Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Global Gene Expression and Systems Biology Analysis of Bovine Monocyte-Derived Macrophages in Response to In Vitro Challenge with Mycobacterium bovis

  • David A. Magee ,

    Contributed equally to this work with: David A. Magee, Maria Taraktsoglou

    Affiliation Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

  • Maria Taraktsoglou ,

    Contributed equally to this work with: David A. Magee, Maria Taraktsoglou

    Affiliation Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

  • Kate E. Killick,

    Affiliation Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

  • Nicolas C. Nalpas,

    Affiliation Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

  • John A. Browne,

    Affiliation Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

  • Stephen D. E. Park,

    Affiliation Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

  • Kevin M. Conlon,

    Affiliation UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland

  • David J. Lynn,

    Affiliation Animal Bioscience Centre, Teagasc, Grange, Dunsany, County Meath, Ireland

  • Karsten Hokamp,

    Affiliation Smurfit Institute of Genetics, Trinity College Dublin, Trinity College, Dublin, Ireland

  • Stephen V. Gordon,

    Affiliations Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland

  • Eamonn Gormley,

    Affiliation Tuberculosis Diagnostics and Immunology Research Centre, UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland

  • David E. MacHugh

    david.machugh@ucd.ie

    Affiliations Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland

Global Gene Expression and Systems Biology Analysis of Bovine Monocyte-Derived Macrophages in Response to In Vitro Challenge with Mycobacterium bovis

  • David A. Magee, 
  • Maria Taraktsoglou, 
  • Kate E. Killick, 
  • Nicolas C. Nalpas, 
  • John A. Browne, 
  • Stephen D. E. Park, 
  • Kevin M. Conlon, 
  • David J. Lynn, 
  • Karsten Hokamp, 
  • Stephen V. Gordon
PLOS
x

Abstract

Background

Mycobacterium bovis, the causative agent of bovine tuberculosis, is a major cause of mortality in global cattle populations. Macrophages are among the first cell types to encounter M. bovis following exposure and the response elicited by these cells is pivotal in determining the outcome of infection. Here, a functional genomics approach was undertaken to investigate global gene expression profiles in bovine monocyte-derived macrophages (MDM) purified from seven age-matched non-related females, in response to in vitro challenge with M. bovis (multiplicity of infection 2∶1). Total cellular RNA was extracted from non-challenged control and M. bovis-challenged MDM for all animals at intervals of 2 hours, 6 hours and 24 hours post-challenge and prepared for global gene expression analysis using the Affymetrix® GeneChip® Bovine Genome Array.

Results

Comparison of M. bovis-challenged MDM gene expression profiles with those from the non-challenged MDM controls at each time point identified 3,064 differentially expressed genes 2 hours post-challenge, with 4,451 and 5,267 differentially expressed genes detected at the 6 hour and 24 hour time points, respectively (adjusted P-value threshold ≤0.05). Notably, the number of downregulated genes exceeded the number of upregulated genes in the M. bovis-challenged MDM across all time points; however, the fold-change in expression for the upregulated genes was markedly higher than that for the downregulated genes. Systems analysis revealed enrichment for genes involved in: (1) the inflammatory response; (2) cell signalling pathways, including Toll-like receptors and intracellular pathogen recognition receptors; and (3) apoptosis.

Conclusions

The increased number of downregulated genes is consistent with previous studies showing that M. bovis infection is associated with the repression of host gene expression. The results also support roles for MyD88-independent signalling and intracellular PRRs in mediating the host response to M. bovis.

Introduction

Bovine tuberculosis (BTB), an infectious disease caused by the intracellular pathogen Mycobacterium bovis, continues to impact the welfare of domestic herds, despite the implementation of surveillance and control programmes in many countries. The aetiology and host immune response to M. bovis infection is similar to M. tuberculosis infection in humans [1], [2]. M. bovis is transmitted primarily via aerosolised respiratory secretions containing infectious bacilli, with the natural site of infection being the respiratory tract. Following initial exposure, the pathogen is encountered by host alveolar macrophages, which serve as key effector cells in activating the innate and adaptive immune responses required to determine the outcome of infection [3].

Macrophage recognition of mycobacteria occurs through the interaction of mycobacterial pathogen-associated molecular patterns (PAMPs) with host pathogen recognition receptors (PRRs), such as the Toll-like receptors (TLRs), expressed on the macrophage cell surface [4]. PRR activation induces signalling pathways resulting in the production of endogenous NF-κB-inducible cytokines that promote an adaptive immune response characterised by the release of proinflammatory interferon-gamma (IFN-γ) from T cells and natural killer (NK) cells [5], [6]. In turn, IFN-γ induces microbicidal activity in infected macrophages and enhances the expression of major histocompatibility complex (MHC) class I and II molecules necessary for the presentation of mycobacterial antigens on the macrophage surface to T cells [4], [5].

These molecular mechanisms culminate in the formation of granulomas―organised complexes of immune cells comprised of lymphocytes, non-infected macrophages and neutrophils that contain mycobacterial-infected macrophages and prevent the dissemination of bacilli to other organs and tissues―however, in most cases the pathogen is not eliminated by the host [4]. The survival of mycobacteria in host macrophages (within granulomas) is believed to be achieved through a diverse range of molecular mechanisms that subvert the host immune response. These include the prevention of host phagosome maturation, the inhibition of apoptosis in infected macrophages and the suppression of innate cell signalling pathways and cytokine production necessary to activate adaptive immune responses [7][9].

This persistence of mycobacteria can result in progression to active tuberculosis, particularly in cases where the immune response to infection is compromised [2], [4]. Furthermore, it has been proposed that mycobacteria can exploit several host defence mechanisms, such as cytokine-induced necrosis, resulting in immune pathology, dissemination of infection through the host and ultimately shedding of the pathogen from the host, thus maintaining the cycle of infection [10], [11]. Consequently, analysis of the macrophage transcriptome in response to mycobacterial infection may provide insight into the cellular mechanisms governing mycobacteria-macrophage interactions, enabling further understanding of how modulation of these pathways can result in pathology. In addition, identification of transcriptional markers of infection may lead to the development of novel diagnostics for BTB, providing new molecular tools for disease control and eradication [12].

The recent availability of a complete Bos taurus genome [13], coupled with the continuing development of high-throughput genomic technologies, have enabled analysis of the transcriptional changes induced in bovine macrophages following challenge with M. bovis in vitro [14], [15]. In the current study, purified monocyte-derived macrophages (MDM) from seven age-matched unrelated Holstein-Friesian females were used for in vitro challenge experiments with cultured M. bovis (multiplicity of infection [MOI] 2∶1). Total cellular RNA was extracted from challenged and non-challenged control MDM samples at intervals of 0 hours (i.e. pre-infection) and 2 hours, 6 hours and 24 hours post-challenge and prepared for global gene expression analyses using the high-density Affymetrix® GeneChip® Bovine Genome Array. This microarray contains 24,072 probe sets representing over 23,000 gene transcripts. Differentially expressed genes, identified through comparison of the gene expression profiles from the M. bovis-challenged MDM and the non-challenged control MDM at the 2 hour, 6 hour and 24 hour time points, were subsequently analysed with the Ingenuity® Pathway Analysis Knowledge Base (http://www.ingenuity.com) and used to reconstruct the macrophage cellular pathways underlying M. bovis infection. These data add a novel layer of information regarding the complex macrophage-specific molecular pathways elicited upon phagocytosis of M. bovis and the role these pathways play in establishing the host immune response to BTB.

Materials and Methods

Ethics statement

All animal procedures were carried out according to the provisions of the Irish Cruelty to Animals Act (Department of Health and Children licence number B100/3939) and ethical approval for the study was obtained from the UCD Animal Ethics Committee (protocol number AREC-P-07-25-MacHugh).

Animals

Seven age-matched (three-year old) unrelated Holstein-Friesian females were used in this study. All animals were maintained under uniform housing conditions and nutritional regimens at the UCD Lyons Research Farm (Newcastle, County Kildare, Ireland). The animals were selected from an experimental herd without a recent history of bovine tuberculosis infection and all animals tested negative for the single intradermal tuberculin test (SICTT). These cattle were also negative for infection with Brucella abortus, M. avium subsp. paratuberculosis, Salmonella Typhimurium, bovine herpesvirus 1 (BHV-1) and bovine viral diarrhoea (BVD) virus.

Monocyte extraction and culture of bovine MDM

For monocyte isolation, 300 ml of whole blood was collected in acid citrate dextrose buffer (Sigma-Aldrich Ireland Ltd., Dublin, Ireland) in sterile bottles. Blood was layered onto Accuspin™ tubes containing Histopaque® 1077 (Sigma-Aldrich Ireland Ltd., Dublin, Ireland), and following density gradient centrifugation (500 g for 20 minutes) performed at room temperature, peripheral blood mononuclear cells (PBMC) were collected. Contaminating red blood cells (RBC) were removed following resuspension and subsequent incubation of the PBMC in RBC lysis buffer (10 mM KHCO3, 150 mM NH4Cl, 0.1 mM EDTA pH 8.0) for 5 minutes at room temperature. After incubation, PBMC were washed twice with sterile phosphate-buffered saline (PBS; Invitrogen™, Life Technologies Corporation, Paisley, UK) before resuspending cells in PBS containing 1% bovine serum albumin (BSA; Sigma-Aldrich Ireland Ltd., Dublin, Ireland). Monocytes were then isolated using the MACS® protocol and MACS® MicroBeads conjugated to mouse anti-human CD14 antibodies (Miltenyi Biotec Ltd., Surrey, UK), which has been shown to be cross-reactive with bovine monocytes [16]. The MACS® protocol was performed according to the manufacturers' instructions.

The identity and purity of monocytes was confirmed by flow cytometry using an anti-CD14 fluorescein-labelled antibody (data not shown). This method has been previously shown by us to yield a purity of CD14+ cells ≥99% [17]. Purified monocytes were seeded at 1×106 per well on 24-well tissue culture plates in RPMI 1640 medium (Invitrogen™, Life Technologies Corporation, Paisley, UK) containing 15% heat inactivated foetal calf serum (FCS; Sigma-Aldrich Ireland Ltd., Dublin, Ireland), 1% non-essential amino acids (NEAA; Sigma-Aldrich Ireland Ltd., Dublin, Ireland), gentamicin (5 µg/ml; Sigma-Aldrich Ireland Ltd., Dublin, Ireland) and incubated at 37°C, 5% CO2. Following 24 hours incubation (day one) the media was replaced with 1 ml fresh antibiotic-containing media to remove any non-adhered cells. On day three, media was replaced with 1 ml antibiotic-free culture media (RPMI 1640 medium containing 15% heat inactivated FCS and 1% NEAA only). To ensure that the same number of MDM were subjected to M. bovis-challenge, cells were dissociated on day five using 1× non-enzymatic cell dissociation solution (Sigma-Aldrich Ireland Ltd., Dublin, Ireland), counted and then re-seeded at 2×105 cells per well in 24-well tissue culture plates (Sarstedt Ltd., County Wexford, Ireland) using antibiotic-free culture media. By day eight, 80–100% confluent monolayers of MDM were generated that displayed the characteristic macrophage morphology as confirmed by Giemsa staining (data not shown). On day eight, MDM were used for the in vitro challenge experiments with M. bovis.

Culture of M. bovis

The M. bovis M2137 strain was used in this study. This strain is a clinical isolate from a tuberculous badger, isolated in 2001. The strain bears the SB0142 spoligotype, a strain type frequently isolated from badgers and cattle in Ireland. This isolate of M. bovis was supplied by Mr. Eamon Costello (Irish Department of Agriculture, Food and the Marine, Backweston Laboratory Campus, Celbridge, Co. Kildare).

Culturing of M. bovis was performed in a Biosafety Containment Level 3 (CL3) laboratory and conformed to the national guidelines on the use of Hazard Group 3 infectious organisms. M. bovis stocks for the in vitro MDM-challenge experiments were cultured in Middlebrook 7H9 medium (Difco™, Becton, Dickinson Ltd., Oxford, UK) containing 10% (volume/volume) Middlebrook albumin-dextrose-catalase (ADC) enrichment (Difco™, Becton, Dickinson Ltd., Oxford, UK), 0.05% Tween 80 (Sigma-Aldrich, Dublin, Ireland) and 0.40% (weight/volume) sodium pyruvate (Sigma-Aldrich Ltd., Dublin, Ireland) at 37°C. Bacterial cultures were grown to mid-logarithmic phase as determined by spectrophotometric analysis prior to the challenge experiments using the purified bovine MDM. Colony-forming unit (cfu) counting was performed using Middlebrook 7H11 medium (Difco™, Becton-Dickinson Ltd., Dublin, Ireland) containing 0.40% (volume/volume) sodium pyruvate (Sigma-Aldrich, Dublin, Ireland) and 10% (volume/volume) Middlebrook (ADC) enrichment (Difco™, Becton-Dickinson Ltd., Dublin, Ireland).

