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The gene-expression profile of renal medulla in ISIAH rats with inherited stress-induced arterial hypertension

  • 1,
  • 1,
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  • 1, 2,
  • 1, 2 and
  • 1Email author
BMC GeneticsBMC series – open, inclusive and trusted201617 (Suppl 3) :151

https://doi.org/10.1186/s12863-016-0462-6

  • Published:

Abstract

Background

The changes in the renal function leading to a reduction of medullary blood flow can have a great impact on sodium and water homeostasis and on the long-term control of arterial blood pressure. The RNA-Seq approach was used for transcriptome profiling of the renal medulla from hypertensive ISIAH and normotensive WAG rats to uncover the genetic basis of the changes underlying the renal medulla function in the ISIAH rats being a model of the stress-sensitive arterial hypertension and to reveal the genes which possibly may contribute to the alterations in medullary blood flow.

Results

Multiple DEGs specifying the function of renal medulla in ISIAH rats were revealed. The group of DEGs described by Gene Ontology term ‘oxidation reduction’ was the most significantly enriched one. The other groups of DEGs related to response to external stimulus, response to hormone (endogenous) stimulus, response to stress, and homeostatic process provide the molecular basis for integrated responses to homeostasis disturbances in the renal medulla of the ISIAH rats. Several DEGs, which may modulate the renal medulla blood flow, were detected. The reduced transcription of Nos3 pointed to the possible reduction of the blood flow in the renal medulla of ISIAH rats.

Conclusions

The generated data may be useful for comparison with those from different models of hypertension and for identifying the common molecular determinants contributing to disease manifestation, which may be potentially used as new pharmacological targets.

Keywords

  • Stress-sensitive hypertension
  • Renal medulla
  • Transcriptional profiling
  • RNA-Seq
  • ISIAH rats

Background

The study of molecular-genetic mechanisms of hypertension is an important task for biology and medicine. Multifactorial etiology of hypertension complicates the solution of this problem, and despite of numerous studies, the basal molecular mechanisms of essential hypertension remain not fully elucidated. However, it has long been known that renal dysfunction underlies the development of all forms of hypertension in experimental animals and humans [14].

The ISIAH (Inherited Stress-Induced Arterial Hypertension) rat strain was developed to study the genetic background of the stress-sensitive form of arterial hypertension and its complications. Selection of the ISIAH rats from an outbred Wistar stock was performed for a systolic arterial blood pressure (BP) elevation induced by 0.5 h emotional stress, which was caused by keeping the rats restricted in a small wire mesh cage. The ISIAH rats acquire the elevated basal systolic arterial BP at the age of 6 weeks (175.0 ± 3.5 mmHg in males and 165.0 ± 3.0 mmHg in females) and systolic arterial BP in these rats dramatically increases under the restriction conditions [5, 6]. The development of the hypertensive state in ISIAH rats is also accompanied by a hypertrophy of the left ventricle, increase in the wall thickness of the small arteries, and changes in the electrocardiographic pattern [6]. The studies on the kidney histology showed the alterations, which were indicative of an increase in filtration barrier functional load and of processes leading to the development of glomerular [7] and medullary sclerosis in ISIAH rats [8].

The unraveling of the genetic basis of the renal function in ISIAH rats may be useful for understanding the mechanisms underlying the stress-sensitive hypertension development and for identifying the molecular determinants, which may be potentially used as the therapeutic targets for pharmacological intervention.

Recently, using the next-generation RNA sequencing (RNA-Seq) approach, we analyzed the renal cortex transcriptome in ISIAH rats [9]. The results of this study showed that the functioning of the renal cortex in ISIAH rats is based on the changes in transcriptional activity of multiple genes related to different biological processes and metabolic pathways. However, in a number of studies it was demonstrated that the initial changes leading to the pathology of renal function begin from medullary blood flow reduction, and these changes may exert significant effects on sodium and water homeostasis and on the long-term control of arterial BP [2, 10, 11].

Thus, the goal of the current study was to uncover the genetic basis of the changes underlying the renal medulla function in the ISIAH rats and to reveal the features which possibly may be related to the alterations in medullary blood flow. To achieve this goal the RNA-Seq approach was used for transcriptome profiling of the renal medulla from hypertensive ISIAH and normotensive WAG rats. The differentially expressed genes (DEGs) related to stress-sensitive hypertension and possibly to the alterations in medullary blood flow as well as the metabolic pathways contributing to the inter-strain differences in renal medulla functions were detected. Several DEGs, which may modulate the renal medulla blood flow, were identified and their possible impact on the process was discussed.

Results

The expression of 13,646 genes was detected in the renal medulla of analyzed rats. The comparative analysis of their expression in the renal medulla of ISIAH and WAG rats revealed 960 DEGs (listed in the Additional file 1). The Additional file 2 represents the heatmap for the DEGs. Approximately a half of these genes (524 genes, i.e., 54.7%) were down-regulated in the renal medulla of ISIAH rats. The expression of 11 genes was detected in renal medulla of only one rat strain (Additional file 3). One of these genes (Retn, resistin) is known as associated with hypertension. Its expression was detected in renal medulla of ISIAH rats but not in WAG. The list of the top 40 genes with the highest differences in their expression in renal medulla of ISIAH and WAG rats included two genes (Acsm3, acyl-CoA synthetase medium-chain family member 3; and Ephx2, epoxide hydrolase 2, cytoplasmic), which were annotated in Rat Genome Database (RGD) as genes related to hypertension (Table 1).
Table 1

Top 40 genes with the greatest difference in expression between ISIAH and WAG renal medulla

Gene symbol

NCBI gene ID

Gene definition

log2 (fold_change) ISIAH/WAG

LOC102546948

102546948

uncharacterized LOC102546948

−5.73

Galnt13

311039

UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 13 (GalNAc-T13)

−5.56

Pdilt

293544

protein disulfide isomerase-like, testis expressed

−5.17

LOC501110

501110

similar to Glutathione S-transferase A1 (GTH1) (HA subunit 1) (GST-epsilon) (GSTA1-1) (GST class-alpha)

−4.59

Slc10a2

29500

solute carrier family 10 (sodium/bile acid cotransporter), member 2

−4.40

Pcdh9

306091

protocadherin 9

−4.23

LOC100909561

100909561

nuclease-sensitive element-binding protein 1-like

−3.74

Car5a

54233

carbonic anhydrase 5a, mitochondrial

−3.74

LOC100361907

100361907

complement factor H-related protein B

−3.73

Sphkap

316561

SPHK1 interactor, AKAP domain containing

−3.65

LOC102552001

102552001

uncharacterized LOC102552001

−3.62

LOC102550987

102550987

uncharacterized LOC102550987

−3.57

Kcnj5

29713

potassium inwardly-rectifying channel, subfamily J, member 5

−3.23

Upk2

689093

uroplakin 2

3.12

LOC102555352

102555352

uncharacterized LOC102555352

3.13

LOC102551856

102551856

uncharacterized LOC102551856

3.21

Spta1

289257

spectrin, alpha, erythrocytic 1 (elliptocytosis 2)

