Biology subjects

Pociot, F.

Publications and source records attributed to Pociot, F..

6 recordsLinked to original sources

bioRxiv2026

Stem-Cell-Derived Islets as a Model of Human Islet Inflammation: A Comparative Analysis of Pro-inflammatory Cytokine Responses

BackgroundInflammation-induced pancreatic islet-cell death and dysfunction are key aspects of both type 1 and type 2 diabetes. Stem cell-derived islets (SC-islets) are an emerging tool in diabetes research, however, our understanding of how inflammation affects SC-islet function is incomplete. We therefore aimed to thoroughly characterize how SC-islets respond to pro-inflammatory cytokines at the functional and transcriptomic levels in comparison with human primary islets and EndoC-{beta}H5 cells. MethodA 7-stage differentiation protocol was used to generate SC-islets with insulin-, glucagon-, and somatostatin-positive cells. SC-islets, primary human islets and EndoC-{beta}H5 cells were exposed to different doses of pro-inflammatory cytokines (IL-1{beta} + IFN{gamma} + TNF) including a high dose for up to 48 h and a low dose up to 144 h to mimic the intense islet inflammation in T1D and chronic low-grade inflammation in T2D, respectively. Differential gene expression (RNA-seq), cell death, activation of key signalling proteins, hormones, and chemokine secretion were determined. ResultsBasal expression of key islet-cell identity genes in SC-islets correlated well with that of primary islets and EndoC-{beta}H5 cells. In SC islets, cytokines dose-dependently induced activation of key proximal signalling pathways (NF{kappa}B, STAT1, and JNK), upregulation of major histocompatibility complex (MHC) class I, and increased cell death (cytotoxicity and caspase 3/7 activity). In head-to-head experiments, SC-islets displayed similar cytokine responses particularly as primary islets regarding induction of cell death, chemokine secretion, differential gene expression, and protein levels of cell death executioners (gasdermin D and caspase-7). Cytokines increased insulin release in SC-islets and primary islets, while diminishing insulin secretion in EndoC-{beta}H5 cells. Cytokines reduced glucagon release in SC-islets, which was partially restored by treatment with the incretin hormone glucose-dependent insulinotropic peptide (GIP) with or without a glucagon-like peptide 1 (GLP-1) receptor agonist (liraglutide). ConclusionSC-islets are highly responsive to inflammation with a high degree of similarity to primary islets. Our results support the use of SC-islets as a valid tool in inflammation and diabetes research.

molecular biology↗

bioRxiv2022

Characterisation of the functional and transcriptomic effects of pro-inflammatory cytokines on human EndoC- βH5 beta cells

ObjectiveEndoC-{beta}H5 is a newly established human beta-cell model which may be superior to previous models of native human beta cells. Exposure of beta cells to proinflammatory cytokines is a widely used in vitro model of immune-mediated beta-cell failure in type 1 diabetes and we therefore performed an in-depth characterisation of the effects of cytokines on EndoC-{beta}H5 cells. MethodsThe sensitivity profile of EndoC-{beta}H5 cells to the toxic effects of the pro-inflammatory cytokines interleukin-1{beta} (IL-1{beta}), interferon {gamma} (IFN{gamma}) and tumour necrosis factor- (TNF) was examined in titration and time-course experiments. Cell death was evaluated by caspase 3/7 activity, cytotoxicity, viability, TUNEL assay and immunoblotting. Mitochondrial function was evaluated by extracellular flux technology. Activation of signalling pathways and major histocompatibility complex (MHC) class I expression were examined by immunoblotting, immunofluorescence, and real-time quantitative PCR (qPCR). Glucose-stimulated insulin secretion (GSIS) and cytokine-induced chemokine secretion were measured by ELISA and Meso Scale Discovery multiplexing electrochemiluminescence, respectively. Global gene expression was characterised by stranded RNA sequencing. ResultsCytokines increased caspase activity and cytotoxicity in EndoC-{beta}H5 cells in a time- and dose-dependent manner. The proapoptotic effect of cytokines was primarily driven by IFN{gamma}. Cytokine exposure caused impaired mitochondrial function, diminished GSIS, and induced secretion of chemokines. At the signalling level, cytokines increased the phosphorylation of signal transducer and activator of transcription 1 (STAT1) but not c-jun N-terminal kinase (JNK) and did not cause degradation of nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor (I{kappa}B). MHC class I was induced by cytokines. Cytokine exposure caused significant changes to the EndoC-{beta}H5 transcriptome including upregulation of HLA genes, endoplasmic reticulum stress markers, and non-coding RNAs. Among the differentially expressed genes were several type 1 diabetes risk genes. ConclusionsOur study provides detailed insight into the functional and transcriptomic effects of cytokines on EndoC-{beta}H5 cells. This knowledge will be helpful for future investigations studying cytokine effects in this cell model.

