Genome-Wide DNA Methylation in Mixed Ancestry Individuals with Diabetes and Prediabetes from ...

12 Pages·2016·1.84 MB·English

Hindawi Publishing Corporation
International Journal of Endocrinology
Volume 2016, Article ID 3172093, 11 pages
http://dx.doi.org/10.1155/2016/3172093
Research Article
Genome-Wide DNA Methylation in Mixed Ancestry Individuals
with Diabetes and Prediabetes from South Africa
TandiE.Matsha,1CarmenPheiffer,2StephenE.Humphries,3JunaidGamieldien,4
RajivT.Erasmus,5andAndreP.Kengne6,7
1DepartmentofBiomedicalSciences,FacultyofHealthandWellnessSciences,CapePeninsulaUniversityofTechnology,
CapeTown7535,SouthAfrica
2BiomedicalResearchandInnovationPlatform,SouthAfricanMedicalResearchCouncil,CapeTown7505,SouthAfrica
3CentreforCardiovascularGenetics,BritishHeartFoundationLaboratories,InstituteofCardiovascularScience,TheRayneBuilding,
UniversityCollegeLondon,LondonWC1E6JF,UK
4SouthAfricanNationalBioinformaticsInstitute,UniversityoftheWesternCape,CapeTown7535,SouthAfrica
5DivisionofChemicalPathologyandNationalHealthLaboratoryService(NHLS),FacultyofMedicineandHealthSciences,
UniversityofStellenbosch,CapeTown7505,SouthAfrica
6Non-CommunicableDiseasesResearchUnit,SouthAfricanMedicalResearchCouncil,CapeTown7505,SouthAfrica
7DepartmentofMedicine,UniversityofCapeTown,CapeTown8000,SouthAfrica
CorrespondenceshouldbeaddressedtoTandiE.Matsha;[email protected]
Received23December2015;Revised6June2016;Accepted28June2016
AcademicEditor:MarcoBugliani
Copyright©2016TandiE.Matshaetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Aims.Toconductagenome-wideDNAmethylationinindividualswithtype2diabetes,individualswithprediabetes,andcontrol
mixedancestryindividualsfromSouthAfrica.Methods.Weusedperipheralbloodtoperformgenome-wideDNAmethylation
analysisin3individualswithscreendetecteddiabetes,3individualswithprediabetes,and3individualswithnormoglycaemia
from the Bellville South Community, Cape Town, South Africa, who were age-, gender-, body mass index-, and duration of
residency-matched. Methylated DNA immunoprecipitation (MeDIP) was performed by Arraystar Inc. (Rockville, MD, USA).
Results.HypermethylatedDMRswere1160(81.97%)and124(43.20%),respectively,inindividualswithdiabetesandprediabetes
whenbothwerecomparedtosubjectswithnormoglycaemia.Ourdatashowsthatgenesrelatedtotheimmunesystem,signal
transduction, glucose transport, and pancreas development have altered DNA methylation in subjects with prediabetes and
diabetes.PathwayanalysisbasedonthefunctionalanalysismappingofgenestoKEGGpathwayssuggestedthatthelinoleicacid
metabolismandarachidonicacidmetabolismpathwaysarehypomethylatedinprediabetesanddiabetes.Conclusions.Ourstudy
suggeststhatepigeneticchangesarelikelytobeanearlyprocessthatoccursbeforetheonsetofovertdiabetes.Detailedanalysisof
DMRsthatshowsgradualmethylationdifferencesfromcontrolversusprediabetestoprediabetesversusdiabetesinalargersample
sizeisrequiredtoconfirmthesefindings.
1.Introduction thus methylation at these sites is associated with changes
in gene expression. Hypermethylation represses transcrip-
Deoxyribonucleicacid(DNA)methylationisabiochemical tion, thereby reducing gene expression, while hypomethy-
process catalyzed by DNA methyltransferase enzymes to lation is associated with transcriptional activation of the
󸀠
covalently add a methyl group at the 5 position of cyto- affected genes [2, 3]. DNA methylation is the most char-
sine DNA nucleotides, creating 5-methylcytosine in CpG acterized of the epigenetic processes, which also include
dinucleotidesconcentratedingenepromoters[1]. TheCpG histonemodification,chromatinremodeling,andnoncoding
dinucleotidesingenepromotersarenotgenerallymethylated; RNAs. Epigenetics, defined as mechanisms that affect gene
2 InternationalJournalofEndocrinology
transcriptionand/orexpressionintheabsenceofalterations 3.MaterialsandMethods
to the DNA sequence, provides a plausible link between
genetic and environmental determinants of health status. 3.1.EthicalApprovaloftheStudy. Thisinvestigationisbased
Currentstudieshaveshowncongruenceofepigeneticlociand on the Bellville South (Ward 009) cohort from Cape Town
gene polymorphisms in diseases [4–7]. In type 2 diabetes, that has been approved by the Research Ethics Committees
for example, 19 single nucleotide polymorphisms (SNPs) of the Cape Peninsula University of Technology and Stel-
associatedwiththediseaseintroducedorremovedpotential lenbosch University (resp., NHREC: REC-230 408-014 and
sites for DNA methylation [7]. Furthermore, the genetic- N14/01/003).ThestudywasconductedaccordingtotheCode
epigenetic-environment link has been demonstrated in a of Ethics of the World Medical Association (Declaration of
report that showed an association between the nuclear- Helsinki). All participants signed written informed consent
encoded gene, NADH dehydrogenase [ubiquinone] 1 beta after all the procedures had been fully explained in the
subcomplex subunit 6 (NDUFB6) polymorphism, DNA languageoftheirchoice.
methylation,age,andtheexpressionofNDUFB6inhuman
skeletalmuscle[4]. 3.2. Study Procedures. All participants received a standard-
Estimates from the International Diabetes Federation ized interview, blood pressure, and anthropometric mea-
(IDF) suggest that the population of people with diabetes surements.Participantswithnohistoryofdoctor-diagnosed
isgrowingrelativelyfasterinAfricacomparedtoanywhere diabetes mellitus underwent a 75g oral glucose tolerance
else [8]. Emerging evidence suggests that this process is test (OGTT) as recommended by WHO [12]. Further, the
not entirely accounted for by the traditional drivers of the following biochemical parameters were analyzed at an ISO
diabetes epidemic. In mixed ancestry South Africans, for 15189 accredited pathology practice (PathCare, Reference
instance,whoarelargelyoverweightorobeseandathighrisk Laboratory, Cape Town, South Africa): plasma glucose,
ofdiabetes[9],thedistributionoftraditionalriskfactorsfor serum insulin, serum creatinine, total cholesterol (TC),
diabetesisnotappreciablydifferentbetweenindividualswith high density lipoprotein cholesterol (HDL-c), triglycerides
diabetes and those without diabetes, while the accelerated (TG), low density lipoprotein cholesterol (LDL), C-reactive
deterioration of glucose tolerance status over time is not protein (CRP), 𝛾-glutamyl transferase (GGT), AST, ALT,
explained by the known powerful determinants of diabetes and glycated haemoglobin (HbA1c), certified by National
occurrence[10].Inthiscontext,thecontributionofemerging Glycohemoglobin Standardization Program (NGSP). Full
risk factors for diabetes including epigenetic changes has bloodcountwasalsoassessedonallparticipants.Inaddition,
been postulated [11] but remains largely uninvestigated. It, an EDTA blood sample was collected and stored at −20
therefore, became our primary aim to investigate nontradi- degreesforDNAextractionandanalysis.
