Integrated Diagnostic Centre for Infectious Diseases, Guangzhou Huayin Medical Laboratory Center, Guangzhou, P.R. China.
Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Sciences, Medical School, Nanjing University, Nanjing, P.R. China.
J Microbiol Biotechnol. 2024 Aug 28;34(8):1617-1626. doi: 10.4014/jmb.2402.02004. Epub 2024 Jun 20.
Various antibiotic-resistant bacteria (ARB) are known to induce repeated pulmonary infections and increase morbidity and mortality. A thorough knowledge of antibiotic resistance is imperative for clinical practice to treat resistant pulmonary infections. In this study, we used a reads-based method and an assembly-based method according to the metagenomic next-generation sequencing (mNGS) data to reveal the spectra of ARB and corresponding antibiotic resistance genes (ARGs) in samples from patients with pulmonary infections. A total of 151 clinical samples from 144 patients with pulmonary infections were collected for retrospective analysis. The ARB and ARGs detection performance was compared by the reads-based method and assembly-based method with the culture method and antibiotic susceptibility testing (AST), respectively. In addition, ARGs and the attribution relationship of common ARB were analyzed by the two methods. The comparison results showed that the assembly-based method could assist in determining pathogens detected by the reads-based method as true ARB and improve the predictive capabilities (46% > 13%). ARG-ARB network analysis revealed that assembly-based method could promote determining clear ARG-bacteria attribution and 101 ARGs were detected both in two methods. 25 ARB were obtained by both methods, of which the most predominant ARB and its ARGs in the samples of pulmonary infections were (), (), (), and (). Collectively, our findings demonstrated that the assembly-based method could be a supplement to the reads-based method and uncovered pulmonary infection-associated ARB and ARGs as potential antibiotic treatment targets.
已知各种抗生素耐药菌（ARB）可引起反复肺部感染，增加发病率和死亡率。为了临床治疗耐药性肺部感染，透彻了解抗生素耐药性至关重要。在这项研究中，我们根据宏基因组下一代测序（mNGS）数据使用基于读长的方法和基于组装的方法，揭示了肺部感染患者样本中 ARB 和相应抗生素耐药基因（ARGs）的谱。共收集了 144 例肺部感染患者的 151 份临床样本进行回顾性分析。通过基于读长的方法和基于组装的方法，分别与培养方法和抗生素药敏试验（AST）比较，评估了 ARB 和 ARGs 的检测性能。此外，通过这两种方法分析了 ARGs 和常见 ARB 的归属关系。比较结果表明，基于组装的方法可以辅助确定基于读长的方法检测到的真正的 ARB，并提高预测能力（46%>13%）。ARG-ARB 网络分析表明，基于组装的方法可以促进确定明确的 ARG-细菌归属，两种方法共检测到 101 个 ARGs。两种方法均获得 25 株 ARB，其中肺部感染样本中最主要的 ARB 及其 ARGs 为（）、（）、（）和（）。总之，我们的研究结果表明，基于组装的方法可以作为基于读长的方法的补充，并揭示了与肺部感染相关的 ARB 和 ARGs，它们可能是抗生素治疗的潜在靶点。
