Promoters adopt distinct dynamic manifestations depending on transcription factor context

Abstract Cells respond to external signals and stresses by activating transcription factors (TF), which induce gene expression changes. Prior work suggests that signal‐specific gene expression changes are partly achieved because different gene promoters exhibit distinct induction dynamics in response to the same TF input signal. Here, using high‐throughput quantitative single‐cell measurements and a novel statistical method, we systematically analyzed transcriptional responses to a large number of dynamic TF inputs. In particular, we quantified the scaling behavior among different transcriptional features extracted from the measured trajectories such as the gene activation delay or duration of promoter activity. Surprisingly, we found that even the same gene promoter can exhibit qualitatively distinct induction and scaling behaviors when exposed to different dynamic TF contexts. While it was previously known that promoters fall into distinct classes, here we show that the same promoter can switch between different classes depending on context. Thus, promoters can adopt context‐dependent “manifestations”. Our analysis suggests that the full complexity of signal processing by genetic circuits may be significantly underestimated when studied in only specific contexts.

(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. Depending on transfer agreements, referee reports obtained elsewhere may or may not be included in this compilation. Referee reports are anonymous unless the Referee chooses to sign their reports.) Please note that this manuscript was previously reviewed at another journal. Since the original reviews are not subject to The EMBO Journal's transparent review process policy, these initial reports and author response to them cannot be published here. 6th June 2020 Editorial Correspondence Dear Christoph, Thank you again for your patience. We have now had the chance to discuss the study at our editorial meeting. We would like to invite you to formally submit the paper to the journal via the submission site (msb.msubmit.net). We would kindly ask you to address the following points: 1. It might be useful to reiterate in the discussion the difference between this study (ie. Intrinsic dynamics of the same promoter can change depending on stimulus patters and context) and previous studies (such as the ones mentioned by reviewer #2) that showed that one promoter can filter or discriminate stimuli patterns). This may help some readers to better understand the main message of your paper.
2. I would also suggest you to consider including some further discussion about the foundation of the concept of 'promoter state', which is the framework used to describe promoter dynamics. The fundamental assumption underlying this study (and irrespectively of any modeling details) is that promoter dynamics can be captured by representing them as adopting *discrete* states and characterizing the switching dynamics between such states. I wonder whether the phenomenon observed in this study, ie. different context-dependent 'manifestations', could also reveal that this discretization of promoter dynamics into 'states' is too coarse grained to capture the full complexity underlying the dynamical properties of promoters. Could your observations also be interpreted as suggesting that a promoter 'state' (or some of its features) can in fact not be reduced to discrete states but can only be described as a continuous state (something like a non-linear function)? The justification of discrete states vs continuous states representation will eventually be motivated by knowing how well they represent the molecular mechanisms that determine promoter dynamics. Is it possible that the observation of context-dependent 'manifestations' reveals that at least some rate-limiting biochemical events are better represented as continuous functions rather than discrete states? I realize that these questions were not mentioned by any of the four referees but I thought that it could be insightful to have some further thoughts on these issues as it may reveal some path forward in terms of conceptualizing and modeling transcription and finding the molecular explanations of the observed context-dependent promoter dynamical properties. Would this make sense? 4. The issue related to whether the system is 'feedback-less' is best addressed by some toning down and clarifying that formal exclusion of any feedback is difficult if not impossible.
Once the manuscript has been submitted, we will NOT send the paper for a new round of review and will aim at making a final decision as fast as possible. Here, we show that promoters can switch between classes depending on context. We show that even under these relatively simple conditions, the same promoter can exhibit context-dependent scaling and induction behaviors. To describe this observation, we introduce the concept of context-dependent "manifestations". The underlying number of molecular states of a promoter is potentially enormous: when we measure a doseresponse, we likely observe only certain rate-limiting regimes or manifestations of the system. What we show here is that the particular observed rate-limiting manifestation is highly context-dependent and very distinct quantitative behaviors can be observed under different contexts -even in systems that are seemingly simple." We hope these new text segments more clearly explain this difference, but we would be happy to take your recommendation on additional ways to clearly lay out this point or on way to modify these text segments. Clearly communicating this difference between prior studies (fixed promoter classes) and this study (promoter class switching depending on context) is foundational to communicating the conceptual advance of this study. We also emphasize this point now in a sentence in the abstract.
2. I would also suggest you to consider potentially including some further discussion about the foundation of the concept of 'promoter state', which is the framework used to describe promoter dynamics. The fundamental assumption underlying this study (and irrespectively of any modeling details) is that promoter dynamics can be captured by representing them as adopting *discrete* states and characterizing the switching dynamics between such states. I wonder whether the phenomenon observed in this study, ie. different contextdependent 'manifestations', could also reveal that this discretization of promoter dynamics into 'states' is too coarse grained to capture the full complexity underlying the dynamical properties of promoters. Could your observations also be interpreted as suggesting that a promoter 'state' (or some of its features) can in fact not be reduced to discrete states but can only be described as a continuous state (something like a non-linear function)? The justification of discrete states vs continuous states representation will eventually be motivated by knowing how well they represent the molecular mechanisms that determine promoter dynamics. Is it possible that the observation of context-dependent 'manifestations' reveals that at least some rate-limiting biochemical events are better represented as continuous functions rather than discrete states? I realize that these questions were not mentioned by any of the four referees but I thought that it could be insightful to have some further thoughts on these issues as it may reveal some path forward in terms of conceptualizing and We have removed all claims of "feedback free" from the manuscript.

15th Aug 2020 1st Authors' Response to Reviewers
The Authors have made the requested editorial changes.

Accepted 25th Aug 2020
Dear Christ oph, Thank you again for sending us your revised manuscript . We are now sat isfied wit h the modificat ions made and I am pleased to inform you that your paper has been accept ed for publicat ion.
Thank you again for submit ting your work to Molecular Syst ems Biology. We are now sat isfied wit h the changes made to the manuscript and I am pleased to inform you that we will be able to accept your paper for publicat ion pending t he minor amendments. Do the data meet the assumptions of the tests (e.g., normal distribution)? Describe any methods used to assess it.

No animals. N/A
The single-cell trajectories rely on imperfect image analysis, cell segmentation and cell tracking code. After analyses, the segmentation and tracking was manually inspected, and cases of missegmentation or tracking manually removed. Fewer than 5% of cells were removed in this way.

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