From genotype to human beta cell phenotype

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www.landesbioscience.com Islets 323

Islets 4:5, 323–332; September/October 2012; © 2012 Landes Bioscience

REVIEW REVIEW

Introduction

Type 2 diabetes mellitus (T2DM) is a multi-factorial disease due to the interplay between genes and the environment.1-6 The importance of genetic factors is documented by solid evidence. The risk of developing the disease in offspring of one parent with T2DM is approximately 40% and almost twice as much if both parents are diabetic.7 In addition, concordance for diabetes is more than 60% in monozygotic twins and less than 20% in dizygotic twins.8,9 Over the past few decades, much effort has been made to discover the genetic factors responsible of the pre-disposition of T2DM. Essentially, three approaches have been adopted: the evaluation of linkage peaks from family studies, the identification of candidate genes on biological basis and, more recently, the genome-wide association study (GWAS) approach.

Linkage analysis relies on genetic markers in family pedi-gree to identify the chromosomal regions showing linkage with T2DM. This approach is very useful when the disease follows a monogenic pattern of inheritance and, in fact, causative genetic variations of several forms of maturity-onset diabetes of the young were successfully identified using this approach.10,11 In addition, linkage peaks near CAPN10 gene were found by this strategy in the polygenic common form of T2DM.12 The candidate gene approach is based on case-control association studies, focusing on specific candidate genes or a selected genetic region, chosen on the consideration of known biological functions. Many dif-ferent candidate genes have been investigated by this approach,

*Correspondence to: Piero Marchetti; Email: [email protected]: 06/14/12; Revised: 09/17/12; Accepted: 09/18/12http://dx.doi.org/10.4161/islets.22282

Polygenic type 2 diabetes mellitus (T2DM) is a multi-factorial disease due to the interplay between genes and the environment. Over the years, several genes/loci have been associated with this type of diabetes, with the majority of them being related to β cell dysfunction. In this review, the available information on how polymorphisms in T2DM-associated genes/loci do directly affect the properties of human islet cells are presented and discussed, including some clinical implications and the role of epigenetic mechanisms.

From genotype to human β cell phenotype and beyond

Piero Marchetti,* Farooq Syed, Mara Suleiman, Marco Bugliani and Lorella Marselli

Department of Endocrinology and Metabolism; University of Pisa; Pisa, Italy

Keywords: b cell, genes, genotype, polymorphism, type 2 diabetes

and three (PPARG, KCNJ11 and TCF7L2) have been shown to be associated with T2DM.13-15 A major breakthrough has been the introduction of the use of genome-wide association stud-ies (GWAS) in the year 2007.16 This was made possible by the improved knowledge of human genetic variations derived from the International HapMap project, the technical advances in microarray genotyping methods and the progress in biostatistic methods to handle large amounts of data. By GWAS, hundreds of thousands of single-nucleotide polymorphisms (SNPs) can be tested for association with a disease, such as T2DM, in thousands of individuals. After the initial GWAS results demonstrating some genetic loci to be associated with T2DM,16 approximately 70 loci (including those previously identified by linkage analysis and candidate gene approach) have now been reported to modify the risk of polygenic T2DM and/or influence glycemic traits with genome-wide significance (Table 1).12,13,17-49 The role of many of them still needs to be confirmed, and for the majority the bio-logical and molecular mechanisms, are far from being clearly understood. Nevertheless, it soon became clear that the most of the so-far-described genetic variations were associated with defec-tive insulin secretion in vivo (Table 2),17,18,23,25,32,36,41,50-64 implying reduced β cell function and/or mass. In the present review, we will analyze the changes occurring at the islet cell level in the presence of mutations in loci associated with T2DM and directly affecting the β cell, with the focus on studies performed with human pancreatic islets. However, it has to be kept in mind that some polymorphisms may cause systemic effects (for example, by increasing circulating fatty acids), which, in turn, could lead to β cell dysfunction.45,52,60,65-67 Some clinical implications will also be discussed, and a few considerations on the role of epigenetics will be briefly presented. Information on these topics can also be found in a few articles published recently.65-67

From Genotype to Human β Cell Phenotype

The first gene to be linked to T2DM was CAPN10, encoding for a cystein protease, calpain-10.68 Calpains are calcium-depen-dent, intracellular, non-lysosomal proteases that can hydrolyze substrates and are important in calcium-regulated signaling pathways, this possibly affecting cell function and survival.69 Although the mechanisms involved are not clear yet, calpain-10 has been suggested to influence both insulin secretion and

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324 Islets Volume 4 Issue 5

Table 1. Genes/loci associated with type 2 diabetes and/or glycemic traits reported according to the year of discovery

Year Locus (nearest gene) Full name Index variant Ref.

2000 CAPN 10 calpain 10 rs2953171 12

2000 PPARG peroxisome proliferator-activated receptor gamma rs1801282 13

2003 KCNJ11 potassium inwardly-rectifying channel, subfamily J, member 11 rs5215 17

2006 TCF7L2 transcription factor 7-like 2 rs7901695; rs7903146 18, 19

2007 FTO fat mass and obesity associated rs8050136 20

2007 HHEX/IDE hematopoietically expressed homeobox/ insulin-degrading enzyme rs1111875

212007 CDKN2A/2B cyclin-dependent kinase inhibitor 2A/2B rs10811661

2007 IGF2BP2 insulin-like growth factor 2 mRNA binding protein 2 rs4402960

2007 HNF1B hepatocyte nuclear factor 1-β (transcription factor 2, hepatic) rs121918673 22

2007 SLC30A8 solute carrier family 30 member 8 rs13266634; rs11558471 23, 24

2007 CDKAL1 CDK5 regulatory subunit associated protein 1-like 1 rs10946398 25

2007 WFS1 Wolfram syndrome 1 rs10010131 26

2008 JAZF1 JAZF zinc finger 1 rs864745

27

2008 CDC123/CAMK1Dcell division cycle 123 homolog; calcium/calmodulin-dependent

protein kinase IDrs12779790

2008 TSPAN8/LGR5tetraspanin 8/ leucine-rich repeat containing G protein-coupled

receptor 5rs7961581

2008 THADA thyroid adenoma associated rs7578597

2008 ADAMTS9 ADAM metallopeptidase with thrombospondin type 1 motif, 9 rs4607103

2008 NOTCH2 notch 2 rs10923931

2008 KCNQ1 potassium voltage-gated channel, KQT-like subfamily, member 1 rs2237892 28

2008 MC4R melanocortin 4 receptor rs17782313 29

2008 HK1 Hexokinase 1 rs7072268 30

2009 IRS1 Insulin receptor substrate 1 rs2943641 31

2009 MTNR1B Melatonin receptor 1B rs10830963 32

2009 GCK glucokinase (hexokinase 4) rs4607517 33

2010 PEPD peptidase D rs10425678 34

2010 C2CD4A/B C2 calcium-dependent domain-containing 4B rs7172432 35

2010 UBE2E2 ubiquitin-conjugating enzyme E2E 2 rs7612463

2010 DGKB/TMEM195 Diacylglycerol kinase β Transmembrane protein 195 rs2191349 36

2010 ZBED3 Zinc finger BED domain-containing protein 3 rs4457053

37

2010 BCL11A B-cell lymphoma ⁄ leukemia 11A rs243021

2010 KLF14 Krueppel-like factor 14 rs972283

2010 TP53INP1 Tumour protein p53-inducible nuclear protein 1 rs896854

2010 CHCHD9Coiled-coil-helix-coiled-coil-helix domain-containing protein 9, mito-

chondrialrs13292136

2010 CENTD2Arf-GAP with Rho-GAP domain, ANK repeat and PH domain- containing

protein 1rs1552224

2010 KCNQ1b Potassium voltage-gated channel, KQT-like subfamily, member 1 rs231362

2010 HMGA2 High mobility group protein HMGI-C rs1531343

2010 HNF1A Hepatocyte nuclear factor 1-α rs1531343

2010 PRC1 Protein regulator of cytokinesis 1 rs8042680

2010 ZFAND6 AN1-type zinc finger protein 6 rs11634397

2010 DUSP9 Dual specificity protein phosphatase 9 rs5945326

2010 RBMS1 RNA binding motif, single stranded interacting protein 1 rs7593730 38

2010 SRR serine racemase rs39130039

2010 PTPRD protein tyrosine phosphatase, receptor type, D rs17584499

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Table 1. Genes/loci associated with type 2 diabetes and/or glycemic traits reported according to the year of discovery

Year Locus (nearest gene) Full name Index variant Ref.

