WGS-aqua: Capacity building for the widespread adoption of whole genome sequencing (WGS) for the molecular epidemiology of aquaculture pathogens.
WGS-aqua: Capacity building for the widespread adoption of whole genome sequencing (WGS) for the molecular epidemiology of aquaculture pathogens.
批准号:
BB/M026388/1
负责人:
Edward Feil
金额:
$31.64万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2015
资助国家:
英国
项目状态:
已结题
起止时间:
2015 至 --
中文摘要
全球人口迅速增长,加上环境压力和资源有限,意味着为每个人可持续地生产和分配足够数量的健康、安全食品将成为人类面临的最大挑战之一。由于全球监管和管理不善,我们的许多自然资源被毫无希望地过度开采,特别是在过去几十年里。渔业就是一个很好的例子,几十年的短期主义导致了整个渔业的灾难性崩溃。这不可避免地导致对养殖(水产养殖)的依赖日益增加,目前全球水产养殖业消费的海产品(鱼类和贝类)超过捕捞部门。水产养殖商业损失的一个主要原因是传染病,特别是密集养殖做法会增加风险。以高密度饲养应激动物大大增加了农场爆发疾病的可能性,病原体可能会传播到其他农场甚至野生鱼类。此外,鸡蛋和活鱼的国际贸易增加了全球疾病传播的可能性,并将外来病原体引入脆弱的本地物种。除了虱子等寄生虫外,细菌和病毒等微生物也会引起严重疾病。为了更有效地检测和管理这些感染,我们迫切需要更多关于病原体遗传学的数据,为什么它们会引起疫情,以及它们如何在地理上或不同鱼类之间传播。这些挑战中有许多与人类疾病类似,和公共卫生传染病流行病学家面临着理解为什么一种新的“超级细菌”(如MRSA)出现,以及它传播的可能性有多大。幸运的是,在过去的几年里,我们见证了巨大的技术进步,这为我们提供了更有信心地解决这些问题的方法。这项技术可以非常快速和相对便宜地解码不同细菌和病毒菌株的整个遗传内容(基因组)。基因组中的微小变异使得追踪这些病原体的传播成为可能,并且通过能够识别基因组中存在的所有基因,可以预测给定菌株是否具有高毒性,或者由于抗生素耐药性而难以治疗。该项目将利用人类病原体基因组数据生成和分析方面的进展,并将相同或类似的技术应用于水产养殖病原体。通过这样做,希望其他学者,利益相关者和公司将认识到这种方法的好处,并将被广泛采用。新测序技术面临的主要挑战之一是大量数据迅速变得笨重,难以有效管理和分析。为了解决这个问题,该项目的一个主要重点将是修改为公共卫生病原体基因组数据开发的直观软件工具。为了确保该系统尽可能有用,我们将首先举办一个研讨会,汇集公共卫生和水产养殖等不同领域的专家,以确定该系统的关键要求。非常重要的是,现在开发出这样一个易于使用但功能强大的系统,这样来自不同研究的数据就可以有效地结合起来,我们可以真正了解不同菌株的出现和传播。一旦我们优化了软件,我们将通过生成三种严重水产养殖病原体(两种细菌和一种病毒)的基因组数据并将数据上传到系统来说明其有用性。这将显示不同菌株之间的关系,它们在地图上的分布,以及它们包含哪些疾病和抗性基因。
英文摘要
The rapidly increasing global population, combined with mounting environmental pressures and resource limitation, means that the sustainable production and distribution of adequate quantities of healthy, safe food for everyone is set to become one of mankind's biggest challenges. Due to poor global regulation and management, many of our natural resources have been hopelessly over-exploited, particularly so over the last few decades. The fishing industry is a perfect example, where catastrophic collapses of whole fisheries have resulted from decades of short-termism. This has inevitably resulted in an increasing reliance on farming (aquaculture), and this industry globally now accounts for more seafood (fish and shellfish) consumed than the capture sector. A major cause of commercial losses in aquaculture is infectious disease, and intensive farming practices in particular will increase the risk. Rearing stressed animals at high densities greatly increases the probability of disease outbreaks on a farm, and pathogens may spread to other farms or even to wild fish. Moreover, the international trade in eggs and live fish increases the likelihood of global disease spread, and the introduction of exotic pathogens into vulnerable native species. In addition to parasites such as lice, serious diseases are also caused by microbes such as bacteria and viruses. In order to detect and manage these infections more efficiently, we urgently require more data on the genetics of the pathogens, why they cause outbreaks when they do, and how they can transmit geographically or between different species of fish.Many of these challenges are analogous to human diseases, and public health infectious disease epidemiologists are faced with understanding why