Dissecting cell surface protein diversity to enhance leptospiral vaccine efficacy.

由钩端螺旋体细菌引起的钩端螺旋体病是一种世界性的严重传染病，影响几种不同的宿主物种，包括牛、狗和人。在全球范围内，牛受到严重影响，造成严重的经济损失、粮食安全下降、大量使用抗微生物药物以及动物传染给人（人畜共患）。英国每年的经济成本为2230万英镑，预计热带地区（包括许多中低收入国家）的成本要高得多，因为热带地区的疾病负担更重，疾病更严重。目前的牛钩端螺旋体病（BL）疫苗具有有限的特异性，需要冷链运输和储存，这在许多热带地区，通常是疾病负担最重的中低收入地区是一个问题。使疫苗具有更广泛的保护作用和更容易获得，将增加全球的吸收，减少全球抗生素使用和抗微生物药物耐药性的发展。这对钩端螺旋体病尤其重要，人们认为钩端螺旋体病正在全球范围内出现/重新出现，并受到全球变暖和相关极端气候事件（如洪水）增加的驱动。细菌表面蛋白被认为是对一系列钩端螺旋体和血清型提供交叉保护和持久免疫的重要靶点。钩端螺旋体的免疫逃避被认为与这些细菌用宿主分子包裹自己有关。虽然与疾病有关的不同钩端螺旋体细菌是多种多样的，但它们必须具有几乎相同的免疫逃避机制，这种机制必须存在于细菌表面，以允许宿主结合和/或破坏，因此代表理想的疫苗目标。因此，关键细菌表面蛋白的特征，特别是那些参与免疫逃避和确定其相互作用机制应该允许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一种重要的人类病原体疫苗的开发途径，该疫苗现已获得许可，现在可以应用于兽医病原体。在这里，我们结合合成生物学、人工智能和硅（生物信息学）方法来指导关键细胞表面蛋白的工程设计，以开发一种具有广泛钩端螺旋体特异性和增强效力的新型热稳定性疫苗。本研究将1)调查钩端螺旋体物种间候选疫苗的多样性，包括调查来自某些物种的变异是否对来自特定宿主物种的分子表现出粘附偏好，从而产生已知的宿主特异性和鉴定，来自共生（无害）亲缘体的变异是否缺乏附着宿主分子的能力；2)使用序列多样性/保守性和粘附能力的差异与人工智能（AI）生成的结构模型结合计算机方法来设计表面蛋白以限制宿主相互作用，这与最近的人类病原体疾病工作一致，应该允许更有效的疫苗，3)使用序列多样性与人工智能生成的结构模型和计算机方法一起合成具有增强稳定性的表面蛋白。4)利用啮齿动物的疾病模型来识别那些工程细菌表面蛋白，这些蛋白最有可能提供保护，免受一系列致病细菌的侵害。通过上述多样化和全面的方法调查BL候选疫苗，应有助于确定致病细菌的特征，提高对疾病的了解，大大推进疫苗开发管道和/或确定新的治疗方法。这些研究既及时又非常需要，以便能够预防甚至根除这一严重的、重要的全球疾病。

Leptospirosis, caused by Leptospira bacteria, is a worldwide, severe infectious disease affecting several different host species including cattle, dogs and man. Globally, cattle are greatly afflicted resulting in severe economic losses, reduced food security, substantial antimicrobial use and animal to man (zoonotic) transmission. Economic cost to the UK is £22.3 million/year with much greater costs expected for tropical regions, including many low to middle income countries (LMICs), due to substantially greater disease burden and more severe disease. Current bovine leptospirosis (BL) vaccines have a limited range of specificity and require cold chain transport and storage, which is problematic in the many tropical, frequently LMIC regions with greatest disease burden. Making vaccines more broadly protective and easily accessible will increase uptake globally, decreasing global antibiotic use and antimicrobial resistance development. This is especially important for leptospirosis which is considered to be emerging/re-emerging globally and being driven by global warming and associated increases in extreme climatic events, such as flooding. Bacterial surface proteins are considered important targets to provide cross-protective and long lasting immunity against a range of Leptospira species and serovars. Immune evasion by leptospires, is considered to involve these bacteria coating themselves with host molecules. Whilst the different leptospire bacteria involved in disease are diverse, they must have near identical machinery for this immune evasion which must be present on the bacterial surface to allow for host binding and/or damage and therefore represent ideal vaccine targets. Thus, characterisation of key bacterial surface proteins, especially those involved in immune evasion and determining their mechanism of interaction should allow for 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 pathway for an important human pathogen vaccine which is now licensed and can now be applied to veterinary pathogens. Here, we combine synthetic biology, artificial intelligence and in silico (bioinformatic) approaches to guide engineering of key cell surface proteins to develop a novel thermostable vaccine with broad Leptospira specificity and enhanced efficacy.This study will 1) investigate vaccine candidate diversity across leptospire species including surveying whether variants from some species exhibit adhesion preference for molecules from specific host species resulting in known host specificity and identify, whether variants from commensal (harmless) relatives lack ability to attach to host molecules, 2) use sequence diversity/conservation and differences in adhesion ability together with artificial intelligence (AI) generated structural models with in silico approaches to engineer the surface proteins to restrict host interaction which in line with recent human pathogen disease work should allow for more effective vaccines, 3) use sequence diversity together with AI generated structural models and in silico approaches to synthesise surface proteins with enhanced stability, 4) use a rodent models of disease to identify those engineered bacterial surface proteins most likely to offer protection from a range of the disease causing bacteria.Investigating BL vaccine candidates by the diverse and comprehensive methods described above, should help characterise the causal bacteria, improve understanding of the disease, substantially progress the vaccine development pipeline and/or identify novel therapeutics. Such studies are both timely and much needed to enable the prevention or even eradication of this severe, important global disease.