Multi-omics tools for studying microbial biofilms: current perspectives and future directions. (1st November 2020)

• Record Type: Journal Article
• Authors: Seneviratne, Chaminda J.; Suriyanarayanan, Tanujaa; Widyarman, Armelia Sari; Lee, Lye Siang; Lau, Matthew; Ching, Jianhong; Delaney, Christopher; Ramage, Gordon
• Is Part Of: Critical reviews in microbiology. Volume 46:Number 6(2020)
• Page Start: 759
• Page End: 778
• Publication Date: 2020-11-01
• Subjects: Biofilms -- OMICS biology -- metabolomics -- drug resistance; Microbiology -- Periodicals; Microbiology -- Periodicals; Review Literature -- Periodicals; 579
• DOI: 10.1080/1040841X.2020.1828817
• Languages: English
• ISSNs: 1040-841X

Abstract

Abstract: The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes.