intramammary P recise Precise quarter-level estimation of the impact of quarter non-aureus staphylococcal intramammary infection on udder health and milk yield in dairy heifers infection - level Dimitri Valckenier estimation on udder of health the im p act and

milk of non yield - aureus in dairy sta p hylococcal heifers

D imitri V alckenier

2021

Precise Quarter-Level Estimation of the Impact of Non-Aureus Staphylococcal Intramammary

Infection on Udder Health and Milk Yield in

Dairy Heifers

Dimitri Valckenier

Merelbeke, 2021

“A pessimist sees difficulty in every opportunity;

an optimist sees opportunity in every difficulty.”

Winston Churchill

“The pessimist complains about the wind; the optimist expects it to change;

the realist adjusts the sails.”

William Arthur Ward

Precise Quarter-Level Estimation of the Impact of Non-Aureus Staphylococcal

Intramammary Infection on Udder Health and Milk Yield in Dairy Heifers

Dimitri Valckenier

Cover design: Creativision, Erpe-Mere

Printing: University Press, Wachtebeke

ISBN number: 9789464202434

Department of Reproduction, Obstetrics, and Herd Health

Faculty of Veterinary Medicine

Ghent University

Precise Quarter-Level Estimation of the Impact

of Non-Aureus Staphylococcal Intramammary

Infection on Udder Health and Milk Yield in

Dairy Heifers

Dimitri Valckenier

Dissertation submitted to Ghent University in the fulfillment of the requirements for the degree of Doctor in Veterinary Sciences (PhD)

June 18, 2021

Promotors Prof. dr. Sarne De Vliegher Faculty of Veterinary Medicine, Ghent University, Belgium Prof. dr. Sofie Piepers Faculty of Veterinary Medicine, Ghent University, Belgium

Members of the Examination Committee Prof. dr. Edwin Claerebout, chairman Faculty of Veterinary Medicine, Ghent University, Belgium Dr. Anneleen De Visscher Flanders Research Institute for Agriculture, Fisheries, and Food (ILVO), Belgium Prof. dr. Marie Joossens Faculty of Sciences, Ghent University, Belgium Prof. dr. Gerrit Koop Department Population Health Sciences, Utrecht University, the Netherlands Prof. dr. Bart Pardon Faculty of Veterinary Medicine, Ghent University, Belgium Prof. dr. Ynte H. Schukken GD Animal Health, the Netherlands – Department of Animal Sciences, Wageningen University, the Netherlands – Department of Population Health Sciences, Utrecht University, the Netherlands

Table of Contents

Chapter 1 General introduction 1

Chapter 2 Scope and aims of the thesis 29

Chapter 3 Effect of intramammary infection with non-aureus staphylococci 33 in early lactation in dairy heifers on quarter somatic cell count and quarter milk yield during the first 4 months of lactation

Chapter 4 The effect of intramammary infection in early lactation with non- 65 aureus staphylococci in general and Staphylococcus chromogenes specifically on quarter milk somatic cell count and quarter milk yield

Chapter 5 Longitudinal study on the effects of intramammary infection with 103 non-aureus staphylococci on udder health and milk production in dairy heifers

Chapter 6 General discussion 143

Summary 191

Samenvatting 197

Curriculum vitae and publications 205

Dankwoord 213

List of Abbreviations

AMS Automatic milking system AS Ali and Schaeffer BMSCC Bulk milk somatic cell count CFU Colony forming units cIMI Cured intramammary infection CM Clinical mastitis d Days DCC Direct cell counter DHI Dairy herd improvement DHIA Dairy Herd Improvement Association DIM Days in milk IMI Intramammary infection LnSCC Natural log-transformed somatic cell count LSM Least squared means MALDI-TOF Matrix-assisted laser desorption/ionization time of flight MLST Multilocus sequence typing Mo Months MS Mass spectrometry MY Milk yield NAGase N-acetyl-β-D-glucosaminidase NAS Non-aureus staphylococci nIMI New intramammary infection PCR Polymerase chain reaction PFGE Pulsed-field gel electrophoresis pIMI Persistent intramammary infection PMNL Polymorphonuclear leukocytes PRL Prolactin q Quarter qMY Quarter milk yield qPRL Quarter milk prolactin qSCC Quarter milk somatic cell count RAPD-PCR Random amplification of polymorphic DNA - polymerase chain reaction RIA Radioimmunoassay S. Staphylococcus SCC Somatic cell count SCM Subclinical mastitis SD Sampling day SE Standard error tDNA-PCR Transfer RNA intergenic spacer - polymerase chain reaction tIMI Transient intramammary infection

Chapter 1

General introduction

D. Valckenier

Department of Reproduction, Obstetrics, and Herd Health

Faculty of Veterinary Medicine,

Ghent University, Merelbeke, Belgium

Chapter 1 General introduction

1. Bovine mastitis

1.1. What is bovine mastitis? Bovine mastitis, defined as an inflammation of the mammary gland parenchyma (Fig. 1), is an immunological reaction to infectious or noninfectious injury (e.g., physical trauma or toxic agents). The majority of mastitis cases are associated with bacteria, with over 137 species, subspecies and serovars that have been isolated from the mammary gland (Watts, 1988). A minority of mastitis cases can be attributed to other organisms such as yeasts, fungi, algae (Watts, 1988) and viruses (Gourlay et al., 1974; Wellenberg et al., 2002; Çomakli and Özdemir, 2019). Intramammary infections (IMI) are usually caused by bacteria invading the bovine mammary gland through the teat canal by growth or propulsion (Blowey and Edmondson, 2010) and subsequently adhering to the mammary tissue. Based on the clinical presentation, mastitis can be categorized as subclinical mastitis (SCM) or clinical mastitis (CM). In cases of SCM there are no visible changes to the milk, udder, and cow (Ruegg, 2011); however, there is an inflammatory reaction resulting in an influx of white blood cells, primarily polymorphonuclear neutrophil leukocytes. The most common method to detect this type of mastitis is by measuring the somatic cell count (SCC) in the milk. Inflammation in the mammary gland combined with the potential adverse effects of invading bacterial pathogens in the udder parenchyma can result in suboptimal milk production in the affected quarter (Koldeweij et al., 1999; Seegers et al., 2003; Halasa et al., 2007). Conversely, in cases of CM, a variety of symptoms can be observed such as altered milk composition (e.g., flakes, watery milk, …), local symptoms in the udder (e.g., swelling, loss of the function of a quarter, …), systemic symptoms, and even death (Ruegg, 2011).

3 Chapter 1 General introduction

Figure 1: Schematic illustration of the anatomy of one udder quarter from the bovine mammary gland. Adapted from http://www.ubrocare.com/content/udder-anatomy

1.2. Importance of bovine mastitis in general and heifer mastitis in specific After more than 30 years, the dairy sector in the European Union entered a new economic context in 2015 with the termination of the milk quota system. As milk prices become more volatile and resources for milk production (e.g., nutrition, ground, labor, ...) more expensive, optimizing the milk production and management on dairy farms is more crucial than ever. Additionally, an increasing trend in herd size and specialization in the dairy industry requires ever-increasing investments of the herd owner. The income of most dairy farms relies almost entirely on the selling of raw milk; therefore, prevention of diseases that affect dairy cow milk

4 Chapter 1 General introduction production is of the utmost importance. Producing large quantities of milk requires a well- developed and healthy udder to meet the quality standards. For example, infection of the udder tissue in the developing udder (Trinidad et al., 1990a) or during the first lactation (De Vliegher et al., 2005b) could potentially threaten the milk production capacity of the developing udder and might impair the udder health for the duration of the productive life of the dairy cow (Rupp et al., 2000). Heifers, i.e., cows in their first lactation, form the future of each dairy herd. They replace older cows at the end of their productive life, both for milking and breeding. On well-managed dairy farms, implementation of continuous genetic selection and well-considered breeding programs results in new generations of animals with higher genetic merit for milk production than previous generations. Therefore, the goal of each dairy herd should be to raise bred heifers in optimal conditions to maximize the expression of their genetic potential. The IMI status of pre-calving heifers is seldomly checked because pre-existing IMI before calving is unexpected (Nickerson, 2009) and it is less feasible to collect mammary secretion samples. Unfortunately, heifers often have IMI before their first calving. Depending on the herd and region, estimated IMI prevalences range from 12% to 75% of the quarters of heifers before and around calving (Oliver and Mitchell, 1983; Trinidad et al., 1990b; Roberson et al., 1994; Parker et al., 2007; Fox, 2009). Infections of the udder in late gestation or in lactation form a threat to the genetic potential of heifers (De Vliegher et al., 2012). The impact of IMI depends on, amongst others, the form of mastitis (subclinical versus clinical), the pathogenicity and virulence of the invading bacteria (major versus minor pathogens), the time and duration of the infection relative to calving, and the host’s immunity (De Vliegher et al., 2012). Heifer mastitis results in extra costs and/or financial losses that can be ascribed to milk production losses, additional labor, use of drugs, veterinary needs, premature culling, production of nonsaleable milk and risk of residues. The combined costs of an elevated SCC in early lactation that decreased again, an elevated SCC at calving that evolved in SCM, and a clinical heifer mastitis case associated with an elevated SCC at calving, resulted in an average total cost of €31 per heifer present on a farm (range: €0 - €220) (Huijps et al., 2009). However, not all reports on IMI in early lactating heifers show a negative effect. When compared to noninfected heifers, IMI caused by non-aureus staphylococci (NAS) in early

5 Chapter 1 General introduction lactation results in a higher milk yield (MY), a lower risk of culling, and fewer cases of clinical mastitis (Piepers et al., 2009, 2010, 2013). In many countries and regions, raw bovine milk must meet the high quality standards described by legislation to be fit for human consumption. Regarding these legal requirements in the European Union, Council Directive 92/46/EEC states that raw milk may not originate from cows whose general state of health is impaired or have a recognizable inflammation of the udder, and that the bulk tank milk’s geometric average SCC of 4 measurements per month over a period of 3 months must be lower than 400,000 cells/mL milk. In the United States of America, the legislation is based on the federal Pasteurized Milk Ordinance and implemented by a series of rules and regulations by the different states. Globally, the general bovine udder health has improved greatly by implementing diverse control programs and preventive measures (e.g., the 10 point plan of the National Mastitis Council) (National Mastitis Council, 2011), with a significant reduction in the incidence of IMI by Staphylococcus (S.) aureus, streptococci and coliforms. However, mastitis remains the most important disease in the dairy sector worldwide. And in a context of a continuously increasing worldwide concern regarding the usage of antibiotics and the emergence of antimicrobial resistance, the importance of animal health should not be overlooked as 50% of the total amount of antibiotics used in the European Union were administered to animals (van den Bogaard and Stobberingh, 2000). All animal production systems are currently contributing to lower their usage of antibiotics. A good udder health management is of utmost importance because IMI are one of the most frequent reasons for antimicrobial therapy in dairy herds worldwide (Pol and Ruegg, 2007; Brunton et al., 2012; Stevens et al., 2016a). In a cohort of Flemish dairy herds, about 29% of the total amount of antibiotics were used for the intramammary treatment of subclinical and clinical mastitis cases and 33% for dry cow therapy via long acting antimicrobial preparations (Stevens et al., 2016a). However, the finding that a better udder health management and implementation of preventive measures is not always associated with a lower antimicrobial usage, and vice versa, was quit strikingly (Stevens et al., 2016b).

6 Chapter 1 General introduction

2. Role of non-aureus staphylococci in bovine mastitis Non-aureus staphylococci, consisting of more than 50 (sub)species, form a heterogeneous group of Gram-positive bacteria (Piessens et al., 2011). Until recently, NAS were generally termed coagulase-negative staphylococci. In most clinical laboratories the differentiation of staphylococci was performed by the tube coagulase test in which the ability to clot plasma by converting fibrinogen to fibrin was tested. At the time, all Staphylococcus species other than S. aureus were often regarded as coagulase-negative. However, some Staphylococcus species have the (variable) ability to clot plasma such as S. delphini, S. agnetis, S. lutrae, S. pseudintermedius, S. schleiferi subsp. coagulans, S. hyicus, S. intermedius, and S. chromogenes (Roberson et al., 1996; Vanderhaeghen et al., 2015; Santos et al., 2016). Before genotypic methods were commonly available, a scale of phenotypic methods, such as API Staph ID 20 (Carretto et al., 2005) or 32 (Ieven et al., 1995), Vitek Gram-Positive Identification Card (Bannerman et al., 1993), and several others, were used for the identification of NAS species. However, these tests were mainly validated for human NAS species, depend on the variable expression of certain phenotypic characteristics of the bacteria, and were rather subjective to interpret, making them less accurate than genotypic methods such as gene sequencing (Ruegg, 2009). One of the more recent developments in identification methods is the matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry, which has proven to be a reliable assay for identification of NAS species from ruminants when using a database of spectra of bovine mastitis pathogens (Cameron et al., 2018; Gosselin et al., 2018; Mahmmod et al., 2018b).

2.1. Prevalence of non-aureus staphylococci According to studies carried out in 100 Belgian dairy herds (De Visscher et al., 2017) and in all 4,258 Danish dairy herds (Katholm et al., 2012), it is estimated that NAS species are present in practically all dairy herds. In the last 2 decades, NAS have become the most isolated bacteria from cases of SCM on well-managed dairy farms that have controlled contagious major mastitis pathogens and have a low bulk milk SCC (BMSCC) (Pitkälä et al., 2004; Bradley et al., 2007; Piepers et al., 2007; Pyörälä and Taponen, 2009; Reyher et al., 2011). In a meta-

7 Chapter 1 General introduction analysis of studies published between 1971 and 2000, the prevalence of IMI caused by NAS varied between 5.5% and 27.1% at the quarter level (Djabri et al., 2002). At least 26 different NAS species have been isolated from bovine milk samples: S. agnetis, S. arlettae, S. auricularis, S. capitis, S. caprae, S. chromogenes, S. cohnii, S. devriesei, S. epidermidis, S. equorum, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hyicus, S. lentus, S. nepalensis, S. pasteuri, S. pseudintermedius, S. saprophyticus, S. sciuri, S. simulans, S. succinus, S. vitulinus, S. warneri, and S. xylosus (Persson Waller et al., 2011; Piessens et al., 2011; Taponen et al., 2011; Youn et al., 2011; De Visscher et al., 2014, 2016; Fry et al., 2014; Mahmmod et al., 2018a). Although the prevalence and distribution of the different species is herd- and regional- dependent, the 5 NAS species that have been most frequently isolated from bovine mastitis cases and identified by molecular identification techniques are S. chromogenes, S. simulans, S. haemolyticus, S. xylosus and S. epidermidis (Vanderhaeghen et al., 2014). However, studies using the more reliable genotypic identification methods and reporting NAS species-specific prevalence data are scarce. Staphylococcus chromogenes appears to be the predominant species amongst the group of NAS, with more than 40% of the isolates belonging to this species in heifers and multiparous cows (Supré et al., 2011; Fry et al., 2014; Tomazi et al., 2015; De Visscher et al., 2016; Condas et al., 2017a). Risk factors at the herd, cow, and quarter level for NAS IMI have been identified for multiple species (Piessens et al., 2011; Bexiga et al., 2014; De Visscher et al., 2016). For example, heifers were shown to be more prone to NAS IMI than multiparous cows, especially in early lactation (Matthews et al., 1992; Bradley, 2002; Tenhagen et al., 2006; Sampimon et al., 2009; De Vliegher et al., 2012). Previous studies reported a proportion of SCM caused by NAS of 21.8% to 39.0% at first calving (Roberson et al., 1994; Fox et al., 1995; Nickerson et al., 1995) and of 19.3% to 35.3% in early lactation (Aarestrup and Jensen, 1997; Piepers et al., 2010) in heifers.

8 Chapter 1 General introduction

2.2. Impact of intramammary infections caused by non-aureus staphylococci on udder health Due to variations in study design and in identification methods (i.e. phenotypic methods vs. genotypic methods), many studies reported contradictory conclusions regarding the relevance of NAS for udder health (Vanderhaeghen et al., 2015). In general, NAS were considered to be minor mastitis pathogens or even harmless commensals (Piepers et al., 2009; Schukken et al., 2009; Vanderhaeghen et al., 2014), and their clinical relevance was under debate (Compton et al., 2007). However, in most studies published in the past 10-15 years, NAS are generally considered minor pathogens that cause a moderate increase of the SCC, and to a lesser extent able to cause mild cases of CM (Schukken et al., 2009; Fry et al., 2014; Tomazi et al., 2015). More ambiguity exists on the potential species differences in impact for udder health. In 2 Finnish studies, one reported that clinical symptoms were related to the NAS species involved and were most severe when caused by S. hyicus (Honkanen-Buzalski et al., 1994), whereas the other study showed no such relation (Taponen et al., 2006). Some studies found no significant differences in quarter milk SCC (qSCC) between the different NAS species (Hogan et al., 1987; Bexiga et al., 2014), whereas others reported relevant species differences (Supré et al., 2011; Fry et al., 2014). Staphylococcus chromogenes, S. simulans, and S. xylosus were called the species “more relevant for udder health” due to their ability to raise the qSCC to a level comparable to that of S. aureus (Supré et al., 2011), although the power in that study was low. This is however a common issue in many studies investigating the impact of NAS IMI, especially because many different NAS species occur and the differences in SCC and MY compared with noninfected animals or quarters are relatively small, thus resulting in the need for huge numbers of animals to have sufficient IMI cases with the different NAS species in order to have sufficient power in the analysis. Staphylococcus chromogenes was also amongst the NAS species that resulted in a significantly higher qSCC compared with noninfected quarters and was more prevalent in high than in low SCC quarters (Fry et al., 2014; Condas et al., 2017b). Heifers were at greater risk to be infected with the “more relevant” species when compared with multiparous cows (Taponen et al., 2007; De Visscher et al., 2015). Although heifers with an elevated SCC in early lactation had a higher culling risk (De Vliegher et al., 2005a), heifers having an IMI caused by NAS (as a group) had a significantly lower incidence

9 Chapter 1 General introduction of CM (Piepers et al., 2010), leaving the question open whether control programs should also focus on NAS prevention or not. Still, the number of studies identifying the NAS isolates at the species level and determining the IMI status and milk SCC at the quarter level are limited, and most of them lack a longitudinal follow-up.

2.3. Impact of intramammary infections caused by non-aureus staphylococci on milk yield The effect of IMI caused by NAS on MY is the subject of controversy and conflicting conclusions in the past 40 years (Table 1 and 2), and has yet to be unequivocally clarified. Most remarkable is the finding that IMI with NAS had a positive association with MY, despite an increased SCC (Wilson et al., 1997; Compton et al., 2007; Schukken et al., 2009; Piepers et al., 2010, 2013). This suggests that NAS are capable of causing IMI resulting in an elevated SCC yet not in less milk; on the contrary (Piepers et al., 2009, 2010). One of the potential explanations for this higher MY is that NAS IMI might lead to a higher local production of prolactin, the main lactogenic hormone in ruminants (Lacasse et al., 2016), in the udder. Indeed, the bovine mammary gland is able to synthesize prolactin (Piccart, 2016), but despite a trend towards a higher milk prolactin level in NAS-infected quarters, the prolactin gene expression is not different compared with noninfected quarters, thus leaving many questions about the prolactin level and its potential role in the higher MY of NAS-infected quarters. However, it might also be possible that high-yielding animals were more prone to NAS IMI, although it has been shown that a higher MY was no significant risk factor (Dolder et al., 2017; Piepers et al., 2011). On the other hand, a plethora of studies have found no association between IMI with NAS and MY (Eberhart et al., 1982; Kirk et al., 1996; Paradis et al., 2010; Pearson et al., 2013; Tomazi et al., 2015; Heikkilä et al., 2018), as can be expected taking into account the status of NAS as minor pathogens. This would also confirm that not all NAS isolated from the udder cause IMI but rather act as commensals (Isaac et al., 2017). Other studies, however, detected a slight decrease in milk production due to IMI with NAS (Timms and Schultz, 1987; Gröhn et al., 2004; Thorberg et al., 2009; Simojoki et al., 2011). These findings are likely explained by the fact that an increased SCC caused by NAS IMI results in a proportional decrease in MY (Koldeweij et al., 1999; De Vliegher et al., 2005b).

10 Chapter 1 General introduction

The association between NAS IMI and MY is influenced by a whole complex of other factors, e.g. type of infection (clinical vs. subclinical mastitis), parity and stage of lactation of the studied animals, … Differences and even some flaws in the design of the aforementioned studies might explain why no general conclusions could be drawn about the association between NAS IMI and MY. First of all, most studies have considered NAS as a group rather than scrutinizing the individual NAS species, or at least the most prevalent species, separately. Due to the diversity of this group of bacteria with species that differ in pathogenicity and virulence and have a species-dependent effect on (q)SCC (Vanderhaeghen et al., 2014, 2015), the association with MY could have depended on the type of NAS species. If the predominant NAS species was different between the studies, and these species had not the same effect on milk production, this may explain why a negative association with MY was found in some studies, whereas no association or a positive association was observed in other studies. However, when multiple species had a similar prevalence within a study, the effects could be just averaged out, thus resulting no overall association between MY and NAS IMI. Several studies have also included both heifers and multiparous cows, whereas it has been shown that the odds of being infected with the “more relevant species” S. chromogenes, S. simulans and S. xylosus is higher in heifers (De Visscher et al., 2016). However, the conclusions of studies including only heifers were not unambiguous either. Also, most of the previous studies, except 2, were observational studies that did not allow to determine the causal relationship between NAS IMI and MY. Thus it could also have been possible that the cows with the highest MY were more susceptible to NAS IMI. The fact that in 2 experimental challenge studies (Piccart et al., 2015; Simojoki et al., 2011) a negative association was found, albeit with a small study group, may have complicated the interpretations of the results even more. If more high-yielding animals were having NAS IMI, and these infections resulted in a lower MY, these animals could have had a MY that was no longer different from that of uninfected herd mates, as was observed in the study by Gröhn et al. (2004). Furthermore, in almost all studies, MY was measured at the cow level (e.g., via Dairy Herd Improvement data). The total production of an animal measured in those studies is the result of the combined production of 4 separate mammary quarters. This could lead to distorted results because it has been shown that a reduced or absent milk production in one quarter can be partially compensated by a higher production of milk in the

11 Chapter 1 General introduction

3 S. Most Most prevelant prevelant epidermidis, epidermidis, NAS species chromogenes chromogenes S. simulans, S. S. simulans, S.

species species 2 tests) tests) Yes (via (via Yes biochemical biochemical identification NAS

Quarter No / / No Quarter / No Quarter composite composite Quarter or or Quarter milk samples milk

Clinical or or Clinical subclinical Subclinical Composite No / / No Subclinical Composite Subclinical Subclinical / No Quarter Subclinical and clinical clinical and clinical and

Stage of of Stage lactation l tgs lncl ure N / No Quarter / Clinical stages All No Composite Subclinical stages All

efr Peripartum Heifers Separate Separate Parity of of Parity separately separately Heifers and and Heifers multiparous multiparous analyses for for analyses cows analyzed analyzed cows heifers and cows and heifers included animals included

4

status) 56 with with 56 Not available All parities All stages Subclinical Quarter No / / No Quarter Subclinical stages All parities All available Not 764 (of which which (of 764 unknown IMI unknown No. of quarters of No.

Not Not No. of of No. animals available available

of of 1 2 139 554 All parities All stages stages All Peripartum parities All Heifers 554 / 139 339 2 1 cases 692 3,071 2 29 Peripartum Heifers Quarter 2,664 Subclinical stages All 708 parities All 30 2,303 587 11 191 20 herds ,0 1832 Alprte Alsae Sblncl opst N / No Composite Subclinical stages All parities All / 108,312 1,601 / 352,614 4,200 No.

Design Observational Observational Observational Observational Observational

List of publications studying the association between IMI caused by NAS and milk yield in dairy cows: overview of the study designs.

Study 1982 Eberhart and Timms 1987 Schultz Observational 1996 Kirk Observational 1997 Wilson Observational 2004 Gröhn Compton 2007 Schukken 2009 Thorberg 2009 Observational 2010 Piepers Table Table 1. (continued on next page)

12 Chapter 1 General introduction

S. S. Most Most prevelant prevelant NAS species chromogenes chromogenes

) ) 5 cation RFLP NAS species NAS species identifi Yes (via PCR- (via Yes

Quarter / / / / Quarter / / / Quarter No Quarter composite composite Quarter or or Quarter milk samples milk

clinical clinical Mild to to Mild mastitis mastitis signs of of signs (separate (separate moderate moderate analyses) No visual visual No Clinical or or Clinical subclinical Subclinical Composite No / / No / Composite / Subclinical No No Quarter Subclinical Quarter Subclinical Subclinical and clinical clinical and in all animal animal all in clinical signs signs clinical

Mid- Mid- Stage of of Stage lactation lactation lactation Polymerase chain reaction - fragment restriction length polymorphism. 5

Heifers Heifers Heifers animals included included Parity of of Parity

5 Hies Peripartum Heifers 154 Intramammary Intramammary infection. 4 quarter) quarter) as control as control per animal) animal) per every fourth fourth every 16 (2 quarters (2 quarters 16 (3 quarters 24 quarter served served quarter No. of of quarters No. per animal, and and animal, per

No. of of No. 38 (19 38 animals tic twins) twins) tic monozygo- monozygo- Staphylococcus. 3

8 1 1 8 1 0 ,9 / efr Peripartum Heifers / Peripartum Heifers 1,691 Quarter 50 1,354 Subclinical stages All parities All 344 1,140 30 285 21 herds 3,953 20,234 Not available All parities All stages stages All parities All available Not 20,234 3,953 No. of of No.

List of publications studying the association between IMI caused by NAS and milk yield in dairy cows: overview of the of overview cows: dairy in yield milk and NAS by caused IMI between association the studying publications of List . Design challenge challenge challenge Experimental Experimental Experimental Observational

Non-aureus staphylococci. 2 continued

Study Observational 2010 Paradis Simojoki 2011 Observational 2013 Piepers Observational 2013 Pearson Observational 2015 Tomazi 2015 Piccart 2018 Heikkilä Number.

1 Table 1 Table designs. study

13 Chapter 1 General introduction

: -6.6, 0.2 kg/d). After After 0.2 kg/d). -6.6, : 8 -value < 0.01) lower production than production lower < 0.01) -value P of 2: lactation curve was slightly lower. Largest Largest lower. slightly was curve lactation 2: of 7 infected and noninfected quarters/animals noninfected and infected - had significantly ( significantly had 9 -value < 0.05) for all parities. Decreases of 776, 940, and 658 kg kg 658 and 940, 776, of Decreases all parities. for 0.05) < -value P at a median DIM median a at 6 -value < < 0.05). -value P -value < 0.01) higher milk production was observed among persistently infected cows. infected persistently among observed was milk production higher < 0.01) -value P Difference in MY between NAS between MY in Difference ( period 305 d kg over -821 uninfected with compared lactation animals or greater third and second, first, d for 305 in milk ( animals NAS CM with Heifers CI (95% diagnosis following immediately the week in kg/d) (3.2 drop kg/d. 3 and 1 between fluctuated losses daily that, kg/d) (2.5 herdmates healthy their outproduced significantly NAS CM with cows Multiparous healthy as level same the to down they dropped diagnosis, After CM. of diagnosis before cows the If supposed. be first at might than greater is cases in loss such milk Therefore, cows. higher. even been have would yield milk their CM, contracted not had SCM nonpersistent with Cows significantly SCM, persistent or nonpersistent with cows all comparing When cows. healthy (

2 and MY MY and

3 Positive +41 kg on d. kg on 305 +41 Positive Negative Negative Negative Negative Association Association IMI between NAS between kg/d. 0 association No

during during 5 record No association (not available). available). (not association No record

4 data data to 1995 to interval interval with 4 week 4 week with 5 months via via months 5 Period of MY of Period DHIA records records DHIA measurements entire lactation lactation entire 2 DHIA records records DHIA 2 DHIA from 1991 from DHIA Weekly milk yield yield milk Weekly Every 28 d 28 Every

measured measured 1 Animal Animal lactation. entire over 0.7 kg/d and recording DHIA at first kg/d +0.6 Positive kg/d. +0.45 Positive lactation Whole Animal lactation Whole Animal Animal at animal or or animal at quarter level quarter MY Continued on next page) Continued List of publications studying the association between IMI caused by NAS and milk yield in dairy cows: overview of the results and results the of overview cows: dairy in yield milk and NAS by caused IMI between association the studying publications of List

brat18 Aia 1 DHIA Animal Study 1982 Eberhart Animal and Timms 1987 Schultz Animal 1996 Kirk 1997 Wilson Animal 2004 Gröhn Compton 2007 kg/d. +2.9 Schukken Positive 2009 DIM 285 Until Thorberg 2009 Animal 2010 Piepers Table 2. Table conclusions.

14 Chapter 1 General introduction

as n milk. in Days 7

group and 0.41 kg/d in the in and 0.41 kg/d group -value = 0.06). = -value P simulans Clinical mastitis. mastitis. Clinical 6 11 S. Day(s). Day(s). 5 infected and noninfected quarters/animals noninfected and infected - was 0.33 kg/d in the in kg/d 0.33 was 10 -value = 0.07). 0.07). = -value P -value > 0.1). > -value P Least Least square means. = 1.73 and 1.98 kg/milking, respectively; respectively; kg/milking, 1.98 and 1.73 = 12 group, when production 2 d before the challenge was compared with that on d 7 7 on d that with compared was challenge the dbefore 2 production when group, 12 ar Hr Ipoeet Association. Improvement Herd Dairy Difference in MY between NAS between MY in Difference qMY in decrease average The epidermidis S. post-challenge. control than in quarters NAS challenged the in lower be to tended qMY mean overall The (LSM quarters without and with lactations curves for lactation the between difference significant no SCM: when kg/d) (-1.8 MY the305-d of -5.7% CM: kg lower). 330 to 81 was MY (305-d mastitis DIM. 120 and 54 between when kg/d) (-1.0 -3.2% and DIM, 53 before occured CM 4 Staphylococcus. 11

and MY MY and Negative Negative Negative Association Association No association +0.6 kg/d (not significant, significant, (not kg/d +0.6 association No ( 200 DIM kg over -51 association No milking. single at a kg +0.049 association No association No between NAS IMI IMI NAS between

Quarter yield. milk 10 nrmmay infection. Intramammary 3 DIM DIM per year) per At least 2 least At Period of MY of Period measurements post-challenge post-challenge post-challenge from 0 d to d 7 0 to d from from -2 d to 7 to-2 7 d d from At each milking milking each At milking each At DHIA data from from data DHIA the first lactation lactation first the lactations per cow cow per lactations records DHIA (12 Between 2 and 200 and 2 Between

Subclinical mastitis. 9 List of publications studying the association between IMI caused by NAS and milk yield in dairy cows: overview of the of overview cows: dairy in yield milk and NAS by caused IMI between association the studying publications of List . Animal Animal at animal or or animal at quarter level quarter MY measured measured MY o-ues staphylococci. Non-aureus 2 continued

Piepers 2013 Animal Until 285 DIM Positive +2.05 kg/d. kg/d. +2.05 Animal Study Positive 2010 Paradis DIM 285 Until Simojoki 2011 Animal 2013 Piepers Animal day on 1 milking 1 2013 Pearson Quarter 2015 Tomazi Quarter 2015 Piccart Animal 2018 Heikkilä Milk yield. yield. Milk Confidence interval.

1 8 Table 2 Table andresults conclusions.

15 Chapter 1 General introduction other quarters (Hamann and Reichmuth, 1990; Hamann and Gyodi, 1994; Skarbye et al., 2018), thus leveling out possible changes in the total MY of an animal. And in relation to the previous argument, in many studies the IMI status was determined at the cow level rather than the quarter level by either analyzing composite milk samples or by aggregating the different quarter IMI statuses into one cow status. To the best of our knowledge, no study has identified NAS to the species level by using genotypic methods and scrutinized the association between NAS species on MY in which both the IMI status and MY were determined at the quarter level with a longitudinal follow-up of several months.

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about?. Vet. Microbiol. 134:9–14. https://doi.org/10.1016/j.vetmic.2008.09.014. Seegers, H., C. Fourichon, and F. Beaudeau. 2003. Production effects related to mastitis and mastitis economics in dairy cattle herds. Vet. Res. 34:475–491. https://doi.org/10.1051/vetres:2003027. Simojoki, H., T. Salomaki, S. Taponen, A. Iivanainen, and S. Pyorala. 2011. Innate immune response in experimentally induced bovine intramammary infection with Staphylococcus simulans and S. epidermidis. Vet. Res. 42:49. https://doi.org/10.1186/1297-9716-42-49. Skarbye, A.P., M.A. Krogh, and J.T. Sørensen. 2018. The effect of individual quarter dry-off in management of subclinical mastitis on udder condition and milk production in organic dairy herds: A randomized field trial. J. Dairy Sci. 101:11186–11198. https://doi.org/10.3168/jds.2018-14794. Stevens, M., S. Piepers, K. Supré, J. Dewulf, and S. De Vliegher. 2016a. Quantification of antimicrobial consumption in adult cattle on dairy herds in Flanders, Belgium, and associations with udder health, milk quality, and production performance. J. Dairy Sci. 99:2118–30. https://doi.org/10.3168/jds.2015-10199. Stevens, M., S. Piepers, and S. De Vliegher. 2016b. Mastitis prevention and control practices and mastitis treatment strategies associated with the consumption of (critically important) antimicrobials on dairy herds in Flanders, Belgium. J. Dairy Sci. 99:2896–2903. https://doi.org/10.3168/jds.2015-10496. Supré, K., F. Haesebrouck, R.N.N. Zadoks, M. Vaneechoutte, S. Piepers, and S. De Vliegher. 2011. Some coagulase-negative Staphylococcus species affect udder health more than others. J. Dairy Sci. 94:2329–2340. https://doi.org/10.3168/jds.2010-3741. Taponen, S., J. Koort, J. Björkroth, H. Saloniemi, and S. Pyörälä. 2007. Bovine intramammary infections caused by coagulase-negative staphylococci may persist throughout lactation according to Amplified Fragment Length Polymorphism-based analysis. J. Dairy Sci. 90:3301–3307. https://doi.org/10.3168/jds.2006-860. Taponen, S., H. Simojoki, M. Haveri, H.D. Larsen, and S. Pyörälä. 2006. Clinical characteristics and persistence of bovine mastitis caused by different species of coagulase- negative staphylococci identified with API or AFLP. Vet. Microbiol. 115:199–207. https://doi.org/10.1016/j.vetmic.2006.02.001.

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Taponen, S., K. Supre, V. Piessens, E. van Coillie, S. de Vliegher, J.M.K. Koort, K. Supré, V. Piessens, E. van Coillie, S. de Vliegher, J.M.K. Koort, K. Supre, V. Piessens, E. van Coillie, S. de Vliegher, and J.M.K. Koort. 2011. Staphylococcus agnetis sp. nov., a coagulasevariable species from bovine subclinical and mild clinical mastitis. Int. J. Syst. Evol. Microbiol. 62:61–65. https://doi.org/10.1099/ijs.0.028365-0. Tenhagen, B.-A., G. Köster, J. Wallmann, and W. Heuwieser. 2006. Prevalence of mastitis pathogens and their resistance against antimicrobial agents in dairy cows in Brandenburg, Germany. J. Dairy Sci. 89:2542–2551. https://doi.org/10.3168/jds.S0022-0302(06)72330- X. Thorberg, B.-M., M.-L. Danielsson-Tham, U. Emanuelson, and K. Persson Waller. 2009. Bovine subclinical mastitis caused by different types of coagulase-negative staphylococci. J. Dairy Sci. 92:4962–4970. https://doi.org/10.3168/jds.2009-2184. Timms, L.L., and L.H. Schultz. 1987. Dynamics and significance of coagulase-negative staphylococcal intramammary infections. J. Dairy Sci. 70:2648–2657. https://doi.org/10.3168/jds.S0022-0302(87)80335-1. Tomazi, T., J.L. Goncalves, J.R. Barreiro, M.A. Arcari, and M. V dos Santos. 2015. Bovine subclinical intramammary infection caused by coagulase-negative staphylococci increases somatic cell count but has no effect on milk yield or composition. J. Dairy Sci. 98:3071– 3078. https://doi.org/10.3168/jds.2014-8466. Trinidad, P., S.C. Nickerson, and R.W. Adkinson. 1990a. Histopathology of Staphylococcal Mastitis in Unbred Dairy Heifers. J. Dairy Sci. 73:639–647. https://doi.org/10.3168/jds.S0022-0302(90)78715-2. Trinidad, P., S.C. Nickerson, and T.K. Alley. 1990b. Prevalence of Intramammary Infection and Teat Canal Colonization In Unbred and Primigravid Dairy Heifers. J. Dairy Sci. 73:107–114. https://doi.org/10.3168/jds.S0022-0302(90)78652-3. Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher. 2014. Invited review: Effect, persistence, and virulence of coagulase-negative Staphylococcus species associated with ruminant udder health. J. Dairy Sci. 97:5275– 5293. https://doi.org/10.3168/jds.2013-7775. Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher.

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2015. Identification, typing, ecology and epidemiology of coagulase negative staphylococci associated with ruminants. Vet. J. 203:44–51. https://doi.org/10.1016/j.tvjl.2014.11.001. van den Bogaard, A. E. and E. E. Stobberingh. 2000. Epidemiology of resistance to antibiotics - Links between animals and humans. Int. J. Antimicrob. Agents 14(4):327-335. https://doi.org/10.1016/s0924-8579(00)00145-x. Watts, J.L. 1988. Etiological agents of bovine mastitis. Vet. Microbiol. 16:41–66. https://doi.org/10.1016/0378-1135(88)90126-5. Wellenberg, G.J., W.H.M. Van Der Poel, and J.T. Van Oirschot. 2002. Viral infections and bovine mastitis: A review. Vet. Microbiol. 88:27–45. https://doi.org/10.1016/S0378- 1135(02)00098-6. Wilson, D.J., R.N. Gonzalez, and H.H. Das. 1997. Bovine Mastitis Pathogens in New York and Pennsylvania: Prevalence and Effects on Somatic Cell Count and Milk Production. J. Dairy Sci. 80:2592–2598. https://doi.org/10.3168/jds.S0022-0302(97)76215-5. Youn, J., L.K. Fox, K. Seok, M.A. Mcguire, Y. Ho, F.R. Rurangirwa, W.M. Sischo, G.A. Bohach, J.Y. Park, L.K. Fox, K.S. Seo, M.A. Mcguire, Y.H. Park, F.R. Rurangirwa, W.M. Sischo, and G.A. Bohach. 2011. Comparison of phenotypic and genotypic methods for the species identification of coagulase-negative staphylococcal isolates from bovine intramammary infections. Vet. Microbiol. 147:142–148. https://doi.org/10.1016/j.vetmic.2010.06.020.

27

Chapter 2

Scope and aims of the thesis

D. Valckenier

Department of Reproduction, Obstetrics, and Herd Health

Faculty of Veterinary Medicine,

Ghent University, Merelbeke, Belgium

Chapter 2 Aims of the thesis

In dairy cows, a healthy udder is a prerequisite to allow a lifelong production of large quantities of high-quality milk. Heifers form the foundation and are the future of every dairy herd. Due to the implementation of mastitis control programs that focus mostly on the reduction of major pathogens, the relative importance of the so-called minor pathogens has increased. In the last decades, non-aureus staphylococci have become the most isolated group of bacteria in samples taken for routine culturing of cases of subclinical mastitis. Many studies have been performed on the effects of intramammary infections caused by non-aureus staphylococci on udder health and production of affected animals. However, equivocal and even contradictory results have been reported. This thesis will provide deeper insights in the impact of intramammary infections caused by non-aureus staphylococci on the quarter milk yield and quarter somatic cell count. The main aims of this thesis were:

 To study the impact of intramammary infections caused by non-aureus staphylococci as a group within the first 4 days after calving on the future milk yield and udder health in dairy heifers (Chapter 3);

 To determine the effect of intramammary infections in dairy heifers within the first 18 days in milk with all non-aureus staphylococci species in general and Staphylococcus chromogenes more specifically on quarter milk yield and quarter somatic cell count in the first 130 days in milk (Chapter 4);

 To evaluate the effect of transient and persistent subclinical intramammary infections in dairy heifers caused by non-aureus staphylococci during the first 130 days in milk on quarter milk yield and quarter somatic cell count (Chapter 5).

