RB loss modulates chromatin organization by regulating cohesin-dependent loops and enhancer-promoter interactions

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: major_revision

Abstract
The retinoblastoma protein (RB) is a well-characterized repressor of E2F
transcriptional activity that controls genes involved in cell proliferation during the G1 phase.
Here, we examine the effect of RB on chromatin organization and uncover a non-canonical role
for RB in which it promotes the removal of cohesin from insulators and thereby induces the
expression of adjacent genes. We identify that RB colocalizes with cohesin across the human
genome. During mitosis, RB facilitates the removal of cohesin from CTCF sites, with this effect
persisting into early G1, impacting loop formation and extrusion at topologically associating
domain (TAD) boundaries. Chromosome conformation capture assays reveal that RB reduces
insulation at TAD boundaries and promotes enhancer-promoter interactions marked by histone 3
lysine 27 acetylation. In this way, RB enhances the transcription of hundreds of genes beyond
the E2F transcriptional program, a finding that expands our understanding of this key tumor
suppressor and cell cycle regulator.

Introduction
The retinoblastoma gene RB1 was the first tumor suppressor to be molecularly defined and is often
described as a prototypical tumor suppressor1,2. Its protein product, RB, is a critical cell cycle regulator
that represses the transcriptional program required for cell proliferation. Active RB associates with E2F-
regulated promoters and represses the transcription of cell cycle genes, preventing cell cycle
advancement1. Cyclin-dependent kinase (CDK) activity phosphorylates RB, inhibiting its association with
E2F3-6 and promoting E2F-dependent transcription and cell cycle progression2,7,8.

RB1 is genetically or functionally inactivated in most types of cancer9,10. More frequently, cancer
cells acquire mutations that increase cyclin D-CDK4/6 or cyclin E-CDK2 kinase activity, leading to
hyper-phosphorylation and functional inactivation of RB10. However, some cancers can directly target RB
through loss-of-function mutations and loss of heterozygosity in the RB1 gene itself11-13. RB1 mutations
are often late events during tumor evolution14,15, and RB-deficient tumors exhibit unique characteristics
that are not fully explained by the RB/E2F model and its role in the regulation of cell proliferation16.
These include the histological trans-differentiation of adenocarcinoma to neuroendocrine tumors and the
development of acquired resistance to targeted therapies14,15,17-19, increased genomic instability associated
with compromised sister chromatid cohesion20-22, and heightened metastatic potential in lung and prostate
cancers14,15,19. These non-canonical roles of RB raise the question of whether it might have additional
functions in transcriptional regulation beyond the direct inhibition of E2F-targeted genes.

Recently, we discovered that RB’s association with E2F-bound promoters represents only a portion of
the chromatin-bound population of RB23. RB is also associated with AP-1-bound enhancers and CTCF
insulators23, which may suggest an emerging model in which RB regulates transcription by interacting
with different types of cis-regulatory elements. Fluctuations in RB phosphorylation at CDK sites control
RB distribution between promoters and enhancers but have less influence on RB’s association with
insulators23,24. Microscopic studies, including our own, have revealed that RB plays an important role in
regulating nuclear morphology25,26. Since CTCF sites are the primary anchors for the cell’s loop extrusion
machinery27-30, which regulates transcription partly through chromatin insulation31-33, and since RB’s
association with CTCF sites is mostly cell cycle-independent and conserved across cell types23,34, we
hypothesized that RB might exert non-canonical transcriptional regulation by modulating DNA loops.

Here, by employing chromosome conformation capture with nucleosome resolution35, we reveal that
RB is an essential regulator of cohesin-dependent loops at CTCF insulators. Loss of RB increased the
number and size of DNA loops and the stability of topologically associating domains (TADs). We
observed that RB and cohesin were associated genome-wide at accessible chromatin regions. In RB-
deficient cells, cohesin was less effectively removed from CTCF insulators, resulting in its accumulation
and enhanced insulation. Cell cycle synchronization experiments showed that the RB-mediated
deregulation of cohesin distribution peaked during mitosis and persisted into early G1. The enhanced
insulation at CTCF sites following RB loss diminished enhancer-promoter interactions assessed by
histone 3 lysine 27 acetylation (H3K27ac) HiChIP36. Overall, we demonstrate that by facilitating
cohesin removal from insulators and promoting enhancer-promoter interactions, RB activates hundreds of
genes outside of the E2F transcriptional program.

Results
RB is a regulator of chromatin organization
To characterize the impact of RB in chromatin organization, we employed Micro-C35, a chromosome
conformation capture method with nucleosome resolution. We conducted two complementary sets of
experiments in human retinal pigment epithelial cells immortalized by telomerase reverse transcriptase
(hTERT-RPE1) (Figs. 1a and b) to compare the effects of (1) conditional gain of constitutively active RB
and (2) chronic RB deficiency. Cell cycle synchronization was used to minimize potential confounding
variables between the two experimental systems. In the first set of experiments, we studied the role of
unphosphorylated RB, which has been described to trigger visible changes in nuclear architecture and
promote chromatin dispersion25. To examine these microscopic findings in chromosome conformation
capture, we utilized the same cellular model, replacing the endogenous RB with a constitutively active
FLAG-tagged variant (RBΔCDK) via a doxycycline-inducible system23,25,37. The RBΔCDK variant harbors 14
missense mutations that substitute all known endogenous CDK phosphorylation sites with alanine37,38.
Following 24 h of doxycycline treatment, cells entered the G0/G1 phase, and upon 72 h, they exited the
cell cycle and developed a chromatin dispersion phenotype25. In the second set of experiments, we aimed
to distinguish RB’s role in chromatin organization from changes related to cell cycle. To model RB loss,
we utilized RB1 knockout hTERT-RPE1 cells generated with the CRISPR/Cas9 system (Supplementary
Fig. 1)39. These cells were synchronized in G1 phase via 6 days of contact inhibition and then
subsequently released to S phase via trypsinization (Fig. 1b and Supplementary Fig. 2).

Chromosome conformation capture recapitulated many of the expected features of chromatin
organization, including chromosome territories40, nuclear compartments41, topologically associating
domains (TADs)42,43, cohesin-dependent loops27,44, and architectural stripes45 (Fig. 1a, Supplementary
Figs. 3 and 4). To identify the overall scale at which RB-dependent changes in chromatin organization
occur, we measured the contact probability (P(s)), which assesses the likelihood of contact formation as a
function of genomic separation (Fig. 1c)44,46. The contact probability curve has been shown to have at
least two different regimes in mammalian cells, among which a shallow decay for s < 200 kb has been
reported to be produced by cohesin-dependent loop formation44,47. In our chromosome conformation
capture, we identified three major regimes in the contact probability curve: the cohesin-dependent decay
for s < 200 kb (P(s) ≈ s-0.8), a steeper decay for 200 kb < s < 3 Mb (P(s) ≈ s-1.3), and a variable far-cis
scaling for s > 3Mb (Fig. 1c).

Expression of RB∆CDK caused significant changes in chromatin structures at s > 3 Mb, often
associated with far-cis contacts between nuclear compartments44,47. Accordingly, when RB∆CDK was
expressed, the most striking change in chromatin organization occurred in the distribution of nuclear
compartments and their interactions (Fig. 1d). Nuclear compartments are chromatin features that are
thought to be cohesin-independent44. Although changes that occurred between 0h and 24h or 72h of
RB∆CDK expression may be associated with G1-arrest, most compartmental changes occurred between 24
h and 72 h of RB∆CDK expression, coinciding with the irreversible cell cycle exit, as previously
described25. We observed the spreading of compartment B to adjacent regions and increased spatial
distance within compartment B (Fig. 1d), concordant with the previously described heterochromatin
dispersion phenotype (Fig. 1e)25.

In contrast, the loss of RB in G1-synchronized cells affected chromatin interaction at s < 200 kb and
200 kb < s < 3 Mb (Fig. 1c). In both regimes, RB loss led to steeper decays, shifting the contact probability
curves to P(s) ≈ s-0.85 (s < 200 kb) and P(s) ≈ s-1.35 (200 kb < s < 3 Mb). The steeper decay at the cohesin-
dependent regime for s < 200 kb suggested that RB may regulate cohesin-dependent loop formation
independently from its effect on cell cycle. Indeed, in cells synchronized in the G1 phase, RB loss increased
the number of cohesin-dependent loops by 40.5% (Fig. 1f; p-value = 3.66×10-7, two-tailed paired Student’s
t-test) and increased the average size of these loops by 16.4 kb (Fig. 1g; p-value = 1.86×10-2, two-tailed
Student’s t-test). In RB-depleted cells, these loops also produced stronger domains, as evidenced by
increased TAD strength in RBKO compared to RBWT (Fig. 1h). These changes were not observed in cells
accumulated in S phase (Figs. 1f and g). Furthermore, three-dimensional reconstruction of the chromatin
architecture, via a cell population-based algorithm, revealed that RB loss weakens the spatial clustering of
acrocentric chromosomes harboring nucleolus organizer regions (NORs). Specifically, the average distance
between centromeres of acrocentric chromosomes grew from ~1.81 µm in RBWT to ~2.52 µm in RBKO
(Supplementary Figs. 5–7). This Micro-C data corroborated previous microscopic observations26,
positioning RB as a significant regulator of the cohesin-dependent interphase chromatin architecture. In
summary, we observe that RB exerts broad effects on chromatin organization (Fig. 1i), with RB∆CDK
expression promoting primarily heterochromatin spreading and dispersion (Figs. 1d–e), and RB loss in G1-
arrested cells altering cohesin-dependent loop number, loop length, and TAD boundary strength (Figs. 1g–
h).

Genome-wide association of RB and cohesin
Given that the loss of RB promoted cohesin-dependent loop formation, we assessed whether
chromatin-bound RB is associated with genomic loci bound by cohesin. For this, we performed chromatin
immunoprecipitation sequencing (ChIP-seq) for SMC3 in hTERT-RPE1 cells. We also performed ChIP-
seq experiments for various histone modifications to annotate active promoters and enhancers (H3K4me3,
H3K4me1, H3K27ac) and heterochromatin (H3K27me3) and for transcription factors previously reported
to colocalize with RB at cis-regulatory elements (E2F1, CTCF, and c-Jun)23. To assess chromatin
accessibility at these loci, we performed assays for transposase-accessible chromatin using sequencing
(ATAC-seq).

Strikingly, RB showed extensive colocalization with SMC3 in the human genome (Figs. 2a and b):
68.7% of RB binding sites exhibited SMC3 binding as well (Fig. 2a). This overlap was strongest at
insulators, where 99.6% of RB binding sites showed SMC3 binding. Notably, the overlap between RB
and SMC3 was significantly more extensive than that between RB and E2F1, which was only evident in
promoter regions. Besides the association with SMC3, all chromatin regions bound by RB were highly
accessible (Fig. 2b).

Cohesin is a loop extrusion machinery that utilizes energy from ATP hydrolysis to form chromatin
loops28,29. We wondered, therefore, if RB binding sites also demonstrate insulation of chromatin contacts
at a comparable level to that observed at cohesin binding sites. To quantify the strength of insulation,
we measured the insulation score from chromosome conformation capture, with more negative values
indicating stronger insulation48,49. Indeed, RB binding sites were significantly more insulating compared
to the genome-wide average (Fig. 2c; p-value < 0.0001, two-tailed Student’s t-test), and were comparable
to the insulation strength at SMC3 binding sites. At CTCF binding sites, which showed the strongest
insulation on average (Fig. 2c; p-value < 0.0001, two-tailed Student’s t-test), the co-occurrence of RB
binding further increased insulation (Fig. 2c; p-value < 0.0001, two-tailed Student’s t-test). Notably, RB-
bound CTCF sites were significantly more accessible compared to non-RB-bound CTCF sites (Fig. 2d; p-
value < 0.0001, two-tailed Student’s t-test), which may contribute to the observed differences in
insulation50.

At promoters, RB and SMC3 localization both occurred at a region ~65 bp upstream of the
transcription start site (TSS) (Fig. 2e). RB-bound promoters were more insulating compared to non-RB-
bound promoters (Fig. 2f and Supplementary Fig. 8; p-value = 9.0×10-240, two-tailed Student’s t-test) and
were often co-occupied by E2F1 (Supplementary Fig. 9), leading to decreased nucleosome occupancy at
the TSS and more active transcription (Figs. 2g and h; p-value < 0.0001, two-tailed Student’s t-test).
Notably, the increased insulation at RB-bound promoters was significant when it was compared to non-
RB-bound promoters with similar transcriptional activity (Supplementary Fig. 8; p-value < 0.0001, two-
tailed Student’s t-test).

In brief, we report that RB-bound chromatin loci were also occupied by SMC3 and exhibited
increased insulation. This extensive colocalization of RB and SMC3 on chromatin raises the question of
whether RB-mediated regulation of cohesin-dependent loops results from RB’s direct influence on
cohesin activity, and whether this regulation is confined to RB-bound loci or occurs genome-wide.

RB promotes cohesin removal from CTCF sites across chromosomes
The extensive co-localization of chromatin-bound RB and SMC3, along with the observed impact of
RB loss on cohesin-dependent loop formation and TAD boundary strength, prompted us to investigate
RB’s role in cohesin localization. Upon RB depletion, a clear change in the distribution of SMC3 was
observed genome-wide. Outside DNA repeats, the binding of SMC3 occurred at three distinct types of
cis-regulatory elements: promoters, insulators, and enhancers (Supplementary Fig. 10). In G1-
synchronized cells, RB loss led to an average +14.4% increase (95% confidence interval (CI), 11.1%–
17.7%; n = 4) in the SMC3 signal across 21,498 insulators (Fig. 3a). We reproduced this finding in
biological replicate experiments as well as in different RB1 knockout clones (Fig. 3a). To rule out the
possibility that the observed increase is an indirect effect of RB loss, we compared the proteomes of
RBWT and RBKO cells and found no significant differences in the protein levels of SMC3 or other core
components of the cohesin complex (Supplementary Fig. 11). To exclude the possibility that the observed
changes resulted from normalization artifacts, we utilized spike-in DNA from Escherichia coli as an
additional control and confirmed the increase in SMC3 signal at insulators (Fig. 3a). Notably, we did not
observe analogous increases in SMC3 signal at promoters (+2.8% upon RB loss; n = 4) or enhancers (-
3.4% upon RB loss; n = 4), further supporting the specificity of RB’s regulatory role at insulator sites.
Notably, the enrichment of signal at insulators occurred across the chromosome and was not confined to
RB-bound loci (Supplementary Fig. 10), suggesting that the observed functional interplay between RB
and SMC3 might be distinct from the physical interaction.

To further characterize the functional interplay between RB and SMC3, we performed SMC3 ChIP-seq
experiments in RBWT and RBKO hTERT-RPE1 cells synchronized at different phases of the cell cycle: G1,
S, and prometaphase (Fig. 3b). Synchronization in prometaphase was achieved via nocodazole treatment.
As expected, prometaphase-synchronized cells exhibited weakest SMC3 binding at CTCF insulators,
reflecting the prophase pathway, whereby chromatin-bound cohesin is released from chromosome arms
through a Wapl-dependent mechanism51,52. We also reproduced the reduction in cohesin enrichment at
CTCF insulators upon G1/S transition, thought to result from the dissolution of CTCF-chromatin
interactions by the DNA replication machinery53. Importantly, we identified that RB loss significantly
disturbs cohesin release in prometaphase cells, leading to an average +57.6% increase (95% CI, 42.1%–
73.2%; n = 2) in the SMC3 signal across 21,498 insulators (Fig. 3b). This change was specific (p-value =
1.26×10-3, one-way ANOVA) to insulators and was less pronounced in promoters (+12.2% upon RB loss;
n = 2) and enhancers (+8.2% upon RB loss; n = 2). SMC3 ChIP-seq in RBWT and RBKO hTERT-RPE1
cells synchronized in prometaphase using inhibition of Eg5 kinesin activity, as an alternative to
nocodazole-induced arrest, similarly revealed cohesin enrichment at insulators following RB loss
(Supplementary Fig. 12).

We observed that the RB-mediated deregulation of cohesin distribution peaked during mitosis and
persisted into early G1 (Fig. 3b). Supporting this model, we found that the fold change in SMC3 binding
upon RB depletion was positively correlated between G1 and prometaphase cells, while S-phase cells
showed little to no correlation with either (Fig. 3c and Supplementary Fig. 10). Notably, a higher
correlation was observed in regions that gained SMC3 upon RB loss, compared to regions that lost
SMC3. Since RBKO hTERT-RPE1 cells synchronized in prometaphase exhibited a dramatic increase in
SMC3 binding at insulators, we wondered which sites lost SMC3 during this process. Therefore, we
performed a DNA motif analysis on regions that gained or lost SMC3 signal upon RB loss (Fig. 3d). We
observed that regions that gained SMC3 were highly enriched with CTCF and BORIS binding motifs,
corroborating our findings that SMC3 gain primarily occurs at insulators. Notably, in regions that lost
SMC3, we did not observe DNA motif enrichment at a similar level to those that gained SMC3 (Fig. 3d).
While AP-1 binding motifs showed statistical significance, AP-1-bound enhancers exhibited an overall
increase in SMC3 signal upon RB loss (Supplementary Fig. 13), and only the strongest AP-1-bound
enhancers (n = 3,652) showed slight reductions in SMC3 signal (-2.3% change upon RB loss). Due to the
relatively small number of these sites and the minimal differences in SMC3 signal, we conclude that AP-
1-bound enhancers are not the primary source of SMC3 loss.

We then assessed repetitive DNA sequences, where a significant fraction of cohesin may localize
(Supplementary Fig. 14)54. As RBWT hTERT-RPE1 cells entered prometaphase, we observed a specific
3.5-fold increase in SMC3 ChIP-seq reads at centromeric α-satellites, with 2.8% of all reads in
prometaphase cells arising from these regions. However, RB loss led to an increased SMC3 binding
(+10.6% upon RB loss) at centromeric α-satellites in prometaphase cells, ruling out the possibility that the
repetitive DNA sequences are the primary source of SMC3 loss upon RB depletion.

Cohesin-dependent loop extrusion is a dynamic process, with the vast majority of cohesin being
partially extruded and not statically localized to TAD boundaries (Fig. 3e)28,30. We therefore hypothesized
that partially extruded cohesin might be the source of SMC3 loss upon RB depletion. Supporting this
hypothesis, in a pooled analysis, we observed a chromosome-wide decrease in SMC3 signal at
intermediary non-SMC3 sites upon RB loss (Fig. 3f). These changes were specific to G1 and
prometaphase cells and not to cells synchronized in S phase. Sequencing of the input DNA showed no
differences in the same regions, confirming that the observed changes in SMC3 signal at intermediary
non-SMC3 sites upon RB loss were not due to differential sequencing coverage (Supplementary Fig. 15).
Overall, we conclude that RB loss perturbs cohesin release in prometaphase, leading to cohesin
enrichment at CTCF insulators and cohesin depletion at intermediary non-SMC3 sites, with these effects
persisting into G1.

RB reduces insulation at TAD boundaries
While RB-mediated changes in SMC3 distribution peaks during the mitosis, these cells typically lack
nuclear compartments and TADs46. Notably, we observed that this deregulation of SMC3 distribution
persists into the G1 phase. This led us to investigate whether RB-mediated changes in SMC3 distribution
affect chromatin organization in G1. A strong negative correlation was found between the difference in
SMC3 ChIP-seq signal and the change in the insulation score (Spearman’s ρ = -0.87, p-value = 1.17×10-
3), suggesting that regions that gained SMC3 following RB loss showed increased insulation (Fig. 3g).
Further analysis of insulation changes at the entire SMC3-bound or RB-bound genomic loci revealed that
RB depletion specifically induced insulation at CTCF-bound insulators and TAD boundaries, while
having the opposite effect in promoters and enhancers (Supplementary Fig. 15).

These changes in insulation were also evident from the differential analysis of chromosome
conformation capture. Reflecting the overall increase in the number of loops upon RB loss, we observed a
4.7-fold increase in the number of loops specific to RBKO compared to RBWT (Supplementary Fig. 16).
Inspection of individual regions in chromosomes 9 and 11 revealed visually striking gains in chromatin
contacts and showed that the formation of differential loops in RBKO is driven by an increase in cohesin
signal at insulators, including SMC3 and STAG1 (Figs. 3h and i). Additional SMC3 ChIP-quantitative
PCR further confirmed the increase of SMC3 binding at chromosome 9 locus following RB loss
(Supplementary Fig. 17). Replicate experiments, using spike-in DNA from Escherichia coli as an
additional control, corroborated the increased SMC3 binding at these insulators upon RB loss.

RB enhances the non-E2F transcriptional program
Insulators are cis-regulatory elements that act as barriers for enhancer-promoter interactions55.
Therefore, we wondered whether the observed interplay between RB and SMC3 at insulators has
implications for enhancer-promoter interactions. To quantify enhancer-promoter interactions, we utilized
H3K27ac HiChIP in RBWT and RBKO hTERT-RPE1 cells synchronized in G136.

