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. Suppose a cell at baseline expresses 100 copies of mRNA of gene X. If you give signal A, the cell expresses 200 copies of gene X. Give signal B, and you see 300 copies. What happens when you give both signals at the same time? Do the effects add (gene X increases to 400 copies)? Multiply (600 copies)? Additive and to some extent multiplicative phenomenological models have seen widespread use due to their simple mechanistic basis. However, there is little systematic empirical evidence that either of these phenomenological models of combined responses are in general valid or should be favored in any way.Part of the appeal of the additive and multiplicative phenomenological models is their emergence from simple and natural mechanistic models of transcriptional regulation. For instance, additive behavior naturally emerges from a model in which transcription factors can independently recruit polymerase to the promoter . SpecifiE. coli experiments after λcI- and CRP-binding sides were placed adjacent to a lacZ promoter  have been shown to admit a wide variety of behaviors, ranging from sub-addition to super-multiplication . A systecis-regulatory elements. For instance, if each signal results in the binding of a specific set of transcription factors at a particular regulatory region individually, then do these two different sets of factors bind with the same probability when both signals are applied? Or are these probabilities affected by potential regulatory interactions between the signals? And how might these binding probabilities and potential interactions affect expression of the target genes? There is only limited transcription-factor-binding data available for experiments where cells receive multiple signals simultaneously , but it is difficult to exclude the possibility of the other model, especially when only a limited number of genes are considered . With cuHere, we profiled MCF-7 cells with paired RNA-seq and ATAC-seq measurements after we exposed them to retinoic acid, TGF-β, and both signals. We found that while genes’ transcriptional responses exhibit a wide variety of behaviors when combining these two signals, they generally tended toward either addition or multiplication. ATAC-seq peaks, on the other hand, appeared to prefer addition as the default operation for combining two signal effects, although a minority of peaks clearly showed sub-additive or super-additive behavior. Genes with super-additive ATAC-seq peaks nearby were more likely to have a multiplicative or super-multiplicative transcriptional responses to retinoic acid and TGF-β. These data provide a comprehensive and systematic view of transcriptional responses to combined signal treatments.To quantitatively measure how gene regulation depends on multiple input signals, we performed three replicates of a paired RNA-seq and ATAC-seq experiment using MCF-7 cells . Prior to sequencing, we treated these cells with three different doses of TGF-β , retinoic acid , or both signals  for 72 hr . We waitA > 0 and ΔB > 0), in order to maintain a consistent mapping between our categorical description of combined responses We defined a master set of 1398 genes by selecting the set of genes that were significantly upregulated in any dose of the combination treatment  and that had increased expression in all doses of each individual signal . If we hicative’ ) and ourreatment , leavingIn our analysis of combined transcriptional responses, we assumed that retinoic acid and TGF-β exhibited their effects on common target genes through distinct transcription factors. To justify this assumption, we confirmed that there was little cross-activation of pSMAD2  by performing immunofluorescence targeting pSMAD2 upon the addition of TGF-β and retinoic acid individually . We saw Within our master set of 1398 upregulated genes, we found a variety of different combined transcriptional response behaviors ranging from sub-addition to super-multiplication . A transIn order to quantitatively describe the combined transcriptional response characteristics of any gene without any presupposition of additive or multiplicative behavior, we defined a continuous parameter, hereby referred to as a gene’s combined response factor or ‘c’ value, that places the gene in an exact location on the spectrum of possible combined response behaviors  and c = 1 (multiplication), the tails of the observed c value distribution are clearly heavier . These hWe next wondered how a gene’s combined response factor  depended on dosage of the input signals. In theory, the c value might remain stable as dosage increases, monotonically increase or decrease as dosage increases, or may appear to be ‘random’ with respect to dose, perhaps due to complex unobserved dose-dependent gene regulatory interactions. To distinguish between these possibilities, we plotted how a set of upregulated genes’ c values changed as they moved from low to medium to high dose of combination treatment with retinoic acid and TGF-β . To genebaseline . Since ΔTranscriptional regulation is thought to occur largely via the binding of transcription factors, but it remains unknown how the transcription factors associated with the effects of individual signals might interact upon the addition of both signals simultaneously. We performed ATAC-seq on the same populations described earlier, reasoning that the observation that changes in chromatin accessibility have been shown to correlate with changes in aggregate transcription-factor-binding activity  meant thWe then wondered how well simple additive and multiplicative phenomenological predictions corresponded to the increase in chromatin accessibility at upregulated peaks in the combined treatment. We found that an additive model was generally highly predictive and matched the observed increases in ATAC-seq fragment counts more accurately than the multiplicative model; the multiplicative model generally predicted larger changes in accessibility than we experimentally observed . To quanGiven the general accuracy of the additive model for upregulated peaks, we wondered to what extent deviations from additive model predictions represented true deviations as opposed to just measurement error. We produced randomly generated simulated data that matched the statistical properties of our actual data, assuming that the combined treatment would result on average in perfectly additive peak sizes . We found that our observed data are more widely dispersed than the simulations, indicating that a fair number of peaks are significantly sub-additive or super-additive . We founcis-regulatory element activity that may dictate how a gene’s regulatory behavior would encode the observed integration of the transcriptional effects of two signals. We reasoned that the number of upregulated ATAC-seq peaks near a gene or the manner in which the nearby peaks themselves integrated the two signals’ effects may predict the gene’s combined transcriptional response behavior. For each transcriptionally upregulated gene, we counted the number of sub-additive, additive, and super-additive ATAC-seq peaks within 100 kb of its transcription start site. We found that, on average, genes that were transcriptionally additive had 2.7x more sub-additive ATAC-seq peaks nearby than genes with multiplicative transcriptional responses . Genes with multiplicative and super-multiplicative transcriptional responses had 2.5x or 2.6x, respectively, more super-additive ATAC-seq peaks nearby than genes with additive transcriptional responses  and an independent recruitment model . To increase the likelihood of selecting retinoic acid and TGF-β-exclusive transcription-factor-binding events, we searched near genes for upregulated peaks that responded exclusively to either retinoic acid or TGF-β.  