2010 DGKB diacylglycerol kinase, β 90kDa rs7788248 40

2010 PROX1 Prospero homeobox protein 1 rs340874

24

2010 GCKR Glucokinase (hexokinase 4) regulator rs780094

2010 ADCY5 Adenylate cyclase, 5 rs11708067

2010 G6PC2 glucose-6-phosphatase, catalytic, 2 rs560887

2010 MADD MAP kinase-activating death domain protein rs7944584

2010 ADRA2A Alpha-2A adrenergic receptor rs10885122

2010 CRY2 Cryptochrome-2 rs11605924

2010 SLC2A2 solute carrier family 2 (facilitated glucose transporter), member 2 rs11920090

2010 FADS1 Fatty acid desaturase 1 rs174550

2010 GLIS3 Zinc finger protein GLIS3 rs7034200

2010 IGF1 Insulin-like growth factor I rs35767

2010 GIPR Gastric inhibitory polypeptide receptor rs10423928 41

2010 C2CD4B C2 calcium-dependent domain-containing 4B Rs1436955 42

2010 FN3K Fructosamine-3 kinase rs1056534 43

2010 ANK1 Ankyrin 1, erythrocytic rs6474359

442010 SPTA1 Spectrin, α, erythrocytic 1 elliptocytosis 2 rs2779116

2010 ATP11A ⁄ TUBGCP3 ATPase type 11A rs7998202

2011 GATAD2A GATA zinc finger domain containing 2A rs3794991

452011 SREBF1 sterol regulatory element binding transcription factor 1 rs4925115

2011 TH/INS tyrosine hydroxylase/ insulin rs10770141

2011 BCL2 B-cell CLL/lymphoma 2 rs12454712

2011 VPS13C Vacuolar protein sorting 13 homolog C rs4502156

46

2011 ARAP1 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1 rs11603334

2011 PCSK1 proprotein convertase subtilisin/kexin type 1 rs6235

2011 LARP6 La ribonucleoprotein domain family, member 6 rs1549318

2011 SGSM2 small G protein signaling modulator 2 rs4790333

2011 SNX7 sorting nexin 7 rs9727115

2011 ST6GAL1 ST6 β-galactosamide α-2,6-sialyltranferase 1 rs16861329

47

2011 VPS26A vacuolar protein sorting 26 homolog A rs1802295

2011 HMG20A high mobility group 20A rs7178572

2011 AP3S2 adaptor-related protein complex 3, sigma 2 subunit rs2028299

2011 HNF4A hepatocyte nuclear factor 4, α rs4812829

2011 GRB14 growth factor receptor-bound protein 14 rs3923113

2012 ADIPOQ adiponectin, C1Q and collagen domain containing rs1648707

482012 ZNF664 zinc finger protein 664 rs863750

2012 CMIP c-Maf inducing protein rs2927322

2012 GNL3 guanine nucleotide binding protein-like 3 (nucleolar) rs1108842

2012 LYPLAL1 lysophospholipase-like 1 rs3001032; rs2785980 48, 49

2012 COBLL1-GRB14 COBL-like 1 rs7607980

492012 PPP1R3B protein phosphatase 1, regulatory subunit 3B rs4841132

2012 UHRF1BP1 UHRF1 binding protein 1 rs4646949

(continued)

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arginine-stimulated insulin release in islets from non-diabetic (but not T2DM) donors. Although there was no association between SNP 43 or SNP 44 and calpain-10 gene expression, the G/G variant of SNP-43 was associated with reduced insulin release in response to glucose in non-diabetic donors.

