Characterization of the Bacterial Communities of the Tonsil of the Soft Palate of Swine

Shaun Kernaghan

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

CHARACTERIZATION OF THE BACTERIAL COMMUNITIES OF THE TONSIL OF THE SOFT PALATE OF SWINE

Shaun Kernaghan Advisor: University of Guelph, 2013 Professor Janet I. MacInnes

Terminal restriction fragment length polymorphism (T-RFLP) analysis and pyrosequencing were used to characterize the microbiota of the tonsil of the soft palate of 126 unfit and 18 healthy pigs. The T-RFLP analysis method was first optimized for the study of the pig tonsil microbiota and the data compared with culture-based identification of common pig pathogens. Putative identifications of the members of the microbiota revealed that the phyla Firmicutes, Proteobacteria and Bacteroidetes were the most prevalent. A comparison of the T-RFLP analysis results grouped into clusters to clinical conditions revealed paleness, abscess, PRRS virus, and Mycoplasma hyopneumoniae to be significantly associated with cluster membership. T-RFLP analysis was also used to select representative tonsil samples for pyrosequencing. These studies confirmed Actinobacteria, Bacteroidetes, Firmicutes,

Fusobacteria, and Proteobacteria to be the core phyla of the microbiota of the tonsil of the soft palate of pigs.

Acknowledgements

I would like to thank my advisor Janet MacInnes for her support and endless patience during this project. I would like to thank my committee, Patrick Boerlin and Emma Allen-Vercoe, for their insights and support, as well as Zvoninir Poljak for his help through this project. I would also like to thank the members of the MacInnes lab, Allen-Vercoe lab, AHL diagnostic lab, and Laboratory Services for their help. Finally, a big thank you to my family for their patience and support throughout my studies.

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Declaration of Work Done

All the work described in this thesis was performed by me with the exception of:

1. Julie McDonald aided in DNA extraction comparison by providing two commercial

kits and explaining the use of the bead-beater.

2. Dr. Zvonimir Poljak wrote the STATA code for statistical comparisons.

3. Charlotte Swanson performed DNA extractions of several tonsil cultures.

4. Pyrosequencing was performed by Marcio Costa in the laboratory of Dr. Scott Weese.

Table of Contents Acknowledgements ...... iii Declaration of Work Done ...... iv Table of Contents ...... v List of Tables ...... viii List of Figures ...... viii List of Abbreviations ...... xi

Chapter 1 Review of the Literature ...... 1 1.1. Methods for Profiling Bacterial Communities ...... 1 1.1.1. Study of Microbial Communities using 16S rRNA Genes ...... 1 1.1.2. Clone Library Analysis ...... 2 1.1.3. Biases Associated with Clone and PCR-Based Studies ...... 3 1.1.4. Denaturing and Temperature Gradient Gel Electrophoresis ...... 6 1.1.5. Terminal Restriction Fragment Length Polymorphism Analysis ...... 8 1.1.6. Diversity Microarrays ...... 11 1.1.7. 16S rRNA Gene Pyrosequencing...... 13 1.2. Studying Bacterial Communities...... 16 1.2.1. Studying Bacterial Communities in Macro-Organisms...... 16 1.2.2. Tonsil of the Soft Palate of Pigs ...... 18 1.2.3. Bacteria associated with Pig Tonsils ...... 19 1.3. References ...... 22

Chapter 2 Objectives ...... 39

Chapter 3 Optimization of the terminal restriction fragment length polymorphism analysis (T- RFLP) method for the characterization of bacterial communities at the tonsil of the soft palate of swine ...... 41

3.1. Introduction ...... 41 3.2. Materials & methods ...... 43 3.2.1. Sample collection and processing ...... 43 3.2.2. Comparison of DNA extraction kits ...... 44 3.2.3. Checking lysis of ‘difficult-to-lyse’ bacteria ...... 45 3.2.4. Tonsil tissue homogenization ...... 45 3.2.5. T-RFLP analysis PCR amplification and digestion ...... 46 3.2.6. Capillary electrophoresis ...... 47 3.2.7. Comparison of tissue and culture samples ...... 48 3.2.8. Mock community analysis ...... 48 3.2.9. Filtering and binning of results...... 49 3.2.10. Creation of database for bacterial pathogen identification ...... 49 3.2.11. Diagnostic Comparison ...... 50 3.3. Results ...... 50 3.3.1. DNA extraction kit comparison ...... 50 3.3.2. Evaluation of the Qiagen-bb protocol ...... 51 3.3.3. Tissue homogenization protocol comparison ...... 51 3.3.4. PCR optimization ...... 51 3.3.5. Mock community analysis ...... 52 3.3.6. Comparison of T-RFLP analysis and culture identifications ...... 52 3.4. Discussion ...... 53 3.5. References ...... 62

Chapter 4 T-RFLP analysis of the bacterial communities associated with pig tonsils ...... 76 4.1. Introduction ...... 76 4.2. Material & methods ...... 77 4.2.1. Sample collection and T-RFLP analysis...... 77 4.2.2. Non-metric multidimensional scaling (NMS) analysis and beta diversity ...... 78 4.2.3. Putative identification of T-RFLP analysis fragments ...... 79 4.2.4. Cluster analysis and comparison with clinical information ...... 80 vi

4.3. Results & Discussion ...... 80 4.3.1. Comparison of OTUs observed in both healthy and unfit pigs ...... 80 4.3.2. The ability of T-RFLP analysis to match fragments to putative identifications ...... 83 4.3.3. Putative identifications of the bacterial communities from healthy and unfit pigs ..... 84 4.3.4. Comparison of bacterial community data to clinical information collected from unfit pigs ...... 88 4.4. Conclusion ...... 90 4.5. References ...... 91

Chapter 5 Characterization of the Tonsil Microbiota of Unfit Pigs by 454 Pyrosequencing ...... 107 5.1. Introduction ...... 107 5.2. Material & Methods ...... 108 5.2.1. Tonsil collection, DNA extraction, T-RFLP analysis, and sample selection ...... 108 5.2.2. Sequencing and analysis ...... 108 5.3. Results ...... 109 5.3.1. OTU analysis ...... 109 5.3.2. Taxonomic identifications ...... 110 5.3.3. Foodborne and “pig pathogens” identified ...... 112 5.4. Discussion ...... 112 5.5. References ...... 116

Chapter 6 Conclusions and future directions ...... 125 References ...... 130

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List of Tables

Table 1.1. Pathogens associated with the tonsils of the soft palate of pigs...... 34

Table 1.2. Bacteria associated with the tonsil of the soft palate of…...... 35

Table 3.1. Comparison of DNA extraction protocols...... 64

Table 3.2. T-RFLP analysis of output of forward and reverse fragments seen using

DNA prepared with different extraction kits...... 65

Table 3.3. Comparison of total number of T-RFLP bands seen using different homogenization protocols in duplicate experiments...... 66

Table 3.4. Comparison of richness and reproducibility of T-RFLP analysis output using different annealing times and number of cycles in duplicate experiments...... 67

Table 3.5. Comparison of forward and reverse primers used in the Phusion Bacterial

Profiling kit with corresponding sequences of 16S rRNA genes in representative species...... 68

Table 3.6. Comparison of selected genera identified by T-RFLP analysis of tissue samples with culture results...... 69

Table 3.7. Comparison of selected genera identified by T-RFLP analysis of culture samples with culture results...... 70

Table 3.8. Comparison between T-RFLP analysis of tissue samples and T-RFLP analysis of corresponding culture samples...... 71

Table 4.1. The mean number of unique forward (TRF-F) and reverse (TRF-R) fragments per farm in microbiota of unfit pigs...... 93

Table 4.2. The mean number of forward (TRF-F) and reverse (TRF-R) fragments per farm in microbiota of healthy pigs...... 94

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Table 4.3. Prevalence (%) of phyla identified with T-RFLP analysis in microbiota of both unfit pigs and healthy pigs...... 95

Table 4.4. Prevalence (%) of the five classes most frequently identified by T-RFLP analysis in microbiota of both unfit pigs and healthy pigs...... 95

Table 4.5. Prevalence (%) of the five orders most frequently identified by T-RFLP analysis in microbiota of both unfit pigs and healthy pigs...... 96

Table 4.6. Prevalence (%) of the five families most frequently identified by T-RFLP analysis in the microbiota of both unfit pigs and healthy pigs...... 97

Table 4.7. Prevalence (%) of the ten genera most frequently identified by T-RFLP analysis in the microbiota of both unfit pigs and healthy pigs...... 98

Table 4.8. Percentage of forward and reverse fragments in tonsil microbiota of unfit and healthy pigs that were unmatched when analysed with the “pig specific” and

Phusion Bacterial Profiling kit databases...... 99

Table 4.9. Prevalence (%) of ten foodborne and “pig pathogens” found in pig tonsils in this study...... 100

Table 4.10. Comparison between cluster membership and presence of specific clinical condition using Fisher’s exact test...... 101

Table 4.11. Comparison between cluster membership and presence of PRRSV, PCV-2, and Mycoplasma hyopneuomiae using Fisher’s exact test...... 102

Table 5.1. Diversity in tissue and culture derived tonsil microbial communities...... 116

Table 5.2. Range of abundances of sequences of pig or zoonotic pathogens in tonsil tissue samples...... 117

Supplementary Table 4.1...... 129

List of Figures

Fig. 1.1a. Bacterial community profiling by sequencing...... 32

Fig. 1.1b. Fingerprinting techniques used for bacterial community profiling...... 33

Fig. 3.1. Electropherograms of filtered T-RFLP analysis outputs of tonsil tissue

“spiked” with Mycobacterium smegmatis, Pasteurella multocida, and Staphylococcus aureus...... 72

Fig. 3.2. Comparison of input and output ratios of Mycobacterium smegmatis,

Pasteurella multocida, and Streptococcus suis...... 73

Fig. 3.3. Characterization of an “even” mock community by T-RFLP analysis...... 74

Fig. 4.1. Non-metric multidimensional scaling (NMS) analysis of microbiota of pigs from closeout group of finisher herds and pigs from slaughterhouse…………...……103

Fig. 4.2. Non-metric multidimensional scaling (NMS) analysis of tonsil microbiota of pigs from unfit pigs and healthy pigs grouped intofarms…………….....……………104

Fig. 4.3. Dendrogram of cluster analysis of the T-RFLP analysis data from diseased pigs...... 105

Fig. 5.1. Rarefaction curves of six pig tonsil tissue samples...... 118

Fig. 5.2. Rarefaction curves of six pig tonsil culture samples...... 119

Fig. 5.3. Relative proportion of phyla in microbiota of tissue and culture samples..120

Fig. 5.4. Relative proportions of the top 10 genera in pig tonsil tissue...... 121

Fig. 5.5. Relative proportions of the top 10 genera in pig tonsil culture pyrosequencing samples...... 122

List of Abbreviations

ARDRA - Amplified rDNA Restriction Analysis

OTU - Operational Taxonomic Units

RDP - Ribosomal Database Project

DGGE Denaturing Gradient Gel Electrophoresis

TGGE - Temperature Gradient Gel Electrophoresis

T-RFLP - Terminal Restriction Fragment Length Polymorphism

POA - Phylogenetic Oligonucleotide Microarrays

HMP - Human Microbiome Project

PRRS - Porcine Reproductive and Respiratory Syndrome

PCV2 - Porcine Circovirus 2

RDC - Routine Diagnostic Culture

Columbia CNA - Columbia Colostin and Naladixic Acid

PEA Phenylethyl Alcohol Agar

SBA Sheep Blood Agar

Qiagen-bb - Qiagen kit (using the Gram-positive protocol and bead-beating)

AHL - Animal Health Laboratory

T-REX - T-RFLP Analysis Expedited

PAT - Phylogentic Assignment Tool

HP1-4 - Homogenization Protocols 1-4

AP1-5 - Annealing Protocols 1-5

NMS - Non-Metric Multidimensional Scaling

PAST - Paleontological Statistics

UPGMA - Unweighted Pair Group Method with Arithmetic Mean

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Chapter 1

Review of the Literature

1.1. Methods for Profiling Bacterial Communities

1.1.1. Study of Microbial Communities using 16S rRNA Genes

Ribosomal RNA genes have been used in community profiling studies for over 20 years and molecular methods using rRNA genes are considered by many to be the ‘gold standard’ (Case et al. 2007). The number of 16S rRNA genes per genome can vary between one and 15, with 4.1 genes per genome being the average (Case et al. 2007).

Within a given species, rRNA genes are usually identical, but Case et al. (2007) found that sequence divergence between intragenomic 16S rRNA gene copies can range between 0 and 11.6%. Such variation may cause problems when profiling bacterial communities because most researchers use between 97 and 98% similarity as the cut- off for species identification. Furthermore, there are certain phylogenetic groups, such as the genus Staphylococcus, that require a more conservative cut-off (Rossello-Mora and Amann, 2001), suggesting that the rRNA gene has a limited ability in identifying bacteria to the species level (Rajendhran and Gunasekaran, 2011). The limitations of using 16S rRNA genes can also be seen in the genus Bacillus, where B. globisporus and

B. psychrophilus have 99.8% rRNA sequence identity, but on the basis of DNA-DNA hybridization, are clearly separate species (Fox et al. 1992). Other target genes including cpn60, rpoB, gyrB and dnaK have been used for evolutionary and profiling

1 studies, but they have not been used and validated to the same extent as the 16S rRNA gene (Rajendhran and Gunasekaran, 2011).

1.1.2. Clone Library Analysis

One of the first approaches employed in bacterial profiling was the use of 16S rRNA clone libraries (Schmidt et al. 1991; Fig.1a.). Using efficient cloning vectors and hosts, libraries of community DNA could be isolated and then sequenced providing a way to create a profile of members of a bacterial community and place them in their phylogenetic context. In this method, universal primers targeting conserved regions of the 16S rRNA gene are used to amplify the gene in almost all bacterial species present.

Either blunt or sticky end cloning vectors are used to clone the different 16S amplicons.

The recombinant plasmids are then transformed into host cells, usually Escherichia coli, where transformants are selected on the basis of antibiotic resistance encoded by the vector. Because sequencing is the most expensive part of the method, screening clone libraries for identical sequences is done to reduce the amount of high-quality sequencing needed while still allowing for detection of rare species. Fingerprinting methods such as Amplified rDNA Restriction Analysis (ARDRA) and hybridization techniques are used for such screening (Nocker et al. 2007). Another common approach for screening is to partially sequence the target gene and then take representatives from groupings for longer sequencing. Once sequence information has been obtained, analysis of the community can be performed using a sequence database such as the

Ribosomal Database Project (Maidak et al. 2001). Profiling of communities includes determining how many species are present in each sample (richness). Calculating the

2 coverage of each clone library using accumulation curves or rank-abundance plots provides an estimate of the diversity present in the sample and whether the library is a good representation of the species composition (Rajendhran and Gunasekaran, 2011).

Studies of the human gastrointestinal tract, vagina and oral cavity have been undertaken to identify the core species of healthy individuals (Tap et al. 2009) and to compare the microbiota of healthy to diseased individuals (Rolph et al. 2001; Saito et al. 2006). Animal studies using clone libraries have been mostly limited to the rumen or gut. A good example of such studies is one by Leser et al. (2002) who looked at the pig gastrointestinal tract. Ileum, cecum and colon samples were taken from 24 pigs given 4 different diets. Two of these pigs were experimentally infected with Salmonella enterica serovar Typhimurium. Partial sequencing of the insert DNA of the 4270 clones allowed grouping of sequences, with representatives from each group being sequenced with 6 different primers to attain near full length sequences. Chimera checking (see below) excluded 65 sequences and using a 97% sequence similarity cut-off, 375 unique sequences remained. Of these 375 operational taxonomic units (OTU), 309 (82%) were novel. Interesting features of this study included the inability to establish an association between phylotypes and animal status and the large proportion of unknown species found (Leser et al. 2002).

1.1.3. Biases Associated with Clone and PCR-Based Studies

Cloning and sequencing allows high phylogenetic resolution and provides data for use in other studies. This method, however, has a number of biases which limit its ability to completely profile bacterial communities (von Wintzingerode et al. 1997;

Stackebrandt et al. 1999). These biases or limitations include: PCR bias, cloning bias, and sequencing bias. Together with handling and DNA extraction artefacts, these biases can alter the apparent community profile.

The first potential bias is introduced with the PCR amplification of the target gene.

The primers designed for 16S rRNA gene amplification were originally thought to be universal, but as the database of 16S sequences grew it became increasingly apparent these primers had limitations (Baker et al. 2003). The primers used today to amplify

16S rRNA genes are all based on the conserved regions of cultured and uncultured organisms with 16S genes that are similar to primers for their amplification, which means that they may miss divergent species. Primers with degenerate positions have been used to increase sensitivity; however, even these primers will not bind to all sequences and increasing degenerate positions can lead to binding of non-target genes

(Baker et al. 2003). It has been suggested that using multiple primers and then pooling the product may reduce this bias. Another way to reduce primer bias is to reduce the size of the primer. Isenbarger et al. (2008) designed 10-nt miniprimers together with a new polymerase that can recognize the small primer sequence (Isenbarger et al. 2008).

While recognizing that smaller primers may increase the chance for non-target sequences to be amplified, Isenbarger et al. (2008) reported higher sensitivity using the

“miniprimers”, potentially allowing for amplification of rRNA genes from previously unknown bacteria. Sergeant et al. (2012), however, reported no difference between these primers and conventional ones so, not surprisingly, researchers have continued use of conventional primers.

Other biases may be introduced during the PCR step. Factors that can affect the PCR amplification fidelity include template concentration, number of amplification cycles,

G+C content, rRNA gene copy number, and rRNA intragenome heterogeneity

(Reysenbach et al. 1992; Farrelly et al. 1995; Suzuki and Giovannoni, 1996; Wilson and Blitchington, 1996; Chandler et al. 1997). Another problem is seen in studies with

PCR amplifications is the formation of chimeric sequences, which occur when two transcripts join to create one transcript. There are programs, however, designed to detect chimeric sequences such as the Ribosomal Database Project (RDP)

Check_Chimera program (Cole et al. 2005).

The cloning procedure itself can also introduce bias. For example, genes with atypical G+C content are sometimes not stable in E. coli, which can lead to skewed community profiles (Rajendhran and Gunasekaran, 2011).

Limiting the amount of full length sequencing can also introduce bias. For example, picking only 100 clones to represent a sample will lower the sequencing costs, but high diversity communities will not be accurately represented. Screening the clones using fingerprinting methods or partial sequencing may help, but with these methods, closely related bacteria aren’t differentiated.

Using a “clone and sequence” approach, the number of moderately abundant organisms detected may be reduced to rare or non-existent levels if severely affected by these biases. Nevertheless, this technique has greatly increased the number of near full length 16S rRNA genes in public databases, which are useful for other profiling methods. Although cloning and sequencing has lost its once prominent role in

5 community profiling, an understanding of this methodology and its limitations is necessary to extrapolate past information to present studies.

1.1.4. Denaturing and Temperature Gradient Gel Electrophoresis

As mentioned above, the costs involved in cloning and sequencing limits thorough profiling of environments. Creating community fingerprints using the 16S rRNA gene can provide a way to characterize bacterial communities quickly and reasonably thoroughly. Denaturing gradient gel electrophoresis was first used to profile a marine ecosystem (Muyzer et al. 1993) and has since been used to characterize many different environments, being most useful in observing shifts in population structure of communities. In denaturing and temperature gradient gel electrophoresis (DGGE and

TGGE) methods, 16S rRNA genes are separated based on their respective melting behaviours (Muyzer and Smalla, 1998). With conventional non-denaturing gel electrophoresis, 16S amplicons of the same size run at the same speed, but if a denaturing gradient is applied to the gel using formamide, urea, or even increasing temperature, fragments with different nucleotide sequences will separate at different times into ssDNA and stop migrating. In this method, PCR-derived fragments of around

200 to 700 bp from a variable region of the 16S rRNA gene are used. To prevent the fragments from separating completely in the gel, a GC clamp is part of the sequence of one of the primers, keeping a short region of the fragment together when the rest has separated. The GC clamp, a 40 bp double-stranded region with only guanine and cytosine, is usually positioned next to the highest melting domain of the sequence to ensure the melting process is in one direction. In the DGGE method, the PCR products

6 run initially according to the log of their molecular weight, but as they migrate they encounter increasing concentrations of denaturing agent. Once the PCR fragments are fully separated except for the GC-clamp, the ‘Y’ shaped molecule stops migrating. The melting behaviour of the fragment depends mainly on the size of the fragment and its nucleotide sequence. Sequences with high melting temperatures, such as species with high %G+C content, migrate further before denaturing in the gel than those with lower

%G+C content. After electrophoresis, the positions of the bands are visualized by silver staining. The main advantage of this method is the low cost compared to cloning and sequencing, and the ability to select unique bands that can be excised for sequencing.

This method has to be coupled with sequencing for meaningful identifications. Once the method has been optimized, it is a fairly easy to use. However, a lot of work is needed to set up the protocol, including picking the best primers to give an optimal melting map and establishing the optimal gradient. Problems with DGGE are mainly due to

PCR amplification anomalies and reproducibility (Nocker et al. 2007). In addition, the presence of a GC-clamp in one of the primers can lower PCR amplification efficiency, increasing artefacts such as heteroduplexes. Another problem encountered is the use of a gel for analysis, which reduces the reproducibility of the method. Background staining can vary and even hide small bands. Finally, fragments with the same melting temperature will co-migrate on the gel, leading to an underestimation of community diversity.

Nevertheless, DGGE proved to be useful early in the molecular evolution in studying bacterial communities, and is most useful comparing changes in community composition such as observing the changes in human fecal microbiota after a

7 chemotherapy cycle (Zwielehner et al. 2011). This method finds its place alongside the other methods as a quick, inexpensive, high throughput method providing a snapshot of bacterial communities.

1.1.5. Terminal Restriction Fragment Length Polymorphism Analysis

Terminal restriction fragment length polymorphism (T-RFLP) analysis is another fingerprinting method that is increasingly being used (Fig. 1.1b). It was first applied to study sludge, aquifer sand, and termite gut bacterial communities (Liu et al. 1997). T-

RFLP analysis has undergone significant improvement over the last decade to become a highly versatile method to explore community differences. In this method, the 16S rRNA gene is amplified by primer pairs where one or both primers have a fluorescent dye attached. The PCR products are then digested using one or more restriction enzymes (usually ones having a 4-base recognition site). The products are then separated by capillary electrophoresis together with labelled DNA markers that are used to size each fragment. The size data for each sample has to be standardized in order to make meaningful comparisons between samples. First, true peaks must be separated from background noise. This can be done by simply setting a defined threshold that is applied to all samples, or by using the variable levels of background noise in each sample to determine an individual threshold for each sample (Schutte et al. 2008). Due to running differences that can arise during capillary electrophoresis, the fragments then need to be “binned” together when a small difference in size is observed. Once the data has been standardized across all samples, the samples can then be compared. The simplest way to use T-RFLP analysis data is to use the fragments only as operational taxonomic units and look for the presence or absence of fragments (Schutte et al. 2008).

