A dynamic model of proteome changes reveals new roles for transcript alteration in yeast
2011; Springer Nature; Volume: 7; Issue: 1 Linguagem: Inglês
10.1038/msb.2011.48
ISSN1744-4292
AutoresM Violet Lee, Scott Topper, Shane L. Hubler, James Hose, Craig D. Wenger, Joshua J. Coon, Audrey P. Gasch,
Tópico(s)Fungal and yeast genetics research
ResumoArticle19 July 2011Open Access A dynamic model of proteome changes reveals new roles for transcript alteration in yeast M Violet Lee M Violet Lee Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Scott E Topper Scott E Topper Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Present address: Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA Search for more papers by this author Shane L Hubler Shane L Hubler Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author James Hose James Hose Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Craig D Wenger Craig D Wenger Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Joshua J Coon Corresponding Author Joshua J Coon Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Audrey P Gasch Corresponding Author Audrey P Gasch Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author M Violet Lee M Violet Lee Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Scott E Topper Scott E Topper Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Present address: Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA Search for more papers by this author Shane L Hubler Shane L Hubler Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author James Hose James Hose Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Craig D Wenger Craig D Wenger Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Joshua J Coon Corresponding Author Joshua J Coon Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Audrey P Gasch Corresponding Author Audrey P Gasch Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Author Information M Violet Lee1,‡, Scott E Topper2,‡, Shane L Hubler1, James Hose2, Craig D Wenger1, Joshua J Coon 1,3 and Audrey P Gasch 2,3 1Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA 2Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA 3Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA ‡These authors contributed equally to this work *Corresponding authors. Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA. Tel.: +1 608 263 1718; Fax: +1 608 262 0453; E-mail: [email protected] of Genetics, University of Wisconsin-Madison, Madison, WI, USA. Tel.: +1 608 265 0859; Fax: +1 608 262 1069; E-mail: [email protected] Molecular Systems Biology (2011)7:514https://doi.org/10.1038/msb.2011.48 Present address: Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info The transcriptome and proteome change dynamically as cells respond to environmental stress; however, prior proteomic studies reported poor correlation between mRNA and protein, rendering their relationships unclear. To address this, we combined high mass accuracy mass spectrometry with isobaric tagging to quantify dynamic changes in ∼2500 Saccharomyces cerevisiae proteins, in biological triplicate and with paired mRNA samples, as cells acclimated to high osmolarity. Surprisingly, while transcript induction correlated extremely well with protein increase, transcript reduction produced little to no change in the corresponding proteins. We constructed a mathematical model of dynamic protein changes and propose that the lack of protein reduction is explained by cell-division arrest, while transcript reduction supports redistribution of translational machinery. Furthermore, the transient ‘burst’ of mRNA induction after stress serves to accelerate change in the corresponding protein levels. We identified several classes of post-transcriptional regulation, but show that most of the variance in protein changes is explained by mRNA. Our results present a picture of the coordinated physiological responses at the levels of mRNA, protein, protein-synthetic capacity, and cellular growth. Synopsis By characterizing dynamic changes in yeast protein abundance following osmotic shock, this study shows that the correlation between protein and mRNA differs for transcripts that increase versus decrease in abundance, and reveals physiological reasons for these differences. Natural microenvironments change rapidly, and living creatures must respond quickly and efficiently to thrive within this flux. At all cellular levels—signaling, transcription, translation, metabolism, cell growth, and division—the response is dynamic and coordinated. Some aspects of this response, such as dynamic changes of the transcriptome, are well understood. But other aspects, like the response of the proteome, have remained obscured primarily because of previous limitations in technology. Without