- Research article
- Open Access
Distinct functional enrichment of transcriptional signatures in pigs with high and low IFN-gamma responses after vaccination with a porcine reproductive and respiratory syndrome virus (PRRSV)
© The Author(s) 2016
- Received: 17 March 2016
- Accepted: 5 October 2016
- Published: 20 October 2016
Little is known about the host factor in the response to PRRSV vaccination. For this purpose, piglets were immunized with a commercial PRRSV-live vaccine and classified as high responders (HR) or low responders (LR) as regards to the frequencies of virus-specific IFN-γ-secreting cells. Six weeks post vaccination, PBMCs isolated from three individuals with the most extreme responses in each HR and LR groups and 3 unvaccinated controls, were either stimulated with phytohaemagglutinin, challenged with the vaccine or mock treated for 24 h, prior conducting transcriptional studies, gene ontology and pathway analyses. The LR group had very low neutralizing antibody levels and showed a higher number of down-regulated transcripts compared with the HR group (FDR < 0.2, P < 0.001). Down-regulated genes encoded chemoattractants, proinflammatory cytokines and the interferon-inducible GBP family, and showed enrichment in wounding (FDR < 3.6E-13), inflammation (FDR < 8E-12), defence (FDR < 8.7E-09) and immunity (FDR < 7.6E-08), suggesting immune response impairment. In the HR group, down-regulated genes were involved in protein transport (FDR < 4.77E-03), locomotory behavior (FDR < 5.47E-3), regulation of protein localization (FDR < 1.02E-02), and regulation of TNF superfamily member 15 and miR181. In contrast, the HR group presented up-regulated transcripts associated with wounding (FDR < 4.95). Moreover, IFN-γ was predicted to be an inhibited upstream regulator since IFN-γ pathways were associated with higher number of down-regulated genes in the LR (n = 40) than the HR (n = 10). Divergent responses to PRRSV-vaccination may be the result of the genetic background of the host.
- False Discovery Rate
- High Responder
- Guanylate Binding Protein
- Biolayout Express3D
- High Responder Group
Porcine reproductive and respiratory syndrome (PRRS) is arguably the most endemic infectious disease challenge for the pig industry and causes very significant economic losses worldwide [1, 2]. The causative agent is an enveloped, single-stranded 15-kb positive-sense RNA virus belonging to the Arteriviridae family in the order Nidovirales  known as PRRS virus (PRRSV). The extensive genetic diversity characteristic of PRRSV encodes considerable antigenic diversity which presents a highly variable challenge to the host’s immune system [4–12]. Moreover PRRSV is known to modify the host’s innate immune response, particularly by inhibiting type-I interferons (IFNs), affecting cytokine production or by down regulating toll-like receptor (TLRs) expression [13–19].
The most widely used strategy for reducing the incidence and limiting the impact of PRRSV-infection damages is vaccination. Several commercial vaccines (including live attenuated and inactivated) are available on the market but their efficacies are considered to be at best partial as they cannot provide full protection against the wide diversity of PRRSV strains circulating in the field [20, 21]. PRRSV vaccines are routinely applied to breeding herds to prevent reproductive problems but the beneficial effect of vaccination to piglets is still controversial. After immunization of a group of pigs with a given vaccine, a variety of immune response are often generated among pigs and only some of them are being protected against infection. Thus, the genetics of the host (pigs) and of the pathogen (PRRSV) appear to play an important role on the efficacy of vaccination in the control of PRRS.
The interaction of PRRSV with the immune system is believed to be of critical importance for defining immunological and clinical outcomes of the infection and, along with other factors, is associated with an inefficient development of the adaptive immunity. The prototypical adaptive response to PRRSV is characterized by a weak and delayed production of neutralizing antibodies and by relatively low levels of IFN-γ producing T-cells [22, 23]. Moreover, there is increasing evidence that the genetic variation of both host and virus also have impacts on the response to infection [24–29].
Therefore, in order to understand immune responses to PRRSV infection and/or vaccination it is necessary to examine both sources of variation: host (pig) and pathogen (PRRSV). Analysis of the host response using transcriptional profiling of pigs with different immune status against PRRSV may help to understand why vaccines offer only partial protection. The objective of this study was to identify transcriptional signals and molecular pathways associated with responses to a PRRSV vaccine . Systems biology approaches have been used successfully to identify early gene signatures that predict immune responses in humans vaccinated with the live attenuated vaccine YF-17D against yellow fever virus and inactivated seasonal influenza vaccine against influenza virus with accuracy [31, 32]. In order to explore this approach for PRRSV vaccines pigs were vaccinated with a modified live (ML) PRRSV vaccine and later classified into two groups, namely “high responders” (HR) and “low responders” (LR) on the basis of the frequencies of virus-specific interferon-γ secreting cells (IFN-γ SC). Subsequently, the humoral response against the virus and the transcriptional profiles of peripheral blood mononuclear cells (PBMC) of both groups were examined in order to identify genes, molecular functions and biological pathways associated with the vaccine response.
