Mastitis is a costly disease which hampers the dairy industry. Inflammation of the mammary gland is commonly caused by bacterial infection, mainly Escherichia coli, Streptococcus uberis and Staphylococcus aureus. As more bacteria become multi-drug resistant, one potential approach to reduce the disease incidence rate is to breed selectively for the most appropriate and potentially protective innate immune response. The genetic contribution to effective disease resistance is, however, difficult to identify due to the complex interactions that occur. In the present study two published datasets were searched for common differentially expressed genes (DEGs) with similar changes in expression in mammary tissue following intra-mammary challenge with either E. coli or S. uberis. Additionally, the results of seven published genome-wide association studies (GWAS) on different dairy cow populations were used to compile a list of SNPs associated with somatic cell count. All genes located within 2 Mbp of significant SNPs were retrieved from the Ensembl database, based on the UMD3.1 assembly. A final list of 48 candidate genes with a role in the innate immune response identified from both the DEG and GWAS studies was further analyzed using Ingenuity Pathway Analysis. The main signalling pathways highlighted in the response of the bovine mammary gland to both bacterial infections were 1) granulocyte adhesion and diapedesis, 2) ephrin receptor signalling, 3) RhoA signalling and 4) LPS/IL1 mediated inhibition of RXR function. These pathways comprised a network regulating the activity of leukocytes, especially neutrophils, during mammary gland inflammation. The timely and properly controlled movement of leukocytes to infection loci seems particularly important in achieving a good balance between pathogen elimination and excessive tissue damage. These results suggest that polymorphisms in key genes in these pathways such as SELP, SELL, BCAR1, ACTR3, CXCL2, CXCL6, CXCL8 and FABP may influence the ability of dairy cows to resist mastitis.
Innate Immunity Disease Resistance E. coli S. Uberis SNP Somatic Cell Count1. Introduction
Mastitis in dairy cows, characterized by a high cell count in the milk, is one of the most economically serious diseases of livestock worldwide. In the UK there are around 40 cases of clinical mastitis per 100 cows. It is estimated that 70% of these are mild (being treated by the farmer), 29% are severe (requiring a visit from the veterinarian) and 1% are fatal [1] . Continuous genetic selection for milk production in modern high yielding dairy cows has been associated with an increased incidence of mastitis [2] [3] . Poor health reduces longevity and causes serious economic losses [4] . The bacteriological aetiology of mastitis has changed over recent years from primarily contagious forms (such as Staphylococcus (St.) aureus) to environmental pathogens (such as Escherichia (E.) coli and Streptococcus (S.) uberis) [5] . Conventional treatment requires antibiotic therapy but traditional mastitis control strategies are not fully efficient against environmental pathogens [6] . This raises major concerns over antibiotic residues in food products as well as an ever increasing rate of antimicrobial resistance [7] . Alternative options are urgently needed for efficiency, health and sustainability of the dairy industry. This need will be even more urgent in future as antibiotic usage in farm livestock becomes increasingly restricted.
Many studies have indicated that the host defence status is a key factor determining the severity of mastitis [8] [9] . The innate response offers the first line of defence in the bovine mammary gland, potentially helping the host to eliminate invading pathogens at an early stage, thus minimizing adverse effects [10] . This rapid early response is of key importance as adaptive immune responses seem to fail to induce long-lasting protection [11] . A more sustainable approach to decrease the incidence of disease and improve health in dairy cattle is thus by genetic selection, utilizing gene polymorphisms for enhanced innate immunity [12] . Such genetic improvement could potentially result in cumulative, permanent and cost-effective change. The candidate genes for genetic selection are, however, difficult to identify due to the complex interactions that must occur for effective disease resistance.
