Time-resolved multiomics profiling reveals chromatin O-GlcNAc modification promotes senescence-associated transcriptional program

Domain: chemistry
Published: 2026-01-08
Editorial outcome: major_revision

Abstract
O-GlcNAc modification is a key cellular signal, but its role in regulating senescence-associated transcription remains poorly understood. Here, we apply a time-resolved chemical genomics strategy to map dynamic O-GlcNAc chromatin-associated proteins (OCPs) during oncogene-induced senescence (OIS) in primary human fibroblasts. Chromatin O-GlcNAc modification continues to accumulate, while 1,987 senescence-associated OCPs undergo dynamic shifts in genomic occupancy across diverse epigenetic chromatin states and display bimodal regulatory activities within the 3,466-gene senescence transcriptome. O-GlcNAc facilitates the formation of dual-function complexes: TF–SWI/SNF activates senescence-associated secretory phenotype (SASP) genes at promoters, whereas NuRD enforces the repression of cell-cycle regulators at enhancers. Furthermore, we identify O-GlcNAc modified JUN and GATAD2A as key regulators of OIS phenotypes in both in vitro and in vivo models of senescence-driven tumorigenesis. These findings reveal dynamic regulation and chromatin organization principles of O-GlcNAc–related epigenetic factors, providing insights into cellular senescence and potential therapeutic strategies.

Introduction
Cellular senescence, which involves the irreversible transition of cells from the cell cycle to a state of proliferative arrest due to internal and external stresses, is a primary promoter of age-related diseases such as cancer1,2. Cellular senescence is predominantly categorized into replicative senescence and premature senescence. An increase in cell division frequency and telomere shortening can induce replicative senescence, whereas diverse stimuli, such as oxidative stress, DNA damage, and oncogene activation, can trigger premature senescence3.

Cellular senescence is a complex process that occurs over multiple stages and is characterized by dynamic shifts. During this process, senescent cells undergo a series of transformations that result in distinctive phenotypes. These transformations include the development of an enlarged flat morphology, cessation of the cell cycle, and an increase in senescence-associated β-galactosidase (SA-β-Gal) activity4,5. Concurrently, these cells also exhibit an altered secretion phenotype characteristic of the senescence-associated secretory phenotype (SASP), which has been associated with promoting neoplastic cell proliferation and metastasis in many cancers6–9. Further investigation revealed that these abnormal biological activities occurring during the senescence process are driven by transcriptional characteristics and temporal shifts in the gene expression profile, a phenomenon modulated predominantly through epigenetic regulation10–12. Therefore, clarifying the pivotal gene-regulatory networks governing senescent cell fate could identify potential targets for fighting aging-related diseases.

O-linked β-N-acetylglucosamine (O-GlcNAc) modification, a posttranslational modification (PTM) enriched in the nucleus and cytoplasm, contributes substantially to the regulation of chromatin assembly and gene expression13–15. O-GlcNAc is a ubiquitous and reversible monosaccharide modification that occurs on protein serine/threonine residues and is catalyzed by the corresponding enzymes: the glycosyltransferase OGT and the glycosidase OGA. Cells can efficaciously modulate the O-GlcNAc levels of an array of pivotal enzymes and signaling molecules, thereby altering the activity and biological functionalities of the substrate proteins, to respond adeptly to alterations in environmental conditions and signal stimulation16–20. We and others have substantiated that chromatin complexes commonly contain O-GlcNAc modifications, significantly impacting the activity of various epigenetic factors, such as transcription factors (TFs) and chromatin remodeling factors21–27. Dynamic alterations in O-GlcNAc levels, which occur by modulating the stability of these chromatin-binding proteins as well as protein–protein interactions and protein–DNA interactions, influence gene expression on a genomic scale, reconfigure cellular transcriptional patterns, and drive phenotypic manifestations in cells. Recent studies have indicated that O-GlcNAc modification plays an important role in the process of cellular senescence28,29. Moreover, O-GlcNAc can promote DNA damage repair in breast cancer cells and inhibit tumor therapy-induced cellular senescence (TIS)30. STAT3 also contains O-GlcNAc, and downregulating the glycosylation of this TF in neuronal cells can activate target gene expression and induce senescence phenotypes31. The repression complex TRIM28 needs OGT for its assembly on methylated promoters, with O-GlcNAc stimulating its functions and promoting cellular senescence phenotypes32. O-GlcNAc also stabilizes GATA4, promoting SASP acquisition and chronic inflammation; thus, O-GlcNAc plays a key role in osteoarthritis progression33. These findings suggest that the process of cellular senescence requires the involvement of various O-GlcNAc modified epigenetic factors, driving the formation of a characteristic senescence gene expression profile. However, revealing the integrated mechanism through which O-GlcNAc modification orchestrates multiple epigenetic factors and sequentially shapes the character of the senescent cell transcriptome remains challenging.

