Response of the goat mammary gland to infection with Staphylococcus aureus revealed by gene expression profiling in milk somatic and white blood cells

Intra-mammary S. aureus growth following experimental infection

Following intra-mammary experimental infection with S. aureus, the pathogen could be cultured from the milk of all 10 experimentally infected animals at 6 hours, and at the final sampling 30h post-infection, S. aureus could be cultured from 9 of the 10 infected animals. S. aureus present in milk samples reached a maximum at 18h post-infection with a mean of 5.8 log10 CFU/ml (Colony Forming Unit/ml) in the Low SCS (LSCS) line animals and 6.1 log10 CFU/ml the High SCS (HSCS) selection line goats. The number of S. aureus then remained constant for the rest of the study period (Figure 1). Strain analysis using the RAPD-PCR (Randomly Amplified Polymorphic DNA-PCR) method confirmed that S. aureus isolated from infected udders were the same as the strain used for the infection (data not shown). The control PBS-infused udders remained free from detectable infection throughout the study. No significant difference was observed in S. aureus counts at different time points in milk from LSCS and HSCS goats.

Systemic and localized inflammatory responses to S. aureus intra-mammary infection

As an indicator of a systemic response to S. aureus infection, rectal temperatures were monitored throughout the study. Body temperature showed an upward trend from 0 to 18 h post-infection, however, the increased temperature was only significant (p=0.05) at the 24 and 30 h time points. Temperatures reached a maximum 30 h after infection and reached a peak mean (± S.D.) of 40.2 (±0.5) and 40.7 (±0.6) °C in LSCS and HSCS goats, respectively, which is not a significant difference (Figure 2).

The systemic response to intra-mammary infection with S. aureus was characterized by a significant decrease (p=0.05) of total blood leukocyte (TBL) and of neutrophil (NEU) numbers 18h post-infection. The TBL and NEU counts were 13.7 and 6.0 x 10³ cells/ml in LSCS goats and 11.5 and 4.0 x 10³ cells/ml in HSCS goats. The lowest blood cell counts were reached at 30h post-infection, when the TBL and NEU counts were 9.8 and 4.2 x 10³ and 5.6 and 2.0 x 10³ cells/ml in LSCS and HSCS goats, respectively (Figure 3).

Changes in milk SCC (Somatic Cell Count) were monitored as an indicator of local response. Mean milk SCCs (x10³/ml), before S. aureus intra-mammary infection, were 6.0 x10⁵ and 4.5x10⁵ for LSCS left and right udder halves respectively, and 8.8 x10⁵ and 5.2 x10⁵ for HSCS left and right udder halves respectively at T5. Increases in milk SCC were initially observed after 18 h of infection. Milk SCC in the right hand control udders, infused with saline, remained unchanged throughout the study in LSCS goats, while milk SCC in right hand udder halves of HSCS goats increased significantly (p=0.05) (Figure 4).

Therefore, statistically different increase and variations in somatic cell composition were seen in the in right hand udder halves of HSCS vs LSCS goats (Figure 4 and Table 1), whereas there were no statistically significant differences in either blood or milk between the HSCS and LSCS goats for the other phenotypic responses (i.e. rectal temperature, milk SCC, TBL, NEU) at any of the time points after infection.

Gene expression analysis of milk samples

Canonical pathway

At T4 and T5, 19 and 11 networks were identified using Ingenuity pathway analysis, respectively. The T4 networks contained a total of 244 differentially expressed genes which were involved in pathways and functions including the Cellular Movement, Haematological System, Immune Cell Trafficking, Haematopoiesis, Tissue Development, Antigen Presentation, Cellular Compromise, Cellular Function and Maintenance and Inflammatory Response. The T5 networks contained a total of 109 differentially expressed genes involved in pathways and functions including Inflammatory Response, Digestive System Development and Function, Hepatic System Development and Function, Cell-To-Cell Signalling and Interaction, Tissue Development, Haematological System Development and Function, Kidney Failure, Organismal Injury and Abnormalities, Renal and Urological Disease, Tumor Morphology, Amino Acid Metabolism, Small Molecule Biochemistry, Cell Death, Cellular Compromise.

