Systems infection biology: a compartmentalized immune network of pig spleen challenged with Haemophilus parasuis
- Ming Zhao†1,
- Xiang-dong Liu†1,
- Xin-yun Li1,
- Hong-bo Chen1, 2,
- Hui Jin3,
- Rui Zhou3,
- Meng-jin Zhu1Email author and
- Shu-hong Zhao1
© Zhao et al.; licensee BioMed Central Ltd. 2013
Received: 7 October 2011
Accepted: 18 January 2013
Published: 22 January 2013
Network biology (systems biology) approaches are useful tools for elucidating the host infection processes that often accompany complex immune networks. Although many studies have recently focused on Haemophilus parasuis, a model of Gram-negative bacterium, little attention has been paid to the host's immune response to infection. In this article, we use network biology to investigate infection with Haemophilus parasuis in an in vivo pig model.
By targeting the spleen immunogenome, we established an expression signature indicative of H. parasuis infection using a PCA/GSEA combined method. We reconstructed the immune network and estimated the network topology parameters that characterize the immunogene expressions in response to H. parasuis infection. The results showed that the immune network of H. parasuis infection is compartmentalized (not globally linked). Statistical analysis revealed that the reconstructed network is scale-free but not small-world. Based on the quantitative topological prioritization, we inferred that the C1R-centered clique might play a vital role in responding to H. parasuis infection.
Here, we provide the first report of reconstruction of the immune network in H. parasuis-infected porcine spleen. The distinguishing feature of our work is the focus on utilizing the immunogenome for a network biology-oriented analysis. Our findings complement and extend the frontiers of knowledge of host infection biology for H. parasuis and also provide a new clue for systems infection biology of Gram-negative bacilli in mammals.
KeywordsPig model Haemophilus parasuis Spleen Immunogenome Network Quantitative topology Scale-free, C1R
Glässer's disease is caused by Haemophilus parasuis (shortened as H. parasuis or HPS), a model Gram-negative bacillus. This disease is an important cause of economic loss in the world's pig industry, which is clinically characterized by fibrinous polyserositis, polyarthritis and meningitis . To date, the major focus of studies of porcine Glässer's disease has centered on clinical symptoms, pathology and diagnosis, susceptibility and epidemiology, pathogenic biology, vaccine development, and evaluation of virulence-associated factors [2–6]. Many of these investigations have highly focused on the major aspects of biology and pathogenesis for the H. parasuis bacterium itself. Aside from several recent studies [7–11], the molecular mechanisms of the pig host that are involved in the response to the H. parasuis invasion have not been well addressed. More importantly, the pig is an excellent biomedical model because it has a closer phylogenetic and physiological relationship to humans than rodent models . In addition to being a potential asset for undiscovered clinical and therapeutic needs, pigs infected with H. parasuis could also serve as mammalian and human models for bacterial infectious diseases.
It is well known that the immune genes (hereafter referred to as immunogenes) have played central roles in the regulation of pathogen-induced host processes in vivo, including those of Glässer's disease. Systems biology (also referred to as network biology) approaches have brought a research paradigm for infectious diseases; for example, a systems biology program was recently initiated by the National Institute of Allergy and Infectious Diseases . Systems biology investigations of the transcriptome of host immunogenome could provide a profound exploration of the molecular events occurring, for example, the three- or even four-dimensional relationships between genes during a response to pathogen infection. This would increase our understanding of host resistance/susceptibility genes, immune response mechanisms, and molecular basis of host-pathogen interactions [14, 15]. So, the systems biology approaches can also provide us with powerful tools for uncovering the molecular immune mechanisms that defend against H. parasuis infection.
Custom-build gene chips have been widely applied in a variety of investigations [16–21]. On many occasions, a reduced fragment of microarray data could work more efficiently to reveal more subtle insights into the target biological phenomena than the non-reduced global genome data do [22–24]. As a consequence, an analysis focusing on immunogenes could give a more precise exploration of the transcriptomic landscape of infection-induced immune processes in hosts.
