review article pharmacogenomics of drug response in type …

Review ArticlePharmacogenomics of Drug Response in Type 2 DiabetesToward the Definition of Tailored Therapies

Carla Pollastro12 Carmela Ziviello1 Valerio Costa1 and Alfredo Ciccodicola12

1 Institute of Genetics and Biophysics ldquoAdriano Buzzati-Traversordquo National Research Council Via Pietro Castellino 11180131 Naples Italy2DiST Department of Science and Technology ldquoParthenoperdquo University of Naples Centro Direzionale Isola C480143 Naples Italy

Correspondence should be addressed to Alfredo Ciccodicola alfredociccodicolaigbcnrit

Received 3 March 2015 Accepted 24 May 2015

Academic Editor Todd Leff

Copyright copy 2015 Carla Pollastro et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Type 2 diabetes is one of the major causes of mortality with rapidly increasing prevalence Pharmacological treatment is the firstrecommended approach after failure in lifestyle changes However a significant number of patients showsmdashor develops alongtime and disease progressionmdashdrug resistance In addition not all type 2 diabetic patients have the same responsiveness to drugtreatment Despite the presence of nongenetic factors (hepatic renal and intestinal) most of such variability is due to geneticcauses Pharmacogenomics studies have described association between single nucleotide variations and drug resistance eventhough there are still conflicting results To date the most reliable approach to investigate allelic variants is Next-GenerationSequencing that allows the simultaneous analysis on a genome-wide scale of nucleotide variants and gene expression Herewe review the relationship between drug responsiveness and polymorphisms in genes involved in drug metabolism (CYP2C9)and insulin signaling (ABCC8 KCNJ11 and PPARG) We also highlight the advancements in sequencing technologies that to dateenable researchers to perform comprehensive pharmacogenomics studiesThe identification of allelic variants associated with drugresistance will constitute a solid basis to establish tailored therapeutic approaches in the treatment of type 2 diabetes

1 Introduction

Diabetes is one of the leading causes of mortality in thecontemporary society [1] The last report of the InternationalDiabetes Federation in 2013 indicates an onset rate of about84 in adults and a total number of 382 million cases ofdiabetes worldwide This number is estimated to criticallyrise up to 592 million cases by 2035 [1] so that the WorldHealth Organization (WHO) has defined this phenomenonas a ldquoglobal outbreakrdquo There are two more frequent forms ofdiabetes both due to defects of insulin action type 1 diabetesmellitus (T1D) also called ldquoinsulin-dependent diabetesrdquo orldquojuvenile diabetesrdquo [2] and type 2 diabetes mellitus (T2D)also known as ldquononinsulin-dependent diabetesrdquo This formeris characterized by early onset and occurs because of absolutedeficiency of insulin [3] whereas the latter (themost frequentform) with onset in older age occurs because of an insulindefective function [4] T2D affects more than 5 of the

population of developed countries and its predominanceincreases worldwide

Diabetes is a chronic disease which over time leadsto cardiovascular and blood vessels damage and neuro-nephro- and retinopathy with a dramatic impact on healthand high costs for all National Health Systems [2]

Intensive programs that consider lifestyle changes toreduce T2D risk have revealed a moderate efficacy inreducing diabetes incidence in at-risk individuals [5] Whenlifestyle changes are not sufficient to ameliorate the clini-cal features of T2D patients it is necessary to design anappropriate pharmacological approach In this scenario thepharmacogenomics is a discipline that studies the impor-tance of optimal treatment to patients starting from theknowledge about the genetic and molecular etiology of thedisease Several studies have shown a widespread variabilityin glycemic response tolerability and a plethora of variableeffects in patients treated with similar antidiabetic drugs

Hindawi Publishing CorporationPPAR ResearchVolume 2015 Article ID 415149 10 pageshttpdxdoiorg1011552015415149

2 PPAR Research

[6 7] These lines of evidence represent the starting point ofpharmacogenomics [4] Generally interindividual variabilityis mainly determined by single nucleotide polymorphisms(SNPs) Specifically a relevant fraction of the genetic variabil-ity observed in T2D patients has been found in genes directly(or indirectly) related to the activity (or to the metabolism)of oral antidiabetic drugs (OAD) The assumption of thesedrugs is the first intervention step in T2D management afterthe failure of lifestyle changes Therefore the identificationof genetic variants associated to altered drug responsivenessis a key point in diabetes research since it is expected toameliorate the therapeutic approach in a tailored mannerHowever other biological nongenetic factors can influ-ence pharmacodynamics of OADs such as hepatic renaland intestinal functions These considerations highlight theimportance of considering both the phenotype (clinical andpathophysiological parameters) and the genotype of T2Dpatients in order to choose the most appropriate therapeuticapproach [5]

In the last decade the advent of genome-wide associ-ation studies (GWAS) has gradually shifted the genetics ofT2D to a step forward definitely turning pharmacogeneticsinto pharmacogenomics Indeed whereas the former mainlyfocuses on single drug-gene interaction the latter faces therelationship among inherited nucleotide variations and drugresponse also taking into account gene expression othergenomics features and epigenetics factors underlying inter-and intraindividual variability [4 5] Despite many GWAShave revealed the association among genetic variants andcomplex traitsdiseases many factors are still underestimatedor unexplored clearly deserving further investigation Forinstance a renewed interest is emerging from the so-calledldquojunkrdquo DNA [8] Indeed it is known that the vast majorityof nucleotide variants that are associated to complex traitsincluded T2D localizes into noncoding regionsThus a smallfraction of intragenic and intergenic noncoding RNAs (ncR-NAs) with still undefined regulatory functions [9 10] mayplay a role in the onset andor progression of multifactorialdiseases Noncoding RNAs levels may also account for thevariable drug responsiveness observed in T2D patients

In this review we describe the relationship between drugresponsiveness in T2D patients and SNPs also describingthe organs that have a major role in drug metabolismor activity (Figure 1) We also discuss the recent advance-ments in sequencing technologies highlighting how theycan provide significant contributions to pharmacogenomicsstudies The new technological frontiers in the identificationof allelic variants associated with altered drug responsivenesswill surely constitute a solid basis to design personalizedtherapeutic approaches in T2D treatment

2 Pharmacogenomics of Antidiabetic Drugs

Currently the more widely used drugs in T2D treatmentare the sulphonylureas metformin and thiazolidinediones(troglitazone pioglitazone and rosiglitazone) Figure 2schematizes the main proteins that are involved in theuptake and metabolism of oral antidiabetic drugs or that areactivated upon their administration

Generally pharmacogenetics studies consider some clin-ical endpoints to evaluate drug responsiveness Among themthe achievement ofHbA1c levelslt7 as defined in the guide-lines and an overall reduction in HbA1c represent the mostappropriate parameters to consider inT2Dpharmacogeneticsstudies [11] Another crucial consideration is whether thedrug of interest has been used at early or late stages of thedisease where there is a very low probability to reach asignificant therapeutic effect

In Table 1 we summarize a schematic catalogue of SNPsthat according to GWASs are commonly associated toaltered drug responsiveness in T2D In many cases thesestudies have revealed the absence of a significant associationamong SNPs and the expression levels of the closest geneshowing a wide variability Such association studies havealso underlined the ethnic-specific expression profile ofSNPs in tissues crucially involved in glucose homeostasis[12]

