Enhancer activity drives cell differentiation and cell fate determination, but it remains unclear how enhancers cooperate during these processes. Here we investigate enhancer cooperation during transdifferentiation of human leukemia B-cells to macrophages. Putative enhancers are established by binding of the pioneer factor C/EBPα followed by chromatin opening and enhancer RNA (eRNA) synthesis from H3K4-monomethylated regions. Using eRNA synthesis as a proxy for enhancer activity, we find that most putative enhancers cooperate in an additive way to regulate transcription of assigned target genes. However, transcription from 136 target genes depends exponentially on the summed activity of its putative paired enhancers, indicating that these enhancers cooperate synergistically. The target genes are cell type-specific, suggesting that enhancer synergy can contribute to cell fate determination. Enhancer synergy appears to depend on cell type-specific transcription factors, and such interacting enhancers are not predicted from occupancy or accessibility data that are used to detect superenhancers. Enhancers are cis-regulatory DNA elements that drive the transcription activity of target gene promoters . EnhanceDrosophila . TT-seq combines short-term metabolic RNA labeling (5 min) with sequencing of newly synthesized RNA fragments and provides a genome-wide unbiased view of RNA synthesis activity . The fraTT-seq can monitor changes in enhancer and promoter activities over time with great sensitivity. During T-cell stimulation, transcription from enhancers and promoters of responsive genes is activated simultaneously . EnhanceTo address the question of enhancer cooperation during cell type determination, we used a transdifferentiation system driven by a single TF . In thisHere we report a genome-wide multi-omics data set for C/EBPα-induced transdifferentiation of human B cells to macrophages . In-depth analysis of the data provided general insights into the order of events that establish active enhancers. Furthermore, TT-seq allowed us to identify transcriptionally active enhancers, to pair these with their putative target promoters, and to analyze the changes in transcription activity of enhancers and promoters over time. Our analyses revealed that most enhancers that drive the expression of a common target gene do so in an additive manner. However, for about one-fifth of the enhancers tested , the change in transcription activity of the target gene was larger than the change attributable to the sum of the enhancer activities, indicating that enhancers cooperate synergistically at these loci to drive target gene transcription.To study the temporal order of gene regulatory events during transdifferentiation of human precursor leukemia B cells to macrophage-like cells, the master TF C/EBPα fused to estrogen receptor were activated by addition of beta estradiol . As reveChanges in gene expression occurred in two major transitions, as indicated by principal component analysis and Euclidean distance analysis of RNA-seq data . The firWe performed TT-seq at 0 hr, 12 hr, 24 hr, and 96 hr post induction to monitor changes in newly synthesized RNA, including eRNAs . We alsoKLF4 (We then used TT-seq data to annotate transcription units (TUs) as described , and ideKLF4 .The resulting 7624 transcriptionally active regions correspond to putative enhancers (referred to as ‘enhancers’ for simplicity). These enhancers had a median length of ~1 kb and were predominantly located in intergenic regions . Among tIn order to investigate how C/EBPα may induce the observed changes in enhancer landscape and gene transcription, we mapped C/EBPα-binding sites genome-wide by ChIP-seq at 0, 12, 24, and 96 hr. The majority of the obtained 14,561 ChIP-seq peaks (FDR < 0.05) fell into intergenic or intronic regions . To inveKLF4 and the +349 kb MAFB enhancer  >1, FDR < 0.05) occupied by C/EBPα according to changes in eRNA synthesis , and theat 96 hr .To further investigate the order of events, we analyzed the temporal relationship between C/EBPα binding and eRNA synthesis for all enhancers bound by C/EBPα (1487 out of 7624 enhancers). Of these, 965 were differentially transcribed during transdifferentiation compared to 0 hr (log2(FC) >1, FDR < 0.05, We next aimed at identifying the target promoters for the enhancers. We paired all 7624 enhancers with potential target promoters using two different approaches  were paired with more than one enhancer when we used the neighboring or 1 Mb pairs, respectively, as exemplified for CD14 . ObtainiCD14 . This shCD14 , as exemnd LMO2, . In summnd LMO2, .To investigate the temporal changes in transcription activity of target genes that were paired with multiple enhancers, we used the amount of eRNA production as a proxy for enhancer activity. We first examined whether target gene transcription changes relative to 0 hr can be explained by the activity changes of a single enhancer or whether they can only be explained when multiple enhancers were taken into account. Generally, target genes paired with more than one enhancer showed higher mRNA synthesis , consist2) of a linear regression, and this was independent of the method of promoter–enhancer pairing s in mRNA synthesis and the sum of eRNA synthesis of the paired enhancers. There was a clear trend that promoter activity changes are better explained by the sum of activity changes in paired enhancers . General pairing . These rWe next asked whether the contributions of individual enhancers to target gene activation are always additive or whether they can also be non-additive. This requires an analysis at the level of individual loci, which is only possible with time course data as available here. For robust curve fitting, we included TT-seq data for three additional time points, resulting in a total of seven time points . We fitted these data to three models , an addiTo investigate which of the three models best explains transcription changes at target genes that are paired with multiple enhancers we estimated the relative BIC for 773 target genes that were paired with 2–20 enhancers according to the neighboring method. Of these target genes, 277, 92, and 250 were best described by the additive, synergistic, and logistic models, respectively . For theMyc, which is known to be regulated in an additive manner  were regulated in an additive manner by multiple enhancers. These genes included e manner . We alsoe manner , and incED2, LYZ , and B cand TLE1 . In summTo investigate the phenomenon of synergistic enhancer activity further, we carried out additional analyses. To exclude that the observed synergistic behavior is a consequence of a noisy relative BIC distribution, we computed the relative BIC of an additive model with respect to a logarithmic model, which is not meaningful . StrikinSince the number of enhancers paired to each gene varies between the neighboring and 1 Mb methods, only a small number of genes could be tested for enhancer synergy. We therefore used the third method of promoter–enhancer pairing referred to as ‘TAD pairing’. TAD pairing pairs all promoters and enhancers that lie within a TAD that was experimentally identified with Hi-C data . We assuWe further tested whether we had underestimated the number of enhancers because this could lead to incorrect assignment of synergistic behavior. We extended our list of putative enhancers by including 7863 additional merged ncRNA TUs that lacked H3K4me1 or ATAC-seq signals. Using the neighboring method, this increased the number of paired enhancers to 1184 out of 1790 TUs. We found that 97 of the 136 previously identified synergistically regulated genes (71%) were again detected to be regulated in a synergistic manner . This inWe also asked whether one highly active enhancer could dominate gene activation for synergistic enhancers. This was not the case because the median contribution from the strongest enhancer to the total eRNA signal was the same for additive and synergistic enhancers . As an aMYB, BLNK, VPREB1, and IGLL5  were cell type-specific genes, that is, genes that were specifically expressed either in B cells (60 TUs) or in macrophage-like cells (56 TUs) according to RNA-seq data . B cell-nd IGLL5 . Macrophnd ITGAX . Macrophnd ITGAX . Gene onnd ITGAX . Thus, tFurther analysis showed that the delayed but rapid induction of synergistically regulated genes was not a consequence of late chromatin opening. Although the average chromatin accessibility changed more at synergistic enhancers compared to additive enhancers , the kinetics of chromatin opening were similar for both types of enhancers at late upregulated genes . The iniWe further tested whether our synergistic enhancers may be described as ‘superenhancers’, because these can also target cell type-specific genes. Superenhancers are generally defined by their high occupancy with specific TFs and coactivators . We collFurthermore, the superenhancers detected by occupancy or accessibility signals did not correspond to the synergistic enhancers we identified here based on RNA synthesis changes. Indeed, previously identified superenhancers from the dbSUPER database  overlappWe finally investigated whether enhancer synergy depends on binding of specific TFs to enhancer regions. According to our RNA-seq data, different sets of TFs are expressed in cascades from 0 hr to 96 hr during transdifferentiation . We locaIn contrast, additive enhancers contained motifs of only two TF families that were enriched compared to synergistic enhancers, in particular the E2F and FOS . TFs of In general, synergistic enhancers were enriched for macrophage-specific TFs, whereas additive enhancers were not. To confirm this, we investigated the expression of TFs that belong to the same subfamilies with the enriched TFs in additive or synergistic enhancers. Indeed, of the TFs that were enriched in synergistic enhancers and differentially expressed, about half were macrophage specific, whereas TFs of E2F family that were enriched in additive enhancers were not . This suHow enhancers cooperate to drive target gene expression and to determine cell types is a central question in the field of genomic regulation. However, the question of functional enhancer cooperativity could thus far not be addressed in a systematic manner in a dynamic system. A route to such an analysis has now been provided by the availability of an efficient and simple transdifferentiation system and the development of TT-seq to measure genome-wide RNA synthesis from both enhancers and target gene promoters. Here we investigated enhancer cooperation over time during transdifferentiation of human B-cells to macrophage-like cells. We examined this process in a quantitative manner, assuming eRNA synthesis to be a proxy for enhancer activity. We identified transcriptionally active enhancers, their temporal activity changes, and used correlations to predict how multiple enhancers cooperate to regulate their target genes. These analyses indicated that multiple enhancers often act in an additive manner, and that synergistic enhancer action occurs at ~20% of the tested putative target genes. Target genes of synergistic enhancers were almost exclusively cell type-specific.The synergistic enhancers identified here were different from previously reported superenhancers. We identified synergistic enhancers from changes in RNA synthesis activity during the time course, whereas identification of superenhancers generally uses TF and coactivator occupancy measurements at one time point. Whereas our synergistic enhancers are not necessarily arranged next to each other, superenhancers are generally linear clusters of constituent enhancers. In contrast to our time-resolved study, previous studies investigated enhancer cooperation in steady state or at an end point, and at selected genes, rather than genome-wide . It was Drosophila are regulated by multiple enhancers, and deleting one of these enhancers has little effect on the gene expression