# Transcriptomic analysis of milk somatic cells in mastitis resistant and susceptible sheep upon challenge with Staphylococcus epidermidis and Staphylococcus aureus.

Cécile Bonnefont, Mehdi Toufeer, Cécile Caubet, Eliane Foulon, Christian Tasca, Marie-Rose Aurel, Dominique Bergonier, Séverine Boullier, Christèle Robert-Granié, Gilles Foucras, et al.

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Cécile Bonnefont, Mehdi Toufeer, Cécile Caubet, Eliane Foulon, Christian Tasca, et al. Transcriptomic analysis of milk somatic cells in mastitis resistant and susceptible sheep upon challenge with Staphylococcus epidermidis and Staphylococcus aureus. BMC Genomics, 2011, 12 (1), pp.208. 10.1186/1471-2164-12-208. inserm-00593968

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Transcriptomic analysis of milk somatic cells in mastitis resistant and susceptible sheep upon challenge with *Staphylococcus epidermidis* and *Staphylococcus aureus*

Cécile MD Bonnefont¹,<fnref n="2" />,³, Mehdi Toufeer<fnref n="2" />,³, Cécile Caubet<fnref n="2" />,³, Eliane Foulon<fnref n="2" />,³, Christian Tasca<fnref n="2" />,³, Marie-Rose Aurel⁴,
Dominique Bergonier<fnref n="2" />,³, Séverine Boullier<fnref n="2" />,³, Christèle Robert-Granié¹, Gilles Foucras<fnref n="2" />,³*† and Rachel Rupp¹†

**Abstract**

**Background:** The existence of a genetic basis for host responses to bacterial intramammary infections has been widely documented, but the underlying mechanisms and the genes are still largely unknown. Previously, two divergent lines of sheep selected for high/low milk somatic cell scores have been shown to be respectively susceptible and resistant to intramammary infections by *Staphylococcus spp.* Transcriptional profiling with an 15K ovine-specific microarray of the milk somatic cells of susceptible and resistant sheep infected successively by *S. epidermidis* and *S. aureus* was performed in order to enhance our understanding of the molecular and cellular events associated with mastitis resistance.

**Results:** The bacteriological titre was lower in the resistant than in the susceptible animals in the 48 hours following inoculation, although milk somatic cell concentration was similar. Gene expression was analysed in milk somatic cells, mainly represented by neutrophils, collected 12 hours post-challenge. A high number of differentially expressed genes between the two challenges indicated that more T cells are recruited upon inoculation by *S. aureus* than *S. epidermidis*. A total of 52 genes were significantly differentially expressed between the resistant and susceptible animals. Further Gene Ontology analysis indicated that differentially expressed genes were associated with immune and inflammatory responses, leukocyte adhesion, cell migration, and signal transduction. Close biological relationships could be established between most genes using gene network analysis. Furthermore, gene expression suggests that the cell turn-over, as a consequence of apoptosis/granulopoiesis, may be enhanced in the resistant line when compared to the susceptible line.

**Conclusions:** Gene profiling in resistant and susceptible lines has provided good candidates for mapping the biological pathways and genes underlying genetically determined resistance and susceptibility towards *Staphylococcus* infections, and opens new fields for further investigation.

**Background**

Mastitis is defined as an inflammation of the udder, mainly caused by an infection by various bacterial species. Amongst infectious diseases, intramammary infections (IMI) are of major importance in dairy ruminants because of their high frequency and the increased production costs that they incur (loss of milk, treatment,

culling). The most prevalent etiological group causing mastitis in sheep is *Staphylococcus* with 78.9% of positive cultures [1]. Coagulase-Negative *Staphylococci* (CNS) are particularly frequent and represent 74.8% of all isolates, most of which are *S. epidermidis* (Se) [2]. CNS are considered to be minor pathogens, causing moderate inflammatory responses and often subclinical infections in dairy ruminants [1]. On the contrary, Coagulase-Positive *Staphylococci* (CPS), largely represented by *S. aureus* (Sa), are major pathogens in all dairy species. They are principally associated with both chronic

<footnote>* Correspondence: g.foucras@envt.fr</footnote>

<footnote>† Contributed equally</footnote>

<footnote n="2">²INRA, UMR1225, IHAP, F-31076 Toulouse, France
Full list of author information is available at the end of the article</footnote>
and clinical forms of mastitis [3,4], some of which can
be very severe and in the worst case lead to a high mor-
tality rate.

Although much work has been carried out in dairy
ruminants to understand the complex physiological and
cellular events that occur in the mammary gland in
response to pathogens [3-5], the protective mechanisms
are still obscure. Schematically, when pathogens enter
the udder lumen via the teat canal, they are detected by
both immune and non-immune cells, and this is fol-
lowed by the release of chemoattractants. As a conse-
quence, neutrophils migrate from the blood flow to the
infection site [4]. These cells can phagocyte bacteria
and exert bactericidal activities by releasing potent oxi-
dative products [6]. This massive recruitment of neutro-
phils in the udder incurs a dramatic increase in the
milk somatic cell count (SCC) [5]. Accordingly, SCC
has been widely advocated as an easy-to-measure tool
for predicting mastitis and discriminating between
chronically infected and non-infected animals [5,7].
Recent advances in microarray technology, that nowa-
days enable the expression analysis of thousands of
infection-related genes, have provided novel insights
into host response to pathogens. Microarray analysis is
a well-adapted technology to investigate the gene regu-
lation mechanisms underlying immunity against patho-
gens. Previously, gene expression profiles for challenged
mammary tissue [8-11], milk cells [12] and peripheral
blood mononuclear cells [13] have been studied using
microarrays.

There is overwhelming evidence that the host's response to IMI is under genetic control, as extensively described in earlier studies [14-18]. Genetic parameters have been established for milk somatic cell scores (SCS) and occurrence of clinical mastitis, thus indicating that five to twenty percent of all variability between individuals is of genetic origin. Additionally, numerous quantitative trait loci (QTL) for udder health traits have been identified [16,19]. However, up to date, only one of these QTL - the forebrain embryonic zinc finger-like gene -has been fully characterised [20]. Apart from this QTL, the genetic basis of resistance is still largely unknown.

Nevertheless, breeding programmes for mastitis resis-
tance have been implemented throughout the world in
dairy cattle [14,16] and sheep [21] using indirect predic-
tor traits such as clinical mastitis and SCS. To assess
the effect of SCS-based selection for resistance or sus-
ceptibility to IMI, two divergent lines of dairy sheep
were created on the basis of their parents' breeding
values for SCS [22]. Evaluation of the frequency and
duration of mastitis in the two lines demonstrated that
selection for decreased SCS is associated with a decrease
of IMI [22].

In the present study, we performed transcriptomic
analysis of milk somatic cells (MSC), collected from
mastitis resistant and susceptible ewes using a generic
15K oligonucleotide chip. MSC were collected after
challenge with *Se* and *Sa* during the first and second
lactations respectively. Our objective was to use this ani-
mal-model of divergent-SCS-lines in order to identify
some of the genes and molecular mechanisms involved
in the genetic basis of the protective host response to
staphylococcal IMI.

Results

Experimental challenges

Clinical examination of the mammary gland, bacteriolo-
gical analyses and SCC confirmed that all animals were
free from udder infections before challenges.

At the beginning of the first infection with *Se*, the SCS increased rapidly in the inoculated half udder (Figure 1A). It then increased at a slower rate after the second infection with *Sa* in the contra-lateral half udder (Figure 1B). Consequently, the SCS were higher upon *Se* than upon *Sa* challenge at 12 hours (μ<sub>Se</sub> = 10.1 ± 0.7, μ<sub>Sa</sub> = 6.6 ± 1.0, p < 0.001), although the contrary was observed at 24 hours (μ<sub>Se</sub> = 9.3 ± 3.7, μ<sub>Sa</sub> = 10.8 ± 0.19, p = 0.18). Bacteriological titres (Figure 1C and 1D) were significantly higher after *Sa* challenge than after *Se* challenge over the period from 24 to 48 hours (μ<sub>sa</sub> = 12.9 ± 2.0, μ<sub>se</sub> = 4.1 ± 4.1, p < 0.001).

No significant differences were observed for milk SCS between the resistant and susceptible lines, either before or after the two successive challenges.

By contrast, the bacteriological titres were significantly
higher in the susceptible line than in the resistant line
over the period from 24 to 48 hours upon *Sa* challenge
(μsusceptible = 13.7 ± 1.7, μresistant = 12.2 ± 2.1, p = 0.048).
Although it was not significant, the bacteriological titre
was also higher at 48 hours upon *Se* challenge in the sus-
ceptible line when compared to the resistant line (μsuscep-
tible = 3.9 ± 4.6, μresistant = 0.86 ± 2.1, p = 0.27). These
results suggest that bacterial clearance is more efficient
in the resistant than in the susceptible line.

Furthermore, a difference in cell viability between
challenges was observed in the milk cells collected by
cisternal lavage 12 hours after challenge, with respec-
tively 92.1% ± 6.1 and 55% ± 17.1 of viable cells in *Se*
and *Sa* challenges (p < 0.001). Notably, the proportion
of lymphocytes was higher after *Sa* than after *Se* infec-
tion (7.8% ± 4.9 and 4.1% ± 11, respectively, p < 0.001)
as illustrated in Figure 2 for one representative ewe.
Despite the differences observed between the challenges,
the profile of cell types was not significantly different
between the two divergent lines, except for monocytes/
macrophages whose proportion tended to be higher in
susceptible animals (p = 0.28).
<figure><img src="image_2.png" /><figcaption>**Figure 1** Evolution of SCS and bacteriology titres before and after *S. epidermidis* and *S. aureus* challenges. A and B. SCC were measured in the inoculated half-udder from 48 hours before to 48 hours after challenge. SCS were computed from the SCC with a log-2 transformation and their time evolution are drafted on the graph. C and D. Bacteriology counts were measured at the same time points (the time 12 hours post-inoculation with *S. aureus* is missing). All mammary glands were free of infection before inoculation. The positive values were transformed in score by a log-10 formula. The resistant line is represented in open symbols and the susceptible line in closed symbols. Figures A and C correspond to *S. epidermidis* and Figures B and D correspond to *S. aureus* challenge.</figcaption></figure>

## Comparison of the host response to *S. aureus* and *S. epidermidis* challenges

### Microarray analysis and biological interpretation

The functional roles of MSC in response to different *Sta-phylococcus spp* were explored through gene expression profiling in the divergent sheep lines successively infected with *Se* during the first lactation, and *Sa* during the second lactation. The multifactorial ANOVA applied probe by probe identified 5,573 probes as differentially expressed according to the challenge effect (FDR of 5% and absolute fold-change (aFC) > 1.5; the FC is the ratio between *Sa* and *Se* challenges). Among these, 261 probes had an aFC > 5 (Additional file 1). The DE probe list enabled a perfect segregation between animals challenged by *Se* and *Sa* as shown by the hierarchical clustering in Figure 3. This

probe list corresponded to 210 annotated genes with 95 and 115 genes that were more expressed in *Sa* and *Se* infections, respectively. The main functions of the genes expressed at a higher level in *Sa* than in *Se* infections were associated with the immune response: hematopoiesis (p = 0.010), cell-mediated immune response (p = 0.009), cell death (p = 0.001), immunological disease (p = 0.001) and inflammatory response (p = 0.010). Genes whose expression was higher after *Se* challenge were linked to cellular growth and proliferation (p = 0.048), infectious disease (p = 0.032), lipid metabolism (p = 0.042), molecular transport (p = 0.049) and small molecule biochemistry (p = 0.048) (Ingenuity Pathway Analysis - IPA - data, not shown).

