High intake of dietary fat and chronic exposure to environmental arsenic (As) are independent risk factors for liver disease, but there have been no studies on how these two risk factors interact to damage the liver. The majority of obese people in the United States have steatosis (fatty liver), but less than 20% of people with steatosis progress to the much more serious condition of steatohepatitis (fatty liver with inflammation and liver injury), indicating that other environmental factors have an impact on inflammatory liver injury. The current proposal examines the role of environmental As in the progression from steatosis to steatohepatitis. A diet high in fat places a metabolic burden on the liver, making it susceptible to injury by a second insult. In the studies proposed here, the hypothesis to be tested is that chronic As exposure contributes to liver disease progression by altering metabolic pathways related to inflammation. Specifically, this team will examine the basis for their recent observation that As exposure results in inflammatory liver injury in mice fe a high fat diet, whereas the same concentration of As is not toxic in mice fed a low fat diet. The plan is to identify pathways altered by high fat, As, and the combination of the two by measuring changes in liver at the level of metabolomics and proteomics signatures. By constructing a model integrating both the enzymatic activities and metabolites, one will be able to identify the pathways where these two stresses interact. These studies are expected to reveal metabolic, signaling and regulatory pathways that are associated with inflammatory liver injury in a model of diet-environment interaction that is relevant to the United States population, where high fat diets are common and As exposure levels are typically below the threshold for overt hepatotoxicity. This new information will be used as a basis for future studies examining the molecular targets of As that are responsible for increased sensitivity to inflammation. These studies may be applicable to other As- associated illnesses such as cardiovascular disease and cancer that are driven by chronic inflammation.
Most obese people have fatty liver disease to at least some extent. The proposed studies will examine how environmental As contributes to progression to more serious forms of liver disease.
|Shi, Xue; Wei, Xiaoli; Koo, Imhoi et al. (2014) Metabolomic analysis of the effects of chronic arsenic exposure in a mouse model of diet-induced Fatty liver disease. J Proteome Res 13:547-554|
|Koo, Imhoi; Yao, Sen; Zhang, Xiang et al. (2014) Comparative analysis of false discovery rate methods in constructing metabolic association networks. J Bioinform Comput Biol 12:1450018|
|Wei, Xiaoli; Koo, Imhoi; Kim, Seongho et al. (2014) Compound identification in GC-MS by simultaneously evaluating the mass spectrum and retention index. Analyst 139:2507-14|
|Watson, Walter H; Burke, Tom J; Doll, Mark A et al. (2014) S-adenosylhomocysteine inhibits NF-?B-mediated gene expression in hepatocytes and confers sensitivity to TNF cytotoxicity. Alcohol Clin Exp Res 38:889-96|
|Koo, Imhoi; Shi, Xue; Kim, Seongho et al. (2014) iMatch2: compound identification using retention index for analysis of gas chromatography-mass spectrometry data. J Chromatogr A 1337:202-10|
|Wei, Xiaoli; Shi, Xue; Koo, Imhoi et al. (2013) MetPP: a computational platform for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry-based metabolomics. Bioinformatics 29:1786-92|
|Koo, Imhoi; Kim, Seongho; Zhang, Xiang (2013) Comparative analysis of mass spectral matching-based compound identification in gas chromatography-mass spectrometry. J Chromatogr A 1298:132-8|
|Kim, Seongho; Zhang, Xiang (2013) Comparative analysis of mass spectral similarity measures on peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry. Comput Math Methods Med 2013:509761|