Differential Expressed Genes in ECV304 Endothelial-Like Cells Infected with Herpes Simplex Virus Type 2
Yuqi Xu1, Meiling Gong1, Wenling Zheng2, Wenli Ma2, Yali Zhang3, Xiaoyang Mo4, Huanying Zheng5, Changwen Ke5, Meilan Liu1, Diaodiao Shi1, Hui Zhang1,6, Haiquan Zhao1*, Yaqiong Ye1*
1School of Animal Science and Technology, Foshan University, Foshan, China.
2Institute of Genetic Engineering, Southern Medical University, Guangzhou, China.
3Department of Clinical Laboratory Science, Guiyang Medical College, Guiyang, China.
4The Center for Heart Development, Key Lab of National Education Ministry, College of Life Sciences, Hunan Normal University, Changsha, China.
5Guangdong Province Center of Disease Control Virology Section, Guangzhou, China.
6College of Animal Science and Technology, Jiangxi Agriculture University, Nanchang, China.
DOI: 10.4236/jbm.2024.1211034   PDF    HTML   XML   32 Downloads   230 Views  

Abstract

Herpes simplex virus (HSV), the viral agent causing human genital herpes, recurs easily and poses significant harm to patients, while also being associated with atherosclerosis (AS). Currently, no effective therapy or vaccine exists to combat HSV. Previous studies have demonstrated the presence of HSV and its DNA in AS-diseased tissue, yet the precise pathogenesis of HSV involvement remains unclear. To investigate the genetic mechanism of HSV-induced vascular endothelial injury and AS, a type of human umbilical vein endothelial cells (ECV-304 cells) cultured in vitro were infected with herpes simplex virus type 2 (HSV-2). The effect of HSV-2 on differential gene expression in ECV304 cells was investigated by gene microarray technology during the early stages of infection. The results revealed a total of 462 differentially expressed genes, with 318 genes exhibiting up-regulated expression and 144 genes showing down-regulated expression. Furthermore, bioinformatics analysis revealed that all 462 differentially expressed genes were implicated in 237 distinct biological processes. Notably, 79 of these biological processes demonstrated statistically significant differences (P < 0.05), encompassing critical functions such as protein synthesis, ribosome biogenesis and assembly, as well as DNA and mRNA metabolism. Our findings have unveiled the differentially expressed genes of HSV-2 in ECV304 cells during infection, offering crucial insights into the pathogenic mechanisms underlying HSV-2 invasion of endothelial cells and the pathobiology of AS.

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Xu, Y. , Gong, M. , Zheng, W. , Ma, W. , Zhang, Y. , Mo, X. , Zheng, H. , Ke, C. , Liu, M. , Shi, D. , Zhang, H. , Zhao, H. and Ye, Y. (2024) Differential Expressed Genes in ECV304 Endothelial-Like Cells Infected with Herpes Simplex Virus Type 2. Journal of Biosciences and Medicines, 12, 407-432. doi: 10.4236/jbm.2024.1211034.

1. Introduction

Herpes simplex virus (HSV) belongs to the α subfamily of herpesviridae [1]. Human infections caused by the herpes simplex virus (HSV) can be categorized into two distinct types: HSV-1 and HSV-2 [2] [3]. Herpes Simplex Virus type 2 (HSV-2) is implicated not only in genital and neonatal infections but also in the pathogenesis of cervical cancer and atherosclerosis (AS) [4] [5]. Furthermore, HSV-2 infection exhibits a synergistic relationship with Human Immunodeficiency Virus (HIV) infection [6]. Prior research has demonstrated the presence of HSV antigens and their corresponding DNA sequences at sites of lesions in AS, specifically within vascular smooth muscle cells (SMCs) and endothelial cells (ECs) [7]. The prevalence of positive serum HSV antibodies is markedly elevated in patients with AS. This evidence suggests that HSV infection may play a significant role in the initiation and progression of AS. The detection of HSV DNA within the vascular wall of AS further indicates a potential association between HSV infection and the development of atherosclerosis.

To date, the detailed pathological mechanisms of HSV involvement in AS, in particular the relationship of HSV with EC and SMC, the major cells of the vessel wall, in vitro is unclear. The effect of the virus on target cells in vitro could reflect the direct effect of the virus on cells in vivo. Studying the biological effects of HSV infection on vascular endothelial cells in vitro is important to elucidate the mechanisms of HSV induction.

DNA expression profiling microarrays are designed to monitor the presence or expression of various genes in samples [8] [9]. Thus, by analyzing these data, we can determine whether the tested specimen is infected with the pathogen and the extent of the infection. Microarray technology has also been used to study the effects of other viruses on ECV304 endothelial cells [10] [11]. Due to the expensive equipment and the complexity of the experiments, there are no reports on the effect of HSV on the gene expression profile of endothelial cells.

In this study, our aim is to identify the differentially expressed genes in endothelial cells induced by HSV-2, and to explore the genetic mechanisms by which HSV-2 causes vascular endothelial injury, dysfunction, and atherosclerosis. We used a high-density microarray containing 18,000 genes to compare the gene expression profiles of HSV-2-treated ECV-304. This allowed us to determine the differential expression of HSV-2-induced endothelial cell genes and to explore the genetic mechanisms by which HSV-2 causes vascular endothelial injury and dysfunction and AS.

2. Materials and Methods

2.1. Cell Culture and Virus Infection

ECV304 cells were preserved and provided by the laboratory of the Department of Nephrology, Southern Hospital. Herpes simplex virus type II (HSV-2) strain was obtained from the General Hospital of Guangzhou Military Region of the Chinese People’s Liberation Army. The HSV-2 strain was inoculated into Vero cells, and the virus was collected after 6 days and the titer was measured (50% tissue cell infection, TCID50 of 105.5/mL). ECV304 cells were maintained in a maintenance medium (MEM, Gibco, US) supplemented with 10% fetal bovine serum, glutamine, sodium bicarbonate, penicillin, and streptomycin in a 37˚C, 5% CO2 humidified incubator. Controls were added directly to the maintenance culture under the same conditions. Cultured cells and their controls were sampled at 2, 4 and 6 days after virus inoculation. Sample three times continuously within thirty minutes for subsequent experiments.

2.2. DNA Extraction and PCR

The LAT gene was amplified by PCR using the primer set: (forward: 5’-GTCAACACGGACACACTCTTTT-3’) and (reverse: 5’-CGAGGCCTGTTGGTCTTTATC-3’), which could be used to produce a 150-bp fragment. DNA was extracted from cells of the control group and infected groups at 12 h and 24 h post-infection using a DNA extraction kit for PCR detection of the HSV LAT gene. The total volume of the amplification reaction system was 50 µL, including 5 µL of DNA sample, 25 µL of 2 × Premix PCR premix buffer, 20 μmol/L of upstream and downstream primers of 1 µL each, and 18 µL of ddH2O. Cycling conditions were as follows: pre-denaturation of 5 min at 94˚C, main cycle (1 min at 94˚C, 30 s at 65˚C, 1 min at 72˚C) for 30 cycles, and extension of 5 min at 72˚C. The amplification product was identified by 1.5% agarose gels (TaKaRa company, China).