The KCNJ11 gene encodes the pore-forming subunit (Kir6.2) of the ATP-sensitive potassium channel, which is present also in pancreatic β cells. Closure of this channel leads

resistance, and polymorphisms in this gene have been associated with T2DM in some (but not all) populations.70-72 In a study performed with pancreatic islets isolated from non-diabetic and T2DM donors, calpain-10 gene expression was assessed, CAPN10 SNP-43 and SNP-44 were genotyped and the relationships with ex vivo insulin release investigated.73 The authors found that the mRNA level of calpain-10 was significantly increased in T2DM islets. Moreover, calpain-10 expression correlated positively with

Table 2. Genes/loci associated with reduced insulin secretion in vivo

Author/Journal Genes Test Observations Ref.

Nielsen EM et al., Diabetes 2003

KCNJ11 OGTT 17

Saxena R et al., Diabetes 2006

TCF7L2 OGTT 18

Lyssenko V et al., J Clin Invest 2007

TCF7L2 OGTT, IVGTT 50

Staiger H et al., Plos ONE 2007

SLC30A8, HHEX OGTT, IVGTT No association with insulin sensitivity 23

Steinthorsdottir V et al., Nat Genet 2007

CDKAL1 OGTT 25

Pascoe L et al., Diabetes 2007

CDKAL1 HHEX/ IDE OGTT No association with insulin sensitivity 57

Grarup N et al., Diabetes 2008

JAZF1, CDC123/CAMK1D, TSPAN 8 OGTTTHADA, ADAMTS9, NOTCH2 not

associated; TSPAN8 associated also with reduced insulin sensitivity

52

Boesgaard TW et al., Diabetologia 2008

SLC30A8 OGTT, IVGTTNon-diabetic off springs of T2DM

patients53

Sparso T et al., Diabetologia 2008

WSF1 OGTT 54

Staiger H et al., Diabetes 2008

HHEX OGTT, IVGTT 55

Pascoe et al., Diabetologia 2008

TCF7L2, CDKA1L, HHEX/IDE, KCNJ11, IGF2BP2, CDKN2A/2B, SLC30A8

OGTTIncreased effects with risk allele

combination56

Groenewound MJ et al., Diabetologia 2008

CDKA1L, IGF2BP2 Hyperglycemic clamp 57

Staiger H et al., PLoS ONE 2008

MTNR1BOGTT, IVGTT, euglycemic–

hyperinsulinemic clamp58

Holmkvist J et al., Plos ONE 2009

KCNQ1 OGTT 59

Stancakova A et al., Diabetes 2009

TCF7L2, SLC30A8, HHEX, CDKN2B, CDKAL1, KCNJ11, IGF2BP2

OGTT

PPARG, WFS1, JAZF1, TSPAN8, HNF1B, THADA, ADAMTS9, NOTCH2, KCNQ1 not associated; HHEX associated with insulin

sensitivity; increased effects with risk allele combination

60

Lyssenko V et al., Nat Genet 2009

MTNR1B OGTT, IVGTT 32

Langenberg C et al., Diabetologia 2009

MTNR1BOGTT, euglycemic-hyperinsu-

linemic clampDefective insulin release due to

decreased β cell glucose sensitivity61

Saxena R et al., Nat Genet, 2010

GCKR, ADCY5, TCF7L2, VPS13C, GIPR OGTT GIRP—blunted insulin response 41

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response to oral and intravenous glucose and predicts future T2DM.32 This receptor is localized also in β cells (Fig. 1), and islets from subjects carrying the risk allele show increased expres-sion of the receptor.32 Since melatonin, which is released from the pineal gland in the brain, reduces cAMP production, it is not surprising that increased expression of the MTNR1B receptor in the islets (as it occurs in the presence of the risk allele) plays an important role in inhibiting insulin release from the β cell. Accordingly, MTNR1B is present at higher levels in islets from T2DM patients, and its expression correlates with ex-vivo islet insulin secretion inversely.32

