Genetic factors affecting susceptibility to udder pathogens

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Genetic factors affecting susceptibility to udder pathogens

J.C. Detilleux

To cite this version:

J.C. Detilleux. Genetic factors affecting susceptibility to udder pathogens. Veterinary Microbiology,

Elsevier, 2009, 134 (1-2), pp.157. �10.1016/j.vetmic.2008.09.023�. �hal-00532483�

Accepted Manuscript

Title: Genetic factors affecting susceptibility to udder pathogens

Author: J.C. Detilleux

PII: S0378-1135(08)00378-7

DOI: doi:10.1016/j.vetmic.2008.09.023

Reference: VETMIC 4163

To appear in: VETMIC

Please cite this article as: Detilleux, J.C., Genetic factors affecting susceptibility to udder pathogens, Veterinary Microbiology (2008), doi:10.1016/j.vetmic.2008.09.023 This is a PDF file of an unedited manuscript that has been accepted for publication.

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Accepted Manuscript

Genetic factors affecting susceptibility to udder pathogens 1

2

J.C. Detilleux 3

4

Faculté de Médecine Vétérinaire, Université de Liège, Liège, Belgium 5

6

Corresponding author: Detilleux J., Faculty of Veterinary Medicine, University of Liège, 7

Department of Quantitative Genetics, Liege 4000, Belgium, email: jdetilleux@ulg.ac.be, Phone:

8

+ 32 4 366 42 15, Fax: + 32 4 366 4122 9

10

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Accepted Manuscript

Abstract 11

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Many studies have identified genetic factors underlying resistance or susceptibility to 13

mastitis in dairy cows and heifers. Some authors focused on polygenic variation while others 14

searched for genes and/or quantitative trait loci with major effects on mastitis. Classical traits related 15

to mastitis include somatic cell counts, electrical conductivity and clinical cases of the disease. With 16

the development of automatic milking devices and ‘-omics’ technologies, new traits are considered, 17

such as acute phase proteins, immunological assays, and milk flow patterns, and new biological 18

pathways are discovered, for example the role of mammary epithelium and the nervous system. The 19

usefulness of these traits for identification of resistant cows is discussed in relation to the biological 20

mechanisms underlying the development of the disease. In addition, the utility of these traits for 21

genetic improvement is reviewed. Finally, the problem of durability in resistance is addressed, 22

including co-evolution and the cost of resistance.

23 24

Key words: Mastitis; Genetics; Heifer 25

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Accepted Manuscript

1. Introduction 26

27

The use of breeding as a measure to combat mastitis in dairy cattle has been the subject 28

of considerable research effort over the past 20 years or so. Genetic studies may be classified 29

according to two major modes of action for the genes underlying traits associated with mastitis 30

resistance: the polygenic and major gene approaches, respectively. The results from studies on both 31

modes of action will be reviewed, based upon research papers found in PubMed with the key words 32

‘genetic’ and ‘mastitis’, published mainly after 2005 and with particular attention to heifer mastitis.

33

The distinction between modes of action is theoretical as, in reality, few characteristics 34

are purely Mendelian, purely polygenic or purely environmental. Most depend on some mix of major 35

and minor genetic determinants, together with environmental influences. Pure Mendelian and pure 36

polygenic characteristics occupy opposite ends of a spectrum. In between are disease traits governed 37

by one or a few major susceptibility loci, possibly operating against a polygenic background, and 38

possibly subject to major environmental influences.

39 40

2. The polygenic hypothesis 41

42

The inheritance of quantitative traits is typically viewed in terms of what is called 43

polygenic inheritance (Mendelian inheritance at many loci): the traits are the expression of a large 44

number of genes, each with small, additive and relatively equal effects. Under this hypothesis, 45

heritability (h²) estimates have been computed for different traits measuring the susceptibility of 46

cows to mastitis. Genetic selection is more effective in improving traits with a high heritability than 47

in improving traits with a low heritability. By definition, h² measures the degree to which the 48

phenotypic variation is reflected by the genetic variation.

