Evidence map›Paper›PMID 38956426›Full record

ArticleNature ecology & evolution2024

Metabolic complexity drives divergence in microbial communities.

Michael R Silverstein, Jennifer M Bhatnagar, Daniel Segrè

Abstract read
In one paragraph

Article in Nature ecology & evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
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  18. Review
  19. Review
  20. mSystems · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael R SilversteinBioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2205-3624
Jennifer M BhatnagarBioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0001-6424-4133
Daniel SegrèBioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, MA, USA. dsegre@bu.edu.ORCID http://orcid.org/0000-0003-4859-1914

Funding

Identifying protective omics profiles in centenarians and translating these into preventive and therapeutic strategiesUH2AG064704 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI PERLS, THOMAS T, SEBASTIANI, PAOLA · 2019 to 2021
$9.4M
Predoctoral Training in Bioinformatics and Computational BiologyT32GM100842 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI TULLIUS, THOMAS D · 2012 to 2022
$2.8M
A platform for mining, visualization and design of microbial interaction networksR01GM121950 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI DELISI, CHARLES, HU, ZHENJUN · 2017 to 2019
$1.1M
Synthetic Biology and Biotechnology (SB2) Predoctoral Training ProgramT32GM130546 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI CHEN, CHRISTOPHER S, KHALIL, AHMAD SAMIR · 2019 to 2023
$1.0M
National Science Foundation (NSF) 1457695NIA NIH HHS UH2 AG064704NIGMS NIH HHS R01 GM121950NIGMS NIH HHS T32 GM100842NIGMS NIH HHS T32 GM130546U.S. Department of Energy (DOE) DE-AC02-05CH11231U.S. Department of Energy (DOE) DE-SC0012704U.S. Department of Energy (DOE) DE-SC0020403U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32GM100842U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R01GM121950U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) UH2AG064704
6 · The paper itself

Abstract

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Indexed as

MicrobiotaBacteriaModels, Biological

Identifiers

PMID38956426
PMCPMC13619369

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.