Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing

Application of in-silico approaches in subunit vaccines: Overcoming the challenges of antigen and adjuvant development

Subunit vaccines are crucial in preventing modern diseases due to their safety, stability, and ability to elicit targeted immune responses. However, challenges in antigen and adjuvant design hinder their development. Recent advancements in in-silico approaches, including reverse vaccinology, structural vaccinology, and machine learning, have revolutionized vaccine development from empirical practices to rational design approaches. This review summarizes the transformative impact of in-silico approaches on subunit vaccine development. We address the challenges of antigen identification and designation, highlighting how advanced computational techniques are employed to accelerate antigen acquisition. We also examine the challenges in adjuvant discovery and illustrate how machine learning helps overcome these barriers. Finally, we explore potential future directions for subunit vaccines, highlighting the importance of combining computational methods with other technologies to tackle the challenges associated with subunit vaccine development.

Fluctuations in serogroup B meningococcal vaccine antigens prior to routine MenB vaccination in France

BACKGROUND

Invasive meningococcal disease (IMD) of serogroup B is preventable by protein-based vaccines targeting one (Bivalent rLP2086 vaccine) or several variable proteins (4CMenB vaccine) at the bacterial surface. The 4CMenB was licensed in Europe in 2013 but has been recommended and reimbursed in France for infants over 2 months old since April 2022. The bivalent rLP2086 vaccine was licensed in Europe in 2017 for subjects of 10 years and older. Evaluating strain coverage and fluctuations prior to large scale vaccine use is highly informative.

METHODS

We analysed invasive isolates at the French National Reference Centre for meningococci between 1975 and 2022. The 1691 recovered isolates were sequenced. We scored sex, and age groups of subjects. We also scored clonal complexes (CC) and the predicted coverage rates of the corresponding isolates using the genetic Meningococcal Antigen Typing System (gMATS) and the Meningococcal Deduced Vaccine Antigen Reactivity (MenDeVAR).

RESULTS

The period was divided into four periods 1975-1986, 1987-1998-1999-2010 and 2011-2022. Our data clearly show significant differences in the distribution of alleles encoding the vaccine-covered antigens between these four periods. The clonal complex (CC) distribution also differed between the two periods with the disappearance of CC8 since 2011 and drastic decreases in CC11 since 1999. MenDeVar-predicted coverage fluctuated between 46.8% and 60.6% during the four periods for the 4CMenB and between 63.4% and 81.3% for rLP2086. For 4CMenB, coverage was higher using gMATS and varied between 74.5% and 85.0%. Fluctuations were also observed for all age groups.

CONCLUSIONS

IMD epidemiology is continuously changing with fluctuation in vaccine strain coverage over the 48 years prior to the routine implementation of the vaccines.

Construction of an immunoinformatics-based multi-epitope vaccine candidate targeting Kyasanur forest disease virus

Kyasanur forest disease (KFD) is one of the neglected tick-borne viral zoonoses. KFD virus (KFDV) was initially considered endemic to the Western Ghats region of Karnataka state in India. Over the years, there have been reports of its spread to newer areas within and outside Karnataka. The absence of an effective treatment for KFD mandates the need for further research and development of novel vaccines. The present study was designed to develop a multi-epitope vaccine candidate against KFDV using immunoinformatics approaches. A total of 74 complete KFDV genome sequences were analysed for genetic recombination followed by phylogeny. Computational prediction of B- and T-cell epitopes belonging to envelope protein was performed and epitopes were prioritised based on IFN-Gamma, IL-4, IL-10 stimulation and checked for allergenicity and toxicity. The eight short-listed epitopes (three MHC-Class 1, three MHC-Class 2 and two B-cell) were then combined using various linkers to construct the vaccine candidate. Molecular docking followed by molecular simulations revealed stable interactions of the vaccine candidate with immune receptor complex namely Toll-like receptors (TLR2-TLR6). Codon optimization followed by in-silico cloning of the designed multi-epitope vaccine construct into the pET30b (+) expression vector was carried out. Immunoinformatics analysis of the multi-epitope vaccine candidate in the current study has potential to significantly accelerate the initial stages of vaccine development. Experimental validation of the potential multi-epitope vaccine candidate remains crucial to confirm effectiveness and safety in real-world conditions.

Reverse vaccinology: A strategy also used for identifying potential vaccine antigens in poultry

Vaccination of livestock plays a major role in improving animal health, welfare and productivity, but also in public health by preventing zoonotic diseases. Advances in bioinformatics and whole-genome sequencing techniques since the 2000s have led to the development of genome-based vaccinology, called reverse vaccinology. Reverse vaccinology is a rapid and competitive strategy that uses pathogen genome sequences to screen for and identify potential vaccine antigens and, unlike conventional methods, does not require culturing the pathogenic microorganism, at least initially. Based on in silico approaches and dedicated software, reverse vaccinology has led to the identification of a wide range of proteins as putative vaccine candidates against human pathogens and has been applied more recently to several animal diseases. After a brief overview of the principle of the approach and its applications in human medicine, this review focuses on the use of reverse vaccinology for the development of vaccines specifically for poultry, a representative example of livestock vaccination, and discusses the important points to consider when using this method.

Meningococcal Vaccination in the United States: Past, Present, And Future

Meningococcal disease is rare but serious, often striking previously healthy adolescents or young adults, with substantial morbidity and mortality. The incidence of meningococcal disease in the USA declined even prior to the issuance of routine recommendations for vaccination, although an uptick in incidence has occurred since 2022. Routine recommendations for adolescent MenACWY vaccination were issued in 2005, and recommendations for adolescent MenB vaccination based on shared clinical decision-making (SCDM) were issued in 2015. Although meningococcal vaccines are safe and effective, their limited duration of protection coupled with low disease incidence result in a high cost per case averted by vaccination, most notably with MenB vaccines. The low cost-effectiveness raises ethical concerns about resource use and the role of economic analyses in policy decisions. However, the potential for substantial public health impact remains. Outer membrane vesicle (OMV)-containing MenB vaccines provide some protection against gonorrhea infections. The recent development of pentavalent ABCWY vaccines provide the opportunity to reduce the number of injections and simplify implementation, provided MenACWY and MenB vaccine schedules are harmonized. Vaccine attributes, implementation issues, and resource utilization will be important considerations in optimization of the US adolescent meningococcal vaccination strategy.

Unveiling the role of adhesin proteins in controlling Acinetobacter baumannii infections: a systematic review

Combating multidrug-resistant Acinetobacter baumannii is considered a priority by the World Health Organization. Virulence mechanisms, such as biofilm formation, multidrug resistance, and high adherence to both biotic and abiotic surfaces, underscore the urgency of exploring approaches to control this pathogen. The search for new antibiotic compounds and alternative strategies like immunotherapies and vaccination offers potential solutions to address this pressing health concern. In this context, adhesins play a crucial role in the pathogenicity and virulence of A. baumannii, making them potential targets for therapeutic interventions. To address this, we conducted a systematic review of A. baumannii adhesin research from the last decade (2013-2023). We reviewed 24 papers: 6 utilizing reverse vaccinology bioinformatic tools to predict adhesin targets for vaccine construction, 17 employing DNA recombinant techniques for in vivo active and passive immunization or in vitro antibody-mediated therapy assays, and 1 paper exploring the impact of pyrogallol therapy on A. baumannii virulence mechanisms. Our review identified over 20 potential targets with significant findings. We screened and summarized these targets to aid in further exploration of therapies and prevention.

