Understanding the Relationship Between the Tonsil Microbiota and Clinical suis Infection in Nursery

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 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 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 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 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 , 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 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 , 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, multocida, Streptococcus suis, Streptococcus dysgalactiae,

Staphylococcus aureus, Staphylococcus epidermidis and 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 , , 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., (73.4%), (17.8%), (5.6%),

Actinobacteria (1.2%), and (0.8%) (Lowe et al., 2012). Most notably, species from the families and 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 (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 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 , 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 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 , with co-infection of Staphylococcus aureus, ,

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 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 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%), (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%),

(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.

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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

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

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.

46

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APPENDICES

Date: ______Name(s): ______

Owner ID

Assigned Farm Number

F1 F2 F3

Address

Phone

E-mail

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

57

Date: ______Name(s): ______

Farm ID Age Found in

Hospital pen Pig ID Genetic lineage Alley Normal pen Current feed ration Other: ______

CASE (check all that apply) CONTROL (check all that apply) DOA

Ataxia Healthy (good body Peracute death due to Incoordination condition, laying in S. suis Tremor/shaking groups, eating, drinking, Other: Opisthotonus/unusual socializing behavior) ______posture Blindness Other: Loss of hearing ______ Paralysis Dyspnea Convulsions Nystagmus Erythema

Other: ______

Body Condition PHOTO VIDEO

Bacteriology Virome Microbiome Nasal swab Nasal swab Nasal swab Tonsilar swab Tonsilar swab Tonsilar swab Meningeal swab Fecal swab Serology + Host Whole Blood Serum Histopathology Fresh Fixed

Brain Tonsil Lung Heart Liver Spleen Ileum Lymph node(s)

***Please fill out ONE PER PIG***

Appendix 2: Pig level survey

58

farm_idPig_idClass Age (in weeks)Sex Estimate weightTreated? (kg) in-water medication in-feed medication? pen_typegroup_sizeataxiaincoordinationtremoropisthotonusblinddeafparalysisdyspneaconvulsionsnystagmuserythemalamenessother notes Death on Arrival?sample collect at visit/ total visits at farm F1 F1H10Control 6 female 12 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 of 5 F1 F1H11Control 5 female 15 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 of 5 F1 F1H12Control 5 male 8 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 of 5 F1 F1H8ControlMISG MISG MISG No No lincomycin, chlortetracycline, tiamulinMISG MISG MISGMISG MISGMISG MISGMISGMISG MISG MISG MISG MISG MISG n/a 0 3 of 5 F10 F10H1Control 4 male 5 No penicillin tylosin hospital 150 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 1 of 2 F10 F10H2Control 4 male 5 No penicillin tylosin hospital 150 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 1 of 2 F10 F10H4Control 3 female 10 No penicillin tylosin production 150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F10 F10H5Control 7 female 12 No penicillin tylosin production 150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F11 F11H1Control 6 female 9 No potpen/neomycin/amoxcillin 0 production 240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F11 F11H2Control 6 female 6 No potpen/neomycin/amoxcillin 0 production 240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F12 F12H1Control 7 female 8 No penicillin/predef yes production 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F12 F12H2Control 7 male 15 No penicillin/predef yes production 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F13 F13H1Control 6 male 15 No penicillin yes alley 200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F13 F13H2Control 6 female 20 No penicillin yes alley 200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F7 F7H10Control 6 male 15 No No chlortetracycline, sulfamethazine,hospital penicillin 30 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 2 of 2 F7 F7H11Control 6 female 13.5 No No chlortetracycline, sulfamethazine,production penicillin 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F7 F7H4Control 6 female 10 No No chlortetracycline, sulfamethazine,hospital penicillin 4 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 1 of 2 F7 F7H5Control 7 female 20 No No chlortetracycline, sulfamethazine,hospital penicillin 4 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 1 of 2 F8 F8H1Control 4 male 10 excede, predef potpen/chlor production 150 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 1 of 1 F8 F8H2Control 4 male 10 excede, predef potpen/chlor production 150 0 0 0 0 0 0 0 0 0 0 0 0 euthanized$ 0 1 of 1 F1 F1S1 DiseaseMISG MISG 15 No No lincomycin, chlortetracycline, tiamulinother MISG 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 of 5 F1 F1S10Disease 6 male 25 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 0 0 0 0 1 0 0 0 0 0 0 0 4 of 5 F1 F1S12Disease 5 male 10 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 1 1 0 0 0 0 0 0 0 0 0 0 5 of 5 F1 F1S13Disease 5 male 10 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 0 0 0 0 1 0 1 0 0 0 0 0 5 of 5 F1 F1S6 Disease 9 male 15 No No lincomycin, chlortetracycline, tiamulinproduction 100 0 0 1 1 0 0 0 0 0 1 0 0 tail bitten, ear tip necrosis, paddling 0 2 of 5 F1 F1S7 Disease 9 male 15 No No lincomycin, chlortetracycline, tiamulinproduction 50 1 1 1 0 0 0 0 0 0 1 0 0 mild ataxia, incoordination, tremor, nystagmus; cold extremities0 2 of 5 F1 F1S8 DiseaseMISG MISG MISG No No lincomycin, chlortetracycline, tiamulinMISG MISG MISGMISG MISGMISG MISGMISGMISG MISG MISG MISG MISG MISG 0 0 3 of 5 F10 F10S1Disease 4 female 5 No penicillin tylosin hospital 150 1 1 0 1 0 0 0 0 0 0 0 0 head tilt 0 1 of 2 F11 F11S1Disease 6 male 3 No potpen/neomycin/amoxcillin 0 production 240 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 of 1 F11 F11S4Disease 6 female 5 No potpen/neomycin/amoxcillin 0 production 240 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 of 1 F11 F11S5Disease 3 female 4.5 No potpen/neomycin/amoxcillin 0 production 240 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F13 F13S1Disease 6 male 15 No penicillin yes alley 200 1 1 1 1 1 1 1 1 1 1 1 1 paddling 0 1 of 1 F13 F13S2Disease 6 male 15 No penicillin yes alley 200 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 of 1 F7 F7S1 Disease 4 male 5 No No chlortetracycline, sulfamethazine,production penicillin 30 0 1 0 0 0 0 0 1 0 0 0 1 hairy, swollen joints 0 1 of 2 F7 F7S11Disease 6 female 13 No No chlortetracycline, sulfamethazine,hospital penicillin 30 1 1 1 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F7 F7S3 Disease 6 male 15 No No chlortetracycline, sulfamethazine,production penicillin 30 1 0 0 0 0 0 0 0 0 0 1 0 swollen joints 0 1 of 2 F7 F7S4 Disease 6 male 4 No No chlortetracycline, sulfamethazine,alley penicillin 30 1 1 1 1 0 0 1 1 1 1 1 1 near death 0 1 of 2 F7 F7S5 Disease 4 female 6 No No chlortetracycline, sulfamethazine,production penicillin 30 1 1 0 0 0 0 0 0 0 0 0 0 head tilt 0 1 of 2 F7 F7S7 Disease 6 female 5 No No chlortetracycline, sulfamethazine,alley penicillin 30 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F7 F7S9 Disease 6 male 8 No No chlortetracycline, sulfamethazine,alley penicillin 30 1 1 1 0 0 0 0 0 1 0 0 0 0 0 2 of 2 F8 F8S1 Disease 4 male 6 excede, predef potpen/chlor production 150 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 of 1 F1 F1S11Probable 5 male 8 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 1 0 1 0 0 0 0 0 0 0 0 0 0 5 of 5 F1 F1S14Probable 5 male 8 No No lincomycin, chlortetracycline, tiamulinproduction 176 0 0 0 0 0 0 0 0 1 0 0 0 0 0 5 of 5 F10 F10S2Probable 4 male 7 No penicillin tylosin hospital 150 0 1 0 0 0 0 0 0 0 0 0 0 swollen joints, walking on front knees 0 1 of 2 F10 F10S4Probable 3 male 5 No penicillin tylosin production 150 0 0 0 0 0 0 0 1 0 0 0 0 poor doing 0 2 of 2 F10 F10S5Probable 3 female 5 No penicillin tylosin production 150 0 0 0 0 0 0 0 0 0 0 0 1 swollen joints 0 2 of 2 F10 F10S6Probable 3 female 5 No penicillin tylosin production 150 1 1 0 0 0 0 0 1 0 0 1 0 poor doing 0 2 of 2 F10 F10S7Probable 3 female 5 No penicillin tylosin production 150 0 1 0 0 0 0 0 0 0 0 0 0 head tilt 0 2 of 2 F10 F10S8Probable 3 MISG 5 No penicillin tylosin production 150 1 0 0 0 0 0 0 1 0 0 0 0 poor doing 0 2 of 2 F11 F11S2Probable 6 female 3 No potpen/neomycin/amoxcillin 0 production 240 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 of 1 F11 F11S3Probable 6 male 4 No potpen/neomycin/amoxcillin 0 production 240 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 of 1 F12 F12S1Probable 7 male 10 No penicillin/predef yes hospital 500 0 0 0 0 0 0 0 0 0 0 0 0 poor doing 0 1 of 1 F12 F12S2Probable 7 male 10 No penicillin/predef yes hospital 500 0 0 0 0 0 0 0 0 0 0 0 0 poor doing 0 1 of 1 F2 F2S1 Probable 7 male 10 No No 0 productioneric 1 1 0 1 0 0 0 0 0 0 0 0 swollen hocks 0 1 of 1 F6 F6S2 Probable 6 female 30 penicillin, dexamethasoneNo 0 hospital 589 1 1 0 1 0 0 0 0 0 0 0 0 from room 7 0 1 of 1 F7 F7S10Probable 6 MISG 12 No No chlortetracycline, sulfamethazine,alley penicillin 30 1 1 1 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F7 F7S2 Probable 6 female 12 No No chlortetracycline, sulfamethazine,production penicillin 30 1 1 0 0 0 0 0 0 0 0 0 0 head tilt 0 1 of 2 F7 F7S6 Probable 4 male 4 No No chlortetracycline, sulfamethazine,alley penicillin 30 0 0 0 0 0 0 0 0 0 0 0 0 doa 1 1 of 2 F7 F7S8 Probable 6 female 10 No No chlortetracycline, sulfamethazine,alley penicillin 30 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 of 2 F8 F8S2 Probable 4 male 10 excede, predef potpen/chlor production 150 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 of 1 F10 F10S3Probable 4 female 6 No penicillin tylosin hospital 150 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 of 2 F6 F6S3 Probable 4 male 15 penicillin, dexamethasoneNo 0 hospital 582 1 1 1 1 0 0 0 0 0 0 0 0 from room 3 0 1 of 1 F6 F6S4 Probable 6 female 30 penicillin, dexamethasoneNo 0 hospital 589 0 0 1 0 0 0 0 0 0 0 0 0 from room 7 0 1 of 1 F6 F6S5 Probable 6 male 30 penicillin, dexamethasoneNo 0 hospital 589 0 0 1 0 0 0 0 0 0 0 0 0 from room 7 0 1 of 1

Appendix 3: Pig and farm level data (0=no, 1=yes, MISG=missing data)

59

ATCC 20 even-mix community

5% Acinetobacter baumannii (ATCC 17978)

5% Actinomyces odontolyticus (ATCC 17982)

5% cereus (ATCC 10987)

5% Bacteroides vulgatus (ATCC 8482)

5% Bifidobacterium adolescentis (ATCC 15703)

5% Clostridium beijerinckii (ATCC 35702)

5% Cutibacterium acnes (ATCC 11828)

5% Deinococcus radiodurans (ATCC BAA-816)

5% Enterococcus faecalis (ATCC 47077)

5% Escherichia coli (ATCC 700926)

5% (ATCC 700392)

5% Lactobacillus gasseri (ATCC 33323)

5% Neisseria meningitidis (ATCC BAA-335)

5% Porphyromonas gingivalis (ATCC 33277)

5% Pseudomonas aeruginosa (ATCC 9027)

5% Rhodobacter sphaeroides (ATCC 17029)

5% Staphylococcus aureus (ATCC BAA-1556)

5% Staphylococcus epidermidis (ATCC 12228)

5% Streptococcus agalactiae (ATCC BAA-611)

5% Streptococcus mutans (ATCC 700610) Appendix 4: 20 even mix bacterial species ATCC (Manassas, VA 20110 USA)

60

Controls # OF READS Disease # OF READS Probable # OF READS

F10H1 78230 F10S1 87022 F10S2 110327

F10H2 87661 F11S1 46805 F10S3 68763

F10H4 83371 F11S5 36972 F10S4 97530

F10H5 117107 F13S1 146219 F10S5 72163

F11H1 6783 F13S2 83421 F10S6 67374

F11H2 4358 F1S1 84678 F10S6T 132902

F12H1 76570 F1S10 117075 F10S7 84660

F12H2 5205 F1S12 23362 F10S8 71860

F13H1 42623 F1S13 136352 F11S2 144718

F13H2 40452 F1S6 117847 F11S3 21702

F1H10 81801 F1S7 9 F12S1 67433

F1H11 32843 F1S8 54754 F12S2 9002

F1H12 94241 F7S1 50116 F1S11 108917

F1H8 11035 F7S11 2260 F1S14 24025

F7H10 137644 F7S3 140890 F2S1 87144

F7H11 106392 F7S4 782 F6S2 96187

F7H4 133655 F7S5 171663 F6S3 65440

F7H5 25531 F7S7 82546 F6S4 8663

F8H1 155375 F7S9 59615 F6S5 11

F8H2 90765 F11S4 47513 F7S10 89267

F8S1 84613 F7S2 93712

F7S6 103547

MOCK 104735 F7S8 78488

F8S2 84956 Appendix 5: Number of reads per sample

61

Phylum median 1 Firmicutes 0.398278 2 Proteobacteria 0.299773 3 Bacteroidetes 0.172361 4 Fusobacteria 0.023652 5 Actinobacteria 0.009243 6 Tenericutes 0.005075 7 Bacteria_unclassified 0.001541 8 TM7 0.000272 9 9.06E-05 10 0 11 0 12 SR1 0 13 0 14 0

