Biology subjects

Szymczak, F.

Publications and source records attributed to Szymczak, F..

3 recordsLinked to original sources

bioRxiv2023

Interferons are the key cytokines acting on pancreatic islets in type 1 diabetes

The pro-inflammatory cytokines IFN, IFN{gamma}, IL-1{beta} and TNF may contribute to innate and adaptive immune responses during islet inflammation (insulitis) in type 1 diabetes (T1D). We used deep RNA-sequencing analysis to characterize the response of human pancreatic beta cells to each cytokine individually and compared the signatures obtained with those present in islets of individuals affected by T1D. IFN and IFN{gamma} had a much greater impact on the beta cell transcriptome when compared to IL-1{beta} and TNF. The IFN-induced gene signatures have a strong correlation with those observed in beta cells from T1D patients, and the level of expression of specific IFN-stimulated genes is positively correlated with proteins present in islets of these individuals, regulating beta cell responses to "danger signals" such as viral infections. These data suggest that IFN and IFN{gamma} are the central cytokines at the islet level in T1D, contributing to the triggering and amplification of autoimmunity.

immunology↗

bioRxiv2022

RedRibbon: A new rank-rank hypergeometric overlap pipeline to compare gene and transcript expression signatures

MotivationHigh throughput omics technologies have generated a wealth of large protein, gene and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at both gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. ResultsWe tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anti-correlation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. Additionally, the package is a drop-in replacement to previous packages as a compatibility mode is included, allowing to re-run older studies with close to no change to existing pipelines. RedRibbon holds the promise to accurately extricate detailed information from large analyses. AvailabilityRNA-sequencing datasets are available through the Gene Expression Omnibus (GEO) portal with accession numbers GSE159984, GSE133218, GSE137136, GSE98485, GSE148058 and GSE108413. The C libraries and R package code are open to the community with a permissive licence (GPL3) and available for download from GitHub https://github.com/antpiron/ale, https://github.com/antpiron/cRedRibbon and https://github.com/antpiron/RedRibbon. Contactanthony.piron@ulb.be

bioinformatics↗

bioRxiv2021

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis

Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression. We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-{beta}H1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples. We identified a total of 264 genes stably expressed in EndoC-{beta}H1 cells and human islets following cytokine- or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells.

systems biology↗