Biology subjects

Arner, E.

Publications and source records attributed to Arner, E..

14 recordsLinked to original sources

bioRxiv2026

Extracellular GPX3 drives insulin receptor expression in adipose tissue independent of its peroxidase activity

Obesity-associated insulin resistance is driven in part by adipose tissue dysfunction and oxidative stress, yet the role of extracellular mechanisms in modulating insulin signaling remains poorly understood. Here, we show that extracellular Glutathione Peroxidase 3 (GPX3) enhances adipocyte insulin sensitivity and functionalities independent of its canonical peroxidase function. GPX3, a secreted selenoprotein highly expressed in white adipose tissue (WAT), is suppressed in obesity, but whether it actively regulates insulin sensitivity had not been established. We find that WAT GPX3 expression closely mirrors insulin receptor (InsR) expression across a broad range of metabolic parameters in humans and increases dynamically following weight loss. Prolonged therapeutic selenium supplementation in diet-induced obese mice selectively suppressed epididymal WAT oxidative damage, restored GPX3 and InsR expression, and enhanced insulin-stimulated AKT phosphorylation, improving oral glucose tolerance. Mechanistically, recombinant GPX3 extracellularly administered at physiological concentrations induced InsR expression across multiple adipocyte models, including pre-adipocytes, epididymal WAT explants, and adipocyte organoids, by promoting binding of the transcription factor Specificity Protein 1 (SP1) to the Insr promoter, while knockdown of GPX3 completely blocks SVF adipogenesis. These effects rely on the structural integrity, rather than selenocysteine-dependent activity. Thus, we identify a GPX3-SP1-InsR signaling axis in adipose tissue insulin sensitivity, suggesting GPX3 is a novel paracrine mediator of metabolic health and a potential therapeutic target to reinstate WAT insulin sensitivity in obesity and type 2 diabetes.

physiology↗

bioRxiv2026

Fast and Slow Gene Expression Changes in Blood Following Acute Social Stress

Social stress is a risk factor for psychiatric disorders and also influences immune function. While it is known that acute social stress impacts the number of immune cells in circulation, the temporal dynamics of stress induced immune-related transcriptional changes in human blood remain unclear. To investigate changes in gene expression, we exposed 26 adults to the Trier Social Stress Test (TSST), and collected blood at baseline, as well as 5, 30, 60 and 90 min after stress. Whole-blood gene expression was profiled using a 5 targeted RNA-sequencing method (STRT). Differential expression was analyzed using linear and cubic models. We observed a total of 54 differentially expressed genes following stress. Fast responses, with a transient peak immediately following stress, were enriched for cytotoxic T cell, NK cell and dendritic cell functions (e.g., GZMB, GNLY, CCL4 and GZMA) and paralleled lymphocyte count changes. In contrast, gradual, linear responses without any evident peak were enriched for neutrophil related genes (e.g., FPR2, PLAUR, CXCR2, AQP9, and QPCT) and did not mirror neutrophil counts, indicating cell intrinsic transcriptional changes. From pathway and transcription factor enrichment analyses, IL-12 family mediated signaling is inferred as a central mechanism linking stress to immune gene regulation. Our results show that acute psychosocial stress induces both fast and slower changes in gene expression in different immune cell populations. The involvement of the IL-12-STAT4 axis and genes such as PLAUR and FPR2 suggests molecular mechanisms through which stress-related immune activation may contribute to vulnerability for anxiety and depressive disorders.

genetics↗

bioRxiv2023

Molecular profiling of high-level athlete skeletal muscle after acute exercise - a systems biology approach