Appendix 6: Median of aggregate relative abundance at phylum level of controls

62

Phylum median 1 Proteobacteria 0.309379 2 Firmicutes 0.29932 3 Bacteroidetes 0.199366 4 Fusobacteria 0.047531 5 Actinobacteria 0.010648 6 Bacteria_unclassified 0.009968 7 Tenericutes 0.002401 8 TM7 0.000272 9 Chlamydiae 0.000181 10 Spirochaetes 0 11 SR1 0 12 Synergistetes 0

Appendix 7: Median of aggregate relative abundance at phylum level of confirmed cases

63

Phylum median 1 Proteobacteria 0.36058 2 Firmicutes 0.272134 3 Bacteroidetes 0.167648 4 Fusobacteria 0.026371 5 Tenericutes 0.018215 6 Actinobacteria 0.010693 7 Bacteria_unclassified 0.00444 8 Chlamydiae 0.000634 9 TM7 0.000272 10 Spirochaetes 9.06E-05 11 Chloroflexi 0 12 Deferribacteres 0 13 SR1 0 14 Synergistetes 0 15 Verrucomicrobia 0

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

Family median Family median 1 Streptococcaceae 0.2657 Actinomycetales_unclassified 0 2 Enterobacteriaceae 0.185682 Aeromonadaceae 0 3 Bacteroidaceae 0.072315 Bacillaceae_1 0 4 Pasteurellaceae 0.065066 Bacteroidetes_unclassified 0 5 Fusobacteriaceae 0.023652 Bifidobacteriaceae 0 6 Porphyromonadaceae 0.022293 Burkholderiales_unclassified 0 7 Veillonellaceae 0.009515 Cardiobacteriaceae 0 8 Flavobacteriaceae 0.00879 Chitinophagaceae 0 9 Actinomycetaceae 0.007793 Chloroflexi_unclassified 0 10 Staphylococcaceae 0.006434 Clostridiales_Incertae_Sedis_XIII 0 11 Moraxellaceae 0.005437 Comamonadaceae 0 12 Neisseriaceae 0.005165 Desulfovibrionaceae 0 13 0.005075 Firmicutes_unclassified 0 14 Peptostreptococcaceae 0.004712 Hyphomicrobiaceae 0 15 Lachnospiraceae 0.004531 Lactobacillales_unclassified 0 16 Campylobacteraceae 0.002809 Leuconostocaceae 0 17 Enterococcaceae 0.002809 Oxalobacteraceae 0 18 Bacillales_Incertae_Sedis_XI 0.002628 Paenibacillaceae_1 0 19 Prevotellaceae 0.002628 Peptococcaceae_1 0 20 Clostridiales_Incertae_Sedis_XI 0.001631 Planococcaceae 0 21 Bacteria_unclassified 0.001541 Propionibacteriaceae 0 22 Lactobacillaceae 0.001359 Pseudomonadaceae 0 23 Clostridiales_unclassified 0.000544 Spirochaetaceae 0 24 Coriobacteriaceae 0.000453 SR1_family_incertae_sedis 0 25 Micrococcaceae 0.000362 Sutterellaceae 0 26 Aerococcaceae 0.000362 Synergistaceae 0 27 TM7_family_incertae_sedis 0.000272 Verrucomicrobiaceae 0 28 Eubacteriaceae 0.000181 Xanthomonadaceae 0 29 Alcaligenaceae 9.06E-05 30 Carnobacteriaceae 9.06E-05 31 Chlamydiaceae 9.06E-05 32 Clostridiaceae_1 9.06E-05 33 Corynebacteriaceae 9.06E-05 34 Erysipelotrichaceae 9.06E-05 35 Leptotrichiaceae 9.06E-05 36 Ruminococcaceae 9.06E-05 37 Acidaminococcaceae 0 38 39 40 41 42 43 44 Appendix 12: Median of aggregate relative abundance at family level of controls 45 46 47 68 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Family median Family median 1 Streptococcaceae 0.210829 Burkholderiaceae 0 2 Enterobacteriaceae 0.097372 Burkholderiales_incertae_sedis 0 3 Bacteroidaceae 0.065202 Burkholderiales_unclassified 0 4 Pasteurellaceae 0.061894 Cardiobacteriaceae 0 5 Fusobacteriaceae 0.047213 Chitinophagaceae 0 6 Moraxellaceae 0.035206 Clostridia_unclassified 0 7 Porphyromonadaceae 0.032261 Clostridiales_Incertae_Sedis_XIII 0 8 Flavobacteriaceae 0.011781 Comamonadaceae 0 9 Bacteria_unclassified 0.009968 Dermabacteraceae 0 10 Veillonellaceae 0.009017 Desulfovibrionaceae 0 11 Actinomycetaceae 0.006072 Desulfovibrionales_unclassified 0 12 Staphylococcaceae 0.004576 Firmicutes_unclassified 0 13 Neisseriaceae 0.00435 Hyphomicrobiaceae 0 14 Peptostreptococcaceae 0.004078 Lactobacillales_unclassified 0 15 Prevotellaceae 0.003217 Legionellaceae 0 16 Clostridiales_Incertae_Sedis_XI 0.003172 Leuconostocaceae 0 17 Mycoplasmataceae 0.002401 Microbacteriaceae 0 18 Alcaligenaceae 0.00222 Nocardiaceae 0 19 Campylobacteraceae 0.00213 Oxalobacteraceae 0 20 Lachnospiraceae 0.002084 Peptococcaceae_1 0 21 Lactobacillaceae 0.002039 Planococcaceae 0 22 Aerococcaceae 0.001133 Propionibacteriaceae 0 23 Bacillales_Incertae_Sedis_XI 0.00077 Pseudomonadaceae 0 24 Micrococcaceae 0.000498 Spirochaetaceae 0 25 Carnobacteriaceae 0.000408 SR1_family_incertae_sedis 0 26 Leptotrichiaceae 0.000362 Synergistaceae 0 27 Coriobacteriaceae 0.000317 Xanthomonadaceae 0 28 Clostridiales_unclassified 0.000272 29 Enterococcaceae 0.000272 30 TM7_family_incertae_sedis 0.000272 31 Erysipelotrichaceae 0.000227 32 Chlamydiaceae 0.000181 33 Clostridiaceae_1 0.000136 34 Corynebacteriaceae 9.06E-05 35 Eubacteriaceae 4.53E-05 36 Ruminococcaceae 4.53E-05 37 Acidaminococcaceae 0 38 Actinomycetales_unclassified 0 39 Bacillaceae_2 0 40 Bacillales_unclassified 0 41 Bacilli_unclassified 0 42 Bacteroidales_unclassified 0 43 Bacteroidetes_unclassified 0 44 Bifidobacteriaceae 0 45 Bradyrhizobiaceae 0 46 47 48 49 50 51 Appendix 13: Median of aggregate relative abundance at family level of confirmed cases

69

Family median Family median 1 Streptococcaceae 0.206615315 Actinomycetales_unclassified 0 2 Enterobacteriaceae 0.154689624 Aeromonadaceae 0 3 Pasteurellaceae 0.07711826 Anaerolineaceae 0 4 Bacteroidaceae 0.062800181 Bacilli_unclassified 0 5 Porphyromonadaceae 0.034073403 Bacteroidales_unclassified 0 6 Fusobacteriaceae 0.024014499 Bacteroidetes_unclassified 0 7 Moraxellaceae 0.022111464 Bifidobacteriaceae 0 8 Mycoplasmataceae 0.018214771 Burkholderiales_incertae_sedis 0 9 Peptostreptococcaceae 0.007068419 Burkholderiales_unclassified 0 10 Prevotellaceae 0.006887177 Cardiobacteriaceae 0 11 Campylobacteraceae 0.006434073 Chitinophagaceae 0 12 Flavobacteriaceae 0.006162211 Clostridia_unclassified 0 13 Neisseriaceae 0.00598097 Clostridiales_Incertae_Sedis_XII 0 14 Staphylococcaceae 0.005527866 Clostridiales_Incertae_Sedis_XIII 0 15 Lachnospiraceae 0.005527866 Deferribacteraceae 0 16 Clostridiales_Incertae_Sedis_XI 0.004712279 Deltaproteobacteria_unclassified 0 17 Actinomycetaceae 0.004440417 Dermabacteraceae 0 18 Bacteria_unclassified 0.004440417 Desulfovibrionaceae 0 19 Veillonellaceae 0.003896692 Dietziaceae 0 20 Bacillales_Incertae_Sedis_XI 0.003171726 Firmicutes_unclassified 0 21 Clostridiales_unclassified 0.001812415 Helicobacteraceae 0 22 Enterococcaceae 0.001449932 Hyphomicrobiaceae 0 23 Lactobacillaceae 0.001359311 Lactobacillales_unclassified 0 24 Clostridiaceae_1 0.001087449 Leuconostocaceae 0 25 Aerococcaceae 0.000906208 Microbacteriaceae 0 26 Micrococcaceae 0.000815587 Nocardiaceae 0 27 Chlamydiaceae 0.000634345 Oxalobacteraceae 0 28 Alcaligenaceae 0.000543725 Peptococcaceae_1 0 29 Carnobacteriaceae 0.000543725 Planococcaceae 0 30 Coriobacteriaceae 0.000362483 Propionibacteriaceae 0 31 Eubacteriaceae 0.000362483 Pseudomonadaceae 0 32 Leptotrichiaceae 0.000362483 Shewanellaceae 0 33 Ruminococcaceae 0.000362483 Sphingobacteriaceae 0 34 Corynebacteriaceae 0.000362483 Sphingobacteriales_unclassified 0 35 Erysipelotrichaceae 0.000362483 Sphingomonadaceae 0 36 TM7_family_incertae_sedis 0.000271862 SR1_family_incertae_sedis 0 37 Comamonadaceae 0.000181242 Succinivibrionaceae 0 38 Spirochaetaceae 9.06208E-05 Synergistaceae 0 39 Acholeplasmataceae 0 Verrucomicrobiaceae 0 40 Acidaminococcaceae 0 Xanthomonadaceae 0 41 42 43 44 45 46 Appendix 14: Median of aggregate relative abundance at family level of probable cases 47 48 49 50 51 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

Genus median Genus median Genus median Streptococcus 0.2657 Acidaminococcus 0 Faecalibacterium 0 Escherichia_Shigella 0.120707 Acidovorax 0 Firmicutes_unclassified 0 Bacteroides 0.072315 Acinetobacter 0 Flavobacterium 0 Fusobacterium 0.023652 Actinomycetales_unclassified 0 Gemmiger 0 Actinobacillus 0.022111 Aerococcus 0 Globicatella 0 Porphyromonas 0.018396 Aeromonas 0 Granulicatella 0 Pasteurellaceae_unclassified 0.013049 Alcaligenaceae_unclassified 0 Herbaspirillum 0 Enterobacteriaceae_unclassified 0.012053 Alcaligenes 0 Howardella 0 Pasteurella 0.010059 Alkanindiges 0 Jeotgalicoccus 0 Veillonella 0.008971 Anaerococcus 0 Kingella 0 Staphylococcus 0.006162 Anaerorhabdus 0 Lachnospiracea_incertae_sedis 0 Trueperella 0.006162 Anaerovorax 0 Lactobacillales_unclassified 0 Mycoplasma 0.005075 Anoxybacillus 0 Lactococcus 0 Moraxella 0.00444 Arcanobacterium 0 Leptotrichia 0 Lachnospiraceae_unclassified 0.003897 Bacteroidetes_unclassified 0 Leptotrichiaceae_unclassified 0 Neisseriaceae_unclassified 0.003353 Bifidobacterium 0 Macrococcus 0 Peptostreptococcus 0.003262 Blautia 0 0 Campylobacter 0.002809 Bordetella 0 Megasphaera 0 Gemella 0.002628 Bulleidia 0 Moraxellaceae_unclassified 0 Bacteria_unclassified 0.001541 Burkholderiales_unclassified 0 Morganella 0 Enterococcus 0.001359 Butyricicoccus 0 Myroides 0 Lactobacillus 0.001359 Cardiobacteriaceae_unclassified 0 Oscillibacter 0 Prevotella 0.001178 Carnobacteriaceae_unclassified 0 Paenibacillus 0 Parvimonas 0.001087 Carnobacterium 0 Peptococcus 0 Flavobacteriaceae_unclassified 0.000906 Catonella 0 Peptostreptococcaceae_incertae_sedis 0 Neisseria 0.000816 Chitinophagaceae_unclassified 0 Planococcaceae_incertae_sedis 0 Actinomyces 0.000634 Chloroflexi_unclassified 0 Propionimicrobium 0 Clostridiales_unclassified 0.000544 Chryseobacterium 0 Proteiniclasticum 0 Veillonellaceae_unclassified 0.000544 Cloacibacillus 0 Proteus 0 Rothia 0.000362 Clostridiaceae_1_unclassified 0 Providencia 0 Enterococcaceae_unclassified 0.000272 Clostridiales_Incertae_Sedis_XIII_unclassified 0 Pseudomonas 0 Helcococcus 0.000272 Clostridium_XI 0 Psychrobacter 0 Prevotellaceae_unclassified 0.000272 Clostridium_XlVa 0 Raoultella 0 TM7_genus_incertae_sedis 0.000272 Comamonadaceae_unclassified 0 Roseburia 0 Aerococcaceae_unclassified 0.000181 Coprococcus 0 Ruminococcaceae_unclassified 0 Capnocytophaga 0.000181 Coriobacteriaceae_unclassified 0 Schwartzia 0 Eubacterium 0.000181 Delftia 0 Solobacterium 0 Tannerella 0.000181 Desulfovibrio 0 SR1_genus_incertae_sedis 0 Atopobium 9.06E-05 Desulfovibrionaceae_unclassified 0 Stenotrophomonas 0 Bergeyella 9.06E-05 Dorea 0 Sutterella 0 Chlamydia 9.06E-05 Dysgonomonas 0 Suttonella 0 Clostridium_sensu_stricto 9.06E-05 Empedobacter 0 Treponema 0 Corynebacterium 9.06E-05 Erysipelotrichaceae_incertae_sedis 0 Vagococcus 0 Filifactor 9.06E-05 Erysipelotrichaceae_unclassified 0 Verrucomicrobiaceae_unclassified 0 Porphyromonadaceae_unclassified 9.06E-05 Facklamia 0 Weissella 0 Wohlfahrtiimonas 0