2.3. Detection of HSV Infection by Indirect Immunofluorescence

HSV-2 pp65 protein expression was detected by immunofluorescence assay. ECV304 cell crawl sheets were prepared and inoculated with virus for 24 h. Infected and uninfected cells were fixed at room temperature with a fixed solution containing 2% paraformaldehyde and 0.1% Triton X-100 for 30 min, washed twice with PBS, blocked with normal goat serum for 20 min, and washed three times with PBS. Cell crawls were incubated with mouse anti-HSV-2 pp65 (US Biological, USA) for 30 min at 37˚C, and washed five times with PBS. The secondary antibody conjugated with FITC was subsequently added and incubated for 30 min at 37˚C, washed three times with PBS and observed under a fluorescence microscope (Nikon TE-2000, US).

2.4. Microarray Hybridization and Image Analysis

Total RNA was extracted using the Trizol extraction kit (Life Technologies INC.) and the concentration and total amount of RNA was quantified by UV spectrophotometry at 260 and 280 nm. RNA samples with an A260/A280 ratio between 1.8 and 2.0 were selected. Total RNA from uninfected and infected ECV304 cells was reverse transcribed using the RNA Fluorescent Linear Amplification Kit (Agilent) and labeled with Cy5-dCTP and Cy3-dCTP, respectively. Cy5-labeled and Cy3-labeled targets were purified using Rneasy Mini Spin Columns (Qiagen) and then mixed and hybridized to Oligo Microarray Kit (Qiagen). The hybridization volume was 400 µL and consisted of 0.75 µg of each Cy3-labeled and Cy5-labeled linearly amplified cRNA, 50µL of 10 × control target and 225 µL of 2 × hybridization buffer. The mixture was vibrated and centrifuged, pipetted onto a cover glass, and covered with Agilent human 1B oligonucleotide chip. The hybridization box was hybridized at 60˚C and 4 rpm for 16 h, washed twice in 6 × SSC and 0.005% Triton X-102 solution at room temperature for 10 min each time, and then wash it in cold solution (0.1 × SSC, 0.005% Triton X-102) for 5 min. The slides were dried with nitrogen and stored in dark. The chip is scanned in the Agilent 2565BA gene chip scanner (Agilent, Palo Alto, CA, USA). The default parameters of the scanner are used for parameter setting. The scanned data are analyzed and homogenized by Feature extraction software. According to Cy3 (g processed signal) and Cy5 (r processed signal) Log ratio P-Value, P < 0.01 indicated a significant difference in gene expression between the virus-infected group and the control group. G processed signal < r processed signal was defined as up-regulation of gene expression, and g processed signal > r processed signal was defined as down-regulation of gene expression.

2.5. Validation of Microarray Results by Real-Time qPCR

Total RNA was extracted from ECV304 cells infected with HSV-2 and the control group for 6 h. Prepare the RT reaction solution (on ice) as per the reverse transcription kit. The reverse transcription was carried out under the following conditions: 10 min at 42˚C, and 2 min at 95˚C. The chip was verified by real-time qPCR, and a Rotor Gene 3000 Fluorescence quantitative detection system was detected by the SYBR RT-PCR kit. The genes to be tested and the sequence of GAPDH primers are shown in Table 1. The final volume of the 20 μL reaction mixture consisted of PCR mixture, diluted cDNA, and specific primers for two up-regulated genes and two down-regulated genes selected from the differentially expressed genes. The PCR reaction was carried out under the following conditions: an initial denaturation of 2 min at 95˚C; 30 cycles, each cycle consisting of 2 min at 95˚C, 94˚C, 1 min at 54˚C, and 1 min at 72˚C. At the end of the procedure, the specificity of the primer group was confirmed by the analysis of the neutrophilic curve. RT-PCR results were compared at different dilutions (100, 101, 102, 103, 104, 105 copies/reaction) to estimate the copy number of the target gene and GAPDH mRNA. Taking the housekeeping gene GAPDH as the internal standard (IS), the quantitative results of GAPDH in the HSV-infection Group and the Control Group were calculated through the standard curve of the housekeeping gene, and the error of RNA quantity (relative quantity) was calculated. Each target gene was quantified by its standard curve, and the errors of the two groups of housekeeping genes were corrected. The corrected quantitative results of the target gene were compared to obtain the relative ratio of the two.

Table 1. Specific primers used in qRT-PCR analysis.

Gene name

Forward primer (5’ - 3’)

Downstream primers (5’ - 3’)

GAPDH

GCACCGTCA AGGCTGAGA AC

ATGGTGGTGAAGACGCCAGT

SP100

AAAGTTGAGTGCCAAGCCCAAG

TCTAAGGGCTCATCAACGTCAGTG

RPS24

GACAACTGGCTTTGGCATGATTTA

CCA ACA TTGGCCTTTGCAGTC

HNRPA1

AGGCTGGCAGATACG TTCGTC

CCTCAGGCTCTCATCAGTTGTTTC

RTP801

GCAGGACGCACTTGTCTTAGCA

CCA AAGGCTAGGCATGGTGAG

2.6. Statistical Processing

Statistical analysis was performed using SPSS10.0. One-Way ANOVA test was used to compare group means and differences between multiple groups. A P-value < 0.05 was considered significant.

3. Results

3.1. LAT Gene Testing

The HSV LAT gene is the first gene expressed after the virus invades the host cell. Since the presence of viral mRNA in the host cell is an important indicator of viral replication, the detection of viral LAT mRNA expression indicates active HSV infection. In the present study, PCR amplification of HSV-2 LAT gene after infection of cells with virus and viral supernatant was consistent with the expected results, indicating the possibility of direct virus infection to cells (Figure 1).

M: DNA Marker; 1, 4: Control; 2, 3: 12 h Post-infection; 5, 6: 24 h Post-infection.

Figure 1. Electrophoreosis of PCR products of HSV-2 LAT gene.

3.2. Detection of LAT Protein

Two days after viral infection, most cells showed a large fluorescent signal around the nucleus (Figure 2(A)). The control group had a homogeneous cell background with no strong fluorescent signal (Figure 2(B)). This finding suggests that HSV-2 can infect ECV304 cells and proliferate in their cytoplasm.

Figure 2. Immunofluorescence was used to detect the expression of the LAT gene in ECV304 cells 2 days after ECV304 infection (200×). (A) Control group; (B) ECV304 cells (green fluorescence showed positive LAT protein in infected cells).

