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Journal ArticleDOI

Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression

01 Nov 2013-Genes & Development (Cold Spring Harbor Lab)-Vol. 27, Iss: 21, pp 2380-2396
TL;DR: This work developed a sequencing method called 3'-seq to quantitatively map the 3' ends of the transcriptome of diverse human tissues and isogenic transformation systems and found that cell type-specific gene expression is accomplished by two complementary programs.
Abstract: More than half of human genes use alternative cleavage and polyadenylation (ApA) to generate mRNA transcripts that differ in the lengths of their 3' untranslated regions (UTRs), thus altering the post-transcriptional fate of the message and likely the protein output. The extent of 3' UTR variation across tissues and the functional role of ApA remain poorly understood. We developed a sequencing method called 3'-seq to quantitatively map the 3' ends of the transcriptome of diverse human tissues and isogenic transformation systems. We found that cell type-specific gene expression is accomplished by two complementary programs. Tissue-restricted genes tend to have single 3' UTRs, whereas a majority of ubiquitously transcribed genes generate multiple 3' UTRs. During transformation and differentiation, single-UTR genes change their mRNA abundance levels, while multi-UTR genes mostly change 3' UTR isoform ratios to achieve tissue specificity. However, both regulation programs target genes that function in the same pathways and processes that characterize the new cell type. Instead of finding global shifts in 3' UTR length during transformation and differentiation, we identify tissue-specific groups of multi-UTR genes that change their 3' UTR ratios; these changes in 3' UTR length are largely independent from changes in mRNA abundance. Finally, tissue-specific usage of ApA sites appears to be a mechanism for changing the landscape targetable by ubiquitously expressed microRNAs.

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Citations
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Journal ArticleDOI
12 Aug 2015-eLife
TL;DR: It is shown that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical.
Abstract: Proteins are built by using the information contained in molecules of messenger RNA (mRNA). Cells have several ways of controlling the amounts of different proteins they make. For example, a so-called ‘microRNA’ molecule can bind to an mRNA molecule to cause it to be more rapidly degraded and less efficiently used, thereby reducing the amount of protein built from that mRNA. Indeed, microRNAs are thought to help control the amount of protein made from most human genes, and biologists are working to predict the amount of control imparted by each microRNA on each of its mRNA targets. All RNA molecules are made up of a sequence of bases, each commonly known by a single letter—‘A’, ‘U’, ‘C’ or ‘G’. These bases can each pair up with one specific other base—‘A’ pairs with ‘U’, and ‘C’ pairs with ‘G’. To direct the repression of an mRNA molecule, a region of the microRNA known as a ‘seed’ binds to a complementary sequence in the target mRNA. ‘Canonical sites’ are regions in the mRNA that contain the exact sequence of partner bases for the bases in the microRNA seed. Some canonical sites are more effective at mRNA control than others. ‘Non-canonical sites’ also exist in which the pairing between the microRNA seed and mRNA does not completely match. Previous work has suggested that many non-canonical sites can also control mRNA degradation and usage. Agarwal et al. first used large experimental datasets from many sources to investigate microRNA activity in more detail. As expected, when mRNAs had canonical sites that matched the microRNA, mRNA levels and usage tended to drop. However, no effect was observed when the mRNAs only had recently identified non-canonical sites. This suggests that microRNAs primarily bind to canonical sites to control protein production. Based on these results, Agarwal et al. further developed a statistical model that predicts the effects of microRNAs binding to canonical sites. The updated model considers 14 different features of the microRNA, microRNA site, or mRNA—including the mRNA sequence around the site—to predict which sites within mRNAs are most effectively targeted by microRNAs. Tests showed that Agarwal et al.'s model was as good as experimental approaches at identifying the effective target sites, and was better than existing computational models. The model has been used to power the latest version of a freely available resource called TargetScan, and so could prove a valuable resource for researchers investigating the many important roles of microRNAs in controlling protein production.

5,365 citations


Cites background from "Ubiquitously transcribed genes use ..."

  • ...…1—figure supplement 1A GSM210897, GSM210898, GSM210901, GSM210903, GSM210904, GSM210907, GSM210909, GSM210911, GSM210913, GSM37599, GSM37601 (Lim et al., 2005; Grimson et al., 2007) Figure 1—figure supplement 1B, Figure 3, Figure 3—figure supplement 1B,C, Figure 4 74 datasets compiled in…...

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  • ...…GSM210907, GSM210909, GSM210911, GSM210913, GSM37599, http://psilac.mdc-berlin.de/download/ (let7b_32h, miR-30_32h, miR-155_32h, miR-16_32h) (Lim et al., 2005; Grimson et al., 2007; Linsley et al., 2007; Selbach et al., 2008) Figure 1H, Figure 6K,L E-MTAB-2110 (Tan et al., 2014) Figure 1I,…...

