Integrating bioinformatics and physiology to describe genetic effects in complex polygenic diseases

Type 2 diabetes mellitus (T2DM) results from interaction between genetic and environmental factors. The worldwide prevalence of T2DM is increasing rapidly due to reduction in physical activity, increase in dietary intake, and the aging of the population. This thesis has focused on dissecting the genetic contribution in T2DM using largescale genomic approaches with a particular emphasis on analysis of gene transcripts in different tissues, predominantly muscle. In paper I, we identified TXNIP as a gene whose expression is powerfully suppressed by insulin yet stimulated by glucose. In healthy individuals, its expression was inversely correlated to total body measures of glucose uptake. Forced expression of TXNIP in cultured adipocytes significantly reduced glucose uptake, while silencing with RNA interference in adipocytes and in skeletal muscle enhanced glucose uptake, confirming that the gene product is also a regulator of glucose uptake. TXNIP expression is consistently elevated in the muscle of pre-diabetics and diabetics, although in a panel of 4,450 Scandinavian individuals, we found no evidence for association between common genetic variation in the TXNIP gene and T2DM. TXNIP regulates both insulindependent and insulin-independent pathways of glucose uptake in human skeletal muscle. Combined with recent studies that have implicated TXNIP in pancreatic β-cell glucose toxicity, our data suggest that TXNIP might play a key role in defective glucose homeostasis preceding overt T2DM. In paper II, we investigated molecular mechanisms associated with insulin sensitivity in skeletal muscle by relating global skeletal muscle gene expression to physiological measures of the insulin sensitivity. We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle. Most notably, genes involved in a mammalian target-of-rapamycin signaling pathway were positively whereas genes encoding extracellular matrix structural constituent such as extracellular matrix-receptor, cell communication, and focal adhesion pathways were inversely correlated with insulin sensitivity. More specifically, expression of CPT1B was positively and that of LEO1 inversely correlated with insulin sensitivity, a finding which was replicated in an independent study of 9 non-diabetic men. These data suggest that a high capacity of fat oxidation in mitochondria is reflected by a high expression of CPT1B which is a marker of insulin sensitivity. In paper III, we investigated molecular mechanisms associated with maximal oxygen uptake (VO2max) and type 1 fibers in human skeletal muscle. We identified 66 genes positively and 83 genes inversely correlated with VO2max and 171 genes positively and 217 genes inversely correlated with percentage of type 1 fibers in human skeletal muscle. Genes involved in oxidative phosphorylation (OXPHOS) showed high expression in individuals with high VO2max, whereas the opposite was not the case in individuals with low VO2max. Instead, genes such as AHNAK and BCL6 were associated with low VO2max. Also, expression of the OXPHOS genes, NDUFB5 and ATP5C1, increased with exercise training and decreased with aging. In contrast, expression of AHNAK in skeletal muscle decreased with exercise training and increased with aging. These findings indicate that VO2max closely reflects expression of OXPHOS genes, particularly that of NDUFB5 and ATP5C1 in skeletal muscle and high expression of these genes suggest good muscle fitness. In contrast, a high expression of AHNAK was associated with a low VO2max and poor muscle fitness. In paper IV, we combined results from the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC) genome-wide association (GWA) studies with genome-wide expression profiling in pancreas, adipose tissue, liver, and skeletal muscle in patients with or without T2DM or animal models thereof to identify novel T2DM susceptibility loci. We identified 453 single nucleotide polymorphisms (SNPs) associated with T2DM with P < 0.01 in at least one of the GWA studies and 150 genes that were located in vicinity of these SNPs. Out of these 150 genes, we identified 41 genes differentially expressed using publicly available gene expression profiling data. Most notably, we were able to identify four genes namely IGF2BP2, CDKAL1, TSPAN8, and NOTCH2 for which SNPs located in vicinity of these genes have shown association with T2DM in different populations. In addition, we identified a SNP (rs27582) in the CAST gene which was associated with future risk of T2DM (odds ratio (OR) = 1.10, 95% CI: 1.00-1.20, P < 0.05) in a prospective study of 16,061 Swedish individuals followed for more than 25 years; this association was stronger in lean individuals (OR = 1.19, 95% CI: 1.03-1.36, P = 0.024). Moreover in the Botnia Prospective Study (BPS) involving 2,770 individuals followed for more than 7 years, carriers of the A-allele were more insulin resistant than carriers of the G-allele as indicated by higher fasting insulin concentrations (regression coefficient (β) = 0.048, P = 0.017) and higher HOMA-IR index (β = 0.044, P = 0.025) as well as lower insulin sensitivity index during OGTT (β = -0.039, P = 0.039) at follow-up. In conclusion, using gene expression in different tissues from patients with T2DM and animal models is a powerful tool for prioritizing SNPs from GWA studies for replication studies. We thereby identified association of a variant (rs27582) in the CAST gene with T2DM and insulin resistance

Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have emerged as a powerful approach for identifying susceptibility loci associated with polygenetic diseases such as type 2 diabetes mellitus (T2DM). However, it is still a daunting task to prioritize single nucleotide polymorphisms (SNPs) from GWAS for further replication in different population. Several recent studies have shown that genetic variation often affects gene-expression at proximal (<it>cis</it>) as well as distal (<it>trans</it>) genomic locations by different mechanisms such as altering rate of transcription or splicing or transcript stability.</p> <p>Methods</p> <p>To prioritize SNPs from GWAS, we combined results from two GWAS related to T2DM, the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC), with genome-wide expression data from pancreas, adipose tissue, liver and skeletal muscle of individuals with or without T2DM or animal models thereof to identify T2DM susceptibility loci.</p> <p>Results</p> <p>We identified 1,170 SNPs associated with T2DM with <it>P </it>< 0.05 in both GWAS and 243 genes that were located in the vicinity of these SNPs. Out of these 243 genes, we identified 115 differentially expressed in publicly available gene expression profiling data. Notably five of them, <it>IGF2BP2</it>, <it>KCNJ11</it>, <it>NOTCH2</it>, <it>TCF7L2 </it>and <it>TSPAN8</it>, have subsequently been shown to be associated with T2DM in different populations. To provide further validation of our approach, we reversed the approach and started with 26 known SNPs associated with T2DM and related traits. We could show that 12 (57%) (<it>HHEX</it>, <it>HNF1B</it>, <it>IGF2BP2</it>, <it>IRS1</it>, <it>KCNJ11</it>, <it>KCNQ1</it>, <it>NOTCH2</it>, <it>PPARG</it>, <it>TCF7L2</it>, <it>THADA</it>, <it>TSPAN8 </it>and <it>WFS1</it>) out of 21 genes located in vicinity of these SNPs were showing aberrant expression in T2DM from the gene expression profiling studies.</p> <p>Conclusions</p> <p>Utilizing of gene expression profiling data from different tissues of individuals with or without T2DM or animal models thereof is a powerful tool for prioritizing SNPs from WGAS for further replication studies.</p

Two common genetic variants near nuclear-encoded OXPHOS genes are associated with insulin secretion in vivo

