Boruta Tomasz
Lodz University of Technology, Faculty of Process and Environmental Engineering, Department of Bioprocess Engineering, ul. Wólczańska 213, 93-005 Łódź, Poland.
Comput Struct Biotechnol J. 2023 Aug 16;21:4021-4029. doi: 10.1016/j.csbj.2023.08.011. eCollection 2023.
Co-cultivation is an effective method of inducing the production of specialized metabolites (SMs) in microbial strains. By mimicking the ecological interactions that take place in natural environment, this approach enables to trigger the biosynthesis of molecules which are not formed under monoculture conditions. Importantly, microbial co-cultivation may lead to the discovery of novel chemical entities of pharmaceutical interest. The experimental efforts aimed at the induction of SMs are greatly facilitated by computational techniques. The aim of this overview is to highlight the relevance of computational methods for the investigation of SM induction via microbial co-cultivation. The concepts related to the induction of SMs in microbial co-cultures are briefly introduced by addressing four areas associated with the SM induction workflows, namely the detection of SMs formed exclusively under co-culture conditions, the annotation of induced SMs, the identification of SM producer strains, and the optimization of fermentation conditions. The computational infrastructure associated with these areas, including the tools of multivariate data analysis, molecular networking, genome mining and mathematical optimization, is discussed in relation to the experimental results described in recent literature. The perspective on the future developments in the field, mainly in relation to the microbiome-related research, is also provided.
共培养是诱导微生物菌株产生特殊代谢产物（SMs）的一种有效方法。通过模拟自然环境中发生的生态相互作用，这种方法能够触发在单培养条件下无法形成的分子的生物合成。重要的是，微生物共培养可能会导致发现具有药物价值的新型化学实体。计算技术极大地推动了旨在诱导特殊代谢产物的实验工作。本综述的目的是强调计算方法对于通过微生物共培养研究特殊代谢产物诱导的相关性。通过探讨与特殊代谢产物诱导工作流程相关的四个领域，即仅在共培养条件下形成的特殊代谢产物的检测、诱导的特殊代谢产物的注释、特殊代谢产物产生菌株的鉴定以及发酵条件的优化，简要介绍了与微生物共培养中特殊代谢产物诱导相关的概念。结合近期文献中描述的实验结果，讨论了与这些领域相关的计算基础设施，包括多元数据分析工具、分子网络、基因组挖掘和数学优化。还提供了对该领域未来发展的展望，主要涉及与微生物组相关的研究。
