Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.', Methods in molecular biology (Clifton, N.J.), vol. 2183, pp. 29-42.View/Download from: Publisher's site
Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user's choice.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2018, 'A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen.', Frontiers in Genetics, vol. 9.View/Download from: Publisher's site
Over the last two decades, various in silico approaches have been developed and refined that attempt to identify protein and/or peptide vaccines candidates from informative signals encoded in protein sequences of a target pathogen. As to date, no signal has been identified that clearly indicates a protein will effectively contribute to a protective immune response in a host. The premise for this study is that proteins under positive selection from the immune system are more likely suitable vaccine candidates than proteins exposed to other selection pressures. Furthermore, our expectation is that protein sequence regions encoding major histocompatibility complexes (MHC) binding peptides will contain consecutive positive selection sites. Using freely available data and bioinformatic tools, we present a high-throughput approach through a pipeline that predicts positive selection sites, protein subcellular locations, and sequence locations of medium to high T-Cell MHC class I binding peptides. Positive selection sites are estimated from a sequence alignment by comparing rates of synonymous (dS) and non-synonymous (dN) substitutions among protein coding sequences of orthologous genes in a phylogeny. The main pipeline output is a list of protein vaccine candidates predicted to be naturally exposed to the immune system and containing sites under positive selection. Candidates are ranked with respect to the number of consecutive sites located on protein sequence regions encoding MHCI-binding peptides. Results are constrained by the reliability of prediction programs and quality of input data. Protein sequences from Toxoplasma gondii ME49 strain (TGME49) were used as a case study. Surface antigen (SAG), dense granules (GRA), microneme (MIC), and rhoptry (ROP) proteins are considered worthy T. gondii candidates. Given 8263 TGME49 protein sequences processed anonymously, the top 10 predicted candidates were all worthy candidates. In particular, the top ten included ROP5 and...
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2017, 'On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates.', International journal for parasitology, vol. 47, no. 12, pp. 779-790.View/Download from: Publisher's site
Reverse vaccinology has the potential to rapidly advance vaccine development against parasites, but it is unclear which features studied in silico will advance vaccine development. Here we consider Neospora caninum which is a globally distributed protozoan parasite causing significant economic and reproductive loss to cattle industries worldwide. The aim of this study was to use a reverse vaccinology approach to compile a worthy vaccine candidate list for N. caninum, including proteins containing pathogen-associated molecular patterns to act as vaccine carriers. The in silico approach essentially involved collecting a wide range of gene and protein features from public databases or computationally predicting those for every known Neospora protein. This data collection was then analysed using an automated high-throughput process to identify candidates. The final vaccine list compiled was judged to be the optimum within the constraints of available data, current knowledge, and existing bioinformatics programs. We consider and provide some suggestions and experience on how ranking of vaccine candidate lists can be performed. This study is therefore important in that it provides a valuable resource for establishing new directions in vaccine research against neosporosis and other parasitic diseases of economic and medical importance.
Goodswen, SJ, Barratt, JLN, Kennedy, PJ & Ellis, JT 2015, 'Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine', International Journal for Parasitology, pp. 305-318.View/Download from: Publisher's site
Neospora caninum is an apicomplexan parasite which can cause abortion in cattle, instigating major economic burden. Vaccination has been proposed as the most cost-effective control measure to alleviate this burden. Consequently the overriding aspiration for N. caninum research is the identification and subsequent evaluation of vaccine candidates in animal models. To save time, cost and effort, it is now feasible to use an in silico approach for vaccine candidate prediction. Precise protein sequences, derived from the correct open reading frame, are paramount and arguably the most important factor determining the success or failure of this approach. The challenge is that publicly available N. caninum sequences are mostly derived from gene predictions. Annotated inaccuracies can lead to erroneously predicted vaccine candidates by bioinformatics programs. This study evaluates the current N. caninum annotation for potential inaccuracies. Comparisons with annotation from a closely related pathogen, Toxoplasma gondii, are also made to distinguish patterns of inconsistency. More importantly, a mRNA sequencing (RNA-Seq) experiment is used to validate the annotation. Potential discrepancies originating from a questionable start codon context and exon boundaries were identified in 1943 protein coding sequences. We conclude, where experimental data were available, that the majority of N. caninum gene sequences were reliably predicted. Nevertheless, almost 28% of genes were identified as questionable. Given the limitations of RNA-Seq, the intention of this study was not to replace the existing annotation but to support or oppose particular aspects of it. Ideally, many studies aimed at improving the annotation are required to build a consensus. We believe this study, in providing a new resource on gene structure and annotation, is a worthy contributor to this endeavour.