Conflicting results have been obtained as for the properties of islet cells in the presence of the rs13266634 polymorphism of SLC30A8. This gene encodes for zinc transporter 8, a fundamen-tal protein for zinc transport and insulin granules formation,78 and its association with T2DM in humans has been consistently observed. The risk allele is associated with morphological and functional alterations in rodent β cells.79 However, in human islets, the rs13266634 SNP is not associated with alterations of ex vivo insulin secretion or ZnT8 gene expression.80

Genes involved in insulin signaling do also affect β cell sur-vival and function. The common Gly(972) → Arg amino acid polymorphism of insulin receptor substrate 1 (IRS-1, a mol-ecule immediately downstream the insulin receptor), has been found to be associated with alterations of β cell turnover and insulin secretion. Pancreatic islets isolated from carriers of this polymorphism exhibited impaired IRS-1-associated PI 3-kinase activity, increased apoptosis and appeared resistant to the anti-apoptotic effect of insulin as compared with wild-type con-trols.81,82 In addition, the variant was associated with reduced glucose stimulated insulin secretion, lower glucose oxidation and reduced amount of mature insulin granules (Fig. 2).81 Of interest, it has been more recently observed that two additional

to β cell membrane depolarization, opening of the voltage-dependent calcium channels, calcium entry and eventually exocytosis of the insulin granules.74 The substitution of lysine for glutamic acid at the 23rd amino acid (E23K) leads to a gain-of-function mutation, causing more sustained opening of the potassium channel, partial inhibition of β cell depolariza-tion and reduced insulin secretion. However, this risk variant seems to manifest its direct effects on human β cells under conditions of stress. In fact, islets carrying the polymorphism have been shown to release less insulin (in particular to sul-phonylurea stimulation) only after they have been cultured for 24 h in the presence of increased glucose concentrations (glucotoxicity).75

A gene that was originally found to be associated with T2DM by the candidate gene approach, and then consistently and strongly confirmed in such a direction, is TCF7L2, which is a crucial component of Wnt-β catenin signaling and is implicated in cell proliferation and the incretin effect. It has been shown that the CT/TT genotypes of SNP rs7903146 (T being the risk allele) strongly predicted future diabetes in independent cohorts of patients and that TCF7L2 mRNA expression in human type 2 diabetic islets was increased 5-fold.50 In addition, increased TCF7L2 expression was associated with reduced ex vivo insu-lin secretion, while the presence of the TT genotype determined increased TCF7L2 gene expression.50 However, in other studies, TCF7L2 protein expression was found to be markedly reduced in type 2 diabetic islets,76 and downregulation of TCF7L2 in non-diabetic islet cells was associated with impaired insulin secre-tion and reduced β cell survival.77 Therefore, the relationships between TCF7L2 genotype, gene and protein expression and direct effects on human islet cells need further investigation.

The rs10830963 SNP in the gene for the melatonin recep-tor 1B gene (MTNR1B) is associated with reduced early insulin

Table 2. Genes/loci associated with reduced insulin secretion in vivo

Author/Journal Genes Test Observations Ref.

