FACT regulates pluripotency through proximal and distal regulation of gene expression in murine embryonic stem cells

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1. Version published to 10.1186/s12915-023-01669-0

   Aug 4, 2023
2. 

   Review Commons

   Aug 29, 2022

   Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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   Reply to the reviewers

   Reviewer #1 (Evidence, reproducibility and clarity (Required)):

   Summary: Klein and colleagues generate an ES cell model system with inducible FACT depletion to understand how loss of FACT affects gene regulation in ES cells. They find that FACT is critical for ES cell maintenance through multiple mechanisms including direct regulation of key pluripotency transcription factors (Sox2, Oct4, and Nanog), maintaining open chromatin at enhancers, and regulated enhancer RNA transcription. The paper is well-written, the experiments are generally well-controlled and appropriately interpreted and placed within the context of the field.

   We appreciate the Reviewer’s …

   Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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   Reply to the reviewers

   Reviewer #1 (Evidence, reproducibility and clarity (Required)):

   Summary: Klein and colleagues generate an ES cell model system with inducible FACT depletion to understand how loss of FACT affects gene regulation in ES cells. They find that FACT is critical for ES cell maintenance through multiple mechanisms including direct regulation of key pluripotency transcription factors (Sox2, Oct4, and Nanog), maintaining open chromatin at enhancers, and regulated enhancer RNA transcription. The paper is well-written, the experiments are generally well-controlled and appropriately interpreted and placed within the context of the field.

   We appreciate the Reviewer’s support of this manuscript.

   Major comments:

   1. In general, the ChIP-seq and CUT&RUN data are not that similar. Although correlation seems reasonable (S2A), looking at the heatmaps in S2B/C these seem pretty different. It's not very clear if this is a case where CUT&RUN has higher specificity (and signal-to-noise, which is very clear from example tracks) or if these two methods are picking up biologically different sites. Could the authors include some overlap analysis of peaks and comment on these discrepancies. Looking at the example tracks in Figure 2B, it seems likely that prior SPT16 and SSRP1 ChIP-seq were relatively high-noise.

   We have identified overlapping peaks between the two techniques, and while CUT&RUN identified substantially more peaks overall, percentage of peaks shared between datasets were relatively consistent (1-6% of total) between the individual ChIP-seq datasets and the CUT&RUN dataset (Response Figure 1). We note that the biological classes identified through all datasets were remarkably consistent (Fig. 2D), and therefore attribute the discrepancies to the greater number of reproducible peaks called from CUT&RUN data. As discussed in the paper, peak calling algorithms designed for the specific data types were used, and therefore peak calling could also contribute to differences.

   Response Figure 1. ChIP-seq and CUT&RUN peak overlap. Pie chart depicting the unique and overlapping peaks called from V5-SPT16 CUT&RUN data and FACT ChIP-seq data. These data are included in the revised manuscript (as a new Figure panel 2E). Peaks must have been identified in at least two technical or biological replicates.

   Are motifs described in Figure 2E CUT&RUN only, and do prior ChIP-seq experiments also identify these motifs?

   The motifs shown in Figure 2E (now 2F) are indeed CUT&RUN peaks only. We were unable to confidently assign enriched motifs to the ChIP-seq datasets (the most enriched motifs were approximately p = 10-18). By analyzing all SPT16 ChIP-seq peaks, rather than only intersected SPT16 ChIP-seq peaks, we were able to identify motifs recognized by two of the top three CUT&RUN motif hits (SOX2 and OCT4/SOX2/TCF/NANOG); however, enrichment was quite poor (p = 10-3). By limiting the analysis to intergenic regions, we were able to identify strong enrichment for motifs recognized by CTCF and BORIS (p = 10-58 and 10-51, respectively). As validation, we also called motifs from peak files published as supplementary material to the original Tessarz lab manuscript but were still unable to confidently call motifs (all p > 10-7 for SPT16 peaks, p > 10-15 for SSRP1 peaks). Related to major comment 1, we suspect that the weak motif enrichment is due to high background in ChIP-seq datasets compared to CUT&RUN datasets.

   The authors state that FACT depletion affects eRNA transcription and measured this using TT-seq. The analysis in Figure 3B seems to be all the different types of sites looked at together (genes, PROMPTs, etc). Is there evidence that eRNAs specifically are regulated by FACT loss.

   We apologize for the confusion and have clarified that Figure 3B (now 3A) is referring to mRNAs only in the text and figure. Our analysis of eRNA regulation by FACT is predominantly contained within Fig. 4B (TT-seq from DHSs, but no histone mark overlap assessment), Supp Fig. S4 (as in Fig 4B, but at DHSs overlapping H3K27ac or H3K4me1), Fig. 5E (FACT localization to putative enhancers, defined as in S4), and Fig. 6D (ATAC-seq demonstrating loss of accessibility at putative enhancers upon FACT depletion). Based on these results, we believe there are many eRNAs specifically misregulated by FACT loss and that potential direct targets (based on change in depletion and containing FACT binding) are in Fig 5E.

   Could these be compared to DHS sites that lack FACT binding to support a direct role for FACT at these sites?

   We appreciate the suggestion and have performed this analysis (see Response Figure 2). Relatedly, we analyzed putative silencers, defined as DHSs marked by H3K27me3, for FACT binding and expression changes (measured by TT-seq) following FACT depletion (Supp Fig. S7). As expected, FACT does not bind these putative silencer DHSs and transcription does not markedly increase or decrease from these regions after FACT depletion. Complicating the matter, FACT binds at many DHSs, even those that did not to meet our stringent peak-calling criteria (see Response Figure 2, middle cluster).

   __Response Figure 2. Overlap between FACT binding sites and gene-distal DHSs. __Individual clusters are sorted by V5-SPT16 binding. Clusters were assigned based on direct overlap between called V5-SPT16 peaks and assigned gene-distal DHSs. Overall, 17.6% of DHSs overlapped a FACT peak identified in at least one CUT&RUN replicate (8.5% of DHSs overlapped a peak present in multiple replicates).

   One mechanism proposed for how FACT regulates enhancers is that it is required for maintaining a nucleosome free area, and when FACT is depleted nucleosomes invade the site (Figure 7). It wasn't clear if they compared distal DHS sites were FACT normal bound to those without FACT binding in the MNase experiments, which could help support the direct role or specificity of FACT in regulating those enhancers (or a subset of them).

   We have subset the V5-SPT16 CUT&RUN peaks and distal DHSs into groups and have identified increased nucleosome occupancy after depletion at both FACT-bound and FACT-unbound DHSs suggesting both direct and indirect regulation (Fig. 6A, D). There is disruption to nucleosome arrays at non-FACT-bound DHSs (although more modest relative to the FACT bound locations), and therefore we speculate that a nucleosome remodeler is involved downstream of FACT (possibly CHD1, per recent work out of Patrick Cramer and François Robert’s labs, among others).

   1. Data quality for nucleosome occupancy was a little strange (Figure 7F), where the two clones had very different MNase patterns at TSS sites. Could the authors comment on why there is such a strong difference between clones here.

   We agree that the trends identified by visualizing differential MNase-seq signal near TSSs do not fully replicate; however, in examining the nondifferential MNase-seq heatmaps, we see a more expected distribution (see new Figure 7A). Per our newly-added Supp Fig. S9B, all MNase-seq replicates had a pairwise Pearson correlation value of at least 0.73 (SPT16-depleted clone 1/rep 1 vs untagged rep 3), and the vast majority of samples had pairwise correlations of above 0.85, suggesting that these discrepancies are not due to strong differences in sequencing depth or MNase-protected regions. We therefore suspect that the clonal distinctions are a result of different background occupancy of nucleosomes near the TSS, resulting in an array with increased occupancy in one clone and more generalized increased occupancy in the other clone. We also added the MNase-seq data over TSSs in a non-differential form in Fig 7A, and believe the difference between the clones is due to the differential analysis, and have commented accordingly in the revised manuscript.

   More details on some of the analysis steps would be really helpful in evaluating the experiments. Specifically, was any normalization done other than depth normalization? I ask this because the baseline levels for many samples in metaplots look quite different. For example, see Figure 7B where either clone 1 has a globally elevated (at least out 2kb) ratio of nucleosome in the IAA samples relative to the EtOH, or there is some technical difference in MNase. One suggestion is to look at methods in the CSAW R package to allow TMM based normalization strategies which may help.

   We appreciate the suggestion – we have expanded our explanation of normalization methodology in the paper. We initially used quartile and RPGC normalizations to attempt to mitigate technical differences in MNase-seq data. Size distribution plots did not suggest differences in MNase digestion between samples, and neither quartile/RPGC nor TMM-based normalization fully resolved this issue. Because our ATAC-seq datasets agree with the general trends identified by MNase-seq (which are consistent, despite technical differences between clones), we do not believe that the differences constitute true biological difference, but rather experimental noise.

   1. I appreciated the speculation section, and the possible relationship between FACT and paused RNAPII is interesting. While further experiments may be outside the scope of this work and I am not suggesting they do them, I am wondering if others have information on locations of paused RNAPII in ESC that would allow them to test if genes with paused RNAPII have a special requirement for FACT that they could use their current data to assess.

   We agree that experiments to test the relationship between paused RNAPII and FACT are an intriguing next step, and plan to dissect those in the near future.

   Minor comments:

   1. When describing the peaks found in the text related to Figure 2 they refer to 'nonunique' peaks. Does this mean the intersection of the independent peak calls? Could they clarify this.

