Proteomic and Immunological Identification of Diagnostic Antigens from Spirometra erinaceieuropaei Plerocercoid

Human sparganosis is a food-borne parasitic disease caused by the plerocercoids of Spirometra species. Clinical diagnosis of sparganosis is crucial for effective treatment, thus it is important to identify sensitive and specific antigens of plerocercoids. The aim of the current study was to identify and characterize the immunogenic proteins of Spirometra erinaceieuropaei plerocercoids that were recognized by patient sera. Crude soluble extract of the plerocercoids were separated using 2-dimensional gel electrophoresis coupled with immunoblot and mass spectrometry analysis. Based on immunoblotting patterns and mass spectrometry results, 8 antigenic proteins were identified from the plerocercoid. Among the proteins, cysteine protease protein might be developed as an antigen for diagnosis of sparganosis.

Putative antigenic proteins identified by comparative and subtractive reverse vaccinology in necrotic enteritis-causing Clostridium perfringens isolated from broiler chickens

Background Avian necrotic enteritis (NE) caused by Clostridium perfringens is a disease with a major economic impact, generating losses estimated to 6 billion of dollars annually for the poultry industry worldwide. The incidence of the disease is particularly on the rise in broiler chicken flocks eliminating the preventive use of antibiotics. To date, no alternative allows for the efficient prevention of NE and a control of the disease using a vaccinal strategy would be mostly prized. For this purpose, comparative and subtractive reverse vaccinology identifying putative immunogenic bacterial surface proteins is one of the most promising approaches. Results A comparative genomic study was performed on 16 C. perfringens strains isolated from healthy broiler chickens and from broilers affected with necrotic enteritis. Results showed that the analyzed genomes were composed of 155,700 distinct proteins from which 13% were identified as extracellular, 65% as cytoplasmic and 22% as part of the bacterial membrane. The evaluation of the immunogenicity of these proteins was determined using the prediction software VaxiJen®. Conclusions For the most part, proteins with the highest scores were associated with an extracellular localisation. For all the proteins analyzed, the combination of both the immunogenicity score and the localisation prediction led to the selection of 12 candidate proteins that were mostly annotated as hypothetical proteins. We describe 6 potential candidates of higher interest due to their antigenic potential, their extracellular localisation, and their possible role in virulence of C. perfringens.

A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum

Avian mycoplasma is a bacterial disease causing chronic respiratory disease (CRD) in poultry industries with high economic losses. The eradication of this disease still remains as a challenge. A multi-epitope prophylactic vaccine aiming the antigenic proteins of Mycoplasma gallisepticum can be a capable candidate to eradicate this infection. The present study is focused to design a multi-epitope vaccine candidate consisting of cytotoxic T-cell (CTL), helper T-cell (HTL), and B-cell epitopes of antigenic proteins, using immunoinformatics strategies. The multi-epitopic vaccine was designed, and its tertiary model was predcited, which was further refined and validated by computational tools. After initial validation, molecular docking was performed between multi-epitope vaccine construct and chicken TLR-2 and 5 receptors, which predicted effective binding. The in silico results specify the structural stability, precise specificity, and immunogenic response of the designed multi-epitope vaccine, and it could be an appropriate vaccine candidate for the M. gallisepticum infection.

ArVirInd - a database of arboviral antigenic proteins from the Indian subcontinent

Studies on antigenic proteins for arboviruses are important for providing diagnostics and vaccine development. India and its neighbouring countries have huge burden of arboviral diseases. Data mining for country-specific sequences from existing databases is cumbersome and time-consuming. This necessitated the development of a database of antigenic proteins from arbo-viruses isolated from the countries of the Indian subcontinent. Arboviral antigenic protein sequences were obtained from the NCBI and other databases. In silico antigenic characterization was performed (Epitope predictions) and data incorporated in the database. The front end is designed and developed using HTML, CSS and PHP. For the backend of the database, we have used MySQL. A database, named ArVirInd, is created as a repository of information on antigenic proteins. This enlists sequences by country and year of outbreak or origin of the viral strain. For each entry antigenic information is provided along with functional sites, etc. Researchers can search this database by virus/protein name, country and year of collection (or in combination). It is available publicly via Internet at http://www.arvirind.co.in. ArVirInd will be useful in the study of immuno-informatics, diagnostics and vaccinology for arboviruses.

Identification of a Novel Antigen for Serological Diagnosis of Scrub Typhus

Scrub typhus is an acute infectious disease caused by the bacterium Orientia tsutsugamushi, which is widely distributed in northern, southern, and eastern Asia. Early diagnosis is essential because the average case fatality rate is usually >10% but can be as high as 45% if antimicrobial treatment is delayed. Although an O. tsutsugamushi 56-kDa type-specific antigen (TSA) is commonly used for serological diagnosis of scrub typhus, the 56-kDa TSA shows variations among O. tsutsugamushi strains, which may lead to poor diagnostic results. Therefore, the discovery of new antigenic proteins may improve diagnostic accuracy. In this study, we identified an O. tsutsugamushi 27 kDa antigen through an immunoinformatic approach and verified its diagnostic potential using patient samples. Compared with the O. tsutsugamushi 56-kDa antigen, the new 27-kDa antigen showed better diagnostic specificity with similar diagnostic sensitivity. Therefore, the O. tsutsugamushi 27-kDa antigen shows potential as a novel serological diagnostic antigen for scrub typhus, providing higher diagnostic accuracy for O. tsutsugamushi than the 56-kDa antigen.

Inferring the intrinsic mutational fitness landscape of influenza-like evolving antigens from temporally ordered sequence data

There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. For finding such a broadly protective immunization strategy it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenza-like evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method we are able to infer single- as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. This approach may in the future be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.