The nucleolus is sensitive to stress and can orchestrate a chain of cellular events in response to stress signals. Despite being a growth factor, FGF2 has antiproliferative and tumor-suppressive functions in some cellular contexts. In this work, we investigated how the antiproliferative effect of FGF2 modulates chromatin-, nucleolus- and rDNA-associated proteins. The chromatin and nucleolar proteome indicated that FGF2 stimulation modulates proteins related to transcription, rRNA expression and chromatin-remodeling proteins. The global transcriptional rate and nucleolus area increased along with nucleolar disorganization upon 24 h of FGF2 stimulation. FGF2 stimulation induced immature rRNA accumulation by increasing rRNA transcription. The rDNA-associated protein analysis reinforced that FGF2 stimulus interferes with transcription and rRNA processing. RNA Pol I inhibition partially reversed the growth arrest induced by FGF2, indicating that changes in rRNA expression might be crucial for triggering the antiproliferative effect. Taken together, we demonstrate that the antiproliferative FGF2 stimulus triggers significant transcriptional changes and modulates the main cell transcription site, the nucleolus.

The nucleolus is a membrane-less nuclear organelle composed of three main regions – the fibrillar center (FC), the dense fibrillar component (DFC) and the granular component (GC). These structures reflect the compartmentalization of machinery related to rDNA transcription, rRNA processing and ribosomal subunit formation (40S and 60S) (Hernandez-Verdun, 2011). The nucleolus is responsible for more than 60% of RNA synthesized in cells. There are ∼400 copies of rDNA genes in human cells, and only ∼50% of these genes are transcribed (Birch and Zomerdijk, 2008; Schöfer and Weipoltshammer, 2018).

The rRNA precursor (47S) is transcribed by RNA polymerase I (Pol I) and the cooperation of at least two transcription factors, the upstream-binding factor (Ubf1; also known as Ubtf) and the selective factor 1 (SL1) complex (TATA-binding protein, TAF1A, TAF1B, and TAF1C) (Grummt, 2003; Moss et al., 2019; Miller et al., 2001). A complex sequence of processing steps is required to form mature rRNA, including incorporating ∼79 ribosomal proteins. The genes that give rise to nascent 47S transcripts consist of the 28S, 18S and 5.8S rRNAs flanked by the 5′ and 3′ external transcribed spacer sequences (5'ETS and 3'ETS, respectively), and are separated by two internal transcribed spacer sequences (ITS1 and ITS2, respectively). Additionally, association of ribonucleoproteins, preribosomal factors, modifications (pseudouridine and 2′-O-methylation) and snoRNPs (small nucleolar ribonucleoproteins) are required to form the three mature rRNAs (28S, 18S, and 5.8S) (Henras et al., 2015; Schöfer and Weipoltshammer, 2018).

Active and inactive rDNA copies are organized by different chromatin conformations, epigenetic marks, topological organization and nucleolar location (Németh and Längst, 2011; Grummt and Längst, 2013). Three protein complexes are involved in the epigenetic regulation of rDNA the energy-dependent nucleolar silencing complex (eNoSC), nucleolar remodeling complex (NoRC), and nucleosome remodeling and deacetylase (NuRD). The eNoSC and NuRD complexes have histone deacetylation and methylation activity, and the NoRC has chromatin-remodeling activity that helps facilitate silencing of rDNA transcription by histone-modifying enzymes or DNA methyltransferases (Srivastava et al., 2016).

Cellular stresses can lead to the reorganization of the nuclear architecture, and the structural integrity of nucleoli plays an essential role in coordinating cellular stress responses. For example, nucleolar disorganization has been proposed as a standard feature in cellular responses that activate the p53 pathway (Rubbi and Milner, 2003a,b; Mayer and Grummt, 2005; Boulon et al., 2010).

The presence and activity of growth factors in the nucleolus have already been described. For example, nucleolin phosphorylation by fibroblast growth factors (FGFs) has been linked to the activation of ribosome production (Pederson, 1998). In skeletal precursors, nucleolar fibroblast growth factor receptor 2 (FGFR2) directly activates rDNA transcription via interaction with FGF2 and upstream binding factor 1 (Ubf1) (Neben et al., 2014). The 18 kDa FGF2 isoform, an isoform of FGF2 in the nucleolus, can directly govern rRNA transcription in vivo (Sheng et al., 2005; Hung et al., 2017; Gaviraghi et al., 2019).

The stimulation of mouse adrenocortical carcinoma cells (Y1) (Schwab et al., 1983; Kimura and Armelin, 1988; Forti et al., 2002) with physiological levels of FGF2, promotes the transition between the G0-G1 phases, delays the S phase and irreversibly blocks the cell cycle in G2-M of the cell cycle, leading to the appearance of cell senescence markers (SA-βGal). FGF2 sensitizes cells overexpressing the k-Ras proto-oncogene to treatment with proteasome inhibitors and DNA damage response (DDR) checkpoints, which results in cell death. Stimulation with FGF2 potentiates replication and proteotoxic stress in these tumor cells. In response, the cells need checkpoint control to survive (Dias et al., 2019). Large-scale analyses have shown that the changes caused by the antiproliferative effect of FGF2 on Y1 cells are consistent with oncogene-induced senescence, demonstrating the relationship between FGF2 growth stimulation and k-Ras oncogene amplification (Lund et al., 2021). Furthermore, FGF2 stimulation causes changes in the expression of transcription factors, especially Fosb and Junb, which might be associated with the DNA replication delay caused by FGF2 in Y1 cells (Vitorino et al., 2018).

Considering the crucial role chromatin plays in the connection between cell signaling and gene expression, we investigated how the antiproliferative effect of FGF2 modulates chromatin and nucleolus-associated proteins. We found that FGF2 stimulation modulates transcriptional regulation, particularly rRNA expression and chromatin-remodeling proteins. Changes in rRNA expression could be crucial for triggering the antiproliferative effect induced by FGF2 given that inhibiting RNA Pol I, which is responsible for rRNA expression, partially reverses the growth arrest induced by FGF2.

FGF2 stimulation modulates chromatin-associated proteins involved in transcriptional regulation

We were interested in studying how extracellular stimulation induced by FGF2 can lead to changes in chromatin-associated proteins resulting in altered gene expression. Thus, chromatin-associated proteins were extracted and analyzed by label-free quantitative proteomics (Fig. S1A–E; Table S1A,B). We treated G0-arrested Y1 cells with fetal bovine serum (FBS) or FBS plus FGF2 (hereafter denoted SF) for 1, 5 or 24 h, which corresponds to cells reaching to G1 (1 h and 5 h for both treatments) and G2/M (for 24 h SF treatment) or G2/M/G1 (for 24 h FBS treatment). Out of 6060 identified proteins, only those identified in at least two replicates were considered, resulting in 5891 proteins used for downstream analysis. Although the number of chromatin-associated proteins is higher than annotated in the UniProt database, it agrees with previous chromatin proteomics analyses (Kustatscher et al., 2014a).

Statistical analysis by QSPEC (Choi et al., 2008) identified several proteins with statistically relevant changes in abundances by pairwise analyses of samples stimulated with FBS or SF. The two early time points (1 and 5 h of FGF2 stimulation) showed proteins with changes in levels that were mostly upregulated with SF versus FBS, whereas the late time point (24 h) there were predominantly downregulated for this comparison (Fig. 1A). Of note, the differentially abundant proteins are likely related to their chromatin-binding status and/or differences in their expression.

Fig. 1.

Effect of FGF2 stimulation on chromatin-associated proteins. (A) Statistical analysis of protein levels measured in FBS+FGF2 samples (SF 1 h, SF 5 h and SF 24 h) compared to FBS-only samples (S 1 h, S 5 h and S 24 h) identified 669 abundant proteins (636 non-redundant proteins) after FGF2 stimulation (FC≥±2, FDR≤0.05, unpaired two-tailed t-test, n=4 biological replicates). Most upregulated proteins were identified in the early incubation times (1 and 5 h), whereas most proteins were downregulated in the 24 h incubation period dataset. (B) Enrichment of GO terms for the up- and down-regulated proteins are represented in a bubble plot. (C) A functionally grouped network was generated with the statistically significant proteins using the ClueGO plugin (V2.5) for Cytoscape (V3.4.0). Each node represents a biological GO process, and the colors represent the GO groups. Twelve GO groups are present within the network. (D) Based on ClueGO classification (Cytoscape software), proteins related to transcription and rRNA processes were subjected to hierarchical clustering using log fold change (SF/S) values ​​for each time point (1, 5 and 24 h). See Fig. S1 and Table S1 for more details on this dataset.