Upon RB loss, we observed a general loss of contacts between sites marked by H3K27ac (Figs. 4a–c).
The number of H3K27ac HiChIP peaks decreased 3.3-fold (Fig. 4a and Supplementary Fig. 18), and the
average strength of HiChIP interactions was weakened in both biological replicate experiments (Fig. 4b).
Using ChIP-seq tracks for histone modifications, we further categorized these interactions as enhancer-
enhancer, enhancer-promoter, and promoter-promoter interactions (Methods). Among the H3K27ac
HiChIP peaks shared between RBWT and RBKO hTERT-RPE1 cells, 83.3% represented enhancer-enhancer
interactions, 12.0% were enhancer-promoter interactions, and 4.8% were promoter-promoter interactions.
Although RB1 knockout cells display slightly increased H3K27ac ChIP-seq signal compared to RB1 wild-
type hTERT-RPE1 cells, consistent with transcriptional de-repression23, RB depletion reduced the
strength of H3K27ac HiChIP peaks across all three interaction groups (Fig. 4c and Supplementary Fig.
15). As an orthogonal approach, we performed MED15 ChIP-seq to assess mediator recruitment and
observed reduced MED15 occupancy at enhancers and promoters following RB loss (Fig. 4d and
Supplementary Fig. 19). To further investigate whether the role of RB in regulating enhancer-promoter
interactions associated with its activity in repressing insulation at TAD boundaries, we compared
interactions between enhancer-promoter pairs that cross a TAD boundary (inter-TAD) with those that do
not (intra-TAD). To control distance-dependent effects, we selected a subset of enhancer-promoter pairs
matched for distance (Supplementary Fig. 20). As expected, intra-TAD enhancer-promoter interactions
were stronger and more well-defined, while inter-TAD interactions were weaker and less distinct in both
RBWT and RBKO hTERT-RPE1 cells (Supplementary Fig. 20). Comparing RBWT and RBKO cells revealed
that both intra- and inter-TAD interactions weaken upon RB loss, with the RB-dependent effect appearing
more pronounced when the enhancer-promoter pair spans a TAD boundary. Consistent with this, MED15
ChIP-seq analysis demonstrated that in RBWT cells, TAD boundaries impose a distance-dependent effect
on MED15 ChIP-seq signal and enhancer activity, whereas this pattern is lost in RBKO cells
(Supplementary Figs. 20c and d). Together, these findings suggest that RB-mediated repression of
insulation at TAD boundaries facilitates increased enhancer-promoter interactions in the presence of wild-
type RB, although additional mechanisms may also contribute. Motif enrichment analysis of enhancers
with decreased MED15 binding upon RB loss revealed enrichment of AP-1 and TEAD transcription
factors motifs (Supplementary Fig. 19). Given that RB has been reported to associate with these enhancer
regions23,56, these results raise the possibility that RB regulates enhancer activity through additional
mechanistically distinct pathways.

To assess the transcriptional impact of the RB-mediated changes in H3K27ac interactions, we
compiled a set of 2,656 genes with insulators located between active enhancers and an active promoter
(Fig. 5a). From this set, we identified 1,754 genes that showed no RB or E2F1 binding at the promoter.
We reasoned that this gene set would be ideal for assessing the transcriptional effects of the RB-induced
chromatin rearrangements, as potential complications from RB-mediated repression of E2F activity would
be minimal. Within this gene set, 181 genes showed increased SMC3 binding at the insulator (cohesin
gain), and 44 genes showed decreased SMC3 binding at the insulator (cohesin loss) across experiments,
consistent with the overall increase in SMC3 binding at insulators upon RB loss (Fig. 5b). Although RB
depletion elevates the average expression levels of genes in both RB/E2F-bound and non-RB/non-E2F-
bound promoters (Fig. 5b, left panel), the expression of genes within the ‘cohesin gain’ gene set was
significantly lower compared to the average expression level of genes with non-RB/non-E2F-bound
promoters (Fig. 5b; p-value = 0.0368, two-tailed Student’s t-test) or compared to the ‘cohesin loss’ gene
set (Fig. 5b; p-value = 9.16×10-4, two-tailed Student’s t-test). This indicates that cohesin release at
insulators facilitated by RB impacts transcription and that RB acts via this mechanism to safeguard and
enhance the expression of multiple non-E2F target genes. As an example, we demonstrate RB’s role in
enhancing the expression of lysyl oxidase-like 1 (LOXL1) that correlates with reduced cohesin binding at
an insulator site located between LOXL1 promoter and enhancer (Supplementary Fig. 21).

Gene set enrichment and gene ontology analyses of the 181 genes regulated by cohesin gain at an
adjacent insulator revealed that RB-mediated dysregulation of SMC3 distribution primarily affects genes
regulating epithelial-mesenchymal transition by maintaining epithelial characteristics (Fig. 5c and
Supplementary Fig. 22; p-value = 4.84×10-4, gene set enrichment analysis), and more specifically
extracellular matrix organization (Fig. 5c; p-value = 1.41×10-3, gene ontology: biological processes).
Consistent with these results, we also observed an increased cellular detachment rate in both RBKO clones
assessed compared to RBWT in hTERT-RPE1 cells (Fig. 5d and Supplementary Fig. 23; p-value < 0.0001,
two-tailed Student’s t-test). Consistent with this, wound healing assays revealed an elevated migration
rate in RBKO cells compared to RBWT cells (Fig. 5e and Supplementary Fig. 24). We also observed
enrichment of genes associated with myogenesis (Fig. 5c; p-value = 1.27×10-2, gene set enrichment
analysis), intriguing in light of previous suggestions that RB behaves as a transcriptional activator during
this process57-59. While these phenotypes do not represent comprehensive functional validation of RB-
regulated chromatin architecture, these cell cycle-independent effects of RB may be a good starting point
for connecting architectural chromatin changes to cellular behavior of RB-deficient cells.

To place these transcriptional changes in context, we clustered the RB-deregulated genes based on the
transcriptional patterns observed in synchronized populations of RBWT and RBKO cells. We identified five
major categories of RB-mediated transcriptional regulation (Fig. 5f). Cluster 3 represents RB-bound
genes suppressed by RB in G1, with promoters enriched in E2F binding motifs (Supplementary Fig. 25).
Cluster 1 includes cell cycle-dependent genes suppressed in G1 in both RBWT and RBKO cells, whose
promoters are enriched in the CHR motif, indicating late cell cycle genes controlled by DREAM and
MMB-FOXM160. The other three groups of the RB-deregulated genes show less RB binding, and their
transcriptional regulation doesn’t follow the canonical RB/E2F model. Of these, cluster 5, a group of G1-
specific genes whose expression is enhanced by RB, exhibited the strongest association with the ‘cohesin
gain’ gene set: 54.5% of differentially expressed genes in this gene set belonged to cluster 5 (Fig. 5f; p-
value = 5.35×10-3, Fisher’s exact test). Notably, the gene set enrichment and gene ontology analyses for
cluster 5 genes revealed similar signatures compared to the ‘cohesin gain’ gene set (Supplementary Fig.
26). Overall, our data indicate that rather than targeting random genes, this previously unrecognized RB
function may regulate specific biological processes. To further investigate whether these RB-associated
transcriptional changes occur population-wide or are driven by a specific cell population, we performed
single-cell RNA-seq (scRNA-seq) experiments in RBWT and RBKO hTERT-RPE1 cells synchronized in
the G1 phase via contact inhibition. Principal component analysis revealed that all cells clustered
together, with a modest separation between RBWT and RBKO cells, primarily driven, as expected, by
differences in RB/E2F target gene expression – cluster 3 (Supplementary Fig. 26). Notably, the scRNA-
seq analysis validated the differential expression of cluster 5 genes, where RB functions as a
transcriptional activator during G1 (Supplementary Figs. 27 and 28). While the scRNA-seq experiment
corroborated non-canonical transcriptional activation by RB, it did not reveal additional distinct
transcriptional profiles at the single-cell level that would support RB’s effect on cellular heterogeneity.

Methods
Cell culture
All cells were cultured at 37ºC with 5% CO2. Non-transformed human retinal pigment
epithelial cells immortalized by human telomerase reverse transcriptase (hTERT-RPE1) were a
gift from Dr. David Pellman’s laboratory (Dana Farber Cancer Institute, RRID: CVCL_4388).
Human embryonic kidney cells (HEK293T) were obtained from ATCC (Cat. # CRL-3216,
RRID: CVCL_0063). hTERT-RPE1 cells were cultured in Dulbecco’s Modified Eagle’s
Medium (DMEM) supplemented with 25 mM glucose, 2 mM glutamine, 5% fetal bovine serum,
and antibiotics (penicillin, 100 units/mL; streptomycin, 100 μg/mL). HEK293T cells were
cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 25 mM glucose,
2 mM glutamine, 10% fetal bovine serum, and antibiotics (penicillin, 100 units/mL;
streptomycin, 100 μg/mL). RB1 knockdown or the replacement of endogenous RB with the
constitutively active RB (i.e., RB∆CDK) were done in cells transduced with pINDUCER11
(Addgene, Cat. # 44363) or pINDUCER20 (Addgene, Cat. # 44012) constructs as previously
described14,16. Doxycycline induction was performed by treating cells with doxycycline (Sigma-
Aldrich, Cat. # D9891) at 0.5 μg/mL concentration for 48 h. G1 synchronization was performed
via contact inhibition through maintaining cells for 6 d in a confluent state. Cells were
subsequently released to S phase via trypsinization. For mitotic synchronization in
prometaphase, cells were synchronized to G1 by contact inhibition through maintaining cells in a
confluent state for 6 days. Cells were subsequently released to S phase by trypsinization and
were transferred to T175 (large) flasks to sub-confluent state. After 16 h, cells were treated with
0.1 µg/mL nocodazole (Cell signaling, Cat. #2190) for 6 h or with 5 µM kinesin Eg5 inhibitor
(Eg5i) S-Trityl-L-Cysteine (PMID: 16507573) (Tocris, Cat. #2191) for 24 h. Mitotic cells were
collected by gentle shaking and tapping of the flask. RB1 knockout cell lines were generated by
CRISPR/Cas9 and were previously described21.

Transfections
pINDUCER11 (Addgene, Cat. # 44363, RRID: Addgene_44363), pINDUCER20
(Addgene, Cat. # 44012, RRID: Addgene_44012), pLenti-CMVtight-Puro-DEST (Addgene, Cat.
# 26430, RRID: Addgene_26430), and pLenti-CMVtight-eGFP-Puro (Addgene, Cat. # 26431,
RRID: Addgene_26431) constructs were packaged in HEK293T cells by transient transfection.
Envelope plasmid pCMV-VSV-G (Addgene, Cat. # 8454, RRID: Addgene_8454) and packaging
plasmid pCMV-dR8.2 dvpr (Addgene, Cat. # 8455, RRID: Addgene_8455) were utilized for
transient transfection. Transfections were carried out using X-tremeGENE 9 DNA Transfection
Reagent (Sigma-Aldrich, Cat. # 6365787001). All cell lines were infected with lentiviral
particles in the presence of 5 μg/ml polybrene (Sigma-Aldrich, Cat. # 107689). Stable cell lines
were selected for expression of the green fluorescence protein by fluorescence-activated cell
sorting or for resistance to specific antibiotics: pINDUCER20, 400μg/ml G-418 (Gibco, Cat. #
10131035); and pLenti-CMVtight-Puro-DEST, 6 μg/ml puromycin (Sigma-Aldrich, Cat. #
P9620).

Cell cycle analysis
The cell cycle was analyzed for hTERT-RPE1 cells in various conditions. Cells were
incubated with 20 μM 5-ethynyl-2’-deoxyuridine (EdU; Life technologies, Cat. # A10044) for 2
h, fixed with 3.7% formaldehyde in PBS for 15 min, blocked with 3% BSA in PBS for 1 min,
permeabilized with 0.5% Triton X-100 in PBS for 30 min, and stained with ClickIT reaction
(100mM Tris-HCl pH 7.5, 3mM CuSO4, 50 mM Ascorbic Acid, and 2.5 μM Alexa Fluor-647
azide; Life Technologies, Cat. # A10277) for 30 minutes and 3 μM 4',6-Diamidino-2-
Phenylindole Dihydrochloride (DAPI; Life Technologies, Cat. # D1306) in staining buffer
(100mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM CaCl2, 0.5 mM MgCl2, and 0.1% NP-40) for
15 minutes. Flow cytometry experiments were performed with LSR II flow cytometer (BD
Biosciences) and were analyzed using the FCS Express software (v7.18.0025).

Heterochromatin dispersion analysis
Coverslips with adhered hTERT-RPE1 cells were washed with PBS and fixed by
sequentially incubating in 70, 85, and 100% ethanol for 2 min each. Coverslips were dried, and
probe mix containing FISH probe for chromosome 7 α-satellite (Cytocell, Cat. # LPE007G) and
hybridization buffer B (Cytocell, Cat. # H1000L) was added. Coverslips were affixed to slides
and sealed using rubber cement. Slides were denatured at 75°C and incubated at 37°C for 16 h.
Coverslips were removed and washed in 0.25× SSC at 72°C and 2× SSC (with 0.05% Tween-20)
for 2 min each. Coverslips were mounted using Vectashield (with DAPI) and sealed. A Zeiss
LSM 710 Laser Scanning Confocal Microscope and Zeiss ZEN software were used to acquire
FISH images. Images were captured using parameters for each probe that remained consistent
across samples and experiments. Images were acquired at room temperature, and a Plan-
Apochromat 63×/1.40 Oil DIC M27 (Zeiss, Cat. # 420782-9900-000) was used to capture Z-
stacks (6–12 sections of 0.5-µM thickness). FITC/Alexa Fluor 488 filter was used to acquire
images for chromosome 7 α-satellite. Maximum projections of Z-stacks were used for image
analysis. No other image processing was performed. Images were analyzed using Fiji (ImageJ)
software. To effectively quantify the dispersion phenotype, we used a skeleton dot length plugin,
and macros were made to execute the steps as previously described (14). The following macro
steps were written and executed in FIJI: open();setOption(“BlackBackground”, false);
run(“Make Binary”); run(“Dilate”); run(“Dilate”); run(“Dilate”); run(“Skeletonize”);
run(“Analyze Skeleton (2D/3D)”, “prune=none calculate show display”). Cutoffs of skeleton dot
length were used to define the foci categories as compact, diffused, and dispersed: compact,
<0.75 µm; diffused, 0.75–1.25 µm; and dispersed, >1.25 µm.

Cell detachment rate analysis
For the cell detachment experiment, hTERT-RPE1 cells were seeded on 24-well plates and
synchronized to G1 in confluent culture for 6 days. Cells were collected after treatment with
0.25% trypsin (Corning, Cat. # 25-053-CI) for 1 to 6 minutes, and the number of cells was
assessed using the TC20 automated cell counter (Bio-Rad). Brightfield images of the cells that
remained attached on the plate surface after trypsinization were obtained using the Diaphot 300
inverted microscope (Nikon), 4x objective lens.

Wound healing assay
hTERT-RPE1 cells were seeded on 6-well plates and synchronized to G1 in confluent
culture for 6 days. On the sixth day of contact inhibition, wounds were generated in a straight
line using a 1 mm pipette tip. After scratch, the cell monolayer was washed with PBS to remove
detached cells and replenished with fresh reduced DMEM media supplemented with 25 mM
glucose, 2 mM glutamine, antibiotics (i.e., penicillin, 100 units/mL; streptomycin, 100 μg/mL)
and 1% fetal bovine serum. Serum starvation at 1% fetal bovine serum was performed to prevent
cells from proliferating, and its concentration was determined by assessing the minimum
concentration of serum that prevents cells from dying. At 0.5% and 0.05% fetal bovine serum,
we observed that most cells died due to the lack of sufficient serum. The images were acquired
immediately following media replacement (t = 0 hours) and after 2 days (t = 48 hours) using an
inverted microscope (Nikon, Diaphot 300), 4x objective lens. A Bandpass filter was applied to
the images (Process > FFT > Bandpass Filter Image, large structure equal to 40 pixels, small
structure equal to 3.0 pixels, tolerance = 5%).

Wound healing image analysis
Wound areas were assessed using ImageJ (v1.54d). Briefly, images were first converted to
8-bit (Image > Type > 8-bit) and the threshold was reset (Image > Adjust > Threshold > Reset).
A Bandpass filter was applied to the images (Process > FFT > Bandpass Filter Image, large
structure equal to 40 pixels, small structure equal to 3.0 pixels, tolerance= 5%) and the threshold
was adjusted to 85% (Image > Adjust > Threshold). An additional filter of minimum radius
equal to 5.0 pixels was used (Process > Filters > Minimum > Radius). The area was selected
using the wand tool and measured in pixels (Analyze > Measure).

Micro-C
Micro-C experiments were performed using the Micro-C kit (10X wash buffer, NWB
solution, chromatin capture beads, streptavidin beads, 20% SDS, crosslink reversal buffer,
MNase enzyme mix, 10X nuclease digest buffer, 100 mM MgCl2, 0.5 M EGTA, end polishing
buffer, end polishing enzyme mix, intra-aggregate ligation buffer, intra-aggregate ligation
enzyme mix, bridge, 5X bridge ligation buffer, bridge ligase, proteinase K, 250 mM DTT, and
HotStart PCR Ready Mix; Dovetail Genomics, Cat. # 21006) via Dovetail Genomics’ Micro-C
service (Cat. # 20020). In short, frozen hTERT-RPE1 cells were resuspended in 1X PBS,
crosslinked with crosslinked using 3 mM disuccinimidyl glutarate (DSG; Thermo Fisher
Scientific, Cat. # A35392) and 1% formaldehyde. MNase reaction was performed using 0.5 μL
of MNase enzyme mix in a 100 μL solution of 1X nuclease digest buffer and was stopped by
addition of 5 μL of 0.5 M EGTA. Digested chromatin was solubilized by supplementing with 3
μL of 20% SDS and was captured by chromatin capture beads. End polishing was performed
using the end polishing enzyme mix at the end polishing buffer, and crosslinks were reversed by
1X crosslink reversal buffer supplemented with proteinase K. DNA was purified using
SPRIselect beads (Beckman Coulter, Cat. # B23317). The sequencing library was prepared using
the Dovetail Library Module for Illumina (end repair enzyme mix, end repair enzyme buffer,
ligation enhancer, ligation enzyme mix, adaptor for Illumina, and USER enzyme mix; Dovetail
Genomics, Cat. # DG-LIB-001). End repair enzyme mix and end repair buffer supplemented
with 12 mM DTT were utilized for end repair, which were followed by adapter ligation and
USER digest. 2.5 μL adaptor for Illumina, 30 μL ligation enzyme mix, and 1 μL ligation
enhancer were used for the 33.5 μL reaction mixture. DNA was then purified with SPRIselect
beads (Beckman Coulter, Cat. # B23317), ligation captured via streptavidin beads, and
underwent Index PCR using the HotStart PCR ready mix. Size selection was performed using
SPRIselect beads (Beckman Coulter, Cat. # B23317). The final libraries were run on the
NovaSeq 6000 system (Illumina) in technical quadruplicates on a paired-end mode. On average,
1.3 billion reads were sequenced per sample to achieve nucleosome resolution.

Bioinformatics processing for Micro-C
Library quality control was performed on the first 2 million reads via the get_gc.py code
provided by Dovetail Genomics (https://github.com/dovetail-genomics/Micro-C). Sequencing
reads were aligned to the hg38 human reference build using bwa mem (v0.7.17-r1198-dirty).
Pairtools (v0.3.0) were utilized to parse, sort, and deduplicate ligation sequencing reads to valid
pairs. On average, 80.0% of sequencing reads were mapped as valid pairs, 13.3% were single-
sided, and 6.5% were unmapped. Juicer Tools pre (v1.22.0) was utilized to generate .hic files for
the chromosome conformation capture maps at 5 kb resolution. JuiceBox (v1.11.08) was utilized
to visualize chromosome conformation capture maps in the .hic format. For .hic files, the
aggregate peak analysis was performed using the APA (v1.22.0) algorithm. Most of the
subsequent analyses were performed after converting the .hic files to .cool and .mcool formats
using hic2cool (v0.5.1) at 10 kb resolution. The .cool files were matrix balanced using cooler
balance (v0.9.2). For analyses that does not require full resolution, .cool files were coarsened
using cooler coarsen (v0.9.2) to 1 Mb resolution and was matrix balanced using cooler balance
(v0.9.2). Expected number of cis contacts were quantified using the cooltools expected_cis
function (v0.5.2) and were utilized for log observed/expected visualization. The visualization of
.cool files were performed using cooltools pileup (v0.5.2) and coolpup.py (v1.0.0). For the
visualization of chromosome conformation capture map simultaneously with other sequencing
tracks, we utilized pyGenomeTracks (v3.8). The .hic files were converted to .h5 format using
HiCExplorer hicConvertFormat (v2.2.1.1) for usage. Normalization methods utilized in this
study were as follows: .hic files, Knight-Ruiz matrix balancing; .cool and .mcool files, iterative
correction and eigenvector decomposition (ICE); and .h5 files, Knight-Ruiz matrix balancing.

Contact probability
Contact probability, also known as P(s) (22, 24), was measured using cooltools (v0.5.2) at
10 kb resolution. Smoothed aggregate curve was produced using cooltools expected_cis (v0.5.2)
and its first derivative was assessed using NumPy gradient (v1.22.3). The visualization of the
curves was performed using loglog and semilogx functions of Matplotlib (v3.4.2).

Nuclear compartment analysis
The visualization of Pearson correlation map was performed using JuiceBox (v1.11.08).
Nuclear compartment analysis was performed by quantifying the eigenvector of the chromosome
conformation capture matrix using cooltools eigs_cis (v0.5.2). Eigenvector tracks were then
exported to bigWig via Bioframe to_bigwig (v0.3.3). The assignment of compartments A/B was
performed by comparing the eigenvector track to the ATAC-seq data for hTERT-RPE1 cell lines
generated in this study. Saddle plots, generated from cooltools saddle (v0.5.2), were utilized to
assess the distribution of compartments and interactions between compartments.

