Research

Ongoing research projects are focused on the following areas:

 

Alpha-aminoadipic Acid in Diabetes

Diabetes affects ~10% of the US population, and is one of the leading causes of death in the US and worldwide. Early intervention can prevent or slow down diabetes progression, but we do not have reliable ways of predicting who will develop diabetes, and the therapeutic options are limited. Strategies to modulate pathogenesis before and during onset of overt disease would have significant impact in reducing mortality, morbidity, and healthcare costs. However, we do not yet understand the pathogenesis underlying many cases of diabetes. Alpha-aminoadipic acid (2-AAA) has been established as a biomarker of diabetes risk through metabolomic profiling in epidemiological cohorts. Increased plasma 2-AAA in healthy individuals predicted increased future risk of diabetes after 12 years of follow-up. 2-AAA is generated from the catabolism of lysine, an essential dietary amino acid. However, dietary lysine or total protein do not predict 2-AAA levels, and the genetic or other determinants of 2-AAA have not yet been identified. We are studying the determinants of 2-AAA in the population, and probing the genetic underpinnings of 2-AAA variation, to better understand the mechanisms linking 2-AAA to diabetes. 

 

Virtual Metabolomics for Cardiovascular Disease

Cardiometabolic diseases are a leading global cause of morbidity and mortality.  Circulating markers such as LDL- or HDL-cholesterol can point to disease mechanisms, enable risk stratification, and, in some cases, enable therapeutic risk modification. However, disease progression still occurs despite optimal therapy, and up to a third of individuals with an acute cardiac event do not exhibit known risk markers. We hypothesize that circulating metabolites may function as biomarkers for this unexplained disease risk. However, conducting large-scale, unbiased metabolomics studies for multiple diseases and sub-phenotypes is prohibitively expensive, time-consuming, and often not feasible. We have developed a "virtual metabolite" approach using genetic predictors of metabolite concentrations to efficiently and rapidly identify novel metabolite-disease associations, and prioritize genes and metabolites for further functional interrogation.

 

The Human Microbiome

The human microbiome has been associated with health status, and risk of disease development. While the etiology of microbiome-mediated disease remains to be fully elucidated, one mechanism may be through microbial metabolism. Metabolites produced by commensal organisms, particularly in response to host diet, may affect host metabolic processes, with potentially protective or pathogenic consequences. We use multi-omic phenotyping in human studies, in addition to functional experiments, to understand how diet, the microbiome, and metabolism interact to modulate risk of cardiometabolic diseases. 

Our lab collaborates with microbiome researchers across the Vanderbilt and VUMC campus, and is part of the Vanderbilt Microbiome Initiative: https://my.vanderbilt.edu/microbiome/

 

Genetics of Fever

Sepsis, trauma, Systemic Inflammatory Response Syndrome (SIRS), and other acute inflammatory diseases account for >1 million annual deaths in the US, and place a considerable financial burden on the healthcare system. Development of effective therapies is hampered by the complexity and dynamic nature of the inflammatory response, and clinical trials for new drugs have thus far proved disappointing. Given involvement of multiple components that may be protective or harmful depending on the setting, improved knowledge of genetic regulation of the inflammatory response and underlying dynamic molecular mechanisms is urgently needed. Fever is a clinically relevant biological mediator which impacts outcomes in acute infections, sepsis and trauma, yet the genetic determinants of fever are poorly understood.We discovered a novel locus on chr7p11.2 associated with febrile responses, acute mortality from trauma and sepsis, and with SIRS. We are using multiple models to better understand the causal mechanisms at this locus.