We then considered how likely genes with different combined transcriptional response behaviors were to have at least one retinoic acid-dominant and one TGF-β-dominant peak nearby (<100 kb to the transcription start site). We found that at each dose, genes with multiplicative transcriptional responses were the most likely to have at least one retinoic-acid-dominant and one TGF-β-dominant upregulated peak nearby  that represents the percentage change in ATAC-seq fragment counts in all peaks that contain the given transcription factor’s motif  and 7% fewer total motif matches per 150 bp when using the entire cisBP database (p=1.5e−8), suggesting that sub-additive peaks are slightly depleted for motifs overall while being even more depleted for the motifs in our enriched set (−15) as well as AP-1 subunit motifs such as JUN  and FOS  and a depletion of several ETS family factors  . Here, wWe hypothesized that cooperative interactions between transcription factors may lead to super-additive increases in chromatin accessibility. To evaluate if our data supported this hypothesis, we tested if super-additive peaks were more likely to have both a retinoic-acid-enriched motif and a TGF-β-enriched motif. We defined retinoic acid-enriched factors to be retinoic acid receptor, FOX, and ETS-family factors, and we defined TGF-β-enriched motifs to be SMAD, AP-1, BACH, BATF, SMARCC1, NFE2, NFE2L2, MAFF, and MAFK factors. We found that all categories of peaks (including super-additive) were less likely to have dual-motifs than expected based on a null distribution we generated by randomly shuffling motif matches across peaks  . HoweverOur combined transcriptional responses were measured using bulk RNA-sequencing, which averages the transcriptional effects of retinoic acid and TGF-β across millions of cells. Heterogeneity in the response of individual cells could mean that what we observed, for instance, as a multiplicative transcriptional response at the population level is actually a mixture of sub and super-multiplicative transcriptional responses at the single-cell level. Future studies might combine microfluidic delivery of cell signals with live imaging of transcription to measure the response to both individual and combined signal treatments in the same single cells, thereby revealing the extent to which the combined response factor for a given gene displays cell-to-cell heterogeneity . High amIn our dataset, the combined response factor remained largely constant over a range of doses. This constancy suggests that whatever the functional interaction is between the factors responsible for the particular mode of combined response, that interaction is quantitatively maintained through doses (with some evidence for saturation at high dose). Such behavior may constrain potential models for interactions, because in principle the interactions could be highly dose dependent. Another open question is whether the mode of combined response for a particular gene depends on the particular signals applied or contextual factors that may vary between cell lines. Further studies may reveal these dependencies.Another interesting feature of our data was the general lack of strong correspondence between changes in chromatin accessibility and changes in transcriptional output. While we were able to identify some trends, we could not find any strict rules for e.g. what transcription factors associated with what types of combined responses. We found this lack of correspondence surprising, given that transcription factors are the dominant form of transcriptional regulation. There are many potential explanations for this observation. One is that the degree of chromatin accessibility is not as correlated with aggregate transcription factor occupancy levels as we expected. For instance, it may be that accessibility may only change for some types of transcription factor-DNA interactions and not others. Another possibility is that our analysis does not take into account precisely which peaks near a given gene correspond to regulatory elements and which ones do not. This mapping remains largely unknown, although information about what pieces of chromatin spatially contact which other ones may help narrow down the choices . FinallyWe acquired one vial of MCF-7 cells from ATCC (lot 64125078), which we expanded in DMEM/F12 with 5% FBS and 1% penicillin/streptomycin. Prior to adding retinoic acid and TGF-β, the cells experienced a total of 13 passages and 1 freeze/thaw cycle. Because normal FBS can have significant amounts of retinoic acid , we cultFor our dose-response experiment, we split two ~ 80% confluent 10 cm dishes equally into 12 different 10 cm dishes, and waited 24 hr prior to adding media containing retinoic acid , TGF-β , or both signals. Because the cells grew faster when exposed to retinoic acid and slower when exposed to TGF-β, we included two additional control conditions that had 50% and 150% of the starting cell density to test for potential cell-density effects . We treated cells for 72 hr in three doses of retinoic acid , TGF-β , or both signals . The medium dose we chose for TGF-β, 5 ng/ml, is used in several studies of MCF-7 cells , and theFor immunofluorescence experiments, we seeded eight-well glass chambers (Lab-tek 12-565-470) with hormone-starved MCF-7 cells for 24 hr before treating the cells with the medium dose of TGF-β (5 ng/ml), retinoic acid (200 nM), or vehicle (0.0125% ethanol). Following treatment, we fixed cells for 12 min in 3.7% formaldehyde (Sigma F1635) diluted in 1x PBS. We stored samples at 4C in 1x PBS, then performed the immunofluorescence protocol exactly as described by Cell Signaling Technology, using a dilution of 1:800 for the primary anti-pSMAD2 antibody , 1:200 for the primary anti-RARA antibody (Sigma HPA058282), and 1:1000 for the goat anti-rabbit secondary antibody conjugated with Alexa Fluor 647 (Thermo Fisher Scientific A-21244). In brief, we blocked samples with 5% goat serum for 60 min, incubated with primary antibody overnight at 4C, washed three times with 1X PBS for 10 min each, incubated with secondary antibody at room temperature for 90 min in the dark, then washed the cells another three times in 1X PBS. We stained cellular nuclei with DAPI prior to imaging. We imaged the cells with an inverted Nikon TI-E microscope with a 20x Plan-Apo λ (Nikon MRD00205) objective and with DAPI and Atto647N filter sets. We collected all images at 20x magnification.To quantify the nuclear pSMAD2 and RARA signal in our immunofluorescence experiments, we developed a custom image analysis pipeline in Python that was centered around the usage of Cellpose  to detecWe extracted RNA from previously frozen MCF-7 cell Qiazol lysates using the Qiagen miRNeasy kit (217004). We then used the NEBNext Ultra II RNA Library Prep Kit for Illumina (E7770) with the NEBNext Poly(A) mRNA Magnetic Isolation Module (E7490) and NEBNext Multiplex Oligos for Illumina (E7600) to prepare individual libraries. We then pooled our three replicates’ libraries together and performed paired-end sequencing on an Illumina NextSeq 500, using a 75-cycle NextSeq 500/550 High Output Kit v2.5 (20024906), yielding ~15 million read pairs per sample.We aligned reads to the hg38 assembly using STAR v2.7.1a and counted uniquely mapped reads with HTSeq v0.6.1 and the hg38 GTF file from Ensembl (release 90). We performed differential expression analysis using DESeq2 v1.22.2  in R 3.5https://github.com/emsanford/combined_responses_paper (swh:1:rev:e25f3d9eefd72ac1ab2885d9b0f3ad0c3cf0b3b8). We then performed paired-end sequencing using one 75-cycle NextSeq 500/550 High Output Kit v2.5 (20024906) for each replicate, yielding ~42 million read pairs per sample.At the endpoint of each cell condition, we immediately performed the Omni-ATAC protocol  on 50,00ENCODE ATAC-seq v1 pipeline specifications. Briefly, we aligned our ATAC-seq reads to the hg38 assembly using bowtie2 v2.3.4.1, filtered out low-quality alignments with samtools v1.1, removed duplicate read pairs with picard 1.96, and generated artificial single-ended text-based alignment files containing inferred Tn5 insertion points with custom Python scripts and bedtools v2.25.0. To call peaks, we used MACS2 2.1.1.20160309 with the command, ‘macs2 callpeak --nomodel--nolambda--keep-dup all --call-summits -B--SPMR--format BED -q 0.05 --shift 75--extsize 150’. While we created this pipeline for use on the Penn Medicine Academic Computing Services’ high performance cluster, it is also publicly available at github.com/arjunrajlaboratory/atac-seq_pipeline_paired-end (swh:1:rev:c4c819e3ad5828b953fbd2ec05163e590518ae4b). Our pipeline generates a series of post-sequencing quality control metrics, which we have provided in We created a paired-end read analysis pipeline using the Since we had three biological replicates per ATAC-seq condition, we used an established ‘majority rule’ to retain only the peak summits that were found in at least two replicates   value for one replicate was drawn from a Gaussian distribution. We estimated the parameters of these Gaussian distributions to create an 80% confidence interval for which to compare additive and multiplicative predictions. For each dosage of the combination treatment, we classified a gene as sub-additive if the additive and multiplicative predictions were higher than the 80% confidence interval, additive if only