Identification of putative pan-streptococcal vaccine targets
Identification of putative pan-streptococcal vaccine targets
批准号:
2595188
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
链球菌在农业、水产养殖业中造成重大的经济和种群损失,并对人类健康产生严重影响。粮食生产持续集约化带来的这些主要威胁是由人:牛:猪:鱼的界面不断扩大以及这些畜牧业的集约化所驱动的。由于这种人畜共患病原体的耐药性日益增强和基因转移的威胁,抗生素控制疾病变得越来越不可行,迫切需要替代控制策略。人畜共患链球菌存在有限的菌株、流行率和序列信息,这些菌株的功能特征将为潜在的未来控制策略提供关键的毒力相关信息。识别参与感染过程的细菌的关键成分将有助于开发防止传播的替代疗法。或者,可以将这些成分纳入诊断测试,以确定自然携带的更有可能引起感染的菌株,从而在传播更有可能导致疾病的情况下采用更有针对性的治疗方法。任何一种方法都将大大减少人类和牲畜链球菌感染的发生率,从而降低与该疾病相关的死亡率。该项目将利用SVMS链球菌研究小组(SRG)产生的大量随机诱变高通量测序数据,描述人类、鱼类和牛的泛链球菌发病机制所必需的基因/蛋白质。使用pGh9:ISS1插入诱变系统为具有代表性的菌株生成了单个细菌突变池,并使用我们开发的新的定制实验室和生物信息学分析程序PIMMS V2进行了分析。PIMMS V2将用于绘制细菌基因组内的插入序列,并识别有条件的重要/必需序列,这些序列将被翻译为代谢途径和生化过程,以进行进一步的比较分析。产生描述链球菌必需基因组和/或有条件的重要基因组和代谢/生化途径和过程的信息,这些信息可作为新的疾病控制策略的基础。该项目将首先完成8种目标链球菌基因组的泛基因组分析(ubercoccus、iniae、suis、无乳链球菌、肺炎链球菌、garviae和无乳链球菌),并利用现有的PIMMS数据进行完整的表型泛基因组分析,从而确定假定的泛链球菌目标。这些靶标的附加特征将在实验室使用SRG持有的突变体库进行,以评估假设的靶标是否确实推断出生长抑制作用。该项目的总体产出将是经过实验证实的数据分析管道,该管道将与现有的PIMMS管道合作,纳入多个数据集,为未来更大规模的疫苗开发赠款提案制定目标。
英文摘要
Streptococci cause significant economic and stock losses in the agriculture, aquaculture and have a serious impact on human health. These key threats from the continued intensification of food production are driven by the growing human:cattle:swine:fish interface and intensification of these livestock sectors. Antibiotic control of disease is becoming less feasible due to growing resistance and the threat of gene transfer within this zoonotic pathogen and alternative control strategies are urgently required. Limited strain, prevalence and sequence information exists for zoonotic Streptococcus and functional characterisation of these strains will provide key virulence related information for potential future control-based strategies. Identification of the key components of bacteria that are involved in the infection process will allow the development of alternative therapies that prevent transmission. Alternately, these components may be incorporated into diagnostic tests to identify those strains naturally carried that are more likely cause infection, allowing a more targeted approach for treatment in those situations where transmission leading to disease is more likely. Either approach would greatly reduce the incidence of streptococcal infection in humans and livestock and thus reduce mortality associated with this disease. This project will characterise genes/proteins essential for pan-streptococcal pathogenesis in humans, fish and cattle using vast archive of random mutagenesis high throughput sequencing data which have been generated in the Streptococcal Research Group (SRG) at SVMS. Individual bacterial mutant pools have be generated for representative strains using the pGh9:ISS1 insertional mutagenesis system, and analysed using the new bespoke laboratory and bioinformatic analysis programme we have developed called PIMMS V2. PIMMS V2 will be used to map insertions within the bacterial genomes and identify conditionally important / essential sequences that will be translated to metabolic pathways and biochemical processes for further comparative analysis. Producing information that describes a pan streptococcal essential genome and/or conditionally important genome and metabolic/ biochemical pathways and processes against which new disease control strategeies could be based.The project will initially complete a pangenome analysis of 8 target streptococcal genomes (Streptococcus uberis, iniae, suis, agalactiae, pneumoniae, garviae and dysgalactiae) and incorporate a complete phenotypic pangenome analysis using the existing PIMMS data available leading to the identification of putative pan-streptococcal targets. Additional characterisation of these targets will take place in the laboratory using mutant banks held by SRG, to assess if the putative targets do infer a growth inhibiting effect. The overall output for the project will be a experimentally confirmed data analysis pipeline which will work with the existing PIMMS pipeline to incorporate multiple datasets, producing targets for future larger grant proposal in vaccine development.
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