tional context specific diabetes risk factors and pathophys-
iological pathways underlying the excess risk of diabetes in
3.3. Genome-Wide DNA Methylation. Genomic DNA was
thispopulation.Herein,wereporttheSouthAfricanmixed extractedfromperipheralbloodusingtheWizard(cid:2)Genomic
ancestry population’s specific differentially methylated sites
DNAPurificationKit(Promega,Madison,WI,USA)accord-
andmetabolicpathwaysaffectedbyDNAmethylationinthis
ing to the manufacturer’s instructions. Briefly, white blood
population.
cells were lysed; thereafter, cellular proteins were removed
by salt precipitation, and high molecular weight genomic
DNA left in solution was then concentrated and desalted
2.Subjects
by isopropanol precipitation. At least 2𝜇g of DNA (con-
centrations ranging between 70ng/𝜇L and 130ng/𝜇L) with
Participants were members of a cohort study conducted in
A260/A280andA260/A230ratios≥1.8wasshippedfrozen
Ward009,CapeTown,SouthAfrica.ThecityofCapeTown
on dry ice, as instructed by Arraystar Inc. (Rockville, MD,
defines Ward 009 as a mixed ancestry township formed in
USA).MethylatedDNAimmunoprecipitation(MeDIP)was
the late 1950s. According to the 2011 population census, its
performedbyArraystarInc.(Rockville,MD,USA)according
population stands at approximately 29,301 with an average
toDownetal.[13]withminormodificationsasfollows.
householdsizeof4.84individuals.Thepopulationispredom-
inantlyofmixedancestryorcoloured(76%)followedbyblack
Africans(18.5%)andCaucasianandAsianswhomakeonly 3.4. Sequencing Library Preparation. For MeDIP, genomic
1.5%.Mostoftheresidentsinthiscommunityhavelivedthere DNAwassonicatedto∼200–900bpwithaBioruptorsonica-
for more than five years, while others have been there for tor(Diagenode,Denville,NJ,USA).Thereafter,800ngofson-
theirentirelives.Thesocioeconomicconditionofthepeople icatedDNAwasend-repaired,A-tailed,andligatedtosingle-
isaveragewith37%ofhouseholdshavingamonthlyincome endadaptersfollowingthestandardIlluminagenomicDNA
ofZAR3,200orless.TherecruitmentoftheWard009cohort protocol. After agarose size selection to remove unligated
was initiated in April 2014, from which 3 individuals with adapters,theadaptor-ligatedDNAwasusedforimmunopre-
screendetecteddiabetes,3individualswithprediabetes,and cipitationusingahumanmonoclonalanti-5-methylcytosine
3individualswithnormoglycaemiawhowereage-,gender-, antibody (Diagenode). For this, DNA was heat-denatured
∘
body mass index- (BMI-), duration of residency-matched at 94 C for 10min, rapidly cooled on ice, and immunopre-
were selected for the current study. All participants were cipitated with 1𝜇L of primary antibody overnight at 4∘C
females. with rocking agitation in 400𝜇L of immunoprecipitation
InternationalJournalofEndocrinology 3
buffer(0.5%BSAinPBS).Torecovertheimmunoprecipitated (2)Genebodypeaks:thegenebodyregionwasdefinedas
DNA fragments, 100𝜇L of protein G magnetic beads (Life +2000bp downstream of the transcription start site
Technologies,Carlsbad,CA,USA)wasaddedandincubated (TSS)tothetranscriptionterminationsite(TTS).
∘
foradditional2hoursat4 Cwithagitation.Afterimmuno-
(3)Intergenic peaks: intergenic regions were defined as
precipitation, a total of five immunoprecipitation washes
theothergenomicregionsnotincludedintheabove
wereperformedwithice-coldimmunoprecipitationbuffer.A
2regions.Peakswhosecenterswerelocatedinthese
nonspecifichumanIgGimmunoprecipitationwasperformed
intergenic regions were defined as intergenic peaks.
inparalleltomethylDNAimmunoprecipitationasanegative
MAnormwasusedtocalculatedifferentiallymethy-
control. Washed beads were resuspended in TE buffer with
∘ latedregionswithstatisticalsignificance.
0.25%SDSand0.25mg/mLproteinaseKfor2hoursat65 C
andthenallowedtocooldowntoroomtemperature.MeDIP
andsupernatantDNAwerepurifiedusingQiagenMinElute 3.7. Solexa CHASTITY Quality Filter. Individual bases gen-
columns and eluted in 16𝜇L EB (Qiagen, Germantown, erated from original image files have quality scores, which
MD, USA). Fourteen cycles of PCR were performed on reflect the probability whether base calling is correct or
5𝜇L of the immunoprecipitated DNA using the single-end not. The score is calculated by CHASTITY Formula. The
IlluminaPCRprimers.Theresultingreactionswerepurified CHASTITY(C)ofeachbaseintheshortreadsisdetermined
with Qiagen MinElute columns, after which a final size bytheintensityoffourcolours(I ,I ,I ,andI here),and
A C G T
selection (300–1,000bp) was performed by electrophoresis theformulameans“theratioofthehighest(I here)ofthe
C
in 2% agarose. Libraries were quality controlled with the four(basetype)intensitiestothesumofhighesttwo(I and
C
Agilent2100Bioanalyzer(AgilentTechnologies,SantaClara, I here).”TheCHASTITY(C)shouldbenolessthan0.6in
G
CA, USA). An aliquot of each library was diluted in EB thefirst25bases.
(Qiagen) to 5ng/𝜇L and 1𝜇L was used in real-time PCR
reactions to confirm the enrichment for methylated region.
3.8. Gene Ontology (GO) Analysis. The Gene Ontology
TheenrichmentofDNAimmunoprecipitationwasanalyzed
project provides a controlled vocabulary to describe gene
by qPCR using specific methylated sites at H19 locus and
and gene product attributes in any organism (http://www
nonmethylatedsitesatGAPDH.
.geneontology.org/).Theontologycoversthreedomains:bio-
logicalprocess,cellularcomponent,andmolecularfunction.
