Distribution of Staphylococcus Non-Aureus Isolated from Bovine Milk in Canadian Herds

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2016 Distribution of Staphylococcus non-aureus isolated from bovine milk in Canadian herds

Condas, Larissa

Condas, L. (2016). Distribution of Staphylococcus non-aureus isolated from bovine milk in Canadian herds (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/25729 http://hdl.handle.net/11023/3441 master thesis

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Distribution of Staphylococcus non-aureus isolated from bovine milk

in Canadian herds

Larissa Anuska Zeni Condas

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN VETERINARY MEDICAL SCIENCES

CALGARY, ALBERTA

OCTOBER, 2016

Abstract

The Staphylococci non-aureus (SNA) species are among the most prevalent isolated from bovine milk. However, the role of each species within the SNA group still needs to be fully understood. Knowing which SNA species are most common in bovine intramammary infections

(IMI), as well as their epidemiology, is essential to the improvement of udder health on dairy farms worldwide. This thesis is comprised of two studies on the epidemiology of SNA species in bovine milk, and used molecular methods to identify of isolates obtained from the Canadian

Bovine Mastitis and Milk Quality Research Network. The first study focused on the prevalence of SNA species on Canadian dairy farms and potential associations of SNA positive mammary quarters with bulk milk somatic cell count (BMSCC), barn type, parity, month of lactation and quarter location. Overall SNA represented 9% of the isolates from culture positive mammary quarters and the most common species were S. chromogenes, S. simulans, S. xylosus, S. haemolyticus, and S. epidermidis. Province and barn type were associated with SNA species distribution; Albertan bedded-packs were mostly affected by S. chromogenes, Maritimes free- stall herds by S. epidermidis, and Ontario and Quebec tie-stalls by S. xylosus. Staphylococcus arlettae, S. cohnii, and S. gallinarum were isolated from quarters of herds with high BMSCC.

Fresh heifers and cows in later lactation were most frequently infected by S. chromogenes. The second study focused on the distribution of the same species in SNA positive-quarters according to udder inflammation status, classified according to low and high SCC and clinical mastitis.

Average somatic cell count (SCC) for the SNA as a group was 70,000 cells/mL, driven mostly by S. chromogenes, S. haemolyticus, S. xylosus and S. epidermidis. Species-specific prevalence of SNA-positive quarters was higher in high (≥ 200,000 cells/mL) than in low SCC (< 200,000 ii

cells/mL) samples for the 11 most frequently isolated SNA species. Staphylococcus sciuri was more frequently isolated from clinical mastitis samples.

Considering SNA as a group will misrepresent the role of individual species on farms.

Ultimately, adopting molecular identification of SNA species along with future research in species-specific risk factors are necessary to fully elucidate the importance of of the different

SNA species on udder health and possible species-specific interventions.

iii

Acknowledgements

Firstly, I would like to thank my supervisors Herman Barkema and Jeroen De Buck for their support and patience guiding me through such an ambitious project. Since my first year in

Calgary, Herman encouraged me to persevere in research and I have undoubtedly learned a lot.

Also, special thank you to Jeroen for encouraging me to improve my presentation skills in weekly meetings and lab group presentations.

I also would like to thank my committee members, whose experience and insights contributed for making this thesis and publications undeniably better. I would like specially thank Dr. John Kastelic for his astonishing writing skills translated into edits of manuscripts and scientific writing courses at UofC and for all the encouraging words in the worst moments, when they were so needed.

This research was only possible with the collaboration of the Natural Sciences and

Engineering Research Council of Canada (NSERC) Industrial Research Chair in Infectious

Diseases of Dairy Cattle. Also, thank you all of the dairy producers, animal health technicians, and Canadian Bovine Mastitis and Milk Quality Research Network (CBMQRN) regional coordinators (Trevor De Vries, University of Guelph, Canada; Jean-Philippe Roy and Luc Des

Côteaux, University of Montreal, Canada; Kristen Reyher, University of Prince Edward Island,

Canada; and Herman Barkema, University of Calgary, Canada) who participated in the data collection. The bacterial isolates were provided by the CBMQRN. The CBMQRN pathogen and data collections were financed by the Natural Sciences and Engineering Research Council of

Canada (Ottawa, ON, Canada), Alberta Milk (Edmonton, AB, Canada), Dairy Farmers of New

Brunswick (Sussex, New Brunswick, Canada), Dairy Farmers of Nova Scotia (Lower Truro, NS, iv

Canada), Dairy Farmers of Ontario (Mississauga, ON, Canada) and Dairy Farmers of Prince

Edward Island (Charlottetown, PE, Canada), Novalait Inc. (Quebec City, QC, Canada), Dairy

Farmers of Canada (Ottawa, ON, Canada), Canadian Dairy Network (Guelph, ON, Canada),

Agriculture and Agri-Food Canada (Ottawa, ON, Canada), Public Health Agency of Canada

(Ottawa, ON, Canada), Technology PEI Inc. (Charlottetown, PE, Canada), Université de

Montréal (Montréal, QC, Canada) and University of Prince Edward Island (Charlottetown, PE,

Canada), through the CBMQRN (Saint-Hyacinthe, QC, Canada).

Thank you as well for Annik L’Esperance for her effort in organizing and shipping all the

6000 SNA isolates to Calgary and promptly answering gazillions of e-mails about barcodes.

Moreover, I would like to express my appreciation to Dr. Simon Dufour for guiding me with the cohort dataset.

I would like to thank Uliana Kanevets and Aaron Lucko for the great work done with

PCRs. Their help turned my lab experience happier and less repetitive.

This thesis was also possible due to effort and statistical knowledge of Diego Borin

Nobrega. Thank you so much for the uncountable discussions about the cohort, and all the statistical input. Also, thank you to Domonique Autumn Carson, whose writing skills and support helped me throughout the countless prevalence tables and graphs. I cannot forget the remaining “CNS Gang”, Sohail Naushad, Ana Paula Alves Monteiro, Ali Naqvi and Liu Gang.

I would also like to thank the awesome colleagues at my office Caroline C., Caroline R.,

Marija, Emily, Taya, Christina, Robert, Laura, Barbara, Vineet, Nidhi, Verocai, Casey, and

Janneke. I certainly will have good memories of our laughs, and talks.

Further thank you to Dr. Karin Orsel, that provided continuous feedback related to several epidemiological topics throughout our regular Epi Journal Club. Certainly the meetings contributed in my improvement as a scientist.

I would like to thank my friends in Calgary, which have been family to me (and

Guilherme) for all these years. You are special and made this time so much more enjoyable. I will never forget such dearest moments with you. I’m so thankful to sweet Ana Carolina Rasera,

Luis Saviolli, Christina Tse, Anderson Macedo Silva, Estela Costa, Alysson Macedo Silva, Ana

Luisa Bras, Marina Chueiri, Diego, Larissa Ozeki, and Tulio Alcantara.

My deep gratitude and love to my husband, Guilherme Borges Bond, who brought me to

Canada in a new endeavour, shared all difficult times, and gave me all the strength necessary to conquer this outcome. We have certainly achieved many milestones together which lead us to amazing personal growth. Literally, a lovely lifetime experience at your side. Finally, my dearest family, from which I was physically distant for a good purpose, but with them always in my thoughts. Thank you so much for your support in every moment.

“Sou um pouco de todos que conheci, um pouco dos lugares que

fui, um pouco das saudades que deixei e sou muito de tudo que

apreciei” (Antonie de Saint-Exupéry).

Dedication

To my family,

in particular my husband Guilherme Borges Bond, and my parents Sara Jane Soares Zeni Condas and Luiz Carlos Condas

who always supported my passion for learning

vii

Table of Contents

Abstract ...... ii Acknowledgements ...... iv Dedication ...... vii Table of Contents ...... viii List of Tables ...... x List of Figures and Illustrations ...... xi List of Symbols, Abbreviations and Nomenclature ...... xiii Preface ...... xiv

CHAPTER ONE: GENERAL INTRODUCTION ...... 1 1.1 Impact of mastitis on the dairy industry ...... 4 1.2 Staphylococcus non-aureus ...... 6 1.2.1 Phenotypic identification ...... 8 1.2.2 Genotypic identification ...... 9 1.3 Relevance of Staphylococcus non-aureus in intramammary infection ...... 10 1.3.1 Prevalence of SNA IMI ...... 10 1.3.2 Effect of SNA IMI on udder health and milk production...... 12 1.4 Thesis outline ...... 14 1.4.1 Distribution of SNA in different herd, cow and quarters ...... 14 1.4.2 SNA species and SCC ...... 15

CHAPTER TWO: PREVALENCE OF STAPHYLOCOCCUS NON-AUREUS SPECIES ISOLATED FROM INTRAMAMMARY INFECTION IN CANADIAN DAIRY HERDS ...... 16 2.1 Abstract ...... 16 2.2 Introduction ...... 18 2.3 Materials and methods ...... 20 2.3.1 Herds and cows ...... 20 2.3.2 Sampling ...... 21 2.3.3 Laboratory analyses ...... 21 2.3.4 Definition of intramammary infection...... 22 2.3.5 SNA isolates identification ...... 23 2.3.6 Statistical analyses...... 24 2.3.6.1 Prevalence ...... 24 2.3.6.2 Intraclass correlation coefficients ...... 25 2.3.6.3 Multilevel multivariable analysis ...... 25 2.4 Results ...... 26 2.4.1 Distribution of SNA species positive-quarters ...... 26 2.4.2 Prevalence of SNA species IMI ...... 27 2.4.2.1 Overall ...... 27 2.4.2.2 Cow-level associations ...... 27 2.4.2.3 Quarter-level association ...... 28 2.4.2.4 Province, housing and bulk milk SCC ...... 29

viii

2.4.3 Multivariable analysis ...... 30 2.5 Discussion ...... 32 2.6 Conclusions ...... 38

CHAPTER THREE: DISTRIBUTION OF SNA SPECIES IN QUARTERS WITH LOW AND HIGH SOMATIC CELL COUNTS, AND CLINICAL MASTITIS ...... 60 3.1 Abstract ...... 60 3.2 Introduction ...... 61 3.3 Materials and methods ...... 63 3.3.1 Herds and cows ...... 63 3.3.2 Sampling ...... 64 3.3.3 Laboratory analyses ...... 64 3.3.4 Definition of intramammary infection...... 65 3.3.5 Dataset ...... 65 3.3.6 SNA species identification...... 66 3.3.7 Statistical analyses...... 67 3.3.7.1 SCC estimates ...... 67 3.3.7.2 Distribution estimates ...... 68 3.3.7.3 Prevalence estimation ...... 69 3.4 Results ...... 70 3.4.1 Somatic cell count ...... 70 3.4.2 Distribution of SNA species ...... 71 3.4.3 Prevalence of SNA species ...... 71 3.5 Discussion ...... 72 3.6 Conclusions ...... 79

CHAPTER FOUR: SUMMARIZING DISCUSSION...... 91 4.1 SNA species identification...... 91 4.2 Most common SNA species in milk samples ...... 93 4.3 Distribution of CNS species according to herd level factors ...... 94 4.4 Distribution of SNA species according to cow factors ...... 97 4.5 Distribution of SNA species in quarters ...... 100 4.6 Distribution of SNA species according to inflammation status ...... 101 4.7 Conclusions and future research ...... 104

REFERENCES ...... 109

List of Tables

Table 2-1. Distribution of Staphylococcus non-aureus species intramammary infection from bovine milk in Canadian dairy herds and intraclass correlation (ICC) of quarters within cow, cows within herd, and among herds...... 39

Table 2-2. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus species intramammary infection, according to quarter location...... 41

Table 2-3. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection within region in 91 ...... 43

Table 2-4. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection within 3 barn types...... 45

Table 2-5. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection according to bulk milk somatic cell count category...... 47

Table 2-6. Final multilevel model for the quarter-level prevalence of the 4 most prevalent Staphylococcus non-aureus intramammary infections in 91 Canadian dairy herds...... 49

Table 3-1. Mean, lower and upper limits of beta-distributions used in the present study according to various estimates of sensitivity (Se) and specificity (Sp) for several pathogens (or pathogen group)...... 80

Table 3-2. Somatic cell count (SCC) of quarters culture-positive for Staphylococcus non- aureus (SNA) species, major pathogens and culture-negative quarters from 91 dairy herds in 4 regions of Canada...... 81

Table 3-3. Distribution of Staphylococcus non-aureus (SNA) species isolated within SNA as a group from bovine milk from 91 Canadian dairy herds in quarters with low (<200,00 cell/mL) and high (≥ 200,000 cells/mL) somatic cell count, and clinical mastitis...... 82

Table 3-4. Prevalence of Staphylococcus non-aureus (SNA) species, major pathogens and culture-negative quarters in quarters with a low (< 200,000 cells/mL) and high (≥ 200,000 cells/mL) somatic cell count...... 84

List of Figures and Illustrations

Figure 2-1. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection across parities...... 51

Figure 2-2A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection across parities for the five most frequently isolated species...... 52

Figure 2-2B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection across parities for the 6th to 10th most frequently isolated species...... 53

Figure 2-3. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection over the lactation...... 54

Figure 2-4A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection over the lactation for the 5 most frequently isolated species...... 55

Figure 2-4B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection over the lactation for the 6th to 10th most frequently isolated species...... 56

Figure 2-5. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection in the first 30 DIM...... 57

Figure 2-6A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection in the first 30 DIM for the 5most frequently isolated species...... 58

Figure 2-6B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non- aureus intramammary infection in the first 30 DIM for the 6th to 10th most frequently isolated species...... 59

Figure 3-1. Estimates of sensitivity and specificity for Staphylococcus non-aureus (SNA) according to the structure of the dataset...... 86

Figure 3-2. Distribution of somatic cell count (SCC) of culture-negative, Staphylococcus non-aureus (SNA, figure legend states CNS not SNA), and Staphylococcus aureus- positive quarters...... 86

Figure 3-3. Distribution of somatic cell count (SCC) of CNS (legend shows CNS, but rest of paper uses SNA), Streptococcus dysgalactiae, Streptococcus uberis, and Klebsiella pneumoniae positive quarters...... 87

Figure 3-4. Distribution of somatic cell count (SCC) of Staphylococcus chromogenes, S. simulans, and S. xylosus-positive quarters...... 88

Figure 3-5. Distribution of somatic cell count (SCC) of Staphylococcus cohnii, S. epidermidis, and S. haemolyticus-positive quarters...... 89

Figure 3-6. Distribution of somatic cell count (SCC) of Staphylococcus capitis, S. gallinarum, and S. sciuri-positive quarters...... 90

xii

List of Symbols, Abbreviations and Nomenclature

Symbol Definition AB Alberta BMSCC Bulk milk somatic cell count CBMRN Canadian Bovine Mastitis Research Network CBMQRN Canadian Bovine Mastitis and Milk Quality Network CM Clinical mastitis CNS Coagulase-negative staphylococci CPS Coagulase-positive staphylococci d Day DHI Dairy herd improvement DIM Days in milk DNA Deoxyribonucleic acid IMI Intramammary infection MALDI-TOF Matrix-assisted laser desorption/ionization-time of flight mo Month NMC National Mastitis Council ON Ontario OR Odds ratio PCR Polymerase chain reaction PFGE Pulse-field gel electrophoresis SD Standard deviation QC Québec rDNA Ribosomal DNA RNA Ribonucleic acid SCC Somatic cell count SCM Subclinical mastitis SNA Staphylococci non-aureus WGS Whole genome sequencing wk Week yr Year

xiii

Preface

This dissertation consists of two manuscripts – both will be submitted for publication at the time of defense. For both manuscripts, the first author was involved with study concept and design, acquisition of isolates and data, laboratory analysis, analysis and interpretation of data, drafting of the manuscript, and critical revision. This was done under the guidance of the senior author, supervisor and co-supervisor. All authors provided critical reviews of the manuscripts and contributed intellectual content. Both manuscripts were reproduced in their entirety as chapters in this dissertation.

Manuscript I) Larissa A.Z. Condas, Jeroen De Buck, Diego B. Nobrega, Domonique A. Carson,

Sohail Naushad, Sarne de Vliegher, Ruth N. Zadoks, John R. Middleton, Simon Dufour, John P.

Kastelic, and Herman W. Barkema. Prevalence of Staphylococcus non-aureus species isolated from intramammary infection in Canadian dairy herds. To be submitted in Journal of Dairy

Science.

Manuscript II) Larissa A.Z. Condas, Jeroen De Buck, Diego B. Nobrega, Domonique A. Carson,

Jean-Philippe Roy Greg P. Keefe, Trevor DeVries, John R. Middleton, Simon Dufour, and

Herman W. Barkema. Distribution of Staphylococcus non-aureus species in samples from quarters with low and high somatic cell count, and clinical mastitis. To be submitted in Journal of Dairy Science.

xiv

Chapter One: General Introduction

Mastitis, inflammation of the mammary gland, is a multifactorial disease, mainly caused by pathogenic microorganisms. Mastitis can be classified as clinical or subclinical. The former

(CM) is defined as visibly abnormal milk or abnormalities of the udder. Nevertheless, when clinical signs are not present, the inflammation can be assessed by the measurement of somatic cell count (SCC) in milk samples in order to diagnose subclinical mastitis cases (SCM) (Bradley et al., 2002).

Although the distinction between SCM and CM is clearly defined; SCM and intramammary infection (IMI) are unfortunately still interchanged or used as a synonym

(Barkema et al., 1997). Intramammary infection is defined as presence of microorganisms in the udder, whereas SCM is an inflammation of the udder as determined by an increase in SCC. The most frequently used cut-off to differentiate normal from SCM is 200,000 cells/mL, with or without detection of an IMI (Barkema et al., 1997, Dohoo et al., 2011b).

Several pathogens are implicated in bovine mastitis, including bacteria, viruses, mycoplasma, fungi, and algae. These microorganisms are usually classified in two groups, corresponding to their transmission characteristics, in environmental or contagious pathogens, and according to the severity of clinical signs into major and minor pathogens (Bradley et al.,

2002, Ruegg, 2003). The most common major udder pathogens are Staphylococcus aureus,

Streptococcus uberis, Streptococcus dysgalactiae, Streptococcus agalactiae, Escherichia coli, and Klebsiella spp. (Radostits et al., 2007). Streptococcus agalactiae and S. aureus are the most common causes of contagious mastitis, which usually spread from infected to non-infected udder quarters during milking (Radostits et al., 2007). Environmental pathogens as E. coli, Klebsiella 1

spp. and Strep uberis (Munoz et al., 2007, Olde Riekerink et al., 2008) are ubiquitous in the cows’ environment, in reservoirs such as feces, soil, and bedding (Bradley, 2002). These pathogens are also capable of inducing major clinical signs, such as granulomas, hemorrhages and intense tissue damage. The major contagious pathogens are comprised by S. aureus, Strep. dysgalactiae and Strep. agalactiae; while major environmental pathogens are comprised by E. coli, Klebsiella spp. and Strep. uberis (Bradley et al., 2002). On the contrary, minor pathogens such as, Corynebacterium spp. and Staphylococcus non-aureus, are usually associated with mild clinical signs or subclinical mastitis, without extensive lesion in mammary gland (Bradley, 2002,

Radostits et al., 2007). These pathogens can also be associated with protective effects, since are usually part of microbiota, acting as contagious opportunistic pathogens under certain conditions

(Ruegg, 2003).

Microbiological culture, is still the main methodology for identifying pathogens in the udder, although typically not always replicate ideally the bacterial load present in mammary gland (Sears and McCarthy, 2003, Hogan et al., 2009). Because of this limitation, the National

Mastitis Council (NMC) and the International Dairy Federation published guidelines for IMI and

SCM classification. The current criteria include ≥1 cfu/10 µL from a single milk sample to achieve a reasonable balance of sensitivity (Se) and specificity (Sp) in IMI definition (NMC,

2012). Nevertheless, several definitions were already applied making the comparison among studies more complicated (Zadoks et al., 2002). Dohoo et al. (2011a) concluded that the cost associated with multiple sampling can be too high and does not substantially increases Se and

Sp. Therefore, the same authors, comparing consecutive and single samplings in association to criteria such as microorganism isolated in pure or mixed culture, SCC cut-off (minimum of

200,000 cells/mL), and number of colonies cultured (Dohoo et al., 2011b) was able to determine 2

Se and Sp values according to each pathogen group, which represents an additional resource for

IMI definition and comparison among studies. Hopefully, it will be possible in a near future to have technologies able to detect bacteria directly from milk, and determine which load of a specific pathogen are truly associated with and infective state.

In the last half-century, major advances have been made in understanding mastitis.

Identification and characterization of etiologic agents of IMI (Zadoks and Watts, 2009, Zadoks et al., 2011), determination of prevalence and incidence of IMI (Olde Riekerink et al., 2008,

Oliveira et al., 2013), and development of innovative research to control and treat IMI (Makovec and Ruegg, 2003, Hillerton and Berry, 2005, LeBlanc et al., 2006) have contributed to our current understanding (NMC, 1996). Although there have been considerable advances in genetic potential for milk production, management strategies, and milking technologies, more research is needed to elucidate the changing epidemiology of mastitis and to develop new mastitis prevention and control technologies. Advancements in molecular techniques are facilitating more precise characterization of existing and emerging IMI pathogens and development of control strategies. Bio-prospecting is leading to discovery of new antimicrobial agents to replace or complement current antibiotics. Advances in our understanding of mammary gland immunology, vaccinology, proteomics, and genomics are facilitating manipulation and enhancement of the cow’s immune response to IMI and mastitis. Overall such efforts aim to decrease the impact of pathogens in the mammary gland, and consequently impair this disease of causing detrimental losses for dairy industry.