Quantification of insulation score
The insulation score (30-33) was measured from .mcool files using cooltools insulation
(v0.5.2) at 10 kb resolution using four different window sizes: 30 kb, 50 kb, 100 kb, and 250 kb.
For subsequent analyses, insulation scores measured from the window size of 100 kb were
utilized. Insulation scores were then filtered for good bins via the “is_bad_bin” column of the
insulation table and were subsequently exported to bigWig via Bioframe to_bigwig (v0.3.3). The
assessment of average insulation score at a region was assessed by deeptools
multiBigwigSummary (v3.5.1).

Analyses of cohesin-dependent loops, topologically associating domains, and architectural
stripes
The HiCCUPS algorithm (v1.22.0) was utilized to identify chromatin loops from
chromosome conformation capture maps, and the HiCCUPS Diff algorithm (v1.22.0) was
utilized to identify differential loop formation in G1-synchronized RBWT and RBKO hTERT-
RPE1 cells. For the assessment of topologically associating domains, the insulation table
acquired from .mcool files through cooltools insulation (v0.5.2) was utilized. From the insulation
table, good bins and strong boundaries were filtered by “is_bad_bin” and “is_boundary_100kb”
columns, respectively, and were utilized as topologically associating domain boundaries. For
visualization, topologically associating domains with sizes from 300 kb to 400 kb were
aggregated using cooltools (v0.5.2). The architectural stripes were identified by the Stripenn
algorithm (v1.1.65.15) on .mcool files at 10 kb resolution with default parameters.

3D reconstruction of nuclear organization
3D reconstruction of nuclear organization from chromosome conformation capture maps
were performed using the PGS algorithm (v0.0.1). The topologically associating domains from
cooltools insulation (v0.5.2) were utilized, and the PGS algorithm simulated 100 structures at a 1
Mb resolution. The following values of theta were utilized for modeling: 1, 0.2, 0.1, 0.05, 0.02,
and 0.01. The nucleus radius were simulated as 5 μm, and the average location of each
topologically associating domain beads were assessed by taking the average of 100 structures
that were generated. For visualization, .pqr files were generated using the PGS hms_to_pdb.py
(v0.0.1) function, and a PyMOL (v2.5.2) code for each file was generated to alter the radius of
each topologically associating domain beads. Mesh visualization was used to create figures, and
the structures were ray traced using POV-Ray (v3.7.0.msvc10).

HiChIP
The HiChIP MNase library was prepared using the Dovetail HiChIP MNase Kit for
mammalian cells (Cat. # 21007), according to the manufacturer’s protocol. Briefly, 5×106 frozen
hTERT-RPE1 cells were used and chromatin was crosslinked with 0.3 M disuccinimidyl
glutarate (DSG; Thermo Fisher Scientific, Cat. # A35392) and 1% formaldehyde (Sigma-
Aldrich, Cat. # F8775). The cross-linked chromatin was digested in situ with micrococcal
nuclease. The MNase reaction was performed using 0.5 μL of MNase enzyme mix in a 100 μL
solution of 1X nuclease digest buffer and was stopped by addition of 10 μL of 0.5 M EGTA
(Dovetail Mico-C Module Box 2, Cat. # DG-NUC-001). The cross-linked, digested chromatin
was then extracted upon cell lysis with 1X RIPA and solubilized with 1 μL of 20% SDS
(Dovetail Chromatin Immunoprecipitation Core Box 1, Cat. # DG-NUC-001). Chromatin
fragments from 1000 ng of lysates were incubated with 300 ng of antibody against H3K27ac
(Cell Signaling, Cat. # 8173) overnight for chromatin immunoprecipitation. The antibody-
protein-DNA complex was pulled down with protein A/G-coated beads (Dovetail Chromatin
Immunoprecipitation Core Box 1, Cat. # DG-NUC-001). Next, the chromatin ends were repaired
and ligated to a biotinylated bridge adapter followed by proximity ligation of adapter-containing
ends as described by the manufacturer (Dovetail Mico-C Module Box 2, Cat. # DG-NUC-001).
After proximity ligation, the crosslinks were reversed, and the associated proteins were degraded
by 1X crosslink reversal buffer supplemented with proteinase K (Dovetail Chromatin
Immunoprecipitation Core Box 1 and Dovetail Mico-C Module Box 2, Cat. # DG-NUC-001).
DNA was purified using SPRI select beads (Beckman Coulter, Cat. # B23317), and then
converted into a sequencing library using Illumina-compatible adaptors (Dovetail Dual Index
Primer Set #1 for Illumina, Cat. # DG-PRS-002 and Dovetail Library Module for Illumina, Cat.
# DG-LIB-001). Biotin-containing fragments were isolated using streptavidin beads prior to PCR
amplification (Dovetail Chromatin Immunoprecipitation Core Box 1 and Dovetail Mico-C
Module Box 2, Cat. # DG-NUC-001). Size selection was performed using SPRIselect beads
(Beckman Coulter, Cat. # B23317). The libraries were sequenced on the NovaSeq 6000
(Illumina) platform to generate 300 million read pairs with length of 150 bp.

Bioinformatics processing for HiChIP
Adapter trimming was performed using trim-galore in a paired-end mode to remove
Illumina adapter sequences. Sequencing reads were aligned to the hg38 human reference build
using bwa mem (v0.7.17-r1188). Pairtools (v0.3.0) were utilized to parse, sort, and deduplicate
ligation sequencing reads to valid pairs. Juicer Tools pre (v1.22.01) was utilized to generate .hic
files for the chromosome conformation capture maps. JuiceBox (v1.11.08) was utilized to
visualize chromosome conformation capture maps in the .hic format. For .hic files, the aggregate
peak analysis was performed using the APA (v1.22.01) algorithm. Most of the subsequent
analyses were performed after converting the .hic files to .cool and .mcool formats using
hic2cool (v0.5.1) at 100 kb resolution. The .cool files were matrix balanced using cooler balance
(v0.9.2). Expected number of cis contacts were quantified using the cooltools expected_cis
function (v0.5.2) and were utilized for log observed/expected visualization. The visualization of
.cool files were performed using cooltools pileup (v0.5.2) and coolpup.py (v1.0.0). For the
visualization of chromosome conformation capture map simultaneously with other sequencing
tracks, we utilized pyGenomeTracks (v3.8). The .hic files were converted to .h5 format using
HiCExplorer hicConvertFormat (v2.2.1.1) for usage. Normalization methods utilized in this
study were as follows: .hic files, Knight-Ruiz matrix balancing; .cool and .mcool files, iterative
correction and eigenvector decomposition (ICE); and .h5 files, Knight-Ruiz matrix balancing.
H3K27ac HiChIP peaks were called using FitHiChIP (v11.0) using valid pairs. The peak file for
H3K27ac ChIP-seq was prepared using MACS (v2.2.7.1) using default parameters. The
following parameters for FitHiChIP were utilized: BINSIZE, 100 kb; LowDistThr, 200 kb;
UppDistThr, 10 Mb; UseP2PBackgrnd, 0; BiasType, 1; MergeInt, 1; and QVALUE, 0.01. To
assign each H3K27ac HiChIP peaks as enhancer-enhancer, enhancer-promoter, and promoter-
promoter interactions, we assessed ATAC-seq peaks and ChIP-seq peaks for H3K4me3,
H3K4me1, H3K27ac, and H3K27me3 modifications and identified enhancers and promoters for
each bin. For each H3K27ac HiChIP peak, we measured the number of enhancer-enhancer,
enhancer-promoter, and promoter-promoter pairs, and we assessed the maximum number of
pairs using a custom MATLAB (vR2023a) code. The code is available at our GitHub repository
(https://github.com/hanjunlee21/Lee-et-al-2023).

ChIP-seq
ChIP assays were carried out on hTERT-RPE1 cell cultures using approximately 5 million
cells per sample and per epitope. Cells were washed once with PBS and fixed with 1.5 mM
ethylene glycol bis(succinimidyl succinate) (Thermo Scientific, Cat. # 21565) for 30 min at 25ºC
and 1% formaldehyde (Sigma, Cat. # F8775) for 10 min at 37ºC. 20 mM Tris-HCl (pH 7.5) and
150 mM glycine were used to quench cross-linking. Cell pellets were washed twice with ice-cold
PBS, and lysates were prepared using 300 μL lysis buffer (50mM Tris-HCl (pH 7.4), 1% SDS,
and 0.25% DOC) supplemented with protease and phosphatase inhibitors cocktail (PPI) (Thermo
Scientific, Cat. # 78442) for 10 min on ice. Cell lysates were diluted with 900 μL ChIP dilution
buffer (50mM Tris-HCl (pH 7.4), 0.1% SDS, 150 mM NaCl, and 1.84% Triton-X) supplemented
with PPI. Chromatin was fragmented to a size of 200–700 bases with Branson 250 sonifier.
Solubilized chromatin was further diluted with 1800 μL ChIP dilution buffer and
immunoprecipitated with the indicated antibodies overnight at 4°C. Anti-DYKDDDDK Tag
(Cell Signaling, Cat. # 14793) was used for FLAG-RB ChIP, and for other proteins, their
primary antibodies listed in the Antibodies subheadings were utilized for ChIP. Antibody-
chromatin complexes were pulled down with protein G-Dynabeads (Life Technologies, Cat. #
10004D) for 2 hours at 4°C and washed 3 times with RIPA 150 Wash Buffer (0.1% SDS, 0.1%
DOC 1% Triton X-100, 1 mM EDTA, 10 mM Tris-HCl (pH 8), and 150mM NaCl), 3 times with
RIPA 500 Wash Buffer (0.1% SDS, 0.1% DOC, 1% Triton X-100, 1 mM EDTA, 10 mM Tris-
HCl (pH 8), and 500 mM NaCl), 3 times with LiCl wash buffer (10 mM Tris-HCl (pH 8), 250
mM LiCl, 0.5% Triton X-100, and 0.5% DOC) and once with ice-cold 10 mM Tris-HCl (pH 8).
Chromatin complexes were eluted from the beads using elution buffer (10 mM Tris-HCl (pH 8),
0.1% SDS, 150 mM NaCl, and 5 mM DTT) for 1 hour at 65°C and were treated with RNase A
(Roche, Cat. # 11119915001) for 30 min at 37°C. Proteinase K (Thermo Scientific, Cat. #
25530049) treatment and reverse cross-linking was performed for 3 hours at 65°C. After reversal
of the crosslinks, immunoprecipitated DNA was extracted with AMP Pure XP beads (Beckman
Coulter, Cat. # A63881). ChIP DNA was quantified with Qubit dsDNA HS Assay kit (Thermo
Scientific, Cat. # Q32851). 1–2 ng ChIP DNA samples were used to prepare sequencing
libraries, using the Ovation Ultralow System IV2 kit (TECAN, Cat. # 0344NB-A01). ChIP DNA
and input controls were sequenced using the NextSeq 500/550 High Output Kit v2.5 (75 Cycles)
(Illumina, Cat. # 20024906) with the NextSeq 500 sequencer (Illumina) or with the NovaSeq
6000 sequencer (Illumina). For spike-in controls, DNA fragments from genomic DNA of TOP10
strain of Escherichia coli (Invitrogen, Cat. # C404010) were added to the samples before library
preparation. 5% of spike-in DNA was added compared to the samples to be sequenced.

ChIP- quantitative PCR
Anti-SMC3 (Abcam, Cat. # ab9263) ChIP assays were carried out on hTERT-RPE1 cell
cultures synchronized in pro-metaphase using approximately 5 million cells per
immunoprecipitation, as described in the ChIP-seq method. 0.1-0.2 ng ChIP DNA samples were
analyzed, along with input DNA, using the Roche LightCycler 480 system, as described before
(PMID: 35019938). ChIP real-time PCR primers that were used; CHR9_SMC3_peak_F: 5’-
GACAGGGAGTTGGGGTATGAAA-3’, CHR9_SMC3_peak_R: 5’-
CATTAAGAACAACGGGGCTGAC-3’, CHR9_non-target_F: 5’-
GGTGCTTGTAAAAGGATGCTTAGAT-3’, CHR9_non-target_R: 5’-
CTGGCAGGAACAGAAACAGGG-3’. Average scores from three technical replicates were
calculated. P-values were calculated with ordinary one-way ANOVA multiple comparisons
using GraphPad Prism8 software.

Bioinformatics processing for ChIP-seq
Adapter trimming was performed using trim-galore in a paired-end mode to remove
Illumina adapter sequences. Sequencing reads were then aligned using bwa mem (v0.7.17-r1188)
either to the hg38 human reference genome build or the chimera build of hg38 human reference
genome build and NZ_CP080620.1 Escherichia coli TOP10 strain reference genome build. After
the aligned .bam file was sorted and indexed using samtools (v1.10), sambamba (v0.7.1) was
utilized to sort, mark duplicate reads, and index .bam files. Then, we filtered reads in the .bam
files to produce a file that only contains reads that are not PCR duplicates, with less than or equal
to two mismatches, and with mapping quality that is greater than or equal to 30. After indexing
the .bam file, deeptools bamCoverage (v3.5.1) was utilized to normalize the number of reads
based on counts per million (CPM), and .bigWig files were generated at a 1 bp resolution. Peak
calling was performed as previously described (15), using bedGraph2Cluster with the following
parameters: fold change, 2; normalization, QNorm; distance method, sqeuclidean; and clustering
method, symmetry-collapsed profile and scalar. Alternatively, MACS (v2.2.7.1) was utilized to
call peaks. For samples with spike-in controls, spike-in normalization was performed by
multiplying the data column in each bedGraph file with a scaling factor. The scaling factor was
determined by dividing the number of reads that are aligned to the NZ_CP080620.1 Escherichia
coli TOP10 strain reference genome build by the total number of aligned reads and further
multiplying the value by 1,000,000 and rounding it. For the assessment of repetitive sequences,
sequencing reads were realigned to the hg38 human reference genome build via bowtie
(v2.3.5.1) and were analyzed using RepEnrich (v2) using the Python programming language
(v2.7.18). The fold change upon RB loss was assessed using the read counts calculated from
RBWT and RBKO samples in hTERT-RPE1 cells, and their z-scores were measured. Statistical
significance was measured using the z-score, and the p-values were utilized to generate the
Manhattan plot. For the identification of cohesin gain and cohesin loss gene set, a custom
MATLAB code was utilized. The code is available at our GitHub repository
(https://github.com/hanjunlee21/Lee-et-al-2023). In brief, the list of promoters from Ensembl
(v105) and the list of enhancers from histone modification-based clustering analysis of H3K27ac
ChIP-seq peaks were assessed. Promoters that were non-RB-bound and non-E2F1-bound and
those that were within 60 kb from the nearest enhancer were utilized.

Motif analysis
Motif analysis was performed using the HOMER (v4.11.1) algorithm. Absolute motif
analysis was performed using HOMER findMotifsGenome.pl (v4.11.1) without a preset
background and a size parameter given by the peak file. Differential motif analysis was
performed using HOMER fnidMotifsGenome.pl (v4.11.1) using a different peak file as a
background and a size parameter that is given by the peak file.

ATAC-seq
hTERT-RPE1 cells were synchronized to G1 by contact inhibition for 6 days and
subsequently released to S phase via trypsinization. Cells were harvested and counted using an
automated cell counter (Bio-Rad, TC20). The viability of the cells was assessed at 95% of the
total number of cells after staining them with trypan blue (Thermo Fisher Scientific, Cat.
#15250061). For each reaction, 100,000 cells were collected and washed with PBS. The ATAC-
seq was performed using an ATAC-seq kit (Active Motif, Cat. # 53150) as described by the
manufacturer. Briefly, cells were resuspended in cold lysis buffer provided by the ATAC-seq kit
(Active Motif, Cat. # 53150) and centrifuged at 1000xg for 10 min at 4°C. The nuclei pellet was
resuspended in the tagmentation buffer provided by the ATAC-seq kit (Active Motif Cat. #
53150) and the reaction was incubated at 37°C for 30 min. Tagmented DNA was purified and
used to generate sequencing libraries following the manufacturer’s instructions. Libraries were
sequenced to a minimum depth of 25 million paired end reads with an NovaSeq 6000 sequencer
(Illumina).

Bioinformatics processing for ATAC-seq
Adapter trimming was performed using trim-galore in a paired-end mode to remove
Illumina adapter sequences. Sequencing reads were then aligned using bwa mem (v0.7.17-r1188)
to the hg38 human reference genome build. After the aligned .bam file was sorted and indexed
using samtools (v1.10), sambamba (v0.7.1) was utilized to sort, mark duplicate reads, and index
.bam files. Then, we filtered reads in the .bam files to produce a file that only contains reads that
are not PCR duplicates, with less than or equal to two mismatches, and with mapping quality that
is greater than or equal to 30. After indexing the .bam file, deeptools bamCoverage (v3.5.1) was
utilized to normalize the number of reads based on counts per million (CPM), and .bigWig files
were generated at a 1 bp resolution. Peak calling was performed as previously described (15),
using bedGraph2Cluster with the following parameters: fold change, 2; normalization, QNorm;
distance method, sqeuclidean; and clustering method, symmetry-collapsed profile and scalar.
Alternatively, MACS (v2.2.7.1) was utilized to call peaks.

Validation of RB1 mutation
For the validation of RB1 mutations, RBWT and RBKO hTERT-RPE1 cells were harvested
from culture, and approximately 5 million cells per sample were utilized. After the chromatin
was isolated from cells, the chromatin was digested with micrococcal nuclease. 1–2 ng MNase-
digested DNA samples were used to prepare sequencing libraries, using the Ovation Ultralow
System V2 kit (TECAN, Cat. # 0344NB-A01) and were subsequently sequenced using the
NovaSeq 6000 sequencer (Illumina) using the S4 flow cell. Adapter trimming was performed
using trim-galore in a paired-end mode to remove Illumina adapter sequences. Sequencing reads
were then aligned using bwa mem (v0.7.17-r1188) to the hg38 human reference genome build.
After the aligned .bam file was sorted and indexed using samtools (v1.10), sambamba (v0.7.1)
was utilized to sort, mark duplicate reads, and index .bam files. Then, we filtered reads in the
.bam files to produce a file that only contains reads that are not PCR duplicates, with less than or
equal to two mismatches, and with mapping quality that is greater than or equal to 30.

RNA-seq
hTERT-RPE1 cells after 6 d of contact inhibition or trypsinization were used for RNA-seq
experiments. The RNA-seq experiment for each experimental condition was performed in
biological triplicates. Total RNA was extracted from cells using the Direct-zol RNA MiniPrep
Plus kit (Zymo Research, Cat. # R2073). Multiplexed RNA sequencing libraries were prepared
using the TruSeq library prep kit (Illumina, Cat. # RS-122-2301/2), which utilizes Oligo-dT
beads to capture polyA tails. Paired-end 150 bp reads were collected by the NovaSeq 6000
sequencer (Illumina).

Bioinformatics processing for RNA-seq
Adapter trimming was performed using trim-galore in a paired-end mode to remove
Illumina adapter sequences. Sequencing reads were then aligned using STAR (v2.7.1a) to the
hg38 human reference genome build. A two-pass alignment pipeline was utilized, where the first
pass was performed using the sjdbOverhang of 149 and the sjdbGTFfile from Ensembl (v105)
and the second pass was performed using the SJ.out.tab files generated from the first pass
alignment. Separately, STAR-Fusion (v2.7.1a) was utilized to identify gene fusions using the
RNA-seq dataset. The .bam files generated by the STAR aligner were sorted and indexed via
samtools (v1.10) and were subsequently merged via samtools merge (v1.10) to create a single
file per experimental condition. Deeptools bamCoverage (v3.5.1) was then utilized to normalize
the number of reads based on counts per million (CPM), and .bigWig files were generated at a 1
bp resolution. The calculation of the expression level, on the other hand, was performed on
individual replicate experiments using the RSEM (v1.3.1) algorithm in a paired-end mode.
After RSEM calculation, the read count table was exported to R programming language (v4.3.0),
and DESeq2 (v1.40.2) was utilized to assess the differentially expressed genes. k-means clustering of
the RNA-seq expression profiles generated from this pipeline was utilized using a custom
MATLAB algorithm on the transcripts per million values calculated from RSEM (v1.3.1). The
custom code is available at our GitHub repository [https://github.com/hanjunlee21/Lee-et-al-
2023]. In brief, the k-means clustering was performed using a variation of the kmeans function of
MATLAB (vR2023a) and identified five major categories of transcriptional regulation in our
experiments. The gene set enrichment and gene ontology analyses were performed using Enrichr
and were done by providing the gene symbols generated from analyses, including the cohesin
gain and cohesin loss gene sets as well as the five transcriptional regulation categories.

Single-cell RNA-seq
RBWT and RBKO RPE1 cells arrested in G1 phase by 6 days of contact inhibition were
collected for single-cell RNA sequencing (scRNAseq). Cells were washed twice with RPMI
containing 10% heat-inactivated FCS, filtered through a 50 μm filter (Sysmex, Cat#04-004-2327),
followed by cell concentration and viability assessment using trypan blue with a Bright-Line
hemocytometer (Cat#1492), with both samples reaching 95% cell viability. Gene expression
libraries were constructed using the 10x Genomics GEM-X Universal 3’ Gene Expression v4
reagent kits (1000691, 1000690, and 1000215) according to the manufacturer's guidelines. Quality
control was assessed for both cDNA and gene-expression libraries using Qubit dsDNA High
Sensitivity Kit (Invitrogen, Cat#Q32854) and the Agilent High Sensitivity Bioanalyzer DNA Kit
(Cat#5067-4626). Both samples were sequenced on a single lane of an Illumina NovaSeq X 25B
flow-cell to yield a total of ~2 billion reads, using the following read configurations: Read 1- 28
cycles; Read 2- 90 cycles, and dual barcodes with 10 cycles each (i7 and i5 primers).

