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

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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.