49 50

2.1. Clinical signs and bacteriological analysis 51

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In the context of susceptibility to mastitis, an evident phenotype is the presence or absence of clinical 53

signs of mastitis (CM). This seems straightforward but studies differ greatly in their definition of CM 54

and in the models used to compute h². As pointed out by Gasqui and Barnouin (2003), the type of 55

model used depends on the unit of observation or the study level: herd, lactation, animal, udder and 56

quarter. Clinical signs may be observed at the animal (de Haas et al., 2002) or quarter level (Nash et 57

al., 2000). Heifers are often classified CM positive if they experience at least one episode of CM but 58

some authors have used the number of clinical cases, the number of veterinary treatments or the 59

number of days from calving to CM (Carlen et al., 2006). A veterinarian and/or the producer may 60

diagnose the disease. The period of observation may vary from before calving up to anywhere until 61

the end of lactation. Heritabilities have been computed assuming CM follows a binary (Klungland et 62

al., 2001) or a normal distribution (after log-transformation). Recent developments in statistical 63

analyses have lead to threshold models (Heringstad et al., 2004). Under such models, it is assumed 64

there is an unobserved latent variable, which reflects the liability to CM and a conceptual threshold at 65

or above which clinical disease is observed. Liability is a latent continuous variable that underlies the 66

expression of a disease. It represents the contribution of all genetic and environmental influences.

67

Individuals whose liability exceeds a certain threshold manifest the disorder, with the more severely 68

affected individuals assumed to have a higher liability than the less severely affected individuals 69

Summarizing studies on CM (reviewed by Detilleux, 2002; Rupp and Boichard, 2003), 70

one may consider h² for CM to be around 5%, although it may reach 15% under the liability scale 71

(Zwald et al., 2006). In heifers, h² varied with the species of bacteria infecting the gland (de Haas et 72

al., 2002; Nash et al., 2000) but estimates are not consistent across studies. Values of h

also vary 73

with the period of observation with respect to calving, with h² ranging from 0.24 to 0.73 (Heringstad 74

et al., 2004). Although a few h² estimates were high, it is unlikely that selection based on CM will be 75

highly effective because, on average, estimates for h

are low. Moreover, only Norway, Denmark and 76

Sweden have field recording systems with records of all veterinary treatments (since the early 80s) 77

that allow them to implement national genetic evaluation based on such data. For example, a 78

selection experiment in Norway showed, after 5 cow-generations, a decrease in mastitis frequency

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from 11 to 5% in a cow line selected for little veterinary treatment from 15d before to 120d after 80

calving (Heringstad et al., 2007).

81

Occurrence of mastitis (with or without clinical signs) can also be identified by 82

bacteriological analyses but h² values for this measure are scarce. They vary from 2% (Weller et al., 83

1992) to 10% (Detilleux et al., 1995) and 20% (Wanner et al., 1998). Bacteriological analyses are 84

impractical for selection purposes because they are not available on a large scale and costly to obtain.

85 86

2.2. Somatic cell counts 87

88

To counter the weakness of CM and bacteriological culture as measures of mastitis, the 89

use of indirect biomarkers of intra-mammary infection has been proposed. A popular one is the 90

somatic cell score (SCS), i.e., a logarithmic transformation of somatic cell count (SCC). SCC is a key 91

measure of milk quality. Selection based on SCS rather than CM may be advantageous as SCC is 92

routinely recorded every month in most dairy cattle recording systems and h² for SCS is higher than 93

for CM. In fact, international genetic evaluations for SCS in dairy cattle have been available, through 94

Interbull since May 2001. Heritabilities range from 10 to 15% for lactation average SCS (average 95

individual test-day records), and from 5 to 14% for single monthly test-day SCS (Detilleux et al., 96

2002; Rupp and Boichard, 2003). For first-parity cows, some authors obtained low h

(4 to 5%) at the 97

beginning of the lactation and higher (11 to 13%) at the end (Haile-Mariam et al., 2001; Mrode and 98

Swanson, 2001; Odegard et al., 2003) while others observed a U-shaped trajectory with a minimum 99

around the 50

or 100

day in milk (Negussie et al., 2006; de Roos et al., 2003).

