Innate gene repression associated with Mycobacterium bovis infection in cattle: toward a gene signature of disease
© Meade et al; licensee BioMed Central Ltd. 2007
Received: 11 May 2007
Accepted: 31 October 2007
Published: 31 October 2007
Bovine tuberculosis is an enduring disease of cattle that has significant repercussions for human health. The advent of high-throughput functional genomics technologies has facilitated large-scale analyses of the immune response to this disease that may ultimately lead to novel diagnostics and therapeutic targets. Analysis of mRNA abundance in peripheral blood mononuclear cells (PBMC) from six Mycobacterium bovis infected cattle and six non-infected controls was performed. A targeted immunospecific bovine cDNA microarray with duplicated spot features representing 1,391 genes was used to test the hypothesis that a distinct gene expression profile may exist in M. bovis infected animals in vivo.
In total, 378 gene features were differentially expressed at the P ≤ 0.05 level in bovine tuberculosis (BTB)-infected and control animals, of which 244 were expressed at lower levels (65%) in the infected group. Lower relative expression of key innate immune genes, including the Toll-like receptor 2 (TLR2) and TLR4 genes, lack of differential expression of indicator adaptive immune gene transcripts (IFNG, IL2, IL4), and lower BOLA major histocompatibility complex – class I (BOLA) and class II (BOLA-DRA) gene expression was consistent with innate immune gene repression in the BTB-infected animals. Supervised hierarchical cluster analysis and class prediction validation identified a panel of 15 genes predictive of disease status and selected gene transcripts were validated (n = 8 per group) by real time quantitative reverse transcription PCR.
These results suggest that large-scale expression profiling can identify gene signatures of disease in peripheral blood that can be used to classify animals on the basis of in vivo infection, in the absence of exogenous antigenic stimulation.
Mycobacterium bovis infection is the cause of bovine tuberculosis, an important health problem in cattle with zoonotic potential for transmission to humans. In cattle this infection can be slowly progressive, with limited outward signs of disease, making diagnosis and eradication of tuberculosis difficult. Current diagnostic techniques often involve an in vivo single intradermal comparative tuberculin test (SICTT), alone, or combined with an in vitro ELISA based interferon-γ assay (IFN-γ) [1, 2]. However, problems remain with the sensitivity of current diagnostics leading to a failure to detect all infected animals [3, 4].
Following initial exposure to M. bovis, a specific T-cell immune response develops characterized by the release of proinflammatory cytokines including IFN-γ [5, 6]. The loss of this early proinflammatory cytotoxic response is thought to be associated with an inability to control infection, resulting in progression to clinical disease [6, 7]. The persistence of infection leading to chronic tuberculosis may be due to an ineffective immune response that involves suppression of specific immune mechanisms .
The immune response to tuberculosis is a complex process and studies in the bovine model have primarily focused on the adaptive immune response. Although the T-cell response is critical in controlling tuberculosis infection in cattle , studies in mice and humans suggest a significant role for innate immune mechanisms in mounting early and effective immune responses to mycobacterial infection [8–10]. Development of an effective adaptive immune response is dependent on innate immune activation. The innate immune response is regulated via receptors for antigen recognition known as pathogen recognition receptors (PRRs) and antigen presentation molecules. PRRs including the Toll-like receptors (TLRs) have been implicated in the immune response to M. bovis BCG [11, 12], specifically TLR-2 and TLR-4 . A diverse range of mechanisms used by mycobacteria to subvert the host immune response have also been described [13, 14]. Mycobacteria can inhibit host cell signalling via the TLRs and other mediators of the innate immune response ; they may also interfere with maturation of the phagosome in infected macrophages, thereby reducing the ability of the host to successfully eliminate the pathogen [16, 15]. Failure or subversion of an appropriate innate immune response may therefore be critical to the establishment of infection and progression to disease .
