Mechanisms of gene regulation by SRCAP and H2A.Z

Domain: chemistry
Published: 2026-03-06
Editorial outcome: major_revision

Abstract
Discriminating regulatory functions of chromatin composition from those of chromatin-modifying complexes is a central problem in gene regulation. This question remains unexplored in the context of histone variants and their dedicated chromatin remodelers. Here we dissect the distinct and cell cycle-dependent functions of Snf2 Related CREBBP Activator Protein (SRCAP) and H2A.Z in gene regulation of pluripotent stem cells. Using acute degradation of endogenous SRCAP, we uncover dynamic changes of H2A.Z occupancy and continuous requirement of SRCAP over the cell cycle. We also engineered an SRCAP mutant, defective for H2A.Z deposition, allowing us to distinguish H2A.Z-dependent and independent functions of SRCAP. We discover that SRCAP exhibits essential H2A.Z-independent functions in inhibiting DNA binding of dozens of pioneer transcription factors at enhancers by steric hindrance. In contrast, H2A.Z acts mainly as a transcriptional repressor gatekeeping the expression of lineage-specific genes. Our study establishes the catalytic-independent role of a chromatin remodeler in broadly regulating transcription factor binding, and demonstrates how a chromatin remodeler-histone variant pair orchestrates transcription to maintain self-renewal and plasticity of pluripotent stem cells.

Introduction
In Eukaryotes, most DNA is wrapped around nucleosomes made of histone octamers. While two copies of the four canonical histones make up most nucleosomes, histone variants can replace them in specific positions of the genome. H2A.Z is a highly conserved H2A histone variant essential for zygotic genome activation in drosophila and zebrafish and for post-implantation development in the mouse 1–3. In vertebrates, there are two different isoforms of H2A.Z (H2A.Z.1 and H2A.Z.2) that differ by only 3 amino acids 4. H2A.Z plays a central role in self-renewal and lineage commitment of multiple types of stem cells, including mouse pluripotent stem cells (PSCs) 5–7. Both Snf2 Related CREBBP Activator Protein (SRCAP) and Tip60/p400 remodeler complexes ensure H2A.Z deposition in mammals 8,9. In mouse PSCs, H2A.Z deposition is mainly achieved by the SRCAP complex, made of the SRCAP protein bearing the catalytic ATPase activity and 9 other subunits 10. The SRCAP complex binds to canonical nucleosomes and swaps the H2A-H2B dimer with a H2A.Z-H2B dimer 8. It was also reported to facilitate the recruitment of some transcriptional regulators, but whether this function is independent of its role in depositing H2A.Z is unclear 11–13.

In PSCs, H2A.Z is broadly enriched at active and bivalent/poised promoters and enhancers 14,15. H2A.Z has been proposed to regulate transcription at multiple levels, i.e. by modulating chromatin accessibility, repressing DNA methylation, altering transcription factor (TF) binding and enhancing RNA Polymerase II pausing 16–20. H2A.Z nucleosomes protect shorter DNA segments compared to canonical nucleosomes, which was suggested to reflect nucleosome destabilization and to facilitate protein-DNA interactions 21.

Mechanistic understanding of transcriptional regulation by SRCAP and H2A.Z remains obscured by conflicting evidence and technical challenges. First, H2A.Z was reported to either increase or decrease both chromatin accessibility and TF binding 5,16,18,22,23. Second, whether H2A.Z-containing nucleosomes are evicted or replaced with canonical nucleosomes when H2A.Z is depleted is unclear 5,24,25. Thus, the specific function of H2A.Z with respect to H2A has not yet been addressed in vivo. Third, the proposed role of H2A.Z in inhibiting DNA methylation in mammals is only supported by weak evidence that is either correlative or uses slow H2A.Z knockdown approaches may cause indirect effects 26,27. Fourth, depletion of H2A.Z causes genome-wide redistribution of the yeast SRCAP homolog (SWR1) 28, and nucleosomal H2A.Z inhibits human SRCAP binding 23, potentially leading to SRCAP-mediated changes in recruiting transcriptional regulators 29.

Here, we use rapid degradation of endogenous SRCAP to dissect the direct and acute consequences of SRCAP loss and analyze the function of H2A.Z with respect to the canonical H2A histone. Overexpressing a SRCAP mutant defective for H2A.Z deposition allowed us to uncouple H2A.Z deposition from other SRCAP functions in transcriptional regulation. Our study reveals how SRCAP and H2A.Z coordinate their distinct functions to orchestrate gene regulation governing self-renewal and plasticity.

Results
Dynamic and cell cycle-dependent SRCAP-mediated H2A.Z positioning
To study the dynamics of H2A/H2A.Z exchange by SRCAP, we generated a CGR8 mouse embryonic stem cell (mESC) line allowing rapid degradation of the catalytic subunit of the SRCAP complex (SRCAP), using the auxin-inducible protein degradation system 30. We knocked-in YPet-AID (YA) into the 3’end of both alleles of srcap and stably expressed the OsTir1 receptor to generate the SRCAP-Ypet-AID Tir1 (SYAT) cell line. We validated the pluripotency of SYAT cells by Alkaline Phosphatase assay and RT-QPCR of pluripotency TFs (Supplementary Fig.S1a), nuclear interphase localization and previously-described mitotic chromosome association of SRCAP 31 (Supplementary Fig.S1b). CUT&RUN and CUT&TAG revealed SRCAP-YA enrichment at promoters and preferential colocalization with active transcription start sites (TSS) and H2A.Z peaks (Supplementary Fig.S1c, S1d and 1a), as reported for SWR1 in yeast 28. Additionally, by re-analyzing published data 32, we confirmed that SRCAP-YA was enriched at the same genomic locations than endogenous SRCAP in wt mESCs (Supplementary Fig. S1e). Upon addition of indole-3-acetic acid (IAA), SRCAP-YA was rapidly depleted (Fig.1b and Supplementary Fig.S1f), and H2A.Z signal on mitotic chromosomes was decreased (Supplementary Fig.S1g).

We then performed H2A.Z and H2A ChIP-seq (see Methods) at 2, 4, 6 or 8 hours after IAA addition. H2A.Z was depleted and replaced by H2A (Fig.1c and d) at 98% of H2A.Z ChIP-seq peaks, with the majority of peaks (72%) losing most H2A.Z within 2 h (Supplementary Fig.S1f). H2A.Z turnover at regions specifically enriched for H2A.Z.1 or H2A.Z.2 (using data from 20) was similar, suggesting a comparable impact of SRCAP depletion on H2A.Z.1 and H2A.Z.2 (Supplementary Fig.S1h). The rate of SRCAP-mediated H2A.Z exchange was highest at active promoters, followed by active enhancers, and slightly slower at bivalent and poised enhancers (Fig.1e- f). The pace of H2A.Z loss was particularly fast at nucleosomes surrounding TSS of active genes (Fig.1g) and scaled with their transcriptional activity (Supplementary Fig.S1i). At bivalent promoters, while H2A.Z occupancy was high, SRCAP-mediated H2A.Z turnover was slower. This is in line with slower eviction of H2A.Z leading to a less pronounced recruitment of SRCAP at these loci (Fig.1a). Also note that we cannot exclude that Ep400-Tip60 also contribute to H2A.Z deposition at bivalent TSS. Loci with the highest H2A.Z turnover were initially highly enriched for SRCAP, the SWI/SNF, NuRD and Ep400 chromatin remodelers, and several PSC TFs (Fig.1h and Supplementary Fig.S1j), suggesting their role in H2A.Z turnover regulation. We next examined H2A.Z co-localization with the large number of regions depending on the pioneer TFs (pTFs) OCT4 and SOX2 for chromatin accessibility maintenance in PSCs 33,34. H2A.Z was rapidly lost in these regions upon SRCAP depletion (Supplementary Fig.S1k). Furthermore, knockdown of OCT4 (see Methods and 35) rescued H2A.Z occupancy at OCT4-occupied regions (Supplementary Fig.S1l), suggesting that OCT4 mediates H2A.Z eviction. These findings are in line with H2A.Z nucleosome eviction by the transcriptional machinery and gene regulatory factors 36,37.

As TF binding, chromatin remodeler activity and transcriptional activity vary across the cell cycle, we determined H2A.Z positioning in early G1 (EG1), late G1 (LG1), S, G2/M phases, or mitosis (Methods and 34, 38). Global variations of H2A.Z through the cell cycle were modest (Fig.2a) and H2A.Z was mostly located at active and bivalent/poised regulatory elements (Supplementary Fig.S2a-b). H2A.Z was lost at active promoters and enhancers at the mitosis-EG1 transition, but accumulated at bivalent and poised enhancers (Fig.2b). We neither observed a mitotic shift of H2A.Z on the TSS nor a mitotic H2A.Z depletion at promoters as reported in different cell types 39, 40, suggesting cell type-specificity of H2A.Z positioning. We then classified regulatory elements according to changes in H2A.Z occupancy between G2 phase and mitosis (Fig.2c and Supplementary Fig.S2c). Global H2A.Z levels were equalized during mitosis at promoters and enhancers, in line with a quantitative resetting of the H2A.Z landscape before cells enter the next cell cycle (Fig.2d and Supplementary Fig.S2d). Promoters in cluster 1 and cluster 3 showed enrichment for Gene Ontology (GO) terms associated with lineage specification and general cellular functions (Supplementary Fig.S2e), respectively. Highly-expressed genes were more enriched in cluster 3 (Fig.2e), but enhancer activity levels were comparable in all enhancer clusters (Supplementary Fig.S2f). Next, we performed ChIP-seq on the acetylated form of H2A.Z (acH2A.Z), associated with active regulatory elements and positively correlated with transcription levels 41. Mitotic acH2A.Z levels were decreased in all three promoter clusters (Fig.2f and Supplementary Fig.S2g, S2h), reflecting a shift in the equilibrium between acetylation and deacetylation during mitosis.

We next investigated SRCAP-dependent H2A.Z positioning during mitosis. We treated cells with IAA for 1 h and sorted asynchronous and mitotic cells depleted for SRCAP. 1 h IAA treatment decreased H2A.Z occupancy genome-wide (Fig.2g and Supplementary Fig.S2i) at bivalent/poised promoters and enhancers in mitosis but not in interphase (Fig.2h and Supplementary Fig.S2j). While we noted a small increase in H2A.Z enrichment at bivalent promoters in asynchronous cells upon 1h IAA treatment, the biological meaning of this finding is unclear (Fig.2h). In contrast, 1 h IAA treatment in both mitosis and interphase decreased H2A.Z levels at active promoters and enhancers, in line with their high H2A.Z turnover rate (Fig.2h and Supplementary Fig.S2j). This suggests that SRCAP-mediated H2A.Z turnover is accelerated in mitosis despite very low transcriptional activity 42, indicating transcription-independent mitotic H2A.Z eviction.

SRCAP depletion unleashes the expression of master regulators of cell fate
We next performed nuclear RNA-seq after IAA treatment for 2, 4, 6 or 8 h in SYAT cells or wt CGR8 cells for 4 h to account for unspecific effects of IAA. We quantified both intronic reads, which accurately reflect acute transcriptional activity 43, and exonic reads. In wt CGR8 cells, 4 h of IAA treatment did not impact transcription (Supplementary Fig.S3a). In SYAT cells, changes in intronic reads were already apparent after 2 h and peaked after 6 h of IAA treatment, with 211 and 276 genes up- and downregulated, respectively (Fig.3a and Supplementary Data 1). Mature transcripts were more gradually affected starting from 2 h of IAA treatment (Supplementary Fig.S3b). These results contrast with H2A.Z knockdown in mESCs, which mainly results in upregulated genes 20,44 that poorly overlap with the genes we identified (Supplementary Fig.S3c). The pace of H2A.Z removal was comparable between transcriptionally-affected and unaffected genes (Supplementary Fig.S3d). Bivalent genes were markedly over-represented among upregulated genes upon SRCAP depletion (Fig.3b), but no functional gene category was overrepresented in downregulated genes (Supplementary Fig.S3e). Upregulated genes were highly enriched in DNA-binding proteins, notably in lineage-specific transcription factors (Fig.3c and Supplementary Fig.S3f). Among those were TFs associated with differentiation towards germ layers, especially mesoderm (Gata3, Gata6, BMP4, Meis1, Lefty2), or with pluripotency maintenance (Nanog, Myc, Trim28, Zfp42) (Supplementary Fig.S3g, Supplementary Data 1). In contrast, downregulated genes were enriched in ubiquitously expressed genes (Fig.3d). We next quantified the number of promoter-enhancer (P-E) loops formed for each gene as well as the P-E loop strength (Methods). Upregulated genes formed more promoter-enhancer loops as well as slightly stronger loops than downregulated genes (Fig.3e and Supplementary Fig.S3h), in line with enhancer-dependent regulation of lineage-specific genes 45. We then quantified changes in the proteome of the 4h IAA-treated cells by Mass Spectrometry (Methods). Only 52 and 30 proteins were significantly increased and decreased in their levels, respectively (Fig.3f, Supplementary Data 2). GO terms associated with DNA binding and transcriptional regulation were enriched in upregulated proteins (Supplementary Fig.S3i), in line with upregulated mRNAs. Note that we expect proteins levels to reflect changes that we observed at the mRNA levels only at longer timescales.

We next investigated the impact of mitotic SRCAP depletion on gene reactivation during EG1 (Methods). We treated cells with IAA for 1 h, followed by sorting of EG1 cells or LG1 cells (undergoing SRCAP depletion during mitosis and G1, respectively). Mitotic SRCAP depletion resulted mainly in downregulation of genes in EG1 (Fig.3g), many of which were housekeeping genes (Supplementary Fig.S3j). In contrast, SRCAP depletion during G1 resulted in a smaller number of downregulated genes (Fig.3h). This suggests that SRCAP activity during mitosis and at mitotic exit is mainly required for transcriptional reactivation in early G1.

