Hilgendorf, P. | bioRxiv Science

bioRxiv2023

DNA methylation profiling identifies TBKBP1 as potent amplifier of cytotoxic activity in CMV-specific human CD8+ T cells

Epigenetic mechanisms stabilize gene expression patterns during CD8+ T cell differentiation. However, although adoptive transfer of virus-specific T cells is clinically applied to reduce the risk of virus infection or reactivation in immunocompromised individuals, the DNA methylation pattern of virus-specific CD8+ T cells is largely unknown. Hence, we here performed whole-genome bisulfite sequencing of cytomegalovirus-specific human CD8+ T cells and found that they display a unique DNA methylation pattern consisting of 79 differentially methylated regions when compared to bulk memory CD8+ T cells. Among them was TBKBP1, coding for TBK-binding protein 1 that can interact with TANK-binding kinase 1 (TBK1) and mediate pro-inflammatory responses in innate immune cells downstream of intracellular virus sensing. Since TBKBP1 has not yet been reported in T cells, we aimed to unravel its role in virus-specific CD8+ T cells. TBKBP1 demethylation in terminal effector CD8+ T cells correlated with TBKBP1 expression and was stable upon long-term in vitro culture. TBKBP1 overexpression resulted in enhanced TBK1 phosphorylation upon stimulation of CD8+ T cells and significantly improved their virus neutralization capacity. Collectively, our data demonstrate that TBKBP1 modulates virus-specific CD8+ T cell responses and could be exploited as therapeutic target to improve adoptive T cell therapies.

immunology↗

bioRxiv2023

Predicting T Cell Receptor Functionality against Mutant Epitopes

Cancer cells or pathogens can escape recognition by T cell receptors (TCRs) through mutations of immunogenic epitopes. TCR cross-reactivity, i.e., recognition of multiple epitopes with sequence similarities, can be a factor to counteract such mutational escape. However, cross-reactivity of cell-based immunotherapies may also cause severe side effects when self-antigens are targeted. Therefore, the ability to predict the effect of mutations in the epitope sequence on T cell functionality in silico would greatly benefit the safety and effectiveness of newly-developed immunotherapies and vaccines. We here present "Predicting T cell Epitope-specific Activation against Mutant versions" (P-TEAM), a Random Forest-based model which predicts the effect of point mutations of an epitope on T cell functionality. We first trained and tested P-TEAM on a comprehensive dataset of 36 unique murine TCRs in response to systematic single-amino acid mutations of their target epitope (representing 5.472 unique TCR-epitope interactions). The model was able to classify T cell reactivities, corresponding to in vivo recruitment of T cells, and quantitatively predict T cell functionalities for unobserved single-point mutated altered peptide ligands (APLs), or even unseen TCRs, with consistently high performance. Further, we present an active learning framework to guide experimental design for assessing TCR functionality against novel epitopes, minimizing primary data acquisition costs. Finally, we applied P-TEAM to a novel dataset of 7 human TCRs reactive to the tumor neoantigen VPSVWRSSL. We observed a similarly robust performance for these human TCRs as for the murine TCRs recognizing SIINFEKL, thus providing evidence that our approach is applicable to therapeutically relevant TCRs as well as across species. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

bioinformatics↗