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2014, 'Discovering a vaccine against neosporosis using computers: is it feasible?', Trends In Parasitology, vol. 30, no. 8, pp. 401-411.View/Download from: Publisher's site
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2014, 'Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores', PLOS ONE, vol. 9, no. 12.View/Download from: Publisher's site
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2014, 'Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology', Bioinformatics, vol. 30, no. 16, pp. 2381-2383.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2013, 'A guide to in silico vaccine discovery for eukaryotic pathogens', Briefings in Bioinformatics, vol. 14, no. 6, pp. 753-774.View/Download from: Publisher's site
In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available; therefore, the highest-scoring proteins from the list require laboratory validation.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2013, 'A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms', BMC Bioinformatics, vol. 14, no. 1, pp. 315-327.View/Download from: Publisher's site
An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2013, 'A review of the infection, genetics, and evolution of Neospora caninum: from the past to the present', Infection, Genetics and Evolution, vol. 13, no. 1, pp. 133-150.View/Download from: Publisher's site
This paper is a review of current knowledge on Neospora caninum in the context of other apicomplexan parasites and with an emphasis on: life cycle, disease, epidemiology, immunity, control and treatment, evolution, genomes, and biological databases and web resources. N. caninum is an obligate, intracellular, coccidian, protozoan parasite of the phylum Apicomplexa. Infection can cause the clinical disease neosporosis, which most notably is associated with abortion in cattle. These abortions are a major root cause of economic loss to both the dairy and beef industries worldwide. N. caninum has been detected in every country in which a study has been specifically conducted to detect this parasite in cattle. The major mode of transmission in cattle is transplacental (or vertical) transmission and several elements of the N. caninum life cycle are yet to be studied in detail. The outcome of an infection is inextricably linked to the precise timing of the infection coupled with the status of the immune system of the dam and foetus. There is no community consensus as to whether it is the dams pro-inflammatory cytotoxic response to tachyzoites that kills the foetus or the tachyzoites themselves. From economic analysis the most cost-effective approach to control neosporosis is a vaccine. The perfect vaccine would protect against both infection and the clinical disease, and this implies a vaccine is needed that can induce a non-foetopathic cell mediated immunity response. Researchers are beginning to capitalise on the vast potential of -omics data (e.g. genomes, transcriptomes, and proteomes) to further our understanding of pathogens but especially to identify vaccine and drug targets. The recent publication of a genome for N. caninum offers vast opportunities in these areas.
Ellis, JT, Goodswen, SJ, Kennedy, PJ & Bush, SA 2012, 'The Core Mouse Response to Infection by Neospora Caninum Defined by Gene Set Enrichment Analyses', Bioinformatics and Biology Insights, vol. 6, pp. 187-202.View/Download from: Publisher's site
.In this study, the BALB/c and Qs mouse responses to infection by the parasite Neospora caninum were investigated in order to identify host response mechanisms. Investigation was done using gene set (enrichment) analyses of microarray data. GSEA, MANOVA, Romer, subGSE and SAM-GS were used to study the contrasts Neospora strain type, Mouse type (BALB/c and Qs) and time post infection (6 hours post infection and 10 days post infection). The analyses show that the major signal in the core mouse response to infection is from time post infection and can be defined by gene ontology terms Protein Kinase Activity, Cell Proliferation and Transcription Initiation. Several terms linked to signaling, morphogenesis, response and fat metabolism were also identified. At 10 days post infection, genes associated with fatty acid metabolism were identified as up regulated in expression. The value of gene set (enrichment) analyses in the analysis of microarray data is discussed.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2012, 'Evaluating High-Throughput Ab Initio Gene Finders to Discover Proteins Encoded in Eukaryotic Pathogen Genomes Missed by Laboratory Techniques', PLOS ONE, vol. 7, no. 11.View/Download from: Publisher's site