Rapid Identification of Potential Drugs for Diabetic Nephropathy Using Whole-Genome Expression ...

14 Pages·2016·2.14 MB·English

Hindawi Publishing Corporation
BioMed Research International
Volume 2016, Article ID 1634730, 13 pages
http://dx.doi.org/10.1155/2016/1634730
Research Article
Rapid Identification of Potential Drugs for
Diabetic Nephropathy Using Whole-Genome
Expression Profiles of Glomeruli
JingsongShi,SongJiang,DandanQiu,WeiboLe,XiaoWang,YinhuiLu,andZhihongLiu
NationalClinicalResearchCenterofKidneyDiseases,ResearchInstituteofNephrology,JinlingHospital,
NanjingUniversitySchoolofMedicine,Nanjing210016,China
CorrespondenceshouldbeaddressedtoZhihongLiu;[email protected]
Received23October2015;Revised25January2016;Accepted8February2016
AcademicEditor:GianluigiZaza
Copyright©2016JingsongShietal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Objective. To investigate potential drugs for diabetic nephropathy (DN) using whole-genome expression profiles and the
ConnectivityMap(CMAP).Methodology.EighteenChineseHanDNpatientsandsixnormalcontrolswereincludedinthisstudy.
Whole-genomeexpressionprofilesofmicrodissectedglomeruliweremeasuredusingtheAffymetrixhumanU133plus2.0chip.
Differentiallyexpressedgenes(DEGs)betweenlatestageandearlystageDNsamplesandtheCMAPdatabasewereusedtoidentify
potentialdrugsforDNusingbioinformaticsmethods.Results.(1)Atotalof1065DEGs(FDR<0.05andfoldchange>1.5)were
foundinlatestageDNpatientscomparedwithearlystageDNpatients.(2)Piperlongumine,15d-PGJ2(15-deltaprostaglandinJ2),
vorinostat,andtrichostatinAwerepredictedtobethemostpromisingpotentialdrugsforDN,actingasNF-𝜅Binhibitors,histone
deacetylase inhibitors (HDACIs), PI3K pathway inhibitors, or PPAR𝛾 agonists, respectively. Conclusion. Using whole-genome
expressionprofilesandtheCMAPdatabase,werapidlypredictedpotentialDNdrugs,andtherapeuticpotentialwasconfirmed
bypreviouslypublishedstudies.Animalexperimentsandclinicaltrialsareneededtoconfirmboththesafetyandefficacyofthese
drugsinthetreatmentofDN.
1.Introduction Whole-genome expression profiling is the simultaneous
measurement of the expression of thousands of genes by
Diabetic nephropathy (DN), which is clinically character- microarray technology (or RNA-Seq) to create a global
ized by proteinuria and morphological and ultrastructural pictureoftissueorcellularfunction.Comparingthewhole-
changes in the kidney, is a serious complication of diabetes genomeexpressionprofilesoftissues(orcells)underphysio-
mellitusandisamajorcauseofend-stagerenaldiseaseworld- logicandpathologicconditionsmayrevealthepathogenesis
wide.DNisamultifactorialprogressivediseasewithcomplex of DN. In addition to identifying differentially expressed
pathogenesis, involving hyperglycemia, advanced glycation and coexpressed genes, from which one can generate new
end products (AGEs), hemodynamic disorder, metabolic hypotheses about the molecular mechanism of complex
abnormalities,inflammatoryfactors,andoxidativestress[1]. diseases, whole-genome expression data are also used to
Although our knowledge of DN is continuouslyincreasing, identify therapeutic drugs. The Connectivity Map (CMAP)
no treatment strategies specifically target the pathogenesis database is a collection of genome-wide transcriptional
of DN beyond controlling glucose levels, blood lipid levels, expression data from cultured human cells treated with
and high blood pressure [2]. As a result, the prognosis for bioactive small molecules [3]. Lamb et al. findings showed
most DN patients is poor, especially for those in late stages thatgenomicsignaturesintheCMAPdatabasecanbeused
ofthedisease.Theidentificationofpotentialdrugstargeting to identify potential new therapeutics, and signatures are
themolecularpathogenesisofDNiscriticalforimprovingthe often conserved across diverse cell types [3]. Therefore, the
prognosisandsurvivalofpatientswithDN. CMAPdatabasecanbeusedwithwhole-genomeexpression
2 BioMedResearchInternational
profiles to identify potential drugs for DN in the glomeruli Table1:ThebaselineclinicalandpathologiccharacteristicsofDN
ofpatients.UsingtheCMAPdatabase,Zhongetal.predicted patients.
that the combination of an angiotensin-converting enzyme
Earlystage Latestage 𝑃
(ACE) inhibitor and a histone deacetylase inhibitor would
𝑛 6 12 —
maximallyreversethedisease-associatedexpressionofgenes
in a mouse model of HIV-associated nephropathy (Tg26 Age(years) 45.9±6.4 51.0±6.8 0.103
mice),andtherenoprotectiveeffectofthecombineduseof Sex(female,%) 3(50%) 3(25%) 0.344
theseinhibitorswasproveninTg26mice[4].Itisfeasibleto Ethnicity Han Han —
utilizegeneexpressionprofilesoftissuesundernormaland BMI(kg/m2) 25.3±1.5 24.9±1.3 0.892
physiopathological conditions to investigate potential ther- Serumcreatinine(mg/dL) 0.63±0.13 2.34±0.74 0.000
apeutic drugs based on bioinformatics methods. However, eGFR(mL/min) 112.8±8.1 32.9±13.3 0.000
this kind of therapeutic drug identification in DN research Proteinuria(g/24h) 0.82±0.47 5.18±2.01 0.000
is lacking. In our study, we utilized the gene expression
HbA1C(%) 8.4±1.6 7.2±1.9 0.146
profiles of microdissected glomeruli from DN patients and
BUN(mg/dL) 15.4±4.8 35.8±11.8 0.003
exploredpotentialtherapeuticdrugsusinginsilicoscreening
Glomerularlesions 0.000
approaches.
