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4 result(s) for "Luijten, Robbie J."
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Induction of broad multifunctional CD8+ and CD4+ T cells by hepatitis B virus antigen-based synthetic long peptides ex vivo
IntroductionTherapeutic vaccination based on synthetic long peptides (SLP®) containing both CD4+ and CD8+ T cell epitopes is a promising treatment strategy for chronic hepatitis B infection (cHBV).MethodsWe designed SLPs for three HBV proteins, HBcAg and the non-secreted proteins polymerase and X, and investigated their ability to induce T cell responses ex vivo . A set of 17 SLPs was constructed based on viral protein conservation, functionality, predicted and validated binders for prevalent human leukocyte antigen (HLA) supertypes, validated HLA I epitopes, and chemical producibility.ResultsAll 17 SLPs were capable of inducing interferon gamma (IFNɣ) production in samples from four or more donors that had resolved an HBV infection in the past (resolver). Further analysis of the best performing SLPs demonstrated activation of both CD8+ and CD4+ multi-functional T cells in one or more resolver and patient sample(s). When investigating which SLP could activate HBV-specific T cells, the responses could be traced back to different peptides for each patient or resolver.DiscussionThis indicates that a large population of subjects with different HLA types can be covered by selecting a suitable mix of SLPs for therapeutic vaccine design. In conclusion, we designed a set of SLPs capable of inducing multifunctional CD8+ and CD4+ T cells ex vivo that create important components for a novel therapeutic vaccine to cure cHBV.
HLA I immunopeptidome of synthetic long peptide pulsed human dendritic cells for therapeutic vaccine design
Synthetic long peptides (SLPs) are a promising vaccine modality that exploit dendritic cells (DC) to treat chronic infections or cancer. Currently, the design of SLPs relies on in silico prediction and multifactorial T cells assays to determine which SLPs are best cross-presented on DC human leukocyte antigen class I (HLA-I). Furthermore, it is unknown how TLR ligand-based adjuvants affect DC cross-presentation. Here, we generated a unique, high-quality immunopeptidome dataset of human DCs pulsed with 12 hepatitis B virus (HBV)-based SLPs combined with either a TLR1/2 (Amplivant®) or TLR3 (PolyI:C) ligand. The obtained immunopeptidome reflected adjuvant-induced differences, but no differences in cross-presentation of SLPs. We uncovered dominant (cross-)presentation on B-alleles, and identified 33 unique SLP-derived HLA-I peptides, several of which were not in silico predicted and some were consistently found across donors. Our work puts forward DC immunopeptidomics as a valuable tool for therapeutic vaccine design.
Empirical Evaluation of the Use of Computational HLA Binding as an Early Filter to the Mass Spectrometry-Based Epitope Discovery Workflow
Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made to rescue peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored peptides. Most of these peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for epitope discovery and reveals the extent of peptides to be rescued by recently developed algorithms.