An in-depth analysis and exploreation with focus on the biofilm in Staphylococcus aureus

Abstract

Research into the biolfilm formation in Staphylococcus aureus has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The accumulation of these data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in the biofilm formation of Staphylococcus aureus through machine learning strategies and co-expression analyses. Herein, we harnessed all available RNA sequencing data related to Staphylococcus aureus biofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state via employing machine learning and differential expression analyses. By weighted gene co-expression analysis and our previously developed predictor, important functional modules, potential biofilm-associated proteins and subnetwork of biofilm formation pathway were found. By constructing a protein-protein interaction (PPI) network, we discovered several hitherto unreported novel protein interactions within these functional modules. To make these data more straightforward to experimental biologists, an online database named SAdb was developed (http://sadb.biownmcli.info/). IMPORTANCEIn this work, we conducted a comprehensive and in-depth exploration of RNA sequencing data in biofilm research through differential expression analysis, machine learning, WGCNA, and biofilm-associated protein predictive analysis, which has also illuminated novel analytical perspective for other research into bacterial phenotypes. And, to provide researchers with unimpeded access to these data, we developed a database name SAdb for the storage and analysis of Staphylococcus aureus omics data. We believe that this study will captivate the interest of researchers in the field of bacteriology, particularly those studying biofilms, which play a crucial role in bacterial growth, pathogenicity, and drug resistance.