New Results

Different NIPBL requirements of cohesin-STAG1 and cohesin-STAG2

Dácil Alonso Gil, Ana Cuadrado, Daniel Giménez-Llorente, Miriam Rodríguez-Corsino, Ana Losada
doi: https://doi.org/10.1101/2022.11.29.518367

Abstract

Cohesin organizes the genome through the formation of chromatin loops. NIPBL activates cohesin’s ATPase and is essential for loop extrusion, but its requirement for cohesin loading is currently unclear. Here we have examined the effect of reducing NIPBL levels on the behavior of the two cohesin variants carrying STAG1 or STAG2 by combining a flow cytometry assay to measure chromatin-bound cohesin with analyses of its genome-wide distribution and genome contacts. We show that NIPBL depletion results in increased cohesin-STAG1 on chromatin that further accumulates at CTCF positions while cohesin-STAG2 diminishes genome-wide. Our data support a model in which NIPBL is not required for initial association of cohesin with chromatin but it is for loop extrusion, which in turn facilitates stabilization of cohesin-STAG2 at CTCF positions after being loaded elsewhere. In contrast, cohesin-STAG1 is loaded and stabilized at CTCF sites even under low NIPBL levels, but genome folding is severely impaired.

Introduction

Cohesin mediates sister chromatid cohesion and organizes the genome through the formation of chromatin loops 1–3. It consists of four subunits, SMC1A, SMC3, RAD21 and STAG. Additional proteins interact with cohesin and regulate its behavior, most prominently NIPBL, PDS5A/B, WAPL, SORORIN, ESCO1/2, HDAC8 and CTCF. Moreover, two versions of the complex carrying either STAG1 or STAG2 coexist in vertebrate cells and show overlapping and specific functions 4–10. Importantly, both are required to fulfill embryonic development 11,12. The two complexes also display different chromatin association dynamics that determine their contributions to loop formation and stability. Cohesin-STAG1 displays longer residence time on chromatin that depends on CTCF and ESCO1 and establishes longer, long-lived chromatin loops together with CTCF 13. Cohesin-STAG2 shows a preferential interaction with WAPL and mediates shorter loops involved in tissue-specific transcription.7,8,14

The heterodimer of NIPBL-MAU2 is currently viewed as the cohesin loader, necessary for activation of the cohesin ATPase 15,16. Cells with low levels of the NIPBL have reduced cohesin on chromatin and display altered genome folding, with loss of topological associating domains (TADs) and increased compartmentalization 17–19. In vitro, NIPBL is also required both for topological entrapment of plasmid DNA and for loop extrusion by cohesin 20–22. PDS5 proteins compete with NIPBL for binding cohesin and are, together with WAPL, required for cohesin unloading23–25. Thus, NIPBL-bound, presumably loop extruding cohesin, cannot be unloaded. In view of these results, it has been suggested that NIPBL might not function as a cohesin loader but as an extrusion processivity factor that promotes retention of cohesin on chromatin 2. However, other reports indicate that chromatin remodelers, Mediator or the chromatin regulator BRD4 promote the recruitment and/or stabilization of NIPBL on chromatin, which in turn is important for cohesin loading and function26–28. NIPBL is detected preferentially at TSS and enhancers, suggesting that these could be loading sites in which nucleosome depletion would facilitate binding of cohesin to DNA26,29–33.

The two potential functions of NIPBL, loading and extrusion, are difficult to separate. Here, by looking at the specific behavior of cohesin-STAG1 and cohesin-STAG2 after NIPBL knock down (KD), we provide evidence that initial association of cohesin with chromatin is independent of NIPBL and that the two cohesins have different requirements for the putative loader.

Results

NIPBL KD does not prevent cohesin loading and has opposite effects on STAG1 and STAG2

To assess cohesin loading throughout the cell cycle in individual cells, we adapted a flow cytometry protocol in which soluble proteins are extracted before fixation and combined it with a barcoding strategy to multiplex samples from different treatments prior to staining34,35. As control, we monitored the behavior of the replicative helicase component MCM3, which increases on chromatin during G1 and decreases as S phase progresses while total levels are maintained36 (Figure S1A, first column). In contrast, the profiles of cohesin subunits were similar in extracted (chromatin-bound) and permeabilized (total) conditions (Figure S1A), consistent with chromatin fractionation results (Figure S1B), and showed massive loading during G1 that further increased during S phase and G2. Strikingly, knock down (KD) of NIPBL only decreased chromatin association of STAG2 while that of STAG1 even increased (Figure 1A, top, compare colored and grey maps for each protein). Immunoblot analysis of chromatin fractions showed some differences although not as clearly and reproducibly as the flow cytometry (Figure 1B) while immunostaining further confirmed the opposite behavior of the two cohesin variants after NIPBL KD (Figure 1C). Similar results were obtained in two additional cell lines (Figure S2A, B) and with different siRNAs (Figure S2C). The increase of STAG1 on chromatin occurred in the context of a full cohesin complex, as it was abrogated by co-depletion of SMC1A (Figure 1D). Taken together, our results clearly show that a strong reduction in NIPBL levels does not prevent the association of cohesin with chromatin but affects the two cohesin variants in opposite ways.

Figure S1.
Figure S1. Chromatin association of the two cohesin variants by flow cytometry

A. Flow cytometry analysis of asynchronously growing HeLa, Ewing sarcoma A673 and mammary epithelial MCF10A cells with the indicated antibodies. Results are shown as contour plots. Cells were either pre-extracted with detergent before fixation to measure chromatin-bound protein levels (Chromatin) or permeabilized after fixation to assess total levels in the cell (Total). For each map, the cell cycle profile according to DNA content appears on top while the distribution of antibody intensities is plotted on the right.

