Evidence map›Paper›PMID 39856391›Full record

ArticleNature microbiology2025

Longitudinal phage-bacteria dynamics in the early life gut microbiome.

Michael J Tisza, Richard E Lloyd, Kristi Hoffman, Daniel P Smith, Marian Rewers, Sara J Javornik Cregeen, Joseph F Petrosino

Abstract read
In one paragraph

Article in Nature microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Observational
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Alternating High-Fat and Polysaccharide Diets Modulates Gut Phage-Bacterial Interplay.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Single cell viral tagging ofGut microbes · 2025
    Article
  14. Review
  15. A prevalent huge phage clade in human and animal gut microbiomes.bioRxiv : the preprint server for biology · 2025
    Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Review
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

7 authors.

Michael J TiszaThe Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.ORCID http://orcid.org/0000-0003-1168-1617
Richard E LloydThe Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.
Kristi HoffmanThe Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.
Daniel P SmithThe Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.ORCID http://orcid.org/0000-0002-2479-2044
Marian RewersBarbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO, USA.ORCID http://orcid.org/0000-0003-3829-9207
Sara J Javornik CregeenThe Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA. cregeen@bcm.edu.ORCID http://orcid.org/0009-0000-2698-6478
Joseph F PetrosinoThe Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA. jpetrosi@bcm.edu.ORCID http://orcid.org/0000-0002-4046-6898

Funding

Limited Competition: Continued Follow-up of Subjects and Initiation of a Second Case-control Cohort in The Environmental Determinants of Diabetes in The Young Study (TEDDY)U01DK128847 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI JEFFREY P KRISCHER · 2021 to 2026
$79.2M
Data Coordinating Center (DCC)for the Consortium for Identification of EnvironmenUC4DK095300 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2011 to 2011
$56.0M
NIH Prior Approval Process ProfessionalUL1TR002535 · NCATS · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2018 to 2022
$51.1M
Data Coordinating CenterU01DK063790 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2002 to 2006
$50.1M
Follow-up on Subjects, Integrative Data Analysis and Measurement of Viral Antibodies in The Environmental Determinants of Diabetes in The Young Study (TEDDY)U01DK124166 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2020 to 2022
$31.1M
NIDDK Follow-up on Subjects and Immunological Assessments in The Environmental Determinants of Diabetes In The Young Study (TEDDY) (UC4)UC4DK117483 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2017 to 2020
$30.4M
Establish a Data Coordinating Center (DCC) for the Consortium for Identification UC4DK100238 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2014 to 2014
$28.9M
The Study of Epigenetics and Infections in The Environmental Determinants of Diabetes in the Young (TEDDY)UC4DK112243 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2016 to 2016
$27.0M
Data Coordinating Center (DCC) for the Consortium for Identification of Environmental Determinants of Diabetes in the Young (TEDDY) StudyUC4DK106955 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2015 to 2015
$20.0M
Translational Research Support CoreP30ES030285 · NIEHS · BAYLOR COLLEGE OF MEDICINE · PI Cheryl L. Walker · 2019 to 2026
$14.7M
The Environmental Determinants of Diabetes in Young (TEDDY0U01DK063863 · NIDDK · HOSPITAL DISTRICT OF SOUTHWEST FINLAND · PI TOPPARI, JORMA · 2003 to 2022
$13.5M
The Environmental Triggers of Diabetes (TEDDY) in SwedenU01DK063861 · NIDDK · UNIVERSITY OF WASHINGTON · PI LERNMARK, AKE · 2003 to 2022
$13.4M
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR002535NIDDK NIH HHS U01 DK063790NIDDK NIH HHS U01 DK063821NIDDK NIH HHS U01 DK063829NIDDK NIH HHS U01 DK063836NIDDK NIH HHS U01 DK063861NIDDK NIH HHS U01 DK063863NIDDK NIH HHS U01 DK063865NIDDK NIH HHS U01 DK124166NIDDK NIH HHS U01 DK128847NIDDK NIH HHS UC4 DK063821NIDDK NIH HHS UC4 DK063829NIDDK NIH HHS UC4 DK063836NIDDK NIH HHS UC4 DK063861NIDDK NIH HHS UC4 DK063863NIDDK NIH HHS UC4 DK063865NIDDK NIH HHS UC4 DK095300NIDDK NIH HHS UC4 DK100238NIDDK NIH HHS UC4 DK106955NIDDK NIH HHS UC4 DK112243NIDDK NIH HHS UC4 DK117483NIEHS NIH HHS P30 ES030285NLM NIH HHS HHSN267200700014CU.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) U01 DK63821U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) U01 DK63829U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) U01 DK63865
6 · The paper itself

Abstract

Microbial colonization of the human gut occurs soon after birth, proceeds through well-studied phases and is affected by lifestyle and other factors. Less is known about phage community dynamics during infant gut colonization due to small study sizes, an inability to leverage large databases and a lack of appropriate bioinformatics tools. Here we reanalysed whole microbial community shotgun sequencing data of 12,262 longitudinal samples from 887 children from four countries across four years of life as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We developed an extensive metagenome-assembled genome catalogue using the Marker-MAGu pipeline, which comprised 49,111 phage taxa from existing human microbiome datasets. This was used to identify phage marker genes and their integration into the MetaPhlAn 4 bacterial marker gene database enabled simultaneous assessment of phage and bacterial dynamics. We found that individual children are colonized by hundreds of different phages, which are more transitory than bacteria, accumulating a more diverse phage community over time. Type 1 diabetes correlated with a decreased rate of change in bacterial and viral communities in children aged one and two. The addition of phage data improved the ability of machine learning models to discriminate samples by country. Finally, although phage populations were specific to individuals, we observed trends of phage ecological succession that correlated well with putative host bacteria. This resource improves our understanding of phage-bacteria interactions in the developing early life microbiome.

Indexed as

BacteriaBacteriophagesGastrointestinal MicrobiomeChild, PreschoolDiabetes Mellitus, Type 1FecesFemaleHumansInfantInfant, NewbornLongitudinal StudiesMachine LearningMaleMetagenome

Identifiers

PMID39856391
PMCPMC11790489

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.