文章基本信息

标题：Stochastic particle unbinding modulates growth dynamics and size of transcription factor condensates in living cells
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作者：Gorka Muñoz-Gil ; Catalina Romero-Aristizabal ; Nicolas Mateos 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：31
DOI：10.1073/pnas.2200667119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance Living cells organize internal compartments by forming molecular condensates that operate as versatile biochemical “hubs.” Their occurrence is particularly relevant in the nucleus where they regulate, amongst others, gene transcription. However, the biophysics of transcription factor (TF) condensation remains highly unexplored. Through single-molecule experiments in living cells, theory, and simulations, we assessed the diffusion, growth dynamics, and sizes of TF condensates of the nuclear progesterone receptor (PR). Interestingly, PR condensates obey classical growth dynamics at shorter times but deviate at longer times, reaching finite sizes at steady-state. We demonstrate that condensate growth dynamics and nanoscale-size arrested growth is regulated by molecular escaping from condensates, providing an exquisite control of condensate size in nonequilibrium systems such as living cells. Liquid–liquid phase separation (LLPS) is emerging as a key physical principle for biological organization inside living cells, forming condensates that play important regulatory roles. Inside living nuclei, transcription factor (TF) condensates regulate transcriptional initiation and amplify the transcriptional output of expressed genes. However, the biophysical parameters controlling TF condensation are still poorly understood. Here we applied a battery of single-molecule imaging, theory, and simulations to investigate the physical properties of TF condensates of the progesterone receptor (PR) in living cells. Analysis of individual PR trajectories at different ligand concentrations showed marked signatures of a ligand-tunable LLPS process. Using a machine learning architecture, we found that receptor diffusion within condensates follows fractional Brownian motion resulting from viscoelastic interactions with chromatin. Interestingly, condensate growth dynamics at shorter times is dominated by Brownian motion coalescence (BMC), followed by a growth plateau at longer timescales that result in nanoscale condensate sizes. To rationalize these observations, we extended on the BMC model by including the stochastic unbinding of particles within condensates. Our model reproduced the BMC behavior together with finite condensate sizes at the steady state, fully recapitulating our experimental data. Overall, our results are consistent with condensate growth dynamics being regulated by the escaping probability of PR molecules from condensates. The interplay between condensation assembly and molecular escaping maintains an optimum physical condensate size. Such phenomena must have implications for the biophysical regulation of other nuclear condensates and could also operate in multiple biological scenarios.
关键词：enliquid–liquid phase separationtranscription factorBrownian motion coalescencebiomolecular condensatessingle particle tracking