Network analysis with IPA produced two networks. In the first network (Additional file 2A), which is
<figure><img src="image_3.png" /><figcaption>**Figure 2** Cell population in the milk after *S. epidermidis* and *S. aureus* challenges. After incubation with propidium iodide, cells from cisternal lavages were analysed by flow cytometry. Dead cells were electronically gated out, and cell types (granulocytes, monocytes/ macrophages and lymphocytes) were analysed on the forward and side scatter intensity profiles. The results from a resistant ewe after *Se* (A) or *Sa* (B) are presented.</figcaption></figure>

characterised by cell-to-cell signalling and interaction
and cell-mediated immune response, the T cell receptor
signalling pathway (with the genes: *cd247*, *cd3d*, *cd3e*,
*cd3g*, *ctlα4*, *itk*, *ppp3cc*, *rasgrp1*, *tra@*, *zap70*) and the
major histocompatibility complex - MHC - pathway
(with six sub-units: *dna*, *dqa1* (three probes), *dqa2* (two
probes), *dra*, *drb1* (two probes) and *drb3*) are highly
represented. In a second network, defined by inflamma-
tory disease and response and haematological disease,
both the IL1 receptor pathway and the TNF pathway
are well represented with *il1r1*, *il1rap* and *irak4*, and
*traf2-3-5* respectively (Additional file 2B).

Real-time qPCR validation of the differentially-expressed
genes between *Sa* and *Se* challenges

Real-time qPCR was used to confirm the gene expres-
sion differences between the two challenges. The most
expressed gene in *Se* challenges was *cpb2*. We also
examined the differential expression of chemokine (C-C
motif) receptor 3 (*ccr3*) and interleukine 1 receptor type
II (*il-1r2*) for their role in pathogen detection, as well as
myxovirus resistance 2 (*mx2*) and granzyme H (*gzmh*)
for their role in immune response. Hence, five genes
were selected, three of which were more expressed in
the *Sa* condition (*ccr3*, *gzmh* and *mx2*) and two in the
*Se* condition (*cpb2* and *il-1r2*). Chemokine (C-X-C
motif) ligand 10 (*cxcl10*) was added to this gene list
since it is known to induce T cell recruitment in
inflamed tissue, and because it had been discarded from
the microarray analysis due to missing data (data not
shown). The differential expression between the two
challenges was significantly (p < 0.05) confirmed for five

out of the six genes (Table 1). Among those genes, the
expression of *cxcl10* was 60-fold higher in *Sa* than in *Se*
challenges. Although not significant (p = 0.12), the
expression of *gzmh* was higher in the *Sa* than the *Se*
condition (Table 1).

Comparison of gene expression profiles between mastitis resistant and susceptible lines

Microarray analysis

Using a multifactorial ANOVA model applied probe by
probe, 57 probes were shown to be significantly DE
between the resistant and susceptible lines (FDR 5% and
aFC > 1.5) (Table 2). These probes corresponded to 52
annotated genes, with 33 and 19 genes expressed at a
higher level in the resistant and susceptible line, respec-
tively. This list of 52 genes is further named as the
"main list". Out of the 33 genes with higher expression
in the resistant line, eight had a FC ≥ 2 (*cryl1*, *tp53*, *a
non-classical mhc-I*, *slc40a1*, *eif4ebp1*, *ppapdc1b*, *slc46a3*
and *loc784517*). Only three out of the 19 genes that
were expressed at a higher level in the susceptible line
had an aFC > 2 (*gtpbp4*, *mapre1*, *tmem87b*).

As the gene expression of MSC response differs
between challenges with two different *Staphylococcus*
species, the line effect was also analysed independently
within each challenge. The lesser amount of data in
separate analyses made it necessary to loosen the signifi-
cance threshold to find differentially expressed genes
within *Se* or *Sa* challenges. Accordingly, a total of 152
probes (138 annotated genes) was considered to be DE
between the lines after *Se* challenge and 235 probes
<figure><img src="image_4.png" /><figcaption>**Figure 3 Heatmap of differentially expressed probes in samples from S. epidermidis and S. aureus challenges.** Hierarchical clustering was performed using Pearson-centred unsupervised statistics with GeneSpring®. Gene expression intensities are in rows (n = 261, FDR q-value < 0.05 and aFC > 5). Each column represents a sample. The level of gene expression is proportional to the colour scale. The genes in the top part of the graph are over-expressed in Se when compared to Sa samples, whereas it is the contrary for the bottom part of the graph. The cluster tree of the genes (left) illustrates the nodes of genes co-regulated in each *Staphylococcus* infection and their main functions are indicated. The cluster tree of samples above the heatmap graph enabled a perfect discrimination between S. aureus and S. epidermidis-challenged samples.</figcaption></figure>

(204 annotated) after Sa challenge (p-value ≤ 0.01 and aFC > 1.5) (Additional file 3). The latter two lists were compared to each other and also to the main list (57 probes) by generating a Venn diagram (Figure 4). Nine genes (*bola-nc1*, *ccdc125*, *eif4ebp1*, *kdm4b*, *mapre1*, *ppapdc1b*, *ppil3*, *timm8a*, *tmem87*) and also *loc784517* and an unannotated probe were common to the three

<table><caption>**Table 1 RT-qPCR results for the differentially expressed genes between S. aureus and S. epidermidis challenges**</caption><thead><tr><td>Gene</td><td>S. aureus</td><td>S. epidermidis</td></tr></thead><tbody><tr><td>CXCL10</td><td>91.46 ± 70.08***</td><td>1.46 ± 1.79</td></tr><tr><td>IL1R2</td><td>0.12 ± 0.11***</td><td>2.09 ± 2.35</td></tr><tr><td>CPB2</td><td>0.05 ± 0.04***</td><td>1.40 ± 1.84</td></tr><tr><td>CCR3</td><td>6.69 ± 6.80**</td><td>1.35 ± 0.83</td></tr><tr><td>MX2</td><td>4.64 ± 4.75**</td><td>1.00 ± 1.07</td></tr><tr><td>GZMH</td><td>12.41 ± 12.12</td><td>2.98 ± 2.52</td></tr></tbody></table>

The results represent the mean ± standard deviation of the relative expression in qPCR of six differentially expressed genes identified in the microarray analyses between Se and Sa challenges. A non parametric Wilcoxon test was performed with SAS to identify the differentially expressed genes.

lists. Forty other genes belonged to the Main List and one of the two single-challenge lists (Figure 4).

To gain further insight into the biological significance of the differences between the resistant and susceptible lines, information from the separate analyses in Sa and Se was added to the main list. The three probe lists were pooled together (Additional file3), resulting in a super-list of 335 annotated genes (380 probes). In this list, further named as the “pooled list”, 209 and 169 genes were expressed at a higher level in the resistant and the susceptible animals, respectively.

**Real-time qPCR validation of the differentially-expressed genes between resistant and susceptible lines**

To confirm the line effect, seventeen genes were analysed by qPCR. Genes were chosen because they belonged to the main list (*cryl1*, *eif4ebp1*, *gtpbp4*, *mapre1*, *ppapdc1b*, *rarα*, *slc40a1*, *tmem87b* and *tp53*), or had been identified in Sa (*ccl5*, *itgb6*, *s100a2*, *saa2* and *tlr2*) or Se conditions (*capn3*, *psmd4* and *st3gal4*) (Table 3). Real-time qPCR confirmed the significant (p < 0.05) differential expression of eight genes (*capn3*, *cryl1*, *itgb6*, *psmd4*, *rarα*, *saa2*, *st3gal4* and *tp53*). Six