B. Immunoblot analysis of the indicated cellular fractions from HeLa and MCF10A cells. ORC2, a chromatin bound protein, and MEK2, a cytoplasmic kinase, were used as controls for the fractionation procedure.

Figure S2.
Figure S2. NIPBL KD affects cohesin-STAG1 and cohesin-STAG2 in opposite ways also in MCF10A and A673 cells

A-C. Flow cytometry contour plots for chromatin-bound levels of the indicated proteins in control (grey plots) and NIPBL KD (colored plots) in MCF10A cells (A), A673 cells (B) and HeLa cells (C). In the latter experiment, a mixture of 4 siRNAs (smart pool) was used.

Figure 1.
Figure 1. NIPBL KD affects cohesin-STAG1 and cohesin-STAG2 in opposite ways

A. Asynchronously growing HeLa cells mock transfected (control) or transfected with siRNA against NIPBL (NIPBL KD) were analyzed by flow cytometry 72 h later. Contour plots of the indicated proteins in control (grey plots) and NIPBL KD cells (colored plots) were overlapped for comparison. For each map, the cell cycle profile according to DNA content appears on top while the distribution of antibody intensities is plotted on the right.

B. Immunoblot analysis of chromatin fractions (Chr) and total cell extracts from control and NIPBL KD cells. Increasing amounts of total extract from control cells were loaded to better quantitate the extent of depletion. NIPBL partner MAU2 also decreases after NIPBL KD.

C. Quantitative immunofluorescence (a.u., arbitrary units) of pre-extracted HeLa cells control (-) or NIPBL KD (+) stained with antibodies against STAG1, STAG2 and SMC1A. n=372 cells were analyzed per condition. A Mann Whitney non-parametric test with confidence intervals of 99% was performed; *** p<0.001.

D. Flow cytometry contour plots for chromatin-bound STAG1 and STAG2 in control (grey contour plots), SMC1 KD and double NIPBL/SMC1A KD (colored contour plots) HeLa cells. The immunoblot on the left shows remaining protein levels in total cell extracts in the different conditions.

Different chromatin association dynamics of STAG1 and STAG2 do not dictate their different response to NIPBL KD

We reasoned that the increased presence of STAG1 on chromatin in the NIPBL KD condition could be the result of its more stable association, as a more stable complex would be less dependent on the loader. However, increased chromatin association of cohesin-STAG1 persisted after co-depletion of NIPBL and either CTCF or ESCO1 (Figure 2A and Figure S3). Similarly, reducing the dynamic behavior of cohesin-STAG2 by co-depleting WAPL together with NIPBL did not alter the chromatin flow cytometry profiles of either variant compared with the single depletion of NIPBL (Figure 2A and Figure S3). Thus, the opposite effect of NIPBL KD in the loading of cohesin-STAG1 and cohesin-STAG2 is not simply a consequence of their different chromatin association/dissociation dynamics imposed by CTCF, ESCO1 and WAPL.

Figure S3.
Figure S3. Effect of cohesin regulators on chromatin association of the two variants

A. HeLa cells mock transfected (control) or transfected with siRNAs against CTCF, ESCO1 or WAPL (KD) were analyzed 72 h post-transfection by immunoblot.

B. RNA was extracted from the same cells for qRT-PCR analyses of the indicated genes. Results are represented as fold change of each KD condition compared to their respective controls and normalized to GAPDH. Data come from 3 experiments.

C. Contour plots for chromatin bound proteins in control (grey) and KD cells (colored) in each condition were overlapped for comparison.

Figure 2.
Figure 2. Effect of cohesin regulators in the response of cohesin variants to NIPBL KD

A. Flow cytometry contour plots for chromatin-bound STAG1 and STAG2 in control (grey plots) and KD (colored plots) HeLa cells. For each double KD condition, a single KD condition was also analyzed (see Figure S3). The NIPBL KD plot shown corresponds to the experiment co-depleting NIPBL and CTCF.

B. Left, Scheme of the experiment. Right, flow cytometry contour plots comparing total and salt-resistant chromatin-bound levels of STAG1, STAG2 and MCM3.

C. Quantification of salt-resistant versus total chromatin-bound levels of STAG1 and STAG2 in G1 and G2 (n=5 experiments). The graph shows average and standard deviation of the log2FC of median antibody intensity (salt res vs total).

D. Changes in total and salt-resistant chromatin-bound levels of STAG1 and STAG2 in G1 cells after NIPBL KD (n=4 experiments) or CTCF KD (n=3 experiments). The graph shows average and standard deviation of the log2FC of median antibody intensity (KD vs control).

