Understanding the Relationship Between the Tonsil Microbiota and Clinical Streptococcus suis Infection in Nursery Pigs
by
Sarah Victoria Hill
A Thesis presented to The University of Guelph
In partial fulfilment of requirements for the degree of Master of Science in Pathobiology
Guelph, Ontario, Canada
© Sarah Hill, September 2019 ABSTRACT
UNDERSTANDING THE RELATIONSHIP BETWEEN THE TONSIL MICROBIOTA AND CLINICAL STREPTOCOCCUS SUIS INFECTION IN NURSERY PIGS Sarah Hill Advisor(s): University of Guelph, 2019 Dr. Nicole Ricker Dr. Vahab Farzan
The objective of this study was to determine if the tonsillar microbiota is different in nursery pigs with Streptococcus suis disease compared with that of healthy pigs. Fifty- six pigs from 9 farms were classified as confirmed cases, probable cases and healthy controls. Illumina MiSeq-sequencing of the 16S V3-V4 hypervariable region was done to assess the microbiota composition and the mothur pipeline was used for clustering and taxonomical assignment. Overall, 453, 600 and 334 different taxa were identified in confirmed cases, probable cases and controls, respectively. The beta diversity differed significantly between the farms (p=0.035). Alpha diversity differed between probable cases and control (p<0.001) and tended to be different between confirmed and probable cases (p=0.088). The inverse Simpson tended to be different between confirmed cases and controls (p=0.083). Phylogenetic analysis demonstrated no differences between the microbiota of nursery pigs with clinical S. suis infection and that of heathy animals.
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ACKNOWLEDGEMENTS
First and foremost, I’d like to thank my entire advisory committee: Dr. Vahab Farzan, Dr. Nicole Ricker, Dr. Janet MacInnes and Dr. Emma Allen-Vercoe for all their guidance and support throughout my master’s.
I would like to thank Cole Siegel and his entire family for their unconditional support. Cole, all the late nights spent editing my papers and the chores you did around the house were so helpful and I cannot thank you enough.
Mum, thank you for all your love and unconditional support; the daily phone calls kept me sane. Pregent Family, thank you for always hosting family gatherings, its always nice to see you all and it’s a nice break from school!
The Hill family, thank you for everything you have done for me. Uncle Dale and Aunt Sonja, I would not be here today without your support. Laura and Michelle, you were the sisters I never had; hanging out with you both, whether it was in Ottawa or Toronto would always cheer me up. Oma, thank you for always treating me like a grand- daughter. I would also like to thank Oma for my first car, it was a saving grace for commuting back and forth during my master’s. Dad and Amanda, thank you for all the effort you’ve put in for me to have a relationship with my baby brothers Logan and Atlas. I hope one day when they are older, they too follow their passions and enjoy school. Aunt Andi and Uncle Dave, thank you for all your Facebook message checkups. It was really nice knowing I had family who care so much about my well-being.
I would like to thank the entire pig group for being so welcoming, the coffee and the Friday afternoon visits. Dr. Bob Friendship, thank you for always having your door open; from the assistance in pig knowledge to the casual chats, I really appreciate it all. Linda Kraemer, thank you for giving me a desk in the pig palace- it was super convenient being close to my friends and the pig family. Outbreaks softball team, thank you having me on your team- the weekly Tuesday games were so much fun and a good break from school work!
I would also like to thank the Canadian First Research Excellence Fund for financial support and the Ontario pork producers who very generously granted us access to their facilities and for their patient assistance during the sampling processes.
Finally, King George and Eli - thank you for always being the best fur friends one could have.
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DECLARATION OF WORK
All the work described was performed by me with the exception of:
1. Leann Denich, Jeremy Wong, Chris McLaren-Almond, Maria Amezcua,
Kaushalya Kuruppu, Eric Perrin, Karen De Bruyn, assisted in farm visits
2. Leann Denich and Emily Arndt performed the culture-dependent study and
identified S. suis isolates
3. Jordan Buchan and Alison Jeffery assisted in tonsil preparation for DNA
extraction
4. Jeff Gross from Advanced Analytical Centre at the University of Guelph
performed Qubits for most of the samples and performed Illumina MiSeq on
all samples
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TABLE OF CONTENTS
Abstract ...... ii
Acknowledgements ...... iii
Declaration of Work ...... iv
Table of Contents ...... v
List of Tables ...... viii
List of Figures ...... ix
List of Abbreviations ...... x
List of Appendices ...... xi
1 Literature Review ...... 1
1.1 Introduction ...... 1
1.1.1 Streptococcus suis ...... 1
1.1.2 Clinical diagnosis ...... 1
1.1.3 Epidemiology ...... 2
1.1.4 Virulence ...... 3
1.1.5 Treatment and management ...... 4
1.2 Tonsils, a natural habitat of S. suis ...... 5
1.2.1 Anatomy and the role of tonsils of the soft palate ...... 5
1.3 Tonsil microbiome ...... 6
1.3.1 Bacterial communities in the tonsils of the soft palate ...... 6
1.3.2 Presence of bacteria and viruses associated with swine diseases ...... 9
1.4 Roles within bacterial communities ...... 10
1.4.1 Microbial interactions ...... 10 v
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1.4.2 Polymicrobial infections ...... 13
1.5 Methods for identifying microbial communities ...... 14
1.5.1 DNA extraction kits ...... 14
1.5.2 Quantification of DNA ...... 15
1.5.3 16S rRNA gene sequences ...... 15
1.5.4 DNA sequencing ...... 17
1.5.5 Analysis of microbiota data ...... 17
1.6 Conclusion ...... 18
1.7 Figures ...... 19
1.8 Tables ...... 20
2 A descriptive analysis of the microbiota of the tonsil of the soft palate in nursery pigs in Ontario farms ...... 21
2.1 Introduction ...... 21
2.2 Methods and materials ...... 23
2.2.1 Farm and Pig Selection ...... 23
2.2.2 Sample collection ...... 23
2.2.3 S. suis isolation and identification ...... 24
2.2.4 DNA extraction from tonsil tissues ...... 25
2.2.5 16rRNA sequencing ...... 25
2.2.6 Data analysis ...... 26
2.2.7 Statistical analysis ...... 27
2.3 Results and discussion ...... 29
2.4 Figures ...... 39
2.5 Tables ...... 43
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3 Summary and conclusion ...... 45
References ...... 47
Appendices ...... 57
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LIST OF TABLES
Table 1-1: Summary table of core microbial communities found in studies by (Lowe et al. 2011; Lowe et al., 2012; Kernaghan, 2013)...... 20
Table 2-1: Number of controls, confirmed cases and probable cases from each farm .. 43
Table 2-2: Sum of clinical signs in confirmed cases (n=21) and probable cases (n=24)44
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LIST OF FIGURES
Figure 1-1: 16S rRNA gene (Modified from Fadrosh et al., 2014; Highlander, 2012) .... 19
Figure 2-1: Mean observed richness and inverse Simpson index in confirmed cases, probable cases and controls ...... 39
Figure 2-2: NMDS of Bray-Curtis matrix of confirmed cases and controls ...... 40
Figure 2-3: NMDS of Bray-Curtis matrix of probable cases and controls ...... 41
Figure 2-4: NMDS of Bray-Curtis matrix of probable and confirmed cases ...... 42
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LIST OF ABBREVIATIONS
Fbps – Fibronectin-fibrinogen binding proteins
Gdh – Glutamate dehydrogenase
IUPAC – International Union of Pure and Applied Chemistry
MALT – Mucosal- associated lymphoid tissue
NMDS - Non-metric multidimensional scaling
OTU – Operational taxonomic unit
PCR – Polymerase chain reaction
PERMANOVA – Permutational multivariate analysis of variance
PPM – Parts per million
URT – Upper respiratory tract
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LIST OF APPENDICES
Appendix 1: Farm level survey ...... 57
Appendix 2: Pig level survey ...... 58
Appendix 3: Pig and farm level data ...... 59
Appendix 4: 20 even mix bacterial species ATCC (Manassas, VA 20110 USA) ...... 60
Appendix 5: Number of reads per sample ...... 61
Appendix 6: Median of aggregate relative abundance at phylum level of controls ...... 62
Appendix 7: Median of aggregate relative abundance at phylum level of confirmed cases ...... 63
Appendix 8: Median of aggregate relative abundance at phylum level of probable cases ...... 64
Appendix 9: Top 5 median aggregate relative abundances at the phylum level in both control and confirmed cases ...... 65
Appendix 10: Top 5 median aggregate relative abundances at the phylum level in both control and probable cases ...... 66
Appendix 11: Top 10 median aggregate relative abundances at the phylum level in both confirmed and probable cases ...... 67
Appendix 12: Median of aggregate relative abundance at family level of controls ...... 68