31

Chapter 3

Effect of intramammary infection with non-aureus

staphylococci in early lactation in dairy heifers on

quarter somatic cell count and quarter milk yield

during the first 4 months of lactation

D. Valckenier,1 S. Piepers,1 A. De Visscher,1 R.M. Bruckmaier,2

and S. De Vliegher1

1M-team & Mastitis and Milk Quality Research Unit, Department of Reproduction,

Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke,

Belgium B-9820.

2Veterinary Physiology, Vetsuisse Faculty, University of Bern, Bern,

Switzerland CH-3001.

Adapted from Journal of Dairy Science, 2019, 102:6442-6453

https://doi.org/10.3168/jds.2018-15913

Chapter 3 IMI with NAS in the first 4 DIM

ABSTRACT

A longitudinal study in 3 dairy herds was conducted to assess to what extent intramammary infection (IMI) with non-aureus staphylococci (NAS) within the first 4 days (d) after calving in dairy heifers affects quarter milk yield (qMY) and quarter milk somatic cell count (qSCC) during the first 4 months (mo) of lactation. In total, 324 quarters from 82 Holstein Friesian heifers from 3 commercial dairy herds equipped with an automatic milking system were included and followed from calving up to 4 mo in lactation. The automatic milking system allowed us to precisely determine the daily qMY. A milk sample from each quarter was collected in early lactation (between 1 and 4 d in milk) for bacteriological culturing and measurement of the qSCC. Subsequently, milk samples were taken on a biweekly basis for measurement of the qSCC. The milk prolactin level in early lactation was measured, and the relation with NAS IMI was determined. Overall, NAS IMI in early lactation caused only a slight but significant increase in qSCC compared with milk from noninfected quarters during the first 4 mo in lactation, whereas no significant difference in daily qMY was present between NAS- infected and noninfected quarters. The milk prolactin level in early lactation did not differ between NAS-infected and noninfected quarters either.

Our data suggest that IMI with NAS (as a group) present shortly after calving had no effect on later production, despite an elevated qSCC. The milk prolactin concentrations were equally high in NAS-infected and noninfected quarters.

Key words: non-aureus staphylococci, quarter milk yield, quarter somatic cell count, prolactin

35 Chapter 3 IMI with NAS in the first 4 DIM

INTRODUCTION

A large proportion of heifers freshen with IMI and prevalence varies widely, with 12.3 to 74.6% of quarters being infected (reviewed by De Vliegher et al., 2012). A common denominator in all studies reporting on IMI in early-lactating dairy heifers is the large proportion of infections caused by NAS. Previous studies have shown a percentage of NAS- positive mammary quarters at first calving of up to 45.5% (Oliver et al., 2003).

Non-aureus staphylococci are a heterogeneous group consisting of more than 50 species and subspecies (Vanderhaeghen et al., 2015). Thus far, more than 10 species of NAS have been isolated from bovine milk (Supré et al., 2011, Vanderhaeghen et al., 2014, De Visscher et al., 2016). However, the effect of IMI caused by NAS on milk yield (MY) remains inconclusive. Some studies have classified NAS as an important cause of bovine mastitis with potentially negative effects on MY (Timms and Schultz, 1987, Gröhn et al., 2004, Taponen et al., 2006) whereas others consider them to be minor mastitis pathogens that only slightly increase milk SCC but do not affect MY (Paradis et al., 2010, Pearson et al., 2013, Tomazi et al., 2015). A more recent study concluded that the negative effect of NAS, identified using PCR, on udder health and MY, should not be underestimated (Heikkilä et al., 2018). Some studies, however, observed higher test-day MY in NAS-infected dairy heifers and multiparous cows compared with noninfected herd mates (Schukken et al., 2009, Piepers et al., 2013). Still, in all but 1 study (i.e., Tomazi et al., 2015), both SCC and MY were measured at the animal level, making it difficult to relate the differences in test-day milk SCC and MY between animals to the infection status of a specific quarter or quarters infected with a specific mastitis pathogen or pathogens in (early) lactation. A drawback of the study of Tomazi et al. (2015) is that the quarter MY (qMY) was determined only at one single milking. To unequivocally determine the association between NAS IMI in early lactation and milk SCC and MY further in lactation, a longitudinal study is needed in which the IMI-status as well as the SCC and MY are measured (repeatedly) at the quarter level.

One of the hypotheses to explain the higher MY in NAS-infected heifers compared with noninfected herd mates is that NAS IMI might enhance the local production of prolactin (PRL) in the mammary gland. Prolactin is a hormone involved in a broad range of biological processes

36 Chapter 3 IMI with NAS in the first 4 DIM and is crucial for the initiation and maintenance of lactation in ruminants (Lacasse et al., 2016). Milk production significantly decreases when dairy cows receive long-term treatment with the selective dopamine receptor agonist quinagolide (Lacasse et al., 2011). In general, serum PRL lies between 10 and 60 ng/mL in adult dairy cows (Koprowski et al., 1972; Fulkerson et al., 1980; Marcek and Swanson, 1984). Prolactin is transported from the bloodstream to the milk across mammary epithelial cells via the transcytosis pathway. After binding on the membrane receptor on these cells, PRL is internalized; carried throughout endosomes, multivesicular bodies and the Golgi apparatus; and subsequently released into the milk through secretory vesicles (Ollivier-Bousquet, 1998). The milk PRL concentration is overall lower than the circulating PRL level (Malven and McMurtry, 1974). The mammary gland can function as a self-regulating endocrine organ that is largely independent from systemic influences (Wilde and Peaker, 1990; Weaver and Hernandez, 2016). Nevertheless, the biological significance of autocrine PRL, and its potential correlation with the milk secretion, has not been studied extensively in cattle. Damage to the mammary tissue due to mastitis increases the tight junction permeability, thereby enabling the paracellular transport of blood-borne components (Nguyen and Neville, 1998), which might result in the leaking of PRL from the bloodstream into the milk. Previous studies could not find a difference in blood PRL level between healthy cows, cows with clinical mastitis (CM), and cows with subclinical mastitis (SCM; Hockett et al., 2000; Boutet et al., 2007). On the contrary, the SCC of chronically infected quarters was found to be positively correlated with the milk PRL concentration (Boutet et al., 2007). More recently, the average milk PRL level tended to be higher in NAS-infected quarters than in noninfected quarters in an experimental infection trial (Piccart et al., 2015). Piccart (2016) confirmed that, like in other ruminants such as sheep and goats (Le Provost et al., 1994), the bovine mammary gland is able to synthesize PRL, but the PRL gene expression was not higher in NAS-challenged mammary epithelial cells compared with unchallenged control cells. On the other hand, the general correlation between mRNA and the final protein can be low, and the MAC-T cells that were used to study the PRL gene expression might not be a reliable reflection of the complete dairy cow udder.

The main objective of this study was to unravel to what extent IMI with NAS in quarters from dairy heifers in early lactation truly affect the qMY and quarter milk somatic cell count

37 Chapter 3 IMI with NAS in the first 4 DIM

(qSCC) during the first 4 mo of lactation. A longitudinal study on 3 dairy herds equipped with an automatic milking system (AMS) was conducted, allowing us to measure the daily MY at the quarter level. The second objective was to scrutinize the association between early-lactation NAS IMI and the quarter milk PRL concentration.

MATERIALS AND METHODS

Sample Size

Prior to the study, a sample size calculation was performed using SPSS SamplePower 3.0 (IBM, Armonk, NY) for clustered data. Assuming that 35% of the quarters were infected with NAS and that 50% of the quarters was noninfected between 1 and 4 DIM (Piepers et al., 2010), an α of 0.05, an intraclass correlation coefficient of 0.5 at the quarter level, and a standard deviation of 1.5 kg, 72 heifers were needed to detect a difference in milk production of 0.4 kg between NAS-infected and noninfected quarters using a linear mixed regression model taking the clustering of the 10 observations within quarters into account with a power of 80%. The intraclass correlation coefficient and standard deviation were derived from a dataset obtained from the Ghent University dairy farm (Biocentrum-Agrivet, Melle, Belgium) equipped with an AMS. As clustering of quarters within heifers was not taken into account, the sample size calculation was most probably slightly underestimated. Therefore, and to compensate for nonfunctional quarters at calving, contaminated milk samples, and heifers getting culled before the end of the study, the number of heifers included in the study was increased to 82.

Herds, Animals and Study Design

The study was conducted between the end of August 2013 and the end of October 2014. Eighty-two Holstein Friesian dairy heifers were included from 3 commercial dairy herds in the province of West Flanders (Belgium) equipped with an AMS. Herd owners were approached by the first author and asked whether they were willing to participate. None of the 3 herds treated their end-term heifers with antimicrobials before calving. None of the herds participated in the local dairy herd improvement program.

38 Chapter 3 IMI with NAS in the first 4 DIM

In all herds, cows were milked automatically. Herd 1 and 3 had 2 AMS each, whereas there was only 1 AMS in herd 2. In herd 1 and 3, the lactating cows were housed in a separate group per AMS. The average number of lactating cows during the study period was 128, 60 and 115 in herd 1, 2 and 3, respectively. All cows were black and white Holstein Friesians. The average daily milk production per cow was 29.9, 28.8, and 27.5 kg in herds 1, 2, and 3, respectively. The average number of milkings per cow per day in the 3 herds during the study period varied between 1.93 and 2.11.

In all 3 herds, all lactating cows and heifers were housed in freestall barns with a concrete slatted floor and cubicles bedded with sawdust. The cubicles were cleaned, and fresh bedding was added at least once a day. In all herds, the slatted floors were automatically cleaned at least twice per day with a robotic scraper. Hairs from the udders were clipped at least 2 times per year.

Heifers with signs of impending calving were separated in a calving pen on straw. In herd 1, those heifers were kept separated from cows close to calving and sick or injured animals which were housed in two different (calving) pens. In herd 2 and 3, cows and heifers close to calving were kept together and housed in the same pen as the sick animals.

The first author visited the herds 2 times per week, on Monday and Thursday, to perform quarter milk sampling of the heifers that calved since the previous visit. Every heifer was thus sampled between 1 and 4 d after calving. After the first sampling, all heifers were followed up until 127 to 130 DIM.

During the trial period, every heifer that calved was included in the study. No further inclusion or exclusion criteria at the herd or heifer level were applied. When a heifer was sold, was culled, or died within the first 4 mo of lactation, it was not replaced by another animal. Nonfunctional quarters or quarters with CM before or at the first sampling were excluded because the main objective of this study was to investigate the effect of NAS IMI causing SCM on qSCC and qMY. For this reason, 2 quarters with CM and 2 nonfunctional quarters were excluded from the final data set.

39 Chapter 3 IMI with NAS in the first 4 DIM

Advice with respect to mastitis prevention and control was not given to the farmers or their herd veterinarians before or during the trial period. In addition, the culture results and SCC data were not made available to avoid alterations in the udder health management or antimicrobial treatment based on these data.

Sampling and Data Recording

One milk sample was taken within 1 to 4 d after calving (referred to as early lactation throughout the article) from each quarter of the heifers. All samples were taken aseptically according to the guidelines of the National Mastitis Council (Hogan et al., 1999). Briefly, gross contamination was removed from the teat skin and teat end with a dry paper towel. Subsequently, all teats were forestripped (3-5 streams), and the milk was inspected for visual abnormalities and discarded. The teat end and the bottom third of the teat were scrubbed with a cotton gauze or paper cloth moistened with ethanol (96%; VWR International BVBA, Leuven, Belgium). When needed, more than 1 gauze was used per teat. Approximately 6 to 8 mL of milk was collected per sample in sterile vials. Postsampling, teats were dipped with a chlorite- based dipping solution (Uddergold Platinum, Ecolab Europe GmbH, Wallisellen, Switzerland). All samples were transported in a cooled box (4°C) to the laboratory of the Mastitis and Milk Quality Research Unit (Ghent University, Merelbeke, Belgium) for bacteriological culturing and determination of qSCC. Quarter milk production per milking was available through the herd management software of the AMS (DelPro, DeLaval International AB, Tumba, Sweden).

Farmers were asked to record all CM cases that occurred during the first 4 mo after calving in one of the included heifers and to aseptically collect milk samples (approximately 6 to 8 mL) of every CM case (visible abnormalities in the udder or milk, such as presence of flakes or a swollen or painful quarter). Those milk samples were stored at -18°C until the next herd visit and then collected by the first author for further processing.

40 Chapter 3 IMI with NAS in the first 4 DIM

Microbiology

Standard culturing of all milk samples was performed according to the guidelines of the National Mastitis Council (Hogan et al., 1999). Briefly, a 0.01-mL loop of milk was spread on both an esculin blood agar plate and a MacConkey agar plate (Thermo Fisher Diagnostics N.V., Groot-Bijgaarden, Belgium) and aerobically incubated at 37°C. The plates were phenotypically examined after 24 h and again after 48 h. Quarters were considered to be infected if 1 or more colonies were observed (≥ 100 CFU/mL) in the milk sample. Identification of bacteria was done by Gram staining, inspection of the colony morphology, and biochemical testing. Catalase tests were performed to differentiate Gram-positive cocci as catalase-positive or catalase-negative cocci. Staphylococci (Gram-positive, catalase-positive cocci) were identified as Staphylococcus aureus or NAS by colony morphology, coagulase testing, hemolysis patterns, and DNase tests. Isolates of the Streptococcus-Enterococcus group were differentiated as esculin-positive or esculin-negative cocci. Christie, Atkins, and Munch-Petersen tests were used to differentiate esculin-negative cocci as Streptococcus agalactiae or Streptococcus dysgalactiae. Gram-negative bacteria were differentiated in oxidase-negative and oxidase- positive bacteria, and further identified using the EnteroPluri-Test (Liofilchem, Roseto degli Abruzzi, Italy) or Oxi/FermPluri-Test (Liofilchem) identification systems, respectively, and classified as either Escherichia coli (E. coli), Klebsiella spp., or other Gram-negative bacteria.

Non-aureus staphylococci and Corynebacteria spp. were considered to be minor pathogens. Staphylococcus aureus, esculin-positive and esculin-negative cocci, Trueperella pyogenes, E. coli, Klebsiella spp. and other Gram-negative bacteria were regarded as major pathogens. A quarter yielding a major and a minor pathogen was classified as infected with the major pathogen, whereas a quarter yielding 2 major or 2 minor pathogens was considered to be infected with the bacteria with the highest colony-forming units per milliliter. Samples yielding 3 or more different bacterial species were considered to be contaminated.

After a loop of milk was spread on the agar plates and the qSCC was determined, 1 mL of each sample was stored at -20 °C in Eppendorf cups for determination of the PRL concentration.

41 Chapter 3 IMI with NAS in the first 4 DIM

Clinical Mastitis and Culling

One quarter was immediately dried-off after CM between 85 and 102 DIM, but no milk sample was collected by the herd owner. The left hind quarter from a heifer in herd 3 was dried- off at 85 DIM because of a teat end injury. The samples taken before these quarters were dried- off were included in the analysis.

Two heifers from herd 3 were culled after a severe case of CM between 57 and 74 DIM. Streptococcus dysgalactiae was identified as the causative pathogen in the former quarter, whereas no bacteria could be cultured from the milk sample in the latter case. Two heifers (herd 2 and 3) were sold between 85 and 102 DIM. The samples taken before these 4 animals were culled were included in the analysis.

Quarter-Level IMI

From the 82 heifers included in this study, 324 quarters were eligible for sampling in early lactation (i.e., 1 – 4 DIM). Of these 324 quarters, the qIMI status could not be defined for 15 quarters because the milk samples were considered to be contaminated, and these quarters were excluded from the entire study. A quarter was defined as having an IMI with NAS, Bacillus spp., Corynebacterium spp., S. aureus, Streptococcus spp., Trueperella pyogenes, or Gram- negative bacteria at calving when the sample collected between 1 and 4 DIM contained ≥ 100 CFU/mL of the specific bacteria (Dohoo et al., 2011). A total of 220 quarters were noninfected in early lactation, whereas 89 quarters were infected with any pathogen (Table 1). The majority of the infected quarters at this sampling were infected with NAS (n = 68; 76.4% of infected quarters). Major pathogens (S. aureus, esculin-positive cocci and E. coli) accounted for only 10.1% of the infected quarters. Six quarters were infected with Corynebacterium spp. and 6 were infected with Bacillus spp. Only quarters that were noninfected (n = 220) or infected with either NAS (n = 68) or a major pathogen (n = 9) in early lactation were retained for further analyses.

42 Chapter 3 IMI with NAS in the first 4 DIM

Quarter SCC

After the early-lactation sampling (within 1 – 4 DIM), quarter milk samples were collected every 14 d for 9 consecutive times (10 so-called sampling days in total). The qSCC of each sample was measured using a DeLaval Cell Counter DCC (DeLaval International AB, Tumba, Sweden) in the laboratory of the Mastitis and Milk Quality Research group at the Faculty of Veterinary Medicine of Ghent University (Merelbeke, Belgium).

Quarter MY

The estimated daily qMY at the first sampling day (i.e., between 1 and 4 d after calving) was calculated by first summing the qMY of all milkings from calving up to d 7 after the first sampling, and then dividing this total amount of milk produced during this period by the number of days. Subsequently, the estimated daily qMY on the next 9 sampling days (with an interval of 14 d) was also calculated by dividing the sum all the quarter milk productions from 7 d before until 7 d after each sampling by 14 (i.e., the number of days in this period).

Milk PRL Concentration

All frozen (-20 °C) quarter milk samples collected in early lactation (1 – 4 DIM; n = 324) were thawed and centrifuged (25 min at 3000 × g at 20°C). The lower lipid-depleted aqueous phase was used to determine milk PRL by RIA as previously described by Bruckmaier et al. (1992).

Statistical Analyses

All data were entered in an electronic spreadsheet program (Excel 2010, Microsoft Corp., Redmond, WA) and were checked for unlikely values.

Quarter Milk SCC and MY. The association between quarter IMI status (qIMI) in early lactation (determined between 1 and 4 DIM; predictor variable of main interest) and the sampling-day qSCC and sampling-day qMY (outcome variables) throughout the first 4 mo of

43 Chapter 3 IMI with NAS in the first 4 DIM lactation, respectively, was determined fitting two separate linear mixed models (PROC MIXED) in SAS (version 9.4; SAS Institute Inc., Cary, NC). A natural logarithmic transformation of the quarter SCC (qLnSCC) was performed to obtain a normal distribution. All models included DIM (between 1 and 130 DIM) and its quadratic term as continuous predictor variables. The 3 qIMI status levels (noninfected, infected with NAS, and infected with 1 or more major pathogens) was forced in all models as categorical predictor variable of main interest. Quarter position (2 levels: front vs. hind) was added to the models as categorical predictor variable. Additionally, the model for sampling day qMY was fit with quarter qLnSCC at sampling day as continuous predictor variable. Herd was forced into all models as a fixed effect to correct for potential clustering of heifers within herds. Heifer was added as random effect to account for clustering of quarters within heifer. In all linear mixed models, a first-order autoregressive correlation structure was used to account for clustering of the repeated observations (i.e., 10 sampling days) within a quarter.

The initial linear mixed model with qSCC as outcome variable was: qLnSCCijkl = β0 + β1 qIMIjkl + β2 Quarter positionjkl + β3 Herdl + β4 DIMijkl + β5 DIM²ijkl +

µHeifer kl(j) + µQuarter jkl(i) + eijkl, [1] where qLnSCCijkl is the natural logarithm of SCC for the ith sample (i = 1 – 10) of the jth quarter

(j = 1 – 4) of the kth heifer (k = 1 – 82) from the lth herd (l = 1 – 3); β0 is the intercept (overall mean); β1 to β5 are the regression coefficients of the fixed effects: IMI status in early lactation, quarter position, herd, DIM and DIM quadratic, respectively; µHeifer kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer; µQuarter jkl(i) was added to correct for within-quarter correlation of subsequent biweekly sampling days i (repeated statement) for quarter j of heifer k from herd l and eijkl is the random error term.

The initial model with outcome variable daily qMY was: qMYijkl = β0 + β1 qIMIjkl + β2 Quarter positionjkl + β3 Herdl + β4 DIMijkl + β5 DIM²ijkl + β6 qLnSCCijkl + µHeifer kl(j) + µQuarter jkl(i) + eijkl, [2] where qMYijkl is the qMY for the ith sample (i = 1 – 10) of the jth quarter (j = 1 – 4) of the kth heifer (k = 1 – 82) from the lth herd (l = 1 – 3); β0 is the intercept (overall mean); β1 to β6 are

44 Chapter 3 IMI with NAS in the first 4 DIM the regression coefficients of the fixed effects: IMI status in early lactation, quarter position, herd, DIM, DIM quadratic and the natural logarithm of the qSCC, respectively; µHeifer kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer;

µQuarter jkl(i) was added to correct for within-quarter correlation of subsequent biweekly sampling days i (repeated statement) for quarter j of heifer k from herd l and eijkl is the random error term.

Quarter Milk PRL. The association between the qIMI status (predictor variable of main interest) and the quarter milk PRL (qPRL) concentration (outcome variable; ng/mL) in early lactation (determined between 1 and 4 DIM) was studied using a linear mixed model (PROC MIXED) in SAS (version 9.4; SAS Institute Inc., Cary, NC). The model was fit with season of calving (4 levels: January – March, April – June, July – September, October – December) and quarter position (2 levels: front vs. hind) as categorical predictor variables. DIMearly (4 levels: 1 DIM, 2 DIM, 3 DIM, 4 DIM) was included in the model as categorical predictor variable to correct for the expected rapid decrease of the milk PRL concentration within the first days after calving (Koprowski et al., 1972; Edgerton and Hafs, 1973; Marcek and Swanson, 1984). Daily qMY in early lactation was included in the model as continuous predictor variable. The qIMI status (3 levels: noninfected, infected with NAS or infected with 1 or more major pathogens) was forced in the model as categorical predictor variable of main interest. Herd was forced into the model as a fixed effect to correct for potential clustering of heifers within herds. Heifer was added as random effect to account for clustering of quarters within heifer.

The initial linear mixed model with PRL as outcome variable was: qPRLjkl = β0 + β1 qIMIjkl + β2 Quarter positionjkl + β3 Herdl + β4 DIMearly kl + β5 Seasonkl +

β6 qMYjkl + µHeifer kl(j) + ejkl, [3] where qPRLjkl is the predicted milk PRL concentration of the jth quarter (j = 1 – 4) of the kth heifer (k = 1 – 82) from the lth herd (l = 1 – 3); β0 is the intercept (overall mean); β1 to β6 are the regression coefficients of the fixed effects: IMI status in early lactation, quarter position, herd, DIM at the sampling day in early lactation, season of calving and the qMY, respectively;

µHeifer kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer and ejkl is the random error term.

45 Chapter 3 IMI with NAS in the first 4 DIM

For all linear mixed models, the goodness-of-fit measures included −2 × log-likelihood, the Akaike information criterion, and the Bayesian information criterion. Residuals were evaluated graphically and plotted against the predicted values. A Bonferroni’s correction was used to correct for multiple comparisons. Significance was assessed at P ≤ 0.05. Non-significant variables (P > 0.05) were omitted using a backward stepwise approach. Confounding was assessed by examining the effect of each variable on the estimates of other explanatory variables (Dohoo et al., 2003). No variables included in any final model resulted in substantial changes (>20%) of the estimates of other explanatory variables, indicating that confounding was not a problem.

Likelihood of NAS IMI. The association between the likelihood of NAS IMI (noninfected vs. NAS infected; outcome variable) and quarter position (front versus hind; predictor variable) at the first sampling was studied using a logistic mixed regression model (PROC GLIMMIX) in SAS (version 9.4; SAS Institute Inc., Cary, NC). Herd was forced into the model as a fixed effect to correct for potential clustering of heifers within herds. Heifer was added as random effect to account for clustering of quarters within heifer. Significance was assessed at P ≤ 0.05. The odds ratio and 95% confidence interval were calculated.

The logistic mixed model used for NAS IMI was: logit(pjkl) = β0 + β1 Quarter positionjkl + β2 Herdl + µHeifer kl(j) + ejkl, [4]

where logit(pjkl) = ln , and pjkl = P(qIMINAS jkl) denotes the probability of quarter j from heifer k in herd l having a NAS

IMI; qIMINAS jkl is a binary qIMI status observation (noninfected vs. NAS infected) of the jth quarter (j = 1 – 4) of the kth heifer (k = 1 – 82) from the lth herd (l = 1 – 3); β0 is the intercept

(overall mean); β1 and β2 are the regression coefficients of the fixed effects (quarter position and herd, respectively); µHeifer kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer and ejkl is the random error term.

46 Chapter 3 IMI with NAS in the first 4 DIM

2200 9 2000 8 1800

1600 7

1400 6

1200 5

1000 (kg) milkyield quarter Daily 4 800 3 600 Geometric mean SCC (x 1000 cell/mL)(xSCC 1000 mean Geometric 2 400

200 1

0 0

Days in milk

Noninfected (n = 220) (qSCC) Infected with NAS (n = 68) (qSCC)

Infected with major pathogen (n = 9) (qSCC) Noninfected (n = 220) (qMY)

Infected with NAS (n = 68) (qMY) Infected with major pathogen (n = 9) (qMY)

Figure 1. Evolution of average daily quarter milk yield (qMY) and geometric mean quarter milk somatic cell count (qSCC), both calculated from the original data, during the first 4 mo of lactation in quarters that were noninfected, infected with NAS, or infected with a major pathogen in early lactation (1-4 DIM). Error bars represent the positive standard error for each IMI status on each sampling day.

47 Chapter 3 IMI with NAS in the first 4 DIM

0

511 853

4,62 1,004 2,705 4,046 3,587 1,505 Maximum

Trueperella

1 8 8 7

71 25 16 601 sampling day

Minimum

Second 64 40 63 96 96 716 438 178 mean Geometric

000 cells/mL) , Resulting in the IMI status “infected with “infected status IMI the in Resulting

3

678 5,078 4,463 2,648 4,738 1,174 5,037 1,313

qSCC 1 (x Maximum

2

13 92 11 56 162 535 495 sampling day

Minimum

First

353 116 784 210 394 244 mean 1,190 1,531 Geometric

— — 6.74 6.74 2.25 2.25 5.62 % of % 76.4 positive samples samples = 89)(n

0 1.85 1.85 0.62 0.62 1.54 4.63 Resulting in the IMI status “infected with major pathogen”. major with “infected status IMI the in Resulting 67.9 20.99 2 (n = 324)(n % of % quarters

6 6 2 2 5 68 15 220 No. of

quarters

2

2 spp.

2 aureus , and 2 nonfunctional quarters were omitted.

1 coli .

4 E. coli spp positive cocci -

3 Overview of the bacteriological culturing results from samples collected in early lactation (1-4 DIM) and the associated quarter milk SCC milk quarter associated the and DIM) (1-4 lactation early in collected samples from results culturing bacteriological the of Overview and negative More than 2 phenotypically different bacterial colony types. Two quarters with clinical mastitis in early lactation, caused by caused lactation, early in mastitis clinical with quarters Two types. colony bacterial different phenotypically 2 than More 4 orynebacterium sculin scherichia taphylococcus C Bacillus S E E NAS

Culture positiveCulture Contaminated “noninfected”. status IMI the in Resulting 1 Table 1. Table (qSCC) at first (1-4 DIM) and second (15–18 DIM) sampling days 324in quarters 82 from dairy heifers NAS”. pyogenes

48 Chapter 3 IMI with NAS in the first 4 DIM

RESULTS

Likelihood of NAS IMI

With almost 21% of quarters eligible for sampling in early lactation (1 – 4 DIM) being infected with NAS (Table 1), this group of pathogens has the highest prevalence in our study. The likelihood of having an IMI with NAS in early lactation in a hind quarter was 76% higher compared to a front quarter (odds ratio = 1.76; 95% CI = 0.98-3.16; P = 0.06).

Table 2. Final linear mixed regression model describing the association between the natural log- transformed quarter milk SCC (outcome variable) during the first 4 mo of lactation and quarter-level IMI (qIMI) status in early lactation (1-4 DIM; main predictor of interest) of 297 quarters from 82 dairy heifers in 3 herds

Predictor variable No. of quarters Estimate SE P-value LSM Intercept — 5.36 0.11 <0.001 — 1 Herd 0.20 Herd 1 148 Referent — — 4.46 Herd 2 60 0.005 0.15 0.97 4.47 Herd 3 89 0.23 0.14 0.09 4.69 DIM — -0.05 0.002 <0.001 — DIM x DIM — 0.0003 0.00002 <0.001 — qIMI in early lactation <0.001 Noninfected 220 Referent — — 4.19 Infected with NAS 68 0.30 0.08 <0.001 4.49 Infected with MP2 9 0.75 0.22 <0.001 4.94 1Herd was forced in the model to correct for potential clustering of heifers within herds. 2Major pathogen.

Effect of Quarter-Level IMI on qSCC

The geometric mean qSCC in milk from noninfected quarters at the first (1 – 4 DIM; early lactation) and second sampling days (15 – 18 DIM) was 353,000 and 64,000 cells/mL, respectively (Table 1). Milk from NAS-infected quarters had a slightly higher geometric mean qSCC (394,000 and 96,000 cell/mL at the first and second sampling days, respectively),

49 Chapter 3 IMI with NAS in the first 4 DIM whereas the geometric mean qSCC in milk from quarters infected with a major pathogen was 480,000 and 285,000 cells/mL at the first and second sampling days, respectively.

The evolution of the qSCC over the first 4 mo of lactation in milk from noninfected quarters and quarters infected with NAS or a major pathogen in early lactation is depicted in Figure 1.

The variable quarter position was not significant and was omitted from the final model. Milk from quarters infected with NAS had a significantly higher sampling day qLnSCC than noninfected quarters (LSM = 4.49 and 4.19, respectively; P < 0.001; Table 2), and a not statistically significant lower sampling day qLnSCC than milk from quarters infected with a major pathogen (LSM = 4.94; P = 0.14).

200 180 160 140 120 100 80 60 40

Milk prolactin concentration (ng/mL) concentration prolactin Milk 20 0 1 2 3 4 Days in milk

Noninfected quarters (n = 220) Quarters infected with NAS (n = 68)

Figure 2. Evolution of the quarter milk prolactin concentration of quarters sampled in early lactation (1-4 DIM) as a function of IMI status (noninfected vs. infected with NAS). Error bar = ±1 standard deviation.

50 Chapter 3 IMI with NAS in the first 4 DIM

Effect of Quarter-Level IMI on qMY

The average daily MY of a noninfected quarter at the first sampling day (1 - 4 DIM; early lactation) was 4.62 kg (Table 3). The evolution of the daily qMY at sampling day as a function of the qIMI statuses is depicted in Figure 1. The daily MY over the first 4 mo of the first lactation did not differ between quarters infected with NAS (7.39 kg/d) and noninfected quarters [7.35 kg/d; standard error (SE) = 0.19; P = 0.99]. Excluding quarter position from the model as a predictor variable resulted in larger estimates of NAS IMI on MY (0.19 kg/day; SE = 0.20; P = 0.33). Hind quarters produced on average 0.93 kg more per day compared with front quarters (P < 0.001). Noninfected quarters produced 0.51 kg more per day during the remainder of the first lactation compared with quarters infected with a major pathogen in early lactation (SE = 0.51; P = 0.92). Excluding quarter position as a predictor variable from the model increased this difference to 0.75 kg/d (SE = 0.52; P = 0.16; Table 3).

PRL

The average qPRL concentration in milk in early lactation (1 – 4 DIM) was 100.43 ng/mL, with a strong decrease of the qPRL concentration over time. Samples that were collected at 1 DIM had an average qPRL concentration of 144.71 ng/mL, whereas at 2 DIM the concentration was 91.69 ng/mL. The lowest levels were reached at 3 and 4 DIM with 51.52 and 58.43 ng/mL, respectively. The qPRL concentration in early lactation as a function of the IMI status (noninfected quarters vs. quarters infected with NAS) is depicted in Figure 2.

The variable season was not significant and was omitted from the final model. The average qPRL concentration did not differ significantly between the 3 herds (P = 0.52; Table 4). Also, the qIMI status and season of calving were not significantly associated with qPRL concentration (P = 0.98 and P = 0.16, respectively). Still, the quarter position had an influence on qPRL concentration. The PRL concentration in milk collected from a hind quarter was 7.71 ng/mL higher (P < 0.001) than in milk collected from a front quarter. The daily qMY was negatively associated with the qPRL concentration (estimate: -2.93; SE = 1.01; P = 0.004).

51 Chapter 3 IMI with NAS in the first 4 DIM

— — — — — — 7.02 6.92 7.46 7.32 7.51 6.57

LSM

— — — — — value 0.16 0.73 0.10 0.19 0.33 0.16 - <0.001 <0.001 <0.001 <0.001 P

— — — — SE

0.19 0.30 0.26 0.002 0.00002 0.20 0.52 0.01

Natural log-transformed quarter milk

3 Model withoutModel position quarter

— —

5.00 0.44 0.08 0.19 0.10 0.0005 0.75 0.07 - - - - Estimate Referent Referent

Major pathogen. 2 — — — — 7.11 6.95 7.54 7.35 7.39 6.84 6.73 7.66 LSM

— — — value 0.13 0.58 0.10 0.56 0.83 0.31 - <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P

Full Full model — — — SE

0.19 0.29 0.26 0.002 0.00002 0.19 0.50 0.14 0.01

0.16 0.0005 0.51 0.07

4.62 0.43 0.08 0.04 0.92 - - - - Estimate Referent Referent Referent

9 quarters — 60 89 — — 68 — 148 220 152 145 o. of o. of N

2

at samplingat day

3

Final linear mixed regression model describing the association between quarter milk yield (outcome variable) during the first 4 mo of of mo 4 first the during variable)(outcome yield milk quarterbetween association the describing model regression mixed linear Final DIM

1

Herd 1 Herd 2 Herd 3 Herd Noninfected Infected with NAS Infected with MP Front Hind Predictor variable Intercept Herd DIM DIM x qIMI early lactation in Quarter position qLnSCC wasHerd forced in the model correctto for potential clustering heifersof within herds. 1 Table 3. Table 3 in heifers dairy 82 from quarters 297 of interest) of predictor main DIM; (1-4 lactation early in status (qIMI) IMI quarter-level and lactation herds SCC.

52 Chapter 3 IMI with NAS in the first 4 DIM

Table 4. Final linear mixed regression model describing the association between quarter milk prolactin concentration (ng/mL) (outcome variable) and quarter IMI (qIMI) status (main predictor of interest) in early lactation (1-4 DIM) of 297 quarters from 82 dairy heifers in 3 herds

Predictor variable No. of quarters Estimate SE P-value LSM Intercept — 154.37 7.83 <0.001 — Herd1 0.52 Herd 1 148 Referent — — 87.33 Herd 2 60 8.05 10.12 0.43 95.38 Herd 3 89 -4.36 8.71 0.62 82.96 DIM in early lactation — <0.001 — 1 DIM 124 Referent — — 145.57 2 DIM 70 -51.03 9.61 <0.001 94.54 3 DIM 80 -92.84 9.45 <0.001 52.72 4 DIM 23 -84.18 14.99 <0.001 61.39 qIMI status at calving 0.98 Noninfected 220 Referent — — 88.19 Infected with NAS 68 0.27 2.01 0.89 89.02 Infected with MP2 9 0.84 5.86 0.89 88.45 Quarter position <0.001 Front 152 Referent — — 84.70 Hind 145 7.71 1.32 <0.001 92.41 qMY3 at sampling day — -2.93 1.01 0.004 — 1Herd was forced in the model to correct for potential clustering of heifers within herds. 2Major pathogen. 3Quarter daily milk yield (kg).

DISCUSSION

This is the first longitudinal study that investigated the effect of the IMI status of quarters of early-lactating heifers, and more specifically the presence of NAS, on the qMY and qSCC throughout lactation in 3 herds equipped with an AMS. The latter approach allowed for matching the qIMI status in early lactation to the qMY and qSCC during the first 4 mo of lactation. By doing so, the major drawback of previous studies in which MY was measured at the animal level rather than per quarter and where the IMI status of the heifers was an aggregate of the quarter-level IMI statuses was elegantly circumvented. With 21% of the 324 quarters

53 Chapter 3 IMI with NAS in the first 4 DIM enrolled in this study being NAS infected, this group of pathogens was clearly the most prevalent cause of IMI in the early-lactation heifers, as expected (De Vliegher et al., 2012).

Quarters were considered as having an IMI if ≥100 CFU/mL was cultured (definition A in Dohoo et al., 2011). The goal of the authors was to identify as many existing infections as possible and to reduce the chances of an infected quarter being misclassified as noninfected. We opted for the threshold with the highest sensitivity and thus the lowest percentage of false- negative results because the main goal of our study was to compare the effect of IMI with NAS on the qSCC and qMY against noninfected quarters or quarters infected with a major pathogen. The higher SCC in milk from quarters considered to be infected with NAS using the threshold of 100 CFU/mL compared with noninfected quarters indicated that most quarters that were culture-positive for NAS were truly infected with NAS. Also, a huge and overlapping variation was seen in the number of colony-forming units per milliliter between the 25% lowest producing quarters and the 25% highest producing quarters (min = 100, max = 1,600; data not shown), indicating that the impact of NAS infections on qMY did not depend on the number of isolated colonies from the milk sample.

The increase in SCC in milk as a result of the inflammatory response due to an infection of the quarter with NAS was less pronounced than the response to IMI caused by major pathogens, which is in line with previous findings (Piepers et al., 2010; Supré et al., 2011). The overall geometric mean qSCC steeply decreased between the first and second sampling day, as was expected based on the findings of previous studies in which either composite (Dohoo, 1993; Laevens et al., 1997; Piepers et al., 2010) or quarter (Barkema et al., 1999; De Visscher et al., 2016) milk samples were collected. In the study of Barkema et al. (1999), the geometric mean qSCC in milk from noninfected quarters was as low as 42,000 cells/mL at the sixth milking after calving versus 170,000 cells/mL in milk from NAS-infected quarters and 1,257,000 cells/mL in milk from quarters infected with a major pathogen. Still, the effect of NAS on the qSCC in general (Piccart et al., 2016) and after parturition in particular might differ among the different NAS species (De Visscher et al., 2016).

Despite the higher qSCC, quarters infected with NAS in early lactation did not produce less milk than noninfected quarters. Numerically, NAS-infected quarters even outproduced

54 Chapter 3 IMI with NAS in the first 4 DIM noninfected quarters, whereas quarters infected with major pathogens produced less milk than noninfected quarters. This is in accordance with previous findings showing that heifers infected with NAS in early lactation produced 2 kg milk/d more than heifers that were not infected in early lactation (Piepers et al., 2013). The discrepancy between both studies in terms of significance might be partially explained by a lack of power (17% rather than 80%; data not shown) in our study because the assumptions made for the sample size calculation did not completely fit with the data obtained in our study. The difference in MY between NAS-infected and noninfected quarters was much lower than what was expected based on Piepers et al. (2010; 0.5 kg/d). Also, the standard deviation of the data in the present study was larger compared to the test data set (1.82 and 1.50, respectively), and the proportion of NAS-infected quarters in the present study was lower compared with the prevalence obtained in the study of Piepers et al. (2010; 21% and 33%, respectively), resulting in a lower total number of NAS-infected quarters than estimated in the sample size calculation. The lack of power in our study might also partially explain why the numerically lower daily MY in quarters infected with a major pathogen was not significantly different from the MY of noninfected quarters. Actually, only 9 quarters were infected with a major pathogen in early lactation. Moreover, more than half of these infections were caused by E. coli, which contrasts with the study of Piepers et al. (2010). In the latter study, Staph. aureus was the causative pathogen in 37% of the major pathogen- infected quarters. The higher prevalence of Staph. aureus might also explain the higher milk losses at the heifer level in that study because according to Heikkilä et al. (2018), SCM with Staph. aureus can lead to average milk losses of 2.2kg/day until 305 DIM, whereas mastitis caused by E. coli had an effect only on the 305-d MY in the case of CM.

