Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus

Advancing understanding of microbial biofilms through machine learning-powered studies

Microbial biofilms are prevalent in various environments and pose significant challenges to food safety and public health. The biofilms formed by pathogens can cause food spoilage, foodborne illness, and infectious diseases, which are difficult to treat due to their enhanced antimicrobial resistance. While the composition and development of biofilms have been widely studied, their profound impact on food, the food industry, and public health has not been sufficiently recapitulated. This review aims to provide a comprehensive overview of microbial biofilms in the food industry and their implication on public health. It highlights the existence of biofilms along the food-producing chains and the underlying mechanisms of biofilm-associated diseases. Furthermore, this review thoroughly summarizes the enhanced understanding of microbial biofilms achieved through machine learning approaches in biofilm research. By consolidating existing knowledge, this review intends to facilitate developing effective strategies to combat biofilm-associated infections in both the food industry and public health.

Targeted Anti-Biofilm Therapy: Dissecting Targets in the Biofilm Life Cycle

Biofilm is a crucial virulence factor for microorganisms that causes chronic infection. After biofilm formation, the bacteria present improve drug tolerance and multifactorial defense mechanisms, which impose significant challenges for the use of antimicrobials. This indicates the urgent need for new targeted technologies and emerging therapeutic strategies. In this review, we focus on the current biofilm-targeting strategies and those under development, including targeting persistent cells, quorum quenching, and phage therapy. We emphasize biofilm-targeting technologies that are supported by blocking the biofilm life cycle, providing a theoretical basis for design of targeting technology that disrupts the biofilm and promotes practical application of antibacterial materials.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.