SBIR Phase I: Rapid Pathogen Diagnostics and Biosurveillance using Multiplexed High-throughput Sequencing

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是提高我们对抗传染病的能力，这些传染病对农业产量产生负面影响，降低全球粮食生产和分配系统的效率。通过利用高通量测序和生物信息学的力量，这一创新将加强科学和技术的理解，提供一种病原体鉴定和监测工具，证明其对已知和未知感染因子的有效性。该平台快速、灵敏且具有成本效益，可用于任何动物样本检测几乎所有可能的微生物。甚至是以前从未被描述过的微生物。可以快速筛选数百个样品，而无需依赖已知的微生物遗传/基因组数据。预计2012 - 2017年全球分子诊断市场复合年增长率（CAGR）将超过14%，其中传染病检测是领先的应用，占26%的份额，因此该项目的商业机会是巨大的。该项目旨在解决PCR、杂交阵列和培养等低通量靶向检测技术带来的问题。目前的诊断方法依赖于已知的目标微生物遗传学，并且以已知目标序列的存在/缺失的形式提供有限的信息。提出的研究目标与降低基于DNA测序和贝叶斯统计的高通量无偏病原体检测平台相关的技术风险有关。最终目标是开发、标准化和验证我们的宏基因组病原体鉴定平台，用于农业检测和生物监测环境，利用水产养殖相关鱼类及其感染因子作为相关应用。本项目拟：1)对病原鉴定测序平台的相对性能进行表征；2)对我们的农业检测平台进行验证和基准测试，使用鱼类样本和使用既定方法诊断的尖刺；3)评估宿主基因表达特征作为感染支持证据的使用。该小组将从受感染的鱼类中获得宏基因组序列数据，将其分析结果与现有方法进行比较，并利用已知数量的致病物质确定检测极限。

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.