ClassI 3 0
2.MaterialsandMethods ClassIIa 3 0
ClassIIb 0 0
2.1.Patients. Theclinicalstudyusedacross-sectionaldesign. ClassIII 0 7
The control and DN kidney samples were obtained from ClassIV 0 5
leftoverportionsofdiagnostickidneybiopsies.Forthekidney IFTA 0.006
biopsies,informedconsentwasobtainedfromthedonorsand
0 2 0
patients. All of the participants provided written informed
1 4 2
consent. The institutional review board of Jinling Hospital
2 0 4
specificallyapprovedthisstudy[5].
3 0 6
Atotalof18DNpatientsdiagnosedbyrenalbiopsywere
Interstitialinflammation 0.004
enrolledinthestudy.Thebaselineclinicalcharacteristicsof
theDNpatientsarelistedinTable1. 0 2 0
Foreachbiopsyspecimen,lightmicroscopy,immunoflu- 1 3 1
orescence, and electron microscopy were routinely per- 2 1 11
formed. Sections for light microscopy were stained with Arteriolarhyalinosis 0.333
hematoxylineosin,periodicacid-Schiff,Masson’strichrome, 0 0 0
and periodic acid methenamine silver. All of the patients
1 1 0
werecategorizedbasedonthepathologicclassificationofthe
2 5 12
Renal Pathology Society [5]. The glomerular classifications
Arteriosclerosis 0.504
were as follows: class I, glomerular basement membrane
NA 0 0
thickening; class IIa, mild mesangial expansion; class IIb,
0 2 1
severemesangialexpansion;classIII,nodularsclerosis;and
class IV, global glomerulosclerosis in >50% of glomeruli. 1 1 2
Interstitial fibrosis and tubular atrophy (IFTA) were scored 2 3 9
as follows: 0, absent; 1, <25%; 2, 25–50%; and 3, >50% of BMI:bodymassindex;eGFR:estimatedglomerularfiltrationrate,calculated
thetotalarea.Interstitialinflammationwasscoredasfollows: usingtheEPI-CKDformula;HbA1C:glycatedhemoglobin;BUN:bloodurea
nitrogen.Valuesarepresentedas𝑛ormeans±SD.𝑃valueswereobtained
0, absent; 1, inflammation only in relation to IFTA; and 2,
usingtheWilcoxonranksumtestforcontinuousvariablesandFisher’sexact
inflammation in areas without IFTA. Arteriolar hyalinosis
testforcategoricalvariables.
wasscoredasfollows:0,absent;1,atleastoneareaofarteriolar
hyalinosis;and2,morethanoneareaofarteriolarhyalinosis.
Arteriosclerosiswasscoredasfollows:NA,absenceoflarge
glomeruli isolated from the renal tissue of early stage DN
vessels; 0, no intimal thickening; 1, intimal thickening less
patients who were identified with eGFR > 90mL/min; late
than thickness of media; and 2, intimal thickening greater
stage DN group (𝑁 = 12), glomeruli isolated from the
than thickness of media. All of the specimens were scored
renal tissue of late stage DN patients with eGFR between
by the same pathologist (Dr. Feng Xu) who was blinded to
15mL/min and 60mL/min. Control samples (𝑁 = 6) were
the clinical findings. In order to assess the reliability and
obtainedfromlivingdonorkidneybiopsies.Controlsubjects
reproducibilityoftheclassification,biopsyslideswerescored
weredefinedashavinganeGFRofmorethan90mL/min,the
independently by another pathologist (Dr. Dandan Liang).
absenceofproteinuria,normalserumcreatinine,andBUN.
The pathologic characteristics of the DN patients are listed
inTable1.
The DN patients were divided into 2 groups according 2.2. Tissue Handling and Microdissection. Tissues placed in
to the following criteria: early stage DN group (𝑁 = 6), RNALater (SIGMA, St. Louis, MO, USA) were manually
BioMedResearchInternational 3
∘
microdissectedat4 Cforglomeruli.Ingeneral,10glomeruli Table2:ThebaselineclinicalandpathologiccharacteristicsofDN
werecollectedfromeachbiopsytissueandwereplacedinto patientsforvalidation.
cold RNeasy lysis buffer solution (Qiagen, Valencia, CA,
Earlystage Latestage 𝑃
USA).