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  • ...…16012097016667, 16012097016668, 16012097016669, 16012097017938, 16012097017939, 16012097017952, 16012097017953, 16012097018568, 251209725411) (Lim et al., 2005; Birmingham et al., 2006; Schwarz et al., 2006; Jackson et al., 2006a; Jackson et al., 2006b; Grimson et al., 2007; Anderson et…...

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  • ...D, Figure 6I,J GSM538818, GSM538819, GSM538820, GSM538821 (Hafner et al., 2010) Figure 1G GSM156524, GSM156532, GSM210897, GSM210898, GSM210901, GSM210903, GSM210904, GSM210907, GSM210909, GSM210911, GSM210913, GSM37599, http://psilac.mdc-berlin.de/download/ (let7b_32h, miR-30_32h, miR-155_32h, miR-16_32h) (Lim et al., 2005; Grimson et al., 2007; Linsley et al., 2007; Selbach et al., 2008) Figure 1H, Figure 6K,L E-MTAB-2110 (Tan et al., 2014) Figure 1I, Figure 1—figure supplement 2B, Figure 6E GSM1479572, GSM1479576, GSM1479580, GSM1479584 (Eichhorn et al., 2014) Figure 1—figure supplement 1A GSM210897, GSM210898, GSM210901, GSM210903, GSM210904, GSM210907, GSM210909, GSM210911, GSM210913, GSM37599, GSM37601 (Lim et al., 2005; Grimson et al., 2007) Figure 1—figure supplement 1B, Figure 3, Figure 3—figure supplement 1B,C, Figure 4 74 datasets compiled in Supplementary data 4 of Garcia et al. (2011), used as is or after normalization (Supplementary file 1); GSM119707, GSM119708, GSM119710, GSM119743, GSM119745, GSM119746, GSM119747, GSM119749, GSM119750, GSM119759, GSM119761, GSM119762, GSM119763, GSM133685, GSM133689, GSM133699, GSM133700, GSM134325, GSM134327, GSM134466, GSM134480, GSM134483, GSM134485, GSM134511, GSM134512, GSM134551, GSM210897, GSM210898, GSM210901, GSM210903, GSM210904, GSM210907, GSM210909, GSM210911, GSM210913, GSM37599, GSM37601; E-MEXP-1402 (1595297366, 1595297383, 1595297389, 1595297394, 1595297399, 1595297422, 1595297427, 1595297432, 1595297491, 1595297496, 1595297501, 1595297507, 1595297513, 1595297518, 1595297524, 1595297530, 1595297535, 1595297564, 1595297588, 1595297595, 1595297605, 1595297614, 1595297621, 1595297627, 1595297644, 1595297650, 1595297662); E-MEXP-668 (16012097016666, 16012097016667, 16012097016668, 16012097016669, 16012097017938, 16012097017939, 16012097017952, 16012097017953, 16012097018568, 251209725411) (Lim et al., 2005; Birmingham et al., 2006; Schwarz et al., 2006; Jackson et al., 2006a; Jackson et al., 2006b; Grimson et al., 2007; Anderson et al., 2008) Figure 1—figure supplement 1C GSM95614, GSM95615, GSM95616, GSM95617, GSM95618, GSM95619 (Giraldez et al., 2006) Figure 1—figure supplement 1D,F GSM1269344, GSM1269345, GSM1269348, GSM1269349, GSM1269350, GSM1269351, GSM1269354, GSM1269355, GSM1269356, GSM1269357, GSM1269360, GSM1269361, GSM1269362, GSM1269363 (Nam et al., 2014) Figure 1—figure supplement 3E, Figure 6H http://icb.med.cornell.edu/faculty/betel/lab/betelab_v1/Data. html (Lipchina et al., 2011) Figure 1—figure supplement 4B http://psilac.mdc-berlin.de/media/database/release-1.0/protein/ pSILAC_all_protein_ratios_OE.txt (miR155) (Selbach et al., 2008) Figure 3—figure supplement 1A GSM416753 (Mayr and Bartel, 2009) Figure 5, Figure 5—figure supplement 1 GSM156522, GSM156580, GSM156557, GSM156548, GSM156533, GSM156532, GSM156524, processed and normalized (Supplementary file 2) (Linsley et al., 2007) Figure 6A GSM37601 (Lim et al., 2005) Figure 6F,G GSM363763, GSM363766, GSM363769, GSM363772, GSM363775, GSM363778 (Hausser et al., 2009) DOI: 10.7554/eLife.05005.022 Agarwal et al. eLife 2015;4:e05005....

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  • ...In addition, many nonconserved interactions also function to reduce mRNA levels and protein output (Farh et al., 2005; Krutzfeldt et al., 2005; Lim et al., 2005; Baek et al., 2008; Selbach et al., 2008)....

