Dual promoter–enhancer activities reflect a unified regulatory logic

Domain: neuroscience
Published: 2026-01-30
Editorial outcome: minor_revision

Abstract
Accumulating evidence indicates that both putative promoters and enhancers can exert dual promoter and enhancer functions. However, how these activities are related within individual elements and what determines this dual functionality remain poorly understood. We developed a massively parallel dual reporter assay that enables simultaneous assessment of the intrinsic promoter and enhancer potential exerted by the same sequence. Parallel quantification for thousands of elements reveal that canonical human promoters and enhancers can act as both promoters and enhancers under the same contexts, and that promoter activity may be necessary but not sufficient for enhancer function. Perturbations to element transcription factor binding motifs lead to disruptions in both activities, implicating a shared syntax for the two regulatory functions. Combinations of elements with different minimal promoters reveal reciprocal activity modulation, which, together with a strong correlation between promoter and enhancer functions, imply a bidirectional feedback loop to sustain environments of high transcriptional activity. Finally, we validate this reciprocity and correlation in situ using CRISPR activation at the human β-globin locus. Our approach reveals that the functional convergence between promoters and enhancers arises from a shared regulatory logic and sequence syntax, advancing a unified model for regulatory element biology.

Introduction
Cellular processes are precisely controlled through complex gene expression programs that integrate signals from gene-proximal promoters and gene-distal enhancers. Traditionally, promoters and enhancers have been considered separate classes of transcriptional regulatory elements (TREs), often distinguished by their functions, chromatin environments, and genomic locations. Increasing evidence, however, has revealed broad similarities between both the architecture and function of these two regulatory elements, complicating their classification as distinct types1,2.

Promoters are classically defined as DNA sequences that drive RNA polymerase II (RNAPII) transcription initiation at gene-proximal transcription start sites (TSS)3. The region around the TSS (typically defined as the ±50 base pair (bp) region surrounding the TSS) is known as the core promoter, which contains core promoter motifs, such as the Goldberg-Hogness box (TATA box) and the Initiator (Inr)4. These motifs are recognized and bound by general transcription factors (GTFs), such as TBP and TFIIB, that recruit and assemble the RNAPII pre-initiation complex (PIC). RNAPII transcription initiation, promoter-proximal pausing, and pause-release into productive elongation are further facilitated by gene-, cell-, and state-specific transcription factors (TFs) and co-factors that bind at the promoter and at other regulatory sequences such as enhancers to regulate gene transcription5.

Conversely, enhancers are defined as DNA sequences that stimulate transcription at a distance, irrespective of their position and orientation with respect to their target gene6. The first enhancer discovered was a 72 bp sequence from the SV40 genome that could dramatically increase the transcription of the β-globin gene in a distance- and orientation-independent manner7–9. Enhancers were soon after discovered in Mammalia10 and are now recognized as key elements in the regulation of eukaryotic transcription. Today, enhancers are generally thought of as TF-binding regulatory regions that come into proximity to their target gene promoters via three-dimensional DNA looping11.

Numerous findings now point to promoters and enhancers being far more similar than previously thought, spearheaded by the discovery of widespread transcription at enhancer loci12,13. Detailed characterization has revealed that when active, both are encompassed within nucleosome-depleted regions (NDRs), bound by sequence-specific TFs that facilitate the assembly of PICs at core promoter regions, and undergo divergent transcription initiation and pausing, with protein-coding gene promoters divergently transcribing mRNAs and upstream antisense RNAs (uaRNAs) in either direction and enhancers divergently transcribing enhancer RNAs (eRNAs) in both14–19. These remarkable similarities in chromatin and sequence architecture have led to a proposed unified model for transcriptional regulatory elements2,14,16.

Prior attempts at defining promoters and enhancers have largely assumed that their functions are distinct and non-overlapping. However, enhancers possess intrinsic promoter potential12,13,20, and numerous studies are now reporting that promoters can exert distal enhancer activity21–24. Understanding how these two activities within the same element relate and elucidating their shared determinants is necessary to ensure that current models of gene regulation capture the full complexity of regulatory element functionality.

To date, experiments that measure both the promoter and enhancer potentials of the same set of sequences are lacking. One of the few studies to have employed such a strategy on a large scale was Nguyen et al., which used separate massively parallel reporter assays (MPRAs) to measure promoter and enhancer activities for the same elements25. This attempt, however, like all existing MPRAs, inherently decouples the intrinsic promoter and enhancer potential from one another, regulatory potentials that are likely interrelated in their native contexts. One method developed by Mikhaylichenko et al. detected promoter and enhancer activities simultaneously, though it was limited to a small test set of elements given the utilization of fluorescent in situ hybridization (FISH) as their functional readout26.

For these reasons, we developed a Quantitative Unifying Assay for Simultaneously Active Regulatory Regions by sequencing (QUASARR-seq), a massively parallel dual reporter assay that allows for the simultaneous measurement of intrinsic promoter and enhancer potentials exerted by the same element, for thousands of elements in a single experiment. We perform systematic functional comparisons between intrinsic promoter and enhancer functions exerted by canonical promoter and enhancer elements, revealing a degree of entanglement between the two activities, across element types. Our findings suggest that regulatory potential is embedded within element sequences, with impairments in element promoter activity showing compounding effects by also impairing enhancer function. Finally, using combinations of elements with different minimal promoters (minPs), we find that paired elements exert reciprocal regulatory influences to establish promoter and enhancer function of each element, and validate this phenomenon in situ using CRISPR activation (CRISPRa) data at a human enhancer-promoter pair. Our results support the principle that promoters and enhancers are not separate classes of regulatory elements, with their functional convergence underpinned by shared regulatory logic and sequence syntax.

Results
To investigate the relation and determinants of promoter and enhancer element dual functionality, we sampled a set of TREs in the human immortalized myelogenous leukemia cell line K-562. We previously demonstrated that Precision Run-On and capped nascent RNA sequencing (PRO-cap), a technique that maps TSSs genome-wide by capturing and sequencing the 5′ capped ends of nascent transcripts, provides high predictive performance using 500 bp distance cutoffs from GENCODE annotations, a high-quality, comprehensive reference database of human genes, to distinguish promoter and enhancer elements (see Methods)27,28. Thus, we selected candidate active promoters defined as GENCODE-proximal PRO-cap TSSs and candidate active enhancers as distal TSSs (Fig. 1a). Additionally, we selected a set of candidate inactive promoters defined as proximal PRO-cap untranscribed DNase-seq DNase I hypersensitive sites (DHSs) and candidate inactive enhancers as distal untranscribed DHSs. In total, we tested 2,151 elements, ranging from 65 to 703 bp, with a median length of 254 bp (Supplementary Fig. 1a).

Hereafter, for clarity, we use the terms ‘promoter activity’ to refer to the candidate TREs ability to drive transcription locally (i.e., initiation at the TRE) and ‘enhancer activity’ to refer to the candidate TREs ability to drive transcription from a distance (i.e., initiation at the minP).

QUASARR-seq simultaneously measures an element’s intrinsic promoter and enhancer activity

To systematically evaluate the functional duality of transcriptional regulatory elements, we developed a Quantitative Unifying Assay for Simultaneously Active Regulatory Regions by sequencing (QUASARR-seq), a massively parallel dual reporter assay that enables the simultaneous measurement of intrinsic promoter and enhancer potentials exerted by the same regulatory element molecule (Fig. 1a). QUASARR-seq adopts the principles of classical reporter assays where candidate regulatory sequences are cloned into expression vectors but incorporates two functional reporter systems each used to independently quantify promoter and enhancer activity for the same sequence (see Methods). Enhancer activity measurements are obtained by the reporter system positioned upstream of the TRE that contains a minP that drives the expression of enhancer activity barcodes (eaBCs) located within the 3′ untranslated region (UTR) of an enhancer activity transcript (eaT). Similarly, promoter activity measurements are obtained by the reporter system positioned downstream of the TRE where the TRE itself drives the expression of promoter activity barcodes (paBCs) located within the 5′ UTR of a promoter activity transcript (paT). As in all MPRAs, QUASARR-seq quantifies activity as the ratio of counts of aBC RNA transcripts to aBC DNA input vectors, from which boost indices are calculated by basal expression level normalization using negative controls (see Methods).

QUASARR-seq promoter activity measurements [log2 (RNA paBCs/DNA paBCs)] across replicates demonstrated a high degree of reproducibility (R2 = 0.98; Fig. 1b). As expected, promoter activity varied significantly across GENCODE classes, with PRO-cap transcribed proximal elements (i.e., active promoters) exhibiting the highest activity, followed by transcribed distal elements (i.e., active enhancers), and with untranscribed elements and negative control open reading frames (ORFs) exhibiting little or no activity, respectively (P-value < 0.001, Student’s t-test; Fig. 1c). Interestingly, many of the proximal elements with the highest promoter activities were found to correspond to the promoters of long non-coding RNAs (lncRNAs) and small nuclear RNAs (snRNAs). The top three included: 1. the divergent promoter for LINC01138 (lncRNA)/RNVU1-21 (snRNA) and ENSG00000224481 (uncharacterized lncRNA), 2. the promoter for RNVU1-2A (snRNA), and 3. the divergent promoter for SNHG1 (lncRNA) and SLC3A2 (protein coding).

Methods
To investigate the relation and determinants of promoter and enhancer element dual functionality, we sampled a set of TREs in the human immortalized myelogenous leukemia cell line K-562. We previously demonstrated that Precision Run-On and capped nascent RNA sequencing (PRO-cap), a technique that maps TSSs genome-wide by capturing and sequencing the 5′ capped ends of nascent transcripts, provides high predictive performance using 500 bp distance cutoffs from GENCODE annotations, a high-quality, comprehensive reference database of human genes, to distinguish promoter and enhancer elements (see Methods)27,28. Thus, we selected candidate active promoters defined as GENCODE-proximal PRO-cap TSSs and candidate active enhancers as distal TSSs (Fig. 1a). Additionally, we selected a set of candidate inactive promoters defined as proximal PRO-cap untranscribed DNase-seq DNase I hypersensitive sites (DHSs) and candidate inactive enhancers as distal untranscribed DHSs. In total, we tested 2,151 elements, ranging from 65 to 703 bp, with a median length of 254 bp (Supplementary Fig. 1a).

Hereafter, for clarity, we use the terms ‘promoter activity’ to refer to the candidate TREs ability to drive transcription locally (i.e., initiation at the TRE) and ‘enhancer activity’ to refer to the candidate TREs ability to drive transcription from a distance (i.e., initiation at the minP).

QUASARR-seq simultaneously measures an element’s intrinsic promoter and enhancer activity

To systematically evaluate the functional duality of transcriptional regulatory elements, we developed a Quantitative Unifying Assay for Simultaneously Active Regulatory Regions by sequencing (QUASARR-seq), a massively parallel dual reporter assay that enables the simultaneous measurement of intrinsic promoter and enhancer potentials exerted by the same regulatory element molecule (Fig. 1a). QUASARR-seq adopts the principles of classical reporter assays where candidate regulatory sequences are cloned into expression vectors but incorporates two functional reporter systems each used to independently quantify promoter and enhancer activity for the same sequence (see Methods). Enhancer activity measurements are obtained by the reporter system positioned upstream of the TRE that contains a minP that drives the expression of enhancer activity barcodes (eaBCs) located within the 3′ untranslated region (UTR) of an enhancer activity transcript (eaT). Similarly, promoter activity measurements are obtained by the reporter system positioned downstream of the TRE where the TRE itself drives the expression of promoter activity barcodes (paBCs) located within the 5′ UTR of a promoter activity transcript (paT). As in all MPRAs, QUASARR-seq quantifies activity as the ratio of counts of aBC RNA transcripts to aBC DNA input vectors, from which boost indices are calculated by basal expression level normalization using negative controls (see Methods).

