Int. J. Mol. Sci. 2013, 14(5), 10626-10660; doi:10.3390/ijms140510626

Article
Transcriptional Profiling of Swine Lung Tissue after Experimental Infection with Actinobacillus pleuropneumoniae
Zhicai Zuo 1,2, Hengmin Cui 1,2,*, Mingzhou Li 3, Xi Peng 1,2, Ling Zhu 1,2, Ming Zhang 3, Jideng Ma 3, Zhiwen Xu 1,2, Meng Gan 1,2, Junliang Deng 1,2, Xuewei Li 3 and Jing Fang 1,2
1
College of Veterinary Medicine, Sichuan Agricultural University, Ya’an 625014, Sichuan, China; E-Mails: zzcjl@126.com (Z.Z.); pengxi197313@163.com (X.P.); abtczl72@126.com (L.Z.); abtcxzw@126.com (Z.X.); gm0320@163.com (M.G.); dengjl213@126.com (J.D.); fangjing4109@163.com (J.F.)
2
Laboratory of Animal Disease and Human Health, Sichuan Agricultural University, Ya’an 625014, Sichuan, China
3
College of Animal Science and Technology, Sichuan Agricultural University, Ya’an 625014, Sichuan, China; E-Mails: mingzhou.li@163.com (M.L.); zhm3000@163.com (M.Z.); jideng_ma@sina.com (J.M.); xuewei.li@sicau.edu.cn (X.L.)
*
Author to whom correspondence should be addressed; E-Mail: cui580420@sicau.edu.cn or cuihengmin2008@sina.com; Tel.: +86-13608264628; Fax: +86-0835-2882-340.
Received: 1 April 2013; in revised form: 9 May 2013 / Accepted: 10 May 2013 /
Published: 21 May 2013

Abstract

: Porcine pleuropneumonia is a highly contagious respiratory disease that causes great economic losses worldwide. In this study, we aimed to explore the underlying relationship between infection and injury by investigation of the whole porcine genome expression profiles of swine lung tissues post-inoculated with experimentally Actinobacillus pleuropneumoniae. Expression profiling experiments of the control group and the treatment group were conducted using a commercially available Agilent Porcine Genechip including 43,603 probe sets. Microarray analysis was conducted on profiles of lung from challenged versus non-challenged swine. We found 11,929 transcripts, identified as differentially expressed at the p ≤0.01 level. There were 1188 genes annotated as swine genes in the GenBank Data Base. GO term analysis identified a total of 89 biological process categories, 82 cellular components and 182 molecular functions that were significantly affected, and at least 27 biological process categories that were related to the host immune response. Gene set enrichment analysis identified 13 pathways that were significantly associated with host response. Many proinflammatory-inflammatory cytokines were activated and involved in the regulation of the host defense response at the site of inflammation; while the cytokines involved in regulation of the host immune response were suppressed. All changes of genes and pathways of induced or repressed expression not only led to a decrease in antigenic peptides presented to T lymphocytes by APCs via the MHC and alleviated immune response injury induced by infection, but also stimulated stem cells to produce granulocytes (neutrophils, eosinophils, and basophils) and monocyte, and promote neutrophils and macrophages to phagocytose bacterial and foreign antigen at the site of inflammation. The defense function of swine infection with Actinobacillus pleuropneumoniae was improved, while its immune function was decreased.
Keywords:
porcine pleuropneumonia; infection; injury; Actinobacillus pleuropneumoniae; agilent porcine genechip; microarray analyses; cytokine; host defense response

1. Introduction

Porcine pleuropneumonia (PP) is a highly contagious respiratory disease that causes great economic losses worldwide [1]. The disease, which occurs in swine of all ages, is highly infectious, often fatal, and characterized by necrotizing, hemorrhagic bronchopneumonia and serofibrinous pleuritis [1]. Actinobacillus pleuropneumoniae (APP) is the causative agent of PP and can spread quickly by air-borne particles and/or touching a contaminated surface, and often kills infected animals in the acute phase when extensive lung hemorrhage and necrosis occur. Swine that survive often develop pleurisy, the sequelaes of local necrosis of the pleura, or became healthy carriers of APP.

The porcing lung infected with APP has previously been reported to result in local production of proinflammatory proteins or to mRNA encoding the cytokines interleukin (IL)-1α, IL-1α, IL-6 and the chemokine IL-8 [2]. Likewise, bioactive protein and/or mRNA code IL10, IL12p35, TNF-α and INF-α have shown to be up-regulated after infection with APP in vivo or in vitro[24]. Using cDNA microarrays, Moser and co-workers found 307 anonymous transcripts in blood leukocytes from swine that were significantly affected by experimental infection with APP [5]. Hedegaard et al. investigated the molecular characterization of the early response in pigs to experimental infection with APP serotype 5B, using cDNA microarrays [6]. In this study, two-colour microarray analysis was conducted to identify genes being significantly differently expressed in non-inflamed lung tissue compared with inflamed lung tissue sampled from the same animal [6]. The samples of lung tissue were studied by manual hybridization to the pig array DIAS_PIG_27K2 that contains 5375 PCR products amplified from unique cDNA clones [6]. Hedegaard and co-workers found three subsets of genes consistently expressed at different levels depending upon the infection status, and a total of 357 genes differed significantly in their expression levels between infected and non-infected lung tissue from infected versus non-infected animals [6]. Mortensen et al. studied the local transcriptional response in different locations of lung from pigs experimentally infected with the respiratory pathogen APP 5B, using porcine cDNA microarrays (DJF Pig 55 K v1) representing approximately 20,000 porcine genes printed in duplicate [7]. Within the lung, Mortensen and co-workers found a clear division of induced genes as, in unaffected areas a large part of differently expressed genes were involved in systemic reaction to infections, while differently expressed genes in necrotic areas were mainly concerned with homeostasis regulation [7]. However, a limited number of genes relative to the whole Porcine Genome have been studied in previous documents by using cDNA microarrays [57]. Thus, transcriptional profiling of whole porcine genome in lung tissue sampled from inoculated versus non-inoculated swine would lead to greater knowledge of the host response dynamics to bacterial infection in the lung. This knowledge is important to obtain a more complete picture of the lung-specific host reactions in the pathogenesis of respiratory infection.

In the present study, the Agilent Whole Porcine Genome Oligo (4 × 44 K) Microarrays (one-color platform), which is a commercially available Agilent Porcine Genechip that included 43,603 probe sets, were used to detect the changes in gene expression of infected pigs’ lungs from non-inoculated animals. Ten transcripts (top six up-regulated and top four down-regulated in microarray data) were selected to verify the accuracy and reproducibility of the microarray data by real-time qRT-PCR.

2. Results

2.1. Clinical Symptoms and Necropsy Findings

The symptoms of lung lesions in the TG were typical after swine infected with APP. Swine showed hyperthermia (40.6–42.0 °C), dyspnea and anorexia after inoculation with APP 24–48 h. Two swine died with respiratory distress at post-inoculation 36–48 h. In the autopsy, the lungs were found to be severely damaged by acute, multifocal, fibrino-necrotizing and hemorrhagic pneumonia complicated by acute diffuse fibrinous pleuritis. The tracheobronchial lymphoid nodes were enlarged and congested.

No lesions were observed in CG lung (Figures 1A and 2). Lung showed swelling, bleeding and fibrinous exudate sticking to the lung surface in TG (Figure 1B). The histopathologic changes were characterized by hemorrhage, lymphocyte infiltration, fibrinous exudation vascular thrombosis, necrotic focus and edema in TG (Figure 3).

2.2. Microarray Profiling

Expression profiling was conducted using a commercially available Agilent Porcine Genechip that included 43,603 probe sets. The transcriptome of the lung was determined. Expression was detected for 30,574 probes (70.12% of all probe sets) of the CG. A total of 31,957 probes (73.29% of all probe sets) were expressed in TG. When probe set intensities were normalized and filtered, there were still 26353 probes used to significantly identify DE genes. There were 11,929 genes identified as DE at the p ≤ 0.01 level by comparing the log2 (normalized signal) of the two groups using T-test analysis.

Hierarchical clustering was applied to the mean log-ratio of the replicated spots from the DE genes by the average linkage and using euclidean distance as the similarity metric (Figure 4). The expression profiles of samples were divided into two groups—one from the non-inoculated swine (M-P-1, M-P-2, M-P-3) and the other group from the inoculated swine (M-P-4, M-P-5, M-P-6).

The principal component map to three-dimensional space, also found that the distance of CG (samples M-P-1, M-P-2, M-P-3) is short, and the gene expression pattern is more consistent. Also the distance of TG (samples M-P-4, M-P-5, M-P-6) is relatively discrete because of the differences in the degree of lesion, and the gene expression pattern is similar (Figure 5).

The six samples were set as variables, the principal component analysis (PCA) of the co-expressed differentially genes (CG vs TG) showed the contribution rate of the first principal component which reached 96.95%, the first three principal components of the total contribution rate reach 99.754% (Table 1).

2.3. DE Genes Profiling

Of the 11,929 DE genes, 1188 were annotated as swine genes in the GenBank Database (DB). GO and KEGG pathway analyses of the 1188 DE gene lists were conducted using DAVID. There were 89 biological process (BP) categories (Table 2), 82 cellular components (Table 3), and 182 molecular functions (Table 4) were significantly affected by infection with APP (p = 0). The BP of the test for over-representation of specific GO terms among the affected genes related to the immune responses were at least 27 (Table 5). Furthermore, a number of BP related to metabolism were also identified.

A total of 513 genes were analyzed using gene set enrichment analysis (GSEA). Three hundred and thirty (64.3%) database genes correlated with TG, while the other 183 (35.7%) genes correlated with CG. One hundred and thirty pathways remained for further analysis after size filtering (2 ≤ sizes ≤ 20). Altogether, 102 pathways (Table 6) were enriched and up-regulated in the TG and down-regulated in the CG. One pathway (SSC04664) was significantly enriched at a false discovery rate <25%. Eight pathways (i.e., SSC04664, SSC04930, SSC04914, SSC00140, SSC04621, SSC05221, SSC05218 and SSC03040) were significantly enriched at nominal p values of less than 1% and 5%.

Twenty-eight pathways (Table 7) were down-regulated in the TG but upregulated in the CG. Six pathways (i.e., SSC05320, SSC04940, SSC05330, SSC04530, SSC04260 and SSC05412) were significant at a false discovery rate of <25%. Five pathways (i.e., SSC05320, SSC04940, SSC05330, SSC04530 and SSC04260) were significantly enriched at a nominal p value of less than 1% and 5%.

Further analysis revealed that several immune response genes were induced by leading edge analysis for the 13 significant pathways (Figure 6). The genes included those encoding CCL2, GM-CSF, HLA-B associated transcript 1, IGF-1, IL-6, IL-8, IL-18, TNF, Hsp70s, Hsp70.2, Fc fragment of IgE, MAP2K1, PIK3R5, MAPK 14, STAT3 and STAT5B, among others. Many genes related with metabolism as well as ribosomal protein genes were also induced in the inflamed lung. These genes included adiponectin, the Saccharomyces pombe cell division cycle 25 homolog C, cytochrome P450 (3A29, 3A39 and 3A46), FYN oncogene related to SRC, FGR and YES (FYN), the phosphatase and tensin homolog PTEN, PDK, Snrpa, Syk, SPI1, and v-Ha-ras, c-Myc, avian, among others.

The repressed genes comprised those encoding members of the MHC, including (SLA- 2, SLA-3, SLA-6, SLA-8, SLA-DRA, SLA-DQA1, SLA-DRB1, SLA-DMB, SLA-DQA, SLA-DMA, SLA-DQB1), CD40 molecule, CD40, IL-12B, IL-2, myosin, MYH, MYH2, CACNB4, CACNA2D, CPE, and FXYD2, among others.