Appendix 13: Median of aggregate relative abundance at family level of confirmed cases ...... 69
Appendix 14: Median of aggregate relative abundance at family level of probable cases ...... 70
Appendix 15: Top 10 median aggregate relative abundances at the family level in both control and confirmed cases ...... 71
Appendix 16: Top 10 median aggregate relative abundances at the family level in both confirmed and probable cases ...... 72
Appendix 17: Top 10 median aggregate relative abundances at the family level in both confirmed and probable cases ...... 73
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Appendix 18: Median of aggregate relative abundance at genus level of controls ...... 74
Appendix 19: Median of aggregate relative abundance at genus level of confirmed cases ...... 75
Appendix 20: Median of aggregate relative abundance at genus level of probable cases ...... 76
Appendix 21: Top 15 median aggregate relative abundances at the genus level in both control and confirmed cases ...... 77
Appendix 22: Top 15 median aggregate relative abundances at the genus level in both probable and confirmed cases ...... 78
Appendix 23: Top 15 median aggregate relative abundances at the genus level in both control and probable cases ...... 79
Appendix 24: Observed richness in all samples ...... 80
Appendix 25: Inverse Simpson of all samples ...... 81
Appendix 26: Kruskal- Wallis rank sum test at phylum level between control and confirmed cases ...... 82
Appendix 27: Kruskal- Wallis rank sum test at phylum level between control and probable cases ...... 83
Appendix 28: Kruskal- Wallis rank sum test at phylum level between probable and confirmed cases ...... 84
Appendix 29: Kruskal- Wallis rank sum test at family level between control and confirmed cases ...... 85
Appendix 30: Kruskal- Wallis rank sum test at family level between control and probable cases ...... 86
Appendix 31: Kruskal- Wallis rank sum test at family level between probable and confirmed cases ...... 87
Appendix 32: Kruskal- Wallis rank sum test at genus level between control and confirmed cases ...... 88
Appendix 33: Kruskal- Wallis rank sum test at genus level between control and probable cases ...... 89
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Appendix 34: Kruskal- Wallis rank sum test at genus level between probable and confirmed cases ...... 90
Appendix 35: Tonsil extraction protocol ...... 91
Appendix 36: E. coli sequence provided with highlighted sectioned used for primer design (Schloss, P.D. 2016.http://blog.mothur.org/2016/07/07/Customization-for-your- region/. accessed: 01/09/2019) ...... 92
Appendix 37: Mothur MiSeq SOP code modified from (Kozich, Westcott, Baxter, Highlander, & Schloss, 2013; https://www.mothur.org/wiki/MiSeq_SOP, accessed: 08/08/2019) ...... 93
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1 Literature Review
1.1 Introduction
1.1.1 Streptococcus suis
Streptococcus suis is a Gram-positive bacterium that is a common resident of the upper respiratory tract of pigs found primarily in the nasal and tonsillar cavities (Goyette-
Desjardins et al., 2014; Lowe et al., 2011). S. suis is one of the most important swine bacterial pathogens causing major economic loss in the pork industry (Chuzeville et al.,
2017; Segura et al., 2017). While most weanling pigs are colonized with S. suis and remain healthy carriers, a small percentage develop clinical S. suis infection. Why some pigs become infected is not well understood.
1.1.2 Clinical diagnosis
While S. suis is most commonly found in the upper respiratory tract, it is also sometimes present in the genital and alimentary tracts of pigs (Gottschalk et al., 2007; Goyette-
Desjardins et al., 2014). The typical presentations of an S. suis infection are sequelae to septicemia and include neurological signs associated with meningitis, cyanosis, lethargy and lameness due to arthritis (Gottschalk et al., 2007; Wertheim et al., 2009). Other manifestations include endocarditis, which can result in laboured breathing and sudden death caused by pneumonia (Gottschalk et al., 2007). Pigs 5 to 10 weeks of age are most often affected (Zimmerman et al., 2012). Clinical signs of the infection include poor appetite, head tilt, inability to stand, paddling, convulsions, lameness and conjunctivitis
(Gottschalk, 2012). 1
1.1.3 Epidemiology
The first reported case of S. suis in pigs was reported in 1954 and the first reported human case in 1968 (Feng et al., 2014; Wertheim et al., 2009). Infection of S. suis in pigs is globally distributed with cases reported in North America, South America, Asia, and Europe (Feng et al., 2014; Goyette-Desjardins et al., 2014; Wertheim et al., 2009).
Thirty-five serotypes of S. suis have been described; however, some are no longer considered S. suis (Auger & Gottschalk, 2017). Serotype 2 is the most important worldwide as it can cause severe porcine and human infection (Auger & Gottschalk,
2017). Transmission of S. suis is believed to occur by a variety of means including vertical transmission where piglets acquire an infection from sows during the birth process, direct pig to pig contact, and transmission from the environment. Entrance of
S. suis into the pig can occur via inhalation, orally via the digestive tract, through wounds, or from navel or genital infection (Seitz et al., 2016). Transmission of S. suis among animals is mainly through the respiratory route, but in humans the main route is through contact of cutaneous lesions with contaminated animals, meat or carcasses
(Goyette-Desjardins et al., 2014). While the majority of human cases have been found in Southeast Asia (Gottschalk et al., 2007; Goyette-Desjardins et al., 2014; Ye et al.,
2009), a recent human case in occurred in Canada in a 69-year-old farmer (Gomez-
Torres et al., 2017). Over the past decade, S. suis has become an emerging zoonotic pathogen as sporadic cases in humans have begun to appear at an increasing rate
(Gottschalk et al., 2007; Goyette-Desjardins et al., 2014; Ye et al., 2009).
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1.1.4 Virulence
The virulence mechanisms of S. suis are still poorly understood and there have been discrepancies between various studies (Segura et al., 2017). In a recent review by
Fittipaldi et al. (2012), 67 confirmed and putative virulence factors are described. These include capsular polysaccharide biosynthesis, adhesins, metal uptake systems, transcriptional regulators, metabolic regulators, and genes associated with resistance to toxicity (Fittipaldi et al., 2012). Capsule polysaccharides (CPS) can assist in survival in the bloodstream as they may allow the bacteria to travel undetected (Fittipaldi et al.,
2012). However, there has been evidence of non-capsulated strains also surviving in the blood (Fittipaldi et al., 2012). CPS are typically comprised of various sugars, including sialic acid (Baums & Valentin-Weigand, 2009). Serotypes 1, 2, 1/2 , 14 and
27 carry genes that are responsible for the synthesis of sialic acid (Baums & Valentin-
Weigand, 2009; Smith et al., 2000) which may help S. suis to adhere to monocytes which results in camouflage for S. suis. (Fittipaldi et al., 2012). Nine adhesion factors have been described: i.e., Fibronectin-fibrinogen binding proteins (Fbps), Enolase,
Dipeptidylpeptidase IV, Amylopullulanase, Glutamine synthetase, Gylceraldehye-3- phosphate dehydrogenase, 6-phosphogluconate-dehydrogenase, glutamine synthetase and SrtF and SrtG pilus (Fittipaldi et al., 2012). Another confirmed virulence factor is the haemolysin suilysin (Baums & Valentin-Weigand, 2009; Fittipaldi et al., 2012; Jacobs et al., 1994). S. suis has a number of metal uptake systems and regulators for zinc (AdcR and Lipoprotein 103), iron (Fur and FeoB) and manganese (TroA) (Fittipaldi et al.,
2012). LuxS, which is responsible for quorum sensing and biofilm formation may also
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play an important role in S. suis virulence (Fittipaldi et al., 2012; Wang et al,. 2011).
Researchers demonstrated that an S. suis serotype 2 mutant with a deletion of the luxS gene had significantly reduced biofilm formation, hemolytic activity, and cell adhesion
(Wang et al., 2011). This finding suggests that disrupting the communication influences the virulence of S. suis ( Wang et al., 2011).
While numerous virulence factors have been described that may have some relevance in the pathogenicity of S. suis, a number of difficulties are associated with their characterization. First, some pigs with no clinical signs can be healthy carriers of virulent strains (Segura et al., 2017). The second issue is the questionable accuracy of defining virulence based on experimental animal models (Segura et al., 2017). Species that have been used include amoeba (Dictyostelium discoideum), zebrafish, rabbits, guinea pigs and mice (Segura et al., 2017). As infection produces different pathologies in different species, it is difficult to use these studies to make conclusions about the role of particular “virulence factors” in swine and human S. suis infection (Segura et al.,
2017).