T-RFLP analysis can also be used to identify members of a bacterial community by matching fragments generated from a sample with a database of in silico digested gene sequences. These two ways of using T-RFLP are called ‘peak-profile T-RFLP’ and

‘database T-RFLP’, respectively (Dickie and FitzJohn, 2007).

There are a number of places where biases or limitations can be introduced in T-

RFLP analysis. As with other typing methods, primer selection can introduce bias leading to the detection of “known” organisms. In addition, if only one fluorescently labelled primer is used, each bacterial species will have only one piece of information to contribute to its identification. In this case bacterial species that share the same cut- site closest to the primer will be grouped together as one fragment, making profiling of some community member impossible (Schutte et al. 2008). Methods using more than one labelled primer including labelling forward and reverse, or two different forward or using a multiplex PCR with many labelled primers increase the information that can be obtained with T-RFLP analysis (Singh et al. 2006). Digestion of the amplified target

DNA using restriction enzymes with frequent cut sites may allow for better identification (Osborn et al. 2000). When Engebretson and Moyer compared 18 restriction enzymes they found that four enzymes were able to efficiently resolve their model community. Moreover, they found that no single enzyme is able to resolve communities with more than 50 different OTUs and concluded that T-RFLP is best used to analyze communities of low to medium species richness (Engebretson and

Moyer, 2003). A number of free online programs has been developed to find the best primer-RE match for either specific communities or entire 16S databases (Collins and

Rocap, 2007; Szubert et al. 2007; Shyu et al. 2007; Stres et al. 2009).

Phylogenetic identification using T-RFLP is much more direct than other fingerprinting methods where cloning and sequencing of bands is required. Also, capillary electrophoresis is a much more reproducible and accurate method for determining band lengths (+/-1 bp) and allows for comparison with in silico databases of full length 16S rRNA gene sequences. Many researchers using T-RFLP develop their own database (usually based on clone libraries created from the same sample) to match their fragments with 16S rRNA gene sequences (Hommez et al. 2004; Saito et al.

2009). In addition, many free online programs have been developed for matching fragments to 16S sequences, including TAP-TRFLP (Marsh et al. 2000), PAT-TRFLP

(Kent et al. 2003), TRF-CUT TReFID (Ricke et al. 2005), MiCA (Shyu et al. 2007),

TRAMPR (Fitzjohn and Dickie, 2007), TRiFle (Junier et al. 2008), T-RFPred

(Fernandez-Guerra et al. 2010), and PyroTRF-ID (Weissbrodt et al. 2012). Programs such as PAT-TRFLP, TReFID and MiCA use data from multiple digests to match to corresponding sequences from online databases. In studies by Marsh et al. (2000) PAT-

TRFLP was used to make identifications with T-RFLP analysis and these were found to be comparable to those made using clone library data. Similarly, MiCA uses the latest sequence database from websites such as RDP (Cole et al. 2005), but can also take user inputted primer sequences and restriction enzyme sites to create a database for comparison (Shyu et al. 2007). TRAMPR is an application of the R statistical program which allows multiple T-RFLP samples to be analyzed concurrently. An advantage to this program is that the downstream statistical applications can be done with the output file; however, TRAMPR is not a very “user-friendly” program (Fitzjohn and Dickie,

2007). The T-RFLPred program matches partial 16S sequences generated from clone

10 libraries or pyrosequencing to full length sequences, which are then digested in silico to create a database of fragments that is matched against the user’s input fragments

(Fernandez-Guerra et al. 2010). Though helpful tools, these programs have weaknesses.

Differences between expected fragment sizes for a species and experimentally derived sizes will always be present, and approaches to match them lowers the resolution of the methods. Moreover, T-RFLP cannot provide the taxonomic resolution that full 16S rRNA gene sequences can, but it is a high-throughput method that is inexpensive, easy to use, reproducible, and is considered optimal for statistical analysis (Forney et al.

2004).

1.1.6. Diversity Microarrays

Microarrays are very widely used to study gene expression from bacterial species in various environments. However, similar to the other methods described above, microarrays were first used to study bacterial communities. Three types of microarrays frequently used to study bacterial communities are: community genome arrays, functional gene arrays, and phylogenetic oligonucleotide microarrays (Zhou. 2003).

Community genome microarrays use genomic DNA to detect bacterial species in either simple or complex communities (Wu et al. 2004), but they have not been used extensively. Functional gene microarrays, such as the GeoChip (He et al. 2007), use common genes used in essential biochemical pathways to characterize bacterial communities. Phylogenetic oligonucleotide microarrays (POA) use the 16S rRNA gene sequences from online databases to profile bacterial communities (Zhou. 2003). POAs have been used more frequently than community genome microarrays due to the ease of

11 access to 16S rRNA gene sequences. The original POAs, such as Phylochip (Brodie et al. 2006), were developed for general use in all environments, and targeted all known bacterial species (Palmer et al. 2007).While no POAs have been used to study microbiota in pigs, several different types have been developed for the characterization of human bacterial communities. Many POAs have been developed to specifically target the intestinal microbiome of humans including the Microbiota array (Paliy et al.

2009), HITChip (Rajilic-Stojanovic et al. 2009), AUS-HIT Chip (Kang et al. 2010), and others (Wang et al. 2004; Manges et al. 2010). Two microarrays have been developed to target the oral microbiome (Preza et al. 2009; Crielaard et al. 2011) as well as one for the study of the vaginal microbiome (Dols et al. 2011). POAs can be useful for studying bacterial communities in that they allow profiling multiple samples simultaneously, provide quantitative information of each identification, and are less expensive than pyrosequencing methods. For example, the PhyloChip microarray (together with clone libraries) was used to analyze the bacterial communities in lungs of intubated patients colonized with Pseudomonas aeruginosa. From this study (Flanagan et al. 2007) determined that as the lung community complexity disappeared, pneumonia developed in the patients, suggesting that antibiotics were actually causing a shift to a more serious infection.

A potential problem for POAs is cross-hybridization, leading to more than one bacterial species hybridizing to the same DNA probe. This problem is avoided by using a perfect match-mismatch (PM-MM) method whereby only PM fluorescence that is 1.3 times the MM intensity is counted as positive (DeSantis et al. 2007). The main

12 limitation of POAs is the inability to detect novel bacterial species as they rely upon previously identified sequences.

1.1.7. 16S rRNA Gene Pyrosequencing

The first molecular methods applied to profile bacterial communities revealed complexity not seen by culturing. Application of next-generation sequencing technology to this field has been the next paradigm shift. Pyrosequencing technology has been around since the mid 1990’s, but it wasn’t used for community profiling until

2006 when Sogin and colleagues utilized this platform for 16S rRNA profiling (Sogin et al. 2006). Instead of sequencing the entire 16S rRNA gene, only the V6 region was used to profile eight different ocean environmental samples, producing ~118,000 sequences (16S pyrotags) (Fig. 1.1a). This method has since been optimized for the study of other environments using different variable regions of the 16S rRNA gene.

Roche 454 (formerly 454 Life Sciences) pyrosequencing allows for the creation of libraries of sequences without the need for cloning by instead using tiny-beads that attach to single DNA molecules which can be amplified creating a “polony” (clonal amplification of single DNA molecule) (Shendure and Ji, 2008). The biggest problem with this technology at this time is that comparatively short read lengths are produced.

The first generation 454 platform produced 100 bp reads, but newer platforms can give reads exceeding 400 bp (Petrosino et al. 2009).

The other common next-generation sequencing technology is the Illumina platform.

This sequencing by synthesis method originally could give reads of only ~35 bp, but read length has recently increased to 100 bp. In addition, Illumina has the ability to use

13 a unique paired-end strategy sequencing the DNA molecule from both ends thus increasing the length of sequence reads (Zhou et al. 2011). Regardless, with all these platforms, short pieces of the 16S rRNA gene are used for profiling instead of the full length sequence.

Several studies have been done to compare the use of these shorter sequences

(variable regions of 16S rRNA gene) against either full length sequences or clone library analysis to determine their ability to place bacteria phylogenetically (Liu et al.

2007; Huse et al. 2008). In studies by Liu et al. (2007) and Jeraldo et al. (2011), numerous carefully chosen shorter reads of the 16S rRNA gene are a comparable alternative to the full length sequences. Regions V2, V4 and V6 have been used most often withV2 and V4 found to give the lowest error rates for classification (Hamady and Knight, 2009). In the pyrosequencing method, unique four base-pair sequences in the primers (4 bp sequences usually referred to as barcodes or multiplex identifiers) permit multiplexing of samples (Andersson et al. 2008). Pyrosequencing allows the profiling of low-abundance organisms in a community and has been shown to provide much greater depth than full-length sequencing while providing equivalent taxonomic identifications.

In a recent study by (Claesson et al. 2010) Illumina-based analysis was compared with 454 Titanium-based analyses to profile a human fecal sample (Claesson et al.

2010). These researchers used six tandem combinations of 16S rRNA gene variable regions as targets and found that results from both methods were consistent. The

Illumina-based method, however, had less ability to produce sequences that were able to identify members of the community down to the genus level due to shorter sequence

14 reads and higher error rates. Nevertheless, it was noted that because Illumina produces far more sequence reads per run, the coverage was greater and improvements to read length could make this method unparalleled (Claesson et al. 2010). Several papers have since been published outlining strategies for using Illumina to profile bacterial communities, and it is being seen as a less-expensive method providing millions of sequences per run (Gloor et al. 2010; Zhou et al. 2011; Ram et al. 2011; Degnan and

Ochman, 2012).

Even though the direct sequencing approach avoids cloning bias and provides a very thorough analysis of a community, it is still dependent on PCR so biases associated with primer selection and amplification problems remain (von Wintzingerode et al.

1997). The method, however, has proven to be much more sensitive than cloning and sequencing, and it has been proposed that miniprimers could lead to improved sensitivity (Isenbarger et al. 2008). Another problem associated with short sequence length of the reads is that the method is reliant upon previously established full-length sequences to classify each variable region amplified. In fact, it was shown that regions which have been best characterized, such as the human intestine, have better taxonomic classifications than less well characterized environments (Huse et al. 2008). This problem should be solved with newer sequencing technologies that provide longer reads without sacrificing the total number of reads, such as the SMRT platform from Pacific

Biosciences which produces more than 1000 bp per run (Eid et al. 2009). Even with their limitations, high-throughput sequencing platforms are considered the future of human genetics and microbial ecology (Shendure and Ji, 2008).

1.2. Studying Bacterial Communities

1.2.1. Studying Bacterial Communities in Macro-Organisms

Bacteria have often been studied as simple unicellular organisms capable of causing disease in humans and animals. Increasingly, however, bacteria are being investigated in their natural environment as members of complex microbiological communities.

Mammals have been described as being super-organisms, with microbial cells making up ten times the numbers of human cells, providing one hundred times more genes than the human genome (Fujimura et al. 2010). The improvement of molecular techniques to study bacterial communities has led to a better understanding of the important role that they play in human and animal health. An excellent example of increased interest in microbial communities was the United States National Institutes of Health supported

Human Microbiome Project (HMP).The main goals of this initiative were to determine whether individuals share a core microbiota and to understand whether changes in the human microbiota can be correlated with changes in human health (Blaser and Falkow,

2009).

Bacterial communities have been described using the ecological concept of community structure, which describes the identity and abundance of each member in a community (Little et al. 2008). The community structure of the human microbiota is made up of 4 dominant phyla: Actinobacteria, Firmicutes, Proteobacteria, and

Bacteroidetes (Dethlefsen et al. 2007; Costello et al. 2009). Each body site in mammals provides a different environment that selects for a unique bacterial community (Costello et al. 2009). For example, bacterial communities found in the oral cavity are different from those found in the gastrointestinal (GI) tract. Recent work characterizing oral

16 bacterial communities suggests that this site could have a relatively simple core microbiota with the predominant members being Firmicutes (Costello et al. 2009;

Zaura et al. 2009). On the other hand, the GI tract is a very complex environment, increasing in bacterial density from the stomach (101-3 CFU/ml) to the colon (1011-12

CFU/ml)(O'Hara and Shanahan, 2006). The microbiota of the GI tract is much more variable than the oral cavity, and is thought to only have a core of functional genes rather than particular organisms (Turnbaugh et al. 2009).

As noted above, each body site provides a slightly different environment for bacterial growth. The communities that fully develop at particular body sites are dependent on primary colonizers. These primary colonizers have the appropriate adhesins to attach and are able to utilize available nutrients. These primary colonizers can, in turn, provide nutritional resources, as well as new areas for attachment for secondary colonizers. This succession of colonizers continues until a climax community develops around an equilibrium composition (Gonzalez et al. 2011). The climax community interacts with the host to maintain an equilibrium in which it is involved in the modulation of the immune system (preventing overgrowth of either immune cells or bacterial cells), maintenance of the epithelial barrier, and provision of absorbable monosaccharides broken down from indigestible oligosaccharides (Sekirov et al. 2010).

The climax community provides another benefit to the host which is protecting it from invading pathogens. Termed colonization resistance, climax communities can prevent the invasion of pathogens through a number of negative interactions, such as competition for attachment sites and nutrients (Wardwell et al. 2011). Other ways growth of pathogens and other invading species is prevented is through the production

17 of antimicrobial substances such as short-chain fatty acids, reactive oxygen species, and bacteriocins (Wardwell et al. 2011).

1.2.2. Tonsil of the Soft Palate of Pigs

The oral cavity is a complex region of the mammalian body that contains physiologically many different tissue types (e.g. soft and hard palate, buccal mucosa) colonized each by a variety of metabolically different organisms (Segata et al. 2012). In pigs, the tonsil of the soft palate is a mucosa-associated lymphoid tissue consisting of aggregations of lymphoid cells that is located on the ventral surface of the soft palate

(Casteleyn et al. 2011). It contains both innate and adaptive immune elements and is a site of complement activation and lymphocyte expansion to produce plasma cells. The strategic position at the entrance to the respiratory and gastrointestinal tracts and its function as a secondary lymphoid tissue are important reasons to study the bacterial community found in this tissue. The tonsil of the soft palate of pigs contains numerous deep invaginations known as crypts which sample debris entering the respiratory and digestive tract such as food and microorganisms. The tonsillar crypts are involved in immune surveillance, containing components of both innate and acquired immunity including mucin, complement, antimicrobial peptides, macrophages, T and B cells

(Horter et al. 2003). These structures have a thinner epithelial layer than the surface of the tonsil and are intervened by M cells and goblet cells (Belz and Heath, 1996). The tonsil of the soft palate in pigs contains 10 times more crypts than the corresponding human tonsil (palatine), increasing the surface area for bacteria interactions with the epithelium and provides many more isolated environments for slightly different

18 bacterial communities to develop (Belz and Heath, 1996; Lowe et al. 2011). These crypts provide an anaerobic environment, promoting growth of obligate and facultative anaerobic bacteria (Horter et al. 2003) and have been known to contain biofilms in human subjects (Chole and Faddis, 2003).

1.2.3. Bacteria associated with Pig Tonsils

The palatine tonsil is the most microbiologically diverse (i.e., greatest number of species) site within the oral cavity of humans (Segata et al. 2012) and is known to harbour pathogens such as Haemophilus influenzae and Streptococcus pneumoniae

(Lindroos. 2000). The tonsil of the soft palate of swine is also thought to contain a large number of species including pathogens capable of causing systemic, respiratory tract, intestinal infections in pigs and some organisms implicated in foodborne illness in humans (Table 1.1). Pathogens such as Streptococcus suis and Salmonella enterica serovar Typhimurium use the tonsil as a portal of entry or initial site of colonization

(Horter et al. 2003), but have also been isolated from clinically healthy pigs residing next to non-pathogenic bacteria. In a recent survey of common swine pathogens,

MacInnes et al. (2008) tested tonsillar swabs collected from 50 different farms in

Ontario and found a high prevalence of Haemophilus parasuis (96%), Actinobacillus pleuropneumoniae (78%), Actinobacillus suis (>78%), and S. suis (48%)(MacInnes et al. 2008). In tonsils collected from pigs at slaughter, O’Sullivan et al. (2011) detected a number of pathogenic and non-pathogenic bacteria, observing S. suis as the most prevalent bacterium isolated at a rate slightly lower to that seen by MacInnes et al.

(2008). The difference in prevalence in these two studies is most likely due to the tests used, with MacInnes and colleagues using a sensitive PCR-based test and O’Sullivan

19 relying on culture-based identification. Although quite a bit is known about the presence of primary and opportunistic pathogens, the normal microbiota of this body site needs to be studied further to elucidate what, if any, associations may contribute to pig health and disease.

In studies to date, the microbiota of the healthy porcine tonsil has been found to contain both pathogenic and non-pathogenic bacteria. However, the precise roles these bacteria play in the health and disease of pigs has not yet been determined. The bacterial phyla that have been found in the porcine tonsil of the soft palate are listed in

Table 1.2. To date, 17 phyla, 127 genera, and 131 species have been described in addition to many more uncultured and unclassified bacteria. Four studies have looked at the bacterial community found in the porcine tonsil. The first two studies searched for only Gram-positive bacteria and relied on culture for bacterial community growth and identification (Devriese et al. 1994; Baele et al. 2001). In one of these early studies,

Baele and colleagues looked at piglets before and after weaning and observed a slight change in the Gram-positive flora, but bacteria such as S. suis were consistently associated with the tonsil. In a more recent study, Lowe et al. (2011) used a culture- independent profiling method to characterize the tonsil microbiota. The clone library used in these studies contained a diverse range of organisms similar to those reported in the human tonsil (Segata et al. 2012). In the 8 different pigs sampled, the species richness varied between 15.5 and 44.5 per tonsil with the members of the family

Pasteurellaceae being the most prevalent. Lowe et al. (2011) also noted that there was unsampled diversity that was not included in their clone library and that there were a number of sequences that could not be matched to any known genera. In further work,

Lowe et al. used 454 pyrosequencing to profile tonsillar bacterial communities from the same 8 animals in their 2011 study as well as four additional pigs (Lowe et al. 2012). In this latter study they recovered 13,152 reads per sample, with one sample having

43,770 reads. They found an average of 230 OTUs per sample, a much greater number than was found in their previous study (Lowe et al. 2011). While most of their identifications were not at the species level, they identified core bacteria that were found in virtually all their samples. Of the 17 phyla detected, five of the them were determined to be core (Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria,

Proteobacteria). They also found 8 core classes (Gammaproteobacteria, Clostridia,

Fusobacteria, Bacilli, Betaproteobacteria, Actinobacteria, Alphaproteobacteria,

Bacteroidia), 10 core orders (Pasteurellales, Clostridiales, Pseudomonadales,

Fusobacteriales, Lactobacillales, Neisseriales, Enterobacteriales, Actinomycetales,

Burkholderiales, Bacteroidales), 8 core families (Pasteurellaceae, Moraxellaceae,

Fusobacteriaceae, Veillonellaceae, Neisseriaceae, Peptostreptococcaceae,

Enterobacteriaceae, Streptococcaceae), and 8 core genera (Actinobacillus sp.,

Alkanindiges sp., Fusobacterium sp., Haemophilus sp., Pasteurella sp., Veillonella sp.,

Peptostreptococcus sp., Streptococcus sp.).

Taken together, these studies suggest that many members of the bacterial communities of the tonsil of the soft palate of have not yet been described and much more study is needed.

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Shearing metagenomic DNA by nebulization and isolation of 400 to 800 bp DNA fragments

Ligating to adapters for in vitro amplification

Fig. 1.1a. Bacterial community profiling by sequencing. Left: Clone library analysis.

Right: Pyrosequencing. Figure taken from Rajendhran and Gunasekaran et al. 2011.

Fig. 1.1b. Fingerprinting techniques used for bacterial community profiling:

DGGE/TGGE and T-RFLP analysis. Figure taken from Rajendhran and Gunasekaran et al. 2011.

Table 1.1 Pathogens detected at the tonsils of the soft palate of pigs. Pathogen Reference Streptococcus suis Pasteurella multocida Actinobacillus suis MacInnes et al. 2008; Lowe et al. 2011 Actinobacillus pleuropneumoniae Haemophilus parasuis Erysipelothrix rhusiopathiae O’Sullivan et al. 2011 Mycoplasma hyopneumoniae Marois et al. 2008 Lawsonia intracellularis Jensen et al. 2000 Mycobacterium avium Hibiya et al. 2010 Salmonella sp. Horter et al. 2003 Yersinia enterocolitica De Boer and Nouws, 1991 Berzins et al. 2009; Fredriksson-Ahomaa Listeria monocytogenes et al. 2009 Staphylococcus hyicus Baele et al. 2001; O’Sullivan et al. 2011 Staphylococcus aureus

Table 1.2. Bacteria detected at the tonsil of the soft palate of pigs. Compiled based on information in de Boer and Nouws, 1991; Devriese et al. 1994; Offermann et al. 1999;

Jensen et al. 2000; Baele et al. 2001; Chiers et al. 2001; Autio et al. 2004; Marois et al.

2008; MacInnes et al. 2008; Fredriksson-Ahomaa et al. 2009; Ortiz Martinez et al.

