Open Access

Transcription analysis on response of swine lung to H1N1 swine influenza virus

  • Yongtao Li1,
  • Hongbo Zhou1, 2,
  • Zhibin Wen1,
  • Shujuan Wu1,
  • Canhui Huang2,
  • Guangmin Jia1,
  • Huanchun Chen1, 2 and
  • Meilin Jin1, 2Email author
Contributed equally
BMC Genomics201112:398

DOI: 10.1186/1471-2164-12-398

Received: 25 February 2011

Accepted: 8 August 2011

Published: 8 August 2011

Abstract

Background

As a mild, highly contagious, respiratory disease, swine influenza always damages the innate immune systems, and increases susceptibility to secondary infections which results in considerable morbidity and mortality in pigs. Nevertheless, the systematical host response of pigs to swine influenza virus infection remains largely unknown. To explore it, a time-course gene expression profiling was performed for comprehensive analysis of the global host response induced by H1N1 swine influenza virus in pigs.

Results

At the early stage of H1N1 swine virus infection, pigs were suffering mild respiratory symptoms and pathological changes. A total of 268 porcine genes showing differential expression (DE) after inoculation were identified to compare with the controls on day 3 post infection (PID) (Fold change ≥ 2, p < 0.05). The DE genes were involved in many vital functional classes, mainly including signal transduction, immune response, inflammatory response, cell adhesion and cell-cell signalling. Noticeably, the genes associated with immune and inflammatory response showed highly overexpressed. Through the pathway analysis, the significant pathways mainly concerned with Cell adhesion molecules, Cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway and MAPK signaling pathway, suggesting that the host took different strategies to activate these pathways so as to prevent virus infections at the early stage. However, on PID 7, the predominant function classes of DE genes included signal transduction, metabolism, transcription, development and transport. Furthermore, the most significant pathways switched to PPAR signaling pathway and complement and coagulation cascades, showing that the host might start to repair excessive tissue damage by anti-inflammatory functions. These results on PID 7 demonstrated beneficial turnover for host to prevent excessive inflammatory damage and recover the normal state by activating these clusters of genes.

Conclusions

This study shows how the target organ responds to H1N1 swine influenza virus infection in pigs. The observed gene expression profile could help to screen the potential host agents for reducing the prevalence of swine influenza virus and further understand the molecular pathogenesis associated with H1N1 infection in pigs.

Background

Swine influenza is a respiratory disease in pigs characterized by fever, anorexia, tachypnea, dyspnea and coughing [1]. The causative agent, swine influenza virus (SwIV) contains three key subtypes: H1N1, H3N2 and H1N2 circulating in pigs worldwide [25]. In pig flocks, H1N1, as the predominant subtype in clinical surveys or in the NCBI Influenza resource, has been further categorized into classical swine H1N1 virus, human-like H1N1 virus and European avian-like H1N1 virus [68]. In March 2009, a novel swine-origin H1N1 influenza virus containing a unique combination of gene segments of the North American and Eurasian SwIV lineages has continued to circulate in humans and raised severe concerns about pandemic developments. And the finding indicates the significant role of H1N1-subtype SwIV in the evolution of new viruses with pandemic potential [9].

SwIV replication is mainly restricted to epithelial cells in the respiratory tract with the lung being the major target organ [10]. As a short-lasting disease, swine influenza manifests itself with an incubation period of 1-3 days, then the recovery follows, which is limited to 6 or 7 days after infection [11]. During the acute phase of the disease, SwIV induces an overwhelming and simultaneous pro-inflammatory cytokines in the lungs of infected animals [12]. Recently, researchers have found that after H1N1 SwIV infection, secretion of innate, pro-inflammatory, Th1, Th2, and Th3 cytokines was observed in infected pig lungs, which could be useful indicator for host anti-viral response and SwIV pathogenesis [13]. Among the cytokines, several have been demonstrated to be related with anti-viral functions or tissue damage at the acute stage of SwIV infection, such as IFN-alpha [14], tumour necrosis factor-alpha [15], interleukins [16]. As mentioned above, swine influenza infection has the characteristics of lasting for a short period and quick recovery, which suggests that great number anti-viral molecules, such as IFN-stimulated genes, may play a central role in the infection course, although the detailed information remains to be obtained. Moreover, cytokines are suggested to be able to partly explain why invasive SwIV could cause severe tissue damage by influx of inflammatory cells, which makes animals more susceptible to secondary bacterial infections. In the clinical surveillance, SwIV was a frequent co-infecting agent in swine pneumonia cases [17]. In the previous studies, the experimental dual infection in pigs with a H1N1 SwIV and other pathogens were carried out, and the results determined the contributions of SwIV infection to the enhanced susceptibility to secondary bacteria induced pneumonia [18, 19]. To date, investigators have mainly concentrated their attention on the implications of swine influenza for public health. However, it is equally worth elucidating basic viral pathogenesis of swine influenza, for swine influenza is one of main immunesuppressive diseases in pig flocks and causes the serious harm to the swine industry. Along this line, it is also indispensable to study the host response against SwIV infection and analyze the potential pathogenesis of SwIV infection.

Moreover, virus infections are generally associated with numerous changes in gene expression of a specific tissue or organ that determines the fate of the ultimate outcome of the infected host. As a high density technology, microarray gene expression profiling has been increasingly used to evaluate the status of gene expressions of pigs after being infected by different pathogens [20, 21]. In the analysis of the gene regulation patterns, attention has been given to genes related to the key factor in the clinical course and pathology of the disease, particularly when the host was infected with zoonosis, like SwIV [22].

To obtain the sufficient information on host response of H1N1 SwIV infection in pigs, the present study focused on the global genomic analysis of transcriptional responses of pig lungs to H1N1 virus infection using Agilent Porcine Oligo Microarray containing more than 42,034 transcripts. The comparison of H1N1 SwIV-infected and mock-infected pig lungs indicated that 268 and 214 DE genes were differently expressed respectively on PID 3 and PID 7 (Fold change ≥ 2, p < 0.05). As a result, these data would enable us to better understand the underlying pathogenesis of H1N1 SwIV infection in pigs.