the additive prediction laid in the confidence interval, multiplicative if only the multiplicative prediction laid in the interval, super-multiplicative if both additive and multiplicative predictions were below the confidence interval, and ambiguous if both the additive and the multiplicative prediction laid within the interval.To estimate the mean expression value of a gene in an experimental condition (e.g. 200 nM retinoic acid), we simply calculated the average TPM value across the three replicates. To improve our variance estimates, we took advantage of an observation we made during extensive manual review that the coefficient of variation (CV) appeared to be the same between each dosage we tested for retinoic acid, TGF-β, and combined treatment . We thenWhere To classify ATAC-seq peaks as sub-additive, additive, or super-additive, we used the same approach described above for RNA-seq TPM values, but with a given peak’s normalized fragment count value. We then classified peaks as sub-additive or super-additive if the additive prediction was higher than or lower than (respectively) the estimated Gaussian distribution’s 80% confidence interval.To simulate new ATAC-seq and RNA-seq measurements, for each gene and condition we randomly sampled three new observations from a folded Gaussian distribution  with the parameters we previously estimated for the purpose of categorically classifying combined response behaviors. For the combined treatment, we set the mean of the distribution to be either a perfectly additive or perfectly multiplicative prediction. We then calculated the average of the three new simulated observations and used these average values to determine a gene’s c value at a given dose or an ATAC-seq peak’s d-value at a given dose. Using this process, we calculated 250 simulated c values for each dose of each upregulated gene in our master set and 10 simulated d values for each ATAC-seq peak that was upregulated individually by retinoic acid and TGF-β. In the simulated data mixture model where genes can be strictly additive or multiplicative, at each we randomly assigned a gene to be additive or multiplicative based on the ratio of the dose-specific frequencies we observed in the categorical classification of the combined response.To generate a hypothetical plot of observed c values in which the primary peak of additive responses centered at c = 0 was depleted, we subtracted the additive component of a c value histogram generated by simulated data. These simulated c values were generated using gene and condition-specific Gaussian distributions in a process outlined above and in We also estimated the probability of obtaining the number of combined transcriptional responses in each bin of our observed c value histogram if all combined responses were additive. To do this, we scaled the peak height of our observed data at c = 0 to the peak height of an additively simulated distribution of c values. We then repeatedly (1000 times) ran new simulations of additive combined responses, simulating one observation per gene in our master set of 1384 genes. We used a bin width of 0.25 and allowed for overlapping bins. Because the probability of obtaining the observed number of counts was extremely low for many bins and because the variability in the number of observations in a given bin was well described by a Poisson distribution (outside the range of −0.3 < c < 0.3), we used a Poisson cumulative density function to estimate the probability of witnessing the number of observed counts (or greater) in each c value bin of the simulated additive data.To generate a null distribution for dual-motif matches, we first separated our set of upregulated peaks into sub-additive, additive, and super-additive peaks. Within these peak subsets, we counted the number of retinoic acid-dominant , TGF-β-dominant , and neither-signal-dominant  motifs at each peak. Due to similar features of their position-weight matrices, we avoided over-counting similar motifs by reporting the maximum number of motif matches for a single type of motif within a group of motifs. The motif groups we used were as follows: retinoic acid receptor consisted of RARA, group FOX consisted of FOXA1, FOXA2, FOXA3, FOXC2, FOXD3; group ETS consisted of SPI, SPIB, SPIC, EHF, ELF1, ELF2, ELF3, ELF4, ELF5; group SMAD consisted of SMAD3, SMAD4, SMAD9; group AP-1 consisted of JUN, JUNB, JUND, JDP2, FOS, FOSB, FOSL1, FOSL2, BACH1, BACH2, BATF ; group SMARCC1 consisted of SMARCC1; group NFE consisted of NFE2, NFE2L2; group MAF consisted of MAFF, MAFK; group HOX consisted of HOXA13, HOXB13, HOXC10, HOXC12, HOXC13, HOXD13; group NFKB consisted of NFKB1, REL, RELA; group CDX consisted of CDX1, CDX2; group CTCF consisted of CTCF; group BCL consisted of BCL11A, BCL11B; group GRHL1 consisted of GRHL1. For example, if a peak had three JUN motifs, two FOS motifs, two JDP2 motifs, and one BACH1 motif, we would count this as three AP-1 motifs. We then randomly shuffled these grouped motif matches within each peak set, with each peak retaining its original number of total motif matches . After each of 1000 random shuffles, we calculated the fraction of peaks in each peak set that contained both a retinoic acid-dominant and a TGF-β dominant motif.DeSeq2’s adjusted p value and our manually calculated p value for the null distribution we generated for dual-motif matches at upregulated ATAC-seq peaks, we calculated all reported p values in the figures and main text using Welch's unequal variances t-test in R. (Note that we did not correct for multiple comparisons.)With the exception of https://github.com/emsanford/combined_responses_paper. The ATAC-seq pipeline we used is available at https://github.com/arjunrajlaboratory/atac-seq_pipeline_paired-end (copy archived at swh:1:rev:c4c819e3ad5828b953fbd2ec05163e590518ae4b). The RNA-seq pipeline we used is available at https://github.com/arjunrajlaboratory/RajLabSeqTools (swh:1:rev:c8b8c79b2ec9c1bd9eb7ced427bb2aec25f19506).All custom data analysis code is available at  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The work investigates an interesting question in gene regulation: when two independent transcription induction signals on one gene are combined, will it result in an additive or multiplicative gene expression level? Using genomic approaches to assay both mRNA expression and chromatin accessibility, the work finds that while there is a large range of responses, most genes favor either additive or multiplicative outcome. This work connects the phenomenological description of gene expression with a mechanistic insight of how two independent transcription signals could work together to regulate one gene.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Naama Barkai as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Hernan Garcia (Reviewer #1); Angela H DePace (Reviewer #2).Thank you for submitting your article "Gene regulation gravitates towards either addition or multiplication when combining the effects of two signals" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without substantial additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below mainly address clarity and presentation, with a few minor experiments if possible.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as 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-β 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 vs. 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.Essential revisions:1) Presentations of the logic and data: it is hard to follow the math in the absence of equations:a) Please show the equations featured in Figure 1A in the main text together with some derivation or explanation that can build intuition.b) The simulations results shown in Figure 1 and Figure 2 are a key part of the argument. Yet, their details are buried in the SI, making it hard to follow their justification . Please explain these simulations in the context of the main text.c) "For each dosage of the combination treatment, we classified a gene as sub-additive if the additive and multiplicative predictions were higher than the 80% confidence interval, additive if only the additive prediction laid in the confidence interval, multiplicative if only the multiplicative prediction laid in the interval, super-multiplicative if both additive and multiplicative predictions were below the confidence interval, and ambiguous if both the additive and the multiplicative prediction laid within the interval." Please show this graphically and/or with an equation.2) Clarifications of experimental conditions and results:a) 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-β treatment showed a transient, dose dependent SMAD response in the first 4 h after TGF-β treatment, with a strong early peak in the nuclear/cytoplasmic ratio of SMAD2/4 . In addition, TGF-β signaling has been suggested to depend on cell density and cell cycle stage , 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).b) MCF7 cells were treated with three different doses of TGF-β  and RA . As it seems that the selected doses are higher than what has been used in previous studies, the authors should comment on their choice.c) 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  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 Figure 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.d) Figure 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.e) 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 signaling pathways that could interact also at the level of signal transduction . 