3.3. Identifying the Quality of RNA

The RNA quantification results are as follows: The RNA of the control group is 1149 ng/µL, with an A260/A280 ratio of 1.8. The RNA of the HSV-2 infection group is also 1149 ng/µL, with an A260/A280 ratio of 1.81. The samples were subjected to agarose gel electrophoresis to identify the quality of RNA, and electrophoresis showed three clear RNA bands with visible 28S, 18S and 5S, and the samples were of high purity and integrity, in accordance with the DNA microarray requirements (Figure 3). Qualified samples were stored at −80˚C.

M: Marker; 1: Control; 2: HSV-2 infection.

Figure 3. Electrophoresis result of total RNA sample.

3.4. Quality Control of Microarray Hybridization

In the Agilent 2565BA gene microarray scanner, the microarrays were scanned with the default parameters of the scanner, and the scanned data were analyzed and homogenized using the feature extraction software. The scanned results of the hybridized gene expression profiling microarrays were in accordance with the standard, with high signal intensity and uniform background (Figure 4).

Figure 4. Microarray hybridization scanning picture.

3.5. Scatter Plots of Hybridizing Signal

Figure 5. Scatter plot of Cy3/Cy5 hybridization intensity in the microarray. The fluorescence intensity of the chip was analyzed by scattergraph. Taking process signal (Cy3) as ordinate and process signal (Cy5) as abscissa, a scatter plot of fluorescence intensity of all points was drawn.

Gene Chip scatter plot analysis elucidates the intensity of fluorescent hybridization signals. As depicted in Figure 5, the horizontal axis represents Cy3 fluorescence intensity values, while the vertical axis represents Cy5 fluorescence intensity values. Each data point on this plot signifies the hybridization signal emanating from a specific gene locus on the microarray. The color of these data points conveys information about differential expression: yellow indicates no differential expression, whereas blue or red hues signify differential expression. The ECV-304 cells, subject to experimental (Cy3) and control (Cy5) conditions, exhibit a two-color fluorescent marker overlay. When these two fluorescent signals overlap at a single point, the resultant color provides insights into gene expression trends. Specifically, a stronger Cy3 signal renders the point green, suggesting an up-regulation trend. Conversely, a stronger Cy5 signal results in a red point, indicating a down-regulation trend. When the intensities of both signals are comparable, the point appears yellow.

3.6. Differential Expressed Genes in HSV-2 Infected ECV304 Cells

When the fluorescence signal reaches a certain intensity, data points with ratios greater than 1.37 or less than 0.7 are selected. Data points with signal intensities greater than 5 × 108 were considered valid data points, and data points with ratios greater than 1.37 or less than 0.7 were considered to have significant expression changes. Based on the selection criteria, 17,575 valid data points were extracted from the microarray results, and a total of 462 differentially expressed genes were extracted. 318 genes were up-regulated (ratio more than 1.37); 144 genes were down-regulated (ratio less than 0.7) (Table 2). Positive values indicate up-regulation, while negative values describe down-regulation.

Table 2. Differential display of genes expressed in ECV 304 infected with HSV-2.

Gene Symbol

Ratio (g/r)

Gene Symbol

Ratio (g/r)

Gene Symbol

Ratio (g/r)