QUASARR-seq promoter activity measurements [log2 (RNA paBCs/DNA paBCs)] across replicates demonstrated a high degree of reproducibility (R2 = 0.98; Fig. 1b). As expected, promoter activity varied significantly across GENCODE classes, with PRO-cap transcribed proximal elements (i.e., active promoters) exhibiting the highest activity, followed by transcribed distal elements (i.e., active enhancers), and with untranscribed elements and negative control open reading frames (ORFs) exhibiting little or no activity, respectively (P-value < 0.001, Student’s t-test; Fig. 1c). Interestingly, many of the proximal elements with the highest promoter activities were found to correspond to the promoters of long non-coding RNAs (lncRNAs) and small nuclear RNAs (snRNAs). The top three included: 1. the divergent promoter for LINC01138 (lncRNA)/RNVU1-21 (snRNA) and ENSG00000224481 (uncharacterized lncRNA), 2. the promoter for RNVU1-2A (snRNA), and 3. the divergent promoter for SNHG1 (lncRNA) and SLC3A2 (protein coding).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "As a followed up of previous studies [Nguyen, T.A. et al. High-throughput functional comparison of promoter and enhancer activities. Genome Res 26, 1023-33 (2016) and Mikhaylichenko, O. et al. The degree of enhancer or promoter activity is reflected by the levels and directionality of eRNA transcription. Genes Dev 32, 42-57 (2018)], the originality of this manuscript is that both enhancers and promoters’ activity has been quantified (high-throughput sequencing using barcodes) together using the same reporter system (QUASARR-Seq). The limitation of the study is that it is not done in a chromatin context (plasmid), and this is acknowledged (briefly) in the Discussion section.\nMajor comments\nAfter reading the abstract, it is still difficult to understand what the main conclusions are. A shorten version of the abstract may help to focus on the key take home message. Same as for the last paragraph of the introduction (lanes 85-90).\nWhile I don’t have the capacity and the skills to check all the bioinformatics and statistics in this study, the work presented here is colossal and good fit for the journal. However, the narrative is very explorative by testing a variety of hypothesis which disconnects the reading interest. This reflects the above comments about the abstract. The last part of the manuscript (Discussion Section) ends the same way by the lack of key highlights.\nThe distinction between enhancers and promoters is always blurred and this is well presented in the introduction. Enhancers are (usually) bidirectional and promoters mostly unidirectional (as these must tell the PolII machinery which direction the coding gene is). In a chromatin context, they can be distinguished by deposition of high H3K4me1 at enhancers and high H3K4me3 at promoters. However, in contrast to enhancers and regardless of chromatin context, promoters have been classified in subgroups based on consensus sequences located at specific distances from TSS (e.g. TATA box at -25bp, CAAT Box at -75bp etc…). Some promoters have CpG Islands (usually constitutive, unless repressed by CpG), some have a TATA Box, and others an Initiator element (INR). Without additional lab work, the authors may find benefit by pushing the analysis further in separating the effect of all promoters tested based on these categories and hopefully find sometime more conclusive.\nAdditional comment (for future studies)\nEnhancers are usually primed first, suggesting that the ‘transcription signal’ is primarily launched remotely to eventually reach the promoter. In the future, perhaps a key experiment on a single locus would be to swap enhancer and promoter and see what happens, but this is out of the scope of this manuscript.", "reviewer_2": "The study by Paramo et al. investigated enhancer and promoter activity of human transcriptional regulatory elements (TREs) with QUASARR-seq, a newly developed massively parallel reporter assay. The TREs were defined by nascent transcription start sites, which were demonstrated to be good predictors of regulatory activity. The authors found that TREs often drive both distal and proximal expression, i.e., the promoter and enhancer activities of the same element are often correlated. The regulatory activity can be modulated by different minimal promoters.\nI found that the data are sufficient to support the authors’ conclusion. The study strengthened the previous notion that promoters and enhancers might not be functionally distinct elements. Once in a microenvironment with high regulatory activity (or the “all-by-all” model as the authors put it), i.e., high concentration of RNAP, TFs, open chromatin, etc., the regulatory elements may function either as promoters or enhancers.\nHowever, it was not very surprising that most TREs showed correlated promoter and enhancer activities, especially when the enhancer activity was evaluated by its ability to drive expression of a nearby minP in a synthetic construct. Traditionally, one feature that distinguishes enhancers from promoters is that its ability to drive expression at a distance. From the paper, it was not very clear how far apart the TREs were from the minPs and whether the physical distance should be considered when we evaluate the distal regulatory activity, which could be added to the methods and discussion respectively. Another discerning feature of enhancers is its cell-type specificity, which is not evaluated by the current assay but might worth discussion. Furthermore, it was not surprising that the regulatory activity can be modulated by minPs, either. It would be interesting to learn what sequence features, besides GC content, were involved in the interaction, if there were enough data for such analysis.\nIn terms of clarity, I would appreciate more details in terms of the library design, e.g., how long the TREs are, how many TREs were included in the library, how they are chosen (Random? Representatives of different cell types? Different information content?), if they consist of known TF binding sites, etc.\nMinor points:\nLine 96: GENCODE annotations: it would be helpful to explain what they are for people who are not familiar with this database. Similarly, the PRO-cap technique needs to be explained on first mention (what is the abbreviation short for?).\nFig. 1a: it would be helpful to show “candidate active promoters” “candidate active enhancers” “candidate inactive promoters” and “candidate inactive enhancers” as a table, along with their corresponding diagrams. Also, it wasn’t very clear what the peak height means in the diagrams and how it is related to proximal/distal classification.\nFig. 1c: is “GENCODE distal & PRO-cap untranscribed” equivalent to “Distal DHS & PRO-cap untranscribed” as in Fig. 1a and Line 100? In general, I was a bit confused about how GENCODE class was defined and how it was related to DHS.\nLine 219: SOLARR-seq was only introduced after line 290, so referencing the SOLARR-seq data here is a bit confusing.\nLine 350: “a small delta boost index…”: it might be clearer to say “a low delta boost index” or “a negative delta boost index indicated a disrupting effect”.\nLine 354: it would be nice to report effect size along with the P-value.\nFig. 5: “a, b, c” could be moved to the top-left corner of each panel. Legend for (a) was not very clear in terms of what “left” and “right” refer to. Also, there is no error bar or scatter points in (a) that indicate the distribution of data.\nLine 685: “R” was not defined. Should it be “Bal”?\nExtended Data Figs and Supplementary figures: usually, extended data figures go into supplementary. So, it was a bit confusing that some supp. figures are named “extended data fig” and others “supplementary fig”.", "reviewer_3": "Evidence has been accumulating that enhancers and promoters may in fact be two sides of the same coin. This manuscript systematically addresses this hypothesis by testing E and P activity of a collection of regulatory elements simultaneously in a dual reporter construct. This is novel and exciting. The design of the experiments is very elegant, and the (often challenging) computational analyses were done with great care. The findings support the hypothesis that E and P activities of single regulatory elements are indeed tightly linked, and the authors favor the interpretation that E activity requires P activity - which seems reasonable given the results with a set of mutants. Moreover, they provide evidence that three Ps differ in their dynamic range of response to Es, and reciprocally affect E activity to different degrees. Overall, this manuscript is of high quality and of substantial novelty, and will be of interest to a broad readership interested in the fundamental biology of Es and Ps. That said, I have a few points that could strengthen it further.\nMajor points:\nline 205+: Potential readthrough of the pA sites is indeed a risk. I did not understand the logic of the orientation analysis. How can this estimate readthrough? Please explain better; perhaps a cartoon could help? If I understand it correctly, the results point to some degree of (rho = 0.16) of readthrough? Then the authors' conclusion seems a bit too strong: \"supporting the case that the high correlation of promoter and enhancer activities is not an artifact of measuring the same function\"\". Please tone down. This conclusion could perhaps be strengthened by RT-qPCR of plasmid backbone sequences downstream of the 2nd pA, or of sequences in EGFP when no TRE is inserted? Or, perhaps simpler, what is the ratio paBC over eaBC counts in RNA when no TRE is inserted?\nOne of the most interesting concepts arising from this work is that promoters may reciprocally activate nearby enhancers (Figure 5d). But of course the experimental setting used here is quite artificial (as acknowledged in the Discussion). It seems to me that it should not be too difficult to test the P-->E effect in a native genomic context: delete a P by CRISPR/ and test whether the activity of a nearby E (using ATAC-seq, H3K27ac or PRO-Cap as proxy readout) is reduced. Possibly such data is already publicly available, but no-one bothered to look at it this way? Addition of a small set of such experiments could greatly strengthen this manuscript. I should point out that even a negative result would be insightful (albeit a bit disappointing, of course); publication should not depend on a positive result.\nThe P and E boost indices show a substantial difference in dynamic range (Extended data figure 1a). The authors then z-normalize these to enable the calculation of a P/E balance index. Although these differences in dynamic range are puzzling, this seems a reasonable approach when only a single minP is analyzed in the first part of the manuscript. However, I found this much more difficult to interpret when the three minPs have different dynamic ranges - which I gather from figure 6a (but it would be good to show the equivalent of extended data fig 1a for the three minPs). With these differences in dynamic range, can the balance indices still be compared? Is it fair to remove these differences by z-normalization? The interpretation becomes even more complicated to me when the authors switch to ranked balance indices and show quite puzzling scatter plots (figs 6c,d,e). My overall feeling is that the authors push the data analysis a little too far in figure 6; I am afraid that most readers may not be able to deeply understand these results. Because the manuscript is already quite long and detailed, I suggest to remove Figure 6; this will help to focus the manuscript on the key messages.\nMinor points:\nline 180+: GC content analysis -- this seems similar to ref 25. Please discuss similarities/differences.\nline 219+: SOLARR-seq data are discussed here, but the assay is only introduced/explained in lines 290+.\nline 344+: Please indicate how many variants in each of the mentioned classes was tested. Please also add n to figure 4b. If this plot is based on the same small dataset as figure 4d, then the violin/boxplot representation is not appropriate; a beeswarm plot would be better (but redundant with 4d).\nline 351+: it seems an overstatement that \"Variants reliably disrupted\". While for the entire population the effect may be statistically significant, there seem many promoters for which the variant does not lead to loss of activity. This is even more the case for enhancer activity, for which the delta boost-index on average seems barely affected.\nlines 364-369: I found these two sentences difficult to follow, in particular \"the intrinsic enhancer activity of the enhancer itself\" and \"other regulatory elements, such as its associated minimal promoter\". Please clarify.\nline 383: to support this conclusion, please color the dots in Supp Figure 5a according to the three minPs.\nWe reported previously (PMID 39466837) that promoters differ in their response dynamic range to a collection of Es (or pairs of Es, in that case), with a housekeeping promoter showing the smallest dynamic range. This seems similar to figure 5c. Please discuss similarities/differences with this earlier report. It would also be informative to provide pairwise scatter plots that compare boost indices between the three minPs: any evidence for differences in E-minP compatibility?\n===============Reviewed by Bas van Steensel, review task accepted: 3 June 2025; completed 10 June 2025. It is my standard policy to sign and date *all* of my manuscript review reports, regardless of my comments and recommendations. All correspondence about this manuscript should go via the editor. PLEASE DO NOT REMOVE THIS NOTE ===========", "reviewer_4": "Multiple studies in recent years have demonstrated that enhancers and promoters are much more similar than previously regarded. Nevertheless, experimental settings that enable the quantitative measurements of both the promoter and enhancer potentials of the same set of sequences are lacking. The authors have elegantly designed and developed “Quantitative Unifying Assay for Simultaneously Active Regulatory Regions by sequencing (QUASARR-seq)”, a massively parallel dual reporter assay that enables the simultaneous measurement of the intrinsic promoter and enhancer potentials exerted by the same element.\nThe authors rigorously demonstrate that canonical human promoters and enhancers can indeed act as both promoters and enhancers under the same contexts, and that promoter activity may be necessary but insufficient for enhancer function. They examined whether the regulatory potential is embedded within element sequences and discovered that variants with strong clinical/functional evidence for imparing promoter activity, also impair enhancer activity. Using combinations of transcription regulatory elements with three different minimal promoters (GAPDH, MYC and APOBEC3F) they identified reciprocal regulatory effects between the associated elements, and a strong positive correlation between promoter and enhancer activities. They further discovered that the promoter-enhancer activity balance is determined by both sequence-intrinsic properties and contextual regulatory effects.