| 1. |
- Frith, Martin C., et al.
(författare)
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Pseudo-messenger RNA : Phantoms of the transcriptome
- 2006
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Ingår i: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7390 .- 1553-7404. ; 2:4, s. 504-514
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Tidskriftsartikel (refereegranskat)abstract
- The mammalian transcriptome harbours shadowy entities that resist classification and analysis. In analogy with pseudogenes, we define pseudo-messenger RNA to be RNA molecules that resemble protein- coding mRNA, but cannot encode full-length proteins owing to disruptions of the reading frame. Using a rigorous computational pipeline, which rules out sequencing errors, we identify 10,679 pseudo - messenger RNAs ( approximately half of which are transposonassociated) among the 102,801 FANTOM3 mouse cDNAs: just over 10% of the FANTOM3 transcriptome. These comprise not only transcribed pseudogenes, but also disrupted splice variants of otherwise protein- coding genes. Some may encode truncated proteins, only a minority of which appear subject to nonsense- mediated decay. The presence of an excess of transcripts whose only disruptions are opal stop codons suggests that there are more selenoproteins than currently estimated. We also describe compensatory frameshifts, where a segment of the gene has changed frame but remains translatable. In summary, we survey a large class of non- standard but potentially functional transcripts that are likely to encode genetic information and effect biological processes in novel ways. Many of these transcripts do not correspond cleanly to any identifiable object in the genome, implying fundamental limits to the goal of annotating all functional elements at the genome sequence level.
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| 2. |
- Horie, Masafumi, et al.
(författare)
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Integrative CAGE and DNA Methylation Profiling Identify Epigenetically Regulated Genes in NSCLC
- 2017
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Ingår i: Molecular Cancer Research. - : AMER ASSOC CANCER RESEARCH. - 1541-7786 .- 1557-3125. ; 15:10, s. 1354-1365
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Tidskriftsartikel (refereegranskat)abstract
- Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of cancer driver mutations have been identified; however, relevant epigenetic regulation involved in tumorigenesis has only been fragmentarily analyzed. Epigenetically regulated genes have a great theranostic potential, especially in tumors with no apparent driver mutations. Here, epigenetically regulated genes were identified in lung cancer by an integrative analysis of promoter-level expression profiles from Cap Analysis of Gene Expression (CAGE) of 16 nonsmall cell lung cancer (NSCLC) cell lines and 16 normal lung primary cell specimens with DNA methylation data of 69 NSCLC cell lines and 6 normal lung epithelial cells. A core set of 49 coding genes and 10 long noncoding RNAs (lncRNA), which are upregulated in NSCLC cell lines due to promoter hypomethylation, was uncovered. Twenty-two epigenetically regulated genes were validated (upregulated genes with hypomethylated promoters) in the adenocarcinoma and squamous cell cancer subtypes of lung cancer using The Cancer Genome Atlas data. Furthermore, it was demonstrated that multiple copies of the REP522 DNA repeat family are prominently upregulated due to hypomethylation in NSCLC cell lines, which leads to cancer-specific expression of lncRNAs, such as RP1-90G24.10, AL022344.4, and PCAT7. Finally, Myeloma Overexpressed (MYEOV) was identified as the most promising candidate. Functional studies demonstrated that MYEOV promotes cell proliferation, survival, and invasion. Moreover, high MYEOV expression levels were associated with poor prognosis. (C) 2017 AACR.
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| 3. |
- Horie, Masafumi, et al.
(författare)
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Transcriptome analysis of periodontitis-associated fibroblasts by CAGE sequencing identified DLX5 and RUNX2 long variant as novel regulators involved in periodontitis
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Periodontitis is affecting over half of the adult population, and represents a major public health problem. Previously, we isolated a subset of gingival fibroblasts (GFs) from periodontitis patients, designated as periodontitis-associated fibroblasts (PAFs), which were highly capable of collagen degradation. To elucidate their molecular profiles, GFs isolated form healthy and periodontitis-affected gingival tissues were analyzed by CAGE-seq and integrated with the FANTOM5 atlas. GFs from healthy gingival tissues displayed distinctive patterns of CAGE profiles as compared to fibroblasts from other organ sites and characterized by specific expression of developmentally important transcription factors such as BARX1, PAX9, LHX8, and DLX5. In addition, a novel long non-coding RNA associated with LHX8 was described. Furthermore, we identified DLX5 regulating expression of the long variant of RUNX2 transcript, which was specifically active in GFs but not in their periodontitis-affected counterparts. Knockdown of these factors in GFs resulted in altered expression of extracellular matrix (ECM) components. These results indicate activation of DLX5 and RUNX2 via its distal promoter represents a unique feature of GFs, and is important for ECM regulation. Down-regulation of these transcription factors in PAFs could be associated with their property to degrade collagen, which may impact on the process of periodontitis.
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| 4. |
- Ravasi, Timothy, et al.
(författare)
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An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man
- 2010
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Ingår i: CELL. - : Elsevier BV. - 0092-8674 .- 1097-4172. ; 140:5, s. 744-752
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Tidskriftsartikel (refereegranskat)abstract
- Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.
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| 5. |
- Yu, Nancy Yiu-Lin, et al.
(författare)
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Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium
- 2015
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:14, s. 6787-6798
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene expression correlation was high between the 22 corresponding tissues analyzed (R > 0.8). For genes ubiquitously expressed across all tissues, the two data sets showed high genome-wide correlation (91% agreement), with differences observed for a small number of individual genes indicating the need to update their gene models. Among the identified single-tissue enriched genes, up to 75% showed consensus of 7-fold enrichment in the same tissue in both methods, while another 17% exhibited multiple tissue enrichment and/or high expression variety in the other data set, likely dependent on the cell type proportions included in each tissue sample. Our results show that RNA-Seq and CAGE tissue transcriptome data sets are highly complementary for improving gene model annotations and highlight biological complexities within tissue transcriptomes. Furthermore, integration with image-based protein expression data is highly advantageous for understanding expression specificities for many genes.
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