3 Impact of Polymorphisms in ABCC8KCNJ11 TCF7L2 CYP2C9 IRS1 andCAPN10 Genes on Sulphonylureas Effects

Sulphonylureas (SUs) are widely used drugs in the treatmentof T2D Despite the wide use of these drugs in the clinicalpractice different side effects such as weight gain andincreased risk of hypoglycemia have been frequently [13 14]Glibenclamide gliclazide glipizide and glimepiride are themain SUs currently used for T2D treatment [15]

All SUs bind to sulphonylurea receptor 1 (SUR1) andenhance glucose-stimulated insulin release from the pancre-atic120573-cellsTherefore SUs act by inducing the closure ofATP-sensitive potassium (KATP) channel through the bindingwith the proteins that form it Four K+ ions are located inthe inner pore of KATP channel whereas outside the channelis formed by four SUR1 molecules [16] The ATP producedby glucose oxidation in mitochondria causes the closure ofKATP channel with the consequent depolarization of 120573-cellsmembrane the increased entry of Ca2+ ions followed bythe release of presynthesized insulin from 120573-cells Ultimatelysulphonylureas induce the closure of these channels and therelease of insulin through the binding to the specific receptoroutside the KATP channel

Nucleotide variations in genes encoding KATP channelproteins such as potassium channel inwardly rectifyingsubfamily J member 11 (KCNJ11) and ATP-binding cassettesubfamily C member 8 (ABCC8) are associated with theonset of neonatal diabetes mellitus Studies on SUs revealedthat these drugsmight effectively act in response to the defectinduced by KCNJ11 and ABCC8 mutations in T2D patients[17 18] KCNJ11 gene encodes the potassium inward rectifier62 subunit (Kir62) of KATP channel which is implicatedin glucose-dependent insulin secretion in pancreatic 120573-cells GWAs have revealed a strong association between thepolymorphism rs5219 inKCNJ11 (CT nucleotide substitutionthat leads to K23E amino acid change) and T2D [19] IndeedJavorsky et al (2012) have demonstrated the impact of K23Eamino acid substitution on SUs therapeutic effects in a cohort

PPAR Research 3

TZD

TZD damage

SUSU

SU

SU

SU

SU

TZD damage

Metformin

Metformin

MetforminPPARG

(Pro12Ala)

SLC22A1(420del)

(R61C)(G401S)(G465R)SLC22A2

(S270A)

KCNJ11(K23E)(A250I)

CYP2C9(R144C)(I359L)

ABCC8(A1369S)

IRS1(G971R) TCF7L2

Adipose tissue Heart Liver Pancreas Kidney

SU

Figure 1 Interactions between gene products and OADs on target organs Genes and the related ldquoat-riskrdquo SNPs (in brackets) are shown inthe upper part Arrows indicate if a SNP has a negative impact on the responsiveness to a given drug in a specific organ Red arrows indicateincreased drug resistance (or altered drug metabolism) whereas green arrows indicate a beneficial effect of such a SNP Dashed lines indicateside effects of a given drug TZD = thiazolidinedione SU = sulphonylureas MET = metformin

Table 1 Schematic catalogue of SNPs commonly associated with T2D

Chr Position Localization Gene Alleles SNP ID Protein change Association

6160543148160560824160560881160575837

ExonExonExonExon

SLC22A1

CTAGndashA

ACG

rs12208357rs34130495rs35167514rs34059508

R61CG401S420delG465R

Metforminmetabolism

T2D susceptibility

6 160670282 Exon SLC22A2 GT rs316019 S270AMetforminmetabolism

T2D development

11 1740957217408630

ExonExon KCNJ11 TC

GArs5219rs5215

K23EA250I

SulphonylureasmetabolismT2D onset

10 9670204796741053

ExonExon CYP2C9 CT

ACrs1799853rs1057910

R144CI359L

Sulphonylureasmetabolism

T2D susceptibility

11 17418477 Exon ABCC8 GT rs757110 A1369SSulphonylureasmetabolismT2D onset

3 12393125 Exon PPARG CG rs1801282 P12ATZD metabolism

T2D onset-development

10 114808902114758349

IntronIntron TCF7L2 GT

CTrs12255372rs7903146

mdashmdash


T2D development

2 227093745227660544

IntergeneExon IRS1 CT

GArs2943641rs1801278

mdashG971R

SulphonylureastreatmentT2D onset

2241534293241531174241542703

IntronIntronIntron

CAPN10IndelAGCT

rs3842570rs3792267rs5030952

mdashmdashmdash

Probably involved inSulphonylureas

response

4 PPAR Research

Sulphonylureas PI3KAKT1GSK3 andMAPK cascade

Metformin

OCT

Sulphonylureas

IRS-1 CAPN10 Depolarization

Mitochondrion

TZD

TCF4

Nucleus

GLP-1

PI3K

Insulin

CYP2C9

Ca++

KK+

120573-catenin

PPAR120574-RXR

H3CN

CH3

HN

NH NH

NH2

R

OOO

S NH

NH

R2

SUR1

SUR1

Kir62

R

O OS

O

NH

NH

R2

SO

NHO

5998400 59984005998400 5998400

3998400 3998400 39984003998400

IRIR

+

Figure 2 Main proteins involved in uptake and metabolism of OADs IR = insulin receptor GLP-1 = glucagon-like peptide-1 SUR1 =sulphonylureas receptor 1 Kir62 = potassium inward rectifier 62 subunit PI3K = phosphoinositide 3-kinase TCF4 = transcription factor 4RXR = retinoid X receptor PI3KAKT1GSK3 = phosphoinositide 3-kinaseRAC-alpha serinethreonine-protein kinaseglycogen synthasekinase 3 MAPK = mitogen-activated protein kinases

of 101 Caucasian patients The study has revealed that ldquoK-allelerdquo homozygous carriers had a higher reduction in HbA1clevels after 6months of therapy than ldquoEErdquo carriers (104plusmn010versus 079 plusmn 012 119901 = 0036) [20] A similar study hasbeen carried out also onChinese population In this study 100

patients were treated for 24 weeks with repaglinide [21] Theauthors have reported a significant decrease in HbA1c levelsin ldquoEKrdquo and ldquoKKrdquo patients compared to ldquoEErdquo carries (ldquoEErdquo152 plusmn 103 ldquoEKrdquo 233 plusmn 153 and ldquoKKrdquo 265 plusmn 173119901 = 0022)

PPAR Research 5

Several studies have reported that sulphonylureas (andalso glinides) are able to ameliorate in T2D patientsthe defective insulin secretion Nonetheless it has beenfrequently observed that long-term treatment leads toa progressive decrease in SUs effectiveness This phe-nomenon might result from a progressive lack of the insulin-producing capacity of pancreatic 120573-cells In addition SUshave proven to be particularly beneficial if combined withmetformin which decreases the extent of insulin resistance[15]

Nucleotide variations in TCF7L2 gene have been widelyassociated with T2D onset as well as the effectiveness inSU treatments Shu et al (2008) reported that TCF7L2 isnecessary for maintaining the glucose-stimulated insulinsecretion (GSIS) and 120573-cell survival Thus variations in thelevel of active TCF7L2 in 120573-cells may play a crucial role indetermining a progressive deficit in the insulin secretion aswell as in accelerating T2D progression [22]