\nThe study addresses a fundamental question in gene regulation and will be of broad interest to researchers in the field. It is novel, elegantly designed and rigorously executed, making it highly suitable for Nature Communications.\nMinor Comments:\n1. I suggest elaborating in the discussion on the following issues:\na. The study was conducted using the human K562 cell line. How might the findings differ if the study were performed in a different cellular context?\nb. Potential outcomes if the cells were induced to differentiate\n2. Cells were harvested 6 hours post electroporation. While this time point is appropriate for capturing immediate early responses, it may not be optimal for assaying enhancer function. I suggest including a brief rationale for the choice of the 6-hour time point.\n3. Fig. 1 legend - indicate which minimal promoter was used\n4. Fig. 5a legend – left and right should be top and bottom"}, "author_response": "Point-by-Point Response Letter\nRef1.1 – “the originality of this manuscript is that both\nenhancers and promoters’ activity has been quantified […]\ntogether using the same reporter system…” –\nReviewer As a followed up of previous studies [Nguyen, T.A. et al. High-throughput functional comparison of promoter and enhancer\nComment activities. Genome Res 26, 1023-33 (2016) and Mikhaylichenko, O. et al. The degree of enhancer or promoter activity\nis reflected by the levels and directionality of eRNA transcription. Genes Dev 32, 42-57 (2018)], the originality of this\nmanuscript is that both enhancers and promoters’ activity has been quantified (high-throughput sequencing using barcodes)\ntogether using the same reporter system (QUASARR-Seq). The limitation of the study is that it is not done in a chromatin\ncontext (plasmid), and this is acknowledged (briefly) in the Discussion section.\nAuthor We thank the reviewer for highlighting the originality of our approach.\nResponse\nTo clarify, QUASARR-seq goes a step beyond quantifying promoters’ and\nenhancers’ singular activities using the same reporter system. Instead, it enables\nsimultaneous measurement of both promoter and enhancer functions exerted by the\nsame sequence. For each tested element (including annotated promoters and\nenhancers) we assess its capacity to drive transcription locally (promoter activity)\nand its capacity to stimulate transcription of another element from a distance\n(enhancer activity), in parallel. This design enables a direct and unbiased element-\nwide comparison of regulatory potential.\nWe also acknowledge, as noted in the Discussion, that the assay is plasmid-based\nand therefore does not fully capture chromatin-mediated regulation. Nevertheless,\nwe believe this system provides valuable information on the intrinsic functional\ncapabilities of gene regulatory sequences.\nRef1.2 – Suggests shortening abstract and introduction to\nbetter highlight key message. –\nReviewer After reading the abstract, it is still difficult to understand what the main conclusions\nComment are. A shorten version of the abstract may help to focus on the key take home\nmessage. Same as for the last paragraph of the introduction (lanes 85-90).\nAuthor We thank the reviewer for the opportunity to clarify our main objectives and\nResponse conclusions of our study. The manuscript is organized around three central\nquestions, inspired by the increasing body of evidence suggesting that both\npromoters and enhancers are capable of exerting promoter and enhancer\nfunctionalities.\n1. How can the promoter and enhancer activities exerted by the same element\nbe measured simultaneously?\na. We developed QUASARR-seq, a massively parallel dual reporter\nassay that simultaneously measures both activities exerted from the\nsame sequence.\n2. What is the relationship between promoter and enhancer activities within\nindividual elements?\na. Annotated promoters and enhancers can concurrently exert both\nactivities under the same contexts and are thus not mutually\nexclusive functions of the same element.\nb. The two activities exerted by the same element are positively\ncorrelated (i.e., the stronger the element’s enhancer activity, the\nstronger its promoter activity).\n3. What are the determinants of this dual promoter-enhancer functionality?\na. Promoter activity is necessary for an element to have enhancer\nfunction, but it is not sufficient.\nb. Transcript features, including termination signals, defining\nannotated promoters and enhancers do not confer regulatory activity.\nc. Intrinsic-sequence features, including GC content and transcription\nfactor motifs, influence both functions in an element, suggesting a\nshared sequence syntax.\nd. Both activities in an element are modulated by the reciprocal\nregulatory influence of its paired element (i.e., as an enhancer\nstimulates its target promoter’s transcription, the promoter in turn\nstimulates its enhancer’s transcription).\nIn all, our results support the unifying conclusion that promoters and enhancers may\nnot be separate classes of regulatory elements, since a shared regulatory logic and\nsequence syntax underlie their functional convergence.\nTo improve clarity, we have revised the Abstract and the final paragraph of the\nIntroduction to better emphasize the main conclusions. We hope this reframing\nhelps convey the main takeaways of the work, and we thank the reviewer again for\nthe valuable suggestion.\nRevised Abstract\nExcerpt Accumulating evidence indicates that both promoters and enhancers are capable of\nexerting promoter and enhancer functions; however, the relationship between these\ntwo activities within individual elements and the determinants of this dual\nfunctionality remain poorly understood. We developed a massively parallel dual\nreporter assay that enables simultaneous assessment of the intrinsic promoter and\nenhancer potential exerted by the same sequence. Parallel quantification for\nthousands of elements reveals that canonical human promoters and enhancers can\nact as both promoters and enhancers under the same contexts, and that promoter\nactivity may be necessary but not sufficient for enhancer function. Perturbations to\n2\nelement transcription factor binding motifs lead to disruptions in both activities,\nimplicating a shared syntax for the two regulatory functions. Combinations of\nelements with different minimal promoters reveal reciprocal activity modulation,\nwhich, together with a strong correlation between promoter and enhancer functions,\nimply a bidirectional feedback loop to sustain environments of high transcriptional\nactivity. Finally, we validate this reciprocity and correlation in situ using CRISPR\nactivation at the human β-globin locus. Our approach reveals that the functional\nconvergence between promoters and enhancers arises from a shared regulatory logic\nand sequence syntax, advancing a unified model for regulatory element biology.\nIntroduction\nFor these reasons, we developed a Quantitative Unifying Assay for Simultaneously\nActive Regulatory Regions by sequencing (QUASARR-seq), a massively parallel\ndual reporter assay that allows for the simultaneous measurement of intrinsic\npromoter and enhancer potentials exerted by the same element, for thousands of\nelements in a single experiment. We perform systematic functional comparisons\nbetween intrinsic promoter and enhancer functions exerted by canonical promoter\nand enhancer elements, revealing a degree of entanglement between the two\nactivities, across element types. Our findings suggest that regulatory potential is\nembedded within element sequences, with impairments in element promoter activity\nshowing compounding effects by also impairing enhancer function. Finally, using\ncombinations of elements with different minimal promoters (minPs), we find that\npaired elements exert reciprocal regulatory influences to establish promoter and\nenhancer functions of each element, and validate this phenomenon in situ using\nCRISPR activation (CRISPRa) data at a human enhancer-promoter pair. Our results\nsupport the principle that promoters and enhancers are not separate classes of\nregulatory elements, with their functional convergence underpinned by shared\nregulatory logic and sequence syntax.\nRef1.3 – “ …the work presented here is colossal and good fit\nfor the journal.” –\nReviewer While I don’t have the capacity and the skills to check all the bioinformatics and\nComment statistics in this study, the work presented here is colossal and good fit for the\njournal. However, the narrative is very explorative by testing a variety of hypothesis\nwhich disconnects the reading interest. This reflects the above comments about the\nabstract. The last part of the manuscript (Discussion Section) ends the same way by\nthe lack of key highlights.\nAuthor We thank the reviewer for their thoughtful feedback and for recognizing the scope\nResponse and relevance of the work. We agree that the study is explorative in nature, which\nreflects its hypothesis-driven structure. QUASARR-seq is a new method that\nenables us to directly test long-standing and debated questions about transcriptional\nregulatory elements, questions that we argue previously lacked the appropriate tools\nfor systematic dissemination.\nDiscussion to more clearly reflect the central hypotheses tested and the main\nconclusions drawn. We hope this framing helps clarify the structure and intent of\nthe manuscript, and we thank the reviewer again for their valuable input.\nRevised Discussion\nExcerpt Our findings further challenge the conventional distinction between promoters and\nenhancers and instead support a unified model arising from a shared regulatory logic\nand sequence syntax present across element classes. Using QUASARR-seq, we\ndemonstrate that both canonical promoters and enhancers can drive transcription\nlocally and at a distance under the same regulatory contexts, finding evidence that\npromoter activity may be necessary, but not sufficient for enhancer function. This\ndual potential is unifyingly rooted by intrinsic sequence features and regulatory\ncontext.\nThe strong positive correlation between promoter and enhancer activities exerted by\nthe same element could be indicative of a positive feedback loop, where reciprocal\nmodulation between associated elements reinforces their regulatory functions. This\nbidirectional feedback may increase and maintain local concentrations of RNAPII,\nTFs, and co-activators to establish environments with high transcriptional activity.\nTo this end, we propose an ‘all-by-all’ model for transcriptional regulatory elements\n(Fig. 6g), in which all elements (both canonical gene promoters and enhancers)\nexhibit functional duality to reciprocally regulate one another. This framework\nextends existing hub/condensate models by adding a bidirectional feedback\ncomponent, where rather than unidirectional enhancer-to-promoter activation, all\nelements (i.e., enhancer-to-promoter, promoter-to-enhancer, enhancer-to-enhancer,\nand promoter-to-promoter) reinforce one another through shared recruitment of\nfactors, sustaining self-organized environments of high transcriptional activity. We\nnote that the concept of regulatory element co-regulation is supported by a recent\nstudy1. Using single-cell live imaging in flies, Levo et al. elegantly demonstrated\nthat physically distant promoters can undergo coordinated transcriptional bursting.\nThese findings indicate that regulatory crosstalk between TREs may be a\nwidespread feature of gene regulation in vivo.\nVariants that disrupted promoter activity simultaneously impaired enhancer\nfunction, revealing the interconnected nature of these activities. This shared\nsusceptibility has significant implications for understanding disease-associated\nvariants as they may have compounding effects on gene regulation. These findings\nmay underscore the need for therapeutic approaches that address the dual roles of\nregulatory elements.\nWhile our study provides compelling evidence for a unified framework of TREs,\nwe acknowledge several limitations. Read-through past the first pA accounted for\n~16% of egfp signal. Next generation designs could integrate capped RNA\nenrichment to detect transcripts arising exclusively from the TREs and enable\npositional TSS mapping, similar to the recently developed TSS-MPRA2,3.\nMoreover, the transient nature of our assay does not fully recapitulate endogenous\n4\nchromatin dynamics that influence regulatory interactions, in particular the much\nlonger genomic distances spanned by enhancer-promoter contacts. Additionally,\nbroader testing of TREs, minPs, and mutants of the two will be essential to validate\nand extend our findings and further elucidate the contextual factors that shape\nfunctional duality. Future work should move toward in vivo models and explore\ndiverse cellular contexts, including shifts with cell identity and induced\ndifferentiation. Still, we argue that a reductionist system that can isolate and dissect\nactivities like QUASARR-seq is essential for elucidating fundamental principles of\nregulatory element logic as complex endogenous systems can be subjected to\nimmense regulatory confounders.\nTo this end, our study advances our understanding of transcriptional regulation by\ndemonstrating that promoters and enhancers are not separate classes of regulatory\nelements, as their functional convergence is underpinned by a shared regulatory\nlogic and sequence syntax. This paradigm shift provides new insights into the\nmechanisms of gene regulation and the functional consequences of regulatory\nvariants in health and disease.\nReferences\n1. Levo, M. et al. Transcriptional coupling of distant regulatory genes in living\nembryos. Nature 605, 754-760 (2022).\n2. Guzman, C. et al. Combining TSS-MPRA and sensitive TSS profile\ndissimilarity scoring to study the sequence determinants of transcription\ninitiation. Nucleic Acids Res 51, e80 (2023).\n3. Duttke, S.H. et al. Position-dependent function of human sequence-specific\ntranscription factors. Nature 631, 891-898 (2024).\nRef1.4 – Suggests analyzing promoters by consensus\nsequence features to strengthen conclusions. –\nReviewer The distinction between enhancers and promoters is always blurred and this is well\nComment presented in the introduction. Enhancers are (usually) bidirectional and promoters\nmostly unidirectional (as these must tell the PolII machinery which direction the\ncoding gene is). In a chromatin context, they can be distinguished by deposition of\nhigh H3K4me1 at enhancers and high H3K4me3 at promoters. However, in contrast\nto enhancers and regardless of chromatin context, promoters have been classified in\nsubgroups based on consensus sequences located at specific distances from TSS\n(e.g. TATA box at -25bp, CAAT Box at -75bp etc…). Some promoters have CpG\nIslands (usually constitutive, unless repressed by CpG), some have a TATA Box,\nand others an Initiator element (INR). Without additional lab work, the authors may\nfind benefit by pushing the analysis further in separating the effect of all promoters\ntested based on these categories and hopefully find sometime more conclusive.\nAuthor We appreciate the reviewer’s detailed comment and the opportunity to clarify key\nResponse points regarding the distinction between enhancers and promoters.