Context Mitochondrial ATP production is important in the regulation of glucose-stimulated insulin secretion. Genetic factors may modulate the capacity of the β-cells to secrete insulin and thereby contribute to the risk of type 2 diabetes. OBJECTIVE: The aim of this study was to identify genetic loci in or adjacent to nuclear encoded genes of the oxidative phosphorylation (OXPHOS) pathway that are associated with insulin secretion in vivo. DESIGN AND METHODS: To find polymorphisms associated with glucose-stimulated insulin secretion, data from a genome-wide association study (GWAS) of 1467 non-diabetic individuals, the Diabetes Genetic Initiative (DGI), was examined. 413 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) ≥0.05 located in or adjacent to 76 OXPHOS genes were included in the DGI GWAS. A more extensive population based study of 4323 non-diabetics, the PPP-Botnia, was used as a replication cohort. Insulinogenic index during an oral glucose tolerance test (OGTT) was used as a surrogate marker of glucose-stimulated insulin secretion. Multivariate linear regression analyses were used to test genotype-phenotype associations. RESULTS: Two common variants were indentified in the DGI, where the major C-allele of rs606164, adjacent to NDUFC2 (NADH dehyrogenase (ubiqinone) 1 subunit C2), and the minor G-allele of rs1323070, adjacent to COX7A2 (cythochrome c oxidase subunit VIIa polypeptide 2), showed nominal associations with decreased glucose-stimulated insulin secretion (p=0.0009 respective p=0.003). These associations were replicated in PPP-Botnia (p=0.002 and p=0.05). CONCLUSION: Our study shows that genetic variation near genes involved in oxidative phosphorylation may influence glucose-stimulated insulin secretion in vivo

Relationship between insulin sensitivity and gene expression in human skeletal muscle

BackgroundInsulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood.MethodsTo explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR).ResultsWe identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g.PPARGC1A.ConclusionsThe muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g.SIRT2, and genes regulating autophagy and mTOR signaling, e.g.FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.Peer reviewe

Metabolic pathways and immunometabolism in rare kidney diseases

Objectives To characterise renal tissue metabolic pathway gene expression in different forms of glomerulonephritis. Methods Patients with nephrotic syndrome (NS), antineutrophil cytoplasmic antibody-associated vasculitis (AAV), systemic lupus erythematosus (SLE) and healthy living donors (LD) were studied. Clinically indicated renal biopsies were obtained at time of diagnosis and microdissected into glomerular and tubulointerstitial compartments. Microarray-derived differential gene expression of 88 genes representing critical enzymes of metabolic pathways and 25 genes related to immune cell markers was compared between disease groups. Correlation analyses measured relationships between metabolic pathways, kidney function and cytokine production. Results Reduced steady state levels of mRNA species were enriched in pathways of oxidative phosphorylation and increased in the pentose phosphate pathway (PPP) with maximal perturbation in AAV and SLE followed by NS, and least in LD. Transcript regulation was isozymes specific with robust regulation in hexokinases, enolases and glucose transporters. Intercorrelation networks were observed between enzymes of the PPP (eg, transketolase) and macrophage markers (eg, CD68) (r=0.49, p<0.01). Increased PPP transcript levels were associated with reduced glomerular filtration rate in the glomerular (r=-0.49, p<0.01) and tubulointerstitial (r=-0.41, p<0.01) compartments. PPP expression and tumour necrosis factor activation were tightly co-expressed (r=0.70, p<0.01). Conclusion This study demonstrated concordant alterations of the renal transcriptome consistent with metabolic reprogramming across different forms of glomerulonephritis. Activation of the PPP was tightly linked with intrarenal macrophage marker expression, reduced kidney function and increased production of cytokines. Modulation of glucose metabolism may offer novel immune-modulatory therapeutic approaches in rare kidney diseases

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression.

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.The TEDDY Study is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Centers for Disease Control and Prevention (CDC), and JDRF. This work supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082). KGCS is a Lister Prize fellow and is supported by a Wellcome Trust Senior Investigator award (200871/Z/16/Z). EFM is a Wellcome-Beit prize fellow (10406/Z/14/A) supported by the Wellcome Trust and Beit Foundation (10406/Z/14/Z) and by the National Institutes for Health Research Biomedical Research Centre (Cambridge). LPX’s affiliation changed after completion of the manuscript and is now Département d'informatique et de recherche opérationnelle, Université de Montréal, Montréal, Canada and Mila, Quebec Institute for Learning Algorithms, Montréal, Canada

High-throughput muscle fiber typing from RNA sequencing data

Background Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. Methods By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). Results The correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13–0.67], [95% CI], and rmyosin = 0.83 [0.61–0.93], with p = 5.70 × 10–3 and 2.00 × 10–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads. Conclusions This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.journal articl