<footnote>*** p-value < 0.01; ** p-value < 0.05.</footnote>
<table><caption>Table 2 List of the differentially expressed genes between the resistant and susceptible lines</caption><thead><tr><th>ProbeName</th><th>Genbank</th><th>Genes</th><th>Fold change<sup>1</sup></th><th>FDR</th><th>Description</th></tr></thead><tbody><tr><td>A_70_P018246</td><td>FE029767</td><td>CRYL1</td><td>4.7</td><td>0.030</td><td>crystallin, lambda 1</td></tr><tr><td>A_70_P062021</td><td>EE803126</td><td>TP53</td><td>4.6</td><td>0.031</td><td>tumor protein p53</td></tr><tr><td>A_70_P001626</td><td>CN824748</td><td>BOLA-NC1</td><td>3.5</td><td>0.027</td><td>non-classical MHC class I antigen</td></tr><tr><td>A_70_P007316</td><td>EE851499</td><td>SLC40A1</td><td>2.6</td><td>0.046</td><td>solute carrier family 40 member 1-like iron-regulated transporter</td></tr><tr><td>A_70_P029426</td><td>CN822074</td><td>EIF4EBP1</td><td>2.4</td><td>0.014</td><td>eukaryotic translation initiation factor 4E binding protein 1</td></tr><tr><td>A_70_P049136</td><td>DY522411</td><td>KIAA2013</td><td>2.2</td><td>0.024</td><td></td></tr><tr><td>A_70_P054531</td><td>EE849541</td><td>PPAPDC1B</td><td>2.2</td><td>0.000</td><td>phosphatidic acid phosphatase type 2 domain containing 1B</td></tr><tr><td>A_70_P013986</td><td>EE808866</td><td>SLC46A3</td><td>2.1</td><td>0.040</td><td>solute carrier family 46, member 3</td></tr><tr><td>A_70_P010846</td><td>EE822719</td><td></td><td>2.0</td><td>0.047</td><td></td></tr><tr><td>A_70_P006576</td><td></td><td>LOC784517</td><td>2.0</td><td>0.030</td><td>similar to cationic amino acid transporter 5;</td></tr><tr><td>A_70_P059451</td><td>EE748438</td><td>RARA</td><td>2.0</td><td>0.027</td><td>retinoic acid receptor, alpha</td></tr><tr><td>A_70_P066641</td><td>FE023374</td><td>CCDC125</td><td>1.9</td><td>0.019</td><td>coiled-coil domain containing 125</td></tr><tr><td>A_70_P019936</td><td>EE776127</td><td>KDM4B</td><td>1.8</td><td>0.023</td><td>lysine (K)-specific demethylase 4B</td></tr><tr><td>A_70_P038196</td><td>EE746703</td><td>SULT1A1</td><td>1.8</td><td>0.041</td><td>sulfotransferase family, cytosolic, 1A, phenol-preferring, member 1</td></tr><tr><td>A_70_P031756</td><td>EE767595</td><td>YPEL3</td><td>1.8</td><td>0.040</td><td>yippee-like 3</td></tr><tr><td>A_70_P062891</td><td>EE747727</td><td>PIGR</td><td>1.8</td><td>0.027</td><td>polymeric immunoglobulin receptor</td></tr><tr><td>A_70_P021746</td><td>EE782837</td><td>ACTN4</td><td>1.8</td><td>0.040</td><td>actinin, alpha 4</td></tr><tr><td>A_70_P055391</td><td>DY492111</td><td>FAM100B</td><td>1.8</td><td>0.023</td><td>Family with sequence similarity 100, member B</td></tr><tr><td>A_70_P021086</td><td>EE826005</td><td>PCID2</td><td>1.7</td><td>0.048</td><td>PCI domain containing 2</td></tr><tr><td>A_70_P069621</td><td>EE856030</td><td></td><td>1.7</td><td>0.049</td><td></td></tr><tr><td>A_70_P054671</td><td>EE765024</td><td>LOC781337</td><td>1.7</td><td>0.019</td><td></td></tr><tr><td>A_70_P038536</td><td>EE827115</td><td>GABARAPL1</td><td>1.7</td><td>0.026</td><td>GABA(A) receptor-associated protein like 1</td></tr><tr><td>A_70_P022126</td><td>EE826386</td><td>RERE</td><td>1.7</td><td>0.040</td><td>similar to atrophin-1 like protein</td></tr><tr><td>A_70_P059286</td><td>DY500392</td><td>PLOD1</td><td>1.6</td><td>0.027</td><td>procollagen-lysine 1, 2-oxoglutarate 5-dioxygenase 1</td></tr><tr><td>A_70_P064491</td><td>EE836176</td><td>SERINC3</td><td>1.6</td><td>0.040</td><td>serine incorporator 3</td></tr><tr><td>A_70_P024181</td><td>DQ239612</td><td>TUBA1A</td><td>1.6</td><td>0.045</td><td>tubulin, alpha 1a</td></tr><tr><td>A_70_P011346</td><td>EE790238</td><td>STAB1</td><td>1.6</td><td>0.041</td><td>stabilin 1</td></tr><tr><td>A_70_P016501</td><td>EE873028</td><td>PTTG1IP</td><td>1.6</td><td>0.030</td><td>pituitary tumor-transforming 1 interacting protein</td></tr><tr><td>A_70_P060561</td><td></td><td></td><td>1.6</td><td>0.030</td><td></td></tr><tr><td>A_70_P042031</td><td>GO760287</td><td>VAMP5</td><td>1.6</td><td>0.041</td><td>vesicle-associated membrane protein 5</td></tr><tr><td>A_70_P045551</td><td>EE792489</td><td>LOC507126</td><td>1.6</td><td>0.042</td><td>basement membrane-induced gene</td></tr><tr><td>A_70_P033276</td><td>EE812467</td><td>PPIL3</td><td>1.6</td><td>0.019</td><td>peptidylprolyl isomerase cyclophilin-like 3</td></tr><tr><td>A_70_P007306</td><td>EE843558</td><td></td><td>1.6</td><td>0.030</td><td></td></tr><tr><td>A_70_P019896</td><td>EE823241</td><td>UQCRQ</td><td>1.6</td><td>0.025</td><td>low molecular mass ubiquinone-binding protein (9.5 kD)</td></tr><tr><td>A_70_P023216</td><td>EE864116</td><td>CNNM2</td><td>1.6</td><td>0.040</td><td>cyclin M2</td></tr><tr><td>A_70_P066801</td><td>EE865060</td><td>DEF8</td><td>1.6</td><td>0.030</td><td>differentially expressed in FDCP 8 homolog</td></tr><tr><td>A_70_P049271</td><td>EE806359</td><td>GIYD1</td><td>1.5</td><td>0.030</td><td>GIY-YIG domain containing</td></tr><tr><td>A_70_P060881</td><td>EE824343</td><td>ZNF259</td><td>-1.5</td><td>0.040</td><td>zinc finger protein 259</td></tr><tr><td>A_70_P060761</td><td>EE749912</td><td>ARMC1</td><td>-1.5</td><td>0.019</td><td>armadillo repeat containing 1</td></tr><tr><td>A_70_P064541</td><td>EE833852</td><td>PPIG</td><td>-1.6</td><td>0.049</td><td>peptidylprolyl isomerase G cyclophilin G</td></tr><tr><td>A_70_P063461</td><td>FE022716</td><td>NVL</td><td>-1.6</td><td>0.040</td><td>nuclear VCP-like</td></tr><tr><td>A_70_P062791</td><td>EE823755</td><td>POLR2D</td><td>-1.6</td><td>0.025</td><td>polymerase (RNA) II (DNA directed) polypeptide D</td></tr><tr><td>A_70_P050356</td><td>EE777866</td><td>CYP51A1</td><td>-1.6</td><td>0.025</td><td>cytochrome P450, family 51, subfamily A, polypeptide 1</td></tr><tr><td>A_70_P009601</td><td>EE747016</td><td>STT3A</td><td>-1.6</td><td>0.040</td><td>STT3, subunit of the oligosaccharyltransferase complex, homolog A</td></tr><tr><td>A_70_P050201</td><td>FE030100</td><td>DNTTIP2</td><td>-1.7</td><td>0.040</td><td>deoxynucleotidyltransferase, terminal, interacting protein 2</td></tr><tr><td>A_70_P046246</td><td>EE777707</td><td>TIMM8A</td><td>-1.7</td><td>0.026</td><td>translocase of inner mitochondrial membrane 8 homolog A</td></tr><tr><td>A_70_P061706</td><td>EE815731</td><td>USP10</td><td>-1.7</td><td>0.045</td><td>ubiquitin specific peptidase 10</td></tr><tr><td>A_70_P057996</td><td>EE827511</td><td>FYN</td><td>-1.7</td><td>0.041</td><td>FYN oncogene related to SRC</td></tr><tr><td>A_70_P060371</td><td>EE849850</td><td>AHCYL1</td><td>-1.7</td><td>0.000</td><td>adenosylhomocysteinase-like 1</td></tr><tr><td>A_70_P049176</td><td>EE746595</td><td>HMGCS1</td><td>-1.7</td><td>0.013</td><td>3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 (soluble)</td></tr><tr><td>A_70_P049891</td><td>CF118151</td><td>ITGA2</td><td>-1.8</td><td>0.040</td><td>integrin, alpha 2</td></tr></tbody></table>
<table><caption>Table 2 List of the differentially expressed genes between the resistant and susceptible lines (Continued)</caption><tr><td>A_70_P034661</td><td>EE780305</td><td>HOOK1</td><td>-1.9</td><td>0.030</td><td>hook homolog 1 (Drosophila)</td></tr><tr><td>A_70_P011861</td><td>FE031423</td><td>MUC12</td><td>-1.9</td><td>0.033</td><td>mucin 12, cell surface associated</td></tr><tr><td>A_70_P055431</td><td>EE849843</td><td>FYN</td><td>-1.9</td><td>0.040</td><td>FYN oncogene related to SRC</td></tr><tr><td>A_70_P010631</td><td>EE756345</td><td>GTPBP4</td><td>-2.1</td><td>0.019</td><td>GTP binding protein 4</td></tr><tr><td>A_70_P006201</td><td>FE031048</td><td>MAPRE1</td><td>-3.1</td><td>0.014</td><td>microtubule-associated protein, RP/EB family, member 1</td></tr><tr><td>A_70_P057056</td><td>EE780570</td><td>TMEM87B</td><td>-3.5</td><td>0.030</td><td>transmembrane protein 87B</td></tr></table>

others (ccl5, gtpbp4, ppapdc1b, s100a2, slc40a1 and tmem87b) were close to signification (p < 0.10). The genes tlr2, eif4ebp1 and mapre1 were not significantly confirmed by qPCR (0.20 < p < 0.30), however the relative gene expression between resistant and susceptible was in accordance with microarray analysis (Table 3). **Clustering of the differentially expressed genes between the resistant and susceptible lines**

Principal component analysis (PCA) of the probes from the pooled list revealed that the first two principal components, that represented 19% of the total variations, could separate the sheep samples into three clusters: resistant animals infected by Sa, susceptible animals infected by Sa and animals infected by Se (Figure 5A). The principal component 1 (PC1) explained 11.2% of the total variations and clearly discriminated the *Staphylococcus spp* within the differentially expressed genes between the lines (Figure 5A). The gene expression of *capn3* was mainly associated with Se challenge whereas the gene expression of *ccl5* and *cd36* was linked to Sa challenge (Figure 5B). The PC2 explained 7.8% of the total variations and generally tended to separate the resistant and susceptible

animals whatever the challenge (Figure 5A). Gene expression of *tp53*, *tlr2*, *map3k3*, *selplg* and *bola-nc1* was associated with the resistant animals whereas gene expression of *plekhb2*, *tmem87b* and *csf3* was linked to the susceptible ones (Figure 5B).

**GO and pathway analysis of the differentially expressed genes between resistant and susceptible lines**

Additional biological information was obtained using the Database for Annotation, Visualization and Integrated Discovery (DAVID v6.7) with human ortholog gene names - HUGO nomenclature (n = 306 out of 335 genes recognized by DAVID). First, Gene Ontology (GO) analysis of the biological process terms was performed. The up-regulated genes in the resistant line showed a significant enrichment of leukocyte adhesion and activation, cell death regulation, intracellular signaling cascades and negative regulation of macromolecule metabolic processes and catalytic activities (Additional file 4). On the contrary, the down-regulated genes showed a significant enrichment in positive regulation of inflammatory response (p = 0.029) (Additional file 4). Both up and down-regulated genes showed an over-representation of genes involved in the regulation of transcription and RNA maturation and in cell motion (Additional file 4).

Two BIOCARTA pathways were well-represented: apoptotic signalling in response to DNA damage (p = 0.029, with *akt1*, *eif2s1*, *cycs*, *tp53*), and adhesion and diapedesis of granulocytes (p = 0.087, with *csf3*, *selp*, *selplg*). Four KEGG pathways were also identified: lysosome (p = 0.010, with *tcirg1*, *sgsh*, *cd68*, *smpd1*, *ctsd*, *ctsa*, *ctsb*, *fuca1*), adipocytokine signalling pathway (p = 0.012, with *akt1*, *cd36*, *mapk8*, *acsl4*, *acsl3*, *acsl5*), hematopoietic cell lineage (p = 0.032, with *csf3*, *cd36*, *itga5*, *cd59*, *itga2*, *csf1r*) and focal adhesion (p = 0.054, with *akt1*, *lama3*, *ccnd3*, *actn4*, *itga5*, *fyn*, *itgb6*, *itga2*, *mapk8*).

