Label-free quantitative proteomics reveals regulation of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) and 5'-3'-exoribonuclease 2 (XRN2) during respiratory syncytial virus infection

A large quantitative study was carried out to compare the proteome of respiratory syncytial virus (RSV) infected versus uninfected cells in order to determine novel pathways regulated during viral infection. RSV infected and mock-infected HEp2 cells were lysed and proteins separated by preparative isoelectric focussing using offgel fractionation. Following tryptic digestion, purified peptides were characterized using label-free quantitative expression profiling by nano-ultra performance liquid chromatography coupled to electrospray ionisation mass spectrometry with collision energy ramping for all-ion fragmentation (UPLC-MSE). A total of 1352 unique cellular proteins were identified and their abundance compared between infected and non-infected cells. Ingenuity pathway analysis revealed regulation of several central cellular metabolic and signalling pathways during infection. Selected proteins that were found regulated in RSV infected cells were screened by quantitative real-time PCR for their regulation on the transcriptional level. Synthesis of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) and 5'-3'-exoribonuclease 2 (XRN2) mRNAs were found to be highly induced upon RSV infection in a time dependent manner. Accordingly, IFIT3 protein levels accumulated during the time course of infection. In contrast, little variation was observed in XRN2 protein levels, but different forms were present in infected versus non-infected cells. This suggests a role of these proteins in viral infection, and analysis of their function will shed further light on mechanisms of RNA virus replication and the host cell defence machinery

Characterization of the Theileria parva sporozoite proteome

East Coast fever is a lymphoproliferative disease caused by the tick-borne protozoan parasite Theileria parva. The sporozoite stage of this parasite, harboured and released from the salivary glands of the tick Rhipicephalus appendiculatus during feeding, invades and establishes infection in bovine lymphocytes. Blocking this initial stage of invasion presents a promising vaccine strategy for control of East Coast fever and can in part be achieved by targeting the major sporozoite surface protein p67. To support research on the biology of T. parva and the identification of additional candidate vaccine antigens, we report on the sporozoite proteome as defined by LC–MS/MS analysis. In total, 4780 proteins were identified in an enriched preparation of sporozoites. Of these, 2007 were identified as T. parva proteins, representing close to 50% of the total predicted parasite proteome. The remaining 2773 proteins were derived from the tick vector. The identified sporozoite proteins include a set of known T. parva antigens targeted by antibodies and cytotoxic T cells from cattle that are immune to East Coast fever. We also identified proteins predicted to be orthologs of Plasmodium falciparum sporozoite surface molecules and invasion organelle proteins, and proteins that may contribute to the phenomenon of bovine lymphocyte transformation. Overall, these data establish a protein expression profile of T. parva sporozoites as an important starting point for further study of a parasitic species which has considerable agricultural impact

Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data

Peptide binding to MHC class I molecules is the single most selective step in antigen presentation and the strongest single correlate to peptide cellular immunogenicity. The cost of experimentally characterizing the rules of peptide presentation for a given MHC-I molecule is extensive, and predictors of peptide-MHC interactions constitute an attractive alternative. Recently, an increasing amount of MHC presented peptides identified by mass spectrometry (MS ligands) has been published. Handling and interpretation of MS ligand data is, in general, challenging due to the polyspecificity nature of the data. We here outline a general pipeline for dealing with this challenge and accurately annotate ligands to the relevant MHC-I molecule they were eluted from by use of GibbsClustering and binding motif information inferred from in silico models. We illustrate the approach here in the context of MHC-I molecules (BoLA) of cattle. Next, we demonstrate how such annotated BoLA MS ligand data can readily be integrated with in vitro binding affinity data in a prediction model with very high and unprecedented performance for identification of BoLA-I restricted T-cell epitopes. The prediction model is freely available at http://www.cbs.dtu.dk/services/NetMHCpan/NetBoLApan . The approach has here been applied to the BoLA-I system, but the pipeline is readily applicable to MHC systems in other species

Immunopeptidomic Analysis of BoLA-I and BoLA-DR Presented Peptides from Theileria parva Infected Cells

