Neutralising antibody response in domestic cats immunised with a commercial feline immunodeficiency virus (FIV) vaccine

Across human and veterinary medicine, vaccines against only two retroviral infections have been brought to market successfully, the vaccines against feline leukaemia virus (FeLV) and feline immunodeficiency virus (FIV). FeLV vaccines have been a global success story, reducing virus prevalence in countries where uptake is high. In contrast, the more recent FIV vaccine was introduced in 2002 and the degree of protection afforded in the field remains to be established. However, given the similarities between FIV and HIV, field studies of FIV vaccine efficacy are likely to advise and inform the development of future approaches to HIV vaccination.<p></p> Here we assessed the neutralising antibody response induced by FIV vaccination against a panel of FIV isolates, by testing blood samples collected from client-owned vaccinated Australian cats. We examined the molecular and phenotypic properties of 24 envs isolated from one vaccinated cat that we speculated might have become infected following natural exposure to FIV. Cats vaccinated against FIV did not display broadly neutralising antibodies, suggesting that protection may not extend to some virulent recombinant strains of FIV circulating in Australia.<p></p&gt

Variation in dengue virus plaque reduction neutralization testing: systematic review and pooled analysis.

BackgroundThe plaque reduction neutralization test (PRNT) remains the gold standard for the detection of serologic immune responses to dengue virus (DENV). While the basic concept of the PRNT remains constant, this test has evolved in multiple laboratories, introducing variation in materials and methods. Despite the importance of laboratory-to-laboratory comparability in DENV vaccine development, the effects of differing PRNT techniques on assay results, particularly the use of different dengue strains within a serotype, have not been fully characterized.MethodsWe conducted a systematic review and pooled analysis of published literature reporting individual-level PRNT titers to identify factors associated with heterogeneity in PRNT results and compared variation between strains within DENV serotypes and between articles using hierarchical models.ResultsThe literature search and selection criteria identified 8 vaccine trials and 25 natural exposure studies reporting 4,411 titers from 605 individuals using 4 different neutralization percentages, 3 cell lines, 12 virus concentrations and 51 strains. Of 1,057 titers from primary DENV exposure, titers to the exposure serotype were consistently higher than titers to non-exposure serotypes. In contrast, titers from secondary DENV exposures (n = 628) demonstrated high titers to exposure and non-exposure serotypes. Additionally, PRNT titers from different strains within a serotype varied substantially. A pooled analysis of 1,689 titers demonstrated strain choice accounted for 8.04% (90% credible interval [CrI]: 3.05%, 15.7%) of between-titer variation after adjusting for secondary exposure, time since DENV exposure, vaccination and neutralization percentage. Differences between articles (a proxy for inter-laboratory differences) accounted for 50.7% (90% CrI: 30.8%, 71.6%) of between-titer variance.ConclusionsAs promising vaccine candidates arise, the lack of standardized assays among diagnostic and research laboratories make unbiased inferences about vaccine-induced protection difficult. Clearly defined, widely accessible reference reagents, proficiency testing or algorithms to adjust for protocol differences would be a useful first step in improving dengue PRNT comparability and quality assurance

An overview of bioinformatics tools for epitope prediction: Implications on vaccine development

AbstractExploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned

A novel design of multi-epitope based vaccine against Escherichia coli

Background: Multi-valent based vaccines have advantage over conventional vaccines because of its multi-faceted action targeted at antigen; thereby raising hope of a more sustained actions against allergens. Escherichia coli (E. coli) is a bacterium that is commonly found in the gut of humans and warm-blooded animals. An increasing number of outbreaks are associated with the consumption of fruits and vegetables (including sprouts, spinach, lettuce, coleslaw, and salad) thereby contamination may be due to contact with faeces from domestic or wild animals at some stages during cultivation or handling. Due to the reported increase in resistance to antibiotics used for Escherichia coli control; an effective vaccine is a would-be alternative of proven interest. Hence, a need for a rational, strategic, and efficient vaccine candidate against E.coli is of paramount necessity by the use of the most current bioinformatics tools to achieve this task. Method: In this study, immunoinformatics tools mined from diverse molecular databases were used  for a novel putative epitope based oral vaccine against E.coli. The prospective vaccine proteins were carefully screened and validated to achieve a high thorough-put three-dimensional protein structure. The eventual propsective vaccine candidate proteins was evaluated for its non-allergenicity, antigenicity, solubility, appropriate molecular weight testing and isoelectric point evaluation. Conclusion: The resultant vaccine candidate could serve as a promising anti-E.coli vaccine candidate. Immunoinformatics is a new field over pharmaco-therapeutics; this newest technology should continue to be a rescue from age-long traditional approach in vaccine developments