Herein, to elucidate the regulatory role of O-GlcNAc modification in the oncogene-induced senescence (OIS) process, we employed a chemical reporter-based multiomics strategy for time series proteomic profiling and genome-wide mapping of O-GlcNAc chromatin-associated proteins (OCPs) in primary human fibroblasts. Through dynamic analysis of the transcriptome and epigenome profiles, we demonstrated that OCPs undergo a genomic occupation switch across diverse epigenetic chromatin states and exhibit sequential regulatory activity in the senescence transcriptome. We demonstrate that O-GlcNAc modification facilitates the development of bimodal epigenetic regulatory complexes that control both the SASP and the cell cycle arrest-related genes during OIS. We further identified O-GlcNAcylated JUN and GATAD2A as regulatory epigenetic factors that influence OIS phenotypes using both in vitro and in vivo models of senescence and tumor growth. Collectively, our findings offer evidence for the dynamic behavior and chromatin organization principles of O-GlcNAc related epigenetic factors in driving the senescence process. Our findings also reveal potential avenues for therapeutic manipulation of aging-related diseases, including cancer.

Results
Dynamic accumulation of chromatin O-GlcNAc modifications is associated with the progression of premature senescence in human cells

Primary human lung and skin fibroblasts (derived from individuals; LU1015 and SK0304) and normal human epithelial cell lines (MCF10A, mammary origin; HaCaT, skin origin) were used to investigate the role of O-GlcNAc modification during cellular senescence. To avoid complications associated with the loss of telomeric sequences during premature senescence3, we immortalized the primary fibroblasts by transduction of a lentivirus expressing hTERT (generating monoclonal populations; Supplementary Fig. 1a). Oncogene-induced premature senescence (OIS) models were subsequently established using a doxycycline (Doxy)-responsive Tet-On human H-RASG12V expression system (abbreviated as LU-RAS, SK-RAS, MCF-RAS, and HaC-RAS cells), and the classic biomarkers12 of cellular senescence were assessed (Fig. 1a, b, Supplementary Fig. 1b–i). LU-RAS and SK-RAS fibroblasts undergoing OIS exhibited G2/M arrest, which is consistent with many established features of cellular senescence. In contrast, MCF-RAS epithelial cells were arrested in S phase, in agreement with prior reports35, and HaC-RAS epithelial cells exhibited a similar phenomenon. These differences may reflect cell type–specific programs and the distinct modalities by which senescence is induced.

To elucidate the cellular O-GlcNAc modification signatures of OIS, we performed a time series analysis of these models at six time points: Days 0, 2, 4, 6, 8, and 10 (D0–D10). Notably, the formation of a senescence phenotype was accompanied by dynamically elevated levels of O-GlcNAc in both total cell lysates and chromatin precipitates (defined as O-GlcNAc chromatin-associated proteins, OCPs). Consistent with the findings of previous studies29, OGT expression increased with the induction of oncogenic H-RASG12V. Moreover, OGA expression was attenuated during OIS progression (Fig. 1c). We further revealed that chromatin-associated OGT is upregulated concomitant with OIS. These findings suggest that excess O-GlcNAc modification on chromatin, which is catalyzed by OGT, may be involved in regulating the senescence program.