Fig. 1.

Effect of FGF2 stimulation on chromatin-associated proteins. (A) Statistical analysis of protein levels measured in FBS+FGF2 samples (SF 1 h, SF 5 h and SF 24 h) compared to FBS-only samples (S 1 h, S 5 h and S 24 h) identified 669 abundant proteins (636 non-redundant proteins) after FGF2 stimulation (FC≥±2, FDR≤0.05, unpaired two-tailed t-test, n=4 biological replicates). Most upregulated proteins were identified in the early incubation times (1 and 5 h), whereas most proteins were downregulated in the 24 h incubation period dataset. (B) Enrichment of GO terms for the up- and down-regulated proteins are represented in a bubble plot. (C) A functionally grouped network was generated with the statistically significant proteins using the ClueGO plugin (V2.5) for Cytoscape (V3.4.0). Each node represents a biological GO process, and the colors represent the GO groups. Twelve GO groups are present within the network. (D) Based on ClueGO classification (Cytoscape software), proteins related to transcription and rRNA processes were subjected to hierarchical clustering using log fold change (SF/S) values ​​for each time point (1, 5 and 24 h). See Fig. S1 and Table S1 for more details on this dataset.

To obtain an overview of the functional protein pathways induced by FBS or SF stimulation, we performed a gene ontology analysis to identify those biological processes enriched for the group of up- and down-regulated proteins (Fig. 1B). Terms that were significantly overrepresented in both up- and down-regulated proteins included cell cycle, cellular response to DNA damage stimulus, cellular response to stress, chromatin organization, DNA repair, DNA replication, intracellular transport, positive regulation of transcription by RNA polymerase II, ribosome biogenesis, and translation. Furthermore, terms associated with histone modification, mitotic G1 DNA damage checkpoint signaling, and mitotic G1/S transition checkpoint signaling were exclusively overrepresented among the upregulated proteins. Terms associated with cytoskeleton organization and protein stabilization were exclusively classified in the downregulated proteins, as well as gene expression and RNA processing. Overall, our results highlight a relationship between FGF2 stimulation and RNA metabolism, specifically mRNA and rRNA processing.

To better understand the pathway enrichment among the differentially abundant chromatin-associated proteins, we generated an interaction network grouped by nonredundant biological terms by using the ClueGO plug-in (Bindea et al., 2009). These analyses also showed that the terms related to ‘cell cycle’, ‘stress response’, ‘transcription’ and ‘rRNA processing’ were overrepresented (Fig. 1C). Proteins classified in terms related to transcription and RNA processing were further subjected to hierarchical clustering by using log values of fold changes for each time point (1, 5 and 24 h) (Fig. 1D). The clustering analysis showed that most of these proteins were downregulated after 24 h of FGF2 stimulation. These results correlate with the prevalence of downregulated chromatin-associated proteins after 24 h of FGF2 stimulation identified in the statistical analysis (Fig. 1A). These results suggest that FGF2 stimulation modulates the levels of chromatin-associated proteins, such as Med1, Med12, Polr2c, Polr2g, Taf4 and Gtf2e2, specifically involved in transcription and RNA processes.

FGF2 stimulus modulates global transcription as well as nucleus and nucleolus size

To verify whether there was a global modulation in the transcription rates induced by the FGF2 stimulus, we evaluated the global transcription rates of stimulated cells using run-on assays with 5-ethynyluridine (EU). As expected by the cell cycle progression, changes in the abundance of newly synthesized transcripts (Basier and Nurse, 2023) were observed over time in FBS-treated cells. To minimize the expected transcriptional cell cycle differences, comparisons were performed between stimuli at similar time points. We identified a statistically significant decrease in the level of newly synthesized transcripts 1 h and 5 h after the addition of FGF2 to the cells, followed by an increase after 24 h of FGF2 stimulation (Fig. 2A,B). These results demonstrate that an FGF2 stimulus modulates global transcription, showing opposite patterns in short and long FGF2 incubation durations.

Fig. 2.

Effect of FGF2 stimulation on global transcription levels. (A) EU incorporation of Y1 cells after FBS or FBS+FGF2 (FS) stimulation at the indicated times. The uridine analog was labeled with a fluorophore (Alexa Fluor 488) and analyzed by fluorescence microscopy. The green color corresponds to the RNA (EU) and the blue color to DNA (DAPI). The experiment was performed with three biological replicates. Scale bar: 20 µm. (B) Quantification of EU labeling was obtained using ImageJ (mean gray value) and analyzed using GraphPad Prisma v.5. Results show mean±s.e.m. (n=3). *P<0.05, **P<0.01, ****P<0.0001 (unpaired two-tailed t-test). See Fig. S1 for nucleus and nucleolus size measurements.

Fig. 2.

Effect of FGF2 stimulation on global transcription levels. (A) EU incorporation of Y1 cells after FBS or FBS+FGF2 (FS) stimulation at the indicated times. The uridine analog was labeled with a fluorophore (Alexa Fluor 488) and analyzed by fluorescence microscopy. The green color corresponds to the RNA (EU) and the blue color to DNA (DAPI). The experiment was performed with three biological replicates. Scale bar: 20 µm. (B) Quantification of EU labeling was obtained using ImageJ (mean gray value) and analyzed using GraphPad Prisma v.5. Results show mean±s.e.m. (n=3). *P<0.05, **P<0.01, ****P<0.0001 (unpaired two-tailed t-test). See Fig. S1 for nucleus and nucleolus size measurements.

Despite causing cell cycle arrest, FGF2 stimulation causes increases in cell size and promotes cell growth (Dias et al., 2019). It is well established that the cell nucleus increases in size according to overall cell size, maintaining a constant nucleus-to-cytoplasm ratio. Changes in this ratio are associated with several pathologies (Mukherjee et al., 2016). Therefore, we measured nuclear area and found an increase after 24 h of FGF2 stimulation (Fig. S1E). Considering that FGF2 induces changes in global transcription, we also evaluated the nucleolus number and area upon FGF2 stimulation. Regarding nucleoli numbers per cell, no significant differences were found between stimuli (data not shown). To further evaluate the nucleolus, we used an antibody against the fibrillarin protein, which is a methyltransferase enzyme that participates in the first stage of rRNA processing, pre-rRNA modification and ribosome assembly (Monaco et al., 2018), and is therefore a nucleolar marker. We observed that the nucleolus also decreased upon 1 h of FGF2 stimulation and increased in size after 24 h (Fig. S1F). These data corroborate the results of the run-on assays with EU (Fig. 2) given that nucleolus size is directly related to transcriptional rate (Ma et al., 2016). To investigate whether this effect occurs in other cancer cell types, we evaluated fibrillarin labeling intensity in a MCF7 cell line (Fig. S1G), which was also subjected to FGF2 antiproliferative treatment. In agreement with the findings in the Y1 cell line, fibrillarin labeling increased significantly upon FGF2 stimulation. To better understand the effect of FGF2 stimulation, we next analyzed the nucleolus proteome.

FGF2 antiproliferative stimulus modulates the nucleolar proteome

Our chromatin-enrichment proteomics results indicated that FGF2 modulates the levels of proteins associated with chromatin that are related to transcription, RNA processing and ribosome biogenesis. Furthermore, global transcription rates, preferentially detected in the nucleolus, and nucleolus size were altered upon FGF2 stimulation. Therefore, we hypothesized that stimulation by FGF2 could modulate the rates of rRNA transcription and/or processing by altering the nucleolar proteome and its structure. Thus, we applied a quantitative TMT-labeled proteomics approach to evaluate changes in protein levels in Y1 cells stimulated with FBS or SF for 0, 1, 5 and 24 h. A total of 1229 proteins were identified, and after applying the filters (identification in at least two out of the three biological replicates), 614 proteins were further considered for pathway analysis (Fig. S2A–E, Table S3). The number of identified proteins concurs with the nucleolar proteome database described by Ahmad et al. (2009).