T-cell transcription factor 4 (TCF4) the protein encodedby TCF7L2 gene is a high mobility group (HMG) box-containing transcription factor implicated in blood glucosehomeostasis It acts through the binding with 120573-cateninand it mediates Wnt signaling It is also involved in pan-creas development during embryogenesis and it affects thesecretion of glucagon-like peptide 1 (GLP1) by L-cells inthe small intestine [23] Two allelic variants in this geners790314 and rs12255372 (CT and GT nucleotide variationsresp) have been associated with T2D In particular it hasbeen demonstrated that such variants are the most importantpredictors of T2D with a 40 increased risk per allele [2425] Genetics of Diabetes Audit and Research Tayside Studies(GoDARTS) has also revealed the relationship between thesetwo allelic variants and therapeutic outcomes in T2D patientstreated with sulphonylureas The GoDARTS study enrolled901 Scottish T2D patients carrying rs12255372 homozygotesfor TT genotype Patients were treated with sulphonylureasfor 3ndash12 months and compared to individuals with the GGgenotype The results revealed that the TT patients undergo-ing early SUs treatment had approximately two-fold higherprobability to fail (57 versus 17 for TT versus GG resp)[26] These results were confirmed by another independentstudy on 101 Slovakian patients In this study T2D patientswere supplied six months with SUs Patients with CT (119899 = 41HbA1c baseline 801 plusmn 013) and TT (119899 = 9 HbA1c baseline806plusmn027) genotypes showed a significantly lower reductionof HbA1c levels than CC homozygous patients (119899 = 51HbA1c baseline 806 plusmn 014) [27]

Sulphonylureas are metabolized in the liver by thecytochrome P450 isoenzyme 2C9 encoded by CYP2C9 gene[28 29] Therefore it is clear that some allelic variants inCYP2C9 are likely to be associated with T2D susceptibilityandor altered drug responsiveness to SUs The major riskalleles so far described for this gene areCYP2C92 (rs1799853CT Arg144Cys) and CYP2C93 (rs1057910 CT Ile359Leu)[28]

GoDARTS study has highlighted for the first time alsofor CYP2C9 gene the relationship between its variants andthe therapeutic response to sulphonylureas Indeed treating1073 T2D patients with SUs the authors observed that 6 of

themmdashcarrying two variant alleles (22 or 23 or 33)mdashhad a 05 higher reduction in HBA1c levels than 11homozygous and had 3-4 fold higher probability to reachHbA1c levels lt7 [30]

Some studies have also investigated the effects of thirdgeneration SUs treatment (combined with metformin) inpatients with polymorphisms in CYP2C9 KCNJ11 andABCC8 genes Klen et al in 2014 have reported a study ona cohort of 156 Slovenian T2D patients (18ndash72 years old)treated with SUs monotherapy (119899 = 21) or in combinationwith metformin (119899 = 135) Glucose levels were monitored(hematic HbA1c) and patients were genotyped for rs1799853and rs1057910 (2 and 3 allele resp) in CYP2C9 for rs5219and rs5215 in KCNJ11 and for rs757110 in ABCC8 Thestudy revealed that none of these SNPs significantly affectedglucose levels Nonetheless CYP2C93 genotype inducedslight hypoglycemic episodes in elderly patients (gt60 yearsold) treated with second-generation SUs more frequentlythan third generation drugs Specifically such difference hasbeen reported for glimepiride treatment used instead of gli-clazide indicating that CYP2C9 genotypes are relevant to thepharmacokinetics of sulphonylureas [31 32] However theauthors could not find any association between hypoglycemicepisodes and SNPs in ABCC8 and KCNJ11 genes [33]

Additionally a nucleotide variant (G971A) in IRS1(Insulin Receptor Substrate 1) gene has been extensively stud-ied due to its relation with SUs responsiveness In particularexperimental evidences have shown its association with anincreased risk of secondary failure to SUs treatment Theallele frequency of this variant is 2-fold higher in patientswith secondary failure to SUs compared to T2D patientsthat normally respondmdashin terms of glycemic controlmdashtooral therapy with SUs IRS1 gene product acts to stimulatethe PI3KAKT1GSK3 signaling pathway and in turn glucosetransport and glycogen synthesis The Arg971 polymorphismdecreases the phosphorylation of the substrate and allowsIRS1 acting as an inhibitor of PI3K [19 31 34]

Finally there are some SNPs in CAPN10 gene such asrs3842570 (intronic indel) rs3792267 (intronic nucleotidechange AG) and rs5030952 (intronic nucleotide changeCT) that are associated to SU responsiveness inT2DpatientsHowever the exact mechanisms that underlie such phe-nomenon are still unknown [19]

4 Impact of Polymorphisms in SLC22A1 Geneon Metformin Effects

Metformin is a frequently used drug in the treatment of T2Das much as SU It is positively charged at physiological pH soit turns in hydrophilic changing its pharmacokinetic prop-erties [35] Metformin is not metabolized in the liver suchas sulphonylureas but it is excreted in the urine Thereforethe metformin glucose lowering effect is not influenced bygenetic variants in genes encoding metabolizing enzymesEven in this case pharmacogenetics has taken advantagefrom GWASs to understand the impact of SNPs in genesencoding metformin transporters on its clinical effects

6 PPAR Research

Zhou et al performed a GWAS analyzing about 700Kpolymorphisms in 1024 patients treatedwithmetformin sub-sequently to GoDARTS [36 37] Researchers usedGoDARTSand United Kingdom Prospective Diabetes Study (UKPDS)populations for genotyping purposes and obtained the sameresults [5] In particular they demonstrated that nucleotidevariations in genes involved in DNA repair and cell cyclecontrol determine altered response to metformin in termsof glycemic response [37] and considering HbA1c lt 7 astreatment achievement Among them SLC22A1 is the moststudied gene as it is involved in the response to metformin Itencodes the organic cation transporter 1 (OCT1) Shu et al(2008) analyzed the effect of SLC22A1 gene allelic variantson plasma glucose levels after metformin administrationin animal models and healthy volunteers They identifiedfour polymorphisms in this gene that are associated toT2D susceptibility that is R61C G401S 420del and G465R(details about these nucleotide variants are reported inTable 1) Interestingly they found that glucose lowering wascompromised in presence of these SNPs [38 39]

Moreover R61C and 420del have been extensively studiedsince these are the most frequent allelic variants in the Cau-casian population The presence of R61C amino acid changehas been demonstrated to determine a reduced expression ofOCT1 protein [40]

Interestingly Christensen et al studied the effect of elevenpolymorphisms in genes encoding other membrane trans-porters in association with their effects on plasma glucoselevels in 151 T2D patients [41] Metformin was providedto these patients after insulin treatment They found that420del carriers had a more significant decrease in glucoseplasma levelmdashafter the assumption ofmetforminmdashcomparedto noncarriers [41] indicating a beneficial effect of such SNPon metformin activity

The frequency occurrence of R61C and 420del in theAsiatic population is lower than in Caucasians and noneof the SLC22A1 polymorphisms has been associated witha reduced transporter activity [42] Interestingly in thispopulation the genetic variants in SLC22A2 gene (encodingOCT2) seem to have a stronger association with metforminresponsiveness compared to SNPs in SLC22A1 [5]