2010; O'Sullivan et al. 2011; Lowe et al. 2011; Lowe et al. 2012; Makhanon et al. 2012

Phylum Genus Species Gp22 Acidobacteria Gp4 Gp6 Actinomyces hyovaginalis, unclassified Arcanobacterium pyogenes Atopobium Bifidobacterium Cellulomonas Corynebacterium Actinobacteria Dietzia Gardnerella Mycobacterium avium Rothia nasimurium Turicella Yaniella Unclassified genus Bacteroides fragilis, new OTU1-4, vulgatus Bergeyella Capnocytophaga Chryseobacterium New OTU Dysgonomonas New OTU Paludibacter Parabacteroides New OTU Bacteroidetes catoniae, endodontalis, gulae, new Porphyromonas OTU1-3 buccalis, heparinolytica, new Prevotella OTU1-6 Riemerella Tannerella forsythensis Terrimonas

Chlamydia Chlamydiae Unclassified genus Chloroflexi Caldilinea Cyanobacteria Unclassified genus Acetobacterium Aerococcus Anaerococcus Atopostipes Centipeda Clostridium disporicum Dialister avium, faecalis, faecium, Enterococcus gallinarum Erysipelothrix rhusiopathiae,new OTU Eubacterium Facklamia Filifactor villosus Finegoldia Gemella palaticanus Helcococcus Jeotgalicoccus agilis, animalis, crispatus, Lactobacillus johnsonii, reuteri, salivarius, vaginalis Firmicutes Listeria innocua, monocytogenes Megasphaera Parvimonas Pediococcus pentosaceus Pelosinus Peptococcus Peptoniphilus Peptostreptococcus New OTU, stomatis Planococcus New OTU Proteocatella Ruminococcus Spartobacteria genera incertae_sedis SR1 genera incertae sedis aureus, chromogenes, epidermidis, haemolyticus, hominis, hyicus, Staphylococcus pasteuri, pseudintermedius, simulans, warneri agalactiae, bovis, cricetus, Streptococcus dysgalactiae, equisimilis,

gallolyticus, hyointestinalis, mitis, new OTU, plurextorum, porcinus, suis, zooepidermicus Subdivision3 genera incertae sedis TM7 genera incertae sedis Trichococcus Turicibacter Veillonella caviae, parvula Unclassified genus necrophorum, new OTU, Fusobacterium nucleatum, russii, simiae Fusobacteria Leptotrichia Sneathia Gemmatimonadetes Gemmatimonas Planctomycetes Unclassified genus Acidovorax Acinetobacter venetianus indolicus, lingieresii, minor, Actinobacillus pleuropneumoniae, porcinus, porcitonsillarum, rossii Aggregatibacter Alkanindiges Alysiella Azoarcus Bisgaard taxon 10 Bradyrhizobium Brevundimonas Burkholderia New OTU Campylobacter mucosalis, rectus Proteobacteria Citrobacter freundii Desulfovibrio Enterobacter Aerogenes, cloacae Escherichia coli Escherichia/Shigella Geobacter Haemophilus parasuis, felis, new OTU Helicobacter Herbaspirillum Herminiimonas Ignatzschineria Kingella Klebsiella pneumoniae Lawsonia intracellularis

Massilia Methylobacterium Moraxella canis Morganella morganii Neisseria Paracoccus aerogenes, canis, mairii, multocida, Pasteurella new OTU Phenylobacterium Polynucleobacter Proteus mirabilis, vulgaris alcalifaciens, rustiganii, stuartii, Providencia vermicola Pseudomonas New OTU Psychrobacter Roseomonas Salmonella Suttonella indologenes Yersinia enterocolitica, pseudotuberculosis Unclassified genus Spirochaetes Treponema New OTU, pedis, vincetti SR1 Unclassified genus Synergistetes Pyramidobacter hyohinis, hyopneumoniae, Mycoplasma Tenericutes hyosynoviae TM7 Unclassified genus Prosthecobacter Verrucomicrobia Unclassified genus n=17 n=127 n=131

Chapter 2

Objectives

The tonsil of the soft palate harbours bacterial and viral pathogens known to be associated with many of the most significant diseases in swine. Despite its potential importance, the microbial community of this site has received limited attention. To date, there have only been two studies from bacterial community relying primary on culture as well as two studies using culture-independent methods. In these culture- independent studies, samples from only a limited number of healthy pigs were analyzed. In an attempt to fill parts of this major gap in our knowledge, I undertook studies of the microbiome of the tonsil of the soft palate of 146 pigs using Terminal

Restriction Length Polymorphism (T-RFLP) analysis.

The main objectives of my research were to:

1 optimize the DNA extraction and T-RFLP analysis protocols for porcine tonsils. To

do this I used a mock community to identify potential biases in the protocol and

compared the T-RFLP analysis protocol with the results of routine diagnostic

characterization done by the Animal Health Laboratories at the University of

Guelph.

2. use the optimized T-RFLP analysis protocol to analyze the pig tonsil communities of

128 unthrifty pigs from 29 farms as well as 18 healthy slaughter pigs. Putative

identifications were made with T-RFLP analysis using an in silico database. The

results from the T-RFLP analysis were grouped into clusters and statistical analysis

was performed to compare clinical data with cluster membership.

3. do 454 pyrosequencing. Pyrosequencing was performed on six DNAs recovered

directly from tissue and seven DNAs prepared from plate cultures. These samples

were selected as being the most diverse communities as determined by T-RFLP

analysis. The pyrosequencing results were used in turn to aid in identifications done

by T-RFLP analysis.

Chapter 3

Optimization of the terminal restriction fragment length polymorphism analysis

(T-RFLP) method for the characterization of bacterial communities

at the tonsil of the soft palate of swine

3.1. Introduction

Recent culture-independent profiling studies have demonstrated that the tonsils of the soft palate of swine have a microflora that is much more complex than earlier culture-based studies had indicated (Lowe et al. 2011; Lowe et al. 2012). In the first of these studies, a clone library sequencing method was used to profile the bacterial communities present on the tonsils of eight (18 to 20 wk old) swine. In this study many

Proteobacteria and Bacteroidetes were found, but Gram-positives such as Firmicutes were not detected. In a second study, 454 pyrosequencing was used to profile 12 tonsil samples; the microbiota was found to be dominated by members of the family

Pasteurellaceae and the core genera (found in all or most pigs) included Actinobacillus,

Alkanindiges, Fusobacterium, Haemophilus, Pasteurella, Veillonella,

Peptostreptococcus, and Streptococcus (Lowe et al. 2012). Although the cost of sequencing methods for microbiota characterization has been decreasing, these techniques are still prohibitively expensive for the analysis of large numbers of samples. Terminal restriction fragment length polymorphism (T-RFLP) analysis, an alternative method for the analysis of bacterial communities, can be done for a fraction of the cost of sequencing. T-RFLP analysis uses a DNA fingerprinting approach where

16S rRNA genes are amplified with fluorescently labelled primers, digested with one or

41 more restriction enzymes, and the resultant fragments characterized by capillary electrophoresis.

Regardless of the method, recovery of DNA that best represents the bacterial community under study is the end goal in optimizing an extraction protocol for a particular environment (Ariefdjohan et al. 2010). A common approach to optimize

DNA preparation is to compare the different protocols and kits, evaluate the quality and quantity of the DNA by spectrometry, and measure the richness of the community using an available profiling method (Li et al. 2007).‘Difficult to lyse’ bacteria are typically tested to ensure that the most representative community is obtained (Rantakokko-Jalava and Jalava, 2002). To verify that a protocol for the preparation of DNA from a particular environment is appropriate, the quality of DNA recovered must be characterized and the preparation tested to determine if there are any inhibitory substances that affect any downstream reactions (Li et al. 2007). To check the accuracy of a particular profiling method in representing a bacterial community, samples

“spiked” with a mock community can be tested. A mock community (with known numbers of 16S rRNA genes per species present in the DNA mix) can help to determine whether there is bias in the profiling method (Diaz et al. 2012).

In the current study, the T-RFLP analysis method was optimized and putative identifications made using T-RFLP were compared with classical bacterial culture characterization.

3.2. Materials & methods

3.2.1. Sample collection and processing

Tonsils of the soft palate were collected as part of a study on risk-based surveillance of respiratory infections in growing pigs by Poljak et al. (2012). Tonsil tissue, lung, lymph node, serum, and clinical information were obtained from 128 unfit animals from closeout groups in 29 finisher facilities in Southwestern Ontario. These samples were sent to Animal Health Laboratories at the University of Guelph and screened for specific bacterial and viral pathogens. Tonsil swabs were plated on MacConkey agar to selectively grow Gram-negative bacteria, on Columbia Colostin and Naladixic Acid

(Columbia CNA) or phenylethyl alcohol agar (PEA) for selection of Gram-positive bacteria, and on non-selective sheep blood agar (SBA) with a Staphylococcus streak.

From these plates, Actinobacillus pleuropneumoniae, Actinobacillus suis, Haemophilus parasuis, Streptococcus suis, Pasteurella multocida, and Yersinia enterocolitica were identified using routine diagnostic culture (RDC). Sera were screened for porcine reproductive and respiratory syndrome (PRRS) virus antibodies using a 2XR ELISA

(IDEXX Laboratories, Inc., Markham, Ontario). Sera and lung tissues were also tested for the presence of PRRS virus using real-time PCR (Wasilk et al. 2004). In addition, sera and lymph nodes were screened for porcine circovirus type 2 (PCV2) using real- time PCR and immunohistochemistry (Carman et al. 2008).

After preliminary processing, bacterial communities (excluding the Staphylococcus streak) were washed off of the original streak plates in 2.5 ml of phosphate buffered saline and frozen at -70 oC following the addition of glycerol to a final concentration of

10% (2.5 ml 20% glycerol solution/2.5 ml PBS). Tonsil tissues were stored at -70 oC.

3.2.2. Comparison of DNA extraction kits

A piece (0.5 g) of tonsil of the soft palate from a healthy pig was homogenized in 2 ml of PBS by hand using a mortar and pestle and used to optimize the protocol for the preparation of community DNA. To try to ensure that the DNA being used for downstream analysis was representative of the bacterial populations, four commercially available DNA extraction kits with an added bead-beating step were compared. DNA was extracted from the tissue homogenate in duplicate using the PowerSoil® DNA

Isolation Kit (Mobio Laboratories , Inc., Carlsbad, CA), the E.Z.N.A.® Stool DNA Kit

(Omega Bio-Tek, Inc., Norcross, GA), the Maxwell® System (Promega Corporation,

Madison, WI), and the DNeasy Blood & Tissue kit, Gram-positive protocol (Qiagen

Inc., Toronto, ON) according to the manufacturers’ instructions. In addition, the

E.Z.N.A.® Stool DNA kit combined with the Maxwell® System was tested (Table 3.1).

Except for the Powersoil kit (which already had a bead beating step), 0.25 g of 0.1 mm beads (Biospec Products, Inc., Bartlesville, OK) were added to 2 ml screw capped tubes

(Corning Inc., Tewksbury, MA) and 200 μl of homogenized tissue was suspended in the first solution of the different kits and pulsed six times for 30 s in a Mini-Beadbeater-16

(BioSpec Products, Inc., Bartlesville, OK). Following the bead-beating step, DNA samples were extracted according to the manufacturers’ instructions. The resultant

DNA preparation was stored at -20oC. Once all of the DNA samples had been prepared,

T-RFLP analysis was performed on duplicate DNA preparations (see below) and the number of fragments from both forward and reverse primers was counted to evaluate the richness of the output. Based on this analysis, the Qiagen kit (using the Gram-

44 positive protocol and bead-beating) (Qiagen-bb) was used for all subsequent DNA extractions.

3.2.3. Checking lysis of ‘difficult-to-lyse’ bacteria

The effectiveness of the Qiagen-bb method to extract DNA from different Gram negative and Gram positive bacteria including a “difficult-to-lyse” species was evaluated. Pasteurella multocida was used as a representative Gram-negative bacterium as well as a common resident of swine tonsils while another swine bacterium,

Streptococcus suis, was used as a representative Gram-positive bacterium.

Mycobacterium smegmatis was used as a representative of bacteria known to be extremely difficult to lyse. In the first experiment, DNA was extracted from the bacteria using the Qiagen-bb protocol in duplicate cultures and evaluated by T-RFLP analysis to check for fragments of predicted size. In the second experiment, tonsil tissue (0.25 g) was minced into 10 to 12 pieces (~0.5 mm3) and placed in a screw-capped tube with 0.5 mm beads (see below). This tissue preparation was spiked with 10 µl of an overnight broth culture of each bacterial species. The tissue samples were homogenized (see below) and DNA was extracted using the Qiagen-bb method. T-RFLP analysis was performed to determine if the fragments from the three test species could be identified amongst the fragments of resident bacteria.

3.2.4. Tonsil tissue homogenization

Once the DNA extraction protocol was optimized, four different protocols were evaluated for their ability to rapidly dissociate tonsil tissue (Table 2). Duplicate samples of minced tonsil tissue (0.25 g and 0.5 g) were placed in screw-cap tubes (Axygen, Inc.,

Union City, Cal) containing 0.5 ml PBS and 0.5 g of 0.5 mm beads (BioSpec Products,

Inc.) and disrupted in a Mini-Beadbeater-1 (BioSpec Products, Inc., Bartlesville, OK) for 5 min at room temperature. Following bead beating, the supernatant was transferred to a new tube with 0.25 g of 0.1 mm beads and DNA was extracted using the Qiagen-bb protocol (see above). Minced tonsil tissue samples (0.25 g and 0.5 g) were also disrupted by bead beating in tubes containing both 0.5 mm and 0.1 mm beads. The supernatant was removed and DNA was extracted as described above. The DNA samples were evaluated by T-RFLP analysis to compare the richness of the DNA preparations obtained using the different protocols (see below). In these experiments the DNA samples derived directly from the tonsil tissue samples were characterized by

T-RFLP analysis in duplicate.

3.2.5. T-RFLP analysis PCR amplification and digestion

DNA concentration was estimated using a NanoDrop 2000 spectrophotometer

(Thermo Scientific, Waltham, MA) and dilutions (37.5 ng/µl) were prepared in

UltraPureDNase/RNase-Free Distilled Water (Life Technologies Inc., Burlington, ON).

DNA samples were stored at -20oC in a separate clean room where the preparation of the PCR mastermix was done. T-RFLP analysis was done using the Phusion Bacterial

Profiling kit from Thermo Fisher Biosciences (Burlington, ON). The master mix solution was prepared by combining 9.5 µl of the Phusion Bacterial Profiling kit solution 1 with 9.5 µl of the Phusion Bacterial Profiling kit solution 2. Solution 1 contains Phusion® Hot Start High-Fidelity DNA Polymerase, dNTPs and Phusion

Bacterial PCR buffer with MgCl2 while Solution 2 contains the 16S rRNA gene universal primers that are differentially labelled with the blue 6-FAM (8F - 5'

AGAGTTTGATCCTGGCTCAG 3') and the yellow NED (926R – 5'

CCGTCAATTCCTTTRAGTTT 3') fluorescent dyes. Eighteen µl of the master mix was aliquoted into 0.2 ml PCR tubes (Life Technologies Inc.) and 2 µl (75 ng) of DNA was added. Streptococcus suis DNA and UltraPureDNase/RNase Free distilled water

(Life Technologies Inc., Burlington, ON) were used as positive and negative controls, respectively. For routine T-RFLP analysis, PCR amplification was done in a thermocycler (TPersonal, Biometra, Goettingen, Germany). Following amplification,

2.5 µl of the endonuclease solution (containing MspI and HinP1I) was added and the tubes were returned to the thermocycler and held for 2.25 h at 37 oC.

As per the manufacturer’s instructions, five PCR protocols with different annealing times and number of cycles were compared. These programs included: 30 s annealing with 30 cycles, 30 s annealing with 35 cycles, 45 s annealing with 45 cycles, 60 s annealing with 30 cycles, and 60 s annealing with 35 cycles. These PCRs were done in duplicate and the number of T-RFLP analysis fragments was counted to compare the richness of the output. The optimal conditions were determined to be: 3 min initial denaturation at 98 oC, followed by 35 cycles of denaturation (30 s at 98 oC), annealing

(30 s at 59 oC), extension (30 s at 72 oC), with a final extension at 72 oC for five min.

The optimal conditions were then used in all subsequent experiments.

3.2.6. Capillary electrophoresis

Following PCR amplification and restriction digestion, the samples were analyzed at the University of Guelph, Animal Health Laboratory (AHL) using an ABI PRISM®

Genetic Analyzer (Applied Biosystems, Carlsbad, Cal). For these experiments, the

GeneScan 1200 LIZ Size Standard was edited according to the manufacturer’s

47 instructions. The raw results, including background noise were obtained for each sample and processed as described below.

3.2.7. Comparison of tissue and culture samples

To determine if DNA samples derived from all cultures from a single animal could be pooled, T-RFLP analysis was performed on combinations of DNA preparations of culture plates of 8 pigs (Pigs 1 to 4 from Farm 1 and Pigs 1 to 4 from Farm 2). For each pig, DNA samples from cultures obtained from MacConkey agar and PEA, MacConkey agar and SBA, PEA and SBA, and MacConkey agar and PEA and SBA plates were pooled. T-RFLP analysis was run on these preparations and the results were compared with those from individual plates. Based on this experiment, PEA and SBA DNA samples were analyzed together (n=138) and community DNA samples from

MacConkey plates were analyzed separately. T-RFLP analyses of culture samples were performed once.

3.2.8. Mock community analysis

To determine if the Phusion Bacterial Profiling kit could provide an accurate representation of the microbial communities, a collection of swine pathogens, foodborne pathogens, as well as low GC and high GC% bacteria were combined to make up mock communities. Two different types of communities were tested. Mock community #1 was comprised of equal concentrations of three different bacterial DNA samples (Fig. 3.2) while mock community #2 contained the same number of the respective 16S rRNA genes of five species (Fig. 3.3) The mock communities and individual species DNA were analyzed using the Phusion Bacterial Profiling kit and the fragments corresponding to each species were identified. The relative abundance of

48 each species was then calculated (band height/total height) and the results were compared with expected results for each mock community.

3.2.9. Filtering and binning of results

The Phusion Bacterial Profiling kit produced reverse primer fragments that had significantly more background noise than the forward primer fragments. To deal with this problem, four standard deviations of the sum of squares of all band heights for each sample were used as the cut-off. This ensured that the forward and reverse band results in each sample had a cut-off point whereby true fragments could be distinguished from background noise. This analysis was done using T-RFLP analysis Expedited (T-REX) software (Culman et al. 2009). The true peaks in the results were filtered and placed in an Excel file using the Filter Noise function of T-REX. The output files from T-REX were then analyzed with an algorithm created by Ingo Fetzer (based on Abdo et al.

(2006) using the R statistical software package (Fetzer. 2011). The Fetzer algorithm bins fragments in the same sample that are within 1 bp (fragments 50 bp to 250 bp) or 2 bp (fragments >250 bp) together by combining the fragment height values within the range and averages the fragment size. Once all fragments within a single sample had been binned, the algorithm then performs the same function between samples.

3.2.10. Creation of database for bacterial pathogen identification

To compare identifications made using conventional culture with the T-RFLP results, a database of the possible forward and reverse band combinations was constructed using the Phusion Bacterial Profiling kit database. Fragment matches for the bacterial genera under study were collected to form a database. This database was

49 then used for the identification of bacterial pathogens using the Phylogentic Assignment

Tool (PAT) online program (Kent et al. 2003)

3.2.11. Diagnostic Comparison

T-RFLP analysis was performed on DNA isolated directly from the tonsil tissue as well as DNA isolated from corresponding culture plates. The agreement between these methods was measured using the kappa statistic in STATA (College Station, Texas).

The kappa score is a way to measure if there is agreement between two methods, with the null hypothesis being that there is no agreement. Since the T-RFLP analysis method did not permit identification at the species level, comparisons were done at the genus level.

3.3. Results

3.3.1. DNA extraction kit comparison

The number of fragments from both forward and reverse labelled primers was used as the basis of comparison of the DNA extraction kits. The greatest number of T-RFLP fragments was detected using DNA prepared using the Qiagen kit with additional bead beating steps. Almost twice as many fragments were seen using this kit compared with the four other kits tested, (Table 3.1). With the exception of the Powersoil kit, DNA could be extracted from most of the dominant members of the community (Table 3.2).

The Qiagen kit with additional bead beating step, however, was the only kit where less prevalent members of the community were detected and thus it was used for all further experiments.

3.3.2. Evaluation of the Qiagen-bb protocol

The ability of Qiagen-bb protocol to extract DNA from “difficult-to-lyse” bacteria was tested using Mycobacterium smegmatis as the test subject, with Staphylococcus aureus and P. multocida as controls. The DNA samples of these bacterial species were extracted using the Qiagen-bb protocol from individual pure cultures and from spiked tissue samples (Fig. 3.1). Each of the bacterial species used to spike the tonsil tissue was found using T-RFLP analysis, although the signal intensity of the fragments representing P. multocida was much lower than that of the other two species.

3.3.3. Tissue homogenization protocol comparison

Four tissue homogenization protocols (HP1-4) were compared for differences in fragment richness as demonstrated by T-RFLP analysis (Table 3.3). In these experiments two weights of tissue (0.25 and 0.5 g) were tested following simultaneous and sequential bead beating steps with 0.5 mm and 0.1 mm beads. There were differences between duplicate runs of HP1 and HP3, while protocol HP4 (0.25 g of tissue with sequential bead beating steps) had the largest number of both forward and reverse fragments and few inconsistent fragments in the duplicate runs. Accordingly, the HP4 protocol was used for subsequent experiments.

3.3.4. PCR optimization

Five protocols with different annealing times (AP1-5) and number of cycles were compared to determine any differences in T-RFLP analysis output. With the exception of the AP1 protocol, there were not any major differences amongst the five protocols

(Table 3.4). Approximately 70 forward and 25 reverse fragments were found consistently using these protocols. Protocols AP2 and AP3 were both highly

51 reproducible, but as slightly more forward and reverse fragments were seen, protocol

AP2 was used for subsequent experiments.

3.3.5. Mock community analysis

Mock communities were made using DNA samples from pure cultures of known swine pathogens and from low and high GC% Gram-positive bacteria. When the same concentration of purified Mycobacterium smegmatis, Pasteurella multocida, and

Streptococcus suis DNA was used (Fig. 3.2), P. multocida was not seen and M. smegmatis and S. suis were detected in a ratio of 1:2 respectively by T-RFLP analysis.

Similarly, when samples with an equal concentration of 16S rRNA DNA from five different species (i.e., S. suis, A. pleuropneumoniae, H. parasuis, M. smegmatis, F. nucleatum) were used as the input, the two members of the family Pasteurellaceae family were not detected by T-RFLP (Fig. 3.3).

3.3.6. Comparison of T-RFLP analysis and culture identifications

The species identified by routine bacterial culture included: Streptococcus suis,

Pasteurella multocida, Actinobacillus suis, Actinobacillus pleuropneumoniae,

Arcanobacterium pyogenes, Haemophilus parasuis, and Yersinia enterocolitica. The most prevalent genus identified by both culture and T-RFLP analysis was Streptococcus spp. (Table 3.5). Arcanobacterium spp., Haemophilus spp., and Yersinia spp. were found most frequently with T-RFLP analysis of tissue samples while Pasteurella spp. and Actinobacillus spp. were found more frequently by routine culture. The kappa values of Streptococcus spp., Pasteurella spp., and Arcanobacterium spp. show slight agreement between T-RFLP analysis of tissues and routine culture (Table 3.5).

As with direct tissue T-RFLP analysis, culture based T-RFLP analysis appeared to be much more sensitive than routine diagnostic culture for the identification of selected genera, with the exception of Pasteurella spp. (Table 3.6). The kappa score suggests very slight agreement between the two methods for Streptococcus spp., Pasteurella spp., Actinobacillus spp., and Arcanobacterium spp.

Streptococcus spp., Pasteurella spp., Actinobacillus spp., and Haemophilus spp. were detected more often, while Arcanobacterium spp., and Yersinia spp. were seen less frequently by T-RFLP analysis in community DNA samples prepared from plates versus those prepared from tonsil tissue samples (Table 3.7). The kappa values for

Pasteurella spp., Arcanobacterium spp., and Haemophilus spp. suggest slight agreement between these methods, but their p-values are not significant. The kappa value for Streptococcus spp. suggests a fair agreement (kappa value between 0.21 and

0.4) in identifying this genus and the p-value was significant (insert Viera 2005) (Table

3.7).

3.4. Discussion

The study of microbial communities has benefited greatly from the introduction of molecular techniques. These techniques allow for the analysis of communities without the need for culture. However, much work must be done to ensure that the bacterial community under study is being represented as fully as possible in the output of a molecular technique. To ensure this, every step of a method studying bacterial communities, from DNA extraction to the handling of the results, needs to be optimized.

