Studies on the Impact of Probiotic Bacteria on Enteric Microbial Diversity and Immune Response

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Studies on the impact of probiotic bacteria on enteric microbial diversity and immune response

Xi-Yang Wu University of Wollongong

Wu, Xi-Yang, Studies on the impact of probiotic bacteria on enteric microbial diversity and immune response, PhD thesis, School of Biological Sciences University of Wollongong, 2006. http://ro.uow.edu.au/theses/596

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STUDIES ON THE IMPACT OF PROBIOTIC BACTERIA ON ENTERIC MICROBIAL DIVERSITY AND IMMUNE RESPONSE

A thesis submitted in fulfilment of the requirements for the award of the degree of

Doctor of Philosophy in Biological Sciences

from

UNIVERSITY OF WOLLONGONG

Xi-Yang Wu (Master of Science)

School of Biological Sciences University of Wollongong Australia

2006

Abstract

The mechanism of action of probiotics is based on competitive exclusion and immune modulation. However, the literature is scant on supporting data because of the failure to adopt a systems approach to probiotic functionality. This has been partially addressed in this thesis by taking into consideration the tripartite interaction between bacteria and bacteria in the enteric community; between bacteria and the host animal and finally, between the host immune response (innate or acquired) on the plethora of microbes that inhabit the gastrointestinal tract.

A trial involving newly inducted cattle in a feedlot, formed the basis of initial attempts to assess the benefits of a commercial probiotic formulation – Protexin on intestinal health by enumeration of a select subset of cultivable bacteria species and by assessment of immune modulation. The results failed to demonstrate a significant change in the population dynamics of cultured faecal microbes but did show that Protexin stimulated immune responsiveness in T cells. Carcass analysis demonstrated a significant reduction in marbling or intramuscular fat deposition.

In the course of examining the faecal microflora from feedlot cattle, the presence of high levels of Bacillus spores suggested that one possible reason for the lack of a growth benefit may be attributed to a high endogenous level of bacilli. Since there were no reliable methodologies for identifying Bacillus species, an alternative procedure was developed involving amplified ribosomal DNA restriction analysis (ARDRA). With this protocol, we were able to show that cattle faeces contained large numbers of Bacillus spores representing different mesophilic species, where B. subtilis, B. licheniformis and B. clausii dominated.

The presence of a stable population of coliforms in cattle faeces that was not altered by probiotic feeding highlighted the importance of developing better techniques to characterise diversity in E. coli, a potential food-borne pathogen of economic significance to the cattle industry. The use of virulence genes to genotype coliforms provided a method for differentiating between pathogenic, clinical and commensal

i isolates of E. coli. Altogether, a combination of uni- and multiplex PCR assays was developed to screen for 50 virulence genes (VGs) from 8 pathotypes of E. coli. There was a significant association between phylogroupings and VG ownership. This result showed clearly that the lack of or possession of VGs in member isolates of each phylogenetic group can be used to assess diversity and potential pathogenesis of E. coli.

To understand better the importance of pathogenic enteric coliforms, an alternative animal model involving pigs with post-weaning diarrhoea was used to investigate the relationship between pathogenicity and commensalism by VG profiling. Porcine enterotoxigenic E. coli (ETEC) were found to carry VGs identified in E. coli that cause extraintestinal infection. Furthermore, by using the appropriate methods of statistical analysis, VG profiling had the capacity to predict the pathogenic and commensal status of individual clones. By developing the capacity to rapidly characterise and genotype virulence and commensalism in E. coli, it is now feasible to examine how probiotic feeding can modulate the population dynamics of different community members in pigs with enteric disease, as well as changes in the coliform populations.

Finally, another arm of the tripartite interaction involving bacteria and host interaction was modelled in vitro by examining the primary signalling events between bacteria and intestinal epithelial cells. These investigations focused on the judicious selection of T84 as the reporter intestinal epithelial cell line because of low level expression of inflammatory transcripts from 6 other epithelial cell lines. Using a panel of coliforms genotyped for virulence or lack of virulence, the signalling events that followed on from the primary interaction between bacterium and cell, showed there was a lack of correlation between VGs and gene activation. Nonetheless, all the coliform strains tested varied in their capacity to signal transduce T84, confirming that this differential bioactivity can be exploited in the ranking of candidate probiotic strains. The differential responses seen with different E. coli strains and the lower and more consistent activation patterns recorded by LABs for both cytokine and chemokine gene activation, demonstrate that a semi-quantitative ranking of microbial bioactivity can be obtained. Such an approach if adopted in conjunction with an even wider panel of genes in a standardised in vitro environment can provide invaluable information on the selection of appropriate strains to be further tested in vivo.

Certification

I, Xi-Yang Wu, declare that this thesis, submitted in fulfilment of the requirement for the award of Doctor of Philosophy, in the School of Biological Sciences, University of Wollongong, is wholly my own work unless otherwise referenced or acknowledged. The document has not been submitted for qualification at any other academic institution

Xi-Yang Wu

Signature: ………………………………….

Date: ………………………………………...

iii Acknowledgements

I would like to deeply thank my supervisor, A/Prof. James Chin at Elizabeth Macarthur Agricultural Institute, NSW Department of Primary Industry, for his outstanding knowledge, encouragement, guidance and constructive advice throughout this study.

I am very grateful to Prof. Mark Walker, School of Biological Science, University of Wollongong, for his supervision and attention which have contributed to the success of this study.

I express my great gratitude to Chris Lawlor and Kevin Healey, International Animal Health, Pty. Ltd., for their invaluable advice and long-term support.

I acknowledge Dr. Barbara Vanselow and CRC Beef Quality for the sustainment during my work.

Especially thankful to Dr. Darren Trott, Dr. Michael Hornitzky and Dr. Steve Djordjevic for their scientific guidance.

I offer my warmest thanks to Toni Chapman, Bernadette Turner, Jannine Petterson, Sameer Dixit and all the members at the ‘JC’s Group’ for the friendship and assistance.

Very special thanks to my parents and my wife, Xiaoyin Wang, for your endless love, much needed understanding and unconditional support ever since then.

Last, but not least my lovely daughter Bethany, for all the happiness in the world.

iv List of Publications

This thesis is based on the following articles

Xi-Yang Wu, Mark J Walker, Michael Hornitzky and James Chin (2006): Identification of Bacillus species from environmental sources using amplified ribosomal DNA restriction analysis (ARDRA). Journal of Microbiological Methods 64: 107-119

Xi-Yang Wu, James Chin, Aida Ghalayini and Michael Hornitzky (2005): Pulsed-field gel electrophoresis typing and oxytetracycline sensitivity of Paenibacillus larvae subsp. larvae isolates of Australian origin and those recovered from honey imported from Argentina. Journal of Apiculture Research 44(2): 87-92

Xi-Yang Wu, Mark Walker, Barbara Vanselow, Ri-Liang Chao and James Chin (2006): Characterization of mesophilic bacilli in faeces of feedlot cattle. Journal of Applied Microbiology (Published article online: 15-Aug-2006)

Toni Chapman, Xi-Yang Wu, Idris Barchia, Karl Bettelheim, Steven Driesen, Darren Trott, Mark Wilson and James J-C Chin (2006): A comparison of virulence gene profile between E. coli strains isolated from healthy and diarrheic swines. Applied & Environmental Microbiology 72(7):4782-95

Do T, Stephens C, Townsend K, Wu X-Y, Chapman T, Chin J, Mccormick B, Bara M and Trott DT (2005): Rapid identification of virulence genes in enterotoxigenic Escherichia coli isolates associated with diarrhoea in Queensland piggeries. Australian Veterinary Journal 83: 25-31

S. M. Dixit, D. M. Gordon, X-Y. Wu, T. Chapman, K. Kailasapathy and J. J.-C. Chin (2004): Diversity analysis of commensal porcine Escherichia coli - associations between genotypes and habitat in the porcine gastrointestinal tract. Microbiology 150: 1735-1740

v X-Y. Wu, T. Chapman, D. Gordon, D.N. Thuy, S. Driesen, M. Walker and J. Chin (2003): Molecular virulence gene typing of clinical E. coli isolates from pigs with postweaning diarrhoea. In: Paterson, J.E. (ed). "Manipulating pig production IX", pp59. Proceedings of the ninth biennial conference of the Australasian pig science association (Inc.)

X-Y. Wu, T. Chapman, M-K, Tan, D. Trott, M. Walker and J. Chin (2003): Comparison of pig enterotoxigenic E. coli isolates by pulsed-field gel electrophoresis (PFGE). In: Paterson, J.E. (ed). "Manipulating pig production IX", pp68. Proceedings of the ninth biennial conference of the Australasian pig science association (Inc.)

T. Chapman, X-Y. Wu, D. Broek, D. Jordan, M. Wilson and J. Chin (2003): Haemolytic bacterial population analysis in rectal swabs from healthy and scouring neonates. In: Paterson, J.E. (ed). "Manipulating pig production IX", pp35. Proceedings of the ninth biennial conference of the Australasian pig science association (Inc.)

T. Chapman, X-Y. Wu, R. Smith, D. Broek, D. Jordan, M. Wilson and J. Chin (2003): Population analysis of haemolytic bacteria in rectal swabs from healthy and scouring weaners. In: Paterson, J.E. (ed). "Manipulating pig production IX", pp29. Proceedings of the ninth biennial conference of the Australasian pig science association (Inc.)

X-Y. Wu, T. Chapman, D. Trott, D.N. Thuy, S. Driesen, M. Walker and J. Chin (2006): Virulence gene profiling of enterotoxigenic Escherichia coli isolates associated with post-weaning diarrhoea in pigs. Applied & Environmental Microbiology (In press)

X-Y. Wu, T. Chapman, D. Gordon, K. Bettelheim, M. Walker and J. Chin (2006): Validation of Escherichia coli phylogenetic assignments based on virulence gene ownership (submitted to Microbiology)

James Chin, Toni Chapman and Xi-Yang Wu (2006): A re-appraisal of early signalling events and gene activation in a human intestinal epithelial cell line in vitro by probiotic and enteric pathogenic bacteria species (Manuscript)

vi Table of Contents

Frontispiece Abstract …………………………………………………………………….…. i Certification ……………………………………………………………….…. iii Acknowledgements ………………………………………………………… iv List of Publications ………………………………………………………… v Table of Contents …………………………………………………………... vii List of Tables ………………………………………………………………... xiv List of Figures …………………………………………………………….… xvi Abbreviations ………………………………………………………...... xix

Chapter 1 – Literature Review ……………………………………...... 1 1.1 What are probiotics? …………………………………………….….. 2 1.1.1 Definition of probiotics……………………………………………….… 2 1.1.2 Use of probiotics as alternatives to growth promotants for intensive animal industries …………………………………………………….…. 2 1.1.3 Selection criteria for a probiotic strain………………………………….. 6 1.1.4 Safety assessment of probiotics - the accurate identification of probiotic strains…………………………………………………………………… 8 1.1.5 Bacillus probiotic strains…………………………………………….…. 11 1.1.6 Escherichia coli (E. coli) probiotic strains...... 14 1.1.7 Mechanisms of probiotic action/efficacy……………………………..… 17 1.1.7.1 Adhesion and competitive exclusion…………………………….… 17 1.1.7.2 Immunomodulation………………………………………………... 19

vii 1.2 Intestinal microbial diversity……………………………………….. 19 1.2.1 Intestinal microbial ecosystem………………………………………..… 19 1.2.2 Traditional approaches for the analysis microbial diversity……….….... 22 1.2.3 Molecular techniques used to investigate gut microflora diversity….…. 23 1.3 Virulence genes versus adaptive genes – the difference between commensals and pathogens……………………………… 27 1.3.1 Definition of virulence genes and adaptive genes…………………….... 27 1.3.2 Possession of virulence genes in E. coli…………………………………….. 30 1.3.3 E. coli phylogeny and diversity……………………………………….... 33 1.3.4 Detection of virulence genes………………………………………….… 35 1.4 Host-bacteria interactions………………………………………….... 36 1.4.1 The structure of the gut epithelium……………………………………… 36 1.4.2 The role of enterocytes in intestinal barrier integrity………………….... 38 1.4.3 Inflammatory response in the gut…………………………………..…... 40 1.4.4 Immunomodulative function of probiotics……………………………… 42 1.5 Thesis objectives……………………………………………………….. 44

Chapter 2 – Materials and Methods…………………………………….. 46 2.1 List of ATCC and commercially sourced bacteria strains …. 47 2.2 Bacterial culture media………………………………………...... 48 2.2.1 Non-selective bacterial culture media………………………………..…. 48 2.2.2 Selective culture media (SCM)………………………………………..… 49 2.3 Cell culture media……………………………………….…………..… 50 2.4 Bacterial growth and storage……………………………………….. 50 2.4.1 Recovery of bacteria from environmental samples…………………….... 50 2.4.2 Freezedown storage…………………………………………………….... 50 2.4.3 Microbead storage……………………………………………………..… 51 2.5 Bacterial counting………………………………………………….….. 51

viii 2.6 Cell culture and storage…………………………………………….... 51 2.6.1 Revival and growth of cell Lines from frozen storage…………………... 51 2.6.2 Maintenance and subculture of cells…………………………………….. 52 2.6.3 Preparation of frozen stocks………………………………..…………… 52 2.7 DNA and RNA extractions………………………………………….. 53 2.7.1 Bacterial genomic DNA extraction…………………………………..…. 53 2.7.2 RNA extraction………………………………………………………….. 54 2.8 PCR and gel electrophoresis………………………………………... 54 2.9 DNA cloning and sequencing…………………………………….…. 55 2.9.1 Competent cell preparation………………………………………..……. 55 2.9.2 Ligation and transformation…………………………………………..... 55 2.9.3 Plasmid DNA extraction and sequencing……………………………..... 56 2.10 Statistic analysis………………………………………………………. 56

Chapter 3 – Field Assessment of the Function of a Panel of Probiotic Strains in Feedlot Cattle…………………………………….… 57 3.1 Introduction……………………………………………………….……. 58 3.2 Materials and methods…………………………………………..…… 59 3.2.1 Description of the experimental substance ……………………………... 59 3.2.2 Trial farm and animals ………………………………………………….. 60 3.2.3 Experimental design………………………………………………..…… 61 3.2.4 Feeding regime……………………………………………………..……. 61 3.2.5 Faecal flora determination…………………………………………..….. 62 3.2.6 Assessment of immune responses…………………………………..…... 63 3.2.7 Statistical analysis…………………………………………………..…… 63 3.3 Results…………………………………………………………………….. 64 3.3.1 Faecal bacterial enumeration using culture media…………………….... 64 3.3.2 Immune response…………………………………………………..….… 65

ix 3.3.3 Animal growth performance……………………………………………. 65 3.4 Discussion………………………………………………………………… 70

Chapter 4 – Bacillus as Candidate Probiotics – Molecular Characterisation………………………………………………………………. 74 4.1 Introduction…………………………………………………..……..…… 75 4.2 Materials and methods……………………………………………….. 78 4.2.1 Reference strains and culture conditions …………………………..…… 78 4.2.2 Morphological and biochemical identification of Bacillus strains………. 78 4.2.3 DNA isolation……………………………………………………….…... 78 4.2.4 Primer design and PCR amplification…………………………………… 79 4.2.5 ARDRA analysis of PCR amplicons………………………………….... 83 4.2.6 Bacillus spore isolation from environmental sources…………………... 83 4.2.7 Pulsed-field gel electrophoresis (PFGE) for Bacillus isolates…………... 84 4.2.8 DNA sequence analysis………………………………………………..… 84 4.3 Results…………………………………………………………………….. 84 4.3.1 Selection and validation of a Bacillus specific forward and reverse primer set………………………………………………..………………. 84 4.3.2 Genus-specific amplification of reference Bacillus strains……….…….. 87 4.3.3 Speciation of reference Bacillus strains by ARDRA……………….…… 87 4.3.4 Validation of ARDRA by speciation of probiotic strains from commercial sources………………………………………………..……. 90 4.3.5 Identification of test Bacillus isolates from environmental sources…..… 90 4.3.6 Determination of the intraspecies clonal variation of B. licheniformis by PFGE……………………………………………………………..……... 93 4.4 Discussion………………………………………………………………… 95

x Chapter 5 – Validation of Escherichia coli Phylogenetic 102 Assignments Based on Virulence Gene Ownership…………….……. 5.1 Introduction………………………………………….………..………… 103 5.2 Materials and methods………………………………………….…..... 105 5.2.1 Bacterial strains and clinical characteristics…………………………..… 105 5.2.2 Serotyping……………………………………………………………..… 105 5.2.3 Phylogenetic grouping…………………………………………………… 105 5.2.4 Virulence genotypes…………………………………………………..… 108 5.2.5 Pulsed-field gel electrophoresis (PFGE) for coliforms…………………. 111 5.2.6 Biometric analysis…………………………………………………..…… 111 5.3 Results…………………………………………………………………….. 112 5.3.1 Validation of primer set for virulence gene detection…………….…….. 112 5.3.2 Phylogenetic status of E. coli isolates…………………………………… 112 5.3.3 Presence of virulence genes……………………………………………… 114 5.3.4 Prevalence of significant virulence genes in isolates belonging to different phylogroups………………………………………………..….. 115 5.3.5 Phylogenetic distribution of virulence genes…………………………… 116 5.3.6 Principal coordinate (PCO) analysis……………………………………. 116 5.3.7 Phylogenetic grouping based on PFGE DNA fingerprinting………….... 117 5.4 Discussion………………………………………………………………… 122

Chapter 6 – Comparative Analysis of Virulence Genes, Genetic Diversity and Phylogeny Between Commensal and Enterotoxigenic E. coli from Weaned Pigs………………………..….... 128 6.1 Introduction………………………………………………………….…. 129 6.2 Materials and methods………………………………………….….... 131 6.2.1 E. coli strains used in this study…………………………………………. 131 6.2.2 Serotyping porcine E. coli isolates…………………………………...…. 131

xi 6.2.3 Detection of virulence genes and rapid phylogenetic analysis by PCR…………………………………………………………………….... 131 6.2.4 Statistic analysis……………………………………………………….... 132 6.3 Results…………………………………………………………………..... 133 6.3.1 Prevalence of virulence genes in porcine E. coli PWD and commensal isolates……………………………………………………………….….. 133 6.3.2 Phylogenetic group analysis of porcine E. coli PWD and commensal 134 isolates…………………………………………………………………… 6.3.3 Clustering analysis of porcine E. coli based on virulence gene attributes…………………………………………………..…………….. 135 6.3.4 PFGE analysis of PWD isolates…………………………………………. 135 6.3.5 Analysis of porcine commensal isolates by PFGE and serotyping…….... 136 6.4 Discussion……………………………………………………….…..…… 139

Chapter 7 – A Re-appraisal of Early Signalling Events and Gene Activation in a Human Intestinal Epithelial Cell Line in Vitro by Probiotic and Enteric Pathogenic Bacteria…………………………….. 146 7.1 Introduction……………………………………………………………... 147 7.2 Materials and Methods………………………………………….…..... 148 7.2.1 Growth of human cell lines ………………………………………...…… 148 7.2.2 Preparation of bacteria ………………………………………………….. 151 7.2.3 Bacteria-cell interaction…………………………………………..…….. 151 7.2.4 Detection of gene activation by semi-quantitative reverse transcriptase- PCR (RT-PCR)……………………………………………………….…. 152 7.2.4.1 cDNA synthesis………………………………………..……………. 152 7.2.4.2 PCR with gene-specific primers…………………………………….. 152 7.2.5 Detection of NF-κB activation by electrophoretic mobility shift assay (EMSA)………………………………………………………………..... 154

xii 7.2.5.1 DNA probe labelling………………………………………….……. 154 7.2.5.2 Nuclear protein extraction…………………………………………... 154 7.2.5.3 Assay of NF-κB activation……………………………………..…… 154 7.3 Results…………………………………………………………………….. 155 7.3.1 Selection and validation of probiotic and pathogenic Gram-negative coliforms …………………………………………………………….….. 155 7.3.2 Basal levels of gene activation in 7 different cell lines ……………….… 157 7.3.3 Activation of NF-κB………………………………………………...…… 158 7.3.4 Activation of multi-functional genes by Gram-negative coliforms……… 158 7.3.5 Activation of multi-function genes by a panel of LABs…………..…….. 159 7.4 Discussion……………………………………………………………....… 163

Summary……………………………………………………………….………. 167 References……………………………………………………………………… 173 Appendix……………………………………………………………………….. 217

xiii List of Tables

Table 1.1 Infection potential of probiotic microorganisms……………… 9 Table 1.2 Microbes used for probiotics…………………………………… 11 Table 1.3 Selective media used for the culture of different strains……... 23 Table 1.4 Comparison of some molecular techniques which have been frequently used for the study of the intestinal microflora……. 28 Table 1.5 Review of the major pathotypes of pathogenic E. coli 31 Table 1.6 Review the ability of some probiotic bacteria to modulate immune responses in vitro……………………………………… 43 Table 2.1 Reference strains used in this study…………………………… 47 Table 3.1 Microbial composition of Protexin…………………………….. 60 Table 3.2 Faecal flora determination using bacterial culture medium…. 63 Table 4.1 List of bacterial strains used as a source of genomic DNA for the validation of primer pairs K-B1/F and K-B1/R1…………. 80 Table 4.2A List of Bacillus taxa providing 16S rDNA gene sequences from GenBank for the design of primers……………………… 81 Table 4.2B List of non-Bacillus genera providing 16S rDNA gene sequences from GenBank for the design of primers………….. 82 Table 4.3 Speciation of Bacillus isolates in cattle faeces and feedlot feed………………………………………………………………. 93 Table 5.1 List of bacterial strains used in the analysis…………………... 107 Table 5.2 Virulence genes included in the uni/multiplex combinatorial 109 PCR assays……………………………………………………… 110

xiv Table 5.3 Ranking the relative importance of the virulence genes (top 21 out of 50) based on the deviance value contributed to by phylogroup differences…………………………………………. 115 Table 6.1 Additional virulence genes tested in this study……………….. 132 Table 6.2 The prevalence of VGs detected in porcine PWD and commensal isolates……………………………………………… 134 Table 7.1 List of human cell lines used in this study…………………….. 149 Table 7.2 Culture conditions for growing human cell lines…………….. 150 Table 7.3 Bacteria strains used in this study……………………………... 151 Table 7.4 Oligonucleotide primers and PCR product size………………. 153 Table 7.5 Characteristics (serotype, pathotype and phylogroup) of coliforms…………………………………………………………. 156 Table 7.6 Baseline level of gene transcripts in different cell lines determined by RT-PCR………………………………………… 157

List of Figures

Figure 1.1 Anatomical regions of the human gastrointestinal tract………. 20 Figure 1.2 Sequence structure of bacterial 16S ribosomal RNA gene…….. 24 Figure 1.3 Various culture-independent molecular approaches to analyse the human intestinal microbial community…………………….. 26 Figure 3.1 Counts of total aerobic bacteria on blood agar (A), lactic acid bacteria on MRS agar (B), coliform bacteria on MAC agar (C), streptococci and enterococci bacteria on KEA agar (D), and enterococci bacteria on CATC agar (E), recovered from -1 cattle faeces during the Protexin DFM trial (log10 CFU g )….... 66 Figure 3.2 Effects of ovalbumin (OVA) vaccination on the serum IgG response of cattle against OVA………………...... 67 Figure 3.3 Assessment of cellular immunity of Protexin feeding by PHA activation………………………………………………………….. 67 Figure 3.4 Average daily feed intake (ADFI) for 10 cattle fed rations containing Protexin versus 10 cattle fed a control diet………… 68 Figure 3.5 Average daily weight gain (ADWG) for 10 cattle fed rations containing Protexin versus 10 cattle fed a control diet………… 68 Figure 3.6 Meat quality of animals was investigated by measuring the P8, 12/13th rib and EMA values using ultrasound scanning……….. 69 Figure 3.7 Percentage of intramuscular fat at three sites – IMF 1, 2 and 3. 69 Figure 4.1A Alignment of primer K-B1/F with homologous target sequences of 16S rDNA (E. coli numbering scheme - 255 and 273) from Bacillus, Paenibacillus, Brevibacillus and other genera, obtained from GenBank………………...... 86 Figure 4.1B Alignment of primer K-B1/R1 with homologous target sequences of 16S rDNA (E. coli numbering scheme - nucleotides 1350 and 1368) from Bacillus, Paenibacillus, Brevibacillus and other genera………...... 86

xvi

Figure 4.2A AluI restriction profiles of amplified regions of the 16S rRNA genes of Bacillus reference strains…………………………...... 88 Figure 4.2B TaqI restriction profiles of Bacillus reference strains………….. 88 Figure 4.2C Theoretical prediction of ARDRA profiles generated with restriction enzymes AluI (a) and TaqI (b) based on published 16S rDNA sequences from GenBank…………………………..... 89 Figure 4.3 ARDRA analysis of 17 commercial probiotics strains 92 corresponding to AluI digestion……………………………...... Figure 4.4 Counts of aerobic spores on TSA media recovered from cattle -1 faeces during feedlotting (log10 CFU g )………………………... 92 Figure 4.5A Pulsed-field gel electrophoresis (PFGE) with ApaI showing the intraspecies clonal variation of 12 cattle isolates in B. licheniformis species………………………………………...... 94 Figure 4.5B Dendrogram clustering showing similarity among 12 cattle B. licheniformis isolates based on their PFGE profiles described in 4.5A………………………………………………………...... 94 Figure 5.1 Agarose gel electrophoresis of uni/multiplex combinatorial PCR amplified E. coli virulence genes…………………………... 113 Figure 5.2 Clustering of 42 E. coli isolates based on their virulence gene profiles and phylogenetic groups………………………………... 118 Figure 5.3 PCO analysis with 37 VGs (included 3 Clermont genes)………. 119 Figure 5.4 PCO analysis with 34 VGs (excluded 3 Clermont genes)……… 120 Figure 5.5 Clustering of 42 E. coli isolates based on their PFGE profiles with XbaI digestion……………………………………………….. 121 Figure 5.6 Venn diagram describing the virulence gene distribution in EXPEC, IPEC and commensal isolates…………………………. 123 Figure 6.1 Clustering analysis of the genetic variation between PWD and commensal porcine E. coli isolates based on the virulence gene attributes………………………………………………………….. 137

xvii

Figure 6.2 Clustering analysis of porcine PWD isolates from different geographic origin (NSW, Qld and Vietnam) based on PFGE patterns obtained by digestion of bacterial genomic DNA with NotI...... 138 Figure 7.1 Representative EMSA demonstrating NF-κB activation in bacterially stimulated T84 cells………………………………….. 160 Figure 7.2 Gene activation in T84 cells incubated with 7 different E. coli strain………………………………………………………………. 161 Figure 7.3 Gene activation in T84 cells incubated with 4 different LAB strain………………………………………………………………. 162

xviii Abbreviations

ARDRA Amplified ribosomal DNA restriction analysis bp Basepair(s) BSA Bovine serum albumin cDNA Complementary DNA CE Competitive exclusion CFU Colony forming units Da Dalton(s) DFM Direct-fed microbial DNA Deoxyribonucleic acid dNTP Deoxyribonucleic triphosphate DTT Dithiothreitol, threo-1,4-Dimercapto-2,3-butandiol EaggEC Enteroaggregative E. coli EDTA Ethylendiamin-N,N,N',N'-tetra acid EHEC Enterohemorrhagic E. coli EIEC Enteroinvasive E. coli EMSA Electrophoretic mobility shift assay EPEC Enteropathogenic E. coli ETEC Enterotoxigenic E. coli ExPEC Extraintestinal pathogenic E. coli FCS Fetal calf serum GIT Gastrointestinal tract h Hour IEC Intestinal epithelial cell IL Interleukin IPEC Intestinal pathogenic E. coli kb Kilobasepair(s) LAB Lactic acid bacteria M Molar Mb Megabase

xix min Minutes MLEE Multilocus enzyme electrophoresis MOI Multiplicity of infection

MQ-H2O Milli-Q filtered deionised water mRNA Messager RNA NMEC Neonatal Meningitis E. coli O.D. Optic density PBS Phosphate buffered saline PCR Polymerase chain reaction PFGE Pulsed-field gel electrophoresis PWD Post-weaning diarrhoea RNA Ribonucleic acid RNase Ribonuclease rpm Revolution per minute RT Room temperature RT-PCR Reverse transcriptase polymerase chain reaction sec Second(s) TBE Tris-borate-EDTA TE Tris-EDTA TEMED N,N,N',N'-tetramethylethylendiamin µg Microgram UPEC Urinary pathogenic E. coli V Voltages v/v Volume for volume VG Virulence gene Vol Volume

xx Chapter 1

Literature Review

1.6 What are probiotics? 1.6.1 Definition of probiotics 1.6.2 Use of probiotics as alternatives to growth promotants for intensive animal industries 1.6.3 Selection criteria for a probiotic strain 1.6.4 Safety assessment of probiotics - the accurate identification of probiotic strains 1.6.5 Bacillus probiotic strains 1.6.6 Escherichia coli (E. coli) probiotic strains 1.6.7 Mechanisms of probiotic action/efficacy 1.7 Intestinal microbial diversity 1.7.1 Intestinal microbial ecosystem 1.7.2 Traditional approaches for the analysis microbial diversity 1.7.3 Molecular techniques used to investigate gut microflora diversity 1.8 Virulence genes versus adaptive genes – the difference between commensals and pathogens 1.8.1 Definition of virulence genes and adaptive genes 1.8.2 Possession of virulence genes in E. coli 1.8.3 E. coli phylogeny and diversity 1.8.4 Detection of virulence genes 1.9 Host-bacteria interactions 1.9.1 The structure of the gut epithelium 1.9.2 The role of enterocytes in intestinal barrier integrity 1.9.3 Inflammatory response in the gut 1.9.4 Immunomodulative function of probiotics 1.10 Thesis objectives

1 1.1 What are probiotics? 1.1.1 Definition of probiotics The term ‘probiotics’ is derived from the Greek meaning ‘for life’. The probiotic concept was born at the end of 19th century by Elie Metchnikoff at the Pasteur Institute in Paris. Since then, scientists have been attempting to explain and define the probiotic effect. In 1965, Lilly and Stillwell described probiotics as substances secreted by one microorganism which stimulated the growth of another microorganism (Lilly and Stillwell, 1965). Later, it was used to describe tissue extracts that stimulated microbial growth (Sperti, 1971). Parker defined probiotics as ‘organisms and substances which contribute to intestinal microbial balance’ (Parker, 1974). In 1989, Fuller defined probiotics as ‘a live microbial feed supplement which beneficially affects the host animal by improving its intestinal microbial balance’ (Fuller, 1989). This is now recognised as the generally accepted and widely used definition for a probiotic (Tannock, 1999). In 1992, Havenaar suggested that probiotics be defined as ‘mono- or mixed cultures of live microorganisms which, when applied to animal or man, beneficially affect the host by improving the properties of the indigenous microflora’ (Havenaar et al., 1992). Havennar’s definition was the first that applied the term probiotics to both humans and animals, and expended ‘probiotic’ activities to microbial communities to include sites of the body beyond the intestinal microflora. In consideration of the current applications and proven effects of probiotics, Salminen et al. proposed a new definition, ‘Probiotics are microbial cell preparations or components of microbial cells that have a beneficial effect on the health and well-being of the host’ (Salminen et al., 1999). This definition included microbial cells (viable or non-viable) and parts of cells as probiotics, but not metabolites such as antibiotics. It also indicated the application of probiotics beyond use in foods.

1.1.2 Use of probiotics as alternatives to growth promotants for intensive animal industries Growth promoting feed additives are used extensively in the livestock industry to decrease overall production costs by influencing digestive tract microflora and environment, which in-turn promotes better animal health and/or enhances the

2 efficiency of feed conversion. Some feed additives have secondary benefits which include reducing the incidence of acidosis, coccidiosis, and grain bloat, while others suppress estrus, reduce liver abscesses, or control footrot problems (Mader and Lechtenberg, 2000). In general, most feed additives used for performance enhancement can be divided into five main categories: (1) ionophores, (2) antibiotics, (3) estrus suppressants, (4) buffers, and (5) probiotics. Each feed additive has its own characteristics and feeding limitations.

The term ‘antibiotic growth promoter’ is used to describe any medicine that destroys or inhibits bacteria and is administered at a low, subtherapeutic dose. Infectious agents reduce the yield of farmed food animals and, to control these, the administration of sub-therapeutic antibiotics and antimicrobial agents has been shown to be effective. The use of antibiotics as feed additives was boosted in 1950’s due to the effects of improving animal growth and feed conversion. Antibiotics have been shown to improve weight gain and feed conversion in cattle 3-5%, on the average (Krehbiel et al., 2003). However, the detrimental side effects of using antibiotic as growth promotants were emerged subsequentially, based on the following evidences:

• Induce diarrhoea in animals • Increase the antibiotic-resistant pathogens • Increased the colonisation of the chicken gut by Salmonellae • Acquisition of antibiotic resistance genes from such bacteria by human bacterial pathogens (Barton et al., 1998) • Antibiotics present in the food chain will create a potential public health problem

In 1969, the Swann Committee recommended that antibiotics in animal feeds be restricted to those not used therapeutically, and there is increasing international pressure to reduce antibiotic usage either voluntarily or by legislation. This created a void and stimulated the use of probiotics for farm animals. Probiotics, when used as a feed additive, fall into a class of compounds called direct-fed microbials (DFMs). Bacterial probiotics such as lactobacilli, streptococci, enterococci, bacilli and E. coli have been effectively used in chickens, pigs and pre-ruminant calves whereas fungal

3 probiotics such as Saccharomyces and Aspergillus have shown promise in adult ruminants. Modes of action of DFM are not entirely clear. Aside from competition with undesirable organisms, DFM are thought to produce antimicrobial agents to inhibit undesirable microbial growth and prevent the build-up of toxic compounds, such as amines and ammonia, which irritate the digestive tract and inhibit immune function. Feed consumption tends to be enhanced and morbidity reduced by feeding DFM. In addition, DFM have been found to be useful under conditions of animal stress. The potential benefits of application of probiotics to farm animals are summarised as follows (Fuller, 1999):

• Greater resistance to infection diseases • Increased growth rate • Improved feed conversion • Improved digestion • Better absorption of nutrients • Increased milk yield and quality • Increased egg production and quality • Improved carcass quality and less contamination

In general, administration of probiotics may be most effective in the following situations:

• After birth, to encourage the early establishment of beneficial microflora • Following antibiotic treatment • When enteric pathogens, such as E. coli, Salmonella or coccidia, are present • During periods of environmental or management stress

In calves, it can be most effective to give probiotics after birth, when changing to bucket feeding, before and after transportation, at weaning, following over-eating or after administration of antibiotics. In adult cattle, probiotics may be most effective in cases of ketosis or bloat, after antibiotic treatment or after difficult calving. In piglets, administration of probiotics may be most effective after birth, if piglets are

4 weak, during tailing and castration, for gastrointestinal problems and at weaning. Older pigs may benefit most following antibiotic treatment, at farrowing and before transport or mixing (Fuller, 1999).

It has been widely accepted that different probiotics may be more suitable for different animal species, and the type and dose of probiotic given may also be related to the age and health condition of the animal (Wolter et al., 1987). For example, in the ruminant the two most active probiotics are yeast (Saccharomyces cerevisiae) and fungi (Aspergillu. oryzae). The feeding of yeast and fungal has shown beneficial effects for young calves. Parra and DeConstanzo (1992) reported that greater weight gains (1.48 vs. 1.23 kg day-1) and feed conversion efficiencies [7.73 vs. 8.80 kg dry matter (DM) kg-1 gain] were noted from 32 to 58 days in feed-lotting steers fed a live yeast culture (YC2, A-Max) compared with controls. Hudyma and Gray (1993) also found significantly higher weight gains, feed intakes and feed conversion when feedlotting crossbred steers were fed with YC1 (Diamond-V XP) in a corn silage diet. Two factors, the type of diet and the age of feeder cattle, may be influencing the response of feedlot cattle to probiotics (Newbold et al., 1992).

In contrast to cattle, the most important reason for using a probiotic product for pigs is the claim that probiotics influence the indigenous microflora resulting in inhibition of enteric pathogens in young piglets (Gedek, 1991). Among the potential pathogens, E. coli was considered to cause the most serious problems in neonatal or weaning piglets (Mulder et al., 1997; Noamani et al., 2003). Numbers of beneficial microorganisms were investigated in order to demonstrate the improvement of colonization resistance against pathogenic E. coli in pigs (reviewed by: Havenaar and Huis in’t Veld, 1992; Fuller, 1986, 1989; Sissons, 1989). Enterococcus faecalis (BIO-4R) was demonstrated to significantly reduce the coliform counts in faecal samples of piglets (Ozawa et al., 1983). Feeding neonatal piglets with a Bacillus cereus-based probiotic (CenBiot) notably reduced diarrhoea which was equal to that achieved with furazolidone (Zani et al., 1988). In addition, a combination of Lactobacillus casei and Enterococcus faecium was capable of reducing the coliforms in the ileal content of piglets (Tortuero et al., 1995).

5 Although there are numerous commercial probiotic products for farm animals in the market, very little unequivocal scientific data with regard to the underlying mechanism for health improvement by these products has been published. Therefore it is difficult to assess the importance of probiotics to support widespread use (Barrow, 1992). In order to improve the credibility of the probiotic concept, more information on the following issues is essential.

• Development of selection criteria for probiotics based on scientific evidence which provides an explanation for the expected probiotic effect • Appropriate in vitro and animal models must be available for screening and validation • Sensitive and reproducible methods are necessary for identification of the specific probiotic strain and to quantify the expected effects

1.1.3 Selection criteria for a probiotic strain There are many approaches for assessing a new probiotic strain. Fuller (1992) gave a basic selection criterion, in which a probiotic strain is required to possess at lease the following four characteristics:

• Is capable of being prepared as a viable product on an industrial scale • Remains stable and viable for long periods under storage and field conditions • Has the ability to survive (not necessarily grow) in the intestine • Produces a beneficial effect in the host animal

In recent years, more sophisticated criteria have emerged for the selection and assessment of probiotics, considering factors related to health promotion/sustainment, safety assessment and technological features (Holzapfel et al., 1998). In 1998, the participants involved in the LABIP (The Lactic Acid Bacteria Industrial Platform) workshop on probiotics supported criteria outlined by Guarner and Schaafsma (1998) for the selection and assessment of probiotic bacteria. These criteria were summarised below, together with some suggested selection criteria from other authors (Klaenhammer and Kullen, 1999; Salminen et al., 1998).

6 General microbiological criteria • Accurate taxonomic identification • Nonpathogenic behaviour, generally recognised as safe (GRAS) • Human origin for human probiotics • Genetically stable and amenable to gastric acidity and bile toxicity • Adhesion to gut epithelial tissue, ability to persist within the gastrointestinal tract

Technological aspects • Amenable to mass production and storage • Adapted to a suitable carrier or fermentable substrate • Has an acceptable shelf-life and sensory attributes • Viability at high concentration (preferred at 106 to 108 CFU g-1)

Functional effects • Production of antimicrobial substances • Antagonistic toward pathogenic bacteria • Ability to modulate immune responses and strengthen the gut mucosal barrier • Ability to influence metabolic activities (e.g. cholesterol assimilation, lactase activity and vitamin production) • Able to exert one or more clinically documented health benefits (e.g. lactose tolerance) • Anti-mutagenic and anti-carcinogenic

In addition, appropriate in vivo tests in animal models and even human clinical trials are necessary for screening and validation of potential probiotic strains. The demonstration of probiotic activity of a certain strain for human use requires well- designed, double-blind, placebo-controlled human studies (Guarner and Schaafsma, 1998). These requirements were outlined by Berg (1998) and Salminen et al (1996 & 1998) as follows: (i) each potential probiotic strain should be documented and assessed independently, extrapolation of data from closely related strains is not acceptable, (ii) only well-defined strains, products, and study populations should be used in trials, (iii) when possible, all human studies should be randomised, double-

7 blind, and placebo-controlled, and (iv) results should be confirmed by independent research groups, and preferentially, the study should be published in a peer-reviewed journal. Just recently, Dunne et al. have developed criteria for in vitro selection of probiotic bacteria that reflect certain in vivo effects for the human host, such as modulation of gastrointestinal tract microflora (Dunne et al., 2001). Among the 1500 bacterial strains isolated from resected human terminal ilea, the Lactobacillus salivarius subsp. salivarius strain UCC118 was found to be resistant to bile acid and gastric acidity, adhere to human intestinal cell lines and have antimicrobial effects. Further, in vivo mouse feeding and human feeding studies showed this strain could successfully colonise the murine and human gastrointestinal tract with considerable efficacy in influencing gut flora.

1.1.4 Safety assessment of probiotics - the accurate identification of probiotic strains

Traditional probiotic dairy strains of lactic acid bacteria (LAB) such as some members of bifidobacteria and Lactobacillus have a long history of safe use without any obvious adverse effects. These strains are commonly placed in the category ‘GRAS’ (Generally Recognised as Safe) (Donohue and Salminen, 1996; Fuller, 1992; Saxelin et al, 1996). With growing consumer requirement and interest, new and more specific microorganisms with probiotic attributes are being induced into food products for human and feed additive products for animals. It cannot be assumed that these novel probiotic organisms share the historical safety of these traditional strains. Before incorporation into products, new strains should be carefully assessed and tested for safety and efficacy of proposed use. As yet, no general guidelines exist for safety testing of probiotics. Theoretically, probiotics may, as living micro-organisms, be responsible for four types of side-effect in susceptible individuals: infections, deleterious metabolic activities, excessive immune stimulation and gene transfer (Salminen et al. 1998). The infection potential of probiotic microorganisms are summarised in (Table 1.1).