100

There is no consensus on the use of SCC as an indirect means for reducing incidence of 101

mastitis (Mrode and Swanson, 1996). Indeed, SCC data are usually collected on a monthly basis 102

while some infections (especially by coliforms) can occur and completely disappear between 2 103

adjacent samplings. In some studies, milk SCC prior to experimental challenge with Staphylococcus 104

aureus was higher in cows that resisted infection than in cows that eventually became infected 105

(Beaudeau et al., 2002; Piccinini et al., 1999; Schukken et al., 1999; Shuster et al., 1996;

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Suriyasathaporn et al., 2000). This may not reflect the natural situation because the teat end, which is 107

the first line of defense, has been bypassed in many infection experiments. Using mathematical 108

models, we showed SCC should be in a limited range, not too low and not too high, to clear infection 109

by S. aureus (Detilleux et al., 2004) in experimental studies. On the other hand, Rupp and Boichard 110

(2000) found, in field studies, heifers with low SCC at the first test day had the lowest probability of 111

CM later in the lactation. Similarly, De Vliegher et al. (2004) showed, in heifers, high SCC measured 112

in the first 2 wk after calving was associated with elevated test-day SCC in the subsequent months.

113

An argument against the use of SCC in selection against CM is the fact that genetic correlation 114

between lactation SCS and CM is less than 100%, which suggests SCS and CM, are different traits.

115

In first parity cows, genetic correlation varied from 30% (Poso et al., 1996) to 70% (Koivula et al., 116

2005; Rupp and Boichard, 2000).

117

Some argue SCS is not the same trait in cows with or without CM. In the healthy gland, 118

the predominant cell type is macrophages (34-79%) followed by lymphocytes (16-28%), PMN (3- 119

26%) and epithelial cells (2-15%) but these percentages change dramatically after infection as the 120

permeability of the milk barrier increases (Lindmark-Mansson et al., 2006). Based on a data set of 121

499,878 heifers, Heringstad et al. (2006) calculated that h² for SCS in cows with CM (h² = 0.02) was 122

significantly lower than h² for healthy cows (h² = 0.08). The genetic correlation between SCS of 123

cows with and without CM was 78%, significantly lower than the 100% expected if SCS were the 124

same trait in cows with or without mastitis. The polygenic nature of both CM and SCS, with some 125

genes in common and some genes linked to one of the traits only, may explain this imperfect 126

correlation. In mixture models, cows are assigned to different subpopulations (e.g., healthy or 127

diseased) via posterior probabilities estimated from the SCS data, rather than on crude SCS. Since the 128

publication of the first model (Detilleux and Leroy, 2000), mixture models have been improved 129

(Odegard et al., 2003, 2005) and use to analyze real data (Boettcher et al., 2007). Recently, we 130

proposed the hidden Markov model which is an expansion of the mixture models and allows the 131

estimation, for each cow, of the individual future probability of getting infected or remaining or 132

becoming healthy given the current SCS (Detilleux, 2007).

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134

2.3. Automatic milking records 135

136

The development in recent years of techniques for automated sampling has lead to the 137

in-line measurement of biomarkers for CM, including milk electrical conductivity (EC), milk flow 138

patterns (Tančin et al., 2007) and enzymes such as N-acetyl-beta-D-glucosaminidase (NAGase) and 139

L-lactate dehydrogenase (LDH). NAGase is an intracellular, lysosomal enzyme that is released into 140

milk from neutrophils during phagocytosis and cell lysis, and to some degree, from damaged 141

epithelial cells (Pyörälä, 2003). LDH, an enzyme involved in energy production in cells, originates 142

mainly from somatic cells, and from damaged mammary epithelial and interstitial cells (Babaei et al., 143

