Promoter Decoding of Transcription Factor Dynamics Involves a Trade-Off between Noise and Control of Gene Expression

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Molecular Systems Biology Peer Review Process File

Promoter Decoding of Transcription Factor Dynamics

Involves a Trade-Off between Noise and Control of Gene

Expression

Anders S. Hansen and Erin K. O'Shea

Corresponding author: Erin K. O'Shea, Harvard University

Review timeline: Submission date: 07 August 2013

Editorial Decision: 29 August 2013 Revision received: 15 September 2013

Accepted: 24 September 2013

Editor: Thomas Lemberger

Transaction Report:

(Note: With the exception of the correction of typographical or spelling errors that could be a source of ambiguity, letters and reports are not edited. The original formatting of letters and referee reports may not be reflected in this compilation.)

1st Editorial Decision 29 August 2013

Thank you again for submitting your work to Molecular Systems Biology. We have now heard back from the three referees who agreed to evaluate your manuscript. As you will see from the reports below, the referees find the topic of your study of potential interest. The reviewers raise, however, several concerns on your work, which should be convincingly addressed in a revision.

Some of the major points refer to the following issues:

- to which extent do the measurements reflect Msn-dependent direct regulatory effects rather than indirect or even Msn-independent PKA-mediated effects?

- should regulation of mRNA half-life be taken into account?

- the proposed signal decoding system should be discussed in the context of yeast's physiology. Reviewer #3 was perplexed by the proposed segregation of promoters in discrete classes. While the clusters shown in Figure 2B are clear, Figure 1A may appear to show more of a continuum in response behavior and this would prompt for additional explanations in the text.

On a more editorial level, we would kindly ask you to supply the quantitative data shown in Figure 1C and Supplementary Figure S6A as 'Source data files' in Excel, tab-delimited or csv format so that others can reproduce your analysis, re-visualize or perform new analyses of your data. See our guide to authors at <http://www.nature.com/msb/authors/index.html#a3.4.3>.

Supplementary Figure 1D appears to be identical to Figure 1C in the main text. If this is indeed the case, we would ask you to remove Sup Fig S1D to avoid confusing readers.

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<http://www.nature.com/msb/authors/index.html#a3.4.6>. Essentially, Supplementary text and figures and short tables should be included in a single PDF file that starts with a Table of Content. If you feel you can satisfactorily deal with these points and those listed by the referees, you may wish to submit a revised version of your manuscript. Please attach a covering letter giving details of the way in which you have handled each of the points raised by the referees. A revised manuscript will be once again subject to review and you probably understand that we can give you no guarantee at this stage that the eventual outcome will be favorable.

--- Reviewer #1:

In this manuscript entitled "Promoter Decoding of Transcription Factor Dynamics ..." Hansen and O'Shea explore the impact of cyclical nuclear localization of transcription factors (TF) on the noise characteristics of target gene expression. Usually transcription factors get localized for a sustained period into the nucleus, where they bind to the regulatory regions of the genes and initiate transcription. In certain cases, such as in circadian rhythms, and the case studied by the authors in yeast, cells show cyclical entry and exit of TFs into the nucleus. It is believed that information is encoded in the amplitude and frequency of these waves and different patterns of TF dynamics results in induction of a distinct set of genes. While the research on the mechanism of this coding is undergoing, a question arises as to how these cycles interact with stochasticity of expression of target genes.

The studies show that in the case of yeast transcription factor Msn2 the oscillatory and sustained TF nuclear localizations induce different set of genes. The former regimen leads to higher noise than the latter. The mechanisms that produce noise in eukaryotic transcription are still not clear. The present work, particularly the observation that slow promoter transition produces higher noise and how that is related to nucleosome occupancy at the promoter, has implications for that fundamental issue as well.

This is a beautiful paper. It is well written and was a pleasure to read. It is rich in concept and in detail. Large amount of data is condensed and presented very well. The study utilizes a rare combination of microfluidic device engineering, genomics, mathematical modeling, molecular biology, imaging and organic chemistry, all focused on dissection of transcriptional noise. Scientists in the fields of gene expression noise, transcription factors, signaling and allied fields will find it exciting.

I have only a few minor concerns:

1. Their modeling predicts that four different classes of promoters that respond differently to amplitudes and duration of pulses should exist. They explore in some details examples of two classes. However, the genes for these were selected from a microarray analysis for their ability to be strongly induced by Msn2. Is it possible to identify representatives of the other two classes from the same screen?

2. There is something wrong with the scaling of the figures. Their sizes are very large.

3. The oscillations in this study are rapid. What would happen in the case of slower ones, such as in circadian oscillations?

Reviewer #2:

Referee report for 'Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression' by Hansen and O'Shea.

Background of this study:

Extracellular inputs are often encoded into dynamics of transcriptional factors, which are

subsequently decoded into target expression. Hao and O'Shea (2012) showed that the yeast general stress regulator Msn2 exhibits distinct shuttling dynamics between cytoplasm and nucleus in response to different stresses. A technique based on analog-sensitive kinase (also used in current manuscript) was developed to control the shuttling of Msn2. This technique allowed them to reveal

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the dependence of gene expression on promoter kinetics under oscillatory Msn2 at the population level. In their later study, Hao and O'Shea (2013) continued to show that the dynamics of Msn2 are largely dependent on, and can be modulated by, the phosphorylation states of the PKA target residues on the NLS/NES of Msn2.

