UNIVERSITY OF CALGARY. Distribution of Staphylococcus non-aureus isolated from bovine milk. in Canadian herds. Larissa Anuska Zeni Condas A THESIS

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1 UNIVERSITY OF CALGARY Distribution of Staphylococcus non-aureus isolated from bovine milk in Canadian herds by 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 Larissa Anuska Zeni Condas 2016

2 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 freestall 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

3 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

4 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

5 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 s 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. v

6 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). vi

7 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

8 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 Impact of mastitis on the dairy industry Staphylococcus non-aureus Phenotypic identification Genotypic identification Relevance of Staphylococcus non-aureus in intramammary infection Prevalence of SNA IMI Effect of SNA IMI on udder health and milk production Thesis outline Distribution of SNA in different herd, cow and quarters SNA species and SCC CHAPTER TWO: PREVALENCE OF STAPHYLOCOCCUS NON-AUREUS SPECIES ISOLATED FROM INTRAMAMMARY INFECTION IN CANADIAN DAIRY HERDS Abstract Introduction Materials and methods Herds and cows Sampling Laboratory analyses Definition of intramammary infection SNA isolates identification Statistical analyses Prevalence Intraclass correlation coefficients Multilevel multivariable analysis Results Distribution of SNA species positive-quarters Prevalence of SNA species IMI Overall Cow-level associations Quarter-level association Province, housing and bulk milk SCC viii

9 2.4.3 Multivariable analysis Discussion Conclusions CHAPTER THREE: DISTRIBUTION OF SNA SPECIES IN QUARTERS WITH LOW AND HIGH SOMATIC CELL COUNTS, AND CLINICAL MASTITIS Abstract Introduction Materials and methods Herds and cows Sampling Laboratory analyses Definition of intramammary infection Dataset SNA species identification Statistical analyses SCC estimates Distribution estimates Prevalence estimation Results Somatic cell count Distribution of SNA species Prevalence of SNA species Discussion Conclusions CHAPTER FOUR: SUMMARIZING DISCUSSION SNA species identification Most common SNA species in milk samples Distribution of CNS species according to herd level factors Distribution of SNA species according to cow factors Distribution of SNA species in quarters Distribution of SNA species according to inflammation status Conclusions and future research REFERENCES ix

10 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 Table 2-2. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus non-aureus species intramammary infection, according to quarter location Table 2-3. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection within region in Table 2-4. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection within 3 barn types Table 2-5. Quarter- and cow-level prevalence of Staphylococcus non-aureus species intramammary infection according to bulk milk somatic cell count category 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 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) Table 3-2. Somatic cell count (SCC) of quarters culture-positive for Staphylococcus nonaureus (SNA) species, major pathogens and culture-negative quarters from 91 dairy herds in 4 regions of Canada 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 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 x

11 List of Figures and Illustrations Figure 2-1. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection across parities Figure 2-2A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection across parities for the five most frequently isolated species Figure 2-2B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection across parities for the 6th to 10th most frequently isolated species Figure 2-3. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection over the lactation Figure 2-4A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection over the lactation for the 5 most frequently isolated species Figure 2-4B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection over the lactation for the 6 th to 10 th most frequently isolated species Figure 2-5. Overall quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection in the first 30 DIM Figure 2-6A. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection in the first 30 DIM for the 5most frequently isolated species Figure 2-6B. Quarter-level prevalence (per 100 quarters at risk) of Staphylococcus nonaureus intramammary infection in the first 30 DIM for the 6 th to 10 th most frequently isolated species Figure 3-1. Estimates of sensitivity and specificity for Staphylococcus non-aureus (SNA) according to the structure of the dataset Figure 3-2. Distribution of somatic cell count (SCC) of culture-negative, Staphylococcus non-aureus (SNA, figure legend states CNS not SNA), and Staphylococcus aureuspositive quarters 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 xi

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

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

14 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

15 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

16 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

17 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. 3

18 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

19 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

20 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

21 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

22 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 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. 8

23 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

24 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 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

25 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 11

26 (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) 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

27 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

28 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 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. 14

29 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. 15

30 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 16

31 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 17

32 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

33 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