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BMC Veterinary Research

, 15:151 | Cite as

Proteomics analysis reveals heat shock proteins involved in caprine parainfluenza virus type 3 infection

  • Chunyan Zhong
  • Jizong LiEmail author
  • Li Mao
  • Maojun Liu
  • Xing Zhu
  • Wenliang Li
  • Min Sun
  • Xinqin Ji
  • Fang Xiao
  • Leilei Yang
  • Wenwen Zhang
  • Zheng Liao
Open Access
Research article
Part of the following topical collections:
  1. Virology

Abstract

Background

Caprine parainfluenza virus type 3 (CPIV3) is major pathogen of goat herds causing serious respiratory tract disease and economic losses to the goat industry in China. We analyzed the differential proteomics of CPIV3-infected Madin-Darby bovine kidney (MDBK) cells using quantitative iTRAQ coupled LC-MS/MS. In addition, four DEPs were validated by qRT-PCR and western blot analysis.

Results

Quantitative proteomics analysis revealed 163 differentially expressed proteins (DEPs) between CPIV3-infected and mock-infected groups (p-value < 0.05 and fold change > 1.2), among which 91 were down-regulated and 72 were up-regulated. Gene ontology (GO) analysis showed that these DEPs were involved in molecular functions, cellular components and biological processes. Biological functions in which the DEPs were involved in included diseases, genetic information processing, metabolism, environmental information processing, cellular processes, and organismal systems. STRING analysis revealed that four heat shock proteins (HSPs) included HSPA5, HSPA1B, HSP90B1 and HSPA6 may be associated with proliferation of CPIV3 in MDBK cells. qRT-PCR and western blot analysis showed that the selected HSPs were identical to the quantitative proteomics data.

Conclusion

To our knowledge, this is the first report of the proteomic changes in MDBK cells after CPIV3 infection.

Keywords

Caprine parainfluenza virus type 3 Madin-Darby bovine kidney cells Proteomic analysis iTRAQ LC-MS/MS 

Abbreviations

COG

Cluster of orthologous groups of proteins

CPE

Cytopathic effect

CPIV3

Caprine parainfluenza virus type 3

DEPs

Differentially expressed proteins

DMEM

Dulbecco’s modified Eagle’s medium

FDR

False discovery rate

GO

Gene ontology

HSPs

Heat shock proteins

iTRAQ

Isobaric tags for relative and absolute quantification

KEGG

Kyoto encyclopedia of genes and genomes

MDBK

Madin-Darby bovine kidney

MOI

Multiplicity of infection

Background

In August 2013, an outbreak of severe goat respiratory disease occurred throughout the major goat herd regions of eastern China. The causative agent was identified as a novel strain of parainfluenza virus type 3 (PIV3) and was designated as caprine parainfluenza virus type 3 (CPIV3) strain JS2013 [1]. The infected goats exhibited high fever, coughing, nasal discharge and dyspnea. Necropsy of the infected goats showed mild to moderate gross lesions in the lungs, and increased amounts of secretion in the tracheas and bronchia were also observed. Genome sequence alignment and phylogenetic analysis revealed that the genome of CPIV3 strain JS2013 showed only 73.3–75.5% identity with BPIV3 and HPIV3 strains [2]. Based on phylogenetic analysis, this pathogen was designated as CPIV3, a member of the PIV3 group belonging to the Respirovirus genus within the Paramyxiviridae family. Moreover, we further demonstrated that CPIV3 strain JS2013 can be transferred horizontally between adjacent pens [3]. Recently, a seroprevalence study using 2919 serum samples in China reported a CPIV3 prevalence of 39.9% in goats [4]. Another study reported that 35% of nasal swabs and serum samples from clinically diseased goats were positive for CPIV3 by quantitative RT-PCR (qRT-PCR) [5]. It is noteworthy that the spread of CPIV3 has caused heavy economic losses in China [6].

To understand the pathogenesis of viral infection, research on virus-host interaction is critical. Virus infection can dramatically affect host cell morphology, transcription and translation patterns, the cytoskeleton, the cell cycle and innate immune responses of the host, the apoptosis pathway, and may also cause inflammation and alter stress responses [7]. Many functional and morphological changes in host cells are associated with significant changes in the patterns of expression of host cells. Therefore, information on proteome changes in the host following CPIV3 infection may be crucial to understand the host response to viral pathogenesis. In recent years, comparative proteomic analysis has emerged as a valuable tool for the establishment of the global host protein profiles in response to virus infection [8]. This technique has been widely used to investigate proteome changes in cow, yak, buffalo, goat and camel milk [9], and peste des petits ruminants virus (PPRV)-infected Vero cells [10], based on the isobaric tags for relative and absolute quantification (iTRAQ) method. In addition, this technique has also been widely employed to examine the mechanisms of viral infection through comparative investigation of the proteome changes, for example, in the case of Crimean-Congo hemorrhagic fever virus (CCHFV) [11] and bovine respiratory syncytial viruses (BRSV) [12].

However, to the best of our knowledge, no previous study has analyzed the proteomic changes in CPIV3-infected MDBK cells. Proteomic techniques are effective tools to characterize protein expression profiles, and have been widely used to investigate disease-associated proteins [13, 14]. Among current proteomics methods, quantitative high-throughput proteomics approaches are useful for the analysis of infection-associated proteins [15, 16]. In our current study, we used a quantitative proteomics approach based on an iTRAQ tandem mass spectrometry (MS/MS) technique to identify differentially expressed proteins (DEPs) between CPIV3-infected and mock-infected MDBK cells. The functions of the DEPs were analyzed to determine whether they might be associated with CPIV3 infection [17]. Our findings provide valuable insight into the changes in cellular processes that occur during CPIV3 infection.

Results

CPIV3 propagation in MDBK cells

The kinetics of CPIV3 propagation in MDBK cells were observed by monitoring the CPE at 24, 48 and 72 h post infection (hpi) (Fig. 1a), a minimal CPE was visible at 24 hpi, whereas an obvious CPE was observed at 48 hpi, and at 72hpi, almost all cells were disrupted. The TCID50 showed that the viral titer reached 103.5 TCID50/ml at 24 hpi, peaked at 107.0 TCID50/ml at 72 hpi and then declined (Fig. 1b). To ensure a higher proportion of infected cells and to avoid an excessive CPE, we selected 24 hpi as the time point under our infection conditions for further proteomic analysis.
Fig. 1

Confirmation of CPIV3 infection in MDBK cells. a A CPE was observed in MDBK cells at 24, 48 and 72 h after CPIV3 infection (MOI = 1), with mock-infected cells included as a control. b One-step growth curve of CPIV3 strain JS2013 in MDBK cells

