Abstract
Cellular RNA levels typically fluctuate and are influenced by different transcription rates and RNA degradation rates. However, the understanding of the fundamental relationships between RNA abundance, environmental stimuli, RNA activities, and RNA age distributions is incomplete. Furthermore, the rates of RNA degradation and transcription are difficult to measure in transcriptomic experiments in living organisms, especially in studies involving humans. A model based on activity demands and RNA age was developed to explore the mechanisms of RNA level fluctuations. Using single-cell time-series gene expression experimental data, we assessed the transcription rates, RNA degradation rates, RNA life spans, RNA demand, accumulated transcription levels, and accumulated RNA degradation levels. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge.
Highlights
Mechanisms underlying the maintenance of specific RNA levels in a given cell are a target of transcriptomics studies [1,2,3]
We developed a model based on the demands of RNA, RNA age, transcription, and RNA degradation to explain the mechanism underlying intracellular RNA level fluctuations
We developed a model based on the demands of RNA, RNA age, pulse transcription, and RNA degradation to explain the mechanism underlying intracellular RNA level fluctuations
Summary
Mechanisms underlying the maintenance of specific RNA levels in a given cell are a target of transcriptomics studies [1,2,3]. Detected cellular RNA levels usually fluctuate [4,5,6]. Studies have provided diverse explanations for the mechanisms of RNA level fluctuations, such as stochastic pulsing of transcription and environmental determinants [5,7,8,9,10,11,12,13,14]. The limitations of transcriptomic techniques mean that some potential fundamental drivers of RNA level fluctuations remain unknown. The challenge involves linking RNA level fluctuations with the effects of various environmental stimuli, the demands for RNA, and transcription
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