Bilateral BBSRC-FAPESP: Defining the Genetic and Semiochemical Basis of Tick Resistance in Cattle

蜱和蜱传疾病对牲畜、人类和伴侣动物有重大影响。特别是，牛蜱在世界各地的高温和高湿度地区发现。蜱虫感染导致体重减轻、贫血和继发性感染，以及传播巴贝斯虫病、泰勒虫病和无形体病等疾病。传统上，化学品（杀螨剂）是主要的蜱控制策略，但这种方法越来越有问题，主要是由于进化杀螨剂抗性蜱和化学残留物的产生。由于疫苗只取得了好坏参半的成功，因此迫切需要新的控制措施。牛的蜱虫负荷差异很大，已知这种差异大部分受宿主的遗传组成控制，因此有可能进行提高蜱虫抗性的育种。我们假设，宿主牛在蜱抗性方面不同的主要手段是通过它们在皮肤表面上产生引诱剂/驱避剂挥发性化学物质，即化学信息素，这最终决定了有多少蜱仍然附着在皮肤表面。识别这些化学物质和导致不同动物产生不同化学物质的遗传因素将为我们提供强大的新控制机会。我们可以培育对蜱虫更有抵抗力的动物，或者我们可以设计驱虫剂来减少蜱虫的数量。该项目旨在实现这一目标：目标是了解巴西牛对蜱虫感染的抗性遗传变异的潜在因素，包括遗传和生物化学水平。首先，我们将在巴西的1000多头商业Giroplasty牛上收集有关蜱虫负荷、生物样本和可能影响蜱虫负荷的因素的详细数据。这些数据将概述影响蜱虫负担的因素。从血液中提取DNA后，我们将使用称为高密度SNP芯片的工具对每只动物进行基因分型。这使我们能够同时对整个基因组中超过750，000个位置的遗传标记进行基因分型。对这些数据的分析将告诉我们基因组中哪些基因对蜱虫负担有很大影响，从而对蜱虫抗性有很大影响。但它不能告诉我们是哪些基因。 为了弄清楚哪些基因是重要的，我们将更详细地研究极端动物。使用从具有一致的最高和最低蜱负荷的动物获得的皮肤摩擦，我们将使用色谱法和光谱分析来检测这两组动物产生的行为改变化学物质（化学信息素）。我们将检验蜱虫对与叮咬蝇相同的化学信息素（如6-甲基-5-庚烯-2-酮）的反应这一假设。我们还将购买具有我们预测代表极端易感性和抗性的基因型的动物，并用蜱虫挑战这些动物。然后，我们将使用皮肤活检观察这些动物在激发前后的RNA谱。RNA图谱将告诉我们哪些基因正在表达，我们可以比较耐药和易感动物之间的这些图谱。这将告诉我们哪些基因和生化途径是耐药性的预测。利用生物信息学，我们将把我们所有的结果放在一起。我们将结合联合收割机的基因表达和信息化学的研究，以确定哪些基因是最有可能的抗性变异的基础。然后，我们将这些结果与SNP芯片结果相关联：如果可能的基因落在已被证明影响蜱抗性的基因组区域，那么我们就有非常强有力的证据表明基因（和遗传标记）影响抗性。然后，我们将通过对另外200头表型奶牛进行基因分型来验证我们的结果，并设计育种策略来提高抗性。我们还将根据我们的数据调查基于化学信息素或畜牧业的干预措施。

Ticks and tick-borne diseases have major impacts on livestock, humans and companion animals. In particular, the cattle tick is found worldwide in areas with high temperature and humidity. Tick infestations lead to weight loss, anaemia and secondary infections, as well as transmitting diseases such Babesiosis, Theileriosis and Anaplasmosis. Traditionally, chemicals (acaricides) are the main tick control strategy, but this approach is increasingly problematic, mainly due to the evolution acaricide-resistant ticks and generation of chemical residues. Because vaccines have only had mixed success, there is an urgent requirement for new control measures. Cattle vary greatly in the tick loads, and much of this variation is known to be controlled by the genetic composition of the host, therefore breeding for increased tick resistance is possible. We hypothesize that the primary means by which host cattle differ in the tick resistance is through their production of attractant/repellent volatile chemicals on the skin surface, i.e. semiochemicals, which ultimately determine how many ticks remain attached to the skin surface. Identification of these chemicals and the genetic factors that lead to different chemicals being produced by different animals would give us powerful new control opportunities. We could either breed animals that will be more resistant to ticks or we could devise repellents to reduce tick load. This project is designed to achieve this: the goal is to understand the factors underlying genetic variation in the resistance of Brazilian cattle to tick infestations, at both the genetic and biochemical levels. Firstly, on more than 1000 commercial Girolando cattle in Brazil we will collect detailed data on tick load, biological samples and factors which conceivably influence tick load. These data will provide an overview of factors influencing tick burden. After extracting DNA from the blood, we will genotype each animal using a tool known as a high density SNP chip. This allows us to genotype animals simultaneously for genetic markers at more than 750,000 locations throughout the genome. Analyses of these data will tell us where in the genome are the genes which have a large influence on tick burden, and hence tick resistance. But it doesn't tell us which genes. To work out which genes are important, we will then examine the extreme animals in much more detail. Using skin rubbings obtained from the animals with the consistently highest and lowest tick burdens we will characterise the behaviour-modifying chemicals (semiochemicals) produced by these two groups of animals using chromatography and spectroscopic analysis. We will test the hypothesis that ticks respond to the same semiochemicals as for biting flies (e.g. 6-methyl-5-hepten-2-one). We will also purchase animals with genotypes that we predict to represent the extremes of susceptibility and resistance, and challenge these animals with ticks. We will then look at the RNA profile of these animals before and after challenge, using skin biopsies. The RNA profiles will tell us which genes are being expressed, and we can compare these profiles between resistant and susceptible animals. This will tell us which genes and biochemical pathways are predictive of resistance. Using bioinformatics we will then bring all our results together. We will combine the gene expression and the semiochemistry studies to determine which genes are most likely to underpin variation in resistance. We will then relate these results to the SNP chip results: if likely genes fall in regions of the genome that have been shown to influence tick resistance then we have very strong evidence for genes (and genetic markers) that influence resistance. We will then validate our results by genotyping a further 200 phenotyped cows, and devise breeding strategies to improve resistance. We will also investigate semiochemical based or husbandry interventions from our data.