In the body's immune system, spleen is an important target organ for studies of immune mechanisms. It has been well documented that the spleen is a crucial immune organ to protect the body against a variety of diseases and infections [25–27]. The spleen, known as the blood cleaner for its role in capturing foreign antigens and destroying old red blood cells, is made up of a variety of immune cells and blood cells, including B cells, T cells, macrophages, dendritic cells, natural killer cells and red blood cells [28–31]. When migratory macrophages and dendritic cells bring antigens to the spleen, the immune cells (e.g., T- and B-lymphocytes) become activated and trigger a series of immune responses [32–35]. Although not obligatory for survival, it has been proven that the spleen plays a key role in mounting immune responses to antigens, and in the absence of the spleen, the body would be more susceptible to infections . Consequently, the spleen is one of the ideal organ models for studying host immune responses to pathogenic challenges, including the H. parasuis infection.
Our previous study has used the Affymetrix Porcine Genechip™ to profile the differentially expressed genes between spleens with and without administrations with the H. parasuis. There were totally 931 differentially expressed transcripts, of which only a small fragment has been annotated as immunogenes. The result showed that the unfocused global expression profiling based on a full-genome array couldn’t reveal the subtle roles of immunogenes. In the present study, we aim to clarify the subtle roles of immunogenes in the host response to H. parasuis challenge. Using the pig (Sus scrofa) as an in vivo model, we first characterized the microarray expression dataset of the spleen's immunogenome. Based on the partitioned immunogenome dataset, we performed a comprehensive immunomic analysis, which included reconstruction of the immune network and evaluation of network parameters and quantitative topological properties. Our investigation revealed a vital network component in response to H. parasuis infection. To our knowledge, this is the first network biology analysis of the spleen immunogenome upon challenge with a Gram-negative bacterium in mammals.
Results and Discussion
Characterization of the immunogene dataset
We used the GeneChip® Porcine Genome Array (Affymetrix) to measure gene expressions of porcine spleen from three normal and three H. parasuis-infected samples from six separate piglets. By extracting immune pathways from KEGG and reactome databases (see the Additional file 1), a total of 1,999 transcripts from the 20,201 transcripts arrayed on the chip were targeted as immunogenes according to the pathway annotation results. The basic annotation information of Affymetrix probesets and corresponding transcripts of immunogenes is shown in the Additional file 2. Among the subsection of 1,999 transcripts of immunogenes, a total of 1,115 transcripts were detected to call Present in both normal and H. parasuis-infected samples. Additional file 3: Table S1 gives the descriptive statistical parameters used to evaluate the expression of the signals of the immunogenes on at least one chip using the mas5calls method.
Identification and GO annotation of differentially expressed immunogenes
One of the routine goals for transcriptomic analysis is to identify differences in the expression between phenotypic covariates of samples. To improve the detection power of the differential expression test [23, 24], the inter-quartile range (IQR) filter was used to remove those uninformative genes. The differentially expressed immunogenes between the control and H. parasuis-infected groups were identified by empirical Bayes correction of the linear model , in which the cutoffs of p-value and logFC (log2-fold-change) were set as 0.05 and 1, respectively. The logFC, AveExpr (average log2-expression), t-statistic, p-value, adjusted p-value (q-value) and B-value (log odds value) for each gene can be found in the Additional file 5.
According to the cutoff criteria, a total of 36 immunogenes were detected to be differentially expressed. The estimates of t-value, p-value and logFC for all IQR-filtered immunogenes are given in Figure 2A. The hierarchical clustering presented in Figure 2B gives a distinct occurrence of differential expression pattern of these 36 immunogenes in which, compared to the control group, there were 9 down-regulated genes and 27 up-regulated genes (see Additional file 3: Table S2), respectively. The visual distinction between the up- and down-regulated immunogenes of the two-way hierarchical clustering pattern supports the reliability of the results of the differential expression test. In addition, a comparison of expression values of differentially expressed immunogenes between samples is displayed in Figure 2C, from which one can observe that the H. parasuis infection has mainly resulted in increased activation of immunogenes.