8 Table 1.1 Infection potential of probiotic microorganisms Species Infection potential Lactobacillus Mainly non-pathogens, some may cause opportunistic infections Lactococcus Mainly non-pathogens Leuconostoc Mainly non-pathogens, some isolated cases of infection Streptococcus Oral streptococci mainly non-pathogens (including Streptococcus thermophilus); some may cause opportunistic infections Enterococcus Some strains are opportunistic pathogens with haemolytic activity and antibiotic resistance Bifidobacterium Mainly non-pathogens, some isolated cases of human infection Propionibacterium Dairy propionibacteria group mainly non-pathogens, some cutaneous propionibacteria strains may cause opportunistic infections Saccharomyces Mainly non-pathogens, some isolated cases of human infection

According to Salminen et al. (1998), three approaches can be used to assess the safety of probiotic strain: (i) studies on the intrinsic properties of the strain, (ii) studies on the pharmacokinetics of the strain, and (iii) studies searching for interactions between the strain and the host. The determination of the intrinsic properties of probiotic candidates is crucial because it is the starting point for the whole process of probiotic safety assessment. Thus this review will focus on this issue in the following section.

The current state of evidence suggests that probiotic effects are strain specific. Strain identity is important to link a strain to a specific health effect as well as to enable accurate surveillance and epidemiological studies (Fuller, 1992). Several commercial probiotic products have been reported with inaccuracies in labelling of species including members of Lactobacillus and Saccharomyces (Canganella et al., 1997), Pediococcus and Enterococcus (Hamiton-Miller et al., 1999) and Bacillus (Green et al., 1999). More cases are reviewed by Yeung et al. (2002). Safety consideration was aroused in these products because certain species of Enterococcus, Streptococcus and Bacillus are opportunistic pathogens (Donohue and Salminen, 1996; Salminen et al., 1998). On the other hand, accurate species labelling is essential for responsible quality control efforts and to build consumer confidence. The guidelines for the

9 evaluation of probiotics, set forth by participants of the 2002 Joint FAO/WHO Working Group, suggested that speciation of bacteria is essential for evaluation of probiotics, and this must be established using the most current, valid methodology with a combination of phenotypic and genetic tests. The detail information was summarised below:

• Nomenclature of the bacteria must conform to current, scientifically recognised names

• DNA-DNA hybridization is the reference method for speciation, and DNA sequencing of the 16S rRNA gene is a suitable substitute

• Some key biochemistry phenotypes should be investigated for identification purposes

• Strain typing has to be performed with a reproducible genetic method or using a unique phenotypic trait. Pulsed-field gel electrophoresis (PFGE) is the gold standard

• All strains should be deposited in an internationally recognised culture collection

Genetic variation exists in different strains within a species. In some bacterial species such as E. coli, pathogenicity may be acquired by single plasmid transfer. Therefore the intrinsic properties of probiotic candidates must be investigated genetically and phenotypically for safety ensuring. Such intrinsic properties include:

• Genes encoding for virulence factors such as those required for toxin, invasion, adhesion, protection and antibiotic resistance

• Ability to produce virulence factors and cause antibiotic resistance

• Plasmid transfer

• Harmful metabolic activities (e.g. haemolytic activity, D-lactate production, bile salt deconjugation)

10 1.1.5 Bacillus probiotic strains A diverse group of microbes have been used as probiotic supplements, as listed in Table 1.2. The widely used probiotic bacteria include strains from the genera Lactobacillus and Bifidobacterium. These are the most abundant genera in probiotic- containing food products. Less commonly, species of other lactic acid bacteria, Saccharomyces, E. coli and Bacillus have been suggested for probiotic effects.

Table 1.2 Microbes used for probiotics (modified from Shah, 2000; Young, 1998) Lactobacillus Bifidobacterium Other lactic acid Other species bacteria L. acidophilus B. adolescentis Enterococcus Bacillus spp. faecalis L. brevis B. animalis Enterococcus E. coli faecium L. casei B. bifidum Leuconostoc lactis Propionibacterium freudenreichii L. cellobiosus B. breve Leuconostoc Saccharomyces mesenteroides cerevisiae L. crispatus B. infantis Pediococcus acidilactici L. delbrueck B. lactentis Sporolactobacillus subsp. inulinus bulgaricus L. fermentum B. longum Streptococcus cremoris L. gallinarum B. thermophilum Streptococcus intermedius L. gasseri Streptococcus lactis L. GG Streptococcus thermophilus L. lactis L. plantarum L. reuteri L. rhamnosus

Strains in the genus Bacillus are sporeforming, catalase-positive aerobic bacteria, commonly associated with soil. Among the 130 currently recognised species of this

11 genera, only a few have been evaluated for probiotic functionality. Those available commercially for human and animal use include B. subtilis, B. coagulans, B. clausii, B. cereus, B. laterosporus, B. licheniformis, and B. pumilis (Duc et al., 2004; Sanders et al., 2003). Unlike other probiotic bacteria, Bacillus probiotic strains are present in commercial products as spores rather than being consumed as vegetative cells, with the advantage that spores can easily survive transit through the stomach intact. Using a murine model, spores of B. subtilis were found to germinate in the mouse small intestine (Casula and Cutting, 2002). Additionally, the inherent resistance of spores to environmental stress is an attractive attribute for commercial application, especially in the animal industry (Mazza, 1994). The potential probiotic attributes of Bacillus include competitive exclusion (CE) of pathogens, production of antimicrobials, enzyme production and modulation of immune function. The mechanism of CE will be further discussed in section 1.1.6.

Recent studies have demonstrated that certain Bacillus spores may be successful competitive exclusion agents. La Ragione et al (2001) reported that oral inoculum of B. subtilis PY79 spores can successfully suppress E. coli O78:K80 infection in day old chicks. Also, it was found that Vibrio harveyi in shrimp is suppressed by spores of various Bacillus species (Rengpipat et al., 1998; Vaseeharan and Ramasamy, 2003). In pig industry, feeding weaned piglets with a probiotic LSP 122 (Alpharma) which contains spores of B. licheniformis and another product Toyocerin (contains spores of B. cereus) significantly reduced the incidence and severity of diarrhoea caused by ETEC (enterotoxigenic E. coli) (Kyriakis et al., 1999). Supplemented the sows and neonatal piglets with probiotic CenBiot (contains spores of B. cereus) was found to reduce the prevalence of diarrhoea and E. coli K88 counts in faeces, and significantly improved feed efficiency in piglets (Zani et al., 1998).

One of the possible functions of Bacillus feeding may be the production of antimicrobial substances. Dozens of different peptide antibiotics exhibiting antagonism against a broad spectrum of microorganisms have been identified from the Bacillus strains (Mazza, 1994; Nacleerio et al., 1993). A probiotic strain, B. subtilis 3, was able to produce at least two antibiotics with anti-H. pylori activity (Princhuk et al., 2001). One of these was amicoumacin A, an isocoumarin group

12 antibiotic. Another bacteriocin-like compound, coagulin, produced by B. coagulans

I4 was identified by Hyronimus and colleagues as plasmid-encoded, heat-stable, protease sensitive, with activity against Gram-positive bacteria such as Leuconostoc, Oenococcus, Listeria, Pediococcus and Enterococcus (Hyronimus et al., 1998). Most of the Bacillus bacteriocins were only investigated at the basic level, the effectiveness of these bacteriocins produced in vivo on pathogen inhibition has rarely been investigated (Sanders et al., 2003).

Studies suggest that Bacillus probiotic efficacy is also mediated through metabolites or enzymatic activities. For example, some Bacillus species produce enzymes (e.g. xylanases, cellulases or amylases), proteases such as subtilism, and vitamin K2 (Hosoi and Kiuchi, 2003), which aid digestion and reduce allergenicity especially in domestic animals. These enzymes contribute to the disintegration of complex organic compounds in fodder into low-molecular compounds. The animals can then make better use of these compounds. Appetite improves, whilst the fodder conversion rates and production performance also increased. Additional functions of Bacillus probiotics: increase cellulolytic digesting bacterial numbers, increase fibre digestion, enhance utilization of lactic acid by rumen bacteria and stabilise the rumen pH (Fuller, 1999).

In some countries such as Italy and Vietnam, Bacillus probiotics are used by humans as an ‘over-the counter’ supplement for oral bacteriotherapy and for bacterioprophylaxis of mild gastrointestinal disorders (Hoa et al., 2001; Mazza, 1994). One of these pharmaceutical products, Enterogermina (sanofiSynthelabo SpA, Milan, Italy), is an aqueous suspension of B. clausii spores (originally labelled as B. subtilis). It has been marketed in Italy for more than 30 years due to its purported activity to prevent or treat infectious bacterial diarrhoea (Cliffo, 1984). Studies have suggested that the stimulation of secretory A immunoglobulins (Fiorini et al., 1985), immunostimulatory activity such as increased interferon production ex vivo by stimulated peritoneal and spleen cells (Muscettola et al., 1992) and the in vitro mitogenic activity (Ciprandi et al., 1986) contribute to the therapeutic efficacy of Enterogermina (Senesi et al., 2001).

13 Compared with the lactic acid bacteria, the published literature substantiating the health effects of Bacillus probiotic is sparse. Major safety issues were raised based on the following concerns: • Bacillus is not a normal inhabitant of the gastrointestinal tract (Sanders et al., 2003) • Some strains in B. cereus species cause gastrointestinal infections (Granum and Lund, 1997) • Product mislabelling – improper taxonomic characterisation of the bacteria in the product (Hoa et al., 2000)

1.1.6 Escherichia coli (E. coli) probiotic strains The majority of current probiotic bacteria are Gram-positive. This is largely because of the ability of these bacteria to persist within the gut ecosystem, produce organic acid (e.g. lactate and acetate) and antimicrobials (Rastall and Maitin, 2002). Comparably, Gram-negative probiotics are few in number. Recently, attention has turned to less fastidious microorganisms and recent reports have cited the use of E. coli as a probiotic.

E. coli are described as Gram-negative, non-acid-fast, non-sporing rods or bacilli, which are between 0.3-1.0 µm in diameter and between 1.0-6.0 µm in length. This species is a natural inhabitant of the mammalian gut, being present in the intestinal tract of all humans and most other warm blooded animals, generally in numbers around 108 to 109 per gm of large intestinal contents (Bettelheim and Thompson, 1987). In humans, babies were found to have E. coli in their faeces within the first few days of life, before they left the maternity ward (Full, 1989). In pigs, during the first day of life the intestinal tract is flooded with large numbers of E. coli as well as other bacteria. Colonisation with pathogenic E. coli strains such as ETEC in the small intestinal of neonatal and weaned piglets will cause severe diarrhoea.

Most of the work on probiotic E. coli centres on a particular strain, known as Nissle 1917. It was isolated by Alfred Nissle in World War I from a German soldier who survived a severe outbreak of diarrhoea, even though all people in the vicinity succumbed, and later was patented as the Mutaflor trademark. It has been designated

14 as DSM 6601 (serotype O6:K5:H1) in the German Collection for Microorganisms (Blum et al., 1995). The strain is well characterised by means of microbiological, biochemical, serological as well as molecular genetic typing (Grozdanov et al., 2000; Stentebjerg-Olesen et al., 1999). This strain has been using as a therapeutic agent in medicine for treatment of various intestinal disorders and is known to be a successful coloniser of the human gut (Lodinova-Zadnikova, and Sonnenborn, 1997; Lodinova- Zadnikova et al., 1992). A few controlled human clinical trials have been performed to analyse the influence of oral administration of Nissle 1917 in premature infants (Cukrowska et al., 2002) and in patients with inflammatory bowel disease (IBD) (Kruis et al., 1997; Rembacken et al., 1999). Just recently, genome sequence, lipopolysaccharide structure and induced host function of this strain have been documented (Grozdanov et al., 2004; Otte and Podolsky, 2004; Wehkamp et al., 2004). The probiotic efficiencies of this strain is summarised below based on the published references (Lodinova-Zadnikova et al., 1992; Cukrowska et al., 2002; Rembacken et al., 1999; Otte and Podolsky, 2004)

Probiotic efficiencies of an E. coli strain - Nissle 1917 Safety • Non-pathogenic • Does not produce known protein toxin • Lack of defined virulence factors • Exhibit a semi-rough lipopolysaccharide phenotype and serum sensitivity • High level of tolerance and lack of adverse effects • Sensitive to all antibiotics directed against Gram negative bacteria • In conjugation experiments, it was shown to be a poor recipient for foreign plasmid DNA

Antagonistic action • Expresses microcin M and H47 • Stimulates the intestinal innate defence through the upregulation of inducible antimicrobial peptides • Greatly reduces the invasion of epithelial cells by enteroinvasive E. coli (EIEC) • Inhibition of S. typhimurium invasion into intestinal cells

15 Colonising and surviving in gut • Expresses type 1 and F1C fimbriae • Possesses a number of iron uptake system

Functional modulation of enterocytes • Immunomodulatory • Competition for contact with the epithelial surface • Stabilization of the cytoskeleton and barrier function • Induction of mucin expression

Clinical effect • Remission maintenance of ulcerative colitis • Protects gnotobiotic pigs against S. typhimurium infection • Immunogen: stimulate specific humoral and cellular response and induces non- specific natural immunity

Nissle 1917 was also applied in veterinary medicine and in raising animals. It was administered to piglets (Sharma et al., 2005) and cattle (Von Buenau et al., 2003) with nutritional disorders and diarrhoea, and reported great success. In addition to Nissle 1917, other E. coli probiotic stains have also been used in domestic animals. Probactrix, a liquid food additive probiotic, containing saprophytic E. coli strain (ATCC 202226) was capable of decreasing the incidence of diarrhoea and the death rate of weaned piglets (Hadani et al., 2002). This product was also shown to relieve patients of IBD in a pilot trial (Hamilton-Miller, 2001), but the mechanism by which this occurs is still unknown. In cattle, Zhao et al. (2003) reported that several strains of probiotic E. coli could inhibit the growth of enterohaemorrhagic E. coli (EHEC) strains including O157:H7, O26:H11 and O111:NM isolates in vitro and reduce or eliminate faecal shedding of EHECs in weaned calves. These probiotic E. coli were also used in follow-up studies and were shown to significantly reduce E. coli O157:H7 colonisation of weaned calves, however not for neonatal calves (Tkalcic et al., 2003; Zhao et al., 2003). This inhibition was caused by microbial colicins (Schamberger et al., 2004) which killed sensitive E. coli that surround the producing cells. Colicins are a specific subset of bacteriocins, being substances produced by

16 some strains of E. coli which will kill other E. coli strains (Schamberger and Diez- Gonzalez, 2004).

Some research has also been undertaken to characterise an E. coli strain A034/86 of serotype O83:K24:H31, which was initially isolated from porcine faeces. This strain has been safely and effectively used in Czech paediatric clinics over the past three decades for prophylactic and therapeutic colonization of the intestine of several thousand premature and newborn infants at risk of nosocomial infections and diarrhoea (Lodinova-Zadnikova et al., 1998; Lodinova et al., 1967 & 1980). This strain has been approved in the Czech and Slovak Republics for routine use as a live oral vaccine preparation for infants (Hejnova et al., 2005). It has been suggested that gut colonization by this stain contributes to the beneficial effect, by displacing intestinal pathogens in infected carriers, assisting the re-establishment of a normal gut flora (Lodinova-Zadnikova et al, 1995 & 1998; Lodinova et al., 1980), and by stimulating local antibody formation in the gut and saliva (Lodinova-Zadnikova et al., 1991). Moreover, subjects colonised by this strain early after birth appear to be significantly less prone to repeated infections and development of allergies later in life (Lodinova-Zadnikova et al., 2003).

1.1.7 Mechanisms of probiotic action/efficacy Although probiotic microorganisms are considered to promote health, the actual mechanisms involved have not yet been fully elucidated. The beneficial effect of probiotics and the underlying mechanisms are yet to be substantiated by well- controlled in vivo studies or clinical trials. Different bacteria may have unique mechanisms to act as probiotics in different host species. Two important issues defined as desirable for probiotics are briefly discussed below.

1.1.7.1 Adhesion and competitive exclusion Adhesion to the gut surface is considered an important property of indigenous probiotic strains, such as some Lactobacillus, Bifidobacteria and E. coli strains, which are able to perform colonisation and immune stimulation (Havenaar et al., 1992). This is mediated either non-specifically by physico-chemical factors, or specifically by bacterial surface molecules and epithelial receptor molecules. For

17 instance, evidence indicates that hydrophobic interactions mediated by lipoteichoic acids are involved in the adherence of bifidobacteria to intestinal epithelial cells (IEC) (Op den Camp et al., 1985), whereas Reid et al. (1993) observed that three different L. casei strains produce an extracellular protein-like adhesin to adhere to human uroepithelial and colonic epithelial cells (Reid et al., 1993). Umesake and colleagues (1997) reported that intestinal microflora can influence host epithelial cells in the small intestine to express fucosylated glycoconjugates, which may serve as receptors for attachment by organisms such as Bacteroides thetaiotaomicron.

Adherence of pathogens to mucosal surfaces in the gastrointestinal tract (GIT) assists in preventing the removal of bacteria by host secretions and luminal flow. Many intestinal pathogens produced multiple adhesins such as the fimbrial adhesins CFA I & II, K88, and Fim in E. coli (Smith, 1995). The abilities of probiotic bacteria to colonise epithelial surfaces thereby exclude pathogenic species. On the other hand, those probiotic strains are not able to colonise the gut, including the non-indigenous strains (e.g. Bacillus), non-intestinal origin strains (e.g. Saccharomyces and fungi) and some LABs, are capable of protecting against pathogens by CE effect. The CE effect of microorganisms in resistance to Salmonella colonisation was firstly reported by Nurmi and Rantala (1973), who reported that the resistance could be produced by dosing newly hatched chickens with gut contents from healthy adult birds. Subsequent studies have shown that CE can also protect against some other pathogens such as E. coli, Campylobacter jejuni, Clostridium perfringens and Yersinia enterocolotica. Moreover, the CE approach have been studied in pigs using pure culture preparations such as strains of E. faecalis, E. faecium (Ozawa et al., 1983) and B. cereus (Zani et al., 1988). Recently, Saccharomyces boulardii, a non- intestinal origin probiotic strain, was reported to express a protease that cleaves the C. difficile toxin receptor sites from the surface of intestinal epithelial cells (Czerucka and Rampal, 2002).

The underlying mechanism of CE effect is still not fully clarified but various antimicrobial factors such as pH, H2S, bacteriocins, fatty acids, deconjugated acids, competition for nutrients or for adhesion receptors on the gut wall may be involved. Schneitz and Mead (2000) summarised the CE effect by one or more of 4 general

18 principle actions: the creation of a restriction physiological environment, competition for bacterial receptor sites, production of antibiotics, and depletion of essential substrates.

1.1.7.2 Immunomodulation It appears that adhesive probiotic strains and their components make contact with gut associated lymphoid tissue (GALT) to provoke immune effects. In two separate human trials, L. johnsonii LJ-1 and L. salivarius UCC 118 stimulated a mucosal IgA response and increased phagocytic activity (Saarela et al., 2000; Schiffrin, 1997). Some probiotic strains have been shown to enhance serum IgA responses to pathogens such as attenuated Salmonella typhi TY21a (Collins et al., 1998). The immunomodulation mediated by these strains was not linked to an inflammatory response or general modification of immune responsiveness that could potentially have harmful effects, but was rather associated with transient or longer term colonisation through adhesion and aggregation without invasion (Collins, 1993). Although differences between probiotic bacteria in respect to their immunomodulatory effects have been observed (Pessi et al., 1998), it appears that many of these are mediated through immune regulation and especially through balance control of pro-inflammatory and anti-inflammatory cytokines (see Section 1.4 for more information). Therefore probiotics are considered as innovative tools to alleviate intestinal inflammation, normalise gut mucosal dysfunction, and down- regulate hypersensitivity (Isolauri et al., 2001).

1.2 Intestinal microbial diversity 1.2.1 Intestinal microbial ecosystem The intestinal ecosystem is by far the most complicated ‘system’ within the mammalian body. The human intestine is more densely populated with microorganisms than any other organ and is a site where the microflora may have a pronounced impact on our biology. The mucosal surface of the human GIT is about 200-300 m2 and is colonised by 1013 to 1014 CFU bacteria – some 10 times the amount of host cells, by 400 different species and subspecies and with 30 to 40 species accounting for 99% of the total microflora (Berg, 1996; Savage, 1987). The overall number of genes in the flora can be estimated to be at least two orders of

19 magnitude greater than the gene number in the host’s own DNA (Hopper et al., 2001). The prevalence of bacteria in different parts of the GIT (as shown in Figure 1.1) appears to be dependent on several factors such as pH, peristalsis, redox potential, bacterial adhesion, bacterial cooperation, mucin secretion, nutrient availability, diet, and bacterial antagonism. Because of the low pH of the stomach and the relatively swift peristalsis through the stomach and the small bowel, the stomach and the upper two-thirds of the small intestine (duodenum and jejunum) contain only low numbers of microorganisms, which range from 103 to 104 CFU/ml of the gastric or intestinal contents, mainly acid-tolerant lactobacilli and streptococci. The intestinal microflora of healthy humans is confined to the distal ileum and the colon (Tannock, 1999). In the distal small intestine (ileum), the microflora begins to resemble those of the colon, with around 107 to 108 CFU/ml of intestinal contents. With decreased peristalsis, acidity, and lower oxidation-reduction potentials, the colon maintains a more diverse microflora and a higher bacterial population. The colon is the primary site of microbial colonization in humans, reaching to 1011 to 1012 CFU/ml of contents and with the majority being obligate anaerobes. The most frequently identified anaerobic microorganisms are Bacteroides spp., Bifidobacterium spp., Eubacterium spp., Peptostrpetococcus spp., and Fusobacterium spp. (Simon and Gorbach, 1986).

Figure 1.1 Anatomical regions of the human gastrointestinal tract (adapted from: www.answers.com/ topic/intestine-png)

20 Savage (1977) has defined and categorised the intestinal microflora into two types, autochthonous flora (indigenous flora) and allochthonous flora (transient flora). Autochthonous microorganisms colonise particular habitats, i.e. physical spaces in the intestine, whereas allochthonous microorganisms cannot colonise particular habitats except under abnormal conditions. Most pathogens are allochthonous microorganisms. Nevertheless, some pathogens can be autochthonous to the ecosystem and normally live in harmony with the host, except when the system is disturbed. Establishment and maintenance of the intestinal ecosystem is a complex process and can be influenced by diet, method of birth, and microbe-microbe and microbe-host interactions (Mackie et al., 1999; Savage, 2001). Although the composition of the intestinal microbiota can be quite different among various mammalian species, several aspects are shared by most mammals. For example, establishment of a microflora begins at birth and the first microorganisms to colonise postnatal babies are aerobic and facultative bacteria, such as coliforms, lactobacilli and streptococci (Cooperstock and Zedd, 1983). Colonization by these species is believed to lower the oxidation-reduction potential in the intestine, thereby allowing the establishment of anaerobes such as Bacteroides spp. and Bifidobacterium spp. By introduction of solid food, the intestinal microbiota becomes increasingly similar to that of adults, eventually reaching a stable community in which anaerobes predominate.

Molecular approaches are providing considerable insight into the diversity of the complex gastrointestinal microflora. Much progress has been made towards identifying species that are encountered in the intestinal tract of infants, children and adults. However, there are still doubts about the type of microbes and their numbers in the intestinal tract. Furthermore, the succession of microflora in an individual from infancy to old age is unknown. Finally, the roles of the different microbial cells in the intestinal tract have yet to be addressed. Only further progress in high throughput strategies can place information concerning the intestinal microbes and subsequent impact on our health in context.

21 1.2.2 Traditional approaches for the analysis of microbial diversity To date, much of our knowledge of the intestinal microflora of humans and animals is derived from bacteriological studies that utilised traditional techniques of culture, microscopy and biochemical assays (Finegold, 1983; Holdeman and Moore, 1972; Moore et al., 1978). Culture techniques are used to isolate cultivable bacteria from faecal or intestinal samples. In 1972, Holdeman and Moore simplified and commercialised the strictly anaerobic culture technique devised by Hungate (Holdeman and Moore, 1972). This technique and the later developed anaerobic chamber (glovebox) technology have greatly increased the possibilities for isolation and enumeration of anaerobes from the GIT (Aranki and Freter, 1972). Since then, it became clear that E. coli and enterococci were no longer the principal inhabitants of the human colon: these were outnumbered at least 1000-fold by populations of obligate anaerobic bacteria (Savage, 1977).

Enumeration of specific bacterial genera is generally achieved by plating on selective culture medium (SCM) which is designed to isolate bacteria of a particular type or from a particular nutritional niche based on the metabolism of intestinal strains. SCM are used in investigation of bacterial infections in which a limited number of members of the intestinal microflora are the aetiological agent (Finegold, 1977). SCM are also used in studies of commensal species such as bifidobacteria, lactobacilli and E. coli in the intestine. For example, MacConkey and Ecosine Methylene Blue agars are used for E. coli isolation and both types of media are selective for faecal coliforms (Adami and Cavazzoni, 1996; Baron et al., 1992). Table 1.3 listed some commercially available SCM for different bacterial species. SCM are valuable tools for analysis of the ecology of the intestinal microflora, and development of novel SCM to isolate and enrich uncultivable microorganisms would greatly help to explore the intestinal microflora and also, help in the discovery of new types of probiotic strains. However, the complex nature of the bacterial population in the gut makes it difficult to correlate nutritional functions with the activities of specific microorganisms, therefore SCM have the inherent disadvantages of not absolute selectively and toxicity against certain strains within the genus (O’Sullivan, 2000).

22 Table 1.3 Selective media used for the culture of different strains Bacteria Media* Culture condition E. coli and coliform bacteria MAC, EMBA aerobic Bifidobacterium spp. MRS anaerobic Lactobacillus spp. MRS facultative anaerobic B. fragilis BPRM anaerobic Enterococci CATC aerobic Streptococci KEA aerobic Clostridia NCA, CCA anaerobic

*MAC, MacConkey agar; EMBA, Ecosine Methylene Blue agar; MRS, Man, Rogosa and Sharpe medium; BPRM, Bacteroides Phage Recovery Medium; CATC, Citrate Azide Tween Carbonate medium; KEA, Kanamycin Esculin Azide Medium; NCA, novobiocin colistin agar; CCA, colistin crystal violet agar

1.2.3 Molecular techniques used to investigate gut microflora diversity Traditional approaches have contributed in significant ways to the understanding of gut microflora diversity, but are inherently limited by lack of precision, low levels of reproducibility and are labour intensive, thus limiting effectiveness for analysing a large number of individuals. The advent of nucleic acid-based molecular tools has greatly expanded the ability to reliably identify isolates and also to calculate the evolutionary relatedness between strains. The target analysed within the bacterial chromosome is usually the 16S rRNA gene. Woese et al. (1987) reported that small ribosomal subunit RNA (16S rRNA in bacteria) contains regions of nucleotide base sequence that are highly conserved and that these are interspersed with hypervariable regions, known as V regions. Figure 1.2 illustrates the variable regions (V regions) and the conserved (C regions) of the 16S rRNA gene. These V regions contain the signatures of phylogenetic groups. The taxonomic information contained in 16S rRNA genes forms the basis of molecular analytical methods (Tannock, 2002). The sequence analysis of the 16S rRNA gene (about 1.5 kb in size) has been used as a tool for classifying organisms and evaluating evolutionary relatedness, which has revolutionised the field of microbial ecology and has allowed meaningful phylogenetic relationships between microbes in natural ecosystems to be discerned

23 (Olsen et al., 1994). For instance, the use of 16S RNA sequence analysis has contributed enormously to the phylogeny for the two major genera in the human intestine, Bacteroides and Bifidobacterium (Leblond-Bourget et al., 1996; Shah and Collins, 1989).

Figure 1.2 Sequence structure of bacterial 16S ribosomal RNA gene. The gene is about 1.5 kb in size (from 3’ to 5’ end). The most variable regions (V regions) are in black while the most conserved (C regions) are in red (adapted from Lodish et al., 2000)

The development of a number of genotypic fingerprinting techniques has been a major advantage in deciphering complex mammalian intestinal ecosystems by rapid analysis of colony isolates. Those which techniques have been frequently used for the study of the intestinal microflora are colony hybridization (colony hybridization is the screening of a library with a labelled probe to identify a specific sequence of

24 DNA or RNA), pulsed-field gel electrophoresis (PFGE), ribotyping, and randomly amplified polymorphic DNA (RAPD).

The above methods require prior cultivation of bacteria. However, approximately 60% of the total microscopic count in the human intestine cannot be cultured (Tannock et al., 2000). Only those organisms whose niche could relatively easily be mimicked in the laboratory have been isolated and identified. The low growth rates and anaerobiosis of the majority of gut bacteria make these extremely difficult to study by culture-based approaches (Raskin et al., 1994). These problems have been solved to some extent through the use of recently developed culture-independent molecular technologies. Figure 1.3 shows various molecular approaches for culture- independent analyses of the human intestinal microflora. All of these approaches rely on directly amplifying 16S rRNA/rDNA from intestinal samples using PCR. Denaturing gradient gel electrophoresis (DGGE) is a gel-electrophoretic separation procedure for double stranded DNA of equal size but with different base-pair composition or sequence (Muyzer and Smalla, 1998). DGGE of 16S rRNA amplicons is an exceptional tool to study the bacterial species composition of unknown samples. Since individual bands can be excised and sequenced, the identity of bacteria present in the sample can be determined without cultivation. By inter- sample comparison, dominant shifts in population composition monitored and bacterial population dynamics can be studied in more detail. For example, using primers specific for Lactobacillus 16S rRNA sequences, the Lactobacillus community in three adults over a 2-year period showed variation in composition and stability depending on the individual, while successional change of the Lactobacillus community was observed during the first 5 months of an infant's life (Heilig et al., 2002). Furthermore, DGGE of faecal 16S rDNA amplicons using a Bifidobacterium genus-specific primers from five adult individuals showed host-specific populations of bifidobacteria that were stable over a period of 4 weeks, and B. adolescentis was found to be the most common species in faeces of the human adult subjects in the study (Satokari et al., 2001).

GI Tract Ecosystem Enumeration FISH and/or Flow Cytometry

DNA/rRNA Isolation

Cloning PCR RT-PCR (of PCR products)

DGGE/T-RFLP Sequence Quantitative RT-PCR Quantitative Dot-Blot Hybridisation 16S rDNA Sequences Band Extraction Monitor Specific Species

Primer Design Comparative Analysis Probe Design

Figure 1.3 Various culture-independent molecular approaches to analyse the human intestinal microbial community (Adapted from Vaughan et al., 2000).

Terminal restriction fragment length polymorphism (T-RFLP) uses fluorescence- labelled primer to amplify the 16S rDNA. Following digestion of the fluorescent PCR products with restriction enzymes, the fragments are separated through gel or capillary electrophoresis. The abundance and length of only the fluorescent terminal fragments is determined using an automated sequencer. This yields a pattern or ‘community fingerprint’ similar to the case of DGGE which can be used for assessing the diversity and structure of intestinal microflora populations (Kaplan et al., 2001; Osborn et al., 2000). Dot blot hybridisation is useful to measure the amount of a specific 16S rRNA in a mixture relative to the total amount of rRNA using 16S RNA-based species-specific probes. The relative quantity of rRNA provides a reasonable measure of the relative physiological activity of a specific population. In contrast to most molecular methods referred to above, whole cell fluorescence in situ hybridisation (FISH) with rRNA-targeted oligonucleotide probes is quantitative using FISH, fluorescent labelled oligo probes are hybridised directly to cells fixed on a matrix and are visualised by fluorescence microscopy. Thus enumeration of species in the intestinal tract is best addressed by this approach.

26 Flow cytometry is a rapid and sensitive technique that can determine cell numbers and measure various physiological characteristics of individual cells (Bunthof and Abee, 2002). Specific bacterial species may be detected with antibodies or fluorescent dye-labelled rRNA probes. Several studies have demonstrated the use of flow cytometry in association with FISH and the general conclusion is that this combination is a very powerful tool for the rapid and automated analysis of mixed microbial communities (Davey and Kell, 1996; Porter et al., 1996; Thomas et al., 1997). The feasibility of this technique has been reported for enumeration of bacterial species in the human gut microflora whereby intestinal bifidobacteria were enumerated with good precision when compared with microscopic counts (Schut and Tan, 1999).

Table 1.4 summarises the comparison of some molecular techniques which have been frequently used for the study of the intestinal microflora. Although there are still some technical challenges ahead to make such technology more accessible and affordable, microarray technologies offers great advantages for the future. The potential of microarray technology in microbial ecology studies was demonstrated using microchips containing oligonucleotides complementary to 16S rRNA sequences of nitrifying bacteria that could detect and identify the DNA or RNA isolated from samples containing the target bacteria (Guschin et al., 1997).

1.3 Virulence genes versus adaptive genes – the difference between commensals and pathogens 1.3.1 Definition of virulence genes and adaptive genes Virulence (from the Latin word ‘for poisonous’) or pathogenicity denotes the ability of a microorganism to cause disease. In general, virulence results from the cumulative impact of one or several special properties, or virulence factors. Thus virulence factor is defined as a bacterial product, mechanism or strategy that contributes to virulence and serves to distinguish potential pathogens from harmless strains (Johnson, 1991; Salyers and Whitt, 1994). The particular gene possessed by a

Table 1.4 Comparison of some molecular techniques which have been frequently used for the study of the intestinal microflora, summarised in this study

Colony PFGE Ribotyping DGGE T-RFLP FISH 16S rRNA hybridization sequencing Requires culturing yes yes yes no no no no Rapid technique no no no yes yes no no Discriminatory power high very high medium high very high very high very high Reproducibility high very high very high high high high very high Labour intensive yes yes no no no no no Procedure has been automated no no yes no yes no yes Cost low high low high high high high Required use of restriction enzymes no yes yes no no no no Requires sequence knowledge no no no yes yes no yes Surveys the entire genome rather no yes no no no no no than a sub-section

28 pathogen which encodes a virulence factor is defined as a virulence gene (VG), which includes genes encoding adhesins, host cell surface-modifying factors, invasins, toxins and secretion systems involved in bacterial pathogenicity. These VGs are often carried by a variety of pathogenicity islands (PAIs), bacteriophages, plasmids and/or transposons (Nataro and Kaper, 1998; Ochman and Jones, 2000). In contrast to VG, bacteria also possess a repertoire of adaptive genes in order to perform essential metabolic function for survival or improvement of bacterial fitness in novel environmental niches. These include some housekeeping genes which encode certain housekeeping enzymes that are ubiquitously present in almost all living organisms and perform essential metabolic functions for the purpose of survival, structural genes and essential genes which are generally not regarded as virulence genes. However, the border between virulence genes and adaptive genes remains poorly defined (Wassenaar and Gaastra, 2001). Recent reports have shown that certain housekeeping enzymes are found on the surface of microbial pathogens and perform a variety of unrelated functions, and may act as putative virulence factors and targets for the development of new strategies to control infection (Alderete et al. 2001; Chhatwal, 2002; Pancholi and Chhatwal, 2003). For instance, the secretion of a housekeeping enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), was critical to initiate signal transduction events in epithelial cells by enteropathogenic E. coli (EPEC) (Kenny and Finlay, 1995). The problem is enlarged when considering the numbers of genes existing in both pathogens and non- pathogenic bacteria. For example, genes for pathogens obtaining nutrient and energy from the host body are normally regarded as virulence genes since it is clearly essential for infection. Nonetheless the same genes exist in non-pathogenic strains with only the purpose of bacterial survival (Wassenaar and Gaastra, 2001).

There is still not complete agreement on what criteria are necessary and sufficient to distinguish virulence genes from adaptive genes. However, the criteria designated as ‘Koch’s postulates for genes’ have come to be widely accepted as listed below (Koch, 1893).

29 ‘Kochs Postulates for Genes’ • The gene or its product should be found in strains of bacteria that cause the disease and not in avirulent strains of the bacterium • Disruption of the gene should reduce virulence or on transfection into an avirulent strain should increase virulence • The gene should be shown to be expressed by the bacterium at some point during the infectious process when it is in the host • Adaptive genes can be regarded as virulence genes, when their inactivation results in attenuation of virulence

Virulence genes can be loosely classified into two categories: those that promote bacterial colonization and invasion and those that cause damage to the host. In each category, different pathogenic bacteria possess unique repertoire of virulence genes to cause diseases. For the purpose of this thesis, the virulence genes displayed by E. coli strains that promote the establishment of infection and damage the host to produce the disease state will be reviewed in detail below.

1.3.2 Possession of virulence genes in E. coli E. coli is the predominant facultative anaerobe found in the GIT of human and animals. While commensal E. coli strains are a typical component of colonic flora, the pathogenic types of E. coli can cause both enteric and diarrhoeal disease, as well as extraintestinal infections of the urinary tract, neonatal sepsis and meningitis (Kaper and Hacker, 1999). On the basis of their distinct virulence properties and the clinical symptoms of the host, pathogenic E. coli strains are divided into eight pathotypes. Pathotype is defined as a classification of E. coli into groups that have a similar mode of pathogenesis and cause clinically similar forms of disease (Donnenberg and Whittam, 2001). Strains of the same pathotype are genetically similar and carry the same set of VGs involved in infection (Bekal et al., 2003). Table 1.5 summarised the clinical symptoms, pathogenicity mechanisms, virulence factors and strains serotypes of the eight major pathotypes of pathogenic E. coli, based on the published information (Donnenberg and Whittam, 2001; Kaper and Hacker, 1999; Gyles, 1992; Savarino et al., 1996).

30 Table 1.5 Review of the major pathotypes of pathogenic E. coli Extraintestinal Intestinal pathogenic E. coli pathogenic E. coli (IPEC) (ExPEC) Type of Urinary Tract Neonatal Gastrointestinal Disease infection Infections Meningitis (GID) (UTI) (NM)

Uropathogenic Enterotoxigenic Enteroinvasive Ec Enterohemorrhagic Enteropathogenic Enteroaggregative Diffusely Pathotype NMEC Ec. (UPEC) Ec (ETEC) (EIEC) Ec (EHEC) Ec (EPEC) Ec (EAggEC) adherent Ec (DAEC)

Clinical (UTI) in Meninges watery diarrhea dysentery-like pediatric diarrhea, infantile diarrhea; persistent diarrhea mild, no-bloody Syndrome anatomically- inflammation in infants diarrhea (mucous, copious bloody watery diarrhea in young children diarrhea normal, &travelers; no blood), severe discharge similar to ETEC, without unobstructed inflammation, no inflammation, fever (hemorrhagic colitis), some inflammation, no urinary tracts fever intense inflammatory inflammation, no fever response fever

Virulence P fimbria K-1 antigen fimbrial adhesins outer membrane fimbriae non fimbrial ST-like toxin efa factors Type 1 fimbriae siderophore LT & ST protein: ipaH Shiga -like toxins adhesin ( intimin) (east-1) siderophores endotoxin enterotoxin Shiga-like toxins aggC hemolysins bfpA hemolysin K antigen

Pathogenicity adherenceadherence adherence, non- nonfimbrial adherence, non fimbrial adherence, non- adherence, mechanisims invasive adhesins, invasive moderately invasive adhesin invasive non-invasive

O:H serotype O1.H4 O1.H6 O18ac:H7:K1 O6.H16 O8.H9 O28.H- O112a,c.H- O4.H- O5.H- O16.H6 O18a,c.H7 O20.H26 O7.H- O77.H18 O1.H7 O1.H- O11.H27 O15.H11 O124.H30 O26.H11 O26.H21 O20.H34 O25.H1 O86.H- O126.27 O2.H1 O2.H4 O20.H- O25.H42; O124.H32 O124.H- O26.H32 O46.H31 O26.H11 O26.H- O4.H5 O6.H1 O25.H- O27.H7 O136.H-O143.H- O48.H21 O55.H7 O44.H34 O55.H6 O127.H2. O7.H4 O7.H6 O78.H11 O78.H12 O144.H- O152.H- O91.H10 O91.H21 O55.H7 O55.H- O7.H- O128.H7 O159.H-O167.H4 O98.H- O111.H2 O86.H27 O86.H34 O18a,c.H7 O148.H28 O167.H5. O111.H8 O111.H- O86.H- O91.H7 O18a,c.H- O149.H10 O113.H21 O117.H14 O91.H- O111.H2 O22.H1 O25.H1 O159.H20 O118.H12 O119.H6 O111.H12 O111.H- O75.H5 O75.H-. O16,O148.H28 O125.H- O126.H8 O114.H10 O173.H-. O128.H2 O145.H- O114.H32 O119.H6 O157.H7 O157.H- O119.H- O125.H21 O172.H-. O125.H- O126.H2 O126.H- O127.H9 O127.H21 O127.H40 O127.H- O128.H2 O128.H7 O128.H8 O128.H12 O128.H- O142.H6 31 O158.H23.

As previously mentioned, virulence gene can be loosely classified into two classes: (i) those that cause damage to the host and (ii) those that promote bacterial colonization and invasion. Toxins represent the first class of VG, which are the most obvious VGs found in practically all pathogenic E. coli. Some toxins show a strong association with specific pathotypes. The heat-labile toxins LT1 and LT2 (gene designations eltIA and eltIIA respectively) and heat-stable toxins STa and STb (gene designations stIA and stIB respectively) are associated with enterotoxigenic E. coli (ETEC) (Gyles and Barnum, 1969; Gyles, 1992). These toxins can bind to the surface of enterocytes in the gut and cause the accumulation of cAMP (LT) or cGMP (ST), which indirectly leads to massive loss of electrolyte and water from cells (Nataro and Kaper, 1998). The Shiga toxins Stx1 and Stx2, encoded by the genes stx1 and stx2 respectively, are prominent toxins and give rise to the Shiga toxin- producing E. coli pathotype STEC. After internalization into cells, Stx1 and Stx2 depurinate specific residues of the host cell ribosomal RNA, thereby blocking the binding of aminoacyl tRNA to the ribosome and inhibiting protein synthesis (Endo et al, 1988; Saxena et al., 1989). The α-hemolysin (hlyA) is a pore-forming toxin of the RTX (repeats in toxin) family (Welch et al., 1992) and is predominantly detected in UPEC strains. It is also a marker of most porcine ETEC and STEC, which cause edema disease in pigs. The heat-stable toxin EAST1 (astA), which seems to have an active site and a mechanism of action similar to STa, has been found distributed in all types of enteric E. coli of clinical significance (Savarino et al., 1996).

