Identifying Risk Factors for Antibiotic Resistance via Integration of Epidemiology and Metagenomics

通过流行病学和宏基因组学的整合识别抗生素耐药性的风险因素

• 批准号: 10371163
• 负责人: Jessica Rhea Galloway-Pena
• 金额: $ 10.8万
• 依托单位: TEXAS A&M AGRILIFE RESEARCH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-18 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371163
• 关键词: 16S ribosomal RNA sequencing; Acute Myelocytic Leukemia; Acute leukemia; Address; Affect; Algorithms; Anti-Infective Agents; Antibiotic Resistance; Antibiotics; Antimicrobial Resistance; Area; Automobile Driving; Bioinformatics; Biometry; Blood Circulation; Characteristics; Classification; Clinical; Clinical Data; Collection; Communities; Data; Decision Trees; Development; Elements; Ensure; Epidemiology; Event; Foundations; Freezing; Future; Genomics; Goals; Health; Hematologic Neoplasms; Human; Immunocompromised Host; Infection; Infection Control; Institution; Intestines; K-Series Research Career Programs; Knowledge; Longitudinal cohort study; Mediating; Medicine; Mentors; Mentorship; Metadata; Metagenomics; Methods; Microbiology; Modeling; Molecular Epidemiology; Morbidity - disease rate; Outcome; Patients; Performance; Population; Predisposition; Prevention; Public Health; ROC Curve; Recovery; Research; Research Design; Research Personnel; Research Training; Resistance to infection; Resources; Risk; Risk Factors; Role; Sampling; Sampling Studies; Sensitivity and Specificity; Shotguns; Source; System; Techniques; Testing; Time; Training; Trees; Validation; Weight; antibiotic resistant infections; antimicrobial; bacterial resistance; base; career; chemotherapy; cohort; colonization resistance; commensal microbes; drug resistant pathogen; effective therapy; emerging antimicrobial resistance; genomic epidemiology; gut microbiome; gut microbiota; high risk; high risk population; improved; infection risk; leukemia; metagenomic sequencing; microbial; microbiome; microbiota; mortality; multi-drug resistant pathogen; multiple omics; pathogen; patient stratification; patient subsets; prediction algorithm; predictive modeling; preventive intervention; programs; rRNA Genes; random forest; resistance gene; risk stratification; skills; stool sample; tool

PROJECT SUMMARY Given the growing burden of antimicrobial resistance (AR) and lack of effective therapies for multi-drug resistant organisms, the development of new tools or models which risk-stratify patients for colonization and infection by AR bacteria is of paramount importance, particularly in high-risk populations. The significance of the gut microbiome in mediating colonization resistance against drug resistant pathogens as well as the role of microbiota-depleting antibiotics in the development of AR infections is being increasingly appreciated. However, there is currently a deficiency of methods integrating microbiome and antibiotic factors into AR- predictive algorithms. Thus, the overall objective of the proposed research is to improve understanding of the factors driving the epidemiology of AR-colonization and infection by incorporating metagenomic and antibiotic administration data of a well-defined clinical cohort. In this proposal, we focus on patients with acute myelogenous leukemia (from whom we have already collected extensive longitudinal stool samples and performed 16S rRNA gene sequencing) because of the high rates of AR pathogen colonization and severe risk for infection. The overarching hypothesis that will be tested is that the baseline presence of a limited number of key bacterial species and antibiotic resistance genes (ARGs) are critical for the risk of colonization and/or infection with an AR pathogen when combined with the administration of specific antimicrobials. We will begin our research by comprehensively determining the epidemiology of AR pathogen colonization and AR infection in our cohort via culture based stool sample analyses and clinical chart review, respectively. Using shotgun metagenomics, we will establish whether the baseline intestinal microbiome species and resistome characteristics are associated with the acquisition of AR pathogens colonizing or causing infection. Similarly, we will ascertain the relationship between antimicrobial exposure, microbiome disruption, and subsequent AR emergence. The data from these studies will be integrated into Decision Tree (DT) and Random Forest (RF) models to improve the prediction of AR pathogen colonization and AR infection outcomes. The proposed career development award, which utilizes the expertise of a superlative mentorship team and a uniquely designed research and training plan, will enable me the opportunity to build upon my current expertise in microbiology, genomics, and molecular epidemiology by adding advanced training in shotgun metagenomic analyses, bioinformatics, and biostatistical modeling. Moreover, the numerous resources and support provided by my institution and mentoring team will ensure my successful transition to an independent investigator as well as establish a strong foundation for my long-term goals of understanding and mitigating the impact of antimicrobial resistance in human health via integration of multiple –omics platforms and provision of personalized genomic-based medicine.

项目摘要 鉴于抗菌耐药性（AR）的燃烧日益增长和多药的有效疗法 抗性生物，开发新工具或模型，这些新工具或模型可能会使患者殖民化和 AR细菌感染至关重要，尤其是在高风险人群中。的意义 肠道微生物组在介导对耐药病原体的定殖耐药性以及 越来越多地欣赏到AR感染发展中的微生物群抗生素。 但是，目前存在将微生物组和抗生素因子整合到Ar-的方法的缺陷 预测算法。这是拟议研究的总体目标是提高对 通过纳入宏基因组和抗生素来推动AR殖民化和感染流行病学的因素 定义明确的临床队列的管理数据。在此提案中，我们专注于急性患者 髓质白血病（我们已经从中收集了广泛的纵向粪便样本和 由于AR病原体定植和严重风险的速度很高，进行了16S rRNA基因测序 感染。将要测试的总体假设是数字有限的基线存在 关键细菌物种和抗生素抗性基因（ARGS）对于定植和/或 与特定抗菌剂的给药结合时，会感染AR病原体。我们将开始 我们的研究通过全面确定AR病原体定植和AR感染的流行病学 在我们的队列中，分别通过培养粪便样品分析和临床图表审查。使用shot弹枪 宏基因组学，我们将确定基线肠道微生物组和树脂组是否是否 特征与获得AR病原体的殖民化或引起感染有关。相似地， 我们将确定抗菌暴露，微生物组破坏与随后的AR之间的关系 出现。这些研究的数据将集成到决策树（DT）和随机森林（RF）中 改善AR病原体定植和AR感染结果预测的模型。提议 职业发展奖，它利用了最高级的Mentalship团队的专业知识和独特的专业知识 设计的研究和培训计划将使我有机会建立我当前的专业知识 微生物学，基因组学和分子流行病学通过添加shot弹枪元基因组学的高级训练 分析，生物信息学和生物统计建模。此外，提供的众多资源和支持 我的机构和心理团队将确保我成功地过渡到独立调查员 以及为我理解和减轻影响的长期目标建立了坚实的基础 通过整合多种 - 组平台和提供的抗菌素抵抗 个性化基因组药物。