Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.

Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.
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DOI:
10.15252/msb.202110588
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发表时间:
2022-03
影响因子:
9.9
通讯作者:
Lahav G
Lahav G
中科院分区:
生物学1区
文献类型:
--
作者:
Jiménez A;Lu D;Kalocsay M;Berberich MJ;Balbi P;Jambhekar A;Lahav G

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细胞应激反应转录因子 p53 影响其靶基因的表达和随后的细胞反应,部分取决于其动态(水平随时间的变化)。将 p53 动力学解码为后续目标 mRNA 和蛋白质动力学的机制仍不清楚。我们使用 RNA 测序和 TMT 质谱法,在两种 p53 动态机制(振荡和上升)下,系统地量化了 p53 靶标 mRNA 和蛋白质随时间的表达。振荡动力学为 mRNA 和蛋白质提供了更多样的动力学模式。经验数据的数学模型揭示了解码 p53 动态的三种不同机制。这些机制在转录和转录后水平上的特定组合使得能够在特定动力学下独家诱导蛋白质。此外,p53 诱导的增加导致蛋白质的诱导增加,无论其功能类别如何,包括促进增殖停滞的蛋白质,这是 p53 增加时的主要细胞结果。我们的结果强调了细胞用来区分复杂转录因子动力学以调节基因表达的多种机制。时间序列转录组学和蛋白质组学揭示转录因子 p53 的不同动态会产生不同的 p53 靶标 mRNA 和蛋白质表达模式。数学模型揭示了解码 p53 动态的独特机制。
The cell stress‐responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA‐sequencing and TMT mass spectrometry. Oscillatory dynamics allowed for a greater variety of dynamical patterns for both mRNAs and proteins. Mathematical modeling of empirical data revealed three distinct mechanisms that decode p53 dynamics. Specific combinations of these mechanisms at the transcriptional and post‐transcriptional levels enabled exclusive induction of proteins under particular dynamics. In addition, rising induction of p53 led to higher induction of proteins regardless of their functional class, including proteins promoting arrest of proliferation, the primary cellular outcome under rising p53. Our results highlight the diverse mechanisms cells employ to distinguish complex transcription factor dynamics to regulate gene expression. Time‐series transcriptomics and proteomics reveal that different dynamics of the transcription factor p53 generate diverse p53 target mRNA and protein expression patterns. Mathematical modeling uncovers distinct mechanisms that decode p53 dynamics.
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