Ingelsson E et al., Diabetes 2010

MADD, VPS13C, TCF7L2, SLC30A8, GIPR, C2CD4B, MTNR1B, GCK, FADS1, DGKB,

PROX1, G6PC2, GCKR, IGF1, ADCY5, ADRA2A, CRY2, SLC2A2, GLIS3

FSIGT, OGTT, euglycemic-hyper-insulinemic clamp

MADD, VPS13C—abnormal insulin processing

TCF7L2, SLC30A8, GIPR, C2CD4B—higher proinsulin and lower insulin secretion

MTNR1B, GCK, FADS1, DGKB, PROX1, G6PC2—abnormalities in early insulin

secretion

GCKR, IGF1—reduced insulin sensitivity

62

Boesgaard TW et al., Diabetologia 2010

DGKB/TMEM195, ADCY5, MADD, ADRA2A, FADS1, CRY2,SLC2A2, GLIS3,

PROX1, C2CD4B, IGF1OGTT

DGKB/TMEM195, ADRA2A, GLIS3 C2CD4B—decreased glucose stimulated

insulin release36

Hu C et al., PLoS One 2010

GIPR, ADCY5, ADCY5, TCF7L2, VPS13C, DGKB,

OGTT

GIPR, TCF7L2, DGKB, MADD, CRY2, GLIS3, PROX1, SLC30A8 and IGF1—associated

with fasting insulin ADCY5, TCF7L2, SLC2A2 and ADRA2A—no association

63

Renström F et al, Diabetes 2011

MTNR1B, G6PC2, GCK, CRY2, DGKB-TMEM195, SLC30A8, GCKR,TCF7L2,

ADRA2A,FADS1,SLC2A2, PROX1, MADD, GLIS3, ADCY5, C2CD4B

OGTTMTNR1B- Insulin sensing/insulin release

G6PC2, GCK, DGKB-TMEM195—predic-tion of worsening glucose control

64

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susceptibility variants.87 The authors found that variants near TCF7L2 and ADRA2A were associated with reduced glucose-induced insulin secretion, whereas susceptibility variants near ADRA2A, KCNJ11, KCNQ1 and TCF7L2 were associated with reduced depolarization-evoked insulin exocytosis. In addition, KCNQ1, ADRA2A, KCNJ11, HHEX/IDE and SLC2A2 variants affected granule docking.87

Therefore, human islet cells from subjects carrying some of the common risk variants in genes/loci associated with T2DM show several alterations in their function and molecular prop-erties, this providing the basis for further studies aimed to dis-cover the mechanisms leading from genetic alterations to β cell dysfunction.

Clinical Implications

Despite the incredible work done over the past few years to find genes associated with T2DM, only roughly 10% of disease her-itability can be accounted for by the gene/loci variants identi-fied so far, suggesting that much remains to be discovered. In this regard, continuing international collaborations on multiple cohorts, combinations of different genotyping platforms, search-ing for less common variants with increased pathogenetic roles and improving technologies (such as the use of next-generation sequencing) are tools that soon will add key information. To date, a few follow-up studies have examined up to 20 loci asso-ciated with T2DM and/or have used genetic scores calculated according to the number of risk alleles.88-91 The results have shown no improvement of predictive power by adding the genetic risk score to classical prediction models using clinical and bio-chemical factors including age, gender, family history of diabetes, body mass index, fasting glucose level, systolic blood pressure and lipid profile. However, genetic testing might be useful in spe-cific subsets of subjects and/or clinical settings. In a study,92 it has been reported that the genetic score marginally improved the ability to predict future diabetes in subjects younger than 50 y. In the Diabetes Prevention Program, the subjects carrying both copies of the TCF7L2 risk allele did not develop diabetes when randomized to the lifestyle intervention arm,93 suggesting that intensive prevention approaches might be successfully employed in individuals at high T2DM genetic risk. In addition, it has been suggested that having a high probability of developing T2DM from genetic testing might more effectively motivate subjects to adopt lifestyle changes.94 Finally, the presence of certain risk alleles for T2DM has been associated with the development of new-onset diabetes after transplantation.95-97 Since a few anti-rejection agents are considered more diabetogenic than others,98 genotyping patients undergoing transplantation might guide the choice of immunosuppression therapy. Evidence is increasing to indicate that the efficacy of T2DM oral antidiabetic therapy may be affected by the presence of certain risk alleles. A study75 in Caucasian individuals with T2DM found a higher frequency of the K allele of KCNJ11 in patients who failed sulfonylurea ther-apy (67%) compared with those who did not (58.0%). This was associated, as mentioned above, with lower glibenclamide-stimu-lated insulin secretion from islets isolated from patients carrying

genes involved in insulin signaling could also influence β cell properties. The ectonucleotide pyrophosphatase phosphodies-terase 1 (ENPP1) gene is a class II transmembrane 18 glyco-protein, which inhibits insulin receptor signaling and has been proposed as a candidate for insulin resistance.83 The presence of the non-synonymous K121Q polymorphism (rs1044498) con-fers a gain of function due to the less common Q allele that is associated with reduced insulin secretion both in vivo and ex vivo.84 In addition, although obtained with a β cell line, data show that this allele can reduce β cell survival by promoting caspase 3 activation. One more gene involved in the insulin signal cascade, TRIB3, has also been associated with β cell dysfunction. This gene encodes for a molecule that inhibits insulin-stimulated Akt phosphorylation and subsequent insu-lin action.85 Human islets carrying the TRIB3 gain-of-func-tion polymorphism, Q84R (rs2295490), are less sensitive to glucose stimulation than the wild-type counterpart.86 Finally, in a recent study, glucose-stimulated insulin secretion and exocytosis were evaluated in islets isolated from non-diabetic and T2DM individuals, to be correlated with several disease