Appendix 18: Median of aggregate relative abundance at genus level of controls

74

Genus median Genus median Genus median Genus median Streptococcus 0.210784 Leptotrichia 9.06E-05 Comamonadaceae_unclassified 0 Parabacteroides 0 Escherichia_Shigella 0.0942 Leptotrichiaceae_unclassified 9.06E-05 Comamonas 0 Pediococcus 0 Bacteroides 0.065156 Porphyromonadaceae_unclassified 9.06E-05 Coprococcus 0 Peptococcus 0 Fusobacterium 0.047213 Eubacterium 4.53E-05 Coriobacteriaceae_unclassified 0 Peptoniphilus 0 Porphyromonas 0.031808 Acetanaerobacterium 0 Delftia 0 Peptostreptococcaceae_incertae_sedis 0 Moraxella 0.031581 Acidaminococcus 0 Dermabacteraceae_unclassified 0 Petrimonas 0 Actinobacillus 0.030766 Acidovorax 0 Desemzia 0 Phascolarctobacterium 0 Pasteurellaceae_unclassified 0.012053 Acinetobacter 0 Desulfovibrio 0 Planococcaceae_incertae_sedis 0 Flavobacteriaceae_unclassified 0.010739 Actinomycetales_unclassified 0 Desulfovibrionaceae_unclassified 0 Planococcaceae_unclassified 0 Bacteria_unclassified 0.009968 Aerococcus 0 Desulfovibrionales_unclassified 0 Planomicrobium 0 Veillonella 0.007839 Anaerococcus 0 Dialister 0 Propionibacteriaceae_unclassified 0 Pasteurella 0.00734 Anaerorhabdus 0 Dorea 0 Proteus 0 Enterobacteriaceae_unclassified 0.004259 Anaerovorax 0 Enterococcaceae_unclassified 0 Pseudomonadaceae_unclassified 0 Staphylococcus 0.004033 Aquabacterium 0 Erysipelotrichaceae_incertae_sedis 0 Pseudomonas 0 Peptostreptococcus 0.003489 Arcanobacterium 0 Erysipelotrichaceae_unclassified 0 Psychrobacter 0 Trueperella 0.003036 Arcobacter 0 Facklamia 0 Roseburia 0 Mycoplasma 0.002401 Atopostipes 0 Faecalibacterium 0 Ruminococcaceae_unclassified 0 Alcaligenaceae_unclassified 0.00222 Bacillaceae_2_unclassified 0 Firmicutes_unclassified 0 Ruminococcus 0 Prevotella 0.00213 Bacillales_unclassified 0 Flavobacterium 0 Schwartzia 0 Lactobacillus 0.002039 Bacilli_unclassified 0 Gemmiger 0 Selenomonas 0 Campylobacter 0.001948 Bacteroidales_unclassified 0 Globicatella 0 Sharpea 0 Lachnospiraceae_unclassified 0.00145 Bacteroidetes_unclassified 0 Gordonia 0 Solobacterium 0 Neisseriaceae_unclassified 0.001405 Bifidobacterium 0 Herbaspirillum 0 SR1_genus_incertae_sedis 0 Prevotellaceae_unclassified 0.001269 Bilophila 0 Howardella 0 Stenotrophomonas 0 Neisseria 0.001223 Blautia 0 Jeotgalicoccus 0 Suttonella 0 Parvimonas 0.001178 Bordetella 0 Kingella 0 Treponema 0 Helcococcus 0.000861 Brachybacterium 0 Kocuria 0 Turicibacter 0 Gemella 0.00077 Brachymonas 0 Lachnospiracea_incertae_sedis 0 Vagococcus 0 Aerococcaceae_unclassified 0.000725 Bradyrhizobium 0 Lactobacillaceae_unclassified 0 Weissella 0 Bergeyella 0.000634 Bulleidia 0 Lactobacillales_unclassified 0 Wohlfahrtiimonas 0 Actinomyces 0.000498 Burkholderiales_unclassified 0 Lactococcus 0 Xanthomonadaceae_unclassified 0 Rothia 0.000498 Butyricimonas 0 Legionella 0 Alkanindiges 0.000453 Cardiobacteriaceae_unclassified 0 Limnobacter 0 Filifactor 0.000453 Catonella 0 Mannheimia 0 Granulicatella 0.000408 Chitinophagaceae_unclassified 0 Megamonas 0 Atopobium 0.000317 Chryseobacterium 0 Megasphaera 0 Clostridiales_unclassified 0.000272 Cloacibacillus 0 Microbacteriaceae_unclassified 0 Tannerella 0.000272 Clostridia_unclassified 0 Mitsuokella 0 TM7_genus_incertae_sedis 0.000272 Clostridiaceae_1_unclassified 0 Moraxellaceae_unclassified 0 Capnocytophaga 0.000181 Clostridiales_Incertae_Sedis_XI_unclassified 0 Morganella 0 Chlamydia 0.000181 Clostridium_IV 0 Moryella 0 Clostridium_sensu_stricto 0.000136 Clostridium_XI 0 Myroides 0 Veillonellaceae_unclassified 0.000136 Clostridium_XlVa 0 Olsenella 0 Corynebacterium 9.06E-05 Clostridium_XlVb 0 Oribacterium 0 Enterococcus 9.06E-05 Collinsella 0 Oscillibacter 0

Appendix 19: Median of aggregate relative abundance at genus level of confirmed cases

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Genus median Genus median Genus median Genus median Genus median Streptococcus 0.206615 Aerococcus 9.06E-05 Clostridiales_Incertae_Sedis_XI_unclassified 0 Myroides 0 Tessaracoccus 0 Escherichia_Shigella 0.106932 Alkanindiges 9.06E-05 Clostridiales_Incertae_Sedis_XIII_unclassified 0 Naxibacter 0 Turicibacter 0 Bacteroides 0.060172 Capnocytophaga 9.06E-05 Clostridium_IV 0 Nosocomiicoccus 0 Vagococcus 0 Porphyromonas 0.026552 Filifactor 9.06E-05 Clostridium_XI 0 Olsenella 0 Verrucomicrobiaceae_unclassified 0 Pasteurellaceae_unclassified 0.025464 Leptotrichia 9.06E-05 Clostridium_XlVa 0 Oribacterium 0 Wautersiella 0 Pasteurella 0.02483 Leptotrichiaceae_unclassified 9.06E-05 Collinsella 0 Oscillibacter 0 Weeksella 0 Fusobacterium 0.024014 Treponema 9.06E-05 Comamonadaceae_unclassified 0 Paenalcaligenes 0 Weissella 0 Actinobacillus 0.022565 Acetanaerobacterium 0 Comamonas 0 Parabacteroides 0 Wohlfahrtiimonas 0 Mycoplasma 0.018215 Acholeplasma 0 Coprococcus 0 Paraprevotella 0 Xanthomonadaceae_unclassified 0 Moraxella 0.013049 Acidaminococcus 0 Coriobacteriaceae_unclassified 0 Pediococcus 0 Yaniella 0 Campylobacter 0.006434 Acidovorax 0 Deltaproteobacteria_unclassified 0 Peptococcus 0 Lampropedia 0 Staphylococcus 0.005528 Acinetobacter 0 Desulfovibrio 0 Peptoniphilus 0 Macrococcus 0 Lachnospiraceae_unclassified 0.004712 Actinomycetales_unclassified 0 Desulfovibrionaceae_unclassified 0 Peptostreptococcaceae_unclassified 0 Mannheimia 0 Bacteria_unclassified 0.00444 Aeromonadaceae_unclassified 0 Dialister 0 Petrimonas 0 Megasphaera 0 Peptostreptococcus 0.003897 Aeromonas 0 Dietzia 0 Phascolarctobacterium 0 Microbacteriaceae_unclassified 0 Flavobacteriaceae_unclassified 0.003534 Anaerococcus 0 Dorea 0 Planococcaceae_incertae_sedis 0 Mitsuokella 0 Porphyromonadaceae_unclassified 0.003262 Anaerolineaceae_unclassified 0 Dysgonomonas 0 Propionibacteriaceae_unclassified 0 Mogibacterium 0 Prevotella 0.003262 Anaerorhabdus 0 Elizabethkingia 0 Propionibacterium 0 Moraxellaceae_unclassified 0 Gemella 0.003172 Anaerovibrio 0 Empedobacter 0 Propionimicrobium 0 Morganella 0 Trueperella 0.002537 Anaerovorax 0 Enhydrobacter 0 Proteus 0 Mucispirillum 0 Veillonella 0.002266 Aquabacterium 0 Erysipelothrix 0 Providencia 0 Neisseriaceae_unclassified 0.001994 Arcanobacterium 0 Erysipelotrichaceae_incertae_sedis 0 Pseudomonadaceae_unclassified 0 Enterobacteriaceae_unclassified 0.001812 Arcobacter 0 Erysipelotrichaceae_unclassified 0 Pseudomonas 0 Clostridiales_unclassified 0.001812 Atopostipes 0 Eubacteriaceae_unclassified 0 Pseudoramibacter 0 Lactobacillus 0.001359 Bacilli_unclassified 0 Facklamia 0 Psychrobacter 0 Neisseria 0.001269 Bacteroidales_unclassified 0 Faecalibacterium 0 Raoultella 0 Parvimonas 0.001087 Bacteroidetes_unclassified 0 Finegoldia 0 Roseburia 0 Prevotellaceae_unclassified 0.001087 Bergeyella 0 Firmicutes_unclassified 0 Ruminococcus 0 Actinomyces 0.000816 Bifidobacteriaceae_unclassified 0 Flavobacterium 0 Salinicoccus 0 Helcococcus 0.000816 Bifidobacterium 0 Gallicola 0 Schlegelella 0 Rothia 0.000816 Blautia 0 Gelidibacter 0 Schwartzia 0 Clostridium_sensu_stricto 0.000725 Bordetella 0 Gemmiger 0 Selenomonas 0 Chlamydia 0.000634 Brachybacterium 0 Globicatella 0 Sharpea 0 Alcaligenaceae_unclassified 0.000544 Bulleidia 0 Gordonia 0 Shewanella 0 Enterococcaceae_unclassified 0.000453 Burkholderiales_unclassified 0 Guggenheimella 0 Solobacterium 0 Enterococcus 0.000453 Butyricicoccus 0 Helicobacter 0 Sphingobacteriales_unclassified 0 Atopobium 0.000362 Cardiobacteriaceae_unclassified 0 Herbaspirillum 0 Sphingobacterium 0 Eubacterium 0.000362 Carnobacteriaceae_unclassified 0 Ignatzschineria 0 Sphingomonadaceae_unclassified 0 Granulicatella 0.000362 Carnobacterium 0 Jeotgalicoccus 0 SR1_genus_incertae_sedis 0 Corynebacterium 0.000362 Catonella 0 Kingella 0 Stenotrophomonas 0 Ruminococcaceae_unclassified 0.000272 Chitinophagaceae_unclassified 0 Kocuria 0 Succinivibrio 0 Peptostreptococcaceae_incertae_sedis 0.000272 Chryseobacterium 0 Kurthia 0 Suttonella 0 TM7_genus_incertae_sedis 0.000272 Cloacibacillus 0 Lachnospiracea_incertae_sedis 0 Tannerella 0 Aerococcaceae_unclassified 0.000181 Clostridia_unclassified 0 Lactobacillales_unclassified 0 Veillonellaceae_unclassified 0.000181 Clostridiaceae_1_unclassified 0 Lactococcus 0

Appendix 20: Median of aggregate relative abundance at genus level of probable cases

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Appendix 21: Top 15 median aggregate relative abundances at the genus level in both control and confirmed cases

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Appendix 22: Top 15 median aggregate relative abundances at the genus level in both probable and confirmed cases

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Appendix 23: Top 15 median aggregate relative abundances at the genus level in both control and probable cases

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Observed Richness Control Samples mean +/- sd Disease Samples mean +/- sd Probable Sample mean +/- sd F10H1 105.31+/-3.46 F10S1 77.45+/-3.85 F10S2 69.38+/-3.64 F10H2 68.4+/-3.15 F11S1 127.55+/-3.21 F10S3 126.6+/-4.31 F10H4 115.65+/-3.64 F11S4 75.4+/-3.46 F10S4 156.1+/-5.15 F10H5 105.1+/-3.6 F11S5 87.2+/-2.22 F10S5 106.8+/-3.10 F12H1 116.43+/-3.47 F13S1 158.66+/-4.37 F10S6 106.91+/-3.31 F13H1 86.25+/-2.72 F13S2 175.27+/-5.04 F10S6T 140.04+/-4.26 F13H2 87.45+/-2.87 F1S1 61.69+/-3.25 F10S7 236.82+/-5.17 F1H10 97.47+/-3.13 F1S10 100.76+/-3.06 F10S8 88.33+/-2.68 F1H11 67.78+/-2.06 F1S12 116.84+/-2.52 F11S2 108.81+/-4.08 F1H12 81.06+/-3.27 F1S13 99.78+/-3.52 F11S3 104.15+/-2.38 F1H8 84.65+/-2.31 F1S6 87.71+/-3.12 F12S1 134.62+/-4.33 F7H10 100.69+/-3.34 F1S8 99.69+/-2.59 F1S11 108.59+/-3.35 F7H11 84.06+/-3.75 F7S1 86.37+/-2.72 F1S14 111.43+/-3.39 F7H4 66.76+/-3.47 F7S3 84.5+/-3.33 F2S1 89.82+/-2.91 F7H5 81.43+/-2.15 F7S5 61.39+/-3.54 F6S2 104.6+/-2.87 F8H1 94.29+/-3.28 F7S7 151.16+/-6.12 F6S3 130.5+/-4.15 F8H2 65.21+/-3.55 F7S9 107.95+/-3.37 F7S10 185.16+/-4.58 Minimum Mean 65.21 F8S1 116.35+/-3.89 F7S2 113.34+/-3.26 Maximum Mean 116.43 Minimum Mean 61.39 F7S6 93.3+/-2.08 Average 88.71 Maximum Mean 175.27 F7S8 110.67+/-4.69 Average 104.21 F8S2 104.41+/-2.86 Minimum Mean 69.38 Maximum Mean 236.82 Average Mean 120.5

