Bioinformatics for Immune Response Biosignature Analyses

用于免疫反应生物特征分析的生物信息学

• 批准号: 6881710
• 负责人: Kenneth L Drake
• 金额: $ 49.98万
• 依托单位: SERALOGIX
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6881710
• 关键词: Bacillus anthracis; Poxviridae; animal data; bioinformatics; biomarker; bioterrorism /chemical warfare; computer assisted sequence analysis; computer data analysis; computer program /software; computer system design /evaluation; host organism interaction; immune response; mathematical model; messenger RNA; microarray technology; model design /development; molecular biology information system; staphylococcal enterotoxin; statistics /biometry

DESCRIPTION (provided by applicant): This Phase II proposal continues the development and validation of a bioinformatics and diagnostic platform for rapid detection and pre-symptomatic identification of biological agents on the basis of host gene and proteomic expression response profiles. Our collaborators preliminary host-pathogen studies indicate that exposed individuals display idiosyncratic peripheral blood mononuclear cell (PBMC) mKNA expression and serum protein temporal patterns to pathogenic agents prior to the onset of full illness. New tools are needed to determine the patterns from the volumes of complex, variable, and noisy genomic/proteomic data generated from host-pathogen studies. Our computational tools are based on the probabilistic power of dynamic Bayesian networks (DBNs) which are utilized to learn, model and recognize the dynamic pattern-of-change of mRNA and proteins ("biosignature") of the hostpathogen innate immune response. A unique feature of our approach is the inclusion of "time" combined with prior quantitative and qualitative knowledge that improves the recognition accuracy between different pathogenic agents. Phase I s/w prototype demonstrated proof-of-concept that our computational approach produced correct DBN models from time-course data mimicking the innate immune response to nine different pathogen types. The prototype performed remarkably well in both the representation of the time-course biosignatures as DBN models and for correctly identifying an unknown biosignature to the correct infectious agent with better than 98% accuracy. Phase II main objective is to statistically show that Seralogix's computational framework can extract and model unique host-pathogen biosignature patterns in the face of host heterogeneities using real time-course PBMC mRNA and protein datasets for the pathogens/toxin B. anthracis, cowpow, and staphylococcal enterotoxin B. in animal and non-human primate models. Existing time course data will be provided by our collaborators at the Walter Reed Army Institute of Research and the Biosignature Consortium (comprised of the University of New Mexico, the University of Texas Southwestern Med. Ctr., and Lawrence Livermore Labs). Seralogix believes that our DBN based computational tools will be important for: 1) deciphering the cellular signaling pathways and mechanisms of virulence and toxicity of pathogens/toxins; 2) creating new diagnostics for real-time, pre-symptomatic pathogenic identification, and 3) understanding the progressive stages of a disease to aid in creating new intervening drugs and therapeutic strategies.

描述（由申请人提供）：该II期申请继续开发和验证基于宿主基因和蛋白质组学表达反应谱的生物信息学和诊断平台，用于快速检测和症状前鉴定生物制剂。我们的合作者初步的宿主-病原体研究表明，暴露的个体在完全发病之前对病原体表现出特殊的外周血单个核细胞（PBMC） mKNA表达和血清蛋白时间模式。需要新的工具来从宿主-病原体研究产生的大量复杂、可变和嘈杂的基因组/蛋白质组学数据中确定模式。我们的计算工具基于动态贝叶斯网络（dbn）的概率能力，该网络用于学习，建模和识别宿主病原体先天免疫反应的mRNA和蛋白质的动态变化模式（“生物特征”）。该方法的一个独特之处在于将“时间”与先前的定量和定性知识相结合，从而提高了不同病原体之间的识别准确性。第一阶段的s/w原型证明了我们的计算方法从模拟九种不同病原体类型的先天免疫反应的时间过程数据中产生正确的DBN模型。该原型在将时间过程生物标记表示为DBN模型以及将未知生物标记正确识别为正确的感染因子方面表现得非常好，准确率超过98%。第二阶段的主要目标是统计显示Seralogix的计算框架可以在面对宿主异质性的情况下，使用实时过程的PBMC mRNA和蛋白质数据集提取和建模独特的宿主-病原体生物特征模式，用于动物和非人灵长类动物模型中的病原体/毒素炭疽芽孢杆菌、奶牛和葡萄球菌肠毒素芽孢杆菌。现有的时间课程数据将由我们在沃尔特里德陆军研究所和生物签名联盟（由新墨西哥大学，德克萨斯大学西南医学中心组成）的合作者提供。和劳伦斯利弗莫尔实验室)。Seralogix认为，我们基于DBN的计算工具将在以下方面发挥重要作用：1)破译细胞信号通路和病原体/毒素的毒力和毒性机制；2)为实时、症状前的病原鉴定创造新的诊断方法；3)了解疾病的进展阶段，以帮助创造新的干预药物和治疗策略。