**Gene network analysis of the differentially expressed genes between resistant and susceptible lines**

Systemic identification and grouping of line-associated genes into biological networks was performed with IPA using HUGO names (n = 331 out of 335 genes,

<figure><img src="image_5.png" /></figure>

<footnote n="1">¹ In the fold-change, the enumerator is the resistant line and the denominator is the susceptible line.</footnote>

<footnote>ANOVA models with Line and Challenge effects were applied probe by probe with GeneSpring® (n = 57 probes, n = 52 genes, FDR q-value < 0.05, absolute FC¹ > 1.5).</footnote>
<table><caption>Table 3 RT-qPCR of the differentially expressed genes between resistant and susceptible lines</caption><thead><tr><th rowspan="2">Microarray result</th><th rowspan="2">Genes</th><th colspan="2">All data</th><th colspan="2">Sa data</th><th colspan="2">Se data</th></tr><tr><th>Resistant</th><th>Susceptible</th><th>Resistant</th><th>Susceptible</th><th>Resistant</th><th>Susceptible</th></tr></thead><tbody><tr><td rowspan="10">Main List</td><td>CRYL1</td><td>26.50 ± 29.98**</td><td>4.65 ± 5.68</td><td>40.39 ± 37.92**</td><td>6.20 ± 6.66</td><td>12.61 ± 8.71*</td><td>3.10 ± 4.57</td></tr><tr><td>TP53</td><td></td><td></td><td>4.53 ± 3.51**</td><td>1.16 ± 0.72</td><td></td><td></td></tr><tr><td>RARα</td><td>1.34 ± 0.82**</td><td>0.76 ± 0.37</td><td>0.95 ± 0.58*</td><td>0.51 ± 0.31</td><td>1.72 ± 0.90*</td><td>1.02 ± 0.20</td></tr><tr><td>SLC40A1</td><td></td><td></td><td>5.07 ± 8.07*</td><td>1.29 ± 1.10</td><td></td><td></td></tr><tr><td>GTPBP4</td><td></td><td></td><td>0.59 ± 0.47*</td><td>1.21 ± 0.92</td><td></td><td></td></tr><tr><td>TMEM87B</td><td></td><td></td><td>0.73 ± 0.14*</td><td>1.19 ± 0.86</td><td></td><td></td></tr><tr><td>PPAPDC1B</td><td>0.43 ± 0.39</td><td>0.81 ± 0.92</td><td>0.25 ± 0.13</td><td>0.26 ± 0.19</td><td>0.61 ± 0.50*</td><td>1.36 ± 1.05</td></tr><tr><td>EIF4EBP1</td><td>1.54 ± 0.80</td><td>1.27 ± 0.75</td><td>1.93 ± 0.75</td><td>1.49 ± 1.01</td><td>1.14 ± 0.67</td><td>1.05 ± 0.33</td></tr><tr><td>MAPRE1</td><td></td><td></td><td>0.87 ± 0.57</td><td>1.16 ± 0.76</td><td></td><td></td></tr><tr><td>SAA2</td><td></td><td></td><td>0.33 ± 0.31**</td><td>1.86 ± 2.30</td><td></td><td></td></tr><tr><td>ITGB6</td><td></td><td></td><td>0.42 ± 0.31**</td><td>1.49 ± 1.49</td><td></td><td></td></tr><tr><td rowspan="4">Sa list</td><td>S100A2</td><td></td><td></td><td>0.58 ± 0.39*</td><td>1.28 ± 0.94</td><td></td><td></td></tr><tr><td>CCL5</td><td></td><td></td><td>0.55 ± 0.36*</td><td>1.35 ± 1.02</td><td></td><td></td></tr><tr><td>TLR2</td><td></td><td></td><td>2.04 ± 2.17</td><td>1.09 ± 0.47</td><td></td><td></td></tr><tr><td>CAPN3</td><td>3.63 ± 2.71**</td><td>1.52 ± 0.98</td><td>4.07 ± 3.23</td><td>1.66 ± 0.81</td><td>3.19 ± 2.28*</td><td>1.38 ± 1.18</td></tr><tr><td rowspan="2">Se list</td><td>PSMD4</td><td>0.82 ± 0.28**</td><td>1.06 ± 0.31</td><td>0.96 ± 0.27</td><td>1.08 ± 0.33</td><td>0.67 ± 0.22**</td><td>1.04 ± 0.32</td></tr><tr><td>ST3GAL4</td><td>1.56 ± 1.31**</td><td>0.82 ± 0.50</td><td>0.96 ± 0.73</td><td>0.54 ± 0.37</td><td>2.16 ± 1.55**</td><td>1.10 ± 0.48</td></tr></tbody></table>

The results represent the mean ± standard deviation of the relative expression in qPCR of seventeen differentially expressed genes identified in the microarray analyses with all data (main list), *S. aureus* (*Sa* list) or *S. epidermidis* data (*Se* list). qPCR were performed with *Sa* or *Se* samples or with both *Sa* and *Se* samples (All data). A non parametric Wilcoxon test was performed with SAS to identify the differentially expressed genes. ** p-value < 0.05; * p-value < 0.10.

recognized by IPA). Five networks were obtained with a score superior to 30. Twenty-two to twenty-eight genes were involved in each network. The two first networks are presented in the Figure 6. Network 1 is characterised by lipid metabolism, molecular transport and small molecule biochemistry (Figure 6A). It presents *tp53*, the

second most up-regulated gene in the resistant line (Table 2 and Additional file 3), as a hub which means that *tp53* regulates or is regulated by a large proportion of the identified DE genes (Figure 6A). Network 2 is defined by cellular movement, haematological system development and function, and immune cell trafficking

<figure><img src="image_6.png" /><figcaption>**Figure 5** Principal component analysis of the differentially-expressed probes between resistant and susceptible lines. PCA was performed with R on the 380 probes that are differentially expressed between the lines from the pooled list. (A) All samples from the four conditions - Low-SCS animals infected by *Se* (open triangle), Low-SCS animals infected by *Sa* (open circle), High SCS animals infected by *Se* (closed triangle) and High SCS animals infected by *Sa* (closed circle) were separated based on Line-Challenge along the principal component 1 (PC1) and PC2 axes. PC1 explained 11.2% of the total variations and mainly discriminates the challenges whereas the PC2 explained 7.8% of the total variations and segregates between the lines. (B) The 380 probes were projected on PC1 and PC2.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>A. Network 1</figcaption></figure>

**Figure 6** Network analysis of the differentially-expressed genes between resistant and susceptible lines. Network analysis was performed with IPA (n = 335 genes, n = 287 IPA network eligible genes). The colours represent the expression level: the genes over-expressed in resistant animals are in red whereas the down-regulated genes are in green. (A) Twenty-eight genes belonged to network A that scores 42. The main biological functions are lipid metabolism, molecular transport and small molecule biochemistry. (B) Thirty-five genes are present in network B. The original network involved twenty-six genes and scores 39. It is characterised by cell movement, haematological system development and function, and immune cell trafficking. We could add seven DE expressed genes of interest (*akt1*, *cd59*, *eif4ebp1*, *hspa6*, *itga5*, *osmr* and *rarα*) to this network through five other genes, with direct relationships with genes involved in this network.

<figure><img src="image_8.png" /><figcaption>B. Network 2</figcaption></figure>

(Figure 6B). It highlights a central position for *tlr2*, linked to *cd36*, that are of major importance for pathogen recognition. Other genes were included in this network *eif4ebp1* and *itga5*, *nfkb* (immune response), *ccl5* (diapedesis) and *rarα* and *ahr* involved in the retinoic acid pathway (Figure 6B).

### Communal transcription factor analysis

To enhance the understanding of the genetic basis of the DE genes, the potential transcription factors that can regulate these genes were investigated by looking for transcription factor-binding sites (TFBPS) that were over-represented in the promoter sequences of the DE genes. Using 260 genes recognised in InnateDB, significant enrichment was demonstrated for 11 transcription factors with a p < 0.05 (CEBPB, PAX5, E4F1, CREB1, ETV7, HIF-1, SMAD1, FOXO4, NRF-2, NRF1 and NFIC), and nine other transcription factors with p < 0.10 (AP-1, ETS1, 120-kDa CRE-binding protein, FOXO1A, NKX2-5, ATF, ATF3, XBP1 and PBX1). Furthermore, the transcription factors AHR, which showed lower expression levels in the resistant animals, and CEBPA, which showed higher expression levels, could bind the promoters of thirty-three and seven genes, respectively, that were more expressed in the susceptible line (p = 0.07 and p < 0.1, respectively). TP53, which showed higher expression levels in the resistant

animals, could bind fourteen up-regulated genes (p = 0.16).

### Discussion

#### Gene expression of milk somatic cells

In the present study, we compared the gene expression in MSC upon mammary challenge. Up to date, only a small number of studies have analysed the transcriptome of MSC, by comparing infected and non-infected glands [16,17]. MSC populations, however, are highly modified after infection: whereas mononuclear cells are preponderant in healthy glands [23], neutrophils represent the main cell type in infected glands [4,13,24]. Because of the profound modifications within the MSC population, the comparison of MSC gene expression before and after challenge should be interpreted with caution as they are the result of mixed molecular and cellular effects. Hence, our study focused on the transcriptomic differences within homogeneous cell populations between the lines upon challenge. It is well known that the development of *S. aureus* in any tissue implies host cell apoptosis [25,26], and so RNA breakdown occurs. To circumvent the difficulty, cisternal lavage was performed after emptying the mammary gland to gather recently emigrated and living MSC. This protocol enabled us to recover a higher proportion of living cells
and obtain good quality RNA, despite the short half-life
of milk neutrophils.

Physiological mechanisms that are mobilised during
the early response are considered as determinant for the
outcome of IMI. Therefore, we selected an early time
point (12 hours) for cell collection, during the first wave
of cell recruitment, just before the bacteria titres
diverge, and the side effects, on the mammary tissue
due to bacteria growth, appear. In addition at 12 hours
post inoculation, the milk cell population was much
more homogeneous than at later time points, i.e. 95% of
the cells were neutrophils. Therefore, despite the small
number of animals used and the fact that only one time
point was considered, we identified lists of significant
and biological relevant differentially expressed genes in
MSC. The results gave some insight into the differential
response of the host to IMI according to the mastitis
causing pathogen and its genetic background.