IL9

111.7176

PSMA7

4.3616

RPS5

3.1848

NAG-7

36.2037

RPS23

4.3444

HOXA3

3.1625

ATP6V0A2

30.6709

UQCRH

4.2248

PTMA

3.1382

I_1100038

8.7079

HSPC016

4.1982

VIM

3.0837

RPLP1

8.2655

RPL34

4.1579

RPL7A

3.0814

RPS10

7.8284

I_1000200

4.1499

LAMR1

3.0735

MT2A

7.4679

MT1E

4.0840

RPL7

3.0290

I_1109418

7.0383

RPL10A

4.0734

RPL13A

3.0256

FTH1

6.9516

ZFPM1

4.0569

APP

2.9978

RPS20

6.7500

RPL35

3.9349

RPS24

2.9796

RPL36A

6.6287

RPS11

3.9325

LU

2.9455

RPL19

6.4475

RPS14

3.9083

PABPC1

2.9446

RPL38

6.2619

RPS25

3.8745

IGFBP7

2.9329

RPL23A

6.1302

RPL8

3.8569

ANAPC11

2.8797

RPL26

6.0291

MT1B

3.8338

BTF3

2.8327

RPS27A

5.9390

UBC

3.7614

NSEP1

2.8307

RPS29

5.8372

RPS17

3.7474

RPL11

2.8280

S100A6

5.6553

EEF2

3.7165

RPS8

2.7911

RPS18

5.6445

MIF

3.6554

RPLP0

2.7563

RPL27

5.5780

NQO1

3.6068

I_962007

2.7405

EEF1B2

5.5078

OAZ1

3.5915

I_1847600.FL1

2.7162

RPL31

5.4858

MT1H

3.4805

I_958592

2.7154

RPL37A

5.4226

RPS15A

3.4709

GPX1

2.7026

SUI1

5.4220

RPL39

3.4672

MT1A

2.6876

RPL26

5.3642

LRRN1

3.4660

HIST1H4C

2.6689

I_1002391

5.3147

CALM2

3.4604

RPL30

2.6677

CAM-KIIN

5.3061

RPL24

3.4533

PTMS

2.6490

I_963838

5.2654

RPS28

3.4451

I_966336

2.6435

RPL12

5.2032

MAP3K10

3.4417

I_1109768

2.6310

RPL9

5.0539

RPS6

3.3936

KIAA0616

2.6284

LOC51142

5.0194

NME2

3.3849

NM_000983.2

2.6241

RPS21

5.0185

RPL41

3.3441

FLJ22184

2.6229

ATP5E

4.9273

NM_139020.1

3.3386

HES7

2.6205

RPS12

4.9041

TUBA6

3.3351

EIF3S3

2.6144

RPL37

4.8491

RPS16

3.3208

NM_178438.1

2.6101

C21orf6

4.7727

MT1K

3.3205

GAPD

2.6071

RPS27

4.7202

RPL27A

3.2840

GLTSCR2

2.6041

RPL35A

4.6320

I_963575

3.2436

MTCO2

2.6002

NM_006082.1

4.4970

RPL17

3.2110

VGF

2.5959

RPS13

4.4756

RPL23

3.1938

NM_178430.1

2.5736

RPS2

2.5501

HSPA5

2.1935

DTYMK

1.9531

PP2447

2.5492

EBNA1BP2

2.1859

TMSB10

1.9490

H2AFZ

2.5349

NM_178511.1

2.1853

COX6A1

1.9465

RPS15

2.5000

NM_000398.3

2.1832

I_1201840

1.9410

MT1J

2.4955

EEF1D

2.1763

S100A10

1.9405

RPS3A

2.4749

I_1000395

2.1550

TUBA1

1.9351

ID1

2.4594

MTND1

2.1545

PROL2

1.9144

GRIN2D

2.4444

NDUFC2

2.1482

MRPS24

1.8799

HCN2

2.4259

I_961758

2.1462

ATP5G3

1.8573

FAU

2.4240

MGC14353

2.1321

NM_003017.2

1.8535

HES1

2.4212

FBL

2.1311

H3F3B

1.8280

NDUFB2

2.4136

DUX4

2.1310

DIA1

1.8203

NM_001743.3

2.4102

NM_005251.1

2.1301

PRO0478

1.8193

PARD6G

2.3801

DKFZp434N0650

2.1238

LOC51219

1.8170

NDUFS5

2.3533

LSAMP

2.1204

GNG11

1.8153

RPL21

2.3337

RPL32

2.1155

EGR1

1.8146

CKS2

2.3328

COX7C

2.1142

HSPA1A

1.8122

SFRS9

2.3299

I_1152056

2.1133

NM_145293.1

1.8055

I_959447

2.3260

TPT1

2.1102

SSR2

1.8048

MT1X

2.3242

SNK

2.1065

H2AV

1.7792

DKFZp434N074

2.3097

MTND3

2.1002

LDHB

1.7750

FLJ14464

2.3068

SP100

2.0971

I_1000283

1.7707

STMN1

2.3054

ATP5O

2.0886

RPL14

1.7664

DBI

2.3020

COX7A2

2.0827

DAP

1.7645

HSPA8

2.2911

I_931617

2.0797

I_964413

1.7492

DRD4

2.2910

COX8

2.0775

FTL

1.7473

NM_152350.1

2.2761

SLC25A5

2.0500

CCT5

1.7348

HSPE1

2.2726

MT2A

2.0420

THOC4

1.7294

HINT1

2.2725

KRT18

2.0291

RoXaN

1.7275

EEF1A1

2.2678

NCL

2.0277

HMGB1

1.7216

CASKIN1

2.2615

ARF1

2.0275

DKFZp762E1312

1.7183

H2AFX

2.2557

CKLFSF3

2.0187

NOLA2

1.7032

POLR2L

2.2444

NNMT

2.0158

TXN

1.7009

GSTP1

2.2436

RAB34

2.0139

ATP5L

1.6978

LGALS1

2.2373

TK1

1.9980

I_1000105

1.6892

LBX1

2.2227

RPL4

1.9735

ZFP36L2

1.6889

VHL

2.2149

I_960618

1.9724

I_931932

1.6873

RPS4X

2.2147

HIST1H4L

1.9623

NM_003358.1

1.6863

CCT4

2.1969

SNRPG

1.9589

NXT1

1.6851

SEPW1

1.6782

NM_173609.1

1.5329

RBM8A

0.7166

TPSG1

1.6712

I_964798

1.5291

SNRPD1

0.7151

SLC25A3

1.6579

PCNA

1.5230

WBSCR1

0.7001

FLJ20308

1.6578

PCBP1

1.5212

APPBP1

0.6981

POLR2A

1.6538

CMT2

1.5142

PSMB6

0.6954

HNRPM

1.6532

YWHAB

1.5060

PYCR1

0.6948

RPL10

1.6495

TNFRSF1A

1.5048

HSPCB

0.6936

PLP2

1.6392

NDUFB4

1.5034

ANXA2

0.6895

TEBP

1.6368

EIF3S9

1.4982

DNAJC9

0.6855

NM_003769.1

1.6275

C20orf24

1.4963

CCT3

0.6848

MT1H

1.6255

MRPS12

1.4917

GARS

0.6845

UNRIP

1.6048

NM_022833.1

1.4869

PDHA1

0.6839

RPL36AL

1.6005

SERF2

1.4865

NDUFS6

0.6838

OCSP

1.5976

SLC21A12

1.4849

SF3B1

0.6758

QP-C

1.5974

I_957363

1.4813

I_1110347

0.6753

I_932488

1.5964

LOC51685

1.4707

I_1109809

0.6738

UBL1

1.5957

NACA

1.4686

MCM3

0.6704

U2AF1

1.5939

DNCL1

1.4640

ITGAM

0.6673

TGFA

1.5907

MORF4L2

1.4540

MTHFD2

0.6672

SEC61B

1.5839

I_951081

1.4475

FLJ11323

0.6591

RPL15

1.5826

LAMP1

1.4449

LMNA

0.6591

HIST2H2AA

1.5816

BLCAP

1.4415

HSPA9B

0.6588

SF1

1.5815

UQCR

1.4393

MGC4308

0.6568

NME1

