Singapore Oral Microbiomics Initiative (SOMI), National Dental Research Institute Singapore, National Dental Centre, Singapore, Singapore.
Duke NUS Medical School, Singapore, Singapore.
Crit Rev Microbiol. 2020 Nov;46(6):759-778. doi: 10.1080/1040841X.2020.1828817. Epub 2020 Oct 8.
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.
组学技术的出现极大地提高了我们对微生物生物学的理解，尤其是在过去的二十年中。然而，微生物生物膜领域相对较新，于 20 世纪 80 年代得到巩固。微生物从浮游生物表型向生物膜表型的形态发生转换赋予了它们许多生存优势，例如抗干燥、抗生素、杀生物剂、紫外线辐射和宿主免疫反应的能力，从而使治疗病原微生物的策略变得复杂。因此，了解控制生物膜表型的机制可以导致针对介导该过程的分子标记物的有效治疗策略。组学技术在研究微生物生物膜中的应用相对较少，但在进一步了解生物膜生物学方面具有很大的潜力。在这篇综述中，我们概述了组学工具（如转录组学、蛋白质组学和代谢组学）以及多组学方法在当前文献中研究微生物生物膜的应用。我们还强调了如何通过多组学平台整合针对生物信息流各个阶段（从基因到代谢物）的组学工具，以提供生物膜生物学的整体视图。之后，我们提出了一个未来基于人工智能的多组学平台，可以预测与不同生物膜表型相关的途径。