Animals and study design
Peripheral blood mononuclear cell isolation
Heparinized blood samples were used for separation of PBMC by density-gradient centrifugation with Histopaque 1.077 (Sigma). PBMC were subsequently cultured using RPMI medium supplemented with 10% foetal calf serum (FCS) (Invitrogen, Madrid, Spain), 1 mM non-essential amino acids (Invitrogen), 1 mM sodium pyruvate (Invitrogen), 5 Mm 2-mercaptoethanol (Sigma), 50 000 IU penicillin (Invitrogen), 50 mg streptomycin (Invitrogen) and 50 mg gentamicin (Sigma) (complete RPMI). Trypan blue was used to assess cell viability.
ELISPOT assays were performed as described previously [12, 33] using commercial mAbs (P2G10 and biotin P2C11; BD Biosciences Pharmingen). Peripheral blood mononuclear cells (PBMC) were stimulated for 24 h (5 × 105 PBMC/well, in triplicate) with the ML vaccine strain at a multiplicity of infection (m.o.i.) of 0.1 as a recall antigen, phytohaemagglutinin (PHA, 10 μg/mL) or culture medium. To calculate the frequencies of virus specific IFN-γ-SC, average counts of spots in unstimulated wells were subtracted from average counts obtained in antigen stimulated wells. Results were expressed as the number of PRRSV-specific IFN-γ-SC per 5 × 105 PBMC.
A commercially available ELISA (Idexx PRRS X3 Ab Test) was used for measuring non neutralizing anti-PRRSV antibodies in blood samples taken at the different time-points of the study. Results were reported as a ratio of optical densities between the results of a given sample and the positive control (S/P ratio) included in the kit (cut-off: S/P ≥ 0.4). All samples were tested the same day and in the same batch of plates.
In parallel, homologous (against the vaccine virus) neutralizing antibodies (NA) titers were determined in a viral neutralization test (VNT). The VNT assays were done in MARC-145 cells using log2 serial dilutions (from 2−1 to 2−8) as described before running samples in duplicate [34, 35].
In vitro treatment of peripheral blood mononuclear cells
At 6 weeks PV (Figure 1), PBMC isolated from the selected pigs of each group (HR and LR) and from unvaccinated pigs were seeded at 5 × 105 cells/well in 96 well-plates and were cultured in triplicates with complete RPMI alone (negative controls), complete RPMI with phytohaemagglutinin (PHA, 10 μg/mL, positive controls) or the PRRSV vaccine strain (m.o.i. 0.1, specific stimulus) for 24 h. After that period, cells were harvested, centrifuged and cell pellets were resuspended in 1 mL of Trizol (Life Technologies) and frozen at −80 °C until processing for RNA extraction, microarray and downstream analysis.
Total RNA from PBMC of selected group pigs at 6 weeks PV was extracted using Trizol (Invitrogen, Paisley UK) according to the manufacturer’s instructions and was later purified using the Qiagen RNeasy minikit (Qiagen, Crawley, UK). RNA was eluted from the spin column in 30 µL of RNase-free water and stored as aliquots at −80 °C. The quantity and quality of RNA were assessed using a Nanodrop spectrophotometer (NanoDrop Technologies Inc, Wilmington, DE, USA) and Agilent 2100 bioanalyser (Agilent Technologies, Palo Alto, CA, USA) respectively.