Methods

Animals and viruses

A total of 30 piglets with high-health status were obtained from a commercial herd and were raised in isolated facilities. To confirm that all piglets were free of swine influenza virus prior to challenge, Hemagglutination Inhibition Test (HI) and Enzyme-linked immunosorbent assay (ELISA) were performed on all pigs' sera and the sera were confirmed negative for the antibodies against H1 and H3 swine influenza viruses (unpublished data). The influenza viruses were stored at -80°C. The biological properties of swine influenza strains including EID50 and TCID50 were also determined (unpublished data). The representative of these strains, designated as A/swine/Hubei/101/2009(H1N1) was sequenced and submitted into the Genbank (Accession numbers: CY083005, CY083006, CY083007, CY083008, CY083009, CY083010, CY083011, CY083012).

Virus infection and tissue collection

All procedures for animal infection and tissue collection were performed according to protocols approved by Biological Studies Animal Care and Use Committee in Hubei Province, PR China. At the age of day 35, 15 pigs were randomly allocated to the non-infected group and another 15 to the infected group. Each piglet in the infected group was intranasaly challenged with A/swine/Hubei/101/2009(H1N1) strain at a dose of 107.0EID50 in 2 ml of phosphate buffered saline (PBS) in separate HEPA filtered containment facilities. Each piglet of the non-infected group was treated similarly with an identical volume of PBS as control. Clinical symptoms, including lethargy, anorexia, coughing, hyperpnea or dyspnea, nasal and ocular discharge, and their body temperature were recorded daily. 7 from the infected pigs and 7 from the control pigs were respectively euthanized on PID 3 and 7 by intravenous administration of pentobarbital, and their lungs were removed completely. The percentage of the surface of each pulmonary lobe affected by macroscopic lesions was estimated visually. Virus isolation and H1N1 specific quantitative real-time PCR (qRT-PCR) were performed after the pigs were sacrificed on PID 3 and 7. Quantification was achieved by relating viral Ct value to the Ct value on a standard curve of a measured copy number of a plasmid bearing a 140 bps fragment of the NP gene, amplified by using the following primers: 5-CACTCACCTGATGATTTGGCA-3 (forward) and 5- CAGCAGCTCCAGATCTCCTTG-3 (reverse). Some lungs from both groups were frozen and stored in liquid nitrogen immediately after collection until RNA extraction, and the remaining were fixed in formalin for further histopathology evaluation.

RNA extraction, reverse transcription, RNA labelling and cRNA hybridization Total

RNA extraction from the lungs prepared from the same lesion, areas of localized infection (with viral mRNA present) was performed using TRIzol by following the standard instructions (Invitrogen, Carlsbad, CA) and a clean-up was carried out using RNeasy columns (Qiagen, Valencia, CA). Both RNA integrity and concentration were evaluated by Agilent 2100 Bioanalyzer by following the manufacturer's instructions (Agilent Technologies, Palo Alto, CA). Reverse transcription of 2 μg total RNA and synthesis of Cy3-labelled cRNA with one round of amplification were carried out by a commercial Agilent array service (Shanghaibio, China) following the standard one-cycle protocol according to the manufacturer's instructions. Transcriptional profiles were assessed using Agilent 4 × 44 K Porcine Oligo Microarray which contains more than 42,034 transcripts of pig from the database of RefSeq, Unigene and TIGR. Hybridization and scanning of the arrays were carried out according to standard protocols using a G2565BA Scanner (Agilent Technologies, Palo Alto, CA).

Expression microarray analysis and bioinformatics

Raw data and statistical analyses were performed with Feature Extraction software. Normalization was performed per chip (normalized to 50th percentile) and per gene (normalized to the median) respectively. A statistical analysis of variance (ANOVA) model was applied to the data and the significance was showed by accepting a false discovery rate (FDR) of 0.05. A further cut-off threshold was applied based on a fold change of 2.0 between infected and control pigs. Then all the DE genes were performed for hierarchical cluster (Ver.3.0) and TreeView (Ver.1.1.1) analyses. Genes with significant similarities to the transcripts in nr database based on BLASTX searches were selected for GO analysis, performed by MAS 3.0 software which was based on DAVID database (CapitalBio, Beijing, China) [23]. Annotation results were obtained by inputting the list of gene symbol as identifier. The Pathway analysis was done using the MAS 3.0 software which was based on the Kyoto Encyclopedia of Genes and Genomes database (KEGG) (CapitalBio, Beijing, China). The raw and processed data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE28871.

qRT-PCR for confirmation

Microarray results were validated by qRT-PCR using SYBR green-based detection with an ABI PRISM 7500 cycler (Applied Biosystems, Foster City, CA). Total lung RNA (1 μg) prepared as described above was reversely transcribed in a 20 ul reaction mixture containing 2ul avian myeloblastosis virus (AMV) buffer, 50 pM Oligo18T, 0.5 mM dNTPs, 10 U RNase inhibitor and 20 U AMV reverse transcriptase (TAKARA, Japan). All primers were designed using Primer Premier 5.0 software based on the sequences of the corresponding porcine mRNAs in GenBank (Additional file 1). The GAPDH gene was used for expression normalization, using the primers described previously [23]. PCRs were triplicated to guarantee the reproducibility of amplification of the lung cDNA sample from each animal. Changes in gene expression revealed by qRT-PCR were calculated by following the t-test reported previously [24], and a P-value < 0.05 was considered significant. Reaction specificity was ascertained by performing the Melt procedure at the end of the amplification protocol; and the efficiency of the PCR reaction was 92-99% for all reactions (slope standard line between-3.3 and-3.6) according to the manufacturer's instructions (Applied Biosystems, Foster City, CA).

Results

Clinical and Pathological evaluation

By monitoring the clinical signs of all pigs, it was intriguing to find that most infected pigs just showed mild signs, such as coughing, nasal discharge and dyspnea on PID 1-3 and recovered on PID 5-7. The average body temperature of infected pigs started to increase on PID 1, peaked around PID 3 with 40.8°C, and returned to the initial temperature (about 39°C) until PID 5 (P < 0. 05). As expected, the temperature of the six PBS-mock infected pigs rose slightly on PID 1 and kept stable all the remaining days. To investigate pathological damage to lungs of infected pigs, 7 from the infected pigs and 7 from the control pigs were respectively euthanized on PID 3 and 7 by intravenous administration of pentobarbital, and the macroscopic lesions of lungs tissues were estimated visually (Figure 1A). Then lungs of pigs in each group were fixed by submerging them in 4% neutral buffered formalin and embedding them in paraffin. Three-micron sections were made before they were stained with hematoxylin and eosin (H&E). Lung tissues in lesion areas of PID 3 showed dominant pneumonia with increased lung elastance, severe edema formation, and pathological responses, such as large amount infiltration of inflammatory cells, alveolar wall thickening and bleeding. The pathological changes of lung tissues on PID 7 were investigated, they just showed slightly alveolar wall thickening with few inflammatory cells, and the PBS-mock infected pigs didn't show lung lesions (Figure 1B).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-398/MediaObjects/12864_2011_Article_3554_Fig1_HTML.jpg
Figure 1