In vitro challenge of bovine MDM with M. bovis and RNA extraction

A schematic outlining the experimental design used in the current study is depicted in Figure 1. All in vitro challenge experiments including the preparation of non-challenge control MDM samples were performed in the CL3 laboratory. Prior to the M. bovis challenge experiments, MDM from two adjacent culture plate wells were lysed using RLT buffer from the RNeasy Mini kit (Qiagen Ltd., Crawley, UK) supplemented with 1% β-mercaptoethanol (Sigma-Aldrich Ireland Ltd., Dublin, Ireland) and pooled. These samples constituted the 0 hour control MDM.

thumbnail
Figure 1. Schematic depicting the experimental design used in the current study.

MDM were cultured in 24-well tissue culture plates (2×105 cells per well) and challenged with M. bovis (MOI 2∶1). RNA was extracted from M.bovis-challenged and non-challenged control MDM at three time points post-challenge: 2 hours, 6 hours and 24 hours. In addition, RNA was extracted from a 0 hour non-challenged control to assess potential non-experimental changes in gene expression. The MDM lysates from replicate tissue culture wells (shaded) were pooled for RNA extraction. Global gene expression for the control and M. bovis-challenged MDM was analysed using the Affymetrix® GeneChip® Bovine Genome Array.

https://doi.org/10.1371/journal.pone.0032034.g001

MDM (seeded at 2×105 cells per well) were challenged with M. bovis (4×105 cells per well) prepared in antibiotic-free culture media (RPMI 1640 medium containing 15% heat inactivated FCS and 1% NEAA only). M. bovis cell counts were performed using a Petroff Hausser chamber (Fisher Scientific Ltd., Dublin, Ireland) following sterile filtering to prevent clumping using a 5 micron filter (Millipore Ireland Ltd., County Cork, Ireland). M. bovis cell numbers were then adjusted and 4×105 cells were added to the appropriate wells giving an MOI of 2∶1. Subsequent cfu counting also yielded a mean MOI of 2∶1. Once challenged, MDM were then incubated at 37°C, 5% CO2 for 2 hours, 6 hours and 24 hours. For the non-challenged control samples at each time-point, antibiotic-free culture media (RPMI 1640 medium containing 15% heat inactivated FCS and 1% NEAA only) was added to each well. After 2 hours post-challenge, the media from all 6 hour and 24 hour challenge experiments was replaced with 0.5 ml fresh antibiotic-free culture media per well and re-incubated at 37°C, 5% CO2 until MDM were required for harvesting. Challenged and non-challenged control MDM were lysed and harvested using RLT-1% β-mercaptoethanol buffer (Qiagen Ltd., Crawley, UK) at the designated time points. For each treatment, MDM lysates from two culture plate wells were pooled and stored at −80°C until required for RNA extraction.

RNA extraction and microarray analysis

All RNA extractions were performed in the CL3 laboratory using an RNeasy kit incorporating an on-column DNase treatment step (Qiagen Ltd., Crawley, UK) according to the manufacturer's instructions. RNA quantity and quality was assessed using a NanoDrop™ 1000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) and on an Agilent 2100 Bioanalyzer using an RNA 6000 Nano LabChip kit (Agilent Technologies Ltd., Cork, Ireland). All samples displayed a 260/280 ratio greater than 2.0 and RNA integrity numbers (RIN) greater than 8.5.

Total cellular RNA extracted from non-challenged control and M. bovis-challenged MDM from all seven animals at the 0 hour, 2 hour, 6 hour and 24 hour time points (giving a total of 49 samples) was prepared for global gene expression analyses using the pan-genomic high-density Affymetrix® GeneChip® Bovine Genome Array (Affymetrix UK Ltd., High Wycombe, UK; http://www.affymetrix.com). This array contains 24,072 probe sets representing over 23,000 gene transcript and includes approximately 19,000 UniGene clusters. cDNA labelling, hybridisation and scanning for the microarray experiments were performed by Almac Diagnostics Ltd. (Craigavon, Co. Armagh, Northern Ireland) using a one-cycle amplification/labelling protocol.

Statistical analysis of microarray data

Affymetrix® GeneChip® Bovine Genome Array data were analysed using the BioConductor software project [18] (http://www.bioconductor.org) contained within the R statistical environment (http://www.r-project.org). Quality control analysis was performed on all 49 microarrays using the Simpleaffy software package [19] contained within BioConductor. Normalisation of raw data was performed using the Factor Analysis for Robust Microarray Summarization (FARMS) algorithm. The FARMS algorithm uses only perfect match probes and a quantile normalization procedure, providing both P-values and signal intensities [20]. Normalised data were then further subjected to filtering for informative probe sets using the Informative/Non-informative calls (I/NI-calls) software package within R [21]. I/NI-calls uses multiple probes for the same gene as repeated measures to quantify the signal-to-noise ratio of that specific probe set. Principal component analysis (PCA) was performed using data from all informative probe sets following all data filtering steps from the M. bovis-challenged and non-challenged control samples at the 2, 6 and 24 hour time points. The MultiExperiment Viewer version 4.7 software package [22] was used for PCA with Euclidean distance as the distance metric.

Differentially expressed genes were identified using the Linear Models for Microarray Data (LIMMA) package [23] contained within R. Genes displaying differential expression patterns between non-challenged control and M. bovis-challenged MDM at the 2, 6 and 24 hour time points were annotated using the Affymetrix® bovine gene annotation (http://www.affymetrix.com). The Benjamini-Hochberg multiple testing correction method [24] was applied to all differentially expressed genes to minimise the false discovery rate (FDR) and adjusted P-values for differentially expressed genes were calculated (adjusted P-value threshold 0.05).

Geometric mean fold-changes in gene expression are presented in the current study following back-transformation of mean log2 fold-changes obtained from statistical analysis of microarray data. For replicate probes for single genes, the average log2 expression fold-change was used and subsequently back-transformed. The negative reciprocals of the geometric fold-changes were calculated and are presented for downregulated genes. All microarray data were MIAME compliant [25] and have been submitted to the NCBI Gene Expression Omnibus (GEO) database [26] with experiment series accession number GSE33309.

Systems biology analyses

Ingenuity® Systems Pathway Analysis (IPA; Ingenuity Systems, Redwood City, CA, USA; http://www.ingenuity.com) was used to identify canonical pathways and functional processes of biological importance within the list of differentially expressed genes. The Ingenuity® Knowledge Base contains the largest database of manually-curated and experimentally-validated physical, transcriptional and enzymatic molecular interactions. Furthermore, each interaction in the Ingenuity® Knowledge Base is supported by previously published information.

For IPA analysis, the Affymetrix® GeneChip® Bovine Genome Array was used as a reference gene set. All differentially expressed genes with an adjusted P value≤0.05 were included in the uploaded gene list and only differentially expressed genes mapping to molecules in the Ingenuity® Knowledge Base were used for systems analysis. Functional analysis of genes was performed using IPA to characterise the biological functions of the differentially expressed genes between the control and M. bovis-challenged MDM. For this, IPA performed an over-representation analysis that categorises the differentially expressed genes within the uploaded list into functional gene ontology (GO) categories. Each GO category in IPA is ranked based on the number of differentially expressed genes falling into each functional group. Right-tailed Fisher's exact tests were used to calculate a P-value for each of the biological functions assigned to the list of differentially expressed genes.

IPA contains a large library of known canonical pathways that were overlaid with the differentially expressed genes to identify major biological pathways associated with M. bovis infection in MDM. The significance of the association between differentially expressed genes and the canonical pathway was assessed using two methods: (1) a ratio of the number of molecules from the differentially expressed gene data set that map to the pathway, compared to the total number of molecules that map to the canonical pathway based on the reference gene list; and (2) a Fisher's exact test that generates a P-value for the assignment of the differentially expressed genes to a particular canonical pathway compared to the reference gene list. Canonical pathways were then overlaid with the expression values of the differentially expressed genes.

Conventional cDNA preparation for real time quantitative reverse transcription PCR (qRT-PCR) analysis

cDNA was prepared from 80 ng of total RNA isolated from each sample analysed in the microarray study (including the 0 hour control RNA samples) using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems®, Life Technologies Corporation, Warrington, UK). cDNA conversions were performed in 20 µl reaction volumes using random primers as per the manufacturer's instructions. In addition, 80 ng of pooled RNA (comprising ∼10 RNA samples per pool) was included in non-reverse transcriptase (non-RT) control reactions to test for the presence of contaminating genomic DNA during real-time qRT-PCR analysis. All cDNA samples and non-RT controls were diluted 1∶8 (final concentration cDNA 0.5 ng/µl) using RNAse- and DNAse-free water and stored at −20°C prior to real time qRT-PCR analysis.

Whole-transcriptome amplified cDNA preparation for real time quantitative reverse transcription PCR (qRT-PCR) analysis

To generate a repository of cDNA from each RNA sample, whole-transcriptome linearly amplified cDNA was prepared using the WT-Ovation™ RNA Amplification System (NuGEN Technologies Inc., Bemmel, the Netherlands). For this, 25 ng of template RNA was included in each amplification reaction (40 µl final volume) and all procedures were performed according to the manufacturer's instructions. In addition, non-template controls that included RNAse- and DNAse-free water instead of RNA template were also prepared using the WT-Ovation™ RNA Amplification System kit. Once amplification reactions were completed, the amplified cDNA and non-template controls were diluted 1∶1,000 using RNAse- and DNAse-free water and stored at −20°C prior to real time qRT-PCR analysis.

Real time quantitative reverse transcription PCR (qRT-PCR) analysis and validation of microarray results

Intron-spanning primers were designed for each gene using the Primer3Plus package [27] and commercially synthesised (Eurofins MWG Operon, Ebersberg, Germany). Table S1 provides sequence information for all primer pairs used. Real time qRT-PCR reactions (20 µl final volume) were performed on 96-well plates using Fast SYBR® Green Master Mix (Applied Biosystems®, Life Technologies Corporation, Warrington, UK) on a 7500 Fast Real Time PCR System (Applied Biosystems®, Life Technologies Corporation, Warrington, UK) as per manufacturer's instructions. Real time qRT-PCR amplifications contained as template either: (a) 2 µl of the diluted conventionally-prepared cDNA/non-RT control (equivalent to 1.0 ng of total RNA); or (b) 2 µl of the diluted linearly amplified cDNA/non-RNA template control. A final concentration of 300 nM of each forward and reverse primer was included in each amplification reaction. Non-template real time qRT-PCR controls and a seven-point standard curve prepared from 1∶2 serial dilutions of pooled conventionally-prepared cDNA or linearly amplified cDNA from each M. bovis-challenged MDM sample were included on every real time qRT-PCR plate.

PCR thermal cycling conditions for each amplicon comprised one cycle at 50°C for 2 minutes, one cycle at 95°C for 20 seconds, followed by 40 cycles at 95°C for 3 seconds and 60°C for 30 seconds. A dissociation step was included for all amplifications to confirm the presence of single discrete PCR products of the expected size; this was further confirmed by visualisation of the amplification products on 2% agarose gels stained with 0.5 µg/ml ethidium bromide (Invitrogen™, Life Technologies Corporation, Paisley, UK).

Statistical analysis of real time qRT-PCR data

All real time qRT-PCR data, including primer efficiency estimations using the standard curves, were analysed using the qbasePLUS software package [28] (Biogazelle NV, Zwijnaarde, Belgium). Real time qRT-PCR efficiency correction and normalisation was performed using the peptidylprolyl isomerase A (cyclophilin A) gene (PPIA) as a reference gene based on GeNorm analysis performed by us in a previous study involving bovine MDM [17]. PPIA expression profiles were generated for both the conventionally-prepared and linearly amplified cDNA templates for all samples to ensure appropriate real time qRT-PCR efficiency correction and normalisation analyses were performed for all genes-of-interest.

Calibrated normalised relative quantities (CNRQ) of gene expression for each analysed sample, as generated by the qbasePLUS package, were used to calculate fold-changes in expression for each gene. For this, the CNRQ value generated for each M. bovis-challenged MDM sample was divided by the CNRQ value generated for the non-challenged control MDM sample at the corresponding post-challenge time point from the same animal. Log2 fold-changes in gene expression were then used for the statistical analysis of all genes analysed using real time qRT-PCR. All statistical analyses were performed using the SPSS statistical package (IBM Corporation, Armonk, NY, USA) and the Minitab version 16 statistical package (Minitab Ltd., Coventry, UK).

Anderson-Darling tests of normality were applied to the residuals of the log2 fold-change values at each time point prior to statistical analysis to ensure the data conformed to a normal distribution—no significant departures from normality were observed for any of the genes analysed (P≥0.05). Two-tailed paired Student's t-tests were used to assess significant differences in mean log2 fold-changes in gene expression between the M. bovis-challenged MDM and the non-challenged control MDM at each time point post-challenge. Geometric mean fold-changes in expression were generated for each gene by back-transformation of the log2 fold-change data. The negative reciprocals of the geometric fold-changes were calculated and are presented for downregulated genes.

To validate the performance of the linearly amplified cDNA in real time qRT-PCR amplifications, the expression profiles of four genes (CCL5, CCL20, IL1B and IL6) were generated using both conventionally-prepared cDNA and linearly amplified cDNA. The log2 fold-changes in gene expression in the M. bovis-challenged MDM (relative to the control MDM) were then compared for both template types using regression analysis.