Extraction of DNA from pig tonsil bacterial communities was optimized through the comparison of various commercial kits as well as testing for the ability to extract DNA from ‘hard-to-lyse’ bacterial species. A number of studies in the past have used a comparative procedure to pick the best DNA extraction protocol for their samples.

These comparisons take different forms, whether it is the comparison of commercial kits (Li et al. 2007; Ariefdjohan et al. 2010; Biesbroek et al. 2012), or comparing non- commercialized methods with kits (Li et al. 2003; Carrigg et al. 2007; Yang et al. 2008;

Tang et al. 2008; Vanysacker et al. 2010; Wu et al. 2010; Salonen et al. 2010), or simply comparing different methods (Polgarova et al. 2010). Often, to ensure the optimal extraction of DNA, these studies have to take the possible presence of PCR inhibitors into account by adjusting the method accordingly. This problem is addressed in studies analyzing soil or stool samples (Li et al. 2003; Ariefdjohan et al. 2010b), and has been addressed with saliva samples (Polgarova et al. 2010). The testing of various

DNA extraction kits, an important step for analysis of bacterial communities, is usually not cited to occur in the majority of studies.

The comparison of different DNA extraction kits (Table 3.1) revealed that the kits designed for use with soil or stool samples performed poorly when compared to the kit designed for DNA extraction from blood and tissue (Qiagen). More than twice as many fragments were produced by the DNeasy® Blood & Tissue kit combined with bead- beating than with other kits. This method was able to provide a DNA solution that, when analyzed by T-RFLP analysis, was more reflective of the original bacterial community than that obtained using other methods tested (Table 3.2). This result was surprising, because although Qiagen kits have been used successfully in the past, the

DNeasy® Blood & Tissue kit has only been used in combination with the stool kit to study bacterial communities (Biesbroek et al. 2012). Another reason that these results were surprising was that one of the kits compared, MoBio Powersoil, has been used extensively in other microbiota studies such as the most recent pig tonsil community analysis (Lowe et al. 2011) as well as the Human Microbiome Project (McInnes and

Cutting, 2010). A possible reason for the Powersoil kit’s underperformance was that its bead-beating step had both large (~0.5 mm) and small (~0.1 mm) beads combined together in one tube as opposed to the Qiagen kit’s use of only small beads for bacterial lysis. Further studies of tissue homogenization revealed that separate steps of tissue homogenization with large beads followed by bacterial lysis with smaller beads performed better than having these steps combined (Table 3.3). A further reason that our results were surprising was the perceived absence of inhibiting substances. The stool and soil kits which were compared in this work have specific steps designed to remove PCR inhibitors, whereas the Qiagen kit did not have this capability. Even though saliva, thought to contribute to debris entering tonsillar crypts (Horter et al.

2003), is known to be rich in PCR inhibitors, it did not appear to contribute to any difficulties in evaluating the pig tonsil bacterial community.

In this work, a T-RFLP analysis kit was used to study the bacterial communities of the pig tonsil. The advantage of using a kit for T-RFLP analysis is that it provided a largely optimized protocol with quality controlled reagents. The Phusion Bacterial

Profiling kit had a particular advantage in that only one PCR cycle had to be run because the forward and reverse primers were fluorescently labelled. Additionally, 2 restriction enzymes were used together in the same reagent, streamlining the protocol

55 and reducing the chances of contamination. Because a kit was used, it was only possible to optimize a minor element of the PCR protocol, namely the annealing time and the number of cycles. The ability to characterize multiple types of bacteria at once was tested using two mock communities of bacterial species associated with pigs (Fig. 3.2-

3). A greater number of PCR cycles, as opposed to increasing the annealing time was found to be more useful in our studies (Table 3.4). Although it has been found that greater number of cycles in PCR may increase the likelihood of bias (Polz and

Cavanaugh, 1998), using 35 vs. 30 PCR cycles increased the number of fragments recovered from T-RFLP analysis, and as well increased the reproducibility of obtaining these fragments.

The 8F and 926R primers that were used in our study were developed by Ben Dov et al. (2006), who derived them from the 16S rRNA gene sequences of culturable bacterial species. Ben Dov et al. (2006) found that these primers, when used to amplify bacterial species from various phyla, optimally amplified Proteobacteria, a finding that is not supported by the current study. The mock communities that were tested revealed a bias against the members of the family Pasteurellaceae (Fig. 3.2 & 3.2). When the 8F and

926R primer sequences were compared with those of the annealing site for each bacterial species, a mismatch was found in both forward and reverse primers with members of the family Pasteurellaceae (Table 3.5). This mismatch, however, was not enough to completely prevent the amplification of species from the family

Pasteurellaceae, because when in pure culture or in high numbers, these bacteria were amplified (Fig. 3.1).

Further studies by Mao et al. (2012) tested the coverage rates of the same primers as were used in our study (i.e., 8F and 926R) and found that these primers had variable rates of coverage of various 16S rRNA gene databases. Mao et al. (2012) showed that the 926R primer appeared to have a much broader hybridization ability (~98% coverage of RDP sequences) than the 8F primer (~85% coverage of RDP sequences), suggesting that it is the forward primer that is primarily associated with the bias in our data (Mao et al. 2012).

The bias that was observed in our optimization studies aided the interpretation of the profiling results (Table 3.2-3). The number of pigs found to have Pasteurella spp. and

Actinobacillus sp. were much lower than expected. This result is consistent with the predicted primer bias; recent studies of pig tonsil bacterial communities have shown that primer bias is a common problem. For example, in their clone library analysis,

Lowe et al. (2011) initially recovered no sequences of Gram-positive bacteria, however, sequencing of samples from two pigs with an additional primer revealed some Gram- positive species. In pyrosequencing studies of the same eight samples as well as four additional ones, Lowe et al. (2012) found very few Bacteroidetes sequences, in contrast to their earlier clone library analysis. Although the pyrosequencing studies probably provided a more complete picture of the bacterial communities, including Gram- positives, Lowe et al. (2012) suggested that their inconsistent results could have been due to primer bias. While showing the effect of different primers on community profiles, these studies demonstrated that universal primers (especially those designed in the 1990s), have some limitations. In order to correctly interpret our T-RFLP analysis

57 data, it is necessary to be aware of, and potentially correct for, the effects of primer bias.

Following optimization of the DNA extraction and T-RFLP analysis protocols, the utility of the Phusion Bacterial Profiling kit as a diagnostic profiling tool was tested.

Routine diagnostic characterization (RDC) is a commonly used method to assess the presence of six different bacterial pathogens using classical culturing techniques:

Streptococcus suis, Pasteurella multocida, Actinobacillus suis, A. pleuropneumoniae,

Haemophilus parasuis, and Yersinia enterocolitica. T-RFLP analysis, and specifically its ability to characterize multiple species within a single run, was compared against

RDC to identify these species. T-RFLP analyses of both tissue and culture community

DNA samples were found to be more sensitive than RDC, with the exception of detection of Pasteurella spp. (both tissue and culture) and Actinobacillus spp. (tissue samples only). While primer bias (explained above) may explain why some species were found less often, the greater sensitivity of T-RFLP analysis is simply a common feature of molecular based profiling methods.

Because of the nature of T-RFLP analysis, multiple microbial species can share the same fragment profile. Studies using T-RFLP analysis for diagnostic purposes have used carefully chosen primers and restriction enzymes that are specifically tailored to create fragments that are unique to the species of interest. Brugger et al. (2012) created a T-RFLP analysis protocol both to study the nasopharyngeal microbiota as well as to differentiate Streptococcus pneumoniae from all other streptococci. Elliott et al. (2012) developed a multiplex T-RFLP analysis kit that was able to identify Salmonella

58 enterica as well as six Listeria species. This kit had the typical sensitivity of molecular methods, but lacked specificity for particular bacterial species.

Pairwise comparisons were made between RDC, T-RFLP analysis of DNA samples extracted directly from tissue, and T-RFLP analysis of DNA samples extracted directly from culture (Tables 3.2-4). The level of agreement between all these methods was generally very low. T-RFLP analysis that directly uses tissue DNA samples is expected to differ substantially from RDC because RDC is more selective because of culture pre- steps. RDC was also expected to differ from T-RFLP analysis of culture because of its need to sub-culture before identification, a labour intensive step which limits the number of colonies that are tested. When tissues were analysed by T-RFLP in comparison to RDC, similar results for two bacterial species were obtained, but when

T-RFLP analysis was done on culture samples there was no result overlap with RDC.

Surprisingly, the identification of Pasteurella sp. by T-RFLP analysis of tissue samples and by RDC showed statistically significant agreement (Table 3.5), which seems to contradict the presence of primer bias; however (as previously mentioned) in sufficient numbers, Pasteurellaceae could be identified using T-RFLP analysis. Thus, the identification of Pasteurella sp. by both T-RFLP analysis and RDC suggests that, in the tissue, these species were present in detectable numbers. T-RFLP analysis of culture and RDC showed no agreement, highlighting the effects of such different techniques

(Table 3.6). RDC methods only support the growth of non-fastidious mesophilic anaerobes. Additionally, RDC requires a level of subjectivity in choosing particular colonies for sub-culturing, which are then pursued for identification. On the other hand,

T-RFLP analysis has the opportunity to amplify everything on the plate, but is hampered by primer bias and a lack of specificity to the species level.

The comparison between T-RFLP analysis of tissue and culture DNA samples showed agreement with the identification of Streptococcus spp. (Table 3.7). This was not surprising because culture is highly likely to select for certain bacterial species and changes the structure of the original bacterial community. Rogers et al. (2009) compared T-RFLP analysis of direct tissue and culture plate DNA samples and found significant differences between the results. Although particular identifications were not compared, these authors clearly demonstrated the experimental limitation of using culture plates as starting material.

Although T-RFLP analysis has great potential to be useful for profiling in the diagnostic laboratory; Elliott et al. (2012) showed that when T-RFLP analysis was optimized for species identification, the ability to study bacterial communities was decreased. Although the Phusion Bacterial Profiling kit lacked the specificity to supplant RDC as the main diagnostic profiling tool, the method may have future applicability, for example as a tool for diagnostically profiling entire communities for their potential to cause disease. Along these lines, Horz et al. (2012) published intriguing work suggesting that T-RFLP analysis could be used to measure community structure that may lead to disease in the oral cavity.

Although the Phusion Bacterial Profiling kit showed promise profiling the microbiota of pig tonsils, future profiling studies should utilize this technique’s flexibility to create a protocol that can reliably differentiate targeted bacterial species.

In summary, a T-RFLP analysis protocol has been optimized for the study of pig tonsil bacterial communities. We demonstrate that a modified Qiagen kit protocol is optimal for DNA extraction for our analyses. We also have delineated the limits of the

T-RFLP analysis method for this kind of work. We conclude that T-RFLP analysis should not be considered as a replacement for RDC, but may be an effective tool for the study of bacterial communities at the pig tonsil.

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Table 3.1. Comparison of DNA extraction protocols.

Extraction Qiagen- Omega- Maxwell- Omega/Maxwell- Powersoil Protocol bb bb bb bb Company Qiagen Omega Promega Omega/Promega Mobio DNeasy PowerSoil® Blood & EZNA DNA Tissue Maxwell EZNA & Kit name Stool Isolation Kit system Maxwell DNA kit Kit (Gram +

protocol) Bead-beating Yes Yes Yes Yes Included step added?a # of fragments bF:23 F:11 F:8 F:9 F:4 duplicate 1 R:19 R:11 R:13 R:11 R:3 # of fragments F:21 F:10 F:9 F:6 F:4 duplicate 2 R:18 R:8 R:13 R:9 R:3 Ability to lyse ‘difficult-to- Yes NA NA NA NA ‘lyse’ bacteriac Used in tissue homogenization Yes No No No No protocol testingd a original protocol of commercial kit did not include bead-beating step, 0.1 mm beads were used in a bead-beating step (6 x 30 s) that preceded the regular protocol. b Forward (F) and reverse (R) fragments distinguished from noise using 4 standard deviations in the algorithm developed by Abdo et al. (2006). c Mycobacterium smegmatis was used as test organism. d Test described in Table 3.3.

Table 3.2. T-RFLP analysis of output of forward and reverse fragments seen using DNA prepared with different extraction kits. Cells with numbers show the relative abundance as a percentage. Cells without numbers are counted as zero. Qiagen-bb Omega-bb Maxwell-bb Omega/Maxwell Powersoil ab Fragment c (bp) D1 D2 D1 D2 D1 D2 D1 D2 D1 D2 55F 3.7 6 25.3 19.4 60F 6 4.5 7.5 8.5 16.6 15.9 9.1 11.9 64F 6 5.3 4.7 5.5 7.3 7.4 87F 1.6 94F 2.6 4.6 18.8 13.5 100F 1.1 10.9 3.3 35.8 26 27 33.9 39 103F 3.8 2.3 9.6 11.8 9.2 10.4 12.4 17.7 145F 4 4.1 5.8 8.2 6.1 147F 2.4 2.6 4.5 150F 2.4 2.8 170F 1 1.8 4.1 3.5 4.1 4.4 172F 2 3.1 5.9 7.4 4.8 4.9 6.8 7.9 189F 15.3 11.2 7.6 8.6 9.7 9.8 8.3 9.3 198F 2.3 3.5 4 6.3 224F 4.8 4 237F 15.5 21.1 242F 40.4 46 274F 1.6 1.2 287F 0.85 299F 3.2 4.2 302F 0.85 458F 1.8 3.9 486F 1.5 547F 6.9 5.3 11.5 12 8.9 12.2 14.2 555F 9.1 1.2 6.6 14.3 11.6 6.8 577F 6.3 5 60R 3.2 5.7 26.9 21.1 66R 7.7 8.6 7.1 10 7.2 7.6 7.6 8.5 68R 1.7 2.2 4.3 6.2 4.4 72R 1 1.9 4.5 8.2 2.6 2.6 4.8 4.6 73R 2.2 3.8 7.3 11.6 3.7 4 7.6 8.1 82R 5 8.9 4 5.6 5.7 6.5 3.8 164R 0.94 26 28.9 180R 0.94 201R 1.6 203R 1.2 1.6 3.1 3.2 206R 3 2.9 4.8 4 208R 1.1 228R 1.4 2.2 245R 9.2 8.2 12.2 18.3 10.7 10.9 12.1 13.3 266R 18.4 15.6 16.4 20.7 16.4 16.9 16.7 18.3 280R 1.7 1.9 301R 12.2 9.8 19.3 19.3 11.7 11.1 17.4 19.2 303R 17.7 15.3 7.9 7.4 7.2 7.3 8 47.1 50 347R 2 3.2 2.4 369R 1.8 4.4 5.1 7.9 7 5.1 5.4 371R 8.8 4.3 11.9 15.5 16.5 13.2 14.6 a length of fragment (in bp) attached to either the forward (F) or reverse (R) primer. b Forward (F) and reverse (R) fragments distinguished from noise using 4 standard deviations in the algorithm developed by Abdo et al. (2006). c D1 and D2 refer to the results for duplicates analyzed for each protocol.

Table 3.3. Comparison of total number of T-RFLP fragments observed using different homogenization protocols in duplicate experiments.

# of # of

Protocol Tissue Homogenization Protocol (HP) fragments fragments

D1ab D2ab

HP1 0.5 g tissue, F:10 F:20 0.5 mm bead-beating step followed by 0.1 mm bead- R:8 R:10 beating step

HP2 0.5 g tissue F:24 F:22 0.5 mm beads combined with 0.1 mm beads bead- R:10 R:13 beating step

HP3 0.25 g tissue F:11 F:18 0.5 mm beads combined with 0.1 mm beads bead- R:8 R:10 beating step

HP4 0.25 g tissue, F:29 F:32 0.5 mm bead-beating step followed by 0.1 mm bead- R:14 R:16 beating step a Duplicates 1 and 2. b Forward (F) and reverse (R) fragments distinguished from noise using 4 standard deviations in the algorithm developed by Abdo et al. (2006).

Table 3.4. Comparison of richness and reproducibility of T-RFLP analysis output using different annealing times and numbers of cycles in duplicate experiments.

# of # of Reproducibility Protocol PCR Protocol fragments fragments %c D1ab D2ab

F:43 F:32 F:74.4 AP1 Annealing: 30 s - # of cycles: 30 R:16 R:13 R:81.3

F:72 F:74 F:92.1 AP2 Annealing: 30 s - # of cycles: 35 R:24 R:26 R:92.3

F:74 F:69 F:90.7 AP3 Annealing: 45 s - # of cycles: 35 R:26 R:25 R:89.7

F:57 F:68 F:78.6 AP4 Annealing: 60 s - # of cycles: 30 R:21 R:25 R:70.4

F:69 F:73 F:84.4 AP5 Annealing: 60 s - # of cycles: 35 R:24 R:29 R:82.8 a Duplicates 1 and 2. b Forward (F) and reverse (R) fragments distinguished from noise using 4 standard deviations in the algorithm developed by Abdo et al. (2006). c Reproducibility calculated by dividing all the fragments that were found in both duplicates by the total number of unique fragments.

Table 3.5. Comparison of selected genera identified by T-RFLP analysis of tissue samples (n=101) with RDC results using the kappa statistic.

# T-RFLP # Expected Identified generaa Agreement Kappa P-valuec Tissueb RDCb Agreement

Streptococcus 79 66 56.69% 50.48% 0.1255 0.0742

Pasteurella 19 51 62.2% 56.9% 0.1231 0.0436*

Actinobacillus 4 6 92.13% 92.42% -0.0393 0.6746

Arcanobacterium 31 7 77.95% 72.77% 0.1903 0.0014*

Haemophilus 5 3 93.7% 93.89% -0.0304 0.6387

Yersinia 11 1 90.55% 90.69% -0.0146 0.6214 a T-RFLP analysis was only able to identify isolates to the genus level. Culture results are reported at the species level (i.e., Streptococcus suis, Pasteurella multocida,

Actinobacillu suis, Arcanobacterium pyogenes, Haemophilus parasuis, Yersinia enterocolitica). b Number of genera identified by T-RFLP analysis or by routine diagnostic culture c significant difference shown as P-value less than 0.05.

Table 3.6. Comparison of selected genera identified by T-RFLP analysis of culture samples with AHL culture results. T-RFLP analysis was only able to identify isolates only to the genera level.

# T-RFLP # Expected Identified generaa Agreement Kappa P-valuec Cultureb RDCb Agreement

Streptococcus 78 59 55.26% 50.65% 0.0935 0.1443

Pasteurella 21 48 58.77% 54.99% 0.0841 0.1455

Actinobacillus 12 6 85.96% 85.32% 0.044 0.3073

Arcanobacterium 23 7 77.19% 76.16% 0.0433 0.2839

Haemophilus 25 3 75.44% 76.59% -0.0493 0.8239

Yersinia 4 1 95.61% 95.68% -0.0142 0.5759

a Culture results were reported at the species level (i.e., Streptococcus suis, Pasteurella multocida, Actinobacillus suis, Arcanobacterium pyogenes, Haemophilus parasuis,

Yersinia enterocolitica). b Number of genera identified by T-RFLP analysis or by routine diagnostic culture. c significant difference shown as P-value less than 0.05.

Table 3.7. Comparison between results of T-RFLP analysis of tissue samples and T-

RFLP analysis of corresponding culture samples.

# # Expected Identified generaa Agreement Kappa P-valuec Tissueb Cultureb Agreement

Streptococcus 67 78 65.79% 53.23% 0.2685 0.0017*

Pasteurella 16 21 74.56% 72.71% 0.0677 0.2320

Actinobacillus 2 12 87.72% 88.09% -0.031 0.6877

Arcanobacterium 27 23 68.42% 65.7% 0.0794 0.1970

Haemophilus 3 25 77.19% 76.59% 0.0256 0.3143

Yersinia 7 4 90.35% 90.78% -0.0467 0.6987

a T-RFLP analysis was able to identify isolates only to the genera level. Culture results were reported at the species level (i.e., Streptococcus suis, Pasteurella multocida,

Actinobacillus suis, Arcanobacterium pyogenes, Haemophilus parasuis, Yersinia enterocolitica).b Number of genera identified by T-RFLP analysis or by routine diagnostic cultur. c significant difference shown as P-value less than 0.05.

Table 3.8. Comparison of forward and reverse primers used in the Phusion Bacterial

Profiling kit with corresponding sequences of 16S rRNA genes in representative species. Mismatches are underlined and in bold.

Forward Region Reverse Region

8F/926R primers agagtttgatcctggctcag aaactraaaggaattgacgg

Actinobacillus agagtttgatcatggctcag aaactcaaatgaattgacgg pleuropneumoniae Actinobacillus suis agagtttgatcatggctcag aaactcaaatgaattgacgg

Fusobacterium nucleatum agagtttgatcctggctcag aaactcaaaggaattgacgg

Haemophilus parasuis agagtttgatcatggctcag aaactcaaatgaattgacgg

Mycobacterium smegmatis agagtttgatcctggctcag aaactcaaaggaattgacgg

Pasteurella multocida agagtttgatcatggctcag aaactcaaatgaattgacgg

Streptococcus suis agagtttgatcctggctcag aaactcaaaggaattgacgg

Fig. 3.1. Electropherograms of filtered T-RFLP analysis outputs of tonsil tissue

“spiked” with Mycobacterium smegmatis, Pasteurella multocida, Staphylococcus aureus. Fragment size is in basepairs. Peak height is in fluorescent units. Blue lines represent fragments attached to the forward primer and the yellow lines represent fragments attached to the reverse primer.

MS 33%

SS 67% PM 0%

Fig. 3.2. Comparison of input and output ratios of Mycobacterium smegmatis (MS),

Pasteurella multocida (PM), and Streptococcus suis (SS). The input contained 25 ng

DNA of each organism. Signal height in the T-RFLP analysis output. The output ratio was calculated by comparing both forward and reverse fragment signal heights from each species to one another and then averaging the result.

SS, 4%

APP FN 0% 87% HP 0% MS 9%

Fig. 3.3. Characterization of an “even” mock community by T-RFLP analysis. The input DNA (86.6 ng) contained an equal molar amount of 16S rRNA genes of each community member (SS = 8.78 ng, APP = 6.23 ng, HP = 6.22 ng, MS = 57.4 ng, FN =

7.99 ng). relative % of 16S rRNA genes as determined by signal height in the T-RFLP analysis output. SS=Streptococcus suis, APP=Actinobacillus pleuropneumoniae,

HP=Haemophilus parasuis, MS=Mycobacterium smegmatis, FN=Fusobacterium nucleatum.

