In Silico Prediction of Linear B-Cell Epitopes on Proteins

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

Research progress on the N protein of porcine reproductive and respiratory syndrome virus

Porcine reproductive and respiratory syndrome (PRRS) is a highly contagious disease caused by the porcine reproductive and respiratory syndrome virus (PRRSV). PRRSV exhibits genetic diversity and complexity in terms of immune responses, posing challenges for eradication. The nucleocapsid (N) protein of PRRSV, an alkaline phosphoprotein, is important for various biological functions. This review summarizes the structural characteristics, genetic evolution, impact on PRRSV replication and virulence, interactions between viral and host proteins, modulation of host immunity, detection techniques targeting the N protein, and progress in vaccine development. The discussion provides a theoretical foundation for understanding the pathogenic mechanisms underlying PRRSV virulence, developing diagnostic techniques, and designing effective vaccines.

Designing a multi-epitope vaccine against Peptostreptococcus anaerobius based on an immunoinformatics approach

Peptostreptococcus anaerobius is an anaerobic bacterium, which has been found selectively en-riched in the fecal and mucosal microbiota of colorectal cancer (CRC) patients. Emerging evidence suggest P. anaerobius may contribute to the development of CRC in human. In this study, we designed a multi-epitope chimeric vaccine against P. anaerobius PCWBR2, a recently identified adhesin that interacts directly with colon cell lines by binding α2/β1 integrin frequently overexpressed in human CRC tumors and cell lines. Immunoinformatics tools predicted six cytotoxic T lymphocyte epitopes, five helper T lymphocyte epitopes, and six linear B lymphocyte epitopes. The predicted epitopes were joined with AAY or GPGPG linkers and a previously reported TLR4 agonist was added to the vaccine construct's N terminal as an adjuvant using EAAAK linkers and the order of epitopes was optimized. Further in silico analysis revealed that the vaccine construct possesses satisfactory antigenicity, allergenicity, solubility, physicochemical properties, adjuvant-TLR4 molecular docking, and immune profile characteristics. Our study provided a promising design for vaccines against P. anaerobius.

Epitopes and Mimotopes Identification Using Phage Display for Vaccine Development against Infectious Pathogens

Traditional vaccines use inactivated or weakened forms of pathogens which could have side effects and inadequate immune responses. To overcome these challenges, phage display has emerged as a valuable tool for identifying specific epitopes that could be used in vaccines. This review emphasizes the direct connection between epitope identification and vaccine development, filling a crucial gap in the field. This technique allows vaccines to be engineered to effectively stimulate the immune system by presenting carefully selected epitopes. Phage display involves screening libraries of random peptides or gene/genome fragments using serum samples from infected, convalescent, or vaccinated individuals. This method has been used to identify epitopes from various pathogens including SARS-CoV-2, Mycobacterium tuberculosis, hepatitis viruses, H5N1, HIV-1, Human T-lymphotropic virus 1, Plasmodium falciparum, Trypanosoma cruzi, and Dirofilaria repens. Bacteriophages offer advantages such as being immunogenic carriers, low production costs, and customization options, making them a promising alternative to traditional vaccines. The purpose of this study has been to highlight an approach that encompasses the entire process from epitope identification to vaccine production using a single technique, without requiring additional manipulation. Unlike conventional methods, phage display demonstrates exceptional efficiency and speed, which could provide significant advantages in critical scenarios such as pandemics.

Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus

Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%-100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine's effectiveness and potential for further development.

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.

An immunoinformatics approach to epitope-based vaccine design against PspA in Streptococcus pneumoniae

BACKGROUND

Streptococcus pneumoniae (SPN) is the agent responsible for causing respiratory diseases, including pneumonia, which causes severe health hazards and child deaths globally. Antibiotics are used to treat SPN as a first-line treatment, but nowadays, SPN is showing resistance to several antibiotics. A vaccine can overcome this global problem by preventing this deadly pathogen. The conventional methods of wet-laboratory vaccine design and development are an intense, lengthy, and costly procedure. In contrast, epitope-based in silico vaccine designing can save time, money, and energy. In this study, pneumococcal surface protein A (PspA), one of the major virulence factors of SPN, is used to design a multi-epitope vaccine.