Proteomics
Cell pellets were processed as previously described75. Briefly, cell pellets were lysed in
500 µL of lysis buffer (75 mM NaCl, 3 % SDS, 1 mM NaF, 1 mM beta-glycerophosphate, 1 mM
sodium orthovanadate, 10 mM sodium pyrophosphate, 1 mM PMSF and 1X Roche Complete
Mini EDTA free protease inhibitors in 50 mM EPPS, pH 8.5). Proteins were then reduced with 5
mM DTT at 56C for 30 min and alkylated with 15 mM iodoacetamide in the dark at room
temperature for 20 min. The reaction was stopped by adding DTT (5 mM final concentration)
and incubated in the dark at room temperature for 15 minutes. Proteins were precipitated by
adding one part trichloroacetic acid to four parts protein solution, followed by 10 minutes of
incubation on ice. The precipitated proteins were pelleted by centrifugation (15,000 g, 10 min,
4°C) and washed twice with prechilled acetone (-20°C, 300 µL, 15,000 g, 10 min, 4°C). The
protein pellets were then resuspended in 1M urea in 50mM EPPS, ph8.5 and digested in a two-
step process (overnight digestion at room temperature with 1 µg/µL of Lys-C (Wako) followed
by six hours of digestion with trypsin (sequencing grade, Promega) at a final concentration of 1
ng/μL for 6 hr at 37 ˚C). The digest was acidified with 10 % trifluoroacetic acid (TFA) and
peptides were desalted on C18 solid-phase extraction (SPE) (Sep-Pak, Waters) columns. The
concentration of the desalted peptide solutions was measured with a BCA assay, and 25 ug of
peptides were aliquoted. The samples were dried under vacuum, and stored at -80 ˚C. The TMT
labeling was performed with 18-plex tandem mass tag (TMT) reagents (Thermo Scientific) as
previously described75. The 25 µg of peptide aliquotes were resuspended in 25 µL of 200 mM
EPPS (pH 8.5), 30% acetonitrile (ACN). Labeling was performed by adding 70 μg TMT reagent
in anhydrous ACN and incubating at room temperature for 1 h. The reaction was stopped by
addition of 5% (w/v) hydroxylamine in 200 mM EPPS (pH 8.5) to a final concentration of 0.5%
hydroxylamine and incubation at room temperature for 15 min. Samples were acidified with 1%
TFA, and samples were combined. The pooled samples were desalted using Sep- Pak C18 SPE
cartridges and vacuum dried. The multiplexed samples were fractionated using high-performance
liquid chromatography (HPLC) under basic pH, as previously described75, and dried. The dried
peptides were resuspended in 5 % ACN/5 % formic acid and analyzed in 3-hour runs via
reversed phase LC-MS2/MS3 on an Orbitrap Fusion mass spectrometer using the Simultaneous
Precursor Selection (SPS) supported MS3 method76. MS2 spectra were assigned using a
SEQUEST-based in-house built proteomics analysis platform using a target-decoy database-
based search strategy to assist filtering for a false-discovery rate (FDR) of protein identifications
of less than 1 %77. Peptides that matched to more than one protein were assigned to that protein
containing the largest number of matched redundant peptide sequences following the law of
parsimony. TMT reporter ion intensities were extracted from the MS3 spectra, selecting the most
intense ion within a 0.003-m/z window centered at the predicted m/z value for each reporter ion,
and spectra were used for quantification if the sum of the S/N values of all reporter ions divided
by the number of analyzed channels was ≥10 and the isolation specificity for the precursor ion
was ≥0.75. Protein intensities were calculated by summing the TMT reporter ions for all peptides
assigned to a protein. The full proteome data was normalized in a two-step process, a row
(protein) normalization followed by a column (sample) normalization. The row normalization
involved scaling the row intensity values by the global median of all row means divided by each
rows mean, adjusting each row mean to equal the global median. Similarly, the column
normalization involved scaling the column intensities so that each column median would be
equal the average of all sample medians. The proteome profiles were generated from
quadruplicate RBWT and RBKO RPE1 cell cultures arrested in G1 phase by 6 days of contact
inhibition. The normalized protein intensities were further processed with Perseus software
(Version 1.6.15.0)78. Identified proteins were compared for deregulation (FDR-threshold < 0.15
and log fold change > 0.7 or < 0.7 between samples) and visualized by using VolcaNoseR
package in R-studio.

