Evidence map›Paper›PMID 34057024›Full record

ArticleGut microbes

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of

Johannes Hertel, Almut Heinken, Filippo Martinelli, Ines Thiele

Open access · goldAbstract read
In one paragraph

Article in Gut microbes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
2.3field-weighted citation impact, top 11% of its field
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

26 citing papers in PubMed, 35 citations in OpenAlex.

  1. mBio · 2026
    Article
  2. Article
  3. Review
  4. Toward the Next Generation of In Silico Modeling of Dynamic Host-Microbiota Interactions in the Skin.JID innovations : skin science from molecules to population health · 2025
    Review
  5. Article
  6. Characterising functional redundancy in microbiome communities via relative entropy.Computational and structural biotechnology journal · 2025
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. pymgpipe: microbiome metabolic modeling in Python.Journal of open source software · 2023
    Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
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

4 authors at 3 institutions in 2 countries.

Johannes HertelSchool of Medicine, National University of Galway, Galway, Ireland.
Almut HeinkenSchool of Medicine, National University of Galway, Galway, Ireland.
Filippo MartinelliSchool of Medicine, National University of Galway, Galway, Ireland.
Ines ThieleSchool of Medicine, National University of Galway, Galway, Ireland.
Ollscoil na Gaillimhe – University of Galway · IEAPC Microbiome Institute · IEUniversitätsmedizin Greifswald · DE

Funding

Project 4 - Mechanistic studies on the role of the gut microbiome in models for Alzheimer's diseaseU19AG063744 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Rima F Kaddurah-Daouk · 2019 to 2026
$54.1M
Gut Liver Brain Biochemical Axis in Alzheimer's DiseaseRF1AG058942 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, VAN DUIJN, CORNELIA MARJA · 2018 to 2018
$3.4M
NIA NIH HHS RF1 AG058942NIA NIH HHS U19 AG063744
6 · The paper itself

Abstract

Characterizing the metabolic functions of the gut microbiome in health and disease is pivotal for translating alterations in microbial composition into clinical insights. Two major analysis paradigms have been used to explore the metabolic functions of the microbiome but not systematically integrated with each other: statistical screening approaches, such as metabolome-microbiome association studies, and computational approaches, such as constraint-based metabolic modeling. To combine the strengths of the two analysis paradigms, we herein introduce a set of theoretical concepts allowing for the population statistical treatment of constraint-based microbial community models. To demonstrate the utility of the theoretical framework, we applied it to a public metagenomic dataset consisting of 365 colorectal cancer (CRC) cases and 251 healthy controls, shining a light on the metabolic role of

Indexed as

Gastrointestinal MicrobiomeAgedBacteriaButyratesCase-Control StudiesColorectal NeoplasmsFecesFemaleFusobacteriumHumansMaleMetabolomicsMiddle AgedButyratesflux balance analysisfusiobacteriaMetabolic modelingmetagenomic datamicrobiome

Identifiers

PMID34057024
PMCPMC8168482
OpenAlexW3164970195

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.