Evidence map›Paper›PMID 35597888›Full record

ArticleThe ISME journal2022

Ecological dynamics of the gut microbiome in response to dietary fiber.

Hongbin Liu, Chen Liao, Lu Wu, Jinhui Tang, Junyu Chen, Chaobi Lei, Linggang Zheng, Chenhong Zhang, Yang-Yu Liu, Joao Xavier and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in The ISME journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
16.2field-weighted citation impact, top 1% 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

62 citing papers in PubMed, 143 citations in OpenAlex.

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

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 at 5 institutions in 2 countries.

Hongbin Liu *CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Chen Liao *Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-8474-1196
Lu WuCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Jinhui TangGuangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Junyu ChenCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Chaobi LeiCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Linggang ZhengCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Chenhong ZhangState Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0002-4462-892X
Yang-Yu LiuChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.ORCID http://orcid.org/0000-0003-2728-4907
Joao XavierProgram for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0003-3592-1689
Lei DaiCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. lei.dai@siat.ac.cn.ORCID http://orcid.org/0000-0002-5598-5308
Chinese Academy of Sciences · CNMemorial Sloan Kettering Cancer Center · USBrigham and Women's Hospital · USShanghai Jiao Tong University · CNShenzhen Institutes of Advanced Technology · CN

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Functional Redundancy of Human Microbiome and its Implication in Fecal Microbiota TransplantationR01AI141529 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI LIU, YANG-YU · 2019 to 2023
$4.5M
China Postdoctoral Science Foundation 2020M682968National Natural Science Foundation of China (National Science Foundation of China) 31971513National Natural Science Foundation of China (National Science Foundation of China) 32061143023NCI NIH HHS P30 CA008748NIAID NIH HHS R01 AI141529
6 · The paper itself

Abstract

Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual's response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.

Indexed as

Gastrointestinal MicrobiomeAnimalsDietary FiberFatty Acids, VolatileFecesHumansInulinMiceResistant StarchDietary FiberFatty Acids, VolatileInulinResistant Starch

Identifiers

PMID35597888
PMCPMC9296629
OpenAlexW4281252130

What OpenQuestion holds

Textmetadata
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.