coordinated time-course data, it has remained impossible to correctly characterize the correlations and dependencies between these two essential levels of cell biology. This work presents an extended picture of the coordinated response of the transcriptome and proteome as cells respond to an abrupt environmental change. To assay proteomic dynamics, we developed a strategy for large-scale, multiplexed quantitation using isobaric tags and high mass accuracy mass spectrometry. This sensitive yet efficient platform allows for the expedient collection of quantitative time-course proteomic data at six time points, sufficiently reproducible to permit meaningful interpretation of variation across biological replicates. Time-course transcriptome data were generated from paired biological samples, allowing us to examine the relationships between changes in mRNA and protein for each gene in terms of direction and intensity, as well as the characteristics of the temporal profiles for each gene. It was immediately obvious that a single measure of correlation across the entire data set was a meaningless metric. We therefore analyzed relationships between mRNA and protein for different subsets of data. In response to osmotic shock, hundreds of transcripts are highly induced, and their temporal pattern reveals a transient peak of maximal induction, which resolves into a new elevated level as cells acclimate (Figure 2). For this group of genes, there is extremely high correlation between peak mRNA change and protein change (R2∼0.8). But the dynamics of the molecules differ: while mRNA levels transiently overshoot their final levels, proteins gradually rise in abundance toward their new, elevated state. We observed, however, that a measure of efficiency connects the two profiles. The time it takes for a protein to acclimate to its new state correlates with the magnitude of the excess mRNA induction. Thus, the cell imparts an urgency to protein induction by transiently producing excess transcript. The most surprising result, however, involves transcripts that decrease in abundance. In response to osmotic shock, the cell transiently reduces over 600 transcripts, many of which are among the most highly expressed in unstressed cells. But protein levels for these genes remain, for the most part, almost completely unchanged. The stark absence of protein repression is independent of basal protein abundance, independent of reported protein half-lives, reproducible across biological replicates, and validated by quantitative western blots. Furthermore, since we do detect a handful of proteins whose abundance is significantly reduced, our technology is capable of identifying protein loss. Thus, we conclude that transcript reduction serves another purpose besides reducing protein levels. To explore alternate interpretations of the consequence of transcriptional repression, we devised a mass-action kinetic model, which describes protein changes based on mRNA dynamics in the context of transient changes in the rates of cell division. The model successfully recapitulated the observed data, allowing us to alter modeling parameters to test various hypotheses. In response to osmotic shock, overall rates of translation temporarily decrease and cell growth transiently arrests before resuming at a slower rate. We reasoned that mRNA reduction might lower the rate of new protein synthesis, but that retarded production is balanced by reduced cell division. We explored both aspects of this logic with our model. As expected, removing cell division from our model led to a calculated decrease of protein levels, indicating that reduced growth is necessary for maintaining protein levels. However, when we computationally held mRNA levels stable and calculated protein levels in the absence of mRNA repression, we did not find the expected increase in protein abundance. We then considered the possibility that one function of the regulated repression of these highly abundant transcripts was to liberate proteins essential for translation, such as ribosomes or translation initiation factors. To explore this, we examined a mutant lacking the Dot6p/Tod6p transcriptional repressors, which fails to properly repress ∼250 genes in response to osmotic shock. In the wild type, the mRNA for a Dot6p/Tod6p target (ARX1) decreased seven-fold, and the remaining transcript was generally unassociated with poly-ribosomes. In the mutant, however, the mRNA levels were reduced only two-fold, while the remaining transcript continued to bind ribosomes. Therefore, failure to reduce transcript levels led to a persistent association with poly-ribosomes, thereby consuming translational machinery. Our hypothesis is, therefore, that widespread changes in the transcriptome