   We apologize for the confusion and have clarified in the text that nonunique peaks does indeed refer to the intersection of independent peak calls (now specified on manuscript page 8, line 15).

   In the text they refer to H3K56ac data in S2D and I don't see that panel. The color scheme for the 1D heatmaps (Figure 5A) is tough to appreciate the differences. I'd suggest something more linear rather than this spectral one might be easier to see.

   We apologize for the confusion and removed the remaining H3K56ac-related data and references in the text. We appreciate the suggestion regarding the 1D heatmap color scheme and have adjusted the colors to a linear (white à red) scheme.

   For the 2D heatmaps of binding, could they include the number of elements they are looking at for each group?

   We appreciate the suggestion and have included numbers of elements visualized wherever applicable in the figure panels and legends.

   1. Also for 2D heatmaps, I think the scale is Log2 (IAA/EtoH), but could they confirm that and include it in the figure?

   We apologize for the confusion; the only heatmaps displaying log2(IAA:EtOH) are those in Fig. 6; for those panels, we have clarified the scale in the figure and legend.

   Reviewer #1 (Significance (Required)):

   • The use of degrader based approaches to depleting a protein allows refined kinetic and temporal assays which I think are important. Several papers showed a rapid invasion of nucleosomes after SWI/SNF loss using these kinds of approaches and revealed surprisingly fast replacement of SWI/SNF. This paper is consistent with those models, showing that another remodeler behaves the same, suggesting there may be general requirements for active chromatin remodeling to maintain the expression of these genes. It also highlights a key gap in how specificity works to target these enzymes remains somewhat unknown.

   • This work will be of interest to those studying detailed mechanisms of gene regulation. Compared to some other chromatin regulators, FACT is understudied and so this work will allow comparison between different chromatin remodeling complexes.

   • My experience: chromatin, gene regulation, cancer, genomics

   We appreciate the thorough review and hope that we have sufficiently addressed your concerns.

   Reviewer #2 (Evidence, reproducibility and clarity (Required)):

   The authors propose that the FACT complex can regulate pluripotency factors along with their regulatory targets through non-genic locations. They find that acute depletion of FACT leads to a "reduction" in pluripotency in mouse embryonic stem cell by disrupting transcription of master regulators of pluripotency. They also show FACT depletion affected the transcription of gene distal regulatory sites, but not silencers. They also stated that SPT16 depletion resulted in both, a reduction of chromatin accessibility and increase of nucleosome occupancy over FACT bound sites.

   Overall the study appears technically well executed. The use of an Auxin induced depletion system is a good model to study the acute effects of FACT depletion. However, I have a number of concerns relating to specificity and interpretation of the results that need to be addressed. We appreciate the careful review and have addressed your comments below:

   Major points o Authors claimed that depletion of the FACT complex "triggers a reduction in pluripotency". As evidence supporting this statement they present images of alkaline phosphatase assays of a time course performed upon depletion of FACT. These experiments indeed show that ESCs are destabilized in the absence of SPT16. However, some key questions regarding the phenotype remain unresolved: o What is are the kinetics of expression of selected naïve pluripotency and early differentiation markers? Are differentiation markers upregulated, consistent with normal differentiation upon FACT depletion?

   We appreciate the suggestion and have emphasized the decrease in pluripotency factor expression, accompanied by an increase in differentiation marker expression across all three germ layers. We graphed 7 pluripotency factors and 7 differentiation markers for each germ layer; generally speaking, pluripotency factors are decreased while differentiation markers are increased (Response Figure 4; pluripotency factors are included in the new Fig. 3B, while differentiation markers are included in the new Supp Fig. S3 F-H).

   We have also performed an immunocytochemistry (ICC) timecourse, per Reviewer 3’s suggestion. This ICC timecourse allows us to orthogonally assess decreased pluripotency factor expression, to pair with the OCT4 Western blot shown in Supp Fig. S1B. These new ICC data are shown in the new Fig. 1D and included here for convenience (Response Figure 5). In addition, we have added alkaline phosphatase staining at 12 hours of depletion to Fig. 1C.

   __Response Figure 4. Plots of DESeq2 analysis across experimental timecourse. __Shown are lineage markers denoting: A. Pluripotency B. Endoderm C. Mesoderm and D. Ectoderm. Generally, expression of pluripotency factors decrease over time, while differentiation markers of each lineage increase over time. These data are shown in Figure 3B and Supplemental Figure S3F-H.

   __Response Figure 5. Immunocytochemistry timecourse depicting DAPI staining (left panels, blue) and OCT4 immunofluorescence (right panels, green). __Images are representative of plate-wide immunofluorescence changes.

   O Is only ESC identity affected or does loss of FACT impair viability also of cells that have exited pluripotency? To address this, growth curves and/or cell cycle analysis upon FACT depletion could be performed. Alternatively, the authors could utilize surface markers to distinguish naïve pluripotent form differentiated cells in the cell cycle analysis experiments to identify a potential differential response of pluripotent and differentiated cells to FACT depletion.

   We have performed a growth curve with FACT depletion as suggested; as the two points are related, we will explain further below:

   o Another key question is whether it is only the metastable pluripotent state of ESCs in heterogeneous FCS/LIF conditions which is affected by FACT loss, and whether cells cultured in the more homogeneous and more robust 2i-LIF conditions can tolerate FACT removal. If that is indeed the case it would enable the authors to address one main concern I have with this manuscript, which is that it is nearly impossible to distinguish the direct effect of FACT loss from differences induced by differentiation (and maybe cell death, see comment above). This is a critical concern that needs to be addressed and discussed appropriately.

   We apologize for the confusion – all original experiments for this project were performed in the presence of LIF as well as GSK and MEK inhibitors CHIR99021 and PD0325091, respectively (2i+LIF conditions). To address the reviewers question, we have now performed a timecourse growth assay under both LIF-only and 2i+LIF conditions (Response Figure 6 and new Supp Fig S1F), and as suggested by the reviewer, observe a stronger effect of FACT depletion on cell viability in LIF-alone (FACT-depletion results in ~90% death within ~24 hours, with differences in growth observed by 12 hours) than in 2i+LIF (FACT-depletion results ~80% death within 48 hours, with differences in growth observed starting around 18 hours). Overall, ES cells in LIF alone are indeed more sensitive to FACT loss, supporting our decision to perform the experiments throughout the manuscript in 2i+LIF conditions.

   LIF alone __ LIF + 2i __

   Response Figure 6. __Growth assays in LIF (left) and 2i+LIF (right) conditions. __Cells were treated with either EtOH or 3-IAA and counted at the indicated times. Viability was assessed using trypan blue exclusion. Error bars indicate standard deviation for biological triplicate experiments.

   o A further major concern is about the specificity of the effect of FACT depletion. The authors claim that FACT is required to maintain pluripotency. From the data presented this is unclear. FACT appears to be part of the general transcription machinery in ESCs. It appears generally associated with active promoters and active genes, according to the data in this manuscript. Whether there is any specific link to pluripotency remains to be shown. It is unclear how enrichment analyses have been performed. If they haven't been performed using a background list of genes actively transcribed in ES cells, they will obviously show enrichment of ESC specific GO categories, because ESCs express ESC specific genes robustly expressed in ESCs?

   We apologize for the confusion and have updated our methods section to include more comprehensive details on our pathway enrichment analyses. We have confirmed that pluripotency-related categories are still highly enriched in FACT-regulated DEGs, even when using a background dataset of all transcribed genes, per our TT-seq datasets (baseMean ≥ 1 in DESeq2 output).

   In line with this: the authors show that FACT bound loci well overlap with Oct4 bound regions. But which proportion of FACT targets loci are actually Oct4 bound too?Is FACT binding exclusive to Oct4 regulated enhancers and promoters? In other words, will FACT be recruited to all actively transcribed genes in ES cells? In that case, a specific effect on pluripotency network regulation cannot be claimed.

   We appreciate the suggestion, and have added the number of OCT4/SOX2/NANOG-bound FACT peaks and vice versa in the text and legend of Fig 3E-F. We have also summarized this information in Response Table 1, below (and included these data as Table 2 in the revised manuscript).

   OCT4 peaks

   Sox2 Peaks

   Nanog Peaks

   Any of OSN

   V5 Peaks

   8,544

   5,948

   5,307

   9,682

   OSN Peaks

   45,476

   19,211

   16,817

   52,899

   % of OSN peaks bound by FACT

   18.33%

   30.72%

   31.40%

   17.91%

   % of V5 peaks bound by pluripotency factor(s)

   52.41%

   36.85%

   32.94%

   59.63%

   V5-bound promoters

   4,261

   2,719

   2,327

   4,452

   OSN-bound promoters

   6,550

   1,542

   666

   6,948

   V5- and OSN-bound promoters

   2,040

   801

   343

   2,202

   OSN-bound gene-distal peaks

   38,926

   17,669

   16,151

   45,938

   V5-bound gene-distal OSN peaks

   6,504

   5,147

   4,964

   7,480

   __Response Table 1. Overlapping CUT&RUN and ChIP-seq peaks shared between OCT4, SOX2, NANOG, and V5-SPT16 under various stratifications. __Shown are numbers or percentages of peaks overlapping between V5 and OSN. The last column are peaks containing any of OCT4, SOX2, and/or NANOG. The first four rows include all peaks, regardless of location, and the last five rows are broken down by promoter (as defined by an annotated mRNA) or gene-distal location (defined by a minimum of +/- 1kb from a gene).