Life-long high-level exercise training leads to improvements in physical performance and multi-tissue adaptation following changes in molecular pathways. While skeletal muscle baseline differences between exercise-trained and untrained individuals have been previously investigated, it remains unclear how acute exercise multi-omics are influenced by training history. We recruited and extensively characterized 24 individuals categorized as endurance athletes, strength athletes or control subjects. Multi-omics profiling was performed from skeletal muscle before and at three time-points after endurance or resistance exercise sessions. Timeseries multi-omics analysis revealed distinct differences in molecular processes such as fatty- and amino acid metabolism and for transcription factors such as HIF1A and the MYF-family between both exercise history and acute form of exercise. Furthermore, we found a "transcriptional specialization effect" by transcriptional narrowing and intensification. Finally, we performed multi-omics network analysis and clustering, providing a novel resource of skeletal muscle transcriptomic and metabolomic profiling in highly trained and untrained individuals.

physiology↗

bioRxiv2023

Ancient loss of catalytic selenocysteine spurred convergent adaptation in a mammalian oxidoreductase

Selenocysteine (Sec), the 21st amino acid specified by the genetic code, is a rare selenium-containing residue found in the catalytic site of selenoprotein oxidoreductases. Sec is analogous to the common cysteine (Cys) amino acid but its selenium atom offers physicalchemical properties not provided by the corresponding sulfur atom in Cys. Catalytic sites with Sec in selenoproteins of vertebrates are under strong purifying selection but one enzyme, Glutathione Peroxidase 6 (GPX6), independently exchanged Sec for Cys less than one hundred million years ago in several mammalian lineages. We reconstructed and assayed these ancient enzymes before and after Sec was lost and up to today, and found them to have lost their classic ability to reduce hydroperoxides using glutathione (GSH). This loss of function, however, was accompanied by bursts of amino acid changes in the catalytic domain, with protein sites concertedly changing under positive selection across distant lineages abandoning Sec in GPX6. This demonstrates that when sulfur in Cys impairs catalysis a narrow evolutionary path is followed, with epistasis and pleiotropy leading to convergent evolution and triggering enzymatic properties likely beyond those in classic GPXs. These findings are an unusual example of adaptive convergence towards unexplored oxidoreductase functions during mammalian evolution.

molecular biology↗

bioRxiv2022

xcore: an R package for inference of gene expression regulators

Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is one of the most common questions asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types. Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that the modeling can be done using ChiP-seq "signatures" directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows Transcription Factor activity modeling based on their ChiP-seq signatures and users gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChiP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using TGF-beta induced epithelial-mesenchymal transition and rinderpest infection time-course CAGE data as examples. Availability and Implementationxcore and xcoredata R packages are freely available at www.github.com/bkaczkowski/xcore and www.github.com/mcjmigdal/xcoredata. xcore user guide is available www.bkaczkowski.github.io/xcore/articles/xcore_vignette.html. Contactb.kaczkowski@gmail.com

bioinformatics↗

bioRxiv2021

Redox regulation of PTPN22 affects the severity of T cell dependent autoimmune inflammation

Chronic autoimmune diseases are associated with mutations in PTPN22, a modifier of T cell receptor signaling. As with all protein tyrosine phosphatases the activity of PTPN22 is redox regulated, but if or how such regulation can modulate inflammatory pathways in vivo is not known. To determine this, we created a mouse with a cysteine-to-serine mutation at position 129 in PTPN22 (C129S), a residue proposed to alter the redox regulatory properties of PTPN22 by forming a disulfide with the catalytic C227 residue. The C129S mutant mouse showed a stronger T cell-dependent inflammatory response and development of T cell dependent autoimmune arthritis due to enhanced TCR signaling and activation of T cells, an effect neutralized by a mutation in Ncf1, a component of the NOX2 complex. Activity assays with purified proteins suggest that the functional results can be explained by an increased sensitivity to oxidation of the C129S mutated PTPN22 protein. We also observed that the disulfide of native PTPN22 can be directly reduced by the thioredoxin system, while the C129S mutant lacking this disulfide was less amenable to reductive reactivation. In conclusion, we show that PTPN22 functionally interacts with Ncf1 and is regulated by oxidation via the non-catalytic C129 residue and oxidation-prone PTPN22 leads to increased severity in the development of T cell-dependent autoimmunity. Significance statementA hitherto unstudied aspect of PTPN22 biology is its regulation by cell redox states. Here we created a mouse model where PTPN22 was mutated to respond differentially to redox levels in vivo and found that PTPN22 function is regulated by reactive oxygen species and that redox regulation of PTPN22 impacts T-cell-dependent autoimmune inflammation.

