Next Generation Sequencing for the Detection of Foodborne Microbial Pathogens

Abstract

The rapid detection and typing of DNA belonging to known and emerging pathogens represents one of the most fundamental and frequently encountered tasks by state and national health laboratories. Over the past decade, next generation sequencing (NGS) platforms have been incorporated into a range of public health programs responsible for surveilling, detecting, and investigating/responding to infectious disease outbreaks. NGS has been rapidly integrated into the field of pathogenic foodborne microbiology as both a primary and supportive detection tool and is routinely used in the analysis of isolates from many prominent foodborne bacterial pathogens including Salmonella, Listeria, Escherichia coli, Shigella, and neurotoxigenic Clostridium. Collectively, 31 major foodborne pathogens are estimated to result in 9.4 million instances of illness leading to 55,961 hospitalizations and 1351 deaths per year in the United States; figures dwarfed by estimates over the same period for unspecified agents responsible for 38.4 million cases of acute gastroenteritis, 473,832 hospitalizations, and 5072 deaths [1, 2]. The relatively well-defined and studied major foodborne pathogens often are associated with established regulatory procedures for their detection and verification and are often the focus of major public health programs. A number of these bacteria have been the subject of large multi-center NGS-enabled whole genome sequencing (WGS) initiatives, which have begun to fundamentally change the landscape of disease surveillance. While a diversity of factors is responsible for the many cases of acute gastroenteritis with unspecified etiology, the possibility exists that a substantial number are attributable to uncharacterized, cryptic, or conditional pathogens that currently evade identification. The steady growth in WGS and metagenomic sequence data from pathogenic and non-pathogenic organisms already has provided critical insight into horizontally mobile genomic elements and revealed that some critical virulence factors may have a broader distribution than was previously understood. NGS is a transformative technology, and the sequence data produced by NGS are impacting the field of pathogen detection in profound ways. This chapter explores what NGS platforms are, the types of sequence data they can produce, and how sequence data are being leveraged to enhance the detection of foodborne bacterial pathogens. In the first part of the chapter we begin with a brief history of the emergence of NGS technology and its early integration into the field of bacterial pathogenesis. Next, we provide an overview of core concepts used in the preparation of nucleotide data for whole genome sequencing before transitioning into an over- view of several commonly encountered NGS platforms and the state of the sequence data that is produced by them. In the second half, we focus on the utilization of NGS data as a tool for pathogen typing and detection. The process by which viruses, organisms, and/or their toxic factors drive pathogenesis can be immensely complex and diverse. Although WGS can be highly complementary to pathogen detection goals, there is no one-size-fits- all answer for how to utilize WGS data. Options will differ based on a range of factors that are often specific to the organism genome being sequenced. To provide a broad overview we discuss gnome assembly and the applications of NGS data in the context of two foodborne pathogens, Salmonella enterica (S. enterica) and Clostrid- ium botulinum (C. botulinum). These two organisms differ substantially in regard to underlying biology, disease outbreak frequency, pathogenesis, and detection goals. These differences allow us to explore the immense variety of options an investigator faces once WGS data are acquired, highlight how these data can be applied to detection goals, and demonstrate how these goals can vary from organism to organism. We explore (1) the use of WGS as a high-resolution molecular typing tool, (2) its compatibility with other typing schemes, (3) and ways to utilize the data encoded within the genome to detect and explore virulence factors.