Appendix 24: Observed richness in all samples

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Inverse Simpson Control Samples mean +/- sd Disease Samples mean +/- sd Probable Sample mean +/- sd F10H1 5.27+/-0.1 F10S1 3.83+/-0.05 F10S2 2.27 +/- 0.03 F10H2 3.48+/-0.04 F11S1 14.96+/-0.19 F10S3 11.77 +/- 0.16 F10H4 5.06+/-0.09 F11S4 8.26+/-0.08 F10S4 14.91 +/- 0.22 F10H5 13.1+/-0.17 F11S5 15.69+/-0.18 F10S5 9.15 +/- 0.13 F12H1 7.01+/-0.1 F13S1 24.31+/-0.35 F10S6 1.90 +/- 0.02 F13H1 9.91+/-0.14 F13S2 11.78+/-0.24 F10S6T 12.16 +/- 0.23 F13H2 7.05+/-0.09 F1S1 2.67+/-0.03 F10S7 17.60 +/- 0.39 F1H10 12.54+/-0.15 F1S10 20.75+/-0.22 F10S8 9.09 +/- 0.11 F1H11 6.74+/-0.11 F1S12 14.12+/-0.19 F11S2 4.72+/- 0.08 F1H12 14.48+/-0.17 F1S13 6+/-0.1 F11S3 9.56 +/- 0.18 F1H8 8.28+/-0.1 F1S6 14.12+/-0.18 F12S1 8.49 +/- 0.13 F7H10 7.73+/-0.13 F1S8 17.68+/-0.21 F1S11 10.50 +/- 0.18 F7H11 3.56+/-0.05 F7S1 11.8+/-0.15 F1S14 12.26 +/- 0.19 F7H4 7.71+/-0.1 F7S3 4.39+/-0.05 F2S1 8.51 +/- 0.13 F7H5 14.32+/-0.16 F7S5 4.1+/-0.05 F6S2 7.41+/- 0.14 F8H1 12.83+/-0.17 F7S7 11.07+/-0.14 F6S3 11.91 +/- 0.17 F8H2 5.45+/-0.06 F7S9 18.18+/-0.29 F7S10 6.74 +/- 0.13 Minimum Mean 3.48 F8S1 14.49+/-0.19 F7S2 8.62 +/- 0.17 Maximum Mean 14.48 Minimum Mean 2.67 F7S6 16.52+/- 0.23 Average 8.5 Maximum Mean 24.31 F7S8 3.51 +/- 0.05 Average 12.12 F8S2 13.95 +/- 0.23 Minimum Mean 1.9 Maximum Mean 17.6 Average Mean 9.6

Appendix 25: Inverse Simpson of all samples

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Phylum statistic p.value p.value.adj Bacteria_unclassified 6.46222596 0.01101915 0.17630644 Chloroflexi 1.05882353 0.30348366 0.52981993 Deferribacteres 1.05882353 0.30348366 0.52981993 Firmicutes 1.74291939 0.18676935 0.52981993 Fusobacteria 1.33442266 0.24801994 0.52981993 0.94444444 0.33113746 0.52981993 Spirochaetes 2.60859978 0.10628557 0.52981993 SR1 1.55614389 0.21223072 0.52981993 TM7 1.2118753 0.27096104 0.52981993 Verrucomicrobia 2.18181818 0.1396494 0.52981993 Actinobacteria 0.52730697 0.46774127 0.68035094 Bacteroidetes 0.13180828 0.71656447 0.93544668 Chlamydiae 0.09327806 0.76005043 0.93544668 Proteobacteria 0.00108932 0.97367067 0.97367067 Synergistetes 0.00803121 0.92859157 0.97367067 Tenericutes 0.01816177 0.8927972 0.97367067

Appendix 26: Kruskal- Wallis rank sum test at phylum level between control and confirmed cases

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Phylum statistic p.value p.value.adj Spirochaetes 5.473325788 0.019308848 0.30894156 Tenericutes 3.636924651 0.056511229 0.45208983 Bacteria_unclassified 2.258185053 0.132909748 0.53163899 Firmicutes 2.541353383 0.110899692 0.53163899 Chlamydiae 1.070180254 0.300904734 0.56825359 Chloroflexi 0.761904762 0.382733089 0.56825359 OD1 1.3125 0.251942515 0.56825359 Proteobacteria 0.736842105 0.390674346 0.56825359 SR1 1.407926917 0.235400842 0.56825359 Synergistetes 0.761904762 0.382733089 0.56825359 TM7 1.08538206 0.297496665 0.56825359 Actinobacteria 0.158891091 0.690179521 0.90240179 Bacteroidetes 0.015037594 0.902401786 0.90240179 Deferribacteres 0.024470899 0.875692695 0.90240179 Fusobacteria 0.060150376 0.806258423 0.90240179 Verrucomicrobia 0.055059524 0.814482216 0.90240179

Appendix 27: Kruskal- Wallis rank sum test at phylum level between control and probable cases

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Phylum statistic p.value p.value.adj Tenericutes 4.24526333 0.03935997 0.55103951 Chloroflexi 0.85714286 0.35453948 0.82725879 Deferribacteres 0.85714286 0.35453948 0.82725879 Fusobacteria 1.46746032 0.22574683 0.82725879 Spirochaetes 1.05666007 0.30397818 0.82725879 Synergistetes 1.44930371 0.22863971 0.82725879 Bacteroidetes 0.38412698 0.53540278 0.92161544 Chlamydiae 0.40288478 0.52560319 0.92161544 Proteobacteria 0.28650794 0.59246707 0.92161544 Actinobacteria 0.13414056 0.71417701 0.9223295 Bacteria_unclassified 0.1240456 0.72468747 0.9223295 Firmicutes 0.01607306 0.89911488 1 SR1 0 1 1 TM7 0.00184298 0.96575737 1

Appendix 28: Kruskal- Wallis rank sum test at phylum level between probable and confirmed cases

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Family statistic p.value p.value.adj Family statistic p.value p.value.adj Alcaligenaceae 9.40534138 0.00216354 0.17308339 Peptococcaceae_1 0.50920428 0.47548352 0.75036098 Aerococcaceae 5.14597825 0.02330028 0.35124065 Porphyromonadaceae 0.52738084 0.46771009 0.75036098 Bacteria_unclassified 6.46222596 0.01101915 0.35124065 Bacteroidaceae 0.39324619 0.53059798 0.81630459 Dermabacteraceae 5.31339301 0.0211621 0.35124065 Clostridiaceae_1 0.32524639 0.56847169 0.82767047 Erysipelotrichaceae 4.93336002 0.02634305 0.35124065 Firmicutes_unclassified 0.32431921 0.56902344 0.82767047 Micrococcaceae 5.91962525 0.01497314 0.35124065 Xanthomonadaceae 0.33669834 0.56174123 0.82767047 Moraxellaceae 4.39304447 0.03608582 0.41240938 Eubacteriaceae 0.24557408 0.62020889 0.88601271 Actinomycetales_unclassified 1.1559633 0.28230429 0.60206811 Lactobacillaceae 0.21377709 0.64382293 0.90361113 Anaerolineaceae 1.05882353 0.30348366 0.60206811 Ruminococcaceae 0.17777778 0.67328998 0.92867583 Bacillaceae_2 0.94444444 0.33113746 0.60206811 Acidaminococcaceae 0.16096394 0.68827058 0.93324825 Bacillales_Incertae_Sedis_XI 2.47024368 0.11601994 0.60206811 Neisseriaceae 0.13199314 0.71637437 0.95516582 Bacilli_unclassified 0.94444444 0.33113746 0.60206811 Chlamydiaceae 0.09327806 0.76005043 0.96468941 Bacteroidales_unclassified 1.94444444 0.16318677 0.60206811 Comamonadaceae 0.10877072 0.74154802 0.96468941 Bifidobacteriaceae 1.05882353 0.30348366 0.60206811 Desulfovibrionaceae 0.08414906 0.77175153 0.96468941 Bradyrhizobiaceae 0.94444444 0.33113746 0.60206811 Leuconostocaceae 0.08908663 0.76534155 0.96468941 Burkholderiaceae 0.94444444 0.33113746 0.60206811 Coriobacteriaceae 0.0618462 0.80360139 0.97509393 Burkholderiales_incertae_sedis 1.94444444 0.16318677 0.60206811 Staphylococcaceae 0.06130027 0.8044525 0.97509393 Burkholderiales_unclassified 1.05882353 0.30348366 0.60206811 Actinomycetaceae 0.00027248 0.98682985 1 Caulobacteraceae 0.94444444 0.33113746 0.60206811 Bacteroidetes_unclassified 0.00672818 0.93462644 1 Clostridiales_Incertae_Sedis_XI 3.30121759 0.06922855 0.60206811 Campylobacteraceae 0.00246409 0.96040955 1 Clostridiales_unclassified 0.988407 0.32013203 0.60206811 Carnobacteriaceae 0.04053272 0.84044259 1 Deferribacteraceae 1.05882353 0.30348366 0.60206811 Clostridia_unclassified 0.0016835 0.96727158 1 Desulfovibrionales_unclassified 0.94444444 0.33113746 0.60206811 Clostridiales_Incertae_Sedis_XIII 0 1 1 Enterobacteriaceae 1.04684096 0.30623577 0.60206811 Mycoplasmataceae 0.01816177 0.8927972 1 Enterococcaceae 1.72650321 0.18885809 0.60206811 Oxalobacteraceae 0.00672818 0.93462644 1 Fusobacteriaceae 1.25925926 0.26179135 0.60206811 Pasteurellaceae 0.00108932 0.97367067 1 Helicobacteraceae 0.94444444 0.33113746 0.60206811 Peptostreptococcaceae 0.00435974 0.94735527 1 Hyphomicrobiaceae 2.14528944 0.14300806 0.60206811 Planococcaceae 0 1 1 Lactobacillales_unclassified 3.34703026 0.06732638 0.60206811 Propionibacteriaceae 0.00672818 0.93462644 1 Legionellaceae 0.94444444 0.33113746 0.60206811 Synergistaceae 0.00803121 0.92859157 1 Leptotrichiaceae 1.42118957 0.23320771 0.60206811 Veillonellaceae 0.00681019 0.93423015 1 Microbacteriaceae 3.00326603 0.08309685 0.60206811 Nocardiaceae 0.94444444 0.33113746 0.60206811 Paenibacillaceae_1 2.1799308 0.13982075 0.60206811 Planctomycetaceae 0.94444444 0.33113746 0.60206811 Prevotellaceae 1.04830918 0.30589682 0.60206811 Pseudomonadaceae 1.81206316 0.17826093 0.60206811 Rikenellaceae 0.94444444 0.33113746 0.60206811 Spirochaetaceae 2.60859978 0.10628557 0.60206811 SR1_family_incertae_sedis 1.55614389 0.21223072 0.60206811 Streptococcaceae 2.51015548 0.11311477 0.60206811 Subdivision3_family_incertae_sedis 1.05882353 0.30348366 0.60206811 TM7_family_incertae_sedis 1.2118753 0.27096104 0.60206811 Verrucomicrobiaceae 1.05882353 0.30348366 0.60206811 Cardiobacteriaceae 0.8880045 0.34601864 0.61514425 Flavobacteriaceae 0.82391714 0.36403737 0.63310847 Corynebacteriaceae 0.6696562 0.4131715 0.68861916 Lachnospiraceae 0.68111407 0.40920336 0.68861916 Chitinophagaceae 0.50261097 0.47835513 0.75036098

Appendix 29: Kruskal- Wallis rank sum test at family level between control and confirmed cases