In recent years, high-throughput genomic analyses have facilitated identification of transcriptional regulatory networks involved in the orchestration of the immune response [17, 18]. Gene expression studies of host responses to infection can provide a powerful tool for understanding the interactions between pathogens and the host immune system and may be particularly powerful in identifying specific molecules or pathways that have been targeted by pathogens for immune evasion . One desirable outcome of genomic analyses across large gene subsets is the identification of an infection expression signature that may be used to differentiate groups based on their infection status . Microarrays have recently been applied to the study of M. tuberculosis infection  and unique host gene expression signatures have been attributed to specific strains of M. avium in human macrophages . In cattle, microarray studies of peripheral blood mononuclear cells (PBMC) from M. avium subsp. paratuberculosis (MAP)-infected cattle have revealed MAP-associated gene profiles, which include cytokines and other putative biomarkers that are indicative of infection status [22, 23]. These investigations also revealed that differential gene expression patterns were identifiable irrespective of whether PBMC were stimulated with antigen. Differential gene expression patterns may therefore provide useful novel diagnostic and prognostic tools [20, 24].
We have previously used a bovine targeted immunospecific cDNA microarray to study gene expression changes in PBMC from bovine tuberculosis-(BTB-) infected cattle cultured in vitro in the presence of bovine and avian tuberculins . Stimulation with tuberculin antigens induced significant expression changes in a range of immune genes. In addition, the pattern of expression of many other genes provided evidence of an M. bovis-specific signature of infection. In the present study, we have used an expanded microarray platform to investigate gene expression differences that exist between infected and healthy control cattle in vivo, in the absence of in vitro antigenic stimulation. The results have yielded insights regarding the immune response to bovine tuberculosis, indicating that the expression of innate immune genes in in vivo infected animals is suppressed. This innate immune gene repression may limit the initiation of an appropriate adaptive immune response, which may contribute to progression of the disease. This study has demonstrated the involvement of a number of genes previously not associated with host defence or inflammation and has used stringent microarray analysis methods to detect and validate a robust gene signature of infection. The results highlight the usefulness of large-scale genomics approaches to detect biomarkers of disease and gene signatures of infection that in future may form the basis for novel diagnostics and/or therapeutics.
Analysis of leukocyte cell population subsets and IFN-γ release from control and BTB-infected animals
Microarray gene expression profile in BTB-infected cattle
Microarray analysis of mRNA was compared in the PBMC of six M. bovis infected cattle and six non-infected controls to investigate differential gene expression. The expression data generated from the microarray experiment were deposited in the NCBI Gene Expression Omnibus (GEO) repository  with experiment series accession GSE8857.
261 of the spot features represent 122 genes, where at least two replicate gene spot features were found to be significantly differentially expressed; 21 of these genes were identified as BOTL clones with no current gene match (see Additional file 2).
Furthermore, 90 of the 122 genes were expressed at lower levels in BTB-infected animals compared with non-infected controls. Among the genes reduced in expression with immune-related functions were PRKCB1, PRKCA, AKT1, AKT2, EEF2, EEF1G, GATA4 and IER5. Other genes normally associated with a proinflammatory immune response including CSF2 (-3.67 fold), CD14 (-3.08 fold), CCL1 (-4.86 fold), CHUK (-1.85 fold), NFKB1 (-2.89 fold), TBK1 (-1.63 fold), MIF (-1.91 fold), CCR7 (-2.49 fold), BOLA (-4.32 fold) and BOLA-DRA (-1.69) genes all displayed lower expression levels in BTB-infected animals relative to the control animal group (P ≤ 0.05).
Messenger RNA (mRNA) transcripts for only 32 of the 122 genes showed higher levels of expression in BTB-infected animals. Most of these genes were EST sequences, the functions of which remain to be elucidated. Genes with increased expression and well characterised functions include the platelet-derived growth factor family, represented by the PDGFA and PDGFB genes (1.70 and 1.61 fold, respectively) and ECGF1 (1.77 fold). Also significantly increased were G protein-coupled receptor family 1 members MCHR1 (1.84 fold) and GPR98 (2.07 fold), a member of the receptor tyrosine kinase subfamily AXL (1.59 fold), a member of the Ig superfamily CD84 (1.53 fold) and the cytokine, CCL15 (1.60 fold) [and represented by replicate significant gene features at P ≤ 0.05].
