Global increases in the use of carbapenems have resulted in several strains of Gram-negative bacteria acquiring carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is a common carbapenem-resistant pathogenic bacterium that is widely studied to identify novel antibiotic resistance mechanisms and drug targets. Antibiotic-resistant clinical isolates generally harbor many genetic alterations, and the identification of responsible mutations would provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance on the basis of expression changes in their local subnetworks, hypothesizing that mutated genes that show significant expression changes among the corresponding functionally associated genes are more likely to be involved in the carbapenem resistance. For network-based gene prioritization, we developed KlebNet (www.inetbio.org/klebnet), a genome-scale cofunctional network of K. pneumoniae genes. Using KlebNet, we reconstructed the functional modules for carbapenem resistance and virulence and identified the functional association between antibiotic resistance and virulence. Using complementation assays with the top candidate genes, we were able to validate a novel gene that negatively regulated carbapenem resistance and four novel genes that positively regulated virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antibiotic resistance and virulence of human-pathogenic bacteria. IMPORTANCE Klebsiella pneumoniae is a major bacterial pathogen that causes pneumonia and urinary tract infections in human. K. pneumoniae infections are treated with carbapenem, but carbapenem-resistant K. pneumoniae has been spreading worldwide. We are able to identify antimicrobial-resistant genes among mutated genes of the antibiotic-resistant clinical isolates. However, they usually harbor many mutated genes, including those that cause weak or neutral functional effects. Therefore, we need to prioritize the mutated genes to identify the more likely candidates for the follow-up functional analysis. For this study, we present a functional network of K. pneumoniae genes and propose a network-based method of prioritizing the mutated genes of the resistant clinical isolates. We also reconstructed the network-based functional modules for carbapenem resistance and virulence and retrieved the functional association between antibiotic resistance and virulence. This study demonstrated the feasibility of network-based analysis of clinical genomics data for the study of K. pneumoniae infection.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) via grants funded by the South Korean Government (MSIT) (NRF-2018M3C9A5064709 and NRF-2018R1A5A2025079 to I.L.); the Nano Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (grant 2017M3A7B4039936); the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI17C1807); and the BioNano Health-Guard Research Center funded by the Ministry of Science, ICT, Future Planning (MSIP) of Korea as a Global Frontier Project (H-GUARD_2014M3A6B2060509). This research was partially supported by Graduate School of YONSEI University Research Scholarship Grants in 2018.
© 2019 Lee et al.
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Modelling and Simulation
- Molecular Biology
- Computer Science Applications