Results

A summary of differential gene expression at each time point post-challenge with M. bovis

In total, 12,339 probe sets on the microarray passed filtering for informative probe sets using the I/NI-calls R package. PCA was performed using all informative transcripts that passed the data filtering process for all samples at the 2, 6 and 24 hour time points (Figure 2). Principal component 1 (PC1) primarily separates the challenge status (i.e. M. bovis-challenged and non-challenged controls) of the MDM samples, while PC2 delineates the M. bovis-challenged MDM with respect to time post-challenge. PC1 and PC2 account for 23.31% and 17.05% of the total variation in gene expression, respectively.

thumbnail
Figure 2. Principal component analysis for all individual control and M. bovis-challenged MDM at the 2 hour, 6 hour and 24 hour time points.

Principal component (PC) 1 and PC2 are shown (accounting for 23.31% and 17.05% of the total variation, respectively). PCA was performed using data for all genes whose probes passed the data filtering process with Euclidean distance as the distance metric.

https://doi.org/10.1371/journal.pone.0032034.g002

The gene expression data were then used to compare the gene expression profiles between the M. bovis-challenged MDM and non-challenged MDM samples across all time points, the results of which are summarised in Figure 3. This shows that the number of differentially expressed genes increased with each time point post-challenge, with the highest number of differentially expressed genes identified at the 24 hour time point. Furthermore, the number of downregulated genes exceeded the number of upregulated genes displaying increased gene expression in the challenged MDM at each time point.

thumbnail
Figure 3. The number of significantly differentially expressed genes at each time point post-M. bovis challenge.

The number of upregulated and downregulated genes in the M. bovis-challenged MDM relative to the control MDM at each time point are shown (adjusted P-value 0.05).

https://doi.org/10.1371/journal.pone.0032034.g003

Analysis of global gene expression after 2 hours post-challenge with M. bovis

At 2 hours post-challenge, a total of 3,529 transcripts, representing 3,064 unique genes, were identified as being differentially expressed between the M. bovis-challenged and non-challenged MDM (adjusted P-value≤0.05). Of these, 1,253 transcripts (representing 1,031 genes) were upregulated while the remaining 2,276 transcripts (representing 2,033 genes) were downregulated in the M. bovis-challenged MDM. Notably, the relative fold-change in expression was markedly higher for the upregulated genes compared to the downregulated genes at this time point. A complete list of the differentially expressed genes and the relative fold-change in expression at the 2 hour time point post-challenge are provided in Table S2.

Further inspection of the differentially expressed genes revealed that many of the highly upregulated genes at this time point had immune-related functions. These included NF-κB-inducible chemokine and cytokine genes, such as the (C-C motif) ligand 20 gene (CCL20); the interleukin 1, beta gene (IL1B); the chemokine (C-X-C motif) ligand 2 gene (CXCL2); the interleukin 1, alpha gene (IL1A); the tumour necrosis factor gene (TNF) and the interleukin 6 gene (IL6).

Several genes encoding proteins associated with TLR signalling were also differentially expressed 2 hours post-M. bovis challenge. These included TLR2 (upregulated); the myeloid differentiation primary response (88) gene (MYD88) [downregulated]; the interleukin-1 receptor-associated kinase 1 gene (IRAK1) [downregulated]; the interleukin-1 receptor-associated kinase 4 gene (IRAK4) [downregulated]; the toll-like receptor adaptor molecule 1 gene (TICAM1) [upregulated; note: this gene was not present on the microarray but was analysed using real time qRT-PCR]; the TNF receptor-associated factor 6 gene (TRAF6) [upregulated]; the TNF receptor-associated factor 3 gene (TRAF3) [upregulated]; the TGF-beta activated kinase 1/MAP3K7 binding protein 2 gene (TAB2) [upregulated] and the nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha gene (NFKBIA) [upregulated]. The genes encoding NF-κB1 (NFKB1) and NF-κB2 (NFKB2) were also upregulated in the M. bovis-challenged MDM at this time point (Figure 4). TLR4 and the gene encoding IFN-γ (IFNG)―two genes previously reported to play an important role in the immune response to mycobacterial infection [4]―were not differentially expressed at the 2 hour time point.

thumbnail
Figure 4. Differential gene expression in the TLR signalling pathway 2 hours post-M. bovis challenge.

Genes within the TLR signalling pathway showing differential expression are highlighted in colour. Colour intensity indicates the degree of upregulation (red) or downregulation relative to the control MDM. Grey shading indicates genes that were not differentially expressed; white shading represents genes in the pathway not represented on the microarray; mycobacterial PAMPs are shaded orange.

https://doi.org/10.1371/journal.pone.0032034.g004

Of the 3,064 differentially expressed genes identified at 2 hours post-challenge, 1,916 mapped to unique molecules represented in the Ingenuity® Knowledge Base. Functional categorisation analysis using IPA showed that the differentially expressed genes had roles involved in the regulation of gene transcription, the immune response and cell signalling (Table S3). IPA was also used to identify the top ranking canonical pathways enriched for differentially expressed genes in response to M. bovis challenge. Many of the top ten ranking canonical pathways identified had an immunological role and included Interleukin 10 signalling (1st ranked pathway), B cell activating factor signalling (2nd ranked pathway), CD40 signalling (4th ranked pathway) and IL6 signalling (5th ranked pathway) (Table S4).

Analysis of global gene expression after 6 hours post-challenge with M. bovis

Comparison of the M. bovis-challenged MDM and non-challenged control MDM 6 hour post-challenge, revealed 5,211 differentially expressed transcripts, comprising 4,451 differentially expressed genes. Of these, 2,458 transcripts (representing 2,027 unique genes) were upregulated and 2,753 transcripts (representing 2,424 unique genes) were downregulated in the M. bovis-challenged MDM (Table S5). As with the 2 hour time point, the relative fold-change in expression was markedly higher for the upregulated genes compared to the downregulated genes after 6 hours post-challenge.

Immune-related genes were among the differentially expressed genes showing the highest relative increase in expression. The identity of the genes displaying the highest fold-change in expression at the 6 hour time point were similar to those detected at the 2 hour time point, among which were CCL20, IL1B, TNF and IL6. IFNG was also upregulated in the M. bovis-challenged MDM at this time point. In addition, a small number of genes encoding proteins belonging to TLR signalling pathways displayed differential expression in the M. bovis-challenged MDM, included TLR3 (upregulated), IRAK1 (downregulated), IRAK4 (downregulated), TICAM2 (upregulated) and TRAF3 (upregulated). The genes encoding TLR2, TLR4, MyD88, TICAM1 and TRAF6 were not differentially expressed at this time point.

Of the 4,451 differentially expressed genes identified at 6 hours post-challenge, 2,850 mapped to unique molecules represented in the Ingenuity® Knowledge Base. Functional analysis comparing M. bovis-challenged and non-challenged MDM transcriptomes revealed an enrichment of genes with immunological, apoptotic and cellular signalling functions (Table S6). Cellular pathways associated with the immune response were also among the top ranking canonical pathways identified by IPA, several of which were similar to those identified at the 2 hour time point. These included IL6 signalling (5th ranked pathway), CD40 signalling (8th ranked pathway) and IL10 signalling (11th ranked pathway).

Death receptor signalling was identified as the fourth highest ranking canonical pathway 6 hours post M. bovis-challenge. Given the previously identified role of programmed cell death during mycobacterial infection in vitro [14], [15], the pattern of differential gene expression in this pathway is depicted in Figure 5. Interestingly, the Role of retinoic-acid-inducible I (RIGI)-like receptors in anti-viral innate immunity (14th ranked pathway), a cellular pathway primarily involved in the host recognition of viral PAMPs was also listed among the top ranking canonical pathways after 6 hours post-challenge with M. bovis (Table S7).

thumbnail
Figure 5. Differential gene expression associated with apoptosis 6 hours post-M. bovis challenge.

Genes associated with apoptosis showing differential expression are highlighted in colour. Colour intensity indicates the degree of upregulation (red) or downregulation relative to the control MDM. Grey shading indicates genes that were not differentially expressed; white shading represents genes in the pathway not represented on the microarray.

https://doi.org/10.1371/journal.pone.0032034.g005

Analysis of global gene expression changes 24 hours post-challenge with M. bovis

6,150 differentially expressed transcripts representing 5,267 unique genes were identified through comparison of the global gene expression profiles of the M. bovis-challenged MDM and the non-challenged control MDM 24 hours post-challenge (Table S8). Of these, 2,537 transcripts (comprising 2,093 unique genes) were upregulated and 3,613 transcripts (comprising 3,174 unique genes) were downregulated in the M. bovis-challenged MDM. Among the genes with immunological functions displaying high levels of increased relative gene expression in the M. bovis-challenged MDM included the chemokine (C-C motif) ligand 8 gene (CCL8), the chemokine (C-X-C motif) ligand 11 gene (CXCL11), IL1B and IL6. TNF and IFNG also displayed increased relative expression in the M. bovis-challenged MDM at this time point.

Of the 5,267 differentially expressed genes identified at 24 hours post-challenge, 3,377 mapped to unique molecules represented in the Ingenuity® Knowledge Base. As with the 2 hour and 6 hour time points, functional categorisation of the differentially expressed genes 24 hours post-challenge revealed an enrichment of genes involved in the host immune response, particularly the activation and development of the adaptive immune response (Table S9). Furthermore, many of the top ranking canonical cellular pathways identified 24 hours post-M. bovis challenge were associated with adaptive immune function. These included Communication between innate and adaptive immune cells (3rd ranked pathway); Interferon signalling (8th ranked pathway); T helper cell differentiation (9th ranked pathway) and IL6 signalling (12th ranked pathway) (Table S10).

As with the 6 hour post-challenge results, systems analysis also identified intracellular PRRs as playing a role in M. bovis infection in vitro. This was evidenced by the identification of Activation of interferon regulatory factors by cytosolic pattern recognition receptors (5th ranked pathway, presented in Figure 6) and the Role of RIG-I-like receptors in antiviral innate immunity (7th ranked pathway) among the top ranking canonical pathways at the 24 hour time point.

thumbnail
Figure 6. Differential gene expression associated with intracellular pathogen recognition receptors 24 hours post-M. bovis challenge.

Genes associated with intracellular PRR signalling showing differential expression are highlighted in colour. Colour intensity indicates the degree of upregulation (red) or downregulation relative to the control MDM. Grey shading indicates genes that were not differentially expressed; white shading represents genes in the pathway not represented on the microarray; viral PAMPs are shaded orange.

https://doi.org/10.1371/journal.pone.0032034.g006

Real time quantitative reverse transcription PCR (qRT-PCR) analysis and validation of microarray results

A panel of the 16 top ranking differentially expressed genes identified through analysis of the microarray data were selected for real time qRT-PCR analysis and validation. Selection of these genes was based largely on the highest fold-change in increased and reduced relative expression and the lowest adjusted P-value across all three time points. These genes were: AREGB, CCL4, CCL5, CCL20, CD40, CFB, CXCL2, FOS, IL1B, IL6, IL15, IRF1, NFKB2, PIK3IP1, SPRY2 and TNF. In addition, the expression profile of TICAM1, which was not represented on the Affymetrix® GeneChip® Bovine Genome Array, was analysed using real time qRT-PCR.

Of the total of 17 genes analysed using real time qRT-PCR, eight genes (CCL4, CD40, CFB, CXCL2, IL15, IRF1, NFKB2 and TNF) were analysed using conventionally-prepared cDNA template only; five genes (AREGB, FOS, PIK3IP1, SPRY and TICAM1) were analysed using the linearly amplified cDNA template only; while the remaining four genes (CCL5, CCL20, IL1B and IL6) were analysed using both the conventionally-prepared cDNA and the linearly amplified cDNA templates in order to validate the performance of the linearly amplified cDNA in real time qRT-PCR analyses.

To validate the performance of the linearly amplified cDNA in real time qRT-PCR amplifications, the expression profiles generated for the CCL5, CCL20, IL1B and IL6 genes using conventionally-prepared and linearly amplified cDNA were compared. For this, regression analysis was performed using the log2 fold-changes in relative gene expression for the M. bovis-challenged MDM analysed for the two different cDNA templates across all three post-challenge time points, revealing a high correlation for all four genes (r2≥0.65, P≤0.001) (Figure S1). Furthermore, the geometric mean fold-changes in expression and Student's t-test statistics in the M. bovis-challenged MDM relative to the control MDM for these genes were similar across all three time points (Figure S2 and Table S11). These results suggest that no preferential amplification bias of gene transcripts or amplification artefacts occurred during the preparation of the linearly amplified cDNA. Consequently, we concluded that the use of the linearly amplified cDNA was suitable in the current study.