Furthermore, we modulated cellular O-GlcNAc levels by altering OGT expression using shRNA-mediated knockdown and an OGT overexpression construct (pcDNA-OGT) and by pharmacologically inhibiting OGT and OGA with the potent inhibitors OSMI-4 and Thiamet-G (TMG), respectively. Indeed, SA-β-Gal activity and proliferation arrest assays revealed that the OIS process was markedly slowed when the O-GlcNAc level was decreased in both fibroblasts and epithelial cells (Fig. 1d, e, Supplementary Fig. 2). In addition, the expression of bona fide markers of senescence, such as p53, p21WAF1/Cip1, and p16INK4A, was decreased, whereas compared with that in control group fibroblasts undergoing OIS, the expression of LMNB1 and MKI67 increased in the absence of O-GlcNAc modification (Fig. 1f, g). In contrast, increasing O-GlcNAc modification approximately doubled the number of SA-β-Gal–positive cells, reduced the number of EdU-positive cells by approximately twofold, and effectively accelerated the acquisition of the senescence phenotype. Taken together, these results clearly show that O-GlcNAc modification facilitates the participation of human cells in senescence.

Time-resolved multiomics profiling reveals that OCPs impact transcriptional signatures in fibroblasts undergoing OIS

Accumulating evidence has demonstrated that the genomic binding of OCPs serves as a sensor for the rapid induction of transcriptome reprogramming in cell fate decisions18,36,37. We employed a metabolic chemical reporter-based multiomics strategy developed in a previous study18 to systematically elucidate the mechanisms by which OCPs orchestrate the OIS program. An optimal chemical reporter, 1,6-Pr2GalNAz, which is capable of efficient metabolic incorporation into O-GlcNAc modified proteins while preventing the nonspecific S-glycosylation modification38, was added during the culture of senescent LU-RAS cells. We subsequently performed chemoselective conjugation using an alkyne–biotin-tag for specific isolation and hierarchical analysis of azide-labeled O-GlcNAc (O-GlcNAz) chromatin. We profiled the presence of nucleus-localized OCPs using quantitative proteomics and mapped the chromatin occupation dynamics of the interrelated OCPs using next-generation sequencing (chemoselective O-GlcNAc chromatin sequencing, COGC-seq) in a time series pattern during OIS progression. To further elucidate the transcriptomic and epigenomic dynamics signatures of OIS, RNA-seq, histone modification ChIP-seq, and whole-genome DNA methylation (WGBS) analyses were performed at the time points indicated in Fig. 2a. Crosslinked chromatin complexes were fractionated and extracted, with decrosslinking performed prior to proteomic analysis to minimize potential nonspecific carryover (Supplementary Fig. 3a). The successful incorporation of azides into OCPs was verified through immunoblotting of cell lysates, chromatin precipitates, and the biotin–streptavidin pulldown fraction from LU-RAS cells undergoing OIS and cultured with 1,6-Pr2GalNAz (Fig. 2b).