Hind quarters outproduced the front quarters by 0.93 kg/d, as expected, yet interestingly the likelihood of having a NAS IMI in a hind quarter tended to be 1.76 higher (P = 0.06) compared with a front quarter. Correcting the model with qMY as outcome variable for the quarter position effect by adding this potential confounder as a categorical variable to the model further decreased the average difference in qMY between NAS-infected and noninfected quarters from 0.19 to 0.04 kg/d. In the studies by De Visscher et al. (2015, 2016), however, no significant difference in the distribution of NAS infections between front and hind quarters was found in general, although hind quarters tended (P = 0.087) to be more infected with the less relevant

55 Chapter 3 IMI with NAS in the first 4 DIM

NAS species (De Visscher et al., 2015). Still, one should take into account that, by dividing the NAS species into a more and a less relevant group, the number of quarters per group was relatively small and that in these data sets both heifers and multiparous cows were considered. The different infection dynamics during the dry period (e.g., IMI that were already present at dry-off and that did not cure during dry period) and the use of long-acting antimicrobials at drying-off in the multiparous cows only might have influenced the prevalence of NAS infections within the first days after calving as well. Also, in another study, the likelihood of NAS IMI in early-lactating heifers was not affected by quarter position (Piepers et al., 2011), leaving us with the question whether the finding in our study that hind quarters are more prone to NAS IMI is a coincidence. High-yielding multiparous dairy cows might inherently be more prone to NAS infections, but this effect was not found in heifers (Gröhn et al., 2004). In the latter study, multiparous cows with clinical NAS mastitis produced significantly more milk (between 2.3 and 2.7 kg/d) 1 mo before diagnosis than their noninfected herd mates. According to Piepers et al. (2013), the association between MY and NAS IMI was only partially confounded by the genetic potential for milk production, as including the breeding index for MY did not fully explain the observed difference in MY. These findings are reinforced by our data. Out of the 82 heifers included in our study, 28 heifers were culture-negative in all functional quarters at the first sampling day. Also, excluding these heifers from the dataset had little to no effect on the association between NAS IMI and the sample day qMY and qSCC (data not shown).

An inflammatory reaction in the udder, mostly caused by an IMI, results in an elevated qSCC, and it is generally accepted that a higher (q)SCC is associated with a lower (q)MY. NAS-infected quarters had a higher milk qSCC compared with noninfected quarters in our study, which indicates there is an inflammatory reaction in most of these quarters. Thus, it is remarkable that these NAS infections had little effect on the qMY estimates for NAS-infected quarters. One of the potential explanations for this somewhat awkward finding is that the increase in qSCC could have been mainly due to an increase in epithelial cells or macrophages and lymphocytes instead of neutrophils, which generally cause the greatest damage to the udder tissue (Capuco et al., 1986; Paape et al., 2002). Differential cell counting would probably have given more insight into the distribution of the different cell populations. Still, at the onset of

56 Chapter 3 IMI with NAS in the first 4 DIM this study, no routine method for differential cell counting (Damm et al., 2017) was available as it is now.

A possible explanation for the finding that IMI with NAS did at least not have a negative effect on qMY could be found in the effect of NAS IMI on the PRL levels. Recent studies (Lacasse et al., 2011; Lacasse and Ollier, 2015; Lacasse et al., 2016) have shown the importance of PRL measured in circulating plasma as a galactopoietic hormone in cattle. In our study, however, no difference could be found in the qPRL concentration between noninfected or NAS- infected quarters. This might support our finding that the qMY between noninfected or NAS- infected quarters did not significantly differ. The higher milk PRL levels in hind quarters, having the highest daily qMY, compared with front quarters supports the positive correlation between PRL level and MY. Results from a trial in which heifers in mid lactation were experimentally infected with 3 strains of NAS showed a slightly higher milk PRL level in infected quarters, although the difference with the control quarters was not significant (Piccart et al., 2015). Future studies should further unravel the local production of PRL in the (infected) bovine mammary gland and its role in the inflammatory pathways (e.g., neutrophil activation and migration) in bovine mastitis.

CONCLUSION

Non-aureus staphylococci as a group were the most prevalent mastitis pathogens, accounting for 76.4% of the IMI in early-lactating heifers. Our data support the status of NAS (as a group) as minor pathogens, only slightly elevating the qSCC in milk from infected quarters compared with noninfected quarters. No significant difference in the daily qMY could be found between NAS-infected and noninfected quarters. The odds of the hind quarters being NAS- infected were 76% higher compared with front quarters, explaining at least partly why NAS- infected quarters had a numerically slightly higher qMY compared to noninfected quarters in this study. The milk PRL level in early lactation did not differ between NAS-infected and noninfected quarters.

To further scrutinize the ability of (certain) NAS species to cause persistent IMI and the effect of transient versus persistent infections in freshly calved heifers on qMY and qSCC

57 Chapter 3 IMI with NAS in the first 4 DIM during the remainder of the lactation, further identification to the species level of the isolated NAS is needed.

58 Chapter 3 IMI with NAS in the first 4 DIM

REFERENCES

Barkema, H. W., H. A. Deluyker, Y. H. Schukken, and T. J. Lam. 1999. Quarter-milk somatic cell count at calving and at the first six milkings after calving. Prev. Vet. Med. 38(1):1-9. Boutet, P., J. Sulon, R. Closset, J. Detilleux, J. F. Beckers, F. Bureau, and P. Lekeux. 2007. Prolactin-induced activation of nuclear factor kappaB in bovine mammary epithelial cells: role in chronic mastitis. J. Dairy Sci. 90(1):155-164. Bruckmaier, R. M., D. Schams, and J. W. Blum. 1992. Aetiology of disturbed milk ejection in parturient primiparous cows. J. Dairy Res. 59(4):479-489. Capuco, A. V., M. J. Paape, and S. C. Nickerson. 1986. In vitro study of polymorphonuclear leukocyte damage to mammary tissues of lactating cows. Am. J. Vet. Res. 47(3):663-668. Damm, M., C. Holm, M. Blaabjerg, M. N. Bro, and D. Schwarz. 2017. Differential somatic cell count-A novel method for routine mastitis screening in the frame of Dairy Herd Improvement testing programs. J. Dairy Sci. 100(6):4926-4940. De Visscher, A., S. Piepers, F. Haesebrouck, and S. De Vliegher. 2016. Intramammary infection with coagulase-negative staphylococci at parturition: Species-specific prevalence, risk factors, and effect on udder health. J. Dairy Sci. 99(8):6457-6469. De Visscher, A., S. Piepers, K. Supré, F. Haesebrouck, and S. De Vliegher. 2015. Short communication: Species group-specific predictors at the cow and quarter level for intramammary infection with coagulase-negative staphylococci in dairy cattle throughout lactation. J. Dairy Sci. 98(8):5448-5453. De Vliegher, S., L. K. Fox, S. Piepers, S. McDougall, and H. W. Barkema. 2012. Invited review: Mastitis in dairy heifers: nature of the disease, potential impact, prevention, and control. J. Dairy Sci. 95(3):1025-1040. Dohoo, I. R. 1993. An evaluation of the validity of individual cow somatic cell counts from cows in early lactation. Prev. Vet. Med. 16(2):103-110. Dohoo, I., W. Martin, and H. Stryhn. 2003. Veterinary Epidemiologic Research. AVC Inc., Charlottetown, PEI, Canada. Dohoo, I. R., J. Smith, S. Andersen, D. F. Kelton, S. Godden, and Mastitis Research Workers' Conference. 2011. Diagnosing intramammary infections: evaluation of definitions based on a single milk sample. J. Dairy Sci. 94(1):250-261.

59 Chapter 3 IMI with NAS in the first 4 DIM

Edgerton, L. A. and H. D. Hafs. 1973. Serum luteinizing hormone, prolactin, glucocorticoid, and progestin in dairy cows from calving to gestation. J. Dairy Sci. 56(4):451-458. Fulkerson, W. J., G. J. Sawyer, and C. B. Gow. 1980. Investigations of ultradian and circadian- rhythms in the concentration of cortisol and prolactin in the plasma of dairy-cattle. Aust J Biol Sci. 33(5):557- 561. Gröhn, Y. T., D. J. Wilson, R. N. Gonzalez, J. A. Hertl, H. Schulte, G. Bennett, and Y. H. Schukken. 2004. Effect of pathogen-specific clinical mastitis on milk yield in dairy cows. J. Dairy Sci. 87(10):3358-3374. Heikkilä, A. M., E. Liski, S. Pyörälä, and S. Taponen. 2018. Pathogen-specific production losses in bovine mastitis. J. Dairy Sci. 101(10):9493-9504. Hockett, M. E., F. M. Hopkins, M. J. Lewis, A. M. Saxton, H. H. Dowlen, S. P. Oliver, and F. N. Schrick. 2000. Endocrine profiles of dairy cows following experimentally induced clinical mastitis during early lactation. Anim. Reprod. Sci. 58(3-4):241-251. Hogan, J. S., R. N. Gonzales, R. J. Harmon, S. C. Nickerson, S. P. Oliver, J. W. Pankey, and K. L. Smith. 1999. Laboratory Handbook on Bovine Mastitis. Rev. ed. National Mastitis Council. Madison, WI. Koprowski, J. A., H. A. Tucker, and E. M. Convey. 1972. Prolactin and growth hormone circadian periodicity in lactating cows. Proc. Soc. Exp. Biol. Med. 140(3):1012-1014. Lacasse, P., V. Lollivier, R. M. Bruckmaier, Y. R. Boisclair, G. F. Wagner, and M. Boutinaud. 2011. Effect of the prolactin-release inhibitor quinagolide on lactating dairy cows. J. Dairy Sci. 94(3):1302-1309. Lacasse, P. and S. Ollier. 2015. The dopamine antagonist domperidone increases prolactin concentration and enhances milk production in dairy cows. J. Dairy Sci. 98(11):7856- 7864. Lacasse, P., S. Ollier, V. Lollivier, and M. Boutinaud. 2016. New insights into the importance of prolactin in dairy ruminants. J. Dairy Sci. 99(1):864-874. Laevens, H., H. Deluyker, Y. H. Schukken, L. De Meulemeester, R. Vandermeersch, E. De Muelenaere, and A. De Kruif. 1997. Influence of parity and stage of lactation on the somatic cell count in bacteriologically negative dairy cows. J. Dairy Sci. 80(12):3219- 3226.

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Le Provost, F., C. Leroux, P. Martin , P. Gaye and J. Dijane 1994. Prolactin gene expression in ovine and caprine mammary gland. Neuroendocrinol 60(3): 305-313. Malven, P. V. and J. McMurtry. 1974. Measurement of prolactin in milk by radioimmunoassay. J. Dairy Sci. 57(4):411-415. Marcek, J. M. and L. V. Swanson. 1984. Effect of photoperiod on milk production and prolactin of Holstein dairy cows. J. Dairy Sci. 67(10):2380-2388. Nguyen, D.D. and M.C. Neville M.C. (1998) Tight junction regulation in the mammary gland. J. Mammary Gland Biol. Neoplasia 3(3): 233-246. Oliver, S. P., M. J. Lewis, B. E. Gillespie, H. H. Dowlen, E. C. Jaenicke, and R. K. Roberts. 2003. Prepartum antibiotic treatment of heifers: milk production, milk quality and economic benefit. J. Dairy Sci. 86(4):1187-1193. Ollivier-Bousquet, M. 1998. Transferrin and prolactin transcytosis in the lactating mammary epithelial cell. J. Mammary Gland Biol Neoplasia 3(3):303–313. Paape, M., J. Mehrzad, X. Zhao, J. Detilleux, and C. Burvenich. 2002. Defense of the bovine mammary gland by polymorphonuclear neutrophil leukocytes. J Mammary Gland Biol Neoplasia 7(2):109-121. Paradis, M. E., E. Bouchard, D. T. Scholl, F. Miglior, and J. P. Roy. 2010. Effect of nonclinical Staphylococcus aureus or coagulase-negative staphylococci intramammary infection during the first month of lactation on somatic cell count and milk yield in heifers. J. Dairy Sci. 93(7):2989-2997. Pearson, L. J., J. H. Williamson, S. A. Turner, S. J. Lacy-Hulbert, and J. E. Hillerton. 2013. Peripartum infection with Streptococcus uberis but not coagulase-negative staphylococci reduced milk production in primiparous cows. J. Dairy Sci. 96(1):158-164. Piccart, K. 2016. The host-pathogen interaction of ecologically diverse coagulase-negative staphylococci in bovine mastitis, with a focus on prolactin. PhD thesis. Ghent University, Ghent, Belgium. Piccart, K., S. Piepers, J. Verbeke, N. M. de Sousa, J. F. Beckers, and S. De Vliegher. 2015. Milk prolactin response and quarter milk yield after experimental infection with coagulase-negative staphylococci in dairy heifers. J. Dairy Sci. 98(7):4593-4600. Piccart, K., J. Verbeke, A. De Visscher, S. Piepers, F. Haesebrouck, and S. De Vliegher. 2016.

61 Chapter 3 IMI with NAS in the first 4 DIM

Local host response following an intramammary challenge with Staphylococcus fleurettii and different strains of Staphylococcus chromogenes in dairy heifers. Vet. Res. 47(1):56. Piepers, S., G. Opsomer, H. W. Barkema, A. de Kruif, and S. De Vliegher. 2010. Heifers infected with coagulase-negative staphylococci in early lactation have fewer cases of clinical mastitis and higher milk production in their first lactation than noninfected heifers. J. Dairy Sci. 93(5):2014-2024. Piepers, S., K. Peeters, G. Opsomer, H. W. Barkema, K. Frankena, and S. De Vliegher. 2011. Pathogen group specific risk factors at herd, heifer and quarter levels for intramammary infections in early lactating dairy heifers. Prev. Vet. Med. 99(2-4):91-101. Piepers, S., Y. H. Schukken, P. Passchyn, and S. De Vliegher. 2013. The effect of intramammary infection with coagulase-negative staphylococci in early lactating heifers on milk yield throughout first lactation revisited. J. Dairy Sci. 96(8):5095-5105. Schukken, Y. H., R. N. Gonzalez, L. L. Tikofsky, H. F. Schulte, C. G. Santisteban, F. L. Welcome, G. J. Bennett, M. J. Zurakowski, and R. N. Zadoks. 2009. CNS mastitis: nothing to worry about? Vet. Microbiol. 134(1-2):9-14. Supré, K., F. Haesebrouck, R. N. Zadoks, M. Vaneechoutte, S. Piepers, and S. De Vliegher. 2011. Some coagulase-negative Staphylococcus species affect udder health more than others. J. Dairy Sci. 94(5):2329-2340. Taponen, S., H. Simojoki, M. Haveri, H. D. Larsen, and S. Pyörälä. 2006. Clinical characteristics and persistence of bovine mastitis caused by different species of coagulase- negative staphylococci identified with API or AFLP. Vet. Microbiol. 115(1-3):199-207. Timms, L. L. and L. H. Schultz. 1987. Dynamics and significance of coagulase-negative staphylococcal intramammary infections. J. Dairy Sci. 70(12):2648-2657. Tomazi, T., J. L. Goncalves, J. R. Barreiro, M. A. Arcari, and M. V. dos Santos. 2015. Bovine subclinical intramammary infection caused by coagulase-negative staphylococci increases somatic cell count but has no effect on milk yield or composition. J. Dairy Sci. 98(5):3071- 3078. Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher. 2014. Invited review: effect, persistence, and virulence of coagulase-negative Staphylococcus species associated with ruminant udder health. J. Dairy Sci. 97(9):5275-

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5293. Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher. 2015. Identification, typing, ecology and epidemiology of coagulase negative staphylococci associated with ruminants. Vet. J. 203(1):44-51. Weaver, S. R. and L. L. Hernandez. 2016. Autocrine-paracrine regulation of the mammary gland. J. Dairy Sci. 99(1):842-853. Wilde, C. J. and M. Peaker. 1990. Autocrine control in milk secretion. J. Agr. Sci. 114:235- 238.

63

Chapter 4

The effect of intramammary infection in early

lactation with non-aureus staphylococci in general

and Staphylococcus chromogenes specifically on

quarter milk somatic cell count

and quarter milk yield

D. Valckenier,1 S. Piepers,1 A. De Visscher,1,2 and S. De Vliegher1

1M-team & Mastitis and Milk Quality Research Unit, Department of Reproduction, Obstetrics

and Herd Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke,

Belgium B-9820

2Flanders research institute for agriculture, fisheries, and food (ILVO), Technology and Food

Science, Agricultural Engineering, Merelbeke, Belgium B-9820

Adapted from Journal of Dairy Science, 2020, 103:768-782

https://doi.org/10.3168/jds.2019-16818

Chapter 4 IMI with NAS in the first 18 DIM

ABSTRACT

This longitudinal study aimed to evaluate the impact of subclinical intramammary infection (IMI) with non-aureus staphylococcal (NAS) species in the first 18 days (d) in milk (DIM) on the quarter milk somatic cell count (qSCC) and quarter milk yield (qMY) during the first 4 months (mo) of lactation in Holstein Friesian heifers.

Quarter milk samples were collected from 82 heifers from 1 to 4 DIM until 130 DIM on a biweekly (14 d) basis for determination of the qSCC; qMY data was available through the automatic milking systems. The quarter samples collected on the first (1-4 DIM) and second (15-18 DIM) sampling days were used for bacteriological culturing to determine the IMI status.

In this study, 324 quarters from 82 heifers were enrolled, of which 68 were NAS-infected at the first sampling day. Only 16 (23.5%) of these quarters were still NAS-infected at the second sampling day, demonstrating the high spontaneous cure rate of these infections shortly after calving; 9 of these 16 cases were infected with the same NAS species. Interestingly, none of the NAS-infected quarters at the first sampling day acquired a new infection with a major pathogen at the second sampling day, whereas 2.3% of the noninfected quarters did. All 102 isolates phenotypically identified as NAS were further identified to the species level. Staphylococcus (S.) chromogenes was the most prevalent species at the first (29.4% of all NAS) and second sampling day (52.9%).

Quarters infected with S. chromogenes at the first sampling day had a significantly higher qSCC in later lactation than noninfected quarters, whereas this was not true for quarters infected with all other NAS species (i.e. as a group of species). The average daily qMY in the first 4 mo of lactation did not differ between noninfected quarters and quarters infected with S. chromogenes or all other NAS species at the first sampling day.

Persistently NAS species-infected quarters in the first 18 DIM (i.e., infected with the same NAS species on the first and second sampling days) had the highest qSCC later in lactation, followed by quarters with a new NAS IMI (i.e., noninfected at the first sampling day and infected with NAS at the second sampling day). The qSCC from transiently NAS species- infected quarters (i.e., not infected with the same NAS species at the second sampling day) was not significantly higher in later lactation compared with that in noninfected quarters. The IMI

67 Chapter 4 IMI with NAS in the first 18 DIM status of quarters in the first 18 DIM, combining culture results at 1 to 4 and 15 to 18 DIM (new, persistent, and transient IMI), was not significantly associated with the daily qMY in the first 4 mo after calving.

In general, NAS should be considered minor pathogens with no adverse effect on daily qMY in quarters of heifers infected in the first 18 DIM and with a high spontaneous cure rate. Staphylococcus chromogenes was the most prevalent species, causing an increase in qSCC comparable to the level of quarters infected with a major pathogen; S. chromogenes caused most infections that persisted through at least the first 18 DIM.

Key words: non-aureus staphylococci, species-specific infections, quarter milk yield, quarter somatic cell count

68 Chapter 4 IMI with NAS in the first 18 DIM

INTRODUCTION

As a group, NAS are the most prevalent (ranging from 9.1% to 16.6% of milk samples) causative agents of IMI in dairy cows in many regions (Wilson et al., 1997; Pitkälä et al., 2004; Tenhagen et al., 2006), and are typically more isolated from subclinical than from clinical cases of mastitis (Persson Waller et al., 2011; Heikkilä et al., 2018). The relevance of NAS IMI with regard to the impact on the milk production in affected quarters is still under debate as inconsistent results have been reported (Timms and Schultz, 1987; Gröhn et al., 2004; Taponen et al., 2006; Schukken et al., 2009; Paradis et al., 2010). Part of the contradiction can be attributed to differences in the length of the follow-up period, breed, parity, diagnostic methods, and definition of IMI status among the studies. More recent studies, considering only heifers (Valckenier et al., 2019) or cows of all parities (Tomazi et al., 2015; Heikkilä et al., 2018), still consider NAS (as a group) as minor pathogens, only slightly elevating the quarter milk somatic cell count (qSCC) in case of subclinical mastitis, and with little or no effect on the quarter milk yield (qMY).

The inconclusive results might also be, at least partly, explained by the diversity of this group of bacteria. Over 50 species and subspecies have been identified (http://www.bacterio.net/staphylococcus.html, accessed April 10, 2019), of which more than 19 have been isolated from bovine milk samples (Capurro et al., 2009; Tomazi et al., 2014; Vanderhaeghen et al., 2014; De Visscher et al., 2016). In many countries (reviewed by Vanderhaeghen et al., 2015), Staphylococcus (S.) chromogenes is the most prevalent species in milk samples from healthy primi- and multiparous cows and animals with subclinical (SCM) or clinical (CM) mastitis, although differences among countries exist. In cases of SCM in Scandinavian countries such as Sweden, S. epidermidis was more frequently isolated than S. chromogenes (Persson Waller et al., 2011; Nyman et al., 2018), whereas in Finland S. simulans was the predominant species (Taponen et al., 2016).

The prevalence of the different NAS species isolated from bovine milk samples depends not only on the country or region but also varies with parity and stage of lactation of the animal. Heifers are more prone to IMI with NAS before or at the beginning of their lactation compared with multiparous cows (37.5% vs. 5.8% of the quarters, respectively) (Taponen et al., 2007).

69 Chapter 4 IMI with NAS in the first 18 DIM

Up to 45.5% of quarters might be NAS-infected at first calving (Oliver et al., 2003). In Sweden, 38, 26 and 36% of the 658 quarter milk samples that were positive for the 7 most common NAS species in that study were from first-parity, second-parity and older cows, respectively (Nyman et al., 2018). In heifers, S. chromogenes, S. xylosus and S. simulans were more commonly found compared with third-parity or older cows (Thorberg et al., 2009; De Visscher et al., 2016; Condas et al., 2017a; Nyman et al., 2018), and the association with stage of lactation varies among the different NAS species (Condas et al., 2017a). In Flanders, S. chromogenes is also the predominant species both at parturition and in lactation in randomly selected heifers and older animals, followed by S. sciuri and S. cohnii at parturition (De Visscher et al., 2016) and by S. simulans, S. xylosus, S. epidermidis and S. haemolyticus in lactation (Piessens et al., 2011; Supré et al., 2011).

Although the spontaneous cure rate of untreated subclinical NAS IMI in both primiparous and multiparous cows ranges between 39.5% (Taponen et al., 2006) and 64.5% to 72% (McDougall, 1998; Wilson et al., 1999), respectively, some NAS infections may persist for an entire lactation (Aarestrup and Jensen, 1997; Chaffer et al., 1999; Taponen et al., 2007). Up to 33% of quarters were classified as having a persistent IMI with a NAS species in Sweden, and the infection persisted in almost 40% of the quarters from whom S. chromogenes was isolated (Nyman et al., 2018). Based on the ability to cause persistent infections and the genetic heterogeneity and clonality, some NAS species appear to be more adapted to the cow’s mammary gland, such as S. chromogenes, S. epidermidis, S. simulans and S. hyicus, and might hypothetically have a different impact on qSCC and qMY than other less cow-adapted NAS species (Taponen et al., 2008; Piessens et al., 2012; Fry et al., 2014; Nyman et al., 2018).

The effect of subclinical NAS IMI on SCC and milk yield (MY) has been studied almost exclusively at the heifer or cow level. Only a few studies have been conducted at the quarter level. Supré et al. (2011) called S. chromogenes, S. simulans and S. xylosus the species “more relevant for udder health” with regard to the SCC of quarters being subclinically infected with these species, although no qMY data were available. The effect on the qMY was elaborated by Tomazi et al. (2015) by milking the quarters (n = 82) individually using a bucket milking system to measure average qMY on sampling day. Subclinical IMI caused by S. chromogenes was

70 Chapter 4 IMI with NAS in the first 18 DIM found to have no effect on the qMY, despite an increased qSCC, in a cohort of both primiparous and multiparous cows at different stages of lactation. As these two studies either did not measure the qMY or did not have a longitudinal follow-up, the question remains whether the “more relevant” species have a long-term impact on qSCC and qMY when quarters are infected in the first 18 d after calving.

This longitudinal study aimed at evaluating (1) the effect of subclinical IMI with all NAS species at the first sampling day (i.e., 1-4 DIM) on qSCC and qMY during the first 4 mo of lactation in heifers (i.e., animals in first lactation) in 3 dairy herds milking with an automated milking system (AMS), and (2) the impact of transient, persistent and new subclinical IMI with all NAS species or with S. chromogenes in the first 18 d after calving by including culture results available at 15 to 18 DIM, on qSCC and qMY during the first 4 mo of lactation.

MATERIALS AND METHODS

Herds, Animals and Study Design

The data in this study are part of the data set described in Valckenier et al. (2019). In short, the study was conducted from August 2013 until the end of October 2014. Eighty-two Holstein Friesian dairy heifers were included from 3 commercial dairy herds in the province of West Flanders (Belgium) equipped with an AMS. Herd owners were approached by the first author and asked whether they were willing to participate. None of the 3 herds treated their end-term heifers with antimicrobials before calving, and none of the herds participated in the local dairy herd improvement program.

The first author visited the herds 2 times per week, on Mondays and Thursdays, to perform quarter milk sampling of the heifers that calved since the previous visit. Every heifer was thus sampled between 1 and 4 DIM. After the first sampling, all heifers were followed up until 127- 130 DIM by collecting milk samples every 14 d another 9 times after the first sampling and recording all CM events.

During the sampling period, every heifer that calved was included in the study. No further inclusion or exclusion criteria were applied at the herd or heifer level. When a heifer was sold,

71 Chapter 4 IMI with NAS in the first 18 DIM culled, or died within the first 4 mo of lactation, it was not replaced by another animal. Nonfunctional quarters or quarters with CM before or at the first sampling were excluded because the aim of this study was to investigate the effects of IMI with NAS causing subclinical mastitis. For this reason, 2 quarters with CM and 2 nonfunctional quarters were excluded.

Sampling, Quarter SCC, and Quarter MY

Quarter milk samples were collected aseptically every 14 d on 10 occasions starting at 1 to 4 DIM according to the guidelines of the National Mastitis Council (Hogan et al., 1999). The time interval between the last milking before sampling and the sampling itself varied because all animals were milked voluntarily by the AMS. The qSCC of each sample was measured using a Direct Cell Counter DCC (DeLaval International AB, Tumba, Sweden) in the laboratory of the Mastitis and Milk Quality Research group at the Faculty of Veterinary Medicine of Ghent University (Merelbeke, Belgium). Milk samples taken within 1 to 4 d after calving (referred to as the first sampling day) and between 15 and 18 DIM (referred to as the second sampling day) were used for bacteriological culturing to determine the IMI status of each quarter.

Quarter milk production per milking was available through the herd management software of the AMS (DelPro, DeLaval International AB). The estimated daily qMY at the first sampling day (1-4 DIM) was calculated by first summing the qMY of all milkings from calving up to d 7 after the sampling, and then dividing this total amount of milk produced during this period by the number of days. Subsequently, the estimated daily qMY on the next 9 sampling days (with an interval of 14 d) was also calculated by dividing the sum all the quarter milk productions from 7 d before until 7 d after each sampling by 14 (i.e., the number of days in this period).

Microbiology

Standard bacteriological culture was performed as described elsewhere (Valckenier et al., 2019). Briefly, a 0.01-mL loop of milk was spread on a quadrant of both an esculin blood agar plate and a MacConkey agar plate (Thermo Fisher Diagnostics N.V., Groot-Bijgaarden, Belgium) and aerobically incubated at 37°C. The plates were phenotypically examined after 24

72 Chapter 4 IMI with NAS in the first 18 DIM h and again after 48 h. Identification of bacteria was done by Gram staining, inspection of colony morphology, and biochemical testing. Quarters were considered infected if one or more colonies were observed (≥ 100 CFU/mL) in the milk sample.

Staphylococcus aureus, esculin-positive and esculin-negative cocci, Trueperella pyogenes, Escherichia coli, Klebsiella spp., and other Gram-negative bacteria were regarded as major pathogens. A quarter yielding a major pathogen and NAS, Bacillus spp., or Corynebacterium spp. was classified as infected with the major pathogen, whereas a quarter yielding 2 different major pathogens or 2 different minor pathogens was considered infected with the pathogen that had the highest count (CFU/mL). Samples yielding 3 or more different bacterial colony types were considered to be contaminated.

All isolates phenotypically identified as NAS during the trial were stored in Microbank vials (Pro-Lab diagnostics, Richmond Hill, ON, Canada) at −80°C until the end of the sampling period. These isolates were identified at the species level using transfer RNA intergenic spacer PCR (tDNA- PCR) or, if no identification was obtained, sequencing of the 16S rRNA or rpoB gene, as described by Supré et al. (2009).

Clinical Mastitis and Culling

Two heifers from herd 3 were culled after a severe case of CM between 57 and 74 DIM. One quarter from a heifer in herd 1 was immediately dried off after a CM case occurring between 85 and 102 DIM. Two heifers (herd 2 and 3) were sold between 85 and 102 DIM. The left hindquarter from a heifer in herd 3 was dried off at 85 DIM because of a teat end injury. The samples taken before these animals were culled or dried off were included in the analysis.

Quarter-Level IMI

From the 82 heifers included in this study, 324 quarters were eligible for sampling at the first sampling day (1–4 DIM). A quarter was defined as having an IMI at the first sampling day with a NAS species, Bacillus spp., Corynebacterium spp., S. aureus, Streptococcus spp., or

73 Chapter 4 IMI with NAS in the first 18 DIM

Gram-negative bacteria when the sample collected between 1 and 4 DIM contained ≥ 100 CFU/mL of the specific bacteria (Dohoo et al., 2011).

Combining the culture results from the first and second sampling days allowed us to define quarters as having a transient IMI (tIMI) with a NAS species if the NAS species isolated at the first sampling day was not recovered at the second sampling day. A persistent IMI (pIMI) was defined when the quarter was infected with the same NAS species at the second sampling day. Quarters that were noninfected at the first sampling day and infected with a NAS species at the second sampling day were defined as having a new IMI (nIMI). A quarter was considered to be noninfected on the first and at second sampling days if both milk samples were culture- negative for any pathogen.

Overall, 648 samples were collected from 324 quarters from 82 heifers (Table 1). Based on standard bacteriological culturing, 220 (67.9%) quarters were noninfected at the first sampling day (1–4 DIM), whereas 245 (75.6%) quarters were noninfected at the second sampling day. Overall, NAS were the most prevalent group of bacteria with 21.0 and 10.5% of quarters being infected at the first and at second sampling days, respectively.

To investigate the effect of IMI at the first sampling day with S. chromogenes or other NAS species, only quarters that were noninfected or infected with either NAS or a major pathogen at the first sampling day (1–4 DIM) were retained for further analysis. If the NAS isolate could not be identified to the species level (e.g., by contamination of the Microbank vial or no growth after culturing of the sample), the corresponding quarter (n = 18) was omitted from the analysis.

Only quarters considered to be having a tIMI, pIMI, or nIMI with a specific NAS species or that were noninfected at the first and second sampling days were retained to evaluate the effect of these infections at the qSCC and daily qMY during the first 4 mo of lactation.

74 Chapter 4 IMI with NAS in the first 18 DIM

— — — 9.4 1.6 25.0 10.9 53.1

Resulting in the in Resulting 4 samples (nsamples 64)= % of % culture-positive

— 1.9 4.9 0.3 2.2 4.6 75.6 10.5 Second Second daysampling (n = 324)(n % of % samples

6 1 7 N 16 — 34 15 245

—

samples (nsamples 89)= % of % culture-positive Resulting in the IMI status “infected with major pathogen”. major with “infected status IMIthe in Resulting 3

First samplingFirst day . 6.7 6.7 2.3 2.3 5.6 1.9 — 1.9 0.6 0.6 1.5 4.6 67.9 76.4 21.0 (n = 324)(n % of % samples

1 6 6 2 2 5 68 15 N 220 More than 2 bacterial colony types. 6

3

3 spp. Resulting in the IMI status “noninfected”. status IMI the in Resulting 2

3

2

5 spp. positive cocci -

4 negative positive - - . Overview of bacteriological culture results from samples (n = 324) collected at the first (1-4 DIM) and second (15-18 DIM) samplingsecondDIM) (15-18 DIM) (1-4 and first the collected at 324) = from(n samplesculture results Overviewof bacteriological . Corynebacterium Bacillus Staphylococcus aureus Esculin Escherichia coli NAS Bacteriological cultureBacteriological result Culture Culture Contaminated samples. of Number 1

Table 1 Table day IMI “infected with status NAS”.

75 Chapter 4 IMI with NAS in the first 18 DIM

Statistical Analyses

All data were entered in an electronic spreadsheet program (Excel 2016, Microsoft Corp., Redmond, WA) and were checked for unlikely values.

Common Features of the Statistical Models. The associations between quarter IMI (qIMI) status, sampling day qSCC, and sampling day qMY (outcome variables) throughout the first 4 mo of lactation, respectively, were determined fitting two separate linear mixed models with herd (forced into the model as fixed effect), heifer (random effect), and quarter (repeated statement) to correct for potential clustering of heifers within herds, for clustering of quarters within heifer, and for clustering of observations (10 repeated samplings, including that at 1-4 DIM) within quarters, respectively. A natural logarithmic transformation of the qSCC (qLnSCC) was performed to obtain a normal distribution. The models included sampling day (10 levels: 1 to 10) and quarter position (2 levels: front vs. hind) as categorical predictor variables.

The initial linear mixed model with qSCC as outcome variable was: qLnSCCijkl = β0 + β1 qIMIjkl + β2 Quarter positionjkl + β3 Herdl + β4 Sampling dayijkl +

µHeifer_kl(j) + µQuarter_jkl(i) + eijkl, [SCC model] where qLnSCCijkl is the natural logarithm of qSCC for the ith sample (i = 1–10) of the jth quarter (j = 1–4) of the kth heifer (k = 1–82) from the lth herd (l = 1–3); β0 is the intercept

(overall mean); β1 to β4 are the regression coefficients of the fixed effects: quarter IMI status, quarter position, herd and sampling day, respectively; µHeifer_kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer; µQuarter_jkl(i) was added to correct for within-quarter correlation of subsequent biweekly sampling days i (repeated statement) for quarter j of heifer k from herd l; and eijkl is the random error term.

The initial linear mixed model with daily qMY as outcome variable was: qMYijkl = β0 + β1 qIMIjkl + β2 Quarter positionjkl + β3 Herdl + β4 Sampling dayijkl + β5 qLnSCCijkl + µHeifer_kl(j) + µQuarter_jkl(i) + eijkl, [MY model] where qMYijkl is the quarter milk yield for the ith sample (i = 1–10) of the jth quarter (j = 1–4) of the kth heifer (k = 1–82) from the lth herd (l = 1–3); β0 is the intercept (overall mean); β1 to

76 Chapter 4 IMI with NAS in the first 18 DIM

β5 are the regression coefficients of the fixed effects: quarter IMI status, quarter position, herd, sampling day, and the natural logarithm of the qSCC at sampling day, respectively; µHeifer_kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer;

µQuarter_jkl(i) was added to correct for within-quarter correlation of subsequent biweekly sampling days i (repeated statement) for quarter j of heifer k from herd l; and eijkl is the random error term.

Linear mixed models were fit in SAS (PROC MIXED; version 9.4; SAS Institute Inc., Cary, NC). For all linear mixed models, the goodness-of-fit measures included −2 × log-likelihood, the Akaike information criterion, and the Bayesian information criterion. Residuals were evaluated graphically and plotted against the predicted values. A Bonferroni’s correction was used to correct for multiple comparisons. Significance was assessed at P ≤ 0.05. Non-significant variables (P > 0.05) were omitted using a backward stepwise approach. Confounding was assessed by examining the effect of each variable on the estimates of other explanatory variables (Dohoo et al., 2003). No variables included in any final model resulted in substantial changes (>20%) of the estimates of other explanatory variables, indicating that confounding was not a problem. In all linear mixed models, a first-order autoregressive correlation structure was used to account for the clustering of repeated sampling days within a quarter.

To evaluate the proportion of variance in qLnSCC and qMY occurring at the heifer, quarter and sampling day levels, 3-level null models (intercept only) were fit. The total variance was estimated as follows:

Var(Zijk) = var(μHeifer(k)) + var(μQuarter(jk)) + var(μSample(ijk)) where Zijk is the qLnSCC or qMY for the ith sample (i = 1–10) of the jth quarter (j = 1–4) of the kth heifer (k = 1–82); var(μHeifer(k)) is the variance occurring at the heifer level, var(μQuarter(jk)) is the variance occurring at the quarter level, and var(μSample(ijk)) is the variance occurring at the sampling day level.

Effect of IMI with S. chromogenes Versus All Other NAS at the First Sampling Day on qSCC and qMY. Because the number of observations was too small to analyze individual species other than S. chromogenes, these were included as a group (named “all other NAS

77 Chapter 4 IMI with NAS in the first 18 DIM species”) in the statistical analyses. Associations between quarter IMI status at the first sampling day (categorical predictor variable of main interest with 4 levels: noninfected, infected with S. chromogenes, infected with all other NAS species or infected with a major pathogen) and the outcome variable sampling day qSCC and sampling day qMY throughout the first 4 mo of lactation, respectively, were determined fitting two separate linear mixed models.

Effect of tIMI, pIMI, or nIMI with NAS on qSCC and qMY. The associations between qIMI status based on the first two sampling days (categorical predictor variable of main interest with 4 levels: noninfected at the first and second sampling days, transient infection with a NAS species, persistent infection with a NAS species, or new infection with a NAS species) and sampling day qSCC and sampling day qMY (2 separate outcome variables) throughout the first 4 mo of lactation, respectively, were determined fitting two separate linear mixed models.

Effect of tIMI, pIMI, or nIMI with S. chromogenes on qSCC and qMY. The association between qIMI status based on the first two sampling days (categorical predictor variable of main interest with 4 levels: noninfected at the first and second sampling days, transient infection with S. chromogenes, persistent infection with S. chromogenes, or new infection with S. chromogenes) and either sampling day qSCC or sampling day qMY (outcome variables) throughout the first 4 mo of lactation, respectively, was determined fitting two separate linear mixed models.

RESULTS

Intramammary Infection after Calving

Of the 220 quarters that were noninfected at the first sampling day, 179 (81.4%) remained noninfected, 15 (6.8%) had a nIMI with NAS and 5 (2.3%) had a major pathogen IMI at the second sampling day (Table 2). Forty-eight of the 68 (70.6%) quarters that were initially infected with NAS at the first sampling day were no longer infected with NAS at the second sampling day. None of the 68 NAS-infected quarters at the first sampling day became infected

78 Chapter 4 IMI with NAS in the first 18 DIM with a major pathogen at the second sampling day. Sixteen (23.5%) quarters infected with NAS at the first sampling day were still NAS-infected at the second sampling day, 9 of which were infected with the same NAS species at both sampling days and were therefore considered to have a pIMI. The IMI-status of 4 (5.9%) quarters infected with NAS at the first sampling day could not be determined at the second sampling day because of contamination of the milk samples. In 45 quarters that were infected with a NAS species at the first sampling day, the same NAS species was no longer present at the second sampling day (i.e., tIMI).

NAS Species Distribution

In total, 102 isolates were phenotypically identified as NAS and further identified to the species level. Staphylococcus chromogenes was the most prevalent species at the first sampling day (29.4% of 68 isolates), followed by S. xylosus, S. vitulinus and S. sciuri (each 8.8%). At the second sampling day, S. chromogenes was still the predominant species (52.9% of 34 isolates), followed by S. xylosus (11.8%; Figure 1). One isolate, initially phenotypically misclassified as NAS, was identified as S. aureus. Eighteen of the isolates (17.7%), i.e. 14 obtained at the first sampling day and 4 at the second sampling day, could not be identified to the species level due to contamination of the Microbank vial or could not be cultured from the Microbank, and these quarters were omitted for further statistical analyses.