A recent UK survey showed that S. uberis and E. coli were the predominant pathogens isolated from clinical mastitis cases and S. uberis from subclinical cases [13] . Different bacterial species lead to different host responses [14] . Differentially expressed gene (DEGs) profile studies have provided much useful information for understanding dairy cow mastitis. These profiles have been derived from cattle challenged in vivo with St. aureus [15] , E. coli [16] [17] and S. uberis [18] [19] . Additional in vitro studies have investigated the responses of bovine monocyte-derived macrophages to St. aureus [20] and mammary epithelial cells to both St. aureus and E. coli [21] [22] . These studies have revealed some important differences between Gram-negative and Gram-positive bacterial species in terms of both the time course and magnitude of the response as well as which genes are differentially expressed. Gram-negative E. coli infections are generally of short duration with a high bacterial count and induce a rapid and strong rise in the pro-inflammatory cytokines TNFα, IL1β and IL6 in mammary tissue via TLR4-dependent lipopolysaccharide (LPS) induced signalling. This results in a fast influx of neutrophils to inhibit bacterial growth [11] . On the other hand, Gram-positive bacteria such as S. uberis cause a slower and less dramatic response [23] while St. aureus is better able to evade the host immune response leading to a more persistent infection. In these instances, TLR signalling increases IL6 expression but does not up-regulate TNFα and IL1β [11] . Despite these species-specific responses, similarities still exist across bacterial species, such as the up-regulation of genes related to the innate immune response and down-regulation of genes related to fat metabolism [16] [18] . Indeed, several innate immune response pathways show evolutionary conservation independent of the infecting pathogens [24] . This increases the feasibility of finding common defence mechanisms against mastitic infections caused by different bacterial species, which is necessary to develop a more efficient breeding strategy for dairy cows.
Allele-phenotype association studies, especially genome-wide association studies (GWAS), offer another source of valuable information for understanding genetic mechanisms underlying different dairy cow phenotypes. This provides a powerful tool to map QTL of important dairy traits onto the genome. Several GWAS using bovine SNP chips have investigated associations between mastitis incidence and somatic cell count (SCC) in different dairy cow populations [25] -[33] . SCC is generally more informative than clinical case recording for this purpose as it is recorded regularly and consistently from all cows in the herd in a more extensive and reliable manner. In general, mastitis incidence increases with increasing SCC [34] and the genetic correlation between SCC and clinical or subclinical mastitis is positive [35] [36] . There is however a U shaped distribution as cows with a very low SCC may have fewer immune cells present in their udders, making them more susceptible to infection [11] . Results from GWAS studies thus present us with a fragmented but key insight into which areas of the genome may be associated with improved resistance to mastitis.
We hypothesize that important common innate immune defence mechanisms exist in dairy cows to different sources of intra-mammary infection and that variance in the key genes associated with these mechanisms can lead to differences in disease resistance. Identifying these common and bacterial-species independent pathways is an essential step in developing an appropriate future breeding strategy. Supporting that this approach is feasible comes from a comparison of the responses of mammary epithelial cells derived from two groups of German Holstein heifers which are selected for high or low susceptibility to mastitis using marker assisted selection for a haplotype on BTA18 which is associated with SCC [22] [37] . Gene expression profiles found that the more resistant animals showed a quicker and stronger response to both E. coli and St. aureus with greater expression of the cytokines IL1β, IL6, IL8 and TNFα, and the chemokines CXCL2 and CXCL3 and NFKB1A. These studies suggested that RELB, a transcription regulator in the NF-κB family, was the gene most likely to be responsible for the quantitative trait locus (QTL).
In the present paper, we have undertaken a systematic integrated analysis on expression and association profiling to search for such potential common defence mechanisms (innate immunity) and different genetic mechanisms (gene polymorphisms) existing in dairy cows which alter their responses to invading pathogens within the mammary gland.
