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Impact of antimicrobial treatments on sprouting alfalfa seed contaminated with Salmonella enterica Dai, Yue 2018

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  IMPACT OF ANTIMICROBIAL TREATMENTS ON SPROUTING ALFALFA SEED CONTAMINATED WITH SALMONELLA ENTERICA by  Yue Dai  B.Sc., The University of British Columbia, 2016  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Food Science)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2018  © Yue Dai, 2018  ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: Impact of Antimicrobial Treatments on Sprouting Alfalfa Seed Contaminated with Salmonella enterica  submitted by Yue Dai  in partial fulfillment of the requirements for the degree of Master of Science in Food Science  Examining Committee: Dr. Siyun Wang, Food Science Supervisor  Dr. Pascal Delaquis, Agriculture and Agri-Food Canada Supervisory Committee Member   Supervisory Committee Member Dr. David Kitts, Food Science Additional Examiner   Additional Supervisory Committee Members: Dr. Vivien Measday, Food Science Supervisory Committee Member  Supervisory Committee Member iii  Abstract  Consumption of alfalfa sprouts has increased worldwide due to the nutritional value and health benefits of sprouts. However, alfalfa sprouts contaminated with Salmonella enterica (S. enterica) have been the source of many foodborne outbreaks in Europe and North America. Antimicrobial treatments for sprouted seeds are recommended by the Canadian Food Inspection Agency but the influence of different antimicrobial sanitation seed treatments on the behaviour of S. enterica during seed germination remains unclear. The goals of this study were (1) to investigate the ability of S. enterica to grow on sprouting alfalfa seeds after three different sanitation seed treatments to reduce microbial load, and (2) to understand how colonization by S. enterica and different antimicrobial treatments affect metabolites released by sprouting alfalfa seed.  Alfalfa seeds inoculated with five strains of S. enterica were subject to three different seed treatments: (1) sodium hypochlorite (NaClO), (2) hydrogen peroxide (H2O2), and (3) an organic treatment involving a hot water dip, treatment with H2O2 and acetic acid. The disinfected seeds were sprouted to identify the growth characteristics of S. enterica after sanitation stress (n = 3). The populations of all five S. enterica strains which were present at <10 CFU/g immediately after sanitation treatment increased to 4 - 8 log CFU/g after 6 days of germination. After 6 days of germination, most S. enterica strains recovered from sprouts germinated from seeds treated with H2O2 or the organic treatment were lower than those recovered from sprouts germinated from seeds treated with NaClO. Additionally, metabolites were identified by rinsing seeds after 24 hours of germination (n = 4). Almost all of the 535 identified compounds were affected by the application of antimicrobial treatments. Specifically, the NaClO treatment diminished the levels iv  of metabolites on uninoculated, S. enterica Agona and Typhimurium colonized sprouting seed by almost half, possibly via oxidative destruction. The organic treatment increased and decreased similar numbers of metabolites, around 30% of all detected metabolites.  This study provided new insight on the ecology of S. enterica on germinating seeds, an important consideration in the development of better strategies to lessen the risk associated with sprouted vegetables. v  Lay Summary  Sprouted vegetables have been recognized as a significant source of foodborne disease in recent decades. Seed is the most likely source of contamination and sanitation treatments for seeds are endorsed by the Canadian Food Inspection Agency to reduce the food safety risk of sprouts. To date, however, there is no sanitation treatment that can guarantee pathogen-free seed. Moreover, human pathogens, such as Salmonella, can recover from sanitation stress and grow on sprouting seed. To address this food safety gap, we explored the behaviour of Salmonella on sprouting alfalfa seed after different seed treatments, including government-recommended treatments and an organic intervention, and investigated the survival mechanisms that Salmonella utilizes to recover from these sanitation treatments on sprouting alfalfa. This study provided new insight into the post-sanitation behaviour of foodborne pathogens on germinating seeds, an important consideration in the development of better strategies to lessen the risk associated with sprouted vegetables.    vi  Preface  Subsections 2.2.3, and 2.2.4, Seed inoculation and antimicrobial treatments, were based on protocols developed in Summerland Research and Development Centre of Agriculture and Agri-Food Canada by Dr. Pascal Delaquis. The author, Yue Dai, performed the laboratory work and was responsible for modifying the protocols to adjust the inoculation levels and the sample sizes, and to fulfill the sampling requirements for metabolomic analysis.  Subsection 3.2.3, Metabolite analysis, identification, and quantification, was based on work conducted by Metabolon, Inc., Morrisville, NC, USA. The author, Yue Dai, submitted the sprout wash samples to be tested.  The rest of this research was designed, carried out, and analyzed by the author, Yue Dai, under the guidance of Dr. Siyun Wang.  This work is original and has not been previously published. vii  Table of Contents  Abstract ......................................................................................................................................... iii  Lay Summary ............................................................................................................................... iii Preface ........................................................................................................................................... vi  Table of Contents ........................................................................................................................ vii  List of Tables ............................................................................................................................... xii  List of Figures ........................................................................................................................... xviii  List of Symbols ........................................................................................................................... xxi  List of Abbreviations ................................................................................................................ xxii  Acknowledgements .................................................................................................................. xxiv  Dedication ................................................................................................................................. xxvi  Chapter 1: Introduction and Literature Review.........................................................................1 1.1 Introduction ..................................................................................................................... 1  1.2 Salmonella enterica ........................................................................................................ 1 1.2.1 Microbial characteristics ............................................................................................. 1 1.2.2 Classification............................................................................................................... 2  1.2.3 Pathogenicity............................................................................................................... 4  1.2.4 Produce-related foodborne outbreaks ......................................................................... 6 1.2.5 Response to stresses .................................................................................................... 8  1.2.5.1 Oxidative stress ................................................................................................... 8 1.2.5.2 Desiccation stress ................................................................................................ 9 1.2.5.3 Heat stress ......................................................................................................... 10 viii  1.2.6 Cross-protection ........................................................................................................ 11 1.3 Sprouts .......................................................................................................................... 12 1.3.1 The natural microbiota of sprouted vegetables ......................................................... 12 1.3.2 Sprout production practices ...................................................................................... 13 1.3.3 Persistence of pathogens in sprouts .......................................................................... 14 1.3.4 Current sprout safety interventions ........................................................................... 15  1.3.5 Organic sprout production ........................................................................................ 18 1.4 Metabolomics and its application in food microbiology .............................................. 20 1.4.1 Instruments ................................................................................................................ 21 1.4.2 Applications in pathogen detection and identification .............................................. 23 1.4.3 Assessing the Metabolic State of a Microbial Community ...................................... 24 1.4.4 Host–Microbe Interactions........................................................................................ 25 1.5 Research purpose .......................................................................................................... 25  1.5.1 Research hypotheses ................................................................................................. 26 1.5.2 Research objectives ................................................................................................... 26  Chapter 2: Recovery of Salmonella enterica on Sprouting Alfalfa after Seed Sanitation .....27 2.1 Introduction ................................................................................................................... 27  2.2 Material and methods .................................................................................................... 29  2.2.1 Bacterial strains ......................................................................................................... 29  2.2.2 Alfalfa seeds.............................................................................................................. 29 2.2.3 Seed Inoculation........................................................................................................ 30  2.2.4 Antimicrobial treatments .......................................................................................... 30 2.2.5 Sprout germination.................................................................................................... 32  ix  2.2.6 Microbiological analyses .......................................................................................... 32 2.2.7 Statistical analysis ..................................................................................................... 33 2.3 Results and discussion .................................................................................................. 34 2.3.1 Effectiveness of antimicrobial treatments against S. enterica .................................. 34 2.3.2 Post sanitation behaviour of S. enterica on sprouting alfalfa seed ........................... 37 2.3.2.1 Baranyi and Roberts Model fitting and the diauxic growth of S. enterica Typhimurium .................................................................................................................... 37 2.3.2.1.1 μmax of S. enterica on sprouting alfalfa seed ............................................... 38 2.3.2.1.2 Nmax of S. enterica in sprouting alfalfa seed ............................................... 39 2.3.2.2 N24 of S. enterica on sprouting alfalfa seed ...................................................... 42 2.3.2.3 N6d of S. enterica on alfalfa sprouts .................................................................. 43 2.3.3 Effectiveness of antimicrobial treatments against indigenous aerobic bacteria ....... 46 2.3.4 Post sanitation behaviour of indigenous aerobic bacteria on sprouting alfalfa seed 49 2.3.4.1 Baranyi and Roberts Model fitting ................................................................... 49 2.3.4.1.1 μmax of indigenous aerobic bacteria on sprouting alfalfa seed .................... 49 2.3.4.1.2 Nmax of indigenous aerobic bacteria on sprouting alfalfa seed .................... 52 2.3.4.2 N24 of indigenous aerobic bacteria on sprouting alfalfa seed ........................... 54 2.3.4.3 N6d of indigenous aerobic bacteria on sprouting alfalfa seed ........................... 57 2.4 Conclusions ................................................................................................................... 59 Chapter 3: Impact of antimicrobial treatments on sprouting alfalfa seed contaminated with Salmonella enterica revealed by metabolomics .........................................................................61 3.1 Introduction ................................................................................................................... 61  3.2 Materials and methods .................................................................................................. 63  x  3.2.1 Preparation of sprouting alfalfa seed ........................................................................ 63 3.2.2 Preparation of lyophilized sprout washes ................................................................. 64 3.2.3 Metabolite analysis, identification, and quantification ............................................. 64 3.2.4 Statistical analysis ..................................................................................................... 66 3.3 Results and Discussion ................................................................................................. 67  3.3.1 Metabolite summary ................................................................................................. 67  3.3.2 Principal component analysis ................................................................................... 72 3.3.3 Impact of disinfection treatments on metabolite profiles of sprouting alfalfa seed .. 74 3.3.3.1 Effect of antimicrobial treatment exposure....................................................... 74 3.3.3.2 The CLO treatment ........................................................................................... 77 3.3.3.3 The HPA treatment ........................................................................................... 78 3.3.4 Impact of S. enterica colonization on metabolite profiles of sprouting alfalfa seed 79 3.3.4.1 Summary of S. enterica colonization ................................................................ 79 3.3.4.2 Impact of S. enterica colonization after the HPA treatment ............................. 81 3.3.4.2.1 Amino acid metabolism .............................................................................. 82 3.3.4.2.2 Phospholipids .............................................................................................. 83 3.3.4.2.3 Carbohydrates.............................................................................................. 84 3.3.4.3 Impact of strain type ......................................................................................... 84 3.4 Conclusions ................................................................................................................... 86 Chapter 4: Conclusion and Future Directions ..........................................................................89  4.1 Conclusion .................................................................................................................... 89 4.2 Future Directions .......................................................................................................... 91 References .....................................................................................................................................93  xi  Appendices ..................................................................................................................................107  Appendix A Post-sanitation recovery curves of S. enterica on sprouting alfalfa seed ........... 107 A.1 Comparisons of antimicrobial treatments ............................................................... 107 A.2 Comparisons of S. enterica strains .......................................................................... 112 Appendix B Post-sanitation recovery curves of indigenous aerobic bacteria on sprouting alfalfa seed .............................................................................................................................. 115  B.1 Comparisons of antimicrobial treatments ............................................................... 115 B.2 Comparisons of S. enterica strains .......................................................................... 120 Appendix C Significantly altered metabolites ........................................................................ 123  xii  List of Tables  Table 2.1 Identification, serotypes, origins and stock sources of S. enterica used in this study. . 29 Table 2.2 Steps, chemicals and exposure durations used for the antimicrobial treatment applied in this study. N/A means not applicable. ...................................................................................... 32 Table 2.3 N0 and calculated population reduction (log CFU/g) for 5 different S. enterica strains on alfalfa seeds subjected to 3 different antimicrobial treatments. Results are summarized by mean ± standard deviation (SD) for the bacterial strains tested in triplicates. Means with the same letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with less than (<) have at least one replicate with a population of S. enterica below the detection limit (10 log CFU/g). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ......................................................................................... 36 Table 2.4 μmax (log CFU/g/h) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a - d) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A, B) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ........ 39 Table 2.5 Nmax (log CFU/g) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a-c) in the same column are not statistically different from xiii  each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A-C) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ........ 41 Table 2.6 N24 (log CFU/g) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized as mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a-c) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A-C) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ........ 43 Table 2.7 N6d (log CFU/g) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A-C) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ........ 45 Table 2.8 N0 (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Counts of indigenous bacteria were calculated by subtracting Salmonella counts from total APCs. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). xiv  Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ......................................................................................... 48 Table 2.9 μmax (log CFU/g/h) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A, B) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ......................................................................................... 51 Table 2.10 Nmax (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ......................................................................................... 53 Table 2.11 N24 (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the xv  same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ......................................................................................... 56 Table 2.12 N6d (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. ......................................................................................... 58 Table 3.1 Combinations of microbiota types and antimicrobial treatments with assigned sample group abbreviations. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. ....................................................... 67 Table 3.2 Numbers of significantly up- or down-regulated metabolites in the antimicrobial-treated groups (CLO or HPA), with 3 different types of microbiota, compared to the corresponding controls (CTL) (p ≤ 0.05, Welch’s two-sample t-test). Sig: significantly. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents xvi  microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. .................................................................................................... 75  Table 3.3 Numbers of significantly up- or down-regulated metabolites in S. enterica - colonized groups (ST or SA), with different types of antimicrobial treatments, compared to the corresponding controls (IM). There was no significantly altered metabolites shared by both types of S. enterica - colonized microbiota and all 3 types of antimicrobial treatments. Sig: significantly. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. ....................................................... 80 Table 3.4 Numbers of significantly up- or down-regulated metabolites in the SA groups, with different types of antimicrobial treatments, compared to the corresponding ST groups. Sig: significantly. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. ....................................................... 86 Table C.1 Metabolites significantly down-regulated in all washing samples of sprouting alfalfa seed treated with antimicrobial treatments (CLO or HPA) regardless of microbiota type compared to corresponding CTL at 24 h of germination. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. .................................................................................................................................... 123  Table C.2 Metabolites significantly up-regulated in all washing samples of sprouting alfalfa seed treated with antimicrobial treatments (CLO or HPA) regardless of microbiota type compared to xvii  corresponding CTL at 24 h of germination. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. ...... 126 Table C.3 Fold changes and metabolic pathways of significantly up-regulated amino acids in the washing samples of sprouting alfalfa seed with SA or ST after the HPA treatment at 24 h of germination. As detailed in table 2.2, HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. .................................... 127 Table C.4 Fold changes of significantly down-regulated phospholipids in the washing samples of sprouting alfalfa seed with SA or ST after the HPA treatment at 24 h of germination. As detailed in table 2.2, HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. .................................................................................... 128    xviii  List of Figures  Figure 3.1 Heat map of metabolite scaled intensities in samples grouped by major metabolite class. Log-transformed metabolite concentrations were scaled to the median value (1.0) of all samples for each compound and represented as different colours (red or blue) with different colour intensity. A continuously increasing intensity of red represents values ranging from 1.0 to 4.0 and blue represents values from 1.0 to 0.25. The maximum-intensity red represents all values ≥4.0 and maximum blue stands for ≤0.25. Carbos stands for carbohydrates. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. ........................................................................................................................... 71  Figure 3.2 Principal Component Analysis of the metabolite profiles of samples colonized with 3 different types of microbiota and after 3 different antimicrobial treatments. Comp.: principal component. The sample dots were coloured by different types of antimicrobial treatments and shaped by different types of microbiota. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. ................................ 73 Figure A.1 Populations of S. enterica Agona (PARC 5) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. ................................................................................................................. 107  xix  Figure A.2 Populations of S. enterica Agona (FSL S5-517) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. ............................................................................................. 108  Figure A.3 Populations of S. enterica Enteriditis (LMFS-S-JF-005) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. ............................................................................................. 109  Figure A.4 Populations of S. enterica Daytona (LMFS-S-JF-009) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. ............................................................................................. 110  Figure A.5 Populations of S. enterica Typhimurium (LMFS-S-JF-001) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. Characteristics of diauxic growth were observed with all the growth curves of S. enterica Typhimurium LMFS-S-JF-001 (Roseman & Meadow, 1990). ..................................................................................................... 111  Figure A.6 Populations of S. enterica Agona PARC 5, Agona FSL S5-517, Enteriditis LMFS-S-JF-005, Daytona LMFS-S-JF-009, and Typhimurium LMFS-S-JF-001 on alfalfa sprouts germinated from seeds treated with (A) CTL (B) CLO (C) HPO (D) HPA over 6 days of germination. Error bars indicate the SD of three replicates. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment, and HPA represents the organic treatment. ............................................................... 114  Figure B.4 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Daytona (LMFS-S-JF-009) and germinated from seeds treated with different xx  antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. .................................................................................................................................... 118  Figure B.5 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Typhimurium (LMFS-S-JF-001) and germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. .................................................................................................................................... 119  Figure B.6 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Agona PARC 5, Agona FSL S5-517, Enteriditis LMFS-S-JF-005, Daytona LMFS-S-JF-009, and Typhimurium LMFS-S-JF-001 and germinated from seeds treated with (A) CTL (B) CLO (C) HPO (D) HPA over 6 days of germination. Error bars indicate the SD of three replicates. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment, and HPA represents the organic treatment. .................................................................................................................................... 122  xxi  List of Symbols  H2O2 N0 N24 N6d Nmax NaClO μmax log h t Hydrogen peroxide Population density immediately after seed treatment Population density at 24 hours of germination Final population density after 6 days of germination Maximum population density achieved at stationary phase Sodium hypochlorite Maximum growth rate of exponential phase Log10, logarithm Hours Germination time   List of Abbreviations  ANOVA BC BPW CFIA CFU CLO FSMA GC HPA HPO HSD IM MALDI – TOF MS MS/MS2 PAA PAI PBS ROS SA SD Analysis of variance British Columbia Buffered peptone water Canadian Food Inspection Agency Colony forming units The sodium hypochlorite treatment Food Safety Modernization Act Gas chromatography The organic treatment The hydrogen peroxide treatment Honest Significant Difference Indigenous microbiota matrix-assisted laser desorption ionization time - of – flight Mass spectrometry Tandem mass spectrometry Peracetic acid Pathogenicity island Phosphate buffered saline Reactive oxygen species Microbiota with S. enterica Agona PARC 5 Standard deviation xxiii  ST APC TSA TSB UHPLC US XLD Microbiota with S. enterica Typhimurium LMFS-S-JF-001 Aerobic plate count Tryptic soy agar Tryptic soy broth Ultra-high-performance liquid chromatography United States Xylose-lysine-deoxycholate  xxiv  Acknowledgments  First and foremost, I would like to acknowledge my supervisor, Dr. Siyun Wang for being an excellent mentor throughout this project. I would like to thank her for accepting me as her student and allowing me to pursue my Master of Science in the Wang Lab of Molecular Food Safety. I thank Dr. Wang for enlarging my vision of science and providing coherent answers to my endless questions.  