The macroscopic and microscopic lung lesions in pigs infected with H1N1 SwIV. (A) Pigs were PBS-mock infected or infected with H1N1 SwIV. On PID 3 and 7, extensive areas of macroscopic lesions of lungs tissues were observed in lungs of virus-infected pigs. (B) Hematoxylin- and eosin-stained sections of lungs from control pigs and the pigs infected with H1N1 SwIV. The microscopic lesions of lung tissues collected on PID 3 and 7 showed acute pneumonia with pathological changes: alveolar wall thickening, bleeding, debris in the lumen, and the accumulation of inflammatory cells, indicated by an arrow (panel ii and iii). The lungs of PBS-mock infected pigs showed normal bronchial epithelial lining and absence of inflammatory cells (panel i).

Virus isolation and confirmation

The virus could be isolated from lung tissues in all infected pigs on PID 3 and PID 7 but could only be found in nasal swabs of pigs before PID 3. As expected, no virus could be isolated in the controls. To investigate the tissue distribution of virus, samples of heart, liver, spleen, lung, kidney, trachea, tonsil, intestine and serum from pigs were collected on PID 3 and 7 after inoculation, then detected to manifest whether any virus could be isolated from these tissues. Results showed that in most infected pigs, the virus could be isolated only from the trachea and lung. No virus was isolated from heart, liver, intestine, spleen, tonsil or kidney in any infected pigs. The above results were confirmed by qRT-PCR using specific primers for the NP gene (unpublished data). Based on these results, we chose three pigs from each group to perform the following studies.

Gene expression alterations in the lung tissue

To analyse the genomic expression of SwIV-infected pigs, total RNA was extracted from the infected pig lungs (with viral mRNA present) and from the control pig lungs at the same area. The transcriptome analysis indicated that the detection ratios of all probe sets in the chip representing "expression transcripts" were 75-85% in different samples, which could exactly meet the quality control standards. The global expression profile of porcine lungs of the infected group was compared with that of the control group. After quantile normalization and statistical analyses, a total of 534 and 467 transcripts at acute and recovery phases were respectively identified to be differentially expressed (Fold change ≥ 2, p < 0.05). The functions of the DE genes were analysed by MAS 3.0 software. Surprisingly, about half of the transcripts at both time points were not functionally annotated, with only a total of 268 genes showing clear functional annotation on PID 3 and 214 genes on PID 7, and all DE genes were performed for hierarchical cluster (Figure 2A and 2C). Amongst the DE genes on PID 3, a set of 160 genes were up-regulated and the rest of 108 genes were down-regulated. The DE genes mainly clustered into functional groups: signal transduction (e.g. TLRs, RLRs), immune response (e.g. GBP1, OAS2), inflammatory response (e.g. CXCL9, CXCL10), cell adhesion (e.g. SELL, SELE), cell-cell signaling (e.g. CCR5, TGF-β2), biological process (e.g. RETN, APOBEC3A), transport (e.g. Haptocorrin, AQP7), oxidation reduction (e.g. DUOX2, ALOX15), interspecies interaction between organisms (e.g. IFIH1, STAT1), response to virus (e.g. MX1, ISG20) and so on (Figure 2B). Particularly, the genes associated with immune and inflammatory response were highly overexpressed among the up-regulated genes, which indicated that they may play important roles in host defense response at the early stage of infection (Table 1). Through similar analysis, on PID 7, it was found that the predominant groups of DE genes contained signal transduction, metabolism, transcription, development and transport, however, the DE genes related with immune and inflammatory responses became the minority in number (Table 2). The results presented here reflected that it was beneficial for the host to prevent excessive inflammatory damage and recover the normal state by activating these clusters of genes mentioned above (Figure 2D). When comparing the DE genes on PID 3 and 7, only 23 genes were present in the two time points, and they may exert a continuous impact on host at both early and recovery stages (Additional file 2). The complete microarray dataset of the two time points with functional annotations and the changes at expression level is summarized in Supplementary Table (Additional file 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-398/MediaObjects/12864_2011_Article_3554_Fig2_HTML.jpg
Figure 2

Clustering and characterization of the differential expression of genes. (A) The DE genes showing clear functional annotation on PID 3 have been selected for cluster analysis which is described in methods. Amongst the DE genes on PID 3, a set of 160 genes were up-regulated and the rest of 108 genes were down-regulated. Each row represents a separate gene and each column represents a separate piglet. Color legend is on the left, the color scale ranges from saturated green for log ratios -3.0 and above to saturated red for log ratios 3.0 and above. Red indicates increased gene expression levels; green indicates decreased levels compared with normal samples. (B) Categories of annotated genes based on biological process GO term. On PID 3, the DE genes mainly clustered into 34 functional groups with varied numbers. (C) The genes showing clear functional annotation on PID 7 have been selected for cluster analysis. (D) On PID 7, the DE genes mainly clustered into 33 functional groups with varied numbers. Many categories shared the same genes.

Table 1

The DE genes associated with immune and inflammatory responses on PID 3

Functional

classification

Gene description

Gene symbol

Fold Change

Gen Bank ID

 

CD45 antigen isoform 3 Fragment

CD45

38.62459

AK230523

 

Sus scrofa antibacterial protein

PMAP-23

21.70289

EW419136

 

Sus scrofa peptide antibiotic PR39

PR39

13.50724

NM_214450

immune

Sus scrofa chemokine ligand 26-like mRNA

CCL26

8.645769

NM_001078665

response

Sus scrofa protegrin 4 NPG4, mRNA

NPG4

8.237142

NM_213863

 

Sus scrofa immunoglobulin kappa variable group

IGKV

6.440473

CB286369

 

Sus scrofa 2'-5'-oligoadenylate synthetase-like mRNA

OASL

5.803129

NM_001031790

 

Interferon-stimulated gene 20 kDa protein

ISG20

5.414398

NM_001005351.2

 

Sus scrofa chemokine C-X-C motif ligand 10 mRNA

CXCL10

5.329808

NM_001008691

 

Rep: IL-1 receptor 2-Bos taurus Bovine, partial 98%

IL1R2

5.321422

NM_001046210.1

 

Sus scrofa MHC class II histocompatibility antigen

SLA-DQA1

5.232868

AK230483

 

Sus scrofa cathepsin L1-like

CTSL1

4.814094

AK230898

 

Sus scrofa interferon regulatory factor 3 (IRF3), mRNA

IRF3

4.320395

NM_213770

 

Sus scrofa Fc fragment of IgG, low affinity IIb, receptor, mRNA

FCGR2B

3.622257

NM_001033013

 

Sus scrofa CD14 molecule, mRNA

CD14

3.544918

NM_001097445

 

Sus scrofa interferon stimulated gene 15 mRNA

ISG15

3.45

EU584557.1

 

Sus scrofa IgG heavy chain LOC396781, mRNA

IgG

3.438582

NM_213828

 

Sus scrofa interferon-induced protein with tetratricopeptide repeats 1

IFI56

3.386147

XM_001928724

 

Sus scrofa inducible T-cell co-stimulator ICOS, mRNA

ICOS

3.353116

NM_001044546

 

Sus scrofa interferon regulatory factor 7 IRF7, mRNA

IRF7

3.249327

NM_001097428

 

Sus scrofa myxovirus influenza virus resistance 1, interferon-inducible protein p78 MX1, mRNA

MX1

3.237013

NM_214061

 

Sus scrofa MHC class I antigen

SLA-2

3.187732

AK231553

 

Sus scrofa cytotoxic T-lymphocyte-associated protein 4 CTLA4, mRNA

CTLA4

3.184663

NM_214149

 

Sus scrofa DEAD Asp-Glu-Ala-Asp box polypeptide 58 DDX58, mRNA

DDX58

3.122048

NM_213804

 

Rep:Tumor necrosis factor receptor superfamily member 17-Homo sapiens

TNFRSF17

3.071191

NM_001192.2

 

Sus scrofa CD8a molecule CD8A, mRNA

CD8A

2.834259

NM_001001907

 

Similar to melanoma cell adhesion molecule

CD146

-2.819245

BX915975

 

similar to guanylate binding protein 4

GBP4

2.780124

AK239735

 

Sus scrofa guanylate binding protein 1, interferon-inducible, 67 kDa GBP1, mRNA

GBP1

2.765026

NM_001128473

 

Sus scrofa ubiquitin specific peptidase 18, mRNA

USP18

2.732302

NM_213826

 

Sus scrofa interferon-induced protein with tetratricopeptide repeats 2 mRNA

IFI54

2.729199

AK230663

 

mast cell immunoreceptor signal transducer-like

CLNK

2.704822

AK237804

 

Rep:CD274 molecule CD274, mRNA- Homo sapiens

CD274

2.648947

EW655654

 

Sus scrofa CD163 molecule CD163, mRNA

CD163

2.633336

NM_213976

 

Sus scrofa interferon-induced protein 44 mRNA

IFI44

2.629777

AK233687

 

Sus scrofa interleukin 12A natural killer cell stimulatory factor 1, IL12A, mRNA

IL12A

2.580563

NM_213993

 

Sus scrofa guanylate binding protein 2, interferon-inducible GBP2, mRNA

GBP2

2.561492

NM_001128474

 

Sus scrofa interferon-induced protein with tetratricopeptide repeats 3, mRNA

IFI60

2.465327

XM_001928703

 

Sus scrofa interferon-induced transmembrane protein 1

IFITM1

2.407903

AK237820

 

Sus scrofa chemokine C-C motif receptor 5, mRNA

CCR5

2.374719

NM_001001618

 

Sus scrofa chemokine C-C motif receptor 10, mRNA

CCR10

2.36215

NM_001044563

 

Sus scrofa chemokine C-C motif ligand 28, mRNA

CCL28

2.330211

NM_001024695

 

Sus scrofa chemokine C-X-C motif ligand 9 mRNA

CXCL9

2.312722

BP169836

 

Sus scrofa transporter 1, ATP-binding cassette, sub-family B MDR/TAP TAP1, mRNA

TAP1

2.15635

NM_001044581

 

Sus scrofa Fas ligand TNF superfamily, member 6, mRNA

FAS

2.098484

NM_213806

 

Sus scrofa immunoglobulin-associated alpha CD79A, mRNA

CD79A

2.086904

NM_001135962

 

Sus scrofa interferon induced with helicase C domain 1 IFIH1, mRNA

IFIH1

2.075703

NM_001100194

 

Sus scrofa aquaporin 9, mRNA

AQP9

2.067154

NM_001112684

 

Sus scrofa tumor necrosis factor superfamily member 18 mRNA, partial cds

TNFSF18

2.065184

DQ995649

 

Sus scrofa 2'-5'-oligoadenylate synthetase 2, 69/71kDa OAS2, mRNA

OAS2

2.054377

NM_001031796

 

similar to cystatin F

CST7

2.042642

CJ009409

 

Sus scrofa Ig gamma 2b chain constant region

IGG2B

2.017483

AK237225

 

Sus scrofa CD86 molecule CD86, mRNA

CD86

2.007655

NM_214222

 

Sus scrofa vitronectin VTN, mRNA

VTN

-2.006228

NM_214104

 

Sus scrofa chemokine ligand 26-like protein LOC780409, mRNA

CCL26

8.645769

NM_001078665

 

Sus scrofa Toll-like receptor 3

TLR3

7.331916

NM_001097444.1

 

Sus scrofa chemokine C-X-C motif ligand 10, mRNA

CXCL10

5.329808

NM_001008691

 

Sus scrofa selectin L (SELL), mRNA

SELL

4.497706

NM_001112678

inflammatory

Sus scrofa inflammatory response protein 6, mRNA

IRG6

3.840661

NM_213817

response

Sus scrofa C1q-related factor LOC780424, mRNA

CRF

3.627351

NM_001078676

 

Sus scrofa CD14 molecule CD14, mRNA

CD14

3.544918

NM_001097445

 

Sus scrofa alpha-1-antichymotrypsin 2 SERPINA3-2

SERPINA3

3.119463

NM_213787

 

Sus scrofa complement C4 C4, mRNA

C4

2.949702

NM_001123089

 

Sus scrofa selectin E SELE, mRNA

SELE

-2.879741

NM_214268

 

Sus scrofa toll-like receptor 2 (TLR2), mRNA

TLR2

2.8723

NM_213761

 

Similar to melanoma cell adhesion molecule

CD146

-2.819245

BX915975

 

Sus scrofa toll-like receptor 8 TLR8, mRNA

TLR8

2.675719

NM_214187

 

Sus scrofa CD163 molecule CD163, mRNA

CD163

2.633336

NM_213976

 

Rep: transforming growth factor, beta 2- Macaca mulatta

TGFB2

-2.554227

EV858461

 

Sus scrofa chemokine C-C motif receptor 5, mRNA

CCR5

2.374719

NM_001001618

 

Sus scrofa chemokine C-C motif ligand 28, mRNA

CCL28

2.330211

NM_001024695

 

Sus scrofa chemokine C-X-C motif ligand 9

CXCL9

2.312722

BP169836

 

Sus scrofa serpin peptidase inhibitor, clade E member 1 SERPINE1, mRNA

SERPINE1

2.225002

NM_213910

 

Sus scrofa Toll-like receptor 9 TLR9, mRNA

TLR9

2.200666

NM_213958

 

Rep: arachidonate 15-lipoxygenase Alox15 gene- Mus musculus

Alox15

-2.168114

AK238353

 

Sus scrofa signal transducer and activator of transcription 1 STAT1, mRNA

STAT1

2.116864

NM_213769

 

Sus scrofa S100 calcium binding protein A2

S100A2

2.04835

XM_001929556

 

Sus scrofa serpin peptidase inhibitor, clade G1 inhibitor, member 1 SERPING1, mRNA

SERPING1

2.022489

NM_001123194

The DE genes associated with immune and inflammatory responses were assigned based on GO term and manual annotation. Manual annotations were listed in italics. Most genes could also be classified into other categories but only immune and inflammatory responses were considered.

Table 2

The DE genes associated with immune and inflammatory responses on PID 7

Functional

classification

Gene description

Gene symbol

Fold Change

Gen Bank ID

 

Rep:CD274 molecule CD274, mRNA- Homo sapiens

CD274

2.648947

EW655654

immune

Sus scrofa complement component C6

C6

2.660923

NM_001097449

response

Sus scrofa lymphocyte cytosolic protein 2

LCP2

2.461111

AK230876

 

Rep:Sus scrofa tumor necrosis factor superfamily member 18 mRNA

TNFSF18

2.5221772

DQ995649

 

Sus scrofa interleukin 2 receptor, alpha

IL2RA

3.3108058

NM_213835

 

Sus scrofa C-type lectin domain family 5, member A

CLEC5A

4.702034

NM_213990

 

Sus scrofa interferon stimulited exonuclease gene 20 kDa

ISG20

2.1768405

NM_001005351

 

Sus scrofa MHC class II DR-alpha (SLA-DRA)

SLA-DRA

2.0392556

NM_001113706.1

 

Sus scrofa secreted phosphoprotein 1

SPP1

3.411632

NM_214023

 

Sus scrofa lymphocyte-activation gene 3

LAG3

2.0111256

NM_001105306

 

Sus scrofa selectin E

SELE

9.232597

NM_214268

 

Sus scrofa chemokine ligand 26-like mRNA

CCL26

8.645769

NM_001078665

 

Sus scrofa Toll-like receptor 3

TLR3

7.331916

NM_001097444.1

 

Sus scrofa matrix metallopeptidase 25

MMP25

3.32788

NM_213905

 

Sus scrofa toll-like receptor 7

TLR7

3.262197

NM_001097434

inflammatory response

Sus scrofa interleukin 13

IL13

2.4685268

NM_213803

 

Sus scrofa sphingosine 1-phosphate receptor 3-like

S1PR3

2.293045

BP173206

 

chemokine (C-C motif) ligand 8

CCL8

2.0915823

NM_001164515.1

 

Sus scrofa S100 calcium binding protein A2

S100A2

2.04835

XM_001929556

 

Sus scrofa Toll-like receptor 5

TLR5

- 2.10299

NM_001123202

 

Rep: transforming growth factor, beta 2- Macaca mulatta

TGFB2

- 2.180843

EV858461

 

Rep: leukocyte cell-derived chemotaxin 2

LECT2

- 2.381497

NM_002302.2

 

Sus scrofa S100 calcium binding protein A16

S100A16

- 13.31639

NM_001190208.1

The DE genes associated with inflammatory response were assigned based on GO term and manual annotation. Manual annotations were listed in italics. Most genes could also be classified into other categories but only immune and inflammatory responses were considered.

Pathway analysis

To gain insight into the different biological processes of swine influenza infection at the different time points, a pathway analysis was performed on DE genes. We selected most genes for further examination, based on their potential implication in immune and inflammatory response processes. On PID 3, by using the KEGG database, the significant pathways mainly contained Cell adhesion molecules, Cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway and MAPK signaling pathway, which suggested that at the early stage, the host took different strategies to activate immune and inflammatory response so as to prevent from virus infections. However, on PID 7, PPAR signaling pathway and Complement and coagulation cascades became the most significant pathways, indicating that the host start to repair excessive tissue damage by anti-inflammatory functions at the recovery stage (Table 3).
Table 3

The key pathways that contain an over-representation of regulated genes in pig lungs after incubation with H1N1 SwIV on PID 3 and 7

KEGG pathway(PID 3)

Gene number

KEGG pathway(PID 7)

Gene number

Cell adhesion molecules (CAMs)

17

PPAR signaling pathway

8

Cytokine-cytokine receptor interaction

15

Focal adhesion

8

Toll-like receptor signaling pathway

13

Cytokine-cytokine receptor interaction

8

MAPK signaling pathway

13

Complement and coagulation cascades

7

Focal adhesion

12

Wnt signaling pathway

6

Complement and coagulation cascades

10

Adherens junction

4

T cell receptor signaling pathway

8

ECM-receptor interaction

4

Antigen processing and presentation

7

Toll-like receptor signaling pathway

4

PPAR signaling pathway

5

Cell cycle

4

ECM-receptor interaction

5

Cell adhesion molecules (CAMs)

4

Jak-STAT signaling pathway

4

Natural killer cell mediated cytotoxicity

4

B cell receptor signaling pathway

4

Oxidative phosphorylation

4

At each time-point, the pathway classes were more than the results present in the table. Only these containing relative high gene numbers and low p-Value were chose for analysis.