Figure 1. Immunohistochemistry localization of MTNR1B (green) and insulin (red) in human pancreatic islets. Merging shows colocalization in yellow. Adapted from reference 32.

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Conclusions

Many genes/loci are associated with T2DM, and poly-morphisms identified in most of them are accompanied by altered insulin secretion in vivo. A number of studies have been performed to evaluate whether this reflected changes in the properties of pancreatic islet cells, showing that some poly-morphisms were indeed associated with reduced β cell func-tion and survival. In a few cases, insights on the molecular mechanisms have also been provided. Whereas it must be taken into account that the use of isolated islets has potential lim-itations due to the procedures for their preparation and dif-ferences between centers in their handling, nevertheless, the available information is contributing to better understanding the relationships between genotype and human β cell pheno-type, with the aim of identifying specific defects to be tackled by targeted approaches for the prevention and treatment of the disease.

the variant allele. Accordingly, another study has reported that the K allele was associated with higher HbA1c levels compared with the E allele (p = 0.04) in type 2 diabetic patients treated with sulphonyureas.99

Similar results have been obtained in the presence of the risk alleles at rs12255372 and/or rs7903146 of TCF7L2. A study involving 4,469 participants from the Genetics of Diabetes Audit and Research Tayside showed that patients with either risk variant were less likely to respond to sulfonylurea treatment with a target HbA1c < 7% and achieve a target HbA1c of 7% within 1 y of initiating sulfonylurea.100 On the same line, when the effects of sulphonylurea therapy in addition to metformin, according to KCNQ1 genotypes, were evaluated in patients who failed to achieve glycemic control on metformin monotherapy, it was found that the T allele of KCNQ1 was associated with more marked improvement of glycemic values.101 Finally, the role of the Gly972Arg risk marker of IRS1 was investigated in another study, and the authors found that frequency of this genotype was almost twice as high in patients with secondary sulfonylurea failure.102

Therefore, although still at its dawn, work on the relevance of the T2DM genotype in providing information useful to the clinical practice holds many promises and deserves supportive attention.

The Role of Epigenetics

Epigenetics consists of the heritable changes in gene functions, which occur without modifications in the nucleotide sequence. Epigenetic modifications can be passed from one cell genera-tion to the next (mitotic inheritance) or between generations of individuals (meiotic inheritance). These types of epigenetic effects might explain some of the missing genetic variance com-ponent of complex diseases. Epigenetic effects can also occur during life, for example, in response to some environmental conditions, thus influencing the effects of genetic variants and thus acting as a mechanism of gene-environment interaction. Histone modifications and DNA methylation have been shown to be able to influence gene expression. For instance, changes in the structure of chromatin can modulate the access of pro-teins to binding with transcription factors, as demonstrated for TCF7L2 (103). Furthermore, it has been observed that the expression of PPARGC1α (a key regulator of mitochondrial genes) was reduced in the islets of T2DM subjects which was due, at least in part, to increased DNA methylation in the pro-moter of the gene.104 More recently,105 DNA methylation pro-filing was performed in pancreatic islets from non-diabetic and T2DM individuals. Almost 300 CpG loci affiliated to promoters of 254 genes were found to show differential DNA methylation in diabetic islets, with a subgroup being associated with transcriptional changes. Clearly, investigating the role of epigenetics in the link between genes and environment in deter-mining β cell failure in T2DM is a fascinating field of research, carrying much expectation.

Figure 2. Electron microscopy of control (upper) and Gly972→Arg IRS-1 (lower) islets, showing a greater number of more immature granules in the latter. Adapted from reference 81.

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From genotype to human beta cell phenotype