Do Porto D, Custódio FL, Trevizani R, Nicolás MF. PAPreC: A Pipeline for Antigenicity Prediction Comparison Methods across Bacteria

Antigenicity prediction plays a crucial role in vaccine development, antibody-based therapies, and diagnostic assays, as this predictive approach helps assess the potential of molecular structures to induce and recruit immune cells and drive antibody production. Several existing prediction methods, which target complete proteins and epitopes identified through reverse vaccinology, face limitations regarding input data constraints, feature extraction strategies, and insufficient flexibility for model evaluation and interpretation. This work presents PAPreC (Pipeline for Antigenicity Prediction Comparison), an open-source, versatile workflow (available at https://github.com/YasCoMa/paprec_nx_workflow) designed to address these challenges. PAPreC systematically examines three key factors: the selection of training data sets, feature extraction methods (including physicochemical descriptors and ESM-2 encoder-derived embeddings), and diverse classifiers. It provides automated model evaluation, interpretability through SHapley Additive exPlanations (SHAP) analysis, and applicability domain assessments, enabling researchers to identify optimal configurations for their specific data sets. Applying PAPreC to IEDB data as a reference, we demonstrate its effectiveness across the ESKAPE pathogen group. A case study involving Pseudomonas aeruginosa and Staphylococcus aureus shows that specific feature configurations are more suitable for different sequence types, and that ESM-2 embeddings enhance model performance. Moreover, our results indicate that separate models for Gram-positive and Gram-negative bacteria are not required. PAPreC offers a comprehensive, adaptable, and robust framework to streamline and improve antigenicity prediction for diverse bacterial data sets.

In Silico Discovery of Antigenic-Secreted Proteins to Diagnostic Human Toxocariasis

BACKGROUND

Human toxocariasis is a helminthic zoonosis caused by infection of Toxocara canis or T. cati. Humans can be infected by through ingestion of embryonated eggs from contaminated water, food or soil. Diagnosis is challenging, immunodiagnosis tests are commonly implemented with major pitfalls in the cross-reactivity with other pathogens, particularly in endemic areas.

METHODS

With the aim of identify species-specific genes encoding for highly expressed antigenic proteins, a list of parasites that may infect humans and that might present similar clinical symptoms to T. canis infections was built. Only organisms whose genomes were completely sequenced and the proteome predicted were included. First, orthologous proteins were detected and the subcellular localization of T. canis proteins was predicted. In order to identify differentially expressed genes encoding proteins in larvae L3, pair-wise comparisons among transcriptomes from body parts and genders were performed. Finally, all secreted proteins classified as species-specific of T. canis, whose genes were upregulated in larvae L3 were included in an antigenic prediction.

RESULTS

Twenty-eight parasites were included in the analyses, proteins of T. canis were clustered in 11,399 groups, however, 279 were species-specific groups which represent 816 proteins. Three hundred and twenty-two proteins were predicted to be secreted and upregulated in larvae L3, however, after filtering these proteins by their orthology inference, only three proteins met all the features included in this study (species-specific, upregulated, secreted, and antigenic potential). To conclude, our strategy in the study is a rational approach for discovering antigenic proteins to be used in diagnosis.

Innovations in vaccine design: Computational tools and techniques

The advancements in computational tools have revolutionized vaccine development by organizing and analyzing large-scale immunological data through immuno-informatics. This field combines computational and mathematical approaches to model molecular interactions during antigen presentation and processing. These tools have significantly accelerated vaccine development, making it more efficient and cost-effective. Applications such as SCWRL and SCAP help in side chain and backbone modeling to improve antibodies and forecast secondary structures. Multi-graft and multivalent scaffolds present antigens to elicit strong immune responses; antibodyomics studies the sequences of antibodies to find antibodies that can neutralize. It is another traditional way of doing vaccines where the pathogen's genome is scanned by diacide such as Vaxign to identify the likely vaccine agents. Codon optimization, as implemented with the aid of COOL and OPTIMIZER tools, enhances the output of proteins among which vaccines are needed. These tools also allow for predicting epitope structures the more accurately, or so. Prediction tools that include immunogenicity screening tests that map B-cell epitope and T-cell epitope such as ElliPro and DiscoTope aid in drug design, while the application of Fusion technologies facilitates vaccine development and kit diagnostics. The percentage of time trying to identify possible vaccine candidates is reduced alongside the costs with the application of these tools allowing the improvement in the prediction of vaccine candidates. The purpose of this chapter is to emphasize the invention of computational tools and methods that together are revolutionizing vaccine design and development and to underline the importance of tissue engineering and immunology advances.

Cyclic di AMP phosphodiesterase nanovaccine elicits protective immunity against Burkholderia cenocepacia infection in mice

Burkholderia cenocepacia causes life-threatening infections in immunocompromised patients. Treatment is challenging due to intrinsic antibiotic multiresistance, so vaccination provides an alternative approach. We aimed to identify vaccine candidates using reverse vaccinology and evaluate their efficacy as protein-loaded chitosan: pectin nanoparticles (C:P NPs) in a vaccine model. Applying strict subtractive channels, three proteins were shortlisted: WP_006481710.1 (LY), WP_012493605.1 (KT), and WP_006492970.1 (BD). Proteins were cloned, purified as His-tagged proteins, and loaded onto C:P NPs. Vaccinated mice had significantly higher systemic IgG and mucosal IgA antibody responses and induced IL-6 and IL-17A. 6x-His-LY-CS:P NPs and 6x-His-KT-CS:P NPs vaccines induced TNF-α. Vaccines conferred significant protection against B. cenocepacia intranasal infections. In conclusion, cyclic-di-AMP phosphodiesterase (WP_012493605.1) is a promising vaccine candidate that elicited IgG and IgA antibodies, Th1, Th2, and Th17 cellular immunity in BALB/c mice and protected against B. cenocepacia infection. This provides hope for saving lives of people at high risk of infection.

NIAID Workshop Report: Systematic Approaches for ESKAPE Bacteria Antigen Discovery

On 14-15 November 2023, the National Institute of Allergy and Infectious Diseases (NIAID) organized a workshop entitled "Systematic Approaches for ESKAPE Bacteria Antigen Discovery". The goal of the workshop was to engage scientists from diverse relevant backgrounds to explore novel technologies that can be harnessed to identify and address current roadblocks impeding advances in antigen and vaccine discoveries for the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). The workshop consisted of four sessions that addressed ESKAPE infections, antigen discovery and vaccine efforts, and new technologies including systems immunology and vaccinology approaches. Each session was followed by a panel discussion. In total, there were over 260 in-person and virtual attendees, with high levels of engagement. This report provides a summary of the event and highlights challenges and opportunities in the field of ESKAPE vaccine discovery.

NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface

BACKGROUND

Vaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills.

RESULTS

We report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org ) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop-Razor, to recover fragments of promising vaccine candidates or Epitope Prediction for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment.

CONCLUSIONS

With its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.

Developing a universal multi-epitope protein vaccine candidate for enhanced borna virus pandemic preparedness

Introduction

Borna disease virus 1 (BoDV-1) is an emerging zoonotic RNA virus that can cause severe acute encephalitis with high mortality. Currently, there are no effective countermeasures, and the potential risk of a future outbreak requires urgent attention. To address this challenge, the complete genome sequence of BoDV-1 was utilized, and immunoinformatics was applied to identify antigenic peptides suitable for vaccine development.

