Genetic variants in protein kinase C zeta gene and type 2 diabetes risk: a case-control study of a Chinese Han population

Blood-Based DNA Methylation Biomarkers for Type 2 Diabetes: Potential for Clinical Applications

Type 2 diabetes (T2D) is a leading cause of death and disability worldwide. It is a chronic metabolic disorder that develops due to an interplay of genetic, lifestyle, and environmental factors. The biological onset of the disease occurs long before clinical symptoms develop, thus the search for early diagnostic and prognostic biomarkers, which could facilitate intervention strategies to prevent or delay disease progression, has increased considerably in recent years. Epigenetic modifications represent important links between genetic, environmental and lifestyle cues and increasing evidence implicate altered epigenetic marks such as DNA methylation, the most characterized and widely studied epigenetic mechanism, in the pathogenesis of T2D. This review provides an update of the current status of DNA methylation as a biomarker for T2D. Four databases, Scopus, Pubmed, Cochrane Central, and Google Scholar were searched for studies investigating DNA methylation in blood. Thirty-seven studies were identified, and are summarized with respect to population characteristics, biological source, and method of DNA methylation quantification (global, candidate gene or genome-wide). We highlight that differential methylation of the TCF7L2, KCNQ1, ABCG1, TXNIP, PHOSPHO1, SREBF1, SLC30A8, and FTO genes in blood are reproducibly associated with T2D in different population groups. These genes should be prioritized and replicated in longitudinal studies across more populations in future studies. Finally, we discuss the limitations faced by DNA methylation studies, which include including interpatient variability, cellular heterogeneity, and lack of accounting for study confounders. These limitations and challenges must be overcome before the implementation of blood-based DNA methylation biomarkers into a clinical setting. We emphasize the need for longitudinal prospective studies to support the robustness of the current findings of this review.

PKCζ Is Essential for Pancreatic β-Cell Replication During Insulin Resistance by Regulating mTOR and Cyclin-D2

Adaptive β-cell replication occurs in response to increased metabolic demand during insulin resistance. The intracellular mediators of this compensatory response are poorly defined and their identification could provide significant targets for β-cell regeneration therapies. Here we show that glucose and insulin in vitro and insulin resistance in vivo activate protein kinase C ζ (PKCζ) in pancreatic islets and β-cells. PKCζ is required for glucose- and glucokinase activator-induced proliferation of rodent and human β-cells in vitro. Furthermore, either kinase-dead PKCζ expression (KD-PKCζ) or disruption of PKCζ in mouse β-cells blocks compensatory β-cell replication when acute hyperglycemia/hyperinsulinemia is induced. Importantly, KD-PKCζ inhibits insulin resistance-mediated mammalian target of rapamycin (mTOR) activation and cyclin-D2 upregulation independent of Akt activation. In summary, PKCζ activation is key for early compensatory β-cell replication in insulin resistance by regulating the downstream signals mTOR and cyclin-D2. This suggests that alterations in PKCζ expression or activity might contribute to inadequate β-cell mass expansion and β-cell failure leading to type 2 diabetes.

QTLs influencing IGF-1 levels in a LOU/CxFischer 344F2 rat population. Tracks towards the metabolic theory of Ageing

OBJECTIVE

Since a reduction of the insulin/IGF-1 signaling cascade extends life span in many species and IGF-1 signaling might partly mediate the effects of caloric restriction (CR), an experimental intervention for increasing longevity, the purpose of the present study was to use quantitative trait loci (QTL) analysis, an unbiased genetic approach, to identify particular regions of the genome influencing plasma IGF-1 levels in an F2 intercross between F344 and LOU/C rats; the latter being an inbred strain of Wistar origin, considered as a model of healthy aging since it resists to age (and diet)-induced obesity.

DESIGN

F1 hybrids were obtained by crossbreeding LOU/C with F344 rats, and then F1 were bred inter se to obtain the F2 population, of which 93 males and 94 females were studied. Total plasma IGF-1 levels were determined by radioimmunoassay. A genome scan of the F2 population was made with 100 microsatellite markers) selected for their polymorphism between LOU/C and F344 strains (and by covering evenly the whole genome.

RESULTS

By simple interval mapping sex-dependent QTLs were found on chromosome 17 in males and on chromosome 18 in females. By multiple interval mapping, additional QTLs were found on chromosomes 1, 4, 5, 6, 12, 15 and 19 in males and on chromosomes 3, 5, 6, 12 and 17 in females. Only the markers D1Rat196 and D12Mgh5 were found in both males and females. The majority of QTLs corresponded to metabolic syndrome (cardiac function: n = 45 (30%), obesity/diabetes: n = 22 (15%), inflammation: n = 19 (13%) and only a limited number to body weight: n = 13 (9%), proliferation (n = 10 (7%) or ossification: n = 7 (5%). Ninety-six candidate genes were located on the different QTLs. A significant proportion of these genes are connected to IGF-1 production and receptor pathways (n = 18) or metabolic syndrome (n = 11).

CONCLUSIONS

Subsequent studies are necessary to determine whether the genetic networks underscored are also involved in age-associated obesity, diabetes and inflammation as well as cardiovascular impairments.

Hypermethylation of the PRKCZ Gene in Type 2 Diabetes Mellitus

Objectives. To study the correlation between the methylation of protein kinase C epsilon zeta (PRKCZ) gene promoters and type 2 diabetes mellitus (T2DM). Methods. The case-control method was implemented in 272 unrelated to one another cases in Shiyan People's Hospital. Of those, 152 were diagnosed as T2DM cases, and the other 120 cases were healthy individuals visiting the hospital for a physical examination. The subjects were first divided into two groups: the T2DM group and the normal control (NC) group. Next, methylated DNA immunoprecipitation chip (MeDIP-chip) was used for detection. Bisulfite sequencing PCR (BSP) and gene sequencing were then performed to detect and analyze the correlation between PRKCZ gene promoter methylation and T2DM. Finally, Western blotting was applied to determine the serum level of PRKCZ. The data were then analyzed with the statistics analyzing software SPSS 17.0. Results. In contrast with cases in NC, T2DM patients showed a high level of methylation, with 7 of 9 CpG sites were shown to be methylated, whereas, in the control group, only one CpG site was found to be methylated. The methylated CpG sites for the two groups showed marked differences (P < 0.01). Additionally, the level of PRKCZ was decreased in T2DM subjects, and the difference between the two groups was statistically significant (P < 0.05). Discussion. This study suggests that the PRKCZ gene is the hypermethylated gene of T2DM and the hypermethylation PRKCZ gene may be involved in the pathogenesis of T2DM.

Prioritizing candidate disease genes by network-based boosting of genome-wide association data

Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.