Genes were frequently induced in the TG included p101, MAP2K1, H-RAS, TNF, MAPK14 and IGF1, while the genes such as CD40, IL12B, IL-2, SLA-2, SLA-3, SLA-6, SLA-8, SLA-DRA, LA-DQA1, SLA-DRB1, SLA-DMB, SLA-DQA, SLA-DMA and SLA-DQB1 were frequently suppressed.

2.4. Verification of Gene Expression Pattern from Microarray Data Using Real-Time QRT-PCR

Ten genes (i.e., RETN, ADAM17, GPNMB, CHRM1, ALDH2, IL6, KLRK1, DUOX2, OAS2 and KCNAB1) were selected to confirm expression patterns using real-time qRT-PCR. The results indicate that the expression patterns of all the genes were consistent with the microarray data (r = 0.905 ± 0.125, Figure 7).

3. Discussion

In the present study, we revealed 11,929 DE genes using Agilent Whole Porcine Genome Oligo Microarrays (one-color platform) that contain 43,603 probes. There were 1188 genes annotated as swine genes in the GenBank Data Base (DB). GO term analysis identified that a total of 89 BP categories, 82 cellular components and 182 molecular functions were significantly affected and at least 27 BP categories were related to the host immune response.

The NOD-like receptor signaling pathway, Fc epsilon RI signaling pathway, acute myeloid leukemia, melanoma, progesterone-mediated oocyte maturation, spliceosome, type II diabetes mellitus and steroid hormone biosynthesis etc. were significantly enriched in inflamed lung. Five pathways as type I diabetes mellitus, autoimmune thyroid disease, allograft rejection, tight junction and cardiac muscle contraction were significantly enriched in non-inflamed lung. The NOD-like receptor signaling pathway is one of the most important pathways associated with microbial recognition and host defense [8,9]. The innate immune system comprises several classes of pattern recognition receptors, including Toll-like receptors (TLRs), NOD-like receptors (NLRs) and RIG-1-like receptors. Two NLRs, NOD1 and NOD2, sense the cytosolic presence of the peptidoglycan fragments, meso-DAP and muramyl dipeptide, respectively, and drive the activation of MAPK and the transcription factor NF-kappaB (NF-κB). A different set of NLRs induces caspase-1 activation through the assembly of large protein complexes named inflammasomes [9]. Inflammasomes are critical for generating mature proinflammatory cytokines in concert with Toll-like receptor signaling pathways [10]. Nod proteins fight off bacterial infections by stimulating proinflammatory signaling and cytokine networks and by inducing antimicrobial effectors, such as nitric oxide and antimicrobial peptides [11]. Fc epsilon RI-mediated signaling pathways in mast cells are initiated by the interaction of antigen with IgE bound to the extracellular domain of the alpha chain of the Fc epsilon RI [1216]. The activation pathways are regulated by mast cells that release histamines and proteoglycans (especially heparin), lipid mediators such as leukotrienes (LTC4, LTD4 and LTE4) and prostaglandins (especially PDG2), and cytokines such as TNF-alpha, IL-4 and IL-5. These mediators and cytokines contribute to inflammatory response [14].

The pathways activated in infected lung tissues also include the acute myeloid leukemia pathway characterized by uncontrolled proliferation of clonal neoplastic cells and accumulation in the bone marrow of blasts with an impaired differentiation program [1722], progesterone-mediated oocyte maturation pathway involved in endocrine system either insulin/IGF-1 or the steroid hormone progesterone regulation [2325], steroid hormone biosynthesis pathway involved in lipid metabolism [2629], type II diabetes mellitus involved in endocrine and metabolic diseases [3033], spliceosome pathway involved in genetic information processing and transcription [3436], and melanoma pathway involved in cancer arising from the malignant transformation of melanocytes [3740].

The NOD-like receptor signaling pathway, Fc epsilon RI signaling pathway, acute myeloid leukemia pathway, progesterone-mediated oocyte maturation pathway were strongly linked to the MAPK signaling pathway, which are involved in environmental information processing and signal transduction [4143]; the acute myeloid leukemia pathway progesterone-mediated oocyte maturation pathway, melanoma pathway were all strongly linked to the cell cycle pathway, which plays an important role in the regulation of cell growth and death [4446]; and the NOD-like receptor signaling pathway, acute myeloid leukemia pathway, type II diabetes mellitus pathway, melanoma pathway were all directly or indirectly linked to the apoptosis pathway, which plays an important role in the regulation of apoptosis (programmed cell death) [4749]. Hence, the pathways linked to the cell function regulation, including the MAPK signaling pathway, apoptosis and Cell cycle pathway, were also affected directly or indirectly by the process of the body’s resistance to infection.

Many cytokines as shown by leading edge analysis were activated at the site of inflammation, including IL-6, IL-8, IL-18, TNF, GM-CSF, CCL2, p101 protein, HLA-B associated transcript 1, Fc fragment of IgE, MAPK14, MAP2K1, IGF-1, STAT3 and STAT5B, etc. IL-6 is responsible for stimulating acute phase protein synthesis, as well as the production of neutrophils in the bone marrow. IL-8 is synthesized by macrophages, endothelial cells and epithelial cells as host defenses against severe infection [50,51]. It serves as a chemical signal that attracts neutrophils to the site of any inflammation. Significant increases in IL-8 and IL6- mRNA after infection with APP have previously been observed in lung lavage as well as lung tissue using northern blotting and in situ hybridization [52,53]. IL-18 plays multiple roles in chronic inflammation and in a number of infections and enhances both Th-1- and Th-2-mediated immune response [54]. IL-18 is able to induce IFN gamma, GM-CSF, TNF-α and IL-1 in immunocompetent cells to activate killing by lymphocytes and to up-regulate the expression of certain chemokine receptors. GM-CSF stimulates stem cells to produce granulocytes (neutrophils, eosinophils, and basophils) and monocytes. Monocytes exit the circulation and migrate into tissues, whereupon they mature into macrophages. Thus, these cells play a part in the immune-inflammatory cascade, by which activation of a small number of macrophages can rapidly lead to an increase in their number, a process crucial for fighting infection. CCL2 recruits monocytes, memory T cells and dendritic cells to the sites of tissue injury, infection, and inflammation [55,56]. TNF-α can promote inflammatory response by inducing the production of other proinflammatory cytokines at the vicinity of the infection [57], and increase the expression of endothelial surface HLA-B by activation of the nuclear transcription factor NF-κB [58,59]. P101 protein is a single regulatory subunit of the phosphoinositide 3-kinase gamma (PI3Kα), which plays a crucial role in inflammatory and allergic processes [60,61], including neutrophil chemotaxis, mast cell degranulation, and cardiac function [62,63].

Genes involved in a variety of cellular function, including proliferation, differentiation, growth arrest or apoptosis of normal cells were affected including those encoding HLA-B associated transcript 1 [64,65], Fc fragment of IgE [66,67], MAPK14 [68], MAP2K1 [69], H-ras [70,71], IGF-1 [72], STAT3 and STAT5B [73]. Activations of all these genes can stimulate stem cells to produce granulocytes (neutrophils, eosinophils, and basophils) and monocytes, and also induce neutrophils and macrophages to phagocytose bacterial and foreign antigens.

Immunomodulatory cytokines were significantly suppressed at the site of inflammation. In this study, genes encoding IL2, IL12B, CD40, members of the MHC (SLA-2, SLA-3, SLA-6, SLA-8, SLA-DRB1, SLA-DMB, SLA-DQA, SLA-DMA and SLA-DQB1), as well as SLA-DRA and SLA-DQA1 in a previous study [74], were significantly down-regulated at the site of inflammation.

IL-2 is a type of cytokine immune system signaling molecule which is a leukocytotrophic hormone made in response to microbial infection that can identify the difference between self and non-self [75,76]. When environmental substances (molecules or microbes) gain access to the body, these substances (termed antigens) are recognized as foreign by antigen receptors that are expressed on the surface of lymphocytes. MHC can present antigenic peptides to T lymphocytes, which are responsible for a specific immune response that can destroy the pathogen producing those antigens [77]. CD40 is a co-stimulatory protein found on antigen presenting cells (APC) and is essential in mediating a broad variety of immune and inflammatory responses including T cell-dependent immunoglobulin class switching, memory B cell development, and germinal center formation [78]. The macrophage can express more CD40 and TNF receptors on its surface, which can increase the level of activation culminating in the induction of potent microbicidal substances in the macrophage; these include reactive oxygen species and nitric oxide, leading to the destruction of the ingested microbe [7982]. IL-12 is an essential inducer of Th1 cell development, and has an important role in sustaining a sufficient number of memory/effector Th1 cells to mediate long-term protection against an intracellular pathogen [83]. The suppression of these immunomodulatory cytokines leads to a decrease in antigenic peptides presented to T lymphocytes by APC via the MHC, as well as to alleviate immune response injury induced by infection at the site of inflammation.

Many genes encoding metabolism as well as ribosomal proteins were affected at the site of inflammation. Genes related to metabolism and ribosomal proteins synthesis were induced in the inflamed lung, including adiponectin, cell division cycle 25 homolog C, cytochrome P450 (CYP 3A29, CYP 3A39 and CYP 3A46), FYN, PTEN, PDK, Snrpa, Syk and SPI1, among others. The repressed genes comprised those encoding MYH1, MYH2, tropomyosin (alpha, beta), troponin I type 3 (cardiac), CACNB4, CACNA2D1, CPE and FXYD2; while the CYP2E1 and the CYP3A29 were known to be down-regulated during inflammation in another study [84].

SOCS3 and CISH, both found to be up-regulated in the present study, are members of the suppressor of cytokine signaling (SOCS) family of proteins whose members regulate protein turnover by targeting proteins for degradation [42]. Expression of the members of the SOCS family is induced by cytokines such as IL-6 and IL-10, both found to be up-regulated in this study; both function as negative feed- back regulators of cytokine signaling [85,86]. The statistically significant increase in mRNA coding for the anti-inflammatory cytokine IL-10, found in inflamed areas of the lung, is probably due to the function of IL-10 in counteracting the host mediated tissue damage caused by proinflammatory and chemotactic cytokines [87]. The lower expression levels observed for genes encoding ribosomal proteins could be due to a general downregulation of ribosomal biogenesis in the necrotic areas of the lung. Previous studies have shown that 41 out of 54 genes encoding ribosomal proteins were down-regulated in Pseudomonas aeruginosa after treatment with H2O2 induced oxidative stress [88].

As described above, we found that: (1) A total of 89 biological process categories, 82 cellular components and 182 molecular functions were significantly affected, and more than 27 biological process were involved in the host immune response; (2) At the site of inflammation, 13 pathways associated with host responses were affected significantly; many proinflammatory-inflammatory cytokines were activated and several immunomodulatory cytokines were suppressed at the gene expression level reflecting the complex machinery at work during an infection; (3) Many genes which were involved in a variety of cellular functions-proliferation, differentiation, growth arrest or apoptosis of normal cells that activated, could stimulate stem cells to produce granulocyte (neutrophil, eosinophil, and basophil) and monocyte. All changes of the genes and pathways which induced or repressed expression, not only led to decrease in antigenic peptides presented to T lymphocytes by APC via the MHC and alleviated immune response injury induced by infection, but also stimulated stem cells to produce granulocyte (neutrophil, eosinophil, and basophil ) and monocyte, and promote neutrophil and macrophages to phagocytose bacterial and foreign antigen at the site of inflammation. Additional work including more animals and time points is clearly needed to further delineate the host response to APP infection and will contribute to a more detailed description of the dynamics of host responses in general.

