Dataset Information

Clustering of rare peptide segments in the HCV immunome.

ABSTRACT: Our previous research and a comprehensive meta-analysis of data from the literature on epitope mapping has revealed that the B cell epitope repertoire is allocated to rare peptide motifs, i.e., antigenic peptide sequences endowed with a low level of similarity to the host proteome. From a clinical point of view, low-similarity peptides able to evoke an immune response appear to be of special interest for the rational design of vaccines for poorly treatable diseases such as hepatitis-C virus (HCV) infection. Indeed, low similarity peptides would guarantee the highest specificity and lowest cross-reactivity, i.e., effectiveness without adverse side-effects. In this study, aimed at gaining further information for the development of effective anti-HCV peptide-based vaccines, the HCV epitopes recognized by human antibodies and currently catalogued in the Immune Epitope Data Base (IEDB) were examined for pentamer sequence similarities to the human proteome. We report that the analyzed HCV determinants are characterized by the presence of fragment absent from (or scarcely represented in) human proteins. These data confirm the low-similarity hypothesis, according to which a low-similarity to the host proteome defines the nonself character of microbial antigens and modulates peptide immunogenicity. Moreover, this study indicates a concrete and safe immunotherapeutic approach which might be used in a universal anti-HCV vaccine.

SUBMITTER: Stufano A

PROVIDER: S-EPMC3065674 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Clustering of rare peptide segments in the HCV immunome.

Stufano Angela A Capone Giovanni G Pesetti Barbara B Polimeno Lorenzo L Kanduc Darja D

Self/nonself 20100203 2

Our previous research and a comprehensive meta-analysis of data from the literature on epitope mapping has revealed that the B cell epitope repertoire is allocated to rare peptide motifs, i.e., antigenic peptide sequences endowed with a low level of similarity to the host proteome. From a clinical point of view, low-similarity peptides able to evoke an immune response appear to be of special interest for the rational design of vaccines for poorly treatable diseases such as hepatitis-C virus (HCV ...[more]

PMID: 21487517

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Project description:BackgroundHepatitis C virus (HCV) genomic variability is a major challenge to the generation of a prophylactic vaccine. We have previously shown that HCV specific T-cell responses induced by a potent T-cell vaccine encoding a single strain subtype-1b immunogen target epitopes dominant in natural infection. However, corresponding viral regions are highly variable at a population level, with a reduction in T-cell reactivity to these variants. We therefore designed and manufactured second generation simian adenovirus vaccines encoding genomic segments, conserved between viral genotypes and assessed these for immunogenicity.MethodsWe developed a computer algorithm to identify HCV genomic regions that were conserved between viral subtypes. Conserved segments below a pre-defined diversity threshold spanning the entire HCV genome were combined to create novel immunogens (1000-1500 amino-acids), covering variation in HCV subtypes 1a and 1b, genotypes 1 and 3, and genotypes 1-6 inclusive. Simian adenoviral vaccine vectors (ChAdOx) encoding HCV conserved immunogens were constructed. Immunogenicity was evaluated in C57BL6 mice using panels of genotype-specific peptide pools in ex-vivo IFN-ϒ ELISpot and intracellular cytokine assays.ResultsChAdOx1 conserved segment HCV vaccines primed high-magnitude, broad, cross-reactive T-cell responses; the mean magnitude of total HCV specific T-cell responses was 1174 SFU/106 splenocytes for ChAdOx1-GT1-6 in C57BL6 mice targeting multiple genomic regions, with mean responses of 935, 1474 and 1112 SFU/106 against genotype 1a, 1b and 3a peptide panels, respectively. Functional assays demonstrated IFNg and TNFa production by vaccine-induced CD4 and CD8 T-cells. In silico analysis shows that conserved immunogens contain multiple epitopes, with many described in natural HCV infection, predicting immunogenicity in humans.ConclusionsSimian adenoviral vectored vaccines encoding genetic segments that are conserved between all major HCV genotypes contain multiple T-cell epitopes and are highly immunogenic in pre-clinical models. These studies pave the way for the assessment of multi-genotypic HCV T-cell vaccines in humans.

Project description:BACKGROUND: Along with the improvement of high throughput sequencing technologies, the genetics community is showing marked interest for the rare variants/common diseases hypothesis. While sequencing can still be prohibitive for large studies, commercially available genotyping arrays targeting rare variants prove to be a reasonable alternative. A technical challenge of array based methods is the task of deriving genotype classes (homozygous or heterozygous) by clustering intensity data points. The performance of clustering tools for common polymorphisms is well established, while their performance when conducted with a large proportion of rare variants (where data points are sparse for genotypes containing the rare allele) is less known. We have compared the performance of four clustering tools (GenCall, GenoSNP, optiCall and zCall) for the genotyping of over 10,000 samples using the Illumina's HumanExome BeadChip, which includes 247,870 variants, 90% of which have a minor allele frequency below 5% in a population of European ancestry. Different reference parameters for GenCall and different initial parameters for GenoSNP were tested. Genotyping accuracy was assessed using data from the 1000 Genomes Project as a gold standard, and agreement between tools was measured. RESULTS: Concordance of GenoSNP's calls with the gold standard was below expectations and was increased by changing the tool's initial parameters. While the four tools provided concordance with the gold standard above 99% for common alleles, some of them performed poorly for rare alleles. The reproducibility of genotype calls for each tool was assessed using experimental duplicates which provided concordance rates above 99%. The inter-tool agreement of genotype calls was high for approximately 95% of variants. Most tools yielded similar error rates (approximately 0.02), except for zCall which performed better with a 0.00164 mean error rate. CONCLUSIONS: The GenoSNP clustering tool could not be run straight "out of the box" with the HumanExome BeadChip, as modification of hard coded parameters was necessary to achieve optimal performance. Overall, GenCall marginally outperformed the other tools for the HumanExome BeadChip. The use of experimental replicates provided a valuable quality control tool for genotyping projects with rare variants.

Project description:ObjectiveIdentifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing and/or platform based therapeutic development. Toward that aim, we utilized an integrative knowledge graph-based approach to constructing clusters of rare diseases.Materials and methodsData on 3,242 rare diseases were extracted from the National Center for Advancing Translational Science (NCATS) Genetic and Rare Diseases Information center (GARD) internal data resources. The rare disease data was enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were used to convert nodes into vectors upon which k-means clustering was applied. We validated the disease clusters through semantic similarity and feature enrichment analysis.ResultsA node embedding model was trained on the ontology enriched rare disease KG and k-means clustering was applied to the embedding vectors resulting in 37 disease clusters with a mean size of 87 diseases. We validate the disease clusters quantitatively by looking at semantic similarity of clustered diseases, using the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters were shown to be highly related.DiscussionWe demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and approved or investigational drugs are enumerated for follow-up efforts.ConclusionOur study lays out a method for clustering rare diseases using the graph node embeddings. We develop an easy to maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems. Detailed subnetwork analysis and in-depth review of individual clusters may lead to translatable findings. Future work will focus on incorporation of additional data sources, with a particular focus on common disease data.

Dataset Information

Clustering of rare peptide segments in the HCV immunome.

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Clustering of rare peptide segments in the HCV immunome.

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