2007). Chagunda et al. (2006a, 2006b) showed that LDH activity was better than NAGase activity at 144

classifying CM and combined LDH activity with mastitis risk factors such as days from calving, 145

breed, parity, milk yield, udder characteristics, other disease records, electrical conductivity, and herd 146

characteristics to obtain a daily estimate for each cow of the risk of having mastitis. Such an index 147

may be valuable for selection as h² for serum LDH activity in replacement heifers at weaning, 148

yearling, and prebreeding stages were 0.22, 0.32, and 0.13, respectively (Brown et al., 2000) 149

Electrical conductivity (EC) is also an indicator of mastitis because the concentration of 150

Na

and Cl

increases in the milk from infected quarters, leading to increased EC. The h² estimates 151

(0.22 to 0.39) for daily measures from Holstein heifers suggest this indicator is a potential trait for 152

breeding programs, especially because its genetic correlation with CM is 75%.

153 154

2.4. Cell-mediated and antibody-mediated immunity 155

156

Heritability estimates for selected immune mechanisms underlying mastitis resistance 157

have also been computed. Kelm et al. (1997) showed cows with low expected breeding values for 158

SCS tended to have neutrophils that had greater functional ability around calving, low serum IgG

, 159

and high numbers of circulating mononuclear cells. Unfortunately, results are variable depending

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upon the mechanisms studied and the time of sampling. For example, the concentration of blood 161

polymorphonuclear neutrophils (PMN) after parturition is highly heritable (h

= 0.87) but h

for 162

blood PMN ability to migrate in vitro either randomly (area in square millimeters) or directly 163

(directed millimeters/random millimeters) in response to chemotactic factors is almost zero 164

(Detilleux et al., 1994). The h² for the PMN cytochrome C reduction assay, a measure of PMN 165

production of superoxide anion, was estimated at 0.22, 0.88 and 0.99 before, around, and after 166

calving (Detilleux et al., 1994). More recently, Young et al. (2005) reported moderate heritabilities 167

for S. aureus-induced, PHA-induced, and control proliferation of peripheral blood mononuclear cells 168

(h

= 0.20, 0.20 and 0.30, respectively). With regards to antibody-mediated immunity, it was found 169

that h² is high for IgG

concentration in colostrum (Gilbert et al., 1988), for serum antibody response 170

to injection of ovalbumin and to E. coli J5 antigen, and for hemolytic complement activity (Wagter et 171

al., 2000). Heritability estimates for antibody to ovalbumin were 0.62, 0.32, 0.50, and 0.58 at wk -3, 172

0, 3, and 6 relative to parturition, respectively. Heritability estimates for antibody to E. coli J5 173

antigen at wk -3, 0, 3, and 6 relative to parturition were 0.13, 0.88, 0.32, 0.5, and 0.88, respectively 174

(Wagter et al., 2000). Heritability of 10.2% was reported for the ability to produce antibodies to 175

Mycobacterium avium subsp. paratuberculosis (Mortensen et al., 2004).

176

Practically, the ability of cows to mount an efficient immune response cannot be 177

measured on the millions of cows registered in national breeding programs, even if tests measuring 178

mechanisms such as the respiratory burst activity may be performed on milk rather than blood PMN 179

(Mehrzad et al., 2002). To solve this issue, some authors have proposed to restrict the collection of 180

immunological data to AI bulls. Indeed, if a strong correlation exists between immunocompetence, 181

i.e. the ability to mount an efficient immune response in sires and resistance to mastitis in their 182

daughters, then only samples from bulls would be necessary for selecting resistant cows (Kelm et al., 183

1997). Others have proposed to restrict the samplings to a small number of assays considered as 184

important in the development of the response to udder pathogens. To do so, the fate of leukocytes 185

and bacteria during an inflammatory reaction was quantified mathematically and it was found that