To assess the “quality” of the chromatin association measured by flow cytometry, we modified the protocol to include an incubation in high-salt buffer before fixation. This extra step reduced the amounts of both STAG1 and STAG2 on chromatin, but affected STAG2 more severely, further supporting their different behavior. In contrast, the MCM3 profile was unchanged (Figure 2B). When we segregated G1 and G2 cells, we observed that the difference between the salt resistant fraction and the total chromatin-bound protein was reduced in G2 for both STAG1 and STAG2, consistent with the stabilization resulting from cohesion establishment (Figure 2C). We focused then on G1 to avoid interference of this cohesive cohesin. After NIPBL KD, the increase in “total” chromatin-bound STAG1 was not paralleled by an increase in “salt-resistant” STAG1 (Figure 2D, left). For STAG2, there was little protein left in NIPBL KD cells after the high-salt incubation. For comparison, we repeated the experiment with CTCF KD cells, a condition that also increases the amount of STAG1 on chromatin (Figure S3C). In this case, however, “salt-resistant” and “total” chromatin-bound STAG1 increased to the same extent (Figure 2D, right). We conclude that NIPBL is required to stabilize the binding of both cohesin variants to chromatin, maybe by promoting topological entrapment, even before cohesion establishment. Recent results suggest that (pseudo) topological embrace may be important for CTCF-cohesin loops37.

The response of cohesin-STAG1 and cohesin-STAG2 to NIPBL KD does not depend on the presence of the other variant

We next asked about the crosstalk between STAG1 and STAG2 loading. In the presence of NIPBL, the reduction of one of the variants did not affect significantly the amount of the other variant on chromatin (Figure S4). We also generated A673 cells carrying a single complex and found that when only cohesin-STAG2 is present (STAG1 KO cells), NIPBL KD still reduced cohesin levels on chromatin (Figure 3A). More importantly, in cells with only cohesin-STAG1 (STAG2 KO cells), reduction of the putative cohesin loader increased the amount of chromatin-bound cohesin (Figure 3B). We conclude that NIPBL promotes or stabilizes the association of STAG2 with chromatin but restricts that of STAG1. The latter effect is independent of the presence of STAG2 and may rely, at least in part, on repression of STAG1 transcription, as increased STAG1 mRNA levels are detected after reduction of NIPBL in several contexts, including blood cells from Cornelia de Lange (CdLS) patients with NIPBL mutations18,19,38–40 (Table S1). A corollary from the results presented so far is that at least cohesin-STAG1 complexes have the ability to associate with chromatin independently of NIPBL, either on its own or aided by a different loader yet to be identified.

Figure S4.
Figure S4. STAG1 behavior is not the same in NIPBL KD and STAG2 KD cells

A. Quantification of mRNA levels of STAG1, STAG2 and NIPBL in the indicated KD cells expressed as fold change compared to their respective controls and normalized to GAPDH. Data come from 3 experiments.

B. Immunoblot analyses of total cell extracts from control and KD HeLa cells.

C. Flow cytometry contour plots for the indicated chromatin-bound proteins in control cells (grey plots) and cells KD for NIPBL, STAG1 or STAG2 (colored) were overlapped for comparison.

Figure 3.
Figure 3. NIPBL KD increases cohesin loading in STAG2 KO cells

A. Top, contour plot profiles of chromatin bound STAG1 and STAG2 in A673 cells with (WT) or without (KO) STAG1 in control (grey) and NIPBL KD (colored) condition. Bottom, immunoblot analyses of the same cells.

B. As in A, for A673 cells with (WT) or without (KO) STAG2.

Cohesin-STAG1 further accumulates at CTCF sites upon reduction of NIPBL levels

Current models propose that cohesin is loaded at sites bound by NIPBL, often transcription start sites (TSS)s or active enhancers, and then moves away extruding DNA until stopped and stabilized at CTCF sites20,21. Given the requirement of NIPBL for loop extrusion, we expected that cohesin-STAG1 complexes present on chromatin in NIPBL KD cells would be less able to reach CTCF sites and would instead accumulate at their loading sites. Calibrated chromatin immunoprecipitation sequencing (ChIP-seq) in MCF10A cells showed that STAG1 was present, and even increased, at the same CTCF-bound sites in NIPBL KD and control cells. In contrast, STAG2 signals were significantly decreased at both CTCF and non-CTCF cohesin positions (Figure 4A, 4B). As a result of this opposite behavior of the two variants, total cohesin detected with anti-SMC1A was reduced genome-wide, but not to the same extent as STAG2. Assuming that NIPBL is required for loop extrusion, the most likely explanation for this result is that cohesin-STAG1 is preferentially loaded at or near CTCF positions. Strikingly, after CTCF KD, cohesin-STAG1 still accumulates at CTCF sites in which remaining CTCF is also bound while STAG2 is drastically reduced, similar to what happens in NIPBL KD condition (Figure 4C, 4D). We speculate that STAG1 associates with chromatin in a NIPBL-independent manner at CTCF sites and its interaction with CTCF prevents WAPL-mediated unloading. In contrast, a significant fraction of STAG2 may be loaded elsewhere in the genome, possibly also without NIPBL, but requires NIPBL to arrive to CTCF sites by loop extrusion. In cells with full NIPBL but reduced CTCF levels (CTCF KD condition, around 20% of CTCF left, Figure S5A), STAG1 is upregulated (Figure S3B) and cohesin-STAG1 preferentially occupies remaining CTCF-bound positions. Even if NIPBL-bound cohesin-STAG2 can reach those sites, they would be already occupied by cohesin-STAG1 and STAG2 would not arrest there5. It is also possible that the STAG2-CTCF interaction is outcompeted by WAPL binding more easily than the STAG1-CTCF interaction, particularly when the levels of CTCF are reduced, consistent with our previous proposal of differential affinities of STAG1 and STAG2 for CTCF and WAPL8. Co-depletion of NIPBL and CTCF further reduced the presence of cohesin-STAG2 genome-wide while cohesin-STAG1 was maintained at CTCF sites (Fig. S5B,C).