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 clearer from the beginning and should be taken into account when interpreting the data.f) 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– Figure 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 4.3) Title: 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 Figure 1E. Stronger evidence  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. Essential revisions:1) Presentations of the logic and data: it is hard to follow the math in the absence of equations:a) Please show the equations featured in Figure 1A in the main text together with some derivation or explanation that can build intuition.We thank the reviewers for pointing out this lack of clarity and intuition in the presentation of our equations in Figure 1A. To address this issue, we created an appendix referenced early in the main text, which performs a step-by-step derivation of the additive combined response, multiplicative combined response, the precise difference term between the additive and multiplicative combined responses, and the combined response factor . Please see “Appendix 1: mathematical description of additive versus multiplicative combined transcriptional responses”b) The simulations results shown in Figure 1 and Figure 2 are a key part of the argument. Yet, their details are buried in the SI, making it hard to follow their justification . Please explain these simulations in the context of the main text.The reviewers are right that the simulations are critical and lend substance to our arguments. We have now described them in further detail in the main text and have created explanatory schematics  to help flesh out the motivation and methodology for our analysis and conclusions. We have also performed a far more rigorous analysis of the “c-value” histogram; see point #3 below for further discussion of that point. See below for our updated main text describing how we performed these simulations:“At all doses of combination treatment, we observed a wide peak centered around c=0 (additive), with a hint of a secondary peak at c=1 (multiplicative), suggesting that the integration of the effects of two signals is preferentially additive or multiplicative . […] The resultant residual distribution was a broad peak centered roughly around c=1 , consistent with our multiplicative simulated data .”c) "For each dosage of the combination treatment, we classified a gene as sub-additive if the additive and multiplicative predictions were higher than the 80% confidence interval, additive if only the additive prediction laid in the confidence interval, multiplicative if only the multiplicative prediction laid in the interval, super-multiplicative if both additive and multiplicative predictions were below the confidence interval, and ambiguous if both the additive and the multiplicative prediction laid within the interval." Please show this graphically and/or with an equation.The reviewers are correct that our original presentation did not do a good job of illustrating our statistical classification scheme. We have now incorporated a supplementary figure  with a schematic that we hope illustrates the point better, which we now reference in the main text. We thank the reviewer for suggesting this clarification.We have also updated the main text to reduce the confusion caused by the very long sentence quoted by the reviewers in this point. The updated text is now as follows:“We classified a combined transcriptional response as sub-additive, additive, multiplicative, or super-multiplicative by comparing where a “perfect” hypothetical additive or multiplicative response lay with respect to the 80% confidence interval of the combined treatment's expression value . If both the hypothetical additive and the hypothetical multiplicative predictions lay within the confidence interval, we classified the response as ambiguous .”We thank the reviewers for directing our attention to this point of confusion and hope that our new schematics and changes to the main text add clarity to how we classified genes as sub-additive, additive, multiplicative, super-multiplicative, or ambiguous.2) Clarifications of experimental conditions and results:a) 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-β treatment showed a transient, dose dependent SMAD response in the first 4 h after TGF-β treatment, with a strong early peak in the nuclear/cytoplasmic ratio of SMAD2/4 . In addition, TGF-b signaling has been suggested to depend on cell density and cell cycle stage , 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).The reviewers have brought up a critical point about the timescales we used in our experiments. It is true that there are likely secondary effects at play, however, in principle such secondary effects do not per se affect the conclusions we draw. Our goal was not to elucidate the precise mechanism by which genes are regulated, but rather to ascertain how they responded to combinations of regulatory signals, regardless of the precise origin of those signals. Indeed, we chose a longer timescale so as to have more genes displaying differential expression, thus enabling us to add more genes to our analysis. It is true that one situation in which secondary effects could muddy our interpretations would be if the signals were largely overlapping , but our chromVAR analysis of our paired ATAC-seq data  showed that the changes in gene expression we measured were associated with largely distinct sets of transcription factors. We apologize for not making this rationale clear in our original manuscript, and we have now added discussion of this point to the main text.Regarding cell density and cell cycle stage, it is true that these factors could affect the signaling dynamics, and we did not test these factors would have altered our results. We have added some text discussing this caveat in the main text:“Note that our ethanol “vehicle” controls were performed at three different cell concentrations, and there were no significantly differentially expressed genes between concentrations. We did not, however, add the signals to different concentrations of cells or cells at different points in the cell cycle, in which context the signals may exert differential effects.”The reviewers have also brought up the specific dynamics of the TGF-β and retinoic acid signaling pathways. We performed a series of immunofluorescence experiments aimed at showing the extent to which the signals were independent and their timescales. We found that in response to TGF-β, nuclear pSMAD2 levels  increased rapidly (by 40 minutes) and continued to stay above baseline at 72 hours ; however, retinoic acid had no effect on these levels, demonstrating that there was little-to-no cross-activation of SMADs by TGF-β . ; however, we saw no evidence of activation of RARA by TGF-β in our chromVAR analysis .) In total, we believe these results suggest that the signaling pathways activated by our signals act through largely independent mechanisms, making it possible to interpret our results despite the presence of secondary effects.We added a new paragraph to our main text to summarize the results of our immunofluorescence experiments and ATAC-seq analysis:“In our analysis of combined transcriptional responses, we assumed that retinoic acid and TGF-β exhibited their effects on common target genes through distinct transcription factors. […] This same motif analysis also suggested that retinoic acid and TGF-β largely increased the activity of distinct transcription factors at the 72 hour time point, meaning that the secondary effects of retinoic acid and TGF-β are likely mediated through the activity of distinct transcription factors.”b) MCF7 cells were treated with three different doses of TGF-β  and RA . 