Identification and annotation of proteins

We detected 8153 proteins and quantified 4109 proteins, including 28,815 peptides (Additional file 1: Figure S1). Detected proteins were annotated according to the GO database in the following categories: cellular components (CC), biological processes (BP), and molecular functions (MF) (Additional file 2: Figure S2). The top 20 pathways containing the largest number of proteins among the 8153 proteins were annotated according to KEGG (Additional file 3: Figure S3). Based on the KOG, 830 of the proteins were annotated as being involved in information storage and processing, 1545 were annotated as cellular processes and signaling, 581 were annotated as metabolism, and 699 were annotated as poorly characterized (Additional file 4: Figure S4 and Data Sheet 5). Furthermore, the cutoff criteria considered for the DEPs were set with an adjusted p-value of < 0.05 and a ratio of > 1.2-fold difference. Among the DEPs, 163 proteins from the two sets of biological replicates overlapped and were subsequently adjusted for multiple testing according to the stringent method of Benjamini and Hochberg [18]. Of these, 72 proteins were up-regulated and 91 proteins were down-regulated based on our criteria for the identification of DEPs in the MDBK-infected and mock-infected groups using the iTRAQ-MS/MS approach. Protein ratios were presented as CPIV3-infected/mock-infected. An average V/C ratio > 1 represented up-regulated proteins and an average V/C ratio < 1 represented down-regulated proteins. A list of DEPs information is shown in Table 1. DEPs between the two groups are shown as heat map and scatterplot (Additional file 5: Figure S6 and S7). Finally, the DEPs displaying the greatest increase and decrease in expression in the CPIV3-infected MDBK cells were FAM81B protein (1:0.118) and the DEP displaying the greatest decrease in expression in the CPIV3-infected MDBK cells was carboxypeptidase (1:1.206).
Table 1

Statistically significant DEPs identified by iTRAQ analysis of MDBK cells infected with CPIV3

Accession

Protein name

CPIV3-infected

Mock-infected

FC (CPIV3-infected vs_Mock-infected)

regulate

Q0VCX2

Endoplasmic reticulum chaperone BiP (HSPA5)

1

0.488

2.049180328

up

Q95M18

Endoplasmin (HSP90B1)

1

0.744

1.344086022

up

F1MEN8

Protein disulfide-isomerase A4 (PDIA4)

1

0.816

1.225490196

up

E1B748

Hypoxia up-regulated protein 1 precursor (HYOU1)

1

0.704

1.420454545

up

Q27965

Heat shock 70 kDa protein 1B (HSPA1B)

1

0.793

1.261034048

up

A6QR28

Phosphoserine aminotransferase (PSAT1)

1

0.814

1.228501229

up

F1MWU9

Uncharacterized protein (HSPA6)

1

0.726

1.377410468

up

Q3ZCA7

G protein subunit alpha i3 (GNAI3)

1

0.739

1.353179973

up

Q1LZA3

Asparagine synthetase [glutamine-hydrolyzing] (ASNS)

1

0.721

1.386962552

up

P80513

Mesencephalic astrocyte-derived neurotrophic factor (MANF)

1

0.794

1.259445844

up

Q2KHU0

Phosphoserine phosphatase (PSPH)

1

0.81

1.234567901

up

Q3T0L2

Endoplasmic reticulum resident protein 44 (ERP44)

1

0.765

1.307189542

up

Q08DL0

SLC3A2 protein (SLC3A2)

1

0.78

1.282051282

up

A5PK96

ACP1 protein (ACP1)

1

0.78

1.282051282

up

P13909

Plasminogen activator inhibitor 1 (SERPINE1)

1

0.807

1.239157373

up

P68301

Metallothionein-2 (MT2)

1

0.491

2.036659878

up

Q27955

Voltage-gated potassium channel subunit beta-2 (KCNAB2)

1

0.797

1.254705144

up

A5D7C1

Probable ATP-dependent RNA helicase DDX52 (DDX52)

1

0.814

1.228501229

up

A6H797

MLEC protein (MLEC)

1

0.807

1.239157373

up

F1N1R3

Mitochondrial ribosomal protein L40 (MRPL40)

1

0.824

1.213592233

up

E1BPL3

ATP binding cassette subfamily B member 7 (ABCB7)

1

0.721

1.386962552

up

A5PJN8

Splicing factor 3A subunit 2 (SF3A2)

1

0.551

1.814882033

up

Q2KIN6

Protein Mpv17 (MPV17)

1

0.801

1.248439451

up

Q3SZZ0

Ribosome biogenesis protein BRX1 homolog (BRIX1)

1

0.799

1.251564456

up

A6QLR4

Flotillin-2 (FLOT2)

1

0.476

2.100840336

up

Q17QI2

RNA polymerase II subunit A C-terminal domain phosphatase SSU72 (SSU72)

1

0.796

1.256281407

up

Q0VCS9

Ankyrin repeat and MYND domain-containing protein 2 (ANKMY2)

1

0.812

1.231527094

up

A2VE10

Protein CASC4 (CASC4)

1

0.829

1.206272618

up

A7MB19

NLRX1 protein (NLRX1)

1

0.804

1.243781095

up

Q6EVI2

eIF4GI protein (eIF4GI)

1

0.825

1.212121212

up

Q3SZ99

Peptidylprolyl isomerase (AIP)

1

0.774

1.291989664

up

E1BD11

Chromosome 11 open reading frame 84 (SPINDOC)

1

0.825

1.212121212

up

A4FUC0

39S ribosomal protein L37, mitochondrial (MRPL37)

1

0.815

1.226993865

up

Q2TA30

Ninjurin 1 (NINJ1)

1

0.594

1.683501684

up

E1BN60

Solute carrier family 30 member 1 (SLC30A1)

1

0.77

1.298701299

up

Q3T093

Adaptin ear-binding coat-associated protein 1 (NECAP1)

1

0.83

1.204819277

up

G3N3D6

Phosphoinositide phospholipase C(PLCH1)

1

0.823

1.215066829

up

Q2YDF6

28S ribosomal protein S35, mitochondrial(MRPS35)

1

0.809

1.236093943

up

Q08DH9

CCCTC-binding factor(CTCF)

1

0.802

1.246882793

up

Q08DK7

Mitochondrial basic amino acids transporter(SLC25A29)

1

0.798

1.253132832

up

F1MBD5

Surfeit 2(SURF2)

1

0.833

1.200480192

up

G3X6N3

Serotransferrin (TF)

1

0.76

1.315789474

up

F1MG47

Peroxisomal N(1)-acetyl-spermine/spermidine oxidase (PAOX)

1

0.706

1.416430595

up

E1BH45

RB1 inducible coiled-coil 1 (RB1CC1)

1

0.682

1.46627566

up

E1BMF4

Kinase D interacting substrate 220 (KIDINS220)

1

0.811

1.233045623

up

E1BI11

ELM2 and Myb/SANT domain containing 1 (ELMSAN1)

1

0.736

1.358695652

up

Q5E9T1

GDP-D-glucose phosphorylase 1 (GDPGP1)

1

0.823

1.215066829

up

A7Z023

CCDC132 protein (CCDC132)

1

0.819

1.221001221

up

A6QR26

UBAP1 protein (UBAP1)

1

0.702

1.424501425

up

A5PJZ7

Histone deacetylase (HDAC6)

1

0.832

1.201923077

up

Q148F0

Ubiquitin-related modifier 1 (URM1)

1

0.402

2.487562189

up

F1MRI6

Lemur tyrosine kinase 2 (LMTK2)

1

0.42

2.380952381

up

Q0V882

Bax inhibitor 1 (TMBIM6)