2. Materials and Methods2.1. Data and Differential Expression Analysis
Two gene expression profiles from the Gene Expression Omnibus (GEO) database (accession numbers GSE15025 and GSE15344) were used to search for common DEGs whose expression in mammary tissue samples was altered following intra-mammary challenge with either E. coli (using Affymetrix Bovine Genome Array, platform GPL2112) [17] or S. uberis (using UIUC Bostaurus 13.2 K 70-mer oligoarray (condensed), made by W.M. Keck Center, University of Illinois Urbana-Champaign, platform GPL8776) [18] . The results of these two studies were chosen for analysis because: 1) E. coli and S. uberis are representative gram negative and gram positive bacteria respectively; 2) they are both common environmental pathogens causing mastitis [5] [13] ; 3) the expression profiles were generated at similar times after the start of infection (20 - 24 h), which is the stage associated with innate immunity. The microarray data were analysed using a moderated student t-test with the Benjamini-Hochberg (BH) adjustment for false discovery rate (FDR) control using GeneSpring GX12.5 software (Agilent Technologies, Santa Clara, CA), according to gene accession number and experimental group as described in the GEO database and related articles [17] [18] . Fold changes were calculated as: Infected/Control where Infected ≥ Control (up-regulation) and -Control/Infected where Infected < Control (down-regulation). P values were calculated based on a moderated t-test (tow sided) built in GeneSpring V12.5. The moderated variance was calculated as following where Sm1-m2 is the variance across the conditions, m1 and m2 are the mean expression values for gene g within groups, df(m1-m2) = n1 + n2 - 2, Sglobal and dglobal are the prior variance and degrees of freedom, respectively:
The moderated t value was calculated as:
The data obtained at 20 h and 24 h respectively following intra-mammary bacterial inoculation were used for the analysis. At this time point it was considered that the treatment would have triggered an innate immune response in defence against the invading pathogens. However, as relatively few neutrophils had invaded the mammary gland by 20 h following S. uberis infection [18] , DEG data may reflect mainly differences in gene expression of mammary epithelial cells. All the DEGs with similar expression patterns in both data sets and fold changes in response to E. coli infection > 1.5 or S. uberis infection > 2 were selected (Table S1). These different cut-off values were chosen due to the different microarray platforms used in the two previous experiments (E. coli: Affymetrix one colour/channel array; S. uberis: two colour/channel customised array) [17] [18] . Based on the nature of the microarray platforms, the data for the Affymetrix array were normalised using the Robust Multi-array Average (RMA) method whereas those for the two colour array were normalised with Lowess regression. Use of the different cut-off values in our study thus provided an appropriate number of genes for further analysis from each of the original data sets.
2.2. Candidate Genes Based on Genome-Wide Association Study (GWAS)
Significant SNPs identified by GWAS in seven different dairy cow populations were used to select candidate genes associated with somatic cell count (SCC) (Table 1). Genes located within 2 Mbp upstream and downstream of significant SNPs were retrieved from the Ensembl database with the position based on the UMD3.1 assembly at http://www.ensembl.org.The SNPs located on the same chromosome within 2 Mbp were considered as a block when looking for candidate genes. All the SNPs and adjacent genes included are listed in Table S2.
2.3. Data Integration of Gene Expression and GWAS
The two datasets from the differential expression and GWAS analyses were integrated. The 48 common genes which appeared on both lists represented genes with a putative role in responding to infection with organisms causing mastitis (Figure 1 and Table 2).
2.4. Selection of Candidate Genes Involved in the Innate Immune Response
Annotation was initially provided by GeneSpring Technology files updated on May 2014 and gene function clustering via Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Redwood City, CA. http://www.ingenuity.com). The 48 shortlisted candidate genes were further annotated using a number of databases available online, including the NetAffx™ analysis center toolbar on the Affymetrix website (http://www.affymetrix.com) and GeneCard (http://www.genecards.org). The genes were also compared with the innate immune genes database InnateDB (http://www.innatedb.com). After screening, only genes contributing to “inflammatory response” or “innate immunity response” were considered for further analysis.
