Intelligently predicting viral spillover risks from bats and other wild mammals

智能预测蝙蝠和其他野生哺乳动物的病毒溢出风险

• 批准号: 10289637
• 负责人: DeeAnn Reeder
• 金额: $ 24.45万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-22 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10289637
• 关键词: Address; Artificial Intelligence; Attention; Base Sequence; Bioinformatics; COVID-19; COVID-19 pandemic; Chiroptera; Clinical Treatment; Computing Methodologies; Coronavirus; Data; Data Set; Databases; Disease; Disease Outbreaks; Ecology; Ecosystem; Epidemiologist; Event; FAIR principles; Feces; Filovirus; Foundations; Future; Generations; Goals; Grain; Graph; Human; Immune Tolerance; Immune response; Immunological Models; Infectious Diseases Research; Information Retrieval; Infrastructure; Intelligence; Investigation; Knowledge; Lead; Life; Link; Logic; Mammals; Metadata; Methods; Molecular; Muridae; Names; National Institute of Allergy and Infectious Disease; Natural Language Processing; Nature; Outcome; Paramyxovirus; Pathogenicity; Public Health; Publications; Publishing; Research; Research Personnel; Resources; Risk; Risk Estimate; Rodent; Sampling; Serology; Services; Source; Structure; Taxon; Taxonomy; Testing; Time; Tissues; Trees; Uncertainty; Urine; Viral; Viral Load result; Viral reservoir; Virus; Work; Zoonoses; base; bioinformatics pipeline; bioinformatics resource; data reuse; digital; flexibility; global health; high risk; improved; inclusion criteria; indexing; innovation; insight; interest; knowledge base; knowledge graph; novel; pathogenic virus; prevent; response; secondary analysis; spillover event; surveillance study; trait; transmission process; virus host interaction

PROJECT SUMMARY The transmission or ‘spillover’ of wildlife viruses to humans is a critical threat to global health, with outbreaks of viral pathogens like filoviruses, paramyxoviruses, and coronaviruses all originating in wild mammals. A key outstanding question is whether specific taxonomic groups, such as bats, warrant extra surveillance as ‘special reservoirs’ of viruses that are potentially pathogenic to humans. However, existing host-virus datasets are not sufficiently resolved to predict fine-grain risk for species or genera. An effective response must therefore address two core aims: (i) synthesizing knowledge regarding virus-to-mammal interactions; and (ii) using that knowledgebase to robustly predict future spillover events (i.e., zoonotic risk). To enable robust analysis and reusability of public datasets of NIAID’s Bioinformatics Resource Center (BRC; especially NCBI Virus and Virus Pathogen Resources, ViPR), the project will develop Host-Virus Data Intelligence to address three main problems for data reuse: confidence of the taxonomic assignments of mammals and viruses in observations; confidence in the evidence for proposed mammal-virus interactions; and connecting all the relevant data in published texts that are hidden from existing databases. The project team will construct a novel bioinformatic pipeline that will digitally connect taxonomic knowledge, use it to search dark data to find evidence of potential host-virus interactions, and then link it together using metadata layers (‘data about the data’) to form a more expansive host-virus knowledge graph than previously feasible. The project’s computational approach leverages information extraction methods in natural language processing as well as novel applications of artificial intelligence methods such as probabilistic inductive logic programming. A key anticipated outcome is to expand the dataset of host-virus interactions by 3-fold compared to comprehensive existing datasets. The proposed project will lay the foundation for a new generation of work reusing host-virus interaction data to test previously inaccessible hypotheses about how species’ traits impact viral spillover to humans. Shifting the paradigm to graph-based analyses, compared to purely taxonomic representations of host-virus interactions, will allow researchers to directly investigate the impact of ecosystem structure and human encroachment upon viral loads. Determining whether all mammals have equal risk of viral spillover, or whether some groups have higher taxon-specific zoonotic risk (e.g., horseshoe bats, murid rodents), is critical information for public health workers and epidemiologists. More definitive risk quantification will also help researchers identify which ecophysiological adaptations predispose certain groups to tolerating more viruses, which may in turn lead to clinical treatments by modeling the immune responses of wild mammals. Filling the identified gaps in host-virus knowledge is therefore essential to aid the progress of zoonotic disease research in the wake of COVID-19.

项目概要 野生动物病毒向人类传播或“溢出”对全球健康构成严重威胁， 丝状病毒、副粘病毒和冠状病毒等病毒病原体均起源于野生哺乳动物。一把钥匙 悬而未决的问题是，特定的分类群体（例如蝙蝠）是否需要作为“特殊”进行额外的监视 对人类具有潜在致病性的病毒的储存库。然而，现有的宿主病毒数据集并不 充分解决了预测物种或属的细粒度风险的问题。因此，有效的应对措施必须 解决两个核心目标：（i）综合有关病毒与哺乳动物相互作用的知识； (ii) 使用该 稳健预测未来溢出事件（即人畜共患风险）的知识库。为了实现稳健的分析和 NIAID 生物信息学资源中心（BRC；特别是 NCBI 病毒和 病毒病原体资源 (ViPR)，该项目将开发宿主病毒数据智能，以解决三个主要问题 数据重用的问题：观察中哺乳动物和病毒分类分配的置信度； 对拟议的哺乳动物与病毒相互作用的证据有信心；并连接所有相关数据 现有数据库中隐藏的已发表文本。项目团队将构建一个新颖的生物信息学 管道将以数字方式连接分类知识，用它来搜索暗数据以寻找潜在的证据 宿主-病毒相互作用，然后使用元数据层（“关于数据的数据”）将其链接在一起，形成更多 比以前可行的更广泛的宿主病毒知识图。该项目的计算方法 利用自然语言处理中的信息提取方法以及新颖的应用 人工智能方法，例如概率归纳逻辑编程。一个关键的预期结果是 与现有的综合数据集相比，宿主-病毒相互作用的数据集扩大了三倍。这 拟议的项目将为新一代利用宿主-病毒相互作用数据进行测试的工作奠定基础 关于物种特征如何影响病毒向人类传播的假设，是以前无法实现的。移动 与宿主-病毒相互作用的纯粹分类学表示相比，基于图形的分析范式， 将使研究人员能够直接调查生态系统结构和人类侵占的影响 病毒载量。确定是否所有哺乳动物都具有相同的病毒溢出风险，或者某些群体是否具有相同的病毒溢出风险 较高的特定类群人畜共患病风险（例如马蹄蝠、鼠科啮齿动物）是公共卫生的关键信息 工人和流行病学家。更明确的风险量化也将帮助研究人员确定哪些 生态生理适应使某些群体容易耐受更多病毒，这反过来可能导致 通过模拟野生哺乳动物的免疫反应进行临床治疗。填补宿主病毒中已发现的空白 因此，了解 COVID-19 后的人畜共患疾病研究的进展至关重要。