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      Genome-wide analysis of FoxO1 binding in hepatic chromatin: Potential involvement of FoxO1 in linking retinoid signaling to hepatic gluconeogenesis

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          Abstract

          The forkhead transcription factor FoxO1 is a critical regulator of hepatic glucose and lipid metabolism, and dysregulation of FoxO1 function has been implicated in diabetes and insulin resistance. We globally identified FoxO1 occupancy in mouse hepatic chromatin on a genome-wide level by chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq). To establish the specific functional significance of FoxO1 against other FoxO proteins, ChIP-seq was performed with chromatin from liver-specific FoxO1 knockout and wild-type mice. Here we identified 401 genome-wide FoxO1-binding locations. Motif search reveals a sequence element, 5′ GTAAACA 3′, consistent with a previously known FoxO1-binding site. Gene set enrichment analysis shows that the data from FoxO1 ChIP-seq are highly correlated with the global expression profiling of genes regulated by FoxO1, demonstrating the functional relevance of our FoxO1 ChIP-seq study. Interestingly, gene ontology analysis reveals the functional significance of FoxO1 in retinoid metabolic processes. We show here that FoxO1 directly binds to the genomic sites for the genes in retinoid metabolism. Notably, deletion of FoxO1 caused a significantly reduced induction of Pck1 and Pdk4 in response to retinoids. As Pck1 and Pdk4 are downstream targets of retinoid signaling, these results suggest that FoxO1 plays a potential role in linking retinoid metabolism to hepatic gluconeogenesis.

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          Most cited references42

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          ChIP-seq accurately predicts tissue-specific activity of enhancers.

          A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover because they are scattered among the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here we present the results of chromatin immunoprecipitation with the enhancer-associated protein p300 followed by massively parallel sequencing, and map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases demonstrated reproducible enhancer activity in the tissues that were predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities, and suggest that such data sets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.
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            Detecting differential gene expression with a semiparametric hierarchical mixture method.

            Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.
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              Identification of the differential distribution patterns of mRNAs and consensus binding sequences for mouse DAF-16 homologues.

              daf-16 is a forkhead-type transcription factor, functioning downstream of insulin-like signals, and is known to be critical to the regulation of life span in Caenorhabditis elegans. Mammalian DAF-16 homologues include AFX, FKHR and FKHRL1, which contain a conserved forkhead domain and three putative phosphorylation sites for the Ser/Thr kinase Akt/protein kinase B (PKB), as well as for DAF-16. To assess the function of the homologues, we examined tissue distribution patterns of mRNAs for DAF-16 homologues in mice. In the embryos, expressions of AFX, FKHR and FKHRL1 mRNAs were complementary to each other and were highest in muscle, adipose tissue and embryonic liver. The characteristic expression pattern remained in the adult, except that signals of FKHRL1 became evident in more tissues, including the brain. In order to clarify whether each DAF-16 homologue had different target genes, we determined the consensus sequences for the binding of DAF-16 and the mouse homologues. The binding sequences for all four proteins shared a core sequence, TTGTTTAC, daf-16 family protein-binding element (DBE) binding protein. However, electrophoretic mobility shift assay showed that the binding affinity of DAF-16 homologues to the core sequence was stronger than that to the insulin-responsive element in the insulin-like growth factor binding protein-1 promoter region, which has been identified as a binding sequence for them. We identified one copy of the DBE upstream of the first exon of sod-3 by searching the genomic database of C. elegans. Taken together, DAF-16 homologues can fundamentally regulate the common target genes in insulin-responsive tissues and the specificity to target genes of each protein is partially determined by the differences in their expression patterns.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                December 2012
                12 October 2012
                12 October 2012
                : 40
                : 22
                : 11499-11509
                Affiliations
                1Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA, 2Metabolic Signaling and Disease Program, Sanford-Burnham Medical Research Institute, Orlando, FL 32827, USA and 3Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
                Author notes
                *To whom correspondence should be addressed. Tel: +407 745 2000; Fax: +407 745 2001; Email: djshin@ 123456sanfordburnham.org
                Article
                gks932
                10.1093/nar/gks932
                3526316
                23066095
                ff322885-21e8-4491-9749-a8167cabf889
                © The Author(s) 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.

                History
                : 30 March 2012
                : 3 September 2012
                : 14 September 2012
                Page count
                Pages: 11
                Categories
                Genomics

                Genetics
                Genetics

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