1.5814

NM_178352.1

1.4379

PPIA

0.6507

CDC42

1.5759

AKAP2

1.4374

NEUGRIN

0.6498

MGC10974

1.5756

RPL5

1.4328

PSA

0.6488

SFPQ

1.5729

MGAT1

1.4283

NASP

0.6393

FHL2

1.5616

PSME2

1.4211

CD59

0.6383

NDUFS3

1.5587

HIST3H3

1.4201

FLJ23209

0.6341

RPS3

1.5585

I_1152035

1.4183

RRM1

0.6339

HIS1

1.5579

NM_138425.1

1.4156

PSMC1

0.6310

EEF1G

1.5558

I_1000097

1.4115

RARS

0.6260

PC4

1.5514

PTTG1

1.4052

GSTTLp28

0.6246

NEDD8

1.5479

PABPC3

1.4025

SFRS7

0.6226

PCBP2

1.5460

I_932347

1.3941

DKFZp566H0824

0.6220

PSMB1

1.5439

CCT2

1.3919

RHO

0.6208

SOD1

1.5437

LENG5

1.3876

SNRPA1

0.6155

I_930805

1.5384

EIF2S2

1.3812

STMN4

0.6154

ZFP36L1

1.5363

CBS

1.3766

LOC147700

0.6118

PFDN2

0.5141

YEA

0.0009

I_960911

0.6109

EIF3S2

0.5094

BAIAP1

0.0008

PCK2

0.6105

I_960077

0.4929

HSD3B7

0.0008

PAICS

0.6084

DDB1

0.4832

ALDOC

0.0008

HNRPDL

0.6054

I_1000514

0.4793

I_964921.FL2

0.0008

FLJ90165

0.6008

I_1110080

0.4735

I_1000255

0.0008

UBCE7IP5

0.5999

EIF4A1

0.4678

GML

0.0008

I_931957

0.5981

I_1000329

0.4639

WBP4

0.0008

MRPL22

0.5980

HUMAUANTIG

0.4554

DNASE2

0.0008

I_1000009

0.5951

ANXA1

0.4480

PPIL4

0.0008

GP2

0.5926

ACTB

0.4374

NEU4

0.0008

RBM3

0.5903

TPM1

0.4280

TRNT1

0.0008

I_1151867

0.5899

C20orf97

0.4213

MTERF

0.0008

PSMB7

0.5887

CLIC1

0.3868

MFNG

0.0007

CEBPB

0.5884

NM_173624.1

0.3830

MGC21738

0.0007

I_928538

0.5884

HSPCA

0.3799

GGCX

0.0007

I_965066

0.5844

HNRPA1

0.3795

ZNF333

0.0007

I_1109622

0.5842

RTP801

0.3263

FLJ20333

0.0007

FLJ20700

0.5794

CELSR1

0.0017

ZNF215

0.0007

ARHGEF15

0.5783

FLJ20695

0.0014

CACH-1

0.0007

FLJ10097

0.5759

SORCS3

0.0013

G22P1

0.0007

FLJ21174

0.5743

SPP1

0.0012

ACAS2L

0.0007

CENPF

0.5694

PIGO

0.0011

FUT5

0.0007

I_962171

0.5617

I_1110369

0.0011

I_1000094

0.0006

SSRP1

0.5593

PROSC

0.0011

LOC51185

0.0006

I_962014

0.5588

I_961649

0.0010

NM_144963.1

0.0006

RPL6

0.5583

eQC

0.0010

BRUNOL6

0.0006

IL30

0.5571

MCCC2

0.0010

NM_138350.1

0.0006

I_965611

0.5539

I_966078

0.0009

C22orf2

0.0006

HEC

0.5538

I_963210

0.0009

ZNF267

0.0006

THOC3

0.5445

I_934625

0.0009

PIG3

0.0005

NM_006088.2

0.5425

DBR1

0.0009

SPP1

0.0005

RA410

0.5376

SCGB1A1

0.0009

NM_145300.1

0.0005

HMGN2

0.5271

MLANA

0.0009

I_931899

0.0003

DNAJA1

0.5266

ANKRA2

0.0009

CYCS

0.5248

NM_173501.1

0.0009

3.7. Verification of Gene Chip Results

Table 3. Relative quantitation results of real time RT-PCR.

IS (GAPDH)

Target Gene

Relative

Quantitative Results (copies)

Relative (X)

Quantitative Results (Y) (copies)

Quantitative results after correction (Y/X)

SP100 gene

Control

12,698,991

1

8,212,159

8212159.0

1

Infect

6,223,228

0.490

8,800,188

17957484.0

2.187

RPS24 gene

Control

12,698,991

1

12,835,309

12835309.0

1

Infect

6,223,228

0.490

10,901,163

22244690.7

1.733

RTP801 gene

Control

12,698,991

1

479358511

479358511.0

1

Infect

6,223,228

0.490

49557264

101125541.9

0.211

HNRPA1 gene

Control

12,698,991

1

30478756

30478756.0

1

Infect

6,223,228

0.490

24397428

49784894.2

1.633

Table 4. Compared the Results of Real Time RT-PCR and Microarray.

Real Time RT-PCR Quantitative Results

Gene chip test results

SP100 gene

2.187

2.097

RPS24 gene

1.733

2.980

RTP801 gene

0.211

0.326

HNRPA1 gene

1.633

0.380

Four genes were selected and validated by real-time quantitative PCR using double-stranded DNA combined with SYBR Green I. The quantitative results were generally consistent with the gene microarray detection results, which illustrated the reliability of the human whole-genome oligonucleotide expression profiling microarray in screening differentially expressed genes (Table 3 & Table 4).

3.8. Analysis of Differential Genes Involved in Biological Processes

The differential genes were uploaded to the Internet bioinformatics analysis professional website to analyze the biological processes involved in the differential genes (https://panther.appliedbiosystems.com/), and it was found that 462 differential genes were involved in a total of 237 biological processes, 79 of which were significantly different (P < 0.05), as shown in Table 5. The results showed that 73 genes related to protein synthesis, such as RPS, RPL, EEF and SUI1, were generally up-regulated 6 h after HSV-2 infection of ECV304 cells. HSPA8, HSPE1, CCT2, CCT4 and CCT5 genes related to protein elongation and folding were up-regulated, and 9 genes such as HSPCB, HSPCA, CCT3 and PPIA were down-regulated; 12 genes related to cell signaling such as CASK, APP and HINT1 were up-regulated, and 5 genes such as RHO and CELSR1 were down-regulated.

Table 5. Analysis of gene expression profiles in ECV 304 infected with HSV-2.