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 reviewers have raised an important point about the doses that we used. Most other studies we have found using TGF-β in MCF-7 cells used a concentration of 5 ng/mL , which was our “medium” dose used, and which gave a robust transcriptional response in our hands. Other studies using retinoic acid in MCF-7 cells used concentrations higher than ours, on the order of 1µM , but we found that concentration to be lethal in combination with TGF-β, so after some empirical testing, we settled on the range of concentrations we used because they elicited a robust and dose-dependent transcriptional response. We have now commented on the choice of doses and model system in the main text.c) 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  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 Figure 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.We thank the reviewers for pointing out our lack of clarity in describing the gene sets we used. Indeed, the full set of genes in the Venn diagram totalled to 2,246 genes, which included any gene that was upregulated in the combined treatment. However, for the reasons we outline below, we had a further requirement that the transcriptional changes from both individual signals be > 0 in order to be readily interpretable in the additive/multiplicative framework; these restrictions narrowed the gene set to roughly 1,398 genes, as described in our updated text below :“We defined a master set of 1,398 genes by selecting the set of genes that were significantly upregulated in any dose of the combination treatment  and that had increased expression in all doses of each individual signal . […] ”We also added a new panel to Figure 1  to graphically illustrate this set using an annotated version of the Venn diagram from the top-right of Figure 1B.Note that in our analyses of the c values of the combined responses in this master set, we removed an additional 14 genes from this set that had an expression of exactly 0 TPM in the control condition. We removed these genes because when the control TPM is zero, the estimation of the c value also becomes exactly zero, implying a perfectly additive combined response when such a response may not be the case . We have added asterisks with appropriate explanatory footnotes in these cases to the figures and the main text. We hope these changes help clarify the gene sets we used.d) Figure 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.We apologize for the confusion regarding the definition of our master set of upregulated genes, which we now go over in detail as described in the previous point (point 2c above). The reviewers are right that genes significantly upregulated in response to both signals but not in response to either of the signals individually should be included in the analysis, and we did in fact include most, but not all, of these genes (we included 493 of the 752 genes (66%) significantly upregulated in both signals but not either signal individually). The reason we excluded some of these genes relates to our analysis framework, in which we require the effects due to the individual signals to be greater than zero  or order to develop an analysis framework with a consistent mapping between our c values and categories of combined responses . We now include a discussion of the selection of the master set and how it relates to the Venn diagram in Figure 1B in the main text, shown below:“If we had selected the full set of all genes upregulated in any dose of the combined treatment, we would have analyzed a set of 2246 genes . […] Inclusion of genes with negative changes after individual signal treatments would require a more elaborate analysis framework to encompass the much larger variety of categorizations of potential responses that would be difficult to characterize with the number of genes in our analysis.”e) 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 signaling pathways that could interact also at the level of signal transduction . 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 clearer from the beginning and should be taken into account when interpreting the data.The reviewers have made a great point about the potential for cross-interaction. Indeed, this comment motivated us to perform the cross-interactivity immunofluorescence experiments we described in our response to point 2a above. Our interpretation, based on those data in addition to the ATAC-seq analyses, is that it is likely that there is not appreciable cross-activity in the signaling pathways or their downstream effectors. We have made this point also in the main text as per our response to 2a, as well as including appropriate caveats. We appreciate the reviewers raising these concerns, as addressing them has enhanced the manuscript.f) 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– Figure 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.The reviewers are totally right that the extent to which there is a quantitative relationship between transcription factor occupancy and chromatin accessibility is still unclear, and that our manuscript was too heavily implying that changes in chromatin accessibility are a direct readout of aggregate changes in transcription factor occupancy. We have reviewed and revised the portions of the manuscript (which includes the four instances mentioned by the reviewers as well as the Abstract), presenting the combined responses of ATAC-seq peaks more precisely as changes in chromatin accessibility and tempering any implications of a direct connection between ATAC-seq changes and transcription factor binding. We have included two examples of more significant edits to the manuscript that we hope address the reviewers’ concerns:“We performed ATAC-seq on the same populations described earlier, reasoning that the observation that changes in chromatin accessibility have been shown to correlate with changes in aggregate transcription factor binding activity  meant that we could infer something about transcription factor binding at these sites. […] Reassuringly, our initial motif enrichment analysis revealed that retinoic acid receptor alpha (RARA) and three TGF-β pathway transcription factor motifs  were highly enriched in their respective individual signal treatment conditions .”“Super-multiplicative combined responses may reflect cooperative interactions between retinoic acid and TGF-β induced factors, in which binding of a retinoic acid factor to DNA strengthens the binding of a TGF-β factor to nearby DNA or vice versa. […] However, given that ATAC-seq peaks likely have additional routes to super-additive increases in accessibility , further work would be needed to demonstrate that super-multiplicative transcriptional responses are indeed a result of direct binding interactions at enhancers between each signal’s induced transcription factors.”3) Title: 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 Figure 1E. Stronger evidence  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 reviewers are absolutely right that a rigorous c-value analysis is critical for the claims we are making in the paper, and that our treatment of the c-values could have been considerably more rigorous. To make our conclusions more sound, we performed several additional analyses of our data to show that the c-value distribution has a strong overrepresentation at c=1 . The details are in the Materials and methods, but the overall scheme was to fit the primary additive peak (c=0) to an additive model, and then subtract that distribution from the actual observed distribution. We then fit a Gaussian to the remainder, finding that the residuals contained a peak at or near c=1 for both the medium and high dose (1.28 for low-dose).We updated our main text to describe the new analyses performed above, which can be seen below:“At all doses of combination treatment, we observed a wide peak centered around c=0 (additive), with a hint of a secondary peak at c=1 (multiplicative), suggesting that the integration of the effects of two signals is preferentially additive or multiplicative .[…] Overall, while there is the possibility of further peaks within our data, our data most strongly support the existence of two peaks in the c-value histogram, one corresponding most closely with an additive model, and the other with a multiplicative model.”We thank the reviewers for pointing out this deficiency in our original manuscript, and feel that these additional analyses have greatly enhanced our manuscript.


