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      lincRNAs act in the circuitry controlling pluripotency and differentiation

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          Abstract

          While thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of-function studies on most lincRNAs expressed in mouse embryonic stem cells (ESC) and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ESC regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ESCs and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ESC state.

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          Most cited references46

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          The transcriptional landscape of the mammalian genome.

          This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
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            Long noncoding RNA as modular scaffold of histone modification complexes.

            Long intergenic noncoding RNAs (lincRNAs) regulate chromatin states and epigenetic inheritance. Here, we show that the lincRNA HOTAIR serves as a scaffold for at least two distinct histone modification complexes. A 5' domain of HOTAIR binds polycomb repressive complex 2 (PRC2), whereas a 3' domain of HOTAIR binds the LSD1/CoREST/REST complex. The ability to tether two distinct complexes enables RNA-mediated assembly of PRC2 and LSD1 and coordinates targeting of PRC2 and LSD1 to chromatin for coupled histone H3 lysine 27 methylation and lysine 4 demethylation. Our results suggest that lincRNAs may serve as scaffolds by providing binding surfaces to assemble select histone modification enzymes, thereby specifying the pattern of histone modifications on target genes.
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              Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

              Y. H. Yang (2002)
              There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                28 July 2011
                28 August 2011
                15 March 2012
                : 477
                : 7364
                : 295-300
                Affiliations
                [1 ]Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge MA 02142
                [2 ]Department of Biology, Massachusetts Institute of Technology, Cambridge MA 02139
                [3 ]Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge MA 02142
                [4 ]Genomics Research and Development, Agilent Technologies, Santa Clara, CA 95051
                [5 ]Stem Cell and Regenerative Biology, Harvard University, Cambridge MA 02138
                [6 ]Department of Systems Biology, Harvard Medical School, Boston MA 02114
                Author notes
                []Correspondence and requests for materials should be addressed to Mitchell Guttman ( mguttman@ 123456mit.edu ) or Eric S. Lander ( lander@ 123456broadinstitute.org )
                [*]

                These authors contributed equally to this work

                Article
                nihpa314381
                10.1038/nature10398
                3175327
                21874018
                76e76ec3-ea0a-4501-ba3e-2730edf9329e

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