   Of the 45,865 OCT4 peaks, 3,688 are located at promoters, and 1,209 of these peaks are bound by V5-SPT16 (32.8%). Inversely, 13,228 of 42,177 gene-distal OCT4 peaks are called as SPT16-V5 peaks in at least one CUT&RUN replicate (31.36%), suggesting a relationship between OCT4 binding and FACT binding, which has long been identified with genic transcription, but has roles extending beyond gene-proximal regulation. We observe similar trends with NANOG and SOX2.

   o It is disappointing that neither raw data (GEO submission set to private) nor any Supplemental Tables containing differentially expressed transcripts and ChIP or Cut and Run peaks and associated genes were made available. This strongly reduces the depth of review that can be performed.

   We apologize if the reviewer token in the cover letter was not accessible. The GEO datasets (including differentially expressed transcripts, raw fastq files, and analyzed datasets) will be made public upon publication; in the meantime, the GEO entry (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181624) can still be accessed using the previously provided reviewer token: wvkvwmwynjeffux.

   o To what extent do FACT bound loci overlap with genes differentially expressed 24h after FACT depletion? This analysis would help determine the direct targets of FACS regulation.

   We appreciate the suggestion. This analysis can be found in the original Figure S6, broken down by FACT-repressed (expression increased upon FACT depletion), unchanged, and FACT-stimulated (expression decreased upon FACT depletion) DESeq2 results (ordered left-to-right, respectively). Figure S6A-C shows that V5-SPT16 binding is enriched, but not exclusive to, genes with FACT-regulated expression, while Fig. S6D-F shows TT-seq data for each group, sorted by log2-fold change assigned by DESeq2.

   o The paper mainly relies on NGS analysis. Therefore, it is crucial that authors show as Supplemental Material some basic QC of these data. PCA analyses to show congruency of replicates are the minimum requirement.

   We appreciate the suggestion and have included a new Supp. Fig S9, with pairwise comparative Pearson correlation scatterplots and heatmaps for replicates in each dataset, in addition to the scatterplots shown for CUT&RUN and ChIP-seq data in the original Supp Fig. S2A.

   o Did the authors perform any filtering for gene expression levels before analysis? Are genes in the analysis robustly expressed in at least one of the conditions?

   We apologize for the confusion. Due to the sensitive nature of TT-seq and the germ layer-inconsistent pattern of cell differentiation following FACT depletion, we did not perform filtering for gene expression prior to any analyses. For the vast majority of genes analyzed, however, we are able to identify transcription via TT-seq, even in those that do not significantly change expression upon FACT depletion (see Supp Fig S6E). As discussed above, we did include a cutoff for expressed genes in our revised pathway analysis.

   o Wherever p values were reported for enrichment analyses, adjusted p values should be used

   We apologize for the oversight; the p values were in fact adjusted p values and have updated the text and figures to make it explicit that the adjusted p values were used wherever applicable.

   o I cannot follow the logic used by the authors to explain discrepant results from Chen et al about the role of FACT in ESCs. Chen et al showed that FACT disruption by SSRP1 depletion is compatible with ESC survival and leads to ERV deregulation. The authors of the present study attribute these differences to potential FACT independent roles of SSRP1. However, I would assume that if there are indeed FACT independent roles of SSRP1, then the phenotype of SSRP1 KOs in which FACT and other processes should be dysfunctional should be even stronger than a plain FACT KO. This needs a proper and careful explanation.

   We apologize that our discussion of FACT-independent roles of SSRP1 was not clear and have clarified our wording in the text (page 4, line 49 – page 5, line 4)in the revised manuscript); we intended to reconcile the results of Chen *et al. *2020 with Goswami *et al. *2022 and Cao *et al. *2003; despite SSRP1 knockout viability in embryonic stem cells, SSRP1 knockout is lethal in mice between 5-40 weeks and general SSRP1 knockout is lethal 3.5 days post-conception (per Goswami et al. 2022). We therefore posit that the general requirement for SSRP1 may be due to distinct roles from those carried out by the FACT complex in ES cells, as discussed by Spencer et al. 1999, Zeng et al. 2002, Li *et al. *2007, and Marciano *et al. *2018.

   We note that our findings are in agreement with papers from the Gurova lab and others in that depletion of mRNA or protein of SPT16 leads to concomitant loss of SSRP1; we therefore do not expect total SSRP1 loss to have a stronger effect than SPT16 depletion. We therefore expect, and confirmed via Western blotting (Figure 1B, Supplemental Figure 1), that depletion of SPT16 leads to loss of both FACT subunits, and therefore all FACT subunit activity, complex-dependent or -independent.

   Also, did the authors observe any evidence for ERV deregulation upon acute SPT16 depletion?

   We did indeed observe ERV deregulation upon SPT16 depletion. When reviewing our TT-seq datasets, 7.1% of ERVs were derepressed, while 2.4% decreased in expression upon 24h FACT depletion (mm10 ERVs sourced from gEVE, Nakagawa and Takahashi, 2016). Further, we identified increased chromatin accessibility after FACT depletion at annotated LTR elements, as shown in the table below (Response Table 2). Here we are displaying the calculated enrichment score for accessibility detected at these locations. A negative value indicates lower accessibility than expected by region size, while a positive score indicates that reads are more enriched than expected at the indicated region.

   ATAC-seq enrichment score for locations losing accessibility with FACT depletion

   3h

   6h

   12h

   24h

   LTR Enrichment

   -1.445

   -1.299

   -0.917

   -0.559

   Intergenic Enrichment

   -6.046

   -4.765

   -3.926

   -2.972

   Promoter Enrichment

   3.335

   2.789

   2.726

   2.233

   ATAC-seq enrichment score for locations gaining accessibility with FACT depletion

   3h

   6h

   12h

   24h

   LTR Enrichment

   -1

   -0.436

   1.103

   1.13

   Intergenic Enrichment

   -1

   0.134

   0.435

   0.236

   Promoter Enrichment

   -1

   -3.585

   1.171

   1.39

   __Response Table 2. Changes in ATAC-seq peak enrichment for selected regions, annotated via HOMER. __At regions differentially accessible between SPT16-depleted and SPT16-undepleted samples, regions were assigned to an annotated genomic feature using HOMER annotatePeaks.pl and assigned an enrichment score based on the ratio of ATAC-seq signal to region size. Over time, LTR elements become more enriched among the ATAC-seq peaks both gaining and losing accessibility, indicating a role for FACT in maintaining LTR accessibility.

   We do wish to note, however, that Lopez *et al. *2016 identified SPT16-independent regulation of LEDGF/HIV-1 replication by SSRP1, and therefore cannot rule out effects on ERV dysregulation due to SSRP1 loss that accompanies SPT16 depletion.

   Minor points o Figure S2A is very small and resolution is low. Page 10: "...while all four Yamanaka factors (Pou5f1, Sox2, Klf4, and Myc) and Nanog were significantly 24 reduced after 24 hours (Fig. 3A, S3A-B)". No data for myc is being shown.

   We apologize for the figure resolution and have included a larger image. Because pairwise comparative scatterplots are not space-efficient, we opted to display the Pearson correlations for the datasets including more samples (TT-seq and ATAC-seq timecourses) as heatmaps in the new Supp Fig S9. We have added Myc labeling to the volcano plot (now in Fig. 3A) and included a trace of Myc expression over time to the new pluripotency factor graph in Fig. 3B.

   o Are the two bands in the middle in figure 1B is supposed to be a ladder? This should be clarified.

   We thank the reviewer for noticing this and apologize for the oversight.

   o Figure 3C- This Figure is complicated to read. Also, information appears redundant with the Table 1, I recommend to remove this panel.

   We have moved the panel to the supplement (now Supp Fig. S3A). While the information is somewhat redundant with Table 1, we chose to include the former panel 3C as a visual representation of the consistent deregulation over depletion time across transcript categories.

   o Figure 6 and figure 7 could be presented in one single figure since both aspects are complementary and target related aspects.

   While we thank the reviewer for this suggestion, we do not believe that the information contained in Figs. 6 and 7 can effectively be conveyed in a single figure. While both figures focus on chromatin accessibility and nucleosome occupancy, Fig. 6 is designed to address the changes in chromatin accessibility over time, while Fig.­­­ 7 is more relevant to the biological mechanism through which FACT co-regulates targets of the core pluripotency network (OCT4/SOX2/NANOG) after 24 hours of depletion.

   o Are the authors certain that the effects observed are directly linked to the FACT complex in contrast to FACT independent roles of SPT16, if any exist? The experiment to address this would be to deplete SSRP1 and investigate whether the effects are identical, which would be the hypothesis to be tested.

   We thank the reviewer for this suggestion. We did attempt to create additional SSRP1-AID-tagged lines; however, generating these lines proved to be technically challenging, and comparison of the FACT-dependent and -independent roles of the individual subunits is beyond the scope of this work. Further complicating the matter, SSRP1 is effectively depleted within 6 hours of 3-IAA addition in SPT16-AID lines due to the interdependence of FACT subunits. We again thank the reviewer for their suggestion and will consider this work for a future study.

   Reviewer #2 (Significance (Required)):

   My expertise is pluripotency and GRNs.

   I would judge the significance of the study as presented as low, mainly because at this moment it remains unclear what FACT indeed does concerning regulation of pluripotency.