1

0.766

1.305483029

up

G3X6Y2

Chromosome X open reading frame 38 (CXHXorf38)

1

0.81

1.234567901

up

G3MYB9

UNC homeobox (UNCX)

1

0.793

1.261034048

up

G3N0M5

Uncharacterized protein

1

0.698

1.432664756

up

Q3SZN3

Metalloendopeptidase OMA1, mitochondrial (OMA1)

1

0.304

3.289473684

up

A7YWG9

PHLDA1 protein (PHLDA1)

1

0.654

1.529051988

up

A0JNQ0

Allograft inflammatory factor 1-like (AIF1L)

1

0.808

1.237623762

up

Q2YDD1

FGFR1 oncogene partner (FGFR1OP)

1

0.664

1.506024096

up

F1MN39

Interferon related developmental regulator 1 (IFRD1)

1

0.521

1.919385797

up

Q0II90

Protein FAM81B (FAM81B)

1

0.118

8.474576271

up

Q75V95

Calcitonin receptor-stimulating peptide 1 (CRSP1)

1

0.818

1.222493888

up

F1MSI9

Discs large MAGUK scaffold protein 5 (DLG5)

1

0.795

1.257861635

up

E1BFR6

Transmembrane protease, serine 13 (TMPRSS13)

1

0.827

1.209189843

up

E1BC24

Midasin (MDN1)

1

0.579

1.727115717

up

Q08DG0

Nuclear receptor binding factor 2 (NRBF2)

1

0.434

2.304147465

up

Q2KI89

LisH domain-containing protein ARMC9 (ARMC9)

1

0.411

2.433090024

up

F1MNN5

Sortilin related VPS10 domain containing receptor 1 (SORCS1)

1

0.759

1.317523057

up

A0A140T882

Uncharacterized protein CLBA1 (CLBA1)

1

0.635

1.57480315

up

F1MH73

Transmembrane protein 131 (TMEM131)

1

0.793

1.261034048

up

Q28037

Vitamin D3 receptor (VDR)

1

0.826

1.210653753

up

F1N2K8

Periplakin (PPL)

1

1.423

0.702740689

down

F6RJG0

3-hydroxy-3-methylglutaryl coenzyme A synthase (HMGCS1)

1

1.371

0.729394602

down

Q5KR49

Tropomyosin alpha-1 chain (TPM1)

1

1.227

0.814995925

down

G3MWV5

Histone cluster 1 H1 family member e (HIST1H1E)

1

1.264

0.791139241

down

A7MAZ5

Histone H1.3 (HIST1H1D)

1

1.258

0.79491256

down

Q3SYV6

Importin subunit alpha (KPNA2)

1

1.22

0.819672131

down

Q28178

Thrombospondin-1 (THBS1)

1

1.406

0.711237553

down

F1N3A1

Thrombospondin-1 (THBS1)

1

1.555

0.643086817

down

A4FV94

KRT6A protein (KRT6A)

1

1.212

0.825082508

down

A6QPB5

PGM1 protein (PGM1)

1

1.272

0.786163522

down

G3N0V2

Keratin 1 (KRT1)

1

1.499

0.667111408

down

E1BNE7

Caveolae associated protein 1 (CAVIN1)

1

1.213

0.824402308

down

Q3YJF3

MHC class I antigen (Fragment) (BoLA)

1

1.277

0.783085356

down

Q2HJJ0

Kinesin light chain 4 (KLC4)

1

1.209

0.827129859

down

F1MX88

Solute carrier family 25 member 13 (SLC25A13)

1

1.212

0.825082508

down

F1 N688

V-type proton ATPase subunit B, kidney isoform (ATP6V1B1)

1

1.352

0.73964497

down

Q0VCZ8

Acyl-CoA synthetase long-chain family member 1 (ACSL1)

1

1.333

0.750187547

down

A6QNZ7

Keratin 10 (Epidermolytic hyperkeratosis; keratosis palmaris et plantaris) (KRT10)

1

1.387

0.720980534

down

F1N4K3

Uncharacterized protein

1

1.474

0.678426052

down

F1MTJ9

Terpene cyclase/mutase family member (LSS)

1

1.243

0.804505229

down

Q867D1

Stearoyl-CoA desaturase (Scd)

1

1.427

0.700770848

down

F1MH31

Nucleoporin 214 (NUP214)

1

1.241

0.805801773

down

G3N1R5

Uncharacterized protein

1

1.454

0.687757909

down

Q32PA5

Ubiquitin-conjugating enzyme E2 C (UBE2C)

1

1.589

0.629326621

down

Q0P5J6

Keratin, type I cytoskeletal 27 (KRT27)

1

1.375

0.727272727

down

A7MB38

SFRS4 protein (SRSF4)

1

1.22

0.819672131

down

A7YW33

DNA polymerase delta interacting protein 3 (POLDIP3)

1

1.267

0.789265983

down

Q3ZCI0

Coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD9)

1

1.298

0.770416025

down

E1BJC9

Uncharacterized protein (C18H19orf33)

1

1.24

0.806451613

down

A5D7N6

Kinesin-like protein (KIF23)

1

1.373

0.728332119

down

F2Z4H2

Non-histone chromosomal protein HMG-17 (HMGN2)

1

1.242

0.805152979

down

A3KLR9

Superoxide dismutase (SOD3)

1

1.36

0.735294118

down

G8JKY5

Thymosin beta-4 (TMSB4X)

1

1.547

0.646412411

down

Q08DI5

Ras-related protein Rap-2c (RAP2C)

1

1.207

0.828500414

down

A4IF70

GPR56 protein (GPR56)

1

1.233

0.811030008

down

P15103

Glutamine synthetase (GLUL)

1

1.265

0.790513834

down

E1BKT0

Leucine zipper protein 1 (LUZP1)

1

1.353

0.7390983

down

F1MFW9

Keratin 24 (KRT24)

1

2.313

0.432338954

down

Q0VC74

Trimethyllysine dioxygenase, mitochondrial (TMLHE)

1

1.217

0.821692687

down

F1MLZ1

Cytochrome b reductase 1 (CYBRD1)

1

1.252

0.798722045

down

F1MP14

Forkhead box K1 (FOXK1)

1

1.208

0.82781457

down

F1MYS2

FCH domain only 2 (FCHO2)

1

1.253

0.798084597

down

Q3T0J9

Guanine nucleotide-binding protein-like 3-like protein (GNL3L)

1

1.336

0.748502994

down

Q2NKZ9

Carboxypeptidase (SCPEP1)

1

1.206

0.829187396

down

F1N6L1

Valyl-tRNA synthetase 2, mitochondrial (VARS2)

1

1.272

0.786163522

down

G5E5Q8

SET binding factor 1 (SBF1)

1

1.286

0.777604977

down

Q2KHW7

Regulator of G-protein signaling 10 (RGS10)

1

1.219

0.820344545

down

F1N4R2

Uncharacterized protein (MORF4L1)

1

1.222

0.818330606

down

Q5E9Q1

Protein O-glucosyltransferase 1 (POGLUT1)

1

1.234

0.810372771

down

Q29RZ9

WD repeat-containing protein 92 (WDR92)