Differences between *S. epidermidis* and *S. aureus*
challenges

Although genetically *Sa* and *Se* are closely related [27],
the host cell viability after *Sa* challenge was lower than
after *Se* challenge and the proportion of T-cells was
higher. This is correlated with the striking differences in
the gene expression profiles of infected MSC at twelve
hours post-inoculation. Indeed, 5,573 genes were differ-
entially expressed in MSC infected either by *Sa* or *Se*.
The greatest absolute fold changes (aFC) were achieved
when *Sa* was compared to *Se* - 91% of the genes with
an aFC > 10 were over-expressed after *Sa* challenge. A
high number of DE genes underlined T cell recruitment
after *Sa* challenge. Indeed, two major genes that are up-
regulated after *Sa* challenge are involved in T-cell
responses: the chemokine (C-X-C motif) ligand 10
(*cxcl10*) [28] and the cytotoxic T lymphocyte-associated
protein 4 gene (*ctla4*). The up-regulation of *ctla4* in
MSC of cows with chronic *Sa* mastitis has already been
reported [13]. In addition, the components of the che-
mokine signalling and cell adhesion molecule pathways
were over-represented after *Sa* challenge. These path-
ways play important roles in blood neutrophil arrest and
diapedesis across the endothelium [6]. Furthermore,
cytokine-cytokine receptor interactions are also notice-
able and cytokines are known to tightly regulate neutro-
phil functions during inflammatory response [6]. Thus,
pro-inflammatory cytokines lead to the activation of the
mitogen-activated protein kinase pathway (MAPK) in
neutrophils and promote leukocyte recruitment to
inflammation sites. Furthermore, *S. aureus* exerted an
oxidative priming and a pro-apoptotic effect on neutro-
phils, contrary to *S. epidermidis* strains [29]. *S. aureus*
cytotoxicity mainly depends on proteases, hyaluroni-
dases, lipases and nucleases that facilitate tissue

destruction, membrane-damaging toxins that cause cyto-
lytic effects in host cells, and superantigens that contri-
bute to the symptoms of septic shock [30]. Moreover,
three sub-units of the major histocomptibility complex
(MHC) class II (DQA1, DQA2 and DRB1) were up-
regulated after *Sa* challenge suggesting recruitment of
antigen-presenting cells or activation of T cells. The
higher proportion of lymphocytes after *Sa* challenge
might be partly responsible for the large variations of
gene expression observed between *Sa* and *Se* challenges.

Altogether, the gene expression profiles of MSC upon
challenge with two distinct pathogens differed widely in
relation with the severity of the mastitis, bacterial counts
and milk SCC [7,22]. Furthermore, it should be noted
that all the differences of MSC gene expression infected
either by *Sa* or *Se* might not be caused by the *Staphylo-
coccus* species. Actually, other factors may explain these
differences since the animals were infected twice, and a
memory response could have developed consequently to
the first challenge. However, this is improbable due to
the recurrence of infections frequently reported in
apparently susceptible animals and the poor effect of
vaccination with living bacteria on the occurrence and
severity of subsequent infections. The animals' physiolo-
gical conditions (age, effect of previous infection, *etc*. ) at
the second lactation could also influence the pattern of
response. So partial confusion with other effects not
related to the bacterial strain cannot be totally excluded.

Transcriptome differences associated with mastitis
resistance or susceptibility

To our knowledge, this is the first report of a high-
throughput gene expression profiling of MSC from ani-
mals genetically selected for their resistance to mastitis.
Although, Griesbeck-Hilch et al. [31] have already com-
pared the expression of a few genes in mammary epithe-
lial cells (MEC) of mastitis resistant and susceptible cows
selected on the basis of the breeding values for SCS or
genetic markers. The genetic lines used in this study
were created from a one-generation selection of Lacaune
dairy ewes based on their parents' breeding values for
SCS. The divergence achieved was high, i.e. 3 genetic
standard deviations [22], and was confirmed over succes-
sive cohorts raised in the same flock between 2005 and
2009 (RR, unpublished data). The selection criterion used
is the SCC, it is based on few repeated measures (usually
3 to 4 times per lactation) and is able to discriminate
chronically infected animals from non-infected animals.
SCC-based selection, therefore, does not give any infor-
mation about the underlying mechanisms selected, but
previously published results have shown that such selec-
tion improves resistance to IMI [22].

In the present study, whereas milk SCC were not sig-
nificantly different between the lines in the early course
of *Staphylococcus* challenges, bacteria titres were lower
in the resistant line than in the susceptible line. The dif-
ference between lines increased from 12 to 48 hours
upon challenge. Accordingly, differentially expressed
genes in milk cells at twelve hours post-inoculation
might provide useful information about the early
mechanisms underlying the genetic control of mastitis
in the resistant line, that portend differences of the mas-
titis issue that were later observed.

Cytokines and chemokines

Some soluble factors such as chemokines form gradients from the blood towards infected sites and can enhance neutrophil attraction and migration. In our study, we found no expression differences for major chemokines such as *il-1β*, tumour necrosis factor alpha (*tnfα*), *il-6* and *il-8* (also named *cxcl8*, one of the main chemoattractants for neutrophils [4]), contrary to Griesbeck-Zilch in MEC of mastitis resistant cows selected on QTL alleles [31]. However, integrin beta 6 (*itgb6*), serum amyloid A 2 (*saa2*), a marker of acute inflammation [32,33], S100 calcium binding protein A2 (*s100a2*), a member of the S100 family that is highly correlated with somatic cell count [10], and the chemokine C-C motif ligand 5 (*ccl5*, also named *rantes*) that triggers adhesion and transmigration of blood monocytes to/ through endothelial cells were expressed at higher levels in susceptible animals. On the contrary, the expression of calpain 3 (*capn3*) was higher in resistant animals. This protein was shown to play a role in resting neutrophils and to operate as a negative regulator of protrusion and migration [34]. In the present study, although pro-inflammatory molecules have been widely shown to play an important role in neutrophil recruitment and IMI outcome [7,14,28], their expression patterns at 12 hours post-challenge were contrasted in resistant and susceptible lines and not correlated to differences in milk cell concentrations.

Neutrophil diapedesis

Furthermore, a group of genes related to cell adhesion
and movement, including ST3 beta-galactoside alpha-
2,3-sialyltransferase 4 (*st3gal4*), but also activated leuko-
cyte cell adhesion molecule (*alcam*), integrin alpha 5
(*itga5*), selectin platelet (*selp*) and its ligand (*selplg*),
were over-expressed in the resistant line. *st3gal4* is
involved in the synthesis of selectin ligand [35]. The
selectins are fundamental for the attachment of neutro-
phils to the endothelial cell surface [4,36] before diaped-
esis. These results suggest that neutrophil diapedesis
might be more efficient in the resistant line.

TLR pathways

The genes *cd36* and *tlr2* that were expressed at a higher
level in resistant animals and that are central in the net-
work analysis collaborate together in the Toll-like recep-
tor (TLR) signalling pathway [37]. TLR2 is dedicated to

Gram-positive bacteria recognition [38] and it may
enhance the activation of neutrophil phagocytosis [39].
Griesbeck-Hilch et al. [31] also found that *tlr2* was up-
regulated in mastitis resistant cows selected on markers
for one QTL. The TLR2 signalling pathway may activate
a cascade of intracellular events that may initiate the
transcription of pro-inflammatory cytokine genes
through the ERK/MAPK signalling pathway with consis-
tent up-regulation of *eif4ebp1* and *itga5* in the resistant
animals. This pathway is involved in oxidative stress
which has been shown to contribute to the variability of
susceptibility to IMI in cattle [6]. Altogether, the modifi-
cation of TLR2 and MAPK signalling pathways might be
responsible for a higher uptake of bacteria by phago-
cytes, and therefore might contribute to the more effi-
cient clearance of the infection in the resistant line.

Transcription factors

A noteworthy fact is that considerable differences
between lines were related to transcriptional activity
within MSC. The activator protein 1 (AP-1) transcrip-
tion factor is considered as an immediate-early response
gene and is thought to be involved in a wide range of
transcriptional regulatory processes linked to cellular
proliferation and differentiation. RARα, TP53, and AHR
were also transcription factors of interest.

RARα, the alpha receptor of retinoic acid, a com-
pound derived from vitamin A metabolism, is a nuclear
receptor. It can affect several aspects of innate immunity
by enhancing the function of neutrophils, macrophages
and natural killer cells [40]. It has also been shown to
play a role in helping the development of T helper cells,
B-cells and, thus antibody-mediated response [40], and
more recently in modulating antigen-presenting cells
[41]. Retinoic acid metabolism has been shown to play a
part in IMI outcome, and dietary vitamin A supplemen-
tation has been reported to have a protective effect
against experimental *S. aureus* mastitis in mice [42].
Moreover, we found that the aryl hydrocarbon receptor
(*ahr*) was under-expressed, which is consistent with sev-
eral studies that have demonstrated its interaction with
RA-signalling pathways [43]. The involvement of AHR
in the control of inflammatory responsiveness has been
reported previously [44]. To our knowledge the role of
TP53 in the response to bacterial infection has yet to be
studied whereas it has been shown elsewhere to be asso-
ciated with mastitis infected quarters [45]. *tp53* was the
gene that displayed the second highest ratio between
lines and was over-expressed in the resistant line; it was
highly associated with resistant animals in PCA and was
represented as a hub in IPA networks since it interacts
with numerous other DE genes. *tp53* can be regulated
by S100A2 proteins [46] and JNK [47], and *in vitro* it
was shown to regulate TNFα and other cytokines [48].
TP53 has been largely studied in the cancer context for
its role in cell cycle arrest, apoptosis, DNA repair and production of antioxidants [49]. Furthermore, TP53 has already been shown to play a pivotal role in determining cellular response to stress via NFκB [50] and TNFα [48]; it may also enhance transcription of the complement regulator CD59 [51]. These observations suggest an important role of TP53 during bacterial infections.

**Granulopoiesis, cell proliferation, apoptosis**

In addition, particular cell functions such as granulopoie-
isis, cell proliferation and apoptosis seemed to explain
some of the differences between the lines. Strong
increases in neutrophil efflux from the bone marrow are
followed by intense granulopoiesis and efflux of band
cells that will later be recruited at the infection site:
these cells may show higher transcriptional activity than
mature neutrophils. Gene expression analysis demon-
strated that the transcription factors CCAAT/enhancer
binding protein (C/EBP), alpha (CEBPA) and RARα
were up-regulated in the resistant line. CEBPA is crucial
for the differentiation of granulocytes [52] and RARα
has been shown to be directly involved in some aspects
of the immune response by enhancing granulopoiesis
[53,54]. Collins *et al.* (60) showed that RARα can
enhance granulocytic differentiation through a molecular
pathway that is independent of CEBPA [55] suggesting
that two different pathways stimulating granulopoiesis
were up-regulated in the resistant line.

