Biology subjects

Alvelos, M. I.

Publications and source records attributed to Alvelos, M. I..

5 recordsLinked to original sources

bioRxiv2024

Pharmacological inhibition of tyrosine protein-kinase 2 reduces islet inflammation and delays type 1 diabetes onset in mice

Tyrosine protein-kinase 2 (TYK2), a member of the Janus kinase family, mediates inflammatory signaling through multiple cytokines, including interferon- (IFN), interleukin (IL)-12, and IL-23. Missense mutations in TYK2 are associated with protection against type 1 diabetes (T1D), and inhibition of TYK2 shows promise in the management of other autoimmune conditions. Here, we evaluated the effects of specific TYK2 inhibitors (TYK2is) in pre-clinical models of T1D. First, human {beta} cells, cadaveric donor islets, and iPSC-derived islets were treated in vitro with IFN in combination with a small molecule TYK2i (BMS-986165 or a related molecule BMS-986202). TYK2 inhibition prevented IFN-induced {beta} cell HLA class I up-regulation, endoplasmic reticulum stress, and chemokine production. In co-culture studies, pre-treatment of {beta} cells with a TYK2i prevented IFN-induced activation of T cells targeting an epitope of insulin. In vivo administration of BMS-986202 in two mouse models of T1D (RIP-LCMV-GP mice and NOD mice) reduced systemic and tissue-localized inflammation, prevented {beta} cell death, and delayed T1D onset. Transcriptional phenotyping of pancreatic islets, pancreatic lymph nodes (PLN), and spleen during early disease pathogenesis highlighted a role for TYK2 inhibition in modulating signaling pathways associated with inflammation, translational control, stress signaling, secretory function, immunity, and diabetes. Additionally, TYK2i treatment changed the composition of innate and adaptive immune cell populations in the blood and disease target tissues, resulting in an immune phenotype with a diminished capacity for {beta} cell destruction. Overall, these findings indicate that TYK2i has beneficial effects in both the immune and endocrine compartments in models of T1D, thus supporting a path forward for testing TYK2 inhibitors in human T1D.

physiology↗

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↗

bioRxiv2019

The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides new insights into the genetics of type 1 diabetes

Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic {beta} cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the {beta}-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the {beta}-cell transcriptome, proteome and 3D chromatin structure. Our data indicates that the {beta} cell response to cytokines is mediated by the induction of novel regulatory regions as well as the activation of primed regulatory elements pre-bound by islet-specific transcription factors. We found that T1D-associated loci are enriched of the newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human {beta} cells. Our study illustrates how {beta} cells respond to a pro-inflammatory environment and implicate a role for stimulus-response islet enhancers in T1D.

genomics↗