Expression analysis of loci associated with type 2 diabetes in human tissues
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- Cotsapas, C., Prokunina-Olsson, L., Welch, C. et al. Diabetologia (2010) 53: 2334. doi:10.1007/s00125-010-1861-2
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Genetic mapping has identified over 20 loci contributing to genetic risk of type 2 diabetes. The next step is to identify the genes and mechanisms regulating the contributions of genetic risk to disease. The goal of this study was to evaluate the effect of age, height, weight and risk alleles on expression of candidate genes in diabetes-associated regions in three relevant human tissues.
We measured transcript abundance for WFS1, KCNJ11, TCF2 (also known as HNF1B), PPARG, HHEX, IDE, CDKAL1, CDKN2A, CDKN2B, IGF2BP2, SLC30A8 and TCF7L2 by quantitative RT-PCR in human pancreas (n = 50), colon (n = 195) and liver (n = 50). Tissue samples were genotyped for single nucleotide polymorphisms (SNPs) associated with type 2 diabetes. The effects of age, height, weight, tissue and SNP on RNA expression were tested by linear modelling.
Expression of all genes exhibited tissue bias. Immunohistochemistry confirmed the findings for HHEX, IDE and SLC30A8, which showed strongest tissue-specific mRNA expression bias. Neither age, height nor weight were associated with gene expression. We found no evidence that type 2 diabetes-associated SNPs affect neighbouring gene expression (cis-expression quantitative trait loci) in colon, pancreas and liver.
This study provides new evidence that tissue-type, but not age, height, weight or SNPs in or near candidate genes associated with increased risk of type 2 diabetes are strong contributors to differential gene expression in the genes and tissues examined.
KeywordsColoneQTLHHEXIDELivermRNAPancreasSLC30A8SNPType 2 diabetes
Centre d’Etude du Polymorphisme (Utah residents with northern and western European ancestry)
Expression quantitative trait locus
Haemopoietically expressed homeobox protein
Minor allele frequency
Solute carrier family 30 (zinc transporter), member 8
Single nucleotide polymorphism
Type 2 diabetes is a common disease with significant risk heritability. Over 20 loci in the human genome have been identified in the past few years as underlying this risk . Attention is now being focused on translating these findings into understanding mechanisms of pathogenesis. Functional genomic experiments are an effective approach to elucidating biological mechanisms. One of the most popular strategies is expression quantitative trait locus (eQTL) analysis, where genetic effects on transcript levels are mapped to regions of the genome. If disease associated variants affect RNA abundance, this may represent a mechanism through which the genetic variant regulates the disease phenotype . Studies determining whether genetic variants, height, weight and age may be associated with RNA abundance in tissues relevant to type 2 diabetes are lacking. Furthermore, information on translation from DNA to where the protein is produced within a tissue is also lacking.
In the present study, we investigated whether genetic variants robustly associated with type 2 diabetes also modulate expression levels of candidate genes nearby. We measured transcript abundance of eleven such genes (IDE, CDKAL1, CDKN2A, CDKLN2B, IGF2BP2, SLC30A8, TCF7L2, HHEX, TCF2 [also known as HNF1B], KCNJ11, PPPARG and WFS1) by RT-PCR in three relevant human tissues (colon, liver and pancreas) and correlated expression levels to tissue, age at sampling, height, weight and genotype. We chose three human tissues in which genes near the loci associated with increased risk may be influencing the risk of disease. Pancreas was chosen given the evidence to date of a role for these genes in insulin secretion [1, 3]. Colon was chosen as a second representative neuroendocrine tissue, given the presence of incretins released from the gut, including glucagon-like peptide-1, glucagon-like peptide-2 and gastric inhibitory polypeptide. Liver was chosen on the basis of its gluconeogenic properties. Our current understanding of developmental biology in endoderm morphogenesis, and genes and signalling pathways that control transitions to epithelium also supported a decision to study colon and liver.
