Dissecting treponemal immune-modulation to enable disease control.

数字皮炎（DD）被认为是由螺旋体细菌引起的，是一种全球性的严重传染病，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，牛最常患脚后跟球之间的炎症病变，导致严重跛行。这种疾病非常痛苦，导致动物福利不佳，因此具有重要意义。虽然局部抗生素治疗允许一些愈合，病变经常复发，没有单一的有效治疗。此外，产奶量和繁殖能力下降造成严重的经济损失，而抗生素使用增加和化学足疗对全球健康造成影响。英国每年的经济损失为7400万英镑，全世界有数千万只动物被感染，每年至少花费10亿美元。为重要的牲畜地方病生产负担得起的疫苗，可促进全球吸收，提高动物健康、福利和生产力，同时减少抗生素使用和抗微生物药物耐药性。这对牛DD尤其重要，因为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性螺旋体细菌的保护性免疫。螺旋体的免疫逃避被认为涉及到这些细菌用宿主分子包裹自己。虽然DD型密螺旋体多种多样，但它们必须具有几乎相同的免疫调节机制，这种机制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点。因此，对细菌表面蛋白，特别是那些参与免疫逃避的表面蛋白进行表征，将有助于开发新的疫苗或治疗方法。最近的研究，突变细菌表面蛋白以防止宿主分子的结合，以及增强蛋白质的稳定性，增加了这些细菌成分用作疫苗时的保护能力。这种新型蛋白质工程的应用已被用于开发一种重要人类病原体的疫苗，这种疫苗现已获得许可，现在可以应用于兽医病原体。此外，人工智能（AI）在结构预测方面的最新进展非常准确，现在可以用来指导此类工程。在这里，我们将人工智能、合成生物学和计算机方法结合起来，指导关键细胞表面蛋白的鉴定和工程，利用尖端的增强反向疫苗学管道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)利用人工智能生成的结构模型识别候选疫苗，并研究功能多样性，包括量化来自不同物种的同源物是否表现出不同的粘附能力，以及来自共生体的同源物是否缺乏对关键宿主分子的粘附能力；2)利用序列多样性/保守性和粘附能力的差异与人工智能生成的结构模型和计算机方法合成具有限制性宿主附着的突变表面蛋白；3)利用序列多样性与人工智能生成的结构模型和计算机方法合成具有增强稳定性的表面蛋白；4)利用疾病模型确定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。利用上述协同方法确定和改造DD候选疫苗，应该能更好地表征致病细菌，提高对疾病的认识，并产生保护性疫苗。为了预防这一严重的、重要的全球疾病，非常需要这样的研究。此外，这种新颖的、增强的管道应该可以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学研究中的动物使用。

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.