pattern . For our time course experiments, cells were treated with beta estradiol (100 nM) , human IL-3 (10 ng/ml) and human CSF-1 (10 ng/ml) . Cells were harvested at various time points after treatment .We used BLaER1 cells derived from RCH-ACV, precursor leukemia B cells, stably expressing estrogen-inducible C/EBPα ; these c8 cells were labeled with 0.5 mM 4-thiouridine  for 5 min and harvested by centrifuging at 500 g for 2 min. Harvested pellets were lysed with QIAzol . We generated RNA spike-ins based on sequences of six ERCC RNA spike-in mix – ERCC-00043, ERCC-00170, ERCC-00136, ERCC-00145, ERCC-00092, and ERCC-00002, and prepared spike-in mix as described previously  according to the manufacturer’s instructions. Total RNA (300 μg) was fragmented to 1500–5000 bp in size using Covaris S220 Ultrasonicator. Fraction of total RNA was set aside for total-RNA-seq. Newly synthesized RNAs were purified as described . Formaldehyde was quenched with 0.2 M glycine for 5 min. Chromatin was sheared to 200–500 bp in size using the Covaris S220 ultrasonicator. A fraction of the resulting chromatin was used as input control. Sheared chromatin  was incubated with 5 μg of antibodies – anti-C/EBPα , anti-H3K4me1 , and anti-BRD4  – bound to protein-A beads . Samples were incubated at 65°C overnight to reverse the crosslinks, followed by RNase and proteinase K treatments. Purified ChIP and input DNA were quantified by Qubit 2.0 Fluorometer . Libraries were generated using NEB Ultra DNA Library kit  and amplified by nine cycles with KAPA Real-Time Library Amplification Kit . The libraries of BRD4-ChIP-seq were sequenced at 2 × 75 bp on an Illumina NextSeq machine in house. The other libraries were sequenced at 2 × 50 bp on HiSeq2000 . The average read numbers obtained were 4 × 107 for C/EBPα ChIP-seq, 7 × 107 for H3K4me1 ChIP-seq, and 2 × 107 for BRD4 ChIP-seq.ChIP-seq was performed from two biological replicates at four time points. At each time point, 3 × 104 cells at each time point were harvested and treated with Nextera Tn5 Transposase  for 45 min at 37°C. Library fragments were amplified using 1× NEBNext High-Fidelity 2× PCR Master Mix  and 1.25 μM of custom Nextera PCR primers 1 and 2. PCR amplification was done with 11 cycles, determined by KAPA Real-Time Library Amplication Kit  to stop prior to saturation. Then, the samples were purified using Qiagen MinElute PCR Purification Kit  and with Agencourt AMPure XP beads  in 3:1 ratio. The libraries were sequenced at 2 × 50 bp on HiSeq2000 . The average read numbers obtained were 3 × 107.ATAC-seq was performed from two biological replicates at four time points as described . BrieflyCells were harvested, washed with PBS, and blocked with Human FC Receptor Binding Inhibitor . Cells were incubated with antibodies, APC-cy7 Mouse Anti-Human CD19 , PE Mouse Anti-Human CD14 . After washing with PBS, cells were resuspended in 7AAD  diluted with PBS. The analysis was performed on BD Accuri C6 flow cytometer (BD Biosciences) according to standard protocols with following changes in filters. We used filter 675/25 at F3 and 670/LP at F4.GAPDH expression. Relative expression levels were calculated at each time points compared to 0 hr for B cell markers or to 168 hr for macrophage markers. Primers used for qRT-PCR are listed in Cells were harvested with QIAzol and purified using RNeasy Mini Kit . The purified RNA was quantified using a Nanodrop spectrophotometer (Thermofisher), and cDNA was generated by reverse transcription PCR. The resulting cDNA was used as a template for quantitative PCR . Ct values were normalized with https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Read quality was screened using FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). After alignments, Samtools1.2  using STAR2.4.2 . For cov--BAMPE’ options of MACS2.1.1. To catalogue the binding sites over all the time points, we merged and reduced all peaks, and removed those falling within ±4 Mb of centromeric heterochromatin regions. Duplicates were removed. Read numbers, Spearman's correlation coefficients between replicates, FRiP, and peak numbers are tabulated in Trimmed reads were aligned to GRCh38 genome assembly using Bowtie2.2.5 . ChIP-se--shift 37 --extsize 73’ options of MACS2.1.1. Peaks called with ‘--broad’ and ‘--narrow’ were combined and used for further analysis. Duplicates were removed.Trimmed reads were aligned to GRCh38 genome assembly without mitochondrial chromosome (ChrM) using Bowtie1.0.0 . Peaks whttp://ggplot2.org). Loadings were computed, and top 1000 genes with high loadings were selected for component 1 and 2. Among 1000 genes, the ones that were not found in the other group were retained for further analysis. Heatmaps were generated from gene expression data with pheatmap (https://CRAN.R-project.org/package=pheatmap). Gene ontology (GO) analysis was performed with DAVID  were ncRNAs that lie within 1 kb downstream of protein-coding genes or products from stepwise termination of RNA polymerase II ; ncRNAs Each eRNA was paired with the putative target mRNAs by two methods; the neighboring method and 1 Mb method. For both methods, the eRNAs and mRNAs were taken from our annotation with TT-seq data and had to be synthesized at least at one time point. The neighboring method paired the eRNAs with mRNAs based on distances. First, intragenic eRNAs were paired with the mRNA in whose transcribed region they lay. The rest of the eRNAs was paired with both the closest upstream and downstream mRNAs. Of 12,900 pairs, 5786 pairs (45%) with differentially synthesized mRNAs were used . As a result, 4721 of 7624 enhancers were paired to 1790 genes . The 1 Mhttp://promoter.bx.psu.edu/hi-c/).TAD information of BLaER cell line during transdifferentiation was obtained from 2) were obtained.Enhancer–promoter (EP) pairs were classified based on the number of enhancers paired with the same promoter. We obtained the log2 fold change of length normalized mRNA and eRNA synthesis from 0 hr. For Theoretical models were suggested by The additive relationship is described as follows:Promoter activity = α × (sum of enhancer activities) + βThe synergistic  relationship is described as:Promoter activity = exp[α × (sum of enhancer activities) + β]The logistic relationship is described as follows:Promoter activity = δ/(1 + exp[−(α × (sum of enhancer activities) + β)])The constants α, β, and δ describe the effect size of enhancer activities on their target promoter and vary between promoters. It can be assumed that these constants are fixed for each enhancer−promoter pair, even upon stimulation. The promoter activity was predicted from the sum of enhancer activities using generalized linear models. The additive and synergistic models were fitted with the use of normal error mode using the DEoptim package in R/Bioconductor and codes adapted from Superenhancers are predicted as suggested in TF bound motifs were identified by PIQ, a TF footprinting method . First,  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The authors provide insight into genome control mechanisms that are used to generate context-specific patterns of gene activity that underlie the distinct functions of cells and tissues. The study's novelty lies in the application of eRNA kinetics during differentiation and the finding that synergistic enhancer/promoter sets are enriched for both lineage-determining genes and binding of cell type-specifying transcription factors.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Harinder Singh (Reviewer #2).Thank you for submitting your work entitled "Enhancer synergy can drive human cell fate determination" for consideration by eLife.Our decision has been reached after extensive consultation among the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.The reviewers appreciated the study's attempt at addressing several important and timely questions regarding the developmental control of mammalian gene transcription. In particular, they were supportive of the rich data sets, the kinetic analyses and the use of TT-seq to analyze eRNAs (as a proxy of endogenous enhancer activity) and promoter output. Nevertheless, the three reviewers expressed a variety of concerns (see below) which, following extensive discussion, led to a decision to decline your submission. From reading the reviewers' comments you will see that a variety of further experiments would have been necessary to satisfy them that some conclusions are fully warranted. When coming to this decision, we and the reviewers took into consideration the likely time required to perform these experiments. Reviewers expressed an interest in seeing a new submission containing additional data that support (or otherwise) your initial manuscript's predictions, should you choose to submit this to Reviewer #1:The manuscript tackles an interesting question, but it completely lacks experimental verification of the hypothesis generated by the very intricate but poorly explained analysis. Without experimental validation of the prediction that some enhancer sets act synergistically while other are only additive, the major conclusions of the manuscript remain preliminary.1) The manuscript is devoid of performance metrics for the sequencing experiments, making it impossible to gauge data quality. No comparison/reproducibility analysis of the replicates is presented, nor data on IP quality, peak numbers, read counts, IP efficiency. The super-enhancer analysis is not internally validated by assessing whether the super-enhancers identified using BRD4, C/EBPa or ATAC-seq make biological sense (are the identified super-enhancers lineage genes?), which would be important for the conclusion that synergistic enhancers are distinct from super-enhancers.2) For validation, enhancer cooperativity could be tested in reporter assays with a handful of enhancers of either type, alone or in combination. Alternatively, Cas9-mediated knockout of increasing numbers of enhancers in a given gene/enhancer set would be predicted to have linear or non-linear effects on their target gene, depending on whether they are synergistic or additive.3) The use of estrogen to activate the fusion protein could lead to estrogen receptor-dependent enhancer activation. The authors should clarify whether they see these effects, and if so, how they discern them from the C/EBPa-mediated effects.4) The Discussion lacks discussion of previous findings; for example, inducible TF binding has previously been shown induce histone methylation (H3K4me1) at enhancers and nucleosome remodeling , which is corroborated by the current manuscript. For short-term transcriptional responses, eRNA transcription elongation has been shown to be required for MLL4-mediated H3K4me1/2 deposition at enhancers, while in this manuscript eRNA transcription occurs after H3K4me1 deposition – how do the authors explain this mechanistic difference?Reviewer #2:The authors are attempting to address several fundamental issues concerning the developmental control of mammalian gene transcription by deploying various genome wide methodologies and computationally integrating their findings. They have previously described a useful model system to analyze such regulatory processes. The experimental system involves a B cell line that undergoes transdifferentiation into a macrophage by the ectopic and inducible expression of the transcription factor C/EBPa. Using this system and performing transient transcriptome sequencing (TT-seq), to analyze eRNAs and promoter output, as well as C/EBPa ChIP-seq, H3K4me1 ChIP-seq and ATAC-seq they are able to put together a temporal sequence of molecular events for C/EBPa targeted genes. The experiments are rigorous and enhanced by the kinetic analyses.Such temporal analysis has been performed in other systems and several of the key conclusions reached are similar to those of earlier studies. In particular that C/EBPa binds to a class of genomic sites which are accessible as well as those that are inaccessible (ATAC-seq). For