   We respect the reviewer’s opinion and hope that our revisions have made more clear how the FACT complex prevents nonspecific differentiation from occurring, thereby maintaining pluripotency and self-renewal in embryonic stem cells. Importantly, neither untagged cells treated with 3-IAA nor tagged cells treated with vehicle display the growth defects, loss of pluripotency factor expression, increased differentiation marker expression, phenotypic evidence of differentiation, and reduced alkaline phosphatase staining that the FACT-depleted cells do, highlighting a key requirement for FACT in pluripotent cells. Beyond this, we believe the novel gene distal regulatory role we have identified for FACT presents an exciting new role for this complex in gene regulation.

   Reviewer #3 (Evidence, reproducibility and clarity (Required)):

   In this manuscript, Klein, et al. addressed function of FACT complex in mouse ESCs, using cut&run, TT-seq, ATAC-seq, MNase-seq, together with Auxin-mediated FACT degradation system. The authors first reported that efficient and acute depletion of SPT16 with the Auxin-mediated degradation system resulted in over 5,000 up- and 5,000 down-regulated genes within 24 hours, including down-regulation of pluripotent gens. Then, they demonstrated that many of FACT binding sites overlap with Oct4, Sox2, Nanog binding sites by Cut&Run, and those loci increase nucleosome occupancy 24 hour after removal of FACT.

   The Auxin-mediated degradation system seems to be working very well (while I would like to see an over exposed version of Western blotting), and efficient degradation might explain the different phenotypes from the previous reported phenotypes by shRNA and the chemical inhibitor, which might not deplete FACT function completely and/or might have off-target effects. The Cut&Run data also have much sharper peaks than previously reported SSRP1, SPT16 ChIP-seq data. Doing ATAC-seq, MNase-seq upon removal of FACT is excellent. WIth the excellnet degradation system, depletion of FACT resulted in loss and gain of gene expression and differentiation. However, unfortunately it was not very clear to me what was the direct consequences of FACT removal and its mechanisms, waht was consequence of differentiation.

   We appreciate the kind words regarding our choice and execution of techniques and the reviewer’s time spent on this manuscript. We have made a number of changes to the manuscript in order to clarify the direct role of FACT and the consequences of FACT loss on embryonic stem cells.

   Although we did not develop the blots for a longer period when we performed the Westerns, we have artificially overexposed our V5-SPT16 Western blot from Figure S1 (in Adobe Illustrator) to highlight the more subtle bands at later depletion timepoints; we hope that this helps to clarify the effectiveness of the degron system.

   Response Figure 7. V5-SPT16 Western blot with adjusted exposure. We manually adjusted the entire blots’ exposures using Adobe Illustrator. L indicates ladders, and the timecourse depletion is shown above the blot.

   In my opinion, doing many of the analysis 24 hours after FACT depletion, where differential expressed (coding) genes (DEGs) are >10,000 (Table 1)), is too late to understand what the direct consequences are. Seeing 214 up- and 174 down-regulated DEGs 6 hours after FACT depletion, I do agree that FCAT seems to do both suppression and activation of target genes. It could have been really interesting to investigate what % of FACT bindign sites change chromatin accesibility and nucleosome occupancy at that time point, if those loci are close to any of the up- or down-regualted DEGs.

   We appreciate the suggestion and have included more information regarding the percentage of FACT binding sites with altered chromatin accessibility, as well as included some analyses to address the directness of FACT’s contribution to DEGs at all timepoints (see Supp Figs S4, S6). We would like to note that, we performed the TT-seq and ATAC-seq experiments at 0, 3, 6, 12, and 24 hours post 3-IAA treatment in order for us to explore the progressive change in both the transcriptome and chromatin accessibility, with only the MNase-seq limited to 24 hours. As originally shown in our Sankey plot in Supp Fig 4, we see a progressive change in expression for a small subset of genes over our timecourse running from 0-24 hours, with the largest effect observed at 24 hours, once the FACT protein levels are almost entirely depleted. Similarly, we see a progressive change in ATAC-seq signal over the same regions, with the strongest effects over the same regions visible at 24 hours post-depletion. Due to our observation that SPT16 is not depleted at 3 or 6 hours, with significant depletion seen at 24 hours (see Response Figure 7) and because we intended to study the FACT complex’s role in preventing differentiation, we were most interested in the effects at 24 hours of depletion, which allow us to analyze both the disruption of pluripotency factor expression and the facilitation of differentiation marker expression across all three germ layers (see Response Figure 4).

   Followings are reasons of above my judgement and suggestions to improve the manuscript.

   Major points

   1. Figure 1. ALP staining is not very sensitive way to evaluate ESC differentiation. I recommend Immunofluorescence for pluripotency genes (NANOG and/or SOX2) and quantification. Or present changes of pluripotency genes in graphs over time course from RNA-seq data.

   We appreciate the suggestions and have taken both into account. We have included a new panel in Figure 3 (new 3B) to display the changes of pluripotency factor expression over our timecourse. We have also included some data showing differentiation factors as part of a response to Reviewer 1, which can be found above (Response Figure 4). In addition, we performed immunocytochemistry to examine OCT4 abundance over a depletion timecourse and have added a 12-hour to our alkaline phosphatase assay to address the sensitivity of differentiation over time (Figure 1C, D and Response Figure 5).

   1. Fig 2A, 3E, 3F. How many transcription start sites are shown here? (Throughout the manuscript, it is hard to know how many loci are shown in the heatmaps. It should be described within the figures)

   We apologize for the omission and have added numbers of loci shown to relevant Figure panels throughout the paper.

   It is nice to see nascent transcription high sites have high FACT binding, but can you also show actual nascent transcription of these loci as a heatmap, before and after FACT depletion? These heatmaps should be shown in a descending order of FACT Cut&Run signalling, as FACT binding is important in this manuscript.

   We appreciate the suggestion and have plotted those data below (see Response Figure 8).

   Response Figure 8. Nascent transcription from sites with high FACT binding. Top: TPM-normalized TT-seq signal after 12-hour treatment, oriented to mRNA strand and plotted as entire mRNA length, ± 500 bp. Data are sorted by SPT16 CUT&RUN signal over 1kb upstream of annotated TSSs. n = 1 over 22,597 rows (RefSeq Select mRNAs). Bottom: TPM-normalized TT-seq signal after 24-hour treatment, oriented to mRNA strand and plotted as entire mRNA length, ± 500 bp. Data are sorted by SPT16 CUT&RUN signal over 1kb upstream of annotated TSSs. n = 3 (mean) over 22,597 rows (RefSeq Select mRNAs).

   Strong FACT binding sites have strong transcription. Is FACT really supressing transcription?

   We agree that it is very difficult to disentangle FACT function due to its binding correlation with transcription; however, we see a clear trend of FACT binding at promoters that are sensitive to FACT depletion (Supp Fig. S6A/D and C/F). Intriguingly, the genes that see the greatest derepression by DESeq2 analysis are those that are directly bound by FACT (per ChIP-seq and CUT&RUN; Supplemental Figure S6A/D), while the greatest decrease in expression occurs at genes that are less bound by FACT (Supp Fig S6C/F). In our opinion, this trend lends credence to both direct repression by FACT and distal gene regulation. We note that others (e.g., Kolundzic *et al. *2018) have shown direct repression of gene expression by FACT, in line with that aspect of our data.

   1. Fig 3ABD. It is more important to show 3h, 6h 12 h time points. The same apply to Fig 4. What %, how many of DEGs (coding and non-coding) at each time point had FACT binding nearby in ESCs?

   We agree that the early timepoints are important and have added volcano plots to the supplemental material for earlier timepoints, with genes of interest specifically annotated. We have also examined pluripotency and differentiation markers at earlier timepoints, per other reviewers’ suggestions, and have included the percentage of DEGs with nearby FACT binding in the manuscript. Specifically, 2013 replicated V5 peaks (out of 16,054; 12.54%) occurred within 1000 bp of a RefSeq Select TSS.

   Timepoint

   Total DEGs (up)

   V5-bound DEGs (up)

   Total DEGs (down)

   V5-bound DEGs (down)

   3h

   58

   16 (27.59%)

   5

   1 (20%)

   6h

   214

   38 (17.76%)

   174

   31 (17.82%)

   12h

   1366

   123 (9.00%)

   1932

   281 (14.54%)

   24h

   5398

   431 (7.98%)

   5000

   663 (13.26%)

   __Response Table 3. Table of DESeq2-assigned DEGs that are bound by SPT16-V5. __To be defined as V5-SPT16-bound, a DEG must have SPT16-V5 binding within 1000 bp upstream of its RefSeq-select annotated TSS.

   We believe that these earliest depletion timepoints are in line with FACT-mediated gene regulation occurring distal to the regulated genes’ promoters.

   Fig 3EF. Interesting data and the overlap between SPT16 binding sites and pluripotency binding sites look very strong. But it is difficult to know what % is overlapping from these figures.

   We appreciate the difficulty in quantifying the overlap between pluripotency factor binding sites and FACT binding sites; we have added those data to the manuscript below Figure 3E for OCT4; for other pluripotency factors, these data can be found in Response Figure 9 and Response Table 1. Briefly, 18.33% of OCT4 ChIP-seq peaks are bound by V5-SPT16 and 52.41% of V5-SPT16 peaks are bound by OCT4. Interestingly, 34.6% of gene-distal OCT4 ChIP-seq peaks are bound by V5-SPT16, implying greater convergence between FACT and pluripotency factors at gene-distal sites, in line with known trends for OCT4 binding. Overall, 59.63% of V5-SPT16 peaks are co-bound by at least one of OCT4, SOX2, or NANOG.