1

1.26

0.793650794

down

F1N5R4

Conserved oligomeric Golgi complex subunit 8 (COG8)

1

1.271

0.786782061

down

F1ML71

Nedd4 family interacting protein 2 (NDFIP2)

1

1.254

0.797448166

down

G3 N266

G protein signaling modulator 1 (GPSM1)

1

1.235

0.809716599

down

F1N0K0

Collagen alpha-1(XI) chain (COL11A1)

1

1.212

0.825082508

down

F1MGF2

Chromodomain helicase DNA binding protein 1 (CHD1)

1

1.254

0.797448166

down

A6QQK2

MAP3K7IP1 protein (MAP3K7IP1)

1

1.339

0.74682599

down

E1BDA1

Ras and Rab interactor 1 (RIN1)

1

1.29

0.775193798

down

E1B8R7

HPS5, biogenesis of lysosomal organelles complex 2 subunit 2 (HPS5)

1

1.252

0.798722045

down

A8E646

CARD11 protein (CARD11)

1

1.222

0.818330606

down

Q32KL9

B-cell receptor-associated protein 29 (BCAP29)

1

1.435

0.696864111

down

E1BGG6

Regulatory factor X5 (RFX5)

1

1.233

0.811030008

down

Q3T0N3

Calcium load-activated calcium channel (TMCO1)

1

1.295

0.772200772

down

E1BC89

Oxysterol-binding protein (OSBPL5)

1

1.241

0.805801773

down

F1MQ45

Solute carrier organic anion transporter family member (SLCO2A1)

1

1.234

0.810372771

down

Q32P76

Small EDRK-rich factor 1 (SERF1)

1

1.447

0.691085003

down

A6QQS5

WHSC2 protein (WHSC2)

1

1.289

0.77579519

down

F1MNT2

Protein RTF2 homolog (RTF2)

1

1.278

0.782472613

down

F1MEY2

Enoyl-[acyl-carrier-protein] reductase, mitochondrial (MECR)

1

1.387

0.720980534

down

A6QNX2

DPP7 protein (DPP7)

1

1.287

0.777000777

down

E1BE80

Transmembrane protein 236 (TMEM236)

1

1.247

0.801924619

down

A4IFD1

PDCD4 protein (PDCD4)

1

1.209

0.827129859

down

A1A4R8

Cell division cycle protein 23 homolog (CDC23)

1

1.267

0.789265983

down

E1BG49

Centromere protein E (CENPE)

1

1.324

0.755287009

down

P07926

ATP synthase F(0) complex subunit C2, mitochondrial (ATP5MC2)

1

1.497

0.668002672

down

Q402A0

Aggrus (PDPN)

1

1.293

0.773395205

down

Q17QI1

Trafficking protein particle complex subunit 1 (TRAPPC1)

1

1.265

0.790513834

down

E1BKA4

Uncharacterized protein (HAUS4)

1

1.3

0.769230769

down

Q2KHT6

F-box only protein 32 (FBXO32)

1

1.227

0.814995925

down

F1MS44

Doublecortin domain containing 2 (DCDC2)

1

1.277

0.783085356

down

E1BIR2

Dipeptidase (DPEP2)

1

1.211

0.825763832

down

A5PKA5

Sorting nexin-27 (SNX27)

1

1.31

0.763358779

down

A6H7C1

MORF4L2 protein (MORF4L2)

1

1.213

0.824402308

down

A6QLZ5

Protein FAM177A1 (FAM177A1)

1

1.23

0.81300813

down

P13384

Insulin-like growth factor-binding protein 2 (IGFBP2)

1

1.748

0.57208238

down

A5D974

Acyl-Coenzyme A dehydrogenase family, member 9 (ACAD9)

1

1.217

0.821692687

down

F1N2N9

Coiled-coil domain containing 114 (CCDC114)

1

1.224

0.816993464

down

E1BBH4

Protein unc-93 homolog B1 (UNC93B1)

1

1.666

0.600240096

down

A5PJX0

F-box protein 22 (FBXO22)

1

1.272

0.786163522

down

E1BEG4

Zinc finger FYVE-type containing 16 (ZFYVE16)

1

1.225

0.816326531

down

E1BEI6

ATM serine/threonine kinase (ATM)

1

1.507

0.663570007

down

P0C914

Overexpressed in colon carcinoma 1 protein homolog

1

1.213

0.824402308

down

GO analysis of the DEPs

The molecular functional classes and subcellular locations of the 163 DEPs were analyzed using UniProt and the GO database. The 163 DEPs were annotated into the categories: cellular component, biological process, or molecular function, and the distribution of up-regulated and down-regulated proteins among these GO annotations are shown in Additional file 6: Figure S8. GO enrichment annotation comparisons were performed to elucidate the characteristics of the altered proteins in MDBK cells induced by CPIV3 infection, to determine any associations with virulence and pathogenicity. In terms of biological process annotation, DEPs were mainly involved in cell aggregation, cellular processes, cellular component organization or biogenesis, locomotion, metabolic processes, multicellular organismal processes and reproductive processes; in terms cellular component annotation, DEPs were mainly involved in the cell part, extracellular region part, membrane part, organelle part, protein-containing complex and supramolecular complex; in terms of molecular function annotation, DEPs were mainly involved in binding, catalytic activity, molecular carrier activity and transporter activity (Fig. 2).
Fig. 2

GO enriched histogram of DEPs. Each column in the figure is a GO terms, the abscissa text indicates the name and classification of GO, and the height of the column indicates the enrichment rate. The color indicates the significance of the enrichment (p-value). The darker the color, the more significant the enrichment of the GO term (*P < 0.05; **P < 0.01; ***P < 0.001)

KEGG (Kyoto encyclopedia of genes and genomes) pathway analysis of the DEPs

The KEGG pathway is a collection of pathway maps that represent molecular interactions and reaction networks in cell line. The 93 DEPs identified were annotated, and mapped to a total of six KEGG pathway categories, which included metabolism, disease, genetic information processing, cellular processes, environmental information processing, and organismal systems pathway categories (Additional file 7 Data Sheet 9). The enrichment annotation protein pathway information is shown in Fig. 3. The results showed that most of the abundant KEGG terms were involved in biological processes such as the p53 signaling pathway, microRNAs in cancer, alanine, aspartate and glutamate metabolism, nitrogen metabolism, the estrogen signaling pathway, mineral absorption and thyroid hormone synthesis. Functional classification by KEGG showed that the upregulated and downregulated proteins could be divided among six distinct functional sets: environmental information processing, cellular processes, metabolism, genetic information processing, organismal systems and human diseases (Fig. 4).
Fig. 3

KEGG enrichment annotation of the DEPs. Each column in the figure is a pathway. The abscissa text indicates the name and classification of the pathway, and the height of the column indicates the enrichment rate. The color indicates the significance of the enrichment (p-value). The darker the color, the more significant the enrichment of the pathway (*P < 0.05; **P < 0.01; ***P < 0.001)

Fig. 4

Functional characterization of DEPs. a Cellular processes, metabolism and organismal systems. b Environmental information processing, genetic information processing and human diseases. More information is available in Additional file 5: Figure S7