SRCAP depletion alters histone modifications and nucleosome properties
To understand the mechanisms underlying changes in transcriptional activity upon SRCAP depletion, we analyzed changes in histone post-translational modifications by ChIP-seq and nucleosome properties by MNase-seq. At 4 h of IAA, H3K4me3 levels were down in 66.6% of H3K4me3-enriched regions (Fig 4a and Supplementary Fig.S4a) and 66.8% of H3K4me3 peaks also enriched for H2A.Z (Supplementary Fig.S4b), with a marked decrease at bivalent promoters (Fig.4b). This is in line with the role of H2A.Z in recruiting SET1/COMPASS complexes 5, which allows H3K4me3 maintenance 46. Changes in H3K4me3 enrichment at +1 nucleosomes were mildly correlated with transcriptional changes for both active and bivalent genes (Fig.4c and Supplementary Fig.S4c), and promoters of downregulated genes displayed a more pronounced decrease of H3K4me3 (Supplementary Fig.S4d), in line with the role of H3K4me3 in fostering transcriptional activity 47,48.

H3K27me3 levels decreased at 27.6% of regions (4799 peaks) and increased at 19.6% of regions (3402 peaks), including at bivalent promoters (Fig.4a, d and Supplementary Fig.S4e). H3K27me3 peaks also enriched for H2A.Z presented the same trend (Supplementary Fig.S4f). H2AK119ub1 displayed a massive increase at most enriched loci (52.6%, 20881 peaks), especially at bivalent promoters (Fig.4a, e and Supplementary Fig.S4g). This trend was even more clear at H2AK119ub1 and H2A.Z shared peaks (Supplementary Fig.S4h). Additionally, changes in H3K27me3 and H2AK119ub1 levels do not explain transcriptional changes (Supplementary Fig.S4i and S4j).

We next performed MNase-seq using high or low concentrations of MNase (Methods) on SYAT cells treated with control or IAA for 4 h. We classified nucleosomes in four classes according to initial H2A.Z enrichment scores. H2A.Z enrichment scaled with nucleosome occupancy at regulatory elements (Fig.4f and Supplementary Fig.S4k). However, this coupling was also observed upon SRCAP/H2A.Z depletion, suggesting that nucleosome occupancy is not primarily regulated by H2A.Z but is mostly dependent on the genomic context. The size of nucleosome-protected fragments increased upon SRCAP degradation independently of initial H2A.Z levels in regions containing these nucleosomes (Fig.4g and Supplementary Fig.S4l). While agreeing with the reported unwrapped state of H2A.Z nucleosomes 21, this also raises the possibility that SRCAP unwraps nucleosomes independently of H2A.Z deposition, similarly to SWR1 in vitro 49,50. We then focused on fragile nucleosomes, defined by a high signal ratio between MN-low versus MN-high digestion and enriched around TSS and TF binding sites 51,52. Nucleosome fragility scaled with H2A.Z levels at active and bivalent regulatory regions (Fig.4h and Supplementary Fig.S4m), in agreement with reported H2A.Z enrichment in fragile nucleosomes 53–55. Surprisingly, IAA did not induce a global decrease of nucleosome fragility (Fig.4h and Supplementary Fig.S4m). Furthermore, nucleosome fragility was unchanged at +1 nucleosomes of downregulated genes and increased at upregulated genes (Supplementary Fig.S4n), suggesting that fragility depends on the genomic context but not on H2A.Z occupancy. Therefore, alteration of nucleosome properties does not explain the transcriptional changes mediated by SRCAP depletion.

SRCAP depletion unleashes transcription factor binding
We next performed ATAC-seq after treating SYAT cells for 2, 4, 6 or 8 h with IAA. Overall chromatin accessibility was unchanged within the first 6 h of SRCAP degradation (Supplementary Fig.S5a). Promoters were mostly unaffected in their accessibility, while many distal intergenic regions displayed increased or decreased accessibility (Fig.5a-b). To understand the mechanisms underlying these changes, we analyzed TF footprints in accessible regions using the TOBIAS bioinformatic pipeline 56. In wt CGR8 cells, a 4 h IAA treatment had an anecdotal impact on TF footprinting (Supplementary Fig.S5b, Supplementary Data 3). In SYAT cells, hundreds of TF motifs already displayed significant changes in footprinting after 2 h of IAA treatment, and these alterations were very consistent across time points (Fig.5c and Supplementary Fig.S5c). About four times more motifs had their footprints enriched (up motifs) than depleted (down motifs) at all time points after IAA treatment (Fig.5c and Supplementary Fig.S5c). Up and down motifs were mostly located in distal regulatory regions and promoter regions, respectively (Fig.5d and Supplementary Fig.S5d). As promoter- and enhancer-regulated genes tend to be downregulated and upregulated upon SRCAP/H2A.Z depletion, respectively (Fig.3e), this suggests that changes in TF binding could contribute to transcriptional changes caused by SRCAP depletion. We next asked whether the predominant transcriptional downregulation after mitotic IAA treatment (Fig.3g) could be caused by a shift towards more downregulated TF footprints. We performed 1 h of IAA or control treatment, and sorted cells enriched for the end of M-phase (see Methods) and in EG1 to perform ATAC-seq prior to and concurrently to EG1 transcriptional changes, respectively. Changes in TF footprinting were more pronounced for down relative to up motifs as compared to longer IAA treatments in asynchronous cells (Fig.5e), in line with transcriptional downregulation upon mitotic SRCAP/H2A.Z loss.

As pTFs can bind nucleosomal DNA, we reasoned that their binding might be particularly sensitive to nucleosome composition. We quantified the number of pTFs enriched in up vs downregulated motifs, using a classification of pTFs based on 57,58 (see Methods). 87 out of 376 up-TFs (23.1%) but only 7 out of 97 (7.2%) down-TFs after 4 h of IAA treatment were known pTFs (Supplementary Fig.S5e). Therefore, pTF binding is generally impaired by the presence of SRCAP and/or H2A.Z.

To gain a broader view of changes in protein-DNA interactions induced by SRCAP depletion, we treated cells with IAA for 4 h and analyzed changes in proteins bound to chromatin (chromatome) (59 and Methods). We found 174 proteins significantly up or downregulated. In line with TOBIAS analysis, more TFs were upregulated than downregulated (Fig.5f). TF protein levels were unaffected (Fig.3f and Supplementary Data 2), suggesting that changes in TF binding are not mediated by changes in their concentrations. In contrast to SRCAP, H2A.Z was only slightly decreased in the whole chromatome (Fig.5f and Supplementary Data 2), suggesting low or SRCAP-independent H2A.Z turnover in regions that are not recovered in ChIP-seq experiments, such as sonication-resistant heterochromatin 60 and repeated regions that are filtered our during processing of ChIP-Seq reads such as centromeres and telomeres. Because H2A.Z is not only enriched at accessible regions together with SRCAP but also in constitutive heterochromatin where SRCAP is absent, it is expected to see only partial loss of total chromatin-associated H2A.Z upon SRCAP depletion.

We then performed ChIP-seq on selected TFs with altered footprints and/or enrichment in the chromatome. 28.2% of OCT4 and 42% of SOX2 binding sites were more occupied upon IAA treatment, in line with TOBIAS and chromatome analysis (Fig.5g and h). However, OCT4 was shown to bind H2A.Z nucleosomes 61 and to lose binding upon H2A.Z knockdown 5. Accordingly, sites bound by OCT4-only lost binding upon H2A.Z swapping with H2A, in contrast to sites bound by both OCT4 and SOX2 (Supplementary Fig.S5f), in line with SOX2-dependent OCT4 binding on canonical nucleosomes 62,63. Furthermore, SOX2 knockdown (Methods, 64) decreased the number of OCT4 binding sites, and the remaining bound sites were highly enriched for H2A.Z (Supplementary Fig.S5g). Therefore, without SOX2, OCT4 favors binding to H2A.Z over H2A nucleosomes, also explaining the largely intact pioneer activity of OCT4 in the absence of SOX2 34. NANOG, YY1 and NFY-a also displayed altered genome occupancy in line with TOBIAS and/or chromatome analysis (Fig.5g) while NANOG protein levels were unchanged after SRCAP depletion (Supplementary Fig.S5h), and OCT4, SOX2 and NANOG sites displayed increased binding in distal regulatory regions (Supplementary Fig.S5i).

Since changes in TF binding were already maximal 2 h after IAA addition (Supplementary Fig.S5c), we reasoned that TFs could drive subsequent changes in chromatin accessibility (Fig.5a). Loss and gain of TF binding were associated with corresponding changes in chromatin accessibility (Fig.5i), in line with the high number of pTFs with increased footprints (Supplementary Fig.S5e). Furthermore, loci with increased OCT4 or SOX2 binding displayed more pronounced increased accessibility than loci with stable or decreased binding (Supplementary Fig.S5j). This suggests that alterations in pTF binding drive changes in chromatin accessibility upon SRCAP/H2A.Z depletion.

DNA methylation changes do not explain changes in TF binding upon SRCAP depletion
Since H2A.Z was proposed to antagonize DNA methylation 26,65, we reasoned that SRCAP loss may increase DNA methylation, which could subsequently mediate changes in TF binding. CpGs were over-represented in down vs up motifs (Methods and Fig.5j), and TFs with decreased footprints included methyl-sensitive NRF1 and ZBTBP33 66,67 (Supplementary Data 3).

We quantified changes in DNA methylation at H2A.Z-occupied loci after 4 h of IAA treatment using Enzymatic Methyl Sequencing (EM-seq). Global DNA methylation displayed only minor changes (Supplementary Fig.S5k). While some regulatory elements displayed increased or decreased CpG methylation upon IAA treatment (Supplementary Fig.S5l), their accessibility was unaffected (Supplementary Fig.S5m). Next, we treated SYAT cells with the GSK-3484862 DNMT1 inhibitor for 5 days, which decreases DNA methylation by 80% in PSCs 68 and performed ATAC-seq after 4 h of IAA or control treatment. DNMT1 inhibition only slightly dampened the impact of IAA on increased TF footprints and did not impact decreased TF footprints (Supplementary Fig.S5n), suggesting that changes in DNA methylation plays a minor role in regulating TF binding. To determine if TF binding changes could lead to changes in DNA methylation, we identified enriched TF binding motifs in hyper- and hypomethylated H2A.Z peaks at promoters and enhancers. Many TF motifs were enriched in hypomethylated enhancers (Fig.5k). Our results suggest that acute changes in DNA methylation are mainly secondary to TF activity and play a minor role in further enhancing TF binding at hypomethylated motifs.

SRCAP and H2A.Z have distinct, complementary roles in transcriptional regulation
To determine H2A.Z-independent functions of SRCAP, we generated a catalytically-dead SRCAP protein (cdSRCAP) deficient for H2A.Z deposition by mutating 3 amino acid residues in its ATPase domain (Supplementary Fig.S6a). We then engineered the SYAT cell line for doxycycline (dox)-inducible expression of either wtSRCAP or cdSRCAP fused to mCherry (Methods), hereafter referred to as wtSRCAP and cdSRCAP cell lines. We induced expression of wtSRCAP-mCherry or cdSRCAP-mCherry for 24 h and treated cells with IAA or control for 4 h. We FACSorted cells with the same average mCherry fluorescence to ensure identical expression levels of wt and cdSRCAP and performed ChIP-seq for H2A.Z (Fig.6a). cdSRCAP cells displayed lower global H2A.Z enrichment than wtSRCAP cells (Supplementary Fig.S6b), and H2A.Z levels were rescued within <10% of the control condition at 44,769 peaks in wtSRCAP but not cdSRCAP cells (see Methods) (Fig.6b). In addition, overexpression of cdSRCAP either in CTRL or IAA condition led to a lower enrichment of H2A.Z at H2A.Z peaks, confirming that cdSRCAP is defective in depositing H2A.Z, with a slight dominant negative effect on H2A.Z deposition (Supplementary Fig. S6c).

We then compared transcriptional changes in IAA-treated cells expressing wtSRCAP or cdSRCAP. wtSRCAP compensated transcriptional changes mediated by endogenous SRCAP degradation more than cdSRCAP (Supplementary Fig.S6d). We clustered genes with intronic counts significantly differing between any pair of samples, according to their changes as a function of H2A.Z and SRCAP levels in wtSRCAP or cdSRCAP cells (Fig.6c). Genes in clusters A and B were negatively regulated by H2A.Z, while genes in clusters D and E were positively regulated by SRCAP. Clusters C and F reflected conflicting effects of H2A.Z and SRCAP on gene regulation, resulting in no significant differences in expression upon SRCAP degradation in SYAT cells (Fig.6d). Genes upregulated by SRCAP (clusters D and E) were associated with a general cellular function GO term (Supplementary Fig.S6e) and included 70 housekeeping genes, indicating an H2A.Z-independent role of SRCAP in activating ubiquitously-expressed genes. H2A.Z played a dominant role in transcriptional repression of lineage-specific genes, confirmed by direct comparison of cdSRCAP versus wtSRCAP-expressing cells in both control and IAA conditions (Fig.6e-f and Supplementary Fig.S6f-g). We next asked whether the H2A.Z-independent SRCAP function in stimulating transcription is determinant for gene reactivation at mitotic exit. Affected genes from all clusters stimulated by SRCAP were downregulated upon IAA treatment (Fig.6g). In contrast, 1 h-IAA treatment in LG1 cells led to transcriptional changes closer to those of longer IAA treatments in asynchronous cells (Supplementary Fig.S6h). This suggests that transcriptional reactivation at mitotic exit is dominated by the stimulatory effect of SRCAP, and that the inhibitory role of H2A.Z comes into play only later during G1 phase.

We then performed ATAC-seq after 24 h of dox and 4 h of IAA treatment in wtSRCAP and cdSRCAP cell lines. Overall changes in chromatin accessibility upon IAA treatment were minor and comparable in both cell lines (Supplementary Fig.S6i). Changes in chromatin accessibility caused by SRCAP depletion were abolished upon wtSRCAP but also cdSRCAP overexpression (Supplementary Fig.S6j), suggesting that these are mostly H2A.Z-independent. We then used TOBIAS to analyze TF footprints in the wtSRCAP or cdSRCAP cell lines in control or IAA conditions. K-mean clustering of TF footprinting scores yielded 6 clusters displaying different dependencies to SRCAP and/or H2A.Z levels (Fig.6h). We identified two clusters with a predominant role of SRCAP in increasing (cluster 1) or decreasing (cluster 2) TF binding (Fig.6i), as well as a role for H2A.Z in positive (cluster 3 and 4) or negative regulation (cluster 5) of TF binding at hundreds of motifs (Fig.6i). Overall, SRCAP strongly prevented TF association with their motifs while H2A.Z had both enhancing and inhibitory effects on TF binding (Fig.6j). We then compared TF footprinting in rescued peaks in wtSRCAP cells treated with control or IAA, allowing us to determine the impact of different SRCAP levels on TF footprinting independently of H2A.Z levels. Both up and down motifs overlapped substantially with those identified upon IAA treatment of SYAT cells, confirming the prominent H2A.Z-independent role of SRCAP in regulating TF binding (Supplementary Fig.S6k). SRCAP and to a lesser extent H2A.Z tended to hinder TF binding at distal regulatory elements, as seen from the mostly distal localization of the TF motifs in clusters 2 and 5 (Supplementary Fig.S6l). Conversely, both SRCAP and H2A.Z tended to stimulate TF binding at promoters, as seen from the larger fraction of TF motifs located in promoters in their main activatory clusters 1 and 4 (Supplementary Fig.S6l). In addition, SRCAP strongly disfavored pTF association with their motifs (112 SRCAP- vs 5 SRCAP+ motifs) (Supplementary Fig.S6m). Taken together, this suggests that H2A.Z-independent activity of SRCAP plays a major role in restricting pTF binding.