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milk SCC was mostly sensitive to variations in the rate of bacterial killing and the rate of production 187

of inflammatory cells (Detilleux et al., 2006) 188

Serum concentrations of various bovine acute phase proteins (APP) were correlated 189

with the severity of clinical signs of experimentally induced E. coli mastitis (Hirvonen et al., 1999) 190

and could serve as potential biomarkers. Eckersall et al. (2006) showed that haptoglobin (Hp) and 191

mammary-associated serum amyloid A (SAA) appear in milk during mastitis, and that Hp and SAA 192

increase in the serum during mastitis. In humans, h² for basal serum levels of APP ranges from 24 to 193

26% (Best et al., 2004). Specific values for dairy heifers are not available. In beef cattle, Qiu et al.

194

(2007) found breed differences in APP concentrations in response to weaning and transportation.

195 196 197

3. The oligenic hypothesis 198

199

Under this hypothesis, a few genes with major effects will confer resistance and/or 200

susceptibility. The search for such genes centers on two major techniques, linkage mapping and the 201

candidate gene approach. Linkage mapping is the process of systematically scanning the genome to 202

identify a quantitative trait locus (QTL), i.e., a region of DNA closely linked to the genes that 203

underlie the disease phenotype. A QTL is often not the actual gene underlying the disease trait, but 204

rather a region of DNA that is closely linked with the gene(s). The candidate gene approach involves 205

assessing the association between a particular allele (or set of alleles) of a gene and the disease 206

process. The choice of the candidate gene is based on an understanding of the mechanisms 207

underlying the disease.

208 209

3.1. Quantitative trait loci 210

211

In cattle, many QTL for CM and SCC were found (reviewed by Khatkar et al., 2004;

212

Klungland et al., 2001; Rupp and Boichard, 2003). They are localized on almost all 30 bovine

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chromosomes. Some of these associations were only found in single cattle populations, and some 214

associations were found only for SCC and not CM or vice versa. This diversity of results is 215

confusing and may be due to the different environmental conditions (exposure to pathogens) and 216

genetic backgrounds of studied populations (host and pathogen).

217

In contrast to QTL for SCC, no QTL for CM were localized on Chr. 23 (Boichard et al., 218

2003; Holmberg and Andersson-Eklund, 2004), although Chr. 23 harbors genes of the major 219

histocompatibility complex (MHC). This supports the hypothesis that SCC and CM are not the same 220

genetic trait. QTL for both CM and SCC were localized on Chr. 3, 4, 9, 11, 18 and 27, which 221

recommends them as candidate chromosomes for fine mapping. This was done by Brunner et al.

222

(2003) and Mömke et al. (2005) for Chr. 18. They observed synteny between the telomeric region of 223

Chr. 18 (containing QTL for mastitis) and the HSA19q region in humans. Such discoveries should 224

improve selection of candidate genes for mastitis susceptibility in dairy cattle.

225 226

3.2. MHC candidate genes 227

228

MHC genes are plausible candidates because of the important role played by MHC 229

molecules in the regulation of both antibody- and cell-mediated immune responses to infection. The 230

results of the class I association studies are inconsistent with many different class I alleles 231

(haplotypes) appearing to confer susceptibility or resistance. A likely explanation for this is that 232

resistance is controlled by a linked class II gene rather than by a class I gene. Among class II genes, 233

the gene DRB3 has been extensively evaluated because it is the most polymorphic. In a review by 234

Rupp and Boichard (2003), three studies showed significant association of allele DRB3.2*24 with 235

susceptibility to mastitis, intramammary infections with major pathogens, clinical mastitis and high 236

SCC.

237

The situation is confusing as alleles DRB3.2*23, DRB3.2*8 and DRB3.2*16 have been 238

associated with either higher or lower SCC and/or CM. For example, Dietz et al. (1997) and Kelm et

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al. (1997) found that DRB3.2*16 was significantly associated with higher SCS while Starkenburg et 240

al. (1997) and Sharif et al. (1998) demonstrated an association with lower SCS. In heifers, 241

Starkenburg et al. (1997) observed associations between DRB3.2*23 and SCS (positive) and CM 242

(negative), but these were non-significant.