Fold change differences for differentially expressed genes on the microarray ranged from a decrease of 5.13 fold (the major histocompatibility complex, class I, A gene [BOLA] to an increase in expression of 2.14 fold (the growth arrest and DNA-damage-inducible, alpha gene [GADD45A]) in the BTB-infected cattle relative to control animals.
Analysis of the microarray experimental false discovery rate (type 1 error)
Investigation of the experiment-specific false discovery rate (FDR) using exact multivariate permutations tests based on 462 available permutations demonstrated that the probability of obtaining at least 151 genes significant by chance (at the P ≤ 0.01 level) if there are no real differences between the classes is 0.011. Furthermore, permutation-based analysis of the data using the Significance Analysis of Microarrays (SAM) package with 403 differentially expressed spot features (comparable to the 378 spot features obtained using conventional statistical analyses), demonstrated that only 15 of these 403 spot features were false positives (data not shown).
Real time quantitative PCR (qRT-PCR) supports a trend of innate immune gene repression in BTB-infected cattle
Gene expression fold-change differences between BTB-infected animals (n = 8) and control animals (n = 8) using real time qRT-PCR
Gene ontology (GO) function/s
Infected vs control group: relative expression
Eukaryotic translation elongation factor 1 gamma gene
Protein binding, translation elongation factor activity
1.46 ± 0.07
ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) gene
Metal ion binding, metalloendopeptidase activity, protein binding, zinc ion binding
-2.24 ± 0.28
Chemokine (C-X-C motif) receptor 3 gene
C-X-C chemokine receptor activity, receptor activity, rhodopsin-like receptor activity
-2.22 ± 0.18
Immediate early response 5 gene
Molecular function unknown
-2.28 ± 0.26
Prohibitin 2 gene
Estrogen receptor binding, protein binding, receptor activity, specific transcriptional repressor activity
-1.97 ± 0.19
Serine/threonine kinase 17b (apoptosis-inducing) gene
ATP binding, nucleotide binding, protein serine/threonine kinase activity, transferase activity
-1.39 ± 0.11
CD84 antigen gene
Molecular function unknown
1.77 ± 0.48
Myeloid cell leukemia sequence 1 (BCL2-related) gene
Protein binding, protein channel activity, protein heterodimerization activity
-1.07 ± 0.31
Chemokine (C-C motif) ligand 1 gene
-1.33 ± 0.09
TANK-binding kinase 1 gene
ATP binding, nucleotide binding, protein serine/threonine kinase activity, signal transducer activity, transferase activity
-1.56 ± 0.19
V-akt murine thymoma viral oncogene homolog 1 gene
ATP binding, nucleotide binding, protein kinase activity, serine/threonine kinase activity, transferase activity
-1.49 ± 0.16
Interleukin 8 gene
Chemokine activity, interleukin-8 receptor binding, protein binding
-1.64 ± 0.64
Toll-like receptor 2 gene
Gram-positive bacterial binding, lipopolysaccharide receptor activity, peptidoglycan binding, transferase activity
-2.37 ± 0.89
Protein kinase C, beta 1 gene
ATP binding, calcium ion binding, diacylglycerol binding, protein kinase C activity, transferase activity, zinc ion binding
-1.13 ± 0.35
Toll-like receptor 4
Lipopolysaccharide binding, protein binding, transferase activity, transmembrane receptor activity
-1.22 ± 0.55
Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105) gene
Protein binding, transcription factor activity
-1.02 ± 0.50
Interleukin 16 (lymphocyte chemoattractant factor) gene
Cytokine activity, protein binding
1.33 ± 0.17
Ribosomal protein S6 kinase, 70 kDa, polypeptide 2 gene
ATP binding, nucleotide binding, protein kinase activity, protein serine/threonine kinase activity, transferase activity
-0.92 ± 0.29
B-cell CLL/lymphoma 2 gene
Identical protein binding
-1.62 ± 0.61
Tumor necrosis factor (TNF superfamily, member 2) gene
Protein binding, tumor necrosis factor receptor binding
0.89 ± 0.46
CD81 molecule gene
-1.23 ± 0.41
Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 4 gene
Transcription coactivator activity, transcription factor activity
13.22 ± 6.42
Cluster analysis identifies a gene expression signature of BTB infection
List of 15 genes significantly differentially expressed at the P < 0.001 level between BTB-infected cattle (n = 6) and control cattle (n = 6) from the BOTL-5 microarray data
Array feature/Clone ID
Gene ontology (GO) function/s
Infected vs control animal relative expression
Nuclear receptor co-repressor 1
DNA binding, protein binding, transcription corepressor activity
PP2A protein phosphatase 2A B56-beta
Protein phosphatase type 2A regulator activity
Uncoupling protein 2 (UCP2) (mitochondrial, proton carrier)
Binding, transporter activity
Unc-84 homolog B
Zinc finger, DHHC-type containing 19
Acyltransferase activity, metal ion binding, transferase activity, zinc ion binding
Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1
Protein binding, transcription factor activity
Giant axonal neuropathy (gigaxonin)
Splicing factor proline/glutamine rich (polypyrimidine tract binding protein associated)
DNA, RNA, nucleotide and protein binding
Nuclear envelope membrane protein
Unknown – limited similarity to Formin 2
Fibroblast growth factor receptor 1
Fibroblast growth factor receptor 1
ATP, nucleotide and protein binding. Receptor and tranferase activity.
