T-cell epitope mapping for the design of powerful vaccines

Characterization of T-Cell Epitopes in Food Allergens by Bioinformatic Tools

The identification of T-cell epitopes is a critical step in the understanding of the immunologic mechanisms such as food allergy. Epitope screening in silico by bioinformatic tools can be used to identify T-cell epitopes, which can save time and resources. In this chapter, a multiparametric approach to predict and assess major histocompatibility complex (MHC) class II binding T-cell epitopes using bioinformatics was introduced for food allergens. Furthermore, the ability of predicted T-cell epitopes to induce interleukin (IL)-4, as well as the allergenicity potential based on the sequence analysis and population coverage of epitopes were also determined. The molecular docking approach was further used to explore the binding ability between epitopes and human leukocyte antigen (HLA) class II molecules. The amino acids that might be responsible for binding to HLA class II molecules and their binding interactions were analyzed.

Mouse models in COVID-19 research: analyzing the adaptive immune response

The emergence of SARS-CoV-2, the severe acute respiratory syndrome coronavirus type 2 causing the COVID-19 pandemic, resulted in a major necessity for scientific countermeasures. Investigations revealing the exact mechanisms of the SARS-CoV-2 pathogenesis provide the basis for the development of therapeutic measures and protective vaccines against COVID-19. Animal models are inevitable for infection and pre-clinical vaccination studies as well as therapeutic testing. A well-suited animal model, mimicking the pathology seen in human COVID-19 patients, is an important basis for these investigations. Several animal models were already used during SARS-CoV-2 studies with different clinical outcomes after SARS-CoV-2 infection. Here, we give an overview of different animal models used in SARS-CoV-2 infection studies with a focus on the mouse model. Mice provide a well-established animal model for laboratory use and several different mouse models have been generated and are being used in SARS-CoV-2 studies. Furthermore, the analysis of SARS-CoV-2-specific T cells during infection and in vaccination studies in mice is highlighted.

In silico formulation of a next-generation multiepitope vaccine for use as a prophylactic candidate against Crimean-Congo hemorrhagic fever

BACKGROUND

Crimean-Congo hemorrhagic fever (CCHF) is a widespread disease transmitted to humans and livestock animals through the bite of infected ticks or close contact with infected persons' blood, organs, or other bodily fluids. The virus is responsible for severe viral hemorrhagic fever outbreaks, with a case fatality rate of up to 40%. Despite having the highest fatality rate of the virus, a suitable treatment option or vaccination has not been developed yet. Therefore, this study aimed to formulate a multiepitope vaccine against CCHF through computational vaccine design approaches.

METHODS

The glycoprotein, nucleoprotein, and RNA-dependent RNA polymerase of CCHF were utilized to determine immunodominant T- and B-cell epitopes. Subsequently, an integrative computational vaccinology approach was used to formulate a multi-epitopes vaccine candidate against the virus.

RESULTS

After rigorous assessment, a multiepitope vaccine was constructed, which was antigenic, immunogenic, and non-allergenic with desired physicochemical properties. Molecular dynamics (MD) simulations of the vaccine-receptor complex show strong stability of the vaccine candidates to the targeted immune receptor. Additionally, the immune simulation of the vaccine candidates found that the vaccine could trigger real-life-like immune responses upon administration to humans.

CONCLUSIONS

Finally, we concluded that the formulated multiepitope vaccine candidates would provide excellent prophylactic properties against CCHF.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

Identification of B and T Cell Epitopes to Design an Epitope-Based Peptide Vaccine against the Cell Surface Binding Protein of Monkeypox Virus: An Immunoinformatics Study

Background

Although the monkeypox virus-associated illness was previously confined to Africa, recently, it has started to spread across the globe and become a significant threat to human lives. Hence, this study was designed to identify the B and T cell epitopes and develop an epitope-based peptide vaccine against this virus's cell surface binding protein through an in silico approach to combat monkeypox-associated diseases.

Results

The analysis revealed that the cell surface binding protein of the monkeypox virus contains 30 B cell and 19 T cell epitopes within the given parameter. Among the T cell epitopes, epitope "ILFLMSQRY" was found to be one of the most potential peptide vaccine candidates. The docking analysis revealed an excellent binding affinity of this epitope with the human receptor HLA-B^∗15:01 with a very low binding energy (-7.5 kcal/mol).

