- Research article
- Open Access
Transcriptome analysis of porcine PBMCs after in vitro stimulation by LPS or PMA/ionomycin using an expression array targeting the pig immune response
© Gao et al; licensee BioMed Central Ltd. 2010
- Received: 29 July 2009
- Accepted: 11 May 2010
- Published: 11 May 2010
Designing sustainable animal production systems that better balance productivity and resistance to disease is a major concern. In order to address questions related to immunity and resistance to disease in pig, it is necessary to increase knowledge on its immune system and to produce efficient tools dedicated to this species.
A long-oligonucleotide-based chip referred to as SLA-RI/NRSP8-13K was produced by combining a generic set with a newly designed SLA-RI set that targets all annotated loci of the pig major histocompatibility complex (MHC) region (SLA complex) in both orientations as well as immunity genes outside the SLA complex.
The chip was used to study the immune response of pigs following stimulation of porcine peripheral blood mononuclear cells (PBMCs) with lipopolysaccharide (LPS) or a mixture of phorbol myristate acetate (PMA) and ionomycin for 24 hours. Transcriptome analysis revealed that ten times more genes were differentially expressed after PMA/ionomycin stimulation than after LPS stimulation. LPS stimulation induced a general inflammation response with over-expression of SAA1, pro-inflammatory chemokines IL8, CCL2, CXCL5, CXCL3, CXCL2 and CCL8 as well as genes related to oxidative processes (SOD2) and calcium pathways (S100A9 and S100A12). PMA/ionomycin stimulation induced a stronger up-regulation of T cell activation than of B cell activation with dominance toward a Th1 response, including IL2, CD69 and TNFRSF9 (tumor necrosis factor receptor superfamily, member 9) genes. In addition, a very intense repression of THBS1 (thrombospondin 1) was observed. Repression of MHC class I genes was observed after PMA/ionomycin stimulation despite an up-regulation of the gene cascade involved in peptide processing. Repression of MHC class II genes was observed after both stimulations. Our results provide preliminary data suggesting that antisense transcripts mapping to the SLA complex may have a role during immune response.
The SLA-RI/NRSP8-13K chip was found to accurately decipher two distinct immune response activations of PBMCs indicating that it constitutes a valuable tool to further study immunity and resistance to disease in pig. The transcriptome analysis revealed specific and common features of the immune responses depending on the stimulation agent that increase knowledge on pig immunity.
- Major Histocompatibility Complex
- Major Histocompatibility Complex Class
- Phorbol Myristate Acetate
- Peptide Processing
- Ingenuity Pathway Analysis System
Understanding resistance to disease is a major concern for all living organisms. Thus, it is necessary to design strategies to address related questions according to scientific and economic contexts. In farm animals like pig, zootechnical performances including growth, meat quality, feed intake or prolificacy have increased considerably during the last 25 years as a result of both the application of rational genetic selection schemes , and the improvement of feed formulations and sanitary conditions in breeding units. However at the same time, diseases have emerged that can cause substantial economic loss. Intensive research is carried out to better understand the etiology of emerging as well as endemic diseases in pig and raises questions on host pathogen interactions, pathogen latency, pathogen shedding, vaccine efficiency and host immune response. Thus, producing efficient methods and tools for these studies and improving basic knowledge on immune response in pig are major issues.
With the explosion of information on genome sequences and the emergence of functional genomics, it is now possible to study the expression of many genes in a single experiment. The development of DNA chips for genome-wide expression studies  and the next generation sequencing (NGS) technology for much deeper transcriptome analyses  are complementary approaches to conduct functional genomics research . DNA chip-based transcriptome analyses are efficient to study host-pathogen interactions using either pathogen transcriptomes  or host transcriptomes [6–9] or both pathogen and host modifications of the transcriptome during infection . Thus, DNA chips are still highly valuable to analyze large numbers of samples and in the case of domestic animals, it is essential to develop well-annotated DNA chips and sequence-based transcriptome using the NGS technology.
One major concern in designing a DNA chip-based experiment is to use the most appropriate and relevant array. For human and laboratory animals like mouse, the genomes are almost fully annotated, thus chips representing all the annotated genes are commercially available. In pig, the genome sequence is in progress and a first assembly has been released . Today, many commercial and custom-made genome-wide microarrays exist for pig but probe annotation of these arrays is still poor because of the limited availability of full length cDNA sequences in pig . Available porcine DNA chips include a 9 K cDNA-based microarray on nylon membranes , a 1789 DNA/cDNA microarray including a subset of probes specific for the SLA locus, a subset of immune response genes outside the SLA complex, and a subset of randomly chosen probes , the ARK-Genomics Sus scrofa Immune Array 3 K v1.0 , the Sus scrofa AROS (Array-Ready Oligo Sets) V1.1 (Operon Biotechnologies Inc., USA), the GeneChip® Porcine Genome Array (Affymetrix, USA), a 25 K porcine long-oligonuclotide DNA microarray , and the Swine Protein-Annotated Oligonucleotide Microarray . The immune system represents a complex network involving many regulation points and the genome-wide generic arrays that have been developed in pig only partially cover the genome and lack many immune response genes. As an example, the Major Histocompatibility Complex (MHC), which plays a key role in innate, adaptive immune response as well as in inflammation in mammals, is only poorly represented on existing pig expression arrays.
The objectives of our study were first to produce a generic array enriched in MHC and immunity-related genes and second to study transcriptome modifications of porcine peripheral blood mononuclear cells (PBMCs) after in vitro stimulation of the immune response. We describe the SLA-RI/NRSP8-13K chip that combines the generic Qiagen-NRSP8-13K set  with a long-oligonucleotide set comprising all the genes and pseudogenes annotated for the pig MHC referred to as the SLA (Swine Leucocyte Antigen) complex as well as immune response genes outside the SLA complex. We report the use of this array to investigate the differential expression of genes in PBMCs stimulated with lipopolysaccharide (LPS) or a mixture of Phorbol Myristate Acetate (PMA) and ionomycin for 24 hours. LPS is part of the outermost layer of gram-negative bacteria and is a pathogen-associated molecular pattern (PAMP) used for in vitro studies of the innate immune response after bacterial infection. PMA, a phorbol diester, is a potent tumor promoter often used in biomedical research to activate the signal transduction enzyme protein kinase C and a potent mitogen for PBMCs. Ionomycin is a ionophore that stimulates the intracellular production of the cytokines IL-2 and IL-4 in conjunction with PMA. Both these stimulations with either LPS or PMA/ionomycin were chosen because they are widely used to stimulate immune response in vitro. Our results show that some biological pathways and gene networks are differentially expressed in PBMCs according to stimulation. They provide new data on pig immunity and validate the relevance of the SLA-RI/NRSP8-13K chip for further studies on immunity and immune response to stimuli and pathogens in pig.
