Evidence map›Paper›PMID 34006865›Full record

ArticleNature communications2021

Gene-level metagenomic architectures across diseases yield high-resolution microbiome diagnostic indicators.

Braden T Tierney, Yingxuan Tan, Aleksandar D Kostic, Chirag J Patel

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 pooled it
5.2field-weighted citation impact, top 3% 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

40 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Comparative metagenomics using pan-metagenomic graphs.bioRxiv : the preprint server for biology · 2025
    Article
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  7. Observational
  8. Review
  9. Review
  10. Article
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  12. Article
  13. Co-evolution of vaginal microbiome and cervical cancer.Journal of translational medicine · 2024
    Article
  14. Article
  15. Article
  16. The Next Generation Fecal Microbiota Transplantation: To Transplant Bacteria or Virome.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Review
  17. Article
  18. Article
  19. Article
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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 2 institutions in 1 country.

Braden T TierneyDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-7533-8802
Yingxuan TanDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Aleksandar D KosticSection on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, MA, USA. Aleksandar.Kostic@joslin.harvard.edu.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. chirag_patel@hms.harvard.edu.ORCID 0000-0002-8756-8525
Harvard University · USJoslin Diabetes Center · US

Funding

SPECIAL ASSAY COREP30DK036836 · NIDDK · JOSLIN DIABETES CENTER · PI ROHIT N. KULKARNI · 1986 to 2026
$50.5M
Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan AfricaR01AI127250 · NIAID · STANFORD UNIVERSITY · PI BENDAVID, ERAN, PATEL, CHIRAG J. · 2017 to 2020
$2.7M
Harvard Training Program in Bioinformatics Applied to Diabetes, Obesity and Metabolism.T32DK110919 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI FLOREZ, JOSE CARLOS, PATEL, CHIRAG J. · 2017 to 2021
$1.3M
NIAID NIH HHS R01 AI127250NIDDK NIH HHS P30 DK036836NIDDK NIH HHS T32 DK110919
6 · The paper itself

Abstract

We propose microbiome disease "architectures": linking >1 million microbial features (species, pathways, and genes) to 7 host phenotypes from 13 cohorts using a pipeline designed to identify associations that are robust to analytical model choice. Here, we quantify conservation and heterogeneity in microbiome-disease associations, using gene-level analysis to identify strain-specific, cross-disease, positive and negative associations. We find coronary artery disease, inflammatory bowel diseases, and liver cirrhosis to share gene-level signatures ascribed to the Streptococcus genus. Type 2 diabetes, by comparison, has a distinct metagenomic signature not linked to any one specific species or genus. We additionally find that at the species-level, the prior-reported connection between Solobacterium moorei and colorectal cancer is not consistently identified across models-however, our gene-level analysis unveils a group of robust, strain-specific gene associations. Finally, we validate our findings regarding colorectal cancer and inflammatory bowel diseases in independent cohorts and identify that features inversely associated with disease tend to be less reproducible than features enriched in disease. Overall, our work is not only a step towards gene-based, cross-disease microbiome diagnostic indicators, but it also illuminates the nuances of the genetic architecture of the human microbiome, including tension between gene- and species-level associations.

Indexed as

BacillotaBacteriaCluster AnalysisColorectal NeoplasmsComputational BiologyDiabetes Mellitus, Type 2Gastrointestinal MicrobiomeHumansInflammatory Bowel DiseasesMetagenomeMetagenomicsMicrobiotaPhylogenySpecies Specificity

Identifiers

PMID34006865
PMCPMC8131609
OpenAlexW3161341786

What OpenQuestion holds

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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.