Chapter 4

T-RFLP analysis of the bacterial communities associated with pig tonsils

4.1. Introduction

The tonsil of the soft palate of swine is a secondary lymphoid organ containing many openings leading to crypts which run into the tissue (Horter et al. 2003). Its position at the beginning of the digestive and respiratory tract allows for sampling and effective immune surveillance of materials entering the mouth or nares (Horter et al. 2003). The tonsil of the soft palate is the site of colonization of many pig bacterial pathogens including Streptococcus suis, Pasteurella multicoda, Actinobacillus pleuropneumoniae,

Actinobacillus suis, Haemophilus parasuis, and Mycoplasma hyopneumoniae (Wesley et al. 2008; Marois et al. 2008; Lowe et al. 2011; O'Sullivan et al. 2011). Foodborne pathogens such as Salmonella enterica., Yersinia enterocolitica, and Escherichia coli

O157:H7 have also been isolated from this site (O'Sullivan et al. 2011). Likewise, viral pathogens such as porcine circovirus type 2 (PCV-2) and porcine reproductive and respiratory syndrome (PRRS) virus have been shown to colonize the tonsil of the soft palate (O'Sullivan et al. 2011; Wills et al. 2003). Early work to profile the microbiota of the swine tonsils relied primarily on methods for the culture of facultative anaerobes

(Devriese et al. 1994; Baele et al. 2001). Using such methods, 70 species belonging to

14 families were reported (Kernaghan et al. 2012). The recent application of culture- independent methods has greatly expanded our understanding of the tonsil microbiota.

In a clone library study of eight animals, Lowe et al. (2011) identified 50 species belonging to six families, and in later pyrosequencing studies by this same group, the

76 tonsil microbiota of 12 animals was characterized. The researchers study 101 genera belonging to 61 families, dominated by Pasteurellaceae. The core genera (found in all or most pigs) they reported were Actinobacillus, Alkanindiges, Fusobacterium,

Haemophilus, Pasteurella, Veillonella, Peptostreptococcus, and Streptococcus (Lowe et al. 2012). In total, 119 genera have been identified in the pig tonsil up to 2012.

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a relatively inexpensive DNA fingerprinting tool that allows for investigation of bacterial communities at the level of operational taxonomic units (OTUs), and putative identification to various taxonomic levels (Schutte et al. 2008). In the current study, T-

RFLP analysis was used to characterize the tonsil microbiota of 128 unfit pigs from closeout groups in 29 finisher and weaning herds, and 18 age-matched healthy pigs. In addition to studying the distribution of anonymous OTUs, attempts were made to assign putative identifications to members of the tonsil microbiota in these two groups. Further analysis was also carried out to determine if there was a statistically significant correlation between bacterial community type and either disease condition or presence of PRRSV, PCV-2, or Mycoplasma hyopneumoniae.

4.2. Material & methods

4.2.1. Sample collection and T-RFLP analysis

Tonsils of the soft palate were collected as part of a study on risk-based surveillance of respiratory infections in growing pigs by Poljak et al. (2012). Lungs, lymph nodes, serum, clinical information, and tonsil tissues were obtained from 128 unfit pigs in the closeout group in 29 finisher facilities in Southwestern Ontario. Pigs from the finisher

77 facilities suffered from various clinical conditions (Table 4.10) which made them unfit for slaughter. These samples were screened for specific bacterial and viral pathogens by the Animal Health Laboratories at the University of Guelph as described in Chapter 3.

Briefly, bacterial cultures were grown on MacConkey agar, Columbia Colostin and

Naladixic Acid (Columbia CNA) or phenylethyl alcohol (PEA) agar, and non-selective sheep blood agar (SBA) with a Staphylococcus streak. In addition, sera and tissues were tested for the presence of PRRSV by ELISA and PCR, and for porcine circovirus type 2 (PCV2) by PCR and immunohistochemistry (Chapter 3). Tonsil tissues were also collected from 18 age-matched healthy slaughterhouse pigs from different farms than the unfit pigs and analysed by T-RFLP as described above.

For T-RFLP analysis, tissue was first homogenized by bead beating and the DNA extracted using a Qiagen kit with an additional bead beating step as described previously (Chapter 3). The Phusion Bacterial Profiling kit was used as described previously (Chapter 3). Briefly, labelled 8F and 907R primers were used to amplify a

900 bp region of the 16S rRNA genes. The amplicons were then digested with MspI and HinP1I and the resultant forward and reverse fragments (attached to forward and reverse primers) characterized by capillary electrophoresis (Laboratory Services,

Guelph). Capillary electrophoresis results were processed using the method of Abdo et al. (2006) as described previously (Chapter 3).

4.2.2. Non-metric multidimensional scaling (NMS) analysis and beta diversity

NMS analysis was performed on the T-RFLP analysis data using the Paleontological

Statistics (PAST) program (Hammer et al. 2001) to visualize similarities and differences of the bacterial communities found in between samples. Whittaker’s beta-

78 diversities, a measure of how communities differ by species presence, were calculated for the unfit and healthy pig groups, as well as for each farm of healthy and unfit pigs, using the PAST program (Hammer et al. 2001).

4.2.3. Putative identification of T-RFLP analysis fragments

A “pig tonsil-specific” database, consisting of forward and reverse fragments, was created to assign putative identifications to bacteria detected using the Phusion

Bacterial Profiling kit. To create this database, a list of bacterial genera known to be associated with the pig tonsil was made based on the scientific literature (Table 1.2) and the sizes of the predicted 16S rRNA fragments, the latter either obtained from the

Phusion Bacterial Profiling kit database or calculated using data available at the

Ribosomal Database Project website (Cole et al. 2005). This pig tonsil specific database contained 82 genera and was expanded to 200 genera using pyrosequencing data from representative samples (see Chapter 5). The T-RFLP Phylogenetic

Assignment Tool (PAT) program (https://secure.limnology.wisc.edu/trflp/index.jsp) was then used to match the forward and the reverse T-RFLP fragments to the database to provide putative identifications (Kent et al. 2003). Using this program, the forward fragments were first matched to putative identifications. Next genera that had corresponding reverse fragments in the sample were identified and the list was generated. Genera sharing the same forward and reverse fragment matches (e.g.,

Campylobacter and Helicobacter) were grouped. The pig tonsil specific database was used exclusively for putative identifications with the assumption that fragments would match with bacterial species that have already been identified at the pig tonsil.

Unmatched fragments were re-evaluated using PAT with a broader database that is provided with the Phusion Bacterial Profiling kit.

4.2.4. Cluster analysis and comparison with clinical information

The Euclidean distances between T-RFLP analysis profiles were calculated and hierarchically clustered using the UPGMA (unweighted pair group method with arithmetic mean) algorithm to create a dendrogram (Abdo et al. 2006). In addition, three clustering algorithms (Cubical Clustering Criteria, the pseudo F statistic, and a pseudo T2 statistic) were used to determine a statistically meaningful number of groups from the T-RFLP analysis data (Abdo et al. 2006).

Fisher’s exact test was performed using STATA (StataCorp LP, College Station,

Texas) to test for associations between clusters and clinical data.

4.3. Results & Discussion

4.3.1. Comparison of OTUs observed in both healthy and unfit pigs

Analysis of the tonsil microbiota of 120 unfit pigs from 27 farms revealed considerable diversity. In total, 152 forward and 67 reverse-labelled unique fragments were detected (Table 4.1). The most prevalent OTUs based on forward and reverse fragments (forward and reverse fragment OTU) in unfit pigs were found in 81% and

69% of the samples, respectively; however, 30% of the forward fragment and 33% of the reverse fragment OTUs were present in only one sample. On average, 15 forward fragment OTUs were detected per animal, but there was considerable pig to pig variation. For example, the microbial community of Pig one on Farm 12c, which had the greatest number of forward fragment OTUs, was comprised of 46 OTUs, while in

80 the sample from Pig five on Farm 23c, only one OTU was detected. Similar results were found when the reverse fragment OTUs were characterized. Overall, less diversity was observed in the 18 healthy pigs sampled. The most prevalent forward and reverse fragment OTUs in the healthy pigs were found in 100% and 94% of the samples, respectively. Sixty-four forward and 22 reverse unique fragments were detected in the tonsil microbiota of these animals (Table 4.2). On average, 14 forward fragments and 4 reverse fragment OTUs were observed per animal, but again there was pig-to-pig variation. For example, the microbiota of Pig one from Farm 5h, had the highest number of fragments (28 and 9 forward and reverse associated, respectively), while Pig two from Farm 4h had the smallest number of fragments (7 and 1, forward and reverse associated respectively). These results exceeded previous estimates for the tonsil microbiota complexity (Horter et al. 2003), but are not unexpected since recent sequencing studies of both pig and human tonsil microbiota indicate higher diversity that previously appreciated (Lowe et al. 2011; Lowe et al. 2012).

Comparison of the OTUs obtained through this analysis from unfit and healthy pigs did reveal some differences. The beta-diversity measurements obtained from the healthy pigs, an indication of the variety of species present in these samples, was lower than that of the unfit pigs (3.9 versus 9.4), a result that was reflected by NMS analysis of the same samples. When considering the data statistically, the area covered by samples from the healthy pig group (indicated by a convex hull) was considerably smaller than that covered by the data obtained from the unfit pig cohort. The healthy pig data, however, were all found within the unfit pig data, suggesting a high degree of similarity (Fig. 4.1). These differences may have been due to the larger number of

81 samples in the unfit pig category. The similarity between the unfit and healthy pigs is perhaps not surprising. First, the high level of diversity observed within and between samples made it difficult to discern differences between the two groups. NMS analysis, which has been described as an exploratory method to view relationships between samples (Schutte et al. 2008), was negatively affected by the high level of diversity

(Culman et al. 2008). The fact that there were multiple diseases present in the unfit pig group being compared to healthy pigs made differences difficult to discern; microbiota studies usually focus on a specific disease, such as vaginosis (Thies et al. 2007) or inflammatory bowel disease (Andoh et al. 2011). The unfit pigs used for comparison had various ailments, including respiratory, gastrointestinal, and physical, alone and in combination.

When farm-to-farm comparisons were made, the beta-diversity of the farms with unfit pigs ranged from 0.5 to 2.4 with an average of 1.4 while the farms with healthy pigs ranged from 0.7 to 1.1 with an average of 0.9. The OTU data of various farms with unfit or healthy pigs were analyzed with NMS to illustrate the variation between farms

(Fig. 4.2). The samples within each farm group together in Fig. 4.2 indicating similarity between the pig data within farms. These results show that there is considerable diversity between pigs within farms. Although little work has been done, Fig. 4.2 supports the assumption that there is greater relation between bacterial communities of pigs within farms rather than pigs between farms. A recent study looking at skin, oral, and fecal microbiota of 60 different families found that individuals within a family, and even their dogs, shared more microbiota signatures with one another than with individuals of other families (Song et al. 2013). In this study, the skin microbiota was

82 most similar within families, and it was suggested that direct contact between family members led to transfer of the microbiota, that then led to transient detection. If the results from Song et al. (2013) are applied to a farm situation where pigs come into close contact with one another, this would suggest the sharing of the microbiota of each pig within a farm. This would be seen more readily with the unfit pigs sampled, as they were part of the closeout group of finisher herds, meaning they were spending a significant amount of time together. Testing the tonsil microbiota of unfit and healthy pigs from the same farm would likely give a clearer picture as to the differences between these two groups.

4.3.2. The ability of T-RFLP analysis to match fragments to putative identifications

Putative identifications of the members of the tonsil microbiota were made by comparison of the T-RFLP data with two databases. The majority of both forward and reverse fragments of the unfit and healthy pig microbiota were matched to a bacterial identification in the pig-specific database (Table 4.3). Within the unfit pig samples, the reverse fragments had a consistently lower matched rate than the forward fragments

(33.3%-100% vs. 11.1-88.5). The lowest matched rates were seen in unfit Farm 22c which also had the smallest number of forward and reverse OTUs. Within the unfit pig samples, the largest proportion of matched forward OTUs was seen in Farm 3c which also had a high rate for the reverse fragment matching. Interestingly, this farm had the highest average number of forward and reverse OTUs (Table 4.1). The matched rates for forward fragments in healthy pigs varied less but on average were similar to the

83 unfit pigs. The reverse fragments in healthy pigs, on average, had less unmatched fragments.

When the unmatched OTUs were evaluated using the Phusion Bacterial Profiling database, the matched rate for both forward and reverse fragments increased (Table

4.3). Many of the identifications made using this second database were of uncultured bacteria. Uncultured Bacteroidetes had the highest prevalence using the second database and was found in 31% of the unfit pigs.

Although having all the fragments matched to putative identifications would be ideal, this is not the reality of bacterial profiling studies. Previous T-RFLP analysis studies have described variable number of fragments that were matched to a bacterial identification. In work by Rogers et al. (2004, 2010), the number of T-RFLP analysis fragments that matched to an identification rose from 20% in 2004 to 75% in 2010 which was largely due to improved characterization of their study site. . Similar to the work performed by Rogers et al. (2004), our results indicate that the pig specific database was incomplete and in need of expansion. The variable number of matched fragments suggests that there are many bacteria unidentified and that there is a considerable diversity to be found in the pig tonsil microbiota, indicating that there is a need for further sampling to expand the pig specific database for improved T-RFLP analysis.

4.3.3. Putative identifications of the bacterial communities from healthy and unfit pigs

Eight phyla of bacteria were detected in the tonsil microbiota of unfit and healthy pigs examined (Table 4.4, Table S1). Members of the phylum Firmicutes were detected

84 in all of the healthy pigs and most of the unfit pigs tested; the prevalence of

Bacteroidetes, Proteobacteria, and Tenericutes was similar in both groups. In contrast,

Actinobacteria and Fusobacteria were detected more often in unfit than healthy pigs while the prevalence of Spirochaetes was greater in healthy pigs.

Nineteen classes of bacteria were identified in the tonsil microbiota of unfit and healthy pigs. The Gram-positive classes Bacilli, Clostridia, and Flavobacterii were the most prevalent in both unfit and healthy pigs (Table 4.5, Table S1) while the prevalence of Actinobacteridae (65% vs. 39%), Cytophagia (59% vs. 27%), and Spirochaetales

(2% vs. 11%) was significantly different in unfit and healthy pigs.

At the level of order, Gram-positives were again the most prevalent (Table 4.6,

Table S1). Members of 31 different orders of bacteria were found in the microbiota of unfit pigs while 24 orders were present in the healthy pigs. The prevalence of six different orders were significantly different in unfit and healthy pigs: Actinomycetales

(65% vs. 39%), Cytophagales (59% vs. 28%), Desulfuromonadales (37% vs. 11%),

Fusobacteriales (52% vs. 17%), Lactobacillales (81% vs. 100%), and Spirochaetales

(2% vs. 11%).

Members of 71 different families of bacteria were found in the microbiota of the unfit and healthy pigs examined. Forty-eight of these 71 families were found in the healthy pigs while all were found in the microbiota of unfit pigs (Table 4.7, Table S1).

Although most families were found in both groups, the prevalence of many of these families differed significantly. For example, these families were found more often in unfit pigs: Clostridiales Family XI Incertae Sedis (56% vs. 21%), Enterococcaceae

(94% vs. 42%), Spirochaetaceae (11% vs. 2%), and Staphylococcaceae (56% vs. 26%)

85 were detected significantly more frequently in healthy pigs while Actinomycetaceae

(39% vs. 11%), Cytophagaceae (59% vs. 28%), Fusobacteriaceae (52% vs. 11%),

Geobacteraceae (37% vs. 11%), Mycoplasmataceae (42% vs. 17%), and

Streptomycetaceae (45% vs. 11%).

A total of 150 genera of bacteria were putatively identified in at least one unfit or healthy pig. A total of 85 genera were identified in the healthy pigs while 149 genera were detected in the microbiota of unfit animals. Again, there are similarities between the top identifications; with six of the top 10 genera being detected in both groups

(Table 4.8, Table S1).The prevalence of 29 genera was significantly different in the two groups with 19 genera more prevalent in the healthy pigs while 10 genera were significantly more prevalent in unfit pigs (Supplementary Table 4.1). While many of these significant differences had a p-value well below the normal 5% cut-off, the values approaching this number should be interpreted with caution due to the large number of comparisons made using the z-test.

There were no bacteria in the unfit pigs that met the 95-100% prevalence criteria that has been used to define the members of the core microbiota (Huse et al. 2012). Only

Streptococcus and Enterococcus, whose species shared many of the same fragments, were found to be core genera of the healthy samples. These results differ from the results of Lowe et al. (2012) who found that 5 phyla, 8 classes, 10 orders, 8 families, and 8 genera made up the core microbiota of the analyzed samples. In the study by

Lowe et al. (2012), the bacteria that qualified as core members of a community were found in most or all of the 12 pigs sampled. This disparity is probably due to T-RFLP analysis having a lower sensitivity than the pyrosequencing used by Lowe et al. (2012).

Another reason that could have contributed to the disparity between the two studies was the differences in sample size. We sampled 138 pigs from 34 different farms while

Lowe et al. (2012) sampled 12 animals from two farms. There is a reasonable expectation that samples across two farms are going to show more similarity than samples across 34 farms. Additionally, the majority of the pigs in our study were unfit for slaughter, suffering from many different ailments that could potentially alter the tonsillar microbiota, so that the core members were undetected by T-RFLP analysis.

Although T-RFLP analysis did not reveal any core bacteria, the top five phyla, five of the top eight classes, and six of the top 10 orders identified were the same as the core members found by Lowe et al. (2012). The limited number of pigs sampled for analysis

(12 pigs) by Lowe et al. (2012) cannot be considered exhaustive or an accurate representative of the pig microbiota. Due to this, the core bacteria identified by Lowe et al. (2012) could be the most prevalent members of the pig tonsil microbiota or the simply the most dominant members of the two farms sampled. The T-RFLP analysis results reject the idea of a core microbiota; however, there may be a core metabolome

(set of functional genes) present at the pig tonsils which are regularly found in the most prevalent members of the pig tonsil microbiota. Recent work by Uttenhower et al.

(2012) has shown the presence of a core metabolome that unites individuals with wide ranging microbiota.

Analysis of selected foodborne and “pig pathogens” revealed that the number of members of the family Pasteurellaceae (i.e., Actinobacillus, Haemophilus, Pasteurella) was quite low and didn’t differ significantly between unfit and healthy pigs (Table 4.9,

Table S1). The prevalence of genera of known zoonotic pathogens including

Streptococcus, Escherichia, Salmonella, and Yersinia, was higher in healthy than in unfit pigs. Interestingly, the prevalence of these genera was significantly higher in the healthy pig group, suggesting that they may be normal members of the healthy pig tonsil microbiota. Alternatively, the healthy pigs could have been contaminated with these genera during slaughter. The unfit pigs were euthanized at their respective farms, and only respiratory tract tissue was collected preventing any possible contamination from the full slaughter of the animal. Although these genera are only putative identifications, and further do not directly imply a pathogenic species, the issue of cross-contamination at the slaughterhouse is a known problem for Salmonella, Yersinia enterocolitica, and Escherichia coli (Botteldoorn et al. 2003; Bohaychuk et al. 2011;

Bonardi et al. 2013). With the exception of Streptococcus, the prevalence of all genera was less than 50% in both unfit and healthy pigs. Actinobacillus and Haemophilus were found in a small number of pigs, consistent with the results seen by O’Sullivan et al.

(2011), but in contrast to results reported by MacInnes et al. (2008). The numbers of

Pasteurella in unfit pigs corresponded with O’Sullivan et al. (2011) while the numbers seen in healthy pigs aligned with the results from MacInnes et al. (2008); this difference could be due to the fact that O’Sullivan et al. (2011) used culture profiling (a less sensitive method) while MacInnes et al. (2008) used PCR.

4.3.4. Comparison of bacterial community data to clinical information collected from unfit pigs

A total of 10 clusters (containing >2 samples) were identified using the method described by Abdo et al. 2006 (Fig. 4.3). From the 120 samples analyzed, 100 could be

88 placed in one of these 10 clusters. The 10 clusters were comprised of three large groups

(16 to 31 samples) and seven small groups (3 to 6 samples). Statistical comparison using the Fisher’s exact test indicated that of the 13 clinical conditions studied, only the presence of paleness and abscesses were significantly associated with cluster membership (Table 4.10). Descriptive statistics show the prevalence of the identified genera in each of the clusters (Supplementary Table 4.2).

Pigs positive for paleness (n=27) were significantly associated with cluster membership, particularly with clusters II, VI, and VII (Table 4.10). The presence of paleness usually indicates that the pig is suffering from a gastric ulcer or systemic infection. This may have an indirect effect on the microbiota of the tonsil due to abnormal iron levels, an effect shown to occur in salivary microbiota (Wang et al.

2012).

Twenty-six pigs were positive for the presence of an abscess; this condition was significantly associated with cluster membership, particularly with clusters I, V, VI, and

IX (Table 4.10). The clinical definition of an abscess used in this study was very broad and included wounds caused by fighting or secondary bacterial infections, which made the association with tonsil communities difficult to interpret.

The pigs were investigated for signs of infection with Mycoplasma hyopneumoniae,

PRRS virus, and PCV-2 using several different tests, including RT-PCR, ELISA, and immunohistochemistry. When these results were compared to cluster membership, a number of significant associations were detected (Table 4.11). PRRSV was found in the pooled tissue in 23 pigs and was significantly associated with clusters II, III, and VI

(Table 4.11). M. hyopneumoniae was found in 20 pigs and was significantly associated

89 with clusters III, V, VIII, IX, and X (Table 4.11). PRRS virus and M. hyopneumoniae are pathogens known to cause acute infection, have immunosuppressive capabilities and cause secondary infections such as porcine respiratory disease complex and enzootic pneumonia, respectively (Opriessnig et al. 2011). The significant associations for both of these pathogens were with PCR analyses of tissue, suggesting that when they are present in the respiratory tract, the tonsil bacterial community is affected. The tonsil microbiota is known to harbour bacterial species that cause secondary infections in the lungs of pigs, suggesting that disruption of the normal microbiota by factors such as

PRRSV could make the tonsil a source of disease. The data suggest that there is an interaction occurring between these pathogens and the tonsil microbiota; what remains to be determined is whether this interaction is direct or indirect.

4.4. Conclusion

The T-RFLP analysis results confirmed the high level of diversity of the microbiota of the pig tonsil. Although no core microbiota was observed, the same phyla as reported as core by Lowe et al. (2012) were observed in the current study. Additionally, community membership was observed to be significantly associated with two clinical conditions, paleness and abscess. The presence of M. hyopneumoniae and PRRS virus was also found to be significantly associated with cluster membership. Whether there is a core metabolome, or how pathogenic species such as PRRSV affect the tonsil community, are questions that need to be studied in the future.

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Song, S.J., Lauber, C., Costello, E.K., Lozupone, C.A., Humphrey, G., Berg-Lyons, D., Caporaso, J.G., Knights, D., Clemente, J.C., Nakielny, S., Gordon, J.I., Fierer, N., Knight, R., 2013. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2, e00458.

Thies, F.L., Konig, W., Konig, B., 2007. Rapid characterization of the normal and disturbed vaginal microbiota by application of 16S rRNA gene terminal RFLP fingerprinting. J. Med. Microbiol. 56, 755-761.

Wang, R., Kaplan, A., Guo, L., Shi, W., Zhou, X., Lux, R., He, X., 2012. The influence of iron availability on human salivary microbial community composition. Microb. Ecol. 64, 152-161.