Summary of studies using GWAS to identify SNPs associated with somatic cell count in different dairy cow populations
Population
SNP chip
No. significant SNPs
Reference
Canadian Holsteins
1536 SNP Marker Chip
11
Kolbehdari et al. [26]
U.S. Holsteins
BovineSNP50 Bead Chip
20
Cole et al. [28]
Norwegian Red cattle
Affymetrix 25K MIP Array
29
Sodeland et al. [29]
Netherlands Holsteins
BovineSNP50 Bead Chip
1
Wijga et al. [30]
Irish Holstein-Friesian
BovineSNP50 Bead Chip
5
Meredith et al. [31]
Nordic Holsteins
BovineSNP50 Bead Chip
21
Sahana et al. [32]
German Holstein
Unknown
10
Abdel-Shafy et al. [33]
List of 48 candidate genes with significant changes in expression in mammary tissue in response to both E. coli and S. uberis and which were also located close to significant SNPs in gene association studies with SCC. See Materials and Methods section for details on calculations of significance. <sup>#</sup>Bold denotes the genes located within 1Mbp around significant SNPs associated with SCC. The remainder were within 2 Mbp
Gene symbol#
E. coli
S. uberis
Significant SNPs
P
Fold change
P
Fold change
FASN
2.40E−06
−4.39
8.83E−03
−2.51
rs41257403
FABP4
3.18E−05
−2.89
2.85E−05
−2.93
rs41629827
RHPN2
3.52E−06
−2.71
5.48E−04
−2.73
rs29020544
ACSS2
4.86E−06
−2.48
7.37E−03
−2.27
rs41576572
NOV
8.64E−05
−2.10
4.87E−03
−2.36
rs41629827
ROGDI
4.29E−05
−2.08
6.60E−03
−2.32
Hapmap25382−BTC−000577
CBFA2T3
7.95E−06
−2.06
2.93E−03
−2.23
rs110754697
SORBS1
2.09E−04
−2.03
9.37E−03
−2.46
rs41650611
ALDH18A1
7.01E−07
−1.88
2.36E−03
−2.34
rs41650611
BDH2
1.03E−04
−1.82
5.96E−03
−2.40
rs41664497
FAM110A
9.80E−04
−1.62
1.08E−03
−2.30
rs41601522
HSF1
1.75E−05
−1.61
7.46E−03
−2.15
rs109421300
EMX2
1.63E−03
−1.55
5.62E−03
−2.37
rs41606777
GNAS
2.59E−04
−1.52
9.46E−03
−2.27
rs41694067
CST6
5.04E−04
−1.52
6.63E−03
−2.21
rs29027496
SRL
2.89E−03
1.51
1.05E−03
2.21
Hapmap25382−BTC−000577
SLC25A16
7.48E−03
1.52
1.50E−04
2.31
rs41655339
MRPL12
6.69E−03
1.53
8.58E−03
2.27
rs41636878
EIF4E
2.24E−05
1.54
2.11E−03
2.35
rs110927426
MAPRE1
1.44E−04
1.58
3.23E−04
2.44
rs29022774
GSPT1
7.42E−04
1.59
5.60E−03
2.42
BFGL−NGS−119848
CRNKL1
1.49E−04
1.62
4.51E−03
2.26
rs41628293
DDX27
1.15E−04
1.63
4.46E−04
2.33
rs109934030
BCAR1
2.77E−03
1.66
3.06E−05
2.54
rs29014958
TRIP10
1.78E−04
1.72
1.61E−04
2.71
rs110066189
RBM14
4.16E−05
1.76
6.57E−04
2.35
rs29027496
TARS
3.15E−04
1.78
5.80E−04
2.39
rs41578305
ZNFX1
9.71E−05
1.81
9.50E−04
2.53
rs109934030
SULF2
1.81E−05
1.89
3.73E−03
2.33
rs109934030
B4GALT5
1.21E−03
1.93
7.58E−05
2.53
rs109934030
CDC42SE2
3.07E−07
1.99
6.06E−04
2.37
rs41657989
RSL1D1
1.75E−06
2.00
1.20E−04
2.65
BFGL−NGS−119848
SLC6A9
1.11E−05
2.16
6.49E−03
2.35
rs41628293
TPM4
7.88E−06
2.22
4.11E−03
2.39
rs110213141
SELP
2.57E−03
2.24
1.03E−04
4.69
rs41579632
SPRY1
1.59E−04
2.27
8.90E−04
2.32
rs41616806
ANTXR2
2.80E−06
2.41
3.73E−03
2.35
rs41653149
ACTR3
1.26E−07
2.49
1.89E−05
2.69
BTA−47902
ZFP36L2
2.08E−07
2.75
3.29E−04
2.91
rs43673004
EHBP1L1
2.58E−07
2.76
8.50E−04
2.92
rs29027496
PIK3AP1
3.10E−06
3.03
4.64E−05
2.44
rs41650611
CTSZ
1.31E−07
3.10
1.31E−03
2.25
rs41694067
PGS1
3.22E−08
3.51
5.73E−03
2.42
rs41636878
SELL
3.70E−09
9.41
4.88E−05
2.63
rs41579632
CXCL6
3.54E−10
36.81
6.16E−04
4.01
rs41617692
EMR1
2.94E−11
63.30
8.90E−05
6.02
rs110066189
CXCL8
1.02E−09
82.99
1.33E−04
5.64
rs41617692
CXCL2
2.02E−09
94.97
4.47E−04
3.08
rs41617692
2.5. Gene Function and Pathway Analysis by IPA
The two separate and one combined gene lists were each analysed using IPA to mine the relationships via grouping DEG into known functions, pathways, and networks. Information in IPA is based primarily on human and rodent studies but is still relevant to the cow. The fold change in response to E. coli infection and the associated P-value data were used for the IPA analysis. All the DEGs were included without fold-change cut-off. Most genes were mapped to their corresponding gene object in the IPA Knowledge Base considering both the direct and indirect relationship. Several analyses were run including Functional Analysis, Network Generation, Canonical Pathway Analysis and Upstream Regulators Effects Analysis.Fisher’s exact test with BH-FDR control at P < 0.05 was used to determine the biological functions and canonical pathways significantly altered by the treatment. These analyses integrate data from a variety of experimental platforms and provide insight into the most likely molecular and chemical interactions between the DEG which have been identified.
