gene expression profiling reveals renin mrna ... · gene expression profiling reveals renin mrna...

Gene Expression Profiling Reveals Renin mRNAOverexpression in Human Hypertensive Kidneys and a Role

for MicroRNAsFrancine Z. Marques, Anna E. Campain, Maciej Tomaszewski, Ewa Zukowska-Szczechowska,

Yee Hwa J. Yang, Fadi J. Charchar, Brian J. Morris

Abstract—The kidney has long been invoked in the etiology of essential hypertension. This could involve alterations inexpression of specific genes and microRNAs (miRNAs). The aim of the present study was to identify, at thetranscriptome-wide level, mRNAs and miRNAs that were differentially expressed between kidneys of 15 untreatedhypertensive and 7 normotensive white male subjects of white European ancestry. By microarray technology we found14 genes and 11 miRNAs that were differentially expressed in the medulla. We then selected and confirmed by real-timequantitative PCR expression differences for NR4A1, NR4A2, NR4A3, PER1, and SIK1 mRNAs and for the miRNAshsa-miR-638 and hsa-let-7c. Luciferase reporter gene experiments in human kidney (HEK293) cells confirmed thepredicted binding of hsa-let-7c to the 3� untranslated region of NR4A2 mRNA. In the renal cortex we found differentialexpression of 46 genes and 13 miRNAs. We then confirmed expression differences for AIFM1, AMBP, APOE, CD36,EFNB1, NDUFAF1, PRDX5, REN, RENBP, SLC13A1, STX4, and TNNT2 mRNAs and for miRNAs hsa-miR-21,hsa-miR-126, hsa-miR-181a, hsa-miR-196a, hsa-miR-451, hsa-miR-638, and hsa-miR-663. Functional experiments inHEK293 cells demonstrated that hsa-miR-663 can bind to the REN and APOE 3� untranslated regions and can regulateREN and APOE mRNA levels, whereas hsa-miR-181a regulated REN and AIFM1 mRNA. Our data demonstrated forthe first time that miRNAs can regulate renin expression. The observed downregulation of 2 miRNAs in hypertensioncould explain the elevation in intrarenal renin mRNA. Renin, CD36, and other mRNAs, as well as miRNAs andassociated pathways identified in the present study, provide novel insights into hypertension etiology. (Hypertension.2011;58:1093-1098.) ● Online Data Supplement

Key Words: microarrays � microRNAs � renin angiotensin system � kidney � hypertension

Solving the molecular etiology of essential hypertensionhas been challenging.1 Studies of selected blood pressure

(BP) genes and large genome-wide association studies haveidentified only a handful of gene variants of small effectsizes. The interplay between genetic and environmentalfactors that contributes to the heterogeneity of hypertensionhas made the identification of causative alleles, genes, andtranscripts difficult.1 A further impediment has been thedifficulty in obtaining suitable human tissue samples otherthan blood.

Guyton and colleagues2,3 theorized that hypertension iscaused by a primary defect in the kidney. This idea has gainedsupport from rare monogenic forms of hypertension, in whichsingle mutations in genes responsible for renal sodiumreabsorption4 result in BP elevation.3

MicroRNAs (miRNAs) are important for physiological andpathophysiological processes in various diseases.5 These 18to 30 nucleotides noncoding RNAs regulate the expressionand translation of half of protein-coding mRNAs in humansby binding to target sites in the 3� untranslated region (UTR)to destabilize them or impede translation.5 If miRNAs mod-ulate mRNAs for BP genes, they could form the basis fornovel antihypertensive therapies.6,7 Knowledge of effects ofmiRNAs on BP is, however, rudimentary.

The present study tested the hypothesis that discovery ofgenes and miRNAs differentially expressed in the renalcortex and medulla in hypertension should reveal importantpathophysiological mechanisms. To do this we used atranscriptome-wide functional genomics approach involvinga unique set of renal samples from untreated hypertensive and

Received August 3, 2011; first decision August 29, 2011; revision accepted October 7, 2011.From the Basic & Clinical Genomics Laboratory, School of Medical Sciences and Bosch Institute (F.Z.M., B.J.M.), and School of Mathematics and

Statistics, The University of Sydney (A.E.C., Y.H.J.Y.), Sydney, New South Wales, Australia; Department of Cardiovascular Science, University ofLeicester, and Leicester National Institute for Health Research Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital (M.T.), Leicester,United Kingdom; Department of Internal Medicine, Diabetology and Nephrology, Silesian School of Medicine (E.Z.-S.), Zabrze, Poland; School ofHealth Sciences, University of Ballarat (F.J.C.), Ballarat, Victoria, Australia.

B.J.M. and F.J.C. contributed equally to this work as senior authors.Correspondence to Brian J. Morris, Basic & Clinical Genomics Laboratory, School of Medical Sciences and Bosch Institute, Building F13, University

of Sydney, Sydney, New South Wales 2006, Australia. E-mail [email protected]© 2011 American Heart Association, Inc.

Hypertension is available at http://hyper.ahajournals.org DOI: 10.1161/HYPERTENSIONAHA.111.180729

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normotensive subjects. The ability of selected miRNAs toregulate stability of specific mRNAs of interest was thentested in cultured renal cells.

Materials and MethodsFor a full description see the online Data Supplement(http://hyper.ahajournals.org).

SamplesBecause of the functional and anatomic demarcation between themedulla and cortex, gene expression profiles for each differ, so wereexamined separately. Samples were from the Silesian Renal TissueBank, collected to study genetic aspects of human cardiovasculardisease,8–10 and consisted of 42 Polish individuals of white Europeanancestry who underwent elective unilateral nephrectomy because ofnoninvasive renal cancer. Recruitment and phenotyping were asdescribed.10,11 Diagnosis of hypertension was as stipulated for theSilesian Hypertension Study.8,9 Four classified as having secondaryhypertension were excluded. Male patients only (15 untreatedhypertensive and 7 normotensive; age: 56.6�12.8 years; body massindex [BMI]: 26.8�3.7 kg/m2) were included.8,9 Age and BMI didnot differ significantly between each group. Samples were from apole of kidney unaffected by the neoplastic process. Identity asmedulla or cortex was confirmed by markers (Table S1, available inthe online Data Supplement). All of the subjects gave informedconsent, and the study was approved by ethics committees ateach institution.

Microarray ExperimentsAffymetrix GeneChip Human Gene 1.0 ST Arrays were used fortranscriptome-wide gene expression analysis. MicroRNA studiesused the Agilent Human miRNA Microarray V3, release 12.0.Microarrays (1 per sample, with no pooling) were performed onmedulla and cortex samples from each of 14 (9 hypertensive) and 8(5 hypertensive) Silesian Renal Tissue Bank individuals, respec-tively, at the Ramaciotti Centre for Gene Function Analysis, Uni-versity of New South Wales (Sydney, New South Wales, Australia).Medulla and cortex were from the same subjects. The datasetobtained was deposited in the National Center for BiotechnologyInformation Gene Expression Omnibus database (SuperSeries acces-sion No. GSE28260).

Differentially expressed genes were identified using a robustleast-squares approach applied by the Limma package.12 For renalmedulla, differences were tested by linear analysis involving 2models, one which considered the effect of age (young, �55 years,n�6; old, �55 years, n�8) and the other BMI (lean, BMI �25kg/m2, n�5; overweight, BMI �25 kg/m2, n�9). Older subjectsonly were also compared directly (n�8). For renal cortex, because ofthe smaller sample size, we compared hypertensive and normoten-sive data directly. The arrays generated pilot data that were validatedin a larger set of medulla and cortex samples from the same subjects.Differentially expressed genes and miRNAs were ranked by falsediscovery rate (FDR) value. Fold-difference of these was also noted.Based on these rankings, the individual lists of genes and miRNAswere analyzed, and different cutoffs based on FDR were determined(please see Tables for individual FDR values). Genes that had thehighest fold-differences irrespective of FDR values were also con-sidered. The Gene Ontology (GO) database13 was used to furtherinterpret the dataset and identify overrepresented functional groupsof genes. For all of the genes ranked via FDR adjustment, a gene settest was used to highlight pathways differentially represented as aset. The gene expression lists generated were combined with themiRNA list in an in silico analysis using the prediction databasesmiRanda14 and picTar15 to identify miRNAs having the potential toregulate the genes identified.

Validation by Real-Time QuantitativePCR (qPCR)Quantitative PCR (qPCR) involved an expanded sample set of 22men (15 hypertensive and 7 normotensive). For primers and qPCR

conditions see Table S2 (mRNAs) and Table S3 (miRNAs). Signif-icance was assessed by ��CT.16 Data for hypertensives and normo-tensives were compared by independent sample t tests, with P�0.05regarded as significant. Our sample of 22 subjects had 64% power todetect a significant association at P�0.05.

3�UTR Reporter and Luciferase Reporter AssaysmiExpress precursor miRNA clones, constructed in nonviral vector-based systems with enhanced green fluorescent protein reportergene, for hsa-let-7c, hsa-miR-181a, hsa-miR-663, and “scrambled”control were obtained from Gene Copoeia, as were luciferasereporter gene constructs containing the 3�UTR of an miRNA targetgene (Table S4). HEK293 kidney cells in 12-well plates werecotransfected with the 3�UTR construct (400 ng per well) andmiRNA mimics or scrambled vector (1000 ng per well) usingLipofectamine 2000 (Invitrogen). Firefly and Renilla luciferaseactivities were measured using the Luc-Pair miR Luciferase Assaykit (Gene Copoeia) with 6 replicates. Independent sample t tests wereused to compare scrambled and miRNA mimic groups for APOE,AIFM1, and NR4A2. For REN, ANOVA was used to comparebetween groups, followed by Bonferroni to correct for multipletesting.

Transfection of Cells With miRNA MimicHEK293 cells were similarly transfected with each miRNA mimic orscrambled control. RNA was extracted 24 hours later using TRIzol(Invitrogen). Experiments were run in triplicate. qPCR was used tomeasure mRNAs. Statistical tests were as above.

ResultsRenal MedullaMicroarrays revealed differential expression of 12 protein-coding genes between hypertensive and normotensive sub-jects after adjustment for age and 3 genes after adjustment forBMI (Table S5). In the expanded sample (n�22), qPCR of 9mRNAs validated nuclear receptor subfamily 4 group Amember 1 (NR4A1), 2 (NR4A2) ,and 3 (NR4A3); periodhomolog 1 (PER1), and salt-inducible kinase 1 (SIK1; Table1). For individuals aged �55 years, the difference for NR4A3mRNA (fold-difference: 9.1) was significant (FDR: 0.006)and was confirmed by qPCR in the expanded sample set(Table 1). The genes for 13 of these 15 mRNAs were in QTLs

Table 1. Validation by Quantitative PCR of Array Results forSelected mRNAs and miRNAs Differentially Expressed in RenalMedulla Between 15 Hypertensive and 7 Normotensive Subjects

Transcript TypeMean�SE in

NormotensivesMean�SE inHypertensives

Fold-Difference* P

mRNA

NR4A1 522�190 1140�206 �2.2 0.03

NR4A2 77�35 154�20 �2.0 0.03

NR4A3 210�95 550�114 �2.6 0.009

PER1 274�46 551�102 �1.7 0.04

SIK1 98�37 178�23 �1.8 0.04

miRNA

hsa-miR-638 32 700�19 800 4260�2990 �7.7 0.02

hsa-let-7c 46 000�5700 27 600�5870 �1.7 0.04

Shown are mean relative expression �SE, fold-difference, and P values formRNAs and miRNAs based on relative abundance (see Methods for statisticaltests employed). miRNA indicates microRNA.

*Positive values indicate higher expression in hypertensives, and negativevalues indicate higher expression in normotensives.

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associated previously with BP or hypertension (Table S6).The functions of the validated mRNAs appear in Table S7.Figure S1A shows the mRNAs and miRNAs not validated.GO analysis revealed enrichment in hypertension of the termsregulation of signaling (GO:0007165, GO:0023060, andGO:0023046), regulation of transcription (GO:0006357,GO:0045449, and GO:0003700), and regulation of celldeath (GO:0042981, GO:0043067, and GO:0010941), amongothers (Table S8). Gene set test analysis indicated overrepre-sentation of sodium ion transport (GO:0006814) and binding(GO:0031402; Table S9). In silico molecular networks areshown in Figure S2 and hierarchical clustering in Figure S3.

Arrays identified 11 differentially expressed miRNAs (Ta-ble S10). Of 4 selected, hsa-miR-638 and hsa-let-7c werevalidated by qPCR in the larger sample set (Table 1).

Renal CortexArrays revealed differential expression of 46 protein-codinggenes (Table S11). Twenty seven of these were located in ornear BP or hypertension QTLs (Table S6). Twelve mRNAswere selected, and the 10 validated by qPCR in the largersample-set were as follows: apoptosis-inducing factormitochondrion-associated 1(AIFM1), �1-microglobulin/bikunin precursor (AMBP), apolipoprotein E (APOE),ephrin-�1 (EFNB1), NADH dehydrogenase (ubiquinone) 1�-subcomplex assembly factor 1 (NDUFAF1), peroxiredoxin5 (PRDX5), renin binding protein (RENBP), solute carrierfamily 13 (sodium/sulfate symporters) member 1 (SLC13A1),syntaxin 4 (STX4), and troponin T type 2 (TNNT2; Table 2).Although not significant in the microarray analysis, themRNAs for renin (REN) and CD36 antigen (CD36) had highfold-differences (2.7 and 2.0, respectively; Table S12) andwere validated by qPCR (Table 2). Table S7 summarizes theirfunctions. Figure S1B shows the mRNAs not validated. GOanalysis indicated overrepresentation of induction of apopto-sis (GO:0008629, GO:0006917, and GO:0012502), responseto reactive oxygen species (GO:0000302 and GO:00346),response to oxidative stress (GO:0006979), activation of theimmune system (GO:0002682, GO:0051251, andGO:0002684), sodium ion transport activity (GO:0006814and GO:0015081), and regulation of NO synthase activity(GO:0050999), among others (Table S13). Gene set testanalysis showed enrichment of terms for mitochondrial elec-tron transport chain (including, among others, GO:0022900,GO:0006120, GO:0005747, and GO:0009055) and renin-an-giotensin system (KEGG 4614; Table S14). In silico molec-ular networks are shown in Figure S4 and hierarchicalclustering in Figure S5.

Arrays revealed 13 differentially expressed miRNAs (Ta-ble S15). The 7 selected and validated by qPCR in theexpanded sample set were as follows: hsa-miR-21, hsa-miR-126, hsa-miR-181a, hsa-miR-196a, hsa-miR-451, hsa-miR-638, and hsa-miR-663 (Table 2).

Prediction of miRNA Target GenesThese are listed in Table S16. For medulla, hsa-let-7c waspredicted to regulate NR4A2 mRNA (Figure S6). For cortex,potential target sequences for hsa-miR-181a and hsa-miR-663 were present in the 3�UTRs of AIFM1 (Figure S7), APOE

(Figure S8), and REN (Figure S9) mRNAs. The 3�UTRhomology regions of these are not polymorphic.

3�UTR Reporter AssaysThese confirmed that hsa-let-7c binds to the 3�UTR ofNR4A2, as did hsa-miR-181a for AIFM1, hsa-miR-663 forAPOE, and hsa-miR-181a and hsa-miR-663 alone or incombination for the REN 3�UTR (Figure A). Figure S10Ashows reduction in reporter (luciferase) activity when hsa-miR-181a and hsa-miR-663 bind to the REN 3�UTR in aplasmid construct containing this sequence.

Effect of miRNAs on Specific mRNA LevelsAlthough the effect of hsa-let-7c on the level of NR4A2mRNA was marginal, those of hsa-miR-181a on AIFM1mRNA, hsa-miR-663 on APOE mRNA, and hsa-miR-181a,hsa-miR-663, or a combination on REN mRNA level were allsignificant (Figure B).

DiscussionThis is the first transcriptome-wide study of differentialexpression of mRNAs and miRNAs in kidney in humanhypertension. Several of the mRNAs identified had miRNAtargets in their 3�UTR. Functional experiments in cultured

Table 2. Validation by Quantitative PCR of Array Results forSelected mRNAs and miRNAs Differentially Expressed in RenalCortex Between 15 Hypertensive and 7 Normotensive Subjects

Transcript TypeMean�SE in

NormotensivesMean�SE inHypertensives

Fold-Difference* P

mRNA

AIMF1 3800�1070 8480�1380 �2.2 0.03

AMBP 10�3.3 27�5.2 �2.6 0.008

APOE 13 700�5360 43 700�5920 �3.2 0.003

CD36 91�34 41�8.3 �2.2 0.03

EFNB1 258�53 728�150 �2.8 0.03

NDUFAF1 70�19 160�25 �2.3 0.007

PRDX5 3740�900 6950�879 �1.9 0.02

REN 288�159 1700�389 �5.9 0.002

RENBP 556�140 2470�584 �4.4 0.003

SLC13A1 2590�645 4950�664 �1.9 0.03

STX4 703�249 1720�368 �2.4 0.02

TNNT2 362�246 848�152 �2.4 0.05

miRNA

hsa-miR-21 0.2�0.1 2.6�1 �13 0.007

hsa-miR-126 0.4�0.2 69�26 �170 0.02

hsa-miR-181a 8.0�4.3 1.3�0.3 �6.0 0.04

hsa-miR-196a 8.0�3.4 26�7.0 �3.3 0.04

hsa-miR-451 1.6�1.0 973�403 �610 �0.001

hsa-miR-638 0.11�0.04 0.04�0.01 �2.7 �0.001

hsa-miR-663 8.7�6.1 1.1�0.4 �8.2 0.004

Shown are mean relative expression �SE, fold-difference and P valuesbased on relative abundance (see Methods for statistical tests employed).miRNA indicates microRNA.

*Positive values indicate higher expression in hypertensives, and negativevalues indicate higher expression in normotensives.

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Figure. A, Effect of microRNAs (miRNAs) on 3� untranslated region (UTR) of specific mRNAs (n�6). An interaction between a specificmiRNA and a specific 3�UTR cloned into an expression construct downstream of a luciferase reporter gene was evident as a decreasein relative luciferase activity (mean�SE) vs control construct containing a scrambled version of the particular 3�UTR. Shown is effect ofhsa-let-7c on NR4A2 (P�0.002), hsa-miR-181a on AIFM1 (P�0.01), hsa-miR-663 on APOE (P�0.001), and hsa-miR-181a (P�0.05),hsa-miR-663 (P�0.004), or both (P�0.001) on REN 3�UTR. See Methods for statistical tests used. B, Effect of miRNAs on expressionof specific mRNA (n�3). An interaction between an miRNA and the 3�UTR of an mRNA was expected to cause a decrease in themRNA level (mean�SE) of a given gene when compared with construct with control (scrambled) sequence. Shown is effect of hsa-let-7c on NR4A2 (P�0.07), hsa-miR-181a on AIFM1 (P�0.03), hsa-miR-663 on APOE (P�0.04), and hsa-miR-181a (P�0.003), hsa-miR-663 (P�0.001), or both (P�0.001) on REN 3�UTR. *P�0.05, **P�0.01, ***P�0.001.



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kidney cells showed that 2 selected miRNAs may exertposttranscriptional control of REN, APOE, and AIFM1mRNAs via their 3�UTRs. The reduced expression of hsa-miR-181a and hsa-miR-663 in hypertensive kidneys couldexplain the renin mRNA elevation seen.