TANK-binding kinase 1
ATP and nucleotide binding. Protein kinase and signal transducer activity.
28S ribosomal RNA gene
G protein-coupled receptor 98
G-protein coupled receptor activity, calcium ion binding
Neuropilin 1 (NRP1)
Receptor activity, vascular endothelial growth factor receptor activity
The 15 genes used for the cluster analysis, expression for four of which was increased in the infected animals, included some genes with functions that are not well described in any species. These include the NRM (nurim [nuclear envelope membrane protein]), ZDHHC19 (zinc finger, DHHC-type containing 19), UCP2 (uncoupling protein 2 [mitochondrial, proton carrier]) and GAN (giant axonal neuropathy [gigaxonin]) genes. However, the panel also included well characterized genes of immunological relevance such as the FGFR1 (fibroblast growth factor receptor 1) gene, the transcription factor NFKB1 gene and the TBK1 gene, a mediator of the action of NF-κB.
Estimates of the experimental FDR using the SAM package and permutation analyses showed that the probability of getting at least 17 genes significant by chance at the P ≤ 0.001 level, if there are no real differences between the classes, was 0.013 (data not shown). Furthermore, 13 of these 15 genes were represented by two or more significant gene features in the original BOTL-5 data. The real time qRT-PCR verification performed for the NFKB1 and the TBK1 genes, both of which are represented in the infection signature panel, supports the reliability of this method for the detection of a gene infection signature for BTB (see Table 2 and Fig. 3).
The accuracy of the 15 genes, estimated to be predictive of disease status in peripheral blood was further analyzed using leave-one-out cross-validation (LOOCV)  as implemented in BRB ArrayTools version 3.0. LOOCV analysis confirmed these gene predictors using a number of analyses including the diagonal linear discriminant, which classified the gene infection signature with a sensitivity and specificity of 0.833 between BTB-infected and control animal groups (data not shown). From the 15 gene list, 100% correct classification rate was obtained for the expression levels of four genes: NCOR1, ZDHHC19, GAN and an unknown gene represented by clone identifier BOTL0100013_F01 (Table 2).
The inability of infected cattle to eliminate M. bovis suggests that the host immune response is inadequate to control infection in these animals. The specific immune cell signalling pathways that are involved in the immune response to intracellular infectious agents are highly complex and poorly characterized in cattle. Although cell-mediated immunity is known to be critical for the control of mycobacterial infections; the role of the innate immune system has only recently been addressed in human and murine studies [11, 8, 9]. Cells and molecules of the innate immune system play a fundamental role in the detection of pathogen-associated molecular patterns, in phagocytosis, pathogen destruction, antigen presentation to T-lymphocytes that drive the production of proinflammatory cytokines, and the subsequent activation of an effective adaptive immune response. Interference in, or suppression of these molecular mechanisms, due to a change in the balance of cytokines, or in the pathogen-induced suppression of cell regulatory pathways may be a decisive factor in determining the progression of M. bovis infection in cattle [5, 7, 28].