𝑛 5 4 —
Age(years) 43.1±11.8 52.1±7.3 0.226
2.3.RNAExtractionandAmplification. Dissectedglomeruli
Sex(female,%) 3(60%) 2(50%) 1
were homogenized, and RNA was prepared using RNeasy
mini columns (Qiagen, Valencia, CA, USA), according to Ethnicity Han Han —
the manufacturer’s instructions. RNA quality and quantity BMI(kg/m2) 25.5±4.7 22.9±2.3 0.342
were determined using the Laboratory-on-Chip Total RNA Serumcreatinine(mg/dL) 0.65±0.15 1.76±0.54 0.003
Pico Kit Agilent Bioanalyzer. Samples without evidence of eGFR(mL/min) 112.3±4.3 42.1±17.4 0.000
degradationwerefurtheramplifiedusingtheOvationRNA Proteinuria(g/24h) 0.50±0.23 4.43±0.41 0.000
amplificationsystemkit(NuGEN,SanCarlos,CA,USA). HbA1C(%) 6.5±0.9 7.3±1.0 0.285
BUN(mg/dL) 14.2±2.7 30.0±6.8 0.002
2.4. Affymetrix Microarray Data and Preprocessing. The
Glomerularlesions 0.016
Affymetrixmicroarrayplatform(HumanU133Plus2.0)was
ClassI 1 0
used to produce the whole-genome gene expression profile
ClassIIa 4 0
data. Quality control and data processing were performed
ClassIIb 0 0
using R [6] and Bioconductor [7]. The CEL source files
ClassIII 0 2
were processed into expression estimates, and background
correction and quartile data normalizationwere performed ClassIV 0 2
usingtheRMA(robustmultiarrayaverage)algorithm[8]. IFTA 0.087
0 1 0
2.5.ScreeningofDifferentiallyExpressedGenes(DEGs). The 1 4 1
limmapackage[9]inRlanguagewasusedtoscreenDEGsby 2 0 3
pairwisecomparisonbetweengroups.Thestatisticalmethod 3 0 0
implementedinthelimmapackageisbasedonanapproach Interstitialinflammation 0.286
called linear models. We used the method proposed by
0 1 0
Benjamini-Hochberg (BH) for multiple testing correction.
1 4 2
The adjusted 𝑃 values were the false discovery rates (FDR).
2 0 2
ThethresholdcriterionisacombinationofFDR<0.05and
foldchange>1.5.TheDEGsbetweenlatestageandearlystage Arteriolarhyalinosis 1
0 0 0
DNsampleswerechosenforDNdrugidentification.
1 0 0
2 5 4
2.6. Validation of Microarray Expression Data. The relative
mRNA levels of 10 genes were validated in new selected Arteriosclerosis 1
glomerularsamples.Theclinicalandpathologiccharacteris- NA 0 0
ticsoftheseDNpatientsarelistedinTable2.Theprocesses 0 1 0
usedforpatientscreening,tissuehandling,microdissection, 1 2 1
and total RNA were performed as previously described. 2 2 3
The mRNA levels of the target genes were analyzed by
quantitativereal-timeRT-PCR(qRT-PCR)usingtheApplied
Biosystems(cid:2)7900HTFastReal-TimePCRSystem(Thermo between the late and early stage DN samples, and the
FisherScientific,Waltham,MA,USA).TheqRT-PCRresults upregulated probes and the downregulated probes were
were normalized to 18S ribosomal RNA using the 2−ΔΔCT savedinGRPfiles.TheCMAPwebsiteprovidesaKSstatistic
method[10],andsignificancewassetto𝑃<0.05. algorithm; we uploaded the GRP files as the query gene
signature, which was then compared to each rank-ordered
2.7. Drug Identification Using a Kolmogorov-Smirnov (KS) listtodeterminewhetherupregulatedquerygenesappeared
Statistic Algorithm. From the CMAP database (http:// nearthetopofthelistanddownregulatedquerygenesnear
www.broadinstitute.org/cmap/),wedownloadedtheranked the bottom (positive connectivity) or vice versa (negative
listsofprobetables.Thistableisfreelyavailablefordownload connectivity), yielding a connectivity score ranging from 1
and provides the ranks of all genes in the form of probes to −1. A high negative connectivity score indicated that the
basedonthechangeofgeneexpressionafterapproximately corresponding perturbagen reversed the expression of the
6000 drug perturbation experiments. The table contains querysignatureandmighthavethepotentialtotreatDN.
22283 probes, which have 22277 common probes with
HumanU133Plus2.0.Finally,weobtainedatablecontaining 2.8.DrugIdentificationUsingaMatchingAlgorithm[4]. The
24 samples and 22277 probes. With the limma package in IDsoftheprobetabledownloadedfromtheCMAPdatabase
R language, we obtained the 1000 largest changing probes wereconvertedtoEntrezgenesymbolsusingtheAffymetrix
4 BioMedResearchInternational
lookuptableassociatedwiththeplatform.Ifmorethanone AsshowninFigure3,qRT-PCRanalysiswasperformed
probe ID corresponded to the same gene, the gene rank toconfirmthedegreeanddirectionoftheexpressionchanges
was condensed to the median of the probe ranks. Then, in 10 genes. All 10 genes assayed by qRT-PCR were found
we extracted the 500 top and bottom DEGs. Potential DN tobesignificantlydifferentiallyexpressedinthemicroarray
drugsshouldreversetheDEGsofDN;genesupregulatedin analysis between the late and early stage DN samples. As
DN should be downregulated by a potential DN drug and determined by qRT-PCR analysis, 10 out of the 10 selected
vice versa. The DEGs (FDR < 0.05 and fold change > 1.5) genes demonstrated a change in expression in the same
betweenthelateandearlygroupswereusedtocalculatethe direction(i.e.,up-ordownregulated)(Figure3(a)).Similarly,
reversingscoresusingformula(1).Theperturbagenwiththe the direction of the change in gene expression determined
highestreversingscoresmighthavethepotentialtotreatDN. byqRT-PCRanalysisagreedwiththedirectionsobtainedfor
Consider the genes that were found to be significantly differentially
Score𝑑𝑖 =[(up∩down𝑑𝑖)+(down∩up𝑑𝑖)] expressedbymicroarrayanalysisbetweentheearlystageDN
samplesandthecontrols(5outof5genes,Figure3(b))and
(1)
−[(up∩up𝑑𝑖)+(down∩down𝑑𝑖)], betweenthelatestageDNsamplesandthecontrols(10outof
10genes,Figure3(c)).