1.1.5 Treatment and management
Pigs with S. suis infection require treatment with appropriate antimicrobials (Seitz et al.,
2016). The antimicrobials commonly used are beta-lactams such as penicillin, ceftriaxone, and ceftiofur, but a wide range of other antibiotics can be used (Seitz et al.,
2016). Diseases can be limited through maximizing immunity and minimizing challenge
(Friendship & Deckert, 2017). Piglets are often weaned between the third and fourth
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week of life. At this time, these pigs have lost their passive immunity from the colostrum acquired from birth to relying on an immature active immunity (Friendship & Deckert,
2017). A recent study examined whether management practices influenced the tonsillar microbiota (Pena Cortes et al., 2018b). These researchers demonstrated that early stressors such as moving pigs to new batches, weaning and changing from milk to solid plant-based food caused a dramatic shift in the tonsillar microbiome of the weanling pigs (Pena Cortes, et al., 2018b). Management factors that minimize the impact and spread of disease include: all-in/all-out management of pig flow, proper ventilation, minimizing overcrowding, keeping the facility sanitary, washing and disinfecting between batches, and maintaining strict biosecurity (Friendship & Deckert, 2017; Varela et al., 2013). Practices that help to establish maximum immunity in a herd include: good nutrition, minimizing stress and the use of vaccinations (Friendship & Deckert, 2017).
1.2 Tonsils, a natural habitat of S. suis
1.2.1 Anatomy and the role of tonsils of the soft palate
The tonsil of the soft palate is composed of lympho-epithelium, lymphoid follicles, connective tissue, B and T-lymphocytes, dendritic cells and macrophages (Horter et al,
2003). Deep invaginations in the tonsils of the soft palate called crypts are about 90 to
500 µm in diameter and extend into lymphoid tissue containing neutrophils (Belz &
Heath, 1996; Horter et al., 2003). The crypts are lined by epithelial cells and may contain food particles and various microbes (Belz & Heath, 1996; Horter et al., 2003).
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The tonsils of the soft palate are secondary lymphoid organs. They can survey, detect and initiate an immune response to pathogens (Horter et al., 2003; Belz and Heath,
1996). At the same time, tonsils are a frequent site of colonization and may provide a portal of entry for bacteria and viruses to systemic sites (Horter et al., 2003; Belz and
Heath, 1996).
1.3 Tonsil microbiome
1.3.1 Bacterial communities in the tonsils of the soft palate
The microbiota of the tonsils has been examined using both culture-dependent and culture-independent methods. In culture-dependent studies, tonsils are swabbed and the bacteria grown in-vitro—usually on blood plates or other rich media. In these studies, the analysis relies on the ability of the bacteria to grow in an in-vitro environment which only partially mimics conditions in-vivo. In culture-independent methods, bacterial DNA is extracted from swabs or homogenized samples and 16S rRNA sequences are analyzed to determine which bacteria are present. Although not limited by the ability to grow in in-vitro, culture-independent methods also have weaknesses (see below).
In a relatively recent study, Lowe et al. examined eight 18- to 20-week old pigs from two herds (Lowe et al., 2011). Both herds were healthy; herd 1 had no recent history of respiratory disease while herd 2 did (Lowe et al., 2011). Bacterial communities were evaluated using culture-dependent and culture-independent methods. In the culture dependent analysis, 253 individual isolates were characterized and in over half the pigs
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in both herds, Pasteurella multocida, Streptococcus suis, Streptococcus dysgalactiae,
Staphylococcus aureus, Staphylococcus epidermidis and Escherichia coli were isolated
(Lowe et al., 2011). Using 16S rRNA gene clone library analysis, similar genera and families were found in both herds, but their prevalence varied (Lowe et al., 2011). In both herds, the dominant genera were Actinobacillus, Haemophilus, Pasteurella,
Porphyromonas, Fusobacterium, Bacteroides and Prevotella (Lowe et al., 2011).
A study done in 2012 described the ‘core’ microbiome of the tonsils of twelve 18- to 20- week old healthy pigs (Lowe et al., 2012). This analysis was done using DNA extracted from tissue or brush swab samples followed by pyrosequencing of amplicons of the V4-
6 region of the 16S rRNA gene (Lowe et al., 2012). Five core microbial phyla were identified: i.e., Proteobacteria (73.4%), Firmicutes (17.8%), Fusobacteria (5.6%),
Actinobacteria (1.2%), and Bacteroidetes (0.8%) (Lowe et al., 2012). Most notably, species from the families Pasteurellaceae and Streptococcaceae were identified.
Actinobacillus indolicus, A. minor, A. porcitonsillarum and Haemophilus parasuis were found in all samples (Lowe et al., 2012) and A. porcinus, A. rossii, H. felis, Pasteurella aerogenes, P. canis, P. multocida, and S. suis were found in most samples (Lowe et al.,
2012).
A T-RFLP study together with 454 pyrosequencing of amplicons of the V3-5 region of the 16S rRNA gene of culture-dependent and tissue-derived samples was done by
Kernaghan, 2013. In this study, 3 times more diversity was detected in the tissue- derived samples when compared to culture-dependent samples (Kernaghan, 2013).
The core phyla of the microbiota of the soft palate identified in the tissue-derived 7
together with the culture-dependent samples included: Firmicutes, Bacteroidetes,
Fusobacteria, Proteobacteria and Actinobacteria (Kernaghan, 2013). Kernaghan also evaluated the samples for the prevalence of ten foodborne and pig pathogens using T-
RFLP analysis of the microbiota of 120 unfit pigs and 18 healthy pigs (2013) and found that there was a significant differences (Kernaghan, 2013). Surprisingly, there was a higher prevalence of foodborne pathogens in heathy pigs including species belonging to the genera Streptococcus, Escherichia, Salmonella, and Yersinia (Kernaghan, 2013).
Identifying potentially pathogenic microbes present in both healthy and sick pigs can be used to anticipate, prevent and control infections (Kernaghan, 2013).
These studies characterized the common bacterial species present in a healthy tonsil tissue in swine (Table 1-1). While there is a difference between the diversity results of culture-dependent and culture-independent methods, there seems to be a consensus regarding the core microbiota of the tonsils of the soft palate in swine.
More recently, a culture-independent study examined the tonsillar microbiome development of 24 pigs (6 sows, 4 piglets each) from newborn to weanling (Pena
Cortes et al., 2018a). The researchers swabbed tonsils of the piglets immediately after birth and 8 hours after birth as well as the vagina, teat skin and tonsils of the sows
(Pena Cortes, et al., 2018a). The objective of this study was to follow the development of the tonsillar microbiota as piglets were exposed to their surroundings (Pena Cortes et al., 2018a). This study found that the bacterial communities of tonsillar swab samples of piglets immediately after birth resembled that of the sow’s vagina (Pena Cortes et al.,
2018). Eight hours after birth the microbiota were similar to the vaginal and teat skin 8
samples (Pena Cortes et al., 2018a).The study also illustrated that piglets from different sows had different bacterial communities in their tonsils immediately after birth and 8 hours after birth, but within 3 weeks of age the microbiota had a similar composition
(Pena Cortes et al., 2018a). At the 2 to 3 week mark, Staphylococcaceae,
Fusobacteriaceae and Leptotrichiaceae were abundantly present, but by the fourth week their presence was reduced (Pena Cortes et al., 2018a). The most prevalent species found in the tonsils of newborn to 4 week old piglets were members of the families Pasteurellaceae, Moraxellaceae and Streptococcaceae (Pena Cortes et al.,
2018a). By comparing the tonsil microbiomes to the sows’ teat and vaginal microbiomes, these researchers concluded that Pasteurellaceae and Streptococcaceae were most likely inherited from the vaginal tract during birth while Staphylococcaceae and Moraxellaceae were most likely inherited from teat skin (Pena Cortes et al., 2018a).
1.3.2 Presence of bacteria and viruses associated with swine diseases
The tonsils are a common site of microbial colonization and the presence of a variety of potentially pathogenic bacteria and viruses in the tonsils has been evaluated by a number of researchers. For example, MacInnes et al. (2008) estimated the prevalence of S. suis, toxigenic strains of Pasteurella multocida, Actinobacillus pleuropneumoniae,
A. suis, and Haemophilus parasuis in the upper respiratory tract of asymptomatic nursery aged pigs (MacInnes et al., 2008). Swabs of tonsils of healthy pigs were cultured and PCR tests were used to look for putative pathogens (MacInnes et al.,
2008). All but 1 herd tested positive for S. suis and 48% were serotype 2 and/or 1/2
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positive; 96% tested positive for H. parasuis; 78% of herds tested positive for A. pleuropneumoniae; and at least 16% tested positive for A. suis (MacInnes et al., 2008).