SRCAP inhibits TF binding by steric hindrance
The catalytically-independent inhibition of TF binding mediated by SRCAP could be caused by physically blocking the access of TFs to their binding sites (steric hindrance) and/or by decreasing TF affinity for their binding sites through chemical modification of chromatin or physical mechanisms acting through deformation of the DNA double-helix 69,70. Several arguments favor a steric hindrance mechanism. First, the SRCAP complex covers a large fraction on the nucleosome 71–74. Using a published structure 71, we computed the surface of DNA accessible to molecules considering either DNA in a free or SRCAP complex-bound nucleosome. For TFs (approximated as pseudo-spheres with radii of 20-30 Å 75,76), DNA was 2-3-fold less accessible on SRCAP-bound nucleosomes (Fig.7a). Second, SWR1 complexes remain bound to nucleosomes in vitro for several minutes 50,77. When crossing our SRCAP mapping and MNase data, we found that nucleosomes and SRCAP peaks were strongly colocalizing (Supplementary Fig.S7a), confirming that SRCAP mostly occupies nucleosomal DNA in vivo. Third, the motifs of TFs increasing their binding upon SRCAP loss (cluster 2 in Fig.6) colocalize with SRCAP (Supplementary Fig.S7b). NANOG, which strongly increased its binding upon SRCAP depletion (Fig.5g-h), displayed maximal SRCAP enrichment at the center of its ChIP-seq peaks (Fig.7b). Fourth, we reasoned that a steric hindrance mechanism implies its immediate mitigation after SRCAP degradation. After 2 h of IAA treatment, changes in TF footprinting were already maximal (Supplementary Fig.S5c). Furthermore, NANOG ChIP-seq after 2 h of treatment with different doses of IAA (Supplementary Fig.S7c and d) also revealed increased NANOG occupancy in regions initially co-occupied by SRCAP (Fig.7c).

However, these results do not fully exclude that SRCAP loss may induce rapid changes in local chromatin chemistry or physical properties, which could alter TF binding affinity. The genomic occupancy of TFs depends both on their binding frequency and residence time on specific sites. While alterations of chemical or physical properties of chromatin would alter TF residence time, steric hindrance is expected to decrease TF binding frequency by hiding TF binding sites. To discriminate between these scenarios, we determined the impact of SRCAP on DNA-binding dynamics of NANOG using single-molecule fluorescence tracking (SMT) in live cells (Fig.7d). We expressed a Halo-NANOG fusion protein at low levels in SYAT cells, cdSRCAP cells – dox, cdSRCAP + dox and wtSRCAP + dox (Methods). We first determined the overall bound fraction of dye-labeled Halo-NANOG by tracking molecules at 85Hz 78. We analyzed the distribution of molecular frame-to-frame jump distances with a multi-component diffusion model79 (Supplementary Fig.S7e,f), which revealed a higher bound fraction of NANOG in the absence of SRCAP (Supplementary Fig.S7g and Methods). This increase in bound fraction was counteracted by expression of wt or cdSRCAP (Supplementary Fig.S7h-j). Next, we determined the spectrum of residence times of Halo-NANOG by tracking bound molecules at 0.5Hz using several time-lapse conditions 80 (Supplementary Fig.S7k and Methods). We then focused our analysis on molecules binding longer than 50s, which are mostly specifically-bound molecules. As expected, the fraction of long-binding molecules was increased in absence of SRCAP, and this effect was suppressed by expression of wt or cdSRCAP (Fig.7e, Supplementary Fig.S7m). From the bound fractions and residence times, we calculated the binding frequencies using a kinetic model including diffusing, transiently binding, and stably binding NANOG molecules 81,82 (Supplementary Fig.S7l and Methods). Strikingly, the binding frequency of NANOG increased four-fold upon SRCAP degradation, and this effect was suppressed by expression of wt or cdSRCAP (Fig.7f, Supplementary Fig.S7n). Interestingly, NANOG residence time was decreased upon SRCAP depletion, (Supplementary Fig.S7o), in line with the extension of the NANOG binding landscape to lower affinity regions displaying low accessibility and OCT4/SOX2 occupancy (Supplementary Fig.S7p). These results indicate that increased binding frequency of NANOG drives increased NANOG binding upon SRCAP depletion, in line with SRCAP inhibition of TF binding by steric hindrance.

