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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

基本信息

批准号：
10435545
负责人：
DeeAnn Reeder
金额：
$ 18.8万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-22 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10435545
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 graph knowledgebase novel pathogenic virus prevent response secondary analysis spillover event surveillance study trait transmission process virus host interaction

项目摘要

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.

项目摘要野生动物病毒向人类的传播或“溢出”是对全球健康的重大威胁，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。一个关键一个悬而未决的问题是，特定的分类群体，如蝙蝠，是否需要额外的监测，因为“特殊” 对人类具有潜在致病性的病毒的储存库。但是，现有的宿主病毒数据集并不足以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：（i）综合有关病毒与哺乳动物相互作用的知识;（ii）利用知识库来稳健地预测未来的溢出事件（即，人畜共患病风险）。为了实现强大的分析和 NIAID的生物信息学资源中心（BRC）的公共数据集的可重用性;特别是NCBI病毒和病毒病原体资源，ViPR），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再利用的问题：观察中哺乳动物和病毒分类归属的可信度; 对拟议的哺乳动物-病毒相互作用证据的信心;并将所有相关数据从现有数据库中隐藏的已发布文本。该项目团队将构建一个新的生物信息学管道，将数字连接分类学知识，用它来搜索暗数据，以找到潜在的证据主机-病毒交互，然后使用元数据层（“关于数据的数据”）将其链接在一起，以形成一个更详细的扩展主机病毒知识图比以前可行。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人工智能方法，如概率归纳逻辑编程。一个关键的预期成果是与现有的综合数据集相比，将宿主-病毒相互作用的数据集扩大了3倍。的拟议的项目将为新一代的工作奠定基础，重用主机-病毒交互数据进行测试关于物种特征如何影响病毒对人类的溢出的以前无法实现的假设。转移范例到基于图形的分析，与宿主-病毒相互作用的纯粹分类学表示相比，将使研究人员能够直接调查生态系统结构和人类入侵的影响，病毒载量确定是否所有哺乳动物都有相同的病毒溢出风险，或者是否有些群体有较高的分类群特异性人畜共患病风险（例如，马蹄形蝙蝠，鼠形啮齿动物），是公共卫生的重要信息工作人员和流行病学家。更明确的风险量化也将有助于研究人员确定生态生理适应使某些群体倾向于容忍更多的病毒，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。填补宿主病毒中已确定的空白因此，在COVID-19之后，知识对于帮助人畜共患病研究的进展至关重要。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Holistic understanding of contemporary ecosystems requires integration of data on domesticated, captive and cultivated organisms.

对当代生态系统的整体理解需要整合有关驯化，俘虏和栽培生物的数据。

DOI：
10.3897/bdj.9.e65371
发表时间：
2021
期刊：
Biodiversity data journal
影响因子：
1.3
作者：
Groom Q;Adriaens T;Bertolino S;Phelps K;Poelen JH;Reeder DM;Richardson DM;Simmons NB;Upham N
通讯作者：
Upham N

The macroevolutionary impact of recent and imminent mammal extinctions on Madagascar.

DOI：
10.1038/s41467-022-35215-3
发表时间：
2023-01-10
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Michielsen, Nathan M.;Goodman, Steven M.;Soarimalala, Voahangy;van der Geer, Alexandra A. E.;Davalos, Liliana M.;Saville, Grace, I;Upham, Nathan;Valente, Luis
通讯作者：
Valente, Luis

Genomics expands the mammalverse

基因组学扩大了哺乳动物的范围