Evidence map›Paper›PMID 37577626›Full record

ArticlebioRxiv : the preprint server for biology2023

Metabolic complexity drives divergence in microbial communities.

Michael Silverstein, Jennifer M Bhatnagar, Daniel Segrè

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael SilversteinBioinformatics Program, Boston University, Boston, MA.
Jennifer M BhatnagarBioinformatics Program, Boston University, Boston, MA.
Daniel SegrèBioinformatics Program, Boston University, Boston, MA.

Funding

Predoctoral Training in Bioinformatics and Computational BiologyT32GM100842 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI TULLIUS, THOMAS D · 2012 to 2022
$2.8M
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
NIGMS NIH HHS T32 GM100842NIGMS NIH HHS T32 GM130546
6 · The paper itself

Abstract

Microbial communities are shaped by the metabolites available in their environment, 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. To this end, 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 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 diverse 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 smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.

Identifiers

PMID37577626
PMCPMC10418233

What OpenQuestion holds

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Registered trials

None linked

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.