Conclusion

The outcome of this research will aid the development of a T cell epitope-based peptide vaccine, and the discovered B and T cell epitopes will facilitate the creation of other epitope and multi-epitope-based vaccines in the future. This research will also serve as a basis for further in vitro and in vivo analysis to develop a vaccine that is effective against the monkeypox virus.

Engineered microbial systems for advanced drug delivery

The human body is a natural habitat for a multitude of microorganisms, with bacteria being the major constituent of the microbiota. These bacteria colonize discrete anatomical locations that provide suitable conditions for their survival. Many bacterial species, both symbiotic and pathogenic, interact with the host via biochemical signaling. Based on these attributes, commensal and attenuated pathogenic bacteria have been engineered to deliver therapeutic molecules to target specific diseases. Recent advances in synthetic biology have enabled us to perform complex genetic modifications in live bacteria and bacteria-derived particles, which simulate micron or submicron lipid-based vectors, for the targeted delivery of therapeutic agents. In this review, we highlight various examples of engineered bacteria or bacteria-derived particles that encapsulate, secrete, or surface-display therapeutic molecules for the treatment or prevention of various diseases. The review highlights recent studies on (i) the production of therapeutics by microbial cell factories, (ii) disease-triggered release of therapeutics by sense and respond systems, (iii) bacteria targeting tumor hypoxia, and (iv) bacteria-derived particles as chassis for drug delivery. In addition, we discuss the potential of such drug delivery systems to be translated into clinical therapies.

Prediction and characterization of the T cell epitopes for the major soybean protein allergens using bioinformatics approaches

Protein allergens is a health risk for consumption of soybeans. To understand allerginicity mechanism, T cell epitopes of 7 soybean allergens were predicted and screened by abilities to induce cytokine interleukin (IL) 4. The relationships among amino acid composition, properties, allergenicity, and pepsin hydrolysis sites were analyzed. Among the 138 T cell epitopes identified, YIKDVFRVIPSEVLS, KDVFRVIPSEVLSNS, DVFRVIPSEVLSNSY of Gly m 6.0501 (P04347), and AKADALFKAIEAYLL, ADALFKAIEAYLLAH of Gly m 4.0101 (P26987) were the most possible epitope candidates. In T cell epitopes pattern, the frequencies of amino acids Q, D, E, P, and G decreased, while F, I, N, V, K, H, A, L, and S increased. Hydrophobic residues at positions p1 and p2 and positively charged residues in positions p13 might contribute to allergenicity. Most of epitopes could be hydrolyzed by pepsin into small polypeptides within 12 residues length, and the anti-digestive epitope regions contained I, V, S, N, and Q residues. T cell epitopes EEQRQQEGVIVELSK from Gly m 5.03 (P25974) showed resistance to pepsin hydrolysis and would cause a higher Th2 cell response. This research provides basis for the development of hypoallergenic soybean products in the soybean industry as well as for the immunotherapy design for protein allergy.

Immunoproteomic and Immunopeptidomic Analyses of Histoplasma capsulatum Reveal Promiscuous and Conserved Epitopes Among Fungi With Vaccine Potential

As there are more than 6 million human deaths due to mycoses each year, there is an urgent need to develop fungal vaccines. Moreover, given the similarities among pathogenic fungi, it may be possible to create a multi-fungi vaccine. In this study, we combined immunoproteomic and immunopeptidomic methods, for which we have adapted a technique based on co-immunoprecipitation (Co-IP) that made it possible to map Histoplasma capsulatum epitopes for the first time in a natural context using murine dendritic cells (DCs) and macrophages (Mφ). Although polysaccharide epitopes exist, this research focused on mapping protein epitopes as these are more immunogenic. We used different algorithms to screen proteins and peptides identified by two-dimensional electrophoresis (2-D) and Co-IP. Seventeen proteins were revealed by 2-D gels, and 45 and 24 peptides from distinct proteins were presented by DCs and Mφ, respectively. We then determined which epitopes were restricted to MHC-I and II from humans and mice and showed high promiscuity, but lacked identity with human proteins. The 4 most promising peptides were synthesized, and the peptides with and without incorporation into glucan particles induced CD4+ and CD8+ T cell proliferation and produced a Th1 and Th17 response marked by the secretion of high levels of IFN-γ, IL-17 and IL-2. These epitopes were from heat shock protein 60, enolase, and the ATP-dependent molecular chaperone HSC82, and they each have a high degree of identity with proteins expressed by other medically important pathogenic fungi. Thus, the epitopes described in this study have the potential for use in the development of vaccines that could result in cross-protection among fungal species.

