The mitochondrial rhomboid protease PSARL is a new candidate gene for type 2 diabetes

Aims/hypothesis This study aimed to identify genes that are expressed in skeletal muscle, encode proteins with functional significance in mitochondria, and are associated with type 2 diabetes.
Methods We screened for differentially expressed genes in skeletal muscle of Psammomys obesus (Israeli sand rats), and prioritised these on the basis of genomic localisation and bioinformatics analysis for proteins with likely mitochondrial functions.
Results We identified a mitochondrial intramembrane protease, known as presenilins-associated rhomboid-like protein (PSARL) that is associated with insulin resistance and type 2 diabetes. Expression of PSARL was reduced in skeletal muscle of diabetic Psammomys obesus, and restored after exercise training to successfully treat the diabetes. PSARL gene expression in human skeletal muscle was correlated with insulin sensitivity as assessed by glucose disposal during a hyperinsulinaemic–euglycaemic clamp. In 1,031 human subjects, an amino acid substitution (Leu262Val) in PSARL was associated with increased plasma insulin concentration, a key risk factor for diabetes. Furthermore, this variant interacted strongly with age to affect insulin levels, accounting for 5% of the variation in plasma insulin in elderly subjects.
Conclusions/interpretation Variation in PSARL sequence and/or expression may be an important new risk factor for type 2 diabetes and other components of the metabolic syndrome.

GAD2 on chromosome 10p12 is a candidate gene for human obesity

The gene GAD2 encoding the glutamic acid decarboxylase enzyme (GAD65) is a positional candidate gene for obesity on Chromosome 10p11–12, a susceptibility locus for morbid obesity in four independent ethnic populations. GAD65 catalyzes the formation of γ-aminobutyric acid (GABA), which interacts with neuropeptide Y in the paraventricular nucleus to contribute to stimulate food intake. A case-control study (575 morbidly obese and 646 control subjects) analyzing GAD2 variants identified both a protective haplotype, including the most frequent alleles of single nucleotide polymorphisms (SNPs) +61450 C>A and +83897 T>A (OR = 0.81, 95% CI [0.681–0.972], p = 0.0049) and an at-risk SNP (−243 A>G) for morbid obesity (OR = 1.3, 95% CI [1.053–1.585], p = 0.014). Furthermore, familial-based analyses confirmed the association with the obesity of SNP +61450 C>A and +83897 T>A haplotype (χ2 = 7.637, p = 0.02). In the murine insulinoma cell line βTC3, the G at-risk allele of SNP −243 A>G increased six times GAD2 promoter activity (p < 0.0001) and induced a 6-fold higher affinity for nuclear extracts. The −243 A>G SNP was associated with higher hunger scores (p = 0.007) and disinhibition scores (p = 0.028), as assessed by the Stunkard Three-Factor Eating Questionnaire. As GAD2 is highly expressed in pancreatic β cells, we analyzed GAD65 antibody level as a marker of β-cell activity and of insulin secretion. In the control group, −243 A>G, +61450 C>A, and +83897 T>A SNPs were associated with lower GAD65 autoantibody levels (p values of 0.003, 0.047, and 0.006, respectively). SNP +83897 T>A was associated with lower fasting insulin and insulin secretion, as assessed by the HOMA-B% homeostasis model of β-cell function (p = 0.009 and 0.01, respectively). These data support the hypothesis of the orexigenic effect of GABA in humans and of a contribution of genes involved in GABA metabolism in the modulation of food intake and in the development of morbid obesity.

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

Background
Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.

Results
Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.

Conclusion
The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Obesity and diabetes gene discovery approaches

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and  biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the  pathogenesis of these diseases is critical to this process, however  identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Novel gene discovery in the muscle of obese-diabetic Psammomys obesus

Metabolism in Psammomys obesis, a polygenic animal model of obesity and type 2 diabetes is associated with dysregulated nocturnal fat oxidation in diabetic animals. Furthermore, a new gene called AGT-203 has been identified. Evidence indicates that AGT-203 is involved in abnormal glucose metabolism leading to the proposition that AGT-203 is a new candidate gene for type 2 diabetes.

Mutation of the LUNATIC FRINGE gene in humans Causes spondylocostal dysostosis with a severe vertebral phenotype

The spondylocostal dysostoses (SCDs) are a heterogeneous group of vertebral malsegmentation disorders that arise during embryonic development by a disruption of somitogenesis. Previously, we had identified two genes that cause a subset of autosomal recessive forms of this disease: DLL3 (SCD1) and MESP2 (SCD2). These genes are important components of the Notch signaling pathway, which has multiple roles in development and disease. Here, we have used a candidate-gene approach to identify a mutation in a third Notch pathway gene, LUNATIC FRINGE (LFNG), in a family with autosomal recessive SCD. LFNG encodes a glycosyltransferase that modifies the Notch family of cell-surface receptors, a key step in the regulation of this signaling pathway. A missense mutation was identified in a highly conserved phenylalanine close to the active site of the enzyme. Functional analysis revealed that the mutant LFNG was not localized to the correct compartment of the cell, was unable to modulate Notch signaling in a cell-based assay, and was enzymatically inactive. This represents the first known mutation in the human LFNG gene and reinforces the hypothesis that proper regulation of the Notch signaling pathway is an absolute requirement for the correct patterning of the axial skeleton.