Meta-analysis

The meta-analysis of expression data from the milk samples cells across all the time points identified twenty differentially expressed genes (p value <0.0001). These genes could be placed into 5 pathways, with 1 gene (NFKB1) in common. Two genes (TLR2 and NFKBIA) were common to 3 pathways: Toll like receptor signalling, role of pattern recognition receptors in recognition of bacteria and viruses, production of nitric oxide and reactive oxygen species in macrophages. The remaining 17 genes were distributed as follows: 9 genes (HDAC6, BTK, ARHGEF4, RALA, RALB, FCER1G, LYN, MYL10 and ITGA3) in phospholipase C signalling pathway, 2 genes (IFNGR2 and IRF8) in production of nitric oxide and reactive oxygen species in macrophages, 2 genes (IL1RN and IL10RB) in IL-10 signalling, 1 gene (TIRAP) in toll like receptor signalling and 3 genes (PTX3, CLEC7A and NOD2) in role of pattern recognition receptors in recognition of bacteria and viruses (Figure 5; Additional file 5).

Gene expression analysis in White Blood Cells (WBC)

Real-time PCR analyses for microarray data validation

The expression of RPL13A (Ribosomal Protein L13A) showed no significant variation among time points for both milk and blood cells on the array and was therefore used as reference gene for qPCR for both milk and blood samples.

Real-time qPCR was used to confirm the gene expression differences in expression of some genes between 24 h (T4) and 30 h (T5) post challenge for milk derived cells and 30 h (T5) for blood cells.

The gene with the greatest increase in expression from the array data was the PTX3 (Pentraxin 3), which showed a logFC of 5.6 in the milk derived cells at T4 and a logFC of 5.0 in blood cells at T5. The strong increase in gene expression of this gene was confirmed by qPCR with a logFC of 8.6 (T4 vs T0) and 8.9 (T5 vs T0) for milk and 7.2 (T5 vs T0) for blood samples.

Changes in expression observed at 30 h after challenge (T5 vs T0), from the microarray data were confirmed by qPCR for following genes: S100 calcium-binding protein A8 (logFC 3.0), hormone receptor-like 1 (logFC 3.0), hormone receptor-like 1 precursor (logFC 3.3) genes for blood and colony stimulating factor 3 receptor (logFC 4.4), Complement C3 precursor (logFC 5.6), IL-18 receptor beta (logFC 5.9), leukocyte elastase inhibitor (logFC 3.9), myeloid differentiation (logFC 3.2), pellino protein (logFC 4.3), T-cell activation Rho (logFC 4.4), Thrombomodulin fragment (logFC 3.9), Toll-like receptor 4 precursor (logFC 5.5), tumor necrosis factor (logFC 6.7) genes for milk samples (Additional files 6 and Additional file 7).

Transcriptome differences between high and low SCC animal groups

Somatic cell score is strongly correlated with intra-mammary infection, and mastitis incidence in cattle. Somatic cells score is used in genetic selection with the hope that this will select animal genetically less susceptible to mastitis. Pathogens can only multiply in sufficient quantity to become resident in the gland if the defence mechanisms fail, or are slow to respond to the infection. Therefore, control of invading pathogens within the mammary gland is crucial to prevent the establishment of an infection in the udder, therefore immune cell recruitment is an important part of mammary gland defence against pathogens. Understanding the genetic basis of activation and recruitment of these cells is therefore important to understand the role of these cells in mastitis defence.

Goats from two lines selected for high and low SCS were investigated in this study with the hypothesis that the mammary response and function may differ between the lines. A significant difference in macrophage numbers in the udder was observed between the two divergent selection lines prior to infection (T0). As macrophages are the first cellular type involved in the elimination of invading bacteria, the difference may result in dissimilarities between the HSCS and LSCS in early response to natural infection. However, no differences in gene expression in the milk derived cells between the two lines were observed following the experimental challenge carried out in this study. The changes in the numbers and ratios between of cell types are variables dependent on the individual with as much variation seen between compared within line.