The relative fold-changes in expression between the M. bovis-challenged and control MDM for the IL1B, IL6, NFKB2 and TNF genes as determined through real time qRT-PCR analysis (using the conventionally-prepared cDNA as template) are depicted graphically in Figure 7, while fold-changes in expression for all other genes analysed are shown graphically in Figure S3. In addition, comparisons between the results from the real time qRT-PCR and microarray analyses for all genes are given in Table S12. These results showed that twelve genes (CCL4, CCL5, CCL20, CD40, CFB, CXCL2, IL1B, IL6, IL15, IRF1, NFKB2 and TNF) were significantly upregulated across all three time points (P≤0.01). Two genes (AREGB and FOS) displayed significant downregulation in the M. bovis-challenged MDM samples across all time points (P≤0.05). TICAM1 was differentially expressed (upregulated) only at the 2 hour time point (P≤0.001). PIK3IP1 showed significant decreased relative expression in the M. bovis-challenged samples at the 2 hour and 6 hour time points (P≤0.05); however, this gene was not differentially expressed at the 24 hour time point. SPRY2 displayed significant (P≤0.05) decreased relative expression in the M. bovis-challenged MDM at the 2 hour time point but was not differentially expressed after 6 hours and 24 hours post-challenge. Comparison of the real time qRT-PCR expression profiles of each gene for the 2 hour, 6 hour and 24 hour non-challenged control MDM relative to the 0 hour non-challenged control MDM did not reveal significant differential expression (P≥0.05) for 16 of the 17 analysed genes; only the expression profile of the AREGB gene in the 0 hour control MDM differed from the control MDM at 2 hours and 6 hours (P≤0.01).

thumbnail
Figure 7. Real time qRT-PCR analysis of the IL1B, IL6, NFKB2 and TNF genes following M. bovis challenge.

Log2 fold-changes in expression in the M. bovis-challenged MDM relative to the non-challenged control MDM at all three time points are shown. For comparison, the expression profiles for these genes based on the microarray data are also shown. The significance of the fold-changes in expression for each gene based on the real time qRT-PCR analysis only are denoted by asterisks in the figure (*P≤0.05, **P≤0.01, ***P≤0.001). The fold-changes calculated for each gene from the microarray data in the M. bovis-challenged MDM for each gene at each time point were significant (adjusted P-value ≤ 0.05). In addition, the log2 fold-change in expression for the control MDM at each time point relative to the 0 hour control MDM are also shown for both the microarray and real time qRT-PCR data; no significant differences in gene expression between the control MDM relative to the 0 hour control was observed for these genes at each time point (P≥0.05).

https://doi.org/10.1371/journal.pone.0032034.g007

Gene expression profiles for all 16 genes analysed using both real time qRT-PCR and microarray platforms were largely the same across all three time points. Only the expression profile of the SPRY gene at 6 hours and 24 hours post-challenge and the PIK3IP1 gene at 24 hours post-challenge did not display significant differential expression (P≥0.05) between the M. bovis-challenged and non-challenged control MDM based on real time qRT-PCR analysis but displayed significant reduced relative expression (adjusted P-value threshold ≤0.05) based on the microarray results at these time points. The observed discrepancies between the microarray and real time qRT-PCR data for the SPRY2 and PIK3IP1 genes at the 6 hour and 24 hour time points may reflect differences in the sensitivity of the two analytical methods used and/or differences in the mRNA transcripts targeted by the probes (microarray) and primer pairs (real time qRT-PCR) used for the two forms of gene expression analysis [29], [30].

Discussion

The host immune response to mycobacterial infection is highly complex and involves interaction between innate and adaptive immune mechanisms. The macrophage is among the first innate immune cell types to encounter mycobacteria and the response elicited by these cells is key to the control of infection [3], [31]. Consequently, investigation of gene transcriptional changes in macrophages following mycobacterial challenge in vitro provides a model for understanding the complex host-pathogen interactions and innate cellular signalling pathways that underlie the host immune response to mycobacterial infection. Therefore, for the current study, we have investigated gene expression in bovine MDM in response to M. bovis challenge in vitro using the pan-genomic high-density Affymetrix® GeneChip® Bovine Genome Array at 2, 6 and 24 hours post-challenge. The gene expression data from these analyses were used to investigate and reconstruct key host regulatory networks involved during early mycobacterial infection.

It is important to note that MDM are not the primary cell type that recognise M. bovis during the course of natural infection and that bovine alveolar macrophages would be a more appropriate cell type to analyse the host transcriptional response to M. bovis challenge in vitro. However, MDM are widely used for in vitro challenge experiments involving mycobacteria as they best mimic the in situ status of infection compared to other cell types used for experimental infection (e.g. cell lines), particularly in experiments where alveolar macrophages are difficult to source [32][38]. Furthermore, bovine MDM are easily prepared from peripheral blood mononuclear cells using standard procedures, whereas the preparation of bovine alveolar macrophages requires post-mortem lung lavages [14], [39]. Finally, blood macrophages do recognise mycobacteria during secondary phases of infection that occur when bacilli disseminate from infected primary alveolar macrophages [40]; therefore, the results presented in the current study may also provide insight into the macrophage immune mechanisms that occur during the progression of infection.

It should also be noted that the culturing of M. bovis as performed in this study involved the use of growth media containing Tween 80, a non-ionic surface-active detergent that affects the mycobacterial cell wall composition, causing a reduction in virulence [41]. However, Tween 80 is routinely added to liquid media to reduce cell clumping (which can affect colony-forming unit [cfu] counts), therefore enabling homogeneous cell suspensions of mycobacteria to be obtained for in vitro macrophage challenge experiments [41], [42]. As such, the current study is directly comparable to recent studies investigating the interaction of M. tuberculosis complex bacilli with host macrophages [14], [32], [33], [43][45].

The patterns of global gene expression support the association of M. bovis infection with repression of host gene expression

Comparison of the gene expression profiles in the M. bovis-challenged MDM relative to the control MDM at each time point revealed an increase in the number of differentially expressed genes across the three post-challenge time points analysed. Notably, the number of downregulated genes exceeded the number of upregulated genes at each time point; however, the fold-change in expression for the upregulated genes was much greater than that for the downregulated genes at each time point. These findings support previous work by us and others showing that mycobacterial infection in vitro and in vivo is associated with pan-genomic and immuno-specific gene repression [46][49]. However, at each time point post-challenge, the mean fold-change in expression for upregulated genes was markedly higher than the mean fold-change in expression for downregulated genes. Furthermore, many of the upregulated genes displaying the highest fold-increase in expression at each time point post-challenge had a known immunological role, while many of the genes displaying the greatest reduction in expression had no obvious immune function―a finding that highlights the complex nature of the interaction between the macrophage and M. bovis during infection.

Analysis of global gene expression signatures 2 hours post-challenge with M. bovis support a role for MyD88-independent signalling

Host innate immune recognition of mycobacterial pathogens is largely regulated through the engagement of macrophage PRRs with mycobacterial PAMPs. TLRs are among the most important PRRs for the recognition of mycobacterial PAMPs during the early stages of infection, particularly the cell surface TLR2 (acting alone or as a heterodimeric complex with TLR1 or TLR6) and TLR4 receptors [4], [50]. More recently, membrane-associated cytosolic TLRs, such as TLR8 and TLR9, and the cell surface TLR5 receptor have also been shown to play a role in host detection of mycobacteria [51]-[53].

TLR2 and TLR4 signalling is predominantly mediated through the adaptor protein, MyD88, the activation of which results in the initiation of an inflammatory signalling cascade involving IRAK1, IRAK4 and TRAF6. Activated TRAF6 can interact with the regulatory components of the nuclear receptor subfamily 2, group C, member 2 (NR2C2) complex (also called the TGF-b activated kinase [TAK1] complex), culminating in the activation of TAK1, which can phosphorylate downstream targets, such as the IκB kinases (IKKs). Phosphorylation of the IKK complex catalyses the degradation of the NF-κB inhibitors (IKBs) resulting in the release of the NF-κB complex, which translocates to the nucleus and directs the transcription of many immune-related genes, including inflammatory cytokines and chemokines necessary to prime the adaptive immune response [54], [55].

A MyD88-independent pathway (also called the TRIF-dependent pathway) has also been reported as an alternative signalling route for TLR4 and as the primary route for the membrane-associated cytosolic TLR3 receptor. MyD88-independent signalling involves the recruitment of at least two adaptor proteins: TICAM1 (also called TRIF) and TICAM2 (also called TRAM). TICAM1 can activate TRAF3 to induce interferon regulatory factor 3 (IRF3)-mediated transcription of type I interferons (IFN), or it can form heterodimeric signalling complexes with the TNFRSF1A-associated via death domain (TRADD) and ralA binding protein 1 (RALBP1, also called RIP1) proteins that induce both NF-κB- and mitogen-activated protein kinases (MAPK)-mediated transcription of inflammatory cytokines [54], [55].

In the current study, inspection of the differentially expressed genes at the 2 hour time point confirm a role for TLR-mediated recognition of M. bovis in the challenged MDM. The only differentially expressed TLR gene at this time point was TLR2 (upregulated), while many of the genes encoding proteins involved in TLR signalling were also shown to be differentially expressed in the M. bovis-challenged MDM at this time point. These included MYD88 (downregulated); IRAK4 (downregulated); IRAK1 (downregulated); TICAM1 (upregulated); TRAF6 (upregulated); TRAF3 (upregulated); TAB2 (upregulated) and NFKBIA (upregulated). TAB3 and NR2C2 were not differentially expressed.

The induction of TLR2 2 hours post-challenge suggest that this PRR is involved in the early macrophage recognition of M. bovis. However, the suppression of the genes encoding the MyD88 adaptor and the IRAK4 and IRAK1 kinases at this time point post-challenge suggests that MyD88-dependent signalling is repressed during M. bovis infection. Alternatively, these results may point to the activation of a macrophage TLR2-dependent/MyD88-independent signalling pathway involving alternative adaptor protein(s), such as TICAM1, during M. bovis-challenge in vitro. Furthermore, the increased relative expression of TRAF6 and TAB2 2 hours post-infection suggests that this signalling cascade bypasses TRADD (the gene encoding TRADD was downregulated) and RALBP1 (the gene encoding RALBP1 was downregulated) and is relayed directly to the IKKs, inducing NF-κB-directed transcription. Although there is little published work on TLR2-dependent/MyD88-independent pathway(s), previous research involving in vitro infections of murine peritoneal macrophages with heat-killed M. tuberculosis was shown to result in the expression of the secretory leukocyte peptidase inhibitor gene (SLPI) via a TLR2-dependent/MyD88-independent pathway [56]. Similarly, microarray-based analysis of the murine bone marrow-derived macrophage transcriptome following infection with virulent M. tuberculosis in vitro was also shown to induce MyD88-independent signalling pathways, possibly involving TLR2 [57].

The repression of MyD88-dependent signalling may represent a transcriptional signature of M. bovis-mediated subversion of host immune responses. Indeed, inhibition of IFN-γ-induced MHC class II expression in M. tuberculosis-infected macrophages has been shown to involve MyD88-dependent signalling mediated through TLR2 [58], [59]. Thus, the suppression of TLR2-MyD88-dependent signalling may represent one mechanism for immuno-evasion by the mycobacterial pathogen enabling persistence within the host macrophage during infection. In contrast, the activation of MyD88-independent signalling may present an alternative route used by host macrophages to circumvent suppressed MyD88-dependent signalling and induce downstream transcription factors which promote chemokine and cytokine production during infection.

It is also important to note that as this is a transcriptomics study, the functional role of various pre-existing TLR adaptor proteins in macrophage cellular pathways during M. bovis infection cannot be assessed at the RNA level; consequently, their involvement in these pathways cannot be fully excluded. Further work involving both transcriptomic and proteomic platforms is required to investigate fully a role for macrophage TLR2-dependent/MyD88-independent signalling pathways in response to mycobacterial infection.

A role for intracellular PRR-signalling in the control of M. bovis infection in vitro

Inspection of the list of differentially expressed genes demonstrated that the expression of several genes encoding intracellular PRRs―which are generally associated with the detection of viral PAMPs, such as viral RNA―is modulated during M. bovis challenge in vitro. This was further supported by systems analysis, which identified Signalling by RIG-I-like receptors in anti-viral innate immunity and Activation of interferon regulatory factors by cytosolic pattern recognitions receptors among the top ranking canonical pathways at 6 hours and 24 hours post-M. bovis challenge, respectively.

RIG-I-like receptors (RLRs) represent a group of RNA helicases that function as cytosolic sensors of PAMPs within viral RNA or processed self RNA ligands to elicit innate immune responses to control infection. To-date, three RLR protein members have been identified: retinoic acid-inducible gene I (RIG-I, encoded by DDX58); melanoma differentiation associated factor 5 (MDA5, encoded by IFIH1); and laboratory of genetics and physiology 2 and a homolog of mouse D11lgp2 (LPG2, encoded by DHX58) [60]. In general, intracellular signalling mediated by RLRs results in the activation of downstream transcription factors that culminates in the production of type I IFN and antiviral proteins that control viral infection [60]. Notably, all three genes were differentially expressed in the current study at the 6 hour and 24 hour time points: DDX58 (downregulated at 6 hours and upregulated at 24 hours), IFIH1 (upregulated at 6 hours and 24 hours) and DHX58 (upregulated at 24 hours).

Genes encoding members of the TLR3 signalling pathway also displayed differential expression in the M. bovis-challenged MDM at the 6 hour and 24 hour time points. TLR3 encodes a membrane-bound intracellular PRR that localises to endosomes and is involved in the recognition of viral PAMPs, such as double-stranded reoviral RNA [54]. TLR3-mediated signalling occurs through the TICAM1 adaptor which recruits TRAF3 to activate IRF3-mediated transcription of type I IFN. In the current study, TLR3 was upregulated in the M. bovis-challenged MDM samples at 6 hours and 24 hours post-challenge, while upregulation of TRAF3, IRF3 and INFB1 was also observed at the 24 hour time point. TICAM1 was not differentially expressed at either the 6 hour or 24 hour time points.