Design of the porcine SLA-RI/NRSP8-13K chip
Construction of SLA-RI/NRSP8-13K chip
Number of probes
Number of genes
gene or pseudogenes
Lucidea Universal ScoreCard
SpotReport Alien Control
Position or Negative Control
Differentially expressed genes in PBMCs stimulated with LPS or PMA/ionomycin
Number of probes differentially expressed by PBMCs stimulated with LPS or PMA/ionomycin
Cluster analysis of common differentially expressed genes in PBMCs stimulated with LPS or PMA/ionomycin
Top ten genes found differentially up- or down-regulated after LPS or PMA/ionomycin stimulation
Overview and comparison of affected biological functions in PBMCs during LPS or PMA/ionomycin stimulation
LPS and PMA/ionomycin stimulation: top biological functions from the catalogs Diseases and Disorders1, Molecular and Cellular Functions2 and Physiological System Development and Function3 identified by IPA and number of focus genes
Top biological functions
Cellular Growth and Proliferation2
2.25E-18 - 7.17E-07
2.00E-27 - 9.59E-07
3.40E-46 - 9.59E-07
6.00E-23 - 9.56E-07
2.58E-45 - 1.06E-06
Hematological System Development and Function3
6.70E-28 - 8.53E-07
Cell-To-Cell Signaling and Interaction2
2.44E-24 - 4.29E-07
6.70E-28 - 8.33E-07
Immune and Lymphatic System Development and Function3
3.43E-19 - 8.03E-07
Skeletal and Muscular Disorders1
2.52E-39 - -2.26E-07
1.66E-15 - 1.08E-06
Connective Tissue Disorders1
2.52E-39 - -2.26E-07
4.02E-20 - 8.53E-07
6.06E-18 - 5.30E-07
1.60E-32 - 1.08E-06
5.41E-25 - 5.07E-08
Cellular Growth and Proliferation2
3.15E-48 - 1.18E-08
1.10E-32 - 5.07E-08
3.61E-32 - 4.90E-08
Hematological System Development and Function3
1.34E-35 - 4.73E-08
1.34E-35 - 4.73E-08
1.83E-52 - 3.92E-08
Immune and Lymphatic System Development and Function3
1.34E-35 - 2.91E-08
5.57E-36 - 4.26E-09
4.35E-26 - 4.78E-08
Skeletal and Muscular Disorders1
5.57E-36 - 3.41E-08
1.10E-32 - 2.30E-08
Connective Tissue Disorders1
5.57E-36 - 5.29E-09
1.10E-15 - 2.41E-08
1.30E-29 - 6.66E-09
LPS-related gene networks
List of KEGG biological pathways associated with the genes differentially expressed after LPS or PMA/ionomycin stimulation
Fisher p value
Cytokine-cytokine receptor interaction
Antigen processing and presentation
Toll-like receptor signaling pathway
Cell adhesion molecules
Hematopoietic cell lineage
Type I diabetes mellitus
B cell receptor signaling pathway
Adipocytokine signaling pathway
Small cell lung cancer
T cell receptor signaling pathway
Complement and coagulation cascades
Epithelial cell signaling in Helicobacter pylori infection
Acute myeloid leukemia
Cytokine-cytokine receptor interaction
Cell adhesion molecules
Jak-STAT signaling pathway
Natural killer cell mediated cytotoxicity
Toll-like receptor signaling pathway
Leukocyte transendothelial migration
Hematopoietic cell lineage
T cell receptor signaling pathway
Antigen processing and presentation
Chronic myeloid leukemia
TGF-beta signaling pathway
B cell receptor signaling pathway
Fc epsilon RI signaling pathway
Adipocytokine signaling pathway
Renal cell carcinoma
Acute myeloid leukemia
Pathogenic Escherichia coli infection - EHEC
Pathogenic Escherichia coli infection - EPEC
Type I diabetes mellitus
Citrate cycle (TCA cycle)
Glycosphingolipid biosynthesis - globoseries
Fatty acid elongation in mitochondria
Phenylalanine, tyrosine and tryptophan biosynthesis
PMA/ionomycin-related gene networks
After PMA/ionomycin stimulation, 37 KEGG pathways with a Fisher Exact P-Value < 0.05 were identified (Table 5, and Additional file 6: SLA_RI_Table_S6.doc). The most represented pathways are cytokine-cytokine receptor interaction, oxidative phosphorylation, ribosome, cell adhesion molecules (CAMs), Jak-STAT signaling pathway, natural killer cell mediated cytotoxicity and cell cycle. The Toll-like receptor signaling pathway occupies the eighth position with 40 genes. Interactions between pathways with their relative importance are presented in Figure 5B. Globally, PMA-ionomycin stimulation mostly modifies pathways associated with the immune system, signaling molecules and interactions, human diseases and metabolism like LPS stimulation but it also affects additional pathways associated to metabolism, cell growth and death and signal transduction.
Expression of probes mapping to the SLA complex
Since sense and antisense probes for all SLA annotated transcripts were present on the DNA chip (Table 1), it was possible to perform an in-depth analysis of the expression profile of all annotated transcripts mapping to the locus. For probes targeting protein coding genes, only the differential expression was studied. For antisense and non-coding transcripts, expression and differential expression between stimulation and mock-stimulation were both analyzed.
List of differentially expressed genes in MHC class I and class II presentation pathways after stimulation by LPS or PMA/ionomycin
Gene symbol (Pig)
MHC I pathway
MHC II pathway
MHC I pathway
MHC II pathway
In order to analyze anti-sense oligonucleotide and non-coding RNA probe expression, the A value (A = 1/2(log2(Cy3*Cy5))) was used. Since the average A value of probes corresponding to negative controls was 7.8, probes were considered as expressed for A values higher than 8.8 that corresponded to signal intensities twice as high as for the controls. With such a threshold, about 30% of the anti-sense oligonucleotide probes were found expressed. After LPS stimulation, 135 probes corresponding to anti-sense sequences derived from 93 genes are expressed. After PMA/ionomycin stimulation, 124 probes corresponding to anti-sense sequences from 85 genes are expressed among which 121 are expressed by PBMCs in both stimulation conditions. Anti-sense sequences of eight genes (ABCF1, C7H6orf27, LTA, NRM, SFTPG, snoRNAU52 (RF00276), SLA-1 and SLA-DOB) are specifically expressed in LPS-stimulated PBMCs. For non-coding RNA, sense probes targeting mir-219 (RF00251) and snoRNAU84 are expressed by PBMCs stimulated by LPS or PMA/ionomycin and the anti-sense probe targeting snoRNAU52 (RF00276) is specifically expressed in LPS-stimulated PBMCs. Differential analysis revealed that no non-coding RNA is differentially expressed whatever the stimulation and that antisense probes are regulated only after PMA/ionomycin stimulation. Four probes are up-regulated (anti-sense sequences of BAT3, IER3, EGFL8 and PSMB9) and nine probes are down-regulated (anti-sense sequences of OLF42-3, STK19, LSM2, AIF1, STK19, BAT1, RPP21, SKIV2L and PPT2).
Validation of differentially expressed genes at the RNA level
Primer sequences for qRT-PCR validation
Gene name (Symbol)
Amplicon size (bp)
Validation of differentially expressed genes at the protein level
Comparison of gene expression fold change between stimulated and non stimulated PBMCs at the protein (ELISA tests) and RNA levels (microarray)
LPS stimulation fold change1
PMA/ionomycin stimulation fold change1
Differential expression of MHC class I and class II molecules was validated by fluorescence-activated cell sorting (FACS). FACS analysis confirmed a significant down-regulation of MHC class I molecules at the surface of PBMCs stimulated with PMA/ionomycin for 24 hours compared to mock-stimulated PBMCs. The MHC class I mean fluorescence intensity of PBMCs after PMA/ionomycin stimulation was 52.6% of that of mock-stimulated PBMCs (p = 0.0096). As expected from microarray results, no change in MHC class I molecule expression was detected at the surface of LPS stimulated PBMCs for 24 hours. In contrast, MHC class II molecules were found down-regulated at the surface of PBMCs in both stimulation conditions compared to mock-stimulated cells. The MHC class II mean fluorescence intensity of LPS-stimulated PBMCs was 68.9% of that of mock-stimulated PBMCs (p = 0.033) and 72.1% of that of mock-stimulated PBMCs (p = 0.054) after PMA/ionomycin stimulation.
The objectives of this study were first to produce a well annotated and an easy to use DNA chip to analyze the immune response in pig and second to validate its relevance by investigating transcriptome modifications in PBMCs stimulated with LPS or PMA/ionomycin for 24 hours. The same seven biological replicates from seven distinct animals were used for transcriptome analysis, qRT-PCR and ELISA validation, and another set of seven animals was used for validation by FACS analysis. Reproducibility of the results was good.
