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.
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 [
Many studies have indicated that the host defence status is a key factor determining the severity of mastitis [
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 [
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 [
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 [
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.
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) [
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 [
Significant SNPs identified by GWAS in seven different dairy cow populations were used to select candidate genes associated with somatic cell count (SCC) (
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 (
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.
Population | SNP chip | No. significant SNPs | Reference |
---|---|---|---|
Canadian Holsteins | 1536 SNP Marker Chip | 11 | Kolbehdari et al. [ |
U.S. Holsteins | BovineSNP50 Bead Chip | 20 | Cole et al. [ |
Norwegian Red cattle | Affymetrix 25K MIP Array | 29 | Sodeland et al. [ |
Netherlands Holsteins | BovineSNP50 Bead Chip | 1 | Wijga et al. [ |
Irish Holstein-Friesian | BovineSNP50 Bead Chip | 5 | Meredith et al. [ |
Nordic Holsteins | BovineSNP50 Bead Chip | 21 | Sahana et al. [ |
German Holstein | Unknown | 10 | Abdel-Shafy et al. [ |
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 |
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.
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 (
Based on the seven different GWAS studies listed in
A total of 1635 protein-coding genes were located within 2 Mbp of the 93 significant SNPs (
When the gene lists from the two sources were compared (
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-