Wesley, I.V., Bhaduri, S., Bush, E., 2008. Prevalence of Yersinia enterocolitica in market weight hogs in the United States. J. Food Prot. 71, 1162-1168.

Wills, R.W., Doster, A.R., Galeota, J.A., Sur, J.H., Osorio, F.A., 2003. Duration of infection and proportion of pigs persistently infected with porcine reproductive and respiratory syndrome virus. J. Clin. Microbiol. 41, 58-62.

Table 4.1. The mean number and range of unique forward (TRF-F) and reverse (TRF-

R) fragments per farm in microbiota of unfit pigs.

Farm TRF-F TRF-R n=120a n=152b n=67b 1c (n=4) 10 (3-21) 5 (2-10) 2c (n=4) 13 (9-17) 7 (4-10) 3c (n=4) 31 (22-43) 14 (10-19) 4c (n=7) 28 (19-38) 13 (10-17) 5c (n=5) 15 (3-29) 6 (2-9) 6c (n=4) 11 (2-22) 4 (2-8) 7c (n=6) 5 (3-6) 2 (1-4) 8c (n=6) 18 (2-32) 8 (1-12) 9c (n=2) 16 (11-21) 7 (5-8) 10c (n=6) 10 (4-25) 3 (1-9) 11c (n=6) 17 (12-20) 8 (6-11) 12c (n=5) 23 (13-46) 10 (6-13) 14c (n=6) 14 (6-19) 5 (1-9) 15c (n=5) 21 (14-33) 7 (6-8) 16c (n=2) 5 (4-5) 2 (1-2) 17c (n=5) 18 (6-25) 6 (0-10) 18c (n=3) 10 (7-17) 3 (1-4) 19c (n=2) 10 (3-16) 4 (1-6) 20c (n=3) 8 (5-13) 4 (3-6) 21c (n=3) 9 (6-12) 4 (3-5) 22c (n=2) 5 (4-5) 2 (1-2) 23c (n=6) 6 (1-15) 1 (0-4) 24c (n=6) 11 (2-23) 3 (1-7) 25c (n=4) 20 (11-34) 6 (4-10) 26c (n=3) 18 (13-22) 11 (8-16) 27c (n=5) 13 (4-28) 5 (2-10) 29c (n=6) 19 (8-32) 7 (3-12) Avg. /pig 15 (1-46) 6 (0-19)

a number of pigs sampled b total number of unique forward fragments in all samples c total number of reverse fragments in all samples

Table 4.2. The mean number and range of forward (TRF-F) and reverse (TRF-R) fragments per farm in microbiota of healthy pigs.

Farm* TRF-F TRF-R n=18a n=64b n=22c 1h (n=3) 12 (8-15) 3 (2-3)

2h (n=3) 13 (10-17) 3 (3-3)

3h (n=3) 17 (12-21) 4 (3-6)

4h (n=2) 12 (7-17) 5 (1-8)

5h (n=1) 28 9

6h (n=2) 12 (6-17) 3 (2-4)

7h (n=4) 14 (10-21) 6 (4-9)

Avg./pig 14 (6-28) 4 (1-9)

a number of pigs sampled b total number of unique forward fragments in all samples c total number of reverse fragments in all samples

Table 4.3. Percentage of forward and reverse fragments in tonsil microbiota of unfit and healthy pigs that were matched to an identification when analysed with the “pig specific” and Phusion Bacterial Profiling kit databases.

Pig specific database Phusion Bacterial Profiling database Avg. (Range) Avg. (Range) Unfit pigs: 69.5 (11.1-88.5) 82.2 (22.2-97.5) Forward fragments Healthy pigs: 74.5 (57.9-87.7) 74.5 (57.9-87.7) Forward fragments Unfit pigs: 83.2 (33.3-100) 92.1 (33.3-100) Reverse fragments Healthy pigs: 96.2 (88.9-100) 96.2 (88.9-100) Reverse fragments

Table 4.4. Prevalence (%) of phyla identified by T-RFLP analysis in the tonsil microbiota of unfit and healthy pigs.

Phyla Unfit Pigs (n=120) Healthy Pigs (n=18)

Actinobacteria* 66 39

Bacteroidetes 70 72

Firmicutes 85 100

Fusobacteria* 53 17

Proteobacteria 72 72

Spirochaetes* 2 11

Synergistetes 1 0

Tenericutes 43 44

* Significantly different (p<0.05)

Table 4.5. Prevalence (%) of the five classes most frequently identified by T-RFLP analysis in the tonsil microbiota of unfit and healthy pigs.

Unfit Pigs Healthy Pigs

Class % Pigs Class % Pigs

(top 5 of 19) (n=120) (top 5 of 17) (n=18)

Bacilli 83 Bacilli 100

Clostridia 70 Clostridia 78

Actinobacteridae 65 Flavobacteriia 72

Flavobacteriia 64 Deltaproteobacteria 61

Cytophagia 59 Gammaproteobacteria 61

Table 4.6. Prevalence (%) of the five orders most frequently identified by T-RFLP analysis in the tonsil microbiota of unfit and healthy pigs.

Unfit Pigs Healthy Pigs

Order % Pigs Order % Pigs

(top 5 of 31) (n=120) (top 5 of 24) (n=18)

Lactobacillales 81 Lactobacillales 100

Clostridiales 70 Clostridiales 78

Actinomycetales 65 Flavobacteriales 72

Flavobacteriales 64 Bacillales 61

Bacillales 59 Pseudomonadales 61

Table 4.7. Prevalence (%) of the five families most frequently identified by T-RFLP analysis in the tonsil microbiota of unfit and healthy pigs.

Unfit Pigs Healthy Pigs

Family % Pigs Family % Pigs

(top 5 of71) (n=120) (top 5 of48) (n=18)

Streptococcaceae 73 Enterococcaceae 94

Flavobacteriaceae 64 Streptococcaceae 94

Clostridiaceae 63 Clostridiaceae 72

Cytophagaceae 59 Flavobacteriaceae 72

Bacillaceae 57 Bacillaceae 56

Table 4.8. Prevalence (%) of the ten genera most frequently identified by T-RFLP analysis in the tonsil microbiota of unfit and healthy pigs.

Unfit Pigs Healthy Pigs

Genus % Pigs Genus % Pigs

(top 10 of 149) (n=120) (top 10 of 85) (n=18)

Streptococcus 69.17 Enterococcus 94.4

Flavobacterium 64.17 Streptococcus 94.4

Clostridium 63.33 Flavobacterium 66.7

Cytophaga 59.17 Lactococcus 61.1

Arthrobacter 55 Bacillus 55.6

Lactobacillus 53.33 Clostridium 55.6

Lactococcus 53.33 Desulfovibrio 55.6

Bacillus 52.5 Pseudomonas 55.6

Fusobacterium 52.5 Staphylococcus 55.6

Pseudomonas 52.5 Capnocytophaga 50

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Table 4.9. Prevalence (%) of ten foodborne and “pig pathogens” genera found in this study.

Unfit Pigs Healthy Pigs

% Pigs % Pigs Genus Genus (n=120) (n=18)

*Streptococcus 69.2 *Streptococcus 94.4

Actinobacillus 4.2 Actinobacillus 0

Bordetella. 4.2 Bordetella 5.6

Campylobacter 11.7 Campylobacter 0

*Escherichia 1.7 *Escherichia 27.8

Haemophilus 5 Haemophilus 5.6

Mycoplasma 42.5 Mycoplasma 44.4

Pasteurella 16.7 Pasteurella 5.6

*Salmonella 14.2 *Salmonella 33.3

*Yersinia 3.3 *Yersinia 38.9

* Significantly different (p<0.05)

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Table 4.10. Comparison between cluster membership and presence of specific clinical condition using Fisher’s exact test.

Cluster Clinical Condition Total I II III IV V VI VII VIII IX X P-value (n=101) (n=5) (n=31) (n=16) (n=3) (n=22) (n=6) (n=3) (n=5) (n=4) (n=6) Diarrhea 30 0 11 6 2 7 2 0 1 0 1 0.581 Dyspnea 55 3 14 11 0 14 3 2 3 1 4 0.432 Rectal Stricture 8 0 4 1 0 1 1 0 0 1 0 0.781 Weight loss 80 2 22 13 2 19 6 2 5 4 5 0.279 Neurological 11 0 4 0 1 3 1 0 0 0 2 0.374 Depression 36 2 8 6 1 8 1 1 1 3 5 0.239 Head Tilt (common sequel of 6 0 2 0 0 1 1 1 0 0 1 0.369 S. suis infection) Paleness* 27 1 5 4 0 7 5 0 3 0 2 0.039 Traumatic injury 20 2 8 3 1 4 0 0 0 0 1 0.794 Abscess* 26 5 9 3 1 2 0 0 1 3 2 0.002 Lameness 42 4 15 6 1 10 0 1 1 2 2 0.374 Rough hair coat 29 1 7 4 0 10 1 0 1 2 3 0.514 Fever 6 0 3 0 1 1 1 0 0 0 0 0.493 * condition significantly associated with cluster membership

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Table 4.11. Comparison between cluster membership and presence of PRRSV, PCV-2, and Mycoplasma hyopneuomiae using

Fisher’s exact test.

Cluster Pathogen test Total I II III IV V VI VII VIII IX X P-value (n=101) (n=5) (n=31) (n=16) (n=3) (n=22) (n=6) (n=3) (n=5) (n=4) (n=6) PRRSV 23 2 17 8 2 5 4 1 0 3 4 0.080 ELISA Serum PRRSV 11 0 1 3 0 2 3 0 0 0 2 0.067 PCR Serum PRRSV 23 0 3 6 0 4 4 2 0 1 3 0.009 PCR Pooled Tissue* PCV-2 30 0 7 4 0 9 3 0 1 1 5 0.072 PCR Serum PCV-2 15 0 2 4 0 6 0 0 1 0 2 0.297 IHC Lymphoid Tissue PCV-2 64 4 18 13 2 15 3 2 1 1 5 0.238 PCR Pooled Tissue Mycoplasma 20 0 6 1 0 2 0 1 5 2 3 <0.001 PCR Lung Tissue* * pathogen presence is significantly associated with cluster membership

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Fig. 4.1. Non-metric multidimensional scaling analysis of tonsil microbiota from unfit pigs (red) and healthy pigs (blue).

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Fig. 4.2. Non-metric multidimensional scaling analysis of tonsil microbiota from various unfit pigs (multiple colours) and healthy (black) pigs grouped into farms.

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Average Distance Between Clusters

1 2 3 4 5 6 7 8 9 10

Fig. 4.3. Dendrogram of cluster analysis of the T-RFLP analysis tonsil microbiota data of unfit pigs. Forward and reverse fragments were combined to create one dataset for input. Euclidean distances were calculated and then clustered by the unweighted-pair group method using arithmetic averages (UPGMA). The boxes and numbers indicate the clusters that were found using 3 different criteria.

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Chapter 5

Characterization of the Tonsil Microbiota of Unfit Pigs by 454 Pyrosequencing

5.1. Introduction

The tonsil of the soft palate of swine is a secondary lymphoid organ containing many openings leading to crypts that run into the tissue (Horter et al. 2003). Its position at the beginning of the digestive and respiratory tracts allows for sampling and effective immune surveillance of materials entering the mouth or nares (Horter et al. 2003). The tonsil of the soft palate is known as a site of colonization for many pig bacterial pathogens including Streptococcus suis, Pasteurella multicoda, Actinobacillus pleuropneumoniae, Actinobacillus suis, Haemophilus parasuis, and Mycoplasma hyopneumoniae (Kernaghan et al. 2012). Foodborne pathogens such as Salmonella enterica, Yersinia enterocolitica, and Escherichia coli have also been isolated from this site. While previous work to profile the microbiota of the swine tonsils have relied primarily on culture, the recent application of culture-independent methods has greatly expanded our understanding of the tonsil microbiota. Sequencing work performed by

Lowe et al. (2011, 2012) has shown the tonsil microbiota to be very diverse, bringing the total number of genera identified to 119. In these studies, pyrosequencing of tonsil samples from 12 healthy pigs revealed the microbiota to be dominated by

Pasteurellaceae and further defined the core genera (found in most or all pigs) to include:

Actinobacillus, Alkanindiges, Fusobacterium, Haemophilus, Pasteurella, Veillonella,

Peptostreptococcus, and Streptococcus (Lowe et al. 2011; Lowe et al. 2012). To further characterize the diversity of the tonsil microbiota in swine, samples from six animals that

107 were representative of the diversity seen by T-RFLP analysis of 128 “unfit” pigs” animals were selected for further analysis by pyrosequencing of the V3-V5 region. In addition, pyrosequencing of seven representative tonsil cultures was done.

5.2. Material & Methods

5.2.1. Tonsil collection, DNA extraction, T-RFLP analysis, and sample selection

Tonsil collection, bacterial DNA preparation of tonsil tissue and combined culture samples, and T-RFLP analysis was done as described previously (Chapter 3). Once the data matrices of the T-RFLP analysis of both the tissue and culture-extracted DNAs were created, the systematic cover method of (Abdo et al. 2006) was used to select samples from the both matrices that would represent the highest diversity. In total, six samples from the tissue-extracted T-RFLP analysis matrix and seven samples from the culture- extracted T-RFLP analysis matrix were selected for further study by 454 pyrosequencing.

5.2.2. Sequencing and analysis

Using the same DNA as used for the T-RFLP analysis, the microbiota of the tonsil tissue and culture samples were analyzed using a 454 GS Junior Titanium System as described in Costa et al. (2012). Briefly, the V3-V5 region of the 16S rRNA gene was amplified by PCR using the primers 357F (CCTACGGGAGGCAGCAG) and 926R

(CGTATCGCCTCCCTCGCGCCA). Forward primers were designed with adaptor A sequence (CGTATCGCCTCCTCGCGCCA) as well as a key sequence (TCAG) and reverse primers with the adaptor B sequence (CTATGCGCCTTGCCAGCCGG) as well

108 as the a key sequence (TCGA) as recommended by the 454 Sequencing Technical

Bulletin No. 013-200. The PCR mixture underwent the following conditions: five min at

95oC for denaturing, and 28 cycles of 15 s at 95oC for denaturing, 45 sec at 56 oC for annealing and 60 sec at 72oC for elongation followed by a final eight min at 72 oC and held at 4 oC. Sequencing errors and chimeras were removed using the mother software

(Schloss et al. 2009). The output files were uploaded to the MG-RAST server (Meyer et al. 2008 ref) and sequence identification were done using the following criteria: an e- value of 10-30, a minimum alignment length of 75 bp and a minimum percentage identity of 97% as cut-off values using the SILVA Small Subunit rRNA Database (SSU) as the reference. The Ribosomal Database Project (RDP) pipeline (Cole et al. 2009) was used to cluster the sequences into OTUs using a 0.03 cutoff value. Rarefaction curves were created to attain the values necessary to be graphed in Microsoft Excel. The Chao-1 and

Shannon indices, the inverse of the Simpson diversity, and Simpson evenness indices for alpha diversity analysis were calculated as described in Schloss et al. (2009).

5.3. Results

5.3.1. OTU analysis

A total of 28,768 and 36,233 reads were recovered after filtering from the tissue- derived and culture-derived DNA samples, respectively. Within the tissue-derived group, the number of reads ranged from a low of 2635 reads to a high of 6577 reads (Table 5.1).

The culture-derived group ranged from 3659 to 6660 reads. The rarefaction curves seen in Fig. 5.1 and 5.2 as well as the coverage in Table 5.1 show that the samples had sufficient depth of coverage from the number of reads. As expected, there was much

109 more diversity seen in the tissue-derived samples as compared to the culture-derived samples, with 3 times more OTUs being observed. The number of OTUs seen in the tissue-derived samples never fell below 123 whereas the number seen in the culture- derived samples peaked at 68.

With the exception of sample T4, the number of OTUs in tissue-derived samples was relatively similar (Table 5.1). The Chao-1 indices indicated a species richness of approximately 200 and the Shannon diversity indices were consistently high. The culture- derived samples had expectantly lower Chao-1 indices as well as lower Shannon diversity indices. The Simpson’s evenness index for both groups of samples indicated uneven communities for all samples.

5.3.2. Taxonomic identifications

From the 65,001 reads obtained from the tissue and culture-derived samples, a total of

816 OTUs were found and from these OTUs, nine phyla were identified (Fig. 5.3). The tissue samples were dominated by Firmicutes and Bacteroidetes, which made up 72% of the total reads. The next three most abundant phyla, Fusobacteria, Proteobacteria, and

Actinobacteria, comprised a further 15.6% of the total reads. A sizable number (12.4%) of the reads were “unclassified”. In the culture samples, a marked shift in relative abundances of the most abundant phyla was observed with Firmicutes and

Proteobacteria making up 80% of the total reads. In the culture samples, Bacteroidetes made up only 1% of the total reads and Fusobacteria 0.05%.

In total, one hundred and forty-two genera were identified in the tissue and culture- derived samples. In tissue-derived samples, 129 different genera were identified. Eight

110 percent of total reads could not be identified to the genus level and were placed in a higher taxonomic group. The 10 most abundant genera (Bacteroides, Fusobacterium,

Streptococcus, Lactobacillus, Clostridium, Unclassified derived from

Peptostreptococcaceae, unclassified derived from Ruminococcaceae, Marinilabilia, and

Prevotella) made up 64.4% of the total reads (Fig. 5.4). In the culture-derived samples,

57 genera were identified; four identifications that could not be made to the genus level and made up 0.7% of reads total. The 10 most abundant genera (Staphylococcus,

Pseudomonas, Pasteurella, Escherichia, Streptococcus, Kurthia, Corynebacterium,

Streptomyces, Proteus, and Brevundimonas) make up 80% of the total reads (Fig. 5.5).

The taxa that were found in all six of the tissue-derived samples make up the core microbiota of our pig tonsils. Five phyla were identified in all six tissue-derived samples, including: Firmicutes, Bacteroidetes, Fusobacteria, Proteobacteria, and Actinobacteria.

These five phyla make up 87.5% of the total reads, and were found in all the culture- derived samples as well. Fifteen families were identified in all 6 tissue-derived samples, including: Bacteroidaceae, Fusobacteriaceae, Streptococcaceae, Peptostreptococcaceae,

Lactobacillaceae, Clostridiaceae, Porphyromonadaceae, Ruminococcaceae,

Lachnospiraceae, Pasteurellaceae, Prevotellaceae, Clostridiales Family XI. Incertae

Sedis, Eubacteriaceae, Staphylococcaceae, and Veillonellaceae. These families made up

75% of the total reads with 13 of these being found, in different abundances, in the culture samples as well. Eleven genera were identified in all 6 tissue-derived samples, including: Bacteroides, Fusobacterium, Streptococcus, Lactobacillus, Clostridium sp.,

Porphyromonas, Prevotella, Peptostreptococcus, Eubacterium, Pasteurella,

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Actinobacillus. These 11 genera made up 60% of the total reads and all but Prevotella sp. were found in the culture samples as well, albeit at slightly different abundances.

5.3.3. Foodborne and “pig pathogens” identified

Seven species known or suspected to be either a foodborne or pig pathogen were identified in at least one tissue-derived sample (Table 5.2). The percentage of reads of these pathogens varied, ranging from as low as 0.004% (C. perfringens) to 11.8% (S. suis). Clostridium perfringens and Shigella flexneriwere found in only one pig each and at very low numbers whereas the Pasteurella multocida, Haemophilus parasuis,

Erysipelothrix rhusiopathiae, and S. suis were found in many more pigs, sometimes being a major component in a particular pig tonsil bacterial community.

5.4. Discussion

Pyrosequencing of community DNA obtained from the pig tonsil directly from culture plates provided a glimpse of the diversity seen at this site as well as reminder of the notable difference between culture-independent and culture-dependent populations. The samples selected for this study were chosen from a large group of unfit pigs with varying disease conditions. Both tissue and culture-derived samples were chosen based upon T-

RFLP analysis results which indicated a high level of diversity in all the samples chosen.

Although the tissue and culture-derived samples were not from the same animals (with exception of T2 and C1), there is a profound difference in the community composition between the two sets of samples. This is most readily seen in the lower proportion of

Proteobacteria as well as the higher Bacteroidetes levels observed in the tissue samples when compared to the culture samples (Fig. 5.3). A feature of the data that was common

112 to both sets of samples was the large proportion of unclassified sequences. Twelve percent of the total sequences of both tissue and culture-derived samples were

“unclassified but derived from bacteria” according to MG-RAST. In sample T5 over 50% of the sequences recovered were unclassified. The high number of unclassified reads from culture-derived samples is suggestive that, despite the strict filter applied for quality control and cleaning, these reads were still not correctly classified. This issue has been observed when one read is assigned to more than one phylum.

Pyrosequencing has been used in only one other study of the microbiota of pig tonsils.

Lowe et al. (2012) found an average of 13,152 reads corresponding to 230 OTUs per pig in tonsil samples from 12 animals from two farms. In the current study (with an average

4,795 reads and 192 OTUs per sample) a similar number of OTUs was found. Where

Lowe et al. (2012) amplified the V4 region of the 16S rRNA gene; we studied the V3 to

V5 region. This difference in amplified region size has been shown to affect OTU richness and could explain some of the discrepancies between our sets of data

(Engelbrektson et al. 2010). Studying different regions of the 16S rRNA gene has also been shown to affect identifications of genera (Kumar et al. 2011). The similarities between our data and Lowe et al. (2012) can be seen in the core bacteria that are found in the pig tonsils. The five phyla found in all of our samples (Actinobacteria, Bacteroidetes,

Firmicutes, Fusobacteria, Proteobacteria) also made up the core found by Lowe et al.

(2012). Five of the eight core families as well as five of the eight core genera were also core in our samples. The difference in the results of the two studies is found between the abundances of these identifications. The reads recovered by Lowe et al. (2012) were dominated by Proteobacteria, with 73.4% of their reads being part of that phylum.

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Proteobacteria were less abundant in our samples with only 3.6% of sequences being attributed to that phylum. A difference is also seen when comparing the phylum

Bacteroidetes. Bacteroidetes was the second most abundant phylum in our samples with

34% of reads while only 0.8% of reads were attributed to this phylum in the results reported by Lowe et al. (2012). Lowe et al. (2012) noted that the absence of

Bacteroidetes was suspicious because a previous clone library study with eight of the same pigs (with same DNA samples) found higher numbers of Bacteroidetes: the tonsil communities that we analyzed showed them to be much more significant member. The high abundance of Bacteroidetes in our samples, in comparison to Lowe et al. (2012), could be the result of bias caused by sampling pigs that showed high diversity in T-RFLP analysis results. The lower abundance of Proteobacteria in our data may suggest bias in our method or could be the result of pigs being of low health status.

The foodborne pathogens identified from the tissue samples are not the usual species identified associated with pigs. Although pathogens such as Salmonella enterica, Listeria monocytogenes, and Yersinia enterocolitica have recently been shown to be present in pig tonsils in Ontario (O'Sullivan et al. 2011), none of these were identified in our samples.