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

In silico Vaccine Design against Dengue Virus Type 2 Envelope Glycoprotein

Dengue fever is caused by the mosquito-borne virus termed (DENV). However, DENV-2 has been identified as the most prevalent amongst the Indonesian pediatric urban population, in contrast with the other four serotypes. Therefore, it is important to reduce severe infection risk by adopting preventive measures, including through vaccine development. The aim of this study, therefore is to use various in silico tools in the design of epitope-based peptide vaccines (T-cell and B-cell types), based on the DENV-2 envelope glycoprotein sequences available. Therefore, in silico methods were adopted in the analysis of the retrieved protein sequences. This technique was required to determine the most immunogenic protein, and is achieved through conservancy analysis, epitope identification, molecular simulation, and allergenicity assessment. Furthermore, B4XPM1, and KAWLVHRQW were identified from positions 204-212, while the 77 to 85 peptide region was considered the most potent T-cell and B-cell epitopes. The interaction between KAWLVHRQW and HLA-C*12:03 occurs with maximum population coverage, alongside high conservancy (96.98%) and binding affinity. These results indicated a potential for the designed epitopes to demonstrate high immunity against DENV-2

Sequence analysis methods for the design of cancer vaccines that target tumor-specific mutant antigens (neoantigens)

The human adaptive immune system is programmed to distinguish between self and non-self proteins and if trained to recognize markers unique to a cancer, it may be possible to stimulate the selective destruction of cancer cells. Therapeutic cancer vaccines aim to boost the immune system by selectively increasing the population of T cells specifically targeted to the tumor-unique antigens, thereby initiating cancer cell death.. In the past, this approach has primarily focused on targeted selection of ‘shared’ tumor antigens, found across many patients. The advent of massively parallel sequencing and specialized analytical approaches has enabled more efficient characterization of tumor-specific mutant antigens, or neoantigens. Specifically, methods to predict which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell recognition improve predictions of immune checkpoint therapy response and identify one or more neoantigens as targets for personalized vaccines. Selecting the best/most immunogenic neoantigens from a large number of mutations is an important challenge, in particular in cancers with a high mutational load, such as melanomas and smoker-associated lung cancers. To address such a challenging task, Chapter 1 of this thesis describes a genome-guided in silico approach to identifying tumor neoantigens that integrates tumor mutation and expression data (DNA- and RNA-Seq). The cancer vaccine design process, from read alignment to variant calling and neoantigen prediction, typically assumes that the genotype of the Human Reference Genome sequence surrounding each somatic variant is representative of the patient’s genome sequence, and does not account for the effect of nearby variants (somatic or germline) in the neoantigenic peptide sequence. Because the accuracy of neoantigen identification has important implications for many clinical trials and studies of basic cancer immunology, Chapter 2 describes and supports the need for patient-specific inclusion of proximal variants to address this previously oversimplified assumption in the identification of neoantigens. The method of neoantigen identification described in Chapter 1 was subsequently extended (Chapter 3) and improved by the addition of a modular workflow that aids in each component of the neoantigen prediction process from neoantigen identification, prioritization, data visualization, and DNA vaccine design. These chapters describe massively parallel sequence analysis methods that will help in the identification and subsequent refinement of patient-specific antigens for use in personalized immunotherapy

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development

Vaxign is the first web-based vaccine design system that predicts vaccine targets based on genome sequences using the strategy of reverse vaccinology. Predicted features in the Vaxign pipeline include protein subcellular location, transmembrane helices, adhesin probability, conservation to human and/or mouse proteins, sequence exclusion from genome(s) of nonpathogenic strain(s), and epitope binding to MHC class I and class II. The precomputed Vaxign database contains prediction of vaccine targets for >70 genomes. Vaxign also performs dynamic vaccine target prediction based on input sequences. To demonstrate the utility of this program, the vaccine candidates against uropathogenic Escherichia coli (UPEC) were predicted using Vaxign and compared with various experimental studies. Our results indicate that Vaxign is an accurate and efficient vaccine design program