Reconstruction, visualization, and statistical evaluation of the immunogene network
Based on mutual information between immunogenes, the C3NET algorithm was used to estimate the adjacency matrix of the gene network . Here, all of the immunogenes that passed the IQR filter were used to make the network inference. There are two reasons why we used the IQR-passed genes rather than the differentially expressed genes for network inference: 1) the number of differentially expressed immunogenes was relatively small, and, more importantly, the differential expression revealed the dimension-reduced or projected relationships between genes on the phenotypic covariate axis, which was not equivalent to the context of high-dimensional relationships of members in the network; and 2) it is accepted that not all members from a network will be simultaneously detected to be differentially expressed in real biological samples, and thus a network reconstructed only by differentially expressed genes was inadequate to depict the real topological architecture.
Topological parameters for the immunogene network
Betweenness score (edge)*
Betweenness score (vertex)*
Average path length
Burt’s constraint score*
Dice similarity coefficient*
Kleinberg’s hub score
The holistic properties of the network topology were further evaluated. The logarithmic forms of degrees and their probabilities (proportions) are graphically plotted in Figure 4C, two of which obviously represent a linear relationship. This means that the degree distribution satisfies a power law. If the degree distribution follows a power law, we say that the network is scale-free . So, it can conclude that the network we reconstructed in this study is scale-free. Figure 4D displays the distribution of average path lengths of random networks (10000-time simulations based on the Erdös-Renyi model ), in which the vertical broken red line locates the estimation of average path length of the H. parasuis infection network. The location of the vertical broken red line indicates that the reconstructed network has a much smaller average path length than those of simulated random network. In addition, the clustering coefficient of the H. parasuis infection network is estimated to be zero, which is also much smaller than the average value of those from the randomly simulated networks. According to the complex network theory, the estimation results of average path length and clustering coefficient suggest that the immune network we reconstructed is not small-world.
There are two possibilities to explain these results. One is that the nodes in the reconstructed network did not cover the list of all potential immunogenes, and the absence of absent members decreases the global connectivity of the network. The other is that not all real biological networks are always small-world, and the network of H. parasuis infection might be strongly segmented or compartmentalized. Regarding the latter possibility, similar to Mycobacterium bovis bacillus Calmette-Guerin , the H. parasuis infection might initiate independent signalling cascades of various immune regulatory pathways that lead to a sparse-splitting immunogene network. Although many biological networks have been proven to belong to the small-world category, there have also been studies to support the second possibility. These include the long-range interaction networks in protein structure , the metabolic network of E. coli as defined by atomic mappings , the KSHV PPI network , the global network of Avian Influenza outbreaks , the sequence-based chemoinformatics threshold networks for drug target , and the network for phenolic secondary metabolism of T. cacao . Therefore, the small-world property might be typical of networks, but might not be true for all real biological networks.
Detection of the infection-induced network components
Here, by combining gene network ideas with differential expression, the network components involving differentially expressed immunogenes are considered to participate in, or at least be tightly associated with, the biological process of H. parasuis infection. Through mapping the members of differentially expressed immunogenes into the reconstructed network, we found that there were seventeen hub genes (of which the degree is defined here to be not lower than three), in which nine hubs (i.e., C1R, ADM, ARG2, BCL6, CD46, CD3E, CD163, CD1D, and LYZ) were involved in differentially expressed immunogenes. Although they were identified as being involved in the infection process of H. parasuis, most of the hub immunogenes themselves had no significant expression changes in the differential expression test. There were only two members (i.e., CD1D and CD163) that were identified to be differentially expressed. As can be seen in Figure 4A, when challenged with H. parasuis, CD1D was down-regulated, coupling with down-regulations of CD3D and PSEN2 and up-regulation of TNFRSF1B. Despite up-regulation of CD163, its linked neighbours were not detected to have significant changes in statistics. Furthermore, there were a total of 16 network clusters that had included the differentially expressed genes, and the network clusters mediated by C1R, CD1D and BCL6 were involved in at least four differentially expressed genes (see Figure 4A). In these clusters, 6 clusters involved down-regulated genes and 13 involved up-regulated ones. This means that there are three clusters being involved in both down-regulated and up-regulated genes.
