Developing genome-scale fluxomics to uncover metabolic reprogramming in human pathogens during infection - Human 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 contributes to virulence and identifying metabolic targets that inform the development of anti-virulence strategies. Current multi-omics methods (transcriptomics, proteomics, metabolomics) provide valuable but indirect and predominantly static snapshots of cellular state; they do not measure the actual fluxes (i.e., reaction rates) that reveal how the metabolic network is dynamically reprogrammed. Consequently, metabolic targets derived from these data are often inaccurate. Genome-scale fluxomics—the direct, quantitative measurement of metabolic reaction rates across the entire network—offers the dynamic, functional perspective required to understand pathogenic metabolic reprogramming. Yet two major barriers prevent routine, genome-scale application of fluxomics. First, current isotope-tracing workflows demand labor-intensive, expert-dependent manual data processing. Second, current metabolic flux analysis is predominantly limited to small networks (e.g., central carbon metabolism), leaving the majority of metabolic fluxes unquantified. The objective of this proposal is to develop the first genome-scale fluxomics technology and to automate its data processing so that flux measurements become as routine and accessible as other omics. To address these two challenges, we will specifically: (1) automate mass spectrometry data processing to compute mass isotopologue distributions for all detected metabolites (untargeted profiling), and (2) develop a scalable framework that integrates isotope-tracing data with other multi-omics data to infer reaction rates at the genome scale. We will apply this technology to characterize metabolic reprogramming in a major fungal pathogen Candida albicans during its yeast-to-hyphae transition, a key virulence trait that contributes to tissue invasion, biofilm formation, and immune evasion. By targeting reactions with altered fluxes, we will test whether reversing these flux changes can block the morphological transition and hyphal growth. This program builds on my interdisciplinary expertise in metabolic modeling, machine learning, isotope tracing, computational mass spectrometry, and multi-omics integration, and leverages the experimental and computational infrastructure of my hybrid laboratory at Dartmouth College. By transforming fluxomics into a routine tool, this work will enable mechanistic studies of host–pathogen metabolic interactions, reveal how intracellular metabolic reprogramming drives pathogenic adaptation and virulence, and facilitate the discovery of novel metabolic targets for anti- virulence therapies.