the latter set, chromatin accessibility follows C/EBPa binding and H3K4me1.The new element in this work is the genome-wide analysis of eRNAs, used as a proxy for endogenous enhancer activity, and their correlation with the activities of nearest (or within I Mb) promoters. Based on such analysis the authors conclude that for promoters that are acted on by multiple enhancers, most enhancers function additively. However for a smaller set of genes, enhancers appear to function synergistically and these genes are enriched for cell type specific functions.A current model for super enhancer action involving liquid-liquid phase transition  is suggested to underlie the basis of enhancer synergy.There are two major issues to be addressed:1) In this study, enhancers are inferred to be connected to their cognate promoters on the basis of nearest neighbors or by using an arbitrary genomic distance (I Mb). Although such criteria are widely used they have been supplanted by Hi-C or promote capture Hi-C analysis as a means of directly analyzing enhancer-promoter interactions. Given that this connectivity is critical for the subsequent analysis and conclusions focusing on enhancer action (additive versus synergistic) on different types of target genes, the authors need to use of one of the above approaches.2) Although not emphasized in the Abstract the authors fail to uncover evidence for super enhancers  operating in their cellular system in the selective control of key differentiation genes. They suggest that their method of utilizing eRNAs rather than mediator binding revealed by ChIP-seq is better at distinguishing functionally distinct classes of enhancers. This major conclusion needs to be elaborated and deserves to be featured in the Abstract. From a mechanistic standpoint there appears to be a problem that needs to be better discussed by the authors. As noted above a current model for super enhancer action involving liquid-liquid phase transition is invoked by the authors to underlie the basis of enhancer synergy. However in their study the identified super enhancers appear to function either additively or synergistically. Can these different types of analyses be reconciled or perhaps not?Reviewer #3:In this manuscript, Choi and colleagues study enhancer cooperativity – i.e. how multiple enhancers collaboratively regulate the transcription of their target genes – during transdifferentiation of human B cells to macrophages at a genome-wide scale. To do so, they used an estrogen-inducible system driven by the C/EBPα pioneer factor and monitored transcriptional and chromatin dynamics over time. By using RNA-Seq, they first show that two main waves of transcriptional changes underline B cell to macrophage transdifferentiation. Then, they combined Transient Transcriptome Sequencing (TT-Seq) with newly generated ATAC-Seq and ChIP-seq for the H3K4me1 enhancer mark to identify enhancer elements that are transcribed in at least one of the timepoints, highlighting a subset of enhancers whose transcription changes upon transdifferentiation. To further disentangle the order of events underpinning transdifferentiation they performed ChIP-seq for the C/EBPα TF, whose binding is associated with chromatin opening and later transcriptional activation, further supporting the proposed role of C/EBPα as a pioneering factor.In order to tackle enhancer cooperative behaviors, the authors used two distinct assignment strategies to pair enhancers to their target promoter(s) and report that (1) mRNA transcription of target genes correlate with the number of assigned enhancers and that (2) the sum of eRNA transcription at all assigned enhancers outperforms eRNA transcription at individual loci in predicting mRNA transcription at multiple enhancer genes. By fitting additive, synergistic and logistic models to their time-course datasets, the authors find enhancers predicted to cooperate in an additive but also synergistic fashion, meaning that mRNA output of their assigned target gene(s) is higher than expected from the sum of eRNA transcription at cognate enhancers. Finally, they show that synergistic enhancers are mostly associated with cell-type specific genes and are enriched for cell-type specific TF binding motifs, although they do not show higher C/EBPα, chromatin accessibility or BRD4 levels, as previously reported for cell-type specific "super-enhancer" hubs.The authors address an important and timely question in the field of gene regulation, i.e. how do multiple enhancers cooperate to regulate their target genes. To achieve this goal, the authors produce a compelling and rich dataset, analyze various aspects of it and apply a modeling approach to assign enhancers to additive and synergistic modes.Overall, this study presents a comprehensive characterization of transcription and various chromatin aspects  for a transdifferentiation timecourse from human B lineage cells to macrophages – a true tour-de-force. We however find that the authors partly overstate their findings and draw overly strong conclusions without adequately discussing caveats. We therefore recommend that the authors carefully revise their manuscript prior to publication to address two major concerns. The two major concerns relate to 1. some important untested assumptions that underlie the key finding of the manuscript, i.e. enhancer synergy and 2. partly overly strong conclusions that for example incorrectly imply causation from correlative analyses.1) Potential caveats to the key finding – enhancer synergyThe additive and synergistic cooperative behaviors reported by the authors heavily rely on (i) eRNAs – or TT-seq signals – accurately reflecting enhancer activity, (ii) a truly comprehensive set of enhancers, and (iii) enhancer-promoter assignment strategies. The authors need to address or discuss these points in more depth, as their key finding, i.e. the existence of enhancer synergy, crucially relies on all three assumptions being correct – any underestimation of the number of enhancers or their activities will unavoidably make the enhancers appear synergistic even if they are not. We comment on each of these points separately and then discuss some implications in more depth below.Drosophila. Whether or not transcription rates truly reflect enhancer activities is far from clear: Mikhaylichenko et al. for example state "the levels and directionality of transcription are highly varied among active enhancers. […] enhancer RNA (eRNA) production and activity are not always strictly coupled", and in human HeLa and HepG2 cells, CAGE-eRNA-signals do not quantitatively correlate with enhancer activities . In addition (more minor), at the core of TT-Seq is the transient sparse labeling of newly synthetized RNAs and their subsequent enrichment by affinity purification. We recommend discussing in more detail TT-seq's sensitivity and ability to reliably detect short, unstable and lowly abundant eRNAs (compared to more stable and longer mRNA molecules).(i) The authors treat this caveat rather superficially, referring to two recent publications  that study transcription at enhancers in ii) The authors define enhancers as TT-Seq Transcription Units (TUs) and further require them to be enriched for both H3K4me1 and ATAC-Seq signals. Through these latter criteria, only 8,165 of 26,130 TUs are defined as enhancers, leaving the vast majority of about 18,000 TUs unassigned. While stringent filtering makes much sense in general, a systematic underestimation of the number of enhancers might strongly confound the analysis of enhancer cooperativity. As the H3K4me1 mark is not required for enhancer activity , the authors should consider whether these unannotated TUs might also stem from enhancer elements and e.g. from gene loci that show synergistic effects.iii) In addition to potentially missing enhancers by an overly stringent enhancer definition (point ii), the number of enhancers per gene can also be underestimated at the step of enhancer-to-gene assignment. To mitigate this problem, the authors use two complementary assignment strategies, "neighboring" and "1Mb", which make sense yet have their caveats. While the 1Mb method should be more inclusive, it for example imposes a 0.4 correlation coefficient between eRNA and mRNA changes, thus limiting the pertinence of this approach for further hypothesis testing (see major concern 2). More importantly though, the two approaches don't seem to yield very consistent results: 136 genes  were classified as synergistic by the neighboring method and 194 by the 1Mb method (subsection “Enhancer synergy is a robust phenomenon”), yet only 57 could be tested by both methods of which 33 were consistently classified as synergistic. While the authors claim that this corresponds to a strong overlap , it is unclear why so few genes can be tested by both methods and why of the 136 or 194 synergistically regulated genes, respectively, only 33 are consistent between methods. At the very least, these numbers suggest that the classification to the additive or synergistic models strongly depends on the enhancer-to-gene assignment.With these caveats in mind, it is not clear whether the manuscript conclusively demonstrates the existence of synergistic enhancer cooperativity and what to recommend. A systematical under-estimation of enhancer activities or their dynamics  would bias the analysis towards seemingly synergistic enhancer cooperativity, especially for genes that are highly expressed and/or strongly upregulated.To reach strong conclusions, one would probably have to directly test combinations of enhancers or perform CRISPR-Cas9-mediated deletion of additive and synergistic enhancers with similar individual activities (as has been done in papers claiming or refuting enhancer synergy of e.g. superenhancers). One would expect that the deletion of an additive enhancer would have a limited impacted on downstream mRNA transcription and transdifferentiation efficiency, while the deletion of a synergistic enhancer would lead to stronger effects and might potentially impede transdifferentation.However, especially given the data-richness of the manuscript, such major additional efforts seem to be beyond the scope of this project. Maybe the authors could more openly discuss the caveats listed above and how they might confound the analyses and/or perform additional sanity checks, maybe some of the following:– simulations to assess the potential influence of underestimating enhancer activities or enhancer number on the additivity vs. synergy estimates. For example, the authors could determine how a simulated increase of eRNA transcription  impacts the assignment of gene loci to additive, synergistic and/or logistic classes.– a systematic analysis of potential correlations of synergistic genes with the number of enhancers. The author used gene loci containing 2 to 20 enhancers for model fitting and it would be informative whether synergistic loci overall contain more enhancers compared to additive ones.2) Data presentation and overly strong conclusionsThe authors describe and analyze their datasets by a combination of rule-based filtering and direct comparisons. This strategy is valid, yet can easily suffer from circular logic if the rules influence the comparisons, which can render results trivial. We recommend that the authors carefully revise their manuscript to remove analyses that might suffer from circular logic or discuss such caveats and tone down their conclusions.For example, the authors require "nucleosome depletion" to define enhancers and subsequently study 4550 C/EBPa binding sites "with low or undetectable chromatin accessibility at 0h", which unavoidably leads to the observation that chromatin opens. Similarly, the "1Mb" method to assign enhancers to promoters includes a requirement that the activities need to be correlated, which means that correlations  cannot be seen as results.Similarly, the authors draw strong causal conclusions from correlative analyses , which should be avoided.