3.5.Sequencing. Thelibrarywasdenaturedwith0.1MNaOH Fisher’sexacttestwasusedtofindiftherewasmoreoverlap
togeneratesingle-strandedDNAmoleculesandloadedonto betweentheDElistandtheGOannotationlistthanwould
channelsoftheflowcellat8pMconcentration,amplifiedin beexpectedbychance.The𝑃valuedenotesthesignificance
situusingTruSeqRapidSRClusterKit(Illumina,SanDiego, of GO terms enrichment in the DE genes. The lower the 𝑃
CA,USA).Sequencingwascarriedoutbyrunning100cycles value,themoresignificanttheGOTerm;a𝑃value≤0.05was
on Illumina HiSeq 2000 according to the manufacturer’s consideredsignificant.
instructions. The Agilent 2100 Bioanalyzer was used for
accurate assessment of the quality and concentration of
3.9.PathwayAnalysis. Pathwayanalysisisafunctionalanal-
the sequencing library, while the size and concentration
ysismappingofgenestoKEGGpathways.The𝑃value(EASE
of each sample were determined after sequencing library
score,Fisher’sPvalue,orhypergeometricPvalue)denotesthe
preparation.
significanceofthepathwaycorrelatedtotheconditions.The
lowerthe𝑃valueis,themoresignificantthepathwayis;a𝑃
3.6.DataAnalysis. Afterthesequencingplatformgenerated value≤0.05wasconsideredsignificant.
thesequencingimages,thestagesofimageanalysisandbase
calling were performed using Off-Line Basecaller software
4.Results
(OLB V1.8). After passing Solexa CHASTITY quality filter,
the clean reads were aligned to the human genome (UCSC
4.1.GeneralCharacteristicsofParticipants. Thegeneralchar-
HG19)usingBOWTIEsoftware(V2.1.0).MeDIPpeakswere
acteristics of the nine female participants are presented
identifiedbyMACS2andMAnormidentifiedDMRs.Statis-
in Table 1 for each participant and further summarized
tically significant MeDIP-enriched regions (peaks) detected
across subgroups defined by the glucose tolerance status.
by MACS2 were identified by comparison to a Poisson
−2 All participants had reported no menstrual periods for 6
background model, using a q-value threshold of 10 . The
months or more prior to taking part in this study. As
peaks in samples were annotated by the nearest gene (the
expected from the study design, age and BMI were mostly
nearest TSS to the canter of peak region) using the newest
similar across subgroups, with all participants being obese.
UCSCRefSeqdatabase.Peaksweredividedinto3classeson
Hip circumferences were mostly similar across subgroups,
thebasisoftheirdistancestoUCSCRefSeqgenes:
while waist circumference and waist-to-hip ratio decreased
(1)Promoter peaks: promoters were defined as 2000bp withimprovedglucosetolerance.Bloodpressurelevelswere
upstream and downstream from the transcription lowest in normotolerant subjects and highest in those with
start site (TSS). Peaks whose centers were located prediabetes. The lipid profile and indicators of glycaemia
inthesepromoterregionsweredefinedaspromoter improved with improving glucose tolerance status, while
peaks. fastinginsulinlevelsdecreasedaccordingly(Table1).
4 InternationalJournalofEndocrinology
Table1:Characteristicsofallparticipants.
Diabetes Prediabetes Controls
Case1 Case2 Case3 Mean Case1 Case2 Case3 Mean Case1 Case2 Case3 Mean
Age,years 62 52 52 55.3 52 50 55 52.3 62 52 52 55.3
2
BMI,kg/m 35.3 39.3 31.2 35.3 32.6 35.2 30.4 32.7 41.3 34.0 31.7 35.6
WaistC,cm 120 103 116 113 104 108 116 109 112 93 89 98
HipC,cm 118 124 113 118 108 119 129 118 127 124 106 119
WHR 1.02 0.83 1.02 0.96 0.96 0.91 0.90 0.93 0.88 0.75 0.84 0.82
SBP,mmHg 153 183 143 160 178 164 163 168 142 127 137 135
DBP,mmHg 78 130 61 90 109 101 78 96 72 75 92 80
S-Creat.,𝜇mol/L 45 75 35 52 69 47 52 56 62 58 50 57
∗
GGT,U/L 18 42 295 118 22 20 12 18 22 20 99 47
ALT,U/L 19 86 61 55 22 16 17 18 12 12 30 18
AST,U/L 24 45 50 40 21 18 18 19 21 16 30 22
Smoking,yes/no Yes Yes Yes Yes No No Yes Yes No
Cotinine,ng/mL∗ 160 >500 76.3 245.4 94 <10 <10 38 94.4 338 <10 147.5
TC,mmol/L 6.0 6.0 8.3 6.8 4.9 4.1 5.5 4.8 3.6 5.2 4.7 4.5
TG,mmol/L 1.49 3.35 2.51 2.45 2.10 1.21 1.04 1.45 1.22 1.23 1.27 1.24
HDL-C,mmol/L 1.09 1.1 1.87 1.35 1.09 1.17 1.42 1.23 1.35 1.08 1.42 1.28
LDL-C,mmol/L 3.9 4.2 5.1 4.4 3.1 2.1 3.5 2.9 1.6 3.4 2.7 2.6
FSI,mIU/L 97.9 68.7 47.7 71.4 77.3 29.9 110.2 72.5 72.8 20.8 30.1 41.2
∗
FSI,mIU/L 20.7 21.2 21.0 5.7 5.5 11.5 7.6 10.4 7.6 5.3 7.8
HbA1c,% 7.2 7.5 6.1 6.9 5.0 6.1 6.4 5.8 5.9 5.6 6.1 5.9
HbA1c,mmol/mol 55.2 58.5 43.2 52.3 31.1 43.2 46.4 40.2 41.0 37.7 43.2 40.6
FBG,mmol/L 9.0 8.1 7.8 8.3 4.7 6.2 6.1 5.7 5.4 2.8 5.8 4.7
2-hourPG,mmol/L 16.4 13.8 5.9 12.0 7.8 10.3 9.4 9.2 7.4 5.0 6.1 6.2
U-CRP,mg/L 3.2 10.4 5.3 6.3 5.2 15.6 21.6 14.1 23.4 8.2 5.9 12.5
RBC,×1012/L 4.56 5.07 4.37 4.67 4.86 4.42 4.88 4.72 3.75 4.42 4.82 4.33
Plateletcount,×109/L 284 217 369 290 238 286 305 276 281 152 256 230
WCC,×109/L 8.6 10.0 7.8 8.8 7.1 8.0 8.9 8.0 6.0 8.3 4.6 6.3
Hb,g/dL 13.0 15.1 12.6 13.6 13.6 13.2 13.5 13.4 6.1 14.4 13.6 11.4
Haematocrit,L/L 0.39 0.46 0.37 0.41 0.42 0.39 0.41 0.41 0.22 0.42 0.41 0.35
MCV,fL 85 90 85 87 86 89 83 86 57 95 86 79
MCH,pg 29 30 29 29 28 30 28 29 16 33 28 26
MCHC,g/dL 34 33 34 34 33 34 33 33 29 34 33 32
RDW,% 14.0 15.0 15.2 14.7 12.7 14.1 14.0 13.6 25.3 14.8 15.9 18.7
Neutrophils,×109/L 5.6 7.2 4.6 5.8 4.5 4.5 5.4 4.8 2.8 5.7 2.9 3.8
Lymphocytes,×109/L 2.3 2.0 2.4 2.2 2.1 3.0 2.8 2.6 2.3 1.9 1.4 1.9
Monocytes,×109/L 0.50 0.30 0.40 0.40 0.30 0.30 0.30 0.30 0.72 0.46 0.20 0.46
Eosinophils,×109/L 0.10 0.40 0.40 0.30 0.20 0.20 0.40 0.27 0.12 0.14 0.10 0.12
Basophils,×109/L 0.01 0.01 0.10 0.04 0.01 0.01 0.01 0.01 0.06 0.04 0.01 0.04
2-hourPG,after2-hourplasmaglucose;2-hourSI,after2-hourseruminsulin;ALT,alanineaminotransferase;AST,aspartateaminotransferase;DBP,diastolic
bloodpressure;FBG,fastingplasmaglucose;FSI,fastingseruminsulin;GGT,gammaglutamyltranspeptidase;Hb,haemoglobin;HDL-C,highdensity
lipoproteincholesterol;HipC,hipcircumference;LDL-C,lowdensitylipoproteincholesterol;MCH,meancellhaemoglobin;MCHC,meancorpuscular
haemoglobinconcentration;MCV,meancellvolume;RBC,redbloodcellcount;RDW,redcelldistributionwidth;S-Creat.,serumcreatinine;SBP,systolic
bloodpressure;TC,totalcholesterol;TG,triglycerides;U-CRP,ultrasensitiveC-reactiveprotein;WaistC,waistcircumference;WCC,whitecellcount;WHR,
waist-to-hipratio.