4. Experimental Section

4.1. Animals, Bacterial Inoculation and Samples

All animal procedures were performed according to protocols approved by the Biological Studies Animal Care and Use Committee of Sichuan Province, China. Twenty 12-week-old male castrated Danish Landrace/Yorkshire/Duroc crossbred swine from a healthy herd free from APP were divided equally into a control group (CG) and the treatment group (TG). APP serotype I (Strain provided by the Animal Biotechnology Center, Laboratory of Animal Disease and Human Health, Sichuan Agricultural University) was cultivated overnight at 37 °C in air on trypticase soy broth (TSB) (Hangwei, Hangzhou, China). Bacterial counts of the suspensions were performed at the same time as the start of the inoculation. The inoculation was performed by holding the pigs (1–10) from the TG in an upright sitting position and spraying 0.25mL diluent containing (3.5–4) × 107 CFU/mL APP per kilogram weight into the nostrils during inspiration. Swine from the CG (swines 11–20) were inoculated with physiological saline (0.9% wt/vol NaCl) by the same means. In the TG, lung tissue was collected from three swine (swines 1, 2 and 3) after abattage and used for total RNA extraction and pathological analysis. Another three swine (swines 11, 12 and 13) from the CG were sacrificed 48 h post-inoculation and their lung tissues were collected. The remaining swine were used for other trials.

4.2. Microarray Hybridizations and Data Analysis

Total RNA was extracted from tissues using Trizol reagent (Invitrogen, Carlsbad, CA, USA). RNA was purified and DNase treated using the RNeasy QIAGEN RNeasy® Mini Kit. cDNA was synthesized from 2 μg of total-RNA using the direct cDNA Labeling System. Aminoallyl-cRNA was synthesized from cDNA using the Superscript Indirect cDNA Labeling System. The cRNA was purified and DNase treated using RNeasy QIAGEN RNeasy® Mini Kit. RNA integrity was confirmed with a bioanalyzer (model 2100; Agilent Technologies, Palo Alto, CA, USA) according to the manufacturer’s protocol. Labeling and hybridization of the cRNA was performed with Agilent Whole Porcine Genome Oligo (4 × 44 K) Microarrays (one-color platform) at the National Engineering Center for Biochip at Shanghai, according to the manufacturer’s protocols. The slides were scanned and analyzed using the histogram method with default settings in an Agilent G2565AA and Agilent G2565BA Microarray Scanner System with SureScan Technology. The array data were submitted to GEO [89].

Comparisons between the CG and TG were carried out using three biological replicates for each group. CG samples and TG samples were used for microarray analysis. The six Microarray data were normalized using the quantile normalization method [90] with WebarrayDB (http://www.webarraydb.org/webarray/) [91] and were filtered and assessed by the MIDAW online analysis program (http://www.webarraydb.org/webarray/) [92] using the method of weighted K-nearest neighbor [93]. T-tests and hierarchical cluster analyses of the significantly differentially expressed (DE) genes (clustering method: complete linkage; similarity measure: Pearson product momentum correlation; ordering function: average value) for microarray data were carried out by a MultiExperiment Viewer (MeV) software package (Version 4.5, Dana-Farber Cancer Institute, Boston, MA, USA, 2009) [94].

Tests for statistical significance (p < 0.05), overrepresentation of Gene ontology (GO) terms [74,95], and pathway in Kyoto Encyclopedia of Genes and Genomes (KEGG) DB [10,96] (http://www.genome.jp/kegg/) both induced and repressed genes were conducted using the ErmineJ [97] and the Database for Annotation, Visualization and Integrated Discovery (DAVID) Online platform (http://david.abcc.ncifcrf.gov/) with a threshold of a minimum three genes annotated at each node. The leading edge analysis for the pathway of differential expression in microarray data with a threshold of a minimum of two genes and maximum of 20 genes annotated at each node was conducted using the GSEA V2.06 package [98,99]. More detailed descriptions of the microarray experiments are available at the NCBI Gene Expression Omnibus [100102].

4.3. Real-Time QRT-PCR

In order to confirm the reliability the expression profile in the microarray analyses, the expression level 10 gene (six up-regulated and four down-regulated) were performed by real-time qRT-PCR. Sequences for primers were obtained from Genbank and NCBI. Primers were designed using Primer 5 and synthesized at Invitrogen (Shanghai, China) (Table 1). Extracted RNA was converted into cDNA by reverse transcription of 1 μL total RNA using SYBR® PrimeScriptTM RT-PCR Kit (TaKaRa, Japan) according to the manufacturer’s protocol and then cDNA was stored at −20 °C until use. Quantitative PCR was performed in a 25 μL reaction volume (2 μL cDNA, 12.5 μL of SYBR® Premix Ex TaqTM (2×) TaKaRa, Japan), 0.5 μL of 10 μM upstream and downstream primers respectively, and added ddH2O to 25 μL) on the BIO-RAD IQ5 System (BIO-RAD, Hercules, CA, USA). Real-time PCR conditions were as follows: 30 s at 95.0 °C, 40 cycles of denaturation at 95 °C for 5 s followed by 30 s annealing and elongation at 51.2–60 °C (Table 8). Efficiency of primer pairs is reported in Table 1. Melting curves were obtained at the end of each run to confirm a single PCR product. All samples were run in triplicate. Non-template controls were included in each run to exclude contamination and nonspecific amplification. Expression levels of samples were normalised by using a normalisation factor calculated by the program geNorm. This normalisation factor was calculated based on RT-qPCR results for three selected reference genes, ACTB, TOP2B and TBP.

This allowed quantification of the target gene in one sample relative to that in another (the calibrator) using the “2−ΔΔCt method” of calculating fold changes in gene expression [103]. Correlation analysis between qRT-PCR and microarray was conducted.

5. Conclusions

We have generated reliable mRNA transcriptomes of swine lung tissues from APP-infected and negative control pigs. We have identified a set of differentially expressed (DE) genes in our current case-control study, and a functional enrichment analysis indicated that these DE genes mainly related to “host immune response” and “host response”. In addition, we also found that, in the APP-infected lung tissues, many proinflammatory-inflammatory cytokines were activated and involved in the regulation of the host defense response at the site of inflammation, while the cytokines involved in regulation of the host immune response were suppressed. The current study provides data that can be used in future studies to decipher the molecular mechanism of the systematic influences from porcine pleuropneumonia. Our findings will also help promote the further development of therapy for porcine pleuropneumonia.

Acknowledgments

We thank the farmer that provided swine. This work was supported financially by the Natural Science Foundation of Science and Technology Department of Sichuan Province.

Conflict of Interest

The authors declare no conflict of interest. Our experiments involving the use of swine, and the use of pigs and all experimental procedures involving animals were approved by Sichuan Agricultural University Animal Care and Use Committee.