5 Pro12Ala Polymorphism in PPARG andResponsiveness to Thiazolidinediones

The nuclear receptor Peroxisome Proliferator-ActivatedReceptor (PPARG) is a transcription factor that plays arelevant role in glucose and lipid metabolism It is able toactivate the transcription of several metabolic target genessuch as lipoprotein lipase fatty-acid transcript protein andaquaporin which mediate triglyceride hydrolysis fatty acidand glycerol uptake [43 44] PPARG is a master gene ofadipogenesis and its functions are very complex due tothe huge number of target genes ligands and coregulators(coactivators or corepressors) and to the presence ofseveral isoforms even with opposite or dominant negativeactivity [44 45] Indeed different studies have revealed thepresence of a relevant number of PPARG transcripts strongly

suggesting that alternative splicing has an important role inthe functioning of such a nuclear receptor [44 45]

In the last decade PPARG polymorphismsmdashboth incoding and regulatory regionsmdashhave been largely analyzedfor their possible association to pathologic phenotypes suchas T2D [46ndash49]

One of the most studied polymorphisms is Pro12Ala(rs1801282) frequently associated with clinical consequencesand alterations of the physiological metabolic status [44]The amino acid modification has been predicted to beresponsible of a significant change in the secondary structureof the protein Thus it might also affect its functionality[50] Phenotypically Pro12Ala has been associated with adecreased risk of T2D even though conflicting results havebeen reported in the literature [51 52] A study by Hara et alhas shown the association between Pro12Ala and a reducedrisk of developing T2DThe authors performed a case-controlstudy on 415 diabetic subjects and 541 nondiabetic subjects inthe Japanese population (gt60 years old) They revealed thatPro12Ala frequency was significantly lower in the diabetic(0018 119901 lt 0005) compared to the nondiabetic group (0043119901 lt 0005) In detail T2D individuals carrying ProPro allelewere 400 (964) whereas ProAla-AlaAla were 15 (36)conversely nondiabetic patients carrying ProPro allele were496 (917) instead ProAla-AlaAla were 45 (83) [53]

These results are not in agreement with the FinnishDiabetes Prevention Study (FDPS) [54] Indeed in this study522 individuals with impaired glucose tolerance (IGT) wereanalyzed after placebo assumption and lifestyle interventionAt the same time some clinical parameters (weight gainwaist and hip circumferences etc) have been measured inthese patients enrolled for the study A two-fold increase inthe risk of developing T2D was reported for Ala carriers inthe placebo arm when compared to ProPro homozygousMoreover weight gain has been identified as predictor forT2D development and it has been associated with Pro12AlaSNP Indeed AlaAla homozygous patients were more obesethan ProPro homozygous ones The therapeutic response inpresence of Pro12Ala variant has been also evaluated Suchevaluation is crucial to design optimal therapeutic strategiesfor T2D treatment [55]

A study by Hsieh et al on 250 diabetic patients (120 menand 130 women) has recently demonstrated the associationbetween Ala allele and a stronger reduction of HbA1c andfasting glucose plasma levels after treatment with thiazo-lidinediones (TZD such as pioglitazone troglitazone androsiglitazone) These patients assumed 30mgday of piogli-tazone for 6 months One hundred fifty-four patients outof 250 (616) positively responded to the treatment Levelsof HbA1c were 856divide179 for responders and 824divide188 fornonresponders (119901 = 0179) [56] These findings were furtherconfirmed by a study of Kang et al (198 diabetic patients) inwhich 183 T2D patients carried ProPro 15 carried ProAlaand none of themwas AlaAlaThe diabetic patients carryingAla12 allele had a higher decrease in fasting glucose plasmalevels than ProPro individuals (506 plusmn 278mgdL versus243 plusmn 419mgdL 119901 = 0026) [57]

Moreover a study by Bluher and colleagues [58] thatenrolled 131 T2D patients revealed no significant differences

PPAR Research 7

between ProPro homozygous and Ala carriers in terms ofTZD response (defined as HbA1C levels gt15 andor fastingblood glucose decreasegt20after 12 or 26weeks of treatmentwith pioglitazone) Furthermore a larger study performedon 340 T2D patients revealed that Pro12Ala is not correlatedwith any significant difference in troglitazone response [55]

Despite these conflicting results the association betweenPro12Ala polymorphism and TZD responsiveness has to bestill investigated at the molecular level Indeed the causalrelation between this SNP and the altered TZD responsehas to be functionally proven yet Notably TZD have beenfrequently described to cause significant side effects Indeedtroglitazone has been recently withdrawn from sale world-wide due to clinical cases of liver damage Rosiglitazonehas been withdrawn from sale in Europe and put underrestriction in USA due to its increased cardiovascular riskassociated with its administration [5] In August 2008 theAmerican Food and Drug Administration recommendedmonitoring patients under TZDs treatment for increasedrisk of myocardial ischemia whose association has beenfound in several studies [59] Thus in light of these con-siderations it is particularly relevant to assess by targetedpharmacogenomics studies whether TZD side effects mayderive from genotype-based differential drug responsivenessSuch consideration holds true also for previously describeddrugs commonly used to treat T2D and its complications

6 Next-Generation Sequencing and Diabetes

In the last few years genotyping and transcriptomics-basedstudies have gradually shifted from hybridization-based tosequencing-based approaches Indeed thanks to the intro-duction of Next-Generation Sequencing (NGS) techniqueand of new advanced sequencing platforms a growingnumber of studies have shown how polymorphisms canaffect gene expression variation among populations [38 60]These findings have confirmed that GWAS alone cannotcompletely capture the complexity of T2D and other multi-factorial diseases Indeed in the absence of functional studiesthe potential causative role of SNPs in complex diseasessusceptibility is only predictable Clearly the combination ofdifferent NGS applications (such as RNA- ChIP- and DNA-Seq) may help clinicians to dissect the genetic and epigeneticcomplexity that underlies complex traitsdiseases as well ascancer [61]

Currently NGS is the most common and powerfulapproach for genome sequencing for gene expression studiesand to study epigenetic marks In the last years this sequenc-ing technology has dramatically reduced the experimentalcosts significantly increasing the amount of data outputAmong its applications RNA-Seq has provided a significantimprovement in transcriptome analysis thanks to its abilityto detect and quantify low expressed genes alternativesplicing events posttranscriptional RNA editing and SNPsexpression [60 62] thanks to the type of sequence (readlength) the sequence quality the high throughput and its lowcost [63]

Notably as widely discussed in this review SNPs havebeen associated with individual pharmacotherapy response

In this light NGS technology is an optimal candidate tosimultaneously explore SNPs and gene expression on awidespread scale NGS-based studies have been recentlyperformed in animal models to explore the alteration ofimmunologic and metabolic functions in diabetes UsingRNA-Seq Kandpal and colleagues investigated the retinaltranscriptome of streptozotocin-induced diabetic mice toassess the efficacy of two candidate drugs Through thisapproach they found differentially expressed transcripts andquantified the relative abundance of ldquodrug-inducedrdquo isoformsafter treatment with inhibitors of the advanced glycation end-product receptors and p38 MAP kinase [64] Similarly usingRNA-Seq to profile human pancreatic islets transcriptomeEizirik and colleagues revealed that most of candidate genemdashidentified by GWASs as associated with T1D susceptibilitymdashare expressed in human pancreatic islets and are significantlyaltered after inflammatory stimuli [65] Due to the higher sen-sitivity compared to hybridization-based approaches RNA-Seq has been also used to identify new transcripts potentiallyimplicated in diabetic nephropathy [66]

However despite the fact that these pioneer studies havestarted to highlight the potential of NGS for this kind ofanalyses to date none of these has still faced the relationshipbetween SNPs and drug responsiveness in T2D patients