79 Chapter 4 IMI with NAS in the first 18 DIM

4

4 0 0 1 10 E. coli- E. Undefined

5 0 0 1 1 EPC

Phenotypically identified

2 0 0 1 0 0 aureus

S.

sp.

8 2 0 0 0 Bacillus -infected, n = 2; EPC-infected, n = 2; = n EPC-infected, 2; = n -infected,

3

3 1 0 0 0 spp. C. S. aureus S.

3 — — — — possible No species No species identification identification

4 — — — —

qIMI at second status sampling day 2 sampling day spp. asspp. on first Different NAS NAS

9 — — — — as on as first sampling day Same NAS spp.Same

1

1 0 1 15 16 As a group

Contaminated samples (more than 2 phenotypically different colony types). 4 0 1 2 45 179 spp. spp. Noninfected

)

coli

EPC

Corynebacterium n = 5) at the first sampling at the first n = 5) day, and thus forrelevant statistical further scope in analysis of the study, demonstrated. this are 3 , . Overview of quarter-level the IMI the (qIMI) status sampling at first day (1-4 and second DIM) the associated qIMI the sampling day at (15–18 DIM)

cocci cocci ( aureus

scherichia qIMI at first status sampling day Noninfected NAS S. Esculin-positive E ( pathogen-infected major and 68), = (n NAS-infected 220), = (n noninfected were that quarters Only 1

Table 2 Table 297 in quarters from 82 heifers 3 dairy herdsin infected NAS.as

80 Chapter 4 IMI with NAS in the first 18 DIM

species identification and relative distribution of the isolates phenotypically identified as NAS at the first (1-4 DIM) and second and DIM)(1-4 first the NASat phenotypicallyas isolates identified the of distributionand relative identification species Staphylococcus . Figure 1 Figure (15-18 DIM) sampling day (quarters whichfrom the isolates could not be identified species to the level were omitted for statisticalfurther analysis).

81 Chapter 4 IMI with NAS in the first 18 DIM

Effect of IMI with S. chromogenes Versus All Other NAS at the First Sampling Day on qSCC and qMY

The variable quarter position was not significantly associated with qLnSCC in the initial model and omitted from the final model. Quarters infected with S. chromogenes at the first sampling day (n = 20; 144,000 cells/mL; 1-4 DIM) had a significantly higher sampling day qLnSCC than noninfected quarters (n = 220; 67,000 cells/mL, P < 0.001; Table 3). Interestingly, we found no difference between the SCC in quarters infected with S. chromogenes and quarters infected with a major pathogen (n = 9; 162,000 cells/mL; P = 0.99), and between quarters infected with any other NAS species than S. chromogenes (n = 34; 66,000 cells/mL) at the first sampling day and noninfected quarters (P = 0.93).

The variable qLnSCC was not significantly associated with qMY in the initial model and omitted from the final model. The qMY over the first 4 mo of lactation did not differ between noninfected quarters (n = 220; 7.37 kg/d) and quarters infected with S. chromogenes (n = 20; 7.31 kg/d; P = 0.85) or with any other NAS species (n = 34; 7.35 kg/d; P = 0.94; Table 4). Hind quarters produced, on average, 0.89 kg/d more than front quarters (P < 0.001). Noninfected quarters produced 0.42 kg/d more in the first 4 mo of the lactation compared with quarters infected with a major pathogen (n = 9) at the first sampling day (P = 0.47).

For the LnSCC-model, only 11.6 and 9.4% of the variation resided at the heifer and quarter level, respectively, whereas 79.0% of the total variation occurred at the level of the repeated samplings (sampling day). For the MY-model, the variation was the smallest at the heifer level (6.0%) and slightly larger at the quarter level (16.6%). The largest proportion of the variation also resided at the level of the repeated samplings (77.4 %).

82 Chapter 4 IMI with NAS in the first 18 DIM

Table 3. Final linear mixed regression model describing the association between the natural log- transformed quarter milk somatic cell count (qLnSCC; outcome variable) during the first 4 mo of lactation and quarter-level IMI (qIMI) status at the first sampling day (1-4 DIM; main predictor of interest) of 283 quarters from 82 dairy heifers in 3 herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 5.70 0.11 <0.001 — 2 Herd 0.15 Herd 1 141 Referent — — 4.50 Herd 2 56 0.10 0.16 0.54 4.60 Herd 3 86 0.27 0.14 0.05 4.77 Sampling day <0.001 1 (1-4 DIM) Referent — — 6.23 2 (15-18 DIM) -1.55 0.09 <0.001 4.68 3 (29-32 DIM) -1.86 0.09 <0.001 4.38 4 (43-46 DIM) -1.91 0.10 <0.001 4.32 5 (57-60 DIM) -1.84 0.10 <0.001 4.40 6 (71-74 DIM) -1.87 0.10 <0.001 4.36 7 (85-88 DIM) -1.84 0.10 <0.001 4.39 8 (99-102 DIM) -1.83 0.10 <0.001 4.41 9 (113-116 DIM) -1.94 0.10 <0.001 4.29 10 (127-130 DIM) -1.51 0.10 <0.001 4.72 qIMI status on first sampling day <0.001 Noninfected 220 Referent — — 4.21 Infected with S. chromogenes 20 0.76 0.13 <0.001 4.97 Infected with a NAS species other than S. chromogenes 34 -0.009 0.10 0.93 4.20 Infected with major pathogen3 9 0.87 0.22 <0.001 5.09 1Number of quarters. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Escherichia coli¸ Staphylococcus aureus, esculin-positive cocci. Quarters for whom the NAS isolate could not be identified to the species level (n = 14) were omitted from the analysis.

83 Chapter 4 IMI with NAS in the first 18 DIM

Table 4. Final linear mixed regression model describing the association between the daily quarter milk yield (outcome variable) during the first 4 mo of lactation and quarter-level IMI (qIMI) status at the first sampling day (1-4 DIM; main predictor of interest) of 283 quarters from 82 dairy heifers in 3 herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 4.34 0.19 <0.001 — 2 Herd 0.16 Herd 1 141 Referent — — 7.14 Herd 2 56 -0.11 0.30 0.72 7.03 Herd 3 86 0.44 0.26 0.10 7.58 Sampling day <0.001 1 (1-4DIM) Referent — — 4.76 2 (15-18 DIM) 2.04 0.04 <0.001 6.81 3 (29-32 DIM) 2.99 0.05 <0.001 7.75 4 (43-46 DIM) 3.22 0.06 <0.001 7.99 5 (57-60 DIM) 3.19 0.07 <0.001 7.96 6 (71-74 DIM) 3.01 0.08 <0.001 7.77 7 (85-88 DIM) 2.75 0.08 <0.001 7.51 8 (99-102 DIM) 2.61 0.09 <0.001 7.37 9 (113-116 DIM) 2.55 0.09 <0.001 7.32 10 (127-130 DIM) 2.47 0.09 <0.001 7.23 qIMI status on first sampling day 0.88 Noninfected 220 Referent — — 7.37 Infected with S. chromogenes 20 -0.06 0.32 0.85 7.31 Infected with a NAS species other than S. chromogenes 34 -0.02 0.26 0.94 7.35 Infected with major pathogen3 9 -0.37 0.51 0.47 6.95 Quarter position <0.001 Front 148 Referent — — 6.80 Hind 135 0.88 0.14 <0.001 7.69 1Number of quarters. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Escherichia coli¸ Staphylococcus aureus, esculin-positive cocci.

84 Chapter 4 IMI with NAS in the first 18 DIM

Effect of tIMI, pIMI, or nIMI with All NAS Species on qSCC and qMY

Besides the 8 pIMI caused by S. chromogenes, only S. xylosus was responsible for 1 other pIMI with NAS at the first sampling day. Species other than S. chromogenes were the cause of more tIMI and nIMI cases. The evolution of mean qSCC and mean daily qMY over the first 4 mo of lactation for quarters having a tIMI, pIMI, and nIMI with NAS at the first and second sampling day are depicted in Figure 2.

The variable quarter position was not significantly associated with qLnSCC in the initial model and omitted from the final model. The SCC in quarters having a tIMI with a NAS species (n = 45; 73,000 cells/mL) was not significantly higher than that of noninfected quarters (n = 177; 63,000 cells/mL; P = 0.10; Table 5). Quarters with a pIMI with a NAS species (n = 9) had the highest SCC (226,000 cells/mL; P < 0.001), followed by quarters with a nIMI with a NAS species (n = 15) at the second sampling day (119,000 cells/mL; P < 0.001).

The variable qLnSCC was not significantly associated with qMY in the initial model and omitted from the final model. The qIMI status at the first and second sampling day was not significantly associated with the daily qMY in the first 4 mo after calving (Table 6). Noninfected quarters (n = 177) and quarters with a tIMI with NAS (n = 45) had the same daily qMY (7.37 kg/d vs. 7.37 kg/d; P = 0.99), whereas qMY was slightly but not significantly different in quarters having a pIMI (n = 9; 7.33 kg/d; P = 0.93) or a nIMI (n = 15; 7.72kg/d; P = 0.35).

85 Chapter 4 IMI with NAS in the first 18 DIM

9 1800

8 1600

7 1400

6 1200

5 1000

800 4 Mean daily qMY (kg)qMY daily Mean

Mean qSCC (x1000 cells/mL)(x1000 qSCC Mean 600 3

400 2

200 1

0 0

Days in milk Noninfected¹ (n = 177) (qSCC) Transient IMI² (n = 45) (qSCC) Persistent IMI³ (n = 9) (qSCC) New IMI⁴ (n = 15) (qSCC) Noninfected (n = 177) (qMY) Transient IMI (n = 45) (qMY) Persistent IMI (n = 9) (qMY) New IMI (n = 15) (qMY)

Figure 2. Evolution of the mean quarter milk somatic cell count (qSCC) and mean daily quarter milk yield (qMY) during the first 4 mo of lactation for quarters having a transient, persistent and new quarter- level IMI (qIMI) with NAS on the first (1-4 DIM) and second (15-18 DIM) sampling day after calving.

1Noninfected was defined as a culture-negative test result at the first and second sampling day. ²Transient IMI was defined as a culture-positive test result for a NAS species at first sampling day and a culture- negative test result for the same NAS species at the second sampling day. ³Persistent IMI was defined as a culture-positive test result for the same NAS species at the first and second sampling days. 4New IMI was defined as a culture-negative test result at the first sampling day and a culture-positive test result at the second sampling day. Error bars represent the positive standard error of qSCC and qMY for each IMI status on each sampling day.

86 Chapter 4 IMI with NAS in the first 18 DIM

Table 5. Final linear mixed regression model describing the association between the natural log- transformed quarter milk somatic cell count (qLnSCC; outcome variable) during the first 4 mo of lactation and transient, persistent and new quarter-level IMI (qIMI) with any NAS species at the first (1-4 DIM) and second (15-18 DIM) sampling day after calving (main predictor of interest) of 246 quarters from 80 dairy heifers in 3 herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 5.66 0.11 <0.001 — 2 Herd 0.09 Herd 1 126 Referent — — 4.56 Herd 2 52 -0.005 0.16 0.97 4.56 Herd 3 68 0.30 0.14 0.04 4.86 Sampling day <0.001 1 (1-4DIM) Referent — — 6.27 2 (15-18 DIM) -1.58 0.10 <0.001 4.69 3 (29-32 DIM) -1.88 0.10 <0.001 4.39 4 (43-46 DIM) -1.93 0.11 <0.001 4.35 5 (57-60 DIM) -1.83 0.11 <0.001 4.44 6 (71-74 DIM) -1.87 0.11 <0.001 4.40 7 (85-88 DIM) -1.83 0.11 <0.001 4.44 8 (99-102 DIM) -1.79 0.11 <0.001 4.48 9 (113-116 DIM) -1.90 0.11 <0.001 4.38 10 (127-130 DIM) -1.50 0.11 <0.001 4.77 qIMI status on first & second sampling days <0.001 Noninfected3 177 Referent — — 4.15 Transient infection4 with any NAS species 45 0.14 0.09 0.10 4.29 Persistent infection5 with any NAS species 9 1.27 0.17 <0.001 5.42 New infection6 with any NAS species 15 0.63 0.14 <0.001 4.78 1Number of quarters. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Based on a culture-negative test result on the first and second sampling days. 4Based on a culture- positive test result for a bacterial species at first sampling day and a culture-negative test result for the same bacterial species at the second sampling day. 5Based on a culture-positive test result for the same bacterial species on the first and second sampling days. 6Based on a culture-negative test result at the first sampling day and a culture-positive test result at the second sampling day.

87 Chapter 4 IMI with NAS in the first 18 DIM

Table 6. Final linear mixed regression model describing the association between the daily quarter milk yield (outcome variable) during the first 4 mo of lactation and transient, persistent and new quarter-level intramammary infection (qIMI) with any NAS species at the first (1-4 DIM) and second (15-18 DIM) sampling day after calving (main predictor of interest) of 246 quarters from 80 dairy heifers in 3 herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 4.25 0.20 <0.001 — 2 Herd 0.12 Herd 1 126 Referent — — 7.29 Herd 2 52 -0.07 0.31 0.83 7.23 Herd 3 68 0.53 0.28 0.06 7.82 Sampling day <0.001 1 (1-4DIM) Referent — — 4.94 2 (15-18 DIM) 2.05 0.04 <0.001 6.99 3 (29-32 DIM) 3.02 0.05 <0.001 7.97 4 (43-46 DIM) 3.21 0.07 <0.001 8.16 5 (57-60 DIM) 3.23 0.07 <0.001 8.17 6 (71-74 DIM) 3.03 0.08 <0.001 7.97 7 (85-88 DIM) 2.77 0.09 <0.001 7.72 8 (99-102 DIM) 2.64 0.09 <0.001 7.58 9 (113-116 DIM) 2.58 0.10 <0.001 7.52 10 (127-130 DIM) 2.50 0.10 <0.001 7.44 qIMI status on first & second sampling days 0.81 Noninfected3 177 Referent — — 7.37 Transient infection4 with any NAS species 45 -0.004 0.24 0.99 7.37 Persistent infection5 with any NAS species 9 -0.04 0.46 0.93 7.33 New infection6 with any NAS species 15 0.35 0.37 0.35 7.72 Quarter position <0.001 Front 128 Referent — — 6.99 Hind 118 0.92 0.16 <0.001 7.91 1Number of quarters. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Based on a culture-negative test result on both the first and second sampling days. 4Based on a culture-positive test result for a bacterial species at the first sampling day and a culture-negative test result for the same bacterial species at the second sampling day. 5Based on a culture-positive test result for the same bacterial species on the first and second sampling days. 6Based on a culture-negative test result at the first sampling day and a culture-positive test result at the second sampling day.

88 Chapter 4 IMI with NAS in the first 18 DIM

Effect of tIMI, pIMI, or nIMI with S. chromogenes on qSCC and qMY

The variable quarter position was not significantly associated with qLnSCC in the initial model and omitted from the final model. Transient (n = 12; 93,000 cells/mL), persistent (n = 8; 340,000 cells/mL) as well as new (n = 5; 302,000 cells/mL) IMI with S. chromogenes resulted in a significantly higher sampling day qLnSCC in affected quarters compared with noninfected quarters (n = 177; 63,000 cells/mL; P = 0.01; P < 0.001 and P < 0.001, respectively; Table 7). The SCC in quarters with a nIMI with S. chromogenes was not different compared with that of quarters with pIMI (P = 0.99).

The variable qLnSCC was not significantly associated with qMY in the initial model and omitted from the final model. Quarters that were noninfected at the first and second sampling days (n = 177) had an average daily MY of 7.38 kg in the first 4 mo of lactation (Table 8). Persistent IMI with S. chromogenes (n = 8; 7.11 kg/d) had no significant effect on the daily qMY (P = 0.60). The daily qMY in quarters newly infected with S. chromogenes (n = 5; 7.92 kg/d) was not significantly different from that of noninfected quarters (P = 0.41) during the first 4 mo of lactation.

89 Chapter 4 IMI with NAS in the first 18 DIM

Table 7. Final linear mixed regression model describing the association between the natural log- transformed quarter milk somatic cell count (qLnSCC; outcome variable) during the first 4 mo of lactation and transient, persistent and new quarter-level IMI (qIMI) with Staphylococcus chromogenes on the first (1-4 DIM) and second (15-18 DIM) sampling days after calving (main predictor of interest) of 202 quarters from 74 dairy heifers in 3 herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 5.62 0.11 <0.001 — 2 Herd 0.05 Herd 1 108 Referent — — 4.91 Herd 2 39 0.13 0.14 0.36 5.04 Herd 3 55 0.32 0.13 0.02 5.22 Sampling day <0.001 1 (1-4 DIM) Referent — — 6.67 2 (15-18 DIM) -1.61 0.11 <0.001 5.06 3 (29-32 DIM) -1.86 0.12 <0.001 4.82 4 (43-46 DIM) -1.96 0.12 <0.001 4.71 5 (57-60 DIM) -1.83 0.12 <0.001 4.84 6 (71-74 DIM) -1.94 0.12 <0.001 4.74 7 (85-88 DIM) -1.78 0.12 <0.001 4.90 8 (99-102 DIM) -1.77 0.12 <0.001 4.91 9 (113-116 DIM) -1.92 0.12 <0.001 4.76 10 (127-130 DIM) -1.49 0.12 <0.001 5.18 qIMI status on first & second sampling days <0.001 Noninfected3 177 Referent — — 4.15 Transient infection4 with S. chromogenes 12 0.38 0.15 0.01 4.53 Persistent infection5 with S. chromogenes 8 1.68 0.18 <0.001 5.83 New infection6 with S. chromogenes 5 1.56 0.22 <0.001 5.71 1Number of quarters. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Based on a culture-negative test result on the first and second sampling days. 4Based on a culture- positive test result for a bacterial species at the first sampling day and a culture-negative test result for the same bacterial species at the second sampling day. 5Based on a culture-positive test result for the same bacterial species on the first and second sampling days. 6Based on a culture-negative test result at the first sampling day and a culture-positive test result at the second sampling day.

90 Chapter 4 IMI with NAS in the first 18 DIM

Table 8. Final linear mixed regression model describing the association between the daily quarter milk yield (qMY; outcome variable) during the first 4 mo of lactation and transient, persistent and new quarter-level IMI (qIMI) with Staphylococcus chromogenes at the first (1-4 DIM) and second (15-18 DIM) sampling day after calving (main predictor of interest) of 202 quarters from 74 dairy heifers in 3 herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 4.31 0.21 <0.001 — 2 Herd 0.29 Herd 1 108 Referent — — 7.34 Herd 2 39 -0.06 0.35 0.86 7.28 Herd 3 55 0.44 0.30 0.15 7.78 Sampling day <0.001 1 (1-4 DIM) Referent — — 4.96 2 (15-18 DIM) 2.06 0.05 <0.001 7.02 3 (29-32 DIM) 3.06 0.06 <0.001 8.02 4 (43-46 DIM) 3.23 0.08 <0.001 8.19 5 (57-60 DIM) 3.23 0.09 <0.001 8.19 6 (71-74 DIM) 3.03 0.09 <0.001 7.99 7 (85-88 DIM) 2.75 0.10 <0.001 7.71 8 (99-102 DIM) 2.64 0.11 <0.001 7.60 9 (113-116 DIM) 2.56 0.11 <0.001 7.52 10 (127-130 DIM) 2.50 0.11 <0.001 7.46 qIMI status on first & second sampling days 0.80 Noninfected3 177 Referent — — 7.38 Transient infection4 with S. chromogenes 12 0.08 0.43 0.86 7.46 Persistent infection5 with S. chromogenes 8 -0.27 0.52 0.60 7.11 New infection6 with S. chromogenes 5 0.54 0.66 0.41 7.92 Quarter position <0.001 Front 106 Referent — — 7.03 Hind 96 0.86 0.19 <0.001 7.90 1Number of quarters. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Based on a culture-negative test result both on the first and second sampling days. 4Based on a culture-positive test result for a bacterial species at the first sampling day and a culture-negative test result for the same bacterial species at the second sampling day. 5Based on a culture-positive test result for the same bacterial species on the first and second sampling days. 6Based on a culture-negative test result at the first sampling day and a culture-positive test result at the second sampling day.

91 Chapter 4 IMI with NAS in the first 18 DIM

DISCUSSION

Most studies have reported that IMI caused by NAS results in a higher SCC compared with that in noninfected quarters, whereas there is more ambiguity about the effect of NAS IMI on the MY. Some studies have found a negative association between NAS IMI and MY (Timms and Schultz, 1987; Gröhn et al., 2004), whereas others found no association (Tomazi et al., 2015; Heikkilä et al., 2018) or even a positive association (Compton et al., 2007; Schukken et al., 2009; Piepers et al., 2010, 2013). These conflicting results can be explained at least in part by the fact that, in many studies, NAS were considered a homogeneous group of different species, whereas enough evidence now exists to show that the different species differ in pathogenicity, virulence, ecology and epidemiology [reviewed by Vanderhaeghen et al. (2014, 2015)]. In our previous study, we investigated the effect of IMI with NAS, as a group, in the first 4 d after calving on the qSCC and qMY in the first 4 mo of lactation in dairy heifers (Valckenier et al., 2019). Non-aureus staphylococci IMI at 1 to 4 DIM was found to cause a slight increase in qSCC (89,000 cells/mL) during the first 130 DIM compared with that in noninfected quarters (66,000 cells/mL), whereas it was not associated with daily qMY. The aim of the present study was to provide a more in-depth analysis by studying species-specific relationships with qSCC and qMY. Furthermore, rather than only focusing on IMI at the first sampling day, IMI status at both 1 to 4 DIM and 15 to 18 DIM were considered, allowing us to study the impact of new, transient and persistent IMI with NAS, and S. chromogenes in particular, on milk production and udder health during the first 4 mo of lactation.

As expected (Fox, 2009; De Vliegher et al., 2012), NAS were the most prevalent cause of IMI in fresh heifers in our study, with 68 of the 324 quarters being NAS-infected at the first sampling day. Also not surprisingly, S. chromogenes was the most prevalent species (20 out of 68 quarters), followed by S. xylosus, S. vitulinus, and S. sciuri, each of which were isolated from 6 different quarters. By way of comparison, in the study of De Visscher et al. (2016), S. chromogenes accounted for 41.4% of NAS species isolated from heifers and multiparous cows within 4 d after parturition. Up to 47,5% and 74.1% of NAS isolates were identified as S. chromogenes in the studies of Fry et al. (2014) and Tomazi et al. (2015), respectively.

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The effect of NAS IMI at the first sampling day on qSCC in later lactation depends on the NAS species, whether the infections persists for at least 2 weeks, and the time after calving when infection occurs (1-4 DIM vs. 15-18 DIM). The qSCC in quarters having a tIMI with NAS at the first sampling day did not differ significantly from that of noninfected quarters when all isolated NAS species were taken into account. Both pIMI and nIMI resulted in a significantly higher qSCC in the first 130 DIM compared with that of noninfected quarters or quarters having a tIMI with NAS. This is in contrast to the results of Thorberg et al. (2009), where no difference between nonpersistent and persistent subclinical IMI caused by NAS was observed, although this study was based on the composite SCC measured at only 2 milk recordings. When only S. chromogenes was considered, the persistence of IMI and the time of infection in the first 18 d after calving appeared less decisive because tIMI, pIMI, and nIMI resulted in a significantly higher qSCC compared with that of noninfected quarters, although pIMI and nIMI resulted also in the highest qSCC in affected quarters. Previous studies at the quarter level (Fry et al., 2014; Condas et al., 2017b) or a combination of cow and quarter level (Nyman et al., 2018) also showed that S. chromogenes was among the NAS species that resulted in a significantly higher SCC and was more prevalent in high- than in low-SCC quarters. We acknowledge strain-typing of the NAS isolates cultured at different time points from the same quarter is imperative to consider IMI a persistent infection. However, Fry et al. (2014) have demonstrated that, if a NAS species was isolated multiple times from the same quarter, in 91.4% of these quarters the isolates had the same pulsed-field gel electrophoresis banding pattern. This supports our approach to consider the infection as persistent if the same NAS species was isolated from 2 successive samplings.

In contrast to the effect of IMI with NAS on qSCC, NAS IMI at the first sampling day or 2 weeks later and average daily qMY in the first 4 mo of lactation were not associated. This is expected, because most of the IMI cured spontaneously during the first 2 weeks after calving, and is in line with the results of Tomazi et al. (2015). The fact that the negative association between daily qMY and IMI caused by major pathogens was not significant can be attributed in part to a lack of power. Part of the lack of power in our study can be explained by the smaller than expected number of IMI during the sampling period and the very small numerical difference in the estimated MY between NAS-infected and noninfected quarters. To avoid a

93 Chapter 4 IMI with NAS in the first 18 DIM lack of power, future studies with larger numbers of samples could be more easily conducted due to the availability of new diagnostic techniques, such as the MALDI-TOF MS, which allows for a more rapid, much cheaper, and reliable identification of NAS to the species level (Cameron et al., 2018).

Results of bacteriological culturing showed that of the 68 quarters subclinically infected with NAS at the first sampling day, only 23.5% were still infected with NAS at the second sampling day whereas 70.6% had spontaneously cured. This high level of spontaneous cure is slightly higher than reported by McDougall (1998), Wilson et al. (1999) and Nyman et al. (2018), and almost twice the level (39.5%) observed in the study of Taponen et al. (2006). A first explanation for this discrepancy might be the different type of sampling; in the current study, all quarters were sampled after calving, whereas in the study of Taponen et al. (2006), field mastitis cases were sampled. A second reason might be the difference in interval between 2 samplings: 30 d in Taponen et al. (2006) compared with 14 d in our study. The longer interval potentially increases the risk of re-infection. It is also important to consider that the ability to persist in the udder for longer periods varies greatly among different NAS species (Aarestrup and Jensen, 1997; Chaffer et al., 1999). At least 11 different NAS species have been found to cause persistent IMI (S. chromogenes, S. simulans, S. epidermidis, S. haemolyticus, S. xylosus, S. cohnii, S. capitis, S. nepalensis, S. warneri, S. hyicus, and S. saprophyticus) (Thorberg et al., 2009; Mørk et al., 2012; Fry et al., 2014; Nyman et al., 2018), with several strains within each species being able to cause these persistent infections (Mørk et al., 2012).

In our study, none of the quarters infected with a NAS species at the first sampling day became infected with a major pathogen at the second sampling day, although this finding is not significantly different from noninfected quarters (odds ratio = 2.79; 95% confidence interval = 0.15- 51.36). Further investigation using a larger study population would be necessary to come to definitive conclusions regarding the potential protective effects of IMI with NAS against IMI with major pathogens. Several factors might contribute to this protective effect; for example, the production of bacteriocins (Nascimento et al., 2005; Braem et al., 2014; Carson et al., 2017). This might partly explain the higher MY in animals having an IMI with NAS in the study of Piepers et al. (2010).

94 Chapter 4 IMI with NAS in the first 18 DIM

Besides the high spontaneous cure rate in the period between the first and second sampling days, 15 of the 220 noninfected quarters acquired a nIMI with a NAS species by the second sampling day. These nIMI cases came with a higher qSCC during the first 4 mo of lactation compared with quarters having a tIMI. Of the 15 nIMI cases, 5 were caused by S. chromogenes, and the qSCC until 130 DIM was not significantly lower compared with quarters having a pIMI with S. chromogenes. A potential explanation could be that these nIMI cases at the second sampling day might persist for longer in the lactation. This highlights the importance of preventing heifer mastitis, not only at the end of gestation and around calving but also during lactation. In our previous study (Valckenier et al., 2019), the effect of NAS IMI on the qSCC during the first 4 mo of lactation was underestimated because it was determined based only on IMI status at 1 to 4 DIM. Thus, quarters having a nIMI at the second sampling day were considered to be among the noninfected quarters at 1 to 4 DIM. These results stress the importance of determining the IMI status not only within the first days after calving but also at a later time point.

Interestingly, quarters having a nIMI with NAS at the second sampling day had the highest daily qMY in the first 4 mo of lactation, although the difference was not significant, most probably due to the small number of samples. This raises the question whether animals having the highest (genetic) predisposition are more susceptible for IMI with NAS, or whether these NAS IMI might have a protective effect against IMI with major pathogens, which partly explained the higher MY in NAS-infected heifers (Piepers et al., 2010). Our previous findings showed no difference in prolactin level in milk from NAS-infected quarters compared with noninfected quarters (Valckenier et al., 2019), which puts the positive effect of NAS IMI on MY further under debate. More research is needed to investigate the infection dynamics of NAS IMI further into lactation to fully understand the impact (and potential protective effects) of these infections for milk production and udder health.

95 Chapter 4 IMI with NAS in the first 18 DIM

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Piessens, V., S. De Vliegher, B. Verbist, G. Braem, A. Van Nuffel, L. De Vuyst, M. Heyndrickx, and E. Van Coillie. 2012. Intra-species diversity and epidemiology varies among coagulase-negative Staphylococcus species causing bovine intramammary infections. Vet. Microbiol. 155:62–71. https://doi.org/10.1016/j.vetmic.2011.08.005. Piessens, V., E. Van Coillie, B. Verbist, K. Supré, G. Braem, A. Van Nuffel, L. De Vuyst, M. Heyndrickx, and S. De Vliegher. 2011. Distribution of coagulase-negative Staphylococcus species from milk and environment of dairy cows differs between herds. J. Dairy Sci. 94:2933–2944. https://doi.org/10.3168/jds.2010-3956. Pitkälä, A., M. Haveri, S. Pyörälä, V. Myllys, and T. Honkanen-Buzalski. 2004. Bovine mastitis in finland 2001—Prevalence, distribution of bacteria, and antimicrobial resistance. J. Dairy Sci. 87:2433–2441. https://doi.org/10.3168/jds.S0022-0302(04)73366-4. Schukken, Y.H., R.N. Gonzalez, L.L. Tikofsky, H.F. Schulte, C.G. Santisteban, F.L. Welcome, G.J. Bennett, M.J. Zurakowski, and R.N. Zadoks. 2009. CNS mastitis: Nothing to worry about?. Vet. Microbiol. 134:9–14. https://doi.org/10.1016/j.vetmic.2008.09.014. Supré, K., S. De Vliegher, O.C.C. Sampimon, R.N.N. Zadoks, M. Vaneechoutte, M. Baele, E. De Graef, S. Piepers, and F. Haesebrouck. 2009. Technical note: Use of transfer RNA- intergenic spacer PCR combined with capillary electrophoresis to identify coagulase- negative Staphylococcus species originating from bovine milk and teat apices. J. Dairy Sci. 92:3204–3210. https://doi.org/10.3168/jds.2008-1923. Supré, K., F. Haesebrouck, R.N.N. Zadoks, M. Vaneechoutte, S. Piepers, and S. De Vliegher. 2011. Some coagulase-negative Staphylococcus species affect udder health more than others. J. Dairy Sci. 94:2329–2340. https://doi.org/10.3168/jds.2010-3741. Taponen, S., J. Björkroth, and S. Pyörälä. 2008. Coagulase-negative staphylococci isolated from bovine extramammary sites and intramammary infections in a single dairy herd. J. Dairy Res. 75:422–429. https://doi.org/10.1017/S0022029908003312. Taponen, S., J. Koort, J. Björkroth, H. Saloniemi, and S. Pyörälä. 2007. Bovine intramammary infections caused by coagulase-negative staphylococci may persist throughout lactation according to Amplified Fragment Length Polymorphism-based analysis. J. Dairy Sci. 90:3301–3307. https://doi.org/10.3168/jds.2006-860. Taponen, S., S. Nykäsenoja, T. Pohjanvirta, A. Pitkälä, and S. Pyörälä. 2016. Species

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distribution and in vitro antimicrobial susceptibility of coagulase-negative staphylococci isolated from bovine mastitic milk. Acta Vet. Scand. 58:1–13. https://doi.org/10.1186/s13028-016-0193-8. Taponen, S., H. Simojoki, M. Haveri, H.D. Larsen, and S. Pyörälä. 2006. Clinical characteristics and persistence of bovine mastitis caused by different species of coagulase- negative staphylococci identified with API or AFLP. Vet. Microbiol. 115:199–207. https://doi.org/10.1016/j.vetmic.2006.02.001. Tenhagen, B.-A., G. Köster, J. Wallmann, and W. Heuwieser. 2006. Prevalence of mastitis pathogens and their resistance against antimicrobial agents in dairy cows in Brandenburg, Germany. J. Dairy Sci. 89:2542–2551. https://doi.org/10.3168/jds.S0022-0302(06)72330- X. Thorberg, B.-M., M.-L. Danielsson-Tham, U. Emanuelson, and K. Persson Waller. 2009. Bovine subclinical mastitis caused by different types of coagulase-negative staphylococci. J. Dairy Sci. 92:4962–4970. https://doi.org/10.3168/jds.2009-2184. Timms, L.L., and L.H. Schultz. 1987. Dynamics and significance of coagulase-negative staphylococcal intramammary infections. J. Dairy Sci. 70:2648–2657. https://doi.org/10.3168/jds.S0022-0302(87)80335-1. Tomazi, T., J.L. Goncalves, J.R. Barreiro, M.A. Arcari, and M. V dos Santos. 2015. Bovine subclinical intramammary infection caused by coagulase-negative staphylococci increases somatic cell count but has no effect on milk yield or composition. J. Dairy Sci. 98:3071– 3078. https://doi.org/10.3168/jds.2014-8466. Tomazi, T., J.L. Goncalves, J.R. Barreiro, P.A. d. C. Braga, L.F. Prada e Silva, M.N. Eberlin, and M. V. dos Santos. 2014. Identification of Coagulase-Negative Staphylococci from Bovine Intramammary Infection by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry. J. Clin. Microbiol. 52:1658–1663. https://doi.org/10.1128/JCM.03032-13. Valckenier, D., S. Piepers, A. De Visscher, R.M. Bruckmaier, and S. De Vliegher. 2019. Effect of intramammary infection with non-aureus staphylococci in early lactation in dairy heifers on quarter somatic cell count and quarter milk yield during the first 4 months of lactation. J. Dairy Sci. 102:6442–6453. https://doi.org/10.3168/jds.2018-15913.

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Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher. 2014. Invited review: Effect, persistence, and virulence of coagulase-negative Staphylococcus species associated with ruminant udder health. J. Dairy Sci. 97:5275– 5293. https://doi.org/10.3168/jds.2013-7775. Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher. 2015. Identification, typing, ecology and epidemiology of coagulase negative staphylococci associated with ruminants. Vet. J. 203:44–51. https://doi.org/10.1016/j.tvjl.2014.11.001. Wilson, D.J., R.N. Gonzalez, K.L. Case, L.L. Garrison, and Y.T. Grohn. 1999. Comparison of seven antibiotic treatments with no treatment for bacteriological efficacy against bovine mastitis pathogens. J. Dairy Sci. 82:1664–1670. Wilson, D.J., R.N. Gonzalez, and H.H. Das. 1997. Bovine Mastitis Pathogens in New York and Pennsylvania: Prevalence and Effects on Somatic Cell Count and Milk Production. J. Dairy Sci. 80:2592–2598. https://doi.org/10.3168/jds.S0022-0302(97)76215-5.

101

Chapter 5

Longitudinal study on the effects of intramammary

infection with non-aureus staphylococci on udder

health and milk production in dairy heifers

D. Valckenier,1 S. Piepers,1 Y.H. Schukken,² A. De Visscher,1,3 F. Boyen,4

F. Haesebrouck,4 and S. De Vliegher1

1M-team & Mastitis and Milk Quality Research Unit, Department of Reproduction, Obstetrics

and Herd Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke,

Belgium B-9820

2GD Animal Health, PO Box 9, Deventer, the Netherlands 7400 AA; Department of Animal

Sciences, Wageningen University, Wageningen, the Netherlands 6708 PB; Department of

Population Health Sciences, Utrecht University, Utrecht, the Netherlands 3584 CL.

3Flanders research institute for agriculture, fisheries, and food (ILVO), Technology and Food

Science, Agricultural Engineering, Merelbeke, Belgium B-9820

4Department of Pathology, Bacteriology, and Avian Diseases, Faculty of Veterinary

Medicine, Ghent University, Merelbeke, Belgium B-9820.

Adapted from Journal of Dairy Science, 2021, 104:899-914

https://doi.org/10.3168/jds.2020-18685

Chapter 5 IMI with NAS in the first 130 DIM

ABSTRACT

We conducted a longitudinal study to evaluate the effect of non-aureus staphylococci (NAS) causing subclinical intramammary infections (IMI) on the quarter milk somatic cell count (qSCC) and quarter milk yield (qMY). In total, 324 quarters of 82 Holstein Friesian heifers were followed from calving up to 130 days (d) in milk (DIM) and were sampled 10 times each at 14-d intervals. The IMI status of each quarter was determined based on bacterial culture results on the current and previous or next sampling day, or both. The qSCC was determined on each sampling day and the average qMY on sampling day was available through stored daily milk weight data in the management program of the automatic milking system.

A transient IMI (tIMI) was defined as a case where a specific pathogen was isolated from a quarter on only 1 sampling day and not on the previous or next sampling day. When the same bacterial strain, as defined by random amplification of polymorphic DNA - polymerase chain reaction (RAPD-PCR), was isolated from the same quarter on multiple sampling days, it was defined as a persistent IMI (pIMI) status on those sampling days. A ‘pIMI episode’ was defined as the combination of multiple consecutive pIMI statuses with the same bacterial strain on different sampling days. During this study, 142 subclinical IMI with NAS occurred in 116 different quarters from 64 animals, yielding in total 304 NAS isolates belonging to 17 different species. The prevalence of NAS was highest in the first 4 DIM. Overall, the predominant species was Staphylococcus (S.) chromogenes (52% of the isolates), followed by S. epidermidis (9.2%), S. xylosus (8.2%) and S. equorum (5.9%). Staphylococcus chromogenes was the only species for which an effect on qSCC and qMY could be analyzed separately; the other NAS species were considered as a group because of their low prevalence.

Eighteen out of 40 IMI (45%) caused by S. chromogenes persisted over at least 2 sampling days, whereas only 10 out of 102 (9.8%) IMI caused by the other NAS species persisted for at least 2 sampling days. The average duration of the pIMI episodes was 110.4 d for S. chromogenes and 70 d for the other NAS species. Remarkably, 17 of the 18 pIMI episodes with S. chromogenes started within the first 18 DIM.

The qSCC was highest in quarters having a pIMI with a major pathogen, followed by quarters having a pIMI with S. chromogenes and a pIMI with other NAS. Transient IMI with

105 Chapter 5 IMI with NAS in the first 130 DIM other NAS or with a major pathogen caused a small but significantly higher qSCC, whereas the qSCC in quarters having a tIMI with S. chromogenes was not statistically different compared with noninfected quarters. No significant differences in qMY were observed between quarters having a pIMI or tIMI with S. chromogenes or with the group of other NAS species compared with noninfected quarters, despite the higher qSCC. Quarters having a pIMI with major pathogens showed a significantly lower daily milk production. Surprisingly, quarters that cured from an IMI with S. chromogenes had a significantly lower qMY compared with noninfected quarters.

Key words: non-aureus staphylococci, species-specific intramammary infection, quarter milk yield, quarter somatic cell count

106 Chapter 5 IMI with NAS in the first 130 DIM

INTRODUCTION

Mastitis is the most important disease in the dairy sector worldwide and one of the most frequent reasons for antimicrobial therapy in dairy herds (Brunton et al., 2012; Stevens et al., 2016). In the past few decades, non-aureus staphylococci (NAS) have become the most common pathogen isolated from subclinical mastitis cases in dairy cows in many regions (Pitkälä et al., 2004; Piepers et al., 2007; Pyörälä and Taponen, 2009; Reyher et al., 2011). This group of staphylococci is present in almost every dairy herd. They were recovered from 299 out of 300 bulk milk samples from 100 Belgian dairy herds (De Visscher et al., 2017), and 100% of all 4,258 Danish dairy herds (Katholm et al., 2012). Risk factors at the herd, cow, and quarter level have been identified for multiple species (Piessens et al., 2011; Bexiga et al., 2014; De Visscher et al., 2016). Heifers are more prone to NAS IMI than multiparous cows (Matthews et al., 1992; Tenhagen et al., 2006; Sampimon et al., 2009; De Vliegher et al., 2012).