References

  1. Taylor, D.J. Actinobacillus Pleuropneumoniae. In Diseases of Swine; Straw, B.E., d’Allaire, S., Mengeling, W.L., Taylor, D.J., Eds.; Iowa State University Press: Ames, IA, USA, 1999; Volume 26, pp. 343–354.
  2. Baarsch, M.J.; Foss, D.L.; Murtaugh, M.P. Pathophysiologic correlates of acute porcine pleuropneumonia. Am. J. Vet. Res 2000, 61, 684–690.
  3. Cho, W.S.; Chae, C. Expression of nitric oxide synthase 2 and tumor necrosis factor alpha in swine naturally infected with Actinobacillus. pleuropneumoniae. Vet. Pathol 2002, 39, 27–32.
  4. Cho, W.S.; Jung, K.; Kim, J.; Ha, Y.; Chae, C. Expression of mRNA encoding interleukin (IL)-10, IL-12p35 and IL-12p40 in lungs from swines experimentally infected with Actinobacillus pleuropneumoniae. Vet. Res. Commun 2005, 29, 111–122.
  5. Moser, R.J.; Reverter, A.; Kerr, C.A.; Beh, K.J.; Lehnert, S.A. A mixedmodel approach for the analysis of cDNA microarray gene expression data from extreme-performing swines after infection with Actinobacillus pleuropneumoniae. J. Anim. Sci 2004, 82, 1261–1271.
  6. Hedegaard, J.; Skovgaard, K.; Mortensen, S.; Sorensen, P.; Jensen, T.; Hornshoj, H.; Bendixen, C.; Heegaard, P.M.H. Molecular characterisation of the early response in pigs to experimental infection with Actinobacillus pleuropneumoniae using cDNA microarrays. Acta Vet. Scand 2007, 49, 11.
  7. Mortensen, S.; Skovgaard, K.; Hedegaard, J.; Bendixen, C.; Heegaard, P.M. Transcriptional profiling at different sites in lungs of pigs during acute bacterial respiratory infection. Innate Immun 2011, 17, 41–53.
  8. Shaw, M.H.; Reimer, T.; Kim, Y.G.; Nunez, G. NOD-like receptors (NLRs): Bona fide intracellular microbial sensors. Curr. Opin. Immunol 2008, 20, 377–382.
  9. Kanneganti, T.D.; Lamkanfi, M.; Nunez, G. Intracellular NOD-like receptors in host defense and disease. Immunity 2007, 27, 549–559.
  10. Aoki-Kinoshita, K.F.; Kanehisa, M. Gene annotation and pathway mapping in KEGG. Methods Mol. Biol 2007, 396, 71–91.
  11. Tattoli, I.; Travassos, L.H.; Carneiro, L.A.; Magalhaes, J.G.; Girardin, S.E. The Nodosome: Nod1 and Nod2 control bacterial infections and inflammation. Semin. Immunopathol 2007, 29, 289–301.
  12. Kawakami, T.; Galli, S.J. Regulation of mast-cell and basophil function and survival by IgE. Nat. Rev. Immunol 2002, 2, 773–786.
  13. Siraganian, R.P. Mast cell signal transduction from the high-affinity IgE receptor. Curr. Opin. Immunol 2003, 15, 639–646.
  14. Turner, H.; Kinet, J.P. Signalling through the high-affinity IgE receptor Fc epsilonRI. Nature 1999, 402, B24–B30.
  15. Gilfillan, A.M.; Tkaczyk, C. Integrated signalling pathways for mast-cell activation. Nat. Rev. Immunol 2006, 6, 218–230.
  16. Nadler, M.J.; Matthews, S.A.; Turner, H.; Kinet, J.P. Signal transduction by the high-affinity immunoglobulin E receptor Fc epsilon RI: Coupling form to function. Adv. Immunol 2000, 76, 325–355.
  17. Steffen, B.; Muller-Tidow, C.; Schwable, J.; Berdel, W.E.; Serve, H. The molecular pathogenesis of acute myeloid leukemia. Crit. Rev. Oncol. Hematol 2005, 56, 195–221.
  18. Tenen, D.G. Disruption of differentiation in human cancer: AML shows the way. Nat. Rev. Cancer 2003, 3, 89–101.
  19. Martelli, A.M.; Nyakern, M.; Tabellini, G.; Bortul, R.; Tazzari, P.L.; Evangelisti, C.; Cocco, L. Phosphoinositide 3-kinase/Akt signaling pathway and its therapeutical implications for human acute myeloid leukemia. Leukemia 2006, 20, 911–928.
  20. Lennartsson, J.; Jelacic, T.; Linnekin, D.; Shivakrupa, R. Normal and oncogenic forms of the receptor tyrosine kinase kit. Stem Cells 2005, 23, 16–43.
  21. Lorsbach, R.B.; Downing, J.R. The role of the AML1 transcription factor in leukemogenesis. Int. J. Hematol 2001, 74, 258–265.
  22. Mizuki, M.; Schwäble, J.; Steur, C.; Choudhary, C.; Agrawal, S.; Sargin, B.; Steffen, B.; Matsumura, I.; Kanakura, Y.; Böhmer, F.D. Suppression of myeloid transcription factors and induction of STAT response genes by AML-specific Flt3 mutations. Blood 2003, 101, 3164–73.
  23. Nebreda, A.R.; Ferby, I. Regulation of the meiotic cell cycle in oocytes. Curr. Opin. Cell. Biol 2000, 12, 666–675.
  24. Andersen, C.B.; Sakaue, H.; Nedachi, T.; Kovacina, K.S.; Clayberger, C.; Conti, M.; Roth, R.A. Protein kinase B/Akt is essential for the insulin- but not progesterone-stimulated resumption of meiosis in Xenopus oocytes. Biochem. J 2003, 369, 227–238.
  25. Abrieu, A.; Doree, M.; Fisher, D. The interplay between cyclin-B-Cdc2 kinase (MPF) and MAP kinase during maturation of oocytes. J. Cell. Sci 2001, 114, 257–267.
  26. Ghayee, H.K.; Auchus, R.J. Basic concepts and recent developments in human steroid hormone biosynthesis. Rev. Endocr. Metab. Disord 2007, 8, 289–300.
  27. Holmes, M.C.; Seckl, J.R. The role of 11beta-hydroxysteroid dehydrogenases in the brain. Mol. Cell. Endocrinol 2006, 248, 9–14.
  28. Thomas, J.L.; Duax, W.L.; Addlagatta, A.; Brandt, S.; Fuller, R.R.; Norris, W. Structure/function relationships responsible for coenzyme specificity and the isomerase activity of human type 13 beta-hydroxysteroid dehydrogenase/isomerase. J. Biol. Chem 2003, 278, 35483–35490.
  29. Tomlinson, J.W.; Stewart, P.M. Cortisol metabolism and the role of 11beta-hydroxysteroid dehydrogenase. Best Pract. Res. Clin. Endocrinol. Metab 2001, 15, 61–78.
  30. Gual, P.; le Marchand-Brustel, Y.; Tanti, J.F. Positive and negative regulation of insulin signaling through IRS-1 phosphorylation. Biochimie 2005, 87, 99–109.
  31. Kaneto, H.; Matsuoka, T.-a.; Nakatani, Y.; Kawamori, D.; Miyatsuka, T.; Matsuhisa, M.; Yamasaki, Y. Oxidative stress, ER stress, and the JNK pathway in type 2 diabetes. J. Mol. Med 2005, 83, 429–439.
  32. Sakai, K.; Matsumoto, K.; Nishikawa, T.; Suefuji, M.; Nakamaru, K.; Hirashima, Y.; Kawashima, J.; Shirotani, T.; Ichinose, K.; Brownlee, M. Mitochondrial reactive oxygen species reduce insulin secretion by pancreatic beta-cells. Biochem. Biophys. Res. Commun 2003, 300, 216–222.
  33. Robertson, R.P. Chronic oxidative stress as a central mechanism for glucose toxicity in pancreatic islet beta cells in diabetes. J. Biol. Chem 2004, 279, 42351–42354.
  34. Wahl, M.C.; Will, C.L.; Luhrmann, R. The spliceosome: Design principles of a dynamic RNP machine. Cell 2009, 136, 701–718.
  35. Chen, Y.-I.G.; Moore, R.E.; Helen, Y.G.; Young, M.K.; Lee, T.D.; Stevens, S.W. Proteomic analysis of in vivo-assembled pre-mRNA splicing complexes expands the catalog of participating factors. Nucleic Acids Res 2007, 35, 3928–3944.
  36. Ritchie, D.B.; Schellenberg, M.J.; Macmillan, A.M. Spliceosome structure: Piece by piece. Biochim. Biophys. Acta 2009, 1789, 624–633.
  37. Levy, C.; Khaled, M.; Fisher, D.E. MITF: Master regulator of melanocyte development and melanoma oncogene. Trends Mol. Med 2006, 12, 406–414.
  38. Tsao, H.; Goel, V.; Wu, H.; Yang, G.; Haluska, F.G. Genetic interaction between NRAS and BRAF mutations and PTEN/MMAC1 inactivation in melanoma. J. Invest. Dermatol 2004, 122, 337–341.
  39. Gruss, C.; Herlyn, M. Role of cadherins and matrixins in melanoma. Curr. Opin. Oncol 2001, 13, 117–123.
  40. Lázár-Molnár, E.; Hegyesi, H.; Tóth, S.; Falus, A. Autocrine and paracrine regulation by cytokines and growth factors in melanoma. Cytokine 2000, 12, 547–554.
  41. Tanoue, T.; Nishida, E. Docking interactions in the mitogen-activated protein kinase cascades. Pharmacol. Ther 2002, 93, 193–202.
  42. Yang, S.H.; Sharrocks, A.D.; Whitmarsh, A.J. Transcriptional regulation by the MAP kinase signaling cascades. Gene 2003, 320, 3–21.
  43. Zhu, X.; Lee, H.G.; Raina, A.K.; Perry, G.; Smith, M.A. The role of mitogen-activated protein kinase pathways in Alzheimer’s disease. Neurosignals 2002, 11, 270–281.
  44. Smith, M.L.; Chen, I.T.; Zhan, Q.; Bae, I.; Chen, C.Y.; Gilmer, T.M.; Kastan, M.B.; O’Connor, P.M.; Fornace, A.J., Jr. Interaction of the p53-regulated protein Gadd45 with proliferating cell nuclear antigen. Science 1994, 266, 1376–1380.
  45. Diehl, J.A.; Cheng, M.; Roussel, M.F.; Sherr, C.J. Glycogen synthase kinase-3beta regulates cyclin D1 proteolysis and subcellular localization. Genes Dev 1998, 12, 3499–3511.
  46. Klein, E.A.; Assoian, R.K. Transcriptional regulation of the cyclin D1 gene at a glance. J. Cell. Sci 2008, 121, 3853–3857.
  47. Sprick, M.R.; Walczak, H. The interplay between the Bcl-2 family and death receptor-mediated apoptosis. Biochim. Biophys. Acta 2004, 1644, 125–132.
  48. Steelman, L.S.; Pohnert, S.C.; Shelton, J.G.; Franklin, R.A.; Bertrand, F.E.; McCubrey, J.A. JAK/STAT, Raf/MEK/ERK, PI3K/Akt and BCR-ABL in cell cycle progression and leukemogenesis. Leukemia 2004, 18, 189–218.
  49. Martinon, F.; Holler, N.; Richard, C.; Tschopp, J. Activation of a pro-apoptotic amplification loop through inhibition of NF-κB-dependent survival signals by caspase-mediated inactivation of RIP. FEBS Lett 2000, 468, 134–136.
  50. Wolff, B.; Burns, A.R.; Middleton, J.; Rot, A. Endothelial cell “memory” of inflammatory stimulation: Human venular endothelial cells store interleukin 8 in Weibel-Palade bodies. J. Exp. Med 1998, 188, 1757–1762.
  51. Utgaard, J.O.; Jahnsen, F.L.; Bakka, A.; Brandtzaeg, P.; Haraldsen, G. Rapid secretion of prestored interleukin 8 from Weibel-Palade bodies of microvascular endothelial cells. J. Exp. Med 1998, 188, 1751–1756.
  52. Baarsch, M.J.; Scamurra, R.W.; Burger, K.; Foss, D.L.; Maheswaran, S.K.; Murtaugh, M.P. Inflammatory cytokine expression in swine experimentally infected with Actinobacillus pleuropneumoniae. Infect. Immun 1995, 63, 3587–3594.
  53. Myers, M.J.; Baarsch, M.J.; Murtaugh, M.P. Effects of pentoxifylline on inflammatory cytokine expression and acute pleuropneumonia in swine. Immunobiology 2002, 205, 17–34.
  54. Nakanishi, K.; Yoshimoto, T.; Tsutsui, H.; Okamura, H. Interleukin-18 regulates both Th1 and Th2 responses. Annu. Rev. Immunol 2001, 19, 423–474.
  55. Carr, M.W.; Roth, S.J.; Luther, E.; Rose, S.S.; Springer, T.A. Monocyte chemoattractant protein 1 acts as a T-lymphocyte chemoattractant. Proc. Natl. Acad. Sci. USA 1994, 91, 3652–3656.
  56. Xu, L.L.; Warren, M.K.; Rose, W.L.; Gong, W.; Wang, J.M. Human recombinant monocyte chemotactic protein and other C–C chemokines bind and induce directional migration of dendritic cells in vitro. J. Leukoc. Biol 1996, 60, 365–371.
  57. Toews, G.B. Cytokines and the lung. Eur. Respir. J 2001, 34, S3–S17.
  58. Krönke, M.; Schütze, S.; Scheurich, P.; Pfizenmaier, K. TNF signal transduction and TNF-responsive genes. Immunol. Ser 1992, 56, 189–216.
  59. Schütze, S.; Potthoff, K.; Machleidt, T.; Berkovic, D.; Wiegmann, K.; Kronke, M. TNF activates NF-kappa B by phosphatidylcholine-specific phospholipase C-induced “acidic” sphingomyelin breakdown. Cell 1992, 71, 765–776.
  60. Brock, C.; Schaefer, M.; Reusch, H.P.; Czupalla, C.; Michalke, M.; Spicher, K.; Schultz, G.; Nürnberg, B. Roles of G beta gamma in membrane recruitment and activation of p110 gamma/p101 phosphoinositide 3-kinase gamma. J. Cell. Biol 2003, 160, 89–99.
  61. Voigt, P.; Brock, C.; Nürnberg, B.; Schaefer, M. Assigning functional domains within the p101 regulatory subunit of phosphoinositide 3-kinase γ. J. Biol. Chem 2005, 280, 5121–5127.
  62. Wymann, M.P.; Zvelebil, M.; Laffargue, M. Phosphoinositide 3-kinase signalling—Which way to target? Trends Pharmacol. Sci 2003, 24, 366–376.
  63. Crackower, M.A.; Oudit, G.Y.; Kozieradzki, I.; Sarao, R.; Sun, H.; Sasaki, T.; Hirsch, E.; Suzuki, A.; Shioi, T.; Irie-Sasaki, J.; et al. Regulation of myocardial contractibility and cell size by distinct PI3K-PTEN signaling pathways. Cell 2002, 110, 737–749.
  64. Peelman, L.J.; Chardon, P.; Nunes, M.; Renard, C.; Geffrotin, C.; Vaiman, M.; Zeveren, A.V.; Coppieters, W.; Weghe, A.V.; Bouquet, Y.; et al. The BAT1 gene in the MHC encodes an evolutionarily conserved putative nuclear RNA helicase of the DEAD family. Genomics 1995, 26, 210–218.
  65. Spies, T.; Blanck, G.; Bresnahan, M.; Sands, J.; Strominger, J.L. A new cluster of genes within the human major histocompatibility complex. Science 1989, 243, 214–217.
  66. Von Bubnoff, D.; Novak, N.; Kraft, S.; Bieber, T. The central role of FcɛRI in allergy. Clin. Exp. Dermatol 2003, 28, 184–187.
  67. Gounni, A.; Lamkhioued, B.; Ochiai, K.; Tanaka, Y.; Delaporte, E.; Capron, A.; Kinet, J.P.; Capron, M. High-affinity IgE receptor on eosinophils is involved in defence against parasites. Nature 1994, 367, 183–186.
  68. Gesslein, B.; Hakansson, G.; Carpio, R.; Gustafsson, L.; Perez, M.T.; Malmsjo, M. Mitogen-activated protein kinases in the porcine retinal arteries and neuroretina following retinal ischemia-reperfusion. Mol. Vis 2010, 16, 392–407.
  69. Kubota, Y.; O’Grady, P.; Saito, H.; Takekawa, M. Oncogenic Ras abrogates MEK SUMO ylation that suppresses the ERK pathway and cell transformation. Nat. Cell. Biol 2011, 13, 282–291.
  70. Reuther, G.W.; Der, C.J. The Ras branch of small GTPases: Ras family members don’t fall far from the tree. Curr. Opin. Cell. Biol 2000, 12, 157–165.
  71. Malumbres, M.; Pellicer, A. RAS pathways to cell cycle control and cell transformation. Front. Biosci 1998, 3, 887–912.
  72. Yilmaz, A.; Davis, M.E.; Simmen, R.C. Reproductive performance of bulls divergently selected on the basis of blood serum insulin-like growth factor I concentration. J. Anim. Sci 1999, 77, 835–839.
  73. Kisseleva, T.; Bhattacharya, S.; Braunstein, J.; Schindler, C.W. Signaling through the JAK/STAT pathway, recent advances and future challenges. Gene 2002, 285, 1–24.
  74. Mootha, V.K.; Lindgren, C.M.; Eriksson, K.-F.; Subramanian, A.; Sihag, S.; Lehar, J.; Puigserver, P.; Carlsson, E.; Ridderstråle, M.; Laurila, E. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet 2003, 34, 267–273.
  75. Cantrell, D.A.; Smith, K.A. The interleukin-2 T-cell system: A new cell growth model. Science 1984, 224, 1312–1316.
  76. Smith, K.A. Interleukin-2: Inception, impact, and implications. Science 1988, 240, 1169–1176.
  77. Steinman, R.M.; Inaba, K.; Turley, S.; Pierre, P.; Mellman, I. Antigen capture, processing, and presentation by dendritic cells: recent cell biological studies. Human immunol 1999, 60, 562–567.
  78. Entrez Gene: CD40 CD40 molecule, TNF receptor superfamily member 5, Available online: http://www.ncbi.nlm.nih.gov/gene/958 (Accessed on 14 May 2013).
  79. Tojo, T.; Aoki, T.; Kobayashi, N.; Ohishi, T.; Watanabe, T.; Yamamoto, T.; Inoue, J. TRAF5, a novel tumor necrosis factor receptor-associated factor family protein, mediates CD40 signaling. Proc. Natl. Acad. Sci. USA 1996, 93, 9437–9442.
  80. Cheng, G.; Schoenberger, S.P. CD40 signaling and autoimmunity. Curr. Dir. Autoimmun 2002, 5, 51–61.
  81. Dallman, C.; Johnson, P.W.; Packham, G. Differential regulation of cell survival by CD40. Apoptosis 2003, 8, 45–53.
  82. O’Sullivan, B.; Thomas, R. Recent advances on the role of CD40 and dendritic cells in immunity and tolerance. Curr. Opin. Hematol 2004, 10, 272–278.
  83. Oppmann, B.; Lesley, R.; Blom, B.; Timans, J.C.; Xu, Y.M.; Hunte, B.; Vega, F.; Yu, N.; Wang, J.; Singh, K.; et al. Novel p19 protein engages IL-12p40 to form a cytokine, IL-23, with biological activities similar as well as distinct from IL-12. Immunity 2000, 13, 715–725.
  84. Jover, R.; Bort, R.; Gomez-Lechon, M.J.; Castell, J.V. Down-regulation of human CYP3A4 by the inflammatory signal interleukin 6: Molecular mechanism and transcription factors involved. FASEB. J 2002, 16, 1799–1801.
  85. Kile, B.T.; Schulman, B.A.; Alexander, W.S.; Nicola, N.A.; Martin, H.M.E.; Hiton, D.J. The SOCS box: A tale of destruction and degradation. Trends Biochem. Sci 2002, 27, 235–241.
  86. Krebs, D.L.; Hilton, D.J. SOCS proteins: Negative regulators of cytokine signaling. Stem Cells 2001, 19, 378–387.
  87. Morrison, D.F.; Foss, D.L.; Murtaugh, M.P. Interleukin-10 gene therapy-mediated amelioration of bacterial pneumonia. Infect. Immun 2000, 68, 4752–4758.
  88. Palma, M.; DeLuca, D.; Worgall, S.; Quadri, L.E.N. Transcriptome analysis of the response of Pseudomonas aeruginosa to hydrogen peroxide. J. Bacteriol 2004, 186, 248–252.
  89. Series GSE42317, Available online: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42317 (Accessed on 15 November 2012).
  90. Hu, J.; He, X. Enhanced quantile normalization of microarray data to reduce loss of information in gene expression profiles. Biometrics 2007, 63, 50–59.
  91. Xia, X.; McClelland, M.; Wang, Y. WebArray: An online platform for microarray data analysis. BMC Bioinf 2005, 6, 306.
  92. Romualdi, C.; Vitulo, N.; Favero, M.D.; Lanfranchi, G. MIDAW: A web tool for statistical analysis of microarray data. Nucleic Acids Res 2005, 33, W644–W649.
  93. Troyanskaya, O.; Cantor, M.; Sherlock, G.; Brown, P.; Hastie, T.; Tibshiran, R.; Bostein, D.; Altman, R.B. Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17, 520–525.
  94. Saeed, A.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.; Klapa, M.; Currier, T.; Thiagarajan, M. TM4: A free, open-source system for microarray data management and analysis. Biotechniques 2003, 34, 374–378.
  95. Bild, A.; Febbo, P.G. Application of a priori established gene sets to discover biologically important differential expression in microarray data. Proc. Natl. Acad. Sci. USA 2005, 102, 15278–15279.
  96. Dennis, G.; Sherman, B.T.; Hosack, D.A.; Yang, J.; Gao, W.; Lane, H.C.; Lempicki, R.A. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol 2003, 4, 1–11.
  97. Lee, H.K.; Braynen, W.; Keshav, K.; Pavlidis, P. ErmineJ: Tool for functional analysis of gene expression data sets. BMC Bioinf 2005, 6, 269.
  98. Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gilette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545–15550.
  99. Dopazo, J. Functional interpretation of microarray experiments. OMICS 2006, 10, 398–410.
  100. Davis, S.; Meltzer, P.S. GEOquery: A bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007, 23, 1846–1847.
  101. Barrett, T.; Suzek, T.O.; Troup, D.B.; Wilhite, S.E.; Ngau, W.-C.; Ledoux, P.; Rudnev, D.; Lash, A.E.; Fujibuchi, W.; Edgar, R. NCBI GEO: Mining millionsof expression profiles-database and tools. Nucleic Acids Res 2005, 33, 562–566.
  102. Edgar, R.; Domrachev, M.; Lash, A.E. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30, 207–210.
  103. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001, 25, 402–408.
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Figure 1. (A) Normal lung from healthy swine; (B) Damaged lung after APP infection (Lung in TG showing swelling, bleeding and fibrinous exudate sticking to the lung surface; while no lesion in CG).