1.1 Impact of mastitis on the dairy industry

Udder health is a constant concern for dairy farms, due to the widespread deleterious impact of IMI on milk production, milk quality, and public health. Mastitis is the major cause of decreased milk yield (Bradley, 2002), and is the one of the most common and economically important diseases affecting the dairy industry (Schukken et al., 2009b, Rollin et al., 2015). The annual cost of clinical mastitis varies according to several criteria, but estimates range from $100 to $900 per cow affected depending on pathogen, stage of lactation, and parity (Grohn et al.,

2005, Schukken et al., 2009b, Rollin et al., 2015). For the Canadian industry, annual losses due to mastitis exceeded $400M in 2003 (CDIC, 2003). Furthermore, mastitis is the second leading cause of culling of dairy cows in Québec (Baillargeon et al., 2009). Finally, CM is the leading cause of antibiotic use in lactating dairy cows, with associated economic losses due to bacteriological and antimicrobial resistance tests, duration of antibiotic usage and supportive treatment, labour and veterinarian costs, and withholding milk from sale (Saini et al., 2012,

Oliveira and Ruegg, 2014).

Cows with mastitis not only produce less milk, but also produce milk with lower dietary and processing values (Harmon, 1994, Lindmark-Månsson et al., 2003). Mastitis alters milk composition, with decreases in proportions of casein, lactose, and fats, and increases in soluble proteins, Na+ and Cl- ions, and SCC compared to milk from unaffected cows (Lindmark-

Månsson et al., 2003, Ogola et al., 2007).

The incidence of CM and prevalence of SCM is, in general, higher in multiparous cows compared to first-calf heifers, and, particularly in the latter group, is highest in the weeks around calving (Barkema et al., 1998, De Vliegher et al., 2012, Piepers et al., 2013). Also, the 4

prevalence of IMI in heifers range widely during the periparturient period (De Vliegher et al.,

2012). In a Belgian study, 27% of heifers had a high SCC (>200,000 cells/mL) in early lactation, suggesting they had IMI during the peripartum period (De Vliegher et al., 2004). Every year, approximately five million dairy heifers in North America calve and enter dairy herds. There is anecdotal evidence that heifer mastitis occurs frequently in Canada and the USA, although there are no Canadian studies documenting the incidence of heifer mastitis. Data collected in the cohort of dairy herds of the CBMRN and in Canada-wide studies (Olde Riekerink et al., 2008) provide an excellent opportunity to determine the pathogen-specific occurrence of SNA heifer mastitis in Canadian dairy herds, and perhaps new knowledge to reduce the economic impact of this disease.

In the middle of last century, IMI with major pathogens (e.g. S. aureus and particularly

Strep. agalactiae) was extremely prevalent worldwide, causing a high bulk milk SCC (BMSCC)

(Barkema et al., 1998, Waage et al., 1999, Sol et al., 2000, Oliveira et al., 2013). In the 1960s,

Neave et al. (1969) published the 5-point schedule for mastitis control, focusing predominantly on management strategies to control contagious mastitis. In the decades that followed, herds in many developed countries were able to control these pathogens, particularly S. agalactiae, decreasing their impact on udder health, decreasing BMSCC, and contributing to increases in milk production. Modern mastitis control schemes, which focus on major contagious pathogens but seem to be less effective against minor pathogens, may have contributed to the marked increase in the prevalence of SNA IMI (Pitkälä et al., 2004, Tenhagen et al., 2006, Sampimon et al., 2009a). Twenty percent of milk samples collected on CBMRN farms were SNA-positive

(Reyher and Dohoo, 2011, Dufour et al., 2012). Unfortunately, it is not possible to determine whether SNA were always part of the udder microbiota, or whether prevalence of these bacteria 5

has truly increased. Regardless it is clear that currently this group of microorganism is a challenge for some dairy herds (Sampimon et al., 2009a). Therefore, there is a considerable effort to understand this group and the separate species within this group, so that mastitis control can be improved.

To characterize effects of SNA on udder health and production in heifers and cows, appropriate identification of these organisms is essential. Thereafter, full implications of infections with specific SNA species can be determined and studied in relation to subsequent udder infections with major mastitis pathogens, udder health, milk production, and culling. Initial studies, SNA are regarded as a large, heterogeneous group of staphylococci. Unfortunately, considering them as group rather than studying them at a species level has contributed to apparent discrepancies among studies. It is likely that individual SNA species interact differently with the host and the environment; as a consequence, they are expected to have specific effects on their host, with disparate courses of udder infection and distinct patterns of transmission.

1.2 Staphylococcus non-aureus

Staphylococci non-aureus are Gram-positive and catalase-positive cocci that occur mostly in tetrads or short chains in a grape-like format. They are non-motile, facultative anaerobes, and catalase-positive (Götz et al., 2006). These bacteria belong to phylum Firmicutes, order

Bacillales, class Bacilli, family Staphylococcaceae, genus Staphylococcus (LPSN, 2016).

This group classification was based on the phenotypic characteristic of S. aureus to coagulate plasma, often applied at a time when only S. aureus was characterized as pathogenic and the other SNA species were considered minor pathogens (Becker et al., 2014). However, this 6

system classification grouped species that are not necessarily phylogenetically related and does not correctly represent variability within the genus Staphylococcus (Becker et al., 2014). Most studies have included staphylococcal species that are variable in the coagulase test, e.g. S. agnetis, in the group of coagulase-negative staphylococci (CNS) (Taponen et al., 2012).

Therefore, since some SNA species vary in their response to the coagulase test, we prefer to use the name SNA for these bacteria.

Currently, there are 47 staphylococcal species, which includes 38 species that can be coagulase-negative. Of these 38 species, 11 display variable coagulase test results, including S. aureus which is most often considered a coagulase-positive Staphylococcus sp. (CPS) (Becker et al., 2014, LPSN, 2016). From cows, approximately 25 staphylococcal species have been isolated, with S. chromogenes being most frequently isolated from milk and skin microbiota

(Vanderhaeghen et al., 2015).

The SNA are categorized as skin and mucous membrane opportunists of humans and animals (Götz et al., 2006), and are ubiquitously distributed in soil, water, air, meat and dairy products (Piessens et al., 2011, De Visscher et al., 2014). In cows, they are isolated mainly from teat skin and, to a lesser extent, the hair-covered udder and body surfaces (White et al., 1989). In a recent metagenomics study, SNA isolated from humans preferred areas with higher humidity

(Grice et al., 2009).

The heterogeneity of the staphylococci group leads to a diverse number of phenotypic and genotypic methods that allows its identification. Hitherto, the general isolation and identification of SNA as a group has generally been accepted; however, awareness of variable effects of different SNA species on udder health and productivity in heifers and cows, has resulted in a clear need for appropriate identification and speciation of these organisms (Supré et al., 2011). 7

There are two basic principles in bacterial species identification, the first based on phenotypic methods and the second based on molecular methods; these are often combined to obtain a final definitive identification.

1.2.1 Phenotypic identification

Routine veterinary microbiology laboratories are able to diagnose the most important pathogens isolated from milk using basic phenotypic characteristics (e.g. colony morphology,

Gram staining, hemolytic patterns, and catalase and coagulase tests) (De Visscher et al., 2013,

Markey et al., 2013). These steps, that enable identification of SNA as a group, are being used as a starting point for subsequent phenotypic and genotypic methods.

A plethora of phenotypic tests are available to identify bacterial species. Several commercial kits include a combination of tests that were primarily developed to identify SNA species isolated from humans. Since they were convenient to use, some of these kits were used to characterize isolates of bovine origin, e.g. API Staph ID system and the Staph-Zym system

(Taponen et al., 2006, Taponen et al., 2008, Ruegg, 2009, Sampimon et al., 2009b, Piessens et al., 2011). Unfortunately, these kits performed poorly on bovine isolates with considerable variation in metabolic reactions (Zadoks and Watts, 2009, De Visscher et al., 2013). The low typeability, accuracy, and repeatability of these kits in comparison to genotypic methods makes them unsuitable for identifying Staphylococcus spp. originating from dairy production systems

(Supré et al., 2011). Therefore, in most recent studies, identification of SNA species has been based on molecular methods.

1.2.2 Genotypic identification

Several genotypic methods have been applied in recent years to improve studies of SNA species in milk and dairy environment. These techniques involve sequencing housekeeping genes, high-resolution melt analysis, ribotyping, the use of restricted or amplified fragments, the use of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF), and whole genome sequencing (WGS) (Supré et al., 2009, Zadoks et al., 2011, Ajitkumar et al., 2012,

Piessens et al., 2012, Tomazi et al., 2014). Some genotypic methods have advantages over others, including being able to differentiate at subspecies or strain type levels; this is the case for techniques such as DNA sequencing, amplified fragment length polymorphism (AFLP), random amplification of polymorphic DNA (RAPD-PCR), pulse-field gel electrophoresis (PFGE), multiple locus sequence typing (MLST), MALDI-TOF and WGS (Tenover et al., 1995, Enright et al., 2000, Calcutt et al., 2014, Tomazi et al., 2014, Zhang et al., 2014, Naushad et al., 2016). It is noteworthy that PFGE, and MALDI-TOF has proven to be reproducible, and provides comprehensible recommendation for species identification (Taponen et al., 2008, Rajala-Schultz et al., 2009, Tomazi et al., 2014, Argemi et al., 2015). Lately, after adoption of next generation sequencing (NGS) technologies, average nucleotide identity (ANI), average amino acid distance

(AAI) and genome to genome distance generated by WGS are known to be robust methods for species identification. Nevertheless, these methods are still expensive, and require specialized expertise (Zhang et al., 2014).

The DNA sequencing of housekeeping genes is considered one of the most reliable, less expensive, and less demanding molecular techniques. DNA sequencing of housekeeping genes is being applied in several studies related to human and veterinary infections caused by SNA

(Taponen et al., 2008, Becker et al., 2014, Calcutt et al., 2014, Tomazi et al., 2014). Various 9

targets are available, which most commonly include hsp60, rpoB, sodA, gap, and tuf genes

(Ghebremedhin et al., 2008). The rpoB is the most frequently amplified gene for isolates obtained from milk samples (Sampimon et al., 2009a, Park et al., 2011a, Piessens et al., 2011,

Supré et al., 2011, Ajitkumar et al., 2013, Fry et al., 2014), following the protocol described by

Drancourt and Raoult (2002) and Mellmann et al. (2006). This gene encodes a subunit of RNA polymerase, an important enzyme in transcription. In addition, in bacteria, it is responsible for synthesizing mRNA, rRNA and tRNA (Mellmann et al., 2006). The rpoB sequencing is an ideal method for SNA identification since it has already been used for identifying taxonomic relationships among S. aureus strains (Adekambi et al., 2009). Furthermore, sequencing of the rpoB gene is a reliable method to support the 16S rDNA-based phylogenetic tree of many bacteria genera, being highly discriminative, as it is a less conserved gene than 16S ribosomal

DNA (Drancourt and Raoult, 2002, Adekambi et al., 2009).

1.3 Relevance of Staphylococcus non-aureus in intramammary infection

1.3.1 Prevalence of SNA IMI

The worldwide reported prevalence of SNA from bovine milk samples ranges from 10 to

50% at the mammary quarter-level (Barkema et al., 1999, Pitkälä et al., 2004, Østerås et al.,

2006, Tenhagen et al., 2006, Gillespie et al., 2009, Sampimon et al., 2009a, Schukken et al.,

2009a, Piessens et al., 2011, Piessens et al., 2012), 9 to 35% at the cow-level (Sampimon et al.,

2009a, Schukken et al., 2009a), and 2 to 25% at the herd-level (Schukken et al., 2009b, Supré et al., 2011). However, it is unclear why SNA represent the most frequently isolated organisms from milk (Piepers et al., 2007). 10

The SNA rarely cause major CM, and when it occurs it is usually associated with mild symptoms. More often, these bacteria are associated with IMI with relatively low SCC and SCM.

In CM samples, the proportion of SNA isolated ranges from 5.4 to 10.8% (Oliver and Jayarao,

1997, De Haas et al., 2002, Bradley et al., 2007, Olde Riekerink et al., 2008, Steeneveld et al.,

2008, Botrel et al., 2010, Piessens et al., 2011), whereas in SCM 14 to 41% of quarters are affected (Chaffer et al., 1999, Bradley et al., 2007, Piepers et al., 2007, Botrel et al., 2010). It seems that short-term infections are most common, possibly more associated with IMI with a relatively low SCC (Taponen et al., 2006); however, persistent SCM throughout lactation is also frequently reported (Taponen et al., 2007, Piessens et al., 2011, Supré et al., 2011, Mork et al.,

2012). Factors that lead to their elimination or persistence are not fully understood, but certainly involve host and pathogen factors (Piessens et al., 2011). For example, aspects such as biofilm formation and intracellular infection are virulence mechanisms employed by SNA species that might be involved in infection persistency (Becker et al., 2014, Vanderhaeghen et al., 2014).

Apparently, heifers are more prone to IMI with SNA species present in the environment than species present in their microbiota, and the infecting strains can lead to a pronounced inflammatory reaction that can persist throughout lactation (Piepers et al., 2009).

The most prevalent bovine SNA species reported worldwide are S. chromogenes, S. simulans, S. hyicus, S. haemolyticus, S. epidermidis, and S. xylosus (Vanderhaeghen et al., 2014).

Their ranking differed among studies, which is among other reasons, related to extraneous factors such as diagnostic criteria including, definition of IMI and species identification methods.

Other factors that are likely associated with differences in distribution are management practices such as post-milking teat disinfection, housing system, parity, lactation stage, and climate

(Taponen et al., 2006, Taponen et al., 2007, Sampimon et al., 2009a, Thorberg et al., 2009,

Piessens et al., 2011, Koop et al., 2012, Piepers et al., 2013).

A thorough knowledge of possible environmental sources of SNA species is necessary for adequate mastitis prevention (De Vliegher et al., 2003, Piessens et al., 2011). The most frequently isolated species, S. chromogenes, was frequently isolated from skin and teats of prepartum heifers not previously exposed to milking and not frequently isolated from other environmental sources. It is therefore most likely host-adapted (Thorberg et al., 2009, Piessens et al., 2011). Other species like S. haemolyticus or S. sciuri are most frequently isolated from bedding material and milking machines, consistent with them being environmental opportunists

(De Visscher et al., 2014).

1.3.2 Effect of SNA IMI on udder health and milk production

The significance of SNA isolation from both the udder and the teat canal remains a topic of debate. Several studies indicate that SNA are the principal cause of IMI on modern dairy farms

(Piepers et al., 2007, Sampimon et al., 2009a) especially in first-lactation heifers (Fox et al.,

1995, Nickerson et al., 1995, Oliver and Jayarao, 1997, Piepers et al., 2007, Sampimon et al.,

2009a).

When IMI with SNA causes mastitis, it usually results in SCM and less often CM, with a moderate increase in SCC (Vanderhaeghen et al., 2014). Experiments evaluating the immunological response of SNA observed an increased infiltration of neutrophils and leukocytes in quarters with SNA compared to quarters without microorganism growth in culture. In most experiments, the increase in SCC was mild, corresponding to mild tissue lesions (Benites et al.,

2002, De Vliegher et al., 2005). Nevertheless, considering that SNA are highly prevalent bacteria 12

in IMI, in most studies its frequency is also intrinsically related to increases in BMSCC, which results in economic losses for producers (Schukken et al., 2009a).

According to Schukken et al. (2009a), an increase in quarter SCC >200,000 cells/mL corresponds to milk yield loss. In contrast, other studies report higher milk production in cows infected by SNA (Schukken et al., 2009b, Paradis et al., 2010, Piepers et al., 2010).

Unfortunately, most milk production data are only available at the cow-level; therefore, milk production by uninfected quarters could mask lower milk production by SNA infected quarters

(Piccart et al., 2015). Most experimental data present a milk loss as consequence of IMI by SNA

(Simojoki et al., 2009, Piccart et al., 2015); however, observational data using quarter milk yield are still unavailable (Vanderhaeghen et al., 2015).

At a species level, Thorberg et al. (2009) reported that S. epidermidis, S. simulans and S. chromogenes were responsible for significantly increased SCC in cows with persistent SCM, in comparison to S. haemolyticus and S. xylosus. Simojoki et al. (2009) and Simojoki et al. (2011), also concluded that S. chromogenes and S. epidermidis were capable of inducing greater inflammatory response and more clinical signs. Recently, Piccart et al. (2016) observed that SCC of experimentally infected quarters with S. chromogenes isolated from teat apex and IMI increased SCC, whereas this did not occur in quarters inoculated with an environmental isolate of S. fleuretti or negative-control quarters.

Conversely, a naturally occurring IMI with SNA was previously shown to have a protective effect against intramammary challenge with S. aureus (Matthews et al., 1990, De

Vliegher et al., 2004), and suppressed colonization by other mastitis-causing pathogens

(Matthews et al., 1991, De Vliegher et al., 2004, De Visscher et al., 2016b). Piepers et al. (2010) recently reported that heifers with a SNA infection in early lactation resulted in a lower 13

incidence of CM. Additionally, in an in vitro study, 2 of 10 S. chromogenes isolates from the udders of dairy heifers caused variable inhibition (decreasing intensity) of all S. aureus, all Strep. dysgalactiae, and all Strep. uberis isolates tested (De Vliegher et al., 2004). This study implicated in vitro production of inhibitory substances by some S. chromogenes isolates as a possible protective mechanism when these strains colonize the teat apex in vivo (De Vliegher et al., 2003).

1.4 Thesis outline

The overall aim of this thesis research was to describe the distribution of SNA species on

Canadian dairy farms. The current thesis has a descriptive character, which is important to raise questions regarding potential risk factors (Dohoo et al., 2009). The study consisted of two parts with distinct (albeit complementary) aims and hypotheses.

1.4.1 Distribution of SNA in different herd, cow and quarters

Aim: Using molecular identification of ≥ 6,000 SNA isolates from the Mastitis Pathogen

Collection of the Canadian Bovine Mastitis and Milk Quality Network (CBMQRN), determine the species of SNA isolated from bovine milk samples (Canadian dairy herds), their distribution in herds and among scows and mammary quarters.

Hypothesis: Distribution of SNA species will be different across Canada, and will be associated with herd and farm characteristics such as BMSCC and barn type, and cow characteristics such as days in milk and parity, and quarter location.

The research to address this aim and hypothesis is described in Chapter 2.

1.4.2 SNA species and SCC

Aim: Using the CBMQRN cohort database, analyze the distribution of SNA species causing IMI with low SCC (< 200,000 cells/mL), high SCC (≥ 200,000 cells/mL), and CM to determine the pathogenic potential of each species. Furthermore, determine the association of

SNA species with SCC.

Hypotheses: 1) Some SNA species will be more frequently observed in samples with high

SCC and CM than in samples with a low SCC; and 2) some species will increase SCC more than others.

The research to address this aim and hypothesis is described in Chapter 3.

Chapter Two: Prevalence of Staphylococcus non-aureus species isolated from intramammary infection in Canadian dairy herds

2.1 Abstract

Staphylococcus non-aureus (SNA), the most frequently isolated microorganism from bovine milk worldwide, are a heterogeneous group of numerous species. However, due to variety of species that this group comprises, their epidemiology is still under investigation. In the present study, SNA intramammary infection (IMI) was defined as milk samples containing >1,000 cfu/mL isolated in pure culture, obtained from a cohort of cows assembled by the Canadian

Bovine Mastitis Research Network (CBMRN). Overall, 9.17% of 98,227 milk samples from

5,149 cows and 20,305 quarters were SNA-positive. Of the 9,173 SNA positive milk samples,

5,508 (60%) had SNA isolated at >1,000 cfu/mL in pure culture and were stored by the CBMRN

Mastitis Pathogen Collection. Of these 5,508 isolates, 5,434 isolates were identified to the species-level as SNA using partial sequencing of the rpoB housekeeping gene. Prevalence of each SNA species IMI at the mammary quarter and cow-level during the period of the cohort study were estimated using binomial regression using Fisher’s quasi-maximum likelihood with a logit link with presence of a specific SNA species as the outcome. Quarter-level and cow-level

SNA IMI prevalence adjusted for 150 DIM was 8.9 and 23.9%, respectively. The most prevalent species isolated were Staphylococcus chromogenes (48.95%), S. simulans (16.8%), S. xylosus

(11.6%), S. haemolyticus (7.9%), and S. epidermidis (4.1%). Furthermore, overall prevalence of

SNA and specifically S. chromogenes and S. arlettae IMI was highest in first-lactation heifers. In contrast, isolation of Staphylococcus haemolyticus, S. sciuri, and S. saprophyticus IMI increased

from the first to the third lactations. Early in lactation, predominant IMI species were S. chromogenes, S. simulans, S. haemolyticus, S. gallinarum, S. cohnii, and S. capitis, whereas prevalence of S. haemolyticus, S. xylosus, and S. cohnii IMI increased during lactation.

Prevalence of Staphylococcus chromogenes, S. haemolyticus, and S. epidermidis IMI was highest in high BMSCC herds, whereas S. sciuri IMI were more prevalent in herds with intermediate or low BMSCC. Distribution of SNA species differed among the four provinces of

Canada, where S. chromogenes was most prevalent in Alberta and Maritimes, S. simulans and S. xylosus in Ontario and Quebéc; whereas S. haemolyticus and S. epidermidis were even isolated in all provinces. Staphylococcus arlettae, S. capitis, S. cohnii, S. xylosus, and S. chromogenes positive-quarters were most prevalent in tie-stall barns, S. epidermidis most prevalent in free-stall barns, and S. sciuri, S. gallinarum and S. chromogenes IMI were highest in cows housed in bedded-pack barns. In conclusion, distribution differed considerably among SNA species and accurate identification (species level) was essential for studying epidemiology of SNA.

Key-words: Staphylococcus non-aureus, intramammary infection, prevalence, dairy, mastitis

2.2 Introduction

Staphylococcus non-aureus (SNA) species are considered pathogens of minor importance in dairy production, particularly compared to major udder pathogens such as Staphylococcus aureus, streptococci, and coliforms. Notwithstanding, SNA are the most frequently isolated bacteria from udder quarters in all recent subclinical mastitis surveys worldwide (Piepers et al.,

2007, Pyörälä and Taponen, 2009, Sampimon et al., 2009a, Thorberg et al., 2009, De Vliegher et al., 2012). Additionally, because SNA intramammary infection (IMI) moderately increases SCC and more stringent regulations calling for reductions in bulk milk SCC (BMSCC) have increased the relative importance of understanding the epidemiology of SNA IMI (Makovec and Ruegg,

2003, Piepers et al., 2007, Abrahmsén et al., 2014).