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Family statistic p.value p.value.adj Family statistic p.value p.value.adj Actinomycetales_unclassified 5.256813977 0.021861003 0.411924158 Dermabacteraceae 0.76190476 0.38273309 0.53582632 Clostridiales_Incertae_Sedis_XI 4.158221656 0.041432884 0.411924158 Eubacteriaceae 1.04746018 0.30609276 0.53582632 Clostridiales_unclassified 4.059135271 0.043932991 0.411924158 Gammaproteobacteria_unclassified 0.76190476 0.38273309 0.53582632 Erysipelotrichaceae 7.175946966 0.007388744 0.411924158 Hyphomicrobiaceae 0.88766184 0.34611171 0.53582632 Leptotrichiaceae 4.698270649 0.030192983 0.411924158 Microbacteriaceae 0.76190476 0.38273309 0.53582632 Micrococcaceae 4.051399901 0.044134731 0.411924158 Nocardiaceae 0.76190476 0.38273309 0.53582632 Prevotellaceae 4.475154543 0.034391092 0.411924158 Porphyromonadaceae 0.84596494 0.35769635 0.53582632 Ruminococcaceae 4.206732925 0.040263815 0.411924158 Sphingobacteriales_unclassified 0.76190476 0.38273309 0.53582632 Spirochaetaceae 5.473325788 0.019308848 0.411924158 Sphingomonadaceae 0.76190476 0.38273309 0.53582632 Mycoplasmataceae 3.636924651 0.056511229 0.437847183 Synergistaceae 0.76190476 0.38273309 0.53582632 Streptococcaceae 3.612781955 0.057337131 0.437847183 TM7_family_incertae_sedis 1.08538206 0.29749666 0.53582632 Acidaminococcaceae 1.745157385 0.186486677 0.529079282 Staphylococcaceae 0.73701684 0.39061817 0.53790043 Alcaligenaceae 1.97198203 0.160237638 0.529079282 Pseudomonadaceae 0.708582 0.39991495 0.54182026 Bacillaceae_1 1.3125 0.251942515 0.529079282 Actinomycetaceae 0.68571937 0.40762421 0.54349895 Bacteria_unclassified 2.258185053 0.132909748 0.529079282 Propionibacteriaceae 0.54646353 0.45976631 0.60344328 Bacteroidaceae 1.393257261 0.237855795 0.529079282 Peptococcaceae_1 0.3865922 0.53409615 0.69021656 Bifidobacteriaceae 2.415048727 0.120174231 0.529079282 Coriobacteriaceae 0.33658566 0.56180671 0.71502672 Burkholderiales_incertae_sedis 1.566137566 0.210768821 0.529079282 Enterococcaceae 0.25758396 0.61178489 0.75573427 Campylobacteraceae 1.903646804 0.167670693 0.529079282 Lactobacillales_unclassified 0.26267627 0.60828746 0.75573427 Carnobacteriaceae 2.657997033 0.103030402 0.529079282 Neisseriaceae 0.21154139 0.64556195 0.7859015 Clostridiaceae_1 1.599415405 0.205986076 0.529079282 Brucellaceae 0.15102041 0.69756212 0.79182727 Clostridiales_Incertae_Sedis_XIII 1.346925935 0.245816249 0.529079282 Burkholderiales_unclassified 0.15102041 0.69756212 0.79182727 Comamonadaceae 1.511999094 0.218834296 0.529079282 Enterobacteriaceae 0.15883459 0.69023176 0.79182727 Corynebacteriaceae 2.470997474 0.115964311 0.529079282 Flavobacteriaceae 0.15885342 0.69021435 0.79182727 Desulfovibrionaceae 2.020945001 0.155142564 0.529079282 Helicobacteraceae 0.1771455 0.67383792 0.79182727 Dietziaceae 1.566137566 0.210768821 0.529079282 Lactobacillaceae 0.13549897 0.7127973 0.79833297 Firmicutes_unclassified 2.194515306 0.13850277 0.529079282 Leuconostocaceae 0.08220869 0.77432628 0.84471958 Lachnospiraceae 2.25791721 0.132932741 0.529079282 Oxalobacteraceae 0.08490406 0.77075841 0.84471958 Moraxellaceae 1.903646804 0.167670693 0.529079282 Chitinophagaceae 0.06659239 0.79636445 0.85762325 OD1_family_incertae_sedis 1.3125 0.251942515 0.529079282 Verrucomicrobiaceae 0.05505952 0.81448222 0.86603172 Paenibacillaceae_1 1.3125 0.251942515 0.529079282 Bacillales_Incertae_Sedis_XI 0.04606355 0.83006032 0.87156333 Pasteurellaceae 2.256578947 0.13304769 0.529079282 Aeromonadaceae 0.0244709 0.87569269 0.89705105 Peptostreptococcaceae 1.861799309 0.172417278 0.529079282 Deferribacteraceae 0.0244709 0.87569269 0.89705105 Planococcaceae 1.469313279 0.225454074 0.529079282 Cardiobacteriaceae 0.00961086 0.92190452 0.93301181 Sphingobacteriaceae 1.566137566 0.210768821 0.529079282 Fusobacteriaceae 0.00093985 0.97554311 0.97554311 SR1_family_incertae_sedis 1.407926917 0.235400842 0.529079282 Succinivibrionaceae 2.415048727 0.120174231 0.529079282 Sutterellaceae 1.3125 0.251942515 0.529079282 Veillonellaceae 2.076373952 0.149595177 0.529079282 Xanthomonadaceae 1.572114678 0.209900177 0.529079282 Acholeplasmataceae 0.761904762 0.382733089 0.535826324 Aerococcaceae 1.158318426 0.281814557 0.535826324 Anaerolineaceae 0.761904762 0.382733089 0.535826324 Bacteroidales_unclassified 0.761904762 0.382733089 0.535826324 Bacteroidetes_unclassified 1.119617656 0.290000797 0.535826324 Betaproteobacteria_unclassified 0.761904762 0.382733089 0.535826324 Chlamydiaceae 1.070180254 0.300904734 0.535826324 Clostridia_unclassified 0.761904762 0.382733089 0.535826324 Cytophagaceae 0.761904762 0.382733089 0.535826324

Appendix 30: Kruskal- Wallis rank sum test at family level between control and probable cases

86

Family statistic p.value p.value.adj Family statistic p.value p.value.adj Alcaligenaceae 5.49749102 0.01904378 0.538731 Bacteroidales_unclassified 0.62804484 0.42807339 0.66452145 Bacillales_Incertae_Sedis_XI 4.53870821 0.03313659 0.538731 Bifidobacteriaceae 0.61653117 0.43233926 0.66452145 Clostridiales_unclassified 4.50762829 0.033744 0.538731 Carnobacteriaceae 0.64838353 0.4206912 0.66452145 Eubacteriaceae 5.03390163 0.02485584 0.538731 Microbacteriaceae 0.64030765 0.42359941 0.66452145 Lachnospiraceae 4.00241406 0.04543514 0.538731 Prevotellaceae 0.69095434 0.40583996 0.66452145 Mycoplasmataceae 4.24526333 0.03935997 0.538731 Propionibacteriaceae 0.58195351 0.44554762 0.67237187 Pseudomonadaceae 4.65006384 0.03105239 0.538731 Neisseriaceae 0.55790595 0.4551052 0.67453092 Acholeplasmataceae 0.85714286 0.35453948 0.61305785 Chlamydiaceae 0.40288478 0.52560319 0.75830769 Actinomycetaceae 0.94550415 0.33086632 0.61305785 Clostridiales_Incertae_Sedis_XIII 0.39457995 0.52990176 0.75830769 Aeromonadaceae 0.85714286 0.35453948 0.61305785 Aerococcaceae 0.25874034 0.61098688 0.84519852 Anaerolineaceae 0.85714286 0.35453948 0.61305785 Helicobacteraceae 0.26849846 0.6043408 0.84519852 Bacillaceae_2 1.16666667 0.28008721 0.61305785 Moraxellaceae 0.21611518 0.64201604 0.8735628 Bacillales_unclassified 1.16666667 0.28008721 0.61305785 Burkholderiales_incertae_sedis 0.18218218 0.66950458 0.89627226 Bacilli_unclassified 1.16666667 0.28008721 0.61305785 Lactobacillaceae 0.16720355 0.68260916 0.89931048 Bradyrhizobiaceae 1.16666667 0.28008721 0.61305785 Bacteria_unclassified 0.1240456 0.72468747 0.93894285 Brucellaceae 0.85714286 0.35453948 0.61305785 Enterobacteriaceae 0.11428571 0.73531669 0.93894285 Burkholderiaceae 1.16666667 0.28008721 0.61305785 Bacteroidetes_unclassified 0.09939804 0.75255321 0.94639268 Burkholderiales_unclassified 1.7593985 0.18469956 0.61305785 Streptococcaceae 0.07936508 0.77815969 0.96398886 Campylobacteraceae 3.25276902 0.07130291 0.61305785 Acidaminococcaceae 0.03266296 0.85658032 0.97783138 Cardiobacteriaceae 1.06910208 0.30114835 0.61305785 Bacteroidaceae 0.01984127 0.8879813 0.97783138 Caulobacteraceae 1.16666667 0.28008721 0.61305785 Clostridiales_Incertae_Sedis_XI 0.02858879 0.86573199 0.97783138 Chitinophagaceae 1.0233287 0.31173073 0.61305785 Coriobacteriaceae 0.00982565 0.92103949 0.97783138 Clostridia_unclassified 0.85714286 0.35453948 0.61305785 Erysipelotrichaceae 0.03950871 0.84244424 0.97783138 Clostridiaceae_1 2.94913768 0.08592339 0.61305785 Firmicutes_unclassified 0.0364439 0.8486017 0.97783138 Clostridiales_Incertae_Sedis_XII 0.85714286 0.35453948 0.61305785 Hyphomicrobiaceae 0.0080713 0.92841402 0.97783138 Comamonadaceae 1.05704085 0.30389107 0.61305785 Leptotrichiaceae 0.00181157 0.96605028 0.97783138 Corynebacteriaceae 1.04578721 0.30647933 0.61305785 Leuconostocaceae 0.01434962 0.90464959 0.97783138 Deferribacteraceae 0.85714286 0.35453948 0.61305785 Nocardiaceae 0.00543024 0.94125691 0.97783138 Dermabacteraceae 2.71799338 0.09922268 0.61305785 Peptococcaceae_1 0.00286178 0.95733705 0.97783138 Desulfovibrionaceae 1.6529212 0.19856181 0.61305785 Porphyromonadaceae 0.01269841 0.91027852 0.97783138 Dietziaceae 1.7593985 0.18469956 0.61305785 Staphylococcaceae 0.0285859 0.86573872 0.97783138 Enterococcaceae 1.70929504 0.19107686 0.61305785 TM7_family_incertae_sedis 0.00184298 0.96575737 0.97783138 Flavobacteriaceae 0.94512116 0.33096428 0.61305785 Xanthomonadaceae 0.01321805 0.90846908 0.97783138 Fusobacteriaceae 1.6797051 0.19496364 0.61305785 SR1_family_incertae_sedis 0 1 1 Lactobacillales_unclassified 1.66847381 0.19646308 0.61305785 Legionellaceae 1.16666667 0.28008721 0.61305785 Micrococcaceae 0.86859352 0.35134498 0.61305785 Oxalobacteraceae 1.7593985 0.18469956 0.61305785 Pasteurellaceae 3.25079365 0.07138888 0.61305785 Peptostreptococcaceae 1.17722295 0.27792207 0.61305785 Planococcaceae 1.65113396 0.19880466 0.61305785 Ruminococcaceae 0.89057433 0.34532172 0.61305785 Sphingobacteriaceae 1.7593985 0.18469956 0.61305785 Sphingomonadaceae 0.85714286 0.35453948 0.61305785 Spirochaetaceae 1.05666007 0.30397818 0.61305785 Succinivibrionaceae 1.7593985 0.18469956 0.61305785 Synergistaceae 1.44930371 0.22863971 0.61305785 Veillonellaceae 1.46850075 0.22558239 0.61305785 Actinomycetales_unclassified 0.80474657 0.369678 0.62618927

Appendix 31: Kruskal- Wallis rank sum test at family level between probable and confirmed cases