Secondly, I would like to thank Dr. Monika Trząskowska at the Warsaw University of Life Sciences, Poland, Ms. Carman Walking at Eatmore Sprouts & Greens Ltd., and Dr. Pascal Delaquis at the Agriculture and Agri-food Canada for introducing me to the sprout safety topic as well as the technical support and inspiring discussion on the way.  I also gratefully acknowledge my committee members, Dr. Vivien Measday and Dr. Pascal Delaquis, for their innovative ideas and probing questions. Your feedback has been very helpful for improving my experimental design and result interpretation.  To all members of the Wang lab, thank you so much for your help and support in the last two years. I really appreciate lab tips and professional guidance from Karen Fong, Justin Falardeau, Dr. Patricia Hinstong, and Dr. Aljosa Trmcic. Thank you Huihui Chen, Catherine Wong and Yvonne Ma for being my labbies and riding this MSc. journey together. Technical support from Alfred Ke and thesis writing guidance from Dr. Kristie Keeney are gratefully acknowledged. xxv  I also would like to offer my enduring gratitude to the faculty, staff and my fellow students at UBC, who have inspired me to continue my work in this field. Especially, I would like to thank Dr. Xiumin Chen at Jiangsu University, China, Dr. David Kitts at UBC Food Science and Dr. Emily Drummond at UBC Botany for introducing me to academic research.  Thank you to Organic Agriculture Centre of Canada and Agriculture and Agri-Food Canada's Growing Forward 2 Policy Framework for funding that made this project possible.  Finally, I owe particular thanks to my parents, whose have supported me throughout my years of education, both morally and financially. Also, to my fiancé, Leonel Yuan, I am especially appreciative of your continued encouragement, support, and love. xxvi  Dedication  I dedicate this work to my mom, Lili Zhu. Without her love, unselfish support and example, the completion of this work would not have been possible.1  Chapter 1: Introduction and Literature Review  1.1 Introduction The most critical aim of food microbiology has always been to ensure food safety. Nowadays, consumers prefer fresh, exotic and instantly available food products (Law, Ab Mutalib, Chan, & Lee, 2015). Food ingredients in finished food products often originate from various geographic origins around the world and there is a complex manufacturing and distribution chain between the farm and the consumer (Ercsey-Ravasz, Toroczkai, Lakner, & Baranyi, 2012). As a result, foodborne pathogens can enter the global food supply network at multiple nodes (Law et al., 2015). Additionally, conventional interventions that ensure long-lasting microbial stability, such as thermal processing, are no longer preferred by the food industry due to the compromised organoleptic quality after processing (Fu & Li, 2014). All these new changes, however, favour microbial growth and potentially contribute to increased food safety risk. In outbreaks of foodborne illness, foodborne pathogens can lead to substantial illness, hospitalizations and even death.  1.2 Salmonella enterica 1.2.1 Microbial characteristics Salmonellae are Gram-negative, rod-shaped, facultative anaerobes that cause a foodborne infection known as salmonellosis (Jay et al., 2005). Salmonella is a human pathogen of global importance, causing 93.8 million enteric infections and 155,000 diarrheal deaths worldwide annually (Majowicz et al., 2010). This bacterium is also a common zoonotic pathogen, causing enterocolitis, fever, or asymptomatic carriage in a wide range of warm-blooded animals. These 2  infected animals can serve as pathogen reservoirs for human infection. Most Salmonellae are motile with peritrichous flagella and can produce hydrogen sulfide from thiosulfate and sulfite (Barrett & Clark, 1987; Kwon, Park, Birkhold, & Ricke, 2000). Selective agars, e.g. xylose-lysine-deoxycholate (XLD), that differentiate Salmonella from other microorganisms are formulated based on its characteristic hydrogen sulfide production. The generation of hydrogen sulfide leads to formation of colonies with black centers on the surface of XLD agar (Zajc-Satler & Gragas, 1977). Salmonella is generally considered to be unable to ferment lactose, however, the ability to ferment this sugar is occasionally observed, notably in environmental isolates (Blackburn & Ellis, 1973). Salmonellae tend to have a broad host range, but some serotypes possess unique pathogenicity profiles that may be more host-restricted. For instance, typhoidal Salmonella, including serotypes Typhi , Paratyphi A, Paratyphi B and Paratyphi C, are particularly well-adapted to humans and have no other known hosts (Tauxe & Pavia, 1998), while serotype Pullorum is highly host-adapted to chickens and causes the systemic pullorum disease in poultry (Buxton & Fraser, 1977).  1.2.2 Classification The genus Salmonella consists of only two species, S. bongori and S. enterica. Individual strains of S. enterica are classified using the subspecies and serotype systems. Six subspecies of S. enterica have been identified thus far based on their biochemical characteristics and DNA-DNA hybridization patterns (Brenner, Villar, Angulo, Tauxe, & Swaminathan, 2000). Subspecies I, enterica, count for the majority of the mammalian pathogens, including those that cause disease in humans. Almost all clinical isolates belong to one of 50 serotypes within S. enterica subsp. enterica. S. enterica can be separated into serotypes based on the antigens present on the 3  bacterial cell membrane (O antigen) and a slender threadlike part of the flagella (H antigen). More than 2,500 serotypes have been identified so far varying in both their host range and disease outcomes (Uzzau et al., 2000). Regardless of serotype, all hosts are typically infected orally, via ingestion of contaminated food or water.  Typhimurium and Enteritidis are the top two serotypes causing the most cases of illness in humans worldwide (Mattick et al., 2000) and are also capable of colonizing the intestines of a wide variety of animals (Field, 1958). Serotype Enteritidis has been frequently isolated from animal source food, especially poultry (Angulo & Swedlow, 1998), while human illness caused by S. Typhimurium has been predominantly linked to bovine meat, porcine meat, and poultry (European Centre for Disease Prevention and Control, 2011).  Host-restricted S. enterica serotypes are more commonly associated with invasive, severe systemic infections in their specialized hosts whereas serotypes with broad host ranges tend to cause local, self-limiting gastroenteritis. Serotype Typhi, responsible for human typhoid fever, demonstrates host-specialization. Infection of S. Typhi leads to a systemic disease characterized by prolonged fever, generalized pain, diarrhea, and general malaise in humans (Ohl & Miller, 2001); no similar symptoms were observed after oral infection of rabbits, guinea pigs, and monkeys (Edsall et al., 1960). In contrast, serotypes Typhimurium and Enteritidis infect a broad range of host animals, causing localized self-limiting enterocolitis in humans as well as calves, poultry, and swine (Ohl & Miller, 2001). S. enterica serotype Typhimurium causes a systemic typhoid-like disease in susceptible mouse strains that serves as an experimental model for human typhoid fever.  4  1.2.3 Pathogenicity After the consumption of contaminated food or water, the host is most commonly infected with Salmonella via ingestion and entry of the enterocytes in the small intestine (Hurley, McCusker, Fanning, & Martins, 2014; van der Heijden & Finlay, 2012). The pathogenesis of S. enterica relies on many host and pathogenic factors in humans and warm-blooded animals. Host factors include the host species, sensors such as those in the intestinal epithelium, and innate and adaptive mucosal immunity. Pathogenic factors include whether the pathogen is host-specific or host-adapted, pathogenicity islands (PAIs) on the chromosome, virulence-encoding sequences on plasmids and the presence of Salmonella bacteriophages (Arya et al., 2017).  All S. enterica serotypes possess two common PAIs, known as Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2), which are located in proximity to tRNA genes as part of the chromosome and may be excisable. SPI-1 and SPI-2 encode two different type III secretion systems (T3SS), T3SS-1 and T3SS-2, respectively (Nieto et al., 2016). A total of 23 SPIs have been identified so far and some PAIs, other than SPI-1 and SPI-2, are found only in certain S. enterica serotypes (Hayward et al., 2014).  As discussed above, non-typhoidal Salmonella, such as S. enterica Typhimurium, typically induce a self-limiting inflammatory diarrhoea. First, once the Salmonella reaches the intestines, its flagella bring it in close proximity to the intestinal epithelium (van der Heijden & Finlay, 2012). Next, mediated by the T3SS-1, Salmonella produces a specialized syringe-with-needle complex to bind to non-phagocytic epithelial cells, form a translocon pore in the epithelial cell membrane, and inject virulence proteins (effectors) into host cells (Bergeron et al., 2013). 5  Salmonella invades by triggering cytoskeletal rearrangement that leads to membrane ruffles that engulf bacteria (Galán, 2001). Once inside the host cell, the Salmonella cell reside within a modified lysosome called a Salmonella-containing vacuole (SCV), which protects it from degradation (Kidwai et al., 2013). Salmonella proliferates in the SCV during early biogenesis and maturation stages and continuously injects SPI-1 T3SS effectors. The SopE effectors, SopE2 and SopB stimulate the synthesis of Rho-family GTPases. Rho-family GTPases activate mitogen-activated protein kinase (MAPK), NF-κB signalling, and the production of pro-inflammatory cytokines, such as interleukin (IL)-8, IL-1β and tumor necrosis factor (TNF)-α. Eventually, with the accumulation of polymorphonuclear leucocytes (PMNs), acute intestinal inflammation was induced (Bruno et al., 2009; Lei, Wang, Xia, & Liu, 2016). The infected gut epithelial cells express a sensor, a caspase-1 inflammasome, that triggers the production of IL-18. IL-18 is a pro-inflammatory cytokine that leads to the advanced recruitment of natural killer cells into the infected mucosa, possibly via a perforin-mediated cytotoxic response. This innate immune response leads to the expulsion of infected epithelial cells into the gut lumen, causing more severe inflammation characterized by crypt abscesses, tissue damage and leukocyte infiltration (Müller et al., 2016).  The intracellular survival and replication of Salmonella in the SCV were facilitated by the SPI-2 encoded T3SS-2. Approximately 30 effectors with virulence-associated functions are produced by T3SS-2. These effectors are required for forming the Salmonella-induced filaments and maintaining the integrity of the SCV membrane, preventing fusion of the SCV with lysosomes, and avoiding exposure to and degradation of the bacteria by reactive oxygen and nitrogen species (ROS and RNS) (Joris van der, Bosman, Reynolds, & Finlay, 2015). T3SS-2 effectors also play 6  important roles in interfering with antigen presentation on dendritic cells, facilitating the efficient proliferation of Salmonella in macrophages, and delaying host cell death by apoptosis following intracellular infection (Hurley et al., 2014; Kidwai et al., 2013; van der Heijden & Finlay, 2012).  1.2.4 Produce-related foodborne outbreaks Globally, non-typhoidal Salmonella infection cases have been estimated at 93.8 million annually (Majowicz et al., 2010). In Canada, it has been estimated that 19 people were infected with Salmonella per 100,000 population in 2000 - 2001 (Thomas et al., 2006), but this rate has increased to 21.5 cases per 100,000 people in 2016 (BC Centre for Disease Control, 2017). Salmonella is the number 3 cause of bacterial foodborne disease overall in Canada, contributing to 5% of illnesses, 24% of hospitalizations, and 16% of the deaths resulting from foodborne illness (Public Health Agency of Canada, 2016). The most commonly observed S. enterica serotypes associated with foodborne outbreaks in Canada are Enteritidis, Heidelberg, and Typhimurium (Public Health Agency of Canada, 2015), with Enteritidis accounting for approximately half of illnesses reported in British Columbia in 2014 - 2015 (BC Centre for Disease Control, 2017).   Food-borne pathogens, such as S. enterica, are traditionally acquired by humans via the consumption of contaminated food, especially poultry and dairy products. Nevertheless, in recent years, a variety of foodborne outbreaks have been associated with produce, primarily leafy greens and sprouts. Between 1998 and 2008, approximately 50% of the cases of foodborne illness in the US were attributed to contaminated fresh and minimally processed produce, more than meat, poultry, dairy, and eggs combined (Painter et al., 2013). In the US, contaminated 7  sprouts, cantaloupe, cucumbers, peppers, tomatoes, and leafy greens have been implicated numerous times, including in multistate foodborne outbreaks that have sickened thousands (Centers for Disease Control and Prevention, 2018a). It has been concluded that fresh and minimally processed produce is the riskiest food commodity for foodborne illness nowadays with S. enterica listed as one of the most common bacterial pathogens associated with produce-related foodborne outbreaks (Doyle & Erickson, 2008). In addition to the public health burden of foodborne illnesses and short-term economic losses, increased regulatory burden and reduced consumer confidence can lead to negative long-term economic impacts on the produce farmers (Teplitski, Noel, Alagely, & Danyluk, 2012).  Currently, thermal processing and antimicrobial treatments are the most commonly used interventions in the food industry to minimize the risk of foodborne pathogens on food products. However, the production of fresh and minimally processed produce does not usually involve a pathogen-killing step, e.g. intensive heat or harsh chemicals that destroys enteric human pathogens in food. Current food safety interventions used in the produce industry focus on surface sanitation, which do not effectively reduce or eliminate irreversibly attached or internalized S. enterica from produce. S. enterica attached to plant surface cannot be effectively removed by washing and scrubbing. In addition, antimicrobial treatments involving chlorine, surfactants, and detergents reduce but do not eliminate enteric human pathogens from produce (Wiedemann, Wiedemann, & Virlogeux, 2015). The low effectiveness of chemical sanitation interventions targeting plant surfaces could be associated with the internalization capability of many foodborne pathogens, such as S. enterica, into plant tissues (Ge & Bohrerova, 2013; Poza-Carrion, Suslow, & Lindow, 2013; Wang, 2013). Irradiation has been shown to be an effective 8  method for reducing internalized pathogens in produce; however, consumer acceptance, regulatory approval, and potential negative impacts on nutritional quality impede its widespread adoption (Poza-Carrion et al., 2013). Moreover, produce is often eaten raw, which eliminates cooking as a practical strategy for ensuring food safety at the household level. With the current healthy eating trend encouraging consumption of fresh and minimally processed produce (Fabrega, Fàbrega, & Vila, 2013), and the absence of promising food safety strategies, there is a pressing need to develop novel approaches that eliminate bacteria from contaminated fruits and vegetables. More in-depth understanding of the survival mechanisms human pathogens utilize on plants, an ecological niche, is required to devise more-targeted interventions.  1.2.5 Response to stresses Generally, bacterial stress can be defined as a physical, chemical, or nutritional condition insufficiently severe to eliminate but leading to reduction and sub-lethal injury of microbes (Wesche, Gurtler, Marks, & Ryser, 2009). Some common types of stress pathogens encounter in agricultural practices and food processing include oxidative, desiccation, and heat stresses (Shepherd, Liang, Jiang, Doyle, & Erickson, 2010; Singh & Jiang, 2012; Singh, Jiang, & Luo, 2010).  1.2.5.1 Oxidative stress Although diverse chemical treatments, such as sodium hypochlorite (NaOCl), hydrogen peroxide (H2O2), and ozone (O3), are being widely used as sanitation practices in the fresh produce industry, the underlying antimicrobial mechanisms of these sanitizers is based on inducing oxidative damage in microbes (Gómez-López & Ebooks, 2012). Strong oxidising molecules, 9  including reactive oxygen species (ROS), are usually blocked by the outer membrane of gram-negative bacteria via decreased membrane permeability and bacterial survival under harsh circumstances depends on their immediate response to environmental threats. A study investigating the real-time influx of H2O2 in S. enterica led to the discovery of two novel mechanisms by which they rapidly control outer membrane permeability. When oxidative stress from ROS is present, S. enterica responses immediately by closing pores in two major outer membrane proteins, OmpA and OmpC, by forming disulfide bonds in the periplasmic domain of OmpA and TrxA, an oxidation-sensitive protein thioredoxin, respectively. In addition, Salmonella mutants with higher outer membrane permeability were more sensitive to treatment with antibiotics. The influx of ROS in S. enterica for defense against oxidative stress could be therapeutically targeted in the development of novel interventions to increase the bactericidal effectiveness of conventional antibiotics (van der Heijden et al., 2016).  1.2.5.2 Desiccation stress Desiccation stress is the limiting factor influencing the survival and persistence of bacterial pathogens in low-water-activity (low-aw) environmental habitats, such as soil, sand, and most plant surfaces (Shepherd et al., 2010). The rdar morphotype is defined as red, dry, and rough colonies that are observed with most S. enterica and E. coli isolates when grown on Congo red agar. This morphotype has been shown to play an important role on non-host desiccation resistance and survival (Zaragoza, Noel, & Teplitski, 2012), and is characterized by the expression of adhesive extracellular matrix components, including cellulose and curli fimbriae (Brandl, Cox, & Teplitski, 2013). Both rdar and non-rdar morphotypes, including the saw 10  (smooth and white) morphotype, can be found from produce, animal, and clinical sources (Solomon, Niemira, Sapers, & Annous, 2005).  In low-aw foods, S. enterica exhibits long-term survival and shows a strain-specific response. For example, S. Hartford and S. Thompson were identified as persistent in peanut oil, chia seeds, and peanuts, while S. Typhimurium was identified as the least persistent serotype (Fong & Wang, 2016). Moreover, pre-exposure to six days of desiccation in peanut oil and/or 45°C heat for three minutes significantly improved the resistance of S. enterica to 70°C heat treatment. Under the desiccation and heat treatments, serotypes Hartford and Thompson demonstrated the highest up-regulation of otsB and fadA expression which are genes actively involved in desiccation response, consistent with the persistence of these serotypes the low-aw foods (Fong & Wang, 2016).  1.2.5.3 Heat stress Heat shock is encountered when microorganisms are exposed to sub-lethal temperatures above their normal growth range (Farber & Brown, 1990). Salmonella typically encounters various thermal stresses that can be host-specific and can be part of the overall immune and physiological response to infection. Nevertheless, Salmonella has developed thermal resistance mechanisms to overcome these changes in host temperature through the induction of stress response mechanisms. Sigma factors play a leading role in bacterial thermal stress response. Moreover, differential expression of dnaK, a gene important for heat-tolerance was also observed with S. enterica strains under heat stress (Fong & Wang, 2016). Pre-exposure to thermal stress can lead to an increase in pathogenic potential through activation and regulation of genes 11  associated with thermal stress. This thermal stress response can influence the activation of genes associated with virulence and the general stress response allowing for Salmonella to overcome host defenses and establish infection (Dawoud et al., 2017). Many stressed pathogens either retain or exhibit enhanced virulence and invasion, thus making their inactivation crucial to ensure food safety (Humphrey, 2004). S. enterica Enteritidis PT4 which has enhanced heat and acid resistance has been reported to be more virulent in mice and more invasive in chickens than the non-resistant reference strain (Humphrey et al., 1996).  1.2.6 Cross-protection Bacteria typically respond to stresses by altering their cellular morphology, membrane composition, biological metabolism, and virulence. Such stressed microorganisms produce a series of stress responses that can afford cross-protection against other stresses, indicating that the adaptation to a single sub-lethal stress may also enhance the tolerance to multiple lethal stresses. In fact, bacteria, especially foodborne pathogens, are frequently exposed to environmental stresses that cross-protect them against various other stresses (Wesche et al., 2009). Bacterial cells can gradually adapt to the hostile sub-lethal conditions, causing an adaptive response accompanied by a temporary physiological change that may result in an enhanced stress tolerance (Yousef & Courtney, 2002).  The general stress response identified in most Gram-negative bacteria, such as Escherichia coli, and S. enterica, is regulated by the sigma factor, RpoS (σS) (Abee & Wouters, 1999). Up-regulation of RpoS enhances bacterial resistance to environmental stresses, such as extreme temperatures, prolonged starvation, osmotic shock, acid stress, and oxidative stress (Suh et al., 12  1999). Bacteria defective in the gene rpoS for RpoS synthesis have proved to be more sensitive to different environmental stresses (Cheville, Arnold, Buchrieser, Cheng, & Kaspar, 1996). RpoS, as the master controller of the bacterial stress response, regulates the expressions of over 50 genes involved in the responses to various stresses. These response pathways extensively overlap, and bacteria exposed to one sub-lethal stress may thus develop cross-protection against other stresses (Battesti, Majdalani, & Gottesman, 2011).  1.3 Sprouts 1.3.1 The natural microbiota of sprouted vegetables The use of sprouted seeds and beans as a source of food originated in Asia and its cultivation has spread around the world (Weiss, Hertel, Grothe, Ha, & Hammes, 2007). A wide range of seed types is now used to produce sprouted vegetables that are valued for their high content of nutrients, e.g., phytochemicals and phenolic compounds, which may have positive effects on human health (Kim, Jeong, Gorinstein, & Chon, 2012). When calculated on a solids basis, the protein content of alfalfa sprouts was higher than that of the seeds due in part to a loss of leachable sugars and seed coats during the sprouting procedure and partly due to protein synthesis. Riboflavin content of the sprouts on a dry weight basis increased to three times the original content in alfalfa seed (Kylen, Kylen, & McCready, 1975). A study of mung bean sprouts showed that the nutrient content is differentiated in sprouts compare to mature plants. For example, mean vitamin C content was 2.7-fold higher at the sprouting stage compared to mature mung beans. In contrast, the content of the phenolic compounds caffeic acid and kaempferol, and antioxidant activity was much higher in mung bean grain than in sprouts (Ebert, Chang, Yan, & Yang, 2017). 13  Recently, there has been renewed interest in the indigenous microbiota of sprouting seeds and the environment in which they are produced. Species such as Bacillus spp., Pseudomonas spp., Lactococcus spp., and indicators such as coliforms often contribute to the sprout microbiota (Asakura et al., 2016; Cai, Ng, & Farber, 1997; Pao, Khalid, & Kalantari, 2005; Weiss et al., 2007). The presence of specific genera, such as Lactococcus and Pseudomonas, may limit or even prevent the growth of pathogenic organisms (Cai et al., 1997; Matos & Garland, 2005; Weiss et al., 2007). Studies have demonstrated that germinating seeds in the presence of bacterial communities derived from used sprout water hinders the growth of foodborne pathogens (Matos & Garland, 2005; Weiss et al., 2007). Sprout-derived organisms may be better adapted to the sprouting environment and limit the presence of foodborne pathogens through competitive exclusion or through the production of antagonistic compounds (Weiss et al., 2007).  1.3.2 Sprout production practices According to the Code of Practice for the Hygienic Production of Sprouted Seeds, seed should be rinsed thoroughly before application of an antimicrobial treatment to remove dirt and increase efficacy (Canadian Food Inspection Agency, 2007). There is currently no treatment available that can guarantee pathogen-free seeds. An antimicrobial treatment for seed that can achieve a minimum 3 log reduction of the microbial pathogens of concern is required in sprout production. The CFIA-recommended treatments include 2,000 ppm of calcium hypochlorite or sodium hypochlorite for 15-20 minutes or 6-10% hydrogen peroxide for 10 minutes. After the treatment, the seeds must be thoroughly rinsed with potable water to ensure complete removal of chemical residue. Pre-germination soaking is often done to improve germination. During germination, it is critical to keep the irrigation water, environment and equipment clean to avoid potential 14  contamination. Collection of spent irrigation water after 48 h of germination and analysis for the presence of microbial pathogens is highly recommended as a means to monitor the presence of pathogens in the finished product. After harvesting, a final rinse with cold water is required to remove hulls and to further reduce microbial contamination. The sprouts should then be placed in a cold room after drying to lower the temperature as quickly as possible (Canadian Food Inspection Agency, 2007).  1.3.3 Persistence of pathogens in sprouts Unfortunately, sprouts represent a considerable public health challenge. Enteric bacterial human pathogens have been shown to survive on seeds and conditions during the germination process are conducive to microbial growth (Sadler‐Reeves et al., 2016; Symes, Goldsmith, & Haines, 2015). Numerous outbreaks of foodborne illness have been linked to sprouted vegetables, which are often consumed raw (Ding & Fu, 2016). A summary of outbreaks reported between 2000-2011 shows that alfalfa sprouts were the most common vehicle of transmission, followed by bean sprouts, including mung bean sprouts (Yang et al., 2013). S. enterica and E. coli O157:H7 were the infectious agents responsible for most of the outbreaks, although Listeria monocytogenes was also implicated in some incidents (Centers for Disease Control and Prevention, 2018a; Food and Drug Administration, 2016).  