Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.01.31.478157

In silico analysis of predicted differential MHC binding and CD8+ T-cell immune escape of SARS-CoV-2 B.1.1.529 variant mutant epitopes

Riesgo-Ferreiro, P.·Ranganath, G.·Bukur, T.·Sorn, P.·Rosler, T.·Schrors, B.·Lower, M.
View original source ↗Export metadata ↓

Abstract

IntroductionThe B.1.1.529 (Omicron) SARS-CoV-2 variant has raised global concerns due to its high number of mutations and its rapid spread. It is of major importance to understand the impact of this variant on the acquired and induced immunity. Several preliminary studies have reported the impact of antibody binding and to this date, there are few studies on Omicrons CD8+ T-cell immune escape. MethodsWe first assessed the impact of Omicron and B.1.617.2 (Delta) variant mutations on the SARS-CoV-2 spike epitopes submitted to the Immune Epitope Database (IEDB) with positive out-come on MHC ligand or T-cell assays (n=411). From those epitopes modified by a mutation, we found the corresponding homologous epitopes in Omicron and Delta. We then ran the netMHCpan computational MHC binding prediction on the pairs of IEDB epitopes and matching homologous epitopes over top 5 MHC I alleles on some selected populations. Lastly, we applied a Fisher test to find mutations enriched for homologous epitopes with decreased predicted binding affinity. ResultsWe found 31 and 78 IEDB epitopes modified by Delta and Omicron mutations, respectively. The IEDB spike protein epitopes redundantly cover the protein sequence. The WT pMHC with a strong predicted binding tend to have homologous mutated pMHC with decreased binding. A similar trend is observed in Delta over all HLA genes, while in Omicron only for HLA-B and HLA-C. Finally, we obtained one and seven mutations enriched for homologous mutated pMHC with decreased MHC binding affinity in Delta and Omicron, respectively. Three of the Omicron mutations, VYY143-145del, K417N and Y505H, are replacing an aromatic or large amino acid, which are reported to be enriched in immunogenic epitopes. K417N is common with Beta variants, while Y505H and VYY143-145del are novel Omicron mutations. ConclusionIn summary, pMHC with Delta and Omicron mutations show decreased MHC binding affinity, which results in a trend specific to SARS-CoV-2 variants. Such epitopes may decrease overall presentation on different HLA alleles suggesting evasion from CD8+ T-cell responses in specific HLA alleles. However, our results show B.1.1.529 (Omicron) will not totally evade the immune system through a CD8+ immune escape mechanism. Yet, we identified mutations in B.1.1.529 (Omicron) introducing amino acids associated with increased immunogenicity. AvailabilityAll the code and results from this study are available at https://github.com/TRON-bioinformatics/omicron-analysis.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Riesgo-Ferreiro, P., Ranganath, G., Bukur, T., Sorn, P., Rosler, T., Schrors, B., Lower, M.. 2022-01-31. In silico analysis of predicted differential MHC binding and CD8+ T-cell immune escape of SARS-CoV-2 B.1.1.529 variant mutant epitopes. https://doi.org/10.1101/2022.01.31.478157

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