\nhistone modifications like H3K4me1 and H3K4me3 provide a strict, categorical\ndistinction between enhancers and promoters. Instead, several lines of evidence\nsuggest that these marks scale with local transcriptional activity, irrespective of\nregulatory class:\n1. In human cells, we have shown that histone marks at enhancers and\npromoters scale with Pol II intensity (Core & Martins et al.1).\n2. In both Drosophila and mESCs, most active enhancers harbor H3K4me3\nrather than H3K4me1 and the level of H3K4 methylation corresponds to the\namount of transcription at that locus (Henriques et al.2).\n3. Similarly, H3K4me1:H3K4me3 ratios and H3K4me1 levels are negatively\nassociated with endogenous CAGE expression levels in flies (Rennie &\nDalby et al.3).\nThese data suggest that chromatin-based annotations may more accurately reflect\nthe level of promoter activity at that locus rather than fundamentally distinguishing\nbetween enhancers and promoters. Since enhancers generally exhibit lower levels\nof intrinsic promoter activity and thus show distinct histone mark profiles, studies\nsuch as Henriques et al. demonstrate that when enhancers are highly active, these\nchromatin marks converge with those typically seen at promoters, limiting their\nreliability as distinguishing features. This is consistent with our results in this study,\nwhich show that active enhancers also generally exert higher levels of intrinsic\npromoter activity, further blurring the distinction between these regulatory\nfunctions.\nSecond, we appreciate the reviewer’s suggestion to further investigate the influence\nof core promoter architecture on dual regulatory activity. In response, we calculated\nmotif match scores (see Methods) for all tested elements (both annotated promoters\nand enhancers) within specified windows relative to their GRO-cap-detected TSSs\nin K-562 cells, including TATA (-40 to -20 bp), Inr (-2 bp), MTE (+5 to +35 bp),\nand DPE (+5 to +35 bp). To assess whether specific core promoter motifs contribute\nto establishing either element class (proximal vs. distal) or activity bias (promoter-\ndominant, balanced, or enhancer-dominant), we compared motif match scores with\npromoter-enhancer balance index categories across GENCODE classes.\nAs expected, the distributions for proximal (i.e., promoters) and distal (i.e.,\nenhancers) TREs showed similar relationships between core promoter motif match\nscores and activity balance categories (Supplementary Fig. 4f) consistent with\nprevious evidence of a unified sequence architecture for promoters and enhancers1.\nStill, some differences emerged, primarily across balance index categories. In\nproximal TREs, promoter-dominant elements had significantly higher Inr and MTE\nscores than balanced elements (P-value < 0.05, Wilcoxon rank-sum test with\nBonferroni correction). In distal TREs, promoter-dominant elements also had higher\nInr scores than balanced (P-value < 0.01) in addition to enhancer-dominant (P-value\n< 0.001) elements. By contrast, promoter-dominant elements had lower TATA\n6\nscores than balanced (P-value < 0.001) and enhancer-dominant (P-value < 0.01)\nelements, whereas DPE was enriched in balanced relative to enhancer-dominant\nelements (P-value < 0.05).\nTATA Inr MTE DPE\nNS. NS. NS. NS.\nNS. * * NS.\nNS. NS. NS. NS.\n** *** NS. NS.\n*** ** NS. NS.\nNS. NS. NS. *\nSupplementary Fig. 4: GC content and Inr drive functional bias in activity\nbalance across GENCODE classes.\nf, Plus-strand motif match scores by activity category and GENCODE class. For\nproximal, Inr and MTE promoter-dominant vs. balanced (P-value < 0.05, Wilcoxon\nrank-sum test with Bonferroni correction). For distal, Inr promoter-dominant vs.\nbalanced (P-value < 0.01) and vs. enhancer-dominant (P-value < 0.001), TATA\npromoter-dominant vs. balanced (P-value < 0.001) and vs. enhancer-dominant (P-\nvalue < 0.01) elements, DPE balanced vs. enhancer-dominant (P-value < 0.05).\nSignificance levels indicated by asterisks (* P-value < 0.05, ** P-value < 0.01, ***\nP-value < 0.001).\nTogether, these findings suggest that Inr is the most informative motif for predicting\npromoter-biased activity across proximal and distal TREs. In contrast, TATA is a\nweak predictor of promoter dominance, likely due to strong TATA-containing\nelements making up only ~10% of TREs. MTE and DPE showed modest, context-\ndependent contributions, influencing activity bias only in specific TRE classes.\nWe thank the reviewer for their suggested analysis which we have incorporated in\nthe revised manuscript.\nReferences\n1. Core, L.J. et al. Analysis of nascent RNA identifies a unified architecture of\ninitiation regions at mammalian promoters and enhancers. Nat Genet 46, 1311-\n20 (2014).\nDistal\n4\n0\n−4\n−8\n4\n0\n−4\n−8\nEnhancer−do\nminant\nBala\nP\nnc\ro\ne\nm\nd\noter−do\nminan\nE\nt\nnhancer−do\nminant\nBala\nP\nnc\ro\ne\nm\nd\noter−do\nminan\nE\nt\nnhancer−do\nminant\nBala\nP\nnc\ro\ne\nm\nd\noter−do\nminan\nE\nt\nnhancer−do\nminant\nBala\nP\nnc\ro\ne\nm\nd\noter−do\nminan\nerocs\nhctam\nfitoM\n2. Henriques, T. et al. Widespread transcriptional pausing and elongation control\nat enhancers. Genes Dev 32, 26-41 (2018).\n3. Rennie, S. et al. Transcription start site analysis reveals widespread divergent\ntranscription in D. melanogaster and core promoter-encoded enhancer activities.\nNucleic Acids Res 46, 5455-5469 (2018).\nRevised Results\nExcerpt Next, to investigate the influence of core promoter architecture on dual regulatory\nactivity, we calculated motif match scores (see Methods) for elements within\nspecified windows relative to their GRO-cap-detected TSSs in K-562, including\nTATA (-40 to -20 bp), Inr (-2 bp), MTE (+5 to +35 bp), and DPE (+5 to +35 bp). To\nassess whether specific core promoter motifs contribute to establishing either\nactivity bias or element class, we compared motif match scores with balance index\ncategories across GENCODE classes. As expected, the distributions for proximal\nand distal TREs showed similar relationships between core promoter motif match\nscores and balance index categories (Supplementary Fig. 4f), consistent with\nprevious evidence of a unified sequence architecture for promoters and enhancers1.\nStill, some differences emerged, primarily across balance index categories. In\nproximal TREs, promoter-dominant elements had significantly higher Inr and MTE\nscores than balanced elements (P-value < 0.05, Wilcoxon rank-sum test with\nBonferroni correction). In distal TREs, promoter-dominant elements also had higher\nInr scores than balanced (P-value < 0.01) in addition to enhancer-dominant (P-value\n< 0.001) elements. By contrast, promoter-dominant elements had lower TATA\nscores than balanced (P-value < 0.001) and enhancer-dominant (P-value < 0.01)\nelements, whereas DPE was enriched in balanced relative to enhancer-dominant\nelements (P-value < 0.05).\nTogether, these data suggest that GC content and Inr are the most informative\nsequence predictors of promoter-biased activity across proximal and distal TREs. In\ncontrast, TATA is a weak predictor of promoter dominance, likely due to strong\nTATA-containing elements making up only ~10% of TREs. MTE and DPE showed\nmodest, context-dependent contributions, influencing activity bias only in specific\nTRE classes.\nMethods\nMotif match score calculation\nFor a given sequence 𝑥, the motif match score, 𝑚𝑎𝑡𝑐ℎ(𝑥), was calculated as the\nlog-odds ratio between the probability of observing the sequence under a position\nweight matrix (PWM) distribution, and the probability of observing the sequence\nunder a background model:\n𝑃𝑊𝑀(𝑥)\n𝑚𝑎𝑡𝑐ℎ(𝑥) = 𝑙𝑜𝑔 - 3\n!\" 𝐵𝐺(𝑥)\nWhere:\nproduct of the probabilities of each base in the sequence based on the PWM:\n'\n𝑃𝑊𝑀(𝑥) = 4𝑃𝑊𝑀 ,𝑥 = {𝑥 ,𝑥 ,…,𝑥 }\n#,% ! & '\n!\n#(!\nPWMs used in this calculation were obtained from Adato & Sloutskin et al.1\nThe probability under the background distribution, 𝐵𝐺(𝑥), was calculated as the\nproduct of the fractions of each base observed across all GRO-cap peaks in K-562\nusing data from Core & Martins et al.2\n'\n𝐵𝐺(𝑥) = 4𝑏𝑔(𝑥 ),𝑥 = {𝑥 ,𝑥 ,…,𝑥 }\n# ! & '\n#(!\nWhere:\nThe background probabilities, 𝑏𝑔(𝑥 ) for each base were defined as:\n#\n0.2256,𝑥 = 𝐴\n#\n0.2743,𝑥 = 𝐶\n𝑏𝑔(𝑥 ) = : #\n# 0.2752,𝑥 = 𝐺\n#\n0.2249,𝑥 = 𝑇\n#\nReferences\n1. Adato, O. et al. ElemeNT 2023: an enhanced tool for detection and curation of\ncore promoter elements. Bioinformatics 40(2024).\n2. Core, L.J. et al. Analysis of nascent RNA identifies a unified architecture of\ninitiation regions at mammalian promoters and enhancers. Nat Genet 46, 1311-\n20 (2014).\nRef1.5 – “Additional comment (for future studies)” –\nReviewer Enhancers are usually primed first, suggesting that the ‘transcription signal’ is\nComment primarily launched remotely to eventually reach the promoter. In the future, perhaps\na key experiment on a single locus would be to swap enhancer and promoter and\nsee what happens, but this is out of the scope of this manuscript.\nAuthor We appreciate the reviewer’s suggestion and note the long-standing view that\nResponse enhancers are often primed before their target promoters, consistent with the idea\nthat a transcriptional “signal” originates distally and propagates toward the\npromoter. While we agree that this experiment could provide valuable insights into\nthe dynamics between enhancers and promoters, it is indeed outside the scope of the\ncurrent manuscript.\nlocus to further probe this enhancer-promoter priming relationship. As expected,\ntargeting of the HS2 enhancer with dCas9-CBP activated HS2 eRNA transcription\nand H3K27 acetylation, as well as mRNA transcription and H3K27 acetylation of\nits target gene promoter HBG1 (Fig. 6c-d). Interestingly, targeting the HBG1\npromoter had reciprocal effects, elevating HS2 enhancer transcription and\nchromatin acetylation (Fig. 6e-f). With CBP being an acetyltransferase, these\nmanipulations may mimic the natural “priming” of regulatory elements, and these\ndata suggest that the observed reciprocity reflects a shared ability of enhancers and\npromoters to initiate such capabilities. Taken together, these results raise the\npossibility that promoter-enhancer priming can be bidirectional, consistent with the\nreciprocal crosstalk observed in our QUASARR-seq data.\n***\n1000\n***\n100\n10\n1\n10\nlangis\nRCPq−TR\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\n01\nc.\n*\n3\n*\n2\neRNA mRNA\nlangis\nRCPq−PIhC\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\ndCas9-CBP\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA + H3K27ac mRNA + H3K27ac\nd.\nHS2 HBG1\nH3K27ac H3K27ac\nCRISPRa targeting HBG1promoter\ndCas9-CBP\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA + H3K27ac mRNA + H3K27ac\n***\n1000\n100\n**\n10\nactivities of both elements.\nc-d, RT-qPCR measured HS2 eRNA and HBG1 mRNA signals (c) and ChIP-qPCR\nmeasured HS2 H3K27ac and HBG1 H3K27ac signals (d), when HS2 enhancer is\ntargeted by dCas9-CBP. e-f, RT-qPCR measured HS2 eRNA and HBG1 mRNA\nsignals (e) and ChIP-qPCR measured HS2 H3K27ac and HBG1 H3K27ac signals\n(f), when HBG1 promoter is targeted by dCas9-CBP. For bar plots, bars show mean\nRT-qPCR signal (Cas9-CBP relative to Cas9), error bars ±1 SD of log -transformed\n10\nvalues. Points represent individual biological replicates. Significance was assessed\nusing One-sample t-test on log values. Significance levels indicated by asterisks\n10\n(* P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001). CRISPRa data from\nWang et al.1\nWe thank the reviewer again for this constructive suggestion and agree that the\nconcept of priming between regulatory elements warrants its own broad and\nthorough investigation.\nReferences\n1. Wang, K. et al. Systematic comparison of CRISPR-based transcriptional\nactivators uncovers gene-regulatory features of enhancer-promoter interactions.\nNucleic Acids Res 50, 7842-7855 (2022).\n11\nlangis\nRCPq−TR\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\ne.\n* **\n10\n3\neRNA mRNA\nlangis\nRCPq−PIhC\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\nf.\nHS2 HBG1\nH3K27ac H3K27ac\nRef2.1 – “The TREs were defined by nascent transcription\nstart sites, which were demonstrated to be good predictors of\nregulatory activity.” –\nReviewer The study by Paramo et al. investigated enhancer and promoter activity of human\nComment transcriptional regulatory elements (TREs) with QUASARR-seq, a newly\ndeveloped massively parallel reporter assay. The TREs were defined by nascent\ntranscription start sites, which were demonstrated to be good predictors of\nregulatory activity. The authors found that TREs often drive both distal and\nproximal expression, i.e., the promoter and enhancer activities of the same element\nare often correlated. The regulatory activity can be modulated by different minimal\npromoters.\nAuthor We thank the reviewer for their clear and accurate summary of our study. We\nResponse appreciate their recognition of the key findings regarding TREs, their dual activities,\nand the modulation of regulatory function by different minimal promoters.\nRef2.2 – “I found that the data are sufficient to support the\nauthors’ conclusion.” –\nReviewer I found that the data are sufficient to support the authors’ conclusion. The study\nComment strengthened the previous notion that promoters and enhancers might not be\nfunctionally distinct elements. Once in a microenvironment with high regulatory\nactivity (or the “all-by-all” model as the authors put it), i.e., high concentration of\nRNAP, TFs, open chromatin, etc., the regulatory elements may function either as\npromoters or enhancers.\nAuthor We thank the reviewer for their thoughtful feedback and are pleased that they found\nResponse the data sufficient to support our conclusions. We're also glad that our proposed\nmodel resonates with their interpretation, particularly regarding the dual\nfunctionality of regulatory elements within highly active transcriptional\nenvironments.\nRef2.3 – Suggests clarifying TRE-minP distances, discussing\ncell-type specificity, and exploring additional sequence\nfeatures influencing activity.\nReviewer However, it was not very surprising that most TREs showed correlated promoter\nComment and enhancer activities, especially when the enhancer activity was evaluated by its\nability to drive expression of a nearby minP in a synthetic construct. Traditionally,\none feature that distinguishes enhancers from promoters is that its ability to drive\nexpression at a distance. From the paper, it was not very clear how far apart the\nTREs were from the minPs and whether the physical distance should be considered\nwhen we evaluate the distal regulatory activity, which could be added to the methods\n12\nand discussion respectively. Another discerning feature of enhancers is its cell-type\nspecificity, which is not evaluated by the current assay but might worth discussion.\nFurthermore, it was not surprising that the regulatory activity can be modulated by\nminPs, either. It would be interesting to learn what sequence features, besides GC\ncontent, were involved in the interaction, if there were enough data for such\nanalysis.