Apart from the increasing prevalence of SNA species isolated from the udder, their importance remains a topic of debate (Oliver and Jayarao, 1997, Piepers et al., 2007, Fox, 2009,

Nickerson, 2009, Sampimon et al., 2009a, Schukken et al., 2009a). Some authors consider SNA a main cause of subclinical and persistent mastitis (Sampimon et al., 2009a, Fry et al., 2014), whereas other reports suggest SNA have a protective effect against major pathogen IMI

(Matthews et al., 1990, De Vliegher et al., 2004). Additionally, milk production was higher in heifers with SNA IMI compared to uninfected heifers (Schukken et al., 2009a, Piepers et al.,

2010); however, no effect on milk production (Tomazi et al., 2015) or decreased milk production associated with SNA IMI have also been reported (Taponen and Pyörälä, 2009).

Apparently contrasting findings among studies regarding impact of SNA on udder health and milk production could be the result of regarding the SNA as one group (Woodward et al.,

1987, Woodward et al., 1988, Matthews et al., 1990). However, SNA are a large and 18

heterogeneous group (Vanderhaeghen et al., 2015), and it is therefore expected that individual

SNA species interact differently with the host and the environment. Consequently, SNA likely have variable effects on udder health and production (Piepers et al., 2009, Vanderhaeghen et al.,

2014, Piccart et al., 2016). For example, IMI with Staphylococcus chromogenes, S. simulans and

S. xylosus have a greater impact on increasing SCC compared to IMI with other species, e.g. S. cohnii and S. sciuri (Taponen et al., 2007, Supré et al., 2011, Fry et al., 2014). Some species, such as S. chromogenes and S. epidermidis, seem to be host-adapted, whereas others, such as S. simulans, seem to be environmentally adapted and can act as opportunists (Piessens et al., 2011).

Geometric mean BMSCC and housing of lactating cows differ by geographical region

(Barkema et al., 2015). Based on the National Cohort of Dairy Farms conducted in Canada during 2007 and 2008, Dufour et al. (2012) reported no difference in prevalence of overall SNA

IMI among tie-stall, free-stall, and bedded pack barns, considering SNA as a single group.

However, Olde Riekerink et al. (2008) reported a difference between tie- and free-stalls in incidence of clinical mastitis caused by SNA species. Perhaps differences in management practices among housing systems impact prevalence of IMI with specific SNA species.

Within-herd prevalence of IMI with various SNA species is influenced by parity and lactation stage (Sampimon et al., 2009a, De Visscher et al., 2016a). Staphylococcus simulans and

S. epidermidis are most commonly isolated from multiparous cows (Taponen and Pyörälä, 2009,

Mork et al., 2012), whereas S. chromogenes is more frequently isolated from heifers. In the latter, prevalence is usually higher close to calving, but persistency is also common (Taponen et al., 2007). In that regard, S. simulans can persist for long intervals throughout lactation, whereas prevalence of S. chromogenes IMI decreases shortly after calving (Piessens et al., 2011).

However, there are apparently no North-American data on parity and DIM distribution of SNA 19

species IMI in bovine milk. Furthermore, although Barkema et al. (1997) isolated SNA more frequently in rear versus front quarters, there are no reports on species-specific SNA quarter distribution.

A large field cohort study was conducted during 2007 and 2008 by the Canadian Bovine

Mastitis and Milk Quality Research Network (CBMQRN). Data and isolates from this study enabled further investigation on relevance of SNA species for the dairy industry. The first objective of the current study was to determine distribution of SNA species isolated from bovine milk on Canadian dairy farms. The second objective was to evaluate potential associations of species-specific SNA IMI with BMSCC, barn type, and cow characteristics, such as parity, DIM, and quarter location.

2.3 Materials and methods

2.3.1 Herds and cows

Data and samples were collected in the National Cohort of Dairy Farms (NCDF) conducted in Canada during 2007 and 2008, as described (Reyher et al., 2011). Briefly, the study included 91 farms allocated into four regions: Maritimes, Québec, Ontario, and Alberta).

Eligibility was primarily based on BMSCC and housing system. Farms were randomly selected based on average BMSCC during the last 12 mo, classified as low, intermediate and high

(<150,000, 150,000-300,000 and >300,000 cells/mL, respectively). Herds enrolled had to match

(within 15 percentage points) the proportion of free-stall systems of their respective regional free-stall percentages. Additionally, each herd was comprised of at least 80% lactating Holstein-

Friesian cows milked twice a day and participated in a DHI recording system. A total of 91 herds

were sampled, 17 in Alberta, 27 in Ontario, 29 in Québec, and 18 in the Maritimes (Prince

Edward Island, New Brunswick, and Nova Scotia).

Among the 91 herds, 60% housed lactating cows in a tie-stall, 33% in a free-stall, and 6% were housed on a bedded-pack. One farm in Québec was excluded from the analysis to determine the effect of barn type on SNA distribution, as it had a mixed-barn design. Most herds in Alberta and the Maritimes were housed in free-stalls (12 and 9 herds, respectively), with a few bedded-pack barns, whereas Ontario and Québec herds most often had tie-stalls (20 and 25 herds, respectively), followed by free-stall barns (6 and 3 herds).

2.3.2 Sampling

Field sampling and corresponding applied techniques have been described (Reyher et al.

(2011). Briefly, the CBMQRN field technicians and producers followed a standard protocol, performing sampling over 2 y (2007 and 2008). In the first year, 2 periods of intensive milk sampling were done with 3 sample collections during the winter and 7 collections during the summer. Intensive quarter milk sampling was done on 10 random lactating cows and the 5 most recently calved cows (1 wk intervals). Additionally, quarter samples were collected (by the farmer) from 15 cows per herd 4 wk before dry-off, 2 wk before dry-off, at calving, and 2 wk after calving.

2.3.3 Laboratory analyses

Bacteriological culture was done on sheep blood agar and MacConkey agar. An inoculum of 10 μL of milk was spread using a sterile 10-μL inoculating loop. All plates were incubated at

37°C and examined for bacterial growth at 24 and 48 h. Following 48 h of incubation, colonies

were enumerated and species were presumptively identified using recommended phenotype- based bacteriologic procedures (Hogan et al., 2009). Milk samples with growth of ≥ 3 different microorganisms were considered contaminated (Reyher et al., 2011), whereas SNA presenting at least 10 phenotypically identical colonies of SNA in pure culture were stored (Reyher et al.,

2011). A total of 9,173 SNA positive quarters were recorded. From these quarters, a total of

5,508 isolates were harvested from cultures yielding ≥1000 cfu/mL and stored as pure cultures in individual cryovials at -80oC for later identification. Isolates from the remaining 3,665 SNA positive cultures were not stored because culture yields were below the 1000 cfu/mL threshold.

Somatic cell count was determined using a Fossomatic cell counter (Fossomatic 4000 series, Foss Electric, Hillerød, Denmark). Briefly, after obtaining 10µL of milk aliquots for bacteriological analyses, the remaining sample was preserved with the addition of 2-Bromo-2- nitropropane- 1,3-diol tablet (Broad Spectrum Microtabs II, D&F Control Systems Inc., Dublin

CA). The samples were then shipped to Maritime Quality Milk at the University of Prince

Edward Island (Charlottetown, PE, Canada) and kept frozen or refrigerated up to 5 d after shipment, until SCC analysis was completed (Fry et al., 2014).

2.3.4 Definition of intramammary infection

At the quarter-level, all samples stored by the National Cohort of Dairy Farms Mastitis

Pathogen Collection followed the criteria that considered a quarter as infected when SNA yielded ≥1000 cfu/mL of milk in pure culture. However, for this study, all mixed SNA cultures described in the cohort dataset were also included in our definition, with proportion of non- characterized isolates implement in the logistic regression model as explained in next section. At

the cow level, IMI of a CNS species in at least 1 quarter at a sampling was considered a positive result for that species.

2.3.5 SNA isolates identification

All 5,508 SNA isolates were shipped in lyophilized form in vials labeled with individual isolate barcodes to the University of Calgary. Bacteria were re-suspended with sterile ultra-pure distilled water, plated on 5% defribinated sheep blood agar plates, and incubated at 37°C for 24 h. Morphology and phenotypic tests (catalase, coagulase, and Gram-stain) were used to differentiate bacterial groups. Subsequent PCR amplification and sequencing with universal primers 27F-1392R were done to confirm identification of non-SNA bacterial groups previously misclassified during collection (Chakravorty et al., 2007). Extraction of DNA was done as described (Sampimon et al., 2009a).

Partial sequencing of the rpoB gene was performed as described (Mellmann et al., 2006).

Staphylococcus-specific primer sets used for amplification were staph_rpoB_1418F and staph_rpoB_3554R, which resulted in an amplicon of 899 bp. Thermal cycling conditions were 5 min at 94°C as the first denaturation step, followed by 35 cycles of denaturating at 94°C for 45 s, annealing at 52°C for 60 s, and extension at 72°C for 90 s with a final extension step at 72°C for

10 min. Primers for sequencing were 1418F and 1975R (500 bp; Mellman et al., 2006). Sanger sequencing for all amplicons was performed at University of Calgary Core DNA Services

(UCDNA). Pairwise aligned sequence data were compared to sequence data in GenBank using the nucleotide BLAST algorithm of the National Center for Biotechnology Information

(http://blast.ncbi.nlm.nih.gov). The SNA species were identified with > 97% identity to database sequences, as described Drancourt and Raoult (2002) and Mellmann et al. (2006), except when 23

the coverage identity was ≤ 50%, or high identity (> 97%) was observed with 2 different species.

In these cases, partial rpoB gene sequences were blast against whole-genome sequences of 450 bovine intramammary SNA isolates selected from the current study to represent all SNA species present in the collection and verified by their full 16S rDNA sequences (Naushad et al., 2016).

2.3.6 Statistical analyses

Data were analyzed using STATA version 13.0 (StataCorp, College Station, TX, USA), with P < 0.05 considered statistically significant. Bulk milk SCC categories were determined using the monthly geometric mean BMSCC from the 2y duration of the study. Three categories were created: ≤ 150,000, 151,000-250,000, and > 250,000 cells/mL, based on criteria already published by Sampimon et al. (2009a).

2.3.6.1 Prevalence

The prevalence of each CNS species IMI (cow and quarter levels) was estimated using binomial regression using Fisher’s quasi-maximum likelihood with a logit link with presence of a specific CNS species as the outcome. Prevalence estimates were presented for cows at 150

DIM, except models estimating prevalence at various DIM (not centered at 150 DIM).

Additionally, due to a high proportion of IMI at the start and end of lactation for almost all CNS species, quadratic terms for DIM were forced into models after visual inspection of the data. To account for repeated milk samplings, robust standard errors considered within-herd clustering.

Samples characterized. According to our definition of IMI, 9,173 quarters were diagnosed as SNA IMI. From those, 5,434 CNS were identified to the species-level. The proportion of samples that were stored and characterized was estimated for housing types,

BMSCC categories, and regions using logistic regression including DIM and DIM2 as independent variables. This proportion was used to estimate a sampling weight for each prevalence estimate at each level of potential explanatory independent variables and 95% confidence intervals were obtained using the initially observed robust standard errors (Zeileis,

2006) resulting in a conservative approach in order to avoid Type I errors. Additionally, because a proportion of the characterized isolates proved not to be a CNS species, the CNS prevalence

(overall and species) in not-characterized isolates was corrected for this proportion using sampling weights as well.

2.3.6.2 Intraclass correlation coefficients

Level of clustering at the herd, cow and quarter (repeated samplings) levels was assessed using intraclass correlation coefficients (ICC) and respective 95% confidence intervals were calculated for each species with mixed effects logistic regression using the latent variable approach (Rodríguez and Elo, 2003). For CNS species where the model did not converge, a maximum of 7 interactions was allowed.

2.3.6.3 Multilevel multivariable analysis

The association of quarter-level prevalence of CNS species and overall CNS prevalence with potential explanatory independent variables was estimated using generalized estimating equations. The variables included were region, barn type, herd size (recorded monthly) and average monthly milk production at herd-level; and DIM, parity (binary, heifers vs. older cows) and DIM x DIM at cow-level. Two-way interactions were considered based on biological plausibility and possible modification effects (involving region, barn type, parity and average

milk production). No 3-term interaction was evaluated. Region was introduced as a fixed-effect to address clustering at the province level since no other predictor existed at this specific level.

Robust standard errors were calculated using sandwich estimators (Maas and Hox, 2004). An exchangeable covariance matrix was considered with correlated errors at the herd level.

Although this strategy does not properly address autocorrelation of repeated measures, initial results were comparable to the multilevel logistic regression model with an unstructured correlation matrix (results not shown) and it was reported to be a fast and efficient way to deal with analysis of repeated-measures designs (Dohoo et al., 2009). For this analysis, an important assumption was that samples identified and not identified did not differ with regards to any association evaluated.

2.4 Results

2.4.1 Distribution of SNA species positive-quarters

Overall, 9.17% of the 98,227 milk samples from 5,149 cows and 20,305 quarters were

SNA-positive (Table 2-1). Of the 9,173 previously identified SNA infections, 5,508 (60.3%) isolates were stored. However, when characterized, 74 (1.54%) of these isolates were not SNA, including 32 S. aureus, 18 Corynebacterium spp., 2 Bacillus spp., 3 Brachybacterium spp., 7

Enterococcus spp., 3 Micrococcus spp., 1 Moraxella spp., and 1 yeast. Seven isolates did not grow after storage. Therefore, 5,434 SNA isolates were available for further characterization.

The 10 most frequently isolated SNA species were S. chromogenes, S. simulans, S. xylosus, S. haemolyticus, S. epidermidis, S. cohnii, S. sciuri, S. gallinarum, S. capitis, and S. arlettae, with S. chromogenes accounting for approximately 50% of isolates (Table 2-1).

2.4.2 Prevalence of SNA species IMI

2.4.2.1 Overall

Quarter-level SNA IMI prevalence adjusted for 150 DIM was 8.89 (95% CI: 8.09-9.77) per 100 quarters, whereas cow-level SNA IMI prevalence adjusted for 150 DIM was 23.86 (95%

CI: 21.93-25.92) per 100 cows.

2.4.2.2 Cow-level associations

Quarter-level ICC, measuring correlation in the same quarter for repeated sampling, was high for all species, but particularly for S. simulans, S. capitis, S. warneri and S. chromogenes, and S. saprophyticus. Clustering within cow was highest for S. warneri, S. epidermidis, S. arlettae, S. sciuri, S. cohnii, S. capitis, and S. simulans. Herd-level clustering was highest for S. cohnii, S. arlettae, S. warneri, S. sciuri, and S. capitis, whereas the ICC was relatively low for S. chromogenes, and S. saprophyticus (Table 2-1). When ignoring quarter-level clustering due to lack of convergence, cow-level ICC was 0.55 (95% CI: 0.51 - 0.58) for first-lactation heifers, compared to 0.44 (95% CI: 0.41 - 0.46) per 100 cows with parity ≥ 2. In the first week of lactation, the cow-level ICC was 0.34 (95% CI: 0.28 - 0.41) compared to 0.48 (95% CI: 0.46 -

0.51) thereafter.

Overall prevalence of SNA IMI in first lactation heifers was 10.6 per 100 quarters compared to a range of 7.46 - 7.90 per 100 quarters in multiparous cows (Figure 1-1). Quarter- level prevalence of S. chromogenes IMI was highest in first lactation heifers (6.72 per 100 quarters; 95% CI: 5.71-7.89%; adjusted for 150 DIM) compared to second parity cattle (3.35 per

100 quarters; 95% CI: 2.57-4.35%; adjusted for 150 DIM) in second parity (Figure 2A).

Prevalence of S. xylosus, S. haemolyticus, S. epidermidis, S. cohnii, S. sciuri, and S. gallinarum

IMI increased with increasing parity (Figures 2-2A,B).

Prevalence of SNA as a group increased throughout lactation, from 7.98 per 100 quarters in the first month of lactation to 13.08 per 100 quarters for quarters with ≥ 12 mo of lactation

(Figure 2-3). The same pattern was also observed at the species-level for S. chromogenes, S. xylosus, S. haemolyticus, S. cohnii, and S. arlettae (Figures 2-4A, B). In contrast, S. simulans

IMI was highest in the first third of lactation, whereas S. sciuri IMI was most prevalent in the middle third (Figure 2-4B). Staphylococcus chromogenes was the most frequently isolated species immediately after calving, then decreased in prevalence until 5 to 9 DIM, whereas thereafter its prevalence increased (Figure 2-6A). Staphylococcus simulans and S. epidermidis had the next highest prevalence at calving and maintained that over the first month of lactation, as well as throughout lactation (Figure 2-4A, 2- 6A). Staphylococcus haemolyticus also had a high prevalence at calving, but prevalence decreased in the following days, which was also observed with S. cohnii, S. gallinarum, and S. capitis (Figures 2-6A, B).

2.4.2.3 Quarter-level association

Overall SNA IMI prevalence was not different among udder quarters, primarily due to the even distribution of S. chromogenes, S. simulans, S. xylosus, S. haemolyticus, and S. epidermidis across all quarters (Table 2-2). Nevertheless, S. cohnii, S. sciuri, and S. arlettae had lower prevalence in left front quarters.

2.4.2.4 Province, housing and bulk milk SCC

Overall prevalence of SNA IMI was highest in Ontario and Alberta (Table 2-3). Species- specific prevalence of S. chromogenes, S. simulans, S. xylosus, S. cohnii, S. arlettae, S. agnetis, and S. nepalensis IMI was highest in Ontario. Staphylococcus equorum IMI was most prevalent in Alberta, whereas S. capitis and S. saprophyticus were not isolated in that province. The prevalence of S. gallinarum and S. pasteuri IMI was highest in Québec, whereas S. chromogenes and S. succinus had its lowest prevalence in that province. Prevalence of S. chromogenes and S. sciuri IMI was highest in the Maritimes, although S. xylosus had its lowest prevalence in this location. The prevalence of other species such as S. epidermidis, S. haemolyticus and S. warneri did not differ among provinces.

The prevalence of SNA IMI was higher in bedded-pack and tie-stall herds (Table 2-4).

Prevalence of S. simulans, S. xylosus, S. cohnii, S. arlettae, S. capitis, S. warneri, S. saprophyticus, and S. nepalensis IMI was highest in tie-stall barns. Staphylococcus sciuri and S. chromogenes IMI was highest in cows housed on bedded-packs, whereas S. epidermidis had its lowest prevalence in this barn type. Staphylococcus equorum was most prevalent in free-stall barns. Prevalence of S. haemolyticus and S. gallinarum IMI was similar across all barn-types.

Overall prevalence of SNA IMI, and prevalence of S. simulans, S. epidermidis, S. cohnii,

S. arlettae, and S. gallinarum IMI was lowest in low BMSCC herds, whereas S. devriesei IMI prevalence was highest in this BMSCC herd category (Table 2-5). Prevalence of other species such as S. chromogenes, S. xylosus, S. sciuri, S. gallinarum, and S. capitis did not differ among herds in different BMSCC categories, whilst S. warneri was most prevalent in high BMSCC herds.

2.4.3 Multivariable analysis

The GEE models could be fitted for overall SNA and the 4 most frequently isolated SNA species (Table 6). There were distinct associations for the factors and each SNA species. When treating SNA as a group, the odds associated with the housing system depended on the BMSCC.

Samples from tie-stall herds had lower odds of being SNA-positive in the low BMSCC category compared to the intermediate category (OR = 0.69), although the same was not observed for free-stalls. In high BMSCC herds, SNA were more frequently isolated from cattle housed in tie- stalls than in free-stalls (OR = 1.57). The SNA were more frequently isolated in Ontario than in

Alberta herds. Heifers were more frequently SNA-positive than multiparous cows (OR = 1.27).

The overall species-specific SNA prevalence was associated with DIM, but the shape of the association differed among SNA species (Table 2-6).

Quarters from bedded pack herds were more frequently S. chromogenes-positive than samples from tie-stall herds in Alberta and Maritimes, regardless of parity (OR = 1.97 for heifers and 3.32 for multiparous cows). Quarters from free-stall herds were 1.79 times more likely to be

S. chromogenes-positive than quarters from tie-stall herds for multiparous cows, irrespective of province. Also, Québec quarters were 2.03 times more often S. chromogenes-positive than quarters collected in Alberta herds. Finally, the odds of isolating S. chromogenes from a quarter increased with an increasing average herd-level milk production. The odds of isolating S. chromogenes were not associated with BMSCC category (Table 2-6).

Quarters from heifers were less frequently S. xylosus-positive than quarters from multiparous cows in tie-stalls (OR = 0.60). Comparison between free-stall and tie-stall depended on BMSCC category and on parity. For quarters from cows in herds with a BMSCC < 150,000 cells/mL or 150,000- 250,000 cells/mL, there was no difference between the 2 housing systems. 30

However, in herds with a high BMSCC, quarters from free-stalls had lower odds of being S. xylosus-positive, irrespective of parity (OR = 0.11). Regardless, the odds of isolating S. xylosus was lower in quarters from bedded-pack herds in contrast to tie-stall quarters for multiparous cows (OR = 0.15), and also for herds with low BMSCC (OR = 0.10). Quarters from high

BMSCC herds had lower odds of S. xylosus than quarters from intermediate BMSCC herds in free-stall herds (OR = 0.21).

Quarters from heifers were more often S. simulans-positive than quarters from multiparous cows (OR = 1.66), irrespective of stall type. In Maritimes herds, only 1 quarter from more than 1,950 quarters collected from 2 bedded-pack herds was S. simulans-positive.

Staphylococcus simulans was most frequently isolated from intermediate BMSCC herds, regardless of stall type or province.