Statistics & Reproducibility
All statistical analysis was performed using the OriginPro (v2023b) software. For
comparison of one group against a specific value, two-tailed one-sample Student’s t-test was
utilized (Fig. 2c). For comparison of a paired dataset, two-tailed paired Student’s t-test was
utilized (Fig. 1f). Comparisons of two groups were performed using a two-tailed two-sample
Student’s t-test (Figs. 1g, 2c, 2d, 2h, 5b, 5d, and 5e). To assess statistical significance of DNA
motif enrichment, HOMER79 algorithm’s default two-tailed hypergeometric test followed by a
Benjamini-Hochberg multiple hypotheses correction was utilized. Other enrichment analyses
were performed using a two-tailed Fisher’s exact test. No statistical method was used to
predetermine sample size, and no data were excluded from the analyses. The experiment were
not applicable for randomization, while differential analysis algorithms for Micro-C, HiChIP,
ChIP-seq, ATAC-seq, and RNA-seq experiments were blinded to the conditions. The calculated
statistical significance is indicated as ∗p-value<0.05; ∗∗p-value<0.01; and ∗∗∗p-value<0.001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "RB is a well-established cell cycle regulator whose canonical mechanism of action is to bind E2F transcription factors at the\npromoters of cell cycle genes and recruit chromatin regulatory complexes to suppress transcription. RB has been implicated\nin many other cellular functions beyond the cell cycle and E2F, however, including cellular differentiation and cancer lineage\ninfidelity. The mechanisms underlying such non-canonical functions are largely unknown. This manuscript investigates\nwhether RB influences chromatin looping dynamics and gene expression independent of E2F. This work extends the\ngroups prior Mol. Cell paper that used engineered immortalized cells to perform high quality RB Chip-seq to demonstrate Rb\nbinds enhancers and insulators independent of E2F, as well as promoters in an E2F dependent fashion. The current paper\nutilizes Micro-C to analyze the same engineered cell system to assess the impact of Rb on higher order chromatin structure.\nA key finding in this paper is that RB loss prevents normal removal of CTCF sites during mitosis. This results in an elevated\nnumber of cohesion-defined chromosome loops with stronger chromatin interactions within them. Another important finding\nis that RB-bound CTCF sites show increased chromatin accessibility, transcriptional activity, and stronger insulator\nboundary strength compared to CTCF sites not bound by RB. This suggests a novel transcriptional co-activator mechanism\nfor RB at these regions. Overall, this work represents an important and significant area of inquiry as it has potential to shift\nparadigms in understanding RB function and the impact of RB deficiency in cancer.\nThere are some conceptual and technical limitations, however, that should be addressed before publication in Nature\nCommunications. The authors demonstrate RB loss increases the number of chromosome loops and strengthens\ninteractions within those loops. To a first approximation this may be expected to increase the number of enhancer/promoter\ninteractions within a larger number of TADs, increasing the fraction of genes expressed per cell. A standard scRNA-seq\nanalysis could be used to test this. The data would also have the benefit of testing whether RB affects molecular\nheterogeneity between cells. Molecular heterogeneity could confound interpretation of peak calls in bulk Micro-C, Hi-C, and\nChIP-seq data if a larger number of potential genome interactions sites are spread across a larger fraction of the genome.\nThis could explain why the strength of H3K27ac HiChIP peaks is reduced upon RB loss despite a stronger H3K27ac ChIP-\nseq signal. Another issue that is not directly addressed is the stability of higher order chromatin structures across cell cycles.\nThis chromatin structure presumably needs to be recreated with some fidelity after every cell division cycle to maintain\nstable phenotypes. It would be interesting to know if RB loss decreases the fidelity of this process as the findings may be\nrelevant to important non-canonical RB functions like cancer lineage fidelity.\nAdditional Strengths:\n1) The generation and analysis of data is robust and well controlled, with exceptions noted below. The experimental\napproaches and methodology used are appropriate.\n2) The computational approaches for all omics analyses were sound and justified based on the methodology and\nhypotheses. Gold-standard chromatin conformation pipelines such as ‘Juicer/JuiceBox’, ‘cooltools’, ‘HiCCUPS’ were\ndescribed appropriately for reproducible analyses.\n3) Complementary RB knockout and RB activation models are employed in a non-transformed, genetically stable cell line so\nRB-dependent effects can be cleanly identified.\n4) Use of Micro-Capture technology, which provides nucleotide-resolution and higher sensitivity for chromatin interactions,\noffered a distinct advantage in detecting the subtle (but significant) genome-wide RB1-dependent chromatin organization\neffects while minimizing digestion bias which can sometimes hinder interpretations when other chromatin conformation\nassays are used. The use of Hi-ChIP (H3K27ac) is also appropriate for investigating RB’s impact on enhancer-promoter\ninteractions.\nAdditional Weaknesses:\n1) It would be nice to see the distribution of mapped sequencing reads across the genome to get a sense of how the MicroC,\nHiC, and ChIP signals are distributed in the presence and absence of RB.\n2) The authors claim that RB promotes removal of cohesion, a significant finding, but no complementary or orthogonal\nexperiments were performed to confirm this.\n3) The mechanisms by which RB removes cohesion or co-occupies SMC3 sites independent of E2F is unknown, but the\nauthors could speculate in the discussion section. Potential interactions with Wapl could also be discussed,\n4) The figures are difficult to interpret easily given their size and resolution. For example, it was hard to discern the subtle\ndifferences in TAD boundaries between RB KO and RB WT (Figure 1H and 2F).\n5) The descriptions provided in the figure legends are cursory, making it difficult to interpret figures without frequent\nconsultation of the main text.\n6) The deltaCDK RB activation experiments do not control for cell cycle phase distribution differences as far as I can tell. Pre\nand post Dox induction time points are compared, but only the Dox induced cell will undergo cell cycle arrest. This could be\ncontrolled by comparing confluent, G1 arrested Dox induced and Dox uninduced cells.\n7) While the engineered cell model is a strength, it is the only model employed so the generality of the findings are unclear.\nWhether findings are applicable in the more clinically relevant context of RB deficiency in cancer is unclear. Targeted\nexperiments to validate some key observations in other cell lines would be helpful.\n8) The manuscript would benefit from a clearer and more detailed graphical abstract which depicts the RB-dependent effects\non TAD boundaries, enhancer-promoter interactions, and gene expression. The current graphical abstract does not clearly\nconvey the high-level chromatin organizational changes that the paper is demonstrating.\n9) The gene set enrichment and functional analysis of EMT needs clarification. The PI focuses on 181 genes showing an\nincrease in SMC3 binding in the absence of RB. These genes are enriched for EMT genes and the PI provides functional\nevidence that RB loss causes EMT properties. However, the authors also conclude that RB depletion significantly\ndecreases expression of the “cohesion gain” gene set. These conclusions seem at odds with one another.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "In this manuscript, Lee et al. investigate Retinoblastoma protein (RB) dependent effects on chromatin and chromosome\norganization. They utilize comparisons between control and knock out cells as well as an exogenous expression system to\ninduce a non-phosphorylatable version of RB. They use chromosome conformation capture based methods to investigate\nchromosome organization in the different genotypes of cells under varying conditions related to RB\nphosphorylation or cell cycle position. The also undertake SMC3 ChIP-seq and H3K27ac HiChIP experiments to further elucidate RB status effects\non chromosome organization and dynamics. Analysis of these experiments is blended with previously reported data from the\nsame cell systems and organized into the present manuscript. The authors argue based on their data that RB status\ninfluences DNA and chromosome topology and in particular this effect reorganizes Cohesin complexes and further\ninfluences the ability of enhancer-promoter contacts to be made, ultimately altering gene expression patterns.\nThis was a difficult paper to review from a number of perspectives that prevent me from being enthusiastic about the\npublication of this study. In its present form it looks like an article from Plos One or Scientific Reports where data is reported\nfor the record, and future investigators may or may not see value in it, but the current take home message is unclear. I’ve\nlisted the biggest drawbacks to this study below to help the authors improve their work.\n1) The authors, for the most part, compare RB wild type status cells with knock out cells. The resulting molecular\ncharacteristics observed indicate that alterations take place in RB’s absence. It is an overinterpretation to assume that RB\nhas a deliberate regulatory role related to Cohesin positioning and chromatin binding when it is present. The title of the\npaper includes this error. It is just as likely that the data in this study captures random downstream consequences of RB loss\nas compared to a deliberate function as the authors hope. The model diagram in figure 5 indicates the authors conclusion of\nan ‘RB/cohesin pathway’, but there is no evidence that this pathway exists from data in this paper. Indirect influence of\nCohesin by RB status has also been reported by others previously, making this a very modest conceptual advancement.\n2) The paper in many places is an unconnected series of observations. Figure 1 is the best example of this as it attempts to\nlink different observations from exogenous non-phosphorylatable RB and RB deficiency. However, the staging of the\ndifferent RB status cells are starkly different either because of time point choices or manipulation by trypsinization and re-\nplating. The narrative of the results section that corresponds to this figure states a number of different changes at varying\nstages of the cell cycle, but the illustrations are more about experimental design than illustrating cell cycle consequences of\nRB status. The biggest admission from the authors that this figure is incoherent is panel I where they offer brief conclusions\nas they already expect that readers of their paper won’t come to these conclusions on their own from the data presented. The\nnarrative of the results indicates RB gain of function causes downstream alterations in heterochromatin structure, but the\nmicroscopy image in figure 1E offers a rather superficial look at gain of RB function in the regulation of heterochromatin and\nso I struggle to find a way to agree with the conclusions in panel I. In most figures the data displays appear to be mostly a\ncollection of comparisons that show statistically significant differences even though they don’t offer a singular conclusion.\nInstead the more I read on, the more I encountered seemingly random measurements that support the broad conclusion that\nchromatin structure changes when RB is lost. The figures also don’t relate well to one another as there is little reason to\nexpect that figures 1 and 2 lead to a need to understand Cohesin occupancy as a logical progression of the work in figure 3.\nMany other factors could have been profiled and found to be altered as well.\n3) In many places the author’s narrative in the results describe changes to chromatin structure and they describe a statistical\ntest and significance level to go with it. However, the data display panel that they cite along with it often shows little evidence\nof change due to RB status. In figure 1, panels D and H. Panel 2F, panels 3A and B, and 4B and C all contain genotype\ncomparisons that suffer from looking very similar, if not virtually identical. In some cases, very modest changes are illustrated\nby graphs such as figure 1G. Even if a statistically significant difference does exist in the cases of these displays, what does\nsuch a small difference mean? Are these subtle changes really meaningful in biological terms? Highly similar data in control\nand RB knock out makes the value of this study hard to appreciate.\n4) Somewhat related to point 3 and some earlier concerns, the manuscript largely lacks biological consequences to go with\nthe observations of chromatin structure. Subtle changes in chromatin structure may be important biologically, or they could\nbe largely inconsequential. I was surprised to read to the end and see that the authors really don’t have key biological\nrelevance for the consequence for Cohesin misregulation by RB loss. Given all of the biological consequences of RB loss\nthat are already published and RB’s key role in human cancer, the subtle change in adherence of cells to a culture dish and\nscratch assay closure are quite minimal functional experiments. Even in the case of these observations, does the data\npresented really indicate that altered Cohesin loops causes these effects compared to alterations in other aspects of RB\nfunction? Some gene categories that they discover in their analysis are interesting. RB loss has been shown to alter\nproperties of advanced cancer cells that are consistent with EMT and RB has also been documented to have a role in\ntranscriptional regulation of myogenesis. The opportunity to connect to more meaningful biological scenarios exists, but at\npresent the manuscript stops short of connecting the chromatin structure details to functions that yield a better understanding\nof RB.\nSome smaller, but also important issues are:\n5) The scratch wound experiment either contains an error or it doesn’t represent what the authors hope. The Y-axis is a\nmeasure of closure of the scratch (presumably through cell migration). However, the control cells display measurements that\nexceed 100%. Does this mean that control cells migrate in the opposite direction? Without these outlier and counterintuitive\ndata points included, the statistically significant difference with RB knock out would likely be lost. Something is wrong with\nthis display/experiment.\n6) The authors offer a comparison with Wapl effects on Cohesin (SMC3) levels. ‘Notably, these results resembled the\nchanges observed with cohesin accumulation at TAD boundaries following Wapl depletion (ref 47), albeit at a weaker\nmagnitude.’ Without a side-by-side comparison experiment look to look at Wapl depletion, statements like this have little\nvalue. This is but one example of many descriptive connections between the observations from their data and the literature\nthat are very much opinion and not relevant to a results section.\n7) Most figures contain unreadable text and numbers embedded in them. Sometimes these are labels, sometimes\nmeasurements or statistical values. I’m not sure if it is important to be able to read them. Are they already stated in the\nnarrative of the results? Do I need to read them? This deficiency adds to the lack of clarity.", "reviewer_4": "The manuscript by Lee et al homes in on the dynamics of chromatin structure and its role in shaping the expressivity of the\ngenome. Specifically, it addresses the regulation of the interplay between chromatin-bound CTCF and the cohesin complex\nassigned roles in the partitioning of the genome into chromatin domains. Having previously focused on the inner workings of\nthe tumor repressor protein RB1, the authors perceived that there could be a non-canonical role for this factor going beyond\nits well-known function of repressing E2F- dependent functions. To deal with the E2F-dependent noise provided by this\npivotal function in cell cycle progression, the team resorted to extensive Chip-seq and Micro-C analyses of cells carrying\ninducible mutant RB1 expression vectors. The key outcome is the documentation that RB1 functions to antagonize the\nchromatin-bound CTCF-cohesin complexes to remodel the partitioning of a subset of TAD domains and that\nphosphorylation of RB1 controls this process. Importantly, it describes a painstaking strategy to uncover that CTCF-cohesin\ncomplexes are stabilized in the absence of an RB1 function and that RB1 functions to release the cohesion complex from\nchromatin-bound CTCF specifically in prometaphase cells. It is of significant interest that chromatin loops tend to get smaller\nand more numerous in RB1-deficient cells. The authors have adopted heroic efforts to highlight that these outcomes are\ndifferent from the canonical RB1 function that serves to repress E2F-dependent gene expression. Taken together, the data\nsupports the view that a subset of chromatin domains is rewired in the absence of an RB1 function in a non-canonical\nmanner. I was also intrigued by the discussion that RB1 might control also the role of the condensin complex and that the\nabsence of an RB1 function might generate chromosome instability analogous to its role in the CTCF-cohesin complex. The\nobservations of the manuscript have obvious implications for cancer in instances where the RB1 function is perturbed,\ngenetically or otherwise. The surprising outcome of this reasoning is, however, that the plasticity of the genome function to\nenable adaptation to changing environments might be compromised.\nTechnically, the manuscript describes cutting edge approaches that include a balanced consideration of alternative\nexplanations. The experiments appear well executed and with appropriate controls.\nTo sum up, this is an excellent although dense report, which provides yet another surprising twist to the regulation of the\nCTCF function. It provides a much needed insight into the dynamics of the formation of chromatin domains that underlie the\nfunction of the genome. However, there are a number of loose ends, listed below, that the authors might wish to consider.\n• It is not crystal clear to me why the Extended data Figs S5-S7 are treated so summarily. Although I appreciate the space\nlimit of the manuscript format it makes little sense to include this data without explanations.\n• I lack an insightful discussion about how the absence of an RB1 function might affect cancer evolution. Although loss of\nRB1 function occurs late in cancer development as alluded to by the authors, its absence would be expected to\nfreeze/stabilize chromatin interactions underlying the expressivity of the genome running counter to the general view that\ncancer cells evolve by readily adapting to a changing environment. Even though this is not a topic of the manuscript,\nits observations have strong implications on this topic. For example, the authors could consider to analyze single cell RNA-seq\ndata to explore the non-canonical function of RB1 even though I realize that the space of the manuscript format limits such\noptions.\n• On a similar note, I lack a few sentences discussing the observation that genes encoding factors involved in the EMT\nprocess are strongly overrepresented in two of the non-canonical clusters. This is especially relevant as RB1 has been\nimplicated to control this process, which likely underlies the ability of cancer cells to establish metastatic niches.\n• In the manuscript it is stated “RB depletion reduced the strength of H3K27ac HiChIP peaks across all three interaction 256\ngroups despite slightly stronger H3K27ac ChIP-seq signal (Fig. 4c and Extended Data Fig. 15).” I lack a sentence or two\nexplaining this discrepancy.\nRolf Ohlsson"}, "author_response": "Point-by-point response to the critiques\nWe sincerely thank the reviewers for their thoughtful and constructive feedback, which has significantly\nimproved the clarity and overall quality of the manuscript, and helped us better highlight the novel aspects\nof RB’s non-canonical activity at chromatin insulators. Our point-by-point responses to the reviewers’\ncomments are provided below:\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nRB is a well-established cell cycle regulator whose canonical mechanism of action is to bind E2F\ntranscription factors at the promoters of cell cycle genes and recruit chromatin regulatory complexes\nto suppress transcription. RB has been implicated in many other cellular functions beyond the cell\ncycle and E2F, however, including cellular differentiation and cancer lineage infidelity. The\nmechanisms underlying such non-canonical functions are largely unknown. This manuscript\ninvestigates whether RB influences chromatin looping dynamics and gene expression independent of\nE2F. This work extends the groups prior Mol. Cell paper that used engineered immortalized cells to\nperform high quality RB Chip-seq to demonstrate Rb binds enhancers and insulators independent of\nE2F, as well as promoters in an E2F dependent fashion. The current paper utilizes Micro-C to analyze\nthe same engineered cell system to assess the impact of Rb on higher order chromatin structure. A\nkey finding in this paper is that RB loss prevents normal removal of CTCF sites during mitosis. This\nresults in an elevated number of cohesion-defined chromosome loops with stronger chromatin\ninteractions within them. Another important finding is that RB-bound CTCF sites show increased\nchromatin accessibility, transcriptional activity, and stronger insulator boundary strength compared\nto CTCF sites not bound by RB. This suggests a novel transcriptional co-activator mechanism for RB\nat these regions. Overall, this work represents an important and significant area of inquiry as it has\npotential to shift paradigms in understanding RB function and the impact of RB deficiency in cancer.\nThere are some conceptual and technical limitations, however, that should be addressed before\npublication in Nature Communications. The authors demonstrate RB loss increases the number\nof chromosome loops and strengthens interactions within those loops. To a first approximation this\nmay be expected to increase the number of enhancer/promoter interactions within a larger number\nof TADs, increasing the fraction of genes expressed per cell. A standard scRNA-seq analysis could\nbe used to test this. The data would also have the benefit of testing whether RB affects molecular\nheterogeneity between cells. Molecular heterogeneity could confound interpretation of peak calls in\nbulk Micro-C, Hi-C, and ChIP-seq data if a larger number of potential genome interactions sites are\nspread across a larger fraction of the genome. This could explain why the strength of H3K27ac\nHiChIP peaks is reduced upon RB loss despite a stronger H3K27ac ChIP-seq signal.\nAnother issue that is not directly addressed is the stability of higher order chromatin structures across\ncell cycles. This chromatin structure presumably needs to be recreated with some fidelity after every\ncell division cycle to maintain stable phenotypes. It would be interesting to know if RB loss\ndecreases the fidelity of this process as the findings may be relevant to important non-canonical RB\nfunctions like cancer lineage fidelity.\nWe thank Reviewer 1 for recognizing the significance of our study in advancing the understanding\n1 of 19\nof the non-canonical, E2F-independent functions of RB. We especially appreciate their\nacknowledgment that this is the first report to describe a transcriptional co-activator role for RB,\nrepresenting a paradigm shift in the field.\nAs the reviewer correctly noted, our analysis of the Micro-C data demonstrated that RB loss increases\nthe number of the cohesin-dependent chromosome loops and strengthens the interactions within\nTADs. Importantly, our H3K27ac HiChIP revealed that these changes are associated with a reduction\nin enhancer-promoter interactions. Given that RB loss promotes the gain of cohesin binding and\nincreased insulation at these loci, we interpret the reduction in enhancer-promoter interactions as a\nconsequence of the increased chromatin insulation, as illustrated in the model presented in Figure 5.\nWe agree with the reviewer that a limitation of our approach is that bulk Micro-C, Hi-C, and ChIP-\nseq data may mask potential cell-to-cell heterogeneity in RB’s effects on chromatin organization. To\naddress this limitation, we performed single-cell RNA-seq (scRNA-seq) analysis in RB wild-type\nand RB knockout cells synchronized in the G1 phase.\nThe scRNA-seq experiment corroborated non-canonical transcriptional activation by RB, but it did\nnot reveal additional distinct transcriptional profiles at the single-cell level that would support RB’s\neffect on heterogeneity. Principal component analysis showed that all cells clustered together, with a\nmodest separation between RB wild-type and knockout cells, primarily driven, as expected, by\ndifferences in RB/E2F target gene expression – cluster 3 (Fig. R1). Notably, the scRNA-seq analysis\nvalidated the differential expression of cluster 5 genes, where RB functions as a transcriptional\nactivator during G1 (Fig. R1D). Cluster 5 genes exhibit cohesin gain at insulators upon RB loss,\nwhich correlates with reduced expression in RB-deficient cells. In addition, we observed that RB loss\nnot only reduces the expression of cluster 5 genes but also reduces the number of genes expressed\nper cell upon normalization with UMI (Fig. R1B). This is in line with our H3K27ac HiChIP results\nwhich showed that RB loss leads to decreases in enhancer-promoter interactions. We present the\nscRNA-seq analysis in the new Extended Data Fig. 25 and discuss these findings in the revised\nmanuscript in the paragraph “RB enhances the non-E2F transcriptional program” (page 8, line 39 to\npage 9, line 5). “To further investigate whether these RB-associated transcriptional changes occur\npopulation-wide or are driven by a specific cell population, we performed single-cell RNA-seq\n(scRNA-seq) experiments in RBWT and RBKO hTERT-RPE1 cells synchronized in the G1 phase via\ncontact inhibition. Principal component analysis revealed that all cells clustered together, with a\nmodest separation between RBWT and RBKO cells, primarily driven, as expected, by differences in\nRB/E2F target gene expression – cluster 3 (Extended Data Fig. 25). Notably, the scRNA-seq analysis\nvalidated the differential expression of cluster 5 genes, where RB functions as a transcriptional\nactivator during G1 (Extended Data Fig. 25). While the scRNA-seq experiment corroborated non-\ncanonical transcriptional activation by RB, it did not reveal additional distinct transcriptional profiles\nat the single-cell level that would support RB’s effect on cellular heterogeneity.”\nWe would also like to acknowledge and thank the reviewer for their insightful comment regarding\nthe stability of higher-order chromatin structures across the cell cycle in RB knockout cells. This is\nindeed an important and intriguing area of investigation, particularly in the context of understanding\nRB’s role in maintaining cell lineage fidelity. Our data indicate that the altered chromatin distribution\nof cohesin upon RB loss is rescued by the cell cycle progression since the differential binding of\nSMC3 at chromatin insulators resolves during the S-phase (Fig. 3B). However, to determine precisely\nwhether these cell cycle-dependent changes in cohesin distribution result in more persistent defects\nin higher-order chromatin architecture, additional Micro-C profiling at multiple time points following\nacute RB loss would be required. While this question lies beyond the scope of the current manuscript\n2 of 19\nand we couldn’t answer it within the time frame of this revision process, we agree it represents an\nimportant direction for future studies. We now discuss this point in the second paragraph of the\nrevised Discussion (page 10, lines 14-19), stating that “Notably, our data indicate that the altered\nchromatin distribution of cohesin upon RB loss is resolved during cell cycle progression, as the\ndifferential binding of SMC3 at chromatin insulators diminishes in S phase. Future studies employing\nMicro-C profiling at multiple time points following acute RB loss may answer whether the cell cycle\nand RB-dependent changes in cohesin distribution result in more persistent defects in higher-order\nchromatin architecture.”