The BTB-infected animals used in this study displayed a significant 29% increase in the relative proportion of lymphocytes in their blood (P < 0.001, Fig. 1), accompanied by a small decrease in the proportion of monocytes (4%). The production of IFN-γ after in vitro antigenic stimulation indicated the presence of M. bovis-specific T-lymphocytes in the BTB-infected lymphocyte populations. However, despite the influx of lymphocytes, the gene expression data presented here did not reveal a proinflammatory immune response in PBMC from these BTB-infected animals in vivo. In addition, the BOTL-5 microarray results showed that genes detected with decreased expression outnumbered genes detected with increased expression by a factor of two, suggesting gene repression (Fig. 2).
Estimation of the microarray platform-specific false discovery rate (FDR) provided information regarding the reliability of the 378 differentially expressed spot features detected using the BOTL-5 microarray platform under the specific experimental design and conditions. With 1,391 genes spotted on the array approximately 70 and 14 false positive genes would be expected using standard statistical tests at the P ≤ 0.05 and P ≤ 0.01 levels respectively. However, because each gene on the BOTL-5 microarray is represented twice, individual spot features are not strictly independent observations. Furthermore, the array is a targeted immunospecific platform, and as such, represents a subset of bovine genes that are known to participate in the immune response and ancillary processes. Therefore, there is likely to be a relatively high degree of functional overlap and co-regulation, such that many of these genes are not strictly independent of one another. Previous work has shown that the experimental FDR for a porcine brain microarray platform followed statistical expectations without the addition of a fold cut-off and that additional selection criteria could be used to virtually eliminate false positives . Taken together, the analyses of the FDR in this study suggest that the microarray data is reliable and that the FDR was lower than random expectations with the experimental conditions described.
Ninety of the 122 genes represented by significant replicate spot features were expressed at lower levels in BTB-infected animals (Fig. 2). Furthermore, the 25 genes examined by real time qRT-PCR confirmed the BOTL-5 microarray results and supported an overall trend of repression of the immune response, which may be manifested primarily through decreased expression of innate immune genes (Fig. 3). Reduced expression of key indicator genes, with well established roles in the bovine immune response to BTB, associated with BTB-infection was particularly suggestive of innate immune gene repression in vivo. The expression of Toll-like receptor genes, TLR2 and TLR4 was reduced by 2.4-fold and 1.2-fold respectively in PBMC from the BTB-infected animals ex vivo (see Table 1 and Fig. 3). These results suggest that TLR expression associated with pathogen recognition and reaction to mycobacteria  was suppressed in PMBC of animals with advanced BTB infection. Significantly, the NFKB1 gene, a central mediator of the proinflammatory immune response and a gene that encodes a mediator of NF-κB action (TBK1) were both expressed at significantly reduced levels in BTB-infected animals with P values of 0.023 and 0.004, respectively (see Table 1 and Fig. 3). In addition, the microarray data indicated reduced expression of CHUK, a gene that also regulates NF-κB activation, (P = 0.005), further supporting the trend of immune gene repression in the BTB-infected animals. NF-κB is a key transcription factor for many of the genes involved in the immune response , and as such may be a key mediator of the gene repression detected in PBMC from the BTB-infected group.
The CCL1 gene, which encodes a cytokine that displays chemotactic activity for monocytes  also exhibited reduced expression (Fig. 3). Two genes that encode members of the G protein-coupled receptor family involved in chemotactic T-cell migration, dendritic cell maturation and recruitment of inflammatory cells (CCR7 and CXCR3)  are also expressed at significantly lower levels in BTB-infected animals based on the microarray data.
Expression of the IL8 gene, which encodes a neutrophil recruiting chemokine – a key mediator of the inflammatory response – was also reduced (Fig. 3). This observation was consistent with the reduced NFKB1 gene expression in the infected animals; NF-κB is a well characterised mediator of IL8 expression . Furthermore, despite the relative expansion of lymphocytes in the PBMC from BTB-infected cattle (Fig. 1), a majority of genes are expressed at lower levels with no change in the expression of the proinflammatory IFNG, IL2 or IL4 genes detected using the BOTL-5 microarray or real time qRT-PCR (P = 0.487, 0.772 and P = 0.385 respectively for qRT-PCR results).