Based on a serological assay, one herd was positive for toxigenic strains of P. multocida (MacInnes et al., 2008).
A study by O’Sullivan et al. (2011) determined the prevalence of important pathogenic bacteria and viruses in tonsils from carcasses of pigs (264 normal and 180 abnormal) originating from 264 farms and collected at a slaughter plant. Using culture-dependent methods, the most frequently isolated bacterial species was S. suis (53.7%) and the most common viruses present were porcine respiratory and reproductive syndrome
(PRRS) virus (22.9%) and porcine circovirus 2 (PCV-2 ) (11.9%) (O’Sullivan et al.,
2011). Many commensal organisms and opportunistic bacteria were identified in both normal and abnormal carcasses including: Streptococcus porcinus, S. dysgalactiae,
Staphylococcus aureus, P. multocida, and Staphylococcus hyicus (O’Sullivan et al.,
2011). Interestingly, this study showed that tonsils which were positive for PRRS virus and/or PCV-2 did not have an increased likelihood of being positive for S. suis
(O’Sullivan et al., 2011).
1.4 Roles within bacterial communities
1.4.1 Microbial interactions
Bacteria are often designated as commensals, opportunistic pathogens, or true pathogens (Wassenaar & Gaastra, 2001). Bacterial virulence factors are often classed 10
into 3 sub-categories: true virulence gene, virulence-associated genes and virulence life-style genes. True virulence genes can be genes which are directly responsible for pathological damage and are absent in bacteria that do not cause pathogenesis.
Virulence- associated gene can be genes which regulate expression of virulence or required for the activity of true-virulence genes. Finally, virulence life-style genes can be genes which enable intracellular survival, or enable colonization of the host, or utilize host factors to aid in bacterium survival. However, the terminology of whether bacteria are pathogens or commensal can become ambiguous because of the nature of the virulence life-style genes (example, commensal bacteria can colonize host as well)
(Wassenaar & Gaastra, 2001). In addition, microbes live in heterogenous environments, resulting in social interaction (Leid & Cope, 2011; Xavier, 2011). Such interactions allow bacteria such as S. suis to acquire or contribute products of expressed genes that facilitate microbial survival in host environments (Leid & Cope, 2011; Xavier, 2011).
Bacterial cells can communicate and interact with each another in their host environment through the mechanism of quorum sensing in two ways: intraspecies communication and interspecies communication (Pflughoeft & Versalovic, 2012). By signaling one another with the use of small molecules, bacteria can sense the same species present (intraspecies communication) or whether other bacterial species are present (interspecies communication) (Pflughoeft & Versalovic, 2012). Intraspecies communication can result in regulation of virulence factors and nutrient availability, and can affect community composition through the production of antimicrobials (Pflughoeft &
Versalovic, 2012). Interspecies communication can also result in modulation of immune
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response in host, regulation of transcription factors, and alterations in nutrient availability (Pflughoeft & Versalovic, 2012). Bacteria can also interact with one another through gene transfer. Genes that can be transferred include; antibiotic resistance, virulence factors and metabolism related genes (Pflughoeft & Versalovic, 2012).
Studies by Allison Barre demonstrated a one-sided synergistic effect between S. suis and H. parasuis in co-culture (Barre, 2015). S. suis serotype 2 had increased growth when in planktonic co-culture with H. parasuis, while H. parasuis had decreased growth when in planktonic co-culture with S. suis (Barre, 2015). In co-culture biofilms, S. suis and H. parasuis formed significantly less biofilm together in comparison to when they were grown alone as planktonic, suggesting that there was a competition for nutrients between the two species (Barre, 2015). It was hypothesized that S. suis produces an acidic, toxic environment which inhibits the survival of H. parasuis, resulting in reduced biomass growth (Barre, 2015). Barre also examined whether quorum sensing was involved in the effects observed on planktonic and biofilm co-culture growth (Barre,
2015). Preliminary studies concluded that soluble molecules contributed to the interaction of the two bacteria, but the identity of the molecules involved was not determined (Barre, 2015). While Barre’s research suggested a very complex relationship between 2 species, the intricate communication between a diverse group of microbes in the tonsils of the soft palate is likely far more complex (Barre, 2015).
In a recent review Short et al. (2014) summarized numerous human diseases where particular bacteria are dominant in the microbiome and the consequences of inter- bacterial interactions (Short et al., 2014). For example, cystic fibrosis, device-related 12
infections, urinary tract infections, pneumonia, otitis media, periodontitis, diabetic ulcers/wound infections and inflammatory bowel disease are all are examples [of the consequences] of polymicrobial infections (Short et al., 2014). Co-infection of
Streptococcus pneumoniae and H. influenzae can lead to an increased biofilm formation and synergistic induction of proinflammatory cytokines (Ratner et al., 2005; Shak et al.,
2013; Short et al., 2014). The dominant bacteria in cystic fibrosis are Pseudomonas aeruginosa, with co-infection of Staphylococcus aureus, Haemophilus influenzae,
Burkholderia cepacia complex or Stenotrophomonas maltophila (Bhagirath et al., 2016;
Short et al., 2014). This has been shown to be influenced by inter-bacterial interactions between these organisms, including biofilm growth and quorum sensing (Bhagirath et al., 2016; Short et al., 2014).
1.4.2 Polymicrobial infections
As mentioned above, the tonsils are home to a variety of commensal organisms as well as opportunistic and primary pathogens. S. suis is an opportunistic pathogen; there are pigs that carry the bacterium but always remain asymptomatic while some will develop clinical disease. The mechanism of this transition is still unknown, but it is reasonable to think that other microbes might be involved. When the immune system is challenged by one organism, another may take the opportunity to invade the host. For example, some of the most common and important pathogenic viruses present in the Canadian pig population are PRRS virus, PCV-2 and influenza A virus (IAV) which can often be isolated from tonsils. Bacterial pathogens including A. pleuropneumoniae, H. parasuis,
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P. multocida and M. hypopneumoniae are also often present in pig tonsils. When two or more of these bacterial or viral pathogens are present in pigs with respiratory clinical signs, the disease is sometimes referred to as porcine respiratory disease complex
(PRDC) (Opriessnig et al., 2011). PRDC is a result of various combinations of respiratory pathogens and is characterized by decreased feed intake, fever, cough and dyspnea (Opriessnig et al., 2011). The factors involved in PRDC include environmental factors, the virulence of the pathogen, the affected pig’s age and immunological status, and production management (Opriessnig et al., 2011).
1.5 Methods for identifying microbial communities
1.5.1 DNA extraction kits
There are a multitude of DNA extraction kits, all differing in effectiveness but all heavily reliant on the quality of the starting sample. The study done by Kernaghan (2013) compared 4 DNA extraction kits to determine the most effective method for characterizing tonsil microbiota of pigs (Kernaghan, 2013). DNA was extracted following the manufacturer’s instructions with two added bead-beating steps—the first to disrupt the tissue and the second to disrupt the bacteria (Kernaghan, 2013). The
DNeasy Blood & Tissue kit with the Gram-positive protocol was shown to be the best kit as it was the only one where the infrequent members of the community could be detected (Kernaghan, 2013). The choice of DNA extraction kits is heavily reliant on factors such as sample type (swab, feces, tissue etc.) and costs (Highlander, 2012).
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1.5.2 Quantification of DNA
For 16S rRNA gene sequence analysis, the quantity of intact DNA needs to be evaluated. The NanoDrop (Thermo Scientific) ultra-violet spectrophotometer and the
Qubit florescence system (Life Technologies) (Simbolo et al., 2013) are often used to measure DNA concentration. Both the NanoDrop and the Qubit are relatively fast, easy and cost effective (Simbolo et al., 2013). However, the NanoDrop can over-estimate
DNA concentration and the Qubit doesn’t automatically provide information about impurities in DNA samples; factors which can negatively affect sequencing results
(Simbolo et al., 2013). It is suggested to combine both technologies for a more accurate quantification analysis of the DNA samples (Simbolo et al., 2013).
1.5.3 16S rRNA gene sequences
16S rRNA and 18S rRNA sequences can be used to look at the phylogeny of prokaryotes and eukaryotes, respectively (Woese & Fox, 1977). Using 16S rRNA genes as a marker for the identity of bacterial species is an effective approach for surveying complex microbial communities (Tremblay et al., 2015). The 16S rRNA gene contains highly conserved regions, which can be used to amplify the 9 hypervariable regions
(Figure 1-1) (Yang et al., 2016). Since it is not easy to sequence the whole gene, partial gene regions are sequenced by selecting desired hypervariable regions. The choice between specific hypervariable regions and combinations of hypervariable regions has been long debated.