IL1B, where de novo cohesin binding is involved in new loop formation. Similar IL1B induction with de novo cohesin appearance and new loop formation are observed in terminally differentiated macrophages, but not TNFα-treated cells. These results suggest that RAS-induced senescence represents a cell fate determination-like process characterised by a unique gene expression profile and 3D genome folding signature, mediated in part through cohesin redistribution on chromatin.Senescence is a state of stable proliferative arrest, generally accompanied by the senescence-associated secretory phenotype, which modulates tissue homeostasis. Enhancer-promoter interactions, facilitated by chromatin loops, play a key role in gene regulation but their relevance in senescence remains elusive. Here, we use Hi-C to show that oncogenic RAS-induced senescence in human diploid fibroblasts is accompanied by extensive enhancer-promoter rewiring, which is closely connected with dynamic cohesin binding to the genome. We find de novo cohesin peaks often at the 3′ end of a subset of active genes. RAS-induced de novo cohesin peaks are transcription-dependent and enriched for senescence-associated genes, exemplified by  Senescence is a state of stable proliferative arrest. Here, the authors perform Hi-C analysis on oncogenic RAS-induced senescence in human fibroblasts and characterize the changes in the 3D genome folding associated with the senescence-specific gene expression profile, which are mediated in part through cohesin redistribution on chromatin. While senescence plays a role in limiting the propagation of damaged cells, persistence of senescent cells in vivo can have deleterious effects on tissue integrity3. Ample evidence suggests that this is primarily mediated through the senescence-associated secretory phenotype (SASP). While the composition of SASP can be different depending on cell types and senescence inducers, the major SASP factors, mostly identified in human diploid fibroblast models, include inflammatory cytokines and chemokines and extracellular matrix remodelling factors4.Cellular senescence is a program of persistent cell-cycle exit, triggered by diverse stimuli, including genotoxic stress and excessive oncogenic signalling5 or the appearance of new ‘super-enhancers’6. In addition, the three-dimensional chromatin structure of senescent cells has been characterised using Hi-C technologies, mostly in terms of macro-domain structures, such as megabase-sized topologically associating domains (TADs) and larger-scale chromatin organization, A/B compartments, which represent ‘open’ (A) and ‘closed’ (B) chromatin states11. For example, the first study showed an increase in long-range interaction with a limited alteration in TAD borders during oncogene-induced senescence7. A/B compartments also appear to be stable8, although some changes between the two compartments were also reported10. These studies have provided insights into how large-scale 3D chromatin landscapes can be altered during senescence. However, the relevance of the dynamic chromatin interaction in the gene regulation remains elusive.Senescence has been linked to dynamic alterations of the chromatin state, through the formation of senescence-associated heterochromatic foci (SAHFs), altered distributions of histone modifications and chromatin accessibility12, such as chromatin loops14 and enhancer-promoter (EP) contacts16. Vast majority of loop anchors (loop ends) are bound by CCCTC binding factor (CTCF) and the cohesin complex, which consists of three core subunits 14. It is thought that loops are a structural unit of gene regulation and enhancers and promoters within same loop domains are more likely to bind17. EP interactions can be dynamically altered during cell differentiation18, playing a key role in cell type-specific gene expression. It has been shown that regulation of acute stress-responsive genes can be achieved through transcription factor (TF) recruitment to largely pre-existing EP contacts, suggesting that perturbation-responsive gene expression can occur under conserved EP networks in given cell types20. However, whether or not a similar mechanism is employed during senescence is unknown.The recent advance in Hi-C technologies and high-resolution Hi-C data have revealed additional structural unitsIL1B can be induced in both TNFα-treated and RIS human fibroblasts, and yet cohesin accumulation on the IL1B gene accompanied by new loop formation and EP alterations at the IL1 locus are only observed in the RIS condition. Our data suggest that senescence can activate fundamentally different gene regulatory machinery from non-senescent stress responses.Here we characterize high-order chromatin structure alteration during oncogenic RAS-induced senescence (RIS), integrating Hi-C and high-resolution capture Hi-C (cHi-C) for selected regions, including senescence-relevant genes. We particularly focus on the dynamic nature of chromatin loops and EP contacts during senescence and highlight the extensive alteration of loop structures, which correlates with differential binding of cohesin, but not CTCF. Decreased cohesin binding is enriched around loop anchors. In contrast, de novo cohesin peaks in RIS cells are mostly associated with highly active genes within loop domains in a transcription-dependent and CTCF-independent manner. These de novo cohesin peaks tend to interact with neighbouring cohesin peaks, suggesting they might contribute to the new loop formation, whereby affecting local EP contact, in RIS cells. HRAS-G12V-induced senescent (RIS) IMR90 human diploid fibroblasts (HDFs), using the 4-hydroxytamoxifen (4OHT)-inducible oestrogen receptor (ER) HRAS fusion system (ER:HRASG12V)21. Matching cHi-C libraries were generated for 62 selected genomic regions of interest  in normal growing and oncogenic 7, TAD borders were similar between conditions . We also found virtually no differences in the distribution of A/B compartments  exhibited significant interaction changes, out of which 1621 were A compartment TADs . The NRG1 gene was largely (except for a few isoform-specific 5′ exons) encompassed in a H3K27me3-dense TAD in growing cells. However, the interactions within the gene body and with the nearby regions were almost entirely lost in RIS cells. As illustrated in our 3D TADbit30 modelling (see “Methods”), the data suggest that the NRG1 gene body is released from the heterochromatic TAD   as well as qPCR analysis  for gene bodies (gene probes) and the other end of the respective TADs (TAD probes) as depicted in Fig. 32.Using the Hi-C data, we identified 3488 and 3535 TADs in growing and RIS conditions, respectively, at 40 kb resolution. In agreement with a previous studyADs Fig. , similarene Fig. , which wTAD Fig. . This waTAD Fig. , bottom.6 (details in “Methods”). Promoters  cHi-C data to identify any differential EP pairs. Within the captured regions , while maximising the EP changes annotated both in Hi-C and cHi-C   as well as with the gene expression log-fold change .The distances between interacting enhancers and promoters from both Hi-C and cHi-C were below 2 Mb, consistent with the previous studiesnes Fig. . AlthougGene set enrichment analysis  and several other genes (such as CKAP2L) on chr2q13. Both IL1A and IL1B encode key proximal SASP components, which are integral parts of SASP regulation35. The localization of CKAP2L (encoding a mitotic spindle protein) within the IL1 cluster is highly conserved and the expression of CKAP2L is tightly controlled during the cell cycle36. Our cHi-C showed dynamic sharing of enhancers between IL1A, IL1B, and CKAP2L during RIS. The differential interaction matrix of cHi-C at the IL1 locus showed new loop formation, compared with loops that previously defined by high-resolution Hi-C in IMR90 cells13, segregating IL1A and CKAP2L from IL1B and therefore increasing the specificity of their enhancer-associations. Consistently, IL1A and IL1B began to interact more frequently with enhancers located within their respective new loops in RIS  Hi-C maps, Jin et al.15 have shown that a transient TNFα treatment of IMR90 cells leads to upregulation of IL1A and IL1B with increased binding of NF-κB (a major inflammatory TF) to active enhancers of its targets. In addition, IL1A and CKAP2L were shown to be induced simultaneously via shared enhancer binding. The authors concluded that gene expression alterations mostly occur via TF binding to ‘pre-existing’ EP complexes, at least upon TNFα treatment15. We re-analysed the Hi-C data from this study using the analysis pipelines described in this study and, like Jin et al., did not observe any significant changes upon TNFα treatment , chromatin structural proteins associated with chromatin loopsRIS Fig. . In contRIS Fig. . SimilarRIS Fig. . Thus, a13, Fig. 13 were indeed enriched for CTCF and cohesin binding in our data. 90% of the loop anchors were bound by both cohesin and CTCF within 10 kb of the anchor. 