\nFigure R1. Single cell RNA sequencing of G1-arrested RBWT and RBKO hTERT-RPE1 cells further validates\nthe non-canonical RB transcriptional activation, without detecting distinct transcriptional profiles at the\nsingle-cell level A, principal component analysis (PCA) of single-cell transcriptomes from RBWT and RBKO hTERT-\nRPE1 cells, arrested in G1 by 6 days of contact inhibition. Each dot on the plot represents an individual cell, colored\naccording to its genotype; RBWT (cyan) and RBKO (red). The plot visualizes the principal components (PC1 and PC2)\nthat capture the greatest variance within the dataset. Values on the axes after the principal components indicate\nexplained variance. B, quantification of unique gene counts per cell in G1 arrested RBWT and RBKO hTERT-RPE1\ncells reveals that RBKO cells exhibit fewer number of genes per cell compared to RBWT. Gene counts were calculated\nby Unique Molecular Identifiers (UMI) and normalized to account for differences in sequencing depth. Box plot\nshows the distribution of the number of unique genes detected per cell in RBWT (cyan) and RBKO (red) hTERT-RPE1\ncells. Horizontal lines indicate mean values. Statistical significance was determined by a two-tailed Student’s t-test,\n3 of 19\np<0.0001****. C and D, average expression levels of cluster 3 and cluster 5 genes in G1 arrested RBWT and RBKO\nhTERT-RPE1 cells reveal that upon RB loss, cluster 3 genes get upregulated while cluster 5 genes get downregulated.\nBox plots showing the average normalized expression levels of genes belonging to cluster 3 (C) or cluster 5 (D) in\nRBWT (cyan) and RBKO (red) cells. Median is indicated by the horizontal lines. Statistical significance was determined\nby a two-tailed Student’s t-test, p<0.0001****. E, average expression levels of cluster 3 and cluster 5 genes reveal\nmodest transcriptional shifts in RBKO G1 arrested hTERT-RPE1 cells. Scatter plot visualizes the single-cell\nrelationship between the average expression scores for genes within cluster 3 (x axis) and cluster 5 (y axis). Each dot\nrepresents an individual cell, colored by genotype; pink for RBWT and maroon for RBKO. A modest, but statistically\nsignificant negative Spearman correlation (ρ= -0.25, p<0.0001****) is observed between clusters 3 and 5 scores\nacross total cells population, indicated by the dashed trend line. The plot annotates the inverse transcriptional\nregulation of these two gene sets (cluster 3 and cluster 5) at the single-cell level.\nAdditional Strengths:\n1) The generation and analysis of data is robust and well controlled, with exceptions noted\nbelow. The experimental approaches and methodology used are appropriate.\n2) The computational approaches for all omics analyses were sound and justified based on the\nmethodology and hypotheses. Gold-standard chromatin conformation pipelines such as\n‘Juicer/JuiceBox’, ‘cooltools’, ‘HiCCUPS’ were described appropriately for reproducible\nanalyses.\n3) Complementary RB knockout and RB activation models are employed in a non-transformed,\ngenetically stable cell line so RB-dependent effects can be cleanly identified.\n4) Use of Micro-Capture technology, which provides nucleotide-resolution and higher sensitivity\nfor chromatin interactions, offered a distinct advantage in detecting the subtle (but significant)\ngenome-wide RB1-dependent chromatin organization effects while minimizing digestion bias which\ncan sometimes hinder interpretations when other chromatin conformation assays are used. The use\nof Hi-ChIP (H3K27ac) is also appropriate for investigating RB’s impact on enhancer- promoter\ninteractions.\nWe thank the reviewer for highlighting the additional strengths of our study.\nAdditional Weaknesses:\n1) It would be nice to see the distribution of mapped sequencing reads across the genome to get a\nsense of how the MicroC, HiC, and ChIP signals are distributed in the presence and absence of RB.\nIn Fig. R2, we present the distribution of mapped sequencing reads from Micro-C and ChIP-seq\nexperiments across chromosome 11 and the representative genomic region in chromosome 11\ndescribed in the main Fig. 3h. Overall, we did not observe significant differences in sequencing\ncoverage across experiments. However, in the absence of RB, we observed specific enrichment of\ncohesin at insulators at regions of differential chromatin loops, supporting our observation that RB\nloss increases insulator activity.\n4 of 19\nFigure R2. Overview of sequencing coverage for Micro-C and SMC3 ChIP-seq experiments across\nchromosome 11. Genome browser tracks display the normalized sequencing coverage (Counts Per Million, CPM)\nfor various experiments (as indicated) across a broad region of human chromosome 11. Red arrows indicate the\nregions where significant changes in chromatin loop formation are observed upon RB loss (Micro-C contact maps\nare not shown here). Overall sequencing coverage is comparable across experimental conditions (upper panel).\nHowever, SMC3 Chip-seq tracks reveal differential enrichment patterns at specific genomic loci (lower panel, red\narrows).\n2) The authors claim that RB promotes removal of cohesion, a significant finding, but no\ncomplementary or orthogonal experiments were performed to confirm this.\nWe thank the reviewer for the suggestion to add complementary and orthogonal experiments. In the\nrevised manuscript, we performed additional experiments to corroborate RB’s role in cohesin\ndistribution, chromatin organization, and transcriptional regulation. More specifically:\n1. While the redistribution of SMC3, a core component of the cohesin complex, suggested cohesin\nalterations upon RB loss, additional data on other cohesin subunits would strengthen our\nconclusions. In the revised manuscript, we have included ChIP-seq data for two additional\nproteins essential for cohesin function, STAG1 and STAG2. In Figure R3, we present the SMC3,\nSTAG1, and STAG2 ChIP-seq tracks at the representative genomic region in chromosome 11\ndescribed in Fig. 3h. We observed stronger STAG1 signals compared to STAG2, in line with\nprevious reports that STAG2 is primarily associated with sister chromatin cohesion. Notably, we\nobserved redistribution of STAG1 upon RB loss at regions of differential chromatin loop\n5 of 19\nformation, supporting the model that RB loss leads to increased cohesin binding at insulators.\nThis redistribution was observed genome-wide across insulators (Fig. R3B). The new STAG1\nChIP-seq data are shown in the revised Figure 3 h and i.\n2. To confirm that the observed RB-mediated transcriptional regulation is due to the enrichment of\ncohesin at insulators and not the differences in cohesin expression, we performed proteomics\nexperiment and confirmed that protein levels of the cohesin complex components SMC3,\nSMC1A, PDS5B, PDS5A, RAD21, WAPL, STAG1, and STAG2 were comparable between\nRBWT and RBKO cells (Extended Data Fig. 11).\n3. We also performed complementary SMC3 ChIP-quantitative PCR experiment to confirm the\nincreased SMC3 binding upon RB loss at the locus of differential chromatin loop formation\ndescribed in Fig. 3h.\nWe discuss these new data in the revised text on page 7, lines 11-14: “Inspection of individual regions\nin chromosomes 9 and 11 revealed visually striking gains in chromatin contacts and showed that the\nformation of differential loops in RBKO is driven by an increase in cohesin signal at insulators,\nincluding SMC3 and STAG1 (Figs. 3h and i). Additional SMC3 ChIP-quantitative PCR further\nconfirmed the increase of SMC3 binding at chromosome 9 locus following RB loss”, and page 5,\nlines 22-31: “To rule out the possibility that the observed increase is an indirect effect of RB loss, we\ncompared the proteomes of RB wild-type and RB knockout cells and found no significant differences\nin the protein levels of SMC3 or other core components of the cohesin complex (Extended Data Fig.\n11).”\n6 of 19\nFigure R3. SMC3, STAG1 and STAG2 ChIP-seq reveal redistribution of STAG1 upon RB loss. A, STAG1\nand STAG2 ChIP-seq tracks across region of differential chromatin loop formation in chr11. Genome browser\ntracks display the normalized sequencing coverage (Counts Per Million, CPM) for various experiments (as\nindicated) across a broad region of human chromosome 11. Red arrows indicate regions where significant changes\nin chromatin loop formation are observed upon RB loss (Micro-C contact maps, not show here). Both SMC3 and\nSTAG1 exhibit redistribution upon RB loss in distinct genomic loci (red arrows), indicating that RB loss enriches\nthe cohesin complex at insulators. B, Aggregate plots showing the average STAG1 ChIP-seq signal across indicated\ngenomic features (Promoter, Insulator, Enhancer) in G1 arrested RBWT (grey) and RBKO (maroon) hTERT-RPE1\ncells. The x-axis represents genomic distance centered at the respective feature and the y-axis represents normalized\nChIP-seq signal. The plots reveal a distinct increase of STAG1 enrichment at insulator regions upon RB loss.\n7 of 19\nFigure R4. ChIP-qPCR confirms differential SMC3 recruitment to locus of chromatin loop gain at\nchromosome 9. ChIP-qPCR experiment was performed at the site of differential chromatin loop formation in\nchromosome 9 identified in Fig. 3i. Primer pairs targeting the SMC3-bound locus was used to probe SMC3 binding\nat the locus, while primer pairs targeting the non-targeting locus 500 bp away from the SMC3-bound locus were\nutilized as the negative control. ChIP-qPCR signal for SMC3 increased upon RB loss, corroborating our ChIP-seq\nand Micro-C results (not shown here). Statistical significance was determined by one-way ANOVA, p<0.05*.\n4. In addition, we performed orthogonal assays to corroborate reduced enhancer-promoter\ninteraction upon RB loss. To a first approximation, reduced enhancer-promoter interactions\nobserved in H3K27ac HiChIP experiment should lead to reduced mediator complex recruitment\nto enhancers and promoters. We therefore performed MED15 ChIP-seq on RBWT and RBKO\nhTERT-RPE1 cells and confirmed reduced MED15 signal specifically at promoters and\nenhancers (Figure R5). The new MED15 ChIP-seq data are shown in the revised Figure 4d.\nWe discuss these new data in the revised text, page 7, lines 33-34: “As an orthogonal approach, we\nperformed MED15 ChIP-seq to assess mediator recruitment and observed reduced MED15\noccupancy at enhancers and promoters following RB loss (Fig. 4d).”\nFigure R5. MED15 recruitment to promoters and enhancers is reduced upon RB loss. Aggregate plots\nshowing the average MED15 ChIP-seq signal across indicated genomic features (Promoter, Insulator, Enhancer)\nin G1 arrested RBWT (grey) and RBKO (red) hTERT-RPE1 cells. The x-axis represents genomic distance centered\nat the respective feature and the y-axis represents normalized ChIP-seq signal. The plots reveal a distinct decrease\nof MED15 enrichment at promoter and enhancer regions upon RB loss, in line with H3K27ac HiChIP data (Fig.\n4b).\n8 of 19\n3) The mechanisms by which RB removes cohesion or co-occupies SMC3 sites independent of E2F\nis unknown, but the authors could speculate in the discussion section. Potential interactions with\nWapl could also be discussed,\nWe have expanded our discussion to include this point on page 11, lines 3-5, noting that “Although\nRB and SMC3 co-occupy the same chromatin loci, the presence of RB is associated with reduced\ncohesin binding. This supports the hypothesis that RB may promote cohesin removal at TAD\nboundaries during prophase by facilitating the recruitment of Wapl.”\n4) The figures are difficult to interpret easily given their size and resolution. For example, it was hard\nto discern the subtle differences in TAD boundaries between RB KO and RB WT (Figure 1H and\n2F).\nWe have increased the size and resolution of Figures 1h and 2f to more clearly highlight the\ndifferences described in the text.\n5) The descriptions provided in the figure legends are cursory, making it difficult to interpret figures\nwithout frequent consultation of the main text.\nWe have revised the figure legends to make them more descriptive.\n6) The deltaCDK RB activation experiments do not control for cell cycle phase distribution\ndifferences as far as I can tell. Pre and post Dox induction time points are compared, but only the\nDox induced cell will undergo cell cycle arrest. This could be controlled by comparing confluent,\nG1 arrested Dox induced and Dox uninduced cells.\nThe goal of this experiment was to compare cells treated with DOX for 24 hours versus 72 hours,\nboth of which are arrested in G1. The 24-hour treatment induces a reversible G1 arrest, whereas the\n72-hour treatment leads to a non-reversible G1 arrest associated with chromatin dispersion. We agree\nwith the reviewer that differences observed between pre- and post-DOX induction time points may\nresult from RB∆CDK-induced cell cycle arrest. We have revised the text on page 3, lines 33-35, to\nclarify this point, stating that “Although changes occurred between 0 h and 24 h or 72 h of RB∆CDK\nexpression may be associated with G1-arrest, most compartmental changes occurred between 24 h\nand 72 h of RB∆CDK expression, coinciding with the irreversible cell cycle exit, as previously\ndescribed.”\n7) While the engineered cell model is a strength, it is the only model employed so the generality of\nthe findings are unclear. Whether findings are applicable in the more clinically relevant context of\nRB deficiency in cancer is unclear. Targeted experiments to validate some key observations in other\ncell lines would be helpful.\nWe appreciate the reviewer’s comment that the role of RB in regulating chromatin conformation\nwould have greater clinical relevance if examined specifically in the context of RB-deficient cancer\ncells. However, this study represents the first demonstration of a novel role for RB in higher-order\nchromatin organization, and extending these findings broadly across cancer models is beyond the\nscope of the current manuscript.\n9 of 19\nTo strengthen our conclusions and validate key observations, we have included the following new\ndata in the revised manuscript:\ni) SMC3 ChIP-seq in RB wild-type and RB knockout cells synchronized in prometaphase using\ninhibition of Eg5 kinesin activity, as an alternative to nocodazole-induced arrest (Fig. R6).\nNotably, SMC3 accumulation at chromatin insulators upon RB loss is observed regardless of\nthe synchronization method or RB knockout clones used. These new data have been shown in\nthe revised Extended Data Fig. 12.\nFigure R6. Prometaphase arrest by Eg5 inhibition reveals SMC3 redistribution toward insulators upon RB\nloss. Aggregate plots showing the average SMC3 ChIP-seq signal across indicated genomic features (Promoter,\nInsulator, Enhancer) in prometaphase arrested RBWT (grey) and RBKO (red) hTERT-RPE1 cells. Prometaphase\narrest was performed by Eg5 inhibition. The x-axis represents genomic distance centered at the respective feature\nand the y-axis represents normalized ChIP-seq signal. The plots reveal a distinct increase of SMC3 enrichment at\ninsulator regions upon RB loss, similar to the results obtained by nocodazole-induced prometaphase arrest (Fig.\n3b).\nii) MED15 ChIP-seq in RB wild-type and RB knockout cells synchronized in G1 (Fig. R5). The\nobserved reduction in mediator complex binding at promoters and enhancers upon RB loss\nsupports the enhancer-promoter interaction defects identified by H3K27ac HiChIP.\nWe discuss these new data in the revised text at page 6, lines 2-5: “SMC3 ChIP-seq in RBWT and\nRBKO hTERT-RPE1 cells synchronized in prometaphase using inhibition of Eg5 kinesin activity, as\nan alternative to nocodazole-induced arrest, similarly revealed cohesin enrichment at insulators\nfollowing RB loss (Extended Data Fig. 12)”, and page 7, lines 33-35: “As an orthogonal approach, we\nperformed MED15 ChIP-seq to assess mediator recruitment and observed reduced MED15\noccupancy at enhancers and promoters following RB loss (Fig. 4d).”\nTo extend our analysis to additional normal immortalized cell lines, we generated genetic knockouts\nof RB in hTERT-BJ1 fibroblasts. However, we were unable to obtain high-quality ChIP-seq or\nCUT&RUN data, largely due to the limited number of mitotically arrested cells. In Figure R7, we\npresent a CUT&RUN experiment we performed in RBWT and RBKO hTERT-BJ1 cells following\nprometaphase arrest via Eg5 inhibition.\n10 of 19\nFigure R7. SMC3 CUT&RUN experiment in hTERT-BJ1 cells yielded very low signal-to-noise ratio.\nGenome browser tracks display the normalized sequencing coverage (Counts Per Million, CPM) for mitotically\narrested RBWT and RBKO hTERT-BJ1 cells across a broad region of human chromosome 9 (top) and 11 (bottom).\nBoth experiments yielded very low signal-to-noise ratio, due to a small fraction of cells arrested in prometaphase.\n8) The manuscript would benefit from a clearer and more detailed graphical abstract which depicts\nthe RB-dependent effects on TAD boundaries, enhancer-promoter interactions, and gene expression.\nThe current graphical abstract does not clearly convey the high-level chromatin organizational\nchanges that the paper is demonstrating.\nWe have revised Figure 5 to illustrate the RB-dependent effects on chromatin insulation and E/P\ninteractions.\n9) The gene set enrichment and functional analysis of EMT needs clarification. The PI focuses on\n181 genes showing an increase in SMC3 binding in the absence of RB. These genes are enriched for\nEMT genes and the PI provides functional evidence that RB loss causes EMT properties. However,\nthe authors also conclude that RB depletion significantly decreases expression of the “cohesion gain”\ngene set. These conclusions seem at odds with one another.\nWe apologize for not explaining this more clearly in the original text. While the gene set is enriched\nfor EMT-related genes, the majority are involved in maintaining epithelial characteristics, including\ngenes associated with extracellular matrix organization and cell adhesion. We have revised the text\non page 8, line 12, to clarify this distinction: “…genes regulating epithelial-mesenchymal transition\nby maintaining epithelial characteristics…”\nReviewer #1 (Remarks on code availability):\nMy co-reviewer reviewed the code and felt it appropriate based on standards in the field. The paper\nmakes use of publicly available code and thus is generally available to the community.\nWe thank the co-reviewer for taking the time to review the code used for our data analysis and its\navailability.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\n11 of 19\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nIn this manuscript, Lee et al. investigate Retinoblastoma protein (RB) dependent effects on\nchromatin and chromosome organization. They utilize comparisons between control and knock\nout cells as well as an exogenous expression system to induce a non-phosphorylatable version of\nRB. They use chromosome conformation capture based methods to investigate chromosome\norganization in the different genotypes of cells under varying conditions related to RB\nphosphorylation or cell cycle position. The also undertake SMC3 ChIP-seq and H3K27ac\nHiChIP experiments to further elucidate RB status effects on chromosome organization and\ndynamics. Analysis of these experiments is blended with previously reported data from the same\ncell systems and organized into the present manuscript. The authors argue based on their data\nthat RB status influences DNA and chromosome topology and in particular this effect\nreorganizes Cohesin complexes and further influences the ability of enhancer-promoter contacts\nto be made, ultimately altering gene expression patterns.\nThis was a difficult paper to review from a number of perspectives that prevent me from being\nenthusiastic about the publication of this study. In its present form it looks like an article from Plos\nOne or Scientific Reports where data is reported for the record, and future investigators may or\nmay not see value in it, but the current takefsc home message is unclear. I’ve listed the biggest\ndrawbacks to this study below to help the authors improve their work.\nWe thank the reviewer for their time and effort in reviewing our manuscript. We appreciate the\nspecific concerns raised and have carefully considered each of them, as discussed below. However,\nwe respectfully disagree with the comment that our manuscript presents data without drawing\nsignificant or novel conclusions. This study provides the first evidence that RB and cohesin co-\noccupy specific genomic regions and that RB loss leads to enrichment of cohesin at insulators.\nOur\nMicro-C data, representing the first such dataset generated in RB-deficient cells, demonstrate that\nRB-dependent regulation of cohesin alters chromatin insulation, disrupts enhancer-promoter\ninteractions, and reveals a previously unrecognized mechanism by which RB acts as a transcriptional\nactivator. Given the mechanistic insights provided and the novelty of the findings, we believe that\nthis manuscript will be of significant interest to the scientific community.\n1) The authors, for the most part, compare RB wild type status cells with knock out cells. The\nresulting molecular characteristics observed indicate that alterations take place in RB’s absence.\nIt is an overinterpretation to assume that RB has a deliberate regulatory role related to Cohesin\npositioning and chromatin binding when it is present. The title of the paper includes this error. It\nis just as likely that the data in this study captures random downstream consequences of RB loss\nas compared to a deliberate function as the authors hope. The model diagram in figure 5 indicates\nthe authors conclusion of an ‘RB/cohesin pathway’, but there is no evidence that this pathway exists\nfrom data in this paper. Indirect influence of Cohesin by RB status has also been reported by\nothers previously, making this a very modest conceptual advancement.\nWhile our study provides for the first time a comprehensive analysis of the chromatin architecture of\n12 of 19\nRB wild-type and RB knockout cells, we agree with the reviewer that these models cannot sufficiently\ndistinguish direct from indirect regulatory effects. We have performed several new orthogonal assays\nto further strengthen our conclusions, moderated the language throughout the manuscript and the title,\nand revised Figure 5 to incorporate the reviewer’s suggestions. Specifically, in the revised figure we\nremoved the section on the “RB/cohesin pathway” and focused on the RB-dependent changes in\nTADs, enhancer-promoter interactions, and gene expression, observations that are directly supported\nby our experiments. We believe that the revised schematic provides a more thorough and\nscientifically accurate interpretation of our findings and proposed model. We also revised the title of\nthe manuscript according to the reviewer’s suggestion. The revised version of the manuscript now\nhighlights the effect of RB loss: “RB loss modulates chromatin organization by regulating\ncohesin-dependent loops and enhancer-promoter interactions”. In addition, we performed the\nfollowing orthogonal assays to corroborate our observations:\ni) ChIP-seq experiments on other cohesin subunits (STAG1 and STAG2). We show, in the\nrevised manuscript, that most SMC3 binding sites are co-occupyied by STAG1 and that the\neffect of RB loss on SMC3 is also observed in STAG1 (Fig. 3h-i and Fig. R3). RB loss leads\nto redistribution of both SMC3 and STAG1 towards insulators, supporting the interpretation\nthat RB loss alters the chromatin localization of the cohesin complex.\nii) SMC3 ChIP-seq experiments after different prometaphase arrest protocols (Eg5\ninhibition). While we previously showed that RB loss has the strongest effect on insulators\nduring prometaphase, the generalizability of this observation was limited as only the\nnocodazole-induced arrest was tested. In the revised manuscript, we utilize Eg5 inhibitor as\nan alternative and orthogonal method of arresting RB wild-type and RB knockout cells in\nprometaphase. SMC3 ChIP-seq experiments after Eg5 inhibitor-induced prometaphase arrest\nshow that the effect of RB loss is the strongest in prometaphase cells, regardless of the\nsynchronization method, and that RB loss leads to significant enrichment of cohesin towards\ninsulators (Extended Data Fig. 12).\niii) MED15 ChIP-seq experiments to validate enhancer-promoter interaction. While our\nH3K27ac HiChIP data suggested that RB loss led to reduced enhancer-promoter interaction,\nwhich correlated with our Micro-C, ChIP-seq, and RNA-seq experiments, the limitation of\nthis experiment has been the lack of orthogonal assays. As a first approximation, reduced\nenhancer-promoter interaction should lead to reduced mediator complex enrichment in\nenhancers and promoters. To test this, we performed MED15 ChIP-seq experiments and found\nthat RB loss indeed leads to reduced mediator occupancy at enhancers and promoters (Fig.\n4d). Together with the H3K27ac HiChIP results (Fig. 4a-c), these data support the\ninterpretation that RB loss leads to reduced enhancer-promoter interactions.\niv) SMC3 ChIP-qPCR on sites of differential chromatin contact. For the region of differential\nchromatin contact presented in Fig. 3i, we further performed SMC3 ChIP-qPCR experiments\nand validated the SMC3 enrichment upon RB loss.\nv) Proteomics analysis of cohesin subunits upon RB loss. We performed proteomics analysis\nin G1-arrested RB wild-type and RB knockout cells in biological quadruplicates and found\nthat none of the cohesin comlex components, including SMC3, SMC1A, PDS5B, PDS5A,\nRAD21, WAPL, STAG1, and STAG2, exhibited statistically significant changes upon RB\nloss (Extended Data Fig. 11). This new analysis confirm that RB does not significantly alter\nthe expression or stability of cohesin subunits, supporting the conclusion that RB directly\ninfluences cohesin association with chromatin. This data, combined with our observation that\nthe effect of RB loss on cohesin is specific to insulators and is the strongest in the mitotic\nphase, highlights the specificity of this deregulatory mechanism and argues against a\n13 of 19\nnonspecific effect. We have revised the text on page 5, lines 22-32, to reflect and clarify these\npoints: “To rule out the possibility that the observed increase is an indirect effect of RB loss,\nwe compared the proteomes of RBWT and RBKO cells and found no significant differences in\nthe protein levels of SMC3 or other core components of the cohesin complex (Extended Data\nFig. 11). To exclude the possibility that the observed changes resulted from normalization\nartifacts, we utilized spike-in DNA from Escherichia coli as additional control and confirmed\nthe increase in SMC3 signal at insulators (Fig. 3a). Notably, we did not observe analogous\nincreases in SMC3 signal at promoters (+2.8% upon RB loss; n = 4) or enhancers (-3.4% upon\nRB loss; n = 4), further supporting the specificity of RB’s regulatory role at insulator sites.”\n2) The paper in many places is an unconnected series of observations. Figure 1 is the best example\nof this as it attempts to link different observations from exogenous non- phosphorylatable RB and\nRB deficiency. However, the staging of the different RB status cells are starkly different either\nbecause of time point choices or manipulation by trypsinization and re- plating. The narrative of the\nresults section that corresponds to this figure states a number of different changes at varying stages\nof the cell cycle, but the illustrations are more about experimental design than illustrating cell cycle\nconsequences of RB status. The biggest admission from the authors that this figure is incoherent is\npanel I where they offer brief conclusions as they already expect that readers of their paper won’t\ncome to these conclusions on their own from the data presented. The narrative of the results\nindicates RB gain of function causes downstream alterations in heterochromatin structure, but the\nmicroscopy image in figure 1E offers a rather superficial look at gain of RB function in the regulation\nof heterochromatin and so I struggle to find a way to agree with the conclusions in panel I. In most\nfigures the data displays appear to be mostly a collection of comparisons that show statistically\nsignificant differences even though they don’t offer a singular conclusion. Instead the more I read\non, the more I encountered seemingly random measurements that support the broad conclusion that\nchromatin structure changes when RB is lost. The figures also don’t relate well to one another as\nthere is little reason to expect that figures 1 and 2 lead to a need to understand Cohesin occupancy\nas a logical progression of the work in figure 3. Many other factors could have been profiled and\nfound to be altered as well.\nThe overarching goal of Fig. 1 was to compare the effects of RB loss versus gain of non-\nphosphorylated RB (RB∆CDK) in the same cell type, focusing specifically on chromatin structural\nchanges across distinct cell cycle stages. These models were chosen to provide complementary\ninsight: RB knockout cells represent a chronic RB-deficient state, while RB∆CDK expression allows\nfor temporally controlled RB activation which, depending on the activation time, can cause G1 arrest\nor trigger cell cycle exit. While the experimental systems differ in setup, we used cell cycle\nsynchronization to mitigate confounding variables. We now clarify these details in the revised text\n(page 3, lines 2-6) to help readers more easily follow the logic and biological relevance of each\ncomparison: “We conducted two complementary sets of experiments in human retinal pigment\nepithelial cells immortalized by telomerase reverse transcriptase (hTERT1-RPE1) (Figs. 1a and b) to\ncompare the effects of (1) chronic RB deficient state with (2) conditional gain of constitutively active\nRB. Cell cycle synchronization was used to minimize potential confounding variables between the\ntwo experimental systems.”