Expression profiles differed between the renal medulla andcortex, consistent with their anatomic demarcation and dif-ferent biological functions. In the medulla, transcriptionfactor and sodium transport GO categories were highlighted,whereas data for cortex showed enrichment in pathwayscontributing to reactive oxygen species generation, immunesystem function, and the renin-angiotensin system. Two ofthe biological processes that were overrepresented, cell deathand regulation of apoptosis, were also identified in a recentlarge-scale gene-centric analysis of mean 24-hour diastolicBP.17 Thus, genes and transcripts of these pathways appearrelevant to human hypertension and merit further in-depthanalyses.

In medulla, SIK1 could raise BP via its ability to increaseactive cell sodium transport in response to angiotensin II.18

SIK1 activity is elevated in proximal tubule cells from Milanhypertensive rats, and SIK1 blockade modulates active so-dium transport.19

The nuclear transcription factor family NR4A (genesNR4A1, NR4A2, and NR4A3) is important for cytokineregulation20 and may contribute to inflammation in renalmedulla in hypertension. The activation of NR4A2 in re-sponse to angiotensin II is, moreover, modulated by SIK1.18

BP is altered by proximal tubule disruption of epithelialsodium transport by targeted deletion of angiotensin II type 1receptors,21 as well as renin inhibitor therapy,22 and trans-genic overexpression23 or knockout24 of renin. Our data are,however, the first to implicate endogenous REN overexpres-sion in human hypertension. Just as CD36 was downregulatedin the renal cortex here, Cd36 is downregulated in spontane-ously hypertensive rat kidney and predisposes this strain tohypertension.25

miRNAs have been implicated in cardiovascular disease,26

cancer,27 and, for one (miR-155), hypertension, where miR-155 targets a hypertension-associated SNP (rs5186) in theangiotensin II type 1 receptor 3�UTR.28,29 Elevated angioten-sin II type 1 receptor in young hypertensives homozygous forthe rs5186 C allele tracks positively with BP and negativelywith hsa-miR-155 expression.30 hsa-miR-155 was not differ-entially expressed in our study.

Not only have we identified miRNAs in human hyperten-sive kidneys, but we demonstrate the potential for some ofthese, hsa-let-7c, hsa-miR-181a, and hsa-miR-663, to bind tothe 3�UTR and regulate AIFM1, APOE, REN, and NR4A2mRNAs. Confirmation of the target sequence was not at-tempted. Although miRNAs primarily affect mRNA stabilityrather than translation in animals (Figure S10B),5 we did notdetermine which was involved.

Most of the genes that we found have not been implicatedpreviously in hypertension. This was also the case for genesidentified in the large genome-wide association studies. Only12% of the genes identified here were present in the Af-fymetrix Genome-wide Human SNP Array 6.0 chip used inthe genome-wide association studies (Table S17).

A strength of our study was the ability to obtain humankidneys from untreated hypertensive patients. However, thescarcity of kidneys meant that sample size was low. This wascompounded by our decision to reduce variability by restrict-ing our sample to men. Our findings should now invigoratethe acquisition of suitable samples from larger cohorts ofuntreated hypertensive subjects in other settings, despite thechallenges that this presents. Protein was not available fromour subjects, but should in future studies help corroborate themRNA findings.

Although our subjects had renal cancer, the effect of thiswould have been similar in normotensives and hypertensives.We carefully dissected out healthy tissue unaffected by theneoplastic process, and samples were collected in the sameway for all of the subjects.

In conclusion, this is the first study to documenttranscriptome-wide differences in gene and miRNA expres-sions in kidneys of hypertensive subjects. The findings wereconfirmed by qPCR, as well as functional experimentsinvolving miRNAs predicted to target mRNAs of greatesthistorical interest. A major discovery was evidence for RENmRNA regulation via binding of miRNAs hsa-miR-181a andhsa-miR-663 to its 3�UTR.

PerspectivesIt has for many decades been argued that renin is involved inthe etiology of essential hypertension. Molecular geneticshas, however, failed to implicate REN polymorphisms. Thus,our finding of REN overexpression, coupled with the discov-ery of downregulation of 2 miRNAs that bind to and affectREN mRNA levels, is the first real evidence to implicaterenin. The mechanism involved could be exploited in devel-oping novel antihypertensive therapies. Our findings alsoimplicate other miRNAs in hypertension. We show, more-over, for the first time, that CD36 is downregulated in humanhypertensive kidneys as it is in rat genetic hypertension,where reduced Cd36 is causative. Furthermore, preliminaryfindings show significant differential renal expression ofAIFM1, APOE, CD36, REN, RENBP, PRDX5, NR4A1, andNR4A3, as well as miRNAs hsa-let-7c and hsa-miR-181a, infemale Silesian Renal Tissue Bank kidneys in hypertension(Table S18). To confirm effects on BP of the mRNAs andmiRNAs identified here, conditional knockdown in animalmodels may be informative. Although we expect that themRNAs and miRNAs that we have identified include caus-ative genes, some gene expression changes are likely second-ary effects to maintain or counteract the elevated BP or arefrom high BP-induced kidney damage. Our findings thusprovide scope for extensive further investigations.

AcknowledgmentsWe thank the Ramaciotti Centre for Gene Function Analysis for helpwith arrays.

Sources of FundingThis work was supported in part by a University of Sydney ResearchInfrastructure block grant (to B.J.M.), grants from L.E.W. Carty andthe National Health and Medical Research Council of Australia (toF.J.C.), Australian Research Council grant DP0770395 (toY.H.J.Y.), an Endeavour International Postgraduate Research Schol-

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arship (to F.Z.M.), and an Australian Postgraduate Award (toA.E.C.). The Silesian Renal Tissue Bank was supported by NationalInstitutes of Health Fogarty International Research Collaborationaward R03 TW007165 (to M.T. and E.Z.-S.). This study is part of theresearch portfolio supported by the Leicester National Institute forHealth Research Biomedical Research Unit in Cardiovascular Dis-ease. The Clive and Vera Ramaciotti Foundation and Prostate CancerFoundation of Australia cofunded the LightCycler480 qPCR ma-chine, and the Cancer Institute of New South Wales funded theFLUOstar Omega Microplate Reader.

DisclosuresNone.

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3. Guyton AC. Abnormal renal function and autoregulation in essentialhypertension. Hypertension. 1991;18:III49–III53.

4. Hamm LL, Hering-Smith KS. Pivotal role of the kidney in hypertension.Am J Med Sci. 2010;340:30–32.

5. Huntzinger E, Izaurralde E. Gene silencing by microRNAs: contributionsof translational repression and mRNA decay. Nat Rev Genet. 2011;12:99–110.

6. Schroen B, Heymans S. MicroRNAs and beyond: the heart reveals itstreasures. Hypertension. 2009;54:1189–1194.

7. Liang M, Liu Y, Mladinov D, Cowley AW Jr, Trivedi H, Fang Y, Xu X,Ding X, Tian Z. MicroRNA: a new frontier in kidney and blood pressureresearch. Am J Physiol Renal Physiol. 2009;297:F553–F558.

8. Tomaszewski M, Brain NJ, Charchar FJ, Wang WY, Lacka B, Padma-nabahn S, Clark JS, Anderson NH, Edwards HV, Zukowska-Szczechowska E, Grzeszczak W, Dominiczak AF. Essential hypertensionand �2-adrenergic receptor gene: linkage and association analysis.Hypertension. 2002;40:286–291.

9. Tomaszewski M, Charchar FJ, Lacka B, Pesonen U, Wang WY,Zukowska-Szczechowska E, Grzeszczak W, Dominiczak AF. Epistaticinteraction between �2-adrenergic receptor and neuropeptide Y genesinfluences LDL-cholesterol in hypertension. Hypertension. 2004;44:689–694.

10. Tomaszewski M, Charchar FJ, Lynch MD, Padmanabhan S, Wang WY,Miller WH, Grzeszczak W, Maric C, Zukowska-Szczechowska E,Dominiczak AF. Fibroblast growth factor 1 gene and hypertension: fromthe quantitative trait locus to positional analysis. Circulation. 2007;116:1915–1924.

11. Tomaszewski M, Charchar FJ, Nelson CP, Barnes T, Denniff M, KaiserM, Debiec R, Christofidou P, Rafelt S, van der Harst P, Wang WY, MaricC, Zukowska-Szczechowska E, Samani NJ. Pathway analysis showsassociation between FGFBP1 and hypertension. J Am Soc Nephrol. 2011;22:947–955.

12. Smyth GK. Linear models and empirical Bayes methods for assessingdifferential expression in microarray experiments. Stat Appl Genet MolBiol. 2004;3:article 3.

13. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, DavisAP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-TarverL, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin

GM, Sherlock G. Gene ontology: tool for the unification of biology–theGene Ontology Consortium. Nat Genet. 2000;25:25–29.

14. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. HumanmicroRNA targets. PLoS Biol. 2004;2:e363.

15. Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, Mac-Menamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N. Com-binatorial microRNA target predictions. Nat Genet. 2005;37:495–500.

16. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the com-parative CT method. Nat Protoc. 2008;3:1101–1108.

17. Tomaszewski M, Debiec R, Braund PS, Nelson CP, Hardwick R,Christofidou P, Denniff M, Codd V, Rafelt S, van der Harst P,Waterworth D, Song K, Vollenweider P, Waeber G, Zukowska-Szczechowska E, Burton PR, Mooser V, Charchar FJ, Thompson JR,Tobin MD, Samani NJ. Genetic architecture of ambulatory blood pressurein the general population: insights from cardiovascular gene-centric array.Hypertension. 2010;56:1069–1076.

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Yee Hwa J. Yang, Fadi J. Charchar and Brian J. MorrisFrancine Z. Marques, Anna E. Campain, Maciej Tomaszewski, Ewa Zukowska-Szczechowska,

Kidneys and a Role for MicroRNAsGene Expression Profiling Reveals Renin mRNA Overexpression in Human Hypertensive

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Updated January 6, 2012

Online Supplement

Gene Expression Profiling Reveals Renin mRNA Overexpression in

Human Hypertensive Kidneys and a Role for MicroRNAs

Francine Z. Marques; Anna E. Campain; Maciej Tomaszewski; Ewa Zukowska-

Szczechowska; Yee Hwa J. Yang; Fadi J. Charchar; Brian J. Morris

Short title: Gene and miRNA expression in human hypertension

From the Basic & Clinical Genomics Laboratory, School of Medical Sciences and Bosch

Institute (F.Z.M., B.J.M.) and School of Mathematics and Statistics, The University of

Sydney, Sydney, New South Wales, Australia (A.E.C., Y.H.Y.); Department of

Cardiovascular Science, University of Leicester, and Leicester NIHR Biomedical Research

Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, UK (M.T.); Department of

Internal Medicine, Diabetology and Nephrology, Silesian School of Medicine, Zabrze,

Poland (E.Z.-S.); and School of Health Sciences, University of Ballarat, Victoria 3350,

Australia (F.J.C.)

B.J.M. and F.J.C. contributed equally to this work as senior authors.

Correspondence: Brian J. Morris, D.Sc., Basic & Clinical Genomics Laboratory, School of

Medical Sciences and Bosch Institute, Bldg F13, The University of Sydney NSW 2006,

Australia. E-mail: [email protected]; Tel: +61-2-9351-3688; Fax: +61-2-9351-

2227

Gene and miRNA expression in human hypertension

S2

Methods

Subjects

We used samples of renal cortex and medulla from the Silesian Renal Tissue Bank (SRTB), which had been collected to study candidate genes in human cardiovascular disease.1-3 The recruitment strategy and phenotyping were described in detail previously.3, 4 Briefly, Polish individuals of white European ancestry who underwent elective unilateral nephrectomy because of non-invasive renal cancer were recruited into the SRTB. All patients underwent a thorough medical examination prior to surgery. Diagnosis of hypertension was established based on to a protocol validated previously in the Silesian Hypertension Study.1, 2 Four classified as having secondary hypertension were excluded. Male patients only (15 untreated essential hypertensive and 7 normotensive) were included. The subjects were aged 56.6±12.8 S.D. years and their BMI was 26.8±3.7 S.D. kg/m2. There was no statistical difference in age or BMI between the hypertensive and normotensive groups.

All patients signed an informed consent for participation, and the study was approved by the local bioethical committee of the Medical University of Silesia, as well as by the human ethics committees at the University of Sydney and the University of Ballarat.

RNA Extraction and Quality/Quantity Assessment

Approximately 1 cm3 of tissue from the healthy (unaffected by cancer) pole of the kidney was obtained immediately after surgery. Medulla and cortex were identified initially by eye. Later, comparison between genes expressed in medulla and cortex showed that there was no overlap in any of those found. Markers specific for each tissue5 were, moreover, identified (Table S1), confirming complete and accurate separation of medulla and cortex samples during collection. Tissues were transferred into receptacles containing RNAlater (Ambion) before storage at –70°C. RNA from medulla and cortex was assessed for quality based on a RNA integrity number (RIN) higher than 8 by electrophoresis involving an Agilent 2100 Bioanalyzer at the Ramaciotti Centre for Gene Function Analysis, University of New South Wales, Sydney, Australia. RNA was quantified by spectrophotometry using a NanoDrop® ND-100 spectrophotometer (Thermo Scientific) in the Laboratory of F.Z.M. and B.J.M. at the University of Sydney. mRNA was extracted using a commercially available kit (RNeasy, Qiagen).

Microarray Experiments

mRNA was converted to single-stranded DNA, labeled and one microarray was performed for each sample, with no pooling. Samples were hybridized to Affymetrix GeneChip® Human Gene 1.0 ST Arrays, which simultaneously analyze 28,869 gene transcripts using 764,885 probe sets (on average 27 probes per gene), all according to the manufacturer’s instructions, and with the assistance of the Ramaciotti Centre. miRNA was processed in a similar manner and each sample was applied to a separate Agilent Human miRNA Microarray (V3, release 12.0), which analyzed 866 human and 89 human viral miRNAs represented by an average of 16 probes for each miRNA. For renal medulla, samples from 8 hypertensives and 6 normotensives were subjected to microarray analysis, while for renal cortex the samples analyzed were from 5 hypertensives and 3 normotensives. The data-set obtained has been


S3

deposited in the NCBI Gene Expression Omnibus database according to MIAME guidelines with SuperSeries accession number GSE28260.

Statistical Analyses of Gene Expression in Microarrays

Samples were normalized using the robust multi-array analysis (RMA)6 algorithm implemented in the Affymetrix7 package. The probe annotation package “hugene10sttranscriptcluster.db” from Bioconductor8 was used. Differentially expressed genes were identified using a robust least squares approach applied by the Limma package.9 For renal medulla, linear models were developed looking at differences between the two groups. The main effects in the additive model were those of age (young, ≤55 years; old, >55 years) and BMI, where BMI ≤25 kg/m2 was considered lean, and BMI >25 kg/m2was considered overweight. Data for old patients only were also evaluated by direct comparison. For renal cortex, a direct comparison between hypertensive and normotensive samples was performed because the sample size was smaller. It is important to emphasize that data from 15 hypertensive and 7 normotensive subjects were used to later validate the results by semi-quantitative real-time PCR (qPCR). Differentially expressed genes were ranked based on their false discovery rate (FDR) and fold difference (FD), each of which are relevant to gene discovery from expression arrays. Hierarchical clustering used Euclidean distance and was performed with TMeV 4.5.10

The Gene Ontology (GO) database11 was used to further interpret the differentially expressed gene data set and to identify over- and under-represented functional groups of genes. A hypergeometric test using GOstats12 was used to determine if particular GO terms were more significantly over- or under-represented in the differentially expressed gene list than the gene list of the entire array. Up-regulated and down-regulated mRNAs were examined separately. GO terms were ranked based on their FDR value, with a small ranking being indicative of them likely being of significant interest in the differentially expressed gene list. For each gene, annotations performed included molecular function, biological process and cellular component. A gene set test (GST) was applied through a Wilcoxon rank sum test. This is considered as a complete ranked list if particular GO terms are more over- or under-represented as a set. For this analysis GST was implemented via the Limma package.9

Using the “Core Analysis” function in the Ingenuity Pathway Analysis (IPA, Ingenuity®

Systems, www.ingenuity.com) application, molecular networks were built. Briefly, a data set containing differentially expressed genes and respective fold differences were uploaded into the application. These genes were then correlated based on a previous association between genes or proteins and known functional roles of genes. The biological relationship between two genes, represented as nodes, is shown as a line. Nodes with different shapes indicate different functional classes.

Statistical Analyses of miRNA Expression Data with miRNA Target Prediction.

Samples were normalized using the robust multi-array analysis (RMA).6 All 16 probe values for each miRNA were combined using their mean expression value before the analysis, resulting in one reading for each miRNA. Differentially expressed miRNAs were identified using a linear regression with robust least squares applied by the Limma package.9 Selection of differentially expressed miRNAs was based of FD and FDR.


S4

The gene expression lists generated were combined with the miRNA list in an in silico analysis using the prediction database miRanda13 and picTar14 in order to identify if any of the particular miRNAs we found could be predicted to regulate the mRNAs in the gene lists we identified.

Validation by Real-time Quantitative PCR (qPCR)

qPCR was conducted to confirm the results for both gene and miRNA expression in samples from 15 EH and 7 NT subjects. The first-strand complementary synthesis reaction was performed using the SuperScript® VILO™ cDNA Synthesis Kit (Invitrogen) for mRNA (as its cDNA) and using the NCode™ VILO™ miRNA cDNA Synthesis Kit (Invitrogen) for miRNA (as its cDNA). Amplification reactions used the EXPRESS SYBR® GreenER™ qPCR reagent system (Invitrogen) in a LightCycler 480 qPCR machine (Roche). Primers for gene expression were designed specifically for each gene around the most differentially expressed probe location using Primer3.15 Primers for miRNA expression were designed in the NCode™ miRNA Database (Invitrogen) based on the Sanger Institute miRBase (release 10.0). Primers and qPCR conditions are described in Table S2 for gene expression and in Table S3 for miRNA expression. The specificity of the qPCR was ensured by melting curve analysis for both gene and miRNA expression and electrophoresis in agarose gels for gene expression only (data not shown). β-actin (ACTB) mRNA was used as an internal reference transcript for gene expression and RNA U6 small nuclear 2 (RNU6B) for miRNA expression.

Statistical Analysis of qPCR Results

The SPSS for Windows (Release 17.0, 2008) statistical package was used for statistical analyses. The comparative ∆∆CT statistical method was used to assess significance.16 qPCR results were tested for normal distribution using the Skewness and Kurtosis test. Non-normally distributed data were logarithmically transformed to achieve normal distribution. Data for individual mRNA and miRNAs were submitted to Levene’s test for Equality of Variances. Independent sample t-tests were used to compare data for samples from the hypertensive and normotensive groups. Significance was set at P<0.05.

3’-Untranslated Region Reporter and Luciferase Reporter Assays

miExpressTM precursor miRNA clones for hsa-let-7c, hsa-miR-181a, hsa-miR-663 or the scrambled control, and that had been constructed in non-viral vector based systems with eGFP reporter gene, were obtained from Gene Copoeia. They were transformed into BL21-Gold competent cells (Stratagene). Constructs were cultivated in plates of Luria-Bertani (LB) medium containing agar and ampicillin (50 µg/ml). Individual colonies were selected and cultured in LB medium overnight, and plasmids were extracted using a PureLink™ HiPure Plasmid Filter Kit (Invitrogen).

Human embryonic kidney 293 (HEK293) cells were grown using Dulbecco's modified Eagle’s medium supplemented with 10% fetal bovine serum and 4 mM L-glutamine (all from Invitrogen). Cell culture medium was replaced every 48–72 hours. Luciferase reporter gene constructs containing the 3’-untranslated region (3’UTR) of a miRNA target gene were acquired from Gene Copoeia (Table S4). Constructs were cultivated in plates of LB medium containing agar and kanamycin (50 µg/ml). Individual colonies were selected and cultured in LB medium overnight, and plasmids were extracted using a PureLink™ HiPure Plasmid


S5

Filter Kit (Invitrogen). HEK293 cells were cultured in 12-well plates and co-transfected with the 3’UTR construct or the negative control vector for pEZX-MT01 (400 ng per well, GeneCopoeia) and miRNA mimics or scrambled (1000 ng per well) using Lipofectamine 2000TM (Invitrogen) in cells at 80–90% confluence. After 18 hours of transfection, cells were transferred to 96-well plates, where experiments were performed with 6 replicates. Firefly and Renilla luciferase activities were measured in a FLUOstar Omega Microplate Reader using the Luc-Pair™ miR Luciferase Assay Kit (Gene Copoeia) according to the manufacturer’s instructions. Renilla activity was used to normalize firefly activity to control for transfection efficiency and cell density. Preliminary experiments were used to determine the best concentration of cells, reagents and vectors, and time to measure luciferase after transfection (data not shown). A minimum concentration of vectors, determined according to the manufacturer’s recommendations, was used. SPSS was used to test for Equality of variances. Independent sample t-tests were used to compare scrambled and miRNA mimic groups for APOE, AIFM1 and NR4A2. Analysis of variance, followed by correction for multiple testing using the Bonferroni post-hoc test, was used to compare scrambled and each miRNA mimic group for REN. Significance was set at P<0.05.

Culture and transfection of renal cells with miRNA mimic

HEK293 cells at 80–90% confluence were transfected with each miRNA mimic or scrambled control (1000 ng/well) using Lipofectamine 2000TM (Invitrogen) following the manufacturer’s protocol. Untransfected and mock experiments were also performed. RNA was extracted using TRIzol® (Invitrogen) after 24 hours. The experiments were run in triplicate. qPCR, as described above, was used to assess the expression of the target genes. Preliminary experiments were used to determine the best concentration of cells, reagents and vectors, and time to extract RNA after transfection (data not shown). The minimum concentration of vector, according to the manufacturer’s recommendations, was used. qPCR, as described above, was used to assess the expression of the target genes. After statistical analysis using the SPSS package, results were submitted to Levene’s Test for Equality of Variances. Independent sample t-tests were used to compare scrambled and miRNA mimic groups for APOE, AIFM1 and NR4A2. A one-way analysis of variance, followed by correction for multiple testing using the Bonferroni post-hoc test, was used to compare scrambled and each miRNA mimic group for REN. Significance was set at P<0.05.

Results and Discussion This online Supplement also contains Tables S5–S18 referred to in these sections of the main manuscript. The caption to each of the supplementary Tables describes what each is about.


S6

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Table S1. Genes differentially expressed between renal medulla and cortex that are established tissue-specific markers for each of these tissues.5 Negative values represent higher expression in cortex, while positive values represent higher expression in medulla. With the exception of 2 genes (ALDOB and MT1B), all differences between medulla and cortex were statistically significant (P<0.05).

Probeset number

Fold difference

FDR value EntrezID Official Symbol

Gene name Tissue Marker

8111474 –2.95 0.02 64902 AGXT2 alanine-glyoxylate aminotransferase 2 Cortex 8143054 +2.91 0.04 231 AKR1B1 aldo-keto reductase family 1, member β1 (aldose

reductase) Medulla

8162884 –3.72 0.095 229 ALDOB aldolase B, fructose-bisphosphate Cortex 7955290 +2.08 0.003 359 AQP2 aquaporin 2 (collecting duct) Medulla 7901565 –3.44 0.01 1733 DIO1 deiodinase, iodothyronine, type I Cortex 7914075 –1.85 0.01 8547 FCN3 ficolin (collagen/fibrinogen domain containing) 3

(Hakata antigen) Cortex

8033043 –2.53 0.03 2528 FUT6 fucosyltransferase 6 (α1,3 fucosyltransferase) Cortex 8071927 –1.92 0.005 2678 GGT1 gamma-glutamyltransferase 1 Cortex 8127065 –4.04 0.02 2939 GSTA2 glutathione S-transferase α2 Cortex 8040802 –3.55 0.008 3795 KHK ketohexokinase (fructokinase) Cortex 8136662 –2.18 0.05 8972 MGAM maltase-glucoamylase (α-glucosidase) Cortex 7995806 –1.30 0.04 4489 MT1A metallothionein 1A Cortex 7995820 –1.27 0.094 4490 MT1B metallothionein 1B Cortex 7995813 –1.88 0.01 326343 MT1DP metallothionein 1D (pseudogene) Cortex 7995797 –1.87 0.02 4493 MT1E metallothionein 1E Cortex 7995825 –2.06 0.05 4494 MT1F metallothionein 1F Cortex 7995829 –3.21 0.02 4496 MT1H metallothionein 1H Cortex 7995834 –1.44 0.02 644314 MT1IP metallothionein 1I (pseudogene) Cortex 8062119 –2.33 0.009 4498 MT1JP metallothionein 1J (pseudogene) Cortex 7995803 –1.98 0.004 4498 MT1JP metallothionein 1J (pseudogene) Cortex


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7995793 –1.65 0.05 4500 MT1L metallothionein 1L (gene/pseudogene) Cortex 7995787 –2.86 0.03 4499 MT1M metallothionein 1M Cortex 7925413 –2.16 0.001 645745 MT1P2 metallothionein 1 pseudogene 2 Cortex 7995838 –1.96 0.02 4501 MT1X metallothionein 1X Cortex 8095376 –1.58 0.02 4502 MT2A metallothionein 2A Cortex 7995783 –1.56 0.01 4502 MT2A metallothionein 2A Cortex 7995776 –1.23 0.0004 4504 MT3 metallothionein 3 Cortex 7995772 –1.31 0.02 84560 MT4 metallothionein 4 Cortex 8111490 –2.68 0.03 5618 PRLR prolactin receptor Cortex 8142628 –3.17 0.01 6561 SLC13A1 solute carrier family 13 (sodium/sulfate

symporters), member 1 Cortex

8064613 +3.37 0.004 83959 SLC4A11 solute carrier family 4, sodium borate transporter, member 11

Medulla

7934215 –1.41 0.005 9806 SPOCK2 sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 2

Cortex


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Table S2. Primers and conditions used to validate microarray gene expression results.

Official gene symbol

GenBank Accession #

Tissue Primer Sequence (5’ 3’) Concentration Product length

Annealing temperature

ACTB NM_001101 Control Fw: CGCGAGAAGATGACCCAGAT 200 nM 55-65ºC Rv: GAGTCCATCACGATGCCAGT BTG2 NM_006763.2 Medulla Fw: ATGAGCCACGGGAAGGGA 500 nM 152 bp 55ºC Rv: TTGTAGTGCTCTGTGAGTGCC DUSP6 NM_001946.2 Medulla Fw: AGGTTACCAAGTGCGGAATTGG 500 nM 120 bp 55ºC Rv: GCTGTACACATGGGTACAGAGC HSPA1B NM_005346.4 Medulla Fw: GCGGTCCCAAGGCTTTCCAG 200 nM 150 bp 60ºC Rv: GTGCCCTGCTCTGTGGGC NR4A1 NM_173157.1 Medulla Fw: CGCTGTGCTGTGTGTGGGGA 200 nM 150 bp 58ºC Rv: CCGCCTCTTGTCCACAGGGC NR4A2 NM_006186.2 Medulla Fw: TGTAAGTGTTGTATGTACCGTTG 200 nM 155 bp 52ºC Rv: AGTGCTCAGTTATTTCCAGGG NR4A3 NM_006981.2 Medulla Fw: GGGAGCCGCTGGGCTTG 200 nM 132 bp 61ºC Rv: CAGTGGGCTTTGAGTGCTGTG PER1 NM_002616.2 Medulla Fw: GTGGGGGAGGTTGGTGGAC 500 nM 89 bp 60ºC Rv: ATGCCATCGGCAGAGGGTAC RCAN1 NM_004414.5 Medulla Fw: ACTCTCTACTGGTAGGAAGAG 500 nM 130 bp 55ºC Rv: TACATGCACAAACATGAGAAC SIK1 NM_173354.3 Medulla Fw: TCCAGACCATCTTGGGGCAG 200 nM 86 bp 58ºC Rv: AAGGGGAAGGGGTTTTGTGTTG SPRED1 NM_152594.2 Medulla Fw: AGAGACAGTTGTTACCAGTGAGCCT 200 nM 127 bp 58ºC Rv: GCCTGGCTGACCAAATGTTATCTGA AIFM1 NM_004208.3 Cortex Fw: TCCATCCGGGCTCGGGATC 200 nM 76 bp 58ºC Rv: GAGGTCGCATGTACGGCAGC AMBP NM_001633.3 Cortex Fw: CTCGTTGGCGGAAAGGTGTC 200 nM 101 bp 55ºC


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Rv: GGTTATGTTCCATTTGGATTTGTGATAG APOE NM_000041.2 Cortex Fw: CCAATCACAGGCAGGAAGATGAAGG 200 nM 99 bp 55ºC Rv: CTCTGTCTCCACCGCTTGCTC CD36 NM_001001548.2 Cortex Fw: TGGTTTCTGGGTGGCCAATTCAGA 200 nM 112 bp 58ºC Rv: AGGGCAGCTACTGGGATGATGGT EFNB1 NM_004429.4 Cortex Fw: CTCCCTCAACCCCAAGTTCCTGA 200 nM 133 bp 58ºC Rv: CCGCACCAGGTACAGCTTGTAGT GPD1 NM_005276.2 Cortex Fw: TGTGACCAGCTCAAGGGCCA 200 nM 124 bp 55ºC Rv: GGATGCCGAGGCGCTCCC NDUFAF1 NM_016013.2 Cortex Fw: TTCTGGCACATGGTAGAATTGGGC 200 nM 140 bp 55ºC Rv: TTTCTGAGAAAATAAGTACCACGCAGC PRDX5 NM_012094.3 Cortex Fw: GTCCAGGTGGTGGCCTGTCTGA 200 nM 95 bp 58ºC Rv: GCCAGGAGCCGAACCTTGCCTT REN NM_000537.3 Cortex Fw: AGCCAGGACATCATCACCGTGG 200 nM 125 bp 55ºC Rv: TCAATGAAGCCCATGCCCACAA RENBP NM_002910.5 Cortex Fw: TGACCTCAAGTATGTGTGGCTGCA 200 nM 127 bp 55ºC Rv: CAGCAAGAACTCACCACCTGCT SLC13A1 NM_022444.3 Cortex Fw: ACGTGTTTGTGCATTGCCTACTCT 200 nM 113 bp 55ºC Rv: CGACAGTCAGGATAGCGTGTATTGAA SLC5A9 NM_001135181.1 Cortex Fw: TGGTGGGCAGAGTGTTTGTGGT 200 nM 193 bp 58ºC Rv: AGAGCGGTGATGGGTGGGGC STX4 NM_004604.3 Cortex Fw: ACGAGGAGTTCTTCCACAAGGTCC 200 nM 101 bp 55ºC Rv: GGCCAGGATGGTGACCTGCT TNNT2 NM_000364.2 Cortex Fw: GGCCCAGACAGAGCGGAAAAGTG 200 nM 140 bp 55ºC Rv: GCCACAGCTCCTTGGCCTTC

*Fw: forward, Rv: reverse.


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Table S3. Primers and conditions used to confirm microarray miRNA expression results.

Official miRNA symbol

Mature miRNA Accession #

Tissue Primer Sequence (5’ 3’) Concentration Annealing temperature

RNU6B - Control Fw: CGCTTCAAGTAATTCAGGATAGGT 200 nM 60-64ºC hsa-miR-22 MIMAT0000077 Medulla Fw: AAGCTGCCAGTTGAAGAACTGT 200 nM 64ºC hsa-let-7c MIMAT0000064 Medulla Fw: CGCTGAGGTAGTAGGTTGTATGGTT 200 nM 64ºC

hsa-miR-21 MIMAT0000076 Cortex Fw: CGGTAGCTTATCAGACTGATGTTGA 200 nM 60ºC hsa-miR-126 MIMAT0000445 Both Fw: TCGTACCGTGAGTAATAATGCG 200 nM 64ºC hsa-miR-451 MIMAT0001631 Cortex Fw: CGAAACCGTTACCATTACTGAGTT 200 nM 64ºC hsa-miR-663 MIMAT0003326 Cortex Fw: GCGGGACCGCAAAAA 200 nM 64ºC hsa-miR-196a MIMAT0000226 Cortex Fw: GCTAGGTAGTTTCATGTTGTTGGG 100 nM 64ºC hsa-miR-181a MIMAT0000256 Cortex Fw: ATTCAACGCTGTCGGTGAGT 100 nM 64ºC hsa-miR-638 MIMAT0003308 Both Fw: GGGTGGCGGCCTAAAAA 200 nM 60ºC

*Fw: forward. Universal primer was used as reverse.


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Table S4. MicroRNAs and predicted gene targets used for in vitro experiments.

miRNA miRNA mimic number

Gene target Target gene accession

3’UTR construct number

hsa-let-7c HmiR0348-MR04 NR4A2 NM_006186.2 HmiT011968-MT01 hsa-miR-181a HmiR0023-MR04 AIFM1 NM_004208.2 HmiT022136-MT01 REN NM_000537.2 HmiT016215-MT01 hsa-miR663 HmiR0348-MR04 APOE NM_000041.2 HmiT009556-MT01 REN NM_000537.2 HmiT016215-MT01


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Table S5. Transcriptome-wide gene expression array results showing genes differentially expressed in renal medulla between 9 hypertensive and 5 normotensive subjects adjusted by age and BMI (false discovery rate <0.16).

Gene symbol

Probe cluster ID

Fold- difference*

FDR† value Adjustment

ARL4D 8007493 +1.6 0.11 Age BTG2 7908917 +3.4 0.05 Age

DUSP6 7965335 +1.8 0.15 Age GOLGA9P 7982206 +2.3 0.10 BMI HSPA1B 8179324 +1.6 0.16 Age

IER2 8026163 +2.2 0.08 Age/BMI NR4A1 7955589 +4.4 0.13 Age NR4A2 8055952 +6.8 0.05 Age PER1 8012349 +1.8 0.13 Age

RCAN1 8070182 +1.7 0.16 Age SIK1 8070665 +2.3 0.12 Age

SPRED1 7982564 +1.4 0.15 BMI SSB 8046201 –1.3 0.16 Age

TGIF1 8180318 +2.0 0.05 Age

*Positive values indicate higher expression in hypertensives, and negative values indicate higher expression in normotensives.

† FDR: false discovery rate.


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Table S6. Quantitative trait locus (QTL) markers previously associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) or essential hypertension (HT) in humans, and that correspond to the loci housing genes we identified as exhibiting altered expression in renal medulla or in renal cortex of HT subjects. Location of genes was identified in centi-Morgans (cM) based on single nucleotide polymorphisms (SNPs) (estimated by SNP Genetic Mapping at http://integrin.ucd.ie/cgi-bin/rs2cm.cgi).

Gene symbol Chromosome Region (cM) Location relative to QTL for SBP, DBP or HT

Reference for QTL

Medulla ARL4D 17q12–q21 85.50 Close to QTL for SBP and DBP Levy et al., 2000;17

de Lange et al., 200418 BTG2 1q32 218.02 Close to QTL for HT Hunt et al., 200219

DUSP6 12q22–q23 103.29 Close to QTL for DBP and HT Rice et al., 200020 Hunt et al., 200219

GOLGA9P 15q11.2 * HSPA1B 6p21.3 50.70 Close to QTL for HT, SBP and DBP Pankow et al.,;

Wu et al., 200621 IER2 19p13.2 41.45 Close to QTL for SBP Rice et al., 200020

NR4A1 12q13 68.89 Close to QTL for HT Hunt et al., 200219 NR4A2 2q22–q23 169.01 Close to QTL for HT Perola et al., 2000;22

Zhu et al., 2001;23 Caulifield et al., 200324

NR4A3 9q22 151.49–151.55 Close to QTL for HT Caulifield et al., 200324 PER1 17p13.1–p12 37.25 Close to QTL for SBP and DBP Xu et al, 1999;25

Hottenga et al., 2007 26


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RCAN1 21q22.12 37.24 – 37.59 Close to QTL for SBP Atwood et al., 2001;27 Simino et al., 201128

SIK1 21q22.3 61.33 None SPRED1 15q14 34.99 – 35.01 None

SSB 2q31.1 180.70 Close to QTL for HT, SBP and DBP Hsueh et al., 2000;29 Perola et al., 2000;22 Zhu et al., 2001;23

Caulifield et al., 200324

TGIF1 18p11.3 8.16 Close to QTL for DBP Levy et al., 200017

Cortex AIFM1 Xq26.1 138.74 None

ALDH1B1 9p11.1 62.54 None AMBP 9q32–q33 153.11–153.14 Close to QTL for HT Caulifield et al., 200324 APOE 19q13.2 81.03 None ASL 7q11.2 8.14 Close to QTL for SBP Adeyemo et al., 200530

C14orf183 14q21.3 45.79 Close to QTL for DBP Hottenga et al., 2007 26 C8orf84 8q21.11 103.92 Close to QTL for DBP Simino et al., 201128 C9orf106 9q34.11 171.76 Close to QTL for HT Caulifield et al., 200324

CD36 7q11.2 93.84–93.88 Close to QTL for DBP Adeyemo et al., 200530 CDCP1 3p21.31 67.68 Close to QTL for HT and SBP Wu et al., 200621 EFNB1 Xq12 86.83 None


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FAM154B 15q25.2 83.23 Close to QTL for HT, SBP and DBP Krushkal et al., 1999;31 Xu et al, 1999;25

Hunt et al., 200219 FAM90A1 12p13.31 22.46 Close to QTL SBP and DBP Gong et al., 200332 FRMD6 14q22.1 47.52 None

GADD45A 1p31.2 104.52 In and close to QTL for SBP Rice et al., 2000;20 Harrap et al., 200233

GHRHR 7p14 49.52–49.54 Close to QTL for HT Hunt et al., 200219 GPD1 12q12–q13 66.83 Close to QTL for HT Hunt et al., 200219 GRIA3 Xq25 130.13–130.65 None HIRIP3 16p11.2 61.26 In QTL for SBP Harrap et al., 200233

HNRNPD 4q21 96.17 In QTL for SBP Harrap et al., 200233 KIAA1841 2q14 84.71 None KIAA2022 Xq13.3 91.52 None LRRC37A2 17q21.31 87.02 Close to QTL for SBP and DBP Levy et al., 200017 NDUFAF1 15q11.2–q21.3 39.09 None

NPL 1q25 194.65 Close to QTL for HT Hunt et al., 200219 PHF21A 11p11.2 79.04 None PIK3C2G 12p12 36.60 Close to QTL SBP and DBP Gong et al., 200332 PRDX5 11q13 85.59 In QTL for SBP Rice et al., 200020 PSMC3 11p11.2 79.46–79.47 None

REN 1q32 221.37 Close to QTL for HT Hunt et al., 200219 RENBP Xq28 187.70 None


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RNF133 7q31.32 † RNF216L 7p22.1 * RNPC3 1p21 135.88–135.89 Close to QTL for SBP Rice et al., 200020 SART1 11q13.1 86.60 In QTL for SBP Rice et al., 200020

SLC13A1 7q31–q32 137.93 – 138.01 Close to QTL for SBP and HT Adeyemo et al., 2005;30 Rice et al., 2000;20 Hunt et al., 200219

SLC5A9 1p33 81.59 In and close to QTL for SBP Rice et al., 2000;20 Harrap et al., 200233

SPDYE8P 7q11.23 * STX17 9q31.1 123.44 Close to QTL for HT Caulifield et al., 200324 STX4 16p11.2 61.75 In QTL for SBP Harrap et al., 200233

TAF15 17q11.1–q11.2 65.25 In QTL for SBP Levy et al., 200017 TBC1D3P2 17q23.2 *

TNNT2 1q32 215.64 Close to QTL for HT Hunt et al., 200219 TRIM41 5q35.3 217.23–217.25 None TRIM44 11p13 68.30–68-59 None TUFM 16p11.2 60.43 In QTL for SBP Harrap et al., 200233 XRRA1 11q13.4 95.04 Close to QTL for SBP Rice et al., 200020

ZFY Yp11.3 † None

*No SNPs in the genes indicated have been described, so the specific region where the gene is located could not be found.

†SNPs could not be mapped.


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Table S7. Summary of the function of the genes differentially expressed between renal tissue from hypertensive and normotensive subjects and also validated by qPCR.

Gene name Gene symbol Function relevant to hypertension

Medulla Nuclear receptor subfamily 4, group A, member 1, 2 and 3

NR4A1, NR4A2, NR4A3

Transcription factors with role in inflammation + can regulate aldosterone synthase gene.34

Period homolog 1 PER1 Involved in the regulation of the renal epithelial Na+ channel and implicated in the circadian clock control of Na+ balance.35

Salt-inducible kinase 1 SIK1 Activity of Na+,K+-ATPase pump in renal proximal tubules is dependent on SIK1 and this might increase Na+ reabsorption.36

Cortex Apoptosis-inducing factor, mitochondrion-associated, 1

AIFM1 Apoptotic and NADH* oxidase activities.37

α1-microglobulin/bikunin precursor

AMBP Higher levels in urine were suggested as a marker of renal insufficiency38 and in hypertension as a marker of inflammation.39

Apolipoprotein E APOE Well-known involvement in atherosclerosis. CD36 CD36 Renal deficiency in CD36 leads to hypertension in a rodent model.40 Ephrin-β1 EFNB1 Can enhance inflammatory response.41 NADH dehydrogenase 1α subcomplex, assembly factor 1

NDUFAF1 Mitochondrial enzyme essential for the assembly and stability of mitochondrial Complex I.42

Peroxiredoxin 5 PRDX5 Antioxidant,43 activated by an increase in ROS†.44 Renin REN Rate-limiting enzyme for generation of Ang II; well-known role in BP§

regulation. Renin binding protein RENBP Inhibits renin activity in vitro by forming a dimer with renin,45 but there is no

evidence for an effect on BP‡ in vivo.46


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Solute carrier family 13 (sodium/sulfate symporters), member 1

SLC13A1 Involved in sodium/sulfate cotransport in kidney.

Syntaxin 4 STX4 Involved in vasopressin-regulated water absorption pathway in nephron.47 Troponin T type 2 TNNT2 Associated with cardiomyopathies.

* NADH: nicotinamide adenine dinucleotide

† ROS: reactive oxygen species

‡ BP: blood pressure


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Table S8. Gene ontology analysis of the gene list for renal medulla in hypertensive compared to normotensive subjects.

Ontology Representation GOCCID FDR value

Odds Ratio

Count Size Term Genes

BioP Over GO:0030154 0.05 9.94 6 1009 cell differentiation BTG2, C15orf2, DUSP6, NR4A2, RCAN1, SIK1

BioP Over GO:0048869 0.05 9.10 6 1091 cellular developmental process

BTG2, C15orf2, DUSP6, NR4A2, RCAN1, SIK1

BioP Over GO:0010243 0.05 54.2 2 40 response to organic nitrogen BTG2, NR4A2 BioP Over GO:0007165 0.05 8.04 7 1671 signal transduction ARL4D, DUSP6, NR4A1,

NRA42, PER1, RCAN1, SIK1

BioP Over GO:0048523 0.05 8.29 6 1183 negative regulation of cellular process

BTG2, HSPA1B, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0048519 0.05 7.62 6 1272 negative regulation of biological process

BTG2, HSPA1B, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0031323 0.05 7.45 8 2462 regulation of cellular metabolic process

BTG2, DUSP6, NR4A1, NR4A2, PER1, RCAN1,

SIK1, TGIF1 BioP Over GO:0023060 0.05 7.08 7 1852 signal transmission BTG2, DUSP6, NR4A1,

NR4A2, PER1, RCAN1, SIK1

BioP Over GO:0023046 0.05 7.07 7 1853 signaling process ARL4D, DUSP6, NR4A1, NRA42, PER1, RCAN1,

SIK1 BioP Over GO:0006357 0.05 9.78 4 518 regulation of transcription

from RNA polymerase II promoter

NR4A1, NR4A2, SIK1, TGIF1

BioP Over GO:0019222 0.05 7.02 8 2571 regulation of metabolic BTG2, DUSP6, NR4A1,


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process NR4A2, PER1, RCAN1, SIK1, TGIF1

BioP Over GO:0050794 0.05 12.0 10 4244 regulation of cellular process ARL4D, BTG2, DUSP6, HSPA1B, NR4A1, NRA42,

PER1, RCAN1, SIK1, TGIF1

BioP Over GO:0032502 0.05 6.62 7 1953 developmental process BTG2, C15orf2, DUSP6, NR4A2, RCAN1, SIK1,

TGIF1 BioP Over GO:0043066 0.05 13.0 3 264 negative regulation of

apoptosis BTG2, HSPA1B, NR4A2

BioP Over GO:0043069 0.05 12.7 3 269 negative regulation of programmed cell death

BTG2, HSPA1B, NR4A2

BioP Over GO:0050790 0.05 8.70 4 578 regulation of catalytic activity DUSP6, NR4A1, NRA42, RCAN1

BioP Over GO:0060548 0.05 12.6 3 272 negative regulation of cell death

BTG2, HSPA1B, NR4A2

BioP Over GO:0042981 0.05 8.62 4 583 regulation of apoptosis BTG2, HSPA1B, NR4A1, NR4A2

BioP Over GO:0050789 0.05 11.1 10 4416 regulation of biological process

ARL4D, BTG2, DUSP6, HSPA1B, NR4A1, NRA42,

PER1, RCAN1, SIK1, TGIF1

BioP Over GO:0043067 0.05 8.49 4 591 regulation of programmed cell death

BTG2, HSPA1B, NR4A1, NR4A2

BioP Over GO:0010941 0.05 8.44 4 594 regulation of cell death BTG2, HSPA1B, NR4A1, NR4A2

BioP Over GO:0051789 0.05 26.0 2 81 response to protein stimulus HSPA1B, NR4A2 BioP Over GO:0006366 0.06 7.70 4 647 transcription from RNA NR4A1, NR4A2, SIK1,


S25

polymerase II promoter TGIF1 BioP Over GO:0065007 0.06 10.1 10 4647 biological regulation ARL4D, BTG2, DUSP6,

HSPA1B, NR4A1, NRA42, PER1, RCAN1, SIK1,

TGIF1 BioP Over GO:0023052 0.06 5.56 7 2234 signaling ARL4D, DUSP6, NR4A1,

NRA42, PER1, RCAN1, SIK1

BioP Over GO:0042069 0.06 233 1 5 regulation of catecholamine metabolic process

NR4A2

BioP Over GO:0042423 0.06 233 1 5 catecholamine biosynthetic process

NR4A2

BioP Over GO:0070841 0.06 233 1 5 inclusion body assembly HSPA1B BioP Over GO:0065009 0.06 7.36 4 675 regulation of molecular

function DUSP6, NR4A1, NRA42,

RCAN1 BioP Over GO:0017085 0.06 186 1 6 response to insecticide NR4A2 BioP Over GO:0021952 0.06 186 1 6 central nervous system

projection neuron axonogenesis

NR4A2

BioP Over GO:0042026 0.06 186 1 6 protein refolding HSPA1B BioP Over GO:0016481 0.06 9.49 3 357 negative regulation of

transcription PER1, SIK1, TGIF1

BioP Over GO:0007275 0.06 5.34 6 1714 multicellular organismal development

BTG2, C15orf2, NR4A2, RCAN1, SIK1, TGIF1

BioP Over GO:0006355 0.06 5.72 5 1189 regulation of transcription, DNA-dependent

NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0001975 0.06 155 1 7 response to amphetamine NR4A2 BioP Over GO:0033238 0.06 155 1 7 regulation of cellular amine

metabolic process NR4A2


S26

BioP Over GO:0051602 0.06 155 1 7 response to electrical stimulus BTG2 BioP Over GO:0006915 0.06 6.49 4 757 apoptosis BTG2, HSPA1B, NR4A1,

NR4A2 BioP Over GO:0051252 0.06 5.51 5 1228 regulation of RNA metabolic

process NR4A1, NR4A2, PER1,

SIK1, TGIF1 BioP Over GO:0012501 0.06 6.41 4 766 programmed cell death BTG2, HSPA1B, NR4A1,

NR4A2 BioP Over GO:0010629 0.06 8.46 3 398 negative regulation of gene

expression PER1, SIK1, TGIF1

BioP Over GO:0045934 0.06 8.46 3 398 negative regulation of nucleobase, nucleoside,

nucleotide and nucleic acid metabolic process

PER1, SIK1, TGIF1

BioP Over GO:0016311 0.06 15.7 2 132 dephosphorylation DUSP6, RCAN1 BioP Over GO:0051172 0.06 8.37 3 402 negative regulation of

nitrogen compound metabolic process

PER1, SIK1, TGIF1

BioP Over GO:0021955 0.06 116 1 9 central nervous system neuron axonogenesis

NR4A2

BioP Over GO:0042752 0.06 116 1 9 regulation of circadian rhythm PER1 BioP Over GO:0043666 0.06 116 1 9 regulation of phosphoprotein

phosphatase activity RCAN1

BioP Over GO:0045449 0.07 4.88 6 1843 regulation of transcription BTG2, NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0010558 0.07 8.02 3 419 negative regulation of macromolecule biosynthetic

process

PER1, SIK1, TGIF1

BioP Over GO:0006351 0.07 5.16 5 1300 transcription, DNA-dependent NR4A1, NR4A2, PER1, SIK1, TGIF1


S27

BioP Over GO:0016070 0.07 4.80 6 1868 RNA metabolic process HSPA1B, NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0032774 0.07 5.15 5 1302 RNA biosynthetic process NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0031327 0.07 7.82 3 429 negative regulation of cellular biosynthetic process

PER1, SIK1, TGIF1

BioP Over GO:0008219 0.07 5.81 4 838 cell death BTG2, HSPA1B, NR4A1, NR4A2

BioP Over GO:0009890 0.07 7.65 3 438 negative regulation of biosynthetic process

PER1, SIK1, TGIF1

BioP Over GO:0042417 0.07 93.0 1 11 dopamine metabolic process NR4A2 BioP Over GO:0043154 0.07 93.0 1 11 negative regulation of caspase

activity NR4A1

BioP Over GO:0016265 0.07 5.78 4 841 death BTG2, HSPA1B, NR4A1, NR4A2

BioP Over GO:0006350 0.07 4.63 6 1921 transcription BTG2, NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0010921 0.07 84.5 1 12 regulation of phosphatase activity

RCAN1

BioP Over GO:0019219 0.07 4.45 6 1982 regulation of nucleobase, nucleoside, nucleotide and

nucleic acid metabolic process

BTG2, NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0006916 0.07 12.3 2 167 anti-apoptosis BTG2, HSPA1B BioP Over GO:0051171 0.07 4.41 6 1998 regulation of nitrogen

compound metabolic process BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0010466 0.07 71.5 1 14 negative regulation of

peptidase activity NR4A1

BioP Over GO:0021954 0.07 71.5 1 14 central nervous system neuron development

NR4A2


S28

BioP Over GO:0042551 0.07 71.5 1 14 neuron maturation NR4A2 BioP Over GO:0010556 0.08 4.37 6 2012 regulation of macromolecule

biosynthetic process BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0010033 0.08 6.84 3 487 response to organic substance BTG2, HSPA1B, NR4A2 BioP Over GO:0010468 0.08 4.26 6 2052 regulation of gene expression BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0000188 0.08 62.0 1 16 inactivation of MAPK activity DUSP6 BioP Over GO:0031326 0.08 4.22 6 2066 regulation of cellular

biosynthetic process BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0009605 0.08 6.47 3 513 response to external stimulus BTG2, NR4A2, PER1 BioP Over GO:0009889 0.08 4.20 6 2076 regulation of biosynthetic

process BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0021953 0.08 58.1 1 17 central nervous system neuron

differentiation NR4A2

BioP Over GO:0043086 0.09 10.4 2 196 negative regulation of catalytic activity

DUSP6, NR4A1

BioP Over GO:0000122 0.09 10.2 2 200 negative regulation of transcription from RNA polymerase II promoter

SIK1, TGIF1

BioP Over GO:0006584 0.09 48.9 1 20 catecholamine metabolic process

NR4A2

BioP Over GO:0009712 0.09 48.9 1 20 catechol metabolic process NR4A2 BioP Over GO:0018958 0.09 48.9 1 20 phenol metabolic process NR4A2 BioP Over GO:0034311 0.09 48.9 1 20 diol metabolic process NR4A2 BioP Over GO:0035303 0.09 48.9 1 20 regulation of

dephosphorylation RCAN1

BioP Over GO:0014075 0.09 46.4 1 21 response to amine stimulus NR4A2 BioP Over GO:0042401 0.09 46.4 1 21 cellular biogenic amine NR4A2


S29

biosynthetic process BioP Over GO:0007242 0.09 4.68 4 1016 intracellular signaling cascade ARL4D, DUSP6, RCAN1,

SIK1 BioP Over GO:0031324 0.09 5.87 3 562 negative regulation of cellular

metabolic process PER1, SIK1, TGIF1

BioP Over GO:0010605 0.09 5.73 3 575 negative regulation of macromolecule metabolic

process

PER1, SIK1, TGIF1

BioP Over GO:0007417 0.10 9.01 2 226 central nervous system development

NR4A2, RCAN1

BioP Over GO:0045768 0.10 40.4 1 24 positive regulation of anti-apoptosis

BTG2

BioP Over GO:0051336 0.10 8.97 2 227 regulation of hydrolase activity

NR4A1, RCAN1

BioP Over GO:0008285 0.10 8.81 2 231 negative regulation of cell proliferation

BTG2, HSPA1B

BioP Over GO:0032501 0.10 3.78 6 2250 multicellular organismal process

BTG2, C15orf2, NR4A2, RCAN1, SIK1, TGIF1

BioP Over GO:0001764 0.10 38.7 1 25 neuron migration NR4A2 BioP Over GO:0019722 0.10 38.7 1 25 calcium-mediated signaling RCAN1 BioP Over GO:0007585 0.10 37.1 1 26 respiratory gaseous exchange NR4A2 BioP Over GO:0043407 0.10 37.1 1 26 negative regulation of MAP

kinase activity DUSP6

BioP Over GO:0009892 0.10 5.41 3 606 negative regulation of metabolic process

PER1, SIK1, TGIF1

BioP Over GO:0045944 0.10 8.47 2 240 positive regulation of transcription from RNA polymerase II promoter

NR4A1, NR4A2

BioP Over GO:0008344 0.10 35.7 1 27 adult locomotory behavior NR4A2


S30

BioP Over GO:0006139 0.10 3.70 7 2994 nucleobase, nucleoside, nucleotide and nucleic acid

metabolic process

BTG2, HSPA1B, NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0044092 0.10 8.33 2 244 negative regulation of molecular function

DUSP6, NR4A1

BioP Over GO:0007623 0.10 34.4 1 28 circadian rhythm PER1 BioP Over GO:0045767 0.10 34.4 1 28 regulation of anti-apoptosis BTG2 BioP Over GO:0007399 0.10 5.26 3 623 nervous system development BTG2, NR4A2, RCAN1 BioP Over GO:0051346 0.10 32.0 1 30 negative regulation of

hydrolase activity NR4A1

BioP Over GO:0048741 0.11 30.9 1 31 skeletal muscle fiber development

RCAN1

BioP Over GO:0080090 0.11 3.55 6 2358 regulation of primary metabolic process


BioP Over GO:0060255 0.11 3.54 6 2362 regulation of macromolecule metabolic process


BioP Over GO:0030182 0.11 7.53 2 269 neuron differentiation BTG2, NR4A2 BioP Over GO:0009612 0.11 28.1 1 34 response to mechanical

stimulus BTG2

BioP Over GO:0045892 0.11 7.30 2 277 negative regulation of transcription, DNA-dependent

SIK1, TGIF1

BioP Over GO:0043524 0.11 27.3 1 35 negative regulation of neuron apoptosis

NR4A2

BioP Over GO:0048747 0.11 27.3 1 35 muscle fiber development RCAN1 BioP Over GO:0051253 0.11 7.19 2 281 negative regulation of RNA

metabolic process SIK1, TGIF1

BioP Over GO:0030534 0.11 26.5 1 36 adult behavior NR4A2 BioP Over GO:0042398 0.11 26.5 1 36 cellular amino acid derivative

biosynthetic process NR4A2


S31

BioP Over GO:0009719 0.11 7.11 2 284 response to endogenous stimulus

BTG2, NR4A2

BioP Over GO:0009791 0.12 23.8 1 40 post-embryonic development NR4A2 BioP Over GO:0048699 0.13 6.65 2 303 generation of neurons BTG2, NR4A2 BioP Over GO:0006479 0.13 22.6 1 42 protein amino acid

methylation BTG2

BioP Over GO:0008213 0.13 22.6 1 42 protein amino acid alkylation BTG2 BioP Over GO:0006807 0.13 3.27 7 3254 nitrogen compound metabolic

process BTG2, HSPA1B, NR4A1,

NR4A2, PER1, SIK1, TGIF1 BioP Over GO:0048469 0.13 22.1 1 43 cell maturation NR4A2 BioP Over GO:0009636 0.13 21.1 1 45 response to toxin NR4A2 BioP Over GO:0055002 0.13 21.1 1 45 striated muscle cell

development RCAN1

BioP Over GO:0034645 0.13 3.18 6 2559 cellular macromolecule biosynthetic process


BioP Over GO:0055001 0.13 20.6 1 46 muscle cell development RCAN1 BioP Over GO:0009306 0.13 20.1 1 47 protein secretion ARL4D BioP Over GO:0019220 0.13 6.18 2 325 regulation of phosphate

metabolic process DUSP6, RCAN1

BioP Over GO:0051174 0.13 6.18 2 325 regulation of phosphorus metabolic process

DUSP6, RCAN1

BioP Over GO:0045893 0.13 6.16 2 326 positive regulation of transcription, DNA-dependent