3.28

LOC686967

686967

similar to olfactory receptor 1442

3.34

Acsm3 a

24763

acyl-CoA synthetase medium-chain family member 3

3.40

LOC100911960

100911960

UDP-glucuronosyltransferase 1-9-like

3.44

Thrsp

25357

thyroid hormone responsive

3.51

Sprr1a

499660

small proline-rich protein 1A

3.51

Hpgd

79242

hydroxyprostaglandin dehydrogenase 15 (NAD)

3.52

LOC102553290

102553290

collagen alpha-1(III) chain-like

3.62

Ly6al

362935

lymphocyte antigen 6 complex, locus A-like

3.65

Akr1b8

286921

aldo-keto reductase family 1, member B8

3.70

Shisa3

498356

shisa family member 3

3.83

Nefh

24587

neurofilament, heavy polypeptide

3.90

Tcerg1l

361669

transcription elongation regulator 1-like

4.09

Krt19

360626

keratin 19

4.18

Ephx2 a

65030

epoxide hydrolase 2, cytoplasmic

4.52

Ubd

29168

ubiquitin D

4.61

Resp18

50561

regulated endocrine-specific protein 18

4.65

Serpinb12

304692

serpin peptidase inhibitor, clade B (ovalbumin), member 12

5.38

Car3

54232

carbonic anhydrase 3

5.54

Fam111a

499322

family with sequence similarity 111, member A

5.86

LOC100362069

100362069

ribosomal protein L28-like

6.11

Gys2

25623

glycogen synthase 2

6.38

Stk32c

365381

serine/threonine kinase 32C

6.41

RGD1565131

498143

60S ribosomal protein L15-like

8.94

aGenes are annotated in Rat Genome Database (http://rgd.mcw.edu/) as associated with hypertension. ISIAH and WAG – rat strains used in the study

The genes making the strongest contribution to the inter-strain differences were detected by the partial-least squares discriminant analysis (PLS-DA). The distances between ISIAH and WAG rats are shown in Fig. 1a, and the results of the correlation analysis between gene expression and PLS-DA Axis 1 are presented in Fig. 1b. The DEGs are shown in red in Fig. 1b. The DEGs in the most polar position contribute the most to the inter-strain variations. The correlation coefficients for Acsm3 and Ephx2 were 0.996 and 0.994, correspondingly. Hence, Acsm3 and Ephx2 may be considered as genes making the strongest contribution to the inter-strain differences. The differential expression of these and several other genes was validated by real-time PCR (Fig. 2). The correlation between the gene expression estimated by two methods (RNA-Seq and real-time PCR) was 0.99 (Fig. 3).
Fig. 1
Fig. 1

a Axes maximizing the distances between ISIAH and WAG rats. b The distribution of expressed genes along the first axis. The data are based on the Pearson correlation coefficients between the first axis and the level of genes expression

Fig. 2
Fig. 2

The relative mRNA abundance measured by qPCR. The normalized mRNA level in control samples of the WAG rats was assigned a value of 1. Vertical bars show the standard error of the mean, and significance of inter-strain difference is indicated by *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 3
Fig. 3

Comparison of gene expression level measurements obtained by RNA-seq and qPCR

According to the RGD annotations, 58 DEGs found in the current study are associated with hypertension (Table 2). Seven of them (Agtr1a, Fn1, Gja1, Lama2, Mmp2, Mmp9, Nos3) are known as being associated with renal hypertension. The functional annotation in Database for Annotation, Visualization and Integrated Discovery (DAVID) additionally revealed four DEGs (Col1a2, Guca2b, P2rx4, Pcsk5) related to BP regulation. The transcription of most of genes associated with hypertension (67.2%) was reduced in the renal medulla of ISIAH rats. About a half of the DEGs related to hypertension are also known as associated with diabetic nephropathy and insulin resistance, and two DEGs were found to be related to nephrosclerosis (Table 2). Many of DEGs listed in the Table 2 are related to the diseases of immune system. The study revealed 76 DEGs referred to in RGD as associated with different renal diseases, such as renal insufficiency, nephrosclerosis, diabetic nephropathy, and renal fibrosis (Table 3).
Table 2

Genes differentially expressed in ISIAH and WAG renal medulla and referred to in Databases as associated with hypertension and blood pressure regulation

Gene symbol

NCBI gene ID

Gene name

log2 fold_change ISIAH/WAG

Rat Genome Database

Acsm3 e

24763

acyl-CoA synthetase medium-chain family member 3

3.40

Adipoq c,d,e

246253

adiponectin, C1Q and collagen domain containing

1.17

Adra1b c

24173

adrenoceptor alpha 1B

−1.31

Adra2a

25083

adrenoceptor alpha 2°

−1.15

Agtr1a a,c,d,e

24180

angiotensin II receptor, type 1°

−0.47

Alas1

65155

aminolevulinate, delta-, synthase 1

0.74

Angpt2 e

89805

angiopoietin 2

−0.79

Aqp2 d

25386

aquaporin 2 (collecting duct)

−0.47

Aqp4 e

25293

aquaporin 4

−0.59

Clu d,e

24854

clusterin, transcript variant X1

−0.80

Cnr1 c

25248

cannabinoid receptor 1 (brain)

−0.58

Comt

24267

catechol-O-methyltransferase

−0.67

Corin

289596

corin, serine peptidase

−1.04

Cst3 c,d,e

25307

cystatin C

−0.50

Cyp1a1 e

24296

cytochrome P450, family 1, subfamily a, polypeptide 1

−0.51

Cyp4f1

56266

cytochrome P450, family 4, subfamily f, polypeptide 1

0.60

Dio2 c

65162

deiodinase, iodothyronine, type II

−2.56

Dusp1 e

114856

dual specificity phosphatase 1

−0.71

Ebag9

299864

estrogen receptor binding site associated, antigen, 9

0.49

Emilin1

298845

elastin microfibril interfacer 1

−0.65

Ephx1 e

25315

epoxide hydrolase 1, microsomal (xenobiotic)

