Im Hanhyeok, Lee Ju-Hoon, Choi Sang Ho
National Research Laboratory of Molecular Microbiology and Toxicology, Department of Agricultural Biotechnology, Seoul National University, Seoul, South Korea.
Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Seoul National University, Seoul, South Korea.
Front Microbiol. 2022 Jun 24;13:953404. doi: 10.3389/fmicb.2022.953404. eCollection 2022.
The elucidation of the transcriptional regulatory networks (TRNs) of enterohemorrhagic (EHEC) is critical to understand its pathogenesis and survival in the host. However, the analyses of current TRNs are still limited to comprehensively understand their target genes generally co-regulated under various conditions regardless of the genetic backgrounds. In this study, independent component analysis (ICA), a machine learning-based decomposition method, was used to decompose the large-scale transcriptome data of EHEC into the modulons, which contain the target genes of several TRNs. The locus of enterocyte effacement (LEE) and the Shiga toxin (Stx) modulons mainly consisted of the Ler regulon and the Stx prophage genes, respectively, confirming that ICA properly grouped the co-regulated major virulence genes of EHEC. Further investigation revealed that the LEE modulon contained the hypothetical Z0395 gene as a novel member of the Ler regulon, and the Stx modulon contained the and locus genes in addition to the Stx prophage genes. Correspondingly, the Stx prophage genes were also regulated by thiamine and copper ions known to control the and locus genes, respectively. The modulons effectively clustered the genes co-regulated regardless of the growth conditions and the genetic backgrounds of EHEC. The changed activities of the individual modulons successfully explained the differential expressions of the virulence and survival genes during the course of infection in bovines. Altogether, these results suggested that ICA of the large-scale transcriptome data can expand and enhance the current understanding of the TRNs of EHEC.
阐明肠出血性大肠杆菌（EHEC）的转录调控网络（TRNs）对于理解其发病机制和在宿主中的存活情况至关重要。然而，目前对TRNs的分析仍局限于在不考虑遗传背景的情况下，全面了解其在各种条件下共同调控的靶基因。在本研究中，独立成分分析（ICA），一种基于机器学习的分解方法，被用于将EHEC的大规模转录组数据分解为模体，其中包含几个TRNs的靶基因。肠上皮细胞脱落位点（LEE）和志贺毒素（Stx）模体分别主要由Ler调控子和Stx前噬菌体基因组成，证实ICA正确地将EHEC共同调控的主要毒力基因进行了分组。进一步研究表明，LEE模体包含假设的Z0395基因作为Ler调控子的一个新成员，并且Stx模体除了Stx前噬菌体基因外还包含和位点基因。相应地，Stx前噬菌体基因也分别受已知控制和位点基因的硫胺素和铜离子调控。这些模体有效地将共同调控的基因聚集在一起，而不考虑EHEC的生长条件和遗传背景。各个模体活性的变化成功地解释了牛感染过程中毒力基因和存活基因的差异表达。总之，这些结果表明，大规模转录组数据的ICA可以扩展和增强目前对EHEC的TRNs的理解。