Figure S5.
Figure S5. Cohesin-STAG1 persists at CTCF sites after reduction of CTCF and NIPBL levels

A-B. Immunoblot analyses of CTCF KD cells used for ChIP-seq analyses.

C. Heatmaps showing genome-wide distribution of STAG1 and STAG2 in MCF10A cells treated as in B. Reads from calibrated ChIP-seq are plotted in a 5-kb window centered in the summits of cohesin positions with and without CTCF. A single replicate for each condition is plotted.

D. Normalized read density plots for cohesin subunits ±2.5 kb of the summit in the different KD conditions.

Figure 4.
Figure 4. Cohesin-STAG1 accumulates at CTCF sites in NIPBL KD cells

A. Distribution of three cohesin subunits in control and NIPBL KD MCF10A cells. Reads from calibrated ChIP-seq are plotted in a 5-kb window centered in the summits of cohesin positions with and without CTCF (24,912 and 14,607 positions, respectively). Datasets used are listed in Table S2.

B. Distribution of CTCF, STAG1 and STAG2 in control and CTCF KD MCF10A cells, as in A. See also Figure S5.

C. Normalized read density plots for cohesin subunits ±2.5 kb of the summit in the different KD conditions.

Cohesin-STAG1 cannot form loops in the absence of NIPBL

We next asked if cohesin-STAG1 present at CTCF sites in NIPBL KD cells was able to form and extrude loops. For that, we performed in situ Hi-C analyses in mock transfected (control) MCF10A cells, and cells treated with different siRNAs against NIPBL (Figure S6A,B). After confirmation of a high correlation among replicates, data from control (3 replicates) and KD cells (4 replicates) were merged for subsequent analyses (Figure S6C, Table S3). Interaction frequencies in the 0.1-1.2 Mb range decreased in NIPBL KD cells compared to control cells and increased at higher genomic distances, suggesting loss of cohesin-mediated loops and enhanced compartmentalization (Figure 5A, left; Figure S6D). The latter was confirmed by visual inspection of Hi-C matrices of whole chromosomes in which the checkerboard pattern was better defined in the NIPBL KD condition (Figure 5A, middle). Zooming in, many loops seen in control cells were reduced or lost in NIPBL KD cells (Figure 5A, right). Genome-wide, the number of called loops decreased after KD, and among differential loops detected in the two conditions, lost loops were clearly longer than shared and gained loops (Figure 5B; Table S4). Metaplots of loops of different sizes confirmed some gain of interactions at short distances, very close to loop anchors, while for longer loops interactions were drastically reduced in NIPBL KD cells (Figure 5C). We reckon that remaining NIPBL levels in these cells may allow cohesin-STAG1 to perform loop extrusion to certain extent, forming short loops, but further extension is severely impaired. An example is shown in Figure 5D, in which a prominent loop in the center of the matrix in control cells is clearly reduced while a shorter loop is maintained and even slightly increased after NIPBL KD. Moreover, next to this loop one can observe the disappearance of a stripe after NIPBL KD (Figure 5D). These changes in chromatin organization are not just the result of reduced levels of cohesin on chromatin (Figure S6B). Changes in STAG1 and STAG2 genome-wide distribution after NIPBL KD are similar to those previously observed in STAG2 KD cells, with little accumulation of STAG2 anywhere in the genome and STAG1 present mainly at CTCF-cohesin sites and slightly increased with respect to the control condition8 (Figure 5E, snapshots below matrices). Despite this similar distribution of cohesin, differential Hi-C matrices provide evidence for the specific changes in chromatin folding caused by NIPBL KD and STAG2 KD cells. In particular, we observed that longer loops, which depend specifically on STAG1 and are prominent in STAG2 KD cells8,13 do require also NIPBL since they are lost in NIPBL KD cells (Figure 5E). Consistent with more dramatic changes in chromatin architecture in NIPBL KD cells, 3 times more differentially expressed genes (DEGs) were detected in these cells, although a number of them were common between the two conditions [3,340 DEGs in NIPBL KD; 1,154 in STAG2 KD; 685 common DEGs; Figure S7A,B; Tables S5 and S6]. Importantly, the transcriptional changes observed after NIPBL KD, but not after STAG2 KD, resemble those found in blood cells from CdLS patients carrying NIPBL mutations39 (Figure S7C).

Figure S6.
Figure S6. Correlation among Hi-C replicates

A. Immunoblot analyses of the NIPBL KD cells used for in situ Hi-C. Cells were transfected with a single oligonucleotide (NIPBL KD_o) or with a smart pool of four oligonucleotides (NIPBL KD_sp).

B. Contour plots showing chromatin-bound SMC1A levels in NIPBL KD and control cells used in in situ Hi-C.

C. Hierarchical clustering of Hi-C data showing correlation among the replicates for the control (3) and NIPBL KD (4) conditions.

D. Contact probability as a function of genome distance in replicates of control and NIPBL KD cells.

Figure S7.
Figure S7. Gene deregulation in NIPBL KD cells resembling Cornelia de Lange Syndrome

A. Heatmap of significant gene expression changes observed in MCF10A cells in control and NIPBL KD condition (3 replicates each) and comparison with the changes detected in STAG2 KD cells8. FDR<0.05, ∣log2FC∣>0.5.

B. Venn diagram showing Differentially Expressed Genes (DEGs) in the two KD conditions.

C. GSEA was used to compare gene deregulation in NIPBL KD and STAG2 KD in MCF10A cells with that observed in lymphocyte cell lines from CdLS patients carrying mutations in NIPBL39. Only NIPBL KD deregulated genes showed significant enrichment in gene sets encompassing CdLS upregulated (top) and downregulated (bottom) genes.

