Association Analysis Of LEP Signaling Pathway And ADIPO Signaling Pathway With Type 2 Diabetes Mellitus

Research background and objectiveSurvey data show that, the incidence of diabetes is rapidly rising in the world, Type 2 diabetes mellitus is becoming a global pandemic, the number of diabetes patients will be close to 500 million by 2030, Diabetes has become a major public health problem in China, a country now has the largest number of diabetes patients around the world. It has brought a heavy burden to the society, economy and people’s health. The etiology and pathogenesis of type 2 diabetes are still not fully understood, which makes it difficult for people to prevent and treat it. It has been increasingly accepted that type 2 diabetes arise from numerous environmental and genetic factors working together. Each of these risk factors can, via largely undefined mechanisms, lead to skeletal muscle, adipose and hepatic insulin resistance, and/or β-cell dysfunction. Ultimately, insulin resistance accompanied by inadequate insulin secretory responses results in postprandial and fasting hyperglycaemia. In turn, diabetes-related hyperglycaemia and associated metabolic abnormalities can further alter signal transduction and gene expression (glucolipotoxicity), thus contributing to a vicious cycle. The rapid increase in the prevalence of type 2 diabetes may partially attribute to environmental and lifestyle factors, including overnutrition, obesity, physical inactivity and so on, furthermore, overwhelming data support that type 2 diabetes has an evident genetic component and genetic factors influence the disease susceptibility. First, twin studies demonstrated a markedly higher concordance for type 2 diabetes in monozygotic compared with dizygotic twins. Second, type 2 diabetes clusters within families and first-degree relatives have, compared with the general population, higher risk to develop the disease. Finally, certain ethnic minorities and indigenous groups with low population admixture show exceptionally high type 2 diabetes prevalence.In recent years, the domestic and foreign scholars have conducted a large number of genetic epidemiological studies, applying a variety of strategies and approaches, in order to locate and identify susceptible genes of type 2 diabetes and better understand its genetics architecture and pathogenesis. We review some commonly used methods in the following sections.(1) Linkage analysis:Linkage analysis was the primary methods to link genotype and phenotype in early time. Genome-wide scans have been performed to search for microsatellite markers associated with type 2 diabetes in more than 20 ethnic groups (such as the Finns, Mexico Americans, Caucasian American, British, Japanese, French, Chinese northern and Southern Han people and so on) The results show that multiple susceptibility loci of type 2 diabetes positioning on different chromosomes and some of them have been duplicated in different populations.(2) Candidate-gene association study:Candidate gene approach directly tests the effects of genetic variants of a potentially contributing gene in an association study. These studies, which may include members of an affected family or unrelated cases and controls, can be performed relatively quickly and inexpensively and may allow identification of genes with small effects. However, the candidate gene approach is limited by how much is known of the biology of the disease being investigated. As researchers select potential candidate genes based on the results of linkage analysis, chromosomal location or functional information of gene products. Since insulin gene was reported twenty years ago, more and more candidate genes were studied in different populations. Candidate-gene association studies showed that the genes for the peroxisome proliferator activated receptor gamma(PPARG) and the potassium inwardly rectifying channel subfamily J member 11 (KCNJ11) were 2 candidate susceptibility genes. Both genes encode targets of anti-diabetes medications and harbor missense variants associated with T2D. To date, only these two loci (PPARG, KCNJ11) were robustly implicated in T2D susceptibility.