Methods

Immunoinformatics and antigenicity-focused protein screening were employed to predict B-cell linear epitopes, B-cell conformational epitopes, and cytotoxic T lymphocyte (CTL) epitopes. Only overlapping epitopes with antigenicity greater than 1 and non-toxic, non-allergenic properties were selected for subsequent vaccine construction. The epitopes were linked using GPGPG linkers, incorporating β-defensins at the N-terminus to enhance immune response, and incorporating Hit-6 at the C-terminus to improve protein solubility and aid in protein purification. Computational tools were used to predict the immunogenicity, physicochemical properties, and structural stability of the vaccine. Molecular docking was performed to predict the stability and dynamics of the vaccine in complex with Toll-like receptor 4 (TLR-4) and major histocompatibility complex I (MHC I) receptors. The vaccine construct was cloned through in silico restriction to create a plasmid for expression in a suitable host.

Results

Among the six BoDV-1 proteins analyzed, five exhibited high antigenicity scores. From these, eight non-toxic, non-allergenic overlapping epitopes with antigenicity scores greater than 1 were selected for vaccine development. Computational predictions indicated favorable immunogenicity, physicochemical properties, and structural stability. Molecular docking analysis showed that the vaccine remained stable in complex with TLR-4 and MHC I receptors, suggesting strong potential for immune recognition. A plasmid construct was successfully generated, providing a foundation for the experimental validation of vaccines in future pandemic scenarios.

Discussion

These findings demonstrate the potential of the immunoinformatics-designed multi-epitope vaccines for the prevention and treatment of BoDV-1. Relevant preparations were made in advance for possible future outbreaks and could be quickly utilized for experimental verification.

Whole-genome sequencing of Neisseria meningitidis collected in Chile from pediatric patients during 2016-2019 and coverage vaccine prediction

BACKGROUND

Over the past few years, whole-genome sequencing (WGS) has become a valuable tool for global meningococcal surveillance. The objective of this study was to genetically characterize Neisseria meningitidis strains isolated from children in Chile through WGS and predicting potential vaccine coverage using gMATS and MenDeVAR.

METHODS

WGS of 42 N.meningitidis from pediatric patients were processed and assembled using different software. We analyzed genomes with BIGSdb platform hosted at PubMLST.org, and predicted vaccine coverage using MenDeVAR and gMATS tools.

RESULTS

Among 42 strains, 25 were MenB, 16 MenW, and 1 MenC. The cc11 and cc 41/44 were the most frequents. The main frequent deduced peptide sequence for PorA was P1.5,2 (40 %), peptide P1.4 was present in one MenB strain; NHBA-29 (64 %), none having peptide 2; fHbp-2 (76 %), one strain had peptide-1, and two had peptide 45; NadA was detected in 52 %, peptide-6 was present in 84 %, none had peptide 8. The MenDeVAR index predicted a coverage in MenB strains for 4CMenB 8 % exact matches, 12 % cross-reactivity, 8 % not coverage and 64 % had insufficient data. gMATS predicted 16 % was covered, 8 % not covered and 76 % unpredictable, and overall coverage of 54 %. For rLP2086-fHbp, the MenDeVAR index predicted exact match in 8 %, cross-reactivity in 64 %, and insufficient data in 28 % and an overall coverage of 72 %. In non-MenB strains, the MenDeVAR index predicted for 4CMenB vaccine: cross-reactivity 88 %, 6 % for not covered and insufficient data. For rLP2086-fHbp, predicted cross-reactivity 12 % and insufficient data in 88 %. gMATS predicted an overall coverage of 50 % for Non-MenB.

CONCLUSION

genetic variability of the Chilean strains that its different from other countries, and until now limit the coverage prediction of vaccine with the available tools like gMATS and MenDeVAR.

Identification of potential antigenic proteins and epitopes for the development of a monkeypox virus vaccine: an in silico approach

Virus assembly, budding, or surface proteins play important roles such as viral attachment to cells, fusion, and entry into cells. The present study aimed to identify potential antigenic proteins and epitopes that could be used to develop a vaccine or diagnostic assay against the Monkeypox virus (MPXV) which may cause a potential epidemic. To do this, 39 MPXV proteins (including assembly, budding, and surface proteins) were analyzed using an in silico approach. Of these 39 proteins, the F5L virus protein was found to be the best vaccine candidate due to its signal peptide properties, negative GRAVY value, low transmembrane helix content, moderate aliphatic index, large molecular weight, long-estimated half-life, beta wrap motifs, and being stable, soluble, and containing non-allergic features. Moreover, the F5L protein exhibited alpha-helical secondary structures, making it a potential "structural antigen" recognized by antibodies. The other viral protein candidates were A9 and A43, but A9 lacked beta wrap motifs, while A43 had a positive GRAVY value and was insoluble. These two proteins were not as suitable candidates as the F5L protein. The KRVNISLTCL epitope from the F5L protein demonstrated the highest antigen score (2.4684) for MHC-I, while the GRFGYVPYVGYKCI epitope from the A9 protein exhibited the highest antigenicity (1.754) for MHC-II. Both epitopes met the criteria for high antigenicity, non-toxicity, solubility, non-allergenicity, and the presence of cleavage sites. Molecular docking and dynamics (MD) simulations further validated their potential, revealing stable and energetically favorable interactions with MHC molecules. The immunogenicity assessment showed that GRFGYVPYVGYKCI could strongly induce immune responses through both IFN-γ and IL-4 pathways, suggesting its capacity to provoke a balanced Th1 and Th2 response. In contrast, KRVNISLTCL exhibited limited immunostimulatory potential. Overall, these findings lay the groundwork for future vaccine development, indicating that F5L, particularly the GRFGYVPYVGYKCI epitope, may serve as an effective candidate for peptide-based vaccine design against MPXV.

Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.

Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review

Significant populations in tropical and sub-tropical locations all over the world are severely impacted by a group of neglected tropical diseases called leishmaniases. This disease is caused by roughly 20 species of the protozoan parasite from the Leishmania genus. Disease prevention strategies that include early detection, vector control, treatment of affected individuals, and vaccination are all essential. The diagnosis is critical for selecting methods of therapy, preventing transmission of the disease, and minimizing symptoms so that the affected individual can have a better quality of life. Nevertheless, the diagnostic methods do eventually have limitations, and there is no established gold standard. Some disadvantages include the existence of cross-reactions with other species, and limited sensitivity and specificity, which are mostly determined by the type of antigen used to perform the tests. A viable alternative for a more precise diagnosis is the application of recombinant antigens, which have been generated using bioinformatics approaches and have shown increased diagnostic accuracy. This approach proves valuable as it spans from epitope selection to predicting the interactions within the antibody-antigen complex through docking analysis. As a result, identifying potential new antigens using bioinformatics resources becomes an effective technique since it may result in an earlier and more accurate diagnosis. Consequently, the primary aim of this review is to conduct a comprehensive overview of the most significant in silico tools developed over time, with a focus on evaluating their efficacy and exploring their potential applications in optimizing the selection of highly specific molecules for a more effective diagnosis of leishmaniasis.

Novel Antibody-Based Protection/Therapeutics in Staphylococcus aureus

Staphylococcus aureus is a commensal of the skin and nares of humans as well as the causative agent of infections associated with significant mortality. The acquisition of antibiotic resistance traits complicates the treatment of such infections and has prompted the development of monoclonal antibodies. The selection of protective antigens is typically guided by studying the natural antibody responses to a pathogen. What happens when the pathogen masks these antigens and subverts adaptive responses, or when the pathogen inhibits or alters the effector functions of antibodies? S. aureus is constantly exposed to its human host and has evolved all these strategies. Here, we review how anti-S. aureus targets have been selected and how antibodies have been engineered to overcome the formidable immune evasive activities of this pathogen. We discuss the prospects of antibody-based therapeutics in the context of disease severity, immune competence, and history of past infections.