NR4A1, NR4A2

BioP Over GO:0051254 0.13 6.08 2 330 positive regulation of RNA metabolic process

NR4A1, NR4A2

BioP Over GO:0009059 0.13 3.12 6 2591 macromolecule biosynthetic process


BioP Over GO:0022008 0.13 6.07 2 331 neurogenesis BTG2, NR4A2


S32

BioP Over GO:0006402 0.13 18.2 1 52 mRNA catabolic process HSPA1B BioP Over GO:0007519 0.13 18.2 1 52 skeletal muscle tissue

development RCAN1

BioP Over GO:0031668 0.13 18.2 1 52 cellular response to extracellular stimulus

NR4A2

BioP Over GO:0043281 0.13 18.2 1 52 regulation of caspase activity NR4A1 BioP Over GO:0060538 0.13 18.2 1 52 skeletal muscle organ

development RCAN1

BioP Over GO:0071496 0.13 18.2 1 52 cellular response to external stimulus

NR4A2

BioP Over GO:0052548 0.14 17.5 1 54 regulation of endopeptidase activity

NR4A1

BioP Over GO:0006986 0.14 17.1 1 55 response to unfolded protein HSPA1B BioP Over GO:0006576 0.14 16.8 1 56 cellular biogenic amine


BioP Over GO:0052547 0.15 16.2 1 58 regulation of peptidase activity

NR4A1

BioP Over GO:0009309 0.15 15.9 1 59 amine biosynthetic process NR4A2 BioP Over GO:0043523 0.15 15.9 1 59 regulation of neuron apoptosis NR4A2 BioP Over GO:0021700 0.15 15.4 1 61 developmental maturation NR4A2 BioP Over GO:0042221 0.15 3.76 3 847 response to chemical stimulus BTG2, HSPA1B, NR4A2 BioP Over GO:0043414 0.15 14.9 1 63 macromolecule methylation BTG2 BioP Over GO:0045941 0.15 5.21 2 383 positive regulation of

transcription NR4A1, NR4A2

BioP Over GO:0006401 0.15 14.7 1 64 RNA catabolic process HSPA1B BioP Over GO:0006469 0.15 14.7 1 64 negative regulation of protein

kinase activity DUSP6

BioP Over GO:0007243 0.15 5.16 2 386 protein kinase cascade DUSP6, SIK1


S33

BioP Over GO:0051402 0.15 14.4 1 65 neuron apoptosis NR4A2 BioP Over GO:0070997 0.15 14.2 1 66 neuron death NR4A2 BioP Over GO:0010628 0.15 5.08 2 392 positive regulation of gene

expression NR4A1, NR4A2

BioP Over GO:0044260 0.15 3.05 8 4349 cellular macromolecule metabolic process

BTG2, DUSP6, HSPA1B, NR4A1, NR4A2, PER1,

SIK1, TGIF1 BioP Over GO:0010467 0.15 2.82 6 2782 gene expression BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0033673 0.15 13.8 1 68 negative regulation of kinase

activity DUSP6

BioP Over GO:0032259 0.16 13.2 1 71 methylation BTG2 BioP Over GO:0051146 0.16 13.2 1 71 striated muscle cell

differentiation RCAN1

BioP Over GO:0048511 0.16 13.0 1 72 rhythmic process PER1 BioP Over GO:0051348 0.16 12.8 1 73 negative regulation of

transferase activity DUSP6

BioP Over GO:0030308 0.16 12.7 1 74 negative regulation of cell growth

HSPA1B

BioP Over GO:0006793 0.16 3.45 3 915 phosphorus metabolic process DUSP6, RCAN1, SIK1 BioP Over GO:0006796 0.16 3.45 3 915 phosphate metabolic process DUSP6, RCAN1, SIK1 BioP Over GO:0045935 0.16 4.68 2 424 positive regulation of

nucleobase, nucleoside, nucleotide and nucleic acid

metabolic process

NR4A1, NR4A2

BioP Over GO:0014070 0.17 12.1 1 77 response to organic cyclic substance

BTG2

BioP Over GO:0048468 0.17 4.56 2 435 cell development NR4A2, RCAN1


S34

BioP Over GO:0045792 0.17 11.7 1 80 negative regulation of cell size HSPA1B BioP Over GO:0010557 0.17 4.52 2 438 positive regulation of

macromolecule biosynthetic process

NR4A1, NR4A2

BioP Over GO:0051173 0.17 4.51 2 439 positive regulation of nitrogen compound metabolic process

NR4A1, NR4A2

BioP Over GO:0031328 0.18 4.35 2 454 positive regulation of cellular biosynthetic process

NR4A1, NR4A2

BioP Over GO:0045926 0.18 10.7 1 87 negative regulation of growth HSPA1B BioP Over GO:0044237 0.18 3.21 9 5442 cellular metabolic process BTG2, DUSP6, HSPA1B,

NR4A1, NR4A2, PER1, SIK1, TGIF1

BioP Over GO:0009891 0.18 4.29 2 460 positive regulation of biosynthetic process

NR4A1, NR4A2

BioP Over GO:0006725 0.18 10.2 1 91 cellular aromatic compound metabolic process

NR4A2

BioP Over GO:0014706 0.18 10.2 1 91 striated muscle tissue development

RCAN1

BioP Over GO:0009952 0.18 10.1 1 92 anterior/posterior pattern formation

BTG2

BioP Over GO:0042692 0.19 9.80 1 95 muscle cell differentiation RCAN1 BioP Over GO:0043405 0.19 9.70 1 96 regulation of MAP kinase

activity DUSP6

BioP Over GO:0060537 0.19 9.70 1 96 muscle tissue development RCAN1 BioP Over GO:0001666 0.19 9.60 1 97 response to hypoxia NR4A2 BioP Over GO:0007346 0.19 9.60 1 97 regulation of mitotic cell cycle SIK1 BioP Over GO:0043170 0.19 2.70 8 4632 macromolecule metabolic

process BTG2, DUSP6, HSPA1B, NR4A1, NR4A2, PER1, RCAN1, SIK1, TGIF1


S35

BioP Over GO:0006470 0.19 9.40 1 99 protein amino acid dephosphorylation

DUSP6

BioP Over GO:0042127 0.19 3.98 2 494 regulation of cell proliferation BTG2, HSPA1B BioP Over GO:0006730 0.19 9.21 1 101 one-carbon metabolic process BTG2 BioP Over GO:0070482 0.19 9.21 1 101 response to oxygen levels NR4A2 BioP Over GO:0044249 0.19 2.44 6 3072 cellular biosynthetic process BTG2, NR4A1, NR4A2,

PER1, SIK1, TGIF1 BioP Over GO:0006575 0.19 8.85 1 105 cellular amino acid derivative


BioP Over GO:0044087 0.19 8.85 1 105 regulation of cellular component biogenesis

HSPA1B

BioP Over GO:0051129 0.20 8.68 1 107 negative regulation of cellular component organization

HSPA1B

BioP Over GO:0043687 0.20 2.92 3 1062 post-translational protein modification

BTG2, DUSP6, SIK1

BioP Over GO:0019932 0.20 8.44 1 110 second-messenger-mediated signaling

RCAN1

MF Over GO:0003707 0.03 62.4 2 37 steroid hormone receptor activity

NR4A1, NR4A2

MF Over GO:0004879 0.03 57.5 2 40 ligand-dependent nuclear receptor activity

NR4A1, NR4A2

MF Over GO:0005126 0.04 0.018 1 73 cytokine receptor binding SPRED1 MF Over GO:0003700 0.05 8.59 4 619 transcription factor activity NR4A1, NR4A2, RCAN1,

TGIF1 MF Over GO:0030528 0.05 7.03 5 1050 transcription regulator activity NR4A1, NR4A2, RCAN1,

SIK1, TGIF1 MF Over GO:0043565 0.08 9.41 3 381 sequence-specific DNA

binding NR4A1, NR4A2, TGIF1

MF Over GO:0046982 0.08 16.8 2 131 protein heterodimerization NR4A1, NR4A2


S36

activity MF Over GO:0017017 0.08 109 1 10 MAP kinase

tyrosine/serine/threonine phosphatase activity

DUSP6

MF Over GO:0033549 0.08 109 1 10 MAP kinase phosphatase activity

DUSP6

MF Over GO:0016564 0.19 8.76 2 246 transcription repressor activity SIK1, TGIF1 KEGG Over 4010 0.00 51.1 3 190 MAPK signaling pathway DUSP6, HSPA1B, NR4A1 KEGG Over 4710 0.04 93.4 1 13 Circadian rhythm - mammal PER1 KEGG Over 4612 0.09 24.1 1 47 Antigen processing and

presentation HSPA1B

KEGG Over 3040 0.17 9.04 1 121 Spliceosome HSPA1B KEGG Over 4144 0.18 6.78 1 159 Endocytosis HSPA1B

*Ontology: BioP, biological process; MF, molecular function; KEGG: Kyoto Encyclopedia of Genes and Genomes; GOID: Gene Ontology identification. Shown is the P value adjusted by Benjamini-Hochberg false discovery rate (FDR) (≤0.2=significant); OR, odds ratio: p(Ontology W in the list)/p(Ontology W in the universe); ExpCount, expected count; Count, number of genes found that belong to ontology; Size, total number of genes in ontology; Term, ontology-associated term; Gene Symbol, official gene symbol.


S37

Table S9. Gene set tests (GST), based on gene ontology, of the gene list for renal medulla comparing samples from hypertensive and normotensive subjects.

Ontology Representation GOCCID P-value (Bonferroni adjusted)

Count Term

BioP Over GO:0055085 P<0.001 331 transmembrane transport BioP Over GO:0006811 P<0.001 248 ion transport BioP Over GO:0006814 P<0.001 71 sodium ion transport BioP Over GO:0055114 0.001 390 oxidation reduction BioP Under GO:0045449 P<0.001 802 regulation of transcription BioP Under GO:0006511 0.006 137 ubiquitin-dependent protein catabolic process BioP Under GO:0019941 0.01 316 modification-dependent protein catabolic process BioP Under GO:0006397 0.01 203 mRNA processing BioP Under GO:0008380 0.01 236 RNA splicing CC Over GO:0016021 P<0.001 2244 integral to membrane CC Over GO:0016020 P<0.001 2552 membrane CC Over GO:0005739 0.003 916 mitochondrion CC Over GO:0016324 0.006 103 apical plasma membrane CC Over GO:0005743 0.009 216 mitochondrial inner membrane CC Under GO:0005634 P<0.001 3849 nucleus CC Under GO:0005622 P<0.001 1505 intracellular CC Under GO:0005737 P<0.001 3477 cytoplasm CC Down GO:0005730 P<0.001 618 nucleolus CC Under GO:0016607 0.001 115 nuclear speck CC Under GO:0005681 0.04 126 spliceosomal complex MF Over GO:0005215 P<0.001 181 transporter activity MF Over GO:0031402 P<0.001 66 sodium ion binding


S38

MF Over GO:0015293 P<0.001 70 symporter activity MF Under GO:0003677 P<0.001 954 DNA binding MF Under GO:0008270 P<0.001 1711 zinc ion binding MF Under GO:0046872 P<0.001 1841 metal ion binding MF Under GO:0005515 P<0.001 4158 protein binding MF Under GO:0003723 P<0.001 514 RNA binding MF Under GO:0000166 P<0.001 1582 nucleotide binding MF Under GO:0005524 0.005 1166 ATP binding MF Under GO:0003682 0.03 100 chromatin binding

KEGG 5332 0.006 22 Graft-versus-host disease KEGG 5320 0.02 23 Autoimmune thyroid disease KEGG 5330 0.02 23 Allograft rejection KEGG 5310 0.03 16 Asthma

*Ontology: BioP, biological process; MF, molecular function; KEGG: Kyoto Encyclopedia of Genes and Genomes; GOID: Gene Ontology identification. Shown is Term, ontology-associated term; Count, number of genes found that belong to ontology; P-value, P value adjusted by Benjamini-Hochberg (≤0.05=significant).


S39

Table S10. Transcriptome-wide expression array findings for microRNAs differentially expressed in renal medulla between 5 hypertensive and 3 normotensive subjects (false discovery rate ≤0.24).

miRNA name Fold- difference*

Microarray FDR† value

hsa-miR-196a +1.4 0.003 hsa-miR-26b +1.9 0.02 hsa-miR-21 +1.5 0.07 hsa-miR-638 –1.5 0.07 hsa-miR-10a +1.4 0.08 hsa-miR-16 +1.4 0.12 hsa-miR-126 +1.5 0.15

hsa-let-7c –1.2 0.18 hsa-let-7g +1.5 0.18

hsa-miR-22 +1.6 0.18 hsa-miR-181a –1.5 0.24




S40

Table S11. Transcriptome-wide gene expression array data showing mRNAs differentially expressed in renal cortex between 5 hypertensive and 3 normotensive subjects (false discovery rate ≤0.1).

Gene symbol

Probe cluster ID

Fold-difference*

FDR† value Gene symbol

Probe cluster ID

Fold-difference*

FDR† value

AIFM1 8175052 +1.8 0.08 NPL 7908003 +1.5 0.06 ALDH1B1 8155327 +1.4 0.08 PHF21A 7947624 –1.3 0.10

AMBP 8163535 +1.5 0.05 PIK3C2G 7954208 –1.4 0.07 APOE 8029530 +1.7 0.08 PRDX5 7940996 +1.6 0.08 ASL 8133122 +1.4 0.10 PSMC3 7947867 +1.4 0.09

C14orf183 7978917 –1.4 0.09 RENBP 8175933 +2.8 0.008 C8orf84 8151369 –1.3 0.09 RNF133 8142618 –1.6 0.06 C9orf106 8158539 –1.4 0.07 RNF216L 8131286 –1.3 0.09 CDCP1 8086517 –1.6 0.09 RNPC3 7903404 –1.4 0.09 EFNB1 8168045 1.4 0.09 SART1 7941478 +1.6 0.08

FAM154B 7985398 –1.9 0.03 SLC13A1 8142628 +1.6 0.09 FAM90A1 8144448 +2.3 0.02 SLC5A9 7901316 +1.6 0.09 FRMD6 7974316 –1.4 0.06 SPDYE8P 8140211 +1.4 0.08

GADD45A 7902227 +1.6 0.09 STX17 8156861 –1.4 0.10 GHRHR 8132130 +1.3 0.10 STX4 7995017 +1.6 0.08 GPD1 7955348 +1.9 0.09 TAF15 8006573 +1.6 0.04 GRIA3 8169717 +1.5 0.09 TBC1D3P2 8017173 +2.0 0.10 HIRIP3 8000748 +1.5 0.08 TNNT2 7923332 +1.8 0.08

HNRNPD 8101324 +1.5 0.09 TRIM41 8110649 +1.5 0.08 KIAA1841 8042168 –1.4 0.08 TRIM44 7939368 +1.6 0.03 KIAA2022 8173621 –1.4 0.06 TUFM 8000603 +1.3 0.09


S41

LRRC37A2 8007867 –1.6 0.07 XRRA1 7950447 +1.5 0.08 NDUFAF1 7987642 +1.4 0.08 ZFY 8176384 –1.3 0.09




S42

Table S12. Transcriptome-wide gene expression array data showing mRNAs differentially expressed in renal cortex between hypertensive and normotensive subjects based on fold difference values higher than 2. Gene Symbol Gene Name Entrez ID Probe cluster ID Fold difference

ALB albumin 213 8095628 +2.1 APOH apolipoprotein H (beta-2-glycoprotein I) 350 8017766 +2.8

C2 complement component 2 717 8179331 +2.1 CCDC144A coiled-coil domain containing 144A 9720 8013272 –2.0

CD36 CD36 molecule (thrombospondin receptor) 948 8133876 –2.0 CDH6 cadherin 6, type 2, K-cadherin (fetal kidney) 1004 8104663 +2.0

CDK11B cyclin-dependent kinase 11B 984 8180291 +2.7 CYP24A1 cytochrome P450, family 24, subfamily A,

polypeptide 1 1591 8067140 +4.2

CYP4F11 cytochrome P450, family 4, subfamily F, polypeptide 11

57834 8035095 +2.7

CYP8B1 cytochrome P450, family 8, subfamily B, polypeptide 1

1582 8086457 +2.0

DRD5 dopamine receptor D5 1816 7905025 –2.0 FAM40B family with sequence similarity 40, member B 57464 8136115 +2.6 FAM90A1 family with sequence similarity 90, member A1 55138 8144448 +2.3

FCN3 ficolin (collagen/fibrinogen domain containing) 3 (Hakata antigen)

8547 7914075 +2.0

HAVCR2 hepatitis A virus cellular receptor 2 84868 8115464 +2.8 HHLA2 HERV-H LTR-associating 2 11148 8081488 +2.0 IGHA1 immunoglobulin heavy constant alpha 1 3493 7981722 +2.1 IGLJ3 immunoglobulin lambda joining 3 28831 7981730 +2.0

KRTAP4-9 keratin associated protein 4-9 1E+08 8007112 +2.1


S43

LOC146336 hypothetical LOC146336 146336 7998405 –2.2 LOC162632 TL132 pseudogene 162632 8005225 –2.4 LOC375010 ankyrin repeat domain 20 family, member A

pseudogene 375010 7919139 –2.2

MUC13 mucin 13, cell surface associated 56667 8090180 +2.3 NKAIN4 Na+/K+ transporting ATPase interacting 4 128414 8067602 +2.3

PLTP phospholipid transfer protein 5360 8066619 +2.9 REG1A regenerating islet-derived 1 alpha 5967 8042986 +2.2

REN renin 5972 7923608 +2.7 RENBP renin binding protein 5973 8175933 +2.8 RNU4-2 RNA, U4 small nuclear 2 26834 7967028 +2.0

RPS9 ribosomal protein S9 6203 8031152 –2.1 SLC5A2 solute carrier family 5 (sodium/glucose

cotransporter), member 2 6524 7995222 +2.7

SPINK1 serine peptidase inhibitor, Kazal type 1 6690 8114964 +2.2 TBC1D3P2 TBC1 domain family, member 3 pseudogene 2 440452 8017173 +2.0

TGIF1 TGFB-induced factor homeobox 1 7050 8180318 –2.0 VNN1 vanin 1 8876 8129618 +2.8 VPS52 vacuolar protein sorting 52 homolog (S.

cerevisiae) 6293 8178917 +2.2

*Positive fold difference values indicate higher expression in kidneys of hypertensive subjects; negative values indicate higher expression in normotensive kidneys.


S44

Table S13. Gene ontology (GO) analysis of the gene list for renal cortex comparing hypertensive to normotensive subjects.