3154 (out of 7647) loops showed changes in either CTCF or cohesin binding. Most of the loops correlated with cohesin changes exhibited cohesin losses at their ends (1857 loops), followed by loops with cohesin gains inside the loop (823 loops)  and found a trend of decreased interaction between loop ends in RIS compared to growing cells, suggesting a global correlation between the decreased cohesin binding at loop anchors and reduced loop formation  centred on selected loop ends Fig. , Methodsion Fig. , left. Wion Fig. , right. NA2 Fig. .IL1 locus belong to the same loop identified in normal IMR90 cells , as well as loop reorganization around the new cohesin peak  mouse embryonic fibroblasts (MEFs) of Ctcf and the cohesin release factor Wings apart-like (Wapl). The authors proposed that cohesin is loaded onto chromatin at the TSSs of a large number of active genes and is then relocated though transcription: if there is no CTCF in the way and no efficient cohesin release at the 3′ end of active genes, cohesin accumulates at the 3’ end of these genes37. A similar pattern of cohesin binding has been reported in wild-type yeast, which lacks a CTCF equivalent40. Thus, we hypothesized that genes highly active in RIS somehow allow for the accumulation of cohesin at their 3’ ends in a transcription-dependent manner, potentially promoting loop reorganization. To test this, we compared transcript abundance and cohesin binding at the 3’ end of genes. Both convergent (genes on opposite strands that terminate in the same place) and isolated (no overlap with other genes) genes in RIS IMR90 cells exhibited cohesin island-like binding and cohesin binding positively correlated with gene expression , the genes with cohesin islands were enriched in pathways that have been implicated in senescence, such as Wnt, Autophagy and NF-κB signalling42  treatment, a transcription elongation inhibitor. The de novo cohesin peaks at the ely Fig. . To gainely Fig. . 531 islely Fig. . Note, wely Fig. . Whetherely Fig. . MoreoveCtcf-Wapl DKO MEFs37 the lack of CTCF binding of cohesin islands in RIS cells appeared to a general trend  cohesin islands and surrounding cohesin peaks (4497 interactions) were increased in RIS in the Aggregate Peak Analysis  and after (macrophages) phorbol myristate acetate (PMA) treatment. We reanalysed these datasets and found that the new loop formation in the IL1 locus in THP-1 macrophages was similar to RIS  might facilitate transcription of genes in this locus.Finally, we asked whether changes similar to those observed at the RIS Fig. , concomiL1B Fig. . Genome-L1B Fig. . 65 geneL1B Fig. . Togethe44. Mechanistically, our data suggest that this can be at least partly explained by the formation of transcription-dependent cohesin islands. We also observed the induction of cohesin islands during macrophage terminal differentiation  than those in Ctcf-Wapl DKO MEFs37 (up to 70 kb). Localization of RIS-associated cohesin islands appeared less restricted to 3′-ends of genes and they were often observed on gene bodies. The reasons behind these apparent differences are not clear but one possibility is the difference between the two genetic backgrounds. Further studies are required to understand how cohesin islands are formed in the presence of CTCF and WAPL in RIS fibroblasts and THP-1 macrophages. Importantly, in addition to their susceptibility to the DRB treatment, the RIS cohesin islands exhibited a strong correlation with Pol II ChIP-seq profiles, supporting the same mechanism, the transcription-dependent cohesin relocation, as proposed in Ctcf-Wapl DKO cells37.Despite the common features, there are some differences between cohesin islands between RIS and Although the precise mechanism of how cohesin islands are triggered during senescence is unclear, it is tempting to speculate that the initial de novo cohesin accumulation promotes new loop formation, and thus, increased gene expression. This would further promote transcription-dependent cohesin accumulation, constituting a gene amplification feed-forward mechanism and eventually contributing to forming the phenotype specific gene expression profile. The enrichment of active enhancer marks on cohesin islands in RIS cells might also contribute to the ability of cohesin islands to form new interactions  genotyping. Cells were regularly tested for mycoplasma contamination and always found to be negative. The following compounds were used in cultures: 100 nM 4-hydroxytamoxifen (4OHT) , 100 μM 5,6-dichloro-1-β-D-ribofuranosylbenzimidazole (DRB) , 10 ng/mL tumour necrosis factor alpha (TNFα)  as indicated in individual figures. TNFα was added to the culture media to the final concentration of 10 ng/mL for 1 h before harvesting for ChIP-seq and immunofluorescence studies.IMR90 and WI38 HDFs (ATCC) were cultured in Dulbecco’s modified Eagle’s medium (DMEM)/10% foetal calf serum (FCS) in a 5% OER:HRASG12V , pBabe-puro for HRASG12V. Senescence was induced using the ER:RAS system unless otherwise mentioned.The following retroviral vectors were used: pLNCX2 (clontech) for 46. Cells were plated onto glass coverslips the day before fixation. Cells were pre-treated with digitonin (150 μg/ml) in Cytoskeletal (CSK) Buffer , then fixed in 4% paraformaldehyde. Cells were permeabilized with 0.2% (v/v) Triton X-100  in PBS, soaked in liquid nitrogen, treated in 0.1 M HCl and dehydrated in EtOH. We used the fluorescent labelled probes corresponding to the following BAC clones (Empire Genomics): 5-fluorescein-labelled RP11-57I3 , 5-TAMRA-labelled RP11-451O18 , 5-fluorescein-labelled RP11-185D13  and 5-TAMRA-labelled RP11-63F4 . Confocal images were obtained using a Leica TCS SP8 microscope.DNA-FISH was performed as previously describedhttps://scikit-image.org]), by filtering noise using Yen filters for the image channels with FISH signal and Li filter for the DAPI channel. The Clear Border segmentation algorithm was used both for determining individual cell nuclei from DAPI signal, as well as for detecting areas with high FISH signal. We determined the average distance between the peaks of the two types of FISH signals in each cell and then performed two-sided Student t-tests to determine whether distances between probes are significantly different between conditions.Analysis of FISH signal was performed using scikit-image  the day before fixation. Cells were fixed in 1% (v/v) paraformaldehyde in PBS for 1 h at 4 °C and permeabilised with 0.2% (v/v) Triton X-100  in PBS for 5 min at room temperature. The primary antibody used was anti-RELA  in PBS + 0.1% Tween 20 (Sigma P1379) (PBS-T). The secondary antibody used was goat anti-rabbit IgG  in PBS-T. Cells were counter-stained with DAPI at 1 μM in the secondary antibody solution. Coverslips were mounted onto Superfrost Plus slides  with Vectashield Antifade mounting medium (Vector Laboratories Ltd. H-1000). Fluorescence images were obtained using Leica DMI6000B epifluorescence light microscope.Immunofluorescence staining was performed as described previously48 for the following antibodies: anti-H3K27ac , anti-H3K27me3 , anti-CTCF , anti-RAD21  and anti-SMC3 , anti-Pol II . Libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina  according to the manufacturer’s instructions except that size selection was performed after PCR amplification using AMPure XP beads . Samples were sequenced paired-end using 50 bp reads on the Illumina platforms.Chromatin immunoprecipitation (ChIP) was performed as previously described31 on an Applied Biosystems Quantstudio 6 by the 2−ΔΔCt method using β-actin (ACTB) as an internal control. The primers used are mentioned in Supplementary Data RNA was prepared using the Qiagen RNeasy Plus Kit  according to the manufacturer’s instructions and reverse-transcribed to cDNA using the Applied Biosystems High-Capacity Reverse Transcription Kit . Relative expression was calculated as previously described53 using the in-nucleus ligation protocol54. For each sample and replicate 50 million IMR90 cells were used. For capture Hi-C, biotinylated 120-mer RNA baits complementary to both ends of each target HindIII restriction fragment of interest were designed. Target sequences were valid if they contained no more than two