Methods
Table1. List of resources
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
Rabbit anti-H2A.Z Abcam #ab4174 RRID:AB_304345
Acetyl-histone H2A.Z ThermoFisher Scientific RRID:AB_2609565
(K4,K7,K11) #PA540095
Rabbit anti-H2A Abcam #ab18255 RRID:AB_470265
Rabbit anti-GFP Abcam #ab290 RRID:AB_2313768
Rabbit anti histone H3K4me3 Abcam #ab8580 RRID:AB_306649
Rabbit anti histone Abcam #ab195477 E RRID:AB_2819023
H3K27me3 R
Rabbit anti histone Cell Signaling Technology #8240 RRID:AB_10891618
H2AK119ub1
Rabbit anti-YY1 Cell SignalingN Technology #46395 RRID:AB_2799302
Mouse anti NF-Ya Santa-Cruz Biotechnology #sc- RRID:AB_628018
17753 E
Rabbit anti-Oct4 CCell Signaling Technology #5677 N/A
Rabbit anti-Sox2 Cell Signaling Technology #23064 RRID:AB_2714146
Rabbit anti-Nanog Cell Signaling Technology #8822 RRID:AB_11217637
Rabbit anti-SRCAP Kerafast #ESL103 RRID:AB_3086743
Biological samples
Chemicals, peptides, and recombinant proteins
Indole-3-Acetic Acid (IAA) Sigma-Aldrich #I5148 CAS: 6505-45-9
Auxinole MedChemExpress #HY-111444 CAS:86445-22-9
GSK-3484862 MedChemExpress #HY-135146 CAS: 2170136-65-7
Deposited data
GSE64825 De Dieuleveult et al. 2016 93 N/A
GSE48519 Hon et al. 2014 94 N/A
GSE143737 Mylonas et al. 202120 N/A
Experimental models: Cell lines
CGR8 (ESC Mus musculus) Sigma-Aldrich #07032901 N/A
SYA (ESC Mus musculus) This study N/A
SYAT (ESC Mus musculus) This study N/A
SYAT-wtSRCAP (ESC Mus This study N/A
musculus)
SYAT-cdSRCAP (ESC Mus This study N/A
musculus)
ZHBTc4 (ESC Mus Niwa et al., 2000 35 RRID: CVCL_C715
musculus)
HEK-293T ATCC RRID:CVCL_0063
Oligonucleotides : List provided in Table S1
Recombinant DNA
psPAX2 Addgene RRID:
Addgene_12260
pMD2.G Addgene RRID:
Addgene_12259
pX330 Addgene RRID:Addgene_422
30
pMK-Ypet-AID-P2A-Hygro This study N/A
pLV-EF1a-OsTir1-SNAP- This study N/A
P2A-Puro
pLV-PGK-Ypet-MD Deluz et al. 2016 95 N/A
pLV-PGK-mCherry-MD This study P N/A
pLV-rtTA3G-Bsd Deluz et al. 2016 95 N/A
pLV-TRE3G-wtSRCAP- This study N/A
mCherry
pLV-TRE3G-cdSRCAP- This study N/A
mCherry
pLV-TRE3G-Halo-Nanog This study N/A
pLV-pGK-Halo-Nanog This study N/A
Software and algorithms
FiJi Version 2.14.0/1.54f Schindelin et al., 2012 96 RRID:SCR_002285
STAR Version 2.7.6a Dobin et al., 2013 97 RRID:SCR_015899
Picard Version 2.23.7 Broad Institute RRID:SCR_006525
MACS Version 3.0.0a7 Zhang et al., 2008 98 RRID:SCR_013291
DeepTools Version 3.5.1 Ramírez et al., 2016 99 RRID:SCR_016366
Bedops Version 2.4.41 Neph et al., 2012 100 RRID:SCR_012865
BEDtools Version 2.30.0 Quinlan and Hall, 2010 101 RRID:SCR_006646
SAMtools Version 1.14 Li et al., 2009 102 RRID:SCR_002105
DANPOS2 Version 3.1.1 Chen et al. 2013 103 RRID:SCR_015527
Bwa-Meth Pedersen et al. 2014 104 RRID:SCR_010910
MethylDackel Version 0.5.3 https://github.com/dpryan79/Methyl
Dackel
MethylKit Version 1.28.0 Akalin et al. 2012 105 RRID:SCR_005177
DeSeq2 Version 1.42.1 Love M.I et al. 2014 106 RRID:SCR_015687
edgeR Version 4.0.11 Robinson et al., 2010 107 RRID:SCR_012802
limma Version 3.58.1 Ritchie et al., 2015 108 RRID:SCR_010943
R studio Version RRID:SCR_000432
2023.12.0+369
Cluster Profiler Version Yu et al. 2012 109 RRID:SCR_016884
4.10.1
ggplot2 Version 3.5.0 Wickham, 2009 110 RRID:SCR_014601
ggrepel Version 0.9.5 Slowikowski K, 2024
biomaRt Version 2.58.2 Durinck et al., 2005 111 RRID:SCR_002987
TOBIAS Version 0.13.3 Bentsen et al.,2020 56
SEA Version 5.5.5 Bailey and Grant, 2021 112 RRID:SCR_001783
FreeSASA Version 2.1.2 Mitternacht, 2016 113
Cell culture
CGR8 mouse ES cells (Sigma, Cat#07032901‐1VL) and ZHBTc4 mouse ES cells35 were routinely cultured at 37 °C and 5% CO on cell culture-treated dishes coated with 0.1% gelatin (Sigma 2 #G9391-100G) using the following culture medium: GMEM (Sigma #G5154-500ML) supplemented with 10% ES cell-qualified fetal bovine serum (Gibco #16141–079), 1% nonessential amino acids (Gibco #11140–050), 2 mM L-glutamine (Gibco #25030–024), 2 mM sodium pyruvate (Sigma #S8636-100ML), 100 μM 2-mercaptoethanol (Sigma #63689–25 ML-F), 1% penicillin and streptomycin (BioConcept #4–01 F00-RH), in-house produced leukemia inhibitory factor (LIF), CHIR99021 (Merck #361559–5 MG) at 3 μM and PD184352 (Sigma #PZ0181-25MG) at 0.8 μM. Cells were passaged by trypsinization (Sigma #T4049-100ML) every two to three days.
Before all experiments, mESC medium was changed to Serum LIF medium: GMEM (Sigma #G5154-500ML) supplemented with 10% ES cell-qualified fetal bovine serum (Gibco #16141–079), nonessential amino acids (Gibco #11140–050), 2 mM L-glutamine (Gibco #25030–024), sodium pyruvate (Sigma #S8636-100ML), 100 μM 2-mercaptoethanol (Sigma #63689–25 ML-F), penicillin and streptomycin (BioConcept #4–01 F00-H), in-house produced leukemia inhibitory factor (LIF). SYAT cells were cultured routinely with 200 µM Auxinole (MedChemExpress #HY-111444) (Control condition) to avoid basal degradation of SRCAP. For SRCAP degradation, SYAT cells were treated for 1, 2, 4, 6 or 8 h with 500 µM Indole-3-Acetic Acid (IAA, Sigma-Aldrich #I5148). For induction of Oct4 knockdown in ZHBTc4 cells, 1 µg/mL Doxycycline was added for 9 or 12 h prior cell collection. For DNMT1 inhibition, SYAT cells were treated with 2 µM GSK-3484862 (MedChemExpress #HY-135146) for 5 days before performing ATAC-seq. All used cell lines were tested every 6 months for the absence of Mycoplasma by Eurofins Genomics.
For imaging, cells were plated on Laminin (1/10 dilution of Biolaminin (BioLamina, LN511-0202) in PBS). Prior imaging, medium was replaced by FluoroBrite DMEM (ThermoFisher, #A18967–01) supplemented with 10% ES cell-qualified fetal bovine serum (ThermoFisher, #16141079), 2 mM sodium pyruvate (Sigma-Aldrich, #113–24–6), 1% non-essential amino acids (ThermoFisher, #11140035), 1% penicillin/streptomycin (BioConcept, #4–01F00H), 2 mM L-glutamine (ThermoFisher, #25030–024), 100 μM 2-mercaptoethanol (Sigma-Aldrich, #63689–25ML-F) and LIF.
Lentiviral vector production
Lentiviral vectors were produced by transfection of HEK 293T cells with the envelope (psPAX2, Addgene #12260), packaging (pMD2.G, Addgene #12259) 114, and lentiviral construct of interest using Calcium Phosphate transfection, as described previously 115. Viral vectors were concentrated 120-fold by ultracentrifugation at 20000 x g for 120 min at 4°C. 50000 cells in 500 µL of medium in a 24-well plate were transduced with 50 μL of concentrated lentiviral vector particles to generate stable cell lines.
Generation of SYA and SYAT cell lines
The SYA cell line was generated using CRISPR/Cas9‐mediated homology‐directed repair (HDR). A SRCAP gRNA designed using CRISPOR 116 to target the C-terminal part of mouse SRCAP was added in pX330-gRNA vector (pX330-gRNA was a gift from Charles P. Lai (Addgene plasmid #158973)) by digestion with BbsI to build pX330_SRCAP. The Ypet_AID_P2A_Hygromycin-resistance cassette was amplified with primers containing 60bp homology arms corresponding to the carboxy-terminal part of SRCAP and 2µg of amplicons were co-transfected in 1.5x106 CGR8 mouse ES cells together with 2 µg of pX330-SRCAP using Lipofectamine 3000 (ThermoFisher Scientific #L3000008). Cells were selected by addition of 150 µg/mL Hygromycin 72 h post-transfection for a week before individual clones were picked. gDNA was extracted using Direct PCR Lysis (Viagen Biotech, #301-C) and incubated 2 h at 55°C, 30 min at 85°C followed by 5 min at 95°C. Clones were characterized by PCR using primers annealing to the SRCAP locus and in the knock-in cassette (Table S1), followed by visualization of the Ypet signal intensity and nuclear localization by live imaging using the YFP channel on a IN Cell Analyzer 2200 apparatus (GE healthcare). One homozygous clone of SYA cells was then transduced with a lentiviral vector containing EF1a-OsTir1-SNAP-P2A-puro and selected with 2 μg/ml of Puromycin (Gibco A11138‐03) for a week. To select for cells efficiently degrading SRCAP upon IAA treatment, SYAT cells were treated for 2 h with IAA prior sorting for Ypet-negative cells.
SRCAP constructs and cell line generation
SRCAP was amplified from cDNA of CGR8 cells in 3 separate PCR fragments. To generate the catalytically-dead mutant, mutations were introduced in the Rev primer of the first fragment and the Fw primer of the second fragment (see Supplementary Table S2 for primer sequences). The three fragments and the pLV-TRE3G-mCherry cut with SalI upstream of the mCherry coding sequence were assembled using In-fusion cloning (Takara Bio # 638947) and the obtained products were transformed into home-made HB101 competent bacteria. Clones were then screened by PCR for the presence of full length SRCAP and sequenced to verify the presence of the mutated sequence. Correct clones were used to produce lentiviral particles to transduce SYAT cells together with a lentiviral vector expressing rtTA3G and a blasticidin resistance. After selection with 5 µg/mL blasticidin for a week, cells were treated with 1µg/mL of dox for 24 h to induce expression of SRCAP-mCherry, prior to cell sorting for mCherry-positive cells and isolation of single cells. One clone for wtSRCAP and one clone for cdSRCAP were selected by FACS for similar mCherry levels. Prior to all experiments, wtSRCAP and cdSRCAP were induced with 1 µg/mL Doxycyline for 24 h, treated with 500 µM IAA for 4 h or left in 200 µM Auxinole (Control) and sorted for similar and high mCherry expression just before performing ChIP-Seq, ATAC-Seq or nuclear RNA-Seq.
FACS for different cell cycle phases
For mitotic ChIP, 12x106 cells were collected by mitotic shake-off, fixed 10 min with 1% formaldehyde (ThermoFisher #28908) at room temperature (RT) prior to quenching with 250 mM Tris-HCl pH8. Next, staining with 50 mM Hoechst33342 (Thermo Fisher Scientific #H3570) was performed for 15 min at RT. Cells were then permeabilized by addition of ice-cold 70% ethanol and stained with an anti-H3S10P antibody (Merck-Millipore, 05-1336) diluted 1:1000 for 1 h at 4°C. Samples were stained with Alexa-Fluor 647 anti-rabbit (H+L) secondary antibody (ThermoFisher Scientific #A-31571) for 45 min at 4°C. Mitotic cells were selected by FACS as Hoechst-high and H3S10P-positive. For other cell cycle phases, we used DNA content and mitotic degron-based FACS. Mitotic degrons (MDs) have been extensively used in previous publications 34,38,117 as fusion to fluorescent proteins, inducing their degradation at the metaphase-anaphase transition. As a result, the level of the fluorescent protein remains very low until the middle of G1 phase, thereby allowing distinction between early G1 and late G1 cells. Here, we used CGR8 cells stably expressing Ypet -MD or SYAT cells stably expressing mCherry-MD, which were stained with Hoechst, followed by sorting according to Hoechst and Ypet/mCherry intensities: Hoechst-low/Ypet-low/mCherry-low cells being in early G1 phase, Hoechst-low/Ypet-high/mCherry-high in late G1 phase, intermediate-Hoechst in S phase and Hoechst-high in G2M phase. For sorting cells at the end of mitosis, SYAT cells expressing mCherry-MD and stained with Hoechst were selected for Hoechst-high/mCherry-low intensities. All FACS were performed on a FACSAria II or a FACSAria Fusion (BD Biosciences).
Immunofluorescence
SYAT or wt CGR8 cells were plated on 1:10 diluted Biolaminin (BioLamina, LN511-0202) in DPBS (Gibco, 14040091) in 96-well plates with black walls or 3.5 mm dishes. Cells were fixed with 2% Formaldehyde diluted in DPBS for 30 min at RT. Permeabilization was then performed by addition of a solution of 0.5% Triton X-100 (AppliChem, A1388,0500) for 30 min before addition of blocking solution (BSA 1% (Sigma-Aldrich, A7906) in DPBS) for 30 min. An anti-H2A.Z antibody (Abcam ab4174) was diluted 1:1000 in blocking solution prior to incubation overnight at 4°C, followed by three washes with 0.1% Tween-20 diluted in DPBS. The secondary antibody Alexa-Fluor-647 Chicken anti-rabbit IgG (H+L) (ThermoFisher Scientific #A-21443), was diluted 1:1000 in blocking buffer and incubated for 1 h at RT. Cells were then washed three times with 0.1% Tween-20 diluted in DPBS and twice with DPBS before addition of DAPI-Fluoromount G (SouthernBiotech, #0100-20). Imaging was performed on an IN Cell Analyzer 2200 apparatus (GE healthcare) using a 20X magnification objective and the YFP fluorescence channel for Ypet detection, the Cy5 fluorescence channel for AF-647 detection and DAPI fluorescence channel for DAPI detection.
For high magnification imaging of mitotic chromosomes, imaging was performed on a spinning disk microscope (Visitron CSU-W1) with a 60X objective. For image quantification, the Fiji software96 was used to subtract background and manually determine the area of the region of interest (ROI) and the intensities for the different channels. For each channel, integrated intensities were calculated by multiplying the mean fluorescence intensity by the area of the ROI. Around 50 cells were quantified for each treatment condition and phase.
Pluripotency assay and RT-qPCR
600 SYAT or CGR8 cells were plated in 6-well plates and cultured in GMEM-Serum-2i-LIF for 5 days. Cells were stained with Phosphatase Alkaline staining kit (Merck-Millipore, SCR004) according to the manufacturer instructions. Colony forming assays were performed in three biological replicates.
For RT-qPCR experiments, RNAs from three biological replicates were extracted from 3x106 SYAT or CGR8 cells with the Rneasy Plus Mini Kit (Qiagen, #74134). Reverse transcription was performed on 1 µg RNA using oligodT (Thermo Fisher Scientific #SO131) and Maxima H minus First Strand cDNA synthesis (Thermo Fisher Scientific #K1652). The RT reaction was diluted 1/10 prior to qPCR. Primers used to amplify Rps5, Zfp42, Pou5f1, Sox2, Nanog, Esrrb and c-Myc are detailed in Supplementary Table S1. Plates containing three technical replicates were assembled using an automated Hamilton liquid handling platform and analyzed on a QuantStudio 7 (Applied Biosystem). Relative quantifications were obtained by normalization on Rps5 mRNA levels.
Western-Blot
Wt CGR8 or SYAT mESCs in control conditions or treated with IAA for 2, 4, 6 and 8h were collected and lysed in RIPA buffer (50mM Tris pH8, 1%NP-40, 0.5% NaDoc, 0.1% SDS, 150mM NaCl supplemented with Protease Inhibitor Cocktail and 25U of Benzonase (Sigma 70746). 20 or 40µg of whole protein extracts were loaded on a 4-20% SDS-Page gel (Biorad) and transferred on ethanol-activated PVDF membrane. Membranes were blocked in TBS-BSA 5% and incubated with the following antibodies diluted in TBS-Tween 0.1% : SRCAP (Kerafast ESL103), GFP (Abcam ab290), NANOG (Cell Signalling Technology 8822) and GAPDH (Santa-Cruz Biotechnology sc-32233). Membranes were then stained with either anti-mouse or anti-rabbit IgG-HRP and revealed using Clarity Western ECL substrate (BioRad) on a Amersham Imager 680 system (GE Healthcare).
ChIP-Seq
ChIP-Seq experiments against H2A.Z, H2A, H3K4me3, OCT4 and SOX2 were performed in duplicates, except for the ChIP-seq against H2A.Z after 1h of IAA treatment in mitotic versus asynchronous cells that were performed as single replicates. ChIP-Seq against H3K27me3, H2AK119ub1, NANOG, YY1 and NF-Ya were performed as single replicates.
For histones and histone post-translational modifications, cells were fixed for 10 min with 1% formaldehyde at RT. For transcription factors, cells were fixed with 2 mM disuccinimidyl glutarate (Thermo Fisher Scientific #20593) for 50 min and in 1% formaldehyde for 10 min. In both cases, fixation was quenched with 250 mM Tris-HCl pH8. All fixed cell pellets except mitotic samples were kept on ice and resuspended in Lysis Buffer 1 (50 mM HEPES-KOH pH 7.4, 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 10% Glycerol, 0.5% NP-40, 0.25% Triton X-100 supplemented with Protease inhibitor cocktail (Sigma #P8340-1ML) at 1:100 dilution), incubated 10 min at 4°C, spun down at 1700 x g, and resuspended in LB1 a second time, spun down and resuspended in Lysis Buffer 2 (10 mM Tris-HCl pH 8.0, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA supplemented with Protease inhibitor cocktail (Sigma #P8340-1ML) at 1:100 dilution), incubated for 10 min at 4°C, spun down and washed without disturbing the pellet twice with SDS shearing buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA, 0.15% SDS supplemented with Protease inhibitor cocktail (Sigma #P8340-1ML) at 1:100 dilution)) and finally resuspended in SDS shearing buffer. For mitotic samples, cell pellets were washed twice in SDS shearing buffer and then resuspended in SDS Shearing buffer. Chromatin was sonicated for 20 min at 5% duty cycle, 140 W, 200 cycles or for sorted samples for 10 min at 10% duty, 75W, 200 cycles on a Covaris E220 focused ultrasonicator. ChIPs were then performed using ChIP-IT High Sensitivity kit (Active motif, #53040), following manufacturer instructions. 10 ng of Drosophila spike-in chromatin (Active motif, 53083) and 0.5µg of spike-in antibody (anti-H2Av, Active motif, 61686) were processed together with mouse ES cell chromatin (1.5 µg for ChIP-seq on mitotic cells and matching asynchronous cells, 650ng for ChIP-Seq after 1h of IAA treatment, 4 µg for the other H2A.Z and H2A ChIP-seq, 2 µg for H3K4me3, H3K27me3 and H2AK119ub1 and 5 µg for ChIP-seq against TFs) as an internal experiment calibrator 118. Libraries were prepared with the NEBNext Ultra II DNA Library Prep Kit (NEB #E7645) and sequenced on an Illumina NextSeq 500 using 75-nucleotide read length paired-end sequencing.