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For example, Yang et al. (2016) examined the phylogenetic relationship, or geodesic distance, between the hypervariable regions. They demonstrated that V4-V6 had the shortest geodesic distance and was the most reliable for representing full 16S rRNA genes (regions V1-V9) (Yang et al., 2016). The phylogenetic relationship of the V2 and
V8 was the least similar and had the longest geodesic distance from the full gene (Yang et al., 2016).
Another study illustrated the validity of using specific hypervariable regions in comparison to using the whole 16S rRNA gene (Martínez-Porchas et al., 2016). These researchers looked at primers covering V1-V9 (whole gene) and gene fragments V3-V4,
V4-V5 and V3-V5. Using V3-V4 region, only 45.4% of the sequences had the same classification in comparison to that obtained when V1-V9 was used. Only 41.1% and
52.4% of V4-V5 and V3-V5, respectively, matched with V1-V9. The sensitivity and specificity of the V3-V5 for species was 56.41% and 86.34, respectively (Martínez-
Porchas et al., 2016). The sensitivity and specificity of V4-V5 for species was 44% and
78.86%, respectively. Finally, the sensitivity and specificity of V3-V4 for species was
48.32% and 80.13%, respectively. The study concluded that sequencing the whole gene produced the best results; but since that is not always feasible, the V3-V5 fragment loses the least amount of sensitivity and specificity (Martínez-Porchas et al.,
2016).
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1.5.4 DNA sequencing
Over the last several decades, DNA sequencing technologies have become important tools. Methods for DNA sequencing have evolved and the common limitations for each include lengths of reads, cost, and processing time (Franca et al., 2002; Liu et al.,2012).
In 2012, the MiSeq platform was launched by Illumina Inc. It is used for small genomes and targeted sequencing making it ideal for bacterial DNA sequencing (Illumina, 2017).
Illumina NGS uses a sequencing by synthesis approach that involves four main steps: library preparation, cluster generation, sequencing and data analysis (Illumina, 2017). In the library preparation step, adapter ligated fragments are PCR amplified (Illumina,
2017). In the cluster generation step, the library is then put into a flow cell where amplicons are then bound to oligosaccharides complementary to the library adapters, followed by bridge amplification (Illumina, 2017). Afterwards, sequencing uses reversible terminator dNTPs incorporated into the DNA template strands (Illumina,
2017). Lastly, the data analysis and alignment step is done using a wide variety of bioinformatics software (Illumina, 2017).
1.5.5 Analysis of microbiota data
The software used for microbiota analysis needs to be able to handle a large amount of data, be flexible, perform at an effective speed, and be easily maintained (Schloss et al.,
2009). There are numerous pipeline software packages used for analyzing sequences.
A review in 2014 compared 7 pipelines: Mothur, WATERS, Genboree, QIIME, VAMPS,
RDPipeline, and SnoWMAN (Nilakanta et al., 2014). Mothur and QIIME were the most
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comprehensive (Nilakanta et al., 2014). QIIME uses a minimum read length of 200 bp, a maximum of 1000 bp, eliminates homopolymers of > 6 bp and filters for a minimum quality score of 25, while the restrictions of Mothur are user defined (Nilakanta et al.,
2014). Mothur is a comprehensive software platform that was first introduced in 2009 and was developed to overcome limitations in flexibility, speed and sequence capacity that were present in other pipelines (Schloss et al., 2009). It is an object-oriented, free platform which continuously releases updates, allowing it to evolve with research needs
(Schloss et al., 2009). Features worth noting by the developer include: the availability of tools to calculate a and b diversity, visual tools to generate Venn diagrams, heat maps and dendrograms, and a pairwise sequence distance calculator (Kozich et al., 2013).
1.6 Conclusion
Characterization of the bacterial communities in the tonsils of soft palate could assist in identifying dominant bacteria that can sequentially interact negatively and/or positively with S. suis.
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1.7 Figures
Figure 1-1: 16S rRNA gene (Modified from Fadrosh et al., 2014; Highlander, 2012)
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1.8 Tables
Table 1-1: Summary table of core microbial communities found in studies by (Lowe et al. 2011; Lowe et al., 2012; Kernaghan, 2013).
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2 A descriptive analysis of the microbiota of the tonsil of the soft palate in nursery pigs in Ontario farms
2.1 Introduction
Streptococcus suis is a Gram-positive bacterium that is a common inhabitant of the nasal and tonsillar cavities of the upper respiratory tract (URT) of swine and is also reported to colonize the digestive and genital tract (Goyette-Desjardins et al., 2014). S. suis has been reported to be present in the URT of 98% of healthy Ontario nursery- aged swine (MacInnes et al., 2008). While most pigs are asymptomatic carriers of the bacterium, approximately 5% of pigs develop clinical signs of S. suis infection
(Gottschalk, 2012). As an opportunistic pathogen, S. suis can cause septicemia, which in turn can lead to meningitis, endocarditis and arthritis (Gottschalk, 2012). Weanling pigs, ages 5- to 10-weeks old (Gottschalk, 2012), are most susceptible to infection.
Currently, it is not known why some pigs develop clinical signs of S. suis while others remain healthy.
A change in exposure to the host environment, such as change in feed, temperature and medication can affect the microbial environment, potentially altering the presence of bacteria species abundance (Pflughoeft & Versalovic, 2012). A recent study examined the development of the tonsil microbiota in healthy pigs from newborn to 19 weeks of age and demonstrated that the stress of changes in in-feed medication and movement from farrowing to the nursery room was associated with a shift in bacterial composition and affected the development of the microbiome (Pena Cortes et al.,2018b).
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The tonsils of the soft palate are an important pharyngeal mucosal-associated lymphoid tissue (MALT) (Belz & Heath, 1996). These tonsils have crypts which are small pockets in the lymphoid tissue, approximately 90 – 500 µm in diameter (Belz & Heath, 1996;
Horter et al., 2003). The tonsils play an important role in innate immunity and can provide an immediate defense mechanism against pathogens (Belz & Heath, 1996;
Horter et al., 2003). Paradoxically, the tonsils are also an important niche for colonization and can be a portal of entry for a variety of microbes including those that are pathogenic (Horter et al., 2003).
Previous studies have shown that the 5 most predominant phyla in the tonsil microbiota in pigs are Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria and Proteobacteria.
(Kernaghan, 2013; Lowe, et al., 2012). In one study looking at whether the core microbiota is shared among pigs that are ‘fit’ (healthy) and ‘unfit’ pigs ( (lameness, diarrhea and rough hair coat) the unfit pigs had greater operational taxonomic units
(OTU) richness as well as a higher beta diversity than the fit pigs (Kernaghan, 2013).
While the researchers’ goal was to characterize and profile the tonsil microbiota, they concluded that it was too early to conclude the composition of the core tonsil microbiota and further studies need to be done (Kernaghan, 2013).
The first objective of the current study is to characterize the tonsillar microbial communities of healthy pigs and those exhibiting clinical signs of S. suis infection. The second objective it to determine whether there is an association between the tonsil microbiota and clinical S. suis infection.
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The hypothesis of this study is that there will be greater diversity in the tonsillar microbiota of pigs with clinical S. suis infection than healthy swine.
2.2 Methods and materials
All animal research performed in this study was approved by the Animal Care
Committee at the University of Guelph.
2.2.1 Farm and Pig Selection
Farms in Southwestern Ontario, Canada which had on-going S. suis cases were identified by several swine veterinarians. These farms were contacted and where producers were willing to participate, sampling was done over a 16-month period. A survey was conducted to record farm practices and pig level information (Appendix 1).
All pigs selected for this study were at the nursery stage (3 - 10 weeks of age). Cases were selected based on presence of clinical signs including ataxia, tremors, opisthotonus, paralysis, dyspnea, convulsions, nystagmus, lameness, erythema, and paddling (Appendix 2). The control pigs were selected from a group of low value pigs that were unthrifty or which had problems such as hernias or rectal prolapse, but which did not exhibit clinical signs of S. suis. Depending on the frequency of on-going S. suis cases at each farm, some farms were visited multiple times (Appendix 3).
2.2.2 Sample collection
Bacterial swabs (BBL culture Ò (Sparks) and BD EswabÒ (Sparks)) were collected from the nasal, tonsils of the soft palate and fecal. The cases were anesthetized and euthanized and the following tissue samples were collected: heart, lung, liver, ileum and
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tonsil of the soft palate (Appendix 3). The meninges were swabbed using a BBL culture swab, this was done carefully to avoid potential contamination. Controls were euthanized and tonsils of the soft palate and ileum samples were collected. All samples were maintained at 4oC until brought back to the laboratory. Tissues and swabs were stored at -20°C until further processing.