7 Conclusions

Since a growing number of studies are pointing out the roleof ncRNAs into human diseases NGS could significantlyhelp researchers to improve the knowledge about SNPs drugresponse and the noncoding fraction of the human genome[67 68]

To the best of our knowledge any systematic analysisof SNPs in regulatory regions that may affect (abrogate orcreate) new binding sites for microRNAs (miRNAs) andtranscription factors andor affect nucleotide methylation orchromatin remodeling has not yet been described In lightof this consideration the usage of NGS to explore this newpotential avenue appears crucial (Figure 3)

Overall we predict that NGS will significantly improvethe identification of genetic variants associated with altereddrug responsiveness in T2D and that a systematic investi-gation of how these variations affect gene expression andepigenetic mechanisms is expected to guide better drug usein clinic [62]

Disclosure

Dr Carla Pollastro is PhD student in environmentresources and sustainable development at Department ofScience and Technology ldquoDiSTrdquo at ldquoParthenoperdquo Universityof Naples

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT

Figure 3 SNPs affecting gene expression and epigenetic mechanism Schematic representation of how SNPs can potentially affect differentprocesses (1) A single nucleotide variation (eg C to A substitution) may prevent miRNA from binding to its target site in the 31015840 untranslatedregion (UTR) of a gene (2) SNP may also abrogate the binding site of a transcription factor (TF) andor the binding of proteins involved intranscription as well as in chromatine remodelling DNMT = DNA methyltranferase CH3 = methyl group RNA pol II = RNA polymeraseII

Authorsrsquo Contribution

Carla Pollastro and Carmela Ziviello contributed equally tothis work

Acknowledgments

The authors would like to thank the Italian Ministry ofEducationUniversity andResearch (MIUR) ProjectNationalOperational Programme for ldquoResearch andCompetitivenessrdquo2007ndash2013 PON0102460 for the financial support Supportfrom ldquoProgetto Invecchiamento CNRrdquo Regione CampaniaPO FESR 20072013 is also acknowledged

References

[1] J E Shaw R A Sicree and P Z Zimmet ldquoGlobal estimates ofthe prevalence of diabetes for 2010 and 2030rdquoDiabetes Researchand Clinical Practice vol 87 no 1 pp 4ndash14 2010

[2] World Health Organization Diabetes World Health Organiza-tion Geneva Switzerland 2015

[3] A Hattersley J Bruining J Shield P Njolstad and K CDonaghue ldquoThe diagnosis and management of monogenicdiabetes in children and adolescentsrdquo Pediatric Diabetes vol 10no 12 pp 33ndash42 2009

[4] G C Mannino and G Sesti ldquoIndividualized therapy for type2 diabetes clinical implications of pharmacogenetic datardquoMolecular Diagnosis ampTherapy vol 16 no 5 pp 285ndash302 2012

[5] M L Becker E R Pearson and I Tkac ldquoPharmacogenetics oforal antidiabetic drugsrdquo International Journal of Endocrinologyvol 2013 Article ID 686315 10 pages 2013

[6] J KDi Stefano andRMWatanabe ldquoPharmacogenetics of anti-diabetes drugsrdquo Pharmaceuticals vol 3 no 8 pp 2610ndash26462010

[7] L K Billings and J C Florez ldquoThe genetics of type 2 diabeteswhat have we learned from GWASrdquo Annals of the New YorkAcademy of Sciences vol 1212 pp 59ndash77 2010

[8] L Wang H L McLeod and R M Weinshilboum ldquoGenomicsand drug responserdquo The New England Journal of Medicine vol364 no 12 pp 1144ndash1153 2011

PPAR Research 9

[9] V Costa C Angelini I De Feis and A Ciccodicola ldquoUncover-ing the complexity of transcriptomes with RNA-Seqrdquo Journal ofBiomedicine and Biotechnology vol 2010 Article ID 853916 19pages 2010

[10] W F Doolittle and C Sapienza ldquoSelfish genes the phenotypeparadigm and genome evolutionrdquoNature vol 284 no 5757 pp601ndash603 1980

[11] A Vella ldquoPharmacogenetics for type 2 diabetes practicalconsiderations for study designrdquo Journal of Diabetes Science andTechnology vol 3 no 4 pp 705ndash709 2009

[12] H P Kang X Yang R Chen et al ldquoIntegration of disease-specific single nucleotide polymorphisms expression quantita-tive trait loci and co-expression networks reveal novel candidategenes for type 2 diabetesrdquoDiabetologia vol 55 no 8 pp 2205ndash2213 2012

[13] J Belsey and G Krishnarajah ldquoGlycaemic control and adverseevents in patients with type 2 diabetes treated with met-formin + sulphonylurea a meta-analysisrdquoDiabetes Obesity andMetabolism vol 10 no 1 pp 1ndash7 2008

[14] P L Drury and T Cundy ldquoGlycemic management of type 2diabetes mellitusrdquo The New England Journal of Medicine vol367 no 2 pp 182ndash183 2012

[15] M Rendell ldquoThe role of sulphonylureas in the management oftype 2 diabetes mellitusrdquo Drugs vol 64 no 12 pp 1339ndash13582004

[16] S-L Shyng and C G Nichols ldquoOctameric stoichiometry of theKATP channel complexrdquo Journal of General Physiology vol 110no 6 pp 655ndash664 1997

[17] E R Pearson I Flechtner P R Njoslashlstad et al ldquoSwitching frominsulin to oral sulphonylureas in patients with diabetes due toKir62 mutationsrdquo The New England Journal of Medicine vol355 no 5 pp 467ndash477 2006

[18] M Rafiq S E Flanagan A-M Patch et al ldquoEffective treatmentwith oral sulfonylureas in patients with diabetes due to sulfony-lurea receptor 1 (SUR1) mutationsrdquo Diabetes Care vol 31 no 2pp 204ndash209 2008

[19] X Sun W Yu and C Hu ldquoGenetics of type 2 diabetes insightsinto the pathogenesis and its clinical applicationrdquo BioMedResearch International vol 2014 Article ID 926713 15 pages2014

[20] M Javorsky L Klimcakova Z Schroner et al ldquoKCNJ11 geneE23K variant and therapeutic response to sulfonylureasrdquo Euro-pean Journal of Internal Medicine vol 23 no 3 pp 245ndash2492012

[21] Y-Y He R Zhang X-Y Shao et al ldquoAssociation of KCNJ11 andABCC8 genetic polymorphisms with response to repaglinide inChinese diabetic patientsrdquo Acta Pharmacologica Sinica vol 29no 8 pp 983ndash989 2008

[22] L Shu N S Sauter F T Schulthess A V Matveyenko JOberholzer and K Maedler ldquoTranscription factor 7-like 2regulates 120573-cell survival and function in human pancreaticisletsrdquo Diabetes vol 57 no 3 pp 645ndash653 2008

[23] F Yi P L Brubaker and T Jin ldquoTCF-4 mediates cell type-specific regulation of proglucagon gene expression by 120573-cateninand glycogen synthase kinase-3120573rdquo Journal of Biological Chem-istry vol 280 no 2 pp 1457ndash1464 2005

[24] S F A Grant G Thorleifsson I Reynisdottir et al ldquoVariant oftranscription factor 7-like 2 (TCF7L2) gene confers risk of type2 diabetesrdquo Nature Genetics vol 38 no 3 pp 320ndash323 2006

