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
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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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