{"title":"Systematic pairwise co-cultures uncover predominant negative interactions among human gut bacteria.","authors":"Jiaying Zhu, Min-Zhi Jiang, Xue Chen, Min Li, Yu-Lin Wang, Chang Liu, Shuang-Jiang Liu, Wei-Hua Chen","doi":"10.1186/s40168-025-02156-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Understanding pairwise bacterial interactions in the human gut is crucial for deciphering the complex networks of bacterial interactions and their contributions to host health. However, there is a lack of large-scale experiments focusing on bacterial interactions within the human gut microbiome.</p><p><strong>Methods: </strong>We investigated the pairwise interactions of 113 bacterial strains isolated from healthy Chinese volunteers, selected for their high abundance and functional representation of the human gut microbiome. Using mGAM agar plates, a rich medium designed to maintain community structure, we established the \"PairInteraX\" dataset, which includes 3233 pair combinations of culturable human gut bacteria. This dataset was analyzed to identify interaction patterns and the key factors influencing these patterns.</p><p><strong>Results: </strong>Our analysis revealed that negative interactions were predominant among the bacteria in the PairInteraX dataset. When combined with in vivo gut metagenome datasets, we noted a diminishing mutualism and an increasing competition as microbial abundances increased; consequently, the maintenance of community diversity requires the participation of various types of interactions, especially the negative interactions. We also identified key factors influencing these interaction patterns including metabolic capacity and motility.</p><p><strong>Conclusions: </strong>This study provides a comprehensive overview of pairwise bacterial interactions within the human gut microbiome, revealing a dominance of negative interactions. Besides, metabolic capacity and motility were identified as the key factors to influence the pairwise interaction patterns. This large-scale dataset and analysis offer valuable insights for further research on microbial community dynamics and their implications for host health. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"161"},"PeriodicalIF":13.8000,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235815/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbiome","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s40168-025-02156-0","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Understanding pairwise bacterial interactions in the human gut is crucial for deciphering the complex networks of bacterial interactions and their contributions to host health. However, there is a lack of large-scale experiments focusing on bacterial interactions within the human gut microbiome.
Methods: We investigated the pairwise interactions of 113 bacterial strains isolated from healthy Chinese volunteers, selected for their high abundance and functional representation of the human gut microbiome. Using mGAM agar plates, a rich medium designed to maintain community structure, we established the "PairInteraX" dataset, which includes 3233 pair combinations of culturable human gut bacteria. This dataset was analyzed to identify interaction patterns and the key factors influencing these patterns.
Results: Our analysis revealed that negative interactions were predominant among the bacteria in the PairInteraX dataset. When combined with in vivo gut metagenome datasets, we noted a diminishing mutualism and an increasing competition as microbial abundances increased; consequently, the maintenance of community diversity requires the participation of various types of interactions, especially the negative interactions. We also identified key factors influencing these interaction patterns including metabolic capacity and motility.
Conclusions: This study provides a comprehensive overview of pairwise bacterial interactions within the human gut microbiome, revealing a dominance of negative interactions. Besides, metabolic capacity and motility were identified as the key factors to influence the pairwise interaction patterns. This large-scale dataset and analysis offer valuable insights for further research on microbial community dynamics and their implications for host health. Video Abstract.
期刊介绍:
Microbiome is a journal that focuses on studies of microbiomes in humans, animals, plants, and the environment. It covers both natural and manipulated microbiomes, such as those in agriculture. The journal is interested in research that uses meta-omics approaches or novel bioinformatics tools and emphasizes the community/host interaction and structure-function relationship within the microbiome. Studies that go beyond descriptive omics surveys and include experimental or theoretical approaches will be considered for publication. The journal also encourages research that establishes cause and effect relationships and supports proposed microbiome functions. However, studies of individual microbial isolates/species without exploring their impact on the host or the complex microbiome structures and functions will not be considered for publication. Microbiome is indexed in BIOSIS, Current Contents, DOAJ, Embase, MEDLINE, PubMed, PubMed Central, and Science Citations Index Expanded.