87

Genus statistic p.value p.value.adj Genus statistic p.value p.value.adj Genus statistic p.value p.value.adj Aerococcaceae_unclassified 7.66649179 0.00562557 0.30615785 Naxibacter 1.05882353 0.30348366 0.58027898 Lachnospiraceae_unclassified 0.09846324 0.75368166 0.92451617 Alcaligenaceae_unclassified 8.10754912 0.00440813 0.30615785 Nosocomiicoccus 0.94444444 0.33113746 0.58027898 Chlamydia 0.09327806 0.76005043 0.92615417 Alkanindiges 9.03934107 0.00264231 0.30615785 Olsenella 0.94444444 0.33113746 0.58027898 Weissella 0.08908663 0.76534155 0.92646609 Providencia 7.36363636 0.00665561 0.30615785 Oribacterium 1.05882353 0.30348366 0.58027898 Staphylococcus 0.06977541 0.79166406 0.95206659 Bacteria_unclassified 6.46222596 0.01101915 0.40550481 Oscillibacter 0.94444444 0.33113746 0.58027898 Actinomyces 0.03309111 0.85565366 0.97717864 Moraxella 5.57046014 0.01826596 0.47910376 Paenibacillus 2.1799308 0.13982075 0.58027898 Anaerorhabdus 0.03211009 0.85778625 0.97717864 Prevotellaceae_unclassified 5.33645044 0.02088396 0.47910376 Pediococcus 3.00505051 0.08300539 0.58027898 Desulfovibrio 0.03095444 0.86034206 0.97717864 Rothia 5.1394561 0.02338797 0.47910376 Peptostreptococcaceae_incertae_sedis 0.94585749 0.33077598 0.58027898 Erysipelotrichaceae_incertae_sedis 0.04617817 0.82985225 0.97717864 Solobacterium 5.13601185 0.02343442 0.47910376 Planctomyces 0.94444444 0.33113746 0.58027898 Kingella 0.03095444 0.86034206 0.97717864 3_genus_incertae_sedis 1.05882353 0.30348366 0.58027898 Planomicrobium 0.94444444 0.33113746 0.58027898 Mannheimia 0.03212483 0.85775397 0.97717864 Acetanaerobacterium 0.94444444 0.33113746 0.58027898 Propionibacteriaceae_unclassified 0.94444444 0.33113746 0.58027898 Moraxellaceae_unclassified 0.0420289 0.83756469 0.97717864 Acinetobacter 1.8174626 0.17761561 0.58027898 Propionimicrobium 1.05882353 0.30348366 0.58027898 Trueperella 0.04608851 0.83001498 0.97717864 Actinomycetales_unclassified 1.1559633 0.28230429 0.58027898 Proteiniclasticum 1.05882353 0.30348366 0.58027898 Veillonella 0.04604976 0.83008536 0.97717864 Alcaligenes 1.05882353 0.30348366 0.58027898 Proteus 1.75 0.18587673 0.58027898 Atopobium 0.02252252 0.8807054 0.98945561 Alistipes 0.94444444 0.33113746 0.58027898 Pseudomonadaceae_unclassified 0.94444444 0.33113746 0.58027898 Peptostreptococcus 0.0220681 0.88190609 0.98945561 Anaerococcus 3.00326603 0.08309685 0.58027898 Pseudomonas 1.12984218 0.287809 0.58027898 Mycoplasma 0.01816177 0.8927972 0.99560415 Anaerolineaceae_unclassified 1.05882353 0.30348366 0.58027898 Pyramidobacter 0.94444444 0.33113746 0.58027898 Acidaminococcus 0.00672818 0.93462644 1 Aquabacterium 1.94444444 0.16318677 0.58027898 Raoultella 2.1799308 0.13982075 0.58027898 Acidovorax 0.00089236 0.97616887 1 Arcanobacterium 4.12625567 0.04222268 0.58027898 Ruminococcus 0.94444444 0.33113746 0.58027898 Aerococcus 0.00201288 0.96421479 1 Arcobacter 0.94444444 0.33113746 0.58027898 Selenomonas 1.94444444 0.16318677 0.58027898 Anaerovorax 0 1 1 Atopostipes 0.94444444 0.33113746 0.58027898 Sharpea 0.94444444 0.33113746 0.58027898 Bacteroidetes_unclassified 0.00672818 0.93462644 1 Bacillaceae_2_unclassified 0.94444444 0.33113746 0.58027898 SR1_genus_incertae_sedis 1.55614389 0.21223072 0.58027898 Bordetella 0 1 1 Bacilli_unclassified 0.94444444 0.33113746 0.58027898 Streptococcus 2.51015548 0.11311477 0.58027898 Campylobacter 0.00684471 0.93406405 1 Bacteroidales_unclassified 1.94444444 0.16318677 0.58027898 Suttonella 1.99779964 0.15752774 0.58027898 Clostridia_unclassified 0.0016835 0.96727158 1 Bifidobacterium 1.05882353 0.30348366 0.58027898 TM7_genus_incertae_sedis 1.2118753 0.27096104 0.58027898 Clostridiales_Incertae_Sedis_XI_unclassified 0.0016835 0.96727158 1 Brachybacterium 0.94444444 0.33113746 0.58027898 Treponema 2.60859978 0.10628557 0.58027898 Clostridium_XI 0.00687386 0.93392408 1 Brachymonas 1.94444444 0.16318677 0.58027898 Turicibacter 0.94444444 0.33113746 0.58027898 Clostridium_XVIII 0.0016835 0.96727158 1 Bradyrhizobium 0.94444444 0.33113746 0.58027898 Veillonellaceae_unclassified 3.46808049 0.06256387 0.58027898 Collinsella 0 1 1 Brevundimonas 0.94444444 0.33113746 0.58027898 Verrucomicrobiaceae_unclassified 1.05882353 0.30348366 0.58027898 Flavobacterium 0.00672818 0.93462644 1 Bulleidia 1.99158249 0.15817551 0.58027898 Xanthomonadaceae_unclassified 3.00326603 0.08309685 0.58027898 Herbaspirillum 0.00672818 0.93462644 1 Burkholderiales_unclassified 1.05882353 0.30348366 0.58027898 Yersinia 2.18181818 0.1396494 0.58027898 Jeotgalicoccus 0.00089236 0.97616887 1 Butyricicoccus 1.1564938 0.28219388 0.58027898 Dorea 0.82084691 0.36493267 0.63174411 Planococcaceae_incertae_sedis 0 1 1 Butyricimonas 0.94444444 0.33113746 0.58027898 Facklamia 0.80556994 0.36943325 0.63174411 Psychrobacter 0.00040183 0.98400696 1 Carnobacteriaceae_unclassified 1.35727398 0.24401048 0.58027898 Neisseriaceae_unclassified 0.80096059 0.37080632 0.63174411 Vagococcus 0.00063152 0.97995125 1 Carnobacterium 1.05882353 0.30348366 0.58027898 Leptotrichia 0.76966976 0.38031923 0.64200678 Wohlfahrtiimonas 0 1 1 Catonella 1.94612795 0.16300471 0.58027898 Clostridium_XlVa 0.74313307 0.38865909 0.64757374 Clostridiales_unclassified 0.988407 0.32013203 0.58027898 Prevotella 0.73689948 0.3906559 0.64757374 Clostridium_IV 1.94612795 0.16300471 0.58027898 Corynebacterium 0.6696562 0.4131715 0.66687329 Clostridium_XlVb 0.94444444 0.33113746 0.58027898 Phascolarctobacterium 0.68647641 0.40736547 0.66687329 Comamonas 1.94444444 0.16318677 0.58027898 Tannerella 0.67798593 0.41028113 0.66687329 Coprococcus 1.98856951 0.15849053 0.58027898 Bergeyella 0.65544989 0.41817091 0.66907346 Delftia 1.05882353 0.30348366 0.58027898 Lachnospiracea_incertae_sedis 0.56892068 0.45068813 0.71488462 Dermabacteraceae_unclassified 4.12625567 0.04222268 0.58027898 Chitinophagaceae_unclassified 0.50261097 0.47835513 0.73347786 Desulfovibrionales_unclassified 0.94444444 0.33113746 0.58027898 Coriobacteriaceae_unclassified 0.50920428 0.47548352 0.73347786 Dialister 1.94444444 0.16318677 0.58027898 Morganella 0.50920428 0.47548352 0.73347786 Dysgonomonas 1.05882353 0.30348366 0.58027898 Peptococcus 0.50920428 0.47548352 0.73347786 Enterobacteriaceae_unclassified 2.73749746 0.09801816 0.58027898 Parvimonas 0.46030293 0.49748243 0.75650221 Enterococcaceae_unclassified 1.55923743 0.21177691 0.58027898 Porphyromonas 0.43572985 0.50919071 0.76795976 Enterococcus 1.36746937 0.24224716 0.58027898 Cloacibacillus 0.41683162 0.51852225 0.7704591 Erysipelotrichaceae_unclassified 4.00401569 0.04539199 0.58027898 Neisseria 0.41543756 0.51922244 0.7704591 Filifactor 2.30740741 0.12875865 0.58027898 Bacteroides 0.39324619 0.53059798 0.78104023 Flavobacteriaceae_unclassified 3.06506422 0.079992 0.58027898 Bilophila 0.3338945 0.56337481 0.78547186 Flavonifractor 0.94444444 0.33113746 0.58027898 Blautia 0.36209394 0.54734561 0.78547186 Fusobacterium 1.25925926 0.26179135 0.58027898 Chryseobacterium 0.33369623 0.56349068 0.78547186 Gemella 2.47024368 0.11601994 0.58027898 Clostridium_sensu_stricto 0.36458333 0.54597172 0.78547186 Gemmiger 2.14528944 0.14300806 0.58027898 Desulfovibrionaceae_unclassified 0.33369623 0.56349068 0.78547186 Globicatella 0.94444444 0.33113746 0.58027898 Parabacteroides 0.37410926 0.54077284 0.78547186 Gordonia 0.94444444 0.33113746 0.58027898 Peptoniphilus 0.3338945 0.56337481 0.78547186 Helcococcus 3.83219313 0.05027711 0.58027898 Firmicutes_unclassified 0.32431921 0.56902344 0.7872204 Helicobacter 0.94444444 0.33113746 0.58027898 Roseburia 0.29405587 0.58763308 0.80689915 Howardella 0.94444444 0.33113746 0.58027898 Comamonadaceae_unclassified 0.26603325 0.6060052 0.82029912 Kocuria 0.94444444 0.33113746 0.58027898 Stenotrophomonas 0.26558624 0.60630804 0.82029912 Lactobacillaceae_unclassified 0.94444444 0.33113746 0.58027898 Eubacterium 0.24557408 0.62020889 0.82708518 Lactobacillales_unclassified 3.34703026 0.06732638 0.58027898 Porphyromonadaceae_unclassified 0.24542665 0.62031388 0.82708518 Lactococcus 1.11762789 0.2904298 0.58027898 Faecalibacterium 0.22631301 0.63427125 0.83437242 Legionella 0.94444444 0.33113746 0.58027898 Granulicatella 0.2255429 0.63484858 0.83437242 Leptotrichiaceae_unclassified 4.2225014 0.03989131 0.58027898 Escherichia_Shigella 0.21350763 0.64403195 0.83452027 Limnobacter 0.94444444 0.33113746 0.58027898 Lactobacillus 0.21377709 0.64382293 0.83452027 Megasphaera 3.34172481 0.06754377 0.58027898 Clostridiaceae_1_unclassified 0.18826694 0.66436296 0.85484465 Microbacteriaceae_unclassified 3.00326603 0.08309685 0.58027898 Actinobacillus 0.15686275 0.69206149 0.86960077 Mitsuokella 1.94444444 0.16318677 0.58027898 Capnocytophaga 0.15400675 0.69473539 0.86960077 Moryella 0.94444444 0.33113746 0.58027898 Pasteurellaceae_unclassified 0.15686275 0.69206149 0.86960077 Mucispirillum 1.05882353 0.30348366 0.58027898 Ruminococcaceae_unclassified 0.16838631 0.68154994 0.86960077 Myroides 1.0936384 0.29566646 0.58027898 Pasteurella 0.13184521 0.71652648 0.89081671 Cardiobacteriaceae_unclassified 0.10626186 0.74444076 0.9193094

Appendix 32: Kruskal- Wallis rank sum test at genus level between control and confirmed cases

