title: Stimulus-induced modulation of transcriptional bursting in a single mammalian gene.
abstract: Mammalian genes are often transcribed discontinuously as short bursts of RNA synthesis followed by longer silent periods. However, how these "on" and "off" transitions, together with the burst sizes, are modulated in single cells to increase gene expression upon stimulation is poorly characterized. By combining single-cell time-lapse luminescence imaging with stochastic modeling of the time traces, we quantified the transcriptional responses of the endogenous connective tissue growth factor gene to different physiological stimuli: serum and TGF-β1. Both stimuli caused a rapid and acute increase in burst sizes. Whereas TGF-β1 showed prolonged transcriptional activation mediated by an increase of transcription rate, serum stimulation resulted in a large and temporally tight first transcriptional burst, followed by a refractory period in the range of hours. Our study thus reveals how different physiological stimuli can trigger kinetically distinct transcriptional responses of the same gene.
authors.affiliation: Institute of Bioengineering, School of Life Sciences, Swiss Institute of Bioinformatics, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
authors.forename: Nacho
authors.initials: N
authors.lastname: Molina
authors.forename: David M
authors.initials: DM
authors.lastname: Suter
authors.forename: Rosamaria
authors.initials: R
authors.lastname: Cannavo
authors.forename: Benjamin
authors.initials: B
authors.lastname: Zoller
authors.forename: Ivana
authors.initials: I
authors.lastname: Gotic
authors.forename: Félix
authors.initials: F
authors.lastname: Naef
mesh_terms: D000818:Animals; D051379:Mice; D008954:Models, Biological; D041681:NIH 3T3 Cells; D013269:Stochastic Processes; D014158:Transcription, Genetic / Q000502:physiology*; D053773:Transforming Growth Factor beta1 / Q000378:metabolism*
chemical_list: C508961:Tgfb1 protein, mouse; D053773:Transforming Growth Factor beta1
keywords: computational modeling; single-cell dynamics; stochastic gene expression