Intelligently predicting viral spillover risks from bats and other wild mammals

项目总结 野生动物病毒对人类的传播或“溢出”是对全球健康的严重威胁，爆发了 病毒病原体，如丝状病毒、副粘病毒和冠状病毒，都起源于野生哺乳动物。一把钥匙 悬而未决的问题是，特定的分类类群，如蝙蝠，是否有理由将额外的监视视为 对人类有潜在致病性的病毒的蓄水池。然而，现有的主机病毒数据集不是 足够的分辨率来预测物种或属的细粒风险。因此，有效的应对措施必须 解决两个核心目标：(1)综合关于病毒与哺乳动物相互作用的知识；(2)利用 知识库，以有力地预测未来的溢出事件(即，人畜共患病风险)。要实现可靠的分析和 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，该项目将开发主机-病毒数据智能，以解决三个主要问题 数据重复使用的问题：观察中哺乳动物和病毒的分类分配的可信度； 对所提出的哺乳动物-病毒相互作用的证据有信心；以及将所有相关数据连接到 对现有数据库隐藏的已发布文本。项目团队将构建一种新的生物信息学 将数字连接分类知识的管道，使用它来搜索暗数据以找到潜在的证据 主机与病毒的交互，然后使用元数据层(有关数据的数据)将其链接在一起，以形成更多 扩展的主机-病毒知识图谱比以前可行。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 概率归纳逻辑编程等人工智能方法。一个关键的预期结果是 与全面的现有数据集相比，将主机与病毒交互的数据集扩大3倍。这个 提议的项目将为新一代工作奠定基础，重复使用主机-病毒交互数据来测试 以前无法理解的关于物种特征如何影响病毒对人类溢出的假设。正在转移 从范例到基于图表的分析，与主机-病毒相互作用的纯分类表示法相比， 将使研究人员能够直接调查生态系统结构和人类入侵对 病毒载量。确定是否所有哺乳动物都有同样的病毒溢出风险，或者某些群体是否有 较高的特定分类群人畜共患病风险(例如，马蹄蝠、鼠类)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 生态生理适应使某些群体更容易耐受更多的病毒，这可能反过来导致 通过模拟野生哺乳动物的免疫反应进行临床治疗。填补主机病毒中已发现的空白 因此，在新冠肺炎之后，知识对于帮助人畜共患病研究取得进展至关重要。

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.