(3) Genome-wide association study, GWAS:Limited success of candidate gene approach and linkage analysis in identifying the genetic background of type 2 diabetes has caused many research groups to apply the genome-wide association studies approach in large case-control cohorts. In a typical GWAS, hundreds of thousands of SNPs are genotyped for thousands of individuals. By comparisons of differences in the DNA variations between the normal and affected individuals, the SNPs can be ordered according to their degrees of association. The common approach is to select dozens of the most significant SNPs in the list for further investigations. Nearly 60 susceptibility loci associated with type 2 diabetes have been successfully identified and replicated since 2007. Substantial progress in our knowledge of the genetic basis of T2D has been elucidated by T2D GWAS, but there remains a large portion of unexplained genetic heritability. This may attribute to some limitations of GWAS. Recently, scientists have focused on performing further analysis by utilizing the genome-wide genotyping data to identify more susceptibility genes of complex diseases. Many strategies and methods have been applied in the following GWAS, such as gene-gene and gene-environment interaction, pathway analysis, and epistasis study and so on. The application of these strategies and methods compensates the limitation of the traditional GWAS and provides new insights into genetics basis of complex diseases.(4) SNP-SNP interaction:GWAS have been successful in identifying individual variants in a variety of genes that may play a role in the etiology of T2D. However, because of practical and statistical challenges, none of the GWAS have considered interactions among the thousands of variants. Gene-gene interaction mainly refers to the nonallelic gene interactions, also known as epistatic effect, which may be one of the most important factors influencing susceptibility of complex diseases. Complex diseases are affected by minor genes, these effects are more likely to be detected by the interaction analysis. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. One study showed that there was a two-locus interaction between the UCP2 and PPARγ genes among 23 loci in the candidate genes of Type 2 diabetes In another research the results suggest that the single nucleotide polymorphisms from the obesity candidate genes may contribute to the risk of T2D in an interactive manner.(5) Pathway-based analysis:The pathway-based association analysis is to take a pathway as a basic unit of analysis. The pathway may come from KEGG (Kyoto Encyclopedia of Genes and Genomes) or Gene Ontology database, which was defined based on existing knowledge in biological processes. The pathway-based approach aims to simultaneously study association of a group of genetic variants in the same biological pathway, which help us to holistically unravel complex genetic structure of common disease to gain insight into the biological processes and disease mechanism. It is well known that genes do not work in isolation; instead, complex molecular networks and cellular pathways are often involved in disease susceptibility and disease progression. Therefore, by taking into account prior biological knowledge about genes and pathways, we may have a better chance to identify the genes and mechanisms that are involved in disease pathogenesis.In previous study we found that adipocytokine genes and related signaling pathways have close relationship with insulin signaling transduction, and variants in these genes or pathways may lead to insulin resistance, which plays an important role in the pathophysiology of type 2 diabetes. Therefore, we select LEP signaling pathway and ADIPO signaling pathway and propose (1) to investigate associations between single SNPs of these genes and type 2 diabetes; (2) to identify SNP-SNP interactions; (3)to assess the association of the LEP signaling pathway and ADIPO signaling pathway with type 2 diabetes mellitus by using a pathway-based approach; (4) to further estimate the combined effects of these SNPs and predictive power for type 2 diabetes. We hope to find novel variants associated to the disease. The results of this study will provided the theoretical basis for further revealing the pathogenesis of type 2 diabetes mellitus, identifying potential drug targets, predicting higher risk individuals and preventing type 2 dianbetes mellitus.Materials and MethodsThis study employed a case-control study design. The cases were patients with type 2 diabetes which were recruited from endocrinology departments of 10 hospitals in Guangdong province (Affiliated Hospital of Guangdong Medical College, Maoming City People’s Hospital, Shaoguan City People’s Hospital, Dongguan Bo