Big Data approaches to identifying potential sources of emerging pathogens in humans, domesticated animals and crops
Big Data approaches to identifying potential sources of emerging pathogens in humans, domesticated animals and crops
批准号:
MR/R024898/1
负责人:
Maya Wardeh
金额:
$33.01万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --
中文摘要
新出现的传染病继续对人类、动物和植物构成重大威胁。近年来,几种新出现的疾病大规模爆发,从众所周知的(埃博拉病毒和橄榄快速衰退综合征),到以前鲜为人知的(寨卡病毒),再到全新的(Schmallenberg),仅举几例。已经确定的是,病原体感染多个宿主,特别是不同分类目或野生动物中的宿主的能力是人类和牲畜病原体出现的风险因素。新出现的野生动物疾病也与人类或家养动物的“溢出”有关。尽管跨物种疾病传播的重要性,但相对较少关注哪些物种是跨社区的最重要来源(例如,人畜共患病、野生动物到家养、植物到其他界),哪些是最多产的载体,这些物种如何获得病原体,以及疾病通过何种方式进入新物种或种群。这种有限的理解的一个主要原因是缺乏关于动物和植物种群中病原体的全面数据,并且在大多数情况下,关于它们如何传播的信息记录不足,包括人类。在这个奖学金中,我将改进和利用利物浦大学开发的一种新的生物信息学资源,他们驯养的动物和作物与其他物种的病原体宿主有关,以及这些病原体如何从宿主传播到重点人群。生物信息学资源,由我与BBSRC的资金开发,是增强型传染病数据库(EID2)。EID2利用最先进的文本和数据挖掘程序从多个来源提取信息,包括伴随基因序列和科学出版物的数百万元数据记录。经过处理后,EID2提供了超过60,000种宿主和病原体之间相互作用的证据,是关于人类,动物和植物已知病原体及其地理范围的最全面的数据源。在这个奖学金期间,我的目标是调查导致病原体出现的因素,提出以下问题:1.通过共享病原体连接物种的网络有哪些特征?人类及其驯养的动物和农作物在这些网络中有多重要,这些社区中的每一个与哪些其他物种联系最密切?2.不同的病原体传播途径对这些网络的性质有什么作用?直接、食源性、水媒和病媒病原体的潜在种间传播途径是否不同?3.哪些因素决定了病原体的宿主范围?宿主物种是否更有可能暴露于感染广泛物种的病原体?从基因上与它们相近的物种身上?或者是来自于它们经常接触的物种?4.我们遗漏了什么?鉴于网络、传播途径和宿主范围,新物种中出现的每种病原体的风险是什么?哪些病原体可以被优先考虑为未来更有可能出现?
英文摘要
Emerging infectious diseases continue to pose major threats to humans, animals and plants. Recent years have seen significant outbreaks of several emerging diseases, ranging from the well-known (Ebola and Olive quick decline syndrome), to the previously little known (Zika), to the entirely novel (Schmallenberg), to name but a few. It is well established that the ability of a pathogen to infect multiple hosts, particularly hosts in different taxonomic orders or wildlife, is a risk factor for emergence in human and livestock pathogens. Emerging wild-life diseases have also been linked to 'spill-overs' from humans or domesticated animals. Despite the importance of cross-species disease transmission, there has been relatively little attention paid to which species are the most important sources cross communities (e.g., zoonotic, wild-life to domestic, plants to other kingdoms), which are the most prolific vectors, how those species acquired the pathogens, and by what means the diseases entered new species or populations. A major reason for this limited understanding is the lack of comprehensive data on the pathogens in animal and plant populations and, in most cases, poorly documented information on how they are transmitted, including to humans.In this fellowship, I will improve and exploit a novel bioinformatic resource developed at the University of Liverpool to investigate how humans, their domesticated animals and crops are connected to the pathogen reservoir in other species, and how these pathogens pass from that reservoir to the focus populations. The bioinformatic resource, developed by me with funding from BBSRC, is the Enhanced Infectious Disease Database (EID2). EID2 utilises state-of-the-art, text and data mining procedures to extract information from multiple sources, including millions of metadata records accompanying genetic sequences and scientific publications. After processing, EID2 provides evidence for over 60,000 interactions between species of hosts and pathogens and is the most comprehensive data source on the known pathogens of humans, animals, and plants and their geographical ranges.During this fellowship, I aim to investigate the factors which lead to emergence of pathogens, asking the following questions:1. What are the characteristics of the networks that connect species via shared pathogens? How central are humans and their domesticated animals and crops in these networks and which other species are each of those communities most closely connected to?2. What is the role of different pathogen transmission routes on the nature of these networks? Are the potential species-to-species transmission pathways different for direct, food-borne, water-borne and vector-borne pathogens?3. What factors determine the host ranges of pathogens? Are host species more likely to become exposed to pathogens that infect a wide range of species? From species that are closer to them genetically? Or from those species with which they often interact? 4. What are we missing? Given the networks, transmission routes and host ranges, what is the risk associated with each pathogen emerging in new species? What are the pathogens that can be prioritised as more-likely to emerge in the future?
期刊论文(9)
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Identifying life-history patterns along the fast-slow continuum of mammalian viral carriers
识别哺乳动物病毒携带者快慢连续体的生活史模式
DOI:
10.21203/rs.3.rs-2722217/v1
发表时间:
2023
期刊:
影响因子:
--
作者:
[Tonelli A]
通讯作者:
Tonelli A
Features that matter: evolutionary signatures that predict viral transmission routes
重要的特征:预测病毒传播途径的进化特征
DOI:
10.1101/2023.11.22.568327
发表时间:
2023
期刊:
影响因子:
--
作者:
[Wardeh M]
通讯作者:
Wardeh M
Electronic supplementary notes and materials from Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs
来自共享病原体网络和机器学习集成的电子补充说明和材料揭示了人畜共患疾病的关键方面并预测了哺乳动物宿主
DOI:
10.6084/m9.figshare.11665581
发表时间:
2020
期刊:
影响因子:
--
作者:
[Wardeh M]
通讯作者:
Wardeh M
DOI:
10.1038/s41467-021-24085-w
发表时间:
2021-06-25
期刊:
Nature communications
影响因子:
16.6
作者:
[Wardeh M, Blagrove MSC, Sharkey KJ, Baylis M]
通讯作者:
Baylis M
Predicting mammalian hosts in which novel coronaviruses can be generated
预测可产生新型冠状病毒的哺乳动物宿主
DOI:
10.1101/2020.06.15.151845
发表时间:
2020
期刊:
影响因子:
--
作者:
[Wardeh M]
通讯作者:
Wardeh M
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