Gene regulation gravitates toward either addition or multiplication when combining the effects of two signals

  1. Eric M Sanford
  2. Benjamin L Emert
  3. Allison Coté
  4. Arjun Raj

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Abstract

Two different cell signals often affect transcription of the same gene. In such cases, it is natural to ask how the combined transcriptional response compares to the individual responses. The most commonly used mechanistic models predict additive or multiplicative combined responses, but a systematic genome-wide evaluation of these predictions is not available. Here, we analyzed the transcriptional response of human MCF-7 cells to retinoic acid and TGF-β, applied individually and in combination. The combined transcriptional responses of induced genes exhibited a range of behaviors, but clearly favored both additive and multiplicative outcomes. We performed paired chromatin accessibility measurements and found that increases in accessibility were largely additive. There was some association between super-additivity of accessibility and multiplicative or super-multiplicative combined transcriptional responses, while sub-additivity of accessibility associated with additive transcriptional responses. Our findings suggest that mechanistic models of combined transcriptional regulation must be able to reproduce a range of behaviors.

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  1. Version published to 10.7554/elife.59388 on eLife
  2. Version published to 10.1101/2020.05.26.116962v2 on bioRxiv
  3. eLife

    ###Reviewer #2:

    In this paper the authors use a genomics approach to tackle the question of how the combined transcriptional response to two signals compares to the responses to the two treatments individually. They treat MCF-7 cells with TGF-beta and retinoic acid, and find that the combined response at the level of gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) may encompass additivity, multiplicativity but also a wide range of other intermediate or more extreme behaviours.

    The work is conceptually very interesting, and the manuscript text and figures were extremely clear and a pleasure to read. We suggest that the following major points be addressed to clarify the assumptions and limitations of the study.

    The authors treat the cells for 72h. This is a very long time where secondary effects may be dominating the results. The choice of this time point should, at the very least, be justified and discussed. For example, previous studies that quantitatively characterized distinct temporal dynamics in SMAD signaling after TGF-beta treatment showed a transient, dose dependent SMAD response in the first 4 h after TGF-beta treatment, with a strong early peak in the nuclear/cytoplasmic ratio of SMAD2/4 (Clarke & Liu, 2008; Schmierer et al, 2008; Zi et al, 2011; Zi et al, 2012; Strasen et al., 2018). In addition, TGF-b signaling has been suggested to depend on cell density and cell cycle stage (Zieba et al, 2012), which may also affect the results. Also it would be helpful to have a quantitative measure of the corresponding nuclear TF levels at the selected time-point after 72h (e.g for main affected TFs such as pSMAD2 and RARA levels).

    MCF7 cells were treated with three different doses of TGF-beta (1.25, 5, and 10 ng/mL) and RA (50, 200 and 400 nM). As it seems that the selected doses are higher than what has been used in previous studies, the authors should comment on their choice. The authors state that "We defined a master set of 1,398 upregulated genes by selecting the set of genes that were differentially expressed in any dose of the combination treatment (log FC {greater than or equal to} 0.5 and padj {less than or equal to} 0.05) and that had increased expression in each dose of each individual signal." It is unclear how this gene set relates to the top-right Venn diagram in Fig 1B, in which only 303 genes are shown as being upregulated in all three treatments and the total according to the numbers in the diagram are >1398.

    Fig 1B shows that a large proportion of genes were differentially expressed in response to both signals, but not to either of the signals individually. Their responses are presumably more non-additive than the responses of genes upregulated in response to all three treatments. Restricting analysis to the latter group therefore introduces a bias for certain modes of combinatorial regulation. The justification for this choice should be discussed.

    The authors suggest a bimodal distribution for the observed c values, with peaks at 0 and 1. The authors write that "Our simulated c value distributions bear a moderate resemblance to our observed c value distributions". This conclusion is central to the paper's claim that "Gene regulation gravitates towards either addition or multiplication when combining the effects of two signals" (title) and that "the combined responses exhibited a range of behaviors, but clearly favored both additive and multiplicative combined transcriptional responses" (abstract). However, the additional peak at c=1 is not obvious from the data in Fig. 1E. Stronger evidence (i.e. statistical analysis of the observed distributions) would be needed to demonstrate overrepresentation of c values ~1. Alternatively, the title and abstract could be revised to better reflect the strength of the findings.