88

Genus statistic p.value p.value.adj Genus statistic p.value p.value.adj Genus statistic p.value p.value.adj Actinomycetales_unclassified 5.25681398 0.021861 0.56503502 Kurthia 3.31111629 0.06881273 0.59016858 Globicatella 0.32883476 0.56634603 0.76005408 Anaerococcus 5.26287196 0.02178504 0.56503502 Lachnospiracea_incertae_sedis 1.63851074 0.20052992 0.59016858 Lactococcus 0.33058388 0.56531548 0.76005408 Arcanobacterium 5.26287196 0.02178504 0.56503502 Lachnospiraceae_unclassified 1.82019617 0.17728993 0.59016858 Alkanindiges 0.26678112 0.60549925 0.80309847 Clostridium_XI 5.21618243 0.02237762 0.56503502 Leadbetterella 0.76190476 0.38273309 0.59016858 Lactobacillales_unclassified 0.26267627 0.60828746 0.80309847 Porphyromonadaceae_unclassified 5.62083369 0.0177482 0.56503502 Leptotrichiaceae_unclassified 4.44282084 0.03504829 0.59016858 Leptotrichia 0.22523742 0.63507792 0.83302428 Prevotellaceae_unclassified 5.90479884 0.01509969 0.56503502 Megasphaera 3.14212689 0.07629426 0.59016858 Veillonellaceae_unclassified 0.2163649 0.64182375 0.83644128 Solobacterium 5.91106062 0.01504611 0.56503502 Microbacteriaceae_unclassified 0.76190476 0.38273309 0.59016858 Actinobacillus 0.15883459 0.69023176 0.83873541 Treponema 5.47332579 0.01930885 0.56503502 Mitsuokella 0.76190476 0.38273309 0.59016858 Anaerorhabdus 0.16484584 0.6847337 0.83873541 Acetanaerobacterium 0.76190476 0.38273309 0.59016858 Mogibacterium 0.76190476 0.38273309 0.59016858 Atopobium 0.17918925 0.67207091 0.83873541 Acholeplasma 0.76190476 0.38273309 0.59016858 Moraxella 1.21818952 0.26971625 0.59016858 Burkholderiales_unclassified 0.15102041 0.69756212 0.83873541 Acidaminococcus 2.41504873 0.12017423 0.59016858 Mycoplasma 3.63692465 0.05651123 0.59016858 Catonella 0.1771455 0.67383792 0.83873541 Aerococcus 1.6501858 0.19893365 0.59016858 OD1_genus_incertae_sedis 1.3125 0.25194252 0.59016858 Collinsella 0.15102041 0.69756212 0.83873541 Alcaligenaceae_unclassified 3.04162359 0.08115471 0.59016858 Oribacterium 2.41504873 0.12017423 0.59016858 Coprococcus 0.15102041 0.69756212 0.83873541 Alcaligenes 1.3125 0.25194252 0.59016858 Paenalcaligenes 0.76190476 0.38273309 0.59016858 Erysipelotrichaceae_incertae_sedis 0.166771 0.68299761 0.83873541 Anaerolineaceae_unclassified 0.76190476 0.38273309 0.59016858 Paenibacillus 1.3125 0.25194252 0.59016858 Kingella 0.15844209 0.69059492 0.83873541 Anaerovibrio 1.56613757 0.21076882 0.59016858 Paenochrobactrum 0.76190476 0.38273309 0.59016858 Macrococcus 0.1771455 0.67383792 0.83873541 Anoxybacillus 1.3125 0.25194252 0.59016858 Paraprevotella 0.76190476 0.38273309 0.59016858 Peptostreptococcaceae_incertae_sedis 0.16622022 0.6834931 0.83873541 Aquabacterium 1.56613757 0.21076882 0.59016858 Pasteurellaceae_unclassified 2.35018124 0.12526844 0.59016858 Petrimonas 0.15102041 0.69756212 0.83873541 Arcobacter 1.56613757 0.21076882 0.59016858 Peptoniphilus 2.26392086 0.13241842 0.59016858 Raoultella 0.15844209 0.69059492 0.83873541 Atopostipes 2.42016807 0.11978208 0.59016858 Peptostreptococcaceae_unclassified 0.76190476 0.38273309 0.59016858 Jeotgalicoccus 0.14614878 0.70224323 0.83936764 Bacteria_unclassified 2.25818505 0.13290975 0.59016858 Peptostreptococcus 0.81858338 0.36559468 0.59016858 Lactobacillus 0.13549897 0.7127973 0.84697091 Bacteroidales_unclassified 0.76190476 0.38273309 0.59016858 Phascolarctobacterium 0.8917368 0.34500708 0.59016858 Coriobacteriaceae_unclassified 0.12683199 0.72173963 0.84762445 Bacteroides 1.32182022 0.25026564 0.59016858 Prevotella 2.54497354 0.11064576 0.59016858 Pediococcus 0.12710084 0.72145713 0.84762445 Bacteroidetes_unclassified 1.11961766 0.2900008 0.59016858 Propionibacteriaceae_unclassified 0.76190476 0.38273309 0.59016858 Psychrobacter 0.11997324 0.72906352 0.85127648 Betaproteobacteria_unclassified 0.76190476 0.38273309 0.59016858 Providencia 1.12122766 0.28965426 0.59016858 Comamonadaceae_unclassified 0.11015682 0.73996566 0.8590406 Bifidobacteriaceae_unclassified 0.76190476 0.38273309 0.59016858 Pseudomonadaceae_unclassified 0.76190476 0.38273309 0.59016858 Tannerella 0.10248929 0.7488626 0.8644014 Bifidobacterium 2.41504873 0.12017423 0.59016858 Pseudomonas 0.82178741 0.36465809 0.59016858 Stenotrophomonas 0.08517217 0.77040689 0.8836944 Bordetella 0.82178741 0.36465809 0.59016858 Roseburia 2.41504873 0.12017423 0.59016858 Weissella 0.08220869 0.77432628 0.8836944 Brachybacterium 0.76190476 0.38273309 0.59016858 Rothia 3.57168167 0.05877276 0.59016858 Escherichia_Shigella 0.07612782 0.78261512 0.88813626 Bulleidia 1.4698065 0.22537623 0.59016858 Ruminococcaceae_unclassified 2.98627154 0.08397331 0.59016858 Acidovorax 0.06660426 0.7963467 0.88876033 Butyricicoccus 1.73044218 0.18835444 0.59016858 Ruminococcus 0.76190476 0.38273309 0.59016858 Capnocytophaga 0.06666166 0.79626089 0.88876033 Butyrivibrio 0.76190476 0.38273309 0.59016858 Salinicoccus 0.76190476 0.38273309 0.59016858 Chitinophagaceae_unclassified 0.06659239 0.79636445 0.88876033 Campylobacter 1.81998035 0.17731562 0.59016858 Schlegelella 1.56613757 0.21076882 0.59016858 Herbaspirillum 0.05505952 0.81448222 0.89771789 Carnobacterium 1.11849015 0.2902438 0.59016858 Schwartzia 0.76190476 0.38273309 0.59016858 Neisseriaceae_unclassified 0.05311841 0.81772323 0.89771789 Chlamydia 1.07018025 0.30090473 0.59016858 Selenomonas 1.56613757 0.21076882 0.59016858 Verrucomicrobiaceae_unclassified 0.05505952 0.81448222 0.89771789 Cloacibacillus 0.76190476 0.38273309 0.59016858 Sharpea 1.56613757 0.21076882 0.59016858 Aeromonas 0.0244709 0.87569269 0.89886165 Clostridia_unclassified 0.76190476 0.38273309 0.59016858 Sphingobacteriales_unclassified 0.76190476 0.38273309 0.59016858 Blautia 0.02943777 0.86377216 0.89886165 Clostridiaceae_1_unclassified 1.85871272 0.17277344 0.59016858 Sphingobacterium 1.56613757 0.21076882 0.59016858 0.0244709 0.87569269 0.89886165 Clostridiales_Incertae_Sedis_XI_unclassified 3.31111629 0.06881273 0.59016858 Sphingomonadaceae_unclassified 0.76190476 0.38273309 0.59016858 Chryseobacterium 0.02410714 0.8766126 0.89886165 Clostridiales_unclassified 4.05913527 0.04393299 0.59016858 SR1_genus_incertae_sedis 1.40792692 0.23540084 0.59016858 Delftia 0.0244709 0.87569269 0.89886165 Clostridium_sensu_stricto 1.17003975 0.27939307 0.59016858 Staphylococcus 0.96263424 0.32652408 0.59016858 Dysgonomonas 0.0244709 0.87569269 0.89886165 Clostridium_XVIII 1.3125 0.25194252 0.59016858 Streptococcus 3.61278195 0.05733713 0.59016858 Gemella 0.04606355 0.83006032 0.89886165 Comamonas 0.76190476 0.38273309 0.59016858 Succinivibrio 2.41504873 0.12017423 0.59016858 Moraxellaceae_unclassified 0.04110356 0.83933815 0.89886165 Corynebacterium 2.47099747 0.11596431 0.59016858 Sulfuricurvum 1.3125 0.25194252 0.59016858 Mucispirillum 0.0244709 0.87569269 0.89886165 Desemzia 0.76190476 0.38273309 0.59016858 Sutterella 1.3125 0.25194252 0.59016858 Naxibacter 0.03826531 0.84491136 0.89886165 Desulfovibrio 0.76454082 0.38191121 0.59016858 Suttonella 0.91828856 0.33792474 0.59016858 Oscillibacter 0.0244709 0.87569269 0.89886165 Desulfovibrionaceae_unclassified 2.69791667 0.10047947 0.59016858 Tessaracoccus 0.76190476 0.38273309 0.59016858 Propionimicrobium 0.0244709 0.87569269 0.89886165 Dialister 1.56613757 0.21076882 0.59016858 Tissierella 0.76190476 0.38273309 0.59016858 Yersinia 0.03826531 0.84491136 0.89886165 Dietzia 1.56613757 0.21076882 0.59016858 TM7_genus_incertae_sedis 1.08538206 0.29749666 0.59016858 Aerococcaceae_unclassified 0.01636866 0.89819639 0.91634178 Dorea 1.56613757 0.21076882 0.59016858 Trueperella 1.0250759 0.31131801 0.59016858 Vagococcus 0.01493452 0.90273518 0.91634425 Empedobacter 0.76190476 0.38273309 0.59016858 Turicibacter 1.56613757 0.21076882 0.59016858 Bergeyella 0.00651249 0.93568051 0.94503732 Enterobacteriaceae_unclassified 3.62265881 0.05699771 0.59016858 Veillonella 1.43052872 0.23167817 0.59016858 Fusobacterium 0.00093985 0.97554311 0.97554311 Enterococcaceae_unclassified 1.4971269 0.22111397 0.59016858 Wautersiella 2.41504873 0.12017423 0.59016858 Proteus 0.0011499 0.97294876 0.97554311 Enterococcus 0.99082953 0.31953972 0.59016858 Weeksella 0.76190476 0.38273309 0.59016858 Erysipelothrix 0.76190476 0.38273309 0.59016858 Wohlfahrtiimonas 1.34692593 0.24581625 0.59016858 Erysipelotrichaceae_unclassified 3.56494698 0.05901163 0.59016858 Xanthomonadaceae_unclassified 0.76190476 0.38273309 0.59016858 Eubacteriaceae_unclassified 0.76190476 0.38273309 0.59016858 Yaniella 1.56613757 0.21076882 0.59016858 Eubacterium 1.04746018 0.30609276 0.59016858 Anaerovorax 0.7275402 0.39368167 0.59062071 Facklamia 1.47401183 0.22471378 0.59016858 Mannheimia 0.7275402 0.39368167 0.59062071 Faecalibacterium 4.25838009 0.03905715 0.59016858 Morganella 0.72434684 0.39472176 0.59062071 Finegoldia 0.76190476 0.38273309 0.59016858 Planococcaceae_incertae_sedis 0.7275402 0.39368167 0.59062071 Firmicutes_unclassified 2.19451531 0.13850277 0.59016858 Cardiobacteriaceae_unclassified 0.65547052 0.41816359 0.61656237 Flavobacteriaceae_unclassified 1.65927156 0.1977017 0.59016858 Neisseria 0.66346989 0.41533761 0.61656237 Flavobacterium 1.34873632 0.24549916 0.59016858 Parabacteroides 0.63480392 0.42559866 0.62297775 Gammaproteobacteria_unclassified 0.76190476 0.38273309 0.59016858 Olsenella 0.589548 0.44259396 0.6431941 Gelidibacter 0.76190476 0.38273309 0.59016858 Filifactor 0.58132092 0.44579503 0.64321854 Gemmiger 0.88766184 0.34611171 0.59016858 Parvimonas 0.54238208 0.46144719 0.66108037 Gordonia 0.76190476 0.38273309 0.59016858 Pasteurella 0.49718045 0.48074163 0.6838719 Granulicatella 2.34937503 0.12533325 0.59016858 Clostridium_XlVa 0.4528887 0.50096634 0.70627913 Helcococcus 1.09976345 0.29431802 0.59016858 Myroides 0.44758065 0.50348611 0.70627913 Helicobacter 1.56613757 0.21076882 0.59016858 Actinomyces 0.41689519 0.51849036 0.72231071 Howardella 1.56734694 0.21059272 0.59016858 Carnobacteriaceae_unclassified 0.39866875 0.52777757 0.73021281 Ignatzschineria 0.76190476 0.38273309 0.59016858 Peptococcus 0.3865922 0.53409615 0.73392804 Kocuria 0.76190476 0.38273309 0.59016858 Porphyromonas 0.37598442 0.53976018 0.73669971 Acinetobacter 0.3257722 0.56815924 0.76005408

Appendix 33: Kruskal- Wallis rank sum test at genus level between control and probable cases