Short summary of this manuscript:

In this manuscript, Hansen and O'Shea took a step further and used the aforementioned technique to study the dependence of gene expression on the dynamics of artificially generated Msn2 localization bursts in single cells. Using time-lapse microscopy and mathematical modeling, they identified two types of promoters: high amplitude threshold slow activation (HS) and low amplitude threshold fast activation (LF). A model with three promoter states was used to fit the experimental data and was able to capture the experimental responses. Furthermore, they showed that the noise level in promoter expression depends on the timescale of promoter activation as well as the transcription factor dynamics (i.e., repeated pulses vs. single pulse). Notably, slow promoter activation is largely due to slow nucleosome remodeling. These results paint a picture of how dynamic Msn2 inputs are decoded at the promoter level and how promoter characteristics influence the fidelity of signal decoding.

Overall comment:

These studies were carefully designed and nicely carried out. The data are solid and the analysis is comprehensive. The results address several issues in the field of dynamic signal processing in cells and bring up interesting proposals regarding the role of promoter kinetics and cellular noise in the decoding of dynamic signals. I enjoy reading this manuscript. I would recommend the acceptance of this manuscript if the authors can address the following concerns.

Major comments:

1. The potential problems of the technique using PKA analog sensitive mutant. To manipulate the localization of Msn2, the authors replaced the endogenous PKA with a mutant that can be repeatedly inhibited by 1-NM-PP1. This allows generating artificial Msn2 localization waveforms. While this is very nice, artificial control of PKA may disrupt the normal cellular physiology and affect the measured promoter responses and other activities, potentially weakening the physiological relevance of the study. Since the interaction partners of PKA have been previously reported, I assume the authors can infer how the measurements could be potentially influenced by having this PKA mutant. For example, could PKA inhibition by 1-NM-PP1 affect transcription of some of the targets (not through Msn2)? and how does PKA mutation change other aspects of the cell, like the growth rate etc?

2. The Msn2-mediated indirect regulation of target promoters. Over the timescale of the

measurements (typically > 1hr), the authors assumed that the targets are always directly regulated by Msn2. However, I am not sure if the indirect Msn2 regulation can be ruled out. In other words, Msn2 may transcriptionally activate other regulators that activate or repress the targets. In the presence of such indirect regulation, the target expression may not simply depend on the dynamics of Msn2. One may argue that since the model based on Msn2 direct regulation alone captures the experimental data nicely, the indirect regulation can be ignored. However, it should be noted that, to some extent, the model is merely a fit to the experimental data.

3. The three-state promoter description. I understand the logic underlying the three-state promoter description in the main model. However, a two-state description seems sufficient to describe the state switching of promoters. Indeed, the authors used such a model in Fig. 5 to analytically understand the dependence between noise and promoter parameters. Although the authors

mentioned that three is the minimum number of promoter states that is capable of fitting all the data in the supplement, the authors may want to include more discussion on this in the main text. And how about a four or more-state model?

4. The role of transcript half-life in the decoding. This question is also related to the model. If I understand correctly, the authors have two types of parameters: global and promoter-specific. The global parameters are first fitted and the promoter-specific parameters are then optimized. One of global parameters is the transcript half-life, d3, where a fixed value is used for all promoters. But I am not sure if this is the right approach: the authors have real experimental measurements for the half-life of each mRNA but chose to disregard this data and use a global d3 instead. I understand that it is for the simplicity of the model. However, this could be somewhat misleading because such a model only allows the differences in promoter kinetics as the sole explanation for the differences in expression patterns among promoters. From other studies, people have clearly shown that

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differences in transcript half-life can have strong influence on the dynamics of gene activation (see Tay, S. et al. (2010) for example).

5. The physiological relevance of synthetic Msn2 dynamics. The authors nicely demonstrated the role of promoter kinetics in the decoding of synthetic Msn2 dynamics. I was wondering about the comparison between the physiological Msn2 dynamics and the synthetic counterpart in terms of frequency, duration, or amplitude. In other words, do the synthetic Msn2 dynamics represent any meaningful physiological conditions? I understand that the synthetic dynamics are helpful and unique but the authors may want to have some discussions on this point.

6. The chromatin remodeling timescale. In Figure 7E, the authors examined the rate of nucleosome eviction for different classes of promoters under constant Msn2 nuclear localization. Gasch and colleagues have recently characterize the rate of nucleosome eviction across the Msn2 regulon (under oxidative stress). It appears that the nucleosome eviction occurs much faster in the current data (5min) compared to Gasch's data (>20min) for some promoters such as HXK1 and DCS2. I assume that the response of Msn2 to 0.4mM H2O2 (as used in Gasch paper) is almost immediate, similar to the response to 1-NM-PP1. Therefore, this difference in timescale seems to reflect the lack of physiological relevance of the system. Again, while appreciating the power of the synthetic system, I hope the authors could articulate such concerns clearly in the text.

7. Further interpretations on Figure 8. The authors proposed an elegant signal decoding scheme at the promoter level and its dependence on promoter activation timescale. However, it would be much nicer if the authors could move a step further and discuss about the potential physiological roles in terms of the function of these genes. For example, is there any correlation between the gene function and its activation timescale (and thus its signal decoding capability)?