Figure 5.
Figure 5. NIPBL is required for loop extrusion.

A. Contact probability as a function of genome distance in control and NIPBL KD cells (left) and normalized contact matrices for whole chromosome 17 (middle) and the boxed region within (Chr17:66,700,000-72,500,000; right) that exemplify changes at very long (a) and TAD-scale (b) distances. Resolution is 100 kb/bin in a and 25 kb/bin in b.

B. Box plot for the size of gained (406), lost (1029) and shared (2666) loops between control and NIPBL KD cells. Loops called at 10 kb resolution. See Methods.

C. Metaplots for loops of the indicated sizes in control cells (top) and how they change after NIPBL KD (bottom). The number of loops in each category is indicated below.

D. Representative region in chromosome 4 (chr4:14,830,000-16,060,000) showing contacts (10 kb resolution), distribution of STAG1 and STAG2, and CTCF positions and orientation (top matrix only). In the center, (boxed), a long loop decreases and a shorter one within slightly increases in the NIPBL KD condition (dashed arrows below the matrix). On the right, a stripe is reduced indicated. Arrowheads signal cohesin positions.

E. Comparison of the differential contacts observed in NIPBL KD and STAG2 KD cells (25 kb/bin) in a region of chromosome 7 (chr7:24,000,000-28,000,000) and corresponding distribution of STAG1 and STAG2, as measured by ChIP-seq. The matrix shown on the left corresponds to the control of NIPBL KD cells. Arrow points to a 1.3 Mb-long loop that disappears in NIPBL KD but is maintained (even increased) in STAG2 KD cells8.

Discussion

Here we have addressed changes in chromatin association and genome-wide distribution of cohesin-STAG1 and cohesin-STAG2 after NIPBL KD in human cells. We have found that cohesin-STAG1 levels increase in this condition and the complex further accumulates at CTCF sites although it cannot form long loops. In contrast, cohesin-STAG2 levels decrease genome-wide. These opposite effects on the two variants are independent of the presence of the other variant and epistatic to KD of other regulators of cohesin dynamics. Previous results in yeast had shown that cohesin could be detected at loading sites in scc2 mutants by chromatin immunoprecipitation41. Also, downregulation of MAU2 by siRNA in HeLa cells or its complete knock out (KO) in HAP1 cells reduced considerably the amount of NIPBL but left significant amounts of cohesin on chromatin 17,18. Even genetic deletion of NIPBL in mouse liver cells led to a more severe reduction of SMC1 on chromatin than of STAG1 19. Thus, our results are consistent with previous data showing that significant amounts of cohesin can still be found on chromatin after knock down of NIPBL or MAU2.

While we cannot discard that the small amount of NIPBL left after KD may be sufficient to load all the cohesin that we detect in these cells, it is also possible that association of cohesin with chromatin does not require NIPBL (Fig. 6). Instead, we propose that binding of NIPBL to cohesin promotes retention of the complex on chromatin, an effect that is particularly important for cohesin-STAG2. To date, it is unclear how cohesin engages with DNA to perform its different functions in 3D genome organization and cohesion37,42–47. Even entrapment of DNA by cohesin can take place in the absence of NIPBL, at least in vitro20,48. Structural studies suggest that cohesin has two DNA binding modules, the STAG/hinge module and the NIPBL/SMC head module and it is possible that the former is sufficient for transient chromatin association although not for functional translocation of the complex45,46,49. We propose that this initial and less stable association of cohesin with DNA, independent of NIPBL, can be detected in our flow cytometry assay. When cells are challenged with extra salt, a decrease in chromatin-bound cohesin is observed also for cohesin-STAG1 in NIPBL KD cells, although not as pronounced as the decrease in cohesin-STAG2. Importantly, SMC1A KD decreases the binding of both variants to chromatin without changing their cellular levels, which supports that the assay detects bona-fide chromatin association of the whole complex and not unspecific association of the cohesin subunits (Figure 1D).

Figure 6
Figure 6 New model for the role of NIPBL in loading and extrusion by the two cohesin variants.

In the current model, cohesin (STAG1 or STAG2) is loaded by NIPBL-MAU2 and extrudes DNA until released by WAPL (not depicted) or until arrested by convergent CTCF proteins bound in convergent orientation. In the new model, NIPBL-MAU2 is not required for association of cohesin with chromatin, but it is for loop extrusion. Cohesin-STAG1 binds at/near CTCF sites while cohesin-STAG2 is loaded elsewhere and requires NIPBL to reach them. Image created with BioRender.com.