Statistical analysis identified 67 proteins that were differentially abundant between FGF2-treated samples and FBS-only controls (unpaired two-tailed t-test, P≤0.05). We identified 15, 6 and 10 upregulated proteins and 15, 9 and 12 downregulated proteins in nucleolar extracts after 1, 5 and 24 h of FGF2 stimulation, respectively (Fig. 3A; Fig. S2F). There were 267 proteins identified in both chromatin and nucleolus datasets. Among the statistically significant proteins, there were nine proteins (Hmgb1, Snrpa, Rps10, Hmgb3, Rps7, Rrp12, Ddx56, Rrp1b and Fip1l1) in common between both datasets. In agreement with the chromatin proteomic results, we also identified overexpressed terms related to rRNA processing, transcription and ribosome biogenesis among the statistically significant proteins (Fig. 3B; Fig. S2G). Additionally, several proteins belonging to chromatin-remodeling complexes, such as Gatad2a, Chd4, Trim28, Smarca5 and Baz1b, were differentially abundant in the nucleolus after FGF2 stimulation (Fig. S2H), indicating that FGF2 stimulation might cause rDNA remodeling. Taken together, the modulation of proteins abundance related to transcription and chromatin remodeling that are specifically located in the nucleolus, suggests that FGF2 alters the transcriptional state of rDNA.

Fig. 3.

Effect of FGF2 stimulation on nucleolar proteins. (A) Statistical analysis of changes in protein levels measuring the 1, 5 and 24 h FGF2-treated datasets compared to FBS-only controls represented as volcano plots. Downregulated proteins are highlighted in green, and upregulated proteins are highlighted in red (unpaired two-tailed t-test, P≤0.05, n=4 biological replicates). (B) Bubble plot for the significantly enriched GO terms represented by modulated proteins at the time points. See Fig. S2 and Table S2 for more details on this dataset.

Fig. 3.

Effect of FGF2 stimulation on nucleolar proteins. (A) Statistical analysis of changes in protein levels measuring the 1, 5 and 24 h FGF2-treated datasets compared to FBS-only controls represented as volcano plots. Downregulated proteins are highlighted in green, and upregulated proteins are highlighted in red (unpaired two-tailed t-test, P≤0.05, n=4 biological replicates). (B) Bubble plot for the significantly enriched GO terms represented by modulated proteins at the time points. See Fig. S2 and Table S2 for more details on this dataset.

FGF2 stimulation causes nucleolar disorganization

Considering that most rDNA copies are silenced, changes in proteins responsible for repression and activation through chromatin remodeling can directly impact the nucleolar transcriptional response given that nucleolar transcription is balanced (Boisvert et al., 2007). The nucleolar proteomic results identified modulation in chromatin-remodeling proteins. To further evaluate this observation, we investigated fibrillarin localization upon FGF2 stimulation. Interestingly, we found that fibrillarin disperses significantly after 24 h of FGF2 stimulation in the Y1 cell line, as suggested by the increase in extranucleolar labeling (Fig. 4A,B). Although fibrillarin labeling intensity was detected in another cancer line (Fig. S1G), extranucleolar labeling was not observed in MCF7 cells.

Fig. 4.

Effect of FGF2 stimulation on nucleolar organization. (A) Y1 cells were stimulated with FBS (S) or FBS+FGF2 (SF) for 24 h and subsequently labeled with anti-fibrillarin antibody (red) and DAPI (blue). The experiment was performed with three biological replicates. Scale bar: 20 µm. (B) Fibrillarin extranucleolar (mean gray value×area) and nuclear (mean gray value) intensity values were obtained by analysis of multiple images obtained at different time points of FBS or FBS+FGF2 treatment as described in A. Results show mean±s.e.m. (n=3). (C) Analysis of chromatin structure by transmission electron microscopy. Y1 cells were treated with FBS or FBS+FGF2 for different periods. FC, fibrillar center; DFC, dense fibrillar center; GC: granular center. Arrows indicate an increased area of the DFC after FGF2 stimulus. See Fig. S1G for additional cell line.

Fig. 4.

Effect of FGF2 stimulation on nucleolar organization. (A) Y1 cells were stimulated with FBS (S) or FBS+FGF2 (SF) for 24 h and subsequently labeled with anti-fibrillarin antibody (red) and DAPI (blue). The experiment was performed with three biological replicates. Scale bar: 20 µm. (B) Fibrillarin extranucleolar (mean gray value×area) and nuclear (mean gray value) intensity values were obtained by analysis of multiple images obtained at different time points of FBS or FBS+FGF2 treatment as described in A. Results show mean±s.e.m. (n=3). (C) Analysis of chromatin structure by transmission electron microscopy. Y1 cells were treated with FBS or FBS+FGF2 for different periods. FC, fibrillar center; DFC, dense fibrillar center; GC: granular center. Arrows indicate an increased area of the DFC after FGF2 stimulus. See Fig. S1G for additional cell line.

Alterations in fibrillarin location could indicate changes in rRNA processing, transcription inhibition, segregation or nucleolar fragmentation (Boulon et al., 2010; Burger et al., 2010). Indeed, transmission electron microscopy (TEM) analysis confirmed that FGF2 stimulation leads to intense nucleolar disorganization. Upon 24 h of FGF2 stimulation, the fibrillar center (FC) was less evident, and the area of the DFC was increased (Fig. 4C). Altogether, these results demonstrate that after 24 h of exposure, FGF2 stimulation causes nucleolar disorganization.

FGF2 stimulation modulates RNA polymerase I transcription

To assess whether FGF2 stimulation interferes with transcription and rRNA processing, as indicated by the proteomics results, we performed northern blotting assays using probes targeting the immature rRNA region (47S) and mature 28S regions. There were no discernible changes in the level of mature 28S, whereas those of 47S increased (P<0.05) after 24 h of stimulation (Fig. 5A; Fig. S3A). We confirmed the increase in the immature 47S region after 24 h of FGF2 stimulation by real-time quantitative PCR (RT-qPCR) (Fig. S3B).

Fig. 5.

Effect of FGF2 stimulation on pre-rRNA transcripts. (A) Analysis of immature rRNA transcripts for no treatment (0 h), FBS+FGF2 samples (SF 1 h, SF 5 h and SF 24 h) and FBS-only samples (S 1 h, S 5 h and S 24 h) using probes targeting the 5′ETS region, which corresponds to the 47S rRNA, performed by northern blotting and quantified using ImageJ v1.52 software. Statistical analysis was performed with GraphPad Prisma v.5 software. Results show mean±s.e.m. (n=3). (B) RNA capture assay. The recently synthesized RNAs were incorporated with EU, extracted, biotinylated, captured and submitted to RT-qPCR analysis using primers for the pre-rRNA region (47S). The results were analyzed using GraphPad Prisma v.5. Results show mean±s.e.m. (n=3). (C) Inhibition of RNA Pol I partially rescues cells from the antiproliferative effect caused by FGF2. Left panel: clonogenic assay images, cell colonies stained with crystal violet. Cells were stimulated with FBS or FBS+FGF2 for 1 h and 0.03 μg/ml actinomycin D or 0.2 µM CX5461 was added. Right panel: Quantification of the clonogenic assay images. Values of the FBS+FGF2/FBS ratios of the average of the total area occupied by colonies on plates were used. Results show mean±s.e.m. (n=3). See Fig. S3 for ribosome profile analysis and additional cell lines. *P<0.05; **P<0.01; ***P<0.001 (unpaired two-tailed t-test, n=3 biological replicates).

Fig. 5.

Effect of FGF2 stimulation on pre-rRNA transcripts. (A) Analysis of immature rRNA transcripts for no treatment (0 h), FBS+FGF2 samples (SF 1 h, SF 5 h and SF 24 h) and FBS-only samples (S 1 h, S 5 h and S 24 h) using probes targeting the 5′ETS region, which corresponds to the 47S rRNA, performed by northern blotting and quantified using ImageJ v1.52 software. Statistical analysis was performed with GraphPad Prisma v.5 software. Results show mean±s.e.m. (n=3). (B) RNA capture assay. The recently synthesized RNAs were incorporated with EU, extracted, biotinylated, captured and submitted to RT-qPCR analysis using primers for the pre-rRNA region (47S). The results were analyzed using GraphPad Prisma v.5. Results show mean±s.e.m. (n=3). (C) Inhibition of RNA Pol I partially rescues cells from the antiproliferative effect caused by FGF2. Left panel: clonogenic assay images, cell colonies stained with crystal violet. Cells were stimulated with FBS or FBS+FGF2 for 1 h and 0.03 μg/ml actinomycin D or 0.2 µM CX5461 was added. Right panel: Quantification of the clonogenic assay images. Values of the FBS+FGF2/FBS ratios of the average of the total area occupied by colonies on plates were used. Results show mean±s.e.m. (n=3). See Fig. S3 for ribosome profile analysis and additional cell lines. *P<0.05; **P<0.01; ***P<0.001 (unpaired two-tailed t-test, n=3 biological replicates).

