Evidence map›Paper›PMID 34653368›Full record

ArticleCell systems2022

Functional attractors in microbial community assembly.

Sylvie Estrela, Jean C C Vila, Nanxi Lu, Djordje Bajić, Maria Rebolleda-Gómez, Chang-Yu Chang, Joshua E Goldford, Alicia Sanchez-Gorostiaga, Álvaro Sánchez

Abstract read
In one paragraph

Article in Cell systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing 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

75 citing papers in PubMed.

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15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sylvie EstrelaDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA. Electronic address: sylvie.estrela@yale.edu.
Jean C C VilaDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA.
Nanxi LuDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA.
Djordje BajićDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA.
Maria Rebolleda-GómezDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA.
Chang-Yu ChangDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA.
Joshua E GoldfordPhysics of Living Systems, Massachusetts Institute of Technology, Boston, MA 02139, USA.
Alicia Sanchez-GorostiagaDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, Centro Nacional de Biotecnología, CSIC, UAM Cantoblanco, 28049 Madrid, Spain.
Álvaro SánchezDepartment of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA. Electronic address: alvaro.sanchez@yale.edu.

Funding

Predicting the assembly and function of microbial consortia: a systems biology approachR35GM133467 · NIGMS · YALE UNIVERSITY · PI SANCHEZ DE ANDRES, ALVARO · 2019 to 2021
$1.3M
NIGMS NIH HHS R35 GM133467
6 · The paper itself

Abstract

For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly.

Indexed as

MicrobiotaPopulation DynamicsReproducibility of Resultsalternative statesdynamical systems theorymicrobial community assemblymicrobial metabolismpopulation dynamics

Identifiers

PMID34653368
PMCPMC8800145

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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.