Microarray platform and data analysis
The extracted RNA was then analysed using the Affymetrix Snowball GeneChip® . This GeneChip comprises 23 937 probe sets that interrogate approximately 23 256 transcripts from 20 201 Sus scrofa genes. Sense-strand cDNA was generated from total RNA (500 ng) subjected to two rounds of amplification (Ambion® WT Expression Kit). The resulting cDNA was used for biotin labelling and fragmentation according to the Affymetrix GeneChip® WT Terminal Labelling and Hybridization protocol (Affymetrix UK, High Wycombe). Biotin-labelled fragments of cDNA (5.5 μg) were hybridized to Affymetrix Snowball arrays using the Affymetrix HybWashStain kit and following the manufacturer’s recommendations. After hybridization, the arrays were washed and stained using the Affymetrix Fluidics Station 450 and then scanned in an Affymetrix 7G scanner. Image generation and the resulting CEL files for analysis were produced in AGCC—Affymetrix GeneChip Command Console Software. Initial QCs were performed in Expression Console. All microarray data used in the analyses herein are available from the Array Express repository . The Affymetrix.CEL files were imported into the Partek Genomics Suite software package version 6.13.0213 (Partek, St. Louis, USA) for data analysis. Transcriptional responses were normalised to those from unvaccinated pigs prior to running an ANOVA analysis of the data (Additional file 1). Up-regulated and down-regulated differentially expressed transcripts in HR and LR were selected for further consideration if the false discovery rate (FDR) was ≤0.2.
For network analysis, the normalised array data were uploaded to the software Biolayout Express3D as described previously [36, 38]. Expression variations across treated groups were generated using the gplots package in R.
Quantitative real time RT-PCR validation (qRT-PCR)
List of the genes selected for the quantitative real time RT-PCR validation
Sequence of the primers
Guanylate binding protein 2, interferon-inducible
Interleukin 1, alpha
Chemokine (C–C motif) ligand 4
Serum amyloid A1
Insulin-like growth factor 1
Chemokine (C–C motif) ligand 2
Interleukin 1, beta
Latent transforming growth factor beta binding protein 1
Gene ontology and pathway analysis
Gene ontology and pathway analysis were carried out using DAVID bioinformatics resources  and Ingenuity pathway analysis , respectively. In particular for the identification of upstream regulators using IPA, expression datasets for the HR and the LR pigs were analysed independently with Fisher’s exact test as described by the software. The activation z-score was used to infer the state of activation of upstream regulators based on a comparison with a model that assigns random regulations. The P value overlap, which indicates possible upstream regulators, represents the significance of the overlap between the dataset genes identified and the known targets of transcriptional regulators. Differences were considered significant with P-value <0.05 and activation Z-score ≥2.
Comparison of frequencies of IFN-SC, S/P ratios or titres of NA between groups was done using StatsDirect3 (v3.0.97) and the Kruskal–Wallis test. The coefficient of correlation (R2) between microarray and qRT-PCR techniques was produced by plotting the relative level of gene expression as assessed by microarray against the transcript levels generated by qRT-PCR.
Characterization of the humoral response in HR and LR pigs
Host transcript regulation during PRRSV vaccine challenge
Summary of the number of transcript regulated in PBMCs at 6 weeks post-vaccination
High responder (HR)
Low responder (LR)
High responder (HR)
Unvaccinated pigs + PHA
Low responder (LR)
Unvaccinated pigs + PHA
High responder (HR)
Unvaccinated pigs + vaccine
Low responder (LR)
Unvaccinated pigs + vaccine
Validation of differentially regulated transcripts using quantitative real time PCR (qRT-PCR)
Nine genes for which changes in transcript levels had been observed were selected for validation by qRT-PCR analysis. The selection criteria were: (i), differential regulation in the both groups (fold change ≥2.0, FDR ≤0.2) and ii), involvement in immune functions such as inflammation, antiviral responses and cell growth and recruitment molecules. Thus we selected the following genes: GBP2, IL1A, IL8, CCL4, SAA1, IGF1, CCL2, IL1B and LTBP1. The results showed positive correlation coefficients between the microarray and qRT-PCR techniques in the HR (R2 = 0.76) and LR (R2 = 0.86) groups (Additional file 2).
Gene ontology analyses
In the HR vaccinated group, although the number of genes that were down-regulated per category was lower than the LR, the number of biological processes involved was twice as great as in the LR vaccinated pigs (Figure 5). The biological functional fold enrichments were associated with the regulation of protein transport, locomotory behaviour and regulation of protein localization which were enriched 42.4 FFE (FDR < 4.77E-03), 21.2 FFE (FDR < 5.47E-3) and 35 FFE (FDR < 1.02E-02), respectively. It is noteworthy that down-regulated genes in this group harboured the same trend than in the LR vaccinated group. For example SAA1, CCL3L1 and CCL4 were down-regulated group (Additional file 3). Also transcripts encoding for the tumor necrosis factor (TNF) superfamily member 15 (TNFSF15) (−2.2 fold, P < 7.9E-05) and miR181 (−2.4 fold, P < 2.1E-04) appeared specifically down-regulated in a statistical significant manner. In contrast, up-regulated transcripts in the HR vaccinated group were barely dominated by a biological function that was the response to wounding (50.2 FFE, FDR < 4.95) involving purinergic P2Y G-protein coupled receptor (P2RY12), vanin1 (VNN1) and IGF1 (Additional file 3).