whereScore𝑑𝑖 indicatesthereversingscoreforadrugin𝑖th
experiment in the CMAP database; ∩ indicates the inter-
3.2. Potential DN Drugs Predicted by the KS Statistic Algo-
section between two sets; up indicates a list of upregulated
rithm. Toexplorethepotentialdrugstargetingthemolecular
genesduringdisease;downindicatesalistofdownregulated
mechanisms of DN, we used the DEGs between the late
genes during disease; up indicates genes upregulated by
𝑑𝑖
a drug in the 𝑖th experiment in the CMAP database; and andearlystagesofDN.Thesegeneswereenrichedfortheir
down𝑑𝑖 indicates genes downregulated by a drug in the 𝑖th specificcontributiontonephropathybecausegenesthatare
differentially regulated in human diabetes per se, in the
experimentintheCMAPdatabase.
absenceofnephropathy,wereexcludedbythisstrategy.After
uploading 812 upregulated and 188 downregulated probe
2.9.FunctionalEnrichmentAnalysis. Thefunctionalenrich-
IDs to the CMAP database, the top 20 drug perturbations
mentanalysisofthescreenedDEGsandthegenesreversedby
that most strongly reversed the DRGs are listed in Table3.
potentialdrugswasperformedviatheGeneAnswerspackage
Amongthesedrugs,MG-132[14]andMG-262[15]areprotea-
in R language [11]. The GeneAnswers package functionally
some inhibitors, piperlongumine inhibits PI3K/Akt/mTOR
categorizesgenesbasedonFisher’sexacttestwithannotation signaling [16] and NF-𝜅B activity [17], 15d-PGJ2 (15-delta
librariesofthegeneontology(GO)andtheKyotoEncyclo- prostaglandin J2) activates PPAR𝛾 [18], and vorinostat and
pediaofGenesandGenomes(KEGG).
trichostatinAarehistonedeacetylaseinhibitors(HDACIs).
3.Results
3.3.PotentialDNDrugsPredictedbytheMatchingAlgorithm.
We further utilized a matching algorithm and the DEGs
3.1. DEGs in the Glomeruli of DN Patients. We separately
between early and late stage DN samples to calculate the
comparedthesamplesinthe2stagesofDNwiththecontrols.
reversing score for each drug in the CMAP database. The
Atotalof105DEGswereidentifiedbetweentheearlystage
top 20 drug perturbations that had the highest scores are
DNsamplesandthecontrols,including54upregulatedgenes
listed in Table4. We clustered the drugs by the similarity
and 51 downregulated genes; 2572 DEGs were identified
of reversed DEGs, and the drugs with similar pharmaco-
betweenthelatestageDNsamplesandthecontrols,including
logicalcharacteristicswereclusteredtogether(Figure4).For
1626upregulatedgenesand946downregulatedgenes.
example,HDACIs,includingvorinostat,trichostatinA,and
Only 105 DEGs were identified between the early stage
valproic acid, were clustered together. Among these drugs,
DNsamplesandthecontrols,andtheenrichedGOcategories
piperlongumine, 15d-PGJ2, vorinostat, and trichostatin A
were primarily involved in “response to stimulus.” These
were also identified by the KS statistic algorithm. Valproic
resultswereinaccordancewiththemildpathologicalchanges
acidandparthenolidearealsohistonedeacetylaseinhibitors
intheglomerulifromearlystageDNpatients.
[19],resveratrolinhibitscAMPphosphodiesterase[20],and
In contrast,1065 DEGs were identified between the late
LY-294002inhibitsPI3K[21].
and early stages of DN samples, including 815 upregulated
genesand250downregulatedgenes.TheheatmapinFigure1
shows the expression levels of the top 100 regulated genes 3.4. Functional Enrichment Analysis of the Drug-Reversed
across the 24 samples. As shown in Figure2, the most Genes. Piperlongumine, 15d-PGJ2, vorinostat, and tricho-
enriched GO categories of the DEGs were “extracellular statin A were identified by both algorithms. The genes that
matrix,” “protein binding,” “cell adhesion,” and “immune could be reversed by these drugs are shown in Table5. To
system process.” The most enriched KEGG pathways were indicatethetargetmolecularmechanismsofthesedrugs,we
“ECM-receptor interaction,” “complement and coagulation conductedafunctionalanalysisofthesepotentiallyreversed
cascades,” “focal adhesion,” “cytokine-cytokine receptor genesintheglomeruliofDNpatients(Figure5).Thegenes
interaction,” and “PI3K-Akt signaling pathway.” These GO reversed by piperlongumine are mostly involved in the
categories and KEGG pathways are closely related to DN “immune response,” “response to stimulus,” “complement
progression[12,13]. andcoagulationcascades,”“NF-𝜅Bsignalingpathway,”and
BioMedResearchInternational 5
Groups Groups
12
ALB Ctrl
CYP26B1
SLC23A3 10 Early
AFM Late
APOH
C3
NNMT 8
VCAN
THBS2
CCL21 6
LPAR1
CCDC3
LTF 4
RGS4
COL8A1
APOC1
CD163 2
AGR2
VSIG4
LY86
CTHRC1
NTM
NEFL
LYPD1
GPR39
ADAMTS3
CXCL6
EPHB2
SFRP2
REG1A
CCL19
CCL18
TPSB2
TPSAB1
ISLR
FNDC1
FCGR1B
CCL13
SLC1A3
MS4A4A
CD84
ADAMTS2
KCNMA1
ALDH1A3
RNASE2
TOP2A
DIO2
PLA2G7
FAM132B
ACKR1
TNFAIP6
AHNAK2
GPR34
LCN2
SERPINA3
PRRX1
RARRES1
CPA3
CCL11
FAP
SFRP4
CLEC5A
TMEM119
COMP
NMU
LINC00973
LINC01094
NEIL3
ASTN1
ABCB5
TMEM255A
HMGA2
STMN2
CTSG
GABRP
AGR3
DLGAP5
MND1
MLPH
GREM1
HS3ST2
CNN1
C3orf80
FCGR1A
MSR1
CD28
TREM2
FPR3
REG3G
HMCN1
NLGN4X
GRIN2A
NXPH2
WDR49
LINC00948
DNMT3L
SOX2−OT
MGC40069
SERPINI2
DLGAP2
C01 C02 C03 C04 C05 C06 E01 E02 E03 E04 E05 E06 L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12
Figure1:Heatmapofthetop100DEGsbetweenglomeruliinthelateandearlystagesofDN.