Heifers were less frequently S. haemolyticus quarter-positive than multiparous cows (OR

= 0.76) irrespective of housing. In Ontario, quarters from tie-stall herds were more likely to be positive than quarters from free-stall herds no matter the BMSCC (OR = 3.06). In contrast, quarters from free-stall herds in Alberta were more frequently positive than quarters from tie- stall herds (OR = 9.03). Finally, in Alberta and the Maritimes, S. haemolyticus was more commonly isolated from bedded-pack herds than from tie-stall herds (OR = 4.85 and 7.69 for

Alberta and Maritimes, respectively).

2.5 Discussion

The data and large number of samples collected from 91 dairy herds provided a unique opportunity to study the distribution of SNA IMI across Canada. A total of 25 SNA species were isolated from quarter milk samples. The 5 most prevalent SNA species in this large cohort study were S. chromogenes, S. simulans, S. xylosus, S. haemolyticus, and S. epidermidis consistent with findings of a study using a subset of these isolates (Fry et al., 2014), and also worldwide

(Vanderhaeghen et al. (2014). The order of frequency, however, differed from studies done in other regions; this was attributed to numerous factors influencing pathogen distribution under different management conditions and housing (Taponen et al., 2006, Taponen et al., 2007,

Sampimon et al., 2009a, Thorberg et al., 2009, Piessens et al., 2011, Koop et al., 2012, Piepers et al., 2013).

The prevalence of SNA IMI as a group was 8.9% at quarter-level and 23.9% at cow-level

(adjusted for 150 DIM), which resemble most other studies conducted in North America or

Europe (Barkema et al., 1999, Pitkälä et al., 2004, Østerås et al., 2006, Gillespie et al., 2009,

Sampimon et al., 2009a, Piessens et al., 2011, Piessens et al., 2012). Schukken et al. (2009a) in the USA and Tenhagen et al. (2006) in Germany reported a SNA IMI prevalence of 9% at the quarter-level, whereas Gillespie et al. (2009) reported 11.3% at the quarter-level in 1995, similar to values reported by Pitkälä et al. (2004) in a dataset from 1995 in Finland. However, comparing SNA IMI prevalence in the present study with those of previous reports should be done with caution due to variations in definition of IMI. Østerås et al. (2006) reported a very low prevalence of 3.3% in Norwegian herds, probably the result of a high cut-off for IMI (>4,000 cfu/mL). Other European studies reported SNA IMI at 6 to 12% (Piessens et al., 2011, Piessens 32

et al., 2012) very similar to a report by Sampimon et al. (2009a) where IMI was defined as quarters with consecutive samplings presenting >500 cfu/mL to >1000 cfu/mL.

In this cohort study, SNA IMI was defined as quarters with ≥ 10 colonies isolated from bacterial culture of 10 μL of milk (i.e. ≥ 1,000 cfu/mL) from in a single milk sampling. This strategy had a sensitivity of only 24.2%, but was highly specific (100%) for CNS IMI based on

(Dohoo et al. (2011b). In this analysis we have also corrected the proportions for the presence of not characterized isolates, which also included SNA obtained in mixed culture, increasing the sensitivity (Se) of IMI detection from 24 to 32%. This demonstrated that the selected IMI definition resulted in an underestimation of the prevalence in our study. Nevertheless, our estimates have a higher specificity towards the prevalence observed for each factor studied, which decrease the amount of false-positive IMI that might not be associated with these variables. Moreover, although other studies point to SNA as a commensal opportunistic group of bacteria, the Se and Sp criteria established in Dohoo et al., 2011b do not address the possible variations for each SNA species behaviour. Therefore, it was decided to not include these Se and

Sp in our prevalence estimates.

The species-specific SNA IMI prevalence differed considerably among parities. The overall prevalence of SNA IMI was higher in heifers (10.6% at quarter-level), consistent with other studies (White et al., 1989, Fox, 2009, Mork et al., 2012). Although the 10 most common

SNA species in our study were all prevalent in heifers, S. chromogenes was more prevalent in heifers (quarter-level, 6.7%) than in older cows (3.3%). These results are also in agreement with previous reports, which observed higher SNA positive quarters in heifers, mainly caused by S. chromogenes and S. simulans (Piepers et al., 2011, De Visscher et al., 2016a). Moreover, to a lesser extent, S. simulans, and S. arlettae also had their peak prevalence in this parity, whereas S. 33

simulans, S. xylosus S. haemolyticus, S. epidermidis, and S. sciuri IMI increased in prevalence with increasing parity. Previous studies did not focus on prevalence of SNA species throughout parity, even though Thorberg et al. (2009) reported persistence of IMI by S. chromogenes, S. simulans, S. xylosus, S. epidermidis, and S. cohnii in multiparous cows. Given SNA’s ubiquitous presence in the environment and its capacity for transmission, increasing prevalence with age can be attributed to a combination of persistence and increased susceptibility for re-infection (Fox,

2009). It is not clear why some SNA species persisted longer than others, or had a higher incidence of re-infection.

The prevalence of IMI with all SNA species was high at calving, but decreased rapidly during the early postpartum period (Figures 5, 6A, 6B), which was also described by (Matthews et al., 1992, Aarestrup et al., 1995, Piepers et al., 2010). Species with peak prevalence at calving were also the most prevalent species recently reported by De Visscher et al. (2016a). Exceptions in that study were S. simulans and S. epidermidis, which could have been due to, not only fewer herds, but also the sampling methodology which included collection of samples 4 d after calving or species that are more frequently isolated later in lactation (e.g. S. epidermidis). However, in contrast to the study reported by Piepers et al. (2010), a steady increase of prevalence of SNA

IMI was noted throughout lactation. Additionally, during lactation, the prevalence of IMI of the most prevalent SNA species increased reaching a peak at the end of the lactation. According to

Taponen et al. (2007), 55% of S. chromogenes and 67% of S. simulans persisted throughout lactation. Furthermore, Mork et al. (2012) and Piessens et al. (2011) also reported persistence of

S. haemolyticus and S. epidermidis, mainly in multiparous cows, whereas Supré et al. (2011) reported an average of 150 d duration of SNA IMI by S. chromogenes, S. simulans and S. xylosus. These species may have the ability to re-infect or persist in the udder due to their 34

environmental reservoir and opportunistic behaviour, adaptation to animal microbiota, (Taponen et al., 2007), or capacity to produce biofilms, which increase cellular adhesion and survival as well as resistance to antimicrobial treatment (Tremblay et al., 2013).

Although our statistical analyses included DIM as an independent variable, for presentation purposes, the prevalence of SNA IMI was presented at 150 DIM. The distribution over lactation differed among species (Fig. 3, 4A, 4B); therefore, tables do not provide a valid comparison for other stages of lactation. In particular, distribution differed in the early postpartum period (Fig. 5, 6A, 6B).

The SNA as a group were equally prevalent in all quarters. Although some small differences in distribution were identified for particular SNA species, these differences could have been due to multiple sampling of the same quarter. The even distribution of S. chromogenes and most other species among quarters might have resulted from its colonization of udder skin as reported by De Vliegher et al. (2003), as well as due to differences in teat end shapes or teat hygiene (De Visscher et al., 2016a). Within-cow clustering was high for all SNA species, particularly when compared to herd-level clustering. The high within-cow clustering likely results from cow-related factors that determine whether a quarter acquires a SNA infection, but could also indicate that there was a high rate of within-cow quarter-to-quarter infection.

In agreement with other studies (Taponen et al., 2006, Taponen et al., 2008, Capurro et al., 2009, Sampimon et al., 2009a, Piessens et al., 2011, Supré et al., 2011), prevalence of overall

SNA IMI and quarter prevalence of the 5 most frequently isolated species were highest in herds with a higher BMSCC. The distribution of SNA species differed among the 3 BMSCC categories, with S. simulans, S. epidermidis, S. cohnii, S. arlettae, and S. gallinarum considerably less prevalent in low BMSCC herds (Table 5). Most SNA species cause only a moderately 35

increased SCC (Vanderhaeghen et al. (2014). Furthermore, contribution of SNA IMI to BMSCC decrease as BMSCC increases (Schukken et al. (2009a). It is therefore likely that the BMSCC, particularly in high BMSCC herds, was not increased due to a higher prevalence of SNA IMI, but rather that SNA IMI is relevant in herds with an increased BMSCC because of management practices that not only promote an elevation in SNA IMI, but also increase prevalence of major udder pathogens such as S. aureus and Streptococcus dysgalactiae.

The distribution of barn types in this study was similar to that in the regions represented

(Canadian Dairy Information Centre, 2016) and also to a previous Canadian study on clinical mastitis (Olde Riekerink et al., 2006). Farms in Alberta and the Maritimes mostly had free-stall and bedded-pack barns, whereas herds in Ontario and particularly Québec had a higher proportion of tie-stall barns, with variations in BMSCC among the 4 regions. We corrected for these differences in the multivariate analysis, although some differences among the 4 regions remained, likely due to differences in management practices among these regions. The distribution of SNA species was different in herds with different housing types (Tables 4 and 6).

Staphylococcus epidermidis was more frequently isolated from cows housed in free-stalls, similar to a report in non-lactating heifers (White et al., 1989). That same study reported, similar to the findings of the present study in lactating cows, that S. chromogenes and S. sciuri were more prevalent in heifers housed on bedded-pack barns than in free-stall herds. Staphylococcus simulans, S. haemolyticus, S. xylosus, and S. arlettae were most prevalent in tie-stall barns.

Staphylococcus xylosus and S. simulans IMI has been associated with sawdust bedding

(Thorberg et al., 2006, Pyörälä and Taponen, 2009), which can be used in tie-stalls and in free- stalls, but not in bedded straw-packs. The prevalence of these 2 SNA species was indeed lower in bedded-pack herds, whereas Staphylococcus chromogenes and S. sciuri IMI were more prevalent 36

in the same housing system. Regardless, further observational and controlled studies are needed to determine the role of bedding and other risk factors, e.g. hygiene and management, on the distribution and prevalence of SNA species IMI (De Visscher et al., 2016a).

Various factors evaluated resulted in different associations according to bacterial species.

Still, unmeasured factors associated with provinces, housing systems and/or BMSCC categories could explain part of the residual variance in our analysis, and our results only represented indirect associations. Although other factors may be responsible for the observed effects (i.e. the association in question does not exist once we introduce possible confounders, or the factor in hand is a confounder of a non-evaluated association), our study represented a first step in identifying specific risk factors for each SNA species.

The current study had some limitations. When evaluating results for samples from bedded-pack herds, any associations should be evaluated carefully due to the low number of herds under this housing system in the present study. Whenever an interaction was present involving housing system, the stratification of bedded pack herds into smaller categories may reflect an individual herd factor as deemed responsible for the result (or absence of significant result). Further studies are needed to evaluate prevalence of various SNA species in bedded-pack herds.

The multivariable analysis was vulnerable to non-differential misclassification bias.

Samples that were SNA-positive, but not identified, were considered SNA-negative in the species-specific models. This could introduce a bias in the estimates towards the null value (i.e.

OR = 1). However, there is no reason to believe that there was an association of non- identification of SNA and any exposure status.

2.6 Conclusions

Based on these data, treating SNA as a single group would lead to erroneous conclusions for some species. This large and consistent dataset facilitated determination of the disparate association of species-specific SNA IMI to predictors at herd and cow levels, when compared to

SNA as an overall pathogen.

In recent years, molecular identification has improved knowledge regarding SNA IMI epidemiology and facilitated correct identification of species in this study. Consistent with other studies, S. chromogenes was the most prevalent species; however, S. simulans, S. xylosus, S. epidermidis, and S. haemolyticus IMI differed in order of prevalence from other countries.

Prevalence of most SNA IMI was high at calving, particularly in heifers. Prevalence thereafter decreased for a short interval, but increased again during lactation.

From a herd perspective, management related to different barn types and BMSCC affected SNA species distribution and influenced distribution among provinces. Staphylococcus chromogenes and S. epidermidis IMI were most prevalent in Alberta and the Maritimes, respectively, and in bedded-pack herds and free-stall herds. Staphylococcus simulans and S. xylosus were more prevalent in tie-stalls, mostly in Ontario and Québec. Furthermore, SNA IMI was associated with high BMSCC herds, mainly due to S. chromogenes, S. haemolyticus, and S. epidermidis. Reporting the prevalence of SNA species in different levels of production helped identify important risk factors which can influence strategic management decisions that can ultimately be used to improve udder health and milk quality in Canadian herds.

Herds (n=91) Cows (n=5,149) Quarters (n=20,305) Species N %1 N %2 ICC N %3 ICC N %4 ICC 2,660 48.95 91 100.0 0.04 (0.02 - 0.06) 980 19.03 0.34 (0.30 - 0.38) 1,301 6.41 0.81 (0.79 - 0.83) S. chromogenes 913 16.80 81 89.0 0.09 (0.06 - 0.14) 347 6.74 0.46 (0.39 - 0.53) 433 2.13 0.87 (0.84 - 0.89) S. simulans 628 11.56 71 78.0 0.13 (0.09 - 0.20) 353 6.86 0.37 (0.30 - 0.45) 422 2.08 0.75 (0.71 - 0.78) S. xylosus 428 7.88 78 85.7 0.06 (0.03 - 0.10) 277 5.38 0.29 (0.21 - 0.38) 304 1.50 0.75 (0.71 - 0.79) S. haemolyticus 225 4.14 50 54.9 0.14 (0.08 - 0.24) 130 2.52 0.48 (0.37 - 0.58) 151 0.74 0.79 (0.74 - 0.83) S. epidermidis 139 2.56 33 36.3 0.33 (0.20 - 0.49) 102 1.98 0.50 (0.35 - 0.65) 112 0.55 0.79 (0.72 - 0.84) S. cohnii 121 2.23 44 48.4 0.18 (0.10 - 030) 87 1.69 0.53 (0.41 - 0.65) 102 0.50 0.69 (0.61 - 0.76) S. sciuri 50 0.92 23 25.3 0.11 (0.03 - 0.31) 35 0.68 0.37 (0.17 - 0.64) 37 0.18 0.76 (0.65 - 0.84) S. gallinarum 45 0.83 18 19.8 0.17 (0.06 - 0.38) 28 0.54 0.47 (0.20 - 0.76) 30 0.15 0.87 (0.78 - 0.92) S. capitis 44 0.81 20 22.0 0.24 (0.11 - 0.47) 36 0.70 0.48 (0.26 - 0.71) 39 0.19 0.73 (0.61 - 0.82) S. arlettae 31 0.57 14 15.4 0.24 (0.10 - 0.49) 21 0.41 0.67 (0.43 - 0.84) 25 0.12 0.81 (0.68 - 0.89) S. warneri 30 0.55 20 22.0 0.03 (0.00 - 0.85) 28 0.54 0.16 (0.00 - 0.99) 30 0.15 - S. saprophyticus 24 0.44 14 15.4 - 16 0.31 - 17 0.08 - S. agnetis 24 0.44 15 16.5 0.11 (0.00 - 0.78) 24 0.47 - 24 0.12 - S. equorum 17 0.31 13 14.3 - 15 0.29 - 17 0.08 - S. succinus 12 0.22 9 9.9 - 11 0.21 - 11 0.05 - S. hominis 9 0.17 8 8.8 - 9 0.17 - 9 0.04 - S. devriesei 8 0.15 6 6.6 - 8 0.16 - 8 0.04 - S. pasteuri 7 0.12 2 2.2 - 3 0.06 - 4 0.02 - S. nepalensis S. vitulinus 6 0.11 6 6.6 - 6 0.12 - 6 0.03 - 39

4 0.07 4 4.4 - 4 0.08 - 4 0.02 - S. auricularis 4 0.07 4 4.4 - 4 0.08 - 4 0.02 - S. hyicus 2 0.04 2 2.2 - 2 0.04 - 2 0.01 - S. caprae 2 0.04 1 1.1 - 2 0.04 - 2 0.01 - S. fleuretti 1 0.02 1 1.1 - 1 0.02 - 1 0.00 - S. kloosii Characterized5 5,434 Not 3,665 characterized6 Total SNA 9,099 91 100 0.05 (0.04 - 0.08) 2,599 50.48 0.28 (0.25 - 0.30) 4,397 21.65 0.71 (0.70 - 0.72) 1Percentage of SNA species from the 5,434 characterized SNA isolates at species-level; 2Percentage of herds with at least 1 quarter positive for a species; 3Percentage of cows with at least 1 quarter positive for a species o; 4Percentage of quarters positive for a species; 5SNA isolated characterized at the species level; 6SNA isolates obtained from IMI, but not stored and not characterized at species-level.

Table 2-2. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus species intramammary infection, according to quarter location.

Left front Right front Left rear Right rear Species N Prevalence N Prevalence N Prevalence N Prevalence 1 720 4.68 702 4.50 632 4.37 606 4.47 S. chromogenes 1 262 1.44 223 1.50 225 1.52 203 1.23 S. simulans S. xylosus1 153 0.85 145 0.85 146 0.90 184 1.14

1 117 0.68 115 0.72 103 0.65 93 0.59 S. haemolyticus 1 54 0.34 49 0.31 56 0.41 66 0.48 S. epidermidis 1 19 0.137 39 0.27 42 0.31 39 0.26 S. cohnii S. sciuri1 15 0.086,8,9 34 0.19 32 0.1810 40 0.39

1 13 0.05 11 0.05 18 0.08 8 0.03 S. gallinarum 1 15 0.07 14 0.127 6 0.03 10 0.07 S. capitis 1 6 0.047,9 7 0.07 18 0.18 13 0.14 S. arlettae S. warneri1 7 0.08 10 0.14 7 0.07 7 0.04

S. saprophyticus2 11 0.07 5 0.03 6 0.04 8 0.06

2 5 0.03 6 0.04 6 0.04 7 0.05 S. agnetis 2 2 0.0110 7 0.05 7 0.05 8 0.06 S. equorum 2 3 0.02 4 0.03 6 0.04 4 0.03 S. succinus 2 4 0.03 2 0.01 2 0.01 4 0.03 S. hominis 2 3 0.02 2 0.01 1 0.01 3 0.02 S. devriesei 2 4 0.03 2 0.01 0 0 2 0.01 S. pasteuri 2 1 0.01 2 0.01 4 0.03 0 0 S. nepalensis 2 0 0 1 0.01 3 0.02 2 0.01 S. vitulinus 2 0 0 2 0.01 2 0.01 0 0 S. auricularis S. hyicus2 1 0.01 0 0 1 0.01 2 0.01

2 0 0 1 0.01 1 0.01 0 0 S. caprae 2 1 0.01 0 0 0 0 1 0.01 S. fleuretti 2 0 0 1 0.01 0 0 0 0 S. kloosii Characterized3 1,416 1,384 1,324 1,310 Not characterized4 872 958 949 886

Total SNA1 2,288 8.54 2,342 8.77 2,273 8.88 2,196 9.05 1Prevalence estimated for 150 DIM; 2Prevalence not adjusted for DIM due to low number of species; 3SNA isolated characterized at

the species level; 4SNA isolates obtained from IMI, but not stored and not characterized at species-level; 5Different (P < 0.05) from

right front quarters; 6Tendency for difference (0.05 ≤ P < 0.10) from right front quarters; 7Different (P < 0.05) from left rear quarter;

8Tendency for difference (0.05 ≤ P < 0.10) from left rear quarter; 9Different (P < 0.05) from right rear quarter; 10Tendency for

difference (0.05 ≤ P < 0.10) from right rear quarter.

Table 2-3. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection within region in 91

Canadian dairy herds.

Alberta (17 herds) Maritimes (18 herds) Ontario (27 herds) Québec (29 herds) Species N Quarter1 Cow2 N Quarter1 Cow2 N Quarter1 Cow2 N Quarter1 Cow2 3 545 5.6911 15.8411 607 3.90 11.61 885 5.2711 14.6811 623 3.60 9.72 S. chromogenes 3 64 0.789 2.649 165 0.929 2.709 408 2.5111 6.8911 276 1.10 3.35 S. simulans 3 65 1.037 3.067 67 0.329,11 1.119,11 287 1.40 4.7612 209 0.99 3.16 S. xylosus 3 61 0.69 2.20 91 0.50 1.75 135 0.71 2.37 141 0.67 2.17 S. haemolyticus 3 19 0.34 1.21 47 0.31 0.97 74 0.39 1.37 85 0.46 1.48 S. epidermidis 4 4 0.059,12 0.129,11 13 0.089 0.289 94 0.5611 1.6411 28 0.15 0.49 S. cohnii 3 30 0.369,11 1.009,11 55 0.439,11 1.179,11 20 0.08 0.27 16 0.07 0.21 S. sciuri 4 3 0.0312 0.1212 10 0.06 0.22 15 0.09 0.28 22 0.12 0.39 S. gallinarum 4 0 09,12 0.009,12 10 0.06 0.21 19 0.11 0.36 16 0.09 0.29 S. capitis 4 4 0.059 0.168,10 1 0.019 0.029 36 0.2111 0.6111 3 0.02 0.06 S. arlettae 4 2 0.02 0.08 10 0.06 0.20 8 0.05 0.15 11 0.06 0.19 S. warneri 4 0 08 0 11 0.07 0.20 9 0.05 0.16 10 0.05 0.16 S. saprophyticus 4 2 0.02 0.06 3 0.0210 0.06 10 0.06 0.16 9 0.05 0.16 S. agnetis 4 15 0.177,9,11 0.487,9,11 1 0.01 0.02 5 0.03 0.10 3 0.02 0.06 S. equorum 4 3 0.03 0.1211 6 0.0412 0.1311 7 0.0412 0.0912 1 0.01 0.01 S. succinus 4 0 0 0 2 0.01 0.04 2 0.01 0.03 8 0.04 0.13 S. hominis 4 2 0.02 0.06 3 0.02 0.07 4 0.02 0.07 0 0 0 S. devriesei 4 1 0.01 0.03 1 0.01 0.02 0 012 0 6 0.03 0.11 S. pasteuri 4 0 0 0 0 010 0 7 0.0412 0.1212 0 0 0 S. nepalensis 43

4 2 0.02 0.08 3 0.02 0.06 1 0.01 0.02 0 0 0 S. vitulinus 4 1 0.01 0.04 2 0.01 0.04 1 0.01 0.02 0 0 0 S. auricularis 4 0 0 0 1 0.01 0.02 1 0.01 0.02 2 0.01 0.04 S. hyicus 4 0 0 0 1 0.01 0.02 1 0.01 0.02 0 0 0 S. caprae 4 2 0.02 0.04 0 0 0 0 0 0 0 0 0 S. fleuretti 4 0 0 0 0 0 0 1 0.01 0.02 0 0 0 S. kloosii 5 Characterized 825 1,110 2,030 1,469 6 Not characterized 857 342 1,403 1,063 3 Total SNA 1,682 9.04 7,9 24.46 8,9 1,452 6.72 9 19.25 9 3,433 11.36 7,11 29.96 7,11 2,532 7.56 20.78 1Prevalence per 100 quarters; 2Prevalence per 100 cows; 3Prevalence not adjusted for DIM due to low number of species ; 4Prevalence for this species was, because of the low number, not adjusted for DIM; 5SNA isolated characterized at the species level; 6SNA isolates obtained from IMI, but not stored and not characterized at species-level; 7Different (P < 0.05) from Maritimes (Prince Edward Island,

New Brunswick, and Nova Scotia); 8Tendency for difference (0.05 ≤ P < 0.10) from Maritimes; 9Different (P < 0.05) from Ontario;

10Tendency for difference (0.05 ≤ P < 0.10) from Ontario; 11Different (P < 0.05) from Québec; 12Tendency for difference (0.05 ≤ P <

0.10) from Québec.