2.2.3 S. suis isolation and identification
All samples were cultured for S. suis as described previously (Arndt, 2017). Briefly, tissues, swabs and blood were streaked on phenylethyl alcohol blood plates then incubated for 48 hours at 35 °C in an atmosphere of 5% CO2 (Arndt, 2017). The suspected isolates were sub-cultured on Columbia blood agar plates and incubated for
48 hours at 35 °C (Arndt, 2017). Whole cell DNA was extracted from S. suis isolates using the InstaGene Matrix kit (Bio-Rad) following the manufacturer’s instruction (Arndt,
2017). The glutamate dehydrogenase (gdh) PCR test using purified DNA was used as a preliminary screen for the identification of S. suis (Arndt, 2017). The gdh positive isolates were then evaluated using a recombination/repair protein (recN) gene PCR test
(Arndt, 2017; Ishida et al., 2014). The isolates were confirmed as S. suis if both gdh and recN PCR tests were positive.
The cases were categorized into 2 groups: probable cases and confirmed cases. Pigs that displayed the above clinical signs and where S. suis isolates were obtained from one or more systemic sites (i.e., meninges, spleen, blood) were classified as confirmed cases. Probable cases were defined as pigs which displayed clinical signs and where S.
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suis was obtained from one or more gastrointestinal or respiratory tract sites (i.e., tonsils, nasal cavity, rectum).
2.2.4 DNA extraction from tonsil tissues
Immediately prior to processing, the tonsils were thawed, diced into ~ 2 mm cubes, and placed in 15 mL conical tubes (Sarstedt Inc), with 0.5 mm glass beads and PBS in a
1:1:1 (weight/weight/volume) ratio. The samples were then put into an MP FastPrep-24
5G homogenizer (MP Biomedicals) for 2 cycles at a speed of 4 meter/second for 40 seconds by which time most of the tissue was homogenized. After the remaining solids were allowed to settle out of suspension, 500 µL of the homogenate was placed into a 2 mL centrifuge tube (Qiagen) and DNA was extracted using the Qiagen DNeasy Blood and TissueÒ DNA extraction kit following the Gram-positive protocol. An initial check of the quality of the DNA was performed using a NanoDrop spectrophotometer (Thermo
Fisher Scientific). The quantity of the DNA was checked using a Qubits fluorimeter
(Thermo Fisher Scientific) at the Advanced Analytical Centre at the University of
Guelph.
2.2.5 16rRNA sequencing
16S rRNA gene (V3-V4 region) libraries were prepared at the Advanced Analysis
Center at the University of Guelph, following Illumina’s 16S metagenomic sequencing library preparation protocol (Illumina, 2013). Sixty-six (65 samples and mock community) libraries were then sequenced using Illumina MiSeq technology (Illumina
Inc) with Illumina MiSeq version 3 (paired-ends with 300 bp reads) chemistry.
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2.2.6 Data analysis
The Illumina readings were analyzed using Mothur version 1.41.1 according to the
MiSeq SOP protocol (Kozich, Westcott, Baxter, Highlander, & Schloss, 2013; https://www.mothur.org/wiki/MiSeq_SOP, accessed: 08/08/2019). In the protocol, the
V4 region of the 16S rRNA gene is used; however, in the current study the V3-V4 hypervariable region was amplified (Kozich et al., 2013). To deal with this difference, a few adjustments were made in the code for this study (Appendix 37) (Kozich, Westcott,
Baxter, Highlander, & Schloss, 2013; https://www.mothur.org/wiki/MiSeq_SOP, accessed: 08/08/2019). In addition to adjusting for the 16S target region, the maximum sequence length was set to 500 bp to reduce inaccuracies cause by sequencing and
PCRs errors (Kozich et al., 2013). Finally, singletons and doubletons were removed as recommended in order to reduce the number of unclassified sequences resulting from sequencing errors, as previously recommended (Allen et al., 2016).
R version 3.5.1 “Feather Spray” in RStudioâ version 1.1.463 and Microsoftâ Excel for
Mac version 16.18 software were used for the analysis of microbial communities at the pig and farm level. Nine samples with less than 10,000 reads were removed and not used for analysis. The sequence data were further cleaned through rarefying the dataset using phyloseq package in RStudio. (Appendix 6). Rarefying is a normalization method that subsamples to create an even depth among all samples (Weiss et al.,
2017). In this study, the lowest number of readings in our processed group (11,035) was used to subsample from each sample, resulting in an analysis with an even 11,035
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reads per sample. A replicated random number generator (set.seed) was used in R to ensure there was fair subsampling.
2.2.7 Statistical analysis
Analysis was done examining these samples in 3 cohorts: confirmed cases and controls, confirmed cases and probable cases, and probable cases and controls. The rationale behind dividing the analysis into groups is due to the uncertainty of the probable cases being S. suis cases. They are only probable cases of S. suis, since they had clinical signs, but S. suis disease was not fully confirmed since the bacterium was not present in systemic sites. For a fair analysis of understanding the relationship between the microbiota and infection, it was decided that the probable cases should be considered a separate analysis group.
2.2.7.1 Alpha diversity
Alpha diversity defines the richness and evenness of each individual sample independently (Wagner et al., 2018). Richness is the sum of different taxa within a community (Wagner et al., 2018). Evenness measures whether the taxa present in a community are present in the same numbers (Wagner et al., 2018). With these 2 variables, alpha diversity can be described using inverse Simpson’s index and observed values (Wagner et al., 2018). Observed values are the richness measurement and the inverse Simpson’s index defines the diversity of a community with respect to the average proportional relative abundance of taxa (Wagner et al., 2018). These 2 measurements were calculated using phyloseq package in R. The resulting datasets was then exported to Excel and grouped by case or control. The average and range 27
among diagnostic groups were calculated in Excel. To determine statistical significance among case and control groups, the pairwise Wilcox test was executed in R using the vegan (Oksanen et al., 2019) and tidyverse (Wickham, 2019) packages.
2.2.7.2 Beta diversity
Beta diversity measures the diversity by comparing two samples (Wagner et al., 2018).
This can be measured using species abundance (Barwell et al., 2015) or through presence-absence comparisons. Abundance based measurement compares whether the two groups with the same taxa present have different abundance ranks (Barwell et al., 2015). Beta diversity was performed using the Phyloseq package in R, generating a
Bray-Curtis distance matrix on dissimilarity of OTU abundance, which was visualized as a non-metric multidimensional scaling (NMDA) plot. The statistical significance of Bray-
Curtis distances between control and confirmed cases was measured using permutational multivariate analysis of variance (Adonis).
2.2.7.3 Phylogenetic analysis
Phylogenetic analysis was done primarily using R with tidyverse package. For each sample, the relative abundance at the family and genus level was calculated using total sample size (11,035). The aggregate relative abundance was calculated by using the sum of relative abundance at the phylum, genus or family level. To statistically compare case and control groups, Kruskal-Wallis rank sum test was done on median aggregate relative abundance at the phylum, family and genus level.
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2.3 Results and discussion
The objectives of this study were to characterize the tonsil microbiota of nursery pigs that had clinical signs of Streptococcus suis infection compared to heathy pigs and to then determine whether there was an association between the tonsil microbiota and clinical S. suis infection.
Tonsil samples were collected from 65 pigs on 9 farms including 20 samples from control pigs and 45 samples from pigs with clinical signs (Table 2-1, Appendix 3). The number of samples collected at each farm varied (range 1 to 15 samples) and was dependent on the cases present at the time of visit (Table 2-1). In addition, some farms were visited multiple times (Appendix 3).The pigs with clinical signs were further tested for the presence of S. suis in systemic sites for confirmation of infection. Due to the variable number of samples collected at each farm, this reflected the number of probable cases and confirmed cases (Table 2-1). However, the clinical signs between the two case groups were not substantially different with regards to diagnosing S. suis infection (Table 2-2). Samples from 3 controls, 3 confirmed cases and 3 probable cases that had less than 10,000 reads were removed, leaving a total of 56 valid samples (17 controls, 18 confirmed cases and 21 probable cases) for further analysis (Table 2-1,
Appendix 6).