Upstream regulators using Ingenuity pathway analysis (IPA)
Analysis of upstream regulators using IPA
P value of overlap
P value of overlap
E. coli B5-LPS
It is increasingly recognized that the control of PRRS will require a global approach that will consider relevant factors of the pathogen, the host and the environment. Although valuable tools, current PRRSV vaccines are not fully efficacious and some vaccinated animals may still become infected . It is thought that this partial lack of efficacy may be attributable to the genetic and antigenic diversity of the virus. However, several studies have highlighted that there is high inter-individual variability in the development of the immune response and protection after vaccination with a PRRSV vaccine [12, 44, 45] which may account for some lack of efficacy. Therefore one of the challenges of PRRS control is the development of tools to predict the efficacy of vaccines with greater accuracy.
To date, measurement of humoral immunity (neutralizing antibodies) and cell-mediated immunity (IFN-γ responses levels) have been used for monitoring PRRSV infection [20, 21, 46]. We hypothesized that the generation and the maintenance of protective immunity to PRRSV vaccine are a complex process that entails the regulation of transcriptional pathways leading to the production of neutralizing antibody and the induction of cell-mediated immunity. Thus, in the present study we have focused primarily in the PRRSV-specific IFN-γ responses after vaccination as a mean of discriminating between animals. The three animals with the most extreme results in the ELISPOT were selected. Our results show that in a group of cross-bred pigs of the same origin, responses to PRRSV vaccination may range from low IFN-γ responses and low or nil NA, as in the Low Responders (LR) group, to higher IFN-γ responses and moderate titers of NA, in the High Responders (HR) group. These differences indicate that inter-individual variability in markers of PRRSV vaccine-induced immunity, including neutralizing antibody levels and INF-γ responses, may be regulated, at least in part by host genetic factors . This observation is indeed reminiscent with current trends suggesting that host genetic variation in the outcomes of PRRSV infections occurs in growing pigs as well as during reproduction . Specifically, a QTL on chromosome SSC4 was found to be associated with viraemia levels and the growth rate in experimentally infected pigs . Further, the SNPs defining this QTL have been shown to be associated with resistance and performance in several independent populations [48–50]. Collectively, these studies suggest that immune response to PRRSV is likely the results of multigenic influences and possibly not of a single dominant gene/allele and advocate the need for further genetic investigation of the host response to PRRSV vaccine.
To further delineate which genes and pathways are associated with HR and LR vaccinated pigs we have conducted a genome–wide transcriptional study of PBMCs 6 weeks post vaccination. The selection of the time-points was based on the likelihood of having the minimal influence with residual vaccine virus in blood  and to choose a moment in the development of the immune response when cell-mediated immunity was more or less in a steady state [11, 22].
The number of examined animals per group can represent a critical factor to reach statistical power for comparisons for such studies. Indeed, the high inter-individual variability in gene expression of control animals noticed using Biolayout Express3D software could explain why statistical significance was not achieved among vaccinated and unvaccinated groups after vaccine treatment (Table 2; Figure 4). However, the results of gene expression after vaccine stimulation showed an enrichment of immune-related genes in the LR and HR vaccinated groups as compared to the unvaccinated controls. Most of these genes, CXCL9, CXCL10 and CXCL11 were related with monocyte and T-lymphocyte activation and with interferon-modulation antiviral responses (CYP3A46, CYP3A29 and OAS1) [41, 42]. For this reason we considered that these changes in gene expression were potentially biologically relevant despite not being selected using the Anova test with Partek software. On the other hand and despite the limited number of pigs tested, statistical significance was reached for the other group comparisons (mock and PHA stimulated) suggesting that future transcriptional study of vaccine efficacy could benefit from larger number of animals per experimental group.