Table 2-4. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection within 3 barn types.

Tiestall (54 herds) Freestall (30 herds) Bedded-pack (5 herds) Species N Quarter1 Cow2 N Quarter1 Cow2 N Quarter1 Cow2

S. chromogenes3 1,462 4.209 11.759 930 4.539 12.549 238 8.76 25.00 S. simulans3 694 1.757 5.047,10 187 0.89 2.63 19 0.51 1.08 S. xylosus3 515 1.227 4.147 89 0.43 1.44 11 0.29 2.93 S. haemolyticus3 269 0.70 2.30 133 0.61 2.02 25 0.80 3.34 S. epidermidis4 117 0.339 1.029 100 0.529 1.429 2 0.06 0.23 S. cohnii4 118 0.338,9 0.988,10 19 0.10 0.32 0 0 0 S. sciuri3 47 0.119 0.319 44 0.219 0.679 27 1.18 3.41 S. gallinarum4 34 0.10 0.31 12 0.06 0.21 3 0.09 0.35 S. capitis4 36 0.107 0.337 5 0.03 0.09 0 0 0 S. arlettae4 40 0.117 0.327 1 0.01 0.02 1 0.03 0.12 S. warneri4 25 0.07 0.22 5 0.03 0.09 0 0 0 S. saprophyticus4 28 0.087 0.237 2 0.01 0.04 0 0 0 S. agnetis4 19 0.05 0.16 4 0.02 0.06 1 0.03 0.08 S. equorum4 8 0.027 0.087 16 0.08 0.24 0 0 0 S. succinus4 8 0.02 0.06 8 0.04 0.12 1 0.03 0.12 S. hominis4 9 0.03 0.08 2 0.01 0.04 0 0 0 S. devriesei4 8 0.02 0.07 1 0.01 0.02 0 0 0 S. pasteuri4 6 0.02 0.05 2 0.01 0.03 0 0 0 S. nepalensis4 7 0.02 0.06 0 0 0 0 0 0 S. vitulinus4 2 0.01 0.02 4 0.02 0.07 0 0 0 S. auricularis4 3 0.01 0.03 1 0.01 0.02 0 0 0 S. hyicus4 2 0.01 0.02 2 0.01 0.04 0 0 0 S. caprae4 2 0.01 0.02 0 0 0 0 0 0 S. fleuretti4 0 0 0 2 0.01 0.02 0 0 0 S. kloosii4 1 0 0.01 0 0 0 0 0 0 Characterized5 3,460 1,569 328 Not characterized6 2,339 1,098 187 Total SNA3 5,799 9.26 7 25.08 7 2,667 7.57 9 20.31 9 515 11.32 31.83 1Prevalence per 100 quarters; 2Prevalence per 100 cows; 3Prevalence at 150 DIM; 3Prevalence

not adjusted for DIM due to low number of species; 5SNA isolated characterized at the species

level; 6SNA isolates obtained from IMI, but not stored and not characterized at species-level;

7Different (P < 0.05) from free-stalls; 8Tendency for difference (0.05 ≤ P < 0.10) from free- stalls; 9Different (P < 0.05) from bedded-pack; 10Tendency for difference (0.05 ≤ P < 0.10) from bedded-pack.

Table 2-5. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection according to bulk milk somatic cell count category.

Bulk milk somatic cell count (cells/mL) Species ≤ 150,000 (13 herds) 151,000 - 250,000 (47 herds) > 250,000 (31 herds) N Quarter1 Cow2 N Quarter1 Cow2 N Quarter1 Cow2 S. chromogenes3 351 3.77 9.7710 1,392 4.35 12.56 917 5.11 14.15 S. simulans3 71 0.717,9 2.248,9 570 1.59 4.27 272 1.43 4.51 S. xylosus3 71 0.89 3.03 359 0.91 3.12 198 0.99 2.86 S. haemolyticus3 41 0.45 1.4210 219 0.63 2.15 168 0.83 2.74 S. epidermidis3 7 0.149 0.297,9 124 0.35 1.16 94 0.58 1.44 S. cohnii3 6 0.077,9 0.257,10 88 0.31 1.01 45 0.23 0.71 S. sciuri3 18 0.22 0.68 72 0.24 0.62 31 0.14 0.43 S. gallinarum3 7 07,9 07,9 27 0.05 0.17 16 0.07 0.23 S. capitis4 4 0.05 0.16 28 0.09 0.29 13 0.07 0.23 S. arlettae4 1 0.018,9 0.048,10 26 0.08 0.24 17 0.09 0.27 S. warneri3 7 0.07 0.28 8 0.0310 0.09 16 0.13 0.27 S. saprophyticus4 4 0.05 0.14 18 0.06 0.16 8 0.04 0.14 S. agnetis4 1 0.01 0.038 16 0.05 0.15 7 0.04 0.11 S. equorum4 5 0.06 0.16 14 0.04 0.13 5 0.03 0.09 S. succinus4 4 0.05 0.16 7 0.02 0.06 6 0.03 0.08 S. hominis4 1 0.01 0.04 6 0.02 0.07 5 0.03 0.07 S. devriesei4 5 0.067,9 0.197,9 2 0.01 0.02 2 0.01 0.03 S. pasteuri4 1 0.01 0.03 4 0.01 0.04 3 0.02 0.05 S. nepalensis4 0 0 0 7 0.02 0.06 0 0 0 S. vitulinus4 0 0 0 4 0.01 0.04 2 0.01 0.04 S. auricularis4 2 0.028 0.088 1 0.003 0.01 1 0.01 0.02

S. hyicus4 1 0.01 0.04 1 0.003 0.01 2 0.01 0.04 S. caprae4 0 0 0 2 0.01 0.02 0 0 0 S. fleuretti4 2 0.028 0.058 0 0 0 0 0 0 S. kloosii4 0 0 0 1 0.003 0.01 0 0 0 Characterized5 610 2,996 1,828 Not characterized6 399 1,969 1,297 Total SNA3 1,009 6.45 8,9 17.21 7,9 4,965 8.81 24.33 3,125 9.78 26.01 1Quarter-level prevalence per 100 quarters; 2Cow-level prevalence per 100 cows; 3Prevalence not adjusted for DIM due to low number

of species; 4Prevalence for this species was, because of the low number, not adjusted for DIM; 5SNA isolated characterized at the

species level; 6SNA isolates obtained from IMI, but not stored and not characterized at species-level; 7Different (P < 0.05) from

150,000 to 250,000 cells/mL; 8Tendency for difference (0.05 ≤ P < 0.10) from 150,000 to 250,000 cells/mL; 9Different (P < 0.05)

from > 250,000 cells/mL; 10Tendency for difference (0.05 ≤ P < 0.10) from > 250,000 cells/mL.

Table 2-6. Final multilevel model for the quarter-level prevalence of the 4 most prevalent Staphylococcus non-aureus intramammary infections in 91 Canadian dairy herds.

Overall SNA7 S. chromogenes S. xylosus S. simulans S. haemolyticus

β1 P-value β1 P-value β1 P-value β1 P-value β1 P-value

Intercept -2.36 <0.001 -4.93 <0.001 -4.75 <0.001 -5.01 <0.001 -7.88 <0.001

Region

Alberta ref - ref - ref - ref - ref -

Maritimes -0.16 0.37 0.81 0.00 -0.39 0.42 0.50 0.29 1.79 0.002

Ontario 0.28 0.05 0.90 0.01 0.48 0.28 0.85 0.02 2.64 <0.001

Québec -0.13 0.41 0.71 0.05 -0.13 0.79 0.36 0.35 2.47 <0.001 Housing

Free-stall -0.16 0.26 0.58 0.02 -0.84 0.06 -0.73 0.22 2.20 0.001

Bedded pack 0.14 0.66 1.2 <0.001 -1.88 0.05 0.17 0.77 1.58 0.05

Tie-stall ref - ref - ref - ref - ref -

Parity

Heifer 0.24 <0.001 1 <0.001 -0.51 0.01 0.51 <0.001 -0.28 0.04

Multiparous ref - ref - ref - ref - ref -

Bulk milk SCC2

Low -0.37 0.03 0.17 0.55 -0.14 0.64 -0.85 0.01 -0.26 0.45

Intermediate ref - ref - ref - ref - ref -

High 0.07 0.55 0.11 0.48 -0.20 0.40 -0.52 0.02 0.32 0.14

DIM 0.001 <0.001 0.001 0.01 0.002 <0.001 -0.002 <0.001 0.004 <0.001

4.64 e- -3.65 e- DIM*DIM 1.98 e-06 0.05 -1.98 e-06 0.26 3.93 e-06 0.11 0.14 0.19 06 06 Herd Size <0.001 0.68 -0.003 0.16 <0.001 0.81 -0.002 0.64 0.001 0.63

Avg. Milk Yield (kg)3 0.008 0.35 0.037 0.01 0.01 0.70 -0.014 0.53 0.02 0.49

Region*Housing4

Ontario*Free-stall NS - NS - NS - 0.68 0.30 -3.31 <0.001

Québec *Free-stall NS - NS - NS - 0.09 0.11 -2.07 0.001

Maritimes*Free-stall NS - NS - NS - 0.21 0.30 -1.41 0.06

Maritimes*Bedded Pack NS - NS - NS - -4.01 <0.001 -0.16 0.87

Housing*Parity5

Free-stall*Heifer NS - -0.17 0.44 -0.05 0.89 NS - NS -

Bedded Pack*Heifer NS - -0.52 0.01 1.66 0.04 NS - NS -

Housing*BMSCC6

Free-stall*Low 0.55 0.04 NS - -0.35 0.74 NS - NS -

Free-stall*High -0.29 0.19 NS - -1.34 0.01 NS - NS -

Bedded Pack*Low -0.23 0.49 NS - -0.47 0.63 NS - NS -

Bedded Pack*High 0.15 0.70 NS - 0.82 0.46 NS - NS - Any estimate of the odds ratio could be calculated by calculating the exponential of the coefficients associated with the respective factor, which may include interaction terms with that variable. Interpretation of different estimates is species-specific and depends on which variables were retained in the final models and consequently, reference level. 1Generalized estimating equation with the estimate of the coefficient associated with the respective factor; 22-year average BMSCC (cells/mL): low: ≤ 150,000, intermediate: between 150,000 and 250,000 cells/mL, high ≥ 250,000; 3Average milk production at herd level; and 4Interaction between region and housing type; baseline was samples from tie-stall herds in Alberta (heifers for S. xylosus). 5Interaction between parity and housing; baseline were samples from heifers in tie-stall herds. 6Interaction between housing and BMSCC category; baseline were samples from tie-stall herds with a BMSCC between 150,000 and 250,000 cells/mL. 7Staphylococcus non-aureus grouped as a single category.

abcd

8 abcd

quarter-prevalence Total SNA 6 quarter-prevalence identified SNA quarter-prevalence not identified SNA

4 Prevalence per 100 quarters 100 per Prevalence

0 1 2 3 4 ≥5 Parity

Figure 2-1. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection across parities.

1Prevalence, per 100 quarters, was estimated for 150 DIM; aDifferent (P < 0.05) from second parity; bDifferent (P < 0.05) from third parity; cDifferent (P < 0.05) from fourth parity; dDifferent (P < 0.05) from parity ≥ 5.

51 6.75 abcd 6.70

6.65

6.60

6.55

6.50

6.0 S. chromogenes 5.5 S. simulans 5.0 S. xylosus 4.5 S. haemolyticus 4.0 S. epidermidis 3.5 3.0

Prevalence perquarters 100 2.5 2.0 d 1.5 d c c 1.0 ac 0.5 ac 0.0 1 2 3 4 ≥5 Parity Figure 2-2A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection across parities for the five most frequently isolated species.

52 0.75 0.70 0.65 0.60 0.55 0.50 0.45 0.40 S. cohnii S. sciuri 0.35 S. gallinarum 0.30 S. capitis 0.25

Prevalence perquarters 100 S. arlettae 0.20 c d c 0.15 bd 0.10

0.05 ac 0.00 1 2 3 4 ≥5 Parity

Figure 2-2B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection across parities for the 6th to 10th most frequently isolated species.

1Prevalence, per 100 quarters, was estimated for 150 DIM; aDifferent (P < 0.05) from second parity; bDifferent (P < 0.05) from

third parity; cDifferent (P < 0.05) from fourth parity; dDifferent (P < 0.05) from parity ≥ 5.

53 14

8 quarter-prevalence Total SNA 6 quarter-prevalence identified SNA quarter-prevalence not identified SNA

4 Prevalence per 100 quarters 100 Prevalenceper

0 1 2 3 4 5 6 7 8 9 10 11 ≥12 Month of Lactation

Figure 2-3. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection over the lactation.

54 7.0

6.5

6.0

5.5

5.0

4.5 S. chromogenes 4.0 S. simulans 3.5 S. xylosus 3.0

2.5 S. haemolyticus

2.0 S. epidermidis Prevalence per 100 quarters 100 Prevalenceper 1.5

1.0

0.5

0.0 1 2 3 4 5 6 7 8 9 10 11 ≥12

Month of Lactation Figure 2-4A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection over the lactation for the 5 most frequently isolated species.

55 0.50

0.45

0.40

0.35

0.30 S. cohnii S. sciuri 0.25 S. gallinarum S. capitis 0.20 S. arlettae

Prevalence100quarters per 0.15

0.10

0.05

0.00 1 2 3 4 5 6 7 8 9 10 11 ≥12 Month of Lactation Figure 2-4B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection over the lactation for the 6th to 10th most frequently isolated species.

56 14

12 abcdef

bcd 8 quarter-prevalence Total SNA bcd 6 quarter-prevalence identified SNA

abcdef bcd quarter-prevalence not identified SNA f Prevalence per 100 quarters 100 Prevalenceper 4 f f

0 Calving 01 to 04 05 to 10 11 to 15 16 to 20 21 to 25 26 to 30 Days after calving Figure 2-5. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection in the first 30 DIM.

aDifferent (P < 0.05) from 1 to 4 days after calving; bDifferent (P < 0.05) from 5 to 10 days after calving; cDifferent (P < 0.05) from

11 to 15 days after calving; dDifferent (P < 0.05) from 16 to 20 days after calving; eDifferent (P < 0.05) from 21 to 25 days after

calving; fDifferent (P < 0.05) from 26 to 30 days after calving

57 5.0 bc

4.5 4.0

3.5 3.0 S. chromogenes S. simulans 2.5 S. xylosus 2.0 S. haemolyticus d Prevalence perquarters 100 1.5 bcdef e S. epidermidis 1.0 0.5 e e 0.0 Calving 01 to 04 05 to 10 11 to 15 16 to 20 21 to 25 26 to 30

Days after calving

Figure 2-6A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection in the first 30 DIM for the 5most frequently isolated species.

aDifferent (P < 0.05) from 1 to 4 days after calving; bDifferent (P < 0.05) from 5 to 10 days after calving; cDifferent (P < 0.05) from

11 to 15 days after calving; dDifferent (P < 0.05) from 16 to 20 days after calving; eDifferent (P < 0.05) from 21 to 25 days after

calving; fDifferent (P < 0.05) from 26 to 30 days after calving

58 0.60

0.55 c

0.50

0.45 0.40

0.35 S. cohnii b S. sciuri 0.30 S. gallinarum 0.25 S. capitis

0.20 S. arlettae Prevalence perquaters 100

0.15

0.10 0.05

0.00 Calving 01 to 04 05 to 10 11 to 15 16 to 20 21 to 25 26 to 30

Days after calving Figure 2-6B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus intramammary infection in the first 30 DIM for the 6th to 10th most frequently isolated species.

aDifferent (P < 0.05) from 1 to 4 days after calving; bDifferent (P < 0.05) from 5 to 10 days after calving; cDifferent (P < 0.05) from

11 to 15 days after calving; dDifferent (P < 0.05) from 16 to 20 days after calving; eDifferent (P < 0.05) from 21 to 25 days after

calving; fDifferent (P < 0.05) from 26 to 30 days after calving.

59 Chapter Three: Distribution of SNA species in quarters with low and high somatic cell counts, and clinical mastitis

3.1 Abstract

The role of Staphylococcus non-aureus (SNA) in the bovine udder is controversial.

Overall, SNA intramammary infections (IMI) increase somatic cell count (SCC) with an impact categorized as mild and mostly causing subclinical or mild to moderate signs of clinical mastitis.

However, based on some recent studies, SNA IMI can affect the udder more severely. Some of these apparent discrepancies could be attributed to the large number of species that compose the

SNA group. The objectives of this study were to determine: 1) the SCC of quarters infected by individual SNA species compared to SNA as a group, culture-negative, and major pathogen- infected quarters; 2) the distribution of SNA species in quarters with low SCC (< 200,000 cells/mL), high SCC (≥ 200,000 cells/mL), and clinical mastitis; and 3) the prevalence of SNA species across quarters with low and high SCC. A total of 5,507 SNA isolates, 3,561 with low

SCC, 1,873 with high SCC, and 73 from clinical mastitis cases, were obtained from the National

Cohort of Dairy Farms of the Canadian Bovine Mastitis Research Network. The SNA were isolated from 7.6, 18.5, and 4.3% of quarters with a low SCC, high SCC, and clinical mastitis, respectively. Distribution of isolates was based on relative frequency in these quarters, whereas

SCC distribution was estimated using mixed effect linear regression, and prevalence was consistently estimated using Bayesian analyses. The mean SCC of SNA-positive quarters was

70,000 cells/mL, which was higher than culture-negative quarters and lower than major pathogen-positive quarters (129,000-183,000 cells/mL). Somatic cell counts were higher in quarters positive for S. capitis, S. gallinarum, S. hyicus, S. agnetis, or S. simulans. Considering

60 the distribution of SNA species within group, Staphylococcus xylosus (12.6%), S. cohnii (3.1%), and S. equorum (0.6%) were more frequently isolated from quarters with low SCC, whereas S. sciuri (14%) was most frequently isolated from clinical mastitis cases. Finally, Staphylococcus chromogenes, S. simulans, S. epidermidis and S. haemolyticus were evenly distributed among these three categories. Staphylococcus chromogenes, S. simulans, S. xylosus, S. haemolyticus, S. epidermidis, S. agnetis, S. arlettae, S. capitis, S. gallinarum, S. sciuri and S. warneri were more prevalent in high SCC quarters. Because the SNA are a large, heterogeneous group, considering them as one group rather than at the species, or possibly even subspecies level, has undoubtedly contributed to apparent discrepancies among studies as to their distribution and importance in

IMI and mastitis.

Key-words: mastitis, Staphylococcus non-aureus, somatic cell count, Staphylococcus aureus, coagulase-negative staphylococci

3.2 Introduction

Staphylococci non-aureus (SNA) are the most frequently isolated bacterial group from bovine intramammary infections (IMI) (e.g. Piepers et al., 2009). Several reports attribute the increasing prevalence of SNA to implementation of the five-point contagious mastitis control program, developed in the mid last century in the United Kingdom (Neave et al., 1969). The wide adoption of the management practices included in this control program resulted in a decrease in prevalence of the contagious pathogens Staphylococcus aureus and particularly

Streptococcus agalactiae, and consequently decreased bulk milk SCC (BMSCC) (e.g. Barkema et al., 2009). As a group, SNA are considered minor pathogens, mostly associated with mild 61 inflammation of the mammary gland (Schukken et al., 2009a). However, SNA IMI has been reported to protect the udder against major pathogen mastitis, and to increase milk production of affected heifers (Piepers et al., 2010). In contrast, there are reports that SNA IMI negatively affects high producing heifers, can persist throughout lactation in multiparous cows, and significantly increase BMSCC in low BMSCC herds (De Vliegher et al., 2009, Schukken et al.,

2009a).

At this point, there is good evidence that the various species comprising this group do not behave homogeneously and therefore can contribute to diverse epidemiological findings

(Vanderhaeghen et al., 2014). The most common species isolated worldwide from bovine milk samples are Staphylococcus chromogenes, Staphylococcus simulans, Staphylococcus xylosus,

Staphylococcus epidermidis, and Staphylococcus haemolyticus, with slight variations in distribution depending on the country (Taponen et al., 2007, Sampimon et al., 2009a, Supré et al., 2011, Fry et al., 2014). Some species that are more phylogenetically related (Calcutt et al.,

2014, Naushad et al., 2016) can operate in a similar way, for example S. chromogenes and

Staphylococcus agnetis cause mostly IMI in heifers, likely due to adaptations in udder microbiota (De Visscher et al., 2016a). Other species such as S. epidermidis and S. haemolyticus seem to have greater ability to adapt to the udder and the environment, which can lead to re- infection or chronicity of IMI throughout lactation, mostly in multiparous cows (Piessens et al.,

2012).