Using reverse vaccinology techniques combined with B-cell epitope prediction to screen potential antigenic proteins of the bovine pathogen Clostridium perfringens type A

Clostridium perfringens type A frequently causes necrohaemorrhagic enteritis in cattle, a rapidly progressing disease with a high mortality rate, thus inflicting substantial economic losses in the cattle industry. Effective prevention and control of this disease rely on rapid detection and vaccination strategies, making the screening of antigenic proteins with diagnostic and vaccine potential particularly crucial. In this study, we conducted a pangenomic analysis of 15 bacterial strains, grounded in traditional reverse vaccinology and supplemented with B-cell linear and conformational epitope analysis tools. This approach led to the identification of 2304 core genes and 3606 accessory genes, among which 58 surface-exposed proteins, encoded by core genes, were identified Proteins lacking tertiary structure information were predicted via AlphaFold2, ultimately identifying four target proteins and 14 candidate proteins enriched with linear and conformational epitopes, including virulence proteins such as alpha-toxin, theta-toxin, and alpha-clostripain, and extracellular solute-binding proteins, rhodanese-like proteins, and the accessory gene-encoded lysozyme inhibitor LprI family protein. Our findings demonstrate that the combined use of multiple B-cell epitope analysis tools can help overcome the limitations of any single tool. The proteins selected in this study offer valuable references for rapid diagnostics and the development of genetically engineered vaccines.

Characterisation and Immunogenicity of Neisseria cinerea outer membrane vesicles displaying NadA, NHBA and fHbp from Neisseria meningitidis serogroup B

More affordable and effective vaccines against bacterial meningitis caused by Neisseria meningitidis serogroup B are still required for global prevention. We have previously shown that modified outer membrane vesicles (mOMVs) from commensal Neisseria cinerea can be used as a platform to induce immune responses against meningococcal antigens. The aim of the present study was to use a combination of two genetically engineered mOMVs to express multiple antigens from N. meningitidis known to be involved in protective immunity to meningococcal meningitis (different variants of factor H binding protein (fHbp), Neisseria Heparin Binding Antigen (NHBA) and Neisseria Adhesin A (NadA)). Antigen expression in the mOMVs was confirmed by Western blotting; detoxification of the lipooligosaccharide (LOS) was confirmed by measuring human Toll-like receptor 4 (hTLR4) activation using in vitro cell assays. Mice immunised with a combination of two mOMVs expressing fHbp, NHBA and NadA produced antibodies to all the antigens. Furthermore, serum bactericidal activity (SBA) was induced by the immunisation, with mOMVs expressing NadA displaying high SBA titres against a nadA+ MenB strain. The work highlights the potential of mOMVs from N. cinerea to induce functional immune responses against multiple antigens involved in the protective immune response to meningococcal disease.

An In Silico Design of a Vaccine against All Serotypes of the Dengue Virus Based on Virtual Screening of B-Cell and T-Cell Epitopes

Dengue virus poses a significant global health challenge, particularly in tropical and subtropical regions. Despite the urgent demand for vaccines in the control of the disease, the two approved vaccines, Dengvaxia and TV003/TV005, there are current questions regarding their effectiveness due to an increased risk of antibody-dependent enhancement (ADE) and reduced protection. These challenges have underscored the need for further development of improved vaccines for Dengue Virus. This study presents a new design using an in silico approach to generate a more effective dengue vaccine. Initially, our design process began with the collection of Dengue polyprotein sequences from 10 representative countries worldwide. And then conserved fragments of viral proteins were retrieved as the bases for epitope screening. The selection of epitopes was then carried out with criteria such as antigenicity, immunogenicity, and binding affinity with MHC molecules, while the exclusion criteria were according to their allergenicity, toxicity, and potential for antibody-dependent enhancement. We then constructed a core antigen with the selected epitopes and linked the outcomes with distinct adjuvant proteins, resulting in three candidate vaccines: PSDV-1, PSDV-2, and PSDV-3. Among these, PSDV-2 was selected for further validation due to its superior physicochemical and structural properties. Extensive simulations demonstrated that PSDV-2 exhibited strong binding to pattern recognition receptors, high stability, and robust immune induction, confirming its potential as a high-quality vaccine candidate. For its recombinant expression, a plasmid was subsequently designed. Our new vaccine design offers a promising additional option for Dengue virus protection. Further experimental validations will be conducted to confirm its protective efficacy and safety.

Neisserial adhesin A (NadA) binds human Siglec-5 and Siglec-14 with high affinity and promotes bacterial adhesion/invasion

Neisserial adhesin A (NadA) is a meningococcal surface protein included as recombinant antigen in 4CMenB, a protein-based vaccine able to induce protective immune responses against Neisseria meningitidis serogroup B (MenB). Although NadA is involved in the adhesion/invasion of epithelial cells and human myeloid cells, its function in meningococcal physiology is still poorly understood. To clarify the role played by NadA in the host-pathogen interaction, we sought to identify its cellular receptors. We screened a protein microarray encompassing 2,846 human and 297 mouse surface/secreted recombinant proteins using recombinant NadA as probe. Efficient NadA binding was revealed on the paired sialic acid-binding immunoglobulin-type lectins receptors 5 and 14 (Siglec-5 and Siglec-14), but not on Siglec-9 therein used as control. The interaction was confirmed by biochemical tools with the determination of the KD value in the order of nanomolar and the identification of the NadA binding site by hydrogen-deuterium exchange coupled to mass spectrometry. The N-terminal domain of the Siglec-5 that recognizes the sialic acid was identified as the NadA binding domain. Intriguingly, exogenously added recombinant soluble Siglecs, including Siglec-9, were found to decorate N. meningitidis surface in a NadA-dependent manner. However, Siglec-5 and Siglec-14 transiently expressed in CHO-K1 cells endorsed NadA binding and increased N. meningitidis adhesion/invasion while Siglec-9 did not. Taken together, Siglec-5 and Siglec-14 satisfy all features of NadA receptors suggesting a possible role of NadA in the acute meningococcal infection.IMPORTANCEBacteria have developed several strategies for cell colonization and immune evasion. Knowledge of the host and pathogen factors involved in these mechanisms is crucial to build efficacious countermoves. Neisserial adhesin A (NadA) is a meningococcal surface protein included in the anti-meningococcus B vaccine 4CMenB, which mediates adhesion to and invasion of epithelial cells. Although NadA has been shown to bind to other cell types, like myeloid and endothelial cells, it still remains orphan of a defined host receptor. We have identified two strong NadA interactors, Siglec-5 and Siglec-14, which are mainly expressed on myeloid cells. This showcases that NadA is an additional and key player among the Neisseria meningitidis factors targeting immune cells. We thus provide novel insights on the strategies exploited by N. meningitidis during the infection process, which can progress to a severe illness and death.

An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens

Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

Evaluation of vaccine candidates against Rhodococcus equi in BALB/c mice infection model: cellular and humoral immune responses

Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that mainly causes fatal lung and extrapulmonary abscesses in foals and immunocompromised individuals. To date, no commercial vaccine against R. equi exists. We previously screened all potential vaccine candidates from the complete genome of R. equi using a reverse vaccinology approach. Five of these candidates, namely ABC transporter substrate-binding protein (ABC transporter), penicillin-binding protein 2 (PBD2), NlpC/P60 family protein (NlpC/P60), esterase family protein (Esterase), and M23 family metallopeptidase (M23) were selected for the evaluation of immunogenicity and immunoprotective effects in BALB/c mice model challenged with R. equi. The results showed that all five vaccine candidate-immunized mice experienced a significant increase in spleen antigen-specific IFN-γ- and TNF-α-positive CD4 + and CD8 + T lymphocytes and generated robust Th1- and Th2-type immune responses and antibody responses. Two weeks after the R. equi challenge, immunization with the five vaccine candidates reduced the bacterial load in the lungs and improved the pathological damage to the lungs and livers compared with those in the control group. NlpC/P60, Esterase, and M23 were more effective than the ABC transporter and PBD2 in inducing protective immunity against R. equi challenge in mice. In addition, these vaccine candidates have the potential to induce T lymphocyte memory immune responses in mice. In summary, these antigens are effective candidates for the development of protective vaccines against R. equi. The R. equi antigen library has been expanded and provides new ideas for the development of multivalent vaccines.

Phylogenetic Tracing of Evolutionarily Conserved Zonula Occludens Toxin Reveals a "High Value" Vaccine Candidate Specific for Treating Multi-Strain Pseudomonas aeruginosa Infections

Extensively drug-resistant Pseudomonas aeruginosa infections are emerging as a significant threat associated with adverse patient outcomes. Due to this organism's inherent properties of developing antibiotic resistance, we sought to investigate alternative strategies such as identifying "high value" antigens for immunotherapy-based purposes. Through extensive database mining, we discovered that numerous Gram-negative bacterial (GNB) genomes, many of which are known multidrug-resistant (MDR) pathogens, including P. aeruginosa, horizontally acquired the evolutionarily conserved gene encoding Zonula occludens toxin (Zot) with a substantial degree of homology. The toxin's genomic footprint among so many different GNB stresses its evolutionary importance. By employing in silico techniques such as proteomic-based phylogenetic tracing, in conjunction with comparative structural modeling, we discovered a highly conserved intermembrane associated stretch of 70 amino acids shared among all the GNB strains analyzed. The characterization of our newly identified antigen reveals it to be a "high value" vaccine candidate specific for P. aeruginosa. This newly identified antigen harbors multiple non-overlapping B- and T-cell epitopes exhibiting very high binding affinities and can adopt identical tertiary structures among the least genetically homologous P. aeruginosa strains. Taken together, using proteomic-driven reverse vaccinology techniques, we identified multiple "high value" vaccine candidates capable of eliciting a polarized immune response against all the P. aeruginosa genetic variants tested.

BacScan: a novel genome-wide strategy for uncovering broadly immunogenic proteins in bacteria

In response to the global threat posed by bacterial pathogens, which are the second leading cause of death worldwide, vaccine development is challenged by the diversity of bacterial serotypes and the lack of immunoprotection across serotypes. To address this, we introduce BacScan, a novel genome-wide technology for the rapid discovery of conserved highly immunogenic proteins (HIPs) across serotypes. Using bacterial-specific serum, BacScan combines phage display, immunoprecipitation, and next-generation sequencing to comprehensively identify all the HIPs in a single assay, thereby paving the way for the development of universally protective vaccines. Our validation of this technique with Streptococcus suis, a major pathogenic threat, led to the identification of 19 HIPs, eight of which conferred 20-100% protection against S. suis challenge in animal models. Remarkably, HIP 8455 induced complete immunity, making it an exemplary vaccine target. BacScan's adaptability to any bacterial pathogen positions it as a revolutionary tool that can expedite the development of vaccines with broad efficacy, thus playing a critical role in curbing bacterial transmission and slowing the march of antimicrobial resistance.

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

Optimization of hydrolysis conditions of amino acid analysis for UHPLC-UV antigens content determination: Bexsero vaccine a case study

In the present study the compositional analysis of the amino acids released by the acidic hydrolysis of the vaccine antigens was approached as an alternative to the dye-binding methods, for improvement of the quality control. In particular, the Analytical Quality by Design principles were undertaken in optimizing the hydrolysis conditions of the antigens to be applied prior to the quantitation by UHPLC-UV. Bexsero was used as a case study; it is a recombinant meningococcal B vaccine and one of its critical quality attributes is the content of the three core protein antigens, namely Neisseria Heparin Binding Antigen, factor H binding protein and Neisseria adhesin A, in the final formulation. Conventionally, the proteins quantitation is carried out by dye-binding assays. Analytical Target Profile was defined as the accurate determination of amounts of the Bexsero antigens. The Critical Method Parameters were chosen by means of the cause-effect matrix. A Face Centered Design was used to select the experiments to investigate the process and finally a Method Operable Design Region with a risk of failure of 5% was defined. The selected working point for routine use was: hydrolysis time, 17 hrs; temperature, 112 °C; 6 M HCl volume, 300 µl; antioxidant 90% phenol volume, 5 µl.

In silico discovery of diagnostic/vaccine candidate antigenic epitopes and a multi-epitope peptide vaccine (NaeVac) design for the brain-eating amoeba Naegleria fowleri causing human meningitis

Naegleria fowleri, the brain-eating amoeba, is a free-living amoeboflagellate with three different life cycles (trophozoite, flagellated, and cyst) that lives in a variety of habitats around the world including warm freshwater and soil. It causes a disease called naegleriasis leading meningitis and primary amoebic meningoencephalitis (PAM) in humans. N. fowleri is transmitted through contaminated water sources such as insufficiently chlorinated swimming pool water or contaminated tap water, and swimmers are at risk. N. fowleri is found all over the world, and most infections were reported in both developed and developing countries with high mortality rates and serious clinical findings. Until now, there is no FDA approved vaccine and early diagnosis is urgent against this pathogen. In this study, by analyzing the N. fowleri vaccine candidate proteins (Mp2CL5, Nfa1, Nf314, proNP-A and proNP-B), it was aimed to discover diagnostic/vaccine candidate epitopes and to design a multi-epitope peptide vaccine against this pathogen. After the in silico evaluation, three prominent diagnostic/vaccine candidate epitopes (EAKDSK, LLPHIRILVY, and FYAKLLPHIRILVYS) with the highest antigenicities were discovered and a potentially highly immunogenic/antigenic multi-epitope peptide vaccine (NaeVac) was designed against the brain-eating amoeba N. fowleri causing human meningitis.

Reverse engineering protection: A comprehensive survey of reverse vaccinology-based vaccines targeting viral pathogens

Vaccines have significantly reduced the impact of numerous deadly viral infections. However, there is an increasing need to expedite vaccine development in light of the recurrent pandemics and epidemics. Also, identifying vaccines against certain viruses is challenging due to various factors, notably the inability to culture certain viruses in cell cultures and the wide-ranging diversity of MHC profiles in humans. Fortunately, reverse vaccinology (RV) efficiently overcomes these limitations and has simplified the identification of epitopes from antigenic proteins across the entire proteome, streamlining the vaccine development process. Furthermore, it enables the creation of multiepitope vaccines that can effectively account for the variations in MHC profiles within the human population. The RV approach offers numerous advantages in developing precise and effective vaccines against viral pathogens, including extensive proteome coverage, accurate epitope identification, cross-protection capabilities, and MHC compatibility. With the introduction of RV, there is a growing emphasis among researchers on creating multiepitope-based vaccines aiming to stimulate the host's immune responses against multiple serotypes, as opposed to single-component monovalent alternatives. Regardless of how promising the RV-based vaccine candidates may appear, they must undergo experimental validation to probe their protection efficacy for real-world applications. The time, effort, and resources allocated to the laborious epitope identification process can now be redirected toward validating vaccine candidates identified through the RV approach. However, to overcome failures in the RV-based approach, efforts must be made to incorporate immunological principles and consider targeting the epitope regions involved in disease pathogenesis, immune responses, and neutralizing antibody maturation. Integrating multi-omics and incorporating artificial intelligence and machine learning-based tools and techniques in RV would increase the chances of developing an effective vaccine. This review thoroughly explains the RV approach, ideal RV-based vaccine construct components, RV-based vaccines designed to combat viral pathogens, its challenges, and future perspectives.

mtx-COBRA: Subcellular localization prediction for bacterial proteins

BACKGROUND

Bacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and can induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets.