Furthermore, a number of genes differentially expressed between the sheep lines was related to cell proliferation and apoptosis, e.g. *mapre1*, *znf259*, *gzmh* *cryl1* and *tp53*. These genes exhibited amongst the highest expression differences between lines. The genes encoding *tp53*, and the lambda-crystallin protein (*cryl1*) were expressed up to five times more in the resistant line. Recently, Cheng *et al.* (61) evidenced association between the expression of *CRYL1* and inhibition of cellular proliferation and cell growth [56]. This is in agreement with the decreased expression of both microtubule associated protein RP/EB family member 1 (*mapre1*) and zinc finger protein (*znf259*) in resistant sheep. Indeed, ZNF259 (homologous to ZPR1) has previously been shown to accumulate in the nucleus of proliferating cells [57] and MAPRE1 has been associated during mitosis, with the centrosomes and spindle microtubules. Apoptosis is a critical process necessary to limit or terminate inflammation [6,58] and has previously been shown to be of importance in the response to *S. aureus* by Lutzow *et al.* [10]. The gene expression of *tp53* has been shown to increase in neutrophils during apoptosis [59]. These results suggest that cell proliferation is limited and apoptosis increased in resistant sheep as compared to susceptible sheep. Altogether data support the hypothesis that, as a consequence of apoptosis/

granulopoiesis, the cell turn-over may be enhanced in
the resistant line when compared to the susceptible line.

Collectively, these findings highlight leukocyte adhesion and cell migration, pathogen recognition through the TLR2 signalling pathway, and cell turn-over with the balance between apoptosis and granulopoiesis as possible mechanisms to explain a higher susceptibility or resistance to intramammary infection. Nevertheless they probably give only a partial view as other cell types and compartments may be involved (epithelial cells, dendritic cells, lymphocytes, etc). Also, other conditions (bacteria strain, time point) might have provided some different results. However, these results advocate the use of our differentially expressed gene list as a benchmark to more detailed genetic studies, including genome co-localisation of resistance to mastitis using QTL analysis as reviewed previously [19,22-25] and polymorphism studies.

Conclusion

Resistance to mastitis is the consequence of a fine-tun-
ing of immune and inflammatory processes in a com-
plex network of cell and gene interactions. Our study
has highlighted some of the possible mechanisms, such
as pathogen recognition and neutrophil extravasation
leading to improved immune responses against *Staphy-
lococcus* species and consequently, lower susceptibility
to infection. The list of the differentially expressed genes
between the resistant and susceptible animals provides
relevant information for the identification of candidates
for the genetic basis underlying resistance to mastitis. It
paves the way for further genetic and mechanistic
studies.

Methods

Animals, experimental challenges and sample collection

To provide enhanced insight into the genetic mechan-
isms involved in SCS-based selection, two groups of six
Lacaune ewes were challenged twice with *Staphylococcus*
bacteria. Briefly, primiparous ewes were issued from
divergent selection based on extreme breeding values
for the somatic cell score (SCS) [22]. On a general basis,
the Low SCS ewes are characterised by lower mastitis
susceptibility than the High SCS ewes [22]. *Staphylococ-
cus* genus was chosen since it is the most prevalent etio-
logical group in dairy sheep [1] and in SCS lines as
previously shown [22]. *Se* and *Sa* bacteria used for
inoculation were isolated from ovine chronic mastitis.

After the first lambing, the ewes were inoculated with
10³ cfu of *Se* in a healthy half udder. To clear the infec-
tion, local and systemic treatments with antibiotics were
applied at the end of the survey. One year later, the
same ewes had been mated again, and shortly after the
second lambing, they were inoculated with 10³ cfu of *Sa*
in the opposite half udder.

Milking was performed by hand twice a day. Milk samples were collected for milk somatic cell count (SCC) every 12-hours from 48 h before to 48 h after the inoculation. SCC was determined using a Fossomatic counter. The score of SCC (SCS) was calculated with the following formula: SCS = 3 + log₂ (SCC/100,000) [60]. Samples collected aseptically after inoculation were used for bacteriology counts (except the 12 hours post *Sa* inoculation).

Bacteriological analyses were performed at the Veterinary School of Toulouse (UMR 1225 laboratory) by conventional techniques according to International Dairy Federation (1981) guidelines, with a few additions that have already been described in Rupp *et al*. [22]. The bacteriology titre was calculated by a log-10 transformation of the data.

Twelve hours after challenge, mammary glands were
emptied and teat ends were disinfected with 70% alcohol.
Then, MSC were recovered by cisternal lavage with 100
ml of a saline solution. After centrifugation, cell pellets
were resuspended in TRIzol reagent and stored at -80°C
until further processed. In parallel, cells were immediately
processed for flow cytometry analysis. After incubation
with propidium iodide (1 µg/ml final concentration). Data
were collected on at least 20,000 events on a FACSCalibur
(BD Biosciences) and analysed with FlowJo software.

Statistical analyses of SCS and bacteriological titres
were performed with ANOVA applied to mixed models
(SAS® v.9.1), and with a non-parametric Wilcoxon test
for viability and milk cell population.

Experiments were performed according to French
(Agreement number N°31-2010-67) and European rules,
and following the regulations of the Animal Ethics
Committee for INRA (France).

Microarray analysis

RNA extraction, amplification and labelling

Total RNA was extracted from the 24 cell samples - six
ewes from two lines for two challenges - using a typical
phenol/chloroform extraction method with Trizol
reagent (Invitrogen). Extracted RNA was further purified
on Qiagen RNeasy columns (Mini kit, Qiagen). RNA
quality was assessed using an Agilent 2100 BioAnalyzer
and the RIN (RNA Integrity Number) index was above
7 for all samples.

Hybridisation, scanning and raw data storage

For each of the 24 samples, 200 ng of RNA was con-
verted into double-stranded cDNA using the Amino
Allyl Message Amp II aRNA amplification procedure
(Ambion kit). cDNA was then labelled with Cy3 and
Cy5 to obtain 48 dyed samples. Samples were hybridised
in a two-colour dye-switch experimental design (Geno-
Toul, France, http://biopuce.insa-toulouse.fr/Maquette/

en/) on 22 microarrays of the ovine oligonucleotide
019921 Agilent slide (Agilent Technology).

Chips were hybridised with labelled cDNA at 65°C for
17 hrs and then washed according to the Agilent Tech-
nologies protocol. Intensity values were recorded with a
4000B Axon scanner. Two channel images were
imported into the Agilent Technology Feature Extrac-
tion software for feature spot finding and alignment,
and data were normalised with a Loess procedure.

Annotation of the ovine microarray

A total of 15,008 different probes are present on the Agilent
ovine slide, but only 1,656 genes were annotated by Agilent
(version available in January 2010). Moreover, 8,847 genes
were identified as Human ortholog Gene Nomenclature
Committee (HGNC) by SIGENAE (http://www.sigenae.org/
sheep oligo annotation version 5 of 2009/11/10) [61]. More
information about unannotated probes was obtained
through the Basic Local Alignment Search Tool programme
on the NCBI website (http://blast.ncbi.nlm.nih.gov/Blast.cgi)
and the ENSEMBL website (http://www.ensembl.org/index.
html). After this annotation phase, only a few focus probes
remained unannotated.

Processing data and statistical analysis of microarray data

The Feature Extraction result files (.txt) were imported
into GeneSpring® GX 11 as single-channel values. Gene
expression was analysed probe by probe using an inten-
sity-based model - i.e. working on the intensity of spots
and not on the ratio between conditions. This way of
analysing two-colour-microarray data was shown to
enhance the reproducibility of results and the sensitivity
of the detection of DE genes [62]. Data were filtered
according to spot intensity, saturation and uniformity.
Genes were flagged individually for each of the four
conditions: low SCS *Sa*, low SCS *Se*, high SCS *Sa* and
high SCS *Se*. Only probes that were positively flagged in
all samples for at least one condition were conserved for
further analysis in order to keep only genes that were
very representative of one condition (n = 9,098). Then
data were normalised across arrays with the Gene-
Spring® "scale to median" procedure.

Initially, a two-way ANOVA was performed for each
probe to identify DE genes between the two *Staphylo-
coccus* challenges (*Sa* and *Se*) and between the two
divergent sheep lines (Low SCS and High SCS). The
interaction between the Challenge and the Line effects
was tested but was not significant; therefore, it was
removed from the statistical model. The p-values of the
tests were corrected with a 5% false discovery rate
(FDR) with Benjamini-Hochberg [63] and genes with an
absolute fold-change (aFC) superior to 1.5 were consid-
ered as differentially expressed. For the Line effect, FC
represents the ratio between Low and High SCS; for the
Challenge effects, it symbolises the ratio between *Sa* and
*Se*. The experiment was deposited in GEO at the
identifier number GSE24925 (BioArray Software Environment - version SIGENAE).

As 5,573 genes were differentially expressed for the
Challenge effect, a second analysis was performed con-
sidering only samples infected by one *Staphylococcus*
strain to focus on genetic differences between the lines.
The whole raw data set was divided into two subsets:
MSC from *Sa* and *Se* infections respectively. Data filter-
ing and normalisation were performed as previously for
the ANOVA model and 7,452 and 8,561 probes were
retained for statistical analysis of the *Se* and *Sa* chal-
lenges, respectively. Then, an unpaired Mann Whitney
test was performed probe by probe. Considering a 5%
FDR no probe was identified as significantly differen-
tially expressed between the lines probably because of
the weak number of animals (six in each line). However
to further explore the differences of mastitis susceptibil-
ity, the p-value threshold of statistics was relaxed. The
p-value was rounded to two decimal places, then, the
genes with a p-value ≤ 0.01 and an aFC greater than 1.5
were considered as differentially expressed.

Expression profiles for DE genes were classified using
the hierarchical clustering algorithm in GeneSpring®
based on Pearson-centred gene distances to visualise the
differences between the two conditions. They were also
represented in principal component analysis (PCA) with
the centred data in R (v. 2.9.0) to identify the most impor-
tant genes to explain mastitis resistance or susceptibility.

Biological interpretations of the differentially expressed
genes

Three software programmes were used to interpret the
lists of focus genes obtained from statistical analysis:
Ingenuity Pathway Analysis (IPA), Database for Annota-
tion, Visualization and Integrated Discovery (DAVID
v6.7) [64,65] and Innate Data Base (InnateDB) [30].

IPA software (version 7.5, http://www.ingenuity.com/) was used to generate biological networks from a list of selected genes and to document the functions of these genes and the canonical pathways in which they are involved.

Gene Ontology analysis was performed using DAVID
(http://david.abcc.ncifcrf.gov/) and led to the establish-
ment of relationships between genes with similar biolo-
gical functions. Transcription factors that potentially
regulate several focus genes were identified with Inna-
teDB (http://www.innatedb.com).