Relevance of the SLA-RI/NRSP8-13K chip
DNA chips targeting immunity have been reported for human, mouse and a few domestic species including cow [21, 22], chicken [23, 24] and to a lesser extent pig with a unique report of a nylon membrane comprising less than 100 genes . Designing dedicated chips may be criticized because it is contradictory to the global approach that underlies a transcriptome study. Since no genome-wide expression array exists for pig and since efficient tools are required to study immunity and resistance to disease, we have constructed a generic array enriched in immunity genes. We combined a well-annotated oligonucleotide set referred to as the NRSP8-13K set that partially covers the pig genome  to a set of oligonucleotides referred to as the SLA-RI set that targets all annotated loci within the SLA complex and immunity genes outside the SLA complex. Here, we report that after LPS stimulation, among 258 differentially expressed genes (403 probes, see Table 2), 61 were common to both the generic and the SLA-RI sets and 84 were present only in the SLA-RI set. Similarly, after PMA/ionomycin stimulation, among 2689 differentially expressed genes, 353 were present in both sets and 424 were present only in the SLA-RI set. The SLA-RI set was highly informative for the analyses reported here. The SLA-RI set may be merged with any other generic set and it is anticipated that the number of overlapping probes between sets should increase as a function of the genome coverage in the next generation expression arrays. The SLA-RI/NRSP8-13K chip was shown to be suitable to identify immunity and disease-related biological pathways and functions as well as to construct relevant gene networks. Validation of differential expression was carried out for several genes at the RNA level by qRT-PCR and at the protein level by ELISA tests or FACS analysis. The results show significant correlations between mRNA and protein expression levels, confirming the accuracy of the chip annotation. DNA chips for expression studies are currently replaced by sequence-based transcriptome using the NGS technology, suggesting that the design of genome-wide DNA chips could be skipped and that sequencing could be used directly for transcriptome analysis. However, concentrating all efforts on the NGS technology might hamper the analysis of numerous animals and samples as required for eQTL studies and genetic genomics . We are quite convinced that the NGS technology and well-annotated DNA chips will remain complementary for a while in domestic species. The SLA-RI/NRSP8-13K chip reported here represents an accurately annotated chip dedicated to the pig immune system and will provide a valuable tool for diagnostics and research.
Choice of the in vitro models to study immune response activation
PMA, also known as 12-O-tetradecanoylphorbol-13-acetate (TPA), is a potent tumor promoter often used in biomedical research . Ionomycin is an ionophore produced by Streptomyces conglobatus. PMA in conjunction with ionomycin is known to activate T and B cells and has been used in numerous immune-related studies [25, 28, 29]. LPS is a major structural component of the outer membrane of gram-negative bacteria and is a well-referenced PAMP. LPS stimulation of mammalian cells occurs through a series of interactions with proteins including LPS binding protein, CD14, MD-2 and TLR4 . LPS is one of the best studied immunostimulatory components of bacteria and can induce systemic inflammation and sepsis if excessive signals occur . LPS-stimulation mimics a bacterial infection and has been extensively used to study innate immune response [31, 32]. Two recent studies in pig have reported transcriptome modifications in mesenteric lymph node or spleen after infection by Salmonella enterica serovar Choleraesuis (S. Choleraesuis)  and Haemophilus parasuis (H. parasuis) , respectively. S. Choleraesuis and H. parasuis are both gram-negative bacteria. Our results on LPS-stimulation reveal that many genes already identified after in vivo infection by S. Choleraesuis and H. parasuis are up- or down-regulated confirming that in vitro LPS activation of PBMCs is a good model to study innate immune response to infection with gram negative bacteria in pig.
Indeed, LPS and PMA/ionomycin stimulations were chosen because they are widely used as gold standard in vitro models to measure cytokines released in the medium by PBMCs in many species. A unique time point was studied and we are aware that all the results reported here correspond to this unique time point i.e. 24 hours after stimulation. It has been reported that time points earlier than 24 hours are more relevant to decipher the onset of the response to stimulus as shown in kinetics studies in cow , pig , mouse  or human . Moreover, kinetics studies have revealed that many genes return to their basal expression level by 48 hours of stimulation, suggesting that homeostasis is restored at that time [22, 25]. In this report, we were interested in studying the PBMC transcriptome at the time when cytokines released in the medium are efficiently measured. Our results provide many candidate genes to test for kinetics studies and ongoing complementary studies focus on this topic.
Significant positive correlations have been reported between transcriptomes of total PBMCs and purified monocytes stimulated with LPS, suggesting that for studies focussing on the most differentially expressed genes, separating and analysing cell subpopulations may be unnecessary . Therefore, the results reported here correspond to the most striking transcriptome modifications during immune response activation and may miss some subtle changes that occur in each cell subtype. Identifying transcriptome modifications occurring in each cell subtype is a major objective to better decipher immune response. However, transcriptomic signatures of blood or total PBMCs are of high interest in clinical research and most studies relate to total PBMCs in pig .
Specific transcriptome modifications after LPS stimulation
Almost half of the transcriptome modifications due to LPS stimulation are related to Disease and Disorder biological function (see Figure 3). Most of the up-regulated genes relate to inflammation and innate immune response, as expected. SAA1 and pro-inflammatory chemokines IL8, CCL2, CXCL5, CXCL3, CXCL2 and CCL8 belong to the top-ten most up-regulated genes (see Table 3), SAA1 being the most up-regulated gene with a 27-fold change by comparison to mock-stimulated PBMCs. SAA1 encodes the major acute-phase protein Serum Amyloid A (SAA), the precise role of which is still unclear despite reports suggesting a key role in the establishment and maintenance of inflammation notably as an antiapoptotic agent for neutrophils  and as an opsonin that would facilitate phagocytosis of gram-negative bacteria . SAA1 was also found as the most up-regulated gene in spleen seven days after infection by H. parasuis. The chemokines IL8, CXCL5, CXCL3 and CXCL2 have chemotaxis for neutrophils whereas the chemokines CCL2 and CCL8 have a broader chemotaxis spectrum specific for T, dendritic and NK cells as well as monocytes and basophils . Up-regulation of IL8 has already been reported in pig PBMCs [39, 40] and amnion  after bacterial infection. In human, stimulation of PBMCs with LPS induces the secretion of CCL2 , CXCL3 and CXCL2 . CXCL5 is up-regulated in LPS-challenged bovine mammary epithelial cells . All these results confirm the essential role of chemokines in chemoattraction and cell guidance to the site of infection during bacterial infection. IL1 has been reported to activate chemokine production . In our study, we found that IL1 was moderately up-regulated after 24 hours of stimulation and that it occupies a central position in the LPS-related network 2 providing a global image of inflammation activation (Figure 4B).
We have also found other strongly up-regulated genes after LPS stimulation including SOD2, S100A9 and S100A12. S100A9 and S100A12 are members of the S100 family, which encodes proteins containing two EF-hand calcium-binding motifs and are involved in the regulation of a number of cellular processes such as cell cycle progression and differentiation. In human, S100A9 has been reported to be up-regulated in LPS-stimulated bronchial epithelial cells , suggesting that this gene has a role in innate immune defense. SOD2 (superoxide dismutase 2) is a member of the iron/manganese superoxide dismutase family and it has been shown to be up-regulated in dendritic cells after LPS stimulation . These results suggest that the calcium pathways as well as oxidative processes are strongly affected in LPS stimulated PBMCs. Interestingly, the six genes S100A9, CXCL5, S100A12, IL8 CXCL2 and SOD2 were also found strongly up-regulated in mesenteric lymph nodes of pigs infected by S. Choleraesuis and S100A9, S100A12 and SOD2 were also up-regulated in spleen after H. parasuis infection . Overall these results confirm a predominant role of common genes in the innate immune response of pig to gram-negative bacterial infections.
