Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast

Miller, Christian; Schwalb, Björn; Maier, Kerstin; Schulz, Daniel; Dümcke, Sebastian; Zacher, Benedikt; Mayer, Andreas; Sydow, Jasmin F.; Marcinowski, Lisa; Dölken, Lars; Martin, Dietmar E.; Tresch, Achim; Cramer, Patrick

doi:10.1038/msb.2010.112

Cited by 371 publications

(517 citation statements)

References 62 publications

Supporting

Mentioning

467

Contrasting

Order By: Relevance

“…Among genes that rise to their peaks concomitantly, we observe that their transcript levels may decay at different rates; interestingly, these rates are in rough qualitative agreement with a recent global study of mRNA half-lives (33). For instance, among the closely transcribed genes ASH1, EGT2, AMN1, and DSE3, the half-life of ASH1 is shortest (9.35), the half-lives of A B Fig.…”

Section: Deconvolution Reveals a Large Number Of Transcripts Fluctuatingsupporting

confidence: 89%

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Guo¹,

Bernard²,

Orlando

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cellcycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.O ne of the most fundamental processes in biology is the cell cycle, the intricate progression of events necessary for a cell's division. To better understand how cell-cycle events are regulated, studies in many organisms have monitored the dynamics of various molecular species (e.g., transcript levels, protein levels, nucleosome positions) throughout the cell cycle. Ideally, the dynamics of these species would be studied within individual cells traversing the cell cycle.Unfortunately, current technology enables accurate, genomewide quantification of many molecular species only in populations of cells. To provide insight into the dynamics of cell-cycle processes, the cells in such a population should be as synchronized as possible as they progress through the cell division cycle. To effect this synchrony, cells are arrested or selected at one stage of the cell cycle and then released to progress through subsequent division cycles. Molecular species can then be measured in the population at various time points after release (1-5).Measurements of cell populations would not be substantially different from average measurements of individual cells if the cells in the population were always perfectly synchronized. However, perfect cell synchrony is neither attainable at synchronization nor maintainable after release. Cells exhibit variability even at the time of release, and synchrony deteriorates further over time because individual cells progress through the cell cycle at different rates. Moreover, asymmetric cell division is a major source of synchrony loss in many kinds of cells and especially in budding yeast (6-10). After yeast cell division, newborn daughter cells are smaller than their mothers, and the cycle period of daughters is significantly longer than that of mothers. This is most likely due to mechanisms-not yet we...

show abstract

Section: Deconvolution Reveals a Large Number Of Transcripts Fluctuatingsupporting

confidence: 89%

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Guo¹,

Bernard²,

Orlando

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…4E). Fourth, all four motifs show significant effects in the RNA half-life data set generated by Miller et al (2011), which is also based on 4sU labeling, as well as in the data set of Presnyak et al (2015), which is in contrast based on transcriptional arrest (Supplemental Fig. S7).…”

Section: Sequence Motifs In 3 ′ ′ ′ ′ ′ Utrmentioning

confidence: 99%

“…Moreover, low stability confers high turnover to mRNA and, therefore, the capacity to rapidly reach a new steady-state level in response to a transcriptional trigger (Shalem et al 2008). Hence, stress genes, which must rapidly respond to environmental signals, show low stability (Miller et al 2011;Zeisel et al 2011;Marguerat et al 2014;Rabani et al 2014). In contrast, high stability provides robustness to variations in transcription.…”

Section: Introductionmentioning

confidence: 99%

Cis-regulatory elements explain most of the mRNA stability variation across genes in yeast

Cheng

Maier

Avsec

et al. 2017

RNA

View full text Add to dashboard Cite

The stability of mRNA is one of the major determinants of gene expression. Although a wealth of sequence elements regulating mRNA stability has been described, their quantitative contributions to half-life are unknown. Here, we built a quantitative model for Saccharomyces cerevisiae based on functional mRNA sequence features that explains 59% of the half-life variation between genes and predicts half-life at a median relative error of 30%. The model revealed a new destabilizing 3 ′ ′ ′ ′ ′ UTR motif, ATATTC, which we functionally validated. Codon usage proves to be the major determinant of mRNA stability. Nonetheless, single-nucleotide variations have the largest effect when occurring on 3 ′ ′ ′ ′ ′ UTR motifs or upstream AUGs. Analyzing mRNA half-life data of 34 knockout strains showed that the effect of codon usage not only requires functional decapping and deadenylation, but also the 5 ′ ′ ′ ′ ′ -to-3 ′ ′ ′ ′ ′ exonuclease Xrn1, the nonsense-mediated decay genes, but not no-go decay. Altogether, this study quantitatively delineates the contributions of mRNA sequence features on stability in yeast, reveals their functional dependencies on degradation pathways, and allows accurate prediction of half-life from mRNA sequence.

show abstract

“…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.…”

Section: Resultsmentioning

confidence: 99%

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

Schwabe¹,

Bruggeman²

2014

Nat Commun

View full text Add to dashboard Cite

Individual cells respond very differently to changes in environmental conditions. Stochasticity causes cells to respond at different times, magnitudes or both. Here we disentangle and quantify these two sources of heterogeneity. We track the adaptation dynamics of single Saccharomyces cerevisiae cells exposed to a nutrient shift from methionine to sulphate and back. Using single-molecule RNA fluorescence in situ hybridization, we count the number of transcripts of a methionine-biosynthesis enzyme in single cells during adaptation. The variation of response times between cells is small, yet we find a high transient variability in the messenger RNA copy numbers. Surprisingly, single cells display strongly delayed transcription induction, as we could induce transcription fourfold quicker by direct activation and bypassing the cellular control circuitry. Transcription repression occurs rapidly within several minutes. This study indicates that small variability in response timing combined with high, stochastic transcription activity can cause large cell-to-cell variability in dynamic adaptation responses.

show abstract

Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast

Cited by 371 publications

References 62 publications

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Cis-regulatory elements explain most of the mRNA stability variation across genes in yeast

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

Contact Info

Product

Resources

About