Reverse transcription

cDNA was generated from 300 ng of clean total RNA
from all samples using the Superscript III First Strand
Synthesis System Kit (Invitrogen) following the manu-
facturer's instructions with random hexamer primers
and a RNaseH treatment step.

Reverse transcription quantitative polymerase chain
reaction (RT-qPCR)

The expression of some differentially expressed genes
was verified by qPCR. Primer pairs were designed using
Primer3 [66] based on the relevant ovine sequences and
verified using Primer Express® software. Their specificity
was checked with BLAST (http://blast.ncbi.nlm.nih.gov/
Blast.cgi). Primers were synthesised commercially by
Eurogentec. For genes for which no ovine sequence was
available, a comparative gene alignment of bovine,
human, rat and mouse sequences was made and primers
were then designed on the most conserved regions
between the species. Absence of primer dimers was veri-
fied using melting curve analysis and the efficiency of the
amplification was measured before use. The couples of
primers used in qPCR experiments are listed in Addi-
tional file 5. qPCR reactions were performed on a 7300
Real-Time PCR System (Applied Biosystems). To validate
genes differentially expressed between resistant and sus-
ceptible animals, qPCRs were performed either on both
*Sa* and *Se* samples or only on *Sa* samples. All assays were
carried out in duplicate and each reaction contained 5 µl
of diluted cDNA (1:50) with 2.5 µl (0.5 µm) of each for-
ward and reverse primer along with 12.5 µl of Power
Syber Green PCR Master Mix (Applied Biosystems).

Specific amplification of each target was confirmed by
melting curve analysis. Measured Ct values were
exported from SDS software to Excel for data analysis.
RT-qPCR technical replicates of samples were averaged.
The stability of 7 housekeeping genes, previously cited
in the literature, was checked in the 24 samples and
data was analysed using GeNorm software [67]. The
four most stable genes (rp19, hprt, sdh and gapdh) were
selected for normalisation of RT-qPCR. Fold changes
were calculated by the delta delta Ct method normalised
to the four housekeeping genes [67] with R (version
2.9.0). Statistical analysis was performed using an exact
non parametric Wilcoxon test with SAS (version 9.1).

**Additional material**

**Additional file 1: List of the differentially-expressed genes between S. aureus and S. epidermidis challenges.** Analysis was performed with GeneSpring®. The enumerator of the FC represents the *Sa* samples and the denominator the *Se* samples. Genes were considered as differentially expressed if the FDR q-value < 0.05 and the absolute FC > 5 (n = 261 probes, n = 210 genes).

**Additional file 2: Networks of the differentially expressed genes between S. aureus and S. epidermidis challenges.** Network analysis was performed with IPA. Genes up-regulated after *Sa* challenge are in red whereas genes up-regulated after *Se* challenge are in green. (A). The main biological functions of the network A (molecules: 26, score: 42) are cell-to-cell signalling and interaction, cell-mediated immune response. (B). The main biological functions of the second network (molecules: 15, score 21) are inflammatory disease, inflammatory response and haematological disease.
**Additional file 3: List of the differentially expressed genes between the resistant and susceptible lines.** Analysis was performed with GeneSpring®ª (n = 380 probes, n = 335 genes). The fold change and the p-values of the three analyses are present. ANOVA stands for analysis with all data (corrected p-value with a FDR of 5% and absolute FC > 1.5); *Sa*, for analysis with only *S. aureus* data, and *Se* for analysis with only *S. epidermidis* data (p-value ≤ 0.01 and FC > 1.5).

**Additional file 4: Biological process GO Terms of the differentially expressed genes between the resistant and susceptible lines.**
Analysis was performed by DAVID (n = 335 genes, n = 306 genes recognised by DAVID). Of the 160 and 146 up- and down-regulated genes, 126 and 119 GO terms were identified, respectively. "Count" stands for the number of differentially expressed genes in a GO Term class. "%" represents the number of genes involved in given term divided by the total number of input genes, i.e. percentage of input genes hitting a given term. "Hit" is the number of TF binding sites and the "Enrichment Fold" measures the magnitude of enrichment.

**Additional file 5: Oligonucleotide sequences for quantitative PCR.**
The sequences of the couples of primers to confirm the Challenge effect are listed in the table A and the ones for the Line effect in the table B.

## Acknowledgements and funding

This work was financed by the European Network of Excellence EADGENE, ANR Génanimal and APIS-GENE. The authors would like to thank all the staff of the INRA experimental facility at La Fage (UE321) for breeding the sheep lines. Authors acknowledge the technical support provided by Lidwine Trouilh for microarray experiment in Genomic platform GenoToul Toulouse-Midi-Pyrénées, and by Gaëlle Rauffet and Philippe Bardou from the Sigenae team for annotating the Agilent ovine slide and depositing microarray data in GEO, respectively.

## Author details

¹INRA, UR631, SAGA, F-31326 Castanet-Tolosan, France. ²INRA, UMR1225, IHAP, F-31076 Toulouse, France. ³Université de Toulouse; INP, ENVT, UMR1225, IHAP; F-31076 Toulouse, France. ⁴INRA, UE321, Domaine expérimental de la Fage, F-12250, Roquefort, France.

## Authors' contributions

CB carried out the microarray experiment, performed the statistical analysis and drafted the manuscript. GF and RR designed the experiment, supervised the analysis and participated in writing the paper. RR designed the divergent selection experiment. MRA bred the animals. GF supervised experimental challenges and sample collection. DB, EF, CT and CC participated in the experimental challenges. DB supervised the bacteriology analyses. EF and SB performed flow cytometry analysis. CC prepared RNA for hybridisation. CC, MT and CB hybridised the samples. MT was in charge of the RT-qPCR analysis. RR, CRG and GF helped and discussed the statistical methods implemented. All authors read, helped to edit and approved the final manuscript.

Received: 11 March 2011 Accepted: 28 April 2011

Published: 28 April 2011

## References

1. Ariznabarreta A, Gonzalo C, San Primitivo F: Microbiological quality and somatic cell count of ewe milk with special reference to staphylococci. *J Dairy Sci* 2002, **85**:1370-1375.

2. Gonzalez-Rodríguez MC, Gonzalo C, San Primitivo F, Carmenes P: Relationship between somatic cell count and intramammary infection of the half udder in dairy ewes. *J Dairy Sci* 1995, **78**:2753-2759.

3. Sordillo LM: Factors affecting mammary gland immunity and mastitis susceptibility. *Livest Prod Sci* 2005, **98**:89-99.

4. Paape MJ, Mehrzad J, Zhao X, Detilleux J, Burvenich C: Defense of the bovine mammary gland by polymorphonuclear neutrophil leukocytes. *J Mammary Gland Biol* 2002, **7**:109-121.

5. Kehrl ME, Shuster DE: Factors affecting milk somatic-cells and their role in health of the bovine mammary gland. *J Dairy Sci* 1994, **77**:619-627.

6. Paape MJ, Bannerman DD, Zhao X, Lee JW: The bovine neutrophil: structure and function in blood and milk. *Vet Res* 2003, **34**:597-627.

7. Bergonier D, Berthelot X: New advances in epizootiology and control of ewe mastitis. *Livest Prod Sci* 2003, **79**:1-16.

8. Gunther J, Koczan D, Yang W, Nurnberg G, Repsilber D, Schuberth HJ, Park Z, Magbool N, Molenaar A, Seyfert HM: Assessment of the immune capacity of mammary epithelial cells: comparison with mammary tissue after challenge with Escherichia coli. *Veterinary Research* 2009, **40**.

9. Pareek R, Wellnitz O, Van Dorp R, Burton J, Kerr D: Immunorelevant gene expression in LPS-challenged bovine mammary epithelial cells. *J Appl Genet* 2005, **46**:171-177.

10. Strandberg Lutzow YC, Donaldson L, Gray CP, Vuocolo T, Pearson RD, Reverter A, Byrne KA, Sheehy PA, Windon R, Tellam RL: Identification of immune genes and proteins involved in the response of bovine mammary tissue to *Staphylococcus aureus* infection. *BMC Vet Res* 2008, **4**.

11. Swanson KM, Stelwagen K, Dobson J, Henderson HV, Davis SR, Farr VC, Singh K: Transcriptome profiling of *Streptococcus uberis*-induced mastitis reveals fundamental differences between immune gene expression in the mammary gland and in a primary cell culture model. *J Dairy Sci* 2009, **92**.

12. Pisoni G, Moroni P, Genini S, Stella A, Boettcher PJ, Cremonesi P, Scaccabarozzi L, Giuffra E, Castiglioni B: Differentially expressed genes associated with Staphylococcus aureus mastitis in dairy goats. *Vet Immunol Immunop* 2010, **135**:208-217.

13. Tao WJ, Mallard B: Differentially expressed genes associated with Staphylococcus aureus mastitis of Canadian Holstein cows. *Vet Immunol Immunop* 2007, **120**:201-211.

14. Heringstad B, Klemetsdal G, Ruane J: Selection for mastitis in dairy cattle: a review with focus on the situation of the Nordic countries. *Livest Prod Sci* 2000, **64**:95-106.

15. Mrode R, Swanson G: Genetic and statistical properties of somatic cell count and its suitability as an indirect means of reducing the incidence of mastitis in dairy cattle. *Anim Breed Abstr* 1996, **64**:847-857.

16. Rupp R, Boichard D: Genetics of resistance to mastitis in dairy cattle. *Vet Res* 2003, **34**:671-688.

17. Shook GE: Selection for disease resistance. *J Dairy Sci* 1989, **72**:1349-1362.

18. Detilleux JC: Genetic factors affecting susceptibility of dairy cows to udder pathogens. *Vet Immunol Immunop* 2002, **88**:103-110.

19. Ogorevc J, Kunej T, Razpet A, Dovc P: Database of cattle candidate genes and genetic markers for milk production and mastitis. *Anim Genet* 2009, **40**:832-851.

20. Sugimoto M, Fujikawa A, Womack JE, Sugimoto Y: Evidence that bovine forebrain embryonic zinc finger-like gene influences immune response associated with mastitis resistance. *P Natl Acad Sci USA* 2006, **103**:6454-6459.

21. Rupp R, Boichard D, Barbat A, Astruc JM, Lagriffoul G, Barillet F: Selection for mastitis resistance in french dairy sheep. *7th world congress on genetics applied to livestock production; Montpellier* 2002, 119-122.

22. Rupp R, Bergonier D, Dion S, Hygonenq MC, Aurel MR, Robert-Granié C, Foucas G: Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment sheep. *J Dairy Sci* 2009, **92**:1203-1219.

23. Sarikaya H, Schlamberger G, Meyer HHD, Bruckmaier RM: Leukocyte populations and mRNA expression of inflammatory factors in quarter milk fractions at different somatic cell score levels in dairy cows. *J Dairy Sci* 2006, **89**:2479-2486.