   Can you show 1 heatmap split into 3 groups, a. SPT16-V5 unique, common between SPT16-V5 and Oct4 ChIP-seq, Oct4 ChIP-seq unique, with indication of numbers each group has? Also make the same figures for Sox2 and Nanog. (E is less important. If the authors want, they can use the published FACT ChIP-seq data in the same loci.)

   We appreciate the suggestion and have plotted V5-SPT16 CUT&RUN data and pluripotency factor ChIP-seq over unique and shared regions for OCT4 (top) SOX2 (middle) and NANOG (bottom). Interestingly, although some peaks in the non-overlapping cluster were not called as peaks by the algorithms’ threshold, one can observe that a subset do seem to have overlapping binding. We again appreciate the suggestion and think that this was an excellent way to display the data and have included these data as a new panel (Fig. 3E) but also show below in Response Figure 9.

   Fig. 5. Basic information what % (how many) of SPT16-V5 CUT&RUN peaks belong to this 'enhancer' category is missing.

   We apologize for the oversight and have added numbers to the figure and legend.

   I am not sure the meaning of separating enhancers and TSS of coding genes in the analyses, though. If majority of SPT16-V5 CUT&RUN peaks overlap with Oct4 binding sites, it is not surprising that SPT16-V5 CUT&RUN peaks overlaps with ATAC-seq signal and enhancer marks.

   We agree that it is unsurprising that V5-SPT16 overlaps with accessible chromatin and enhancers, given the extensive overlap with OCT4 ChIP-seq peaks. We wanted to emphasize our novel finding of gene-distal FACT binding, given the more established trend of binding at promoters.

   1. Fig 6A. I could not figure out what % of DHSs overlaps with FACT binding sites.

   We have added this percentage to Fig 5C and included an analysis of altered chromatin accessibility in a new Table 3 (page 20). Briefly, 11,234 replicated V5-SPT16 peaks (out of 16,043; 70%) directly overlap a gene distal DHS. Orthogonally, 11,234 DHSs (out of 132,555; 8.5%) directly overlap a V5-SPT16 peak.

   I do not see the point of showing DHSs which do not overlap with FACT binding sites.

   In agreement with Reviewer 1, we believe that it is important to include FACT-unbound DHSs for a clearer understanding of the direct vs indirect effects of FACT depletion. We have condensed some of these data into a single heatmap, clustered between FACT-bound DHSs, non-FACT-bound DHSs, and FACT-bound non-DHS sites to streamline the information (now shown in Fig 3E).

   Response Figure 9. Heatmaps of clustered SPT16 and OSN binding data. Shown are clustered heatmaps depicting V5-SPT16 CUT&RUN binding overlapping ChIP-seq peaks for OCT4 (top) SOX2 (middle) and NANOG (bottom). In each set of heatmaps the top cluster is pluripotency factor-unique, the middle cluster is shared, and the V5-unique cluster is on the bottom. Each cluster is sorted by descending strength of V5-SPT16 binding (CUT&RUN). Clusters were assigned by directly overlapping peaks.

   How ATAC-seq signal changes upon depletion of FACT at FACT binding sites (Fig 6B) is important. Can you explain why ATAC-seq signals increase at the FACT binding site flanking regions (across +/- 2kb) where FACT binding is strong (without changing the chromatin accessibility at the FACT binding sites)? Perhaps authors need to show actual ATAC-seq track with EtOH or 3-IAA treatment over ~10kb regions flanking FACT binding sites. It is difficult to understand what is happening seeing only the changes (ratio) of ATAC-seq read counts, how big the differences are.

   We agree that the local window and ratio of ATAC-seq signal somewhat muddles the true biological trends. We have plotted non-differential ATAC-seq signal for each SPT16-AID clone over V5 binding sites, ±10 kb, to more accurately depict the local chromatin status (shown below in Response Figure 10). There is an apparent trend at V5-SPT16 CUT&RUN peaks of accessible chromatin, and this high local accessibility very likely contributes to the high ATAC-seq signal immediately flanking V5 binding sites; over the binding sites themselves, however, FACT depletion consistently triggers decreased accessibility (see Fig. 6).

   Can you identify differentially open loci based on 3-IAA- and Et-OH treated ATAC-seq data at each time point, and then how many of them overlap with FACT binding sites? There are a few tools to identify differential open regions with ATAC-seq data. That could help to understand the direct roles of FACT binding.

   We appreciate the suggestion and have performed this analysis using a combination of PEPATAC and HOMER (see Response Tables 4-6 below). FACT depletion leads to the following accessibility changes:

   3-hour

   6-hour

   12-hour

   24-hour

   Decreased accessibility

   220 (0.35%)

   3,713 (5.99%)

   6,885 (11.11%)

   8,441 (13.62%)

   Increased accessibility

   2 (0.00%)

   12 (0.02%)

   276 (0.45%)

   6,031 (9.73%)

   Response Table 4. Accessibility changes over consensus ATAC-seq peaks. Consensus ATAC-seq peaks were defined per PEPATAC standards (peaks called by MACS2 in (n/2)+1 samples, irrespective of condition.

   3-hour

   6-hour

   12-hour

   24-hour

   Decreased accessibility

   848 (1.64%)

   1870 (3.51%)

   2525 (4.83%)

   4,092 (7.90%)

   Increased accessibility

   107 (0.21%)

   283 (0.55%)

   534 (1.03%)

   2,449 (4.73%)

   Response Table 5. Accessibility changes over regions bound by V5-SPT16.

   Response Figure 10. ATAC-seq data shown over a 20kb window. Heatmaps depicting non-differential ATAC-seq data over FACT binding sites for SPT16-AID clones 1 (top) and 2 (bottom). Data are sorted by V5-SPT16 binding strength.

   All

   3-hour

   6-hour

   12-hour

   24-hour

   Decreased accessibility

   3,294 (2.46%)

   3,175 (2.37%)

   3,636 (2.71%)

   7,018 (5.23%)

   Increased accessibility

   102 (0.08%)

   313 (0.23%)

   1,797 (1.34%)

   5,975 (4.45%)

   V5-bound DHSs (11,234 total)

   3-hour

   6-hour

   12-hour

   24-hour

   Decreased accessibility

   1 (0.01%)

   9 (0.08%)

   96 (0.85%)

   2006 (17.86%)

   Increased accessibility

   5 (0.04%)

   28 (0.25%)

   71 (0.63%)

   87 (0.77%)

   __Response Table 6. Accessibility changes over gene-distal DHSs and over only FACT-bound gene-distal DHSs. __

   Together with Fig 1A and Fig 6C, do they mean the more FACT binding, the more transcription (Fig 1A). Also the higher transcription rate, the more increased chromatin accessibility upon depletion of FACT (Fig 6C)?

   While we do see that FACT binding correlates with transcription and with FACT-dependent chromatin accessibility, we do not wish to make the argument that FACT binding alone is indicative of high transcription, nor that transcription is necessarily the deciding factor in FACT-depleted chromatin accessibility changes. We do want to note that transcriptional disruption is a likely contributor to increased chromatin accessibility in the absence of FACT as it pertains to paused RNAPII, as speculated in our discussion, but that experiments to truly test this hypothesis are beyond the scope of this work. That being said, in response to Reviewer 1, we did assess the potential correlation of FACT binding to locations with greater paused RNAPII (Response Figure 3) and see a connection. We are excited to explore this more in future work.

   Perhaps plotting nascent transcripts at 12hr, 24 hr of FACT depletion next to these heatmaps might show if it colleates with transcription changes as well?

   We appreciate the suggestion, and have included this plot in Response Figure 8, sorted by FACT binding to gene promoters; however, we find it difficult to visualize differences in transcription with non-differential heatmaps.

   Sites losing chromatin accessibility (bottom half of Fig 6C) seem not to have FACT binding (bottom half of Fig 1A), thus it is likely to be indirect effects. It is better to make figures focussing on 'direct effects'.

   We agree that there are sites with reduced chromatin accessibility upon FACT depletion that are not bound by FACT; however, given the extensive binding of FACT at gene-distal regulatory regions (F2D, F4A, F5, F6A/D), we would suggest that these “indirect” effects are possibly the result of FACT-dependent gene-distal regulation.

   Fig 1A and Fig 6C indicated that FACT binding sites (i.e. TSS) decrease chromatin accessibility. I thought it does not fit with the idea of increasing nucleosome occupancy. But actually the data (Fig 7F) shows that TSS does not show increased nucleosome occupancy unlike Fig 7A-E. In fact, Fig 6B showed that about bottom 50% of weaker V5 binding sites decreased chromatin accessibility at 24 hr, which fits with increased nucleosome occupancy in Fig 7A. But then if you looked at only top 50% of stronger V5 binding sites, which did not decrease chromatin accessibility, nucleosome occupancy did not change as well? Why don't you make heatmap of MNase-seq next to Fig 6B?

   We have added heatmaps of non-differential MNase-seq data to Fig. 7A to address both concerns. Regarding Figure 6B, we note that the V5-SPT16 peaks themselves invariantly show decreased chromatin accessibility, and that it is the surrounding chromatin, not the V5-SPT16 peak itself, that shifts from increased to decreased chromatin accessibility at 12-24 hours of depletion. We would also like to clarify that the original heatmaps in Fig 6B were sorted by change in chromatin accessibility at 24h, rather than V5 binding.