Microbial populations on sprouted vegetables are usually high. Contaminants on alfalfa seeds can multiply quickly during the germination process, where seeds are usually kept at 20ºC to 30 ºC and provides optimal conditions for bacterial growth (Matthews, 2006). In recent years, the presence of pathogens in retail sprouts has been examined in some detail. A meta-analysis 15  revealed that the incidence of pathogens in retail sprouts in the past 10 years (2001- 2017) was 1.64% in the European market (Silva, Cadavez, Teixeira, & Gonzales-Barron, 2017). Sprouts presented the highest pooled prevalence of Shiga-toxigenic E. coli (1.86%), followed by L. monocytogenes (1.50%) and Salmonella spp. (0.59%). In Australia, a total of 298 seeds samples were collected from retail stores. E. coli was detected in 14.8%, Listeria spp. in 12.3%, and L. monocytogenes in 1.3% of the samples analyzed, but Salmonella was not detected (Symes et al., 2015). In 2014, US Food and Drug Administration (FDA) tested 1,600 sprout samples obtained at three points in the production process (seeds, finished product and spent irrigation water) for three pathogens: Salmonella, L. monocytogenes and E. coli O157:H7. The prevalence of Salmonella in finished products was 0.21%. The US FDA also found that the prevalence of Salmonella in seeds (2.35%) was significantly higher than in sprouted vegetables (0.21%) or spent irrigation water (0.54%). L. monocytogenes was found in 1.28% of finished products, although there was no significant difference in the prevalence of L. monocytogenes at different points in the production process. None of the sprout and irrigation water samples tested were positive for E. coli O157:H7 (Food and Drug Administration, 2017b).  1.3.4 Current sprout safety interventions Current components in achieving the safety of sprouts include antimicrobial treatments, sample testing (seeds, finished product and spent irrigation water), and application of quality management and hazard control systems in the sprout industry. Antimicrobial treatment remains the most crucial step in the production of safe sprouted vegetable products. CFIA regulations and FDA Food Safety Modernization Act (FSMA) currently recommend the application of a sanitation step to achieve a minimum 3 log reduction of microbial pathogens of concern before 16  sprouting (Canadian Food Inspection Agency, 2007; Food and Drug Administration, 2017a). In recent years, both the scientific community and the sprout industry have been searching diligently for new interventions. Several strategies have been explored to minimize the risk associated with bacterial pathogens on seeds and beans used for sprouting, including physical (e.g. dry heat, hot water, high hydrostatic pressure, irradiation), biological (e.g. antagonistic microorganisms and their metabolites) and chemical processes (i.e. chlorine, ozone and organic acids) (Ding & Fu, 2016; Sikin, Zoellner, & Rizvi, 2013). E. coli and Salmonella were most commonly used in research, and multi-hurdle approaches or processes consisting of more than one hurdle to reduce the level of undesirable microorganisms on seed were the preferred approaches. According to Ding et al. (2013), combined treatments are more effective than individual treatments in reducing the level of undesirable microorganisms on seed.  During the last two years, several cutting-edge antimicrobial technologies were involved in the battle for sprout safety. The potential of selected bacteriophages to eliminate pathogens on sprouts has been demonstrated (Fong et al., 2017). A significant reduction of 38.3 ± 3.0% of viable Salmonella cells was observed following two hours of phage treatment. After 2 to 6 days of the sprouting process, reductions of Salmonella were also observed but were not significant compared to the control. In addition, the application of cold plasma disinfection has also been considered. S. Typhimurium populations in plasma-treated radish sprouts sharply decreased during storage (at 4 and 10°C) and fell below the limit of detection (0.5 log CFU/g) after 6 days at both temperatures. Reductions in plasma-treated sprouts may have been due to the dominance of cells that were sublethally injured during treatment. A lower temperature would also be beneficial for reducing S. Typhimurium survival on sprouts. However, these new technologies 17  have their limitations in industry-scale applications and more research is needed to promote their uptake.  Although many emerging treatments appear to be as or more effective than the 20,000 ppm of calcium hypochlorite recommended by the FDA, treating seed to reduce pathogens is unlikely, by itself, to eliminate microbial contaminants on sprouts (Ding & Fu, 2016). The effectiveness of seed sanitizing treatments varies depending on seed or sprout type, pathogen identity, sample size and the materials and methods used. The efficacy of antimicrobial treatments is also hampered by the inability of antimicrobial compounds to reach viable pathogens inside the seed and in seed surface cracks due to the thickness of the seed coat in some plant species. It has been reported that E. coli O157:H7 and several serotypes of Salmonella can become internalized and reside in inner tissues. Internalization is a food safety challenge to the sprout industry as pathogens within the tissues are unaffected by surface sanitization treatments (Shen, Mustapha, Lin, & Zheng, 2017). Additionally, seed coats are often mechanically cracked to improve water uptake and ensure more rapid germination of the seed during the sprouting process. Such cracks allow bacteria to infiltrate the seed where they escape chemical treatment and may enter into a dormant stage (Chavatte, Lambrecht, Van Damme, Sabbe, & Houf, 2016). Even if a pathogen reduction could ensure destruction of most contaminants on the seed, the sprout germination process may stimulate sufficient growth to trigger a food safety hazard.  Consequently, it is presently necessary to implement sampling strategies that incorporate all stages of the sprouting process and to implement prevention-based systems to identify and minimize hazards. One government agency-enforced step in sprout production is the sampling 18  and testing of spent irrigation water. Based on worldwide consumption levels and the assumption that contaminated sprouts that test positive will not enter the market, frequent microbiological testing of spent irrigation water during sprout production could produce similar benefits in risk reduction to that of seed treatment (Ding & Fu, 2016). Although there are continuing needs to search for more effective seed treatments and although sampling and testing programs would improve the effectiveness of risk management programs as justified by this study, the results have also suggested that the industry could rely on current technologies, seed treatment plus testing of spent irrigation water, to achieve an effective control of Salmonella contamination in sprouts (Ding & Fu, 2016). Moreover, prevention-based systems, e.g. Good Agricultural Practice (GAP) and Hazard Analysis Critical Control Point (HACCP), are also foundations of sprout safety in the industry.  1.3.5 Organic sprout production The demand for organically produced food products has increased rapidly worldwide over the past 10 years, potentially due to the notion that improved quality characteristics are linked with organic foods (Willer, 2017). Broadly organic crop production relies on organic manure and biological pest control, excluding the use of synthetic insecticides and fertilizers (Søltoft et al., 2010). Organic food is perceived to be more nutritious, fresher, better tasting, and environmentally friendlier compared to conventionally grown crops (Wang, Chen, Sciarappa, Wang, & Camp, 2008). Compared to conventional systems, organic systems may induce the synthesis of secondary metabolites in crops by increasing the amount of exposure to stresses (Manach, Scalbert, Morand, Rémésy, & Jiménez, 2004). Organic food production is carried out according to standardized principles and guidelines, which prefers mechanical, physical or 19  biological processing methods and minimizes the use of food additives and processing aids (CAN/CGSB-32.310, 2003). The CAN/CGSB guidelines allow certain chemicals to be used to maintain food quality and stability.  Taking into account these requirements, chemical disinfectants sanctioned for use in organic food production or processing and thermal treatments may find value in the formulation of combined treatments for seed disinfection. Acetic acid and hydrogen peroxide (H2O2) are among the agents that can be used for disinfection of organically produced foods (CAN/CGSB-32.311, 2003). Both have well-characterized biocidal properties and are generally recognized as safe (GRAS) (Food and Drug Administration, 2015a, 2015b). The efficacy of acetic acid and H2O2 against bacterial human pathogens on alfalfa seed has been documented. Beuchat (1997) used 6% H2O2 for 30 seconds and observed a 3.7 log CFU/g reduction in S. enterica. Higher H2O2 concentrations (8%) and longer treatment times (10 min) were applied in a study conducted by Holliday et al. (2001), although S. enterica was reduced by only 3.27 log CFU/g. Hong and Kang (2016) applied combined treatments that included dry heat at 60, 70 or 80 ˚C for 0, 12, 18 or 24 h followed by treatment with a 2% H2O2 solution (10 min). S. enterica reductions in this study ranged from 1.66 to 3.60 log CFU/g. Similar to chlorine, H2O2 primarily inhibits microorganisms through chemical oxidation of cellular components but there are considerable differences in the antimicrobial efficacy of these oxidizing agents (Finnegan et al., 2010). Inhibition of microorganisms by acetic acid is believed to involve a number of mechanisms including cell membrane disruption, interference with essential metabolic processes, stress on intracellular pH homeostasis and the accumulation of toxic anions (Brul & Coote, 1999). Peracetic acid, also known as peroacetic acid (PAA), forms upon mixing acetic acid with H2O2 (Zhao, Ting, Yu-Jie, 20  & De-Hua, 2008). A 1% peroxyacetic acid solution was equally effective as the US FDA-recommended 20, 000 ppm Ca(OCl)2 in the reduction of S. enterica on alfalfa seed when the soaked seeds were thoroughly mixed in a commercial seed washer (Buchholz & Matthews, 2007).  1.4 Metabolomics and its application in food microbiology Metabolomics, one of the most recently introduced “omics” approaches, identifies metabolites with a molecular weight of less than 1500 Da in a biological sample. Although originally the main aim of metabolomics was to fully identify all the metabolites produced by a cell or organism (Fiehn, 2002), identification of full metabolite profiles has not been possible so far because of the wide diversity of metabolites, the lack of universal operating procedures, and the lack of fully developed databases adaptive to all matrixes. Rather, recently metabolomics has been used in both a targeted and untargeted manner depending on the research question. Metabolomics is currently considered as a novel technology for obtaining information about as many metabolites as possible in the targeted biological system. Notably, metabolomics has been widely accepted as a hypothesis-generating tool in different areas of biological sciences (Pinu, 2016).  Both targeted and untargeted metabolomics have already emerged as one of the rapid, sensitive, and reliable detection methods of human pathogens at various taxonomic ranks in both clinical microbiology and food science. A typical workflow of metabolomics with a controlled-factor experimental design consists of sample pre-treatments, extraction of metabolites from both a treated and a non-treated (control) biological sample, analysis using appropriate analytical 21  instruments, and spectra matching using reference libraries. Using different statistical and chemometric approaches (e.g. discriminant analyses including principal component analysis and partial least square-discriminant analysis), potential metabolites of interest for targeted metabolomics or full spectra of metabolites for untargeted metabolomics can be identified in order to distinguish between control and treatment. Validation of biomarkers is the subsequent and one of the most important stages in targeted metabolomics, without which these biomarkers cannot be used practically (Pinu, 2016). Once biomarkers are validated, they can be used for the rapid determination of pathogens in a biological sample. Appropriate user-friendly and simpler techniques, e.g. enzymatic or colorimetric, also can be developed for the routine screening and real-time analysis of food materials by food inspectors (Pinu, 2016).  1.4.1 Instruments Mass-spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the two most commonly used detection systems capable of analyzing a wide range of metabolites. NMR possess wide applications in determining the chemical quality and authenticity of various food products (Trimigno, Marincola, Dellarosa, Picone, & Laghi, 2015), while MS has been the most reliable instrumental platform for the study of food pathogens with complex food commodities. MS has been used in combination with other separation techniques, including liquid chromatography (LC) and gas chromatography (GC) for decades. Nevertheless, GC-MS is easier and more convenient than LC-MS. In addition, GC-MS allows the analysis of volatile organic compounds (VOCs) from the headspace of a culture where spoilage and pathogenic microbes are growing. A simple, rapid, and non-invasive technique can be applied to a food sample by analyzing VOCs produced by the food to identify food pathogens and spoilage microorganisms 22  during their early growth stages. Xu et al. (2010) reported a VOC-based GC-MS metabolite profiling approach that can detect Salmonella Typhimurium in pork based on 16 metabolites as spoilage biomarkers. Other primary metabolites including sugars and amino acids (dextrose, glycine, tyrosine, and histidine) were also determined as potential biomarkers for the early detection of E. coli O157:H7 and different strains of Salmonella in ground beef and chicken (Cevallos‐Cevallos, Danyluk, & Reyes‐De‐Corcuera, 2011). Therefore, GC-MS is the most popular system for detecting the presence of pathogenic microorganisms in food with well-established commercial and in-house MS libraries available (e.g. Golm metabolome database) (Hummel, Selbig, Walther, & Kopka, 2007).  In addition to GC-MS, matrix-assisted laser desorption ionization time - of - flight mass spectrometry (MALDI - TOF MS) has emerged as one of the most rapid, sensitive, and reliable methods for the bacterial species identification. It is widely used in clinical microbiology for microbial identification at various taxonomic ranks (Sandrin, Goldstein, & Schumaker, 2013). MALDI-TOF MS used to require culturing of the bacteria to obtain a sufficient amount of cell extract for analysis. The technique has now been improved to enable direct analysis of samples (whole cells), omitting a culturing step. It has been successfully applied in the direct identification of human pathogens in blood cultures and food samples (Wattal & Oberoi, 2016). The huge number of studies classifying clinical isolates of human pathogens using MALDI-TOF MS usually include common foodborne pathogens, such as Bacillus spp., Escherichia coli, Listeria spp., Pseudomonas aeruginosa, S. enterica, Staphylococcus aureus, and Streptococcus spp. (Farfour et al., 2012; Hsieh et al., 2008; Schulthess et al., 2013). Studies targeting species identification of foodborne pathogens by MALDI-TOF MS have been carried out by several 23  research teams. Böhme and his colleagues constructed an open-source spectral library, SpectraBank, based on 58 foodborne bacterial species (Böhme et al., 2013).  1.4.2 Applications in pathogen detection and identification Untargeted metabolomics is generally suitable for identification of common bacterial foodborne pathogens at the species level. Its application in the assignment of subspecies and serotypes varies depending on the analytical instrument, bacterial classification, and the bioinformatics approaches selected for analysis (Kang et al., 2017). For instance, MALDI-TOF MS has been successfully applied to the identification of genus, species, and subspecies of S. enterica based on specific biomarker peaks (Dieckmann, Helmuth, Erhard, & Malorny, 2008; Dieckmann & Malorny, 2011). In a study reported by Kang et al. (2017), a reference database for 7 S. enterica serotypes was established using whole-cell MALDI-TOF MS. After attempting to identifying 82 clinical isolates of S. enterica using the established database, it was found that MALDI-TOF MS provided high accuracy in identification of S. enterica at the species level but was limited to the typing or subtyping of S. enterica serotypes, especially for the subtyping of S. enterica serotype Typhimurium. Serotype-specific biomarkers were not found with whole-cell MALDI-TOF MS (Kang et al., 2017). On the other hand, Ojima-Kato and his colleagues constructed a theoretically calculated mass database of S. enterica subspecies enterica consisting of 12 biomarker proteins. In combination with Strain SolutionTM proteotyping software, the constructed database achieved a serotype-level identification accuracy of 94% by MALDI-TOF MS using 116 S. enterica strains belonging to 23 serotypes. The comparison of these two studies highlights the potential positive impact of the addition of a systematic proteotyping software on the accuracy of subspecies and serotype identification (Ojima-Kato et al., 2017). 24  When applying clinical isolates of the corresponding bacterial species as reference strains in the identification of foodborne pathogens, it has been shown that even isolates from the same strain can differ significantly in their metabolic profiles due to the impact of environmental changes or the source of isolation (Jorge, 2016). Culture media can also affect the metabolic profiles of pathogens which consequently compromises the sensitivity for the differentiation at the strain level. The effect of culture media has been evaluated in the identification of 23 Listeria monocytogenes strains obtained from different dairy and non-dairy food isolates (Jadhav et al., 2015). L. monocytogenes isolates were grown on five different media and it was observed that strain identification varied with the culture media. The highest accuracy of strain identification (91%) was obtained with Agar Listeria Ottaviani Agosti (ALOA) medium and the lowest (50%) with Palcam agar (PA) medium. In addition, proteomic analysis of the mass spectra enabled differentiation of isolates from four different dairy sources (Jadhav et al., 2015).  1.4.3 Interactions within a mixed microbial culture It has been widely concluded that the metabolite profile of a pure microbial culture cannot represent that of a mixed culture, which is very distinct from a simple sum up of the metabolite profiles of the different microbial species composing the community (Sue, Obolonkin, Griffiths, & Villas-Bôas, 2011). After sensing the presence of a different microbial species in their environment, microbes change their metabolic state immediately according to the potential threat posed by the foreign species. This change in metabolic state can be detected through metabolomics, both targeted and non-targeted (Sue et al., 2011). Consequently, metabolomics has become a very informative tool for assessing the metabolic state of microbial communities both in vitro (Ponnusamy, Lee, & Lee, 2013; Sue et al., 2011) as well as in vivo (Badri, Zolla, 25  Bakker, Manter, & Vivanco, 2013; Kimes et al., 2013). However, it is crucial to any metabolomic study involving microbial communities to keep track of the microbial community dynamics in terms of its composition both qualitative and quantitatively.  1.4.4 Host-microbe interaction Metabolomics has an important tool in unraveling the inherent and intimate host-microbe relationships. Coupled with the microbial population profiles derived from culture-independent molecular techniques, this approach has been increasingly applied to non-invasive studies of the molecular mechanisms behind host-microbe interactions. With better understanding of the host-microbial associations, metabolomics demonstrated potential to elucidate the causes of various host-dependent pathologies (Marcobal et al., 2013; Ming, Stein, Barnes, Rhodes, & Guo, 2012), including human enteric infections (J. Han, Antunes, Finlay, & Borchers, 2010). The most widely studied host-microbe interaction network is that between human digestive tract and gut microorganisms. It has been found that the metabolites released by gut microbes affect the luminal environment and alter development, motility, and homeostasis of the digestive tract (J. Han et al., 2010). Therefore, insights regarding the metabolic principles coordinating gut microbiota will guide future manipulation of the gut microbiota to promote human health.   1.5 Research purpose To date, the influence of different antimicrobial seed treatments on the behaviour of S. enterica during seed germination remains unclear. Furthermore, no studies in the area have investigated the relative growth of indigenous microbiota and pathogen on sprouting seed and the impact of 26  antimicrobial treatments on their post-sanitation recovery characteristics. This study aims to bridge the research gap by illuminating any significant relationships between seed treatments, microbiota growth, and plant surface metabolites on alfalfa sprouts contaminated with S. enterica over the course of a 6-day germination.  1.5.1 Research hypotheses Four hypotheses were tested in this study: 1. S. enterica cells can recover from antimicrobial treatments on sprouting alfalfa seeds 2. The post-sanitation growth characteristics of S. enterica on sprouting alfalfa seed is strain-dependent and treatment-dependent. 3. The survival and growth of sanitizer-injured S. enterica on sprouting alfalfa seed are supported by specific metabolic pathways that are linked to stress response. 4. The survival mechanism S. enterica utilized to recover from sprouting alfalfa seed is strain-dependent and treatment-dependent.  1.5.2 Research objectives Two research objectives were identified for this study: 1. To investigate the ability of 5 S. enterica strains to grow on sprouting alfalfa seed after three different antimicrobial seed treatments. 2. To understand how colonization by S. enterica and different antimicrobial treatments affect metabolites released by sprouting alfalfa seed. 27  Chapter 2: Recovery of Salmonella enterica on Sprouting Alfalfa after Seed Sanitation  2.1 Introduction Consumption of alfalfa sprouts has increased worldwide due to its nutrient value and health benefits (Ding, Fu, & Smith, 2013). However, contaminated alfalfa sprouts have been the source of 22 outbreaks of foodborne illness in the US from 2001 to 2016 and resulted in a total of 800 illnesses, 83 hospitalizations, and 2 deaths (Centers for Disease Control and Prevention, 2018b). Sixteen of these outbreaks were associated with alfalfa sprouts contaminated with S. enterica. Unlike other fresh produce, sprout production involves a unique seed germination process that can support the growth of pathogens. Pathogens trapped in or on seeds proliferate exponentially during sprouting, resulting in a high risk of illness, since the warm and humid environment during sprouting provides perfect conditions needed for pathogens to multiply rapidly (Hong & Kang, 2016). The populations of S. enterica on naturally contaminated seeds propagated at 20°C to 30°C may increase up to 100,000-fold during a 4-day germination process and most of the growth occurs during the first 48 hours of propagation (Matthews, 2006).  To help industry minimize microbial hazards in sprouts, application of antimicrobial agents (e.g., sodium hypochlorite or hydrogen peroxide), with concentrations and durations achieving a minimum 3 log reduction, to seeds before germination is recommended as a preventative step by the Canadian Food Inspection Agency (2007). Although these treatments exceed the minimum required reduction of the pathogens immediately after sanitation, previous research has shown a 28  significant increase in the concentration of pathogenic bacteria after 48 h of germination of chlorine-sanitized seeds (Li, 2015). Currently, there is no treatment available that can guarantee pathogen-free seeds (Canadian Food Inspection Agency, 2007). Many seed treatments reduce, but do not eliminate or destroy, microorganisms of public health significance that may be present on the seeds (Food and Drug Administration, 2017a).  In addition, Bacillus spp., Pseudomonas spp., Lactococcus spp. and coliforms often contribute to the sprout microbiota (Asakura et al., 2016; Cai et al., 1997; Pao et al., 2005; Weiss et al., 2007). Studies have demonstrated that germinating seeds in the presence of bacterial communities derived from used sprout irrigation water hinders the growth of foodborne pathogens (Matos & Garland, 2005; Weiss et al., 2007). Sprout-derived organisms may be better adapted to the sprouting environment and limit the presence of foodborne pathogens through competitive exclusion or through the production of antagonistic compounds (Weiss et al., 2007).  Current research is focused on developing effective pre- and post-harvest decontamination treatments and optimizing the efficacy of antimicrobial treatments in eliminating pathogen contaminants from sprouts. To fully understand their persistence, it is therefore critical to understand the survival mechanisms of pathogens in this environment after antimicrobial treatment. However, recovery of injured pathogen cells during seed germination and the interaction between S. enterica and sprouts’ indigenous microbiota have not been given much attention (Sikin et al., 2013). The objective of this study is to investigate the ability of 5 S. enterica strains to grow on sprouting alfalfa seed after three different antimicrobial seed treatments. 29  2.2 Material and methods 2.2.1 Bacterial strains Five strains representing four serotypes of S. enterica were used in this study (Table 2.1). Strains were stored at -80ºC in Brain-Heart-Infusion broth (Becton, Dickinson and Co., East Rutherford, NJ, US) supplemented with 20-25% glycerol. Working stocks were maintained on Tryptic Soy Agar (TSA) (BD Canada, Mississauga, ON, Canada) at 4 ºC for a maximum of one month.  Table 2.1 Identification, serotypes, origins and stock sources of S. enterica used in this study. Identification Serotype Isolation origin Stock source PARC 5 Agona Alfalfa seed Summerland Research and Development Centre, Agriculture and Agri-Food Canada, Canada FSL S5-517 Agona Human Cornell University, US LMFS-S-JF-001 Typhimurium Irrigation water University of British Columbia, Canada LMFS-S-JF-005 Enteritidis Irrigation water University of British Columbia, Canada LMFS-S-JF-009 Daytona Irrigation water University of British Columbia, Canada  2.2.2  Alfalfa seeds Certified organic alfalfa seed was obtained from Mumm’s sprouting seeds (Parkside, SK, Canada) and stored in the dark at room temperature (21.5 ± 0.5°C). To test for the presence of S. enterica, a mixture of 10 g alfalfa seed per batch and 90 mL Buffered Peptone Water (BPW) (BD Canada, Mississauga, ON, Canada) was incubated at 37°C and 170 rpm for 18 ± 2 h, after which 100 µl of the enriched culture was spread onto xylose lysine deoxycholate (XLD) agar 30  (BD Canada, Mississauga, ON, Canada) in triplicate. Plates were incubated at 37°C for 24 ± 2 h (Thermo Fisher Scientific, Waltham, MA, US). The purchased alfalfa seed tested negative for S. enterica.  