Evidence map›Paper›PMID 35921525›Full record

ArticleGut microbes

Modeling spatial interaction networks of the gut microbiota.

Xiaocang Cao, Ang Dong, Guangbo Kang, Xiaoli Wang, Liyun Duan, Huixing Hou, Tianming Zhao, Shuang Wu, Xinjuan Liu, He Huang and 1 more

Abstract 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 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
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  3. A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  7. Review
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  15. Editorial: Engineering probiotics for multiple interventions on intestinal diseases.Frontiers in cellular and infection microbiology · 2023
    Article
  16. Review
  17. 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

11 authors.

Xiaocang CaoDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Ang DongCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Guangbo KangSchool of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China.
Xiaoli WangDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Liyun DuanDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Huixing HouDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Tianming ZhaoDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Shuang WuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Xinjuan LiuDepartment of Gastroenterology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
He HuangSchool of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China.
Rongling WuCenter for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How the gut microbiota is organized across space is postulated to influence microbial succession and its mutualistic relationships with the host. The lack of dynamic or perturbed abundance data poses considerable challenges for characterizing the spatial pattern of microbial interactions. We integrate allometric scaling theory, evolutionary game theory, and prey-predator theory into a unified framework under which quasi-dynamic microbial networks can be inferred from static abundance data. We illustrate that such networks can capture the full properties of microbial interactions, including causality, the sign of the causality, strength, and feedback loop, and are dynamically adaptive along spatial gradients, and context-specific, characterizing variability between individuals and within the same individual across time and space. We design and conduct a gut microbiota study to validate the model, characterizing key spatial determinants of the microbial differences between ulcerative colitis and healthy controls. Our model provides a sophisticated means of unraveling a complete atlas of how microbial interactions vary across space and quantifying causal relationships between such spatial variability and change in health state.

Indexed as

Colitis, UlcerativeGastrointestinal MicrobiomeHumansevolutionary game theoryGut microbiotamicrobial interaction networkquasi-dynamic ordinary differential equationspatial biology

Identifiers

PMID35921525
PMCPMC9351588

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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.