Recent studies of human tuberculosis infection demonstrate that mycobacteria can target cell-signalling pathways to regulate gene expression and subvert the host immune response [reviewed in reference ]. One particular study showed that mycobacteria specifically target the CD209 (DC-SIGN) molecule causing impaired dendritic cell maturation and induction of anti-inflammatory cytokines that promote immunosuppression . In addition, other work has suggested that immune cell signalling suppression may be mediated through TLR-2 . Both of these mechanisms could contribute to the survival of the mycobacteria.
Previous work using mycobacterial infections has demonstrated differential expression of TLR-2, TLR-4 [9, 35], inflammatory cytokines including IFN-γ [36, 37] and IL-8 [38–41], and BOLA MHC molecules [42, 43]. In the present study, there was no discernible difference in expression for proinflammatory molecules between the BTB-infected and control animal groups. This observation suggests that PBMC from BTB-infected cattle display different gene expression program compared to both the healthy control animals and to PBMC exposed to M. bovis antigens in vitro .
The differences in cell subpopulations shown between the BTB-infected and control animal group (Fig. 1) may contribute to some of the gene expression changes detected; however, the data presented here also supports the hypothesis that a host- or pathogen-driven process of innate immune gene repression in BTB-infection in vivo is responsible for the progression of the disease. These results are consistent with recent work involving Johne's disease in cattle caused by M. avium subsp. paratuberculosis (MAP), where suppression of the immune response was detected in late stage infection animals  and a novel gene expression program was identified for PBMC in vivo .
One of the aims of this study was to extract gene expression patterns that are associated with host-pathogen interactions, and that can be interrogated to identify a robust pathogen-specific molecular signature of infection [20, 45]. It is clear that this approach could be problematic because natural gene expression variation for individual animals and their response to M. bovis infection has not been characterized [6, 28]. However, gene expression infection signatures do hold promise; a recent study showed that human gene expression differences due to disease state were significantly greater than variation due to natural factors such as age and gender . In addition, Coussens and co-workers have established two genes (TNFSF8 and SELP) in a gene infection signature for Johne's disease in cattle .
The results presented here suggest that gene expression differences for key immune genes identified using the BOTL-5 microarray and verified using real time qRT-PCR play a role in disease pathogenesis and importantly, that these genes may serve as biomarkers for BTB-infection status. Cluster analysis identified a panel of 15 genes indicative of disease status in PBMC from naturally infected animals, in the absence of antigenic stimulation with tuberculin. In addition, results from class prediction analyses allocated a sensitivity and specificity score of 83% for these gene classifiers as predictive of disease status for the two groups of animals used. Taking these observations into consideration, these genes may therefore represent robust and stable biomarkers for BTB infection. We are currently investigating the sensitivity and specificity of this gene infection signature in a larger cohort of naturally infected and uninfected cattle.
The results from the present study support a primary trend of innate immune gene repression in PBMC from BTB-infected animals. Additionally, a distinct gene expression profile that is predictive of disease state is evident, that also sheds light on the cell regulatory pathways associated with pathogenesis of bovine tuberculosis. However, it is important to note that different patterns of gene expression may be evident in tissues at the sites of active infection. Also, some of the gene expression changes we observed may not be specific for M. bovis infection and may represent a general phenomenon associated with other advanced stage infections or pathologies.
This study highlights the importance of the natural host for M. bovis infection as a model to investigate the immune response to tuberculosis using functional genomics technologies. Genes and cellular regulatory pathways involved in the bovine innate immune response to tuberculosis will likely show evolutionary overlap with mechanisms of response to M. tuberculosis in humans. These results also suggest that clinical strategies that target novel innate immune molecules might be useful in combating mycobacterial infections by shifting the balance between immune activation and suppression to favour the elimination of pathogens.