Physicochemical and structural characterization, epitope mapping and vaccine potential investigation of a new protein containing Tetratrico Peptide Repeats of Acinetobacter baumannii: An in-silico and in-vivo approach

Acinetobacter baumannii is an opportunistic multidrug-resistant pathogen that causes a significant mortality rate. The proteins containing Tetratrico Peptide Repeats (TPRs) are involved in the pathogenicity and virulence of bacteria and have different roles such as transfer of bacterial virulence factors to host cells, binding to the host cells and inhibition of phagolysosomal maturation. So, in this study, physicochemical properties of a new protein containing TPRs in A. baumannii which was named PcTPRs1 by this study were characterized and its 3D structure was predicted by in-silico tools. The protein B and T cell epitopes were mapped and its vaccine potential was in-silico and in-vivo investigated. Domain analysis indicated that the protein contains the Flp pilus assembly protein TadD domain which has three TPRs. The helix is dominant in the protein structure, and this protein is an outer membrane antigen which, is extremely conserved among A. baumannii strains; thus, has good properties to be applied as a recombinant vaccine. The best-predicted and refined model was applied in ligand-binding sites and conformational epitopes prediction. Based on epitope mapping results, several epitopes were characterized which could stimulate both immune systems. BLAST results showed the introduced epitopes are completely conserved among A. baumannii strains. The in-vivo analysis indicates that a 101 amino acid fragment of the protein which contains the best selected epitope, can produce a good protectivity against A. baumannii as well as the whole TPR protein and thus could be investigated as an effective subunit and potential vaccines.

In Silico Designing of a Multitope Vaccine against Rhizopus microsporus with Potential Activity against Other Mucormycosis Causing Fungi

During the current era of the COVID-19 pandemic, the dissemination of Mucorales has been reported globally, with elevated rates of infection in India, and because of the high rate of mortality and morbidity, designing an effective vaccine against mucormycosis is a major health priority, especially for immunocompromised patients. In the current study, we studied shared Mucorales proteins, which have been reported as virulence factors, and after analysis of several virulent proteins for their antigenicity and subcellular localization, we selected spore coat (CotH) and serine protease (SP) proteins as the targets of epitope mapping. The current study proposes a vaccine constructed based on top-ranking cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell lymphocyte (BCL) epitopes from filtered proteins. In addition to the selected epitopes, β-defensins adjuvant and PADRE peptide were included in the constructed vaccine to improve the stimulated immune response. Computational tools were used to estimate the physicochemical and immunological features of the proposed vaccine and validate its binding with TLR-2, where the output data of these assessments potentiate the probability of the constructed vaccine to stimulate a specific immune response against mucormycosis. Here, we demonstrate the approach of potential vaccine construction and assessment through computational tools, and to the best of our knowledge, this is the first study of a proposed vaccine against mucormycosis based on the immunoinformatics approach.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Beyond Just Peptide Antigens: The Complex World of Peptide-Based Cancer Vaccines

Peptide-based cancer vaccines rely upon the strong activation of the adaptive immune response to elicit its effector function. They have shown to be highly specific and safe, but have yet to prove themselves as an efficacious treatment for cancer in the clinic. This is for a variety of reasons, including tumour heterogeneity, self-tolerance, and immune suppression. Importance has been placed on the overall design of peptide-based cancer vaccines, which have evolved from simple peptide derivatives of a cancer antigen, to complex drugs; incorporating overlapping regions, conjugates, and delivery systems to target and stimulate different components of antigen presenting cells, and to bolster antigen cross-presentation. Peptide-based cancer vaccines are increasingly becoming more personalised to an individual's tumour antigen repertoire and are often combined with existing cancer treatments. This strategy ultimately aids in combating the shortcomings of a more generalised vaccine strategy and provides a comprehensive treatment, taking into consideration cancer cell variability and its ability to avoid immune interrogation.