METHODS

For designing the vaccine, the sequence of PspA was retrieved, and then, phylogenetic analysis was performed. Several CTL epitopes, HTL epitopes, and LBL epitopes of PspA were all predicted by using several bioinformatics tools. After checking the antigenicity, allergenicity, and toxicity scores, the best epitopes were selected for the vaccine construction, and then, physicochemical and immunological properties were analyzed. Subsequently, vaccine 3D structure prediction, refinement, and validation were performed. Molecular docking, molecular dynamic simulation, and immune simulation were performed to ensure the binding between HLA and TLR4. Finally, codon adaptation and in silico cloning were performed to transfer into a suitable vector.

RESULTS

The constructed multi-epitope vaccine showed a strong binding affinity with the receptor molecule TLR4. Analysis of molecular dynamic simulation, C-immune simulation, codon adaptation, and in silico cloning validated that our designed vaccine is a suitable candidate against SPN.

CONCLUSION

The in silico analysis has proven the vaccine as an alternative medication to combat against S. pneumoniae. The designated vaccine can be further tested in the wet lab, and a novel vaccine can be developed.

Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinformatics Approach

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools.

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a low throughput. Although a few computational methods have been proposed to address this challenge, there is still a long way to go for practical applications. We proposed a deep learning method called DeepLBCEPred for predicting linear BCEs, which consists of bi-directional long short-term memory (Bi-LSTM), feed-forward attention, and multi-scale convolutional neural networks (CNNs). We extensively tested the performance of DeepLBCEPred through cross-validation and independent tests on training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained state-of-the-art performance. We also investigated the contribution of different deep learning elements to recognize linear BCEs. In addition, we have developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers at: http://www.biolscience.cn/DeepLBCEPred/.

B-Cell Epitope Predictions Using Computational Methods

Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .

Identificationof a novel linear epitope on the porcine reproductive and respiratory syndrome virus nucleocapsid protein, as recognized by a specific monoclonal antibody

Introduction

Porcine reproductive and respiratory syndrome virus (PRRSV) remains one of the most threatening pathogens of swine. The nucleocapsid (N) protein is the major structural protein of the virus and has been used as a PRRSV diagnostic antigen due to its high level of inherent immunogenicity.

Methods

The recombinant PRRSV N protein was generated by the prokaryotic expressing system and used to immunized mice. Monoclonal antibodies against PRRSV were produced and validated by western blot analysis and indirect immunofluorescence analysis. In this study, the linear epitope of a specific monoclonal antibody mAb (N06) was subsequently identified by enzyme-linked immunosorbent assays (ELISA) using the synthesized overlapping peptides as antigens.

Results

According to the results of western blot analysis and indirect immunofluorescence analysis, mAb (N06) was capable of recognizing the native form as well as the denatured form of PRRSV N protein. The results of ELISA showed that mAb N06 recognized the epitope NRKKNPEKPHFPLATE, which was consistent with BCPREDS predictions of antigenicity.

Conclusion

All the data suggested that the mAb (N06) can be used as diagnostic reagents for PRRSV detection, while the recognized linear epitope can be useful in epitope-based vaccines development, which is helpful for the control of local PRRSV infections in swine.

Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets

Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.

A Strategy for Efficient Preparation of Genus-Specific Diagnostic Antibodies for Snakebites

As said by former United Nations Secretary-General Kofi Annan, "Snakebite is the most important tropical disease you've never heard of." Listed as a priority neglected tropical disease by the World Health Organization, snakebite envenoming (SBE) kills in excess of 125,000 people per year. However, due to the complexity and overlap of snake venom compositions, few reliable venom diagnostic methods for genus-/species-specific identification, which is crucial for successful SBE therapy, are available. Here, we develop a strategy to select and prepare genus-specific snake venom antibodies, which allows rapid and efficient clinical diagnosis of snakebite. Multi-omics approaches are used to choose candidate antigens from snake venoms and identify genus-specific antigenic epitope peptide fragments (GSAEPs) with ideal immunogenicity, specificity, and spatial accessibility. Double-antibody sandwich ELISA kit was established by matching a polyclonal antibody against a natural antigen and a monoclonal antibody that was prepared by natural protein as antigen and can specifically target the GSAEPs. The kit shows the ability to accurately identify venoms from similar genera of Trimeresurus and Protobothrops with a detection limit of 6.25 ng/ml on the snake venoms and a little cross-reaction, thus proving high feasibility and applicability.