Not only does SNA distribution vary, but also their effects on udder health. Previous publications concluded that SNA as a group increase SCC when compared to culture-negative quarters, mainly in heifers (Taponen et al., 2007, Schukken et al., 2009a). A recent meta-analysis by Vanderhaeghen et al. (2014) concluded that the most common SNA species in dairy

62 production worldwide are capable of increasing SCC, although species diverge in pathogenic potential regarding udder health. In the first study of SNA species isolated from bovine milk in

Canadian herds, SNA species such as S. chromogenes, S. epidermidis, and S. simulans increased

SCC substantially (Fry et al., 2014).

It is important to determine the distribution of SNA species according to IMI status, and their respective SCC distribution curve in order to verify their current status as minor pathogens.

Consequently, the objectives of this study were to determine: 1) SCC of quarters infected by

SNA species compared to SNA as a group, culture-negative, and major pathogen-infected quarters; 2) distribution of SNA species in quarters with low SCC (< 200,000 cells/mL), high

SCC (SCC ≥ 200,000 cells/mL), and clinical mastitis; and 3) prevalence of SNA species across quarters with a low and high SCC.

3.3 Materials and methods

3.3.1 Herds and cows

Data and samples were collected from the National Cohort of Dairy Farms study conducted by the Canadian Bovine Mastitis Research Network (CBMRN) in 2007 and 2008. The study included 91 farms across 4 regions of Canada: the Maritime Provinces (Prince Edward

Island, Nova Scotia, and New Brunswick), Quebec, Ontario, and the Western provinces, represented by Alberta. The selection criteria are fully described by Reyher et al. (2011). Briefly, herds were selected to represent the provincial proportion of freestall systems to within 15% and to evenly distribute among 3 strata of the last 12 mo BMSCC average, classified as high

(≥300,000 cells/mL), intermediate (<300,000 and >150,000 cells/mL), and low (<150,000 cells/mL). Herds enrolled also had to be comprised of at least 80% lactating Holstein-Friesian

63 cows milked twice a day and herds had to participate in a DHI program. A total of 91 herds were included, 17 in Alberta, 27 in Ontario, 29 in Québec, and 18 in the Maritime Provinces.

3.3.2 Sampling

Detailed sampling is described by Reyher et al. (2011). In short, 3 schematic samplings were done during the 2 y of field collection, divided in 4 periods (March-May 2007, June-August

2007, January-March 2008, and June-August 2008). The first set was composed of 10 random lactating cows and the 5 most recently calved cows with 3 sample collections during the winter and 7 collections during the summer. The second set was composed of 15 cows per herd sampled every 2 wk before dry-off, at calving, and 2 wk after calving. The third set consisted of samples collected by farmers, following a standard protocol, from cows in each herd with clinical mastitis.

3.3.3 Laboratory analyses

Somatic cell count was determined for the non-clinical mastitis samples using a

Fossomatic cell counter (Fossomatic 4000 series, Foss Electric, Hillerød, Denmark). As described by Fry et al. (2014), milk samples were preserved with the addition of 2-Bromo-2- nitropropane- 1,3-diol tablet (Broad Spectrum Microtabs II, D&F Control Systems Inc., Dublin

CA), and shipped to Maritime Quality Milk at the University of Prince Edward Island. The samples were frozen or refrigerated up to 5 days after shipment, until SCC analysis was completed.

Milk samples were also cultured on blood agar and MacConkey media according to standard bacteriological protocols followed by phenotypic identification (Hogan et al., 2009).

Pure cultures with ≥10 colonies of SNA per plate (≥1000 cfu/mL) were stored in individual

64 cryovials at -80oC. Isolates from mixed infections possessing SNA as one of the isolates were not stored, and milk samples with growth of ≥3 species of pathogens were considered contaminated.

Isolates of Staphylococcus aureus, Streptococcus dysgalactiae, Streptococcus uberis, and

Klebsiella pneumoniae were identified according to phenotypic criteria described in the National

Mastitis Council Laboratory Handbook (Hogan et al., 2009), and stored if a milk sample had >1 colony (>100 cfu/mL) in microbiological culture. Milk samples without any growth in standard culture methods were considered negative.

3.3.4 Definition of intramammary infection

Initially, samples stored by the National Cohort of Dairy Farms Pathogen Collection defined a mammary quarter as infected when bacterial culture yielded SNA or other minor pathogens at a concentration of ≥1000 cfu/mL of milk or when major pathogens (S. aureus,

Strep. dysgalactiae, Strep. uberis, Klebsiella pneumoniae) yielded ≥100 cfu/mL of milk in pure culture based on a single sampling.

3.3.5 Dataset

A total of 118,335 milk samples were collected by the CBMRN from 31,941 lactations

(range = 1-15 observation per lactation; mean = 3.7), 21,690 quarters (range = 1-23 observations per quarter; mean 5.5), and 6,058 cows, (range = 1-92 observations per cow; mean = 19.5). Of the 118,335 milk samples collected, 17,368 milk samples were considered unsuitable for laboratory analysis or were defined as contaminated at the time of initial culture. Out of 100,967 milk samples, a total of 6,302 SNA isolates were recorded from 91 herds, 2,139 cows, and 3,225 quarters. A total of 721 of these isolates were not available from storage for further identification, leaving 5,581 SNA isolates for identification. From the 5,581 SNA isolates, 74

65 isolates were identified as another bacterial genus (Condas et al., 2016). Consequently, 5,507

SNA isolates were available to be included in the analyses.

For comparison, 47,996 culture-negative milk samples from 5,108 cows and 16,233 quarters were available. According to our definition of IMI, 2,406 S. aureus were isolated from

885 cows and 1,152 quarters; 296 Strep. dysgalactiae from 189 cows, 215 quarters; 368 Strep. uberis from 232 cows and 253 quarters; and 171 Klebsiella pneumoniae from 135 cows and 148 quarters.

3.3.6 SNA species identification

A total of 5,581 SNA isolates were received at University of Calgary in vials containing lyophilized bacteria; each vial was identified with a barcode representing the isolate identity.

These isolates were then phenotypically characterized as described (Condas et al., 2016).

Extraction of DNA was done as described by Sampimon et al. (2009b). Subsequently, PCR amplification and partial sequencing of 16S rDNA gene was used to confirm the identification of the different bacteria to the genus presumptively identified on the culture plates (Chakravorty et al., 2007). This procedure was followed by partial sequencing of rpoB gene to identify the SNA species (Mellmann et al., 2006). Sanger sequencing for all amplicons was performed at

University of Calgary Core DNA Services (UCDNA), from which, assembly and alignments were performed using Geneious (Biomatters, 2005-2014). Definitive species identification was performed as described (Condas et al. (2016) and Drancourt and Raoult (2002)).

66 3.3.7 Statistical analyses

Two categories based on SCC were created: quarters with SCC < 200,000 cells/mL (low

SCC), quarters with SCC > 199,000 cells/mL (high SCC). A third category was created for clinical mastitis quarters.

3.3.7.1 SCC estimates

Comparison of SCC between species was performed in R. First, SCC was natural log- transformed (LnSCC) to approximate the normal distribution. SNA with less then 10 isolates per species were grouped as other SNA species. Next, the association between DIM and SCC was studied using mixed-effects linear regression and 2-way plots. Models with observations nested in quarter and lactation inside a quarter were evaluated with DIM alone, and with its quadratic and cubic terms, respectively. Results of models and also posterior plots of residual and fitted values suggested the utilization of all 3 terms, resulting in a model with 2 points of inflexion in the curve of LnSCC x DIM (beginning and middle/end of lactation for SNA grouped as a single species). Multilevel mixed-effects linear regression was used to estimate LnSCC values according to different species. Culture-negative quarters were used as a baseline value. Different random structures that dealt with the presence of autocorrelation in SCC measures over time were tested and compared based on the likelihood statistic and residual plots. A 5-level random- intercept model (herd, cow, quarter, lactation and observation level) with random slopes of DIM,

DIM squared and DIM cubic at lactation and quarter levels fitted with maximum-likelihood resulted in the best fit, with successful estimation of all variance and covariance terms between intercept and slopes. Province was included as a fixed effect to deal with the clustering effect of this level. Differences in LnSCC between SNA species and the baseline value (negative quarters) were treated as fixed effects. Parity and average milk production at herd level were included as

67 possible confounders / effect modifiers. Interaction effects of DIM and different SNA species were tested with no significant results. Contrasts of LnSCC values were then used for comparison between species, with Sidak’s adjustment for multiple comparisons (Šidák, 1967).

Those comparisons assumed that the differences between LnSCC for species were common for all parities, provinces, DIM and average milk production, and were based on marginal estimates for each of those factors. Average LnSCC was converted to the geometrical mean SCC for presentation purposes.

3.3.7.2 Distribution estimates

Analyses of distribution of SNA species in each SCC category was performed using

MLWin version 2.36 (Rasbash et al., 2005) considering the total number of SNA isolates as the denominator. For each species of bacteria, differences in the proportion of samples in each category were compared with P < 0.05 considered statistically significant. Three methodologies were used to contrast the distributions due to number of isolates available per species: 1) Mixed- effects logistic regression using 2nd order PQL estimation with herd, cow, and quarter as random effects at the intercept-level and the difference between logits modelled as fixed-effects (S. chromogenes, S. xylosus, S. simulans, S. haemolyticus, S. cohnii, S. gallinarum, S. capitis); 2) 2nd order PQL estimation with herd and cow as random effects and the differences modelled as before (S. epidermidis, S. sciuri); and 3) for species with low occurrence (other SNA identified),

Fisher’s Exact test. All obtained P-values were adjusted for multiple comparisons using the

Benjamini & Hochberg (Benjamini and Hochberg, 1995) method using the p.adjust function in R

(Crawley, 2013).

68 3.3.7.3 Prevalence estimation

Bayesian analyses using the latent class approach were used for prevalence estimation due to imperfect sensitivities and specificities of our IMI definition (Dohoo et al., 2009). Various beta-densities were used to adjust logistic regression analyses. Specificity estimates >99% were considered as 100 (Table 3-1). Distributions were truncated for values within 5 points of the estimated sensitivities and specificities. For the analysis, misclassification was considered independent of SCC status.

Misclassification of SNA species included 3 distinct priors. Initially, 6,302 SNA IMI infections were identified. For unknown reasons, 721 isolates were not stored, leaving 5,581

SNA isolates for identification (0.88 of total SNA samples). From the 5,581 SNA isolates, 74 isolates were identified as another bacterial genus resulting in 5,507 SNA isolates. According to

Dohoo et al. (2009), the estimated sensitivity of using a single sample culture to diagnose an IMI caused by a SNA is 24.2% and specificity is 100%. These values were used to adjust all SNA prevalence estimates. When estimating the prevalence for each SNA species, additional priors dealing with the imperfections in our methodology were used (Figure 3-1). Misclassification of

SNA species was assumed to be independent from the fact that a sample was stored or not and, also, assumed common for all SNA species.

Initial analyses considered the complete structure of our dataset (repeated measures clustered inside quarter, cow and herd), including random slopes of DIM at the quarter level (to address autocorrelation). Random slopes were dropped in final models due to difficulties estimating variance and covariance parameters properly. Final models for species commonly isolated contained random effects for quarter, cow and herd levels. Differences in prevalence of each bacterial species in low and high SCC categories were treated as fixed effects in individual models. Logit values obtained were transformed to population-averaged values (Dohoo et al., 69 2009) and prevalence estimates were obtained using the invert logit function in the associated coefficient in JAGS (Plummer, 2003). For rare outcomes (SNA species rarely isolated), models were built ignoring the clustered nature of the data.

A Markov Chain Monte Carlo method using Gibbs sampling was applied to estimate the posterior densities using the runjags package in R (Denwood and Plummer, 2016). Four chains running in parallel were composed of 100,000 iterations in total for each pathogen. Plots of posterior distribution were visualized in R, and differences in prevalence estimates between SCC categories were considered significant when its respective 95% credible interval did not include zero.

3.4 Results

3.4.1 Somatic cell count

The geometric mean SCC of SNA-positive quarters was 70,000 cells/mL (95% CI:

67,000 – 74,000 cells/mL), which was higher than culture-negative quarters, and lower than quarters positive for major pathogens (Table 3-2; Figures 3-2 and 3-3). Among the SNA species,

S. capitis. Staphylococcus gallinarum, S. hyicus, S. agnetis, and S. simulans positive quarters had a higher geometric mean SCC than SNA as a group (Table 3-2; Figure 3-6). In contrast, S. sciuri,

S. cohnii, S. equorum, S. succinus and other SNA-positive quarters had an SCC that was lower than SNA as a group (Table 3-2; Figures 3-5 and 3-6). Samples positive for each SNA species had, on average, an SCC lower than quarters positive for the major pathogens S. aureus, Strep. dysgalactiae, Strep. uberis, and Klebsiella pneumoniae (Table 3-2).

70 3.4.2 Distribution of SNA species

The 5,507 SNA isolates, used as denominator in our distribution, were obtained from

1,940 cows, and 2,859 quarters, and 3,015 lactations, of which 3,561 were from quarters with low SCC, 1,873 with high SCC, and 73 from clinical mastitis quarters. The proportion of SNA was highest in high SCC positive quarters (18.5%), followed by 7.6% of quarters with low SCC, and 4.3% of quarters with clinical mastitis. Culture-negative quarters were most common among quarters with low SCC (52.8%; Table 3-3).

The 5 most common SNA species within SNA as a group were commonly observed in all

3 categories (Table 3-3). However, whereas S. chromogenes, S. simulans, S. epidermidis, and S. haemolyticus were evenly distributed throughout the 3 categories, S. xylosus (27.6%), S. cohnii

(3.1%), and S. equorum (0.6%) were more frequently isolated from low SCC quarters, and finally S. gallinarum (1.37%) and S. agnetis (1%) were frequently observed in high SCC quarters. Staphylococcus sciuri (13.7%) and S. agnetis (2.74%) were more frequently isolated from quarters with clinical mastitis than from quarters with low or high SCC.

3.4.3 Prevalence of SNA species

Overall SNA IMI, as a group, was more prevalent in high than in low SCC quarters

(50.6%, and 20.6% respectively) (Table 3-4). Quarters with a low SCC were more often culture- negative than high SCC quarters, whereas the opposite was the case for major udder pathogen- positive quarters. The 11 most common SNA species were more prevalent in high SCC quarters.

No SNA species was more prevalent in low than in high SCC quarters (Table 3-4).

71 3.5 Discussion

There was a difference between SCC in culture-negative quarters and quarters with major pathogen IMI in the current study, similar to what is described by previous literature (Pitkälä et al., 2004, Thorberg et al., 2009, Supré et al., 2011). In some studies, culture-negative quarters had SCC that varied from 27,000 to 64,000 cells/mL (Taponen et al., 2006, Supré et al., 2011,

Tomazi et al., 2015), which is below the 100,000 cells/mL threshold reported by Schukken et al.

(2003), but similar to our findings. At the other extreme, IMI caused by major pathogens usually had a SCC ≥ 300,000 cells/mL (Thorberg et al., 2009), higher than our average SCC for major pathogens. The general lower average SCC might be representative of herd management practices such as high quality nutrition, good hygiene, and superior genetics applied by the

Canadian dairy industry (Barkema et al., 2015), which could have resulted in a milder inflammatory response upon infection (Fox, 2009).

The average SCC in the present study might have been affected by other limitations. The first refers to freezing of milk samples prior to measurement of SCC. This procedure destroys cellular structures and can decrease SCC values (Barkema et al., 1997), and was a standard procedure to all stored milk samples as discussed elsewhere (Fry et al., 2014). Another limitation could be resultant of major pathogen misclassification, since we conducted SCC analyses on quarters infected by major pathogens that were only phenotypically identified. It is possible that certain isolates were not true major pathogens, and therefore did not increase SCC at higher levels. Moreover, Fry et al. (2014) also reported a higher average SCC than ours, possibly the selection result of subclinical mastitis cases close to calving as it can take from a few days to 3 wk for SCC to recover to normal values after infection by both minor and major pathogens

(Schukken et al., 2003, Napel et al., 2009). Additionally, persistent infections by any bacterial 72 group will continuously increase SCC, compared to udder infections in remission status (Napel et al., 2009), and decreases the chance of selecting isolates that are only colonizing the teat canal but not causing an infection (Kalmus et al., 2013, Boerhout et al., 2016).

Even though, it was possible to observe that major pathogens can cause 3- to 4-fold increases in SCC compared to culture-negative quarters, similarly to what is described in previous studies (Sampimon et al., 2009a, Fry et al., 2014). There was also a significant difference between SCC in culture-negative quarters and quarters with SNA IMI. According to several studies, the SCC of quarters affected by SNA IMI can increase from 2 to 10-fold, when compared to culture-negative quarters (Oliver and Jayarao, 1997, Schukken et al., 2003, Taponen et al., 2007, Thorberg et al., 2009, Supré et al., 2011). Previous studies classify SNA as a true pathogen, capable of causing moderate to high increases in SCC (Taponen et al., 2007, Thorberg et al., 2009, Supré et al., 2011). Therefore, SNA may have a harmful impact on SCC, consistent with the right skewed tails observed in SCC distribution curves for the present SNA species, compatible with persistent or re-infective cases, might be playing a role in this scenario.

Our average values were low, and, accordingly, SNA IMI might not compromise milk production or milk quality to the extent previously expected. According to Schukken et al.

(2003), quarters with an SCC ≥ 200,000 cells/mL have poor milk quality and production losses.

In addition, for the present study a positive-quarter definition was selected that was likely to overestimate average SCC. As described by Dohoo et al. (2011b), a SNA IMI definition based on a total of 1000 cfu/mL in culture has a sensitivity of 26.8%. Therefore, in addition to having low average SCC increases, our results might represent only 27% of SNA isolates.

Results at the species level in the present study reinforced the concept that SNA species have diverse effects on SCC (Piessens et al., 2011, Supré et al., 2011, Fry et al., 2014, De

73 Visscher et al., 2016a). Previous research described S. chromogenes, S. epidermidis, and S. simulans as SNA species that most likely to increase SCC (Sampimon et al., 2009a, Taponen and

Pyörälä, 2009, Thorberg et al., 2009, Persson Waller et al., 2011), although they were not always consistent. According to Waage et al. (1999) and Taponen et al. (2006), S. simulans is the species with the most capacity to increase SCC as it provokes the strongest inflammatory response based on experimental trials (Simojoki et al., 2011) and previous reports of naturally occurring IMI (Sampimon et al., 2009a, Thorberg et al., 2009, Supré et al., 2011). Thorberg et al.

(2009) described moderate to high increases in SCC by S. epidermidis, which was also demonstrated experimentally (Simojoki et al., 2011).

Staphylococcus chromogenes IMI in reference studies was associated with a significantly higher SCC and is considered an important species in quarters with a high SCC, persistent IMI, and clinical mastitis (Persson Waller et al., 2011, Supré et al., 2011, Fry et al., 2014). In experimental studies, Simojoki et al. (2009) demonstrated that S. chromogenes strains were capable of increasing SCC and causing clinical mastitis. Furthermore, Fthenakis and Jones

(1990) demonstrated intense neutrophil infiltration into mammary tissue after infection with S. chromogenes. Conversely, this species could also be a protective species against IMI by major pathogens as reported by Breyne et al. (2015). In this study, strains isolated from teat apex caused less SCC increase compared to strains isolated from IMI, which might explain variations in SCC and distribution of S. chromogenes in in milk with different SCC levels; this phenomenon requires more investigation.

Other SNA species also had discrepant findings. Tomazi et al. (2015), Fry et al. (2014) and Chaffer et al. (1999) reported that S. haemolyticus had inflammatory potential in the udder.

In contrast, Thorberg et al. (2006), Sampimon et al. (2009a) and Supré et al. (2011) reported that

74 S. haemolyticus was less inflammatory than some of the other SNA species. Previous reports suggest that S. xylosus significantly increased SCC (Sampimon et al., 2009a; Supré et al., 2011, and Fry et al., 2014), but similar to the present study Thorberg et al. (2009) reported low CMT scores that were corroborated by a transient increase of SCC and no tissues abnormalities in another study (Fthenakis and Jones, 1990).

A total of 11 SNA species had higher SCC values than the average SCC of culture- negative quarters. Although the most common SNA species (S. chromogenes, S. xylosus, S. haemolyticus, and S. epidermidis) caused increases in SCC (to 60,000 and 70,000 cells/mL, respectively), only S. agnetis, S. capitis, S. hyicus, S. gallinarum, and S. simulans had the capacity to increase SCC to levels similar to average SCC of the studied major pathogens.

Staphylococcus hyicus, although not one of the most common species, was present in high SCC quarters, thereby displaying the pathogenic potential previously described (Waage et al., 1999,

Persson Waller et al., 2009). The newly described species, S. agnetis (Taponen et al., 2012), that is closely related to S. hyicus and S. chromogenes also has the capacity to increase SCC (Calcutt et al., 2014, Condas et al., 2016, Naushad et al., 2016). In our cohort, S. hyicus and S. agnetis were more frequently isolated from quarters with a high SCC and clinical mastitis, similar to S. chromogenes, indicating potential similarities in pathogenicity. Therefore, future work should be done comparing their epidemiology and species-specific effects.

Species with a significantly lower SCC increase in relation to culture-negative quarters were S. cohnii, S. equorum, S. sciuri, and S. succinus. In other studies, S. cohnii, S. equorum, S. saprophyticus, S. sciuri, S. arlettae, S. succinus, S. pasteuri, S. auricularis, S. nepalensis, S. hominis, and S. gallinarum (Thorberg et al., 2009, Supré et al., 2011, Fry et al., 2014, De

Visscher et al., 2016a). Although our results were consistent with SCC averages for most SNA

75 species, S. gallinarum was associated with an elevated average SCC. We previously demonstrated the importance of this species immediately after calving (Condas et al., 2016).