Minor comments:

8. Combine the right two panels on Figure 3A. I would much prefer to see the measured gene expression data on top of the simulated data.

9. Figure 8 caption not clear. Given that Hao 2012 already characterized the real Msn2 dynamics in different stresses, I wonder if it would be helpful to indicate that the two different types of signal are physiologically relevant (and under what stresses they can be generated).

Reviewer #3:

This paper addresses a very interesting question in gene regulation, namely whether the particular dynamics of an input signal are interpreted differently by different target genes. In particular the authors address whether differences in the dynamics of MSN2 activation leads to differential expression of MSN2 targets.

The authors use a very innovative synthetic system in which they control the nuclear localization of MSN2 with a small molecule. They then provide pulses of this molecule that differ in the amplitude, frequency , and total number of pulses and measure the expression of seven different MSN2 targets at single cell resolution. This is a technical tour de force. I am extremely impressed with the quality of the data.

My main difficulty with this paper is that I do not see evidence for different classes of response genes. The key data is in Figure 1C. Where the authors claim that there are three qualitatively different classes of responses I only see a quantitative difference between the responses. To me it looks as if there are genes which are more or less sensitive to MSN2, but I do not see patterns that indicate qualitatively different responses between genes. Since the main point of the paper is to investigate the origins of the different responses I am at a loss to really comment on the validity of the models, since I don't see the phenomenon that is being modeled in the first place.

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Comments by Dr. Lemberger

Some of the major points refer to the following issues:

- to which extent do the measurements reflect Msn-dependent direct regulatory effects rather than indirect or even Msn-independent PKA-mediated effects?

RESPONSE: We have clarified in the manuscript that the gene expression responses we observed are direct and Msn2-specific. We also discuss this in more detail in the response to reviewer #2.

- should regulation of mRNA half-life be taken into account?

RESPONSE: To be concise, we address this issue in the response to reviewer #2. - the proposed signal decoding system should be discussed in the context of yeast's physiology.

RESPONSE: We now include a new discussion section entitled “Relationship between promoter class and stress-‐specific gene function” where we discuss this.

Reviewer #3 was perplexed by the proposed segregation of promoters in discrete classes. While the clusters shown in Figure 2B are clear, Figure 1A may appear to show more of a continuum in response behavior and this would prompt for additional explanations in the text.

RESPONSE: This is an important point. We agree that we see a continuum of responses. Thus, some promoters show intermediate behavior and are not easily classified (RTN2, for example, is not classified as an HF promoter because it falls too close to the center of Figure 2B). However, other promoters (such as SIP18, HXK1 and DCS2) exhibit such substantial qualitative differences that they can be robustly clustered into distinct promoter classes. We have rewritten the relevant part of the manuscript “Using a mathematical model to cluster promoters into classes” to clarify this point. We have also included additional supplementary figures (Supplementary Figures S2-S5) that further highlight these qualitative differences. Finally, in the response to reviewer #3 we discuss in more detail how we do indeed see qualitative differences between the different promoter classes.

On a more editorial level, we would kindly ask you to supply the quantitative data shown in Figure 1C and Supplementary Figure S6A as 'Source data files' in Excel, tab-delimited or csv format so that others can reproduce your analysis, re-visualize or perform new analyses of your data. See our guide to authors at <http://www.nature.com/msb/authors/index.html#a3.4.3>.

RESPONSE: Together with this letter and the revised manuscript, we have included all the raw single-cell data as source data. Since we profile more than 100,000 single cells over 64 timepoints and quantify both Msn2-mCherry, YFP and CFP reporter expression this amounts to around 20 million single data points. We provide the source data as a zip-compressed folder entitled “source_data.zip” containing csv files and a readme file entitled “source_data_READ_ME.pdf”.

Supplementary Figure 1D appears to be identical to Figure 1C in the main text. If this is indeed the case, we would ask you to remove Sup Fig S1D to avoid confusing readers.

RESPONSE: Figure 1C and Supplementary Figure S1D are slightly different. Figure 1C contains only 22 experiments, whereas Fig S1D contains all the 30 experiments performed for each promoter (10 min pulses and pulse interval modulation experiments are not shown in the main figure because the differences are too fine to adequately visualise in the heatmap format).

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Comments by reviewer #1

Reviewer #1 (Remarks to the Author):

In this manuscript entitled "Promoter Decoding of Transcription Factor Dynamics ..." Hansen and O'Shea explore the impact of cyclical nuclear localization of transcription factors (TF) on the noise characteristics of target gene expression. Usually transcription factors get localized for a sustained period into the nucleus, where they bind to the regulatory regions of the genes and initiate transcription. In certain cases, such as in circadian rhythms, and the case studied by the authors in yeast, cells show cyclical entry and exit of TFs into the nucleus. It is believed that information is encoded in the amplitude and frequency of these waves and different patterns of TF dynamics results in induction of a distinct set of genes. While the research on the mechanism of this coding is undergoing, a question arises as to how these cycles interact with stochasticity of expression of target genes.

The studies show that in the case of yeast transcription factor Msn2 the oscillatory and sustained TF nuclear localizations induce different set of genes. The former regimen leads to higher noise than the latter. The mechanisms that produce noise in eukaryotic transcription are still not clear. The present work, particularly the observation that slow promoter transition produces higher noise and how that is related to nucleosome occupancy at the promoter, has implications for that fundamental issue as well.