The second class of VG include numerous virulence genes encoding adhesins, host cell surface-modifying factors, invasins and secretion systems involved in E. coli colonization, invasion and intracellular residence. Attachment is considered a necessary first step in the colonization of host surfaces which avoids the bacteria from being swept along by the normal flow of body fluids (blood, urine and intestinal contents) and eliminated (Salyers and Whitt, 1994). Strain of a specific pathotype generally contains unique fimbrial genes. For example, P fimbriae (pap/prs), F1C fimbriae (foc) and S fimbriae (sfa) are typical for UPEC. These fimbriae are reported to be responsible for adherence to the host mucosa at different stages of infection (Johnson, 1991). ETEC have a specific adhesion structure, CFA/I (cfa/I) and CFA/II (cfa/II), which allow ETEC to adhere to the intestinal mucosa of the small bowel

(Nataro and Kaper, 1998). EAEC contains an aggregative adherence fimbriae AAF/I (aaf/I), which is a bundle-forming pilus contributing to the lesions caused by EAEC (Nataro et al., 1992). Type I fimbriae (fim/pil) are not restricted to pathogenic E. coli and can be found in most commensal or lab strains such as E. coli K-12. The afimbrial adhesin intimin (eae), responsible for intimate adherence to epithelial cells, is typical for EPEC and EHEC strains. The eae gene is indicative for the presence of a pathogenicity island called the ‘locus of enterocyte effacement’ (LEE) which also encodes a type III secretion system and several secreted proteins (Frankel et al., 1998; McDaniel et al., 1995). In addition to the eae gene, a bundle-forming pili (bfp) encoded by the EAF plasmid is typical for EPEC (Donnenberg et al., 1992; Giron et al., 1991).

Invasion plasmid antigen (ipaH) is typically identified with other invasion-associated factors in the EIEC pathotype and in Shigella, although the function of the ipaH gene remains unknown (Hartman et al., 1990). Two plasmid genes, sepA - encoding serine protease and ospD3 (senA) - encodeing an enterotoxin, were found also to be involved in the pathogenicities of EIEC and Shigella (Nataro et al., 1998). The high efficiency iron uptake system is mediated by the siderophore aerobactin. The gene products of fyuA, iutA and ireA are involved in biosynthesis of aerobactin in ExPEC (Johnson and Stell, 2000), and chuA acts as a heme transport gene found in the EHEC stain O157:H7 (Clermont et al., 2000). Capsule polysaccharides have antiphagocyte activity and protect bacteria against complement-mediated serum killing (Jann and Jann, 1992; Whitfield et al., 1997), which is frequently encountered in UPEC and NMEC. The most frequent capsular types found in those two pathotypes are K1 and K5 (encoded by kpsMT K1 and kpsMT K5 respectively) (Johnson and Stell, 2000).

1.3.3 E. coli phylogeny and diversity A phylogeny is a representation of organisms based on and describing evolutionary relationships. Phylogenetic classification is concerned with grouping individual species into evolutionary categories. Since the early 1980's, phylogenetic classification has been made much more facile by the invention of molecular

taxonomy, which based on the nucleotide sequence divergence at individual loci (genes) (Gupta and Griffiths, 2002).

The ECOR set is a reference collection of 72 wild-type E. coli isolates from humans and 16 other mammalian species obtained from a larger collection of approximately 2,600 isolates and is thought to broadly represent genotypic variation in E. coli (Ochman and Selander, 1984). Phylogenetic analyses have divided these ECOR isolates into four main phylogenetic groups (A, B1, B2, and D) based on multilocus enzyme electrophoresis (MLEE), in which isolates are characterised by the relative mobilities of allelic variants at 11 to 20 metabolic enzyme loci (Ochman and Selander, 1984). These phylogenetic grouping results have been confirmed by ribotyping (Clermont et al., 2001), and recently by using a triplex PCR method developed by Clermont et al (2000).

Pathogenic and commensal E. coli are phylogenetically different. Most E. coli strains responsible for urinary tract infection (UTI) and other extraintestinal infections belong to group B2 or, to a lesser extent, to group D (Bingen et al., 1998; Picard et al., 1999). Group B2 strains carry more virulence genes (e.g. genes encoding fimbriae, aerobactin and the capsular antigens) than do the strains belonging to the other groups. This is a possible reason that group B2 strains have superior capacity to persist in the intestinal microflora (Nowrouzian et al., 2005). Contrary to extraintestinal E. coli, most commensal strains belong to group A or B1. Moreover, each pathotype of intestinal pathogenic E. coli shows phylogenetic diversity and they are distributed among all the phylogenetic groups (Czeczulin et al, 1999; Pupo et al., 1997; Rolland et al., 1998).

E. coli populations consist of stable genetic lineages termed ‘clones’ or ‘strains’. Various intestinal or extraintestinal E. coli infections have been linked to specific clones or groups of related clones. A hypothesis of E. coli as a relatively benign organism obtaining novel sequences from an outside source and thereby becoming pathogenic or switching disease syndrome or host has been supported by many studies (Blum et al., 1995; Hacker et al., 1990; Knapp et al., 1986). For example, Whittam et al. (1993) have proposed that O157:H7 clone emerged when an EPEC

O55:H7-like progenitor was lysogenised by a bacteriophage containing Shiga-like toxin genes. Two mechanisms maybe involved in this virulence gene acquirement: vertical and horizontal gene transfer. Vertical gene transmission gives rise to bacterial lineages that are related by descent. New lineages arise as a consequence of mutation. Bacteria also have the ability to exchange genetic material with cells of different strains or species through the processes of conjugation, transformation and transduction, which is known as horizontal gene transfer (Sprat and Maiden, 1999). Horizontal gene transfer results in genetic diversification through the introduction of new genetic material into a lineage, but it can also lead to genetic homogenization as genetic material becomes distributed among lineages. Thus the genetic structure of a bacterial population or species is the net result of the process of mutation and horizontal gene transfer coupled with the outcomes of selection (Gordon et al. 2002).

1.3.4 Detection of virulence genes The polymerase chain reaction (PCR) allows the fast and direct detection of VGs in a sample even without prior cultivation. The high sensitivity of the PCR assay is particularly useful for the detection of contamination in food and also for the analysis of clinical samples. A major improvement of this technique is the quantitative real- time PCR approach which allows both qualitative and quantitative determination (Heid et al., 1996; Holland et al., 1991). Diagnostic kits targeting the EHEC- associate VG for food-borne pathogenic E. coli using TaqMan real-time PCR have been used in food hygiene (Jothikumar and Griffiths, 2002). Another improvement has been the development of the multiplex PCR technique, which allows detection of several VGs in a single PCR reaction in order to save time and reduce cost. Johnson and Stell (2000) have developed a multiplex PCR technique to screen 30 ExPEC genes. The primer pairs for the 30 VGs were grouped into 5 pools (6 gene primers in each pool), based on primer compatibility and amplicon size. Therefore, a strain could be screened for 30 VGs using only 5 PCR reactions.

While PCR methods are highly specific, they are unable to identify certain variants of the target genes if the variation occurs in the primer sequence. Hence hybridization methods using gene probes are also frequently used. Many probes have been designed and used in hybridization assays for specific detection of virulence

genes. These target either toxin- or adherence factor-encoding genes or markers (Nataro and Kaper, 1998). Using DNA colony blot hybridization, VGs can be screened in an E. coli population, although the standard hybridization procedure normally takes 2 to 3 days to complete (Vaughan et al., 2000).

Since many virulence factors are encoded on mobile genetic elements, strains with new combinations of VGs may emerge in the future. Association of specific VGs with certain categories of E. coli may consequently become more and more difficult. Hence, a system to assess the virulence patterns of E. coli isolates comprising the entire set of known major VGs would be of great help in order to better characterise clinical E. coli isolates as well as strains from food, water and the environment. It will also be useful in gaining a clearer picture of pathogenic potential and to detect newly emerging clones of pathogenic E. coli. New techniques have recently been used for obtaining a complete picture of the virulence attributes of a given strain. For example, Kuhnert et al. (1997) have developed a reverse dot-blot hybridization procedure which was able to detect 24 VGs from different pathotypes (Kuhnert et al., 1997). In brief, 24 unlabelled DNA probes for those VGs were bound to nylon membranes, the filters which represent arrays of E. coli VGs were then hybridised to labelled genomic DNA of E. coli strains to be tested. Bekal et al. (2003) have developed an E. coli virulence factor DNA microarray composed of 105 DNA probes targeting 91 E. coli VGs. All the gene probes were printed on glass slides and arranged in 8 subarrays corresponding to 8 different E. coli pathotypes. Fluorescently labelled genomic DNAs from strains were hybridised to this microarray and the hybridization pattern analysis permitted a rapid assessment of virulence attributes. Virulence genes belonging to two different pathotypes were detected in certain E. coli isolates by both methods. Additionally, the multitude of VGs present on the microarray may greatly facilitate the detection of VGs acquired by horizontal transfer and the identification of emerging pathotypes.

1.4 Host-bacteria interactions 1.4.1 The structure of the gut epithelium A monolayer of epithelial cells lines the luminal aspect of the intestine and serves a number of functions that are essential to the host. These include contributions to

nutrient digestion and absorption, regulation of water and electrolyte transport and protection against luminal microorganisms (Mahida, 2004). The gut epithelium consists of a rapidly renewing epithelial stem cell population that differentiate into four intestinal epithelial cell types (enterocytes, goblet cells, enteroendocrine cells, and Paneth cells). Each of these cell types plays a role in cytoprotection of the intestinal mucosa (Cheng and Leblond, 1974). Enterocytes are formed in both small and large intestine, and comprise > 80% of the epithelial cell types. Goblet cells, producing both mucus and trefoil peptides required for epithelial growth and repair, are present throughout the intestine, but increase in density along the proximal-distal axis (McCracken and Lorenz, 2001). Enteroendocrine cells produce neuroendocrine molecules having a paracrine effect that integrate the intestine with the systemic nervous system and are dispersed throughout the intestine (Furness et al., 1999). The above three types of epithelial cells have a life cycle of about 2-5 days (Cheng and Leblond, 1974; Stappenbeck et al., 1998). The dead cells are exfoliated, along with any adherent bacteria, which may assist in the elimination of pathogens. Paneth cells locate at the base of the small intestinal crypt, are usually absent in the colon and secret the antibiotic peptides known as defensins (McCracken and Lorenz, 2001). Paneth cells have longer life spans, surviving for up to 20 days, whereupon they are phagocytosed (Troughton and Trier, 1969).

The factors that regulate stem cell differentiation into specific lineages of epithelial cells remain to be fully characterised. Some reports suggest that factors such as the resident intestinal flora, intestinal infection with nematodes, T lymphocytes, macrophages and myofibroblasts may be involved. The maintenance of normal interactions between the resident flora and epithelial cells is also dependent on signals to epithelial cells from these other types of cells in the mucosa. Such mucosal cell-cell interactions are facilitated by pores in the basement membrane which not only allow access to secreted products (such as cytokines) but also enable direct contact with epithelial cells.

In the small intestine, the surface of intestinal mucosa projects into the lumen via fingerlike villi and microvilli. At the base of the villi there are crypts which supply cells to the villi. Several crypts surround the base of each villus (Bloom and Fawcett,

1962; Potten and Loeffler, 1990). The large intestine has a flat epithelial surface, which is supplied from several tubular shaped crypts and the cells are extruded directly from the flat surface into the lumen.

1.4.2 The role of enterocytes in intestinal barrier integrity Intestinal epithelial cells are unique in that they represent the only host cells that are constantly interacting with a very large bacterial population in the lumen. Epithelial protective responses may be divided into a secretory barrier designed to avoid microorganisms or their products reaching the cell surface and a physical barrier across the epithelial monolayer. An overlying mucus layer mediates the secretory barrier function. Mucin glycoproteins and members of the trefoil factor family contribute to gel-forming properties and protective functions of the mucus layer overlying epithelial cells (Kindon et al., 1995; Rhodes, 1997). Intestinal epithelial cells protect the host by providing a strong physical barrier and producing a variety of innate antimicrobial defences. This physical barrier is initially established in humans within 48 h of birth through membrane closure, which limits systemic exposure to antigens (Bines and Walker, 1991). Each epithelial cell maintains intimate association with its neighbours and seals the surface of the gut with tight junctions.

Transepithelia penetration occurs by either active or passive mechanisms. The transcellular route is well-adapted for active transport of selected electrolytes and nutrients, but provides an effective barrier to bacteria, and to passive movement of ions and hydrophilic solutes. This selective paracellular permeability across an intact epithelial monolayer is regulated by tight junctions that encircle epithelial cells at the apical end of lateral membranes (Madara, 1998). Tight junctions are dynamic structures whose function can be regulated by external stimuli such as cytokines (Nusrat et al., 2000).

The gut epithelial barrier does not completely prevent luminal antigens from entering the tissue. Antigens can cross the epithelial surface through breaks in tight junctions, perhaps at villus tips where epithelial cells are shed, or through the follicle-associated epithelium (FAE) that overlays the organised lymphoid tissues of the gut wall

(Neutra et al., 2001). FAE contains specialised epithelial cells termed M cells with the function of transporting luminal antigens into the dome area of the follicle. Antigen-presenting dendritic cells (DC) also send process between gut epithelial cells without disturbing tight junction integrity and sample commensal and pathogenic enteric bacteria (Nisess et al., 2005; Rescigno et al., 2001). The gut epithelial barrier therefore represents a highly dynamic structure that limits, but does not exclude, antigens from entering the tissues, whereas the immune system constantly samples gut antigens through the FAE and DC processes.

Despite its robust and multi-faceted nature, the intestinal barrier can be breached. Local infections by bacteria and viruses, exposure to toxins or physical insults, and a variety of systemic diseases lead to disruption (Kim et al., 1998; Mahida et al., 1996). Pathogenic bacteria and toxins therefore may impair epithelial barrier function via an effect on tight junctions (Berkes et al., 2003). For example, B. fragilis secretes a 20 kDa metalloprotease enterotoxin that is capable of disrupting the paracellular barrier function of intestinal epithelial monolayers and inducing intestinal inflammation and diarrhoea in humans and animals (Obiso et al., 1997; Sanfilippo et al., 1998). This toxin specifically cleaves the extracellular portion of E-cadherin present in adherent junctions, which are present below tight junctions in epithelial monolayers (Wu et al., 1998). In addition, enteropathogens such as E. coli, Shigella and salmonellae exploit intestinal epithelial cells in many ways, including the manipulation of normal cellular function, or of cellular structural components, or by the induction of signalling pathways, such as the production of pro-inflammatory cytokines (e.g. tumour necrosis factor-α, TNF-α) (reviewed by Lu and Walker, 2001).

The critical first task for the host following disruption of the intestinal epithelium is to cover the denuded area and re-establish the intrinsic barrier. This rapid restoration of epithelium is accomplished by a process called ‘restitution’ where epithelial cells adjacent to the defect flatten and migrate over the exposed basement membrane. In the small intestine, this process is aided by a rapid contraction and shortening of the affected villi, which reduces the area of basement membrane that must be covered. Restitution provides a rapid mechanism for covering a defect in the barrier and does

not involve proliferation of epithelial cells. It results in an area that, while protected, is not physiologically functional. Healing requires that the epithelial cells on the margins of the defect proliferate, differentiate and migrate into the damaged area to restore normal cellular architecture and function. Restitution has been shown to be stimulated by a number of mostly paracrine regulators. Local prostaglandins and trefoil proteins are clearly involved in this process, and suppression of their production significantly delays restitution. The restitution process can also be enhanced by cytokines such as transforming growth factor-β (TGF-β) derived from underlying myofibroblasts (McKaig et al., 1999), live probiotic strains such as L. rhamnosus GG (Yan and Polk, 2002), and short-chain fatty acids (SCFA) derived from the breakdown of luminal carbohydrates by resident intestinal bacteria (Gibson et al., 1999).

1.4.3 Inflammatory response in the gut In developing countries, many infectious diseases involve the gut. The gut protects itself using the abundant lymphoid tissue and immune cells it harbours. Intestinal pathogens are capable of inciting inflammation in the intestinal mucosa, which cause the release of cytokines, chemokines, and the recruitment of inflammatory cells (Berkes et al., 2003). The means by which enteric pathogens initiate inflammatory signals is complex and only beginning to be elucidated. After bacterial adherence or invasion, epithelial cells respond by secreting or expressing a characteristic pattern of cytokines, adhesion molecules and major histocompatibility complex (MHC) class II molecules. These molecules, whose expression is regulated by various nuclear transcription factors and proteins kinases, recruit a wide variety of effectors cells, including neutrophils, monocytes, lymphocytes, and eosinophils to the site of infection (Mahida, 2004). Host cell uses distinct receptors (designated pattern- recognition receptors) for recognition of highly conserved structures of microorganisms, which are called pathogen-associated molecular patterns (PAMPs) (Medzhitov and Janeway, 2000). Secretion of cytokines is an important component of the innate host response. These cytokines enable regulation of adaptive immunity and facilitate host inflammatory responses. Epithelial cells at mucosal surfaces are capable of responding to pathogens by secreting chemokines that recruit circulating polymorphonuclear cells and monocytes (Mahida and Johal, 2001). The expression

of chemokine genes is mediated via activation of the NF-κB family of transcription factors, following recognition of PAMPs. NF-κB plays a key role in transcriptional activation of various genes involved in mucosal immune and proinflammatory responses (Jobin and Sartor, 2000). Many genes activated by NF-κB were found to possess the consensus binding site: GGGR(C/A/T)TYYCC (Baeuerle, 1991). The lack of responsiveness of intestinal epithelial cells to the resident microflora appears to be due to either absent or low expression of CD14, TLR2 and TLR4 molecules and the co-receptor MD-2 (Abreu et al., 2001; Melmed et al., 2003).

In westernised countries, most infectious diseases of the gut are largely under control, yet gastrointestinal food allergies and idiopathic inflammatory conditions have dramatically increased (Steidler et al., 2000). Unregulated, ongoing mucosal inflammation is characteristic of inflammatory bowel disease (IBD) which proceeds through stages of initiation, amplification, and healing. Two main types of IBD exist: Crohn’s disease and ulcerative colitis (UC). Patients suffer from chronic diarrhoea and weight loss, abdominal pain, fever, and fatigue. There is accumulating evidence from animal models that the luminal flora plays a critical role in the initiation and perpetuation of colitis. For example, the absence of commensal bacterial under germ- free conditions leads to an absence of or reduction in IBD disease (Sartor, 2004). Consistent with evidence from human IBD, there is evidence in some models that CD4 T cell lines reactive to enteric bacterial antigens can cause colitis (Cong et al., 1998). For instance, Crohn’s disease bears the immunological stigmata of an exaggerated CD4 T helper 1 (Th1) response. Intestinal CD4 T cells isolated from Crohn’s patients produce large amounts of the Th1 signature cytokine interferon-γ (IFN-γ) (MacDonald and Monteleone, 2001) and display marked overexpression of the Th1 cell-specific transcription factor, T-bet (Neurath et al., 2002). Mucosal macrophages from Crohn’s patients also produce large amounts of the Th-1 inducing cytokines IL-12 and IL-18 (Monteleone et al, 1997; Monteleone et al., 1999). In addition, the break or lack of tolerance might be due to an imbalance between protective and aggressive luminal bacterial species, a decreased barrier function and an impaired mucosal clearance allowing access of bacteria to the mucosal immune system. Whereas no individual component of the flora has been identified as causal, reduced numbers of Lactobacillus and bifidobacteria have been found in colonic

biopsies of patients with IBD, and the presence of increased numbers of mucosal adherent and perhaps even intraepithelial bacteria in the mucosa of IBD patients has been noted (Schultz and Sartor, 2000).

1.4.4 Immunomodulative function of probiotics Some intestinal commensal strains such as lactobacilli and bifidobacteria have been considered as probiotics with beneficial health effects, including enhanced lymphocyte proliferation (Kirjavainen et al., 1999), innate and acquired immunity (Gill et al., 2000), and anti-inflammatory cytokine production (Pessi et al., 2000). Several experimental models have been used to evaluate the immunomodulative role of bacteria. Perhaps the most reductionist approach is to cultivate cell lines in vitro. Table 1.5 summarises the ability of some probiotic bacteria to modulate immune responses in vitro. These results were obtained by studying interactions between probiotic bacteria or their components with immune cells such as mononuclear cells, splenocytes and human epithelial adenocarcinoma cell lines under controlled laboratory conditions. The use of these cells has been particularly useful for elucidating direct interactions between probiotic bacteria and immune on intestinal epithelial cells. Close encounters between large numbers of probiotic bacteria and discrete populations of immune cells under in vitro conditions can result in cytokine activation as well as increased antibody production (Isolauri, 2001). However, since dietary probiotic bacteria in the intestinal lumen will hardly have direct access to any immune cell because of the intestinal epithelial barrier (with the exception of certain enteric bacteria may gain entry through Peyer’s patches and access to immune cells) (Philpott et al. 2000), one cannot realistically expect that similar levels of immunomodulation actually occur throughout the entire gut wall interphase. Also, results from using epithelial cell lines should be interpreted cautiously because of the absence from these models of immune cells.

A more accurate and realistic picture can be obtained by animal experimentation. By using a mouse model, Chin et al. (2000) have found that splenocytes from mice fed the probiotic strain L. fermentum had significantly higher levels of cellular immune (CMI) responses against ovalbumin compared to the unfed controls. These CMI responses were not observed in mice fed either yeast or S. thermophilus. The results

clearly demonstrated that: (i) different bacterial strains when administered orally can influence the way the immune system responds to further antigenic stimulation, and (ii) an efficacious probiotic bacterium can enhance cellular immune responsiveness without the need for dietary antigen priming. Recently, a food-derived strain of L. rhamonsus HN001 has received research attention as a probiotic strain (Tannock et al., 2000; Zhou et al., 2000). Humans and mice fed HN001 exhibited increased NK cell tumoricidal activity (Gill et al., 2000). Mice that were fed HN001 during antigen sensitization produced higher levels of lymphocyte-derived IFN-γ, IL-4 and IL-5 in comparison to control animals (Cross et al., 2002). The ability of HN001 to invoke mixed lymphocyte cytokine production during an on-going Th2-type immune response in vivo suggested that this probiotic strain is a general immunostimulatory agent.

Table 1.6 Review the ability of some probiotic bacteria to modulate immune responses in vitro. Type of cells Bacteria strains Immune response Reference Peripheral blood L. rhamnosus Production of Th1 Miettinen et mononuclear cells L. bulgaricus cytokines (e.g. IL-12, IL-18 al., 1998 and IFN-γ) Spleen cells L. casei Shirota IL-2, IL-12 and IFN-γ Matsuzaki and Chin, 2000 Peripheral blood L. johnsonii La1 Activate null killer (NK) Haller et al., mononuclear cells cells 2000 Colorectal Nissle 1917 VSL#3 induced mucins but Otte and carcinoma cell not IL-8 secretion Podolsky, line T84 Probiotic 2004 mixture VSL#3- VSL#3 + Nissle 1917 8 LAB strains reduced the IL-8 secretion

VSL#3 induced mucins

VSL#3 and Nissle 1917 diminished S. dublin- induced cell death Colorectal B. subtilis (natto) Induced IL-6 and IL-8 Hosoi et al, adenocarcinoma secretion 2003 cell line Caco-2 Colonic L. rhamnosus Inhibited cytokine-induced Yan and carcinoma cell GG apoptosis Polk, 2002 kine HT 29

Much of the immune function of probiotic bacteria reported have been done in laboratory rodents and care should be taken when extrapolating these findings to humans. However, this type of research does provide a basis for the design of the human trials which are so desperately needed.

1.5 Thesis objectives This thesis is primarily focused upon increasing our knowledge about the tripartite interaction between bacteria and bacteria in the enteric community; between bacteria and the host animal and finally, between the host immune response (innate or acquired) on the plethora of microbes that inhabit the gastrointestinal tract.

The first and second issue of the tripartite interaction involving bacteria to bacteria, and bacteria to host interactions will be addressed in Chapter 3 by designing a field trial that involves feeding a commercial probiotic formulation to newly inducted feedlot cattle. The purpose of doing this will be to: 1. Assess whether the population dynamics of a select subset of cultivable bacteria species will be affected by probiotic feeding 2. Investigate if probiotic feeding will affect the host immune response 3. Answer the question as to whether growth performance and carcase quality of feedlot cattle will be influenced by probiotic feeding

Although there is considerable diversity of microorganisms in the gastrointestinal tract of cattle, there is very little information as to their origin. For example, Bacilli are ubiquitous environmental microbes and yet, their use as a probiotics has been vigorously advocated. The speciation and enumeration of bacilli in cattle faeces will be addressed in Chapter 4 using a new molecular technique that involves amplification and restriction digestion of 16S rDNA (Amplified Ribosomal DNA Restriction Analysis or ARDRA).

E. coli is considered to be one of the most genetically diverse microbes in the GIT of animals. They are mostly commensals and sometimes pathogens. Ingested

pathogenic E. coli must compete against commensal E. coli and to understand this bacteria to bacteria interaction, a different approach will be adopted in Chapter 5 to investigate diversity in this species using a panel of virulence genes. The objectives here are as follows: 1. Develop multiplex PCR to characterise the virulence genes in clinical and commensal E. coli isolates with human origins 2. Determine the phylogenetic relationships of E. coli clones

To further understand the interplay between commensal and pathogen, a pig model of post-weaning diarrhoea will be used in Chapter 6 to: 1. Profile the virulence genes of commensal and pathogenic E. coli 2. Analyse the diversity of porcine E. coli by phylogenetic grouping, serotyping and DNA fingerprinting using pulsed-field gel electrophoresis (PFGE).

Finally, the third arm of the tripartite interaction involving bacteria and host interaction will be modelled in vitro by examining the primary signalling events between bacteria and intestinal epithelial cells. More specifically, Chapter 7 will: 1. Establish the baseline level of expression of 31 genes involved in primary signalling events that engage the innate immune system and its panel of TOLL-like receptors intestinal repair and re-growth factors such as trefoil factor; early inflammatory signal transduction processes involving the participation of NF-κB and cox-2; cytokine activation and chemokine production, vital components in the recruitment of trafficking immune cells to the sub and intra-intestinal epithelium 2. Compare the effects of commensal and pathogenic E. coli on gene activation in a human intestinal epithelial cell line 3. To examine the effects of lactic acid bacteria (LAB) on gene activation in a human intestinal epithelial cell line

Chapter 2

General Materials and Methods

2.1 List of ATCC and commercially sourced bacteria strains 2.2 Bacterial culture media 2.2.1 Non-selective bacterial culture media 2.2.2 Selective culture media (SCM) 2.3 Cell culture media 2.4 Bacterial growth and storage 2.4.1 Recovery of bacteria from environmental samples 2.4.2 Freezedown storage 2.4.3 Microbead storage 2.5 Bacterial counting 2.6 Cell culture and storage 2.6.1 Revival and growth of cell Lines from frozen storage 2.6.2 Maintenance and subculture of cells 2.6.3 Preparation of frozen stocks 2.7 DNA and RNA extractions 2.7.1 Bacterial genomic DNA extraction 2.7.2 RNA extraction 2.8 PCR and gel electrophoresis 2.9 DNA cloning and sequencing 2.9.1 Competent cell preparation 2.9.2 Ligation and transformation 2.9.3 Plasmid DNA extraction and sequencing 2.10 Statistic analysis

2.1 List of ATCC and commercially sourced bacteria strains The ATCC reference bacterial strains and other commercially sourced strains used in this study are listed in Table 2.1.

Table 2.1 Reference strains used in this study

Strain Code Sources* Bacillus badius ATCC14574 ATCC Bacillus cereus ATCC 14579 ATCC Bacillus circulans ATCC 15518 ATCC Bacillus clausii ATCC 700160 ATCC Bacillus coagulans ATCC 7050 ATCC Bacillus laterosporus ATCC 64 ATCC Brevibacillus laterosporus ACM 5117 ACM Brevibacillus licheniformis ATCC 25972 ATCC Bacillus pumilus ATCC 21356 ATCC Bacillus sphaericus ATCC 14577 ATCC Bacillus subtilis ATCC 6633 ATCC Bacillus thuringiensis ATCC 10792 ATCC Bifidobacterium bifidum ATCC 29521 ATCC Campylobacter faecalis ATCC 33710 ATCC Clostridium tertium ATCC 14573 ATCC Enterobacter agglomerans ATCC 27989 ATCC Enterococcus faecium ATCC 19434 ATCC Fusobacterium mortiferum ATCC 25557 ATCC Lactobacillus acidophilus ATCC 4356 ATCC Lactobacillus delbrueckii ATCC 11842 ATCC Lactobacillus rhamnosus ATCC 7469 ATCC Mycoplasma hyopneumoniae ATCC 25934 ATCC Paenibacillus larvae ATCC 9545 ATCC Paenibacillus lentimorbus ATCC 14707 ATCC Paenibacillus polymyxa ATCC 842 ATCC Pediococcus damnosus ATCC11309 ATCC Propionibacterium jensenii ATCC 4868 ATCC Salmonella typhimurium ATCC 23853 ATCC Streptococcus thermophilus ATCC 19528 ATCC Staphylococcus xylosus ATCC 700404 ATCC E. coli Nissle1917 Ardeypharm E. coli ECOR 60 ANU E. coli ECOR 68 ANU E. coli DH5α Promega E. coli BL-21 Promega E. coli XL-10 Promega

*ATCC: American Type Culture Collection, Manassas, USA ACM: Australian Collection of Microorganisms, Department of Microbiology, University of Queensland, Australia ANU: Australian National University, Australia Ardeypharm: Herdecke, Germany Promega: Promega, Australia

2.2 Bacterial culture media 2.2.1 Non-selective bacterial culture media Luria Bertani (LB) medium (Appendix 2.1) A nutrient medium, allowing for the culturing of aerobic Gram-positive and Gram- negative bacteria. In this study, LB medium (broth/agar) was mainly used to culture coliforms such as E. coli and Shigella strains.

Tryptic Soy broth/agar (DIFCO, USA, 211825, Appendix 2.2) A nutrient medium (containing 1.7% Tryptone, 0.3% soytone, 0.5% NaCl, 0.25%

dextrose, 0.25% K2HPO4), used for the enrichment of wide variety of microorganisms. In this study, TSB/TSA was mainly used to culture Bacillus, Brevibacillus and Paenibacillus strains.

Blood agar (Appendix 2.3) A nutrient medium consisting of blood agar base No.2 (OXOID, CM271) and 10% sheep blood for the visualization of haemolytic and non-haemolytic bacteria. Blood agar plates were incubated at 37°C for 18-24 h before enumerating colonies and determining their haemolytic properties. Haemolysis is identified by clearly recognisable haloes around individual colonies; a clear halo is observed for α- haemolysis (or total haemolysis), while a greenish halo is observed for β-haemolysis (or partial haemolysis). A halo is not observed for non-haemolytic colonies.

2.2.2 Selective culture media (SCM) MRS (de Man, Rogosa and Sharpe) medium (MERCK, Darmstat, Germany, 1.10661 Appendix 2.4) For the cultivation and differentiation of Lactobacillus stains. Sodium acetate and ammonium citrate inhibit the growth of Gram-negative bacteria. The pH of 6.2 (±0.2) inhibit Streptococci, considerably reduce the growth of Enterococci while allows for the growth of Lactobacillus at 37°C with 5% CO2 for 36-48 h.

MacConkeys agar (MERCK, 1.05396, Appendix 2.5) Contains bile salts and crystal violet which inhibit the growth of Gram-positive bacteria, making this medium selective for Gram-negative including E. coli, Enterobacteriaceae and Salmonella. The majority of E. coli and Enterobacteriaceae degrade lactose in the medium and absorb the pH indicator neutral red allowing the colony to be identified as pink after incubation at 37°C for 18-24 h.

Kanamycin Esculin Azide (KEA) agar (Oxoid, CM591B, Appendix 2.6) Kanamycin and azide largely inhibit the accompanying bacterial flora. Enterococci colonies are surrounded by a dark zone. D-streptococci are only slightly sensitive to these substances, so they can grow almost normal and hydrolyse the glucoside esculin to give glucose and esculetin. Esculetin forms an olive green to black complex with iron(III) ions.

Citrate Azide Tween Carbonate (CATC) agar (Merck, 1.10279, Appendix 2.7) The high concentrations of citrate and azide almost completely inhibit the growth of the accompanying microbial flora. Enterococci reduce the colourless 2,3,5- triphenyltetrazolium chloride to a red formazan, their colonies thus become red in colour when incubated at 37°C for 24 h.

Bacillus spore generation agar (Amyl Media, Australia, AM717, Appendix 2.8) A medium used to stimulate spore formation of members of the Bacillus genus (including Bacillus, Brevibacillus and Paenibacillus) by 37°C incubation for 3-4 days.

Media for cloning work SOC medium (Appendix 2.9) was used for the propagation of transformed E. coli clones. The antibiotic selective plate was made by adding 50 µg/ml of ampicillin into precooled (~55°C) LB agar before pouring into 10 cm plate. For screening the recombinant colonies, pipet 40 µl of the X-Gal stock solution (Appendix 2.15) and 40 µl of IPTG stock solution (Appendix 2.16) onto the centre of the plate and spread evenly with a sterile spreader. Allow the solution to diffuse into the plate by incubating at 37°C for 20-30 min before used for growing E. coli clones.

2.3 Cell culture media The complete medium (CM) for culturing human colon cell line T84 (ATCC code: CCL-248) consisted of 1:1 DMEM/Hames F12 base medium (JRH Biosciences, CSL) and supplements (Appendix 2.10). In the cell-bacteria interaction study, NBS and antibiotics were excluded from the supplements.

2.4 Bacterial growth and storage 2.4.1 Recovery of bacteria from environmental samples Environmental samples such as faeces and animal feeds were suspended in cold buffered peptone water/10% glycerol (PBWG, Appendix 2.11) at a concentration of 50 mg/ml stock suspension. The sample pellets were completely dispersed by vortexing vigorously for 1 min in the presence of 5-6 sterile glass beads (1mm in diameter). The total vegetative bacterial number in samples were counted by plating serial tenfold dilutions of the suspensions on blood agar and incubated at 37°C for 24 h. Diluted sample suspensions (100 µl) were plated onto selective medium plates such as MacConkey agar, MRS and KEA for the recovery of E. coli, Lactobacilli and Streptococci/Enterococci respectively.

2.4.2 Freezedown storage Single colonies from the culture plate were inoculated into 10 ml culture broth medium (LB, MRS or TSB) in a 50 ml Falcon tube and then incubated at 37°C with shaking (100 rpm) overnight or in sufficient time to reach mid-log phase. 500 µl of bacterial suspensions were then transferred to a cryovial containing the same volume

of cryoprotective media (culture broth medium with 20% v/v sterile glycerol), mixed by vortexing and stored at -80°C freezer. To recover the bacteria from glycerol broth, a sterile wire loop was used to chip the surface of the -80°C stocks and the chipped piece spread on the surface of culture plates.

2.4.3 Microbead storage The Microbank system (PRO-LAB Diagnostics, Canada) is a more reliable method of maintaining bacterial culture than traditional methods of lyophilization or use of glycerol broths. Each Microbank vial contains 25 treated ceramic beads and a specially developed cryogenic preserving solution. Bacterial colonise from pure culture were mixed in the cryo-solution and the beads were coated with bacteria, which maintains the integrity of bacterial cells during prolonged period (years) of frozen storage (-80°C). To recover the bacteria from the Microbank system, remove one bead using a straight wire needle or forcers and roll the bead onto a culture plate.

2.5 Bacterial counting Bacterial suspensions were aliquoted into wells in a sterile 96-well tissue culture plate. Fourfold serial dilutions using sterile 1× PBS were carried out with a multi- channel pipette. Triplicate 10 µl drops representing each dilution were plated onto pre-dried square plates (100 mm × 100 mm, Sarstedt, Ingle farm, Australia) containing culture media such as LA, MAC, KEA, CATC, MRS, BA or TSA. Once dry, the plates were inverted and incubated for 24 h at 37°C for LA, MAC, KEA,

BA; 48 h at 37°C for CATC; 48 h at 37°C in 5 % CO2 for MRS, and for 24 h at 30°C for TSA to grow Bacillus strains. Colony forming units (CFU) distributed over each drop zone were counted and averaged for triplicates with countable bacteria from at least 2 dilutions. The totals from each dilution were averaged to give the final bacterial CFU count.

2.6 Mammalian cell culture and storage 2.6.1 Revival and growth of cell lines from frozen storage Frozen cell stocks were kept in 1.5ml cryovials in liquid nitrogen (-150°C to - 160°C). Frozen cells were revived by transferral from liquid nitrogen to ice, then

rapidly thawed (2-3 min) by gentle agitation in a 37°C water bath. Vials were decontaminated by swabbing with 70% ethanol and then all work was carried out in a biological safety cabinet. The cell suspension was transferred to a sterile 10 ml tube. 1 ml of 1× HBSS buffer (GIBCO) was used to wash and recover any additional cells remaining in the cryovial. The cells were pelleted by centrifugation at 1,000 rpm for 5 min. The supernatant was discarded. 1 ml of complete medium (CM) was used to wash the cells by removing any residual cryoprotective media (CM plus 5% DMSO v/v). The cells were re-pelleted and resuspended in 5 ml of CM, pre-warmed to 37°C. This suspension was then transferred to a 75-cm2 tissue culture flask (Greiner, Interpath) containing 10 ml of CM at 37°C. Flasks were incubated at 37°C with the addition of 5% CO2. CM was replenished twice weekly until 80-90% confluency was reached. This time the cells were passaged at differing split ratios depending upon their growth characteristics.

2.6.2 Maintenance and subculture of cells Cells were subcultured or passaged when they reached 80-90% confluency in a 75- cm2 flask. This was carried out as follows: the supernatant was discarded and the cells washed with 5 ml of 1× HBSS. Trypsin/EDTA (3 ml of 0.5/0.25% v/v, GIBCO) was used to detach cells from the flask. Once trypsin had been added, flasks were returned to the incubator for 5-15 min to release adherent cells into suspension. Trypsin/EDTA was neutralised with 3 ml of CM, the solution was then transferred to a 10 ml tube and centrifuged at 1500 rpm for 5 min. Supernatants were discarded and cells were resuspended in CM. For split ratios of 1:2, cells were resuspended in 2 ml of CM, and then each 1 ml was seeded in a new 75-cm2 flask containing 14 ml of CM (37°C) and transferred to the incubator.

2.6.3 Preparation of frozen stocks To avoid an effect of passage number of the cell line on biological experiments, care was taken to ensure that cells used in all functional assays were restricted to similar passage numbers or at the very least, a defined range of passage numbers. Cells were washed with 1× HBSS, and detached from the plastic by incubation with 3 ml of trypsin/EDTA for 5-15 min at 37°C. The Trypsin/EDTA was neutralised with 3 ml

of CM and centrifuged (1500 rpm for 5 min). The cell pellet was resuspended in 2 ml of CM, of which 1 ml was used to re-inoculated a new 75-cm2 tissue culture flask containing 14 ml of media. The remaining 1 ml was re-centrifuged and the pellet was resuspended in 1.5 ml of cryoprotective media and transferred to a cryovial and allowed to freeze to -80°C overnight then transferred to liquid nitrogen for long term storage. Thereafter, at each subsequent subculture the cells were split at a ratio of 1:4 were 3 cryovials were generated and a flask was seeded for continual passaging of the cell line. All frozen cell stocks were generated and these were labelled according to their passage numbers.

2.7 DNA and RNA extractions 2.7.1 Bacterial genomic DNA extraction A Wizard Genomic DNA extraction kit (Promega, Australia, Cat No. A1120) was used for most Gram-negative microorganism. A single bacterial colony was inoculated into 2 ml of LB broth in a 5 ml tube and 37°C incubated overnight with shaking at 200 rpm. Bacterial DNA was extracted from 1 ml of broth culture following the procedure recommended by the manufacturer and the final DNA was rehydrated with 100 µl of dehydration solution from the kit and stored at -20°C.

Genomic DNA of Gram-positive bacteria such as Bacilli was isolated according to the modified method of Mantynen and Lindstrom (Mantynen and Lindstrom, 1998). In brief, 1.5 ml of an overnight bacterial culture grown in TSB were collected and washed with 5 ml of TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0) and resuspended in 525 µl of TE. 10 µl of proteinase K (10 mg/ml, Sigma) and 40 µl of lysozyme (50 mg/ml, Sigma) are added and incubate for 1 h at 37°C, followed by adding 30 µl of 10% SDS and 100 µl of 10% N-lauroylsarcosine (Sigma, Australia), 37°C incubation for 30 min. Adjust the volume to 3,000 µl by adding 1,420 µl of TE and 420 µl of 5 M NaCl. Then 300 µl of preheated (65°C) 10% CTAB (Sigma) in 0.7 M NaCl is added and incubated for 10 min at 65°C. Add an equal volume of phenol- chloroform-isoamyl alcohol (25:24:1) and centrifuge at 10,000 g for 20 min at 15°C. Followed by chloroform-isoamyl alcohol extraction and precipitation with an equal volume of isopropanol. Spin down the DNA at 10,000 g for 15 min at 4°C and wash with 70% cold ethanol once. Air dry the DNA pellet and resuspended in 100 µl of

TE with 20 µg/ml of RNaseA), 37°C for 30 min to digest the trace RNA and stored at -20°C.

DNA concentration and purity was determined by measuring the absorbance ratio at 260/280 nm using a GeneQuant II photometry (Pharmacia). DNA was diluted with

MQ-H2O to make the OD260 value range between 0.15 and 1.0 to ensure an optimal

measurement. 1 A260 Unit of dsDNA = 50 µg/ml.

2.7.2 RNA extraction TRIzol reagent (Invitrogen, Australia) was used for total RNA extraction in this study. Cells (5 × 105 to 1 × 106 CFU) were washed twice with 1× HBSS and lysed with 1 ml TRIzol reagent by repetitive pipetting before transferring to a 1.5 ml Eppendorf tube. Total RNA was extracted and dissolved in 100 µl of 0.1% DEPC water following the supplier’s protocol before stored at -80°C.

RNA concentration was determined by measuring the absorbance ratio at 260/280 nm using a GeneQuant II photometry (Pharmacia). DNA was diluted with DEPC-

H2O to make the OD260 value range between 0.15 and 1.0 to ensure an optimal

measurement. 1 A260 Unit of dsRNA = 40 µg/ml.

