Whole Genome Sequencing for State Food Testing Laboratories competition B

Wadsworth Center, Menands NY

Investigators

Linked publications & trials

Abstract

The long-term goal of this project is to reduce the frequency of disease outbreaks caused by food and other environmental sources contaminated with enteric pathogens. Identification and removal of sources of contamination is key to achieving this goal. To link sources and patient disease, public health laboratories look for matching genetic fingerprints using pulsed field gel electrophoresis (PFGE). While PFGE based surveillance has been successful at reducing the number of outbreaks, its low resolution is a barrier to further gains. This leads to many sources remaining unidentified. Whole Genome Sequencing (WGS) is being adopted by state and federal public health laboratories because of its greater genetic resolution when compared to PFGE and demonstrated ability to improve source attribution. WGS is described as offering the ultimate resolution since each nucleotide in the genome can be queried and phylogenetic relationships can be inferred using well established computational models. However, to achieve the full promise of this technology large well curated databases are required that harbor sequence and associated metadata from isolates collected at diverse locations and times. Such databases aid in source attribution, understanding the natural history of the organisms, and provide a valuable resource for data mining. One such database is curated by the GenomeTrakr Network. The immediate goal of this project is to add eight hundred unique and diverse draft genomes from food and environmental bacterial pathogens to this database. This will be accomplished by sequencing well characterized isolates at the Wadsworth Center and sharing the sequence and metadata with the GenomeTrakr network and the National Center for Biotechnology Information (NCBI). The expected outcome of this project will be to expand the GenomeTrakr database of high quality scaffold genomes that are linked to metadata. This will allow more accurate subtyping to link patient pathogens to specific foods or environmental sources and ultimately a reduction pathogen borne illness. !