\nRegarding the microscopy image in Figure 1e, this panel was intended as a validation step to\ndemonstrate that our observations on RB∆CDK-induced chromatin changes are consistent with\npreviously published findings of heterochromatin dispersion after 72 hours of RB∆CDK expression\n(Krishnan et al. 2022). We have clarified this point in the revised text (page 3, line 37): “…concordant\nwith the previously described heterochromatin dispersion phenotype (Fig. 1e).\n14 of 19\nWith respect to Figure 1i, we acknowledge the reviewer’s concern and agree that clearer integration\nof the data is warranted. To improve the narrative flow, we have rewritten this section (page 4, lines\n12-15) to guide the reader more explicitly through the results and their interpretation: “In summary,\nwe observe that RB exerts broad effects on chromatin organization (Fig. 1i), with RB∆CDK\nexpression promoting primarily heterochromatin spreading and dispersion (Figs. 1d–e), and RB loss\nin G1-arrested cells altering cohesin-dependent loop number, loop length, and TAD boundary\nstrength (Figs. 1g–h).”\nRegarding the progression to cohesin occupancy in Figure 3, we respectfully note that this direction\nwas motivated by two key observations: (1) our Micro-C analysis in Figure 1 revealed increased\nchromatin loop formation and stronger TAD boundaries upon RB loss, features typically associated\nwith cohesin activity, and (2) our ChIP-seq data in Figure 2 showed extensive co-localization between\nchromatin-bound RB and SMC3. Together, these findings naturally led us to investigate whether RB\nregulates cohesin localization directly. We now emphasize this connection more explicitly in the\nrevised text (page 5, lines 15-17): “The extensive co-localization of chromatin-bound RB and SMC3,\nalong with the observed impact of RB loss on cohesin-dependent loop formation and TAD boundary\nstrength, prompted us to investigate RB’s role in cohesin localization.”\nWhile we acknowledge that many chromatin-associated factors could have been explored, we\nprioritized cohesin due to its direct involvement in the architectural features highlighted by our Micro-\nC data. Moreover, our previous studies have shown that RB depletion does not alter CTCF association\nwith chromatin (Sanidas et al. 2022), further supporting our focus on cohesin as a specific mediator\nof RB’s effect on chromatin architecture.\n3) In many places the author’s narrative in the results describe changes to chromatin structure and\nthey describe a statistical test and significance level to go with it. However, the data display panel\nthat they cite along with it often shows little evidence of change due to RB status. In figure 1, panels\nD and H. Panel 2F, panels 3A and B, and 4B and C all contain genotype comparisons that suffer\nfrom looking very similar, if not virtually identical. In some cases, very modest changes are illustrated\nby graphs such as figure 1G. Even if a statistically significant difference does exist in the cases of these displays, what does\nsuch a small difference mean? Are these subtle changes really meaningful in biological terms? Highly similar data in control\nand RB knock out makes the value of this study hard to appreciate.\nThe chromatin-associated changes presented in the figures mentioned by the reviewer reflect\naggregate signals across the genome. Because these represent averaged differences over thousands\nof loci, changes may appear modest but are statistically significant and biologically consistent.\nImportantly, when we focused on subsets of loci most strongly regulated by RB, such as the top-\nranked insulators in Fig. 3g, or selected genomic domains (Figs. 3h–i), the differences were\nconsiderably stronger. In the revised manuscript, we observe that RB loss leads to a strong effect in\nH3K27ac HiChIP as well as in MED15 ChIP-seq (Fig 4a-d). Through these experiments, we\nsignificantly strengthened our argument that RB loss leads to reduced enhancer-promoter\ninteractions. We also observed strong enrichment of cohesin at insulators upon RB loss in\nprometaphase cells (Fig. 3b, +56% across n=21,498 insulators), and we reproduced these findings\nwhen we tested different knockout clones as well as using different prometaphase arrest methods\n(Extended Data Fig. 12). Subtle alterations in enhancer-promoter proximity or domain insulation can\nsignificantly influence transcriptional output (e.g., Xiao JY et al., 2021, PMID: 34240703). This\nprinciple is reflected in our data as well. In Figure 1g, for example, while the change in loop length\nappears modest, it reflects a consistent trend across the genome that correlates with increased loop\nformation (Fig. 1f) and changes in TAD boundary strength (Fig. 1h), both of which are characteristic\n15 of 19\nof cohesin-mediated chromatin organization. We also observe that in prometaphase cells, where we\nobserve the strongest differences upon RB loss (+56% across n=21,498 insulators), the differential\nenrichment of cohesin at these loci is reproduced, further corroborating our interpretation. Similarly,\nin Figure 4, panels B and C, although the overall changes in contact enrichment between enhancers\nand promoters may appear modest, they were consistently observed across biological replicates. In\nthe revised manuscript, these changes are further supported by corresponding reductions in Mediator\ncomplex enrichment following RB loss at the same loci (Figure 4D), highlighting the functional\nrelevance of these architectural shifts.\n4) Somewhat related to point 3 and some earlier concerns, the manuscript largely lacks biological\nconsequences to go with the observations of chromatin structure. Subtle changes in chromatin\nstructure may be important biologically, or they could be largely inconsequential. I was surprised to\nread to the end and see that the authors really don’t have key biological relevance for the consequence\nfor Cohesin misregulation by RB loss. Given all of the biological consequences of RB loss that are\nalready published and RB’s key role in human cancer, the subtle change in adherence of cells to a culture dish and\nscratch assay closure are quite minimal functional experiments. Even in the case of these observations, does the data\npresented really indicate that altered Cohesin loops causes these effects compared to alterations in other aspects of RB\nfunction? Some gene categories that they discover in their analysis are interesting. RB loss has been shown to alter\nproperties of advanced cancer cells that are consistent with EMT and RB has also been documented to have a role in\ntranscriptional regulation of myogenesis. The opportunity to connect to more meaningful biological scenarios exists, but at\npresent the manuscript stops short of connecting the chromatin structure details to functions that yield a better understanding\nof RB.\nWe agree that connecting chromatin structure changes to downstream biological consequences is a\ncritical next step in understanding the broader significance of RB-mediated regulation. While the\nprimary focus of our study was on uncovering a novel role for RB in modulating higher-order\nchromatin organization through cohesin redistribution, we recognize that the functional implications\nof these structural changes, particularly in disease-relevant contexts, require further investigation.\nImportantly, our transcriptomic analyses revealed that the genes most strongly regulated by RB and\ncohesin are involved in processes such as EMT and extracellular matrix organization. In this initial\nstudy, we chose to explore two phenotypes: 1) cell adhesion and 2) wound-healing, as proof-of-\nconcept readouts for these signatures. However, we acknowledge that these phenotypes are modest\nand do not fully capture the breadth of RB’s biological impact. We now clarify this in the revised text\n(page 8, lines 21-24) by adding the phrase: “While these phenotypes do not represent comprehensive\nfunctional validation of RB-regulated chromatin architecture, these cell cycle-independent effects of\nRB may be a good starting point for connecting architectural chromatin changes to cellular behavior\nof RB-deficient cells.”\nSome smaller, but also important issues are:\n5) The scratch wound experiment either contains an error or it doesn’t represent what the authors\nhope. The Y-axis is a measure of closure of the scratch (presumably through cell migration).\nHowever, the control cells display measurements that exceed 100%. Does this mean that control\ncells migrate in the opposite direction? Without these outlier and counterintuitive data points\nincluded, the statistically significant difference with RB knock out would likely be lost. Something\nis wrong with this display/experiment.\nThe scores in this graph represent changes relative to the average initial wound length in control cells\nat time 0 (n = 8). Because the initial wound sizes varied across samples, some wound measurements\n16 of 19\nat 48 hours, particularly in RBWT cells, fall above 100%, reflecting cases where the wound length\nremained greater than the average baseline. To prevent any confusion, we recalculated the change in\nthe wound length relative to time = 0 for each individual experiment. As a result, all values now fall\nbelow 100%. We have clarified this point in the revised Figure 4i, the revised extended data Figure\n22, and the corresponding figure legends.\n6) The authors offer a comparison with Wapl effects on Cohesin (SMC3) levels. ‘Notably, these\nresults resembled the changes observed with cohesin accumulation at TAD boundaries following\nWapl depletion (ref 47), albeit at a weaker magnitude.’ Without a side-by-side comparison\nexperiment look to look at Wapl depletion, statements like this have little value. This is but one\nexample of many descriptive connections between the observations from their data and the literature\nthat are very much opinion and not relevant to a results section.\nWe are grateful to the reviewer for this suggestion. Our intention was not to imply equivalence\nbetween RB loss and WAPL depletion but rather to provide contextual insight by noting that the\npattern of cohesin accumulation at TAD boundaries upon RB loss qualitatively resembles previously\npublished findings in the WAPL depletion setting (Ref. 47). To avoid potential overinterpretation,\nwe have revised the relevant sentence and moved it from the Results to the Discussion section (page\n10, lines 13-14), where it is now presented as a contextual comparison rather than part of the primary\ndata interpretation: “The changes in cohesin occupancy upon RB loss qualitatively resemble the\naccumulation of cohesin at TAD boundaries reported in WAPL-depleted cells47, albeit at a weaker\nmagnitude.”\n7) Most figures contain unreadable text and numbers embedded in them. Sometimes these are\nlabels, sometimes measurements or statistical values. I’m not sure if it is important to be able to\nread them. Are they already stated in the narrative of the results? Do I need to read them? This\ndeficiency adds to the lack of clarity.\nWe acknowledge that in the original submission, some figure panels contained embedded text, labels,\nor statistical values that were difficult to read due to font size or resolution. To improve readability,\nwe have now provided high-resolution figures. In addition, we have ensured that all key numerical\nvalues and statistical results are explicitly described in the corresponding figure legends and/or the\nmain text so that the information remains accessible even if not visible directly in the figure panels.\nWe hope these revisions significantly improve the clarity and interpretability of the figures.\nReviewer #4 (Remarks to the Author):\nThe manuscript by Lee et al homes in on the dynamics of chromatin structure and its role in shaping\nthe expressivity of the genome. Specifically, it addresses the regulation of the interplay between\nchromatin-bound CTCF and the cohesin complex assigned roles in the partitioning of the genome\ninto chromatin domains. Having previously focused on the inner workings of the tumor repressor\nprotein RB1, the authors perceived that there could be a non-canonical role for this factor going\nbeyond its well-known function of repressing E2F- dependent functions. To deal with the E2F-\ndependent noise provided by this pivotal function in cell cycle progression, the team resorted to\nextensive Chip-seq and Micro-C analyses of cells carrying inducible mutant RB1 expression\nvectors. The key outcome is the documentation that RB1 functions to antagonize the chromatin-\nbound CTCF-cohesin complexes to remodel the partitioning of a subset of TAD domains and that\nphosphorylation of RB1 controls this process. Importantly, it describes a painstaking strategy to\n17 of 19\nuncover that CTCF-cohesin complexes are stabilized in the absence of an RB1 function and that\nRB1 functions to release the cohesion complex from chromatin-bound CTCF specifically in\nprometaphase cells. It is of significant interest that chromatin loops tend to get smaller and more\nnumerous in RB1-deficient cells. The authors have adopted heroic efforts to highlight that these\noutcomes are different from the canonical RB1 function that serves to repress E2F-dependent gene\nexpression. Taken together, the data supports the view that a subset of chromatin domains is\nrewired in the absence of an RB1 function in a non-canonical manner. I was also intrigued by\nthe discussion that RB1 might control also the role of the condensin complex and that the\nabsence of an RB1 function might generate chromosome instability analogous to its role in the CTCF-\ncohesin complex. The observations of the manuscript have obvious implications for cancer in\ninstances where the RB1 function is perturbed, genetically or otherwise. The surprising outcome\nof this reasoning is, however, that the plasticity of the genome function to enable adaptation to\nchanging environments might be compromised.\nTechnically, the manuscript describes cutting edge approaches that include a balanced\nconsideration of alternative explanations. The experiments appear well executed and with\nappropriate controls.\nTo sum up, this is an excellent although dense report, which provides yet another surprising twist\nto the regulation of the CTCF function. It provides a much needed insight into the dynamics of\nthe formation of chromatin domains that underlie the function of the genome. However, there are\na number of loose ends, listed below, that the authors might wish to consider.\nWe thank the reviewer for highlighting the significance of our manuscript in advancing the\nunderstanding of how chromatin-bound CTCF–cohesin complexes and a subset of TAD domains are\nregulated by the cell cycle machinery, and how this regulation may be linked to the non-canonical,\nE2F-independent transcriptional activity of RB.\n• It is not crystal clear to me why the Extended data Figs S5-S7 are treated so summarily.\nAlthough I appreciate the space limit of the manuscript format it makes little sense to include\nthis data without explanations.\nWe thank the reviewer for pointing this out. The reason we did not include a discussion of the analysis\npresented in Extended Data Figs. 5-7 in the main text was solely due to word count limitations. This\nanalysis was designed to estimate the probability of observing specific chromatin architectures based\non our Micro-C data. It confirmed the high quality of our dataset, as we were able to reconstruct\nthree-dimensional chromatin organization with confidence. Notably, the analysis also revealed\npotential differences in the spatial arrangement of acrocentric chromosomes in RB knockout cells.\nTo address this, we have added the following text to the main text on page 4, lines 5-9: “Furthermore,\nthree-dimensional reconstruction of the chromatin architecture via a cell population-based algorithm\nrevealed that RB loss weakens the spatial clustering of acrocentric chromosomes harboring nucleolus\norganizer regions (NORs). Specifically, the average distance between centromeres of acrocentric\nchromosomes grew from ~1.81 µm in RBWT to ~2.52 µm in RBKO (Extended Data Figs. 5–7).”\n• I lack an insightful discussion about how the absence of an RB1 function might affect cancer\nevolution. Although loss of RB1 function occurs late in cancer development as alluded to by\nthe authors, its absence would be expected to\nfreeze/stabilize chromatin interactions underlying the expressivity of the genome running counter to the general view that\ncancer cells evolve by readily adapting to a changing environment. Even though this is not a topic of the manuscript,\nits observations have strong implications on this topic. For example, the authors could consider\nto analyze single cell RNA-seq\ndata to explore the non-canonical function of RB1 even though I realize that the space of the manuscript format limits such\noptions.\nWe agree with the reviewer that this is an important and timely topic for discussion. Recent studies\nhave demonstrated that 3D chromatin reorganization is associated with neuroendocrine\ntransformation in castration-resistant prostate cancer (Lu X. et al., 2024, PMID: 39677680), a process\nthat is also linked to RB1 inactivation (Ku et al. and Mu et al., 2017). To explore whether chromatin\nreorganization following RB loss contributes to cell-to-cell heterogeneity or shifts in lineage\nspecificity, we performed single-cell RNA-seq (scRNA-seq) analysis in RB wild-type and RB\nknockout cells synchronized in the G1 phase. While this analysis confirmed the transcriptional\ndifferences in RB-deregulated genes reported in Figure 4, it did not reveal distinct subpopulations or\ntranscriptional heterogeneity at the single-cell level (Fig. R1; Extended Data Fig. 25; see also\nresponse to Reviewer 1). We acknowledge, however, that in genetically altered cancer models or\nunder therapeutic pressure, we cannot rule out the effect of RB loss on cell-to-cell heterogeneity. We\nnow discuss this point in the revised manuscript on page 10, lines 28-35: “Recent findings showing\nthat 3D chromatin reorganization is linked to neuroendocrine transformation in castration-resistant\nprostate cancer64 suggest that RB’s role in regulating chromatin insulators may contribute to cell-to-\ncell heterogeneity and drive tumor progression. Although our scRNA-seq analysis in RPE1 RBWT\nand RBKO cells did not reveal such heterogeneity, we acknowledge that in genetically altered cancer\nmodels or under therapeutic pressure, these effects could become more pronounced. We appreciate\nthat RB’s involvement in chromatin architecture and its impact on enhancer-promoter interactions\nopens new avenues for exploring its role in the activation of lineage-specific programs seen in lung\nand prostate cancers following RB loss14,15,17-19.”\n• On a similar note, I lack a few sentences discussing the observation that genes encoding factors\ninvolved in the EMT process are strongly overrepresented in two of the non-canonical clusters.\nThis is especially relevant as RB1 has been\nimplicated to control this process, which likely underlies the ability of cancer cells to establish metastatic niches.\nWe thank the reviewer for pointing this out. To address this, we have added the following statement\nto the Discussion on page 10, lines 35-38: “In addition, the newly identified role of RB in regulating\nenhancer-promoter interactions and activating genes involved in maintaining epithelial cell identity\noffers a novel mechanistic perspective on how RB loss may facilitate the ability of cancer cells to\nestablish metastatic niches65,66.”\n• In the manuscript it is stated “RB depletion reduced the strength of H3K27ac HiChIP peaks\nacross all three interaction 256 groups despite slightly stronger H3K27ac ChIP-seq signal\n(Fig.4c and Extended Data Fig. 15).” I lack a sentence or two explaining this discrepancy.\nWe rephrased this sentence to clarify the point on page 7, lines 30-32, as follows: “Although RB1\nknockout cells display slightly increased H3K27ac ChIP-seq signal compared to RB1 wild-type RPE1\ncells, consistent with transcriptional de-repression23, RB depletion reduced the strength of H3K27ac\nHiChIP peaks across all three interaction groups (Fig. 4c and Extended Data Fig. 15).”\n19 of 19"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Revised Manuscript NCOMMS-24-71231A: RB loss modulates chromatin organization by regulating cohesion-dependent\nloops and enhancer-promoter interactions.\nManuscript Summary:\nThis revised manuscript includes additional experimental data and text/figure revisions, including the title, to strengthen the\nrigor of the conclusions drawn and the clarity of the presentation. Key additional data buttressing the conclusions include\nscRNA-seq, STAG1/2 ChIP-seq, MED15 ChIP-seq, proteomics, and quantitative ChIP-PCR. These changes have\nadequately addressed nearly all the issues we raised on prior review. One experiment was attempted to replicate some of\nthe findings in an additional cell line model, as requested, but this experiment did not yield interpretable results. Given the\ntechnical challenges associated with these types of experiments, evidenced by the relative lack of equivalent data in the\npublished literature, I don’t think this should preclude publication of the current manuscript. I support publication of the\ncurrent version of the manuscript in Nature Communication, perhaps with minor revisions listed below.\nIn my opinion the work should be published for several key reasons. This is one of the first papers to address the impact of\nRB on higher order chromatin structure like chromosome looping. A key finding is that RB loss increases the number of\ncohesin-defined chromosome loops and their insulating activity. Given increased interest in non-canonical functions of RB in\ncancer, like phenotypic plasticity, the work presented in this manuscript is an early step in trying to elucidate the molecular\nbasis of such non-canonical functions, even though cancer models are not deployed to generate the data. The data does\nprovocatively align with observations in the cancer plasticity field where RB loss induces an EMT as an intermediate plastic\nstate that evolves into lineage variants, suggesting RB’s impact on higher order chromatin structure may be involved. The\ndata presented is of high quality, especially in light of the difficulty the field has had in generating good RB ChIP-seq data\nwith available antibodies. It has been observed for some time that RB can activate gene expression in addition to its\ncanonical role in gene expression repression. The molecular basis of RB induced gene expression is not well established.\nThis paper provides data to rigorously confirm RB activated gene expression and provides a potential mechanism. Overall,\nthe work presented is important and potentially paradigm shifting.\nMinor issues that could be addressed at the editor’s or author’s discretion:\n1) It would be nice to see UMAPs of the scRNA-seq data, perhaps color coded by the transcriptional clusters identified as\nwell as the number of genes/UMI metric.\n2) The authors focus the insulator discussion on those separating promoters and enhancers, rightfully given the data they\nare interpreting. However, insulators also insulate from encroaching heterochromatin. The authors may want to speculate in\nthe discussion how the increased number and size of chromatin loops upon RB loss may affect the organization of\nheterochromatin.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "The revised manuscript is an improved version of an already excellent study. Given the space limits the authors have done a\ngood job responding to my concerns.", "reviewer_5": "Retinoblasma protein is encoded by a major tumor suppressor able to repress cell cycle genes through mechanisms by\nwhich RB associates with E2F to counteract its role in gene activation. Mutations in CDKs regulating RB association with\nE2F have been associated with tumorigenesis due to such E2F regulation, though additional phenotypes often developing\nin association with RB dysfunction, like sister chromatin cohesion defects, may be due to other reasons than E2F. Here, the\nauthors find a novel role of RB that regulates, on top of E2F, cohesin removal from CTCF insulators. Accordingly, upon\nRB mutations cohesin subunit binding is stabilized at CTCF sites, and probing long-range interactions support a stronger\ninsulation genome-wide. Moreover, long-range contacts engaging enhancers with promoters are perturbed, while CTCF\ninsulation increases. The authors report additional deregulation of genes, in particular for those with no RB in their promoter.\nThey propose a model whereby by regulating cohesin binding, RB may regulate insulation thereby allowing to activate\ngenes involving enhancer-promoter interactions.\nThe paper provides lots of interesting data that, altogether, support a model by which RB controls cohesin binding at CTCF\nsites. Given the major role of cohesin on higher-order chromatin organization, there could be many reasons by which this\nimpedes on gene expression. The proposed model linking the increased insulation with the increasing levels in gene\nexpression is not clearly demonstrated. Although genomic analyses show a tendency for genes being activated to be\nassociated with increased insulation, it is difficult to interpret the data due to the heterogenous nature of the genomic\ncontexts.\nMajor points\n1. In figure 4c, the defects in enhancer-promoter interactions (HiChiP of H3K27ac) should be shown as a function of cohesin\naccumulation at the insulator site that separate E-P’s (as in Fig 4f).\n2. Whereas the split between RB+E2F+ from RB-E2F- is probably of use to analyze the data, there may be many reasons for\ntheir relative change in RNA expression upon RBkd and the comparison is thus not insightful to demonstrate the mechanism\nby which RB functions(Fig 4f). Rather, a more direct enhancer-promoter assay with an intervening insulator would allow a\nmore straightforward comparison.\n3. The defects in B compartment seen for RBcdk (Figure 1d) should be shown upon RBkd. The real question is whether\ngene expression defects (increasing expression levels) could also be the consequence of the defects in B compartments.\n4. The impact of RB-kd should also be shown as a function of the cohesin loss, for panels Fig 4a, 4d.\n5. Boxplot with p-value should be added to test the significance of the changes in binding profiles (fig3a,3b, 4d). Otherwise,\nthere is a risk of looking at the effect of outliers."}, "author_response": "Point-by-point response to the critiques\nWe sincerely thank the reviewers for their thoughtful and constructive feedback, which has\nsignificantly improved the clarity and overall quality of the manuscript. Our point-by-point responses\nto the reviewers’ comments are provided below:\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nRevised Manuscript: RB loss modulates chromatin organization by regulating cohesion-dependent\nloops and enhancer-promoter interactions.\nManuscript Summary:\nThis revised manuscript includes additional experimental data and text/figure revisions, including the\ntitle, to strengthen the rigor of the conclusions drawn and the clarity of the presentation. Key\nadditional data buttressing the conclusions include scRNA-seq, STAG1/2 ChIP-seq, MED15 ChIP-\nseq, proteomics, and quantitative ChIP-PCR. These changes have adequately addressed nearly all the\nissues we raised on prior review. One experiment was attempted to replicate some of the findings in\nan additional cell line model, as requested, but this experiment did not yield interpretable results.\nGiven the technical challenges associated with these types of experiments, evidenced by the relative\nlack of equivalent data in the published literature, I don’t think this should preclude publication of\nthe current manuscript. I support publication of the current version of the manuscript in Nature\nCommunication, perhaps with minor revisions listed below.