24. Riollet C, Rainard P, Poutrel B: Cell Subpopulations and Cytokine Expression in Cow Milk in Response to Chronic Staphylococcus aureus Infection. *J Dairy Sci* 2001, **84**:1077-1084.

25. Nilsdotter-Augustinsson A, Wilsson A, Larsson J, Stendahl O, Ohman L, Lundqvist-Gustafsson H: Staphylococcus aureus, but not Staphylococcus epidermidis, modulates the oxidative response and induces apoptosis in human neutrophils. *Apmis* 2004, **112**:109-118.

26. Piepers S, Opsomer G, Meyer E, Demeyere K, Barkema HW, de Kruif A, De Vliegher S: Heifer and quarter characteristics associated with periparturient blood and milk neutrophil apoptosis in healthy heifers and in heifers with subclinical mastitis. *J Dairy Sci* 2009, **92**:4330-4339.

27. Takeuchi F, Watanabe S, Baba T, Yuzawa H, Ito T, Morimoto Y, Kuroda M, Cui L, Takahashi M, Ankai A, et al: Whole-genome sequencing of Staphylococcus haemolyticus uncovers the extreme plasticity of its
genome and the evolution of human-colonizing Staphylococcal species.
*J Bacteriol* 2005, 187:7292-7308.

28. Dufour JH, Dziejman M, Liu MT, Leung JH, Lane TE, Luster AD: IFN-gamma-inducible protein 10 (IP-10; CXCL10)-deficient mice reveal a role for IP-10 in effector T cell generation and trafficking. *Journal of Immunology* 2002, **168**:3195-3204.

29. Holland DB, Bojar RA, Farrar MD, Holland KT: Differential innate immune responses of a living skin equivalent model colonized by Staphylococcus epidermidis or Staphylococcus aureus. *FEMS Microbiology Letters* 2009, **290**:149-155.

30. Foster TJ: Immune evasion by Staphylococci. *Nat Rev Microbiol* 2005, **3**:948-958.

31. Griesbeck-Zilch B, Osman M, Kuhn C, Schwerin M, Bruckmaier RH, Pfaffl MW, Hammerle-Fickinger A, Meyer HHD, Wellnitz O: Analysis of key molecules of the innate immune system in mammary epithelial cells isolated from marker-assisted and conventionally selected cattle. *J Dairy Sci* 2009, **92**:4621-4633.

32. Grönlund U, Hultén C, Eckersall PD, Hogarth C, Persson Waller K: Haptoglobin and serum amyloid A in milk and serum during acute and chronic experimentally induced Staphylococcus aureus mastitis. *J Dairy Res* 2003, **70**:379-386.

33. Uhlar CM, Whitehead AS: Serum amyloid A, the major vertebrate acute-phase reactant. *Eur J Biochem* 1999, **265**:501-523.

34. Lokuta MA, Nuzzi PA, Huttenlocher A: Calpain regulates neutrophil chemotaxis. *Proc Natl Acad Sci USA* 2003, **100**:4006-4011.

35. Ellies LG, Sperandio M, Underhill GH, Yousif J, Smith M, Priatel JJ, Kansas GS, Ley K, Marth JD: Sialyltransferase specificity in selectin ligand formation. *Blood* 2002, **100**:3618-3625.

36. Duperray A, Mantovani A, Introna M, Dejana E: Endothelial-cell regulation of leukocyte infiltration inflammatory tissues. *Mediat Inflamm* 1995, **4**:322-330.

37. Stuart LM, Deng J, Silver JM, Takahashi K, Tseng AA, Hennessy EJ, Ezekowitz RAB, Moore KJ: Response to Staphylococcus aureus requires CD36-mediated phagocytosis triggered by the COOH-terminal cytoplasmic domain. *J Cell Biol* 2005, **170**:477-485.

38. Takeuchi O, Hoshino K, Kawai T, Sanjo H, Takada H, Ogawa T, Takeda K, Akira S: Differential roles of TLR2 and TLR4 in recognition of Gram-Negative and Gram-Positive bacterial cell wall components. *Immunity* 1999, **11**:443-451.

39. Baranova IN, Kurlander R, Bocharov AV, Vishnyakova TG, Chen Z, Remaley AT, Csako G, Patterson AP, Eggerman TL: Role of human CD36 in bacterial recognition, phagocytosis, and pathogen-induced JNK-mediated signaling. *J Immunol* 2008, **181**:7147-7156.

40. Stephensen CB: Vitamin A, infection, and immune function. *Annu Rev Nutr* 2001, **21**:167-192.

41. Duriancik DM, Lackey DE, Hoag KA: Vitamin A as a regulator of antigen presenting cells. *J Nutr* 2010, **140**:1395-1399.

42. Chew BP, Luedecke LO, Holpich DM: Effect of dietary vitamin A on resistance to experimental Staphylococcus mastitis in mice. *J Dairy Sci* 1984, **67**:2566-2570.

43. Murphy KA, Quadro L, White LA: The intersection between the aryl hydrocarbon receptor (AHR)- and retinoic acid-signaling pathways. *Vitamin A* 2007, **75**:33-67.

44. De Souza VR, Cabrera WK, Galvan A, Ribeiro OG, De Franco M, Vorraro F, Starobinas N, Massa S, Dragani TA, Ibañez OM: Aryl hydrocarbon receptor polymorphism modulates DMBA-induced inflammation and carcinogenesis in phenotypically selected mice. *Int J Cancer* 2009, **124**:1478-1482.

45. Schwerin M, Czernek-Schafer D, Goldammer T, Kata S, Womack J, Pareek R, Pareek C, Walawski K, Brunner R: Application of disease-associated differentially expressed genes - Mining for functional candidate genes for mastitis resistance in cattle. *Genet Sel Evol* 2003, **35**:S19-S34.

46. Mueller A, Schafer BW, Ferrari S, Weibel M, Makek M, Höchli M, Heizmann CW: The calcium-binding protein S100A2 interacts with p53 and modulates its transcriptional activity. *J Biol Chem* 2005, **280**:29186-29193.

47. Das M, Jiang F, Sluss HK, Zhang C, Shokat KM, Flavell RA, Davis RJ: Suppression of p53-dependent senescence by the JNK signal transduction pathway. *Proc Natl Acad Sci USA* 2007, **104**:15759-15764.

48. Tang X, Molina M, Amar S: p53 short peptide (p53pep164) regulates lipopolysaccharide-induced tumor necrosis factor-alfa factor/cytokine expression. *Cancer Res* 2007, **67**:1308-1316.

49. Dave B, Eason RR, Geng Y, Su Y, Badger TM, Simmen RCM: Tp53-associated growth arrest and DNA damage repair gene expression is attenuated in mammary epithelial cells of rats fed whey proteins. *J Nutr* 2006, **136**:1156-1160.

50. Huang WC, Ju TK, Hung MC, Chen CC: Phosphorylation of CBP by IKKalpha promotes cell growth by switching the binding preference of CBP from p53 to NFkB. *Mol Cell* 2007, **26**:75-87.

51. Sampaziotis F, Kokotas S, Gorgoulis VG: P53 possibly upregulates the expression of CD58 (LFA-3) and CD59 (MIRL). *Med Hypotheses* 2002, **58**:136-140.

52. Keeshan K, Santilli G, Corradini F, Perrotti D, Calabretta B: Transcription activation function of C/EBP alpha is required for induction of granulocytic differentiation. *Blood* 2003, **102**:1267-1275.

53. Kastner P, Lawrence HJ, Waltzinger C, Ghyselinck NB, Chambon P, Chan S: Positive and negative regulation of granulopoiesis by endogenous RAR alpha. *Blood* 2001, **97**:1314-1320.

54. Labrecque J, Allan D, Chambon P, Iscove NN, Lohnes D, Hoang T: Impaired granulocytic differentiation in vitro in hematopoietic cells lacking retinoic acid receptors alpha 1 and gamma. *Blood* 1998, **92**:607-615.

55. Collins SJ, Ulmer J, Purton LE, Darlington G: Multipotent hematopoietic cell lines derived from C/EBP-alpha/- knockout mice display granulocyte macrophage-colony-stimulating factor, granulocyte-colony-stimulating factor, and retinoic acid-induced granulocytic differentiation. *Blood* 2001, **98**:2382-2388.

56. Cheng IKC, Ching AKK, Chan TC, Chan AWH, Wong CK, Choy KW, Kwan M, Lai PBS, Wong N: Reduced CRYL1 expression in hepatocellular carcinoma confers cell growth advantages and correlates with adverse patient prognosis. *J Pathol* 2010, **220**:348-360.

57. Galcheva-Gargova Z, Gangwani L, Konstantinov KN, Mikrut M, Theroux SJ, Enoch T, Davis RJ: The cytoplasmic zinc finger protein ZPR1 accumulates in the nucleolus of proliferating cells. *Mol Biol Cell* 1998, **9**:2963-2971.

58. Kobayashi SD, Braughton KR, Whitney AR, Voyich JM, Schwan TG, Musser JM, DeLeo FR: Bacterial pathogens modulate an apoptosis differentiation program in human neutrophils. *Proc Natl Acad Sci USA* 2003, **100**:9948-10953.

59. Barba-Barajas M, Hernandez-Flores G, Lerma-Diaz JM, Ortiz-Lazareno PC, Dominguez-Rodriguez JR, Barba-Barajas L, de Celis R, Jave-Suarez LF, Aguilar-Lemarroy AC, Guevara-Barraza MG, Bravo-Cuellar A: **Gossypol** induced apoptosis of polymorphonuclear leukocytes and monocytes: Involvement of mitochondrial pathway and reactive oxygen species. *Immunopharm Immunot* 2009, **31**:320-330.

60. Ali AKA, Shook GE: An optimum transformation for somatic-cell concentration in milk. *Journal of Dairy Science* 1980, **63**:487-490.

61. Casel P, Moreews F, Lagarrigue S, Klepp C: sigReannot: an oligo-set re-annotation pipeline based on similarities with the Ensembl transcripts and Unigene clusters. *BMC Proc* 2009, **3**:S3.

62. Bossers K, Ylstra B, Brakenhoff RH, Smeets SJ, Verhaagen J, van de Wiel MA: Intensity-based analysis of dual-color gene expression data as an alternative to ratio-based analysis to enhance reproducibility. *Bmc Genomics* 2010, **11**.

63. Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A: False discovery rate, sensitivity and sample size for microarray studies. *Bioinformatics* 2005, **21**:3017-3024.

64. Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. *Nature Protocols* 2009, **4**:44-57.

65. Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. *Nucleic Acids Research* 2009, **37**:1-13.

66. Rozen S, Skaletsky H: Primer3 on the www for general users and for biologist programmers. *Method Mol Biol* 2000, **132**:365-386.

67. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paeppe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. *Genome Biol* 2002, **3**:research0034.0031-research0034.0011.