243

Several explanations could be given for the divergence between studies on association 244

between MHC alleles and indicators of mastitis. First, alleles may be related to resistance or 245

susceptibility according to environmental conditions. Some alleles could confer resistance to a 246

pathogen present in one study and susceptibility to another pathogen present in another study.

247

Conversely, different alleles could confer resistance or susceptibility to the same pathogen. In 248

support of this, Ledwidge (2003) found, in one specific population, that DRB3.2*23 was associated 249

with increased number of cultures positive for E. coli and Strep. spp. and DRB3.2*16 was associated 250

with a decreased number of cultures positive for Strep. spp. Second, the association between MHC 251

polymorphisms and mastitis traits may not be causal but linked to other MHC loci involved in 252

mastitis resistance, which would lead to different associations according to families. Therefore, 253

analysis of effect of MHC haplotypes rather than single locus should be preferred to get a better 254

handle on the links between genotype and resistance to mastitis. For example, Park et al. (2004) 255

found haplotype DH24A was more frequent in a group of 15 cows that received at least 2 treatments 256

for CM over a period of 4 years than in a resistant group with no history of medical treatment of 257

mastitis. The haplotype included DRB3.2*24, DQA*1A, and DQB*1 alleles. Genes of the MHC 258

complex could also be linked to non-MHC genes, as genes for heat-shock protein HSP70, for 259

complement C4, and for TNF have all been mapped on Chr. 23 (Bovmap) 260

The underlying biological mechanisms that lead to MHC-disease associations are not 261

clear. For example, Rupp et al. (2007) showed that associations between BoLA DRB3.2 alleles and 262

immune responses tended to be in the opposite sign for traits measuring type 1 (cellular) and type 2 263

(humoral) immune response: alleles DRB3.2*3 and *24 were associated with higher antibody- 264

mediated but lower cell-mediated responses, whereas the reverse was observed for allele *22. This

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may be related to the bacterial species infecting the gland. Neutrophils that enter in contact with 266

bacteria may communicate with the dendritic cells through cell-surface receptors (Ludwig et al., 267

2006). The latter, being highly potent antigen presenting cells, will drive T helper generation. If, as 268

suggested for BLV, resistant and susceptible alleles at the DRB3.2 locus differ in their amino acids, 269

the MHC molecules may bind differently to helper T cells and trigger either Th

or Th

response.

270 271

3.3. Non-MHC candidate genes 272

273

Genes associated with neutrophil migration are also potential genetic targets for 274

selection. A popular example is the point mutation in the gene encoding the CD18 subunit of the 275

Mac-1 glycoprotein leading to the BLAD syndrome (Shuster et al., 1992). Another example is the 276

association of polymorphism at the chemokine CXCR2 gene with impaired PMN migration and 277

increased frequency of subclinical mastitis (Rambeaud and Pighetti, 2005; Youngerman et al., 2004) 278

although little genetic association was found between breeding values for SCS and genes for 279

chemokine (CCL2 and IL8) and chemokine receptor (IL8RA and CCR2) (Leyva-Baca et al., 2007).

280

Polymorphisms in the gene sequence of IL-8R of Jersey cows (Youngerman et al., 2003) were also 281

associated with CM. Polymorphisms in the gene coding for the Toll-like receptor 4 (TLR4), an 282

important pattern recognition receptor of bacterial endotoxins, were associated with estimated 283

breeding values for SCS in the Canadian Holstein bull population (Sharma et al., 2006). Finally, 284

polymorphisms in the gene sequence of the beta 4-defensin in Holsteins (Bagnicka et al., 2007) were 285

associated with SCC.

