■铜绿假单胞菌具有很强的耐药性,能耐受多种抗生素,这是抗生素耐药感染管理中的一个主要问题。通过基因型直接预测铜绿假单胞菌和临床样本的多药耐药(MDR)表型,有助于及时进行抗生素治疗。
■在研究中,用494株铜绿假单胞菌的全基因组测序(WGS)数据筛选与亚胺培南(IPM)相关的抗微生物耐药(AMR)关键基因,美罗培南(MEM),哌拉西林/他唑巴坦(TZP),通过比较耐药菌株和敏感菌株之间拷贝数差异的基因,以及铜绿假单胞菌对左氧氟沙星(LVFX)的耐药性。随后,为了通过筛选的AMR相关特征直接预测铜绿假单胞菌对四种抗生素的耐药性,我们收集了74份铜绿假单胞菌阳性痰样本,通过宏基因组学下一代测序(mNGS)进行测序,其中1个低质量样品被淘汰。然后,构建了抗性预测模型。
■我们为IPM确定了93、88、80、140个与AMR相关的特征,MEM,TZP,铜绿假单胞菌的LVFX抗性。通过匹配mNGS和WGS数据获得AMR相关基因的相对丰度。具有IPM重要性程度的前20个特征,MEM,TZP,和LVFX电阻被用来建模,分别。然后,利用随机森林算法构建铜绿假单胞菌耐药性预测模型,其中IPM曲线下的区域,MEM,TZP,和LVFX电阻预测模型均大于0.8,表明这些电阻预测模型具有良好的性能。
■总之,mNGS可以通过直接检测AMR相关基因来预测铜绿假单胞菌的耐药性,为临床快速检测病原菌的耐药性提供参考。
UNASSIGNED: Pseudomonas aeruginosa 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 P. aeruginosa isolates and clinical samples by genotype is helpful for timely antibiotic treatment.
UNASSIGNED: In the study, whole genome sequencing (WGS) data of 494 P. aeruginosa 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 P. aeruginosa by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of P. aeruginosa to four antibiotics by the AMR-associated features screened, we collected 74 P. aeruginosa 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.
UNASSIGNED: We identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in P. aeruginosa. 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 P. aeruginosa, 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.
UNASSIGNED: In summary, mNGS can predict the resistance of P. aeruginosa by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria.