Identification of New Features from Known Bacterial Protective Vaccine Antigens Enhances Rational Vaccine Design

The novel antigen, lipopolysaccharide export protein LptH, protects mice against Pseudomonas aeruginosa acute pneumonia in monovalent and multivalent vaccines

Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen that is a leading cause of morbidity and mortality worldwide in susceptible patients, particularly in those with respiratory disorders. The rising prevalence of multidrug-resistant strains and the failure of previous P. aeruginosa vaccine candidates in clinical trials highlight the urgent need to investigate novel vaccine antigens. In this study, we evaluated the protective potential of two antigen candidates, LptH and OprM, previously identified based on their involvement in host-cell attachment in a murine acute pneumonia model. Recombinant Escherichia coli BL21 clones overexpressing these proteins showed 8.8- and 3.5-fold increased attachment to 16HBE14o- cells in vitro, confirming their role in host-cell attachment. Immunisation with rLptH significantly reduced bacterial burden in the lungs by 1.12 log10 CFU and improved animal welfare scores compared to adjuvant-only controls. Serological and immunophenotyping analyses revealed that the monovalent rLptH vaccine stimulated antigen-specific IgG1 and IgG2c isotype production, and enhanced IFN-γ and IL-17 recall responses in the spleen. Moreover, a trivalent vaccine comprising rLptH and two other P. aeruginosa antigens, rFtsZ, and rOpmH, achieved a 2.33 log10 CFU reduction in lung bacterial burden, and 1.85 log10 CFU reduction in dissemination. These encouraging findings support the potential of LptH as a promising antigen for the development of a protective multivalent vaccine against P. aeruginosa infections.

Systematic collection, annotation, and pattern analysis of viral vaccines in the VIOLIN vaccine knowledgebase

Background

Viral vaccines have been proven significant in protecting us against viral diseases such as COVID-19. To better understand and design viral vaccines, it is critical to systematically collect, annotate, and analyse various viral vaccines and identify enriched patterns from these viral vaccines.

Methods

We systematically collected experimentally verified viral vaccines from the literature, manually annotated, and stored the information in the VIOLIN vaccine database. The annotated information included basic vaccine names, pathogens and diseases, vaccine components, vaccine formulations, and their induced host responses. Enriched patterns were identified from our systematical analysis of the viral vaccines and vaccine antigens.

Results

A total of 2,847 viral vaccines against 95 viral species (including 72 RNA viral species and 23 DNA viral species) were collected, manually annotated, and stored in the VIOLIN vaccine database. These viral vaccines used 542 vaccine antigens. A taxonomical analysis found various DNA and RNA viruses covered by the viral vaccines. These vaccines target different viral life cycle stages (e.g., viral entry, assembly, exit, and immune evasion) as identified in top ranked human, animal vaccines, and HPV vaccines. The vaccine antigen proteins also show up in different virion locations in viruses such as HRSV vaccines. Both structural and non-structural viral proteins have been used for viral vaccine development. Protective vaccine antigens tend to have a protegenicity score of >85% based on the Vaxign-ML calculation, which measures predicted suitability for vaccine use. While predicted adhesins still have significantly higher chances of being protective antigens, only 21.42% of protective viral vaccine antigens were predicted to be adhesins. Furthermore, our Gene Ontology (GO) enrichment analysis using a customized Fisher's exact test identified many enriched patterns such as viral entry into the host cell, DNA/RNA/ATP/ion binding, and suppression of host type 1 interferon-mediated signaling pathway. The viral vaccines and their associated entities and relations are ontologically modeled and represented in the Vaccine Ontology (VO). A VIOLIN web interface was developed to support user friendly queries of viral vaccines.

Discussion

Viral vaccines were systematically collected and annotated in the VIOLIN vaccine knowledgebase, and the analysis of these viral vaccines identified many insightful patterns.

Proteomic approach to identify host cell attachment proteins provides protective Pseudomonas aeruginosa vaccine antigen FtsZ

Pseudomonas aeruginosa is an opportunistic Gram-negative pathogen that causes severe nosocomial infections in susceptible individuals due to the emergence of multidrug-resistant strains. There are no approved vaccines against P. aeruginosa infections nor candidates in active clinical development, highlighting the need for novel candidates and strategies. Using a cell-blot proteomic approach, we reproducibly identified 49 proteins involved in interactions with human lung epithelial cells across four P. aeruginosa strains. Among these were cell division protein FtsZ and outer membrane protein OpmH. Escherichia coli BL21 cells overexpressing recombinant FtsZ or rOpmH showed a 66- and 15-fold increased ability to attach to 16HBE14o- cells, further supporting their involvement in host cell attachment. Both antigens led to proliferation of NK and CD8+ cytotoxic T cells, significant increases in the production of IFN-γ, IL-17A, TNF and IL-4 in immunised mice and elicited strong antigen-specific serological IgG1 and IgG2c responses. Immunisation with FtsZ significantly reduced bacterial burden in the lungs by 1.9-log CFU and dissemination to spleen by 1.8-log CFU. The protective antigen candidate, FtsZ, would not have been identified by traditional approaches relying on either virulence mechanisms or sequence-based predictions, opening new avenues in the development of an anti-P. aeruginosa vaccine.

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.

Unveiling promising immunogenic targets in Coxiella burnetii through in silico analysis: paving the way for novel vaccine strategies

BACKGROUND

Coxiella burnetii, an intracellular pathogen, serves as the causative agent of zoonotic Q fever. This pathogen presents a significant threat due to its potential for airborne transmission, environmental persistence, and pathogenicity. The current whole-cell vaccine (WCV) utilized in Australia to combat Q fever exhibits notable limitations, including severe adverse reactions and limited regulatory approval for human use. This research employed the reverse vaccinology (RV) approach to uncover antigenic proteins and epitopes of C. burnetii, facilitating the development of more potent vaccine candidates.

METHODS

The potential immunogenic proteins derived from C. burnetii RSA493/Nine Mile phase I (NMI) were extracted through manual, automated RV, and virulence factor database (VFDB) methods. Web tools and bioinformatics were used to evaluate physiochemical attributes, subcellular localization, antigenicity, allergenicity, human homology, B-cell epitopes, MHC I and II binding ratios, functional class scores, adhesion probabilities, protein-protein interactions, and molecular docking.

RESULTS

Out of the 1850 proteins encoded by RSA493/NMI, a subset of 178 demonstrated the potential for surface or membrane localization. Following a series of analytical iterations, 14 putative immunogenic proteins emerged. This collection included nine proteins (57.1%) intricately involved in cell wall/membrane/envelope biogenesis processes (CBU_0197 (Q83EW1), CBU_0311 (Q83EK8), CBU_0489 (Q83E43), CBU_0939 (Q83D08), CBU_1190 (P39917), CBU_1829 (Q83AQ2), CBU_1412 (Q83BU0), CBU_1414 (Q83BT8), and CBU_1600 (Q83BB2)). The CBU_1627 (Q83B86 ) (7.1%) implicated in intracellular trafficking, secretion, and vesicular transport, and CBU_0092 (Q83F57) (7.1%) contributing to cell division. Additionally, three proteins (21.4%) displayed uncharacterized functions (CBU_0736 (Q83DJ4), CBU_1095 (Q83CL9), and CBU_2079 (Q83A32)). The congruent results obtained from molecular docking and immune response stimulation lend support to the inclusion of all 14 putative proteins as potential vaccine candidates. Notably, seven proteins with well-defined functions stand out among these candidates.

CONCLUSIONS

The outcomes of this study introduce promising proteins and epitopes for the forthcoming formulation of subunit vaccines against Q fever, with a primary emphasis on cellular processes and the virulence factors of C. burnetii.

Improving Protection to Prevent Bacterial Infections: Preliminary Applications of Reverse Vaccinology against the Main Cystic Fibrosis Pathogens

Reverse vaccinology is a powerful tool that was recently used to develop vaccines starting from a pathogen genome. Some bacterial infections have the necessity to be prevented then treated. For example, individuals with chronic pulmonary diseases, such as Cystic Fibrosis, are prone to develop infections and biofilms in the thick mucus that covers their lungs, mainly caused by Burkholderia cepacia complex, Haemophilus influenzae, Mycobacterium abscessus complex, Pseudomonas aeruginosa and Staphylococcus aureus. These infections are complicated to treat and prevention remains the best strategy. Despite the availability of vaccines against some strains of those pathogens, it is necessary to improve the immunization of people with Cystic Fibrosis against all of them. An effective approach is to develop a broad-spectrum vaccine to utilize proteins that are well conserved across different species. In this context, reverse vaccinology, a method based on computational analysis of the genome of various microorganisms, appears as one of the most promising tools for the identification of putative targets for broad-spectrum vaccine development. This review provides an overview of the vaccines that are under development by reverse vaccinology against the aforementioned pathogens, as well as the progress made so far.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

Comparative Reverse Vaccinology of Piscirickettsia salmonis, Aeromonas salmonicida, Yersinia ruckeri, Vibrio anguillarum and Moritella viscosa, Frequent Pathogens of Atlantic Salmon and Lumpfish Aquaculture

Marine finfish aquaculture is affected by diverse infectious diseases, and they commonly occur as co-infection. Some of the most frequent and prevalent Gram-negative bacterial pathogens of the finfish aquaculture include Piscirickettsia salmonis, Aeromonas salmonicida, Yersinia ruckeri, Vibrio anguillarum and Moritella viscosa. To prevent co-infections in aquaculture, polyvalent or universal vaccines would be ideal. Commercial polyvalent vaccines against some of these pathogens are based on whole inactivated microbes and their efficacy is controversial. Identification of common antigens can contribute to the development of effective universal or polyvalent vaccines. In this study, we identified common and unique antigens of P. salmonis, A. salmonicida, Y. ruckeri, V. anguillarum and M. viscosa based on a reverse vaccinology pipeline. We screened the proteome of several strains using complete available genomes and identified a total of 154 potential antigens, 74 of these identified antigens corresponded to secreted proteins, and 80 corresponded to exposed outer membrane proteins (OMPs). Further analysis revealed the outer membrane antigens TonB-dependent siderophore receptor, OMP assembly factor BamA, the LPS assembly protein LptD and secreted antigens flagellar hook assembly protein FlgD and flagellar basal body rod protein FlgG are present in all pathogens used in this study. Sequence and structural alignment of these antigens showed relatively low percentage sequence identity but good structural homology. Common domains harboring several B-cells and T-cell epitopes binding to major histocompatibility (MHC) class I and II were identified. Selected peptides were evaluated for docking with Atlantic salmon (Salmo salar) and Lumpfish MHC class II. Interaction of common peptide-MHC class II showed good in-silico binding affinities and dissociation constants between −10.3 to −6.5 kcal mol−1 and 5.10 × 10−9 to 9.4 × 10−6 M. This study provided the first list of antigens that can be used for the development of polyvalent or universal vaccines against these Gram-negative bacterial pathogens affecting finfish aquaculture.

Vaccine Design by Reverse Vaccinology and Machine Learning

Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction method based on calculating and filtering different criteria of proteins. Vaxign-ML is a new Vaxign machine learning (ML) method that predicts vaccine antigens based on extreme gradient boosting with the advance of new technologies and cumulation of protective antigen data. Using a benchmark dataset, Vaxign-ML showed superior performance in comparison to existing open-source RV tools. Vaxign-ML is also implemented within the web-based Vaxign platform to support easy and intuitive access. Vaxign-ML is also available as a command-based software package for more advanced and customizable vaccine antigen prediction. Both Vaxign and Vaxign-ML have been applied to predict SARS-CoV-2 (cause of COVID-19) and Brucella vaccine antigens to demonstrate the integrative approach to analyze and select vaccine candidates using the Vaxign platform.

Screening of potential vaccine candidates against pathogenic Brucella spp. using compositive reverse vaccinology

Brucella spp. are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and various animals. The threat of brucellosis has increased, yet currently available live attenuated vaccines still have drawbacks. Therefore, subunit vaccines, produced using protein antigens and having the advantage of being safe, cost-effective and efficacious, are urgently needed. In this study, we used core proteome analysis and a compositive RV methodology to screen potential broad-spectrum antigens against 213 pathogenic strains of Brucella spp. with worldwide geographic distribution. Candidate proteins were scored according to six biological features: subcellular localization, antigen similarity, antigenicity, mature epitope density, virulence, and adhesion probability. In the RV analysis, a total 32 candidate antigens were picked out. Of these, three proteins were selected for assessment of immunogenicity and preliminary protection in a mouse model: outer membrane protein Omp19 (used as a positive control), type IV secretion system (T4SS) protein VirB8, and type I secretion system (T1SS) protein HlyD. These three antigens with a high degree of conservation could induce specific humoral and cellular immune responses. Omp19, VirB8 and HlyD could substantially reduce the organ bacterial load of B. abortus S19 in mice and provide varying degrees of protection. In this study, we demonstrated the effectiveness of this unique strategy for the screening of potential broad-spectrum antigens against Brucella. Further evaluation is needed to identify the levels of protection conferred by the vaccine antigens against wild-type pathogenic Brucella species challenge.

Reverse Microbiomics: A New Reverse Dysbiosis Analysis Strategy and Its Usage in Prediction of Autoantigens and Virulent Factors in Dysbiotic Gut Microbiomes From Rheumatoid Arthritis Patients

Alterations in the gut microbiome have been associated with various human diseases. Most existing gut microbiome studies stopped at the stage of identifying microbial alterations between diseased or healthy conditions. As inspired by reverse vaccinology (RV), we developed a new strategy called Reverse Microbiomics (RM) that turns this process around: based on the identified microbial alternations, reverse-predicting the molecular mechanisms underlying the disease and microbial alternations. Our RM methodology starts by identifying significantly altered microbiota profiles, performing bioinformatics analysis on the proteomes of the microbiota identified, and finally predicting potential virulence or protective factors relevant to a microbiome-associated disease. As a use case study, this reverse methodology was applied to study the molecular pathogenesis of rheumatoid arthritis (RA), a common autoimmune and inflammatory disease. Those bacteria differentially associated with RA were first identified and annotated from published data and then modeled and classified using the Ontology of Host-Microbiome Interactions (OHMI). Our study identified 14 species increased and 9 species depleted in the gut microbiota of RA patients. Vaxign was used to comparatively analyze 15 genome sequences of the two pairs of species: Gram-negative Prevotella copri (increased) and Prevotella histicola (depleted), as well as Gram-positive Bifidobacterium dentium (increased) and Bifidobacterium bifidum (depleted). In total, 21 auto-antigens were predicted to be related to RA, and five of them were previously reported to be associated with RA with experimental evidence. Furthermore, we identified 94 potential adhesive virulence factors including 24 microbial ABC transporters. While eukaryotic ABC transporters are key RA diagnosis markers and drug targets, we identified, for the first-time, RA-associated microbial ABC transporters and provided a novel hypothesis of RA pathogenesis. Our study showed that RM, by broadening the scope of RV, is a novel and effective strategy to study from bacterial level to molecular level factors and gain further insight into how these factors possibly contribute to the development of microbial alterations under specific diseases.

Emerging Concepts and Technologies in Vaccine Development

Despite the success of vaccination to greatly mitigate or eliminate threat of diseases caused by pathogens, there are still known diseases and emerging pathogens for which the development of successful vaccines against them is inherently difficult. In addition, vaccine development for people with compromised immunity and other pre-existing medical conditions has remained a major challenge. Besides the traditional inactivated or live attenuated, virus-vectored and subunit vaccines, emerging non-viral vaccine technologies, such as viral-like particle and nanoparticle vaccines, DNA/RNA vaccines, and rational vaccine design, offer innovative approaches to address existing challenges of vaccine development. They have also significantly advanced our understanding of vaccine immunology and can guide future vaccine development for many diseases, including rapidly emerging infectious diseases, such as COVID-19, and diseases that have not traditionally been addressed by vaccination, such as cancers and substance abuse. This review provides an integrative discussion of new non-viral vaccine development technologies and their use to address the most fundamental and ongoing challenges of vaccine development.

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

MOTIVATION

Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility.

RESULTS

This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available.

AVAILABILITY AND IMPLEMENTATION

Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

VIO: ontology classification and study of vaccine responses given various experimental and analytical conditions

Background

Different human responses to the same vaccine were frequently observed. For example, independent studies identified overlapping but different transcriptomic gene expression profiles in Yellow Fever vaccine 17D (YF-17D) immunized human subjects. Different experimental and analysis conditions were likely contributed to the observed differences. To investigate this issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the different variables and relations among these variables systematically. We then evaluated whether the ontological VIO modeling and VIO-based statistical analysis would contribute to the enhanced vaccine investigation studies and a better understanding of vaccine response mechanisms.

Results

Our VIO modeling identified many variables related to data processing and analysis such as normalization method, cut-off criteria, software settings including software version. The datasets from two previous studies on human responses to YF-17D vaccine, reported by Gaucher et al. (2008) and Querec et al. (2009), were re-analyzed. We first applied the same LIMMA statistical method to re-analyze the Gaucher data set and identified a big difference in terms of significantly differentiated gene lists compared to the original study. The different results were likely due to the LIMMA version and software package differences. Our second study re-analyzed both Gaucher and Querec data sets but with the same data processing and analysis pipeline. Significant differences in differential gene lists were also identified. In both studies, we found that Gene Ontology (GO) enrichment results had more overlapping than the gene lists and enriched pathway lists. The visualization of the identified GO hierarchical structures among the enriched GO terms and their associated ancestor terms using GOfox allowed us to find more associations among enriched but often different GO terms, demonstrating the usage of GO hierarchical relations enhance data analysis.

Conclusions

The ontology-based analysis framework supports standardized representation, integration, and analysis of heterogeneous data of host responses to vaccines. Our study also showed that differences in specific variables might explain different results drawn from similar studies.

Molecular features of lipoprotein CD0873: A potential vaccine against the human pathogen Clostridioides difficile

Clostridioides difficile is the primary cause of antibiotic-associated diarrhea and colitis, a healthcare-associated intestinal disease resulting in a significant fatality rate. Colonization of the gut is critical for C. difficile pathogenesis. The bacterial molecules essential for efficient colonization therefore offer great potential as vaccine candidates. Here we present findings demonstrating that the C. difficile immunogenic lipoprotein CD0873 plays a critical role in pathogen success in vivo. We found that in a dixenic colonization model, a CD0873-positive strain of C. difficile significantly outcompeted a CD0873-negative strain. Immunization of mice with recombinant CD0873 prevented long-term gut colonization and was correlated with a strong secretory IgA immune response. We further present high-resolution crystal structures of CD0873, at 1.35–2.50 Å resolutions, offering a first view of the ligand-binding pocket of CD0873 and provide evidence that this lipoprotein adhesin is part of a tyrosine import system, an amino acid key in C. difficile infection. These findings suggest that CD0873 could serve as an effective component in a vaccine against C. difficile.

Cwp22, a novel peptidoglycan cross-linking enzyme, plays pleiotropic roles in Clostridioides difficile

Clostridioides difficile is a Gram-positive, spore-forming, toxin-producing anaerobe pathogen, and can induce nosocomial antibiotic-associated intestinal disease. While production of toxin A (TcdA) and toxin B (TcdB) contribute to the main pathogenesis of C. difficile, adhesion and colonization of C. difficile in the host gut are prerequisites for disease onset. Previous cell wall proteins (CWPs) were identified that were implicated in C. difficile adhesion and colonization. In this study, we predicted and characterized Cwp22 (CDR20291_2601) from C. difficile R20291 to be involved in bacterial adhesion based on the Vaxign reverse vaccinology tool. The ClosTron-generated cwp22 mutant showed decreased TcdA and TcdB production during early growth, and increased cell permeability and autolysis. Importantly, the cwp22 mutation impaired cellular adherence in vitro and decreased cytotoxicity and fitness over the parent strain in a mouse infection model. Furthermore, lactate dehydrogenase cytotoxicity assay, live-dead cell staining and transmission electron microscopy confirmed the decreased cell viability of the cwp22 mutant. Thus, Cwp22 is involved in cell wall integrity and cell viability, which could affect most phenotypes of R20291. Our data suggest that Cwp22 is an attractive target for C. difficile infection therapeutics and prophylactics.

Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery

Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results.

Victors: a web-based knowledge base of virulence factors in human and animal pathogens

Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (http://www.phidias.us/victors) is a novel, manually curated, web-based integrative knowledge base and analysis resource for VFs of pathogens that cause infectious diseases in human and animals. Currently, Victors contains 5296 VFs obtained via manual annotation from peer-reviewed publications, with 4648, 179, 105 and 364 VFs originating from 51 bacterial, 54 viral, 13 parasitic and 8 fungal species, respectively. Our data analysis identified many VF-specific patterns. Within the global VF pool, cytoplasmic proteins were more common, while adhesins were less common compared to findings on protective vaccine antigens. Many VFs showed homology with host proteins and the human proteins interacting with VFs represented the hubs of human-pathogen interactions. All Victors data are queriable with a user-friendly web interface. The VFs can also be searched by a customized BLAST sequence similarity searching program. These VFs and their interactions with the host are represented in a machine-readable Ontology of Host-Pathogen Interactions. Victors supports the 'One Health' research as a vital source of VFs in human and animal pathogens.

Identification of Cross-Protective Potential Antigens against Pathogenic Brucella spp. through Combining Pan-Genome Analysis with Reverse Vaccinology

Brucellosis is a zoonotic infectious disease caused by bacteria of the genus Brucella. Brucella melitensis, Brucella abortus, and Brucella suis are the most pathogenic species of this genus causing the majority of human and domestic animal brucellosis. There is a need to develop a safe and potent subunit vaccine to overcome the serious drawbacks of the live attenuated Brucella vaccines. The aim of this work was to discover antigen candidates conserved among the three pathogenic species. In this study, we employed a reverse vaccinology strategy to compute the core proteome of 90 completed genomes: 55 B. melitensis, 17 B. abortus, and 18 B. suis. The core proteome was analyzed by a metasubcellular localization prediction pipeline to identify surface-associated proteins. The identified proteins were thoroughly analyzed using various in silico tools to obtain the most potential protective antigens. The number of core proteins obtained from analyzing the 90 proteomes was 1939 proteins. The surface-associated proteins were 177. The number of potential antigens was 87; those with adhesion score ≥ 0.5 were considered antigen with "high potential," while those with a score of 0.4-0.5 were considered antigens with "intermediate potential." According to a cumulative score derived from protein antigenicity, density of MHC-I and MHC-II epitopes, MHC allele coverage, and B-cell epitope density scores, a final list of 34 potential antigens was obtained. Remarkably, most of the 34 proteins are associated with bacterial adhesion, invasion, evasion, and adaptation to the hostile intracellular environment of macrophages which is adjusted to deprive Brucella of required nutrients. Our results provide a manageable list of potential protective antigens for developing a potent vaccine against brucellosis. Moreover, our elaborated analysis can provide further insights into novel Brucella virulence factors. Our next step is to test some of these antigens using an appropriate antigen delivery system.