Tissue procurement, DNA and RNA
Clinical characteristics of study samples
58.2 ± 17.2
56.0 ± 12.7
54.3 ± 16.2
1.69 ± 1.03
1.70 ± 1.12
1.70 ± 9.62
80.1 ± 26.1
76.2 ± 18.2
82.9 ± 27.2
28.06 ± 9.42
26.19 ± 5.25
28.50 ± 8.51
All samples were histopathologically normal (non-cancerous) according to pathology reports.
Gene, sample size, covariate associations and eQTL analysis
Sample size (n)
Covariate association (p values)
Detectable effect sizeb
8.84 × 10−12
<2 × 10−16
<2 × 10−16
<2 × 10−16
<2 × 10−16
3.82 × 10−14
<2 × 10−16
<2 × 10−16
9.62 × 10−15
<2 × 10−16
Total RNA was extracted with RNeasy (Qiagen) from frozen tissue specimens and treated with DNAse (Qiagen) according to the manufacturer’s protocols. RNA was reverse-transcribed to cDNA using a kit (SuperScript III; Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s directions. All quantitative RT-PCR reactions were performed on a sequence detection system (7900; Applied Biosystems, Foster City, CA, USA). All Taqman expression assays were purchased from Applied Biosystems. Taqman expression assays included: IGF2BP2, UniGene Hs.35354, Hs01118016_m1; CDKAL1, UniGene Hs.657604, Hs00214949_m1; SLC30A8, UniGene Hs.532270, Hs00545182_m1; CDKN2A/B, UniGene Hs.512599/UniGene Hs.72901, Hs_00793225_m1; IDE, UniGene Hs.500546, Hs00971002_m1; TCF7L2, UniGene Hs.593995, Hs01009053_m1; KCNJ11, UniGene Hs.248141, Hs00265026_s1; PPARG, UniGene Hs.162646, Hs01115513_m1; TCF2, UniGene Hs.191144, Hs01001602_m1; HHEX, UniGene Hs.118651, Hs00242160_m1; and WFS1, UniGene Hs.518602, Hs00903605_m1. Expression of all assays was normalised to cyclophilin A (PPIA Taqman expression assay ID Hs03045993_gH) according to the Δ crossing threshold (Ct) method or relative quantification. Technical duplicates were completed with different genes for normalisation (HPRT and cyclophilin A) resulting in a correlation coefficient of 0.8377. A correlation of replicates of Ct values for a series of pancreas and liver samples performed by the operator on the days of the analyses with the same reagents produced a Pearson correlation coefficient of 0.9927.
Method for immunohistochemistry
Paraffin-embedded sections were mounted on slides (Super Frost Plus; Fisher Scientific, Pittsburgh, PA, USA). Sections were deparaffinised, rehydrated and washed in 0.1 mmol/l PBS, pH 7.2. Sections were treated for antigen retrieval with citrate buffer and by microwaving for 20 min at 95°C. Sections were cooled and endogenous peroxidase was blocked by incubating tissue sections in 3% H2O2 (vol./vol.) for 30 min at ambient temperature. Sections were washed, encircled with a PAP pen and incubated in normal serum for 30 min. Excess serum was removed, and primary antibody applied and the sections incubated overnight at 4°C. Biotinylated secondary antibody was applied for 30 min, the sections washed and the final 3,3′-diaminobenzidine substrate applied for 30 min. Sections were developed using a Vector nickel enhanced method. Antibodies included: insulin-degrading enzyme (IDE) (MA1-91428; Thermo Fisher Scientific, Rockford, IL, USA), haemopoietically expressed homeobox protein (HHEX) (AB4134; Chemicon Millipore, Billerica, MA, USA) and solute carrier family 30 (zinc transporter), member 8 (SLC30A8) (4481.00.02; SDIx, Newark, DE, USA).
For eQTL analysis we used the lm function in R to perform linear modelling including tissue type (as a categorical variable) (http://r-project.org, accessed 6 December 2010), age, height and weight (as continuous variables) on each set of ΔCt gene expression measurements. SNPs were then added as additional covariates. When assessing significance, we corrected for testing 11 genes. We used Genetic Power Calculator  to calculate detectable effect sizes, asking, for each SNP, which proportion of variance of a quantitative trait would have to be explained for us to have 95% power to detect that effect at p = 0.001.