Sixteen cattle were used for this study. The eight infected animals were chosen from herds with a recent history of chronic infection with M. bovis. The animals were selected on the basis of the skin-fold thickness response to bovine and avian tuberculin in the single intradermal comparative tuberculin test (SICTT). The SICTT reactor animals were selected where the skin-fold thickness response to PPD-bovine exceeded that of PPD-avian by at least 12 mm. All of these animals were also measured positive in a whole blood IFN-γ assay . The cattle were confirmed positive for tuberculosis following detailed post-mortem pathological examination and/or culture. Bronchial, mediastinal, submandibular, retropharyngeal, mesenteric and hepatic lymph nodes and lungs were examined macroscopically for tuberculosis lesions. Suspected lesions were cultured on Stonebrinks and Lowenstein-Jensen media at 37°C for eight weeks to detect M. bovis . The eight non-infected control animals were selected from a herd without a recent history of tuberculosis and were SICTT and IFN-γ test negative.
Blood sampling and analysis
400 ml of blood was collected from each animal in sterile heparinised bottles. Five ml of blood was used for haematological analysis using an Abbott CELL-DYN 3500R automated haematology analyzer (Abbott Laboratories). Leukocyte cell population subsets were compared between infected and control groups (n = 8) using Student's t-test.
PBMC separation, RNA extraction and quality control
PBMC were isolated using the Percoll™ gradient method with a standard protocol . PBMC were seeded at 107 per culture plate and cultured in RPMI 1640 medium supplemented with 5% FBS, 0.1% mercaptoethanol and 0.1% gentamicin. All PBMC samples were cultured overnight at 37°C in 5% CO2. Overnight culture was carried out to minimise noise in gene expression measurements potentially introduced by the mechanical disruption of cells associated with PBMC isolation. Residual cells not seeded for culture were immediately suspended in 3 ml TriReagent® (Molecular Research Centre Inc.) and frozen in 1.5 ml cryotubes at -80°C for use later as a common reference RNA (CRR) pool. Total RNA was extracted using a combined TriReagent®, DNase treatment and Qiagen RNeasy® method (Qiagen Ltd.) according to the manufacturers' instructions. The integrity and stability of RNA samples is crucial for gene expression analyses using microarray technology; therefore, RNA yield and quality were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies). The two-step method for RNA extraction described above was found to produce RNA of high yield and quality (ratios of 18S to 28S ribosomal RNA averaged > 1.6).
Microarray experimental design
The 3,888 feature BOTL-5 immunogenetic cDNA microarray system used has been described previously . Technical information with gene content and sequence information for the BOTL-5 array can be downloaded from the 'Links' section on the MSU Center for Animal Functional Genomics website . The NCBI GEO platform accession for the BOTL-5 microarray is: GPL5751. The immunobiology-targeted BOTL-5 array contains 1,391 genes or ESTs spotted in duplicate with multiple additional control features (blank spots, negative spots, housekeeping genes) and is an expanded version of the BOTL-4 array described previously [52, 25]. A reference design was used for microarray hybridizations, such that all RNA samples were labelled using Cy3 and co-hybridized with Cy5 labelled CRR pool. It was hypothesized that the CRR pool would display similar mRNA expression levels and gene coverage as the target samples, therefore allowing accurate and consistent comparison of gene expression data without arbitrarily pairing animals from the two groups . The CRR pool contained equal amounts of total RNA from the treated and control animal groups. Twelve arrays were hybridized in total, representing six individual animals from each treatment group.