286 287

3.4. Potential candidate genes 288

289

A popular way to identify candidate genes is by microarray. The major advantage of 290

this technology is the ability to monitor differential gene expression for thousands of genes 291

underlying complex disease simultaneously, the idea being that the expression of candidate genes

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following disease challenge is different between resistant and susceptible animals. This approach is 293

also useful for elucidating the pathogenesis of the disease by unravelling the regulatory relationship 294

between genes. Zheng et al. (2006) analyzed gene expression patterns of approximately 23,000 295

transcripts 4 h after an intramammary infusion of lipopolysaccharide (LPS) in a mouse model. A 296

total of 489 genes were significantly affected, of which 391 were induced, mostly genes associated 297

with the innate immune response, apoptosis, and cell proliferation (chemokines CXCL1, CXCL2, 298

and S100A8; the acute-phase protein SAA3; and the LPS binding protein CD14). Schwerin et al.

299

(2003) compared mammary gland mRNA patterns in samples from a non-infected and an infected 300

udder quarter of a cow. Among the 532 (out of a total of 704) differentially displayed cDNA bands 301

that show a similarity with previously described genes, the majority exhibited homology to protein 302

kinase encoding genes (STK9, JIK, CDK8, PRKDC and VRK2), to genes involved in the regulation 303

of gene expression (AHCY, HNRPU, SSR1, NRD1, RORA), to growth and differentiation factor 304

encoding genes (OSTF1, RSC1A1, AHNAK, and TP53) and to immune response or inflammation 305

marker encoding genes (COVA1, LY75, PLCE and SAA3).

306

Analyses of the expression of genes in mammary epithelial tissue lead to the discovery 307

of its importance in the early stages of the interaction between pathogens and the host. Larson et al.

308

(2006) observed Gram-negative bacterial lipopolysaccharide (LPS) and Gram-positive bacterial 309

lipoteichoic acid (LTA) substantially up-regulated expression and secretion of serum amyloid A3 310

(SAA3) promoter in bovine mammary epithelial cells. Swanson et al. (2004) observed expression of 311

the beta-defensin (lingual antimicrobial peptide) in bovine mammary epithelial tissue is induced by 312

naturally occurring mastitis, and Pareek et al. (2005) observed the expression of genes 313

RANTES/CCL5, IL-6 and T-PA, 6 h after LPS challenge.

314

Microarray analyses also lead to the discovery of a gene at the crossroads of neuronal 315

development and innate immunity. Sugimoto et al. (2006) found heifers with high SCC have a three- 316

base insertion in a glycine-coding stretch of the forebrain embryonic zinc finger-like gene (FEZL, 317

mapped on BTA22), a transcription factor with a role in neuronal development. They suggested

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mastitis enhances FEZL, FEZL promotes the axon-attracting molecule semaphorin 5A (SEMA5A), 319

and SEMA5A induces TNF- and IL-8 expression.

320

However, microarray identifies all expression in the cell and could be reporting the 321

expression of a gene that is affected by the disease rather than the cause. To disentangle the complex 322

between candidate genes, we are currently studying the feasibility of surrogate end-point analyses 323

and structural equations as tools for identifying the most likely genetic networks that best fit the data.

324

With such models, one may determine whether a biomarker belongs to the causal pathway between 325

gene and disease or whether it lies on a separate pathway.

326 327

4. Durability of Resistance 328

329

Evidence from immunology and genetics demonstrate selective breeding of genetically 330

based resistance is likely to modify the relationships between cows and pathogens, as reviewed 331

above. The challenge is to integrate effectively the information from both fields into existing 332

breeding programs.

333

Genetic engineering is one potential way to increase the host defense against mastitis.

334

In the first published bovine model, cows carrying a gene coding for an antistaphylococcal peptide, 335

lysostaphin, were shown to be resistant to S. aureus (Wall et al., 2005). Cows expressing 336

recombinant human lactoferrin, an iron sequestering protein, in their milk were not protected from 337

experimental infection with E. coli, the bacteria species most susceptible to lactoferrin (Hyvönen et 338

al., 2006). There are public concerns regarding the use of transgenic animals for food production 339

although the approach had little effect on milk composition.