Principal component analysis (PCA) and quantitative analysis of the proteomics data demonstrated both the reproducibility across biological replicates and the significant changes in OCPs over OIS time courses (Fig. 2c). Specifically, compared with that at D0, high-confidence OCPs (identified in at least six of nine biological replicates at any timepoint) exhibited increased chromatin binding and enrichment in cellular senescence-related functions at D10, with 436 upregulated and 93 downregulated OCPs meeting significance thresholds (fold change≥2, p≤0.05; Fig. 2d, e). Furthermore, 1987 OCPs (nucleus-localized) were identified, all of which exhibited a consistent quantitative increase in abundance during OIS progression (Supplementary Data 1). Ontological analyses of these OCPs revealed their enrichment in terms related to transcriptional regulation, chromatin organization, cellular senescence and epigenetic regulation of gene expression, and further protein–protein interaction (PPI) network analysis demonstrated interconnected nodes among these functional complexes, suggesting a role for OCPs in coordinating OIS transcriptional signatures (Supplementary Fig. 3b–d). Subsequently, global gene expression profiling by RNA-seq revealed a total of 10,776 differentially expressed genes (DEGs), including 5430 upregulated and 5346 downregulated genes, between at least two time points during OIS progression (fold change≥2 and FDR≤0.05; Fig. 2f; Supplementary Data 2). The transcriptome data revealed dynamic gene expression trajectories, with a notable shift at D4 (D4–D10 showing similar expression trends), supporting the selection of D0, D2, D4, and D10 as key time points for advanced COGC-seq and epigenome analyses (Fig. 2g and Supplementary Fig. 4a). Both gene ontology (GO) and gene set enrichment analysis (GSEA) of the DEGs revealed that cell cycle-related pathways were significantly downregulated in senescent cells, whereas SASP-associated pathways, such as the TNF-α and inflammatory response pathways, were upregulated (Fig. 2h and Supplementary Fig. 4b). COGC-seq peak calling analysis revealed a progressive increase in both the peak-centered signal intensity and the proportion of peaks mapping to promoters for OCPs during the OIS process, accompanied by distinct peak-associated genes and unique characteristics of OCP loci across the genome (Fig. 2i, Supplementary Fig. 5, and Supplementary Data 3). These data obtained from the chemoselective O-GlcNAz chromatin sequencing analysis suggest that the increasing O-GlcNAc modification during OIS in fibroblasts results in increased binding of OCPs to chromatin, potentially mediating the precise transcriptional regulation of senescence-related genes.

OCPs undergo a genomic occupation switch and exhibit bimodal epigenetic regulatory activity in the senescence transcriptome

To comprehensively investigate the role of O-GlcNAc modification in orchestrating gene expression during OIS cell fate transition, we examined multiple chromatin epigenetic marks and mapped a comprehensive landscape of the dynamic chromatin loci of OCPs. COGC-seq and histone 3 PTM ChIP-seq datasets were integrated, and unsupervised ChromHMM analysis was performed. This approach allowed us to estimate the genome-wide co-occurrence of O-GlcNAc with or without distinct chromatin state markers (H3K4me1, putative enhancers; H3K4me3, putative promoters; H3K27ac, active cis-elements; and H3K27me3, repressed chromatin; Supplementary Fig. 6). Across all four OIS time points (D0, D2, D4, and D10), we delineated 8 chromatin states, each characterized by a distinct enrichment pattern pertaining to one or more markers, including O-GlcNAc (Fig. 3a). The fraction of O-GlcNAc related chromatin states can be categorized into four functional classes: O-GlcNAc promoter, O-GlcNAc enhancer, O-GlcNAc occupied transcriptionally repressed chromatin (O-GlcNAc-repressed), and a class lacking any of the profiled histone modifications (O-GlcNAc-unmarked). Consistent with the genome-wide increase in OCP binding, the cumulative fraction of chromatin states occupied by O-GlcNAc (i.e., the sum of the O-GlcNAc promoter, O-GlcNAc-repressed, and O-GlcNAc-unmarked classes) was increased in OIS-affected cells, from a minimum of 64.1% at D2 to a maximum of 83.5% at D10 of the genomic analysis. Notably, there were two main extensive internal switch directions within the chromatin states of the entire class from D0 to D10, with a high frequency of chromatin state transitions detected at O-GlcNAc-enhancers and a moderate influence observed at O-GlcNAc-promoters (Fig. 3b and Supplementary Fig. 7). During the D0–D2 and D4–D10 intervals, many enhancer-marked regions underwent complete removal of active markers and became either O-GlcNAc-unmarked or O-GlcNAc-repressed chromatin, indicating the repression of these chromatin regions during OIS. Another category exemplified the transition from other chromatin states to the O-GlcNAc promoter state, which resulted in a gradual increase in the proportion of this specific promoter type, indicating the activation of these chromatin regions during OIS. These data indicate that OCPs participate in the bimodal dynamics of both sequential enhancer repression and promoter activation during the OIS process. These results were also confirmed by the increased degree of conservation with the distributions of H3K27me3, H3K4me1, and DNA methylation in COGC-seq–annotated intergenic/enhancer peaks (the subset of COGC-seq peaks annotated as intergenic/enhancer regions, from D0 to D10), while the COGC-seq–annotated promoter peaks (COGC-seq peaks annotated as promoter regions) exhibited increased intensity levels of activating histone markers (H3K4me3 and H3K27ac, Fig. 3c).