   We disagree that the TSSs do not show increased nucleosome occupancy in Fig. 7F, as there is an increase in signal above background directly over the TSS in both replicates, per the differential metaplot shown in Fig. 7B, that is specific to the AID-tagged lines. However, the two clones did show variable results. To address this, we have plotted the non-differential MNase-seq plots (Fig. 7A), which show more consistent trends; it appears that the transformation of the data into differential at this location was the cause of the slightly variable plots over TSSs.

   1. I could not follow based on which data the model in Fig 8 is made. Again it is better to focus in the direct effects.

   Thank you for the suggestion; we have updated our model to focus more on the direct effects.

   Minor points.

   1. Line 1 page 5, Kolundzic paper did not have MEF reprograming data. They reported human fibroblast reprogramming was enhanced by FACT KD.

   We appreciate the correction and have clarified the language to specify that the work of Kolundzic *et al. *included human fibroblast reprogramming and Shen *et al. *performed MEF reprogramming.

   1. Line 3, I disagree with "these data establish FACT as essential in pluripotent cells". One paper said FACT KD increased proliferation of mESCs, the other paper said chemical inhibition of FACT was necessary for passaging ESCs, but not proliferation. Importance of FACT in pluripotent cells was very unclear to me.

   We have clarified our language to specify that pluripotent cells have a FACT dependency that differentiated cells do not. We note that we were unable to recapitulate a relationship between FACT and trypsinization/passaging of ES cells, suggesting a more nuanced role for FACT in pluripotent cells, in line with work from the Tessarz and Gurova labs.

   Line 7 Page 7, reference the paper with the ChIP-seq data.

   We apologize for the oversight and have added the reference.

   Line 16, Page 7. It doesn't seem the the Cut&run and previously published ChIP-seq data agree well.. >50% look different. It is nothing the authors can do, but can you show venn diagram of peak overlap?

   In response to Reviewer 1, we have generated Response Figure 1 where we display a pie chart of the overlap. In addition to displaying this again to the right in Response Figure 11 this, we have included another analysis below in Response Figure 11, to address this comment. Specifically, we have plotted peak overlaps as a Venn diagram to compare peaks identified in at least two experimental replicates from either the CUT&RUN or ChIP-seq data (left). We have also overlapped replicated peaks between the individual targets and displayed them as a pie chart (right; same as Response Figure 1). While the CUT&RUN data do display a greater signal:noise ratio and call far more peaks, we note that more peak conservation between experiments is relatively consistent (1-6%) between all datasets, including the ChIP-seq experiments profiling opposite factors.

   Overall, we see strongly reproducible trends (albeit with less sharp definition in the ChIP-seq), complemented by highly similar biological feature assignment in Fig. 2D and Pearson correlation values of between 0.76 and 0.78 between SPT16 ChIP-seq and V5-SPT16 CUT&RUN (Supp Fig. S2A).

   __Response Figure 11. Overlaps between SPT16-V5 CUT&RUN, SPT16 ChIP-seq, and SSRP1 ChIP-seq. __Called peaks were compared between V5-SPT16 CUT&RUN, SPT16 ChIP-seq, and SSRP1 ChIP-seq, using both our own analysis pipeline (left) and the peaks published with the original manuscript by Tessarz et al. (2018; right). While our ChIP-seq peak-calling appears to have applied more stringent thresholds, trends are generally agreeable.

   Line 12, 22 page 10. Fig.3AB is 24 hrs. Do not match with the text.

   We apologize for the error and have changed the references in the text to the new panel 3C.

   1. Line 23, 24, page 10, Highlight Klf4 and Myc in the volcano plot.

   We have added KLF4 and MYC annotation to the volcano plot in Fig. 3A, as well as plotted their log2FC over time in the new panel 3B.

   1. Line 18, 19, page 16. This is not accurate statement. Sample 2 increased the accessibility at 6 hours. Sample 1 decreased, but even the control did so.

   We apologize for the unclear wording; we intended to suggest that all timepoints after 6 hours (i.e., 12 and 24 hours) display decreased accessibility directly over the DHS. We have corrected the text.

   1. Line 48-50, page 16. Two replicates show very different patterns. Difficult to agree with the statement based on the figure.

   We agree that the differential replicate patterns are not ideal; however, both replicates display an increase in nucleosome-sized reads over the promoter region, consistent with our ATAC-seq results presented in Fig 6C. Size distribution plots did not suggest differences in MNase digestion between samples, and neither quartile/RPGC nor TMM-based normalization fully solved this issue. Because our ATAC-seq datasets agree with the general trends identified by MNase-seq (which are consistent, despite technical differences between clones), we do not believe that the differences constitute biological difference, but rather experimental noise. We have included a heatmap of non-differential MNase-seq signal around TSSs in Fig 7A to highlight the experimental reproducibility between replicates. Based on this analysis it appears that the transformation of the data into differential at this location was the cause of the slightly variable plots over TSSs.

   1. Line 15, page 19. Where does "1.5 times" come from? which is 1.5 times more, and is that different from the proportion of those?

   We apologize for the unclear reference to the altered transcripts in Table 1 and have changed our wording to be more precise.

   1. Line 32, page 19. Is Fig S2B correct figure?

   We appreciate the correction; the text should have referred to Fig. 4 and has been fixed.

   Line 35-39, page 21. I understand FACT does not bind to silenced loci. If FACT does not bind, it is not surprising that expression from those loci does not change upon FACT deletion. I do not understand what the authors said.

   We agree that a lack of binding and unchanged expression after FACT depletion at putative silencers are unsurprising; given FACT’s extensive genic and gene-distal binding, we wished to show a class of transcribed regions unbound by FACT as a control, to show that non-FACT-regulated transcription was not affected by FACT transcription. We have clarified our wording in the text to emphasize that a lack of change was expected at silencers.

   Reviewer #3 (Significance (Required)):

   Previously it has been shown that Oct4 physically interacts with the FAcilitates Chromatin Transactions (FACT) complex. Seemingly contradicting phenotypes have been reporting upon suppression of FACT function in the maintenance and induction of pluripotent cells. Mylonas has reported that knockdown of SSRP1, a component of FACT complex, increased ESC proliferation (2018). Shen has described that chemical inhibition of FACT complex affected passaging of ESCs, but proliferation was not affected without passaging. Kolundzic has found that both SSRP1 and SUPT16H, another component of FACT complex, enhance human fibroblast reprogramming into iPSCs (2018), while Shen has reported that chemical inhibition of FACT blocks mouse iPSC generation form MEFs.

   My expertise lies on pluripotent stem cells and transcriptional regulations. I did like the Auxin-mediated FACT degradation system these authors used and acute depletion of FACT is an excellent way of evaluating FACT function in ESC, compared to previously published shRNA based knockdown or use of a chemical inhibitor. However, as I described above, it was not very clear what could the direct effects and I feel looking at 24 hours after depletion might be to late to address this question.

   We appreciate the review and agree that acute depletion of FACT has great potential to understand the complex’s function in ES cells. We understand that the nature of gene-distal regulation does make it difficult to cleanly elucidate direct regulation, and hope that our revisions have clarified that our goal was to examine direct, gene-distal regulation, rather than indirect effects. We would like to note that we examined transcription and chromatin accessibility after 3, 6, 12, and 24 hours of 3-IAA treatment, with all these data included in the original manuscript, and saw minimal change (likely because FACT was not fully depleted until later timepoints); to capture the true biological effects of FACT depletion, we explored most thoroughly the 24 hour 3-IAA treatment to understand the downstream effects between FACT loss and cellular differentiation. However, we have expanded discussion and analyses of the earlier timepoints in this revised manuscript.

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   Referee #3

   Evidence, reproducibility and clarity

   In this manuscript, Klein, et al. addressed function of FACT complex in mouse ESCs, using cut&run, TT-seq, ATAC-seq, MNase-seq, together with Auxin-mediated FACT degradation system. The authors first reported that efficient and acute depletion of SPT16 with the Auxin-mediated degradation system resulted in over 5,000 up- and 5,000 down-regulated genes within 24 hours, including down-regulation of pluripotent gens. Then, they demonstrated that many of FACT binding sites overlap with Oct4, Sox2, Nanog binding sites by Cut&Run, and those loci increase nucleosome occupancy 24 hour after removal of FACT.

   The Auxin-mediated degradation …

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   Referee #3

   Evidence, reproducibility and clarity

   In this manuscript, Klein, et al. addressed function of FACT complex in mouse ESCs, using cut&run, TT-seq, ATAC-seq, MNase-seq, together with Auxin-mediated FACT degradation system. The authors first reported that efficient and acute depletion of SPT16 with the Auxin-mediated degradation system resulted in over 5,000 up- and 5,000 down-regulated genes within 24 hours, including down-regulation of pluripotent gens. Then, they demonstrated that many of FACT binding sites overlap with Oct4, Sox2, Nanog binding sites by Cut&Run, and those loci increase nucleosome occupancy 24 hour after removal of FACT.

   The Auxin-mediated degradation system seems to be working very well (while I would like to see an over exposed version of Western blotting), and efficient degradation might explain the different phenotypes from the previous reported phenotypes by shRNA and the chemical inhibitor, which might not deplete FACT function completely and/or might have off-target effects. The Cut&Run data also have much sharper peaks than previously reported SSRP1, SPT16 ChIP-seq data. Doing ATAC-seq, MNase-seq upon removal of FACT is excellent. WIth the excellnet degradation system, depletion of FACT resulted in loss and gain of gene expression and differentiation. However, unfortunately it was not very clear to me what was the direct consequences of FACT removal and its mechanisms, waht was consequence of differentiation.