The accumulation of immature rRNA transcripts might indicate an increase in rRNA transcription. To assess this hypothesis, we performed a nascent RNA capture assay followed by RT-qPCR to detect immature rRNAs (5'ETS). There was an increase in immature rRNAs among the more recently synthesized RNAs after 24 h of FGF2 stimulation (Fig. 5B). However, the accumulation of immature rRNA after stimulation by FGF2 did not affect the biogenesis of mature ribosomes, as seen by polysome profiles (Fig. S3C–E). Taken together, these results show that FGF2 stimulation for 24 h increases rRNA transcription without affecting the ribosome profile.

Given that the FGF2 stimulus induces changes in the nucleolus, modulating rRNA transcription, we asked whether inhibition of rRNA transcription could prevent the antiproliferative effect induced by FGF2. To evaluate that, we treated three cell lines (Y1, MCF7 and UM-UC3) with two different RNA Pol I inhibitors, actinomycin D and CX5461, after 1 h of FGF2 stimulation. Inhibition of RNA Pol I led to a partial recovery in the antiproliferative effect in these cell lines (Fig. 5C; Fig. S3G). This result demonstrates that the inhibition of RNA Pol I might be crucial for preventing the antiproliferative effect induced by FGF2.

FGF2 stimulus modulates rDNA-associated proteins

To identify proteins recruited to the rDNA loci after FGF2 stimulation, we performed the Cas9 (CRISPR associated protein) locus-associated proteome (CLASP) assay (Tsui et al., 2018). This protocol is a reverse chromatin immunoprecipitation (ChIP) assay, which uses the recombinant dead Cas9 (dCas9) protein fused to the FLAG tag and guided to the region of interest by a single-guide RNA (sgRNA). Recombinant dCas9 bound to the region of interest, and a pulldown was performed to obtain the DNA of the target region and its associated proteins (Fig. 6A). A pool of six RNA guide sequences for the 28S and 18S regions (Fig. 6B) was used as sgRNA to target the rDNA transcriptional unit. A sample of exponentially growing cells was used as a negative control, using a scrambled sgRNA. The proteins identified by mass spectrometry showed a high Pearson correlation among replicates, and PCR assays validated the enrichment of the target sequences (Fig. S4A–C).

Fig. 6.

Effect of FGF2 stimulation on rDNA-associated proteins. (A) CLASP protocol. (1) Cells are treated with formaldehyde to cross-link proteins with DNA, lysed, and the DNA is fragmented. (2) The FLAG–dCas9+guide RNA complex was added to the extract, and the pulldown of the regions of interest was performed by adding the anti-FLAG antibody. (3) The extract enriched with the regions of interest can be treated with Proteinase K or nuclease to obtain DNA or proteins. (B) Design of guide RNAs. Regions targeted by the guide RNAs are highlighted with red arrows. Regions of the transcriptional block were chosen (18S and 28S). Figures created with BioRender.com. (C) Volcano plot highlighting statistically significant proteins after FGF2 stimulation for 24 h. Eight downregulated and 19 upregulated proteins were found after stimulation (FC≥±2, FDR≤0.05, unpaired two-tailed t-test, n=4 biological replicates). Orange arrows point to Nolc1 and Tcof1 proteins. See Fig. S4 and Table S3 for more details on this dataset.

Fig. 6.

Effect of FGF2 stimulation on rDNA-associated proteins. (A) CLASP protocol. (1) Cells are treated with formaldehyde to cross-link proteins with DNA, lysed, and the DNA is fragmented. (2) The FLAG–dCas9+guide RNA complex was added to the extract, and the pulldown of the regions of interest was performed by adding the anti-FLAG antibody. (3) The extract enriched with the regions of interest can be treated with Proteinase K or nuclease to obtain DNA or proteins. (B) Design of guide RNAs. Regions targeted by the guide RNAs are highlighted with red arrows. Regions of the transcriptional block were chosen (18S and 28S). Figures created with BioRender.com. (C) Volcano plot highlighting statistically significant proteins after FGF2 stimulation for 24 h. Eight downregulated and 19 upregulated proteins were found after stimulation (FC≥±2, FDR≤0.05, unpaired two-tailed t-test, n=4 biological replicates). Orange arrows point to Nolc1 and Tcof1 proteins. See Fig. S4 and Table S3 for more details on this dataset.

A total of 556 proteins were identified, 283 exclusive to samples obtained with the rDNA targeted sgRNA, 53 exclusive to the negative control (scramble sgRNA) and 220 identified in pulldowns with both guides (Fig. S4D, Table S3A). Proteins identified in at least two replicates for the treated samples (FBS and SF) or enriched in the CLASP samples compared to the control replicate exponentially growing cells were considered for statistical analysis. Using a log2 fold change (FC) cutoff of greater than 1 or less than −1 and P≤0.05 identified 27 statistically significant proteins after FGF2 stimulation compared to FBS-only treatment, 19 upregulated and eight downregulated (Fig. 6C).

Among the modulated proteins, we highlight the presence of nucleolar and coiled-body phosphoprotein 1 (Nolc1) and treacle nucleolar phosphoprotein (Tcof1) proteins. Nolc1 plays a role in transcription and rRNA processing. The TCOF1 gene encodes Tcof1, which is homologous to the Nolc1 protein (Meier and Blobel, 1992). The latter participates in the 2′-O-methylation of pre-rRNA through its association with Nop56, a component of the ribonucleoprotein methylation complex (Gonzales et al., 2005). Nop56 was increased in the chromatin proteome dataset with log2 FC>1 at the 1 and 5 h time points (Table S3B). The known involvement of Tcof1 in rDNA transcription and rRNA modification corroborates our results, suggesting once again that the FGF2 antiproliferative effect influences the transcription of rRNA, but now also suggesting that the post-transcriptional rRNA modification pathway might be disrupted too. This evidence, combined with the modulation of Nolc1 and the fibrillarin protein dispersion in the nucleolus, reinforces that FGF2 stimulation could interfere with transcription and rRNA processing and/or modification. These results demonstrate that FGF2 stimulation can also modulate the abundance of rDNA-associated proteins by inducing changes in their expression, affecting the half-life of proteins, or inducing migration and/or recruitment of proteins.

Here, we show that FGF2 stimulation triggers global transcriptional changes, specifically alterations in the abundance of chromatin and nucleolar-associated proteins and profound nucleolar disorganization. Changes in rRNA expression might be crucial for triggering the antiproliferative effect induced by FGF2 given that the inhibition of RNA Pol I, responsible for rRNA expression, partially reverses the growth arrest induced by FGF2.

Considering the crucial role chromatin plays in the connection between cell signaling and gene expression, we started to investigate the antiproliferative effects of FGF2 by evaluating chromatin proteomic alterations. Indeed, the chromatin proteomic changes reflected many phenotypic alterations already reported in previous studies (Costa et al., 2008; Salotti et al., 2013; Dias et al., 2019). The cell cycle arrest and morphological changes induced by FGF2 were corroborated by the enrichment of terms related to the cell cycle, checkpoint signaling and cytoskeletal organization. Among these terms, we detected Cdkn1a, Chek2, Prkdc, Ep300, Mdm2, CDK2, Anapc1 and Anapc2 proteins. Notably, these datasets indicate an unexplored effect of FGF2 stimulation on RNA metabolism, more specifically with mRNA and rRNA transcription and processing.