2.8 PCR and gel electrophoresis In general, 10-50 ng of DNA was used as PCR template and PCR was performed in a PC-960 thermal cycler (CorbettResearch, Australia) using the reagents including Taq

polymerase from Invitrogen, Australia. A MQ-H2O control was included in each PCR batch. The oligonucleotide primers were purchased from Sigma, Australia and dissolved in TE buffer (pH 8.0) to make 50 µM working stock solutions. PCR products were dissolved by agarose gel electrophoresis. A 5 µl sample of PCR product was mixed with 3 µl of loading dye containing 0.25% bromophenol blue in 15% Ficoll solution and loaded onto a 2.0 % agarose gel (Ultrapure Agarose, Life Technology, Australia) containing ethidium bromide (10 µg/ml), using 0.5× TBE buffer (45 mM Tris, 45 mM boric acid, 1 mM EDTA, pH 8.0) as running buffer. Gels were then run under constant electrophoretic conditions of 80 volts, 500 milli-

amps for 2.5 h. DNA in the gel was visualised by exposure to UV light and photographed with a digital capture system (Gel Doc, Bio-Rad, Richmond, California). The sizes of DNA fragments were estimated using a 100 bp+ DNA ladder (Promega, Australia).

2.9 DNA cloning and sequencing 2.9.1 Competent cell preparation Competent cells were prepared following the method described by Sambrook (Sambrook, 2001). A single colony of DH 5α (Promega, Australia) from an overnight LB plate was transferred into 100 ml of LB broth in a 500 ml flask. The culture was incubated for 4 h at 37ºC with vigorous agitation (300 rpm) until the

absorbance (OD600) reached 0.2. Bacterial cells were harvested by centrifugation at 2700 g (4100 rpm in a Sorvall GSA rotor) for 10 min at 4ºC, transferred to an ice- cold 50 ml Falcon tubes, and held in ice for 10 min. Cells were pelleted by centrifugation at 2700 g for 10 min at 4ºC, the supernatant liquid removed and the tubes inverted on a pad of paper towels for 1 min to allow residual culture medium to drain away. The pellets were resuspended by swirling or gentle vortexing in 10 ml

ice-cold 0.1 M CaCl2 and kept on ice for a further 30 min. The cells were again centrifugated at 2700 g for 10 min at 4ºC. Again the tubes were inverted and drained as previously. The resulting pellets were resuspended by swirling or by gentle

vortexing in 2 ml ice-cold CaCl2 (0.1 M) for each 50 ml of original culture.

2.9.2 Ligation and transformation The Original TA Cloning Kit (Invitrogen, Cat No. K2000-01) was used for the ligation and transformation. Following the instructions for ligation and transformation in the kit, fresh-prepared PCR product (less than 1 day old) was purified using QIAGEN PCR purification kit (QIAGEN, Australia). 50 ng of the  purified PCR product was then ligated with pCR 2.1 vector DNA at a 1:1 ratio using T4 DNA ligase in 10 µl reaction volume. The ligation was performed at 14°C overnight. This ligation mixture can be used for transformation immediately or can be stored at -20°C.

2 µl of the ligation reaction was gently mixed with 50 µl of competent cells in a 1.5 ml Eppendorf tube by pipetting followed by incubating the tube on ice for 30 min. The tube was then heat-shocked for 30 s in a 42°C waterbath and kept on ice. 250 µl of SOC medium (room temperature) was then added to the tube and the tube was shaken horizontally at 37°C for 1 h at 225 rpm. The cell suspension was 1:10 diluted with SOC medium and 100 µl of each dilution was spread onto LB agar containing ampicillin, X-Gal and IPTG. The plates were incubated at 37°C for at least 18 h before being shifted to 4°C for 2-3 h to allow for proper colour development.

2.9.3 Plasmid DNA extraction and sequencing Inoculate the white colony in 2 ml LB broth containing 50 µg/ml of ampicillin and grow overnight at 37°C with shaking. Plasmid DNA was extracted from 1.5 ml of culture using QIAGEN Plasmid MinipPrepare kit (QIAGEN, Australia).

PCR product was purified using the QIAGEN Gel Purification kit (QIAGEN, Australia). A total of 70 ng of purified DNA was subjected to AmpliTaq cycle- sequencing reactions using the BigDye terminator cycle-sequencing kit (Applied Biosystems) according to the manufacturer’s instructions. Electrophoresis of DNA sequencing reaction products was performed through 7% polyacrylamide gels with an automated sequencer (model 377, Applied Biosystems). The nucleotide sequences were analysed with the ANALYSIS program (Applied Biosystems) and the ANGIS program.

2.10 Statistic analysis All statistical analyses were carried out with the aid of Dr Idris Barchia (Special biometrician, EMAI). The following statistical methods were used in this study: 1. An agglomerative hierarchical algorithm was used to establish cluster relationships between E. coli strains essentially as described by Kaufman and Rousseeuw (1990) 2. Chi-squared test of deviance (Cox, 1970) 3. Principal Coordinate (PCO) analysis (Gower, 1966)

Chapter 3

Field Assessment of the Function of a Panel of Probiotic Strains in Feedlot Cattle

3.5 Introduction 3.6 Materials and methods 3.6.1 Description of the experimental substance 3.6.2 Trial farm and animals 3.6.3 Experimental design 3.6.4 Feeding regime 3.6.5 Faecal flora determination 3.6.6 Assessment of immune responses 3.6.7 Statistical analysis 3.7 Results 3.7.1 Faecal bacterial enumeration using culture media 3.7.2 Immune response 3.7.3 Animal growth performance 3.8 Discussion

3.1 Introduction Probiotics, when used as a feed additive, fall into a class of compounds called direct- fed microbial (DFM). The aim of the DFM approach is to repair the deficiencies in the gut microflora and restore the animal’s resistance to disease. Such a treatment does not introduce any foreign chemicals into the animal's internal environment and does not run the risk of contaminating the carcass and introducing hazardous chemicals into the food chain. A number of commercial DFM products for domestic animals are available in the retail market and the scientific basis for their effects is a matter of ongoing investigation.

The work on DFMs for cattle (particularly dairy cows) has increased in recent years and positive effects (not always statistically significant) have been found for feed intake, weight gain, milk yield and quality, earlier weaning, decreased scouring, decreased faecal coliform count and reduced demand for antibiotic treatment (Kilmer, 2005; Morrison, 2005; Yoon & Stern, 1995). Nocek et al. (2002) have demonstrated that a DFM supplementation containing yeast and Enterococcus faecium could increase daily dry matter intake (DMI) and milk production during the postpartum period. Feeding a microbial product consisted of B. subtilis was found to reduce scours in dairy calves (Higginbotham and Robinson, 2005) and have a positive effect on feed efficiency during week 1 to 4 and on immediate postweaning gain (Jenny et al., 1991). In the beef cattle studies, the majority were focused on using DFM as a daily feed supplement in controlling the faecal shedding of E. coli O157:H7 (Brashears et al., 2003; Schamberger et al., 2004; Zhao et al., 1998), due to the raised concerns regarding this pathogen’s contamination of foods and the widespread distribution of it in beef cattle. Nevertheless, the underlying mechanisms are largely unclear and the knowledge of the DFM performance on the animal growth and meat quality are minimal.

The commonly used microorganisms in DFM preparations are the lactic acid bacteria (e.g. lactobacilli, streptococci and bifidobacteria), enterococci and Bacillus spores which have effects in the lower digestive tract (Blezinger, 2005). Ingestion of these microorganisms results in colonization of the digestive tract, prevention of pathogen proliferation, neutralization of enterotoxins produced in situ, modulation of some

specific bacterial enzyme activity, enhancement of the small intestinal digestive capacity, and the exertion of adjuvant effects on the immune system (Holzapfel et al., 1998; Salimen et al., 1996). In addition to the bacterial DFMs, yeast cultures (Saccharomyces cerevisiae) have been shown to positively affect animal performance and mineral consumption (Stephen, 2005). Moreover, in vitro and in vivo studies with fungal probiotics such as Aspergillus oryzae have shown an improvement in digestion of fibre and occur without the organisms being able to multiply in the rumen (Gomez-Alarcon, 1990).

It is believed that the beneficial effects of DFM are strain-specific and combinations of different probiotic microorganisms have an advantage over the use of any culture alone (Fuller, 2005; Krehbiel et al., 2003). However, most of the commercial DFM supplements used in cattle only contain 1-2 different species of microorganisms. In contrast, Protexin (International Animal Health Product, Australia) is a multi-strain probiotic containing a blend of 9 different microbial organisms including lactobacilli, streptococci, enterococci, bifidobacteria, yeast and fungi, which has been used in a variety of situations for both household pets as well as intensively farmed animals (Balevi et al., 2001; Denli et al., 2003). The objective of this study was to determine the efficacy of Protexin as a DFM supplement in beef cattle. The hypothesis was that continuous high level dosing with probiotic microorganisms in feedlot cattle would alter the population dynamics of different bacterial species in faeces. It was also the intention to test whether DFM feeding would increase the ability of cattle to mount an immune response against antigenic challenge and to measure the effect on body composition parameters (marbling) through ultrasound scanning.

3.2 Materials and methods The University of New England Animal Ethics Committee gave approval for this trial (AEC01/10).

3.2.1 Description of the experimental substance Protexin is a commercially available multi-strain probiotic product, comprising 7 naturally occurring bacteria, a yeast strain and a fungus strain (Table 3.1). All the component microorganisms of Protexin are registered by the APVMA (Australian

Pesticides and Veterinary Medicines Authority) as safe for use as feed additives when used according to the manufacturer’s instructions and with the target animal categories specified. In this experiment, a commercial batch of Protexin liquid (product no. 3/11/01) was used with a 1.8 × 108 CFU ml-1 of microorganism enumerated by AMS Laboratory, Australia.

Table 3.1 Microbial composition of Protexin Strains Source* Lactobacilus acidophilus ATCC Lactobacillus delbrueckii ATCC Lactobacillus plantarum ATCC Lactobacillus rhamnosus ATCC Streptococcus thermophilus ATCC Bifidobacterium bifidum ATCC Enterococcus faecium ATCC Aspergillus oryzae ATCC Saccharomyces cerevisiae ATCC

*ATCC, American Type Culture Collection

3.2.2 Trial farm and animals Twenty purebred Angus steers from the NSW Agriculture property at Trangie, Australia were used in the trial. Detailed records of their sires and dams and measurements taken at weaning were available. They were healthy after birth (birth weight > 30 kg), aged 12-14 months with an average live weight between 300-350 kg at the commencement of the trial, and pasture fed prior to the trial. Animals were divided equally into 2 groups of 10, so that each group had a similar distribution of body weights just prior to the trial. Other parameters measured at weaning (6-8 months of age), such as weight and size measurements and sire identification were not significantly different for the 2 groups.

Animals were housed in pens at Tullimba Feedlot, which is a research feedlot owned by the University of New England. It is situated 50 km west of Armidale, NSW, Australia. The two groups were housed in adjacent pens, which would normally

house 100 animals each. For the first 21 days the pens were ones without automatic feeders and from day 21 until the end of the trial, they were in pens with automatic feeders. At no time did the two groups share a water trough.

3.2.3 Experimental design Two groups of 10 steers were housed individually in pens. The treatment group was maintained on a daily ration containing 109 CFU of Protexin while the control group was fed normal ration without Protexin. The trial duration was 76 days. Weights, and faecal and blood samples were collected at induction (Day 0) and thereafter on Days 14, 28, 34, 41, 56, 63 and 76 respectively. Faecal samples (10 g) were collected directly from the rectum of each steer using a new glove. All cattle were vaccinated subcutaneously on Days 28 and 41 with 1 ml ovalbumin (500 µg) emulsified in Emulsigen adjuvant. Blood samples (30 ml) were also collected by tail bleeding on the same day that faecal samples were collected. Blood and faecal samples were transported chilled to the laboratory at Elizabeth Macarthur Agricultural Institute (EMAI, NSW DPI, Australia) for analysis. Sample processing began within 24 h of collection.

On day 76, the final day of the trial, all cattle were scanned with an ultrasound probe measuring intramuscular fat (IMF) percentage (same site measured 3 times), P8 (fat depth over rump, position 8), 12/13th rib (fat depth over 12/13th rib) and EMA (Eye muscle area).

At the time of induction, routine management procedures were followed including: 5 in 1 vaccination, anthelmintics (Cydectin and Fasinex), ear tag and weight.

3.2.4 Feeding regime Over the 76 day period 4 different feedlot rations were fed as follows: 1. Starter ration (days 1-7): 60% hay, 31% grain, 8% Molafos, 1% lime Molafos is a molasses based liquid feedlot concentrate containing protein, minerals, vitamins and the ionophor, monensin (Cheetham Rural Pty. Ltd.)

2. Intermediate No 1. Ration (day 8-11): 40.5% hay, 50% grain, 8% Molafos, 1% lime, 0.5% bicarbonate

3. Intermediate no 2. Ration (day 12-15): 25.5% hay, 65% grain, 8% Molfaos, 1% lime, 0.5% bicarbonate

4. Finisher ration (day 16 – 76): 10% hay, 75% grain, 5% protein meal, 8 % Molofos, 1% lime, 0.5% bicarbonate, 0.5% sulphate of ammonia

Animals were fed twice a day, 50% of ration morning and evening. Bunks were cleaned out every 24 hours. Records were kept of residual feed in bunks. Average daily feed intake (ADFI) and average daily weight gain (ADWG) were calculated for each group. Daily intake for 300 kg animal estimated to be 9.5-10.75 kg/day.

The Protexin Liquid was dispersed in maize oil (1:10 v/v) and mixed well before adding to the ration at a rate of 500 ml per half day ration for treatment group (approximately 500 ml per 50 kg feed). Animals in the control group were fed the normal feedlot ration without Protexin. The placebo, maize oil alone, was mixed at a rate of 500 ml per half day ration for the control group.

3.2.5 Faecal flora determination When the faecal samples arrived at the laboratory, two grams of faeces were immediately suspended in cold buffered peptone water plus 10% glycerol at a concentration of 50 mg/ml stock suspension. The faecal pellets were completely dispersed by vortexing for 1 min in the presence of sterile glass beads. This faecal suspension was frozen at –80°C in aliquots and later used for serial dilution and enumeration.

Changes in population dynamics were enumerated by dilution plating onto bacterial culture media (Table 3.2). Faecal suspensions were diluted serially (1: 4) with PBS (pH 7.6) and drop counts of 10 µl were plated in triplicate. The colony forming unit (CFU) were counted individually and the mean value for each animal was calculated.

The number of bacteria for each animal was expressed as log10 of viable bacteria per gram faeces.

Table 3.2 Faecal flora determination using bacterial culture medium Medium Bacterial growth Growth condition Source Sheep Blood Agar all relevant bacteria 37°C OXOID

MRS lactobacilli 37°C, 5% CO2 MERCK CATC enterococci 37°C MERCK KEA streptococci and 37°C MERCK enterococci MacConkey E. coli and coliform 37°C MERCK bacteria

3.2.6 Assessment of immune responses The effect of probiotic feeding on immune function was assessed by intradermal injection of cattle with adjuvanted ovalbumin. Two injections containing 500 µg of ovalbumin (Sigma) adjuvanted in liposomes containing Quil A (Superfos, Denmark) and Emulsigen (MVP Laboratories, USA) were given to each animal 7 days apart after 28 days of probiotic feeding. Blood was collected at fortnightly intervals thereafter to monitor the serological response of cattle to the vaccine. Serum IgG responses against antigen ovalbumin were determined serologically by ELISA as described by Chin et al. (1992).

Cellular immune responses were determined using a blood lymphoproliferation assay (Chin et al., 2000) stimulated with 3 different mitogens, phytohaemagglutinin (PHA, Sigma), concanavalin A (ConA, Sigma) and Pokeweed mitogen (PWM, Sigma) at 2 concentrations (2.5 and 10 µg/ml respectively).

3.2.7 Statistics All bacterial and animal performance data were analysed with each pen as the experimental unit. We required an 85% chance of detecting a difference (p< 0.05) in weight gain per animal in the treatment groups of 12 kg over 100 days feeding. The estimated standard deviation of weight gain is 10 kg over 100 days on feed. Using

Altman’s nomogram for comparison of 2 groups, N= 20. Therefore for each treatment group we required 10 animals. Means (with standard deviation) for individual animals were calculated for all variables. Data for bacterial counts were converted prior to analysis using a log transformation. The parameters were subjected to Pearson’s Chi- squared for the determination of differences among the different groups. The statistical software used was the SAS Package (SAS Release 8.02, SAS Institute Inc., Cary, NC, USA).

3.3 Results 3.3.1 Faecal bacterial enumeration using culture media Total aerobic bacteria counts on sheep blood agar varied from 2.3 × 106 to 4.1 × 108 CFU g-1 (Figure 3.1A). The mean bacterial numbers on two sampling occasions (Day 0 and 14) from both of the control and treated group were higher than the other sampling times. However, there are no significant difference between Protexin treated cattle and controls.

Based on colony-counting on the MRS agar, the numbers of lactic acid bacteria in the faecal samples were significantly increased (P< 0.05) during the feedlotting period (ranged from 3.3 × 106 to 6.2 × 107 CFU g-1), compare with the Day 0 (ranged from 8.0 × 104 to 3.1 × 105 CFU g-1), and the numbers maintained steady throughout the feedlot period. Figure 3.1B shows no significant difference between Protexin treated cattle and controls.

The results of the colony-counting on the MacConkey agar indicated that E. coli and coliform bacteria in cattle faeces were significantly increased (P< 0.05) during the feedlotting period (ranged from 1.0 × 106 to 1.1 × 107 CFU g-1), compared with Day 0 (ranged from 1.9 × 105 to 4.9 × 105 CFU g-1). There are no significant difference between Protexin treated cattle and controls (Figure 3.1C). Moreover, there was generally very little difference in the total numbers of streptococci and enterococci on KEA, CATC and media respectively, between Protexin treated animals and controls, as shown in Figure 3.1D and 3.1E respectively.

3.3.2 Immune response The immune response of the animals was assessed in two ways. Firstly, antibody responses against ovalbumin (OVA) were determined serologically as shown in Figure 3.2. It is clear that all animals developed serum IgG antibodies against OVA post-vaccination but titres were not significantly different between control and Protexin treated cattle. Secondly, cellular immune responses were determined by isolating blood leucocytes and subjecting these to stimulation with mitogens. An increased response of leucocytes to PHA at both indicated concentrations from Protexin-fed cattle was noted compared to controls (Figure 3.3), whereas the responses of leucocytes to ConA and Pokeweed showed no difference between treatments and controls. These results showed that the cellular immune response of the Protexin-supplemented animals was remarkably increased, and that the normal antibody responses of cattle were not adversely affected.

3.3.3 Animal growth performance The average daily feed intake (ADFI) was calculated as the ratio of total pen feed consumption versus number of animal feeding days and the average daily weight gain (ADWG) was defined as the ratio of total pen weight gain versus number of animal feeding days. Overall feedlot performance data were presented in Figure 3.4 and 3.5. The differences in ADFI and ADWG during the feedlotting period were not significant between the 2 experimental groups (P > 0.05). However the daily feed intake of the Protexin group fluctuated less than the control group.

The meat quality of animals was investigated by ultrasound scanning. While P8, 12/13th rib and EMA values were essentially similar between the two groups of cattle (Figure 3.6), intramuscular fat % at 3 sites – IMF 1, 2 and 3 was consistently and significantly lower (P < 0.05) in Protexin treated cattle (Figure 3.7).

A 1.0E+09

1.0E+08

1.0E+07

1.0E+06 Control

g-1 cfu Log 1.0E+05 Protexin

1.0E+04 0 142834415676 Day

B C 1.0E+08 1.0E+08 1.0E+07 1.0E+07

1.0E+06 1.0E+06 Log cfu g-1 Log cfu 1.0E+05 g-1 cfu Logl 1.0E+05

1.0E+04 1.0E+04 0 142834415676 0 142834415676 Day Day

DE 1.0E+08 1.0E+09 1.0E+08 1.0E+07 1.0E+07

1.0E+06 1.0E+06 1.0E+05 Log cfu g-1 Log cfu Logl cfu g-1 cfu Logl 1.0E+05 1.0E+04 1.0E+03 1.0E+04 1.0E+02 0 142834415676 0 142834415676 Day Day

Figure 3.1 Counts of total aerobic bacteria on sheep blood agar (A), lactic acid bacteria on MRS agar (B), coliform bacteria on MAC agar (C), streptococci and enterococci bacteria on KEA agar (D), and enterococci bacteria on CATC agar (E), recovered -1 from cattle faeces during the Protexin DFM trial (log10 CFU g )

Control Protexin 0.5

0.4

Vac 2 0.3

Vac 1 0.2 ELISA O.D.(414nm) ELISA 0.1

0.0 D1D28D34D41D56D63D76 Days

Figure 3.2 Effects of ovalbumin (OVA) vaccination on the serum IgG response of cattle against OVA

PHA (2.5 µg/ml)

Control Protexin 150

120 90 60 30 0 (SI) Index Stimulation Induction Pre-Vac Post-Vac

PHA (10 µg/ml)

150 120 90 60 30

(SI) Index Stimulation 0 Induction Pre-Vac Post-Vac

Figure 3.3 Assessment of cellular immunity of Protexin feeding by PHA activation

17000 Protexin group Control group 16000

15000

14000

13000

12000

11000

10000 Average daily feed intake (g)

9000

8000 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 Days on feed (1 to 53)

Figure 3.4 Average daily feed intake (ADFI) for 10 cattle fed rations containing Protexin versus 10 cattle fed a control diet

500

450

400 Control Protexin

350

300 Mean Weight / Animal (kg) Animal / Weight Mean

250 0 1014283441566376 Days

Figure 3.5 Average daily weight gain (ADWG) for 10 cattle fed rations containing Protexin versus 10 cattle fed a control diet

80 Control Protexin 70 60 50 40 30 20 Meat Depth (mm) 10 0 P8 12/13 EMA

Figure 3.6 Meat quality of animals was investigated by measuring the P8, 12/13th rib and EMA values using ultrasound scanning P8 - Fat depth over rump, position 8 12/13th rib - Fat depth over 12/13th rib EMA - Eye muscle area

7.5 Control Protexin

6.5

5.5

intramuscular percentage fat (%) IMF1 IMF2 IMF3

Figure 3.7 Percentage of intramuscular fat at three sites – IMF 1, 2 and 3

3.4 Discussion The aim of this cattle trial was to assess the effect of feeding a commercial DFM (Protexin) on Angus Murray steers’ growth performance, faecal bacteria population diversity, marbling and immune response to vaccination.

In this study, the main effects that could be attributed to the Protexin treatment were the remarkable improvement in cellular immunity and the impact on the intramuscular fat level. This clearly shows that in-feed probiotic supplementation can have a significant impact on the health status, physiology and carcase quality of feedlot cattle. The growth performance such as average daily feed intake and average daily weight gain was not significantly affected by Protexin treatment, which may have been caused by the high health status of the animals and the low-stress conditions experienced by both groups of cattle, due to the unusually low stocking density. Research by the CRC for Beef Cattle Quality at Tullimba Feedlot has highlighted the important role of stress in determining the outcomes of cattle feedlotting (Blesinger, 2005).

The presence of lactobacilli and bifidobacteria is important for the maintenance of the intestinal microbial balance (Alp et al., 1993; Atherton and Robbins, 1987; Sandine, 1979). If probiotics had an effect on the intestinal flora, the microorganism population, especially lactobacilli and total aerobic bacteria counts, might be expected to increase in the treated animals. The results of this study did not show statistically significant differences in the total number of aerobic and facultative anaerobic bacteria such as lactobacilli, enterococci, streptococci and coliforms among the study groups. Similar results were reported by Siriken et al (2003), who showed that the use of Protexin as feed supplement only suppressed sulphite- reducing anaerobic bacteria in the caecal flora of mature, healthy Japanese quail, but did not change the population dynamics of some bacterial species such as lactobacilli, Enterobacteriaceae, coliforms and enterococci. The population dynamics of the obligate anaerobic bacteria such as clostridia and bifidobacteria were not investigated in the present study, therefore it is impossible to exclude the possibility that continuous dosing with Protexin had altered the population dynamics of these bacteria in the faeces.

Several authors have reported the beneficial effects of DFM feeding in the improvement in feed efficiency and in the inhibition or exclusion of E. coli O157:H7 and coliforms in the gastrointestinal tracts of cattle (Brashears et al., 2003; Ghorbani et al., 2002; Zhao et al., 1998). However, controversial results arose from other studies. For example, Aby-Tarboush et al. (1996) found no differences in faecal coliform counts between dairy calves receiving either a mixture of lactobacilli or a Lactobacillus acidophilus DFM and control animals. Three major factors maybe influence the response of feedlot cattle to probiotic feeding. (i) The age of the selected calves, (ii) The health conditions of animals, and (iii) The composition of microorganisms and/or the dose of feeding in the DMF supplement (Krehbiel, 2003). Newborn and pre-weaning calves have immature microbial gut ecosystems that are more prone to upset by pathogens. Microbes introduced from DFM feeding at this stage are capable of colonising the GIT and remain relatively stable throughout life (Fuller, 2005). Comparably, it is difficult to modify enteric microbial flora once they are established.

It was reported that animals that have been stressed or medicated have a greater response to DFM supplementation than do normal, healthy animals (Blesinger, 2005; Fuller, 1989; Montes and Pugh, 1993), and reference has already been made to the low-stress conditions experienced by both groups of animals in this trial. Newly received beef calves entering the feedlot undergo a variety of stresses, such as recent weaning, transport, fasting, assembly, vaccination, castration, and dehorning. Such stresses can alter the microflora in the rumen and lower gut (Williams and Mahoney, 1984), resulting in decreased performance and increased morbidity and death. Modern rearing methods which include unnatural rearing conditions and diets induce stress. The use of antibiotics and other chemical antibacterial compounds either as growth promoters or as therapeutic agents can cause changes in the composition of the gut microflora. The microbial balance in the animal GIT is thus adversely affected resulting in the overgrowth of coliform and other pathogens. Large doses of probiotics were thought to re-colonise a stressed intestinal environment and return gut function to normal (Fuller 1989; Lizko et al., 1984; Wren, 1989).

The present study used 18-month old healthy cattle for DFM feeding experiment. The indigenous gut flora of these animals was probably in balance therefore the bacterial population may not have been easily influenced by the DFM feeding. It is interesting to note that benefits of DFM feeding need not always be measurable in terms of modifying the gut microbial populations and increasing growth rate or feed efficiency, as in the case of a study by Seymour et al. (1995). In their study, a positive effect was demonstrated by monitoring the days of fever experienced by the animals and the number of antibiotic treatments required maintaining them in health condition. Moreover, DFM supplements were also shown to be able to minimise the risk of acidosis without compromising the high production levels achieved with high concentrate diets in beef cattle (Ghorbani et al., 2002). In the present study, the impact of the DFM supplementation was evident in the immune response and fat deposition.

Studies of bacterial DFM feeding for growing/finished cattle are limited. Results from a few research trials demonstrated that feeding combinations of lactic acid- (e.g. L. acidophilus) and propionic acid-producing (e.g. P. freudenreichii and P. acidipropionici) bacteria can improve feed efficiency and daily gain of feedlot steers (Huck et al., 2000; Rust et al., 2000; Swinney-Floyd et al., 1999). Several mechanisms are likely involved, but the ability for propionate production in the rumen is one mechanism that has been proposed (Nagaraja et al., 1997). These results suggested that growth performance of finishing cattle could be improved by using the appropriate DFM and dosage.

Galyean et al. (2000) found that carcass characteristics were not greatly affected by feeding various Lactobacillus cultures plus Propionibacterium freudenreichii PF-24 to finishing steers. In a report by Ware et al. (1988), L. acidophilus BT1386 did not affect the marbling (intramuscular fat) score, although DFM feeding increased daily gain and improved feed efficiency in yearling steers in the trial. Notably, the present study did show that continuous feeding of probiotic bacteria reduced intramuscular fat. The mechanism for this is uncertain but could relate to alterations in fat metabolism, and in particular, the presence and activity of circulating leptin. Recently, Alexopoulos et al. (2004) reported that feeding pigs with DFM containing

Bacillus spores increased the lean yield during the finishing period. A possible explanation given by the authors was that the DFM feeding optimised the microflora balance in the host gut, thereby leading to a better utilization of nutrients, faster metabolism and transformation of feed into lean meat. Since the daily weight gains and fat depth of both treatment and control groups in the present trial were very similar, and the intramuscular fat was less in the Protexin group, it follows that the probiotic treatment provided a higher yield of meat in this trial The ability of probiotic bacteria feeding to reduce intramuscular fat is surprising and mandates the need for a trial with larger numbers of cattle and more direct measurements of meat composition.

In conclusion, administration of Protexin as a feed supplement in a feedlot gave a remarkable increase in cellular immunity and altered the carcase quality by reducing intramuscular fat. However, it did not influence the population dynamics of the majority of bacterial groups in faeces and the growth performance of cattle in this study, which may be because of the high health status and low stress conditions experienced in the trial. Some important factors should be considered for future study such as age, diet, DFM composition, environmental conditions, stress and concomitant medication. In general, administration of probiotics may be most effective in the following situations: (i) after birth, to encourage the early establishment of beneficial microflora, (ii) following antibiotic treatment, (iii) when enteric pathogens, like E. coli, salmonella or coccidia, are present, and (iv) during periods of environmental or managemental stress.

Chapter 4

Bacillus as Candidate Probiotics – Molecular Characterisation

4.4 Introduction 4.5 Materials and methods 4.5.1 Reference strains and culture conditions 4.5.2 Morphological and biochemical identification of Bacillus strains 4.5.3 DNA isolation 4.5.4 Primer design and PCR amplification 4.5.5 ARDRA analysis of PCR amplicons 4.5.6 Bacillus spore isolation from environmental sources 4.5.7 Pulsed-field gel electrophoresis (PFGE) for Bacillus isolates 4.5.8 DNA sequence analysis 4.6 Results 4.6.1 Selection and validation of a Bacillus specific forward and reverse primer set 4.6.2 Genus-specific amplification of reference Bacillus strains 4.6.3 Speciation of reference Bacillus strains by ARDRA 4.6.4 Validation of ARDRA by speciation of probiotic strains from commercial sources 4.6.5 Identification of test Bacillus isolates from environmental sources 4.3.6 Determination of the intraspecies clonal variation of B. licheniformis by PFGE 4.4 Discussion

4.1 Introduction Members of the genus Bacillus have played a significant dual role in many human activities. On the one hand, species such as B. subtilis, B. amyloliquefaciens and B. licheniformis are used industrially for the production of enzymes, antibiotics, solvents and other molecules (Gerhartz, 1990). Others such as B. thuringiensis and B. sphaericus, because of their insecticidal activity, are used in crop protection (Bourque et al., 1995) while B. mycoides has the ability to promote plant growth (Petersen et al., 1995). In countries like Italy and Vietnam, B. subtilis, B. clausii or B. alcalophilus have been used as an oral bacteriotherapeutic for the treatment of gastrointestinal disorders (Casula and Cutting, 2002; Hoa et al., 2000; Senesi et al., 2001). On the other hand, some strains of B. anthracis and B. cereus are pathogenic to humans and/or animals (Shangkuan et al., 2000) and honeybees are susceptible to American foulbrood, a disease caused by Paenibacillus larvae subsp. larvae (formerly Bacillus larvae) (White, 1920).

In its original classification, the genus Bacillus contained a heterogeneous assembly of aerobic, or facultative anaerobic, Gram-positive, rod-shaped, spore-forming bacteria widely distributed in the environment (Goto et al 2000). Traditionally, Bacillus spp. are identified in the laboratory by biochemical tests and fatty acid methyl ester (FAME) profiling (Bobbie and White, 1980; Vaerewijck et al., 2001). Alternatively, the API (Analytab Products, Inc.) system of identification has been shown to be more reproducible than classical methods (Logan and Berkeley, 1984) and is capable of speciating bacilli using a combination of the 12 tests in the API 20E strip, and 49 tests in the API 50CHB strips. These phenotyping protocols are laborious and time-consuming to undertake and cannot provide a rapid screening system (Wattiau et al., 2001). The shortcomings of phenotypically-based identification methods have led to the development of molecular alternatives based on the microbial genotype or DNA sequence. This approach minimises problems associated with typability and reproducibility, and importantly, facilitates the assembly of large reference databases (Olive and Bean, 1999).

Comparisons of the 16S rRNA sequence is one of the most powerful tools for the classification of microorganisms (Wang et al., 2003; Woese, 1987; Yamada et al.,

1997) and have provided sequence specific primers as gold standards for the identification of pure cultures of Bacillus species (Goto et al., 2000) such as B. subtilis (Wattiau et al., 2001), B. cereus and B. thuringiensis (Hansen et al., 2001), and Paenibacillus alvei (formerly Bacillus alvei) (Djordjevic et al., 2000). However, environmental samples such as those from sewage, water, soil, faeces and even beehives, usually contain mixtures of Bacillus species. The presence of such combinations can be better detected with the use of a group-specific primer that distinguishes as many member species as possible within the genus. Individual species representative of different Bacillus genera can then be characterised by subjecting the amplicons to restriction enzyme digest. Although genus-specific primers have been successfully developed for lactobacilli (Dubernet et al., 2002), mycoplasmas (van Kuppeveld et al., 1992), Bifidobacterium (Matsuki et al., 1999), Pandoraea (Coenye et al., 2001) and Clostridium (Van Dyke and McCarthy, 2002), a group-specific primer pair capable of amplifying a specific sequence of 16S rDNA from all Bacillus taxa has not been developed in accompaniment with restriction digest mapping.

The potential probiotic attributes of Bacillus include competitive exclusion of pathogens, production of antimicrobials, enzyme production and modulation of immune function (Sanders et al., 2003). Unlike other probiotic bacteria such as lactobacilli and bifidobacteria, Bacillus probiotic strains are present in commercial products as spores rather than being consumed as vegetative cells, with the advantage that spores can survive transit through the stomach intact. Although it is still unclear how spores act to enhance the normal microbial flora of the host gastrointestinal tract (GIT), recent evidence has shown that Bacillus spores can germinate in mouse small intestine (Casula and Cutting, 2002; Hoa et al., 2001), and orally ingested B. subtilis spores are immunogenic and can induce gut-associated lymphoid tissue development (Duc et al., 2003; Rhee et al., 2004), suggesting that several Bacillus species may have an important commensal role in the gut microflora.

Although Bacillus spores are currently available as probiotics or CE agents for animals, the presence of mesophilic Bacillus species in animal GIT and the sources of this microbiota remains poorly understood. The faecal flora contents are assumed

to reflect those of the large intestine and cattle faeces are regarded as vehicles for the transmission of pathogenic B. cereus strains, which can contaminate meat during beef processing (Rowan et al., 2001). Prior to the current investigation, there was no known published data characterizing the spore-forming bacilli in cattle faeces. An understanding of the ecological niches of bacilli in cattle is essential to the development of intervention strategies to control contamination at processing. In addition, understanding indigenous Bacillus composition in different hosts will guide the administration of different Bacillus probiotics, and additionally provide the first and necessary stage in the characterization of a selected group of strains with the view to developing safer and more effective Bacillus probiotic/CE agents for animals from indigenous isolates.

In this study, we have focused on a subset of environmental (probiotic and gut- associated) Bacillus strains that are primarily mesophilic spore-forming bacteria capable of growing aerobically on nutrient agar at neutral pH, and between 25-45°C. The strains of interest to us, included species from related genera such as Bacillus, Brevibacillus, Paenibacillus and Geobacillus but not Alicyclobacillus (thermophiles and acidophiles), Amphibacillus and Ureibacillus (alkaliphiles), Halobacillus, Filobacillus, Gracilibacillus, Virgibacillus and Salibacillus (halotolerant or halophilic bacteria) and even several extremophilic, asporogenous and nutritionally fastidious species present in Bacillus, Geobacillus and Paenibacillus. The specificity of our target PCR primers was also validated against related Gram-positive but non- Bacillus species that frequently co-exist with Bacillus in the environment. Species identification of Bacillus, based on amplicons generated by our group-specific PCR primer pair, was accomplished by amplified ribosomal DNA restriction analysis (ARDRA) with AluI and TaqI. The utility of our assay was then used to check for mislabelled identifications if any, in a commercial collection of probiotic Bacillus strains. Furthermore, This ARDRA technique was used to investigate the species identities of aerobic spore-forming Bacillus strains retrieved from the faecal samples of feedlot cattle

4.2 Materials and methods 4.2.1 Reference strains and culture conditions Reference strains used in this study are listed in Table 4.1 including 15 reference Bacillus strains; 18 reference non-Bacillus strains; 17 reference probiotic strains; 50 reference honeybee strains. The identities of those 17 probiotic strains can not be reviewed under confidentiality agreement. Reference strains are defined as those strains that have been speciated using standard taxonomical protocols by ATCC, commercial probiotic companies and diagnostic laboratories.

Bacillus strains were cultivated in tryptic soy broth (TSB) or agar (TSA, DIFCO, Becton Dickinson, Sparks, MD) aerobically at 34°C. The probiotic Bacillus strains were recovered by suspension of dried bacterial preparations in saline followed by plating on TSA. The non-Bacillus strains were cultivated on nutrition agar media as recommended by ATCC. Fifty P. larvae isolates were obtained from the Elizabeth Macarthur Agricultural Institute, Regional Veterinary Laboratory culture collection. These had been cultured and identified as described previously (Hornitzky and Clark, 1991; Hornitzky and Wilson, 1989). All strains were stored at –80oC in 15% glycerol.

4.2.2 Morphological and biochemical identification of Bacillus strains Bacterial isolates were identified as Bacillus spp. based on: (i) Gram stain (Gram positive rods); (ii) spore forming capacity, examined using phase contrast microscopy; and (iii) API tests, using the API 20E and API 50 CHB system (BioMerieux).

4.2.3 DNA isolation Bacterial genomic DNA was isolated according to the modified method of Mantynen and Lindstrom (Mantynen and Lindstrom, 1998). In brief, 1.5 ml of an overnight bacterial culture grown in TSB were collected and washed with TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). Bacterial DNA was extracted by lysozyme (Sigma, Australia) and proteinase K (Promega, Australia) treatment, followed by CTAB- chloroform extraction and isopropanol precipitation. The purified DNA was

resuspended in TE buffer. The quality of the DNA preparation was verified by measuring the absorbance ratio at 260/280 nm.

4.2.4 Primer design and PCR amplification 16S rRNA sequences were retrieved from GenBank, EMBL and RDP (the Ribosomal Database Project) databases (Table 4.2A and 4.2B). Multiple alignments of 16S rRNA from Bacillus spp. and phylogenetically related reference species were constructed with the Pileup program (GCG, University of Wisconsin) accessed through the Australian National Genome Information Service, University of Sydney (ANGIS). Two potential PCR primer sites, starting at position 255 and position 1350 (E. coli 16S rRNA gene numbering) respectively were selected. A 19-mer forward primer (B-K1/F, 5’-TCACCAAGGCRACGATGCG-3’) and an 18-mer reverse primer (B-K1/R1, 5’-CGTATTCACCGCGGCATG-3’) were designed based on data from these sites. Two 16S rRNA universal primers, 341f and 926r (Liu et al., 1997), were used as a PCR control to ensure the quality of the template DNA. The oligonucleotide primers were synthesised commercially (Sigma, Australia).

Amplification was carried out using a PC-960 thermal cycler (Corbett Research, Australia) with a reaction volume of 50 µl. A 2 µl sample of DNA template was added to a mixture containing 0.2 mM of each dATP, dGTP, dCTP and dDTP (Astral

Scientific, Australia), 1× buffer solution (QIAGEN, Australia), 1.5 mM MgCl2, 1.0 µM of each primer (B-K1/F and B-K1/R1 or 341f and 926r) and 1.0 unit of Taq DNA polymerase (QIAGEN, Australia). Each PCR program was conducted using a denaturation step of 3 min at 94oC, followed by 25 cycles of 94oC for 30 s, 63°C (or 56°C for primers 341f/926r) for 30 s and 72oC for 2 min, with an extension step at o 72 C for 10 min. A MQ-H2O control was included in each PCR batch.

PCR products were separated by agarose gel electrophoresis. A 5 µl sample of PCR product was mixed with 3 µl of loading dye and loaded onto a 2.0 % agarose gel (Ultrapure Agarose, Life Technology, Australia) containing ethidium bromide (1 µg/ml), using 0.5× TBE buffer as running buffer. DNA in the gel was visualised by exposure to UV light and photographed with a digital capture system (Gel Doc, Bio- Rad).

