Biology subjects

Wnuk, K.

Publications and source records attributed to Wnuk, K..

3 recordsLinked to original sources

bioRxiv2022

Peptidome Surveillance Across Evolving SARS-CoV-2 Lineages Reveals HLA Binding Conservation in Nucleocapsid Among Variants With Most Potential for T-Cell Epitope Loss In Spike

To provide a unique global view of the relative potential for evasion of CD8+ and CD4+ T cells by SARS-CoV-2 lineages as they evolve over time, we performed a comprehensive analysis of predicted HLA-I and HLA-II binding peptides in spike (S) and nucleocapsid (N) protein sequences of all available SARS-CoV-2 genomes as provided by NIH NCBI at a bi-monthly interval between March and December of 2021. A data supplement of all B.1.1.529 (Omicron) genomes from GISAID in early December was also used to capture the rapidly spreading variant. A key finding is that throughout continued viral evolution and increasing rates of mutations occurring at T-cell epitope hotspots, protein instances with worst case binding loss did not become the most frequent for any Variant of Concern (VOC) or Variant of Interest (VOI) lineage; suggesting T-cell evasion is not likely to be a dominant evolutionary pressure on SARS-CoV-2. We also determined that throughout the course of the pandemic in 2021, there remained a relatively steady ratio of viral variants that exhibit conservation of epitopes in the N protein, despite significant potential for epitope loss in S relative to other lineages. We further localized conserved regions in N with high epitope yield potential, and illustrated HLA-I binding heterogeneity across the S protein consistent with empirical observations. Although Omicrons high volume of mutations caused it to exhibit more epitope loss potential than most frequently observed versions of proteins in almost all other VOCs, epitope candidates across its most frequent N proteins were still largely conserved. This analysis adds to the body of evidence suggesting that N may have merit as an additional antigen to elicit immune responses to vaccination with increased potential to provide sustained protection against COVID-19 disease in the face of emerging variants.

immunology↗

bioRxiv2018

Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples

Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of Regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n=135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.

cancer biology↗

bioRxiv2017

Predicting DNA accessibility in the pan-cancer tumor genome using RNA-seq, WGS, and deep learning

DNA accessibility, chromatin regulation, and genome methylation are key drivers of transcriptional events promoting tumor growth. However, understanding the impact of DNA sequence data on transcriptional regulation of gene expression is a challenge, particularly in noncoding regions of the genome. Recently, neural networks have been used to effectively predict DNA accessibility in multiple specific cell types [14]. These models make it possible to explore the impact of mutations on DNA accessibility and transcriptional regulation.\n\nOur work first improved on prior cell-specific accessibility prediction, obtaining a mean receiver operating characteristic (ROC) area under the curve (AUC) = 0.910 and mean precision-recall (PR) AUC = 0.605, compared to the previous mean ROC AUC = 0.895 and mean PR AUC = 0.561 [14].\n\nOur key contribution extended the model to enable accessibility predictions on any new sample for which RNA-seq data is available, without requiring cell-type-specific DNase-seq data for re-training. This new model obtained overall PR AUC = 0.621 and ROC AUC = 0.897 when applied across whole genomes of new samples whose biotypes were held out from training, and PR AUC = 0.725 and ROC AUC = 0.913 on randomly held out new samples whose biotypes were allowed to overlap with training.\n\nMore significantly, we showed that for promoter and promoter flank regions of the genome our model predicts accessibility to high reliability, achieving PR AUC = 0.839 in held out biotypes and PR AUC = 0.911 in randomly held out samples.\n\nThis performance is not sensitive to whether the promoter and flank regions fall within genes used in the input RNA-seq expression vector.\n\nFinally, we utilize this tool to investigate, for the first time, promoter accessibility patterns across several cohorts from The Cancer Genome Atlas (TCGA) [27].

bioinformatics↗