Specific transcriptome modifications after PMA/ionomycin stimulation
By examining the top-ten most up-regulated genes (Table 3) and the representation of KEGG pathways (see Table 5 and Figure 5B), we have found that after PMA/ionomycin stimulation, T cells are more activated than B cells. Three of the most up-regulated genes are IL2 (fold change close to 10), CD69 (fold change = 6.9) and TNFRSF9 (fold change = 6.6), which are related to T cell activation. B cell markers such as CD40 and INHBA are also up-regulated (Additional file 9: SLA_RI_Table_S9.xls) but with fold changes of 1.6 and 5, respectively. It has been reported that transcription of the cytokine IL2 is the main consequence of T cell activation and that IL2 is produced by T helper cells harboring a Th1 cytokine profile . IL2 is essential for the generation and regulation of immune response. Binding of IL2 activates the Ras/MAPK, JAK/Stat and PI 3-kinase/Akt signaling module pathways (IPA, Ingenuity). IL2 signals through the IL2 receptor and in this study, we have found that the interleukin 2 receptor alpha (IL2RA) is also up-regulated after PMA/ionomycin stimulation as previously reported . The IL-2/IL-2R interaction stimulates the growth, differentiation and survival of antigen-selected cytotoxic T cells . The activation of T lymphocytes, both in vivo and in vitro, induces expression of CD69 that has been reported as the earliest inducible cell surface glycoprotein acquired during lymphoid activation. This molecule is involved in lymphocyte proliferation and functions as a signal-transmitting receptor in lymphocytes, natural killer cells and platelets . TNFRSF9 also known as 4-1BB is a member of the TNF-receptor superfamily and is a CD4+ T cell marker that regulates CD28 co-stimulation to promote Th1 cell response. The expression of this receptor is induced by lymphocyte activation and is involved in T cell division and expansion . In agreement with these findings, IL12B that is known to trigger Th1 response is also found up-regulated with a limited fold change of 1.5. Globally, our results show a strong up-regulation of cytokines and genes related to Th1 response, suggesting a more pronounced activation of the Th1 response compared to a Th2 response after PMA/ionomycin stimulation for 24 hours.
Strikingly, a very strong down-regulation of the THBS1 gene was observed after PMA/ionomycin stimulation for 24 hours with a reduced expression of 66 fold change by comparison to mock-stimulation (Table 3). This gene is also down-regulated in porcine aortic endothelial cells treated with PMA  and in bovine PBMCs stimulated with Concanavalin A . Interestingly, THBS1 down-regulation has been shown to become stronger with time after Concanavalin A stimulation in bovine PBMCs , suggesting a persistent role of this gene repression during immune activation and a delayed response or no response for returning to pre-induction levels. THBS1 encodes an adhesive glycoprotein that mediates cell-to-cell and cell-to-matrix interactions. THBS1 has been described with many diverse functions that may relate to its structure and consequently to its ability to bind to matrix proteins, cell surface receptors or other molecules including cytokines . THBS1 is known as a potent natural inhibitor of angiogenesis and endothelial cell migration . THBS1 has been shown to be regulated by DNA methylation and to be a target for a transcription repression induced by the protein arginine methyltransferase 6 (PRMT6). Our work suggests that the strong repression of THBS1 observed in pig PBMCs may be due to methylation and that the PRMT6 gene may have a role in this repression. Interestingly, CD47, which encodes a membrane protein that is a receptor for the C-terminal cell binding domain of THBS1  was also found slightly down-regulated (fold change of -1.4). Recent findings suggest that THBS1 contributes to the vascular system regulation by acting via its receptor CD47 to inhibit nitric oxide signaling . Our findings suggest a major role of THBS1 repression in T/B cell activation upon stimulation with PMA/ionomycin, by enhancing the ability of cells to proliferate and migrate. Whether this role is connected to CD47 or to other receptors has to be further investigated.
Our study confirms an up-regulation of the pro-inflammatory cytokine IL8 but has not found an over-expression of IFNG as previously reported by Ledger et al..
Down-regulation of MHC mediated antigen presentation pathways after both stimulations
A strong down-regulation of MHC class II or MHC class I and II molecules was observed after LPS or PMA/ionomycin stimulation, respectively. Classical class II molecules are involved in antigen presentation to CD4+ T cells whereas classical class I genes have a double function of antigen presentation to CD8+ T cells and regulation of natural killer cell cytotoxicity by interacting with NK receptors such as NKG2D . In pig, down-regulation of MHC genes has been reported in vivo in the spleen of animals infected by H. parasuis and in vitro in PK15 cells infected by the pseudorabies virus  and in PBMCs stimulated with PMA/ionomycin . In human, such a repression has also been reported in PBMCs infected by bacterial LPS and diverse killed bacteria . Our results show that the repression program includes classical (DR and DQ series) and non classical (DM series) SLA class II genes after LPS and PMA/ionomycin stimulation as reported in human PBMCs. In addition, classical class I genes corresponding to SLA-3 and likely to SLA-1 and SLA-2 are also repressed together with the non classical genes SLA-6 and SLA-7  that map to the SLA complex on chromosome 7 and CD1 that maps to chromosome 4 thus outside of the MHC locus. Strikingly, in our study, after PMA/ionomycin stimulation, biological networks connect the down-regulation of MHC class I molecules to a significant increase in transcription of numerous heat shock proteins known to act as chaperones as well as in transcription of all genes involved in the cascade of peptide processing before loading to the MHC molecule binding groove.
Induction of MHC class I expression is mainly transcriptional and promoters of class I genes contain IFN-stimulated response elements (ISRE) that bind factors of the IFN regulatory factor (IRF) family. Therefore, expression of IRFs influences transcription of class I genes. In our study, IRF1 and IRF8 are found up-regulated after PMA/ionomycin stimulation in contrast to IRF2 and IRF5 that are repressed. IRF8-mediated inhibition of antigen presentation by dendritic cells in the tumor microenvironment has been described in human  but our results are not in concordance with a possible role of IRF8 in MHC class I repression since the repression in peptide presentation by class I molecules was linked with a down-regulation of IRF8 together with a down-regulation of the peptide processing cascade . In contrast, the down-regulation of IRF1 is in agreement with a possible role of this gene in inhibiting transcription of MHC class I genes .
Comparison of transcriptomic signatures specific to LPS and PMA/ionomycin stimulations
In this study, about ten times more genes are found differentially expressed after PMA/ionomycin than after LPS stimulation. This might be related to the fact that LPS targets monocytes and macrophages  expressing CD14 and that PMA/ionomycin have a much wider spectrum of target cells. However, it cannot be ruled out that the significant difference in the number of differentially expressed genes according to stimulation is due to variations in the dynamics of the response. The onset of response may occur much earlier for LPS than for PMA/ionomycin. As a counterpart, the return to basal levels of gene transcription may also occur earlier after LPS stimulation, providing a possible hypothesis for a reduced number of differentially expressed genes after 24 hours stimulation. Additional studies are required to specifically address this question.
Specific and common features in transcriptome modifications were identified for both stimulations at 24 hours. Strikingly, the most significant similarly regulated genes after both stimulations are found down-regulated and many specific genes appear to be up-regulated.
Hierarchical clustering of genes found differentially expressed in both stimulation conditions provided a clear image of genes that were regulated either in the same direction or in opposite directions according to stimulation. In that respect, clusters C2, C4 and C7 (Figure 2) are the most informative to compare signatures and target possible markers that might be regulated in opposite directions according to stimulation. THBS1 (cluster C2), SAA1, chemokines CCL2, CXCL5 and CXCL6 (cluster C4) as well as IL1 receptor, immunoglobulins and LTB (cluster C7) provide a limited subset of genes that are specifically up-regulated after LPS stimulation and down-regulated after PMA/ionomycin stimulation. Similarly, cluster C7 including genes such as the chemokine CXCL10 and IRF8 provides a reservoir of genes specifically up-regulated after PMA/ionomycin stimulation and down-regulated after LPS stimulation.