cDNA labelling, hybridisation and scanning
cDNA synthesis, Cy3 and Cy5 labelling and microarray hybridizations were performed as previously described  with the following modifications. Each labelling reaction contained a total of 8 μg total RNA per sample and 10 μg total RNA from the CRR. Labelled cDNAs were purified to remove unincorporated dyes using a QIAquick® purification kit (Qiagen Ltd.) and concentrated using Microcon® centrifugal filter devices (Millipore Ltd.) according to the manufacturers' instructions. Labelled samples were combined (either an infected or a control sample combined with a CRR sample) and co-hybridized on the BOTL-5 microarrays using SlideHyb Glass Array Hybridization Buffer #3 (Ambion Ltd.). Microarray hybridizations were performed using a Tecan HS400 hybridisation station (Tecan Ltd.) with the following protocol – Step 1: 75°C, wash 10 s, soak 20 s, 1 cycle; probe injection: 85°C; denaturation: 95°C, 2 min; hybridization cycle 1: 65°C, time 35 min, agitation frequency medium; hybridization cycle 2: 55°C, time 35 min, agitation frequency medium; hybridization cycle 3: 50°C, time 2 h 30 m, agitation frequency medium; wash cycle 1: 42°C, wash 10 s, soak 20 s, 2 cycles; wash cycle 2: 33°C, wash 15 s, soak 30 s, 2 cycles; wash cycle 3: 33°C, wash 20 s, soak 40 s, 2 cycles; slide drying: 30°C, 1 min 30 s. Microarrays were scanned immediately using a GenePix 4000B microarray scanner (Molecular Devices Ltd.). Data was captured using GenePix Pro version 5.0 software (Molecular Devices Ltd.).
Data processing, normalization and analysis and clustering
The working signal intensities were generated using the mean foreground intensity values minus the median background intensity values as outputted from the GenePix Pro 5.0 results file. Two methods of data pre-processing were used to flag unreliable data. If the signal intensity in one channel was less than 100 and if the signal intensity for the other channel is less than 200, the spot was flagged. If the signal intensity in one channel was less than 100 and the signal intensity was larger than 200 in the second channel, 100 was assigned to the intensity of the first channel.
Where C jk represents the normalisation factor, S the set of genes for normalization, R jk the observed log ratio of the red (Cy5) channel and G j k the observed log ratio of the green (Cy3) channel [55, 56].
Microarray data analysis was carried out using class comparisons between experimental groups (parametric t-tests) as implemented in BRB ArrayTools version 3.0 .
Microarray platform-specific false discovery rate
The false discovery rate (FDR) for the microarray data was investigated using permutation analysis. Data sets for individual samples were randomly assigned control or infected status to produce new permuted data. This procedure was carried out for 1,000 permutations and the number of differentially expressed genes for each permuted data set was then estimated using BRB ArrayTools. In addition, the experiment-specific FDR was further examined using the Significance Analysis of Microarrays (SAM) version 2.0 package .
Supervised cluster analysis
Gene expression profiles for each animal were clustered using average linkage hierarchical clustering implemented in the BRB ArrayTools version 3.0 package with pairwise Pearson correlations as the distance metric. To test the accuracy of the clustered class predictors whose expression state changed between classes at the P ≤ 0.001 level, a method of class prediction was used as implemented in BRB ArrayTools. Leave-one-out cross-validation (LOOCV) was performed to test the accuracy of each class predictor and compared to the probability of a correct class prediction by chance alone, based on the P-value and total number of genes analyzed .
Real time quantitative reverse transcription PCR
Replicate spot features on the BOTL array were used as a check for the quality control of gene expression data. Each spot was analyzed individually thereby allowing the individual genes to be flagged if expression results from two or more replicates were statistically different. This enabled the identification of differentially expressed genes that had a low probability of being false positives and expedited the choice of target genes for real time qRT-PCR validation of the microarray results. The H3 histone family 3A (H3F3A) gene was used as a quantitative reverse transcription PCR (qRT-PCR) reference gene for the present study. This gene displayed the least gene expression differences among the 12 control and BTB-infected samples analyzed using the BOTL microarray platform (data not shown).
Real time qRT-PCR primer sequences, optimum primer concentrations and amplicon sizes for all validated genes.
Array feature/Clone ID
Forward primer (5'-3')
Reverse primer (5'-3')
Amplicon size (bp)
Primer conc. (nM)
We would like to thank the staff at the UCD Lyons Research Farm for assistance with cattle blood sampling and Grace O'Gorman, Stephen Park and Emmeline Hill for technical assistance and advice. We also thank Frances Quigley and staff at the Mycobacteriology Laboratory, CVRL, Backweston for their assistance in the bacterial culture of M. bovis from infected cattle and Prof. Paul Coussens of the MSU Centre for Animal Functional Genomics for advice and provision of BOTL-5 microarrays. This work was supported by an Investigator Grant from Science Foundation Ireland (Grant No: SFI/01/F.1/B028).
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