Changes expression were also measured in WBC to examine the type and kinetics of systemic in addition to mammary specific responses. Changes in gene expression were observed in the udder from the earliest time point following infection and at 30 hours post infection a large number of genes showed differences. In the blood white cells (WBC) no change in expression was observed at early time points, while at 30 hours post infection only 9 genes were found to be differentially expressed. As may be expected this shows that the systemic response is slower than in the udder following infection with S. aureus and that the changes in expression are more restricted, at least in the early phases. This is not surprising as cells in the udder are becoming activated in themselves to respond directly to the invading pathogen or signalling to activate and recruit other cells to the site of infection. This is seen in the gene pathways that are activated and discussed below. Changes occurring in the blood are more suitable, at least at these early stages of infection. Unfortunately samples were not available to investigate how the response progressed later than 30 hours following infection.

Gene expression of milk somatic cells

The PMN, lymphocytes, macrophages, and epithelial cells in the milk represent important components of the innate immune defence of the mammary gland [10].

These cells release chemokines that stimulate a rapid influx of PMN to combat the bacteria. This response was clearly seen in the present study such that at 24-30h post infection PMN reached over 80% of cells present, while the percentages of macrophages, lymphocytes and epithelial cells significantly decreased. Differences in cell types composition change the genes products that are detected, irrespective of the actual changes in expression occurring within particular cell types. This makes the interpretation of the data difficult, as the cell population, as well as gene expression within cell types have to be taken into account.

The significant change in macrophage percentages among different time points is related to changes in genes detected that are within canonical macrophages specific pathways such as MIF-mediated Glucocorticoid Regulation, MIF Regulation of Innate Immunity, and Production of Nitric Oxide and Reactive Oxygen Species in Macrophages. The expression of these pathways increased in the context that the macrophage cells percentage decreased, then there is an enormous increase in expression of these genes. Therefore, the microarray data from milk SCC in this study partly reflect changes in gene expression as a result of alteration in cell proportions within the mammary gland, and partly changes in expression within cell types.

At 24 h following inoculation with S. aureus transcription levels of 300 genes were significantly changed in milk somatic cells. These genes fell into functional pathways which associated with cell death, cellular movement, cellular growth and proliferation, cell-to-cell signalling and interaction, and lipid metabolism.

The levels of the pro-inflammatory cytokines tumour necrosis factor alpha (TNFα), interleukin 1 alpha (IL-1α), interleukin 8 (IL-8) were increased in the SCC following challenge. These cytokines are associated with inflammatory processes and are involved in recruitment and activation of neutrophils into the infected tissue [18]. This response following the recognition of invading pathogens, is characteristic of macrophages present among the milk somatic cells, and is instrumental in attracting PMN involved in immune defence from the blood to the mammary gland. The response to this signalling is observed in the increased % of PMN found in the infected gland at 30 hours. Expression of the gene coding for CD14 antigen (CD14), a pivotal cell surface protein on macrophages, that interacts with toll-like receptors mediating detection of bacterial cell wall components, was also increased.

The acute phase response is a rapid and a non-specific inflammatory response that provides the initial line of protection against microorganisms and is regulated through the expression of several cytokines [24]. This is consistent with the early stage response observed in the SCC which involves changes in expression of pathways, including IL-10 Signalling (T4) and LXR/RXR activation (T5). A close relationship between polyamine regulation, in particular the sub-group spermine, and IL-10 Signalling has been previously reported in macrophages [25]. An increase of IL-6 and IL-10 expression in somatic cells during mastitis infection has also been previously reported in cattle [18, 26].

The persistence inflammation, induced by cytokines, is harmful for cells [27] and the affected organ. Therefore the observed activation of the IL-10 Signalling, which modulates immune response during the early acute response stage of mastitis infection is important to limit the inflammatory response and tissue damage within the udder. Interleukin-10 is an anti-inflammatory cytokine that blocks NF-kB activity, which leads to suppression of pro-inflammatory mediators such as TNF, IL6, and IL1. Expression was significantly increased for 7 out of 78 putative genes of the IL-10 Signalling pathway present on the microarray following infection. Interleukin 1 receptor antagonist (IL1RN) and interleukin 10 receptor beta (IL10RB) are also part of the IL-10 Signalling pathway and limit inflammatory response, as well as regulating B-cells, T-cells, NK-cells and dendritic cell division and differentiation, which is associated with the immune response.

Lipid metabolism is generally inhibited during the intra-mammary infections [10]. In the present study the LXR/RXR pathway which is involved in the regulation of lipid metabolism, inflammation, and cholesterol to bile acid catabolism [28] showed reduced expression in SCC following infection. The PPAR family consists of PPARα, PPARδ, and PPARγ, each of which act as ligand activated transcriptional regulators. Expression of these genes was found to be reduced following infection in the present study. The PPAR ligands include n-3 and n-6 unsaturated fatty acids and their eicosanoid products, thus PPARs are involved in the regulation intracellular lipid levels. PPAR is highly expressed in adipose tissue and macrophages and primarily regulates adipogenesis, but also interferes with the transcription of proinflammatory factors such as STAT and NF-κB in macrophages [29]. The down regulation of LXR and RXR and the PPAR genes suggest that during the early stage response to mastitis there might be a “general” deregulation of the lipid metabolism. This has also been observed in cattle infected with S. uberis by Moyes et al.[19], where the intra-mammary infection led to the activation of pro-inflammatory pathways, and a marked inhibition of lipid synthesis. The expression of LXR and PPAR and signalling most likely by PPARγ, may provide mechanistic explanation for the inverse relationship between immune response ad fat synthesis. In humans, there is evidence that lipoproteins are part of the innate immune system, and changes in lipid and lipoprotein metabolism that occur during the host response to infection have an anti-infective and anti-inflammatory effect that contributes to the host defence [30].

Moreover, in the stromal vascular fraction, adipose tissue also contains macrophages that secrete pro-inflammatory cytokines, such as TNF-α, IL-1β and IL-6 [31]. Studies focused on the effect of intra-mammary infection in adipose tissue of ruminants are lacking. In a recent work adipose tissue in dairy cows appears to respond in vitro to an immune challenge by increased gene expression of cytokines [32].

Meta-analysis approaches are used to combine or integrate information from independent studies to overcome low statistical power in studies and identify common results to increase confidence in the generality of information [22]. In the present study meta-analysis was used to combine information across samples and time-points, which identified 17 differentially expressed genes (Figure 5) which are involved in five pathways. Pentraxin 3 (PTX3) was significantly associated with response to infection and increased the expression in both SCC and blood PMN. PTX3 is a C-type lectin domain family 7 member A (CLEC7A) and nucleotide-binding oligomerization domain containing 2 (NOD2) belonged to the Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses pathway.

The innate immune system uses a several receptors (PRRs) that recognize conserved microbial structures or pathogen-associated molecular patterns (PAMPS), such as those that occur in the bacterial cell-wall components peptidoglycan and lipopolysaccharide. PPRs are classified as: (i) membrane bound PRRs which include toll-like receptors (TLRs), (ii) extracellular PRRs that include the complement proteins, pentraxins and collectins involved in the process of opsonization, phagocytosis and apoptosis and (iii) cytoplasmic PRRs which include the NOD proteins that regulating the cytokine induction initiated by bacterial ligands.

PTX3 is involved in the initial response of mammary tissue to bacterial infection [18, 33, 34], and is thought to activate the immune system by binding both micro-organisms and C1q, the first component of the classical pathway of complement activation. NOD2, is expressed in monocytes/macrophages, and detects peptidoglican from gram-negative (for example E. coli) and gram-positive bacteria (S. aureus) [35, 36]. Although S. aureus is usually an extracellular bacterium, it has been shown to be internalised by different mammalian cells such as pulmonary epithelial cells, enterocytes and neutrophils in a manner dependent on the expression of the virulence regulators Agr and SarA[37]. The peptidoglycan fragments from cytoplasm-dwelling bacteria could, therefore, be available for recognition by NOD2 and initiate Nod-dependent cellular responses, making this gene a potential marker for this pathogen infection.

