Information technologies for vaccine research

A chimeric vaccine combined with adjuvant system induces immunogenicity and protection against visceral leishmaniasis in BALB/c mice

In Brazil, canine visceral leishmaniasis is an important public health problem due to its alarming growth. The high prevalence of infected dogs reinforces the need for a vaccine for use in prophylactic vaccination campaigns. In the present study, we evaluate the immunogenicity and protection of the best dose of Chimera A selected through the screening of cytokines production important in disease. BALB/c mice were vaccinated subcutaneously with three doses and challenged intravenously with 1 × 10⁷L. infantum promastigotes. Spleen samples were collected to assess the intracellular cytokine profile production, T cell proliferation and parasite load. At first, three different doses of Chimera A (5 μg, 10 μg and 20 μg) were evaluated through the production of IFN-γ and IL-10 cytokines. Since the dose of 20 μg showed the best results, it was chosen to continue the study. Secondarily, Chimera A at dose of 20 μg was formulated with Saponin plus Monophosphoryl lipid A. Vaccination with Chimera A alone and formulated with SM adjuvant system was able to increase the percentage of the proliferation of specific T lymphocytes and stimulated a Th1 response with increased levels of IFN-γ, TNF-α and IL-2, and decreased of IL-4 and IL-10. The vaccine efficacy through real-time PCR demonstrated a reduction in the splenic parasite load in animals that received Chimera A formulated with the SM adjuvant system (92%). Additionally, we observed increased levels of nitric oxide in stimulated-culture supernatants. The Chimera A formulated with the SM adjuvant system was potentially immunogenic, being able to induce immunoprotective mechanisms and reduce parasite load. Therefore, the use of T-cell multi-epitope vaccine is promising against visceral leishmaniasis.

Vaccine Target Discovery

With the rise in novel infectious agents and disease pandemics, a new era of vaccine discovery is necessary. To address this, the new field of immunomics is described, which is synergistically powered by integrating bioinformatics methodologies with technological advances in biology and high-throughput instrumentation. By incorporating biological data from immunology and molecular biology with current genomics and proteomics, immunomics is geared to deliver an insight into immune function, optimal stimulation of immune responses and precise mapping and rational selection of immune targets that cover antigenic diversity. These efforts are expected to contribute towards the development of new generation of vaccines, tailored to both the genetic make-up of the human population and of the pathogen. Vaccine technologies are also being explored for prevention or control of non-communicable diseases.

Literature classification for semi-automated updating of biological knowledgebases

Background

As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature.

Results

We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature.

Conclusion

We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases.

Human leukocyte antigen (HLA) class I restricted epitope discovery in yellow fewer and dengue viruses: importance of HLA binding strength

Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV epitopes were selected using the EpiSelect algorithm to allow for optimal coverage of viral strains. The selected predicted epitopes were synthesized and approximately 75% were found to bind the predicted restricting HLA molecule with an affinity, K_D, stronger than 500 nM. The immunogenicity of 25 HLA-A*02:01, 28 HLA-A*24:02 and 28 HLA-B*07:02 binding peptides was tested in three HLA-transgenic mice models and led to the identification of 17 HLA-A*02:01, 4 HLA-A*2402 and 4 HLA-B*07:02 immunogenic peptides. The immunogenic peptides bound HLA significantly stronger than the non-immunogenic peptides. All except one of the immunogenic peptides had K_D below 100 nM and the peptides with K_D below 5 nM were more likely to be immunogenic. In addition, all the immunogenic peptides that were identified as having a high functional avidity had K_D below 20 nM. A*02:01 transgenic mice were also inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding in shaping the immune response.

In silico modeling and in vivo efficacy of cancer-preventive vaccinations

Cancer vaccine feasibility would benefit from reducing the number and duration of vaccinations without diminishing efficacy. However, the duration of in vivo studies and the huge number of possible variations in vaccination protocols have discouraged their optimization. In this study, we employed an established mouse model of preventive vaccination using HER-2/neu transgenic mice (BALB-neuT) to validate in silico-designed protocols that reduce the number of vaccinations and optimize efficacy. With biological training, the in silico model captured the overall in vivo behavior and highlighted certain critical issues. First, although vaccinations could be reduced in number without sacrificing efficacy, the intensity of early vaccinations was a key determinant of long-term tumor prevention needed for predictive utility in the model. Second, after vaccinations ended, older mice exhibited more rapid tumor onset and sharper decline in antibody levels than young mice, emphasizing immune aging as a key variable in models of vaccine protocols for elderly individuals. Long-term studies confirmed predictions of in silico modeling in which an immune plateau phase, once reached, could be maintained with a reduced number of vaccinations. Furthermore, that rapid priming in young mice is required for long-term antitumor protection, and that the accuracy of mathematical modeling of early immune responses is critical. Finally, that the design and modeling of cancer vaccines and vaccination protocols must take into account the progressive aging of the immune system, by striving to boost immune responses in elderly hosts. Our results show that an integrated in vivo-in silico approach could improve both mathematical and biological models of cancer immunoprevention.