Rank of the betweenness scores of edges and vertices
CP -- C1R
FCN2 -- CFH
S100A8 -- ADM
CAMP -- ADAM17
CCRL2 -- ADAM17
CEBPB -- C1R
CEBPD -- C1R
CFB -- C1R
CXCL2 -- C1R
FCN1 -- C1R
IL10RB -- C1R
IL4R -- C1R
ITGA5 -- C1R
PTPN1 -- C1R
S100A9 -- C1R
THBS1 -- C1R
CASP1 -- C5AR1
TNFRSF17 -- THBD
CD3D -- CD1D
In the reconstructed network, there were 19 edges with betweenness scores greater than 10. According to the estimates of betweenness scores, the importance of the edge between CP and C1R was found to be much higher than others. Except for the edge between C1R and CD55 (only estimated as 1), the betweenness scores of all edges linked to C1R were found to be equal to or greater than 15. More importantly, the C1R-mediated network cluster was also involved in the largest number of differentially expressed immunogenes. It is known that C1R is one of the early complement proteases, which plays a crucial role in immune responses against microbial pathogens. Based on the network-based prioritization, despite not being differentially expressed gene, C1R and its co-expressed genes are considered to be the most important network components associated with H. parasuis challenge. In our opinion, both C1R and its co-expressed members might play key roles in the coordination of host defense mechanisms against the H. parasuis infection.
Evaluation of immunogenes with the higher betweenness scores (at least 10) and bioinformatic validation of the C1R-centered clique
Primer sequences, melting temperatures and product sizes for twelve immunogenes in the qPCR analysis
Primer sequence (5'-3')
Target size (bp)
Due to the mathematical essence of a static network based on mutual information, a network clique simply defines a functionally related gene set, which means that a clique is essentially denotes the extent of functional coupling expressions of a gene set. Given this, we focused on the most important clique and conducted a pathway enrichment analysis (PEA) to detect the possible existence of direct or indirect regulation among the members appearing in the C1R-centered clique. Here, the web tool Gene Set Analysis Toolkit V2 was used to perform pathways enrichment analysis [52, 53], and the parameters of enrichment analyses were set as follows: the statistical test used the hypergeometric method, the multiple test adjustment used the Benjamini and Hochberg method, the significance level was set to 0.05, and the minimum number of genes for a category was set to 2. In terms of option selection, the pathway database resources included the Pathway Commons, Wikipathways, and KEGG pathway databases.
Bioinformatic analysis to identify the existence of potential regulations in the C1R-mediated cluster through pathway enrichment analysis
Enriched genes in pathway
Adjust P value
1. Pathway Commons database
Inflammatory Response Pathway
ITGA5, SPP1, THBS1
TGF Beta Signaling Pathway
Senescence and Autophagy
Initial triggering of complement
Syndecan-4-mediated signaling events
IL6-mediated signaling events
Exocytosis of Alpha granule
IL4-mediated signaling events
Response to elevated platelet cytosolic Ca++
Innate Immunity Signaling
Regulation of p38-alpha and p38-beta
Proteogylcan syndecan-mediated signaling events
p38 MAPK signaling pathway
Plasma membrane estrogen receptor signaling
BMP receptor signaling
Regulation of nuclear SMAD2/3 signaling
TGF-beta receptor signaling
Regulation of cytoplasmic and nuclear SMAD2/3 signaling
Glypican 1 network
2. Wikipathways database
Inflammatory Response Pathway
ITGA5, SPP1, THBS1
TGF Beta Signaling Pathway
Senescence and Autophagy
3. KEGG database
ITGA5, SPP1, THBS1
ITGA5, SPP1, THBS1
Cytokine-cytokine receptor interaction
IL4R, CXCL2, IL10RB
Complement and coagulation cascades
Hematopoietic cell lineage
Jak-STAT signaling pathway
Recently, many studies have focused on the H. parasuis, a model Gram-negative bacterium. However, among these studies, none have paid attention to the host immune network and its quantitative topology. In this work, by targeting the spleen immunogenome, we have reconstructed the immune network and probed the network topology parameters that characterize the immunogenome-wide expression behaviors in response to H. parasuis infection. Our analyses suggest that the reconstructed immune network is scale-free but not small-world. To our knowledge, we report the first investigation into the immunogenome-focused network biology analysis of H. parasuis infection. Compared with our previous investigation , the immunogenome-focused study has mined much new information about the host infection biology of Gram-negative bacterium H. parasuis. Although the number of replicates only met the basic requirements for sample size of microarray studies, the results showed that the immunogenome-focused strategy we used has worked efficiently. In addition, our results are valuable and may have potential applications, for instance, our results might provide new or potential targets for interrupting or alleviating the course of bacterial infections.