\nAuthor We thank the reviewer for their thoughtful and constructive comments. We agree\nResponse that our reporter assay is artificial in nature and cannot fully capture endogenous\nlong-range regulatory dynamics. To address this concern, sought to assess whether\nthe positive relationship between promoter and enhancer functions observed in\nQUASARR-seq could be validated in an endogenous genomic context. To this end,\nwe re-analyzed a published CRISPRa dataset in which the enhancer HS2 was\ntargeted with different dCas9-based transcriptional activators in HEK293T,\nincluding dCas9-VPR, dCas9-SAM, dCas9-SunTag, dCas9-p300, dCas9-CBP, and\ndCas9 as a negative control1. In this in situ context, the enhancer HS2 is located ~30\nkb downstream of, and convergent with, its target gene promoter HBG1. Given that\nactivators exhibit distinct activation strengths, we examined whether varying levels\nof enhancer transcription activation were accompanied by corresponding changes\nin target promoter transcription. Targeting of HS2 with the different activators\nshowed a strong positive correlation between HS2 eRNA activation (i.e., HS2\npromoter activity) and HBG1 mRNA activation (i.e., HS2 enhancer activity,\nSpearman's ρ = 0.79, P-value < 0.001; Fig. 2b). Together with our findings, these\ndata indicate that the positive correlation between promoter and enhancer activities\nholds across endogenous genomic\ncontexts and distances.\ndCas9\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA RT-qPCR mRNA RT-qPCR\nρ=0.79, P<0.001\n1000\n100\n10\n1\n1 10 100\nRelative HS2 eRNA signal\nFig. 2: Promoter and enhancer activities are positively correlated.\nb, Correlation between RT-qPCR measured HS2 eRNA signal and its target gene\npromoter HBG1 mRNA signal (Spearman's ρ = 0.79, P-value < 0.001) when HS2\nenhancer is targeted with different dCas9-based transcriptional activators, including\ndCas9-VPR, dCas9-SAM, dCas9-SunTag, dCas9-p300, dCas9-CBP, and dCas9 as\na negative control. Points represent signal relative to dCas9. CRISPRa data from\nWang et al.1\nRegarding the physical distance between the TREs and the minPs, in our reporter\ndesign, elements are placed a fixed distance downstream of the minP (2,180 bp, or\n2,916 bp when traversing the circular backbone), following standard MPRA design\nconventions. We have now clarified this distance in the Methods section and added\na brief acknowledgment of this limitation in the Discussion.\nWe also appreciate the point about cell-type specificity, which is indeed a critical\nfeature of enhancers. We agree that applying QUASARR-seq across multiple\ncellular contexts will be important for future investigations into cell-type-specific\nregulatory behavior and we now highlight this as a future direction in the\nDiscussion. In fact, this is an active area of ongoing work in our group, which aims\nto understand how promoter-enhancer activity balance varies across cell types and\n14\nlangis\nANRm\n1GBH\nevitaleR\nActivator\ndCas9\ndCas9−VPR\ndCas9−SAM\ndCas9−SunTag\ndCas9−p300\ndCas9−CBP\nduring cell differentiation, potentially providing a mechanism for regulatory\nelement plasticity.\nMoreover, we find the modulation of regulatory activity by minPs to be one of the\nmost interesting findings of our study. Our results suggest that promoters can\nreciprocally influence the promoter activity of their enhancers, revealing a\nbidirectional regulatory relationship. To further explore this idea, we re-analyzed\nthe same CRISPRa dataset described above. As expected, targeting of the HS2\nenhancer with dCas9-CBP increased HS2 eRNA (P-value < 0.001, One-sample t-\ntest; Fig. 6c) and H3K27ac (P-value < 0.05; Fig. 6d), in addition to HBG1 mRNA\n(P-value < 0.001; Fig. 6c) and H3K27ac (P-value < 0.05; Fig. 6d), measured via\nRT-qPCR and ChIP-qPCR, respectively. Strikingly, targeting the HBG1 promoter\nalso elevated not only HBG1 mRNA (P-value < 0.001; Fig. 6e) and H3K27ac (P-\nvalue < 0.05; Fig. 6f) but also HS2 eRNA (P-value < 0.01; Fig. 6e) and H3K27ac\n(P-value < 0.01; Fig. 6f). These results provide direct evidence in a native genomic\ncontext that promoters can reciprocally enhance the promoter activity of their\nenhancers, consistent with the regulatory reciprocity observed in our QUASARR-\nseq data.\n***\n1000\n***\n100\n10\n1\n15\nlangis\nRCPq−TR\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\n01\nc.\n*\n3\n*\n2\neRNA mRNA\nlangis\nRCPq−PIhC\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\ndCas9-CBP\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA + H3K27ac mRNA + H3K27ac\nd.\nHS2 HBG1\nH3K27ac H3K27ac\nCRISPRa targeting HBG1promoter\ndCas9-CBP\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA + H3K27ac mRNA + H3K27ac\n***\n1000\n100\n**\n10\nactivities of both elements.\nc-d, RT-qPCR measured HS2 eRNA and HBG1 mRNA signals (c) and ChIP-qPCR\nmeasured HS2 H3K27ac and HBG1 H3K27ac signals (d), when HS2 enhancer is\ntargeted by dCas9-CBP. e-f, RT-qPCR measured HS2 eRNA and HBG1 mRNA\nsignals (e) and ChIP-qPCR measured HS2 H3K27ac and HBG1 H3K27ac signals\n(f), when HBG1 promoter is targeted by dCas9-CBP. For bar plots, bars show mean\nRT-qPCR signal (Cas9-CBP relative to Cas9), error bars ±1 SD of log -transformed\n10\nvalues. Points represent individual biological replicates. Significance was assessed\nusing One-sample t-test on log values. Significance levels indicated by asterisks\n10\n(* P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001). CRISPRa data from\nWang et al.1\nFinally, we agree that understanding which sequence features beyond GC content\ninfluence the regulatory interaction between TREs and minPs is an important and\ninteresting direction. At the request of Reviewer 3, we have removed Fig. 6 (which\nincluded the GC content analysis) from the revised manuscript to better focus the\nstudy on its core findings. We believe that this streamlining improves the clarity and\naccessibility of the main messages. Nonetheless, we believe these analyses merit\nindependent dissemination due to their standalone interest, and we plan to submit\nthem as a separate, more focused spin-off manuscript that will include motif and\nother sequence feature analyses.\nWe thank the reviewer again for these thoughtful suggestions, which have helped us\nclarify and improve the manuscript.\n16\nlangis\nRCPq−TR\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\ne.\n* **\n10\n3\neRNA mRNA\nlangis\nRCPq−PIhC\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\nf.\nHS2 HBG1\nH3K27ac H3K27ac\nReferences\n1. Wang, K. et al. Systematic comparison of CRISPR-based transcriptional\nactivators uncovers gene-regulatory features of enhancer-promoter interactions.\nNucleic Acids Res 50, 7842-7855 (2022).\nRevised Results\nExcerpt We next sought to assess whether the positive relationship between promoter and\nenhancer functions observed in QUASARR-seq could be validated in an\nendogenous genomic context. To this end, we re-analyzed a published CRISPRa\ndataset in which the enhancer HS2 was targeted with different dCas9-based\ntranscriptional activators in HEK293T, including dCas9-VPR, dCas9-SAM, dCas9-\nSunTag, dCas9-p300, dCas9-CBP, and dCas9 as a negative control1. In this in situ\ncontext, the enhancer HS2 is located ~30 kb downstream of, and convergent with,\nits target gene promoter HBG1. Given that activators exhibit distinct activation\nstrengths, we examined whether varying levels of enhancer transcription activation\nwere accompanied by corresponding changes in target promoter transcription.\nTargeting of HS2 with the different activators showed a strong positive correlation\nbetween HS2 eRNA activation (i.e., HS2 promoter activity) and HBG1 mRNA\nactivation (i.e., HS2 enhancer activity, Spearman's ρ = 0.79, P-value < 0.001; Fig.\n2b). Together with our findings, these data indicate that the positive correlation\nbetween promoter and enhancer activities holds across endogenous genomic\ncontexts and distances.\nDiscussion\nWhile our study provides compelling evidence for a unified framework of TREs,\nwe acknowledge several limitations. Read-through past the first pA accounted for\n~16% of egfp signal. Next generation designs could integrate capped RNA\nenrichment to detect transcripts arising exclusively from the TREs and enable\npositional TSS mapping, similar to the recently developed TSS-MPRA2,3.\nMoreover, the transient nature of our assay does not fully recapitulate endogenous\nchromatin dynamics that influence regulatory interactions, in particular the much\nlonger genomic distances spanned by enhancer-promoter contacts. Additionally,\nbroader testing of TREs, minPs, and mutants of the two will be essential to validate\nand extend our findings and further elucidate the contextual factors that shape\nfunctional duality. Future work should move toward in vivo models and explore\ndiverse cellular contexts, including shifts with cell identity and induced\ndifferentiation. Still, we argue that a reductionist system that can isolate and dissect\nactivities like QUASARR-seq is essential for elucidating fundamental principles of\nregulatory element logic as complex endogenous systems can be subjected to\nimmense regulatory confounders.\nMethods\nIn this design, TREs are positioned 2,180 bp downstream of the minPs (or 2,916 bp\nupstream when traversing the circular backbone), ensuring a fixed distance across\nall constructs.\nReferences\n17\n1. Wang, K. et al. Systematic comparison of CRISPR-based transcriptional\nactivators uncovers gene-regulatory features of enhancer-promoter interactions.\nNucleic Acids Res 50, 7842-7855 (2022).\n2. Guzman, C. et al. Combining TSS-MPRA and sensitive TSS profile\ndissimilarity scoring to study the sequence determinants of transcription\ninitiation. Nucleic Acids Res 51, e80 (2023).\n3. Duttke, S.H. et al. Position-dependent function of human sequence-specific\ntranscription factors. Nature 631, 891-898 (2024).\nRef2.4 – Requests more detail on library design, including\nTRE length, number, and selection criteria.\nReviewer In terms of clarity, I would appreciate more details in terms of the library design,\nComment e.g., how long the TREs are, how many TREs were included in the library, how they\nare chosen (Random? Representatives of different cell types? Different information\ncontent?), if they consist of known TF binding sites, etc.\nAuthor We thank the reviewer for this suggestion. We have included additional details in\nResponse the Methods section to clarify the TRE selection criteria and library design. TREs\nwere selected solely on transcriptional activity (PRO-cap), chromatin accessibility\n(DNase-seq), and proximity to known gene annotations (GENCODE) in K-562. We\nhave also generated Supplementary Fig. 1a. to clarify the number and length\ndistribution of tested TREs.\nn = 2151\n15\nmin = 65 median = 254 max = 703\n10\n5\n0\n100 200 300 400 500 600 700\nLength (bp)\nSupplementary Fig. 1: Length distribution of tested TREs.\na, A total of 2,151 TREs were tested, ranging from 65 to 703 bp, with a median\nlength of 254 bp.\nWe hope these additions improve the clarity of our experimental design, and we\nthank the reviewer for prompting these clarifications.\nRevised Results\nExcerpt In total, we tested 2,151 elements, ranging from 65 to 703 bp, with a median length\nof 254 bp (Supplementary Fig. 1a).\nMethods\n18\nycneuqerF\na.\nTested TRE length distribution\nTo classify promoter and enhancer elements, we based our approach solely on\ntranscriptional activity (PRO-cap), chromatin accessibility (DNase-seq), and\nproximity to known gene annotations (GENCODE) in K-562, given that not all\naccessible regions are transcriptionally active and that histone signatures\ntraditionally used to distinguish promoters and enhancers converge when local\ntranscription is considered.\nThus, to systematically compare promoter and enhancer elements, we defined\ncandidate active promoters as proximal PRO-cap TSSs (distance cutoff < 500 bp\nfrom GENCODE annotation), and candidate active enhancers as distal PRO-cap\nTSSs (distance cutoff > 500 bp from GENCODE annotation). Similarly, we defined\ncandidate inactive promoters as proximal PRO-cap untranscribed DNase-seq DHSs\n(distance cutoff < 500 bp from GENCODE annotation), and candidate inactive\nenhancers as distal PRO-cap untranscribed DNase-seq DHSs (distance cutoff > 500\nbp from GENCODE annotation). Transcribed elements were cloned using\nboundaries set at 60 bp downstream from each divergent TSS peak maximum.\nUntranscribed elements were cloned using boundaries set at DHS peak coordinates.\nRef2.5 – Minor Comment 1 –\nReviewer Line 96: GENCODE annotations: it would be helpful to explain what they are for\nComment people who are not familiar with this database. Similarly, the PRO-cap technique\nneeds to be explained on first mention (what is the abbreviation short for?).\nAuthor We thank the reviewer for this suggestion. In the revised manuscript, we have added\nResponse explanatory text at the first mention of both resources.\nResults\nWe previously demonstrated that Precision Run-On and capped nascent RNA\nsequencing (PRO-cap), a technique that maps TSSs genome-wide by capturing and\nsequencing the 5′ capped ends of nascent transcripts, provides high predictive\nperformance using 500 bp distance cutoffs from GENCODE annotations, a high-\nquality, comprehensive reference database of human genes, to distinguish promoter\nand enhancer elements (see Methods)1,2.\nReferences\n1. Tippens, N.D. et al. Transcription imparts architecture, function and logic to\nenhancer units. Nat Genet 52, 1067-1075 (2020).\n2. Yao, L. et al. A comparison of experimental assays and analytical methods for\ngenome-wide identification of active enhancers. Nat Biotechnol 40, 1056-1065\n(2022).\nRef2.6 – Minor Comment 2 –\nReviewer Fig. 1a: it would be helpful to show “candidate active promoters” “candidate active\nComment enhancers” “candidate inactive promoters” and “candidate inactive enhancers” as a\ntable, along with their corresponding diagrams. Also, it wasn’t very clear what the\n19\npeak height means in the diagrams and how it is related to proximal/distal\nclassification.\nAuthor We thank the reviewer for this suggestion. In the revised manuscript, we have\nResponse converted Fig. 1a into a table showing “candidate active promoters,” “candidate\nactive enhancers,” “candidate inactive promoters,” and “candidate inactive\nenhancers,” alongside the corresponding diagrams for clarity. Additionally, we have\nclarified that the PRO-cap and DNase-seq peak heights in the diagrams reflect the\ntypically higher signals observed at promoters than enhancers.\n1. TRE selection\nPRO-cap\nCandidate active Candidate active\npromoters enhancers\nProximal TSS Distal TSS\nDNase-seq\nCandidate inactive Candidate inactive\npromoters enhancers\nProximal DHS Distal DHS\nFig. 1: QUASARR-seq simultaneously measures an element’s intrinsic\npromoter and enhancer activity.\na, Schematic of the QUASARR-seq workflow. 1, Candidate active promoters were\ndefined as PRO-cap proximal TSSs, active enhancers as distal TSSs, inactive\npromoters as PRO-cap untranscribed, DNase-seq proximal DHSs, and inactive\nenhancers as untranscribed distal DHSs (see Methods). PRO-cap/DNase-seq peak\nheights reflect typically higher signals at promoters than enhancers.