This is a beautiful paper. It is well written and was a pleasure to read. It is rich in concept and in detail. Large amount of data is condensed and presented very well. The study utilizes a rare combination of microfluidic device engineering, genomics, mathematical modeling, molecular biology, imaging and organic chemistry, all focused on dissection of transcriptional noise. Scientists in the fields of gene expression noise, transcription factors, signaling and allied fields will find it exciting.

RESPONSE: We thank reviewer #1 for their careful reading of our manuscript and kind comments. We address the concerns and suggestions below.

I have only a few minor concerns:

1. Their modeling predicts that four different classes of promoters that respond differently to amplitudes and duration of pulses should exist. They explore in some details examples of two classes. However, the genes for these were selected from a microarray analysis for their ability to be strongly induced by Msn2. Is it possible to identify representatives of the other two classes from the same screen?

RESPONSE: This is a great suggestion. Analyzing the microarray data we do indeed find genes likely to represent all 4 classes. However, for all but the seven genes we study in this work, the expression level is too low

for us to reliably quantify gene expression by microscopy. So, with the methodology employed in this paper, we are limited to the study of these seven genes. Below we give more details on the microarray analysis.

In the Msn2-mCherry vs. msn2Δ microarray we identify 23 genes whose expression is strongly dependent on Msn2 (Suppl. Fig S1C). For this list of genes we analyzed the amplitude sensitivity and promoter timescale using previous microarray datasets (Hao & OʼShea, 2012) and

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also our own dataset, and the result is shown on the right.

Here the AM ratio is the integrated mRNA level in response to a single 20 min pulse at 120 nM 1-NM-PP1 divided by the response to a single 20 min pulse at 3 µM 1-NM-PP1. So the higher the AM ratio, the more sensitive the promoter is to low amplitude input and hence the lower the amplitude threshold.

Similarly, the DM ratio is the integrated mRNA level in response to a single 20 min pulse at 3 µM 1-NM-PP1 divided by the response to a single 40 min pulse at 3 µM 1-NM-PP1 (Hao & OʼShea, NSMB, 2012). Thus, the higher the DM ratio, the faster the promoter activation timescale.

Since these two ratios are just based on 3 single data points and since modulating Msn2 dynamics is difficult for the large cell populations required for microarray expression analysis, this scatterplot does not provide enough confidence to reliably classify promoters as in Figure 2B (e.g. TKL2 and ALD3 appears to have a slightly higher amplitude threshold than SIP18 whereas the detailed microscopy analysis shows the opposite to be true). However, this analysis does indicate that natural promoters span the entire space of Figure 2A and that the LS and HF classes likely do exist.

As mentioned above and in the Supplement, we also made orf::CFP/YFP dual reporter strains for GDB1, GND2, HBT1, SPI1, UIP4, but found the expression level in all of these strains much too low to be reliably measured by microscopy. Thus, while the microarray data supports the notion of all 4 classes existing, we were limited for technical reasons to study only the seven genes analyzed in this work.

2. There is something wrong with the scaling of the figures. Their sizes are very large.

RESPONSE: We are sorry about this. In the revised version, all figures are letter sized.

3. The oscillations in this study are rapid. What would happen in the case of slower ones, such as in circadian oscillations?

RESPONSE: We chose rapid oscillations in this study to match the observed Msn2 dynamics in response to natural stresses (Hao and OʼShea, NSMB 2012 & Petrenko et al. MBC 2013). In the case of slow, circadian oscillations we think the promoter kinetics would play less of a role because the promoter activation timescales we observed here (<30 min) are very short compared to the timescale of circadian rhythms such that all promoters would be active for most of the pulse (if the TF is nuclear for ~12 h, whether the activation time is 5 or 30 min is not very important). Instead, the amplitude threshold might be more important. For example, if the promoter has a low amplitude threshold, the circadian pulse amplitude would exceed this for longer resulting in higher gene expression. Conversely, if the promoter has a high amplitude threshold, the circadian pulse amplitude might only exceed this for a short amount of time resulting in much lower gene expression.

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Referee report for 'Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression' by Hansen and O'Shea.

Background of this study:

Extracellular inputs are often encoded into dynamics of transcriptional factors, which are subsequently decoded into target expression. Hao and O'Shea (2012) showed that the yeast general stress regulator Msn2 exhibits distinct shuttling dynamics between cytoplasm and nucleus in response to different stresses. A technique based on analog-sensitive kinase (also used in current manuscript) was developed to control the shuttling of Msn2. This technique allowed them to reveal the dependence of gene expression on promoter kinetics under oscillatory Msn2 at the population level. In their later study, Hao and O'Shea (2013) continued to show that the dynamics of Msn2 are largely dependent on, and can be modulated by, the phosphorylation states of the PKA target residues on the NLS/NES of Msn2.

Short summary of this manuscript:

In this manuscript, Hansen and O'Shea took a step further and used the aforementioned technique to study the dependence of gene expression on the dynamics of artificially generated Msn2 localization bursts in single cells. Using time-lapse microscopy and mathematical

modeling, they identified two types of promoters: high amplitude threshold slow activation (HS) and low amplitude threshold fast activation (LF). A model with three promoter states was used to fit the experimental data and was able to capture the experimental responses. Furthermore, they showed that the noise level in promoter expression depends on the timescale of promoter activation as well as the transcription factor dynamics (i.e., repeated pulses vs. single pulse). Notably, slow promoter activation is largely due to slow nucleosome remodeling. These results paint a picture of how dynamic Msn2 inputs are decoded at the promoter level and how promoter characteristics influence the fidelity of signal decoding.