Table 4.1 List of bacterial strains used as a source of genomic DNA for the validation of primer pairs K-B1/F and K-B1/R1

Bacillus strains Source Non-Bacillus strains (18) Source Probiotic strains (17) Source Honey bee strains (15) B. subtilis ATCC 6633 Clostridium tertium ATCC14573 B. subtilis BS1 Commcial B. licheniformis ATCC 25972 Staphylococcus xylosus ATCC700404 B. subtilis BS2 Commcial P. larvae strains B. pumilus ATCC 21356 E. coli O157H7 EMAI B. subtilis BS3 Commcial (n= 50) B. cereus ATCC 14579 Enterococcus faecium ATCC19434 B. subtilis BS4 Commcial B. thuringiensis ATCC 10792 Bifidobacterium bifidum ATCC 29521 B. subtilis BS5 Commcial B. laterosporus ATCC 64 Fusobacterium mortiferum ATCC25557 B. subtilis BS6 Commcial B. laterosporus ACM 5117 Salmonella typhimurium ATCC23853 B. subtilis BS7 Commcial B. coagulans ATCC 7050 L. acidophilus ATCC4356 B. badius (*B. subtilis BS8) Commcial B. sphaericus ATCC 14577 L. reuteri Commcial B. licheniformis (*B. subtilis BS9) Commcial B. circulans ATCC 15518 Pediococcus damnosus ATCC11309 B. licheniformis BL1 Commcial B. badius ATCC14574 L. casei Commcial B. licheniformis BL2 Commcial B. clausii ATCC 700160 L. delbrueckii ATCC 11842 B. licheniformis BL3 Commcial P. polymyxa ATCC 842 L. rhamnosus ATCC7469 B. licheniformis BL4 Commcial P. larvae ATCC 9545 St. thermophilus ATCC19528 B. circulans BC1 Commcial P. lentimorbus ATCC 14707 Propionibacterium jensenii ATCC 4868 B. thuringiensis BT1 Commcial Campylobacter faecalis ATCC33710 P. polymyxa (*B. macerans BM1) Commcial Mycoplasma hyopneumoniae ATCC25934 B. laterosporus (BLS1) Commcial Enterobacter agglomerans ATCC27989

ATCC: American Type Culture Collection, Manassas, USA; ACM: Australian Collection of Microorganisms, Department of Microbiology, University of Queensland; EMAI: Elizabeth Macarthur Agriculture Institute, Camden, Australia; *Originally mislabelled

Table 4.2A List of Bacillus taxa providing 16S rDNA gene sequences from GenBank for the design of primers

Phylogroupa Species Accession No. I B. subtilis AF198249, AB018486 I B. licheniformis AF234841, AB055006 I B. pumilus AF234856, AB020208 I B. atrophaeus AB021181, AY121428 I B. amyloliquefaciens AY055223, AY055224 I B. cereus AF206326, Z84577 I B. thuringiensis Z84584, Y18473 I B. mycoides AB021192, Z84582 I B. anthracis AF176321, AF290552 I B. megaterium D16273, AB022310 I B. coagulans AF466695, AF346895 I B. badius D78310, X60610 I B. firmus D16268, D78314 I B. lentus AB021189, D16272 I B. circulans X60613, AY043084, Y13064, Y13062 I B. clausii AJ297492, X76440 I B. simplex AJ439078, D78478 II B. sphaericus L14012, AF169495 III P. polymyxa AJ320493, AF355463 III P. l. larvae X60619, U86605 III P. lentimorbus X60622, AB110988 IV Brevi. laterosporus D16271, D78461 IV Brevi. brevis AF424048, D78457 V G. stearothermophilus AY491497, AJ005760 a The phylogenetic grouping is based on Ash et al., (1991)

Table 4.2B List of non-Bacillus genera providing 16S rDNA gene sequences from GenBank for the design of primers

Species Accession No. Clostridium tertium Y18174 Clostridium difficile AB075770 Enterococcus faecium AJ276355 Enterobacter aerogenes AB004750 Bifidobacterium bifidum M38018 Staphylococcus aureus X68417 Campylobacter faecalis AF372091 Streptococcus thermophilus AY188354 Salmonella typhimurium X80681 Pediococcus damnosus AJ318414 Lactococcus lactis AJ488173 Lactobacillus acidophilus M99704 Lactobacillus fermentum AF477499 Lactobacillus plantarum M58827 Lactobacillus reuteri X76328 Listeria monocytogenes AL591981 E. coli O157H7 AB035925 Propionibacterium jensenii X53219 Mycoplasma hyopneumoniae Y00149 Fusobacterium mortiferum X55414

4.2.5 ARDRA analysis of PCR amplicons The restriction enzymes with a 4-bp recognition site used in this study were AluI (AG’CT), TaqI (T’CGA) and HhaI (GCG’C) (Fermentas, Hanover, MD), MspI (C’CGG), MboI (‘GATC) and RsaI (GT’AC) (New England Biolabs, Beverly, MA). PCR products were purified using WIZARD PCR purification columns (Promega, Australia) and were eluted in a final volume of 40 µl. A 5 µl sample of purified PCR amplicon was digested with 5 U of individual restriction enzyme in a 20 µl reaction volume for 4 h using the conditions recommended by the manufacturer. The restriction fragments were electrophoresed through a 2% agarose gel (Ultrapure Agarose) followed by ethidium bromide staining. Digitised gel images were analysed with the GelCompar software (Applied Maths, Belgium, version 3.1) to construct ARDRA profiles for Bacillus strains. Similarity between fingerprints was determined on the basis of the Dice coefficient and a band position tolerance of 1% was set. The theoretical ARDRA patterns were created by means of a MWTOGEL method in the GelCompar. Basically, the restriction band size data was obtained through a Maplot program (ANGIS) analysis for the 16S rRNA gene sequences obtained from GenBank. The data was then imported into GelCompar through the MWTOGEL method to display the fingerprints.

4.2.6 Bacillus spore isolation from environmental sources

Bacillus spores were isolated from faecal samples of 10 feed-lot cattle from control group, as described previously in the Protexin trial (see section 3.2.5 in Chapter 3). Bacillus spores were cultured by heat-treating the faecal suspensions for 10 min at 80°C according to recommended protocols (Swiecicka et al. 2002; Vaerewijck et al. 2001), followed by the preparation of serial tenfold dilutions of the suspensions and plating onto tryptic soy agar (TSA, DIFCO, Becton Dickinson, Sparks, MD). A total of 160 colonies (60 each from Day 0 and Day 76 respectively and 30 from the feedlot feeds) were randomly picked from TSA agar plates (6 colonies per animal) for analysis, using a scheme known as “random sampling without replacement” described by Snedecor and Cochran (1967).

4.2.7 Pulsed-field gel electrophoresis (PFGE) Genomic DNA for PFGE was prepared using the technique of Harrell et al. with some modification (Harrell et al., 1995). In brief, A 10 ml early-log phase culture was grown in TSB at 37°C for 3 h using a Gallenkamp Orbital Incubator (Sanyo, Japan). The cell pellet was resuspended in PIV buffer (10 mM Tris, 1 M NaCl, pH 7.6) and embedded in 2% low melting temperature agarose (Bio-Rad; Cat No. 162- 0017). The resulting agarose plugs were treated with lysis buffer containing 0.5 mg/ml lysozyme (Sigma) and 5 U lysostaphin (Sigma) overnight at 37°C, followed by treatment with proteinase K (0.5 mg/ml, Promega) deproteinising solution at 55°C for 24 h. The agarose-packaged DNA was then digested with 20 U of restriction enzyme ApaI (New England Biolab). The resulting fragments were separated by 1.2% PFGE-grade agarose gel (Bio-Rad) using a GeneNavigator system (Pharmacia). A 5-35 s pulse time at 200 volts for 25 h in 0.5× TBE buffer at 12ºC was employed. Gel images were processed using GelCompar software.

4.2.8 DNA sequence analysis PCR product was purified using the QIAGEN Gel Purification kit (QIAGEN, Australia). A total of 70 ng of purified DNA was subjected to AmpliTaq cycle- sequencing reactions using the BigDye terminator cycle-sequencing kit (Applied Biosystems) according to the manufacturer’s instructions. Electrophoresis of DNA sequencing reaction products was performed through 7% polyacrylamide gels with an automated sequencer (model 377, Applied Biosystems). The nucleotide sequences were analysed with the ANALYSIS program (Applied Biosystems) and the ANGIS program.

4.3 Results 4.3.1 Selection and validation of a Bacillus specific forward and reverse primer set Three criteria were imposed in the search for the forward and reverse primers capable of amplifying a specific 16S rDNA sequence of 3 Bacillus genotypes representing 24 species (Table 4.2A), but not other non-Bacillus genera such as Clostridium, Enterococcus, Bifidobacterium, Staphylococcus, Campylobacter,

Streptococcus, Salmonella, Pediococcus, Lactococcus, Lactobacillus, Listeria, E. coli, Propionibacterium, Mycoplasma and Fusobacterium (Table 4.2B) and normally found associated with environmental samples. Firstly, both primers must have a G+C content > 50%. Secondly, one of the primer set had a deliberate nucleotide mismatch for non-Bacillus species. Using alignment refined from ANGIS (as shown in Figure 4.1A), these conditions were fulfilled by primer K-B1/F corresponding to nucleotide position 255 – 273 of the reference E. coli 16S rDNA. At position 273 (boxed inset), the guanosine nucleotide (bolded) was retained for all bacilli but is replaced by adenosine or cytosine in non-Bacillus genera. The specificity of this primer for Bacillus was further checked with a BLAST program. However, primer suitability was confounded by Clostridium tertium (arrowed) having a guanosine in position 273. To overcome this problem, a third criterion was applied in the design of the reverse primer so that the corresponding Clostridium rDNA sequence would not be amplified when used in conjunction with K-B1/F. As shown in Figure 4.1B, the reverse primer B1-K/R1 spanning the reference E. coli nucleotide positions 1350- 1367 contained an adenosine nucleotide for Clostridium, E. aerogenes and E. coli at position 1350 (arrowed; boxed inset) while all Bacillus species contained a cytosine residue. The presence of cytosine at position 1350 for S. aureus, St. thermophilus, L. reuteri, L. lactis and E. faecium would not generate amplicons because of a mismatched forward primer for these species. The rationale for stringency in the selection of a single mismatch at the 3’ end of a primer is known to interfere strongly with the PCR reaction, while there is a higher degree of tolerance for mismatches at the 5’ end (Kwok et al., 1990).

Primer K-B1/F 5’-TCACCAAGGCAACGATGCG- 3’ A B.sutilis TCACCAAGGCAACGATGCGTAGCCGACCT B.licheformis TCACCAAGGCGACGATGCGTAGCCGACCT B.cereus TCACCAAGGCAACGATGCGTAGCCGACCT B.thuringiensis TCACCAAGGCAACGATGCGTAGCCGACCT B.megaterium TCACCAAGGCAACGATGCGTAGCCGACCT B.coagulans CCACCAAGGCAACGATGCGTAGCCGACCT B.firmus TCACCAAGGCGACGATGCGTAGCCGACCT B.circulans TCACCAAGGCGACGATGCGTAGCCGACCT B.clausii TTACCAAGGCGACGATGCGTAGCCGACCT B.sphaericus TCACCAAGGCAACGATGCGTAGCCGACCT P.polymx CTACCAAGGCGACGATGCGTAGCCGACCT Brev.laterorus TCACCAAGGCGACGATGCGTAGCCGACCT B.stearothermo. TCACCAAGGCGACGATGCGTAGCCGGCCT C.tertium TCACCAAGGCGACGATGCGTAGCCGACCT S.aureus TTACCAAGGCAACGATGCATAGCCGACCT S.thermo. TTACCTAGGCGACGATACATAGCCGACCT L.reuteri TTACCAAGGCGATGATGCATAGCCGAGTT L.lactis TCACCAAGGCGATGATACATAGCCGACCT E.faecium TCACCAAGGCCACGATGCATAGCCGACCT E.aerogenes TTACCTAGGCGACGATCCCTAGCTGGTCT E.coli TCACCTAGGCGACGATCCCTAGCTGGTCT

255 273 Primer K-B1/R 5'-CGTATTCACCGCGGCATG-3’ B complementary sequence CATGCCGCGGTGAATACG B.sutilis AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.liche AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.cereus AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.thuring AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.megaterium AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.coagulans AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.firmus AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.circulans AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.clausii AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.sphaeri AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC P.polymx AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC Brev.latero AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC B.stearothermo AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC C.tertium AATCGCGAATCAGAATGTCGCGGTGAATACGTTCC S.aureus AATCGTAGATCAGCATTCTACGGTGAATACGTTCC S.thermo. AATCGCGGATCAGCACGCCGCGGTGAATACGTTCC L.reuteri AATCGCGGATCAGCATGCCGCGGTGAATACGTTCC L.lactis AATCGCGGATCAGCACGCCGCGGTGAATACGTTCC E.faecium AATCGCGGATCAGCACGCCGCGGTGAATACGTTCC E.aerogenes AATCGTAGATCAGAATGCTACGGTGAATACGGGCC E.coli AATCGTGGATCAGAATGCCACGGTGAATACGTTCC

1350 1367 Figure 4.1 (A) Alignment of primer K-B1/F with homologous target sequences of 16S rDNA (E. coli numbering scheme - nucleotides 255 and 273) from Bacillus, Paenibacillus, Brevibacillus and other genera, sourced from GenBank. Mismatched primer sequences have been underlined. The guanosine nucleotide in position 273 is maintained for all Bacillus genus but changes to either adenosine or cytosine for non-Bacillus genera (see boxed inset). (B) Alignment of primer K-B1/R1 with homologous target sequences of 16S rDNA (E. coli numbering scheme - nucleotides 1350 and 1368) from Bacillus, Paenibacillus, Brevibacillus and other genera, obtained from GenBank. Mismatched primer sequences have been underlined. The box inset shows the different nucleotide in position 1350 between all Bacillus and Clostridium

4.3.2 Genus-specific amplification of reference Bacillus strains The specificity of this primer pair for Bacillus was confirmed by PCR using genomic DNA extracted from 33 reference strains (Table 4.1). All Bacillus, Paenibacillus and Brevibacillus reference strains yielded a PCR product with an amplicon sized around 1114 bp, in agreement with theoretical prediction. The 18 non-Bacillus strains (Table 4.1) were not amplified. PCR amplicons were also generated with 17 probiotic strains as well as all 50 P. larvae honeybee larvae isolates. From all these analyses, it was determined that a 25 cycle amplification required a minimum of 0.5 ng Bacillus genomic DNA for successful amplification.

4.3.3 Speciation of reference Bacillus strains by ARDRA To determine whether genus-specific amplicons from the Bacillus, Paenibacillus and Brevibacillus reference strains could be speciated, restriction digests were carried out with AluI, TaqI, HhaI, MspI, MboI and RsaI. Restriction fragments were generated by all 5 restriction enzymes for amplicons from each of the 15 reference Bacillus strain. However, only AluI and TaqI produced clear ARDRA patterns capable of distinguishing species members in each of the 3 genera - Bacillus, Paenibacillus and Brevibacillus (Figure 4.2A and 4.2B). The ARDRA patterns also matched the theoretical digestion maps generated by simulated restriction digests of published GenBank sequences (Figure 4.2C).

With AluI and TaqI, most Bacillus species could be readily speciated using this ARDRA system by comparing either with the theoretical ARDRA pattern or with actual fragment size profiles as shown in Figure 4.2A, 4.2B and 4.2C. The utility of this procedure was restricted (Figure 4.2C) by its inability to distinguish between three members of the B. subtilis cluster (B. pumilus, B. amyloliquefaciens and B. atrophaeus), and three species in the B. cereus cluster (B. cereus, B. thuringiensis and B. anthracis).

The ARDRA assay could not differentiate between B. circulans from P. larvae. This should not present a problem because they can be simply differentiated by their catalase reaction and growth characteristics. For instance, P. larvae can only grow under facultative anaerobic conditions as described by Hornitzky and Clark (1991).

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Figure 4.2 (A) AluI restriction profiles of amplified regions of the 16S rRNA genes of Bacillus reference strains. (B) TaqI restriction profiles of Bacillus reference strains. Lane M, 100 bp+ DNA ladder (size are indicated on the left in bp); lane 1, B. subtilis ATCC6633; lane 2, B. licheniformis ATCC25972; lane 3, B. pumilus ATCC21356; lane 4, B. cereus ATCC 14579; lane 5, B. thuringiensis ATCC 10792; lane 6, B. laterosporus ATCC 64; lane 7, B. laterosporus ACM 5117; lane 8, B. coagulans ATCC 7050; lane 9, B. sphaericus ATCC 14577; lane 10, B. circulans ATCC 15518; lane 11, B. badius ATCC 14574; lane 12, B. clausii ATCC 700160; lane 13, P. polymyxa ATCC 842; lane 14, P. larvae ATCC 9545; lane 15, P. lentimorbus ATCC 14707

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M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 M 800 700 600 500 bb 400

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Figure 4.2C Theoretical prediction of ARDRA profiles generated with restriction enzymes AluI (a) and TaqI (b) based on published 16S rDNA sequences from GenBank. Lane M, DNA ladder (size are indicated on the right in bp); lane 1, B. subtilis (accession no. AF198249); lane 2, B. licheniformis (AF234841); lane 3, B. pumilus (AF234856); lane 4, B. atrophaeus (AB021181); lane 5, B. amyloliquefaciens (AY055223); lane 6, B. cereus (AF206326); lane 7, B. thuringiensis (Z84584); lane 8, B. mycoides (AB021192); lane 9, B. anthracis (AF176321); lane 10, B. megaterium (AB022310); lane 11, B. coagulans (AF466695); lane 12, B. badius (D78310); lane 13, B. firmus (D16268); lane 14, B. lentus (AB021189); lane 15, B. circulans (Y13062); lane 16, B. clausii (AJ297492); lane 17, B. simplex (AJ439078); lane 18, B. sphaericus (AF169495); lane 19, P. polymyxa (AJ320493); lane 20, P. larvae (X60619); lane 21, P. lentimorbus (X60622); lane 22, B. laterosporus (D16271); lane 23, B. brevis (AF424048); lane 24, G. stearothermophilus (AY491497)

4.3.4 Validation of ARDRA by speciation of probiotic strains from commercial sources The ARDRA assay and API identification kits were used to screen 17 probiotic and 50 honeybee isolates (Table 4.1). The species identity of each of the probiotic strain has been previously labelled by the manufacturer. Of the 17 probiotic strains examined, 3 were found to be misclassified based on ARDRA profiles (Figure 4.3) and API CHB 50 results (data not shown). B. subtilis strain BS9 was observed to possess a typical ARDRA pattern for B. licheniformis. API testing identified Bacillus strain BS9 as being maltose positive (B. subtilis is maltose negative) with 99% similarity to B. licheniformis. Another B. subtilis strain, BS8, was identified as B. badius based on ARDRA and API test. The probiotic B. macerans strain BM1 displayed an ARDRA pattern for P. polymyxa. API testing confirmed the identification of this strain as a P. polymyxa isolate. Sequence analysis of the 16S rDNA gene and BLAST sequence comparison confirmed that strain BS9 is a B. licheniformis isolate, BS8 is a B. badius isolate and strain BM1 is a P. polymyxa isolate (data not shown), validating the ARDRA and API results.

4.3.5 Identification of Bacillus spores from environmental sources Heat treatment is a standard procedure for spore selection. In that way, vegetative cells are killed and only heat-stable spores survive. Although treatment with ethanol was also used for the selective isolation of spore-formers (Barbosa et al. 2005), it was found not an effective method for cattle faecal samples in this study which contained large amount of undigested fibre and grains. Treated samples were subsequently plated and incubated aerobically. Isolates that are capable of growing aerobically on nutrient agar at neutral pH, and between 25-45°C were primarily mesophilic spore-forming Bacillus. As described in Figure 4.4, the numbers of heat- stable spores in the faecal samples were significantly increased during the feedlotting period compared with the Day 0, and the numbers remained steady throughout the feedlot period. The total aerobic spore count in cattle faeces prior to feedlotting (Day 0) and at Day 76 of feedlotting were 4.6 × 104 CFU g-1 (±0.30) and 1.6 × 106 CFU g-1 (±0.20) respectively while vegetative populations enumerated on blood nutrition agar were 3.3 × 107 CFU g-1 (±0.35) and 7.1 × 106 CFU g-1 (±0.26) on Day 0 and Day 76 samples respectively. The ratio of bacterial number on Day 0

relative to Day 76 was 1: 34.8 for spores and 4.6:1 for total vegetative bacteria, demonstrating that feedlot conditions had increased the Bacillus load in the cattle gastrointestinal tract (P< 0.01). When the same heat-treatment condition was used on feedlot feeds, 2 × 106 CFU g-1 (±0.45) of spore formers were recovered from the hay and grain mixture and no heat-stable spores were found in Molafos.

A total of 150 isolates were randomly selected from the heat-stable colonies on TSA plates. Among the 120 cattle isolates, 60 were from the Day 0 and another 60 from the Day 76 feed-lotting faecal samples. Other isolates were from the feedlot feeds. When the genomic DNA of the isolates were subjected to PCR using a Bacillus group-specific primer pair, all gave positive PCR products, confirming that they were all in the genus Bacillus. The PCR amplicons were then subjected to ARDRA identification by restriction enzyme digestions with AluI and TaqI. The results are summarised in Table 4.3. As the ARDRA technique is unable to differentiate three species in the B. cereus cluster (B. cereus, B. thuringiensis and B. anthracis) (Wu et al., 2005), those isolates belonging to the B. cereus cluster according to ARDRA were further speciated by API tests. Results showed the most frequently represented isolates in cattle faeces as B. subtilis strains, with the highest prevalence for both Day 0 (35/60, 58.3%) and Day 76 (30/60, 50.0%). B. cereus isolates were detected in the Day 0 sample with a relatively high prevalence (16/60, 26.7%). However, B. cereus was not found on Day 76. Instead, B. licheniformis (16/60, 26.7%) and B. clausii (12/60, 20.0%) were the other predominant isolates, suggesting that feedlot feeding has modified the Bacillus community in the cattle gastrointestinal tract. Two isolates from Day 0 could not be speciated by ADRDA or API tests. Sequence analysis of the PCR amplicons (generated by the Bacillus group-specific primers) and BLAST sequence comparison indicated they were most likely to be B. sporothermodurans isolates (data not shown). Only two species were identified in the isolates from feedlot feeds, represented by B. subtilis (21/30, 70.0%) and B. licheniformis (9/30, 30.0%).

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 M

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Figure 4.3 ARDRA analysis of 17 commercial probiotics strains corresponding to AluI digestion. Lane M, 100bp+ DNA ladder (size are indicated on the left in bp); lane 1, B. subtilis BS1; lane 2, P. polymyxa strain (originally mislabelled as B. macerans BM1); lane 3, B. licheniformis BL1; lane 4, Brevi. laterosporus BLS1; lane 5, B. licheniformis BL2; lane 6, B. thuringiensis BT1; lane 7, B. circulans BC1; lane 8, B. subtilis BS2; lane 9, B. subtilis BS3; lane 10, B. subtilis BS4; lane 11, B. subtilis BS5; lane 12, B. subtilis BS6; lane 13, B. licheniformis BL2; lane 14, B. licheniformis BL3; lane 15, B. subtilis BS7; lane 16, B. licheniformis strain (originally mislabelled as B. subtilis BS9); lane 17, B. badius strain (originally mislabelled as B. subtilis BS8)

1.0E+07

1.0E+06

1.0E+05

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1.0E+03 Total counts (log10 cfu g-1) cfu (log10 counts Total 014285676 Day

Figure 4.4 Counts of aerobic spores on TSA media recovered from cattle faeces during -1 feedlotting (log10 CFU g )

Table 4.3 Speciation of Bacillus isolates in cattle faeces and feedlot feed. A total of 150 faecal isolates were speciated by ARDRA, including 60 isolates each from Day 0 and Day 76 cattle faecal samples during feed-lotting and 30 from feedlot feed, as described in Materials and Methods. Results showed B. subtilis, B. licheniformis and B. clausii predominated in cattle faecal isolates after 76 days in feedlot, and only the first two species were found in the feed

Source of isolates of Source No. of isolates B. subtilis B. licheniformis B. clausii B. cereus B. coagulans B. sphaericus B. spp.

Day 0 35 4 1 16 1 1 2 Cattle (n=60) (58.3%) (6.7%) (2.0%) (26.7%) (2.0%) (2.0%) (3.3%) faeces Day 76 30 16 12 0 2 0 0 (n=60) (50.0%) (26.7%) (20.0%) (3.3%) Feed n=30 21 9 0 0 0 0 0 (70.0%) (30.0%)

4.3.6 Determination of the intraspecies clonal variation of B. licheniformis by PFGE The ARDRA assay used in this study can discriminate Bacillus isolates only to the species level; therefore it is not capable of characterizing the intraspecies diversity of Bacillus isolates. No significant differences were detected among the B. licheniformis isolates subjected to biochemical properties determined by API tests. In order to clarify the clonal diversity of isolates within this frequently represented Bacillus species, 12 B. licheniformis faecal isolates from Day 76 cattle, identified using ARDRA and API testing, were analysed using PFGE of DNA digested with restriction enzyme ApaI. Nine different PFGE patterns were recognised in the 12 B. licheniformis isolates (Figure 4.5A), indicating that most B. licheniformis isolates examined were heterogeneous at the genotypic level. A dendrogram based on the PFGE patterns (Figure 4.5B) showed that the isolate in lane 3 had an identical PFGE

pattern with 3 isolates in lane 5, 6 and 7 respectively, whilst similar PFGE patterns were observed between lane 4 and lane 12 as well as between lane 8 and lane 9, suggesting that they were genetically closely related clones.

M 1 2 3 4 5 6 7 8 9 10 11 12 A

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B 40 50 60 70 80 90 100 Lane 8

Lane 9 Lane 11

Lane 10 Lane 3

Lane 5 Lane 6

Lane 7 Lane 4

Lane 12 Lane 2

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Figure 4.5 (A). Pulsed-field gel electrophoresis (PFGE) with ApaI showing the intraspecies clonal variation of 12 cattle isolates in B. licheniformis species. Lane M, λ DNA ladder (size in kb indicated on the left); lane 1-12, B. licheniformis isolates from Day 76 cattle faecal samples. (B) Dendrogram clustering showing similarity among 12 cattle B. licheniformis isolates based on their PFGE profiles described in 4.5A. Dendrogram was generated using the Dice coefficient and UPGMA.

4.4 Discussion In recent years, the use of molecular techniques has greatly changed the original taxonomic classification of the Bacillus taxa. Taking advantage of 16S rRNA sequence information, Ash et al. (1991) re-organised the genus into five phylogenetically distinct groups (group 1 through 5). Subsequent re-classifications have created eight or more genera: Alicyclobacillus (Wisotzkey et al., 1992), Aneurinibacillus (Shida et al., 1996), Bacillus, Brevibacillus (Shida et al., 1995; Shida et al., 1996), Gracilibacillus (Waino et al., 1999), Paenibacillus (Ash et al., 1991; Ash et al., 1993), Salibacillus (Waino et al., 1999), and Virgibacillus (Heyndrickx et al., 1998). Under this new scheme, many previous members of the genus Bacillus have been either renamed or regrouped but this re-organised scheme is yet to be accommodated in current assay kits used in the laboratory for the identification of Bacillus species. In this new classification, only a subset of species is environmentally significant, either of use to, and in the service of mankind, or represent a threat as pathogens. Our ultimate goal was to develop a simplified and rapid method for the detection of these environmentally important Bacillus species. Using available information from GenBank, the 16S rDNA sequences of 24 reference Bacillus strains representing all 5 phylogroups (Ash et al., 1991) were aligned. A 19-mer forward primer K-B1/F corresponding to nucleotide position 255 – 273 of the reference E. coli 16S rDNA and a 18-mer reverse primer K-B1/R1 (nucleotides 1350-1367 of E. coli numbering) proved to be selective for Bacillus, Brevibacillus and Paenibacillus genera.

Based on 16S rDNA alignments, the primer set used in this study also showed sequence matches with these new genera such as Gracilibacillus and Geobacillus. It is, however, beyond the scope of this paper since strains from the above genera grow under different conditions from probiotic and the gut-associated bacilli. When PCR of communal DNA (e.g. from faeces) was analysed using universal bacterial primers, only numerically dominant species were amplified whilst minority bacilli were not easily detected. Prior to this study there was no PCR assay for Bacillus group members, although genus-specific PCR primers have been reported for other genera such as Clostridium, Bifidobacterium, Lactobacillus and Mycoplasma (Dubernet et

al., 2002; Matsuki et al., 1999; Van Dyke and McCarthy, 2002; van Kuppeveld et al., 1992).

As a 16S rDNA gene based molecular technique, ARDRA has been used for phylogenetic and taxonomic studies in Mycobacetrium (De Baere et al., 2002), Brevibacillus (Logan et al., 2002), Clostridium (Gurtler et al., 1991), Streptococcus (Jayaro et al., 1991) and Acinetobacter species (Vaneechoutte et al., 1995). Heyndrickx and colleagues have developed an ARDRA method for identification of strains of the genera Alcaligenes, Bordetella, Bacillus and Paenibacillus (Heyndrickx et al., 1996). In that study, a universal bacterial 16S rDNA gene primer set was used to amplify a 1500 bp amplicon, followed by construction of ARDRA patterns using 5 restriction enzymes. The obtained ARDRA patterns for each species were combined to form a database for strain identification in each genus. However, there are several limitations to this ARDRA assay. Firstly, universal bacterial rather than Bacillus- specific primers were used for PCR amplication. Thus subsequent species identification relied on prior genus identification by phenotypic and biochemical methods, FAME analysis and SDS-PAGE profiling. Secondly, compared to the use of Bacillus-specific primers, ARDRA using universal bacterial primers is difficult to assess changes in Bacillus composition. Thirdly, employing 5 restriction enzymes in the ARDRA assay is costly in time, labour and expense, although theoretically the more restriction enzyme used in ARDRA, the more accurate result will be obtained. Heyndrickx also tested different combinations of restriction enzymes in the ARDRA assay and found that 3 enzymes yielded similar results in terms of species identification as did 5 enzymes (Heyndrickx et al., 1996). Some authors have suggested that two restriction enzymes are adequate for ARDRA, especially in analysing species composition changes in complex communities (Gich et al., 2000; Koschinsky et al., 2000; Moyer et al., 1996). In our study, we have found that the ARDRA assay using Bacillus-specific primers and two restriction enzymes, AluI and TaqI, was quite adequate in differentiating most reference bacilli strains.

It was previously reported that ARDRA using universal primers could not separate B. licheniformis from B. pumilus and B. subtilis from B. amyloliquefaciens (Vaerewijck et al., 2001). However, the ARDRA assay developed in this study was able to

differentiate B. subtilis and B. licheniformis from the other species of the “B. subtilis phylogenetic cluster” (B. pumilus, B. amyloliquefaciens and B. atrophaeus). Two species within the “B. cereus phylogenetic cluster”, B. cereus and B. thuringiensis, can not be differentiated on the basis of the 16S rDNA sequence, and therefore cannot also be differentiated by ARDRA assay. On the other hand, one member of this cluster, B. mycoides, showed a high degree of similarity with B. cereus (99.5%) with only 7-9 scattered nucleotides differences in the 16S rRNA gene sequence (Ash et al., 1991; Daffonchio et al., 1998). This was sufficient to allow ARDRA identification in our assay.

Sequence variations in 16S rRNA genes may exist between some Bacillus strains within species. In order to ensure a typical ARDRA pattern correctly represented a particular species, we have retrieved 2 or more 16S rDNA sequences from GenBank for each Bacillus species. The experimentally obtained ARDRA patterns for the Bacillus reference strains matched the theoretical ARDRA patterns in almost every case. There was one exception, B. circulans species, in which the 16S rRNA gene sequences of four individual B. circulans strains selected from GenBank were dispersed at a low similarity level (88.1%). Due to the variant gene sequences of B. circulans and the presence of undetermined nucleotides (represented by n) in the 16S rRNA gene sequences deposited in GenBank, each strain produced a unique ARDRA pattern. The B. circulans reference strain ATCC15518 only matched with one of these (B. circulans strain WSBC 2003, accession no. Y13062). Logan and Berkeley reported that the species B. circulans forms a very diffuse group phenotypically and displays wide biochemical variations between strains (Logan and Berkeley, 1984). Other authors have also found this species to be genetically heterogeneous by 16S rDNA gene analysis (Heyndrickx et al., 1996; Nakamura and Swezey, 1983).

Consumption of certain live microorganisms has been shown to have a beneficial health impact on both humans and animals. A diverse group of Bacillus strains has been evaluated as probiotics and been sold worldwide including subtilis, licheniformis, coagulans, clausii and cereus (Sanders et al., 2003). However, the inaccurate species labelling on some commercial Bacillus probiotic products have been reported recently due to misclassification of some species (Green et al., 1999;

Hoa et al., 2000; Sanders et al., 2003). Our results have shown that 3 out of 17 commercial Bacillus probiotic strains were wrongly identified based on ARDRA assay. This was confirmed by additional API testing and 16S rDNA sequencing. Two misclassifications were in relation to closely related species (eg. B. licheniformis & B. subtilis; and P. polymyxa & B. macerans). The other misclassification was B. badius and B. subtilis, both these species are in phylogenetic group 1. An important aspect of establishing safety of Bacillus probiotics is proper taxonomic characterization of the bacteria in the product. Moreover, accurate species labelling is essential for responsible quality control, and to build consumer confidence in the products.

ARDRA has been used in the analysis of mixed bacterial populations from different environments such as activated sludge, compost samples, marine bacterioplancton and hipersaline environments (Acinas et al., 1999; Gich et al., 2000; Koschinsky et al., 2000; Martinez-Murcia et al., 1995). It has been proven to be sensitive for quick assessment of genotypic changes in the community over time, and successfully used to compare communities subject to different environmental conditions. Comparably, a current molecular community profiling method, denaturing gradient gel electrophoresis (DGGE), represent more powerful tool for studying bacterial diversities in complex environments (Heilig et al., 2002; Muyzer et al., 1993; Satokari et al., 2001). The employment of DGGE in association with our group specific Bacillus PCR/ARDRA technique may provide even better definition between species and such a combined approach would provide a new way to investigating Bacillus diversity in gut microflora as well as in soil, water and sewage samples.

The apparent exclusion of B. cereus by feedlotting is in accordance with the results from other studies which have reported that the feedlot diet may be a contributing factor in reducing the diversity of pathogenic E. coli populations in the faeces of cattle (Bettelheim et al., 2005; Hornitzky et al., 2002; Midgley et al., 1999). In their reports, fewer pathogenic E. coli serotypes were found in the faeces of cattle on feedlot diets, compared with cattle on pasture-based diets. Probiotic strains, including lactic acid bacteria and Bacillus strains, have the property of competitive exclusion

of pathogens. One or more mechanisms could be involved in the exclusion effect: (i) immune exclusion, (ii) competition for adhesion sites, and (iii) growth inhibition by producing antimicrobial compounds (Barbosa et al., 2005). Based on the fact that these probiotic strains were gut-isolated with an animal/human origin and a large amount of non-pathogenic Bacillus spores were found to exist in the feedlot feed in this study, it is understandable that at least a proportion of this Bacillus population have competitive exclusion capacities and are potential probiotic candidates.

It is generally suggested that Bacillus is normally an allochthonous microbe and not a commensal of the GI tract. The Bacillus composition of the GI tract represents a “small transient or supplemental flora” which is most influenced by environmental changes (Casula and Cutting, 2002; Hoa et al., 2001; Sanders et al., 2003). To support this suggestion, we applied the Bacillus-specific PCR-ARDRA system in cattle faecal samples to analyse Bacillus diversity during feedlotting. Results showed that feedlot feeding had modified Bacillus diversity, causing the occurrence of new predominant species such as B. licheniformis and B. clausii, and certain species such as B. cereus, which was present previously, were excluded after a period of feedlotting. Notably, a large numbers of aerobic spores (2 × 106 CFU g-1) were also found in the feed for cattle in this study, represented by two species, B. subtilis and B. licheniformis. This is in accordance with the results of Vaerewijck et al. (2001), who found feed concentrate for dairy cattle, collected from five different farms in Belgium, contained Bacillus predominantly subtilis, pumilus, clausii and licheniformis. Spores in feed can survive in the digestive tract and subsequently be shed in the faeces. This indicates that the feedlot feeds maybe the major source of Bacillus in faeces during feedlotting in the present study, although the clonal identity of those spores in cattle feeds remains to be further clarified. A low level of B. cereus spores (7.0%) were found in the feed concentrate for dairy cattle by Vaerewijck et al. (2001). According to the authors, the reasons could be the contaminations from the ingredients used to produce the feed concentrate or from the production plant. However, B. cereus spores were not found in the feedlot feed in this study. In addition, B. clausii, present in cattle faeces during the feedlotting, has not been found in the feed. Reasons for this could be a very low level of B. clausii spores in feed or

an unknown contamination source at the feedlot (e.g. water, soil, bird/rodent droppings or flies).

The present study showed that the faecal Bacillus community from feedlot cattle are dominated by three species (subtilis, licheniformis and clausii), which raises a question as to whether the members within each species is diverse or homogeneous. Due to the limitation of the currently used techniques such as ARDRA, API test and 16S rDNA sequencing, the diversity analysis for environmentally sourced Bacillus were restricted at the species level. By comparing the DNA fingerprints using PFGE, we have found a high degree of clonal variation in a group of 12 B. licheniformis isolates in this study. Similar heterogeneous clonal diversity was also found in B. subtilis isolates from feedlot cattle (data not shown). Good correlation between whole-cell protein profiles and PFGE patterns of Bacillus spp. has been reported (Kanzawa et al., 1995; Swiecicka et al., 2002). However, because of gene variability and mutability, the usefulness of PFGE may provide more reliable information for comparative studies. DNA fingerprinting by PFGE would be helpful to differentiate the intraspecies Bacillus isolates from different animals and to investigate the genetic relationship between the faecal isolates and the isolates from animal feed supplements within species.

In conclusion, we have developed and validated a Bacillus-specific PCR-ARDRA system, which was successfully applied to identify Bacillus strains from different environmental niches. This simplified PCR-ARDRA methodology targets the environmentally important Bacillus species such as probiotic and gut-associated bacilli. It is easy to conduct and has inherently a high level of discriminatory specificity. To our knowledge, this is the first study in which the predominant Bacillus species in faecal samples of feedlot cattle has been determined. However, the limitation of this study is that comparatively low numbers of isolates and animals were examined, and the animals were from a single feedlot environment. Therefore it is impossible to exclude the possibility that the data and conclusions may only represent the current feedlot situation. In order to better determine the gut bacilli diversity, a broader range of Bacillus strains will need to be isolated and speciated using the ARDRA technique in any future experiment. Moreover, it would be

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intriguing to discover why certain species such as B. subtilis was dominant in both pasture and feedlotting diets. Genotyping such as DNA fingerprinting will be used to investigate the genetic similarities of the isolates from faeces with the same species from animal feed supplements, in order to find out where the GIT Bacillus isolates originate.

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Chapter 5

Validation of Escherichia coli Phylogenetic Assignments Based on Virulence Gene Ownership

5.4 Introduction 5.5 Materials and methods 5.5.1 Bacterial strains and clinical characteristics 5.5.2 Serotyping 5.5.3 Phylogenetic grouping 5.5.4 Virulence genotypes 5.5.5 Pulsed-field gel electrophoresis (PFGE) for coliforms 5.5.6 Biometric analysis 5.6 Results 5.6.1 Validation of primer set for virulence gene detection 5.6.2 Phylogenetic status of E. coli isolates 5.6.3 Presence of virulence genes 5.6.4 Prevalence of significant virulence genes in isolates belonging to different phylogroups 5.6.5 Phylogenetic distribution of virulence genes 5.6.6 Principal coordinate (PCO) analysis 5.6.7 Phylogenetic grouping based on PFGE DNA fingerprinting 5.4 Discussion

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5.1 Introduction Unlike other species that inhabit the intestinal tract, E. coli is probably one of the most genetically diverse species. This conclusion has come from a large number of studies using the technique of MLEE to estimate the genetic diversity and structure in natural populations of E. coli (Selander and Levin, 1980). It is not often appreciated that the soundness of this approach is reliant upon the collection of strains used in the analysis (Whittam et al., 1983) as well as the number of loci analysed. To ensure inherent diversity was attained, strains of E. coli have been assembled from different animal host species in different geographical localities to form the ECOR collection (Ochman and Selander, 1984). This collection of E. coli is frequently used by many researchers to generate diversity estimates ranging from 0.343 to 0.542 (Selander et al., 1987). Surprisingly, the need to use such a diverse collection of strains to establish diversity was recently demonstrated to be unnecessary following MLEE analysis of 151 isolates obtained from the duodenum, ileum, colon and faeces of only eight pigs (Dixit et al., 2004). These studies showed that different regions of the gut regions can be regarded as different ecological niches for E. coli where the mean differentiation score between different regions of the gut was calculated to be 27%, a value that far exceeds the values of 2-5% differentiation observed amongst the ECOR populations isolated from different geographical localities (Whittam et al., 1983).

Based on MLEE data, member clones that have contributed towards this diverse genomic E. coli framework have been subdivided into four major phylogenetic groupings (Herzer et al., 1990). Members in Groups A and B1 are primarily non- diarrhoea faecal isolates from ‘non-clinical’ healthy humans and animals. Therefore clonotypes that form these two groups are considered to be commensals. On the other hand, members found in Group B2 and to a lesser extent in Group D, are dominated by isolates from diseased individuals and are considered to be pathogenic clones (Picard et al., 1999). It is surprising that phylogenetic groupings based on housekeeping enzymes used in MLEE can separate commensal from pathogenic groupings. Clearly, loci variations in housekeeping enzymes have accumulated by various genetic mechanisms (Feil and Spratt, 2001) and under selection pressure, evolved to endow parallel fitness in both groups.

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A key feature associated with the evolution of a pathogen is the acquisition of virulence factors that facilitate adherence, colonization, immune evasion and toxin production (Zhang et al., 2002). E. coli isolated from diarrhoea and non-intestinal diseased tissues are referred to as intestinal and extraintestinal pathogens respectively. Individual strains linked to each specific clinical condition [e.g. travellers & infant diarrhoea, urinary tract infections (UTI), meningitis and osteomyelitis] have been sorted into 8 pathotypes (Nataro and Kaper, 1998), with each pathotype having its own set of specific virulence factors. For example, enteropathogenic E. coli (EPEC) are associated with virulence factors encoded by the LEE (locus of enterocyte effacement) locus and include tir, eaeA and esp genes while extraintestinal pathogenic E. coli (ExPEC) that cause urinary tract infections carry pap, afa/draBC, sfa/focDE, kpsMTII and iutA virulence genes (Johnson et al., 2001).

While diversity studies have utilised E. coli strains from different animal species, almost all of the molecular analysis for the pathogenic E. coli subsets have been based on isolates of human origin. A direct relationship between the distribution of virulence factors and their phylogenetic origin or groupings based on MLEE has not been established. Indeed, the only molecular method that has shown a positive correlation with MLEE phylogenetic groups is the multiplex PCR developed by Clermont et al. (2000). In that study, three gene markers; chuA (a gene involved in haeme uptake found in EHEC, typically O157); yjaA (a non-virulence factor sequence found in K12) and TspE4C2 (an anonymous DNA fragment identified from a subtraction library construct), were able to sort 230 strains (including members from the ECOR collection) into the four major phylogenetic groupings. This was a significant advance since phylogenetic relationships could now be determined simply by an easy to perform assay instead of the more laborious method of MLEE. Unfortunately, Clermont’s three genes do not formally establish a relationship between isolates and pathogenic potential even though isolates categorised as B2 and D are more likely to be considered pathogenic.