CUT&Tag
CUT&Tag experiments were performed in duplicates using the CUT&Tag-IT Assay Kit (Active Motif #53160) and following manufacturer instructions. Briefly, 200,000 SYAT cells were bound on Concanavalin A beads prior to incubation overnight with 1:50 anti-GFP rabbit antibody (Abcam #ab290). Next, cells were incubated with a secondary antibody, pA-Tn5-transposomes, and washed according to manufacturer instructions. Transposition was subsequently performed for 1 h at 37°C, and transposed DNA was purified prior to Library Preparation and size exclusion with SPRI beads using a 0.5X ratio while keeping the unbound fraction and 1X ratio on the left side, taking the bound fraction. Libraries were sequenced on an Illumina NextSeq 500 using 75 nucleotide read-length paired-end sequencing.
CUT&RUN
One replicate of CUT&RUN was performed on 100,000 SYAT cells using an anti-SRCAP antibody (Kerafast #ESL103) following the protocol used in 32.
ATAC-Seq
All ATAC-seq experiments were performed in biological duplicates. 50,000 cells were collected either directly after trypsinization or after sorting as described above, and subjected to ATAC-seq as described previously119. All centrifugation steps were done at 800 x g at 4°C. Briefly, cells were centrifuged for 5 min and washed with cold PBS, then centrifuged for 5 min and resuspended in cold lysis buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40), and centrifuged for 10 min. Subsequently, nuclei were resuspended in a solution of 0.5 mM Tn5 (in-house preparation according to 120) in TAPS-DMF buffer (10 mM TAPS-NaOH, 5 mM Mgcl2, 10% DMF) and incubated for 30 min at 37°C. DNA was immediately purified using column purification (Zymo #D4004) and eluted in 10 µl nuclease-free water. Transposed DNA was amplified in a solution containing 1X NEBNext High Fidelity PCR Master Mix (NEB #M0541L), 0.5 μM of Ad1.1 universal primer, 0.5 μM of Ad2.x indexing primer and 0.6x SYBR Green I (Thermo Fisher Scientific #S7585) using 72°C for 5 min, 98°C for 30 s, and 5 cycles of 98°C for 1 s, 63°C for 30 s, and 72°C for 60 s. 10 μl of amplified DNA was analyzed by qPCR to determine the total number of cycles to avoid amplification saturation and accordingly amplified with additional 3–7 cycles at 98°C for 10 s, 63°C for 30 s, and 72°C for 60 s. DNA was purified using column purification (Zymo #D4004) and size-selected by taking the unbound fraction of 0.55X AMPure XP beads (Beckman Coulter #A63880) followed by the bound fraction of 1.2X AMPure XP beads. Libraries were sequenced on an Illumina NextSeq 500 using 75 nucleotide read-length paired-end sequencing.
Nuclear RNA-Seq
All RNA-Seq experiments were performed in biological duplicates. Following cell collection and wash in PBS, cell pellets were resuspended in 500 μL Nuclear RNA lysis buffer (10 mM Tris pH 7.4, 10 mM NaCl, 3 mM MgCl2, 1% BSA (Merck, #B9000S), 0.1% Tween (Fisher Scientific, #10113103), 1 mM DTT (Merck, #646563), 0.5 U/μl Protector RNase Inhibitor (Merck, #3335399001), 0.1% NP-40, 0.01% Digitonin (Invitrogen, #BN2006)) and incubated 5 min on ice. Then 500 μL Nuclear RNA wash buffer (10 mM Tris pH 7.4, 10 mM NaCl, 3 mM MgCl2, 1% BSA (Merck, #B9000S), 0.1% Tween (Fisher Scientific, #10113103), 1 mM DTT (Merck, #646563), 0.5 U/μl Protector RNase Inhibitor (Merck, #3335402001) were added before spinning down cells at 300 x g 4°C for 5 min. Two more washes with Nuclear RNA wash buffer were performed before proceeding to RNA extraction using the Rneasy Plus Micro Kit (Qiagen, #74034) with the addition of an on-column DNAse digestion (Qiagen, #79254). Sequencing libraries were prepared by the EPFL Gene Expression Core Facility using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB, #E7760S), and sequenced on a NovaSeq6000 using 75-nucleotide read length paired-end sequencing.
MNAse-Seq
All MNAse-Seq experiments were performed in biological triplicates according to the following protocol121. Briefly, Micrococcal Nuclease S7 (Roche #10107921001) was resuspended to 15 U/µL in 20 mM Tris-HCl pH 7.5, 50 mM NaCl, 50% Glycerol. For high digestion condition, MNase was diluted at 100 U/mL in MNase Digestion buffer (50 mM Tris-HCl pH 8, 150 mM sucrose, 50 mM NaCl, 5 mM CaCl ). For low digestion condition, MNase was diluted at 6.25 U/mL in MNase 2 Digestion Buffer. SYAT cells were treated for 4 h with 500 µM IAA or 200 µM Auxinole before collection. 5x106 cells were used for each MNase digestion condition. After cell collection, cell pellets were resuspended in 50 µL Resuspension Buffer (35 mM HEPES pH 7.4, 150 mM sucrose, 80 mM KCl, 5 mM KH PO , 5 mM MgCl , 0.2% NP-40). Then, 500 µL of MNase solution 2 4 2 were added to the cells and incubated for exactly 10 min at 37°C before inactivation of MNase by addition of 11 µL of 0.5 M EDTA to the reaction. Next, 550 µL of Lysis Buffer (50 mM Tris-HCl pH 8, 1% SDS, 10 mM EDTA) was added and samples were incubated on ice for 10 min. After addition of 1.1 mL DNAse-free water, samples were digested for 2 h at 37°C with RNase (50 µg per reaction). Finally, 3200 U of Proteinase K were added, and digestion was performed at 56°C for 2 h. Digested DNA was purified using a DNA Purification kit (Active Motif #58002). Then, 500 ng of DNA for the High digestion condition and 1500 ng of DNA for the Low digestion condition were loaded on a 1.5% agarose gel and the band below 200bp was cut out and gel-purified. Library preparation was carried out on the purified DNA using NEBNext Ultra II DNA Library Prep Kit (NEB #E7645) and sequenced at 100 million reads per sample on a NovaSeq6000 using 75-nucleotide read length paired-end sequencing.
Enzymatic Methyl-Sequencing
EM-Seq experiments were performed in biological duplicates. Genomic DNA was extracted using a DNeasy Blood and Tissue kit (Qiagen #69504) and repurified using a DNA Clean and Concentrator-5 Kit (Zymo #D4004). Next, 200 ng of purified genomic DNA were sonicated for 90 s, 10% duty, 140 W, 200 cycles on a Covaris E220 focused ultrasonicator and were subsequently processed using NEBNext Enzymatic Methyl-Seq kit (NEB #7120) following the manufacturer protocol. Libraries were sequenced at 200 million reads per sample on a NovaSeq6000 using 75-nucleotide read length paired-end sequencing.
Whole Proteome and chromatome analysis by Mass Spectrometry
The whole proteome and chromatome sample preparation, liquid chromatography followed by tandem mass spectrometry (LC-MS/MS) and data analysis were performed as previously described in 59, with a few adjustments: for the whole proteome-chromatome-matched sample collection, 7 x 106 embryonic stem cells were harvested per condition and replicate. The cells were washed with PBS and split into two parts: 2 x 106 cells for whole proteome analysis and 5 x 106 cells for chromatome analysis. Liquid chromatography was performed using the EvosepONE system with a 15 cm × 150 μm column containing 1.9 μm C18 beads (PepSep). Each sample (200 ng) was loaded onto Evotips according to the manufacturer’s protocol. The gradient length was 44 minutes (30SPD). The column temperature was maintained at 40°C, and electrospray ionization was achieved using a stainless-steel emitter (30 µm inner diameter) at 2.2 kV. Mass spectrometry was conducted on an Orbitrap Exploris™ 480 mass spectrometer (Thermo Fisher Scientific) in data-independent acquisition (DIA) mode. MS1 acquisitions were performed as previously described, using an orbitrap resolution of 120,000 with a scan range of 350-1400 m/z and a maximum injection time of 45 ms. For the MS2 acquisition, adjustments were made to accommodate the shorter LC gradient. Specifically, 49 scan events were performed with isolation windows of 13.7 m/z at an orbitrap resolution of 15,000. The normalized AGC target was set to 3000%, and the maximum injection time was 22 ms. The precursor mass range was maintained at 361-1033 m/z.
The list of TF was determined using a list of proteins associated with the GO terms DNA-binding transcription factor activity (GO:0003700) and Sequence-specific DNA binding (GO:0043565).
Single-molecule imaging
We performed single-molecule imaging on SYAT, wtSRCAP and cdSRCAP cells over-expressing Halo-Nanog under the control of doxycycline-inducible TRE3G or constitutive pGK promoters, respectively. We seeded cells on 35 mm glass bottom dishes (Ibidi, 81158) previously coated with a 1:10 dilution of Biolaminin in PBS. 72 h prior imaging, we added 200 µM Auxinole to the culture medium for all cell lines. For SYAT cells, we induced expression of Halo-Nanog by adding 0.5 µg/ml Doxycycline, 24h before imaging. Staining was performed right before Doxycycline induction, 24-25h prior to imaging. We stained SYAT cells for single-molecule tracking with 100 pM or 25 pM HTL-SiR (kindly gifted by Kai Johnsson, MPI Heidelberg), respectively, for 85 Hz or 0.5 Hz movies. To visualize the nucleus, we performed additional staining with 62 nM HTL-TMR (Promega, G8252). After staining, cells were incubated for 30 minutes, then washed twice with PBS and incubated overnight in medium containing 0.5 µg/ml Doxycycline. To induce SRCAP degradation, we washed cells twice with PBS to remove residual auxinole and treated SYAT cells with 500 µM IAA 4h before imaging. Cells were then washed three times with PBS and imaged in OptiMEM. We induced the expression wtSRCAP and cdSRCAP cells by adding 1 µg/ml Doxycycline 24 h before imaging. cdSRCAP cells were stained with 1 pM or 5 pM HTL-SiR, respectively, for 85 Hz or 0.5 Hz movies. wtSRCAP cells were stained with 50 pM HTL-SiR for both types of movies. The incubation time was in all cases 15 minutes. We subsequently washed cells three times with PBS and imaged in OptiMEM.
Single-molecule tracking experiments were conducted on a custom-built inverted microscope with highly inclined laminated optical sheet (HILO) illumination 122. The fluorophores were excited with a 638 nm laser (Omicron Luxx Laser, Omicron-Laserage Laserprodukte Gmbh), which was spatially cleaned by a single mode fiber (kineFLEX HPV, Excelitas Technologies Corp.). The beam was further expanded to a FWHM diameter of around 6mm with a fiber-coupler (60FC-T-4-M50L-0, Schäfter+Kirchhoff GmbH) and its profile was altered from a gaussian to a flattop via a diffractive beam shaper (piShaper 6_6_VIS, AdlOprica GmbH). The laser was focused into the back-focal plane of the objective (CFI SR HP Apochromat TIRF 100xC Öl, Nikon GmbH) over an achromatic lens (AC254-200-A, Thorlabs GmbH) and a dichroic mirror (F73-537, AHF Analysetechnik). The fluorescence light was filtered by a combination of an emission and a notch filter (F67-532 and F40-074, AHF Analysetechnik) and detected on a sCMOS camera (Prime BSI, Teledyne Scientific Imaging GmbH). Pixels on the sCMOS camera were binned 2x2 resulting in an effective pixel size of 130nm. The sample was mounted in a stage incubator (Inkubator XS 2000, PeCon GmbH) with 37°C and 5 % CO2 and was controlled in x-y-z direction with two stacked piezo stages (M-686 and M-562, Physik Instrumente GmbH).
To determine the diffusion coefficients and bound fraction of Halo-NANOG, we performed tracking of molecules with a camera exposure time of 10 ms. Each cell was imaged only once. SYAT cells were imaged with a 638 nm laser at 650 µW for 2500 consecutive frames. cdSRCAP and wtSRCAP cells were selected for low or zero Ypet signal, and additionally for high mCherry signal in the case of + dox measurements, then imaged with a 638 nm laser at 780 µW for 2000 consecutive frames.
To determine the chromatin residence time of Halo-NANOG, we performed measurements with a camera exposure time of 500 ms at continuous illumination (continuous illumination) or alternating between a single illuminated frame and a fixed dark time of 10 s (time-lapse illumination). For both illumination schemes and for all the cell lines, we imaged with a 638 nm laser at 30 µW. Each cell was only imaged once. Global analysis of both illumination schemes allowed us to differentiate between dissociation of molecules and photobleaching or tracking errors, respectively 80.
Analysis of single-molecule movies
Analysis of single-molecule tracking data was performed using TrackIt 79. For 85 Hz movies, spots were detected with a threshold factor of 1 and a tracking radius of 4 px. For 0.5 Hz movies, we selected threshold factors of 1.0, 1.4 and 1.3, respectively for the SYAT, the wtSRCAP and the cdSRCAP cell line. The tracking radii were 3 px for the continuous and 5 px for the time-lapse illumination, for all cell lines. The minimum tracking length was 2 frames for all conditions and cell lines, allowing for one gap frame if the track lasted longer than 2 frames. We determined diffusion coefficients and fractions for Halo-NANOG by fitting the cumulative distribution of squared jump distances obtained from the 85 Hz movies with a 3-component diffusion model 78. To estimate the error for every diffusion coefficient we repeated the analysis 400 times, with 80% of randomly selected jump distances. The overall bound fraction of Halo-NANOG was defined as the amplitude A1 of the slowest apparent diffusion component, originating from the localization error and slow chromatin movement. The other two components A2 and A3 accounted for anomalous diffusion in the nucleus 123,124. Next, we determined the dissociation rates of Halo-NANOG from chromatin using GRID 80. GRID generates a spectrum of dissociation rates by inverse Laplace transformation of the distribution of fluorescence survival times for Halo-NANOG-bound SiR. An estimate of the error associated to each dissociation rate was given by repeating GRID analysis 500 times with 80% of randomly selected data from the survival time distributions. The residence times of Halo-NANOG were calculated as the inverse of the dissociation rates. For further analysis, we focused on the longest residence times, as this is typically associated with the specific function of a transcription factor 125–127. The absolute fraction of long-bound molecules was calculated as the product of the overall bound fraction with the amplitude of the state spectrum associated with the longest binding time. The binding frequencies for Halo-NANOG were calculated as the inverse of the search times determined from bound fractions and dissociation rates as described 81,128.
ChIP-Seq and CUT&Tag analysis
Newly-generated ChIP-sequencing libraries, as well as ChIP-Seq for BRG1, Ep400, CHD4, H3K27ac, H3K4me1, H2A.Z.1 and H2A.Z.2 that were reanalyzed, were mapped to mm10 version of Mus musculus genome or version BDGP6 of Drosophila Melanogaster genome, using STAR 97 with parameters --alignMatesGapMax 2000 --alignIntronMax 1 --alignEndsType EndtoEnd --outFilterMultimapNmax 1. For NANOG ChIP performed on SYAT treated with different doses of IAA for 2 hours, raw sequencing files from two replicates were pooled prior mapping to the genome to increase sequencing depth. Duplicated reads and reads not mapping to chromosome 1-19 and X or Y were removed using Picard (Broad Institute). For the generated Drosophila alignments bam file, the number of reads was determined, and normalization factors were calculated to bring all the Drosophila bam files to the same number of reads. These normalization factors were then applied to downsample alignment files mapped on the mouse genome using SAMTools 102. Peak calling was then performed on these downsampled bam files using MACS2 98 with settings -f BAMPE -g mm, regions contained in mm10 blacklist from ENCODE consortium being excluded. Scores were determined from downsampled bam files using the bamCoverage function from DeepTools 99 with setting --normalizeUsing RPKM.