In summary, we used network biology approaches to quantitatively characterize the nature of immune network responding to H. parasuis infection. Our results for the first time revealed an immunogenome-focused network of porcine spleen challenged with H. parasuis, which also provide a step toward a network biology-based understanding of infection with the Gram-negative bacilli in mammals.
The basic raw data of six Affymetrix chips used for the extraction of immunogenome data came from our previous study , in which the spleen tissues of three HPS infected piglets and three controls were individually used for the experiment. The Affymetrix chip data has been deposited in the NCBI Gene Expression Omnibus (GEO) database under the GSE series accession number GSE11787.
Web resources and tools
The web resources and tools used in this study mainly include the Affymetrix technical files (http://www.affymetrix.com/support/index.affx); the R packages (http://www.r-project.org/); the Bioconductor packages (http://www.bioconductor.org); the annotation tools of WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt/); the KEGG database (http://www.genome.jp/kegg/); the reactome database (http://www.reactome.org/); the GSEA analysis tool (http://www.broadinstitute.org/gsea/downloads.jsp); the igraph R package (http://cneurocvs.rmki.kfki.hu/igraph/); and two-way hierarchical clustering (http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/my.colorFct.R).
R/Bioconductor is open-source, freely available, and widely used for high-throughput data analyses in a variety of biological fields. In this study, the R/Bioconductor packages and self-written procedures in R statistical environment (available upon request: email@example.com) were used to perform the statistical analyses. The affy package was used to perform the low-level processing processes that included quality control, background correction, PM correction, summarization, normalization and probeset filtering. The non-specific filtering, principal components analysis and differentially expressed test were mainly realized by the packages of genefilter, FactoMineR, and limma. The javaGSEA Desktop Application tool was used for GSEA analysis. The packages used for graphical representations mainly included graphics, stats, MASS, misc3d, plotrix and RColorBrewer. The packages of c3net and igraph, and self-written procedures, were used for network reconstruction, including estimations of mutual information, adjacency matrix, network parameters and network topological properties, and graphical representations. All of these packages are free and can be downloaded from the websites: http://www.bioconductor.org and http://cran.r-project.org.
Quantitative real-time PCR
Two-step quantitative RT-PCR (qPCR) was performed on the same spleen RNA samples used for the microarray experiments. Total RNA were treated with DNaseI (Tubo kit, Ambion) and reverse transcribed using the RevertAid™ First Strand cDNA Synthesis Kit (Fermentas) according to the manufacturer's instructions. We used the ribosomal protein L32 (RPL32) gene as an internal control. qPCR was run on the LightCycler® 480 Real-Time PCR System (Roche), in which the SYBR® Green Real-time PCR Master Mix (TOYOBO CO., LTD, Japan) was used as the readout. The cycling parameters were as follows: 95x°C, 2 min; 95°C, 15 s; X°C as appropriate, 15 s, where X is 55, 59 or 65°C depending on the primer pair used; and 72°C, 20 s for 45 cycles. After PCR, a melt-curve analysis of each primer pair was carried out to verify the specificity of the PCR assay. The correct fragment sizes of the PCR products were confirmed using agarose gel electrophoresis (2%). Each primer set amplified a single product as indicated by a single peak present for each gene during melting curve analyses. The relative quantitative gene expression level was evaluated using the comparative Ct method. The ΔCt values were calculated by subtracting the RPL32 Ct value for each sample from the target Ct value of that sample. The duplicates for each sample were averaged, and pairwise t tests were conducted to determine differential expression between control and infection. In all qPCR analyses, the significance level was set at p ≤ 0.05.
- H. parasuis:
Principal components analysis
Geneset enrichment analysis
Normalized enrichment score
False discovery rate
Family-wise error rate
Log fold change
Kyoto Encyclopedia of Genes and Genomes
Pathway Enrichment Analysis
Quantitative real-time polymerase chain reaction.
This work was supported by National Natural Science Foundation of China (30901021), the National High Technology Research and Development Program of China (863 Program) (2013AA102502), and the National Basic Research Program of China (973 Program, 2012CB518802).
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