PIPP Phase I: Center for Emerging Pathogen Prediction and Integration

PIPP 第一阶段：新兴病原体预测和整合中心

• 批准号: 2155222
• 负责人: Joseph Cook
• 金额: $ 99.96万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2155222&HistoricalAwards=false
• 关键词: PIPP; Phase; Center; Emerging; Pathogen

The Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants Center for Emerging Pathogen Prediction and Integration (CEPPI) will lead strategic surveillance of wildlife pathogens across changing environments. Understanding and preventing the emergence of new infectious diseases requires interpreting many types of data (e.g., information on the disease-causing pathogens, interactions between the human and wildlife populations, and changes in their associated environments). The team will use the vast historical data and sampling available in biorepositories, such as museums, to systematically investigate pathogen emergence to enable reliable predictions of zoonotic disease to help prevent or mitigate future pandemics. Many aspects of pathogen biology in wildlife remain poorly documented worldwide, particularly in low-income, biodiverse regions. Pandemics pose a challenge that requires international, collaborative, proactive solutions, so the Center will focus on developing best methods in training the next generation of pathogen biologists, improving basic biodiversity infrastructure, databases, and workflows, and then sharing these new approaches globally. Working especially with partner museums in the Americas, the Center will expand wildlife sampling strategies to develop a more comprehensive, decentralized network of specimen repositories and researchers to improve pathogen identification and surveillance.To improve pathogen detection, surveillance, and mitigation, the Center will promote state-of-the-art biorepositories, genomic screening, and bioinformatic workflows, which are empowered by intuitively interactive visualization web tools, computational and mathematical modeling, and machine learning. Rapid visual and intelligent integration of pathobiology into the vast global, digital infrastructure for wild mammals is the cornerstone of this Center, which will act as an early warning system for pandemic prediction and prevention. In Phase I, the cross-disciplinary team aims to develop internationally scalable multidisciplinary workflows to build: 1) informatics baselines for pathogens and hosts derived from existing archival biorepositories representing decadal sampling of mammalian communities that will be linked directly to targeted strategies for improved monitoring and surveillance; 2) pipelines that streamline sequencing and bioinformatic methods for rapid, affordable, large-scale screening of mammalian host and parasite samples to provide scalable views of diversity and change over space and time; 3) pathogen risk assessments linking novel methods that integrate social and environmental parameters and pathogen diversity to predict the outcomes of accelerating anthropogenic change; 4) collaborations across local biorepositories to develop strategic sampling and digitization protocols for expanded geographic, temporal and taxonomic coverage; and 5) problem-solving networks through educational modules, direct engagement, and collaborative learning. CEPPI helps standardize exploration of pathogens and biodiversity by building new connections with local communities, sequencing and bioinformatic facilities, and public health and natural resource agencies. New capacities for visualization, informatics interpretation, and translation emanate from the development of fast, affordable, and scalable sequencing and bioinformatic technologies. These capacities will stimulate new methods of pathogen detection, discovery, and monitoring that will foster global pathogen surveillance and mitigation. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大流行预防（PIPP）的预测智能I阶段：新兴病原体预测与整合（CEPPI）的开发赠款中心将领导对不断变化的环境中野生动植物病原体的战略监测。了解和预防新的传染病的出现需要解释多种类型的数据（例如，有关引起疾病的病原体的信息，人类和野生动植物种群之间的相互作用以及其相关环境的变化）。该团队将使用博物馆等生物关注剂中可用的大量历史数据和采样，以系统地研究病原体的出现，以实现对人畜共患病的可靠预测，以帮助预防或减轻未来的大流行。野生动植物中病原体生物学的许多方面在全球范围内仍然有很高的文献记载，尤其是在低收入的生物多样性地区。 Pandemics提出了一个需要国际，协作，积极主动的解决方案的挑战，因此该中心将专注于在培训下一代病原体生物学家，改善基本的生物多样性基础设施，数据库和工作流程中，然后在全球共享这些新方法。 Working especially with partner museums in the Americas, the Center will expand wildlife sampling strategies to develop a more comprehensive, decentralized network of specimen repositories and researchers to improve pathogen identification and surveillance.To improve pathogen detection, surveillance, and mitigation, the Center will promote state-of-the-art biorepositories, genomic screening, and bioinformatic workflows, which are empowered by intuitively交互式可视化网络工具，计算和数学建模以及机器学习。 该中心的基石将病理生物学迅速融合到野生哺乳动物的庞大的全球数字基础设施中，它将充当大流行预测和预防的预警系统。在第一阶段，跨学科的团队旨在开发可扩展的多学科工作流程以建立：1）病原体和宿主的信息基线，这些基线来自现有的代表哺乳动物社区deCADAL Sampling的现有档案生物库，这些基线将直接链接到有针对性的策略中，以改善监测和Surveillance和Surveillance和Surveillance; 2）简化测序和生物信息学方法的管道，以快速，负担得起的哺乳动物宿主和寄生虫样品的快速，大规模筛选，以提供可扩展的多样性视图和时间和时间变化； 3）与整合社会和环境参数和病原体多样性相结合的新方法的病原体风险评估，以预测加速人为变化的结果； 4）跨当地生物库的合作，以开发扩展的地理，时间和分类覆盖范围的战略抽样和数字化协议； 5）通过教育模块，直接参与和协作学习解决问题的网络。 CEPPI通过与当地社区，测序和生物信息学设施以及公共卫生和自然资源机构建立新的联系来帮助标准化病原体和生物多样性。可视化，信息学解释和翻译的新能力来自快速，负担得起和可扩展的测序和生物信息学技术的发展。这些能力将刺激病原体检测，发现和监测的新方法，从而促进全球病原体监测和缓解。该奖项得到了跨导向预防I阶段（PIPP）计划的跨导向预测智能的支持，该计划由生物学科学局（BIO）（BIO），计算机信息科学和工程（CISE），工程（ENG），社会，行为和经济科学（SBE）的授权和基金会的支持，并反映了NSF的法定传教士的支持，该计划是由生物学科学和计算机信息科学和工程（CISE），工程和行为和经济科学（ENG）共同资助的。更广泛的影响审查标准。

项目成果

Engaging with the Nagoya Protocol on Access and Benefit-Sharing: recommendations for noncommercial biodiversity researchers

• DOI: 10.1093/jmammal/gyac122
• 发表时间: 2023-06-06
• 期刊: JOURNAL OF MAMMALOGY
• 影响因子: 1.7
• 作者: Colella，Jocelyn P.;Silvestri，Luciana;Lessa，Enrique P.
• 通讯作者: Lessa，Enrique P.