Gene expression white blood cells

The first changes in gene expression seen in WBC were observed at 30h post-infection and coincided with the lowest TBL and NEU counts. At this time point 9 genes were found to be differentially expressed. As for milk somatic cells, in WBC the gene with the highest fold change was PTX3. It was the first long pentraxin identified, an interleukin-1 inducible gene expressed in endothelial cells [38] and is a tumor necrosis factor α (TNF-α) inducible gene in fibroblasts [39]. PTX3 is produced by macrophages and other cell types upon stimulation with primary inflammatory mediators such as LPS, IL-1 or TNF-α [40]. PTX3 plasma levels are very low in normal circumstances (≤2ng/ml) but expression has been shown to increase following pathogen infection [41]. PTX3 is a functional ancestor of antibodies, recognizing microorganisms, activating complement, and facilitating pathogen recognition by phagocytes. Hence it plays a role in resistance against selected pathogens [42]. Mature PTX3 is stored in specific granules by neutrophils and is secreted following recognition of microbial moieties and inflammatory signals [43]. This protein has been previously identified as one of the most up-regulated genes after IMI with S. aureus[18]. PTX3 together with S100A12 act as anti-microbial agents that could assist defence of the mammary gland against chronic and subclinical infections. In the present study PTX3 was the most significantly up-regulated gene in both somatic cells (T4 and T5) and in WBC (T5) in response to S. aureus infection in goats.

Animals

Two groups of five primiparous goats from divergent selection for extreme breeding values for the somatic cell score (one group from the high SCS line and the other from the low SCS line) were selected to have similar milk production (3.2 ± 0.5 kg/d) and provided by INRA. Basically, an experimental genetic evaluation was performed using data from 140,000 primiparous alpine goats in 3625 flock by year combination. The trait considered was the lactation mean of monthly somatic cell scores (LSCS) as detailed in Rupp et al. [12]. Breeding values for LSCS were expressed in genetic standard deviation with inverted sign (positive index are favorable). Two groups of 5 and 6 bucks with extreme high and low breeding values for LSCS (−1.38 ± 0.68 vs 1.17±0.48, respectively) were selected to sire daughters at the INRA experimental facility of Bourges (UE0332, OSMOY, France). Out of 52 born kids, two groups of 5 daughters (sired by 3 and 4 high and low SCC bucks, respectively) were transferred two months before first kidding to “Centro Zootecnico Didattico Sperimentale dell’Università degli Studi di Milano-Italy” (http://www.veterinaria.unimi.it/Facolta/2586_ITA_HTML.html) for experimental infection.

The study was carried out when the goats were of first parity and at the peak of lactation (48 ± 2 days in milking). Goats were monitored for intra-mammary infections (particularly for S. aureus) from parturition to the day of challenge with bacteriological analysis and somatic cell scores measured on milk samples each week as described by Pisoni et al. [21] and Moroni et al.[44]. Fore milk samples were collected on the three days immediately prior to the experimental infection and all animals were shown to be free of any mastitis pathogens and to have SCC below 250,000 cells/mL.

All experimental procedures were performed according to the Italian legislation, following approval by the ethics committee of University of Milan.

Staphylococcus aureus strain

S. aureus strain DV137, which was originally isolated from chronic case of caprine mastitis [45], was used for the experimental infections. S. aureus DV137 is positive for clumping factor, free coagulase, enterotoxins C and L, toxic shock syndrome toxin TSST-1 and leukocidin LukDE.

The S. aureus strain DV137 was grown from an individual colony by transfer to 10 mL of brain heart infusion broth (Becton-Dickinson Diagnostic Systems, Inc., Milan, Italy) and incubated for 6 h at 37°C. Thereafter, 1 mL of the culture was transferred to 99 mL of tryptic soy broth (TSB, Difco, Milan, Italy) and incubated overnight at 37°C. The concentration of the bacterium in this stock culture was maintained at 4°C overnight. Before infection the stock was diluted with sterile pyrogen-free Phosphate Buffered Saline (PBS, Invitrogen, Milan, Italy) to give a final concentration of 10³ Colony Forming Unit/mL (CFU/mL) for the experimental infection.

Intra-mammary challenge

Prior to intra-mammary challenge the goats were milked by hand and their udders emptied; the teat ends were carefully disinfected with chlorhexidine. The left udder half of each goat was infused with 1 mL (10³ CFU/mL) inoculum of S. aureus. The right udder half was infused with 1 mL of sterile pyrogen-free PBS. Inoculation was administered intra-cisternally through the teat canal using a sterile blunt needle. Milk samples were collected from all goats from both udder halves just prior to the challenge (T0) and at 6 (T1), 12 (T2), 18 (T3), 24 (T4) and 30 (T5) hours after the experimental infection. At each time point each goat was recorded for: general demeanour, stance, position, food/water intake and vocalization, temperature and the udder examined for temperature, swelling, colour, pain, lumps, injuries to the teats/udder, milk letdown, milk colour and milk clots. A paired t-test (with threshold for statistical significance set to 0.05) was applied to body temperature of LSCS and HSCS to test if the differences observed at each time point were significant.

Bacteriology and milk somatic cells analysis

Twenty ml milk samples were taken under strictly hygienic conditions from each udder half into two sterile 10 ml tubes. One of the tubes was used for bacteriology, while the other tube of milk was used for SCC analysis. Subsequently, 200 mL of milk were collected from the left infected udders for isolation of cells and further analysis. A paired t-test (with threshold set to 0.05) was applied to milk SCC of right udders in LSCS and HSCS goats to test if the changes at each time point were significant.

Milk SCC were determined in 2-mL of milk, which was heated for 15 min up to 60°C then maintained at 40°C until analysed, in duplicate, on automated fluorescent microscopic somatic cell counter (Bentley Somacount 150, Bentley Instrument, Milan, Italy). For differential cell counts (DCCs), 10 ml of milk were centrifuged for 15 min at 400 x g, the cream layer and supernatant were discarded and the cells were washed twice in PBS. DCCs of isolated cell suspensions were estimated by means of esterase stain [46]. Evaluation of the slides was carried out using light microscopy and oil immersion (100-fold magnification). One-hundred cells of each slide were counted meander-shaped and defined as lymphocytes, macrophages, PMN and epithelial cells. Cell identification was achieved using standard methods [47, 48].

Aseptically collected milk samples were diluted in sterile PBS and plated onto blood agar plates. The number of CFU was determined after 16 h of incubation at 37°C. Colonies displaying haemolysis were initially counted as S. aureus, and subsequently confirmed microscopically and biochemically by the presence of Gram-positive cocci that were both catalase- and coagulase-positive. S. aureus isolates were tested by RAPD-PCR [49] to confirm that they were the strain used in the experimental challenge.

Blood sample analysis

Ten ml of blood samples were collected into EDTA from the jugular vein just prior to the challenge and 6, 12, 18, 24, and 30 hours after inoculation.

Blood samples were analyzed using the ADVIA 120 (Siemens Healthcare) haematology system and the multispecies software provided by the manufacturer. The following parameters were calculated: erythrocyte count (RBC), haemoglobin concentration, haematocrit, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), leukocyte count (WBC), platelet count (Plt), mean platelet volume (MPV). Leukocyte different counts (percentage and number of neutrophils, lymphocytes, monocytes, eosinophils, basophils and large unstained cells or LUC), were also performed. A paired t-test (with threshold set to 0.05) was applied to blood sample parameters of LSCS and HSCS animals to test if differences at each time point were significant.

Isolation of white blood cells (WBC)

Ten ml of fresh blood were transferred into a 50 ml tube and 20 ml of 0.2% NaCl were added, mixed with 5 ml of NaCl 3.7% and then centrifuged at 1000 x g for 10 min at 4°C. The pellet was resuspended in 15 ml of PBS and the solution was centrifuged at 1000 x g for 10 min at 4°C. The supernatant was discarded and the pellet was resuspended in 1 ml of Trizol (Invitrogen, Milan, Italy).

Preparation of milk somatic cells

Fore milk was collected aseptically from both halves of the udder of each goat prior to the challenge and 6, 12, 18, 24, and 30 hours post-infection. Fifty mL of milk were transferred to falcon tubes and centrifuged at 750 x g at 4°C for 10 min. The fat layer and the supernatant were discarded and the cell pellet was re-suspended and washed in PBS pH 7.2. After a centrifugation at 450 x g for 10 min, the supernatant was discarded and the pellet was resuspended in 3 mL of Trizol (Invitrogen, Milan, Italy).

RNA extraction

From WBC and somatic cells total RNA was extracted following the instructions of the supplier (Invitrogen, Milan, Italy), further purified using an RNeasy MinElute spin column (Qiagen, Milan, Italy) and eluted in RNase-free water. RNA was quantified using a NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and quality-checked using a Bioanalyser 2100 (Agilent, Santa Clara, CA). RNA samples with RNA Integrity Number (RIN) values between 7.0 and 10.0 were used for the microarray analysis.

Microrray design

The microarray design has been described previously [23], briefly all available bovine transcript sequence information was downloaded from Ensembl release 50, and Unigene and dbEST databases (Sept. 2008). A bioinformatic pipeline was created to align the sequences and select a unique set of minimally redundant bovine transcripts. This dataset was used to design 43,768 unique probes with a length of 35 nucleotides, each representing a single bovine transcript. The microarray probes were designed at the 3′ end of the bovine sequences. These probes were synthesised in duplicate, along with negative and quality controls, on a 90K feature custom array from CombiMatrix (Seattle, WA).

Array hybridization and statistical analysis

One μg RNA was amplified and labelled with Cy5-ULS using the RNA Amplification and Labelling Kit from CombiMatrix (ampULSe Cat. no. GEA-022; Kreatech Biotechnology, Amsterdam, The Netherlands). All procedures were carried out according to the manufacturer’s protocols. The purified labelled aRNA was quantified using a NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Four μg of labelled RNA were fragmented to a uniform size and hybridized to the custom array following the Combimatrix CustomArray 90K Microarray Hybridization and Imaging Protocol Arrays were stripped and re-hybridised using the CustomArray Stripping Kit for 90K (CombiMatrix Cat. No. 610049) following the protocols of the manufacturer. Each array was used up to 4 times with no deterioration in signal or increase in background.

To verify the powerful of this bovine custom array for the caprine gene expression study, a series of technical replicates were carried out showing a signal intensities correlation higher than 97% (data not shown).

The hybridised arrays were scanned with a GenePix 4000B microarray scanner (Axon, Toronto, CA) and the images (TIF format) were exported to the CombiMatrix Microarray Imager Software, to perform quality checks of the hybridizations and the spots on the slide. Data were extracted and loaded into the R software using the Limma analysis package from Bioconductor. A design matrix was created using Limma functions to describe the experimental samples and replicates. The raw intensities were processed using quantile normalization and data were then transformed in log2 to be used for the statistical analysis.

Limma performs a linear regression analysis on the hybridizations, using a group-means parameterization approach to compare the different conditions and performs a false discovery rate adjustment with Benjamini-Hochberg correction for multiple testing [50]. False discovery rates of 1% and 5% were accepted for SC and WBC respectively and DE genes were selected using an adjusted P-value cut off equal to 0.01 and 0.05.

The microarray data files have been deposited in NCBI’s Gene expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) at the identifier number GPL14856 and the experiment at the identifier number GSE33894.

Assignment of affected genes to pathways, networks and biological functions

Each gene symbol of the affected genes identified with R was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Feeding the lists of affected genes as input to the IPA library identified associated canonical pathways, biological functions and networks which were used to investigate the biological context.