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kevin Struhl as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Harinder Singh (Reviewer #1).Thank you for submitting your article "Additive and synergistic action of mammalian enhancers during cell fate determination" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Choi et al. analyze the transcription changes at enhancers and promoters during estrogen-induced transdifferentiation of a human CEBPa-ER-expressing B cell lymphoma cell line into a macrophage state. By integrating diverse data  at enhancers, the authors propose that C/EBPa binding opens chromatin, increases H3K4me1 in its vicinity, and is followed by eRNA transcription. They conclude that C/EBPa functions as a pioneer factor. The manuscript studies gene transcription, enhancer activities and chromatin properties during transdifferentiation of human B cells to macrophages. While the sums of enhancer activities explain gene transcription for most genes, they do not for over 130 gene loci, suggesting that the enhancers in these loci might cooperate synergistically. The study's novelty lies in the application of eRNA kinetics during differentiation and the finding that synergistic enhancer/promoter sets are enriched for both lineage-determining genes and binding of cell type-specifying transcription factors.The authors define temporally co-regulated pairs and cohorts of enhancers and promoters, and find 3-fold more mRNA/eRNA sets that fit an additive abundance model over those that exhibit an exponential (synergistic) relationship. Although the synergistic sets of enhancers are enriched for cell type-specific genes, similar to what has been described for super-enhancers, they appear to be distinct from super-enhancers defined previously in the same cells. Motif analysis of deeply sequenced ATAC-seq data indicates that synergistic enhancer sets are enriched of additive enhancer sets for cell type-specific transcription factors involved in lineage determination.The authors have substantially revised their manuscript in response to previous comments. In the light that experimental verification of the predicted additive versus synergistic enhancer action was not feasible, the message of the manuscript could be strengthened further in multiple ways. First, the authors should disclose lists of (i) identified additive and synergistic enhancer cohorts and associated genes and (ii) the super-enhancers they identified, including their genomic coordinates. Similarly, the authors should disclose the full results of the GO analysis for all enhancer/super-enhancer set. Additionally, they could provide genome browser tracks and/or more screenshots of example loci. Second, the authors could verify enrichment of lineage-determining transcription factors in synergistic enhancers by ChIP-seq. Finally, the manuscript would benefit from focusing further on the novel aspects it presents and attenuating remaining claims that could be perceived as overstatements.Revisions:1) As the authors could not directly validate enhancer synergy, they indirectly infer its existence by the following logic: They show that the sum of enhancer activities does not match the transcription levels for some genes but leaves some gap. They use this gap to argue that the identified enhancers must be super-additive, i.e. synergistic, in order to fill this gap. While plausible, such an inference is different from the unambiguous demonstration of enhancer synergy. This fact will need to be better reflected in the Title and Abstract. Otherwise, a superficial reader might assume that synergy has been demonstrated directly. This wording could for example directly state the authors' reasoning, namely that a gap in observable enhancer activities suggests the existence of enhancer synergy for some genes. The last sentence of the revised Abstract needs to provide clarity on the comparison being made between synergistically functioning enhancer clusters and super-enhancers. It could read: "Enhancer synergy appears to be dependent on binding of cell type-specific transcription factors and such interacting enhancers are not predicted from occupancy or accessibility data that are used to detect super-enhancers."2) The claim that enhancer synergy "could not be predicted from occupancy or accessibility data that are used to detect super-enhancers" hinges on the comparison of eRNA-derived enhancer cohorts with super-enhancers. This comparison retains two issues that still need to be addressed. First, super-enhancers represent regions of high enhancer activity, originally defined by Med1 or H3K27ac ChIP-seq. Consequently, open chromatin (ATAC-seq) or the locations of a pioneer factor (C/EBPa) that induces open and poised – but not necessarily active – chromatin is not necessarily expected to yield meaningful results in terms of defining genomic regions with cell type-specific and lineage-defining enhancer and gene activity. This would leave the BRD4 ChIP-seq data as the sole signal to define meaningful super-enhancers. However, its high peak count of >98,000 raises concerns about potential ChIP quality issues that unfortunately cannot be assessed because the authors fail to provide additional information on these ChIPs other than the peak count. That the Spearman rho values for BRD4 ChIP replicates fluctuate between 0.72 and 0.99 also do not inspire confidence. Here, a H3K27ac as a broader and more robust ChIP-seq marker of enhancer and transcription activity might help to improve super-enhancer calling, should BRD4 turn out to be sub-optimal. Second, save for a handful of super-enhancer-associated genes that have been included in the revised manuscript, the biological relevance (i.e. gene ontology term enrichment) of the identified super-enhancer-associated genes cannot be verified by readers, since the authors provide neither the corresponding list of genes nor an analysis of the super-enhancer-enriched GO terms. At a minimum, the authors should provide these gene lists for the different super-enhancer methods and additional quality information of the ChIP-seqs used to identify super-enhancers, perform GO term analyses and provide the full list of significantly enriched GO terms, to be able to gauge whether they identify true super-enhancers with the corresponding expected characteristics , and whether these are indeed distinct from synergistic enhancers.3) The analysis of transcription factors in synergistic enhancers versus additive ones is not fully developed and contains inaccuracies and confusing interpretations of the data. Specifically, there is confusion over the two motifs that were determined to be enriched in additive enhancers, for FOS and E2F family factors.a) The motif for "FOS family" factors (TGAc/gTCA) is actually the motif to which most AP-1 family heterodimers . Indeed, AP-1 family factors (and not just FOS) play cell type-specific roles in macrophage development and function and their activity is low or undetectable at B cell enhancers by motif analysis . Consequently, the statement that "additive enhancers were not [enriched for macrophage-specific TFs]" is incorrect, especially in the context of this study of B cell (-like cell) trans-differentiation into macrophage-like cells, and it is not questionable whether FOSL1 as given in Figure 6—figure supplement 1C is indeed the factor that binds these motifs, and which of the Jun-like factors that become upregulated are involved in AP-1 heterodimer binding to them.b) There appears to be a disconnect between the interpretation of the authors' finding that E2F motifs are enriched in additive enhancers, their statement that E2F factors have regulatory functions in macrophages and the expression data in Figure 6B showing that E2F factors are rapidly downregulated upon estrogen addition, which is in line with the cell cycle arrest observed in these cells when leaving the B cell-like state . Together, this indicates that E2F factors would only be able to bind to additive enhancers and play a role in their activation in the proliferative B cell-like state, but not in the macrophage-like state. If so, then why are E2F motifs enriched in late-time point enhancers?4) The number of C/EBPa ChIP-seq peaks is surprisingly low for an overexpressed pioneer transcription factor, especially while other ChIPs in the data set have unusually large numbers of peaks . These peak numbers are unusual and raise data quality concerns, and for C/EBPa might also explain the small number of C/EBPa-defined "super-enhancers". Further, the peak numbers identified by ATAC-seq are not provided, and here the Spearman's rho was only calculated for promoters, which is problematic in a manuscript dedicated to enhancers and raises additional data QC concerns. Providing additional QC data such as FRiP and missing peak numbers, preferentially in a tabulated form, as well as additional genome browser track screenshots at different resolutions  are recommended. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:The manuscript tackles an interesting question, but it completely lacks experimental verification of the hypothesis generated by the very intricate but poorly explained analysis. Without experimental validation of the prediction that some enhancer sets act synergistically while other are only additive, the major conclusions of the manuscript remain preliminary.1) The manuscript is devoid of performance metrics for the sequencing experiments, making it impossible to gauge data quality. No comparison/reproducibility analysis of the replicates is presented, nor data on IP quality, peak numbers, read counts, IP efficiency.7, and 2x108 for TT-seq. Spearman's correlation coefficients between replicates were 0.99 for total RNA-seq and TT-seq on protein coding genes at all time points. The average read numbers obtained were 4x107 for C/EBPa ChIP-seq, 7x107 for H3K4me1 ChIP-seq and 2x107 for BRD4 ChIP-seq. Spearman's correlation coefficients between replicates were 0.94-0.98 for C/EBPα ChIP-seq, 0.96-1 for H3K4me1 ChIP-seq, and 0.72-0.99 for BRD4 ChIP-seq. The number of peaks were 14,561, 55,904 and 98,757 for C/EBPa, H3K4me1 and BRD4, respectively. The average read numbers obtained were 3x107 for ATAC-seq. Spearman's correlation coefficients between replicates were 0.93-0.98 for ATAC-seq on promoters.We have added these metrics to the Materials and methods section. The average read numbers obtained for total RNA-seq were 1x10The super-enhancer analysis is not internally validated by assessing whether the super-enhancers identified using BRD4, C/EBPa or ATAC-seq make biological sense (are the identified super-enhancers lineage genes?), which would be important for the conclusion that synergistic enhancers are distinct from super-enhancers.Superenhancers are identified according to the common procedure as enhancers that show high signals of BRD4 ChIP-seq, C/EBPa ChIP-seq or ATAC-seq. As previously reported, some of these enhancers target cell type-specific genes, such as CD14, DDIT4, JUNB, FOSL2 etc. However, there are some superenhancers commonly found at different cell states, for example at DNMT1, JMJD8 and POLE genes. As shown in Figure 5—figure supplement 3B, some of the superenhancers identified from macrophage-like cells and their target genes are transcribed more in B-cells (0h) than macrophage-like cells (96h). Altogether, the superenhancers identified using BRD4, C/EBPa ChIP-seq and ATAC-seq include both lineage genes and common genes.2) For validation, enhancer cooperativity could be tested in reporter assays with a handful of enhancers of either type, alone or in combination. Alternatively, Cas9-mediated knockout of increasing numbers of enhancers in a given gene/enhancer set would be predicted to have linear or non-linear effects on their target gene, depending on whether they are synergistic or additive.We have spent one year to conduct the suggested experiments, unfortunately without success. We have now decided to submit the revised