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紧迫的公共卫生问题 具有降低或消除治疗感染的药物的有效性的突变的菌株。内酰胺酶 由某些细菌产生的β-内酰胺类抗生素通过降解β-内酰胺提供耐药性，β-内酰胺是最广泛使用的一类抗生素。 抗生素最初仅限于青霉素，突变的β-内酰胺酶赋予抗生素耐药性 包括单环内酰胺类和大多数头孢菌素（称为超广谱β内酰胺酶，或ESBLs） 分布很广目前临床分离株使用抑菌圈试验表征ESBL耐药性 针对一组内酰胺类抗生素，但这些测试产生的结果很难标准化， 并没有持续地转化为临床实践。此外，这些测试通常不会产生关于 观察到的耐药性的遗传基础，以及与其他潜在特征菌株的相关性。 在这里，我们建议开发一个基于序列的分析平台和知识库， 分析最初可以销售的超广谱β-内酰胺酶耐药性的分子特征 卫生机构和公司监测EBSL耐药性的传播。该平台将包括一个 分析试剂盒，其提取阳性选择的变体、β-内酰胺酶序列和其它基因组序列， 来自临床样品的全基因组序列的与ESBL表型相关的信息。共662个 将分析1份样本，其中350份ESBL耐药样本由UC默塞德Mercy中心提供 将进行新的测序，其余的将从牛津大学发表的一项研究中获得。 华盛顿。一个基于云的、可搜索的、具有交互式可视化的数据库将作为存储库 临床样本中发现的ESBL耐药特征。通过利用元数据交换 为了广泛共享人类基因组数据，正在制定标准，迅速扩大的全球基因组数据联盟， 基因组学和健康应用程序接口，我们的工作将代表这个API的初始扩展， 分享以微生物为中心的数据。这个项目的最终目标是建立一个准确的，可预测的阻力 使用β-内酰胺酶基因序列和与已知治疗相关的其他基因组标记的分类器 结果和菌株表型。我们将根据已发表的方法开发这种新的分类器， HIV基因型-表型预测。从临床样本中获得的ESBL耐药特征 在本研究中将用于训练和测试的分类器。Sharable的强大组合 单一平台中的数据库和分析工具将大大提高对耐药性的认识， 细菌，促进ESBL耐药性传播的流行病学监测，并代表关键的第一步， 开发一种诊断工具，利用全基因组序列对抗ESBL耐药性。

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.!