In addition, both proteomics datasets indicated that proteins associated with transcription and processing of RNA Pol II and Pol I are affected by FGF2 stimulation. The Mediator complex, which acts as an RNA Pol II transcriptional coregulator, participates in preinitiation complex (PIC) formation in the promoter region (Soutourina, 2018) and is affected mainly by FGF2 stimulation. Seven proteins belonging to this complex (Med1, Med12, Med13, Med14, Med15, Med19 and Med29) were preferentially decreased after 24 h of stimulation with FGF2. In addition, three Pol II subunits (Polr2c, Polr2g and Polr3e), elongation factors (Elob and Nelfa) and transcription initiation factors (Taf4 and Gtf2e2) were also modulated by FGF2 stimulation. A global run-on assay confirmed that FGF2 stimulation promotes changes in RNA transcription. More specifically, the levels of immature rRNA transcripts increased significantly upon 24 h of stimulus due to an increase in rRNA transcription. The nucleolus size increased concomitantly. Several proteins that participate in rRNA transcription and processing were also modulated. Among these proteins, we highlight that the Utp4 protein Nol11, also involved in transcription and rRNA processing, interacts with Utp4 in these processes (Freed et al., 2012); both were increased after FGF2 stimulation for 1 h (log2 FC 2.0 and 3.1). FGF2 stimulation can also modulate rDNA transcription by decreasing the expression of the RNA Pol I transcription factor Rrn3 (log2 FC −2.5). Another remarkable protein modulated by FGF2 is Nop56, which increased at 1 and 5 h (log2 FC 2.4 and 2.1, respectively). Nop56 forms a complex with Nop58, fibrillarin, Snu13 and Box C/D snoRNAs for rRNA modification and processing. In addition, Nop56 can be controlled by the S6 protein kinase (Chauvin et al., 2014; Monaco et al., 2018). Dias et al. (2019) showed that there were high levels of the S6 protein after stimulation with FGF2, indicating protein synthesis activity. Therefore, the increase in Nop56 levels might be correlated with the increase in phosphorylated S6 after FGF2 stimulation. The level of the critical nucleolar transcription factor UBF increases upon FGF2 overexpression (Fig. S5). Together, the modulation of these proteins shows that stimulation with FGF2 can influence major rRNA processes, such as transcription and processing. In accordance with these results, the effect of FGF2 on rRNA transcription and processing was further confirmed by TEM assays. As rRNA transcription, processing and the formation of precursor ribosomal subunits are compartmentalized inside the nucleolus (Stochaj and Weber, 2020; Lafontaine et al., 2021), we observed profound nucleolar disorganization and an increase in the area of the DFC, indicating a higher abundance in unprocessed transcripts after stimulation with FGF2. The nucleolar disorganization was further confirmed by fibrillarin dispersion. In addition, the nucleolar proteome analysis showed the enrichment of several subunits of chromatin-remodeling complexes (NuRD, NoRC, WICH, SWI/SNF and polycomb) as being modulated by FGF2, indicating that this factor can induce r-chromatin (ribosomal chromatin) remodeling. For example, the Rrp8 protein (upregulated at 1 h, with log2 FC 2.3) is a component of the energy-dependent nucleolar silencing (eNoSC) complex that mediates rDNA silencing by recruiting histone-modifying enzymes (Murayama et al., 2008). Taken together, our results indicated that FGF2 stimulation could induce nucleolar disorganization of rRNA.

Although the FGF2 stimulus delays S-phase and permanently blocks the cells in G2/M transition (Dias et al., 2019), a comparison of FBS-treated and FGF2-treated cells at the same cell cycle phase does not show similar results with regards to their proteome. Thus, the FGF2-induced changes in cell cycle progression cannot fully explain the observed differences between these two treatments. In addition, the FGF2 stimulation presents two phases depending on the early or late stimulation of the cells. Early effects might be related to the cell response to a different transcription regulatory network induced by FGF2 (Vitorino et al., 2018) whereas later timepoints may reflect the cell adaptation to the outcome (cell cycle arrest, senescence and proteotoxic effect) (Costa et al., 2008; Dias et al., 2019) of FGF2 stimulus.

Using CLASP, a cutting-edge approach to retrieve loci-specific proteins, we detected proteomic changes in the proteins bound at rDNA loci. Nolc1 and Tcof1 proteins were upregulated upon FGF2 stimulation. These proteins participate in rRNA processing and post-transcriptional modifications (Chen et al., 1999; Valdez et al., 2004). Proteins associated with rRNA modifications were also detected in the proteomics datasets obtained for the analyses of chromatin or nucleolus fractions. Among these proteins, the Emg1 protein (upregulated at 1 h with log FC 2.1) is involved in 18S rRNA modification and maturation (Wurm et al., 2010; Meyer et al., 2011; Armistead et al., 2014). Accordingly, the Noc4l protein (upregulated at 1 h with log2 FC 2.0) associates with Emg1 (Warda et al., 2016) for the importation of Emg1 into the nucleolus. In addition, we observed increased levels of the fibrillarin protein, which is an rRNA methyltransferase enzyme critical for rRNA processing, and its dispersion through cell nuclei, as discussed above upon FGF2 treatment. Overall, our data suggests that FGF2 stimulation leads to changes in rRNA modifications, which is an unpredicted consequence.

Although we show that FGF2 stimulation increases rRNA transcription and causes nucleolar disorganization, the profile of mature ribosomes was not affected. However, several proteins that bind to rRNA for ribosome maturation and biogenesis are modulated, such as Rpl12, Rpl14, Rpl18, Rpl22, Rpl23, Rpl23a, Rpl3, Rpl7, Rps10, Rps13, Rps15, Rps20 and Rps7 (Fig. S3E). The levels of Emg1 and its associated protein Noc41 (Warda et al., 2016) are increased upon FGF2 stimulation. Both proteins are related to 18S rRNA modification and maturation (Wurm et al., 2010; Meyer et al., 2011; Armistead et al., 2014).

Finally, the inhibition of RNA Pol I using two different inhibitors partially rescued cells from the antiproliferative effect caused by FGF2, suggesting that rRNA transcription might play a critical role. We hypothesize that stimulation with FGF2 increases RNA Pol I activity, leading to nucleolar disorganization. We demonstrate that the antiproliferative FGF2 stimulus triggers significant transcriptional changes and modulation of the main cell transcription site, the nucleolus, directly changing the proteome of the rDNA loci (Fig. 7).

Fig. 7.

Multilayered effects of FGF2 stimulation on Y1 cells. The FGF2 stimulation effects described previously and in this work. The left and right panels highlight the early (1 and 5 h) and late (24 h) effects of the FGF2 stimulus.

Fig. 7.

Multilayered effects of FGF2 stimulation on Y1 cells. The FGF2 stimulation effects described previously and in this work. The left and right panels highlight the early (1 and 5 h) and late (24 h) effects of the FGF2 stimulus.

Cell culture, serum-starvation and growth factor stimulation

The Y1 murine adrenocortical carcinoma (Yasumura et al., 1966), MCF7 and UM-UC-3 cell lines were obtained from the American Type Culture Collection (ATCC). The cell lines were grown at 37°C with a 5% CO2 atmosphere in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% FBS, ampicillin (25 mg l−1), streptomycin (100 mg l−1) and sodium bicarbonate (1.2 g l−1). After plating, the cells were allowed to reach 40% confluence when they were starved by complete removal of FBS from medium for 48 h. Next, the cells were stimulated with DMEM supplemented with 10% FBS, with and without 10 ng ml−1 FGF2 and harvested at the indicated time points.

Chromatin extraction

Y1 cells (four 150 cm2 plates each time point) stimulated with FBS or FS (10 ng ml−1) for 0, 1, 5 and 24 h were obtained in four biological replicates. The ChEP method was performed as previously described (Kustatscher et al., 2014b). Briefly, the cells were rinsed with PBS, 1% (w/v) formaldehyde in PBS (pre-warmed at 37°C) was added to the plates, and incubated for 10 min at 37°C in the CO2 incubator. The cross-linking reaction was halted by adding glycine to a final concentration of 0.25 M and incubation at room temperature (RT) for 5 min. The plates were rinsed with PBS, and the cell suspensions were washed and resuspended in ice-cold cell lysis buffer [25 mM Tris-HCl pH 7.4, 0.1% (v/v) Triton X-100, 85 mM KCl and protease inhibitors (P8340, Sigma-Aldrich)]. Each nuclei pellet was resuspended in 500 μl of cell lysis buffer containing 200 μg/ml RNase A and incubated for 15 min at 37°C. RNase-digested nuclei were resuspended and centrifuged at 2300 g for 10 min at 4°C. The pellet was resuspended in 500 μl of SDS buffer (50 mM Tris-HCl pH 7.4, 10 mM EDTA, 4% SDS and proteases inhibitor), and incubated for 10 min at RT. A urea buffer (1.5 ml) (10 mM Tris-HCl pH 7.4, 1 mM EDTA and 8 M urea) was added, mixed thoroughly by inverting the tube several times, and centrifuged at 16,100 g for 30 min at 25°C. This procedure was performed twice. To wash out urea, the pellet was resuspended twice in 500 μl of SDS buffer. The pellet was covered with 0.5 ml of storage buffer [10 mM Tris-HCl pH 7.4, 1 mM EDTA, 25 mM NaCl, 10% (v/v) glycerol and protease inhibitors] and the samples were sonicated three times alternating 1 min ‘on’ and 1 min ‘off’ intervals, output 7, Duty 70, Tomy ultrasonic disruptor UD-201, in an ice bath. The sonicated samples were spun down at 16,100 g for 30 min at 4°C.

SDS-PAGE and western blotting

Protein fractionation and western blotting were performed as described previously (Vitorino et al., 2018). Blot transparency figures are shown in Fig. S6.