0.70

Ephx2 c,d

65030

epoxide hydrolase 2, cytoplasmic

4.52

F2 d, e

29251

coagulation factor II

−0.93

Fbn1 d

83727

fibrillin 1, transcript variant X1

−0.61

Fn1 a,d,e

25661

fibronectin 1

−0.98

Gja1 a,e

24392

gap junction protein, alpha 1

0.99

Gstm2

24424

glutathione S-transferase mu 2

−0.76

Gstp1 e

24426

glutathione S-transferase pi 1

0.53

Hgf c,d,e

24446

hepatocyte growth factor

−0.77

Hsd17b4 e

79244

hydroxysteroid (17-beta) dehydrogenase 4

0.52

Igf1 d,e

24482

insulin-like growth factor 1

−0.75

Itgav c

296456

integrin, alpha V

−0.60

Lama2 a

309368

laminin, alpha 2

−0.58

Lox

24914

lysyl oxidase

−0.81

Mif c,e

81683

macrophage migration inhibitory factor (glycosylation-inhibiting factor)

0.55

Mmp2 a,e

81686

matrix metallopeptidase 2

−0.54

Mmp9 ad,e

81687

matrix metallopeptidase 9

−2.00

Mthfr b,d,e

362657

methylenetetrahydrofolate reductase (NAD(P)H)

0.49

Nos3 a,c,d,e

24600

nitric oxide synthase 3, endothelial cell

−0.55

Nox4 d

85431

NADPH oxidase 4

0.50

Pappa

313262

pregnancy-associated plasma protein A

−0.68

Pecam1 d,e

29583

platelet/endothelial cell adhesion molecule 1

−0.45

Ptgds b,c,d

25526

prostaglandin D2 synthase (brain)

−0.78

Ptk2b c,e

50646

protein tyrosine kinase 2 beta

−0.79

Retn c,d,e

246250

resistin

expression was detected only in ISIAH rats

Rnpep

81761

arginyl aminopeptidase (aminopeptidase B)

−0.47

RT1-Bb e

309622

RT1 class II, locus Bb

−1.26

Serpine2

29366

serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2

−0.55

Slc26a4

29440

solute carrier family 26 (anion exchanger), member 4

0.59

Slc2a4 c

25139

solute carrier family 2 (facilitated glucose transporter), member 4

−0.76

Slc5a2

64522

solute carrier family 5 (sodium/glucose cotransporter), member 2

0.64

Slc6a19

664630

solute carrier family 6 (neutral amino acid transporter), member 19

0.48

Slc8a1

29715

solute carrier family 8 (sodium/calcium exchanger), member 1

0.53

Slc9a3r2

116501

solute carrier family 9, subfamily A (NHE3, cation proton antiporter 3), member 3 regulator 2

−0.49

Sod3 c,d

25352

superoxide dismutase 3, extracellular

0.56

Tacr3

24808

tachykinin receptor 3

−1.22

Tf c,d,e

24825

transferrin

−1.43

Vcam1 c,d,e

25361

vascular cell adhesion molecule 1

−0.86

DAVID

Col1a2 e

84352

collagen, type I, alpha 2

−0.53

Guca2b

64055

guanylate cyclase activator 2B

2.03

P2rx4

29659

purinergic receptor P2X, ligand-gated ion channel 4

−1.41

Pcsk5

116548

proprotein convertase subtilisin/kexin type 5

0.73

ISIAH and WAG – rat strains used in the study. DAVID – Database for Annotation, Visualization and Integrated Discovery (http://david.abcc.ncifcrf.gov/)

Genes associated with: arenal hypertension; bnephrosclerosis; cinsulin resistance; ddiabetic nephropathy; eimmune system diseases

Table 3

Genes differentially expressed in ISIAH versus WAG renal medulla and annotated in Rat Genome Database as associated with kidney diseases

Gene symbol

NCBI gene ID

Gene name

log2 fold change ISIAH/WAG

Acsm3

24763

acyl-CoA synthetase medium-chain family member 3

3.40

Adipoq c,d

246253

adiponectin, C1Q and collagen domain containing

1.17

Agtr1a a,c,d

24180

angiotensin II receptor, type 1a

−0.47

Ak4 c

29223

adenylate kinase 4

0.63

Alpl

25586

alkaline phosphatase, liver/bone/kidney

0.53

Amacr

25284

alpha-methylacyl-CoA racemase

0.50

Angpt2 a

89805

angiopoietin 2

−0.79

Anxa2

56611

annexin A2

−0.45

Apoh d

287774

apolipoprotein H (beta-2-glycoprotein I)

−1.52

Aqp2 d

25386

aquaporin 2 (collecting duct)

−0.47

Baat

29725

bile acid CoA: amino acid N-acyltransferase (glycine N-choloyltransferase)

0.78

Car2

54231

carbonic anhydrase 2

0.53

Cftr

24255

cystic fibrosis transmembrane conductance regulator

−1.22

Cldn19

298487

claudin 19

−0.53

Clu a,c,d

24854

clusterin

−0.80

Col3a1 a,c

84032

collagen, type III, alpha 1

−0.64

Col4a5

363457

collagen, type IV, alpha 5

−0.45

Comt

24267

catechol-O-methyltransferase

−0.67

Csf1r c,d

307403

colony stimulating factor 1 receptor

−0.73

Cst3 d

25307

cystatin C

−0.50

Cubn d

80848

cubilin (intrinsic factor-cobalamin receptor)

0.68

Cyp1a1 c

24296

cytochrome P450, family 1, subfamily a, polypeptide 1

−0.51

Cyp4a3

298423

cytochrome P450, family 4, subfamily a, polypeptide 3

0.52

Dusp1

114856

dual specificity phosphatase 1

−0.71

Ebag9

299864

estrogen receptor binding site associated, antigen, 9

0.49

Ephx2 c,d

65030

epoxide hydrolase 2, cytoplasmic

4.52

F2 c,d

29251

coagulation factor II

−0.93

Fbn1 a,d

83727

fibrillin 1

−0.61

Fga c

361969

fibrinogen alpha chain

0.85

Fhit

60398

fragile histidine triad

1.58

Fmod d

64507

fibromodulin

−1.09

Fn1 a,c,d

25661

fibronectin 1

−0.98

Gatm c

81660

glycine amidinotransferase (L-arginine:glycine amidinotransferase)

0.86

Gja1

24392

gap junction protein, alpha 1

0.99

Gpc1

58920

glypican 1

−0.72

Gstp1 c

24426

glutathione S-transferase pi 1

0.53

Gtpbp4 c

114300

GTP binding protein 4

3.01

Hao1

311446

hydroxyacid oxidase (glycolate oxidase) 1

−1.60

Hgf d

24446

hepatocyte growth factor

−0.77

Igf1 d

24482

insulin-like growth factor 1

−0.75

Igfbp1 d

25685

insulin-like growth factor binding protein 1

1.26

Il4r

25084

interleukin 4 receptor

0.64

Lama2

309368

laminin, alpha 2

−0.58

Lgals1

56646

lectin, galactoside-binding, soluble, 1

0.88

Lrp1 a

299858

low density lipoprotein receptor-related protein 1

−0.53

Lrp5

293649

low density lipoprotein receptor-related protein 5

−0.47

Ltbp1

59107

latent transforming growth factor beta binding protein 1

−0.57

Mif

81683

macrophage migration inhibitory factor (glycosylation-inhibiting factor)

