PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes

• Published in: Microbiology spectrum 2023-02, Vol.11 (1), p.e0387122-e0387122
• Main Authors: Li, Yangjie, Ma, Bin, Hua, Kexin, Gong, Huimin, He, Rongrong, Luo, Rui, Bi, Dingren, Zhou, Rui, Langford, Paul R, Jin, Hui
• Format: Article
• Language: English
• Subjects: Animals; Bacteria - genetics; dereplication; functional association network inference; Gene Regulatory Networks; Genes, Bacterial; Genetics and Molecular Biology; Methods and Protocols; phylogenetic profiling; Phylogeny; Prokaryotic Cells; prokaryotic genome; Streptococcus suis; Swine
• ISSN: 2165-0497; 2165-0497
• DOI: 10.1128/spectrum.03871-22

Identification of microbial functional association networks allows interpretation of biological phenomena and a greater understanding of the molecular basis of pathogenicity and also underpins the formulation of control measures. Here, we describe PPNet, a tool that uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial gene association networks of a single species. As an exemplar, we have derived a functional association network in the pig pathogen Streptococcus suis using 81 binary similarity and dissimilarity measures which demonstrates excellent performance based on the area under the receiver operating characteristic (AUROC), the area under the precision-recall (AUPR), and a derived overall scoring method. Selected network associations were validated experimentally by using bacterial two-hybrid experiments. We conclude that PPNet, a publicly available (https://github.com/liyangjie/PPNet), can be used to construct microbial association networks from easily acquired genome-scale data. This study developed PPNet, the first tool that can be used to infer large-scale bacterial functional association networks of a single species. PPNet includes a method for assigning the uniqueness of a bacterial strain using the average nucleotide identity and the average nucleotide coverage. PPNet collected 81 binary similarity and distance measures for phylogenetic profiling and then evaluated and divided them into four groups. PPNet can effectively capture gene networks that are functionally related to phenotype from publicly prokaryotic genomes, as well as provide valuable results for downstream analysis and experiment testing.