Dissecting treponemal immune-modulation to enable disease control.
Lead Research Organisation:
University of Liverpool
Department Name: Infection Biology & Microbiomes
Abstract
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.
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.
Technical Summary
1) Identify digital dermatitis (DD) treponeme molecules interacting with complement factor H or plasminogen using in silico, artificial intelligence (AI) co-folding experiments, generate recombinant treponeme surface proteins (RTSPs) and confirm function by ELISA and thermal shift assay (TSA). Generate vaccine candidate orthologs across treponeme species, including non-pathogenic relatives, quantify differences in preference for ligands from different hosts and determine seroreactivity (ELISA/western blotting) using a relevant cattle sera panel (n=80).
2) Map ortholog diversity and function to AlphaFold2 (AI) models for binding site mutagenesis including a) cofold surface proteins with host ligands in silico using AI and b) docking experiments of AI generated structures with host ligands (ClusPro) and c) compare structures to orthologs without function. Using gene synthesis/primer directed mutagenesis produce function-restricted RTSPs, subject to ELISA and TSA to quantify adhesion differences and TSA to confirm no stability loss. Verify mutated RTSPs exhibit equivalent antibody titres with sera panel.
3) Use ortholog diversity and AlphaFold2 models to identify stabilising regions/mutations with FRESCO/FireProt. Produce RTSPs and verify enhanced stability using TSA and intrinsic fluorescence. Verify mutated RTSPs have equivalent antibody titres and no function regain.
4) Compare protective efficacy of enhanced lipidated (if relevant) RTSPs, with treponemal LPS-depleted native antigen vaccines in a murine model of DD with Treponema medium challenge. Use select RTSPs as multivalent vaccines to investigate protection when challenged with polytreponemal mixture or infected tissue. Clinical outcome measures include lesion size and weight loss with bacterial burden measured with qPCR, histopathology and treponeme culture. T-cells to be isolated from splenic tissue for antigen-recall assays. Anti-vaccine IgG response ELISAs/western blotting to be used on pooled sera.
2) Map ortholog diversity and function to AlphaFold2 (AI) models for binding site mutagenesis including a) cofold surface proteins with host ligands in silico using AI and b) docking experiments of AI generated structures with host ligands (ClusPro) and c) compare structures to orthologs without function. Using gene synthesis/primer directed mutagenesis produce function-restricted RTSPs, subject to ELISA and TSA to quantify adhesion differences and TSA to confirm no stability loss. Verify mutated RTSPs exhibit equivalent antibody titres with sera panel.
3) Use ortholog diversity and AlphaFold2 models to identify stabilising regions/mutations with FRESCO/FireProt. Produce RTSPs and verify enhanced stability using TSA and intrinsic fluorescence. Verify mutated RTSPs have equivalent antibody titres and no function regain.
4) Compare protective efficacy of enhanced lipidated (if relevant) RTSPs, with treponemal LPS-depleted native antigen vaccines in a murine model of DD with Treponema medium challenge. Use select RTSPs as multivalent vaccines to investigate protection when challenged with polytreponemal mixture or infected tissue. Clinical outcome measures include lesion size and weight loss with bacterial burden measured with qPCR, histopathology and treponeme culture. T-cells to be isolated from splenic tissue for antigen-recall assays. Anti-vaccine IgG response ELISAs/western blotting to be used on pooled sera.