immunology↗

bioRxiv2021

ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation

Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.

bioinformatics↗

bioRxiv2020

A DeepFake framework for prediction of cell type specific transcriptional states induced by drug treatment

Drug treatment induces cell type-specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.

bioinformatics↗

bioRxiv2020

Efficient Development of Platform Cell Lines Using CRISPR-Cas9 1 and Transcriptomics Analysis

Antibody-drug conjugates offers many advantages as a drug delivery platform that allows for highly specific targeting of cell types and genes. Ideally, testing the efficacy of these systems requires two cell types to be different only in the gene targeted by the drug, with the rest of the cellular machinery unchanged, in order to minimize other potential differences from obscuring the effects of the drug. In this study, we created multiple variants of U87MG cells with targeted mutation in the TP53 gene using the CRISPR-Cas9 system, and determined that their major transcriptional differences stem from the loss of p53 function. Using the transcriptome data, we predicted which mutant clones would have less divergent phenotypes from the wild type and thereby serve as the best candidates to be used as drug delivery testing platforms. Further in vitro and in vivo assays of cell morphology, proliferation rate and target antigen-mediated uptake supported our predictions. Based on the combined analysis results, we successfully selected the best qualifying mutant clone. This study serves as proof-of-principle of the approach and paves the way for extending to additional cell types and target genes.

bioengineering↗

bioRxiv2020

Inducing Human Retinal Pigment Epithelium-like Cells from Somatic Tissue

Regenerative medicine relies on basic research to find safe and useful outcomes that are only practical when cost-effective. The human eyeball requires the retinal pigment epithelium (RPE) for support and maintenance that interfaces the neural retina and the choroid at large. Nearly 200 million people suffer from age-related macular degeneration (AMD), a blinding multifactor genetic disease among other retinal pathologies related to RPE degradation. Recently, autologous pluripotent stem cell-derived RPE cells were prohibitively expensive due to time, therefore we developed a new simplified cell reprogramming system. We stably induced RPE-like cells (iRPE) from human fibroblasts by conditional overexpression of broad plasticity and lineage-specific pioneering transcription factors. iRPE cells showed features of modern RPE benchmarks and significant in-vivo integration in transplanted chimeric hosts. Herein, we detail the iRPE system with comprehensive modern single-cell RNA (scRNA) sequencing profiling to interpret and characterize its best cells. We anticipate that our system may enable robust retinal cell induction for regenerative medicine research and affordable autologous human RPE tissue for cell therapy.

bioengineering↗

bioRxiv2020

Automatic identification of small molecules that promote cell conversion and reprogramming

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of drug-induced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive in silico and in vitro validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.

bioinformatics↗

bioRxiv2019

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

genomics↗

bioRxiv2019

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodelling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed. However, they still present some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared to existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and emphasizes the role of transcription in the establishment of chromatin structure.

genomics↗

bioRxiv2018

C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

Single-cell transcriptomic profiling is a powerful tool to explore cellular heterogeneity. However, most of these methods focus on the 3-end of polyadenylated transcripts and provide only a partial view of the transcriptome. We introduce C1 CAGE, a method for the detection of transcript 5-ends with an original sample multiplexing strategy in the C1 microfluidic system. We first quantified the performance of C1 CAGE and found it as accurate and sensitive as other methods in C1 system. We then used it to profile promoter and enhancer activities in the cellular response to TGF-{beta} of lung cancer cells and discovered subpopulations of cells differing in their response. We also describe enhancer RNA dynamics revealing transcriptional bursts in subsets of cells with transcripts arising from either strand within a single-cell in a mutually exclusive manner, which was validated using single molecule fluorescence in-situ hybridization.

genomics↗