6 BioMedResearchInternational
Enrichment lg(FDR)
Enrichment lg(FDR)
0 −10 −20 −30 −40
0 −5 −10 −15 −20
Extracellular matrix 94
Protein binding 582
Proteinaceous extracellular matrix 76 Protein complex binding 97
Extracellular space 154 Glycosaminoglycan binding 38
Extracellular region 362 Receptor binding 130
Cell periphery 381 Extracellular matrix structural constituent 21
Extracellular region part 310 Heparin binding 28
Plasma membrane 368 Sulfur compound binding 34
Extracellular matrix part 36 Collagen binding 17
Intrinsic component of plasma membrane 141 Cell adhesion molecule binding 28
Plasma membrane part 201 Receptor activity 130
(a) Cellularcomponentontology (b) Molecularfunctionontology
Enrichment lg(FDR)
Enrichment lg(FDR)
0 −5 −10 −15 −20 −25 −30
0 −5 −10 −15
Biological adhesion 157
Amoebiasis 31
Cell adhesion 156
Staphylococcus aureus infection 22
Extracellular matrix organization 85 ECM-receptor interaction 27
Extracellular structure organization 85 Osteoclast differentiation 31
Immune system process 247 Complement and coagulation cascades 20
Single-organism process 829 Focal adhesion 37
Response to external stimulus 219 Pertussis 19
Cellular component movement 191 Cytokine-cytokine receptor interaction 40
Single-multicellular organism process 495 PI3K-Akt signaling pathway 47
Locomotion 175 Malaria 14
(c) Biologicalprocessontology (d) KEGGpathwayenrichment
Figure2:GOcategory(cellularcomponent,molecularfunction,andbiologicalprocess)andKEGGpathwaysenrichedintheDEGsbetween
glomeruliinthelateandearlystagesofDN.
Table3:Top20drugperturbationswithhighnegativeconnectivityscores.
Rank InstanceID CMAPname Dose Cell Score Up Down
1 1274 Bepridil 10𝜇M HL60 −1 −0.077 0.223
2 7063 MG-262 100nM MCF7 −0.96 −0.163 0.125
3 7345 Alcuroniumchloride 5𝜇M MCF7 −0.953 −0.108 0.178
4 942 Prazosin 10𝜇M MCF7 −0.953 −0.111 0.175
5 1764 Piperlongumine 13𝜇M HL60 −0.94 −0.071 0.212
6 7022 Dyclonine 12𝜇M MCF7 −0.931 −0.104 0.175
7 3351 (±)-Catechin 14𝜇M MCF7 −0.923 −0.126 0.151
8 7017 Mesoridazine 7𝜇M MCF7 −0.922 −0.101 0.176
9 909 HCtoxin 100nM MCF7 −0.921 −0.107 0.17
10 1694 Metformin 24𝜇M MCF7 −0.909 −0.102 0.17
11 1656 15d-PGJ2 10𝜇M MCF7 −0.901 −0.112 0.158
12 7020 Xylometazoline 14𝜇M MCF7 −0.898 −0.1 0.17
13 1058 Vorinostat 10𝜇M MCF7 −0.898 −0.12 0.15
14 7178 Tetrandrine 6𝜇M MCF7 −0.897 −0.151 0.118
15 1069 15d-PGJ2 10𝜇M MCF7 −0.895 −0.12 0.149
16 1140 MG-132 21𝜇M MCF7 −0.884 −0.122 0.143
17 1112 TrichostatinA 100nM MCF7 −0.882 −0.104 0.161
18 6936 Chlorpromazine 1𝜇M MCF7 −0.88 −0.115 0.149
19 5310 Puromycin 7𝜇M MCF7 −0.874 −0.111 0.151
20 5304 Moroxydine 19𝜇M MCF7 −0.874 −0.125 0.137
Instance:atreatmentandcontrolpairandthelistofprobesetsorderedbytheirextentofdifferentialexpressionbetweenthistreatmentandcontrolpair;instance
ID:theIDuniquelyidentifyingeachinstance;CMAPname:thenamegiventoaperturbagen;dose:perturbagendose;cell:cellline;up:theupscore,avalue
between+1and−1representingtheabsoluteenrichmentofanuptaglistinagiveninstance;down:thedownscore,avaluebetween+1and−1representing
theabsoluteenrichmentofadowntaglistinagiveninstance;score:theconnectivityscore,acombinationoftheupscoreandthedownscore.Ahighnegative
connectivityscoreindicatesthatthecorrespondingperturbagenreversedtheexpressionofthequerysignature.