STRING analysis of the relationships between DEPs

With the goal of exploring the potential protein network connections for the differentially regulated proteins in detail, the STRING tool was used. The differentially regulated proteins were mainly mapped to four functional networks (Fig. 5). A specific network had at least four “focus” proteins (HSPA5, HSPA1B, HSP90B1 and HSPA6). The networks of interest corresponded to: cell-to-cell signaling, hereditary disorder, cell death and survival, cardiovascular disease, cellular developmental, RNA post-transcriptional modification, cellular growth and proliferation.
Fig. 5

Specific network analysis of proteins significantly altered in CPIV3-infected cells. The network of DEPs with STRING analysis. Each node represents a protein in the graph, each line represents the interaction between proteins, and the wider the line, the closer the relationship

Confirmation of proteomic data by qRT-PCR

Alterations in the expression of a protein may be owing to a change in its mRNA levels. To confirm the results of the proteomic analysis by mRNA expression, transcriptional alterations in four selected proteins were measured by qRT-PCR. The qRT-PCR analysis showed that no difference in the ratio of these mRNAs between the CPIV3 infected group and the mock infected group were consistent with those obtained using quantitative proteomics analysis (Fig. 6). The mRNA expression of HSPA5, HSP90B1, HSPA1B and HSPA6 were increased in CPIV3-infected MDBK cells. Therefore, the trends in the mRNA expression were consistent with those in their corresponding proteins.
Fig. 6

qRT-PCR analysis of mRNA expression in the CPIV3-infected and mock-infected groups. The cells were collected at 24 hpi for qRT-PCR to analyze the relative mRNA expression of the HSPA5, HSP90B1, HSPA1B and HSPA6 genes. The GAPDH gene was included as a control housekeeping gene for the normalization of samples . Error bars represent standard deviations

Western blot analysis of HSPA1B

We analyzed the expression levels of HSPA1B (up-regulated) in CPIV3-infected MDBK cells (Fig. 7) by western blot at 24 h and 48 h. Figure 7 shows that HSPA1B was up-regulated in CPIV3-infected MDBK cells at 24 h and 48 h. The results were consistent with those obtained using the iTRAQ labeled LC-MS/MS system.
Fig. 7

Analysis of HSPA1B expression levels in CPIV3-infected and control cells by western blot analysis at 24 h and 48 h. Protein samples were separated by SDS-PAGE. Western blot analysis was performed using antibodies to the HSPA1B protein.β-actin protein was detected as an internal control

Discussion

Proteomic techniques have become significant methodologies for determining cellular protein interactions and host cellular pathophysiological processes following virus infection [19, 20]. As a general rule, no important host cell membrane rearrangement or cytoskeleton collapse is observed following virus infection but the point at which a high virus yield is obtained is considered as the best time for proteomic analysis [21, 22]. Taking this substantial evidence into consideration, cell samples at 24 hpi were chosen for further proteomic analysis. Based on our study, the expression levels of 163 DEPs were found to be significantly altered in CPIV3-infected cells. The results of GO, KEGG pathway and STRING analysis predicted that these DEPs pertaining to different types of functional categories and signal pathways. Western blot and qRT-PCR were also applied to validate some differential proteins at the mRNA and protein levels. To date, no analysis has been reported of the differential proteomes of MDBK cells infected with CPIV3. Our data may provide an overview of the proteins altered in expression during the host response to CPIV3 infection and may provide insight in the process of CPIV3 pathogenesis.

Studies have shown that HSPs may play an important role in virus host cell interactions during in vivo and in vitro infection [23, 24]. Inhibitors of HSP90 can inhibit herpes simplex virus type 1 (HSV-1) infection [25]. Bovine viral diarrhea virus (BVDV) structural proteins comprise the C nucleocapsid protein and three envelope glycoproteins, Erns, E1 and E2 [26]. A previous study found that HSP110 enhanced the presentation of E2 to CD4 T cells in vitro to improve the immunogenicity of an E2 vaccine in cattle [27]. Previous work demonstrated that HSP70 is actively released into the extracellular milieu and acts as a cytokine and peptide adjuvant, thereby promoting both the innate and adaptive immune responses [28]. In our analysis, four proteins (HSPA5, HSPA1B, HSP90B1 and HSPA6) were identified following CPIV3 infection. HSP90B1 is proposed to be associated with poor survival from hepatocellular carcinoma (HCC), whereas high levels of HSPA5 and HSPA6 may be associated with earlier recurrence of HCC [29]. HSPA1B, also known as heat shock protein 72, is a member of the HSP70 family. HSP70 expression levels rapidly increased in response to cellular stresses such as heat shock, or in response to certain viral infections [30, 31, 32, 33].

In the current study, HSP70 was rarely detected in the mock-infected group, whereas it was notably present in the CPIV3 group. CPIV3 infection resulted in the up-regulated secretion of exosomes and packaging of the viral proteins into exosomes, and these results suggested that CPIV3 infection may enhance HSP70-mediated exosome release (unpublished data). In addition, HSP70 is actively released into the extracellular milieu, thereby promoting innate and adaptive immune responses [34]. In this study, HSPA5, HSPA1B, HSP90B1 and HSPA6 were up-regulated at 24 hpi to various degrees following CPIV3 -infection of MDBK cells. Different expression levels of HSPA1B were detected by western blot analysis at 24 hpi and 48 hpi after CPIV3 -infection of MDBK cells. This may indicate that HSPA1B affects the proliferation of CPIV3 in MDBK cells. HSPA1B is an endogenous ligand for toll-like receptor TLR4, thereby stimulating innate immunity [35], and HSPA1B regulates the NF-κB pathway via TLR2 and TLR4 in fibroblasts. However, fibroblasts and macrophages interact with each other to mediate the immune response. Activation of the NF-κB pathway then results to in enhanced secretion of pro-inflammatory cytokines (TNF-α, IL-6 and IL-1β) and neutrophil chemoattractant MIP-2 and Cxcl1 from macrophages [36]. This evidence indicates that HSPA1B may be associated with the proliferation of CPIV3 in MDBK cells through an ability to interact with key components of the NF-κB pathway, moreover, those involved in innate immunity, but the detailed mechanism remains unknown. However, the detailed functions of these pathways and proteins changes in CPIV3 infection therefore requires further verification.

Conclusions

The proteomic changes in CPIV3-infected MDBK cells were analyzed using iTRAQ combined with LC-MS/MS. To the best of our knowledge, this is the first time proteomics has been used to explore the virus–host protein interaction network in CPIV3-infected MDBK cells. The results revealed 163 DEPs, among which 72 were up-regulated and 91 were down-regulated. In addition, four DEPs were validated by qRT-PCR and HSPA1B was validated by western blot analysis. These results were consistent with those of label-free LC-MS analysis. Our analyses of the DEPs were descriptive, and further functional investigations are required to elucidate the pathogenic mechanisms and cellular responses to CPIV3 infection.