Candidate type 2 diabetes genes exhibit differential expression in colon, liver and pancreas
No effect of age, height, weight or type 2 diabetes risk genotypes on gene expression in tissue
A potential mechanism of action for associated SNPs is to modulate expression of nearby genes . We therefore asked whether risk SNPs identifying our loci affected the expression of proximal genes (cis eQTLs). We were unable to detect any association between the tested SNPs and RNA expression at the level we were powered to detect (Table 2). We did not detect an effect of age, height or weight on gene expression.
We have shown that expression of candidate genes at type 2 diabetes risk loci is tissue-specific in the colon, liver and pancreas. No strong associations were identified between SNPs modulating risk of type 2 diabetes and these transcript levels in cis. Common variables summarised in Table 1 (age, height and weight) also did not exert effects on gene expression in our sample.
SLC30A8, a member of the zinc transporter family, has previously been detected primarily in the secretory vesicles of beta cells  with reports of lower expression levels in other tissues including the liver . A novel finding in the present study was that immunohistochemical staining localised SLC30A8 in the liver, albeit not in hepatic parenchyma, but in nuclei of the sinusoidal fat-storing cells. The sinusoids of the liver radiate from the central vein and are the intervening canals between hepatocytes and/or the bile canaliculi. Within this space and along the luminal surface of the endothelial walls are the mononuclear phagocytic Kupffer cells. Below the endothelium, in the sub-endothelial space known as the ‘space of Disse’, are the fat-storing cells, also known as the ‘fat-storing cells of Ito’. These are stellate cells with the capacity to accumulate exogenously administered vitamin A as retinyl esters in lipid droplets. The role of SLC30A8 in these fat-storing cells of Ito in the liver remains unclear.
While the present work represents one of the largest surveys of candidate gene expression in tissues related to type 2 diabetes, we were unable to document any genetic effects on RNA abundance that were of sufficient magnitude to be detected. We emphasise this point, given that statistical power to detect such effects by genetic mapping is determined by several factors: sample size, allele frequency, effect size and desired level of significance. Other studies have found that cis-eQTLs often explain more than 50% of the expression trait variance . Our study was adequately powered to detect effects of such magnitude; thus we interpret our results as evidence that there are no cis-eQTLs on the target genes in the human tissues examined. This study suggests that caution should be exercised when interpreting eQTLs reported in smaller (and thus lower powered) studies. Replication in independent samples remains the gold standard of proof for associations with any trait, including eQTLs.
We performed an initial survey of candidate gene expression in tissues relevant to type 2 diabetes. Our major findings were of tissue-specific differences in expression of many of these genes in colon, liver and pancreas. These data argue strongly that biologically relevant tissues are critical for gaining insight into disease pathogenesis. Compared with previously published eQTL studies using lymphocytes as the cell/tissue of choice for gene expression, our study is based on a smaller sample size. However, one of its strengths is that it provides data on several tissues related to human diabetes. We recognise that although colon is a relevant tissue for incretin-related biology, samples from the small intestine vs the colon might have been more informative. Our results suggest that these disease-associated genetic variants do not act by dramatically altering expression of nearby genes in type 2 diabetes, although a more thorough dissection in specific cell types is needed, which may account for different transcript and protein isoforms. For example, isolated pancreatic islets and/or neuroendocrine cells could provide a more direct test of the hypothesis vs the entire pancreas and/or gut or small intestine. Analysis of larger collections of relevant tissues will also help understand the molecular effects of disease-associated genetic variants.
We are grateful to S. Schmechel and S. Bowell for help with procurement of tissues from the University of Minnesota Tissue Procurement Facility. We would also like to thank M. Carlson for help in preparing samples for the analysis. The study was supported by an intramural research programme of NCI/NIH and by NIH grant 1R21DK078029-01 (to J. L. Hall).
Duality of interest
J. L. Hall is a financial consultant for Catholic Health Care West. All other authors declare that there is no duality of interest associated with this manuscript.