GSE115539
Network integrative genomic and transcriptomic analysis of carbapenem-resistant Klebsiella pneumoniae strains identifies genes for resistance and virulence
With the global increase in the use of carbapenems, several gram-negative bacteria have acquired carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is one of such notorious pathogen that is being widely studied to find novel resistance mechanisms and drug targets. These antibiotic-resistant clinical isolates generally harbor many genetic alterations, and identification of causal mutations will provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance, in which mutated genes that also show significant expression changes among their functionally coupled genes become more likely candidates. For network-based analyses, we developed a genome-scale co-functional network of K. pneumoniae genes, KlebNet (www.inetbio.org/klebnet). Using KlebNet, we could reconstruct functional modules for antibiotic-resistance, and virulence, and retrieved functional association between them. With complementation assays with top candidate genes, we could validate a gene for negative regulation of meropenem resistance and four genes for positive regulation of virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antimicrobial resistance and virulence of human pathogenic bacteria with genomic and transcriptomic profiles from antibiotic-resistant clinical isolates. Overall design: We used two strains of K. pneumoniae clinical isolates from a single patient who had undergone meropenem treatment: YMC2014/2/R777 (K26) and YMC2014/3/P345 (K56) that were isolated before and after the meropenem administration, respectively. A significant increase in meropenem minimum inhibitory concentration (MIC) was observed in K56 compared with K26 from 0.5 µg/ml to =16 µg/ml.