To understand the diversity within a sample, alpha diversity matrices were used. The mean observed richness for the confirmed, probable and control groups was 104.20,
120.49 and 88.70, respectively (Figure 2-1). The range in richness among the 3 groups
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was 61.39 to 175.27 in confirmed cases, 69.38 to 236.82 in probable cases and 65.21 to 116.43 in the control group. There was no statistical difference between confirmed cases and controls (p=0.16). Similarly, the difference between confirmed cases and probable cases was not statistically significant (p=0.088). The difference between probable cases and controls was statistically significant (p=0.0005). The average inverse Simpson index for confirmed cases, probable cases and controls was 12.12, 9.6 and 8.50, respectively (Figure 2-1). The inverse Simpson index mean ranged from 2.67 to 24.31 in confirmed cases, 3.48 to 14.48 in control group and 1.9 to 17.6 in probable cases. The difference between confirmed cases and controls was not statistically significant (p=0.083), nor was there a difference between confirmed cases and probable cases (p=0.17) or between the probable cases and controls (p=0.46). After examining the alpha diversity among bacterial communities in each sample, it was concluded that there is no difference among diagnostic groups with the exception of observed richness in probable cases in comparison to the control group.
For beta-diversity, a Bray-Curtis dissimilarity distance matrix was used to assess whether there was a difference in the bacterial community of the pigs’ tonsils among confirmed cases, probable cases and controls. In the NMDS plot of the confirmed cases and controls, the samples appeared to be scattered without a pattern (Figure 2-2) and there was no significant difference between tonsil bacterial communities in case and control pigs (p = 0.20). However, there was a significant difference in the beta-diversity between farms (p = 0.035). In the NMDS plot of the probable cases and confirmed cases, the samples also appeared to be scattered without a pattern (Figure 2-4), and
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there was no significant difference in tonsil bacterial communities between confirmed and probable case pigs (p = 0.423). There was also no significant difference in beta- diversity between farms (p = 0.10). In the NMDS plot of the probable cases and controls, the samples appeared to be scattered with an isolation of some probable cases on the left portion of the graph (Figure 2-3), and there was no significant difference in tonsil bacterial communities between probable cases and control pigs (p =
0.457). There was also no difference in beta-diversity between farms (p = 0.421).
Overall, the diversity within each sample and between samples in each diagnostic group were not significantly different from one another. The composition of this diversity was further examined through phylogenetic analysis.
Phylogenetic analysis examined the taxonomic classification of OTUs at the phylum, family and genus level. The top 5 phyla were comprised of at least a relative abundance of 85% at the phylum level in each diagnostic group. The top 10 families were comprised of at least a relative abundance of 58% at the family level in each diagnosis group. Finally, the top 10 genera were comprised of at least a relative abundance of
52% at the genus level in each diagnosis group.
There were a total of 453 different taxa among the confirmed cases. The top 5 phyla in these samples were Proteobacteria (30.9%), Firmicutes (29.9%), Bacteroidetes
(19.9%), Fusobacteria (4.8%) and Actinobacteria (1.1%) (Appendix 8). The top 10 families were Streptococcaceae (21.1%), Enterobacteriaceae (9.7%), Bacteroidaceae
(6.5%), Pasteurellaceae (6.2%), Fusobacteriaceae (4.7%), Moraxellaceae (3.5%),
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Porphyromonadceae (3.2%), Flavobacteriaceae (1.2%), Bacteria_unclassified (1%) and
Veillonellaceae (0.9%) (Appendix 14). The top 10 genera were Streptococcus (21%),
Escherichia/Shigella (9.4%), Bacteroides (6.5%), Fusobacterium (4.7%),
Porphyromonas (3.2%), Moraxella (3.2%), Actinobacillus (3.1%),
Pasteurellaceae_unclassified (1.2%), Flavobacteriaceae_unclassified (1.1%) and
Bacteria_unclassified (1%) (Appendix 20).
A total of 600 different taxa were identified among the probable cases. The top 5 phyla were Proteobacteria (36.6%), Firmicutes (27.2%), Bacteroidetes (16.8%), Fusobacteria
(2.6%) and Tenericutes (1.8%) (Appendix 9). The top 10 families were
Streptoccoccaceae (20.7%), Enterobacteriaceae (15.5%), Pasteurellaceae (7.7%),
Bacteroidaceae (6.3%), Porphyromonadeceae (3.4%), Fusobacteriaceae (2.4%),
Moraxellaceae (2.2%), Mycoplamataceae (1.8%), Peptostreptococcaceae (0.7%)
(Appendix 15). The top 10 genera were Streptococcus (20.7%), Escherichia/shigella
(10.7%), Bacteroides (6%), Porphyromonas (2.6%), Pasteurellaceae_unclassified
(2.5%), Pasteurella (2.5%), Fusobacterium (2.4%), Actinobacillus (2.3%), Mycoplasma
(1.8%) and Moraxella (1.3%) (Appendix 21).
A total of 334 different taxa were identified among the control group. The top 5 phyla were Firmicutes (40%), Proteobacteria (30%), Bacteroidetes (17.2%), Fusobacteria
(2.4%) and Actinobacteria (0.9%) (Appendix 7). The top 10 families were
Streptococcaceae (27%), Enterobacteriaceae (18.6%), Bacteroidaceae (7.2%),
Pasteurellaceae (6.5%), Fusobacteriaceae (2.4%), Porphyromonadaceae (2.2%),
Veillonellaceae (1%), Flavobacteriaceae (0.9%), Actinomycetaceae (0.8%) and 32
Staphylococcaceae (0.6%) (Appendix 13). The top 10 genera were Streptococcus
(26.6%), Escherichia_shigella (12.1%), Bacteroides (7.2%), Fusobacterium (2.4%),
Actinobacillus (2.2%), Porphyromonas (1.8%), Pasteurellaceae_unclassified (1.3%),
Enterobacteriaceae_unclassified (1.2%), Pasteurella (1%) and Veillonella (0.9%)
(Appendix 19). There was no statistically significant difference in taxonomic composition between any of the diagnosis groups at phyla, families, or genera levels (Appendix 27-
35).
The top 5 phyla in both control and confirmed cases were consistent with previous studies (Kernaghan, 2013; Lowe, et al., 2012). Some differences between the current study and previous research at the family and genus level were observed. For example,
Mycoplasmataceae was in the top 10 families and Mycoplasma was in the top 10 genera in the probable group of this study but was not in the top 10 families in previous studies (Kernaghan, 2013; Lowe, et al., 2012). Interestingly, this finding was also not found in the confirmed cases and controls. Several Mycoplasma spp. are found to be pathogenic in swine populations. Most notably, Mycoplasma hyorhinis (M. hyorhinis) is common in swine populations and causes infection primarily in ages 3- 10- week old pigs. It causes polyserositis, arthritis, otitis media pneumonia. Clinical signs include swollen joints, lameness, difficulty breathing, and head tilt (Thacker & Minion, 2012).
(Thacker & Minion, 2012). Since M. hyorhinis share similar clinical signs with S. suis, and the probable cases did not have S. suis present in sterile sites, these findings could suggest that the probable cases could be M. hyorhinis rather than S. suis.
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There were some parallels in the findings at the family level of this study in comparison to previous work. Streptococcaceae, Enterbacteriaceae, Fusobacteriaceae and
Pasteurellaceae were in the top 10 families in this study, consistent with prior research
(Kernaghan, 2013; Lowe et al., 2012). In this study, the, 7 out of the top 10 genera were found in all diagnosis groups were Streptococcus, Escherichia/Shigella,
Bacteroides, Porphyromonas, Fusobacterium, Actinobacillus and unclassified
Pasteurellaceae. Among these 7 genera, 2 of them were not shared with findings from previous studies Escherichia/Shigella and unclassified Pasteurellaceae (Kernaghan,
2013; Lowe et al., 2012). However, after examining the prevalence of putative foodborne pathogen, Kernaghan 2013 demonstrated Escherichia had higher prevalence in healthy pigs (27.8%) in comparison to unfit pigs (1.7%) and concluded that
Escherichia is a common bacteria in the tonsils (Kernaghan, 2013).
In this study, Veillonella was in the top 10 genera in controls as was also shown in the previous study by Lowe et al., 2012. Interestingly, Veillonella was not found in the top
10 genera of confirmed nor probable cases in this study. Previous research has demonstrated that there is a symbiotic relationship between oral biofilm formation between Veillonella and Streptococcus species in human oral cavities (Mashima &
Nakazawa, 2015) . Streptococcus species produce lactate which can be utilized by
Veillonella, resulting in cooperation in biofilm formation (Mashima & Nakazawa, 2015).
