Dynamics of transcriptional and post-transcriptional regulation

Abstract

Despite gene expression programs being notoriously complex, RNA abundance is usually assumed as a proxy for transcriptional activity. Recently developed approaches, able to disentangle transcriptional and post-transcriptional regulatory processes, have revealed a more complex scenario. It is now possible to work out how synthesis, processing and degradation kinetic rates collectively determine the abundance of each gene's RNA. It has become clear that the same transcriptional output can correspond to different combinations of the kinetic rates. This underscores the fact that markedly different modes of gene expression regulation exist, each with profound effects on a gene's ability to modulate its own expression. This review describes the development of the experimental and computational approaches, including RNA metabolic labeling and mathematical modeling, that have been disclosing the mechanisms underlying complex transcriptional programs. Current limitations and future perspectives in the field are also discussed.

Keywords: RNA degradation; RNA metabolic labeling; RNA processing; RNA synthesis; mathematical modeling; nascent RNA.

Figure 1

Mathematical modeling of the RNA life cycle. (A) Schema of the RNA life cycle, including the steps of premature RNA synthesis, its processing into mature RNA, and the degradation of the latter; the corresponding kinetic rates are indicated in parenthesis. (B) Mathematical representation of the RNA life cycle through ODEs. The first equation describes the modulation of premature RNA over time (dP/dt) as the balance between the amount of RNA molecules synthesized (k₁(t)) and those processed to become mature transcripts (k₂(t)⋅P). The second equation describes the modulation of mature RNA (dM/dt) as the balance between the amount of premature RNA molecules processed (k₂(t)⋅P) and the amount of mature RNA transcripts degraded (k₃(t)⋅M). (C) Steady state solution of the ODEs system.

Figure 2

Quantification of a transcript’s half-life through block-of-transcription experiments. Key steps in the estimation of RNA degradation rates through block-of-transcription experiments. First, a drug blocking transcription is provided. Then, the progressive reduction in gene expression is measured at several time points. Finally, the exponential decay coefficient is used to quantify the transcript half-life.

Figure 3

Experimental and computational approaches to RNA kinetic rates quantification through nascent RNA profiling. (A) Alternative experimental approaches to the separation of nascent RNA from its pre-existing form. (B) Alternative approaches to RNA kinetic rates quantification. The corresponding experimental design, computational framework, output, and software are reported.

Figure 4

Similarity between RNA degradation rates quantified by methods relying on block-of-transcription or RNA metabolic labeling. Heatmap of pairwise Spearman’s correlations comparing degradation rates obtained for HEK293 cells using different approaches. Amanitin and Actinomycin D drugs were used to block transcription. Several tools for the analysis of RNA metabolic labeling data were compared, covering various experimental designs: approach to equilibrium, and single metabolic labeling performed at the indicated times (t_L). All degradation rates derive from the Lugowski dataset [12], or from the reanalysis of the same data, with the exclusion of the HEK293 degradation rates determined in an independent study (Mukherjee dataset [68]).