Immunoinformatics based prediction of recombinant multi-epitope vaccine for the control and prevention of SARS-CoV-2

The emergence of SARS-CoV-2 has been reported during December 2019, in the city of Wuhan, China. The transmission of this virus via human to human interaction has already been described. The novel virus has become pandemic and declared as a comprehensive emergency worldwide by World Health Organization due to its exponential spread within and outside China. There is a need of time to create a therapeutic agent and a vaccine to cure and control this lethal SARS-CoV-2. Conventionally, the vaccine development process is time taking, tiresome and requires more economical inputs with manpower. However, bioinformatics offers a key solution to compute the possibilities. The present study focuses on the utilization of bioinformatics platforms to forecast B and T cell epitopes that belong to SARS-CoV-2 spike glycoprotein. The protein is thought to have an involvement in triggering of momentous immune response. NCBI database was explored to collect the surface glycoprotein sequence and was analyzed to determine the immunogenic epitopes. This prediction analysis was carried out using IEDB web based server and the prediction of protein structure was done by homology modeling approach. This study resulted in prediction of 5T cell and 13B cell epitopes. Moreover, GPGPG linker was used to make these predicted epitopes a single peptide prior to further analysis. Afterwards, a 3D model of the final vaccine peptide was constructed, and the structure quality of the final construct was checked by Ramachandran Plot analysis and ProSA-web. Moreover, docking analysis highlighted three interactions of epitope against HLA-B7 including Lys 178, Gol 303 and Thr 31 residues. In conclusion, the predicted multi epitope peptide can be suggested as therapeutic or prophylactic candidate vaccine against SARS-CoV-2 after further confirmation by immunological assays.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus

BACKGROUND

The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity.

METHODS

Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes.

RESULTS

Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability.

CONCLUSION

This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.

Peptides-based vaccine against SARS-nCoV-2 antigenic fragmented synthetic epitopes recognized by T cell and β-cell initiation of specific antibodies to fight the infection

The World Health Organization has declared the rapidly spreading coronavirus to be a global pandemic. The FDA is yet to approve a vaccine for human novel coronavirus. Here, we developed a peptide-based vaccine and used high-throughput screening by molecular dynamics simulation to identify T-cell- and β-cell-recognized epitopes for producing specific antibodies against SARS-nCoV-2. We construct ~ 12 P' antigenic epitope peptides to develop a more effective vaccine and identify specific antibodies. These epitope peptides selectively presented the best antigen presentation scores for both human pMHC class I and II alleles to develop a strong binding affinity. All antigens identified of SARS-nCoV-2 different proteins by each attached specific ~ 1-7 L linker adaptor were used to construct a broad single peripheral peptide vaccine. It is expected to be highly antigenic with a minimum allergic effect. As a result of these exciting outcomes, expressing a vaccine using the intimated peptide was highly promising and positive to be highly proposed as epitope-based peptide vaccine of specific antibody against SARS-nCoV-2 by initiating T cells and β-cells. An in vitro study for the proposed peptide-based vaccine is mostly recommended. Further clinical trials are required to check the efficacy of this vaccine.

The development of ghost vaccines trials

INTRODUCTION

Bacterial ghosts are intact bacterial cell envelopes that are emptied of their content by gentle biological or chemical poring methods. Ghost techniques increase the safety of the killed vaccines, while maintaining their antigenicity due to mild preparation procedures. Moreover, ghost-platforms may express and/or carry several antigens or plasmid-DNA encoding for protein epitopes.

AREAS COVERED

In this review, the development in ghost-vaccine production over the last 30 years is classified and discussed. The different applications of ghost-vaccines, how they trigger the immune system, their advantages and limitations are displayed. The phage-mediated lysis, molecular manipulation of the lysis-genes, and the biotechnological production of ghosts are described. The trials are classified according to the pattern of lysis and to the type of bacteria. Further subdivision includes chronological ordered application of the ghost as alternative-killed vaccine, recombinant antigen platform, plasmid DNA carrier, adjuvants, and dendritic cell inducer. Particular trials for specific pathogens or from distinct research schools are gathered.

