AUTHOR=Cao Lichao , Yang Huilin , Huang Zhigang , Lu Chang , Chen Fang , Zhang Jiahao , Ye Peng , Yan Jinjin , Zhang Hezi 

TITLE=Direct prediction of antimicrobial resistance in Pseudomonas aeruginosa by metagenomic next-generation sequencing

JOURNAL=Frontiers in Microbiology

VOLUME=15

YEAR=2024

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2024.1413434

DOI=10.3389/fmicb.2024.1413434

ISSN=1664-302X

ABSTRACT=<sec id="sec1"><title>Objective</title><p><italic>Pseudomonas aeruginosa</italic> has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of <italic>P. aeruginosa</italic> isolates and clinical samples by genotype is helpful for timely antibiotic treatment.</p></sec><sec id="sec2"><title>Methods</title><p>In the study, whole genome sequencing (WGS) data of 494 <italic>P. aeruginosa</italic> isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in <italic>P. aeruginosa</italic> by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of <italic>P. aeruginosa</italic> to four antibiotics by the AMR-associated features screened, we collected 74 <italic>P. aeruginosa</italic> positive sputum samples to sequence by metagenomics next-generation sequencing (mNGS), of which 1 sample with low quality was eliminated. Then, we constructed the resistance prediction model.</p></sec><sec id="sec3"><title>Results</title><p>We identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in <italic>P. aeruginosa</italic>. The relative abundance of AMR-associated genes was obtained by matching mNGS and WGS data. The top 20 features with importance degree for IPM, MEM, TZP, and LVFX resistance were used to model, respectively. Then, we used the random forest algorithm to construct resistance prediction models of <italic>P. aeruginosa</italic>, in which the areas under the curves of the IPM, MEM, TZP, and LVFX resistance prediction models were all greater than 0.8, suggesting these resistance prediction models had good performance.</p></sec><sec id="sec4"><title>Conclusion</title><p>In summary, mNGS can predict the resistance of <italic>P. aeruginosa</italic> by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria.</p></sec>