Overall comment:

These studies were carefully designed and nicely carried out. The data are solid and the analysis is comprehensive. The results address several issues in the field of dynamic signal processing in cells and bring up interesting proposals regarding the role of promoter kinetics and cellular noise in the decoding of dynamic signals. I enjoy reading this manuscript. I would recommend the acceptance of this manuscript if the authors can address the following concerns.

RESPONSE: We thank reviewer #2 for their very careful reading of our manuscript and their suggestions for improvement. Below we address each concern in detail.

Major comments:

1. The potential problems of the technique using PKA analog sensitive mutant. To manipulate the localization of Msn2, the authors replaced the endogenous PKA with a mutant that can be

repeatedly inhibited by 1-NM-PP1. This allows generating artificial Msn2 localization waveforms. While this is very nice, artificial control of PKA may disrupt the normal cellular physiology and affect the measured promoter responses and other activities, potentially weakening the physiological relevance of the study. Since the interaction partners of PKA have been previously reported, I assume the authors can infer how the measurements could be potentially influenced by having this PKA mutant. For example, could PKA inhibition by 1-NM-PP1 affect transcription of some of the targets (not through Msn2)? and how does PKA mutation change other aspects of the cell, like the growth rate etc?

RESPONSE: This is a very important issue. Here reviewer #2 questions whether (a) inhibition of the PKAas mutant could disrupt normal cell physiology and (b) whether PKA

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inhibition could induce transcription of promoters through transcription factors other than Msn2.

(a): As reviewer #2 points out, PKA is a central signaling molecule controlling many aspects of cell physiology (see e.g. Vandamme et. al. Celullar Signaling, 2012). Here we use the Shokat analogue-sensitive strategy and mutate the PKA catalytic subunits (TPK1M164G, TPK2M147G and TPK3M165G), such that the ATP binding pocket is enlarged. This means that the kinases can still bind ATP normally, but also that the 1-NM-PP1 inhibitor can fit in the ATP pockets of the mutated kinases. The result is an extremely specific nanomolar kinase inhibitor that does not affect other kinases in the cell. Mutating PKA this way could in principle interfere with normal cell physiology, even in the absence of inhibitor. However, at least three lines of evidence suggest that this is not the case. First, lower-than-normal PKA kinase activity would lead to low phosphorylation of Msn2, which would lead to partial nuclear localization of Msn2. By microscopy, we find the Msn2-mCherry is entirely cytoplasmic in standard laboratory medium in the absence of inhibitor (see e.g. initial condition Suppl. Movie S1). Thus, PKAas kinase activity empirically appears to be normal. Secondly, significant loss of PKA function would affect growth rate. But all PKAas strains (except the snf6Δ and gcn5Δ mutants) in this study have measured growth rates (in the absence of inhibitor) of 89±2 min just like the wild-type W303 strain (EY0690) (See also Supplementary Information where the growth rates are mentioned). Thirdly, others have also used the PKAas strategy for other purposes (e.g. Zaman et al., MSB, 2009) and they also found that “In the absence of the inhibitor,

the mutant kinases function essentially normally in the cell and the strains exhibit normal growth and responses.” Thus, both we and other labs find that the PKAas mutation, in the absence of inhibitor, does not significantly affect cell physiology or growth rate.

(b) As reviewer #2 points out, PKA inhibition could indirectly affect gene expression – we find that inhibition causes hundreds of genes to change expression at least 2-fold. Thus, we took care to ensure that the transcriptional responses we study when we inhibit PKA with the inhibitor 1-NM-PP1 are Msn2-specific. By comparing the response to 3 µM 1-NM-PP1 between Msn2-mCherry vs. msn2Δ strains using microarrays we identify 23 genes whose induction is strong when Msn2 is present, but below detection when Msn2 is absent (Suppl. Fig S1C). Of the 7 out of the 23 that showed strong enough induction for reliable detection by microscopy, we see no induction when Msn2 is deleted and all seven promoters are known from previous genome-wide ChIP experiments to directly bind Msn2 (Huebert et al., MCB, 2012). Since the genes we study do not induce in the absence of Msn2 and since the promoters directly bind to Msn2, we conclude that the transcriptional response we focus on is directly dependent on Msn2.

2. The Msn2-mediated indirect regulation of target promoters. Over the timescale of the measurements (typically > 1hr), the authors assumed that the targets are always directly regulated by Msn2. However, I am not sure if the indirect Msn2 regulation can be ruled out. In other words, Msn2 may transcriptionally activate other regulators that activate or repress the targets. In the presence of such indirect regulation, the target expression may not simply depend on the dynamics of Msn2. One may argue that since the model based on Msn2 direct regulation alone captures the experimental data nicely, the indirect regulation can be ignored. However, it should be noted that, to some extent, the model is merely a fit to the experimental data.