Validation of microarray data by qRT-PCR

In order to get an overall validation of the microarray results, 13 genes with different levels of altered expression (10 up-regulated and 3 down-regulated) were selected for qRT-PCR confirmation, based on their involvement in different functional groups or pathways. Results showed that the altered expression of these 13 genes identified by microarray was consistent with the results of qRT-PCR (Figure 3), although the extent of the changes as measured by the two methods did not match exactly due to the different nature of the procedures (Table 4). What's more, qRT-PCR of 15 representative genes associated with anti-viral immune function were performed including TLRs and interferon-induced genes (ISGs) and found the similar tendency as microarray results (Table 5). The data could indicate that the results from the microarray analysis are good indicators of overall changes in gene expression.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-398/MediaObjects/12864_2011_Article_3554_Fig3_HTML.jpg
Figure 3

Validation of the microarray data by qRT-PCR analyses. The genes of TAP1, SAA2, C4, CD2, IRG6, TCN1, ISG20, PR39, PMAP23 and NPG4 were up regulated in SwIV infected pigs compared with normal controls; TGFB2, VEGFA, TLR5 were down regulated. Fold change is calculated based on the mean intensity value from 3 pigs by using the comparative Ct method. Significant levels were analyzed by T-test.

Table 4

An overall validation of microarray data by qRT-PCR

Gene

GenBank ID

Microarray FC

qRT-PCR FC

TAP1

NM_001044581

2.46

2.83

SAA2

CB475095

8.42

2.86

C4

CF366280

2.95

3.53

CD2

NM_213776.1

2.93

6.43

IRG6

NM_213817.1

3.84

32.50

TCN1

BX675338

7.37

3.56

ISG20

NM_001005351.1

5.41

2.15

PR39

NM_214450

13.51

16.48

PMAP23

EW419136

21.7

13.07

NPG4

NM_213863

8.24

21.99

TGFB2

NM_214015.1

-2.55

-3.83

VEGFA

EU714324

-2.42

-6.20

TLR5

NM_001123202

-2.10

-5.78

Fold changes of gene expression in the porcine lung 3 days following H1N1 SwIV infection compared with the controls. The fold changes were calculated by the formula of 2-delta-delta Ct, data was means ± SD of triplicate reactions for each gene transcript. The expression level of GAPDH was assayed for normalization during quantitative PCR.

Table 5

A validation of Toll-like receptors and IFN-stimulated genes by qRT-PCR

Gene

GenBank ID

Microarray FC

qRT-PCR FC

TLR2

NM_213761.1

2.87

4.61

TLR3

NM_001097444.1

7.33

3.42

TLR8

NM_214187

2.68

2.44

TLR9

NM_213958

2.20

1.64

IRF3

NM_213770.1

4.32

6.33

IRF7

NM_001097428

3.25

11.1

Mx1

NM_214061

3.24

10.36

ISG15

EU584557.1

3.45

5.21

ISG20

NM_001005351.1

5.41

2.15

ISG54

XM_001928671.2

2.73

6.27

ISG56

XM_001928724;

3.39

3.43

ISG60

XM_001928703

2.47

5.70

GBP1

NM_001128473

2.78

12.0

OASL

NM_001031790

5.80

2.05

OAS2

NM_001031796

2.05

28.80

qRT-PCR results of 4 Toll-like receptors and 11 IFN-stimulated genes in the SwIV -infected pigs compared with the controls. Gene expression is depicted as relative expression compared to GAPDH gene expression.

Discussion

Swine influenza is an acute respiratory disease caused by influenza A viruses that circulate among pigs with few or no clinical signs. However, after swine influenza infection, the altering host response predisposes to secondary bacterial infection in pigs through complicated mechanisms. At present, the molecular mechanisms of host factors involved in pathogenesis and secondary infection have not been clearly established due to the lack of overall information about host response after influenza infection in pigs. The pathogenesis of swine influenza resembles that of human influenza [25]. In humans, many studies have shown that the severe disease associated with influenza infection might arise through different mechanisms, and one important factor is the augmented host response induced by influenza virus [25]. In view of this, a genomic expression analysis was performed to obtain global information of the host response in lungs of infected pigs at different infection stages. While our clinical and pathological findings are in keeping with the findings of other authors, our unique immunological data allows for the ability to explain the short-lasting clinical signs and severe pathological changes over the course of illness through immune and inflammatory responses of pigs to H1N1 SwIV.

Anti-viral innate immune response

The innate immune system functions as the first line of host defense against numerous pathogens. Pattern recognition receptors (PRRs) are a class of innate immune response expressed sensors that provide the host with the ability to detect and respond to virus infection, then trigger a class of anti-viral signaling cascades, ultimately ensuring production of necessary effectors molecules required for virus elimination, such as type-I interferons (IFNs). In this study, we found that several pattern recognition receptors were over-expressed in the infected pigs on PID 3, including intracellular sensors (MDA5, RIG-I and TLR3,8,9) and extracellular sensors (CD14 and TLR2) with differently increased folds, which was determined by qRT-PCR. With this in mind, we hypothesized that Toll-like receptors (TLRs) and RIG-I like receptors (RLRs) might be activated by SwIV then the receptors triggered the activation of NF-κB and IRF3/7, which cooperated in induction of antiviral type I IFNs [26]. Different TLR family detects distinct microbial pathogen associated molecular patterns (PAMPs) and triggers the activation of specific signaling pathways, leading to induction of interferons [27]. Though interferons were not observed up-regulated on PID 3, we hypothesized that interferons might be induced at the earlier time of infection, which could be indicated by the following enhanced production of ISGs [28]. ISGs are a large family of IFN-signaling and IFN-stimulated immune mediators, which may have a crucial role in antiviral response and host defense [29, 30]. The present study found that on PID 3, ISGs particularly showed the increase of the expression to a relatively high level in response to H1N1 virus infection in pigs, and many showed even higher level, e.g. OASL, OAS2, MX1, IRF7, IRF3, IFIT1, IFIT2, IFIT3, ISG15, ISG20 and ISG44. Several recent microarray results have also highlighted the common involvement of IFN-mediated anti-viral responses in the acute phase of influenza infections in other animal models, and illustrated the role of interferons in the first line of defense against virus infections [31]. In addition, there are several functionally less well characterized interferon-induced genes, such as INDO, CXCL9, CXCL10, GBP1, GBP2, GBP4, IFITM1 and CD274. CD274 is up-regulated on lymphocytes upon IFN-gamma activation and plays a role in T cell co-stimulation and apoptosis during viral infections [32]. GBPs belong to the same families of IFN inducible GTPases as Mx1, and human GBP1 and GBP2 have been implicated in the resistance to VSV and ECMV infection [33]. The role of porcine GBPs in the influenza virus replication is in process of our research, but the experiment results achieved so far could imply that GBP1 has a significant anti-viral effect for influenza (unpublished data). The interferon-inducible transmembrane proteins (IFITMs) have recently been discovered to restrict an early replication of influenza A virus and flaviviruses, including dengue virus and West Nile virus [34]. The up-regulation of IFN-inducible genes after virus infection suggests that the expressions of these genes may play important roles in host antiviral activities. To further confirm the overrepresentation of antiviral genes, qRT-PCR of 15 representative genes was performed including TLRs and ISGs and the similar tendency as microarray results was observed (Figure 4). It was interesting to note that although the majority of up-regulated genes involved in the anti-viral immune response, three inhibitory genes were also up-regulated, including ISG15, USP18 and ISG56, which had been reported as the negative regulators of the RLRs signaling or IFN signaling [3537]. This finding indicated that the production of these negative-regulated genes was an equally important factor of the host response which may keep extremely pulmonary response in check and help restore the host to its normal state when the infection is no longer present.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-398/MediaObjects/12864_2011_Article_3554_Fig4_HTML.jpg
Figure 4

