Illuminating eukaryotic transcription start sites

2010 ◽  
Vol 7 (7) ◽  
pp. 501-503 ◽  
Author(s):  
John A Stamatoyannopoulos
2019 ◽  
Author(s):  
Jin Wang ◽  
Bing Liang Alvin Chew ◽  
Yong Lai ◽  
Hongping Dong ◽  
Luang Xu ◽  
...  

ABSTRACTChemical modification of transcripts with 5’ caps occurs in all organisms. Here we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of the cap epitranscriptome in any organism. The method was piloted with 21 canonical caps – m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG – and 5 “metabolite” caps – NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus,Escherichia coli, yeast, mice, and humans, we discovered four new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and surprisingly high proportions of caps lacking 2’-O-methylation, such as m7Gpppm6A in mammals and m7GpppA in dengue virus, and we did not detect cap m1A/m1Am in humans. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps. The mystery around cap m1A/m1Am analysis remains unresolved.


2019 ◽  
Vol 47 (20) ◽  
pp. e130-e130 ◽  
Author(s):  
Jin Wang ◽  
Bing Liang Alvin Chew ◽  
Yong Lai ◽  
Hongping Dong ◽  
Luang Xu ◽  
...  

Abstract Chemical modification of transcripts with 5′ caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps—m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG—and 5 ‘metabolite’ caps—NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus, Escherichia coli, yeast, mouse tissues, and human cells, we discovered new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and high proportions of caps lacking 2′-O-methylation (m7Gpppm6A in mammals, m7GpppA in dengue virus). While substantial Dimroth-induced loss of m1A and m1Am arose with specific RNA processing conditions, human lymphoblast cells showed no detectable m1A or m1Am in caps. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps.


2019 ◽  
Author(s):  
Bo Yan ◽  
George Tzertzinis ◽  
Ira Schildkraut ◽  
Laurence Ettwiller

AbstractMethodologies for determining eukaryotic Transcription Start Sites (TSS) rely on the selection of the 5’ canonical cap structure of Pol-II transcripts and are consequently ignoring entire classes of TSS derived from other RNA polymerases which play critical roles in various cell functions. To overcome this limitation, we developed ReCappable-seq and identified TSS from Pol-ll and non-Pol-II transcripts at nucleotide resolution. Applied to the human transcriptome, ReCappable-seq identifies Pol-II TSS with higher specificity than CAGE and reveals a rich landscape of TSS associated notably with Pol-III transcripts which have been previously not possible to study on a genome-wide scale. Novel TSS consistent with non-Pol-II transcripts can be found in the nuclear and mitochondrial genomes. By identifying TSS derived from all RNA-polymerases, ReCappable-seq reveals distinct epigenetic marks among Pol-lI and non-Pol-II TSS and provides a unique opportunity to concurrently interrogate the regulatory landscape of coding and non-coding RNA.


2019 ◽  
Author(s):  
Stepan Pachganov ◽  
Khalimat Murtazalieva ◽  
Alexei Zarubin ◽  
Dmitry Sokolov ◽  
Duane Chartier ◽  
...  

As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise - an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at ( http://compubioverne.group /). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.


2019 ◽  
Author(s):  
Stepan Pachganov ◽  
Khalimat Murtazalieva ◽  
Alexei Zarubin ◽  
Dmitry Sokolov ◽  
Duane Chartier ◽  
...  

As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise - an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at ( http://compubioverne.group /). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.


PLoS ONE ◽  
2009 ◽  
Vol 4 (10) ◽  
pp. e7526 ◽  
Author(s):  
Alfredo Mendoza-Vargas ◽  
Leticia Olvera ◽  
Maricela Olvera ◽  
Ricardo Grande ◽  
Leticia Vega-Alvarado ◽  
...  

2022 ◽  
Author(s):  
Edward J Banigan ◽  
Wen Tang ◽  
Aafke A van den Berg ◽  
Roman R Stocsits ◽  
Gordana Wutz ◽  
...  

Cohesin organizes mammalian interphase chromosomes by reeling chromatin fibers into dynamic loops (Banigan and Mirny, 2020; Davidson et al., 2019; Kim et al., 2019; Yatskevich et al., 2019). "Loop extrusion" is obstructed when cohesin encounters a properly oriented CTCF protein (Busslinger et al., 2017; de Wit et al., 2015; Fudenberg et al., 2016; Nora et al., 2017; Sanborn et al., 2015; Wutz et al., 2017), and recent work indicates that other factors, such as the replicative helicase MCM (Dequeker et al., 2020), can also act as barriers to loop extrusion. It has been proposed that transcription relocalizes (Busslinger et al., 2017; Glynn et al., 2004; Lengronne et al., 2004) or interferes with cohesin (Heinz et al., 2018; Jeppsson et al., 2020; Valton et al., 2021; S. Zhang et al., 2021), and that active transcription start sites function as cohesin loading sites (Busslinger et al., 2017; Kagey et al., 2010; Zhu et al., 2021; Zuin et al., 2014), but how these effects, and transcription in general, shape chromatin is unknown. To determine whether transcription can modulate loop extrusion, we studied cells in which the primary extrusion barriers could be removed by CTCF depletion and cohesin's residence time and abundance on chromatin could be increased by Wapl knockout. We found evidence that transcription directly interacts with loop extrusion through a novel "moving barrier" mechanism, but not by loading cohesin at active promoters. Hi-C experiments showed intricate, cohesin-dependent genomic contact patterns near actively transcribed genes, and in CTCF-Wapl double knockout (DKO) cells (Busslinger et al., 2017), genomic contacts were enriched between sites of transcription-driven cohesin localization ("cohesin islands"). Similar patterns also emerged in polymer simulations in which transcribing RNA polymerases (RNAPs) acted as "moving barriers" by impeding, slowing, or pushing loop-extruding cohesins. The model predicts that cohesin does not load preferentially at promoters and instead accumulates at TSSs due to the barrier function of RNAPs. We tested this prediction by new ChIP-seq experiments, which revealed that the "cohesin loader" Nipbl (Ciosk et al., 2000) co-localizes with cohesin, but, unlike in previous reports (Busslinger et al., 2017; Kagey et al., 2010; Zhu et al., 2021; Zuin et al., 2014), Nipbl did not accumulate at active promoters. We propose that RNAP acts as a new type of barrier to loop extrusion that, unlike CTCF, is not stationary in its precise genomic position, but is itself dynamically translocating and relocalizes cohesin along DNA. In this way, loop extrusion could enable translocating RNAPs to maintain contacts with distal regulatory elements, allowing transcriptional activity to shape genomic functional organization.


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