∗referstousingthemedianinsteadofmeanbecause,sincethedataisskewedforthoseparameters,themeanwillgiveawrongmeaning.
4.2. Differentially Methylated Regions. A total of 450,142 intergenicregions,inallgroups.MAnormwasthenusedto
statisticallysignificantMeDIP-enrichedregions(peaks)were calculate the statistical significance of differentially methy-
identified in all the samples. As expected, the promoter latedregions(DMRs)withingenepromoters.Subjectswith
region [TSS − 2000bp; TSS + 2000bp] showed the diabetes showed the highest number of DMRs and these
least number of peaks, compared to the gene body and are summarized in Figure 1. Generally, more than 80% of
InternationalJournalofEndocrinology 5
Total sites = 450142
Diabetics = 260062 (57.77%) Prediabetics = 88037 (19.56%) Controls = 102043 (22.67%)
I = 69.07%GB = 27.24% P = 3.69% I = 67.24% GB = 27.94% P = 4.82% I = 65.88% GB = 28.55% P = 5.57 %
Hyper DMRs (DM) versus Hyper DMRs (pre-DM) versus
prediabetes = 1099/1321 (83.12%) controls = 124/287 (43.20%)
Hyper DMRs (DM) versus controls =
1160/1415 (81.97%)
Figure1:MeDIP-enrichedregions(peaks)identifiedinallthesamples.Totalsitesarethesumofthepeaknumbersforsubjectswithdiabetes,
subjects with prediabetes, and controls and include all the peaks that may be common/redundant across the conditions, as well as the
peaksuniquetoeachcondition.Distributionsofpeaksinintergenic(I),genebody(GB),andpromoter(P)regionsareshown.Statistically
significantMeDIP-enrichedregions(peaks)weredetectedbyMACS2andidentifiedbycomparisontoaPoissonbackgroundmodel,using
−2
aq-valuethresholdof10 .MAnormwasusedtocalculatedifferentiallymethylatedregionswithstatisticalsignificance.Thedifferentially
methylatedregionslocatedwithingenepromoters[TSS−2000bp;TSS+2000bp]areselectedandprovided.Bypeaknumber,therearemore
hypermethylatedpeaksinindividualswithdiabetes(82%)thaninthecontrolsubjects.
DMRsinsubjectswithdiabeteswerehypermethylatedwhen withcellsurfacereceptorsignalingandinflammatorypath-
compared to those with prediabetes or normoglycaemia, ways.Inaddition,glucosetransport,WNTsignaling,muscle
while no differences were observed between subjects with development, pancreas development genes, and insulin sig-
prediabetes or normoglycaemia (Figure 1). Supplementary naling pathway were associated with hypermethylation in
Tables 1 to 6 in Supplementary Material available online subjectswithdiabetesorprediabetes(SupplementaryTables
at http://dx.doi.org/10.1155/2016/3172093 show the DMRs 7–12). Although the I-kappaB kinase/NF-kappaB cascade
in subjects with diabetes, subjects with prediabetes, and wasassociatedwithhypomethylatedDMRsinsubjectswith
controls. To summarize these data, we grouped the DMRs diabetesandhypermethylatedDMRsinsubjectswithpredia-
according to chromosomal location and this is shown in betes,thegenesassociatedwiththesepathwaysweredifferent
Figure2.Comparedtocontrolsandsubjectswithprediabetes, in each group. For example, in subjects with prediabetes,
hypermethylatedDMRsinsubjectswithdiabetesweremore the hypermethylated genes were CHUK, TRIM38, PLK2,
common in chromosomes, 3, 6, 11, 13, and 17, while in TNFRSF19, and ZMYND11, while in subjects with diabetes
chromosome one, there were more hypomethylated DMRs theywereBCL3,IL23A,F2RL1,S100A12,TNFRSF10B,NEK6,
RNF31,SLC35B2,andIRAK1BP1.Pathwayanalysisbasedon
(Figure2).NohypomethylatedDMRswerepresentinchro-
thefunctionalanalysismappingofgenestoKEGGpathways
mosome 13 in subjects with diabetes or prediabetes when
also showed an association with inflammatory pathways
compared with each other or when those with prediabetes
(Table2).Thelinoleicacidmetabolismandarachidonicacid
werecomparedtocontrols(Figure2).
metabolism pathways were progressively hypomethylated
fromprediabetestodiabetes.Ontheotherhand,thehyper-
4.3. Pathway Analysis. We first performed Gene Ontology trophic cardiomyopathy (HCM) pathway was associated
(GO) classification to retrieve the biological process, cel- withhypermethylatedDMRsinsubjectswithdiabeteswhen
lular process, and molecular function of the DMRs and comparedtoeithercontrolsorsubjectswithprediabetes.
these are presented in Supplementary Tables 2–24, while
Figure3showsbiologicalprocessesinthetop10enrichment 5.Discussion
scores for DMRs in subjects with diabetes or prediabetes.