   In my opinion, doing many of the analysis 24 hours after FACT depletion, where differential expressed (coding) genes (DEGs) are >10,000 (Table 1)), is too late to understand what the direct consequences are. Seeing 214 up- and 174 down-regulated DEGs 6 hours after FACT depletion, I do agree that FCAT seems to do both suppression and activation of target genes. It could have been really interesting to investigate what % of FACT bindign sites change chromatin accesibility and nucleosome occupancy at that time point, if those loci are close to any of the up- or down-regualted DEGs.

   Followings are reasons of above my judgement and suggestions to improve the manuscript.

   Major points

   1. Figure 1. ALP staining is not very sensitive way to evaluate ESC differentiation. I recommend Immunofluorescence for pluripotency genes (NANOG and/or SOX2) and quantification. Or present changes of pluripotency genes in graphs over time course from RNA-seq data.
   2. Fig 2A, 3E, 3F. How many transcription start sites are shown here? (Throughout the manuscript, it is hard to know how many loci are shown in the heatmaps. It should be described within the figures) It is nice to see nascent transcription high sites have high FACT binding, but can you also show actual nascent transcription of these loci as a heatmap, before and after FACT depletion? These heatmaps should be shown in a descending order of FACT Cut&Run signalling, as FACT binding is important in this manuscript. Strong FACT binding sites have strong transcription. Is FACT really supressing transcription?
   3. Fig 3ABD. It is more important to show 3h, 6h 12 h time points. The same apply to Fig 4. What %, how many of DEGs (coding and non-coding) at each time point had FACT binding nearby in ESCs?
   4. Fig 3EF. Interesting data and the overlap between SPT16 binding sites and pluripotency binding sites look very strong. But it is difficult to know what % is overlapping from these figures. Can you show 1 heatmap split into 3 groups, a. SPT16-V5 unique, common between SPT16-V5 and Oct4 ChIP-seq, Oct4 ChIP-seq unique, with indication of numbers each group has? Also make the same figures for Sox2 and Nanog. (E is less important. If the authors want, they can use the published FACT ChIP-seq data in the same loci.)
   5. Fig. 5. Basic information what % (how many) of SPT16-V5 CUT&RUN peaks belong to this 'enhancer' category is missing. I am not sure the meaning of separating enhancers and TSS of coding genes in the analyses, though. If majority of SPT16-V5 CUT&RUN peaks overlap with Oct4 binding sites, it is not surprising that SPT16-V5 CUT&RUN peaks overlaps with ATAC-seq signal and enhancer marks.
   6. Fig 6A. I could not figure out what % of DHSs overlaps with FACT binding sites. I do not see the point of showing DHSs which do not overlap with FACT binding sites. How ATAC-seq signal changes upon depletion of FACT at FACT binding sites (Fig 6B) is important. Can you explain why ATAC-seq signals increase at the FACT binding site flanking regions (across +/- 2kb) where FACT binding is strong (without changing the chromatin accessibility at the FACT binding sites)? Perhaps authors need to show actual ATAC-seq track with EtOH or 3-IAA treatment over ~10kb regions flanking FACT binding sites. It is difficult to understand what is happening seeing only the changes (ratio) of ATAC-seq read counts, how big the differences are. Can you identify differentially open loci based on 3-IAA- and Et-OH treated ATAC-seq data at each time point, and then how many of them overlap with FACT binding sites? There are a few tools to identify differential open regions with ATAC-seq data. That could help to understand the direct roles of FACT binding.
   7. Together with Fig 1A and Fig 6C, do they mean the more FACT binding, the more transcription (Fig 1A). Also the higher transcription rate, the more increased chromatin accessibility upon depletion of FACT (Fig 6C)? Perhaps plotting nascent transcripts at 12hr, 24 hr of FACT depletion next to these heatmaps might show if it colleates with transcription changes as well? Sites losing chromatin accessibility (bottom half of Fig 6C) seem not to have FACT binding (bottom half of Fig 1A), thus it is likely to be indirect effects. I beleive it is better to make figures focussing on 'direct effects'.
   8. Fig 1A and Fig 6C indicated that FACT binding sites (i.e. TSS) decrease chromatin accessibility. I thought it does not fit with the idea of increasing nucleosome occupancy. But actually the data (Fig 7F) shows that TSS does not show increased nucleosome occupancy unlike Fig 7A-E. In fact, Fig 6B showed that about bottom 50% of weaker V5 binding sites decreased chromatin accessibility at 24 hr, which fits with increased nucleosome occupancy in Fig 7A. But then if you looked at only top 50% of stronger V5 binding sites, which did not decrease chromatin accessibility, nucleosome occupancy did not change as well? Why don't you make heatmap of MNase-seq next to Fig 6B?
   9. I could not follow based on which data the model in Fig 8 is made. Again it is better to focus in the direct effects.

   Minor points.

   1. Line 1 page 5, Kolundzic paper did not have MEF reprograming data. They reported human fibroblast reprogramming was enhanced by FACT KD.
   2. Line 3, I disagree with "these data establish FACT as essential in pluripotent cells". One paper said FACT KD increased proliferation of mESCs, the other paper said chemical inhibition of FACT was necessary for passaging ESCs, but not proliferation. Importance of FACT in pluripotent cells was very unclear to me.
   3. Line 7 Page 7, reference the paper with the ChIP-seq data.
   4. Line 16, Page 7. It doesn't seem the the Cut&run and previously published ChIP-seq data agree well.. >50% look different. It is nothing the authors can do, but can you show venn diagram of peak overlap?
   5. Line 12, 22 page 10. Fig.3AB is 24 hrs. Do not match with the text.
   6. Line 23, 24, page 10, Highlight Klf4 and Myc in the volcano plot.
   7. Line 18, 19, page 16. This is not accurate statement. Sample 2 increased the accessibility at 6 hours. Sample 1 decreased, but even the control did so.
   8. Line 48-50, page 16. Two replicates show very different patterns. Difficult to agree with the statement based on the figure.
   9. Line 15, page 19. Where does "1.5 times" come from? which is 1.5 times more, and is that different from the proportion of those?
   10. Line 32, page 19. Is Fig S2B correct figure?
   11. Line 35-39, page 21. I understand FACT does not bind to silenced loci. If FACT does not bind, it is not surprising that expression from those loci does not change upon FACT deletion. I do not understand what the authors said.

   Significance

   Previously it has been shown that Oct4 physically interacts with the FAcilitates Chromatin Transactions (FACT) complex. Seemingly contradicting phenotypes have been reporting upon suppression of FACT function in the maintenance and induction of pluripotent cells. Mylonas has reported that knockdown of SSRP1, a component of FACT complex, increased ESC proliferation (2018). Shen has described that chemical inhibition of FACT complex affected passaging of ESCs, but proliferation was not affected without passaging. Kolundzic has found that both SSRP1 and SUPT16H, another component of FACT complex, enhance human fibroblast reprogramming into iPSCs (2018), while Shen has reported that chemical inhibition of FACT blocks mouse iPSC generation form MEFs.

   My expertise lies on pluripotent stem cells and transcriptional regulations. I did like the Auxin-mediated FACT degradation system these authors used and acute depletion of FACT is an excellent way of evaluating FACT function in ESC, compared to previously published shRNA based knockdown or use of a chemical inhibitor. However, as I described above, it was not very clear what could the direct effects and I feel looking at 24 hours after depletion might be to late to address this question.

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   Referee #2

   Evidence, reproducibility and clarity

   The authors propose that the FACT complex can regulate pluripotency factors along with their regulatory targets through non-genic locations. They find that acute depletion of FACT leads to a "reduction" in pluripotency in mouse embryonic stem cell by disrupting transcription of master regulators of pluripotency. They also show FACT depletion affected the transcription of gene distal regulatory sites, but not silencers. They also stated that SPT16 depletion resulted in both, a reduction of chromatin accessibility and increase of nucleosome occupancy over FACT bound sites.

   Overall the study appears technically well executed. The use …

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   Referee #2

   Evidence, reproducibility and clarity

   The authors propose that the FACT complex can regulate pluripotency factors along with their regulatory targets through non-genic locations. They find that acute depletion of FACT leads to a "reduction" in pluripotency in mouse embryonic stem cell by disrupting transcription of master regulators of pluripotency. They also show FACT depletion affected the transcription of gene distal regulatory sites, but not silencers. They also stated that SPT16 depletion resulted in both, a reduction of chromatin accessibility and increase of nucleosome occupancy over FACT bound sites.

   Overall the study appears technically well executed. The use of an Auxin induced depletion system is a good model to study the acute effects of FACT depletion. However, I have a number of concerns relating to specificity and interpretation of the results that need to be addressed.