Methods

Cell culture and virus infection

CPIV3 strain JS2013 isolated in Jiangsu Province was used for virus infection. MDBK cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Sigma, CA, USA) supplemented with 10% fetal bovine serum (FBS; HyClone, UT, USA), at 37 °C in an atmosphere of 5% CO2 [2]. When the cells grow to 70–80% confluence, they were inoculated with CPIV3 at a multiplicity of infection (MOI) of 1. After 1 h of adsorption, infected cells were maintained in fresh medium containing 2% FBS. Uninfected cells were used as a control. The CPIV3- or mock-infected cells were collected at 24 hpi. Viral propagation was confirmed by the observation of a cytopathic effect (CPE).

Protein sample preparation and labeling with iTRAQ reagent

The CPIV3- and mock-infected cell samples were washed three times with cold phosphate-buffered saline (PBS) and then treated with lysis buffer containing 8 M urea, 4% CHAPS, 2 M thiourea, and 30 mM Tris-HCl on ice for 30 min until the cell line were completely lysed. The supernatant was collected by centrifugation at 12000×g for 30 min at 4 °C after ultrasonication treatment for 2 min. The protein concentration in the supernatants was quantified using the Bradford protein assay. After reduction and cysteine-blocking as described in the iTRAQ protocol (AB Sciex, Concord, ON, USA), solutions containing 100 μg protein were digested overnight at 37 °C with sequence grade modified trypsin (Promega, Madison, WI, USA) and then labeled with different iTRAQ tags. The labeled samples were then mixed and dried with a rotary vacuum concentrator.

LC-MS analysis

Ten microliters (μl) of each fraction were analyzed by Q Exactive (Thermo, USA) mass spectrometer coupled to a Proxeon Biosystem Easy-nLC 1200 (Thermo Fisher Scientific, Waltham, MA, USA) in the LC-MS experiments. The peptide mixture (5 g) was loaded onto a C18 column (75 μm × 25 cm, Thermo,USA) packed with RP-C18 (5 m) resin in buffer A (2% ACN with 0.1% formic acid), and eluted with a linear gradient of buffer B (80% ACN with 0.1% formic acid) at a flow rate of 300 nl/min for 120 min using IntelliFlow technology. The equate underwent electrospray ionization for LC-MS analysis. The MS/MS instrument was run in the peptide recognition mode, and the spectra were acquired using a data-dependent top-20 method based on the selection of the most abundant precursor ions from the survey scan (350–1300 m/z) for HCD fragmentation. Determination of the target value was based on the predictive automatic gain control, and the dynamic exclusion duration was 18 s. Survey scans were acquired at a resolution of 70,000 at m/z 200, and the resolution for the HCD spectra was set to 17,500 at m/z 200. The normalized collision energy was 30 eV, and the underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%. Thermo Xcalibur 4.0 (Thermo, USA) was used to collect MS analysis data via DDA mode.

Data analysis

The MS data were analyzed using Proteome Discoverer™ software 2.1. When the library was searched, the raw file was submitted to the Proteome Discoverer server searched against the Uniprot Bos taurus database (197,939 total sequences, downloaded April 26, 2018). The following parameters were used for protein identification: a precursor mass tolerance of 20 ppm; a fragment mass tolerance of 0.05 Da; trypsin digestion; max. Missed cleavage sites of 2; the variable dynamic modifications included oxidation (M), iTRAQ8plex (Y) and acetyl (protein N-terminus), and the fixed static modifications included carbamidomethyl (C), iTRAQ8plex (K) and iTRAQ8plex (N-term). The cutoff for the global false discovery rate (FDR) for peptide and protein identification was set to 0.01. The value of the quantitative ratio for each protein relative to the internal reference was calculated, and averaged to obtain the quantitative ratio (V/C) of the proteins identified in the treatment groups [37]. Proteins with a fold change > 1.2 and a p-value < 0.05 were considered to shows significantly different expression. Auto bias-correction was executed to decrease the artificial error. Statistical analysis was performed using Excel 2007 software. The DEPs were annotated using gene ontology (GO) and KEGG database. The Cluster of Orthologous Groups of proteins (COG or KOG) were retrieved, and mapped to pathways in the KEGG database [38]. In addition, DEPs were analyzed using STRING for predicting functional association networks of proteins.

CPIV3 yield quantification

MDBK cells were seeded in 96-well plates and incubated for 24 h. Then, CPIV3 samples were 10-fold serially diluted and added to each well in quadruplicate. MDBK cells exhibit CPE were scored positive for viral growth and the TCID50 was calculated by the Reed–Muench method [39].

mRNA quantitation by qRT-PCR

Total cellular RNA was extracted from the CPIV3-infected and mock-infected MDBK cells using Transzol UP reagent (Transgen Co. Ltd., Beijing, China) according to the manufacturer’s protocol. Specific primers for amplifying various genes were as follows: for GAPDH mRNA analysis, 5′-GATTGTCAGCAATGCCTCCT-3′ (forward) and 5′-GGTCATA AGTCCCTCCACGA-3′ (reverse) were used; for HSPA5 mRNA analysis, 5′-GTGCCCACCA AGAAGTCTCA-3′ (forward) and 5′-CTTTCGTCAGGGGTCGTTC A-3′ (reverse) were used; for HSP90B1 mRNA analysis, 5′-TCAAGGGTGTTGTGGACTCG-3′ (forward) and 5′-GCT GAAGTGTCTCACGGG AA-3′ (reverse) were used; for HSPA1B mRNA analysis, 5′-AGTC GGACATGAAGCACTGG-3′ (forward) and 5′-TCACCTGCACCTTAGGCTTG-3′ (reverse) were used; and for HSPA6 mRNA analysis, 5′-AGGACAGGCGCAAAGTACAA-3′ (forward) and 5′-TGCTCCAGCTCCCTCTTTTG-3′ (reverse) were used. GAPDH was employed as an internal reference gene. The first-strand cDNA was synthesized via PrimeScript™ RT Master Mix (TaKaRa, Dalian, China). Then qRT-PCR was performed using the SYBR Premix Ex Taq™ II Kit (TaKaRa) on an ABI Step One thermocycler (Applied Biosystems, CA, USA). The relative expression level of each mRNA was calculated by the 2-ΔΔct method. Three independent biological replicates were performed for each gene.

Western blot analysis

To further verify the variation in the DEPs identified by the proteomic approaches, HSPA1B was selected for western blot analysis. The CPIV3- and mock-infected cells were collected at 24 and 48 hpi. Equivalent amounts of cell lysate from each sample were collected. After measuring the protein concentrations, equivalent amounts of cellular proteins were separated by SDS-PAGE and transferred onto nitrocellulose PVDF membranes (Millipore, USA). The membranes were incubated overnight at 4 °C with primary rabbit polyclonal antibodies of anti-HSPA1B (Biyotime, Shanghai, China). Then the membranes were further incubated for 1 h with horseradish peroxidase-conjugated goat anti-rabbit secondary antibody (BIOSS, Beijing, China). The protein bands were detected using the ECL Detection Kit (Vazyme, Nanjing, China). β-actin protein was used as an internal control.

Notes

Acknowledgments

We thank Kate Fox, DPhil, from Liwen Bianji, Edanz Group China (https://www.liwenbianji.cn/), for editing the English text of a draft of this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (31702272, 31802196), Natural Science Foundation of Jiangsu Province, China (BK20170595), Natural Science Foundation of Shandong Province, China (ZR2016CP08), and the National Key R&D Program of China (2016YFD0500908, 2018YFD0502100). The funders had no role in study design, in the collection, analysis and interpretation of data, in the writing the manuscript, or in the decision to submit the article for publication.