Ontology Representation GOCCID FDR value Odds Ratio

Count Size Term Genes

BioP Over GO:0030801 0.08 84.6 2 15 positive regulation of cyclic nucleotide metabolic process

APOE, GHRHR

BioP Over GO:0030804 0.08 84.6 2 15 positive regulation of cyclic nucleotide biosynthetic

process

APOE, GHRHR

BioP Over GO:0030810 0.08 84.6 2 15 positive regulation of nucleotide biosynthetic

process

APOE, GHRHR

BioP Over GO:0045981 0.08 84.6 2 15 positive regulation of nucleotide metabolic process

APOE, GHRHR

BioP Over GO:0010035 0.09 12.5 4 194 response to inorganic substance

APOE, GRIA3, PRDX5, TNNT2

BioP Over GO:0043086 0.13 9.27 4 259 negative regulation of catalytic activity

APOE, GADD45A, PSMC3, TNNT2

BioP Over GO:0009056 0.13 4.36 8 1242 catabolic process AIFM1, AMBP, APOE, ASL, GPD1, HNRNPD,

PSMC3, TRIM41 BioP Over GO:0050865 0.13 11.5 3 152 regulation of cell activation APOE, EFNB1, SART1 BioP Over GO:0044248 0.13 4.36 7 1045 cellular catabolic process AIFM1, AMBP, APOE,

ASL, HNRNPD, PSMC3, TRIM41

BioP Over GO:0044092 0.13 7.47 4 319 negative regulation of molecular function

APOE, GADD45A, PSMC3, TNNT2

BioP Over GO:0043112 0.13 26.1 2 44 receptor metabolic process APOE, GRIA3 BioP Over GO:0008629 0.13 25.5 2 45 induction of apoptosis by AIFM1, SART1


S45

intracellular signals BioP Over GO:0040014 0.13 21.9 2 52 regulation of multicellular

organism growth APOE, GHRHR

BioP Over GO:0043603 0.13 21.9 2 52 cellular amide metabolic process

ASL, GPD1

BioP Over GO:0055114 0.13 5.26 5 580 oxidation reduction AIFM1, ALDH1B1, GPD1, NDUFAF1,

PRDX5 BioP Over GO:0045454 0.13 18.9 2 60 cell redox homeostasis AIFM1, PRDX5 BioP Over GO:0050870 0.13 17.4 2 65 positive regulation of T cell

activation EFNB1, SART1

BioP Over GO:0003013 0.13 8.46 3 204 circulatory system process APOE, RENBP, TNNT2 BioP Over GO:0008015 0.13 8.46 3 204 blood circulation APOE, RENBP, TNNT2 BioP Over GO:0000302 0.13 15.2 2 74 response to reactive oxygen

species APOE, PRDX5

BioP Over GO:0003209 0.13 132 1 5 cardiac atrium morphogenesis TNNT2 BioP Over GO:0003230 0.13 132 1 5 cardiac atrium development TNNT2 BioP Over GO:0006787 0.13 132 1 5 porphyrin catabolic process AMBP BioP Over GO:0010226 0.13 132 1 5 response to lithium ion GRIA3 BioP Over GO:0010875 0.13 132 1 5 positive regulation of

cholesterol efflux APOE

BioP Over GO:0030825 0.13 132 1 5 positive regulation of cGMP metabolic process

APOE

BioP Over GO:0030828 0.13 132 1 5 positive regulation of cGMP biosynthetic process

APOE

BioP Over GO:0033015 0.13 132 1 5 tetrapyrrole catabolic process AMBP BioP Over GO:0034380 0.13 132 1 5 high-density lipoprotein

particle assembly APOE


S46

BioP Over GO:0034382 0.13 132 1 5 chylomicron remnant clearance

APOE

BioP Over GO:0043568 0.13 132 1 5 positive regulation of insulin-like growth factor receptor

signaling pathway

GHRHR

BioP Over GO:0060124 0.13 132 1 5 positive regulation of growth hormone secretion

GHRHR

BioP Over GO:0001919 0.13 105 1 6 regulation of receptor recycling

GRIA3

BioP Over GO:0006072 0.13 105 1 6 glycerol-3-phosphate metabolic process

GPD1

BioP Over GO:0010874 0.13 105 1 6 regulation of cholesterol efflux APOE BioP Over GO:0032373 0.13 105 1 6 positive regulation of sterol

transport APOE

BioP Over GO:0032376 0.13 105 1 6 positive regulation of cholesterol transport

APOE

BioP Over GO:0034447 0.13 105 1 6 very-low-density lipoprotein particle clearance

APOE

BioP Over GO:0042159 0.13 105 1 6 lipoprotein catabolic process APOE BioP Over GO:0006469 0.13 13.3 2 84 negative regulation of protein

kinase activity APOE, GADD45A

BioP Over GO:0051251 0.13 13.3 2 84 positive regulation of lymphocyte activation

EFNB1, SART1

BioP Over GO:0044265 0.13 4.13 5 728 cellular macromolecule catabolic process

AIFM1, APOE, HNRNPD, PSMC3,

TRIM41 BioP Over GO:0033673 0.13 12.7 2 88 negative regulation of kinase

activityAPOE, GADD45A

BioP Over GO:0048585 0.13 12.6 2 89 negative regulation of AMBP, APOE


S47

response to stimulus BioP Over GO:0000050 0.13 87.9 1 7 urea cycle ASL BioP Over GO:0006527 0.13 87.9 1 7 arginine catabolic process ASL BioP Over GO:0010873 0.13 87.9 1 7 positive regulation of

cholesterol esterification APOE

BioP Over GO:0019627 0.13 87.9 1 7 urea metabolic process ASL BioP Over GO:0021984 0.13 87.9 1 7 adenohypophysis development GHRHR BioP Over GO:0032801 0.13 87.9 1 7 receptor catabolic process APOE BioP Over GO:0051000 0.13 87.9 1 7 positive regulation of nitric-

oxide synthase activity APOE

BioP Over GO:0060123 0.13 87.9 1 7 regulation of growth hormone secretion

GHRHR

BioP Over GO:0002696 0.13 12.1 2 92 positive regulation of leukocyte activation

EFNB1, SART1

BioP Over GO:0043085 0.13 4.81 4 486 positive regulation of catalytic activity

APOE, GHRHR, PSMC3, TNNT2

BioP Over GO:0051348 0.13 11.9 2 94 negative regulation of transferase activity

APOE, GADD45A

BioP Over GO:0030802 0.13 11.6 2 96 regulation of cyclic nucleotide biosynthetic process

APOE, GHRHR

BioP Over GO:0030808 0.13 11.6 2 96 regulation of nucleotide biosynthetic process

APOE, GHRHR

BioP Over GO:0050867 0.13 11.5 2 97 positive regulation of cell activation

EFNB1, SART1

BioP Over GO:0001881 0.13 75.3 1 8 receptor recycling GRIA3 BioP Over GO:0006707 0.13 75.3 1 8 cholesterol catabolic process APOE BioP Over GO:0006734 0.13 75.3 1 8 NADH metabolic process GPD1 BioP Over GO:0008340 0.13 75.3 1 8 determination of adult lifespan GHRHR


S48

BioP Over GO:0010543 0.13 75.3 1 8 regulation of platelet activation

APOE

BioP Over GO:0010872 0.13 75.3 1 8 regulation of cholesterol esterification

APOE

BioP Over GO:0010894 0.13 75.3 1 8 negative regulation of steroid biosynthetic process

APOE

BioP Over GO:0016127 0.13 75.3 1 8 sterol catabolic process APOE BioP Over GO:0030826 0.13 75.3 1 8 regulation of cGMP

biosynthetic process APOE

BioP Over GO:0032799 0.13 75.3 1 8 low-density lipoprotein receptor metabolic process

APOE

BioP Over GO:0034384 0.13 75.3 1 8 high-density lipoprotein particle clearance

APOE

BioP Over GO:0043537 0.13 75.3 1 8 negative regulation of blood vessel endothelial cell

migration

APOE

BioP Over GO:0043567 0.13 75.3 1 8 regulation of insulin-like growth factor receptor

signaling pathway

GHRHR

BioP Over GO:0043604 0.13 75.3 1 8 amide biosynthetic process ASL BioP Over GO:0045939 0.13 75.3 1 8 negative regulation of steroid

metabolic process APOE

BioP Over GO:0051044 0.13 75.3 1 8 positive regulation of membrane protein ectodomain

proteolysis

APOE

BioP Over GO:0030799 0.13 11.1 2 100 regulation of cyclic nucleotide metabolic process

APOE, GHRHR

BioP Over GO:0005975 0.13 4.59 4 509 carbohydrate metabolic process

ALDH1B1, GPD1, NPL, RENBP


S49

BioP Over GO:0006140 0.13 10.9 2 102 regulation of nucleotide metabolic process

APOE, GHRHR

BioP Over GO:0009057 0.13 3.78 5 790 macromolecule catabolic process

AIFM1, APOE, HNRNPD, PSMC3,

TRIM41 BioP Over GO:0050863 0.13 10.8 2 103 regulation of T cell activation EFNB1, SART1 BioP Over GO:0002021 0.13 65.9 1 9 response to dietary excess APOE BioP Over GO:0019934 0.13 65.9 1 9 cGMP-mediated signaling APOE BioP Over GO:0030049 0.13 65.9 1 9 muscle filament sliding TNNT2 BioP Over GO:0030823 0.13 65.9 1 9 regulation of cGMP metabolic

process APOE

BioP Over GO:0032370 0.13 65.9 1 9 positive regulation of lipid transport

APOE

BioP Over GO:0032781 0.13 65.9 1 9 positive regulation of ATPase activity

TNNT2

BioP Over GO:0033275 0.13 65.9 1 9 actin-myosin filament sliding TNNT2 BioP Over GO:0045540 0.13 65.9 1 9 regulation of cholesterol

biosynthetic process APOE

BioP Over GO:0070252 0.13 65.9 1 9 actin-mediated cell contraction TNNT2 BioP Over GO:0090181 0.13 65.9 1 9 regulation of cholesterol


BioP Over GO:0009190 0.13 10.7 2 104 cyclic nucleotide biosynthetic process

APOE, GHRHR

BioP Over GO:0050790 0.13 3.76 5 794 regulation of catalytic activity APOE, GADD45A, GHRHR, PSMC3,

TNNT2 BioP Over GO:0007050 0.13 10.5 2 106 cell cycle arrest GADD45A, SART1 BioP Over GO:0006013 0.14 58.6 1 10 mannose metabolic process RENBP


S50

BioP Over GO:0033700 0.14 58.6 1 10 phospholipid efflux APOE BioP Over GO:0019935 0.14 10.1 2 110 cyclic-nucleotide-mediated

signaling APOE, GHRHR

BioP Over GO:0051716 0.14 3.63 5 820 cellular response to stimulus AIFM1, AMBP, GADD45A, GHRHR,

PRDX5 BioP Over GO:0044093 0.14 4.24 4 548 positive regulation of

molecular function APOE, GHRHR, PSMC3, TNNT2

BioP Over GO:0006525 0.14 52.7 1 11 arginine metabolic process ASL BioP Over GO:0010259 0.14 52.7 1 11 multicellular organismal aging GHRHR BioP Over GO:0030816 0.14 52.7 1 11 positive regulation of cAMP

metabolic process GHRHR

BioP Over GO:0030819 0.14 52.7 1 11 positive regulation of cAMP biosynthetic process

GHRHR

BioP Over GO:0032770 0.14 52.7 1 11 positive regulation of monooxygenase activity

APOE

BioP Over GO:0033143 0.14 52.7 1 11 regulation of steroid hormone receptor signaling pathway

GHRHR

BioP Over GO:0034370 0.14 52.7 1 11 triglyceride-rich lipoprotein particle remodeling

APOE

BioP Over GO:0034372 0.14 52.7 1 11 very-low-density lipoprotein particle remodeling

APOE

BioP Over GO:0034433 0.14 52.7 1 11 steroid esterification APOE BioP Over GO:0034434 0.14 52.7 1 11 sterol esterification APOE BioP Over GO:0034435 0.14 52.7 1 11 cholesterol esterification APOE BioP Over GO:0046329 0.14 52.7 1 11 negative regulation of JNK

cascade AMBP

BioP Over GO:0051043 0.14 52.7 1 11 regulation of membrane protein ectodomain proteolysis

APOE


S51

BioP Over GO:0070303 0.14 52.7 1 11 negative regulation of stress-activated protein kinase

signaling pathway

AMBP

BioP Over GO:0009187 0.14 9.57 2 116 cyclic nucleotide metabolic process

APOE, GHRHR

BioP Over GO:0006917 0.14 5.54 3 307 induction of apoptosis AIFM1, APOE, SART1 BioP Over GO:0012502 0.14 5.53 3 308 induction of programmed cell

death AIFM1, APOE, SART1

BioP Over GO:0010038 0.14 9.25 2 120 response to metal ion GRIA3, TNNT2 BioP Over GO:0030252 0.14 47.9 1 12 growth hormone secretion GHRHR BioP Over GO:0034377 0.14 47.9 1 12 plasma lipoprotein particle

assembly APOE

BioP Over GO:0065005 0.14 47.9 1 12 protein-lipid complex assembly

APOE

BioP Over GO:0033554 0.14 4.02 4 577 cellular response to stress AIFM1, AMBP, GADD45A, PRDX5

BioP Over GO:0006814 0.14 8.79 2 126 sodium ion transport SLC13A1, SLC5A9 BioP Over GO:0008272 0.14 43.9 1 13 sulfate transport SLC13A1 BioP Over GO:0010165 0.14 43.9 1 13 response to X-ray XRRA1 BioP Over GO:0010596 0.14 43.9 1 13 negative regulation of

endothelial cell migration APOE

BioP Over GO:0016079 0.14 43.9 1 13 synaptic vesicle exocytosis STX4 BioP Over GO:0018298 0.14 43.9 1 13 protein-chromophore linkage AMBP BioP Over GO:0034375 0.14 43.9 1 13 high-density lipoprotein

particle remodeling APOE

BioP Over GO:0045214 0.14 43.9 1 13 sarcomere organization TNNT2 BioP Over GO:0045940 0.14 43.9 1 13 positive regulation of steroid



S52

BioP Over GO:0048844 0.14 43.9 1 13 artery morphogenesis APOE BioP Over GO:0050999 0.14 43.9 1 13 regulation of nitric-oxide

synthase activity APOE

BioP Over GO:0060840 0.14 43.9 1 13 artery development APOE BioP Over GO:0002682 0.14 5.19 3 327 regulation of immune system

process AMBP, EFNB1, SART1

BioP Over GO:0001775 0.14 5.18 3 328 cell activation APOE, EFNB1, SART1 BioP Over GO:0009124 0.14 8.58 2 129 nucleoside monophosphate

biosynthetic process APOE, GHRHR

BioP Over GO:0051249 0.14 8.58 2 129 regulation of lymphocyte activation

EFNB1, SART1

BioP Over GO:0001937 0.14 40.5 1 14 negative regulation of endothelial cell proliferation

APOE

BioP Over GO:0006182 0.14 40.5 1 14 cGMP biosynthetic process APOE BioP Over GO:0007263 0.14 40.5 1 14 nitric oxide mediated signal

transduction APOE

BioP Over GO:0007271 0.14 40.5 1 14 synaptic transmission, cholinergic

APOE

BioP Over GO:0007267 0.14 3.89 4 595 cell-cell signaling APOE, EFNB1, GHRHR, STX4

BioP Over GO:0043462 0.15 37.6 1 15 regulation of ATPase activity TNNT2 BioP Over GO:0022607 0.15 3.26 5 906 cellular component assembly APOE, NDUFAF1,

SART1, STX4, TNNT2 BioP Over GO:0065009 0.15 3.22 5 918 regulation of molecular

function APOE, GADD45A, GHRHR, PSMC3,

TNNT2 BioP Over GO:0009084 0.15 35.1 1 16 glutamine family amino acid

biosynthetic process ASL

BioP Over GO:0030104 0.15 35.1 1 16 water homeostasis GHRHR


S53

BioP Over GO:0043489 0.15 35.1 1 16 RNA stabilization HNRNPD BioP Over GO:0043535 0.15 35.1 1 16 regulation of blood vessel

endothelial cell migration APOE

BioP Over GO:0043691 0.15 35.1 1 16 reverse cholesterol transport APOE BioP Over GO:0048255 0.15 35.1 1 16 mRNA stabilization HNRNPD BioP Over GO:0003001 0.15 7.84 2 141 generation of a signal involved

in cell-cell signaling GHRHR, STX4

BioP Over GO:0002694 0.16 7.72 2 143 regulation of leukocyte activation

EFNB1, SART1

BioP Over GO:0009123 0.16 7.67 2 144 nucleoside monophosphate metabolic process

APOE, GHRHR

BioP Over GO:0006753 0.16 4.70 3 360 nucleoside phosphate metabolic process

APOE, GHRHR, GPD1

BioP Over GO:0009117 0.16 4.70 3 360 nucleotide metabolic process APOE, GHRHR, GPD1 BioP Over GO:0046068 0.16 32.9 1 17 cGMP metabolic process APOE BioP Over GO:0048009 0.16 32.9 1 17 insulin-like growth factor

receptor signaling pathway GHRHR

BioP Over GO:0006309 0.16 31.0 1 18 DNA fragmentation involved in apoptosis

AIFM1

BioP Over GO:0006706 0.16 31.0 1 18 steroid catabolic process APOE BioP Over GO:0009065 0.16 31.0 1 18 glutamine family amino acid

catabolic process ASL

BioP Over GO:0043409 0.16 31.0 1 18 negative regulation of MAPKKK cascade

AMBP

BioP Over GO:0051055 0.16 31.0 1 18 negative regulation of lipid biosynthetic process

APOE

BioP Over GO:0001755 0.16 29.3 1 19 neural crest cell migration EFNB1 BioP Over GO:0003229 0.16 29.3 1 19 ventricular cardiac muscle TNNT2


S54

tissue development BioP Over GO:0030239 0.16 29.3 1 19 myofibril assembly TNNT2 BioP Over GO:0055010 0.16 29.3 1 19 ventricular cardiac muscle

tissue morphogenesis TNNT2

BioP Over GO:0046486 0.16 7.06 2 156 glycerolipid metabolic process APOE, GPD1 BioP Over GO:0055086 0.16 4.41 3 383 nucleobase, nucleoside and

nucleotide metabolic process APOE, GHRHR, GPD1

BioP Over GO:0016043 0.16 2.35 9 2455 cellular component organization

AIFM1, APOE, EFNB1, GADD45A, HIRIP3, NDUFAF1, SART1,

STX4, TNNT2 BioP Over GO:0003208 0.16 27.7 1 20 cardiac ventricle

morphogenesis TNNT2

BioP Over GO:0006041 0.16 27.7 1 20 glucosamine metabolic process RENBP BioP Over GO:0006044 0.16 27.7 1 20 N-acetylglucosamine

metabolic process RENBP

BioP Over GO:0006921 0.16 27.7 1 20 cell structure disassembly during apoptosis

AIFM1

BioP Over GO:0007098 0.16 27.7 1 20 centrosome cycle GADD45A BioP Over GO:0007595 0.16 27.7 1 20 lactation GHRHR BioP Over GO:0030516 0.16 27.7 1 20 regulation of axon extension APOE BioP Over GO:0032371 0.16 27.7 1 20 regulation of sterol transport APOE BioP Over GO:0032374 0.16 27.7 1 20 regulation of cholesterol

transport APOE

BioP Over GO:0034381 0.16 27.7 1 20 lipoprotein particle clearance APOE BioP Over GO:0048468 0.16 3.46 4 664 cell development APOE, EFNB1,

GHRHR, TNNT2 BioP Over GO:0065003 0.16 3.46 4 665 macromolecular complex APOE, NDUFAF1,


S55

assembly SART1, STX4 BioP Over GO:0006979 0.16 6.84 2 161 response to oxidative stress APOE, PRDX5 BioP Over GO:0006915 0.16 2.99 5 981 apoptosis AIFM1, APOE,

