Regulatory Expression of Peroxisome Proliferator Activated Receptors Genes During Fatty Liver Formation in Geese

In order to investigate the expression pattern of peroxisome proliferator activated receptor （PPAR） genes before and after overfeeding, and estimate the effect of expressed PPAR levels on weights of fatty liver and abdominal fat in geese, the RT-PCR products of PPAR genes in heart, liver, spleen, lung, kidney, stomach, small intestine, brain, breast muscle, leg muscle, and abdominal fat were determined before and after overfeeding. RT-PCR was used to determine the expression levels of PPAR genes. Quantity one software was used to analyze absorbency, and the expression level of GAPDH gene was used as contrast. Expression levels of PPAR-α were relatively high in most of detected tissues but undetectable in abdominal fat tissue before overfeeding, and the level was evidently increased in lung, appeared in abdominal fat tissue, and reduced in the other tissues after overfeeding. Expressed PPAR-γ levels were relatively high in liver, spleen, lung, small intestine, and abdominal fat, and relatively low in the other tissues before overfeeding. Expressed PPAR-γ levels were enhanced in liver, spleen, lung, stomach, and kidney but decreased in abdominal fat and without obvious changes in the other tissues. Expression patterns of PPAR genes show tissue-specific manner. In addition, expression patterns of PPAR-α are different from PPAR-γ after overfeeding. It might suggest that different functions of PPAR subtypes are responsive to overfeeding.

Application of the back-error propagation artificial neural network(BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

Gene-Gene Interaction between PPARd and PPARy Is Associated with Abdominal Obesity in a Chinese Population

The peroxisome proliferator-activated receptors （PPARs） -α, -δ/β and -γ are the ligand-activated transcription factors that function as the master regulators of glucose, fatty acid and lipoprotein metabolism, energy balance, cell proliferation and differentiation, inflarn- marion, and atherosclerosis. The objective of the current study was to examine the main and interactive effect of seven single nucleotide polymorphisms （SNPs） of PPARδ/γ, in contribution to abdominal obesity. A total of 820 subjects were randomly selected and no indi- viduals were related. The selected S NPs in PPARδ （rs2016520 and rs9794） and PPARγ （rs10865710, rs 1805192, rs709158, rs3856806, and rs4684847） were genotyped. Mean difference and 95% confident interval were calculated. Interactions were explored by the method of generalized multifactor dimensionality reduction. After adjustment for gender, age, and smoking status, it was found that the carriers of the C allele （TC ＋ CC） of rs2016520 were associated with a decreased risk of abdominal obesity compared to the carriers of the TT genotype （mean difference = -2.63, 95% CI = -3.61-1.64, P 〈 0.000t）. A significant two-locus model （P = 0.0107） involving rs2016520 and rs 10865710 and a significant three-locus model （P = 0.0107） involving rs2016520, rs9794, and rs 1805192 were observed. Overall, the three-locus model had the highest level of testing accuracy （59.85%） and showed a better cross-validation consistency （9/10） than two-locus model. Therefore, for abdominal obesity defined by waist circumference, we chose the three-locus model as the best interaction model. In conclusion, the C allele in rs2016520 was significantly associated with a lower abdominal obesity. Moreover, an interaction among rs2016520, rs1805192, and rs9794 on incident abdominal obesity could be demonstrated.