42
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Comparison of REST Cistromes across Human Cell Types Reveals Common and Context-Specific Functions

      research-article

      Read this article at

      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

          Recent studies have shown that the transcriptional functions of REST are much broader than repressing neuronal genes in non-neuronal systems. Whether REST occupies similar chromatin regions in different cell types and how it interacts with other transcriptional regulators to execute its functions in a context-dependent manner has not been adequately investigated. We have applied ChIP-seq analysis to identify the REST cistrome in human CD4+ T cells and compared it with published data from 15 other cell types. We found that REST cistromes were distinct among cell types, with REST binding to several tumor suppressors specifically in cancer cells, whereas 7% of the REST peaks in non-neuronal cells were ubiquitously called and <25% were identified for ≥5 cell types. Nevertheless, using a quantitative metric directly comparing raw ChIP-seq signals, we found the majority (∼80%) was shared by ≥2 cell types. Integration with RNA-seq data showed that REST binding was generally correlated with low gene expression. Close examination revealed that multiple contexts were correlated with reduced expression of REST targets, e.g., the presence of a cognate RE1 motif and cellular specificity of REST binding. These contexts were shown to play a role in differential corepressor recruitment. Furthermore, transcriptional outcome was highly influenced by REST cofactors, e.g., SIN3 and EZH2 co-occupancy marked higher and lower expression of REST targets, respectively. Unexpectedly, the REST cistrome in differentiated neurons exhibited unique features not observed in non-neuronal cells, e.g., the lack of RE1 motifs and an association with active gene expression. Finally, our analysis demonstrated how REST could differentially regulate a transcription network constituted of miRNAs, REST complex and neuronal factors. Overall, our findings of contexts playing critical roles in REST occupancy and regulatory outcome provide insights into the molecular interactions underlying REST's diverse functions, and point to novel roles of REST in differentiated neurons.

          Author Summary

          The RE-1 silencing transcription factor (REST) binds to DNA and has been shown to repress neuronal genes in non-neuronal systems, but more recent studies have expanded its functions much beyond this. At the molecular level, REST acts cooperatively with other proteins to execute its transcriptional regulatory roles. The dynamics of REST binding and cofactor recruitment and its association with the underlying DNA sequence remain unclear. Here, we have applied chromatin immunoprecipitation and deep sequencing to identify REST binding across 16 different cell types, including neurons. Our results demonstrate that REST binding events are dynamic and quite distinct among cells and that REST binding is generally associated with low gene expression. Closer examination finds that the context of the DNA sequence at REST bound sites is associated with the lower expression of REST-associated targets and that different contexts correlate with different cofactor recruitment. These in turn have an effect on the expression of REST targets. REST targets in human neurons, however, are drastically different from those in other cell types. These findings provide insights into the effect of genomic and cellular contexts on REST's diverse functions and point to distinct and novel roles for REST in neurons.

          Related collections

          Most cited references70

          • Record: found
          • Abstract: found
          • Article: not found

          Direct conversion of fibroblasts to functional neurons by defined factors

          Cellular differentiation and lineage commitment are considered robust and irreversible processes during development. Recent work has shown that mouse and human fibroblasts can be reprogrammed to a pluripotent state with a combination of four transcription factors. This raised the question of whether transcription factors could directly induce other defined somatic cell fates, and not only an undifferentiated state. We hypothesized that combinatorial expression of neural lineage-specific transcription factors could directly convert fibroblasts into neurons. Starting from a pool of nineteen candidate genes, we identified a combination of only three factors, Ascl1, Brn2, and Myt1l, that suffice to rapidly and efficiently convert mouse embryonic and postnatal fibroblasts into functional neurons in vitro. These induced neuronal (iN) cells express multiple neuron-specific proteins, generate action potentials, and form functional synapses. Generation of iN cells from non-neural lineages could have important implications for studies of neural development, neurological disease modeling, and regenerative medicine.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

            The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Identification of novel transcripts in annotated genomes using RNA-Seq.

              We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                June 2014
                12 June 2014
                : 10
                : 6
                : e1003671
                Affiliations
                [1 ]Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
                [2 ]Howard Hughes Medical Institute, Laboratory of Mammalian Cell Biology & Development, The Rockefeller University, New York, New York, United States of America
                [3 ]Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
                [4 ]Systems Biology Center, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland, United States of America
                [5 ]Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
                [6 ]The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
                Rutgers University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DZ SR. Performed the experiments: SR WHL EP ML GW. Analyzed the data: SR WHL ML. Contributed reagents/materials/analysis tools: KZ HML EF. Wrote the paper: SR DZ.

                [¤a]

                Current address: de Duve Institute, Université Catholique de Louvain, Brussels, Belgium

                [¤b]

                Current address: CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China

                Article
                PCOMPBIOL-D-13-01926
                10.1371/journal.pcbi.1003671
                4055426
                24922058
                60565002-d335-487f-ba57-82122aa760dc
                Copyright @ 2014

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 2 November 2013
                : 1 May 2014
                Page count
                Pages: 17
                Funding
                This work was supported by grants from the National Institutes of Health/National Institute of Mental Health (MH099452 to DZ) and (MH073164 to HML). EF is an Investigator of the Howard Hughes Medical Institute, and received support from National Institutes of Health R01-AR31737 for this work. WHL was supported by a postdoctoral fellowship from the Jane Coffin Childs Foundation and had previously been the recipient of a Harvey L. Karp Discovery Award Fellowship from the Rockefeller University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Genome Expression Analysis
                Genetics
                Genomics
                Functional Genomics
                Systems Biology

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article