Biological process

Unigene ID

Entrez Gene

Symbol

chip Ratio

1

protein biosynthesis

356502

6176

RPLP1

8.2655

406620

6204

RPS10

7.8284

8102

6224

RPS20

6.7500

432485

6173

RPL36A

6.6287

381061

6143

RPL19

6.4475

380953

6169

RPL38

6.2619

419463

6147

RPL23A

6.1302

482144

6154

RPL26

6.0291

311640

6233

RPS27A

5.9390

156367

6235

RPS29

5.8372

546290

6222

RPS18

5.6445

514196

6155

RPL27

5.5780

421608

1933

EEF1B2

5.5078

523670

6160

RPL31

5.4858

433701

6168

RPL37A

5.4226

150580

10209

SUI1

5.4220

408054

6136

RPL12

5.2032

412370

6133

RPL9

5.0539

190968

6227

RPS21

5.0185

546289

6206

RPS12

4.9041

80545

6167

RPL37

4.8491

546291

6232

RPS27

4.7202

182825

11224

RPL35

4.6320

529631

6165

RPL35A

4.6320

446588

6207

RPS13

4.4756

386384

6228

RPS23

4.3444

438227

6164

RPL34

4.1579

148340

4736

RPL10A

4.0734

433529

6205

RPS11

3.9325

381126

6208

RPS14

3.9083

178551

6132

RPL8

3.8569

433427

6218

RPS17

3.7474

515070

1938

EEF2

3.7165

370504

6210

RPS15A

3.4709

546284

6170

RPL39

3.4672

547172

6152

RPL24

3.4533

153177

6234

RPS28

3.4451

408073

6194

RPS6

3.3936

381172

6171

RPL41

3.3441

397609

6217

RPS16

3.3208

523463

6157

RPL27A

3.2840

374588

6139

RPL17

3.2110

406300

9349

RPL23

3.1938

378103

6193

RPS5

3.1848

499839

6130

RPL7A

3.0814

421257

6129

RPL7

3.0290

546356

23521

RPL13A

3.0256

356794

6229

RPS24

2.9796

388664

6135

RPL11

2.8280

546285

6175

RPLP0

2.7563

400295

6156

RPL30

2.6677

492599

8667

EIF3S3

2.6144

356366

6187

RPS2

2.5501

406683

6209

RPS15

2.5000

356572

6189

RPS3A

2.4749

387208

2197

FAU

2.4240

381123

6144

RPL21

2.3337

520703

1915

EEF1A1

2.2678

446628

6191

RPS4X

2.2147

333388

1936

EEF1D

2.1763

265174

6161

RPL32

2.1155

186350

6124

RPL4

1.9735

446522

9045

RPL14

1.7664

401929

6134

RPL10

1.6495

444749

6166

RPL36AL

1.6005

381219

6138

RPL15

1.5826

546286

6188

RPS3

1.5585

144835

1937

EEF1G

1.5558

371001

8662

EIF3S9

1.4982

411125

6183

MRPS12

1.4917

505735

4666

NACA

1.4686

532359

6125

RPL5

1.4328

429180

8894

EIF2S2

1.3812

520943

7458

WBSCR1

0.7001

404321

2617

GARS

0.6845

506215

5917

RARS

0.6260

483924

29093

MRPL22

0.5980

546283

6128

RPL6

0.5583

530096

8668

EIF3S2

0.5094

129673

1973

EIF4A1

0.4678

2

signal transduction

546291

6232

RPS27

4.7202

466743

4294

MAP3K10

3.4417

434980

351

APP

2.9978

155048

4059

LU

2.9455

445015

2906

GRIN2D

2.4444

99922

1815

DRD4

2.2910

483305

3094

HINT1

2.2725

530863

57524

CASKIN1

2.2615

83381

2791

GNG11

1.8153

435136

7295

TXN

1.7009

50425

10728

TEBP

1.6368

279594

7132

TNFRSF1A

1.5048

460978

8883

APPBP1

0.6981

247565

6010

RHO

0.6208

523560

3320

HSPCA

0.3799

252387

9620

CELSR1

0.0017

523732

7356

SCGB1A1

0.0009

3

protein folding

180414

3312

HSPA8

2.2911

1197

3336

HSPE1

2.2726

421509

10575

CCT4

2.1969

520028

3303

HSPA1A

1.8122

1600

22948

CCT5

1.7348

534385

10189

THOC4

1.7294

50425

10728

TEBP

1.6368

189772

10576

CCT2

1.3919

509736

3326

HSPCB

0.6936

523037

23234

DNAJC9

0.6855

491494

7203

CCT3

0.6848

184233

3313

HSPA9B

0.6588

356331

5478

PPIA

0.6507

445203

3301

DNAJA1

0.5266

492516

5202

PFDN2

0.5141

523560

3320

HSPCA

0.3799

551568

85313

PPIL4

0.0008

4

response to unfolded protein

180414

3312

HSPA8

2.2911

1197

3336

HSPE1

2.2726

520028

3303

HSPA1A

1.8122

509736

3326

HSPCB

0.6936

445203

3301

DNAJA1

0.5266

523560

3320

HSPCA

0.3799

5

positive regulation of nitric oxide biosynthesis

509736

3326

HSPCB

0.6936

523560

3320

HSPCA

0.3799

6

protein refolding

523560

3320

HSPCA

0.3799

7

RNA splicing

516076

6637

SNRPG

1.9589

365116

7307

U2AF1

1.5939

355934

6421

SFPQ

1.5729

309090

6432

SFRS7

0.6226

528763

6627

SNRPA1

0.6155

8

nuclear mRNA splicing

369624

8683

SFRS9

2.3299

79110

4691

NCL

2.0277

534385

10189

THOC4

1.7294

365116

7307

U2AF1

1.5939

355934

6421

SFPQ

1.5729

552581

9939

RBM8A

0.7166

464734

6632

SNRPD1

0.7151

471011

23451

SF3B1

0.6758

309090

6432

SFRS7

0.6226

527105

9987

HNRPDL

0.6054

484227

84321

THOC3

0.5445

546261

3178

HNRPA1

0.3795

411300

11193

WBP4

0.0008

348342

60677

BRUNOL6

0.0006

9

mRNA-nucleus export

534385

10189

THOC4

1.7294

552581

9939

RBM8A

0.7166

484227

84321

THOC3

0.5445

546261

3178

HNRPA1

0.3795

10

mRNA metabolism

546271

5094

PCBP2

1.5460

2853

5093

PCBP1

1.5212

458280

5042

PABPC3

1.4025

11

mRNA stabilization

387804

26986

PABPC1

2.9446

12

RNA transcription termination

532216

7978

MTERF

0.0008

13

tRNA 3’-processing

506382

51095

TRNT1

0.0008

14

glycyl-tRNA aminoacylation

404321

2617

GARS

0.6845

15

arginyl-tRNA aminoacylation

506215

5917

RARS

0.6260

16

translational initiation

150580

10209

SUI1

5.4220

371001

8662

EIF3S9

1.4982

429180

8894

EIF2S2

1.3812

17

regulation of translational initiation

150580

10209

SUI1

5.4220

492599

8667

EIF3S3

2.6144

520943

7458

WBSCR1

0.7001

530096

8668

EIF3S2

0.5094

18

regulation of translation

150580

10209

SUI1

5.4220

520703

1915

EEF1A1

2.2678

19

translational elongation

356502

6176

RPLP1

8.2655

421608

1933

EEF1B2

5.5078

546285

6175

RPLP0

2.7563

520703

1915

EEF1A1

2.2678

333388

1936

EEF1D

2.1763

144835

1937

EEF1G

1.5558

20

embryo implantation

523732

7356

SCGB1A1

0.0009

21

cytoplasmic sequestering of NF-kappaB

31210

602

BCL3

2.1500

22

iron ion transport

446345

2495

FTH1

6.9516

517666

1727

DIA1

1.8203

433670

2512

FTL

1.7473

23

ATP synthesis coupled proton transport

546238

514

ATP5E

4.9273

409140

539

ATP5O

2.0886

429

518

ATP5G3

1.8573

486360

10632

ATP5L

1.6978

24

proton transport

201939

23545

ATP6V0A2

30.6709

546238

514

ATP5E

4.9273

409140

539

ATP5O

2.0886

429

518

ATP5G3

1.8573

486360

10632

ATP5L

1.6978

25

mitochondrial electron transport

324250

4708

NDUFB2

2.4136

472185

4725

NDUFS5

2.3533

407860

4718

NDUFC2

2.1482

502528

4722

NDUFS3

1.5587

304613

4710

NDUFB4

1.5034

408257

4726

NDUFS6

0.6838

26

mitochondrial electron transport

481571

7388

UQCRH

4.2248

27

ribosomal protein-nucleus import