To further elucidate the characteristics of the genomic occupation switch of OCPs during the process of OIS, we quantitatively analyzed the peaks derived from COGC-seq datasets (Supplementary Fig. 8 and Supplementary Data 4). Given the increased genomic binding of OCPs, along with their occupancy at promoters, and the decreased signals at intergenic/enhancer regions (Fig. 2i), we further conducted a functional analysis of these quantitatively altered COGC-seq peaks (all MA norm quantitatively differential peaks compared to D0 were included; Fig. 3d). Functional enrichment analysis revealed that genes associated with the quantitatively decreased subset of the OCP chromatin-binding sites (COGC-seq peaks) were linked to cell cycle regulation, whereas numerous genes related to the quantitatively increased subset were annotated as being involved in SASP-related signaling (Fig. 3e). Despite the lack of discernible alterations in the distribution of DNA methylation throughout the entire genome (Supplementary Fig. 9), compared with the peaks at D0, the enhancer peaks quantitatively decreased at D2–D10 exhibited prominently higher levels of DNA methylation, H3K4me1 and the repressive marker H3K27me3 (Fig. 3f, Supplementary Fig. 10). On the other hand, at the quantitatively increased promoter peaks, the intensity of H3K4me3 and H3K27ac were clearly increased, whereas DNA methylation was slightly decreased. These findings underscore the dual regulatory role of OCPs in orchestrating the OIS program. To determine the correlations between OIS-induced OCP chromatin binding and gene expression, we compared differential COGC-seq peak-associated genes with DEGs identified by RNA-seq (Fig. 3g, Supplementary Data 5). Importantly, genes associated with COGC-seq–annotated decreased enhancer peaks (e.g., CCND1, STAG2, CDK6, and E2F8) displayed progressively decreased expression (2187 genes) and were enriched in processes related to the cell cycle and proliferative arrest. Conversely, genes linked to promoter peaks with increased OCP binding according to COGC-seq (e.g., IL6 and IGFBP2) showed temporally upregulated expression (1279 genes) and were associated with cellular stress response- and SASP-related functional terms (Fig. 3h, i), indicating that O-GlcNAc associated enhancer suppression and promoter activation were tightly coordinated with the temporal expression patterns of these genes. Collectively, our findings suggest that during OIS, OCPs exhibit an increase in genome-wide chromatin interactions, undergo a transcriptionally repressed enhancer and activated promoter bidirectional genome-wide binding switch, and thus participate in orchestrating the transcriptomic characteristics of OIS phenotypes.

OCPs assemble distinct epigenetic transcriptional complexes during the progression of OIS

To further elucidate the mechanism by which OCPs regulate the OIS transcriptome, we leveraged quantitative proteomics data to construct an OCP interaction network (Supplementary Fig. 3e), revealing two principal groups of epigenetic transcriptional OCP complexes. PPI analysis demonstrated that the transcriptional repression group contained nodes such as chromatin remodeling proteins and DNA methylation-dependent regulatory factors, which centrally engage the NuRD repressor complex as the core. Another group was centered on the SWI/SNF complex, encompassing multiple TFs, which play an important role in transcriptional activation (Fig. 4a). Furthermore, we confirmed that multiple members of the NuRD and SWI/SNF complexes, along with TFs and OGT, exhibited stronger interactions with chromatin at D10 than at D0 in OIS-affected cells (Fig. 4b). To identify the TFs regulated by O-GlcNAc and involved in gene expression regulation during the OIS process, we subsequently scanned the quantitatively increased COGC-seq–annotated promoter peaks (from D0 to D10) using de novo motif discovery algorithms. After evaluating the potential TF-binding sites and motif enrichment values, we identified a variety of TF motifs that were also identified by OCP proteomics (Fig. 2d), with distinct distribution patterns varying from D0 to D10 (Fig. 4c). bZip TFs, particularly AP-1 superfamily members (e.g., JUN, ATF7, and FOSL2), were found to exhibit increased activity and a continuously increase in chromatin binding during OIS, corroborating previous reports39,40. In addition, substantial changes in the chromatin-binding parameter were identified for the forkhead helix (FOX) family (FOXK1, RFX5) and C2H2 zinc finger family of TFs, implying that O-GlcNAc modification of these epigenetic factors could have an appreciable effect on OIS phenotypes.

