Precision and functional specificity in mRNA decay

Abstract

Posttranscriptional processing of mRNA is an integral component of the gene expression program. By using DNA microarrays, we precisely measured the decay of each yeast mRNA, after thermal inactivation of a temperature-sensitive RNA polymerase II. The half-lives varied widely, ranging from ϳ3 min to more than 90 min. We found no simple correlation between mRNA half-lives and ORF size, codon bias, ribosome density, or abundance. However, the decay rates of mRNAs encoding groups of proteins that act together in stoichiometric complexes were generally closely matched, and other evidence pointed to a more general relationship between physiological function and mRNA turnover rates. The results provide strong evidence that precise control of the decay of each mRNA is a fundamental feature of the gene expression program in yeast.A though initiation of transcription is well studied and its importance in regulation is clear, we know much less about the specificity, precision and regulatory role of mRNA decay. The abundance of each mRNA in the cell is determined not only by the rate at which it is produced, but also by its rate of degradation. mRNA decay rates determine how quickly each message can adapt to a new steady-state level after a change in transcription rate, and dynamic control of the decay of specific transcripts can have an important role in their regulation (1). The decay rates of specific mRNAs can vary by 100-fold or more (2, 3) and are affected by a wide variety of stimuli and cellular signals, including specific hormones (2, 4), iron (5, 6), cell cycle progression (7,8), cell differentiation (9, 10), and viral infection (11). Characterizing this stage in the natural history of each mRNA is an important step toward understanding the logic and molecular mechanisms underlying the regulation of the gene expression program of a genome.With its ease of biochemical and genetic manipulation, yeast makes an excellent model for studying eukaryotic mRNA turnover. Simple and reliable procedures have been developed to measure the decay rates of individual mRNAs in yeast, including global or specific transcriptional shut-off assays (12)(13)(14) and in vivo kinetic labeling (15). However, the global profile of yeast mRNA turnover has not been systematically and quantitatively determined. In this report, we present the results of a genomewide determination of mRNA decay rates, coupling the global transcriptional shut-off assay with DNA microarray analysis. ura3-52 his4-939am rpb1-1), carrying a temperature-sensitive mutation in RNA polymerase II (13), was used in this study. Y262 was grown in 500 ml of yeast extract͞peptone͞dextrose (YPD) medium at 24°C to OD 600 ϳ0.5. The temperature of the culture was abruptly shifted to 37°C by adding an equal volume of YPD medium that had been prewarmed to 49°C. Aliquots of the culture (100 ml) were removed at 0,5,10,15,20,30,40, 50, and 60 min after the temperature shift. Cells were rapidly harvested on a nitrocellulose filter (Whatman no. 141109) followed by immediate freezing in l...

Single yeast cells vary in transcription activity not in delay time after a metabolic shift

“…Previously, values between 13 and 18 min were reported for the half-life of MET5 mRNA [45][46][47] ). Therefore, the intrinsic fluctuations in transcript levels can be expected to have a corresponding autocorrelation time, such that such fluctuations in mRNA persist with a characteristic lifetime of about 7 min.…”

Tuning cell differentiation patterns and single cell dynamics by regulating proteins’ functionalities in a toggle switch

“…Reaction rates k tr = 0.00042 and d rbs = 0.01 are set to match the average observed number of proteins produced per RNA E. coli, i.e., 4.2 [51]. Proteins decay is set to d p = 0.001, ten times slower than mRNA [12,49]. We additionally set heuristically k rep = 1, k unrep = 0.1, k dimer = 5 · 10 −4 and k undimer = 5 · 10 −5 to attain a toggling frequency within the order of magnitude of experimental observations [15].…”

Feedback control of stochastic noise in the yeast galactose utilization pathway

“…The Gillespie solver [18] These simulations again show no great differences between the regulated and unregulated models. However, one expected property of the galactose network [20], and indeed other such networks, is that the structural proteins Gal1p, Gal2p, Gal7p and Gal10p have slow decay times, so perturbations due to stochastic effects lead to lowfrequency fluctuations. Therefore while Figure 3 gives an impression of how individual cells react to induction, the time scale is not sufficiently long to accurately measure the noise properties.…”

“…The If this unregulated model succeeds in reproducing the noise characteristics of the regulated model, one might then ask why the organism doesn't simply maintain a higher concentration of Gal3p and Gal80p at all times. One way to do this is to slow the decay rates for the regulatory mRNA, which in the model decay relatively rapidly compared to structural mRNA [20]. (Another way is to simply speed production, but this might be metabolically constrained.)…”