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: 10552620
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10552620
• 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; Dedications; Development; Elements; Ensure; Epidemiology; Event; Foundations; Freezing; Future; Goals; Health; Hematologic Neoplasms; Human; Immunocompromised Host; Infection; Infection Control; Inpatients; Institution; Intervention; Intestines; K-Series Research Career Programs; Knowledge acquisition; Longitudinal cohort study; Mediating; Medicine; Mentors; Mentorship; Metadata; Metagenomics; Methods; Microbiology; Modeling; Molecular Epidemiology; Morbidity - disease rate; Outcome; Patients; Performance; Population; Predisposition; Prevention; Prevention strategy; Public Health; ROC Curve; Recovery; Research; Research Design; Research Personnel; Resistance to infection; Resources; Risk; Risk Factors; Role; Sampling; Sampling Studies; Shotguns; Source; Specificity; System; Techniques; Testing; Time; Training; Trees; Validation; antibiotic resistant infections; antimicrobial; bacterial resistance; 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; personalized genomics; prediction algorithm; predictive modeling; programs; rRNA Genes; random forest; resistance gene; risk prediction model; 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.

项目概要 鉴于抗菌素耐药性 (AR) 负担日益沉重，且缺乏有效的多药治疗方法 抗性生物体、新工具或模型的开发，对患者进行定植风险分层，以及 AR细菌感染至关重要，特别是在高危人群中。 肠道微生物组在介导耐药病原体定植抵抗中的作用以及 微生物群消耗抗生素在 AR 感染发展中的作用越来越受到重视。 然而，目前缺乏将微生物组和抗生素因素整合到 AR 中的方法。 因此，本研究的总体目标是提高对预测算法的理解。 通过结合宏基因组和抗生素来驱动 AR 定植和感染的流行病学因素 明确的临床队列的管理数据在本提案中，我们关注急性患者。 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并 进行了 16S rRNA 基因测序），因为 AR 病原体定植率高且风险严重 将检验的首要假设是基线存在有限数量。 关键细菌种类和抗生素抗性基因（ARG）对于定植和/或 与特定抗菌药物联合使用时，AR 病原体感染。 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，分别使用基于培养的粪便样本分析和临床图表审查。 宏基因组学，我们将确定基线肠道微生物组物种和耐药组是否 特征与获得定植或引起感染的 AR 病原体有关。 我们将确定抗菌药物暴露、微生物组破坏和随后的 AR 之间的关系 这些研究的数据将被整合到决策树（DT）和随机森林（RF）中。 模型来改善 AR 病原体定植和 AR 感染结果的预测。 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，将使我有机会利用我目前的专业知识 微生物学、基因组学和分子流行病学，增加鸟枪法宏基因组学的高级培训 此外，还提供了大量资源和支持。 我的机构和指导团队将确保我成功过渡为独立调查员 并为我了解和减轻影响的长期目标奠定坚实的基础 通过整合多个组学平台并提供抗菌药物耐药性来影响人类健康 基于基因组的个性化医学。

项目成果

Tools for Analysis of the Microbiome.

微生物组分析工具。

• 发表时间: 2020
• 影响因子: 3.1
• 作者: Galloway;Hanson, Blake
• 通讯作者: Hanson, Blake

Gut Microbiome Signatures Are Predictive of Infectious Risk Following Induction Therapy for Acute Myeloid Leukemia.

肠道微生物组特征可预测急性髓系白血病诱导治疗后的感染风险。

• 发表时间: 2020
• 作者: Galloway;Shi, Yushu;Peterson, Christine B;Sahasrabhojane, Pranoti;Gopalakrishnan, Vancheswaran;Brumlow, Chelcy E;Daver, Naval G;Alfayez, Mansour;Boddu, Prajwal C;Khan, Md Abdul Wadud;Wargo, Jennifer A;Do, Kim;Jenq, Robert R
• 通讯作者: Jenq, Robert R

The gut mycobiome: The overlooked constituent of clinical outcomes and treatment complications in patients with cancer and other immunosuppressive conditions.

肠道菌群：癌症和其他免疫抑制疾病患者临床结果和治疗并发症中被忽视的组成部分。

• 发表时间: 2020
• 影响因子: 6.7
• 作者: Galloway;Kontoyiannis, Dimitrios P
• 通讯作者: Kontoyiannis, Dimitrios P

Fecal Microbiome, Metabolites, and Stem Cell Transplant Outcomes: A Single-Center Pilot Study.

粪便微生物组、代谢物和干细胞移植结果：单中心试点研究。

• 发表时间: 2019-05
• 影响因子: 4.2
• 作者: Galloway;Peterson, Christine B;Malik, Farida;Sahasrabhojane, Pranoti V;Shah, Dimpy P;Brumlow, Chelcy E;Carlin, Lily G;Chemaly, Roy F;Im, Jin Seon;Rondon, Gabriela;Felix, Edd;Veillon, Lucas;Lorenzi, Philip L;Alousi, Amin M;Jen
• 通讯作者: Jen

Observational Cohort Study of Oral Mycobiome and Interkingdom Interactions over the Course of Induction Therapy for Leukemia.

白血病诱导治疗过程中口腔真菌组和界间相互作用的观察队列研究。

• 发表时间: 2020
• 影响因子: 4.8
• 作者: Robinson, Sarah;Peterson, Christine B;Sahasrabhojane, Pranoti;Ajami, Nadim J;Shelburne, Samuel A;Kontoyiannis, Dimitrios P;Galloway
• 通讯作者: Galloway