METHOD

We present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Meta's protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset.

RESULTS

Using benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb.

CONCLUSIONS

mtx-COBRA provides an accessible pipeline that can more efficiently classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.

Immune interface interference vaccines: An evolution-informed approach to anti-bacterial vaccine design

Developing protein-based vaccines against bacteria has proved much more challenging than producing similar immunisations against viruses. Currently, anti-bacterial vaccines are designed using methods based on reverse vaccinology. These identify broadly conserved, immunogenic proteins using a combination of genomic and high-throughput laboratory data. While this approach has successfully generated multiple rationally designed formulations that show promising immunogenicity in animal models, few have been licensed. The difficulty of inducing protective immunity in humans with such vaccines mirrors the ability of many bacteria to recolonise individuals despite recognition by natural polyvalent antibody repertoires. As bacteria express too many antigens to evade all adaptive immune responses through mutation, they must instead inhibit the efficacy of such host defences through expressing surface structures that interface with the immune system. Therefore, 'immune interface interference' (I3) vaccines that target these features should synergistically directly target bacteria and prevent them from inhibiting responses to other surface antigens. This approach may help us understand the efficacy of the two recently introduced immunisations against serotype B meningococci, which both target the Factor H-binding protein (fHbp) that inhibits complement deposition on the bacterial surface. Therefore, I3 vaccine designs may help overcome the current challenges of developing protein-based vaccines to prevent bacterial infections.

Immunoinformatics approaches in developing a novel multi-epitope chimeric vaccine protective against Saprolegnia parasitica

Saprolegnia parasitica is responsible for devastating infections in fish and poses a tremendous threat to the global aquaculture industry. Presently, no safe and effective control measures are available, on the contrary, use of banned toxic compounds against the pathogen is affecting humans via biomagnification routes. This pioneering study aims to design an effective multi-epitope multi-target vaccine candidate against S. parasitica by targeting key proteins involved in the infection process. The proteins were analyzed and linear B-cell epitopes, MHC class I, and class II epitopes were predicted. Subsequently, highly antigenic epitopes were selected and fused to a highly immunogenic adjuvant, 50S ribosomal protein L7/L12, to design a multi-epitope chimeric vaccine construct. The structure of the vaccine was generated and validated for its stereochemical quality, physicochemical properties, antigenicity, allergenicity, and virulence traits. Molecular docking analyses demonstrated strong binding interactions between the vaccine and piscine immune receptors (TLR5, MHC I, MHC II). Molecular dynamics simulations and binding energy calculations of the complexes, further, reflected the stability and favorable interactions of the vaccine and predicted its cytosolic stability. Immune simulations predicted robust and consistent kinetics of the immune response elicited by the vaccine. The study posits the vaccine as a promising solution to combat saprolegniasis in the aquaculture industry.

MA, Tovar-Rosero YY, Oñate AA, O'Ryan M. Two centuries of vaccination: historical and conceptual approach and future perspectives

Over the past two centuries, vaccines have been critical for the prevention of infectious diseases and are considered milestones in the medical and public health history. The World Health Organization estimates that vaccination currently prevents approximately 3.5-5 million deaths annually, attributed to diseases such as diphtheria, tetanus, pertussis, influenza, and measles. Vaccination has been instrumental in eradicating important pathogens, including the smallpox virus and wild poliovirus types 2 and 3. This narrative review offers a detailed journey through the history and advancements in vaccinology, tailored for healthcare workers. It traces pivotal milestones, beginning with the variolation practices in the early 17th century, the development of the first smallpox vaccine, and the continuous evolution and innovation in vaccine development up to the present day. We also briefly review immunological principles underlying vaccination, as well as the main vaccine types, with a special mention of the recently introduced mRNA vaccine technology. Additionally, we discuss the broad benefits of vaccines, including their role in reducing morbidity and mortality, and in fostering socioeconomic development in communities. Finally, we address the issue of vaccine hesitancy and discuss effective strategies to promote vaccine acceptance. Research, collaboration, and the widespread acceptance and use of vaccines are imperative for the continued success of vaccination programs in controlling and ultimately eradicating infectious diseases.

In Vitro Pre-Clinical Evaluation of a Gonococcal Trivalent Candidate Vaccine Identified by Transcriptomics

Gonorrhea, a sexually transmitted disease caused by Neisseria gonorrhoeae, poses a significant global public health threat. Infection in women can be asymptomatic and may result in severe reproductive complications. Escalating antibiotic resistance underscores the need for an effective vaccine. Approaches being explored include subunit vaccines and outer membrane vesicles (OMVs), but an ideal candidate remains elusive. Meningococcal OMV-based vaccines have been associated with reduced rates of gonorrhea in retrospective epidemiologic studies, and with accelerated gonococcal clearance in mouse vaginal colonization models. Cross-protection is attributed to shared antigens and possibly cross-reactive, bactericidal antibodies. Using a Candidate Antigen Selection Strategy (CASS) based on the gonococcal transcriptome during human mucosal infection, we identified new potential vaccine targets that, when used to immunize mice, induced the production of antibodies with bactericidal activity against N. gonorrhoeae strains. The current study determined antigen recognition by human sera from N. gonorrhoeae-infected subjects, evaluated their potential as a multi-antigen (combination) vaccine in mice and examined the impact of different adjuvants (Alum or Alum+MPLA) on functional antibody responses to N. gonorrhoeae. Our results indicated that a stronger Th1 immune response component induced by Alum+MPLA led to antibodies with improved bactericidal activity. In conclusion, a combination of CASS-derived antigens may be promising for developing effective gonococcal vaccines.

Evaluating vaccine-elicited antibody activities against Neisseria gonorrhoeae: cross-protective responses elicited by the 4CMenB meningococcal vaccine

The bacterial pathogen Neisseria gonorrhoeae is an urgent global health problem due to increasing numbers of infections, coupled with rampant antibiotic resistance. Vaccines against gonorrhea are being prioritized to combat drug-resistant N. gonorrhoeae. Meningococcal serogroup B vaccines such as four-component meningococcal B vaccine (4CMenB) are predicted by epidemiology studies to cross-protect individuals from natural infection with N. gonorrhoeae and elicit antibodies that cross-react with N. gonorrhoeae. Evaluation of vaccine candidates for gonorrhea requires a suite of assays for predicting efficacy in vitro and in animal models of infection, including the role of antibodies elicited by immunization. Here, we present the development and optimization of assays to evaluate antibody functionality after immunization of mice: antibody binding to intact N. gonorrhoeae, serum bactericidal activity, and opsonophagocytic killing activity using primary human neutrophils [polymorphonuclear leukocytes (PMNs)]. These assays were developed with purified antibodies against N. gonorrhoeae and used to evaluate serum from mice that were vaccinated with 4CMenB or given alum as a negative control. Results from these assays will help prioritize gonorrhea vaccine candidates for advanced preclinical to early clinical studies and will contribute to identifying correlates and mechanisms of immune protection against N. gonorrhoeae.