This study has addressed this by developing and refining a multiplex PCR assay for 50 virulence genes assembled from 8 major human pathotypes (UPEC, NMEC,

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ETEC, EPEC, EHEC, STEC, EIEC and EaggEC). When applied to a range of E. coli isolates, the combination of 50 representative VGs successfully classified each isolate into groupings that corresponded with the Clermont procedure. This result showed clearly that the lack of or possession of VGs in member isolates of each phylogenetic group can be used to assess diversity of E. coli.

5.2 Materials and methods 5.2.1 Bacterial strains and clinical characteristics A total of 38 E. coli and 4 Shigella isolates were analysed in this study (Table 5.1). The E. coli collection included 21 rectal isolates from patients with diarrhoea or haemolytic-uremic syndrome (HUS) and 6 urinary isolates from Australian women with urinary tract infections (UTI). Two strains (ECOR60 and ECOR68) were from the E. coli Reference (ECOR) collection (Ochman and Selander, 1984). Four strains (DG1-10, FF1-78, STJ1-k4 and JA1-18) were human faecal isolates, TA-165 was a possum faecal isolate and STJ-1 was obtained from a septic tank (Gordon et al.,  2002). E. coli Nissle 1917 (Mutaflor ) was purchased as a commercial preparation from Germany. Three commercial lab strains DH5α, BL-21 and XL-10 were obtained from Integrated Science, Australia. The four Shigella flexneri strains were isolated from human faeces. All strains were grown on Luria Bertani (LB) media and routinely stored at –80ºC in 15% (v/v) glycerol. PCR template DNA were prepared by boiling 1 ml of overnight LB broth culture for 10 min. 2 µl of the supernatant was subjected to PCR.

5.2.2 Serotyping The O and H antigens of the isolates were determined by Microbiological Diagnostic Unit, University of Melbourne, Australia using a standard serological method (Bettelheim and Thompson, 1987; Chandler and Bettelheim, 1974). Nine O antigens that are associated with UTI in human (O1, O2, O4, O6, O7, O16, O18, O25 and O50) were defined as O-UTI antigens (Johnson, 1991).

5.2.3 Phylogenetic grouping The phylogenetic grouping of our isolates was determined based on the PCR method described by Clermont et al. (2000). A two-step triplex PCR was performed using

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three gene primers (chuA, yjaA and TspE4C2) in a PC-960 thermal cycler (CorbettResearch, Australia), under the following conditions: Denaturation for 4 min at 94ºC, followed by 30 cycles of 5 s at 94ºC and 10 s at 59ºC, and a final extension step of 5 min at 72ºC. The following criterion were used for assignment the strains to phylogenetic groups: group B2: chuA+, yjaA+; group D: chuA+, yjaA-; group B1: chuA-, TspE4C2+; group A: chuA-, TspE4C2- (Duriez et al., 2001).

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Table 5.1 List of bacterial strains used in the analysis Original label Identity Serotype Clinical symptom Source DES H1 1 O5:H HUS Human DES B1 2 O26:H11 BD Human DES H24 3 O86:K62:H2 D Human DES H7 4 O91:H10 D Human DES A1 5 O113:H21 HUS Human DES H14 6 O123:H- D Human DES H8 7 O128:H2 D Human DES H19 8 O128:H2 IG Human Ont:H- 9 Ont:H- D Human DES 1644 10 O111:H- D Human DES 1643 11 O157:H7 D Human 5485 12 O55:H- D Human 2665 13 O111:H2 D Human 9090(1) 14 O111:H4 D Human 5480 15 O119:H2 D Human 5483 16 O126:H12 D Human 7413 17 O111:H- HUS Human 5768 18 O128:H2 HUS Human 8469 19 O75:H7 D Human 9983 20 O7:H6 D Human 2766 21 O25:H1 D Human 8982 22 O2:H- UTI Human 1616 23 O2:H4 UTI Human 8985 24 O4:H5 UTI Human 5584 25 O6:H1 UTI Human 8990 26 O75:H- UTI Human ECOR60 27 O4:HN UTI Human ECOR68 28 ON:NM Faecal isolate Giraffe DG1-10 29 NT Faecal isolate Human JA1-18 30 NT Faecal isolate Human STJ1-1 31 NT Faecal isolate Human FF1-78 32 NT Faecal isolate Human TA165 33 Ont:H- Faecal isolate Possum STJ1-K4 34 NT Faecal isolate Septic tank Nissle 1917 35 O6:K5:H1 Probiotic E. coli Human S. flexneri 1 36 serotype 1 Shigella flexneri Human S. flexneri 2 37 serotype 2a Shigella flexneri Human TW4393 38 NT Shigella flexneri Human TW6395 39 NT Shigella flexneri Human DH5a 40 NT Lab strain BL-21 41 NT Lab strain XL-10 42 NT Lab strain

Note: NT= Not tested; D= Diarrhoeal isolates; F= Faecal isolates; HUS= Haemolytic uraemic syndrome; UTI= Urinary tract infection; BD= Bloody diarrhoea; IG= Infantile gastroenteritis

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5.2.4 Virulence genotypes A list of 50 virulence gene DNA markers, reported in the literature to be associated with different E. coli pathotypes, were selected as the panel to be used in our analysis (Table 5.2). The panel consisted of 36 ExPEC-associated and 11 IPEC (intestinal pathogenic E. coli)-associated genes markers. Three gene markers in Clermont PCR were also included in this panel. For some virulence determinants, several genes of the cluster were targeted, such as pap (papAH, papEF, papC, and papG) and sfa (sfa/focDE and sfaS). The use of several genes per cluster assisted in the confirmation of PCR positive results. DNA primers detecting the genetic variants of papG alleles (papGI, papGII and papGIII) were also included. In this study, all 50 VG markers were defined as VGs, in accordance with Johnson and Stell (2000) and Bekal et al. (2003).

ExEPC-associated genes were detected by multiplex PCR testing of 36 different VGs associated with extraintestinal diseases (Johnson and Stell, 2000). Primers were sorted into 6 pools (Pool I to VI) based on primer compatibility and amplicon size. The multiplex PCR assay was modified from Johnson & Stell (2000). Briefly, PCR was performed with 0.2 ml PCR tubes (INTERPATH, Australia) in a PC-960 thermal cycler (CorbettResearch) with a reaction volume of 25 µl. The DNA template (2 µl) was added to a mixture containing 0.6 mM of each dATP, dGTP, dCTP and dDTP (Astral Scientific, Australia), 1× buffer solution (QIAGEN), 4 mM

MgCl2, 0.6 µM of each primer and 1.5 unit of HotStar Taq polymerase (QIAGEN). Each PCR program was preceded by a step of 15 min at 95°C to activate the HotStar Taq polymerase, followed by 25 cycles of 94°C for 30 s, 63°C for 30 s and 68°C for

3 min, with an extension step at 72°C for 10 min. MQ-H2O was used as negative control.

Pool VII comprised the 3 genes of Clermont PCR. Those 11 IPEC-related VGs were PCR amplifications represented by Pool VIII to XII as shown in Table 5.2, which represent different IPEC categories (EHEC, ETEC, EaggEC, EPEC and EIEC) (Do et al., 2005; Gunzburg et al., 1995; Paton and Paton, 1998; Rich et al., 2001; Sethabutr et al., 1994). The PCR conditions for each pool were followed the references.

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Table 5.2 Virulence genes included in the uni/multiplex combinatorial PCR assays Primer Gene name Description/Function Primers Amplicon pool size (bp) (*AT) aI RPAi-F PAI PAI marker malX from strain CFT073 930 (63°C) RPAi-R Major structural subunit of pilus associated with PapA-F papAH 720 pyelonephritis (P fimbriae); defines F antigen PapA-R FimH-F fimH D-mannosespecific adhesin, type 1 fimbriae 508 FimH-R Group III capsular polysaccharide synthesis kpsII-F kps MTIII 392 (e.g., K3, K10, and K54) kpsII-R PapEF-F papEF Minor tip pilins; connect PapG to shaft (PapA) 336 PapEF-R ibe10-F ibeA Invasion of brain endothelium 170 ibe10-R aII Yersinia siderophore receptor (ferric FyuA-F fyuA 880 (63°C) yersiniabactin uptake) FyuA-R bmaE-F bmaE M-agglutinin subunit 507 bmaE-R Central region of sfa (S fimbriae) and foc (F1C sfa 1-F sfa/focDE 410 fimbriae) operons sfa 2-R Ferric aerobactin receptor (iron uptake: AerJ-F iutA 300 transport) AerJ-R AlleleIII-F papG allele III Cystitis-associated (prs or pap-2) papG variant 258 AlleleIII-R K1-fc-F kpsMT K1 Specific for K1 (group II) kpsMT 153 KpsII-R aIII hly-F hlyA α-Hemolysin 1177 (63°C) hly-R rfc-F rfc O4 lipopolysaccharide synthesis 788 rfc-R nfaE-F nfaE Nonfimbrial adhesin I assembly and transport 559 nfaE-R papG allele I Allelel-F (Rare) J96-associated papG variant 461 (internal) Allelel-R Group II capsular polysaccharide synthesis (e.g., kpsII-F kpsMT II 272 K1, K5, and K12) kpsII-R PapC-F papC Pilus assembly; central region of pap operon 200 PapC-R aIV N-acetyl-D-glucosaminespecific (G) fimbriae gafD-f gafD 952 (63°C) adhesin gafD-r ColV-C-F cvaC Colicin V; conjugative plasmids 680 ColV-C-R cdt-a1-F cdt-a2-R cdtB Cytolethal distending toxin 430 cdt-s1-F cdt-s2-R Pilus tip molecule, F1C fimbriae (sialic acid FocG-F focG 360 specific) FocG-R TraT-F traT Surface exclusion, serum survival 290 TraT-R AlleleII-F papG allele II Pyelonephritis-associated papG variant 190 Allele II-R

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Table 5.2 continued Primer Gene name Description/Function Primers Amplicon pool size (bp) (*AT) aV pG-F papG allele I (Rare) J96-associated papG variant 1190 (63°C) pG1”-R papG allele II pG-F J96-associated papG variant 1070 & III pG-R Central region of Dr antigen specific fimbrial Afa-F afa/draBC and afimbrial adhesin operons (e.g. AFA & 559 Afa-R F1845) cnf1-F cnf1 Cytotoxic necrotizing factor 1 498 cnf2-R Pilus tip adhesin, S fimbriae (sialic acid SfaS-F sfaS 240 specific) SfaS-R Specific for non-K1 and non-K2 group II K5-F kpsMT K5 159 kpsMT kpsII-R bVI Universal primer for cytotoxic necrotizing CONCNF-F univcnf 1105 (63°C) factor 1 CONCNF-R Novel nonhemagglutinin adhesin (from IHA-F iha 827 O157:H7 and CFT073) IHA-R IRONEC-F iroN Novel catecholate siderophore 665 E.coli IRONEC-R OMPT-F ompT Outer membrane protein T (protease) 559 OMPT-R ISS-F iss Serum survival gene 323 ISS-R IRE-F ireA Iron-regulated element, a siderophore receptor 254 IRE-R cVII ChuA.1 chuA A heme transport gene found in O157:H7 279 (59°C) ChuA.2 YjaA.1 yjaA An unknown-function gene in K-12 211 YjaA.2 An anonymous DNA fragment associated with TspE4C2.1 TspE4C2 152 NMEC TspE4C2.2 dVIII ehxA-F ehxA Enterohaemolysin 534 (touch ehxA-R down) eaeA-F eaeA Intimin 384 eaeA-R stx2-F stx Shiga toxin II 255 2 stx2-R stx1-F stx Shiga toxin I 180 1 stx1-R eIX LTA-1 696 eltA Heat-labile toxin (LT) (50°C) LTA-2 STb-1 172 estII Heat-stable enterotoxin (STb) STb-2 fX EP1 326 bfpA Type IV bundle-forming pili (50°C) EP2 gXI aggC693 528 aggC Fimbrial antigen-specific gene (45°C) aggC694 east 11a 111 east-1 EaggEC heat-stable enterotoxin east 11b hXII ipaHIII 600 ipah Invasion plasmid antigen (50°C) ipaHIV STa-1 166 estI Heat-stable enterotoxin (STa) STa-2

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*AT, annealing temperature for PCR amplification; a Johnson & Stell (2000); b Personal communication; c Clermont et al (2000); d Paton & Paton (1998); e Do et al, (2005); f Gunzburg et al (1995); g Rich et al, (2001); h Sethabur et al, 1994

PCR products were separated by agarose gel electrophoresis. 5 µl of amplified product was mixed with 3 µl of loading dye containing 0.25% bromophenol blue in 15% Ficoll solution and loaded onto 2% agarose gel electrophoresis (Ultrapure Agarose, Life Technology) with ethidium bromide (1 µg/ml), using 0.5× Tris- Borate-EDTA (TBE) as running buffer. DNA in the gel was visualised by exposing the gel to UV light and was photographed with a digital capture system (Gel Doc, Bio-Rad). Their lengths were verified by a 100 bp+ DNA ladder (Promega).

5.2.5 Pulsed-field gel electrophoresis (PFGE) for coliforms Genomic DNA for PFGE was prepared using the technique of Barrett et al. (Barrett et al., 1994) with some modification. In brief, bacterial colonies from overnight LB agar were embedded in 2% of Low-melting-point agarose (Bio-rad). Genomic DNA were extracted in situ by treatment with lysozyme (0.5 mg/ml, Sigma) and proteinase K (0.5 mg/ml, Sigma) lysis buffers. The agarose-packaged DNA were then digested with endonuclease XbaI or NotI (New England Biolab, Australia). The resulting fragments were separated by 1.2% PFGE-grade agarose gel (Bio-rad) in a GeneNavigator system (Pharmacia), using 5-35 s pulse time and 200 volts for 25 h in 0.5× TBE buffer at 12ºC. Gel images were saved in TIFF format and processed using GelCompar software (Applied Maths, Kortrijk, Belgium version 4.2) for computer analysis. Similarity between fingerprints was determined on the basis of the Dice coefficient. A band position tolerance of 1.0% was set and dendrograms were generated by the unweighted pair group method with arithmetic mean (UPGMA) analysis.

5.2.6 Biometric analysis The Chi-squared test of deviance (Cox, 1970) was used to assess the significance of differences between phylogenetic groups of isolates (A, B1, B2 and D) for each gene and ranking the relative importance of these genes based on the deviance value contributed to by group differences. Distribution of virulence genes among the

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phylogenetic groups were analysed by principal coordinate (PCO) analysis using computer software Genstat 5 Committee (2003) (Genstat, 7th edition, Lawes Agricultural Trust, UK). This method groups the observations with ‘simple matching’ coefficients between pairs of observations forming the similarity matrix. The first principal coordinate scores were plotted against the second principal coordinate scores. The four quadrants split at arbitrary axes were then used to discriminate the observations and the members within quadrants were cross-tabulated against the groups provided by a phylogenetic grouping analysis.

5.3 Results 5.3.1 Validation of primer set for virulence gene detection Figure 5.1 shows representative gels of uni/multiplex combinatorial PCR amplified E. coli virulence genes. Clear PCR products of the expected sizes were observed, consistent with the presence of the 36 ExPEC-associated genes in Pool I to VI, 3 Clermont-PCR genes in Pool VII, and the 11 IPEC-associated genes in Pool VIII to XII. The PCR products were further validated by DNA sequencing.

5.3.2 Phylogenetic status of E. coli isolates E. coli isolates used in this study were grouped into four phylogenetic groups (B2, D, B1 and A) based on the Clermont multiplex PCR. Amongst 42 isolates (38 E. coli and 4 Shigella), 16 belonged to B2 (37.2%), 6 of group D (14%), 14 of B1 (32.6%) and 7 of A (16.3%). All the UTI isolates belonged to group B2, as did 3 human faecal isolates and 1 isolate from a septic tank. This result corresponds to recent phylogenetic studies that most of the ExPEC strains belong to group B2 or D. Most of the 18 IPEC isolates were confined to groups D (22.2%), B1 (66.7%) and A (11.1%), with two EPEC isolates (DES H24 and TW6395, with strain identity of 3 and 39 respectively) in group B2. Three commercial laboratory strains, DH5α, BL- 21 and XL-10 (strain 40, 41 and 42 respectively), belonged to group A and confirms previous findings that these strains are non-pathogenic and unlikely to cause disease (Chart et al., 2000). Notably, Nissle 1917 (strain 35), a faecal-isolate considered to be non-pathogenic and used widely as a probiotic was found to be in group B2. Four Shigella strains, which are closely related to E. coli, were scattered to group A, D and B2.

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Pool I_ Pool II_ Pool III_ Pool IV_ Pool V_ Pool VI_ MM+ + + + + + A

papGI 1200 hlyA papG univcnf gafD 900 II,III PAI iha rfc 700 papA fyuA cvaC ironec 600 afa/dra nfaE BC ompT fimH bmaE 500 cnf1 papG alleleI cdtB 400 sfa/focDE kpsMTIII focG papEF iss 300 iutA traT ireA papG kpsMT II sfaS alleleIII papC 200 papG allele ibeA K5 K1 II

M Pool _VII Pool VIII_ Pool _IX Pool X_ Pool XI_ Pool XII_ M + + + + + + B eae LT 800 ipaH eltA 700 ehxA aggC ipaH 600 ehxA aggC 500 eaeA bfpA 400 ChuA eaeA stx2 YiaA bfpA stx1 STa 300 chuA STb TspE4C2 stx2 VT2e east-1 yjaA 200 stx1 estII estI TspE4C2 east-1 100

Figure 5.1 Agarose gel electrophoresis of uni/multiplex combinatorial PCR amplified E. coli virulence genes. (A) 36 different VG primers were sorted into 6 pools (I-VI), (B) 3 Clermont genes are in pool VII, and 11 diarrhoeal-associated VF gene primers were sorted into 5 pools (VIII-XII). ‘+’ were positive control; ‘-’ were

MQ-H2O control; M were DNA markers (size in base pair)

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5.3.3 Presence of virulence genes The 42 E. coli isolates, including three laboratory strains, were PCR-screened for 36 virulence-associated genes of ExPEC and 11 genes of IPEC as described in Table 5.2. Thirty out of 36 ExPEC genes were found to be present in those 42 isolates, with a prevalence range from 95.2% (fimH, 40/42) to 2.4% (sfaS, 1/42). Six ExPEC genes [bmaE, gafD, nfaE, cdtB, papG alleleI and papG alleleI (internal)] were absent in those isolates. Generally, ExPEC genes were more frequently detectable in ExPEC strains (possession of 27.8%-52.8% of 37 EXPEC genes) than in IPEC (possession of 2.8%-25%). None of the ExPEC isolates harboured VGs typical for IPEC.

The PCR results showed the presence of some ExPEC genes in the IPEC isolates. Twelve out of 36 ExPEC VGs were detected in 22 IPEC isolates, ranging in prevalence from 95.5% (fimH, 21/22) to 4.5% (afa, 1/22). Most of the IPEC isolates possessed typical IPEC genes. Six out of 10 IPEC genes were obtained (stx1, stx2, eaeA, exhA, east-1, bfpA and ipaH) with a prevalence range from 26.2% (eaeA, 11/42) to 4.8% (bfpA and ipaH, 2/42 respectively). LT (heat-labile toxin), STa and STb (heat-stable toxin I and II respectively) genes were not detected, suggesting the tested isolates may not include ETEC strain.

Two E. coli strains (21 and 19), originally isolated from patients with diarrhoea, expressed the uropathogenic serotypes (O25:H1 and O75:H7 respectively) and presented numbers of typical ExPEC genes. The co-occurrence of papGII & III, cnf1 and hlyA has been found in 6 group B2 strains (3 UTI strains: 22, 23, 27; 2 commensal: 29, 30; 1 diarrhoeal isolate, 21). However, strain 24 (UTI) has papG II & III/cnf1 but without hlyA, and strain 19 (diarrhoeal isolate) has hlyA/cnf1 but without any papG allele, including papA, papEF or papC.

The three non-pathogenic laboratory stains, DH5α, BL-21 and XL-10, also possessed ExPEC virulence genes such as fimH, traT and ompT. Interestingly, Nissle 1917 (strain 35) was detected to have a considerable number of ExPEC virulence genes, including PAI, focDG, kpsMTII, K5, fimH, fyuA, iutA, iha, ironec and ompT. Among those Shigella, strain 36 and 37 (both belong to group A) possessed ipah, indicating that they are similar to enteroinvasive E. coli (EIEC). Strain 39 (TW 6395,

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belongs to group B2) seems more likely as an EPEC because it harboured PAI, eae and bfpA. Whereas strain 38 (group D) only present fyuA and east-1 genes.

5.3.4 Prevalence of significant virulence genes in isolates belonging to different phylogroups The Chi-squared test of deviance was used to establish the role and level of significance each gene played in determining similarities and differences between isolates belonging to different phylogroups. The device value (Chi-square) was determined using a likelihood ratio test, as described by Cox in 1970. The comparisons between group means were determined by forming factors of group contrasts, and chi-square value associated with the contrasts were calculated. Table 5.3 shows that only 21 out of 50 virulence genes played a significant role (P< 0.05) in differencing isolate membership in the four different phylogroups. The frequency of occurrence of each gene can be ranked on the deviance value contributed to by group differences. In this study, the Chi-squared ranking varied from 59.59 for chuA to 10.22 for iha. Not surprisingly, all three Clermont-PCR genes, chuA, yjaA and TspE2C4, were ranked in the top 4 at 1, 3 and 4 respectively. Interestingly, the PAI gene was ranked number 2, suggesting that it has a role in complementing the three Clermont-PCR genes. Moreover, among the 21 genes, 15 were ExPEC-associated genes. Three IPEC-associated genes, stx1, stx2 and ehxA were also considered to be significant in differentiating between phylogroups.

Table 5.3 Ranking the relative importance of the virulence genes (top 21 out of 50) based on the deviance value contributed to by phylogroup differences

A B1 B2 D Chi square (n=7) (n=13) (n=16) (n=6) (χ2)

chuA 0 0 1.00 1.00 59.59 PAI 0 0 1.00 0 56.77 yjaA 0.43 0 1.00 0 49.47 TspE4C2 0 1.00 0.81 0 41.32 sfa/focDE 0 0 0.56 0 22.19 fyuA 0 0.43 0.88 0.33 20.77 cnf1 0 0 0.50 0 19.14 kpsMTII 0 0 0.50 0 19.14 univcnf 0 0 0.50 0 19.14 ironec 0 0 0.50 0.33 16.82 stx2 0 0.50 0 0.10 16.5 hlyA 0 0 0.44 0 16.28 K5 0 0 0.44 0 16.28 papA 0 0 0.44 0 16.28 papC 0 0 0.44 0 16.28 papEF 0 0 0.44 0 16.28 papG allele 0 0 0.44 0 16.28 II & III focG 0 0 1150.38 0 13.58 ehxA 0.14 0.43 0 0.17 11.05 stx1 0.14 0.43 0 0.17 11.05 iha 0 0.29 0.44 0 10.22

5.3.5 Phylogenetic distribution of virulence genes The distribution of VGs among four phylogenetic groups in all 42 isolates was examined. Based on the VG profile of each isolate, a UPGMA dendrogram illustrating the pathogenic similarity among strains is shown in Figure 5.2 and outlines two major clusters #1 and #2. Each of the 2 main clusters was subdivided into two subclusters (1α and 1β, 2α and 2β respectively). Isolates in group B2 fell into cluster 1β and isolates in group B1 belonged to a unique cluster 2β. Five isolates in group D were assembled together but they were split into two parts. Strain 14, 31 and 38 were in cluster 1α, which is closer to group B2 (cluster 1β) whereas strain 11 and 12 fell into the same cluster with group B1 strains. (cluster 2β). This result indicates that some group D strains were pathogenically closer to either group B2 or B1, depending on their VF genes distribution. Surprisingly, one group D strain, 20 (serotype O7:H6), was found in group A (cluster 2α) because it only harboured fimH gene.

The PAI marker (pathogenicity-associated islands) was strongly associated with group B2 (100% in B2, 0% in the others, χ2 = 56.77, P < 0.05). Most of the group B2 isolates have typical ExPEC VG profiles as they harboured numbers of ExPEC but no IPEC gene. However, the two EPEC isolates (3 and 39, possessed both eaeA and bfpA) were exceptions in group B2 since they only possessed PAI, fimH, traT and ompT. In addition, when all group B2 strains were compared to group A and B1 strains, pap, sfa, hlyA, kpsMTII, focG and cnf1 genes were significantly more frequent in the group B2 strains (P < 0.05): sfa/focDE (χ2 = 22.19), cnf1 (χ2 = 19.14), kpsMTII (χ2 = 19.14), hlyA (χ2 = 16.28), and pap (χ2 = 16.28). Three VGs, fimH, traT and ompT, were found to broadly present in four phylogenetic groups.

5.3.6 Principal coordinate (PCO) analysis PCO analysis was used to investigate whether groupings defined by VG ownership matched the scheme based on the Clermont triple gene combination. The result of such an analysis for individually strains was projected on a plane defined by two axes (PCO1 and PCO2) giving rise to clustering patterns in 4 quadrants. The PCO grouping was cross-tabulated against the phylogenetic grouping to form a

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contingency table that could then be tested using Pearson’s Chi-squared test. All 37 genes in the E. coli panel gave χ2(df = 9) = 59.44 indicating there was a significant association (P< 0.001) between the two methods of analysis. There was strong agreement between PCO groupings and phylogenetic-based groupings for B1 and B2 strains. As shown in Figure 5.3, all 14 B2 strains were clustered in quadrant 3 and 4, and 12 out of 13 B1 strains were located in quadrant 1. Strains in phylogroup A were mostly in PCO quadrant 2 whereas strains in phylogroup D were scattered in quadrants 1, 2 and 3. Cross-tabulation between phylogenetic and PCO on 34 genes (after exclusion of Clermont’s triple genes) (see Figure 5.4) had a Chi-squared χ2 = 49.40 (P< 0.001). Compared with Figure 5.3, PCO analysis without 3 Clermont-PCR genes resulted in the re-positioning of several strains. For instance, strain 9 and 16 had shifted from quadrant 1 to quadrant 2 because they lacked the STEC genes. Strain 14 has moved to quadrant 3. Meanwhile, strains 3 and 39 have left quadrant 3 for quadrant 2, primarily due to ownership of the eaeA gene. Cross-tabulation between PCO (included Clermont genes) and PCO (excluded Clermont genes) has yielded a Chi-squared χ2 = 96.07 (P< 0.001) with misclassification = 11.63%, indicating that they were significantly associated.

5.3.7 Phylogenetic grouping based on PFGE DNA fingerprinting PFGE was used to investigate genetic diversity of each E. coli isolate. As shown in Figure 5.5, unique PFGE patterns were obtained for each isolate with XbaI digestion. Digestion with restriction enzyme NotI generated identical results (data not shown). Even the strains within the same serogroup (such as 22 and 23 in serogoup O2; 26 and 19 in serogroup O75; 17, 10, 13 and 14 in serogroup O111) had very different PFGE patterns. Particularly with strain 35 and 25, which have similar serotype (O6:K5:H1 and O6:H1 respectively), both in group B2 and possessed identical VG profile, but displayed distinct PFGE patterns. Interestingly, two O111:H- strains, 17 and 10 (HUS and diarrhoeal isolate respectively) has only one VG difference (17 lacked ireA gene) also displayed different PFGE patterns. The UPGMA clustering analysis (Figure 5.5) showed that strains clustered by PFGE could not be assigned to their respective phylogenetic groups.

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e e e e b 30 40 50 60 70 80 90 100 b Shigella type 1 A 36 Shigella type 2a A 37 BL-21 NT A 41 2α 9983 O7:H6 D 20 DH5a NT A 40 ECOR68 ON:NM B1 28 ECOR68 ON:NM B1 28 DES B1 O26:H11 B1 2 DES H19 O128:H2 B1 8 5768 O128:H2 B1 18 #2 DES H14 O123:H- B1 6 DES H8 O128:H2 B1 7 7413 O111:H- B1 17 DES 1644 O111:H- B1 10 DES H7 O91:H10 B1 4 DES A1 O113:21 B1 5 5480 O119:H2 B1 15 2β Ont:H- Ont:H- B1 9 5483 O126:H12 B1 16 DES H1 O5:H- A 1 DES 1643 O157:H7 D 11 5485 O55:H- D 12 1α 9090(1) O111:H4 D 14 STJ1-1 NT D 31 TW4393 NT D 38 DES H24 O86;K61;H- B2 3 TW6395 NT B2 39 STJ1-K4 NT B2 34 #1 FF1-78 NT B2 32 8985 O4;H5 B2 24 DG1-10 NT B2 29 ECOR60 O4;HN B2 27 1β 8982 02;H- B2 22 1616 O2;H4 B2 23 JA1-18 NT B2 30 2766 O25:H1 B2 21 8469 O75;H7 B2 19 5584 O6:H1 B2 25 Nissle1917 O6:K5:H1 B2 35 8990 O75:H- B2 26 TA165 Ont:H- B2 33 2665 O111:H2 A 13 XL-10 NT A 42 Figure 5.2 Clustering of 42 E. coli isolates based on their virulence gene profiles and phylogenetic groups

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0.4 Q1 10 Q4

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-0.1 39 14 32 19 36, 37 26 25, 35 13 20 31 33 -0.2 34 12 40 3 38 -0.3 42 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Figure 5.3. PCO analysis with 37 VGs (included 3 Clermont genes). 42 isolates were split into 4 quadrants (Q1-Q4) based on their VG attributes. Red dots represent isolates belonging to phylogroup B1; Black squares, B2; Blue triangle, D; Green diamond, A

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2 27 0 15 8 29 Q2 13 39 24 Q3 12 3 14 -0.1 9 31 16 32 20 19 41 28 26 -0.2 42 40 34 33 36 25, 35 37 -0.3 38

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Figure 5.4 PCO analysis with 34 VGs (excluded 3 Clermont genes). 42 isolates were split into 4 quadrants (Q1-Q4) based on their VG attributes. Red dots represent isolates belonging to phylogroup B1; Black squares, B2; Blue triangle, D; Green diamond, A

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40 50 60 70 80 90 100 Strain Serotype Phylogroup Identity

9983 O7:H6 D 20 DES H8 O128:H2 B1 7 BL-21 NT A 41 DES A1 O113:21 B1 5 Shigella type 1 A 36 1616 O2;H4 B2 23 8982 02;H- B2 22 JA1-18 NT B2 30 5584 O6:H1 B2 25 8469 O75;H7 B2 19 2766 O25:H1 B2 21 DES H24 O86;K61;H- B2 3 8990 O75:H- B2 26 FF1-78 NT B2 32 TW4393 NT D 38 DES H7 O91:H10 B1 4 ECOR68 ON:NM B1 28 5485 O55:H- D 12 Nissle 1917 O6:K5:H1 B2 35 TA165 Ont:H- B2 33 9090(1) O111:H4 D 14 5768 O128:H2 B1 18 DES H19 O128:H2 B1 8 DES 1643 O157:H7 D 11 7413 O111:H- B1 17 DES 1644 O111:H- B1 10 DES H14 O123:H- B1 6 ECOR60 O4;HN B2 27 8985 O4;H5 B2 24 DES H1 O5:H- A 1 DH 5a NT A 40 XL-10 NT A 42 DG1-10 NT B2 29 ONT:H- Ont:H- B1 9 STJ1-K4 NT B2 34 Shigella type 2a A 37 TW6395 NT B2 39 Figure 5.5 Clustering of 42 E. coli isolates based on their PFGE profiles with XbaDES B1I digestion. O26:H11 Dendrogram B1 was 2 generated 5483 O126:H12 B1 16 using the Dice coefficient and UPGMA 2665 O111:H2 A 13 STJ1-1 NT D 31 5480 O119:H2 B1 15 121

5.4 Discussion In this study, we have characterised 38 E. coli and 4 Shigella isolates using a multiplex PCR assay with respect to one of the largest panels of virulence factors examined to date, including 36 extraintestinal and 11 intestinal VGs, and analysed the prevalence and distribution of these traits among the isolates in different phylogenetic groups. The strain collection was selected to represent minimum bias by including clinical isolates from 6 different pathotypes represented by UPEC, NMEC, EHEC, STEC, EPEC and EIEC that cause UTI, HUS or diarrhoea in humans; as well as non-pathogenic commensal isolates, including a probiotic strain, and laboratory cloning strains. Shigella strains were included as representative EIEC strains because (i) Shigella is so closely related to E. coli that they could be considered members of a single species (Maurelli et al., 1998; Pupo et al., 2000), (ii) Shigella spp. carry identical invasion plasmid (containing virulence gene ipa) with EIEC and cause dysentery that is clinical similar to the diarrhoeal disease caused by EIEC, (iii) EIEC resembles a genetic hybrid between E. coli and Shigella, and even more closely related to Shigella in certain biochemical feature (Sansonetti et al., 1982; Silva et al., 1980), and (iv) Shigellae are forms of EIEC, with the three main clusters representing three successful EIEC clones (Lan et al., 2004). Accordingly, results grouped the two enteroinvasive Shigella strains (serotype 1 and 2a respectively) within the E. coli phylogenetic group A possessed fimH and iutA. Comparably, a non-EIEC type Shigella strain was found in group B2 and possessed EPEC-related virulence genes (bfpA and eae).

It is generally recognised that strains of the same pathotype are genetically similar and carry the same virulence factors needed to be infectious. These virulence genes are ideal targets for the determination of the pathogenic potential of any given E. coli isolates (Bekal et al., 2003). The results of this study support this point of view because different pathotypes possessed unique VG combination. Additionally, some VGs were found to share among pathotypes and commensals. Figure 5.6 summaries the unique or shared virulence genes possessed among ExPEC, IPEC and commensal isolates. Numbers of VGs such as fimH, fyuA, iutA, traT, iha, ompT and ireA were common in all three groups. The virulence gene profiles of the commensal faecal isolates were much more similar to the ExPEC isolates than to the IPEC isolates as

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they were sharing most of the VGs except rfc. Moreover, the three non-pathogenic laboratory stains also possessed ExPEC virulence genes such as fimH, traT and ompT. These genes have been reported in both pathogenic and non-pathogenic E. coli strains (Vandemaele et al., 2004; Zhang et al., 2002), suggesting that they are most likely ‘adaptive genes’ to aid the bacteria to colonise and survive in the host. Interestingly, the reportedly safe probiotic strain, Nissle 1917, was detected to have quite a number of ExPEC virulence genes, including PAI, focDG, kpsMTII, K5, fimH, fyuA, iutA, iha, ironec and ompT. Currently Nissle 1917 has ‘GRAS’ (Generally regarded as safe) status, it is used clinically as a probiotic strain for the treatment of various intestinal disorders ( Grozdanov et al., 2004; Cukrowska et al., 2002). The observation that Nissle 1917 harbours adhesin (focDG, fimH and iha) and siderophore (fyuA, iutA, ironec and chuA) genes rather than toxin and invasion gene is consistent with some recent reports (Blum et al., 1995; Grozdanov et al., 2002; Grozdanov et al., 2004), indicating that some VGs selectively endow some E. coli strains for enhanced colonisation and increased capacity to compete for iron in the gut niche with other bacteria species.

EXPEC (n=6) IPEC (n=21)

Afa/draBC cvaC rfc stx1 stx2 fimH eaeA fyuA ehxA iutA east-1 traT PAI, papA, kpsMTII, ipaH papEF, ibeA, sfa/focDE, iha bfpA papG alleleII, k1, hlyA, ompT kpsMTII, papC, focG, papG alleleIII, cnf1, ireA sfaS, k5, univcnf, inonec, iss, papG alleleII&III

Commensal E. coli (n=12)

Figure 5.6 Venn diagram describing the virulence gene distribution in EXPEC, IPEC and commensal isolates

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Commensal E. coli represents normal and ecologically important inhabitants of the host intestinal tracts since some strains have probiotic effect to benefit the host whereas some other strains may develop into pathogens. It has been long-term realised that pathogenic and commensal E. coli differ in their virulence attributes - the presence of different subsets of virulence-associated genes in pathogens, which occur infrequently amongst commensal E. coli (Hacker & Kaper, 2000; Hentschel & Hacker, 2001). However, it becomes clear that it is difficult to designate true E. coli virulence factors. A considerable fraction of genetic information of ExPEC and IPEC, which has so far been considered as virulence associated, was reported also present in many commensal E. coli isolates. Many of them might contribute to the general adaptability, fitness or ancillary participate in normal metabolic function (Donnenberg & Whittam, 2001). Therefore the existence of these genes seems most likely to let E. coli strain better survive in the host rather than to be harmful. Probiotic E. coli strains need possess this gene arsenal but lack prominent virulence factor to confer the probiotic effect. These silent autochthonous bacteria, while non- pathogenic, could potentially also harbour virulence genes but are incapable of causing disease because they lack the appropriate VG combination. Meanwhile, whether a commensal E. coli will develop into a pathogen depends not only on the acquisition of these adaptive-conferring genes enabling successful colonization of the host, but also on the presence of functional genes directly contributing to pathogenesis.

Investigations into strain virulence in relation to E. coli population structure require a genotyping method that can reveal underlying genetic relationships between different member strains. Traditional O, K, and H serotypes, plasmid profiles, and biotypes in general are unreliable indicators for clonal relationships (Caugant et al., 1985; Eisenstein, 1990; Johnson and O'Bryan, 2000). The reference techniques for phylogenetic studies were considered to be multilocus enzyme electrophoresis (MLEE) (Herzer et al., 1990), ribotyping (Bingen et al., 1996; Desjardins et al., 1995) and recently, multilocus sequence typing (MLST) (Adiri et al., 2003; Maiden et al., 1998). MLEE is based on the principle of allozyme diversity that is brought about by changes in gene loci for a particular enzyme due to environmental conditions (Selander et al., 1986). By analysing the sum of variations at the various

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enzyme loci, the genetic diversity of E. coli isolates can be calculated. MLST determines short nucleotide sequences (450-500 bp) of several (5-7) housekeeping genes that had undergone some evolutionary diversification, leading to polymorphism. Ribotyping is based on the sequence variation in bacterial 16S ribosomal RNA gene determined by restriction fragment length polymorphism (RFLP).

Due to costs and technical limitations, ribotyping or DNA sequencing have been used instead of MLEE for clonotypic analysis of E. coli. Some PCR technologies also fail to give satisfactory results. For example, RAPD (random amplified polymorphic DNA) was found to have poor day-to-day reproducibility and a limited ability to reproduce evolutionary relationship as defined by MLEE (Gordon, 1997). Repetitive-element PCR (rep-PCR) has been shown to inadequately resolve phylogenetic group B1, although it was found to be a reliable technique for sorting strains in groups A, B2 and D (Johnson and O'Bryan, 2000). In our study, we evaluated the PFGE fingerprinting method for phylogenetic analysis of E. coli isolates. PFGE has been used effectively as a molecular subtyping tool in outbreak investigations and surveillance, and the discriminatory power of PFGE has compared favourably to that of other subtyping methods. However, as evident from our study, PFGE did not fulfil a role as a phylogenetic typing tool for E. coli because it could not resolve membership of clones into each of the 4 major phylogroups. Since PFGE is based on the restriction digestion of bacterial genomic DNA and restriction sites may not always be found where VGs are located, it is not surprising that this approach has failed to assign groupings that agree with other methods of phylogenic classification. Certainly, E. coli isolates that harbour different VGs and yet have the same fingerprint, may be more closely clustered than they should be.

The 3-gene PCR method developed by Clermont et al (2000) has proven to be a rapid and accurate tool for E. coli phylogenetic grouping. These three genes alone were capable of providing a phylogenetic classification in the ECOR collection that closely mirrored similar groupings based on a more complex analysis of MLEE. It has been broadly utilised recently in phylogenetic analysis of both pathogenic and commercial strains (Bingen-Bidois et al., 2002; Dobrindt et al., 2003; Duriez et al.,

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2001; Mereghetti et al., 2002; Zhang et al., 2002). The three key genes used in the PCR meet the following criteria for phylogenetic typing as described by the authors (Clermont et al., 2000): (i) The gene is specific for its representative group, (ii) it cannot be transferred horizontally, and (iii) recombination events are rare at these loci. The genotyping results based on 50 VGs in this study was able to assign phylogenetic membership in accordance with the Clermont PCR protocol. Two main conclusions can be drawn from this. Firstly, VG typing reflects the E. coli phylogenetic relationship because their presence or absence truly characterises pathogenic and commensal status. Secondly, additional virulence genes used in this study can be added to the Clermont panel to reinforce phylogenetic typing. Chi- squared test has shown 21 out of 50 VGs were able to resolve differences between several major phylogenetic groups at different levels (Table 5.3), suggesting that they are indeed important for defining pathogenicity.

It is not clear why phylogenetic membership of E. coli isolates can be assigned solely by the panel of VGs. There is some evidence that a specific genetic background is required for acquisition and expression of virulence factors in E. coli (Escobar- Paramo et al., 2004). For example, the concentration of ExPEC virulence genes within group B2 and repeatedly observed recombination events into the group B2, strongly suggest B2 genetic background is critical in the acquisition of ExPEC virulence genes (Escobar-Paramo et al., 2004; Johnson and Stell, 2000). Therefore in theory, the more VGs analysed, the better will be the basis for establishing a phylogenetic relationship between clones. This has been clearly demonstrated in this study with a panel of 50 virulence genes by a more affordable approach of multiplex PCR. Recently, Bekal et al (2003) have developed and validated a microarray approach containing 103 gene probes to detect 91 E. coli virulence genes, but gene chips are expensive and not conducive to the analysis of large numbers of clonal variants.

In summary, we have phylogenetically grouped a human E. coli collection including ExPEC, IPEC and commensal strains on the basis of ownership of 50 VGs and compared this grouping to that generated by Clermont’s triple gene PCR. Though the actual number of strains analysed in our study is small, the similarity in clustering

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patterns based on VGs plus Clermont triple genes, provide a convincing argument that MLEE groupings are co-ordinate by correlated with the possession or absence of virulence factors in each clone’s repertoire. Furthermore, based on a mathematical model involving principal coordinate analysis, we have shown that the phylogenetic groupings generated by both methods were significantly correlated but those generated by PFGE are not.