version without the suggested experiments. We have changed the text and conclusions to reflect the lack of validation by enhancer knockouts/reporter gene assays. Our attempts are summarized below as an explanation to the reviewer. We note we are not able to continue these efforts.a) CRISPR/Cas9 enhancer knockout experiments:We first tried to obtain stable cell lines containing Cas9-mediated knockouts of increasing numbers of enhancers but this turned out to be impossible. Sometimes heterozygous cell lines resulted, and in other cases we could not obtain the desired knockouts. We believe that the enhancers we target are critical for cell growth, preventing analysis by CRISPR knockout.b) Reporter gene assaysDue to these difficulties, we switched to reporter gene assays as suggested by the reviewer. For the luciferase reporter assay, we selected two representative gene candidates with three additive or synergistic enhancers (FTH1 and PELI1 respectively). We cloned target gene promoters alone or in different combinations with one, two or three enhancers (5 constructs per each candidate) into luciferase gene containing plasmid. We performed transdifferentiation of BLaER cells until 96 h and collected induced macrophage-like cells (iMacs). We transfected iMacs with different constructs and measured luciferase activity after 24 h of transfection using Dual-Luciferase Reporter Assay System . In total, we tested two transfection systems: electroporation with Neon Transfection System (Thermo Fisher Scientific) and lipofection with FuGENE HD Transfection Reagent . Unfortunately, it was not possible to get reproducible results for the experimental constructs from the technical and biological replicates due to the complexity of the system. Another complication stems from the need to use (as we did) the endogenous target promoter. Please understand we have to investigate reporter gene activity under transdifferentiation conditions and to our knowledge this has never been achieved. Provided the fluctuations often observed with reporter gene assays, in retrospect it is not surprising that we could not obtain reproducible signals.3) The use of estrogen to activate the fusion protein could lead to estrogen receptor-dependent enhancer activation. The authors should clarify whether they see these effects, and if so, how they discern them from the C/EBPa-mediated effects.We focus our enhancer activation analysis on C/EBPa binding sites . Additionally, the enhancer cooperation we observe would not be a consequence of C/EBPa activation only, but rather a general behavior of enhancers that comes from the cooperative actions of TFs . Thus, estrogen receptor dependent enhancer activation, if any, would not change our conclusion.In addition, a corresponding inducible mouse cell (C10) is available with the exogenous estrogen receptor fused to C/EBPa . The effect of estrogen receptor activation could be tested by comparing the gene expression between an inducible cell line (C10) and the mother cell line (HAFTL) treated with b-estrogen treatment. As shown in a MA (ratio intensity) plot (GSE17316), no significant changes in gene expression were observed . . This is now mentioned in the text.4) The Discussion lacks discussion of previous findings; for example, inducible TF binding has previously been shown induce histone methylation (H3K4me1) at enhancers and nucleosome remodeling , which is corroborated by the current manuscript.We thank the reviewer for pointing this out and we added the missing references and included these points in discussion. It is well agreed that TF binding induces histone methylation (H3K4me1) and nucleosome remodeling at enhancers. However, whether eRNA transcription is required for H3K4me1 deposition at enhancers is still controversial. The study mentioned in the reviewer's comment suggests that inhibition of Pol II elongation reduces H3K4me2 .For short-term transcriptional responses, eRNA transcription elongation has been shown to be required for MLL4-mediated H3K4me1/2 deposition at enhancers, while in this manuscript eRNA transcription occurs after H3K4me1 deposition – how do the authors explain this mechanistic difference?We thank the reviewer for pointing this out. It has also been shown that knockout of MLL3/4 causes reduction of enhancer Pol II occupancy and eRNA synthesis . Moreover, H3K4me1 can precede nucleosomal depletion, which is necessary step prior to transcription . Collectively, H3K4me1 and enhancer transcription likely affect each other. We edited the manuscript accordingly.Reviewer #2:The authors are attempting to address several fundamental issues concerning the developmental control of mammalian gene transcription by deploying various genome wide methodologies and computationally integrating their findings. They have previously described a useful model system to analyze such regulatory processes. The experimental system involves a B cell line that undergoes transdifferentiation into a macrophage by the ectopic and inducible expression of the transcription factor C/EBPa. Using this system and performing transient transcriptome sequencing (TT-seq), to analyze eRNAs and promoter output, as well as C/EBPa ChIP-seq, H3K4me1 ChIP-seq and ATAC-seq they are able to put together a temporal sequence of molecular events for C/EBPa targeted genes. The experiments are rigorous and enhanced by the kinetic analyses.Such temporal analysis has been performed in other systems and several of the key conclusions reached are similar to those of earlier studies. In particular that C/EBPa binds to a class of genomic sites which are accessible as well as those that are inaccessible (ATAC-seq). For the latter set, chromatin accessibility follows C/EBPa binding and H3K4me1.The new element in this work is the genome-wide analysis of eRNAs, used as a proxy for endogenous enhancer activity, and their correlation with the activities of nearest (or within I Mb) promoters. Based on such analysis the authors conclude that for promoters that are acted on by multiple enhancers, most enhancers function additively. However for a smaller set of genes, enhancers appear to function synergistically and these genes are enriched for cell type specific functions.A current model for super enhancer action involving liquid-liquid phase transition  is suggested to underlie the basis of enhancer synergy.There are two major issues to be addressed:1) In this study, enhancers are inferred to be connected to their cognate promoters on the basis of nearest neighbors or by using an arbitrary genomic distance (I Mb). Although such criteria are widely used they have been supplanted by Hi-C or promote capture Hi-C analysis as a means of directly analyzing enhancer-promoter interactions. Given that this connectivity is critical for the subsequent analysis and conclusions focusing on enhancer action (additive versus synergistic) on different types of target genes, the authors need to use of one of the above approaches.Although we used two criteria that are widely accepted, we followed the reviewer’s request and now also incorporated Hi-C data to confirm the enhancer-promoter interactions. TAD boundaries were computed from the Hi-C data and used to study whether the enhancer promoter pairs (EP pairs) are located within the same TAD. Confirming our analysis, 98.3% of the EP pairs obtained by the neighboring method were found within the same TAD, and 96.2% of the EP pairs obtained with 1Mb method were found within the same TAD at one or more time points. We also employed the third pairing method by pairing enhancers to the correlating promoters within the same TAD obtained from Hi-C data. The synergistically regulated genes found with the neighboring and TAD pairing methods significantly overlapped, supporting our conclusions on synergistic enhancer action. This information was added to the text and figures.2) Although not emphasized in the Abstract the authors fail to uncover evidence for super enhancers  operating in their cellular system in the selective control of key differentiation genes. They suggest that their method of utilizing eRNAs rather than mediator binding revealed by ChIP-seq is better at distinguishing functionally distinct classes of enhancers. This major conclusion needs to be elaborated and deserves to be featured in the Abstract. From a mechanistic standpoint there appears to be a problem that needs to be better discussed by the authors. As noted above a current model for super enhancer action involving liquid-liquid phase transition is invoked by the authors to underlie the basis of enhancer synergy. However in their study the identified super enhancers appear to function either additively or synergistically. Can these different types of analyses be reconciled or perhaps not?We agree this is an important finding. As suggested by the reviewer, we have now mentioned this point in the Abstract and discuss it better in the manuscript, but avoiding speculative conclusions. Briefly, superenhancers are identified as enhancers with high signals of BRD4 ChIP-seq, C/EBPa ChIP-seq or ATAC-seq. As previously reported, some of these enhancers target cell type specific genes, such as CD14, DDIT4, JUNB, FOSL2 etc. However, there are some superenhancers commonly found at different cell states, for example at DNMT1, JMJD8 and POLE genes. As shown in Figure 5—figure supplement 3B, some of the superenhancers identified from macrophage-like cells and their target genes are transcribed more in B-cells (0h) than macrophage-like cells (96h). Altogether, the superenhancers identified using BRD4, C/EBPa ChIP-seq and ATAC-seq include both lineage genes and common genes. It has been shown that not all super enhancers act synergistically (see Introduction). Meanwhile, the synergistic enhancers identified with RNA synthesis clearly showed higher frequency of long-range EP interactions than additive enhancers (see above). A recent report also showed that BRD4 inhibition does not disrupt EP interactions (biorxiv: doi: https://doi.org/10.1101/848325), and this was included in the Discussion.Reviewer #3:In this manuscript, Choi and colleagues study enhancer cooperativity – i.e. how multiple enhancers collaboratively regulate the transcription of their target genes – during transdifferentiation of human B cells to macrophages at a genome-wide scale. To do so, they used an estrogen-inducible system driven by the C/EBPα pioneer factor and monitored transcriptional and chromatin dynamics over time. By using RNA-Seq, they first show that two main waves of transcriptional changes underline B cell to macrophage transdifferentiation. Then, they combined Transient Transcriptome Sequencing (TT-Seq) with newly generated ATAC-Seq and ChIP-seq for the H3K4me1 enhancer mark to identify enhancer elements that are transcribed in at least one of the timepoints, highlighting a subset of enhancers whose transcription changes upon transdifferentiation. To further disentangle the order of events underpinning transdifferentiation they performed ChIP-seq for the C/EBPα TF, whose binding is associated with chromatin opening and later transcriptional activation, further supporting the proposed role of C/EBPα as a pioneering factor.In order to tackle enhancer cooperative behaviors, the authors used two distinct assignment strategies to pair enhancers to their target promoter(s) and report that (1) mRNA transcription of target genes correlate with the number of assigned enhancers and that (2) the sum of eRNA transcription at all assigned enhancers outperforms eRNA transcription at individual loci in predicting mRNA transcription at multiple enhancer genes. By fitting additive, synergistic and logistic models to their time-course datasets, the authors find enhancers predicted to cooperate in an additive but also synergistic fashion, meaning that mRNA output of their assigned target gene(s) is higher than expected from the sum of eRNA transcription at cognate enhancers. Finally, they show that synergistic enhancers