Mechanistic targets of weight loss-induced cancer prevention by dietary calorie restriction and physical activity

• Main Author: Standard, Joseph Tabb
• Language: en_US
• Published: Kansas State University 2013
• Subjects: Bioinformatics; Weight control; Cancer prevention; Dietary calorie restriction; Exercise; Mice; Bioinformatics (0715)
• Online Access: http://hdl.handle.net/2097/15937

Master of Science === Department of Human Nutrition === Weiqun Wang === Weight control through either dietary calorie restriction (DCR) or exercise is associated with cancer prevention in animal models. However, the underlying mechanisms are not fully defined. Bioinformatics approaches using genomics, proteomics, and lipidomics were employed to elucidate the profiling changes of genes, proteins, and phospholipids in response to weight loss by DCR or exercise in a mouse skin cancer model. SENCAR mice were randomly assigned into 4 groups for 10 weeks: ad lib-fed sedentary control, ad lib-fed exercise (AE), exercise but pair-fed isocaloric amount of control (PE), and 20% DCR. Two hours after topical TPA treatment, skin epidermis was analyzed by Affymetrix for gene expression, DIGE for proteomics, and lipidomics for phospholipids. Body weights were significantly reduced in both DCR and PE but not AE mice versus the control. Among 39,000 transcripts, 411, 67, and 110 genes were significantly changed in DCR, PE, and AE, respectively. The expression of genes relevant to PI3K-Akt and Ras-MAPK signaling was effectively reduced by DCR and PE as measured through GenMAPP software. Proteomics analysis identified ~120 proteins, with 22 proteins significantly changed by DCR, including upregulated apolipoprotein A-1, a key antioxidant protein that decreases Ras-MAPK activity. Of the total 338 phospholipids analyzed by lipidomics, 57 decreased by PE including 5 phophatidylinositol species that serve as PI3K substrates. Although there were many impacts that we still need to characterize, it appears that both Ras-MAPK and PI3K-Akt signaling pathways are the key cancer preventive targets that have been consistently demonstrated by three bioinformatics approaches.