Biology subjects

Purcell, A. W.

Publications and source records attributed to Purcell, A. W..

26 records · Page 2Linked to original sources

bioRxiv2020

A peptide-signal amplification strategy for the detection and validation of neoepitope presentation on cancer biopsies

Targeting the right cancer-specific peptides presented by Human Leukocyte antigen (HLA) class I and II molecules on the tumor cell surface is a crucial step in cancer immunotherapy. Numerous approaches have been proposed to predict the presentation of potential neoepitopes that may be targeted through immune-based therapies. Often founded on patient specific somatic mutations, the routine validation of their actual appearance on the tumor cell surface is a significant barrier to realising personalized cancer immunotherapy. This can be attributed to the lack of robust and adaptable assays for antigen presentation that offer the required sensitivity to deal with the limited amounts of patient tumor tissue available. Rather than personalize individual assays we propose the use mass spectrometry to identify tumor neoepitopes from HLA-bound peptides directly isolated form the surface of tumor biopsies. We have developed a microscale HLA-peptide complex immunoprecipitation protocol combined with tandem mass tagging (TMT) to directly sequence HLA-bound peptides using mass spectrometry. Using this strategy, we identified HLA-bound peptides from as few as ~1000 cultured cells and from a small piece (~1 mg) of whole melanoma tumour tissue, encompassing epitopes derived from Melanoma-associated antigens and potential neoantigens.

immunology↗

bioRxiv2020

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

Protein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC >0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics.

systems biology↗

bioRxiv2020

HLA-class II specificity assessed by high-density peptide microarray interactions

The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery; something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data is key to the solution of this problem; a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse and representative data. Here, we have used recombinant HLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated. Collectively, with a cost per peptide reduced to a few cents combined with the flexibility of recombinant HLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.

immunology↗

bioRxiv2019

Benchmarking predictions of MHC class I restricted T cell epitopes

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected mice, considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top 0.04% (N = 277) of peptides in the VACV proteome (N = 767,788). These performance metrics provide guidance for immunologists as to which prediction methods to use. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.\n\nAuthor summaryComputational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

immunology↗

bioRxiv2019

Mouse and human microglial phenotypes in Alzheimer’s disease are controlled by amyloid plaque phagocytosis through Hif1α

The important role of microglia, the brains resident immune cells, in Alzheimers disease (AD) is now well recognized, however their molecular and functional diversity and underlying mechanisms still remain controversial. To transcriptionally and functionally characterize the diversity of microglia in AD and aging, we isolated the amyloid plaque-containing (XO4+) and non-containing (XO4-) microglia from an AD mouse model. Transcriptomics analysis unveiled independent transcriptional trajectories in ageing and AD. XO4+ microglial transcriptomes linked plaque phagocytosis to altered [Formula] expression of bona fide late onset AD genetic risk factors. We further revealed that the XO4+ transcriptional program is present in a subset of human microglia from AD patients and is a direct and reversible consequence of A{beta} plaque phagocytosis. Conversely, XO4- microglia in AD displayed an accelerated ageing signature and contained more intracellular post synaptic material than plaque-containing microglia, despite reduced active synaptosome phagocytosis. Mechanistically, we predicted HIF1 as a core regulator of the XO4-/XO4+ axis, and further validated the mechanism in vitro using human stem cell-derived microglia like cells and primary human microglia. Together these findings unveiled the molecular mechanism underpinning the functional diversity of microglia in AD, providing opportunities to develop treatments targeted at subset specific manipulation of the microglial niche.

neuroscience↗

bioRxiv2019

The diversity of the immunogenic components of the melanoma immunopeptidome.

Antigen-recognition by CD8+ T cells is governed by the pool of peptide antigens presented on the cell surface in the context of HLA class I complexes. Recent studies have shown not only a high degree of plasticity in the immunopeptidome, but also that a considerable fraction of all presented peptides is generated through proteasome-mediated splicing of non-contiguous regions of proteins to form novel peptide antigens. Here we used high-resolution mass-spectrometry combined with new bioinformatic approaches to characterize the immunopeptidome of melanoma cells in the presence or absence of interferon-{gamma}. In total, we identified more than 60,000 peptides from a single patient derived cell line (LM-MEL-44) and demonstrated that interferon-{gamma} induced marked changes in the peptidome with an overlap of only [~]50% between basal and treated cells. Around 6-8% of the peptides were identified as cis-spliced peptides, and 2213 peptides (1827 linear, 386 cis-spliced peptides) were derived from known melanoma-associated antigens. These peptide antigens were equally distributed between the constitutive and interferon-{gamma} induced peptidome. We next examined additional HLA-matched patient derived cell lines to investigate how frequently these peptides were identified and found that a high proportion of both linear and spliced peptides were conserved between individual patient tumors, drawing on data amassing to over 100,000 peptide sequences from these extended data sets. Moreover, several of these peptides showed in vitro immunogenicity across multiple melanoma patients. These observations highlight the breadth and complexity of the repertoire of immunogenic peptides that can be exploited therapeutically and suggest that spliced peptides are a major new class of tumor antigens.

immunology↗

bioRxiv2017

Viral hijacking of the nucleolar DNA-damage response machinery: a novel mechanism to regulate host cell biology

Recent landmark studies indicate that nucleoli play critical roles in the DNA-damage response (DDR) via interaction of DDR machinery including NBS1 with nucleolar Treacle protein, a key mediator of ribosomal RNA (rRNA) transcription and processing, implicated in Treacher-Collins syndrome. Here, using proteomics, confocal/super-resolution imaging, and infection under BSL-4 containment, we present the first report that this nucleolar DDR pathway is targeted by infectious pathogens. We find that Treacle has antiviral activity, but that matrix protein of Henipaviruses and P3 protein of rabies virus, highly pathogenic viruses of the order Mononegavirales, interact with Treacle and inhibit its function, thereby silencing rRNA biogenesis, consistent with mimicking NBS1-Treacle interaction during a DDR. These data identify a novel mechanism for viral modulation of host cells by appropriating the nucleolar DDR; this appears to have developed independently in different viruses, and represents, to our knowledge, the first direct intra-nucleolar function for proteins of any mononegavirus.

microbiology↗

bioRxiv2017

Tadpole-like conformations of huntingtin exon 1 with expanded polyglutamine engenders novel interactions in cells

Soluble huntingtin exon 1 (Httex1) with expanded polyglutamine (polyQ) engenders neurotoxicity in Huntingtons disease. To uncover the physical basis of this toxicity, we performed structural studies of soluble Httex1 for wild type and mutant polyQ lengths. Nuclear magnetic resonance experiments show evidence for conformational rigidity across the polyQ region. In contrast, hydrogen-deuterium exchange shows absence of backbone amide protection, suggesting negligible persistence of hydrogen bonds. The seemingly conflicting results are explained by all-atom simulations, which show that Httex1 adopts tadpole-like structures with a globular head encompassing the N-terminal amphipathic and polyQ regions and the tail encompassing the C-terminal proline-rich region. The surface area of the globular domain increases monotonically with polyQ length. This stimulates sharp increases in gain-of-function interactions in cells for expanded polyQ, and one of these interactions is with the stress-granule protein Fus. Our results highlight plausible connections between Httex1 structure and routes to neurotoxicity.

biochemistry↗