Development of a Genotype-linked Antibiotic Resistance Platform for Real Time Pathogen Risk Classification and Epidemiology

项目摘要 由于病原菌的增加，抗生素耐药性已成为一个紧迫的公共卫生问题 具有降低或消除治疗感染药物有效性的突变的菌株。β-内酰胺酶 由一些细菌产生，通过降解β-内酰胺类产生耐药性，β-内酰胺类是使用最广泛的一类 抗生素。最初仅限于青霉素类，即对抗生素产生抗药性的突变β-内酰胺酶 包括单内酰胺类和大多数头孢菌素类(称为超广谱β-内酰胺酶或ESBL) 很普遍。目前临床分离株对超广谱β-内酰胺酶(ESBL)的耐药性采用抑菌圈试验进行表征 针对内酰胺类抗生素，但这些测试产生的结果很难标准化和 没有始终如一地转化为临床实践。此外，这些测试通常不会生成有关以下内容的信息 观察到的耐药性的遗传基础，以及与其他潜在特征菌株的亲缘关系。 在这里，我们提出了一个基于序列的分析平台和知识库的开发 分析超广谱β-内酰胺酶耐药的分子特征，初步可将其推向 卫生机构和公司监测EBSL耐药性的传播。该平台将由一个 提取阳性选择的变异体、β-内酰胺酶序列和其他基因组的分析试剂盒 临床样本全基因组序列中与ESBL表型相关的信息。总数为662 将对样本进行分析；其中350份ESBL耐药样本由加州大学默塞德分校的仁慈中心提供 将进行新的测序，其余的将从加州大学发表的研究中获得 华盛顿。一个基于云的、可搜索的、具有交互可视化功能的数据库将作为存储库 临床样本中发现的耐超广谱β-内酰胺酶特征。通过利用元数据交换 正在为广泛共享人类基因组数据而制定的标准，迅速扩大的全球人类基因组数据联盟 基因组学与健康应用程序接口，我们的工作将代表这一API的初步扩展 共享微生物为中心的数据。这个项目的最终目标是创造一种准确的、可预测的阻力 使用与已知治疗相关的β-内酰胺酶基因序列和其他基因组标记的分类器 结果和菌株表型。我们将基于已发布的方法开发这个新的分类器 对HIV基因-表型预测有效。临床标本中超广谱β-内酰胺酶耐药特征 本研究将用于分类器的训练和测试。可共享内容的强大组合 单一平台中的数据库和分析工具将显著提高对抗生素耐药性的了解 细菌，促进对ESBL耐药性传播的流行病学监测，是关键的第一步 开发一种使用全基因组序列对抗ESBL耐药性的诊断工具。

Project Summary Antibiotic resistance has become a pressing public health concern due to the rise of pathogenic bacterial strains with mutations that reduce or eliminate the effectiveness of drugs to treat infections. Beta-lactamases produced by some bacteria provide resistance by degrading beta-lactams, one of most widely used class of antibiotics. Originally restricted to penicillins, mutant beta-lactamases that confer resistance to antibiotics including monobactams and most cephalosporins (known as extended spectrum beta lactamases, or ESBLs) are widespread. Clinical isolates are currently characterized for ESBL resistance using inhibition zone tests against a panel of lactam antibiotics, but the results produced by these tests are difficult to standardize and do not translate consistently into clinical practice. In addition, these tests typically produce no information about the genetic basis for the observed resistance, nor the relatedness to other potentially characterized strains. Here, we propose the development of a sequence-based analysis platform and knowledgebase for analyzing molecular signatures of extended-spectrum beta-lactamase resistance that can initially market to health institutions and companies monitoring the spread of EBSL resistance. The platform will consist of an analysis kit that extracts positively-selected variants, beta-lactamase sequences, and other genomic information relevant to the ESBL phenotype from whole genome sequences of clinical samples. A total of 662 samples will be analyzed; of which, 350 ESBL-resistant samples provided by the Mercy Center at UC Merced will be newly sequenced and the rest will be obtained from a published study from the University of Washington. A cloud-based, searchable database with interactive visualization will be served as the repository of the ESBL-resistant features identified in the clinical samples. By leveraging the metadata exchange standards being developed for broad sharing of human genomic data, the rapidly expanding Global Alliance for Genomics and Health application program interface, our work will represent the initial extension of this API for sharing microbial-centric data. The ultimate goal of this project is to create an accurate, predictive resistance classifier using beta-lactamase gene sequences and other genomic markers that are linked to known treatment outcomes and strain phenotypes. We will develop this new classifier based on published methods found to be effective with HIV genotype-phenotype prediction. ESBL-resistant features obtained from the clinical samples in this study will be used for training and testing of the classifier. The powerful combination of sharable database and analytic tools in a single platform will significantly advance knowledge of antibiotic-resistant bacteria, facilitate epidemiological monitoring of the spread of ESBL resistance, and represents a key first step to develop a diagnostic tool to counter ESBL resistance using whole-genome sequences.!