Availability of data and materials

The datasets contained in this study are available from the corresponding author upon request.

Authors’ contributions

CZ and JL took part in all the experiments and wrote the manuscript. LM, ML, XZ and WL helped to designed the whole project and draft the manuscript. MS, FX, LY, WZ and ZL conducted cell culture and sample processing for sequencing. XJ conducted data analysis. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12917_2019_1897_MOESM1_ESM.jpg (357 kb)
Additional file 1: Figure S1. Information on the detected proteins in CPIV3-infected MDBK cells (JPG 357 kb)
12917_2019_1897_MOESM2_ESM.jpg (1.4 mb)
Additional file 2: Figure S2. Detected proteins were annotated in the GO database (JPG 1406 kb)
12917_2019_1897_MOESM3_ESM.jpg (738 kb)
Additional file 3: Figure S3. The top 20 pathways annotated by KEGG (JPG 738 kb)
12917_2019_1897_MOESM4_ESM.zip (669 kb)
Additional file 4: Figure S4 and Data Sheet 5. Proteins were annotated based on the KOG (ZIP 669 kb)
12917_2019_1897_MOESM5_ESM.zip (1.1 mb)
Additional file 5: Figure S6 and S7 Heat map and scatterplot (ZIP 1116 kb)
12917_2019_1897_MOESM6_ESM.jpg (2.4 mb)
Additional file 6: Figure S8. GO annotations for the up-regulated and down-regulated proteins (JPG 2482 kb)
12917_2019_1897_MOESM7_ESM.xls (32 kb)
Additional file 7: Data Sheet 9. The 93 DEPs were annotated into six KEGG pathway categories (XLS 32 kb)