Role of non-coding transcripts
After LPS and PMA/ionomycin stimulation, quite a high number of probes corresponding to annotated transcripts in the anti-sense orientation are expressed. It is likely that the expressed antisense probes correspond to either new non annotated transcripts or to antisense transcripts from annotated genes. Interestingly, few of the anti-sense probes are differentially expressed after PMA/ionomycin stimulation suggesting a role in immune response activation that has to be further explored. These preliminary results on the expression of non-coding transcripts mapping to the SLA complex corresponds to a pilot study that would be worth extending to the whole genome.
We have designed a long-oligonucleotide set (SLA-RI) comprising all the genes and pseudogenes annotated for the SLA complex as well as immune response genes outside the SLA complex and produced a generic array (SLA-RI/NRSP8-13K chip) enriched in immunity genes. We have assessed the relevance of this DNA chip by investigating the response of porcine PBMCs to two distinct stimuli LPS and PMA/ionomycin. Ours results reveal common as well as specific gene regulations according to stimulation, confirming some data already reported and providing new insights on the immune response in pig.
Probe selection and oligonucleotide design of the SLA-RI oligonucleotide set
To prepare the 816 probes targeting the SLA complex, all the annotated genes, pseudogenes and putative transcription variants were retrieved from the VEGA database . Oligonucleotides were designed on both DNA strands (see Additional file 1: SLA_RI_Table_S1.xls). To select genes involved in immune response but located outside the SLA complex, a list was drawn up from the Porcine Immunology and Nutrition (PIN) database , the human Immunogenetic Related Information Source (IRIS) , the immune system pathway in KEGG , and immunology microarray resources, such as ARK-Genomics S. scrofa Immune Array 3 K v1.0 , the Affymetrix GeneChip® Human Immune and Inflammation 9 K SNP Kit, Oligo GEArray® Human Autoimmune and Inflammatory Response Microarray, Oligo GEArray® Human Hematology/Immunology Microarray, Oligo GEArray® Human Innate and Adaptive Immune Responses Microarray, Oligo GEArray® Human Inflammatory Cytokines & Receptors Microarray, the PIQOR™ Immunology Microarray for human, and the PIQOR™ Immunology Microarray for mouse. Pig sequences were retrieved by GeneID and RefSeq search or by analysis for sequence similarity by BLAST (see Additional file 1: SLA_RI_Table_S1.xls). In cases where no pig sequence could be identified, a human sequence was used for the oligonucleotide design. Thus, the gene list comprises 2832 pig sequences and 125 human sequences and the final set consists of 2957 oligonucleotides. GO annotations of the probes were retrieved using the corresponding human RefSeq IDs [63, 64]. Oligonucleotides were all designed and synthesized by Operon Company.
Design and production of the SLA-RI/NRSP8-13K chip
The SLA-RI/NRSP8-13K chip was designed by combining the SLA-RI set with the NRSP8-13K set, which was purchased from the Operon Company. Oligonucleotides were resuspended in 0.5× Pronto! Universal Spotting Solution (Corning, USA) at a final concentration of 20 pmol/μL and printed on Corning UltraGAPS slides using a Chipwriter (Virtek, Canada) with 48 microspotting pins (SMP3, TeleChem International, Inc. USA). The Lucidea Universal ScoreCard control samples (GE Healthcare, USA) and SpotReport® Alien® cDNA Array Validation System control samples (Stratagene, USA) were both spotted in four replicates. After spotting, slides were air-dried and DNA was UV-fixed (600 mJ). Slides were stored in dry atmosphere before use. All information on SLA-RI/NRSP8-13 microarray platform has been submitted to the Gene Expression Omnibus (GEO) repository and the accession number is GPL7151. The DNA chips were produced by the French National platform CRB GADIE  and can be purchased upon request.
Cell isolation and stimulation
PBMCs from seven Large White male pigs (~50 kg) were isolated by Ficoll-Hypaque density gradient centrifugation at room temperature. The PBMCs were cultured in RPMI 1640 medium (BioWhittaker, Belgium) supplemented with 10% heat-inactivated FBS (fetal bovine serum) (QB perbio, UK), 2 mmol/L L-glutamine, 100 U/mL penicillin and 100 mg/mL streptomycin. In our experimental conditions, 5 × 106 cells were incubated for 24 hours in culture medium supplemented with 1 μg/mL LPS from E. coli O111:B4 (Sigma, France) or a mixture of PMA (Sigma, France) at 10 ng/mL and ionomycin (Sigma, France) at 1 μg/mL. For mock-stimulation, cells were maintained in the culture medium for 24 hours. PBMCs were further centrifuged for 10 min at 4000 rpm and harvested for RNA extraction. Supernatants were frozen at -20°C for cytokine quantification by ELISA tests.
RNA isolation and quality control
Total RNA was extracted from cells using the RNeasy Midi Kit (Qiagen, USA) and purified by on-column digestion of DNA with DNase I as recommended by the manufacturer (Qiagen, USA) to eliminate residual genomic DNA. RNA concentration was determined by Nanodrop quantification (Thermo Fisher Scientific Inc., USA). RNA quality was checked on an Agilent 2100 Bioanalyzer (Agilent Technologies, Germany). RNAs with a RIN score between 8 and 10 were labeled and used for microarray and qRT-PCR experiments. All RNAs were diluted to a final concentration of 1 μg/μL and stored at -80°C.
RNA labelling, microarray hybridisation and signal quantification
For labelling, 5 μg of total RNA were reverse-transcribed and directly labelled by Cy3 or Cy5 using the ChipShot™ Direct Labeling System (Promega, USA). The CyDye-labelled cDNAs were purified using ChipShot™ Membrane Clean-Up System (Promega, USA). The absorbance at 260, 550 and 650 nm of CyDye-labelled cDNAs was measured by Nanodrop (Thermo Fisher Scientific Inc., USA). Frequency of incorporation (FOI) and labelling efficiency were checked by referring to standards provided by Labeled cDNA Calculator  (Promega, USA). The CyDye-labelled cDNAs were dried by vacuum centrifugation and resuspended at a final concentration of 2.5 pmol/μL in cDNA/long-oligonucleotide hybridization buffer. A dye swap hybridization scheme was designed to compare gene expression between mock-stimulated PBMCs and PBMCs stimulated by either LPS or a mixture of PMA and ionomycin. Each pig/condition RNA was labelled with Cy3 and Cy5 (Additional file 10: SLA_RI_Figure_S10.png). A total of 28 SLA-RI/NRSP8-13K chips were used in our study. Chip hybridization was performed using the Corning hybridization system. Prior to hybridization, the slides were treated with the background-reducing Pronto! Pre-Soak System (Corning, France) and then prehybridized using the Corning "Pronto! Universal Hybridization Solutions and Kits" (Product #40026). Hybridizations were carried out for 16 hours at 42°C in light-protected sealed Corning Hybridization Cassettes (Corning, Product #2551) placed in a water bath. The slides were washed according to the recommended protocol (Corning, Product #40026) and dried by centrifugation at 1600 rpm for 2 min. Slides were scanned using a GenePix™ 4000B array scanner (MDS Inc., Canada) and then array images were processed with the GenePix™ Pro software V6.0 (MDS Inc., Canada) to align spots, to integrate ID data files and to export reports of spot intensity data.
All the results were stored in the BioArray Software Environment (BASE) managed by SIGENAE . The microarray data have been submitted to the GEO and received the accession number GSE17320.
Microarray data statistical analysis
To identify any significant differential expression, the microarray data were analyzed using Limma (Linear Models for Microarray Data)  from the Bioconductor open-source project running under R [69, 70]. After data pre-processing using within-array global loess normalization, the empirical eBayes method in Limma, which computes moderated t-statistics, moderated F-statistics, and log-odds of differential expression, was applied to identify the significance of differential expression in each culture condition. Adjustment for multiple testing was carried out using the false discovery rate (FDR) method  in Limma. Significant changes in gene expression were limited to p < 0.05. Hierarchical clustering analysis (HCL) was performed for gene classification  using the TMeV software .