Upon arrival to CTCF sites, cohesin becomes resistant to WAPL-mediated unloading50. We postulate that cohesin-STAG1 associates with chromatin near CTCF sites or arrives there and becomes arrested even when NIPBL levels are very low (Figure 6). This fact, together with the transcriptional upregulation observed after NIPBL KD, can explain the increased presence of this complex on chromatin. Unlike cohesin-STAG1, cohesin-STAG2 may be preferentially loaded at sites devoid of CTCF, and thus requires binding to NIPBL to translocate and reach CTCF positions. Even in the presence of full NIPBL, only cohesin-STAG1 is found at remaining CTCF-bound sites in CTCF KD cells. Whether the longer residence time of cohesin-STAG1 on chromatin is sufficient to explain our results is unclear. This residence time depends on ESCO1 and CTCF but we here show that KD of any of these two factors along with NIPBL does not modify the results of single NIPBL KD. Likewise, WAPL KD to the levels achieved here (around 25% of normal levels) cannot reverse the NIPBL KD effect on cohesin-STAG2. This is in contrast to results in HAP1 cells that show how WAPL KO can rescue the effects of low NIPBL levels in MAU2 KO cells 18. It is likely that the relative amounts of the two cohesin variants as well as cohesin regulators in different cells and conditions (KD versus KO) affect the final outcome of perturbation experiments. We would like to emphasize, however, that the opposite response of STAG1 and STAG2 to NIPBL KD is similar in the three human cell lines tested here. Importantly, our results confirm the importance of NIPBL for genome folding in vivo18,19, as cohesin-STAG1 complexes found at CTCF positions in NIPBL KD cells have reduced ability to engage in chromatin loop formation.

Methods

Cell culture

HeLa and A673 cells were cultured in DMEM (BE12-604F/U1, Lonza) supplemented with 10% FBS and 1% penicillin-streptomycin. MCF10A cells were cultured in DMEM/F12 (#31330038, ThermoFisher) supplemented with 20 ng/ml EGF, 0.5 mg/ml hydrocortisone, 100 ng/ml cholera toxin, 10 mg/ml insulin and 5% horse serum. All cell lines were grown at 37°C under 90% humidity and 5% CO2.

siRNA treatment

HeLa, A673 and MCF10A cells were transfected with 50 nM siRNAs (Table S7) using DharmaFECT reagent 1 and Gibco Opti-MEM I Reduced Serum Media (#31985047 ThermoFisher). Cells were harvested 72 h after transfection and analyzed by flow cytometry, as described below. Protein and mRNA levels were assessed by immunoblotting and quantitative RT-PCR, respectively.

CRISPR-Cas9 editing

A673 cells expressing inducible Cas9 (A673_iCas9) were generated as described51. A single cassette containing both the rTetR activator under CAG promoter and the Tetracycline Response Element (TRE) promoter driving the expression of Cas9, was inserted in the AAVS1 locus by homologous recombination using the Cas9 nuclease and a guide RNA sequence (gRNA 5′-GGGGCCACTAGGGACAGGAT-3′) against intron 1 of AAVS1. To generate STAG1 and STAG2 KO cell lines, viruses were produced through transfection of 3×106 293T cells with 9 µg of lentiGuide-Puro (Agent, #52963) containing guides for STAG1 or STAG2 (Table S7), 5 μg of psPAX2 and 2.5 μg of pMD2G in Gibco Opti-MEM I Reduced Serum Media with Lipofectamine 2000 Transfection Reagent. After 48 h, viruses were purified through centrifugation at 1500 rpm and filtered. A673_iCas9 cells (considered WT cells for the experiments in Figure 3) were grown in medium with 2 µM doxycycline to induce Cas9 expression and 8 µg/ml of polybrene and transduced with viruses. After 72 h, cells were seeded at low density for clonal selection. Clones were analyzed by immunoblotting with STAG1 and STAG2 antibodies to check protein elimination.

Western blotting and chromatin fractionation

Cells were collected by trypsinization, counted and resuspended in RIPA buffer at 107 cells/ml for 30 min. Upon centrifugation at 14000xg, supernatant was taken, SDS-loading buffer was added and samples boiled. Equal volumes were separated by SDS-PAGE in NuPAGE™ 3-8 % Tris-Acetate gels (#EA0375PK2). Alternatively, samples were resuspended in SDS–PAGE loading buffer at 107 cells/ml, sonicated and boiled before fractionation in in a 7.5% SDS-PAGE gel. Gels were transferred to nitrocellulose membranes in Transfer buffer I (50 mM Tris, 380 nM Glycine, 0.1% SDS, 20% methanol) for 1 h at 100 V and analyzed by immunoblotting. Antibodies and dilutions are listed in Table S8. Chromatin fractionation was performed as described36.

Immunofluorescence

Cells grown on coverslips coated with poly-Lysine were pre-extracted in CSK buffer (10 mM PIPES pH7, 0.1 M NaCl, 0.3 M sucrose, 3 mM MgCl2, 0.5 mM PMSF) with 0.5% Triton X-100 for 5 min before fixation for 15 min in a 2% formaldehyde solution. After incubation for 5 min in CSK-0.5% TX-100, coverslips were blocked with 3% BSA-0.05% Tween-20 in PBS for 30 min. Primary and secondary antibodies were diluted in blocking solution and incubated for 1 h each. DNA was counterstained with 1 □g/ml DAPI. Images were acquired in a TCS-SP5 (AOBS) Confocal microscope (Leica Microsystems) with LAS AF v2.6 acquisition software. Images were analysed with a custom made software programed in Definiens Developer XD v2.5 software (Definiens).