As shown in Figure 3, these hypermethylated DMRs in Emergingdatasupportstheroleofepigeneticmechanismsin
subjectswithdiabetesorprediabeteswerewidelyassociated thedevelopmentofdiabetes;however,todate,genome-wide
6 InternationalJournalofEndocrinology
14.00 14.00
Rs 12.00 Rs 12.00
M M
D 10.00 D 10.00
of 8.00 of 8.00
ge 6.00 ge 6.00
nta 4.00 nta 4.00
Perce 2.00 Perce 2.00
0.00 0.00
Chr1Chr2Chr3Chr4Chr5Chr6Chr7Chr8Chr9Chr10Chr11Chr12Chr13Chr14Chr15Chr16Chr17Chr18Chr19Chr20Chr21Chr22ChrX Chr1Chr2Chr3Chr4Chr5Chr6Chr7Chr8Chr9Chr10Chr11Chr12Chr13Chr14Chr15Chr16Chr17Chr18Chr19Chr20Chr21Chr22ChrX
Chromosomes Chromosomes
Hypermethylated DMRs (DM versus controls) Hypermethylated DMRs (DM versus pre-DM)
Hypomethylated DMRs (DM versus controls) Hypomethylated DMRs (DM versus pre-DM)
(a) (b)
12.00
Rs 10.00
M
D 8.00
e of 6.00
g
nta 4.00
e
erc 2.00
P
0.00
1234567890123456789012X
ChrChrChrChrChrChrChrChrChrChr1Chr1Chr1Chr1Chr1Chr1Chr1Chr1Chr1Chr1Chr2Chr2Chr2Chr
Chromosomes
Hypermethylated DMRs (pre-DM versus controls)
Hypomethylated DMRs (pre-DM versus controls)
(c)
Figure2:Accumulationofdifferentiallymethylatedregions(DMRs)inchromosomes.(a)representsDMRsindiabetesversuscontrols,(b)
representsDMRsindiabetesversusprediabetes,and(c)representsDMRsinprediabetesversuscontrols.
DNA methylation profiling has not involved subjects with 276 DMRs, where 96% of the 254 DMRs located in the
prediabetesordiabetesfromsub-SaharanAfricaorAfricain promoterregionwerehypomethylated[14].TheDMRswere
general.Inthispreliminarygenome-wideDNAmethylation associatedwithbeta-cellfunction,celldeath,andadaptation
analysis of individuals with prediabetes or diabetes from to metabolic stress. Similar DMRs (71) to Volkmar et al.
South Africa, we provide DMRs data and their biological [14]haverecentlybeenreportedinastudy,wheretheywere
pathwaysthatappeartobeaffectedinsubjectswithdiabetes associatedwithpathwaysincancer,axonguidance(SEMA4A
or prediabetes, as well as those that appear to show a trend andSEMA5B),MAPKsignaling(CACNA1H)focaladhesion
from the prediabetes state to diabetes. For instance, the (ITGB4), ECM-receptor interaction (AGRN and TGB4),
linoleic acid metabolism and arachidonic acid metabolism and actin cytoskeleton (TGB4) [7]. Furthermore, the study
pathways were associated with hypomethylated DMRs in showedanincreasedaccumulationoftheDMRsinchromo-
subjects with prediabetes versus controls and in those with somes1and2,whileinchromosome19,DMRswerelessened
diabetes versus those with prediabetes, suggesting that the [7].Inthecurrentstudy,wefoundatotalof1415DMRsinthe
hypomethylationofthesegenesislikelytobeanearlyprocess promoter regions of subjects with diabetes when compared
that occurs before the onset of overt diabetes. Our data tocontrolsubjectsand81.7%ofthesewerehypermethylated.
also shows that genes related to the immune system, signal Similar to Dayeh et al.’s [7] report, the DMRs were mostly
transduction, glucose transport, and pancreas development accumulatedinchromosomes1and2butwereleastinchro-
arehypermethylatedinsubjectswithprediabetesordiabetes. mosome21insubjectswithdiabetescomparedtocontrolsin
When they are investigated further, these DMRs may be ourstudy.Wealsoobservedsimilarfindingbetweensubjects
potentialbiomarkersofdiseaseoccurrence/progressionand withprediabetesandcontrolsshowinghigheraccumulation
alsocouldsuggestpossibletargetsforthedevelopmentofnew in chromosomes 1 and 2, while chromosomes 13, 16, 18, 20,
treatments. 21,and22hadlessthan10DMRs.Alongitudinalstudythat
Whole epigenetic profiling in individuals with type 2 investigatedhypomethylatedDMRsshowedthatprogression
diabetes is relatively in its infancy with the first study from normoglycaemia to a worse glucose tolerance state
reportedin2012.Thestudywasconductedusingpancreatic was associated with early differential methylation prior to
islets of 5 individuals with type 2 diabetes and identified diseasemanifestation[15].Theauthorsanalyzedmethylation
InternationalJournalofEndocrinology 7
Renal system process Regulation of leukocyte proliferation
Positive regulation of vasodilation Regulation of epithelial cell differentiation
Defense response to Gram-negative bacterium Innate immune response
m m
Biological process ter MIo-nkoavpaplaeBnRNt ek eIiginnunutoraleoarsrglteloiae/ognuNnikic Fcoai -nclfk -asba1tylpi0osopt onpeadmr Bto rp dacpranuersoscscpstcauioeodrsrneest Biological process ter RegRuelRgatueigolauntRli aooetnfgi om uonlfoa oktnifeoo rlnnya umtoicnfpl oelhSeacouirygc ktcnyeoeat cldelly itppfftrerrae oonarllecsiinffdteeituvrricaaaatttttiiiiiooooonnnnn
System process Cell surface receptor signaling pathway
Positive regulation of intracellular Negative regulation of epithelial cell differentiation
protein kinase cascade
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.5 1 1.5 2 2.5 3 3.5
Enrichment score Enrichment score
(a) (b)
Regulation of behavior Cell surface receptor signaling pathway
I-kappaB kinase/NF-kappaB cascade Protein localization to endoplasmic reticulum
Regulation of protein transport Regulation of type 2 immune response
m m
Biological process ter PPoossititiivvee r reegguulalattiioonn Po ooff sT iitonivltlre-a lPRrickoeeegesglli uuurtilellvaaactetreii oporpentnrog o ourot fefls a ipipntgrirn oookanttielen iioinannfsg ttes rrp ecaacaanntrshssecpptwaiooodarrnyett Biological process ter CCeyltlouLkl-aianrRs ercT-eeom-ssrhppbeeooidlnncpi ssaaeeetrce itt2ddoo csmcceiyygletltn toodaakkiblffiiionnneleegri c essp nttpaiitmmrtiohauutwciellouausnysss
Regulation of transport Response to organic substance
Regulation of intracellular protein kinase cascade Cellular response to organic substance
3.2 3.4 3.6 3.8 4 4.2 4.4 0 0.5 1 1.5 2 2.5 3 3.5 4
Enrichment score Enrichment score
(c) (d)
Regulation of systemic arterial blood pressure Peptide biosynthetic process
Cellular process Response to hydrogen peroxide
Regulation of potassium ion transport Positive regulation of I-kappaB kinase/
m m NF-kappaB cascade
Biological process ter RegulaPtioosnit oivfe G rs-eipggrunolaatletiPiinonegn pc ptoRoiadufNPt ypfihlAlallw-eot ledmayply seoreitendt cadeieub empmogtrloei aactrnhs ppsuyerrllooaamttctiiebeooislnnnys Biological process ter RRNeeeggguualltaaitvtCiieoo enrnlel uogoflufa ilIIrna-- trtkkireoaaisppnnppps ooiaacnfBB saa eppkk tiioonnopp aatrtssooeeeatt//iicNNcct issFFvii--ggekk nnoaaaaxppllyiippnngaaggeBBn pp cc aasaapttsshheccwwcaaiddaaeyyees
Positive regulation of systemic arterial blood pressure Regulation of apoptotic signaling pathway
Positive regulation of blood pressure Response to ionizing radiation
0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 3 3.5 4
Enrichment score Enrichment score
(e) (f)
Figure3:GOanalysisofthedifferentiallymethylatedgenesindiabetes,prediabetes,andcontrols.Thebarsplotshowsthetoptenenrichment
scorevaluesofthesignificantenrichmentterms.X-axis:GOID’senrichmentscorevalue;itequals−log10(Pvalue);Y-axis:GOcategory.(a)
Hypomethylateddiabetesversuscontrols.(b)Hypermethylateddiabetesversuscontrols.(c)Hypomethylateddiabetesversusprediabetes.