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Chapter 6

Comparative Analysis of Virulence Genes, Genetic Diversity and Phylogeny Between Commensal and Enterotoxigenic E. coli from

Weaned Pigs

6.4 Introduction 6.5 Materials and methods 6.5.1 E. coli strains used in this study 6.5.2 Serotyping porcine E. coli isolates 6.5.3 Detection of virulence genes and rapid phylogenetic analysis by PCR 6.5.4 Statistic analysis 6.6 Results 6.6.1 Prevalence of virulence genes in porcine E. coli PWD and commensal isolates 6.6.2 Phylogenetic group analysis of porcine E. coli PWD and commensal isolates 6.6.3 Clustering analysis of porcine E. coli based on virulence gene attributes 6.6.4 PFGE analysis of PWD isolates 6.6.5 Analysis of porcine commensal isolates by PFGE and serotyping 6.4 Discussion

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6.1 Introduction Enterotoxigenic Escherichia coli (ETEC) is the major cause of post-weaning diarrhoea (PWD) in weaned pigs. ETEC strains colonise the small intestine with the aid of adhesion factors (e.g. F4 or F18 fimbriae). Once attached, the release of heat- labile (LT) or/and heat-stable (STa or STb) enterotoxins induces diarrhoea by effecting the electrolyte balance of the small intestine. The presence of virulence genes (VGs) encoding for these determinants is a key requirement for pathogenicity in ETEC and is generally used to distinguish these pathogens from the non- pathogenic or commensal E. coli normally carried in the intestine (Noamani et al., 2003).

Characterization of isolates from outbreaks of PWD has shown that ETEC strains lacking recognised fimbriae, such as F4 and F18, are becoming more common (Fairbrother et al., 1988; Harel et al., 1991; Soderlind et al., 1988). This maybe related to the widespread use of vaccines incorporating fimbriated strains providing selection pressure for the acquisition and carriage of novel, unrecognised VGs. For example, Noamani et al. (2003) found that most of the PWD isolates recovered in the period 1998-2001 in Ontario, Canada were associated with a new type of O149 ETEC, which possessed VGs for STa and an enteroaggregative heat-stable enterotoxin (EAST1), which were not present in previous O149 PWD ETEC isolates from the same region. Over time, porcine ETEC acquisition of new VGs by associated with PWD may therefore change their VG profile and potentially, their pathogenicity.

Numerous genes encoding virulence factors such as adhesins, host cell surface- modifying factors, invasins, toxins and secretion systems are involved in mechanisms of pathogenicity in E. coli. Strains of the same pathotype and serotype are genetically similar and carry a defined set of VGs that are critical for infection. For instance, enteropathogenic E. coli (EPEC) are associated with virulence factors encoded by the LEE (locus of enterocyte effacement) including tir, eaeA, and esp genes, while extraintestinal pathogenic E. coli (ExPEC) that cause urinary tract infections (UTI) or septicemia carry pap, afa/draBC, sfa/focDE, kpsMTI and iutA genes (Johnson et al., 2003).

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However, using recent technological advances such as microarrays and multiplex PCR to explore the global virulence pattern of strains, combinations of VGs predictive of different pathotypes have been observed in unusual pathogenic and commensal E. coli strain backgrounds (Bekal et al., 2003; Kuhnert et al., 2000). For example, Bekal et al. (2003) found a human EHEC strain harbouring a number of VGs typically involved in extraintestinal infections. Additionally, a bovine ETEC strain was found to contain ExPEC-associated genes (traT, ompT and fimH) and an EHEC-associated gene (etpD) as well as genes for F5 fimbriae and STa. A human ETEC strain (H10407) was also found to contain two invasion genes (tia and tib), which direct the bacterium to invade human intestinal cell lines (Elsinghorst and Kopecko, 1992).

Population analysis of the autochthonous E. coli bacteria inhabiting the gastrointestinal tract of pigs has shown it to be very diverse, with greater diversity being demonstrated between the different regions of the gut than between different animals (Dixit et al., 2004). In terms of microbial fitness, a potential advantage that porcine ETEC have over these commensal E. coli strains are greater amplification and dispersion in pigs with diarrhoea compared to healthy pigs with normal faeces and thus, greater opportunity for acquisition of VGs from other strains (Noamani et al., 2003). Characterisation and comparison of VG profiles between ETEC and gut commensal E. coli can provide important insight into the evolution and spread of VGs in potentially pathogenic lineages. The aims of this work were to determine whether PWD ETECs also carry VGs of other pathotypic E. coli and to assess whether combinations of VGs are associated with different ETEC strains belonging to different serotypes. It was also the intention to examine if any relationships exist between VG combinations and serotype in clinical isolates from different geographic origins and to compare these with commensal isolates. Additionally, molecular variation between the PWD ETEC and commensal isolates have been characterised by pulsed-field gel electrophoresis (PFGE) genomic fingerprinting and contrasted with VG profiling as a predictor of genotype.

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6.2 Materials and methods 6.2.1 E. coli strains used in this study

A total of 205 porcine E. coli were analysed in this study. The collection consisted of 47 isolates recovered from pigs with PWD and 158 commensal E. coli isolated from different parts of the pig GIT described in a previous publication (Dixit et al., 2004). The PWD isolates were obtained from faecal swabs or intestinal contents from animals from different geographic origins. Seventeen isolates were from Southern New South Wales, Australia, 23 from Queensland, Australia and 7 from Vietnam. The isolates were represented by 2 major serogroups - O149 (n = 39) and O141 (n = 8). The 158 commensal E. coli were isolated from 8 healthy 13-week-old male pigs (hybrids of Large White and Landrace) weighing 35-45 kg each from 8 different litters at Elizabeth Macarthur Agricultural Institute, NSW DPI, Australia. On average, 4-5 isolates were taken from three different gut regions (duodenum, ileum and colon) and faecal samples for each individual pig, which were confirmed as E. coli by indole test (+), minimal lactose agar growth (+) and Simmons citrate agar growth (-), as described by Dixit et al. (2004). All strains were grown on Luria Bertani (LB) media and routinely stored at –80ºC in 15% (v/v) glycerol. PCR template DNA were prepared by boiling 1 ml of overnight LB broth culture for 10 min and 2 µl aliquots of the supernatant were subjected to PCR.

6.2.2 Serotyping porcine E. coli isolates

E. coli isolates were serotyped by the Microbiological Diagnostic Unit, University of Melbourne, Australia, using slide agglutination tests to type the O and H antigens, as described previously (Bettelheim et al., 1972; Bettelheim and Thompson, 1987). Strains which failed to achieve motility on semisolid medium were considered nonmotile and designated H-.

6.2.3 Detection of virulence genes and rapid phylogenetic analysis by PCR

ExPEC-associated virulence factors were detected by multiplex PCR testing of 36 different VGs associated with extraintestinal diseases, as described previously in Chapter 5 (Section 5.2.4). DNA from each strain was also subjected to PCR for 18

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different VGs, which represent various E. coli pathotypes causing enteric disease including ETEC, EPEC, EaggEC, EIEC and EHEC. In addition to those 11 IPEC genes used previously in Chapter 5 (see Table 5.2), 7 VGs (F4, F5, F6, F18, F41, VT2e and cdt) associated with porcine ETEC were also included, as described in Table 6.1. The phylogenetic grouping for each isolate was determined based on the PCR method described by Clermont et al. (2000).

Table 6.1 Additional virulence genes tested in this study Gene name Description Amplicon size Reference (bp) F4 fimbriae associated with diarrhoea 86 Frydendahl, 2002 (k88) in weaned pigs F18 fimbriae associated with diarrhoea 128 Frydendahl, 2002 in weaned pigs F5 fimbriae associated with neonatal 450 Do et al., 2005 (k99) diarrhoea F6 fimbriae associated with neonatal 333 Do et al., 2005 diarrhoea F41 fimbriae associated with neonatal 431 Do et al., 2005 diarrhoea VT2e verocytotoxin 2e 139 Frydendahl, 2002

cdt cytolethal distending toxin 108 da Silva and da Silva, 2002

PCR products were separated by agarose gel electrophoresis. 5 µl of amplified product was mixed with 3 µl of loading dye containing 0.25% bromophenol blue in 15% Ficoll solution and loaded onto 2.5 % agarose gel electrophoresis (Ultrapure Agarose, Life Technology) with ethidium bromide (1 µg/ml), using 0.5× Tris- Borate-EDTA (TBE) as running buffer. DNA in the gel was visualised by exposing the gel to UV light and was photographed with a digital capture system (Gel Doc, Bio-Rad). PCR product lengths were verified by comparison with a 100 bp+ DNA ladder (Promega).

6.2.4 Statistical analysis

The Chi-squared test of deviance was used to analyse the correlation between the presence of virulence genes, and the distribution of virulence genes within different serogoups.

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6.3 Results 6.3.1 Prevalence of virulence genes in commensal and pathogenic porcine E. coli Using multiplex PCR screening of 36 ExPEC-associated VGs, 8 genes were identified among the porcine isolates: fimH, fyuA, hlyA, kpsMTII, k5, iha, traT and ompT. The prevalence of these VGs in PWD isolates and commensal isolates is shown in Table 6.2. Two genes, hlyA and iha, occur significantly more frequently among the PWD isolates (P< 0.01). fimH and traT genes were present in most of the isolates but fimH was not found in any PWD isolates belonging to serogroup O149. A number of commensal isolates possessed fyuA or ompT (23 and 13 out of 158 isolates respectively), which were rarely found in PWD isolates.

Nine of 18 enteropathogenic VGs were also detected in the isolates (eltA, estI, estII, VT2e, F4, F18, east-1, eae and stx2). Most PWD isolates contained a typical porcine ETEC fimbrial gene, either F4 or F18, and at least 1 enterotoxin gene such as (eltA, estI and estII), with the exception of one Qld serogroup O149 isolate, which contained F4+, eltA-, estI-, and estII-. The F4 fimbrial gene was exclusively associated with serogroup O149 isolates and all of the O141 isolates harboured the F18 fimbrial gene, except for one isolate in which only the type 1 fimbriae gene (fimH) was found. The east-1 gene, which encodes the heat-stable toxin EAST1 that is unrelated to STa and STb, was found to be particularly associated with isolates of serogroup O149 (Table 6.2). Notably, east-1 was only present in 1 O141 strain and 8 out of 158 commensal isolates. Moreover, none of the O149 isolates possessed any STEC genes (stx1, sx2, eae or exhA), whereas stx2 was of reasonably high prevalence in serotype O141. Comparably, commensal isolates were found on the whole to lack enteric VGs. For example, F4 and F18 were not detected, only 3 strains contained stx2, and estI and east-1 were only detected in 9 and 8 out of 158 isolates respectively, confirming that on the whole, commensal isolates from healthy pigs generally do not carry VG combinations to enable pathogenicity.

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Table 6.2 The prevalence of VGs detected in porcine PWD and commensal isolates

O149 (NSW) O149(Qld) O149 (Vietnam) O141(NSW) Commensals n=9 n=23 n=7 n=8 (n=158) ExPEC VF gene fimH 0 0 0 100% 84.8%(134/158) hlyA 100% 74% (17/23) 85.7% (6/7) 88% (7/8) 0.6%(1/158) traT 100% 61% (14/23) 100% 12% (1/8) 48.7%(77/158) iha 100% 100% 100% 88% (7/8) 1.2%(2/158) fyuA 0 0 0 12% (1/8) 14.5%(23/158) ompT 0 0 0 12% (1/8) 8.2%(13/158) kpsMTII 0 0 0 0 1.2%(2/158) k5 0 0 0 0 1.2%(2/158) Enteric VF gene eltA 100% 100% 100% 0 0 estI 100% 83% (19/23) 100% 88% (7/8) 5.7%(9/158) estII 100% 100% 100% 88% (7/8) 0 VT2e 0 0 0 88% (7/8) 1.9%(3/158) F4 100% 100% 100% 0 0 F18 0 0 0 88% (7/8) 0 east-1 100% 100% 100% 0 5.1%(8/158) stx2 0 0 0 88% (7/8) 1.9%(3/158) eae 0 0 0 0 3.2%(5/158) Clermont PCR gene chuA 0 0 0 12% (1/8) 0.6%(1/158) yjaA 0 0 0 88% (7/8) 51.3%(81/158) tspE4C2 0 0 0 0 31.6%(50/158) Phylo. A A A A (88%) A (67.7%) group B2 (12%) B1 (31.6%) D (0.6%)

6.3.2 Phylogenetic group analysis of porcine E. coli PWD and commensal isolates Phylogenetic analysis using the triplex PCR technique developed by Clermont et al. (2000) revealed that group A E. coli were predominant in the collection of isolates, with a prevalence of 100% in serotype O149, 92.9% in O141, and 70.5% in commensal isolates. Group B1 was only found in commensals (29.5%). Only 1 O141 isolate was identified as group B2 and 1 commensal isolate was found in group D. Interestingly, none of the O149 isolates contained any of the 3 Clermont-PCR gene markers, and most of the O141 and commensal isolates harboured yjaA or TspE4C2.

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6.3.3 Clustering analysis of porcine E. coli based on virulence gene attributes Two major VG combinations were identified among the clinical isolates in this study: O149 (hlyA, iha, traT, east-1, estI, estII, eltA and F4) and O141 (fimH, hlyA, iha, stx2, estI, estII, F18 and yjaA). Thirty VG combinations were present in the 158 commensal isolates, of which 13 were only represented by single isolates. The VG combinations of high prevalence were: 20/158 (12.7%, fimH and yjaA), 19/158 (12.0%, fimH, traT and TspE4C2), 18/158 (11.4%, fimH, fyuA and TspE4C2), 17/158 (10.8%, fimH, traT and yjaA), 13/158 (8.2%, fimH) and 10/158 (6.3%, yjaA). The VG profiles identified in the ETEC isolates were significantly different from the VGs possessed by commensal isolates (P< 0.01). Clustering analysis of the isolates based on VG profiles differentiated the commensals from ETEC isolates by segregation into two differentiated clusters (cluster 1 for all commensal and cluster 2 for all ETECs). The ETEC isolates were further separated into 2 subclusters in accordance with serogroups (O141 and O149), but the ETEC isolates from different geographic origins could not be differentiated based on their VG profiles. As presented in Figure 6.1, commensal isolates were grouped separately into Cluster 1, whereas isolates in O141 and O149 were separated into Cluster 2a and 2b respectively. There was one exception, a serogroup O141 strain was found to be more closely related to the commensal E. coli group because it was the only ETEC strain lacking hlyA, iha, F4 and F18 genes, although it did possess fimH, fyuA, traT, east-1 and estII genes.

6.3.4 PFGE analysis of PWD isolates Forty-five PWD isolates were further analysed by PFGE fingerprinting after restriction enzyme digestion with NotI and XbaI. These isolates included 7 serogroup O141 isolates (NSW), 9 serogroup O149 isolates (NSW), 7 serogroup O149 isolates (Vietnam) and 22 serogroup O149 isolates (Qld). Relatedness of each DNA fingerprint was clustered using GelCompare software, as presented in Figure 6.2. Based on the PFGE pattern, 45 isolates were separated into 2 main clusters (I and II) in accordance with serogroups (O141 and O149). There are three subclusters in serogroup O149, IIa, IIb and IIc, primarily based on geographical origin. Queensland isolates fell into 2 distinct groups, with one group (Qld A in IIa) more closely related to Vietnamese isolates than the second Queensland group (Qld B in IIc). These

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groups were both distinct from the New South Wales isolates, which also fell into a single subcluster (IIb).

6.3.5 Analysis of porcine commensal isolates by PFGE and serotyping PFGE profiles and serotype were obtained for 151 of 158 pig commensal isolates. Seven isolates could not be serotyped or analysed by PFGE. A total of 45 different serotypes were identified, comprising 29 O groups and 19 H types, as well as 58 nonmotile (H-) strains. 17 serotypes were identified as nontypable O antigens (Ont). The predominant commensal serotype was Ont 63 (41.7%), followed by O40, 19 (12.5%); O5, 9 (5.9%); O8, 5 (3.3%); O82, 5 (3.3%); O25, 4 (2.6%); O51, 4 (2.6%); O105, 3 (2.0%); O2, 3 (2.0%) and O71, 3 (2.0%). The typical PWD ETEC serogroups such as O141, O149 or O139 were not identified in any of 151 commensal isolates. Serogroup O8 and O20, which are regarded as potentially associated with neonatal diarrhoeal in piglets, were detected with very low prevalence (5 and 2 out of 151 commensal isolates respectively). Both of the O20 isolates possessed stx2 and east-1 genes, and 1 isolates of O8 harboured both hlyA and iha genes.

Based on the interpretive criteria for PFGE patterns (Tenover et al., 1995), isolates differing by a single genetic event, reflected as a difference of 1 or 2 bands, are considered closely related and were therefore defined as a distinct PFGE pattern in this study. A total of 62 PFGE patterns were distinguished in the 151 commensal isolates, with 30 of the isolates showing a unique PFGE pattern. The predominant PFGE pattern (pattern 20#) was displayed by 16 isolates followed by pattern 30# which was found in 8 isolates. The typical PFGE patterns obtained for the PWD isolates, as shown in Figure 6.2, were not identified in any of the commensals.

Isolates within the same serotype normally possessed identical PFGE patterns. For example, 16 out of 18 isolates within serotype O40:H25 displayed a PFGE pattern 20# regardless of sampling animals or locations. There were some exceptions: 5 isolates with serotype O114:H- had 4 distinct PFGE patterns and 23 isolates belonging to Ont:H- had 15 PFGE patterns.

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Strain Origin eaeeae VT2eVT2e F18F18 F4F4 east-1east-1 estIIestII estIestI eltA eltA tspE4C2 tspE4C2 yjaAyjaA chuAchuA stxstx 2 2 ompT ompT Iha Iha K5K5 traT traT kpsMTkpsMT II II hlyAhlyA fyuA fyuA fimH fimH 20 40 60 80 100 Commensal 20 Commensal 56 Commensal 13 Commensal 17 Commensal 95 Commensal 32 Commensal 58 NSW Commensal 62 healthy pigs Commensal 11 Commensal 26 Commensal 23 Commensal 12 Commensal 157 Commensal 110 Cluster 1 O141 15 NSW Commensal 33 Commensal 8 Commensal 7 Commensal 19 Commensal 1 Commensal 52 Commensal 70 NSW Commensal 53 healthy pigs Commensal 96 Commensal 146 Commensal 97 Commensal 18 Commensal 109 Commensal 30 Commensal 27 Commensal 71 O141 75 NSW O14128 NSW 2a O141 03 NSW 0141706/0 NSW O141 09 NSW 0141 045/0 NSW O141 14 NSW O149 CDT47 Qld O149 CDT91 Qld Cluster 2 O149 P140/2 Viet O149 CDT60 Qld O149 0354 NSW 2b O149 P139/1 Viet O149 VPC2 Viet Figure 6.1 Clustering analysis of the genetic variation between PWD and commensal porcine E. coli isolatesO149 based CDT44 on the Qld virulence gene attributes. O149 CDT43 Qld 30 selected commensal isolates represented 30 different VG combinations were grouped in cluster 1; PWDO149 isolates CDT61 in serogroup Qld O141 were grouped in cluster 2a except one in cluster 1; 11 of serogroup O149 isolates from different geographic origin which O149represented CDT15 7 Qld VG combinations were in a separate cluster 2b.

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Clustering dendrogram PFGE patterns Origin Serogroup

30 40 50 60 70 80 90 100

NSW O141

Qld (A)

Vietnam

O149

NSW

Qld (B)

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6.4 Discussion Porcine ETEC isolates associated with PWD belong to a limited number of serogroups with O8, O138, O139, O141, O147, O149 and O157 being the most commonly reported worldwide (Frydendahl, 2002; Nagy and Fekete, 1999). Serotyping of porcine ETECs for epidemiological purposes utilises surface associated determinants of the bacterium such as lipopolysaccharide, capsular and fimbrial antigens (Bertschinger, 1968). Nonetheless, different serogroups still cause similar pathology and clinical disease, and should therefore share many features that contribute towards their virulence. An important and yet unclear issue is whether these clonal ETEC isolates have in common, VGs typical of enteric E. coli or whether they also possess additional VGs or VG combinations that can be found in other pathotypes such as EHEC, EPEC, EIEC, EaggEC and ExPEC.

It is likely that the acquisition of certain VGs by transferable genetic elements (plasmids and transposons), may endow ETECs with the potential ability to broaden their host range or evolve into new serogroups over time (Fairbrother, 1988; Lee et al., 1985). The deployment of a multi-VG panel assembled from different E. coli pathotypes may provide some insight into the acquisition of VGs in different serogroups of porcine ETECs and whether such diversity may endow better survival attributes because of selection pressure.

In this study, both PWD and commensal E. coli isolates were screened by PCR for a panel of 54 VGs assembled from 7 major human pathotypes (ExPEC, ETEC, EPEC, EHEC, VTEC, EIEC and EaggEC). The PWD strains were confirmed to be ETEC, due to the possession of at least one of the typical porcine ETEC virulence genes encoding fimbriae and/or enterotoxins. VGs such as east-1 and VT2e also were identified in this PWD isolate collection, mirroring previous findings (Choi et al., 2001; Frydendahl, 2002; Osek, 2002). VT2e is normally possessed by porcine VTEC and encodes the verotoxin 2e variant that causes vascular lesions in the intestine, subcutis and brain, leading to edema and neurological symptoms (Imberechts et al., 1992). Certain porcine E. coli strains were found to produce Stx2e and enterotoxins causing diarrhoeal and/or edema disease (ED) (Smith and Halls, 1968). The east-1 gene was initially found in human pathogenic EaggEC and encodes the heat-stable

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toxin termed EAST1 associated with persistent, watery diarrhoea in young children (Vila et al., 2000; Yamamoto and Echeverria, 1996). Recent surveys have shown that east-1 is widely distributed among porcine diarrhoeal-related strains although the role of EAST1 in diarrhoea caused by porcine ETEC is still not determined (Noamani et al., 2003; Vu-Khac et al., 2004). The results of this study showed a significantly higher incidence of east-1 in O149 ETEC isolates, whilst the majority of non-pathogenic porcine commensal E. coli isolates are east-1 negative, providing incentive for further investigating the pathogenic significance of this gene in porcine PWD.

In 1985, Levine et al. suggested that factors in addition to enterotoxin production may be important in the pathogenesis of ETEC infection in animals (Levine et al., 1985). Later efforts have focused on searching for additional factors shown to be important for virulence in other intestinal pathotypes, but the extraintestinal virulence factors have been largely ignored. In the present study, 8 ExPEC-associated VGs were found in the porcine E. coli isolate collection. Among these, fimH, which encodes type 1 fimbriae, was frequently detected in both PWD and commensal isolates with the exception of serogroup O149 isolates. It has been reported that fimH is broadly present both in E. coli isolated from normal flora and in pathogenic isolates with a highly conserved gene sequence among strains from different animal species of origin (Vandemaele et al., 2004). Although type 1 fimbriae mediated adhesion of E. coli to epithelium in the host and could play a role in the initial phase of the infection process, the fimH gene is generally not considered to be a target for assessment of virulence (Kuhnert et al., 2000). fimH was present in the majority of commensal porcine isolates, which may indicate a potential role for type 1 fimbriae in the adherence of resident E. coli to pig GIT via a mannose-resistant mechanism. Interestingly, contrary to the other PWD and neonatal diarrhoeal (ND) serogroups (data not shown) in which fimH prevalence were significantly high, none of the isolates in serogroup O149 contained fimH, which is in agreement with the results of a previous study (Vandemaele et al., 2004).

In this investigation, O149 was shown to have a unique VG profile that differed from O141, O8 and commensals, which lack fimH, fyuA, ompT, chuA, yjaA and TspE4C2

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but generally possess F4, hlyA, iha and east-1. O149 is reported to be the most prevalent serogroup isolated from pigs with PWD in Europe and North America (Frydendahl, 2002). The reason for this dominance is still unclear. According to Hampson (1994), somatic O-antigen or associated factors confers ability to establish and proliferate in the pig intestine, thus strains expressing O149 antigen could possess advantages over other types. O149 dominance could also be related to virulence factor composition if these factors enhance competitiveness in the swine gastrointestinal tract. Larsen (1976) has shown that the PWD O149 strains differed from other serotypes in carbohydrate fermentation, urease activity and colicinogenicity tests. (Larsen, 1976). In the present study, PFGE analysis demonstrated that the DNA fingerprint of O149 isolates differed markedly from other ETEC and non-ETEC isolates, indicating that possession of the unique O149 VG profile is linked with genotype. Interestingly, the PFGE analysis showed that isolates from different geographical origins were highly clonal, and the O149 strains from Vietnam were more related to a group of Qld isolates than NSW isolates, confirming that there has been relatively little movement of E. coli populations in pigs between Qld and NSW (Do et al., 2005). This also suggests that the Vietnamese strains probably originated from stock imported from Qld, most probably during agricultural development programmes sponsored by the Australian government (Trott, DJ. University of Queensland, personal communication). It is in agreement with the previous report from Hampson et al. (1993), which demonstrated that isolates of serogroup O149 from suckling piglets in Scandinavia, Australia and Indonesia belonged to a single clonal grouping based on multilocus enzyme electrophoresis (MLEE) analysis and shared a close genetic relationship with O149 isolates associated with PWD. Clonality of O149 suggests that competitive exclusion via the use of commensal isolates may as a control measure for PWD have a high chance of competing for attachment sites in the gut. However, the possibility that the data and conclusions may only represent a restricted geographical distribution of genotypes cannot be excluded. To support this hypothesis, a broad range of PWD E. coli strains from a wide variety of sources and serotypes will need to be analysed.

Like fimH, traT was also present in both PWD and commensal E. coli in this study, with 100% prevalence in O149 isolates. This gene encodes an outer membrane

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protein which is believed to play a role in serum resistance (Pramoonjago et al., 1992). In addition to E. coli strains of human, cattle or poultry origins, traT was also carried by porcine F165-positive E. coli associated with septicaemia and diarrhoea (Dezfulian et al., 2003). Most F165-positive isolates were neither enterotoxigenic nor haemolytic; negative for F4, F5, F41 and F6; resistant to the bactericidal effect of serum; and possessed many of the ExPEC-associated VGs that were associated with septicaemia (Fairbrother et al., 1993; Harel et al., 1993). Our study has shown that porcine isolates other than F165-positive E. coli possess traT, however, the function of this gene in this genetic background needs to be further clarified.

Two ExPEC genes, hlyA and iha, were present in significantly higher proportions in PWD isolates compared to the commensals. hlyA encodes α-haemolysin which is a Gram-negative bacterial membrane pore-forming cytolytic toxin of the RTX family with glycine-rich repeats (Welch et al., 1992). α-haemolysin is predominantly detected in ExPEC strains. It is also a major marker of most porcine ETEC and STEC causing PWD and edema disease in pigs. Porcine ETEC strains associated with PWD are almost universally haemolytic, and α-haemolysin in these isolates is considered to enhance virulence and colonization (Smith and Linggood, 1971). The cytotoxic necrotizing factor (cnf-1) was reported to be strongly associated with α- haemolysin and virulence in both human ExPEC and pig diarrhoeal isolates. The cnf1 gene is closely linked with hlyA on a pathogenicity island (PAI) (Blanco et al., 1992; Elliott et al., 1998). This co-occurrence, however, was not shown in our porcine isolates. It has been demonstrated that porcine cnf1-producing E. coli also express P, S and F1C fimbriae in strains causing septicaemia and /or diarrhoea (Dozois et al., 1997). Our results indicate that cnf1-producing strains are more likely to be ExPEC strains which possess a repertoire of VGs that differ from the enteric PWD strains.

To our knowledge, this is the first report of the presence of the iha gene in porcine PWD isolates with significantly higher carriage rate compared to gut commensal isolates. iha encodes a novel non-hemagglutinating adhesin found in E. coli O157:H7 which facilitates the adherence of O157:H7 strains to epithelial cells in environment such as the GIT of animals (Tarr et al., 2000). iha also facilitates adherence

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capability to Hela cell when transformed into nonadherent E. coli K-12. Prior to this study, iha has only been found in STEC and UPEC pathotypes. In addition to O157:H7, other STEC strains also contain iha include strains of serotype O111:H-. Comparably, iha occurs significantly more frequently in UTI or bacteremia isolates than human commensal E. coli (Johnson et al., 2003). The role of iha in colonization of both humans and animals is still being elucidated. Porcine ETEC strains harbouring additional fimbrial adhesins such as iha or eae, could possibly exploit several alternative pathways for colonization of the host. The association of iha and hlyA with porcine PWD suggests that these could be used as additional markers of pathogenicity in PWD isolates. More importantly, due to the fact that the use of vaccines against fimbriated strains may allow the emergence of other ETEC strains which lack recognised fimbriae such as F4 and F18, iha might be a potential target for anti-ETEC interventions. Future work will be focused on investigating the role of iha in pig gut colonization by ETEC strains, the mechanisms underlying iha expression and the presence of this gene in other PWD serogroups.

It is widely believed that there is a positive correlation between environmental stability, community stability and high microbial diversity (Pielou, 1975) - a microbial flora with a high diversity may reflect a stable community with greater ‘colonisation resistance’ to enteric pathogens. In the suckling piglet, the main difference between coliforms isolated from normal faeces and those isolated from diarrhoeal infections was that the latter represent a low coliform diversity dominated by pathogens expressing combinations of several virulence-associated factors (Kuhn et al., 1993). The present study investigated 158 commensal E. coli isolates from healthy pigs on the basis of serotype, VG profile and DNA fingerprint. None of the healthy pigs examined were colonised with typical ETEC strains. Very few isolates were of a serotype that is associated with porcine pathogenic E. coli strains and fewer still contained virulence genes such as estI, stx2 and east-1 which may be associated with enteric disease in swine. The majority of commensals only possessed ‘fitness or adaptive’ genes such as fimH, traT and fyuA, which are involved in colonisation, serum resistance and iron-utilisation, respectively. This result corresponds to a previous report by Hinton et al. (1985), in which a complex E. coli microbiota existed in the GIT of healthy weaned pigs with the absence of ETEC serotypes such

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as O149. The present study also established a remarkable within intestinal compartment and between pig diversity among the commensal isolates. This was an interesting finding considering the pigs all shared the same environment feed and water sources. A high level of diversity amongst commensal E. coli has also been observed in cattle and human faeces based on serotype identities (Bettelheim et al., 1972; Bettelheim et al., 2005). By comparing E. coli biochemical fingerprints from 5 healthy piglets from 7 to 63 days of age, (Melin et al., 1997) suggested that a disturbed (i.e. decreased diversity) faecal coliform microbiota close to weaning may reflect a situation contributing to an increased susceptibility to various enteric diseases. This information together with previous work from our laboratory on the diversity of E. coli isolates from different intestinal compartments of the gastrointestinal tract of pigs (Dixit et al., 2005), support the contention that a highly diverse spectrum of commensal E. coli that have colonised the gut epithelial surface can provide a competitive barrier against incoming pathogenic E. coli. The deployment of a large panel of VGs from a wide range of E. coli pathotypes has revealed that porcine ETECs have in the course of their evolution, also acquired extraintestinal VGs. Consequently, VG combinations selected from both the intestinal and extraintestinal pathotypic panel can be used to characterise ETEC serogroups. Equally, the absence of VG combinations can also be used as an epidemiological tool to characterise clonal clusters of commensal E. coli. The advantage of such an approach over serotyping is its potential to evaluate the population structure of entire E. coli communities without the need for multiple analysis of many individual and non-representative clones (Chapman et al., 2006).

In conclusion, virulence genes associated with ExPEC were shown to be present in porcine ETEC associated with PWD and the array of both enteric and extraintestinal virulence genes could be used to distinguish ETEC from commensal E. coli. In addition to the typical porcine ETEC genes, iha and hlyA were significantly more frequently observed in PWD isolates compared with commensals, in which fimH and traT were more prevalent. The main difference between strains isolated from PWD compared to pig commensal strains is that the former represent a limited number of serotypes with combinations of several key virulence gene characteristics, and the gut commensal strains usually possess only one or none of these virulence genes.

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Hence, the normal gut flora may not be an important reservoir for ETEC VGs. This is further emphasised in the demonstrated clonality and reduced genetic diversity of the major pathogenic serogroups O149 and O141. The present study confirmed that healthy pigs possess highly diverse commensal E. coli in the GIT, which may be beneficial to some degree in preventing ETEC infection in the host. On the other hand, possession of a repertoire of VGs will advantage the pathogens survive against the competitors in the gut by intimate colonization of the epithelial surface and initiation of diarrhoea. A characterization of these additional VGs will greatly help in a comprehensive understanding of the pathogenesis of PWD in the pig as well as its treatment, control and prevention.

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Chapter 7

A Re-appraisal of Early Signalling Events and Gene Activation in a Human Intestinal Epithelial Cell Line in Vitro by Probiotic and Enteric Pathogenic Bacteria

7.4 Introduction 7.5 Materials and Methods 7.5.1 Growth of human cell lines 7.5.2 Preparation of bacteria 7.5.3 Bacteria-cell interaction 7.5.4 Detection of gene activation by semi-quantitative reverse transcriptase- PCR (RT-PCR) 7.5.4.1 cDNA synthesis 7.5.4.2 PCR with gene-specific primers 7.5.5 Detection of NF-κB activation by electrophoretic mobility shift assay (EMSA) 7.5.5.1 DNA probe labelling 7.5.5.2 Nuclear protein extraction 7.5.5.3 Assay of NF-κB activation 7.6 Results 7.6.1 Selection and validation of probiotic and pathogenic Gram-negative coliforms 7.6.2 Basal levels of gene activation in 7 different cell lines 7.6.3 Activation of NF-κB 7.6.4 Activation of multi-functional genes by Gram-negative coliforms 7.3.5 Activation of multi-function genes by a panel of LABs 7.7 Discussion

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7.1 Introduction Immune stimulation has been a functional attribute long associated with the consumption of fermenting probiotic species belonging to the lactobacilli family (Isolauri et al., 2001). Evidence for direct immune modulation has come from studies investigating interactions between test probiotic strains and various immune cell types including macrophages (Larsson et al., 1999) and dendritic cells (Kadowaki et al., 2001). However, a valid comparison of the immunomodulating activity of different strains has been confounded because of different mammalian cell types used, varying assay conditions as well as vastly different biological parameters being evaluated. Since the primary interaction between probiotic and the consumer will occur between the bacterium and the epithelial cells of the gastrointestinal tract, it would be more appropriate to simulate this initial interactive event in vitro using the same panel of genes for comparison against that anticipated from a similar interaction with immune cells. At first glance, the events that follow on from such an association between bacterium and intestinal epithelial cell (IEC) may not be dramatically different from that of an enteric pathogen. Yet, downstream physiological changes in vivo that result in clinical symptomatology and increased morbidity suggest that there are pathway differences mediated by either probiotic or pathogen. This raises some key questions concerning how probiotic and pathogenic microorganisms differ in the activation or early signalling process and hence alter downstream pathways that change gene activation events in the same assay system.

While several established human intestinal cell lines such as Caco-2, HT-29, HCT-8 and T84 (Elewaut et al., 1999, Witthoft et al., 1998) have been used for in vitro experiments, very few studies have been carried out to examine bacteria mediated gene activation of these cells with respect to multi-functions such as primary signalling events that engage the innate immune system and its panel of TOLL-like receptors (Aderem and Ulevitch, 2000); intestinal repair and re-growth factors such as trefoil factor; early inflammatory signal transduction processes involving the participation of NF-κB and cox-2; cytokine activation and chemokine production, vital components in the recruitment of trafficking immune cells to the sub and intra- intestinal epithelium. Many of these multi-functional roles are inherent to the bioactivity of epithelial cells in the gastrointestinal tract. Secondly, virtually none of

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the published works utilizing epithelial cell lines has revealed baseline levels of mRNA transcripts involving these multi-functional genes. Data generated in the absence of baseline controls can also confuse interpretation of measurements of gene activation using RT-PCR or quantification of gene products by ELISA, resulting in grossly exaggerated levels of gene activation not consistent with gene product assessments that can be directly attributed to the bacteria-cell interaction.

This study has taken into consideration the need for a more rigorous appraisal of gene activation events following a primary interaction between probiotic or pathogenic bacterium and intestinal epithelial cell. The investigative strategy involved establishing baseline levels of activation of 31 functional genes in 7 human cell lines of which five were of intestinal epithelial origin. One of these was then selected for further assessment of gene activation against a panel of probiotic and pathogenic Gram-negative coliforms characterised by their ownership or not, of a panel of 54 virulence genes, as previously reported in Chapter 6. For comparison, the downstream events following bacteria-cell engagement have been contrasted with a panel of accredited probiotic Gram-positive lactic acid bacteria (LAB) to identify any differences in gene activation between probiotic and pathogen under standardised assay conditions.

7.2 Materials and Methods 7.2.1 Growth of human cell lines Seven different cell lines of human origin are listed in Table 7.1 while their culture conditions are summarised in Table 7.2. Cells were cultured in 75-cm2 flask at 37oC

in 5% CO2 in air atmosphere to 90-95% confluence. Trypsin-EDTA solution (GIBCO) was used to dissociate flasked cells and RNA was extracted only from cells with greater than 95% viability (as determined by trypan blue exclusion test). Approximately 1 × 106 cells from each cell line were used for total RNA extraction.

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Table 7.1 List of human cell lines used in this study

Tumorigenic *Passage ATCC Reference Cell Line Tissue Origin Status Code Small a FHs 74 Int Normal 24 CCL-241 Intestine HuTu 80 Duodenum Adenocarcinoma 19 HTB-40 b Colorectal c HCT116 Colon 10 CCL-247 carcinoma Colorectal d Caco-2 Colon 18 HTB-37 adenocarcinoma Colorectal e T84 Colon 53 CCL-248 carcinoma Hepatocellular f HepG2 Liver 11 HB-8065 carcinoma No-intestinal HeLa g HEp-2 364 CCL-23 epithelium Contaminant

Note: * Passage: generations for subculture when received from ATCC; a Owens et al., b c d e 1979; Seidenfeld et al., 1986; Brattain et al., 1981; Jumarie & Malo 1991; Dharmsathaphorn et al., 1984; f Knowles et al., 1980; g Chen, 1988

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Table 7.2 Culture conditions for growing human cell lines

Complete Media Composition *Split Time to Cell line ratio reach Base media Supplements confluency Modified Dulbecco's Eagle’s medium supplemented with non-essential amino acids, sodium FHs 74 Int DMEM pyruvate (0.5mM), oxaloacetic acid (1mM) and insulin (0.2 units/ml) and 30 ng/ml 1:2 3-8 days epidermal growth factor (EGF), 10% fetal bovine serum Minimum essential medium (Eagle) with 2 mM L-glutamine and Earle's BSS adjusted to HuTu 80 EMEM contain 1.5 g/L sodium bicarbonate, 0.1 mM non-essential amino acids, and 1.0 mM sodium 1:3 3-7 days pyruvate, 10% fetal bovine serum McCoy's 5a medium (modified) with 1.5 mM L-glutamine adjusted to contain 2.2 g/L HCT116 RPMI 1640 1:5 3-5 days sodium bicarbonate, 10% fetal bovine serum Minimum essential medium (Eagle) with 2 mM L-glutamine and Earle's BSS adjusted to Caco-2 EMEM contain 1.5 g/L sodium bicarbonate, 0.1 mM non-essential amino acids, and 1.0 mM sodium 1:2 4-7 days pyruvate, 20% fetal bovine serum A 1:1 mixture of Dulbecco's modified Eagle's medium and Ham's F12 medium containing T84 DMEM/Hams F12 1.2 g/L sodium bicarbonate, 2.5 mM L-glutamine, 15 mM HEPES and 0.5 mM sodium 1:2 6-10 days pyruvate, 5% fetal bovine serum Minimum essential medium (Eagle) with 2 mM L-glutamine and Earle's BSS adjusted to HepG2 RPMI 1640 contain 1.5 g/L sodium bicarbonate, 0.1 mM non-essential amino acids, and 1.0 mM sodium 1:4 3-5 days pyruvate, 10% fetal bovine serum Minimum essential medium (Eagle) with 2 mM L-glutamine and Earle's BSS adjusted to HEp-2 EMEM contain 1.5 g/L sodium bicarbonate, 0.1 mM non-essential amino acids, and 1.0 mM sodium 1:4 3-5 days pyruvate, 10% fetal bovine serum

Note: *Split ratio, passage cells from 1 flask to new flasks (e.g. split ratio 1:3 is 1 flask of confluent cells split into 3 new flasks)

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7.2.2 Preparation of bacteria Seven Gram-negative coliforms including 5 human pathogenic strains, 1 porcine ETEC and a cloning vector DH5α as well as 4 LAB species are listed in Table 7.3.

All bacteria strains were recovered from frozen glycerol stocks and mid-log phase cultures were recovered following expansion of picked single colonies in Luria Broth (LB). Bacteria grown in this medium were washed in ice-cold DMEM basic medium (FCS-free and antibiotic-free) and resuspended at the appropriate dilutions for cell interaction experiments. When necessary, precise colony forming units (CFU) were estimated by limit dilution in Luria agar (LA). LAB strains were grown in MRS broth overnight and phased into DMEM while enumerations were carried out by limit dilution on MRS agar plates.

Table 7.3 Bacteria strains used in this study

Code Strain name Serotype Source Pathotype 1 Nissle 1917 O6:K61:H1 Human Probiotic 2 5584 O6:H1 Human UPEC 3 DES1643 O157:H7 Human EHEC 4 DES H24 O86:K61:H- Human EPEC 5 S. flexneri 1 Serotype I Human EIEC 6 0354/2 O149:K88 Pig ETEC 7 DH5α NT Lab strain Non-pathogenic 8 L. acidophilus NT Commercial Probiotic 9 L. plantarum NT Commercial Probiotic 10 L. paracasei NT Commercial Probiotic 11 L. salivarius NT Commercial Probiotic

7.2.3 Bacteria-cell interactions T84 cells at 90-95% confluency in 24-well microtiter were rinsed gently with HBSS prior to phase replacement with DMEM basal medium (FCS-free and antibiotic-free) for 2 h at a 37°C cell culture incubator. Bacteria were added to the T84 monolayer cells at a MOI of 200 (200 bacteria/cell) and the adhesion/signal activation process was allowed to continue for the specified incubation times as detailed in the respective Tables and Figures. The incubation wells were washed with HBSS before the T84 cell monolayer was lysed with 1 ml of TRIzol reagent (Invitrogen) for total

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RNA extraction. For other studies involving NF-κB, cells were detached and nuclei prepared for nuclear protein extraction.

