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

开发用于实时病原体风险分类和流行病学的基因型相关抗生素耐药性平台

基本信息

批准号：
9203025
负责人：
Patricia Chan
金额：
$ 21.97万
依托单位：
MAVERIX BIOMICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9203025
关键词：
Active Sites Algorithms Antibiotic Resistance Antibiotics Bacteria Bacterial Antibiotic Resistance Base Sequence Carbapenems Categories Cell Wall Cephalosporins Classification Clinical Clinical Data Communicable Diseases Cyclophosphamide Data Databases Development Diagnostic Diagnostic Procedure Disease Outbreaks Drug resistance Effectiveness Enzymes Epidemiologic Monitoring Epidemiology Evolution Exhibits Extended-spectrum β-lactamase Family Future Gene Mutation Generations Genes Genetic Genetic Markers Genome Genomics Genotype Goals HIV Health Healthcare Hospitalization Human Imagery Infection Institution Knowledge Learning Linear Regressions Link Marketing Mediating Metadata Methods Molecular Profiling Monitor Monobactams Mutation Nitrogen Nucleic Acid Regulatory Sequences Penicillin-Binding Proteins Penicillins Peptidoglycan Pharmaceutical Preparations Phase Phenotype Point Mutation Predictive Value Preventive measure Public Health Publishing Reporting Research Personnel Resistance Resort Rest Risk Sampling Structure Superbug Technology Testing Time Training Translating Treatment Failure Treatment outcome Universities Variant Washington Work World Health analog antimicrobial drug application programming interface base beta-Lactam Resistance beta-Lactamase beta-Lactams clinical practice clinical sequencing cloud based data sharing design effective therapy genome sequencing genomic biomarker genomic data health application knowledge base microbial mortality mutant pathogen pathogenic Escherichia coli repository suicide substrates targeted treatment tool whole genome

项目摘要

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.!

项目总结