0.55

Mme

24590

membrane metallo-endopeptidase

1.35

Mmp2 a, c

81686

matrix metallopeptidase 2

−0.54

Mmp9 a, d

81687

matrix metallopeptidase 9

−2.00

Mok

362787

MOK protein kinase

−0.93

Mthfr b, c, d

362657

methylenetetrahydrofolate reductase (NAD(P)H)

0.49

Nos3 c, d

24600

nitric oxide synthase 3, endothelial cell

−0.55

Nox4 d

85431

NADPH oxidase 4

0.50

Nphs2 a

170672

nephrosis 2, idiopathic, steroid-resistant

0.57

Pecam1 d

29583

platelet/endothelial cell adhesion molecule 1

−0.45

Pla2g4a

24653

phospholipase A2, group IVA (cytosolic, calcium-dependent)

−0.69

Ptgds b, d

25526

prostaglandin D2 synthase (brain)

−0.78

Ptk2b

50646

protein tyrosine kinase 2 beta

−0.79

Rap1gap a

313644

Rap1 GTPase-activating protein

−0.60

Retn c, d

246250

resistin

expression was detected only in ISIAH rats

RT1-Bb

309622

RT1 class II, locus Bb

−1.26

Serpinf1 d

287526

serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment epithelium derived factor), member 1

−2.10

Serping1

295703

serpin peptidase inhibitor, clade G (C1 inhibitor), member 1

0.56

Sfrp1

84402

secreted frizzled-related protein 1

−0.61

Slc17a2

306950

solute carrier family 17, member 2

0.59

Slc19a3 c

316559

solute carrier family 19 (thiamine transporter), member 3

1.00

Slc5a2

64522

solute carrier family 5 (sodium/glucose cotransporter), member 2

0.64

Slc6a19

664630

solute carrier family 6 (neutral amino acid transporter), member 19

0.48

Slit2

360272

slit homolog 2 (Drosophila)

0.45

Sod3 d

25352

superoxide dismutase 3, extracellular

0.56

Tf d

24825

transferrin

−1.43

Timp2 d

29543

TIMP metallopeptidase inhibitor 2

−0.52

Vcam1 c,d

25361

vascular cell adhesion molecule 1

−0.86

Vtn d

29169

vitronectin

−1.18

Genes associated with: arenal fibrosis; bnephrosclerosis; crenal insufficiency, ddiabetic nephropathy; ISIAH and WAG – rat strains used in the study

The differential transcription of 59 transcription factor genes was found in the renal medulla of ISIAH and WAG rats (Table 4). No one of these genes is currently referred to in databases as associated with arterial hypertension or kidney diseases.
Table 4

Transcription factor genes differentially expressed in ISIAH and WAG renal medulla

Gene symbol

NCBI gene ID

Gene name

log2 fold_change ISIAH/WAG

Alx1

25401

ALX homeobox 1

−1.13

Arntl

29657

aryl hydrocarbon receptor nuclear translocator-like

−1.01

Bcl6

303836

B-cell CLL/lymphoma 6

−2.02

Bcl6b

360551

B-cell CLL/lymphoma 6, member B

−0.80

Bhlhe22

365748

basic helix-loop-helix family, member e22

−1.08

Bnc2

298189

basonuclin 2

0.61

Btbd11

314675

BTB (POZ) domain containing 11

−0.65

Ccrn4l

310395

CCR4 carbon catabolite repression 4-like (S, cerevisiae)

0.64

Cebpd

25695

CCAAT/enhancer binding protein (C/EBP), delta

−0.83

Dab2

79128

disabled 2, mitogen-responsive phosphoprotein

0.49

Dmrt2

309430

doublesex and mab-3 related transcription factor 2

−0.55

Esrrb

299210

estrogen-related receptor beta

−0.58

Etv5

303828

ets variant 5

−0.70

Fhl1

25177

four and a half LIM domains 1

1.09

Gcfc2

312474

GC-rich sequence DNA-binding factor 2

−0.61

Gne

114711

glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase

−0.51

Grhl1

313993

grainyhead-like 1 (Drosophila)

1.91

Hand2

64637

heart and neural crest derivatives expressed 2

expression was detected only in ISIAH rats

Hes6

316626

hes family bHLH transcription factor 6

−0.72

Id3

25585

inhibitor of DNA binding 3

−0.50

Id4

291023

inhibitor of DNA binding 4

0.47

Ilf2

310612

interleukin enhancer binding factor 2

−0.46

Irf7

293624

interferon regulatory factor 7

0.59

Irf8

292060

interferon regulatory factor 8

−0.58

Khdrbs3

64015

KH domain containing, RNA binding, signal transduction associated 3

−1.23

Lmo2

362176

LIM domain only 2

−0.79

Map2

25595

microtubule-associated protein 2

−1.44

Mcm7

288532

minichromosome maintenance complex component 7

0.61

Mef2c

499497

myocyte enhancer factor 2C

−0.59

Mybl1

297783

myeloblastosis oncogene-like 1

−0.56

Nfkbil1

361794

nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor-like 1

0.98

Notch3

56761

notch 3

−0.50

Npas2

316351

neuronal PAS domain protein 2

−0.90

Nr2f1

81808

nuclear receptor subfamily 2, group F, member 1

−1.31

Nrip3

361625

nuclear receptor interacting protein 3

−0.83

Osr1

298878

odd-skipped related transciption factor 1

−1.29

Osr2

315039

odd-skipped related transciption factor 2

1.17

P8

113900

nuclear proten 1

−0.95

Patz1

305471

POZ (BTB) and AT hook containing zinc finger 1

0.57

Paxbp1

681004

PAX3 and PAX7 binding protein 1

−0.53

Pou2af1

690528

POU class 2 associating factor 1

1.19

Prdm6

307305

PR domain containing 6

1.50

Prox1

305066

prospero homeobox 1

−1.06

Sim1

309888

single-minded family bHLH transcription factor 1

−0.50

Smarcd3

296732

SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 3

−0.58

Spry4

291610

sprouty homolog 4 (Drosophila)

−0.61

Stat5a

24918

signal transducer and activator of transcription 5A

0.63

Tcerg1l

361669

transcription elongation regulator 1-like

4.09

Tcf21

252856

transcription factor 21

−0.48

Tcf4

84382

transcription factor 4

−0.66

Tle2

299636

transducin-like enhancer of split 2 (E(sp1) homolog, Drosophila)

−0.61

Zbtb16

353227

zinc finger and BTB domain containing 16

−1.11

Zdhhc2

246326

zinc finger, DHHC-type containing 2

0.75

Zfhx2

305888

zinc finger homeobox 2

−0.88

Zfp189

313219

zinc finger protein 189

−0.62

Zfp354a

24522

zinc finger protein 354A

0.58

Zfp449

684901

zinc finger protein 449

−0.71

Zfp710

293044

zinc finger protein 710

−0.76

Zfp958

100302405

zinc finger protein 958

−0.64

ISIAH and WAG – rat strains used in the study

Gene Ontology (GO) terms for biological processes defined as significantly enriched within the analysis in DAVID are represented in Additional file 4. GO terms which might be essential for stress-sensitive hypertension development in ISIAH rats are shown in bold in this file, and the details for DEGs from these groups are given in Additional file 5.

