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Transient transcriptome sequencing captures enhancer landscapes immediately after T-cell stimulation
Transient transcriptome sequencing captures enhancer landscapes immediately after T-cell stimulation
Transcription regulation is poorly understood. Transcriptional enhancers produce enhancer RNAs (eRNAs), a class of transient RNAs, whose function remains mainly unclear. To monitor transcriptional regulation in human cells, rapid changes in enhancer and promoter activity must be captured with high sensitivity and temporal reso- lution. Here I show that the recently established protocol TT-seq (‘transient tran- scriptome sequencing’) can monitor rapid changes in transcription from enhancers and promoters during the immediate response of T-cells to ionomycin and phorbol 12-myristate 13-acetate (PMA). Transient transcriptome sequencing (TT-seq) maps eRNAs and mRNAs every 5 minutes after T-cell stimulation with high sensitivity, and identifies many new primary response genes. TT-seq reveals that the synthesis of 1,601 eRNAs and 650 mRNAs changes significantly within only 15 minutes after stimulation, when standard RNA-seq does not detect differentially expressed genes. Transcription of enhancers that are primed for activation by nucleosome depletion can occur immediately and simultaneously with transcription of target gene promot- ers. My results indicate that enhancer transcription is a good proxy for enhancer regulatory activity in target gene activation, and establish TT-seq as a tool for monitoring the dynamics of enhancer landscapes and transcription programs during cellular responses and differentiation. Additionally, I developed a normalization method for TT-seq that scales labeled and total RNA-seq samples relative to each other, allowing to determine absolute half-lives. The method provides a powerful tool to normalize various samples relative to each other on a global scale, and therefore allows to observe global changes in RNA synthesis and degradation. Taken together, metabolical labeling of RNA followed by kinetic modeling enables to quantify RNA metabolism rates and to detect dynamic changes in enhancer landscapes and RNA expression levels.
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Demel, Carina
2017
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Demel, Carina (2017): Transient transcriptome sequencing captures enhancer landscapes immediately after T-cell stimulation. Dissertation, LMU München: Faculty of Chemistry and Pharmacy
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Abstract

Transcription regulation is poorly understood. Transcriptional enhancers produce enhancer RNAs (eRNAs), a class of transient RNAs, whose function remains mainly unclear. To monitor transcriptional regulation in human cells, rapid changes in enhancer and promoter activity must be captured with high sensitivity and temporal reso- lution. Here I show that the recently established protocol TT-seq (‘transient tran- scriptome sequencing’) can monitor rapid changes in transcription from enhancers and promoters during the immediate response of T-cells to ionomycin and phorbol 12-myristate 13-acetate (PMA). Transient transcriptome sequencing (TT-seq) maps eRNAs and mRNAs every 5 minutes after T-cell stimulation with high sensitivity, and identifies many new primary response genes. TT-seq reveals that the synthesis of 1,601 eRNAs and 650 mRNAs changes significantly within only 15 minutes after stimulation, when standard RNA-seq does not detect differentially expressed genes. Transcription of enhancers that are primed for activation by nucleosome depletion can occur immediately and simultaneously with transcription of target gene promot- ers. My results indicate that enhancer transcription is a good proxy for enhancer regulatory activity in target gene activation, and establish TT-seq as a tool for monitoring the dynamics of enhancer landscapes and transcription programs during cellular responses and differentiation. Additionally, I developed a normalization method for TT-seq that scales labeled and total RNA-seq samples relative to each other, allowing to determine absolute half-lives. The method provides a powerful tool to normalize various samples relative to each other on a global scale, and therefore allows to observe global changes in RNA synthesis and degradation. Taken together, metabolical labeling of RNA followed by kinetic modeling enables to quantify RNA metabolism rates and to detect dynamic changes in enhancer landscapes and RNA expression levels.