APPLICATION of QUANTITATIVE RISK ANALYSIS METHODS to IMPROVING MICROBIAL FOOD SAFETY in the U.S. and KENYA a Dissertation Presen
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APPLICATION OF QUANTITATIVE RISK ANALYSIS METHODS TO IMPROVING MICROBIAL FOOD SAFETY IN THE U.S. AND KENYA A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Matthew Stasiewicz May 2015 © 2015 Matthew Stasiewicz APPLICATION OF QUANTITATIVE RISK ANALYSIS METHODS TO IMPROVING MICROBIAL FOOD SAFETY IN THE U.S. AND KENYA Matthew Stasiewicz, Ph. D. Cornell University 2015 Risk analysis is an approach that guides efforts to reduce foodborne disease risk through the distinct activities of risk assessment, risk management, and risk communication. In three projects, I improve two categories of risk analysis tools for two distinct food safety risks in two distinct geographic regions: risk assessment of the bacterial pathogen Listeria monocytogenes in the United States and risk management of the mycotoxins aflatoxin and fumonisin in Kenya. First, I use quantitative microbial risk assessment to show increasing high temperature, short time (HTST) pasteurization temperatures to respond to a bioterror threat would increase the risk of listeriosis from consuming pasteurized fluid milk. I incorporate experimental findings that increased HTST pasteurization temperatures (from 72°C to 82°C) increase outgrowth of L. monocytogenes following post-pasteurization contamination of fluid milk. Conservative estimates show annual listeriosis deaths would increase from 18 to 670, a 38-fold increase, if all U.S. fluid milk were pasteurized at the higher temperature. Second, I apply whole genome sequence technology to identify which repeatedly observed strains of L. monocytogenes are more likely persistent within individual delicatessens or are repeatedly reintroduced from outside sources. Phylogenetic analyses of whole genome single nucleotide polymorphisms (SNPs) distinguish populations of L. monocytogenes that are identical by pulsed field gel electrophoresis. Isolates of persistent strains most often differ by less than 10 individual SNPs and diverge from a most recent common ancestor within the plausible lifetime of the delis. Third, I adapt a multi-spectral optical sorter to remove individual maize kernels that are contaminated with aflatoxin above 10 ppb or fumonisin above 1,000 ppb from maize samples collected from open air markets in Eastern Kenya. Sorting kernels by linear discriminant analysis achieved an 83.3% and 83.7% mean reduction in aflatoxin and fumonisin, respectively, while removing 0 to 25% of a maize sample. The technology has the potential to empower Kenyan maize consumers to manage mycotoxin exposure while subjecting them to minimal food losses. My dissertation reports improved quantitative tools and techniques to assess and manage microbial food safety risks. Improved risk analysis tools will allow for more efficient, efficacious interventions to improve food safety. BIOGRAPHICAL SKETCH Matthew Stasiewicz was born to Duane and Nancy Stasiewicz on May 8th, 1985 in Detroit, Michigan. His family moved to Grosse Pointe Woods, Michigan where he attended Grosse Ponte North High School. Matt went to Michigan State University for undergraduate study, earning both a B.S. in Biosystems Engineering, focusing on food process engineering, and a B.A. in Philosophy, focusing on ethics. During undergraduate studies he was mentored by Dr. Brad Marks on a research project to model the adaption of Salmonella to sublethal thermal heating as it affects subsequent thermal death rates, and therefore process safety. After undergraduate studies, Matt interned with Kellogg Company where Louis Morel supervised work on pilot scale cereal processing refinement. Matt then came to Cornell for Master’s studies with Dr. Martin Wiedmann to understand the microbial pathogens responsible for food safety risks. He worked on a project to first model the synergistic activity of two organic acids to reduce cold-temperature growth of Listeria monocytogenes. Then he explored the mechanisms behind synergistic growth inhibition using transcriptomics. Outside of academics, he participated in cooperative student housing and student groups working to improve local and global food systems. During his Ph. D. with Dr. Wiedmann Matt shifted focus to risk analysis in microbial food safety and took up an international perspective through an NSF funded IGERT in Food Systems and Poverty Reduction. iii ACKNOWLEDGMENTS I want to thank the Food Safety Lab for providing the community in which I built the foundation of the Ph. D. work and from which I was able to expand during my IGERT training. I’d like to thank my research mentors Martin Wiedmann, Rebecca Nelson, and Yrjo Grohn for guiding me along the path from working on a predefined research question to starting to design, fund, and manage a project. On specific projects, Henk den Bakker, Haley Oliver, and James Muthomi have also provided critical guidance on bioinformatics, food industry relations, and the Kenyan research environment, respectively. I hope my work with all these professors has been as interesting to supervise as it has been for me to carry out. I am grateful to the many other students and staff who have gone above and beyond their duties to help me with my work: Steve Warchocki, Janette Robbins, Maureen Gunderson, Sherry Roof, Barbara Bowen, Tom Denes, Laura Strawn, Samuel Mutiga, Titlayo Falade, James Wainaina, Laura Smith, and Laura Morales. Also, let me thank my parents for supporting me on this nearly thirty year journey to obtain my final degree. Their patience has been remarkable. And finally, let me thank my wife Natalie for supporting me at the end of my degree and for helping me through six months of field work in Kenya even before we were married. Funding sources for my dissertation projects are acknowledged at the end of each chapter describing the individual projects iv TABLE OF CONTENTS Dissertation Abstract ........................................................................................................................ i Bibliographical Sketch ................................................................................................................... iii Acknowledgments.......................................................................................................................... iv Table of Contents .............................................................................................................................v List of Figures ................................................................................................................................ vi List of Tables ............................................................................................................................... viii Chapter 1 ..................................................................................................................................1 Chapter 2 ..................................................................................................................................9 Chapter 3 ................................................................................................................................39 Chapter 4 ................................................................................................................................81 Chapter 5 ..............................................................................................................................116 Appendix 1. Supplemental Figures ..............................................................................................122 Appendix 2. Supplemental Tables ...............................................................................................131 v LIST OF FIGURES Main Text Figures Figure 2.1. Differences between modeled lag-phase, λ, and modeled maximum cell density, Nmax at 82°C and 72°C for each of 12 growth trials with paired pasteurization at each temperature ... 21 Figure 2.2. Probability of exposure to a given dose of L. monocytogenes is post-processing contaminated milk when the milk is pasteurized at 72°C or 82°C ............................................... 26 Figure 2.3. Predicted listeriosis mortality due to pasteurization of milk at 82°C and fold-change in mortality from the 72°C to the 82°C pasteurization exposures ................................................ 27 Figure 2.4. Sensitivity analysis for consumer behavior parameters, intrinsic bacterial growth parameters, and supply chain parameters at both 72°C and 82°C pasteurization ......................... 28 Figure 2.5. Detailed sensitivity analysis results for retail-to-consumption storage time, and retail-to-consumption storage temperature ................................................................................... 29 Figure 2.6. Sensitivity of fold-change in mortality when milk is pasteurized at 82°C rather than 72°C using growth parameters from selective and differential media for L. monocytogenes ...... 31 Figure 3.1 Maximum clade credibility tree from 9,376 core genome SNPs differentiating 179 lineage I isolates. ........................................................................................................................... 52 Figure 3.2. Maximum clade credibility tree from 2,993 core genome SNPs differentiating 9 lineage II isolates .......................................................................................................................... 54 Figure 3.3. Representative PFGE patterns for 3 sets of PFGE types that grouped by WGS-SNP