The involvement of RLRs and TLR3 in mediating the macrophage response to M. bovis infection is intriguing given their well-documented role in the detection of viral PAMPs. However, transfection experiments have shown that murine bone marrow-derived macrophage production of type I IFN in response to RNA isolated from Legionella pneumophila (a gram-negative bacterium that replicates in host macrophages and causes severe pneumonia) is mediated via RIG-I [61]; while RNA from Helicobacter pylori (a gram positive bacterium that causes gastritis) was also shown to induce type I IFN production via a RIG-I-mediated signalling pathway in mouse dendritic cells [62]. Moreover, increased TLR3 RNA and protein expression has been reported in human leukocytes following in vitro stimulation with bacterial lipopolysaccharide (LPS), while increased relative TLR3 expression has been reported by us in the transcriptome of peripheral blood leukocytes (PBL) from cattle displaying active BTB compared to the PBL transcriptome of non-infected control animals [48], [62].

Collectively, these findings support a role for cytosolic RLRs and TLR3 during host infection with intracellular bacterial pathogens. It has been proposed that activation of intracellular PRRs by bacterial PAMPs is due to the translocation of bacterial RNA into host cells, or is a consequence of the generation of host-derived RNA ligands caused by the pathogen-mediated disruption of host cellular pathways during infection [61]. Another possibility is that intracellular PRRs mediate autophagy in infected macrophages, which is increasingly regarded as a key anti-mycobacterial host defence mechanism [63][65]. Autophagy is a complex cellular process in which host cell cytosolic components, such as damaged or surplus organelles, are engulfed in double-membrane vesicles called autophagosomes and fused with late endosomes or lysosomes to degrade their contents. However, further work is required to investigate a role for RLR- and TLR3-mediated autophagy in response to mycobacterial infection.

Pro- and anti-apoptotic global transcriptional signatures associated with M. bovis challenge in vitro

Systems analysis of the differentially expressed genes revealed significant enrichment for genes involved in programmed cell death (apoptosis), particularly at 6 hours post-M. bovis challenge. Apoptosis is a complex biological process triggered by the engagement of protein receptors and ligands that initiate a caspase-directed signalling cascade. In particular, caspase activation and subsequent apoptosis is elicited upon the binding of TNF-α and the Fas ligand protein (FASLG) to their respective cell surface receptors, TNF-R1 or TNF-R2 and the FAS (TNF receptor superfamily member 6) receptor. This interaction results in the initiation of a death-inducing signal complex that involves recruitment of FADD, caspase 8 (CASP8) and CASP10, triggering a signalling cascade that culminates in the activation of effector caspases (such as CASP3, CASP6 and CASP7) and other non-caspase enzymes that induce DNA fragmentation, mitochondrial membrane permeability and degradation, and apoptotic vesicle formation [66].

In the current study, genes encoding members of the caspase family of proteins together with several pro- and anti-apoptotic mediators were differentially expressed following challenge with M. bovis. TNF was among the pro-apoptotic genes displaying upregulated gene expression in the M. bovis-challenged MDM at the 6 hour time point; however, the gene encoding the pro-apoptotic TNF-R1 receptor (TNFRSF1A) was downregulated at this time point. In contrast, the gene encoding the pro-apoptotic FAS (TNF receptor superfamily member 6) receptor (FAS) was upregulated in these MDM samples.

FADD and the death-domain associated protein gene (DAXX), which encode adaptor proteins that mediate pro-apoptotic signals from activated TNF-R1 [67] and the FAS receptor [68], respectively, were also upregulated after 6 hours post-challenge. Induction of the BH3 interacting domain death agonist gene (BID) was also observed; the activated form of the BID protein, truncated BID (tBID), forms heterodimeric complexes with other pro-apoptotic proteins causing mitochondrial membrane permeability and the release of cytochrome C, which activates the effector caspases to induce apoptosis [66].

Several anti-apoptotic genes also exhibited differential expression at the 6 hour time point. These included the gene encoding TNF-R2 (TNFRSF1B) [upregulated] that recruits the encoded product of the apoptosis inhibitor baculoviral IAP repeat containing 3 gene (BIRC3; downregulated) [69]. Notably, expression of the B-cell CLL/lymphoma 2 gene (BCL2) that encodes a protein that inhibits mitochondrial membrane permeability and prevents apoptosis [70] was suppressed following M. bovis challenge in vitro at this time point.

Macrophage apoptosis is increasingly regarded as a host innate immune mechanism in controlling mycobacterial infection by containing and limiting mycobacterial growth [71]. Recently, microarray-based studies of mycobacterial infection of bovine macrophages in vitro revealed over-representation of genes associated with apoptosis among the list of differentially expressed genes [14], [15]. The detection of apoptotic signatures of gene expression here supports this earlier work and indicates that apoptosis is an early response of host MDM to M. bovis infection.

Although analysis of the differentially expressed genes following M. bovis-challenge largely supports induction of a pro-apoptotic response in these cells, presumably to eliminate the intracellular pathogen, the increased relative expression of several anti-apoptotic genes suggests that this process is highly regulated. Anti-apoptotic signals may represent a host mechanism to limit the amount of cell death following infection and may also enable improved antigen-presentation by infected macrophages to T cells, resulting in either elimination of the pathogen or enhanced granuloma formation. It is also possible that the pro- and anti-apoptotic signals detected here represent transcriptional signatures of “bystander” apoptosis, whereby uninfected macrophages undergo apoptosis following contact with mycobacteria-infected macrophages [15], [72].

Alternatively, the induction of anti-apoptotic genes may signify an immuno-subversion mechanism used by the pathogen that postpones apoptosis and enables survival and replication within the macrophage [10]. Indeed, recent studies have shown that virulent M. tuberculosis can subvert host apoptotic pathways by interfering with the production of key host signalling molecules [71], [73], resulting in the arrest of apoptosis and induction of necrosis, thereby offering an exit route from the macrophage [10]. Therefore, induction of anti-apoptotic genes as identified by the current study may provide another strategy used by the pathogen for the evasion of the host immune system.

Induction of chemokine and cytokine genes following M. bovis-challenge

The innate immune response to mycobacterial pathogens is largely mediated by immuno-regulatory cytokines and chemokines secreted by various immune cells that regulate the recruitment and expansion of immune cell populations, such as monocytes, neutrophils, T cells and other effector cells from the blood to the site of infection [74]. Macrophage production of cytokines and chemokines in response to mycobacterial infection can be induced through a wide range of PRR-mediated pathways, and their secretion primes the adaptive immune response, which is pivotal in determining pathogenesis [75].

In the current study, several inflammatory chemokine and cytokine genes were shown to display large increases in relative expression following M. bovis-challenge across all of the time points analysed, with systems analysis showing that many of the top ranking GO categories were enriched for large numbers of these genes (see Tables S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, and S12). These results confirm that cytokine and chemokine gene expression is an early response of bovine macrophages to M. bovis-challenge following pathogen recognition by PRRs [14], [76].

Many of the upregulated inflammatory genes detected here have been shown to play an important role in the immune response to mycobacterial infection and several of these direct the transition from innate to adaptive immunity during infection [3]. Indeed, the GO categories identified via systems analysis of the differentially expressed genes here suggest that the expression of macrophage genes involved in the communication between innate and adaptive immune cell types is a key biological process that occurs within the first 24 hours of M. bovis challenge in vitro. For example, TNF-α is a key pleiotropic cytokine produced by macrophages that plays an important role in granuloma formation and maintenance by inducing IFN-γ release from T cells, which in turn, activates anti-mycobacterial function in infected macrophages [4], [77]. IL12 (upregulated at all three time points) encodes a pro-inflammatory cytokine produced by macrophages that activates NK cells and T cells to produce IFN-γ and promote the adaptive immune response to mycobacterial infection [78]. IL-1β (encoded by IL1B, upregulated all time points) is a proinflammatory cytokine secreted largely by innate immune cells in response to mycobacterial infection and studies have shown that IL-1β is produced in excess at the site of infection, suggesting that it plays an important role in granuloma formation and maintenance [79]. Genes encoding β-chemokines (such as CCL20, which was upregulated at all three time points) participate in early immuno-protection against mycobacterial pathogens following infection [80], [81].

IL-6, a pleiotropic inflammatory cytokine expressed by a wide range of immune cells including macrophages in response to mycobacterial infection, has been proposed to play an important role in protection against tuberculosis [82]. IL6 displayed large fold-upregulation at all three time points analysed in the M. bovis-challenged MDM, while the gene encoding the IL-6 receptor (IL6R) displayed downregulation expression in the M. bovis-challenged MDM at 2 hours and 24 hours and was not differentially expressed at the 6 hour time point. This suggests that endogenous IL-6 production by macrophages acts in an endocrine manner, presumably by initiating immune responses in other innate or adaptive immune cells [83]. Indeed, IL-6 has been proposed as a key regulator of the immunological switch between innate and adaptive immune processes [84].

Despite the role of chemokines and cytokines in directing the host immune response to control mycobacterial infection, there is evidence to suggest that their function can be used by mycobacterial pathogens to enable persistence within the host. For example, non-regulated production of TNF-α in lung tissue can result in immunopathology, including destructive inflammation and necrosis, allowing dissemination of the pathogen from infected cells [11], [85]. Furthermore, studies have shown that IL-6 can inhibit T cell responses following infection of macrophages with M. bovis-BCG and M. avium subspecies paratuberculosis [86], while other investigations have reported that M. tuberculosis-induced IL-6 production inhibits the anti-microbial activity of macrophages in response to IFN-γ [87]. IL10, which encodes an anti-inflammatory cytokine that limits local cytokine-induced tissue damage and systemic inflammatory responses during infection [88], was upregulated at 2 hours and 24 hours post-challenge. Interestingly, upregulation of IL10 resulting in the subsequent suppression of host innate immune responses to infection has been proposed as a mechanism which enables enhanced mycobacterial intracellular proliferation [89]. Thus, exploitation of the roles of key pro- and anti-inflammatory cytokines may enable M. bovis to evade elimination by the host adaptive immune response, enabling disease progression.

Conclusions

In the current study, we have analysed the bovine macrophage transcriptome in response to M. bovis-challenge in vitro using a global microarray platform. Inspection and systems analysis of the differentially expressed genes identified several key cellular pathways involved in the host response to M. bovis, including TLR and RLR signalling and apoptosis. These transcriptional signatures highlight the complex interactions between host macrophages and the mycobacterial pathogen during infection. Further analyses involving the comparison of gene expression changes in bovine macrophages to other mycobacterial species, such as M. bovis-BCG and M. avium subspecies paratuberculosis, may also highlight the differential responses of the host macrophage to these pathogens and enable identification of key cellular pathways that contribute to the development of pathology. Furthermore, the identification of gene expression patterns linked to infection and pathology could provide a route to improved BTB diagnostics.

Supporting Information

Figure S1.

Regression analysis of relative gene expression fold-changes in the M. bovis-challenged MDM obtained from real time qRT-PCR analysis using conventionally-prepared and linearly amplified cDNA. Log2 fold-changes in gene expression in the M. bovis-challenged MDM relative to the control MDM were plotted. Data obtained from the conventionally-prepared cDNA are plotted on the y-axis, while data obtained for the linearly amplified cDNA prepared using the WT-Ovation™ RNA Amplification System (OVA) are plotted on the x-axis. The P-value of the slope for all regression lines was≤0.001. Graph construction and analyses were performed using the Minitab version 16 software package. S = standard deviation; R-Sq = r2 value.

https://doi.org/10.1371/journal.pone.0032034.s001

(TIF)

Figure S2.

Comparison of the fold-changes in expression for the CCL5, CCL20, IL1B and IL6 genes based on real time qRT-PCR analysis using conventional and linearly amplified cDNA. Log2 fold-changes in expression in the M. bovis-challenged MDM relative to the control MDM at all three time points are shown. Linearly amplified cDNA template (OVA) was prepared using the WT-Ovation™ RNA Amplification System. For comparison, the expression profiles for these genes as per the microarray data are also shown. The significance of the fold-changes in expression for each gene based on the real time qRT-PCR analysis only are denoted by asterisks in the figure (*P≤0.05, **P≤0.01, ***P≤0.001). The fold-changes calculated for each gene the microarray data in the M. bovis-challenged MDM for each gene were significant (adjusted P-value≤0.05). In addition, the log2 fold-change in expression for the control MDM at each time point relative to the 0 hour control MDM are also shown for both the microarray and real time qRT-PCR data; no significant differences in gene expression between the control MDM relative to the 0 hour control was observed at each time point (P≥0.05).

https://doi.org/10.1371/journal.pone.0032034.s002

(PDF)

Figure S3.

Real time qRT-PCR analysis. Log2 fold-changes in expression in the M. bovis-challenged MDM relative to the control MDM at all three time points are shown. For comparison, the expression profiles for these genes as per the microarray data are also shown. The significance of the mean fold-changes in expression for each gene based on the real time qRT-PCR analysis only are denoted by asterisks in the figure (*P≤0.05, **P≤0.01, ***P≤0.001). The mean fold-changes calculated for each gene based on the microarray data in the M. bovis-challenged MDM for each gene were significant (adjusted P-value≤0.05)―probes for TICAM1 were not present on the microarray and this gene was analysed by real time qRT-PCR only. In addition, the log2 fold-change in expression for the control MDM at each time point relative to the 0 hour control MDM are also shown for both the microarray and real time qRT-PCR data; no significant differences in gene expression between the control MDM relative to the 0 hour control was observed at each time point (P≥0.05).

https://doi.org/10.1371/journal.pone.0032034.s003

(PDF)

Table S1.

Real time qRT-PCR primers used in this study.

https://doi.org/10.1371/journal.pone.0032034.s004

(DOC)

Table S2.

The list of differentially expressed transcripts detected at the 2 hour time point post- M. bovis challenge.

https://doi.org/10.1371/journal.pone.0032034.s005

(XLS)

Table S3.

Gene ontology (GO) categories identified using IPA at the 2 hour time point post-M. bovis challenge. The top ranking GO categories identified by IPA are listed according to P-values.

https://doi.org/10.1371/journal.pone.0032034.s006

(XLS)

Table S4.

Top-ranking canonical pathways identified using IPA at the 2 hour time point post-M. bovis challenge. The top ranking canonical pathways identified by IPA are listed according to P-values. The ratio indicates the number of differentially expressed genes involved in each canonical pathway divided by the total number of genes within each pathway as per the IPA Knowledge Base.

https://doi.org/10.1371/journal.pone.0032034.s007

(XLS)

Table S5.

The list of differentially expressed transcripts detected at the 6 hour time point post- M. bovis challenge.

https://doi.org/10.1371/journal.pone.0032034.s008

(XLS)

Table S6.

Gene ontology (GO) categories identified using IPA at the 6 hour time point post-M. bovis challenge. The top ranking GO categories identified by IPA are listed according to P-values.

https://doi.org/10.1371/journal.pone.0032034.s009

(XLS)

Table S7.

Top-ranking canonical pathways identified using IPA at the 6 hour time point post-M. bovis challenge. The top ranking canonical pathways identified by IPA are listed according to P-values. The ratio indicates the number of differentially expressed genes involved in each canonical pathway divided by the total number of genes within each pathway according to the IPA Knowledge Base.

https://doi.org/10.1371/journal.pone.0032034.s010

(XLS)

Table S8.

The list of differentially expressed transcripts detected at the 24 hour time point post- M. bovis challenge.

https://doi.org/10.1371/journal.pone.0032034.s011

(XLS)

Table S9.

Gene ontology (GO) categories identified using IPA at the 24 hour time point post-M. bovis challenge. The top ranking GO categories identified by IPA are listed according to P-values.

https://doi.org/10.1371/journal.pone.0032034.s012

(XLS)

Table S10.

Top-ranking canonical pathways identified using IPA at the 24 hour time point post-M. bovis challenge. The top ranking canonical pathways identified by IPA are listed according to P-values. The ratio indicates the number of differentially expressed genes involved in each canonical pathway divided by the total number of genes within each pathway according to the IPA Knowledge Base.

https://doi.org/10.1371/journal.pone.0032034.s013

(XLS)

Table S11.

Comparison of relative gene expression fold-changes in the M. bovis-challenged MDM obtained from real time qRT-PCR analysis using conventionally-prepared and linearly amplified cDNA. Geometric mean fold-changes in gene expression (M. bovis-challenged MDM versus control MDM) are given. P-values were obtained by statistical analysis of log2 fold-change gene expression data.

https://doi.org/10.1371/journal.pone.0032034.s014

(DOC)

Table S12.

Comparison of fold-changes in gene expression in the M. bovis-challenged MDM based on microarray and real time qRT-PCR results. Geometric mean fold-changes of gene expression (M. bovis-challenged MDM relative to control MDM) are given for the microarray and real time qRT-PCR data at each time point. All genes at each time point were significantly differentially expressed based on the microarray data (adjusted P-value≤0.05). The P-values obtained from analysis of the real time qRT-PCR analysis are presented. Descriptions of the function of each gene were obtained from the GeneCards version 3 database [90]. ‘Not DE’ indicates that the gene was note differentially expressed according to the real time qRT-PCR data.

https://doi.org/10.1371/journal.pone.0032034.s015

(DOC)

Acknowledgments

We would like to thank Mr. Eamon Costello and staff at the Irish Department of Agriculture, Food and the Marine Backweston Laboratory Campus (Celbridge, Co. Kildare) for providing the isolate of M. bovis. We also would like to thank the staff at the UCD Lyons Research Farm for assistance with blood collection. We also thank members of the UCD Tuberculosis Diagnostics and Immunology Research Centre. Finally, we thank three anonymous reviewers for helpful comments and insights.

Author Contributions

Conceived and designed the experiments: DAM MT JAB EG DEM. Performed the experiments: DAM MT NN JAB KMC. Analyzed the data: DAM MT KEK NN JAB SDEP DJL KH. Wrote the paper: DAM MT KEK NN DJL KH SVG EG DEM.