cell biology↗

bioRxiv2021

Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid disease

For polygenic traits, associations with genetic variants can be detected over many chromosome regions, owing to the availability oflarge sample sizes. Most variants, however, have small effects on disease risk and, therefore, unravelling the causal variants, target genes, and biology of these variants is challenging. Here, we define the Bigger or False Discovery Rate (BFDR) as the probability that either a variant is a false-positive or a randomly drawn, true-positive association exceeds it in effect size. Using the BFDR, we identified 302 previously unreported signals with larger effect associations with type 1 diabetes and autoimmune thyroid disease. Out of 239 genome-wide significant signals in both diseases, only 66 (28%) show evidence for having a large effect using the BFDR, further demonstrating how using a combination of effect size and significance, rather than significance alone, is important in identifying SNPs and candidate genes for further investigation.

genetics↗

bioRxiv2020

Transcriptome profiling of ulcerative colitis mouse model suggests biomarkers and therapeutic targets for human colitis

1.BACKGROUND & AIMSUlcerative colitis (UC) is an inflammatory bowel disorder with unknown etiology. Given its complex nature, in vivo studies to investigate its pathophysiology is vital. Animal models play an important role in molecular profiling necessary to pinpoint mechanisms that contribute to human disease. Thus, we aim to identify common conserved gene expression signatures and differentially regulated pathways between human UC and a mouse model hereof, which can be used to identify UC patients from healthy individuals and to suggest novel treatment targets and biomarker candidates. METHODSTherefore, we performed high-throughput total and small RNA sequencing to comprehensively characterize the transcriptome landscape of the most widely used UC mouse model, the dextran sodium sulfate (DSS) model. We used this data in conjunction with publicly available human UC transcriptome data to compare gene expression profiles and pathways. RESULTSWe identified differentially regulated protein-coding genes, long non-coding RNAs and microRNAs from colon and blood of UC mice and further characterized the involved pathways and biological processes through which these genes may contribute to disease development and progression. By integrating human and mouse UC datasets, we suggest a set of 51 differentially regulated genes in UC colon and blood that may improve molecular phenotyping, aid in treatment decisions, drug discovery and the design of clinical trials. CONCLUSIONGlobal transcriptome analysis of the DSS-UC mouse model supports its use as an efficient high-throughput tool to discover new targets for therapeutic and diagnostic applications in human UC through identifying relationships between gene expression and disease phenotype.

molecular biology↗

bioRxiv2020

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

We report the largest and most ancestrally diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 152 regions associated to false discovery rate < 0.01, including 36 regions associated to genome-wide significance for the first time. Credible sets of disease-associated variants are specifically enriched in immune cell accessible chromatin, particularly in CD4+ effector T cells. Colocalization with chromatin accessibility quantitative trait loci (QTL) in CD4+ T cells identified five regions where differences in T1D risk and chromatin accessibility are potentially driven by the same causal variant. Allele-specific chromatin accessibility further refined the set of putative causal variants with functional relevance in CD4+ T cells and integration of whole blood expression QTLs identified candidate T1D genes, providing high-yield targets for mechanistic follow-up. We highlight rs72938038 in BACH2 as a candidate causal T1D variant, where the T1D risk allele leads to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritise potential drug targets by integrating genetic evidence, functional genomic maps, and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D, including proposed genetic regulatory mechanisms of T1D-associated variants and genetic support for therapeutic targets for immune intervention.

genetics↗

bioRxiv2020

Lipidomics of human adipose tissue reveals diversity between body areas

Background and aimsAdipose tissue plays a pivotal role in storing excess fat and its composition reflects the history of persons lifestyle and metabolic health. Broad profiling of lipids with mass spectrometry has potential for uncovering new knowledge on the pathology of obesity, metabolic syndrome, diabetes and other related conditions. Here, we developed a lipidomic method for analyzing human subcutaneous adipose biopsies. We applied the method to four body areas to understand the differences in lipid composition between these areas. Materials and methodsAdipose tissue biopsies from 10 participants were analyzed using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The method development included the optimization of the lipid extraction, the sample amount and the sample dilution factor to detect lipids in an appropriate concentration range. Lipidomic analyses were performed for adipose tissue collected from the abdomen, breast, thigh and lower back. Differences in lipid levels between tissues were visualized with heatmaps. ResultsLipidomic analysis on human adipose biopsies lead to the identification of 187 lipids in 2 mg of sample. Technical variation of the lipid-class specific internal standards were below 5 %, thus indicating acceptable repeatability. Triacylglycerols were highly represented in the adipose tissue samples, and lipids from 13 lipid classes were identified. Long polyunsaturated triacylglycerols in higher levels in thigh (q<0.05), when compared with the abdomen, breast and lower back, indicating that the lipidome was area-specific. ConclusionThe method presented here is suitable for the analysis of lipid profiles in 2 mg of adipose tissue. The amount of fat across the body is important for health but we argue that also the distribution and the particular profile of the lipidome may be relevant for metabolic outcomes. We suggest that the method presented in this paper could be useful for detecting such aberrations.

biophysics↗