Biology subjects

Szeto, C.

Publications and source records attributed to Szeto, C..

4 recordsLinked to original sources

bioRxiv2023

Type I interferon dynamics determines memory B cell epigenetic identity in chronic viral infection

Memory B cells are key providers of long-lived immunity against infectious disease, yet in chronic viral infection they do not produce effective protection. How chronic viral infection disrupts memory B cell development, and whether such changes are reversible, remains unknown. Here, we uncover type-I interferon (IFN-I) dynamics as a key determinant in shaping chronic memory B cell development. Through single-cell (sc)ATAC-sequencing and scRNA-sequencing, we identified a unique memory subset enriched for IFN-stimulated genes (ISGs) during chronic lymphocytic choriomeningitis virus infection. Blockade of IFNAR-1 early in infection transformed the chromatin landscape of chronic memory B cells, decreasing accessibility at ISG-inducing transcription factor binding motifs and inducing a phenotypic change in the dominating memory B cell subset. However, timing was critical, with memory B cells resistant to intervention after 4 weeks post-infection. Together, our research identifies a key mechanism to instruct memory B cell identity during viral infection. One Sentence Summary: IFN dynamics in chronic versus acute viral infection determines memory B cell development.

immunology↗

bioRxiv2022

COVID-19 vaccine booster induces a strong CD8+ T cell response against Omicron variant epitopes in HLA-A*02:01+ individuals

The >30 mutated residues in the Omicron spike protein have led to its rapid classification as a new SARS-CoV-2 variant of concern. As a result, Omicron may escape from the immune system, decreasing the protection provided by COVID-19 vaccines. Preliminary data shows a weaker neutralizing antibody response to Omicron compared to the ancestral SARS-CoV-2 virus, which can be increased after a booster vaccine. Here, we report that CD8+ T cells can recognize Omicron variant epitopes presented by HLA-A*02:01 in both COVID-19 recovered and vaccinated individuals, even 6 months after infection or vaccination. Additionally, the T cell response was stronger for Omicron variant epitopes after the vaccine booster. Altogether, T cells can recognize Omicron variants, especially in vaccinated individuals after the vaccine booster. One-Sentence SummaryCD8+ T cells response against Omicron variant epitopes is stronger after the vaccine booster.

immunology↗

bioRxiv2021

Molecular basis of a dominant SARS-CoV-2 Spike-derived epitope presented by HLA-A*02:01 recognised by a public TCR

The data currently available on how the immune system recognizes the SARS-CoV-2 virus is growing rapidly. While there are structures of some SARS-CoV-2 proteins in complex with antibodies, which helps us understand how the immune system is able to recognise this new virus, we are lacking data on how T cells are able to recognize this virus. T cells, especially the cytotoxic CD8+ T cells, are critical for viral recognition and clearance. Here we report the X-ray crystallography structure of a T cell receptor, shared among unrelated individuals (public TCR) in complex with a dominant spike-derived CD8+ T cell epitope (YLQ peptide). We show that YLQ activates a polyfunctional CD8+ T cell response in COVID-19 recovered patients. We detail the molecular basis for the shared TCR gene usage observed in HLA-A*02:01+ individuals, providing an understanding of TCR recognition towards a SARS-CoV-2 epitope. Interestingly, the YLQ peptide conformation did not change upon TCR binding, facilitating the high-affinity interaction observed.

immunology↗

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↗