References

  1. 1. Waters WR, Palmer MV, Thacker TC, Davis WC, Sreevatsan S, et al. (2011) Tuberculosis immunity: opportunities from studies with cattle. Clin Dev Immunol 2011: 768542.WR WatersMV PalmerTC ThackerWC DavisS. Sreevatsan2011Tuberculosis immunity: opportunities from studies with cattle.Clin Dev Immunol2011768542
  2. 2. Pollock JM, Welsh MD, McNair J (2005) Immune responses in bovine tuberculosis: towards new strategies for the diagnosis and control of disease. Vet Immunol Immunopathol 108: 37–43.JM PollockMD WelshJ. McNair2005Immune responses in bovine tuberculosis: towards new strategies for the diagnosis and control of disease.Vet Immunol Immunopathol1083743
  3. 3. Iwasaki A, Medzhitov R (2010) Regulation of adaptive immunity by the innate immune system. Science 327: 291–295.A. IwasakiR. Medzhitov2010Regulation of adaptive immunity by the innate immune system.Science327291295
  4. 4. Harding CV, Boom WH (2010) Regulation of antigen presentation by Mycobacterium tuberculosis: a role for Toll-like receptors. Nat Rev Microbiol 8: 296–307.CV HardingWH Boom2010Regulation of antigen presentation by Mycobacterium tuberculosis: a role for Toll-like receptors.Nat Rev Microbiol8296307
  5. 5. Cooper AM (2009) Cell-mediated immune responses in tuberculosis. Annu Rev Immunol 27: 393–422.AM Cooper2009Cell-mediated immune responses in tuberculosis.Annu Rev Immunol27393422
  6. 6. Flynn JL, Chan J, Triebold KJ, Dalton DK, Stewart TA, et al. (1993) An essential role for interferon gamma in resistance to Mycobacterium tuberculosis infection. J Exp Med 178: 2249–2254.JL FlynnJ. ChanKJ TrieboldDK DaltonTA Stewart1993An essential role for interferon gamma in resistance to Mycobacterium tuberculosis infection.J Exp Med17822492254
  7. 7. Józefowski S, Sobota A, Kwiatkowska K (2008) How Mycobacterium tuberculosis subverts host immune responses. Bioessays 30: 943–954.S. JózefowskiA. SobotaK. Kwiatkowska2008How Mycobacterium tuberculosis subverts host immune responses.Bioessays30943954
  8. 8. Hestvik AL, Hmama Z, Av-Gay Y (2005) Mycobacterial manipulation of the host cell. FEMS Microbiol Rev 29: 1041–1050.AL HestvikZ. HmamaY. Av-Gay2005Mycobacterial manipulation of the host cell.FEMS Microbiol Rev2910411050
  9. 9. Netea MG, Van der Meer JW, Kullberg BJ (2004) Toll-like receptors as an escape mechanism from the host defense. Trends Microbiol 12: 484–488.MG NeteaJW Van der MeerBJ Kullberg2004Toll-like receptors as an escape mechanism from the host defense.Trends Microbiol12484488
  10. 10. Behar SM, Divangahi M, Remold HG (2010) Evasion of innate immunity by Mycobacterium tuberculosis: is death an exit strategy? Nat Rev Microbiol 8: 668–674.SM BeharM. DivangahiHG Remold2010Evasion of innate immunity by Mycobacterium tuberculosis: is death an exit strategy?Nat Rev Microbiol8668674
  11. 11. Chen M, Gan H, Remold HG (2006) A mechanism of virulence: virulent Mycobacterium tuberculosis strain H37Rv, but not attenuated H37Ra, causes significant mitochondrial inner membrane disruption in macrophages leading to necrosis. J Immunol 176: 3707–3716.M. ChenH. GanHG Remold2006A mechanism of virulence: virulent Mycobacterium tuberculosis strain H37Rv, but not attenuated H37Ra, causes significant mitochondrial inner membrane disruption in macrophages leading to necrosis.J Immunol17637073716
  12. 12. Walzl G, Ronacher K, Hanekom W, Scriba TJ, Zumla A (2011) Immunological biomarkers of tuberculosis. Nat Rev Immunol 11: 343–354.G. WalzlK. RonacherW. HanekomTJ ScribaA. Zumla2011Immunological biomarkers of tuberculosis.Nat Rev Immunol11343354
  13. 13. Elsik CG, Tellam RL, Worley KC, Gibbs RA, Muzny DM, et al. (2009) The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science 324: 522–528.CG ElsikRL TellamKC WorleyRA GibbsDM Muzny2009The genome sequence of taurine cattle: a window to ruminant biology and evolution.Science324522528
  14. 14. Widdison S, Watson M, Coffey TJ (2011) Early response of bovine alveolar macrophages to infection with live and heat-killed Mycobacterium bovis. Dev Comp Immunol 35: 580–591.S. WiddisonM. WatsonTJ Coffey2011Early response of bovine alveolar macrophages to infection with live and heat-killed Mycobacterium bovis.Dev Comp Immunol35580591
  15. 15. Kabara E, Kloss CC, Wilson M, Tempelman RJ, Sreevatsan S, et al. (2010) A large-scale study of differential gene expression in monocyte-derived macrophages infected with several strains of Mycobacterium avium subspecies paratuberculosis. Brief Funct Genomics 9: 220–237.E. KabaraCC KlossM. WilsonRJ TempelmanS. Sreevatsan2010A large-scale study of differential gene expression in monocyte-derived macrophages infected with several strains of Mycobacterium avium subspecies paratuberculosis.Brief Funct Genomics9220237
  16. 16. Jacobsen CN, Aasted B, Broe MK, Petersen JL (1993) Reactivities of 20 anti-human monoclonal antibodies with leucocytes from ten different animal species. Vet Immunol Immunopathol 39: 461–466.CN JacobsenB. AastedMK BroeJL Petersen1993Reactivities of 20 anti-human monoclonal antibodies with leucocytes from ten different animal species.Vet Immunol Immunopathol39461466
  17. 17. Taraktsoglou M, Szalabska U, Magee DA, Browne JA, Sweeney T, et al. (2011) Transcriptional profiling of immune genes in bovine monocyte-derived macrophages exposed to bacterial antigens. Vet Immunol Immunopathol 140: 130–139.M. TaraktsoglouU. SzalabskaDA MageeJA BrowneT. Sweeney2011Transcriptional profiling of immune genes in bovine monocyte-derived macrophages exposed to bacterial antigens.Vet Immunol Immunopathol140130139
  18. 18. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80.RC GentlemanVJ CareyDM BatesB. BolstadM. Dettling2004Bioconductor: open software development for computational biology and bioinformatics.Genome Biol5R80
  19. 19. Wilson CL, Miller CJ (2005) Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis. Bioinformatics 21: 3683–3685.CL WilsonCJ Miller2005Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis.Bioinformatics2136833685
  20. 20. Hochreiter S, Clevert DA, Obermayer K (2006) A new summarization method for Affymetrix probe level data. Bioinformatics 22: 943–949.S. HochreiterDA ClevertK. Obermayer2006A new summarization method for Affymetrix probe level data.Bioinformatics22943949
  21. 21. Talloen W, Clevert DA, Hochreiter S, Amaratunga D, Bijnens L, et al. (2007) I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data. Bioinformatics 23: 2897–2902.W. TalloenDA ClevertS. HochreiterD. AmaratungaL. Bijnens2007I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data.Bioinformatics2328972902
  22. 22. Saeed AI, Bhagabati NK, Braisted JC, Liang W, Sharov V, et al. (2006) TM4 microarray software suite. Methods Enzymol 411: 134–193.AI SaeedNK BhagabatiJC BraistedW. LiangV. Sharov2006TM4 microarray software suite.Methods Enzymol411134193
  23. 23. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3.GK Smyth2004Linear models and empirical bayes methods for assessing differential expression in microarray experiments.Stat Appl Genet Mol Biol3Article3
  24. 24. Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological 57: 289–300.Y. BenjaminiY. Hochberg1995Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing.Journal of the Royal Statistical Society Series B-Methodological57289300
  25. 25. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, et al. (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29: 365–371.A. BrazmaP. HingampJ. QuackenbushG. SherlockP. Spellman2001Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.Nat Genet29365371
  26. 26. Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, et al. (2011) NCBI GEO: archive for functional genomics data sets-10 years on. Nucleic acids research 39: D1005–1010.T. BarrettDB TroupSE WilhiteP. LedouxC. Evangelista2011NCBI GEO: archive for functional genomics data sets-10 years on.Nucleic acids research39D10051010
  27. 27. Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, et al. (2007) Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res 35: W71–74.A. UntergasserH. NijveenX. RaoT. BisselingR. Geurts2007Primer3Plus, an enhanced web interface to Primer3.Nucleic Acids Res35W7174
  28. 28. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8: R19.J. HellemansG. MortierA. De PaepeF. SpelemanJ. Vandesompele2007qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data.Genome Biol8R19
  29. 29. Draghici S, Khatri P, Eklund AC, Szallasi Z (2006) Reliability and reproducibility issues in DNA microarray measurements. Trends Genet 22: 101–109.S. DraghiciP. KhatriAC EklundZ. Szallasi2006Reliability and reproducibility issues in DNA microarray measurements.Trends Genet22101109
  30. 30. Morey JS, Ryan JC, Van Dolah FM (2006) Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR. Biol Proced Online 8: 175–193.JS MoreyJC RyanFM Van Dolah2006Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR.Biol Proced Online8175193
  31. 31. Pieters J (2008) Mycobacterium tuberculosis and the macrophage: maintaining a balance. Cell Host Microbe 3: 399–407.J. Pieters2008Mycobacterium tuberculosis and the macrophage: maintaining a balance.Cell Host Microbe3399407
  32. 32. Welin A, Eklund D, Stendahl O, Lerm M (2011) Human macrophages infected with a high burden of ESAT-6-expressing M. tuberculosis undergo caspase-1- and cathepsin B-independent necrosis. PLoS One 6: e20302.A. WelinD. EklundO. StendahlM. Lerm2011Human macrophages infected with a high burden of ESAT-6-expressing M. tuberculosis undergo caspase-1- and cathepsin B-independent necrosis.PLoS One6e20302
  33. 33. Wang Y, Zhou X, Lin J, Yin F, Xu L, et al. (2011) Effects of Mycobacterium bovis on monocyte-derived macrophages from bovine tuberculosis infection and healthy cattle. FEMS Microbiol Lett 321: 30–36.Y. WangX. ZhouJ. LinF. YinL. Xu2011Effects of Mycobacterium bovis on monocyte-derived macrophages from bovine tuberculosis infection and healthy cattle.FEMS Microbiol Lett3213036
  34. 34. Lukey PT, Hooker EU (2001) Mycobacterium tuberculosis protocols; Parish T, Stoker NG, editors. Totowa, NJ: Humana. xiv: 403.PT LukeyEU Hooker2001Mycobacterium tuberculosis protocols; Parish T, Stoker NG, editors.Totowa, NJ: Humana.xiv403
  35. 35. Fortune SM, Solache A, Jaeger A, Hill PJ, Belisle JT, et al. (2004) Mycobacterium tuberculosis inhibits macrophage responses to IFN-gamma through myeloid differentiation factor 88-dependent and -independent mechanisms. J Immunol 172: 6272–6280.SM FortuneA. SolacheA. JaegerPJ HillJT Belisle2004Mycobacterium tuberculosis inhibits macrophage responses to IFN-gamma through myeloid differentiation factor 88-dependent and -independent mechanisms.J Immunol17262726280
  36. 36. Dhiman R, Bandaru A, Barnes PF, Saha S, Tvinnereim A, et al. (2011) c-Maf-dependent growth of Mycobacterium tuberculosis in a CD14(hi) subpopulation of monocyte-derived macrophages. Journal of Immunology 186: 1638–1645.R. DhimanA. BandaruPF BarnesS. SahaA. Tvinnereim2011c-Maf-dependent growth of Mycobacterium tuberculosis in a CD14(hi) subpopulation of monocyte-derived macrophages.Journal of Immunology18616381645
  37. 37. Denis M, Keen DL, Parlane NA, Storset AK, Buddle BM (2007) Bovine natural killer cells restrict the replication of Mycobacterium bovis in bovine macrophages and enhance IL-12 release by infected macrophages. Tuberculosis (Edinb) 87: 53–62.M. DenisDL KeenNA ParlaneAK StorsetBM Buddle2007Bovine natural killer cells restrict the replication of Mycobacterium bovis in bovine macrophages and enhance IL-12 release by infected macrophages.Tuberculosis (Edinb)875362
  38. 38. Brooks MN, Rajaram MV, Azad AK, Amer AO, Valdivia-Arenas MA, et al. (2011) NOD2 controls the nature of the inflammatory response and subsequent fate of Mycobacterium tuberculosis and M. bovis BCG in human macrophages. Cell Microbiol 13: 402–418.MN BrooksMV RajaramAK AzadAO AmerMA Valdivia-Arenas2011NOD2 controls the nature of the inflammatory response and subsequent fate of Mycobacterium tuberculosis and M. bovis BCG in human macrophages.Cell Microbiol13402418
  39. 39. Widdison S, Ashley GR, Howard CJ, Coffey TJ (2007) Characterisation of bovine inducible nitric oxide synthase. Vet Immunol Immunopathol 117: 302–309.S. WiddisonGR AshleyCJ HowardTJ Coffey2007Characterisation of bovine inducible nitric oxide synthase.Vet Immunol Immunopathol117302309
  40. 40. van Crevel R, Ottenhoff TH, van der Meer JW (2002) Innate immunity to Mycobacterium tuberculosis. Clin Microbiol Rev 15: 294–309.R. van CrevelTH OttenhoffJW van der Meer2002Innate immunity to Mycobacterium tuberculosis.Clin Microbiol Rev15294309
  41. 41. Masaki S, Sugimori G, Okamoto A, Imose J, Hayashi Y (1990) Effect of Tween 80 on the growth of Mycobacterium avium complex. Microbiol Immunol 34: 653–663.S. MasakiG. SugimoriA. OkamotoJ. ImoseY. Hayashi1990Effect of Tween 80 on the growth of Mycobacterium avium complex.Microbiol Immunol34653663
  42. 42. Sattler TH, Youmans GP (1948) The Effect of “Tween 80,” Bovine Albumin, Glycerol, and Glucose on the Growth of Mycobacterium tuberculosis var. hominis (H37Rv). J Bacteriol 56: 235–243.TH SattlerGP Youmans1948The Effect of “Tween 80,” Bovine Albumin, Glycerol, and Glucose on the Growth of Mycobacterium tuberculosis var. hominis (H37Rv).J Bacteriol56235243
  43. 43. Li AH, Waddell SJ, Hinds J, Malloff CA, Bains M, et al. (2010) Contrasting transcriptional responses of a virulent and an attenuated strain of Mycobacterium tuberculosis infecting macrophages. PLoS One 5: e11066.AH LiSJ WaddellJ. HindsCA MalloffM. Bains2010Contrasting transcriptional responses of a virulent and an attenuated strain of Mycobacterium tuberculosis infecting macrophages.PLoS One5e11066
  44. 44. Ferrer NL, Gomez AB, Neyrolles O, Gicquel B, Martin C (2010) Interactions of attenuated Mycobacterium tuberculosis phoP mutant with human macrophages. PLoS One 5: e12978.NL FerrerAB GomezO. NeyrollesB. GicquelC. Martin2010Interactions of attenuated Mycobacterium tuberculosis phoP mutant with human macrophages.PLoS One5e12978
  45. 45. Dasgupta A, Sureka K, Mitra D, Saha B, Sanyal S, et al. (2010) An oligopeptide transporter of Mycobacterium tuberculosis regulates cytokine release and apoptosis of infected macrophages. PLoS One 5: e12225.A. DasguptaK. SurekaD. MitraB. SahaS. Sanyal2010An oligopeptide transporter of Mycobacterium tuberculosis regulates cytokine release and apoptosis of infected macrophages.PLoS One5e12225
  46. 46. Silver RF, Walrath J, Lee H, Jacobson BA, Horton H, et al. (2009) Human alveolar macrophage gene responses to Mycobacterium tuberculosis strains H37Ra and H37Rv. Am J Respir Cell Mol Biol 40: 491–504.RF SilverJ. WalrathH. LeeBA JacobsonH. Horton2009Human alveolar macrophage gene responses to Mycobacterium tuberculosis strains H37Ra and H37Rv.Am J Respir Cell Mol Biol40491504
  47. 47. Meade KG, Gormley E, Doyle MB, Fitzsimons T, O'Farrelly C, et al. (2007) Innate gene repression associated with Mycobacterium bovis infection in cattle: toward a gene signature of disease. BMC Genomics 8: 400.KG MeadeE. GormleyMB DoyleT. FitzsimonsC. O'Farrelly2007Innate gene repression associated with Mycobacterium bovis infection in cattle: toward a gene signature of disease.BMC Genomics8400