The most significantly enriched GO terms were those related to oxidation reduction, lipid metabolic process, response to external stimulus as well as regulation of response to external stimuli. The analysis showed that the differences in renal medulla function between ISIAH and WAG rats are under control of many genes participating in response to hormone stimulus including genes associated with response to steroid (glucocorticoid) hormone stimulus.

Multiple DEGs were related to transport, (including sodium ion transport, water transport, glucose transport, lipid transport) and regulation of transport. Many DEGs associated with transport are annotated also as responsible for homeostatic process, including ion homeostasis.

Several GO terms were related to BP control. These were the groups of DEGs associated with blood circulation, regulation of BP, and negative regulation of blood coagulation.

Two large groups of DEGs were associated with response to stress and regulation of response to stress. The specificity of the stress types was defined by GO terms related to responses to oxidative stress, to osmotic stress, and to salt stress.

The other processes which might be involved in hypertension development in ISIAH rats were: neurogenesis, regulation of action potential in neuron, angiogenesis, regulation of smooth muscle cell differentiation, cell adhesion and regulation of cell adhesion.

The functional annotation underlined the important role of the immune system process and its regulation. The analysis in the Kyoto Encyclopedia of Genes and Genomes Pathway Database (KEGG) performed within the DAVID highlighted sixteen pathways that were significantly (p < 0.05) enriched (Additional file 6). Several of them were also related to immune system functioning. All of the pathways contained genes annotated in RGD as associated with hypertension or with kidney diseases.

Discussion

The transcriptome profiling of the renal medulla from ISIAH and WAG rats let to identify multiple DEGs and several metabolic pathways contributing to differences between the renal medulla functions in ISIAH rats with stress-sensitive hypertension and normotensive controls. The functional annotation of DEGs within the analysis in Databases demonstrated that many of them are associated with hypertension and regulation of BP. One of these, Retn, was expressed in renal medulla of hypertensive rats but not in controls. However, the low level of expression of this gene was reported in kidney from Fischer 344 male rats, too [12]. So, the inter-strain differences in transcriptional activity of Retn shouldn’t be essential for hypertension development in ISIAH rats.

In recent years, it has become clear that a key determinant of the set point of the renal pressure-natriuresis curve is the balance of reactive oxygen and nitrogen species within the renal medullary region [11]. The experimentally induced elevations of either superoxide or hydrogen peroxide in the renal medulla result in reduction of medullary blood flow, enhanced sodium reabsorption, and hypertension [13].

In the current study, the group of DEGs described by GO term ‘oxidation reduction’ was the most significantly enriched one (Additional file 5). This group contained several genes associated with hypertension and some of them (Cyp1a1, cytochrome P450, family 1, subfamily a, polypeptide 1; Cyp4f1, cytochrome P450, family 4, subfamily f, polypeptide 1; Nos3, nitric oxide synthase 3, endothelial cell; Nox4, NADPH oxidase 4, Sod3, superoxide dismutase 3, extracellular) are known as involved in modulation of vascular tone and renal tubular function.

Cyp1a1 knockout mice are hypertensive. Cyp1a1 metabolizes omega-3 polyunsaturated fatty acids to vasodilators and the loss of these vasodilators may lead to increases in BP [14]. CYP1A1 contributes to eNOS (same as Nos3) activation, nitric oxide (NO) bioavailability, and NO-dependent BP regulation [15]. So, the decreased level of Cyp1a1 transcription in ISIAH renal medulla suggests its contribution to hypertension development in ISIAH rats.

CYP4F1 was characterized as the most critical 4 F isoform involved in the production of 20-hydroxyeicosatetraenoic acid (20-HETE), a potent eicosanoid that modulates vascular tone and renal tubular function [16]. 20-HETE has been shown to play a complex role in BP regulation. In the kidney tubules, 20-HETE inhibits sodium reabsorption and promotes natriuresis, thus, contributing to antihypertensive mechanisms. In contrast, in the microvasculature, 20-HETE plays a pressor role by sensitizing smooth muscle cells to constrictor stimuli and increasing vascular myogenic tone, and by acting on the endothelium to further promote endothelial dysfunction and endothelial activation. In addition, 20-HETE induces endothelial angiotensin-converting enzyme, thus, setting forth a potential feed forward prohypertensive mechanism by stimulating the renin-angiotensin-aldosterone system (RAAS) [17].

Nox4 is a hydrogen peroxide-producing NADPH oxidase isoform highly expressed in the kidney. NAD(P)H oxidase was recognized as the major source of reactive oxygen species (ROS) contributing to salt-induced hypertension [18]. Dahl salt-sensitive hypertensive rats with knockout of Nox4 exhibited a reduced renal injury and attenuated BP response to high salt [19]. Thus, we may suggest that the increased transcription of Nox4 in the renal medulla of ISIAH rats may contribute to increased rate of ROS production and hypertension development.

Superoxide dismutases (SODs) convert superoxide to hydrogen peroxide, which is then removed by glutathione peroxidase or catalase [20]. The oxidative stress appears to be a common feature of hypertensive disorders from diverse origins [21], and it has been long known that oxidative stress induces or enhances the activity of SODs [22]. Thus, the enhanced expression of Sod3 provides a rationale to suggest the presence of the oxidative stress in the renal medulla of ISIAH rats which may require the enhanced activity of SOD3 to prevent the formation of highly aggressive ROS.

Nos3 is known as related to the diversity of biological processes. In the current research, Nos3 was related to GO term groups such as regulation of BP, oxidation reduction, response to stress and to steroid hormone stimulus, regulation of calcium and sodium ion transport, and angiogenesis (Additional file 5). Endothelial nitric oxide synthase (eNOS; NOS3) is physiologically important for vascular homeostasis [23]. It is expressed predominantly in the endothelium of blood vessels where it catalyzes the production of NO. NO regulates vascular tone and local blood flow, platelet aggregation and adhesion, and leukocyte-endothelial cell interactions. Abnormalities in NO production by the vascular endothelium result in endothelial dysfunction, which occurs in hypertension, diabetes, aging, and as a prelude to atherosclerosis [24].