2.2.3 Seed Inoculation Each strain of S. enterica was cultured at 37°C on TSA (BD Canada, Mississauga, ON, Canada) for 24 ± 0.25 h. A single colony of each S. enterica strain was used to inoculate 10 mL Tryptic Soy Broth (TSB) (BD Canada, Mississauga, ON, Canada) which was incubated at 37°C for 18 ± 0.25 h with agitation (170 rpm) to achieve a cell density of approximately 8 log CFU/ml. The overnight culture was spun in a centrifuge at 3000 rpm for 10 min at 22°C; the supernatant was discarded, and the pellet was re-suspended in 10 mL of a 0.85% saline solution. Subsequently, 500 µL aliquots from each culture were added to 9.5 mL 0.85% saline and 10 mL of the diluted culture were added to 100.0 ± 0.1 g alfalfa seeds in a 500 mL sterile Schott bottle, 5 mL at a time with the seeds being stirred with a sterile spatula between each addition. The seeds were mixed by shaking vigorously for 1 min, spread onto sterile aluminum foil and dried in a biological safety cabinet (Esco, Portland, OR, US) for at least 2 h under constant airflow. The air-dried inoculated seeds were mixed well and stored in Whirl-PakTM stand-up bags at 22.0 ± 0.5 °C and used within one week.  2.2.4 Antimicrobial treatments Alfalfa seeds inoculated with each S. enterica strain were subject to three antimicrobial treatments: chlorine (CLO) treatment using 5,000 ppm sodium hypochlorite (NaClO) (2.5%, Ricca Chemical Company, Arlington, TX, US) as active ingredient and a soaking duration of 20 31  minutes (mins), 8% hydrogen peroxide (HPO) (30%, BDH, VWR, Edmonton, Alberta, Canada) treatment for 10 mins, and a treatment compliant with organic production practices (HPA) involving a 10-min soak in water heated to 50°C followed by a 2% hydrogen peroxide - 0.1% acetic acid (white vinegar, 5% acetic acid by volume, Heinz Company, Glenview, IL, US) solution (Table 2.2). Twenty-five grams of alfalfa seeds were weighed into a sterile 500 mL glass bottle and 125 mL of the treatment solution or sterile water (Control, CTL) were added to the bottle at each step. For the HPA treatment, 125 mL preheated sterile distilled water at 50°C was first added to the seeds and the bottle was immediately transferred to a 50°C water bath. After 10 min the bottle was removed, and the water was decanted. For all three antimicrobial treatments, the treatment solution in Step 2 was added by diluting stock chemical solutions with sterile distilled water, with distilled water added to the bottle first. Each bottle was shaken vigorously for 10 seconds every 2 min after the antimicrobial solution was added and the caps were loosened after each shaking. After the exposure duration was reached, the contents of each bottle were transferred to a sterile funnel fitted with a folded sterile Whatman #4 filter paper on top of a 250 mL Erlenmeyer flask to remove the antimicrobial solution. The seeds were immediately rinsed with 125 ml sterile distilled water at room temperature (21.5 ± 0.5°C) and a sterile spatula was used to promote filtration by very gently stirring the bottom of the filter paper cone.    32  Table 2.2 Steps, chemicals and exposure durations used for the antimicrobial treatment applied in this study. N/A means not applicable. Seed treatment Step 1: Heat exposure Step 2: Chemical exposure Step 3: Rinsing CTL N/A N/A N/A CLO N/A 5,000 ppm NaClO for 20 minutes Yes HPO N/A 8% H2O2 for 10 mins Yes HPA 50°C sterile distilled water for 10 mins 2% H2O2 and 0.1% acetic acid for 10mins Yes   2.2.5 Sprout germination Immediately after seed treatment, 20.00 ± 0.05 g treated seeds or untreated CTL seeds were placed into a sterile pipette tip box underlined with a sterile gauze pad and 20 mL sterile distilled water was added to each box. Boxes with the lids closed were stored in the dark at 22.0 ± 0.5°C for 6 days, and 5 mL sterile distilled water was added to each box at 24 and 48 h of germination and every day henceforth.  2.2.6 Microbiological analyses Each box of sprouting seed was sampled at least 8 times over 6 days and at least 6 times within the first 24 h of germination, including immediately after treatment (t = 0 h) and 24 h after 33  treatment (t = 24 h). The minimum time interval between 2 adjacent sampling points was 4 h. At each sampling interval, 3 g seeds were taken from each box and placed in a 50 mL sterile centrifuge tube using a sterile spatula. Twenty-seven mL phosphate-buffered saline (PBS) (Amresco Inc., Solon, OH, US) was added to each tube (10-1 dilution). After mixing on a Vortex for 15 seconds, serial dilutions of the supernatant were prepared with PBS, and 50 µL of the diluted samples were applied to xylose lysine deoxycholate (XLD) (BD Canada, Mississauga, ON, Canada) agar in duplicate using an Easy Spiral Pro spiral plater (Interscience Laboratories Inc., Woburn, MA, US). Duplicate 1 mL aliquots of the 10-1 dilution at t = 0 h were also applied to the surface of XLD agar plates with a spreader. The XLD agar plates were incubated at 37°C for 24 ± 2 h.  Additionally, aerobic plate counts (APCs) on sprouting alfalfa seed were determined after 0, 8, 16, 24, 32 h and 6 days of germination. Samples (3 g) were diluted as described above. After mixing with a vortex, serial dilutions were prepared with PBS and 50 µL aliquots were applied to TSA agar using the Easy Spiral Pro spiral plater. The TSA agar plates were incubated at 37°C for 24 ± 2 h.  2.2.7 Statistical analysis The populations of indigenous aerobic bacteria were determined by subtracting APCs with the S. enterica cell count measured with the same replicate at the same time point. Growth curves for S. enterica and indigenous aerobic bacteria were generated from data derived from three independent trials performed with each combination of S. enterica strain and seed treatment. Cell counts of S. enterica below the detection limit were assigned a value equal to the detection limit 34  (1 log CFU/g for t = 0 h and 2.30 log CFU/g for other time points). The log reduction achieved with each treatment was calculated by subtracting the population density of S. enterica immediately after treatment (N0) from the N0 of the corresponding CTL group. The S. enterica growth curves within the first 48 h of germination (0 – 48 h for CTL and 8 - 48 h for the antimicrobial-treated samples) and the growth curves of indigenous aerobic bacteria within the first 32 h of germination were fitted with the Baranyi and Roberts Model (Baranyi & Roberts, 1994) using online DMFit software (Combase, the University of Tasmania and the USDA Agricultural Research Service, US). Since most of the S. enterica cell counts in the antimicrobial-treated samples at 0 and 4 h of germination were below the detection limit, the t= 0 h and t= 4 h timepoints of all antimicrobial-treated samples were not included in the statistical model fitting. Maximum growth rate (μmax), and maximum population density at the stationary phase (Nmax) were calculated using the Baranyi and Roberts Model (Baranyi & Roberts, 1994). N0, μmax and Nmax, as well as cell count at 24 h of germination (N24) and 6 days of germination (N6d) were examined by one-way ANOVA followed by Tukey’s Honest Significant Difference (HSD)) test (overall α=0.05) using Minitab 17 (Minitab Inc., State College, PA, US).  2.3 Results and discussion 2.3.1 Effectiveness of antimicrobial treatments against S. enterica Calculated population reductions of 5 S. enterica strains achieved with the 3 antimicrobial treatments are summarized in Table 2.3. After seed inoculation, a population density of 4-5 log CFU/g S. enterica was observed on air-dried seeds with serotypes Agona, Enteriditis, and Daytona. Populations of surviving S. enterica Typhimurium (3.41 ± 0.03 log CFU/g) were significantly lower than those of the other serotypes on dried alfalfa seed, approximately 4.5 log 35  CFU/g. Serotype Typhimurium has previously been reported to be less resistant to desiccation stress than other common S. enterica serotypes, such as Agona and Enteriditis (Santillana Farakos, Hicks, Frye, & Frank, 2014). Populations of all 5 S. enterica strains decreased to levels below or equal to the detection limit (10 CFU/g) after each antimicrobial treatment  (Table 2.3) and the calculated population reductions for all strains except S. enterica Typhimurium reached the minimum level (3 logs) required by current seed disinfection guidance (Canadian Food Inspection Agency, 2007). S. enterica was not detected (< 10 CFU/g) in seed inoculated with S. enterica Typhimurium immediately after any of the treatments. Notably, the 2.4-log reduction of S. enterica Typhimurium population after antimicrobial treatments could be underestimated due to the low initial Salmonella count before treatment. The efficacy of the selected NaClO and H2O2 treatments against S. enterica on sprouting seed has been documented in the CFIA regulations (Canadian Food Inspection Agency, 2007). Treatment with 8.7% (v/v) acetic acid at 55ºC for 2-3 h reduced the population of Salmonella inoculated on alfalfa by more than 5.0 log CFU/g (Nei, Latiful, Enomoto, Inatsu, & Kawamoto, 2011). In terms of the HPA treatment, peracetic acid, also known as PAA, forms upon mixing acetic acid with H2O2 (Zhao et al., 2008). In addition to H2O2 and acetic acid, a maximum of 0.13% peracetic acid may also be generated in the mixed solution and act as active antimicrobial compounds. It has been reported that 1% PAA solution was equally effective as the US FDA-recommended 20, 000 ppm Ca(OCl)2 for the reduction of S. enterica on alfalfa seed when the soaked seeds were thoroughly mixed in a commercial seed washer (Buchholz & Matthews, 2017).  36  Table 2.3 N0 and calculated population reduction (log CFU/g) for 5 different S. enterica strains on alfalfa seeds subjected to 3 different antimicrobial treatments. Results are summarized by mean ± standard deviation (SD) for the bacterial strains tested in triplicates. Means with the same letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with less than (<) have at least one replicate with a population of S. enterica below the detection limit (10 log CFU/g). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment.  S. enterica strains Control CLO HPO HPA N0 N0 Reduction N0 Reduction N0 Reduction Agona (PARC 5) 4.59 ± 0.36a <1.00 ± 0.00a >3 <1.03 ± 0.06a >3 <1.38 ± 0.35a >3 Agona (FSL S5-517) 4.59 ± 0.14a <1.18 ± 0.31a >3 <1.00 ± 0.00a >3 <0.93 ± 0.11a >3 Enteriditis (LMFS-S-JF-005) 4.67 ± 0.40a <0.90 ± 0.17a >3 <1.53 ± 0.68a >3 <1.00 ± 0.00a >3 Daytona (LMFS-S-JF-009) 4.22 ± 0.44a <1.23 ± 0.40a >3 <1.30 ± 0.30a >3 <1.26 ± 0.45a >3 Typhimurium (LMFS-S-JF-001) 3.41 ± 0.03b <1.00 ± 0.00a >2.4 <1.00 ± 0.00a >2.4 <1.00 ± 0.00a >2.4 37  2.3.2 Post sanitation behaviour of S. enterica on sprouting alfalfa seed 2.3.2.1 Baranyi and Roberts Model fitting and the diauxic growth of S. enterica Typhimurium The Baranyi and Roberts Model without lag phase was fitted to data derived from the 0 to 48 h germination period for the control (CTL) and the 8 to 48 h germination period for seed subjected to the antimicrobial treatments (CLO, HPO, and HPA) (Baranyi & Roberts, 1994). A R2 coefficient of over 0.9 was achieved with all the growth curves, except for S. enterica Typhimurium (Figures A.1 – A.4). The R2 coefficient of the S. enterica Typhimurium growth curves ranged from 0.7 to 0.9 (Figure A.5). The Baranyi and Roberts Model without lag phase consists of an exponential phase with a maximum growth rate (μmax), and a stationary phase with a maximum cell count (Nmax). However, diauxic growth characteristics were observed with the S. enterica Typhimurium strain on both CTL and antimicrobial-treated seed (Figure A.5). Diauxic growth is typically observed in chemically defined media containing two sugars where cells first grow exponentially using the preferred sugar, and enter a lag phase which followed by a second exponential growth phase fueled by the second sugar (Roseman & Meadow, 1990). In this case, the S. enterica Typhimurium strain reached the end of the first exponential phase at 12 h germination and the second exponential phase started at approximately 20 h germination. Growth parameters for S. enterica Typhimurium were calculated using the Baranyi and Roberts Model without lag phase to allow comparison with the other experimental strains (Baranyi & Roberts, 1994).     38  2.3.2.1.1 μmax of S. enterica on sprouting alfalfa seed In addition to the lowest N0 after seed inoculation, the S. enterica Typhimurium strain also possessed the lowest μmax after sanitation treatment (Table 2.4). Therefore, the recovery of S. enterica Typhimurium after treatment was the slowest among all 5 strains, especially after antimicrobial seed treatment (Figure A.6). The μmax of at least one of the 2 Agona strains was significantly higher than that of S. enterica Typhimurium after all treatments. The μmax of Agona PARC 5 was the highest among all strains in CTL samples, while there was no significant difference among the other 4 strains. PARC 5 was isolated from alfalfa seed and its fast growth on sprouting alfalfa seed highlight the potential risk of this particular strain to the sprout industry.  The antimicrobial treatments exerted significantly different effects on the μmax of some S. enterica strains during post-sanitation growth on sprouting alfalfa seed. Specifically, the CLO treatment increased the μmax of the 2 S. enterica Agona strains compared to the CTL and compared to the HPO and HPA treatment (Table 2.4). The HPO treatment significantly increased the μmax of the S. enterica Enteriditis strain compared to CTL or HPA. Disinfection of seed with CLO is the most commonly used and government-recommended antimicrobial treatment in the US and Canada (Canadian Food Inspection Agency, 2007). The strong recovery of S. enterica Agona after the CLO treatment suggested that it may contribute to the persistence of serotype Agona in outbreaks linked with sprouts contaminated with Salmonella.    39  Table 2.4 μmax (log CFU/g/h) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a - d) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A, B) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 0.25 ± 0.02aB 0.38 ± 0.01aA 0.22 ± 0.04abB 0.23 ± 0.04abB Agona (FSL S5-517) 0.18 ± 0.00bB 0.32 ± 0.04abA 0.24 ± 0.05aAB 0.26 ± 0.05aAB Enteriditis (LMFS-S-JF-005) 0.16 ± 0.01bB 0.21 ± 0.03cdAB 0.32 ± 0.08aA 0.20 ± 0.04abB Daytona (LMFS-S-JF-009) 0.17 ± 0.03bA 0.24 ± 0.05bcA 0.19 ± 0.06abA 0.24 ± 0.00aA Typhimurium (LMFS-S-JF-001) 0.16 ± 0.01bA 0.13 ± 0.04dA 0.10 ± 0.00bA 0.14 ± 0.01bA   2.3.2.1.2 Nmax of S. enterica in sprouting alfalfa seed The population of all 5 S. enterica strains increased from below the detection limit (< 10 log CFU/g) immediately after antimicrobial treatment to maximums of 5.8 – 7.5 log CFU/g after 24 -40  32 h of germination (Table 2.5). The maximum population of S. enterica can reach to 108 CFU/g during sprouting (Gandhi & Matthews, 2003). To determine the basis for the growth of S. enterica on developing alfalfa sprouts, the growth of eight different S. enterica strains isolated from contaminated alfalfa seed was compared to the growth of strains from non-plant sources and the growth of distinct serotypes. The results showed that all the S. enterica strains proliferated up to potentially clinically significant populations (6 – 7 log CFU/g fresh weight), irrespective of strain isolation source (plant or non-plant) or virulence (Howard & Hutcheson, 2003).  In CTL samples, S. enterica Agona PARC 5 achieved the highest Nmax (8.67 ± 0.06 log CFU/g) among all 5 strains while the Typhimurium strain had the lowest Nmax (6.68 ± 0.56 log CFU/g) within the first 48 h of germination. Similarly, the Nmaxs of the 2 Agona strains were significantly higher than that of the Typhimurium strain in seed treated with CLO. However, there was no significant difference between the Nmax of the 5 tested strains in HPO or HPA treated samples.  The negative impact of the HPO treatment on Nmax was observed in seed inoculated with the 2 Agona strains as well as the Enteriditis strain. The HPO treatment applied to the seeds before sprouting limited the rapid recovery and growth of S. enterica Agona and Enteriditis. Additionally, the other 2 antimicrobial treatments (CLO and HPA) also decreased Nmax in sprouts germinated from seeds inoculated with Agona PARC 5, Enteritidis and Daytona compared to CTL. Interestingly, there was no significant effect of antimicrobial treatment on the both the Nmax and μmax of the Typhimurium strain. The diauxic growth of Typhimurium after 41  sanitation stress on sprouting alfalfa seed within the first 48 h of germination was independent of seed treatment type.  Table 2.5 Nmax (log CFU/g) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a-c) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A-C) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 8.67 ± 0.06aA 7.48 ± 0.07aB 5.95 ± 0.47aC 6.98 ± 0.30aB Agona (FSL S5-517) 7.80 ± 0.14bA 7.34 ± 0.43aA 5.78 ± 0.45aB 6.85 ± 0.45aA Enteriditis (LMFS-S-JF-005) 7.56 ± 0.13bA 6.75 ± 0.26abB 5.89 ± 0.31aC 6.95 ± 0.36aAB Daytona (LMFS-S-JF-009) 7.42 ± 0.15bA 6.47 ± 0.35abB 6.72 ± 0.32aAB 6.49 ± 0.49aB Typhimurium (LMFS-S-JF-001) 6.68 ± 0.56cA 6.01 ± 0.80bA 6.14 ± 1.03aA 6.01 ± 0.56aA  42  2.3.2.2 N24 of S. enterica on sprouting alfalfa seed S. enterica populations increased dramatically from below detection limit (<10 CFU/g) to 4 - 9 log CFU/g after 24 h of germination. The populations of S. enterica on naturally contaminated seeds propagated at 20°C to 30°C increased up to 100,000-fold within 24 h and most of the growth occurred during the first 48 h of propagation (Matthews, 2006). In this study, the S. enterica counts at 24 h were compared among 5 strains and 4 treatment types including CTL (Table 2.6). Due to its fast growth rate on sprouting alfalfa seed, Agona PARC 5 achieved the highest cell count, 8.96 ± 0.11 log CFU/g, among all strains in the CTL samples. Among all antimicrobial-treated samples, the N24 populations of S. enterica Typhimurium were significantly lower than the other 4 strains, which is consistent with the low maximal growth rate (μmax) of S. enterica calculated by the Baranyi and Roberts model (Baranyi & Roberts, 1994).  The population of S. enterica at 24 h of germination is treatment-dependent. HPO had the most significant negative effect on the S. enterica count, followed by HPA and CLO. The cell counts of S. enterica after HPO were significantly lower than CTL in all 5 strains. No significant difference was observed among the 3 types of antimicrobial treatments in samples colonized by Daytona and Typhimurium, but they were all lower than the corresponding CTL.   43  Table 2.6 N24 (log CFU/g) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized as mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a-c) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A-C) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 8.96 ± 0.11aA 7.58 ± 0.15aB 6.02 ± 0.64aC 6.24 ± 0.47aC Agona (FSL S5-517) 7.85 ± 0.09bA 7.04 ± 0.48abAB 5.85 ± 0.48aC 6.31 ± 0.51aBC Enteriditis (LMFS-S-JF-005) 7.62 ± 0.22bA 7.03 ± 0.50abA 5.79 ± 0.62aB 6.55 ± 0.42aAB Daytona (LMFS-S-JF-009) 7.52 ± 0.31bA 6.13 ± 0.36bB 6.08 ± 0.34aB 6.05 ± 0.24aB Typhimurium (LMFS-S-JF-001) 7.11 ± 0.81bA 5.09 ± 0.27cB 4.14 ± 0.45bB 4.75 ± 0.28bB   2.3.2.3 N6d of S. enterica on alfalfa sprouts After 6 days of germination, S. enterica populations on alfalfa sprouts achieved 4.5 -7.6 log CFU/g on sprouts germinated from CTL seeds and 3.9 – 7.1 log CFU/g on sprouts germinated 44  from antimicrobial-treated seeds (Table 2.7). S. enterica populations decreased 0 – 2.2 log CFU/g in N6d compared to Nmax, but the number of S. enterica present on the finished products (N6ds) were still above the infectious dose for non-typhoidal Salmonellosis (103 bacilli) (Public Health Agency of Canada, 2011). A study investigating the internalization of S. enterica into alfalfa seeds during germination found significantly larger S. enterica populations on the sprout cotyledon and seed coat tissues compared to sprout stems (D. Liu, Cui, Walcott, & Chen, 2018). After absorbing enough water and swelling to break the seed coat, the first part of the seedling to emerge is the root radicle (Undersander, Hall, Vassalotti, & Cosgrove, 2011).  The decrease in S. enterica population after 48 h could be linked to the significant increase in stem length observed between 48 and 144 h (6 days). Additionally, amino acids are released from sprouting seed with peaks of amino acid concentrations at either 24 h or 48 h, followed by a decline after 48 h of germination (Kwan, Pisithkul, Amador-Noguez, & Barak, 2015). Depletion of nutrients in alfalfa exudates can also lead to the decline in S. enterica populations between 48 h and 6 days.  The N6d of the S. enterica Typhimurium strain (4.46 ± 0.52 and 3.88 ± 0.81 log CFU/g respectively) after the CTL and HPA treatments was significantly lower than those of the other strains. There was no significant difference among the 5 tested strains in the CLO and HPO treated samples. The HPO treatment had the largest negative impact on the N6d of all the strains except S. enterica Typhimurium, followed by the HPA treatment which also had an adverse effect on the N6d of the 2 S. enterica Agona strains. The unique diauxic and slow growth observed with the S. enterica Typhimurium strain indicates that it may utilize different survival mechanisms that are not dependent on the presence and type of antimicrobial treatment applied 45  to the seeds prior to germination, or this strain has a slower growth rate and is therefore less affected by the treatments.  Table 2.7 N6d (log CFU/g) of 5 S. enterica strains on sprouting alfalfa seed after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A-C) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 7.55 ± 0.12aA 7.14 ± 0.32aA 5.32 ± 0.72aB 5.95 ± 0.30aB Agona (FSL S5-517) 6.80 ± 0.21aA 6.45 ± 0.26aAB 4.43 ± 0.23aC 5.82 ± 0.55aB Enteriditis (LMFS-S-JF-005) 6.58 ± 0.11aA 6.64 ± 0.44aA 4.85 ± 0.43aB 6.06 ± 0.96aAB Daytona (LMFS-S-JF-009) 6.79 ± 0.62aA 6.04 ± 0.46aA 4.52 ± 0.72aB 5.65 ± 0.18aAB Typhimurium (LMFS-S-JF-001) 4.46 ± 0.52bA 6.00 ± 1.31aA 4.38 ± 1.21aA 3.88 ± 0.81bA   46  2.3.3 The effectiveness of antimicrobial treatments against indigenous aerobic bacteria Alfalfa seeds inoculated with different strains of S. enterica exhibited total APCs ranging from 4.0 to 5.8 log CFU/g and, after subtracting APCs with Salmonella cell counts, indigenous mesophilic aerobes ranging from 3.8 to 5.7 log CFU/g before germination (Table 2.8). Consistent with our findings, initial populations of mesophilic aerobes ranging between 3.9 – 6.0 log CFU/g were observed in 8 batches of untreated commercial alfalfa seeds (Castro‐Rosas & Escartín, 2000). The microbiota of commercial alfalfa seeds is diverse in composition with the top 3 most abundant bacterial groups being Pseudomonadaceae, Lactobacillaceae, and Nocardiaceae. Other common bacteria contribute to the alfalfa seed microbiota include Bacillaceae, Sphingomonadaceae, and Flavobacteriaceae (Landry, Sela, & McLandsborough, 2018).  The population of indigenous aerobic bacteria on alfalfa seed inoculated with S. Agona PARC 5 was lower than the other 3 strains except for Typhimurium. Agona PARC 5 dominated the microbiota of alfalfa seeds, comprising 86% of the APC, after inoculation while other strains only contributed to approximately 11% of the APCs. The PARC 5 strain was isolated from alfalfa seeds. The dominance of Agona PARC 5 on inoculated alfalfa seed suggested that this strain is highly competitive on the dry seed surface.  Antimicrobial treatments did not have a significant reduction on the population of indigenous aerobic bacteria on alfalfa seed inoculated with S. enterica. However, treatment of the seed with 3,800 ppm of free chlorine from NaOCl reduced the populations of mesophilic aerobes on natural alfalfa seed from 4.08±0.02 log CFU/g to 20 or less CFU/g (Fett, 2002). Subsequently, 47  increased resistance of indigenous aerobic bacteria to sanitation stress was observed after the inoculation of Salmonella onto alfalfa seed. The composition of indigenous bacteria may vary before and after inoculation. Less desiccation-resistant cells in the indigenous microbiota probably die off during the drying period after the Salmonella inoculum was added to the seeds. Additionally, cross-protection could also play a role in the increased stress tolerance of indigenous aerobes after inoculation. Bacterial cells can gradually adapt to the hostile sub-lethal conditions, causing an adaptive response accompanied by a temporary physiological change that may result in an enhanced stress tolerance (Yousef & Courtney, 2002). The general stress response identified in some Gram-negative bacteria, including Salmonella and Pseudomonas, is regulated by the sigma factor, RpoS (Abee & Wouters, 1999; Schnider-Keel, Lejbølle, Baehler, Haas, & Keel, 2001). Induction of RpoS makes bacteria more resistant to environmental stresses, such as high and low temperatures and oxidative stress (Suh et al., 1999). rpoS is required for optimal survival of P. fluorescens, one of the most common surface bacteria on alfalfa seeds, on surfaces of seeds as well as after heat and oxidative stress (Stockwell & Loper, 2005; Suh et al., 1999). The desiccation stress experienced during seed inoculation, drying, and short-term storage could enhance the resistance of indigenous seed surface bacteria to heat stress and oxidative stress involved in antimicrobial treatments through up-regulation of rpoS.    48  Table 2.8 N0 (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Counts of indigenous bacteria were calculated by subtracting Salmonella counts from total APCs. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 3.79 ± 0.34bA 3.87 ± 0.37aA 5.18 ± 0.58aA 4.25 ± 0.81aA Agona (FSL S5-517) 5.18 ± 0.62aA 5.30 ± 0.71aA 5.32 ± 0.82aA 4.71 ± 0.97aA Enteriditis (LMFS-S-JF-005) 5.54 ± 0.60aA 5.42 ± 0.63aA 5.07 ± 0.81aA 4.85 ± 0.91aA Daytona (LMFS-S-JF-009) 5.67 ± 0.25aA 5.25 ± 0.91aA 5.09 ± 0.84aA 4.65 ± 0.69aA Typhimurium (LMFS-S-JF-001) 4.52 ± 0.34abA 4.19 ± 0.15aA 4.06 ± 0.37aA 4.24 ± 0.36aA   49  2.3.4 Post sanitation behaviour of indigenous aerobic bacteria on sprouting alfalfa seed 2.3.4.1 Baranyi and Roberts Model fitting Overall, populations of indigenous aerobic bacteria on alfalfa seeds increased significantly within the first 16 h of germination and almost all samples achieved maximum growth after 16 - 24 h (Figures B.1 – B.5). The Baranyi and Roberts Model without lag phase was fitted to data derived from the 0 to 32 h germination period. An R2 coefficient of over 0.9 was achieved with all the growth curves of indigenous aerobic bacteria on sprouting alfalfa seed.  2.3.4.1.1 μmax of indigenous aerobic bacteria on sprouting alfalfa seed The indigenous aerobic bacteria and S. enterica populations increased simultaneously within the first 24 h of germination (Figures A.1 – A.5 and B.1 – B.5). The increase in bacterial populations is likely driven by the increased levels of monosaccharides, organic acids and amino acids leaching from sprout surface during seed germination (Na Jom, Frank, & Engel, 2011). Sprouting alfalfa seed colonized by S. enterica Typhimurium and Agona PARC 5 exhibited the highest growth rates of indigenous bacteria (Table 2.9). However, the μmax of S. enterica Typhimurium was the slowest among all 5 strains, especially after antimicrobial seed treatment (Table 2.4). Therefore, there is potentially a negative association between the growth rate of S. enterica and the proliferation rate of surrounding indigenous bacteria on sprouting alfalfa seed, which supported the potential of competitive exclusion happened during the exponential growth of Salmonella on sprouted seed. The presence of specific genera in the indigenous microbiota of seeds, such as Lactococcus and Pseudomonas, may limit or even prevent the growth of pathogenic organisms on sprouting seeds (Cai et al., 1997; Matos & Garland, 2005; Weiss et al., 2007). Additionally, it has been reported that the microbiota present on germinated sprouts 50  differs greatly from that on the seeds. After 1 day of germination, Pseudomonadaceae are predominant in the microbiota of sprouting alfalfa seed (Landry et al., 2018). The biocontrol potential of Pseudomonadaceae, a family ubiquitous within soil and produce, has been previously assessed to inhibit S. enterica growth in vitro, on produce, and on alfalfa sprouts specifically (Matos & Garland, 2005; Fett, 2006; Berrios-Rodriguez et al., 2017). Hence, it is possible that competitive exclusion between Pseudomonadaceae and S. enterica negatively impacts the proliferation of S. enterica on sprouting alfalfa seed. Interestingly, the second highest μmax of indigenous bacteria after CTL and CLO was observed with samples inoculated with S. Agona PARC 5, the strain with the highest growth rate on CTL and CLO samples (Table 2.4). Moreover, the dominance of S. PARC 5 on alfalfa seed was replaced by indigenous bacteria during seed germination, which could be further investigated for the development of targeted interventions to inhibit the growth of this particular strain on sprouting seed.   