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Figure 1. (A) Normal lung from healthy swine; (B) Damaged lung after APP infection (Lung in TG showing swelling, bleeding and fibrinous exudate sticking to the lung surface; while no lesion in CG).
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Figure 2. No lesions were observed in CG lung tissue (scale bar = 50 μm, 200×).

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Figure 2. No lesions were observed in CG lung tissue (scale bar = 50 μm, 200×).
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Figure 3. Alveolar cavities were filled with pink serum and red blood cells (A) (scale bar = 50 μm, 200×). and filled with serum, lymphocyte infiltration in the alveolar wall (B) (scale bar = 25 μm, 400×) in the lung of TG.

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Figure 3. Alveolar cavities were filled with pink serum and red blood cells (A) (scale bar = 50 μm, 200×). and filled with serum, lymphocyte infiltration in the alveolar wall (B) (scale bar = 25 μm, 400×) in the lung of TG.
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Figure 4. Hierarchical clustering analysis and clustering segmentation.

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Figure 4. Hierarchical clustering analysis and clustering segmentation.
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Figure 5. Three-dimensional map of principal component analysis (PCA) for mapping samples obtained from clustering segmentation.

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Figure 5. Three-dimensional map of principal component analysis (PCA) for mapping samples obtained from clustering segmentation.
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Figure 6. The heat map shows the clustered genes in the leading edge subsets. In the heat map, expression values are represented as colors, where the range of colors (red, pink, light blue, dark blue) represents the range of expression values (high, moderate, low, lowest) in the CG. This pattern is reversed in the TG.

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Figure 6. The heat map shows the clustered genes in the leading edge subsets. In the heat map, expression values are represented as colors, where the range of colors (red, pink, light blue, dark blue) represents the range of expression values (high, moderate, low, lowest) in the CG. This pattern is reversed in the TG.
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Figure 7. Validation of the microarray data by the real-time qRT-PCR analyses of ten representative genes. The x-axis represents the genes and the y-axis shows their relative expression levels (−ΔCt) values for quantitative real-time RT-PCR; Log (Sample signal, 10) for microarray. Three biological replicates were conducted for both assays. R represents the Pearson correlation coefficient. The significance of differences for gene expression between the CG and the TG was calculated using a two-tailed T-test.

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Figure 7. Validation of the microarray data by the real-time qRT-PCR analyses of ten representative genes. The x-axis represents the genes and the y-axis shows their relative expression levels (−ΔCt) values for quantitative real-time RT-PCR; Log (Sample signal, 10) for microarray. Three biological replicates were conducted for both assays. R represents the Pearson correlation coefficient. The significance of differences for gene expression between the CG and the TG was calculated using a two-tailed T-test.
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Table 1. Eigenvalues and contribution ratio of principal component analysis (PCA) for differential expression genes.

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Table 1. Eigenvalues and contribution ratio of principal component analysis (PCA) for differential expression genes.
Principal componentEigenvaluesContribution ratio
150.1796.95%
21.3392.59%
30.1110.21%
40.0880.17%
50.0270.05%
60.0130.02%
Table 2. The significant gene ontology biological processes in pigs.

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Table 2. The significant gene ontology biological processes in pigs.
NameDescriptionProbeGenes
[GO:0050896]response to stimulus9696
[GO:0051179]Localization9292
[GO:0006810]Transport8888
[GO:0006807]nitrogen compound metabolic process8484
[GO:0019222]regulation of metabolic process6767
[GO:0002376]immune system process5656
[GO:0055114]oxidation reduction5353
[GO:0006955]immune response5353
[GO:0032501]multicellular organismal process5252
[GO:0006950]response to stress5151
[GO:0009056]catabolic process5050
[GO:0032502]developmental process3838
[GO:0065008]regulation of biological quality3535
[GO:0007275]multicellular organismal development3535
[GO:0022610]biological adhesion2929
[GO:0048518]positive regulation of biological process2828
[GO:0016043]cellular component organization2727
[GO:0009605]response to external stimulus2525
[GO:0048856]anatomical structure development2222
[GO:0008219]cell death2222
[GO:0048519]negative regulation of biological process2121
[GO:0033036]macromolecule localization2121
[GO:0048523]negative regulation of cellular process1919
[GO:0048522]positive regulation of cellular process1919
[GO:0044281]small molecule metabolic process1818
[GO:0048869]cellular developmental process1717
[GO:0042221]response to chemical stimulus1717
[GO:0006066]alcohol metabolic process1717
[GO:0006996]organelle organization1616
[GO:0051641]cellular localization1515
[GO:0019882]antigen processing and presentation1515
[GO:0008104]protein localization1515
[GO:0048583]regulation of response to stimulus1313
[GO:0042592]homeostatic process1212
[GO:0032879]regulation of localization1212
[GO:0007049]cell cycle1212
[GO:0048584]positive regulation of response to stimulus1111
[GO:0009893]positive regulation of metabolic process1111
[GO:0002682]regulation of immune system process1111
[GO:0051716]cellular response to stimulus1010
[GO:0019725]cellular homeostasis1010
[GO:0016192]vesicle-mediated transport1010
[GO:0009653]anatomical structure morphogenesis1010
[GO:0002252]immune effector process1010
[GO:0051239]regulation of multicellular organismal process99
[GO:0048731]system development99
[GO:0065009]regulation of molecular function88
[GO:0051704]multi-organism process88
[GO:0051128]regulation of cellular component organization88
[GO:0050778]positive regulation of immune response88
[GO:0044085]cellular component biogenesis88
[GO:0007610]behavior88
[GO:0003008]system process88
[GO:0051301]cell division77
[GO:0030029]actin filament-based process77
[GO:0022607]cellular component assembly77
[GO:0009607]response to biotic stimulus77
[GO:0000003]reproduction77
[GO:0055085]transmembrane transport66
[GO:0051707]response to other organism66
[GO:0051129]negative regulation of cellular component organization66
[GO:0050878]regulation of body fluid levels66
[GO:0050793]regulation of developmental process66
[GO:0023052]signaling66
[GO:0022414]reproductive process66
[GO:0016044]cellular membrane organization66
[GO:0048646]anatomical structure formation involved in morphogenesis55
[GO:0040011]locomotion55
[GO:0019953]sexual reproduction55
[GO:0019637]organophosphate metabolic process55
[GO:0010817]regulation of hormone levels55
[GO:0009892]negative regulation of metabolic process55
[GO:0060348]bone development44
[GO:0044087]regulation of cellular component biogenesis44
[GO:0043933]macromolecular complex subunit organization44
[GO:0042330]taxis44
[GO:0040008]regulation of growth44
[GO:0022402]cell cycle process44
[GO:0070271]protein complex biogenesis33
[GO:0048609]reproductive process in a multicellular organism33
[GO:0046903]secretion33
[GO:0040012]regulation of locomotion33
[GO:0034621]cellular macromolecular complex subunit organization33
[GO:0019748]secondary metabolic process33
[GO:0010605]negative regulation of macromolecule metabolic process33
[GO:0009719]response to endogenous stimulus33
[GO:0009628]response to abiotic stimulus33
[GO:0007017]microtubule-based process33
[GO:0002520]immune system development33
Table 3. The significant gene ontology cellular components in pigs.