Regarding the impact of NAS on udder health, i.e. SCC in case of subclinical mastitis, most studies consider NAS as minor pathogens causing only a moderate increase in SCC or causing mild clinical mastitis (CM) cases (Schukken et al., 2009; Fry et al., 2014; Tomazi et al., 2015; Valckenier et al., 2019). However, more disagreement exists with regard to potential species differences in their effect on udder health. Some studies found no significant differences in quarter milk SCC (qSCC) between different NAS species (Hogan et al., 1987; Bexiga et al., 2014), whereas others noted that important species differences exist (Supré et al., 2011; Fry et al., 2014). Moreover, in our previous study, we concluded that IMI with S. chromogenes starting in the first 18 DIM resulted in a significantly higher qSCC during the first 130 DIM, whereas IMI with the group of other NAS species had no effect on qSCC (Valckenier et al., 2020). Some NAS species, such as Staphylococcus (S.) chromogenes, S. simulans and S. xylosus, are called species “more relevant for udder health” as they are able to increase the qSCC to a level comparable to that of S. aureus (Supré et al., 2011), as substantiated by Fry et al. (2014). Heifers are at greater risk to be infected with the “more relevant” NAS species compared with multiparous cows (De Visscher et al., 2015), especially at the beginning of their lactation (Taponen et al., 2007). Similar to differences observed for the impact on SCC, species-specific

107 Chapter 5 IMI with NAS in the first 130 DIM differences in persistence capacity were reported as well (Supré et al., 2011; Mørk et al., 2012; Nyman et al., 2018).

The impact of NAS infections on milk yield (MY) is even more debated, although most recent research considers NAS as minor pathogens with little to no effect on the quarter MY (qMY) (Tomazi et al., 2015; Heikkilä et al., 2018; Valckenier et al., 2019, 2020). If IMI with all the different NAS species have a negative or no effect on MY, the focus should remain on prevention of these pathogens because NAS IMI lead to an elevated SCC and have been previously identified as a risk factor for developing IMI with S. aureus (Reyher et al., 2012a). On the other hand, if certain NAS species would have a positive effect on MY or if the protective effects of NAS IMI against infections with major pathogens demonstrated in challenge studies would hold true in larger observational studies (Reyher et al., 2012b), they could be interesting in the development of new concepts or strategies in the dairy sector. Studies scrutinizing the association between NAS IMI and MY have come to contradictory conslusions, with some classifying NAS as pathogens with a potential negative effect on MY (Timms and Schultz, 1987; Gröhn et al., 2004; Taponen et al., 2006), some reporting the absence of association with MY (Compton et al., 2007; Paradis et al., 2010; Pearson et al., 2013), and others finding a higher MY in infected animals than in noninfected animals (Schukken et al., 2009; Piepers et al., 2013). Many differences in study designs and methods (e.g., studied population, length of the follow-up period, identification of NAS at the species level or considered as a group, differences in the predominant NAS species, …) might explain the variety of findings. Another common downside of most previous studies is that the IMI status of the cow was an aggregate of the IMI statuses of the quarters, and that MY was also measured at the cow level. This might have distorted the observations and results in other studies: for example, if 1 quarter of animal was NAS-infected, generally this animal was classified as NAS- infected, although one should take into account that the 3 other quarters, producing roughly 75% of the total MY of the animal, might have been noninfected. However, to our knowledge, no research has been performed so far in which both quarter IMI status and quarter MY have been observed simultaneously in a longitudinal study.

108 Chapter 5 IMI with NAS in the first 130 DIM

The question remains whether IMI with the ‘more relevant’ NAS species, in particular S. chromogenes, affect qSCC and qMY. This longitudinal study therefore aimed to evaluate the effect of subclinical NAS IMI during the first 130 DIM on qSCC and qMY in heifers (i.e. animals in first lactation) in 3 dairy herds milking with automated milking systems (AMS).

MATERIALS AND METHODS

Herds and Animals

Data and samples were collected during a longitudinal study conducted from August 2013 until the end of October 2014, as described in Valckenier et al. (2019). Briefly, the study included three Belgian commercial dairy herds, all located in the province of West Flanders and equipped with an AMS. None of the three herds treated their end-term heifers with antimicrobials before calving, nor did they participate in the local dairy herd improvement program.

During the sampling period, 82 heifers calved, and all were included in the study as no further in- or exclusion criteria at the herd or heifer level were applied. When a heifer was sold, culled or died within the first 4 months of lactation, it was not replaced by another animal. Non- functional quarters or quarters with CM before or on the first sampling were excluded as the aim of this study was to investigate the effects of IMI with NAS causing subclinical mastitis. For this reason, 2 quarters with CM and 2 nonfunctional quarters were excluded. In addition, 2 heifers from herd 3 were culled after a severe case of CM between 57 and 74 DIM. One quarter from a heifer in herd 1 was immediately dried off after a CM case occurring between 85 and 102 DIM. Two heifers (herd 2 and 3) were sold between 85 and 102 DIM. The left hind quarter from a heifer in herd 3 was dried off at 85 DIM because of a teat end injury. All quarter samples taken before these animals were culled or dried off were included in the analysis.

Study Design, Quarter Milk Somatic Cell Count, and Quarter Milk Yield

Every included quarter from all heifers was sampled by the first author between 1 and 4 DIM (referred to as “sampling day 1” throughout the paper) and followed up until 127 to 130

109 Chapter 5 IMI with NAS in the first 130 DIM

DIM by collecting milk samples every 14 d for another 9 consecutive times after the first sampling, resulting in 10 repeated samplings per quarter with a 14-d interval (referred to as sampling days). Quarter milk samples were collected aseptically according to the guidelines of the National Mastitis Council (Hogan et al., 1999) and transported in a cooled box to the laboratory of the Mastitis and Milk Quality Research Unit (Ghent University, Merelbeke, Belgium) immediately after sampling for further processing. The heifers were either separated in the selection unit of the AMS if they had been milked in the last few hours before sampling or were searched for in the barn. All heifers were restrained in head lockers at the moment of sampling. The time interval between the last milking before the sampling and the sampling itself varied because all animals were milked voluntarily by the AMS.

The qSCC of each sample was measured using a Direct Cell Counter DCC (DeLaval International AB, Tumba, Sweden).

Quarter milk production per milking was available through the herd management software of the AMS (DelPro, DeLaval International AB, Tumba, Sweden). The estimated daily qMY on the first sampling day (1-4 DIM) was calculated by summing the qMY of all milkings from calving up to d 7 after the sampling and dividing this total amount of milk by the number of days in this period. Subsequently, the estimated daily qMY on the next 9 sampling days (with an interval of 14 d) was also calculated by dividing the sum all the quarter milk productions from 7 d before until 7 d after each sampling by 14 (i.e., the number of days in this period).

Microbiology

All quarter milk samples taken on each sampling day were used for bacteriological culturing to determine the IMI status of each quarter. Standard bacteriological culture was done by spreading a 0.01 mL loop of milk on 5% sheep blood agar and MacConkey agar no. 3 (Thermo Fisher Diagnostics N.V., Groot-Bijgaarden, Belgium), as described elsewhere (Valckenier et al., 2019). All plates were incubated aerobically at 37°C and phenotypically examined after 24 h and again after 48 h. Identification of bacteria was done using recommended phenotype-based bacteriological procedures (Hogan et al., 1999). Milk samples were considered to be culture- positive if one or more colonies were observed (≥ 100 CFU/mL; Dohoo et al., 2011b). Samples

110 Chapter 5 IMI with NAS in the first 130 DIM yielding 3 or more different colony types were considered to be contaminated. Staphylococcus aureus, esculin-positive and esculin-negative Gram-positive cocci, Trueperella pyogenes, Escherichia coli, Klebsiella spp., and other Gram-negative bacteria were regarded as major pathogens. All bacteria not belonging to the group of NAS or to one of the abovementioned major pathogens were considered “other” bacteria. A sample yielding a major pathogen and NAS or other bacteria was classified as culture-positive for the major pathogen, whereas a sample yielding 2 different major pathogens or 2 different minor pathogens was considered as culture-positive for the pathogen with the highest count (CFU/mL; Valckenier et al., 2019, 2020).

NAS Species Identification and Strain-typing

All isolates phenotypically identified as NAS during the trial were stored in Microbank vials (Pro-Lab diagnostics, Richmond Hill, ON, Canada) at −80°C until the end of the sampling period. These isolates were identified at the species level using transfer RNA intergenic spacer PCR (tDNA- PCR) or, if no identification was obtained, sequencing of the 16S rRNA or rpoB gene as described by Supré et al. (2009), or MALDI-TOF MS (Cameron et al., 2017).

If the same NAS species was isolated ≥ 2 times at different sampling days from the same quarter, RAPD-PCR was used for strain-typing (an example is shown in Figure 1). The RAPD- PCR was performed as described by Piessens et al. (2012) with some modifications. The amplified DNA fragments were separated on 1.5% (wt/vol) agarose gels, previously stained with ethidium bromide (10 mg/mL; Sigma Aldrich, St. Louis, MO), at 120V for 75 min. Representative isolates from the same quarter were analyzed in the same PCR run and were run side-by-side on the gel. Gels were photographed by UV transillumination. The images were inspected visually, imported in BioNumerics version 7.6.3 (Applied Maths, Sint-Martens- Latem, Belgium) and analyzed following Adkins et al. (2017) using the Dice similarity coefficient and the unweighted pair group method with arithmetic mean (UPGMA). The optimization and position tolerance were set at 0.5% and 1.0%, respectively, for different isolates to be considered the same strain type. If the NAS isolates from the same quarter belonged to the same RAPD type, the quarter was considered as persistently infected with the

111 Chapter 5 IMI with NAS in the first 130 DIM same NAS strain from the first until the last sampling day on which this NAS strain belonging to the same RAPD type was isolated. A quarter with a culture-negative sampling between two culture-positive samplings with the same RAPD type was still considered as having a pIMI on the culture-negative sampling day.

Figure 1: Example of a random amplification of polymorphic DNA – polymerase chain reaction (RAPD-PCR) fingerprints and dendrogram with the results of 1 quarter from which S. chromogenes was isolated on 8 sampling days (SD). Isolates belonging to the same RAPD type were assigned an arbitrary letter (i.e., A and B in this example) based on clustering.

Sampling Day IMI Status

The sampling day IMI status was defined by combining culture results of the previous, the current and the next sampling day (e.g., to determine the IMI status on sampling day 5, the culture results on sampling day 4, 5 and 6 were used). A quarter was defined as noninfected on the current sampling day if the milk samples were culture-negative on the previous and current sampling days. A quarter was defined as having a transient IMI (tIMI) with a specific pathogen on the current sampling day if the milk sample was culture-positive for a specific pathogen on the current sampling day while the same pathogen was not isolated on the previous and next sampling day. If a quarter was considered infected with the same pathogen on at least 2 sampling days, this quarter was defined as having a persistent IMI (pIMI) on those sampling

112 Chapter 5 IMI with NAS in the first 130 DIM days. Quarters were only considered to have a pIMI with a certain NAS species if the same NAS species isolated on multiple sampling days belonged to the same RAPD-type. A ‘pIMI episode’ is the combination of multiple consecutive pIMI statuses with the same pathogen on different sampling days (i.e., 1 pIMI episode consists of at least 2 and up to 10 consecutive pIMI statuses with the same pathogen in our study). A quarter is defined as having a cured IMI (cIMI) on the current sampling day if it was infected with a specific pathogen on the previous sampling day and the milk sample was culture-negative on the current sampling day and this quarter was not infected with the same bacterial strain on the next sampling day.

The IMI status on the first sampling day was determined by combining the culture results from only the first and second sampling day. A quarter was defined to be noninfected on the first sampling day if the milk sample was culture-negative for any pathogen. When the milk sample on the first sampling day was culture-positive for a specific pathogen but the same pathogen could not be isolated on the second sampling day, the quarter was determined as having a tIMI on the first sampling day. When a quarter was infected with the same pathogen on both the first and second sampling day, the quarter was considered as having a pIMI on both those sampling days.

The IMI status on the last sampling day was determined using the culture results on sampling day 9 and 10. When a quarter was infected with the same pathogen on sampling days 9 and 10, or culture-negative on both sampling days, the quarter was considered as having a pIMI or being noninfected, respectively, on sampling day 10. Statuses of samples that were culture-negative on sampling day 9 and culture-positive on sampling day 10 were defined as missing because it could not be determined if it was a tIMI or a pIMI (right-censoring). A cIMI on sampling day 10 was defined as a quarter being infected with a certain pathogen on sampling day 9 and yielding a culture-negative milk sample on sampling day 10.

The IMI status of sampling days for which the NAS isolate could not be identified to the species level, or when the milk sample was contaminated, or culture-positive for a pathogen belonging to the group of “other” bacteria, or could not be classified into a certain IMI status according to the above definitions, was categorized as “undefined”.

113 Chapter 5 IMI with NAS in the first 130 DIM

Start and End Point of IMI, and Duration of a pIMI Episode

The determination of the start and end point of an IMI episode was adapted from Supré et al. (2011). When a quarter was infected on the first sampling day, the start point of this IMI episode was defined as the first DIM. When a quarter became infected thereafter, the infection was assumed to have started half-way between the previous and current sampling day. The endpoint of infection was considered to be the midpoint between the sampling with IMI and the next sampling without IMI. When the quarter was infected at the end of the follow-up period, an extra period of 7 d was added to the calculation [i.e., the average number of days between 2 samplings, divided by 2].

Statistical Analyses

All data were entered in an electronic spreadsheet program (Excel 2016, Microsoft Corp., Redmond, WA) and were checked for unlikely values.

Common Features of the Statistical Models. The associations between the quarter-level sampling day IMI (qIMI; categorical predictor variable of main interest) status and sampling day qSCC and sampling day qMY (outcome variables) throughout the first 4 months of lactation were determined fitting 2 separate linear mixed models. Herd (forced into the model as fixed effect), heifer (random effect) and quarter (repeated statement) were included in the models to correct for potential clustering of heifers within herds, for clustering of quarters within heifer, and for clustering of observations (10 repeated samplings) within quarters, respectively. As the number of observations was too small for the species other than S. chromogenes, these were included as a group (named “NAS other than S. chromogenes”) in the statistical analyses. By doing so, the qIMI status on sampling day had 11 levels: noninfected; tIMI, pIMI, and cIMI with S. chromogenes; tIMI, pIMI, and cIMI with NAS other than S. chromogenes; tIMI, pIMI, and cIMI with a major pathogen; and quarters with an undefined IMI status on a certain sampling day. A natural logarithmic transformation of the qSCC (qLnSCC) was performed to obtain a normal distribution. The qSCC on sampling day was included as explanatory variable

114 Chapter 5 IMI with NAS in the first 130 DIM in the qMY model. The models included quarter position (2 levels: front vs. hind) as categorical predictor variable. The model with qSCC as outcome variable included sampling day (10 levels: 1 to 10) as categorical predictor variable, whereas the 4 parametric lactation curve function by Ali and Schaeffer (AS; Ali and Schaeffer, 1987) was used for fitting the lactation MY curve in the qMY model.

The linear mixed models were fit in SAS (PROC MIXED; version 9.4; SAS Institute Inc., Cary, NC). The goodness-of-fit measures included -2 x log-likelihood, the Akaike information criterion, and the Bayesian information criterion. Residuals were evaluated graphically and plotted against the predicted values. A Bonferroni’s correction was used to correct for multiple comparisons. Significance was assessed at P ≤ 0.05. Non-significant variables (P > 0.05) were omitted using a backward stepwise approach. In all linear mixed models, a first-order autoregressive correlation structure was used to account for the clustering of repeated sampling days within a quarter. Confounding was assessed by examining the effect of each variable on the estimates of other explanatory variables (Dohoo et al., 2003). No variables included in any final model resulted in substantial changes (>20%) of the estimates of other explanatory variables, indicating that confounding was not a problem.

Effect of tIMI, pIMI and cIMI on qSCC. The initial linear mixed model with qSCC as outcome variable (SCC model; Eq. [1]) was: qLnSCCijkl = β0 + β1 qIMIijkl + β2 quarter positionjkl + β3 herdl + β4 sampling dayijkl + µHeifer kl(j) + µQuarter jkl(i) + eijkl, [1] where qLnSCCijkl is the natural logarithm of SCC for the ith sample (i = 1–10) of the jth quarter

(j = 1–4) of the kth heifer (k = 1–82) from the lth herd (l = 1–3); β0 is the intercept (overall mean); β1 to β4 are the regression coefficients of the fixed effects: quarter-level IMI status on sampling day, quarter position, herd and sampling day, respectively; µHeifer kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer; µQuarter jkl(i) was added to correct for within-quarter correlation of subsequent biweekly sampling days i

(repeated statement) for quarter j of heifer k from herd l and eijkl is the random error term.

115 Chapter 5 IMI with NAS in the first 130 DIM

Effect of tIMI, pIMI and cIMI on qMY. The initial linear mixed model with daily qMY as outcome variable (MY model; Eq. [2]) was: qMYijkl = β0 + β1 qIMIijkl + β2 quarter positionjkl + β3 herdl + β4 AS function term 1ijkl + β5

AS function term 2ijkl + β6 AS function term 3ijkl + β7 AS function term 4ijkl + β8 qLnSCCijkl

+ µHeifer kl(j) + µQuarter jkl(i) + eijkl, [2] where qMYijkl is the quarter milk yield for the ith sample (i = 1–10) of the jth quarter (j = 1–4) of the kth heifer (k = 1–82) from the lth herd (l = 1–3); β0 is the intercept (overall mean); β1 to

β8 are the regression coefficients of the fixed effects: quarter-level IMI status on sampling day, quarter position, herd, AS function terms 1 to 4, and the natural logarithm of the qSCC on sampling day, respectively; µHeifer kl(j) is the random effect of the heifer k from herd l to correct for clustering of quarters within heifer; µQuarter jkl(i) was added to correct for within-quarter correlation of subsequent biweekly sampling days i (repeated statement) for quarter j of heifer k from herd l and eijkl is the random error term. The AS function terms 1 to 4 are (t/130), (t/130)², ln(130/t), and (ln(130/t))², respectively, where t is the DIM on the current sampling day and 130 is the maximum follow-up period in days in this study.

RESULTS NAS Species Distribution

From the 82 heifers included in this study, 324 quarters were eligible for sampling on the first sampling day (1–4 DIM). Four quarters were not eligible because they were nonfunctional or had CM on the first sampling day. In total, 304 isolates could be phenotypically identified as NAS and further identified to the species level (Table 1) across the sampling days. Seventeen different NAS species were cultured. Staphylococcus chromogenes was the most prevalent species (52%). Staphylococcus epidermidis could not be isolated on the first sampling day although it was the second most prevalent species (9.2%) during the entire follow-up period, followed by S. xylosus (8.2%) and S. equorum (5.9%). Thirteen other species had a prevalence of less than 5%. On the first sampling day, 13 different NAS species were isolated, whereas the number of NAS species ranged between 7 and 11 for sampling days 2 to 10. Also, 17.8% of the quarters had an IMI with a NAS species on sampling day 1, whereas this was between 6.9 and 10.5% on the later sampling days. Most of the NAS species with the highest prevalence (e.g.,

116 Chapter 5 IMI with NAS in the first 130 DIM

S. chromogenes, S. epidermidis, S. xylosus, and S. equorum) were found throughout the entire follow-up period, whereas most of the less-prevalent species were predominantly found either before (e.g., S. vitulinus, S. sciuri, and S. pulvereri) or after (e.g., S. arlettae, S. cohnii, and S. warneri) the first 60 DIM.

Prevalence of IMI with NAS

At the first sampling day, 65 (20.1%) of the 324 quarters were infected (i.e., having a tIMI or pIMI) with S. chromogenes (n = 20), other NAS species (n = 36), or a major pathogen (n = 9), and 220 (67.9%) were noninfected, whereas the IMI status was undefined for 39 (12.0%) quarters (Table 2). The number of quarters having an infection on sampling day 2 to 9 ranged between 12 and 14%, whereas the number of quarters not yielding any pathogen (i.e., noninfected quarters and quarters having a cIMI) ranged between 63.2 and 68.6% on those sampling days. On sampling day 10, relatively more quarters had an undefined IMI status because a subsequent sampling day was lacking to correctly define the exact IMI status.

The first tIMI and pIMI episodes with either S. chromogenes or the group of other NAS species were first diagnosed on the first sampling day. The number of tIMI with S. chromogenes and other NAS species was the highest on the first sampling day compared with all later sampling days. During the entire follow-up period, 142 NAS IMI were diagnosed in 116 of the 324 quarters (52 front quarters vs. 64 hind quarters) from 64 of the 82 heifers included in this study.

Half of the tIMI with S. chromogenes (n = 22) started between 1 and 4 DIM, and the last case was estimated to have started at 78 DIM (Table 3). In contrast, new pIMI with S. chromogenes were almost exclusively diagnosed on the first and second sampling days: 9 (50%) and 8 (44.4%) out of 18 episodes, respectively. The average DIM at the start of a new case was 6.2 d, and no new pIMI with S. chromogenes were diagnosed after 36 DIM. Out of 92 tIMI with NAS other than S. chromogenes, 34 (37%) and 6 (6.5%) were first diagnosed on sampling day 1 and 2, respectively, whereas 44 (47.8%) tIMI were diagnosed after 40 DIM. New tIMI with this group of NAS other than S. chromogenes started on average on 38.8 DIM, and pIMI episodes at 35.4 DIM. For both categories, the last IMI were estimated to have started approximately on 109 and 107 DIM, respectively.

117 Chapter 5 IMI with NAS in the first 130 DIM

9.2 8.2 5.9 4.3 3.0 3.0 2.6 2.3 2.3 2.0 1.6 1.0 1.0 0.7 0.7 0.3 52.0 100.0 Total (%)Total

9 9 8 7 7 6 5 3 3 2 2 1 28 25 18 13 158 304 100 Total

130) 6.9 3 2 1 1 1 1 - 12 10 21 (127

.2

116) 9 2 1 1 2 1 3 1 2 1 1 - 9 13 28

(113

102) 8.2 3 2 2 3 1 1 1 8 - 12 25 (99

88) - 6 1 2 2 1 2 1 1 1 7 10.5 15 32

(85

74) - 3 1 3 1 1 1 7.9 6 14 24 (71

60) - 3 3 1 1 1 1 1 2 1 5 Sampling day (DIM) 10.2 17 31 (57

46) - 9.2 3 2 2 1 1 1 4 18 28 (43

32) - 2 2 2 1 1 1 1 1 1 3 10.5 20 32 (29

18) - 3 4 2 1 1 1 9.5 2 17 29 (15

4) - 7 3 3 1 1 6 6 3 1 1 1 1 1 17.8 20 54 (1

. Overview of the NAS species distribution from sampling day 1 (1-4 DIM) to 10 (127-130 DIM) of 324 quarters from 82 dairy heifers in 3 commercial

chromogenes

epidermidis saprophyticus 1

NAS species S. S. S. xylosus S. equorum S. haemolyticus S. simulans S. hominis S. vitulinus S. sciuri S. pulvereri S. arlettae S. cohnii S. warneri S. auricularis S. capitis S. S. devriesei Total (%)Total Staphylococcus 1

Table Table 1 dairy herds

118 Chapter 5 IMI with NAS in the first 130 DIM

.0 0.7 4.6 0.6 2.9 1.6 2.3 2.0 1.5 1.6 61.2 21.0 100 Total (%)Total

22 20 92 51 72 64 46 52 937 167 145 666 , ,

Total 1 3

-

2 5 5 6 7 eclnpstv cocci, esculin-positive , 10 86 185 306 130) 10 (127 -

9 6 8 9 6 6 14 67 181 306 116) 9 (113

-

1 6 6 8 8 5 5 14 57 (99 196 306 102) tpyoocs aureus Staphylococcus 8 4 -

1 1 6 3 4 6 6 15 10 60

88) 203 315 7 (85 -

1 2 5 6 8 9 5 9 14 64 74) 192 315 6 (71 -

2 1 7 5 4 3 8 17 11 74 60) 191 323 5 (57 Sampling day (DIM) -

2 3 7 4 7 7 2 5 18 76 46) ue itaamr infection. intramammary Cured 193 324 3 4 (43 -

4 2 8 5 4 6 5 3 17 71 32) 199 324 3 (29

1 8 6 6 4 5 3 18) - 2 17 25 72 177 324 (15

4)

9 2 6 3 - 1 11 34 39 Quarters that could notclassified be in 1 of the 10 infection on statuses certain sampling a day. 5 220 324 (1

esset nrmmay infection. intramammary Persistent 2

S. chromogenes S. chromogenes

S. chromogenes

4

and other Gram-negative bacteria. chromogenes

S. chromogenes S.

S. chromogenes 5 major major pathogen . Overview of the quarter-level IMI (qIMI) statuses from sampling day 1 (1-4 DIM) to 10 (127-130 DIM) of 324 quarters from 82 dairy heifers in 3 commercial with with with with with with

2 3 1

Sampling day qIMI status Noninfected tIMI pIMI cIMI NAStIMI with other than pIMI NAS other with than cIMI NAS with other than tIMI with pIMI major pathogen with cIMI pathogen with major Undefined Total infection. intramammary Transient 1 Table 2 dairy herds Escherichia coli 119 Chapter 5 IMI with NAS in the first 130 DIM

Persistence of IMI with NAS

Of the 20 quarters that were infected with S. chromogenes on the first sampling day, 11 (55%) were no longer infected with this pathogen on the second sampling day, whereas 9 quarters (45%) were having a pIMI episode. For the NAS species other than S. chromogenes, only 2 (5.6%) of the 36 quarters had a pIMI episode; in the other 34 (94.4%) quarters, the same NAS species was no longer found on the second sampling day (Table 3).

During the entire study, S. chromogenes caused 22 tIMI and 18 pIMI episodes (Table 3). In contrast, NAS other than S. chromogenes caused 92 tIMI and 10 pIMI episodes.

The average duration of pIMI episodes with S. chromogenes was 110.4 d, and ranged between 49.0 and 134.0 d (i.e., the duration of the entire follow-up period; Table 4). The pIMI episodes with NAS other than S. chromogenes had an average duration of 70.0 d, with a minimum and maximum of 21.0 and 133.0 d, respectively. The episodes that were first diagnosed on sampling days 1 and 2 had an average duration of 77.0 and 94.5 d, respectively, whereas the duration of episodes that were diagnosed after 50 DIM varied between 28.0 and 56.0 d on average.

120 Chapter 5 IMI with NAS in the first 130 DIM

78 36 134 133 109 107 Max Max

S. chromogenes .0 6.2 19.8 38.8 35.4 70 of pIMIof episodes 110.4 Average Average (d) (d) or pIMIor episode

DIM of onset a at tIMI 1 1 1 1 49 21 Min Min Duration

and other NAS species in quarter milkotherNASspecies in and

22 18 92 10 (%) 142 (7.0) Total 28.0 (15.5) (12.7) (64.8) 116)

9 (113-

9 1 10

116) 9 (113- 42.0 102) S. chromogenes S. chromogenes 8 (99- first diagnosisfirst

6 1 7 of 102)

8 (99- 7 (85-88)

1

10 11 88) 7 (85-

6 56.0 (71-74)

1 5 1 7

74)

6 (71-

5 42.0

(57-60) 2 7 1 10

60) a tIMI a and a pIMI wasepisode diagnosed

5 (57-

4 98.0

2 1 7 (43-46) 10 46)

4 (43- on which

3

4 8 DIM) (29-32) 12 32) 3 (29-

2 94.5 1 8 6 4 122.5 19 18) (15-18) 2 (15-

Average duration pIMI(d) of episodes at the sampling day

1 9 2 1 77.0 11 34 56 (1-4) 101.1 (1-4) First samplingFirst day ( S.

S. S.

S. chromogenes

S. chromogenes S. chromogenes . Overview of the average duration of persistent intramammary infection (pIMI) episodes with ofintramammaryepisodes persistent duration infection (pIMI) average Overviewofthe . . Overview points start of the of transient intramammary infection (tIMI) persistentand intramammary infection (pIMI) episodes with episode with episode with episode with

chromogenes chromogenes than chromogenes than

IMI status tIMI with pIMI NAStIMI with other than pIMI with NAS episode other Total IMI episode pIMI pIMI with NAS episode other Table 3 Table other NASand species quarter in milk ofsamples 82 heifers sampled from 130 1 to DIM 4 Table ofsamples 82 heifers sampled from 1 to 130 DIM

121 Chapter 5 IMI with NAS in the first 130 DIM

Table 5. Final linear mixed regression model describing the association between the natural log- transformed quarter milk SCC (qLnSCC; outcome variable) and quarter-level IMI status (main predictor of interest) during the first 130 DIM of 324 quarters from 82 dairy heifers in 3 commercial dairy herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 5.66 0.08 <0.001 — Herd² 0.70 Herd 1 1587 Referent — — 4.71 Herd 2 658 0.04 0.07 0.67 4.75 Herd 3 922 0.08 0.08 0.50 4.79 Sampling day <0.001 1 (1-4 DIM) 324 Referent — — 6.42 2 (15-18 DIM) 324 -1.64 0.09 <0.001 4.78 3 (29-32 DIM) 324 -1.88 0.10 <0.001 4.54 4 (43-46 DIM) 324 -1.97 0.10 <0.001 4.46 5 (57-60 DIM) 323 -1.90 0.10 <0.001 4.52 6 (71-74 DIM) 315 -1.93 0.10 <0.001 4.50 7 (85-88 DIM) 315 -1.91 0.10 <0.001 4.51 8 (99-102 DIM) 306 -1.93 0.10 <0.001 4.49 9 (113-116 DIM) 306 -2.01 0.10 <0.001 4.41 10 (127-130 DIM) 306 -1.57 0.10 <0.001 4.85 qIMI status on sampling day <0.001 Noninfected 1937 Referent — — 4.03 tIMI3 with S. chromogenes 22 0.21 0.26 0.41 4.24 pIMI4 with S. chromogenes 145 1.83 0.12 <0.001 5.86 cIMI5 with S. chromogenes 20 0.76 0.27 0.005 4.79 tIMI with NAS other than S. chromogenes 92 0.38 0.13 0.003 4.41 pIMI with NAS other than S. chromogenes 51 1.33 0.20 <0.001 5.36 cIMI with NAS other than S. chromogenes 72 0.12 0.14 0.39 4.15 tIMI with major pathogen6 64 0.42 0.15 0.006 4.45 pIMI with major pathogen 46 2.47 0.20 <0.001 6.49 cIMI with major pathogen 52 0.19 0.17 0.27 4.21 Undefined7 666 0.20 0.06 <0.001 4.23 1Number of measurements. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3Transient intramammary infection. 4Persistent intramammary infection. 5Cured intramammary infection 6Staphylococcus aureus, esculin-positive cocci, Escherichia coli and other Gram-negative bacteria. 7Quarters that could not be classified as 1 of the 10 infection statuses on a certain sampling day.

122 Chapter 5 IMI with NAS in the first 130 DIM

Table 6. Final linear mixed regression model using the Ali and Schaeffer (AS) function describing the association between the daily quarter milk yield (outcome variable) and quarter-level IMI status (qIMI; main predictor of interest) during the first 130 DIM of 324 quarters from 82 dairy heifers in 3 commercial dairy herds

Predictor variable N1 Estimate SE P-value LSM Intercept — 17.52 0.63 <0.001 — Herd² 0.22 Herd 1 1587 Referent — — 7.20 Herd 2 658 -0.04 0.29 0.89 7.16 Herd 3 922 0.41 0.26 0.11 7.61 3 AS function term 1 -14.89 1.08 <0.001 — 4 AS function term 2 4.17 0.50 <0.001 — 5 AS function term 3 -5.75 0.31 <0.001 — 6 AS function term 4 0.63 0.04 <0.001 — qIMI status on sampling day 0.23 Noninfected 1937 Referent — — 7.40 tIMI7 with S. chromogenes 22 -0.10 0.13 0.41 7.30 pIMI8 with S. chromogenes 145 0.08 0.15 0.56 7.49 cIMI9 with S. chromogenes 20 -0.25 0.12 0.04 7.15 tIMI with NAS other than S. chromogenes 92 -0.06 0.06 0.36 7.35 pIMI with NAS other than S. chromogenes 51 -0.001 0.18 0.99 7.40 cIMI with NAS other than S. chromogenes 72 -0.04 0.06 0.47 7.36 tIMI with major pathogen10 64 -0.04 0.07 0.51 7.36 pIMI with major pathogen 46 -0.39 0.17 0.02 7.01 cIMI with major pathogen 52 -0.12 0.08 0.13 7.29 Undefined11 666 0.005 0.03 0.88 7.41 Quarter position <0.001 Front 1598 Referent — — 6.87 Hind 1569 0.89 0.13 <0.001 7.77 1Number of measurements. 2Herd was forced in the model to correct for potential clustering of heifers within herds. 3t/130 where t = stage of lactation in days. 4(t/130)². 5Ln(130/t). 6[Ln(130/t)]². 7Transient intramammary infection. 8Persistent intramammary infection. 9Cured intramammary infection. 10Staphylococcus aureus, esculin-positive cocci, Escherichia coli and other Gram-negative bacteria. 11Quarters that could not be classified as 1 of the 10 infection statuses on a certain sampling day.

123 Chapter 5 IMI with NAS in the first 130 DIM

Effect of tIMI, pIMI, and cIMI on qSCC

Quarter position was not significantly associated with qLnSCC and omitted from the model. The qSCC on sampling day from noninfected quarters (56,000 cells/mL, Table 5) was not different from that of quarters having a tIMI with S. chromogenes (69,000 cells/mL; P = 0.41) and was significantly lower than that from quarters having a tIMI with NAS other than S. chromogenes or a major pathogen (82,000 cells/mL, P = 0.003; and 86,000 cells/mL, P = 0.006, respectively). The sampling day SCC from quarters having a pIMI with S. chromogenes, a pIMI with NAS other than S. chromogenes¸ or a pIMI with a major pathogen was significantly higher (351,000 cells/mL, P < 0.001; 213,000 cells/mL, P < 0.001; and 659,000 cells/mL, P < 0.001, respectively) compared with that of noninfected quarters. The qSCC on sampling days where a quarter was considered cured from an IMI with S. chromogenes was still significantly higher (120,000 cells/mL, P = 0.005) compared with that of noninfected quarters, which is remarkable given the fact that 16 of these 20 cIMI followed a tIMI. In quarters that cured from an IMI with NAS other than S. chromogenes or a major pathogen, qSCC was not different (63,000 cells/ml, P = 0.39; and 67,000 cells/mL, P = 0.27, respectively). The qSCC from quarters having a pIMI with a major pathogen was not significantly different from quarters having a pIMI with S. chromogenes (P = 0.36), although it was significantly higher than qSCC of quarters with a pIMI with NAS other than S. chromogenes (P = 0.002). We observed no difference in qSCC between quarters having a pIMI with S. chromogenes and quarters having a pIMI with NAS other than S. chromogenes (P = 0.99).

Effect of tIMI, pIMI, and cIMI on qMY

The variable qLnSCC was not significantly associated with qMY and was therefore omitted from the model. The qMY on sampling day was significantly higher in noninfected quarters (7.40 kg/d, Table 6) than in quarters having a pIMI with a major pathogen (7.01 kg/d, P = 0.02) or a cIMI with S. chromogenes (7.15 kg/d, P = 0.04). None of the other IMI statuses resulted in a significantly different qMY compared with noninfected quarters. Hind quarters produced on average 0.90 kg/d more milk than front quarters (P < 0.001).

124 Chapter 5 IMI with NAS in the first 130 DIM

DISCUSSION

Because of the diversity of NAS, a group of staphylococci with more than 50 (sub)species (Vanderhaeghen et al., 2015), studies on their association with milk yield have reached contradictory conclusions. Part of the discrepancies among different studies can be attributed partly to the great variation in study design, such as length of the follow-up period, number of herds and animals, definition of IMI, and selection of included animals (e.g., heifers vs. multiparous cows, animals at the start of lactation vs. any lactation stage) but also the identification methods (i.e. phenotypic vs. genotypic methods). In our previous work, we documented that NAS IMI between 1 and 4 DIM and in the first 18 DIM resulted in a higher qSCC during the first 4 months of lactation, but had no effect on qMY (Valckenier et al., 2019, 2020). However, we observed a difference between S. chromogenes and other NAS species in terms of occurrence, persistence, and effect of IMI in the first 18 d after calving on later qSCC and qMY. The aim of this study was to further elaborate on the infection dynamics of NAS IMI in the first 130 DIM and on the effect of NAS IMI on qSCC and qMY during this period. The prevalence and persistence of NAS IMI has been studied extensively in primiparous and multiparous cows, although no qMY data were available in those studies. This is, to the best of our knowledge, the first study to follow quarter IMI status, qSCC, and qMY for several months.

We observed 142 NAS IMI in 116 out of 324 quarters. This is considerably higher than in the study of Taponen et al. (2007) wherein 63 of 328 quarters were NAS-infected. Interestingly, the heifers that had a NAS IMI in that study were infected in 2.9 quarters on average, whereas the heifers in our study had a NAS IMI case in 1.8 quarters on average (data not shown), a finding that might relate to the use of AMS in our study. A significant increase in subclinical mastitis caused by NAS was demonstrated in 18 Danish herds after the introduction of an AMS, and the teat cleaning device was thought to play a role in the increased transmission of pathogens (Pedersen and Bennedsgaard, 2006). Also, in an AMS, a single milking unit is in more intensive use and comes into contact with the teats of all animals in a group at least 2 times per day. Furthermore, the automatic cleaning of the teats is standardized and thus not adapted to the dirtiness of individual animals (Dohmen et al., 2010). The use of automatic post- milking teat spray is often less thorough and precise (Rasmussen and Hemling, 2002),

125 Chapter 5 IMI with NAS in the first 130 DIM potentially allowing more teat skin colonization. The fact that S. equorum, a typical environmental NAS species (Piessens et al., 2011), was the fourth most common species, might be an indication of such an effect.

The number of different NAS species isolated from milk samples during this study (n = 17) is in line with other recent studies (De Visscher et al., 2014, 2016; Mahmmod et al., 2018). The most common NAS species in our study was S. chromogenes. This appears to be the predominant species in most parts of the world (Taponen et al., 2007; Fry et al., 2014; Tomazi et al., 2015), although the distribution of NAS species has been shown to differ greatly between herds (De Visscher et al., 2016; Condas et al., 2017; Dolder et al., 2017; Mahmmod et al., 2018).

Despite the overall mild impact on udder health, it has been shown that NAS possess the ability to cause persistent IMI for longer periods (Taponen et al., 2007; Supré et al., 2011; Mørk et al., 2012). The proportion of NAS IMI that persist varies greatly between different studies: from 33% (Nyman et al., 2018) and 46% (Taponen et al., 2007) up to 85% (Timms and Schultz, 1987; Chaffer et al., 1999). This is higher than the number of pIMI episodes in our study (19.7% of 142 NAS episodes). In studies with a longer follow-up period, IMI caused by NAS could persist for at least 40 w (Taponen et al., 2007), and the mean duration of IMI for all NAS species can be as long as 188 d (Bexiga et al., 2014). Older studies even found average durations of infections of more than 220 d (Rainard et al., 1990; Todhunter et al., 1993), although no reliable strain-typing methods were available for confirmation and NAS species were identified based on phenotypic assays. It should be noted that heifers in our study were followed from calving up to 130 DIM, and thus the maximum possible duration of IMI episodes is limited to this time frame. It is possible that IMI that were present at the end of this follow-up period lasted longer than 130 DIM in the current lactation, and potentially even persisted in the next lactation.