a new strain of a "superbug" (such as MRSA) has emerged, and how likely it is to spread. Fortunately, the last few years have witnessed a huge technological advance which provides to means to address these problems with much more confidence. This technology makes it possible to decode the entire genetic content (genome) of different strains of bacteria and viruses very quickly and relatively cheaply. Tiny variations in the genome makes it possible to track the transmission of these pathogens, and by being able to identify all the genes present in the genome it is possible to predict whether a given strain will be highly virulent, or difficult to treat due to antibiotic resistance. This project will exploit the advances in the generation and analysis of genome data for human pathogens, and will apply the same, or similar, techniques to aquaculture pathogens. By doing so, it is hoped that other academics, stakeholders and companies will recognize the benefits of the approach and it will become widely adopted. One of the major challenges with the new sequencing technology is that the vast amounts of data quickly become unwieldy and difficult to manage and analyse efficiently. To address this, a major focus of the project will be the modification of intuitive software tools developed for the genome data for public health pathogens. In order to make sure the system is as useful as possible, we will first hold a workshop which will bring together experts in different fields, both public health and aquaculture, in order to identify the key requirements of such a system. It is very important that such an easy to use, yet powerful, system like this is developed now, so that data from different studies can be combined efficiently, and we can have a truly global picture of the emergence and spread of different strains. Once we have optimized the software, we will illustrate its usefulness by generating genome data for three serious aquaculture pathogens (two bacterial species and one virus) and uploading the data to the system. This will show the relationships between the different strains, where they are distributed on a map, and which disease and resistance genes they contain.
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DOI:
10.3389/fmicb.2017.00121
发表时间:
2017
期刊:
Frontiers in microbiology
影响因子:
5.2
作者:
[Bayliss SC, Verner-Jeffreys DW, Bartie KL, Aanensen DM, Sheppard SK, Adams A, Feil EJ]
通讯作者:
Feil EJ
DOI:
10.3389/fmicb.2019.02360
发表时间:
2019-10-25
期刊:
FRONTIERS IN MICROBIOLOGY
影响因子:
5.2
作者:
[Merla, Cristina, Rodrigues, Carla, Brisse, Sylvain]
通讯作者:
Brisse, Sylvain
DOI:
10.1099/mgen.0.000055
发表时间:
2016-04
期刊:
Microbial genomics
影响因子:
3.9
作者:
[Brynildsrud O, Gulla S, Feil EJ, Nørstebø SF, Rhodes LD]
通讯作者:
Rhodes LD
DOI:
10.1099/mgen.0.000201
发表时间:
2018-09
期刊:
Microbial genomics
影响因子:
3.9
作者:
[Bayliss SC, Verner-Jeffreys DW, Ryder D, Suarez R, Ramirez R, Romero J, Pascoe B, Sheppard SK, Godoy M, Feil EJ]
通讯作者:
Feil EJ
DOI:
10.1093/gigascience/gix001
发表时间:
2017-03-01
期刊:
GigaScience
影响因子:
9.2
作者:
[Bayliss SC, Hunt VL, Yokoyama M, Thorpe HA, Feil EJ]
通讯作者:
Feil EJ
Tools for the Epidemiology of AMR Plasmids, One-Health Transmission and Surveillance
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