Shek Lung Ai Hospital, Houjie People’s Hospital, Shenzhen Longhua People’s Hospital, Shenzhen Nanshan People’s Hospital, Shenzhen Guanlan People’s Hospital, Shenzhen Xixiang People’s Hospital, Shenzhen Futian People’s Hospital.).The non-diabetic controls were recruited from people who came for general health exams. The cases and controls were matched according to the region and sex.Questionnaire surveys were conducted on subjects who met the inclusion criteria by trained investigators. Age, gender, native place, occupation, history of disease, course of disease, smoking history, family history, complications, diet, exercise and etc. were collected. ACD (anticoagulant citrate dextrose) peripheral blood was collected in the morning, part of blood sample was employed for biochemical detection and another part was used to extract DNA.DNA was extracted from 4 ml ACD (anticoagulant citrate dextrose) peripheral blood. We selected 48 tagging single nucleotide polymorphisms LEP Signaling pathway 15 genes (LEP, LEPR, JAK1, JAK2, STAT3, SHP-2, PGC-la, PRKAA1, PRKAA2, PRKAB2, PRKAG1, PRKAG2, PRKAG3, alpha-MSH, NPY) of 23 SNP (rs4731426, rs2167270, rs12405556, rs17127107, rs108895O2, rs7849191, rs9891119, rs4767860, rs2970847, rs6821591, rs249429, rs3805486, rs1342382, rs6588640, rs6937, rs3766522, rs10783299, rs5017427, rs9648724, rs645163, rs6436094, rs6713532, rs16147) and ADIPO signaling pathway 14 gene (ADIPOQ, ADIPOQR1, ADIPOQR2, PPAR-alpha, PCK1, PCK2, G6PC, ACC2, GLUT1, GLUT4, CPT-1, RXRA, RXRB, RXRC) of 25 SNP (rs266729, rs16861205, rs1342387, rs12733285, rs767870, rs1044471, rs4823613, rs5767743, rs1042531, rs11908628, rs2301336, rs4982856, rs2593595, rs2268388, rs3754219, rs12718444, rs5435, rs16956647, rs11228368, rs11185660, rs1045570, rs2744537, rs2076310, rs1467664, rs3753898) were genotyped. We genotyped the SNPs by SNPscanTM multiple SNP genotyping assays. We checked the demographic, biochemical characteristics and SNP data and integrated them for statistical analysis.Comparisons of all variables between T2DM and control subjects were carried out by chi-square test for nominal variables or t-test for continuous variables. We tested the association of the single SNPs with type 2 diabetes using Pearson chi-square test, Cochran-Armitage trend test, MAX3, logistic regression, etc. LD block in the haploid type uses the unconditioned Logistic regression,Logistic regression was also used for SNP-SNP interactions analysis. We tested the pathway analysis using SNP set analysis based on logistic kernel machine regression. Searching risk SNP combination of type 2 diabetes was performed by genetic algorithm. All statistical analyses were performed by statistical packages including SPSS15.0, PLINK 1.07, R 2.14.2, Haploview 4.2、SNPstatsResults(1) Association analysis of LEP signaling pathway and type 2 diabetes:In association analysis based on single SNPs, rs2167270 in unadjusted for covariates and covariate adjustment dominant model was statistically significant, in unadjusted for covariates and covariate adjustment codominant model, with respect to genotype GG genotype AG were statistically significant.In unadjusted for covariates overdominant model was statistically significant, but there was no statistically significant covariate adjustment in the overdominance model; rs16147 adjustment for covariates before and after adjustment for covariates in recessive model, overdominant model is still statistically significant;By linkage disequilibrium analysis,we found that the rs2167270 and rs4731426 of LEP gene, rs10889502 and rs17127107 of JAK1 gene, rs2970847 and rs6821591 of PGC-1a gene, rs249429 and rs3805486 of PRKAA1 gene, rsl342382 and rs6588640 of PRKAA2 gene, rs3766522 and rs6937 of PRKAB2 gene, rs2970847 and rs6821591 of PRKAG2 gene, rs6436094 and rs645163 of PRKAG3 gene have linkage disequilibrium;Unconditioned Logistic regression analysis did not find the genotype haploid of LD block have obviously significant positive results.We got fifteen statistically significant gene pairs from SNP-SNP interaction analysis, the corresponding gene pairs were:SLC2A1 and LEP, PRKAA2 and PCK2, PRKAA2 and G6PC, JAK1 and PRKAG3, RXRG and NPY, PRKAG3 and DDN. PPARGC1A with LEP, PPARGC1A and PCK2, PRKAA1 and PPARA, PRKAA1 and PPARA, NPY and ADIPOR2, LEP and SLC2A4, LEP and SLC2A4, LEP and PCK1, STAT3 and PCK1, Nine genes such as LEP, PRKAA2, PCK2, PRKAG3, NPY, PPARGC1A, PRKAA1, PPARA, SLC2A4, PCK1 exists in LEP signaling pathways, the