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Table 3. The significant gene ontology cellular components in pigs.
NameDescriptionProbeGenes
[GO:0005886]plasma membrane9898
[GO:0005634]nucleus8686
[GO:0032991]macromolecular complex8686
[GO:0044422]organelle part8484
[GO:0043234]protein complex6262
[GO:0043228]non-membrane-bounded organelle4646
[GO:0044421]extracellular region part4545
[GO:0044459]plasma membrane part4444
[GO:0031090]organelle membrane4141
[GO:0005739]mitochondrion3737
[GO:0005783]endoplasmic reticulum3535
[GO:0005794]Golgi apparatus3333
[GO:0005615]extracellular space2929
[GO:0012505]endomembrane system2525
[GO:0044429]mitochondrial part2323
[GO:0016023]cytoplasmic membrane-bounded vesicle2323
[GO:0005856]cytoskeleton2323
[GO:0031974]membrane-enclosed lumen2222
[GO:0031975]envelope2121
[GO:0005840]ribosome1919
[GO:0044428]nuclear part1818
[GO:0005578]proteinaceous extracellular matrix1717
[GO:0044430]cytoskeletal part1515
[GO:0071212]subsynaptic reticulum1515
[GO:0005740]mitochondrial envelope1515
[GO:0005829]cytosol1414
[GO:0031966]mitochondrial membrane1414
[GO:0019898]extrinsic to membrane1414
[GO:0042611]MHC protein complex1313
[GO:0048770]pigment granule1212
[GO:0044431]Golgi apparatus part1212
[GO:0005773]vacuole1111
[GO:0005764]lysosome1010
[GO:0044432]endoplasmic reticulum part1010
[GO:0005792]microsome99
[GO:0009898]internal side of plasma membrane99
[GO:0005743]mitochondrial inner membrane99
[GO:0005887]integral to plasma membrane88
[GO:0005789]endoplasmic reticulum membrane88
[GO:0005768]endosome88
[GO:0005759]mitochondrial matrix88
[GO:0005654]nucleoplasm88
[GO:0015630]microtubule cytoskeleton88
[GO:0030054]cell junction77
[GO:0031300]intrinsic to organelle membrane77
[GO:0042613]MHC class II protein complex77
[GO:0044451]nucleoplasm part77
[GO:0031301]integral to organelle membrane66
[GO:0005635]nuclear envelope66
[GO:0042612]MHC class I protein complex66
[GO:0015629]actin cytoskeleton66
[GO:0016469]proton-transporting two-sector ATPase complex66
[GO:0031225]anchored to membrane55
[GO:0031968]organelle outer membrane55
[GO:0031965]nuclear membrane55
[GO:0043235]receptor complex55
[GO:0033279]ribosomal subunit55
[GO:0005819]spindle44
[GO:0030173]integral to Golgi membrane44
[GO:0048471]perinuclear region of cytoplasm44
[GO:0005911]cell-cell junction44
[GO:0043292]contractile fiber44
[GO:0000502]proteasome complex44
[GO:0030135]coated vesicle44
[GO:0016459]myosin complex44
[GO:0005874]microtubule44
[GO:0042995]cell projection33
[GO:0045259]proton-transporting ATP synthase complex33
[GO:0044420]extracellular matrix part33
[GO:0015935]small ribosomal subunit33
[GO:0005777]peroxisome33
[GO:0034702]ion channel complex33
[GO:0005901]caveola33
[GO:0045202]synapse33
[GO:0031227]intrinsic to endoplasmic reticulum membrane33
[GO:0016323]basolateral plasma membrane33
[GO:0005681]spliceosomal complex33
[GO:0030141]secretory granule33
[GO:0005667]transcription factor complex33
[GO:0033176]proton-transporting V-type ATPase complex33
[GO:0033177]proton-transporting two-sector ATPase complex, proton-transporting domain33
[GO:0005730]nucleolus33
Table 4. The significant gene ontology molecular functions in pigs.

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Table 4. The significant gene ontology molecular functions in pigs.
NameDescriptionProbeGenes
[GO:0017076]purine nucleotide binding9191
[GO:0003676]nucleic acid binding8888
[GO:0032555]purine ribonucleotide binding8181
[GO:0004872]receptor activity7575
[GO:0004690]cyclic nucleotide-dependent protein kinase activity6868
[GO:0004691]cAMP-dependent protein kinase activity6767
[GO:0016491]oxidoreductase activity6767
[GO:0008270]zinc ion binding6464
[GO:0030554]adenyl nucleotide binding6363
[GO:0005102]receptor binding5757
[GO:0032559]adenyl ribonucleotide binding5353
[GO:0005215]transporter activity5252
[GO:0008233]peptidase activity4646
[GO:0070011]peptidase activity, acting on l-amino acid peptides4343
[GO:0003677]DNA binding4343
[GO:0004888]transmembrane receptor activity4040
[GO:0005509]calcium ion binding3939
[GO:0022892]substrate-specific transporter activity3939
[GO:0004687]myosin light chain kinase activity3838
[GO:0030528]transcription regulator activity3737
[GO:0022857]transmembrane transporter activity3636
[GO:0022891]substrate-specific transmembrane transporter activity3535
[GO:0030234]enzyme regulator activity3535
[GO:0005506]iron ion binding3333
[GO:0004175]endopeptidase activity3333
[GO:0003700]transcription factor activity3232
[GO:0015075]ion transmembrane transporter activity3030
[GO:0019001]guanyl nucleotide binding2828
[GO:0005198]structural molecule activity2828
[GO:0004857]enzyme inhibitor activity2626
[GO:0016788]hydrolase activity, acting on ester bonds2626
[GO:0008324]cation transmembrane transporter activity2525
[GO:0009055]electron carrier activity2424
[GO:0004930]G-protein coupled receptor activity2424
[GO:0003723]RNA binding2323
[GO:0005126]cytokine receptor binding2222
[GO:0016874]ligase activity2222
[GO:0048037]cofactor binding2020
[GO:0016817]hydrolase activity, acting on acid anhydrides1919
[GO:0003735]structural constituent of ribosome1919
[GO:0005125]cytokine activity1818
[GO:0030414]peptidase inhibitor activity1818
[GO:0050662]coenzyme binding1717
[GO:0016757]transferase activity, transferring glycosyl groups1717
[GO:0030246]carbohydrate binding1717
[GO:0008092]cytoskeletal protein binding1616
[GO:0022890]inorganic cation transmembrane transporter activity1616
[GO:0016746]transferase activity, transferring acyl groups1616
[GO:0016879]ligase activity, forming carbon-nitrogen bonds1616
[GO:0000287]magnesium ion binding1515
[GO:0008237]metallopeptidase activity1515
[GO:0016614]oxidoreductase activity, acting on CH–OH group of donors1515
[GO:0022804]active transmembrane transporter activity1515
[GO:0019955]cytokine binding1414
[GO:0017171]serine hydrolase activity1414
[GO:0046906]tetrapyrrole binding1414
[GO:0016616]oxidoreductase activity, acting on the CH–OH group of donors, NAD or NADP as acceptor1414
[GO:0016747]transferase activity, transferring acyl groups other than amino-acyl groups1414
[GO:0008528]peptide receptor activity, G-protein coupled1414
[GO:0003779]actin binding1414
[GO:0004252]serine-type endopeptidase activity1313
[GO:0016705]oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen1313
[GO:0004497]monooxygenase activity1212
[GO:0042578]phosphoric ester hydrolase activity1212
[GO:0008234]cysteine-type peptidase activity1111
[GO:0046873]metal ion transmembrane transporter activity1111
[GO:0008289]lipid binding1111
[GO:0016791]phosphatase activity1111
[GO:0050660]FAD binding1010
[GO:0015078]hydrogen ion transmembrane transporter activity1010
[GO:0016758]transferase activity, transferring hexosyl groups1010
[GO:0004867]serine-type endopeptidase inhibitor activity1010
[GO:0004428]inositol or phosphatidylinositol kinase activity1010
[GO:0016798]hydrolase activity, acting on glycosyl bonds1010
[GO:0005516]calmodulin binding99
[GO:0016810]hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds99
[GO:0042623]ATPase activity, coupled99
[GO:0001871]pattern binding99
[GO:0016776]phosphotransferase activity, phosphate group as acceptor99
[GO:0005179]hormone activity99
[GO:0070851]growth factor receptor binding99
[GO:0004197]cysteine-type endopeptidase activity99
[GO:0005057]receptor signaling protein activity99
[GO:0004950]chemokine receptor activity99
[GO:0004222]metalloendopeptidase activity99
[GO:0043565]sequence-specific DNA binding99
[GO:0005216]ion channel activity88
[GO:0016853]isomerase activity88
[GO:0000826]inositol pyrophosphate synthase activity88
[GO:0019842]vitamin binding88
[GO:0005539]glycosaminoglycan binding88
[GO:0005529]sugar binding88
[GO:0005066]transmembrane receptor protein tyrosine kinase signaling protein activity88
[GO:0020037]heme binding88
[GO:0004356]glutamate-ammonia ligase activity88
[GO:0005507]copper ion binding77
[GO:0016209]antioxidant activity77
[GO:0008238]exopeptidase activity77
[GO:0008009]chemokine activity77
[GO:0016860]intramolecular oxidoreductase activity77
[GO:0004721]phosphoprotein phosphatase activity77
[GO:0015291]secondary active transmembrane transporter activity77
[GO:0016563]transcription activator activity66
[GO:0005244]voltage-gated ion channel activity66
[GO:0008201]heparin binding66
[GO:0031420]alkali metal ion binding66
[GO:0046983]protein dimerization activity66
[GO:0015082]di-, tri-valent inorganic cation transmembrane transporter activity66
[GO:0004312]fatty-acid synthase activity66
[GO:0042802]identical protein binding66
[GO:0016684]oxidoreductase activity, acting on peroxide as acceptor66
[GO:0016829]lyase activity55
[GO:0008047]enzyme activator activity55
[GO:0003924]GTPase activity55
[GO:0004091]carboxylesterase activity55
[GO:0015399]primary active transmembrane transporter activity55
[GO:0005261]cation channel activity55
[GO:0019904]protein domain specific binding55
[GO:0004694]eukaryotic translation initiation factor 2alpha kinase activity55
[GO:0016627]oxidoreductase activity, acting on the CH–CH group of donors55
[GO:0031406]carboxylic acid binding55
[GO:0042803]protein homodimerization activity55
[GO:0004518]nuclease activity55
[GO:0019899]enzyme binding55
[GO:0003774]motor activity55
[GO:0008430]selenium binding55
[GO:0004725]protein tyrosine phosphatase activity55
[GO:0015293]symporter activity55
[GO:0004713]protein tyrosine kinase activity55
[GO:0046915]transition metal ion transmembrane transporter activity44
[GO:0008135]translation factor activity, nucleic acid binding44
[GO:0008134]transcription factor binding44
[GO:0010857]calcium-dependent protein kinase activity44
[GO:0050661]NADP or NADPH binding44
[GO:0060589]nucleoside-triphosphatase regulator activity44
[GO:0008373]sialyltransferase activity44
[GO:0004896]cytokine receptor activity44
[GO:0008083]growth factor activity44
[GO:0016709]oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, NADH or NADPH as one donor, and incorporation of one atom of oxygen44
[GO:0004576]oligosaccharyl transferase activity44
[GO:0008509]anion transmembrane transporter activity44
[GO:0051540]metal cluster binding44
[GO:0004177]aminopeptidase activity44
[GO:0016765]transferase activity, transferring alkyl or aryl (other than methyl) groups44
[GO:0015929]hexosaminidase activity44
[GO:0016741]transferase activity, transferring one-carbon groups44
[GO:0004129]cytochrome-c oxidase activity44
[GO:0004521]endoribonuclease activity44
[GO:0051287]NAD or NADH binding33
[GO:0005385]zinc ion transmembrane transporter activity33
[GO:0004774]succinate-CoA ligase activity33
[GO:0004090]carbonyl reductase (NADPH) activity33
[GO:0005249]voltage-gated potassium channel activity33
[GO:0008757]S-adenosylmethionine-dependent methyltransferase activity33
[GO:0005160]transforming growth factor beta receptor binding33
[GO:0008235]metalloexopeptidase activity33
[GO:0005275]amine transmembrane transporter activity33
[GO:0019840]isoprenoid binding33
[GO:0019838]growth factor binding33
[GO:0005128]erythropoietin receptor binding33
[GO:0016801]hydrolase activity, acting on ether bonds33
[GO:0016701]oxidoreductase activity, acting on single donors with incorporation of molecular oxygen33
[GO:0003712]transcription cofactor activity33
[GO:0015144]carbohydrate transmembrane transporter activity33
[GO:0003995]acyl-CoA dehydrogenase activity33
[GO:0004869]cysteine-type endopeptidase inhibitor activity33
[GO:0031402]sodium ion binding33
[GO:0019888]protein phosphatase regulator activity33
[GO:0016651]oxidoreductase activity, acting on NADH or NADPH33
[GO:0004180]carboxypeptidase activity33
[GO:0030695]GTPase regulator activity33
[GO:0010181]FMN binding33
[GO:0003743]translation initiation factor activity33
[GO:0016861]intramolecular oxidoreductase activity, interconverting aldoses and ketoses33
[GO:0004522]pancreatic ribonuclease activity33
[GO:0015294]solute:cation symporter activity33
[GO:0016790]thiolester hydrolase activity33
[GO:0008026]ATP-dependent helicase activity33
[GO:0030145]manganese ion binding33
[GO:0008417]fucosyltransferase activity33
[GO:0008194]UDP-glycosyltransferase activity33
[GO:0008199]ferric iron binding33
Table 5. Significant GO terms related to the immune responses caused by infection with APP.