   Major points

   • Authors claimed that depletion of the FACT complex "triggers a reduction in pluripotency". As evidence supporting this statement they present images of alkaline phosphatase assays of a time course performed upon depletion of FACT. These experiments indeed show that ESCs are destabilized in the absence of SPT16. However, some key questions regarding the phenotype remain unresolved:
   • What is are the kinetics of expression of selected naïve pluripotency and early differentiation markers? Are differentiation markers upregulated, consistent with normal differentiation upon FACT depletion?
   • Is only ESC identity affected or does loss of FACT impair viability also of cells that have exited pluripotency? To address this, growth curves and/or cell cycle analysis upon FACT depletion could be performed. Alternatively, the authors could utilize surface markers to distinguish naïve pluripotent form differentiated cells in the cell cycle analysis experiments to identify a potential differential response of pluripotent and differentiated cells to FACT depletion.
   • Another key question is whether it is only the metastable pluripotent state of ESCs in heterogeneous FCS/LIF conditions which is affected by FACT loss, and whether cells cultured in the more homogeneous and more robust 2i-LIF conditions can tolerate FACT removal. If that is indeed the case it would enable the authors to address one main concern I have with this manuscript, which is that it is nearly impossible to distinguish the direct effect of FACT loss from differences induced by differentiation (and maybe cell death, see comment above). This is a critical concern that needs to be addressed and discussed appropriately.
   • A further major concern is about the specificity of the effect of FACT depletion. The authors claim that FACT is required to maintain pluripotency. From the data presented this is unclear. FACT appears to be part of the general transcription machinery in ESCs. It appears generally associated with active promoters and active genes, according to the data in this manuscript. Whether there is any specific link to pluripotency remains to be shown. It is unclear how enrichment analyses have been performed. If they haven't been performed using a background list of genes actively transcribed in ES cells, they will obviously show enrichment of ESC specific GO categories, because ESCs express ESC specific genes Will results hold true if these experiments are performed using background lists of genes robustly expressed in ESCs? In line with this: the authors show that FACT bound loci well overlap with Oct4 bound regions. But which proportion of FACT targets loci are actually Oct4 bound too? Is FACT binding exclusive to Oct4 regulated enhancers and promoters? In other words, will FACT be recruited to all actively transcribed genes in ES cells? In that case, a specific effect on pluripotency network regulation cannot be claimed.
   • It is disappointing that neither raw data (GEO submission set to private) nor any Supplemental Tables containing differentially expressed transcripts and ChIP or Cut and Run peaks and associated genes were made available. This strongly reduces the depth of review that can be performed.
   • To what extent do FACT bound loci overlap with genes differentially expressed 24h after FACT depletion? This analysis would help determine the direct targets of FACS regulation.
   • The paper mainly relies on NGS analysis. Therefore, it is crucial that authors show as Supplemental Material some basic QC of these data. PCA analyses to show congruency of replicates are the minimum requirement.
   • Did the authors perform any filtering for gene expression levels before analysis? Are genes in the analysis robustly expressed in at least one of the conditions?
   • Wherever p values were reported for enrichment analyses, adjusted p values should be used
   • I cannot follow the logic used by the authors to explain discrepant results from Chen et al about the role of FACT in ESCs. Chen et al showed that FACT disruption by SSRP1 depletion is compatible with ESC survival and leads to ERV deregulation. The authors of the present study attribute these differences to potential FACT independent roles of SSRP1. However, I would assume that if there are indeed FACT independent roles of SSRP1, then the phenotype of SSRP1 KOs in which FACT and other processes should be dysfunctional should be even stronger than a plain FACT KO. This needs a proper and careful explanation. Also, did the authors observe any
   • evidence for ERV deregulation upon acute SPT16 depletion?

   Minor points

   • Figure S2A is very small and resolution is low. Page 10: "...while all four Yamanaka factors (Pou5f1, Sox2, Klf4, and Myc) and Nanog were significantly 24 reduced after 24 hours (Fig. 3A, S3A-B)". No data for myc is being shown.
   • Are the two bands in the middle in figure 1B is supposed to be a ladder? This should be clarified.
   • Figure 3C- This Figure is complicated to read. Also, information appears redundant with the Table 1, I recommend to remove this panel.
   • Figure 6 and figure 7 could be presented in one single figure since both aspects are complementary and target related aspects.
   • Are the authors certain that the effects observed are directly linked to the FACT complex in contrast to FACT independent roles of SPT16, if any exist? The experiment to address this would be to deplete SSRP1 and investigate whether the effects are identical, which would be the hypothesis to be tested.

   Significance

   My expertise is pluripotency and GRNs.

   I would judge the significance of the study as presented as low, mainly because at this moment it remains unclear what FACT indeed does concerning regulation of pluripotency.

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   Referee #1

   Evidence, reproducibility and clarity

   Summary:

   Klein and colleagues generate an ES cell model system with inducible FACT depletion to understand how loss of FACT affects gene regulation in ES cells. They find that FACT is critical for ES cell maintenance through multiple mechanisms including direct regulation of key pluripotency transcription factors (Sox2, Oct4, and Nanog), maintaining open chromatin at enhancers, and regulated enhancer RNA transcription. The paper is well-written, the experiments are generally well-controlled and appropriately interpreted and placed within the context of the field.

   Major comments:

   1. In general, the ChIP-seq and CUT&RUN data are not that …

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   Referee #1

   Evidence, reproducibility and clarity

   Summary:

   Klein and colleagues generate an ES cell model system with inducible FACT depletion to understand how loss of FACT affects gene regulation in ES cells. They find that FACT is critical for ES cell maintenance through multiple mechanisms including direct regulation of key pluripotency transcription factors (Sox2, Oct4, and Nanog), maintaining open chromatin at enhancers, and regulated enhancer RNA transcription. The paper is well-written, the experiments are generally well-controlled and appropriately interpreted and placed within the context of the field.

   Major comments:

   1. In general, the ChIP-seq and CUT&RUN data are not that similar. Although correlation seems reasonable (S2A), looking at the heatmaps in S2B/C these seem pretty different. It's not very clear if this is a case where CUT&RUN has higher specificity (and signal-to-noise, which is very clear from example tracks) or if these two methods are picking up biologically different sites. Could the authors include some overlap analysis of peaks and comment on these discrepancies. Looking at the example tracks in Figure 2B, it seems likely that prior SPT16 and SSRP1 ChIP-seq were relatively high-noise.
   2. Are motifs described in Figure 2E CUT&RUN only, and do prior ChIP-seq experiments also identify these motifs?
   3. The authors state that FACT depletion affects eRNA transcription and measured this using TT-seq. The analysis in Figure 3B seems to be all the different types of sites looked at together (genes, PROMPTs, etc). Is there evidence that eRNAs specifically are regulated by FACT loss. Could these be compared to DHS sites that lack FACT binding to support a direct role for FACT at these sites?
   4. One mechanism proposed for how FACT regulates enhancers is that it is required for maintaining a nucleosome free area, and when FACT is depleted nucleosomes invade the site (Figure 7). It wasn't clear if they compared distal DHS sites were FACT normal bound to those without FACT binding in the MNase experiments, which could help support the direct role or specificity of FACT in regulating those enhancers (or a subset of them).
   5. Data quality for nucleosome occupancy was a little strange (Figure 7F), where the two clones had very different MNase patterns at TSS sites. Could the authors comment on why there is such a strong difference between clones here.
   6. More details on some of the analysis steps would be really helpful in evaluating the experiments. Specifically, was any normalization done other than depth normalization? I ask this because the baseline levels for many samples in metaplots look quite different. For example, see Figure 7B where either clone 1 has a globally elevated (at least out 2kb) ratio of nucleosome in the IAA samples relative to the EtOH, or there is some technical difference in MNase. One suggestion is to look at methods in the CSAW R package to allow TMM based normalization strategies which may help.
   7. I appreciated the speculation section, and the possible relationship between FACT and paused RNAPII is interesting. While further experiments may be outside the scope of this work and I am not suggesting they do them, I am wondering if others have information on locations of paused RNAPII in ESC that would allow them to test if genes with paused RNAPII have a special requirement for FACT that they could use their current data to assess.

   Minor comments:

   1. When describing the peaks found in the text related to Figure 2 they refer to 'nonunique' peaks. Does this mean the intersection of the independent peak calls? Could they clarify this.
   2. In the text they refer to H3K56ac data in S2D and I don't see that panel. The color scheme for the 1D heatmaps (Figure 5A) is tough to appreciate the differences. I'd suggest something more linear rather than this spectral one might be easier to see.
   3. For the 2D heatmaps of binding, could they include the number of elements they are looking at for each group?
   4. Also for 2D heatmaps, I think the scale is Log2 (IAA/EtoH), but could they confirm that and include it in the figure?

   Significance

   • The use of degrader based approaches to depleting a protein allows refined kinetic and temporal assays which I think are important. Several papers showed a rapid invasion of nucleosomes after SWI/SNF loss using these kinds of approaches and revealed surprisingly fast replacement of SWI/SNF. This paper is consistent with those models, showing that another remodeler behaves the same, suggesting there may be general requirements for active chromatin remodeling to maintain the expression of these genes. It also highlights a key gap in how specificity works to target these enzymes remains somewhat unknown.
   • This work will be of interest to those studying detailed mechanisms of gene regulation. Compared to some other chromatin regulators, FACT is understudied and so this work will allow comparison between different chromatin remodeling complexes.
   • My experience: chromatin, gene regulation, cancer, genomics

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   Version published to 10.1101/2021.07.30.454509v4 on bioRxiv

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   Version published to 10.1101/2021.07.30.454509v3 on bioRxiv

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   Version published to 10.1101/2021.07.30.454509v2 on bioRxiv

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9. 

   Version published to 10.1101/2021.07.30.454509v1 on bioRxiv

   Jul 31, 2021