We then examined the O-GlcNAc state of the NuRD and SWI/SNF components during the OIS process (Fig. 4d). Consistent with the OCP proteomics results (Fig. 2d), nearly all components of the NuRD repressor complex (MTA1, MBD2, CHD3, and GATAD2A) were identified as being O-GlcNAcylated, with the extent of glycosylation increasing as OIS progressed. The results of pulldown assays using succinylated wheat germ agglutinin (sWGA), a lectin that specifically recognizes the O-GlcNAc moiety41, corroborated these findings (Supplementary Fig. 11). Similar results were obtained for the SWI/SNF activator complex (including SMCA4, SMRC1, and SNF5) and TFs (including JUN and FOXK1), indicating the important role of O-GlcNAc modification in the activity of these diverse epigenetic complexes. Co-IP assays further demonstrated that the interactions between NuRD and TF-SWI/SNF complexes were amplified in OIS-affected cells (Fig. 4e). However, inhibiting O-GlcNAcylation with the small molecule OGT inhibitor OSMI-4 markedly disrupted the assembly of these complexes, indicating the important role of O-GlcNAc in the interactions among diverse epigenetic complex components during OIS.

O-GlcNAc modification facilitates the formation of TF-SWI/SNF and NuRD complexes that perform diverse functions in senescence reprogramming

To elucidate the role of O-GlcNAc modification in the assembly of epigenetic complexes and the impact of these complexes on senescence reprogramming, a comprehensive analysis was conducted to identify O-GlcNAc sites in OCPs (Fig. 5a). By utilizing an alkyne–biotin tag with a UV-cleavable linker, we concentrated and examined the O-GlcNAz modified peptides from OIS chromatin through higher-energy collision dissociation product-dependent electron transfer/higher-energy collisional dissociation (HCD pdEThcD)-tandem mass spectrometry (MS). In total, 128, 526, 657, and 688 high-confidence O-GlcNAc sites were identified (present in at least two of three biological replicates at any given time point) in OIS-affected cells at D0, D2, D4, and D10, respectively. Altogether, 969 sites were identified across all the samples, indicating dynamic changes in O-GlcNAc modifications during OIS (Supplementary Data 6). These sites were distributed across 405 proteins, which exhibited significant enrichment in biological processes such as chromatin organization and epigenetic regulation of gene expression (Fig. 5b and Supplementary Fig. 12). Although several O-GlcNAc sites have been reported in the O-GlcNAc Database v2.042, our mass spectrometry data acquired during OIS in LU-RAS cells enabled the pinpointing of potential O-GlcNAc sites on key components of the NuRD and TF–SWI/SNF complexes—specifically, S182 and S625 of GATAD2A, S84 of JUN, T679 of FOXK1, and S1494 of ARID2 (Fig. 5c, and Supplementary Fig. 13). Moreover, a subset of these sites was well conserved across species (e.g., S182/S625 of GATAD2A and S84 of JUN), implying a pivotal functional role. However, previously reported sites near GATAD2A S182 (T175, T180, S182, S185, and T188) and in other proteins were not detected in our MS analysis. A plausible explanation for this discrepancy is the source difference, since many database entries are derived from tumors with elevated O-GlcNAcylation, whereas our normal human embryonic lung fibroblasts have lower O-GlcNAcylation levels, which reduces their detectability even with high-sensitivity MS.