Tripartite motif-containing 55 identified as functional candidate for spontaneous cardiac hypertrophy in the rat locus cardiac mass 22

BACKGROUND: Left ventricular (LV) hypertrophy is a risk factor for cardiovascular death, but the genetic factors determining LV size and predisposition to hypertrophy are not well understood. We have previously linked the quantitative trait locus cardiac mass 22 (Cm22) on chromosome 2 with cardiac hypertrophy independent of blood pressure in the spontaneously hypertensive rat. From an original cross of spontaneously hypertensive rat with F344 rats, we derived a normotensive polygenic model of spontaneous cardiac hypertrophy, the hypertrophic heart rat (HHR) and its control strain, the normal heart rat (NHR).

METHODS AND RESULTS: To identify the genes and molecular mechanisms underlying spontaneous LV hypertrophy we sequenced the HHR genome with special focus on quantitative trait locus Cm22. For correlative analyses of function, we measured global RNA transcripts in LV of neonatal HHR and NHR and 198 neonatal rats of an HHR × NHR F2 crossbred population. Only one gene within locus Cm22 was differentially expressed in the parental generation: tripartite motif-containing 55 (Trim55), with mRNA downregulation in HHR (P < 0.05) and reduced protein expression. Trim55 mRNA levels were negatively correlated with LV mass in the F2 cross (r = -0.16, P = 0.025). In exon nine of Trim55 in HHR, we found one missense mutation that functionally alters protein structure. This mutation was strongly associated with Trim55 mRNA expression in F2 rats (F = 10.35, P < 0.0001). Similarly, in humans, we found reduced Trim55 expression in hearts of subjects with idiopathic dilated cardiomyopathy.

CONCLUSION: Our study suggests that the Trim55 gene, located in Cm22, is a novel candidate gene for polygenic LV hypertrophy independent of blood pressure.

Effects of early-life environment and epigenetics on cardiovascular disease risk in children: highlighting the role of twin studies

Cardiovascular disease (CVD) is the leading cause of death worldwide and originates in early life. The exact mechanisms of this early-life origin are unclear, but a likely mediator at the molecular level is epigenetic dysregulation of gene expression. Epigenetic factors have thus been posited as the likely drivers of early-life programming of adult-onset diseases. This review summarizes recent advances in epidemiology and epigenetic research of CVD risk in children, with a particular focus on twin studies. Classic twin studies enable partitioning of phenotypic variance within a population into additive genetic, shared, and nonshared environmental variances, and are invaluable in research in this area. Longitudinal cohort twin studies, in particular, may provide important insights into the role of epigenetics in the pathogenesis of CVD. We describe candidate gene and epigenome-wide association studies (EWASs) and transgenerational epigenetic inheritance of CVD, and discuss the potential for evidence-based interventions. Identifying epigenetic changes associated with CVD-risk biomarkers in children will provide new opportunities to unravel the underlying biological mechanism of the origins of CVD and enable identification of those at risk for early-life interventions to alter the risk trajectory and potentially reduce CVD incidence later in life.

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Novel therapeutics for cononary artery disease from genome-wide association study data

Abstract
Background: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.
Methods: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatics modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.
Results: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).
Conclusions: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

A syntenic cross species aneuploidy genetic screen links RCAN1 expression to β-cell mitochondrial dysfunction in Type 2 diabetes

Type 2 diabetes (T2D) is a complex metabolic disease associated with obesity, insulin resistance and hypoinsulinemia due to pancreatic β-cell dysfunction. Reduced mitochondrial function is thought to be central to β-cell dysfunction. Mitochondrial dysfunction and reduced insulin secretion are also observed in β-cells of humans with the most common human genetic disorder, Down syndrome (DS, Trisomy 21). To identify regions of chromosome 21 that may be associated with perturbed glucose homeostasis we profiled the glycaemic status of different DS mouse models. The Ts65Dn and Dp16 DS mouse lines were hyperglycemic, while Tc1 and Ts1Rhr mice were not, providing us with a region of chromosome 21 containing genes that cause hyperglycemia. We then examined whether any of these genes were upregulated in a set of ~5,000 gene expression changes we had identified in a large gene expression analysis of human T2D β-cells. This approach produced a single gene, RCAN1, as a candidate gene linking hyperglycemia and functional changes in T2D β-cells. Further investigations demonstrated that RCAN1 methylation is reduced in human T2D islets at multiple sites, correlating with increased expression. RCAN1 protein expression was also increased in db/db mouse islets and in human and mouse islets exposed to high glucose. Mice overexpressing RCAN1 had reduced in vivo glucose-stimulated insulin secretion and their β-cells displayed mitochondrial dysfunction including hyperpolarised membrane potential, reduced oxidative phosphorylation and low ATP production. This lack of β-cell ATP had functional consequences by negatively affecting both glucose-stimulated membrane depolarisation and ATP-dependent insulin granule exocytosis. Thus, from amongst the myriad of gene expression changes occurring in T2D β-cells where we had little knowledge of which changes cause β-cell dysfunction, we applied a trisomy 21 screening approach which linked RCAN1 to β-cell mitochondrial dysfunction in T2D.