GADD45A, PRDX5, SART1

BioP Over GO:0000086 0.16 26.3 1 21 G2/M transition of mitotic cell cycle

GADD45A

BioP Over GO:0006071 0.16 26.3 1 21 glycerol metabolic process GPD1 BioP Over GO:0007215 0.16 26.3 1 21 glutamate signaling pathway GRIA3 BioP Over GO:0030261 0.16 26.3 1 21 chromosome condensation AIFM1 BioP Over GO:0034367 0.16 26.3 1 21 macromolecular complex

remodeling APOE

BioP Over GO:0034368 0.16 26.3 1 21 protein-lipid complex remodeling

APOE

BioP Over GO:0034369 0.16 26.3 1 21 plasma lipoprotein particle remodeling

APOE

BioP Over GO:0044270 0.16 26.3 1 21 cellular nitrogen compound catabolic process

AMBP

BioP Over GO:0050810 0.16 26.3 1 21 regulation of steroid biosynthetic process

APOE

BioP Over GO:0090277 0.16 26.3 1 21 positive regulation of peptide hormone secretion

GHRHR

BioP Over GO:0012501 0.16 2.96 5 990 programmed cell death AIFM1, APOE, GADD45A, PRDX5,

SART1 BioP Over GO:0046483 0.16 4.25 3 397 heterocycle metabolic process AIFM1, APOE,

GHRHR BioP Over GO:0002793 0.16 25.1 1 22 positive regulation of peptide

secretion GHRHR


S56

BioP Over GO:0003231 0.16 25.1 1 22 cardiac ventricle development TNNT2 BioP Over GO:0030048 0.16 25.1 1 22 actin filament-based

movement TNNT2

BioP Over GO:0032768 0.16 25.1 1 22 regulation of monooxygenase activity

APOE

BioP Over GO:0040018 0.16 25.1 1 22 positive regulation of multicellular organism growth

GHRHR

BioP Over GO:0050777 0.16 25.1 1 22 negative regulation of immune response

AMBP

BioP Over GO:0043065 0.16 4.19 3 402 positive regulation of apoptosis

AIFM1, APOE, SART1

BioP Over GO:0043068 0.16 4.16 3 405 positive regulation of programmed cell death

AIFM1, APOE, SART1

BioP Over GO:0006904 0.16 23.9 1 23 vesicle docking during exocytosis

STX4

BioP Over GO:0019400 0.16 23.9 1 23 alditol metabolic process GPD1 BioP Over GO:0019674 0.16 23.9 1 23 NAD metabolic process GPD1 BioP Over GO:0043488 0.16 23.9 1 23 regulation of mRNA stability HNRNPD BioP Over GO:0051353 0.16 23.9 1 23 positive regulation of

oxidoreductase activity APOE

BioP Over GO:0010942 0.16 4.15 3 406 positive regulation of cell death

AIFM1, APOE, SART1

BioP Over GO:0034641 0.16 4.11 3 410 cellular nitrogen compound metabolic process

AMBP, ASL, GPD1

BioP Over GO:0000737 0.16 22.9 1 24 DNA catabolic process, endonucleolytic

AIFM1

BioP Over GO:0006509 0.16 22.9 1 24 membrane protein ectodomain proteolysis

APOE

BioP Over GO:0021983 0.16 22.9 1 24 pituitary gland development GHRHR


S57

BioP Over GO:0030262 0.16 22.9 1 24 apoptotic nuclear changes AIFM1 BioP Over GO:0043487 0.16 22.9 1 24 regulation of RNA stability HNRNPD BioP Over GO:0050728 0.16 22.9 1 24 negative regulation of

inflammatory response APOE

BioP Over GO:0055008 0.16 22.9 1 24 cardiac muscle tissue morphogenesis

TNNT2

BioP Over GO:0060415 0.16 22.9 1 24 muscle tissue morphogenesis TNNT2 BioP Over GO:0044085 0.16 2.86 5 1024 cellular component biogenesis APOE, NDUFAF1,

SART1, STX4, TNNT2 BioP Over GO:0042110 0.16 6.24 2 176 T cell activation EFNB1, SART1 BioP Over GO:0042592 0.16 3.23 4 709 homeostatic process AIFM1, APOE,

GHRHR, PRDX5 BioP Over GO:0006066 0.16 4.02 3 419 alcohol metabolic process APOE, GPD1, RENBP BioP Over GO:0003206 0.16 21.9 1 25 cardiac chamber

morphogenesis TNNT2

BioP Over GO:0006040 0.16 21.9 1 25 amino sugar metabolic process RENBP BioP Over GO:0030195 0.16 21.9 1 25 negative regulation of blood

coagulation APOE

BioP Over GO:0031032 0.16 21.9 1 25 actomyosin structure organization

TNNT2

BioP Over GO:0033344 0.16 21.9 1 25 cholesterol efflux APOE BioP Over GO:0042168 0.16 21.9 1 25 heme metabolic process AMBP BioP Over GO:0048278 0.16 21.9 1 25 vesicle docking STX4 BioP Over GO:0003205 0.17 21.1 1 26 cardiac chamber development TNNT2 BioP Over GO:0008630 0.17 21.1 1 26 DNA damage response, signal

transduction resulting in induction of apoptosis

AIFM1

BioP Over GO:0045582 0.17 21.1 1 26 positive regulation of T cell SART1


S58

differentiation BioP Over GO:0019725 0.17 3.94 3 427 cellular homeostasis AIFM1, APOE, PRDX5 BioP Over GO:0051186 0.17 5.96 2 184 cofactor metabolic process AMBP, GPD1 BioP Over GO:0006094 0.17 20.3 1 27 gluconeogenesis GPD1 BioP Over GO:0010594 0.17 20.3 1 27 regulation of endothelial cell

migration APOE

BioP Over GO:0043534 0.17 20.3 1 27 blood vessel endothelial cell migration

APOE

BioP Over GO:0045862 0.17 20.3 1 27 positive regulation of proteolysis

APOE

BioP Over GO:0048675 0.17 20.3 1 27 axon extension APOE BioP Over GO:0050819 0.17 20.3 1 27 negative regulation of

coagulation APOE

BioP Over GO:0043933 0.17 3.13 4 730 macromolecular complex subunit organization

APOE, NDUFAF1, SART1, STX4

BioP Over GO:0007154 0.17 2.52 6 1417 cell communication AMBP, APOE, EFNB1, GHRHR, GRIA3, STX4

BioP Over GO:0032989 0.17 3.85 3 436 cellular component morphogenesis

APOE, EFNB1, TNNT2

BioP Over GO:0019318 0.17 5.77 2 190 hexose metabolic process GPD1, RENBP BioP Over GO:0045621 0.17 19.5 1 28 positive regulation of

lymphocyte differentiation SART1

BioP Over GO:0045732 0.17 19.5 1 28 positive regulation of protein catabolic process

APOE

BioP Over GO:0014032 0.17 18.8 1 29 neural crest cell development EFNB1 BioP Over GO:0014033 0.17 18.8 1 29 neural crest cell differentiation EFNB1 BioP Over GO:0032368 0.17 18.8 1 29 regulation of lipid transport APOE BioP Over GO:0033619 0.17 18.8 1 29 membrane protein proteolysis APOE


S59

BioP Over GO:0048489 0.17 18.8 1 29 synaptic vesicle transport STX4 BioP Over GO:0051187 0.17 18.8 1 29 cofactor catabolic process AMBP BioP Over GO:0051050 0.17 5.62 2 195 positive regulation of transport APOE, GHRHR BioP Over GO:0008219 0.17 2.70 5 1080 cell death AIFM1, APOE,

GADD45A, PRDX5, SART1

BioP Over GO:0007010 0.17 3.75 3 447 cytoskeleton organization APOE, GADD45A, TNNT2

BioP Over GO:0016265 0.17 2.68 5 1084 death AIFM1, APOE, GADD45A, PRDX5,

SART1 BioP Over GO:0065008 0.17 2.47 6 1444 regulation of biological quality AIFM1, APOE,

GHRHR, PRDX5, RENBP, STX4

BioP Over GO:0000387 0.17 18.2 1 30 spliceosomal snRNP assembly SART1 BioP Over GO:0009880 0.17 18.2 1 30 embryonic pattern

specification EFNB1

BioP Over GO:0010927 0.17 18.2 1 30 cellular component assembly involved in morphogenesis

TNNT2

BioP Over GO:0022406 0.17 18.2 1 30 membrane docking STX4 BioP Over GO:0031348 0.17 18.2 1 30 negative regulation of defense

response APOE

BioP Over GO:0034614 0.17 18.2 1 30 cellular response to reactive oxygen species

PRDX5

BioP Over GO:0045833 0.17 18.2 1 30 negative regulation of lipid metabolic process

APOE

BioP Over GO:0042981 0.17 3.00 4 759 regulation of apoptosis AIFM1, APOE, PRDX5, SART1

BioP Over GO:0006695 0.17 17.5 1 31 cholesterol biosynthetic APOE


S60

process BioP Over GO:0006778 0.17 17.5 1 31 porphyrin metabolic process AMBP BioP Over GO:0033013 0.17 17.5 1 31 tetrapyrrole metabolic process AMBP BioP Over GO:0042102 0.17 17.5 1 31 positive regulation of T cell

proliferation EFNB1

BioP Over GO:0048168 0.17 17.5 1 31 regulation of neuronal synaptic plasticity

APOE

BioP Over GO:0048588 0.17 17.5 1 31 developmental cell growth APOE BioP Over GO:0007409 0.17 5.42 2 202 axonogenesis APOE, EFNB1 BioP Over GO:0002684 0.18 5.36 2 204 positive regulation of immune

system process EFNB1, SART1

BioP Over GO:0043067 0.18 2.97 4 767 regulation of programmed cell death

AIFM1, APOE, PRDX5, SART1

BioP Over GO:0015914 0.18 17.0 1 32 phospholipid transport APOE BioP Over GO:0019218 0.18 17.0 1 32 regulation of steroid metabolic

process APOE

BioP Over GO:0046887 0.18 17.0 1 32 positive regulation of hormone secretion

GHRHR

BioP Over GO:0010941 0.18 2.96 4 770 regulation of cell death AIFM1, APOE, PRDX5, SART1

BioP Over GO:0001824 0.18 16.4 1 33 blastocyst development PSMC3 BioP Over GO:0001936 0.18 16.4 1 33 regulation of endothelial cell

proliferation APOE

BioP Over GO:0019319 0.18 16.4 1 33 hexose biosynthetic process GPD1 BioP Over GO:0021536 0.18 16.4 1 33 diencephalon development GHRHR BioP Over GO:0042311 0.18 16.4 1 33 vasodilation APOE BioP Over GO:0051297 0.18 16.4 1 33 centrosome organization GADD45A BioP Over GO:0044282 0.18 5.21 2 210 small molecule catabolic ASL, GPD1


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process BioP Over GO:0007189 0.18 15.9 1 34 activation of adenylate cyclase

activity by G-protein signaling pathway

GHRHR

BioP Over GO:0010578 0.18 15.9 1 34 regulation of adenylate cyclase activity involved in G-protein

signaling pathway

GHRHR

BioP Over GO:0010579 0.18 15.9 1 34 positive regulation of adenylate cyclase activity by G-protein signaling pathway

GHRHR

BioP Over GO:0030168 0.18 15.9 1 34 platelet activation APOE BioP Over GO:0030193 0.18 15.9 1 34 regulation of blood

coagulation APOE

BioP Over GO:0010741 0.18 15.5 1 35 negative regulation of protein kinase cascade

AMBP

BioP Over GO:0005996 0.19 4.99 2 219 monosaccharide metabolic process

GPD1, RENBP

BioP Over GO:0019932 0.19 4.99 2 219 second-messenger-mediated signaling

APOE, GHRHR

BioP Over GO:0048667 0.19 4.99 2 219 cell morphogenesis involved in neuron differentiation

APOE, EFNB1

BioP Over GO:0006120 0.19 15.0 1 36 mitochondrial electron transport, NADH to

ubiquinone

NDUFAF1

BioP Over GO:0031023 0.19 15.0 1 36 microtubule organizing center organization

GADD45A

BioP Over GO:0051341 0.19 15.0 1 36 regulation of oxidoreductase activity

APOE

BioP Over GO:0006164 0.19 4.96 2 220 purine nucleotide biosynthetic APOE, GHRHR


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process BioP Over GO:0032270 0.19 4.96 2 220 positive regulation of cellular

protein metabolic process APOE, PSMC3

BioP Over GO:0001667 0.19 14.6 1 37 ameboidal cell migration EFNB1 BioP Over GO:0031331 0.19 14.6 1 37 positive regulation of cellular

catabolic process APOE

BioP Over GO:0043407 0.19 14.6 1 37 negative regulation of MAP kinase activity

APOE

BioP Over GO:0048812 0.19 4.87 2 224 neuron projection morphogenesis

APOE, EFNB1

BioP Over GO:0006769 0.19 14.2 1 38 nicotinamide metabolic process

GPD1

BioP Over GO:0032102 0.19 14.2 1 38 negative regulation of response to external stimulus

APOE

BioP Over GO:0042632 0.19 14.2 1 38 cholesterol homeostasis APOE BioP Over GO:0046496 0.19 14.2 1 38 nicotinamide nucleotide

metabolic process GPD1

BioP Over GO:0048638 0.19 14.2 1 38 regulation of developmental growth

APOE

BioP Over GO:0050818 0.19 14.2 1 38 regulation of coagulation APOE BioP Over GO:0055092 0.19 14.2 1 38 sterol homeostasis APOE BioP Over GO:0046364 0.19 13.8 1 39 monosaccharide biosynthetic

process GPD1

BioP Over GO:0051247 0.19 4.76 2 229 positive regulation of protein metabolic process

APOE, PSMC3

BioP Over GO:0042221 0.19 2.46 5 1172 response to chemical stimulus APOE, GHRHR, GRIA3, PRDX5,

TNNT2 BioP Over GO:0016126 0.19 13.5 1 40 sterol biosynthetic process APOE


S63

BioP Over GO:0019362 0.19 13.5 1 40 pyridine nucleotide metabolic process

GPD1

BioP Over GO:0051239 0.20 2.73 4 830 regulation of multicellular organismal process

APOE, GHRHR, SART1, TNNT2

BioP Over GO:0001935 0.20 13.1 1 41 endothelial cell proliferation APOE BioP Over GO:0045834 0.20 13.1 1 41 positive regulation of lipid


BioP Over GO:0090276 0.20 13.1 1 41 regulation of peptide hormone secretion

GHRHR

BioP Over GO:0051246 0.20 3.24 3 515 regulation of protein metabolic process

APOE, GHRHR, PSMC3

BioP Over GO:0009820 0.20 12.8 1 42 alkaloid metabolic process GPD1 BioP Over GO:0019751 0.20 12.8 1 42 polyol metabolic process GPD1 BioP Over GO:0046890 0.20 12.8 1 42 regulation of lipid biosynthetic

process APOE

BioP Over GO:0002791 0.20 12.5 1 43 regulation of peptide secretion GHRHR BioP Over GO:0006641 0.20 12.5 1 43 triglyceride metabolic process APOE BioP Over GO:0034599 0.20 12.5 1 43 cellular response to oxidative

stress PRDX5

BioP Over GO:0051346 0.20 12.5 1 43 negative regulation of hydrolase activity

TNNT2

BioP Over GO:0090087 0.20 12.5 1 43 regulation of peptide transport GHRHR BioP Over GO:0046649 0.20 4.50 2 242 lymphocyte activation EFNB1, SART1 MF Over GO:0004866 0.06 15.6 3 102 endopeptidase inhibitor

activity AMBP, PRDX5,

RENBP MF Over GO:0030414 0.06 14.4 3 110 peptidase inhibitor activity AMBP, PRDX5,

RENBP MF Over GO:0042803 0.06 6.46 4 329 protein homodimerization AMBP, APOE, GPD1,


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activity RENBP MF Over GO:0016833 0.08 120 1 5 oxo-acid-lyase activity NPL MF Over GO:0031013 0.08 120 1 5 troponin I binding TNNT2 MF Over GO:0060228 0.08 120 1 5 phosphatidylcholine-sterol O-

acyltransferase activator activity

APOE

MF Over GO:0004857 0.08 7.07 3 219 enzyme inhibitor activity AMBP, PRDX5, RENBP

MF Over GO:0015081 0.09 96.0 1 6 sodium ion transmembrane transporter activity

SLC13A1

MF Over GO:0030507 0.09 96.0 1 6 spectrin binding STX4 MF Over GO:0015294 0.09 12.4 2 82 solute:cation symporter

activity SLC13A1, SLC5A9

MF Over GO:0016857 0.10 80.0 1 7 racemase and epimerase activity, acting on

carbohydrates and derivatives

RENBP

MF Over GO:0004029 0.10 68.6 1 8 aldehyde dehydrogenase (NAD) activity

ALDH1B1

MF Over GO:0046875 0.10 68.6 1 8 ephrin receptor binding EFNB1 MF Over GO:0051920 0.10 68.6 1 8 peroxiredoxin activity PRDX5 MF Over GO:0008271 0.10 60.0 1 9 secondary active sulfate

transmembrane transporter activity

SLC13A1

MF Over GO:0016840 0.10 60.0 1 9 carbon-nitrogen lyase activity ASL MF Over GO:0031402 0.10 9.69 2 104 sodium ion binding SLC13A1, SLC5A9 MF Over GO:0030234 0.10 3.53 5 754 enzyme regulator activity AMBP, APOE, PRDX5,

RENBP, STX4 MF Over GO:0015116 0.10 53.3 1 10 sulfate transmembrane SLC13A1


S65

transporter activity MF Over GO:0016861 0.10 53.3 1 10 intramolecular oxidoreductase

activity, interconverting aldoses and ketoses

RENBP

MF Over GO:0019865 0.10 53.3 1 10 immunoglobulin binding AMBP MF Over GO:0043027 0.10 53.3 1 10 caspase inhibitor activity PRDX5 MF Over GO:0050750 0.10 53.3 1 10 low-density lipoprotein

receptor binding APOE

MF Over GO:0016854 0.10 48.0 1 11 racemase and epimerase activity

RENBP

MF Over GO:0046983 0.10 3.99 4 523 protein dimerization activity AMBP, APOE, GPD1, RENBP

MF Over GO:0005523 0.10 43.6 1 12 tropomyosin binding TNNT2 MF Over GO:0008200 0.10 43.6 1 12 ion channel inhibitor activity AMBP MF Over GO:0017127 0.10 43.6 1 12 cholesterol transporter activity APOE MF Over GO:0015293 0.11 8.44 2 119 symporter activity SLC13A1, SLC5A9 MF Over GO:0005246 0.11 40.0 1 13 calcium channel regulator

activity AMBP

MF Over GO:0016248 0.11 40.0 1 13 channel inhibitor activity AMBP MF Over GO:0004970 0.11 36.9 1 14 ionotropic glutamate receptor

activity GRIA3

MF Over GO:0005355 0.11 36.9 1 14 glucose transmembrane transporter activity

SLC5A9

MF Over GO:0015248 0.11 36.9 1 14 sterol transporter activity APOE MF Over GO:0070325 0.11 36.9 1 14 lipoprotein receptor binding APOE MF Over GO:0001540 0.11 34.3 1 15 β-amyloid binding APOE MF Over GO:0005234 0.11 34.3 1 15 extracellular-glutamate-gated

ion channel activity GRIA3


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MF Over GO:0042162 0.11 34.3 1 15 telomeric DNA binding HNRNPD MF Over GO:0016829 0.11 7.42 2 135 lyase activity ASL, NPL MF Over GO:0015149 0.12 30.0 1 17 hexose transmembrane

transporter activity SLC5A9

MF Over GO:0015145 0.12 28.2 1 18 monosaccharide transmembrane transporter

activity

SLC5A9

MF Over GO:0005484 0.12 26.6 1 19 SNAP receptor activity STX4 MF Over GO:0016491 0.12 3.39 4 610 oxidoreductase activity AIFM1, ALDH1B1,

