About the Mahmud Lab

The Mahmud Lab is a bioinformatics and computational biology research group dedicated to uncovering the metabolic alterations in inflammation, drug toxicity, and drug resistance mechanisms that underlie major human diseases, including cancer, obesity, cardiovascular disease, aging, diabetes, and childhood malnutrition. By integrating cutting-edge multiomics technologies — genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics — our team decodes disease biology at precision-level resolution, translating complex molecular data into biomarkers, mechanistic insight, and new analytical and computational tools for the scientific community.

Research Focus Areas:

Lipid Metabolism and Cancer. 

A central theme of our research is the role of lipid metabolism in tumor biology. We have shown that the DAXX–SREBP axis drives de novo lipogenesis and tumorigenesis across multiple cancer types, establishing a mechanistic link between chromatin regulators and lipid biosynthesis in cancer cells. Building on this, we have identified how alterations in SPOP, CHD1, SREBP2, ACSL4, and GOT1 converge to reprogram lipid and metabolic pathways in prostate cancer, while OGR1 has emerged as a key driver of lipogenesis in breast cancer, alongside CerS2, which regulates ceramide biology in the same disease. In hepatocellular carcinoma, our work has defined a role for CES1 in shaping the metabolic phenotype of liver tumors. Together, these findings position lipid metabolic rewiring as a unifying vulnerability across diverse cancers.

Obesity, Lipid-Mediated Signaling, and Cancer

Our lab has also uncovered how metabolic signaling connects obesity to cancer risk. We identified chemerin (RARRES2) as a critical adipokine linking obesity to clear cell renal cell carcinoma (ccRCC), and further defined the roles of its receptor network — GPR1, CMKLR1, and ARRB2 — in ccRCC progression. Extending this work into developmental and nutritional biology, we discovered that MMP14, RARRES2, and DRP1 contribute to metabolic dysregulation in both obesity and childhood malnutrition, revealing shared molecular circuitry across seemingly distinct nutritional extremes.

Metabolic and Vascular Mechanisms of Cardiovascular Disease

Our lab investigates how metabolic and mechanotransduction pathways drive vascular disease. We showed that hepatocyte SHMT2 regulates one-carbon metabolism to control VLDL-cholesterol and ceramide synthesis, shaping atherosclerotic plaque size and stability, and used sex-stratified multiomics to link MASH and atherosclerosis through shared biomarkers (cholesterol, CCL2, sphinganine) and TREM2⁺ macrophage signatures conserved in humans. We also identified an endothelial LATS1/2–CD38 axis: disturbed blood flow suppresses LATS1/2, activating CD38-driven mitochondrial reprogramming (SUOX loss, reverse-mode ATP synthase) that produces senescent yet proliferative endothelial cells prone to forming fragile, thrombogenic neovessels—a mechanism confirmed in human plaques. Separately, we found that restoring mitochondrial oxidative metabolism in ACTA2-mutant smooth muscle cells prevents moyamoya-like cerebrovascular occlusion in mice.

Microbiome and immuno-metabolism

Our lab investigates how host and microbial metabolism shapes immune cell function and therapy resistance across cancer and transplantation. In melanoma and hepatocellular carcinoma, we identified circulating lipid signatures and gut microbiome-derived tryptophan metabolites (indolepropionic acid) predicting checkpoint inhibitor response. We showed that PD1-resistant tumors upregulate Fabp7, reprogramming lipid metabolism to block ferroptosis and suppress CD8⁺ T cell antitumor activity—with higher FABP7 predicting worse immunotherapy outcomes in patients. In cervical cancer, tumor-resident Lactobacillus iners drives lactate-induced metabolic rewiring that confers chemoradiation resistance. Extending to transplantation immunology, we found LRP-1 governs T cell proliferation and survival through global lipid remodeling, and that T cell-specific LRP-1 loss abrogates graft-versus-host disease in vivo, positioning lipid metabolism as a convergent immunotherapeutic target. 

Lipid Biochemistry and related-organelle dynamics

Our group is decoding the broader lipid biochemistry landscape — including saturation index, acyl chain length, organelle-specific lipid pools, lipid class distribution, and lipid ontology — to understand how lipid molecules govern mitochondrial and peroxisomal dynamics, membrane and organelle signaling across the ER–Golgi axis, lipid droplet biology, inflammation, and multiple modes of regulated cell death, including ferroptosis, autophagy, and apoptosis. This systems-level view of lipid biochemistry provides a mechanistic foundation that connects our cancer, metabolic, and cardiovascular research programs.

Non-Coding RNA in Metabolic Disease

Complementing our lipid- and protein-focused work, we study how non-coding RNAs — miRNA, lncRNA, and circRNA — regulate metabolic alterations in human disease, including pediatric medulloblastoma, extending our mechanistic reach into RNA-level regulation of the metabolic pathways central to our research.

Analytical Technology Development

To enable rapid, non-invasive biomarker detection, our lab develops novel mass spectrometry-based analytical platforms, including Paper Spray Ionization Mass Spectrometry (PSI-MS) and Segmented Flow Mass Spectrometry (SF-MS). These technologies support fast, minimally invasive detection of metabolic biomarkers for cancer diagnosis, directly translating our mechanistic discoveries into clinically applicable tools.

Computational Biology Tool Development

We are also building the computational infrastructure needed to interpret multiomics data at scale. Our tools include xPEDITE, a platform for multi-omics data interpretation, visualization, and analysis; TARO, an integrated microbiome–metabolite analysis tool; an SBGN-based next-generation pathway-mapping framework; and IROA DUAL-MSTUS, designed to correct for ion suppression in metabolite-based biomarker discovery. Together, these tools support rigorous, reproducible analysis across the multiomics datasets generated by our research.