For score comparison, a bed file regrouping all peaks called in at least one condition was generated, and enrichment was calculated in these defined regions using the multiBigwigSummary function from DeepTools. For classification based on differences of peak amplitude between two time points of IAA treatment or with control, peaks were considered to be different in amplitude if there was a variation of at least 20% of enrichment score in peaks (RPKM-normalized) for H2A.Z, H3K4me3, H3K27me3 and H2AK119ub or at least 30% for YY1, NFYa, OCT4, SOX2, NANOG and H2A.Z in mitotic and asynchronous cells treated with IAA for 1 h or control.
For H2A.Z ChIP-Seq performed across the cell cycle, promoter and enhancer clusters were determined by k-mean clustering of the log2 fold-change of the signal between G2 and M phase together with log2 fold-change of the signal between M and EG1 phases, using the plotHeatmap function of DeepTools.
For H2A.Z ChIP in wtSRCAP or cdSRCAP-expressing SYAT cells, peaks were considered as rescued if the difference between IAA and Control enrichment scores was higher (with a 10% margin) in cdSRCAP compared to wtSRCAP.
In boxplots, the edges of the box represent respectively the first and third quartiles (Q1 and Q3), the bar represent the median value and the dots are the outlier values (lower than Q1-1.5xInterquartile-range, higher than Q3+1.5xInterquartile-range).
Establishment of reference bed files
The list of promoters was determined from a list of TSS in mm10 genomes (obtained from refTSS from the UCSC Genome Browser) that was enlarged from 1kb upstream the TSS and 500pb downstream. To determine the list of active promoters, this list was crossed with H3K4me3 peaks without any repressive histone marks, for bivalent promoters with peaks from H3K4me3 and H3K27me3 ChIP-Seq and for inactive promoters with H3K27me3 peaks that overlapped with no activating histone marks. To identify active enhancers, promoter regions were excluded and then H3K27ac peaks were crossed with H3K4me1 peaks. Poised enhancers were determined by crossing H3K4me1 enrichment with H3K27me3 enrichment. Regions outside promoters and without H3K4me1 enrichment were classified as inactive enhancers. For analysis using all enhancers grouped together, the list from the FANTOM5 consortium was used, or alternatively we used the list from Enhancer Atlas 129 established in J1 mESCs when promoter-enhancer loop information was required. In this database, enhancers were annotated using accessibility data, enrichment for H3K4me1, H3K27ac and p300, and expression of bi-directional eRNA from GRO-Seq data, in different species and cell types. We used the list of enhancers obtained for J1 mouse ESCs. In the Enhancer Atlas, interactions between a given annotated enhancer and promoters are derived from RNA Pol II-based ChIA-PET data. To determine the number of promoter-enhancer loops, the number of enhancers interacting with the promoter from one given gene was computed using the Enhancer Atlas list, separating upregulated genes from downregulated genes upon SRCAP depletion. Additionally, we also re-used the Supplementary Data S9 from 130 presenting all promoter-enhancer loops detected in Micro-C data from wt mESCs together with their strength score, filtering these data for promoters of downregulated and upregulated genes after SRCAP depletion.
The list of TSS was enlarged from 20bp upstream and 20bp downstream of the TSS to determine the position of the TSS-nucleosome. Then the 400pb upstream of this position and 400pb downstream were considered as regions of positioning for the -1 or +1 nucleosomes, respectively. Enhancer RNAs were defined as follows: the middle position of enhancers from the J1 enhancer list was determined and RNA reads falling into 3kb upstream and downstream were extracted, except those falling into exons and long non-coding RNA (list taken from LncRBase, http://bicresources.jcbose.ac.in/zhumur/lncrbase/).
H2A.Z.1-only, H2A.Z.2-only and shared peaks were determined by intersecting bed files generated from ChIP-Seq reanalysis of data from 20 using the bedtools intersect function. OCT4-dependent, SOX2-dependent, and co-dependent regions were extracted from a previous study 34.
MNAse-Seq analysis
MNAse-Seq libraries were mapped and filtered as described for ChIP-Seq. Nucleosome positions were determined using the dpos function from DANPOS2 103. Enrichment scores were computed using the bamCoverage function from DeepTools 99 with the setting --normalizeUsing RPKM. For all nucleosomes determined using DANPOS, the H2A.Z enrichment score was calculated, and nucleosomes were classified into “No H2A.Z” (3% lowest H2A.Z scores), “low H2A.Z” (1st quartile except “No H2A.Z”), “High H2A.Z” (Upper quartile) and “Intermediate H2A.Z” (remaining nucleosomes). These nucleosomes were further classified depending on whether they were in promoters or enhancers and according to the activity of these regulatory regions. Occupancy data were extracted from comparison of MNhigh and MNlow datasets using DANPOS. For MNase sensitivity, MNhigh and MNlow scores were determined for all nucleosomes using the multiBigwigSummary function from DeepTools and the MNlow over MNhigh ratio was calculated. For fragment length analysis, subsets of bam files were generated according to H2A.Z enrichment and regulatory regions using the samtools view function with parameters -b -h -L. The lengths of the fragments in the bam files were then determined with the samtools view -f66 | cut -f 9 command line.
Nuclear RNA-Seq analysis
Nuclear RNA-Seq libraries were mapped to the mm10 mouse genome using STAR. Exon coordinates were gathered from GENCODE annotation from vM25 of the UCSC Genome Browser. Introns coordinates were determined by subtracting exon coordinates from full length gene bodies using a script generated in a previous study (https://github.com/charitylaw/Intron-reads, 131). Gene counts were then determined using the featureCounts function 132 from the R package Rsubread. Differential enrichment analysis as well as normalization of results using the lfcShrink function with type =”normal” were performed using DeSeq2 106. Reads counts were normalized on introns length and in transcript per million (TPM) for analysis of absolute RNA levels in control condition.
The bivalent genes list was determined from the list of bivalent promoters. The list of housekeeping genes was taken from a previous study 133. Finally, the list of genes associated with pluripotency, mesoderm commitment, ectoderm commitment and endoderm commitment were compiled from Datanode using the terms “Mechanism associated with pluripotency (WP1763)”, “Mesodermal commitment pathway (WP2857)”, “Ectoderm differentiation (WP2858)” and “Endoderm differentiation (WP2853)”, respectively.
For k-mean clustering of gene expression between wtSRCAP and cdSRCAP treated with IAA or control, intronic reads count for genes affected in at least one condition were normalized by DeSeq2 using the median of ratios method. Then for each gene, the z-scores across samples were computed. K-mean clustering was next performed using the pheatmap function from pheatmap R package (https://CRAN.R-project.org/package=pheatmap) using a fixed number of 6 clusters.
EM-Seq analysis
Libraries were mapped using bwa-meth from Bwa (from Brent Pedersen, https://github.com/brentp/bwa-meth). CpG methylation was then calculated using MethylDackel (https://github.com/dpryan79/MethylDackel) before analysis of Differential methylation using MethylKit 105. CpG methylation was computed for H2A.Z peaks located in promoters and enhancers and if the percentage of methylation was increased or decreased by 5%, regions were considered as hypermethylated or hypomethylated, respectively.
ATAC-Seq analysis
ATAC-Seq libraries were mapped and filtered as described for ChIP-Seq. The alignment files were subsequently downsampled so that all files contain the same total number of reads. Accessible peaks were then called, and scores were computed as described above for ChIP-Seq. Differentially-accessible regions were determined among peaks that had been called in at least one condition, for scores that varied from at least 30% between control and 4 h of IAA treatment.
TOBIAS analysis
For TOBIAS analysis, alignment files from ATAC-Seq were normalized with trimmed mean of M values (TMM) using calcNormfactors from edgeR with the TMM method followed by downsampling using SAMTools. Then, a correction of Tn5 bias was performed using TOBIAS ATACCorrect in all accessible regions in SYAT treated with control or IAA, excluding mm10 ENCODE-blacklisted regions. Footprints were then assessed with TOBIAS FootprintScore in the same regions. Finally, conditions were compared using TOBIAS BINDetect on the same regions or H2A.Z-rescued regions when specified, using the Jaspar2022 134 non-redundant core list of motifs to which the Nanog motif (UN0383.1) was manually added. The significance threshold of p<0.0001 and |FC|> 0.5 for TOBIAS analysis were determined so that changes in wt CGR8 treated with IAA were very mild and not overlapping with the motifs that we found affected in SYAT cells treated with IAA.
The list of pioneer transcription factors (pTFs) (Supplementary Table S2) was compiled from the list published by Lemma et al. 57 in their Supplementary Table 1 and Peng et al. 58 in their supplementary Table 2 and Supplementary Data 4. In these studies, authors classified TFs that were able to bind nucleosomal DNA and induce the opening of bound regions at least in one cell type as pTFs.
For determination of motifs containing a CpG, power weight matrices (PWM) for all motifs were collected and the CpG score was calculated as follows.
For a motif with N positions :
𝑁−1
(1) 𝐶𝑝𝐺 𝑠𝑐𝑜𝑟𝑒 = ∑𝑃(𝐶) ×𝑃(𝐺) ×(𝑊𝑒𝑖𝑔ℎ𝑡 +𝑊𝑒𝑖𝑔ℎ𝑡 )
𝑖 𝑖+1 𝑖 𝑖+1
𝑖=1
With for each position i,
𝑆𝑐𝑜𝑟𝑒(𝐶)
𝑖
(2) 𝑃(𝐶) = 
𝑖 𝑆𝑐𝑜𝑟𝑒(𝐴) +𝑆𝑐𝑜𝑟𝑒(𝑇) +𝑆𝑐𝑜𝑟𝑒(𝐺) +𝑆𝑐𝑜𝑟𝑒(𝐶)
𝑖 𝑖 𝑖 𝑖
And
𝑆𝑐𝑜𝑟𝑒(𝐴) +𝑆𝑐𝑜𝑟𝑒(𝑇) +𝑆𝑐𝑜𝑟𝑒(𝐺) +𝑆𝑐𝑜𝑟𝑒(𝐶)
𝑖 𝑖 𝑖 𝑖
(3) 𝑊𝑒𝑖𝑔ℎ𝑡 = 
𝑖 mean(𝑆𝑐𝑜𝑟𝑒 )
𝑚𝑎𝑡𝑟𝑖𝑥
mean(𝑆𝑐𝑜𝑟𝑒 ) being the mean of all the scores present in the position weight matrix.
𝑚𝑎𝑡𝑟𝑖𝑥
Then, motifs with a CpG score > 6 were considered as containing at least one CpG.
The P/E score was calculated for each motif as the likelihood that each motif occurrence is present in a promoter rather than an enhancer. For each occurrence of a motif present in a promoter the score is 1, if it is in an enhancer, it is 0. These scores are then averaged over all motif occurrences to give the P/E score of each TF motif.
For k-mean clustering of TF predicted binding scores in wtSRCAP and cdSRCAP treated with IAA or control, TF binding scores were extracted from TOBIAS results for all motifs of the Jaspar2022 database and the Nanog motif. Z-scores were then calculated across samples for each motif and k-mean-clustered using the pheatmap function with a fixed number of 6 clusters.
Motif and Gene Ontology (GO) enrichment
Motif enrichment was performed using the SIEA algorithm 112 from the MEME suite using Jaspar non redundant core database as motif database and the default parameters for the other settings.
GO enrichment analysis was performed using GOnet 135 with the default parameters and either biological process or molecular function filters for GO terms.
Published datasets
BRG1, Ep400, CHD4 ChIP-Seq raw datasets were obtained from GSE64825 93. H3K27ac, H3K4me1, H3K27me3 datasets were obtained from GSE48519 94. H2A.Z.1 and H2A.Z.2 ChIP raw datasets were obtained from GSE143737 20. SRCAP CUT&RUN data in wt mESCs were obtained from GSE256314 32.
Data availability
All sequencing data were deposited at NCBI Gene Expression Omnibus (GEO), accession number GSE269310 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE269305]. Proteomics data were deposited at ProteomeXchange, accession number PXD052934 [https://proteomecentral.proteomexchange.org/ui?pxid=PXD052934]. Source Data have been deposited at Zenodo, under DOI 10.5281/zenodo.18161555.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents a lot of data on SRCAP and H2A.Z. They use a degron line to disentangle the roles of SRCAP and H2A.Z. Although there appear to be interesting findings, it is not always clear how the authors arrive at their conclusions. Some of the presented data is inconsistent, and some conclusions are insubstantially supported by the data.\nComments:\n- In the introduction, the authors state that ‘mechanistic understanding of transcriptional regulation by SRCAP and H2A.Z remains obscured by conflicting evidence and technical challenges’. However, a clear statement, both in the abstract and in the introduction, clarifying how their approach is different compared to these previous studies, and how their approach circumvents these technical challenges, is missing. The most important difference in technical approach in this study is the use of rapid, inducible SRCAP degradation systems, yet this is not mentioned in the abstract, nor in the introduction.\n- Figure 1A: Although this figure shows SRCAP-YA enrichment at H2A.Z peaks and active TSS, it does not show whether the enrichment is similar to WT-SRCAP, or not. In other words, is the enrichment of SRCAP-YA representative of WT-SRCAP enrichment at these peaks?\n- Figure 1A/G: SRCAP-YA does not appear to be enriched at bivalent TSS peaks in figure 1A. Yet, H2A.Z appears the most enriched at bivalent TSS peaks in figure 1G. How can H2A.Z be so strongly enriched at bivalent TSS if SRCAP does not localize there?\n- Figure 2A/2G: Why is the WT asynchronous panel similar to the WT mitotic panel in 2A, but a large difference presented in 2G?\n- Figure 2B/S2B: Why are figures S2B and 2B different? In figure 2B, the H2A.Z enrichment increases at active enhancers, but in figure S2B, it decreases, the latter is also mentioned as a result in the text.\n- Figure 2G/H: How can H2A.Z enrichment increase genome-wide (figure 2G) and specifically at bivalent promotors (figure 2H) in asynchronous cells when SRCAP is depleted? No potential explanation is given for this contradicting result. Why was the time-frame of only 1h IAA treatment chosen here? Figure 1 shows that H2A.Z enrichment is clearly decreased after 2h, but 1h was not tested.\n- Very little detail was given about how promoter-enhancer loops are identified.\n- Figure 4A: ‘At 4 h of IAA, H3K4me3 levels were down in 66.6% of H3K4me3-enriched regions, with a marked decrease at bivalent promoters’. And: ‘H2AK119ub1displayed a massive increase at most enriched loci’. These sentences suggest that the authors only looked at the level of histone marks at loci that were enriched for that histone mark in the control cells. If you are interested in the effect of SRCAP/H2A.Z on these histone marks, why not look at loci that were enriched for SRCAP/H2A.Z in the control cells? After all, in the next part, to determine whether nucleosome properties explain the transcriptional changes, the authors did look at the H2A.Z enriched loci.\n- Fig 4: Can the authors comment on why the amount of up/down regulated genes found by RNA seq are not reflected by the proteome results?\n- “In contrast to SRCAP, H2A.Z was only slightly decreased in the whole chromatome (Fig.5f and Table S2), suggesting low or SRCAP- independent H2A.Z turnover in regions that are not recovered in ChIP-seq experiments, such as sonication-resistant heterochromatin 59.” This is an interesting observation that the authors do not comment on. Why would this be specific to heterochromatin? This seems to relate to the steric hindrance model the authors propose.\n- In the discussion the authors say “Several studies reported that the recruitment and nucleosome-remodeling activity of the BAF complex occur continuously on a time scale of minutes 33,85,86. Here we demonstrate dynamic SRCAP-mediated H2A.Z turnover over active and bivalent/poised regulatory elements on similar time scales.” The authors measure of the course of several hours, which is not the same timescale as minutes, so this comparison is not valid.\n- “We propose steric hindrance as a new mechanism by which bulky DNA-binding complexes can restrict the access of TFs to chromatin.” Although this seems a plausible explanation, steric hindrance by proteins as a mechanism for DNA accessibility is hardly revolutionary (the concept of heterochromatin), and should not be presented as a new mechanism.