340

It is also imperative to incorporate evolutionary considerations into longer-term 341

breeding plans. Host genetics can determine the incidence and severity of disease, and pathogen 342

genetics may influence the host genetic structure. As an example, the so-called ‘conventional 343

wisdom’ evolutionary model predicts that interaction between parasite and host is in the direction of 344

mutualism (reviewed in Levin, 1994). Such evolutionary models can be used to suggest measures to

345

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control mastitis (Dieckmann et al., 2002). Assuming that genetically related cows share genes 346

conferring resistance to mastitis, a genetic-epidemiologic model was proposed to determine the 347

genetic contribution to the spread of S. aureus mastitis among heifers (Detilleux et al., 2005). Using 348

data from Lam et al. (1996) and Zadoks and al. (2001) on S. aureus infected heifers, it was shown 349

that the reproductive number (R

), i.e., the average number of new infective quarters produced by 350

one infective quarter during the mean infective period, increased from 1.7 to 3.6 in herds with a low 351

and high proportion of related individuals, respectively (herds otherwise identical).

352

In the long term, selection for higher MHC polymorphism may be advantageous to 353

protect cows given the genetic heterogeneity among mastitis pathogens. In support of this, Park et al.

354

(2004) have observed higher frequency of haplotypes with a single set of DQ genes than haplotypes 355

with two sets of DQ genes among cows with more frequent treatment against S. aureus mastitis. This 356

lead them to hypothesize that cows expressing a wider range of distinct DQ alleles are more resistant 357

to mastitis.

358

Selection for improved resistance to mastitis may be costly. Furthermore, selection may 359

lead to diminishing productivity. Many studies have shown that unfavorable genetic correlations 360

exist between resistance to mastitis and milk production traits in cattle. More specifically, Banos et 361

al. (2006) observed negative effects of body energy traits on subsequent SCC in heifers, but no effect 362

on subsequent occurrence of CM. Ouweltjes et al. (2007) did not observe any interaction between 363

ration (energy level) and genetic merit effect on the presence of pathogens or SCC in heifers, with 364

the exception of bacteriological cultures showing Staphylococcus or Streptococcus species. The 365

argument has been made that animals may have a limited amount of resources to allocate to disease 366

resistance or productivity. According to this argument, if we increase an animal's ability to resist 367

disease, its resources for productivity or performance may decrease. In addition, it has been 368

suggested that genes for increased resistance may be pleiotropic, i.e. they made code for more than 369

one trait. For example, a pleiotropic gene could code for increased resistance and decreased 370

productivity, or vice versa. Others oppose the idea of competing resources, and the idea that 371

resistance genes could influence both productivity and resistance. In contrast, they propose that once

372

Accepted Manuscript

specific genes for resistance are defined, targeted selection to increase their frequency, e.g. by 373

identifying heifers carrying the gene of interest, will not impinge on productivity traits.

374 375

5. Conclusions 376

377

Sufficient evidence exists that genetic factors underlie resistance or susceptibility to 378

mastitis in dairy heifers. Therefore, selection based on breeding values for quantitative traits, e.g.

379

SCS or EC, or selection based on candidate genes, e.g. QTL or MHC, in conjunction with non- 380

genetic methods of herd health management, e.g. vaccination, will reduce the frequency of mastitis.

381

To evaluate the implications of a breeding strategy comprehensively and to ensure durable results, 382

the evolutionary impact of the interaction between the host and its pathogens need to be investigated.

383

Another key issue for the future will be to integrate ‘-omic’ resources and technologies to extract the 384

maximum of disease-relevant information. The genetic basis for resistance and susceptibility should 385

not only be dissected by studying components of the genetic network individually but by studying 386

the network as a whole.

387 388

Conflict of Interest 389

390

The author (J. Detilleux) has no financial or personal relationship with other people or 391

organizations that could inappropriately influence or bias the paper entitled “Genetic factors 392

affecting susceptibility to udder pathogens”.

393

394

Accepted Manuscript

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