Analysis of global transcription rates by EU incorporation

EU incorporation was performed according to the manufacturer's instructions (Click-iT® RNA Alexa Fluor® 594 imaging kit; cat. no. 10329, Invitrogen). Cells were plated in six-well plates at 2.4×105 cells per well, starved, stimulated with FS and FBS alone, for the given times, followed by incubation with 2 mM EU for 1 h. Then, the cells were fixed, permeabilized as described below, and incubated with Click-iT. DAPI was used for nuclear staining.

Nucleoli extract

Y1 cells (150 cm2 plates) stimulated with FBS or FS (10 ng ml−1 FGF2) for 0, 1, 5 and 24 h were obtained in four biological replicates. The protocol was performed as previously described (Hacot et al., 2010). The purified nucleolar fraction was resuspended in 200 µl of 0.34 M sucrose.

Immunofluorescence

A total of 3×104 cells were plated on eight-well slides (Culture slides, Falcon cat. 354118), starved, and subjected to stimulation with FS and FBS alone, as described above. After stimulation, the cells were washed with ice-cold PBS, fixed with 4% paraformaldehyde for 10 min at room temperature, and washed three times with ice-cold PBS. The cells were permeabilized with 0.1% Triton X-100 in PBS for 10 min at room temperature and washed three times with ice-cold PBS. Blocking with 1% BSA in PBS plus 0.1% Tween-20 was performed for 30 min at room temperature. Primary antibodies diluted as per the manufacturer's instructions in 1% BSA in PBS plus 0.1% Tween-20 were incubated for 1 h at room temperature or overnight at 4°C, and the cells were washed three times with ice-cold PBS. As per the manufacturer's instructions, secondary antibodies diluted in 1% BSA in PBS were incubated for 1 h at room temperature in the dark and washed 3 times with ice-cold PBS. Finally, Vectashield® Antifade mounting medium with DAPI (cat. H-1200) was added, and the coverslip was sealed. The analyses were performed on an Olympus BX51 microscope. Antibodies were from Cell Signaling Technology (anti-fibrillarin #2639S; anti-nucleolin #D4C7O; anti-Polr1a #D6S6S; anti-Rpb1 CTD #4H8) and Abcam (anti-UBF1 ab244287) and were used at a 1:1000 dilution. The analysis was performed using the ImageJ 1.52a software. The mean gray values were used to retrieve the total intensity values of the delimited structure (nucleus or nucleolus). The extra-nucleolar fibrillarin dispersion values were obtained by subtracting the normalized mean gray value of the nucleus (multiplied by its area) from the normalized mean gray value of the nucleolus (multiplied by its area).

Transmission electron microscopy

Y1 cells (∼107) were collected after different times of stimulation with FBS and FGF2 or FBS and fixed in 2.5% glutaraldehyde diluted in 0.1 M sodium cacodylate buffer (pH 7.2) for 30 min at room temperature. After the first fixation, the cells were washed twice with 0.1 M sodium cacodylate, postfixed in 1% osmium tetroxide and 0.8% potassium ferrocyanide diluted in 0.1 M sodium cacodylate (pH 7.2) for 1 h. After washing with 0.1 M sodium cacodylate, the cells were dehydrated in an acetone gradient and embedded in Epon (Electron Microscopy Sciences). Ultrathin sections were stained with uranyl acetate for 40 min and lead citrate for 5 min. The samples were observed under a Zeiss 900 transmission electron microscope (Zeiss).

Northern blotting

Total RNA was extracted from Y1 cells using an Illustra RNAspin mini GE kit (25-0500-72) according to the manufacturer's instructions and quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific). 20 μg of RNA were denatured using Glyoxal (40% or 6 M), separated by electrophoresis on a 1.5% agarose gel and transferred to a nylon membrane (Sambrook and Russell, 2001) (Hybond-N GE cat. RPN303N). Northern hybridization was performed with biotin-labeled oligos, and the membrane was treated with a Chemiluminescent Nucleic Acid Detection Module (Thermo Fisher Scientific) following the manufacturer's protocol. RNA detection after membrane hybridization was performed in a UVITEC Cambridge photodocumentation system. Oligonucleotide sequences were: 5′ETS, 5′-ATCGGGAGAAACAAGCGAGATAGGAATGTCTTA-3′; ITS1, 5′-AAACCTCCGCGCCGGAACGCGACAGCTAGG-3′; ITS2, 5′-CAGACAACCGCAGGCGACCGACCGGCC-3′; 28S, 5′-GAGGGAACCAGCTACTAGATGGTTCGATTA-3′; 18S, 5′-ATCGAAAGTTGATAGGGCAGACGTTCGAAT-3′; and 5S, 5′-CTGCTTAGCTTCCGAGATCAGACGAGATC-3′.

Nascent RNA capture

The protocol was performed according to the manufacturers' instructions (Click-iT™ Nascent RNA Capture Kit, for gene expression analysis Cat. C10365). Briefly, Y1 cells were plated in 100 mm plates at 5×105 cells per well, starved, and stimulated with FBS and FGF2 or FBS alone for 24 h. The nascent RNAs were labeled with 0.5 mM EU by incubation for 1 h. Total RNA was extracted using the Illustra RNAspin mini GE kit (25-0500-72) according to the manufacturer's instructions. The labeled RNAs were biotinylated by incubating 5 µg of EU-RNA with 0.5 mM biotin azide for 30 min, followed by RNA precipitation. 1 μg of precipitated RNA was used for binding with 12 µl of magnetic beads for 30 min. cDNA synthesis was performed using the SuperScript VILO cDNA synthesis kit (Cat. 11754-050), according to the manufacturers' instructions. qPCR was performed as described, using primers (see next section) for the regions 47S and HPRT. Two control samples were used to discount the nonspecific reaction bindings: a sample without EU labeling and a sample with EU labeling without biotin. The protocol was performed on four biological replicates.

RT-qPCR

RNA was extracted from Y1 cells using an illustra RNAspin mini GE kit (25-0500-72) according to the manufacturer's instructions. cDNA was generated using the enzyme SuperScript III Reverse Transcriptase (Thermo) from 4 μg of RNA. 25 ng of cDNA, 200 nM of each primer and 5 μl of SYBR Green reagent (Applied Biosystems) were used. The reaction was performed in triplicate in a Step One Plus thermocycler (Applied Biosystems) with the following program: Stage 1, 1×95°C for 5 min; stage 2, 40×, 95°C for 15 s, 55°C for 20 s and 72°C 20 s; melting curve, 1×95°C for 15 s, 55°C for 20 s, 72°C for 20 s and 95°C for 15 s. The data obtained by Step One Plus software were exported to an Excel spreadsheet, and the efficiency of the primers and CT (threshold cycle) were obtained using the program LinRegPCR (Ruijter et al., 2009). Relative quantification was performed using the Pfaffl method (Pfaffl, 2001). The reactions were performed in triplicate. Primers used were: 47S F, 5′-ACACGCTGTCCTTTCCCTATTA-3′; 47S R, 5′-CCCAAGCCAGTAAAAAGAATAGG-3′; 18S F, 5′-CTATTTTGTTGGTTTTCGGAACTG-3′; 18S R, 5′-TAATGAAAACATTCTTGGCAAATGCT-3′; 28S F, 5′-TACGAATACAGACCGTGAAAGC-3′; 28S R, 5′-CTGTGGTAACTTTTCTGACACC-3′; 5.8S F, 5′-CTCTTAGCGGTGGATCACTC-3′; 5.8S R, 5′-GAAGTGTCGATGATCAATGTGTC-3′; 18S F, 5′-CATTCGAACGTCTGCCCTATC-3′; 18S R, 5′-GCCTCGAAAGAGTCCTGTATTG-3′; HPRT F, 5′-GTTGGATATGCCCTTGACTATAATGAG-3′; HPRT R, 5′-CTGGAAAAGCCAAATACAAAGCC-3′.