Nos3 transcription was found to be reduced in the renal medulla of ISIAH rats. The reduced eNOS expression was earlier reported for different models of hypertension: spontaneously hypertensive rats (SHR) [25], Dahl salt-sensitive rats [26], two-kidney, one-clip hypertensive rats [27]. The reduced level of eNOS and NO production in the renal medulla is associated with a reduction in medullary blood flow and have considerable importance in sodium and water homeostasis and the long-term control of arterial pressure [2729]. Recently, the reduced renal blood flow measured by magnetic resonance angiography was reported in ISIAH rats as compared to normotensive Wistar rats [30]. So, the reduced transcription of the Nos3 in the renal medulla of ISIAH rats may be considered as a key feature possibly leading to the medullary blood flow reduction. This, and the decreased NO availability in serum of ISIAH rats and its negative correlation with the BP reported earlier [31], might indicate the impact of this mechanism on stress-sensitive hypertension development.

One more gene which may participate in regulation of NO availability in the renal medulla of ISIAH rats is Ephx2 encoding the soluble epoxide hydrolase (sEH). In the current study, Ephx2 was one of the DEGs associated with hypertension and showing the highest differences in expression in ISIAH and WAG renal medulla. sEH metabolizes the epoxyeicosatrienoic acids (EETs) having antihypertensive properties. EETs also possess anti-inflammatory actions that could protect the kidney vasculature from injury during renal and cardiovascular diseases [32]. It was shown that P450 eicosanoids are vasodilatory, largely through their ability to activate eNOS and NO release [33]. sEH is a main effector of angiotensin II-induced hypertension [34]. Ephx2 was also recognized as a gatekeeper gene contributing to programmed hypertension [35]. In the current study, the PLS regression method, which is commonly used for biomarker selection in metabolomic [36] and gene expression [37] studies, reckoned Ephx2 as one of the genes making the most significant contribution to the inter-strain differences. All this and the increased transcription of Ephx2 found earlier in hypothalamus [38] and renal cortex [9] of ISIAH rats prompt us to consider Ephx2 as a key candidate for further studies of the mechanisms underlying the stress-sensitive hypertension in ISIAH rats. The sEH was already considered as a suitable target for pharmaceutical intervention in the hypertension treatment [39].

The other groups of DEGs, being among the most significantly enriched and described by GO terms ‘response to external stimulus’, ‘response to hormone (endogenous) stimulus’, ‘response to stress’, and ‘homeostatic process’, provide the molecular basis for integrated responses to homeostasis disturbances in the renal medulla of the ISIAH rats (Additional file 5). Most DEGs associated with BP regulation (Adipoq, Adra1b, Agtr1a, Aqp2, Ephx2, Guca2b, Mif, Nos3, P2rx4, Tacr3) were related to these groups. As it may be seen from the following discussion, some of these genes may contribute to vascular tone and renal blood flow regulation.

Adiponectin (Adipoq gene) stimulates production of nitric oxide in vascular endothelial cells [40] and possess anti-atherogenic properties [41]. The increased expression of Adipoq in renal medulla of ISIAH rats may be protective against the reduction in medullary blood flow.

Agtr1a (angiotensin II receptor, type 1a) encodes the receptor for angiotensin II, which is the main effector of RAAS [42]. AT1A receptors expressed on the renal vasculature and/or renal tubular epithelia play a critical role in sodium and volume homeostasis [43]. AGTR1a is required for mineralocorticoid receptor stimulation to induce vascular remodeling, inflammation and endothelial dysfunction [44]. It was shown that in the euvolemic state the mean arterial pressure was significantly lower in the AT1A receptor-deficient mice compared with wild-type controls [45]. So, the reduced transcription of Agtr1a in ISIAH renal medulla may be considered as a compensatory mechanism against the complications mentioned above.

The alpha1B-adrenoceptors (Adra1b) are involved in blood vessel remodeling [46] and mediate the vasoconstrictor actions of the renal sympathetic nerves in rats with renal failure [47, 48]. Adra1b is one of the sympathetic nervous system (SNS) components [49]. It is known that increased renal SNS activity reduces urinary sodium and water excretion, renal blood flow and glomerular filtration rate [49]. Thus, the reduced transcription of Adra1b in ISIAH renal medulla may be considered as a protective mechanism against the excessive effects of SNS activation reported earlier in ISIAH rats [50].

One more DEG, which may cause the differences in SNS activity in ISIAH and WAG rats is Comt (catechol-O-methyltransferase), which encodes the enzyme involved in the degradation of catecholamines. The inhibition of COMT induces dopamine-dependent natriuresis [51]. The Comt-gene-disrupted mice were resistant to salt-induced hypertension [52]. So, the decreased expression of Comt in the renal medulla of ISIAH rats may lead to increase in renal dopaminergic effects and sodium excretion, and may be considered as an adaptive or protective mechanism for control of hypertension development in ISIAH rats. Earlier, the significantly decreased transcription of Comt was also detected in kidney of 6-month old ISIAH rats [53].

MIF (Macrophage migration inhibitory factor) possesses the ability to directly regulate the immunosuppressive actions of glucocorticoids and thus plays a critical role in the host control of inflammation and immunity [54]. MIF is a key factor in atherogenesis [55, 56]. The upregulation of the podocyte-expressed MIF induces an injury of podocytes and accelerates the progression of glomerulosclerosis [57]. In ISIAH rats, the upregulation of Mif gene was found both in renal cortex [9] and in renal medulla (the current study), and possibly may contribute to development of atherosclerosis as well as to glomerulo- and medullary sclerosis histologically determined earlier [7, 8].

The changes in expression of DEGs associated with renal hypertension (Agtr1a, Fn1, Gja1, Lama2, Mmp2, Mmp9, Nos3) may also be suggested to have a significant impact on disease development in ISIAH rats (Table 2).

Fibronectin 1 is a glycoprotein involved in cell–matrix and cell–cell adhesion [58]. Fibronectin 1 may be induced by different agents including angiotensin II [59] and aldosterone [44]. Both of these stimuli may lead to vascular remodeling and vascular inflammation [44, 60]. So, the reduction in Fn1 expression may be directed against the excessive vascular complications development in ISIAH renal medulla. It is worth also to mention that almost all (33 gene out of 37) of the multiple DEGs related to GO terms ‘cell adhesion’ and ‘cell–matrix adhesion’ were downregulated in ISIAH renal medulla (Additional file 5), and it is of high probability that those changes, at least in a part, are directed towards the prevention of vascular complications.

