Gut-Liver Metabolic Interactions: Implications for Diet, Pollutants, and Cancer
The gut microbiome is a powerful biochemical engine, capable of transforming dietary compounds and environmental pollutants in ways that shape human health. These microbial activities can alter the toxicity or bioavailability of compounds, contributing to cancers such as colorectal cancer in adults and leukemia in children. Environmental pollutants and diet can modify gut microbial activity, sometimes enhancing harmful effects or disrupting protective metabolism.
Our lab combines metabolomics, microbiology, and molecular toxicology to understand how gut microbes interact with the human host and influence cancer development. We use Untargeted metabolomics, in vitro bacterial cultures, Pre clinical models models, and human sample analysis to map microbial and host co-metabolism, identify key metabolites, and evaluate their impact on tumor growth and progression.
Key research approaches include:
- Metabolomic Mapping of Microbial and Host Co-metabolism: Investigating how gut bacteria and liver enzymes transform pollutants and dietary compounds.
- Metabolite Identification and Toxicity Assessment: Detecting microbial-derived metabolites using untargeted metabolomics and assessing their carcinogenic potential.
- Multi-omics Integration: Combining genomics, proteomics, and metabolomics to uncover mechanisms linking microbial metabolism to cancer.
Our ultimate goal is to uncover microbial-metabolite signatures linked to cancer, identify biomarkers for early detection, and guide strategies for cancer prevention and therapy.
Figure 1: Overview of the study design to investigate gut microbiome interactions with pollutants in carcinogenesis
Differential Microbial-Metabolite Effects Across Colorectal cancer subsites
Colorectal cancer (CRC) is highly heterogeneous, with tumors from different colon subsites (cecum, sigmoid, rectum) showing distinct microbiomes, metabolites, molecular features, and clinical behavior. Environmental pollutants and diet influence CRC risk differently across these subsites, highlighting the role of local microenvironments.
Our lab studies how gut microbial metabolism of diet and pollutants drives these subsite-specific effects. Using untargeted metabolomics, shotgun metagenomics, CRC cell lines, organoids, and human cohorts, we identify microbial-derived metabolites and species that modulate tumor biology. Co-culture models reveal how gut bacteria interact with tumors to influence carcinogenesis.
CRC tumors also differ in metastatic behavior, with left- and right-sided tumors showing distinct liver metastasis patterns and outcomes. By integrating metabolomics and microbiome profiling, organoids, and functional assays, we aim to uncover links between diet, pollutants, microbiome, tumor subsite, and metastasis, identify predictive biomarkers, and guide personalized CRC prevention and therapy.
Metabolomics Technology & Novel Metabolite Discovery
Our lab builds advanced metabolomics platforms and computational tools to improve sensitivity, normalization, and metabolite identification. We have established large-scale metabolomics pipelines capable of processing thousands of samples with rigorous QC-based normalization and batch-effect correction. A major focus is the discovery of microbial and host-derived conjugated metabolites that traditional workflows often miss. By combining analytical chemistry, machine-learning–guided annotation, and extensive microbial metabolite libraries, we aim to rapidly identify both known and novel metabolites. These innovations will deepen understanding of gut microbiome–host interactions, pollutant and dietary transformation, and metabolic drivers of cancer.
Metabolomics for Newborn Screening & Early-Life Disease Detection
Current newborn screening (NBS) identifies infants at risk for metabolic disorders using targeted metabolite panels. While effective, these panels capture only a small fraction of the newborn metabolome and can generate false positives, offering limited insight into early disease biology.
We apply untargeted metabolomics to measure hundreds to thousands of metabolites simultaneously, providing a comprehensive view of metabolic activity at birth. This approach helps distinguish true positives from false positives, uncover novel disease-associated pathways, and enhance the accuracy and biological relevance of NBS.
By integrating untargeted metabolomics with existing markers, we aim to identify new biomarkers, refine risk prediction, and map early-life metabolic pathways, paving the way for a more informative, accurate, and biologically grounded newborn screening framework.