Clonogenic assay

For the clonogenic assay, 1000 cells were plated in six-well plates and subjected to stimulation with FGF2 at the indicated times. After the FGF2 incubation period, the culture medium was replaced, and 0.03 μg/ml actinomycin D (A9415, Sigma-Aldrich) was added for 30 min. This concentration and time were previously shown to specifically inhibit RNA Pol I but not RNA Pol II (Fig. S3F). For the CX5461, 0.2 µM was added for 30 min. After the inhibition time, the wells were washed with PBS, and medium with 10% FBS was added to continue with cell culture until visible colonies appeared (10–14 days). After colony growth, the plates were washed with PBS, fixed with 3.7% formaldehyde in PBS for 15 min at room temperature, washed with PBS, stained with 0.5% Crystal Violet (C0775, Sigma-Aldrich) in PBS for 5 min at room temperature, and washed with water. The images of the plates were captured with the Alliance 4.7 blot imaging system (UVITEC Cambridge) and analyzed with ImageJ 1.52a software.

dCas9 expression and purification

The vector pCT310 (Addgene plasmid #111140) was transformed into BL21 (DE3) competent cells (New England BioLabs). Bacterial cultures were induced at optical density (OD) of 0.6 at 600 nm for incubation at 16°C overnight with 0.3 mM isopropyl β-D-1-thiogalactopyranoside (IPTG). HisTag purification was performed according to the manufacturer's instructions (Cobalt resin purification protocol, cat. no. 89964). Briefly, cell pellets were lysed in equilibration/wash buffer (50 mM sodium phosphate, 300 mM sodium chloride, 10 mM imidazole; pH 7.4) supplemented with a 1:100 dilution of 10 mg/ml lysozyme, 0.5 mg/ml DNase I and protease inhibitors. The cells were incubated on ice for 10 min and disrupted using a French press. The lysate was incubated with equilibrated cobalt resin on an end-over-end rotator for 30 min and centrifuged for 2 min at 700 g. The resin was washed four times with two resin-bed volumes of equilibration/wash buffer. The elution was performed five times using one resin-bed volume (each elution) of elution buffer. After HisTag purification (cobalt resin), the eluted fractions were pooled and applied to a size exclusion column (Superose 6 Increase 10/300 GL column, 29091596, GE Healthcare). This was subjected to an isocratic flow of 50 mM HEPES pH 7.5 with 200 mM NaCl or applied to a strong cation exchange column (HiTrap SP HP, 17-115101, GE Healthcare), and subjected to a linear gradient from 0.15 M to 1 M NaCl. Eluted fractions were analyzed by SDS-PAGE, followed by Coomassie Blue or silver staining. Peak fractions were pooled, concentrated, and washed to change buffer to 200 mM NaCl, 50 mM HEPES (pH 7.5), 5% glycerol, and 1 mM dithiothreitol (DTT), using Amicon filters (100 MWCO, Millipore, cat. UFC810096). Samples were aliquoted and flash frozen for storage at −80°C.

CLASP

The CLASP method was performed as previously described (Tsui et al., 2018). Briefly, 5×108 Y1 cells stimulated with FBS or FS (10 ng ml−1 FGF2) for 0 and 24 h were obtained in four biological replicates. Cells were fixed in 1% formaldehyde for 15 min, resuspended in lysis buffer [50 mM HEPES pH 7.9, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% Nonidet P-40, 0.25% Triton X-100 and protease inhibitor cocktail (P8340, Sigma-Aldrich)], incubated on ice for 10 min, spun down, and washed twice with wash buffer (10 mM Tris-HCl pH 8.1, 200 mM NaCl, 1 mM EDTA and 0.5 mM EGTA). The cells pellets were rinsed with shearing buffer (0.1% SDS, 1 mM EDTA, 10 mM Tris-HCl pH 8.1) and sheared in a Covaris S220 instrument until ∼800 bp using the following parameters: Average incident (Power watt), 21; Peak Incident Power (PIP), 140; Duty factor, 15%; Cycles/Burst, 200; Duration, 4 min; Temperature, 6°C; Water level 8. Upon sonication, chromatin was kept in 1% Triton X-100 and 150 mM NaCl as the final concentration. Chromatin was spun down at 12,000 g, 4°C for 10 min. Then 0.18 mg of dCas9-3×FLAG was incubated with guide RNA at a 1:5 molar ratio for 30 min at 37°C in 1× Cas9 buffer (20 mM HEPES pH 7.9, 100 mM NaCl and 5 mM MgCl2). The soluble chromatin was incubated with guide-loaded dCas9 for 1 h under agitation at RT. M2 agarose resin FLAG resin (Sigma) was washed with ultrapure water and then 500 mM NMNT buffer (10× Cas9 buffer pH 7.9, 5 M NaCl and 10% NP40) by resuspending and spinning down at 400 g for 5 min. Resin was added to the chromatin and incubated at RT for 2 h. Resin was washed three times with 500 mM NMNT buffer. Each pull down (5×108 cells) was eluted with 500 μl of elution buffer (Flag peptides), at RT for 3 h by shaking at 1000 rpm. Tubes were incubated at 56°C overnight to decrosslink. 1 µl of salt-activated nuclease (SAN, HL-SAN 25 U/µl; ArcticZymes, cat. no. 70910-202) and 1 µl of Proteinase K were added to remove DNA or protein from samples, respectively. The samples were incubated at 37°C for 2 h while shaking. Samples were frozen and kept at −20°C to be further analyzed by LCMS. Guide RNA sequences were: 18S-T1-A, 5′-GGAGAGGAGCGAGCGACCAA-3′; 18S-T6-B, 5′-CAAAGTCTTTGGGTTCCGGG-3′; 28S-T12-A, 5′-ATTCCCAAGCAACCCGACTC-3′; 28S-T3-B, 5′-GAGGCCTCTCCAGTCCGCCG-3′; 28S-T6-C, GGCGTCCGGTGAGCTCTCGC-3′; 28S-T2-D: 5′-CAGCTCACGTTCCCTATTAG-3′; Scramble: 5′-GGTGTGGACGTATCCGTATC-3′.

Proteomics analysis

Protein precipitation and digestion

100 μg of protein was adjusted to a final volume of 100 μl with 100 mM Tris-HCl pH 8.5. To the sample, 5 μl of 200 mM tris(2-carboethoxy)phosphine hydrochloride (TCEP; Pierce) was added, and the sample was incubated at 55°C for 1 h. 2-Chloroacetamide (CAM; 5 μl of 375 mM; Sigma-Aldrich) was added to the sample and incubated for 30 min in the dark at room temperature. Six volumes (∼600 μl) of prechilled (−20°C) acetone were added, followed by overnight precipitation at 4°C. The samples were centrifuged at 8000 g for 10 min at 4°C. The tubes were carefully inverted to decant the acetone without disturbing the white pellet. The pellet was allowed to dry for 2–3 min. 100 μg of acetone-precipitated protein pellets were resuspended in 100 μl of 100 mM Tris-HCl pH 8.5. Trypsin (1 µg per 100 µg of protein) was added to the sample, and digestion was allowed to proceed overnight at 37°C.

Label-free multidimensional protein identification technology

Chromatin extracts and CLASP samples were analyzed independently by MudPIT, as described previously (Washburn et al., 2001; Florens and Washburn, 2006). Briefly, samples were reduced with TCEP (Pierce) and carbamylated with chloroacetamide (Sigma-Aldrich), then digested with recombinant endoproteinase Lys-C (Promega), followed by sequencing grade modified trypsin (Promega). After digestion, peptide mixtures were pressure-loaded onto 100-µm fused silica microcapillary columns packed first with 9 cm Aqua 5-µm C18 reverse-phase material (Phenomenex), followed by 3 cm Luna 5-µm Strong Cation Exchange material (Phenomenex), followed by 1 cm 5-µm C18 reverse-phase. The loaded microcapillary columns were placed in line with a 1260 quaternary high performance liquid chromatography (HPLC) (Agilent). The application of a 2.5-kV distal voltage electrosprayed the eluting peptides directly into Orbitrap-Velos Pro or Elite hybrid mass spectrometers (Thermo Fisher Scientific) equipped with a custom-made nanoliquid chromatography-electrospray ionization source. Full mass spectrometry (MS) spectra were recorded on the eluting peptides over a 400–1600 mass-to-charge range in the Orbitrap at 60,000 resolution, followed by fragmentation in the ion trap (at 35% collision energy) on the first to fifteenth-most intense ions selected from the full MS spectrum. Dynamic exclusion was enabled for 90 s (Zhang et al., 2009). Mass spectrometer scan functions and HPLC solvent gradients were controlled by the XCalibur data system (Thermo Fisher Scientific). RAW files were extracted into .ms2 file format (McDonald et al., 2004) using RawDistiller v. 1.0, in-house-developed software (Zhang et al., 2011). RawDistiller D(g, 6) settings were used to abstract MS1 scan profiles by Gaussian fitting, and implement dynamic offline lock mass using six background polydimethylcyclosiloxane ions as internal calibrants (Zhang et al., 2011). Tandem mass spectrometry (MS/MS) spectra were first searched using ProLuCID (Xu et al., 2015), with peptide mass tolerances of 6 ppm and 500 ppm for fragment ions. Trypsin specificity was imposed on both ends of candidate peptides during the search against a protein database containing 53,362 Mus musculus proteins [National Center for Biotechnology Information (NCBI), June 27, 2018, release] as well as 193 usual contaminants, such as human keratins, IgGs and proteolytic enzymes. The 3×FLAG and dCas9 sequences were also added to the database. Each protein sequence was randomized to estimate false discovery rates (FDRs), keeping the same amino acid composition and length. The resulting ‘shuffled’ sequences were added to the database for a total search space of 107,114 amino acid sequences. Masses of 57.0215 Da were statically added to cysteine residues to account for carboxyamidomethylcysteine, and 15.9949 Da were differentially added to methionine residues. DTASelect v.1.9 (Tabb et al., 2002) and swallow v.0.0.1, an in-house developed software (available from https://zenodo.org/records/5914885), were used to filter ProLuCID search results at given FDRs (1% or less) at the spectrum, peptide and protein levels. Peptides had to be at least seven amino acids long. The results from each sample were merged and compared using CONTRAST (Tabb et al., 2002) and in-house developed sandmartin v.0.0.1 (available from https://zenodo.org/records/5914885). Combining all four replicates for each time point, proteins had to be detected by at least two peptides or two spectral counts. Proteins that were subsets of others were removed using the parsimony option in DTASelect on the proteins detected after merging all runs. Proteins identified by the same set of peptides (including at least one peptide unique to such a protein group to distinguish between isoforms) were grouped, and one accession number was arbitrarily considered representative of each protein group. NSAF7 (Zhang et al., 2010) was used to create the final reports on all detected peptides and nonredundant proteins identified across the different runs. Spectral and protein-level FDRs were, on average, 0.1±0.09% and 2.4±1.0% (mean±s.e.m.), respectively. NSAF7 (Zhang et al., 2010; available from https://zenodo.org/records/5914885) was also used to generate a list of all PSMs leading to the identification of proteins.