BioMedResearchInternational 7
6 6
5 5
fold changelog2 −012341 ∗ ∗ ∗ ∗ ∗ ∗ ∗∗ ∗∗ ∗ ∗∗ fold changelog2 −012341 ∗ ∗ ∗ ∗∗ ∗
−2 2SFRP 1MOXD 3C VCAN 1FCERA 5MPP NFASC 6ZDHHC 6CERS 2SRGAP −2 2SFRP 1MOXD 3C VCAN 1FCERA 5MPP NFASC 6ZDHHC 6CERS 2SRGAP
qRT‐PCR qRT‐PCR
Microarray Microarray
(a) (b)
6 ∗
5
∗
ge 4 ∗∗
an 3 ∗ ∗
d ch 2 ∗
fol 1
g2 0
o
l −1 ∗∗ ∗ ∗∗ ∗ ∗
−2
2 1 3 N A 5 C 6 6 2
SFRP MOXD C VCA 1CER MPP NFAS DHHC CERS RGAP
F Z S
qRT‐PCR
Microarray
(c)
Figure3:qRT-PCRconfirmation.qRT-PCRwasperformedtoconfirmthedirectionofthefoldchangeinexpression(shownaslog2 fold
change).ThegeneexpressionchangesdeterminedbyqRT-PCRwerecomparedwiththoseobtainedfromthemicroarrayanalysisoflate
versusearlystageDN(a),earlystageDNversusCtrl(b),andlatestageDNversusCtrl(c).∗𝑃<0.05.
“Toll-like receptor signaling pathway.” The genes reversed Themostcommonkidneylesionsinpeoplewithdiabetes
by15d-PGJ2areprimarilyinvolvedin“celldivision,”“FoxO arethosethataffecttheglomeruli,andDNischaracterized
signaling pathway,” and “cytokine-cytokine receptor inter- bymorphologicalandultrastructuralchangesinthekidney,
action,” and the genes reversed by vorinostat are mostly includingexpansionofthemolecularmatrixandlossofthe
involved in the “immune response,” “response to stimu- chargebarrierontheglomerularbasementmembrane[25].
lus,” “signal transduction,” and “osteoclast differentiation.” The gene expression profiles of glomeruli microdissected
The genes reversed by trichostatin A are mostly involved fromDNbiopsysampleswillprovideanexcellentopportu-
in the “immune response,” “response to stress,” “signal nity to explore the molecular mechanisms of this complex
transduction,” “cell migration,” and “osteoclast differentia- diseaseandtoidentifypotentialdrugs.
tion.” In this study, we obtained the whole-genome transcrip-
tome profiles of glomeruli from DN patients and normal
4.Discussion controls.Theglomerulicontainpodocytes,endothelialcells,
and mesangial cells, whereas the CMAP database was built
Current clinical strategies to treat DN focus on the inten- upon four types of cultured human cell lines [3]. Because
sification of glycemic control and the control of blood the signatures of drugs are often conserved across diverse
pressure and blood fat. Renoprotective drugs based on the celltypes[3],wecanutilizetheCMAPdatabasetoidentify
molecular pathogenesis of DN are unavailable because the potentialdrugsforDN.
molecular pathogenesis of DN is complicated. Recently, There were only 105 DEGs between early stage DN
high-throughput transcriptome technology has been used samples and controls, mainly involving “response to stim-
to explore the molecular pathogenesis of complex diseases ulus.” These results indicated that there was minimal gene
[22,23],anddrugscreeningmethodsbasedontranscriptome expression change involving molecular pathogenesis in the
datahaveattractedincreasingattention[4,24]. earlystageofDN.TheDEGsbetweenthelateandearlystages
8 BioMedResearchInternational
LY-294002-6186
Valproic acid-410
Vorinostat-6179
Trichostatin A-1175
Vorinostat-1161
Ellipticine-1765
Piperlongumine-1764
Parthenolide-1736
Ciclopirox-2456
Naproxen-2533
Etoposide-3241
Monobenzone-3391
0173570-0000-7389
Resveratrol-1715
15-delta prostaglandin J2-1231
Resveratrol-662
Withaferin A-3819
Rosiglitazone-2693
Ethoxyquin-3764
Alclometasone-5752
Figure4:Theheatmapandhierarchicalclusterofeffectsofthetop20drugperturbations.Eachrowindicatesadrugperturbation,and
eachcolumnrepresentsagene.Theblueverticalbarsintheheatmapindicatethatthegenewasreversedbythecorrespondingperturbagen,
whereastheredbarsindicatethatthegenewasaggravatedbythecorrespondingperturbagen.Drugperturbationshavingsimilareffectswere
clusteredtogether.
of DN samples were related to “extracellular matrix,” “cell therapeuticdrugs,whichprovidedagoodfoundationforthe
adhesion,”“immunesystemprocess,”“ECM-receptorinter- invitroandinvivostudies.