Moraxellaceae was in the top 10 families in the probable and confirmed cases and was in the top 10 families of previous work done by Lowe, et al., 2012 . Further, at the genus level, Moraxella and Alkanindiges were within the top 10 genera (Lowe et al.,
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2012). However, Moraxella was in the top 10 genera in the probable and confirmed cases in the present study but Alkanindiges was ranked 47 and 33 in probable and confirmed cases, respectively (Lowe et al., 2012). Another study examining the microbiota of saliva in healthy piglets demonstrated that Actinobacillus, Streptococcus, and Moraxella were the top 3 genera in the oral cavity (Murase et al., 2019). Concluding that Moraxella could be a common genera found in the URT.
The lack of statistical significance amongst diagnosis groups could be due to the variability introduced by sampling from multiple farms, as different management practices such as ventilation and in-feed medication might have had an impact on the tonsil bacterial community in samples collected from farms. It has been previously shown that there is higher bacterial similarity between pigs within a single farm than between pigs from different farms (Kernaghan, 2013).
Impacts of management practices on nasal cavity microbial communities was recently illustrated in a study examining the effects of gaseous ammonia exposure (Wang et al.,
2019). Gaseous ammonia is a common pollutant caused by the decomposition of swine feces and gaseous levels are affected by heat and moisture in the barn (Wang et al.,
2019). Researchers swabbed nasal cavities of pigs exposed to either 0, 5, 10, 15, 20 or
25 parts per million (ppm) of gaseous ammonia and found that of the top 6 genera in all samples, there was a significant decrease in Pseudomonas, Lactobacillus, Prevotella and Bacteroides and a significant increase in Moraxella and Streptococcus at 25 ppm of ammonia (Wang et al., 2019). Further, the researchers also demonstrated that
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ammonia levels of 15 to 25 ppm deteriorate the respiratory mucosal barriers in growing pigs and subsequently increase in pathogenic bacteria colonizing (Wang et al., 2019).
This study also did not look at the temperature nor humidity of each room where pigs were sampled.
A recent study demonstrated the effects of management changes on the tonsil microbiota through various ages and after a change of in-feed antibiotics (Pena Cortes et al., 2018b). When the antibiotic carbadox was introduced and the pigs were moved into the weaner barn (week 3) there was an increase in Streptoccaceae and a decrease in Moraxellaceae (Pena Cortes et al., 2018). At week 5, after a change from carbadox to tylosin, there was an increased community richness with a decrease in Streptoccaceae and Proteobacteria, but an increase in other families in the Firmicutes phylum (Pena
Cortes et al., 2018). At week 9, when the use of tylosin was stopped, the community richness was lowered and there was an increase in Clostridales (Pena Cortes et al.,
2018). The study performed by Pena Cortes et al. (2018) demonstrated the microbiota significantly changes due to farm management practices. However, it has been shown previously that the nasal and fecal microbiota undergoes a large transformation within the first 7 weeks of life and doesn’t stabilize until 2 to 3 weeks post weaning (week 5 to
6) (Slifierz et al., 2015). This study did not examine the statistical significance that antibiotic use had on the tonsil microbiota composition due to missing data at the beginning of the study.
36
Future work could examine whether the lack of observed association between tonsil microbiota and S. suis infection was affected by these suggested farm management practices.
With regard to the analysis of tonsil microbiota in case and control groups, there were some limitations that need to be considered. First, the pig sample size was too small to see any significant differences. Since there was not an even number of control and cases from each farm, there is too much variability in the analysis to identify significant differences. The second issue was that it was difficult obtain healthy pigs to euthanize at production farms. To accommodate with the needs of producers, the controls were usually ill thrifty and had conditions such as lameness (of unknown etiology), hernia or rectal prolapse. The limitation with this is that defining our control as healthy may not account for other factors impacting their microbiota. The underlying cause to these unexplained health issues could be caused by a different bacterial pathogen with its own mechanisms for microbial shifts.
Future directions could include: performing a similar microbiota study with a larger and more even sample size of S. suis and control groups from farms with more similar management practices. Metagenomic analysis of these communities might also help to illustrate if differences in the microbiota of each group at the genomic level (Weinstock,
2012). In addition to identifying the genera level of each OTU, identifying virulence genes in each OTU could help in their further categorization, resulting in a greater understanding of the microbial presence (Weinstock, 2012).
37
Another future direction would be to examine the presence of S. suis co-infection with
PRRS virus and SIV. As an opportunistic pathogen, S. suis colonization could result in infection when host environments are vulnerable. It has been previously shown that there is an association with S. suis and viruses as well as other bacteria (Auray et al.,
2016; Meng et al., 2015; Opriessnig et al., 2011). A previous study demonstrated that highly virulent influenza A virus (IAV) infection promotes S. suis adherence, colonization and invasion (Meng et al., 2015). Another study has shown that pre-infected bone marrow derived dendritic cells with porcine reproductive and respiratory syndrome
(PRRS) virus can amplify expression of pro-inflammatory genes caused by S. suis infection (Auray et al., 2016).
Characterization of the microbiota is the start of understanding how S. suis causes infection in some pigs but remains asymptomatic in most animals.
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2.4 Figures
Figure 2-1: Mean observed richness and inverse Simpson index in confirmed cases, probable cases and controls
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Figure 2-2: NMDS of Bray-Curtis matrix of confirmed cases and controls
40
Figure 2-3: NMDS of Bray-Curtis matrix of probable cases and controls
41
Figure 2-4: NMDS of Bray-Curtis matrix of probable and confirmed cases
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2.5 Tables
Table 2-1: Number of controls, confirmed cases and probable cases from each farm
Before processing After processing
Farm # of Controls Confirmed Probable # of Controls Confirmed Probable Samples cases Cases Samples cases Cases
1 13 4 7 2 12 4 6 2
2 1 0 0 1 1 0 0 1
6 4 0 0 4 2 0 0 2
7 15 4 7 4 13 4 5 4
8 4 2 1 1 4 2 1 1
10 13 4 1 8 13 4 1 8
11 7 2 3 2 5 0 3 2
12 4 2 0 2 2 1 0 1
13 4 2 2 0 4 2 2 0
Total 65 20 21 24 56 17 18 21
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Table 2-2: Sum of clinical signs in confirmed cases (n=21) and probable cases (n=24)
Confirmed Probable
Sign
Ataxia 11 10
Incoordination 2 4
Tremor 10 3
Opisthotonus 7 5
Dyspnea 4 3
Convulsions 5 1
Lameness 6 2
Erythema 4 1
Blind 2 0
Deaf 2 0
Paralysis 5 0
Nystagmus 5 0
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3 Summary and conclusion
Technology for examining microbial communities has advanced, increasing the popularity of research on changes in microbial communities and their effects on disease. S. suis is a Gram-positive bacterium that naturally inhabits the upper respiratory tract of many swine. A small proportion of pigs, primarily at weanling age, develop clinical signs of S. suis infection. The pathogenesis of the disease is not fully understood. Previous studies have described the core bacteria present in the tonsils of the soft palate in swine of various ages. The first objective of this study was to characterize the tonsil microbiota of nursery pigs with clinical signs of S. suis. The second objective was to determine whether there was an association between the tonsil microbiota and S. suis infection. From 9 farms, 65 pigs were sampled. S. suis was cultured and its identity confirmed using gdh and recN PCRs. The cases were categorized into 2 groups: probable and confirmed cases. The probable cases had clinical signs, but S. suis isolates were not present in systemic sites. The confirmed cases had typical clinical signs and S. suis isolates were present at systemic sites.
Since many respiratory diseases in swine share similar clinicals signs, the two case groups were analyzed separately. DNA was extracted from the tonsils and Illumina
MiSeq of the V3-V4 hypervariable regions of 16S rRNA was performed. After removing samples with small reads (<10,000), a total of 56 pigs were further analyzed. Alpha diversity, beta diversity and phylogenetics were used to characterize microbial communities and compare differences between the clinical cases and the healthy pigs.
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The core 4 phyla present in all samples were Proteobacteria, Firmicutes, Bacteroidetes,
Fusobacteria, supporting what previous studies have shown as the core microbiota in the tonsils. While this study found that there was no significant difference between diagnosis groups in beta diversity and phylogenetic analysis, more work is needed to examine the relationship between the microbiota and S. suis. Future work could incorporate PRRS virus and SIV data, farm management data and shotgun metagenomics.
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APPENDICES
Date: ______Name(s): ______
Owner ID
Assigned Farm Number
F1 F2 F3
Address
Phone
Herd veterinarian
Relevant History
Avg age at clinical outbreak Duration of problem
Feed Morbidity
In-feed/water antibiotics Mortality
Genetics Treatment(s)
Other Vaccination(s)
Housing
***Please fill out ONE PER FARM***
Appendix 1: Farm level survey
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Date: ______Name(s): ______
Farm ID Age Found in