\nRef2.7 – Minor Comment 3 –\nReviewer Fig. 1c: is “GENCODE distal & PRO-cap untranscribed” equivalent to “Distal DHS\nComment & PRO-cap untranscribed” as in Fig. 1a and Line 100? In general, I was a bit\nconfused about how GENCODE class was defined and how it was related to DHS.\nAuthor We thank the reviewer for pointing out the potential confusion. To clarify,\nResponse “GENCODE distal & PRO-cap untranscribed” is identical to “Distal DHS & PRO-\ncap untranscribed”.\nIncreasing evidence shows that histone marks typically used to distinguish\npromoters and enhancers often converge when accounting for local transcriptional\n20\nactivity1-3. Based on this, we have adopted a definition of promoters and enhancers\nbased solely on transcriptional activity (PRO-cap) and their relative distance to\nknown gene annotations (GENCODE). Specifically, PRO-cap peaks within 500 bp\nof a GENCODE annotation are classified as promoters, whereas PRO-cap peaks\noutside this window are considered distal, and likely enhancers. We have\ndemonstrated that this classification provides a reliable predictor of active element\ntype4.\nRegarding DHSs, while all TSSs are DHSs, not all DHSs contain TSSs (Reviewer\nResponse Fig. 1a). We have shown that DHS peaks lacking TSSs correspond to\ninactive enhancer elements4 and therefore are used as an inactive control set in our\nstudy instead of random genomic regions lacking any features of regulatory\npotential. As true negative controls, we include TSS/DHS-screened human open\nreading frames that showed no cryptic regulatory activity in eSTARR-seq4.\nP ublished Data Figure Redacted\nReviewer Response Fig. 1: Divergent transcription identifies enhancer\nboundaries in high resolution.\na, Features of two candidate regulatory elements in the MYC locus. Raw read counts\nare shown for each track and the ‘candidate elements’ track indicates the cloning\nboundaries used for the luciferase assays of tested sequences. b, Luciferase reporter\nactivity for the regions indicated in a (n = 3 luciferase reactions). P values are from\na one-sided t-test. Figure from Tippens and Liang, et al4.\n1. Core, L.J. et al. Analysis of nascent RNA identifies a unified architecture of\ninitiation regions at mammalian promoters and enhancers. Nat Genet 46, 1311-\n20 (2014).\n2. Henriques, T. et al. Widespread transcriptional pausing and elongation control\nat enhancers. Genes Dev 32, 26-41 (2018).\n3. Rennie, S. et al. Transcription start site analysis reveals widespread divergent\ntranscription in D. melanogaster and core promoter-encoded enhancer activities.\nNucleic Acids Res 46, 5455-5469 (2018).\n4. Tippens, N.D. et al. Transcription imparts architecture, function and logic to\nenhancer units. Nat Genet 52, 1067-1075 (2020).\nRevised Methods\nExcerpt To classify promoter and enhancer elements, we based our approach solely on\ntranscriptional activity (PRO-cap), chromatin accessibility (DNase-seq), and\nproximity to known gene annotations (GENCODE) in K-562, given that not all\naccessible regions are transcriptionally active and that histone signatures\ntraditionally used to distinguish promoters and enhancers converge when local\ntranscription is considered.\nRef2.8 – Minor Comment 4 –\nReviewer Line 219: SOLARR-seq was only introduced after line 290, so referencing the\nComment SOLARR-seq data here is a bit confusing.\nAuthor We appreciate the reviewer’s careful reading. We agree that referencing SOLARR-\nResponse seq prior to its introduction was confusing. In the revised manuscript, we have\nremoved the earlier mention at line 219, as that analysis was replaced with an\nalternative approach. SOLARR-seq is now first introduced in line 402, where it is\ndirectly relevant.\nRef2.9 – Minor Comment 5 –\nReviewer Line 350: “a small delta boost index…”: it might be clearer to say “a low delta boost\nComment index” or “a negative delta boost index indicated a disrupting effect”.\nAuthor We thank the reviewer for this suggestion. We have replaced “small” in the text with\nResponse “low”.\nRevised Results\nExcerpt Here, a low Δ boost index indicated a large mutant disrupting effect\nAs expected, disease-associated variants generally imparted the lowest Δ in dual\nregulatory function (Fig. 5c), with a few population variants unexpectedly showing\nsome high degree of disruption effects.\nRef2.10 – Minor Comment 6 –\nReviewer Line 354: it would be nice to report effect size along with the P-value.\nAuthor We thank the reviewer for the suggestion. We have now included the Cohen’s d\nResponse effect size alongside the P-value.\nRevised Results\nExcerpt Across the population, variants significantly reduced QUASARR-seq promoter\nactivity measurements (P-value < 0.001, One-sample t-test, Cohen’s d = -0.82,\nlarge; Fig. 5b), although the effect was variable for individual elements. Moreover,\nwe observed a similar trend with enhancer activity measurements, though with a\nlesser effect size compared to promoter activity (P-value < 0.05, d = -0.55, medium.\nRef2.11 – Minor Comment 7 –\nReviewer Fig. 5: “a, b, c” could be moved to the top-left corner of each panel. Legend for (a)\nComment was not very clear in terms of what “left” and “right” refer to. Also, there is no error\nbar or scatter points in (a) that indicate the distribution of data.\nAuthor We thank the reviewer for these suggestions. In the revised manuscript, the lettering\nResponse for panels Fig. 5a-c has been moved to the top-left corner of each panel for clarity.\nFig. 5a has been moved to Supplementary Fig. 7g, and we have corrected in the\nlegend that what was previously described as “left” and “right” was actually\nsupposed to refer to the top and bottom of the panel. We have also included error\nbars for Supplementary Fig. 7g calculated as ±1 SEM.\n1.00\n0.75\n0.50\n0.25\n0.00\nH C F\np G A P\nD\np M\nY\nP O B E\nC3\nA\np\nSupplementary Fig. 7: minP-TRE QUASARR-seq.\ng, Top, QUASARR-seq pairing TREs with different minPs, including the promoters\nof a housekeeping gene; GAPDH, and a developmental gene; APOBEC3F, in\n23\nlaminim\ncisnirtnI\nytivitca\nretomorp\nminPs\nHousekeeping pGAPDH\npMYC ORFs\nDevelopmental\npAPOBEC3F eaBC paBC\nluc2 pA egfp pA\naddition to the promoter of MYC used thus far in this study. To distinguish between\nlibraries, unique three-bp barcodes specific to each minP library were incorporated\ndirectly downstream of the paBCs. Bottom, Intrinsic promoter activities for the\nthree minPs, calculated by taking their mean activities when paired with negative\ncontrols ORFs. Values shown are back-transformed from log scale. Error bars\n2\ndenote ±1 SEM.\nRef2.12 – Minor Comment 8 –\nReviewer Line 685: “R” was not defined. Should it be “Bal”?\nComment\nAuthor We thank the reviewer for catching this. The text has been corrected so that “R” is\nResponse now properly labeled as “𝐵𝑎𝑙”.\nRef2.13 – Minor Comment 9 –\nReviewer Extended Data Figs and Supplementary figures: usually, extended data figures go\nComment into supplementary. So, it was a bit confusing that some supp. figures are named\n“extended data fig” and others “supplementary fig”.\nAuthor We thank the reviewer for pointing this out. While the journal from which we\nResponse transferred the manuscript distinguishes among three figure types (Main Figures,\nExtended Data Figures, and Supplementary Figures), Nature Communications does\nnot use Extended Data Figures and instead provides all supporting figures as\nSupplementary Figures. To align with this convention, we have now renamed and\nreorganized all Extended Data Figures in the manuscript as Supplementary Figures.\nexperiments is very elegant, and the (often challenging)\ncomputational analyses were done with great care.” –\nReviewer Evidence has been accumulating that enhancers and promoters may in fact be two\nComment sides of the same coin. This manuscript systematically addresses this hypothesis by\ntesting E and P activity of a collection of regulatory elements simultaneously in a\ndual reporter construct. This is novel and exciting. The design of the experiments is\nvery elegant, and the (often challenging) computational analyses were done with\ngreat care. The findings support the hypothesis that E and P activities of single\nregulatory elements are indeed tightly linked, and the authors favor the\ninterpretation that E activity requires P activity - which seems reasonable given the\nresults with a set of mutants. Moreover, they provide evidence that three Ps differ\nin their dynamic range of response to Es, and reciprocally affect E activity to\ndifferent degrees. Overall, this manuscript is of high quality and of substantial\nnovelty, and will be of interest to a broad readership interested in the fundamental\nbiology of Es and Ps. That said, I have a few points that could strengthen it further.\nAuthor We thank the reviewer for their thoughtful and encouraging evaluation of our work.\nResponse We are especially grateful for their recognition of the novelty of our approach, the\ncare taken in both experimental and computational design, and the significance of\nour findings regarding the coupling of enhancer and promoter activities. We\nappreciate their feedback and look forward to addressing the specific points raised\nto further strengthen the manuscript.\nRef3.2 – Suggests toning down conclusion and proposes\ncontrols to better support claims.\nReviewer line 205+: Potential readthrough of the pA sites is indeed a risk. I did not understand\nComment the logic of the orientation analysis. How can this estimate readthrough? Please\nexplain better; perhaps a cartoon could help? If I understand it correctly, the results\npoint to some degree of (rho = 0.16) of readthrough? Then the authors' conclusion\nseems a bit too strong: \"supporting the case that the high correlation of promoter and enhancer activities is not an artifact of measuring the same function\"\". Please tone down. This conclusion could perhaps be strengthened by RT-qPCR of plasmid backbone sequences downstream of the 2nd pA, or of sequences in EGFP when no TRE is inserted? Or, perhaps simpler, what is the ratio paBC over eaBC counts in RNA when no TRE is inserted?\nAuthor We thank the reviewer for this thoughtful and important comment. We agree that\nResponse the risk of transcriptional readthrough is a key concern, and we appreciate the\nopportunity to clarify our rationale and further support our conclusions.\nFirst, we would like to clarify that the orientation analysis was not intended to\nestimate readthrough directly, but rather to assess whether the enhancer and\npromoter readouts might be driven by a shared underlying transcriptional signal.\npromoter activity is typically orientation-dependent, while enhancer activity is more\noften orientation-independent. We reasoned that if both reporter readouts were\nmeasuring the same transcriptional event (e.g., readthrough), their orientation biases\nshould be more tightly coupled.\nThat said, we agree with the reviewer that this analysis rules out readthrough as an\nartifact and that our claim was too strong. We have toned down the language in the\nmanuscript accordingly and have replaced this analysis with an RT-qPCR\nexperiment that actually estimates read-through as suggested by the reviewer.\nTo directly estimate read-through from the first pA in our dual-reporter system, we\nperformed RT-qPCR targeting luc2 and egfp in both TRE-containing and TRE-\nlacking empty vector constructs (see Methods). Across experiments, we found that\nread-through transcription accounted for 16.47% of egfp expression, indicating that\na relatively small fraction of the promoter activity signal measured reflects\ntranscriptional leakage past the first pA (Supplementary Fig. 5a). Accordingly, we\nhave updated the text to tone down the original claim and added to the Discussion\nthat future work could further address this limitation.\n10.0\n7.5\n5.0\n2.5\n0.0\nTRE Empty\n26\n)QR(\nlangis\npfge\ndezilamroN\na.\nConstructs\nminP TRE\nluc2 pA egfp pA\nTRE\nminP\nluc2 pA egfp pA\nEmpty\nSupplementary Fig. 5: Read-through estimation RT-qPCR.\na, Bottom, RT-qPCR targeting luc2 and egfp in TRE-containing and TRE-lacking\nconstructs. Top, luc2-normalized egfp signal for TRE and empty vectors. Bars show\nmean RT-qPCR signal, error bars ±1 SD. Points represent individual biological\nreplicates.\nRevised Results\nExcerpt Despite each reporter system being insulated with a cleavage and polyadenylation\nsite (pA) to avoid confounding the two activity readouts (Fig. 1a; see Methods), we\nasked whether the high correlation between promoter and enhancer activities that\nwe observed may be due to measuring the same function, perhaps from pA read-\nthrough transcription. To examine this, we performed RT-qPCR targeting luc2\n(upstream of the first pA) and egfp (downstream of the first pA) in both TRE-\ncontaining and TRE-lacking empty vector constructs (see Methods). Across\nexperiments, we found that read-through transcription accounted for 16.47% of egfp\nexpression, indicating that a relatively small fraction of the promoter activity signal\nmeasured reflects transcriptional leakage past the first pA (Supplementary Fig. 5a).\nThese data suggest that QUASARR-seq promoter and enhancer activity\nmeasurements are primarily capturing related, but distinct regulatory functions of\nthe same elements.\nDiscussion\nRead-through past the first pA accounted for ~16% of egfp signal. Next generation\ndesigns could integrate capped RNA\nenrichment to detect transcripts arising exclusively from the TREs and enable\npositional TSS mapping, similar to the recently developed TSS-MPRA1,2.\nMethods\nRT-qPCR\nCell culture and nucleofection were carried out as described above, except 1 x 106\ncells were electroporated with 5 µg plasmid DNA and incubated for 24 h. Total RNA\nwas extracted as described above. cDNA was generated from 1 µg of DNase-treated\nRNA using LunaScript RT SuperMix Kit (E3010; New England Biolabs) and\nRandom Primer Mix (S1330S; New England Biolabs). Parallel no-RT controls were\nperformed to assess plasmid DNA contamination. cDNA was purified with DNA\nClean & Concentrator-25. qPCR was carried out using Luna Universal qPCR\nMaster Mix (M3003; New England Biolabs) with gene-specific primers targeting\nluc2 and egfp on a QuantStudio 7 Pro Real-Time PCR System (A43183; Applied\nBiosystems). Amplification was performed with an initial denaturation at 95 °C for\n1 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 30 s.\nRT-qPCR data processing and read-through calculation\nFor each element and biological replicate, 𝐶 values from technical replicates were\n)\naveraged. Reporter values (luc2 and egfp) were then aligned per replicate, and\nrelative expression was calculated using a ∆𝐶 method, where ∆𝐶 was defined as:\n) )\n∆𝐶 = 𝐶𝑞 −𝐶𝑞\n) *+,- './&\n𝑅𝑄 = 20∆2)\nFor each element, mean 𝑅𝑄, standard deviation (𝑆𝐷), and the standard error of the\nmean (𝑆𝐸𝑀) were calculated across replicates.