406300

9349

RPL23

3.1938

28

cysteine biosynthesis from serine

533013

875

CBS

1.3766

29

bile acid biosynthesis

460618

80270

HSD3B7

0.0008

30

DNA metabolism

471873

1841

DTYMK

1.9531

350966

9232

PTTG1

1.4052

118243

1777

DNASE2

0.0008

31

DNA replication and chromosome cycle

350966

9232

PTTG1

1.4052

497741

1063

CENPF

0.5694

32

base-excision repair, DNA ligation

434102

3146

HMGB1

1.7216

33

regulation of DNA replication

147433

5111

PCNA

1.5230

34

DNA damage response

86161

2765

GML

0.0008

35

dTTP biosynthesis

471873

1841

DTYMK

1.9531

36

nucleotide metabolism

463456

4831

NME2

3.3849

118638

4830

NME1

1.5814

522099

84720

PIGO

0.0011

37

CTP biosynthesis

463456

4831

NME2

3.3849

118638

4830

NME1

1.5814

38

UTP biosynthesis

463456

4831

NME2

3.3849

118638

4830

NME1

1.5814

39

GTP biosynthesis

463456

4831

NME2

3.3849

118638

4830

NME1

1.5814

40

nucleoside triphosphate biosynthesis

463456

4831

NME2

3.3849

118638

4830

NME1

1.5814

41

dTDP biosynthesis

471873

1841

DTYMK

1.9531

42

purine base biosynthesis

518774

10606

PAICS

0.6084

43

polyamine biosynthesis

446427

4946

OAZ1

3.5915

44

aminoglycan biosynthesis

519818

4245

MGAT1

1.4283

45

prostaglandin biosynthesis

407995

4282

MIF

3.6554

50425

10728

TEBP

1.6368

46

“de novo” IMP biosynthesis

518774

10606

PAICS

0.6084

47

cysteine biosynthesis via cystathione

533013

875

CBS

1.3766

48

anaerobic glycolysis

446149

3945

LDHB

1.7750

49

intracellular sequestering of iron ion

446345

2495

FTH1

6.9516

50

immune cell chemotaxis

313

6696

SPP1

0.0012

51

T-helper 1 type immune response

313

6696

SPP1

0.0012

52

regulation of myeloid cell differentiation

313

6696

SPP1

0.0012

53

negative regulation of bone mineralization

313

6696

SPP1

0.0012

54

induction of positive chemotaxis

313

6696

SPP1

0.0012

55

intracellular signaling cascade

209983

3925

STMN1

2.3054

201058

81551

STMN4

0.6154

56

G-protein coupled receptor protein signaling pathway

99922

1815

DRD4

2.2910

83381

2791

GNG11

1.8153

247565

6010

RHO

0.6208

57

sensory perception

298198

123920

CKLFSF3

2.0187

247565

6010

RHO

0.6208

58

rhodopsin mediated signaling

247565

6010

RHO

0.6208

59

phototransduction, visible light

247565

6010

RHO

0.6208

60

phosphoinositide-mediated signaling

83758

1164

CKS2

2.3328

147433

5111

PCNA

1.5230

61

cellular morphogenesis

421597

7428

VHL

2.2149

62

positive regulation of cell differentiation

421597

7428

VHL

2.2149

63

proteolysis and peptidolysis

421597

7428

VHL

2.2149

567440

25823

TPSG1

1.6712

531064

4738

NEDD8

1.5479

64

cellular respiration

437060

54205

CYCS

0.5248

65

caspase activation via cytochrome c

437060

54205

CYCS

0.5248

66

acetyl-CoA metabolism

530331

5160

PDHA1

0.6839

67

peptidyl-glutamic acid carboxylation

77719

2677

GGCX

0.0007

68

spliceosome assembly

516076

6637

SNRPG

1.9589

502829

7536

SF1

1.5815

69

ribosome biogenesis and assembly

499839

6130

RPL7A

3.0814

546285

6175

RPLP0

2.7563

27222

55651

NOLA2

1.7032

70

regulation of macrophage activation

407995

4282

MIF

3.6554

71

regulation of viral genome replication

356331

5478

PPIA

0.6507

72

positive regulation of fibroblast proliferation

275243

6277

S100A6

5.6553

73

positive regulation of translation

387804

26986

PABPC1

2.9446

74

negative regulation of transcriptional preinitiation complex formation

434102

3146

HMGB1

1.7216

75

negative regulation of cyclin dependent protein kinase activity

15299

10614

HIS1

1.5579

76

leucine catabolism

167531

64087

MCCC2

0.0010

77

nascent polypeptide association

505735

4666

NACA

1.4686

78

oxidative phosphorylation

481571

7388

UQCRH

4.2248

79

copper ion homeostasis

418241

4502

MT2A

7.4679

434980

351

APP

2.9978

4. Discussion

Our investigation revealed that HSV-2 is capable of infecting ECV 304 cells, leading to a cascade of modifications in gene expression. These modifications encompass variations in the expression of genes associated with cell signaling, cytoskeletal dynamics and motility, cell cycle regulation, transcription and transcription factors, protein synthesis, ion channel activity, cellular receptors, immune responses, and metabolic processes.

Gene microarrays can monitor the expression levels of thousands of genes simultaneously on a large scale to study the relationship between abundant gene expression and disease. Gene microarray technology can be used to analyze the differential expression of genes during viral infection of host cells, which is important for the treatment of diseases [12].

Previous research showed that ECV304 cells were infected with HSV-2 and morphological changes of the cells were observed by contrast microscopy and tissue staining. It was found that cell necrosis was the predominant form of cell death, and no significant apoptosis was observed. Later, Zhang et al. [13] confirmed that HSV-2 infection of ECV304 cells significantly induced apoptosis. However, the relatively simple information obtained in the single-gene model makes it more difficult to perform a comprehensive in-depth analysis. Mo et al. [14] analyzed the effect of rubella virus (RUBV), human cytomegalovirus (HCMV), and HSV-2 co-infection on mRNA accumulation in ECV304 cells by using microarray technology, and found that 80 genes were up-regulated and 19 genes were down-regulated, including VEGF, WISP2, WISP2 and COL11A2, etc. However, the effect of HSV-2 infection on the mRNA expression of ECV304 cells has not been studied by gene chip technology.

In this study, we used the ECV304 endothelial cell line as a model and human genome-wide oligonucleotide microarray to investigate the inhibitory effect of HSV-2 on ECV304 cells and to elucidate the expression of related genes. HSV-2 may play a role in signal transduction and regulation of gene expression in this process. The results showed that a total of 462 genes were significantly differentially expressed in the experimental group compared with the control group, of which 318 genes were up-regulated and 144 genes were down-regulated. The results showed that differential genes were involved in 237 biological processes, 79 of which were significantly different (P < 0.05).