  48. 48. Killick KE, Browne JA, Park SDE, Magee DA, Martin I, et al. (2011) Genome-wide transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis reveals suppression of host immune genes. BMC Genomics 12: 611.KE KillickJA BrowneSDE ParkDA MageeI. Martin2011Genome-wide transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis reveals suppression of host immune genes.BMC Genomics12611
  49. 49. Galindo RC, Ayoubi P, Naranjo V, Gortazar C, Kocan KM, et al. (2009) Gene expression profiles of European wild boar naturally infected with Mycobacterium bovis. Veterinary Immunology and Immunopathology 129: 119–125.RC GalindoP. AyoubiV. NaranjoC. GortazarKM Kocan2009Gene expression profiles of European wild boar naturally infected with Mycobacterium bovis.Veterinary Immunology and Immunopathology129119125
  50. 50. Quesniaux V, Fremond C, Jacobs M, Parida S, Nicolle D, et al. (2004) Toll-like receptor pathways in the immune responses to mycobacteria. Microbes Infect 6: 946–959.V. QuesniauxC. FremondM. JacobsS. ParidaD. Nicolle2004Toll-like receptor pathways in the immune responses to mycobacteria.Microbes Infect6946959
  51. 51. Bafica A, Scanga CA, Feng CG, Leifer C, Cheever A, et al. (2005) TLR9 regulates Th1 responses and cooperates with TLR2 in mediating optimal resistance to Mycobacterium tuberculosis. J Exp Med 202: 1715–1724.A. BaficaCA ScangaCG FengC. LeiferA. Cheever2005TLR9 regulates Th1 responses and cooperates with TLR2 in mediating optimal resistance to Mycobacterium tuberculosis.J Exp Med20217151724
  52. 52. Davila S, Hibberd ML, Hari Dass R, Wong HE, Sahiratmadja E, et al. (2008) Genetic association and expression studies indicate a role of toll-like receptor 8 in pulmonary tuberculosis. PLoS Genet 4: e1000218.S. DavilaML HibberdR. Hari DassHE WongE. Sahiratmadja2008Genetic association and expression studies indicate a role of toll-like receptor 8 in pulmonary tuberculosis.PLoS Genet4e1000218
  53. 53. Netea MG, Azam T, Lewis EC, Joosten LA, Wang M, et al. (2006) Mycobacterium tuberculosis induces interleukin-32 production through a caspase- 1/IL-18/interferon-gamma-dependent mechanism. PLoS Med 3: e277.MG NeteaT. AzamEC LewisLA JoostenM. Wang2006Mycobacterium tuberculosis induces interleukin-32 production through a caspase- 1/IL-18/interferon-gamma-dependent mechanism.PLoS Med3e277
  54. 54. Kawai T, Akira S (2010) The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat Immunol 11: 373–384.T. KawaiS. Akira2010The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors.Nat Immunol11373384
  55. 55. O'Neill LA, Bowie AG (2007) The family of five: TIR-domain-containing adaptors in Toll-like receptor signalling. Nat Rev Immunol 7: 353–364.LA O'NeillAG Bowie2007The family of five: TIR-domain-containing adaptors in Toll-like receptor signalling.Nat Rev Immunol7353364
  56. 56. Ding A, Yu H, Yang J, Shi S, Ehrt S (2005) Induction of macrophage-derived SLPI by Mycobacterium tuberculosis depends on TLR2 but not MyD88. Immunology 116: 381–389.A. DingH. YuJ. YangS. ShiS. Ehrt2005Induction of macrophage-derived SLPI by Mycobacterium tuberculosis depends on TLR2 but not MyD88.Immunology116381389
  57. 57. Shi S, Nathan C, Schnappinger D, Drenkow J, Fuortes M, et al. (2003) MyD88 primes macrophages for full-scale activation by interferon-gamma yet mediates few responses to Mycobacterium tuberculosis. J Exp Med 198: 987–997.S. ShiC. NathanD. SchnappingerJ. DrenkowM. Fuortes2003MyD88 primes macrophages for full-scale activation by interferon-gamma yet mediates few responses to Mycobacterium tuberculosis.J Exp Med198987997
  58. 58. Noss EH, Pai RK, Sellati TJ, Radolf JD, Belisle J, et al. (2001) Toll-like receptor 2-dependent inhibition of macrophage class II MHC expression and antigen processing by 19-kDa lipoprotein of Mycobacterium tuberculosis. J Immunol 167: 910–918.EH NossRK PaiTJ SellatiJD RadolfJ. Belisle2001Toll-like receptor 2-dependent inhibition of macrophage class II MHC expression and antigen processing by 19-kDa lipoprotein of Mycobacterium tuberculosis.J Immunol167910918
  59. 59. Pai RK, Convery M, Hamilton TA, Boom WH, Harding CV (2003) Inhibition of IFN-gamma-induced class II transactivator expression by a 19-kDa lipoprotein from Mycobacterium tuberculosis: a potential mechanism for immune evasion. J Immunol 171: 175–184.RK PaiM. ConveryTA HamiltonWH BoomCV Harding2003Inhibition of IFN-gamma-induced class II transactivator expression by a 19-kDa lipoprotein from Mycobacterium tuberculosis: a potential mechanism for immune evasion.J Immunol171175184
  60. 60. Loo YM, Gale M Jr (2011) Immune signaling by RIG-I-like receptors. Immunity 34: 680–692.YM LooM. Gale Jr2011Immune signaling by RIG-I-like receptors.Immunity34680692
  61. 61. Monroe KM, McWhirter SM, Vance RE (2009) Identification of host cytosolic sensors and bacterial factors regulating the type I interferon response to Legionella pneumophila. PLoS Pathog 5: e1000665.KM MonroeSM McWhirterRE Vance2009Identification of host cytosolic sensors and bacterial factors regulating the type I interferon response to Legionella pneumophila.PLoS Pathog5e1000665
  62. 62. Rad R, Ballhorn W, Voland P, Eisenacher K, Mages J, et al. (2009) Extracellular and intracellular pattern recognition receptors cooperate in the recognition of Helicobacter pylori. Gastroenterology 136: 2247–2257.R. RadW. BallhornP. VolandK. EisenacherJ. Mages2009Extracellular and intracellular pattern recognition receptors cooperate in the recognition of Helicobacter pylori.Gastroenterology13622472257
  63. 63. Levine B, Deretic V (2007) Unveiling the roles of autophagy in innate and adaptive immunity. Nat Rev Immunol 7: 767–777.B. LevineV. Deretic2007Unveiling the roles of autophagy in innate and adaptive immunity.Nat Rev Immunol7767777
  64. 64. Ni Cheallaigh C, Keane J, Lavelle EC, Hope JC, Harris J (2011) Autophagy in the immune response to tuberculosis: clinical perspectives. Clin Exp Immunol 164: 291–300.C. Ni CheallaighJ. KeaneEC LavelleJC HopeJ. Harris2011Autophagy in the immune response to tuberculosis: clinical perspectives.Clin Exp Immunol164291300
  65. 65. Xu Y, Liu XD, Gong X, Eissa NT (2008) Signaling pathway of autophagy associated with innate immunity. Autophagy 4: 110–112.Y. XuXD LiuX. GongNT Eissa2008Signaling pathway of autophagy associated with innate immunity.Autophagy4110112
  66. 66. Taylor RC, Cullen SP, Martin SJ (2008) Apoptosis: controlled demolition at the cellular level. Nat Rev Mol Cell Biol 9: 231–241.RC TaylorSP CullenSJ Martin2008Apoptosis: controlled demolition at the cellular level.Nat Rev Mol Cell Biol9231241
  67. 67. Tourneur L, Chiocchia G (2010) FADD: a regulator of life and death. Trends Immunol 31: 260–269.L. TourneurG. Chiocchia2010FADD: a regulator of life and death.Trends Immunol31260269
  68. 68. Salomoni P, Khelifi AF (2006) DAXX: death or survival protein? Trends Cell Biol 16: 97–104.P. SalomoniAF Khelifi2006DAXX: death or survival protein?Trends Cell Biol1697104
  69. 69. Deveraux QL, Roy N, Stennicke HR, Van Arsdale T, Zhou Q, et al. (1998) IAPs block apoptotic events induced by caspase-8 and cytochrome c by direct inhibition of distinct caspases. Embo J 17: 2215–2223.QL DeverauxN. RoyHR StennickeT. Van ArsdaleQ. Zhou1998IAPs block apoptotic events induced by caspase-8 and cytochrome c by direct inhibition of distinct caspases.Embo J1722152223
  70. 70. Rolland SG, Conradt B (2010) New role of the BCL2 family of proteins in the regulation of mitochondrial dynamics. Curr Opin Cell Biol 22: 852–858.SG RollandB. Conradt2010New role of the BCL2 family of proteins in the regulation of mitochondrial dynamics.Curr Opin Cell Biol22852858
  71. 71. Divangahi M, Chen M, Gan H, Desjardins D, Hickman TT, et al. (2009) Mycobacterium tuberculosis evades macrophage defenses by inhibiting plasma membrane repair. Nat Immunol 10: 899–906.M. DivangahiM. ChenH. GanD. DesjardinsTT Hickman2009Mycobacterium tuberculosis evades macrophage defenses by inhibiting plasma membrane repair.Nat Immunol10899906
  72. 72. Kelly DM, ten Bokum AM, O'Leary SM, O'Sullivan MP, Keane J (2008) Bystander macrophage apoptosis after Mycobacterium tuberculosis H37Ra infection. Infect Immun 76: 351–360.DM KellyAM ten BokumSM O'LearyMP O'SullivanJ. Keane2008Bystander macrophage apoptosis after Mycobacterium tuberculosis H37Ra infection.Infect Immun76351360
  73. 73. Chen M, Divangahi M, Gan H, Shin DS, Hong S, et al. (2008) Lipid mediators in innate immunity against tuberculosis: opposing roles of PGE2 and LXA4 in the induction of macrophage death. J Exp Med 205: 2791–2801.M. ChenM. DivangahiH. GanDS ShinS. Hong2008Lipid mediators in innate immunity against tuberculosis: opposing roles of PGE2 and LXA4 in the induction of macrophage death.J Exp Med20527912801
  74. 74. Cooper AM, Khader SA (2008) The role of cytokines in the initiation, expansion, and control of cellular immunity to tuberculosis. Immunol Rev 226: 191–204.AM CooperSA Khader2008The role of cytokines in the initiation, expansion, and control of cellular immunity to tuberculosis.Immunol Rev226191204
  75. 75. Kleinnijenhuis J, Oosting M, Joosten LA, Netea MG, Van Crevel R (2011) Innate immune recognition of Mycobacterium tuberculosis. Clin Dev Immunol 405310.J. KleinnijenhuisM. OostingLA JoostenMG NeteaR. Van Crevel2011Innate immune recognition of Mycobacterium tuberculosis.Clin Dev Immunol405310
  76. 76. Widdison S, Schreuder LJ, Villarreal-Ramos B, Howard CJ, Watson M, et al. (2006) Cytokine expression profiles of bovine lymph nodes: effects of Mycobacterium bovis infection and bacille Calmette-Guerin vaccination. Clin Exp Immunol 144: 281–289.S. WiddisonLJ SchreuderB. Villarreal-RamosCJ HowardM. Watson2006Cytokine expression profiles of bovine lymph nodes: effects of Mycobacterium bovis infection and bacille Calmette-Guerin vaccination.Clin Exp Immunol144281289
  77. 77. Flynn JL, Goldstein MM, Chan J, Triebold KJ, Pfeffer K, et al. (1995) Tumor necrosis factor-alpha is required in the protective immune response against Mycobacterium tuberculosis in mice. Immunity 2: 561–572.JL FlynnMM GoldsteinJ. ChanKJ TrieboldK. Pfeffer1995Tumor necrosis factor-alpha is required in the protective immune response against Mycobacterium tuberculosis in mice.Immunity2561572
  78. 78. Okamura H, Kashiwamura S, Tsutsui H, Yoshimoto T, Nakanishi K (1998) Regulation of interferon-gamma production by IL-12 and IL-18. Curr Opin Immunol 10: 259–264.H. OkamuraS. KashiwamuraH. TsutsuiT. YoshimotoK. Nakanishi1998Regulation of interferon-gamma production by IL-12 and IL-18.Curr Opin Immunol10259264
  79. 79. Bergeron A, Bonay M, Kambouchner M, Lecossier D, Riquet M, et al. (1997) Cytokine patterns in tuberculous and sarcoid granulomas: correlations with histopathologic features of the granulomatous response. J Immunol 159: 3034–3043.A. BergeronM. BonayM. KambouchnerD. LecossierM. Riquet1997Cytokine patterns in tuberculous and sarcoid granulomas: correlations with histopathologic features of the granulomatous response.J Immunol15930343043
  80. 80. Saukkonen JJ, Bazydlo B, Thomas M, Strieter RM, Keane J, et al. (2002) beta-chemokines are induced by Mycobacterium tuberculosis and inhibit its growth. Infect Immun 70: 1684–1693.JJ SaukkonenB. BazydloM. ThomasRM StrieterJ. Keane2002beta-chemokines are induced by Mycobacterium tuberculosis and inhibit its growth.Infect Immun7016841693
  81. 81. Rivero-Lezcano OM, Gonzalez-Cortes C, Reyes-Ruvalcaba D, Diez-Tascon C (2010) CCL20 is overexpressed in Mycobacterium tuberculosis-infected monocytes and inhibits the production of reactive oxygen species (ROS). Clinical and experimental immunology 162: 289–297.OM Rivero-LezcanoC. Gonzalez-CortesD. Reyes-RuvalcabaC. Diez-Tascon2010CCL20 is overexpressed in Mycobacterium tuberculosis-infected monocytes and inhibits the production of reactive oxygen species (ROS).Clinical and experimental immunology162289297
  82. 82. Ladel CH, Blum C, Dreher A, Reifenberg K, Kopf M, et al. (1997) Lethal tuberculosis in interleukin-6-deficient mutant mice. Infection and Immunity 65: 4843–4849.CH LadelC. BlumA. DreherK. ReifenbergM. Kopf1997Lethal tuberculosis in interleukin-6-deficient mutant mice.Infection and Immunity6548434849
  83. 83. Fielding CA, McLoughlin RM, McLeod L, Colmont CS, Najdovska M, et al. (2008) IL-6 regulates neutrophil trafficking during acute inflammation via STAT3. J Immunol 181: 2189–2195.CA FieldingRM McLoughlinL. McLeodCS ColmontM. Najdovska2008IL-6 regulates neutrophil trafficking during acute inflammation via STAT3.J Immunol18121892195
  84. 84. Jones SA (2005) Directing transition from innate to acquired immunity: defining a role for IL-6. J Immunol 175: 3463–3468.SA Jones2005Directing transition from innate to acquired immunity: defining a role for IL-6.J Immunol17534633468
  85. 85. Bezuidenhout J, Roberts T, Muller L, van Helden P, Walzl G (2009) Pleural tuberculosis in patients with early HIV infection is associated with increased TNF-alpha expression and necrosis in granulomas. PLoS One 4: e4228.J. BezuidenhoutT. RobertsL. MullerP. van HeldenG. Walzl2009Pleural tuberculosis in patients with early HIV infection is associated with increased TNF-alpha expression and necrosis in granulomas.PLoS One4e4228
  86. 86. VanHeyningen TK, Collins HL, Russell DG (1997) IL-6 produced by macrophages infected with Mycobacterium species suppresses T cell responses. J Immunol 158: 330–337.TK VanHeyningenHL CollinsDG Russell1997IL-6 produced by macrophages infected with Mycobacterium species suppresses T cell responses.J Immunol158330337
  87. 87. Nagabhushanam V, Solache A, Ting LM, Escaron CJ, Zhang JY, et al. (2003) Innate inhibition of adaptive immunity: Mycobacterium tuberculosis-induced IL-6 inhibits macrophage responses to IFN-gamma. J Immunol 171: 4750–4757.V. NagabhushanamA. SolacheLM TingCJ EscaronJY Zhang2003Innate inhibition of adaptive immunity: Mycobacterium tuberculosis-induced IL-6 inhibits macrophage responses to IFN-gamma.J Immunol17147504757
  88. 88. Haddad JJ, Saade NE, Safieh-Garabedian B (2003) Interleukin-10 and the regulation of mitogen-activated protein kinases: are these signalling modules targets for the anti-inflammatory action of this cytokine? Cell Signal 15: 255–267.JJ HaddadNE SaadeB. Safieh-Garabedian2003Interleukin-10 and the regulation of mitogen-activated protein kinases: are these signalling modules targets for the anti-inflammatory action of this cytokine?Cell Signal15255267
  89. 89. Weiss DJ, Souza CD (2008) Review paper: modulation of mononuclear phagocyte function by Mycobacterium avium subsp. paratuberculosis. Vet Pathol 45: 829–841.DJ WeissCD Souza2008Review paper: modulation of mononuclear phagocyte function by Mycobacterium avium subsp. paratuberculosis.Vet Pathol45829841
  90. 90. Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, et al. (2010) GeneCards Version 3: the human gene integrator. Database (Oxford). baq020 p.M. SafranI. DalahJ. AlexanderN. RosenT. Iny Stein2010GeneCards Version 3: the human gene integrator.Database (Oxford)baq020