Systematic review of reverse vaccinology and immunoinformatics data for non-viral sexually transmitted infections

Sexually Transmitted Infections (STIs) are a public health burden rising in developed and developing nations. The World Health Organization estimates nearly 374 million new cases of curable STIs yearly. Global efforts to control their spread have been insufficient in fulfilling their objective. As there is no vaccine for many of these infections, these efforts are focused on education and condom distribution. The development of vaccines for STIs is vital for successfully halting their spread. The field of immunoinformatics is a powerful new tool for vaccine development, allowing for the identification of vaccine candidates within a bacterium's genome and allowing for the design of new genome-based vaccine peptides. The goal of this review was to evaluate the usage of immunoinformatics in research focused on non-viral STIs, identifying fields where research efforts are concentrated. Here we describe gaps in applying these techniques, as in the case of Treponema pallidum and Trichomonas vaginalis.

Exploring the whole proteome of monkeypox virus to design B cell epitope-based oral vaccines using immunoinformatics approaches

In the last few months 85,536 cases and 91 deaths were reported for monkeypox disease from 110 and 71 locations from all over the world, correspondingly. The vaccines of other viruses that belong to the Poxviridae family were recommended for monkeypox. There is no licensed vaccine available for monkeypox that originated from monkeypox virus. In the present study, using the reverse vaccinology approach we have performed whole proteome analysis of monkeypox virus to screen out the potential antigenic proteins that can be used as vaccine candidates. We have also designed 12 B cell epitopes-based vaccine candidates using immunoinformatics approach. We have found a total 15 potential antigenic proteins out of which 14 antigens are novel and can be used for further vaccine development against monkeypox. We have performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidates MPOXVs (MPOXV1-MPOXV12). Further, we have performed molecular docking, in silico immune simulation and cloning of MPOXVs. All MPOXVs are potential vaccine candidate that can potentially activate the innate, cellular, and humoral immune response. However, further experimental validation is required before moving to clinical trials. This is the first oral vaccine reported for monkeypox virus derived from monkeypox proteins.

De novo identification of bacterial antigens of a clinical isolate by combining use of proteosurfaceomics, secretomics, and BacScan technologies

Background

Emerging infectious diseases pose a significant threat to both human and animal populations. Rapid de novo identification of protective antigens from a clinical isolate and development of an antigen-matched vaccine is a golden strategy to prevent the spread of emerging novel pathogens.

Methods

Here, we focused on Actinobacillus pleuropneumoniae, which poses a serious threat to the pig industry, and developed a general workflow by integrating proteosurfaceomics, secretomics, and BacScan technologies for the rapid de novo identification of bacterial protective proteins from a clinical isolate.

Results

As a proof of concept, we identified 3 novel protective proteins of A. pleuropneumoniae. Using the protective protein HBS1_14 and toxin proteins, we have developed a promising multivalent subunit vaccine against A. pleuropneumoniae.

Discussion

We believe that our strategy can be applied to any bacterial pathogen and has the potential to significantly accelerate the development of antigen-matched vaccines to prevent the spread of an emerging novel bacterial pathogen.

Evaluating vaccine-elicited antibody activities against Neisseria gonorrhoeae: cross-protective responses elicited by the 4CMenB meningococcal vaccine

The bacterial pathogen Neisseria gonorrhoeae is an urgent global health problem due to increasing numbers of infections, coupled with rampant antibiotic resistance. Vaccines against gonorrhea are being prioritized to combat drug-resistant N. gonorrhoeae. Meningococcal serogroup B vaccines such as 4CMenB are predicted by epidemiology studies to cross-protect individuals from natural infection with N. gonorrhoeae and elicit antibodies that cross-react with N. gonorrhoeae. Evaluation of vaccine candidates for gonorrhea requires a suite of assays for predicting efficacy in vitro and in animal models of infection, including the role of antibodies elicited by immunization. Here we present assays to evaluate antibody functionality after immunization: antibody binding to intact N. gonorrhoeae, serum bactericidal activity, and opsonophagocytic killing activity using primary human neutrophils (polymorphonuclear leukocytes). These assays were developed with purified antibodies against N. gonorrhoeae and used to evaluate serum from mice that were vaccinated with 4CMenB or given alum as a negative control. Results from these assays will help prioritize gonorrhea vaccine candidates for advanced preclinical to early clinical study and will contribute to identifying correlates and mechanisms of immune protection against N. gonorrhoeae .

Identification of vaccine candidates against rhodococcus equi by combining pangenome analysis with a reverse vaccinology approach

Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that can cause life-threatening infections. The rapid evolution of multidrug-resistant R. equi and the fact that there is no currently licensed effective vaccine against R. equi warrant the need for vaccine development. Reverse vaccinology (RV), which involves screening a pathogen's entire genome and proteome using various web-based prediction tools, is considered one of the most effective approaches for identifying vaccine candidates. Here, we performed a pangenome analysis to determine the core proteins of R. equi. We then used the RV approach to examine the subcellular localization, host and gut flora homology, antigenicity, transmembrane helices, physicochemical properties, and immunogenicity of the core proteins to select potential vaccine candidates. The vaccine candidates were then subjected to epitope mapping to predict the exposed antigenic epitopes that possess the ability to bind with major histocompatibility complex I/II (MHC I/II) molecules. These vaccine candidates and epitopes will form a library of elements for the development of a polyvalent or universal vaccine against R. equi. Sixteen R. equi complete proteomes were found to contain 6,238 protein families, and the core proteins consisted of 3,969 protein families (∼63.63% of the pangenome), reflecting a low degree of intraspecies genomic variability. From the pool of core proteins, 483 nonhost homologous membrane and extracellular proteins were screened, and 12 vaccine candidates were finally identified according to their antigenicity, physicochemical properties and other factors. These included four cell wall/membrane/envelope biogenesis proteins; four amino acid transport and metabolism proteins; one cell cycle control, cell division and chromosome partitioning protein; one carbohydrate transport and metabolism protein; one secondary metabolite biosynthesis, transport and catabolism protein; and one defense mechanism protein. All 12 vaccine candidates have an experimentally validated 3D structure available in the protein data bank (PDB). Epitope mapping of the candidates showed that 16 MHC I epitopes and 13 MHC II epitopes with the strongest immunogenicity were exposed on the protein surface, indicating that they could be used to develop a polypeptide vaccine. Thus, we utilized an analytical strategy that combines pangenome analysis and RV to generate a peptide antigen library that simplifies the development of multivalent or universal vaccines against R. equi and can be applied to the development of other vaccines.

The Screening of the Protective Antigens of Aeromonas hydrophila Using the Reverse Vaccinology Approach: Potential Candidates for Subunit Vaccine Development

The threat of bacterial septicemia caused by Aeromonas hydrophila infection to aquaculture growth can be prevented through vaccination, but differences among A. hydrophila strains may affect the effectiveness of non-conserved subunit vaccines or non-inactivated A. hydrophila vaccines, making the identification and development of conserved antigens crucial. In this study, a bioinformatics analysis of 4268 protein sequences encoded by the A. hydrophila J-1 strain whole genome was performed based on reverse vaccinology. The specific analysis included signal peptide prediction, transmembrane helical structure prediction, subcellular localization prediction, and antigenicity and adhesion evaluation, as well as interspecific and intraspecific homology comparison, thereby screening the 39 conserved proteins as candidate antigens for A. hydrophila vaccine. The 9 isolated A. hydrophila strains from diseased fish were categorized into 6 different molecular subtypes via enterobacterial repetitive intergenic consensus (ERIC)-PCR technology, and the coding regions of 39 identified candidate proteins were amplified via PCR and sequenced to verify their conservation in different subtypes of A. hydrophila and other Aeromonas species. In this way, conserved proteins were screened out according to the comparison results. Briefly, 16 proteins were highly conserved in different A. hydrophila subtypes, of which 2 proteins were highly conserved in Aeromonas species, which could be selected as candidate antigens for vaccines development, including type IV pilus secretin PilQ (AJE35401.1) and TolC family outer membrane protein (AJE35877.1). The present study screened the conserved antigens of A. hydrophila by using reverse vaccinology, which provided basic foundations for developing broad-spectrum protective vaccines of A. hydrophila.