rest exists in ADIPO signaling pathways, only 2 pairs SNP in LEP signaling pathways have statistical significance. LEP、PRKAA2、 PCK、PRKAG3、 NPY、PPARGC1A、PCK1 have more interactions with other genes, especially LEP genes up to 4, suggesting that these genes may have a more important role in lipid metabolism, LEP may be a more inmportmant role. PRKAA1 and PPARA, LEP and SLC2A4 both have two pairs with statistical signficance, which suggest that PRKAA1 and PPARA, LEP and SLC2A4 may have more closely relationship.Also rs16147 and rs2167270 were included in the fifteen statistically significant gene pairs, which can be seen for another evidence associated with type 2 diabetes.There was no statistical significance in LEP signaling pathway analysis no matter the covariates were added or not.(2) Association analysis of ADIPO signaling pathway and type 2 diabetes:In association analysis based on single SNPs, rs3753898 in allelic association analysis, genotype correlation analysis and robust examination were statistically significant after adjustment for covariates and get a stable and consistent results; rs1042531 in unadjusted for covariates and covariate adjustment overdominant model were statistically significant. In codominant model covariate adjustment, TG relative to genotype TT genotype was statistically significant, with respect to genotype GG GG genotype was not statistically significant; rs12718444 is not adjusted for covariates and adjustment covariates were statistically significant in the recessive model, in codominant model covariate adjustment, TG relative to the genotype GG genotype was not significant,and relative to the genotype GG genotype TT there was statistically significant; rs1044471 in unadjusted covariate There are under overdominant genetic model was statistically significant, but after adjustment for covariates both not statistically significant, indicating that they may be affected by covariates.By linkage disequilibrium analysis,we found that the rs16861205 and rs266729 of ADIPOQ gene, rs12733285 and rs1342387 of ADIPOR1 gene, rs1044471 and rs767870 of ADIPOR2 gene, rs4823613 and rs5767743 of PPARA gene, rs1042531 and rs11908628 of PCK1 gene, rs2301336 and rs4982856 of PCK2 gene, rs12718444and rs3754219 of SLC2A1 gene, rs16956647 and rs5435 of SLC2A4 gene, rs2076310 and rs2744537 of RXRB gene, rs1467664 and rs3753898 of RXRG gene have linkage disequilibrium; Unconditioned Logistic regression analysis found that the genotype haploid which consist of rs1467664 allele T and rs3753898 allele A of the RXRG gene is a high risk factor of type 2 diabetes mellitus, It shows the genetic variation of RXRG gene may be the genetic factors of diabetes;Also, It can be seen another evidence for rs3753898 associated with type 2 diabetes.SNP-SNP interactions analysis among 25 SNP of the 14 genes in ADIPO signaling pathway, no statistical significance is found.But in interactions analysis of the LEP signaling pathway genes associated with ADIPO signaling pathway gene, nine genes such as ADIPOQR2, PPARA, PCK1, PCK2, G6PC, GLUT1, GLUT4, RXRG, PRKAG1 of ADIPO pathways of have interactions with genes in LEP signaling pathways.The genes in ADIPO signaling pathway may work with the genes of LEP signaling pathway together. Also rs1042531 was included in the statistically significant gene pairs, which can be seen for another evidence associated with type 2 diabetes.Based on ADIPO signaling pathways of SNP analysis, There was no statistical significance between ADIPO signaling pathway with type 2 diabetes, no matter the covariates were added or notConclusions(1) rs2167270、rs1 6147 in ADIPO signaling pathway are associated with type 2 diabetes; rs3753898. rs104253、rs12718444 of ADIPO signaling pathway are also associated with type 2 diabetes.(2) The allele T of rs1467664 together with the allele T of rs3753898 consisting of haplotype of the RXRG gene in ADIPO signaling pathways, is a high risk factor of type 2 diabetes mellitus(3) The SNPs from adipocytokine and receptor genes have pairs of interactions with SNPs from LEP Signaling pathway and ADIPO Signaling pathway, these interactions are statistical significant, these SNP-SNP interactions will increase the risk of type 2 diabetes. It means that the SNP-SNP interaction is the constituent part of genetic structure of type 2 diabetes.(4) According to the logistic kernel machine regression results, cannot think LEP pathways and ADIPO pathways associated with type 2 diabetes.