The protein encoded by Gja1 (or Cx43, connexin 43) is a component of gap junctions, which permit the passage of ions and chemical mediators from cell to cell [61]. The expression of connexins in renal arterioles is believed to have a profound impact on conducted responses, regulation of arteriolar tonus and renal blood flow. Besides, the cell-to-cell communication mediated by Cx43 channels may contribute to regulating the elasticity of the vascular wall [62]. However, no evidence for an increased abundance of Cx43 in renal arterioles of SHR when compared with normotensive counterparts was found [63]. In the ISIAH renal medulla the increased transcriptional level of Gja1 was detected. Earlier a differential regulation of aortic Cx43 in different models of hypertension was reported [62], and this may also be true for kidney. This may be one of the features distinguishing the mechanism of hypertension development in ISIAH rats from that in SHR.

LAMA2 is an extracellular matrix protein [64] involved in regulation of cell adhesion. Matrix metalloproteinases (MMP2 and MMP9) belong to a family of metalloendopeptidases that cleave the protein components of the extracellular matrix and thereby play a central role in tissue remodeling [65]. MMP2 (Mmp2) and MMP9 (Mmp9) are involved in the vascular smooth muscle cell activation and neointimal formation that characterize arterial tissue remodeling after injury [66]. The overexpression of MMP9 and MMP2 have been observed within plaques [67]. The elevated urine values of MMP-9 was recognized as a marker of atherosclerotic disease [68], and, alternatively, the loss of MMP9 reduces atherosclerotic burden [69]. MMP-2 also contributes to the development of atherosclerosis [70]. Taking that into consideration, the decrease in Mmp2 and Mmp9 expression may be considered as protective against vascular remodeling in ISIAH renal medulla.

So, in ISIAH renal medulla, several DEGs associated with renal hypertension are related to cell–matrix and cell–cell adhesion processes. It is known that in hypertension the increase in arterial stretch stimulates vessel thickening to normalize the tensile forces. This process requires modification of the extracellular matrix and of cell–matrix interactions [71]. The results of the current study are in a good agreement with this conception and highlighted the major DEGs contributing to the modification of the extracellular matrix and of cell–matrix interactions in renal medulla of rats with stress-sensitive hypertension.

Multiple genes with the changed level of transcription related to ion transport and to regulation of ion transport were found in the renal medulla of ISIAH rats. Many of these genes should be essential for maintaining the sodium and water balance in hypertensive state. However, it is not possible to discuss the role of all the DEGs contributing to these processes within the current paper. We just would like to mention that all the processes including such a basic one as sodium and water balance may undergo the strain-specific regulation. This may be seen, for example, in the case of the Aqp2 being regulated by vasopressin [72] and playing a role in body water balance [73]. Mice with reduced expression of Aqp2 have severe polyuria and very low urine osmolality [74]. Aqp2 transcription was reduced in renal medulla of ISIAH rats. The reduced AQP2 in the renal medulla was reported earlier in the clipped kidney of rats with experimental two-kidney, one clip hypertension [75] and in Milan hypertensive rats [76]. However, the medullary expression of AQP2 protein was increased in DOCA-salt hypertensive rats [77] and in SHR [78]. The existence of the strain-specific differences in the molecular mechanisms of the hypertension development points out the importance of the search for the common features contributing to disease manifestation which may be potentially used as new pharmacological targets.

Conclusions

The current study helped to identify a number of DEGs specifying the function of renal medulla in ISIAH rats being a model of the stress-sensitive arterial hypertension. The genes already known as associated with hypertension development were mostly discussed. However, the other genes also may have indirect influence on disease development and maintenance. The discussion demonstrated that the changes in expression of many genes may be considered as protective or adaptive and directed toward the integration and coordination of the general homeostasis of the organism. Several DEGs, which may modulate the renal medulla blood flow were detected. The reduced transcription of the Nos3 points to the possible reduction of the blood flow in the renal medulla of ISIAH rats.

The generated data may be useful for comparison with those from different models of hypertension and for identifying the common molecular determinants contributing to disease manifestation which may be potentially used as new pharmacological targets. Based on discussion, Ephx2 seems to be a promising candidate for further studies of its potential as therapeutic target.

Methods

Animals

The hypertensive ISIAH/Icgn and normotensive WAG/GSto-Icgn rat strains were used in the current study. Rats were bred in the Center for Genetic Resources of Laboratory Animals at the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, (Novosibirsk, Russia, RFMEFI61914X0005 and RFMEFI62114X0010). All rats were kept in standard laboratory conditions and had free access to food and water. The 3-month old ISIAH, and WAG male rats were used in RNA-seq experiments. Each group of experimental animals contained 3 rats. Rats were individually caged one week before the measurement of the systolic arterial BP, which was measured indirectly by the tail-cuff method as it was described earlier [9]. The mean systolic arterial BP was 171.7 ± 1.22 mmHg in the experimental group of ISIAH males and 116.33 ± 1.86 mmHg in WAG. Six days after BP measurement, rats were decapitated and their kidneys were rapidly removed and sectioned. The samples of renal medulla were stored in RNA Later (Qiagen, Chatsworth, CA) at −70 °C until use. The animal experiments were conducted with approval of the Institute’s Animal Care and Use Committee.

RNA-seq analysis

The samples of renal medulla were sent to JSC Genoanalytica (Moscow, Russia), where the technological part of the RNA-seq analysis was conducted as it was described earlier {9}. Dynabeads mRNA Purification Kit (Ambion, USA) was used for mRNA extraction and NEBNext mRNA Library Prep Reagent Set for Illumina (NEB, USA) was used for cDNA libraries construction. All kits were used according to the manufacturer’s protocol. cDNA libraries were sequenced using a HiSeq1500 Sequencing System (Illumina Sequencing, San Diego, USA) in a single end mode with a read length of 50 bases. Three biological replicates were analyzed for each rat strain. The sequencing data were subjected to adapter trimming and low-quality sequence removal and mapped to the rat reference genome (RGSC Rnor_5.0, rn5) with the use of Tophat2 [79]. Quality metrics of the mapped libraries (Additional file 7) were collected using the Picard ‘CollectRnaSeqMetrics’ tool (http://broadinstitute.github.io/picard/). The differential expression analysis was performed using the Cufflinks workflow [80]. A gene was defined as being expressed if it matched the Cufflinks criteria on suitability for statistical testing (test status ‘OK’). Genes with a Benjamini-Hochberg adjusted p-value (q-value) <0.05 were considered to be differentially expressed. Heatmap of the genes differentially expressed (q value < 0.01) in the renal medulla of the ISIAH and WAG rats was built using the core functions of R statistical language (https://cran.r-project.org/); a hierarchical ‘complete linkage’ clustering by Euclidean distance was used to construct the dendrograms. The raw RNA-Seq data are available at the NCBI Short Read Archive database under the Accession number: PRJNA299102.

