Prediction of phenotype from genotype with respect to bacterial infection

根据细菌感染的基因型预测表型

• 批准号: BB/T001933/1
• 负责人: James Leigh
• 金额: $ 67.89万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT001933%2F1
• 关键词: Prediction; phenotype; genotype; respect; bacterial

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

细菌基因组（基因型）的DNA序列编码个体生物体显示的所有性状（表型）。DNA序列现在可以被快速且经济有效地读取，这为利用这些信息控制细菌疾病提供了新的机会。该项目将产生成功预测细菌直接从其基因组引起疾病（毒力）的能力所需的信息。为了实现这一目标，有必要确定毒力所需的细菌组分，它们的变异，并确定这种变异如何影响毒力表型。这项调查将集中在牛乳腺炎，奶牛最常见的传染病（约100万例/年，在英国）。乳腺炎通常是由多种不同的细菌物种之一引起的乳房内感染;在英国最常见的是链球菌（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导致的螺旋状炎症。这种疾病导致产奶组织受损，产奶量不足，产奶量不适合人类食用。仅在英国，乳腺炎就导致相当于约100万吨的产奶量损失。该疾病的地方性特征导致乳制品生产效率低下，对动物福利和乳制品养殖的环境可持续性造成重大经济影响和重大影响。在英国，将乳腺炎控制（预防和治疗）到目前的水平需要大约3吨抗生素/年，迫切需要新的、更有效的预防/治疗方法。导致疾病初始阶段的细胞因子。引入细菌（突变体）的遗传病变（突变）将通过DNA测序定位在基因组上。分析100，000个细菌突变体，每个突变体携带不同的突变，在乳腺定植之前和之后，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鉴定特定的定植阶段（在牛奶中生长，对免疫防御的抵抗）所需的基因。病毒同样具有毒性。一些容易引起乳腺炎，而另一些只是短暂地感染乳腺，数量很少。我们将对600株沙门氏菌进行基因组测序。从患病和未患病动物中分离出500种乳杆菌，并对其中500种乳杆菌定植乳腺、在乳汁中生长和抵抗乳腺免疫防御的能力进行量化。将以各种方式分析这两组数据。对先前确定为对毒力重要的那些基因的详细比较将确定与毒力一致的那些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序列谱，该谱以可量化的方式定义毒性更强和毒性更弱。这些分析的组合将用于预测100 S的毒性。直接从它们的基因组序列中预测菌株，这些菌株将在相关的模型系统中进行测试，以确定预测的准确性。进行这项调查不仅将提供一个关于基因组序列如何以有效和有用的方式与毒力相关的示例研究，而且还将产生一个强大的信息平台，这将支持科学上强大的干预措施的发展，对动物福利和环境可持续粮食生产有重大影响的传染病