\nIn my opinion the work should be published for several key reasons. This is one of the first papers to\naddress the impact of RB on higher order chromatin structure like chromosome looping. A key\nfinding is that RB loss increases the number of cohesin-defined chromosome loops and their\ninsulating activity. Given increased interest in non-canonical functions of RB in cancer, like\nphenotypic plasticity, the work presented in this manuscript is an early step in trying to elucidate the\nmolecular basis of such non-canonical functions, even though cancer models are not deployed to\ngenerate the data. The data does provocatively align with observations in the cancer plasticity field\nwhere RB loss induces an EMT as an intermediate plastic state that evolves into lineage variants,\nsuggesting RB’s impact on higher order chromatin structure may be involved. The data presented is\nof high quality, especially in light of the difficulty the field has had in generating good RB ChIP-seq\ndata with available antibodies. It has been observed for some time that RB can activate gene\nexpression in addition to its canonical role in gene expression repression. The molecular basis of RB\ninduced gene expression is not well established. This paper provides data to rigorously confirm RB\nactivated gene expression and provides a potential mechanism. Overall, the work presented is\nimportant and potentially paradigm shifting.\nWe are grateful to the reviewer for the comments and suggestions.\nMinor issues that could be addressed at the editor’s or author’s discretion:\n1) It would be nice to see UMAPs of the scRNA-seq data, perhaps color coded by the transcriptional\nclusters identified as well as the number of genes/UMI metric.\n1 of 8\nIn the revised manuscript, we have added Extended Data Fig. 27, which presents UMAP\nvisualizations of the scRNA-seq dataset in RBWT and RBKO hTERT-RPE1 cells. To identify whether\nthe expression of each transcriptional cluster is enhanced or repressed by RB, we have color-coded\neach dots based on the genotype (RBWT vs RBKO) and based on the average expression of genes in\neach transcriptional cluster (Figure R1). Overall, our analysis reveals clearer separation of RBWT and\nRBKO hTERT-RPE1 cells and confirms that cluster 5 genes, which is linked to non-canonical\ntranscriptional activation by RB, are preferentially expressed in RBWT cells. In contrast, cluster 3\ngenes, which correspond to the canonical RB/E2F-pathway target genes, are preferentially expressed\nin RBKO cells.\nFigure R1. UMAP visualization of single-cell RNA sequencing of G1-arrested RBWT and RBKO hTERT-RPE1\ncells corroborates differential transcriptional regulation by RB.\nUniform Manifold Approximation and Projection (UMAP) visualization of single-cell transcriptomes from RBWT\nand RBKO hTERT-RPE1 cells, arrested in G1 by 6 days of contact inhibition. Each dot on the plot represents an\nindividual cell. Top left panel is colored according to its genotype: RBWT (cyan) and RBKO (magenta). Average\nexpression levels of clusters 1–5 genes in G1 arrested RBWT and RBKO hTERT-RPE1 cells reveal that cluster 2 and\ncluster 5 gene expression are enhanced in RBWT hTERT-RPE1 cells while cluster 3 gene expression is repressed in\nRBWT hTERT-RPE1 cells. The plot further corroborates the inverse transcriptional regulation of cluster 3 and\ncluster 5 gene sets at the single-cell level.\n2) The authors focus the insulator discussion on those separating promoters and enhancers, rightfully\ngiven the data they are interpreting. However, insulators also insulate from encroaching\nheterochromatin. The authors may want to speculate in the discussion how the increased number and\nsize of chromatin loops upon RB loss may affect the organization of heterochromatin.\nOur chromosome conformation capture data reveal a clear distinction between the effects of transiently\nactive RB in the G1-phase and constitutively active RB∆CDK expression on higher-order chromatin\norganization. In G1, RB modulates cohesin-dependent loop formation in a transient manner that is\nrestored upon S-phase entry. In contrast, sustained RB∆CDK expression induces persistent alterations in\n2 of 8\nnuclear compartments accompanied by heterochromatin dispersion, likely representing an irreversible\nprocess. These findings suggest that the changes in TAD boundary insulation observed upon RB\ndepletion are unlikely to drive compartmental reorganization. This conclusion is consistent with prior\nwork showing that acute cohesin degradation abolishes loop domains but does not disrupt nuclear\ncompartmentalization (PMID: 28985562). Instead, the heterochromatin alterations triggered by\nconstitutive RB∆CDK activity appear independent of cohesin-mediated looping and may arise from the\nformation of far-cis contacts between nuclear compartments (Fig. 1D) and likely involve RB∆CDK-\nmediated recruitment of heterochromatin factors, including HP1, SUV39H1, and DNMT1 (PMID:\n11484059, 17245754).\nWe discuss this observation in the revised discussion page 10, lines 19-28: “In our chromosome\nconformation capture experiments, RB loss led to increased insulation at TAD boundaries and\ndecreased enhancer-promoter interactions without significantly altering the composition or interactions\nwithin nuclear compartments. This observation is consistent with previous work showing that acute\ncohesin degradation abolishes loop domains but does not disrupt nuclear compartmentalization63.\nFurthermore, RB-mediated changes in cohesin-dependent loop formation observed in Micro-C\nexperiments were transient and restored upon S-phase entry, suggesting that it is not likely to be related\nto RB’s role in epigenetic alterations associated with heterochromatin dynamics64,65. Thus, we interpret\nthat the deregulation of gene expression upon RB loss observed in our cell line model is therefore less\nlikely to be linked to changes in heterochromatin dynamics and the position effects they may exert.”\nReviewer #1 (Remarks on code availability):\nMy co-reviewer previously reviewed the code and deemed it appropriate and justified for the data and\nanalyses presented.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe are grateful to the reviewer for their comments throughout the entire revision process. Their\ncomments have helped us significantly improve our manuscript.\nReviewer #4 (Remarks to the Author):\nThe revised manuscript is an improved version of an already excellent study. Given the space limits\nthe authors have done a good job responding to my concerns.\nWe are delighted to hear that the reviewer is positive about our study and the revisions we have\nintroduced according to their suggestions. We are grateful to the reviewer for their comments, which\nhave helped us significantly improve our study.\nReviewer #5 (Remarks to the Author):\n3 of 8\nRetinoblasma protein is encoded by a major tumor suppressor able to repress cell cycle genes through\nmechanisms by which RB associates with E2F to counteract its role in gene activation. Mutations in\nCDKs regulating RB association with E2F have been associated with tumorigenesis due to such E2F\nregulation, though additional phenotypes often developing in association with RB dysfunction, like\nsister chromatin cohesion defects, may be due to other reasons than E2F. Here, the authors find a novel\nrole of RB that regulates, on top of E2F, cohesin removal from CTCF insulators. Accordingly, upon\nRB mutations cohesin subunit binding is stabilized at CTCF sites, and probing long-range interactions\nsupport a stronger insulation genome-wide. Moreover, long-range contacts engaging enhancers with\npromoters are perturbed, while CTCF insulation increases. The authors report additional deregulation\nof genes, in particular for those with no RB in their promoter. They propose a model whereby by\nregulating cohesin binding, RB may regulate insulation thereby allowing to activate genes involving\nenhancer-promoter interactions.\nThe paper provides lots of interesting data that, altogether, support a model by which RB controls\ncohesin binding at CTCF sites. Given the major role of cohesin on higher-order chromatin organization,\nthere could be many reasons by which this impedes on gene expression. The proposed model linking\nthe increased insulation with the increasing levels in gene expression is not clearly demonstrated.\nAlthough genomic analyses show a tendency for genes being activated to be associated with increased\ninsulation, it is difficult to interpret the data due to the heterogenous nature of the genomic\ncontexts.\nWe are grateful to the reviewer for their comments and suggestions. In the revised manuscript, we\nhave included analyses that address the points raised by the reviewer and have edited the text\naccording to the reviewer’s suggestions.\nMajor points\n1. In figure 4c, the defects in enhancer-promoter interactions (HiChiP of H3K27ac) should be shown\nas a function of cohesin accumulation at the insulator site that separate E-P’s (as in Fig 4f).\nIn our study, we report that RB loss leads to increased insulation at TAD boundaries and to reduced\nenhancer-promoter interactions. According to the reviewer’s comment, we aimed to quantify the\nfraction of changes in enhancer-promoter interaction that is attributable to changes in chromatin\ninsulation. Since chromosome conformation capture experiments such as H3K27ac HiChIP did not\nhave the resolution to resolve individual enhancer-promoter interactions, we utilized MED15 ChIP-\nseq signal as a surrogate measure to quantify enhancer activity (Figure R2). MED15 ChIP-seq is an\northogonal experiment we performed to corroborate reduced enhancer-promoter interactions upon RB\nloss. We observed that in RBWT hTERT-RPE1 cells, TAD boundary exerts a distance-dependent\neffect on the enhancer activity. In contrast, in RBKO hTERT-RPE1 cells, the distance-dependent effect\nis abolished. This observation is consistent with RB-mediated repression of insulation at TAD\nboundaries and increased enhancer-promoter interactions associated with wild-type RB. Notably, the\nTAD boundary-dependent effects on MED15 recruitment to enhancers did not fully explain the\ndifference between RBWT and RBKO hTERT-RPE1 cells, suggesting that there may be effects of RB\nloss that are independent of its role in regulating insulation at TAD boundaries. One potential\nmechanism whereby RB may directly regulate enhancer-promoter interactions would be through the\ndirect interaction of RB with AP-1 transcription factors at enhancers (Sanidas et al. 2022). Indeed, we\nobserved that MED15 peaks that are depleted upon RB loss are enriched with AP-1 and TEAD\ntranscription factor binding motifs (Figure R3 and Extended Data Fig. 19). In light of the report that\nRB loss is associated with reduced YAP/TAZ-TEAD activity in cancers (PMID: 34270926) and our\nprevious observation that dominant negative c-Fos prevents RB association with enhancers,\nwe\ninterpret that RB loss may have at least two different mechanisms leading to reduced enhancer-\npromoter interactions. We have revised the main text of the manuscript to include alternative\nmechanisms: page 7, lines 35-38: “Motif enrichment analysis of enhancers with decreased MED15\nbinding upon RB loss revealed enrichment of AP-1 and TEAD transcription factors motifs (Extended\nData Fig. 19). RB has been reported to associate with these enhancer regions23,56, suggesting that RB\nmay regulate enhancer activity through multiple, potentially distinct mechanisms.” We have also\nrevised the schematic presented in Figure 5 to more clearly present our model for non-canonical\ntranscriptional regulation associated with RB.\n4 of 8\nFigure R2. TAD boundary exerts an RB- and distance-dependent effect on MED15 recruitment to\nenhancers.\n(a) Absolute MED15 ChIP-seq signals averaged across enhancers marked with H3K27ac modifications.\nEnhancers were categorized into quartiles based on their distance to nearest TAD boundary.\nAverage\nMED15 ChIP-seq signal is presented as counts per million (CPM). Left panel presents MED15 ChIP-seq\nsignals in RBWT hTERT-RPE1 cells arrested in G1 phase, while right panel present MED15 ChIP-seq\nsignals in RBKO hTERT-RPE1 cells arrested in G1 phase. TAD boundary exerted a distance-dependent\neffect on MED15 recruitment to enhancers in RBWT hTERT-RPE1 cells. This distance-dependent effect\nwas abolished in RBKO hTERT-RPE1 cells. (b) Heatmap of relative MED15 ChIP-seq signal compared to the\nenhancer quartile that is most distal from TAD boundaries. Notably, the TAD boundary-dependent effects on\nMED15 recruitment to enhancers did not fully explain the difference between RBWT and RBKO hTERT-RPE1\ncells, suggesting that there may be effects of RB loss that are independent of its role in regulating\ninsulation at TAD boundaries. Two-tailed Student’s t-test was performed to assess statistical significance,\nand Bonferroni correct was performed for multiple hypotheses correction. *p-value < 0.05, **p-value < 0.01,\nand ***p-value < 0.001.\n5 of 8\nFigure R3. MED15 peaks in enhancers depleted upon RB loss are enriched with AP-1 and TEAD binding\nmotifs.\nMotif enrichment analysis of enhancers with more than 4-fold depletion of MED15 revealed enrichment of DNA\nbindings motifs for AP-1 and TEAD transcription factors.\n2. Whereas the split between RB+E2F+ from RB-E2F- is probably of use to analyze the data, there\nmay be many reasons for their relative change in RNA expression upon RBkd and the comparison is\nthus not insightful to demonstrate the mechanism by which RB functions(Fig 4f). Rather, a more direct\nenhancer-promoter assay with an intervening insulator would allow a more straightforward\ncomparison.\nThis is an excellent experiment and we are grateful to the reviewer for the suggestion. However, it\nwould require the generation of genetically engineered cells suitable for the reporter assay, and this\ncould not be completed within the provided timeframe for the revision.\n3. The defects in B compartment seen for RBcdk (Figure 1d) should be shown upon RBkd. The real\nquestion is whether gene expression defects (increasing expression levels) could also be the\nconsequence of the defects in B compartments.\nThe analysis of the change in nuclear compartments upon RB∆CDK expression and upon RB loss is\nshown in Figure R4. RB∆CDK expression led to dramatic spreading of compartment B as well as reduced\ninteraction within compartment B, consistent with heterochromatin dispersion phenotype that has been\nmicroscopically observed (Krishnan et al. 2022). Notably, the observed change in compartment B\noccurred between 24 h and 72 h of RB∆CDK expression, coinciding with cell cycle exit. In contrast, we\ndid not observe analogous changes in compartment B upon RB loss (Figure R4).\n6 of 8\nFigure R4. RB∆CDK expression but not RB loss induces compartment B spreading and dispersion.\n(a and b) Saddle plot indicating the distribution and the interaction strength between different compartments (A and\nB). (a) The progression from left to right panels shows changes in compartmentalization over time (0 h, 24 h, 72 h),\nupon RBΔCDK expression. Between 24 h and 72 h of RBΔCDK expression, spreading of compartment B as well as\nreduced interaction within compartment B are observed, consistent with heterochromatin dispersion phenotype\nobserved upon cell cycle exit. (b) RBWT and RBKO hTERT-RPE1 cells exhibited similar nuclear\ncompartmentalization. No evidence of heterochromatin dispersion was observed upon RB loss.\nOur chromosome conformation capture data reveal a clear distinction between the effects of transiently\nactive RB in the G1-phase and constitutively active RB∆CDK expression on higher-order chromatin\norganization. In G1, RB modulates cohesin-dependent loop formation in a transient manner that is\nrestored upon S-phase entry. In contrast, sustained RB∆CDK expression induces persistent alterations in\nnuclear compartments accompanied by heterochromatin dispersion, likely representing an irreversible\nprocess. These findings suggest that the changes in TAD boundary insulation observed upon RB\ndepletion are unlikely to drive compartmental reorganization. This conclusion is consistent with prior\nwork showing that acute cohesin degradation abolishes loop domains but does not disrupt nuclear\ncompartmentalization (PMID: 28985562). Instead, the heterochromatin alterations triggered by\nconstitutive RB∆CDK activity appear independent of cohesin-mediated looping and may arise from the\nformation of far-cis contacts between nuclear compartments (Fig. 1D) and likely involve RB∆CDK-\nmediated recruitment of heterochromatin factors, including HP1, SUV39H1, and DNMT1 (PMID:\n11484059, 17245754).\nWe discuss this observation in the revised discussion page 10, lines 19-28: “In our chromosome\nconformation capture experiments, RB loss led to increased insulation at TAD boundaries and\ndecreased enhancer-promoter interactions without significantly altering the composition or interactions\nwithin nuclear compartments. This observation is consistent with previous work showing that acute\ncohesin degradation abolishes loop domains but does not disrupt nuclear compartmentalization63.\nFurthermore, RB-mediated changes in cohesin-dependent loop formation observed in Micro-C\nexperiments were transient and restored upon S-phase entry, suggesting that it is not likely to be related\nto RB’s role in epigenetic alterations associated with heterochromatin dynamics64,65. Thus, we interpret\nthat the deregulation of gene expression upon RB loss observed in our cell line model is therefore less\nlikely to be linked to changes in heterochromatin dynamics and the position effects they may exert.”\n4. The impact of RB-kd should also be shown as a function of the cohesin loss, for panels Fig 4a, 4d.\nWe are grateful to the reviewer’s suggestions. This comment is linked to the reviewer’s 1st major point,\nand we have addressed the points in our reply therein. In short, we have added extra analyses linking\nthe impact of RB loss on enhancer-promoter interactions to TAD boundaries. TAD boundary exerted\nan RB- and distance-dependent effect on MED15 recruitment to enhancers, consistent with increased\ninsulation at TAD boundaries and reduced enhancer-promoter interactions observed upon RB loss.\n5. Boxplot with p-value should be added to test the significance of the changes in binding profiles\n(fig3a,3b, 4d). Otherwise, there is a risk of looking at the effect of outliers.\nWe are very grateful to the reviewer’s suggestions to include box plots. In the revised manuscript, we\nhave included box plots as Extended Data Figures for all the main Figures suggested by the reviewer\n(Figure R5, Extended Data Figs. 10e, 10f, and 19a). Overall, our analyses show that the presented\nchanges in binding profiles are not driven by the effect of outliers and that these changes are statistically\nsignificant.\n7 of 8\nFigure R5. Box plots for SMC3 and MED15 ChIP-seq signals show that the observed differences are not\ndriven by the effect of outliers and are statistically significant.\n(a and b) Box plot of log2 fold change of SMC3 ChIP-seq signals at SMC3 peaks upon RB loss across\nexperiments. SMC3 ChIP-seq signals at insulators in G1 phase (a) and across cis-regulatory elements in G1, S, and\nprometaphase (b) are shown. The data confirms that the observed increase in SMC3 ChIP-seq signal at insulators in\nG1 and prometaphase cells occurs genome-wide and is not driven by outliers. (c) Box plot of log2 fold change of\nMED15 ChIP-seq signals across cis-regulatory elements in hTERT-RPE1 cells. RB loss led to statistically\nsignificant depletion of MED15 at promoters and enhancers. One-sample Student’s t-test was utilized to assess\nstatistical significance and Bonferroni correction was utilized for multiple hypotheses correction. *p-value < 0.05,\n**p-value < 0.01, and ***p-value < 0.001.\n8 of 8"}, {"round": 3, "reviewer_comments": {"reviewer_5": "The authors have addressed most criticisms. The one remaining item may be addressed as explained in the next paragraph.\nThis would strengthen one of the main conclusion of the paper related to the regulation of E-P by RB.\nFormer comment: In figure 4c, the defects in enhancer-promoter interactions (HiChiP of H3K27ac) should be shown as a\nfunction of cohesin accumulation at the insulator site that separate E-P’s (as in Fig 4f).\nFormer authors'response: In our study, we report that RB loss leads to increased insulation at TAD boundaries and to\nreduced enhancer-promoter interactions. According to the reviewer’s comment, we aimed to quantify the fraction of changes\nin enhancer-promoter interaction that is attributable to changes in chromatin insulation. Since chromosome conformation\ncapture experiments such as H3K27ac HiChIP did not have the resolution to resolve individual enhancer-promoter\ninteractions, we utilized MED15 ChIP-seq signal as a surrogate measure to quantify enhancer activity (Figure R2).\nReviewer's comment: The authors solely need to reassess E-P interactions as already measured by the APA in Fig 4c.\nUnlike stated in their answer, there is no need for higher resolution to do so. The APA may be simply redone after splitting E-\nP into two groups, one defines by E-P with an intervening insulator, versus the other defined by E-P couples not separated\nby an insulator. Ideally, this can be done for E-P separated by the same range of distances.\n(Remarks on code availability)"}, "author_response": "Point-by-point response to the critiques\nREVIEWER COMMENTS\nReviewer #5 (Remarks to the Author):\nThe authors have addressed most criticisms. The one remaining item may be addressed as\nexplained in the next paragraph. This would strengthen one of the main conclusion of the paper\nrelated to the regulation of E-P by RB.\nFormer comment: In figure 4c, the defects in enhancer-promoter interactions (HiChiP of H3K27ac)\nshould be shown as a function of cohesin accumulation at the insulator site that separate E-P’s\n(as in Fig 4f).\nFormer authors' response: In our study, we report that RB loss leads to increased insulation at\nTAD boundaries and to reduced enhancer-promoter interactions. According to the reviewer’s\ncomment, we aimed to quantify the fraction of changes in enhancer-promoter interaction that is\nattributable to changes in chromatin insulation. Since chromosome conformation capture\nexperiments such as H3K27ac HiChIP did not have the resolution to resolve individual enhancer-\npromoter interactions, we utilized MED15 ChIP-seq signal as a surrogate measure to quantify\nenhancer activity (Figure R2).\nReviewer's comment: The authors solely need to reassess E-P interactions as already measured\nby the APA in Fig 4c. Unlike stated in their answer, there is no need for higher resolution to do so.\nThe APA may be simply redone after splitting E-P into two groups, one defines by E-P with an\nintervening insulator, versus the other defined by E-P couples not separated by an insulator.\nIdeally, this can be done for E-P separated by the same range of distances.\nWe sincerely thank the Reviewer for this comment, as the suggested analysis enhances the\ninterpretation of the role of RB on E-P interactions. In the revised manuscript, we now compare\nAggregate Peak Analyses (APA) of enhancer-promoter (E-P) interactions that cross a TAD\nboundary (inter-TAD) with those that do not (intra-TAD) (Figure R1). To control distance-\ndependent effects on APA, we randomly pooled a subset of E-P interactions with identical distance\ndistributions (Figure R1b). This led to n=165 E-P interactions for each group.\nWe then generated aggregate peaks for each group using JuicerTools’s APA function at 25 kb\nresolution. As expected,intra-TAD E-P interactions were stronger and more well-defined, while\ninter-TAD E-P interactions were weaker and less distinct in both RBWT and RBKO hTERT-RPE1\ncells (Figure R1a). Comparing E-P interactions between RBWT and RBKO cells showed that both\nintra- and inter-TAD interactions become weaker upon RB loss, with the RB-dependent effect\nappearing more pronounced when E-P interactions cross a TAD boundary.\nDue to the clustered nature of enhancers over several tens or hundreds of kilobases, E-P\ninteractions in HiChIP exhibited wider peaks compared to cohesin-dependent loops, especially\nwhen E-P interactions crossed a TAD boundary. This led quantification using APA P2LL (Peak to\nLower Left) score, which relies on the single pixel on the center, a suboptimal approach for our\nanalysis. Therefore, to better quantify the RB-dependent effects on E-P interaction strength, we\nalso used MED15 ChIP-seq signal as a surrogate measure of enhancer activity and compared it\nagainst the distance from a nearest insulator site. MED15 ChIP-seq is an orthogonal experiment\nwe performed to corroborate reduced E-P interactions observed upon RB loss (Figure 4d). Further\nanalysis of these data demonstrated that in RBWT cells, TAD boundaries impose a distance-\ndependent effect on enhancer activity, whereas in RBKO cells this distance-dependent effect is\nabolished (Figure R1c and d). These findings are consistent with RB-mediated repression of\ninsulation at TAD boundaries and increased E-P interactions in the presence of wild-type RB,\ncorroborating observations from Aggregate Peak Analyses (APA) of H3K27ac HiChIP\nexperiments.\nFigure R1. RB-dependent effects on enhancer-promoter interaction.\n(a) Aggregate Peak Analyses (APA) of H3K27ac HiChIP experiment for inter-TAD and intra-TAD\nenhancer-promoter interactions controlled for enhancer-promoter distance (n=165 each). Intra-\nTAD enhancer-promoter interactions were stronger and more well-defined, while inter-TAD\nenhancer-promoter interactions were weaker and less distinct in both RBWT and RBKO hTERT-\nRPE1 cells. While both intra- and inter-TAD interactions become weaker upon RB loss, RB-\ndependent effect was more pronounced when enhancer-promoter interactions cross a TAD\nboundary. (b) Comparison of enhancer-promoter distance between inter-TAD and intra-TAD\nenhancer-promoter interaction sets controlled for enhancer-promoter distance. The distribution of\nenhancer-promoter distance was highly similar in these sets (p-value=1.0, two-tailed Student’s t-\ntest). (c) Absolute MED15 ChIP-seq signals averaged across enhancers marked with H3K27ac\nmodifications. Enhancers were categorized into quartiles based on their distance to nearest TAD\nboundary. Average MED15 ChIP-seq signal is presented as counts per million (CPM). Left panel\npresents MED15 ChIP-seq signals in RBWT hTERT-RPE1 cells arrested in G1 phase, while right\npanel presents MED15 ChIP-seq signals in RBKO hTERT-RPE1 cells arrested in G1 phase. TAD\nboundary exerted a distance-dependent effect on MED15 recruitment to enhancers in RBWT\nhTERT-RPE1 cells. This distance-dependent effect was abolished in RBKO hTERT-RPE1 cells.\n(d) Heatmap of relative MED15 ChIP-seq signal compared to the enhancer quartile that is most\ndistal from TAD boundaries. Notably, the TAD boundary-dependent effects on MED15 recruitment\nto enhancers did not fully explain the difference between RBWT and RBKO hTERT-RPE1 cells,\nsuggesting that there may be effects of RB loss that are independent of its role in regulating\ninsulation at TAD boundaries. Two-tailed Student’s t-test was performed to assess statistical\nsignificance, and Bonferroni correct was performed for multiple hypotheses correction. *p-value\n< 0.05, **p-value < 0.01, and ***p-value < 0.001.\nThese new analyses are now included in the Extended Data Figure 20 and discussed in the main\ntext of the manuscript page 7, line 35 to page 8, line 10: “To further investigate whether the role\nof RB in regulating enhancer-promoter interactions associated with its activity in repressing\ninsulation at TAD boundaries, we compared interactions between enhancer-promoter pairs that\ncross a TAD boundary (inter-TAD) with those that do not (intra-TAD). To control distance-\ndependent effects, we selected a subset of enhancer-promoter pairs matched for distance\n(Extended Data Fig. 20). As expected, intra-TAD enhancer-promoter interactions were stronger\nand more well-defined, while inter-TAD interactions were weaker and less distinct in both RBWT\nand RBKO hTERT-RPE1 cells (Extended Data Fig. 20). Comparing RBWT and RBKO cells revealed\nthat both intra- and inter-TAD interactions weaken upon RB loss, with the RB-dependent effect\nappearing more pronounced when the enhancer-promoter pair spans a TAD boundary. Consistent\nwith this, MED15 ChIP-seq analysis demonstrated that in RBWT cells, TAD boundaries impose a\ndistance-dependent effect on MED15 ChIP-seq signal and enhancer activity, whereas this pattern\nis lost in RBKO cells (Extended Data Figs. 20c and d). Together, these findings suggest that RB-\nmediated repression of insulation at TAD boundaries facilitates increased enhancer-promoter\ninteractions in the presence of wild-type RB, although additional mechanisms may also contribute.\nMotif enrichment analysis of enhancers with decreased MED15 binding upon RB loss revealed\nenrichment of AP-1 and TEAD transcription factors motifs (Extended Data Fig. 19). Given that RB\nhas been reported to associate with these enhancer regions23,56, these results raise the possibility\nthat RB regulates enhancer activity through additional mechanistically distinct pathways.”"}, {"round": 4, "reviewer_comments": {"reviewer_5": "The authors now include an analysis of E-P long-range interactions in RB WT or KD, for E-P separated by a TAD border or\nnot. The interpretation and conclusions are adequate and satisfactory. It also justify why the authors used Med15 ChIP data.\n(Remarks on code availability)\nA Github linked to the paper is provided with basic functions needed to compute ChIP and Hi-C data, which are clear. A\nsource Matlab code is also provided."}, "author_response": ""}]

Decision letter