(d)Hypermethylateddiabetesversusprediabetes.(e)Hypomethylatedprediabetesversuscontrols.(f)Hypermethylatedprediabetesversus
controls.
levels of candidate DMRs identified by whole epigenetic family 4, subfamily F, polypeptide 3 (CYP4F3), CYP4F8,
profiling in 62 subjects with impaired glucose metabolism Phospholipase A2, group IIC (PLA2G2C), and PLA2G4E
and 64 controls who maintained a normal glucose toler- weredifferentiallymethylated,whileinsubjectswithdiabetes
ancestatusduringfollow-upanddemonstratedsignificantly versusprediabetes,CYP2E1andPLA2G12Awereinvolved.
lower percent of methylation before the appearance of the Lipidsareimportantcomponentsofallmammaliancells
diseaseinthosethatprogressed[15].Similarly,wehavealso andhaveavarietyofbiologicalfunctions,includingserving
observed common DMRs as well as common pathways in as energy reservoirs and mediators of inflammation known
prediabetes and diabetes individuals’ inflammatory genes asoxylipins.OxylipinsresultfromtheoxygenationofPUFAs
including the lipid metabolism pathway, which appeared to by three types of enzymes, cyclooxygenases, lipoxygenases,
beprogressivelymodifiedfromprediabetesstatetodiabetes. and cytochrome P [16]. The type of PUFA oxidized and
We observed that the arachidonic acid (AA) pathway was enzymeinvolveddeterminetheproductionofoxylipins.The
hypomethylatedandappearedtobemodifiedinprediabetic arachidonic acid (AA) generates most of the inflammatory
anddiabeticstates;however,differentisoformsofthegenes molecules involved in cell signaling cascades and is a pre-
were involved in each glycaemic state. For example, in cursor of eicosanoids. Eicosanoids include prostaglandins
subjectswithprediabetesversuscontrols,cytochromeP450, (PGAs), leukotrienes (LTs), and thromboxanes (TXAs) and
8 InternationalJournalofEndocrinology
Table2:KEGGanalysisoftoptenenrichmentscore(−log10(𝑃value))valuesofthesignificantenrichmentpathway.
PathwayID Definition Fisher’s𝑃value Enrichmentscore Genes
Hypomethylateddiabetesversuscontrol
hsa05143 Africantrypanosomiasis 0.0073 2.133882 F2RL1//HPR//THOP1
hsa05340 Primaryimmunodeficiency 0.0086 2.064522 AIRE//CD8A//IGLL1
hsa04640 Hematopoieticcelllineage 0.0200 1.697467 CD1B//CD5//CD8A//CD9
hsa04970 Salivarysecretion,Homosapiens 0.0216 1.665411 ADRB2//CST5//KCNN4//NOS1
hsa00670 Onecarbonpoolbyfolate 0.0229 1.639815 ATIC//MTFMT
hsa04977 Vitamindigestionandabsorption 0.0322 1.490856 SLC19A2//SLC23A1
Hypermethylateddiabetesversuscontrol
CA13//CA5B//CA9//CPS1//GLU
D2
APLN//CHRNA1//CHRNA5//CH
RNB3//CNR1//EDN1//EDNRB//
hsa00910 Nitrogenmetabolism 0.0015 2.818773
FSHB//GABRA6//GABRG2//GA
BRR1//GCG//GHR//GPR156//G
RIA1//GRM4//HCRTR2//LGR4//
NMUR2//NPFF//NPY1R//P2RX6
hsa04080 Neuroactiveligand-receptorinteraction 0.0207 1.683002 //P2RY14//PLG//RXFP1//TAC4
Butirosinandneomycinbiosynthesis GCK//HK2
hsa00524 0.0254 1.594577
Glycosaminoglycanbiosynthesis,heparan EXT1//EXTL2//HS3ST5//XYLT
1
hsa00534 Sulfate/heparin 0.0361 1.441751 CACNB2//EDN1//IGF1//ITGA1//
ITGB1//MYH6//PRKAG2//PRK
hsa05410 Hypertrophiccardiomyopathy(HCM) 0.0362 1.441099 AG3//TPM1
hsa00750 VitaminB6metabolism 0.0368 1.433896 PDXP//PSAT1
hsa04950 Maturityonsetdiabetesoftheyoung 0.0413 1.384024 GCK//HNF1A//NR5A2//PDX1
Hypomethylateddiabetesversusprediabetes
hsa00770 PantothenateandCoAbiosynthesis 0.0137 1.86211 COASY//PANK2
hsa04640 Hematopoieticcelllineage 0.0141 1.850323 CD14//CD1A//CD1B//CD3E
hsa00590 Arachidonicacidmetabolism 0.0304 1.516647 CYP2E1//GGT1//PLA2G12A
hsa03320 PPARsignalingpathway 0.0369 1.433374 ACSL4//NR1H3//PCK2
hsa00591 Linoleicacidmetabolism 0.0378 1.42268 CYP2E1//PLA2G12A
Hypermethylateddiabetesversusprediabetes
CACNA1S//DMD//ITGA4//MY
H6//PRKAB2//PRKAG3//TGFB3
//TNF//TNNI3//TPM4
ACVR1B//CCR6//CCR8//CX3C
hsa05410 Hypertrophiccardiomyopathy(HCM) 0.0063 2.201901
R1//CXCL1//CXCL9//EDAR//G
HR//IFNA2//IFNA21//IL18RAP//
IL3//IL3RA//IL4R//IL9//TGFB3//
TNF//TNFRSF10D//TNFRSF14//
hsa04060 Cytokine-cytokinereceptorinteraction 0.0085 2.071022 TNFRSF9//TNFSF18//TNFSF8
SEC61G//SRP9//SRPR//SRPRB
hsa03060 Proteinexport 0.0210 1.678045 ADCY4//CACNA1S//DMD//ITG
A4//MYH6//TGFB3//TNF//TNNI
3//TPM4
ADCY4//ANAPC11//ATF1//ATF
3//CCNB2//CREM//HLA-
hsa05414 Dilatedcardiomyopathy 0.0251 1.600573
A//HLA-
E//IKBKB//KRAS//MAD2L1//M
RAS//MSX2//NFYB//TGFB3//T
LN2//TNF//WNT16//WNT5B
hsa05166 HTLV-Iinfection 0.0382 1.418455
ACSL6//IKBKB//PRKAB2//PRK
hsa04920 Adipocytokinesignalingpathway 0.0455 1.342336 AG3//PTPN11//SOCS3//TNF
InternationalJournalofEndocrinology 9
Table2:Continued.