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Table 5. Significant GO terms related to the immune responses caused by infection with APP.
NOGO termBiological processNumber PermineJ
1GO:0050896response to stimulus960
2GO:0051179Localization920
3GO:0002376immune system process560
4GO:0006955immune response530
5GO:0055114oxidation reduction530
1GO:0050896response to stimulus960
6GO:0006950response to stress510
7GO:0022610biological adhesion290
8GO:0009605response to external stimulus250
9GO:0008219cell death220
10GO:0033036macromolecule localization210
11GO:0042221response to chemical stimulus170
12GO:0019882antigen processing and presentation150
13GO:0048583regulation of response to stimulus130
14GO:0032879regulation of localization120
15GO:0042592homeostatic process120
16GO:0048584positive regulation of response to stimulus110
17GO:0002682regulation of immune system process110
18GO:0051716cellular response to stimulus100
19GO:0002252immune effector process100
20GO:0050778positive regulation of immune response80
21GO:0009607response to biotic stimulus70
22GO:0023052signaling60
23GO:0040011locomotion50
24GO:0042330taxis40
25GO:0002520immune system development30
26GO:0009628response to abiotic stimulus30
27GO:0009719response to endogenous stimulus30
Table 6. Gene sets enriched in phenotype treatment group (TG).

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Table 6. Gene sets enriched in phenotype treatment group (TG).
NO.PathwaySizeESNESNOM p-valFDR q-valFWER p-valRank at maxLeading edge
1ssc04664: Fc epsilon RI signaling pathway13−0.74−1.830.0010.0840.06489Tags = 77%, list = 17%, signal = 91%
2ssc04930: Type II diabetes mellitus5−0.82−1.620.0170.7150.68542Tags = 60%, list = 8%, signal = 65%
3ssc04914: Progesterone-mediated oocyte maturation12−0.66−1.610.0120.5080.70156Tags = 42%, list = 11%, signal = 46%
4ssc00140: Steroid hormone biosynthesis4−0.85−1.580.0120.5290.81444Tags = 75%, list = 9%, signal = 81%
5ssc04621: NOD-like receptor signaling pathway13−0.62−1.560.0190.490.86149tags = 46%, list = 10%, signal = 50%
6ssc05221: Acute myeloid leukemia11−0.63−1.540.0340.4780.901133tags = 73%, list = 26%, signal = 96%
7ssc05218: Melanoma10−0.64−1.510.0490.5620.955100tags = 50%, list = 19%, signal = 61%
8ssc03040: Spliceosome9−0.67−1.50.0390.5240.961101tags = 44%, list = 20%, signal = 54%
9ssc04640: Hematopoietic cell lineage19−0.53−1.480.0550.550.982120tags = 58%, list = 23%, signal = 73%
10ssc04210: Apoptosis10−0.61−1.480.0660.5040.984158tags = 70%, list = 31%, signal = 99%
11Ssc05214: Glioma11−0.61−1.470.0520.4670.985100tags = 45%, list = 19%, signal = 55%
12ssc04012: ErbB signaling pathway13−0.56−1.450.0760.5390.999102tags = 38%, list = 20%, signal = 47%
13ssc05020: Prion diseases8−0.64−1.440.070.533165tags = 50%, list = 13%, signal = 56%
14ssc04666: Fc gamma R-mediated phagocytosis12−0.56−1.40.0980.6411135tags = 67%, list = 26%, signal = 88%
15ssc04650: Natural killer cell mediated cytotoxicity19−0.52−1.40.0870.605189tags = 42%, list = 17%, signal = 49%
16ssc00650: Butanoate metabolism5−0.72−1.390.0880.58116tags = 20%, list = 1%, signal = 20%
17ssc00410: Beta-Alanine metabolism5−0.71−1.380.0760.60316tags = 20%, list = 1%, signal = 20%
18ssc04660: T cell receptor signaling pathway18−0.49−1.350.1120.6891122tags = 56%, list = 24%, signal = 70%
19ssc04370: VEGF signaling pathway8−0.61−1.350.1520.6641122tags = 63%, list = 24%, signal = 81%
20ssc05219: Bladder cancer10−0.56−1.340.1320.6481147tags = 60%, list = 29%, signal = 82%
21ssc04920: Adipocytokine signaling pathway12−0.53−1.340.1460.634178tags = 42%, list = 15%, signal = 48%
22ssc04114: Oocyte meiosis12−0.54−1.340.1320.608156tags = 25%, list = 11%, signal = 27%
23ssc00750: Vitamin B6 metabolism2−0.86−1.330.090.6172tags = 100%, list = 14%, signal = 116%
24ssc00260: Glycine, serine and threonine metabolism4−0.71−1.320.1310.5971128tags = 75%, list = 25%, signal = 99%
25ssc04960: Aldosterone-regulated sodium reabsorption5−0.69−1.320.1390.5791106tags = 60%, list = 21%, signal = 75%
26ssc00910: Nitrogen metabolism3−0.76−1.290.1640.646191tags = 67%, list = 18%, signal = 81%
27ssc05220: Chronic myeloid leukemia14−0.5−1.270.2060.691172tags = 57%, list = 34%, signal = 84%
28ssc04115: P53 signaling pathway12−0.51−1.270.1970.6871156tags = 75%, list = 30%, signal = 105%
29ssc04630: Jak-STAT signaling pathway19−0.44−1.220.2340.8131105tags = 42%, list = 20%, signal = 51%
30ssc00640: Propanoate metabolism9−0.53−1.220.230.798161tags = 22%, list = 12%, signal = 25%
31ssc05213: Endometrial cancer10−0.51−1.210.2480.7871133tags = 50%, list = 26%, signal = 66%
32ssc00591: Linoleic acid metabolism4−0.66−1.190.270.829144tags = 75%, list = 9%, signal = 81%
33ssc05215: Prostate cancer17−0.44−1.190.2430.8111115tags = 35%, list = 22%, signal = 44%
34ssc00280: Valine, leucine and isoleucine degradation11−0.49−1.180.2690.8261157tags = 45%, list = 31%, signal = 64%
35ssc00620: Pyruvate metabolism7−0.54−1.170.2850.8181239tags = 100%, list = 47%, signal = 185%
36ssc00010: Glycolysis/Gluconeogenesis12−0.47−1.170.260.7961243tags = 83%, list = 47%, signal = 155%
37ssc04150: MTOR signaling pathway7−0.56−1.170.2910.78142tags = 29%, list = 8%, signal = 31%
38ssc00250: Alanine, aspartate and glutamate metabolism5−0.6−1.160.3020.7916tags = 20%, list = 1%, signal = 20%
39ssc00511: Other glycan degradation4−0.63−1.160.3120.7721195tags = 100%, list = 38%, signal = 160%
40ssc05212: Pancreatic cancer13−0.45−1.160.3040.7541172tags = 54%, list = 34%, signal = 79%
41ssc00604: Glycosphingolipid biosynthesis4−0.63−1.150.2990.7551195tags = 100%, list = 38%, signal = 160%
42ssc00052: Galactose metabolism3−0.66−1.120.3380.835152tags = 33%, list = 10%, signal = 37%
43ssc00520: Amino sugar and nucleotide sugar metabolism6−0.54−1.110.3530.8311116tags = 50%, list = 23%, signal = 64%
44ssc04070: Phosphatidylinositol signaling system6−0.55−1.110.3580.8131100tags = 67%, list = 19%, signal = 82%
45ssc04662: B cell receptor signaling pathway12−0.45−1.110.3360.7981120tags = 58%, list = 23%, signal = 74%
46ssc00500: Starch and sucrose metabolism4−0.6−1.10.370.809157tags = 50%, list = 11%, signal = 56%
47ssc05014: Amyotrophic lateral sclerosis (ALS)5−0.57−1.090.4040.812149tags = 40%, list = 10%, signal = 44%
48ssc00310: Lysine degradation4−0.59−1.090.3810.7951149tags = 75%, list = 29%, signal = 105%
49ssc04144: Endocytosis18−0.41−1.080.3840.814126tags = 17%, list = 5%, signal = 17%
50ssc00533: Keratan sulfate biosynthesis2−0.69−1.070.3890.8011162tags = 100%, list = 32%, signal = 146%
51ssc04623: Cytosolic DNA-sensing pathway8−0.47−1.070.40.791130tags = 25%, list = 6%, signal = 26%
52ssc00340: Histidine metabolism5−0.55−1.060.3970.8061149tags = 60%, list = 29%, signal = 84%
53ssc00980: Metabolism of xenobiotics by cytochrome P4509−0.45−1.040.4420.835144tags = 33%, list = 9%, signal = 36%
54ssc00270: Cysteine and methionine metabolism5−0.53−1.040.4780.8241245tags = 100%, list = 48%, signal = 190%
55ssc04330: Notch signaling pathway6−0.5−1.030.4730.83314tags = 17%, list = 1%, signal = 17%
56ssc00330: Arginine and proline metabolism10−0.43−1.030.4470.8241174tags = 50%, list = 34%, signal = 74%
57ssc05223: Non-small cell lung cancer7−0.47−1.010.4870.844189tags = 43%, list = 17%, signal = 51%
58ssc04720: Long-term potentiation8−0.46−1.010.480.84189tags = 25%, list = 17%, signal = 30%
59ssc04110: Cell cycle19−0.36−10.4720.8321220tags = 58%, list = 43%, signal = 98%
60ssc04730: Long-term depression13−0.39−10.4640.822134tags = 15%, list = 7%, signal = 16%
61ssc05211: Renal cell carcinoma13−0.4−10.4670.8121193tags = 62%, list = 38%, signal = 96%
62ssc04912: GnRH signaling pathway13−0.38−0.980.5070.8421122tags = 31%, list = 24%, signal = 39%
63ssc00983: Drug metabolism5−0.5−0.970.5150.838144tags = 60%, list = 9%, signal = 65%
64ssc00450: Selenoamino acid metabolism3−0.58−0.970.5470.841176tags = 67%, list = 34%, signal = 101%
65ssc00071: Fatty acid metabolism8−0.43−0.950.5560.8561244tags = 75%, list = 48%, signal = 141%
66ssc00020: Citrate cycle (TCA cycle)9−0.41−0.950.5330.8451309tags = 100%, list = 60%, signal = 247%
67ssc03018: RNA degradation2−0.6−0.930.5830.8661206tags = 100%, list = 40%, signal = 166%
68ssc00603: Glycosphingolipid biosynthesis6−0.44−0.930.580.8661195tags = 83%, list = 38%, signal = 133%
69ssc00053: Ascorbate and aldarate metabolism3−0.54−0.920.5990.861239tags = 100%, list = 47%, signal = 186%
70ssc04020: Calcium signaling pathway20−0.33−0.920.5780.85129tags = 10%, list = 6%, signal = 10%
71ssc00510: N-Glycan biosynthesis8−0.41−0.910.6070.8631306tags = 100%, list = 60%, signal = 244%
72ssc00903: Limonene and pinene degradation3−0.54−0.910.6310.8511239tags = 100%, list = 47%, signal = 186%
73ssc03320: PPAR signaling pathway13−0.36−0.910.5980.843178tags = 31%, list = 15%, signal = 35%
74ssc04142: Lysosome16−0.34−0.890.6230.8671195tags = 75%, list = 38%, signal = 117%
75ssc05210: Colorectal cancer13−0.35−0.890.5990.857142tags = 15%, list = 8%, signal = 16%
76ssc04622: RIG-I-like receptor signaling pathway12−0.35−0.870.6260.867149tags = 25%, list = 10%, signal = 27%
77ssc04320: Dorso-ventral axis formation2−0.57−0.870.6740.86134tags = 50%, list = 7%, signal = 53%
78ssc04130: SNARE interactions in vesicular transport2−0.57−0.870.6760.8571123tags = 50%, list = 24%, signal = 66%
79ssc00230: Purine metabolism6−0.42−0.850.680.869121tags = 17%, list = 4%, signal = 17%
80ssc00380: Tryptophan metabolism4−0.46−0.850.6990.8651239tags = 75%, list = 47%, signal = 139%
81ssc04910:Insulin signaling pathway16−0.32−0.840.6880.8591104tags = 25%, list = 20%, signal = 30%
82ssc00190: Oxidative phosphorylation17−0.31−0.840.6660.851317tags = 94%, list = 62%, signal = 238%
83ssc00051: Fructose and mannose metabolism4−0.46−0.840.6970.848169tags = 25%, list = 13%, signal = 29%
84ssc04614: Renin-angiotensin system3−0.5−0.840.7150.8391143tags = 33%, list = 28%, signal = 46%
85ssc00860: Porphyrin and chlorophyll metabolism3−0.49−0.830.7090.8361238tags = 67%, list = 46%, signal = 124%
86ssc00350: Tyrosine metabolism5−0.43−0.830.6920.8331263tags = 80%, list = 51%, signal = 163%
87ssc00561: Glycerolipid metabolism6−0.38−0.790.7330.871239tags = 83%, list = 47%, signal = 154%
88ssc00030: Pentose phosphate pathway5−0.41−0.790.7440.868157tags = 20%, list = 11%, signal = 22%
89ssc04540: Gap junction11−0.32−0.770.7640.8841134tags = 27%, list = 26%, signal = 36%
90ssc04916: Melanogenesis14−0.29−0.750.7870.8921145tags = 29%, list = 28%, signal = 39%
91ssc00562: Inositol phosphate metabolism7−0.35−0.750.7750.8841100tags = 43%, list = 19%, signal = 53%
92ssc03050 Proteasome:4−0.4−0.740.8140.887197tags = 25%, list = 19%, signal = 31%
93ssc00600: Sphingolipid metabolism5−0.36−0.710.8210.9191107tags = 40%, list = 21%, signal = 50%
94ssc00630: Glyoxylate and dicarboxylate metabolism2−0.46−0.70.9070.9161142tags = 50%, list = 28%, signal = 69%
95ssc04512: ECM-receptor interaction11−0.29−0.690.8530.91618tags = 9%, list = 2%, signal = 9%
96ssc00531: Glycosaminoglycan degradation4−0.35−0.650.8990.9461240tags = 75%, list = 47%, signal = 140%
97ssc05216: Thyroid cancer8−0.29−0.630.9040.9481133tags = 38%, list = 26%, signal = 50%
98ssc04520: Adherens junction10−0.26−0.610.9020.9571170tags = 40%, list = 33%, signal = 59%
99ssc00564: Glycerophospholipid metabolism5−0.3−0.580.940.9691219tags = 60%, list = 43%, signal = 104%
100ssc00360: Phenylalanine metabolism5−0.26−0.510.9720.9921219tags = 60%, list = 43%, signal = 104%
101ssc04270: Vascular smooth muscle contraction14−0.18−0.460.9880.998134tags = 7%, list = 7%, signal = 7%
102ssc04350: TGF-beta signaling pathway14−0.18−0.450.9820.991172tags = 36%, list = 34%, signal = 52%
Table 7. Gene sets enriched in phenotype control group (CG).