promote efficient translation of new proteins. Transcript increase serves to increase abundance of the encoded proteins, while reduction of some of the most abundant and highly translated mRNAs supports this project by liberating translational capacity. While it is not clear what factors are the limiting elements, it is clear that a full picture of cellular biology requires exploring the dynamics of the cellular response. Introduction Yeast cells remodel a large fraction of their transcriptome to acclimate to the new conditions following environmental shock. Such stressful conditions trigger a common expression program called the environmental stress response (ESR), which includes increased expression of stress-defense genes and reduced expression of protein-synthesis and growth-related messages (Gasch et al, 2000; Causton et al, 2001) that are synthesized at high levels during active growth (Lipson et al, 2009). Increased abundance of stress-defense genes is important for surviving subsequent stressful insults (Berry and Gasch, 2008), while the role of transcript reduction in the ESR is less clear. It has been observed that the abundance of stress-reduced transcripts correlates with growth rate under some (Jorgensen et al, 2002, 2004; Regenberg et al, 2006; Brauer et al, 2008) but not all (Gasch et al, 2000, 2001) conditions, leading to the hypothesis that transcript reduction contributes to resource conservation during stress defense. However, the precise function of mRNA reduction during environmental transition remains enigmatic. Stress responses have been extensively studied at the transcript level because mRNA measurement is robust and broadly accessible, while protein analysis is considerably less well developed. This technology gap has prompted the assumption that changing transcripts mediate proportional protein alterations; however, most recent proteomic studies report poor correlation. For example, many studies comparing absolute abundance (Gygi et al, 1999; Ideker et al, 2001a; Ghaemmaghami et al, 2003; Greenbaum et al, 2003; Washburn et al, 2003) or abundance changes (Ideker et al, 2001a; Griffin et al, 2002; Li et al, 2003; Washburn et al, 2003; de Godoy et al, 2008; Soufi et al, 2009; Fournier et al, 2010) of protein versus mRNA reported only modest correlation between the two. Several smaller investigations cited higher agreement (R2∼0.7), but these were limited to a few hundred mRNA–protein pairs (Futcher et al, 1999; Lu et al, 2007). Many of these studies did not collect mRNA from the same cells from which proteins were measured, and all but a few (Li et al, 2003; Picotti et al, 2009; Soufi et al, 2009; Fournier et al, 2010) neglected temporal changes or did not perform biological replicates. Hence, the true relationship between mRNA and protein levels remains an open question. To understand the dynamic relationship between transcript and protein, we developed a strategy for large-scale, multiplexed quantitation by way of isobaric tags and high mass accuracy mass spectrometry (<5 p.p.m.) (Figure 1A). This platform allowed for the expedient collection of time-course data (response to 0.7 M sodium chloride (NaCl) at six time points over 4 h) at the protein level (two technical replicates of each biological sample in ∼5 days instrument analysis). In contrast to metabolic labeling approaches (Jiang and English, 2002; Ong et al, 2002), which permit up to three-way sample comparisons and require specific growth conditions, this multiplexed method enables practical acquisition of biological data over many samples and in any conditions. Here, we reveal the quantitative and temporal relationships between changes in mRNA and protein levels in cells acclimating to a sudden change in osmolarity. Figure 1.Experimental workflow and mass spectrometry identification summary. (A) Yeast cells were grown to mid-log phase and exposed to 0.7 M NaCl; culture volumes were removed at 30, 60, 90, 120, and 240 min after stress, as well as from unstressed cells (0 min), for microarray and quantitative MS proteomic analysis. Each proteomic sample was lysed, followed by protein extraction and enzyme digestion. Peptides were labeled with isobaric TMT and mixed in equal rations. The labeled mix was then subjected to an orthogonal first-dimension separation: SCX. Fractions were subsequently analyzed on an LTQ Orbitrap Velos mass spectrometer coupled with nano-RP HPLC. Biological replicates were performed in triplicate. Spectra were analyzed with in-house developed software. (B) Peptide and protein identifications across the three biological replicates (BR) are outlined in the above table with the overlap depicted in the Venn diagrams. Of the protein identifications, 60% overlap was observed across all