RESPONSE: This point is related to point (b) above. As mentioned above, we have experimental evidence that the transcriptional response is directly dependent on Msn2: The genes do not induce in the absence of Msn2 and the promoters directly bind Msn2. We agree with reviewer #2 that the fact that the model can nicely fit the experimental data (Suppl. Fig S2-S5), does not prove that no indirect effects exist. Furthermore, it is

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possible for the transcriptional response to be entirely Msn2-specific and for there to be global indirect effects (e.g. on translation) at the same time. While it is impossible to positively prove that no global indirect effects exist, the ability of the model to fit the data suggests that if indirect effects do exist then they are modest. Furthermore, the main claim of this paper is that different promoter classes decode TF dynamics differently. This claim will still hold even if there are global indirect effects – as long as these effects affect all promoters equally.

To make this caveat clearer to the reader, we have changed the main text in the “Identification of specific target genes of Msn2” paragraph to say:

“To identify Msn2-specific target genes, we used microarrays to compare the gene expression response to 1-NM-PP1 in strains with and without Msn2-mCherry and identified 23 genes that showed at least 5-fold up-regulation in the presence of

Msn2-mCherry, but no expression change in an msn2Δ strain (Supplementary Figure S1C). To

measure both gene expression and intrinsic and extrinsic noise components we chose seven of the most strongly induced of these genes and implemented the dual-reporter strategy (Elowitz et al, 2002), replacing the native ORF with fast-maturing CFP and YFP reporters on homologous chromosomes in diploid yeast cells. Finally, although PKAas inhibition might have indirect global effects on gene expression, Msn2 directly controls the transcriptional response of these seven genes: they are not induced in the absence of Msn2 and previous genome-wide ChIP experiments have shown that Msn2 directly binds their promoters (Huebert et al, 2012).”

3. The three-state promoter description. I understand the logic underlying the three-state promoter description in the main model. However, a two-state description seems sufficient to describe the state switching of promoters. Indeed, the authors used such a model in Fig. 5 to analytically understand the dependence between noise and promoter parameters. Although the authors mentioned that three is the minimum number of promoter states that is capable of fitting all the data in the supplement, the authors may want to include more discussion on this in the main text. And how about a four or more-state model?

RESPONSE: Reviewer #2 is exactly right: As we also discuss in the supplement having 2 promoter states is largely acceptable for the LF promoters, but having 3 promoter states is crucial in order to adequately account for the delay/long refractory period we observe for the HS promoters (SIP18, ALD3, TKL2). While we have kept the detailed discussion in the supplement, we now explain why the 2 promoter state model fails in the main text in the section “Using a mathematical model to cluster promoters into classes”, where we now include the sentence: “Models with only 2 promoter states could not adequately account for the long refractory period that we observe for SIP18, ALD3 and TKL2. Thus, our final model contains 3 promoter states. ”.

Adding additional promoter states (e.g. 4 or more) would improve the model fit because there would now be more free, fitted parameters. However, as can be seen from Suppl. Figures S2-S5, the 3 promoter state model can fit the data well. Thus, we feel that including 4 or more promoter states would be overfitting, which we wish to avoid, and is also not biologically justified.

4. The role of transcript half-life in the decoding. This question is also related to the model. If I understand correctly, the authors have two types of parameters: global and promoter-specific. The global parameters are first fitted and the promoter-specific parameters are then optimized. One of global parameters is the transcript half-life, d3, where a fixed value is used for all promoters. But I am not sure if this is the right approach: the authors have real experimental measurements for the half-life of each mRNA but chose to disregard this data and use a global d3 instead. I understand that it is for the simplicity of the model. However, this could be somewhat

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misleading because such a model only allows the differences in promoter kinetics as the sole explanation for the differences in expression patterns among promoters. From other studies, people have clearly shown that differences in transcript half-life can have strong influence on the dynamics of gene activation (see Tay, S. et al. (2010) for example).

RESPONSE: Regarding mRNA transcript dynamics, we want to emphasize that for all reporters we replace the native ORF with the genes encoding CFP and YFP fluorescent proteins followed by the ADH1 terminator. So while the 5ʼ-UTR may differ, all reporters have the same coding sequence and 3ʼ-UTR. So the sequence identity is at least 90%. Since mRNA turnover is controlled primarily by digestion of the 3ʼ poly(A) tail (Garneau, Nature Reviews MCB, 2007), which is common to all reporters, we find it highly unlikely that the mRNA stabilities differ significantly between the different reporter strains. Consistent with this, when we measured the mRNA half-life using qPCR we found that the mRNA half-life estimate differences between strains were the same as between biological replicates for the same strain (see Suppl. Info, p. 30, first paragraph). Our qPCR measurements indicated that the mRNA decay rate was in the range [0.069;0.139] in units of min-1. So we used this as the mRNA decay rate range for the model.

While we believe that the orf::CFP/YFP reporters are unlikely to exhibit different transcript half-lives, reviewer #2 is right that the natural transcripts might well. Native transcript half-lives and splicing effects are certainly important in some cases like NF-κB (e.g. Tay, 2010; Hao & Baltimore, 2009 & 2013). This is also why we chose to replace the native ORF with fluorescent reporters – we can largely ignore native transcript decay dynamics and focus on promoter kinetics.