EXPERT OPINION

Ghosts are highly qualified to act as immune-presenting platforms that express and/or carry several recombinant and DNA vaccines, as well as, being efficient alternative-killed vaccines. The coming years will show more molecular advances to develop ghost-production and to express more antigens.

Designing a multi-epitope vaccine for cross-protection against Shigella spp: An immunoinformatics and structural vaccinology study

Shigellosis is a severe diarrheal disease with high mortality and morbidity rate. Until now, there is no approved vaccine against the disease. Therefore, the present study was planned to design a novel multi-epitope vaccine against Shigella spp., the causative agents of the disease based on the immunoinformatic tools. For this end, firstly seven conserved antigens of the bacteria, including IpaA, IpaB, IpaC, IpaD, OmpC, OmpF and VirG were selected. Then, linear B-cell epitope mapping of these proteins was carried out and top-ranked and shared epitopes were selected based on antigenicity, allergenicity, stability, toxicity and physicochemical properties for further analysis. In next step, B-cell derived T-cell epitopes were determined and appropriate epitopes were selected for incorporation into the final construct. Moreover, the selected epitopes and two mucosal adjuvants including ctxB and LT-IIc were joined using appropriate linkers. The three dimensional structure of the final construct was modeled and evaluated in term of structural quality and presence of conformational B-cell epitopes. Furthermore, binding affinity of the proposed vaccine to MHC I and II molecules were evaluated through molecular docking method using Hex 8.0. as well as the stability of the vaccine-MHC complexes was monitored by molecular dynamics method using the NAMD graphical user interface embedded in visual molecular dynamics. Finally, to evaluate the immunogenicity of the designed protein, the protein was administered to BALB/c mice and the serum IgG was determined by ELISA. The results indicated that the proposed vaccine has high structural quality and binding affinity to both MHC I and II molecules. Moreover, molecular dynamics studies confirmed that the vaccine-MHC docked complexes were stable during simulation time. Animal study showed that the proposed protein is able to evoke mice's humoral immune response. In sum, the results suggested that the proposed candidate vaccine could be considered as a promising anti-shigellosis vaccine.

Fundamentals and Methods for T- and B-Cell Epitope Prediction

Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.

The detection of antigenic determinants of Acinetobacter baumannii

BACKGROUND

Acinetobacter baumannii continues to pose a threat to burdened patients in ICUs all around the world. Lately, infection control techniques are not sufficient to curb A. baumannii's progression and chemotherapeutics are losing their potency against it. Thus, immunization became a key player in providing an ideal solution to the dilemma. None of the vaccines under investigation have reached the market and the search for a tailored vaccine remains a challenge. The notion of unravelling the bacterial antigens to design a novel epitope-based vaccine proved its merits.

METHODS

In this work, the propitious polysaccharide and protein antigenic determinants of A. baumannii were mapped by mimicking the infection. The immune response was evaluated by western blot, ELISA, and cellular proliferation assay techniques.

RESULTS

The screening showed that OMPs induced the most eminent sustained IgG response. In addition, OMP gave the highest cellular proliferation and a fold increase in ELISA that reached up to 10-fold by week 6. Whilst, the LPS gave a rapid IgM response, that reached 5-fold and the response was visible from week 1 in the western blot. The OMPs had a more pronounced effect in eliciting a cellular immune response.

CONCLUSION

The results elaborated the valuable role of using pure OMPs and detoxified LPS together; as a major cornerstone in designing an ideal vaccine against A. baumannii.

Applying proteomics to tick vaccine development: where are we?

INTRODUCTION

Ticks are second to mosquitoes as a vector of human diseases and are the first vector of animal diseases with a great impact on livestock farming. Tick vaccines represent a sustainable and effective alternative to chemical acaricides for the control of tick infestations and transmitted pathogens. The application of proteomics to tick vaccine development is a fairly recent area, which has resulted in the characterization of some tick-host-pathogen interactions and the identification of candidate protective antigens. Areas covered: In this article, we review the application and possibilities of various proteomic approaches for the discovery of tick and pathogen derived protective antigens, and the design of effective vaccines for the control of tick infestations and pathogen infection and transmission. Expert commentary: In the near future, the application of reverse proteomics, immunoproteomics, structural proteomics, and interactomics among other proteomics approaches will likely contribute to improve vaccine design to control multiple tick species with the ultimate goal of controlling tick-borne diseases.