biological replicates. On average, 81.5% of all identifications were quantifiable with an overlap of 55% across all biological replicates. Numbers in each colored circle represent the correspondingly colored sector in the Venn diagrams. Download figure Download PowerPoint Results We followed the response of actively growing Saccharomyces cerevisiae to an osmotic shock of 0.7 M NaCl. This dose of salt provides a robust physiological response but results in high viability and eventual resumption of cell growth. Samples were collected before and at 30, 60, 90, 120, and 240 min after NaCl treatment (measuring the peak transcript changes that occurs at or after 30 min (Berry and Gasch, 2008)), in biological triplicate time courses that captured cells acclimated to both environments and their transition between states. After lysing cells harvested from each time point, we digested the proteins with trypsin, generating peptides to be labeled with one of the six isobaric tags. Tagged samples were then pooled and fractionated via strong-cation exchange (SCX) for LC–MS/MS analysis on an LTQ Orbitrap Velos mass spectrometer (Figure 1). Performing our experiment in biological triplicate generated a total of 454 755 peptide–spectral matches (PSMs), 35 828 unique peptides, and 2965 proteins (1% false discovery rate (FDR); see Materials and methods). We wrote custom software, TagQuant (Wenger et al, 2011), to extract reporter ion intensities and exclude tandem mass spectra containing interference resulting from cofragmentation of multiple precursors. Removal of precursors having ⩾25% interference greatly improved quantitative accuracy, precision, and dynamic range. To obtain maximal noise reduction across all time courses and biological replicates, we used PSMs of unambiguous provenance and required at least two unique peptides per protein. This approach is much more conservative than most proteomic analyses but provides maximal accuracy in peptide quantitation. Following this conservative analysis, we confidently measured the relative abundances of 35 000 unique peptides mapping to 2451 proteins with 60% overlap across biological replicates (Figure 1B). Of the 1814 proteins quantified in at least biological duplicate, 780 (43%) showed statistically significant changes in abundance (5% FDR, modified t-test (Storey and Tibshirani, 2003; Smyth, 2004)). Quantitative western blotting validated these large-scale results for several selected proteins (Supplementary Figure S1). Relationships between changing mRNA and proteins We next generated time-course transcriptome data from the same biological samples and compared maximal changes in mRNA with changes in corresponding protein abundance during NaCl acclimation (Figure 2). For transcripts that increase in abundance, the log–log linear correlation between mRNA change and protein change was significantly higher than previously reported (R2=0.77), indicating that on a global scale nearly 80% of the variance in changing protein abundance is explained by increases in mRNA (Figure 2B and C). Variation in this correlation revealed that some groups of functionally related genes showed even higher agreement while others were unrelated (Supplementary Figure S2; Supplementary Table S1). This result expands on previous studies demonstrating that the correlation between mRNA and protein abundance varies by gene functional group (Washburn et al, 2003). Many of the proteins with the largest changes in abundance function in processes known to be important for NaCl survival, including glycerol and trehalose metabolism and stress defense (Supplementary Figure S3). Figure 2.Correlation between mRNA and protein changes. (A) The average log2 changes in mRNA (left) and protein (right) are shown for each gene, represented as rows, and time point shown as columns within each time course (triangles). Red and green colors indicate increased and decreased abundance, respectively, according to the key. The figure shows all transcripts whose change was statistically significant (FDR <0.05) and whose corresponding proteins were measured in triplicate, amounting to 408 transcripts that increased and 702 transcripts that decreased in abundance. (B) Regression of the maximum average log2 changes in mRNA and protein for transcript–protein pairs shown in (A), where red indicates increased and green represents reduced transcripts. (C) R2-values (shaded according to the key in the center) were calculated as in (B) except that they compared mRNA with protein at each denoted time point for increased (left) and reduced (right) transcripts. Comparisons with the highest correlations are highlighted with asterisks. (D) Average log2 change over time for all mRNAs (solid lines) and corresponding proteins (dashed lines). Download figure Download PowerPoint In stark contrast to the high correlation between protein abundance and increased mRNAs, the poor correlation between protein changes and transcripts that decrease in abundance (R2=0.09; Figure 2B and C) reflects the overall lack of protein reduction over time. Although the decreased abundance of some proteins was statistically significant, the magnitude of change was far less than that of the mRNA (e.g. ∼1.1-fold reduction of ribosomal proteins (RPs) compared with 1.5- to 2-fold reduction of transcripts). Quantitative western blotting validated the lack of protein reduction (Supplementary Figure S1), and furthermore the proteomic results did not suffer from technical inability to measure protein reduction (Supplementary Figure S4). Importantly, the observation was true irrespective of protein abundance or half-life as measured under standard conditions (Supplementary Figure S5). The lack of reduction over this time frame is predicted for long-lived proteins; however, this was not expected for the many proteins with short half-lives (Belle et al, 2006). While the measured changes in mRNA abundance may be influenced by untranslated mRNAs, prior evidence suggests that the majority of most transcripts are associated with ribosomes (Arava et al, 2003), indicating that our measurements largely reflect the translated pool of mRNAs in the cell. Regardless, we conclude that the NaCl-activated reduction in transcript abundance serves another role besides mediating changes in protein abundance. Dynamic modeling suggests alternate roles of transcript reduction We devised a mass-action kinetic model to describe dynamic protein changes, in which protein abundance is a function of new synthesis and of disappearance through degradation and cell division. To calculate translation rates for each protein, we used global measurements of basal protein abundances and protein half-lives as reported by Ghaemmaghami et al (2003) and Belle et al (2006), respectively, along with our own measurements of mRNA and growth rate changes (see Materials and methods). Using this model, we predicted changes in protein abundance based on observed changes in mRNA levels. The model's successful performance in both training and test data sets (Supplementary Appendix) allowed us to use it to test various hypotheses through simulations. Since cell growth was arrested for ∼45 min after NaCl addition before resuming at roughly half the initial rate (Supplementary Figure S6), we first used the model to test the effect of cell-division arrest. The model suggested that the transient growth arrest maintained protein levels despite transcript reduction. As expected, when the model assumed a constant growth rate, it predicted an ∼1.5-fold decrease for virtually all proteins from reduced transcripts (Figure 3, blue trace). Figure 3.Distribution of protein levels predicted from model simulations. Distribution of the minimum log2 change over time (i.e. greatest log2 reduction) for transcripts (red) that decreased in abundance, corresponding protein changes as measured (orange) or calculated using the original model (green), corresponding protein changes calculated under the assumption of a constant growth rate (blue), and protein changes calculated with the original model but predicted from mRNAs simulated without reduction during the time course (purple). Analysis is for 579 triplicated mRNA–protein pairs from the training set for which mRNA was reduced with statistical significance (FDR<0.05). Download figure Download PowerPoint We next tested the consequences of transcript reduction. We reasoned that, if reduced transcript abundance is critical for reducing synthesis of these proteins, then simulating transcripts without a decrease in abundance would lead to increased protein levels during growth arrest. Surprisingly, however, the model predicted little increase in protein abundance in the absence of transcript reduction; most proteins were calculated with only ∼1.2-fold weaker reduction than that predicted from measured mRNA levels (Figure 3, purple trace). Indeed, polysome analysis showed that the maximal reduction in translation initiation (represented by the increase in monosome versus polysome complexes) peaked at 5 min after NaCl exposure (Figure 4A), whereas transcript reduction did not occur until 30 min after treatment. These results strongly suggest that the translational repression, well known to occur during the NaCl response (Uesono and Toh, 2002; Melamed et al, 2008; Warringer et al, 2010), is independent of transcript reduction and is counteracted by transient cell-division arrest, such that corresponding protein levels do not change appreciably. Figure 4.Translational profiles in