A conserved E7-derived cytotoxic T lymphocyte epitope expressed on human papillomavirus 16-transformed HLA-A2+ epithelial cancers

Human Papillomavirus 16 (HPV-16) has been identified as the causative agent of 50% of cervical cancers and many other HPV-associated tumors. The transforming potential/tumor maintenance capacity of this high risk HPV is mediated by two viral oncoproteins, E6 and E7, making them attractive targets for therapeutic vaccines. Of 21 E6 and E7 peptides computed to bind HLA-A*0201, 10 were confirmed through TAP-deficient T2 cell HLA stabilization assay. Those scoring positive were investigated to ascertain which were naturally processed and presented by surface HLA molecules for CTL recognition. Because IFNγ ELISpot frequencies from healthy HPV-exposed blood donors against HLA-A*0201-binding peptides were unable to identify specificities for tumor targeting, their physical presence among peptides eluted from HPV-16-transformed epithelial tumor HLA-A*0201 immunoprecipitates was analyzed by MS(3) Poisson detection mass spectrometry. Only one epitope (E7(11-19)) highly conserved among HPV-16 strains was detected. This 9-mer serves to direct cytolysis by T cell lines, whereas a related 10-mer (E7(11-20)), previously used as a vaccine candidate, was neither detected by MS(3) on HPV-transformed tumor cells nor effectively recognized by 9-mer specific CTL. These data underscore the importance of precisely defining CTL epitopes on tumor cells and offer a paradigm for T cell-based vaccine design.

Controlling influenza by cytotoxic T-cells: calling for help from destroyers

Influenza is a vaccine preventable disease that causes severe illness and excess mortality in humans. Licensed influenza vaccines induce humoral immunity and protect against strains that antigenically match the major antigenic components of the vaccine, but much less against antigenically diverse influenza strains. A vaccine that protects against different influenza viruses belonging to the same subtype or even against viruses belonging to more than one subtype would be a major advance in our battle against influenza. Heterosubtypic immunity could be obtained by cytotoxic T-cell (CTL) responses against conserved influenza virus epitopes. The molecular mechanisms involved in inducing protective CTL responses are discussed here. We also focus on CTL vaccine design and point to the importance of immune-related databases and immunoinformatics tools in the quest for new vaccine candidates. Some techniques for analysis of T-cell responses are also highlighted, as they allow estimation of cellular immune responses induced by vaccine preparations and can provide correlates of protection.

Bioinformatics in new generation flavivirus vaccines

Flavivirus infections are the most prevalent arthropod-borne infections world wide, often causing severe disease especially among children, the elderly, and the immunocompromised. In the absence of effective antiviral treatment, prevention through vaccination would greatly reduce morbidity and mortality associated with flavivirus infections. Despite the success of the empirically developed vaccines against yellow fever virus, Japanese encephalitis virus and tick-borne encephalitis virus, there is an increasing need for a more rational design and development of safe and effective vaccines. Several bioinformatic tools are available to support such rational vaccine design. In doing so, several parameters have to be taken into account, such as safety for the target population, overall immunogenicity of the candidate vaccine, and efficacy and longevity of the immune responses triggered. Examples of how bio-informatics is applied to assist in the rational design and improvements of vaccines, particularly flavivirus vaccines, are presented and discussed.

ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at <igrid-ext.cryst.bbk.ac.uk/immunogrid>.

In silico prediction and immunological validation of common HLA-DRB1-restricted T cell epitopes of Candida albicans secretory aspartyl proteinase 2

ABSTRACT Sap2 is the most abundant virulence factor expressed during Candida infection, and the principal protein known to induce antibody response during Candida infection in humans. Its role in T-cell activation however, has not yet been determined. Sequence analysis revealed that Sap2 contains two variable regions: Var1 and Var2. Computational predictions by the Hotspot Hunter program identified that Var1 contains three candidate T-cell epitopes, whereas Var2 contains four. Thirty-nine overlapping peptides of Sap2 were then synthesized, and tested for their ability to induce proliferation of PBMC from 12 donors. Peptides P11, P17 and P31 exhibited significantly higher proliferative indices when compared with those of other peptides or controls. P17 and P31 are located in the areas of prediction, while P11 is not. There were other peptides outside the prediction areas that could stimulate PBMC proliferation at low levels. Nevertheless, the proliferative noise caused by such peptides was ruled out by IL-2 ELISpot analysis. Only P17 and P31 were shown to induce clonal proliferation of IFN-gamma producing lymphocytes, suggesting that these two peptides contain T cell epitopes. P11, which stimulated IL-2 producing clones, contains a known B-cell epitope. Interestingly, P17 and P31 elicited both Th1 and Th2 cell responses with significant numbers of IL-13 secreting clones in response to stimulation. Taken together, the computer-based T cell epitope prediction method could identify the immunogenic T cell epitopes of C. albicans Sap2 that promiscuously bind to the HLA-DRB1 supertype.

Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research

Background

Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.

Results

Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).

Conclusion

Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction – a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.

Neisseria meningitidis PorB, a TLR2 ligand, induces an antigen-specific eosinophil recall response: potential adjuvant for helminth vaccines?

Efficacious adjuvants are important components of new vaccines. The neisserial outer membrane protein, PorB, is a TLR2 ligand with unique adjuvant activity. We demonstrate that PorB promotes Th2-skewed cellular immune response to the model Ag, OVA, in mice, including Ag-specific recall eosinophil recruitment to the peritoneum. PorB induces chemokine secretion by myeloid cells using both TLR2-dependent and -independent mechanisms, suggesting that anatomical distribution of TLR2(+) cells may not be a limiting factor for potential vaccine strategies. The results from this study suggest that PorB, and other TLR2 ligands, may be ideal for use against pathogens where eosinophilia may be protective, such as parasitic helminths.

Proteome informatics for cancer research: from molecules to clinic

Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.