are mostly associated with cell-type specific genes and are enriched for cell-type specific TF binding motifs, although they do not show higher C/EBPα, chromatin accessibility or BRD4 levels, as previously reported for cell-type specific "super-enhancer" hubs.The authors address an important and timely question in the field of gene regulation, i.e. how do multiple enhancers cooperate to regulate their target genes. To achieve this goal, the authors produce a compelling and rich dataset, analyze various aspects of it and apply a modeling approach to assign enhancers to additive and synergistic modes.Overall, this study presents a comprehensive characterization of transcription and various chromatin aspects  for a transdifferentiation timecourse from human B lineage cells to macrophages – a true tour-de-force. We however find that the authors partly overstate their findings and draw overly strong conclusions without adequately discussing caveats. We therefore recommend that the authors carefully revise their manuscript prior to publication to address two major concerns. The two major concerns relate to 1. some important untested assumptions that underlie the key finding of the manuscript, i.e. enhancer synergy and 2. partly overly strong conclusions that for example incorrectly imply causation from correlative analyses.We thank the reviewer for the careful analysis and kind words. We carefully revised the manuscript to avoid overstating our findings and we now adequately discuss potential caveats.1) Potential caveats to the key finding – enhancer synergyThe additive and synergistic cooperative behaviors reported by the authors heavily rely on (i) eRNAs – or TT-seq signals – accurately reflecting enhancer activity, (ii) a truly comprehensive set of enhancers, and iii) enhancer-promoter assignment strategies. The authors need to address or discuss these points in more depth, as their key finding, i.e. the existence of enhancer synergy, crucially relies on all three assumptions being correct – any underestimation of the number of enhancers or their activities will unavoidably make the enhancers appear synergistic even if they are not. We comment on each of these points separately and then discuss some implications in more depth below.Drosophila. Whether or not transcription rates truly reflect enhancer activities is far from clear: Mikhaylichenko et al. for example state "the levels and directionality of transcription are highly varied among active enhancers. […] enhancer RNA (eRNA) production and activity are not always strictly coupled", and in human HeLa and HepG2 cells, CAGE-eRNA-signals do not quantitatively correlate with enhancer activities . In addition (more minor), at the core of TT-Seq is the transient sparse labeling of newly synthetized RNAs and their subsequent enrichment by affinity purification. We recommend discussing in more detail TT-seq's sensitivity and ability to reliably detect short, unstable and lowly abundant eRNAs (compared to more stable and longer mRNA molecules).(i) The authors treat this caveat rather superficially, referring to two recent publications  that study transcription at enhancers in As recommend, we are now discussing in more detail TT-seq's sensitivity and ability to reliably detect short, unstable and lowly abundant eRNAs . During T-cell stimulation, transcription from enhancers and promoters of responsive genes is activated simultaneously . Enhancers can be paired with their putative target gene promoters based on their proximity . In addition, we have shown that eRNA synthesis measured with TT-seq highly correlated with the putative target gene synthesis , implicating that enhancer transcription activity can indeed reflect enhancer activities. As eRNA production is a very good proxy for enhancer transactivation activity , TTseq can be used to identify active enhancers, to pair enhancers with their putative target promoters, and to measure the transcription activity of enhancers and promoters genome wide.ii) The authors define enhancers as TT-Seq Transcription Units (TUs) and further require them to be enriched for both H3K4me1 and ATAC-Seq signals. Through these latter criteria, only 8,165 of 26,130 TUs are defined as enhancers, leaving the vast majority of about 18,000 TUs unassigned. While stringent filtering makes much sense in general, a systematic underestimation of the number of enhancers might strongly confound the analysis of enhancer cooperativity. As the H3K4me1 mark is not required for enhancer activity , the authors should consider whether these unannotated TUs might also stem from enhancer elements and e.g. from gene loci that show synergistic effects.As suggested, we investigated whether unannotated TUs might also stem from enhancer elements. Among non-coding TUs, 13,952 TUs overlap with H3K4me1 signals, whereas 8,529 TUs overlap with ATAC-seq signals. The restriction rather comes from ATAC-seq, which is required for eRNA synthesis . However, to exclude the possibility that the unassigned TUs are left out due to the low sensitivity of chromatin accessibility detection etc, we have paired these unannotated TUs with the nearest genes, and repeated the analysis to identify synergistic enhancers. After excluding ncRNAs that are downstream RNAs, convergent RNAs, upstream antisense RNAs or that overlap with more than 20% of transcripts annotated in Gencode, 16360 TUs (or 15487 extended EUs after merging TUs within 1kb) remained for further analysis . Using the neighboring pairing method, 1184 of 1790 TUs were paired with more enhancers than previous analysis. In total, we observed 544 and 209 genes were regulated by additive and synergistic enhancers, respectively. Of 136 synergistically regulated genes that have been previously identified, 71% (97 genes) were regulated in synergistic manner . This indicates that the widespread synergistic behavior we observed was not due to an underestimation of the number of enhancers. We edited the text accordingly and this clarifies the reviewer’s concern.iii) In addition to potentially missing enhancers by an overly stringent enhancer definition (point ii), the number of enhancers per gene can also be underestimated at the step of enhancer-to-gene assignment. To mitigate this problem, the authors use two complementary assignment strategies, "neighboring" and "1Mb", which make sense yet have their caveats. While the 1Mb method should be more inclusive, it for example imposes a 0.4 correlation coefficient between eRNA and mRNA changes, thus limiting the pertinence of this approach for further hypothesis testing (see major concern 2). More importantly though, the two approaches don't seem to yield very consistent results: 136 genes  were classified as synergistic by the neighboring method and 194 by the 1Mb method (subsection “Enhancer synergy is a robust phenomenon”), yet only 57 could be tested by both methods of which 33 were consistently classified as synergistic. While the authors claim that this corresponds to a strong overlap , it is unclear why so few genes can be tested by both methods and why of the 136 or 194 synergistically regulated genes, respectively, only 33 are consistent between methods. At the very least, these numbers suggest that the classification to the additive or synergistic models strongly depends on the enhancer-to-gene assignment.The reviewer is correct and the relatively low number of consistent E-P pairs depends on the type of assignment. Briefly, to test enhancer synergy rigorously, we can only use genes that are paired with more than two enhancers. Since the number of enhancers paired to each gene vary between the neighboring and 1Mb methods, only a small number of genes could be tested for enhancer synergy. Nevertheless, our results are consistent between two methods. As in the response to the reviewer #2, we also employed a third pairing method by pairing enhancers to the correlating promoters within the same TAD obtained from Hi-C data. The synergistically regulated genes found with the neighboring and TAD pairing methods significantly overlapped, supporting our conclusions on synergistic enhancer action.This information was added to the text and figures.With these caveats in mind, it is not clear whether the manuscript conclusively demonstrates the existence of synergistic enhancer cooperativity and what to recommend. A systematical under-estimation of enhancer activities or their dynamics  would bias the analysis towards seemingly synergistic enhancer cooperativity, especially for genes that are highly expressed and/or strongly upregulated.Although it is true that Figure 3B shows eRNAs are strongly synthesized at 96h in the LATE Cluster, eRNA synthesis is also observed at earlier time points in other clusters. In the originally submitted manuscript we had already excluded that there is a bias towards highly expressed genes by showing that synergistically regulated genes do not have systematically higher expression levels , and that not all LATE upregulated genes are synergistically regulated. In summary, with all these controls, we conclusively demonstrate enhancer synergy at selected loci.To reach strong conclusions, one would probably have to directly test combinations of enhancers or perform CRISPR-Cas9-mediated deletion of additive and synergistic enhancers with similar individual activities (as has been done in papers claiming or refuting enhancer synergy of e.g. superenhancers). One would expect that the deletion of an additive enhancer would have a limited impacted on downstream mRNA transcription and transdifferentiation efficiency, while the deletion of a synergistic enhancer would lead to stronger effects and might potentially impede transdifferentiation.Please compare our answers to reviewer #1. We spent one year trying both the enhancer knockout approach and reporter gene assays, without success, due to the complex nature of the system, which combines transdifferentiation and enhancer cooperativity. We now resubmit a revised version that is substantially edited and hope the reviewer is fine with publication of this strongly revised version.However, especially given the data-richness of the manuscript, such major additional efforts seem to be beyond the scope of this project. Maybe the authors could more openly discuss the caveats listed above and how they might confound the analyses and/or perform additional sanity checks, maybe some of the following:– simulations to assess the potential influence of underestimating enhancer activities or enhancer number on the additivity vs. synergy estimates. For example, the authors could determine how a simulated increase of eRNA transcription  impacts the assignment of gene loci to additive, synergistic and/or logistic classes.We thank the reviewer for stating that major additional efforts are beyond the scope of this project. We have carried out the suggested test. In the original manuscript we had included more enhancers around a few genes and showed that this did not change conclusions with respect to enhancer synergy. We have now done such testing more systematically and added a statement to the text. Briefly, we simulated increase of eRNA transcription and determined synergistic genes. Synergistically regulated genes were hardly affected by the increase of eRNA transcription .