action,”“complementandcoagulationcascades,”“cytokine- Piperlongumine, 15d-PGJ2, vorinostat, and trichostatin
cytokine receptor interaction,” and “PI3K-Akt signaling Awereidentifiedbybothalgorithms.Themolecularmech-
pathway” (Figure2). These functional categories and path- anisms of the 4 drugs include inhibition of NF-𝜅B activity,
ways have been widely related to the pathogenesis of DN histone deacetylase (HDAC) activity, PI3K-Akt signaling
[13].Coexpressionnetworkanalysisandassociationanalysis pathway,andtheactivationofPPAR𝛾.Thetranscriptionfac-
indicatedthatsomeDEGsbetweenDNsamplesandcontrols torNF-𝜅Bisinducedbyvariouscellstress-associatedstimuli,
exhibit no correlation with the progression or prognosis of including growth factors, vasoactive agents, cytokines, and
DN (data not shown). These genes, which may be differ- oxidative stress. NF-𝜅B in turn controls the regulation of
entially regulated in human diabetes per se, were excluded genesencodingproteinsinvolvedinimmuneandinflamma-
fromthecomparisonofthelateandearlystageDNsamples. tory responses. The activation and nuclear translocation of
Therefore,itismoresuitabletousetheDEGsbetweenthelate NF-𝜅BinhumanDNhavebeendemonstratedintheintrinsic
andearlystagestoidentifypotentialtherapeuticdrugsforDN cells of the kidney [26]. Upregulation of HDACs has been
treatment. reported in the kidneys of patients with DN as well as in
AKSstatisticalgorithmandamatchingalgorithmwere type 1 and type 2 in vivo animal models of diabetes [27].
appliedinthisstudy.However,usingeitheralgorithm,some HDACIs have anti-inflammatory and antifibrotic effects in
drugsamongthetop20areunlikelytohaverenalprotection the kidney and may prove to be a novel class of agents in
onthebasisofpriorclinicalandpharmacologicalknowledge. the treatment of diabetic nephropathy [27]. The PI3K-Akt
Therefore,theresultsbasedonthetwodifferentalgorithms signalingpathwayisdirectlyrelatedtocellularproliferation,
were combined to enhance the reliability of the potential migration, differentiation, and survival. There are many
BioMedResearchInternational 9
Enrichment lg(FDR) Enrichment lg(FDR)
0 −2 −4 −6 −8 −10 0 −1 −2 −3 −4
Immune system process 28 Tuberculosis 7
Immune response 22 Leishmaniasis 5
Regulation of immune system process 20 Phagosome 6
Defense response 22 Legionellosis 4
Regulation of immune response 15 Complement and coagulation cascades 4
Regulation of response to stimulus 28 Pertussis 4
Response to stress 29 NF-𝜅B signaling pathway 4
Positive regulation of immune system process 14 Toll-like receptor signaling pathway 4
Positive regulation of immune response 12 Staphylococcus aureus infection 3
Activation of immune response 11 Osteoclast differentiation 4
(a) GOenrichment (b) KEGGpathwayenrichment
Enrichment lg(FDR)
Enrichment lg(FDR)
0 −5 −10 −15 0 −1 −2 −3 −4
Mitotic nuclear division 19 Cell cycle 6
Cell division 24 Progesterone-mediated oocyte maturation 4
Nuclear division 21 Oocyte meiosis 4
Organelle fission 21 FoxO signaling pathway 4
Chromosome segregation 14 p53 signaling pathway 3
Spindle 15 Cytokine-cytokine receptor interaction 5
Mitotic cell cycle 23 Transcriptional misregulation in cancer 4
Microtubule cytoskeleton 23 MicroRNAs in cancer 5
Mitotic cell cycle process 21 Gap junction 3
Cell cycle process 24 Rheumatoid arthritis 3
(c) GOenrichment (d) KEGGpathwayenrichment
Enrichment lg(FDR)
0 −5 −10 −15 −20
Immune system process 42
Regulation of immune system process 27 Enrichment lg(FDR)
Immune response 28 0 −0.5 −1 −1.5 −2 −2.5 −3
Myeloid leukocyte mediated immunity 9 Osteoclast differentiation 7
Positive regulation of immune system process 20 Staphylococcus aureus infection 4
Myeloid leukocyte activation 11
Tuberculosis 6
Cell periphery 46
Defense response 26 Transcriptional misregulation in cancer 6
Leukocyte activation 18 Proteoglycans in cancer 6
Myeloid cell activation involved in immune response 8 Phagosome 5
(e) GOenrichment (f) KEGGpathwayenrichment
Enrichment lg(FDR)
0 −2 −4 −6 −8 −10 −12
Immune system process 31
Immune response 20
Response to stress 30
Defense response 20
Cell activation 15
Regulation of immune system process 17
Granulocyte migration 6 Enrichment lg(FDR)
Signal transduction 35 0 −0.5 −1 −1.5
Leukocyte migration 9
Cell migration 15 Osteoclast differentiation 5
(g) GOenrichment (h) KEGGpathwayenrichment
Figure5:GOcategoriesandKEGGpathwaysenrichedintheDEGsthatwerereversedbythedrugperturbation.(a),(b)GOcategoriesand
KEGGpathwaysenrichedintheDEGsthatwerereversedbypiperlongumine;(c),(d)GOcategoriesandKEGGpathwaysenrichedinthe
DEGsthatwerereversedby15d-PGJ2;(e),(f)GOcategoriesandKEGGpathwaysenrichedintheDEGsthatwerereversedbyvorinostat;
(g),(h)GOcategoriesandKEGGpathwaysenrichedintheDEGsthatwerereversedbytrichostatinA.
10 BioMedResearchInternational
Table4:Top20drugperturbationswithhighreversingscores. blood glucose levels and protect liver and kidney function
[30].
