5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Human thermogenic adipocyte regulation by the long noncoding RNA LINC00473

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Human thermogenic adipose tissue mitigates metabolic disease, raising much interest in understanding its development and function. Here, we show that human thermogenic adipocytes specifically express a primate-specific long non-coding RNA, LINC00473 which is highly correlated with UCP1 expression and decreased in obesity and type-2 diabetes. LINC00473 is detected in progenitor cells, and increases upon differentiation and in response to cAMP. In contrast to other known adipocyte LincRNAs, LINC00473 shuttles out of the nucleus, colocalizes and can be crosslinked to mitochondrial and lipid droplet proteins. Up- or down- regulation of LINC00473 results in reciprocal alterations in lipolysis, respiration and transcription of genes associated with mitochondrial oxidative metabolism. Depletion of PLIN1 results in impaired cAMP-responsive LINC00473 expression and lipolysis, indicating bidirectional interactions between PLIN1, LINC00473 and mitochondrial oxidative functions. Thus, we suggest that LINC00473 is a key regulator of human thermogenic adipocyte function, and reveals a role for a LincRNA in inter-organelle communication and human energy metabolism.

          Related collections

          Most cited references67

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

            High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
                Bookmark

                Author and article information

                Journal
                101736592
                48119
                Nat Metab
                Nat Metab
                Nature metabolism
                2522-5812
                15 April 2020
                21 May 2020
                May 2020
                01 November 2020
                : 2
                : 5
                : 397-412
                Affiliations
                [1 ]Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
                [2 ]Department of Medicine, Division of Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
                [3 ]The Centre of Inflammation and Metabolism and the Centre for Physical Activity Research, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
                [4 ]Novo Nordisk A/S, Discovery Biology & Technology, Bioinformatics, Maaloev, Denmark
                [5 ]Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
                [6 ]Department of Surgery, Division of Vascular and Endovascular Surgery, University of Massachusetts Medical School, Worcester, MA 01655, USA
                [7 ]Novo Nordisk Research Centre Oxford, University of Oxford, United Kingdom
                [8 ]Co-first authors
                [9 ]Co-senior authors
                Author notes

                Author Contributions

                S.C. and S.N. supervised this work. K.V.T., E.L.B., T.D., C.N.B., S.Y.M., M.L., T.J.L., B.E., B.K.P., T.F., C.S., S.N., S.C.: hypothesis generation, conceptual design, data analysis, and manuscript preparation. K.V.T., E.L.B., T.D., N.Z.J., C.N.B., Q.Y., Z.Y., A.D., S.Y.M., R.R.R., A.F., H.W., M.C.K.S., K.M., A.M.M., A.S.D., S.C., S.N.: conducting experiments and data analysis.

                Article
                NIHMS1584999
                10.1038/s42255-020-0205-x
                7241442
                32440655
                5b320b37-fe07-471e-8e7c-e6cd14b4f8be

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Categories
                Article

                brown,beige,brite,norepinephrine,forskolin,adipocyte,mitochondria,respiration,non-coding rna,fat,lipid droplet,lipolysis,plin1

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content810

                Cited by45

                Most referenced authors1,561