– a systematic analysis of potential correlations of synergistic genes with the number of enhancers. The author used gene loci containing 2 to 20 enhancers for model fitting and it would be informative whether synergistic loci overall contain more enhancers compared to additive ones.We carried out this test and found that there is no difference in enhancer numbers between additive and synergistic loci  (ECDF plot). We added this to the manuscript .2) Data presentation and overly strong conclusionsThe authors describe and analyze their datasets by a combination of rule-based filtering and direct comparisons. This strategy is valid, yet can easily suffer from circular logic if the rules influence the comparisons, which can render results trivial. We recommend that the authors carefully revise their manuscript to remove analyses that might suffer from circular logic or discuss such caveats and tone down their conclusions.We understand the concern and went through the manuscript and made sure there are not analyses that might suffer from circular logic.For example, the authors require "nucleosome depletion" to define enhancers and subsequently study 4550 C/EBPa binding sites "with low or undetectable chromatin accessibility at 0h", which unavoidably leads to the observation that chromatin opens.This may be a misunderstanding. We define enhancers based on enhancer transcription, which can only occur after nucleosome depletion because the Pol II machinery needs to access DNA. Many of these enhancers showed changes in activities . Our question was how enhancers are newly activated from closed chromatin to active transcription. The basic premise here is that enhancers undergo chromatin opening. Then the question is on the temporal relationship of transcription factor binding, H3K4monomethylation, chromatin accessibility and transcription activities at the sites undergoing chromatin opening. In fact, this cannot be observed if chromatin does not open.Similarly, the "1Mb" method to assign enhancers to promoters includes a requirement that the activities need to be correlated, which means that correlations  cannot be seen as results.We agree and thank the reviewer for spotting this. We removed these figure panels and the corresponding text.Similarly, the authors draw strong causal conclusions from correlative analyses , which should be avoided.We changed the title and trust the reviewer agrees to it. We edited the text to make sure there are no overly strong conclusions.[Editors’ note: what follows is the authors’ response to the second round of review.]Revisions:1) As the authors could not directly validate enhancer synergy, they indirectly infer its existence by the following logic: They show that the sum of enhancer activities does not match the transcription levels for some genes but leaves some gap. They use this gap to argue that the identified enhancers must be super-additive, i.e. synergistic, in order to fill this gap. While plausible, such an inference is different from the unambiguous demonstration of enhancer synergy. This fact will need to be better reflected in the Title and Abstract. Otherwise, a superficial reader might assume that synergy has been demonstrated directly. This wording could for example directly state the authors' reasoning, namely that a gap in observable enhancer activities suggests the existence of enhancer synergy for some genes. The last sentence of the revised Abstract needs to provide clarity on the comparison being made between synergistically functioning enhancer clusters and super-enhancers. It could read: "Enhancer synergy appears to be dependent on binding of cell type-specific transcription factors and such interacting enhancers are not predicted from occupancy or accessibility data that are used to detect super-enhancers."We appreciate the comment and changed the Abstract as suggested. We also changed the title to: “Evidence for additive and synergistic action of mammalian enhancers during cell fate determination”.2) The claim that enhancer synergy "could not be predicted from occupancy or accessibility data that are used to detect super-enhancers" hinges on the comparison of eRNA-derived enhancer cohorts with super-enhancers. This comparison retains two issues that still need to be addressed. First, super-enhancers represent regions of high enhancer activity, originally defined by Med1 or H3K27ac ChIP-seq. Consequently, open chromatin (ATAC-seq) or the locations of a pioneer factor (C/EBPa) that induces open and poised – but not necessarily active – chromatin is not necessarily expected to yield meaningful results in terms of defining genomic regions with cell type-specific and lineage-defining enhancer and gene activity. This would leave the BRD4 ChIP-seq data as the sole signal to define meaningful super-enhancers. However, its high peak count of >98,000 raises concerns about potential ChIP quality issues that unfortunately cannot be assessed because the authors fail to provide additional information on these ChIPs other than the peak count. That the Spearman rho values for BRD4 ChIP replicates fluctuate between 0.72 and 0.99 also do not inspire confidence. Here, a H3K27ac as a broader and more robust ChIP-seq marker of enhancer and transcription activity might help to improve super-enhancer calling, should BRD4 turn out to be sub-optimal. Second, save for a handful of super-enhancer-associated genes that have been included in the revised manuscript, the biological relevance (i.e. gene ontology term enrichment) of the identified super-enhancer-associated genes cannot be verified by readers, since the authors provide neither the corresponding list of genes nor an analysis of the super-enhancer-enriched GO terms. At a minimum, the authors should provide these gene lists for the different super-enhancer methods and additional quality information of the ChIP-seqs used to identify super-enhancers, perform GO term analyses and provide the full list of significantly enriched GO terms, to be able to gauge whether they identify true super-enhancers with the corresponding expected characteristics , and whether these are indeed distinct from synergistic enhancers.To provide more support that classically defined superenhancers do not correspond to the synergistic enhancers, we used H3K27Ac ChIP-seq data  as suggested by the reviewers. Consistent with our previous results, the superenhancers derived from H3K27Ac ChIP-seq signal included both synergistic and additive enhancers . Similar to superenhancers derived from other markers for high enhancer/transcription activity, RNA synthesis from the superenhancers which show high H3K27Ac levels at 96h and their paired promoters was not always the highest at 96h compared to other time points . The H3K27Ac level at synergistic enhancers was similar to the level at additive enhancers .Furthermore, we included a list of genes regulated by superenhancers derived from various ChIP-seq data in Supplementary file 1. To ensure these superenhancers correspond to the previously reported superenhancers, we compared the list to dbSUPER . Most of the superenhancers identified in this study could be found in the database (Supplementary file 1). Additionally, GO analysis was performed using superenhancers. GO terms with p values less than 0.05 were included for superenhancers derived from eRNA signal, ATAC-seq signal, BRD4 and C/EBPa ChIP-seq signal in Supplementary files 2, 4 and 5. GO terms with Bonferroni p value less than 0.05 were included for superenhancers derived from H3K27Ac ChIP-seq signal in Supplementary file 3. The resulting GO terms included immune response, inflammatory response and defense response. Finally, FRiP of ChIP-seqs can be found in Supplementary file 7. Additional genome browser screenshots are included in Figure 4—figure supplement 1E and Figure 5—figure supplement 4B.3) The analysis of transcription factors in synergistic enhancers versus additive ones is not fully developed and contains inaccuracies and confusing interpretations of the data. Specifically, there is confusion over the two motifs that were determined to be enriched in additive enhancers, for FOS and E2F family factors.a) The motif for "FOS family" factors (TGAc/gTCA) is actually the motif to which most AP-1 family heterodimers . Indeed, AP-1 family factors (and not just FOS) play cell type-specific roles in macrophage development and function and their activity is low or undetectable at B cell enhancers by motif analysis . Consequently, the statement that "additive enhancers were not [enriched for macrophage-specific TFs]" is incorrect, especially in the context of this study of B cell (-like cell) trans-differentiation into macrophage-like cells, and it is not questionable whether FOSL1 as given in Figure 6—figure supplement 1C is indeed the factor that binds these motifs, and which of the Jun-like factors that become upregulated are involved in AP-1 heterodimer binding to them.The reviewers are right that the motif for FOS family factors is bound by most AP-1 family heterodimers, which have cell type specific roles in macrophage development. We had mentioned this in the originally submitted manuscript. However, this motif was enriched in additive enhancers only at 12h when the cells did not establish macrophage specific program . At later time points in macrophage-like cells, these motifs were found both in additive and synergistic enhancers as no enrichment was shown in Figure 6—figure supplement 1B. We interpret this to mean that the TFs in FOS family and AP-1 family bind both in additive and synergistic enhancers. We improved the text to avoid any misunderstanding.b) There appears to be a disconnect between the interpretation of the authors' finding that E2F motifs are enriched in additive enhancers, their statement that E2F factors have regulatory functions in macrophages and the expression data in Figure 6B showing that E2F factors are rapidly downregulated upon estrogen addition, which is in line with the cell cycle arrest observed in these cells when leaving the B cell-like state . Together, this indicates that E2F factors would only be able to bind to additive enhancers and play a role in their activation in the proliferative B cell-like state, but not in the macrophage-like state. If so, then why are E2F motifs enriched in late-time point enhancers?E2F factors have general regulatory functions in many cell types including macrophages. Indeed, E2F1, a member of E2F factors, is rapidly downregulated upon estrogen addition. Yet, E2F3, E2F4 and E2F6, other members of E2F factors, maintains their expression level during transdifferentiation. Thus, it is possible that E2F motifs were enriched in late-time point enhancers due to the E2F factors without significant expression changes in macrophage-like cells such as E2F3, E2F4 and E2F6.4) The number of C/EBPa ChIP-seq peaks is surprisingly low for an overexpressed pioneer transcription factor, especially while other ChIPs in the data set have unusually large numbers of peaks . These peak numbers are unusual and raise data quality concerns, and for C/EBPa might also explain the small number of C/EBPa-defined "super-enhancers". Further, the peak numbers identified by ATAC-seq are not provided, and here the Spearman's rho was only calculated for promoters, which is problematic in a manuscript dedicated to enhancers and raises additional data QC concerns. Providing additional QC data such as FRiP and missing peak numbers, preferentially in a tabulated form, as well as additional genome browser track screenshots at different resolutions  are recommended.As additional QC data, peak numbers for ATAC-seq, Spearman’s rho for all ATAC-seq peaks and FRiP of ChIP-seq data are provided in Supplementary file 7. Genome browser screenshots of the datasets including BRD4 and H3K27Ac around CD14 are shown in Figure 4D and Figure 4—figure supplement 1). Genome browser screenshots around IRF8 and EBF1 are also shown in Figure 5—figure supplement 4B .