Clinical utility of peptide microarrays in the serodiagnosis of neglected tropical diseases in sub-Saharan Africa: protocol for a diagnostic test accuracy systematic review

INTRODUCTION

Neglected tropical diseases tend to cluster in the same poor populations and, to make progress with their control, they will have to be dealt with in an integrated manner. Peptide microarrays may be a solution to these problems, where diagnosis for co-infection can be detected simultaneously using the one tool. A meta-analysis using hierarchical models will be performed to assess the diagnostic accuracy of peptide microarrays for detecting schistosomiasis (Schistosoma mansoni and S. haematobium), soil-transmitted helminths (Trichuris trichiura, Ascaris lumbricoides and Necator americanus), trachoma (Chlamydia trachomatis), lymphatic filariasis (Wuchereria bancrofti) and onchocerciasis (Onchocerca volvulus) in people residing in sub-Saharan Africa.

METHODS AND ANALYSIS

A comprehensive search of the following databases will be performed: Cochrane Infectious Diseases Group Specialised Register, PubMed, EMBASE and The Web of Science. Studies comparing peptide microarrays with a reference standard from a random or consecutive series of patients will be included in the study. Two review authors will independently screen titles and abstracts for relevance, assess full-text articles for inclusion and carry out data extraction using a tailored data extraction form. The quality Assessment of Diagnostic Accuracy Studies-2 tool will be used to assess the quality of the selected studies. The bivariate model and the hierarchical summary receiver operating characteristic curve model will be performed to evaluate the diagnostic accuracy of the peptide microarrays. Meta-regression analyses will be performed to investigate heterogeneity across studies.

ETHICS AND DISSEMINATION

There is no requirement for ethical approval because the work will be carried out using previously published data, without human beings involvement. Findings will be disseminated through peer-reviewed publication and in conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020175145.

A refined genome phage display methodology delineates the human antibody response in patients with Chagas disease

Large-scale mapping of antigens and epitopes is pivotal for developing immunotherapies but challenging, especially for eukaryotic pathogens, owing to their large genomes. Here, we developed an integrated platform for genome phage display (gPhage) to show that unbiased libraries of the eukaryotic parasite Trypanosoma cruzi enable the identification of thousands of antigens recognized by serum samples from patients with Chagas disease. Because most of these antigens are hypothetical proteins, gPhage provides evidence of their expression during infection. We built and validated a comprehensive map of Chagas disease antibody response to show how linear and putative conformation epitopes, many rich in repetitive elements, allow the parasite to evade a buildup of neutralizing antibodies directed against protein domains that mediate infection pathogenesis. Thus, the gPhage platform is a reproducible and effective tool for rapid simultaneous identification of epitopes and antigens, not only in Chagas disease but perhaps also in globally emerging/reemerging acute pathogens.

•

Genomic shotgun phage display (gPhage) of eukaryotes is feasible and promising.

•

gPhage allows rapid antigen ID and epitope mapping, including 3D structures.

•

Conformation epitopes can be identified and validated by using the gPhage platform.

•

Most Chagas disease antigens are hypothetical proteins rich in repetitive elements.

Detection of Antibodies against Hepatitis A Virus (HAV) by a Surface Plasmon Resonance (SPR) Biosensor: A New Diagnosis Tool Based on the Major HAV Capsid Protein VP1 (SPR-HAVP1)

Hepatitis A (HA) is an acute human infectious disease caused by a positive single-stranded RNA virus (HAV). It is mainly acquired through the fecal–oral route and is primarily spread by contact between people and exposure to contaminated water and food. Recently, large outbreaks of HA have been reported by low and moderate endemicity countries, emphasizing its importance in public health and the need for rapid and large-scale diagnostic tests to support public health decisions on HA. This work proposes a new tool for HAV diagnosis based on the association of surface plasmonic resonance with major capsid protein VP1 (SPR-HAVP1 assay), detecting IgM antibodies for HAV in human serum samples. Structural analyses of VP1 B-lymphocyte epitopes showed continuous and discontinuous epitopes. The discontinuous epitopes were identified in the N-terminal region of the VP1 protein. Both epitope types in the VP1 protein were shown by the reactivity of VP1 in native and denaturing conditions to IgM anti-HAV, which was favorable to tests of VP1 in the SPR assays. SPR-HAVP1 assays showed good performance in the detection of IgM polyclonal antibody anti-HAV. These assays were performed using a COOH5 sensor chip functionalized with VP1 protein. The sensorgram record showed a significant difference between positive and negative serum samples, which was confirmed by analysis of variation of initial and final dissociation values through time (ΔRUd/t). The data gathered here are unequivocal evidence that the SPR-HAVP1 strategy can be applied to detect IgM antibodies in human serum positive to the HAV. This is a new tool to be explored to diagnose human HAV infections.