consecutive N’s, were within 330 bp of the HindIII restriction site and had a GC content ranging between 25 and 65%.Hi-C and capture Hi-C libraries were generated as previously described55 against the hg19 genome build. Artefacts were identified and removed using both HiC-Pro and diffHic28 (R Bioconductor package) and reads were counted into bins at several resolutions (HindIII and 5 kb for cHi-C and 10 kb–100 kb for Hi-C). Read duplicates were removed using samtools56 markdup. We used HiC-Spector22 and HiCRep23 to check for the similarity between biological replicates.Hi-C and cHi-C libraries were quality checked with FastQC and aligned with HiC-Pro24, by performing PCA on distance-corrected, ICE-normalized Hi-C matrices at 100 kb resolution. The principal component which correlated well in absolute value with H3K4me1 ChIP-seq signal was chosen as representative of A/B compartments. The sign of the A/B compartments vector was set to match the sign of the correlation with H3K4me1 signal so that A compartment regions were represented by positive values and B compartment regions were represented by negative values.A/B compartments were called as before30 from Hi-C matrices at 40 kb resolution. A confidence score between 1 and 10 was assigned to each TAD border by TADbit. TADs from different biological replicates were combined in a consensus set per condition using TADbit by only considering common TAD borders with scores over 7 (out of 10). Normalized Mutual Information (NMI) was used to compare the growing and the RIS set of TAD borders for agreement, using the R package NMI (CRAN [https://cran.r-project.org]). The exact positions (bin) of the TAD borders were used to compare growing and RIS borders.TADs were called using TADbit30 to compute 3D models of the interactions of genes released from H3K27me3 neighbourhoods using the ICE-normalized matrices at 20 kb resolution, combined across biological replicates for growing and RIS, respectively. The matrices used correspond to the subset of interactions of one of two TADs around each gene of interest. In each case, we tried several parameter spaces for IMP parameter optimisation, employed by TADbit. For each region, we then chose the parameter subspace which fit the interaction values curve best. Modelling with IMP57 within TADbit was then performed with the parameters optimised for each case. The top 10 models predicted in each case were selected and exported from TADbit as XYZ coordinates.We used TADbitHindIII and 5 kb for cHi-C and 10 kb–100 kb for Hi-C, increasing in 5 kb steps) using diffHic28 (R Bioconductor). Libraries with artefacts and duplicates removed were further filtered for low counts and diagonal entries. Using diffHic, we performed non-linear normalization (LOESS) to remove trended biases between libraries. We tested for significant interaction changes at 5% FDR by using quasi-likelihood F-tests and Benjamini–Hochberg multiple testing correction from diffHic.We performed differential interaction analysis between growing and RIS Hi-C and cHi-C libraries at several resolutions . Only promoters of differentially expressed genes in RIS were considered.Enhancer-promoter interactions were annotated by checking the bins involved in significant differential interactions for overlaps with enhancers and promoters. We used cHi-C EP interactions annotated using HindIII). We wanted to maximise the number of EP interactions detected in the captured regions from both Hi-C and cHi-C and to minimise the interactions detected from Hi-C but not from cHi-C, which were likely false positives. We tried selecting only interactions involving enhancers of large sizes  or genes which were more robustly differentially expressed in RIS (FDR < 0.01), as well as selecting bins without other regulatory elements. All of these filters were applied on EP interactions detected at resolutions between 10 kb and 100 kb as bin size can also affect the accuracy of the interactions detected. Finally, we selected resolutions higher than 30 kb  and interactions involving enhancers larger than 7.5 kb for the EP interactions annotated from Hi-C.In order to annotate EP interactions from Hi-C more robustly, we compared several filtering strategies at different resolutions, using the contacts detected using cHi-C as a baseline for comparison, due to their accuracy at high resolution  using subread65. Differential expression analysis was performed using glmTREAT from edgeR66 and significantly differentially expressed genes were selected at 5% FDR. log-TPM expression values were also calculated for the analysis of transcription and cohesin binding. Gene enrichment analysis of sets of genes of interest was performed using the enrichR R package (CRAN [https://cran.r-project.org]) which queries EnrichR68 against the WikiPathways database.RNA-seq libraries were aligned using STAR28 and further normalized using ICE24 and distance correction as part of the correctedContact functionality. Plotting the matrices was performed using a custom set of R scripts consisting of horizontal rotation of the matrix coordinates and overlaid genomic information such as enhancer and promoter positions, ChIP-seq tracks or interaction arcs. Interactions were coloured using a non-linear scale which represented interaction values above and below the expected values  with ‘warm’ and ‘cold’ colours, respectively. In the case of overlaying cHi-C matrices at HindIII resolution, each interaction unit was represented as equally sized, despite the variable lengths of HindIII fragments. The R scripts used for this visualisation strategy were made available as a package called hicvizR (https://gitlab.com/ilyco/hicvizr). ChIP-seq tracks represented were THOR-normalized  bigWig files produced during the differential binding analysis. The tracks were exported from IGV69 and were scaled to be within the same interval, to allow for comparison between conditions. RNA-seq bigWig files were also produced for visualization of expression and TMM factors were used for normalization of the signal between libraries. Models derived from Hi-C interactions using TADbit30 were visualized using the R package rgl (CRAN [https://cran.r-project.org]). The points corresponding to a model are centred around 0. The curve used to visualize the model is drawn by adding 10 additional points between every pair of points in the set of original coordinates by interpolation with the spline function in R. The radial image of loops with cohesin/CTCF changes in Fig. Hi-C matrices corresponding to combined biological replicates were used for visualization. The raw matrices for each replicate were combined by calculating the overall negative binomial mean contacts with correctedContact from diffHic37, in order to avoid biases caused by genes with overlapping regions post 3′UTR. Small increases in signal were observed before the TES as well caused by short genes which show cohesin binding on their gene bodies as well.We investigated the association between cohesin accumulation and transcription by grouping genes by expression level  and plotting their SMC3 and RAD21 ChIP-seq profile. The binding profile was centred either at their transcription end site (TES) in the case of isolated genes or in the middle of the genomic region bounded by TES of two convergent genes. We focused this analysis on isolated and convergent genes, like in Busslinger et al.We determined cohesin islands by comparing the RIS cohesin ChIP-seq libraries with and without DRB treatment (transcription elongation inhibitor). We then selected significantly differentially bound regions larger than 2 kb. We eliminated possible false positives which can occur due to genes with overlapping 3′ end regions (such as convergent genes) by filtering for highly expressed genes. Genes with cohesin islands were determined by overlapping 10 kb regions starting at the TES with the cohesin islands determined.13. Each interaction pixel was divided by the number of sub-matrices added minus the number of missing values. We selected a 200 kb region around each bin containing a cohesin peak of interest which, at 20 kb resolution, resulted in neighbourhoods of 11 × 11 pixels. Differential aggregated matrices were computed by subtracting the growing-specific aggregated matrix from the RIS one.We identified general trends of certain subsets of interactions by selecting a two-dimensional neighbourhood around each interaction of interest, and summing the corresponding Hi-C sub-matrix , similarly to Aggregate Peak AnalysisFurther information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting Summary