Significant functions and gene network analysis
The differentially expressed genes were analyzed using the IPA software (Ingenuity Systems, USA). Genes with known human locus IDs with corresponding differential expression values were uploaded into the software. Each human locus ID was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Gene networks were algorithmically generated based on their connectivity and assigned a score. Ingenuity Pathways Analysis calculates a significance score for each network. The score is calculated using a p-value calculation for each network, and is displayed as the negative base-ten logarithm of that p-value. It indicates the likelihood that the assembly of a set of focus genes in a network may be explained by random chance alone. In this study, the cut-off significant score was set at 5, which means that a network score of 5 would only have approximately a 10-5 chance of occurring randomly. The KEGG biology pathway information for differentially expressed genes was queried by ArrayTrack [74, 75] using human locus IDs. The interconnectedness information was manually extracted from the KEGG pathways, and for simplicity a line connecting two KEGG pathways was used to represent these interactions. The interaction map was created using CytoScape software  to generate a framework of the interactions of the KEGG biological pathways.
Quantitative real time RT-PCR (qRT-PCR)
Two μg of DNaseI-treated total RNAs were reverse-transcribed using Superscript II enzyme with Oligo(dT) primers. The cDNAs were quantified using a 2100 Bioanalyzer (Agilent Technologies, Germany) and diluted to a working concentration of 4 ng/μL. Duplicate reactions were performed in a final volume of 20 μL with 20 ng cDNA, 300 nM primers and SYBR Green PCR Master Mix (Applied Biosystem, USA), using an ABI PRISM 7900 HT sequence detection system (Applied Biosystem, USA). Primers were chosen either with the Primer Express Software or manually. The gene B2M was chosen as the internal reference gene and the 2-ΔΔCt method was used to calculate the fold change in gene expression .
ELISA test validation
For protein validation by ELISA tests, supernatants of mock-stimulated and stimulated PBMCs from the seven animals used for transcriptome analysis were tested. This means that supernatants for ELISA tests and PBMCs for RNA extraction and transcriptome analysis were collected at the same time from the same culture plates. The concentrations of IL8, IL12, IL1B and TNFA proteins were determined using commercially available ELISA kits (DuoSet, R&D Systems, USA), according to the manufacturer's instructions. Results were reported as the mean values of duplicate ELISA wells.
The anti-porcine MHC Class I monoclonal antibody PT85A (VMRD Inc., USA) and the anti-porcine MHC Class II monoclonal antibody MSA3 (VMRD Inc., USA) were used for FACS analysis. The monoclonal antibody HOPC-1 (IgG2a, Beckman Coulter, USA) was used as a control antibody for isotype. PE-conjugated goat antibodies to mouse IgG2a (Southern Biotech, USA) were used as a secondary antibody.
PBMCs from seven other Large White male pigs were stimulated and mock-stimulated in the same conditions as for microarray analysis. After centrifugation at 1500 rpm for 20 min at 4°C, cells were resuspended and incubated in pig serum for 25 min at 4°C. Cells were washed in PBS (BioWhittaker, Belgium) and incubated with 50 μL of diluted primary antibody (1/200 in FACS buffer) for 25 min at 4°C, then washed again and incubated with 50 μL of diluted PE-conjugated goat antimouse IgG2a (1/600 in FACS buffer) for 25 min at 4°C in light-protected chambers. After a final wash in PBS, PBMCs were fixed in BD CellFix (BD Biosciences, USA) solution and analyzed using a FACS Calibur flow cytometer (BD Biosciences, USA).
The authors wish to thank Dr Patrick Chardon (INRA, UMR GABI, Jouy-en-Josas, France) who contributed to the design of the experiments. The authors would like to acknowledge Florence Jaffrezic (INRA, UMR GABI, Jouy-en-Josas, France) for helpful discussions on transcriptome analysis as well as Karine Hugot (INRA, UMR GABI, CRB GADIE, Jouy-en-Josas, France) for production of the SLA-RI/NRSP8-13K chip and Sylvain Marthey (INRA, UMR GABI, CRB GADIE, Jouy-en-Josas, France) for helping during the GEO submission process. The authors wish to thank Dr Hélène Hayes (INRA, UMR GABI, Jouy-en-Josas, France) for the final read-through of the manuscript. This work was funded by the Agence Nationale de la Recherche (IMMOPIG project, GENANIMAL 2007-2009).
- Tribout T, Caritez J, Gogué J, Gruand J, Bouffaud M, Billon Y, Péry C, Griffon H, Brenot S, Tiran ML: Estimation, par utilisation de semence congelée, du progrès génétique réalisé en France entre 1977 et 1998 dans la race porcine Large White: résultats pour quelques caractères de production et de qualité de tissus gras et maigres. Journées de la Recherche Porcin. 2004, 36: 275-282.Google Scholar
- Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995, 270: 467-470. 10.1126/science.270.5235.467.PubMedView ArticleGoogle Scholar
- Morozova O, Marra MA: Applications of next-generation sequencing technologies in functional genomics. Genomics. 2008, 92: 255-264. 10.1016/j.ygeno.2008.07.001.PubMedView ArticleGoogle Scholar
- Coppee JY: Do DNA microarrays have their future behind them?. Microbes Infect. 2008, 10: 1067-1071. 10.1016/j.micinf.2008.07.003.PubMedView ArticleGoogle Scholar
- Jansen A, Yu J: Differential gene expression of pathogens inside infected hosts. Curr Opin Microbiol. 2006, 9: 138-142. 10.1016/j.mib.2006.01.003.PubMedView ArticleGoogle Scholar
- Jenner RG, Young RA: Insights into host responses against pathogens from transcriptional profiling. Nat Rev Microbiol. 2005, 3: 281-294. 10.1038/nrmicro1126.PubMedView ArticleGoogle Scholar
- Shivers RP, Youngman MJ, Kim DH: Transcriptional responses to pathogens in Caenorhabditis elegans. Curr Opin Microbiol. 2008, 11: 251-256. 10.1016/j.mib.2008.05.014.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang Y, Couture OP, Qu L, Uthe JJ, Bearson SM, Kuhar D, Lunney JK, Nettleton D, Dekkers JC, Tuggle CK: Analysis of porcine transcriptional response to Salmonella enterica serovar Choleraesuis suggests novel targets of NFkappaB are activated in the mesenteric lymph node. BMC Genomics. 2008, 9: 437-10.1186/1471-2164-9-437.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen H, Li C, Fang M, Zhu M, Li X, Zhou R, Li K, Zhao S: Understanding Haemophilus parasuis infection in porcine spleen through a transcriptomics approach. BMC Genomics. 2009, 10: 64-10.1186/1471-2164-10-64.PubMed CentralPubMedView ArticleGoogle Scholar