High-throughput muscle fiber typing from RNA sequencing data

Background: Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. Methods: By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). Results: The correlation between the sequencing-based method and the other two were r(ATpas) = 0.44 [0.13-0.67], [95% CI], and r(myosin) = 0.83 [0.61-0.93], with p = 5.70 x 10(-3) and 2.00 x 10(-6), respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of similar to 10,000 paired-end reads. Conclusions: This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.Peer reviewe

Fine mapping the KLK3 locus on chromosome 19q13.33 associated with prostate cancer susceptibility and PSA levels

Measurements of serum prostate-specific antigen (PSA) protein levels form the basis for a widely used test to screen men for prostate cancer. Germline variants in the gene that encodes the PSA protein (KLK3) have been shown to be associated with both serum PSA levels and prostate cancer. Based on a resequencing analysis of a 56 kb region on chromosome 19q13.33, centered on the KLK3 gene, we fine mapped this locus by genotyping tag SNPs in 3,522 prostate cancer cases and 3,338 controls from five case–control studies. We did not observe a strong association with the KLK3 variant, reported in previous studies to confer risk for prostate cancer (rs2735839; P = 0.20) but did observe three highly correlated SNPs (rs17632542, rs62113212 and rs62113214) associated with prostate cancer [P = 3.41 × 10−4, per-allele trend odds ratio (OR) = 0.77, 95% CI = 0.67–0.89]. The signal was apparent only for nonaggressive prostate cancer cases with Gleason score <7 and disease stage <III (P = 4.72 × 10−5, per-allele trend OR = 0.68, 95% CI = 0.57–0.82) and not for advanced cases with Gleason score >8 or stage ≥III (P = 0.31, per-allele trend OR = 1.12, 95% CI = 0.90–1.40). One of the three highly correlated SNPs, rs17632542, introduces a non-synonymous amino acid change in the KLK3 protein with a predicted benign or neutral functional impact. Baseline PSA levels were 43.7% higher in control subjects with no minor alleles (1.61 ng/ml, 95% CI = 1.49–1.72) than in those with one or more minor alleles at any one of the three SNPs (1.12 ng/ml, 95% CI = 0.96–1.28) (P = 9.70 × 10−5). Together our results suggest that germline KLK3 variants could influence the diagnosis of nonaggressive prostate cancer by influencing the likelihood of biopsy

Telomere length is not a main factor for the development of islet autoimmunity and type 1 diabetes in the TEDDY study

The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8676 children, 3-4 months of age, born with HLA-susceptibility genotypes for islet autoimmunity (IA) and type 1 diabetes (T1D). Whole-genome sequencing (WGS) was performed in 1119 children in a nested case-control study design. Telomere length was estimated from WGS data using five tools: Computel, Telseq, Telomerecat, qMotif and Motif_counter. The estimated median telomere length was 5.10 kb (IQR 4.52-5.68 kb) using Computel. The age when the blood sample was drawn had a significant negative correlation with telomere length (P = 0.003). European children, particularly those from Finland (P = 0.041) and from Sweden (P = 0.001), had shorter telomeres than children from the U.S.A. Paternal age (P = 0.019) was positively associated with telomere length. First-degree relative status, presence of gestational diabetes in the mother, and maternal age did not have a significant impact on estimated telomere length. HLA-DR4/4 or HLA-DR4/X children had significantly longer telomeres compared to children with HLA-DR3/3 or HLA-DR3/9 haplogenotypes (P = 0.008). Estimated telomere length was not significantly different with respect to any IA (P = 0.377), IAA-first (P = 0.248), GADA-first (P = 0.248) or T1D (P = 0.861). These results suggest that telomere length has no major impact on the risk for IA, the first step to develop T1D. Nevertheless, telomere length was shorter in the T1D high prevalence populations, Finland and Sweden.</p