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Tang, Xiaojiao

Mayo Clinic

Mayo Clinic

Citations 2

Gut Microbiota Alter Colonic Expression of Genes Encoding Drug Transporters and Drug-Metabolizing Enzymes in Mice
Nelson, D. , Veerareddy, Vaishnavi, Tang, Xiaojiao, Kashyap, Purna C., Kalari, Krishna R., Kandimalla, Karunya K. , Kandimalla, Karunya K , Kashyap, Purna

International Journal of Molecular Sciences | August 24, 2026 | Volume: 27 | Issue: 17 | Pages: 7583

10.3390/ijms27177583 Get It

Gut microbiota regulate the intestinal expression of drug-metabolizing enzymes, transporters, and barrier properties that determine oral drug absorption and disposition. This study compares germ-free Swiss Webster mice to mice colonized with fecal microbiota from a single human donor, evaluating microbiota-driven differences in colonic gene expression and P-gp protein expression, as well as small intestinal mucosal permeability. Transcriptomic analysis of colonic tissue revealed microbiota-induced upregulation of genes encoding P-gp and other drug transporters, including MCT1 and OCTN2. Immunofluorescence also indicated greater P-gp expression and apical localization in colonized mice. Gene expression of drug-metabolizing enzymes was also higher in colonized mice, including phase I enzymes (carboxylesterase 2 paralogs), phase II enzymes (UDP-glucuronosyltransferases and glutathione S-transferases), and enzymes responsible for synthesizing their co-substrates, UDP-glucuronic acid and glutathione. Small intestinal mucosal explants demonstrated lower permeability to 14C-labeled polyethylene glycol 4000 in colonized mice compared to germ-free mice. In the colon, transcriptomic analysis identified microbiota-dependent changes in genes regulating paracellular tight junctions and actomyosin contractility. These results show that human-derived gut microbiota alter gene expression in the mouse colon, identifying candidate microbiota-responsive genes relevant to oral drug absorption and disposition.
QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid‐Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium
Wang, Zengtao , Veerareddy, Vaishnavi, Tang, Xiao-Jiao, Thompson, Kevin J. , Krishnan, Sunil , Kandimalla, Karunya K. , Tang, Xiaojiao, Kalari, Krishna R , Kandimalla, Karunya K , Krishnan, Sunil , Thompson, Kevin J , Tang, Xiaojiao, Kalari, Krishna R.

CPT: Pharmacometrics & Systems Pharmacology | December 27, 2024 | Volume: 14 | Issue: 3 | Pages: 561-571

10.1002/psp4.13296 Get It

ABSTRACTType 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood–brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K‐AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K‐AKT and MEK–ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with in vitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.