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Journal ArticleDOI
TL;DR: This work presents a census of 1,542 manually curated RBPs that are analysed for their interactions with different classes of RNA, their evolutionary conservation, their abundance and their tissue-specific expression, a critical step towards the comprehensive characterization of proteins involved in human RNA metabolism.
Abstract: Post-transcriptional gene regulation (PTGR) concerns processes involved in the maturation, transport, stability and translation of coding and non-coding RNAs. RNA-binding proteins (RBPs) and ribonucleoproteins coordinate RNA processing and PTGR. The introduction of large-scale quantitative methods, such as next-generation sequencing and modern protein mass spectrometry, has renewed interest in the investigation of PTGR and the protein factors involved at a systems-biology level. Here, we present a census of 1,542 manually curated RBPs that we have analysed for their interactions with different classes of RNA, their evolutionary conservation, their abundance and their tissue-specific expression. Our analysis is a critical step towards the comprehensive characterization of proteins involved in human RNA metabolism.

1,479 citations


Cites background from "Ubiquitously transcribed genes use ..."

  • ...Da Ribosomal protein families (158) Db mRBP families (422) Dc tRNA-binding protein families (130) 2....

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Journal ArticleDOI
TL;DR: A deeper understanding of the complex networks of interactions that ncRNAs coordinate would provide a unique opportunity to design better therapeutic interventions.
Abstract: Thousands of unique non-coding RNA (ncRNA) sequences exist within cells. Work from the past decade has altered our perception of ncRNAs from 'junk' transcriptional products to functional regulatory molecules that mediate cellular processes including chromatin remodelling, transcription, post-transcriptional modifications and signal transduction. The networks in which ncRNAs engage can influence numerous molecular targets to drive specific cell biological responses and fates. Consequently, ncRNAs act as key regulators of physiological programmes in developmental and disease contexts. Particularly relevant in cancer, ncRNAs have been identified as oncogenic drivers and tumour suppressors in every major cancer type. Thus, a deeper understanding of the complex networks of interactions that ncRNAs coordinate would provide a unique opportunity to design better therapeutic interventions.

1,180 citations

Journal ArticleDOI
TL;DR: The roles of APA in diverse cellular processes, including mRNA metabolism, protein diversification and protein localization, and more generally in gene regulation are discussed, and the molecular mechanisms underlying APA are discussed.
Abstract: Alternative polyadenylation (APA) is an RNA-processing mechanism that generates distinct 3' termini on mRNAs and other RNA polymerase II transcripts. It is widespread across all eukaryotic species and is recognized as a major mechanism of gene regulation. APA exhibits tissue specificity and is important for cell proliferation and differentiation. In this Review, we discuss the roles of APA in diverse cellular processes, including mRNA metabolism, protein diversification and protein localization, and more generally in gene regulation. We also discuss the molecular mechanisms underlying APA, such as variation in the concentration of core processing factors and RNA-binding proteins, as well as transcription-based regulation.

758 citations

Journal ArticleDOI
TL;DR: Experiments using sophisticated new methods for analysis of nascent RNA have provided important insights into the relative amount of co- transcriptional and post-transcriptional processing, the relationship between mRNA elongation and processing, and the role of the Pol II carboxy-terminal domain (CTD) in regulating these processes.
Abstract: The cellular transcription, mRNA processing and export machineries seem to have co-evolved to allow spatiotemporal coupling of these processes. Here, the author reviews recent insights into the relative amount of co-transcriptional and post-transcriptional processing, the relationship between mRNA elongation and processing, and the regulating role of the carboxy-terminal domain of RNA polymerase II.

676 citations

References
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Journal ArticleDOI
23 Jan 2004-Cell
TL;DR: Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.

32,946 citations


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Journal ArticleDOI
TL;DR: By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
Abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

31,015 citations

Journal ArticleDOI
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
Abstract: High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.

13,356 citations

Journal ArticleDOI
09 Jun 2005-Nature
TL;DR: A new, bead-based flow cytometric miRNA expression profiling method is used to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers, and finds the miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours.
Abstract: Recent work has revealed the existence of a class of small non-coding RNA species, known as microRNAs (miRNAs), which have critical functions across various biological processes. Here we use a new, bead-based flow cytometric miRNA expression profiling method to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers. The miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours. We observe a general downregulation of miRNAs in tumours compared with normal tissues. Furthermore, we were able to successfully classify poorly differentiated tumours using miRNA expression profiles, whereas messenger RNA profiles were highly inaccurate when applied to the same samples. These findings highlight the potential of miRNA profiling in cancer diagnosis.

9,470 citations

Journal ArticleDOI
29 Jun 2007-Cell
TL;DR: A relatively small set of miRNAs, many of which are ubiquitously expressed, account for most of the differences in miRNA profiles between cell lineages and tissues.

3,687 citations


"Ubiquitously transcribed genes use ..." refers background in this paper

  • ...On the other hand, nine out of 12 miRNAs whose target sites are enriched in the distal 39 UTRs of pAM genes are considered to be widely expressed (Supplemental Table 10; Wienholds et al. 2005; Landgraf et al. 2007)....

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