Design of a Peptide-Carrier Vaccine Based on the Highly Immunogenic Fasciola hepatica Leucine Aminopeptidase

Many studies have shown that the degree of organization and repetitiveness of an antigen correlates with its efficiency to induce a B-cell response and production of neutralizing antibodies. Here we describe the design of a chimeric protein based on the hexamer form of the highly immunogenic Fasciola hepatica leucine aminopeptidase as a carrier system of small peptides with potential use as a multiepitope vaccine.

Designing a multi-epitopic vaccine against the enterotoxigenic Bacteroides fragilis based on immunoinformatics approach

Enterotoxigenic Bacteroides fragilis is an enteric pathogen which is described as a causative agent of various intestinal infections and inflammatory diseases. Moreover, various research studies have reported it to be a leading factor in the development of colorectal cancer. As a part of the normal human microbiome, its treatment has become quite a challenge due to the alarming resistance against the available antibiotics. Although, this particular strain of B. fragilis shows susceptibility to few antibiotics, it is pertinent to devise an effective vaccine strategy for its elimination. There is no vaccine available against this pathogen up to date; therefore, we systematically ventured the outer membrane toxin producing proteins found exclusively in the toxigenic B. fragilis through the in-silico approaches to predict a multi-epitopic chimeric vaccine construct. The designed protein constitutes of epitopes which are predicted for linear B cells, Helper and T cells of outer membrane proteins expected to be putative vaccine candidates. The finalized proteins are only expressed in the enterotoxigenic B. fragilis, thus proving them to be exclusive. The 3D structure of the protein was first predicted followed by its refinement and validation via utilizing the bioinformatic approaches. Docking of the designed protein with the TLR2 receptor forecasted apt binding. Upon immune simulation, notable levels were observed in the expression of the immune cells.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.

Innovative Immunization Strategies for Antivenom Development

Snakes, scorpions, and spiders are venomous animals that pose a threat to human health, and severe envenomings from the bites or stings of these animals must be treated with antivenom. Current antivenoms are based on plasma-derived immunoglobulins or immunoglobulin fragments from hyper-immunized animals. Although these medicines have been life-saving for more than 120 years, opportunities to improve envenoming therapy exist. In the later decades, new biotechnological tools have been applied with the aim of improving the efficacy, safety, and affordability of antivenoms. Within the avenues explored, novel immunization strategies using synthetic peptide epitopes, recombinant toxins (or toxoids), or DNA strings as immunogens have demonstrated potential for generating antivenoms with high therapeutic antibody titers and broad neutralizing capacity. Furthermore, these approaches circumvent the need for venom in the production process of antivenoms, thereby limiting some of the complications associated with animal captivity and venom collection. Finally, an important benefit of innovative immunization approaches is that they are often compatible with existing antivenom manufacturing setups. In this review, we compile all reported studies examining venom-independent innovative immunization strategies for antivenom development. In addition, a brief description of toxin families of medical relevance found in snake, scorpion, and spider venoms is presented, as well as how biochemical, bioinformatic, and omics tools could aid the development of next-generation antivenoms.

Plasmodium vivax Pv12 B-cell epitopes and HLA-DRβ1*-dependent T-cell epitopes in vitro antigenicity

Malaria is an infectious disease caused by parasites from the genus Plasmodium (P. falciparum and P. vivax are responsible for 90% of all clinical cases); it is widely distributed throughout the world’s tropical and subtropical regions. The P. vivax Pv12 protein is involved in invasion, is expressed on merozoite surface and has been recognised by antibodies from individuals exposed to the disease. In this study, B- and T-cell epitopes from Pv12 were predicted and characterised to advance in the design of a peptide-based vaccine against malaria.

For evaluating the humoral response of individuals exposed to natural P. vivax infection from two endemic areas in Colombia, BepiPred-1.0 software was used for selecting B-cell epitopes. B-cell epitope 39038 displayed the greatest recognition by naturally-acquired antibodies and induced an IgG2/IgG4 response. NetMHCIIpan-3.1 prediction software was used for selecting peptides having high affinity binding for HLA-DRβ1* allele lineages and this was confirmed by in-vitro binding assays. T-epitopes 39113 and 39117 triggered a memory T-cell response (Stimulation Index≥2) and significant cytokine production.