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Table 7. Gene sets enriched in phenotype control group (CG).
No.PathwaySizeESNESNOM p-valFDR q-valFWER p-valRank at MaxLeading edge
1ssc05320: Autoimmune thyroid disease150.641.970.0030.0340.058139tags = 87%, list = 27%, signal = 115%
2ssc04940: Type I diabetes mellitus160.591.860.0030.0530.165139tags = 88%, list = 27%, signal = 116%
3ssc05330: Allograft rejection170.541.720.0150.1160.458139tags = 82%, list = 27%, signal = 109%
4Ssc04530: Tight junction170.521.670.0130.1410.6282tags = 12%, list = 0%, signal = 11%
5ssc04260: Cardiac muscle contraction130.551.650.030.1320.683107tags = 46%, list = 21%, signal = 57%
6ssc05412: Arrhythmogenic right ventricular cardiomyopathy120.561.570.0510.1960.853155tags = 75%, list = 30%, signal = 105%
7ssc02010: ABC transporters20.831.340.1360.60.99889tags = 100%, list = 17%, signal = 121%
8ssc05340: Primary immunodeficiency70.541.30.1790.6190.99961tags = 43%, list = 12%, signal = 48%
9ssc05217: Basal cell carcinoma50.561.250.2410.6681114tags = 60%, list = 22%, signal = 76%
10ssc04740: Olfactory transduction30.671.220.2590.6641172Tags = 100%, list = 34%, signal = 150%
11ssc04120: Ubiquitin mediated proteolysis80.471.180.2710.683132Tags = 25%, list = 6%, signal = 26%
12ssc03010: Ribosome170.371.170.2570.6571328Tags = 100%, list = 64%, signal = 268%
13ssc01040: Biosynthesis of unsaturated fatty acids20.731.150.3360.634111Tags = 50%, list = 2%, signal = 51%
14ssc05012: Parkinson’s disease170.351.130.3020.6331337Tags = 100%, list = 66%, signal = 282%
15ssc05332: Graft-versus-host disease150.361.10.3670.6461139Tags = 80%, list = 27%, signal = 107%
16ssc00590: Arachidonic acid metabolism110.391.080.3520.634155Tags = 36%, list = 11%, signal = 40%
17ssc04360: Axon guidance120.361.050.3930.653156Tags = 33%, list = 11%, signal = 37%
18ssc04310: Wnt signaling pathway140.351.030.4620.661132Tags = 43%, list = 26%, signal = 56%
19ssc04340: Hedgehog signaling pathway30.5510.4790.6711114Tags = 67%, list = 22%, signal = 85%
20ssc00982: Drug metabolism120.350.990.4550.651140Tags = 25%, list = 8%, signal = 26%
21ssc04080: Neuroactive ligand-receptor interaction170.290.940.5250.706143tags = 29%, list = 8%, signal = 31%
22ssc00830: Retinol metabolism70.390.940.5240.682116Tags = 29%, list = 3%, signal = 29%
23ssc00565: Ether lipid metabolism40.430.840.6860.8111295Tags = 100%, list = 58%, signal = 233%
24ssc05222: Small cell lung cancer140.260.790.7180.848151Tags = 21%, list = 10%, signal = 23%
25ssc00480: Glutathione metabolism60.340.780.7460.8321339Tags = 100%, list = 66%, signal = 291%
26ssc05310: Asthma110.270.770.7410.8061139Tags = 73%, list = 27%, signal = 98%
27ssc00563: Glycosylphosphatidylinositol (GPI)-anchor biosynthesis20.490.770.7690.7761263Tags = 100%, list = 51%, signal = 204%
28ssc00601: Glycosphingolipid biosynthesis40.330.680.8460.8571164Tags = 75%, list = 32%, signal = 109%
Table 8. Information on the primers used for qRT-PCR.

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Table 8. Information on the primers used for qRT-PCR.
Confirmation objectsGene symbolPrimer sequence (5′→3′)Amplicon length (bp)Ta (°C)GenBank No.
Reference geneACTBTCTGGCACCACACCTTCT11460DQ178122
TGATCTGGGTCATCTTCTCAC
TBPGATGGACGTTCGGTTTAGG12460DQ178129
AGCAGCACAGTACGAGCAA
TOP2BAACTGGATGATGCTAATGATGCT13760AF222921
TGGAAAAACTCCGTATCTGTCTC
Up geneRETNAGTGCGCTGGCATAGACTGG19760NM_213783
CATCCTCTTCTCAAGGTTTATTTCC
ADAM17TTGAGGAAGGGGAAGCC15856NM_001099926
ACGGAGCCCACGATGTT
GPNMBGAGACCCAGCCTTCCTT13051.2NM_001098584
TTGCTTTCTATCGCTTTGTA
CHRM1CGCTGGTCAAGGAGAAGAA18556NM_214034
GCACATGGGGTTGATGGT
ALDH2AAACTGCTCTGCGGTGGA18156NM_001044611
CGTACTTGGAATTGTTGGCTC
IL6GTCGAGGCTGTGCAGATTAG10156NM_214399
GCATTTGTGGTGGGGTTAG
Down geneKLRK1TGATGTGATAAACCGTGGTG10756NM_213813
TGGATCGGGCAAGGAAA
DUOX2CCCTTCTTCAACTCCCTG15851.2NM_213999
CAAAAGTTCTCATAGTGGTGC
OAS2GACACGGCTGAAGGTTT29151.2NM_001031796
TGGCACGTCCCAAGACT
KCNAB1AAGGGAGAAAACAGCAAAAC17656NM_001105294
AACCTGAATGGCACCGA
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