Generally, antimicrobial treatments had a less significant effect on the μmax of indigenous aerobic bacteria compared to that of S. enterica (Figure B.6). No significant difference was observed between CTL and CLO in all 5 strains (Table 2.9). HPA possessed a significant negative effect on the growth rate of indigenous aerobic bacteria, followed by HPO, on S. enterica Agona PARC 5 and Typhimurium – colonized sprouting seed, but not on samples with the other 3 strains. The organic treatment (HPA) retarded the rapid growth of indigenous bacteria on sprouting seeds colonized with S. enterica Agona PARC 5 and Typhimurium. To the contrary, Agona FSL S5-517, Enteriditis and Daytona had no significant difference in growth rate after HPA treatment compared to CTL. The HPA treatment is a multi-hurdle treatment that involves heat, organic acid, and H2O2. The combination of 2 or more treatments, sequentially or simultaneously, can 51  further improve the reduction of foodborne pathogens (Ding et al., 2013). However, the effect of combined treatments on the indigenous bacteria on sprout seed has not been well-documented.  Table 2.9 μmax (log CFU/g/h) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A, B) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 0.32 ± 0.07aA 0.27 ± 0.02aA 0.15 ± 0.03bB 0.16 ± 0.00abB Agona (FSL S5-517) 0.11 ± 0.03bA 0.10 ± 0.02bA 0.11 ± 0.05bA 0.16 ± 0.05bA Enteriditis (LMFS-S-JF-005) 0.12 ± 0.03bA 0.11 ± 0.03bA 0.15 ± 0.05bA 0.14 ± 0.04bA Daytona (LMFS-S-JF-009) 0.13 ± 0.00bA 0.11 ± 0.07bA 0.15 ± 0.06bA 0.14 ± 0.03bA Typhimurium (LMFS-S-JF-001) 0.34 ± 0.02aA 0.36 ± 0.05aA 0.28 ± 0.02aAB 0.25 ± 0.02aB  52  2.3.4.1.2 Nmax of indigenous aerobic bacteria on sprouting alfalfa seed The population of indigenous bacteria increased from 4 – 5 log CFU/g at 0 h of germination to a maximum of approximately 8 log CFU/g within the first 32 h of germination in all samples (Table 2.10). Types of antimicrobial treatment and S. enterica strain type did not have a significant impact on the calculated Nmax of indigenous bacteria within the first 32 h of germination. Large quantities of amino acids are released from sprouting seed within 8 h of seed inhibition, with peaks of amino acid concentrations at either 24 h or 48 h (Kwan et al., 2015). Based on our findings, the population of indigenous aerobic bacteria reached the maximum when the amount of nutrients leaching from sprouting seed maximized (Kwan et al., 2015). Environmental factors from the sprouting environment can influence the microbiota of sprouts (Landry et al., 2018). In addition, the germination of sprouting alfafa seed in a sterile environment promoted a dramatic shift in microbial population (Landry et al., 2018). When sprouted in a commercial setting, more diverse microbiota was detected (Landry et al., 2018). The lack of diversity in the sprouting seed community during sprouting under sterile laboratory conditions could also limit the overall maximum growth capacity of indigenous bacteria. 53  Table 2.10 Nmax (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 8.35 ± 0.28aA 8.14 ± 0.34aA 8.33 ± 0.13aA 8.32 ± 0.06aA Agona (FSL S5-517) 8.20 ± 0.53aA 7.90 ± 0.26aA 8.15 ± 0.30abA 7.99 ± 0.43aA Enteriditis (LMFS-S-JF-005) 7.99 ± 0.35aA 7.58 ± 0.15aA 7.85 ± 0.17abA 7.69 ± 0.33aA Daytona (LMFS-S-JF-009) 8.09 ± 0.34aA 7.87 ± 0.36aA 7.81 ± 0.14bA 7.76 ± 0.15aA Typhimurium (LMFS-S-JF-001) 8.29 ± 0.02aA 8.21 ± 0.07aA 8.30 ± 0.02aA 8.28 ± 0.07aA   54  2.3.4.2 N24 of indigenous aerobic bacteria on sprouting alfalfa seed Populations of indigenous aerobic bacteria on alfalfa seeds increased significantly within the first 16 h of germination and almost all samples achieved a plateau after 16 - 24 h, which explains the high similarity in values between Nmax and N24 (Table 2.11). Indigenous aerobic bacteria reached their maximum growth capacity in antimicrobial-treated samples (Figures B.1 – B.5), while S. enterica was still proliferating at around 16 - 24 h of germination (Figures A.1 – A.5). The nutrient uptake and conversion into biomass in cell proliferation requires substantial energy, and finding and exploiting a suitable energy source is the most important metabolic activity of growing cells (Bumann & Schothorst, 2017). The longer exponential growth phase of S. enterica on sprouting seeds suggested that it is more competitive in acquiring energy from the environment than indigenous bacteria. Salmonella metabolism and virulence mechanisms are highly adaptive to different host cell types and environmental conditions, which benefits its growth and survival under undesired conditions (Bumann & Schothorst, 2017).  Similar to Nmax, N24 (approximately 8 log CFU/g) is independent of the presence of antimicrobial treatments and S. enterica strain type, except that the N24 on CTL sprouting seed colonized by S. enterica Agona FSL S5-517 was significantly higher than that after HPA (Table 2.11). After 24 h of germination, Pseudomonadaceae, Lactobacillaceae, and Bacillaceae comprised 80% of the surface microbiota on alfalfa seed sprouted under sterile laboratory conditions and the sprouting seeds consistently had cell numbers around 8 log CFU/g (Landry et al., 2018). Consistent with Landry et al. (2018), we also observed approximately 8 log CFU/g of indigenous bacteria after 24 h of sprouting (Table 2.11). This finding indicates that the presence of antimicrobial treatments and S. enterica colonization may not affect the maximum growth capacity of 55  indigenous microbes on sprouting seed, although antimicrobial treatments applied on the seed prior to sprouting may contribute to changes in the composition of the indigenous aerobic microbiota.  56  Table 2.11 N24 (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 8.45 ± 0.44aA 8.25 ± 0.07aA 7.78 ± 0.50aA 7.82 ± 0.65aA Agona (FSL S5-517) 7.48 ± 0.08aB 7.62 ± 0.07bAB 7.83 ± 0.24aAB 7.91 ± 0.16aA Enteriditis (LMFS-S-JF-005) 7.80 ± 0.41aA 7.55 ± 0.20bA 7.88 ± 0.14aA 7.67 ± 0.44aA Daytona (LMFS-S-JF-009) 7.92 ± 0.68aA 7.43 ± 0.10bA 7.67 ± 0.16aA 7.50 ± 0.03aA Typhimurium (LMFS-S-JF-001) 8.31 ± 0.21aA 8.25 ± 0.22aA 8.23 ± 0.28aA 8.19 ± 0.11aA   57  2.3.4.3 N6d of indigenous aerobic bacteria on sprouting alfalfa seed Most Salmonella strain-treatment combinations reached a growth plateau after 16 - 24 h of germination and maintained their population at the end of germination (approximately 8 log CFU/g). However, the indigenous bacteria continued to increase after 32 h and reached > 9 log CFU/g on alfalfa sprouts at the end of the 6-day germination period when seed was inoculated with S. enterica Typhimurium or Agona PARC 5 (Table 2.12). On the other hand, S. enterica populations in almost all samples declined after 48 h of germination (Tables 2.5 and 2.7). After 4 days of germination, 8.31 ± 0.09 log CFU /g mesophilic aerobic bacteria, similar to this study, was observed on alfalfa sprouts germinated from seeds treated with chlorine (Fett, 2002). Ninety percent of the microbiota belonged to the family Pseudomonadaceae 4 days after germination on naturally sprouted alfalfa seed (Asakura et al., 2016; Landry et al., 2018). In addition, as one of the most abundant populations in chloraminated water, Pseudomonadaceae is well-adapted to chlorine treatment (Hwang, Ling, L Andersen, Lechevallier, & Liu, 2012). Therefore, it is possible that Pseudomonadaceae is still the leading bacteria family present on alfalfa sprouts after the CLO treatment. As indicated by the elevated indigenous bacteria count between 48 h – 6 days of germination, the dominant indigenous bacteria, probably Pseudomonadaceae, are more resistant to depletion of nutrients than S. enterica Agona PARC 5 and Typhimurium on sprouting alfalfa seed.  Antimicrobial treatments did not have a significant impact on the population of indigenous aerobic bacteria on the finished products (Table 2.12). It has been reported that treatment of the seed with various concentrations of chlorine before sprouting did not affect the final microbial populations on harvested alfalfa sprouts (Fett, 2002).  58  Table 2.12 N6d (log CFU/g) of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with 5 S. enterica strains and after different seed treatments. Results are summarized by mean ± SD for the bacterial strains tested in triplicate. Means with the same lowercase letter (a, b) in the same column are not statistically different from each other (overall α=0.05, Tukey’s HSD test). Means with the same uppercase letter (A) in the same row are not statistically different from each other (overall α=0.05, Tukey’s HSD test). As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment and HPA represents the organic treatment. S. enterica strains CTL CLO HPO HPA Agona (PARC 5) 8.97 ± 0.59abA 9.29 ± 0.35aA 9.37 ± 0.07aA 9.53 ± 0.41aA Agona (FSL S5-517) 8.02 ± 0.19bA 7.89 ± 0.25bA 8.16 ± 0.69abA 8.40 ± 0.62abcA Enteriditis (LMFS-S-JF-005) 7.89 ± 0.63bA 7.86 ± 0.47bA 7.60 ± 0.23bA 7.91 ± 0.30cA Daytona (LMFS-S-JF-009) 8.25 ± 0.19abA 8.23 ± 0.12bA 7.61 ± 0.92bA 8.28 ± 0.63bcA Typhimurium (LMFS-S-JF-001) 9.27 ± 0.39aA 9.13 ± 0.29aA 9.35 ± 0.24aA 9.48 ± 0.02abA     59  2.4 Conclusions The post-sanitation growth of five S. enterica strains and changes in APC were determined in sprouting alfalfa seed. Populations of all 5 S. enterica strains increased from < 10 log CFU/g immediately after antimicrobial treatment to 4.5 -7.6 log CFU/g on sprouts germinated from CTL seeds and 3.9 – 7.1 log CFU/g on sprouts germinated from treated seeds after 6 days of germination. Interestingly, some strains exhibited unique growth characteristics and different antimicrobial treatments had a significant impact on the growth parameters of some strains. The HPO treatment applied to the seeds before sprouting retarded the growth of fast-growing S. enterica Agona and Enteriditis strains. The strong recovery of S. enterica Agona after chlorine treatment suggested that it may contribute to the persistence of serotype Agona in outbreaks linked with sprouts contaminated with Salmonella. The S. enterica Typhimurium strain displayed a unique diauxic growth pattern on sprouting alfalfa seed and had both the lowest N0 after seed inoculation and the lowest μmax irrespective of antimicrobial treatment. It is worth mentioning that the artificial inoculation level of S. enterica used in this study (approximately 5 log CFU/g) was much higher than levels anticipated in naturally contaminated seeds. Moreover, no intervention strategy was used during the germination stage to inhibit pathogen growth (e.g. rinsing with water or disinfectant).  Alfalfa seeds inoculated with different strains of S. enterica also possessed indigenous mesophilic aerobes ranging from 3.8 to 5.7 log CFU/g before germination. Antimicrobial treatments did not have a significant reduction on the populations of indigenous aerobic bacteria. Similar to S. enterica, populations of indigenous aerobic bacteria on sprouting alfalfa seeds increased significantly within the first 24 h of germination and reached 8 - 9 log CFU/g at the 60  end of the 6-day germination period, with the highest cell count observed on sprouts germinated from seed inoculated with S. enterica Typhimurium or Agona PARC 5. The antimicrobial treatments did not affect the population of indigenous aerobic bacteria on the sprouted alfalfa seeds.  Overall, S. enterica was able to recover and grow on sprouting alfalfa seeds. Consequently, multi-hurdle approaches, as well as microbial testing of spent irrigation water, are of uttermost importance to ensure contaminated sprouts do not enter the marketplace. The post-sanitation recovery of S. enterica in sprouts is strain-dependent and treatment-dependent; the mechanism(s) responsible for this effect requires further investigation. An in-depth understanding of S. enterica survival mechanisms on sprouting vegetables is essential for the development of more targeted interventions for the production of pathogen-free sprouted vegetables.  61  Chapter 3: Impact of antimicrobial treatments on sprouting alfalfa seed contaminated with Salmonella enterica revealed by metabolomics  3.1 Introduction Fruit and vegetables that are usually consumed raw can serve as vehicles for the fecal-oral transmission of foodborne pathogens (Fletcher, Leach, Eversole, & Tauxe, 2013). An estimated 50 million cases of produce-linked foodborne illness occur per year in the United States (Centers for Disease Control and Prevention, 2018a). S. enterica can colonize various produce types and plant organs (Delbeke, Ceuppens, Jacxsens, & Uyttendaele, 2015; Guo, Chen, Brackett, & Beuchat, 2001). S. enterica growth on produce appears to be influenced by plant factors, as indicated by the differential growth of this pathogen on different plant commodities and plant parts (S. Han & Micallef, 2014; D. Liu et al., 2018). The results of a recent study demonstrated that significantly larger populations of S. enterica were found on alfalfa sprout root, cotyledon, and seed coat tissues than on sprout stems (D. Liu et al., 2018).  S. enterica is not capable of infecting plants, but its survival on edible plants implies that it can metabolize nutrients leaching from the plant surface. Pathogen colonization on produce depends on the availability of nutrients on the plant surface (Mercier & Lindow, 2000). Germinating seeds are known to release exudates containing various amino acids, sugars, organic acids, and other plant metabolites to the spermosphere, where these nutrients support the growth of the seed microbial communities (S. Liu et al., 2007; Daniel P. Roberts, McKenna, Lohrke, Rehner, & de Souza, 2007). In addition, secondary metabolites, including fatty acids, phenolics, flavonoids, 62  terpenoids, and acyl sugars, are actively exuded by many plants, especially under stress environment (McDowell et al., 2011; Schilmiller et al., 2010). Some plant secondary metabolites play important roles in inhibiting S. enterica multiplication on fruit and vegetables (Greenway & Dyke, 1979; L. L. Wang & Johnson, 1992). Sugars, sugar alcohols, and organic acids were positively correlated with increased S. enterica growth on tomato plants, while fatty acids, including palmitic and oleic acids appeared to have the opposite effect (S. Han & Micallef, 2016).  S. enterica metabolism is thought to be robust in alfalfa seedling exudates (Kwan et al., 2015). Single-amino-acid metabolic pathways are important, but not essential, for the colonization of S. enterica in the spermosphere. Successful colonization and persistence of S. enterica on sprouting alfalfa seed depend on the ability to acquire and metabolize sprout-derived nutrients and to synthesize any essential nutrients not provided by the plant (Kwan et al., 2015). S. enterica also competes with the indigenous microbiota on sprouting seed. Sprout-associated organisms may be better adapted to the sprouting environment and limit the presence of foodborne pathogens through competitive exclusion or through the production of antagonistic compounds (Weiss et al., 2007).  Application of antimicrobial agents (e.g., sodium hypochlorite or hydrogen peroxide) to seeds before germination is required by the Canadian Food Inspection Agency (2007) and US Food and Drug Administration (2017) in industrial sprout production. In Chapter 1, rapid recovery and growth of S. enterica after antimicrobial treatments were observed on sprouting seed. The sprout-derived nutrients that support the rapid growth of S. enterica and the metabolic pathways 63  the bacteria utilize to increase biomass in this post-sanitation germinating-seed environment are both unknown. In this study, the role of seed antimicrobial treatments and S. enterica colonization on the metabolic profile of sprouting alfalfa were investigated. It was hypothesized that strain-dependent and treatment-dependent epiphytic colonization of S. enterica can be explained by variation in the surface metabolic profiles of sprouting alfalfa seeds. The goal was to study how colonization by S. enterica and different antimicrobial treatments affect metabolites released by sprouting alfalfa seed   3.2 Materials and methods 3.2.1 Preparation of sprouting alfalfa seed Based on their distinct growth characteristics observed in Chapter 1, 2 strains, S. enterica Agona PARC 5 and Typhimurium LMFS-S-JF-001 were selected for further investigation using metabolomics. In addition, 2 of the 3 tested antimicrobial treatments in Chapter 1, the chlorine (CLO) treatment and the organic (HPA) treatment, were included in the metabolomics analysis due to their differential impact on the growth characteristics of S enterica.  Procedures followed for seed inoculation, antimicrobial treatments, sprout germination are described in subsections 2.2.3, 2.2.4, and 2.2.5.  After 24 h of germination, 10 g of sprouting seed was added to a sterile FILTRA-BAG®  filter bag (VWR, Edmonton, Alberta, Canada) along with 10 mL sterile distilled water and were gently massaged by hand for 2 minutes. The filtered sprout wash was transferred to a 100 mL flat-bottom specimen container (VWR, Edmonton, Alberta, Canada), 8 mL of 100% ethanol was 64  added, and the samples were stored in a -20°C freezer for a maximum of 24 h. Four independent biological replicates, prepared separately from seed inoculation to germination, were harvested for each sample.  3.2.2  Preparation of lyophilized sprout washes A minimum of 1 h after the addition of ethanol, the containers were dried in a high vacuum chamber for 16 h to remove contained ethanol. After vacuum drying, the containers were stored in a -80°C freezer overnight in preparation for lyophilization. Once frozen, the containers were lyophilized for 24 h in a bulk tray freeze-dryer (Labconco, Kansas City, MO, US). Samples for analysis were prepared by re-dissolving the lyophilized powder in 1mL sterile distilled water, transferring to 1.7 mL microcentrifuge tubes and freezing overnight at -80°C. The samples were then lyophilized again for 24 h in the freeze-dryer and sent to Metabolon (Morrisville, NC, US) for metabolic profiling.   3.2.3 Metabolite analysis, identification, and quantification At the time of analysis, samples were thawed and 100 μL aliquots of each experimental sample were injected for each run and served as technical replicates allowing variability in the quantitation of all consistently detected biochemicals to be determined and overall process variability and platform performance to be monitored. The sample preparation process was carried out using the automated MicroLab STAR® system (Hamilton, Reno, NV, US). Recovery standards were added prior to the first step in the extraction process for quality control purposes. Sample preparation was conducted using a proprietary series of organic and aqueous extractions to remove the protein fraction while allowing maximum recovery of small molecules. The 65  resulting extract was divided into equal fractions for analysis on three independent platforms: ultra-high-performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS2) optimized for basic molecules (LC/MS-NEG), UHPLC/MS/MS2 optimized for acidic molecules (LC/MS-POS), and UHPLC/MS/MS2 optimized for polar molecules (LC/MS-Polar).  Samples were placed briefly on a TurboVap® (Zymark, Biotage, Charlotte, NC, US) to remove the organic solvent. Each sample was then frozen and dried under vacuum. Samples were then prepared for the appropriate platform. The global LC/MS portion of the platform was based on a Waters ACQUITY UPLC (Waters Corporation, Milford, MA, US) and a Thermo-Finnigan LTQ mass spectrometer (ThermoFisher, Grand Island, NY, US), which consisted of an electrospray ionization (ESI) source and linear ion-trap (LIT) mass analyzer. The dried sample extract was reconstituted in acidic or basic LC-compatible solvents, each of which contained 11 or more injection standards at fixed concentrations. One aliquot was analyzed using acidic positive ion optimized conditions and the other using basic negative ion optimized conditions in two independent injections using separate dedicated columns. Extracts reconstituted in acidic conditions were gradient eluted using water and methanol both containing 0.1% formic acid, while the basic extracts, which also used water/methanol, contained 6.5mM ammonium bicarbonate. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion.  The mass spec data files were extracted, and ion peaks were identified and integrated using Metabolon’s proprietary software. Metabolites were then identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries 66  that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra and curated by visual inspection for quality control using software developed at Metabolon. The reference library included known and novel metabolites was developed by analyzing more than 3,000 pure reference standards of known metabolite structures with the identical LC/MS methods and cataloging all the ions that were produced.  3.2.4 Statistical analysis Treatment groups included three types of microbiota, namely: sprouts bearing only indigenous microbiota (IM) as a control, sprouts infected with S. enterica Agona (SA), and S. enterica Typhimurium (ST).  Each group was subjected to three antimicrobial treatments, including a non-treated control (CTL), washing with sodium hypochlorite (CLO), and an organic treatment involving a hot water dip followed by washing with hydrogen peroxide: acetic acid (HPA).  Four biological replicates were provided for each group.  Statistical comparisons were performed between the antimicrobial treatments (CLO or HPA) and control (CTL) within each microbiota type, and between the S. enterica colonized microbiota (SA or ST) and control (IM) within each treatment, resulting in 15 pair-wise Welch’s two-sample t-tests.    67  Table 3.1 Combinations of microbiota types and antimicrobial treatments with assigned sample group abbreviations. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. Microbiota Antimicrobial treatment CTL CLO HPA IM IM_CTL IM_CLO IM_HPA SA SA_CTL SA_CLO SA_HPA ST ST_CTL ST_CLO ST_HPA   Following log transformation, data were scaled to the median value for each compound, then missing values were imputed with the minimum detected value for that compound. An estimate of the false discovery rate (q-value) was calculated to take into account the multiple comparisons that normally occur in metabolomic-based studies. Principal component analysis (PCA) and all other statistical analyses were performed using ArrayStudio (Omicsoft, Cary, NC, US) on natural log-transformed scaled imputed data.   3.3 Results and Discussion 3.3.1 Metabolite summary A total of 535 metabolites of known identity were identified in sprout washing samples, including: 152 amino acids (28.4%), 134 lipids (25.0%), 69 carbohydrates (12.9%), 68 68  nucleotides (12.7%), 52 secondary metabolites (9.7%), 31 cofactors, prosthetic groups, and electron carriers (5.8%), 19 peptides (3.6%), 5 hormone metabolites (0.9%), 3 xenobiotics (0.6%), and 2 partially characterized molecules (0.4%). Most of these metabolite classes have been previously identified in exudates of plants (S. Liu et al., 2007; McDowell et al., 2011; Daniel P. Roberts et al., 2007; Schilmiller et al., 2010). Exudates released by sprouting seed contain many low-molecular-weight metabolites, such as sugars and amino acids, that can be directly used by microbes for carbon and energy. These compounds are the major driver of microbial activity in the spermosphere (Nelson, 2004), enabling members of the plant microbiome, such as S. enterica, to grow and colonize seed and root surfaces. In seed germination, the bulk of seed exudates are generally released within the first 12 h of imbibition (Nelson, 2004), and enteric bacteria grow most rapidly during the first 24 h following seed imbibition (Hao, Willis, Andrews-Polymenis, McClelland, & Barak, 2012; D. P. Roberts, Dery, Yucel, & Buyer, 2000; Stewart, Reineke, Ulaszek, Fu, & Tortorello, 2001).  Amino acids were the most abundant class of metabolites in the washing samples of germinating alfalfa seed. Environmental metabolomics of tomato plant surface revealed that amino acids were in proportion more predominant in seedling exudates, accounting for over 50% of metabolites, than in flowering plant and fruit exudates, which agrees with the high proportion of amino acids detected on sprouting alfalfa seed (Han & Micallef, 2016). A study indicated that, in germinating alfalfa, amino acid concentrations stabilize after 24 h, suggesting that by that time the pool of amino acids available to microbes for growth has already been released (Phillips, Fox, King, Bhuvaneswari, & Teuber, 2004). Out of all amino acids detected, 46 (30.3%) belong to the glutamate family, which suggests the high content of glutamate leaching from sprouting 69  alfalfa seed. Consistent with our finding, it has been reported that glutamate was acquired exogenously by S. enterica from the germinating alfalfa exudates in sufficient quantities to eliminate the need for de novo biosynthesis of the metabolite (Kwan et al., 2015). Other major amino acid families present on sprouting alfalfa seed at 24 h of germination include serine, aromatic amino acid, aspartate, pyruvate derived branched chain amino acid, amine, and glutathione.  In addition, the top three most abundant low-molecular-weight metabolites in the washing samples were stachydrine, homostachydrine, and trigonelline. Stachydrine and homostachydrine belong to the glutamate family; trigonelline is a plant hormone that has been linked to plant cell cycle regulation and oxidative stress response (Garg, 2016).  Trigonelline and stachydrine have been previously identified as major components of alfalfa seed rinse (Phillips, Joseph, & Maxwell, 1992). They are highly soluble in water and especially abundant on dry legume seeds. They are known signaling molecules between alfalfa plant and its nitrogen-fixing bacteria, Rhizobium. A highly coordinated exchange of these signals leads to a gradual and coordinated adjustment of physiology and metabolism in both the plant and the bacteria (Brencic & Winans, 2005). The role of trigonelline and stachydrine in the interactions between alfalfa and foodborne pathogens is unknown.  Out of the 535 identified metabolites, 98.9% of the metabolites possessed significant difference (p ≤ 0.05) in at least one of the 15 pair-wise Welch’s two-sample t-test, comparing antimicrobial treatment (CLO or HPA) to control (CTL) within each microbiota type, and comparing microbiota involving S. enterica (SA or ST) to indigenous microbiota (IM) within each treatment. 70  The finding indicates that the metabolite profile of sprouting alfalfa seed depended heavily on the seed treatment and/or S. enterica colonization. Specifically, 96.1% of metabolites, from all defined pathway classes, were significantly affected by one or both antimicrobial treatments in at least one type of microbiota, while microbiota type had a significant effect on 75.1% of metabolites. Antimicrobial treatments caused changes in a wider range of metabolic pathways than microbiota type did in the washing samples of sprouting alfalfa seed at 24 h of germination.  A heat map was prepared with samples grouped by treatment block and compounds grouped by major biochemical class (Figure 3.1). Uniformity in colour under the same treatment group and the same microbiota type in the heat map indicated relatively high reproducibility among the four biological replicates, which served to strengthen the statistical results. According to the colour variation under different columns in the heat map, antimicrobial treatment led to major changes in metabolite profiles, while the microbiota type had a much subtler effect. Notably, treatment with CLO resulted in generally lower levels of most compounds, as indicated by the high-intensity blue colour under column CLO in the heat map. Moreover, the colour variation in different major metabolite classes indicated that almost all major metabolic pathways were affected by the antimicrobial treatments for alfalfa seed applied prior to germination. Different types of antimicrobial treatment may have different effects on the metabolite profiles of sprouting alfalfa seed at 24 h of germination, as indicated by the different colour patterns under the columns CLO and HPA in the heat map.   71   Figure 3.1 Heat map of metabolite scaled intensities in samples grouped by major metabolite class. Log-transformed metabolite concentrations were scaled to the median value (1.0) of all samples for each compound and represented as different colours (red or blue) with different colour intensity. A continuously increasing intensity of red represents values ranging from 1.0 to 4.0 and blue represents values from 1.0 to 0.25. The maximum-intensity red represents all values ≥4.0 and maximum blue stands for ≤0.25. Carbos stands for carbohydrates. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001.  72  3.3.2 Principal component analysis Principal component analysis (PCA) of the metabolite profiles enabled elimination of uninformative background variables and visualization of small profile changes. The PCA plot revealed the profile differences among sprouting alfalfa seed surface metabolite samples and profile changes associated with different antimicrobial treatments and different types of microbiota (Figure 3.2). The principal components 1 and 2 explained respectively 33.45% and 25.23% of the total variability of the data, accounting for the metabolite composition changes in sprout washing samples.  PCA analysis reiterates similar observations to those indicated by the heat map. The treatment factor accounted for the major portion of the separation of samples in the PCA plot, which indicates the major impact of treatments on sample metabolite profiles.  It is interesting that some separation of the three microbiota types could be discerned in the HPA treatment, while there was essentially no separation of microbiota types in the CTL and CLO treatment groups. S. enterica could utilize different recovery mechanisms after different antimicrobial treatments. This finding supports that the metabolite profile of sprouting alfalfa seed contaminated with S. enterica depends on both the treatment type and microbiota type.   73   Figure 3.2 Principal Component Analysis of the metabolite profiles of samples colonized with 3 different types of microbiota and after 3 different antimicrobial treatments. Comp.: principal component. The sample dots were coloured by different types of antimicrobial treatments and shaped by different types of microbiota. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001.  74  3.3.3 Impact of disinfection treatments on metabolite profiles of sprouting alfalfa seed 3.3.3.1 Effect of antimicrobial treatment exposure The magnitude of metabolic effects caused by specific variables was also judged by summarizing the number of metabolites significantly changed (p ≤ 0.05) in each pair-wise comparison, and the direction of those changes. When testing 535 metabolites at this statistical cut-off (p≤0.05), the problem of false discovery arises, as 25-30 compounds would be expected to meet this criterion by chance alone. All the treatment effects (CLO or HPA) led to more than half of the compounds being significantly altered, far above the number expected by random chance (Table 3.2). The NaClO treatment diminished the metabolites by 44.5%, 49.5%, and 46.9%, and increased metabolites by 10.5%, 8.6%, and 12.9% on uninoculated, S. enterica Agona and Typhimurium - colonized sprouting seeds respectively. As noted above, it is clear that the CLO treatment led to the most significant decline of metabolites. It is interesting that the HPA treatment did not show this effect but tended to increase and decrease similar numbers of metabolites, around 30% of all detected metabolites. This implies that the HPA treatment may have converted existing metabolites into forms that are still recognizable as biochemical compounds in the detection platform, or that it may have liberated compounds from plant cells without destroying them. The difference in the metabolic profiles of the CTL, CLO and HPA - treated samples indicated the impact of antimicrobial treatments on the composition of the alfalfa sprout exudates. The composition of plant exudates results from the net influx and efflux of compounds by the plant (Jones & Darrah, 1994); these influx and efflux rates can change in response to the external chemical environment (Soldal & Nissen, 1978).   75  Table 3.2 Numbers of significantly up- or down-regulated metabolites in the antimicrobial-treated groups (CLO or HPA), with 3 different types of microbiota, compared to the corresponding controls (CTL) (p ≤ 0.05, Welch’s two-sample t-test). Sig: significantly. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. Treatment effects Ratio Total Sig altered  Sig Up | Down-regulated Sig Up | Down - regulated shared by all 3 different types of microbiota Sig Up | Down-regulated shared by all 3 different types of microbiota and both types of antimicrobial treatments CLO  IM_CLO / IM_CTL  294 56 | 238 28 | 185 17 | 81 SA_CLO / SA_CTL 311 46 | 265 ST_CLO / ST_CTL  320 69 | 251 HPA IM_HPA / IM_CTL 310 112 | 198 70 | 111 SA_HPA / SA_CTL 323 147 | 176 ST_HPA / ST_CTL 296 163 | 133 76  Eighty-one out of 535 metabolites (15.1%) were significantly down-regulated by all antimicrobial-treated groups compared to the corresponding CTL groups (Table 3.2). Compounds that were diminished by both the CLO and HPA treatments included a wide range of metabolites from all biochemical classes, especially the amino acids and secondary metabolites (Table C.1). This was likely due to non-specific oxidative destruction of NaClO and H2O2 involved in the antimicrobial treatments. Compounds increased by both treatments relative to the control included some carbohydrates which may be oxidation products of larger biochemical compounds (e.g. oxalate), or breakdown products of cell walls or larger oligosaccharides (e.g. fucitol, maltose, verbascose) (Table C.2). Notably, the metabolites detected in this study only represented compounds that are secreted in large quantities or easily liberated by a water wash.  It is interesting that 3 flavonoids, daidzein, formononetin, and liquirigenin, were up-regulated in both treatments, whereas 2 glycosylated flavonoids, quercetin 3-glucoside and kaempferol 3-O-glucoside/galactoside, were among compounds down-regulated by the treatments (Tables C.1 and C.2). This suggests that both treatments may lead to deglycosylation of the secondary metabolites. Several markers for protein, membrane, and nucleic acid degradation (e.g. dimethylarginine) were higher in the HPA treatment but lower in the CLO treatment. This may be due to the initiation of these macromolecular breakdown processes in the milder HPA treatment, but then subjected to further degradation of the resultant biochemicals in the harsher CLO treatment.   77  3.3.3.2 The CLO treatment The amount of up and down -regulated metabolites in Table 3.2 highlights the dramatic difference between the CLO and HPA treatments, and the strong tendency of CLO to deplete metabolites seen in the CTL groups. One hundred and eighty-five out of 535 metabolites (34.6%) were significantly down-regulated and 28 (5.2%) up-regulated in all CLO-treated groups compared to the corresponding CTL (Table 3.2). There were a few compounds that were increased only in the CLO treatment.  It can be presumed that these compounds are specifically formed by the strong oxidative conditions of the CLO treatment, or that such harsh conditions are necessary to liberate these molecules from larger complexes.  For instance, heme, one of the up-regulated metabolites, is typically a component of hemoglobin, the red pigment in blood, or a substructure of other biologically important hemoproteins such as myoglobin, cytochromes, catalases, and heme peroxidase (Paoli, Marles-Wright, & Smith, 2002). It is possible that the increase of heme after the CLO treatment was due to degradation of larger protein complexes. On the other hand, another one of the upregulated metabolites, 3-hydroxydecanoate, may result from chemical oxygenation of fatty acids (Dhar, W Sepkovic, Hirani, Magnusson, & Lasker, 2008).  Interestingly, N-acetyltryptophan was dramatically higher after the CLO treatment.  It is not the only N-acetylated amino acid up-regulated in CLO samples (e.g. N-acetylphenylalaninine), but the magnitude of the difference was exceptional, reaching 35-fold higher relative to the control or HPA treatment. N-acetyltryptophan is a common metabolite found in plants, as well as in a plant bacterial colonist, Rhizobium (Yu, Hegeman, & Cohen, 2014). The function of N-acetyltryptophan in plant and bacterial metabolism has not been well-studied, but it can prevent 78  protein molecules from oxidative degradation by scavenging oxygen in protein solutions (Fang, Parti, & Hu, 2011). It is possible that N-acetyltryptophan was produced by sprouting alfalfa seed and its surface microbiota to alleviate the strong oxidative stress caused the by the CLO treatment.  3.3.3.3 The HPA treatment In all HPA-treated samples, 70 metabolites were commonly up-regulated at 24 h of germination while 111 metabolites were down-regulated compared to CTL (Table 3.2). As noted above, HPA may also have the effect of changing or destroying biochemical compounds, but the fact that many compounds also increased with HPA treatment may be a result of increased leakage of molecules from plant or bacterial cells. The harsher CLO treatment would cause even more leakage, but the released compounds would have a greater chance of being degraded by the strong oxidant.  In fact, H2O2 has an effect by increasing seed germination rate in pea seeds, in cereal plants such as wheat, and in alfalfa seeds (Ishibashi et al., 2008; Barba-Espin et al., 2010; Hong & Kang, 2016). According to these reports, pre-treatment involving H2O2 caused an increase in the levels of ascorbate -oxidizing enzymes which is correlated with increased germination of seedlings (Hong & Kang, 2016). Also, it has been reported that H2O2 treatment alleviates environmental stresses on growth of the radicle and coleoptile. Hong and Kang (2016) observed that radicals were seen through the edge of H2O2-treated seeds, and those seeds germinated faster than the other samples. Increased seed germination could be linked to the increased number of up-regulated metabolites in HPA-treated samples, compared to CLO-treated samples. On the other 79  hand, organic acids, including acetic acid used in the HPA treatment, are known to have a negative impact on seed germination and promote leakage of ions from roots (Lynch, 1980). However, microbial breakdown of organic acids could decrease the activity of acetic acid (Lynch, 1980).  3.3.4 Impact of S. enterica colonization on metabolite profiles of sprouting alfalfa seed 3.3.4.1 Summary of S. enterica colonization The effects of microbiota type, on the other hand, were much weaker statistically than treatments effects, and in the case of the untreated samples, the number of changes were barely above the number expected in random data (Table 3.3).  Also, it is difficult to make a strong case that the CLO treatment was much different between the S. enterica strains (SA and ST), although there were slightly more compounds affected in the SA samples relative to IM.  However, the presence of Salmonella led to increased numbers of metabolite changes in the HPA treatment, and most of significantly altered metabolites showed an increase in concentration upon treatment. Twenty point two percent and 37.2% of metabolites were significantly up-regulated in S. enterica – colonized samples, SA and ST respectively, after the HPA treatment, while no significant number of metabolites were down-regulated.     80  Table 3.3 Numbers of significantly up- or down-regulated metabolites in S. enterica - colonized groups (ST or SA), with different types of antimicrobial treatments, compared to the corresponding controls (IM). There was no significantly altered metabolites shared by both types of S. enterica - colonized microbiota and all 3 types of antimicrobial treatments. Sig: significantly. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. Treatment Ratio Total Sig altered  Sig Up | Down-regulated Sig Up | Down - regulated shared by both types of S. enterica - colonized microbiota CTL SA_CTL / IM_CTL 57 33 | 24 7 | 4  ST_CTL / IM_CTL 46 8 | 38 CLO SA_CLO / IM_CLO 96 42 | 54 4 | 5 ST_CLO / IM_CLO 47 13 | 34 HPA SA_HPA / IM_HPA 131 108 | 23 70 | 15 ST_HPA / IM_HPA 232 199 | 33  81  Metabolites with higher levels in the CTL - SA and ST samples are likely derived from S, enterica, because of the increase compared to the amount released from plant material with only indigenous microbes (IM) (Table 3.3).  On the other hand, metabolites lower in the SA and ST groups may result from degradation or differential consumption of the plant or IM pools by the added SA and ST microbes.  Sprouting alfalfa seed exudates contained abundant asparagine at 24 h of germination (Kwan et al., 2015). However, asparagine was 20 and 50-fold lower in the SA and ST respectively compared to IM in CTL samples in this study, which means the growth of S. enterica on non-treated seeds consumed almost all the asparagine secreted from sprouting alfalfa seed. Notably, there were 2 polyamines, N-acetylputrescine and (N(1) + N(8))-acetylspermidine, significantly up-regulated in both CTL and CLO - treated S. enterica contaminated samples. Polyamines have been identified as novel environmental signals essential for virulence of S. enterica Typhimurium (Jelsbak, Thomsen, Wallrodt, Jensen, & Olsen, 2012).  3.3.4.2 Impact of S. enterica colonization after the HPA treatment As discussed above, the effect of microbiota types was the most significant in HPA – treated samples. In HPA-treated S. enterica contaminated samples, 70 metabolites were increased and 15 decreased in both SA and ST compared to IM (Table 3.3). The significantly altered metabolites were involved in amino acid metabolism, cofactor biosynthesis, fatty acid biosynthesis and catabolism, sugar metabolism, phospholipid metabolism, purine and pyrimidine metabolism, dipeptide and secondary metabolism. Proteomic analysis also revealed that many of these metabolic pathways were reported to be activated during growth of diverse S. enterica with germinating alfalfa (Kwan et al., 2015). The identified S. enterica proteome indicates that S. enterica acquires and catabolizes plant-derived metabolites, as well as biosynthesizing required 82  nutrients that are presumably absent or limiting in the spermosphere and early rhizosphere. A transcriptomic profile of S. enterica serotype Weltevreden during alfalfa sprout colonization compared to its growth in minimal medium revealed upregulation of genes in many of the same pathways as we discovered (Brankatschk, Kamber, Pothier, Duffy, & Smits, 2014).  3.3.4.2.1 Amino acid metabolism The concentration of 25 amino acids was increased upon S. enterica colonization (both SA and ST) after the HPA treatment while no amino acids were significantly decreased (Table C.3). This observation potentially supported the enhanced amino acid biosynthesis of S. enterica under a mild stress environment on sprouting alfalfa seed, which may contribute to its persistence in sprouts. In addition, the relative importance of biosynthesis versus transport of amino acids to S. enterica growth varied by the specific nutrient availability and the stage of growth. S. enterica not necessarily prefers importing required metabolites rather than synthesizing them de novo (Kwan et al., 2015).  Quantification of amino acids in the exudates of germinating alfalfa seedlings revealed the presence of all 19 of the proteogenic amino acids in concentrations similar in scale to those reported for young crop seedlings (El-Hamalawi, 1986; Simons, Permentier, de Weger, Wijffelman, & Lugtenberg, 1997). However, in the active proteome of S. enterica during growth in the spermosphere and early rhizosphere of germinating alfalfa seedlings, only one of the 22 detected proteins involved in amino acid metabolism was directly involved in amino acid catabolism and 14 detected proteins were strictly involved in the biosynthesis of specific amino acids (Kwan et al., 2015). This finding, together with our finding, suggested that although amino 83  acids were present in the exudates of alfalfa seedlings, they may not be sufficiently available to meet bacterial biomass requirements of Salmonella. Therefore, S. enterica may require de novo biosynthesis of amino acids.  In addition, some necessary amino acids for S. enterica growth, e.g. leucine, were not up-regulated upon S. enterica colonization in HPA-treated samples while some were. The number of amino acid biosynthesis proteins detected in the proteomic survey of S. enterica growing in sprouting alfalfa seed exudate was found disproportionally relative to the number of such proteins in the total S. enterica proteome, suggested that amino acids may be an important but limiting nutrient for S. enterica in the alfalfa spermosphere and early rhizosphere (Kwan et al., 2015). Dependent on the availability of plant amino acids, S. enterica could up-regulated the synthesis of specific limiting amino acids while importing other amino acids from the environment. As mentioned in the same study, sprouting alfalfa stopped releasing amino acids at around 24 h of germination. The proteins S. enterica Typhimurium expressed in the sprouting-alfalfa environment at 24 h of germination indicated that the associated amino acid biosynthetic pathways contributed to S. enterica growth (Kwan et al., 2015).   3.3.4.2.2 Phospholipids Notably, the concentrations of 10 phospholipids were significantly down-regulated in both HPA -treated S. enterica-colonized samples (Table C.4). These decreased phospholipids included 1 phosphatidic acid, 4 phosphatidylcholines (PCs) and 5 phosphatidylethanolamines (PEs). The major alfalfa seed phospholipid was PC, and its level increased during germination. PE is also one type of phospholipid present in alfalfa seed and it increased slightly with germination 84  (Huang & Grunwald, 1988). A metabolomics study revealed that phospholipids, such as PCs and PEs, were important source of carbon and energy source for S. enterica Typhimurium during gallbladder infections of mice and the concentration of all phospholipids was significantly decreased following Salmonella growth (Antunes et al., 2011). In addition, PCs and PEs, especially PEs, are important S. enterica membrane phospholipids. PEs play a role in transport systems and serve as chaperones to help the correct folding of membrane proteins (Antunes et al., 2011).   3.3.4.2.3 Carbohydrates Fructose has been identified as a major sugar in the seed and root exudates of a variety of crop plants (Kamilova et al., 2006; Roberts et al., 1999). Galactose and trehalose have also been reported in seed (Nelson, 2004) and root (Kwan, Charkowski, & Barak, 2013) exudates. S. enterica utilizes plant-derived fructose, galactose, melibiose, trehalose, and propionate (Kwan et al., 2015). However, no significant difference of these sugars was observed between IM and the S. enterica-colonized samples, possibly due to the common role of these sugars as a carbon source in both S. enterica and IM. The availability of these carbon compounds in sprouting seed and the relative importance of each of the nutrients to S. enterica growth in the spermosphere and early rhizosphere remain to be determined.  3.3.4.3 Impact of strain type When comparing ST to SA, strain type possessed a more significant effect on CLO and HPA -treated samples compared to CTL as indicated by the increased total numbers of significantly altered metabolites in Table 3.4. In the case of the untreated (CTL) samples, the number of 85  changes was below the number expected in random data. Therefore, there was no significant difference between the metabolite profiles of CTL samples with the 2 different S. enterica strains. The only metabolite significantly increased in ST compared to SA in all treatments including CTL was diglycerol phosphate, a bacterial metabolite. The function of diglycerol phosphate in Salmonella metabolism has not been well-studied, but it exerted a considerable stabilizing effect against heat inactivation and severe desiccation stress in desiccation-tolerant hyperthermophiles, Archaeoglobus fulgidus and Hydrogenothermus marinus (Beblo-Vranesevic, Galinski, Rachel, Huber, & Rettberg, 2017). It is possible that diglycerol phosphate was significantly up-regulated by S. enterica Typhimurium to alleviate the heat stress caused the by the HPA treatment and the desiccation stress encountered during seed storage.  The increases and decreases in metabolite concentration, when expressed as a ratio of ST/SA, varied a lot between CLO and HPA - treated samples, with 95 (17.7%) identified metabolites increased in ST in HPA samples and 90 (16.8%) metabolites decreased in ST in CLO samples compared to SA (Table 3.4). This finding supports that the survival mechanism of S. enterica after sanitation stress on sprouting alfalfa seed is strain-dependent and treatment-dependent. In addition, the faster recovery of S. enterica Agona PARC 5 when compared to Typhimurium LMFS-S-JF-001 could contribute to the relatively high concentrations of plant metabolites on sprouting alfalfa seed in ST samples after 24 h of germination, as more nutrients leaching from plant were consumed by SA to support its proliferation.    86  Table 3.4 Numbers of significantly up- or down-regulated metabolites in the SA groups, with different types of antimicrobial treatments, compared to the corresponding ST groups. Sig: significantly. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. Treatment Ratio Total Sig altered  Sig Up | Down-regulated Sig Up | Down - regulated shared by all 3 types of antimicrobial treatments CTL ST_CTL / SA_CTL 33 8 | 25 1 | 0 CLO ST_CLO / SA_CLO 129 39 | 90 HPA ST_HPA / SA_HPA 131 95 | 36   3.4 Conclusions In this study, we determined the metabolite profiles of sprouting alfalfa seed surface compounds after antimicrobial treatments to identify metabolic pathways that may be important for S. enterica growth in this environment. A total of 535 compounds of known identity were identified in the sprout washing samples, with amino acid being the most abundant metabolite class. The metabolic profile of the sprout surface washing samples depends on both the antimicrobial 87  treatment type and the microbiota type. The types of treatments had a major impact on sample metabolic profiles, while the effect of microbiota type was much weaker statistically than treatments effect. The CLO treatment led to the significant decline of 44.5%, 49.5%, and 46.9% of metabolites detected on uninoculated, S. enterica Agona and Typhimurium colonized sprouting seed, probably via oxidation. However, the HPA treatment tended to increase and decrease similar numbers (around 30%) of metabolites compared to CTL. 15.1% of the identified metabolites, especially the amino acids and secondary metabolites, were diminished by both antimicrobial treatments.  It is difficult to make a strong case that the CTL and CLO treatment groups were much different between the strains, when expressed as ratios to IM (SA/IM and ST/IM). On the other hand, the presence of the S. enterica led to an increased number of metabolite changes after the HPA treatment which was mostly an increase in metabolite concentration upon treatment. The concentrations of 25 amino acids were increased upon S. enterica colonization (both SA and ST) after the HPA treatment. The potentially enhanced amino acid biosynthesis of S. enterica under a mild stress environment on sprouting alfalfa seed may contribute to its persistence in sprouts. When comparing ST to SA (ST/SA), strain type possessed a more significant effect on CLO and HPA -treated samples compared to CTL samples. There was no significant difference between the metabolite profiles of CTL samples with the 2 different S. enterica strains, while there were 17.7% increases in ST in HPA samples and 16.8% decreases in ST in CLO samples compared to SA.   88  In conclusion, this study provided unique and valuable data on the metabolic environment of sprouting alfalfa seed and supported that the high metabolic adaptivity and robustness of S. enterica contributes to its success as a spermosphere colonist on sprouting alfalfa seed after different types of sanitation treatments. This work presented a novel approach to investigate human pathogen-plant interactions and demonstrated the potential of metabolomics to elucidate the causes of pathogens’ persistence on produce. With better understanding of the interactions among S. enterica, indigenous microbiota, and sprouting seed, we can devise more targeted interventions to enhance sprout safety.  89  Chapter 4: Conclusion and Future Directions 4.1 Conclusion In the present study, the ability of 5 S. enterica strains to grow on sprouting alfalfa seed after three different antimicrobial seed treatments was characterized. Populations of all 5 S. enterica strains increased from below detection limit (< 10 log CFU/g) immediately after antimicrobial treatment to 4.1 – 9.0 log CFU/g after 24 h of germination and 3.9 – 7.1 log CFU/g on sprouts germinated from treated seeds after 6 days of germination. The results confirm the first hypothesis that S. enterica cells can recover from antimicrobial treatments and grow on sprouting alfalfa seeds. Similar to S. enterica, populations of indigenous aerobic bacteria on sprouting alfalfa seeds also increased significantly within the first 24 h of germination and reached 8 - 9 log CFU/g at the end of the 6-day germination period. It is worth mentioning that the artificial inoculation level of S. enterica used in this study was much higher than levels anticipated in naturally contaminated seeds, with no antimicrobial intervention strategy used during seed germination.  The second hypothesis was that the post-sanitation growth characteristics of S. enterica on sprouting alfalfa seed is strain-dependent and treatment-dependent. The hydrogen peroxide treatment applied to the seeds before sprouting retarded the growth of fast-growing S. enterica Agona and Enteriditis strains, while the S. enterica Typhimurium strain displayed a unique and slower diauxic growth pattern on sprouting alfalfa irrespective of antimicrobial treatment. Overall, the hydrogen peroxide treatment was the most effective in inhibiting S. enterica recovery, followed by the organic treatment. These findings suggest that we can accept the hypothesis that the post-sanitation behaviour of S. enterica depends on its strain type and the 90  type of antimicrobial treatments. Furthermore, alfalfa sprouts with the highest count of indigenous bacteria were observed with S. enterica Typhimurium or Agona PARC 5, but the antimicrobial treatments did not significantly affect the population of indigenous aerobic bacteria on the germinated alfalfa sprouts.  In order to understand the survival mechanisms Salmonella utilizes in its post-sanitation recovery on germinating seeds, the impacts of colonization of S. enterica and different antimicrobial treatments on metabolites released by sprouting alfalfa seed were also studied. The third hypothesis was that the survival and growth of sanitizer-injured S. enterica on sprouting alfalfa seed is supported by specific metabolic pathways that are linked to stress response. We also hypothesized that the survival mechanism S. enterica utilized to recover on sprouting alfalfa seed is strain-dependent and treatment-dependent. A total of 535 metabolites were identified in the sprouting seed washing samples, with amino acid being the most abundant metabolite class. Almost all metabolites, covering all major metabolic pathways, were significantly affected by one or both antimicrobial treatments. When compared to the uninoculated control, the chlorine treatment diminished almost half of the metabolites, probably via oxidation, However, the organic treatment tended to increase and decrease similar numbers (around 30%) of metabolites compared to the untreated control. 15.1% of the identified metabolites, especially the amino acids and secondary metabolites, were diminished by both antimicrobial treatments. These results suggested that the metabolite profile of sprouting alfalfa seed is treatment-dependent. In addition, the concentrations of 25 amino acids were increased upon S. enterica colonization after the organic treatment, suggesting the potentially enhanced amino acid biosynthesis of S. enterica. Specifically, S. enterica strain type (Agona PARC 5 or Typhimurium LMFS-S-JF-001) 91  possesses a more significant effect on metabolite profiles after antimicrobial treatments compared to those of the untreated control, which suggests that the metabolite profile of sprouting alfalfa seed is both treatment-dependent and strain-dependent. However, more research is needed to address the underlying mechanisms S. enterica utilizes to recover form sanitation stress on sprouting alfalfa seed.  