Biology subjects

Isaeva, O. I.

Publications and source records attributed to Isaeva, O. I..

3 recordsLinked to original sources

bioRxiv2023

An "alert state" ribosome population acts as a master regulator of cytokine mediated processes

Inflammatory cytokines are pivotal to immune responses. Upon cytokine exposure, cells enter an "alert-state" that enhances their visibility to the immune system. Here, we identified an "alert-state" subpopulation of ribosomes (ASRs) defined by the presence of the P-stalk. We show that ASRs are formed in response to cytokines linked to tumor immunity, and are involved in the preferential translation of mRNAs vital for the cytokine response. Mechanistically, ASRs are required for the efficient translation of transmembrane domains of receptor molecules involved in cytokine-mediated processes. Importantly, loss of the ASR prevents CD8+ T cell recognition and killing, and inhibitory cytokines like TGF{beta} hinder ASR formation, suggesting that the ASR is a central regulatory hub upon which multiple signals converge. Thus, the ASR is an essential mediator of the cellular rewiring that occurs following cytokine exposure, via the translational regulation of this process.

immunology↗

bioRxiv2022

In silico analysis predicts a limited impact of SARS-CoV-2 variants on CD8 T cell recognition

Since the start of the COVID-19 pandemic, mutations have led to the emergence of new SARS-CoV-2 variants, and some of these have become prominent or dominant variants of concern. This natural course of development can have an impact on how protective the previously naturally or vaccine induced immunity is. Therefore, it is crucial to understand whether and how variant specific mutations influence host immunity. To address this, we have investigated how mutations in the recent SARS-CoV-2 variants of interest and concern influence epitope sequence similarity, predicted binding affinity to HLA, and immunogenicity of previously reported SARS-CoV-2 CD8 T cell epitopes. Our data suggests that the vast majority of SARS-CoV-2 CD8 T cell recognized epitopes are not altered by variant specific mutations. Interestingly, for the CD8 T cell epitopes that are altered due to variant specific mutations, our analyses show there is a high degree of sequence similarity between mutated and reference SARS-CoV-2 CD8 T cell epitopes. However, mutated epitopes, primarily derived from the spike protein, in SARS-CoV-2 variants Delta, AY.4.2 and Mu display reduced predicted binding affinity to their restriction element. These findings indicate that the recent SARS-CoV-2 variants of interest and concern have limited ability to escape memory CD8 T cell responses raised by vaccination or prior infection with SARS-CoV-2 early in the pandemic. The overall low impact of the mutations on CD8 T cell cross-recognition is in accordance with the notion that mutations in SARS-CoV-2 are primarily the result of receptor binding affinity and antibody selection pressures exerted on the spike protein, unrelated to T cell immunity.

immunology↗

bioRxiv2020

Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases

MotivationIdentification of differentially expressed genes is necessary for unraveling disease pathogenesis. This task is complicated by the fact that many diseases are heterogeneous at the molecular level and samples representing distinct disease subtypes may demonstrate different patterns of dysregulation. Biclustering methods are capable of identifying genes that follow a similar expression pattern only in a subset of samples and hence can consider disease heterogeneity. However, identifying biologically significant and reproducible sets of genes and samples remains challenging for the existing tools. Many recent studies have shown that the integration of gene expression and protein interaction data improves the robustness of prediction and classification and advances biomarker discovery. ResultsHere we present DESMOND, a new method for identification of Differentially ExpreSsed gene MOdules iN Diseases. DESMOND performs network-constrained biclustering on gene expression data and identifies gene modules -- connected sets of genes up- or down-regulated in subsets of samples. We applied DESMOND on expression profiles of samples from two large breast cancer cohorts and have shown that the capability of DESMOND to incorporate protein interactions allows identifying the biologically meaningful gene and sample subsets and improves the reproducibility of the results. Availabilityhttps://github.com/ozolotareva/DESMOND Contactozolotareva@techfak.uni-bielefeld.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