A Tripartite Microbial-Environment Network Indicates How Crucial Microbes Influence the Microbial Community Ecology.

Yushi Tang, Tianjiao Dai, Zhiguo Su, Kohei Hasegawa, Jinping Tian, Lujun Chen, Donghui Wen

Abstract read

In one paragraph

Article in Microbial ecology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

1.2field-weighted citation impact, top 20% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types.Microorganisms · 2022
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

7 authors at 4 institutions in 2 countries.

Yushi TangCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.

Tianjiao DaiCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.

Zhiguo SuCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.

Kohei HasegawaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.

Jinping TianSchool of Environment, Tsinghua University, Beijing, 100084, China.

Lujun ChenSchool of Environment, Tsinghua University, Beijing, 100084, China.

Donghui WenCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China. dhwen@pku.edu.cn.ORCID http://orcid.org/0000-0002-1301-989X

Peking University · CNHarvard University · USTsinghua University · CNZhejiang University of Technology · CN

Funding

National Natural Science Foundation of China 51678003National Natural Science Foundation of China 51678334

6 · The paper itself

Abstract

Current technologies could identify the abundance and functions of specific microbes, and evaluate their individual effects on microbial ecology. However, these microbes interact with each other, as well as environmental factors, in the form of complex network. Determination of their combined ecological influences remains a challenge. In this study, we developed a tripartite microbial-environment network (TMEN) analysis method that integrates microbial abundance, metabolic function, and environmental data as a tripartite network to investigate the combined ecological effects of microbes. Applying TMEN to analyzing the microbial-environment community structure in the sediments of Hangzhou Bay, one of the most seriously polluted coastal areas in China, we found that microbes were well-organized into 4 bacterial communities and 9 archaeal communities. The total organic carbon, sulfate, chemical oxygen demand, salinity, and nitrogen-related indexes were detected as crucial environmental factors in the microbial-environmental network. With close interactions with these environmental factors, Nitrospirales and Methanimicrococcu were identified as hub microbes with connection advantage. Our TMEN method could close the gap between lack of efficient statistical and computational approaches and the booming of large-scale microbial genomic and environmental data. Based on TMEN, we discovered a potential microbial ecological mechanism that crucial species with significant influence on the microbial community ecology would possess one or two of the community advantages for enhancing their ecological status and essentiality, including abundance advantage and connection advantage.

Indexed as

Bacterial Physiological PhenomenaMicrobial ConsortiaArchaeaBaysChinaGeologic SedimentsMicrobiological TechniquesAbundance advantageConnection advantageLarge-scale environmental dataMetabolic functionMicrobial community structureTripartite network

Identifiers

PMID31428833

DOI10.1007/s00248-019-01421-8

OpenAlexW2969652080

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.