GPD1, PRDX5 MF Over GO:0003746 0.13 25.2 1 20 translation elongation factor

activity TUFM

MF Over GO:0042802 0.13 3.33 4 621 identical protein binding AMBP, APOE, GPD1, RENBP

MF Over GO:0008066 0.13 22.8 1 22 glutamate receptor activity GRIA3 MF Over GO:0016620 0.13 22.8 1 22 oxidoreductase activity, acting

on the aldehyde or oxo group of donors, NAD or NADP as

acceptor

ALDH1B1

MF Over GO:0017046 0.13 22.8 1 22 peptide hormone binding GHRHR MF Over GO:0050662 0.14 5.89 2 169 coenzyme binding AIFM1, GPD1 MF Over GO:0051119 0.15 20.0 1 25 sugar transmembrane

transporter activity SLC5A9

MF Over GO:0015291 0.15 5.68 2 175 secondary active transmembrane transporter

activity

SLC13A1, SLC5A9

MF Over GO:0043028 0.15 18.4 1 27 caspase regulator activity PRDX5 MF Over GO:0000149 0.15 17.8 1 28 SNARE binding STX4


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MF Over GO:0004869 0.15 17.1 1 29 cysteine-type endopeptidase inhibitor activity

PRDX5

MF Over GO:0015144 0.15 17.1 1 29 carbohydrate transmembrane transporter activity

SLC5A9

MF Over GO:0017137 0.15 17.1 1 29 Rab GTPase binding STX4 MF Over GO:0015296 0.16 16.5 1 30 anion:cation symporter activity SLC13A1 MF Over GO:0016903 0.16 16.0 1 31 oxidoreductase activity, acting

on the aldehyde or oxo group of donors

ALDH1B1

MF Over GO:0005215 0.16 2.53 5 1029 transporter activity AMBP, APOE, GRIA3, SLC13A1, SLC5A9

MF Over GO:0015103 0.16 15.5 1 32 inorganic anion transmembrane transporter

activity

SLC13A1

MF Over GO:0008034 0.17 14.5 1 34 lipoprotein binding APOE MF Over GO:0031420 0.17 4.86 2 204 alkali metal ion binding SLC13A1, SLC5A9 MF Over GO:0005231 0.17 13.7 1 36 excitatory extracellular ligand-

gated ion channel activity GRIA3

MF Over GO:0008092 0.17 3.27 3 462 cytoskeletal protein binding APOE, STX4, TNNT2 MF Over GO:0016860 0.17 13.3 1 37 intramolecular oxidoreductase

activity RENBP

MF Over GO:0042562 0.18 12.6 1 39 hormone binding GHRHR MF Over GO:0016830 0.19 12.0 1 41 carbon-carbon lyase activity NPL MF Over GO:0030165 0.19 12.0 1 41 PDZ domain binding GRIA3 MF Over GO:0016209 0.19 11.4 1 43 antioxidant activity APOE MF Over GO:0048037 0.19 4.22 2 234 cofactor binding AIFM1, GPD1 MF Over GO:0051287 0.20 10.9 1 45 NAD or NADH binding GPD1 MF Over GO:0015370 0.20 10.6 1 46 solute:sodium symporter SLC5A9


S68

activity MF Under GO:0016303 0.11 135 1 11 1-phosphatidylinositol-3-

kinase activity PIK3C2G

MF Under GO:0016307 0.11 135 1 11 phosphatidylinositol phosphate kinase activity

PIK3C2G

MF Under GO:0035004 0.11 135 1 11 phosphoinositide 3-kinase activity

PIK3C2G

MF Under GO:0005484 0.14 74.7 1 19 SNAP receptor activity STX17 MF Under GO:0003887 0.14 53.8 1 26 DNA-directed DNA

polymerase activity KIAA2022

MF Under GO:0001727 0.14 51.7 1 27 lipid kinase activity PIK3C2G MF Under GO:0034061 0.14 46.3 1 30 DNA polymerase activity KIAA2022 MF Under GO:0004428 0.15 38.4 1 36 inositol or phosphatidylinositol

kinase activity PIK3C2G

MF Under GO:0005044 0.16 32.0 1 43 scavenger receptor activity C8orf84

*Ontology: BioP, biological process; MF, molecular function; KEGG: Kyoto Encyclopedia of Genes and Genomes; GOID: Gene Ontology identification. Shown is P value adjusted by false discovery rate (FDR) (≤0.2=significant); OR, odds ratio: p(Ontology W in the list)/p(Ontology W in the universe); ExpCount, expected count; Count, number of genes found that belong to ontology; Size, total number of genes in ontology; Term, ontology-associated term; Gene Symbol, official gene symbol.


S69

Table S14. Gene set tests (GST), based on gene ontology, of the gene list for renal cortex comparing hypertensive and normotensive subjects.

Ontology Representation GOCCID P-value (Bonferroni adjusted)

Count Term

BioP Over GO:0055114 P<0.001 453 oxidation reduction BioP Over GO:0008152 P<0.001 402 metabolic process BioP Over GO:0006629 P<0.001 200 lipid metabolic process BioP Over GO:0005975 P<0.001 187 carbohydrate metabolic process BioP Over GO:0022900 P<0.001 88 electron transport chain BioP Over GO:0006631 P<0.001 63 fatty acid metabolic process BioP Over GO:0006457 0.001 140 protein folding BioP Over GO:0006099 0.003 24 tricarboxylic acid cycle BioP Over GO:0006810 0.004 318 transport BioP Over GO:0006508 0.004 340 proteolysis BioP Over GO:0006120 0.02 38 mitochondrial electron transport, NADH to

ubiquinone BioP Under GO:0006355 P<0.001 884 regulation of transcription, DNA-dependent BioP Under GO:0045449 P<0.001 905 regulation of transcription BioP Under GO:0009952 P<0.001 73 anterior/posterior pattern formation BioP Under GO:0046854 0.02 24 phosphoinositide phosphorylation BioP Under GO:0007275 0.03 685 multicellular organismal development BioP Under GO:0045893 0.05 88 positive regulation of transcription, DNA-dependent CC Over GO:0005739 P<0.001 999 mitochondrion CC Over GO:0005783 P<0.001 801 endoplasmic reticulum CC Over GO:0016020 P<0.001 3110 membrane CC Over GO:0016021 P<0.001 2852 integral to membrane CC Over GO:0005759 P<0.001 141 mitochondrial matrix


S70

CC Over GO:0005743 P<0.001 238 mitochondrial inner membrane CC Over GO:0005789 P<0.001 467 endoplasmic reticulum membrane CC Over GO:0005777 P<0.001 86 peroxisome CC Over GO:0005829 P<0.001 1262 cytosol CC Over GO:0005615 P<0.001 414 extracellular space CC Over GO:0005792 P<0.001 189 microsome CC Over GO:0005788 P<0.001 68 endoplasmic reticulum lumen CC Over GO:0005625 P<0.001 247 soluble fraction CC Over GO:0042470 P<0.001 87 melanosome CC Over GO:0005576 0.001 1156 extracellular region CC Over GO:0005624 0.002 400 membrane fraction CC Over GO:0005737 0.01 3993 cytoplasm CC Over GO:0042645 0.01 31 mitochondrial nucleoid CC Over GO:0005747 0.01 38 mitochondrial respiratory chain complex I CC Over GO:0000502 0.02 41 proteasome complex CC Over GO:0005764 0.02 115 lysosome CC Over GO:0045095 0.02 64 keratin filament CC Under GO:0005634 P<0.001 4432 nucleus CC Under GO:0005622 P<0.001 1754 intracellular MF Over GO:0016491 P<0.001 366 oxidoreductase activity MF Over GO:0009055 P<0.001 133 electron carrier activity MF Over GO:0050660 P<0.001 62 FAD binding MF Over GO:0051082 P<0.001 111 unfolded protein binding MF Over GO:0016787 P<0.001 837 hydrolase activity MF Over GO:0016829 P<0.001 86 lyase activity MF Over GO:0051287 0.002 37 NAD or NADH binding


S71

MF Over GO:0008233 0.006 400 peptidase activity MF Over GO:0008137 0.01 40 NADH dehydrogenase (ubiquinone) activity MF Over GO:0004091 0.02 22 carboxylesterase activity MF Over GO:0004866 0.03 6 endopeptidase inhibitor activity MF Over GO:0015020 0.03 11 glucuronosyltransferase activity MF Over GO:0016853 0.05 102 isomerase activity MF Under GO:0003677 P<0.001 1109 DNA binding MF Under GO:0008270 P<0.001 2027 zinc ion binding MF Under GO:0003700 P<0.001 804 transcription factor activity MF Under GO:0046872 P<0.001 2178 metal ion binding MF Under GO:0043565 0.04 413 sequence-specific DNA binding

KEGG 260 0.001 31 Glycine, serine and threonine metabolism KEGG 3320 0.001 64 PPAR signaling pathway KEGG 53 0.002 15 Ascorbate and aldarate metabolism KEGG 4610 0.004 54 Complement and coagulation cascades KEGG 4614 0.005 12 Renin-angiotensin system KEGG 71 0.01 38 Fatty acid metabolism KEGG 380 0.02 33 Tryptophan metabolism KEGG 830 0.02 39 Retinol metabolism

*Ontology: BiolP, biological process; MF, molecular function; KEGG: Kyoto Encyclopedia of Genes and Genomes; GOID: Gene Ontology identification. Shown is: Term, ontology-associated term; Count, number of genes found that belong to ontology; P-value, P value adjusted by Benjamini-Hochberg (≤0.05=significant).


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Table S15. Transcriptome-wide expression array findings showing miRNAs differentially expressed between renal cortex from 5 hypertensive and 3 normotensive subjects (false discovery rate ≤0.25).

miRNA name Fold-difference*

Microarray FDR† value

hsa-miR-638 –1.9 0.002 hsa-miR-196a +1.5 0.002 hsa-miR-451 +1.7 0.01 hsa-miR-663 –1.5 0.02 hsa-miR-630 –1.8 0.02 hsa-miR-26a +1.4 0.02 hsa-miR-26b +1.9 0.14 hsa-miR-126 +1.6 0.18 hsa-miR-130a +1.4 0.18 hsa-miR-572 –1.3 0.20 hsa-miR-21 +1.5 0.21

hsa-miR-181a –1.4 0.24 hsa-miR-22 +1.6 0.25




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Table S16. Microarray findings for microRNAs differentially expressed in renal medulla between hypertensive and normotensive subjects, and potential gene targets predicted by miRanda and PicTar databases.

miRNA name Predicted gene targets based on miRanda database PicTar database

Medulla hsa-miR-196a SSB None hsa-miR-26b None NRIP1 hsa-miR-21 None BTG2 hsa-miR-638 None None hsa-miR-10a None None hsa-miR-16 NR4A1, SSB, HSPA1B BTG2 hsa-miR-126 RCAN1, HSPA1B None

hsa-let-7c DUSP6, ZNF583, NR4A2, NR4A3, HSPA1B BTG2, ZNF583 hsa-let-7g DUSP6, ZNF583 BTG2, ZNF583

hsa-miR-22 None None hsa-miR-181a SSB None

Cortex

hsa-miR-638 GHRHR None hsa-miR-196a GRIA3 None hsa-miR-451 HIRIP3, ALDH1B1 None hsa-miR-663 RENBP, REN, FAM90A1, APOE, PSMC3 None hsa-miR-630 PRDX5, C8orf84 None hsa-miR-26a CD36 PHF21A hsa-miR-26b CD36 PHF21A hsa-miR-126 SART1, C9orf106 None hsa-miR-130a GADD45A GADD45A, FRMD6 hsa-miR-572 APOE, ASL None hsa-miR-21 None None

hsa-miR-181a REN, AIFM1, TAF15, XRRA1, C8orf84 None hsa-miR-22 TNNT2, STX4, AMBP, XRRA1, GHRHR,

TUFMSTX4


S1

Please note: Table S17 has been revised from the originally published version. The new

Table S17 is based on version 32 of the Affymetrix annotation file (released on July 15,

2011), rather than version 31 (released on Aug 30, 2010) that was used originally. The

new analysis shows that 60 of 66 (i.e., 91%) of the genes the authors identified in their

study were also present on the major chip used by others for genome-wide association

studies. The authors apologize for the error in the original Table S17.

Revision posted on January 6, 2012.

Table S17. Genes represented in the Affymetrix Genome-wide Human SNP Array 6.0 chip that were also identified in our study, as listed in Tables 1 and 3. The search was conducted using the GenomeWideSNP_6.na32.annot annotation file from Affymetrix. The name of the gene was entered and then a search of the file was performed. The presence of the gene (based on probe location downstream, upstream, 5’UTR, 3’UTR, intronic or coding region) is shown below as “Yes”, while “No” indicates that it was not present in the file searched.

Tissue Presence on the chip Medulla ARL4D Yes BTG2 Yes

C15orf2 Yes DUSP6 Yes

GOLGA9P No HSPA1A Yes HSPA1B Yes

IER2 Yes NR4A1 Yes NR4A2 Yes NR4A3 Yes

PCDHA13 Yes PER1 Yes

RCAN1 Yes SIK1 Yes

SPRED1 Yes SSB Yes

TGIF1 Yes

Cortex AIFM1 Yes

ALDH1B1 Yes


S2

AMBP Yes APOE No ASL Yes

C14orf183 Yes C8orf84 Yes C9orf106 Yes

CD36 Yes CDCP1 Yes EFNB1 Yes

FAM154B Yes FAM90A1 Yes FRMD6 Yes

GADD45A Yes GHRHR Yes GPD1 No GRIA3 Yes HIRIP3 No

HNRNPD Yes KIAA1841 Yes KIAA2022 Yes LRRC37A2 Yes NDUFAF1 Yes

NPL Yes PHF21A Yes PIK3C2G Yes PRDX5 Yes PSMC3 Yes

REN Yes RENBP Yes RNF133 No

RNF216L Yes RNPC3 Yes SART1 Yes

SLC13A1 Yes SLC5A9 Yes

SPDYE8P No STX17 Yes STX4 Yes

TAF15 Yes TBC1D3P2 Yes

TNNT2 Yes TRIM41 Yes TRIM44 Yes


S3

TUFM Yes XRRA1 Yes

ZFY Yes

Total 60 of 66 (91%)


S76

Table S18. qPCR results for female subjects showing mRNAs and microRNAs differentially expressed between samples from 7 hypertensives and 9 normotensive subjects.

Gene or microRNA symbol Fold difference P value Medulla NR4A1 +2.4 0.03 NR4A3 +3.8 0.02 hsa-let-7c +1.9 0.05 Cortex AIFM1 +1.7 0.04 APOE +1.9 0.04 CD36 –1.8 0.03 REN +7.3 0.003 RENBP +2.4 0.04 PRDX5 +1.5 0.04 hsa-miR-181a +1.5 0.01

*Positive values indicate higher expression in kidneys of hypertensive subjects; negative values indicate higher expression in kidneys from normotensive subjects.


S77

Figure S1. qPCR results for a subgroup of mRNAs that we found to be differentially expressed by microarray analysis, but for which qPCR failed to confirm significant differential expression, i.e., were not validated; (A) medulla and (B) cortex. While the magnitude of differences for BTG2 (P=0.08) and SPRED1 (P=0.06) mRNAs were similar to the ones validated, their P values were of borderline significance. Although DUSP6 data appear to differ between the hypertensive and normotensive groups, the P value was 0.14, probably as a result of the extent of variability in data for the normotensive group. Of miRNAs in medulla, despite differences seen between hypertenwive and normotensive samples, the P values for hsa-miR-22 (P=0.06) and hsa-miR-126 (P=0.09) exhibited only borderline significance.


S78

Figure S2. The major molecular network found for the differentially expressed genes in renal medulla of hypertensive subjects. This showed an enrichment of genes for cell cycle and immunological disease. The network was constructed using the Ingenuity Pathway Analysis (IPA, Ingenuity® Systems, www.ingenuity.com) application. Genes under-expressed in our gene list are represented in the diagram by red.


S79

Figure S3. Hierarchical clustering using Euclidean distance comparing differentially expressed genes adjusted by age in renal medulla of hypertensive subjects (right columns) and normotensive subjects (left columns). Distinctive patterns can be seen for hypertensive versus normotensive. Clusters of genes of similar biological relevance are indicated by square bracketing on far left. Red depicts genes upregulated and green those downregulated. Numbers on the right are probeset numbers.


S80

Figure S4. The major molecular network found for the differentially expressed genes in renal cortex of hypertensive subjects. This showed an enrichment of genes for involvement in lipid metabolism, molecular transport and small molecule biochemsitry. The network was constructed via the Ingenuity Pathway Analysis (IPA, Ingenuity® Systems, www.ingenuity.com) application. Genes over-expressed in our gene list are represented by green and genes under-expressed by red.


S81

Figure S5. Hierarchical clustering using Euclidean distance comparing differentially expressed genes in renal cortex of hypertensive subjects (right columns) and normotensive subjects (left columns). Distinctive patterns can be observed. Clusters of genes of similar biological relevance are indicated by the square bracketing on the far left. Red depicts genes whose expression is upregulated and green those downregulated. Numbers on the right are probeset numbers.


S82

Figure S6. Predicted target sequence for annealing of hsa-let-7c within the 3’UTR of NR4A2 mRNA. Each of these were differentially expressed in renal medulla between hypertensives and normotensives.


S83

Figure S7. Predicted target sequence for annealing of hsa-miR-181a within the 3’UTR of AIFM1 mRNA. Each of these were differentially expressed in renal cortex between hypertensives and normotensives.


S84

Figure S8. Predicted target sequence for annealing of hsa-miR-663 within the 3’UTR of APOE mRNA. Each of these were differentially expressed in renal cortex between hypertensives and normotensives..


S85

Figure S9. Predicted target sequence for annealing of hsa-miR-181a and hsa-miR-663 within the 3’UTR of REN mRNA. Each of these were differentially expressed in renal cortex between hypertensives and normotensives..


S86

Figure S10. (A) Typical action of a miRNA on its target mRNA in mammalian cells. The main effect is to decrease mRNA stability, and thus mRNA levels.48 Repression of translation can also occur. (B) Schematic of strategy used to test whether a miRNA binds to a specific 3’UTR to modulate expression of a linked reporter gene (luciferase). Illustrated is the effect of hsa-miR-181a and hsa-miR-663 which we co-transfected (either alone or in combination) into HEK293 kidney cells along with a recombinant plasmid containing the 3’UTR of renin (REN) cDNA linked to luciferase reporter gene. The results demonstrate that after transcription within the cell these miRNAs each interacted with the REN 3’UTR in the luciferase reporter transcript, leading to a reduction in the stability of the luciferase mRNA. The resulting reduction in level of luciferase mRNA led to lower luciferase activity compared to control cells transfected with “scrambled” vector. The experiment confirms a functional effect of the miRNA(s) via an action of a specific 3’UTR. It was not the purpose of our studies to also confirm the sequence within the 3’UTR to which each miRNA binds.

gene expression profiling reveals renin mrna ... · gene expression profiling reveals renin mrna...

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