Flow cytometry assay

Flow cytometry assays were performed as described34 with some modifications. To analyze chromatin bound proteins, cells were treated for 5 min with a low salt extraction buffer (0.1% Igepal CA-630, 10 mM NaCl, 5 mM MgCl2, 0.1 mM PMSF, 10 mM Potassium Phosphate buffer pH 7.4) and fixed in 1% PFA final concentration. To evaluate the strength of chromatin association, cells were incubated for 5 minutes with salt extraction buffer containing 100 mM NaCl after the 5 min in low salt extraction buffer and before fixation. To analyze total proteins, unextracted cells were fixed in ice-cold 70% ethanol for 2 h. To eliminate antibody staining variation among samples from different conditions, a barcoding strategy was used35. Four different samples were stained with increasing dilutions of Pacific Blue (Invitrogen) for 30 min in the dark at room temperature (RT) and then mixed into one tube. Then, each barcoded sample was blocked in flow buffer (0.1% Igepal CA-630, 6.5 mM Na2HPO4, 1.5 mM KH2PO4, 2.7 mM KCl, 137 mM NaCl, 0.5 EDTA pH 7.5, 4% non-fat milk) for 5 min and consecutively incubated with primary and secondary antibodies, also diluted in flow buffer, for 1 hour each. Finally, DNA staining was performed over night with 125 nM ToPRO3-iodide 642/661 in PBS.

Cells were analyzed on a BD LSRII Fortessa flow cytometer using BD FACSDiva software and four different lasers: 680/30_R laser for ToPRO3 (DNA), 450/50_V for Pacific Blue (barcoding), 586/15_YG for Cy3-labelled secondary antibody and 525/50_B laser for Alexa fluor 488-labelled secondary antibody. For statistical analysis, single cell cycles were gated and at least 10,000 cells were recorded for each population in a barcoded sample. For imaging data, the same number of events were exported for each barcoded population in a FlowJo v10 software. Data quality and fluorescence compensation were assessed in order to correct for emission spectra overlap. Finally, conditions were merged to compare the behavior of the protein of interest throughout cell cycle.

Quantitative RT-PCR

cDNAs were generated using the Superscript II Reverse Transcriptase (Invitrogen) from total RNA (RNeasy Mini Kit, Qiagen) and qRT-PCR analyses were performed using the SYBR Green PCR Master Mix and an ABI Prism® 7900HT instrument (Applied Biosystems®). Reactions were performed in triplicate for each sample and samples came for at least 3 experiments. Expression was normalized to that of the endogenous housekeeping gene GAPDH, using the ΔΔCt method. Primers used are shown in Table S7.

Chromatin-Immunoprecipitation assay

MCF10A cells were grown at high confluence in order to arrest them in G1. Cells in suspension were crosslinked with 1% formaldehyde added to the media for 15 min at RT. After quenching the reaction with 0.125 M Glycine, fixed cells were washed twice with PBS containing 1μM PMSF and protease inhibitors. For chromatin preparation two different protocols were applied depending on the experiment. For experiments labeled in blue in Table S2, cells were pelleted and lysed in lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris-HCl pH 8.1) at a concentration of 2×107 cells/ml. Sonication was performed with a Covaris S220 (shearing time 30 min, 20% duty cycle, intensity 6, 200 cycles per burst and 30 sec per cycle) in a minimum volume of 2 ml. For other experiments, nuclei were isolated before sonication. Cells were incubated for 10 min at 4 °C in 25 ml ice-cold buffer A (10 mM HEPES pH 8.0, 10 mM EDTA pH 8.0, 0.5 mM EGTA, 0.25% Triton X-100 and protease inhibitors), recovered by centrifugation, resuspended in ice-cold buffer B (10 mM HEPES pH 8.0, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.01% Triton X-100 and protease inhibitors) for 10 min at 4 °C and centrifuged again. Nuclei were lysed in chromatin lysis buffer (50 mM Tris-HCl pH 8.0, 10 mM EDTA, 0.25% SDS and protease inhibitors) at a concentration of 2×107 nuclei/ml and stored overnight at 4 °C. Sonication was performed in a Covaris E220 device (shearing time 7 min at 5-7ºC range with 140 peak incident power, 5% duty factor, 200 cycles per burst). In all cases, chromatin from 107 cells was used per immunoprecipitation reaction with 50 μg of antibody as described8. For calibration, 5% of chromatin from mouse ES cells was added to the human chromatin. For library preparation, at least 5 ng of DNA were processed through subsequent enzymatic treatments with “NEBNext Ultra II FS DNA Library Prep Kit for Illumina” from New England BioLabs (cat# E7805). Briefly, a short fragmentation of 10 min was followed by end-repair, dA-tailing, and ligation to adapters. Adapter-ligated libraries were completed by limited-cycle PCR (8-12 cycles). Resulting average fragment size is 300 bp from which 120 bp correspond to adaptor sequences. Libraries were applied to an Illumina flow cell for cluster generation and sequenced on Illumina NextSeq 500 (with v2.5 reagent kits) following manufacturer’s recommendations.

ChIP-sequencing analysis

Alignment of reads to the reference human genome (hg19) was performed using ‘Bowtie2’ (version 2.4.2) under default settings52. Duplicates were removed using GATK4 (version 4.1.9.0) and peak calling was carried out using MACS2 (version 2.2.7.1) after setting the q value (FDR) to 0.05 and using the ‘–extsize’ argument with the values obtained in the ‘macs2 predictd’ step53. “CTCF” and “nonCTCF” positions were defined using cohesin peaks defined by the merge of all the cohesin subunits (STAG1, STAG2 and SMC1A8, see Table S2), and CTCF peaks54 and making two clusters. For analysis of calibrated ChIP-seq, profiles for each antibody were normalized by coverage and then multiplied by the occupancy ratio (OR) = (WmIPh)/(WhIPm), where Wh and IPh are the number of reads mapped to the mouse genome from input (W) and immunoprecipitated (IP) fractions, and Wm and IPm are reads mapped to the human genome from the input and IP fractions used for calibrating55. Mean read-density profiles and read-density heatmaps for different chromatin-binding proteins were generated with deepTools 3.5.056.

