The microRNA Signature in Response to Insulin Reveals Its Implication in the Transcriptional Action of Insulin in Human Skeletal Muscle and the Role of a Sterol Regulatory Element–Binding Protein-1c/Myocyte Enhancer Factor 2C Pathway

International audienceOBJECTIVE: Factors governing microRNA expressions in response to changes of cellular environment are still largely unknown. Our aim was to determine whether insulin, the major hormone controlling whole-body energy homeostasis, is involved in the regulation of microRNA expressions in human skeletal muscle. RESEARCH DESIGN AND METHODS: We carried out comparative microRNA (miRNA) expression profiles in human skeletal muscle biopsies before and after a 3-h euglycemic-hyperinsulinemic clamp, with TaqMan low-density arrays. Then, using DNA microarrays, we determined the response to insulin of the miRNA putative target genes in order to determine their role in the transcriptional action of insulin. We further characterized the mechanism of action of insulin on two representative miRNAs, miR-1 and miR-133a, in human muscle cells. RESULTS: Insulin downregulated the expressions of 39 distinct miRNAs in human skeletal muscle. Their potential target mRNAs coded for proteins that were mainly involved in insulin signaling and ubiquitination-mediated proteolysis. Bioinformatic analysis suggested that combinations of different downregulated miRNAs worked in concert to regulate gene expressions in response to insulin. We further demonstrated that sterol regulatory element-binding protein (SREBP)-1c and myocyte enhancer factor 2C were involved in the effect of insulin on miR-1 and miR-133a expression. Interestingly, we found an impaired regulation of miRNAs by insulin in the skeletal muscle of type 2 diabetic patients, likely as consequences of altered SREBP-1c activation. CONCLUSIONS: This work demonstrates a new role of insulin in the regulation of miRNAs in human skeletal muscle and suggests a possible implication of these new modulators in insulin resistance

Entrer en résonance : Vibrations autour d'un monde commun à partir de rencontres entre le 9e art et la science

What if comics allowed us to understand scientific knowledge (production) differently? Some comics, beyond their aesthetic and playful aspects, raise numerous political, ethical and societal questions that directly resonate with social science work. As part of this ‘making and doing activity’, our goal is to address sociotechnical issues, with the help of comic book writers and other artists, and to collectively rebuild spaces for collective reflection on, and engagement in, open-ended technological futures

Automated quality control tool for high-content imaging data by building 2D prediction intervals on reference biosignatures

Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during lead optimization. Comparison of perturbation biosignatures with biosignatures of annotated compounds can inform on both on- and off-target effects. When building databases with phenotypic profiles of thousands of compounds, it is vital to control the quality of Cell Painting assays over time. A tool for this to our knowledge does not yet exist within the imaging community. In this paper, we introduce an automated tool to assess the quality of Cell Painting assays by quantifying the reproducibility of biosignatures of annotated reference compounds. The tool learns the biosignature of those treatments from a historical dataset, and subsequently, it builds a two-dimensional probabilistic quality control (QC) limit. The limit will then be used to detect aberrations in new Cell Painting experiments. The tool is illustrated using simulated data and further demonstrated on Cell Painting data of the A549 cell line. In general, the tool provides a sensitive, detailed and easy-to-interpret mechanism to validate the quality of Cell Painting assays