Interestingly, a number of “pig pathogens” appear to be a part of the normal microbiota of pig tonsils, with H. parasuis, P. multocida, and S. suis being found in most or all pigs sampled (Table 5.2). The presence of H. parasuis corresponds well with a study by

MacInnes et al. (2008) where H. parasuis was detected by PCR in contrast to a more recent study of pig tonsils by O’Sullivan et al. (2011) in which H. parauis was not isolated very often. The prevalence of S. suis found by O’Sullivan et al. (2011) was approximately 50%, while we identified this species in every pig, corresponding well

114 with the study by MacInnes et al. (2008). The presence of P. multocida in all of our samples differs from the 27% prevalence found by O’Sullivan et al. (2011). The absence of Actinobacillus pleuropneumoniae and A. suis in our samples corresponds well with the results from the study by O’Sullivan et al. (2008), but is not consistent with the studies of

MacInnes et al. (2008) where evidence of these species was found in a large number of samples. These differences could reflect differences in sensitivity of the methods used, the low specificity of pyrosequencing to the species level, but is not likely the result of our pigs being of low health status.

In summary, in addition to identifying new members of the microbiota, this study confirmed the presence of many bacterial species described by Lowe et al. (2012). What can be concluded from our studies (in addition to others) is that the pig tonsil microbiota is highly diverse and deserves continued analysis with high-throughput molecular methods.

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5.5. References

Cole, J.R., Wang, Q., Cardenas, E., Fish, J., Chai, B., Farris, R.J., Kulam-Syed- Mohideen, A.S., McGarrell, D.M., Marsh, T., Garrity, G.M., Tiedje, J.M., 2009. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141-5.

Costa, M.C., Arroyo, L.G., Allen-Vercoe, E., Stampfli, H.R., Kim, P.T., Sturgeon, A., Weese, J.S., 2012. Comparison of the fecal microbiota of healthy horses and horses with colitis by high throughput sequencing of the V3-V5 region of the 16S rRNA gene. PLoS One. 7, e41484.

Engelbrektson, A., Kunin, V., Wrighton, K.C., Zvenigorodsky, N., Chen, F., Ochman, H., Hugenholtz, P., 2010. Experimental factors affecting PCR-based estimates of microbial species richness and evenness. ISME J. 4, 642-647.

Horter, D.C., Yoon, K.J., Zimmerman, J.J., 2003. A review of porcine tonsils in immunity and disease. Anim. Health. Res. Rev. 4:,143-155.

Kernaghan, S., Bujold, A.R., MacInnes, J.I., 2012. The microbiome of the soft palate of swine. Anim. Health. Res. Rev. 13, 110-120.

Kumar, P.S., Brooker, M.R., Dowd, S.E., Camerlengo, T., 2011. Target region selection is a critical determinant of community fingerprints generated by 16S pyrosequencing. PLoS One. 6, e20956.

Lowe, B.A., Marsh, T.L., Isaacs-Cosgrove, N., Kirkwood, R.N., Kiupel, M., Mulks, M.H., 2011. Microbial communities in the tonsils of healthy pigs. Vet. Microbiol. 147, 346-357.

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Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., Sahl, J.W., Stres, B., Thallinger, G.G., Van Horn, D.J., Weber, C.F., 2009. Introducing mothur: open- source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537-7541.

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Table 5.1. Diversity in tissue and culture derived tonsil microbial communities.

% # Chao- Simpsona Pig # Reads Shannona Simpsona Coverage OTUsa 1a evenness

T1 2635 98.18 151 198 3.67 0.05 0.13

T2b 5292 99.21 187 217 3.44 0.07 0.07

T3 5066 99.17 123 177 2.66 0.13 0.06

T4 5240 97.39 326 429 4.21 0.04 0.07

T5 3958 98.74 176 211 3.14 0.12 0.05

T6 6577 99.30 190 228 3.73 0.05 0.12

Tissue 28768 99.28 675 929 4.63 0.02 0.07 totalc C1b 3659 99.63 50 63 2.23 0.19 0.11

C2 5676 99.68 52 73 1.43 0.37 0.05

C3 6660 99.55 68 83 2.27 0.15 0.10

C4 4632 99.68 64 78 2.41 0.13 0.12

C5 5586 99.70 42 65 1.65 0.31 0.08

C6 5712 99.64 39 59 1.02 0.60 0.04

C7 4308 99.77 45 52 1.27 0.43 0.05

Culture 36233 99.80 223 302 2.74 0.11 0.04 totalc a Diversity indices calculated using the RDP pyrosequencing pipeline (0.03 cutoff) b tissue and culture results derived from the same pig c Combined number of reads, with the rest of the columns calculated from a merged file created using Ribosomal Database Project Pyrosequencing Pipeline which was used to analyze the data in mothur.

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Table 5.2. Range of abundances of sequences of pig or zoonotic pathogens in tonsil tissue samples.

Bacterial Species Pig numbera Abundance

(range if >1)b

1 2 3 4 5 6

Clostridium perfringens x 0.004%

Erysipelothrix rhusiopathiae x x x x x 0.05 to 5.3%

Haemophilus parasuis x x x x x 0.2 to 4.2%

Pasteurella multocida x x x x x x 0.02 to 2.4%

Shigella flexneri x 0.1%

Staphylococcus haemolyticus x x 0.6 to 1%

Streptococcus suis x x x x x x 0.3 to 11.8%

a The x represents the presence of the species in the pig microbiota. b Abundance was calculated using the total number of reads found in each pig.

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400 350 300

250 T1 T2 200 T3 No. OTUs No. 150 T4 100 T5 50 T6

282 564 846

2256 2538 5076 1128 1410 1692 1974 2820 3102 3384 3666 3948 4230 4512 4794 5358 Number of Reads

Fig. 5.1. Rarefaction curves comparing the number of reads with the number of OTUs

(0.03 similarity cut-off) found in the DNA obtained from six pig tonsil tissue samples.

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100 90 80

70 C1

60 C2 50 C3

No. OTUs No. 40 C4 30 C5 20 C6 10 C7

264 528 792

1056 1320 1584 1848 2112 2376 2640 2904 3168 3432 3696 3960 4224 4488 4752 5016 5280 5544 Number of Reads

Fig. 5.2. Rarefaction curves comparing the number of reads with the number of OTUs

(0.03 similarity cut-off) found in the DNA obtained from six pig tonsil culture samples.

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100%

90%

80% Actinobacteria 70% Bacteroidetes 60% Cyanobacteria Firmicutes 50% Fusobacteria 40% Proteobacteria Spirochaetes 30% Synergistetes 20% Tenericutes Unclassified 10%

Fig. 5.3. Relative abundances of phyla in the microbiota of 6 tissue (T1-T6) and 7 culture samples (C1-C7) obtained from pig tonsils. Averages of both tissue and culture samples shown as “T total” and “C Total”.

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Bacteroides T1 Fusobacterium

Streptococcus T2 Lactobacillus

T3 Clostridium

Unclassified (derived from T4 Peptostreptococcaceae) Porphyromonas

T5 Unclassified (derived from Ruminococcaceae) Marinilabilia T6 Prevotella

Total Unclassified Other 0% 20% 40% 60% 80% 100%

Fig. 5.4. Relative abundance of the top 10 genera in pig tonsil tissue. Abundance of unclassified sequences as well as the remaining genera also given.

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Staphylococcus C2 Pseudomonas Pasteurella C3 Escherichia

C4 Streptococcus Kurthia C5 Corynebacterium Streptomyces C6 Proteus Brevundimonas C7 Unclassified Other Total

0% 20% 40% 60% 80% 100%

Fig. 5.5 Relative abundance of the top 10 genera in pig tonsil cultures. Abundance of unclassified sequences as well as the remaining genera also given.

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Chapter 6

Conclusions and future directions

The objective of these studies was to characterize the microbiota of the tonsil of the soft palate of pigsA commercial T-RFLP analysis kit (Phusion Bacterial Profiling kit) was used as the primary method for this community analysis. The T-RFLP analysis protocol was first optimized and then tested for its ability to identify pig pathogens, followed by its use to profile and characterize the tonsil bacterial communities, and to identify representative samples for pyrosequencing.

The testing of the T-RFLP analysis protocol during optimization revealed a bias against several genera from the family Pasteurellaceae (Fig. 3.2, 3.3). As seen in Table

3.5 one of the primers used had a mismatch with this family and many other

Proteobacteria. This bias had the potential to underrepresent this entire lineage, which includes many of the important pig pathogens. Nevertheless, Proteobacteria were detected in 72% of both the unfit and healthy pigs using T-RFLP analysis, while

Firmicutes were identified 85% and 100% of the microbiota of these groups, respectively. The prevalence of individual genera such as Actinobacillus and

Haemophilus were found in only 4% and 5% of pigs, respectively. These results were in contrast to recent work by MacInnes et al. (2008) who found high prevalence of

Actinobacillus pleuropneumoniae (78%) and Haemophilus parasuis (96%) in Ontario herds. However, when samples were analyzed by pyrosequencing, Proteobacteria were found in low numbers. These results contrast with those of Lowe et al. (2012), who found

Proteobacteria to be present in very high numbers in the tonsil. Lowe et al. (2012) noted an unexpected absence of Bacteroidetes in their results, suggesting that there was bias in

125 their method. Bacteroidetes were found in relatively high numbers in our pyrosequencing results, suggesting that Bacteroidetes are indeed common member of the pig tonsil microbiota. The bias observed initially with the testing of the T-RFLP analysis protocol appears to not be as significant as originally hypothesized and some of the differences between the current study and that of Lowe et al, 2012 may have been due to differences in the population studied and the actual microbiota examined.

T-RFLP analysis showed Firmicutes to be the most prevalent phylum in our pig tonsil samples, being found in 85% of unfit pigs and 100% of healthy pigs. This is the only phylum that could be considered a core member of the tonsil microbiota based on our T-

RFLP analysis. Pyrosequencing of a smaller number of samples revealed five phyla

(Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria) that were core members of the tonsil microbiota. This results corroborated the results by Lowe et al. (2012) who found the same five phyla to be found in most or all their samples. At the genus level, T-RFLP analysis revealed no core members in the unfit pig samples, while

Enterococcus and Streptococcus were found in 94% of the healthy animal samples.

Eleven genera identified by pyrosequencing (Bacteroides, Fusobacterium, Streptococcus,

Lactobacillus, Clostridium sp., Porphyromonas, Prevotella, Peptostreptococcus,

Eubacterium, Pasteurella, Actinobacillus) were deemed core members while Lowe et al.

(2012) found eight core genera (Actinobacillus, Alkanindiges, Fusobacterium,

Haemophilus, Pasteurella, Veillonella, Peptostreptococcus, and Streptococcus). Our pyrosequencing had all phyla and many genera in common with the results from Lowe et al. (2012). This suggests that the T-RFLP analysis method is lacking the sensitivity to identify some members of the core microbiota. However, 138 tonsil samples were

126 analyzed by T-RFLP analysis while a small number of samples was analyzed in both our pyrosequencing (n=6) and that performed by Lowe et al. (2012) (n=12). These samples chosen for pyrosequencing in our study were selected due to their high level of diversity, meaning that they were not typical representatives of the samples. Additionally, the tonsil microbiota of 120 unfit pig samples were examined by T-RFLP analysis with variable disease conditions that could directly or indirectly affect the normal microbiota, leading to more variability between bacterial communities (higher beta-diversity). These reasons suggest that it may be premature to identify a core microbiota and that additional pyrosequencing studies will make the picture clearer.

Both T-RFLP analysis and pyrosequencing produced results that were unable to be correctly identified in their respective databases. Virtually all the samples examined by T-

RFLP analysis had both forward and reverse fragments that were unmatched in both our custom “pig-specific” and the Phusion Bacterial Profiling kit database. Unclassified reads made up 12% of the pyrosequencing results. While the unmatched fragments from T-

RFLP analysis could be a result of an incomplete database, the unclassified reads in the pyrosequencing results were somewhat unexpected. One possibility is that the unmatched fragments and the unclassified reads represent novel organisms in the pig tonsil. Previous work with 8 pig tonsils identified several novel bacterial species from 831 sequences

(Lowe 2011), a number far below the 28768 sequences in our study. These novel bacteria are thought to be unculturable, were not placed into a taxonomic category.

Respiratory diseases continue to be an important part of pig health management, requiring a proper understanding of pathogen prevalence and the strategies to control them. The pig tonsil, known to harbour both bacterial and viral pathogens, is located at

127 the junction of the respiratory and digestive tracts, suggesting that it has a role to play in the development of disease at both of these body sites. Due to the unknown impact that current prophylactic and therapeutic strategies (primarily vaccines and antimicrobials) have on the microbiota of pig tonsil, this site has the potential to play a unique role in the future application of these strategies. The unfit pigs in our study were tested for PRRS virus. Cluster analysis of the T-RFLP analysis results was used to assess for associations between clusters (community type) and presence of PRRS virus. We found that three clusters were significantly associated with the presence or absence of PRRS virus.

Further work comparing PRRS virus positive pigs with PRRS virus negative pigs is needed to confirm these associations and refine our understanding of the mechanisms behind such associations.

The results from both T-RFLP analysis and pyrosequencing show that the tonsil microbiota needs more in-depth studies to gain a clearer picture. The level of diversity observed in our studies suggests that sequencing with a larger sample size would be necessary for this purpose. Although pyrosequencing is still a relatively expensive method, the costs are decreasing and the average read lengths are increasing. Also, recent work by Segata et al. (2012), sequencing the oral cavity as well as tonsils and gut of humans, grouped together the throat, tongue dorsum, and saliva with the tonsil microbiota. Segata et al. (2012) suggested that the saliva acts as the main vehicle to unify the compositions of these body sites. They also suggested that low levels of pathogenic species could be transferred between body sites or even to respiratory sites though the saliva and mucus. Human and pig tonsils share very similar features, a characteristic that can probably be extended to the oral cavity. The features described for the oral cavity of

128 humans might also apply to pigs. The pig tonsils play host to a number of important pig pathogens, both bacterial and viral, and with further sequencing studies could reveal a microbiota that uses saliva as the main vehicle for transfer between body sites.

Another direction of studies that could lead to a clearer picture of the pig tonsil microbiota’s role in health and disease is the analysis of the bacterial metabolome.

Recently, the Human Microbiome Project Consortium (2012) sequenced the bacterial communities at different body sites of humans and found that while the identity and abundance of main bacterial contributors varied between individuals, the metabolic gene carriage across most individuals was stable. These results suggest that identities of the members of the microbiota are not as important because many different bacteria can perform the same function in a community. Although no study has been performed to the scale of the Human Microbiome Project Consortium (2012), the role of the microbial metabolome is well recognized in the literature (Tang. 2011; Russell et al. 2013). This could be an important step for gaining a better understanding of the pig tonsil microbiota.

These directions for further study could be driven by the need for new therapeutic strategies to manage both bacterial and viral pathogens. Gaining a better understanding of

PRRS virus’ role in the development of the tonsil microbiota, performing metagenomic sequencing, and expanding the focus of study to other body sites, could aid in the development of antimicrobial, vaccine, and probiotic strategies.

Our studies conducted with T-RFLP analysis and pyrosequencing have consolidated the foundation needed to pursue these future studies, as the more we studied the pig tonsil microbiota, the more it was discovered to be highly diverse.

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References

Human Microbiome Project Consortium, 2012. Structure, function and diversity of the healthy human microbiome. Nature. 486, 207-214.

Russell, W.R., Duncan, S.H., Flint, H.J., 2013. The gut microbial metabolome: modulation of cancer risk in obese individuals. Proc. Nutr. Soc. 72, 178-188.

Tang, J., 2011. Microbial metabolomics. Curr. Genomics. 12, 391-403.

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Supplementary Table 4.1. Comparison of the prevalence of each putative identification in both unfit and healthy pigs using z-test.

Unfit Pigs (n=120) Healthy Pigs (n=18) Phyla Prevalence % Prevalence % P-Valuea Actinobacteria 65.83 38.89 0.03 Bacteroidetes 70.00 72.22 0.85 Firmicutes 85.00 100.00 0.08 Fusobacteria 52.50 16.67 0.01 Proteobacteria 71.67 72.22 0.96 Spirochaetes 1.67 11.11 0.03 Synergistetes 0.83 44.44 0.00 Tenericutes 43.33 0.00 0.00

Class Prevalence % Prevalence % P-Value Actinobacteridae 65.00 38.89 0.04 Alphaproteobacteria 32.50 50.00 0.15 Bacilli 83.33 100.00 0.06 Bacteroidia 13.33 11.11 0.79 Betaproteobacteria 50.83 44.44 0.61 Clostridia 70.00 77.78 0.50 Coriobacteridae 8.33 5.56 0.69 Cytophagia 59.17 27.78 0.01 Deltaproteobacteria 51.67 61.11 0.46 Epsilonproteobacteria 25.83 11.11 0.17 Erysipelotrichi 5.83 0.00 0.30 Flavobacteriia 64.17 72.22 0.50 Fusobacteriia 34.17 16.67 0.14 Gammaproteobacteria 58.33 61.11 0.82 Mollicutes 43.33 44.44 0.93 Negativicutes 37.50 44.44 0.57 Sphingobacteriia 29.17 16.67 0.27 Spirochaetales 1.67 11.11 0.03 Synergistia 0.83 0.00 0.70

Order Prevalence % Prevalence % P-Value Acholeplasmatales 1.67 0.00 0.58 Actinomycetales 65.00 38.89 0.04 Anaeroplasmatales 1.67 0.00 0.58 Bacillales 59.17 61.11 0.88 Bacteroidales 13.33 11.11 0.79 Burkholderiales 44.17 44.44 0.98 Campylobacterales 25.83 11.11 0.17 Cardiobacteriales 0.83 0.00 0.70 Caulobacterales 13.33 22.22 0.32 Clostridiales 70.00 77.78 0.50 Coriobacteriales 8.33 5.56 0.69 Cytophagales 59.17 27.78 0.01 131

Desulfobacterales 2.50 0.00 0.50 Desulfovibrionales 39.17 55.56 0.19 Desulfuromonadales 36.67 11.11 0.03 Enterobacteriales 23.33 38.89 0.16 Erysipelotrichales 5.83 0.00 0.30 Flavobacteriales 64.17 72.22 0.50 Fusobacteriales 52.50 16.67 0.01 Lactobacillales 80.83 100.00 0.04 Mycoplasmatales 39.17 44.44 0.67 Neisseriales 25.00 22.22 0.80 Pasteurellales 17.50 11.11 0.50 Pseudomonadales 53.33 61.11 0.54 Rhizobiales 31.67 38.89 0.54 Selenomonadales 37.50 44.44 0.57 Sphingobacteriales 29.17 16.67 0.27 Spirochaetales 1.67 11.11 0.03 Synergistales 0.83 0.00 0.70 Unclassified 19.17 22.22 0.76 Deltaproteobacteria Xanthomonadales 1.67 0.00 0.58

Family Prevalence % Prevalence % P-Value Acholeplasmataceae 1.67 0.00 0.58 Acidaminococcaceae 5.00 0.00 0.33 Actinomycetaceae 39.17 11.11 0.02 Aerococcaceae 39.17 50.00 0.38 Alcaligenaceae 38.33 38.89 0.96 Anaeroplasmataceae 1.67 0.00 0.58 Bacillaceae 56.67 55.56 0.93 Bacillales Family XI. 10.83 0.00 0.14 Incertae Sedis Bacteroidaceae 1.67 0.00 0.58 Bradyrhizobiaceae 11.67 22.22 0.22 Burkholderiaceae 7.50 0.00 0.23 Campylobacteriaceae 11.67 0.00 0.13 Cardiobacteriaceae 0.83 0.00 0.70 Carnobacteriaceae 28.33 11.11 0.12 Caulobacteraceae 13.33 22.22 0.32 Cellulomonadaceae 11.67 0.00 0.13 Chitinophagaceae 0.83 0.00 0.70 Clostridiaceae 63.33 61.11 0.86 Clostridiales Family XI. 20.83 55.56 0.00 Incertae Sedis Clostridiales Family XIV. 2.50 0.00 0.50 Incertae Sedis Comamonadaceae 5.83 0.00 0.30 Coriobacteriaceae 8.33 5.56 0.69 Corynebacteriaceae 0.83 0.00 0.70 Cytophagaceae 59.17 27.78 0.01

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Desulfobulbaceae 2.50 0.00 0.50 Desulfovibrionaceae 39.17 55.56 0.19 Enterobacteriaceae 23.33 38.89 0.16 Enterococcaceae 42.50 94.44 0.00 Erysipelotrichaceae 5.83 0.00 0.30 Eubacteriaceae 34.17 50.00 0.20 Flavobacteriaceae 64.17 72.22 0.50 Fusobacteriaceae 51.67 11.11 0.00 Geobacteraceae 36.67 11.11 0.03 Helicobacteraceae 25.83 11.11 0.17 Heliobacteriaceae 5.00 5.56 0.92 Lachnospiraceae 20.00 5.56 0.14 Lactobacillaceae 55.83 50.00 0.64 Leptotrichiaceae 5.00 5.56 0.92 Leuconostocaceae 1.67 0.00 0.58 Listeriaceae 1.67 0.00 0.58 Marinilabiliaceae 6.67 5.56 0.86 Methylobacteriaceae 28.33 38.89 0.36 Microbacteriaceae 2.50 5.56 0.47 Micrococcaceae 53.33 33.33 0.12 Micrococcineae 25.00 16.67 0.44 Moraxellaceae 36.67 22.22 0.23 Mycobacteriaceae 28.33 50.00 0.07 Mycoplasmataceae 42.50 16.67 0.04 Neisseriaceae 25.00 44.44 0.09 Oxalobacteraceae 10.83 5.56 0.49 Paenibacillaceae 38.33 22.22 0.19 Pasteurellaceae 17.50 27.78 0.30 Peptococcaceae 9.17 5.56 0.61 Peptostreptococcaceae 43.33 44.44 0.93 Planococcaceae 1.67 0.00 0.58 Porphyromonadaceae 13.33 11.11 0.79 Propionibacteriaceae 20.83 11.11 0.33 Pseudomonadaceae 52.50 55.56 0.81 Rikenellaceae 5.00 11.11 0.30 Ruminococcaceae 22.50 22.22 0.98 Sphingobacteriaceae 29.17 16.67 0.27 Spirochaetaceae 1.67 11.11 0.03 Staphylococcaceae 25.83 55.56 0.01 Streptococcaceae 73.33 94.44 0.05 Streptomycetaceae 45.00 11.11 0.01 Synergistaceae 0.83 0.00 0.70 Thermoactinomycetaceae 1.67 0.00 0.58 Unclassified Clostridiales 0.83 0.00 0.70 Unclassified 19.17 22.22 0.76 Deltaproteobacteria Veillonellaceae 35.83 44.44 0.48 Xanthomondaceae 0.83 0.00 0.70

