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.