Transforming vaccine development.

The urgency to develop vaccines against Covid-19 is putting pressure on the long and expensive development timelines that are normally required for development of lifesaving vaccines. There is a unique opportunity to take advantage of new technologies, the smart and flexible design of clinical trials, and evolving regulatory science to speed up vaccine development against Covid-19 and transform vaccine development altogether.

A guide to in silico vaccine discovery for eukaryotic pathogens.

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.

A conserved antigen induces respiratory Th17-mediated broad serotype protection against pneumococcal superinfection.

Several vaccines targeting bacterial pathogens show reduced efficacy upon concurrent viral infection, indicating that a new vaccinology approach is required. To identify antigens for the human pathogen Streptococcus pneumoniae that are effective following influenza infection, we performed CRISPRi-seq in a murine model of superinfection and identified the conserved lafB gene as crucial for virulence. We show that LafB is a membrane-associated, intracellular protein that catalyzes the formation of galactosyl-glucosyl-diacylglycerol, a glycolipid important for cell wall homeostasis. Respiratory vaccination with recombinant LafB, in contrast to subcutaneous vaccination, was highly protective against S. pneumoniae serotypes 2, 15A, and 24F in a murine model. In contrast to standard capsule-based vaccines, protection did not require LafB-specific antibodies but was dependent on airway CD4+ T helper 17 cells. Healthy human individuals can elicit LafB-specific immune responses, indicating LafB antigenicity in humans. Collectively, these findings present a universal pneumococcal vaccine antigen that remains effective following influenza infection.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Leveraging on the genomics and immunopathology of SARS-CoV-2 for vaccines development: prospects and challenges.

The incidence and case-fatality rates (CFRs) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, the etiological agent for Coronavirus Disease 2019 (COVID-19), have been rising unabated. Even though the entire world has been implementing infection prevention and control measures, the pandemic continues to spread. It has been widely accepted that preventive vaccination strategies are the public health measures for countering this pandemic. This study critically reviews the latest scientific advancement in genomics, replication pattern, pathogenesis, and immunopathology of SARS-CoV-2 infection and how these concepts could be used in the development of vaccines. We also offer a detailed discussion on the anticipated potency, efficacy, safety, and pharmaco-economic issues that are and will be associated with candidate COVID-19 vaccines.

Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.

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.

Identification of anti-horn fly vaccine antigen candidates using a reverse vaccinology approach.

BACKGROUND: The horn fly, Haematobia irritans irritans, causes significant production losses to the cattle industry. Horn fly control relies on insecticides; however, alternative control methods such as vaccines are needed due to the fly's capacity to quickly develop resistance to insecticides, and the pressure for eco-friendly options. METHODS: We used a reverse vaccinology approach comprising three vaccine prediction and 11 annotation tools to evaluate and rank 79,542 translated open reading frames (ORFs) from the horn fly's transcriptome, and selected 10 transcript ORFs as vaccine candidates for expression in Pichia pastoris. The expression of the 10 selected transcripts and the proteins that they encoded were investigated in adult flies by reverse transcription polymerase chain reaction (RT-PCR) and mass spectrometry, respectively. Then, we evaluated the immunogenicity of a vaccine candidate in an immunization trial and the antigen's effects on horn fly mortality and fecundity in an in vitro feeding assay. RESULTS: Six of the ten vaccine candidate antigens were successfully expressed in P. pastoris. RT-PCR confirmed the expression of all six ORFs in adult fly RNA. One of the vaccine candidate antigens, BI-HS009, was expressed in sufficient quantity for immunogenicity and efficacy trials. The IgG titers of animals vaccinated with BI-HS009 plus adjuvant were significantly higher than those of animals vaccinated with buffer plus adjuvant only from days 42 to 112, with a peak on day 56. Progeny of horn flies feeding upon blood from animals vaccinated with BI-HS009 plus adjuvant collected on day 56 had 63% lower pupariation rate and 57% lower adult emergence than the control group (ANOVA: F (1, 6) = 8.221, P = 0.028 and F (1, 6) = 8.299, P = 0.028, respectively). CONCLUSIONS: The reverse vaccinology approach streamlined the discovery process by prioritizing possible vaccine antigen candidates. Through a thoughtful process of selection and in vivo and in vitro evaluations, we were able to identify a promising antigen for an anti-horn fly vaccine.

A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen.

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 ROP18, which are T. gondii virulence determinants. The chance of randomly selecting a ROP protein was 0.2% given 8263 sequences. We conclude that the approach described is a valuable addition to other in silico approaches to identify vaccines candidates worthy of laboratory validation and could be adapted for other apicomplexan parasite species (with appropriate data).

On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates.

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.

A Universal Vaccine against Leptospirosis: Are We Going in the Right Direction?

Leptospirosis is the most widespread zoonosis in the world and a neglected tropical disease estimated to cause severe infection in more than one million people worldwide every year that can be combated by effective immunization. However, no significant progress has been made on the leptospirosis vaccine since the advent of bacterins over 100 years. Although protective against lethal infection, particularly in animals, bacterin-induced immunity is considered short term, serovar restricted, and the vaccine can cause serious side effects. The urgent need for a new vaccine has motivated several research groups to evaluate the protective immune response induced by recombinant vaccines. Significant protection has been reported with several promising outer membrane proteins, including LipL32 and the leptospiral immunoglobulin-like proteins. However, efficacy was variable and failed to induce a cross-protective response or sterile immunity among vaccinated animals. As hundreds of draft genomes of all known Leptospira species are now available, this should aid novel target discovery through reverse vaccinology (RV) and pangenomic studies. The identification of surface-exposed vaccine candidates that are highly conserved among infectious Leptospira spp. is a requirement for the development of a cross-protective universal vaccine. However, the lack of immune correlates is a major drawback to the application of RV to Leptospira genomes. In addition, as the protective immune response against leptospirosis is not fully understood, the rational use of adjuvants tends to be a process of trial and error. In this perspective, we discuss current advances, the pitfalls, and possible solutions for the development of a universal leptospirosis vaccine.

Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine.

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.

Discovering a vaccine against neosporosis using computers: is it feasible?

A vaccine is urgently needed to prevent cattle neosporosis. This infectious disease is caused by the parasite Neospora caninum, a complex biological system with multifaceted life cycles. An in silico vaccine discovery approach attempts to transform digital abstractions of this system into adequate knowledge to predict candidates. Researchers need current information to implement such an approach, such as understanding evasion mechanisms of the immune system, type of immune response to elicit, availability of data and prediction programs, and statistical models to analyze predictions. Taken together, an in silico approach involves assembly of an intricate jigsaw of interdisciplinary and interdependent knowledge. In this review, we focus on the approach influencing vaccine development against Neospora caninum, which can be generalized to other pathogenic apicomplexans.

Computer-Aided Vaccine Design.

Vaccines, and their discovery, are topics of singular importance in present-day biomedical science. The discovery of vaccines has hitherto been primarily empirical in nature, and it is only now that this is giving way, albeit very slowly, to a more rational approach, supported and enhanced by computer-based methods. In this context, the book "Computer-Aided Vaccine Design" by Tong and Ranganathan is a welcome new addition to the growing list of biomedical texts that address the computational discovery of vaccines and their components.