- Flori L, Rogel-Gaillard C, Cochet M, Lemonnier G, Hugot K, Chardon P, Robin S, Lefevre F: Transcriptomic analysis of the dialogue between Pseudorabies virus and porcine epithelial cells during infection. BMC Genomics. 2008, 9: 123-10.1186/1471-2164-9-123.PubMed CentralPubMedView ArticleGoogle Scholar
- Ensembl Pig (Sus scrofa). [http://uswest.ensembl.org/Sus_scrofa/Info/Index]
- Tsai S, Cassady JP, Freking BA, Nonneman DJ, Rohrer GA, Piedrahita JA: Annotation of the Affymetrix porcine genome microarray. Anim Genet. 2006, 37: 423-424. 10.1111/j.1365-2052.2006.01460.x.PubMedView ArticleGoogle Scholar
- Bonnet A, Iannuccelli E, Hugot K, Benne F, Bonaldo MF, Soares MB, Hatey F, Tosser-Klopp G: A pig multi-tissue normalised cDNA library: large-scale sequencing, cluster analysis and 9 K micro-array resource generation. BMC Genomics. 2008, 9: 17-10.1186/1471-2164-9-17.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang F, Hopwood P, Abrams CC, Downing A, Murray F, Talbot R, Archibald A, Lowden S, Dixon LK: Macrophage transcriptional responses following in vitro infection with a highly virulent African swine fever virus isolate. J Virol. 2006, 80: 10514-10521. 10.1128/JVI.00485-06.PubMed CentralPubMedView ArticleGoogle Scholar
- Hedegaard J, Hornshøj H, Conley LN, Panitz F, Bendixen C: Design, Production and Usage of a 25 K Porcine Long-Oligo DNA Microarray. 3rd International Symposium on Animal Functional Genomics: 07.04.2008 - 09.04.2008 2008; Edinburgh, United Kingdom. 2008, 64-Google Scholar
- pigoligoarray.org. [http://www.pigoligoarray.org]
- Zhao SH, Recknor J, Lunney JK, Nettleton D, Kuhar D, Orley S, Tuggle CK: Validation of a first-generation long-oligonucleotide microarray for transcriptional profiling in the pig. Genomics. 2005, 86: 618-625. 10.1016/j.ygeno.2005.08.001.PubMedView ArticleGoogle Scholar
- PigQTLdb. [http://www.animalgenome.org/QTLdb/pig.html]
- KEGG. [http://www.genome.jp/kegg/]
- Cambiaggi C, Scupoli MT, Cestari T, Gerosa F, Carra G, Tridente G, Accolla RS: Constitutive expression of CD69 in interspecies T-cell hybrids and locus assignment to human chromosome 12. Immunogenetics. 1992, 36: 117-120. 10.1007/BF00215288.PubMedView ArticleGoogle Scholar
- Tao W, Mallard B, Karrow N, Bridle B: Construction and application of a bovine immune-endocrine cDNA microarray. Vet Immunol Immunopathol. 2004, 101: 1-2. 10.1016/j.vetimm.2003.10.011.PubMedView ArticleGoogle Scholar
- Donaldson L, Vuocolo T, Gray C, Strandberg Y, Reverter A, McWilliam S, Wang Y, Byrne K, Tellam R: Construction and validation of a Bovine Innate Immune Microarray. BMC Genomics. 2005, 6: 135-10.1186/1471-2164-6-135.PubMed CentralPubMedView ArticleGoogle Scholar
- Smith J, Speed D, Hocking PM, Talbot RT, Degen WG, Schijns VE, Glass EJ, Burt DW: Development of a chicken 5 K microarray targeted towards immune function. BMC Genomics. 2006, 7: 49-10.1186/1471-2164-7-49.PubMed CentralPubMedView ArticleGoogle Scholar
- Sarson AJ, Read LR, Haghighi HR, Lambourne MD, Brisbin JT, Zhou H, Sharif S: Construction of a microarray specific to the chicken immune system: profiling gene expression in B cells after lipopolysaccharide stimulation. Can J Vet Res. 2007, 71: 108-118.PubMed CentralPubMedGoogle Scholar
- Ledger TN, Pinton P, Bourges D, Roumi P, Salmon H, Oswald IP: Development of a macroarray to specifically analyze immunological gene expression in swine. Clin Diagn Lab Immunol. 2004, 11: 691-698.PubMed CentralPubMedGoogle Scholar
- Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M: Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009, 10: 184-194. 10.1038/nrg2537.PubMed CentralPubMedView ArticleGoogle Scholar
- Cale JM, Bird IM: Dissociation of endothelial nitric oxide synthase phosphorylation and activity in uterine artery endothelial cells. Am J Physiol Heart Circ Physiol. 2006, 290: H1433-1445. 10.1152/ajpheart.00942.2005.PubMedView ArticleGoogle Scholar
- Rivoltini L, Radrizzani M, Accornero P, Squarcina P, Chiodoni C, Mazzocchi A, Castelli C, Tarsini P, Viggiano V, Belli F: Human melanoma-reactive CD4+ and CD8+ CTL clones resist Fas ligand-induced apoptosis and use Fas/Fas ligand-independent mechanisms for tumor killing. J Immunol. 1998, 161: 1220-1230.PubMedGoogle Scholar
- McCracken SA, Gallery E, Morris JM: Pregnancy-specific down-regulation of NF-kappa B expression in T cells in humans is essential for the maintenance of the cytokine profile required for pregnancy success. J Immunol. 2004, 172: 4583-4591.PubMedView ArticleGoogle Scholar
- Lu YC, Yeh WC, Ohashi PS: LPS/TLR4 signal transduction pathway. Cytokine. 2008, 42: 145-151. 10.1016/j.cyto.2008.01.006.PubMedView ArticleGoogle Scholar
- McDonnell G, Russell AD: Antiseptics and disinfectants: activity, action, and resistance. Clin Microbiol Rev. 1999, 12: 147-179.PubMed CentralPubMedGoogle Scholar
- Wells CA, Ravasi T, Faulkner GJ, Carninci P, Okazaki Y, Hayashizaki Y, Sweet M, Wainwright BJ, Hume DA: Genetic control of the innate immune response. BMC Immunol. 2003, 4: 5-10.1186/1471-2172-4-5.PubMed CentralPubMedView ArticleGoogle Scholar
- Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, Belcher CE, Botstein D, Staudt LM, Brown PO, Relman DA: Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci USA. 2002, 99: 972-977. 10.1073/pnas.231625398.PubMed CentralPubMedView ArticleGoogle Scholar
- Bryant PA, Smyth GK, Robins-Browne R, Curtis N: Detection of gene expression in an individual cell type within a cell mixture using microarray analysis. PLoS One. 2009, 4 (2): e4427-10.1371/journal.pone.0004427.PubMed CentralPubMedView ArticleGoogle Scholar
- Tuggle CK, Wang Y, Couture O: Advances in swine transcriptomics. Int J Biol Sci. 2007, 3: 132-152.PubMed CentralPubMedView ArticleGoogle Scholar
- Christenson K, Bjorkman L, Tangemo C, Bylund J: Serum amyloid A inhibits apoptosis of human neutrophils via a P2X7-sensitive pathway independent of formyl peptide receptor-like 1. J Leukoc Biol. 2008, 83: 139-148. 10.1189/jlb.0507276.PubMedView ArticleGoogle Scholar
- Shah C, Hari-Dass R, Raynes JG: Serum amyloid A is an innate immune opsonin for Gram-negative bacteria. Blood. 2006, 108: 1751-1757. 10.1182/blood-2005-11-011932.PubMedView ArticleGoogle Scholar
- Delves PJ, Martin SJ, Burton DR, Roitt IM: Roitt's Essential Immunology. 2006, Oxford, Blackwell Publishing, EleventhGoogle Scholar
- Royaee AR, Husmann RJ, Dawson HD, Calzada-Nova G, Schnitzlein WM, Zuckermann FA, Lunney JK: Deciphering the involvement of innate immune factors in the development of the host response to PRRSV vaccination. Vet Immunol Immunopathol. 2004, 102 (3): 199-216. 10.1016/j.vetimm.2004.09.018.PubMedView ArticleGoogle Scholar
- Lin G, Pearson AE, Scamurra RW, Zhou Y, Baarsch MJ, Weiss DJ, Murtaugh MP: Regulation of interleukin-8 expression in porcine alveolar macrophages by bacterial lipopolysaccharide. J Biol Chem. 1994, 269: 77-85.PubMedGoogle Scholar