TMT-labeled multiplexed LC/MS analysis

60 μg of soluble protein from nucleolus extracts were transferred to a clean tube, the volume was adjusted to 100 µl with 100 mM TEAB, and cysteines were reduced by 5 mM TCEP for 30 min at room temperature. Cysteine residues were alkylated by 10 mM CAM for 30 min at room temperature in the dark. Proteins were precipitated by adding 600 µl of ice-cold acetone, and the precipitation was allowed to proceed overnight at −20°C. The precipitated proteins were centrifuged for 10 min at 10,000 g, and the supernatant was discarded. Pellets were allowed to air dry and resuspended in 50 µl of 100 mM TEAB. Proteins were digested by adding 10 µl trypsin (Promega, 0.1 mg/ml), and the reaction was allowed to proceed overnight at 37°C with shaking. Following digestion, peptides were quantitated by the Pierce Colorimetric Peptide Assay (Thermo). Solid phase extraction (SPE) was performed to remove excess salt. Tandem mass tag (TMT) (Thompson et al., 2003) labeling was performed according to the manufacturer's instructions (TMT10plex Mass Tag Labeling kit, cat. no. 90110). Briefly, 25 µg of peptides from each sample was resuspended in 40 µl of 100 mM TEAB and 10 µl of 100% acetonitrile. Each TMT label reagent (Thermo Fisher Scientific) was resuspended with 84 µl of 100% acetonitrile in a 0.8 mg vial. 10 μl of the TMT label reagent was added to each sample (0 h,TMT-126; S 1 h, TMT-127N; SF 1 h, TMT-128N; S 5 h, TMT-129N; SF 5 h, TMT-130N; S 24 h, TMT-130C; SF 24 h, TMT-131), and the reaction was allowed to proceed for 1 h at room temperature. Prior to quenching, 10% of the tagged reaction was transferred to a clean autosampler vial and diluted to 25 µl with buffer A [5% acetonitrile (ACN) and 0.1% formic acid (FA)] to check the labeling efficiency by reverse phase LC-MS/MS analysis. After checking that at least >75% of the detected peptides were labeled, the reaction was quenched with 8 µl of hydroxylamine per sample and incubated for 15 min. Equal amounts of each TMT-labeled sample were combined, and the resulting multiplexed mixture was dried using a speed vacuum evaporator. The samples were resuspended in 50 µl of buffer A (5% acetonitrile and 0.1% formic acid). The peptides were loaded onto the trap column [PepMap, 0.3 mm internal diameter (i.d.), 5 mm, 5 µm particles] using the Ultimate 3000 autosampler. Peptides were eluted onto an in-house packed column [75 µm i.d., 15-cm length packed with 1.9 µm ReproSil-Pur C18-AQ resin (Dr Maisch GmbH, Germany)] and eluted with a 1-h gradient from 10%–40% buffer B (80% ACN and 0.1% FA). The flow rate was set to 180 nl/min. A voltage of 2.5 kV was distally applied to the column, which was directly interfaced with a Thermo Orbitrap Eclipse tribrid mass spectrometer with a FAIMS Pro interface. TMT-labeled peptides were detected in the Orbitrap at a resolving power of 120,000, and fragmented in the ion trap in rapid mode by collision-induced dissociation (CID) at 35% normalized collision energy (NCE). The top 10 fragments ions were selected by synchronous precursor selection and fragmented by higher-order collision dissociation (HCD) at 65% HCD (SPS-MS3), to fragment and detect the TMT reporter ions in the Orbitrap at a resolving power of 50,000. Data were analyzed in Proteome Discoverer 2.4 with SEQUEST-HT as the search algorithm (Thermo Fisher Scientific). Peptides were searched with the TMT reporter ion (+229.1629 Da) on the peptide N-terminus and cysteine carboxyamidomethylation (+57.0125 Da) as static modifications, while TMT-labeled lysines (+229.1629 Da) and methionine oxidations (+15.9949 Da) were set as variable modifications.

Cytoscape analysis

A functionally grouped network was generated with the statistically significant proteins. Each node represents a biological GO process, and the colors represent the GO groups. The ClueGO plugin (V2.5) (Bindea et al., 2009) for Cytoscape (V3.4.0) (Shannon et al., 2003) was used with the following settings: Kappa Score threshold=0.5, Min GO level=3, Max GO level=8, Min genes per cluster=3, Min % genes=4, GO Term Fusion=True. The cut-off for enriched terms was set to a familywise error rate of an adjusted P≤0.05.

The authors are grateful to Karin Navarro, Ismael Feitosa Lima, Ivan Novaski Avino and David Pires for technical assistance. The authors thank Drs M. Carolina Elias, Juliana Roson, Carolina Bras, Charles Banks, Melissa Mathews, Selene Swanson, Janet Thornton, Tim Wen, Jennifer Gerton, Tamara Patapova for important discussions. The figure schemes were designed using BioRender.

Author contributions

Conceptualization: F.N.d.L.V., H.A.A., L.A.F., M.P.W., J.P.C.d.C.; Methodology: F.N.d.L.V., C.C.O., L.A.F., M.P.W., J.P.C.d.C.; Software: F.N.d.L.V.; Validation: F.N.d.L.V., M.J.L., R.A.M.W., M.L., M.L.S., M.C.M.M., C.C.O.; Formal analysis: F.N.d.L.V., M.J.L., M.E.S., L.A.F., J.P.C.d.C.; Investigation: F.N.d.L.V., C.C.O., J.P.C.d.C.; Resources: F.N.d.L.V., B.A.G., L.A.F., J.P.C.d.C.; Data curation: F.N.d.L.V., M.J.L., L.A.F.; Writing - original draft: F.N.d.L.V., J.P.C.d.C.; Writing - review & editing: F.N.d.L.V., M.J.L., R.A.W., M. Lopes, M.E.S., M.C.M.M., C.C.O., H.A.A., L.A.F., M.P.W., J.P.C.d.C.; Visualization: F.N.d.L.V., M.J.L.; Supervision: M.P.W., J.P.C.d.C.; Project administration: M.P.W., J.P.C.d.C.; Funding acquisition: H.A.A., M.P.W., J.P.C.d.C.

Funding

This work was funded by the São Paulo Research Foundation [Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, SP, Brazil)] (grants #17/18344-9, #19/17675-7, #11/22619-7, #17/06104-3, #18/15553-9 and #13/07467-1) and Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (CAPES, Brasília, DF, Brazil, financial code 001). M.J.L., M.E.S., L.A.F., and M.P.W. were supported by the Stowers Institute for Medical Research.

Data availability

Mass spectrometry datafiles are available from MassIVE and ProteomeXchange (PXD037300; PXD037339; PXD037310) for chromatin MudPIT, nucleoli TMT and CLASP MudPIT datasets, respectively. Original mass spectrometry data can also be accessed after publication from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1727.

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Competing interests

The authors declare no competing or financial interests.