A Novel Hierarchical Network-Based Approach to Unveil the Complexity of Functional Microbial Genome

Abstract

AbstractBiological networks are pivotal in elucidating intricate biological processes. While substantial research has delved into interspecies environmental interactions within biological networks, intraspecific functional gene interactions within individual microbes remain relatively untapped. The burgeoning availability of microbiome datasets underscores the imperative for a refined examination of microbial genome structures and functions. We innovatively introduce the concept of “Solid Motif Structures (SMS)” through a meticulous biological network analysis of genomes from the same genus, aiming to bridge the gap between the structural and functional intricacies of microbial genomes. Harnessing publicly available data from 162 high-qualityMicrocystisgenomes, a globally prevalent freshwater cyanobacterium instrumental in microbial ecosystems, a comprehensive genome structure network forMicrocystiswas delineated. Employing a state-of-the-art deep learning scheme, we discerned 27 pivotal functional subnetworks and an array of functionally-associated SMS. Incorporating metagenomic data from seven geographically diverse lakes, we embarked on an exhaustive analysis of the functional stability ofMicrocystisacross varied environmental matrices. This culminated in the identification of distinct functional interaction models for each lake. Our research amalgamates these insights into a comprehensive resource repository, furnishing unparalleled perspectives into the functional interplay withinMicrocystis. Leveraging advanced biological network analysis, our study pioneers the delineation of a novel network granularity, facilitating a more lucid comprehension of the dynamic interplay between genome structure and function interactions in microorganisms of the same genus. This study shed light on the plasticity and conservation of microbial functional genomes across diverse environments, offering insights into their evolutionary trajectories.