The smell of infection - detecting infectious disease and determining mechanisms underlying the spread of disease in social networks

感染的气味 - 检测传染病并确定疾病在社交网络中传播的机制

• 批准号: 2597867
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597867
• 关键词: smell; infection; detecting; infectious; disease

Social organization facilitates contacts between individuals altering the probability of parasite transmission (Perkins et al., 2009). Consequently, social animals have evolved mechanisms to mitigate the risks of infection. One mechanism by which parasites may alter contact patterns could be through avoidance of infected individuals by uninfected based on changes in 'smell'. Both non-infectious and infectious diseases can cause a change in volatile organic compounds (VOCs) in many species, including, livestock, wildlife and humans (Penn and Potts, 1998; Balseiro and Correia, 2006). Studies on female mice demonstrate odour-based discrimination between infected and uninfected males for a range of parasites, including, influenza virus, the protozoan, Eimeria vermiformis, and the nematode, Heligomosomoides polygyrus (Kavaliers and Colwell 1995, Penn et al. 1998; Ehman and Scott 2001). Proof of concept in our parasite-cockroach colony system has shown that we can detect distinct scents associated with infection in whole animals (Perkins, Muller et al., in prep) However, parasites may also have evolved to exploit and/or manipulate host behaviour - contact patterns - to improve transmission (Moore, 2002). As such, epidemics may themselves drive contact network patterns.Apiculture worldwide has been threatened by the emergence of infectious diseases (including European and American foulbrood, varroa mite, and several viruses), and associated with effects ranging from reduction in honey productivity to full colony collapse. Using honey bees as a model system we aim to investigate the effect of pathogen load, virulence, mode of transmission on contact networks and the role of 'smell' as a mechanism that alters how epidemics progress in social networks.By combining VOC analysis with detailed behavioural observations, we aim to determine if the odour of infection is a mechanism driving infectious disease dynamics and to tease apart the directional effects of contacts between infected and uninfected individuals - are infected individuals avoided by uninfected individuals thereby reducing parasite transmission. Alternatively, does the parasite manipulate host odour to attract uninfected individuals, so increasing parasite transmission?We will collect headspace air samples in-situ from animals and colonies onto thermal desorption tubes and use state-of-the-art thermal desorption combined with gas chromatography - time-of-flight mass spectrometry (TD-GC-MS-TOF) for analysis of the samples. Behavioural experiments will analyse social network avoidance behaviours of infected and uninfected individuals using behavioural software such as 'Ethovision'.We anticipate that the findings of the project will elucidate key mechanisms in disease transmission and deliver a novel approach to monitoring infections in apiculture and potentially infections in general.

社会组织促进个人之间的接触，改变寄生虫传播的可能性（Perkins等人，2009年）。因此，群居动物进化出了减轻感染风险的机制。寄生虫可能改变接触模式的一种机制可能是基于“气味”的变化，未感染者避开感染者。非传染性疾病和传染性疾病都会导致许多物种（包括牲畜、野生动物和人类）的挥发性有机化合物（VOC）发生变化（Penn和Potts，1998年; Balseiro和Correia，2006年）。对雌性小鼠的研究表明，感染和未感染的雄性小鼠对一系列寄生虫（包括流感病毒、原虫、蠕虫艾美球虫和线虫、多回Heligomosomoides polygyrus）具有基于气味的区分能力（Kavaliers和科尔韦尔，1995; Penn等人，1998; Ehman和Scott，2001）。在我们的寄生虫-蟑螂群体系统中的概念证明已经表明，我们可以检测到与整个动物中的感染相关的不同气味（Perkins，Muller等人，然而，寄生虫也可能已经进化到利用和/或操纵宿主行为-接触模式-以改善传播（摩尔，2002）。世界范围内的养蜂业一直受到感染性疾病（包括欧洲和美洲的污巢病、瓦螨和几种病毒）的威胁，并伴随着从蜂蜜产量下降到蜂群完全崩溃的一系列影响。使用蜜蜂作为模型系统，我们的目标是研究病原体负荷，毒力，传播方式对接触网络的影响，以及“气味”作为改变社交网络中流行病进展方式的机制的作用。通过将VOC分析与详细的行为观察相结合，我们的目标是确定感染的气味是否是驱动传染病动力学的一种机制，并梳理出感染者和未感染者之间的接触-未感染者避免感染者，从而减少寄生虫传播。或者，寄生虫是否操纵宿主的气味来吸引未感染的个体，从而增加寄生虫的传播？我们将从动物和菌落中原位收集顶空空气样品到热解吸管上，并使用最先进的热解吸结合气相色谱-飞行时间质谱法（TD-GC-MS-TOF）对样品进行分析。行为实验将使用行为软件（如“Ethovision”）分析受感染和未受感染个体的社交网络回避行为。我们预计该项目的研究结果将阐明疾病传播的关键机制，并提供一种新的方法来监测养蜂业中的感染和一般的潜在感染。