Research Directions

Direction 1: Metabolic regulation of fungal pathogenesis

Fungal pathogens survive, adapt, and cause disease in hostile host environment by rapidly reprogramming their metabolism. Accurate quantification of how intracellular reaction rates change during host interaction is essential for understanding how metabolic reprogramming drives pathogenesis and identifying metabolic targets that inform antimicrobial therapeutic development. Our project aims to develop an automated, genome-scale fluxomics platform that converts raw gas and liquid chromatography-mass spectrometry (GC/LC-MS) data from isotope tracing experiments into metabolic flux maps. This platform will transform infectious disease research and systems biology by enabling routine, high-resolution analysis of microbe-microbe and host-microbe metabolic interactions. Using this technology, we will investigate clinically relevant questions such as how Candida albicans rewires its metabolism during the pathogenic yeast-to-hyphae transition and how these metabolic adaptations contribute to gut colonization and drug resistance.

Direction 2: Metabolic cross-feeding in the gut microbiome

We aim to develop novel computational models to infer metabolic activities within the human gut microbiome. Instead of focusing on metabolite levels, our goal is to understand the dynamic metabolic behavior of individual microbes and quantify metabolite exchange fluxes between them. Specifically, we ask: given gut microbiota multi-omics data, can we determine which microbes are producing or consuming specific metabolites, and how these activities change over time? To address this question, we will integrate metagenomics, metatranscriptomics, and metabolomics data with community-level metabolic models to infer metabolite uptake and secretion for each microbe. We also plan to expand these models by incorporating spatial effects and water flow dynamics towards a 3D biophysical model. By predicting these microbial metabolic activities, we can identify cross-feeding interactions that are difficult to measure in vivo. This approach provides a new lens for understanding microbiome dysbiosis beyond what can be inferred from metabolite levels alone.

Direction 3: Host-microbiome interactions in colorectal cancer

The gut microbiome holds the power to modulate host immunity and influence how patients respond to cancer immunotherapies, but the underlying mechanisms are poorly understood. Using a combination of mouse models, in vitro co-culture system, and computational modeling, we investigate how gut microbiome composition and function modulate anti-tumor immunity in colorectal cancer. We use gnotobiotic mice colonized with defined microbial communities to manipulate the gut microbiome and measure resulting changes in tumor-associated immune cell populations and cytokine profiles. We also use host-microbe co-culture in vitro systems and apply isotope tracing to identify metabolic crosstalk between microbes and host cells. We combine these experiments with mathematical modeling to simulate host-microbiome interactions and predict which microbes enhance or suppress immunotherapy responses. Our ultimate goal is to track metabolite flow between gut microbiome and host immune cells through in vivo isotope tracing. By integrating experimental and computational approaches, we aim to uncover fundamental principles of microbiome-immune interactions and design a computational tool to faciliate the rationale design of microbiome-based therapies.