3. Results3.1. Differential Expression of Genes Following Intra-Mammary Infection with E. coli or S. uberis
The comparison of the list of genes identified by expression microarrays at 20 - 24 h after intra-mammary infection with either E. coli or S. uberis yielded 505 common genes with a significant fold change (>1.5) in a similar direction and p-values < 0.001. Of these, 348 genes were up-regulated and 157 genes were down-regulated (Table S1). When these genes were imported into IPA, 504 genes were mapped based on annotation to human or mouse within the IPA Knowledge. The top ten most significant signalling pathways are listed in Table S2. These included IL-10 and IL-6, glucocorticoid- and ephrin-receptor signalling and activation of LXR/RXR and PPARα/RXRα. The primary function analysis is given in Table S3. As expected, the most significant functions were associated with inflammatory responses, connective tissue disorders and immune cell trafficking and cell death, survival, proliferation and movement.
3.2. Candidate Genes from Mastitis Association Studies on Different Dairy Cow Populations
Based on the seven different GWAS studies listed in Table 1 that looked for associations with SCC, a total of 94 significant SNPs were identified (Table S4). One SNP (BTB-00495251) could not be found in the NCBI SNP database, so 93 were used in this study. These SNPs were distributed on most chromosomes but with higher density attributed to BTA6 (n = 27), BTA13 (n = 11), BTA20 (n = 9), BTA2 (n = 7), BTA14 (n = 7) and BTA7 (n = 5). Four SNPs located in BTA6 were selected in 2 different populations (rs41588957, rs110707460, rs108988814, rs42766480).The nearest genes to these were TMPRSS11F, DCK, GC and NPFFR2 respectively.
A total of 1635 protein-coding genes were located within 2 Mbp of the 93 significant SNPs (Table S4). These were input for IPA analysis and 1568 of them were mapped and used for further analysis. Function analysis indicated that 209 genes were related with infectious disease and 73 were involved in inflammatory responses including the genes NFKB1 and CXCR2. Primary function analysis of Molecular and Cellular Functions showed that 76 genes were involved in Cell-to-Cell Signalling and Interaction and 76 genes were related to Cellular Movement. At the Physiological System Development and Function level, 181 genes were involved in Haematological System Development and Function and 53 in Immune Cell Trafficking (Table S5).The top 10 canonical pathways included both agranulocyte (mononuclear leukocytes) and granulocyte adhesion and diapedesis (Table S6).
3.3. Common Genes from Both the Gene Expression and SCC Association Studies
When the gene lists from the two sources were compared (Figure 1), 48 common genes emerged. Those genes located around the significant SNPs had similar changes in expression in response to intra-mammary infection caused by either E. coli or S. uberis. Of these genes, 30 were located within 1 Mbp around the significant SNPs (Table 2). These included 5 genes of particular interest, FABP4, SELE, SELP, CXCL2 and CXCL6, since some other genes with similar function from the same families are located in close chromosome areas (Table 3).
Seven genes (EIF4E, HSF1, PIK3AP1, PRMT1, BCAR1, CXCL8, CXCL2) were annotated as having a role in the innate immune response by the innateDB database (http://www.innatedb.com). IPA function analysis indi-
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