PathwayID Definition Fisher’s𝑃value Enrichmentscore Genes
Hypomethylatedprediabetesversuscontrol
CYP4F3//CYP4F8//PLA2G2C//P
hsa00590 Arachidonicacidmetabolism 0.0026 2.593265
LA2G4E
hsa00592 Alpha-linolenicacidmetabolism 0.0211 1.676649 PLA2G2C//PLA2G4E
PLA2G2C//PLA2G4E
hsa00591 Linoleicacidmetabolism 0.0278 1.555232
MYLK3//PLA2G2C//PLA2G4E//
hsa04270 Vascularsmoothmusclecontraction 0.0301 1.521206 RAMP3
hsa04350 TGF-betasignalingpathway 0.0352 1.453226 LEFTY2//SP1//TGFB3
Hypermethylatedprediabetesversuscontrol
hsa05202 Transcriptionalmisregulationincancer 0.0190 1.720526 CCNT1//ELK4//EYA1//RARA
hsa05221 Acutemyeloidleukemia 0.0425 1.371986 CHUK//RARA
theirroleisdependentonthetypeoffattyacidfromwhich for instance, 17 of the 40 type 2 diabetes candidate genes
they are derived [17]. In this regard, we also observed dif- identifiedbygenome-wideassociationstudies(GWAS)were
ferentiallymethylatedsignalsofcytochromeP450andPhos- differentiallymethylatedinpancreaticisletsofsubjectswith
pholipase A2 (PLA2). Inflammatory activationof the PLA2 diabetes[7].Interestingly,inourstudy,KEGGpathwayanaly-
enzyme promotes the release of AA from cell membranes sisidentifiedhypomethylatedDMRsinsubjectswithdiabetes
phospholipids, which in turn is metabolized by cyclooxy- thatwereassociatedwithAfricantrypanosomiasispathway.
genases (COX), yielding eicosanoids [17]. Taken together, African trypanosomiasis is a sleeping sickness caused by
theepigeneticmodificationsofoxylipinsindirectlyrepresent Trypanosoma species from Africa, suggesting a population-
a chronic inflammatory pathway involvement in diabetes specificselectionofDNAmethylationinthispopulationwith
development.Becausefattyacidsarederivedfromthedietin anAfricanancestry.
theformoflinoleicacid,followedbydesaturationandelonga- In this study, DNA methylation was investigated using
tionintospecificfattyacids,theelucidationoflipidpathway whole genome MeDIP sequencing (MeDIP-Seq); though
epigenetics may contribute to the formulation of treatment bisulfite sequencing is currently considered the gold stan-
and prevention strategies. For instance, supplement studies dard for detecting DNA methylation, it does not dis-
usingAA,EPA,and/orALAhavedemonstratedaneffecton tinguish between 5-methylcytosine (5mC) and 5-hydrox-
theproductionofeicosanoidsandinflammatorymarkers.In ymethylcytosine (5hmC). While MeDIP-Seq obviates the
astudycomprisinghealthymales,1.5gofAAincreasedPGE2 need for bisulfite treatment of DNA, a limitation of the
andleukotrieneLTB4[18],whilesupplementationwithfish technique is the inability to detect individual differentially
oil containing EPA and DHA decreased the generation of methylatedCpGsites.Amajorlimitationofthisstudyisthe
TNF𝛼andIL-1by70%and78%,respectively[19].Moreover, lackofverificationofregionsusingothersequencing-based
epigenetic changes are reversible to an extent that in some DNAmethylationprofilingmethods.However,aquantitative
neurologicaldiseasesandcancersepigeneticdrugshavebeen comparison of four sequencing-based DNA methylation
proposed or are currently being used [20, 21]. In cancer, methods including MeDIP-Seq demonstrated comparable
for instance, methylation inhibiting drugs include cytidine methylationcallsofallfourmethodsbutdifferencesinCpG
analogssuchas5-azacitidine[22]andzebularine[23].Inview coverage, resolution, quantitative accuracy, efficiency, and
oftheabove,itisclearthatepigeneticsdooffertremendous cost [30]. Other limitations of the study include the one
opportunitiesfortreatmentandmanagementofdiseases. gender and small number of individuals investigated. The
Considering the current published literature on the pancreatic𝛽-cellsarebelievedtobetheidealtissuefortype2
epigenetics of type 2 diabetes globally, it is encouraging diabetesepigenetics.Wemadeuseofperipheralwhiteblood
to note that a few whole epigenomic studies have been cellsandithasbeenshownthatDNAmethylationisdifferent
conductedinpopulationsfromAfrica.Theseincludestudies between blood cell types [31]; thus our findings should be
that investigated the effect of environment in Moroccans, interpreted with caution. While we carefully matched the
Ethiopians,andEgyptians[24–26],severebladderdamagein participants, we take note that the smoking patterns were
Ghanaians [27], and exposure to famine in offspring from not similar between the groups. Therefore, it is likely that
Gambia [28]. Epigenetic determinants have been shown to some of the DMRs observed are not necessarily due to
differ between populations. For example, in HapMap lym- diabetes or prediabetes but perhaps nicotine. Similarly, all
phoblastoidcelllinesderivedfromindividualsofEuropeanor participantswereobese(BM1>30kg/m2);wecannotdismiss
Africanancestry,population-specificcytosinemodifications the fact that some DMRs are obesity related. Although the
insamplesderivedfromYorubapeoplefromIbadan,Nigeria, population investigated is from Africa, it is noteworthy to
and Caucasian residents of European ancestry from Utah mentionthatthisisauniqueheterogeneousgroup,whichis
were observed [29]. The differences between and within ofmixedgeneticoriginwithcontributionsfromEuropeans,
population groups have been linked to population-disease- South Asians, Indonesians, and a population genetically
specificsinglenucleotidepolymorphisms(SNPs).Indiabetes, close to the isiXhosa sub-Saharan Bantu population [32].
