The role of microglia cells in Alzheimer’s disease (AD) is well recognized, however their molecular and functional diversity remain unclear. Here, we isolated amyloid plaque-containing (using labelling with methoxy-XO4, XO4⁺) and non-containing (XO4⁻) microglia from an AD mouse model. Transcriptomics analysis identified different transcriptional trajectories in ageing and AD mice. XO4⁺microglial transcriptomes demonstrated dysregulated expression of genes associated with late onset AD. We further showed that the transcriptional program associated with XO4⁺microglia from mice is present in a subset of human microglia isolated from brains of individuals with AD. XO4⁻microglia displayed transcriptional signatures associated with accelerated ageing and contained more intracellular post-synaptic material than XO4⁺microglia, despite reduced active synaptosome phagocytosis. We identified HIF1α as potentially regulating synaptosome phagocytosis in vitro using primary human microglia, and BV2 mouse microglial cells. Together, these findings provide insight into molecular mechanisms underpinning the functional diversity of microglia in AD.

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

γ1-Herpesviruses such as Epstein-Barr virus (EBV) have a unique ability to amplify virus loads in vivo through latent growth-transforming infection. Whether they, like α- and β-herpesviruses, have been driven to actively evade immune detection of replicative (lytic) infection remains a moot point. We were prompted to readdress this question by recent work (Pudney, V.A., A.M. Leese, A.B. Rickinson, and A.D. Hislop. 2005. J. Exp. Med. 201:349–360; Ressing, M.E., S.E. Keating, D. van Leeuwen, D. Koppers-Lalic, I.Y. Pappworth, E.J.H.J. Wiertz, and M. Rowe. 2005. J. Immunol. 174:6829–6838) showing that, as EBV-infected cells move through the lytic cycle, their susceptibility to EBV-specific CD8⁺ T cell recognition falls dramatically, concomitant with a reductions in transporter associated with antigen processing (TAP) function and surface human histocompatibility leukocyte antigen (HLA) class I expression. Screening of genes that are unique to EBV and closely related γ1-herpesviruses of Old World primates identified an early EBV lytic cycle gene, BNLF2a, which efficiently blocks antigen-specific CD8⁺ T cell recognition through HLA-A–, HLA-B–, and HLA-C–restricting alleles when expressed in target cells in vitro. The small (60–amino acid) BNLF2a protein mediated its effects through interacting with the TAP complex and inhibiting both its peptide- and ATP-binding functions. Furthermore, this targeting of the major histocompatibility complex class I pathway appears to be conserved among the BNLF2a homologues of Old World primate γ1-herpesviruses. Thus, even the acquisition of latent cycle genes endowing unique growth-transforming ability has not liberated these agents from evolutionary pressure to evade CD8⁺ T cell control over virus replicative foci.

A considerable proportion of HLA-I peptides likely derive from cis- and trans-splicing events.

Current knowledge about the dynamics of antigen presentation to T cells during viral infection is very poor despite being of fundamental importance to our understanding of anti-viral immunity. Here we use an advanced mass spectrometry method to simultaneously quantify the presentation of eight vaccinia virus peptide-MHC complexes (epitopes) on infected cells and the amounts of their source antigens at multiple times after infection. The results show a startling 1000-fold range in abundance as well as strikingly different kinetics across the epitopes monitored. The tight correlation between onset of protein expression and epitope display for most antigens provides the strongest support to date that antigen presentation is largely linked to translation and not later degradation of antigens. Finally, we show a complete disconnect between the epitope abundance and immunodominance hierarchy of these eight epitopes. This study highlights the complexity of viral antigen presentation by the host and demonstrates the weakness of simple models that assume total protein levels are directly linked to epitope presentation and immunogenicity. A major mechanism for the detection of virus infection is the recognition by T cells of short peptide fragments (epitopes) derived from the degradation of intracellular proteins presented at the cell surface in a complex with class I MHC. Whilst the mechanics of antigen degradation and the loading of peptides onto MHC are now well understood, the kinetics of epitope presentation have only been studied for individual model antigens. We addressed this issue by studying vaccinia virus, best known as the smallpox vaccine, using advanced mass spectrometry. Precise and simultaneous quantification of multiple peptide-MHC complexes showed that the surface of infected cells provides a surprisingly dynamic landscape from the point of view of anti-viral T cells. Further, concurrent measurement of virus protein levels demonstrated that in most cases, peak presentation of epitopes occurs at the same time or precedes the time of maximum protein build up. Finally, we found a complete disconnect between the abundance of epitopes on infected cells and the size of the responding T cell populations. These data provide new insights into how virus infected cells are seen by T cells, which is crucial to our understanding of anti-viral immunity and development of vaccines.