The validation of Toll-like receptors and IFN-stimulated genes by qRT-PCR analyses. (A) The genes in this bioset [differentially regulated by 2-fold or more (P < 0.05)] were clustered according to a hierarchical algorithm; red bars represent up-regulated levels of the 15 representative genes after H1N1 infection, including TLRs and interferon-induced genes (ISGs) which were involved in anti-viral signal molecular or effectors. (B) The genes of TLRs and interferon-induced genes (ISGs) were up-regulated at various degrees in H1N1 SwIV infected pigs compared with the normal controls.

IFNs also stimulate the production of the MHC class I and II proteins which play important roles in the immune response to infections. In humans, HLA-B (major histocompatibility complex (MHC); class I; B) and HLA-DRB1 (MHC; class II; DR beta 1) are involved in antigen presentation and the connection of the innate immune system with the adaptive immune system. In the present study, interestingly, both of the MHC genes, SLA-B and SLA-DQA were up-regulated, at 3.2-fold and 5.2-fold respectively. Previous studies have demonstrated that viruses have evolved mechanisms to inhibit MHC class I expression by interfering with the function of the MHC class I assembly machinery in the endoplasmic reticulum and by exploiting endoplasmic-reticulum-associated degradation pathways [38]. Different influenza strains demonstrate various abilities in modulating the mRNA expression of MHC class. On the one hand, H3N2 influenza virus could cause the up-regulation of MHC class I mRNA expression levels [39]. On the other hand, the expression of MHC class I did not increase due to the infection of macrophages with a low pathogenic H7N2 AIV [40]. Thus, it can be suggested that H1N1 SwIV may be unable to inhibit MHC genes expression, which should be an important host factor for easily controlling virus.

Besides the above anti-viral agents, another important defense component of an animal innate immune response is constitutive or inducible production of antimicrobial host defense peptides (HDPs). Besides MAPK and JAK/STAT signaling pathways, interaction between virus and TLRs can trigger the NF-κB transcription factor, thus activating down-stream signaling pathway responsible for producing HDPs [41]. In this study, we detected several high-expression antimicrobial peptides on PID 3, such as PMAP23, PR39, CAMP and NPG4 which were all up-regulated with 21.7-fold, 13.5-fold, 8.3-fold and 8.2-fold respectively. A correlation between the acute SwIV infection and the high level of HDP production in pig lungs was demonstrated by qRT-PCR. To evaluate antiviral activity of the HDPs, PMAP23 and NPG4 were chosen to further characterize the function in vitro. The primary results demonstrated that in most cases, either peptides pre-incubation with virus prior to addition to cells, or simultaneous addition to cells in cell culture medium, yielded similar results with regard to inactivation of viral infectivity in a dose-dependent manner (unpublished data). These findings suggest that HDPs contribute to an early host defense against SIV infection which may be the potential antiviral effectors of pigs.

Inflammatory responses

When successfully overcoming the first barrier of host defense, influenza virus will infect the lung epithelial cells and macrophages then activates the production of pro-inflammatory cytokines and chemokines through distinct signaling pathways [42, 43]. Using DNA microarrays, the gene expression patterns that correlate with inflammatory response of virus infection can be identified. Through such approach, it has been reported that different animals (such as primates, ferrets and mice) with influenza infection have been induced extraordinarily high expression of cytokines and chemokines (e.g. CXCL10, CCL2 and CXCL9) [4446]. In the current report, we found that a large amount of genes involved in inflammatory response were up-regulated at various degrees after H1N1 SwIV infection, such as SERPINA3, CXCL9, CXCL10, CD163, CD14, CCR5, CCL26, C4 and ALOX15. Furthermore, we also found that thirteen genes involved in Toll-like receptors pathway and another thirteen genes in MAPK signaling pathway were regulated, which reflected the up-stream signaling cascades that could lead to secretion of inflammatory cytokines and chemokines. The role of pro-inflammatory cytokines and chemokines in the pathogenesis of H1N1 virus infection in mammal remains controversial. For instance, in response to pro-inflammatory cytokines, immune cells migrate toward the areas of infection where they exert host defense functions by phagocytosing cell debris and invading microorganisms, and eventually controlled the infection. However, the overabundant productions of inflammatory responses are responsible for the signs and pathogenesis of influenza infection [47, 48]. For example, CXCL10 is thought to play a role in the temporal development of innate and adaptive immunity in concert with type I and II IFNs [49]. However, previous studies in vivo have also shown that CXCL10 potentially contributes to lung pathology of H1N1 virus infection [50]. In addition, the receptors of chemokines play an important role in site-directed migration and activation of leukocytes. In our studies, CCR5, CCR10 and CXCR6 were slightly up-regulated at the early infection. Previous studies have proved that avian H1N1 influenza virus could enhance expression of CCR5 in infected adult MDMs [51]. Using knock-down mice model, CCR5 was demonstrated to play a significant role in reducing the mortality rates of mice after influenza virus infection, which indicated that CCR5 significantly affected the course of immune mechanisms as well as the clinical outcome, although in a profound way [52]. In addition, it has been deeply studied that cell adhesion molecules, which can mediate virus-elicited alveolar monocyte accumulation in SwIV-infected pigs, play essential roles in many immune and inflammatory responses [53]. In the current study, through KEGG pathway analysis, we found that cell adhesion molecules (CAMs) pathway was the significant one during the acute virus infection, which contained the families of immunoglobulin superfamily (IGSF21), selectins (SELL, SELE), and others like F11R, CD274, CD2, CR2 and their ligands. Briefly, the above results suggest that the production of inflammatory cytokines and cell adhesion molecules in pigs could not only contribute to the control of virus replication but also elicit significant tissue damage of lung in the context of acute H1N1 virus infection.