\nTo estimate read-through transcription, mean 𝑅𝑄 values for the empty vector control\n(𝑅𝑄 ) and TRE-driven positives (𝑅𝑄 ) were compared. The fraction of egfp\n34-56 783\nsignal attributable to read-through was calculated as:\n𝑅𝑄\n34-56\n𝑅𝑇 =\n595:' 𝑅𝑄\n1. Guzman, C. et al. Combining TSS-MPRA and sensitive TSS profile\ndissimilarity scoring to study the sequence determinants of transcription\ninitiation. Nucleic Acids Res 51, e80 (2023).\n2. Duttke, S.H. et al. Position-dependent function of human sequence-specific\ntranscription factors. Nature 631, 891-898 (2024).\nRef3.3 – “One of the most interesting concepts arising from\nthis work is that promoters may reciprocally activate nearby\nenhancers (Figure 5d).” –\nReviewer One of the most interesting concepts arising from this work is that promoters may\nComment reciprocally activate nearby enhancers (Figure 5d). But of course the experimental\nsetting used here is quite artificial (as acknowledged in the Discussion). It seems to\nme that it should not be too difficult to test the P-->E effect in a native genomic\ncontext: delete a P by CRISPR/ and test whether the activity of a nearby E (using\nATAC-seq, H3K27ac or PRO-Cap as proxy readout) is reduced. Possibly such data\nis already publicly available, but no-one bothered to look at it this way? Addition of\na small set of such experiments could greatly strengthen this manuscript. I should\npoint out that even a negative result would be insightful (albeit a bit disappointing,\nof course); publication should not depend on a positive result.\nAuthor We thank the reviewer for highlighting this important point. We agree that the\nResponse potential for reciprocal activation is one of the more intriguing implications of our\nfindings, and that further testing in a native chromatin context would significantly\nadvance this area of investigation.\nTo independently validate reciprocal regulation in an endogenous genomic context,\nwe re-analyzed a published CRISPRa dataset targeting the human HS2-HBG1\nenhancer-promoter pair of the β-globin locus1. As expected, targeting of the HS2\n28\nenhancer with dCas9-CBP increased HS2 eRNA (P-value < 0.001, One-sample t-\ntest; Fig. 6c) and H3K27ac (P-value < 0.05; Fig. 6d), in addition to HBG1 mRNA\n(P-value < 0.001; Fig. 6c) and H3K27ac (P-value < 0.05; Fig. 6d), measured via\nRT-qPCR and ChIP-qPCR, respectively. Strikingly, targeting the HBG1 promoter\nalso elevated not only HBG1 mRNA (P-value < 0.001; Fig. 6e) and H3K27ac (P-\nvalue < 0.05; Fig. 6f) but also HS2 eRNA (P-value < 0.01; Fig. 6e) and H3K27ac\n(P-value < 0.01; Fig. 6f). These results provide direct evidence in a native genomic\ncontext that promoters can reciprocally enhance the promoter activity of their\nenhancers, consistent with the regulatory reciprocity observed in our QUASARR-\nseq data.\n***\n1000\n***\n100\n10\n1\n29\nlangis\nRCPq−TR\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\n01\nc.\n*\n3\n*\n2\neRNA mRNA\nlangis\nRCPq−PIhC\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\ndCas9-CBP\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA + H3K27ac mRNA + H3K27ac\nd.\nHS2 HBG1\nH3K27ac H3K27ac\nCRISPRa targeting HBG1promoter\ndCas9-CBP\n// < < < < < < < < <\nHS2 ~30 kb HBG1\nEnhancer Promoter\neRNA + H3K27ac mRNA + H3K27ac\n***\n1000\n100\n**\n10\nactivities of both elements.\nc-d, RT-qPCR measured HS2 eRNA and HBG1 mRNA signals (c) and ChIP-qPCR\nmeasured HS2 H3K27ac and HBG1 H3K27ac signals (d), when HS2 enhancer is\ntargeted by dCas9-CBP. e-f, RT-qPCR measured HS2 eRNA and HBG1 mRNA\nsignals (e) and ChIP-qPCR measured HS2 H3K27ac and HBG1 H3K27ac signals\n(f), when HBG1 promoter is targeted by dCas9-CBP. For bar plots, bars show mean\nRT-qPCR signal (Cas9-CBP relative to Cas9), error bars ±1 SD of log -transformed\n10\nvalues. Points represent individual biological replicates. Significance was assessed\nusing One-sample t-test on log values. Significance levels indicated by asterisks\n10\n(* P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001). CRISPRa data from\nWang et al.1\nWe also note that the concept of regulatory element co-regulation is supported by a\nrecent study2. Using single-cell live imaging in flies, Levo et al. elegantly\ndemonstrated that physically distant promoters can undergo coordinated\ntranscriptional bursting. These findings indicate that regulatory crosstalk between\nTREs may be a widespread feature of gene regulation in vivo.\nTogether, our QUASARR-seq results and these complementary observations\nprovide strong evidence that promoter-enhancer reciprocity may be a biologically\nrelevant and generalizable phenomenon.\nReferences\n30\nlangis\nRCPq−TR\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\ne.\n* **\n10\n3\neRNA mRNA\nlangis\nRCPq−PIhC\nevitaleR\n)\ngol\n,9saC/PBC−9saC(\n01\nf.\nHS2 HBG1\nH3K27ac H3K27ac\n1. Wang, K. et al. Systematic comparison of CRISPR-based transcriptional\nactivators uncovers gene-regulatory features of enhancer-promoter interactions.\nNucleic Acids Res 50, 7842-7855 (2022).\n2. Levo, M. et al. Transcriptional coupling of distant regulatory genes in living\nembryos. Nature 605, 754-760 (2022).\nRevised Results\nExcerpt CRISPRa validation of reciprocal activation in the human β-globin locus.\nTo independently validate reciprocal regulation in an endogenous genomic context,\nwe re-analyzed a published CRISPRa dataset targeting the human HS2-HBG1\nenhancer-promoter pair1. The HS2-HBG1 enhancer-promoter pair is a well-\nestablished model for studying tissue-specific gene regulation2. The β-globin locus\ncomprises five β-like globin genes (HBE1, HBG1, HBG2, HBD, and HBB), which\nare developmentally regulated by a shared upstream locus control region (LCR)\nenhancer cluster. This LCR contains five discrete DNase I hypersensitive sites\n(HS1-HS5), among which HS2 functions as a potent erythroid-specific enhancer.\nBecause of its robust enhancer activity and well-defined regulatory interactions, the\nHS2-HBG1 pair provides a canonical system for dissecting enhancer-promoter\ncommunication and transcriptional activation in their native genomic context.\nAs expected, targeting of the HS2 enhancer with dCas9-CBP increased HS2 eRNA\n(P-value < 0.001, One-sample t-test; Fig. 6c) and H3K27ac (P-value < 0.05; Fig.\n6d), in addition to HBG1 mRNA (P-value < 0.001; Fig. 6c) and H3K27ac (P-value\n< 0.05; Fig. 6d), measured via RT-qPCR and ChIP-qPCR, respectively. Strikingly,\ntargeting the HBG1 promoter also elevated not only HBG1 mRNA (P-value < 0.001;\nFig. 6e) and H3K27ac (P-value < 0.05; Fig. 6f) but also HS2 eRNA (P-value < 0.01;\nFig. 6e) and H3K27ac (P-value < 0.01; Fig. 6f). These results provide direct\nevidence in a native genomic context that promoters can reciprocally enhance the\npromoter activity of their enhancers, consistent with the regulatory reciprocity\nobserved in our QUASARR-seq data.\nReferences\n1. Wang, K. et al. Systematic comparison of CRISPR-based transcriptional\nactivators uncovers gene-regulatory features of enhancer-promoter interactions.\nNucleic Acids Res 50, 7842-7855 (2022).\n2. Li, Q., Peterson, K.R., Fang, X. & Stamatoyannopoulos, G. Locus control\nregions. Blood 100, 3077-86 (2002).\nRef3.4 – Notes concerns about comparing balance indices\nacross minPs and suggests removing analysis to improve\nclarity and focus on key messages.\nReviewer The P and E boost indices show a substantial difference in dynamic range (Extended\nComment data figure 1a). The authors then z-normalize these to enable the calculation of a\nP/E balance index. Although these differences in dynamic range are puzzling, this\nseems a reasonable approach when only a single minP is analyzed in the first part\n31\nof the manuscript. However, I found this much more difficult to interpret when the\nthree minPs have different dynamic ranges - which I gather from figure 6a (but it\nwould be good to show the equivalent of extended data fig 1a for the three minPs).\nWith these differences in dynamic range, can the balance indices still be compared?\nIs it fair to remove these differences by z-normalization? The interpretation becomes\neven more complicated to me when the authors switch to ranked balance indices\nand show quite puzzling scatter plots (figs 6c,d,e). My overall feeling is that the\nauthors push the data analysis a little too far in figure 6; I am afraid that most readers\nmay not be able to deeply understand these results. Because the manuscript is\nalready quite long and detailed, I suggest to remove Figure 6; this will help to\nfocus the manuscript on the key messages.\nAuthor We thank the reviewer for this thoughtful and constructive feedback. We agree that\nResponse the interpretation of the balance indices becomes more complex when comparing\nacross multiple minPs with differing dynamic ranges, and we appreciate the concern\nabout overextending the analysis in Fig. 6. In light of this, we have removed Fig. 6\nfrom the revised manuscript to better focus the study on its core findings and avoid\npotential confusion for readers. We believe this streamlining improves the clarity\nand accessibility of the main messages.\nRef3.5 – Minor Comment 1 –\nReviewer line 180+: GC content analysis -- this seems similar to ref 25. Please discuss\nComment similarities/differences.\nAuthor We thank the reviewer for highlighting the connection to Nguyen et al. Consistent\nResponse with their observation that CpG-rich sequences have elevated promoter activity and\npromoter-to-enhancer activity ratios, we find that elements with higher GC content\ntend toward promoter-dominant activity regardless of whether they are classified as\npromoters or enhancers. This result supports one of the main themes of our study;\nthat is, that promoters and enhancers are not distinct element types, but instead share\nsimilar functions, with activity outcomes determined by common sequence features.\nWhile Nguyen et al. emphasized CpG enrichment and motif associations in\npromoter activity for promoters, our analysis frames GC content as a general\ndeterminant of promoter-biased activity across all element classes using a functional\nbalance index.\nRevised Results\nExcerpt These results are consistent with previous observations that CpG-rich sequences\nshow elevated promoter activity and promoter-to-enhancer ratios1, and further\nindicate that GC content drives promoter-dominant functional bias across element\nclasses, revealing a unifying sequence syntax beyond classical promoter or enhancer\ndemarcations.\nReferences\n1. Nguyen, T.A. et al. High-throughput functional comparison of promoter and\nenhancer activities. Genome Res 26, 1023-33 (2016).\nReviewer line 219+: SOLARR-seq data are discussed here, but the assay is only\nComment introduced/explained in lines 290+.\nAuthor We thank the reviewer for pointing this out. We have removed the earlier mention\nResponse of SOLARR-seq at line 219, as that analysis has been replaced with the RT-qPCR\nexperiment to address the reviewer comment Ref3.2. SOLARR-seq is now first\nintroduced in line 402, where it is directly relevant, improving clarity and the flow\nof the manuscript.\nRef3.7 – Minor Comment 3 –\nReviewer line 344+: Please indicate how many variants in each of the mentioned classes was\nComment tested. Please also add n to figure 4b. If this plot is based on the same small dataset\nas figure 4d, then the violin/boxplot representation is not appropriate; a beeswarm\nplot would be better (but redundant with 4d).\nAuthor We thank the reviewer for this suggestion. We have replaced the violin plot in Fig.\nResponse 4b (now Fig. 5b) with a beeswarm (dot) plot to better represent the data points, with\nthe number of variants tested (n = 24) now annotated directly on the figure. We have\nretained Fig. 4d (now Fig. 5d) as we believe it shows the correlation between the\nwhole set of changes, which is not as clearly conveyed in Fig. 5b.\nP <0.001 P <0.05\nd = −0.82 d = −0.55\n1 n = 24 n = 24\n0\n−1\n−2\nPromoter Enhancer\nactivity activity\nFig. 5: Promoter and enhancer effects are correlated in variants disrupting\ndual function.\n33\nxedni\ntsooB.\nb, Change (∆) in promoter and enhancer activity boost indices between mutant and\nwild-type alleles. Dots represent individual elements (n = 24), horizontal bars\nindicate median ∆ boost indices. Significance assessed using One-sample t-test and\neffect size as Cohen’s d.\nRef3.8 – Minor Comment 4 –\nReviewer line 351+: it seems an overstatement that \"Variants reliably disrupted\". While for\nComment the entire population the effect may be statistically significant, there seem many\npromoters for which the variant does not lead to loss of activity. This is even more\nthe case for enhancer activity, for which the delta boost-index on average seems\nbarely affected.\nAuthor We thank the reviewer for this careful reading. We agree that the original wording\nResponse was an overstatement. We have toned down the language to more accurately reflect\nthe data, emphasizing that while variants can have statistically significant effects\nacross the population, the impact on individual promoters and especially on\nenhancer activity can be variable.\nRevised Results\nExcerpt Across the population, variants significantly reduced QUASARR-seq promoter\nactivity measurements (P-value < 0.001, One-sample t-test, Cohen’s d = -0.82,\nlarge; Fig. 5b), although the effect was variable for individual elements. Moreover,\nwe observed a similar trend with enhancer activity measurements, though with a\nlesser effect size compared to promoter activity (P-value < 0.05, d = -0.55, medium).\nRef3.9 – Minor Comment 5 –\nReviewer lines 364-369: I found these two sentences difficult to follow, in particular \"the\nComment intrinsic enhancer activity of the enhancer itself\" and \"other regulatory elements,\nsuch as its associated minimal promoter\". Please clarify.\nAuthor We thank the reviewer for pointing this out. In the revised manuscript, we have\nResponse clarified this section to improve readability. Specifically, we now state that an\nelement’s enhancer activity is influenced by the intrinsic promoter activity of its\nassociated promoter, its own intrinsic enhancer activity, and the three-dimensional\ncontacts between the two, with TRE compatibility contributing additionally. We also\nclarified that an element’s promoter-enhancer dual functionality can be shaped by\ninteractions with other regulatory elements, including its associated promoter.\nRevised Results\nExcerpt Recent studies propose a model in which an element’s enhancer activity is\nestablished by the quantitative tuning of the intrinsic promoter activity of its\nassociated promoter, its own intrinsic enhancer activity, and the three-dimensional\ncontacts between the two, with TRE compatibility appearing to play an additional,\nthough limited role1. It should follow then that an element’s promoter-enhancer dual\nfunctionality can be shaped by its interactions with other regulatory elements,\nincluding its associated promoter.\n1. Bergman, D.T. et al. Compatibility rules of human enhancer and promoter\nsequences. Nature 607, 176-184 (2022).\nRef3.10 – Minor Comment 6 –\nReviewer line 383: to support this conclusion, please color the dots in Supp Figure 5a\nComment according to the three minPs.\nAuthor We thank the reviewer for the suggestion. In the revised manuscript, the dots in\nResponse Supplementary Fig. 5a (now Supplementary Fig. 7h) have been colored\naccording to the three minimal promoters, as requested.