Abstract
Transcript regulation is essential for cell function, and misregulation can lead to disease. Despite technologies to survey the transcriptome, we lack a comprehensive understanding of transcript kinetics, which limits quantitative biology. This is an acute challenge in embryonic development, where rapid changes in gene expression dictate cell fate decisions. By ultra-high-frequency sampling of Xenopus embryos and absolute normalization of sequence reads, we present smooth gene expression trajectories in absolute transcript numbers. During a developmental period approximating the first 8 weeks of human gestation, transcript kinetics vary by eight orders of magnitude. Ordering genes by expression dynamics, we find that "temporal synexpression" predicts common gene function. Remarkably, a single parameter, the characteristic timescale, can classify transcript kinetics globally and distinguish genes regulating development from those involved in cellular metabolism. Overall, our analysis provides unprecedented insight into the reorganization of maternal and embryonic transcripts and redefines our ability to perform quantitative biology.
Highlights
Gene expression is dynamic and tightly regulated
For Clutch A, we sequenced poly(A)+ RNA for the full 66 hr and total RNA depleted of rRNA, containing both poly(A)+ and poly(A)À RNAs, for the first 24 hr
We evaluated the consistency of our proposed absolute normalization on our three datasets independently: Clutch A poly(A)+, Clutch A RNA depleted of rRNA (rdRNA), and Clutch B poly(A)+ (Figure S2B)
Summary
To build a quantitative understanding of gene regulation, direct measurement of transcript kinetics is necessary. Transcript kinetics describe the rate of change of transcript copy numbers with time. In developing systems such as the embryo, dynamic transcript expression precisely coordinates a sequence of stereotypical events that occur in rapid succession. With the ability to measure global transcript kinetics, we can effectively study the impact of different transcript regulation strategies on gene expression; for example, dynamics of chromatin modifications, utilization of cis-regulatory sequences, kinetics of transcription factor binding, and transcript stability. Because the study of gene regulatory networks is limited by the lack of genome-wide kinetic data (Karlebach and Shamir, 2008), a kinetic transcriptome dataset will be transformative to build and test gene regulatory network models in development
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