- Trebichavsky I, Splichal I, Zahradnickova M, Splichalova A, Mori Y: Lipopolysaccharide induces inflammatory cytokines in the pig amnion. Vet Immunol Immunopathol. 2002, 87: 1-2. 10.1016/S0165-2427(02)00025-9.View ArticleGoogle Scholar
- Park HJ, Kim HJ, Ra J, Hong SJ, Baik HH, Park HK, Yim SV, Nah SS, Cho JJ, Chung JH: Melatonin inhibits lipopolysaccharide-induced CC chemokine subfamily gene expression in human peripheral blood mononuclear cells in a microarray analysis. J Pineal Res. 2007, 43: 121-129. 10.1111/j.1600-079X.2007.00452.x.PubMedView ArticleGoogle Scholar
- Ellertsen LK, Hetland G, Johnson E, Grinde B: Effect of a medicinal extract from Agaricus blazei Murill on gene expression in a human monocyte cell line as examined by microarrays and immuno assays. Int Immunopharmacol. 2006, 6: 133-143. 10.1016/j.intimp.2005.07.007.PubMedView ArticleGoogle Scholar
- Pareek R, Wellnitz O, Van Dorp R, Burton J, Kerr D: Immunorelevant gene expression in LPS-challenged bovine mammary epithelial cells. J Appl Genet. 2005, 46: 171-177.PubMedGoogle Scholar
- Yamada T, Fujieda S, Yanagi S, Yamamura H, Inatome R, Yamamoto H, Igawa H, Saito H: IL-1 induced chemokine production through the association of Syk with TNF receptor-associated factor-6 in nasal fibroblast lines. J Immunol. 2001, 167: 283-288.PubMedView ArticleGoogle Scholar
- Henke MO, Renner A, Rubin BK, Gyves JI, Lorenz E, Koo JS: Up-regulation of S100A8 and S100A9 protein in bronchial epithelial cells by lipopolysaccharide. Exp Lung Res. 2006, 32: 331-347. 10.1080/01902140600959580.PubMedView ArticleGoogle Scholar
- Ishii K, Kurita-Taniguchi M, Aoki M, Kimura T, Kashiwazaki Y, Matsumoto M, Seya T: Gene-inducing program of human dendritic cells in response to BCG cell-wall skeleton (CWS), which reflects adjuvancy required for tumor immunotherapy. Immunol Lett. 2005, 98: 280-290. 10.1016/j.imlet.2004.12.002.PubMedView ArticleGoogle Scholar
- Beadling C, Smith KA: DNA array analysis of interleukin-2-regulated immediate/early genes. Med Immunol. 2002, 1 (1): 2-10.1186/1476-9433-1-2.PubMed CentralPubMedView ArticleGoogle Scholar
- Croft M: The role of TNF superfamily members in T-cell function and diseases. Nat Rev Immunol. 2009, 9: 271-285. 10.1038/nri2526.PubMed CentralPubMedView ArticleGoogle Scholar
- Kim SA, Hong KJ: Responsive site on the thrombospondin-1 promotor to down-regulation by phorbol 12-myristate 13-acetate in porcine aortic endothelial cells. Exp Mol Med. 2000, 32: 135-140.PubMedView ArticleGoogle Scholar
- Bornstein P: Diversity of function is inherent in matricellular proteins: an appraisal of thrombospondin 1. J Cell Biol. 1995, 130: 503-506. 10.1083/jcb.130.3.503.PubMedView ArticleGoogle Scholar
- Dawson DW, Pearce SF, Zhong R, Silverstein RL, Frazier WA, Bouck NP: CD36 mediates the In vitro inhibitory effects of thrombospondin-1 on endothelial cells. J Cell Biol. 1997, 138: 707-717. 10.1083/jcb.138.3.707.PubMed CentralPubMedView ArticleGoogle Scholar
- Gao AG, Lindberg FP, Finn MB, Blystone SD, Brown EJ, Frazier WA: Integrin-associated protein is a receptor for the C-terminal domain of thrombospondin. J Biol Chem. 1996, 271: 21-24. 10.1074/jbc.271.1.21.PubMedView ArticleGoogle Scholar
- Isenberg JS, Romeo MJ, Yu C, Yu CK, Nghiem K, Monsale J, Rick ME, Wink DA, Frazier WA, Roberts DD: Thrombospondin-1 stimulates platelet aggregation by blocking the antithrombotic activity of nitric oxide/cGMP signaling. Blood. 2008, 111: 613-623. 10.1182/blood-2007-06-098392.PubMed CentralPubMedView ArticleGoogle Scholar
- Shi FD, Van Kaer L: Reciprocal regulation between natural killer cells and autoreactive T cells. Nat Rev Immunol. 2006, 6: 751-760. 10.1038/nri1935.PubMedView ArticleGoogle Scholar
- Renard C, Hart E, Sehra H, Beasley H, Coggill P, Howe K, Harrow J, Gilbert J, Sims S, Rogers J: The genomic sequence and analysis of the swine major histocompatibility complex. Genomics. 2006, 88: 96-110. 10.1016/j.ygeno.2006.01.004.PubMedView ArticleGoogle Scholar
- Tourkova IL, Shurin GV, Ferrone S, Shurin MR: Interferon regulatory factor 8 mediates tumor-induced inhibition of antigen processing and presentation by dendritic cells. Cancer Immunol Immunother. 2009, 58: 567-574. 10.1007/s00262-008-0579-1.PubMed CentralPubMedView ArticleGoogle Scholar
- Hobart M, Ramassar V, Goes N, Urmson J, Halloran PF: IFN regulatory factor-1 plays a central role in the regulation of the expression of class I and II MHC genes in vivo. J Immunol. 1997, 158: 4260-4269.PubMedGoogle Scholar
- Piriou-Guzylack L, Salmon H: Membrane markers of the immune cells in swine: an update. Vet Res. 2008, 39 (6): 54-10.1051/vetres:2008030.PubMedView ArticleGoogle Scholar
- VEGA. [http://vega.sanger.ac.uk/index.html]
- PINdb. [http://126.96.36.199/fmi/iwp/cgi?-db=PINdb&-loadframes]
- Kelley J, de Bono B, Trowsdale J: IRIS: a database surveying known human immune system genes. Genomics . 85: 503-511. 10.1016/j.ygeno.2005.01.009.Google Scholar
- Wernersson R, Schierup MH, Jorgensen FG, Gorodkin J, Panitz F, Staerfeldt HH, Christensen OF, Mailund T, Hornshoj H, Klein A: Pigs in sequence space: a 0.66× coverage pig genome survey based on shotgun sequencing. BMC Genomics. 2005, 6 (1): 70-10.1186/1471-2164-6-70.PubMed CentralPubMedView ArticleGoogle Scholar
- Thomas JW, Touchman JW, Blakesley RW, Bouffard GG, Beckstrom-Sternberg SM, Margulies EH, Blanchette M, Siepel AC, Thomas PJ, McDowell JC: Comparative analyses of multi-species sequences from targeted genomic regions. Nature. 2003, 424: 788-793. 10.1038/nature01858.PubMedView ArticleGoogle Scholar
- CRB GADIE. [http://crb-gadie.inra.fr/]
- Labeled cDNA Calculator. [http://www.promega.com/applications/ivt/calculator/#ResultsView]
- SIGENAE. [http://www.sigenae.org/]
- Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3: Article3-PubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.PubMed CentralPubMedView ArticleGoogle Scholar
- R Project. [http://www.r-project.org/]
- Reiner A, Yekutieli D, Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 2003, 19: 368-375. 10.1093/bioinformatics/btf877.PubMedView ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-14868. 10.1073/pnas.95.25.14863.PubMed CentralPubMedView ArticleGoogle Scholar
- TMeV. [http://www.tm4.org/mev.html]
- ArrayTrack. [http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm]
- Tong W, Harris S, Cao X, Fang H, Shi L, Sun H, Fuscoe J, Harris A, Hong H, Xie Q: Development of public toxicogenomics software for microarray data management and analysis. Mutat Res. 2004, 549: 1-2.View ArticleGoogle Scholar
- CytoScape. [http://www.cytoscape.org/]
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25: 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
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