Evidence map›Paper›PMID 30195437›Full record

ArticleCell systems2018

Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota.

Gregory L Medlock, Maureen A Carey, Dennis G McDuffie, Michael B Mundy, Natasa Giallourou, Jonathan R Swann, Glynis L Kolling, Jason A Papin

Abstract read
In one paragraph

Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

64 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Constraint-Based Modeling of Microbial Communities for Metabolite Production.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  6. Article
  7. iScience · 2025
    Article
  8. Article
  9. Article
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  11. Article
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  15. Review
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  17. Article
  18. Article
  19. Article
  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Gregory L MedlockDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Maureen A CareyDepartment of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
Dennis G McDuffieDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Michael B MundyCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Natasa GiallourouDepartment of Surgery and Cancer, Division of Integrative Systems Medicine and Digestive Diseases, Faculty of Medicine, Imperial College London, South Kensington, London, UK.
Jonathan R SwannDepartment of Surgery and Cancer, Division of Integrative Systems Medicine and Digestive Diseases, Faculty of Medicine, Imperial College London, South Kensington, London, UK.
Glynis L KollingDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Jason A PapinDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA. Electronic address: papin@virginia.edu.

Funding

Translational Control in the Hepatitis C VirusS06GM008136 · NIGMS · NEW MEXICO STATE UNIVERSITY LAS CRUCES · PI KUEHN, GLENN D · 1989 to 2011
$23.2M
TRAINING IN CELL AND MOLECULAR BIOLOGYT32GM008136 · NIGMS · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI STUKENBERG, P. TODD · 1985 to 2020
$8.5M
Mapping and predicting metabolic fluxes between the ileal microbiome and hostR01GM108501 · NIGMS · UNIVERSITY OF VIRGINIA · PI PAPIN, JASON · 2014 to 2017
$1.4M
Transdisciplinary Big Data Science Training at UVaT32LM012416 · NLM · UNIVERSITY OF VIRGINIA · PI BROWN, DONALD E, LOUGHRAN, THOMAS P. · 2016 to 2020
$1.3M
NIGMS NIH HHS R01 GM108501NIGMS NIH HHS S06 GM008136NIGMS NIH HHS T32 GM008136NLM NIH HHS T32 LM012416
6 · The paper itself

Abstract

The diversity and number of species present within microbial communities create the potential for a multitude of interspecies metabolic interactions. Here, we develop, apply, and experimentally test a framework for inferring metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from a model mouse microbiota. We then apply our framework to dissect emergent metabolic behaviors that occur in co-culture. Based on one of the inferences from this framework, we identify and interrogate an amino acid cross-feeding interaction and validate that the proposed interaction leads to a growth benefit in vitro. Our results reveal the type and extent of emergent metabolic behavior in microbial communities composed of gut microbes. We focus on growth-modulating interactions, but the framework can be applied to interspecies interactions that modulate any phenotype of interest within microbial communities.

Indexed as

Microbial InteractionsAnimalsClostridiumCoculture TechniquesComputer SimulationEubacteriumGastrointestinal MicrobiomeHumansLactobacillusMetabolic Networks and PathwaysMetabolomeMiceModels, BiologicalModels, TheoreticalPrincipal Component Analysisemergent behaviorinterspecies interactionsmathematical modelingmetabolic cross-feedingmetabolismmicrobial communitymicrobial ecologymicrobiomemicrobiotasystems biology

Identifiers

PMID30195437
PMCPMC6166237

What OpenQuestion holds

Textmetadata
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Read underepoch 390

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.