.5 at pH 6 and relative abundance < = 0.03 and = 1 in both pH conditions; n = 3 for all other conditions) in each condition.

Supplementary information

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Supplementary Text Sections 1–5, Supplementary Figs. 1–9 and Supplementary Tables 1–4.

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Crocker, K., Lee, K.K., Chakraverti-Wuerthwein, M. et al. Environmentally dependent interactions shape patterns in gene content across natural microbiomes. Nat Microbiol 9, 2022–2037 (2024). https://doi.org/10.1038/s41564-024-01752-4

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