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首页> 外文学位 >Whole genome sequencing for predicting antibiotic resistance phenotype in clinical isolates of Pseudomonas aeruginosa.
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Whole genome sequencing for predicting antibiotic resistance phenotype in clinical isolates of Pseudomonas aeruginosa.

机译：全基因组测序可预测铜绿假单胞菌临床分离株中的抗生素抗性表型。

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摘要

With the current efforts underway to implement next-generation whole genome sequencing (WGS) in clinical microbiology laboratories, it is critical to understand the scope in which this technology can be applied for clinical isolate characterization. Recently published studies have demonstrated adequate sensitivity and specificity for predicting resistance phenotype to implement WGS in clinical microbiology laboratories; however, these studies have been limited to a small group of common pathogens, i.e. Salmonella spp., Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, and Mycobacterium tuberculosis. Pseudomonas aeruginosa, which causes an estimated 51,000 healthcare-associated infections in the United States per year, is an important as well as challenging organism for which to test antibiotic susceptibility. Our study will guide future efforts for integration of WGS into clinical microbiology laboratories to characterize antibiotic resistance in isolates of Pseudomonas aeruginosa. P. aeruginosa harbors both intrinsic and acquired antibiotic resistance to multiple classes of antibiotics, making it a notoriously difficult pathogen to combat with antibiotic therapy. Chromosomally-encoded resistance determinants, such as efflux pumps and membrane porins, contribute significantly to antibiotic resistance, and differential regulation of these genes plays a role in antibiotic resistance phenotypes. This phenomenon of differential regulation has the potential to confound predictions of antibiotic resistance by WGS alone. In the present study, we evaluate the utility of next-generation WGS for predicting antibiotic resistance phenotype in 109 isolates of P. aeruginosa. We applied WGS analysis of pathogens to identify antibiotic resistance determinants and make direct predictions based on gene presence or absence and then employed logistic regression models on the genomic data to classify organisms as antibiotic "resistant" or "susceptible." The accuracy of leave-one-out cross validation of logistic regression models for predicting resistance to two commonly used antibiotics, the fluoroquinolones ciprofloxacin and levofloxacin, were 0.70 and 0.74, respectively, and model predictions for another class, beta-lactams, were lower with 0.61 accuracy for cefepime and 0.65 accuracy for imipenem. Direct predictions for susceptibility to aminoglycosides using mobile resistance genes were inconsistent, and this is likely due to the contribution of intrinsic resistance mechanisms, such as overexpression of efflux genes, that is not captured with WGS.;Keywords: whole genome sequencing; antibiotic resistance; Pseudomonas aeruginosa.
机译：随着目前在临床微生物学实验室中实施下一代全基因组测序（WGS）的努力，了解该技术可用于临床分离物鉴定的范围至关重要。最近发表的研究表明，在临床微生物学实验室中，预测抗性表型以实施WGS具有足够的敏感性和特异性。但是，这些研究仅限于少数常见病原体，即沙门氏菌，肺炎克雷伯菌，大肠杆菌，金黄色葡萄球菌和结核分枝杆菌。铜绿假单胞菌（Pseudomonas aeruginosa）是每年在美国引起51,000例与医疗保健相关的感染的疾病，是测试抗生素敏感性的重要且具有挑战性的生物。我们的研究将指导今后将WGS整合到临床微生物学实验室中的工作，以表征铜绿假单胞菌分离株中的抗生素抗性。铜绿假单胞菌对多种类抗生素都具有内在的和获得性的抗生素抗性，这使其成为众所周知的难以通过抗生素疗法抵抗的病原体。染色体编码的抗性决定簇，例如外排泵和膜孔蛋白，对抗生素抗性有重要贡献，而这些基因的差异调节在抗生素抗性表型中起作用。这种差异调节现象有可能混淆仅由WGS预测的抗生素耐药性。在本研究中，我们评估了下一代WGS在预测109株铜绿假单胞菌的抗生素耐药性表型中的效用。我们对病原体进行了WGS分析，以鉴定抗生素抗性决定因素，并根据基因存在与否做出直接预测，然后对基因组数据采用逻辑回归模型将生物分类为抗生素“抗药性”或“易感性”。使用Logistic回归模型进行一站式交叉验证以预测对两种常用抗生素的耐药性氟喹诺酮类环丙沙星和左氧氟沙星的准确性分别为0.70和0.74，而另一类β-内酰胺类的模型预测更低。头孢吡肟的准确度为0.61，亚胺培南的准确度为0.65。使用移动抗性基因对氨基糖苷的敏感性的直接预测是不一致的，这可能是由于内在抗性机制的贡献，例如外排基因的过表达，而WGS没有捕获。抗生素耐药性;铜绿假单胞菌。

著录项

  • 作者

    Kelley, Erin J.;

  • 作者单位

    Northern Arizona University.;

  • 授予单位 Northern Arizona University.;
  • 学科 Microbiology.;Molecular biology.
  • 学位 M.S.
  • 年度 2015
  • 页码 89 p.
  • 总页数 89
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 新闻学、新闻事业;
  • 关键词

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