Prediction of phenotype from genotype with respect to bacterial infection

细菌基因组（基因型）的DNA序列编码了单个生物体所显示的所有特征（表型）。DNA序列现在可以快速、低成本地读取，这为利用这些信息控制细菌疾病提供了新的机会。该项目将产生所需的信息，以成功地预测细菌直接从其基因组引起疾病（毒性）的能力。为了实现这一目标，有必要确定毒力所需的细菌成分，它们的变异，并确定这种变异如何影响毒力表型。本次调查将重点关注牛乳腺炎，这是奶牛最常见的传染病（在英国每年约有100万例）。乳腺炎通常是由多种不同细菌的一种引起的乳腺内感染；在英国最常见的是uberis链球菌（约占所有病例的30%），这种细菌将是本研究的主题。乳腺炎的症状是由于免疫防御无法清除感染而引起的螺旋式炎症。这种疾病导致产奶组织受损，牛奶产量不足，以及不适合人类食用的牛奶。仅在英国，乳腺炎导致的牛奶产量损失就相当于100万吨。该疾病的地方性造成了乳制品生产的严重效率低下，导致了重大的经济影响，并对奶牛养殖的动物福利和环境可持续性产生了重大影响。在英国，将乳腺炎控制（预防和治疗）到目前的水平每年需要约3吨抗生素，迫切需要新的、更有效的预防/治疗方法。在这个项目中，突变和DNA测序技术的结合将被用于鉴定uberis中导致疾病初始阶段的基因。遗传病变（突变）引入到细菌（突变）将定位在基因组上的DNA测序。在乳腺定植之前和之后，对100,000个携带不同突变的细菌突变体进行分析，将允许鉴定定植所需的基因。同样的技术将被用于在实验室模型中识别特定阶段（在牛奶中生长，抵抗免疫防御）所需的基因。并非所有的uberis菌株都具有同样的毒性。有些容易引起乳腺炎，而另一些只在少数情况下短暂感染乳腺。我们将对600株来自患病和未患病动物的uberis进行基因组测序，并量化其中500株在乳腺中定植、在乳汁中生长和抵抗乳腺免疫防御的能力。这两个数据集将以不同的方式进行分析。对先前确定的对毒力重要的基因进行详细比较，将确定与毒力一致的基因变异的个体和组合。此外，基于计算机的分析将每个基因组序列的短长度与所有其他基因组序列进行比较，以提供DNA序列的概况，以可量化的方式定义毒性较强和毒性较弱的DNA序列。这些分析的组合将用于直接根据100株uberis菌株的基因组序列预测其毒力，并将在相关模型系统中对这些菌株进行测试，以确定预测的准确性。开展这项调查不仅将为基因组序列如何以有效和有用的方式与毒力相关提供范例研究，而且还将产生一个强有力的信息平台，为科学有力地开发干预措施提供基础，以控制一种对动物福利和环境可持续粮食生产具有重大影响的主要传染病

The DNA sequence of a bacterial genome (genotype) encodes all the traits (phenotypes) displayed by an individual organism. DNA sequences can now be read quickly and cost effectively, providing new opportunities to exploit this information in the control of bacterial diseases.This project will produce the information required to successfully predict the ability of a bacterium to cause disease (virulence) directly from its genome. To achieve this, it will be necessary to identify the bacterial components required for virulence, their variation and determine how such variation impacts the virulence phenotype.This investigation will focus on bovine mastitis, the most common infectious disease of dairy cattle (~1 million cases /year in the UK). Mastitis typically results from intramammary infection by one of a variety of different bacterial species; the most common in the UK being Streptococcus uberis (~30% of all cases) and this bacterium will be the subject of this study.Signs of mastitis result from spiralling inflammation due to the inability of the immune defences to clear the infection. The disease results in damage to milk producing tissue and under production of milk and production of milk that is unfit for human consumption. In the UK alone, mastitis results in lost milk production equivalent to ~1M tonnes. The endemic nature of the disease creates major inefficiencies within dairy production, leading to substantial economic impact and significant effects on animal welfare and the environmental sustainability of dairy farming. Control (prevention and treatment) of mastitis to its current level in the UK requires ~3 tonnes of antibiotic /year and new, more effective methods of prevention / treatment are required urgently.In this project, a combination of mutation and DNA sequencing technology will be used to identify genes in S. uberis that contribute to the initial stages of disease. Genetic lesions (mutations) introduced in to the bacterium (mutant) will be located on the genome by DNA sequencing. Analysis of 100,000 bacterial mutants, each carrying a different mutation, before and after colonisation of the mammary gland will permit identification of genes required for colonisation. The same technology will be used to identify genes required at specific stages of colonisation (growth in milk, resistance to immune defences) in laboratory models.Not all strains of S. uberis are equally virulent. Some readily cause mastitis, while others only transiently infect the mammary gland in low numbers. We will sequence the genome of 600 strains of S. uberis from diseased and non-diseased animals and quantify the ability of 500 of these to colonise the mammary gland, grow in milk and resist the mammary gland immune defences. These two data sets will be analysed in a variety ways. A detailed comparison of those genes previously identified as important for virulence will determine those individual and combinations of gene variants that align with virulence. Also, a computer based analysis will compare short lengths of each genome sequence to all other genome sequences to provide a profile of DNA sequences that defines more virulent from less virulent in a quantifiable manner.A combination of these analyses will be used to predict the virulence of 100 S. uberis strains directly from their genome sequence and these strains will be tested in the relevant model system to determine the accuracy of the prediction.Conducting this investigation will not only provide an example study of how genome sequences may be related to virulence in an effective and useful manner, but will also generate a robust platform of information that will underpin scientifically robust development of interventions to control a major infectious disease with substantial implications for animal welfare and environmentally sustainable food production