[25] Y Tong Y Lin Y Zhang J YangH Liu andB Zhang ldquoAssocia-tion betweenTCF7L2 gene polymorphisms and susceptibility to

type 2 diabetes mellitus a large Human Genome Epidemiology(HuGE) review and meta-analysisrdquo BMCMedical Genetics vol10 article 15 2009

[26] E R Pearson L A Donnelly C Kimber et al ldquoVariationin TCF7L2 influences therapeutic response to sulfonylureas aGoDARTs studyrdquo Diabetes vol 56 no 8 pp 2178ndash2182 2007

[27] M Javorsky E Babjakova L Klimcakova et al ldquoAssocia-tion between TCF7L2 genotype and glycemic control in dia-betic patients treated with gliclaziderdquo International Journal ofEndocrinology vol 2013 Article ID 374858 5 pages 2013

[28] J Kirchheiner J Brockmoller I Meineke et al ldquoImpact ofCYP2C9 amino acid polymorphisms on glyburide kinetics andon the insulin and glucose response in healthy volunteersrdquoClinical Pharmacology andTherapeutics vol 71 no 4 pp 286ndash296 2002

[29] J Kirchheiner S Bauer I Meineke et al ldquoImpact of CYP2C9and CYP2C19 polymorphisms on tolbutamide kinetics and theinsulin and glucose response in healthy volunteersrdquo Pharmaco-genetics vol 12 no 2 pp 101ndash109 2002

[30] K Zhou LDonnelly L Burch et al ldquoLoss-of-functionCYP2C9variants improve therapeutic response to sulfonylureas in type2 diabetes a go-DARTS studyrdquo Clinical Pharmacology andTherapeutics vol 87 no 1 pp 52ndash56 2010

[31] C L Aquilante ldquoSulfonylurea pharmacogenomics in Type2 diabetes the influence of drug target and diabetes riskpolymorphismsrdquo Expert Review of Cardiovascular Therapy vol8 no 3 pp 359ndash372 2010

[32] H-D Yoo M-S Kim H-Y Cho and Y-B Lee ldquoPopulationpharmacokinetic analysis of glimepiride with CYP2C9 geneticpolymorphism in healthy Korean subjectsrdquo European Journal ofClinical Pharmacology vol 67 no 9 pp 889ndash898 2011

[33] J Klen V Dolzan and A Janez ldquoCYP2C9 KCNJ11 and ABCC8polymorphisms and the response to sulphonylurea treatment intype 2 diabetes patientsrdquo European Journal of Clinical Pharma-cology vol 70 no 4 pp 421ndash428 2014

[34] K Almind C Bjorbaek H Vestergaard T Hansen S Echwaldand O Pedersen ldquoAminoacid polymorphisms of insulin recep-tor substrate-1 in non-insulin-dependent diabetesmellitusrdquoTheLancet vol 342 no 8875 pp 828ndash832 1993

[35] G G Graham J Punt M Arora et al ldquoClinical pharmacoki-netics of metforminrdquo Clinical Pharmacokinetics vol 50 no 2pp 81ndash98 2011

[36] K Zhou C Bellenguez C C A Spencer et al ldquoCommonvariants near ATM are associated with glycemic response tometformin in type 2 diabetesrdquo Nature Genetics vol 43 no 2pp 117ndash120 2011

[37] N van Leeuwen G Nijpels M L Becker et al ldquoA gene variantnear ATM is significantly associated with metformin treatmentresponse in type 2 diabetes a replication and meta-analysis offive cohortsrdquo Diabetologia vol 55 no 7 pp 1971ndash1977 2012

[38] S B Montgomery M Sammeth M Gutierrez-Arcelus et alldquoTranscriptome genetics using second generation sequencing inaCaucasian populationrdquoNature vol 464 no 7289 pp 773ndash7772010

[39] A Battle S Mostafavi X Zhu et al ldquoCharacterizing the geneticbasis of transcriptome diversity through RNA-sequencing of922 individualsrdquoGenomeResearch vol 24 no 1 pp 14ndash24 2014

[40] A T Nies H Koepsell S Winter et al ldquoExpression of organiccation transporters OCT1 (SLC22A1) and OCT3 (SLC22A3)is affected by genetic factors and cholestasis in human liverrdquoHepatology vol 50 no 4 pp 1227ndash1240 2009

10 PPAR Research

[41] M M H Christensen C Brasch-Andersen H Green et alldquoThe pharmacogenetics of metformin and its impact on plasmametformin steady-state levels and glycosylated hemoglobinA1crdquo Pharmacogenetics and Genomics vol 21 no 12 pp 837ndash850 2011

[42] H Takane E Shikata K Otsubo S Higuchi and I IeirildquoPolymorphism in human organic cation transporters andmetformin actionrdquo Pharmacogenomics vol 9 no 4 pp 415ndash422 2008

[43] M Agostini E Schoenmakers C Mitchell et al ldquoNon-DNAbinding dominant-negative human PPAR120574 mutations causelipodystrophic insulin resistancerdquo Cell Metabolism vol 4 no4 pp 303ndash311 2006

[44] V CostaM A Gallo F Letizia M Aprile A Casamassimi andA Ciccodicola ldquoPPARG gene expression regulation and next-generation sequencing for unsolved issuesrdquo PPAR Research vol2010 Article ID 409168 17 pages 2010

[45] M Aprile M R Ambrosio V DrsquoEsposito et al ldquoPPARGin human adipogenesis differential contribution of canonicaltranscripts and dominant negative isoformsrdquo PPAR Researchvol 2014 Article ID 537865 11 pages 2014

[46] C Knouff and J Auwerx ldquoPeroxisome proliferator-activatedreceptor-120574 calls for activation in moderation lessons fromgenetics and pharmacologyrdquo Endocrine Reviews vol 25 no 6pp 899ndash918 2004

[47] S Heikkinen J Auwerx andC A Argmann ldquoPPAR120574 in humanandmouse physiologyrdquo Biochimica et Biophysica Acta vol 1771no 8 pp 999ndash1013 2007

[48] W He ldquoPPAR120574211987511990311990012119860119897119886 polymorphism and human healthrdquoPPAR Research vol 2009 Article ID 849538 15 pages 2009

[49] A Ciccodicola V Costa A Casamassimi et al ldquoCharacteri-zation of a novel polymorphism in PPARG regulatory regionassociatedwith type 2 diabetes and diabetic retinopathy in italyrdquoJournal of Biomedicine and Biotechnology vol 2009 Article ID126917 7 pages 2009

[50] H N Gouda G S Sagoo A-H Harding J Yates M S Sandhuand J P T Higgins ldquoThe association between the peroxisomeproliferator-activated receptor-1205742 (PPARG2) Pro12Ala genevariant and type 2 diabetes mellitus a HuGE review and meta-analysisrdquo American Journal of Epidemiology vol 171 no 6 pp645ndash655 2010

[51] S S Deeb L FajasMNemoto et al ldquoAPro12Ala substitution inPPAR1205742 associated with decreased receptor activity lower bodymass index and improved insulin sensitivityrdquo Nature Geneticsvol 20 no 3 pp 284ndash287 1998

[52] E Zeggini J R C Parkinson S Halford et al ldquoExamining therelationships between the Pro12Ala variant in PPARG and type2 diabetes-related traits in UK samplesrdquo Diabetic Medicine vol22 no 12 pp 1696ndash1700 2005