X-inactive-specific transcript (Xist) is a long non-coding RNA (lncRNA) essential for X-chromosome inactivation (XCI) in female placental mammals. Thirty years after its discovery, it is still puzzling how this lncRNA triggers major structural and transcriptional changes leading to the stable silencing of an entire chromosome. Recently, a series of studies in mouse cells have uncovered domains of functional specialization within Xist mapping to conserved tandem repeat regions, known as Repeats A-to-F. These functional domains interact with various RNA binding proteins (RBPs) and fold into distinct RNA structures to execute specific tasks in a synergistic and coordinated manner during the inactivation process. This modular organization of Xist is mostly conserved in humans, but recent data point towards differences regarding functional specialization of the tandem repeats between the two species. In this review, we summarize the recent progress on understanding the role of Xist repetitive blocks and their involvement in the molecular mechanisms underlying XCI. We also discuss these findings in the light of the similarities and differences between mouse and human Xist. X-inactive-specific transcript (Xist) is a long non-coding RNA (lncRNA) responsible for X-Chromosome Inactivation (XCI), the dosage compensation mechanism that equalizes X gene dosage between XX females and XY males in placental mammals. The importance of Xist in XCI regulation was uncovered by loss of function studies showing that lack of dosage compensation for the X chromosome results in female-specific lethality in mice [Xist at the central stage of lncRNA research. in mice ,2. TheseXist is the only gene expressed exclusively from the inactive X chromosome (Xi). This monoallelic expression is established through the concerted action of several cis- and trans-acting players during early embryonic development . Xist ciewed in ,5). How Xist gene produces a spliced ∼17-to-19 kb lncRNA. A remarkable feature of Xist is the existence of several conserved tandem repeat sequences, known as Repeats A-to-F  [Xist repeats seem to act in a synergistic and coordinated manner, orchestrating the process of XCI from its inception to its end. In this review, we summarize recent progress in our understanding of the role of the different Xist repeats and their protein interactors in XCI in the mouse system and discuss how these findings compare to what we know about the human XIST RNA.The s A-to-F . Despites (RBPs) . TogetheXist , a repressive modifying chromatin complex responsible for the tri-methylation of histone H3 on lysine 27 (H3K27me3) that is enriched along the Xi [The mechanistic understanding of the transcriptional silencing imposed by Repeat A has evolved considerably in the past decade. An initial model postulated that transcriptional silencing was achieved through the interaction with g the Xi . This mog the Xi ,19. In ag the Xi ,20,21 alSPlit ENds), a >400 kDa RBP containing a SPOC  transcriptional repressive domain, has been identified as a major player in Xist-dependent transcriptional silencing [RNA Recognition Motifs (RRM) 2–4 [Xist stability and spreading across the X chromosome undergoing inactivation [Xist, SPEN is recruited to both active promoters and enhancers on the X chromosome to effectively initiate gene silencing [Nuclear receptor Co-Repressor/ Silencing Mediator of Retinoic acid and Thyroid hormone receptor) complex [Histone DeACetylase 3) subunit, the NCoR/SMRT complex is responsible for deacetylating histones H3 and H4, one of the first chromatin events during XCI [Nucleosome Remodeling and Deacetylase) complex, RNA polymerase II (PolII) and associated factors, as well as the N6-methyladenosine (m6A) RNA methylation machinery [Among the Repeat A interactors, SPEN  2–4 ,20,22,25tivation . Upon inring XCI . Howeverring XCI , suggestRNA Binding Motif protein 15) and RBM15B also interact with Repeat A [Xist transcript, which includes a m6A hotspot just downstream of Repeat A [YTHDomain Containing 1) m6A reader [Xist spreads along the X chromosome . m6A isRepeat A . This moA reader ,30  protein complexes to the Xi upon initial establishment of Xist-mediated silencing by the Repeat A/SPEN module.The region of mouse A module . RepeatsXist cDNA transgene lacking Repeats B and C, along with Repeat F, called ΔXN, is unable to recruit the PRC2 complex to the Xi, in contrast with a Xist mutant lacking the Repeat A (ΔA) [Polycomb Repressive Complex 1 (PRC1), responsible for ubiquitination of the histone H2A at lysine 119 (H2AK119ub) [The first hint for an involvement of these repeats in PcG protein recruitment came from the observation that an inducible t A (ΔA) . This reAK119ub) . FurtherAK119ub) ,9. HowevAK119ub) ,10,13 (THeterogeneous Nuclear RiboNucleoProtein K) [ChromoBoX homolog) subunit of canonical PRC1 [How the B/C module recruits both PRC1 and PRC2 to the Xi and the corresponding sequence of events are questions that have been widely investigated. Work from different labs showed that cytosine-rich Repeats B and C act as docking sites for HNRNPK (otein K) ,19,33,34otein K)  and servotein K) ,35,36. Tcal PRC1 , thus reXist coating over the Xi and is initially observed at intergenic regions [Spen SPOC mutants will be important to disentangle between a direct or a passive role for Repeat A in PcG recruitment to X-linked genes undergoing silencing.The dynamics of PcG recruitment mediated by Repeats B/C follows  regions ,10,27. S regions  or simpl regions . In thisXist ΔB/C mutants [Xist-mediated gene repression [Xist spreading over the Xi [Structural Maintenance of Chromosomes flexible Hinge Domain containing 1), downstream of the PCGF3/PCGF5–PRC1 complex [Topologically Associating Domains (TADs) [Whereas Repeat A seems to be the main actor on the initial steps of XCI, Repeats B and C appear to take the center stage once XCI has been set in motion. Accordingly, X-linked gene silencing is initiated but is partially defective in  mutants ,41  ,45. A reXist transcript. It is composed of two types of tandem repeats: around 35 C/U/G-rich motifs of ∼16–27 bp at the 5′end and ∼25 C/U-rich motifs of 6–19 bp towards its 3′end. Different Xist ΔE deletions invariably result in delocalization of Xist from the Xi territory that aggravates as cells enter the maintenance stage of XCI [Xist ΔE to coat the X chromosome ultimately results in gene reactivation and loss of PcG marks at the maintenance stage of XCI [Repeat E is localized at the beginning of exon 7 and maps to nucleotides ∼10 275–11 400 of mouse e of XCI  (Table 1Xist to the Xi territory. One of the first interactors identified was CIZ1 (CDKN1A-Interacting Zinc finger protein 1). CIZ1 is a component of the nuclear matrix, a filamentous insoluble protein network that appears to participate in restricting Xist RNA molecules to the Xi [Xist coating after the initial wave of transcriptional silencing and PcG recruitment by Repeat A and Repeats B/C, respectively [in vitro [Xist RNA molecules to the Xi territory , a transcription factor important for Xist expression [Xist RNA, a phenomenon that has previously been documented for some ΔA mutants [XIST from its endogenous locus in male ESCs shows that deletion of this region has a major impact on transcriptional silencing due to loss of tethering of the Xi to Lamin B at the nuclear periphery [The other two repetitive modules, Repeats F and D have attracted less attention. Repeat F is the shortest of all repeats and is made of two copies of a 10 bp motif (UGGCGGGCUU) mapping to nucleotides ∼1540–1575 in the mouse s module ,10,52,53pression  and inte mutants ,16  [The Repeat D is the least characterized repetitive module of mouse use Xist , but up use Xist    : in Xist is conserved between mouse and humans  identified factors known to interact with mouse Xist, such as SPEN, RBM15/RBM15B, HNRNPK or CIZ1 as well as novel functional interactors, like TRIM28  [XIST interactor is not yet known. Besides a shared set of protein interactors, these interactors distribute in a similar fashion along the mouse and human Xist molecules as revealed by enhanced CLIP-seq (UV Cross-Linking and ImmunoPrecipitation coupled with high-throughput sequencing) [Xist in different species.Despite these sequence differences, the higher-order folding of exon 6/7 ,29. Moreeractome ,59. Protuencing) . Small dXIST in cancer cell lines, have started to explore the role of the repeats in the initiation and maintenance stages of human XCI, allowing to further uncover similarities and differences with mouse Xist [XIST gene results in a global up-regulation of X-linked genes [XIST were also made in K562, many of which result in multiple defects at the transcriptional and splicing levels and/or affect XIST coating [XIST mutants with no such defects in K562, it was revealed that exon 5, which does not contain any repeat module, has an important role in XCI maintenance that has not yet been mechanistically explored. In contrast, deletions of Repeat A and Repeat B/C show no or moderate effects on XCI maintenance, respectively, in K562 cells [Recent studies, resorting to segmental deletions of human use Xist ,62. In K coating . ConcentXIST in XCI maintenance in K562 contrasts with the situation in the mouse, where no visible effect on gene silencing is observed when Xist or its repeats are deleted in MEFs [XIST, but not mouse Xist, plays a role in the maintenance of XCI. As a matter of fact, the emerging picture is that the role of Xist in XCI maintenance is complex and cell-type specific, whether in mouse or human [A crucial role for human  in MEFs ,64. Howeor human ,65–68.XIST essential for H3K27me3 and H2AK119ub accumulation on the Xi. In contrast to mouse ESCs, PRC1 and PRC2 recruitment in HT1080 cells, rely on different XIST modules . Continiewed in , will beXist has been under the spotlight of lncRNA research. Although a myriad of intergenic, intronic and antisense lncRNAs have now been identified, the biological relevance of these molecules has remained challenging to define. In no small measure, our understanding of Xist has been central in promoting and inspiring the study of other nuclear-located lncRNAs which, despite being poorly conserved compared with protein-coding genes, share several functions and mechanisms of action. In this sense, the idea that lncRNAs can organize in structural modules, similarly to Xist, is a tantalizing hypothesis to explain how lncRNAs develop their tissue- and species-specific roles. Indeed, it was recently shown that Rsx (RNA-on-the-silent X), the lncRNA responsible for XCI in marsupials but with no homology to Xist, presents multiple tandem repetitive modules reminiscent of Xist repeats [Xist, Rsx might resort to different repetitive modules as functional entities to regulate XCI in marsupials. This example illustrates how further research on Xist RNA might have important implications for our understanding of other lncRNAs.Since its discovery, in the early nineties,  repeats . Just liXist in the past decades is undeniable, many open questions still remain. For example, the crosstalk between the different RNA modules and their protein interactors still needs to be mechanistically explored. Furthermore, while most studies have focused on dissecting Xist function during the establishment phase of XCI, a maintenance role for Xist has just started to be unveiled with cell-type-specific characteristics [XIST and human disease will certainly be one of the areas to explore in the future.Although the knowledge that has been acquired about eristics ,68. Thiseristics ). A possXist lncRNA is the master regulator of X-chromosome inactivation. Discovered thirty years ago, the molecular mechanisms through which Xist renders an entire chromosome into a stable inactive state remain enigmatic.Xist possesses several modules of functional specialization mapping to the conserved Repeats A-to-F. These functional modules execute specific tasks through interaction with various RNA-binding proteins in a coordinated manner during XCI. This modular organization and multitasking nature of Xist is conserved in humans, although the functional specialization of some repeats might have evolved novel functions.A series of deletions in mouse cells revealed that Xist repeats for the control of XCI. Also, the conservation of these events in humans is unknown and will need to be investigated in more appropriate cellular models. Finally, the translation of these findings to other lncRNAs might reveal new molecular insights in the function of these molecules in a vast plethora of biological systems.It still remains to be deciphered the molecular mechanisms underlying the interplay between