89

Genus statistic p.value p.value.adj Genus statistic p.value p.value.adj Genus statistic p.value p.value.adj Acetanaerobacterium 1.16666667 0.28008721 0.67075037 Myroides 1.63266782 0.20133445 0.67075037 Collinsella 0.20903859 0.64752199 0.88875567 Acholeplasma 0.85714286 0.35453948 0.67075037 Oribacterium 1.71454281 0.19039702 0.67075037 Leptotrichiaceae_unclassified 0.21866722 0.64005731 0.88875567 Acidaminococcus 0.87673928 0.34909632 0.67075037 Paenalcaligenes 0.85714286 0.35453948 0.67075037 Ruminococcus 0.20943563 0.64721011 0.88875567 Acinetobacter 2.0663452 0.150582 0.67075037 Paraprevotella 0.85714286 0.35453948 0.67075037 Turicibacter 0.20903859 0.64752199 0.88875567 Aerococcaceae_unclassified 1.96115119 0.16139025 0.67075037 Pasteurella 1.1161844 0.29074154 0.67075037 Prevotellaceae_unclassified 0.19082912 0.66222744 0.90303742 Aerococcus 4.37151767 0.03654443 0.67075037 Pasteurellaceae_unclassified 1.75335207 0.18545589 0.67075037 Aquabacterium 0.18218218 0.66950458 0.90707072 Aeromonas 0.85714286 0.35453948 0.67075037 Peptostreptococcaceae_incertae_sedis 3.30173184 0.06920688 0.67075037 Lactobacillus 0.16720355 0.68260916 0.91503108 Alcaligenaceae_unclassified 4.09445217 0.04302417 0.67075037 Peptostreptococcaceae_unclassified 1.16666667 0.28008721 0.67075037 Neisseria 0.15606102 0.69280924 0.91503108 Alkanindiges 5.75659989 0.01642683 0.67075037 Petrimonas 0.85714286 0.35453948 0.67075037 Olsenella 0.15708566 0.69185398 0.91503108 Anaerolineaceae_unclassified 0.85714286 0.35453948 0.67075037 Planococcaceae_incertae_sedis 0.92721585 0.33558743 0.67075037 Pediococcus 0.15708566 0.69185398 0.91503108 Anaerovibrio 1.7593985 0.18469956 0.67075037 Planococcaceae_unclassified 0.85714286 0.35453948 0.67075037 Dialister 0.15052228 0.69803675 0.91617324 Bacillaceae_2_unclassified 1.16666667 0.28008721 0.67075037 Planomicrobium 1.16666667 0.28008721 0.67075037 Moraxellaceae_unclassified 0.14246571 0.70584196 0.92066343 Bacillales_unclassified 1.16666667 0.28008721 0.67075037 Porphyromonadaceae_unclassified 2.67104867 0.10218867 0.67075037 Clostridiaceae_1_unclassified 0.1292254 0.71923652 0.93234364 Bacilli_unclassified 1.16666667 0.28008721 0.67075037 Propionibacterium 0.85714286 0.35453948 0.67075037 Bacteria_unclassified 0.1240456 0.72468747 0.93364643 Bergeyella 2.18902467 0.13899731 0.67075037 Proteus 1.67137219 0.19607484 0.67075037 Bulleidia 0.11346174 0.7362369 0.93702878 Bilophila 1.16666667 0.28008721 0.67075037 Providencia 3.7088634 0.05412424 0.67075037 Peptoniphilus 0.1134874 0.73620819 0.93702878 Brachymonas 1.16666667 0.28008721 0.67075037 Pseudomonas 3.98519508 0.04590179 0.67075037 Anaerovorax 0.08464339 0.77110075 0.93915824 Bradyrhizobium 1.16666667 0.28008721 0.67075037 Psychrobacter 1.99472553 0.15784766 0.67075037 Bacteroidetes_unclassified 0.09939804 0.75255321 0.93915824 Brevundimonas 1.16666667 0.28008721 0.67075037 Pyramidobacter 2.39473684 0.12174424 0.67075037 Butyricicoccus 0.08515406 0.77043061 0.93915824 Brucella 0.85714286 0.35453948 0.67075037 Raoultella 1.7593985 0.18469956 0.67075037 Enterococcus 0.07954975 0.7779085 0.93915824 Burkholderiales_unclassified 1.7593985 0.18469956 0.67075037 Ruminococcaceae_unclassified 1.58142467 0.2085556 0.67075037 Escherichia_Shigella 0.09604147 0.75663276 0.93915824 Butyricimonas 1.16666667 0.28008721 0.67075037 Salinicoccus 0.85714286 0.35453948 0.67075037 Parvimonas 0.0877665 0.76703603 0.93915824 Butyrivibrio 0.85714286 0.35453948 0.67075037 Schwartzia 0.85714286 0.35453948 0.67075037 Porphyromonas 0.1049922 0.74591911 0.93915824 Campylobacter 3.15191412 0.07583795 0.67075037 Sphingobacteriaceae_unclassified 0.85714286 0.35453948 0.67075037 Selenomonas 0.08464339 0.77110075 0.93915824 Cardiobacteriaceae_unclassified 1.26250456 0.26117754 0.67075037 Sphingobacterium 1.7593985 0.18469956 0.67075037 Streptococcus 0.07936508 0.77815969 0.93915824 Carnobacteriaceae_unclassified 3.67271242 0.0553101 0.67075037 Sphingomonadaceae_unclassified 0.85714286 0.35453948 0.67075037 Comamonas 0.07154443 0.78910122 0.94692146 Carnobacterium 1.7593985 0.18469956 0.67075037 Stenotrophomonas 1.41300577 0.23455806 0.67075037 Anaerococcus 0.06736349 0.79521494 0.94722177 Chitinophagaceae_unclassified 1.0233287 0.31173073 0.67075037 Succinivibrio 1.7593985 0.18469956 0.67075037 Atopobium 0.06525664 0.79837263 0.94722177 Clostridia_unclassified 0.85714286 0.35453948 0.67075037 Tessaracoccus 0.85714286 0.35453948 0.67075037 Acidovorax 0.025756 0.87249766 0.95489832 Clostridiales_Incertae_Sedis_XI_unclassified 1.71454281 0.19039702 0.67075037 Tissierella 0.85714286 0.35453948 0.67075037 Actinomyces 0.02442575 0.8758065 0.95489832 Clostridiales_unclassified 4.50762829 0.033744 0.67075037 Treponema 1.05666007 0.30397818 0.67075037 Atopostipes 0.02172097 0.88283181 0.95489832 Clostridium_IV 0.85714286 0.35453948 0.67075037 Veillonella 1.53837641 0.21485964 0.67075037 Bacteroides 0.01984127 0.8879813 0.95489832 Clostridium_sensu_stricto 3.47818527 0.0621829 0.67075037 Wautersiella 2.70914015 0.09977476 0.67075037 Bifidobacteriaceae_unclassified 0.00543024 0.94125691 0.95489832 Clostridium_XI 2.75676139 0.09684411 0.67075037 Weeksella 0.85714286 0.35453948 0.67075037 Brachybacterium 0.02172097 0.88283181 0.95489832 Clostridium_XlVb 1.16666667 0.28008721 0.67075037 Wohlfahrtiimonas 1.29845315 0.25449597 0.67075037 Cloacibacillus 0.02172097 0.88283181 0.95489832 Comamonadaceae_unclassified 1.25353863 0.26287769 0.67075037 Xanthomonadaceae_unclassified 1.5138802 0.21854796 0.67075037 Coprococcus 0.03220234 0.85758429 0.95489832 Corynebacterium 1.04578721 0.30647933 0.67075037 Yaniella 0.85714286 0.35453948 0.67075037 Coriobacteriaceae_unclassified 0.04078749 0.83994867 0.95489832 Delftia 1.7593985 0.18469956 0.67075037 Actinomycetales_unclassified 0.80474657 0.369678 0.69226137 Desemzia 0.02172097 0.88283181 0.95489832 Dermabacteraceae_unclassified 3.68744076 0.05482368 0.67075037 Tannerella 0.79529924 0.37250255 0.69226137 Facklamia 0.04772996 0.82706147 0.95489832 Desulfovibrionaceae_unclassified 1.26388889 0.26091626 0.67075037 Chryseobacterium 0.73288247 0.39195046 0.72201401 Faecalibacterium 0.01108275 0.91615787 0.95489832 Dietzia 1.7593985 0.18469956 0.67075037 Bacteroidales_unclassified 0.62804484 0.42807339 0.73218745 Firmicutes_unclassified 0.0364439 0.8486017 0.95489832 Dysgonomonas 0.85714286 0.35453948 0.67075037 Bifidobacterium 0.61653117 0.43233926 0.73218745 Gemmiger 0.0080713 0.92841402 0.95489832 Enterococcaceae_unclassified 6.40748764 0.01136401 0.67075037 Bordetella 0.62804484 0.42807339 0.73218745 Gordonia 0.00543024 0.94125691 0.95489832 Erysipelothrix 0.85714286 0.35453948 0.67075037 Microbacteriaceae_unclassified 0.64030765 0.42359941 0.73218745 Granulicatella 0.0162707 0.89849985 0.95489832 Erysipelotrichaceae_incertae_sedis 1.76181935 0.18439775 0.67075037 Morganella 0.6438999 0.42230211 0.73218745 Ignatzschineria 0.00543024 0.94125691 0.95489832 Eubacteriaceae_unclassified 0.85714286 0.35453948 0.67075037 Neisseriaceae_unclassified 0.67003738 0.41303857 0.73218745 Lachnospiracea_incertae_sedis 0.03298546 0.85588175 0.95489832 Eubacterium 5.03230862 0.02487871 0.67075037 Rothia 0.69383478 0.40486308 0.73218745 Mitsuokella 0.01146384 0.91473397 0.95489832 Fastidiosipila 1.16666667 0.28008721 0.67075037 Suttonella 0.67334702 0.41188711 0.73218745 Nosocomiicoccus 0.02172097 0.88283181 0.95489832 Finegoldia 0.85714286 0.35453948 0.67075037 Trueperella 0.62253727 0.43010598 0.73218745 Oscillibacter 0.00543024 0.94125691 0.95489832 Flavobacterium 1.71454281 0.19039702 0.67075037 Veillonellaceae_unclassified 0.68714264 0.40713798 0.73218745 Parabacteroides 0.0257654 0.87247459 0.95489832 Fusobacterium 1.6797051 0.19496364 0.67075037 Anaerorhabdus 0.58165477 0.44566443 0.74726964 Phascolarctobacterium 0.01384684 0.90632707 0.95489832 Gelidibacter 0.85714286 0.35453948 0.67075037 Dorea 0.57478992 0.44836178 0.74726964 Propionibacteriaceae_unclassified 0.00543024 0.94125691 0.95489832 Gemella 4.53870821 0.03313659 0.67075037 Clostridium_XlVa 0.52961968 0.4667668 0.77181911 Propionimicrobium 0.00543024 0.94125691 0.95489832 Globicatella 3.7088634 0.05412424 0.67075037 Capnocytophaga 0.5127707 0.4739419 0.77625676 Pseudomonadaceae_unclassified 0.00543024 0.94125691 0.95489832 Guggenheimella 0.85714286 0.35453948 0.67075037 Solobacterium 0.5060738 0.47684344 0.77625676 Schlegelella 0.00543024 0.94125691 0.95489832 Herbaspirillum 1.7593985 0.18469956 0.67075037 Enterobacteriaceae_unclassified 0.45858173 0.49828755 0.80492605 Sharpea 0.00543024 0.94125691 0.95489832 Howardella 1.16666667 0.28008721 0.67075037 Desulfovibrio 0.44987845 0.50239268 0.80536232 Staphylococcus 0.0198473 0.8879644 0.95489832 Kocuria 0.85714286 0.35453948 0.67075037 Arcanobacterium 0.39672658 0.52878465 0.81567371 Weissella 0.01434962 0.90464959 0.95489832 Kurthia 2.71042471 0.09969445 0.67075037 Chlamydia 0.40288478 0.52560319 0.81567371 Peptococcus 0.00286178 0.95733705 0.96654221 Lachnospiraceae_unclassified 4.11553537 0.04249107 0.67075037 Erysipelotrichaceae_unclassified 0.38741452 0.53366158 0.81567371 TM7_genus_incertae_sedis 0.00184298 0.96575737 0.97037822 Lactobacillaceae_unclassified 1.16666667 0.28008721 0.67075037 Helcococcus 0.42400406 0.51494583 0.81567371 SR1_genus_incertae_sedis 0 1 1 Lactobacillales_unclassified 1.66847381 0.19646308 0.67075037 Jeotgalicoccus 0.39747289 0.52839726 0.81567371 Lactococcus 6.06688541 0.01377396 0.67075037 Prevotella 0.40211131 0.5260009 0.81567371 Lampropedia 0.85714286 0.35453948 0.67075037 Vagococcus 0.38297725 0.53601416 0.81567371 Legionella 1.16666667 0.28008721 0.67075037 Filifactor 0.36088499 0.54801514 0.82202272 Leptotrichia 2.83353724 0.09231483 0.67075037 Peptostreptococcus 0.36693936 0.54467732 0.82202272 Limnobacter 1.16666667 0.28008721 0.67075037 Blautia 0.32799511 0.56684203 0.84141435 Macrococcus 1.7593985 0.18469956 0.67075037 Flavobacteriaceae_unclassified 0.31774977 0.5729631 0.84141435 Mannheimia 0.92755437 0.33549923 0.67075037 Megasphaera 0.32267563 0.57000411 0.84141435 Mogibacterium 0.85714286 0.35453948 0.67075037 Actinobacillus 0.28650794 0.59246707 0.8575106 Moraxella 1.98472963 0.15889304 0.67075037 Arcobacter 0.26849846 0.6043408 0.8575106 Moryella 1.16666667 0.28008721 0.67075037 Catonella 0.26849846 0.6043408 0.8575106 Mucispirillum 0.85714286 0.35453948 0.67075037 Helicobacter 0.26849846 0.6043408 0.8575106 Mycoplasma 4.24526333 0.03935997 0.67075037 Roseburia 0.2770062 0.59867021 0.8575106 Kingella 0.25929814 0.61060275 0.86058106

Appendix 34: Kruskal- Wallis rank sum test at genus level between probable and confirmed cases

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Appendix 35: Tonsil extraction protocol

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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)

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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) make.file(inputdir=., type=fastq, prefix=suis) make.contigs(file=/scratch/shill09/files/suis.files,processors=32) summary.seqs(fasta=suis.trim.contigs.fasta) screen.seqs(fasta=suis.trim.contigs.fasta, group=suis.contigs.groups, maxambig=0, maxlength=500) unique.seqs(fasta=suis.trim.contigs.good.fasta) count.seqs(name=suis.trim.contigs.good.names, group=suis.contigs.good.groups) summary.seqs(count=suis.trim.contigs.good.count_table) pcr.seqs(fasta=silva.bacteria2.fasta, start=6388, end=25316, keepdots=F, processors=8) rename.file(input=silva.bacteria2.pcr.fasta, new=silva.v34.prim2.fasta) summary.seqs(fasta=silva.v34.prim2.fasta) align.seqs(fasta=suis.trim.contigs.good.unique.fasta, reference=silva.v34.prim2.fasta) summary.seqs(fasta=suis.trim.contigs.good.unique.align, count=suis.trim.contigs.good.count_table) screen.seqs(fasta=suis.trim.contigs.good.unique.align, count=suis.trim.contigs.good.count_table, summary=suis.trim.contigs.good.unique.summary, start=1, end=18912, maxhomop=8) summary.seqs(fasta=suis.trim.contigs.good.unique.good.align, count=suis.trim.contigs.good.good.count_table) filter.seqs(fasta=suis.trim.contigs.good.unique.good.align, vertical=T, trump=.) unique.seqs(fasta=suis.trim.contigs.good.unique.good.filter.fasta, count=suis.trim.contigs.good.good.count_table) pre.cluster(fasta=suis.trim.contigs.good.unique.good.filter.unique.fasta, count=suis.trim.contigs.good.unique.good.filter.count_table, diffs=2)

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split.abund(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.fasta, count=suis.trim.contigs.good.unique.good.filter.unique.precluster.count_table, cutoff=2) chimera.vsearch(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abun d.fasta, count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.count_table, dereplicate=t) summary.seqs() remove.seqs(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.fa sta,accnos=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.denovo.vs earch.accnos) summary.seqs(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund. pick.fasta,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.deno vo.vsearch.pick.count_table) classify.seqs(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pic k.fasta,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.denovo. vsearch.pick.count_table, reference=trainset9_032012.pds.fasta, =trainset9_032012.pds.tax, cutoff=80) remove.lineage(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund. pick.fasta,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.deno vo.vsearch.pick.count_table,taxonomy=suis.trim.contigs.good.unique.good.filter.unique. precluster.abund.pick.pds.wang.taxonomy, taxon=Chloroplast-Mitochondria-unknown- Archaea-Eukaryota) summary.tax(taxonomy=suis.trim.contigs.good.unique.good.filter.unique.precluster.abu nd.pick.pds.wang.pick.taxonomy,count=suis.trim.contigs.good.unique.good.filter.unique. precluster.abund.denovo.vsearch.pick.pick.count_table) dist.seqs(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.fasta, cutoff=0.03) cluster(column=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.dist,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.denovo. vsearch.pick.pick.count_table) make.shared(list=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick. pick.opti_mcc.list,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abun d.denovo.vsearch.pick.pick.count_table, label=0.03)

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classify.otu(list=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.opti_mcc.list,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund. denovo.vsearch.pick.pick.count_table,taxonomy=suis.trim.contigs.good.unique.good.filt er.unique.precluster.abund.pick.pds.wang.pick.taxonomy, label=0.03) count.groups(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund. pick.pick.opti_mcc.shared) sub.sample(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pi ck.pick.opti_mcc.shared, size=8663) rarefaction.single(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.ab und.pick.pick.opti_mcc.0.03.subsample.shared, calc=sobs, freq=100) summary.single(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.abu nd.pick.pick.opti_mcc.0.03.subsample.shared, calc=nseqs-coverage-sobs-shannon- invsimpson, subsample=8663) dist.shared(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pi ck.pick.opti_mcc.0.03.subsample.shared, calc=thetayc-jclass, subsample=8663) pcoa(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pick. opti_mcc.0.03.subsample.thetayc.0.03.lt.ave.dist) nmds(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pick. opti_mcc.0.03.subsample.thetayc.0.03.lt.ave.dist) nmds(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pick. opti_mcc.0.03.subsample.thetayc.0.03.lt.ave.dist, mindim=3, maxdim=3) dist.shared(shared=current, calc=braycurtis, subsample=T) amova(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pic k.opti_mcc.0.03.subsample.thetayc.0.03.lt.ave.dist, design=suis.case.design) amova(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pic k.opti_mcc.0.03.subsample.braycurtis.0.03.lt.ave.dist, design=suis.case.design) homova(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.opti_mcc.0.03.subsample.braycurtis.0.03.lt.ave.dist, design=suis.case.design) homova(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.opti_mcc.0.03.subsample.thetayc.0.03.lt.ave.dist, design=suis.case.design)

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corr.axes(axes=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.opti_mcc.0.03.subsample.thetayc.0.03.lt.ave.pcoa.axes, shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pick.opti_ mcc.0.03.subsample.shared, method=spearman, numaxes=3) get.communitytype(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.a bund.pick.pick.opti_mcc.0.03.subsample.shared) metastats(shared=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick .pick.opti_mcc.0.03.subsample.shared, design=suis.case.design) dist.seqs(fasta=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.fasta, output=lt, processors=8) clearcut(phylip=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pick.pi ck.phylip.dist) phylo.diversity(tree=suis.trim.contigs.good.unique.good.filter.unique.precluster.abund.pi ck.pick.phylip.tre,count=suis.trim.contigs.good.unique.good.filter.unique.precluster.abun d.denovo.vsearch.pick.pick.count_table, rarefy=T, processors=32)

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