[53] K Hara T Okada K Tobe et al ldquoThe Pro12Ala polymorphismin PPAR gamma2 may confer resistance to type 2 diabetesrdquoBiochemical and Biophysical Research Communications vol 271no 1 pp 212ndash216 2000

[54] V I Lindi M I J Uusitupa J Lindstrom et al ldquoAssociationof the Pro12Ala polymorphism in the PPAR-1205742 gene with 3-year incidence of type 2 diabetes and body weight change in theFinnish Diabetes Prevention Studyrdquo Diabetes vol 51 no 8 pp2581ndash2586 2002

[55] J C Florez K A Jablonski M W Sun et al ldquoEffects ofthe type 2 diabetes-associated PPARG P12A polymorphismon progression to diabetes and response to troglitazonerdquo The

Journal of Clinical Endocrinology amp Metabolism vol 92 no 4pp 1502ndash1509 2007

[56] M-C Hsieh K-D Lin K-J Tien et al ldquoCommon poly-morphisms of the peroxisome proliferator-activated receptor-120574 (Pro12Ala) and peroxisome proliferator-activated receptor-120574 coactivator-1 (Gly482Ser) and the response to pioglitazonein Chinese patients with type 2 diabetes mellitusrdquo MetabolismClinical and Experimental vol 59 no 8 pp 1139ndash1144 2010

[57] E S Kang S Y Park H J Kim and et al ldquoEffects of Pro12Alapolymorphism of peroxisome proliferator-activated receptor1205742 gene on rosiglitazone response in type 2 diabetesrdquo ClinicalPharmacology and Therapeutics vol 78 no 2 pp 202ndash2082005

[58] M Bluher G Lubben and R Paschke ldquoAnalysis of the relation-ship between the Pro12Ala variant in the PPAR-gamma2 geneand the response rate to therapy with pioglitazone in patientswith type 2 diabetesrdquo Diabetes Care vol 26 no 3 pp 825ndash8312003

[59] C Taylor and F D R Hobbs ldquoType 2 diabetes thiazolidine-diones and cardiovascular riskrdquo The British Journal of GeneralPractice vol 59 no 564 pp 520ndash524 2009

[60] S B Montgomery and E T Dermitzakis ldquoFrom expressionQTLs to personalized transcriptomicsrdquo Nature Reviews Genet-ics vol 12 no 4 pp 277ndash282 2011

[61] V Costa M Aprile R Esposito and A Ciccodicola ldquoRNA-Seq and human complex diseases recent accomplishments andfuture perspectivesrdquo European Journal of Human Genetics vol21 no 2 pp 134ndash142 2013

[62] N K Yadav P Shukla A Omer S Pareek and R K SinghldquoNext generation sequencing potential and application in drugdiscoveryrdquo The Scientific World Journal vol 2014 Article ID802437 7 pages 2014

[63] M L Metzker ldquoEmerging technologies in DNA sequencingrdquoGenome Research vol 15 no 12 pp 1767ndash1776 2005

[64] R P Kandpal H K Rajasimha M J Brooks et al ldquoTran-scriptome analysis using next generation sequencing revealsmolecular signatures of diabetic retinopathy and efficacy ofcandidate drugsrdquoMolecular Vision vol 18 pp 1123ndash1146 2012

[65] D L Eizirik M Sammeth T Bouckenooghe et al ldquoThe humanpancreatic islet transcriptome expression of candidate genes fortype 1 diabetes and the impact of pro-inflammatory cytokinesrdquoPLoS Genetics vol 8 no 3 Article ID e1002552 2012

[66] E P BrennanM JMorine DWWalsh et al ldquoNext-generationsequencing identifies TGF-1205731-associated gene expression pro-files in renal epithelial cells reiterated in human diabeticnephropathyrdquo Biochimica et Biophysica Acta vol 1822 no 4 pp589ndash599 2012

[67] S Marguerat B T Wilhelm and J Bahler ldquoNext-generationsequencing applications beyondGenomesrdquoBiochemical SocietyTransactions vol 36 no 5 pp 1091ndash1096 2008

[68] M Sultan M H Schulz H Richard et al ldquoA global view ofgene activity and alternative splicing by deep sequencing of thehuman transcriptomerdquo Science vol 321 no 5891 pp 956ndash9602008

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


MEDIATORSINFLAMMATION

of


Behavioural Neurology

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

2 PPAR Research

[6 7] These lines of evidence represent the starting point ofpharmacogenomics [4] Generally interindividual variabilityis mainly determined by single nucleotide polymorphisms(SNPs) Specifically a relevant fraction of the genetic variabil-ity observed in T2D patients has been found in genes directly(or indirectly) related to the activity (or to the metabolism)of oral antidiabetic drugs (OAD) The assumption of thesedrugs is the first intervention step in T2D management afterthe failure of lifestyle changes Therefore the identificationof genetic variants associated to altered drug responsivenessis a key point in diabetes research since it is expected toameliorate the therapeutic approach in a tailored mannerHowever other biological nongenetic factors can influ-ence pharmacodynamics of OADs such as hepatic renaland intestinal functions These considerations highlight theimportance of considering both the phenotype (clinical andpathophysiological parameters) and the genotype of T2Dpatients in order to choose the most appropriate therapeuticapproach [5]

In the last decade the advent of genome-wide associ-ation studies (GWAS) has gradually shifted the genetics ofT2D to a step forward definitely turning pharmacogeneticsinto pharmacogenomics Indeed whereas the former mainlyfocuses on single drug-gene interaction the latter faces therelationship among inherited nucleotide variations and drugresponse also taking into account gene expression othergenomics features and epigenetics factors underlying inter-and intraindividual variability [4 5] Despite many GWAShave revealed the association among genetic variants andcomplex traitsdiseases many factors are still underestimatedor unexplored clearly deserving further investigation Forinstance a renewed interest is emerging from the so-calledldquojunkrdquo DNA [8] Indeed it is known that the vast majorityof nucleotide variants that are associated to complex traitsincluded T2D localizes into noncoding regionsThus a smallfraction of intragenic and intergenic noncoding RNAs (ncR-NAs) with still undefined regulatory functions [9 10] mayplay a role in the onset andor progression of multifactorialdiseases Noncoding RNAs levels may also account for thevariable drug responsiveness observed in T2D patients

In this review we describe the relationship between drugresponsiveness in T2D patients and SNPs also describingthe organs that have a major role in drug metabolismor activity (Figure 1) We also discuss the recent advance-ments in sequencing technologies highlighting how theycan provide significant contributions to pharmacogenomicsstudies The new technological frontiers in the identificationof allelic variants associated with altered drug responsivenesswill surely constitute a solid basis to design personalizedtherapeutic approaches in T2D treatment

2 Pharmacogenomics of Antidiabetic Drugs

Currently the more widely used drugs in T2D treatmentare the sulphonylureas metformin and thiazolidinediones(troglitazone pioglitazone and rosiglitazone) Figure 2schematizes the main proteins that are involved in theuptake and metabolism of oral antidiabetic drugs or that areactivated upon their administration

Generally pharmacogenetics studies consider some clin-ical endpoints to evaluate drug responsiveness Among themthe achievement ofHbA1c levelslt7 as defined in the guide-lines and an overall reduction in HbA1c represent the mostappropriate parameters to consider inT2Dpharmacogeneticsstudies [11] Another crucial consideration is whether thedrug of interest has been used at early or late stages of thedisease where there is a very low probability to reach asignificant therapeutic effect

