Literature DB >> 31428833

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

Yushi Tang1,2, Tianjiao Dai1, Zhiguo Su1, Kohei Hasegawa2,3, Jinping Tian4, Lujun Chen4,5, Donghui Wen6.   

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.

Entities:  

Keywords:  Abundance advantage; Connection advantage; Large-scale environmental data; Metabolic function; Microbial community structure; Tripartite network

Mesh:

Year:  2019        PMID: 31428833     DOI: 10.1007/s00248-019-01421-8

Source DB:  PubMed          Journal:  Microb Ecol        ISSN: 0095-3628            Impact factor:   4.552


  59 in total

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Journal:  Curr Opin Biotechnol       Date:  2002-06       Impact factor: 9.740

2.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

Authors:  Weizhong Li; Adam Godzik
Journal:  Bioinformatics       Date:  2006-05-26       Impact factor: 6.937

3.  Serial analysis of rRNA genes and the unexpected dominance of rare members of microbial communities.

Authors:  Matthew N Ashby; Jasper Rine; Emmanuel F Mongodin; Karen E Nelson; Dago Dimster-Denk
Journal:  Appl Environ Microbiol       Date:  2007-05-25       Impact factor: 4.792

Review 4.  Marine microbial community dynamics and their ecological interpretation.

Authors:  Jed A Fuhrman; Jacob A Cram; David M Needham
Journal:  Nat Rev Microbiol       Date:  2015-02-09       Impact factor: 60.633

5.  High-Throughput Sequencing Analysis of the Bacterial Community in Stone Fruit Phloem Tissues Infected by "Candidatus Phytoplasma prunorum".

Authors:  Ales Eichmeier; Tomas Kiss; Tomas Necas; Eliska Penazova; Dorota Tekielska; Marketa Bohunicka; Lucie Valentova; Radek Cmejla; Daniel Morais; Petr Baldrian
Journal:  Microb Ecol       Date:  2018-09-07       Impact factor: 4.552

6.  UCHIME improves sensitivity and speed of chimera detection.

Authors:  Robert C Edgar; Brian J Haas; Jose C Clemente; Christopher Quince; Rob Knight
Journal:  Bioinformatics       Date:  2011-06-23       Impact factor: 6.937

7.  Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data.

Authors:  Kathrin P Aßhauer; Bernd Wemheuer; Rolf Daniel; Peter Meinicke
Journal:  Bioinformatics       Date:  2015-05-07       Impact factor: 6.937

8.  Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

Authors:  David Berry; Stefanie Widder
Journal:  Front Microbiol       Date:  2014-05-20       Impact factor: 5.640

9.  Where less may be more: how the rare biosphere pulls ecosystems strings.

Authors:  Alexandre Jousset; Christina Bienhold; Antonis Chatzinotas; Laure Gallien; Angélique Gobet; Viola Kurm; Kirsten Küsel; Matthias C Rillig; Damian W Rivett; Joana F Salles; Marcel G A van der Heijden; Noha H Youssef; Xiaowei Zhang; Zhong Wei; W H Gera Hol
Journal:  ISME J       Date:  2017-01-10       Impact factor: 10.302

10.  The Ribosomal Database Project: improved alignments and new tools for rRNA analysis.

Authors:  J R Cole; Q Wang; E Cardenas; J Fish; B Chai; R J Farris; A S Kulam-Syed-Mohideen; D M McGarrell; T Marsh; G M Garrity; J M Tiedje
Journal:  Nucleic Acids Res       Date:  2008-11-12       Impact factor: 16.971

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  1 in total

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

Authors:  Haris Zafeiropoulos; Savvas Paragkamian; Stelios Ninidakis; Georgios A Pavlopoulos; Lars Juhl Jensen; Evangelos Pafilis
Journal:  Microorganisms       Date:  2022-01-26
  1 in total

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