GATAD2A plays a crucial role in the assembly of the integrated NuRD complex, significantly contributing to methylated DNA binding and transcriptional repression43–45. Moreover, the AP-1 family of TFs, typified by JUN, plays an important role in cellular senescence39,46. Hence, in this study, their functionalities related to O-GlcNAc modifications were analyzed in depth. LU-RAS cells were stably transduced with lentiviruses containing shRNAs targeting GATAD2A and JUN (shGATAD2A and shJUN, respectively; referred to as LU-RAS/KD cells), and recombinant wild-type constructs (tandem expression of Flag-GATAD2A WT and HA-JUN WT; LU-RAS/KD-WT cells) or mutant constructs (tandem expression of Flag-GATAD2A S182A and HA-JUN S84A; LU-RAS/KD-AA cells) with expression at comparable levels were then reintroduced (Supplementary Fig. 14a, b). The co-IP and sWGA pulldown results demonstrated that mutations at these sites led to a significant decrease in the O-GlcNAc signal on both GATAD2A and JUN (Fig. 5d, e). Similar results were obtained in HEK-293T cells. Residual O-GlcNAc signals were observed in the S182A and S84A mutants, indicating that GATAD2A and JUN are O-GlcNAcylated predominantly at S182 and S84, respectively (Supplementary Fig. 14c).

Previous research has indicated that the bridging of repression components to NuRD core subunits by GATAD2A also relies on the N-terminal coiled-coil domain45,47, which contains the O-GlcNAc site. Our findings showed that GATAD2A WT coimmunoprecipitated multiple NuRD components in OIS-affected cells at D10. Mutation of the glycosylation site in GATAD2A moderately reduces its interaction with MTA1 and HDAC1 but markedly diminished its interaction with the DNA methylation reader MBD248 and the transcriptional repressor CHD449, suggesting that O-GlcNAc regulates the recruitment of repressors to the core subunits of NuRD during the OIS process (Fig. 5f). Furthermore, we performed a fractionation assay on the nuclear extract from OIS-affected cells at D10 using size exclusion chromatography and immunoblot analysis for CHD4, MBD2, MTA1, and the Flag tag (GATAD2A WT or GATAD2A S182A). In the GATAD2A WT group, all four proteins were detected in fractions of very high molecular weight (fractions 4–13), signifying their participation in the complete NuRD repressor complex (Fig. 5g). In contrast, GATAD2A S182A was found in a lower molecular weight complex in fractions containing MTA1 (fractions 13-19) but had minimal overlap with CHD4 (fractions 16-22) and MBD2 (fractions 19-25). This pattern suggests that the lack of O-GlcNAc modification inhibited the formation of the MBD2/CHD4-containing NuRD repressor complex. In the same vein, we observed a comparable scenario in the assembly of JUN and the SWI/SNF transcriptional activator complex, wherein O-GlcNAc is capable of increasing the interaction of JUN with multiple components of the complex (Fig. 5h). Notably, immunofluorescence staining revealed colocalization of Flag-GATAD2A WT:MBD2 and HA-JUN WT:SNF5 in the nucleus in OIS-affected cells. However, the absence of O-GlcNAc resulted in the dissociation of these complexes (Fig. 5i). By expressing truncated forms of GATAD2A (Fig. 5j), we deduced that a region in the N-terminal coiled-coil domain (ΔGATAD2A-1, aa 1–240) is necessary for facilitating the interaction between MTA1 and MBD2. This region lacks a clear domain structure but contains a coiled-coil, which was reported to be O-GlcNAcylated at the terminus with multiple potential modification sites50,51. In the interaction between SNF5 and JUN, the loosely organized N-terminal domain (ΔJUN-1) containing the O-GlcNAc site is crucial. This finding implies the important role of O-GlcNAc modification in the assembly of TF-SWI/SNF and NuRD complexes that perform different functions.

