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.

项目总结 鉴于抗菌素耐药性(AR)的负担日益加重，以及缺乏针对多药的有效治疗 耐药生物，开发新的工具或模型，对患者进行定植和风险分层 AR细菌的感染至关重要，特别是在高危人群中。的重要意义。 肠道微生物群在介导对耐药病原菌定植耐药中的作用 在AR感染的发展过程中消耗微生物区系的抗生素越来越受到重视。 然而，目前还缺乏将微生物组和抗生素因子整合到AR-AR中的方法。 预测算法。因此，拟议研究的总体目标是增进对 合并后基因组和抗生素的AR-定植和感染的流行病学驱动因素 明确的临床队列的管理数据。在这个提案中，我们将重点放在急性胰腺炎患者身上 骨髓性白血病(我们已经从他们那里收集了大量的纵向粪便样本和 进行16S rRNA基因测序)，因为AR病原体定殖率高，风险严重 用来治疗感染。将被检验的最重要的假设是有限数量的基线存在 关键细菌种类和抗生素耐药基因(Args)对定植和/或风险至关重要。 当联合使用特定的抗菌剂时，会感染AR病原体。我们将开始 我们的研究通过综合确定AR病原体定植和AR感染的流行病学 在我们的队列中，分别通过基于培养的粪便样本分析和临床图表审查。使用猎枪 元基因组学，我们将建立肠道微生物组的基线物种和抵抗组 特征与AR病原体的获取、定植或引起感染有关。同样， 我们将确定抗菌素暴露、微生物群破坏和随后的AR之间的关系 浮现。这些研究的数据将被整合到决策树(DT)和随机森林(RF)中 改进AR病原体定植和AR感染结局预测的模型。建议数 职业发展奖，利用最优秀的导师团队的专业知识和独特的 设计的研究和培训计划，将使我有机会在我目前的专业知识基础上 微生物学、基因组学和分子流行病学，增加鸟枪式元基因组学的高级培训 分析、生物信息学和生物统计学建模。此外，提供的大量资源和支持 我的机构和指导团队将确保我成功过渡到独立调查员 并为我的长期目标奠定坚实的基础，以了解和减轻 通过整合多组学平台和提供 个性化的基因组医学。