In summary, this study addressed the importance of pathogens’ post-sanitation recovery in sprout outbreaks. The high metabolic adaptivity and robustness of S. enterica contributes to its success in recovering from sanitation stress and growing on sprouting alfalfa seeds. Our results provide some insight on mechanisms that may be important for pathogen interactions with sprouts after sanitation stress. With better understanding of the interactions among S. enterica, indigenous microbiota, and sprouting seed, more targeted interventions can be devised for the production of pathogen-free sprouted vegetables.  4.2 Future Directions The present work revealed that the persistence of S. enterica in sprouted vegetables could be associated with its fast recovery from sanitation stress during seed germination, but further research is required to investigate the post-sanitation behaviour of Salmonella in an environment more similar to the industrial production. Sprouts germinated under sterile laboratory conditions from seeds with an unnatural contamination level may not represent alfalfa sprouts produced at an industrial scale.   92  Further, sprouting alfalfa was the only type of sprouted vegetables used in this study. A wider range of sprouts should be examined in future studies to gain better insight into this poorly-understood sprout safety issue. There is presently little information about the recovery of Salmonella after sanitation stress during sprout germination.  Lastly, the metabolic mechanisms behind the fast recovery of sanitizer-injured S. enterica on sprouting alfalfa seed were not clearly identified, probably due to the limited number of metabolomics samples assessed. Metabolomics could be a novel approach to investigate Salmonella-sprout interactions to fully characterize the diversity of responses within S. enterica and to develop more promising food safety interventions for the sprout industry. Some of the interesting metabolite observations worth further investigation were: high level of N-acetyltryptophan produced by sprouting alfalfa seed and its surface microbiota after the strong oxidative stress caused by chlorine, elevated levels of polyamines (N-acetylputrescine and (N(1) + N(8))-acetylspermidine) upon S. enterica colonization, increased amino acid metabolism and decreased phospholipid content in S. enterica-colonized alfalfa samples after the organic treatment, and up-regulated synthesis of diglycerol phosphate in S. enterica Typhimurium cells on sprouting alfalfa seed.  93  References  Abee, T., & Wouters, J. A. (1999). Microbial stress response in minimal processing. International Journal of Food Microbiology, 50(1), 65-91. doi:10.1016/S0168-1605(99)00078-1 Angulo, F., & Swedlow, D. (1998). Salmonella Enteritidis infections in the United States. Journal of the American Veterinary Medicine Association, 213, 1729-1731.  Antunes, L. C. M., Andersen, S. K., Menendez, A., Arena, E. T., Han, J., Rosana, B. R. F., . . . Finlay, B. B. (2011). Metabolomics reveals phospholipids as important nutrient sources during Salmonella growth in bile in vitro and in vivo. Journal of Bacteriology, 193(18), 4719-4725. doi:10.1128/JB.05132-11 Arya, G., Holtslander, R., Robertson, J., Yoshida, C., Harris, J., Parmley, J., . . . Poppe, C. (2017). Epidemiology, pathogenesis, genoserotyping, antimicrobial resistance, and prevention and control of Non-Typhoidal Salmonella serovars. Current Clinical Microbiology Reports, 4(1), 43-53. doi:10.1007/s40588-017-0057-7 Asakura, H., Tachibana, M., Taguchi, M., Hiroi, T., Kurazono, H., Makino, S.-I., . . . Igimi, S. (2016). Seasonal and growth-dependent dynamics of bacterial community in radish sprouts: bacterial structures of radish sprouts. Journal of Food Safety, 36(3), 392-401. doi:10.1111/jfs.12256 Badri, D. V., Zolla, G., Bakker, M. G., Manter, D. K., & Vivanco, J. M. (2013). Potential impact of soil microbiomes on the leaf metabolome and on herbivore feeding behavior. New Phytologist, 198(1), 264-273. doi:10.1111/nph.12124 Baranyi, J., & Roberts, T. A. (1994). A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol, 23(3-4), 277-294.  Barba-Espin, G., Diaz-Vivancos, P., Clemente-Moreno, M. J., Albacete, A., Faize, L., Faize, M., . . . Hernández, J. A. (2010). Interaction between hydrogen peroxide and plant hormones during germination and the early growth of pea seedlings. Plant, Cell & Environment, 33(6), 981. doi:10.1111/j.1365-3040.2010.02120.x Barrett, E. L., & Clark, M. A. (1987). Tetrathionate reduction and production of hydrogen sulfide from thiosulfate. Microbiological Reviews, 51(2), 192-205.  Battesti, A., Majdalani, N., & Gottesman, S. (2011). The RpoS-mediated general stress response in Escherichia coli. Annual Review of Microbiology, 65(1), 189-213. doi:10.1146/annurev-micro-090110-102946 BC Centre for Disease Control. (2017). Reportable Disease Dashboard. Retrieved from http://www.bccdc.ca/health-info/disease-system-statistics/reportable-disease-dashboard Beblo-Vranesevic, K., Galinski, E. A., Rachel, R., Huber, H., & Rettberg, P. (2017). Influence of osmotic stress on desiccation and irradiation tolerance of (hyper)-thermophilic microorganisms. Archives of Microbiology, 199(1), 17-28. doi:10.1007/s00203-016-1269-6 Bergeron, J. R. C., Worrall, L. J., Sgourakis, N. G., DiMaio, F., Pfuetzner, R. A., Felise, H. B., . . . Strynadka, N. C. J. (2013). A refined model of the prototypical Salmonella SPI-1 T3SS basal body reveals the molecular basis for its assembly. PLoS pathogens, 9(4), e1003307. doi:10.1371/journal.ppat.1003307 94  Berrios-Rodriguez, A., Olanya, O. M., Annous, B. A., Cassidy, J. M., Orellana, L., & Niemira, B. A. (2017). Survival of Salmonella Typhimurium on soybean sprouts following treatments with gaseous chlorine dioxide and biocontrol Pseudomonas bacteria. Food Science and Biotechnology, 26(2), 513-520. doi:10.1007/s10068-017-0071-9 Blackburn, B. O., & Ellis, E. M. (1973). Lactose-fermenting Salmonella from dried Milk and milk-drying plants. Applied Microbiology, 26(5), 672-674.  Böhme, K., Fernández-No, I. C., Pazos, M., Gallardo, J. M., Barros-Velázquez, J., Cañas, B., & Calo-Mata, P. (2013). Identification and classification of seafood-borne pathogenic and spoilage bacteria: 16S rRNA sequencing versus MALDI-TOF MS fingerprinting. Electrophoresis, 34(6), 877. doi:10.1002/elps.201200532 Brandl, M. T., Cox, C. E., & Teplitski, M. (2013). Salmonella interactions with plants and their associated microbiota. Phytopathology, 103(4), 316.  Brankatschk, K., Kamber, T., Pothier, J. F., Duffy, B., & Smits, T. H. M. (2014). Transcriptional profile of Salmonella enterica subsp. enterica serovar Weltevreden during alfalfa sprout colonization. Microbial Biotechnology, 7(6), 528-544. doi:10.1111/1751-7915.12104 Brencic, A., & Winans, S. C. (2005). Detection of and response to signals involved in host-microbe interactions by plant-associated bacteria. Microbiology and Molecular Biology Reviews, 69(1), 155-194. doi:10.1128/MMBR.69.1.155-194.2005 Brenner, F. W., Villar, R. G., Angulo, F. J., Tauxe, R., & Swaminathan, B. (2000). Salmonella nomenclature. Journal of Clinical Microbiology, 38(7), 2465-2467.  Brul, S., & Coote, P. (1999). Preservative agents in foods. Mode of action and microbial resistance mechanisms. International Journal of Food Microbiology, 50(1-2), 1-17. doi:10.1016/S0168-1605(99)00072-0 Bruno, V. M., Hannemann, S., Lara-Tejero, M., Flavell, R. A., Kleinstein, S. H., & Galán, J. E. (2009). Salmonella Typhimurium type III secretion effectors stimulate innate immune responses in cultured epithelial cells. PLoS pathogens, 5(8), e1000538. doi:10.1371/journal.ppat.1000538 Bumann, D., & Schothorst, J. (2017). Intracellular Salmonella metabolism. Cell Microbiology, 19(10). doi:10.1111/cmi.12766 Buxton, A., & Fraser, G. P. D. (1977). Animal Microbiology. Philadelphia, PA: Blackwell Scientific Publications. Cai, Y., Ng, L. K., & Farber, J. M. (1997). Isolation and characterization of nisin-producing Lactococcus lactis subsp. lactis from bean-sprouts. Journal of Applied Microbiology, 83(4), 499-507. doi:10.1046/j.1365-2672.1997.00262.x CAN/CGSB-32.310. (2003). Organic production systems – General principles and management standards. Retrieved from http://www.tpsgc-pwgsc.gc.ca/ongc-cgsb/programme-program/normes-standards/internet/bio-org/principes-principles-eng.html#a9  CAN/CGSB-32.311. (2003). Organic production systems – Permitted substances lists. Retrieved from http://www.tpsgc-pwgsc.gc.ca/ongc-cgsb/programme-program/normes-standards/internet/bio-org/permises-permitted-eng.html#a71  Canadian Food Inspection Agency. (2007). Code of Practice for the Hygienic Production of Sprouted Seeds. Retrieved from http://www.inspection.gc.ca/food/fresh-fruits-and-vegetables/food-safety/sprouted-seeds/eng/1413825271044/1413825272091 95  Castro-Rosas, J., & Escartín, E. F. (2000). Survival and growth of Vibrio cholerae O1, Salmonella Typhi, and Escherichia coli O157:H7 in alfalfa sprouts. Journal of Food Science, 65(1), 162-165. doi:10.1111/j.1365-2621.2000.tb15973.x Centers for Disease Control and Prevention. (2018a). List of selected multistate foodborne outbreak investigations. Retrieved from http://www.cdc.gov/foodsafety/outbreaks/multistate-outbreaks/outbreaks-list.html Centers for Disease Control and Prevention. (2018b). National Outbreak Reporting System (NORS) Dashboard.  Retrieved from https://wwwn.cdc.gov/norsdashboard/ Cevallos-Cevallos, J. M., Danyluk, M. D., & Reyes-De-Corcuera, J. I. (2011). GC-MS based metabolomics for rapid simultaneous detection of Escherichia coli O157:H7, Salmonella Typhimurium, Salmonella Muenchen, and Salmonella Hartford in ground beef and chicken. Journal of Food Science, 76(4), M238-M246. doi:10.1111/j.1750-3841.2011.02132.x Chavatte, N., Lambrecht, E., Van Damme, I., Sabbe, K., & Houf, K. (2016). Abundance, diversity and community composition of free-living protozoa on vegetable sprouts. Food Microbiology, 55, 55.  Cheville, A. M., Arnold, K. W., Buchrieser, C., Cheng, C. M., & Kaspar, C. W. (1996). RpoS regulation of acid, heat, and salt tolerance in Escherichia coli O157:H7. Applied and Environmental Microbiology, 62(5), 1822-1824.  Dawoud, T. M., Davis, M. L., Park, S. H., Kim, S. A., Kwon, Y. M., Jarvis, N., . . . Ricke, S. C. (2017). The potential link between thermal resistance and virulence in Salmonella: a review. Frontiers in Veterinary Science, 4. doi:10.3389/fvets.2017.00093 Delbeke, S., Ceuppens, S., Jacxsens, L., & Uyttendaele, M. (2015). Survival of Salmonella and Escherichia coli O157:H7 on strawberries, basil, and other leafy greens during storage. Journal of Food Protection, 78(4), 652-660. doi:10.4315/0362-028X.JFP-14-354 Dhar, M., W Sepkovic, D., Hirani, V., Magnusson, R., & Lasker, J. (2008). Omega oxidation of 3-hydroxy fatty acids by the human CYP4F gene subfamily enzyme CYP4F11. Journal of Lipid Research, 49(3), 612-624. doi:10.1194/jlr.M700450-JLR200 Dieckmann, R., Helmuth, R., Erhard, M., & Malorny, B. (2008). Rapid classification and identification of Salmonellae at the species and subspecies levels by whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry. Applied and Environmental Microbiology, 74(24), 7767-7778. doi:10.1128/AEM.01402-08 Dieckmann, R., & Malorny, B. (2011). Rapid screening of epidemiologically important Salmonella enterica subsp. enterica serovars by whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry. Applied and Environmental Microbiology, 77(12), 4136-4146. doi:10.1128/AEM.02418-10 Ding, H., & Fu, T.-J. (2016). Assessing the public health impact and effectiveness of interventions to prevent Salmonella contamination of sprouts. Journal of Food Protection, 79(1), 37-42. doi:10.4315/0362-028X.JFP-15-184 Ding, H., Fu, T. J., & Smith, M. A. (2013). Microbial contamination in sprouts: how effective is seed disinfection treatment? Journal of Food Science, 78(4), R495-R501. doi:10.1111/1750-3841.12064 Doyle, M. P., & Erickson, M. C. (2008). Summer meeting 2007 - the problems with fresh produce: an overview. Journal of Applied Microbiology, 105(2), 317-330. doi:10.1111/j.1365-2672.2008.03746.x 96  Ebert, A. W., Chang, C. H., Yan, M. R., & Yang, R. Y. (2017). Nutritional composition of mungbean and soybean sprouts compared to their adult growth stage. Food Chemistry, 237, 15-22. doi:10.1016/j.foodchem.2017.05.073 Edsall, G., Gaines, S., Landy, M., Tigertt, W. D., Sprinz, H., Trapani, R. J., . . . Benenson, A. S. (1960). Studies on infection and immunity in experimental typhoid fever. I. Typhoid fever in chimpanzees orally infected with Salmonella typhosa. The Journal of Experimental Medicine, 112(1), 143-166. doi:10.1084/jem.112.1.143 El-Hamalawi, Z. A. (1986). Components in alfalfa root extract and root exudate that increase oospore germination of Phytophthora megaspermaf. sp.medicaginis. Phytopathology, 76(5), 508. doi:10.1094/Phyto-76-508 Ercsey-Ravasz, M., Toroczkai, Z., Lakner, Z., & Baranyi, J. (2012). Complexity of the international agro-food trade network and its impact on food safety. PLOS One, 7(5), e37810. doi:10.1371/journal.pone.0037810 European Centre for Disease Prevention and Control. (2011). The european union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2009: EU summary report on trends and sources of zoonoses and zoonotic agents and food-borne outbreaks 2009. EFSA Journal, 9(3), 2090. doi:10.2903/j.efsa.2011.2090 Fabrega, A., Fàbrega, A., & Vila. (2013). Salmonella enterica serovar Typhimurium skills to succeed in the host: virulence and regulation. Clinical Microbiology Reviews, 26(2), 308-341. doi:10.1128/CMR.00066-12 Fang, L., Parti, R., & Hu, P. (2011). Characterization of N-acetyltryptophan degradation products in concentrated human serum albumin solutions and development of an automated high performance liquid chromatography–mass spectrometry method for their quantitation. Journal of Chromatography A, 1218(41), 7316-7324. doi:https://doi.org/10.1016/j.chroma.2011.08.044 Farber, J. M., & Brown, B. E. (1990). Effect of prior heat shock on heat resistance of Listeria monocytogenes in meat. Applied and Environmental Microbiology, 56(6), 1584-1587.  Farfour, E., Leto, J., Barritault, M., Barberis, C., Meyer, J., Dauphin, B., . . . Join-Lambert, O. (2012). Evaluation of the andromas matrix-assisted laser desorption ionization-time of flight mass spectrometry system for identification of aerobically growing gram-positive bacilli. Journal of Clinical Microbiology, 50(8), 2702. doi:10.1128/JCM.00368-12 Fett, W. F. (2002). Reduction of the native microflora on alfalfa sprouts during propagation by addition of antimicrobial compounds to the irrigation water. International Journal of Food Microbiology, 72(1), 13-18. doi:https://doi.org/10.1016/S0168-1605(01)00730-9 Fett, W. F. (2006). Inhibition of Salmonella enterica by plant-associated Pseudomonads in vitro and on sprouting alfalfa seed. Journal of Food Protection, 69(4), 719-728. Fiehn, O. (2002). Metabolomics - the link between genotypes and phenotypes. Plant Molecular Biology, 48(1), 155-171. doi:10.1023/A:1013713905833 Field, H. (1958). Salmonellosis in Animals. Veterinary Research, 70, 1050-1052. Finnegan, M., Linley, E., Denyer, S. P., McDonnell, G., Simons, C., & Maillard, J.-Y. (2010). Mode of action of hydrogen peroxide and other oxidizing agents: differences between liquid and gas forms. The Journal of Antimicrobial Chemotherapy, 65(10), 2108-2115. doi:10.1093/jac/dkq308 97  Fletcher, J., Leach, J. E., Eversole, K., & Tauxe, R. (2013). Human pathogens on plants: designing a multidisciplinary strategy for research. Phytopathology, 103(4), 306-315. doi:10.1094/PHYTO-09-12-0236-IA Fong, K., LaBossiere, B., Andrea, I. M. S., Delaquis, P., Goodridge, L., Levesque, R. C., . . . Wang, S. (2017). Characterization of four novel bacteriophages isolated from British Columbia for control of non-typhoidal Salmonella in vitro and on sprouting alfalfa seeds. Frontiers in Microbiology, 8. doi:10.3389/fmicb.2017.02193 Fong, K., & Wang, S. (2016). Heat resistance of Salmonella enterica is increased by pre-adaptation to peanut oil or sub-lethal heat exposure. Food Microbiology, 58, 139-147. doi:10.1016/j.fm.2016.04.004 Food and Drug Administration. (2015a). GRAS substances (SCOGS) database - select committee on GRAS substances (SCOGS) opinion: acetic acid; sodium acetate; sodium diacetate. Retrieved from http://www.fda.gov/Food/IngredientsPackagingLabeling/GRAS/SCOGS/ucm255100.htm  Food and Drug Administration. (2015b). GRAS substances (SCOGS) database - select committee on GRAS substances (SCOGS) opinion: hydrogen peroxide. Retrieved from http://www.fda.gov/Food/IngredientsPackagingLabeling/GRAS/SCOGS/ucm260427.htm  Food and Drug Administration. (2016). Outbreaks - outbreak investigations. Retrieved from http://www.fda.gov/Food/RecallsOutbreaksEmergencies/Outbreaks/ucm272351.htm  Food and Drug Administration. (2017a). Compliance with and recommendations for implementation of the standards for the growing, harvesting, packing, and holding of produce for human consumption for sprout operations: guidance for industry. Retrieved from https://www.fda.gov/downloads/food/guidanceregulation/guidancedocumentsregulatoryinformation/ucm537031.pdf. Food and Drug Administration. (2017b). FY 2014 – 2016 microbiological sampling assignment summary report: sprouts. Retrieved from https://www.fda.gov/downloads/Food/ComplianceEnforcement/Sampling/UCM566981.pdf Fu, L. L., & Li, J. R. (2014). Microbial source tracking: a tool for identifying sources of microbial contamination in the food chain. Critical Reviews in Food Science and Nutrition, 54(6), 699-707. doi:10.1080/10408398.2011.605231 Galán, J. E. (2001). Salmonella interactions with host cells: type III secretion at work. Annual Review of Cell and Developmental Biology, 17(1), 53-86. doi:10.1146/annurev.cellbio.17.1.53 Gandhi, M., & Matthews, K. R. (2003). Efficacy of chlorine and calcinated calcium treatment of alfalfa seeds and sprouts to eliminate Salmonella. International Journal of Food Microbiology, 87(3), 301-306. doi:https://doi.org/10.1016/S0168-1605(03)00108-9 Garg, R. C. (2016). Fenugreek: multiple health benefits A2 - Gupta, Ramesh C Nutraceuticals. Boston, MA: Academic Press. Ge, C., & Bohrerova. (2013). Inactivation of internalized Typhimurium in lettuce and green onion using ultraviolet C irradiation and chemical sanitizers. Journal of Applied Microbiology, 114(5), 1415-1424. doi:10.1111/jam.12154 Gómez-López, V. M., & Ebooks, C. (2012). Decontamination of fresh and minimally processed produce (1 ed.). Ames, IA: Blackwell Publications. 98  Greenway, D. L. A., & Dyke, K. G. H. (1979). Mechanism of the inhibitory action of linoleic acid on the growth of Staphylococcus aureus. Journal of General Microbiology, 115(1), 233. doi:10.1099/00221287-115-1-233 Guo, X., Chen, J., Brackett, R. E., & Beuchat, L. R. (2001). Survival of Salmonellae on and in tomato plants from the time of inoculation at flowering and early stages of fruit development through fruit ripening. Applied and Environmental Microbiology, 67(10), 4760-4764. doi:10.1128/AEM.67.10.4760-4764.2001 Han, J., Antunes, L. C. M., Finlay, B. B., & Borchers, C. H. (2010). Metabolomics: towards understanding host-microbe interactions. Future Microbiology, 5(2), 153-161. doi:10.2217/fmb.09.132 Han, S., & Micallef, S. A. (2014). Salmonella Newport and Typhimurium colonization of fruit differs from leaves in various tomato cultivars. Journal of Food Protection, 77(11), 1844-1850. doi:10.4315/0362-028X.JFP-13-562 Han, S., & Micallef, S. A. (2016). Environmental metabolomics of the tomato plant surface provides insights on Salmonella enterica colonization. Applied and Environmental Microbiology, 82(10), 3131-3142. doi:10.1128/AEM.00435-16 Hao, L.-y., Willis, D. K., Andrews-Polymenis, H., McClelland, M., & Barak, J. D. (2012). Requirement of siderophore biosynthesis for plant colonization by Salmonella enterica. Applied and Environmental Microbiology, 78(13), 4561-4570. doi:10.1128/AEM.07867-11 Hayward, M. R., AbuOun, M., La Ragione, R. M., Tchórzewska, M. A., Cooley, W. A., Everest, D. J., . . . Woodward, M. J. (2014). SPI-23 of S. Derby: role in adherence and invasion of porcine tissues. PLOS One, 9(9), e107857. doi:10.1371/journal.pone.0107857 Hong, E.-J., & Kang, D.-H. (2016). Effect of sequential dry heat and hydrogen peroxide treatment on inactivation of Salmonella Typhimurium on alfalfa seeds and seeds germination. Food Microbiology, 53, 9-14. doi:https://doi.org/10.1016/j.fm.2015.08.002 Howard, M. B., & Hutcheson, S. W. (2003). Growth dynamics of Salmonella enterica strains on alfalfa sprouts and in waste seed irrigation water. Applied Environmental Microbiology, 69(1), 548-553.  Hsieh, S.-Y., Tseng, C.-L., Lee, Y.-S., Kuo, A.-J., Sun, C.-F., Lin, Y.-H., & Chen, J.-K. (2008). Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS. Molecular & Cellular Proteomics, 7(2), 448-456. doi:10.1074/mcp.M700339-MCP200 Huang, L.-S., & Grunwald, C. (1988). Sterol and phospholipid changes during alfalfa seed germination. Phytochemistry, 27(7), 2049-2053. doi:10.1016/0031-9422(88)80095-5 Hummel, J., Selbig, J., Walther, D., & Kopka, J. (2007). The Golm Metabolome Database: a database for GC-MS based metabolite profiling. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/4735_2007_0229 Humphrey, T. J. (2004). Science and society: Salmonella, stress responses and food safety. Nature Reviews Microbiology, 2(6), 504-509. doi:10.1038/nrmicro907 Humphrey, T. J., Williams, A., McAlpine, K., Lever, M. S., Guard-Petter, J., & Cox, J. M. (1996). Isolates of Salmonella enterica Enteritidis PT4 with enhanced heat and acid tolerance are more virulent in mice and more invasive in chickens. Epidemiology and Infection, 117(1), 79-88. doi:10.1017/S0950268800001151 99  Hurley, D., McCusker, M. P., Fanning, S., & Martins, M. (2014). Salmonella-host interactions - modulation of the host innate immune system. Frontiers in Immunology, 5, 481. doi:10.3389/fimmu.2014.00481 Hwang, C., Ling, F., L Andersen, G., Lechevallier, M., & Liu, W.-T. (2012). Microbial community dynamics of an urban drinking water distribution system subjected to phases of chloramination and chlorination treatments. Applied and Environmental Microbiology, 78(22), 7856-7865. doi:10.1128/AEM.01892-12  Ishibashi, Y., Yamamoto, K., Tawaratsumida, T., Yuasa, T., & Iwaya-Inoue, M. (2008). Hydrogen peroxide scavenging regulates germination ability during wheat (Triticum aestivum L.) seed maturation. Plant Signaling & Behavior, 3(3), 183-188. doi:10.4161/psb.3.3.5540 Jadhav, S., Gulati, V., Fox, E. M., Karpe, A., Beale, D. J., Sevior, D., . . . Palombo, E. A. (2015). Rapid identification and source-tracking of Listeria monocytogenes using MALDI-TOF mass spectrometry. International Journal of Food Microbiology, 202, 1-9. doi:10.1016/j.ijfoodmicro.2015.01.023 Jay, J. M., Loessner, M. J., Golden, D. A., SpringerLink ebooks, C., Materials, S., & Ebook, C. (2005). Modern Food Microbiology (7th ed.). New York, NY: Springer. Jelsbak, L., Thomsen, L. E., Wallrodt, I., Jensen, P. R., & Olsen, J. E. (2012). Polyamines are required for virulence in Salmonella enterica serovar Typhimurium. PLOS One, 7(4), e36149. doi:10.1371/journal.pone.0036149 Jones, D. L., & Darrah, P. R. (1994). Amino-acid influx at the soil-root interface of Zea mays L. and its implications in the rhizosphere. Plant and Soil, 163(1), 1-12. doi:10.1007/BF00033935 Jorge, B. V. (2016). Detection of foodborne pathogens using MALDI-TOF mass spectrometry. In Antimicrobial Food Packaging (pp. 203-214). New York, NY: Elsevier. Joris van der, H., Bosman, E. S., Reynolds, L. A., & Finlay, B. B. (2015). Direct measurement of oxidative and nitrosative stress dynamics in Salmonella inside macrophages. Proceedings of the National Academy of Sciences, 112(2), 560. doi:10.1073/pnas.1414569112 Kamilova, F., Kravchenko, L. V., Shaposhnikov, A. I., Azarova, T., Makarova, N., & Lugtenberg, B. (2006). Organic acids, sugars, and L-tryptophane in exudates of vegetables growing on stonewool and their effects on activities of rhizosphere bacteria. Molecular Plant-Microbe Interactions, 19(3), 250.  Kang, L., Li, N., Li, P., Zhou, Y., Gao, S., Gao, H., . . . Wang, J. (2017). MALDI-TOF mass spectrometry provides high accuracy in identification of Salmonella at species level but is limited to type or subtype Salmonella serovars. European Journal of Mass Spectrometry, 23(2), 70-82. doi:10.1177/1469066717699216 Kidwai, A. S., Mushamiri, I., Niemann, G. S., Brown, R. N., Adkins, J. N., & Heffron, F. (2013). Diverse secreted effectors are required for Salmonella persistence in a mouse infection model. PLOS ONE, 8(8), e70753. doi:10.1371/journal.pone.0070753 Kim, D. K., Jeong, S. C., Gorinstein, S., & Chon, S. U. (2012). Total polyphenols, antioxidant and antiproliferative activities of different extracts in mungbean seeds and sprouts. Plant Foods for Human Nutrition, 67(1), 71-75. doi:10.1007/s11130-011-0273-x Kimes, N. E., Callaghan, A. V., Aktas, D. F., Smith, W. L., Sunner, J., Golding, B., . . . Morris, P. J. (2013). Metagenomic analysis and metabolite profiling of deep-sea sediments from 100  the Gulf of Mexico following the deepwater horizon oil spill. Frontiers in Microbiology, 4, 50. doi:10.3389/fmicb.2013.00050 Kwan, G., Charkowski, A. O., & Barak, J. D. (2013). Salmonella enterica suppresses Pectobacterium carotovorum subsp. carotovorum population and soft rot progression by acidifying the microaerophilic environment. mBio, 4(1), e00557-00512-e00557-00512. doi:10.1128/mBio.00557-12 Kwan, G., Pisithkul, T., Amador-Noguez, D., & Barak, J. (2015). De novo amino acid biosynthesis contributes to Salmonella enterica growth in alfalfa seedling exudates. Applied and Environmental Microbiology, 81(3), 861.  Kwon, Y. M., Park, S. Y., Birkhold, S. G., & Ricke, S. C. (2000). Induction of resistance of Salmonella Typhimurium to environmental stresses by exposure to short-chain fatty acids. Journal of Food Science, 65(6), 1037-1040. doi:doi:10.1111/j.1365-2621.2000.tb09413.x Kylen, A. M., Kylen, A. M., & McCready, R. M. (1975). Nutrients in seeds and sprouts of alfalfa, lentils, mung beans and soybeans. Journal of Food Science, 40(5), 1008-1009. doi:10.1111/j.1365-2621.1975.tb02254.x Landry, K. S., Sela, D. A., & McLandsborough, L. (2018). Influence of sprouting environment on the microbiota of sprouts. Journal of Food Safety, 38(1). doi:10.1111/jfs.12380 Law, J. W. F., Ab Mutalib, N. S., Chan, K. G., & Lee, L. H. (2015). Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Frontiers in Microbiology, 5. doi:10.3389/fmicb.2014.00770 Lei, L., Wang, W., Xia, C., & Liu, F. (2016). Salmonella virulence factor SsrAB regulated factor modulates inflammatory responses by enhancing the activation of NF-κB signaling pathway. Journal of Immunology, 196(2), 792.  Li, Q. (2015). Comparison of the efficacy of three sanitizers with 20,000 ppm calcium hypochlorite for inactivation of Salmonella on artificially contaminated alfalfa seeds. (Dissertation/Thesis), ProQuest Dissertations Publishing. Retrieved from http://ubc.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8MwDLZgXBAHQIB4DMkSVwp9pO16QmgwuMN9ypJUq9R1Zd0O5b_wX7H7GGVIuyD1klaybKVxPjvOZwDPvbOtDZ9AUz8QkZLCBCaIA1J_EslIa1cHsRYVjUG3VOexvRrTTHfrJSvXreeKs-b3Dq1jlwBGGD7kHxb3keLz1qapxi7sOV5gM5v-SxcQreN3is0iCk0Gjt8QP7Xj8I9Xrraa0SG0x85ticnGDcDfNI7_0v4IDp46x_HHsGOyE_garlsT4jxGAohomGdCqrIeL4zBglkzkk_CjsiZXHTtWzIb83yGNOkqWc1wWuZzNeUCv6VBgsaYZHyJok4Bs6Q3mZISXH2F9IJ_4ZrNIi2RC-glF-kQHkaZxvxgQRttcQo3o-f34avVmjtuVkMx_rHVO4NeRqLPAXWgfM-nyFx4UsSOkCbSE619Cgojo2xxAf1tki63f76CfYI2fp0s6UNvuViZ64pZ4Rss786_   Liu, D., Cui, Y., Walcott, R., & Chen, J. (2018). Fate of Salmonella enterica and enterohemorrhagic Escherichia coli cells artificially internalized into vegetable seeds during germination. Applied and Environmental Microbiology, 84(1).  