5. The physiological relevance of synthetic Msn2 dynamics. The authors nicely demonstrated the role of promoter kinetics in the decoding of synthetic Msn2 dynamics. I was wondering about the comparison between the physiological Msn2 dynamics and the synthetic counterpart in terms of frequency, duration, or amplitude. In other words, do the synthetic Msn2 dynamics represent any meaningful physiological conditions? I understand that the synthetic dynamics are helpful and unique but the authors may want to have some discussions on this point.

RESPONSE: We thank reviewer #2 for pointing this out and we now elaborate on this point in the manuscript section “Systematic dissection of how different promoters decode TF dynamics”.

We chose the synthetic dynamics to match the naturally observed Msn2 dynamics in response to oxidative, osmotic and glucose stress (Hao & OʼShea, NSMB, 2012, Figure 1): In response to oxidative stress Msn2 shows prolonged nuclear localization with dose-dependent amplitude and in response to osmotic stress, Msn2 shows constant-amplitude localization with dose-dependent duration. Thus, the 20 experiments where we systematically vary the duration and/or amplitude of a single pulse were chosen to mimic the natural dynamics in response to different intensities of osmotic and oxidative stress.

In response to glucose starvation, Msn2 shows brief pulses of different frequencies and different number of pulses. So the 10 oscillatory experiments were chosen to survey a series of pulse numbers and pulse frequencies. In the revised manuscript we now write: “For each of the seven promoters we performed 30 experiments in which we systematically modulated the amplitude, duration, pulse number and pulse interval of Msn2 nuclear localization (Supplementary Figure S1D) to mimic the naturally observed Msn2 translocation dynamics in response to stress (Hao & O'Shea, 2012). ”

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6. The chromatin remodeling timescale. In Figure 7E, the authors examined the rate of

nucleosome eviction for different classes of promoters under constant Msn2 nuclear localization. Gasch and colleagues have recently characterize the rate of nucleosome eviction across the Msn2 regulon (under oxidative stress). It appears that the nucleosome eviction occurs much faster in the current data (5min) compared to Gasch's data (>20min) for some promoters such as HXK1 and DCS2. I assume that the response of Msn2 to 0.4mM H2O2 (as used in Gasch paper) is almost immediate, similar to the response to 1-NM-PP1. Therefore, this difference in timescale seems to reflect the lack of physiological relevance of the system. Again, while appreciating the power of the synthetic system, I hope the authors could articulate such concerns clearly in the text.

RESPONSE: Following reviewer #2ʼs suggestion, we have included a new section in the Discussion, where we discuss the limitations of the synthetic system entitled “Relationship between promoter class and stress-‐specific gene function”.

As reviewer #2 points out, nucleosome eviction is remarkably rapid for some of our promoters (e.g. HXK1 and DCS2), but very slow for other promoters (e.g. SIP18). In the Gasch paper (Huebert et al., MCB, 2012), some promoters also show very rapid remodeling: remodeling at the CTT1 promoter appears to take place within 2 min (Huebert Fig 7B). Thus, while it is true that nucleosome remodeling is generally slower (20-40 min) in the Gasch data set, there are also several examples of promoters showing equally rapid changes.

We are hesitant to make direct comparisons between our MNase-Seq and Gaschʼs. We used PKA inhibition that induces maximal Msn2 localization. They used 0.4 mM H2O2 which induces only ca. 30-40% of maximal Msn2 localization. Furthermore, oxidative stress can induce many other activators and repressors.

7. Further interpretations on Figure 8. The authors proposed an elegant signal decoding scheme at the promoter level and its dependence on promoter activation timescale. However, it would be much nicer if the authors could move a step further and discuss about the potential physiological roles in terms of the function of these genes. For example, is there any correlation between the gene function and its activation timescale (and thus its signal decoding capability)?

RESPONSE: As reviewer #2 suggests, we have included a new section in the Discussion entitled “Relationship between promoter class and stress-‐specific gene function”. Here we discuss the potential relationship between promoter class and potential stress-specific gene function.

Minor comments:

8. Combine the right two panels on Figure 3A. I would much prefer to see the measured gene expression data on top of the simulated data.

RESPONSE: In the new Figure 3, we show the measured data directly on top of the simulated data. We have updated the figure legend to reflect this change. Everything else (data and simulations) remains the same.

9. Figure 8 caption not clear. Given that Hao 2012 already characterized the real Msn2 dynamics in different stresses, I wonder if it would be helpful to indicate that the two different types of signal are physiologically relevant (and under what stresses they can be generated).

RESPONSE: We have updated the figure 8 legend (caption) to make its meaning clearer and we now discuss the physiological relevance of natural Msn2 dynamics and the

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different promoter classes in the section entitled “Relationship between promoter class and stress-‐specific gene function”.

Regarding Figure 8, we want to distinguish between spurious activation of a TF (signaling noise) and real stress responses. The point we were trying to make in Figure 8 relates to filtering rather than sensitivity to specific TF dynamics. The HS promoters have filtering abilities such that they will filter out both oscillatory stress responses (e.g. Msn2 in response to glucose stress) and spurious/transient activation due to upstream signaling noise. Because the slow genes in the case of p53 and NF-κB appear to induce apoptosis, it is extremely important that these genes are not activated by transient signaling noise in the specific pathway. Thus, these promoters need to filter out transient TF signals. Because filtering relates to both upstream signaling noise and real stress responses (e.g. low frequency oscillations), we feel that this is a more general property and of more general interest than something specific to the yeast Msn2 regulon. We have updated the Figure 8 caption to try and make this distinction clearer.