\n- ‘We thus propose SRCAP/H2A.Z as a pivotal gene regulatory system operating in parallel to other epigenetic mechanisms to maintain cell identity and plasticity’. Given the minor changes in the proteome, this conclusion is not supported by the data. Especially since this is a near-complete knockdown of SRCAP, if it were a pivotal gene regulatory system, larger differences in the proteome would be expected.", "reviewer_2": "In this paper, Tollenaere et al demonstrated the mechanisms of gene regulation by SRCAP and H2A.Z in pluripotent stem cells. More importantly this study establishes the role of chromatin remodeler, which is independent of its catalytic activity, in comprehensively regulating the histone variants orchestrating the binding of transcription factors. This is an interesting finding in the field of cell fate determination for pluripotency by epigenetic regulation. The data are in general technically well performed. However, before publication, there are some points to be better clarified and corrected.\nMajor points\n1.SRCAP-YA was rapidly degraded (Fig. 1b). It was mentioned that SRCAP was rapidly degraded, but the extent of degradation was not confirmed by data such as Western blot. Quantitative analysis of SRCAP protein levels over time (such as Western blot) could be added.\n2.Fig.1c showed the track of H2A.Z ChIP-seq. However, the track image of H2A ChIP-seq was absent, the author should added this track.\n3.Loci with the highest H2A.Z turnover were initially highly enriched for SRCAP, the SWI/SNF, NuRD and Ep400 chromatin remodelers, and several PSC TFs (Fig.1h and S1i), however, why the binding score of Ep400 is declined in the groups of decreased at 4h compared to the group of 2h, and then increased at 6h?\n4.Although the article has analyzed different stages such as EG1, LG1, S, and G2/M, the detailed methods, strategies for cell cycle sorting, and possible cell cycle-dependent background factors have not been described adequately. Regarding cell cycle sorting, supplementary validation data from flow cytometry sorting should be provided in Fig. 2.\n5.The protein levels of key upregulated genes (such as Gata3 and Nanog) have not been verified. The protein expression of key transcription factors can be confirmed by Western blotting in Fig.3f.\n6.Upregulated genes formed more promoter-enhancer loops than downregulated genes (Fig.3e). which method? If used HiC, the author should calculated the E-P loop strength.\n7.Using TOBIAS for TF footprint analysis provides important evidence for revealing the direct influence of SRCAP/H2A.Z on TF binding. However, the relevant parameters (such as significance threshold, pTF classification criteria, etc.) require more detailed explanations in terms of methodology.\n8.This paper used MNase-seq to investigate the changes in nucleosome occupancy, protection fragment length, and \"fragility\". However, the results showed that even when H2A.Z was lost, there was no consistent change in the \"fragility\" of nucleosomes, indicating that this indicator may be more influenced by the genomic background. Further analyze the statistical significance of the structural changes of nucleosomes in different regulatory elements (such as active and bivalent promoters, and distal enhancers), and explore whether there are certain specific regions where the changes in TF binding are highly correlated. If possible, the in vitro experiments on nucleosome sliding or recombination to verify the catalytic independent function of SRCAP in H2A.Z deposition and nucleosome remodeling.\n9.Has cdSRCAP completely lost its ability to deposit H2A.Z? If there is some residual activity, will it cause confusion in the downstream transcriptional effects? It is recommended to provide more quantitative data based on ChIP-seq or biochemical experiments to illustrate this point. For the various regulated genes classified, further discuss the biological significance of this differentiation mechanism in maintaining self-renewal and cell fate determination, as well as whether there are complementary or antagonistic effects.\n10.This paper demonstrates the multi-level, both individual and collaborative, roles of SRCAP and H2A.Z in the cell cycle, TF binding, and transcriptional regulation. However, the discussion section has not yet established a systematic regulatory model. It is suggested to add a comprehensive schematic diagram to summarize the changes in H2A.Z deposition mediated by SRCAP, the modification of nucleosome state, the binding shielding by TF, and its impact on gene expression. This will help readers intuitively understand the interrelationships at various levels.\n11.Considering that some changes in the TF footprints cannot be fully attributed to the direct action of SRCAP, it is suggested that the authors discuss and consider some potential alternative mechanisms (such as whether other chromatin remodeling factors are involved, or whether there are phenomena like RNA polymerase pausing, etc.), and list possible verification directions for the future in the discussion.\nMinor points\n1.Although statistical tests (such as pairwise Wilcoxon test) were conducted for all the data in this paper, but a part figure of the descriptions of key information such as the number of biological replicates, the sample size, and the data normalization method were not sufficient. The author should clearly specify the number of biological and technical replicates for each experiment, and provide more information on data distribution and sample size.\n2.The font in the Fig 6i is too dense to be readable.\n3.All the volcano charts lack the X-axis and Y-axis."}, "author_response": "We thank the reviewer for this comment. We have now stated more clearly the strength of our study compared to previous studies in the abstract and the introduction.\n2) Figure 1A: Although this figure shows SRCAP-YA enrichment at H2A.Z peaks and active TSS, it does not show whether the enrichment is similar to WT-SRCAP, or not. In other words, is the enrichment of SRCAP-YA representative of WT-SRCAP enrichment at these peaks?\nWe thank the reviewer for this point. We have now compared the enrichment of SRCAP in SYAT cells with CUT&RUN data in E14 wt mESCs from Patty et al. Cell Rep 2025 (PMID: 40450687) in revised Fig. S1e, cited on page 3 of the revised manuscript. By mapping the SRCAP signal in SYAT cells on peaks obtained in wt mESCs, we can conclude that SRCAP is enriched at the same locations in SYAT and in wt mESCs. Note that absolute score comparisons are not relevant here given that these experiments were performed in different labs.\n3) Figure 1A/G: SRCAP-YA does not appear to be enriched at bivalent TSS peaks in figure 1A. Yet, H2A.Z appears the most enriched at bivalent TSS peaks in figure 1G. How can H2A.Z be so strongly enriched at bivalent TSS if SRCAP does not localize there?\nWe thank the reviewer for this important point. Indeed, we found very low SRCAP occupancy and high H2A.Z content at bivalent promoters, as well as a slower SRCAP-mediated H2A.Z turnover than at active promoters. The slow loss of H2A.Z upon SRCAP removal (as compared to active TSS) suggests that H2A.Z removal is slow in these regions. A slower eviction of H2A.Z is expected to lead to a slowed-down recruitment of SRCAP, as H2A.Z loading demands at these locations are lower than at active TSS. Therefore, SRCAP enrichment is not expected to scale with H2A.Z occupancy, but with its rate of eviction. The residual levels of H2A.Z after 8 hours of SRCAP removal at bivalent TSS could be explained either by their extremely slow turnover or the deposition of H2A.Z by other chromatin remodelers such as p400/Tip60. We have now explained this better on page 3 of the revised manuscript.\n4) Figure 2A/2G: Why is the WT asynchronous panel similar to the WT mitotic panel in 2A, but a large difference presented in 2G?\nWe understand that comparing these two experiments can be confusing. This is explained by differences in the way these experiments were performed. In Fig. 2a we display results from ChIP-Seq performed on 2µg of chromatin from cells sorted for the different cell cycle phases or collected from an asynchronous sample. In contrast, in Fig. 2g, H2A.Z ChIP-Seq was performed on 650ng of chromatin from cells sorted according to Ypet levels (YPet-positive or negative) and cell cycle phase. Additionally, enrichment from experiments presented in Fig. 2g were mapped on peaks obtained from the experiment presented in Fig. 2a.\nBelow we represent the enrichment of the experiment presented in 2g but now mapped on peaks from that same experiment (respectively peaks obtained in mitosis or asynchronous sample). As expected, the enrichment is now similar in mitotic and asynchronous samples.\n5) Figure 2B/S2B: Why are figures S2B and 2B different? In figure 2B, the H2A.Z enrichment increases at active enhancers, but in figure S2B, it decreases, the latter is also mentioned as a result in the text.\nWe thank the reviewer for raising this point. In Fig. 2b we represent H2A.Z enrichment across the cell cycle in promoters and enhancers, without removing regions where H2A.Z was not detected in order to include inactive regions where H2A.Z is typically absent or present at very low levels. In Fig. S2a and b, the violin plots display H2A.Z enrichment normalized on mean value in promoters and enhancers where at least one H2A.Z peak was detected. We apologize for the confusion and have now clarified this in the corresponding figure legends. Additionally, following the reviewer comment, we believe that the representations formerly presented in Fig. S2a and b are actually more informative, and are now presented as revised Fig. 2b, while former Fig. 2b is now revised Fig. S2b and c.\n6) Figure 2G/H: How can H2A.Z enrichment increase genome-wide (figure 2G) and specifically at bivalent promotors (figure 2H) in asynchronous cells when SRCAP is depleted? No potential explanation is given for this contradicting result. Why was the time-frame of only 1h IAA treatment chosen here? Figure 1 shows that H2A.Z enrichment is clearly decreased after 2h, but 1h was not tested.\nWe thank the reviewer for pointing out this apparently paradoxical result and would like to provide some clarifications. We chose to treat cells for 1h as we wanted to assess the consequences of SRCAP loss at the G2/M transition, and because mitosis typically lasts around 45 minutes in mESCs. We thus decided to compare the changes in H2A.Z levels with asynchronous cells also treated with IAA for 1h. We did not further discuss the increase in H2A.Z levels after 1h of IAA treatment observed both genome-wide and at bivalent promoters because of its fold-change that is too small to be meaningful, even though we cannot fully exclude that such a mild increase is real and due to indirect effects such as increased H2A.Z deposition by the Ep400-Tip60 complex. However, because of the very small magnitude of this change, we prefer not to venture into discussing it in our manuscript.\n7) Very little detail was given about how promoter-enhancer loops are identified.\nWe apologize for this oversight. We have now added more details in the Methods section (page 28 of the revised manuscript) regarding how loops were identified in the Enhancer Atlas database. Additionally, we have now used data from the Supplementary material of Hsieh et al., Molecular Cell 2020 (PMID: 32213323), listing all promoter-enhancer loops detected in their Micro-C data performed in wt mESCs, as well as the strength of these loops. We used these data to compute the number of loops formed by promoters of downregulated or upregulated genes as well as the strength of these loops confirming the results obtained from Enhancer Atlas. These results are now presented as revised Fig. 3e, while the analysis initially presented in the main figure is now displayed in revised Fig. S3h.\n8) Figure 4A: ‘At 4 h of IAA, H3K4me3 levels were down in 66.6% of H3K4me3-enriched regions, with a marked decrease at bivalent promoters’. And: ‘H2AK119ub1displayed a massive increase at most enriched loci’. These sentences suggest that the authors only looked at the level of histone marks at loci that were enriched for that histone mark in the control cells. If you are interested in the effect of SRCAP/H2A.Z on these histone marks, why not look at loci that were enriched for SRCAP/H2A.Z in the control cells? After all, in the next part, to determine whether nucleosome properties explain the transcriptional changes, the authors did look at the H2A.Z enriched loci.\nWe understand the point made by the reviewer, and we have now included (revised Fig S4b, S4f and S4h) the same variation of H3K4me3, H3K27me3 and H2AK119ub1, respectively, in peaks shared with H2A.Z. These results are in line with those obtained when analyzing all peaks enriched for H3K4me3, H3K27me3 or H2AK119ub1.\n9) Fig 4: Can the authors comment on why the amount of up/down regulated genes found by RNA seq are not reflected by the proteome results?\nWe apologize for not making this clear enough. Importantly, these experiments were performed after only 4 hours of IAA treatment. We see that at this time point, there are substantial changes in intronic RNA-Seq results, which reflect acute changes in transcriptional activity. We agree that these changes are expected to lead to corresponding changes in mature mRNAs and proteins, but this is expected to occur on much longer time scales than 4 hours. This is because mature mRNAs and proteins have half-lives on time scales of several hours to days. In line with this, after 4 hours of IAA treatment, we observe very limited changes in mature mRNAs levels (exonic reads), and thus the very minor changes in protein levels after 4 hours of IAA treatment are not surprising. We have now added a sentence on page 5 to clarify this.\n10) “In contrast to SRCAP, H2A.Z was only slightly decreased in the whole chromatome (Fig.5f and Table S2), suggesting low or SRCAP- independent H2A.Z turnover in regions that are not recovered in ChIP-seq experiments, such as sonication-resistant heterochromatin 59.” This is an interesting observation that the authors do not comment on. Why would this be specific to heterochromatin? This seems to relate to the steric hindrance model the authors propose.\nWe thank the reviewer for this comment, however we do not think that this observation relates to our steric hindrance model. As SRCAP is mostly enriched in accessible, active regions of the genome and nucleosome turnover is higher on these regions, we expect the maximum effect on H2A.Z levels on accessible regions that are also the easiest to map by ChIP-Seq. In contrast, closed, constitutive heterochromatin, especially at centromeres and telomeres that are also enriched for H2A.Z, is typically more resistant to chromatin fragmentation by sonication and composed of repeated sequences that are filtered out at the mapping state during ChIP-Seq reads processing. We now explain this better on page 7 of the revised manuscript.\n11) In the discussion the authors say “Several studies reported that the recruitment and nucleosome-remodeling activity of the BAF complex occur continuously on a time scale of minutes 33,85,86. Here we demonstrate dynamic SRCAP-mediated H2A.Z turnover over active and bivalent/poised regulatory elements on similar time scales.” The authors measure of the course of several hours, which is not the same timescale as minutes, so this comparison is not valid.\nWe understand that this was interpreted as an overstatement, which we have now toned down (see revised manuscript, page 12). We however note that the large impact we see on H2A.Z levels as soon as SRCAP is fully degraded (after 2 hours of IAA) is compatible with SRCAP acting on a sub-hour time scale.\n12) “We propose steric hindrance as a new mechanism by which bulky DNA-binding complexes can restrict the access of TFs to chromatin.” Although this seems a plausible explanation, steric hindrance by proteins as a mechanism for DNA accessibility is hardly revolutionary (the concept of heterochromatin), and should not be presented as a new mechanism.\nWe apologize if our statement was interpreted as a novel concept in such a general manner. We do however believe that steric hindrance by a chromatin remodeler that hinders transcription factor binding is an unprecedented finding. We now better specify the nature of our novel findings on page 12 of the revised manuscript.\n13) ‘We thus propose SRCAP/H2A.Z as a pivotal gene regulatory system operating in parallel to other epigenetic mechanisms to maintain cell identity and plasticity’. Given the minor changes in the proteome, this conclusion is not supported by the data. Especially since this is a near-complete knockdown of SRCAP, if it were a pivotal gene regulatory system, larger differences in the proteome would be expected.