Dissecting treponemal immune-modulation to enable disease control.

剖析密螺旋体免疫调节以实现疾病控制。

• 批准号: BB/X016226/1
• 负责人: Nicholas Evans
• 金额: $ 100.48万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX016226%2F1
• 关键词: Dissecting; treponemal; immune; modulation; enable

Digital dermatitis (DD), considered caused by spirochete bacteria called Treponema, is a worldwide, severe infectious disease affecting multiple host species including cattle, sheep and goats. Globally, cattle are most frequently afflicted with inflamed lesions between the heel bulbs of feet causing severe lameness. The disease is of significance as it is extremely painful resulting in poor animal welfare. Whilst topical antibiotic treatment allows some healing, lesions frequently reappear and there is no single effective treatment. Moreover, severe economic losses result from reduced milk yield and reproductive ability whilst global health impacts ensue from increased antibiotic use and chemical footbathing. The UK economic cost is £74 million/year and worldwide tens of millions of animals are infected, annually costing at least a billion dollars. Generating affordable vaccines for important endemic diseases of livestock enables global uptake, increasing animal health, welfare and productivity whilst decreasing antibiotic use and antimicrobial resistance. This is especially important for bovine DD which is increasing in prevalence globally and continuing to emerge in new host species. Bacterial surface proteins are considered important vaccine candidates to provide protective immunity from a range of pathogenic spirochete bacteria. Immune evasion by spirochetes, is considered to involve these bacteria coating themselves with host molecules. Whilst DD treponemes are diverse, they must share near identical machinery for this immune-modulation, which must be present on the bacterial surface to allow host binding and thus represent ideal vaccine targets. Characterisation of bacterial surface proteins, especially those involved in immune evasion should therefore enable development of novel vaccines or therapeutics. Recent research, mutating bacterial surface proteins to prevent binding of host molecules, as well as enhancing protein stability, has increased the protective ability of these bacterial components when used as vaccines. The application of such novel protein engineering has been used in the development of a vaccine for an important human pathogen that is now licensed and can now be applied to veterinary pathogens. Moreover, cutting-edge advances in structure prediction by artificial intelligence (AI) are highly accurate and can now be used to guide such engineering. Here, we combine AI, synthetic biology and in silico approaches to guide identification and engineering of key cell surface proteins to develop a novel efficacious vaccine with broad treponeme specificity using a cutting edge enhanced reverse vaccinology pipeline.This study will 1) identify vaccine candidates using AI generated structural models and investigate functional diversity including quantifying whether orthologs from different species exhibit different adhesion abilities and whether orthologs from commensals lack ability to attach to key host molecules, 2) use sequence diversity/conservation and differences in adhesion ability together with AI generated structural models and in silico approaches to synthesise mutated surface proteins with restricted host attachment, 3) use sequence diversity together with AI generated structural models and in silico approaches to synthesise surface proteins with enhanced stability, 4) use a disease model to identify which engineered bacterial surface proteins are most protective and to decipher immunomodulatory ability of a DD treponeme surface associated sugar.Identifying and engineering DD vaccine candidates using the above synergistic methods, should better characterise causal bacteria, improve disease understanding, and generate a protective vaccine. Such studies are much needed to enable prevention of this severe, important global disease. Moreover, this novel, enhanced pipeline should enable reduced animal use in future vaccinology studies by reducing study candidate numbers using in silico methods.

数字皮炎(DD)被认为是由螺旋体细菌密螺旋体引起的，是一种世界性的严重传染病，影响包括牛、绵羊和山羊在内的多种宿主物种。在全球范围内，牛最常受到脚后跟球部发炎损害的困扰，导致严重跛行。这种疾病意义重大，因为它极其痛苦，导致动物福利很差。虽然局部抗生素治疗有一定的愈合作用，但损伤经常复发，而且没有单一有效的治疗方法。此外，牛奶产量和繁殖能力的下降造成了严重的经济损失，而抗生素使用和化学足浴的增加则对全球健康造成了影响。英国每年的经济成本为7400万GB，全球有数千万动物受到感染，每年至少造成10亿美元的损失。为重要的牲畜地方性疾病生产负担得起的疫苗能够在全球范围内普及，提高动物健康、福利和生产力，同时减少抗生素的使用和抗菌素耐药性。这对牛DD尤其重要，它在全球范围内的流行率正在上升，并继续在新的宿主物种中出现。细菌表面蛋白被认为是重要的候选疫苗，可提供对一系列致病螺旋体细菌的保护性免疫。螺旋体的免疫逃避，被认为是这些细菌用宿主分子包裹自己。虽然DD密螺旋体是多样化的，但它们必须共享几乎相同的免疫调节机制，这种免疫调节必须存在于细菌表面，以允许宿主结合，从而代表理想的疫苗靶标。因此，细菌表面蛋白的特征，特别是那些参与免疫逃避的蛋白，应该能够开发出新的疫苗或疗法。最近的研究，通过突变细菌表面蛋白来防止宿主分子的结合，以及增强蛋白质的稳定性，提高了这些细菌成分用作疫苗时的保护能力。这种新的蛋白质工程的应用已经用于开发一种针对一种重要人类病原体的疫苗，该疫苗现已获得许可，现在可以应用于兽医病原体。此外，人工智能(AI)在结构预测方面的尖端进展具有高度的准确性，现在可以用来指导此类工程。在这里，我们结合人工智能、合成生物学和电子计算机方法来指导关键细胞表面蛋白的鉴定和工程，利用尖端的增强型反向疫苗流水线来开发一种具有广泛密螺旋体特异性的新型有效疫苗。本研究将1)使用人工智能生成的结构模型来确定候选疫苗并研究功能多样性，包括量化来自不同物种的同源基因是否表现出不同的黏附能力以及来自共生基因的同源基因是否缺乏与关键宿主分子的黏附能力；2)利用序列多样性/保守性和黏附能力的差异与人工智能产生的结构模型和在电子技术方法中合成具有受限宿主附着的突变表面蛋白，3)使用序列多样性与人工智能生成的结构模型和在计算机方法中合成具有增强稳定性的表面蛋白；4)使用疾病模型来识别哪些工程细菌表面蛋白最具保护性，并破译DD密螺旋体表面相关糖的免疫调节能力。使用上述协同方法鉴定和设计DD候选疫苗，应能更好地表征致病细菌，提高对疾病的理解，并产生保护性疫苗。为了能够预防这种严重而重要的全球疾病，这种研究是非常必要的。此外，这种新颖的、增强的管道应该能够通过减少使用计算机方法的研究候选数量来减少未来疫苗研究中的动物使用。