\n5.0 ρ=0.75, P<0.001\n2.5\n0.0\n−2.5\n−2.5 0.0 2.5 5.0 7.5\nPromoter activity (log )\nh, Correlation between element promoter and enhancer activities when paired with\nall minPs (Spearman's ρ = 0.75, P-value < 0.001).\nRef3.11 – Minor Comment 7 –\nReviewer We reported previously (PMID 39466837) that promoters differ in their response\nComment dynamic range to a collection of Es (or pairs of Es, in that case), with a housekeeping\npromoter showing the smallest dynamic range. This seems similar to figure 5c.\nPlease discuss similarities/differences with this earlier report. It would also be\ninformative to provide pairwise scatter plots that compare boost indices between the\nthree minPs: any evidence for differences in E-minP compatibility?\n35\n)\ngol(\nytivitca\nrecnahnE\npGAPDH\npMYC\npAPOBEC3F\nAuthor In agreement with earlier reports (Martinez-Ara et al.), we observed that minPs\nResponse differed in their response dynamic range to the same set of elements. For example,\nthe housekeeping minP pGAPDH tended to exhibit a narrower spread of boost\nindices compared to pMYC or pAPOBEC3F (Fig. 5c now Fig. 6a), consistent with\nthe prior finding that housekeeping promoters displayed the smallest dynamic\nrange1. In parallel, our data indicated that differences were not absolute but\nquantitative, where enhancers that were highly compatible with one promoter\ngenerally showed above-baseline activity across all others.\nTo further investigate for potential evidence of enhancer-promoter compatibility, we\ncompared boost indices pairwise across minPs. We found strong correlations in\nenhancer activity across minP libraires (Spearman’s ρ = 0.69 for pMYC vs.\npGAPDH, ρ = 0.69 for pAPOBEC3F vs. pGAPDH, and ρ = 0.78 for pAPOBEC3F\nvs. pMYC, all P-value < 0.001; Supplementary Fig. 8a). These results suggest that\nenhancer-promoter compatibility is broadly conserved, but with minP‐specific\nquantitative shifts that modulate the overall dynamic range. Together, our\nobservations echo the general principle of promoter‐dependent responsiveness\ndescribed previously2,3, finding that enhancer function is not strictly promoter‐\nspecific but instead quantitatively tuned by promoter context.\n5.0\nρ=0.69, P<0.001\n2.5\n0.0\n−2.5\na.\n−1 0 1 2\nρ=0.69, P<0.001\n4\n2\n0\n−2\nF3CEBOPAp\nρ=0.78, P<0.001\n4\n2\n0\n−2\n−1 0 1 2 −2.5 0.0 2.5 5.0\npGAPDH pMYC\nSupplementary Fig. 7: Enhancer-promoter compatibility is quantitative, not\nabsolute.\na, Pairwise correlations of QUASARR-seq enhancer activity measurements across\nminP libraries: pGAPDH vs. pMYC (Spearman's ρ = 0.69, P-value < 0.001; top\nleft), pGAPDH vs. pAPOBEC3F (ρ = 0.69, P-value < 0.001; bottom left), and\npMYC vs. pAPOBEC3F (ρ = 0.78, P-value < 0.001; bottom right).\nResults\nIn agreement with earlier reports, we observed that minPs differed in their response\ndynamic range to the same set of elements. For example, the housekeeping minP\npGAPDH tended to exhibit a narrower spread of boost indices compared to pMYC\nor pAPOBEC3F (Fig. 6a), consistent with the prior finding that housekeeping\npromoters displayed the smallest dynamic range1. In parallel, our data indicated that\ndifferences were not absolute but quantitative, where enhancers that were highly\ncompatible with one promoter generally showed above-baseline activity across all\nothers. To further investigate for potential evidence of enhancer-promoter\ncompatibility, we compared boost indices pairwise across minPs. We found strong\ncorrelations in enhancer activity across minP libraires (Spearman’s ρ = 0.69 for\npMYC vs. pGAPDH, ρ = 0.69 for pAPOBEC3F vs. pGAPDH, and ρ = 0.78 for\npAPOBEC3F vs. pMYC, all P-value < 0.001; Supplementary Fig. 8a). These\nresults suggest that enhancer-promoter compatibility is broadly conserved, but with\nminP‐specific quantitative shifts that modulate the overall dynamic range. Together,\nour observations echo the general principle of promoter‐dependent responsiveness\ndescribed previously2,3, finding that enhancer function is not strictly promoter‐\nspecific but instead quantitatively tuned by promoter context.\nReferences\n1. Martinez-Ara, M., Comoglio, F. & van Steensel, B. Large-scale analysis of the\nintegration of enhancer-enhancer signals by promoters. Elife 12(2024).\n2. Bergman, D.T. et al. Compatibility rules of human enhancer and promoter\nsequences. Nature 607, 176-184 (2022).\n3. Martinez-Ara, M., Comoglio, F., van Arensbergen, J. & van Steensel, B.\nSystematic analysis of intrinsic enhancer-promoter compatibility in the mouse\ngenome. Mol Cell 82, 2519-2531 e6 (2022).\ndeveloped […] ‘QUASARR-seq’…” –\nReviewer Multiple studies in recent years have demonstrated that enhancers and promoters\nComment are much more similar than previously regarded. Nevertheless, experimental\nsettings that enable the quantitative measurements of both the promoter and\nenhancer potentials of the same set of sequences are lacking. The authors have\nelegantly designed and developed “Quantitative Unifying Assay for Simultaneously\nActive Regulatory Regions by sequencing (QUASARR-seq)”, a massively parallel\ndual reporter assay that enables the simultaneous measurement of the intrinsic\npromoter and enhancer potentials exerted by the same element.\nAuthor We thank the reviewer for their supportive comments. We agree that systematic\nResponse methods to quantify both promoter and enhancer activity from the same sequence\nhave been lacking, and we appreciate the recognition of QUASARR-seq as a\nmeaningful step toward addressing this gap.\nRef4.2 – “The authors rigorously demonstrate that canonical\nhuman promoters and enhancers can indeed act as both\npromoters and enhancers under the same contexts…” –\nReviewer The authors rigorously demonstrate that canonical human promoters and enhancers\nComment can indeed act as both promoters and enhancers under the same contexts, and that\npromoter activity may be necessary but insufficient for enhancer function. They\nexamined whether the regulatory potential is embedded within element sequences\nand discovered that variants with strong clinical/functional evidence for imparing\npromoter activity, also impair enhancer activity. Using combinations of\ntranscription regulatory elements with three different minimal promoters (GAPDH,\nMYC and APOBEC3F) they identified reciprocal regulatory effects between the\nassociated elements, and a strong positive correlation between promoter and\nenhancer activities. They further discovered that the promoter-enhancer activity\nbalance is determined by both sequence-intrinsic properties and contextual\nregulatory effects.\nAuthor We thank the reviewer for their engagement with our work. We appreciate their clear\nResponse understanding of the key findings and the broader implications of our study.\nRef4.3 – “The study addresses a fundamental question in\ngene regulation and will be of broad interest to researchers\nin the field.” –\nReviewer The study addresses a fundamental question in gene regulation and will be of broad\nComment interest to researchers in the field. It is novel, elegantly designed and rigorously\nexecuted, making it highly suitable for Nature Communications.\nResponse glad that the study’s novelty, design, and rigor are recognized, and we appreciate\ntheir support for its suitability for publication.\nRef4.4 – Minor Comment 1 –\nReviewer 1. I suggest elaborating in the discussion on the following issues:\nComment a. The study was conducted using the human K562 cell line. How might the findings\ndiffer if the study were performed in a different cellular context?\nb. Potential outcomes if the cells were induced to differentiate\nAuthor We thank the reviewer for raising these important points. We agree that cellular\nResponse context is a critical determinant of regulatory element function. We have expanded\nthe Discussion to note that applying QUASARR-seq across different cell types and\nduring induced differentiation will be important for future work. In fact, this is an\nactive area of ongoing work in our group as we believe that such experiments could\nreveal how the balance between promoter and enhancer activity shifts with cell\nidentity and developmental state, providing insight into mechanisms of regulatory\nelement plasticity.\nRevised Discussion\nExcerpt Future work should move toward in vivo models and explore diverse cellular\ncontexts, including shifts with cell identity and induced differentiation.\nRef4.5 – Minor Comment 2 –\nReviewer 2. Cells were harvested 6 hours post electroporation. While this time point is\nComment appropriate for capturing immediate early responses, it may not be optimal for\nassaying enhancer function. I suggest including a brief rationale for the choice of\nthe 6-hour time point.\nAuthor We thank the reviewer for raising this important point. For our previous study1, we\nResponse performed a series of time-course experiments to determine the optimal post-\ntransfection time point for maximal recovery of plasmid-derived RNA expression\nin K-562 cells. Across three independent experiments testing multiple constructs,\nwe observed that RNA levels peaked consistently at 6 hours post transfection (hpt),\nafter which transcript abundance declined or plateaued.\nFor example, in a time course using our eSTARR-seq vector pDEST-hSTARR-luc\nwith a pMYC minimal promoter (minP) paired with an RSV enhancer, the 6-hour\ntime point yielded the strongest RT-qPCR signal relative to GAPDH internal control\n(Supplementary Fig. 9a). Similar results were obtained when both a pMYC minP\nwas paired with a control TRE and a SCP1 minP was paired with a collection of 713\nTREs (TRE202, Supplementary Fig. 9b), as well as with TRE202 paired with a\npMYC and SCP1 minP (Supplementary Fig. 9c).\nBased on these results, we adopted the 6-hour time point for the present study.\n39\n0.8\npMYC−RSV\n0.6\n0.4\n0.2\n0.0\nSupplementary Fig. 9: Optimal harvest time experiments.\nRelative RT-qPCR signal of plasmid-derived RNA normalized to GAPDH mRNA\nin K-562 transfected with a, pDEST-hSTARR-luc-pMYC-RSV, b, pDEST-\nhSTARR-luc-pMYC-Control1K or pDEST-hSTARR-luc-TRE202, and c, pDEST-\nhSTARR-luc-TRE202 or pDEST-hSTARR-luc-pMYC-TRE202 at varying hours\npost transfection (hpt). Points represent mean of technical triplicates per time point.\nRevised Methods\n40\nlangis\nRCPq−TR\nevitaleR\n)\nANRm\nHDPAG\n/\nANR\ndimsalp(\n6 12 24 36 48\nhpt\n0.04\npMYC−Control\nSCP1−TRE202\n0.03\n0.02\n0.01\n0.00\nlangis\nRCPq−TR\nevitaleR\n)\nANRm\nHDPAG\n/\nANR\ndimsalp(\n246 12 24 36 48\nhpt\n0.020 pMYC−TRE202\nSCP1−TRE202\n0.015\n0.010\n0.005\n0.000\nlangis\nRCPq−TR\nevitaleR\n)\nANRm\nHDPAG\n/\nANR\ndimsalp(\n2 4 6 12 24\nhpt\nExcerpt Optimal harvest time experiments\nTo determine the optimal post-transfection harvest time for plasmid-derived RNA\nexpression, we performed a series of time-course experiments in K-562 as part of\nour previous study27. As a pilot experiment, 1 x 106 cells were electroporated with\n10 μg of pDEST-hSTARR-luc-pMYC-RSV (10 pg/cell) using the Amaxa Cell Line\nNucleofector Kit V with a Lonza Amaxa Nucleofector II Device using program T-\n016. At 6, 12, 24, 36, and 48 hours post transfection (hpt), 5 x 105 cells were\nharvested per time point. Total RNA was extracted with TRIzol Reagent (15596026;\nInvitrogen), treated with DNase I (AM2222; Invitrogen), and purified using RNeasy\nMini Kit (74104; QIAGEN). Reverse transcription was performed using an Oligo\nd(T) mRNA Primer (S1316S; New England Biolabs), and cDNA was quantified\n18\nby qPCR using exon junction-spanning primers specific for plasmid-derived RNA\nand for GAPDH mRNA as an internal control. Technical triplicates (n = 3) were\naveraged per time point. As a follow up experiment, cells were electroporated with\npDEST-hSTARR-luc-pMYC-Control1K or pDEST-hSTARR-luc-TRE202, and 4 x\n105 cells were collected at 2, 4, 6, 12, 24, 36, and 48 hpt. RNA isolation, DNase\ntreatment, purification, reverse transcription, and qPCR were performed as in the\npilot experiment. As a final experiment, cells were electroporated with\npDEST-hSTARR-luc-TRE202 or pDEST-hSTARR-luc-pMYC-TRE202, and 4 x 105 cells\nwere harvested at 2, 4, 6, 12, and 24 hpt. Processing and analysis were performed\nas in the prior experiments. Across all experiments, plasmid-derived transcript\nabundance consistently peaked at 6 hpt and declined or plateaued at later time\npoints. Based on these observations, 6 hpt was selected as the standard harvest point\nfor subsequent assays (Supplementary Fig. 9a-c).\nReferences\n1. Tippens, N.D. et al. Transcription imparts architecture, function and logic to\nenhancer units. Nat Genet 52, 1067-1075 (2020).\nRef4.6 – Minor Comment 3 –\nReviewer 3. Fig. 1 legend - indicate which minimal promoter was used\nComment\nAuthor We thank the reviewer for their suggestion. We have updated the Fig. 1 legend to\nResponse indicate that the minimal promoter used was pMYC.\nRef4.7 – Minor Comment 4 –\nReviewer 4. Fig. 5a legend – left and right should be top and bottom\nComment\nAuthor We appreciate the reviewer’s careful reading. The Fig. 5a (now Supplementary\nResponse Fig. 7g) legend has been corrected to refer to “top” and “bottom” instead of “left”\nand “right.”\nRef1.1 – “Final Comment” –\nReviewer The Authors have addressed all my comments thoroughly. I don't have anything else\nComment to add.\nAuthor We thank the reviewer for their positive assessment and are glad that the revisions\nResponse have addressed all concerns.\nRef2.1 – “Final Comment” –\nReviewer The manuscript has been significantly improved. I have no further comments.\nComment\nAuthor We appreciate the reviewer’s careful evaluation and are pleased to hear that no\nResponse further issues remain.\nRef3.1 – “Final Comment” –\nReviewer The authors have carefully addressed all my comments. I now strongly support\nComment publication in Nature Communications.\nAuthor We sincerely thank the reviewer for the thoughtful feedback provided throughout\nResponse the review process and are grateful for the strong support for publication.\nRef4.1 – “Final Comment” –\nReviewer The authors have fully addressed all of my previous comments.\nComment\nAuthor We thank the reviewer for their positive assessment and are pleased that the revised\nResponse manuscript resolves all prior concerns.\n1"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The Authors have addressed all my comments thoroughly.\nI don't have anything else to add.", "reviewer_2": "The manuscript has been significantly improved. I have no further comments.", "reviewer_3": "The authors have carefully addressed all my comments. I now strongly support publication in Nature Communications.\n===============Reviewed by Bas van Steensel, review task accepted: 15 Oct 2025; completed 18 Oct 2025. It is my standard policy to sign and date *all* my manuscript review reports, regardless of my comments and recommendations. All correspondence about this manuscript should go via the editor. PLEASE DO NOT REMOVE THIS NOTE ===========", "reviewer_4": "The authors have fully addressed all of my previous comments."}, "author_response": ""}]

Decision letter