Deciphering gene regulation in Bacteria

最近使用下一代RNA测序技术对细菌进行的研究发现，这些生物体中的基因调控比以前认为的更复杂。例如，细菌具有：1）大量的调节性非编码小RNA（sRNA）类似于真核生物中的微小RNA，2）具有多个和重叠基因和转录物的操纵子，以及3）在细菌物种之间变化的启动子结构。虽然正在生成大量的RNA测序数据，但缺乏有效整合高通量技术生成的数据的计算方法，以更深入地了解细菌如何控制基因表达。该提案的最终目标是开发智能计算方法来破译细菌中的基因调控。转录起始的控制是基因调控的第一步。启动子是决定何时以及如何启动基因转录的基因组区域。细菌中的基因表达也通过响应特定信号而停止基因转录来调节。终止子是可以终止转录的序列。在这项研究资助期间，我们将专注于开发基于机器学习的方法，以1）找出哪些基因受哪些sRNA的调控，2）识别细菌启动子区域，3）识别细菌终止子。为了促进细菌研究界对我们方法的吸收，我们将把这些方法作为容器化的计算管道分发，以便自然科学研究人员可以轻松使用这些方法。容器化管道是一种可以在无需安装管道使用的所有额外软件的情况下执行的管道。这项研究不仅对促进微生物学研究很重要，这样我们就可以提高对细菌基因调控的理解，而且这项研究的结果也可能被各种行业利用（例如，食品、制药和生物技术）。例如，已鉴定的sRNA可以用作开关，以控制食品工业中某些物质的细菌生产，或作为开发新型抗生素的靶点。

Recent studies in bacteria using next-generation RNA sequencing technology have found that gene regulation is more complex than previously thought in these organisms. For example, bacteria have: 1) a large number of regulatory non-coding small RNAs (sRNAs) analogous to micro-RNAs in eukaryotes, 2) operons with multiple and overlapping genes and transcripts, and 3) promoter structures that vary among bacterial species. Although large amounts of RNA sequencing data are being generated, there is a lack of computational approaches for effectively integrating data generated from high-throughput technologies to gain deeper understanding of how bacteria control gene expression. The ultimate goal of this proposal is to develop smart computational methods to decipher gene regulation in bacteria. Control of transcription initiation is the first step in gene regulation. A promoter is a genomic region that determines when and how transcription of a gene is initiated. Gene expression in bacteria is also regulated by stopping gene transcription in response to specific signals. A terminator is a sequence that can stop transcription. For the duration of this research grant, we will focus on developing machine learning-based approaches to 1) find out what genes are regulated by which sRNAs, 2) identify bacterial promoter regions, and 3) identify bacterial terminators. To facilitate the uptake of our methods by the bacterial research community we will distribute these methods as containerized computational pipelines so that the approaches will be easily used by natural sciences researchers. A containerized pipeline is one that can be executed without having to install all the extra software used by the pipeline. This research is important not only to facilitate microbiology research so that our understanding of gene regulation in bacteria improves, but outcomes of this research could also potentially be exploited by a variety of industries (e.g., food, pharmaceutical, and biotechnology). For example, identified sRNAs could be used as switches to control the bacterial production of certain substances in the food industry, or as targets for the development of novel antibiotics.