More than 10 different NAS species are able to cause persistent IMI (Thorberg et al., 2009; Mørk et al., 2012; Fry et al., 2014; Nyman et al., 2018), but the possibility to cause persistent infections varies with species: S. chromogenes and S. xylosus cause more persistent than transient IMI, whereas other NAS species cause relatively more transient infections according to Supré et al. (2011), although that study did not use strain-typing as we did. This is in line with the results of our study in regard to S. chromogenes: 18 of the 28 pIMI episodes were

126 Chapter 5 IMI with NAS in the first 130 DIM caused by this species, whereas the other 10 episodes were caused by other species (2 S. xylosus, 1 S. hominis, 1 S. simulans, 4 S. epidermidis, 1 S. warneri, and 1 S. haemolyticus). Interestingly, Bexiga et al. (2014) found no significant differences in the mean duration of IMI caused by different NAS species. The number of pIMI episodes in our study was too small to reach definitive conclusions at the species level. However, regarding the ability to cause persistent infections, S. chromogenes also seemed the best adapted to survive in the udder gland in our study. In addition to potential differences in virulence and adaptation to the udder environment, the same strains of S. chromogenes were identified both in mastitic milk and on the udder skin (Taponen et al., 2008). For some NAS species such as S. equorum, the teat skin and apex can thus act as a reservoir increasing the odds of IMI in the same quarter (Mahmmod et al., 2018), although several other studies could not demonstrate such an association for the more prevalent species such as S. chromogenes, S. cohnii, S epidermidis, and S. xylosus (Quirk et al., 2012; Braem et al., 2013; Mahmmod et al., 2018). However, to the best of our knowledge, no study has yet determined the causal relation between NAS skin colonization and IMI.

Aarestrup and Jensen (1997) showed that infections caused by S. chromogenes in heifers disappeared shortly after parturition. This is in contrast to the behavior of S. chromogenes in our study, where 9 out of 20 IMI with this pathogen persisted for, on average, 101 d and at least 49 d. However, there appeared to be no difference in persistence between the 2 dominant species (i.e., S. chromogenes and S. simulans) in Taponen et al. (2007), whereas, in our study, only 2 quarters had an IMI caused by S. simulans (i.e., 1 tIMI case and 1 pIMI). In general, we found about half of the IMI caused by S. chromogenes persisted compared with only 16.4% of IMI caused by the other NAS species. Even higher proportions of pIMI by S. chromogenes (69.6%) and other NAS (31.8%) have been reported (Supré et al., 2011). Differences in persistence of NAS IMI are thus not merely the result of variations in study design and occurrence of certain species, but can also vary between different strains of a NAS species (Piccart et al., 2016).

Subclinical infections with NAS result in a moderately elevated qSCC, supporting the NAS group’s status as minor pathogens. Because of the small number of IMI caused by other NAS species, our results could not be analyzed per species except for S. chromogenes. The qSCC of quarters having a tIMI with S. chromogenes was not significantly higher compared with

127 Chapter 5 IMI with NAS in the first 130 DIM noninfected quarters. On the other hand, tIMI with NAS other than S. chromogenes did result in a significantly yet mildly increased qSCC, with a level comparable to that of tIMI with a major pathogen. When IMI with S. chromogenes, other NAS, or a major pathogen persisted, qSCC during the infection was significantly elevated, although qSCC of quarters having a pIMI with a major pathogen was not significantly higher compared with S. chromogenes. This, in combination with the large proportions of infections that persist for longer periods, confirms the status of S. chromogenes to be “more relevant for udder health”, as was first proposed by Supré et al. (2011) and substantiated by De Visscher et al. (2016) and Fry et al. (2014). Interestingly, qSCC during pIMI and tIMI with NAS were much lower than the values estimated by Taponen et al. (2007) (geometric mean qSCC of 657,600 cells/mL and 649,100 cells/mL, respectively). For S. chromogenes, further investigation is needed to determine whether the strains that cause tIMI without elevated qSCC are different from the strains causing pIMI with a significantly higher qSCC, or that other factors, such as the immunity of the quarter or animal, play a role.

The association between the different IMI statuses and qMY throughout the first 130 DIM was modelled using the 4 parametric AS function. The normal lactation curve of dairy cows is characterized by a steep increase in early lactation and a slower decrease thereafter, thus requiring nonlinear models to predict MY. Several mathematical models were described to predict the lactation curves based on milk yield records. Second-degree polynomial functions, like the 4 parametric AS function, fitted the lactation curve better than other (Ali and Schaeffer, 1987; van der Poel et al., 1995). Compared with 6 other models, AS model had the highest square of the correlation coefficient between actual and predicted milk yield (R²), lowest root mean squared error, and smallest differences between actual and predicted lactation milk yields (Kocak and Ekiz, 2008). Several other analyses have confirmed that this AS function is very suitable to model lactation curves (Buttchereit et al., 2010; Stamer et al., 2011; Melzer et al., 2017).

We can conclude that the MY of quarters having an IMI with either S. chromogenes or other NAS species, either transient or persistent, is not different from that of noninfected quarters, despite the elevated qSCC. Given the strong negative correlation between MY and SCC under

128 Chapter 5 IMI with NAS in the first 130 DIM virtually all circumstances, this is an important result in these data. Despite the presence of an inflammatory response, as shown by the elevated SCC, NAS-infected quarters do not show lower milk production. However, one of the main limitations of our study was the inability to distinguish between teat canal colonization and IMI with NAS, meaning that some of the positive bacteriological examinations will have been the result of teat canal colonization instead of IMI. One option to distinguish between IMI (which causes inflammation and therefore an elevation of the SCC) and teat canal colonization in case of a positive bacteriological examination is to include a certain SCC limit in the IMI definition. We were unable to decide on such a SCC threshold because it would have been impossible to study the impact of a NAS IMI on SCC (e.g., if the qSCC had to be at least 200,000 cells/mL before considering a culture- positive quarter to be truly NAS infected, NAS-infected quarters would always have had an elevated SCC). Other possibilities to consider in future research are to collect duplicate milk samples (Dohoo et al., 2011a), taking milk samples via puncture of the udder wall with a needle (Hiitiö et al., 2016) or via a sterile teat cannula (Friman et al., 2017), or to use another inflammatory parameter that would represent tissue damage, such as N-acetyl-β-D- glucosaminidase (Hovinen et al., 2016). Furthermore, we acknowledge that the detection of CM cases, and especially the mild cases with only changes in milk composition, are a challenge in AMS. It is thus possible that a few of these mild cases remained undetected and were not reported, which might have influenced the analysis of their effect on udder health and milk production. Because of the high number of NAS species identified in our study (n = 17) and the relatively low number of isolates of most NAS species, the effect on qSCC and qMY could not be analyzed per species, with the exception of S. chromogenes.

Remarkably, quarters that cured from an IMI with S. chromogenes had a significantly lower qMY immediately after the pIMI. Interestingly, a higher MY associated with NAS IMI was only found in studies where MY was measured at animal level (Wilson et al., 1997; Schukken et al., 2009; Thorberg et al., 2009; Piepers et al., 2010, 2013). One explanation for the different conclusions might be the aggregation of different IMI statuses of each quarter, resulting in an IMI status at the cow level that does not necessarily represent the IMI status of all quarters of that animal (e.g., an animal could be considered as NAS infected if 1 quarter had a NAS IMI,

129 Chapter 5 IMI with NAS in the first 130 DIM although the 3 other quarters could be noninfected). Another possible explanation is the impossibility of quantifying the physiologically higher milk production of hind quarters when MY is only measured at the animal level, whereas NAS IMI occurred slightly more often in hind quarters in our study. A positive association between NAS IMI and MY could not be found when MY was measured on the quarter level (Tomazi et al., 2015; Valckenier et al., 2019, 2020), and although a negative association between SCC (and thus IMI) and MY is generally substantiated, the elevated qSCC did not result in a lower qMY. A drawback of these studies is that, in contrast to studies measuring MY at the animal level, no longitudinal follow-up of the quarter IMI status was performed, unlike what was done in the current study.

The conclusion that NAS IMI cause a mild inflammatory reaction with a significantly higher qSCC but have no effect on qMY remains intriguing. Structural equation models (Gianola and Sorensen, 2004), which facilitate modelling the simultaneous and recursive effects between phenotypical characteristics, have described 3 main effects that play a role in the relationship between MY and SCC. The infection effect results in a decrease of MY in cows with a higher SCC. Higher-producing cows are more susceptible to IMI, and this stress effect thus results in a higher SCC. Finally, the dilution effect in higher-yielding animals results in a lower SCC (Wu et al., 2007; Jamrozik et al., 2010). No general consensus seems to be found, although most studies have concluded that a model with a negative effect of SCC on MY is favorable (de los Campos et al., 2006; Jamrozik and Schaeffer, 2010), and this in combination with a smaller but positive effect of a higher SCC on MY (Wu et al., 2007; Jamrozik et al., 2010). These effects are larger in the first 60 DIM compared with the period between 61 and 120 DIM (Wu et al., 2007). According to the aforementioned conclusions from structural equation models, one would expect IMI with NAS, resulting in an increased qSCC, to be negatively associated with qMY. The existing of such an underlying positive effect between SCC and MY might explain the positive effect of NAS IMI on MY that was observed in some studies (Piepers et al., 2010, 2013), although other confounding factors, such as a lower incidence of CM in NAS-infected quarters due to protective effects of pre-existing NAS IMI against new IMI caused by major pathogens, should be ruled out first. In the case of IMI with NAS, the underlying negative infection effect might be so small that it had no measurable impact on the association between qSCC and qMY, or the negative infection effect could be of equal magnitude than the positive

130 Chapter 5 IMI with NAS in the first 130 DIM effect of SCC on MY, resulting in no overall effect on MY. In relation to this, we found that tIMI or pIMI with S. chromogenes had no significant effect on qMY but, remarkably, a cIMI resulted in a significantly lower qMY than that of noninfected quarters. Further research should study whether the positive effect on MY due to the presence of this bacterium has disappeared in this situation, which only leaves a negative infection effect and thus a lower MY. Based on our results (i.e., NAS IMI cause a significant higher SCC in infected quarters without a negative effect on qMY), NAS can be considered as minor pathogens. If the underlying cause of the positive effect between SCC and MY can be identified (e.g., protective effects against major pathogen IMI or even production stimulating effects due to some specific NAS strains), this could open perspectives for developing new prevention or treatment concepts in the fight against mastitis.

CONCLUSIONS

The prevalence of NAS is high in early lactating dairy heifers. Most NAS infections seem to be caused by S. chromogenes. The persistence of NAS IMI depends on the NAS species involved. About half of the IMI caused by S. chromogenes persisted, whereas only 9.8% of the IMI caused by other NAS species persisted. Quarters having a persistent IMI caused by S. chromogenes and by the group of other NAS species had a higher SCC than quarters that were transiently infected. However, neither transient nor persistent IMI with NAS were significantly associated with qMY during the first 130 DIM.

131 Chapter 5 IMI with NAS in the first 130 DIM

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https://doi.org/10.3168/jds.2017-14311. Matthews, K.R., R.J. Harmon, and B.E. Langlois. 1992. Prevalence of Staphylococcus Species During the Periparturient Period in Primiparous and Multiparous Cows. J. Dairy Sci. 75:1835–1839. https://doi.org/10.3168/jds.S0022-0302(92)77942-9. Melzer, N., S. Trißl, and G. Nürnberg. 2017. Short communication: Estimating lactation curves for highly inhomogeneous milk yield data of an F 2 population (Charolais × German Holstein). J. Dairy Sci. 100:9136–9142. https://doi.org/10.3168/jds.2017-12772. Mørk, T., H.J. Jørgensen, M. Sunde, B. Kvitle, S. Sviland, S. Waage, and T. Tollersrud. 2012. Persistence of staphylococcal species and genotypes in the bovine udder. Vet. Microbiol. 159:171–180. https://doi.org/10.1016/j.vetmic.2012.03.034. Nyman, A.-K., C. Fasth, and K. Persson Waller. 2018. Intramammary infections with different non-aureus staphylococci in dairy cows. J. Dairy Sci. 101:1403–1418. https://doi.org/10.3168/jds.2017-13467. Paradis, M.E., E. Bouchard, D.T. Scholl, F. Miglior, and J.P. Roy. 2010. Effect of nonclinical Staphylococcus aureus or coagulase-negative staphylococci intramammary infection during the first month of lactation on somatic cell count and milk yield in heifers. J. Dairy Sci. 93:2989–2997. https://doi.org/10.3168/jds.2009-2886. Pearson, L.J., J.H. Williamson, S. Turner, and J.E. Hillerton. 2013. Peripartum infection with Streptococcus uberis but not coagulase- negative staphylococci reduced milk production in primiparous cows. J. Dairy Sci. 96:158–164. https://doi.org/10.3168/jds.2012-5508. Pedersen, L.H., and T.W. Bennedsgaard. 2006. Udder health in dairy herds converting to automatic milking systems - Bacteriology and cell count pattern. Pages 26–31 in Proc. Cattle Consulting Days 2006, Nyborg, Frederiksberg Bogtrykkeri, Denmark. Piccart, K., J. Verbeke, A. De Visscher, S. Piepers, F. Haesebrouck, and S. De Vliegher. 2016. Local host response following an intramammary challenge with Staphylococcus fleurettii and different strains of Staphylococcus chromogenes in dairy heifers. Vet. Res. 47:56–67. https://doi.org/10.1186/s13567-016-0338-9. Piepers, S., L. De Meulemeester, A. de Kruif, G. Opsomer, H.W. Barkema, and S. De Vliegher. 2007. Prevalence and distribution of mastitis pathogens in subclinically infected dairy cows in Flanders, Belgium. J. Dairy Res. 74:478–483.

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https://doi.org/S0022029907002841 [pii]10.1017/S0022029907002841. Piepers, S., G. Opsomer, H.W.W. Barkema, A. de Kruif, and S. De Vliegher. 2010. Heifers infected with coagulase-negative staphylococci in early lactation have fewer cases of clinical mastitis and higher milk production in their first lactation than noninfected heifers. J. Dairy Sci. 93:2014–2024. https://doi.org/10.3168/jds.2009-2897. Piepers, S., Y.H.H. Schukken, P. Passchyn, and S. De Vliegher. 2013. The effect of intramammary infection with coagulase-negative staphylococci in early lactating heifers on milk yield throughout first lactation revisited. J. Dairy Sci. 96:5095–5105. https://doi.org/10.3168/jds.2013-6644. Piessens, V., S. De Vliegher, B. Verbist, G. Braem, A. Van Nuffel, L. De Vuyst, M. Heyndrickx, and E. Van Coillie. 2012. Intra-species diversity and epidemiology varies among coagulase-negative Staphylococcus species causing bovine intramammary infections. Vet. Microbiol. 155:62–71. https://doi.org/10.1016/j.vetmic.2011.08.005. Piessens, V., E. Van Coillie, B. Verbist, K. Supré, G. Braem, A. Van Nuffel, L. De Vuyst, M. Heyndrickx, and S. De Vliegher. 2011. Distribution of coagulase-negative Staphylococcus species from milk and environment of dairy cows differs between herds. J. Dairy Sci. 94:2933–2944. https://doi.org/10.3168/jds.2010-3956. Pitkälä, A., M. Haveri, S. Pyörälä, V. Myllys, and T. Honkanen-Buzalski. 2004. Bovine mastitis in finland 2001—Prevalence, distribution of bacteria, and antimicrobial resistance. J. Dairy Sci. 87:2433–2441. https://doi.org/10.3168/jds.S0022-0302(04)73366-4. Pyörälä, S., and S. Taponen. 2009. Coagulase-negative staphylococci-Emerging mastitis pathogens. Vet. Microbiol. 134:3–8. https://doi.org/10.1016/j.vetmic.2008.09.015. Quirk, T., L.K. Fox, D.D. Hancock, J. Capper, J. Wenz, and J. Park. 2012. Intramammary infections and teat canal colonization with coagulase-negative staphylococci after postmilking teat disinfection: Species-specific responses. J. Dairy Sci. 95:1906–1912. https://doi.org/10.3168/jds.2011-4898. Rainard, P., M. Ducelliez, and B. Poutrel. 1990. The contribution of mammary infections by coagulase-negative staphylococci to the herd bulk milk somatic cell count. Vet. Res. Commun. 14:193–198. https://doi.org/10.1007/BF00347737. Rasmussen, M.D., and T.C. Hemling. 2002. The influence of automatic teat spraying on teat

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condition. Pages 166–167 in NMC Annual Meeting Proc., Orlando, FL., National Mastitis Council, Madison, WI. Reyher, K.K., I.R. Dohoo, D.T. Scholl, and G.P. Keefe. 2012a. Evaluation of minor pathogen intramammary infection, susceptibility parameters, and somatic cell counts on the development of new intramammary infections with major mastitis pathogens. J. Dairy Sci. 95:3766–3780. https://doi.org/10.3168/jds.2011-5148. Reyher, K.K., S. Dufour, H.W. Barkema, L. Des Coteaux, T.J. Devries, I.R. Dohoo, G.P. Keefe, J.P. Roy, and D.T. Scholl. 2011. The National Cohort of Dairy Farms--a data collection platform for mastitis research in Canada. J. Dairy Sci. 94:1616–1626. https://doi.org/10.3168/jds.2010-3180. Reyher, K.K.K., D. Haine, I.R.R. Dohoo, and C.W.W. Revie. 2012b. Examining the effect of intramammary infections with minor mastitis pathogens on the acquisition of new intramammary infections with major mastitis pathogens—A systematic review and meta- analysis. J. Dairy Sci. 95:6483–6502. https://doi.org/10.3168/jds.2012-5594. Sampimon, O.C., H.W. Barkema, I.M.G.A. Berends, J. Sol, and T.J.G.M. Lam. 2009. Prevalence and herd-level risk factors for intramammary infection with coagulase- negative staphylococci in Dutch dairy herds. Vet. Microbiol. 134:37–44. https://doi.org/10.1016/j.vetmic.2008.09.010. Schukken, Y.H., R.N. Gonzalez, L.L. Tikofsky, H.F. Schulte, C.G. Santisteban, F.L. Welcome, G.J. Bennett, M.J. Zurakowski, and R.N. Zadoks. 2009. CNS mastitis: Nothing to worry about?. Vet. Microbiol. 134:9–14. https://doi.org/10.1016/j.vetmic.2008.09.014. Stamer, E., W. Brade, and G. Thaller. 2011. Modelling and estimation of genetic parameters for milk urea content in first and second parity Holstein cows. Zuchtungskunde 83:104– 117. Stevens, M., S. Piepers, K. Supré, J. Dewulf, and S. De Vliegher. 2016. Quantification of antimicrobial consumption in adult cattle on dairy herds in Flanders, Belgium, and associations with udder health, milk quality, and production performance. J. Dairy Sci. 99:2118–30. https://doi.org/10.3168/jds.2015-10199. Supré, K., S. De Vliegher, O.C.C. Sampimon, R.N.N. Zadoks, M. Vaneechoutte, M. Baele, E. De Graef, S. Piepers, and F. Haesebrouck. 2009. Technical note: Use of transfer RNA-

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intergenic spacer PCR combined with capillary electrophoresis to identify coagulase- negative Staphylococcus species originating from bovine milk and teat apices. J. Dairy Sci. 92:3204–3210. https://doi.org/10.3168/jds.2008-1923. Supré, K., F. Haesebrouck, R.N.N. Zadoks, M. Vaneechoutte, S. Piepers, and S. De Vliegher. 2011. Some coagulase-negative Staphylococcus species affect udder health more than others. J. Dairy Sci. 94:2329–2340. https://doi.org/10.3168/jds.2010-3741. Taponen, S., J. Björkroth, and S. Pyörälä. 2008. Coagulase-negative staphylococci isolated from bovine extramammary sites and intramammary infections in a single dairy herd. J. Dairy Res. 75:422–429. https://doi.org/10.1017/S0022029908003312. Taponen, S., J. Koort, J. Björkroth, H. Saloniemi, and S. Pyörälä. 2007. Bovine intramammary infections caused by coagulase-negative staphylococci may persist throughout lactation according to Amplified Fragment Length Polymorphism-based analysis. J. Dairy Sci. 90:3301–3307. https://doi.org/10.3168/jds.2006-860. Taponen, S., H. Simojoki, M. Haveri, H.D. Larsen, and S. Pyörälä. 2006. Clinical characteristics and persistence of bovine mastitis caused by different species of coagulase- negative staphylococci identified with API or AFLP. Vet. Microbiol. 115:199–207. https://doi.org/10.1016/j.vetmic.2006.02.001. Tenhagen, B.-A., G. Köster, J. Wallmann, and W. Heuwieser. 2006. Prevalence of mastitis pathogens and their resistance against antimicrobial agents in dairy cows in Brandenburg, Germany. J. Dairy Sci. 89:2542–2551. https://doi.org/10.3168/jds.S0022-0302(06)72330- X. Thorberg, B.-M., M.-L. Danielsson-Tham, U. Emanuelson, and K. Persson Waller. 2009. Bovine subclinical mastitis caused by different types of coagulase-negative staphylococci. J. Dairy Sci. 92:4962–4970. https://doi.org/10.3168/jds.2009-2184. Timms, L.L., and L.H. Schultz. 1987. Dynamics and significance of coagulase-negative staphylococcal intramammary infections. J. Dairy Sci. 70:2648–2657. https://doi.org/10.3168/jds.S0022-0302(87)80335-1. Todhunter, D.A., L.L. Cantwell, K.L. Smith, K.H. Hoblet, and J.S. Hogan. 1993. Characteristics of coagulase-negative Staphylococci isolated from bovine intramammary infections. Vet. Microbiol. 34:373–380. https://doi.org/10.1016/0378-1135(93)90062-C.

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Tomazi, T., J.L. Goncalves, J.R. Barreiro, M.A. Arcari, and M. V dos Santos. 2015. Bovine subclinical intramammary infection caused by coagulase-negative staphylococci increases somatic cell count but has no effect on milk yield or composition. J. Dairy Sci. 98:3071– 3078. https://doi.org/10.3168/jds.2014-8466. Valckenier, D., S. Piepers, A. De Visscher, R.M. Bruckmaier, and S. De Vliegher. 2019. Effect of intramammary infection with non-aureus staphylococci in early lactation in dairy heifers on quarter somatic cell count and quarter milk yield during the first 4 months of lactation. J. Dairy Sci. 102:6442–6453. https://doi.org/10.3168/jds.2018-15913. Valckenier, D., S. Piepers, A. De Visscher, and S. De Vliegher. 2020. The effect of intramammary infection in early lactation with non-aureus staphylococci in general and Staphylococcus chromogenes specifically on quarter milk somatic cell count and quarter milk yield. J. Dairy Sci. 103:768–782. https://doi.org/10.3168/jds.2019-16818. van der Poel, W.H., M.C. Mourits, M. Nielen, K. Frankena, J.T. Van Oirschot, and Y.H. Schukken. 1995. Bovine respiratory syncytial virus reinfections and decreased milk yield in dairy cattle. Vet. Q. 17:77–81. https://doi.org/10.1080/01652176.1995.9694537. Vanderhaeghen, W., S. Piepers, F. Leroy, E. Van Coillie, F. Haesebrouck, and S. De Vliegher. 2015. Identification, typing, ecology and epidemiology of coagulase negative staphylococci associated with ruminants. Vet. J. 203:44–51. https://doi.org/10.1016/j.tvjl.2014.11.001. Wilson, D.J., R.N. Gonzalez, and H.H. Das. 1997. Bovine Mastitis Pathogens in New York and Pennsylvania: Prevalence and Effects on Somatic Cell Count and Milk Production. J. Dairy Sci. 80:2592–2598. https://doi.org/10.3168/jds.S0022-0302(97)76215-5. Wu, X.-L., B. Heringstad, Y.-M. Chang, G. de los Campos, and D. Gianola. 2007. Inferring Relationships Between Somatic Cell Score and Milk Yield Using Simultaneous and Recursive Models. J. Dairy Sci. 90:3508–3521. https://doi.org/10.3168/jds.2006-762.

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Chapter 6

General discussion

D. Valckenier

Department of Reproduction, Obstetrics, and Herd Health

Faculty of Veterinary Medicine,

Ghent University, Merelbeke, Belgium

Chapter 6 General discussion

1. Introduction and main findings In the past 4 decades, numerous studies have investigated the impact of intramammary infections (IMI) caused by non-aureus staphylococci (NAS) in dairy cows. However, the conclusions regarding their impact and relevance to udder health are contradictory and debatable, especially those regarding the effect on milk yield (MY). The general aim of this thesis was to gain more insights on the effect of IMI caused by NAS by precisely determining, at the quarter level in dairy heifers, the IMI status, somatic cell count (SCC), and MY. In Chapter 3, the presence of IMI caused by NAS as a group in the first 4 days (d) in milk (DIM) was determined at the quarter level in 324 quarters from 82 heifers housed in 3 Flemish dairy herds equipped with an automatic milking system (AMS). Furthermore, the effect of these IMI in the first 4 DIM on the quarter somatic cell count (qSCC) and quarter milk yield (qMY) during the first 4 months of lactation was assessed. In Chapter 4, using the same data, the IMI status was determined at 1-4 DIM and 15-18 DIM, and the isolated NAS were further identified to the species level. In this part of the study, the impact of transient, persistent, and new IMI caused by NAS as a group or by Staphylococcus (S.) chromogenes (the most prevalent NAS species) in the first 18 DIM on the qSCC and on qMY in the first 4 months of lactation was further scrutinized. In Chapter 5, the IMI status was further defined based on milk samples taken at 10 sampling days during the first 130 DIM. This allowed us to determine the infection dynamics in more detail and to precisely study the association of IMI caused by S. chromogenes and the group of other NAS with qSCC and qMY.

The most important conclusions of this thesis are:

• A total of 21% of the quarters sampled within the first 4 DIM were infected with NAS, making this group, as expected, the most prevalent pathogens in early lactating dairy heifers (Chapter 3).

• Quarters with a NAS IMI in the first 4 DIM had a slightly but significantly higher qSCC during the first 4 months of lactation. Despite the slightly elevated qSCC, qMY in NAS- infected quarters was not different from the qMY in noninfected quarters. The milk prolactin level was not different between noninfected and NAS-infected quarters (Chapter 3).

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• Only 23.5% of the quarters having a NAS IMI in the first 4 DIM were still infected 14 days (d) later, demonstrating a high spontaneous cure rate (Chapter 4).

• Staphylococcus chromogenes was the most prevalent NAS species within 1-4 DIM and 15-18 DIM. This was the only NAS species for which the association with qSCC and qMY could be analyzed separately; an IMI in the first 4 DIM resulted in an elevated qSCC in later lactation without having an impact on qMY (Chapter 4).

• The prevalence of NAS is high in early lactation dairy heifers (1-130 DIM) with IMI in 116 quarters from 64 out of 82 heifers. In total 304 NAS isolates belonging to 17 different species were cultured, with S. chromogenes being by far the most common species (52% of the isolates) (Chapter 5).

• The persistence of IMI was different depending on the causative NAS species: 45% of the IMI caused by S. chromogenes persisted for at least 14 days with an average duration of 110.4 days, whereas only 9.8% of 102 IMI caused by the other NAS species persisted at least 14 days and had an average duration of 70 days (Chapter 5).

• Quarters having a persistent IMI with S. chromogenes or the other NAS species have a higher SCC compared with quarters that were transiently infected. However, neither transient nor persistent IMI with NAS were significantly associated with qMY during the first 130 DIM (Chapter 5).

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2. Study design

2.1. Advantages, disadvantages, and possible impact of herds equipped with an automatic milking system A plethora of studies have been conducted that studied the association of NAS IMI and udder health and/or MY, but most of them measured at least 1 of these parameters at the animal level (Eberhart et al., 1982; Kirk et al., 1996; Wilson et al., 1997; Gröhn et al., 2004; Schukken et al., 2009; Thorberg et al., 2009; Paradis et al., 2010; Piepers et al., 2010, 2013; Heikkilä et al., 2018). Several previous studies have investigated the impact of NAS IMI on udder health at the quarter level, but studies scrutinizing the association with qMY are more scarce and only measured the qMY on 1 day (Tomazi et al., 2015). However, to the best of our knowledge, there are no studies in which the qMY was measured during a longer time period for the following obvious reasons: in conventional milking systems only the total amount of milk produced by an animal is measured because milking is udder- rather than quarter-based and the milking cluster is removed for all quarters together when the udder is estimated to be completely milked. Quarter bucket milking systems, such as the system used by Tomazi et al. (2015), do not allow for measuring the qMY of many animals for a longer time period because they are not built-in in the milking parlour and are too labor intensive for use in commercial dairy farms. To overcome this challenge, we chose to collect our data in herds milking with an AMS. In an AMS system, milking is quarter-based (Hovinen and Pyörälä, 2011) and MY is measured for each quarter separately, with data readily available through the management software of the AMS.

Regardless of the benefits of AMS in decreasing the workload of the farmer, mastitis detection and milking hygiene can also pose a challenge for udder health (Hovinen and Pyörälä, 2011). Additionally, udder health in general deteriorates in herds switching from conventional milking to automatic milking (Rasmussen et al., 2001) and the reported incidence of clinical mastitis cases is often lower in AMS herds (Verbeke et al., 2014). The challenges regarding the detection of clinical mastitis, especially mild cases, certainly play a role in that respect. Thus the higher bulk milk SCC in AMS herds can probably be partly explained by a reduced separation of mastitic milk (Hovinen et al., 2009). For this reason, it is possible, in our study,

147 Chapter 6 General discussion that some mild clinical cases were not detected, and could therefore not be taken into account when analyzing the impact of IMI on qMY.

Milk leakage is an important risk factor for mastitis (Schukken et al., 1990; Elbers et al., 1998) and is observed more in AMS herds (39.0%) when compared with other herds (11.2%). It also occurs more often in heifers (62% of the animals) than in multiparous cows (28% of the animals) (Persson Waller et al., 2003). Furthermore, a positive association has been shown between teat skin colonization and IMI with S. aureus and Streptococcus agalactiae in AMS herds (Svennesen et al., 2019). Due to milk leakage, the first barrier against invading pathogens via the teat canal, i.e. the mechanical closure of the canal, remains open and the keratin plug cannot be formed or is continuously flushed out. This could facilitate teat canal colonizations and the occurrence of IMI by pathogens from the environment or by pathogens that have already colonized the teat skin, such as S. chromogenes (De Visscher et al., 2016a). Milk leakage can thus partly explain the high prevalence of this NAS species in AMS herds.

2.1.1. Effect of milking-sampling interval and milking frequency on somatic cell count In the modern dairy sector, the most used indicator to predict whether an animal has an IMI is measuring the milk SCC. The milk SCC is influenced by factors such as age, parity, stage of lactation, season, stress, day-to-day variation, diurnal variation, … (Dohoo and Meek, 1982; Harmon, 1994; Laevens et al., 1997). However, all those factors, except diurnal variations (Dohoo and Meek, 1982), have far less impact on the SCC compared with the IMI status (Dohoo and Meek, 1982; Reneau, 1986; Harmon, 1994). The diurnal variation is higher in high-yielding cows compared with low-yielding cows, suggesting that this effect is caused by a proportional dilution relative to the milking interval (Reneau, 1986). Such dilution effect is expected to be less pronounced in our study because of the higher number of milkings per day in an AMS. Furthermore, polymorphonuclear leukocytes (PMNL) form the predominant cells in IMI cases and it has not yet been investigated that the fluctuation of the relative proportion of these cells during the day is in line with the diurnal variation of the SCC.

148 Chapter 6 General discussion

When milk samples are taken immediately before milking, the resulting SCC are most accurate as indicator for IMI because SCC is higher until 7h after milking compared with the premilking sample (Olde Riekerink et al., 2007). However, it was neither feasible nor desirable in AMS systems in commercial dairy herds to prevent the heifers access to the AMS for milk sample collection as proposed by Olde Riekerink et al. (2007) because irregularly long intervals would also have had an impact on SCC on the one hand and would have had consequences on udder health in general on the other hand (Hamann and Halm, 2004). Thus, in our study, all quarter milk samples were collected in the morning; however, the time interval between last milking and sampling varied. One could argue whether the conclusions regarding the evolution of the SCC between milkings in the study of Olde Riekerink et al. (2007) can be extrapolated to our study population. Olde Riekerink et al. (2007) reported that cows were housed in a tie- stall and milked twice per day with an interval of 9 – 10 hours between the morning and evening milking. In our study, cows were housed in a free stall and were milked more than twice per day on a voluntary basis, resulting in shorter intervals between 2 milkings. Moreover, another study reported that the association between milking interval and SCC in AMS herds is rather small compared with other factors such as production rate and parity, and variations in the milking interval appear to be more important than the interval itself (Mollenhorst et al., 2011). Finally, because of the voluntary milking, the diurnal pattern observed in systems where the cows are milked twice at fixed times is less likely playing a role.

The flushing effect of milking has a positive influence on udder health by removing bacteria from the udder (e.g. milking removes the keratin plug in the teat canal, and thus also bacteria that adhered to it). However, altering the milking interval by changing the milking frequency from 2 to 3 times per day, or vice versa, has no significant effect on SCC (Waterman et al., 1983; Kruip et al., 2002) or on the number of new IMI (Waterman et al., 1983). It should be taken into account that milking also carries a risk. A higher milking frequency might result in more intensive straining on the teat end with detrimental effects on the teat end condition, and increases the risk for environmental pathogens (e.g. via reverse milk flow and impacts) and contagious pathogens (e.g. via more frequent contact with contaminated milking equipment). Presumably, the positive effect of a higher flushing frequency when milking 3 times per day is thus nullified by these increased risks; however, further research is needed to substantiate these

149 Chapter 6 General discussion presumptions. Although bulk milk SCC in general is higher in AMS herds, SCC of cows milked automatically is not different from cows in conventional milking systems (Klungel et al., 2000), implying that the higher herd milk SCC can most likely be attributed to other management factors (e.g., the aforementioned challenges regarding mastitis detection).

In conclusion, we can state that selecting AMS herds, and its consequences on milking frequencies and milking intervals, to study the impact of NAS IMI on SCC most likely had no effect on the external validity of the results in our study compared with other studies performed in herds with conventional milking systems.

2.1.2. Effect of sampling method on somatic cell count In some previous studies, e.g. Piepers et al. (2010), composite milk SCC during the study period was available as part of the Dairy Herd Improvement Association (DHIA) program. In DHIA programs, milk samples are collected using automated sampling devices that take samples continuously or at multiple points throughout the duration of an entire milking. This contrasts with our study, in which the milk samples were taken at 1 instance. However, this did not lead to different results because the SCC level before milking and halfway milking are not significantly different (Olde Riekerink et al., 2007). Taking milk samples continuously or at several points during the same milking is especially useful to correctly estimate the milk fat concentration which, as a result of the oxytocin-mediated milk ejection, increases greatly during milking because milk fat globules are transferred from the alveoli to the cistern during milking. Other milk components like protein and lactose remain consistent during milking (Lollivier et al., 2002).

2.1.3. Milk yield in herds equipped with an automatic milking system Increasing the milking frequency from 2 times per day to 3 times per day results in an increased daily MY of up to 18%, mainly due to a prolonged peak yield (Pearson et al., 1979; Waterman et al., 1983; Stelwagen, 2001). This effect is higher in heifers compared with multiparous cows (Lush and Shrode, 1950; Goff and Gaunya, 1977). In AMS, the milking

150 Chapter 6 General discussion frequency is generally between 2 and 3 milkings per day, and MY therefore rises to an intermediate amount like obtained by milking cows between 2 and 3 times per day (Kruip et al., 2002). In contrast to conventional systems where all cows are milked at regular intervals, the variation in milking interval is logically higher in AMS. Especially milking intervals of 16 hours or more have a negative effect on MY (Hammer et al., 2012; Lyons et al., 2013) and udder health. Only a small percentage (7.5%) of the high yielding animals experience no negative impact from a longer milking interval on MY (Masía et al., 2020). Although the observed MY in our studies are expected to be higher compared with other studies conducted in herds with conventional milking, we do not expect that the observed association between IMI status and MY in our studies is biased merely due to the higher milking frequency because of the very uniform study population, i.e. heifers in early lactation housed in AMS herds.

2.2. Selection of the study population In this thesis, only fresh heifers were enrolled for the following reasons. First, multiparous cows might have suffered from (clinical) mastitis in their previous lactation with damage to the udder parenchyma as a result. This would have biased the analysis of the effect on qMY to some extent. However, only selecting heifers as study population does not fully overcome this potential bias. Pre-calving heifers can also have infections in the developing udder, mainly caused by NAS, S. aureus, and environmental streptococci (Fox, 2009). These IMI can result in histological changes (e.g., scar tissue) in the udder parenchyma, and thus have a negative effect on MY in the first lactation (Trinidad et al., 1990a). Second, infections that originate from the previous lactation might persist throughout the dry period and at calving, making a clear determination of the IMI status of a quarter more complicated and uncertain. Also, even cows that cured during the dry period from an IMI present at dry-off had a higher SCC and a higher chance to develop CM in the next lactation (Lipkens et al., 2019). Cows that had an IMI in the previous lactations but are noninfected after a dry period might still have a negative effect on SCC and MY. Furthermore, according to an online survey conducted in 2014, in 85% of the 549 responding Belgian dairy herds all cows received blanket dry cow therapy with long-acting antibiotics (Lommelen et al., 2014). Residues of these antimicrobials are often still present in

151 Chapter 6 General discussion the milk in the first few days after calving, making standard bacteriological culturing of milk samples collected in these first days of lactation unreliable due to the possibility of false- negative results. As a second consequence of long-acting antimicrobial dry cow treatment, IMI are less present in multiparous cows at the beginning of lactation compared with heifers. Indeed, Taponen et al. (2007) found that 74.2% of quarters from heifers were infected, and only 20.0% in older cows. Assuming that the prevalence of NAS IMI in older cows would also be lower in the herds enrolled in our study, it would have been necessary to enroll more cows to study the association between NAS IMI and qSCC and qMY. And finally, the total MY per lactation increases per parity, thus including cows of different parities would have resulted in more variation and thus also higher numbers of animals needed to be included in the study.

2.3. Bacteriological examination and determination of intramammary infection statuses An important yet difficult step in every study focusing on bovine mastitis is defining the definitions of the different IMI statuses; this includes determining the colony forming units (CFU) threshold in standard bacteriological culture to consider quarters as infected. Many different CFU thresholds have been used in literature, ranging from 100 to 1,000 CFU/mL, and quarters were considered infected if 1, 2, or even more milk samples from consecutive samplings were culture-positive, whether in combination with a SCC threshold of the milk sample (Trinidad et al., 1990b; Taponen et al., 2006; Piepers et al., 2007; Thorberg et al., 2009; Piessens et al., 2011; Supré et al., 2011; Mahmmod et al., 2018a). Our decision to use a 100 CFU/mL threshold in a single milk sample was based on the following quote by Dohoo et al. (2011b): “Consequently, if identifying as many existing infections as possible is important, then the criteria for considering a quarter positive should be a single colony (from a 0.01-mL milk sample) isolated (definition A)”. Our goal was to identify as many IMI as possible, and thus to reduce the likelihood of an infected quarter being misclassified as noninfected. We considered a lower percentage of false-negative results more important (by using the threshold of 100 CFU/mL) than a high specificity and thus fewer false-positive results, because the goal of our study was to compare the effect of IMI with NAS on the qSCC and qMY against noninfected

152 Chapter 6 General discussion quarters or quarters infected with a major pathogen. Furthermore, based on this definition, all 3 studies included in this thesis show that SCC in milk from quarters considered as infected with NAS is higher compared with noninfected quarters, indicating that most of the culture NAS-positive quarters were truly infected with NAS.

In the data set used in Chapter 3, a huge and overlapping variation was seen in the number of CFU/mL between the 25% lowest producing quarters and the 25% highest producing quarters (min = 100, max = 1,600) (data not shown). In total, 68 quarters were considered NAS- infected based on the threshold of 100 CFU/mL. To determine the relationship between the number of CFU/mL at the 1st sampling and the average daily qMY, we divided the 68 NAS- infected quarters into 4 quartiles based on the qMY. The number of CFU/mL varied between 100 to 1,600 in all 4 quartiles. From the 34 quarters with the lowest average daily qMY (quartile 1 and 2), 6 samples yielded 100 CFU/mL and 17 yielded more than 1,000 CFU/mL. From the other 34 quarters (quartile 3 and 4), 6 samples yielded 100 CFU/mL and 15 samples yielded more than 1,000 CFU/mL. This suggests that the impact of NAS infections on qMY did not depend on the number of isolated colonies from the milk sample.