In situ Hi-C

MCF10A cells (3×106 cells per condition) were fixed with 2% formaldehyde (Sigma, #252549) in PBS with 10% FBS for 10 min at RT. Formaldehyde was quenched with 300 mM of glycine at RT for 5 min. Hi-C experiments were performed with Arima-HiC Kit (#A510008) following manufacturer’s instructions. Libraries were prepared with Swift Biosciences Accel-NGS 2S Library Kit (Cat# 21024) and amplified with KAPA Library Amplification Kit (KAPA Cat# KK2620). For all the libraries, 8-9 PCR cycles were used for amplification and they were then sequenced on an Illumina NextSeq550 (82×43 bp).

Hi-C analysis

Sequences were aligned to the reference human genome (hg19) using ‘Bowtie2’ with --local --reorder flags. Then, 5-kb raw matrices were built using hicBuildMatrix from HiCExplorer57. To assess the reproducibility of the replicates, 5-kb raw matrices were summed to obtain 40-kb matrices and these were normalized first by coverage and then by KR algorithm. Given the high correlation of the distribution of contacts as a function of genomic distance among replicates for each condition (control or KD), replicates were merged and analyzed at 5-kb resolution. Contacts at higher resolution (10-kb, 20-kb, 100-kb, etc) were obtained by summing contacts at lower resolution followed by normalization by coverage and KR.

Loops were called at 10-kb resolution in each replicate and only those loops that were called at least twice among all the replicates were considered for subsequent analyses. We next defined “gained” loops as those called only in NIPBL KD replicates and “lost” loops as those called only in wild type replicates. The rest of the loops were considered “shared” loops.

The metaplots of the loops were obtained using coolpup.py58.

Bulk RNA sequencing

Total RNA was extracted with NZY Total RNA Isolation kit (MB13402) following manufacturer’s instructions. Total RNA samples (500ng) were processed with the “NEBNext Single Cell/Low Input RNA Library Prep” kit (NEB #E6420) by following manufacturer instructions. RNA Quality scores were 9.9 on average (range 9.1-10) when assayed on a PerkinElmer LabChip analyzer. Briefly, an oligo(dT) primed reverse transcription with a template switching reaction was followed by double stranded cDNA production by limited-cycle PCR. Non-directional sequencing libraries were completed with the “NEBNext Ultra II FS DNA Library Prep Kit for Illumina” (NEB #E7805) and subsequently analyzed on an Illumina NextSeq 550 with v2.5 reagent kits following manufacturer’s protocols.

RNA-sequencing analysis

Fastq files with 86-nt single-end sequenced reads were quality-checked with FastQC (S. Andrews, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and aligned to the human genome (hg19) with Nextpresso59 executing TopHat-2.0.0 using Bowtie 0.12.7 and Samtools 0.1.16 allowing two mismatches and five multi-hits. The reads were mapped to hg19 genes using HTSeq and the differential expression was obtained using the R package DESeq260. We consider that a gene is expressed if the mean of the reads for the replicates is greater than 2 and changes in the expression of those genes are significant if FDR< 0.05 and absolute log2 fold change > 0.5. Gene Set Enrichment Analysis (GSEA) with GSEA_4.2.3 software61 was used to compare the gene expression changes of NIPBL KD and STAG2 KD cells with the gene sets for “CdLS upregulated genes” and “CdLS downregulated genes” (Figure S6C) comprising the deregulated genes with FDR<0.01 found in lymphoid cell lines from CdLS patients39 (Table S9).

Data availability

The data generated for this study (ChIP-seq, Hi-C and RNA-seq) has been deposited in GEO, accession number GSE207116 (token: sfszqwiuplklroj). See also Table S2 for a list of these and additional datasets used.

Author contributions

AC performed ChIP-seq and Hi-C experiments and generated A673 cell clones used in this study; DG-LL analyzed ChIP-seq, Hi-C and RNA-seq data; DAG performed and analyzed all other experiments; MR-C provided technical help for cloning, immunoblotting and custom antibody generation and characterization; AL supervised the study and wrote the manuscript with input from all authors.

Competing interest

The authors declare no competing interests.

Acknowledgements

We are grateful to R.G. Syljuåsen (Oslo U.H.) and Lola Martínez (Flow Cytometry Unit, CNIO) for advice on the flow cytometry protocol, Diego Megías (Confocal Microscopy Unit) for analysis of microscopy images, Álvaro Quevedo for his contribution to initial ChIP-seq and RNA-seq analyses and the rest of the members of the Chromosome Dynamics and DNA Replication groups at CNIO for helpful discussions. We also thank K. Shirahige (Tokyo University) for the ESCO1 antibody, J. Méndez (CNIO) for MCM3 and ORC2 antibodies, and E. de Alava (IBIS) for the A673 cell line. This work has been funded by the Spanish Research Agency (AEI) through grant PID2019-106499RB-I00 to AL and FPI fellowship (BES-2017-080051) to DAG. DG-LL is supported by a grant from the Spanish Association against Cancer (AECC).

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Posted November 29, 2022.
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Different NIPBL requirements of cohesin-STAG1 and cohesin-STAG2
Dácil Alonso Gil, Ana Cuadrado, Daniel Giménez-Llorente, Miriam Rodríguez-Corsino, Ana Losada
bioRxiv 2022.11.29.518367; doi: https://doi.org/10.1101/2022.11.29.518367
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