Liu, S., Hu, X., Lohrke, S. M., Baker, C. J., Buyer, J. S., de Souza, J. T., & Roberts, D. P. (2007). Role of sdhA and pfkA and catabolism of reduced carbon during colonization of cucumber roots by Enterobacter cloacae. Microbiology, 153(9), 3196-3209. doi:10.1099/mic.0.2006/005538-0 101  Lynch, J. M. (1980). Effects of organic acids on the germination of seeds and growth of seedlings. Plant, Cell and Environment, 3(4), 255-259. doi:10.1111/1365-3040.ep11581824 Majowicz, S. E., Musto, J., Scallan, E., Angulo, F. J., Kirk, M., O'Brien, S. J., . . . Hoekstra, R. M. (2010). The global burden of nontyphoidal Salmonella Gastroenteritis. Clinical Infectious Diseases, 50(6), 882-889. doi:10.1086/650733 Manach, C., Scalbert, A., Morand, C., Rémésy, C., & Jiménez, L. (2004). Polyphenols: food sources and bioavailability. The American Journal of Clinical Nutrition, 79(5), 727.  Marcobal, A., Kashyap, P. C., Nelson, T. A., Aronov, P. A., Donia, M. S., Spormann, A., . . . Sonnenburg, J. L. (2013). A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice. The ISME Journal, 7(10), 1933. doi:10.1038/ismej.2013.89 Matos, A., & Garland, J. L. (2005). Effects of community versus single strain inoculants on the biocontrol of Salmonella and microbial community dynamics in alfalfa sprouts. Journal of Food Protection, 68(1), 40-48. doi:10.4315/0362-028X-68.1.40 Matthews, K. R. (2006). Microbiology of Fresh Produce. Washington, D.C: ASM Press. Mattick, K. L., Jørgensen, F., Legan, J. D., Cole, M. B., Porter, J., Lappin-Scott, H. M., & Humphrey, T. J. (2000). Survival and filamentation of Salmonella enterica serovar enteritidis PT4 and Salmonella enterica serovar Typhimurium DT104 at low water activity. Applied and Environmental Microbiology, 66(4), 1274-1279. doi:10.1128/AEM.66.4.1274-1279.2000 McDowell, E. T., Kapteyn, J., Schmidt, A., Li, C., Kang, J.-H., Descour, A., . . . Gang, D. R. (2011). Comparative functional genomic analysis of Solanum glandular trichome types. Plant Physiology, 155(1), 524-539. doi:10.1104/pp.110.167114 Mercier, J., & Lindow, S. E. (2000). Role of leaf surface sugars in colonization of plants by bacterial epiphytes. Applied and Environmental Microbiology, 66(1), 369-374. doi:10.1128/AEM.66.1.369-374.2000 Ming, X., Stein, T. P., Barnes, V., Rhodes, N., & Guo, L. (2012). Metabolic perturbance in autism spectrum disorders: a metabolomics study. Journal of Proteome Research, 11(12), 5856.  Müller, A. A., Dolowschiak, T., Sellin, M. E., Felmy, B., Verbree, C., Gadient, S., . . . Mikrobiologi. (2016). An NK cell perforin response elicited via IL-18 controls mucosal inflammation kinetics during Salmonella gut infection. PLoS Pathogens, 12(6), e1005723. doi:10.1371/journal.ppat.1005723 Na Jom, K., Frank, T., & Engel, K.-H. (2011). A metabolite profiling approach to follow the sprouting process of mung beans (Vigna radiata). Metabolomics, 7(1), 102-117. doi:10.1007/s11306-010-0236-5 Nei, D., Latiful, B. M., Enomoto, K., Inatsu, Y., & Kawamoto, S. (2011). Disinfection of radish and alfalfa seeds inoculated with Escherichia coli O157:H7 and Salmonella by a gaseous acetic acid treatment. Foodborne Pathogens and Disease, 8, 1089+.  Nelson, E. B. (2004). Microbial dynamics and interactions in the spermosphere. Annual Review of Phytopathology, 42(1), 271-309. doi:10.1146/annurev.phyto.42.121603.131041 Nieto, P. A., Pardo-Roa, C., Salazar-Echegarai, F. J., Tobar, H. E., Coronado-Arrázola, I., Riedel, C. A., . . . Bueno, S. M. (2016). New insights about excisable pathogenicity 102  islands in Salmonella and their contribution to virulence. Microbes and Infection, 18(5), 302-309. doi:10.1016/j.micinf.2016.02.001 Ohl, M. E., & Miller, S. I. (2001). Salmonella: A model for bacterial pathogenesis. Annual Review of Medicine, 52(1), 259-274. doi:10.1146/annurev.med.52.1.259 Ojima-Kato, T., Yamamoto, N., Nagai, S., Shima, K., Akiyama, Y., Ota, J., & Tamura, H.  (2017). Application of proteotyping Strain Solution™ ver. 2 software and theoretically calculated mass database in MALDI-TOF MS typing of Salmonella serotype. Applied Microbiology and Biotechnology, 101(23), 8557-8569. doi:10.1007/s00253-017-8563-3 Painter, J. A., Hoekstra, R. M., Ayers, T., Tauxe, R. V., Braden, C. R., Angulo, F. J., & Griffin, P. M. (2013). Attribution of foodborne illnesses, hospitalizations, and deaths to food commodities by using outbreak data, United States, 1998-2008. Emerging Infectious Diseases, 19(3), 407. doi:10.3201/eid1903.111866 Pao, S., Khalid, M. F., & Kalantari, A. (2005). Microbial profiles of on-line-procured sprouting seeds and potential hazards associated with enterotoxigenic Bacillus spp. in homegrown sprouts. Journal of Food Protection, 68(8), 1648-1653. doi:10.4315/0362-028X-68.8.1648 Paoli, M., Marles-Wright, J., & Smith, A. (2002). Structure-function relationships in heme-proteins. DNA and Cell Biology, 21(4), 271-280. doi:10.1089/104454902753759690 Phillips, D. A., Fox, T. C., King, M. D., Bhuvaneswari, T. V., & Teuber, L. R. (2004). Microbial products trigger amino acid exudation from plant roots. Plant Physiology, 136(1), 2887-2894. doi:10.1104/pp.104.044222 Phillips, D. A., Joseph, C. M., & Maxwell, C. A. (1992). Trigonelline and stachydrine released from alfalfa seeds activate NodD2 protein in Rhizobium meliloti. Plant Physiology, 99(4), 1526-1531. doi:10.1104/pp.99.4.1526 Pinu, F. R. (2016). Early detection of food pathogens and food spoilage microorganisms: application of metabolomics. Trends in Food Science & Technology, 54, 213-215. doi:10.1016/j.tifs.2016.05.018 Ponnusamy, K., Lee, S., & Lee, C. H. (2013). Time-dependent correlation of the microbial community and the metabolomics of traditional barley nuruk starter fermentation. Bioscience, Biotechnology, and Biochemistry, 77(4), 683-690. doi:10.1271/bbb.120665 Poza-Carrion, C., Suslow, T., & Lindow. (2013). Resident bacteria on leaves enhance survival of immigrant cells of Salmonella enterica. Phytopathology, 103(4), 341-351. doi:10.1094/PHYTO-09-12-0221-FI Public Health Agency of Canada. (2011). Pathogen safety data sheets: infectious substances - Salmonella enterica spp.  Retrieved from https://www.canada.ca/en/public-health/services/laboratory-biosafety-biosecurity/pathogen-safety-data-sheets-risk-assessment/salmonella-enterica.html Public Health Agency of Canada. (2015). Executive summary for the national enteric surveillance program 2013 annual report. Retrieved from https://www.nml-lnm.gc.ca/NESP-PNSME/surveillance-2013-eng.html Public Health Agency of Canada. (2016). Yearly food-borne illness estimates in Canada.   Retrieved from https://www.canada.ca/en/public-health/services/food-borne-illness-canada/yearly-food-borne-illness-estimates-canada.html 103  Roberts, D. P., Dery, P. D., Yucel, I., Buyer, J., Holtman, M. A., & Kobayashi, D. Y. (1999). Role of pfkA and general carbohydrate catabolism in seed colonization by Enterobacter cloacae. Applied and Environmental Microbiology, 65(6), 2513-2519.  Roberts, D. P., Dery, P. D., Yucel, I., & Buyer, J. S. (2000). Importance of pfkA for rapid growth of Enterobacter cloacae during colonization of crop seeds. Applied and Environmental Microbiology, 66(1), 87-91.  Roberts, D. P., McKenna, L. F., Lohrke, S. M., Rehner, S., & de Souza, J. T. (2007). Pyruvate dehydrogenase activity is important for colonization of seeds and roots by Enterobacter cloacae. Soil Biology and Biochemistry, 39(8), 2150-2159. doi:10.1016/j.soilbio.2007.03.027 Roseman, S., & Meadow, N. D. (1990). Signal transduction by the bacterial phosphotransferase system. Diauxie and the crr gene (J. Monod revisited). The Journal of Biological Chemistry, 265(6), 2993.  Sadler-Reeves, L., Aird, H., Pinna, E., Elviss, N., Fox, A., Kaye, M., . . . McLauchlin, J. (2016). The occurrence of Salmonella in raw and ready-to-eat bean sprouts and sprouted seeds on retail sale in England and Northern Ireland. Letters in Applied Microbiology, 62(2), 126-129. doi:10.1111/lam.12530 Sandrin, T. R., Goldstein, J. E., & Schumaker, S. (2013). MALDI TOF MS profiling of bacteria at the strain level: A review. Mass Spectrometry Reviews, 32(3), 188-217. doi:10.1002/mas.21359 Santillana Farakos, S., Hicks, J. W., Frye, J. G., & Frank, J. F. (2014). Relative survival of four serotypes of Salmonella enterica in low-water activity whey protein powder held at 36 and 70 degrees C at various water activity levels. Journal of Food Protection, 77(7), 1198-1200. doi:10.4315/0362-028x.jfp-13-327 Schilmiller, A. L., Miner, D. P., Larson, M., McDowell, E., Gang, D. R., Wilkerson, C., & Last, R. L. (2010). Studies of a biochemical factory: tomato trichome deep expressed sequence tag sequencing and proteomics. Plant Physiology, 153(3), 1212-1223. doi:10.1104/pp.110.157214 Schnider-Keel, U., Lejbølle, K. B., Baehler, E., Haas, D., & Keel, C. (2001). The sigma factor AlgU (AlgT) controls exopolysaccharide production and tolerance towards desiccation and osmotic stress in the biocontrol agent Pseudomonas fluorescens CHA0. Applied and Environmental Microbiology, 67(12), 5683-5693. doi:10.1128/AEM.67.12.5683-5693.2001 Schulthess, B., Brodner, K., Bloemberg, G. V., Zbinden, R., Böttger, E. C., & Hombach, M. (2013). Identification of Gram-positive cocci by use of matrix-assisted laser desorption ionization-time of flight mass spectrometry: comparison of different preparation methods and implementation of a practical algorithm for routine diagnostics. Journal of Clinical Microbiology, 51(6), 1834-1840. doi:10.1128/JCM.02654-12 Shen, Z., Mustapha, A., Lin, M., & Zheng, G. (2017). Biocontrol of the internalization of Salmonella enterica and Enterohaemorrhagic Escherichia coli in mung bean sprouts with an endophytic Bacillus subtilis. International Journal of Food Microbiology, 250, 37.  Shepherd, J. M. W., Liang, P., Jiang, X., Doyle, M. P., & Erickson, M. C. (2010). Microbiological analysis of composts produced on South Carolina poultry farms. Journal of Applied Microbiology, 108(6), 2067. doi:10.1111/j.1365-2672.2009.04610.x 104  Sikin, A. M., Zoellner, C., & Rizvi, S. S. H. (2013). Current intervention strategies for the microbial safety of sprouts. Journal of Food Protection, 76(12), 2099-2123. doi:10.4315/0362-028X.JFP-12-437 Silva, B. N., Cadavez, V., Teixeira, J. A., & Gonzales-Barron, U. (2017). Meta-analysis of the incidence of foodborne pathogens in vegetables and fruits from retail establishments in Europe. Current Opinion in Food Science, 18, 21-28. doi:https://doi.org/10.1016/j.cofs.2017.10.001 Simons, M., Permentier, H. P., de Weger, L. A., Wijffelman, C. A., & Lugtenberg, B. J. J. (1997). Amino acid synthesis is necessary for tomato root colonization by Pseudomonas fluorescens strain WCS365. Molecular Plant-Microbe Interactions, 10(1), 102-106. doi:10.1094/MPMI.1997.10.1.102 Singh, R., & Jiang, X. (2012). Thermal inactivation of acid-adapted Escherichia coli O157:H7 in dairy compost. Foodborne Pathogens and Disease, 9(8), 741.  Singh, R., Jiang, X., & Luo, F. (2010). Thermal inactivation of heat-shocked Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes in dairy compost. Journal of Food Protection, 73(9), 1633-1640. doi:10.4315/0362-028X-73.9.1633 Soldal, T., & Nissen, P. E. R. (1978). Multiphasic uptake of amino acids by barley roots. Physiologia Plantarum, 43(3), 181-188. doi:10.1111/j.1399-3054.1978.tb02561.x Solomon, E. B., Niemira, B. A., Sapers, G. M., & Annous, B. A. (2005). Biofilm formation, cellulose production, and curli biosynthesis by Salmonella originating from produce, animal, and clinical sources. Journal of Food Protection, 68(5), 906-912. doi:10.4315/0362-028X-68.5.906 Søltoft, M., Nielsen, J., Holst Laursen, K., Husted, S., Halekoh, U., & Knuthsen, P. (2010). Effects of organic and conventional growth systems on the content of flavonoids in onions and phenolic acids in carrots and potatoes. Journal of Agricultural and Food Chemistry, 58(19), 10323.  Stewart, D., Reineke, K., Ulaszek, J., Fu, T., & Tortorello, M. (2001). Growth of Escherichia coli O157:H7 during sprouting of alfalfa seeds. Letters in Applied Microbiology, 33(2), 95-99. doi:10.1046/j.1472-765x.2001.00957.x Stockwell, V. O., & Loper, J. E. (2005). The sigma factor RpoS is required for stress tolerance and environmental fitness of Pseudomonas fluorescens Pf-5. Microbiology, 151(9), 3001-3009. doi:10.1099/mic.0.28077-0 Sue, T., Obolonkin, V., Griffiths, H., & Villas-Bôas, S. G. (2011). An exometabolomics approach to monitoring microbial contamination in microalgal fermentation processes by using metabolic footprint analysis. Applied and Environmental Microbiology, 77(21), 7605-7610. doi:10.1128/AEM.00469-11 Suh, S.J., Silo-Suh, L., Woods, D. E., Hassett, D. J., Susan, E. H. W., & Ohman, D. E. (1999). Effect of rpoS mutation on the stress response and expression of virulence factors in Pseudomonas aeruginosa. Journal of Bacteriology, 181(13), 3890-3897.  Symes, S., Goldsmith, P., & Haines, H. (2015). Microbiological safety and food handling practices of seed sprout products in the australian State of Victoria. Journal of Food Protection, 78(7), 1387-1391. doi:10.4315/0362-028X.JFP-14-566 Tauxe, R. V., & Pavia, A. T. (1998). Salmonellosis: Nontyphoidal. In A. S. Evans & P. S. Brachman (Eds.), Bacterial Infections of Humans: Epidemiology and Control (pp. 613-630). Boston, MA: Springer US. 105  Teplitski, M., Noel, J. T., Alagely, A., & Danyluk, M. D. (2012). Functional genomics studies shed light on the nutrition and gene expression of non-typhoidal Salmonella and enterovirulent E. coli in produce. Food Research International, 45(2), 576-586. doi:10.1016/j.foodres.2011.06.020 Thomas, M. K., Majowicz, S. E., Sockett, P. N., Fazil, A., Pollari, F., Doré, K., . . . Edge, V. L. (2006). Estimated numbers of community cases of illness due to Salmonella, Campylobacter and Verotoxigenic Escherichia Coli: pathogen-specific community rates. The canadian Journal of Infectious Diseases & Medical Microbiology, 17(4), 229-234. doi:10.1155/2006/806874 Trimigno, A., Marincola, F. C., Dellarosa, N., Picone, G., & Laghi, L. (2015). Definition of food quality by NMR-based foodomics. Current Opinion in Food Science, 4, 99-104. doi:10.1016/j.cofs.2015.06.008 Undersander, D., Hall, M. H., Vassalotti, P., & Cosgrove, D. (2011). Alfalfa Germination & Growth. Retrieved from http://learningstore.uwex.edu/assets/pdfs/A3681.PDF Uzzau, S., Brown, D. J., Wallis, T., Rubino, S., Leori, G., Bernard, S., . . . Olsen, J. E. (2000). Host adapted serotypes of Salmonella enterica. Epidemiology and Infection, 125(2), 229-255. doi:10.1017/S0950268899004379 van der Heijden, J., & Finlay, B. B. (2012). Type III effector-mediated processes in Salmonella infection. Future Microbiology, 7(6), 685-703. doi:10.2217/fmb.12.49 van der Heijden, J., Reynolds, L. A., Deng, W., Mills, A., Scholz, R., Imami, K., . . . Finlay, B. B. (2016). Salmonella rapidly regulates membrane permeability to survive oxidative stress. mBio, 7(4), e01238. doi:10.1128/mBio.01238-16 Wang, L. L., & Johnson, E. A. (1992). Inhibition of Listeria monocytogenes by fatty acids and monoglycerides. Applied and Environmental Microbiology, 58(2), 624-629.  Wang, S. (2013). A colanic acid operon deletion mutation enhances induction of early antibody responses by live attenuated Salmonella vaccine strains. Infection and Immunity, 81(9), 3148-3162. doi:10.1128/IAI.00097-13 Wang, S. Y., Chen, C.T., Sciarappa, W., Wang, C. Y., & Camp, M. J. (2008). Fruit quality, antioxidant capacity, and flavonoid content of organically and conventionally grown blueberries. Journal of Agricultural and Food Chemistry, 56(14), 5788-5794. doi:10.1021/jf703775r Wattal, C., & Oberoi, J. K. (2016). Microbial identification and automated antibiotic susceptibility testing directly from positive blood cultures using MALDI-TOF MS and VITEK 2. European Journal of Clinical Microbiology & Infectious Diseases, 35(1), 75-82. doi:10.1007/s10096-015-2510-y Weiss, A., Hertel, C., Grothe, S., Ha, D., & Hammes, W. P. (2007). Characterization of the cultivable microbiota of sprouts and their potential for application as protective cultures. Systematic and Applied Microbiology, 30(6), 483-493. doi:10.1016/j.syapm.2007.03.006 Wesche, A. M., Gurtler, J. B., Marks, B. P., & Ryser, E. T. (2009). Stress, sublethal injury, resuscitation, and virulence of bacterial foodborne pathogens. Journal of Food Protection, 72(5). doi:10.4315/0362-028X-72.5.1121 Wiedemann, A. S., Wiedemann, A., & Virlogeux, P. (2015). Interactions of Salmonella with animals and plants. Frontiers in Microbiology, 5. doi:10.3389/fmicb.2014.00791 Willer, L. (2017). The World of Organic Agriculture: statistics and emerging rends 2017. Retrieved from https://shop.fibl.org/CHen/mwdownloads/download/link/id/785/?ref=1 106  Xu, Y., Cheung, W., Winder, C. L., & Goodacre, R. (2010). VOC-based metabolic profiling for food spoilage detection with the application to detecting Salmonella Typhimurium-contaminated pork. Analytical and Bioanalytical Chemistry, 397(6), 2439-2449. doi:10.1007/s00216-010-3771-z Yang, Y., Meier, F., Ann Lo, J., Yuan, W., Lee Pei Sze, V., Chung, H. J., & Yuk, H. G. (2013). Overview of recent events in the microbiological safety of sprouts and new intervention technologies. Comprehensive Reviews in Food Science and Food Safety, 12(3), 265-280. doi:10.1111/1541-4337.12010 Yousef, A. E., & Courtney, P. D. (2002). Basics of stress adaptation and implications in new-generation foods. Microbial Stress Adaptation and Food Safety, 1, 1-30.  Yu, P., Hegeman, A. D., & Cohen, J. D. (2014). A facile means for the identification of indolic compounds from plant tissues. The Plant Journal, 79(6), 1065-1075. doi:doi:10.1111/tpj.12607 Zajc-Satler, J., & Gragas, A. Z. (1977). Xylose lysine deoxycholate agar for the isolation of Salmonella and Shigella from clinical specimens. Medizinische Mikrobiologie und Parasitologie, 237(2-3), 196.  Zaragoza, W. J., Noel, J. T., & Teplitski, M. (2012). Spontaneous non-rdar mutations increase fitness of Salmonella in plants. Environmental Microbiology Reports, 4(4), 453.  Zhao, X., Ting, Z., Yu-Jie, Z., & De-Hua, L. (2008). Preparation of peracetic acid from acetic acid and hydrogen peroxide: experimentation and modeling. Journal of Processing Engineering, 8(1), 35-41.  107  Appendices  Appendix A   Post-sanitation recovery curves of S. enterica on sprouting alfalfa seed A.1 Comparisons of antimicrobial treatments  Figure A.1 Populations of S. enterica Agona (PARC 5) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. 01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit108   Figure A.2 Populations of S. enterica Agona (FSL S5-517) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. 01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit109   Figure A.3 Populations of S. enterica Enteriditis (LMFS-S-JF-005) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates.  01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit110   Figure A.4 Populations of S. enterica Daytona (LMFS-S-JF-009) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates.  01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit111   Figure A.5 Populations of S. enterica Typhimurium (LMFS-S-JF-001) on alfalfa sprouts germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. Characteristics of diauxic growth were observed with all the growth curves of S. enterica Typhimurium LMFS-S-JF-001 (Roseman & Meadow, 1990).    01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit112  A.2 Comparisons of S. enterica strains (A)  (B)  01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona FSL S5-517 EnteriditisDaytona Typhimurium Detection Limit01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona S5-517 EnteriditisDaytona Typhimurium Detection Limit113  (C)  (D)  01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona FSL S5-517 EnteriditisDaytona Typhimurium Detection Limit01234567890 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona S5-517 EnteriditisDaytona Typhimurium Detection Limit114  Figure A.6 Populations of S. enterica Agona PARC 5, Agona FSL S5-517, Enteriditis LMFS-S-JF-005, Daytona LMFS-S-JF-009, and Typhimurium LMFS-S-JF-001 on alfalfa sprouts germinated from seeds treated with (A) CTL (B) CLO (C) HPO (D) HPA over 6 days of germination. Error bars indicate the SD of three replicates. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment, and HPA represents the organic treatment.    115  Appendix B   Post-sanitation recovery curves of indigenous aerobic bacteria on sprouting alfalfa seed B.1 Comparisons of antimicrobial treatments  Figure B.1  Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Agona (PARC 5) and germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates. 2.003.004.005.006.007.008.009.0010.000 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit116   Figure B.2 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Agona (FSL S5-517) and germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates.   23456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit117   Figure B.3 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Enteriditis (LMFS-S-JF-005) and germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates.  23456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit118   Figure B.7 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Daytona (LMFS-S-JF-009) and germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates.    23456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit119   Figure B.8 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Typhimurium (LMFS-S-JF-001) and germinated from seeds treated with different antimicrobial treatments over 6 days of germination. Error bars indicate the SD of three replicates.    23456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Control NaClO H2O2 Organic Detection Limit120  B.2 Comparisons of S. enterica strains (A)  (B)   0123456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona FSL S5-517 EnteriditisDaytona Typhimurium Detection Limit0123456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona FSL S5-517 EnteriditisDaytona Typhimurium Detection Limit121  (C)  (D)  0123456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona FSL S5-517 EnteriditisDaytona Typhimurium Detection Limit0123456789100 20 40 60 80 100 120 140 160POPULATION (LOG CFU/G)TIME (H)Agona PARC 5 Agona FSL S5-517 EnteriditisDaytona Typhimurium Detection Limit122  Figure B.9 Populations of indigenous aerobic bacteria on sprouting alfalfa seed inoculated with S. enterica Agona PARC 5, Agona FSL S5-517, Enteriditis LMFS-S-JF-005, Daytona LMFS-S-JF-009, and Typhimurium LMFS-S-JF-001 and germinated from seeds treated with (A) CTL (B) CLO (C) HPO (D) HPA over 6 days of germination. Error bars indicate the SD of three replicates. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, HPO represents the H2O2 treatment, and HPA represents the organic treatment.    123  Appendix C  Significantly altered metabolites Table C.1 Metabolites significantly down-regulated in all washing samples of sprouting alfalfa seed treated with antimicrobial treatments (CLO or HPA) regardless of microbiota type compared to corresponding CTL at 24 h of germination. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. Major pathway Sub pathway Metabolite Name Amino acid Serine family (phosphoglycerate derived) serine Aromatic amino acid metabolism (PEP derived) shikimate tryptophan kynurenine tyramine 3-(4-hydroxyphenyl) propionate Aspartate family (OAA derived) aspartate 2-aminoadipate 6-oxopiperidine-2-carboxylate methionine sulfoxide Glutamate family (alpha-ketoglutarate derived) 1-methyl-4-imidazoleacetate carboxyethyl-GABA glutamate histidine betaine (hercynine) stachydrine homostachydrine Amines and polyamines (N(1) + N(8))-acetylspermidine Glutathione metabolism ophthalmate 5-oxoproline Carbohydrate Glycolysis pyruvate TCA cycle citrate malate Photorespiration tartarate Sucrose, glucose, fructose metabolism galactinol trehalose 124  gluconate Lipids Fatty acid, hydroxy 2-hydroxyheptanoate 3-hydroxyoctanoate 8-hydroxyoctanoate 2-hydroxydecanoate Fatty acid, Dicarboxylate adipate (C6-DC) pimelate (C7-DC) suberate (C8-DC) azelate (C9-DC) sebacate (C10-DC) undecanedioate (C11-DC) dodecanedioate (C12-DC) dodecenedioate (C12:1-DC) Fatty acid conjugate acetylcarnitine (C2) Choline metabolism choline Cofactors, Prosthetic Groups, Electron Carriers CoA metabolism pantothenate Carnitine metabolism deoxycarnitine Ascorbate metabolism ascorbate (Vitamin C) threonate Thiamine metabolism 5-(2-Hydroxyethyl)-4-methylthiazole Vitamin B metabolism (B6 or B12) pyridoxate pyridoxine (Vitamin B6) Nucleotide Purine metabolism allantoic acid allantoin guanine urate xanthosine 8-hydroxyguanine Pyrimidine metabolism 5,6-dihydrouridine 5-methylcytidine cytidine 2' or 3'-monophosphate 2'-O-methylcytidine Hormone metabolism Abscisic acid metabolism abscisate Auxin metabolism indoleacetylaspartate indole-3-carboxylic acid Secondary metabolism Alkaloids salidroside Benzenoids 2,4,6-trihydroxybenzoate 4-hydroxybenzoate 125  gentisic acid-5-glucoside hydroquinone beta-D-glucopyranoside salicylate Fatty acid and sugar derivatives galactarate (mucic acid) Flavonoids dihydroquercetin quercetin 3-glucoside kaempferol 3-O-glucoside/galactoside Phenylpropanoids 4-hydroxycinnamate ferulate sinapate syringic acid vanillate Siderophores deoxymugineic acid Terpenoids mevalonate soyasaponin III Xenobiotics Chemicals trimethylamine N-oxide Partially Characterized Molecules  Partially Characterized Molecules glucuronide of C12H22O4 (1) glucuronide of C12H22O4 (2)    126  Table C.2 Metabolites significantly up-regulated in all washing samples of sprouting alfalfa seed treated with antimicrobial treatments (CLO or HPA) regardless of microbiota type compared to corresponding CTL at 24 h of germination. As detailed in table 2.2, CTL stands for non-treated control, CLO represents the NaClO treatment, and HPA represents the organic treatment. Major pathway Sub pathway Metabolite Name Amino acid Aspartate family (OAA derived) N-acetylasparagine Carbohydrate Photorespiration oxalate (ethanedioate) Inositol metabolism inositol 1-phosphate (I1P) Sucrose, glucose, fructose metabolism fucitol maltose verbascose Lipids Free fatty acid laurate (12:0) docosadienoate (22:2n6) Fatty acid, hydroxy 3-hydroxymyristate 3-hydroxybehenate Nucleotide Purine metabolism adenosine-2',3'-cyclic monophosphate Secondary metabolism Alkaloids caffeine Flavonoids daidzein formononetin liquiritigenin Terpenoids soyasaponin I Xenobiotics Chemicals succinimide 127  Table C.3 Fold changes and metabolic pathways of significantly up-regulated amino acids in the washing samples of sprouting alfalfa seed with SA or ST after the HPA treatment at 24 h of germination. As detailed in table 2.2, HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. Metabolic Pathway Metabolite Name  SA_HPA / IM_HPA ST_HPA / IM_HPA Serine family (phosphoglycerate derived) sulfate 1.32 2.40 cysteine sulfinic acid 2.63 2.58 Aromatic amino acid metabolism (PEP derived) kynurenine 1.38 1.64 phenylpyruvate 4.53 6.02 4-hydroxyphenylpyruvate 8.83 8.22 3-(4-hydroxyphenyl) lactate 1.51 1.66 Aspartate family (OAA derived) threonine 1.24 1.97 2-aminoadipate 1.63 2.26 N6, N6, N6-trimethyllysine 1.90 2.52 N-acetylmethionine 2.80 3.14 Glutamate family (alpha-ketoglutarate derived) 1-methyl-4-imidazoleacetate 1.30 1.69 argininosuccinate 1.56 1.60 dimethylarginine (SDMA + ADMA) 1.86 2.34 gamma-aminobutyrate (GABA) 1.49 3.29 histamine 3.82 19.98 trans-4-hydroxyproline 1.25 1.86 trans-urocanate 1.72 2.46 N-monomethylarginine 1.78 2.17 N-acetylhistamine 1.47 2.08 Branched Chain Amino Acids (pyruvate derived) valine 1.42 2.01 Amines and polyamines putrescine 1.63 1.98 feruloylputrescine 1.45 1.70 1,3-diaminopropane 1.67 1.72 Glutathione metabolism glutathione, oxidized (GSSG) 6.50 7.14 gamma-glutamylvaline 1.94 3.49  128  Table C.4 Fold changes of significantly down-regulated phospholipids in the washing samples of sprouting alfalfa seed with SA or ST after the HPA treatment at 24 h of germination. As detailed in table 2.2, HPA represents the organic treatment. IM stands for indigenous microbiota, SA represents microbiota with S. enterica Agona PARC 5, and ST represents microbiota with S. enterica Typhimurium LMFS-S-JF-001. Metabolite Name  SA_HPA / IM_HPA ST_HPA / IM_HPA 1-linoleoyl-2-linolenoyl-GPA (18:2/18:3) 0.11 0.24 1-palmitoyl-2-linoleoyl-GPC (16:0/18:2) 0.18 0.36 1-palmitoyl-2-oleoyl-GPC (16:0/18:1) 0.18 0.25 1-stearoyl-2-linoleoyl-GPC (18:0/18:2) 0.15 0.21 1-palmitoyl-2-alpha-linolenoyl-GPC (16:0/18:3n3) 0.18 0.36 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) 0.04 0.07 1,2-dioleoyl-GPE (18:1/18:1) 0.03 0.02 1-stearoyl-2-linoleoyl-GPE (18:0/18:2) 0.02 0.01 1,2-dipalmitoyl-GPE (16:0/16:0) 0.06 0.1 1,2-dilinoleoyl-GPE (18:2/18:2) 0.05 0.06    

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