In Chapter 4 and Chapter 5, a quarter was defined as having a persistent IMI with a certain pathogen if that quarter was considered infected with that pathogen on (at least) 2 consecutive sampling days. We acknowledge that a 14-d period might be debatable to consider an IMI to be persistent in terms of udder health. However, no general consensus exists regarding the time interval for consecutive isolations of the same pathogen to define an IMI as persistent or transient. Furthermore, choosing this relatively short sampling interval, especially compared to average DHIA sampling intervals of 4-6 weeks, allowed us to monitor the IMI status of each quarter with improved precision.

If the same pathogen is isolated multiple times, it has been demonstrated via pulsed-field gel electrophoreses (PFGE) that these isolates belong to the same bacterial strain in about 90% of IMI cases (Fry et al., 2014). In Chapter 5, milk samples for bacteriological culturing were taken over a period of 130 days. The avoid the potential misclassification of some IMI as being persistent, a bacterial typing method is useful to confirm whether the different isolates belong to the same bacterial strain. Many different typing techniques exist, all with their specific pro’s

153 Chapter 6 General discussion and contra’s. In general, these typing techniques can be divided in 2 main categories: phenotypic methods and genotypic methods. Phenotypic methods, such as serotype, biotype, phage-type, or antibiogram (Sabat et al., 2013), lack discriminatory power to distinguish closely related strains, and are not advisable to be used in current epidemiological studies. Genotypic methods are more widely used nowadays because of their higher resolution. The genotypic methods can be classified in 3 main categories: methods based on banding patterns, methods based on DNA sequencing, and DNA hybridization methods. The classification and characteristics of most of the available genotypic methods have been described by Foxman et al. (2005), Li et al. (2009), Sabat et al. (2013), and Shokoohizadeh et al. (2016). In Chapter 5, we opted for Random Amplification of Polymorphic DNA (RAPD) as typing technique. This DNA banding pattern-based method is a rapid, low-cost, simple, and labor-extensive technique compared with most methods based on DNA sequencing, and it has been used in several other mastitis research papers [e.g., Piessens et al. (2012), Sabat et al. (2013), Wuytack et al. (2019, 2020), Zadoks and Schukken, (2006)]. The most important limitations of banding pattern-based methods, such as RAPD, are the low inter-laboratory and intra-laboratory reproducibility (Murchan et al., 2003; Sabat et al., 2013). However, we could overcome this limitation by analyzing all the isolates derived from the same quarter at multiple sampling days in the same PCR and gel electrophoresis run. Multilocus sequence typing (MLST) has an excellent reproducibility, but it is currently only available for a few NAS species (Thomas et al., 2007; Chassain et al., 2012; Solyman et al., 2013; Zhang et al., 2013; Kornienko et al., 2016). And the most promising technique with the highest resolution, i.e. whole genome sequencing, is rapidly becoming more and more available for the analyses of larger sample collections (Cunningham et al., 2017).

If a pathogen is only isolated 1 time at a certain sampling day, the question remains whether this should be considered a transient IMI or a teat canal colonization. Taking into account the design of our study (i.e., single milk samples via conventional sampling at 14-d intervals), we cannot be certain about the distinction between both, and we acknowledge that some of the positive bacteriological examinations were the result of teat canal colonization rather than infections of the udder tissue. As was already argued in the discussion section of Chapter 5, we chose not to include a SCC threshold in the IMI definition to distinguish IMI from teat canal

154 Chapter 6 General discussion colonizations because of the negative association between a higher SCC and MY that is generally found. The possibility of other infection indicators, such as N-acetyl-β-D- glucosaminidase (NAGase), as an alternative to SCC data to distinguish teat canal colonizations from IMI is described further in this Chapter in section ‘4. Future research’. Another possibility would have been to take duplicate milk samples, and only consider a quarter as having an IMI if both samples yielded a positive culture result. One should take into account that the sensitivity and specificity of bacteriological culturing is not 100%, thus series interpretation of the results of 2 samples (i.e., same pathogen isolated from both samples) would increase the specificity and decrease the sensitivity (Dohoo et al., 2011a). This would reduce the number of teat canal colonizations that are wrongly classified as IMI, but would also result in an undesired increase of the number of false-negative results. The problem of false-negative results in standard bacteriological culturing could be partly solved by determining the IMI status by using culture- independent molecular methods, such as real-time PCR (Koskinen et al., 2009, 2010; Taponen et al., 2009). Although these kind of methods have proven to be very valuable and accurate, their use is still under debate because these tests present a positive reaction when DNA, and not necessarily viable bacteria, are present in the sample. Remarkably, Taponen et al. (2009) demonstrated that the bacterial concentration in a milk sample is not the main parameter to explain negative standard bacteriological culturing. Even samples in which high quantities of bacteria could be found with real-time PCR can be culture-negative in standard bacteriological examination. Another method to overcome possible ‘contamination’ of the milk during conventional sampling by bacteria that merely colonize the teat canal is collecting samples directly from the udder cistern by puncturing the udder wall with a needle (Hiitiö et al., 2016) or by inserting a sterile cannula through the teat canal (Friman et al., 2017). However, both methods are more expensive and more invasive than conventional sampling, making them less recommended for repeated measurements in commercial dairy herds. Finally, one could also argue about the importance of false-positive culturing results due to bacteria originating from the teat canal. Laevens et al. (1997) showed clearly that even single isolations of NAS resulted in a numerical or statistically significant increase in SCC despite other studies considering a single isolation as a false-positive result (Sheldrake et al., 1983; Rainard et al., 1990).

155 Chapter 6 General discussion

Although estimated at the animal level, few cattle with a composite SCC below 100,000 cells/mL have an IMI (Sordillo et al., 1997), and these cows are estimated not to suffer from a reduction in MY due to inflammation (Green et al., 2006). Further research using quarter milk samples has revealed that only 13.2% of quarters having a SCC of less than 50,000 cells/mL are infected. Remarkably, increasing the threshold up to 100,000 cells/mL causes an increase of the cumulative prevalence of IMI of only 2.4% in these quarters (Petzer et al., 2017). With noninfected quarters having an average SCC of 56,000 cells/mL in our study (Chapter 5), only a minority of quarters will erroneously have been classified as being noninfected using our IMI definitions.

2.4. Should we measure the intramammary infection status, milk yield and somatic cell count at the quarter or animal level? In this thesis, milk samples were collected from the individual quarters of heifers on each sampling day to determine the IMI status and milk SCC at the quarter level. Also, MY could be determined at the quarter level because the AMS measures the milk produced per milking for the individual quarters of an animal. By doing so, we could relate the IMI status of each quarter to its MY and milk SCC which is in contrast with almost all other studies that have been published on the relation between NAS IMI and udder health and milk production. In most previous studies, SCC or MY, or both, were available via DHIA programs which measure and report these parameters at the animal level only. This approach forced the authors of those studies to aggregate IMI statuses of each quarter of an animal at the animal level enabling them to study the association between the 3 aforementioned variables. One could argue that measuring or aggregating the IMI status at the animal level resulted in a loss of information and potentially also distorted the results. It is most common that only 1 or 2 quarters within an animal are infected (e.g., on average 1.65 quarters with a NAS IMI per animal in Chapter 5). However, one should thus take into account that the greatest proportion of the total milk yield is produced by the quarters that are not infected with the pathogen of interest. For example, an animal with 1 NAS-infected quarter and 3 noninfected quarters was typically classified as being NAS-infected. The analysis of the association between IMI and MY will be distorted in this

156 Chapter 6 General discussion case, because the 3 noninfected quarters contributed much more to the total milk SCC and MY of this animal than the 1 infected quarter. And on the other hand, the availability of milk production data only on the animal level can also obfuscate the study of this association. Like is already argued in Chapter 1, a lower MY in 1 quarter can be compensated by a higher MY in the other quarters. Thus, if a NAS IMI would lead to a lower MY in that quarter, this effect might not be reflected in a change of the total MY of the animal.

In order to clarify whether studying the association between IMI status, milk SCC and MY at the animal level could lead to different conclusions than studying these associations at the quarter level, the data from the first study (Chapter 3, studying the impact of IMI at 1-4 DIM on qSCC and qMY in the first 130 DIM in dairy heifers) were re-analyzed after aggregating the IMI status, milk SCC and MY from each quarter at the animal level. This allowed us to compare both types of analyses, i.e. at the animal level versus at the quarter level, using the exact same data.

To do so, the daily qMY at sampling day of the different quarters per heifers were summed, resulting in the new variable ‘Heifer MY/d’. The variable ‘Heifer SCC’ was calculated for each sampling day by summing up the products of the qSCC (in cells/mL) and the daily qMY (in mL) for each quarter within an animal, and then dividing this sum by the total MY (in mL) of this animal at sampling day. Finally, the heifer IMI status in early lactation was defined by aggregating the IMI statuses at the first sampling day (1-4 DIM) of the different quarters within a heifer. This is in line with the methodology of Piepers et al. (2010), although the IMI status was defined based on the culture results at 1-4 DIM and 5-8 DIM. Heifers with at least one quarter infected with a major pathogen (Staphylococcus aureus, esculin-positive cocci, or E. coli) at the first sampling day were classified as major pathogen infected. Heifers with at least one quarter infected with NAS and the other quarters noninfected were classified as NAS infected. If all the quarters were noninfected, these animals were classified as noninfected. When the IMI status could not be defined based on these definitions, e.g. if the milk sample was contaminated or a quarter was infected with any other pathogen, these heifers were excluded from the data set. In total, this resulted in 28 noninfected, 26 NAS-infected and 4 major pathogen-infected heifers. Twenty-four heifers were not assigned an infection status.

157 Chapter 6 General discussion

The associations between the heifer IMI status in early lactation as predictor variable of main interest and the sampling day heifer SCC and sampling day heifer MY, respectively, were determined by fitting the same linear mixed models as described in Chapter 3, yet at the heifer rather than at the quarter level. Briefly, a natural logarithmic transformation of the SCC (in x1000 cells/mL) was performed (LnSCC) (Ali and Shook, 1980), and DIM and its quadratic term were included as continuous predictor variables. The heifer IMI status was included as a categorical predictor variable with 3 levels, as described above. The model for heifer MY was fit with LnSCC at sampling day as an additional predictor. Herd was forced into the models as a fixed effect (3 levels) to correct for potential clustering of heifers within herds, and heifer was added to correct for within-heifer correlation of the 10 biweekly repeated observations per animal (repeated statement). In contrast to the models described in Chapter 3, the categorical variable ‘Quarter position’ (2 levels: front vs. hind) was no longer included because all variables were merged at the heifer level. The results of the models with sampling day heifer SCC and sampling day heifer MY as outcome variables are shown in Tables 1 and 2, respectively.

Because 24 heifers were not assigned an IMI status, data from less animals and therefore also less quarters were available for the analysis as compared with the analysis in Chapter 3. To overcome this potential selection bias, the analysis included in Chapter 3, thus with the quarter level IMI status, qSCC, and qMY, was repeated including only the quarters belonging to the 58 heifers for whom the heifer-level IMI status could be determined in the new data set. The results of the models with sampling day quarter SCC and sampling day quarter MY as outcome variables are shown in Tables 3 and 4, respectively.

158 Chapter 6 General discussion

Table 1. Linear mixed regression model describing the association between the heifer-level natural log- transformed somatic cell count during the first 4 months of lactation and the IMI status in early lactation (1-4 DIM)

No. of Predictor variable heifers Estimate SE P-value LSM1 Intercept — 5.53 0.16 <0.001 — Herd 0.01 Herd 1 30 ref. — — 4.70 Herd 2 11 0.14 0.16 0.38 4.84 Herd 3 17 0.46 0.14 0.01 5.16 DIM2 — -0.05 0.005 <0.001 — DIM*DIM — 0.0003 0.00004 <0.001 — IMI status in early lactation (1-4 DIM) <0.001 Noninfected 28 ref. — — 4.44 NAS-infected 26 0.27 0.12 0.03 4.72 Major pathogen-infected 4 1.10 0.24 <0.001 5.55 1Least Squares Means. 2Days in milk

Table 2. Linear mixed regression model describing the heifer-level association between the heifer-level milk yield during the first 4 months of lactation and the IMI status in early lactation (1-4 DIM)

No. of Predictor variable heifers Estimate SE P-value LSM1 Intercept — 21.14 1.26 <0.001 — Herd 0.70 Herd 1 30 ref. — — 29.36 Herd 2 11 -1.20 1.64 0.46 28.16 Herd 3 17 0.25 1.46 0.86 29.62 DIM2 — 0.32 0.02 <0.001 — DIM*DIM — -0.002 0.0001 <0.001 — IMI status in early lactation (1-4 DIM) 0.87 Noninfected 28 ref. — — 28.49 NAS-infected 26 0.51 1.30 0.70 29.00 Major pathogen-infected 4 1.17 2.51 0.64 29.66 LnSCC3 at sampling day — -0.36 0.10 <0.001 — 1Least Squares Means. 2Days in milk. 3Natural log-transformed heifer somatic cell count (in x1000 cells/mL)

159 Chapter 6 General discussion

In total, 43 quarters of the 26 NAS-infected heifers were infected with NAS at the first sampling day, resulting in an average number of 1.65 NAS-infected quarters in those heifers. The SCC of noninfected heifers (85,000 cells/mL) is significantly lower compared with NAS- infected heifers (112,000 cells/mL, P = 0.03) and major pathogen-infected heifers (257,000 cells/mL, P = 0.001; Table 1). When comparing these results with the results of the analysis at the quarter level (Table 3), the SCC of noninfected and NAS-infected quarters are significantly different (72,000 cells/mL and 91,000 cells/mL, respectively; P = 0.01). These results are strongly in line with those described in Chapter 3.

The MY of NAS-infected heifers and noninfected heifers was not significantly different (29.00 kg/d and 28.49 kg/d, respectively, P = 0.70, Table 2). Also, qMY was not significantly different between NAS-infected and noninfected quarters in the quarter-level model when quarter position (front vs. hind) is accounted for (7.57 kg/d and 7.33 kg/d, respectively, P = 0.32, Table 4). Although there is no significant association between (q)MY and (q)IMI status in these models, they are both based on the same underlying data, which allows us to compare the observed differences in estimated MY in both models. Remarkably, even when considering there were 1.65 NAS-infected quarters within the NAS-infected heifers, the difference in MY observed at the heifer level is 34.4% higher than can be explained by the observed differences at the quarter level.

In conclusion, we can state that analyzing the data set with the IMI status and milk SCC aggregated at the animal level has led to the same conclusions compared with both the analyses performed at the quarter level (i.e., the analysis on the full data set as described in Chapter 3, and the analysis performed only using quarter data for which the IMI status could be determined at the heifer level). However, analyzing the association between the IMI status and daily MY determined at the heifer level has led to different insights. First, there is a larger difference in daily MY when analyzing the full data set and the data set only containing the quarters for which the IMI status could be determined at the heifer level. And second, the difference in daily MY between noninfected and NAS-infected heifers is larger compared with the difference between noninfected and NAS-infected quarters even when taking into account the average number of NAS-infected quarters per heifer. This is intriguing because it could mean that the

160 Chapter 6 General discussion effect of IMI on MY is overestimated when measured at animal level, although this should be studied on a larger scale before definitive conclusions can be drawn. Of the 43 NAS IMI, 18 (41.9%) occurred in front quarters and 25 (58.1%) in hind quarters, and hind quarters tended to have a likelihood of NAS IMI that is 1.76 times higher compared with front quarters (P = 0.06; Chapter 3). The analysis at the heifer level might be partly distorted because hind quarters, which have a higher MY compared with front quarters, are more likely infected with NAS than front quarters. The question remains whether the significant associations between NAS IMI and MY found in studies analyzing animal-level data would hold true if they could be analyzed at the quarter level.

Table 3. Linear mixed regression model describing the association between the quarter-level natural log-transformed quarter somatic cell count during the first 4 months of lactation and the IMI status in early lactation (1-4 DIM). Only the quarters for which the IMI status could be determined at the heifer level were kept in the analysis.

No. of Predictor variable quarters Estimate SE P-value LSM1 Intercept — 5.36 0.11 <0.001 — Herd 0.03 Herd 1 120 ref. — — 4.39 Herd 2 43 0.14 0.17 0.41 4.53 Herd 3 66 0.42 0.15 0.01 4.80 DIM2 — -0.05 0.003 <0.001 — DIM*DIM — 0.0003 0.00002 <0.001 — IMI status in early lactation 0.002 Noninfected 180 ref. — — 4.27 NAS-infected 43 0.23 0.09 0.01 4.51 Major pathogen-infected 6 0.67 0.24 0.005 4.94 1Least Squares Means. 2Days in milk

161 Chapter 6 General discussion

— — — — — —

7.11 6.92 7.50 7.31 7.67 6.56

LSM

— — — — —

value

0.35 0.64 0.24 0.14 0.16 0.24

-

<0.001 <0.001 <0.001 <0.001

P

SE

0.003 0.23 — 0.39 0.34 0.00002 — 0.25 0.63 — — 0.01

mined at the heifer level were kept kept were level heifer the at mined

Model without quarter position quarter without Model

— —

ref. ref.

0.0005

0.08 5.18 0.40 0.36

0.18 0.75 0.08

-

- - -

Estimate

1

— — — —

7.19 7.01 7.60 7.33 7.57 6.90 6.79 7.75

LSM

— — —

value

0.32 0.64 0.22 0.43 0.32 0.47

-

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001

P

SE

Full model Full

0.003 0.24 — 0.39 0.33 0.00002 — 0.24 0.60 — 0.16 0.01

level association between the quarter milk yield during the first 4 months of lactation lactation of months 4 first the during yield milk quarter the between association level

-

ref. ref. ref.

0.0005

0.08 4.74 0.41 0.23 0.96

0.18 0.43 0.08

-

- - -

Estimate

transformed heifer somatic cell count (in x1000 cells/mL) x1000 (in count cell somatic heifer transformed

-

4 DIM). Only the quarters for which the IMI status could be deter be could status IMI the which for quarters the Only DIM). 4

6

-

— — 43 66 — 43 —

120 180 116 113

No. of No.

quarters

Natural log Natural

3

4 DIM) 4

-

Days in milk. in Days

2

infected

-

at sampling day sampling at

3

pathogen

infected

-

Linear mixed regression model describing the quarter the describing model regression mixed Linear

quarter IMI status in early lactation (1 lactation early in status IMI quarter

2

Herd 1 Herd 2 Herd 3 Herd Noninfected NAS Major

Front quarter Front quarter Hind

DIM Predictor variable Predictor Intercept Herd DIM*DIM (1 lactation early in status IMI position Quarter LnSCC Quarter Means. LeastSquares

Table 4. Table the and analysis. inthe 1

162 Chapter 6 General discussion

3. Non-aureus staphylococci and their impact on somatic cell count and milk yield

3.1. The relevance of non-aureus staphylococci in dairy herds Mastitis pathogens are typically divided into major pathogens, such as S. aureus, Streptococcus uberis, Streptococcus dysgalactiae, and coliforms; and minor pathogens, such as NAS and Corynebacterium spp. The distinction is based on the potential of these pathogens to inflict damage to the udder and cause clinical mastitis, on the duration of infections, and on the need for (prolonged) antimicrobial treatment (Reyher et al., 2012). In contrast to major pathogens, NAS in general cause less often and less severe cases of clinical mastitis (Verbeke et al., 2014), and SCC is lower in cases of subclinical mastitis. However, NAS remain a relevant group of pathogens with regard to udder health. They are the most isolated bacteria in milk samples from dairy cows in many countries (Pitkälä et al., 2004; Piepers et al., 2007; Pyörälä and Taponen, 2009; Reyher et al., 2011). Despite their overall lower impact on SCC in individual animals, their high prevalence makes them non-negligible bacteria in herds striving for a milk quality bonus (e.g., by obtaining a bulk milk SCC lower than 300,000 cells/mL) (Rainard et al., 1990), and almost 18% of the total bulk milk SCC can be attributed to NAS IMI in herds with a bulk milk SCC lower than 200,000 cells/mL (Schukken et al., 2009). Typically in herds that have controlled IMI by major pathogens, NAS can become the predominant source of subclinical mastitis in heifers (Oliver and Mitchell, 1983). However, beneficial effects associated with the presence of NAS IMI have been reported, such as a lower likelihood to develop clinical mastitis (Piepers et al., 2010), although the results of studies investigating potential protective effects of IMI with NAS against IMI with major pathogens are contradictory. Some S. chromogenes strains isolated from the teat apex in pre-partum dairy heifers possess the ability in vitro to inhibit the growth of several Gram-positive major pathogens (De Vliegher et al., 2004). The production of bacteriocins by NAS definitely plays a role in the potential protective effects against major pathogens (Nascimento et al., 2005; Braem et al., 2014; Carson et al., 2017). On the other hand, the presence of NAS IMI was defined as a risk factor for developing IMI with S. aureus (Reyher et al., 2012). Also, several NAS species, especially S. chromogenes, possess virulence factors with cytotoxic activity (Zhang and

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Maddox, 2000), although their role in the pathogenesis of mastitis is still unclear. One study even found no significant difference between S. aureus and S. hyicus IMI (Myllys et al., 1994) in NAGase activity as an infection parameter related to cell damage (Berning and Shook, 1992). Furthermore, NAS may also act as a reservoir for antimicrobial resistance (Archer and Climo, 1994), and are feared as contaminants in human surgeries in immunocompromised patients or after foreign-material implantation (Becker et al., 2020).

Beside differences at the host level (e.g., immunity status) and in study designs, many of the contradictory findings are due to the diversity of the group of NAS, consisting of over 50 species and subspecies, all with slightly different characteristics. In our study (Chapter 5), 17 out of at least 26 NAS species that have been isolated from bovine milk (Persson Waller et al., 2011; Piessens et al., 2011; Taponen et al., 2011; Youn et al., 2011; De Visscher et al., 2014, 2016b; Fry et al., 2014; Mahmmod et al., 2018a) were present. Some species have a higher impact on udder health with regard to the caused elevation of the SCC (Supré et al., 2011) and the persistence of IMI (Mørk et al., 2012; Nyman et al., 2018).

Despite the large number of NAS species that can cause IMI in dairy cattle, the vast majority of them are caused by only a handful of species in most regions: S. chromogenes, S. epidermidis, S. simulans, and S. haemolyticus (Thorberg et al., 2009; Persson Waller et al., 2011; Fry et al., 2014; De Visscher et al., 2016b; Raspanti et al., 2016; Condas et al., 2017; Nyman et al., 2018). Still, few studies that have estimated the prevalence of the different NAS species in AMS herds exist. In 8 Danish AMS herds, about half of the NAS isolates belonged to S. epidermidis, followed by S. haemolyticus (15.2%) and S. chromogenes (10.5%) (Mahmmod et al., 2018a). In our field study, S. chromogenes was the most prevalent NAS species (52.0%). This discrepancy in the most prevalent species between both studies is most likely related to the selected animals rather than regional or herd management related differences; animals of all parities that were having a SCC of more than 200,000 cell/mL on the previous DHI recording were included (Mahmmod et al., 2018a). Because results of at least 1 DHI recording had to be available, no animals could be included in the first days after calving, whereas our results have demonstrated that most of the NAS IMI in general and with S. chromogenes more specifically occur shortly after calving. Other studies have also found that S. chromogenes is particularly

164 Chapter 6 General discussion found in heifers around parturition (Trinidad et al., 1990b; Rajala-Schultz et al., 2004). The prevalence of S. chromogenes was highest shortly after calving with 72.5% of the IMI caused by this species diagnosed within the first 18 DIM (Chapter 5), which indicates that our study population in herds with an AMS does not present an atypical infection prevalence; on the contrary.

Several studies, including ours, were conducted to investigate the impact of the individual NAS species on udder health and/or MY; however, all studies suffered from a lack of power to reach definitive conclusions for a large number of different species. In general, only a minority of the cows in a herd are NAS infected, and the prevalence at the quarter level is thus even lower. The already limited number of isolates encompasses 15 or more different species, resulting in a very low number of IMI (and isolates) per species. Taking into account that S. chromogenes is the most prevalent species in this thesis and in many other studies, its impact on SCC, and the possibility to cause IMI that persist for more than 100 d, we believe its status as the most relevant NAS species is justified. Although also S. simulans and S. xylosus have a relevant impact on udder health in individual animals (Supré et al., 2011; Fry et al., 2014), the number of IMI (and isolates) was low in this study as it was in previous studies (De Visscher et al., 2015, 2016b), making it impossible to analyze them as separate species in contrast to S. chromogenes. In addition, their low prevalence makes their status as relevant for udder health at the herd level highly questionable. Altogether, this justifies our approach to study the impact of IMI caused by NAS separately for S. chromogenes and the group of all other NAS.

3.2. Impact of intramammary infections caused by non-aureus staphylococci on somatic cell count During our studies, qSCC was measured 10 times with 14 d intervals and starting at 1-4 DIM. In contrast to multiparous cows in which the SCC increases further in lactation, SCC in heifers typically remains more constant during the entire lactation period (Laevens et al., 1997) after the physiological decrease in SCC during the first days after calving (Dohoo, 1993; Laevens et al., 1997; Barkema et al., 1999), indicating that SCC is not affected by stage of lactation.

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In general, regarding the impact of NAS on SCC, we can endorse their status as minor pathogens and conclude that both species identification of the NAS isolates and a longitudinal follow up are necessary to determine whether the infections are persistent in order to ascertain the impact of NAS IMI on SCC. Indeed, IMI caused by NAS in the first 4 DIM are associated with a higher qSCC (89,000 cells/mL) in the first 4 months of lactation compared with noninfected quarters (66,000 cells/mL; Chapter 3). Still, species identification demonstrated that this is only the case for S. chromogenes whereas the SCC of quarters infected with other NAS species was at the same level compared with noninfected quarters (Chapter 4). Furthermore, the impact on SCC does not merely depend on the species; the persistence of the infection plays a role as well (Chapter 4 and Chapter 5). The persistence of IMI is off course also dependent on the species, as 8 out of 9 IMI that persisted in the first 18 DIM were caused by S. chromogenes, whereas 33 out of 45 transient IMI (i.e., infected at 1-4 DIM and noninfected at 15-18 DIM) were caused by the other NAS species (Chapter 4). These transient IMI with other NAS species had no significant impact on qSCC (data not shown), whereas transient IMI with S. chromogenes (93,000 cells/mL) did result in an elevated qSCC during the first 130 DIM. Also new infections (i.e., quarters that were noninfected at 1-4 DIM and were infected at 15-18 DIM) with NAS in general (119,000 cells/mL) or S. chromogenes more specifically (301,000 cells/mL) resulted in a higher qSCC. The highest qSCC were observed when the IMI caused by S. chromogenes persisted at least until 15-18 DIM (340,000 cells/mL; Chapter 4). When taking into account all IMI at each sampling day (n = 10) during the first 130 DIM, transient IMI caused by S. chromogenes (69,000 cells/mL) were not associated with a higher qSCC compared with noninfected quarters (56,000 cells/mL), whereas on the other hand SCC was higher in quarters having a transient IMI with the other NAS species (82,000 cells/mL). This might indicate that S. chromogenes strains that do not provoke a persistent IMI are less pathogenic and thus result in a smaller increase in qSCC. Nevertheless, qSCC values are considerably higher in persistently infected quarters both for S. chromogenes (351,000 cells/mL) and the other NAS species (213,000 cells/mL; Chapter 5).

166 Chapter 6 General discussion

3.3. Impact of intramammary infections caused by non-aureus staphylococci on milk yield A variety of studies, both experimental (Simojoki et al., 2011; Piccart et al., 2015) and observational [e.g., Gröhn et al. (2004), Piepers et al. (2013), and Tomazi et al. (2015)] investigated the effect of IMI with NAS on test-day MY, but their conclusions were either undefined or even contradictory, as was summarized in Chapter 1. The results suggest NAS range from being true pathogens with a negative effect on both MY and SCC (Timms and Schultz, 1987; Simojoki et al., 2011), over being mildly virulent or rather harmless with little or no effect on MY and SCC (Kirk et al., 1996; Tomazi et al., 2015; Heikkilä et al., 2018), to being a group of bacteria that are even associated with a higher MY in infected animals despite a higher SCC (Compton et al., 2007; Schukken et al., 2009; Piepers et al., 2010, 2013).

Despite the slightly elevated SCC of quarters having an IMI caused by NAS within 1-4 DIM, MY until 130 DIM of these quarters was not significantly different from noninfected quarters (Chapter 3). Furthermore, the prolactin level in milk samples taken at 1-4 DIM was not different between NAS-infected and noninfected quarters. The fact that MY of these quarters having an IMI is at least not lower than noninfected quarters is to be expected. Green et al. (2006) have estimated the negative and linear dilution effect of higher MY on SCC, and the negative and log 10-linear inflammation effect of higher SCC on MY. A composite SCC lower than 100,000 cells/mL had no negative inflammation effect on MY, whereas the true milk loss was approximately 85% of the estimated milk loss if SCC was between 200,000 and 400,000 cells/mL. This phenomenon has not yet been studied at the quarter level. However, it is reasonable to assume that our estimates of the impact of IMI, and thus a higher SCC, on MY are presumable slightly overestimated because we have not applied a correction factor to account for the interplay between SCC and MY (i.e., both a dilution and inflammation effect).

Further species identification of the NAS isolated at 1-4 DIM did not lead to any obvious differences in the impact of these IMI on qMY during the first 130 DIM because the estimated milk production was not different between quarters having an IMI with S. chromogenes or with the other NAS species (Chapter 4). The fact that most NAS IMI present in the first days after calving are transient could further explain why there is only a very small difference in qMY.

167 Chapter 6 General discussion

On the other hand, IMI that persisted at least until 15-18 DIM, and resulted in a significantly higher qSCC, even above the threshold of 100,000 cells/mL (e.g., 340,000 cells/mL for S. chromogenes) at which a negative effect on MY could be expected (Green et al., 2006), do not seem to have a significant effect on qMY in the first 130 DIM (Chapter 4). Also, the low number of persistent IMI and the high variation in qMY challenged this analysis.

In the final chapter of our study, the IMI status was determined at each of the 10 sampling days, and the association with qMY was studied. Although these results provided many detailed insights (e.g., based on strain-typing) in the occurrence and persistence of NAS IMI, and on the impact of these IMI on qSCC, both transient and persistent (even up to 100 days or more) IMI with S. chromogenes or the other NAS species did not lead to a significant difference in qMY compared with noninfected quarters (Chapter 5).

Taking into account our threshold of 100 CFU/mL without a SCC threshold, some quarters, having merely a teat canal colonization rather than a true IMI with NAS, will have been misclassified as having an IMI. Indeed, in standard bacteriological culturing, setting a higher CFU threshold results in a lower sensitivity and higher specificity (Dohoo et al., 2011b), and thus fewer false-positive results. Thus, one could argue that using a low threshold, like in our study, results in a higher chance to misclassify teat canal infections as being an IMI. However, given the fact that in all our analyses the SCC of quarters classified as having an IMI caused by NAS was higher compared with noninfected quarters, that strain-typing demonstrated that even at this low CFU threshold the same bacterial strain could remain present for more than 100 days, and also that the majority of the bacteriological examinations yielded more than 100 CFU/mL, we can assume that most of the quarters were still assigned a correct IMI status.

In general, we conclude that subclinical IMI caused by NAS in general or even persistent IMI caused by the most relevant species S. chromogenes have no negative effect on qMY.

168 Chapter 6 General discussion

4. Future research This observational study has combined a longitudinal follow-up with highly detailed measurements of MY and SCC at the quarter level and strain-typing of the isolated NAS species. Much is already known about NAS in general and the most common species in terms of impact on udder health and MY. In that respect, our results are in line with several other recent studies that showed that the most common NAS species are minor pathogens that result in a limited increase in SCC in early lactating dairy heifers without impairing future MY. In further research it would be more interesting to focus on certain NAS strains that, when causing IMI, have a protective effect against new IMI, and thus can lead to less CM and even culling. On the other hand, it is a challenge to identify such strains, so that further research can be done into the exact mechanisms of such a protective effect. However, it must also be taken into account that certain characteristics expressed by bacteria, be it protective or just virulent, are not immutable, but can change under the influence of environmental factors. An example of such adaptation is the emergence of antimicrobial resistance, e.g., through mutations or via horizontal transfer of genetic elements between bacteria (Gyles and Boerlin, 2014). It is therefore possible that NAS strains can be found carrying certain protective traits, but it will be a challenge to prevent them from losing their protective properties under field conditions, and even to prevent them from acquiring certain virulence factors via other bacteria, i.e. via phage transduction and conjugative transfer (Chan et al., 2011). For example, methicillin-resistant S. aureus clone USA300 has acquired mobile genetic elements from S. epidermidis that have a role in enhancing growth and survival within the host (Diep et al., 2006). In general, more research is needed to further study whether the protective effects of certain NAS strains observed in vitro are also effectively reflected in the field, and whether these strains are worth the effort to introduce them in the herd, taking into account that all NAS IMI also result in an elevated SCC, although to a lower level compared than major pathogens. Also, the fact that the presence of NAS IMI is a risk factor for IMI caused by S. aureus should not be overlooked (Reyher et al., 2012). Another approach apart from using live bacteria, could be to identify the protective factors (e.g., certain bacteriocins) and to develop methods where these can be used.

169 Chapter 6 General discussion

In future studies, it might be interesting to not only measure the total SCC in milk but also to differentiate the cell types via differential cell counting. This has not been performed in our studies because no routine method was available during the sampling period in 2013-2014 (Damm et al., 2017). As SCC increases, the proportion of PMN increases and the proportion of macrophages decreases, whereas the proportion of lymphocytes remains fairly constant. Although the practical interpretation of these varying proportions of cell populations is subject to further investigation, one could estimate that a higher level of PMN, which generally cause the greatest damage to the udder tissue (Capuco et al., 1986; Paape et al., 2002), could result in lower MY in quarters having an IMI with NAS. We hypothesize that the more virulent NAS species would lead to a higher proportion of PMN compared to the less virulent species. So far, no studies have been published investigating the difference of the milk cell population at the quarter level for the different NAS species. Differences in the invoked cell population after the onset of an IMI with the different NAS species might also explain why some infections remain persistent whereas other are readily eliminated.

Current and future studies are benefiting from new developments in diagnostics methods, such as matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry. This technique combines a relatively fast and cheap identification of bacterial isolates to the genus and species level, even for NAS species from ruminants (Cameron et al., 2018; Gosselin et al., 2018; Mahmmod et al., 2018b). As a result, many more isolates can be identified in a shorter period of time with less labor and costs, which can offer advantages to conduct larger studies with a larger number of isolates in order to increase the power. Nucleic acid-based tests such as gene sequencing are still considered the golden standard, and compared with them, MALDI-TOF has a general typeability and accuracy of over 96% and 99%, respectively (Cameron et al., 2018). However, for NAS isolates form ruminants, typeability will be lower if the library is not updated. In our study, a library was used that was updated and validated for the identification of bovine NAS isolates.

To reduce the potential misclassification of teat canal colonizations as IMI, other criteria besides merely bacteriological culturing could be included to define the IMI statuses. Particularly the inclusion of an inflammation parameter that allows to make the difference

170 Chapter 6 General discussion between colonization and infection would be interesting. However, the parameter that is most often used and most readily available as an indicator of infection, i.e., SCC, is not advisable as already discussed above. Thus, other factors that can be used as inflammation indicator are worth considering. In the ideal situation, this indicator should allow to make the difference between infection of udder parenchyma and merely colonization of the teat canal and/or cistern without causing damage to the productive tissue. Furthermore, this indicator should also reflect the amount of damage to the udder tissue. One of the potential infection indicators is NAGase activity due to its high accuracy to detect both clinical and subclinical mastitis and the availability of a reference value for normal milk (Hovinen et al., 2016). Using NAGase activity as infection indicator has the benefit that it represents destruction of udder tissue (Kitchen et al., 1980; Fox et al., 1988) and lysis of neutrophils (Kitchen et al., 1978; Kaartinen et al., 1988), and thus would allow to make the difference between merely teat canal colonization and IMI. The use of an additional parameter would also eliminate the discussion on the use of CFU thresholds in bacteriological culturing. The combination of a threshold of 100 CFU/mL would result in the lowest number of false-negative results, and the infection parameter would allow to determine if this is a true IMI. Distinguishing between colonization and true IMI would make it possible to study the difference in impact on MY and SCC for quarters that have tissue damage (i.e., increased NAGase activity) due to NAS, and even more interestingly to investigate whether there is a difference in protective or just harmful effects of NAS that cause colonization or IMI. In our study, we could conclude that NAS IMI, diagnosed merely based on the presence of NAS in a milk sample, led to higher SCC. But in some quarters the SCC was rather low and it is unclear if these quarters were truly infected or were in fact noninfected quarters whereby the samples were ‘contaminated’ with NAS originating from the teat skin or teat canal. We propose that the species that are more adapted to the cow’s mammary gland, and thus use it as a reservoir and presumably have a more contagious transmission, i.e. S. chromogenes and S. epidermidis, and the other species that are able to cause an elevated SCC to a level comparable with S. aureus, i.e. S. simulans and S. xylosus, are the best candidates for further in-depth research. Moreover, future research should not only study the most relevant NAS species, but also focus on several different strains within these species. For example, a majority of IMI caused by S. chromogenes were persistent in our study (Chapter 5), although

171 Chapter 6 General discussion some other IMI had a duration of less than 14 d. This might be due to differences in virulence of some strains, but also other potential factors such as genetic background of the animal, production level, and immunity status could influence the duration of the IMI. One or more strains might be discovered to have no direct negative effect on MY but can persist in the mammary gland for longer periods, and even have a beneficial effect on udder health and MY, e.g. due to a protective effect against CM or new IMI with major pathogens.

In most situations, the effect of NAS IMI on MY has been studied in observational studies wherein associations between these 2 variables were estimated. Regardless of the different associations that have been found (i.e., either positive, negative, or no association), one of the most crucial limitations is that these associations do not allow determination of the causal relations between the variables. Furthermore, another approach is to work as is done in studies that determine the effect of a specific hormone: either remove or administer a certain hormone to observe the changes that are induced and evaluate if the original situation is restored when this hormone or a replacement is again administered or removed, respectively. For example, in the study of Lacasse et al. (2015), the dopamine antagonist domperidone was administered to 9 mid-lactation Holstein cows, and its effect on prolactin concentration and milk production was studied. Milk production was similar in the treatment and control group before the start of the experiment. During the treatment, milk production was higher in the treated animals, and returned to normal (i.e., the same level as in animals in the control group) when the treatment ended. Thus, in the case where the effect of an IMI with a certain NAS species is determined, one could consider causing an experimentally induced IMI and observe the differences in MY (and SCC) compared to a control group or by comparing the results from the period when the quarter was noninfected and the period in which the NAS IMI was present in that same quarter. And when the pathogen is eliminated from the quarter, it can be observed whether MY (and SCC) returns to pre-infection levels. This would have several benefits beside potential (ethical) issues to conduct these challenge studies in commercial herds with AMS. The exact moment of the onset of the IMI is known, allowing for precise measurements of all variables in the period before and after the onset of IMI. All available resources in a study would be used in a much more efficient way because they can be committed to the period of interest and are not ‘wasted’ to collect samples in periods that are not of interest (i.e., follow-up period in which no infection

172 Chapter 6 General discussion occurs). However, a follow-up period prior to the experimental infection will be necessary to ensure that the quarters have no history of IMI by any pathogen. Additional evidence from studies on the udder microbiome might also reveal factors that play a role in the development of IMI. Furthermore, measurements can be made at shorter intervals (e.g., daily instead of biweekly or monthly), allowing for a more precise estimation of the duration of IMI and the evolution of MY (and SCC) when the IMI has disappeared. This would also reduce the need to follow a whole series of quarters/animals in which IMI is never occurring or are even having an IMI caused by other pathogens. Further research is needed to find the best possible way to create these experimental infections that mimic natural occurring infections. Whether the introduction of bacteria in the mammary gland results in an IMI, and especially the type of IMI (e.g., clinical mastitis, transient or persistent subclinical mastitis, …) is the result of a very complex interplay between pathogen factors (e.g., virulence of the specific bacterial strain), animal factors (e.g., physiological and immunity status), and environmental factors (e.g., infection pressure). To create an infection model under experimental conditions that results in most cases in the type of IMI that are most interesting for future research (e.g., persistent colonization or IMI with a NAS strain that has a potential protective effect against other pathogens or that results in a higher MY) will be particularly challenging and maybe even unachievable because many of the influencing factors are unpredictable or impossible to measure precisely. An example of this are 2 studies in which experimental infections with S. chromogenes were induced. In the first study, 2 different S. chromogenes strains were used (Piccart et al., 2015). One strain was isolated from the teat apex of a heifer with no signs of mastitis, the other strain originated from a cow with a persistent subclinical infection. Using an inoculation dose of 1.0 x 106 CFU, all inoculated quarters from 8 Holstein-Friesian heifers became infected but none of them showed clinical symptoms. Interestingly, the challenged quarters tended to have a lower qMY compared with the control quarters, which is contrary to studies finding a positive association between NAS IMI and MY. In a second study, 6 Holstein Friesian heifers were inoculated in 1 quarter with 2.1 x 106 CFU of S. chromogenes isolated from a case of clinical mastitis (Simojoki et al., 2009). All animals showed mild clinical signs in the challenged quarter. Another consideration is that experimental IMI with NAS are cleared very quickly, as was demonstrated by the milk samples that were collected in the follow-up

173 Chapter 6 General discussion period after the experimental infections in both studies. In the study of Piccart et al. (2016), all bacteria were cleared within 6 days. Only 1 cow developed a persistent mastitis, whereas the bacteria were eliminated 96 hours after inoculation in the other animals in the study of Simojoki et al. (2009). The differences between both studies in clinical outcome and the general short duration of the infections, even with a strain that is able to cause persistent infections, demonstrate the challenges of using experimental infections to further elaborate on the impact of IMI with certain NAS species or their potential protective effects. Both studies also lacked strain-typing to confirm whether the isolated NAS in the follow-up samples were the same as the one that was used for the challenge. Alternatively, instead of introducing bacteria directly through the teat canal into the udder cistern, it is known that teat skin colonization is a risk factor for the occurrence of IMI. Thus, one could try to induce IMI by ‘inoculating’ the teat skin with a NAS isolate that easily colonizes the teat skin. An important consideration is that a NAS isolate should be used that is not already present in the herd to be sure that the induced IMI is truly caused by the specific isolate that is subject of the study. This would of course challenge the selection of a NAS strain. However, much is still to discover about the effectiveness to induce IMI by colonizing the teat skin, and many other factors (e.g., milking hygiene, post milking teat disinfection, …) play a role.