The IPA library items were ranked based on significance of association with the input list of genes. For the canonical pathways this significance was determined based on two parameters: (i) ratio of the number of genes from the input data set that map to the canonical pathway divided by the total number of genes of that pathway and (ii) p-values calculated using Fischer’s exact test determining the probability that the association is explained by chance alone.

For the biological functions and networks the significance was linked to the p-value only, calculated by right-tailed Fisher’s exact test. The p-values for the network analysis take into account the number of affected genes in the network and the size of the network.

Meta-analysis procedures

Data from all time points were loaded into R software, the signal intensities were processed and normalized using standard Limma procedures, then all the expression data were put together.

MetaMA package was used to perform a moderated t-test with a Benjamini Hochberg (BH) correction at a 0.01 % threshold (to take into account the multiple testing problem) to each time point compared with T0. Once obtained, the p-values from study-specific analysis we combined them into one p-value in sense of sum of logs.

The Venn diagram was built using a modified version of R script “Venn”.

http://bioinfo-mite.crb.wsu.edu/Rcode/Venn.R.

Real time PCR validation

For the optimization of all real-time assays, ten milk RNA samples at T0 were pooled and normalized to a final concentration of 200 ng/μl and the same procedure was used for samples from T4 and T5. RNA from 10 WBC samples at T0 were pooled and normalized to a final concentration of 100 ng/μl and the same procedure was applied to T5 samples. Pooled samples were serial diluted and used for the set-up of the standard curve (5 points in triplicates); in each assay a negative control was also included.

Samples were analysed one by one: 1 μg of milk and 0.5 μg of blood samples were individually reverse-transcribed using the Superscript II RT-PCR System (Invitrogen Life technologies) following the manufacturers instructions. Each sample was tested in triplicates.

Eleven genes from the milk samples and four from blood samples showing high FC and p-values different between post (T4, T5) and pre-infection (T0), were selected for quantitative real-time PCR validation of microarray results.

Caprine specific rtPCR primers were designed using goat sequence where this was available. When goat sequence was not available transcript-specific primers were designed for the conserved regions of the bovine sequence using Primer Express software (version 3.0) running standard settings.

ACTB (actin β), GAPD (glyceraldehyde 3-phosphate dehydrogenase), HMBS (Hydroxymethylbilane synthase), RPL13A (Ribosomal protein L13a), and YWHAZ (Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide) were selected from literature [51] and tested in order to choose a reference gene common to milk and blood analyses according to the variation in the array data. Control cDNA dilution series were created for each gene to establish a standard curve; all real-time reactions were performed in triplicate.

The real-time reaction mixture, in a final volume of 10 μl, included 2 μl dilution 1:10 of the cDNA as template, 1X Power SYBR Green Master Mix (Applied Biosystems, Foster City, CA, USA) and 0.5 μM of each primer forward and reverse (Additional files 6 and Additional file 7) except for blood-gene egf-like module containing, mucin-like, hormone receptor-like 1, where final concentration was 0.05 μM for primer forward and 0.9 μM for primer reverse. The real-time PCR reaction set up was made in 384 optical well plates with a Freedom Evo Robot (Tecan) and carried out on an ABI 7900HT Fast Real-Time PCR System (Applied Biosystem) with a standard programme (50°C*2’/95°C*10’/40 cycles 95°C*15” and 60°C*1’). Data were analyzed with the GeneAmp 7900HT sequence detection system software (PerkinElmer Corp., Foster City, USA).The log input amount of the standard curve was plotted versus the output Ct values and the log input amount of each sample was calculated according to the formula (Ct - b)/m, where b is the Y-intercept, and m is the slope. The log input amount was converted to input amount according to the formula 10^(log input amount), and triplicate input amounts were averaged for each sample.

Data were imported and processed into R using the ddCt analysis package from Bioconductor, time point T0 and RPL13A were set as reference sample and reference gene respectively and the 2-ΔΔCt algorithm was applied to find the relative level expression [52].