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This paper addresses a very interesting question in gene regulation, namely whether the particular dynamics of an input signal are interpreted differently by different target genes. In particular the authors address whether differences in the dynamics of MSN2 activation leads to differential expression of MSN2 targets.

The authors use a very innovative synthetic system in which they control the nuclear localization of MSN2 with a small molecule. They then provide pulses of this molecule that differ in the amplitude, frequency , and total number of pulses and measure the expression of seven different MSN2 targets at single cell resolution. This is a technical tour de force. I am extremely impressed with the quality of the data.

My main difficulty with this paper is that I do not see evidence for different classes of response genes. The key data is in Figure 1C. Where the authors claim that there are three qualitatively different classes of responses I only see a quantitative difference between the responses. To me it looks as if there are genes which are more or less sensitive to MSN2, but I do not see patterns that indicate qualitatively different responses between genes. Since the main point of the paper is to investigate the origins of the different responses I am at a loss to really comment on the validity of the models, since I don't see the phenomenon that is being modeled in the first place.

RESPONSE: We thank reviewer #3 for their comments and we apologize if the promoter classifications were not clear from Figure 1C. First, we want to stress that not all promoters will fit one of four promoter classes. Reviewer #3 is correct that there is a continuum of responses, and some promoters will show intermediate behavior. For example, RTN2, DDR2, ALD3 and TKL2 all show more intermediate behavior than do SIP18, HXK1 and DCS2. However, we do observe substantial quantitative differences in the amplitude threshold and promoter activation timescale (e.g. 1 min vs. 25 min) and based on these two properties four extreme classes exist in theory. Our paper experimentally investigates the behavior of the LF and HS classes and, theoretically, the behavior of all four extremes. Future experimental work will be necessary to find examples of the LS and HF classes.

While the heatmap format (Figure 1C) is convenient for summarizing large amounts of data, as reviewer #3 also points out some of the fine detail can be lost. To make the qualitative differences more clear, we now include additional supplementary figures (Suppl. Fig S2-S5). Additionally, we believe that Figure 3 succinctly shows that qualitative differences do exist. Had there not been a qualitative difference in the response of DCS2 and SIP18 to the same Msn2 input, we would not be able to achieve significant differential expression. But as Figure 3 shows, we do see significant differential expression between DCS2 and SIP18. Below we discuss in further detail the qualitative differences between the promoter classes:

Promoter activation timescale: HXK1 shows a clear response to a single 10 min pulse at 690 nM (Fig S3, row 11, column 3) and four 5 min pulses separated by 20 min intervals (Fig S3, row 10, column 6). Thus, even to transient input, fast promoters like HXK1 still induce significantly.

SIP18, on the other hand, does not induce in response to the single 10 min pulse at 690 nM (Fig S2, row 11, column 2) and the four 5 min pulses separated by 20 min intervals (Fig S2, row 10, column 5). Thus, SIP18 completely filters out transient input.

Thus, there is a qualitative difference between the activation time of the different promoters classes: some promoters (e.g. LF) respond to all Msn2 input no matter how transient, whereas others filter out transient input (e.g. HS).

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Promoter amplitude threshold: SIP18 filters out all low amplitude input (100 nM, Suppl. Fig S2 rows 1-5, column 2) regardless of the input duration. For example, a 50 min pulse at 100 nM 1-NM-PP1 is completely filtered out. Even at 275 nM, SIP18 only induces very weakly (275 nM, Suppl. Fig S2 rows 6-10, column 2). Thus, promoters like SIP18, representing the HS class, filter out low amplitude input regardless of the pulse duration. This is in contrast to HXK1 (100 nM, Suppl. Fig S3 rows 1-5, column 3) and DCS2 (100 nM, Suppl. Fig S2 rows 1-5, column 3) which induce significantly in response to 100 nM input and quite strongly to 275 nM input (HXK1: 275 nM, Suppl. Fig S3 rows 6-10, column 3; DCS2: 275 nM, Suppl. Fig S2 rows 6-10, column 3). Thus, promoters like DCS2 and HXK1 representing the LF class do not filter out low amplitude input; instead, they induce even at 100 nM and quite strongly at 275 nM.

Thus, there is a clear qualitative difference in the sensitivity of the HS and LF promoter classes to low amplitude input: The HS promoters filter all low amplitude input, whereas the LF promoter induce significantly in response to low amplitude input.

Thirdly, RTN2 combines a relatively high amplitude threshold, with a relatively fast activation timescale and shows somewhat intermediate behavior. For example, low amplitude input (Suppl Fig S5, rows 1-10, column 2) is largely filtered out (as for the HS promoters), but RTN2 nonetheless induces significantly in response to oscillatory input (8 pulses, Suppl Fig S5, row 6, column 4) and short high amplitude input (10 min, 3 µM 1-NM-PP1, Suppl Fig S5, row 11, column 4). Thus, RTN2 shows somewhat intermediate behavior and borders on the HF promoter class.

Fourthly, we also believe that Figure 2C-F shows qualitative differences in the way expression of SIP18 and DCS2 scales with duration, amplitude, AUC and pulse number. We have updated the relevant paragraph “Using a mathematical model to cluster promoters into classes” of the manuscript to make these points more clearly.