Perspective vaccines for emerging viral diseases in farm animals

The world has watched the emergence of numerous animal viruses that may threaten animal health which were added to the perpetual growing list of animal pathogens. This emergence drew the attention of the experts and animal health groups to the fact that it has become necessary to work on vaccine development. The current review aims to explore the perspective vaccines for emerging viral diseases in farm animals. This aim was fulfilled by focusing on modern technologies as well as next generation vaccines that have been introduced in the field of vaccines, either in clinical developments pending approval, or have already come to light and have been applied to animals with acceptable results such as viral-vectored vaccines, virus-like particles, and messenger RNA-based platforms. Besides, it shed the light on the importance of differentiation of infected from vaccinated animals technology in eradication programs of emerging viral diseases. The new science of nanomaterials was explored to elucidate its role in vaccinology. Finally, the role of Bioinformatics or Vaccinomics and its assist in vaccine designing and developments were discussed. The reviewing of the published manuscripts concluded that the use of conventional vaccines is considered an out-of-date approach in eliminating emerging diseases. However, these types of vaccines are considered the suitable plan especially in countries with few resources and capabilities. Piloted vaccines that rely on genetic-based technologies with continuous analyses of current viruses should be the aim of future vaccinology. Smart genomics of emerging viruses will be the gateway to choosing appropriate vaccines, regardless of the evolutionary rates of viruses.

Specific Proteomic Identification of Collagen-Binding Proteins in Escherichia coli O157:H7: Characterisation of OmpA as a Potent Vaccine Antigen

Escherichia coli is a versatile commensal species of the animal gut that can also be a pathogen able to cause intestinal and extraintestinal infections. The plasticity of its genome has led to the evolution of pathogenic strains, which represent a threat to global health. Additionally, E. coli strains are major drivers of antibiotic resistance, highlighting the urgent need for new treatment and prevention measures. The antigenic and structural heterogeneity of enterohaemorrhagic E. coli colonisation factors has limited their use for the development of effective and cross-protective vaccines. However, the emergence of new strains that express virulence factors deriving from different E. coli diarrhoeagenic pathotypes suggests that a vaccine targeting conserved proteins could be a more effective approach. In this study, we conducted proteomics analysis and functional protein characterisation to identify a group of proteins potentially involved in the adhesion of E. coli O157:H7 to the extracellular matrix and intestinal epithelial cells. Among them, OmpA has been identified as a highly conserved and immunogenic antigen, playing a significant role in the adhesion phenotype of E. coli O157:H7 and in bacterial aggregation. Furthermore, antibodies raised against recombinant OmpA effectively reduced the adhesion of E. coli O157:H7 to intestinal epithelial cells. The present work highlights the role of OmpA as a potent antigen for the development of a vaccine against intestinal pathogenic E. coli.

Identification and Evaluation of Novel Antigen Candidates against Salmonella Pullorum Infection Using Reverse Vaccinology

Pullorum disease, caused by the Salmonella enterica serovar Gallinarum biovar Pullorum, is a highly contagious disease in the poultry industry, leading to significant economic losses in many developing countries. Due to the emergence of multidrug-resistant (MDR) strains, immediate attention is required to prevent their endemics and global spreading. To mitigate the prevalence of MDR Salmonella Pullorum infections in poultry farms, it is urgent to develop effective vaccines. Reverse vaccinology (RV) is a promising approach using expressed genomic sequences to find new vaccine targets. The present study used the RV approach to identify new antigen candidates against Pullorum disease. Initial epidemiological investigation and virulent assays were conducted to select strain R51 for presentative and general importance. An additional complete genome sequence (4.7 Mb) for R51 was resolved using the Pacbio RS II platform. The proteome of Salmonella Pullorum was analyzed to predict outer membrane and extracellular proteins, and was further selected for evaluating transmembrane domains, protein prevalence, antigenicity, and solubility. Twenty-two high-scored proteins were identified among 4713 proteins, with 18 recombinant proteins successfully expressed and purified. The chick embryo model was used to assess protection efficacy, in which vaccine candidates were injected into 18-day-old chick embryos for in vivo immunogenicity and protective effects. The results showed that the PstS, SinH, LpfB, and SthB vaccine candidates were able to elicit a significant immune response. Particularly, PstS confers a significant protective effect, with a 75% survival rate compared to 31.25% for the PBS control group, confirming that identified antigens can be promising targets against Salmonella Pullorum infection. Thus, we offer RV to discover novel effective antigens in an important veterinary infectious agent with high priority.

Historical advances in structural and molecular biology and how they impacted vaccine development

Vaccines are among the greatest tools for prevention and control of disease. They have eliminated smallpox from the planet, decreased morbidity and mortality for major infectious diseases like polio, measles, mumps, and rubella, significantly blunted the impact of the COVID-19 pandemic, and prevented viral induced cancers such as cervical cancer caused by human papillomavirus. Recent technological advances, in genomics, structural biology, and human immunology have transformed vaccine development, enabling new technologies such as mRNA vaccines to greatly accelerate development of new and improved vaccines. In this review, we briefly highlight the history of vaccine development, and provide examples of where advances in genomics and structural biology, paved the way for development of vaccines for bacterial and viral diseases.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Genetic, Functional, and Immunogenic Analyses of the O-Linked Protein Glycosylation System in Neisseria meningitidis Serogroup A ST-7 Isolates

Neisseria meningitidis exhibits a general O-linked protein glycosylation system in which pili and other extracytoplasmic proteins are glycosylated. To investigate glycan antigenicity in humans and the significance of high glycan diversity on immune escape mechanisms, we exploited serogroup A meningococcal strains and serum samples obtained from laboratory-confirmed Ethiopian patients with meningococcal disease. The 37 meningococcal isolates were sequenced, and their protein glycosylation (pgl) genotypes and protein glycosylation phenotypes were investigated in detail. An insertion sequence (IS1655) element in pglH reduced glycan variability in the majority of isolates, while phase variation strengthened glycan variability and microheterogeneity. Homologous recombination events within the pgl genes were identified in eight of the 37 isolates, and the phenotypic consequences ranged from none detected to altered glycoforms in two of the isolates in which the whole pgl locus was exchanged. Immunoblotting of sera against a complete panel of glycan-expressing mutant strains demonstrated that most of these patient sera had IgG antibodies against various neisserial protein glycan antigens. Furthermore, using a bactericidal assay comparing a wild-type meningococcal A strain and a glycosylation-null variant strain, we showed that these protein glycan antigens interfere with bactericidal killing by antibodies in patient sera. Altogether, we were largely able to link pgl genotype with glycosylation phenotype. Our study reveals that protein glycans seem to contribute to the ability of N. meningitidis to resist the bactericidal activity of human serum, possibly by masking protein epitopes important for bactericidal killing and thus protection against meningococcal disease. IMPORTANCE Bacterial meningitis is a serious global health problem, and one of the major causative organisms is Neisseria meningitidis. Extensive variability in protein glycan structure and antigenicity is due to phase variation of protein glycosylation genes and polymorphic gene content and function. The exact role(s) of glycosylation in Neisseria remains to be determined, but increasing evidence, supported by this study, suggests that glycan variability can be a strategy to escape the human immune system. The complexity of the O-linked protein glycosylation system requires further studies to fully comprehend how these bacteria utilize variation in pgl genes to produce such high glycoform diversity and to evade the human immune response.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.