The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve our ability to fight infectious diseases that negatively impact agricultural yields and reduce the efficiency of global food production and distribution systems. This innovation will enhance scientific and technological understanding by leveraging the power of high-throughput sequencing and bioinformatics to provide a pathogen identification and surveillance tool with demonstrated efficacy against known and unknown infectious agents. This platform is fast, sensitive, and cost-effective, and can be used for any animal sample to detect virtually all possible microbes ? even microbes that have never before been characterized. Hundreds of samples can be rapidly screened without relying upon known genetic/genomic data of microbes. The global molecular diagnostics market is expected to grow at a compound annual growth rate (CAGR) of over 14% from 2012 to 2017, with infectious disease testing being the leading application at 26% share, therefore the commercial opportunity of this project is vast.
The proposed project tackles problems arising from low-throughput targeted detection technologies such as PCR, hybridization arrays, and culture. Current diagnostic methods rely on what is already known about target microbe genetics, and provide limited information in the form of presence/absence of a known target sequence. The proposed research objectives are related to lowering the technical risks associated with a high-throughput unbiased pathogen detection platform based on DNA sequencing and Bayesian statistics. The ultimate goal is to develop, standardize and validate our metagenomics pathogen identification platform for use in agricultural detection and biosurveillance contexts, using aquaculture related fish species and their infectious agents as a relevant application. This project proposes to: 1) characterize relative performance of sequencing platforms for pathogen identification; 2) validate and benchmark our agricultural detection platform using fish samples and spike-ins diagnosed using established methods; and 3) evaluate the use of host gene expression signatures as supporting evidence for infection. The team will obtain metagenomic sequence data from infected fish, compare their analysis results against current methods, and establish the limit of detection using known quantities of pathogenic material.
