Department of Biological Sciences, University of North Texas, Denton, Texas, USA.
BioDiscovery Institute, University of North Texas, Denton, Texas, USA.
mSystems. 2023 Dec 21;8(6):e0047323. doi: 10.1128/msystems.00473-23. Epub 2023 Nov 3.
We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in PAO1. The systems-level investigation of PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on virulence and resistance. These could potentially act as molecular determinants of PAO1 pathogenicity and responses to antibiotics.
我们在这里提出了一种新的系统级方法，用于破译与毒力和/或抗生素治疗细菌病原体相关的遗传因素和生物途径。该方法的强大功能通过应用于研究良好的病原体 PAO1 得到了证明。我们基于基因共表达网络的方法揭示了与 PAO1 致病性相关的已知和未知基因及其网络。对 PAO1 的系统级研究有助于确定传统差异基因表达分析方法无法检测到的潜在致病性和耐药相关遗传因素。基于网络的分析揭示了包含以前在毒力和耐药性的几项原始研究中未报道的基因的模块。这些基因可能作为 PAO1 致病性和对抗生素反应的分子决定因素。
