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的影响/商业潜力是提高我们抗击传染病的能力，这些传染病对农业产量产生负面影响，并降低全球粮食生产和分销系统的效率。这一创新将通过利用高通量测序和生物信息学的力量来提高科学和技术的理解，以提供一种病原体识别和监测工具，对已知和未知的传染性病原体具有已证明的功效。该平台快速，灵敏，具有成本效益，可用于任何动物样本，以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，而不依赖于已知的微生物遗传/基因组数据。2012年至2017年，全球分子诊断市场预计将以超过14%的复合年增长率（CAGR）增长，其中传染病检测将以26%的份额成为领先应用，因此该项目的商业机会巨大。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所带来的问题。目前的诊断方法依赖于已知的靶微生物遗传学，并以存在/不存在已知靶序列的形式提供有限的信息。拟议的研究目标是降低与基于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。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，使用水产养殖相关鱼类及其感染因子作为相关应用。该项目建议：1）表征用于病原体鉴定的测序平台的相对性能; 2）使用鱼样品和使用已建立的方法诊断的加标物来验证和基准测试我们的农业检测平台;以及3）评估宿主基因表达特征作为感染的支持证据的使用。该团队将从受感染的鱼类中获得宏基因组序列数据，将其分析结果与当前方法进行比较，并使用已知数量的致病物质建立检测限。

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.