Gene regulation in the LR vaccinated group suggested that in those animals there was a functional impairment in at least some components of the inflammatory response such as CCL2 or IL1A but also that transcript levels or transcription of genes encoding some chemokines that act as strong chemotactic compounds for T cells, NK cell, dendritic cells and monocytes/macrophages are affected (CCL17, CCL22, CCL23). Interestingly down-regulated transcripts encoding guanylate-binding proteins GBP2, 4 and 6 were identified. Members of the GBP gene family are associated with antiviral activity in humans and as discussed previously by Boddicker et al.  they have been highlighted as potential candidate genes for the QTL identified on SSC4 which explains 15% of the variance in genomic estimated breeding values for viral load and 11% for weight gain 42 days post infection . Recently, an intronic SNP in GBP5 has been identify as a strong candidate causal mutation for the SSC4 QTL that controls variation in host response to PRRSV . As regards the HR pigs, either the lower proportion of inhibited up stream regulators of the IFN-γ cascade and the up-regulation of transcripts encoding for molecules involved in anti-apoptosis mechanisms (P2RY12, VNN1 and IGF1), suggests increased chemotactic activity, viral-induced-apoptosis resistance and a better induction of cellular immune response.
Curiously, HR vaccinated animals presented a down-regulation of a member of the tumor necrosis factor superfamily, TNFSF15, and miR181 transcripts. TNFSF15 is produced by inflammatory cytokine-stimulated endothelial cells and by TLR ligands-stimulated antigen presenting cells, and it is involved in apoptosis, cell proliferation and polarization to Th1 and Th17 responses . Recently, increased levels in sera and genetic polymorphisms of TNFSF15 have been observed in inflammatory diseases that are caused by altered immunological reactions such as Crohn’s disease or ulcerative colitis . As regards to miR181, over-expression of this molecule has been recently identified in minimally PRRSV-permissive cells or tissues, and this molecule is able to inhibit PRRSV replication by both mechanisms: binding to a conserved region in the downstream of open reading frame 4  and downregulating the PRRSV receptor CD163 in blood monocytes and porcine alveolar macrophages . The fact that TNFSF15 and miR181 were found down-regulated in HR vaccinated pigs is noteworthy and warrant further studies. It is possible that these factors may be associated with either protective mechanism induced against harmful effects of an exacerbated inflammatory response and/or a higher cell permissibility to the vaccine virus and thus, better priming and immune stimulation.
It is likely that at 6 weeks PV the transcriptional response we have measured may have been mostly associated with the resolution phase rather than the primary response. However, the finding that the transcriptional signature in LR pigs was associated with a down-regulation of the IFN-γ response was interesting for several reasons: this specific transcriptional signature was consistent with the low IFN-γ level found in the LR or the HR pigs maybe resolving some of the vaccine response. On the other hand, the results of the experiment are compatible with either a faster or a stronger response in the HR group. In each case, implications would be a little different. If the differences were related just to the intensity, the outcome of vaccination in LR could maybe be ameliorated by use of adjuvants; if the differences relied in the velocity of the immune response this could be more much difficult to solve. However, in practical terms, in situations where the risk of entering in contact with the virus are high, the point is to achieve the fastest and strongest possible response. As shown here, at 42 days PV a group of animals was far from having that type of immunity and this seemed to be related to some elements in the genetics of the pig.
In summary, this works showed that variable responses to PRRSV vaccination may be the result of the genetic characteristics of the host. If this was to be proven in future challenge studies, genetic selection of pigs could be used to increase resistance to the infection through vaccination. Taken together this work advocate the need for a greater understanding of the host genomics/genetics of PRRSV vaccine response.
The authors declare that they have no competing interests.
ID, FS, EC, ADW, YL, LD contributed to the laboratory analysis. BG analyzed the data using BioLayout Express3D analysis. EM contributed to the study design, discussion of results and manuscript preparation. LD, ALA and TAA contributed to the study design, data analysis, discussion and manuscript preparation. All authors read and approved the final manuscript.
This work was supported by funds from the European Project PoRRSCON-EU (FP7-KBBE-2009-3-245141), the COST-Action (FA-0902) and the Spanish Ministry Science and Innovation (AGL2008-05708-C02-02). LD has been supported by travel grants from the “Agència de Gestió d’Ajuts Universitaris i de Recerca” of the “Generalitat de Catalunya” (2011-BE-100125) and the “Ministerio de Educación, Cultura y Deporte” (Programa Estatal del Talento y su Empleabilidad en I+D+I, Subprograma Estatal de Movilidad, del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016: CAS14/00139). ADW and TAA were supported by BBSRC Institute Strategic Programme grants. We are indebted for the help provided by ARK-Genomics (now part of Edinburgh Genomics) for the microarray hybridizations and support for the data analysis using Partek Software.
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