References

  1. 1.
    Li W, Mao L, Cheng S, Wang Q, Huang J, Deng J, Wang Z, Zhang W, Yang L, Hao F, et al. A novel parainfluenza virus type 3 (PIV3) identified from goat herds with respiratory diseases in eastern China. Vet Microbiol. 2014;174(1–2):100–6.CrossRefGoogle Scholar
  2. 2.
    Yang L, Li W, Mao L, Hao F, Wang Z, Zhang W, Deng J, Jiang J. Analysis on the complete genome of a novel caprine parainfluenza virus 3. Infect Genet Evol. 2016;38:29–34.CrossRefGoogle Scholar
  3. 3.
    Li W, Hao F, Mao L, Wang Z, Zhou T, Deng J, Li J, Zhang W, Yang L, Lv Y, et al. Pathogenicity and horizontal transmission studies of caprine parainfluenza virus type 3 JS2013 strain in goats. Virus Res. 2016;223:80–7.CrossRefGoogle Scholar
  4. 4.
    Mao L, Li W, Zhou T, Yang L, Hao F, Li J, Zhang W, Luo X, Jiang J. Development of a blocking ELISA for caprine parainfluenza virus type 3. J Virol Methods. 2017;250:59–65.CrossRefGoogle Scholar
  5. 5.
    Li J, Li W, Mao L, Hao F, Yang L, Zhang W, Jiang J. Rapid detection of novel caprine parainfluenza virus type 3 (CPIV3) using a TaqMan-based RT-qPCR. J Virol Methods. 2016;236:126–31.CrossRefGoogle Scholar
  6. 6.
    Mao L, Yang L, Li W, Liang P, Zhang S, Li J, Sun M, Zhang W, Wang L, Zhong C, et al. Epidemiological investigation and phylogenetic analysis of caprine parainfluenza virus type 3 in sheep of China. Transbound Emerg Dis. 2019.  https://doi.org/10.1111/tbed.13149.
  7. 7.
    Zheng J, Sugrue RJ, Tang K. Mass spectrometry based proteomic studies on viruses and hosts--a review. Anal Chim Acta. 2011;702(2):149–59.CrossRefGoogle Scholar
  8. 8.
    Han K, Zhao D, Liu Y, Liu Q, Huang X, Yang J, An F, Li Y. Quantitative proteomic analysis of duck ovarian follicles infected with duck Tembusu virus by label-free LC-MS. Front Microbiol. 2016;7:463.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Yang Y, Bu D, Zhao X, Sun P, Wang J, Zhou L. Proteomic analysis of cow, yak, buffalo, goat and camel milk whey proteins: quantitative differential expression patterns. J Proteome Res. 2013;12(4):1660–7.CrossRefGoogle Scholar
  10. 10.
    Pandey A, Sahu AR, Wani SA, Saxena S, Kanchan S, Sah V, Rajak KK, Khanduri A, Sahoo AP, Tiwari AK, et al. Modulation of host miRNAs transcriptome in lung and spleen of Peste des Petits ruminants virus infected sheep and goats. Front Microbiol. 2017;8:1146.CrossRefGoogle Scholar
  11. 11.
    Fernandez de Mera IG, Chaligiannis I, Hernandez-Jarguin A, Villar M, Mateos-Hernandez L, Papa A, Sotiraki S, Ruiz-Fons F, Cabezas-Cruz A, Gortazar C, et al. Combination of RT-PCR and proteomics for the identification of Crimean-Congo hemorrhagic fever virus in ticks. Heliyon. 2017;3(7):e00353.CrossRefGoogle Scholar
  12. 12.
    Hagglund S, Blodorn K, Naslund K, Vargmar K, Lind SB, Mi J, Arainga M, Riffault S, Taylor G, Pringle J, et al. Proteome analysis of bronchoalveolar lavage from calves infected with bovine respiratory syncytial virus-insights in pathogenesis and perspectives for new treatments. PLoS One. 2017;12(10):e0186594.CrossRefGoogle Scholar
  13. 13.
    Sun D, Zhang H, Guo D, Sun A, Wang H. Shotgun proteomic analysis of plasma from dairy cattle suffering from footrot: characterization of potential disease-associated factors. PLoS One. 2013;8(2):e55973.CrossRefGoogle Scholar
  14. 14.
    He Y, Li W, Liao G, Xie J. Mycobacterium tuberculosis-specific phagosome proteome and underlying signaling pathways. J Proteome Res. 2012;11(5):2635–43.CrossRefGoogle Scholar
  15. 15.
    Zeng S, Zhang H, Ding Z, Luo R, An K, Liu L, Bi J, Chen H, Xiao S, Fang L. Proteome analysis of porcine epidemic diarrhea virus (PEDV)-infected Vero cells. Proteomics. 2015;15(11):1819–28.CrossRefGoogle Scholar
  16. 16.
    Linde ME, Colquhoun DR, Ubaida Mohien C, Kole T, Aquino V, Cotter R, Edwards N, Hildreth JE, Graham DR. The conserved set of host proteins incorporated into HIV-1 virions suggests a common egress pathway in multiple cell types. J Proteome Res. 2013;12(5):2045–54.CrossRefGoogle Scholar
  17. 17.
    Sun D, Shi H, Guo D, Chen J, Shi D, Zhu Q, Zhang X, Feng L. Analysis of protein expression changes of the Vero E6 cells infected with classic PEDV strain CV777 by using quantitative proteomic technique. J Virol Methods. 2015;218:27–39.CrossRefGoogle Scholar
  18. 18.
    Cho YE, Singh TS, Lee HC, Moon PG, Lee JE, Lee MH, Choi EC, Chen YJ, Kim SH, Baek MC. In-depth identification of pathways related to cisplatin-induced hepatotoxicity through an integrative method based on an informatics-assisted label-free protein quantitation and microarray gene expression approach. Mol Cell Proteomics. 2012;11(1):M111 010884.CrossRefGoogle Scholar
  19. 19.
    Zhang X, Zhou J, Wu Y, Zheng X, Ma G, Wang Z, Jin Y, He J, Yan Y. Differential proteome analysis of host cells infected with porcine circovirus type 2. J Proteome Res. 2009;8(11):5111–9.CrossRefGoogle Scholar
  20. 20.
    Maxwell KL, Frappier L. Viral proteomics. Microbiol Mol Biol Rev. 2007;71(2):398–411.CrossRefGoogle Scholar
  21. 21.
    An K, Fang L, Luo R, Wang D, Xie L, Yang J, Chen H, Xiao S. Quantitative proteomic analysis reveals that transmissible gastroenteritis virus activates the JAK-STAT1 signaling pathway. J Proteome Res. 2014;13(12):5376–90.CrossRefGoogle Scholar
  22. 22.
    Zhang LK, Chai F, Li HY, Xiao G, Guo L. Identification of host proteins involved in Japanese encephalitis virus infection by quantitative proteomics analysis. J Proteome Res. 2013;12(6):2666–78.CrossRefGoogle Scholar
  23. 23.
    Braga ACS, Carneiro BM, Batista MN, Akinaga MM, Bittar C, Rahal P. Heat shock proteins HSPB8 and DNAJC5B have HCV antiviral activity. PLoS One. 2017;12(11):e0188467.CrossRefGoogle Scholar
  24. 24.
    Rathore AP, Haystead T, Das PK, Merits A, Ng ML, Vasudevan SG. Chikungunya virus nsP3 & nsP4 interacts with HSP-90 to promote virus replication: HSP-90 inhibitors reduce CHIKV infection and inflammation in vivo. Antivir Res. 2014;103:7–16.CrossRefGoogle Scholar
  25. 25.
    Zhong M, Zheng K, Chen M, Xiang Y, Jin F, Ma K, Qiu X, Wang Q, Peng T, Kitazato K, et al. Heat-shock protein 90 promotes nuclear transport of herpes simplex virus 1 capsid protein by interacting with acetylated tubulin. PLoS One. 2014;9(6):e99425.CrossRefGoogle Scholar
  26. 26.
    Patton JT, Chizhikov V, Taraporewala Z, Chen D. Virus replication. Methods Mol Med. 2000;34:33–66.PubMedGoogle Scholar
  27. 27.
    McLaughlin K, Carr VB, Iqbal M, Seago J, Lefevre EA, Robinson L, Prentice H, Charleston B. Hsp110-mediated enhancement of CD4+ T cell responses to the envelope glycoprotein of members of the family Flaviviridae in vitro does not occur in vivo. Clin Vaccine Immunol. 2011;18(2):311–7.CrossRefGoogle Scholar
  28. 28.
    Hunter-Lavin C, Davies EL, Bacelar MM, Marshall MJ, Andrew SM, Williams JH. Hsp70 release from peripheral blood mononuclear cells. Biochem Biophys Res Commun. 2004;324(2):511–7.CrossRefGoogle Scholar
  29. 29.
    Yang Z, Zhuang L, Szatmary P, Wen L, Sun H, Lu Y, Xu Q, Chen X. Upregulation of heat shock proteins (HSPA12A, HSP90B1, HSPA4, HSPA5 and HSPA6) in tumour tissues is associated with poor outcomes from HBV-related early-stage hepatocellular carcinoma. Int J Med Sci. 2015;12(3):256–63.CrossRefGoogle Scholar
  30. 30.
    Cheung RK, Dosch HM. The growth transformation of human B cells involves superinduction of hsp70 and hsp90. Virology. 1993;193(2):700–8.CrossRefGoogle Scholar
  31. 31.
    Lefeuvre A, Contamin H, Decelle T, Fournier C, Lang J, Deubel V, Marianneau P. Host-cell interaction of attenuated and wild-type strains of yellow fever virus can be differentiated at early stages of hepatocyte infection. Microbes Infect. 2006;8(6):1530–8.CrossRefGoogle Scholar
  32. 32.
    Liao WJ, Fan PS, Fu M, Fan XL, Liu YF. Increased expression of 70 kD heat shock protein in cultured primary human keratinocytes induced by human papillomavirus 16 E6/E7 gene. Chin Med J. 2005;118(24):2058–62.PubMedGoogle Scholar
  33. 33.
    Mayer MP. Recruitment of Hsp70 chaperones: a crucial part of viral survival strategies. Rev Physiol Biochem Pharmacol. 2005;153:1–46.CrossRefGoogle Scholar
  34. 34.
    Mansilla MJ, Costa C, Eixarch H, Tepavcevic V, Castillo M, Martin R, Lubetzki C, Aigrot MS, Montalban X, Espejo C. Hsp70 regulates immune response in experimental autoimmune encephalomyelitis. PLoS One. 2014;9(8):e105737.CrossRefGoogle Scholar
  35. 35.
    Rusai K, Banki NF, Prokai A, Podracka L, Szebeni B, Tulassay T, Reusz GS, Sallay P, Kormendy R, Szabo AJ, et al. Heat shock protein polymorphism predisposes to urinary tract malformations and renal transplantation in children. Transplant Proc. 2010;42(6):2309–11.CrossRefGoogle Scholar
  36. 36.
    Zulaziz N, Azhim A, Himeno N, Tanaka M, Satoh Y, Kinoshita M, Miyazaki H, Saitoh D, Shinomiya N, Morimoto Y. Photodynamic therapy mediates innate immune responses via fibroblast-macrophage interactions. Hum Cell. 2015;28(4):159–66.CrossRefGoogle Scholar
  37. 37.
    Unwin RD, Griffiths JR, Whetton AD. Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat Protoc. 2010;5(9):1574–82.CrossRefGoogle Scholar
  38. 38.
    Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–14.CrossRefGoogle Scholar
  39. 39.
    Reed LJ, Muench H. A simple method of estimation of fifty percent endpoint. Am J Epidemiol. 1938;27:493–7.CrossRefGoogle Scholar

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© The Author(s). 2019

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 and Affiliations

  1. 1.Institute of Veterinary Medicine, Jiangsu Academy of Agricultural Sciences, Key Laboratory of Veterinary Biological Engineering and Technology, Ministry of AgricultureNanjingChina
  2. 2.School of Pharmacy, Linyi UniversityLinyiChina
  3. 3.College of Animal ScienceGuizhou UniversityGuiyangChina
  4. 4.Key Lab of Food Quality and Safety of Jiangsu Province-State Key Laboratory Breeding BaseNanjingChina

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