Perturbagenname-instanceID Score Reversed Aggravated 15d-PGJ2 is the endogenous ligand of PPAR𝛾 and can
regulate metabolism of adipose tissue and restrain insulin
Vorinostat-6179 46 75 29 resistance[18].Inratmesangialcells,15d-PGJ2significantly
decreased alpha-smooth muscle actin (𝛼-SMA) expression,
15d-PGJ2-1231 43 65 22
a marker of mesangial cell dedifferentiation. In mouse
Piperlongumine-1764 36 55 19 mesangial cells, 15d-PGJ2 repressed TGF-𝛽1-mediated 𝛼-
SMA, fibronectin, and plasminogen activator inhibitor-1
Resveratrol-662 34 54 20
expression; induced HGF expression; and attenuated Smad
Ciclopirox-2456 33 67 34 nucleartranslocationinresponsetoTGF-𝛽1stimulation[31].
In ratrenalinterstitialfibroblasts, 15d-PGJ2 inhibitedTGF-
Resveratrol-1715 32 59 27 𝛽1-induced renal fibroblast activation, CTGF expression,
LY-294002-6186 32 63 31 and ECM synthesis through abrogating the TGF-𝛽1/Smad
signalingpathway[32].
Valproicacid-410 31 53 22
VorinostatandtrichostatinAbelongtothesamegroup
TrichostatinA-1175 31 59 28 ofHDACIsbasedontheirchemicalstructures(hydroxamic
acid).TheyarebroadinhibitorsofHDACactivityandinhibit
Ethoxyquin-3764 31 57 26 classIandclassIIenzymes[33].VorinostatisFDAapproved
foruseagainstrefractorycutaneousTcelllymphoma[34,35].
0173570-0000-7389 31 68 37
HDACIs have beneficial effects in diabetic nephropathy. In
Parthenolide-1736 30 47 17 culturedproximaltubulecells,vorinostattreatmentreduced
EGFRproteinandmRNAandattenuatedcellularprolifera-
Ellipticine-1765 30 46 16
tion[36].Dailytreatmentofdiabeticratswithvorinostatfor
Etoposide-3241 30 73 43 4 weeks blunted renal growth and glomerular hypertrophy
[36]. In STZ-induced diabetic mice, long-term administra-
WithaferinA-3819 30 68 38
tionofvorinostatdecreasedalbuminuria,mesangialcollagen
Vorinostat-1161 29 57 28 IV deposition, and oxidative-nitrosative stress through an
eNOS-dependentmechanism[37].InSTZ-induceddiabetic
Naproxen-2533 29 63 34
rats, trichostatin A prevented extracellular matrix accumu-
lation and epithelial-to-mesenchymal transition in diabetic
Rosiglitazone-2693 29 55 26
kidney[38].
Monobenzone-3391 29 74 45 Inadditiontothesefourdrugs,wehaveidentifiedpoten-
tial therapeutic drugs to treat DN. Proteasome inhibitors,
Alclometasone-5752 29 63 34
includingMG-132andMG-262,haveanti-inflammatoryand
Perturbagenname:thenamegiventoaperturbagen;instanceID:theID
antifibrotic effects [15]. MG132 alleviates kidney damage
uniquelyidentifyingeachinstance;score:thereversingscorecalculatedusing
formula(1);reversed:numberofgenesinthequerysignature,theexpression by inhibiting Smad7 ubiquitin degradation, SnoN degrada-
ofthesegeneswasreversedbythecorrespondingperturbagen;aggravated: tion, and TGF-𝛽 activation in STZ-induced DN rats [39,
numberofgenesinthequerysignature,theexpressionofthesegeneswas 40]. Valproic acid, another histone deacetylase inhibitor,
aggravatedbythecorrespondingperturbagen. has beneficial effects on proteinuria, glomerulosclerosis,
and renal inflammation in Adriamycin-induced nephro-
pathic mice [41]. LY-294002, a PI3K inhibitor, prevented
the quantitative and distributional changes of CD2AP
knownfactorsthatenhancethePI3K-Aktpathway,including induced by high-glucose and advanced glycosylation end
insulin, EGF, and IGF-1. In rat mesangial cells and db/db products in mouse podocytes [42]. In db/db mice, LY-
mice, high-glucose decreased the expression of MNSOD 294002 decreased the levels of phosphorylated Akt and
via the PI3K-Akt signaling pathway and further aggravated phosphorylated FoxO3a, increased the level of MnSOD
oxidativestress[28].ThenuclearreceptorPPAR𝛾islocated expression, and further decreased oxidative stress [28].
inallthreetypesofglomerularcells,withprominentexpres- Resveratrol,aphosphodiesteraseinhibitor,improveddiabetic
sion in podocytes. PPAR𝛾 agonists, which have emerged nephropathy in several animal models of types 1 and 2
as promising candidates for treating DN, are effective in diabetesthroughitsantioxidativeeffectsresultingfromdirect
delayingandevenpreventingdiseaseprogression. radical scavenging or modulation of antioxidant enzymes
Piperlongumine can inhibit both NF-𝜅B activation and [43].
thePI3K-Aktsignalingpathway[16,17]andincreasemRNA In summary, this study utilized the gene expression
levelsofPPAR𝛾2[29].Piperlongumineisamaincomponent profilesofglomerulifromDNpatientstoidentifypotential
of the root of Piper longum, a plant used by some Indian drugsforDNusingtheCMAPdatabaseandbioinformatics
tribes to treat diabetes, digestive disorders, and obesity methods. Four drugs were identified by two different algo-
[30]. In streptozotocin- (STZ-) induced diabetic rats, Piper rithms,andtherapeuticpotentialwasshowntobepromising
longumrootaqueousextractcansignificantlydecreasefasting byliteratureanalysis.Ourstudyprovidesusefulinformation