Biology subjects

Tscharke, D. C.

Publications and source records attributed to Tscharke, D. C..

3 recordsLinked to original sources

bioRxiv2024

A quantitative approach to poxvirus infection reveals general correlates of antigen presentation and immunogenicity for viral CD8+ T cell epitopes

CD8+ T cells are essential effectors in antiviral immunity that kill infected cells displaying viral peptide epitopes on MHC class I. The pathways underpinning antigen presentation on MHC I are well known but we lack a general understanding of the quantitative and kinetic relationships between source proteins and presented epitopes, and how these relate to immunogenicity. We used quantitative mass spectrometry to interrogate infection with vaccinia virus, the MPOX vaccine, measuring amounts of >40 viral epitopes and their source proteins multiple times after infection in four cell types. This revealed that up to 90% of the epitopes were presented as fast as their source proteins were made and that protein amounts failed to correlate with epitope levels. Further, a significant correlation between epitope levels and immunogenicity required presentation to be quantified ex vivo, with each step away from a matched infection route and natural presenting cell eroding the association.

immunology↗

bioRxiv2023

Genomic location dictates lytic promoter activity during herpes simplex virus latency

Herpes simplex virus 1 (HSV-1) is a significant pathogen that establishes life-long latent infections with intermittent episodes of resumed disease. In mouse models of HSV infection, persistent low-level lytic gene expression has been detected during latency in the absence of spontaneous reactivation events leading to new virus production. This viral activity during latency has been reported using a sensitive Cre-marking model for several lytic gene promoters placed in one location in the HSV-1 genome. Here we extend these findings in the same model by examining first, the activity of an ectopic lytic gene promoter in other places in the genome and second, whether native promoter activity might be detectable. We found that both for ectopic and native lytic gene promoters, Cre expression during latency was detected in our model, but only when the promoter was located near the ends of the unique long genome segment. This location is significant because it is in close proximity to the region from which latency associated transcripts (LAT) are derived. These results show for the first time that native HSV-1 lytic gene promoters can produce protein products during latency, but that this activity is only detectable when they are located close to the LAT locus. Author summaryHSV is a significant human pathogen and the best studied model of mammalian virus latency. Traditionally the active (lytic) and inactive (latent) phases of infection were considered to be distinct, but the notion of latency being entirely quiescent is evolving due to the detection of some lytic gene expression during latency. Here we add to this literature by finding that activity can be found for native lytic gene promotors as well as for constructs placed ectopically in the HSV genome. However, this activity was only detectable when these promoters were located close by a region known to be transcriptionally active during latency. These data have implications for our understanding of HSV gene regulation during latency and the extent to which transcriptionally active regions are insulated from adjacent parts of the viral genome.

microbiology↗

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↗