10 InternationalJournalofEndocrinology
Therefore we cannot rule out the possible role of genetic National Institute for Health Research University College
ancestralcomponentsinthemethylationpatternsobserved. London Hospitals Biomedical Research Centre. The South
However,financialconstraintsandthetechnicalcomplexity African National Research Foundation Unique Grant no.
ofgeneratingancestryinformativemarkers,particularlyfor 87838supportsCP.
thisSouthAfricanpopulationgroup,stillremainachallenge.
In conclusion, our study provides basis for candidate
References
methylation analysis in Africa. Considering that GWAS
studiesofdiabetesinvolvingpopulationsfromAfricaarenot [1] S. Ito, L. Shen, Q. Dai et al., “Tet proteins can convert 5-
available, we recommendmethylationquantitativetrailloci methylcytosine to 5-formylcytosine and 5-carboxylcytosine,”
(meQTL) investigations coupled with ancestry informative Science,vol.333,no.6047,pp.1300–1303,2011.
markerstoaccountforpopulationstratification. [2] A.M.DeatonandA.Bird,“CpGislandsandtheregulationof
transcription,”Genes&Development,vol.25,no.10,pp.1010–
1022,2011.
Disclosure
[3] L. M. Iyer, S. Abhiman, and L. Aravind, “Natural history of
eukaryotic DNA methylation systems,” Progress in Molecular
Any opinion, finding, and conclusion or recommendation
BiologyandTranslationalScience,vol.101,pp.25–104,2011.
expressedinthismaterialisthatoftheauthor(s)andtheMRC
[4] C.Ling,P.Poulsen,S.Simonssonetal.,“Geneticandepigenetic
doesnotacceptanyliabilityinthisregard.
factors are associated with expression of respiratory chain
componentNDUFB6inhumanskeletalmuscle,”TheJournalof
CompetingInterests ClinicalInvestigation,vol.117,no.11,pp.3427–3435,2007.
[5] V. K. Rakyan, T. A. Down, D. J. Balding, and S. Beck,
Theauthorsdeclarethattheyhavenocompetinginterests. “Epigenome-wideassociationstudiesforcommonhumandis-
eases,”NatureReviewsGenetics,vol.12,no.8,pp.529–541,2011.
Authors’Contributions [6] H. Liu, J. Su, J. Li et al., “Prioritizing cancer-related genes
withaberrantmethylationbasedonaweightedprotein-protein
Tandi E. Matsha made substantial contributions to concep- interactionnetwork,”BMCSystemsBiology,vol.5,article158,
2011.
tionanddesign,acquisitionofdata,analysisandinterpreta-
tionofdata,draftingthepaper,finalapprovaloftheversion [7] T.Dayeh,P.Volkov,S.Salo¨etal.,“Genome-wideDNAmethyla-
tionanalysisofhumanpancreaticisletsfromtype2diabeticand
to be published, and agreement to be accountable for all
non-diabetic donors identifies candidate genes that influence
aspectsoftheworkinensuringthatquestionsrelatedtothe
insulin secretion,” PLoS Genetics, vol. 10, no. 3, Article ID
accuracyorintegrityofanypartoftheworkareappropriately
e1004160,2014.
investigatedandresolved.CarmenPheiffermadesubstantial
[8] L. Guariguata, D. R. Whiting, I. Hambleton, J. Beagley, U.
contributionstoconceptionanddesign,acquisitionofdata,
Linnenkamp, and J. E. Shaw, “Global estimates of diabetes
analysis and interpretation of data, revising it critically
prevalencefor2013andprojectionsfor2035,”DiabetesResearch
for important intellectual content, and final approval of
andClinicalPractice,vol.103,no.2,pp.137–149,2014.
the version to be published. Stephen E. Humphries made
[9] R.T.Erasmus,D.J.Soita,M.S.Hassanetal.,“Highprevalence
contribution to revising the paper critically for important
ofdiabetesmellitusandmetabolicsyndromeinasouthafrican
intellectual content and final approval of the version to be mixedancestrypopulation:theBellville-SouthAfricastudy—
published.JunaidGamieldienmadecontributiontorevising baselinedata,”SouthAfricanMedicalJournal,vol.102,pp.841–
the paper critically for important intellectual content and 844,2012.
final approval of the version to be published. Rajiv T. [10] T. E. Matsha, D. J. Soita, M. S. Hassan et al., “Three-year’s
Erasmus made contribution to revising the paper critically changesinglucosetolerancestatusintheBellvilleSouthcohort:
for important intellectual content and final approval of the rates and phenotypes associated with progression,” Diabetes
versiontobepublished.AndreP.Kengnemadecontribution ResearchandClinicalPractice,vol.99,no.2,pp.223–230,2013.
to data acquisition,analysis and interpretation,revising the [11] E. Lontchi-Yimagou, E. Sobngwi, T. E. Matsha, and A. P.
paper critically for important intellectual content, and final Kengne, “Diabetes mellitus and inflammation,” Current Dia-
approvaloftheversiontobepublished.TandiE.Matshaand betesReports,vol.13,no.3,pp.435–444,2013.
CarmenPheiffercontributedequallytothiswork. [12] K.G.M.M.AlbertiandP.Z.Zimmet,“Definition,diagnosis
andclassificationofdiabetesmellitusanditscomplications.Part
1:diagnosisandclassificationofdiabetesmellitus.Provisional
Acknowledgments
reportofaWHOconsultation,”DiabeticMedicine,vol.15,no.7,
pp.539–553,1998.
TheauthorsthanktheBellvilleSouth(Ward009)community
[13] T. A. Down, V. K. Rakyan, D. J. Turner et al., “A Bayesian
for participating in the study. They are also grateful to the
deconvolution strategy for immunoprecipitation-based DNA
Bellville South Community Health Forum for supporting
methylome analysis,” Nature Biotechnology, vol. 26, no. 7, pp.
the engagement with the Bellville South Community. The 779–785,2008.
SouthAfricanMedicalResearchCouncil(MRC)fundedthis
[14] M. Volkmar, S. Dedeurwaerder, D. A. Cunha et al., “DNA
research project with funds from National Treasury under methylation profiling identifies epigenetic dysregulation in
its Economic Competitiveness and Support Package. SEH pancreatic islets from type 2 diabetic patients,” The EMBO
is a BHF Professor and is funded by PG08/008 and by the Journal,vol.31,no.6,pp.1405–1426,2012.