\nHere we disagree, since as we explain in the reply to comment 9, large changes in the proteome are not expected after only 4 hours of IAA, which we have now better emphasized on page 5 of the revised manuscript.\nReviewer #2 (Remarks to the Author):\nIn this paper, Tollenaere et al demonstrated the mechanisms of gene regulation by SRCAP and H2A.Z in pluripotent stem cells. More importantly this study establishes the role of chromatin remodeler, which is independent of its catalytic activity, in comprehensively regulating the histone variants orchestrating the binding of transcription factors. This is an interesting finding in the field of cell fate determination for pluripotency by epigenetic regulation. The data are in general technically well performed. However, before publication, there are some points to be better clarified and corrected.\nMajor points\n1.SRCAP-YA was rapidly degraded (Fig. 1b). It was mentioned that SRCAP was rapidly degraded, but the extent of degradation was not confirmed by data such as Western blot. Quantitative analysis of SRCAP protein levels over time (such as Western blot) could be added.\nSRCAP levels were both assessed by imaging of the Ypet fluorescent protein and mass spectrometry analysis of the proteome, confirming degradation of endogenous tagged SRCAP after 2 hours and 4 hours of IAA treatment, respectively. Following the reviewer comment and despite challenges in detecting the SRCAP protein because of its large molecular weight, we have now performed Western Blotting after different durations of IAA treatment for wt and SYAT mESCS treated with IAA for different time durations. We used both antibodies against SRCAP and GFP (which recognize the Ypet fluorescent protein fused to SRCAP). As expected, the Western Blots show a signal at 400kDa in SYAT in the control condition for both SRCAP and GFP, and these bands are not visible in the IAA-treated conditions. Results are displayed in revised Fig. S1f, and we added a corresponding Methods section on page 20 of the revised manuscript.\n2.Fig.1c showed the track of H2A.Z ChIP-seq. However, the track image of H2A ChIP-seq was absent, the author should added this track.\nWe thank the reviewer for this point. We initially did not include a track for H2A because in contrast to H2A.Z, its enrichment is very diffuse over the whole genome, and differences in overall enrichment are thus much easier to visualize in pile-up profiles. Nevertheless, we have now included genome tracks for H2A in the revised manuscript as Revised Fig. 1c.\n3.Loci with the highest H2A.Z turnover were initially highly enriched for SRCAP, the SWI/SNF, NuRD and Ep400 chromatin remodelers, and several PSC TFs (Fig.1h and S1i), however, why the binding score of Ep400 is declined in the groups of decreased at 4h compared to the group of 2h, and then increased at 6h?\nWe thank the reviewer for raising this interesting point. Loci that lose H2A.Z only after 6h of IAA treatment have a slower nucleosome turnover, which may favor the binding of Ep400. However, this statement is very speculative and we would prefer not including it in the final version of the manuscript.\n4.Although the article has analyzed different stages such as EG1, LG1, S, and G2/M, the detailed methods, strategies for cell cycle sorting, and possible cell cycle-dependent background factors have not been described adequately. Regarding cell cycle sorting, supplementary validation data from flow cytometry sorting should be provided in Fig. 2.\nWe apologize for not describing these sorting strategies enough. Importantly, these were previously validated by several labs (Kadauke et al. 2012 (PMID: 22901805), Friman et al 2019 (PMID: 31794382), Placzek et al. 2025 (PMID: 40153434). We now have added FACS plots as revised Fig. S2a and in the Methods section (page 19 of the revised manuscript), additional information as well as citation of previous work from us and others that have used mitotic degrons and H3S10ph staining.\n5.The protein levels of key upregulated genes (such as Gata3 and Nanog) have not been verified. The protein expression of key transcription factors can be confirmed by Western blotting in Fig.3f.\nWe would like to first state that in whole proteome analysis by Mass spectrometry, we didn’t see any change in NANOG or GATA3 levels (Supplementary Table S2). Additionally, we have now added as revised Fig. S5h a Western Blot showing levels of NANOG in wt CGR8 as well as SYAT cells in control condition and treated with IAA for 2, 4, 6 and 8h, quantified on GAPDH levels, showing no change in total NANOG levels. We tried to analyze GATA3 levels the same way but failed at finding a proper antibody to recognize GATA3 in Western Blot. Also note that even after IAA treatment, we expect the levels of GATA3 to be very low as this transcription factor is typically expressed at later stages of differentiation, thus making it very difficult to blot in the context of pluripotent stem cells, while the high detection sensitivity of mass spectrometry analysis allowed us to detect GATA3.\n6.Upregulated genes formed more promoter-enhancer loops than downregulated genes (Fig.3e). which method? If used HiC, the author should calculated the E-P loop strength.\nInitially we had computed the number of loops formed by promoters of downregulated or upregulated genes using an existing database, Enhancer Atlas, which lists all detected enhancers in a given cell line and the promoter they interact with. This approach was used because genes relying more on enhancers for their regulation tend to form more diverse loops between their promoter and enhancers. We now added more information in the revised Methods section (page 28) to better explain this approach. Additionally, we have now used data from the Supplementary material of Hsieh et al. Mol Cell 2020 (PMID: 32213323), listing all promoter-enhancer loops detected in their Micro-C data performed in wt mESCs, as well as the strength of these loops. We used these data to compute the number of loops formed by promoters of downregulated or upregulated genes as well as the strength of these loops confirming the results obtained from Enhancer Atlas. These results are now presented as revised Fig. 3e while our initial analysis using the Enhancer Atlas is now displayed in revised Fig. S3h.\n7.Using TOBIAS for TF footprint analysis provides important evidence for revealing the direct influence of SRCAP/H2A.Z on TF binding. However, the relevant parameters (such as significance threshold, pTF classification criteria, etc.) require more detailed explanations in terms of methodology.\nWe apologize for not justifying enough our choice for the significance threshold we used for TOBIAS analysis. We decided to place the threshold at p<0.0001 and |FC|>0.05 as with these parameters, changes in the negative control (wt CGR8 treated with IAA or vehicle) were very mild and not overlapping with the motifs that we found to be affected in SYAT cells treated with IAA. Our classification of TFs as pioneer TFs was based on two published lists (Lemma et al. 2022 (PMID: 35440061), Peng et al. 2024 (PMID: 38293962)), in which authors analyzed the ability of TFs to bind to chromatinized DNA and increase chromatin accessibility at bound regions in at least some cell types. We have now added these explanations in the Methods section, page 30 of the revised manuscript.\n8.This paper used MNase-seq to investigate the changes in nucleosome occupancy, protection fragment length, and \"fragility\". However, the results showed that even when H2A.Z was lost, there was no consistent change in the \"fragility\" of nucleosomes, indicating that this indicator may be more influenced by the genomic background. Further analyze the statistical significance of the structural changes of nucleosomes in different regulatory elements (such as active and bivalent promoters, and distal enhancers), and explore whether there are certain specific regions where the changes in TF binding are highly correlated.\nWe apologize that some statistical analyses were missing for the changes in nucleosome fragility, occupancy and fragment length distribution. We have now added statistical tests (Kruskal-Wallis rank sum test and pairwise Wilcoxon rank sum test) for differences in nucleosome occupancy and fragility in regions with different H2A.Z levels (Fig. 4f and h, Fig. S4k,m and n). Statistical analysis of the impact of IAA treatment on nucleosome occupancy and fragility was performed using Welch two samples t-test. Differences in nucleosome-protected fragment length distribution were statistically assessed using the Kolmogorov-Smirnov test. Results from all statistical tests performed in the study are now gathered in a supplementary table (Table S7).\nWe thank the reviewer for raising the potential link between nucleosome fragility and changes in TFs binding. We have now assessed nucleosome fragility at motifs predicted to gain or lose binding in TOBIAS after 4h of IAA treatment. Nucleosome fragility was not significantly changed after IAA treatment for motifs predicted to gain binding in TOBIAS. Because these results are not particularly informative, we decided not to include them in the revised manuscript.\nIf possible, the in vitro experiments on nucleosome sliding or recombination to verify the catalytic independent function of SRCAP in H2A.Z deposition and nucleosome remodeling.\nWe agree that this would be a very exciting experiment, however this would most likely require several years of work to purify and assemble the SRCAP complex in vitro and assay its catalytic-dependent in H2A.Z positioning and chromatin remodelling, and thus we think that this is out of scope of this manuscript.\n9.Has cdSRCAP completely lost its ability to deposit H2A.Z? If there is some residual activity, will it cause confusion in the downstream transcriptional effects? It is recommended to provide more quantitative data based on ChIP-seq or biochemical experiments to illustrate this point. For the various regulated genes classified, further discuss the biological significance of this differentiation mechanism in maintaining self-renewal and cell fate determination, as well as whether there are complementary or antagonistic effects.\ncdSRCAP is an ATPase dead mutant of SRCAP and as the SRCAP ATPase activity was shown to be necessary for H2A-H2B dimer eviction and H2A.Z-H2B deposition (Girvan et al. Nature 2024, PMID: 39506114), cdSRCAP is expected to be defective for H2A.Z deposition. However, to satisfy the reviewer request, we have now compared H2A.Z genomic enrichment by ChIP-Seq between cells overexpressing cdSRCAP or not (+/- dox in the cdSRCAP inducible SYAT cell line), and in the presence or absence of endogenous SRCAP (+/- IAA). We found that cells overexpressing cdSRCAP have a slightly lower level of H2A.Z in regions where H2A.Z is present, indicating that cdSRCAP does not deposit H2A.Z and even plays a slight dominant negative role on H2A.Z deposition. These data are now presented as revised Fig. S6c and mentioned on page 9 of the revised manuscript.\nAmong the six RNA clusters identified, two are independently regulated by SRCAP or H2A.Z. For these clusters (A and E), the depletion of H2A.Z or SRCAP will respectively lead to upregulation of genes from cluster A and downregulation of genes from cluster E, as we observed upon IAA treatment. For all four other clusters, H2A.Z and SRCAP have antagonistic regulatory effects on transcription. For some genes, H2A.Z and SRCAP compensate for each other activity (cluster C) as confirmed by the absence of transcriptional changes upon H2A.Z/SRCAP depletion. We expect the expression of these genes to be buffered by H2A.Z/SRCAP in mESCs, favoring cell identity maintenance. For some genes (cluster B), the repressive impact of H2A.Z dominates on the activatory impact of SRCAP. We observe the opposite for genes in cluster D. SRCAP deposits H2A.Z in H2A-containing nucleosomes, its binding being inhibited by enrichment of H2A.Z. We believe that this feedback loop could create windows of opportunities upon signalling cues for upregulation or downregulation of these genes. We now discuss these results in the discussion section on pages 12-13 of the revised manuscript.\n10.This paper demonstrates the multi-level, both individual and collaborative, roles of SRCAP and H2A.Z in the cell cycle, TF binding, and transcriptional regulation. However, the discussion section has not yet established a systematic regulatory model. It is suggested to add a comprehensive schematic diagram to summarize the changes in H2A.Z deposition mediated by SRCAP, the modification of nucleosome state, the binding shielding by TF, and its impact on gene expression. This will help readers intuitively understand the interrelationships at various levels.\nWe thank the reviewer for this very good suggestion. We have now added a schematic diagram as revised Fig. 8 to summarize our findings on H2A.Z turnover and how SRCAP loss affects nucleosome properties, TF binding and transcription.\n11.Considering that some changes in the TF footprints cannot be fully attributed to the direct action of SRCAP, it is suggested that the authors discuss and consider some potential alternative mechanisms (such as whether other chromatin remodeling factors are involved, or whether there are phenomena like RNA polymerase pausing, etc.), and list possible verification directions for the future in the discussion.\nWe thank the reviewer for their suggestion, we have now included additional discussion points on page 12 of the revised manuscript.\nMinor points\n1.Although statistical tests (such as pairwise Wilcoxon test) were conducted for all the data in this paper, but a part figure of the descriptions of key information such as the number of biological replicates, the sample size, and the data normalization method were not sufficient. The author should clearly specify the number of biological and technical replicates for each experiment, and provide more information on data distribution and sample size.\nFor heatmaps of tag counts and violin plots, we have now added sample sizes corresponding to the number of studied peaks or regulatory regions directly on the plots or in the legend of the figure (in Fig. 1, S1, 2, S2, 4 and S4, 5, 6 and S6). For the number of nucleosomes quantified in the different regulatory regions and depending on H2A.Z levels, we have appended a new table containing this information to the supplementary material (Table S6). Missing numbers of biological or technical replicates were added in the Methods section at the beginning of the paragraph describing the experimental procedure for each given type of experiment.\n2.The font in the Fig 6i is too dense to be readable.\nThe font of Fig. 6i has now been enlarged.\n3.All the volcano charts lack the X-axis and Y-axis.\nWe understand the point, but we believe that keeping the volcano plots without axes that are drawn as lines (noting that the legends and graduations are present) is better for readability."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have responded to our questions adequately. This has significantly improved the manuscript, and we now recommend it for publication.\nOur only remaining confusion is the result in Figure 2 with the increase on bivalent promoters in asynchronous cells. Although we agree with the authors that the magnitude of this change is small, it is not that much smaller than the decrease for mitotic cells. In addition, it is significant, and we would have appreciated acknowledgement of this unexpected result in the manuscript.", "reviewer_2": "The author has addressed all my questions."}, "author_response": "Response to reviewers comments\nReviewer #1 (Remarks to the Author):\nThe authors have responded to our questions adequately. This has significantly\nimproved the manuscript, and we now recommend it for publication.\nOur only remaining confusion is the result in Figure 2 with the increase on bivalent\npromoters in asynchronous cells. Although we agree with the authors that the\nmagnitude of this change is small, it is not that much smaller than the decrease for\nmitotic cells. In addition, it is significant, and we would have appreciated\nacknowledgement of this unexpected result in the manuscript.\nWe have now added the following sentence on page 4 of the manuscript \"While we\nnoted a small increase in H2A.Z enrichment at bivalent promoters in asynchronous\ncells upon 1h IAA treatment, the biological meaning of this finding is unclear\n(Fig.2h).\"\nReviewer #2 (Remarks to the Author):\nThe author has addressed all my questions."}]

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