wild-type and dot6Δtod6Δ cells responding to NaCl. Polysome profiles were measured as described in Materials and methods for (A) wild-type cells and (B) a mutant lacking the Dot6p/Tod6p transcriptional repressors. Absorbance at 260 nm across collected fractions is shown at 0, 5, 30, 45, and 90 min after NaCl treatment, relative to the starting baseline; 40S, 60S, and 80S monosome (M), and polysome (P) peaks are indicated. The monosome/polysome (M/P) ratio was calculated based on the trapezoidal area under the curve. Relative abundance of (C) ARX1 or (D) HSP104 transcript in polysome fractions was measured in wild-type (left) and dot6Δtod6Δ cells (right) before and at 30 min after NaCl treatment. Relative mRNA abundance was measured compared with a doped control mRNA and normalized to baseline abundance measured in the trough between the 2 and 3 polysome peaks (see Materials and methods). Plots are representative of biological duplicates. Download figure Download PowerPoint Although the reduced levels of these transcripts did not influence abundance of the encoded proteins, it may be critical for proper protein levels in the cellular system if translational capacity is limited. To explore this, we characterized the aspects of the NaCl response in a mutant lacking the transcriptional repressors Dot6p and Tod6p (Lippman and Broach, 2009; Zhu et al, 2009). In the absence of stress, the dot6Δtod6Δ mutant grew indistinguishably from wild-type cells (Supplementary Figure S6C) and displayed similar polysome profiles (Figure 4B). However, in response to NaCl treatment, the mutant failed to properly repress ∼250 genes in the yeast ESR (Supplementary Dataset S3). Over 90% of these genes (P=10−66, hypergeometric distribution) contain upstream Dot6p/Tod6p binding elements (GATGAG; Hughes et al, 2008; Zhu et al, 2009), consistent with direct repression by the proteins. The mutant also resumed growth at a slower rate after NaCl treatment (Supplementary Figure S6C), and showed delayed resumption of normal translation profiles (Figure 4B), indicating a specific defect in acclimating to the stress. We followed the polysome association of transcripts encoded by Dot6pTod6p targets, focusing on ARX1 (Figure 4). In unstressed wild-type cells, ARX1 transcript was associated with polysomes, as expected (Arava et al, 2003). We observed an ∼7-fold reduction in ARX1 levels 30 min after NaCl treatment—most of the remaining transcript was found in the monosome peak, suggesting reduced or stalled translation initiation. In contrast, the mutant showed only a 2-fold reduction in ARX1 levels at 30 min after shock. As in wild-type cells, there was a substantial increase in monosome-bound ARX1 mRNA after NaCl exposure, reflecting regulated translation initiation. Surprisingly, however, a large fraction of the remaining mRNA was associated with polysomes at 30 min. We obtained virtually the same result for another Dot6p/Tod6p target, NOP2, which also showed a repression defect in the mutant (data not shown). In contrast, polysome profiles were indistinguishable at induced transcript HSP104 (Figure 4D), although our analysis would miss subtle differences. Unfortunately, we were unable to quantify changes in the corresponding proteins using several available antibodies. Nonetheless, these results show that, although the mutant dramatically reduced global translation initiation immediately after stress, failure to repress high-abundance transcripts led to their continued polysome association as translation was resuming. mRNA dynamics affect protein acclimation time Seventy-four percent of transcripts with increased abundance after NaCl treatment showed a transient ‘burst’ of change before acclimating to final levels (Figures 2D and 5A), consistent with prior studies (Gasch et al, 2000). The majority peaked at 30 min, coincident with the maximal reduction in reduced transcripts. In contrast, only 15% of proteins showed transient change, while most gradually adjusted to final levels. As expected, there was a delay between mRNA changes and protein adjustments; however, we observed a wide range of protein acclimation times, even for those encoded by transcripts peaking at 30 min. Figure 5.Transient mRNA changes produce faster protein changes. (A) Representative changes in mRNA (solid) and protein (dashed) for two pairs with and without transient mRNA induction. (B) In all, 127 transcripts whose increase in abundance peaked at 30 min were binned based on the magnitude of transient burst (see Materials and methods). The percentage with the indicated acclimation times is shown by quartile. (C) Average log2 change of measured mRNA, simulated mRNA without the transient burst, and corresp
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