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Genus Prevalence % Prevalence % P-Value Abiotrophia 19.17 27.78 0.40 Acholeplasma 1.67 0.00 0.58 Achromobacter 5.83 11.11 0.40 Acidovorax 5.83 0.00 0.30 Acinetobacter 13.33 33.33 0.03 Actinobacillus 4.17 0.00 0.38 Actinomyces 27.50 11.11 0.14 Aerococcus 27.50 33.33 0.61 Alcaligenes 5.00 38.89 0.00 Alistipes 4.17 11.11 0.21 Alkaliphilus 12.50 0.00 0.11 Alysiella 16.67 11.11 0.55 Anaerobranca 2.50 0.00 0.50 Anaerococcus 6.67 0.00 0.26 Anaeroplasma 1.67 0.00 0.58 Arcanobacterium 25.83 0.00 0.02 Arthrobacter 55.00 16.67 0.00 Bacillus 52.50 55.56 0.81 Bacteroides 1.67 0.00 0.58 Bergeyella 6.67 5.56 0.86 Bibersteinia 1.67 0.00 0.58 Bordetella 4.17 5.56 0.79 Brachybacterium 3.33 0.00 0.43 Bradyrhizobium 11.67 22.22 0.22 Brevundimonas 13.33 22.22 0.32 Butyricicoccus 1.67 0.00 0.58 Butyrivibrio 9.17 0.00 0.18 Campylobacter 11.67 0.00 0.13 Capnocytophaga 51.67 50.00 0.90 Carnobacterium 12.50 5.56 0.39 Cellulomonas 14.17 0.00 0.09 Centipeda 8.33 38.89 0.00 Chryseobacterium 10.00 38.89 0.00 Citrobacter 7.50 27.78 0.01 Clostridium 63.33 55.56 0.53 Collinsella 1.67 0.00 0.58 Corynebacterium 0.83 0.00 0.70 Cytophaga 59.17 27.78 0.01 Desulfitobacterium 5.00 5.56 0.92 Desulfobulbus 2.50 0.00 0.50 Desulfosporosinus 1.67 0.00 0.58 Desulfotomaculum 17.50 5.56 0.20 Desulfovibrio 35.00 55.56 0.10 Dialister 3.33 0.00 0.43 Dorea 3.33 0.00 0.43 Elizabethkingia 4.17 33.33 0.00 Enterobacter 10.83 38.89 0.00 Enterococcus 41.67 94.44 0.00

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Epulopiscium 0.83 0.00 0.70 Erwinia 0.83 0.00 0.70 Escherichia 1.67 27.78 0.00 Eubacterium 34.17 50.00 0.20 Faecalibacterium 4.17 0.00 0.38 Finegoldia 0.00 16.67 0.00 Flavobacterium 64.17 66.67 0.84 Fusobacterium 52.50 11.11 0.00 Gemella 10.83 0.00 0.14 Geobacillus 20.00 0.00 0.04 Geobacter 36.67 11.11 0.03 Geopsychrobacter 3.33 0.00 0.43 Gordonibacter 8.33 5.56 0.69 Granulicatella 1.67 5.60 0.29 Haemophilus 5.00 5.56 0.92 Halobacillus 18.33 5.56 0.18 Helcococcus 15.00 33.33 0.06 Helicobacter 25.83 11.11 0.17 Heliobacillus 5.00 5.56 0.92 Heliobacterium 0.83 0.00 0.70 Heliorestis 3.33 0.00 0.43 Herbaspirillum 4.17 0.00 0.38 Herminiimonas 6.67 5.56 0.86 Hespellia 2.50 0.00 0.50 Kingella 9.17 0.00 0.18 Klebsiella 0.83 0.00 0.70 Kocuria 50.00 11.11 0.00 Kurthia 1.67 0.00 0.58 Laceyella 0.83 0.00 0.70 Lachnospira 3.33 0.00 0.43 Lactobacillus 53.33 50.00 0.79 Lactococcus 54.17 61.11 0.58 Lawsonia 8.33 11.11 0.70 Leptotrichia 3.33 5.56 0.64 Leucobacter 6.67 5.56 0.86 Listeria 1.67 0.00 0.58 Lysinibacillus 1.67 0.00 0.58 Macrococcus 16.67 38.89 0.03 Mannheimia 2.50 0.00 0.50 Marinilabilia 6.67 5.56 0.86 Massilia 1.67 0.00 0.58 Megamonas 4.17 0.00 0.38 Megasphaera 12.50 33.33 0.02 Methylobacterium 30.00 38.89 0.45 Microbacterium 16.67 11.11 0.55 Mitsuokella 0.83 16.67 0.00 Moraxella 3.33 0.00 0.43 Morganella 3.33 16.67 0.02 Mycobacterium 27.50 16.67 0.33

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Mycoplasma 42.50 44.44 0.88 Myroides 3.33 0.00 0.43 Neisseria 10.00 11.11 0.88 Odoribacter 6.67 5.56 0.86 Oribacterium 9.17 5.56 0.61 Ornithobacterium 3.33 0.00 0.43 Paenibacillus 38.33 22.22 0.19 Pasteurella 16.67 5.56 0.22 Peptostreptococcus 43.33 44.44 0.93 Phascolarctobacterium 5.00 0.00 0.33 Porphyromonas 5.00 11.11 0.30 Propionibacterium 24.17 11.11 0.22 Proteus 9.17 5.56 0.61 Providencia 6.67 0.00 0.26 Pseudobutyrivibrios 1.67 0.00 0.58 Pseudomonas 52.50 55.56 0.81 Psychrobacter 9.17 33.33 0.00 Psychroflexus 21.67 0.00 0.03 Pyramidobacter 0.83 0.00 0.70 Ralstonia 7.50 0.00 0.23 Riemerella 1.67 0.00 0.58 Robinsoniella 7.50 0.00 0.23 Roseburia 3.33 0.00 0.43 Rothia 14.17 0.00 0.09 Ruminococcus 18.33 22.22 0.69 Salmonella 14.17 33.33 0.04 Selenomonas 32.50 44.44 0.32 Serratia 8.33 16.67 0.26 Shigella 1.67 5.56 0.29 Soehngenia 7.50 5.56 0.77 Sphingobacterium 20.00 16.67 0.74 Spirobacillus 15.00 22.22 0.44 Staphylococcus 25.83 55.56 0.01 Stenotrophomonas 1.67 0.00 0.58 Streptobacillus 3.33 5.56 0.64 Streptococcus 69.17 94.44 0.03 Streptomyces 45.00 11.11 0.01 Suttonella 0.83 0.00 0.70 Tepidimicrobium 16.67 22.22 0.56 Terrimonas 0.83 0.00 0.70 Tetragenococcus 5.83 5.56 0.96 Tetrathiobacter 32.50 22.22 0.38 Thermoactinomyces 1.67 0.00 0.58 Tissierella 5.00 22.22 0.01 Treponema 1.67 11.11 0.03 Trichococcus 11.67 0.00 0.13 Turicibacter 5.83 0.00 0.30 Vagococcus 7.50 0.00 0.23 Veillonella 9.17 38.89 0.00

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Virgibacillus 20.83 0.00 0.03 Wautersiella 3.33 0.00 0.43 Weissella 1.67 0.00 0.58 Yersinia 3.33 38.89 0.00 a significant differences is p-value > 0.05.

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Supplementary Table 4.1. Descriptive statistics showing the proportion of genera found in each cluster.

Genus Total Detection frequency in cluster %: I(n=6) II(n=22) III(n=6) IV(n=5) V(n=16) VI(n=31) VII(n=5) VIII(n=3) IX(n=3) X(n=4) Streptococcus 77.2 0 100 100 40 100.0 83.9 100 33.3 0 0 Clostridium 69.3 0 72.7 33.3 40 81.3 96.8 80 100 0 0 Flavobacterium 68.3 0 72.7 16.7 20 10 90.3 80 66.7 0 25 Cytophaga 63.4 0 90.9 16.7 20 93.8 71 60 33.3 0 25 Lactococcus 59.4 0 68.2 33.3 0 100 74.2 40 0 0 50 Pseudomonas 58.4 16.7 68.2 33.3 20 93.8 64.5 40 33.3 0 50 Arthrobacter 57.4 0 50 0 40 75 80.6 100 100 0 0 Lactobacillus 57.4 0 54.5 16.7 20 81.3 87.1 40 66.7 0 0 Bacillus 56.4 0 72.7 50 20 75 67.7 60 33.3 0 0 Fusobacterium 56.4 0 45.5 0 40 68.8 90.3 100 33.3 0 0 Capnocytophaga 55.4 0 40.9 16.7 20 87.5 83.9 80 33.3 0 0 Kocuria 53.5 0 45.5 0 40 75 71 100 100 0 0 Peptostreptococcus 48.5 0 40.9 16.7 0 62.5 77.4 60 66.7 0 0 Streptomyces 48.5 0 54.5 0 20 81.3 64.5 40 33.3 0 0 Enterococcus 47.5 0 68.2 0 20 93.8 48.4 40 0 0 0 Mycoplasma 47.5 0 54.5 0 0 81.3 67.7 40 0.0 0 0 Paenibacillus 41.6 0 40.9 16.7 0 62.5 61.3 40 33.3 0 0 Eubacterium 39.6 0 27.3 16.7 0 56.3 71 40 0 0 0 Geobacter 39.6 0 31.8 0 20 75 54.8 40 33.3 0 0 Desulfovibrio 38.6 0 45.5 0 0 50 58.1 60 0 0 0 Selenomonas 36.6 0 50 0 0 56.3 48.4 40 0 0 0 Methylobacterium 34.7 0 27.3 16.7 0 62.5 51.6 40 0 0 0 Tetrathiobacter 34.7 0 54.5 0.0 20 68.8 29 40 0 0 0 Mycobacterium 30.7 0 18.2 0.0 0 62.5 48.4 40 0 0 0 Aerococcus 29.7 0 27.3 16.7 20 62.5 35.5 0 33.3 0 0 Helicobacter 29.7 0 31.8 0.0 20 37.5 41.9 40 33.3 0 0 Actinomyces 28.7 0 27.3 0.0 0 25.0 54.8 40 0 0 0 Arcanobacterium 26.7 0 27.3 0.0 20 12.5 41.9 100 0 0 0 Propionibacterium 24.8 0 13.6 16.7 0 31.3 41.9 60 0 0 0

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Genus Total I(n=6) II(n=22) III(n=6) IV(n=5) V(n=16) VI(n=31) VII(n=5) VIII(n=3) IX(n=3) X(n=4) Staphylococcus 24.8 0 27.3 16.7 0 31.3 29.0 80.0 0.0 0 0 Virgibacillus 24.8 0 22.7 16.7 20 37.5 35.5 0.0 33.3 0 0 Psychroflexus 22.8 0 22.7 0.0 20 31.3 29.0 40.0 33.3 0 0 Abiotrophia 21.8 0 27.3 0.0 0 18.8 35.5 20.0 0.0 0 25 Halobacillus 21.8 0 18.2 16.7 20 37.5 29.0 0.0 33.3 0 0 Ruminococcus 20.8 0 18.2 0.0 0 25.0 35.5 20.0 0.0 0 25 Sphingobacterium 20.8 0 9.1 16.7 0 31.3 35.5 40.0 0.0 0 0 Desulfotomaculum 19.8 0 18.2 0.0 0 18.8 35.5 40.0 0.0 0 0 Geobacillus 18.8 0 22.7 16.7 0 0.0 32.3 60.0 0.0 0 0 Tepidimicrobium 18.8 0 4.5 16.7 0 25.0 35.5 40.0 0.0 0 0 Alysiella 17.8 0 13.6 16.7 20 37.5 19.4 0.0 33.3 0 0 Pasteurella 17.8 0 13.6 0.0 0 18.8 35.5 0.0 33.3 0 0 Spirobacillus 17.8 0 13.6 0.0 0 18.8 25.8 80.0 0.0 0 0 Macrococcus 16.8 0 13.6 0.0 0 31.3 19.4 60.0 0.0 0 0 Microbacterium 16.8 0 13.6 0.0 0 6.3 29.0 80.0 0.0 0 0 Rothia 16.8 0 4.5 0.0 0 12.5 38.7 20.0 33.3 0 0 Alkaliphilus 14.9 0 4.5 0.0 0 18.8 25.8 40.0 33.3 0 0 Brevundimonas 14.9 0 31.8 16.7 0 12.5 16.1 0.0 0.0 0 0 Helcococcus 14.9 0 4.5 16.7 0 31.3 22.6 20.0 0.0 0 0 Salmonella 14.9 0 31.8 33.3 0 12.5 12.9 0.0 0.0 0 0 Acinetobacter 13.9 0 31.8 16.7 0 12.5 12.9 0.0 0.0 0 0 Campylobacter 13.9 0 22.7 0.0 0 12.5 19.4 0.0 33.3 0 0 Carnobacterium 13.9 0 36.4 0.0 0 18.8 9.7 0.0 0.0 0 0 Cellulomonas 13.9 0 4.5 16.7 0 12.5 29.0 0.0 33.3 0 0 Trichococcus 13.9 0 9.1 0.0 0 25.0 22.6 0.0 33.3 0 0 Gemella 12.9 0 27.3 0.0 0 12.5 9.7 40.0 0.0 0 0 Megasphaera 12.9 0 27.3 0.0 0 12.5 16.1 0.0 0.0 0 0 Bradyrhizobium 11.9 0 22.7 16.7 0 12.5 12.9 0.0 0.0 0 0 Enterobacter 11.9 0 27.3 0.0 0 12.5 12.9 0.0 0.0 0 0 Neisseria 11.9 0 13.6 0.0 0 12.5 19.4 20.0 0.0 0 0

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Genus Total I(n=6) II(n=22) III(n=6) IV(n=5) V(n=16) VI(n=31) VII(n=5) VIII(n=3) IX(n=3) X(n=4) Kingella 10.9 0 4.5 0.0 0 18.8 19.4 20.0 0.0 0 0 Oribacterium 10.9 0 4.5 0.0 0 12.5 19.4 40.0 0.0 0 0 Proteus 10.9 0 4.5 0.0 0 31.3 12.9 20.0 0.0 0 0 Psychrobacter 10.9 0 9.1 0.0 0 18.8 19.4 0.0 0.0 0 0 Butyrivibrio 9.9 0 9.1 16.7 0 0.0 22.6 0.0 0.0 0 0 Chryseobacterium 9.9 0 4.5 0.0 0 12.5 12.9 20.0 33.3 0 25 Centipeda 8.9 0 22.7 0.0 0 12.5 6.5 0.0 0.0 0 0 Gordonibacter 8.9 0 31.8 0.0 0 0.0 6.5 0.0 0.0 0 0 Lawsonia 8.9 0 13.6 0.0 0 25.0 6.5 0.0 0.0 0 0 Robinsoniella 8.9 0 9.1 16.7 0 0.0 19.4 0.0 0.0 0 0 Serratia 8.9 0 13.6 0.0 0 12.5 12.9 0.0 0.0 0 0 Soehngenia 8.9 0 4.5 0.0 0 12.5 16.1 0.0 33.3 0 0 Vagococcus 8.9 0 18.2 0.0 0 12.5 9.7 0.0 0.0 0 0 Veillonella 8.9 0 22.7 0.0 0 12.5 6.5 0.0 0.0 0 0 Alistipes 7.9 0 4.5 0.0 0 0.0 22.6 0.0 0.0 0 0 Anaerococcus 7.9 0 0.0 0.0 0 6.3 19.4 0.0 33.3 0 0 Bergeyella 7.9 0 0.0 0.0 0 12.5 16.1 20.0 0.0 0 0 Citrobacter 7.9 0 9.1 0.0 0 12.5 12.9 0.0 0.0 0 0 Herminiimonas 7.9 0 4.5 0.0 0 6.3 16.1 20.0 0.0 0 0 Marinilabilia 7.9 0 0.0 0.0 0 12.5 12.9 40.0 0.0 0 0 Odoribacter 7.9 0 0.0 0.0 0 12.5 12.9 40.0 0 0 0 Providencia 7.9 0 9.1 0.0 0 25.0 6.5 0.0 0 0 0 Ralstonia 7.9 0 13.6 0.0 0 12.5 9.7 0.0 0 0 0 Acidovorax 6.9 0 4.5 0.0 0 6.3 12.9 20.0 0 0 0 Tetragenococcus 6.9 0 4.5 0.0 0 12.5 12.9 0.0 0 0 0 Turicibacter 6.9 0 4.5 0.0 0 12.5 12.9 0.0 0 0 0 Achromobacter 5.9 0 9.1 0.0 0 6.3 9.7 0.0 0 0 0 Alcaligenes 5.9 0 0.0 0.0 0 0.0 16.1 20.0 0 0 0 Haemophilus 5.9 0 4.5 0.0 0 12.5 9.7 0.0 0 0 0 Heliobacillus 5.9 0 0.0 0.0 0 0.0 19.4 0.0 0 0 0

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Genus Total I(n=6) II(n=22) III(n=6) IV(n=5) V(n=16) VI(n=31) VII(n=5) VIII(n=3) IX(n=3) X(n=4) Leucobacter 5.9 0 0.0 16.7 0 6.3 12.9 0.0 0 0 0 Phascolarctobacterium 5.9 0 0.0 0.0 0 6.3 12.9 20.0 0 0 0 Actinobacillus 5.0 0 0.0 0.0 0 6.3 9.7 20.0 0 0 0 Bordetella 5.0 0 4.5 0.0 0 6.3 9.7 0.0 0 0 0 Desulfitobacterium 5.0 0 4.5 0.0 0 6.3 9.7 0.0 0 0 0 Herbaspirillum 5.0 0 4.5 0.0 0 12.5 6.5 0.0 0 0 0 Megamonas 5.0 0 4.5 0.0 0 0.0 12.9 0.0 0 0 0 Porphyromonas 5.0 0 0.0 0.0 0 0.0 16.1 0.0 0 0 0 Tissierella 5.0 0 0.0 0.0 0 12.5 9.7 0.0 0 0 0 Dialister 4.0 0 18.2 0.0 0 0.0 0.0 0.0 0 0 0 Dorea 4.0 0 0.0 0.0 0 6.3 6.5 20.0 0 0 0 Elizabethkingia 4.0 0 0.0 0.0 0 6.3 6.5 20.0 0 0 0 Faecalibacterium 4.0 0 4.5 0.0 0 0.0 9.7 0.0 0 0 0 Geopsychrobacter 4.0 0 0.0 0.0 0 12.5 3.2 20.0 0 0 0 Heliorestis 4.0 0 4.5 0.0 0 6.3 6.5 0.0 0 0 0 Leptotrichia 4.0 0 0.0 0.0 0 0.0 12.9 0.0 0 0 0 Moraxella 4.0 0 0.0 0.0 0 0.0 9.7 20.0 0 0 0 Morganella 4.0 0 4.5 0 0 6.3 6.5 0.0 0.0 0 0 Roseburia 4.0 0 4.5 0 0 0.0 9.7 0.0 0.0 0 0 Streptobacillus 4.0 0 0.0 0 0 0.0 12.9 0.0 0.0 0 0 Yersinia 4.0 0 18.2 0 0 0.0 0.0 0.0 0.0 0 0 Anaerobranca 3.0 0 0.0 0 0 0.0 9.7 0.0 0.0 0 0 Brachybacterium 3.0 0 0.0 0 0 6.3 6.5 0.0 0.0 0 0 Desulfobulbus 3.0 0 9.1 0 0 0.0 0.0 0.0 33.3 0 0 Hespellia 3.0 0 0.0 0 0 6.3 3.2 20.0 0.0 0 0 Lachnospira 3.0 0 4.5 0 0 0.0 6.5 0.0 0.0 0 0 Mannheimia 3.0 0 0.0 0 0 0.0 6.5 20.0 0.0 0 0 Acholeplasma 2.0 0 0.0 0 0 0.0 6.5 0.0 0.0 0 0 Anaeroplasma 2.0 0 0.0 0 0 6.3 3.2 0.0 0.0 0 0 Bacteroides 2.0 0 0.0 0 0 6.3 3.2 0.0 0.0 0 0

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Genus Total I(n=6) II(n=22) III(n=6) IV(n=5) V(n=16) VI(n=31) VII(n=5) VIII(n=3) IX(n=3) X(n=4) Bibersteinia 2.0 0 0.0 0 0 6.3 3.2 0.0 0.0 0 0 Butyricicoccus 2.0 0 9.1 0 0 0.0 0.0 0.0 0.0 0 0 Collinsella 2.0 0 0.0 0 0 0.0 6.5 0.0 0.0 0 0 Desulfosporosinus 2.0 0 0.0 0 0 0.0 6.5 0.0 0.0 0 0 Escherichia 2.0 0 4.5 0 0 0.0 3.2 0.0 0.0 0 0 Granulicatella 2.0 0 9.1 0 0 0.0 0.0 0.0 0.0 0 0 Kurthia 2.0 0 0.0 0 0 6.3 3.2 0.0 0.0 0 0 Listeria 2.0 0 0.0 0 0 6.3 3.2 0.0 0.0 0 0 Lysinibacillus 2.0 0 0.0 0 0 6.3 3.2 0.0 0.0 0 0 Massilia 2.0 0 4.5 0 0 0.0 3.2 0.0 0.0 0 0 Myroides 2.0 0 4.5 0 0 6.3 0.0 0.0 0.0 0 0 Ornithobacterium 2.0 0 4.5 0 0 6.3 0.0 0.0 0.0 0 0 Weissella 2.0 0 4.5 0 0 6.3 0.0 0.0 0.0 0 0 Pseudobutyrivibrios 2.0 0 0.0 0 0 0.0 6.5 0.0 0.0 0 0 Riemerella 2.0 0 0.0 0 0 0.0 3.2 20.0 0 0 0 Shigella 2.0 0 4.5 0 0 0.0 3.2 0.0 0 0 0 Stenotrophomonas 2.0 0 0.0 0 0 6.3 3.2 0.0 0 0 0 Treponema 2.0 0 0.0 0 0 0.0 6.5 0.0 0 0 0 Wautersiella 2.0 0 4.5 0 0 6.3 0.0 0.0 0 0 0 Weissella 2.0 0 4.5 0 0 6.3 0.0 0.0 0 0 0 Corynebacterium 1.0 0 0.0 0 0 6.3 0.0 0.0 0 0 0 Epulopiscium 1.0 0 0.0 0 0 0.0 3.2 0.0 0 0 0 Erwinia 1.0 0 4.5 0 0 0.0 0.0 0.0 0 0 0 Heliobacterium 1.0 0 0.0 0 0 0.0 3.2 0.0 0 0 0 Klebsiella 1.0 0 4.5 0 0 0.0 0.0 0.0 0 0 0 Laceyella 1.0 0 4.5 0 0 0.0 0.0 0.0 0 0 0 Pyramidobacter 1.0 0 0.0 0 0 0.0 3.2 0.0 0 0 0 Suttonella 1.0 0 0.0 0 0 0.0 3.2 0.0 0 0 0 Terrimonas 1.0 0 0.0 0 0 0.0 3.2 0.0 0 0 0 Thermoactinomyces 1.0 0 4.5 0 0 0.0 0.0 0.0 0 0 0

142