DuPai Cory D, Wilke Claus O, Davies Bryan W
Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, USA.
Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA.
mSystems. 2020 Jul 7;5(4):e00550-20. doi: 10.1128/mSystems.00550-20.
Research into the evolution and pathogenesis of has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The steady accumulation of these data sets now provides a unique opportunity for hypothesis generation via coexpression analysis. Here, we leverage all published RNA sequencing data, in combination with select data from other platforms, to generate a gene coexpression network that validates known gene interactions and identifies novel genetic partners across the entire genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in (e.g., transposon insertion sequencing [Tn-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]), and expands upon microarray-based findings in related Gram-negative bacteria. Cholera is a devastating illness that kills tens of thousands of people annually. , the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of virulence and evolution.
对[病原体名称]的进化和发病机制的研究，因高通量测序数据的产生极大地受益，这些数据推动了分子分析。目前这些数据集的不断积累为通过共表达分析生成假设提供了独特的机会。在这里，我们利用所有已发表的[病原体名称]RNA测序数据，并结合来自其他平台的选定数据，生成一个基因共表达网络，该网络验证已知的基因相互作用，并识别整个[病原体名称]基因组中的新遗传伙伴。这个网络直接洞察影响致病性、代谢和转录调控的基因，进一步阐明先前在[病原体名称]测序实验（如转座子插入测序 [Tn-seq] 和染色质免疫沉淀测序 [ChIP-seq]）的结果，并扩展了基于微阵列在相关革兰氏阴性菌中的发现。霍乱是一种每年导致数万人死亡的毁灭性疾病。[病原体名称]作为霍乱的病原体，是研究细菌发病机制以及水平基因转移对新毒力菌株出现和传播影响的重要模式生物。尽管这种病原体很重要，但大约三分之一的[病原体名称]基因在功能上未得到注释，这使我们对这种微生物的理解存在很大差距。通过对现有RNA测序数据进行共表达网络分析，这项工作开发了一种方法来揭示新的基因-基因关系，并将功能未知的基因置于特定背景中，这将推动我们对[病原体名称]毒力和进化的理解。