Perhaps the average SCC increases observed were due to higher SCC values in the post-calving period thus overestimating its harmful impact on udder health. The same reasoning might be relevant for S. capitis, a species previously reported to affect SCC in 2 studies (Sampimon et al.,

2009a; (Fry et al., 2014).

The distribution of SNA IMI, within the SNA group, in low and high SCC quarters ranged from 20 to 50%, respectively, consistent with most previous studies, where SNA IMI ranged from approximately 19% of isolates (Oliver and Jayarao, 1997, Taponen et al., 2007,

Mullen et al., 2013, Levison et al., 2016) to 27%, considering both low and high SCC quarters

(Supré et al., 2011). Moreover, prevalence at quarter-level ranged from as low as approximately

11% (Gillespie et al., 2009, Sampimon et al., 2009a) to as high as approximately 50% (Dufour et al., 2012). These variations included a wide range of sample selection criteria, sampling methods, laboratorial procedures, and IMI definitions, as well as, intrinsic variation for each herd region under investigation and its respective management practices, all of which could contribute to discrepant results (Barkema et al., 2006, Compton et al., 2007, Sampimon et al., 2009a).

Similar to major pathogens but less pronounced, SNA were most prevalent in high SCC quarters. In some studies, subclinical mastitis caused by SNA was observed in 27% of infected quarters, whereas S. aureus was isolated from 3.1% of subclinical mastitis quarters (Chaffer et al., 1999). Other, recent studies by Botrel et al. (2010) reported prevalence of coagulase-negative staphylococci (CNS) and coagulase-positive staphylococci (CPS) at 13.7 and 30.2%, respectively, followed by Strep. dysgalactiae at 9.3%, and Gram-negatives at lower levels (<

5%). Persson et al. (2011) observed S. aureus at 19% and CNS at 16%, followed by Strep.

76 dysgalactiae and Strep. uberis at 9 and 8% respectively, and E. coli at 2.9% of total of quarters with subclinical mastitis. As mentioned by Oliveira et al. (2013), as major contagious pathogens are better controlled by improved management practices, SNA prevalence increases proportionally, even in high SCC cases.

Staphylococcus chromogenes, S. simulans, S. haemolyticus, and S. epidermidis were evenly distributed across SCC levels, although the prevalence range was different from Thorberg et al. (2009) and Persson Waller et al. (2011). Supré et al. (2011) reported the same most common species; however, low isolate numbers made it impossible to determine significant differences across all IMI types in the previous report. Furthermore, other SNA species such as

S. arlettae, S. devriesei, S. equorum, S. fleuretti, S. hyicus, S. klosii, S. pasteuri, S. warneri were isolated in equal proportions in low SCC and high SCC quarters in the present study, as also described by Thorberg et al. (2009), Persson Waller et al. (2011), and Supré et al. (2011).

Taponen et al. (2006) observed that S. haemolyticus occurred most frequently in subclinical mastitis cases, which coincides with its increased prevalence in high SCC quarters in the present study. It is important to note that S. haemolyticus had an equal proportional distribution in all non-clinical and clinical mastitis samples. The same discrepancies among distribution and prevalence in our study were observed with S. xylosus and S. sciuri. According to Supré et al. (2011) and the prevalence estimate reported herein, S. xylosus is also a relevant species in high SCC cases. However, in other studies these species do not increase SCC

(Sampimon et al., 2009a, Thorberg et al., 2009).

Staphylococcus cohnii and S. equorum were more frequently isolated from low SCC quarters in agreement with previous findings (Supré et al., 2011) although no difference in prevalence was apparent between categories of quarter SCC. Staphylococcus capitis, S. arlettae,

77 and S. warneri had no difference in distribution between levels of quarter SCC but were prevalent in high SCC samples. In contrast, S. gallinarum and S. agnetis had a higher prevalence in high SCC quarters and more IMIs, respectively. These differences indicated that even though the microorganism distribution is usually associated with the degree of SCC elevation, other factors might modify their prevalence according to various levels of quarter SCC.

Worldwide, the prevalence of SNA as a group at the quarter level in clinical mastitis has been reported as 5.4% (Steeneveld et al., 2008), 6 to 7.5% (Olde Riekerink et al., 2008, Piessens et al., 2011), and 9 to 10.8% (Oliver and Jayarao, 1997, De Haas et al., 2002) of cases. Among

SNA species, S. chromogenes was the most common species isolated from cases of clinical mastitis in the present study, similar to Zadoks and Watts (2009). In contrast, other studies have shown S. simulans to be the most frequently isolated SNA species in clinical mastitis (Myllys,

1995, Waage et al., 1999, Taponen et al., 2008, Thorberg et al., 2009). Once again, there are variable reports about the importance of S. xylosus and S. haemolyticus in clinical mastitis.

According to Persson Waller et al. (2011), S. xylosus and S. haemolyticus were observed in clinical mastitis at the same levels as subclinical mastitis, where Taponen et al. (2006) did not detected S. xylosus in clinical mastitis.

Other species, such as S. equorum, S. warneri, S. sciuri and S. hyicus have been reported as causes of clinical mastitis (Taponen et al., 2006, Piessens et al., 2011); however, the lower numbers in other studies cannot support further conclusions. Similarly, S. sciuri, has been reported by other researchers to not increase SCC, but it is more frequently found in clinical mastitis. This warrants further investigation on virulence factors specific to S. sciuri that might influence its pathogenicity.

78 3.6 Conclusions

It is well established that SNA is the most commonly isolated group of bacteria from bovine milk samples worldwide; therefore, understanding their impact on udder health is essential. Even though IMI with SNA increases SCC, the increase is relatively small, and the average SCC increase in this study fell between culture-negative and major pathogen-infected quarters. Some species of SNA, particularly S. sciuri, are involved in clinical mastitis, indicating that they likely play a role as pathogens. The large dataset available in this cohort allowed us to establish that S. chromogenes, S. simulans, and S. sciuri were species most frequently isolated from clinical mastitis, whereas S. agnetis, S. capitis, S. hyicus, S. gallinarum, and S. simulans increased SCC most in subclinical mastitis. Identification of SNA species made it possible to determine how they affect udder health, and will direct further studies on virulence factors, host susceptibility, and management strategies focused on specific characteristics. Furthermore, future research can apply its effort on the investigation of possible species-specific SNA genotypes and how they are related to aspects of mastitis, such as persistence, increased inflammatory response, decreased milk production, and if there are any genotypes that confer protection against major pathogens.

79 Table 3-1. Mean, lower and upper limits of beta-distributions used in the present study according to various estimates of sensitivity (Se) and specificity (Sp) for several pathogens

(or pathogen group).

95% Limits Pathogen Parameter Distribution Mean Lower Upper SNA Culture 1* Se β(66.97, 211.80) 0.24 0.19 0.29 Se β(136.5, 17.8) 0.88 0.83 0.93 SNA Culture 2* Sp β(56.99, 1.5) 0.98 0.93 0.99 Staphylococcus aureus Se β(225.79, 101.87) 0.69 0.64 0.74 Streptococcus dysgalactiae Se β(219.36, 80,12) 0.73 0.68 0.78 Streptococcus uberis Se β(219.36, 80,12) 0.73 0.68 0.78 Klebsiella spp. Se β(152.55, 218.13) 0.41 0.36 0.46 *Culture 1: SNA species comprising isolates stored and not stored by CBMRN, under IMI definition of >1000 cfu/mL; Culture 2: isolates received at UofC and identified as SNA species

80 Table 3-2. Somatic cell count (SCC) of quarters culture-positive for Staphylococcus non- aureus (SNA) species, major pathogens and culture-negative quarters from 91 dairy herds in 4 regions of Canada.

SCC x 1,000 cells/mL Geometrical mean 95% Confidence Interval S. chromogenes 67.86a,d 63.05 73.03 S. simulans 77.39a,c 69.70 85.94 S. xylosus 66.84a,d 59.76 74.76 S. haemolyticus 65.26a,d 57.35 74.25 S. epidermidis 66.99a,d 56.56 79.35 S. cohnii 46.27a,b,d 37.84 56.57 S. sciuri 54.52a,d 44.10 67.40 S. gallinarum 96.62a,c 68.10 137.07 S. capitis 123.36a,c 84.43 180.24 S. arlettae 63.58a,d 45.34 89.15 S. warneri 63.27a,d 42.01 95.28 S. saprophyticus 65.55a,d 42.89 100.18 S. agnetis 81.17a,c 50.79 129.72 S. equorum 40.80a,b,d 25.38 65.57 S. succinus 42.84a,b,d 25.38 72.31 S. hominis 29.40b,d 14.88 58.07 S. hyicus 85.65a,c 68.92 106.45 Other SNA species1 47.52a,b,d 33.12 68.19 Overall SNA 70.36a,d 67.36 74.30 Culture-negative 32.29b,d 30.63 34.03 S. aureus 173.82a,c 160.09 188.73 Streptococcus dysgalactiae 183.45a,c 110.46 151.71 Streptococcus uberis 161.57a,c 134.43 194.20 Klebsiella spp. 132.85a,c 98.75 178.72 1Group of SNA species isolated in lower numbers (< 10 isolates per species): S. devriesei, S. pasteuri, S. nepalensis, S. vitulinus, S. auricularis, S. caprae, S. fleuretti, S. kloosii; aSignificantly higher than negative-culture quarters average SCC (P <0.05); bSignificantly lower than SNA average SCC (P <0.05); cSignificantly higher than SNA average SCC (P <0.05); dSignificantly lower than major pathogens (S. aureus, Strep. dysgalactiae, Strep. uberis, Klebsiella spp.) average SCC (P <0.05).

81 Table 3-3. Distribution of Staphylococcus non-aureus (SNA) species isolated within SNA as a group from bovine milk from 91

Canadian dairy herds in quarters with low (<200,00 cell/mL) and high (≥ 200,000 cells/mL) somatic cell count, and clinical mastitis.

Total Low SCC High SCC Clinical mastitis N % N %1,4 N % N % S. chromogenes1 2,683 48.72 1,794 50.38 866 46.24 23 31.51 S. simulans1 931 16.91 553 15.53 360 19.22 18 24.66 S. xylosus1 630 11.44 450 12.644 178 9.50 2 2.74 S. haemolyticus1 433 7.86 259 7.27 169 9.02 5 6.85 S. epidermidis2 231 4.19 117 3.29 108 5.77 6 8.22 S. cohnii1 140 2.54 109 3.066 30 1.60 1 1.37 S. sciuri2 131 2.38 92 2.585 29 1.555 10 13.70 S. gallinarum1 51 0.93 20 0.566 30 1.60 1 1.37 S. capitis1 46 0.84 23 0.65 22 1.17 1 1.37 S. arlettae3 46 0.84 29 0.81 15 0.80 2 2.74 S. warneri3 31 0.56 17 0.48 14 0.75 . 0 S. saprophyticus3 30 0.54 20 0.56 10 0.53 . 0 S. agnetis3 26 0.47 5 0.144 19 1.017 2 2.74 S. equorum3 24 0.44 22 0.626 2 0.11 . 0 S. succinus3 17 0.31 14 0.39 3 0.16 . 0 S. hominis3 12 0.22 8 0.22 4 0.21 . 0 S. devriesei3 10 0.18 5 0.14 4 0.21 1 1.37 S. pasteuri3 8 0.15 5 0.14 3 0.16 . 0 S. nepalensis3 7 0.13 7 0.20 . 0 . 0 S. vitulinus3 6 0.11 4 0.11 2 0.11 . 0 S. auricularis3 4 0.07 3 0.08 1 0.05 . 0 S. hyicus3 4 0.07 1 0.03 3 0.16 . 0 S. caprae3 3 0.05 1 0.03 1 0.05 1 1.37 S. fleuretti3 2 0.04 2 0.06 . 0 . 0 S. kloosii3 1 0.02 1 0.03 . 0 . 0

82 Total SNA 5507 100% 3561 100% 1873 100% 73 100%

1Significance determined by mixed linear model considering all cluster levels; 2Significance among categories of IMI status determined by mixed linear model without cluster at quarter-level; 3Significance determined by Fisher- exact test due to low number of subjects in each IMI status; 4Significantly different from high SCC (P < 0.05); 5Significantly different from clinical mastitis (P <

0.05); 6Tendency for difference from high SCC (0.05 < P < 0.10); 7Tendency for difference from clinical mastitis (0.05 < P < 0.10).

83 Table 3-4. Prevalence of Staphylococcus non-aureus (SNA) species, major pathogens and culture-negative quarters in quarters with a low (< 200,000 cells/mL) and high (≥ 200,000 cells/mL) somatic cell count.

Low SCC High SCC Species N Prevalence 95% CI3 N Prevalence 95% CI3 S. chromogenes1 1,794 8.66a 7.96 9.50 866 17.69 16.18 19.52 S. simulans1 553 3.50a 3.10 4.01 360 7.87 6.93 8.97 S. xylosus1 450 3.58a 3.04 4.16 178 5.90 4.79 7.00 S. haemolyticus2 259 1.52a 1.22 1.91 169 4.65 3.67 5.92 S. epidermidis1 117 1.07a 0.80 1.38 108 3.47 2.81 4.31 S. cohnii1 109 1.28 0.99 1.62 30 1.51 0.86 2.18 S. sciuri1 92 0.97a 0.72 1.29 29 1.46 1.02 2.02 S. gallinarum1 20 0.34a 0.17 0.53 30 1.20 0.82 1.68 S. capitis2 23 0.13a 0.083 0.21 22 0.61 0.37 0.93 S. arlettae2 29 0.16a 0.11 0.25 15 0.42 0.23 0.69 S. warneri2 17 0.10a 0.06 0.16 14 0.38 0.21 0.65 S. saprophyticus2 20 0.12 0.07 0.18 10 0.28 0.13 0.51 S. agnetis2 5 0.03a 0.01 0.06 19 0.52 0.31 0.83 S. equorum2 22 0.12 0.08 0.20 2 0.05 0.009 0.18 S. succinus2 14 0.08 0.04 0.14 3 0.08 0.02 0.22 S. hominis2 8 0.05 0.020 0.09 4 0.11 0.03 0.26 S. devriesei2 5 0.03 0.09 0.06 4 0.11 0.03 0.27 S. pasteuri2 5 0.03 0.01 0.06 3 0.08 0.02 0.22 S. nepalensis2 7 0.04 0.02 0.08 . 0.003 0 0.08 S. vitulinus2 4 0.02 0.006 0.06 2 0.05 0.007 0.18 S. auricularis2 3 0.02 0.004 0.04 1 0.03 0.001 0.14 S. hyicus2 1 0.005 0 0.02 3 0.08 0.02 0.22 S. caprae2 1 0.006 0 0.03 1 0.03 0 0.13 S. fleuretti2 2 0.011 0.001 0.04 . 0.003 0 0.08 S. kloosii2 1 0.005 0 0.03 . 0.004 0 0.08 Total SNA1 3561 20.60a 17.80 25.46 1873 50.60 43.83 62.09 Culture-negative1 42,727 50.89a 50.23 51.51 4,729 28.01 27.12 28.83 S. aureus1 434 1.82a 1.61 2.05 1642 10.78 9.94 11.71 Strep. dysgalactiae1 21 0.27a 0.18 0.37 164 1.83 1.54 2.16 Strep. uberis1 48 0.41a 0.32 0.54 182 1.83 1.57 2.17 Klebsiella spp.1 22 0.33a 0.22 0.49 47 1.16 0.88 1.51

84 1Prevalence was estimated considering the clustered nature of the data; 2Prevalence was estimated ignoring the clustered nature of the data; 3CI = Bayesian 95% credible interval; a CI differences do not cross zero.

Figure 3-1. Estimates of sensitivity and specificity for Staphylococcus non-aureus (SNA) according to the structure of the dataset. Staphylococcus non-aureus

Staphylococcus non-aureus

Figure 3-4. Distribution of somatic cell count (SCC) of Staphylococcus chromogenes, S. simulans, and S. xylosus-positive quarters.

Figure 3-5. Distribution of somatic cell count (SCC) of Staphylococcus cohnii, S. epidermidis, and S. haemolyticus-positive quarters. 89

Figure 3-6. Distribution of somatic cell count (SCC) of Staphylococcus capitis, S. gallinarum, and S. sciuri-positive quarters.

Chapter Four: Summarizing discussion

The foremost goal of these studies was to gather information on the epidemiology of staphylococci non-aureus (SNA) species isolated from milk samples from Canadian dairy herds.

This bacterial group is a highly prevalent organism isolated from cow’s milk worldwide, but knowledge regarding the distribution of these species and their role in udder health is far from complete (De Vliegher et al., 2003, Taponen and Pyörälä, 2009). The SNA is a heterogeneous group of staphylococci; therefore, considering them as group rather than at species level has undoubtedly contributed to discrepancies among studies on there importance to udder health

(Supré et al., 2011). It is likely that individual SNA species interact differently with the host and the environment, and, as a consequence, they are expected to have different effects on their host with the potential for disparate courses of udder infection and patterns of transmission

(Vanderhaeghen et al., 2014). Therefore, the overall goal of this research was to determine the most prevalent species in milk samples of Canadian dairy herds, and determine how they were distributed according to specific dairy production characteristics.

4.1 SNA species identification

Many (5,507) of SNA isolated from milk samples collected from 91 herds were available in the Mastitis Pathogen Collection of the Canadian Bovine Mastitis and Milk Quality Research

Network (CBMQRN). However, apart from a subset previously reported (Fry et al. 2014), the species of these isolates was not determined. Using the open database, GenBank, which contains sequences of thousands of microorganism’s genes characterized worldwide, 16S rDNA

sequences from bacterial isolates can be compared to reference strains and isolates from other laboratories to determine their identity (Becker et al., 2004, Park et al., 2011a). This approach requires efficient amplification of the discriminatory DNA sequences by PCR, which is sometimes problematic (Park et al., 2011a). Furthermore, there are limitations imposed by the discriminatory power of certain genes. For example, due to hypervariable regions, 16S rDNA gene frequently has limited ability to differentiate between similar species (Drancourt and

Raoult, 2002, Mellmann et al., 2006, Ghebremedhin et al., 2008)

The use of housekeeping genes for sequence-based species identification is also susceptible to the aforementioned limitations (Zadoks and Watts, 2009); nevertheless, their use is still applicable for studies like the present one. In our study, partial sequencing of the rpoB gene was used to identify SNA to the species-level. There was a limitation of rpoB partial sequencing related to discrimination of Staphylococcus chromogenes, S. hyicus and S. agnetis.

Staphylococcus agnetis was recently described by Taponen et al. (2012), and it is possible that many sequences of this microorganism deposited in GenBank are misclassified as either S. hyicus primarily, or secondly as S. chromogenes. Notwithstanding, when comparing results to

441 isolates that underwent whole-genome sequencing (WGS; (Naushad et al., 2016), the accuracy of using rpoB partial sequencing was 100% (data not presented). Furthermore, as the cost of WGS continues decline, it will become the gold standard for species identification

(Zhang et al., 2014). The use of WGS is considered accurate at species and subspecies levels, and is repeatable among laboratories (Hasman et al., 2014), although is still very expensive and demands specific expertise. In that sense, the sequence of the entire bacterial genome will allow not only correct identification, but also determine its phylogeny, and serve as a tool to identify

specific regions that will uniquely identify each SNA species and their virulence factors in the future (Hasman et al., 2014, Zhang et al., 2014).

In conclusion, the sequence method chosen for this study was adequate to reveal the distribution of most common species of SNA in Canada. However, it is important to be aware of continuous development of molecular techniques in this area and be aware of newly discovered

SNA species that may not yet be fully represented in the reference databases, as well as, their diversity due to host factors interaction

4.2 Most common SNA species in milk samples

All species identified in the current study have already been described in previous studies from around the world. Overall, the order of frequency was similar to that in other parts of the world (Sampimon et al., 2009a, Piessens et al., 2011, Supré et al., 2011,Tomazi et al., 2014,

Vanderhaeghen et al., 2015). Staphylococcus chromogenes, comprising approximately 50% of the isolates (Chapter 2) was the predominant SNA species, which is in agreement with most recent studies that applied molecular identification (Vanderhaeghen et al., 2015). Staphylococcus chromogenes is described as a microorganism adapted to bovine skin microbiota, and has been identified as a resistant pathogen to common teat disinfectants and antibiotics, which may contribute to its high prevalence (Pate et al., 2012, Quirk et al., 2012, Tomazi et al., 2014).

Before molecular typing methods were used, S. hyicus was thought to be the most common species. This can be attributed to low accuracy phenotypic methods (Sampimon et al., 2009b), as

S. hyicus is phylogenetically similar to S. chromogenes. Other species such as S. simulans, S. xylosus, S. haemolyticus and S. epidermidis are described in varied order of importance, but also

their adaptation to cattle and their environment has been reported (Thorberg et al., 2006,

Taponen et al., 2008, Piessens et al., 2011, Piessens et al., 2012, Braem et al., 2013, De Visscher et al., 2014).

Staphylococcus arlettae, S. capitis, S. cohnii, S. devriesei, S. equorum, S. sciuri, and S. warneri also are described frequently in other studies, but in lower proportions. Usually, with their low frequency (current and previous studies), they have not been regarded as having a big impact on udder health (Thorberg et al., 2009, Supré et al., 2011, Fry et al., 2014, De Visscher et al., 2016a).

4.3 Distribution of CNS species according to herd level factors

Geographical differences have already been shown to affect SNA species distribution

(Piessens et al., 2011, De Visscher et al., 2014). Very likely, differences in management around the world are the most important determinant of species variation. As an example, the influence of weather and pasture access increased prevalence of SNA IMI in European herds (Sampimon et al., 2009a), which is in contrast to a lower risk of SNA IMI in Canadian herds (Dufour et al.,

2012), particularly in Western Canada where dairy cattle are typically kept indoors. SNA species from udder and cow environments vary among herds (Gillespie et al., 2009, Thorberg et al.,

2009, Piessens et al., 2012) and the variation is mostly linked to management factors. Bulk milk

SCC (BMSCC) and housing type were 2 major herd characteristics evaluated (quarter-level) in this thesis.