The apicomplexan parasite Theileria parva is the causative agent of East Coast fever, usually a fatal disease for cattle, which is prevalent in large areas of eastern, central, and southern Africa. Protective immunity against T. parva is mediated by CD8(+) T cells, with CD4(+) T-cells thought to be important in facilitating the full maturation and development of the CD8(+) T-cell response. T. parva has a large proteome, with >4000 protein-coding genes, making T-cell antigen identification using conventional screening approaches laborious and expensive. To date, only a limited number of T-cell antigens have been described. Novel approaches for identifying candidate antigens for T. parva are required to replace and/or complement those currently employed. In this study, we report on the use of immunopeptidomics to study the repertoire of T. parva peptides presented by both BoLA-I and BoLA-DR molecules on infected cells. The study reports on peptides identified from the analysis of 13 BoLA-I and 6 BoLA-DR datasets covering a range of different BoLA genotypes. This represents the most comprehensive immunopeptidomic dataset available for any eukaryotic pathogen to date. Examination of the immunopeptidome data suggested the presence of a large number of coprecipitated and non-MHC-binding peptides. As part of the work, a pipeline to curate the datasets to remove these peptides was developed and used to generate a final list of 74 BoLA-I and 15 BoLA-DR-presented peptides. Together, the data demonstrated the utility of immunopeptidomics as a method to identify novel T-cell antigens for T. parva and the importance of careful curation and the application of high-quality immunoinformatics to parse the data generated

Long non-coding RNA-derived peptides are immunogenic and drive a potent anti-tumour response

Protein arginine methyltransferase (PRMT) 5 is over-expressed in a variety of cancers and the master transcription regulator E2F1 is an important methylation target. We have explored the role of PRMT5 and E2F1 in regulating the non-coding genome and report here a striking effect on long non-coding (lnc) RNA gene expression. Moreover, many MHC class I protein-associated peptides were derived from small open reading frames in the lncRNA genes. Pharmacological inhibition of PRMT5 or adjusting E2F1 levels qualitatively altered the repertoire of lncRNA-derived peptide antigens displayed by tumour cells. When presented to the immune system as either ex vivo-loaded dendritic cells or expressed from a viral vector, lncRNA-derived peptides drove a potent antigen-specific CD8 T lymphocyte response, which translated into a significant delay in tumour growth. Thus, lncRNA genes encode immunogenic peptides that can be deployed as a cancer vaccine

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue

Identification of antigens presented by MHC for vaccines against tuberculosis

Mycobacterium tuberculosis (M.tb) is responsible for more deaths globally than any other pathogen. The only available vaccine, bacillus Calmette-Guérin (BCG), has variable efficacy throughout the world. A more effective vaccine is urgently needed. The immune response against tuberculosis relies, at least in part, on CD4+ T cells. Protective vaccines require the induction of antigen-specific CD4+ T cells via mycobacterial peptides presented by MHC class-II in infected macrophages. In order to identify mycobacterial antigens bound to MHC, we have immunoprecipitated MHC class-I and class-II complexes from THP-1 macrophages infected with BCG, purified MHC class-I and MHC class-II peptides and analysed them by liquid chromatography tandem mass spectrometry. We have successfully identified 94 mycobacterial peptides presented by MHC-II and 43 presented by MHC-I, from 76 and 41 antigens, respectively. These antigens were found to be highly expressed in infected macrophages. Gene ontology analysis suggests most of these antigens are associated to membranes and involved in lipid biosynthesis and transport. The sequences of selected peptides were confirmed by spectral match validation and immunogenicity evaluated by IFN-gamma ELISpot against peripheral blood mononuclear cell from volunteers vaccinated with BCG, M.tb latently infected subjects or patients with tuberculosis disease. Three antigens were expressed in viral vectors, and evaluated as vaccine candidates alone or in combination in a murine aerosol M.tb challenge model. When delivered in combination, the three candidate vaccines conferred significant protection in the lungs and spleen compared with BCG alone, demonstrating proof-of-concept for this unbiased approach to identifying new candidate antigens

Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR- presented peptides and epitopes

MHC peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parameters that define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein Ags to elicit functional T cell responses. Liquid chromatography–mass spectrometry analysis of MHC-eluted ligand data has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of Ag presentation have reached a high level of accuracy for both MHC class I and class II. In this study, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte Ag class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by eluted ligand data derived from bovine cell lines expressing a range of DRB3 alleles prevalent in Holstein–Friesian populations. The model generated (NetBoLAIIpan, available as a Web server at www.cbs.dtu.dk/services/NetBoLAIIpan) was shown to have unprecedented predictive power to identify known BoLA-DR–restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced mass spectrometry peptidomics with immunoinformatics for characterization of the BoLA-DR Ag presentation system and provide a prediction tool that can be used to assist in rational evaluation and selection of bovine CD4 T cell epitopes