Functional annotation

The DEGs were functionally annotated in DAVID (The Database for Annotation, Visualization and Integrated Discovery) (http://david.abcc.ncifcrf.gov/) [81, 82] with the use of Rattus norvegicus genome as the background list for the over-representation analysis. The significantly enriched biological processes (p < 0.05) were identified using the Gene Ontology option. The most significant to the data set pathways were identified in Kyoto Encyclopedia of Genes and Genomes Pathway Database (KEGG, http://www.genome.jp/kegg/). The annotation of DEGs in the Rat Genome Database Disease Portals (RGD, http://rgd.mcw.edu/wg/portals?100) was used to detect the genes related to arterial hypertension and renal diseases. The DEGs encoding the transcription factors were detected using the atlas of combinatorial transcriptional regulation in mouse and man [83], GenBank (http://www.ncbi.nlm.nih.gov/gene/), and Panther classification system (http://www.pantherdb.org/) [84].

Quantitative real-time PCR (qPCR)

To estimate the relative amount of target mRNA, qPCR analysis was performed on the renal medulla samples from ISIAH (n = 5) and WAG (n = 5) male rats aged as 3-month old. TRI reagent (Molecular research center, USA) was used for the extraction of the total RNA, and DNase I (Promega, USA) treatment was performed to remove the residual genomic DNA. The kits were used according to the manufacturer’s recommendations.

The protocol for the reverse transcription was the following: the reaction was carried out with 40 units of MoMLV (Vektor-Best, Russia) in 50 μl of reaction mixture, which contained reverse transcription (RT) buffer, 0.4 mM dNTPs, 0.25 nmol of random nonanucleotide primers (Biosan, Russia), and 3 μg of total RNA. The cDNA synthesis was performed at 37 °C (1 h), 42 °C (30 min), and 50 °C (10 min) with the following inactivation of the enzyme by heating at 75 °C for 5 min.

The reaction volume for qPCR was 20 μl. It contained a master mix with SYBR Green, 0.15 mM of each forward and reverse primers, 1 unit of HotStart Taq polymerase (Vektor-Best, Russia), and the cDNA template. The Rpl30 (ribosomal protein L30) stably expressed in different tissues from ISIAH and WAG rats was used as a reference gene. The primers sequences and characteristics are given in Additional file 8.

qPCR was performed in an iCycler iQ4 Real-Time PCR Detection System (Bio-Rad Laboratories, USA) using the protocol described earlier [9]. Relative transcript levels were determined by standard-curve quantitation method [85]. The mixture of the aliquots from all synthesized cDNA samples was used as a standard cDNA for calibration curves plotting. The value obtained for the target gene was normalized against the value for reference gene and then the relative mRNA abundance was calculated as a ratio of the normalized mRNA level in the experimental ISIAH samples to the normalized mRNA level in the samples from WAG rats. A value of 1 was assigned to the normalized mRNA level obtained for the samples from control WAG rats.

Statistical methods

Mann-Whitney U-test (Statistica v.8.0, Statsoft, USA) was used for calculations of the statistical significance for qPCR data. Statistical significance was set at p < 0.05. The data were expressed as M ± S.E.M. (means and their standard errors).

The acquired RNA-seq data (FPKM values) were log transformed, centered and normalized to run the partial-least squares discriminant analysis (PLS-DA). First, the scaling of the data sets was performed using principal coordinates method based on Euclidean metric distances. Then, the pattern of co-variation for linear combinations between two blocks of variables [86] was explored to get the separation of groups in PLS-DA. PLS-DA and the following calculation of Pearson correlation coefficients were performed to find a set of variables (expressed genes) that maximize the covariance between gene expression in ISIAH and WAG rat strains and fixed dummy matrix representing group membership [86] for WAG and ISIAH rats, correspondingly. As a result of these procedures the PLS-DA Axes maximizing the distances between hypertensive and normotensive rats were constructed, the correlation between gene expression and PLS-DA Axis 1 was calculated, and the genes contributing the most to differences between ISIAH and WAG transcriptomes were revealed.

Abbreviations

20-HETE: 

20-hydroxyeicosatetraenoic acid

BP: 

Blood pressure

DAVID: 

Database for Annotation, Visualization and Integrated Discovery

DEGs: 

Differentially expressed genes

EETs: 

Epoxyeicosatrienoic acids

eNOS or NOS3: 

endothelial nitric oxide synthase

FPKM: 

Fragments per kilobase of transcript per million mapped reads

GO: 

Gene Ontology

ISIAH: 

Inherited Stress-Induced Arterial Hypertension

KEGG: 

Kyoto Encyclopedia of Genes and Genomes Pathway Database

NO: 

Nitric oxide

PLS-DA: 

Partial-least squares discriminant analysis

qPCR: 

quantitative real time polymerase chain reaction

RAAS: 

Renin-angiotensin-aldosterone system

RGD: 

Rat Genome Database

RNA-seq: 

RNA sequencing

ROS: 

Reactive oxygen species

SABP: 

Systolic arterial blood pressure

sEH: 

soluble epoxide hydrolase

SHR: 

Spontaneously hypertensive rats

SNS: 

Sympathetic nervous system

SODs: 

Superoxide dismutases

WAG: 

Wistar Albino Glaxo rats

Declarations

Acknowledgements

The authors are grateful to JSC Genoanalytica (Moscow, Russia) for conducting the technological part of the experiment and the primary statistical analysis. This work has been supported by the Russian Science Foundation, grant No. 14-15-00118. The Siberian Branch of the Russian Academy of Sciences (SB RAS) Siberian Supercomputer Center is gratefully acknowledged for providing supercomputer facilities.

Declarations

This article has been published as part of BMC Genetics Vol 17 Suppl 3, 2016: Selected articles from BGRS\SB-2016: genetics. The full contents of the supplement are available online at https://bmcgenet.biomedcentral.com/articles/supplements/volume-17-supplement-3.

Funding

Publication of this article has been funded by the Russian Science Foundation (grant 14-15-00118).

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files]. The RNA-Seq data were deposited in the NCBI Short Read Archive database with Accession number: PRJNA299102.

Authors’ contributions

MR and LF performed quantitative real-time PCR, participated in interpretation of data, and helped to draft the manuscript; NE has made substantial contribution to bioinformatics analysis; VE has made substantial contributions to conception and use of multivariate statistical analysis; AM has made substantial contributions to conception and design of the study and participated in interpretation of data; OR participated in interpretation of data and drafted the manuscript. All authors read and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests. The authors alone are responsible for the content and writing of the paper.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All animal experiments were approved by the Institute’s Animal Care and Use Committee.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation
(2)
Novosibirsk State University, Novosibirsk, Russian Federation

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