In Table 1 we summarize a schematic catalogue of SNPsthat according to GWASs are commonly associated toaltered drug responsiveness in T2D In many cases thesestudies have revealed the absence of a significant associationamong SNPs and the expression levels of the closest geneshowing a wide variability Such association studies havealso underlined the ethnic-specific expression profile ofSNPs in tissues crucially involved in glucose homeostasis[12]

3 Impact of Polymorphisms in ABCC8KCNJ11 TCF7L2 CYP2C9 IRS1 andCAPN10 Genes on Sulphonylureas Effects

Sulphonylureas (SUs) are widely used drugs in the treatmentof T2D Despite the wide use of these drugs in the clinicalpractice different side effects such as weight gain andincreased risk of hypoglycemia have been frequently [13 14]Glibenclamide gliclazide glipizide and glimepiride are themain SUs currently used for T2D treatment [15]

All SUs bind to sulphonylurea receptor 1 (SUR1) andenhance glucose-stimulated insulin release from the pancre-atic120573-cellsTherefore SUs act by inducing the closure ofATP-sensitive potassium (KATP) channel through the bindingwith the proteins that form it Four K+ ions are located inthe inner pore of KATP channel whereas outside the channelis formed by four SUR1 molecules [16] The ATP producedby glucose oxidation in mitochondria causes the closure ofKATP channel with the consequent depolarization of 120573-cellsmembrane the increased entry of Ca2+ ions followed bythe release of presynthesized insulin from 120573-cells Ultimatelysulphonylureas induce the closure of these channels and therelease of insulin through the binding to the specific receptoroutside the KATP channel

Nucleotide variations in genes encoding KATP channelproteins such as potassium channel inwardly rectifyingsubfamily J member 11 (KCNJ11) and ATP-binding cassettesubfamily C member 8 (ABCC8) are associated with theonset of neonatal diabetes mellitus Studies on SUs revealedthat these drugsmight effectively act in response to the defectinduced by KCNJ11 and ABCC8 mutations in T2D patients[17 18] KCNJ11 gene encodes the potassium inward rectifier62 subunit (Kir62) of KATP channel which is implicatedin glucose-dependent insulin secretion in pancreatic 120573-cells GWAs have revealed a strong association between thepolymorphism rs5219 inKCNJ11 (CT nucleotide substitutionthat leads to K23E amino acid change) and T2D [19] IndeedJavorsky et al (2012) have demonstrated the impact of K23Eamino acid substitution on SUs therapeutic effects in a cohort

PPAR Research 3

TZD

TZD damage

SUSU

SU

SU

SU

SU

TZD damage

Metformin

Metformin

MetforminPPARG

(Pro12Ala)

SLC22A1(420del)

(R61C)(G401S)(G465R)SLC22A2

(S270A)

KCNJ11(K23E)(A250I)


ABCC8(A1369S)

IRS1(G971R) TCF7L2


SU




6160543148160560824160560881160575837

ExonExonExonExon

SLC22A1

CTAGndashA

ACG

rs12208357rs34130495rs35167514rs34059508


Metforminmetabolism

T2D susceptibility


T2D development

11 1740957217408630

ExonExon KCNJ11 TC

GArs5219rs5215

K23EA250I


10 9670204796741053

ExonExon CYP2C9 CT

ACrs1799853rs1057910

R144CI359L


T2D susceptibility




10 114808902114758349


CTrs12255372rs7903146

mdashmdash


T2D development

2 227093745227660544


GArs2943641rs1801278

mdashG971R


2241534293241531174241542703

IntronIntronIntron

CAPN10IndelAGCT

rs3842570rs3792267rs5030952

mdashmdashmdash


response

4 PPAR Research


Metformin

OCT

Sulphonylureas


Mitochondrion

TZD

TCF4

Nucleus

GLP-1

PI3K

Insulin

CYP2C9

Ca++

KK+

120573-catenin

PPAR120574-RXR

H3CN

CH3

HN

NH NH

NH2

R

OOO

S NH

NH

R2

SUR1

SUR1

Kir62

R

O OS

O

NH

NH

R2

SO

NHO

5998400 59984005998400 5998400

3998400 3998400 39984003998400

IRIR

+




PPAR Research 5












6 PPAR Research













PPAR Research 7









7 Conclusions




Disclosure




8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















PPAR Research 3

TZD

TZD damage

SUSU

SU

SU

SU

SU

TZD damage

Metformin

Metformin

MetforminPPARG

(Pro12Ala)

SLC22A1(420del)

(R61C)(G401S)(G465R)SLC22A2

(S270A)

KCNJ11(K23E)(A250I)


ABCC8(A1369S)

IRS1(G971R) TCF7L2


SU




6160543148160560824160560881160575837

ExonExonExonExon

SLC22A1

CTAGndashA

ACG

rs12208357rs34130495rs35167514rs34059508


Metforminmetabolism

T2D susceptibility


T2D development

11 1740957217408630

ExonExon KCNJ11 TC

GArs5219rs5215

K23EA250I


10 9670204796741053

ExonExon CYP2C9 CT

ACrs1799853rs1057910

R144CI359L


T2D susceptibility




10 114808902114758349


CTrs12255372rs7903146

mdashmdash


T2D development

2 227093745227660544


GArs2943641rs1801278

mdashG971R


2241534293241531174241542703

IntronIntronIntron

CAPN10IndelAGCT

rs3842570rs3792267rs5030952

mdashmdashmdash


response

4 PPAR Research


Metformin

OCT

Sulphonylureas


Mitochondrion

TZD

TCF4

Nucleus

GLP-1

PI3K

Insulin

CYP2C9

Ca++

KK+

120573-catenin

PPAR120574-RXR

H3CN

CH3

HN

NH NH

NH2

R

OOO

S NH

NH

R2

SUR1

SUR1

Kir62

R

O OS

O

NH

NH

R2

SO

NHO

5998400 59984005998400 5998400

3998400 3998400 39984003998400

IRIR

+




PPAR Research 5












6 PPAR Research













PPAR Research 7









7 Conclusions




Disclosure




8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















4 PPAR Research


Metformin

OCT

Sulphonylureas


Mitochondrion

TZD

TCF4

Nucleus

GLP-1

PI3K

Insulin

CYP2C9

Ca++

KK+

120573-catenin

PPAR120574-RXR

H3CN

CH3

HN

NH NH

NH2

R

OOO

S NH

NH

R2

SUR1

SUR1

Kir62

R

O OS

O

NH

NH

R2

SO

NHO

5998400 59984005998400 5998400

3998400 3998400 39984003998400

IRIR

+




PPAR Research 5












6 PPAR Research













PPAR Research 7









7 Conclusions




Disclosure




8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















PPAR Research 5












6 PPAR Research













PPAR Research 7









7 Conclusions




Disclosure




8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















6 PPAR Research













PPAR Research 7









7 Conclusions




Disclosure




8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















PPAR Research 7









7 Conclusions




Disclosure




8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















8 PPAR Research

1

miRNA

miRNA

A

C

RNA pol IITF

DNMTDNMT

CH3

CH3

2

CrarrT




Acknowledgments


References









PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















PPAR Research 9


































10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















10 PPAR Research



































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















review article pharmacogenomics of drug response in type …

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