The occurrence of SNA IMI and mastitis usually do not increase culling rates (Piepers et al., 2009, Piepers et al., 2010). However, SNA IMIs can increase BMSCC, making it difficult for

some herds to achieve some milk quality standards, which can result in economic losses. The proportion of BMSCC caused by SNA IMI has not been frequently evaluated. In a French study,

SNA IMI contributed to 18% of the somatic cells in BMSCC (Rainard et al. 1990). In some large

US herds, while the prevalence of SNA IMI increased with increasing BMSCC, the relative impact of SNA IMI on BMSCC decreased across increasing levels of BMSCC (Schukken et al.

2009a). Although this association might appear paradoxical, the association may be due to not only the prevalence of SNA IMI in herds with a higher BMSCC, but also the higher prevalence of major pathogen IMI in the same herds (Napel et al., 2009, Schukken et al., 2009a).

Usually, SNA species IMI are associated with a low to moderate increase in quarter milk

SCC (Vanderhaeghen et al., 2014) as was observed in Chapter 3. On farms with BMSCC >

250,000 cells/mL recommended mastitis management practices such as blanket dry cow therapy, post-milking disinfection, treatment of clinical cases, and further hygiene practices are less often practiced (Barkema et al., 1999). Other practices may also play a role. For example, common practices such as housing dry cows and pregnant heifers in a single group and contamination of stalls with milk has been shown to increase the risk of having SNA IMI (Sampimon et al.,

2009a). Perhaps, the positive association of prevalence of SNA IMI and prevalence of major pathogens was due to management practices affecting both groups of pathogens. As an example, post-milking teat disinfection practices modified species distribution in cows and herds (Lam et al., 1997, Quirk et al., 2012),whereas quarters of cows that were not disinfected after milking had a higher rate of IMI with both S. aureus and minor pathogens such as SNA and Corynebacterium bovis (Lam et al., 1997).

In Western Canada free-stalls with cubicles and bedded-pack barns are most common and tie-stalls are rare, whereas cows in Ontario and particularly Québec are still often housed in a tie-

stall barns. The current study accounted (multivariable analysis) for differences in herd housing system and BMSCC levels in the 4 regions of Canada. Similar to White et al. (1989), Olde

Riekerink et al. (2006), and Piessens et al. (2011), the present study observed that the distribution of SNA species was associated with different barn types. Staphylococcus chromogenes and S. sciuri were mostly associated with bedded-pack barns, whereas S. simulans, S. xylosus and S. haemolyticus were most frequently isolated in tie-stall barns, and S. epidermidis was most commonly detected in free-stall housing. Therefore, different housing systems and management practices within each barn type are factors that partially explain the present results. For example, the high prevalence of S. xylosus and S. simulans IMI could be linked to sawdust, which is mostly used in tie- and free-stall herds for bedding stalls (Matos et al., 1991, Pyörälä and

Taponen, 2009). According to Sampimon et al. (2009a), this would also explain the increased prevalence of SNA IMI in dairy systems that follow the 5-point program for mastitis control

(Neave et al., 1969). This program did not focus on aspects such as bedding cleanliness, cow pen grouping, and sources of drinking water, which are all environmental aspects associated with high SNA IMI. Therefore, effects of these management practices on the prevalence of SNA IMI warrant further research.

4.4 Distribution of SNA species according to cow factors

Cow factors such as parity and lactation stage also affect distribution of intramammary pathogens. Prevalence of SNA IMI is particularly high at first calving and stays high during the first 14 d of lactation (Barkema et al., 1998, Waage et al., 1999, Fox, 2009). On average, 44 to

87% of quarter samples from heifers had an IMI in early lactation, and in all studies SNA was the main group of bacteria involved being isolated from 22 to 55% of infected quarters (De

Vliegher et al., 2012). Several factors are involved in high susceptibility of heifers to IMI. This is a period with important physiological changes due to rapid udder development as well as associated changes in heifer management (De Vliegher et al., 2012). In addition, the pathogenicity of the species and strains of bacteria also can have a particular role in the infection

(Piccart et al., 2016). Because of the strong association between highly productive heifers and

SNA, proper management of these animals is important (Myllys, 1995, Piepers et al., 2013, De

Visscher et al., 2016a). Heifers are critical aspect of the dairy industry, and therefore the presence of pathogens that are deleterious to udder development and future production is of great economic relevance (De Vliegher et al., 2012).

In our study, the 5 most common species were also the most prevalent in heifers, with S. chromogenes, S. simulans and S. arlettae representing the highest proportion of SNA in this parity. The SNA species previously described in heifers were S. hyicus (Trinidad et al., 1990), S. simulans (Myllys, 1995), S. chromogenes, S. epidermidis and S. xylosus (Aarestrup et al., 1995,

Myllys, 1995). Staphylococcus chromogenes and S. simulans are species described as the most udder-adapted and consequently cause more IMIs in heifers (Vanderhaeghen et al., 2015). There are reports that S. chromogenes is part of the commensal bovine udder microbiota and might

have the capacity to act as an opportunistic pathogen (De Vliegher et al., 2003, Braem et al.,

2013). Because S. chromogenes is an obligate commensal and often infects several quarters within a cow and many cows within herd, it can probably act as a contagious pathogen (Piessens et al., 2012, Braem et al., 2013, De Visscher et al., 2014). Equally, S. simulans might act as an udder-adapted opportunist, since it was not detected from any source in the milking parlour, but was isolated from teat apices of udders in one study (Taponen et al., 2008).

Although SNA are the main group of bacteria infecting the mammary gland of heifers, this group also affects multiparous cows, with S. chromogenes, S. simulans, S. xylosus S. haemolyticus, S. epidermidis, S. sciuri, and S. cohnii being the species with the highest proportions in later lactations (Thorberg et al., 2009, Piessens et al., 2011, Mork et al., 2012).

Staphylococcus haemolyticus seemed to be one of the most ubiquitous pathogens among SNA species, having been isolated from milk, teat apices, several environmental reservoirs, and human skin. Moreover, the high diversity of strains from the environment and milk precluded identification of a specific source. These species also seemed to re-infect the udder several times thus acting as opportunistic pathogens (Braem et al., 2012, Mork et al., 2012, Piessens et al.,

2012). Staphylococcus epidermidis is usually introduced from human origin, although some researchers also identified isolates adapted to the cow’s environment and skin (Vanderhaeghen et al., 2015, De Visscher et al., 2016a). It is unclear if the high prevalence of infection comes from a single human contact or from multiple contacts with various human or environmental sources

(Thorberg et al., 2006, Bexiga et al., 2014, Vanderhaeghen et al., 2015). Staphylococcus xylosus has a diverse habitat, but is mostly environmental in nature (Piessens et al., 2011). Although it has been isolated from bovine skin (Taponen et al., 2008), its primary reservoir is unknown.

Taponen et al. (2008) also reported S. sciuri as a species isolated from extramammary sites,

whereas S. cohnii was recognized as an environmental pathogen (Piessens et al., 2011) causing mostly transient IMI (Supré et al., 2011). These characteristics could allow these species to be introduced from cows and their environment. Given the apparent diverse nature of SNA, species- specific management practices that promote transmission of these organisms should be evaluated in Canadian herds. Fortunately, data collected in the CBMRN cohort study provides an excellent opportunity.

Prevalence of SNA IMI species during lactation differed among various SNA species

(Chapter 2). In several previous studies, the highest prevalence of SNA IMI was found in the early post-calving period, whereas the prevalence of SNA IMI decreased during the ensuing days

(Matthews et al., 1992, Aarestrup et al., 1995, Piepers et al., 2010). According to Piepers et al.

(2010), 72% of subclinical mastitis cases in fresh heifers were caused by SNA IMI, but 1 wk later those isolates were no longer cultured. Staphylococcus chromogenes was the most common species reported (Aarestrup et al., 1995, Piepers et al., 2010). In the present study, among the 5 most commonly isolated species, the species with high peak prevalence at post calving were S. chromogenes, S. simulans, and S. haemolyticus. This can be related to previously mentioned udder adaptation and the ubiquitous nature of their presence in the environment. These were also the most prevalent species recently reported by De Visscher et al. (2016a), except for S. epidermidis. Staphylococcus gallinarum, S. capitis and S. cohnii also had a peak prevalence at calving, although they were less frequent. According to Fox (2009) and Reksen et al. (2012),

SNA occurring in the prepartum period are also more prevalent after calving. A high prevalence of IMI early after calving could be due to infection in the calving pen or an existing IMI acquired earlier in life in the case of heifers or during the dry period in the case of cows. Therefore,

dynamics of infection and transmission of the SNA species from pre- to postpartum should be characterized.

During lactation, contrary to what has been reported (Piessens et al., 2012) the prevalence of the 5 most common SNA species increased, as well as the prevalence of S. sciuri (until 4 mo of lactation), and S. cohnii. Nevertheless, according to Taponen et al. (2007), 55% of S. chromogenes and 67% of S. simulans persisted throughout lactation, where Supré et al. (2011) reported an average of 150 d duration of SNA IMI by S. chromogenes, S. simulans and S. xylosus. Perhaps continuous endemic transmission occurs throughout lactation by transient colonization and IMI of SNA species (Reksen et al., 2012). This also requires further investigation by studying cow management risk factors.

4.5 Distribution of SNA species in quarters

Distribution of SNA species over the 4 udder quarters had a high intraclass correlation within cow and quarters. Therefore, SNA species have cow-related factors (as previously exemplified) that determine whether a quarter acquires a SNA species infection. However, perhaps there is a high rate of quarter-to-quarter infection, indicating a possible contagious characteristic of SNA species (Barkema et al., 1997). Barkema et al. (1997), observed that SNA had a higher prevalence in rear quarters, which differed from studies reported in this thesis

(Chapter 2), and reported by Dufour et al. (2012) who also used the samples collected in the

CBMRN cohort study. In the current study, overall SNA positive-quarters distribution was not different among quarters, with the same outcome for the most frequently isolated species, S. chromogenes, S. simulans, S. xylosus, S. haemolyticus, and S. epidermidis. According to De

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Vliegher et al. (2003), the balanced distribution of S. chromogenes and S. simulans might have resulted from its colonization/ adaptation to udder skin, although no reasonable explanation apparent for the other species. Future research should focus in quarter-level risk factors such as differences in teat-end shape, or post-milking dipping management (De Visscher et al., 2016a), to fully account for differences in transmission dynamics of SNA among quarters (Zadoks et al.,

2001, Reksen et al., 2012).

4.6 Distribution of SNA species according to inflammation status

Previously, presence of SNA in the mammary gland was considered unusual, and this bacterial group was referred to as a mere environmental and skin-adapted organism acting sporadically as an opportunistic pathogen (White et al., 1989, Matthews et al., 1992). More recently, with an increase in their prevalence in IMI, there is a vast body of literature classifying

SNA as a group of pathogens responsible for mild inflammation of the mammary gland, and less frequently cases of mild clinical mastitis (Chaffer et al., 1999, Taponen et al., 2007, Schukken et al., 2009a). During the 2-y period of data collection in the CBMRN cohort, the prevalence of

SNA cases was 21 and 51% in low and high SCC cases, respectively. Furthermore, in comparison to negative-culture quarters, the average SCC of SNA-infected quarters was 2-fold higher than culture-negative quarters.

Recent literature has demonstrated the utmost importance of molecular identification at the species level as the first step towards the investigation of species-specific effects in udder health (Supré et al., 2011, Fry et al., 2014, Tomazi et al., 2015). Methods used in previous research, particularly phenotypic methods, have resulted in species misidentification. For

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example, Sampimon et al. (2009a) reported that S. haemolyticus was one of the most problematic species; partially discrepant results in average SCC might have resulted from this problem.

Perhaps there is a similar problem at the molecular level. Since S. agnetis, S. hyicus and S. chromogenes are phylogenetically close, the effect of S. hyicus on SCC could have been overestimated due to misidentification, whereas effects of S. agnetis are likely under-recognized

(Taponen et al., 2012). It is noteworthy that the evaluation of SCC of SNA IMI has been, over the years, driven by the 4 main SNA species, thereby misrepresenting the effect that other species may have on udder health. For example, Bexiga et al. (2014) reported that, similar to the present study, that 4 main SNA species (S. chromogenes, S. simulans, S. epidermidis, S. haemolyticus) represent the impact of the SNA group in udder health (as measured by average

SCC). However, we believe that the other species should also be considered, in order to accurately represent the inter-species variability in SCC within this group.

Results at species level once more reinforced the different SNA species present diverse effects on SCC (Piessens et al., 2011, Supré et al., 2011, Fry et al., 2014, De Visscher et al.,

2016a). Previous research described S. chromogenes, S. epidermidis, and S. simulans as the species that increased SCC the most (Sampimon et al., 2009a, Taponen and Pyörälä, 2009,

Thorberg et al., 2009, Persson Waller et al., 2011), although they were not always consistent. We also observed that some species had a higher impact, demonstrated by their higher average SCC, higher proportion and higher prevalence in high SCC samples.

Many factors might be responsible for differences related to udder health, either by themselves or in interaction, including species-specific virulence factors, management practices and individual variables at quarter-, cow- and herd-levels (Vanderhaeghen et al., 2014,

Vanderhaeghen et al., 2015). Considering cow factors, it is generally understood that heifers and

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fresh cows are highly susceptible to SNA IMI (Supré et al., 2011, De Visscher et al., 2013,

Tomazi et al., 2015, De Visscher et al., 2016a). However, when species are analyzed separately and not as a group, the influence of cow factors (e.g. lactation stage and parity) began to diverge.

For example, S. epidermidis has been associated with both the immediate post-calving period in heifers (De Visscher et al., 2016a) and the late stage of lactation, mainly in multiparous cows

(Thorberg et al., 2006). In the present study, S. capitis and S. gallinarum were highly prevalent in the period immediately following calving (see Chapter 2), a period generally associated with high SCC (De Vliegher et al., 2005). Moreover, in Chapter 3, the same species produced a high

SCC. Therefore, perhaps IMI were not determined solely by pathogen factors, but in association with host factors.

The environment may also have affected the occurrence of SNA IMI and should be further studied (De Visscher et al., 2014). For example, S. cohnii, which was prevalent in high

BMSCC herds, was also frequently isolated in low SCC quarters. Despite producing different impacts on the mammary gland, both pathogens are isolated from the environment and might be influenced by the same management practices present in high BMSCC herds (Schukken et al.,

2009a, Schukken et al., 2009b). In the cohort study, the SNA isolates from bulk milk were also stored. Comparisons between species and respective genotypes isolated from cows, environment, and from bulk milk might provide insights to aid in the development of common management practices to control SNA IMI at both the individual cow and herd levels.

Previous studies looked into the impact of the colonization of the teat apices on SNA species distribution (De Visscher et al., 2016b). Perhaps teat cleanliness, teat lesions, teat disinfectants, and the flushing effect of removing milk during lactation are factors that influence species strains in the teat (Barkema et al., 1997, Quirk et al., 2012). We observed that S.

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chromogenes, for example, was isolated from all quarters (Chapter 2) and in equal proportions among quarters with low and high SCC, while being most prevalent in quarters with high SCC

(Chapter 3). Some practices may lead to selection of more virulent strains capable of causing high SCC, and these practices should be more fully investigated. It is, however, also possible that cow-specific factors may have the most important role in the ‘selection’ of species.

Specific virulence mechanisms of each species are also expected to affect their impact on udder health and increased prevalence in clinical mastitis (Rall et al., 2014). Staphylococcus sciuri IMI, in our study and others, caused a low average SCC, but it was also one of the most frequent causes of subclinical or clinical mastitis (Gillespie et al., 2009, Thorberg et al., 2009). A possible explanation is the existence of highly virulent strains which can, under certain conditions, cause disease. According to Park et al. (2011b) and de Freitas Guimarães et al.

(2013), S. sciuri has genes for superantigens and enterotoxins which can increase their ability to induce inflammation.

4.7 Conclusions and future research

The studies presented herein contribute towards understanding the role of the different

SNA species isolated from bovine milk in Canadian herds on udder health. Although it was not the first description, using the total number of SNA isolates available from the cohort study allowed us to determine their distribution accurately and according to several characteristics and thus describe the significance of each species.

Chapter one provides a review of SNA and respective importance of these species in the dairy industry worldwide. The most important factors affecting their distribution are BMSCC,

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housing system, and parity (with a peak in heifers in the post-calving period). At the species level, a large range of species-specific distribution patterns have been observed as a result of the infection triad (management practices, host susceptibility, and pathogen virulence mechanisms).

Overall, S. chromogenes, S. simulans, S. xylosus, S. haemolyticus, and S. xylosus were the most common SNA species. Bedded-pack herds in Alberta were more prone to isolate S. chromogenes, whereas in free-stalls in Maritimes were more likely to isolate S. epidermidis, and in Ontario and Quebec tie-stalls had more S. xylosus. These differences suggest that differences in management practices adopted in each region can influence the prevalence of a particular

SNA species and its isolation from milk. Producers aiming to decrease BMSCC should focus on the 5 most common species as well as S. arlettae, S. cohnii, and S. gallinarum. Heifers in the post-calving period are often affected by SNA species, mainly by S. chromogenes. Other species such as S. simulans, S. xylosus, S. haemolyticus, S. epidermidis, and S. sciuri can have a particular impact on multiparous cows. Therefore, molecular identification was imperative to resolve the role of SNA, as treating SNA as a group will likely lead to wrong conclusions.

The relevance of SNA species in relationship to udder health was estimated using NCDF dataset in Chapter 3. The average SCC associated with SNA species (70,000 cells/mL) was between the average of negative-culture and major pathogen-positive quarters. This average was mostly driven by the 4 most common species, S. chromogenes, S. haemolyticus, S. xylosus and S. epidermidis, whereas only S. cohnii, S. equorum and S. succinus had a lower than average SCC.

Along with these species S. capitis, S. gallinarum, S. agnetis, S. simulans, and S. hyicus also increased SCC at higher rates, with the first 3 of these also being most prevalent in high SCC samples. We inferred that SNA species impact udder health differently, with some species having potential to be considered major pathogens, whereas other species leading to higher

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prevalence in high SCC quarters and proportionally higher in clinical mastitis, such as S. chromogenes, S. haemolyticus and S. epidermidis, S. xylosus and S. sciuri.

Some suggestions on future research are described in the following paragraphs. The use of molecular techniques was fundamental to the studies described herein. Clearly, advances in

WGS will allow better discrimination of strain types contributing of the understanding of transmission dynamics (Zadoks et al., 2011, Hasman et al., 2014). Identification of the phylogenetic relationship of SNA species isolated from bovine milk, their virulence mechanisms, and determination of more appropriate genes that can increase the typeability and accuracy for identifying all SNA species involved in bovine mastitis will be the next step towards better understanding the SNA species (Calcutt et al., 2014, Zhang et al., 2014, Naushad et al., 2016). In the past, studies were hampered by technological limitations, including the extensive number of primers needed to detect virulent genes, and for species identification (Vanderhaeghen et al.,

2014).

The prevalence of SNA species isolated from milk with high SCC and cases of clinical mastitis also points to the importance of studying antimicrobial and antiseptic resistance mechanisms that may affect the treatment of IMI, dry period management, and pre- and post- milking disinfection practices (Pate et al., 2012, Quirk et al., 2012, Saini et al., 2012, Taponen et al., 2015). Rising concern on the use of antimicrobials in animals and potential transfer of resistant organisms to humans has stimulated discussions worldwide. Antimicrobial resistance has been reported as highly prevalent (Sampimon et al., 2011, Frey et al., 2013, Taponen et al.,

2015) albeit variable among SNA species (Persson Waller et al., 2011, Sampimon et al., 2011,

Wedley et al., 2014, Taponen et al., 2015). However, there is no further study on antimicrobial resistance in SNA species in Canadian herds. Therefore, studies investigating management

106

strategies that decrease the use of antibiotics (to avoid the selection and spread of antimicrobial resistance genes) in SNA are necessary.

Protective aspect of SNA species, especially of S. chromogenes, was experimentally demonstrated in other studies (De Vliegher et al., 2003, Breyne et al., 2015). Furthermore, a lower incidence of clinical mastitis and higher milk production in heifers infected with SNA was reported (Piepers et al., 2010). Moreover, De Vliegher et al. (2004) demonstrated that various strains of S. chromogenes may confer decreased colonization or inhibition of growth of major pathogens in the udder. Consequently, the study of inhibition patterns of SNA species in vitro, and their associations with virulence mechanisms that might be present in SNA genome needs to be conducted. The interaction between SNA and other mastitis pathogens during co-infections in the udder deserves further attention, whereby this direct interaction between bacterial organisms is discriminated from the host responses to the IMI.

Finally, the NCDF dataset is a goldmine of data that still needs to be explored. Using these data, we need to investigate whether the effect of IMI with SNA species on udder health, milk production, and culling is SNA species-specific. Heifers and cows infected by different SNA species during the post-calving period need to be followed through subsequent lactations.

Heifers in the early post-calving period could either benefit from the putative protective aspects of some SNA species, or be adversely affected by other SNA species (Braem et al., 2014,

De Visscher et al., 2016b). According to De Vliegher et al. (2012), SNA species are isolated from 22 to 71% of heifer quarters, with 30% of cases occurring in the first 2 wk after calving.

Therefore, since heifers are so crucial for farm productivity, more research is needed to identify pathogen-specific risk factors. Previously, some strategies such as control of flies, suckling among young stock, microbiological standards of colostrum fed to calves (Matthews et al., 1992,

107

Piepers et al., 2011, Braem et al., 2013), good nutrition, good hygiene during pregnancy and calving, antibiotic treatment before calving, and genetic susceptibility to infection have been identified as risk factors that warrant investigation in Canada (Piepers et al., 2011, De Vliegher et al., 2012).

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