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https://doi.org/10.3168/jds.S0022-0302(87)80335-1. Tomazi, T., J.L. Goncalves, J.R. Barreiro, M.A. Arcari, and M. V dos Santos. 2015. Bovine subclinical intramammary infection caused by coagulase-negative staphylococci increases somatic cell count but has no effect on milk yield or composition. J. Dairy Sci. 98:3071– 3078. https://doi.org/10.3168/jds.2014-8466. Trinidad, P., S.C. Nickerson, and R.W. Adkinson. 1990a. Histopathology of Staphylococcal Mastitis in Unbred Dairy Heifers. J. Dairy Sci. 73:639–647. https://doi.org/10.3168/jds.S0022-0302(90)78715-2. Trinidad, P., S.C. Nickerson, and T.K. Alley. 1990b. Prevalence of Intramammary Infection and Teat Canal Colonization In Unbred and Primigravid Dairy Heifers. J. Dairy Sci. 73:107–114. https://doi.org/10.3168/jds.S0022-0302(90)78652-3. Verbeke, J., S. Piepers, K. Supré, S. De Vliegher, K. Supre, S. De Vliegher, K. Supré, and S. De Vliegher. 2014. Pathogen-specific incidence rate of clinical mastitis in Flemish dairy herds, severity, and association with herd hygiene. J. Dairy Sci. 97:6926–6934. https://doi.org/10.3168/jds.2014-8173. Waterman, D.F., R.J. Harmon, R.W. Hemken, and B.E. Langlois. 1983. Milking Frequency as Related to Udder Health and Milk Production. J. Dairy Sci. 66:253–258. https://doi.org/10.3168/jds.S0022-0302(83)81784-6. Wilson, D.J., R.N. Gonzalez, and H.H. Das. 1997. Bovine Mastitis Pathogens in New York and Pennsylvania: Prevalence and Effects on Somatic Cell Count and Milk Production. J. Dairy Sci. 80:2592–2598. https://doi.org/10.3168/jds.S0022-0302(97)76215-5. Wuytack, A., A. De Visscher, S. Piepers, F. Boyen, F. Haesebrouck, and S. De Vliegher. 2019. Non-aureus staphylococci in fecal samples of dairy cows: First report and phenotypic and genotypic characterization. J. Dairy Sci. 102:9345–9359. https://doi.org/10.3168/jds.2019-16662. Wuytack, A., A. De Visscher, S. Piepers, F. Haesebrouck, and S. De Vliegher. 2020. Fecal non- aureus Staphylococci are a potential cause of bovine intramammary infection. Vet. Res. 51:32. https://doi.org/10.1186/s13567-020-00761-5. Youn, J., L.K. Fox, K. Seok, M.A. Mcguire, Y. Ho, F.R. Rurangirwa, W.M. Sischo, G.A. Bohach, J.Y. Park, L.K. Fox, K.S. Seo, M.A. Mcguire, Y.H. Park, F.R. Rurangirwa, W.M.

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189

Summary

D. Valckenier

Department of Reproduction, Obstetrics, and Herd Health

Faculty of Veterinary Medicine,

Ghent University, Merelbeke, Belgium

Summary

The dairy sector faces continuously evolving challenges. However, in the changing economical context of this entire industry, mastitis has remained the most important and costly disease in dairy herds worldwide. Primiparous heifers are the new generation of dairy cows in every herd, and a well-developed and healthy udder at the start of their first lactation remains a prerequisite to avoid encumbering the start of their productive life. The implementation of prevention programs has led to a general improvement of the udder health and a reduction of the prevalence and incidence of mastitis caused by the so-called major pathogens in dairy herds. As a side-effect, non-aureus staphylococci (NAS) have become the most prevalent pathogens causing intramammary infections (IMI) in dairy cows in most parts of the world. The current body of literature on IMI caused by NAS has reported debatable and contradictory conclusions on their relevance for udder health and milk production. The aims of this thesis were to precisely estimate the effect of NAS IMI on somatic cell count (SCC) and milk yield (MY) at the quarter level (Chapter 2).

A longitudinal study was conducted in 3 Flemish dairy herds. All herds were equipped with an automatic milking system (AMS) as the different udder quarters are milked independently in such system. This allowed us to precisely measure MY at the quarter level and to avoid the need to aggregate quarter-level IMI statuses at the animal level. In total, 324 quarters from 82 Holstein Friesian heifers were included. Milk samples of each quarter were collected within 1- 4 days in milk (DIM) (i.e., first sampling day), followed by another 9 consecutive samplings at 14-day intervals until 130 DIM. Also, SCC from all quarter milk samples was measured.

The prevalence of IMI caused by NAS (as a group) was determined in the first 4 DIM via bacteriological culturing of the milk samples collected at the first sampling day (Chapter 3). Furthermore, prolactin concentration was measured in milk samples collected at the first sampling day (1-4 DIM). In total, 21% of the quarters were infected with NAS. Hind quarters were more likely to have an IMI with NAS and had a higher MY compared to front quarters. However, quarter position was not significantly associated with quarter SCC (qSCC). Quarters having a NAS IMI had a significantly higher SCC during the first 4 months of lactation compared with noninfected quarters, and non-significantly lower SCC compared with quarters having an IMI caused by major pathogens. During the first 130 DIM, MY was not significantly

193 Summary different between NAS-infected and noninfected quarters. The level of prolactin was highest at 1 DIM and decreased strongly until 4 DIM. However, the milk prolactin concentration did not differ between NAS-infected and noninfected quarters. Based on the results of this study, we concluded that the presence of IMI caused by NAS (as a group) shortly after calving has neither a positive nor negative effect on MY in the first 4 months of lactation in quarters from dairy heifers, despite an elevated qSCC.

The spontaneous cure rate of NAS IMI present in the first days after calving was high: 70% of quarters infected at 1-4 DIM cured before 15-18 DIM (Chapter 4). A number of studies have shown that the effect on udder health and milk production varies depending on the NAS species involved. Therefore, all isolates phenotypically identified as NAS by standard bacteriological culturing of milk samples collected at first (1-4 DIM) and second (15-18 DIM) sampling day were identified at the species level by genotypic tests. Staphylococcus (S.) chromogenes was the most prevalent species. Due to the low prevalence of the other 14 NAS species isolated, their association with qSCC and qMY was analyzed as a group. Quarters infected with S. chromogenes at first sampling day (i.e., 1-4 DIM) had a significantly higher qSCC during later lactation, whereas there was no difference between quarters infected with the group of other NAS species and noninfected quarters. Persistent IMI (pIMI) (i.e., IMI with the same NAS species at the first and second sampling day) and new IMI (nIMI) at the second sampling day resulted in a higher qSCC during the first 4 months of lactation, but transient IMI (tIMI) (i.e., an IMI with a NAS species that was no longer present at the second sampling day) had no effect on later SCC. In addition, pIMI and nIMI with S. chromogenes at the second sampling day also resulted in higher qSCC compared to noninfected quarters. However, none of the different types of infections with NAS was significantly associated with qMY in the first 4 months of lactation.

The IMI status was also determined for each of the 10 sampling days by bacteriological examination of all milk samples collected in this study (Chapter 5). All NAS isolates were identified at the species level through genotypic tests. Furthermore, strain typing via RAPD- PCR was performed when the same NAS species was isolated multiple times from the same quarter to be able to determine whether an IMI was persistent or not. A total of 304 NAS isolates

194 Summary belonging to 17 different species were cultured from 116 quarters of 64 out of the 82 heifers. This indicated that the prevalence of NAS IMI is high during the first 4 months of lactation in dairy heifers. The most prevalent species was S. chromogenes, accounting for 52% of the isolates. The number of IMI that persisted for at least 14 days and the duration of these infections was highest for S. chromogenes compared to the group of other NAS species: 45% of IMI caused by S. chromogenes persisted with an average duration of 110 days compared with 9.8% of IMI caused by other NAS species with an average duration of 70 days. Quarters that were persistently infected with NAS had the highest SCC, whereas the effect on udder health was smaller if an IMI was only present at 1 sampling point (i.e., a transient infection). Analysis of the associations between quarter infection status and qMY during the first 130 DIM did not reveal a positive or negative relation between transient or persistent NAS infections and milk production.

In Chapter 6, all results were summarized and discussed in comparison with these of other studies. In general, the status of NAS as minor pathogens, resulting in a moderately elevated SCC without a negative effect on MY, was substantiated in early-lactating dairy heifers. Future research should focus on 2 areas. First, on specific strains of NAS species for which it has already been demonstrated that they have (long-lasting) protective effects against infections with major pathogens, and on the development of new strategies and concepts to give practical expression to the protective effects of these strains. And secondly, on virulence factors [e.g., transfer of resistance genes between NAS and other (udder) pathogens on dairy farms], and on the control and prevention of NAS infections in herds with already low bulk milk cell counts and where NAS are the most common cause of IMI.

195

Samenvatting

D. Valckenier

Vakgroep Voortplanting, Verloskunde en Bedrijfsdiergeneeskunde

Faculteit Diergeneeskunde,

Universiteit Gent, Merelbeke, België

Samenvatting

De melkveesector wordt voortdurend geconfronteerd met evoluerende uitdagingen. In de veranderende economische context van deze hele industrie is mastitis wereldwijd echter nog steeds de belangrijkste en duurste ziekte op melkveebedrijven. Vaarzen vormen de volgende generatie melkkoeien op elk bedrijf. Een goed ontwikkelde en gezonde uier aan het begin van hun eerste lactatie blijft een voorwaarde om het begin van hun productieve leven niet te hypothekeren. De toepassing van preventieprogramma's heeft geleid tot een algemene verbetering van de uiergezondheid en een vermindering van de prevalentie en incidentie van mastitis veroorzaakt door de zogenaamde ‘major’ pathogenen op melkveebedrijven. Als bijkomend gevolg daarvan zijn de niet-aureus stafylokokken (NAS) inmiddels de meest voorkomende oorzaak van intramammaire infecties (IMI) bij melkvee in de meeste delen van de wereld. In de hedendaagse literatuur op het gebied van IMI veroorzaakt door NAS worden betwistbare en tegenstrijdige conclusies getrokken over hun relevantie voor de uiergezondheid en de melkgift. Het doel van dit proefschrift was om de impact van IMI veroorzaakt door NAS op het somatisch celgetal (SCG) en de melkproductie (MP) op kwartierniveau nauwkeurig in te schatten (Hoofdstuk 2).

Een longitudinale studie werd uitgevoerd op 3 Vlaamse melkveebedrijven. Alle bedrijven waren uitgerust met een automatisch melksysteem (AMS), omdat de verschillende uierkwartieren onafhankelijk van elkaar worden gemolken in een dergelijk systeem. Hierdoor was het mogelijk om de MP op kwartierniveau nauwkeurig te meten en om te voorkomen dat de op kwartierniveau bepaalde IMI-status moest worden samengevoegd op dierniveau. In totaal werden 324 kwartieren van 82 Holstein Friesian vaarzen opgenomen. De melkmonsters van elk kwartier werden binnen 1-4 dagen in lactatie (DIL) genomen (d.w.z. op de eerste bemonsteringsdag), gevolgd door nog eens 9 opeenvolgende bemonsteringen met een interval van 14 dagen tot aan 130 DIL. Het SCG van alle kwartiermonsters werd eveneens bepaald.

De prevalentie van IMI veroorzaakt door NAS (als groep) in de eerste 4 DIL werd bepaald via bacteriologisch onderzoek van de melkmonsters die op de eerste bemonsteringsdag werden genomen (Hoofdstuk 3). Daarnaast werd de prolactineconcentratie gemeten in de melkmonsters die op de eerste bemonsteringsdag (1-4 DIL) werden genomen. In totaal was 21% van de kwartieren geïnfecteerd met NAS. Achterkwartieren hadden een hogere kans op

199 Samenvatting een NAS IMI en een hogere MP in vergelijking met voorkwartieren. Er was echter geen significante associatie tussen kwartierpositie en SCG. Kwartieren met een NAS IMI hadden een significant hoger SCG tijdens de eerste 4 maanden van de lactatie in vergelijking met niet- geïnfecteerde kwartieren. Bovendien was het SCG niet significant lager in vergelijking met kwartieren met een IMI veroorzaakt door major pathogenen. Tijdens de eerste 130 DIL was de MP niet significant verschillend tussen NAS-geïnfecteerde en niet-geïnfecteerde kwartieren. De prolactineconcentratie was het hoogst op 1 DIL en daalde sterk tot 4 DIL. Er was echter geen verschil in prolactineconcentratie tussen NAS-geïnfecteerde en niet-geïnfecteerde kwartieren. Op basis van de resultaten van dit onderzoek concludeerden we dat de aanwezigheid van IMI veroorzaakt door NAS (als groep) kort na het afkalven geen effect heeft op de MP in de eerste 4 maanden van de lactatie in kwartieren van vaarzen, ondanks het verhoogde SCG.

De spontane genezing van NAS IMI aanwezig in de eerste dagen na het afkalven is hoog: 70% van de geïnfecteerde kwartieren op 1-4 DIL waren genezen op 15-18 DIL (Hoofdstuk 4). Uit een aantal studies is gebleken dat het effect op uiergezondheid en melkgift varieert naargelang de betrokken NAS-soort. Daarom werden alle isolaten, fenotypisch geïdentificeerd als NAS door middel van standaard bacteriologisch onderzoek van de melkmonsters die op de eerste (1-4 DIM) en tweede (15-18 DIM) bemonsteringsdag werden genomen, verder geïdentificeerd tot op het soort-niveau aan de hand van genotypische testen. Staphylococcus (S.) chromogenes was de meest voorkomende NAS-soort. Vanwege de lage prevalentie van de 14 andere NAS-soorten werd hun associatie met SCG en MP op groepsniveau geanalyseerd. Kwartieren die geïnfecteerd waren met S. chromogenes op de eerste bemonsteringsdag (1-4 DIL) hadden een significant hoger SCG tijdens de latere lactatie, terwijl er geen verschil was tussen kwartieren geïnfecteerd met de groep van andere NAS-species en niet-geïnfecteerde kwartieren. Persistente IMI (pIMI) (d.w.z. IMI met dezelfde NAS-soort op de eerste en tweede bemonsteringsdag) en nieuwe IMI (nIMI) op de tweede bemonsteringsdag resulteerden in een hoger SCG tijdens de eerste 4 maanden van de lactatie, maar transiënte IMI (tIMI) (d.w.z. een IMI met een NAS-soort die niet meer aanwezig was op de tweede bemonsteringsdag) hadden geen effect op de latere uiergezondheid. Bovendien resulteerden pIMI en nIMI met S. chromogenes op de tweede bemonsteringsdag ook in een hoger SCG in vergelijking met niet-

200 Samenvatting geïnfecteerde kwartieren. Echter, geen van de infecties met NAS was significant geassocieerd met de MP in de eerste 4 maanden van de lactatie.

De IMI-status is ook voor elk van de 10 bemonsteringsdagen bepaald door middel van bacteriologisch onderzoek van alle melkmonsters die in dit onderzoek genomen werden (Hoofdstuk 5). Alle NAS-isolaten werden op species-niveau geïdentificeerd door middel van genotypische testen. Bovendien werd stamtypering via RAPD-PCR uitgevoerd wanneer dezelfde NAS-soort meerdere keren uit hetzelfde kwartier werd geïsoleerd om te kunnen bepalen of er al dan niet sprake was van een pIMI. In totaal werden 304 NAS-isolaten, behorend tot 17 verschillende species, geïsoleerd uit 116 kwartieren van 64 van de 82 vaarzen. Dit gaf aan dat de prevalentie van NAS IMI hoog is gedurende de eerste 4 maanden van de lactatie bij vaarzen. De meest voorkomende soort was S. chromogenes, goed voor 52% van de isolaten. Het aantal IMI dat ten minste 14 dagen aanhield en de duur van deze infecties was respectievelijk hoger en langer voor S. chromogenes in vergelijking met de groep van andere NAS-species: 45% van de IMI veroorzaakt door S. chromogenes persisteerde met een gemiddelde duur van 110 dagen, vergeleken met 9,8% van de IMI veroorzaakt door de andere NAS-species met een gemiddelde duur van 70 dagen. Kwartieren met een persisterende NAS IMI hadden het hoogste SCG, terwijl de impact op de uiergezondheid kleiner was als de IMI slechts op 1 bemonsteringsdag aanwezig was (d.w.z. als er sprake was van een transiënte infectie). De analyse van de associatie tussen IMI en MP tijdens de eerste 130 DIL bracht geen positief of negatief verband aan het licht tussen deze transiënte of persistente NAS-infecties enerzijds en de melkgift anderzijds.

In Hoofdstuk 6 werden alle resultaten samengevat en besproken in vergelijking met deze van andere studies. In het algemeen werd de status van NAS als minor pathogenen, die resulteren in een matig verhoogd SCG zonder een negatief effect op MP, onderbouwd bij melkveevaarzen in vroege lactatie. Toekomstig onderzoek zou zich kunnen richten op 2 gebieden. Ten eerste, op de specifieke stammen van NAS-species waarvoor reeds aangetoond werd dat ze (langdurige) beschermende effecten hebben tegen infecties met major pathogenen, en op de ontwikkeling van nieuwe strategieën en concepten om de beschermende effecten van deze stammen in de praktijk te brengen. En ten tweede, op virulentiefactoren [vb. overdracht

201 Samenvatting van resistentiegenen tussen NAS en andere (uier)pathogenen op melkveebedrijven], en op de controle en preventie van NAS-infecties op bedrijven met reeds een laag tankmelkcelgetal en waar ze de meest voorkomende oorzaak van IMI zijn.

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Curriculum Vitae and Publications

D. Valckenier

Department of Reproduction, Obstetrics, and Herd Health

Faculty of Veterinary Medicine,

Ghent University, Merelbeke, Belgium

Curriculum vitae and publications

CURRICULUM VITAE Dimitri Valckenier werd geboren op 2 februari 1987 te Ninove. Na het behalen van het diploma hoger secundair onderwijs in de richting Wetenschappen-Wiskunde aan het Sint- Jozefsinstituut (Karmelieten) te Geraardsbergen in 2005, vatte hij onmiddellijk de opleiding diergeneeskunde aan de Universiteit Gent aan. Hij behaalde in 2011 het diploma van dierenarts (optie herkauwers) met onderscheiding. Zijn masterproef handelde over het voorkomen en de risicofactoren van urolithiasis bij kalveren en het effect van supplementatie met ammoniumchloride en natriumchloride op de urinaire pH en dichtheid. Na het afstuderen is Dimitri begonnen met een extra opleiding in de vorm van een internship bij het European College of Veterinary Public Health (ECVPH). Van oktober 2012 tot oktober 2019 was Dimitri actief als academisch assistent en vatte hij zijn doctoraatsonderzoek aan bij de “Mastitis and Milk Quality Research Unit” van de vakgroep Voortplanting, Verloskunde en Bedrijfsdiergeneeskunde aan de Faculteit Diergeneeskunde van de Universiteit Gent. De voornaamste doelstelling van zijn onderzoek was om meer inzicht te vergaren over het effect van intramammaire infecties veroorzaakt door boviene non-aureus stafylokokken op de melkproductie en de uiergezondheid bij melkvee vaarzen, en dit onder begeleiding van prof. dr. Sarne De Vliegher en prof. dr. Sofie Piepers. Als dierenarts was hij daarnaast werkzaam in het M-teamUGent waar hij meerdere melkveebedrijven met uiergezondheidsproblemen begeleidde. In de kliniek Verloskunde en aan de Buitenpraktijk+ roteerde hij mee in zowel dag-, avond- en weekenddiensten. Daarnaast was hij actief als bedrijfsbegeleidende dierenarts op verschillende melkveebedrijven. Dimitri ondersteunde als promotor meerdere masterproefstudenten. Van 2015 tot begin 2017 was Dimitri ook werkzaam als diergeneeskundig adviseur bij de Nederlandstalige Gewestelijke Raad van de Orde der Dierenartsen. Sinds januari 2017 is hij actief als Project & Concept Manager bij Mexcellence BVBA (MEX™), een spin-off van de Faculteit Diergeneeskunde. Hier legt hij zich toe op de ondersteuning van rundveepractici bij de uiergezondheidsbegeleiding van melkveebedrijven, de ontwikkeling van softwaretoepassingen, en het organiseren en geven van opleidingen aan dierenartsen. Dimitri is (mede-)auteur van meerdere wetenschappelijke peer-reviewed publicaties en gaf presentaties op (inter)nationale congressen en symposia. Hij gaf voordrachten en opleidingen

207 Curriculum vitae and publications zowel aan studenten en veehouders als aan dierenartsen. Bovendien schreef Dimitri vulgariserende artikels over diverse onderwerpen in de rundveediergeneeskunde voor verschillende landbouwtijdschriften. Hij volgde eveneens verscheidene specialisatiecursussen en mag daardoor het diploma van de “Doctoral Schools of Life Science and Medicine” in ontvangst nemen.

208 Curriculum vitae and publications

PUBLICATIONS IN PEER-REVIEWED JOURNALS Bradley A.J., De Vliegher S., Green M.J., Larrosa P., Payne B., van de Leemput E.S., Samson O., Valckenier D., Van Werven T., Waldeck H.W.F., White V., and Goby L. 2015. An investigation of the dynamics of intramammary infections acquired during the dry period on European dairy farms. Journal of Dairy Science 98 (9): 6029–6047. Put E., Valgaeren B., Pardon B., De Latthauwer J., Valckenier D., and Deprez P. 2015. Surgical correction of pyelonephritis caused by multidrug-resistant Escherichia Coli in a dairy cow. Vlaams Diergeneeskundig Tijdschrift 84 (2): 94–100. Biebaut E., Piepers S., Valckenier D., and De Vliegher S. 2019. Vergelijking van twee California mastitis testen met de elektronische celgetalbepaling voor de detectie van intramammaire infecties in mengmelkstalen van melkvee. Vlaams Diergeneeskundig Tijdschrift 88 (4): 192–200. Valckenier D., Piepers S., De Visscher A., Bruckmaier R.M., and De Vliegher S. 2019. Effect of intramammary infection with non-aureus staphylococci in early lactation in dairy heifers on quarter somatic cell count and quarter milk yield during the first 4 months of lactation. Journal of Dairy Science 102 (7): 6442–6453. Valckenier D., Piepers S., De Visscher A., and De Vliegher S. 2020. The effect of intramammary infection in early lactation with non-aureus staphylococci in general and Staphylococcus chromogenes specifically on quarter milk somatic cell count and quarter milk yield. Journal of Dairy Science 103 (1): 768–782. Valckenier D., Piepers S., Schukken Y.H., De Visscher A., Boyen F., Haesebrouck F., and De Vliegher S. 2021. Longitudinal study on the effects of intramammary infection with non- aureus staphylococci on udder health and milk production in dairy heifers. Journal of Dairy Science 104 (1): 899-914. Van Den Brulle I. and Adriaens I., Piepers S., Valckenier D., Salamone M., D’Anvers L., Geerinckx K., Aernouts B., and De Vliegher S. Key udder health parameters on Belgian and Dutch robotic dairy Farms. Journal of Dairy Science, in progress.

209 Curriculum vitae and publications

ORAL AND POSTER PRESENTATIONS AND CONTRIBUTIONS Valckenier D., Piepers S., and De Vliegher S. 2012. Outline of a new study: Viability of blood and milk neutrophils as indicator of immunity in relation to udder and animal health in dairy cows: dynamics, predictive potential and associated factors. Annual Meeting Mastitis Research Workers, 6-8 November, Chicago, USA. Valckenier D., Piepers S., and De Vliegher S. 2013. Evaluation of the rinsing of teat cup liners with cold water, hot water or steam. Annual Meeting Dutch Mastitis Research Workers, 16 April, Utrecht, The Netherlands. Valckenier D., Piepers S., and De Vliegher S. 2014. Preliminary results: Effect of intramammary infections with CNS in early lactating dairy heifers on the quarter SCC and quarter milk yield throughout first lactation. Annual Meeting Mastitis Research Workers, 7-8 August, Ghent, Belgium. Valckenier D., Piepers S., and De Vliegher S. 2015. Effect of intramammary infections with coagulase-negative staphylococci in early lactating dairy heifers on the quarter somatic cell count and milk yield throughout first lactation. Annual Meeting Mastitis Research Workers, 9-10 November, Bristol, UK. Valckenier D., Piepers S., and De Vliegher S. 2015. Effect of IMI with CNS in early lactating dairy heifers on the quarter SCC and daily milk yield throughout first lactation, British Mastitis Council (BMC) conference, 11 November, Worcester, UK. Valckenier D., Piepers S., and De Vliegher S. 2017. Effect of IMI with CNS in early lactating dairy heifers on the quarter daily milk yield and SCC. Second Seminar on Coagulase- Negative Staphylococci, 18-19 May, Ghent, Belgium. Valckenier D., Piepers S., De Visscher A., and De Vliegher S. 2019. Species-specific effect of non-aureus staphylococci on quarter milk yield and somatic cell count. IDF Mastitis Conference, 14-17 May, Copenhagen, Denmark.

210

Dankwoord

D. Valckenier

Dankwoord

Het laatste hoofdstuk van dit proefschrift verdient het om, zoals in de meeste doctoraten, het meest gelezen onderdeel te worden, want het gaat over de mensen die het mogelijk gemaakt hebben dat dit proefschrift er ooit gekomen is.

Prof. dr. De Vliegher en prof. dr. Piepers, beste Sarne en Sofie, dit werk had er uiteraard nooit gekomen zonder jullie als promotoren. Jullie gedrevenheid en ondernemingszin bleven altijd een bron van inspiratie en motivatie. Bedankt allebei voor het vele vertrouwen, alle kansen om bij te leren en om te kunnen evolueren, en vooral voor jullie nimmer aflatende steun. En uiteraard voor jullie geduld toen het schrijfwerk weeral eens op de achtergrond verdween. Ik vrees dat ik jullie nog vele taarten tegoed heb voor elke keer dat het vertraging opliep …

Misschien komt het er nu toch eens van om dat stomerartikel te schrijven .

Beste examencommissie, prof. dr. Edwin Claerebout, dr. Anneleen De Visscher, prof. dr.

Marie Joossens, prof. dr. Gerrit Koop, prof. dr. Bart Pardon en prof. dr. Ynte Schukken, bedankt voor jullie kritische evaluaties van het proefschrift, en voor jullie vele suggesties en bijdrages om dit werk beter te maken. Veel succes nog met al jullie projecten. Na het lange uitstel door de corona-maatregelen hoop ik dat het uiteindelijk toch mogelijk is om samen het glas te heffen na de openbare verdediging.

Bedankt ook aan de melkveehouders (en hun vaarzen!) die hebben deelgenomen aan dit onderzoek. Meer dan een jaar lang was ik 2 keer per week welkom was om alle vaarzen uit deze studie te bemonsteren. Steeds hadden jullie de dieren al uitgeselecteerd of waren jullie behulpzaam om deze te zoeken in de kudde waardoor deze bemonsteringsperiode zoveel aangenamer verliep.

Ongeveer 10 jaar geleden mocht ik het M-team vervoegen, eerst voor een 1-jarig internship, gevolgd door 7 jaar als assistent. Veel dank dus aan alle ex-collega’s/bureaugenoten om deze periode zo aangenaam te hebben gemaakt: Anneleen, Joren, Kristine, Marina, Reshat, Ameline,

Zyncke, Wannes, Justine, Dagmar, Julie, Lisa, Chloë, Evelien, Bruno, Kristien, Helena en Igor

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Dankwoord

(en nu hoop ik echt dat ik hier niemand vergeten ben!). Hoewel ik het minst van al op de bureau aanwezig was door het werk op de baan en later door mijn andere jobs bij de Orde en MEX, we hebben veel fijne momenten beleefd, (buitenlandse) congressen bijgewoond, een escaperoom overwonnen, lunches gehad in de Eurotuin, en nog te veel andere dingen om hier op te noemen.

Zeker het zwemmen over de middag was een leuke afleiding! Regelmatig bracht ook iemand met veel meer baktalent dan ikzelf iets lekkers mee, en alle kleine en grote succesjes werden steeds met bubbels gevierd. De meeste van jullie zijn ondertussen al een tijdje het M-team nest ontvlogen en bezig met het uitbouwen van een succesvolle verdere carrière. Anneleen, speciaal toch een bedankje voor je ondersteuning en hulp bij het NAS-onderzoek, voor alle antwoorden op kleine en grotere vragen, en voor je bijdrages als lid van de begeleidingscommissie van dit doctoraat. Kristien en Ameline, het is een publiek geheim dat labowerk niet mijn favoriete bezigheid was, dus heel erg bedankt voor jullie hulp bij de stamtyperingen in het laatste deel van dit onderzoek. Aan de huidige doctoraatsstudenten Bruno, Helena en Igor: veel succes en goede moed met jullie onderzoek, en ik duim mee dat jullie ook jullie werk kunnen afronden met een doctoraat. Helena, jou wil ik ook graag bedanken om de Engelstalige teksten van dit proefschrift na te lezen.

Reeds vanaf mijn eerste dagen aan de vakgroep kon ik ook beginnen meedraaien aan de

Buitenpraktijk. Ik herinner mij nog levendig de rush en het enthousiasme wanneer je na een maand of 2-3 ’s nachts alleen kon uitrukken naar een keizersnede (de meeste collega’s uit die periode weten dat dat enthousiasme ook bijzonder snel terug weg was, maar soit ). Een paar maanden na het einde van mijn eerste jaar aan de vakgroep zou prof. dr. Aart De Kruif met emeritaat gaan (zoals dat netjes heet wanneer academici met pensioen gaan). Ik vond het een bijzondere eer dat je me voorstelde om jouw klanten, velen waarbij je al 25 jaar ging, verder op te volgen. Bedankt voor het vertrouwen! Mede hierdoor kon ik als assistent in dienst blijven aan de vakgroep, en mijn onderzoek combineren met het praktische werk aan de Buitenpraktijk

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en bedrijfsbegeleiding. Het was een intensieve periode, en zeker niet altijd met veel enthousiasme wanneer ik uit mijn bed gebeld werd, maar ook met veel leuke momenten en vooral een bijzonder leerrijke periode. In die 7 jaar heb ik mogen samenwerken met heel veel fijne collega’s, en uit vrees om iemand te vergeten vermelden durf ik mij niet wagen om jullie allemaal bij naam te noemen. Vandaar dus een bedankt dus aan alle collega’s en medewerkers uit die periode aan de Buitenpraktijk voor jullie hulp en ondersteuning, en de mogelijkheid om zoveel te kunnen bijleren. Toch in het bijzonder nog een woordje van dank aan beide ‘senior- dierenartsen’ uit die beginperiode. Marcel, je stond bekend om je ‘no-nonsense’ aanpak, maar ik herinner me je vooral als een bijzonder gedreven man die altijd bereid stond om zijn kennis en kunde over te brengen op de jonge collega’s en studenten. Ik kon altijd (ook ’s nachts) op je rekenen als ik in de miserie zat met een keizersnede. Geniet nog volop samen met je vrouw

Claire en je familie, en hopelijk komt er snel een einde aan deze corona-periode zodat je ons terug door Gent kan gidsen met een groep buitenlandse collega’s. En Jef, je rust en koelbloedigheid, zelfs is de meest uitdagende situaties, zullen mij ook altijd bijblijven, en ik zal nog vele jaren ouder moeten worden om die ooit te kunnen evenaren. Je sterke wetenschappelijke kennis en de manier waarop je die kon overbrengen waren een voorbeeld!

Johan, een paar jaar geleden heb ik samen met jou een operatie uitgevoerd die ik nooit zal vergeten: het verwijderen van een nier bij een koe met chronische pyelonefritis. Het was ook geweldig plezant om ’s avonds na de dienst nog even te kickeren tegen de studenten! Wij blijven steeds uitkijken naar de etentjes met jou, je vrouw en de kinderen. Brecht en Bart, nadat jullie een paar jaar in Nederland en Frankrijk gewerkt hadden was het ook heel fijn om 2 studie- en jaargenoten terug te zien op de vakgroep. Veel succes nog met alles wat jullie aanpakken in het leven! Tenslotte toch ook nog een extra woordje van dank voor Sofie, want de eerste paar jaar was je vaak mijn 2e wacht op de Buitenpraktijk, en de ‘uitdagingen’ waren toevallig op die avonden/weekends net iets groter dan gewoonlijk Een paar avonturen (het befaamde

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varkensoor, de repositie van een uterusprolaps en de afgescheurde karunkels, de bloedtransfusies, …) zullen mij altijd bijblijven!

Bij het maken van een doctoraat komen ook nog heel wat andere praktische zaken kijken die niet rechtstreeks aan het onderzoek gekoppeld zijn. Daarom ook een dankjewel aan alle medewerkers van de vakgroep die hulp of ondersteuning geboden hebben tijdens al die jaren:

Sandra bij administratieve uitdagingen, Leïla bij financiële zaken, Steven en Kenny voor de IT- ondersteuning, en de vele andere mensen die dagelijks de vakgroep helpen draaiende houden en het mogelijk maken om onderzoeken te laten doorgaan.

“Een goede vriend is als een klavertje vier, moeilijk om te vinden maar het brengt geluk om deze te hebben”. Vrienden waarop je kan vertrouwen en steunen, die je kennen in je moeilijkere periodes en waarmee je vooral al heel veel leuke momenten hebt beleefd. Jeroen en Lynn C.,

Jonas en Lore, een mens kan zich enkel maar gelukkig prijzen met zulke goede vrienden. Na meer dan 20 jaar vriendschap beschouwen we jullie als familie! Jeroen en Lynn C., de huidige corona-pandemie heeft zowel de planning van mijn doctoraat als van jullie huwelijk behoorlijk in de war gestuurd. Ik duim alvast mee dat de voorbereiding volgend jaar vlotter mag verlopen en dat jullie huwelijk onder een stralende zon zal kunnen plaatsvinden! Het is een eer om samen met Jonas jullie getuige te mogen zijn, en wij kijken al erg uit naar het bijhorende feestje! En

Lore, wij kijken minstens even ongeduldig mee uit naar de dag dat Jonas eens op zijn knie gaat zitten .

Lieve ouders, heel erg bedankt om ons een onbezorgde jeugd te laten hebben, en voor alle vrijheid, kansen en vertrouwen. Het waren soms moeilijke periodes tijdens de studie, maar altijd kon ik op jullie steun blijven rekenen. En nog steeds kunnen we altijd en overal op jullie rekenen. Jullie zijn ook geweldige grootouders voor jullie kleindochters. Bedankt voor alles!

Cédric en Ine, mijn petekindje Nyah en ‘Pluisje’ , we bouwen nu elk aan ons eigen gezin, en ik hoop dat jullie de liefde en geluk blijven vinden bij elkaar. Ook oprecht veel dank aan

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mijn schoonouders en hechte schoonfamilie voor de vele steun die ik van jullie kreeg, en voor de hartelijke opname in jullie familie.

En uiteraard mijn lieve dochters Alina en Olivia! De laatste paar maanden bij het afwerken van dit boekje hebben jullie mij wat minder gezien. In april vorig jaar, op 1 van de eerste mooie lentedagen, waren jullie in de tuin aan het spelen en tijdens een gesprekje met onze buurvrouw hoorden we jullie dit zinnetje letterlijk zeggen: ‘papa is boven aan zijn doctoraat aan het werken aan de computer’ . Ook al spraken jullie het niet letterlijk uit dat jullie me misten toen ik me in het weekend of op vakantiedagen weer eens voor een paar uur boven terugtrok aan de computer (ter info voor wanneer jullie dit later zelf kunnen lezen, jullie waren 4 en bijna 6 jaar op het moment dat ik dit doctoraat schreef), jullie gaven me steeds een extra knuffel en zoen.

En het was altijd ongelooflijk fijn dat jullie me in de armen vlogen wanneer ik terug beneden kwam (tenzij het net een te leuke serie of spelletje was op de tablet natuurlijk). Het doet ons ongelooflijk veel plezier om te zien hoe jullie samen opgroeien en de wereld ontdekken. Jullie zijn allebei echte dametjes met een sterk karakter, en de beste dochters die een papa zich wensen kan!

Maar ook voor jullie mama heeft deze intensieve laatste periode heel wat impact gehad.

Lieve Lynn, ofwel was je gaan werken in het weekend omdat je wachtdienst had in de praktijk, ofwel stond je er de meeste uren van de dag alleen voor omdat ik nu toch wel eindelijk eens dat doctoraat moest afwerken. Ook hier een kleine anekdote . Op de feestdag 1 mei stond mijn computer nog aan, en je had Olivia (toen 3 jaar) eventjes een spelletje laten spelen op een website voor kinderen. Toen ik zei “pas toch maar op dat ze gans mijn doctoraat niet wist tijdens het spelen”, heb je snel die computer afgezet en die oude laptop van onder het stof gehaald zodat ze daar rustig kon op verder spelen . Maar goed, dat is nu dus EINDELIJK helemaal voorbij (al durf ik je niet beloven dat ik hierna geen andere studie ga aanvatten). Oorspronkelijk had ik hier ook geschreven: “En wie weet wordt het na al die jaren toch eens tijd om werk te

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maken van het herinrichten van den beneden…”, maar de corona-vertraging heeft er uiteindelijk voor gezorgd dat dat nog eerder afgewerkt is dan dit doctoraat Lieve schat, we hebben sinds 2008 al heel wat bereikt samen! Je bent een geweldige mama voor onze dochters.

En ook al maak ik regelmatig wel eens plagerige grapjes over het ongelooflijk zware bestaan als gehuwde man: ik hou van je en hoop dat we samen de rest van ons leven nog kunnen delen!

Bedankt voor je ondersteuning, je begrip en geduld op momenten dat het eens wat lastiger ging, en ik kijk uit naar alles dat we nog op ons gezamenlijke pad gaan tegenkomen.

Dimitri.

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Precise quarter-level estimation of the impact of non-aureus staphylococcal Dimitri Valckenier 2021 intramammary infection on udder health and milk yield in dairy heifers