Unraveling the ecology of intestinal fungal expansion in immunocompromised patients through computational modeling and machine learning - PROJECT SUMMARY/ABSTRACT CANDIDATE: I am a Postdoctoral Research Associate at Memorial Sloan Kettering Cancer Center (MSKCC). During my Ph.D. studies, I dedicated myself to developing biology-based mathematical models of bacterial metabolism. My current research extends my interest from single organisms to microbial communities, with a particular focus on the human intestinal microbiome. Since joining MSKCC, I have compiled a large longitudinal microbiome dataset from hospitalized patients who have undergone allogeneic hematopoietic cell transplantation (allo-HCT). I have also acquired bioinformatic skills and data-driven modeling techniques to profile microbiome compositions and quantify their associations with clinical outcomes. Built upon this dataset, my proposed research aligns well with my long-term career goal of establishing an independent laboratory to elucidate mechanistic links between the intestinal microbiota and infectious diseases. To prepare for my transition to independence and developing a competitive computational research program, I have developed a focused career plan to enhance my computational skills in metagenomic/metabolomic data analyses, community metabolic modeling, and development of neural network models. In parallel, I will improve my soft skills, including presentation, networking, grantsmanship, mentorship, leadership, and teaching. RESEARCH: Immunocompromised patients undergoing intensive antimicrobial therapy are at high risk for developing invasive fungal bloodstream infections (BSIs). Between 2016 and 2020, C. parapsilosis was responsible for the most breakthrough BSI cases among allo-HCT recipients at MSKCC. Typically, C. parapsilosis BSI occurs subsequent to its intestinal expansion. This proposal will leverage my mathematical modeling expertise and the vast microbiome dataset of our allo-HCT cohort to elucidate the ecological mechanisms underlying intestinal expansion of C. parapsilosis. My central hypothesis is that altered intestinal metabolic environment enables C. parapsilosis expansion. Specific Aim 1 will identify bacterial secreted metabolites that inhibit C. parasilosis. In Specific Aim 2, I will investigate the impacts of genomic variations across different C. parapsilosis isolates on their ability to utilize nutrients and grow in the human intestine. The Specific Aim 3 will involve building a machine-learning-powered computational framework for the risk assessment of C. parapsilosis expansion and the rational design of antifungal therapy to reduce the risk. ENVIRONMENT: I will complete the K99 phase of this grant in the Computational & Systems Biology Program at MSKCC, a state-of-the-art cancer research institute. My primary mentor, Dr. Joao Xavier, has a proven track record in mathematical modeling of bacterial microbiomes, while my co-mentor, Dr. Tobias Hohl, is an expert in fungal mycobiomes and infectious disease. The two labs will jointly provide a rich and complementary training and research environment that integrates computational, experimental, and clinical resources.