7.2.4 Detection of gene activation by semi-quantitative Reverse Transcriptase- PCR (RT-PCR) 7.2.4.1 cDNA synthesis Message RNA transcripts associated with gene up or down regulation were assessed semi-quantitatively with a two-step RT-PCR. RNA quality and integrity was determined by denaturing formaldehyde agarose gel electrophoresis. Contaminating genomic DNA was eliminated by treating samples with RQ1 RNase-free DNase (Promega). Routinely, 2 µg of total RNA was reverse transcribed to cDNA with 20- mer oligo (dT) primers and 200 U of Superscript III (Invitrogen).

7.2.4.2 PCR with gene-specific primers The cDNA was amplified in 50 µl reaction volumes containing 50 nmol of forward and reverse gene-specific primers and 5 µl of template cDNA corresponding to 20 ng of total RNA. The gene primers used are specified in Table 7.4. Each sample was assayed in triplicate and PCR products (10 µl) were visualised by electrophoresis on a 2% agarose gel followed by staining with ethidium bromide. All RT-PCR bands at the expected size were also directly sequenced to confirm their identity. The intensity of the gel images were analyzed using QuantityOne software (Bio-Rad). The amount of activated mRNA following amplification was normalised relative to β-actin transcripts and the ratios expressed as relative fold increase compared to basal cell levels of gene expression in the absence of microbial exposure. The targeted genes and primer sequence information are summarised in Table 7.4. All of the primer pairs spanned at least one intron in the corresponding genomic DNA therefore only mRNA but not contaminated chromosomal DNA is assayed.

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Table 7.4 Oligonucleotide primers and PCR product size Gene Description Primer (5’ – 3’) PCR size Ref. (bp) TLR-2 Toll-like receptor 2 gccaaagtcttgattgattgg 1 ttgaagttctccagctcctg 346 TLR-4 Toll-like receptor 4 tggatacgtttccttatag 1 gaaatggaggcaccccttc 506 TFF1 trefoil factor 1 tttggagcagagaggaggcaatg 146 2 accacaattctgtctttcacggggg TFF2 trefoil factor 2 gtgttttgacaatggatgctg 101 2 cctccatgacgcactgatc TFF3 trefoil factor 3 aaccggggctgctgctttg 92 2 gaggtgcctcagaaggtgc sprr-2 small proline-rich protein 2a atgtcttatcaacagcagcagtg 219 3 ttacttgctcttgggtggatac iNOS-2 inducible nitric oxide synthase-2 cgaggaggctgccctgcagactgg 1382 4 ctgggaggagctgatggagtagta GATA-3 Th2-specific GATA 3 transcription factor ctgcaatgcctgtgggctc 350 4 gactgcagggactctcgct T-bet Th1-specific T box transcription factor ccccaaggaattgacagttg 317 4 gtttgtgagctttagtttccc FoxP3 Th3-specific Forkhead-family cagctgcctacagtgcccctag 381 5 Transcription factor catttgccagcagtgggtag Cox-2 Cyclooxygenase 2 ttcaaatgagattgtgggaaaattgct 305 6 agatcatctctgcctgagtatctt TNF-α Tumor necrosis factor alpha cagagggaagagttccccag 324 7 ccttggtctggtaggagacg TGF-β Transforming growth factor beta ctccgagaagcggtacctgaac 288 7 cacttgcagtgtgttatccct IFN-γ Interferon γ atatcttggcttttcagctc 489 7 ctcctttttcgcttccctgt IL-1α Interleukin 1, alpha tgagcaatgtgaaatacaac 183 8 ttttagcatcatcctttgat IL-1β Interleukin 1, beta gatcatctgtctctgaatca 387 7 tccagattatgtaatgcagc IL-2 Interleukin 2 actcaccaggatgctcacat 266 8 aggtaatccatctgttcaga IL-4 Interleukin 4 gttcttcctgctagcatgtg 411 7 atttctctctcatgatcgtc IL-5 Interleukin 5 atcaggatgcttctgcatttg 405 8 tcaactttctattatccactcggtgttcattac IL-6 Interleukin 6 atgtagccgccccacacagc 190 8 catccatctttttcagccat IL-10 Interleukin 10 tgatgtctgggtcttggttc 204 7 gcctaacatgcttcgagatc IL-12p40 Interleukin 12 cgtagaattggattggtatccgg 702 7 gctcttgccctggacctgaacgc IL-13 Interleukin 13 gctcctcaatcctctcctgt 485 9 gcaacttcaatagtcaggtcct IL-8 Interleukin 8 ctggccgtggctctcttggcagccttcctg 395 7 ggcaaccctacaacagacccacacaataca ENA-78 epithelial neutrophil-activating peptide 78 gaacccgcgaccgctcgc 331 10 agaaaaggggcttctggatcaa GCP-2 macrophage inflammatory protein 2α ctccacccagctcaggaacc 336 10 gaaaaggggcttccgggtcca (Gro- β) MGSA macrophage inflammatory protein 2β agccacactcaagaatgggcg 473 10 tggcatgttgcaggctcctc (Gro-γ) MIP-2α melanoma growth stimulatory protein atttgttaatatttcttcgtgatgacatatca 323 10 tcgaaacctctctgctctaacac MIP-2β Granulocyte chemotactic protein-2 agaacatccaaagtgtgaatgtaagg 280 10 tcctttccagctgtccctagaa β-actin beta-actin gcatgggccagaaggactcc 362 2 cgtagatgggcaccgtgtgg GAPDH glyceraldehyde-3-phosphate acccagaagactgtggatgg 115 2 dehydrogenase ggatgaccttgcccacag

Note: 1. Yang et al., 2002; 2. dos Santos Silva et al., 2000; 3. Hippo et al., 2001; 4. Vos 1999; 5. Hori et al., 2003; 6. Elewaut 1999; 7. Haller et al, 2000; 8. Actor et al., 1998; 9. Cousins 2002; 10. Rogers et al., 2003

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7.2.5 Detection of NF-κB activation by electrophoretic mobility shift assay (EMSA) 7.2.5.1 DNA probe labelling NF-κB was detected by binding to its specific oligo-DNA probe, which consisted of a double stranded 22-nucleotide oligo (sense, AGTTGAGGGGACTTTCCCAGGC; antisense, GCCTGGGAAAGTCCCCTCAACT) that was dually end labelled with biotin (BTN) using Pierce Biotin 3’ end DNA labelling kit (Pierce Biotechnology, Rockford, USA). 5 µl of each oligonucleotide (1 µM solution) was labelled in a 50µl volume following the supplier’s protocol. The two oligoes were annealed by mixing together the two completed end-labelling reactions and incubating the mixture for 1 h at room temperature (RT) and stored at -20°C.

7.2.5.2 Nuclear protein extraction Since NF-κB activated heterodimer can be found only in the nucleus, its detection was restricted to protein extracts from the cell nucleus of T84. Nuclear extracts were carried out using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Pierce, Cat No. 78833) in the presence of a protease inhibitor cocktail (PMSF 2 mM, aprotinin 10 µg/ml). Protein content of nuclear extracts was quantified by Bradford reagent (Sigma, Australia). The extracts were stored at –80°C. The extracts (2-3 µl or 5 µg of protein) are ready for use in the EMSA.

7.2.5.3 Assay of NF-κB activation DNA binding reactions were carried out in a total volume of 20 µl comprising 5 µg nuclear protein using the reagents from the LightShift Cheniluminescent EMSA kit (Progen, Australia) and followed the manufacturer’s instructions. The binding reactions were incubated at RT for 20 min and then terminated by the addition of 5 µl of 5× loading buffer. Migration differentials between NF-κB/DNA probe complex and free probe was visualised by electrophoresis of samples in 6% polyacrylamide gels. Following electroblot transfer to positively charged nylon membranes (Hybrid- N+, Amersham Pharmacia), biotinylated free and complexed DNA probes were reacted with streptavidin-horseradish peroxidase conjugate and a chemiluminescent substrate (LightShift Cheniluminescent EMSA kit, Progen, Australia).

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Chemiluminescence was detected by exposure to, and development of X-ray films (Kodak Scientific Imaging Film).

7.3 Results 7.3.1 Selection and validation of probiotic and pathogenic Gram-negative coliforms A multiplex PCR assay capable of detecting the presence of 54 virulence genes of E. coli/Shigella, as described in Chapter 5 and 6, was used to confirm the pathogenic status of each of the 7 coliforms used in this study (Table 7.5). The ownership of specific combinations of virulence genes is characteristic of different pathotypes and is also frequently related to serotypes (Chapman et al., 2006). The probiotic strain, Nissle 1917, harboured 11 of the 54 virulence genes and belonged to the same seropathotype as the UPEC isolate 5584. The EHEC representative – DES 1643, commonly identified as a foodborne pathogen O157:H7 has its own unique VG combination represented by ireA, stx1, stx2 and ehxA. The EPEC strain – O86:K61, isolated from a human patient with diarrhoea, is characterised not by a combination of VGs but by the presence of bfpA, a bundle forming pili involved in attachment. Shigella is very close to EIEC because of the presence of the ipaH gene whose gene product plays an important role in cell invasion. ETEC is represented by a porcine strain O149:K88 associated with post-weaning diarrhoea in pigs. This isolate is not normally invasive but carries a panel of unique gene combinations including hlyA, eltA, estI, estII, F4 and east-1. Finally, a control non-pathogenic strain characterised by an absence of unique VGs is represented by DH 5α. A triple gene assay involving ownership of 3 genes – chuA, yjaA and TspE4C2 was used to categorise the phylogenetic status of the 7 coliform strains. The probiotic Nissle 1917, 5584 (UPEC) and DES H24 (EPEC) belong to group B2, while S. flexneri 1 (EIEC), 0354/2 (ETEC) and DH5α belong to group A. DES 1643 (EHEC) was identified as a member of group D.

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Table 7.5 Characteristics (serotype, pathotype and phylogroup) of coliforms

Category Name serotype PAI fimH kpsMTIII fyuA sfa/focDE iutA hlyA kpsMT II focG traT K5 iha ironec ompT ireA stx1 stx 2 eaeA ehxA bfpA ipaH eltA estI estII F4 (k88) east-1 ChuA YjaA TspE4C2 Phylogroup

Probiotic Nissle 1917 O6:K5:H1 + + - + + + - + + - + + + + ------+ + + B2

UPEC 5584 O6:H1 + + - + + + - + + - + + + + ------+ + + B2

EHEC DES 1643 O157:H7 - + ------+ -- - + + + + + + ------+ -- D

EPEC DES H24 O86:K61:H- + + ------+ ---+ - + ------+ + - B2

EIEC S. flexneri serotype 1 - + -- - + ------+ ------A

ETEC 0354/2 O149:K88 ------+ --+ - + ------+ + + + + -- - A

Lab strain DH5a NT - + ------+ ------+ - A

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7.3.2 Basal levels of gene activation in 7 different cell lines Only 17 out of 31 functional genes were found to be constitutively expressed at the basal level in different combinations by each of the 7 cell lines cultured (Table 7.6). Of these, FHs 74 Int was the least active and only expressed the 2 metabolic enzymes β-actin and GADPH. HCT116 was the most metabolically active cell line, constitutively expressing 13 out of 31 genes tested.

The T84 cell line was selected for interactive studies of bacteria induced activation because it did not constitutively express TOLL receptors and was primarily silent for the 4 cytokines IL-1α, IL-1β, IL-12 and IL-13. Although the non-expression of the multifunctional gene panel suggested that FHs 74 Int would be ideal for activation studies, this cell line was in general, not as responsive to microbial activation as T84 (unpublished data) and was not further used in our experiments. Caco-2 also behaved similarly to T84 in terms of its basal metabolic activity but expressed both TLR-2 and cox-2 constitutively.

Table 7.6 Baseline level of gene transcripts in different cell lines determined by RT- PCR. The PCR positives were represented by “+”, the negative results were represented by “-“ gene FHs74 Int HuTu 80 HCT116 Caco-2 T84 Hep-2 HepG2 TLR-2 - - - + - - - TLR-4 - + - - - - + GATA-3 - + + - - - + cox-2 - + + + - + + TGF-β - + + + + + + IL-1α - - + - - + + IL-1β - - + - - - + IL-12p40 - - + - - - - IL-13 - + + - - - - IL-8 - - + - + - + ENA-78 - - - + + - - GCP-2 - - - + + - - MGSA - + + + + - - MIP-2α - + + + + + - MIP-2β - + + + + + - β-actin + + + + + + + GAPDH + + + + + + +

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7.3.3 Activation of NF-κB NF-κB is a transcription factor that plays an integral role as an innate pro- inflammatory mediator in IECs. It exists in an inert form in the cytoplasm complexed with IκB. Once activated by phosphorylation and ubiquitinylation, NF-κB migrates as a heterodimer into the nucleus to bind to its specific promoter DNA sequence. As shown in Fig 7.1A, NF-κB can be detected with a biotinylated oligo probe (a 22 double stranded nucleotide sequence of the NF-κB specific promoter). Binding of NF-κB to the probe reduces the mobility of the bonded probe and shifts the complex to a slower migrating band at the top of the gel (Lane 3) while the free biotinylated oligo-probe runs with the electrophoretic front at the bottom of the gel (Lane 1 - 5). The specificity of NF-κB to its corresponding biotinylated oligo-DNA probe was confirmed by incubating the nuclear extract with biotinylated-oligo probe in the presence of 200-fold excess of unlabelled or cold oligo probe. In this case, the unlabelled probe competed for binding sites with NF-κB and correspondingly prevented association with the biotinyulated oligo-probe (Lanes 4 and 5). It is evident from Fig. 7.1A to G that only 6 out of the 7 coliforms activated NF-κB. The level of activation was very strong for Nissle 1917, 5584 (Fig. 7.1B), DES 1643 (Fig. 7.1C), Shigella (Fig. 7.1E) and even DH5α (Fig. 7.1G). DES H24 (Fig. 7.1D) was considerably weaker in its ability to activate NF-κB while 0354/2 (Fig. 7.1F) failed to do so completely. In all cases, NF-κB was not activated after 1 h exposure to test bacteria and required as least 3 h of incubation before activation could be detected.

7.3.4 Activation of multi-functional genes by Gram-negative coliforms Gene activation by coliform strains are summarised in Fig 7.2. Within a 1-3 h exposure period, different bacteria strains differed in their ability to activate cytokine and chemokine genes. Two cytokine genes – IL-1α and IL-1β were activated from undetectable baseline levels in T84 cells by 6 out of 7 tested bacteria strains. The IL- 1α gene was activated most strongly by probiotic, UPEC and EHEC strains and to a lesser degree by Shigella and ETEC. Surprisingly, the non-pathogen DH5α also activated IL-1α gene expression while the EPEC isolate had no impact at all (Fig. 7.2A). TGF-β mRNA continued to be transcribed unabated at basal levels irrespective of the bacteria species tested (data not shown). However, synthesis of

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both ENA-78 and GCP-2 transcripts was shut down following addition of any one of the 7 coliforms. In contrast, the other 4 chemokines IL-8, MGSA, MIP-2α and MIP- 2β were all further up-regulated above basal levels by the addition of Nissle 1917 and 0354/2 but not significantly by any of the remaining 5 strains examined.

7.3.5 Activation of multi-function genes by a panel of LABs Gene activation by LAB strains are summarised in Fig 7.3. All 4 Lactobacillus species were relatively more uniform in their ability to activate cytokine and chemokine mRNA transcripts. Like coliforms, a similar subset of 6 genes was activated above basal levels by the addition of LAB. However, the level of activation was consistently lower than that generated by the addition of coliforms. L. acidophilus (La) and L. plantarum (Lp) elicited a higher level of mRNA transcripts for IL-1α in 3 h while L. paracasei (Lpc) and L. salivarius (Ls) activated IL-1β transcripts in 1 h of incubation which then declined by 3 h. La and Lp also activated higher levels of MIP-2α transcripts in 3 h compared to Lpc and Ls.

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1 2 3 4 5 (A) NF-kB–biotinylated oligo probe complex (reduced motility)

Free biotinylated oligo probe (rapid motility)

Nissle 1917

1 h 3 h 1 h 3 h 1 h 3 h 1 h 3 h 1 h 3 h 1 h 3 h (B) (C) (D) (E) (F) (G)

5584DES 1643 DES H24 Shigella 0354/2 DH5α

Figure 7.1 Representative EMSA demonstrating NF-κB activation in bacterially stimulated T84 cells. In order to confirm the specificity of the NF-κB signal, a 200-fold excess of unlabelled oligo probe was used as a competitor to inhibit the binding activity of NF-κB (A) Nissle 1917 activates NF-κB in T84 cells after 3h incubation. Lane 1, biotinylated probe only; Lane 2-3, 1 h and 3 h incubation respectively; Lane 4-5, 1 h and 3 h incubation competed with cold probes respectively (B) 5584; (C) DES 1643; (D) DES H24; (E) Shigella; (F) 0354/2; and (G) DH5α

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β IL-1α (A) IL-1 (B)

10 20 1 h 9 18 8 16 3 h 7 14

6 12

5 10

4 8 3 6 2 4

1 2

0 0 Nissle 5584 DES 1643 DES H24 Shigella 0354/2 DH5a Nissle 5584 DES 1643 DES H24 Shigella 0354/2 DH5a

IL-8 (C)30 MGSA (D) 10 25 9 8 20 7 6 15 5 4 10 3 2 5 1 0 0 Nissle 5584 DES 1643 DES H24 Shigella 0354/2 DH5a Nissle 5584 DES 1643 DES H24 Shigella 0354/2 DH5a

30 10 α (E) MIP-2β (F) 9 MIP-2 25

8 20 7

6 15 5

4 10 3 2 5 Fold Increase in mRNAFold Increase in Transcriptsto Controls Relative 1

0 0 Nissle 5584 DES 1643 DES H24 Shigella 0354/2 DH5a Nissle 5584 DES 1643 DES H24 Shigella 0354/2 DH5a

Figure 7.2 Gene activation in T84 cells incubated with 7 different E. coli strain. At 1 or 3 h, total RNA was extracted from cells and gene activations were semi-quantitated by RT-PCR. (A) IL-1α; (B) IL-1β; (C) IL-8; (D) MGSA; (E) MIP-2α; (F) MIP-2β. Results were expressed as the fold increase in (mRNA) relative to levels at 0 h, and data were shown as the means ± SD for triplicate assays

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IL-1α (A)IL-1β (B) 2.0 2.0 1 h

1.5 1.5 3 h

1.0 1.0

0.5 0.5

0.0 0.0

2.0 2.0 IL-8 (C) MGSA (D)

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0

α MIP-2 (E)2.5 β (F) 2.0 MIP-2

2.0 1.5 1.5

1.0 1.0

0.5 Fold Increase in mRNA Transcripts Relative to Controls to Relative mRNA Transcripts in Increase Fold 0.5

0.0 0.0 La Lp Lpc Ls La Lp Lpc Ls

Figure 7.3 Gene activation in T84 cells incubated with 4 different LAB strain: La, L. acidophilus; Lp, L. plantarum; Lpc, L. paracasei; Ls, L. salivarius. At 1 or 3 h, total RNA was extracted from cells and gene activations were semi-quantitated by RT-PCR. (A) IL-1α; (B) IL-1β; (C) IL-8; (D) MGSA; (E) MIP-2α; (F) MIP-2β. Results were expressed as the fold increase in (mRNA) relative to levels at 0 h, and data were shown as the means ± SD for triplicate assays

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7.4 Discussion The primary focus of this work was to investigate the response of a human epithelial cell line T84 following exposure at high multiplicity to pathogenic and non- pathogenic (commensal or probiotic) bacteria. To avoid events that culminate in cellular apoptosis or necrosis (Hosoi et al., 2003), the incubation time for cell and bacteria was restricted to less than 3 h in contrast to some other published studies where incubation times as long as 64 h (Parlesak et al., 2004) have been tested. It is important to recognise that mRNA transcripts activated within this shorter time interval may not have been translated to a detectable intra- or extracellular gene product, thereby eliminating the need to detect expression of any activated genes by antibody-based methodologies. Furthermore, RT-PCR assessment of gene activation is independent of vectorial secretion of proteins and is intended primarily to measure early activation events in the signalling cascade (Delassus, 1997).

Before bacteria-mediated up- or down-regulation of genes can be properly determined, it was essential to establish basal levels of mRNA transcripts in 7 different cell lines with respect to a panel of 31 functional genes. These genes were selected on the basis of functional attributes reported in the literature for intestinal epithelial cell (IEC) lines. For example, IECs such as Caco-2 and T84 are known to produce a number of cytokines and chemokines (Elewaut et al., 1999; Witthoft et al., 1998) following activation by bacteria. Although the use of controls can provide a reference estimate of ongoing gene activity, the basal comparison between cell lines carried out here, has revealed differences that can affect the outcome of their response when exposed to pathogenic or probiotic bacteria strains. Unlike other cell lines such as Caco-2, T84 was selected because it did not express the cyclooxygenase (COX) enzyme constitutively. COX is made up of three subunits encoded by cox-1, cox-2 and cox-3 genes and plays a key role in the conversion of arachidonic acid to prostanoids (Needleman and Isakson, 1997). In studying the primary triggering event between bacterium and IEC, it was important to avoid mechanisms that have evolved to facilitate bacteria translocation into epithelial cells. For example, many epithelial cells in the intestine express the globotriaosylceramide (Gb3) ligand on the apical or lumen face of the cell. Gb3 functions as a receptor to bind EHEC, thereby triggering

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retrograde transport of bacterium via the Golgi complex to the endoplastic reticulum (ER) (Dahan et al., 2002; Sandvig et al., 1992). As the purpose of this work was focused on the primary surface to surface interaction, the absence of Gb3 receptors on T84 was considered to be an advantage.

Another key aspect of this investigation was to verify the pathogenic status of coliforms used to evaluate gene activation in epithelial cells. With the aid of an established panel of virulence genes (VGs) capable of characterising various E. coli pathotypes, it was demonstrated that the widely used human probiotic strain Nissle 1917 (Mutaflor; Kruis et al., 2004), belonged to the Clermont B2 phylogenetic grouping. Members grouped in B2 are normally considered to be pathogens and this was supported by the VG profile of Nissle 1917 which closely matched that of the UPEC isolate 5584. It was therefore not surprising that the Nissle 1917 strain was very prominent in activating both cytokine and chemokine genes in T84. Surprisingly, the sister strain 5584 with an identical serotype and VG profile, was more constrained in its ability to activate chemokine genes MGSA, MIP-2α and MIP-2β. DNA fingerprinting by pulsed-field gel electrophoresis (as shown in Figure 5.5, Chapter 5) have confirmed a separate identity of 5584 from Nissle 1917. The absence of constitutive expression of genes for trefoil factor was not surprising and served as a negative control because these factors are normally expressed only by goblet cells of the small and large intestine (Hauser et al., 1993). Similarly, T-bet is a transcription factor found in activated Th1 cells and thymocytes (Usui et al., 2006) and its mRNA transcripts would not be expressed in IECs. Interestingly, the transcription factor GATA-3 is found in immune cells that regulate antibody production associated with Th2 functionality. Expression of GATA-3 in IECs suggests it has an important role in cross-talk with intra-epithelial lymphocytes in the regulation of Th2 cytokines such as IL-4, IL-5 and IL-13 (Zhu et al., 2006).

While primary activation of the cell can occur upon contact with bacterium, signalling events can also be initiated by soluble components of the bacteria – peptidoglycan, flagella, Lipopolysaccharide (Cario et al., 2000) and even DNA can trigger signalling events (Takeda et al., 2003) via the CpG motif, a pathogen- associated molecular pattern (PAMP). TOLL receptors TLR2 and TLR4 routinely

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survey the enteric lumen for PAMPs such as lipoproteins/peptidoglycan and lipopolysaccharides (Takeuchi et al., 1999) and can thus distinguish between Gram- positive and Gram-negative bacteria respectively. One of the earliest events post recognition/activation is the inflammatory response. In T84, the inflammatory response can be assessed by the level of NF-κB activation. It appears that the level of activation is different between bacteria strains and cannot always be anticipated from the pathogenic status of the test coliform. This finding disputes claims by many groups that different pathotypes of E. coli are all equally capable of activating NF- κB (Dahan et al., 2002; Elewaut et al., 1999; Philpott et al.,2000; Savkovic et al., 1997) and this included even non-pathogenic commensals (Bambou et al., 2004; Wehkamp et al., 2004). A recent report suggests that commensals can activate IECs via still another ligand – TLR-5 (Bambou et al., 2004). While it was not feasible to evaluate all TOLL receptors in this investigation, the important point to note is that different strains and species of bacteria, whether pathogenic or commensal, can signal responses from epithelial cells via different ligands resulting in the activation of different pathways as well.

The results presented here, show quite clearly that pathogenic and commensal bacteria differ in their ability to activate genes in T84. Unfortunately, there appears to be a lack of correlation between the pathogenic status of test coliforms and their ability to activate cytokines and chemokines. For example, the cloning vector DH5α was able to induce mRNA transcripts for IL-1α and IL-1β while the EPEC DES H24 did not. Many pathogenic strains have been reported to be pro-inflammatory in stimulating IL-8, but this bioactivity was confined to only Nissle 1917 and the ETEC stain 0354/2. It is also interesting to note that all tested coliforms failed to suppress basal levels of TGF-β mRNA transcripts but did silence genes encoding ENA-78 (epithelial neutrophil activating peptide 78) and GCP-2 (macrophage inflammatory protein 2β), two chemotactic chemokines responsible for mobilizing neutrophils and macrophages. While the use of monolayer epithelial cell lines to examine primary bacteria-cell signalling events can be challenged as being artificial because it does not take into consideration simulation models that also engage intra-epithelial lymphocytes/leukocytes in cross-talk for cytokine/chemokine activation, the differential responses seen with different E. coli strains and the lower and more

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consistent activation patterns recorded by LABs, demonstrate that a semi- quantitative ranking of microbial bioactivity can be obtained even from such a simple study. Such an approach if adopted in conjunction with an even wider panel of genes in a standardised in vitro environment can provide invaluable information on the selection of appropriate strains to be further tested in vivo. The in vivo testing of orally consumed pre- and probiotics on intestinal microbial health and mucosal immunity will be facilitated by prior knowledge gained from in vitro assessment of candidate strains. In the end, functionality of probiotic formulations must be tempered against the enigma of immune activation versus tolerance (Chin et al., 2000).

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Summary

There is increasing evidence in the literature to suggest that a balanced gut microflora is critical to maintaining intestinal health. Unfortunately, the composition of microflora making up such a balanced and healthy community has never been precisely defined. To validate the concept of probiosis, it must first be argued that an imbalance of the healthy flora predisposes the intestine to colonization by incoming pathogens, or that enteric pathogens have the ability to outcompete even a healthy flora, colonise their own special niche, erode the epithelium and cause clinical disease. Many enteric pathogens accomplish this by a variety of means such as the use of fimbriae for attachment, toxins to erode the epithelial cell wall and other devious strategies yet to be defined.

In view of the lack of understanding about the normal healthy or commensal population as contrasted with well known pathogenic strains, one of the major objectives of this thesis was to investigate the complex tri-partite interaction between commensal, pathogen and host, both in vivo (Chapter 3) and in vitro (Chapter 7).

The main objectives in Chapter 3 were to (i) determine whether continuous high level dosing with probiotic bacteria in feedlot cattle would reduce the incidence of coliforms in faeces and to also alter the population dynamics of different bacteria species in faeces, and (ii) whether probiotic feeding would affect the animal’s capacity to mount an immune response against antigenic challenge. The trial involved feeding inducted feedlot cattle a high dose of probiotic (Protexin) containing a mixture of 9 strains. Such treatment reduced intramuscular fat and also benefited the animals with an enhanced capacity to mount a cellular immune response to vaccinal challenge. Unfortunately, Protexin did not influence the population dynamics of the major cultivable bacterial groups in faeces. These findings have raised some concerns as to benefits of administering probiotics at the time of induction into a feedlot to offset the possibility of animals succumbing to disease; the scientific rationale for probiotic administration and of course the choice of probiotics and dosage.

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In the course of examining the faecal microflora from feedlot cattle in Chapter 3, the presence of high levels of Bacilli suggested that one possible reason for the lack of a discernible growth benefit may be attributed to a high endogenous level of Bacilli. Bacillus species produce proteases that aid digestive processes and they have also been used as probiotics. Their presence in the GIT of feedlot cattle may have subverted an anticipated benefit of probiotic supplements in growth performance until the organisms reached the large intestine where alterations in short chain fatty acids (SCFA), LABs and fungi may have affected carcase composition (Sakata et al., 2003). The presence of Bacilli in feed and in the feedlot environment raised an important issue about the identification of different Bacillus species. Understanding indigenous Bacillus composition in animals will guide the administration of different Bacillus probiotics, and develop safer and more effective Bacillus probiotic/CE agents for animals from indigenous isolates.

Since the identification of Bacillus strains is complex and difficult based on traditional taxonomic approaches, and available molecular techniques did not allow speciation, an alternative speciation strategy based on 16S rDNA sequences was developed. Chapter 4 reports a novel application of ARDRA using a so-called Bacillus-specific primer pair to identify Bacillus species using only 2 restriction enzymes and the analysis of the Bacillus composition of environmental samples without cultivation. Based on alignment of various Bacillus 16S rRNA gene sequences retrieved from published DNA database, a group-specific primer pair was designed to amplify the 16S rRNA gene of representative reference strains from environmentally sourced, mesophilic aerobic spore-forming Bacillus taxa. The PCR generated a 1114 bp amplicon but did not do so with DNA extracted from 16 other Eubacterial species. When amplicons were digested with restriction enzymes AluI or TaqI, different profiles containing between 2 to 5 fragments ranging in size from 76 to 804 base pairs were seen with different Bacillus species. This procedure, known otherwise as amplified ribosomal DNA restriction analysis or ARDRA, produced unique and distinguishable patterns to differentiate between 15 ATCC reference strains (10 Bacillus, 3 Paenibacillus and 2 Brevibacillus member species) as well as 3 misidentified Bacillus probiotic strains in a commercial

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collection. Our simplified PCR-ARDRA protocol provides a facile method for the identification of most environmentally important species of Bacillus.

When applied to Bacillus isolates from feed-lot cattle (n=150, 60 each from Day 0 and Day 76 cattle, 30 from feed), PCR-ARDRA successfully established that cattle faeces contain large numbers of Bacillus spores representing different mesophilic species, in which B. subtilis, B. licheniformis and B. clausii dominated. Stable faecal populations of particular Bacillus species mimicking those found in feed, were subsequently established by feedlotting. The results obtained and methods used in this study provide very useful data of high relevance to our understanding of the occurrence of Bacillus within the ruminant GI tract. This data is needed as we seek to better understand the potential of probiotics to further guide the administration of Bacillus probiotics.

Carriage of foodborne pathogens such as E. coli O157:H7 in cattle presents an important food safety issue for the industry. Zhao et al. (1998) had advocated and tested the use of probiotics to reduce carriage of O157:H7 in beef cattle. However, data in Chapter 3 showed that E. coli and coliforms remained relatively constant in both control and treated groups following enumeration on MacConkey agar, suggesting that Protexin and perhaps other probiotics may not be able to influence E. coli communities in the GIT of cattle. High occurrence and stable E. coli populations are also found in the GIT of other animals such as swine and chicken. Unlike other species that inhabit the intestinal tract, E. coli is probably one of the most genetically diverse. A high diversity of commensal E. coli in the gut microbiota could prevent pathogenic E. coli infection by competing for various attachment sites on the gut epithelial surface. This diversity may form the basis of a new strategy to use E. coli as probiotics to inhibit pathogenic E. coli. A lack of understanding about the differences between pathogens and commensal E. coli has led us to address the role that virulence genes may play in determining the pathogenic status of E. coli strains, and the role of virulence genes as a parameter for phylogenetic membership of clinical E. coli isolates.

In Chapter 5, we have investigated the role of virulence genes as a parameter for phylogenetic membership of human clinical E. coli isolates. This was facilitated by assembling a panel of 50 virulence gene markers found in 8 pathotypes of E. coli known

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to cause intestinal and extraintestinal disease in humans and animals. A combination of uni- and multiplex PCR assays was used to screen 42 E. coli strains including intestinal and extraintestinal human clinical isolates, human and animal commensal isolates and laboratory E. coli strains for these VGs. Results showed that different pathotypes possessed unique VG combinations and a number of the ExPEC VGs were identified in IPEC isolates. Interestingly, a probiotic strain, Nissle 1917, had a considerable number of ExPEC VGs, indicating that some VGs selectively endow sine E. coli strains for enhanced colonization and increased capacity to compete with other bacteria species. With the aid of a Chi-squared test of deviance, 21 out of 50 VG markers played a significant role in determining membership in each of the four different phylogroups (A, B1, B2 and D) (P< 0.05). Principle coordinate analysis (PCO) based on the VG attributes separated isolates into phylogroups identical to those based exclusively on the Clermont triple gene combination. There was a significant association between phylogroups based on VG ownership. This result showed clearly that the lack of or possession of VGs in member isolates of each phylogenetic group can be used to assess diversity of E. coli.

Cattle generally remain immune to infections with E. coli but not other enteric pathogens such as Salmonella. As ruminants, the physiology of the gastrointestinal environment of cattle is completely different from monogastrics such as the pig in which E. coli present a major problem because certain strains and serotypes are responsible for ND or PWD. We subsequently applied the VG concept developed previously in Chapter 5 to examine diversity and differences between pathogenic and commensal E. coli isolates from pigs. In Chapter 6, the VG assay was used to compare the VG profiles between ETEC from porcine PWD and gut commensal E. coli from healthy pigs in order to provide important insight into the evolution and spread of VGs in potentially pathogenic lineages. Data showed that PWD ETEC strains also possess VGs associated with ExPEC. Among these, hlyA (α-haemolysin) and iha (nonhemagglutinating adhesin) occurred significantly more frequently amongst the ETEC isolates (P< 0.01), in comparison to the commensal isolates. To our knowledge, this is the first report of the presence of the hlyA and iha gene in porcine PWD isolates with significantly higher carriage rate compared to gut commensal isolates. Possession of these additional VGs may confer a potential advantage that porcine ETEC have over

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these commensal E. coli strains are greater amplification and dispersion in pigs with diarrhoea compared to healthy pigs with normal faeces. In contrast, most of these VGs were rarely found in commensal isolates, with the exception of some so-called ‘adapted gene’ such as fimH and traT. Moreover, the commensal isolates were heterogenous with respect to both serotype and DNA fingerprint, in agreement with Dixit et al. (2005) based on a MLEE analysis. Taking together, in order to compete with pathogens such as ETEC in the gut, commensal E. coli could possess currently unidentified genes for colonisation. If not, a high diversity of commensal E. coli in the gut microbiota is certainly helpful for preventing pathogenic E. coli colonization as strains of the same species are more likely to have a similar ecological niche in the intestine than are strains of different species.

Finally in Chapter 7, an attempt was made to understand the primary interaction between probiotic or pathogen with intestinal epithelial cells by in vitro modelling. The intestinal epithelium is continually exposed to a high concentration of diverse bacteria. The host mounts an effective immune response to intestinal pathogens and prevents bacterial spread within the organism. However, unlimited immune activation in response to signals from commensal bacteria could pose the risk of inflammation. Immune responses to mucosal microbiota therefore require a precise regulatory control. On the other hand, enteric pathogen infection activates mucosal inflammatory and immune responses and alters epithelial cell functions. This IEC inflammatory program includes the up-regulated expression and production of proinflammatory and chemoattractant cytokines. Although differences between probiotic bacteria in respect to their immunomodulatory effects have been observed (Isolauri et al., 1999), it appears that many of these are mediated through immune regulation and especially through balanced control of pro-inflammatory and anti-inflammatory cytokines. Characterizing how the innate immune system responds to these bacteria in vitro may give an indication as to the likely immunomodulatory events that can be triggered following probiotic administration in vivo. Data gleaned from the experiments in Chapter 7 demonstrate support the contention that enterocytes are capable of responding to probiotic or pathogen. However, the surprise finding was that coliform pathogens and commensals, differ in their ability to signal transduce new gene activations in IECs. By far, Gram-negative coliforms were more effective in triggering gene activation in IECs

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than lactic acid bacteria. Another important point to note was that different strains with very similar virulence gene profiles differed in their ability to activate genes in vitro. This difference was sustained even though most of the strains were able to trigger an inflammatory NF-κB response.

In conclusion, the research described in this thesis emphasises the importance of breaking down complex interactions between host animals and the microbial inhabitants of their gastrointestinal tract (host-microbe), as well as microbe-microbe interactions and microbe-host interactions to increase our understanding of how a healthy gut microbial composition can be sustained.

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Appendix

Culture medium 2.1 Luria Bertani (LB) medium Bacto-Tryptone 10.0 g Bacto-yeast extract 5.0 g NaCl 10.0 g Deionised water made up to 950 ml Adjusted pH to 7.0 with 10 N NaOH (~0.1 ml), and final volume adjusted to 1 litre with deionised water. To prepare a LB agar medium, 1.5% agar was added before autoclaving (121°C for 15 min)

2.2 Tryptic Soy broth/agar Suspend 30 g of TSB (DIFCO, Cat No: 211825) in 1 litre deionised water. To prepare agar medium (TSA), 1.5% agar was added and sterilise by autoclaving at 121°C for 15 minutes

2.3 Blood agar Suspend 40 g of blood agar base No.2 (OXOID, Cat No: CM271) in 1 litre deionised water. Sterilise by autoclaving at 121°C for 15 minutes. Cool to 50°C and add 70 ml of sheep blood to make a concentration of 7%. Mix with gentle rotation and pour into sterile petri dishes

2.4 MRS (de Man, Rogosa and Sharpe) medium Suspend 66.2 g medial powder in 1 litre deionised water to dissolve completely. Sterilise by autoclaving at 121°C for 15 minutes. Cool to 50°C. Mix with gentle rotation and pour into sterile petri dishes

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2.5 MacConkeys agar Suspend 50.0 g medial powder in 1 litre deionised water to dissolve completely. Sterilise by autoclaving at 121°C for 15 minutes. Cool to 50°C. Mix with gentle rotation and pour into sterile dishes

2.6 Kanamycin Esculin Azide (KEA) agar Suspend 47.5 g medial powder in 1 litre deionised water. Bring to the boil to dissolve completely. Sterilise by autoclaving at 121°C for 15 minutes. Cool to 50°C. Mix with gentle rotation and pour into sterile dishes

2.7 Citrate Azide Tween Carbonate (CATC) agar Suspend 56.0 g medial power in 1 litre deionised water. Sterilise by autoclaving at 121°C for 15 minutes. Cool to 50°C and add the following stock solutions (filter- sterilised): • 20 ml 10 % sodium carbonate solution • 10 ml 1 % 2,3,5-triphenyltetrazolium chloride solution • 4 ml 10 % sodium azide solution Mix with gentle rotation and pour into sterile dishes

2.8 Bacillus spore generation agar Suspend 28.3 g medial powder to 1 litre deionised water. Sterilise by autoclaving at 121°C for 15 minutes

2.9 SOC medium Tryptone 20.0 g Yeast extract 5.0 g NaCl 0.5 g KCl (0.25 M KCl solution) 10.0 ml

1 M MgCl2.6H2O 10.0 ml 2 M Glucose 10.0 ml Deionised water made up to 1 litre. Cool the autoclaved solutions (121°C for 20 min) to ~55°C, then add 10 ml of filter- sterile 2 M glucose solution and 10 ml 1 M MgCl2. Store at 4°C

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2.10 Cell culture medium 1:1 DMEM/Hames F12 L-glutamine (GIBCO) 2.5 mM Newborn bovine serum (ThermoTrace) 5%

Buffers and solutions 2.11 Buffered peptone water (OXOID, CM509) Suspend 20 g of powder to 1 litre deionised water. Sterilise by autoclaving at 121°C for 20 min

2.12 10× Phosphate buffered saline (PBS) NaCl 80.0 g KCl 2.0 g

Na2HPO4.7H2O 26.8 g

KH2PO4 2.4 g Adjust to pH 7.4 with HCl and deionised water made up to 1 litre. Sterilise by autoclaving at 121°C for 20 min

2.13 10× Tris-Borate-EDTA buffer (TBE) Tris 108.0 g Boric acid 55.0 g EDTA (0.5 M, pH 8.0) 40 ml Adjust volume to 1 litre with deionised water

2.14 TE buffer (10 mM Tris, 1mM EDTA, pH 7.4 or 8.0) 1 M Tris (pH 7.6 or 8.0) 10.0 ml EDTA (0.5 M, pH 8.0) 2.0 ml Adjust volume to 1 litre with deionised water. Sterilise by autoclaving (121°C for 20 min)

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2.15 X-Gal stock solution (40 mg/ml) Dissovle 400 mg of X-Gal in 10 ml dimethylformamide and store in a brown bottle at - 20°C

2.16 IPTG stock solution (100 mM)

Dissolve 238 mg of IPTG in 10 ml MQ-H2O, filter-sterilise and store in 1 ml aliquots at -20°C

2.17 EDTA stock solution (0.5 M, pH 8.0) (Sambrook and Russell, 2001) EDTA stock solution was prepared by dissolving 186.1 g disodium ethylene diamine tetraacetate.2H2O in 800 ml MQ-H2O, and by stirring vigorously on a magnetic stirrer. pH was adjusted to 8.0 by adding 10N NaOH and MQ-H2O was added to 1 litre

2.18 Ethidium bromide (EB, 10 mg/ml) EB (10 mg/ml) was prepared by dissolving 1 g ethidium bromide (Sigma) in 100 ml

MQ-H2O. The mixture was stirred on a magnetic stirrer for several hours to ensure that the dye was completely dissolved, and the solution was transfer to a brown bottle and stored at 4ºC in dark

2.19 Tris solution (1 M)

Dissolve 121.4 g Tris base (Sigma) in 800 ml MQ-H2O. Adjust pH to the desired value

(7.6 or 8.0) by adding concentrated HCl and adjust volume to 1 litre with MQ-H2O. Sterilise by autoclaving (121°C for 20 min)

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