In order to keep homeostasis of development and metabolism, the host must express anti-inflammatory factors to prevent excessive inflammatory damage caused by infection. In this study, we found several genes were regulated after influenza infection which involved in anti-inflammatory functions through PPAR signaling pathway especially on PID 7. The activation of PPAR pathway can play the significant role in anti-inflammatory process because they reduce expression of several pro-inflammatory cytokines, chemokines, and cell adhesion molecules [54]. Except for genes of PPAR signaling, we also detected up-regulation of other individual anti-inflammatory factors, such as IL1-RA, IL2RA, HSP40 and HSP70. Interestingly, as the anti-inflammatory mediator, TGF-β2 was found to be down-regulated for SwIV infection throughout the whole process, which was demonstrated by qRT-PCR. Previous studies showed that human H1N1 virus could induce a persistent elevation of TGF-β2 mRNA at human pulmonary epithelial cells [55], while highly pathogenic H5N1 virus could cause a down-regulation of TGF-β secretion in mice model which resulted in more severe and widespread lesions [56]. Whether the down-regulation of TGF-β was caused by swine influenza or responsible for acute lung immunopathology of early infection of swine influenza remains to be further studied. From these analyses, we could propose that the infection course is the balance of pro-inflammatory versus anti-inflammatory factors, and these in common affects clinical outcome of influenza virus infections.

Acute-phase response

Acute phase proteins (APPs), such as LTF, SAA2 and coagulation factor XIII, were involved in physiologic reactions initiated early in the inflammatory process [57]. Acute phase proteins were also reported as host antiviral factors against virus infection. In response to influenza virus infection, numerous acute phase proteins in plasma, such as CRP, SAA1, and Orosomucoid, increased dramatically in concentration [58, 59]. In the present study, we found several genes such as SERPINA3, MBL2, F2, CD16 3, SAA2 and lactotransferrin were all up-regulated with 3.12-fold, 2.57-fold, 2.14-fold, 2.63-fold, 8.43-fold and 12.4-fold respectively on PID 3. These up-regulated genes are representative of acute response of host and may help host eliminate the invading viruses. However, it is interesting to note that the genes including Alpha-2-macroglobulin (A2M), Transferrin and Fibronectin were down-regulated on PID 3. A2M has been identified as the inhibitor of influenza A virus in pig serum with virus-neutralizing activity [60]. Recently, using proteomics method, researchers identified A2M in the salivary of people infected with swine origin influenza A virus (S-OIV), and explored A2M as a novel receptor-targeted inhibitor against S-OIV [61]. The cause and effect of down-regulated expression of these genes associated with acute-phase response need further study.

The expression of the immune cell markers

Cellular immune responses are critical for the complete clearance of the influenza virus [62]. Pigs infected with H3N2 and H1N1 viruses have an increased frequency of neutrophils, NK cells, and CD4 and CD8 T cells in the BAL fluid [63]. As pulmonary inflammation after influenza virus infection often involves immune cells infiltration, it raises the question whether the host response occurs primarily in sessile lung cells or could be attributed to infiltrating immune cells. In the current study, several genes as immune cell markers were also detected moderately up-regulated, containing some markers associated with monocytes and macrophages (e.g. CD14, CD163) or T lymphocytes (e.g. CD2, CD3 ε and CD8A) on PID 3 [64]. In some cases these CD molecules do show a parallel expression pattern, as expected if the gene expression responses are caused by cellular influx. Recently, some researchers have found that higher frequencies of cytotoxic T lymphocytes, dendritic cells, activated T cells, and CD4+ and CD8+ T cells could be detected in SwIV-infected pig lungs by flow cytometric analysis [13]. In addition, leukocyte/endothelial cell adhesion molecules are essential mediators of both immune and inflammatory responses, especially implicated in leukocyte recruitment, trafficking and tissue damage [52]. In the current study, some cell adhesion molecules were detected over-expression during the acute virus infection, such as the families of immunoglobulin superfamily (IGSF21), selectins (SELL, SELE), and other molecules like CD274, CR2 and their ligands, which could be considered as the indirect evidences for leukocyte recruitments. Therefore, these suggested that the host immune and inflammatory responses in our study could be partly attributed to immune-related cells influx after virus infection. Other functional classes of DE genes involved in host response might be essentially dependent on gene expression changes in sessile pulmonary cells. Further study is suggested to be focused on the impact of immune cells influx to transcriptional changes of lungs after SwIV infection.

Conclusions

In this work, we compared host clinical signs and pathology changes between infected pigs with H1N1 SwIV and the controls at the different stages, and subsequently determined 268 DE genes on PID 3 and 214 DE genes on PID 7 respectively, through DNA microarray analysis under the specific criteria. Our data explained that a series of genes involved in host defense responses were activated after the H1N1 SwIV invasion, which might be contributors to eliminating virus and severe immunopathology associated with virus infections. The comparison of the overall pattern of antiviral signaling and pro-inflammatory pathways activated by SwIV, it could help to screen the potential host agents for reducing the prevalence of SwIV and further understand the pathogenesis of swine influenza infection in pigs.

Notes

Abbreviations

SwIV: 

swine influenza virus

PID: 

day post infection

DE: 

differential expression

FC: 

fold change

GO: 

Gene Ontology

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

FDR: 

false discovery rate

qRT-PCR: 

quantitative real-time PCR

TLR: 

Toll-like receptors

HDPs: 

host defense peptides.

Declarations

Acknowledgements

The authors would like to thank Cao Yi for his contribution to animal experiments and Professor Liu Yanxiu for critically reading the manuscript. The study was supported by China National Basic Research Program (China ''973'' Program 2011CB505004 & 2010CB534002), National Natural Science Foundation of China (no. 31072154 & 30800832), and China National Science and Technology Major Projects (no. 2008ZX10004-013 and 2009ZX10004-109), the National Transgenic Major Program (2009ZX08009-141B).

Authors’ Affiliations

(1)
Unit of Animal Infectious Diseases, State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University
(2)
College of Veterinary Medicine, Huazhong Agricultural University, P.R. China

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