Combining in-silico, in-vitro and functional assays, two Pv12 protein regions (containing peptides 39038, 39040, 39113 and 39117) have thus been characterised as promising vaccine candidates against P. vivax malaria.

The in Vitro Antigenicity of Plasmodium vivax Rhoptry Neck Protein 2 (PvRON2) B- and T-Epitopes Selected by HLA-DRB1 Binding Profile

Malaria caused by Plasmodium vivax is a neglected disease which is responsible for the highest morbidity in both Americas and Asia. Despite continuous public health efforts to prevent malarial infection, an effective antimalarial vaccine is still urgently needed. P. vivax vaccine development involves analyzing naturally-infected patients' immune response to the specific proteins involved in red blood cell invasion. The P. vivax rhoptry neck protein 2 (PvRON2) is a highly conserved protein which is expressed in late schizont rhoptries; it interacts directly with AMA-1 and might be involved in moving-junction formation. Bioinformatics approaches were used here to select B- and T-cell epitopes. Eleven high-affinity binding peptides were selected using the NetMHCIIpan-3.0 in silico prediction tool; their in vitro binding to HLA-DRB1^*0401, HLA-DRB1^*0701, HLA-DRB1^*1101 or HLA-DRB1^*1302 was experimentally assessed. Four peptides (39152 (HLA-DRB1^*04 and 11), 39047 (HLA-DRB1^*07), 39154 (HLADRB1^*13) and universal peptide 39153) evoked a naturally-acquired T-cell immune response in P. vivax-exposed individuals from two endemic areas in Colombia. All four peptides had an SI greater than 2 in proliferation assays; however, only peptides 39154 and 39153 had significant differences compared to the control group. Peptide 39047 was able to significantly stimulate TNF and IL-10 production while 39154 stimulated TNF production. Allele-specific peptides (but not the universal one) were able to stimulate IL-6 production; however, none induced IFN-γ production. The Bepipred 1.0 tool was used for selecting four B-cell epitopes in silico regarding humoral response. Peptide 39041 was the only one recognized by P. vivax-exposed individuals' sera and had significant differences concerning IgG subclasses; an IgG2 > IgG4 profile was observed for this peptide, agreeing with a protection-inducing role against P. falciparum and P. vivax as previously described for antigens such as RESA and MSP2. The bioinformatics results and in vitro evaluation reported here highlighted two T-cell epitopes (39047 and 39154) being recognized by memory cells and a B-cell epitope (39041) identified by P. vivax-exposed individuals' sera which could be used as potential candidates when designing a subunit-based vaccine.

Identification of Lawsonia intracellularis putative hemolysin protein A and characterization of its immunoreactivity

Despite the recent global increase in fatal endemic outbreaks of proliferative enteropathy (PE) caused by the obligate intracellular bacterium Lawsonia intracelluralis (LI) in the swine industry, development of effective prevention strategies or immunodiagnostic tests has been delayed due to the difficulty of cultivating this pathogen in vitro. Although several genetic analyses have been performed at the level of gene transcription after the complete genome sequence of LI was made available, the mechanism of LI infection and virulence genes remain unidentified. In the present study, we assessed the antigenic features of the LI0004 protein, which we putatively defined as Lawsonia hemolysin A (LhlyA), by employing bioinformatics tools and in vivo and in vitro protein-based molecular assays. The amino acid sequence of LhlyA showed approximately 60% homology to the hemolysin-like proteins of Bilophila wadsworthia and Desulfovibrio piger. Presence of computationally predicted linear antigenic B-cell epitopes on the LhlyA protein was demonstrated by immunoblotting; a band with a molecular mass corresponding to the predicted size of the protein was strongly recognized by sera collected from artificially infected mice. Further, in an in vivo cytotoxicity assay, no splenomegaly was observed in mice inoculated with purified LhlyA. Collectively, the data presented here suggest that the LhlyA protein is a highly immuno-reactive antigen of L. intracellullaris and can potentially be used to develop effective protection strategies against PE.

Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome

Hantaviruses are emerging viral pathogens that causes hantavirus cardiopulmonary syndrome (HCPS) in the Americas, a severe, sometimes fatal, respiratory disease in humans with a case fatality rate of ≥50%. IgM and IgG-based serological detection methods are the most common approaches used for laboratory diagnosis of hantaviruses. Such emerging viral pathogens emphasizes the need for improved rapid diagnostic devices and vaccines incorporating pan-specific epitopes of genotypes. We predicted linear B-cell epitopes for hantaviruses that are specific to genotypes causing HCPS in humans using in silico prediction servers. We modeled the Andes and Sin Nombre hantavirus nucleocapsid protein to locate the identified epitopes. Based on the mean percent prediction probability score, epitope IMASKSVGS/TAEEKLKKKSAF was identified as the best candidate B-cell epitope specific for hantaviruses causing HCPS. Promiscuous epitopes were identified in the C-terminal of the protein. Our study for the first time has reported pan-specific B-cell epitopes for developing immunoassays in the detection of antibodies to hantaviruses causing HCPS. Identification of epitopes with pan-specific recognition of all genotypes causing HCPS could be valuable for the development of immunodiagnositic tools toward pan-detection of hantavirus antibodies in ELISA. J. Cell. Biochem. 118: 2320-2324, 2017. © 2017 Wiley Periodicals, Inc.

The Presence, Persistence and Functional Properties of Plasmodium vivax Duffy Binding Protein II Antibodies Are Influenced by HLA Class II Allelic Variants

Background

The human malaria parasite Plasmodium vivax infects red blood cells through a key pathway that requires interaction between Duffy binding protein II (DBPII) and its receptor on reticulocytes, the Duffy antigen/receptor for chemokines (DARC). A high proportion of P. vivax-exposed individuals fail to develop antibodies that inhibit DBPII-DARC interaction, and genetic factors that modulate this humoral immune response are poorly characterized. Here, we investigate if DBPII responsiveness could be HLA class II-linked.

Methodology/Principal Findings

A community-based open cohort study was carried out in an agricultural settlement of the Brazilian Amazon, in which 336 unrelated volunteers were genotyped for HLA class II (DRB1, DQA1 and DQB1 loci), and their DBPII immune responses were monitored over time (baseline, 6 and 12 months) by conventional serology (DBPII IgG ELISA-detected) and functional assays (inhibition of DBPII–erythrocyte binding). The results demonstrated an increased susceptibility of the DRB1*13:01 carriers to develop and sustain an anti-DBPII IgG response, while individuals with the haplotype DRB1*14:02-DQA1*05:03-DQB1*03:01 were persistent non-responders. HLA class II gene polymorphisms also influenced the functional properties of DBPII antibodies (BIAbs, binding inhibitory antibodies), with three alleles (DRB1*07:01, DQA1*02:01 and DQB1*02:02) comprising a single haplotype linked with the presence and persistence of the BIAbs response. Modelling the structural effects of the HLA-DRB1 variants revealed a number of differences in the peptide-binding groove, which is likely to lead to altered antigen binding and presentation profiles, and hence may explain the differences in subject responses.

Conclusions/Significance

The current study confirms the heritability of the DBPII antibody response, with genetic variation in HLA class II genes influencing both the development and persistence of IgG antibody responses. Cellular studies to increase knowledge of the binding affinities of DBPII peptides for class II molecules linked with good or poor antibody responses might lead to the development of strategies for controlling the type of helper T cells activated in response to DBPII.

Vaccines are a crucial component of the current efforts to eliminate malaria, and much of the vaccine-related research on P. vivax has been focused on the Duffy binding protein II (DBPII), a ligand for human blood stage infection. A high proportion of individuals who are naturally exposed to P. vivax fail to develop neutralizing antibodies, but the host genetic factors modulating this immune response are poorly characterized. We investigated whether DBPII responsiveness was dependent on the variability of human leucocyte antigen (HLA) class II cell surface proteins involved in the regulation of immune responses. To obtain a reliable estimate of DBPII antibodies, we carried out a longitudinal study, collecting serum from the same individuals over a period of 12-months. The results confirmed the heritability of the DBPII immune response, with genetic variation in HLA class II genes influencing both the development and persistence of the antibody response. HLA class II genotype also influenced the ability of DBPII antibodies to block the ligand-receptor interaction in vitro. Computational approaches identified structural specificity between HLA variants, which we propose as an explanation for differences between a good or poor antibody responder. These results may have implications for vaccine development, and might lead to strategies for controlling the type of immune response activated in response to DBPII.