These genes include cell signaling-related genes, cytoskeleton and motility-related genes, cell cycle-related genes, protein synthesis-related genes, transcription and transcription factor-related genes, and cell receptor-related genes. The results of the four differentially expressed genes validated by RT-PCR were generally consistent with those of the differentially expressed genes analyzed by gene microarray, indicating the reliability of the gene microarray data. Several genes that may be involved in endothelial damage and atherosclerosis caused by HSV-2 infection, which were identified for the first time in this study, are discussed below.

Heat shock proteins (HSP) are a family of proteins with important physiological functions that are highly conserved in evolution [15]. HSP can be classified into several families such as HSP110, HSP90, HSP70, HSP60, small molecule HSP and ubiquitin according to their molecular weight and degree of homology [16] [17]. Under adverse conditions such as stress, HSP can induce cell production, improve cell resistance, and play a role in stress protection, so it is also known as stress protein (SP). The main physiological functions of HSP are to promote and maintain the correct folding of new polypeptide chains [18], to participate in cell damage and repair [19], and to regulate cell growth, division, differentiation and death [20] [21]. Our results show that HSV-2 also induces HSP production in ECV304 cells after infection, suggesting that upregulation of HSP may be involved in some HSV-2-mediated cell biological damage.

The ribosome is an important organelle in the cell responsible for protein synthesis and consists of four rRNAs and 80 ribosomal proteins (RP) [22] [23]. RP is an important component of the ribosome and plays an important role in the translation process of the ribosome, such as folding of rRNA to form a functional three-dimensional structure; adjusting the spatial conformation of the ribosome during protein synthesis; and catalyzing protein synthesis in concert with rRNA at the binding site of the ribosome. In our study, the expression of 36 RPL genes and 23 RPS genes related to protein synthesis was elevated, and only the expression of RPL6 gene was decreased, indicating that the ribosomal protein gene is extensively involved in virus-cell interaction during the early stages of HSV-2 infection of ECV304 cells.

The S100 family of proteins is a group of EF-chiral calcium-binding proteins that play a variety of biological roles in vivo through regulation of calcium ions and interaction with target proteins, participating in cell cycle activities, cell differentiation, tumor growth, and extracellular matrix secretory activities [24]. Its distribution is cell- and tissue-specific [25], and several S100 members are abnormally expressed in tumors and are closely associated with tumor infiltration and metastasis. S100A6 is called calcyclin and used to be also known as 2A9, 5B10, and PRA. Chromosome 1q21 has been found to be altered in certain cancers or precancerous lesions, such as breast cancer, lymphoma and leukemia. From the specific expression of the biological function of the S100 protein family in tumors and its chromosomal localization, it can be found that it is closely related to tumors, of which S100A6 has increased expression in most tumor tissues. The results of this study showed that HSV-2 infection of ECV304 cells induced a 5.6-fold increase in cellular S100A6 gene expression. Combined with the results of many studies at home and abroad, which also showed that the development of cervical cancer may be related to HSV-2 infection, it suggests that the S100A6 gene may play some role in the development of cervical cancer. The aberrant expression of S100A6 is intricately associated with cellular proliferation and differentiation processes. Upon stimulation of quiescent cells by serum, epidermal growth factor, platelet-derived growth factor, nerve growth factor, retinoic acid, ischemic injury, and other physiological or pathological stimuli, there is a marked increase in the intracellular levels of S100A6 [26]. Existing literature indicates that S100A6 is significantly overexpressed in a variety of proliferative cell types, including ras-transformed NIH3T3 cells, SV40-transformed mouse fibroblasts, and various human malignancies such as acute myeloid leukemia, endometrial cancer, breast cancer, lung cancer, colorectal tumors, thyroid tumors, malignant fibrous histiocytoma, melanoma, neuroblastoma, squamous cell carcinoma of the oral mucosa, as well as in diverse epithelial-derived tumor cell lines. These cells exhibit elevated S100A6 expression levels in comparison to their differentiated or growth-inhibited counterparts [27].

Osteopontin (OPN) is a secreted acidic glycoprotein with multiple functions, classified as extracellular matrix, which promotes cell adhesion and migration [28]. OPN is present in human blood, urine, breast milk and other body fluids, as well as in the gastrointestinal tract, bladder, pancreas, lungs, bronchi and other tissues [29]. In our study, the expression of the Spp1 gene was significantly reduced, which may be associated with HSV-2 infection, suggesting that the Spp1 gene may play some role in the process of HSV-2 infection.

Calcium/calmodulin-dependent serine protein kinase (CASK) is a family of membrane-associated guanylate kinases. It was first cloned in nematodes [30], and its homologs were subsequently found in Drosophila and mammals [31]. CASK has several distinct protein binding domains and binds to other proteins to form protein complexes involved in the construction of the cell membrane protein backbone, cell signaling, gene regulation and many other important cellular physiological processes [32]. Current research on CASK is focused on the brain nervous system [33] and embryonic development [34]. With the accumulation of research data and advances in proteomics technology, it has become possible to understand the molecular network of various proteins that CASK interacts with in different cells and their related functions. Meanwhile, the establishment of knockout models of this protein in mammals will help to understand the role of CASK in overall biological development as well as in related diseases.

5. Conclusion

Among all biological processes induced by HSV-2 infection of ECV304 cells, 462 differential genes were found to be involved in a total of 237 biological processes, 79 of which were significantly different (P < 0.05). These mainly included biological processes such as protein synthesis, signaling, protein folding, RNA splicing, ion channels, ribosome synthesis and assembly, cellular respiration, DNA and mRNA metabolism. Further analysis revealed that a variety of genes among these differentially expressed genes may be involved in HSV-causing endothelial damage, cervical cancer and atherosclerosis, while the role of most other differentially expressed genes identified for the first time in the pathogenesis of HSV is unclear. Nevertheless, screening for aberrantly expressed genes by gene microarray provides valuable clues for an in-depth study of the mechanisms by which HSV causes endothelial injury and atherosclerosis.

Acknowledgements

We are grateful to Changwen Ke, director of the Institute of Microbiology Laboratory, Guangdong Center for Disease Control, for providing a high-quality laboratory and excellent instruments and equipment for the study of this topic, as well as giving valuable suggestions and enthusiastic guidance.

Funding

This work is supported by grants from the National Natural Science Foundation of China (No.31760716; 31560681), and the Project of Jiangxi Province (No. 20151BBF60007; 20171ACB21028).

Abbreviations

HSV

Herpes simplex virus

AS

Atherosclerosis

ECV-304 cells

Human umbilical vein endothelial cells

HSV-2

Herpes simplex virus type 2

RP

Ribosomal proteins

CASK

Calcium/calmodulin-dependent serine protein kinase

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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