    The authors frame the work on the basis of simple models of gene regulation by pairs of transcription factors that predict either addition or multiplication. However, they are activating two signalling pathways that could interact also at the level of signal transduction (and need not be directly regulating the genes in question, as noted in point 1). How justifiable is it to make inferences about the nature of combinatorial transcriptional regulation from this kind of experimental set up? These issues should be made more clear from the beginning, and should be taken into account when interpreting the data.

    Related to the point above, the authors use chromatin accessibility as a proxy for TF binding. However, this does not need to be the case, especially if the accessibility data are considered quantitatively. For example, TFs may bind and recruit remodeling factors that affect accessibility differentially across the genome, obscuring the relationship between TF binding and accessibility. This is especially pertinent at longer time scales after perturbation. We suggest presenting the data on accessibility as just that, instead of presenting it as data that directly reports on TF binding. The relationship to TF binding can and should still be explored in the analyses, but with clarification for how accessibility data is limited in this case.

    The following are instances where accessibility data is described as directly reporting on TF binding that we recommend revising (the list is not exhaustive):

    -the title of section two

    -Fig.2E

    -the link between models of TF control and the relationship between peaks and expression, such as the reference to the thermodynamic model at the end of section 3

    -remove the implicit assumption between cooperativity of TF binding and super-additive peaks in section 3 and section 4. This may help explain more naturally the lack of dual-motif finding in section 4

  4. eLife

    ###Reviewer #1:

    Cells perform many types of computations to respond to external signals at the transcriptional regulatory level. Often, regulatory sequences read out the concentration of input transcription factors and combine that information to dictate the level of transcriptional output. Yet, for most genes, the quantitative rules for how regulatory regions integrate multiple inputs remain unclear.

    Sanford et al. studied how two signals are interpreted by downstream genes using quantitative tools such as RNA-seq and ATAC-seq. The authors propose two phenomenological models to understand combinational regulation. Specifically, a model in which output gene expression in the presence of two different input signals is the sum of the gene activity in the presence of each signal alone (additive), and an alternate model where the output of the two signals is the product of the output driven by each individual signal (multiplicative).

    The authors performed a genome-wide analysis of thousands of genes and found that most genes responding to either TGF-β or retinoic acid behave in either an additive or multiplicative fashion. The authors further asked whether these additive/multiplicative behaviors can be explained by the accessibility of DNA regulatory regions reported by ATAC-seq. The result reveals that DNA accessibility is mostly additive. However, they also find that multiplicative gene expression is correlated with super-additive accessibility.

    This work provides a platform to quantitatively assess combinatorial transcriptional regulation both at the level of DNA accessibility and transcriptional output. Indeed, one of the exciting aspects of the work is the attempt to use the quantitative values of DNA accessibility reported by ATAC-seq to constrain possible biophysical models of transcriptional regulation. We foresee that this work will set the stage for a better understanding of the molecular relation between transcription factor binding and the gene activity resulting from this binding, in general, and for dissecting the molecular mechanisms of combinatorial regulation, in particular.

  5. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

    ###Summary:

    In this work the authors used a genomic approach to investigate the way cells interpret two combined signals versus two individual signals. The authors used RNA-seq to examine the gene expression outputs from thousands of genes in response to two signal inputs, TGF-b and retinoic acid, either individually or in combination. The authors found that when stimulated with both signals, most cells exhibited additive or multiplicative responses. The authors further used paired chromatin accessibility by ATAC-seq to relate such responses to putative transcription factory binding patterns in these genes. Surprisingly, ATAC-seq revealed that most genes prefer addition to combine two signals as chromatin accessibility is largely additive, although some super-additive accessibility may respond to multiplicative gene expression.

    This work provides a platform to quantitatively assess combinatorial transcription regulation both at the level of DNA accessibility and transcriptional output. Although the concept of additive v.s. multiplicative transcriptional response is phenomenological, it may be used to clarify and constrain certain biophysical models of transcriptional regulation and set the stage for a better understanding of the molecular relation between combinatorial transcription factor binding and corresponding gene activity.

    While the work is written in a clear and concise language, there are places that require further clarification and better presentations.

  6. Version published to 10.1101/2020.05.26.116962v1 on bioRxiv