Using all-atom molecular dynamics simulations, we investigated how O-GlcNAc modulates the binding of GATAD2A and JUN with their partners. O-GlcNAcylated and non-glycosylated GATAD2A-CR1 (aa 137–190) and the JUN N-terminus (aa 69–91) exhibited distinct binding interactions with MBD2 and SNF5, respectively, with O-GlcNAcylation strengthening these interactions (Fig. 5k, l). Specifically, O-GlcNAcylated GATAD2A promoted its interaction with MBD2, stabilizing the MBD2–HDAC1/MTA1 assembly (Supplementary Fig. 15a–e), as indicated by the decreases in Distance I (S85 HDAC1−P157 MBD2, Fig. 5k and Supplementary Fig. 15g) and Distance II (K302 MTA1−E162 MBD2, Supplementary Fig. 15f, g), the more gradual time evolution plots of the C-α root mean square deviation (RMSD) for MBD2 and HDAC1/MTA1, and the increased MBD2–HDAC1/MTA1 contact area, including the changes at key interfacial residues (Supplementary Fig. 15h–k). Similarly, in the JUN system, O-GlcNAcylation stabilized JUN–SNF5 binding and thereby affected SNF5–DNA stability (Supplementary Fig. 16a–f), as supported by the decreased S84 JUN–E59 SNF5 distance (Fig. 5l; Supplementary Fig. 16h), increased contact area (Supplementary Fig. 16g, h), and altered interactions at key interfacial residues (Supplementary Fig. 16i, j). Across the systems, the three replicates yielded consistent results (Supplementary Fig. 15l and m; Supplementary Fig. 16k, l). Consequently, these findings clearly demonstrate that O-GlcNAc facilitates the formation of TF-SWI/SNF and NuRD complexes, which carry out dual transcriptional regulatory functions at promoters and enhancers, respectively, during senescence reprogramming.

O-GlcNAc modification of JUN and GATAD2A plays an important role in intensive chromatin occupation correlated with the SASP and cell cycle arrest

To analyze the influence of O-GlcNAc modification on the dynamic targeting of the SWI/SNF and NuRD complexes in OIS gene reprogramming, we evaluated the chromatin binding patterns of JUN and GATAD2A in OIS-affected cells with varying O-GlcNAc levels. The levels of chromatin-bound JUN WT and GATAD2A WT in LU-RAS/KD-WT cells were markedly greater than those in JUN S84A and GATAD2A S182A-mutant in the LU-RAS/KD-AA cells at both D0 and D10, respectively (Fig. 6a). The levels of JUN S84A and GATAD2A S182A were increased in the nuclear soluble and cytoplasmic fractions, corroborating the finding that glycosylation promotes the chromatin association of JUN and GATAD2A. Moreover, suppressing the glycosylation of JUN and GATAD2A diminished the chromatin accumulation of SNF5 and MBD2 at both D0 and D10, indicating a role for O-GlcNAcylation in mediating the interactions and colocalization among these factors. We next performed ChIP-seq analysis of these variants in OIS-affected cells to precisely determine the target genes of the O-GlcNAc modified SWI/SNF and NuRD complexes (Fig. 6b). Upon comparing the ChIP-seq signals of the JUN and GATAD2A variants, we observed a notable reduction in the intensity of JUN S84A at the COGC-seq–annotated promoter peaks in OIS-affected cells (Fig. 6c). In addition, the removal of O-GlcNAc interfered with the presence of SNF5 (as indicated by SNF5 ChIP-seq) at JUN-bound regions, suggesting that O-GlcNAc is necessary for maintaining the presence of SWI/SNF chromatin and the colocalization of the complex with JUN (Fig. 6d). Moreover, the accumulation of GATAD2A S182A was also decreased at the COGC-seq–annotated intergenic/enhancer peaks, resulting in decreased colocalization of GATAD2A with MBD2. We further revealed greater saturation of active promoter marks (H3K4me3 and H3K27Ac) in JUN WT chromatin-anchored areas than in JUN S84A-bound ChIP-seq peaks. O-GlcNAc-induced GATAD2A chromatin binding was also linked to a notably increased degree of DNA methylation compared with that in GATAD2A S182A-binding regions (Supplementary Fig. 17), consistent with the finding that O-GlcNAc promotes the interaction between GATAD2A and the NuRD complex. Illustrative results from the visual representation of the distinct genomic locations occupied by JUN and GATAD2A variants, including SASP-related genes such as IL6 and IGFBP2 and cell cycle-related genes such as CCND1 and STAG2, show O-GlcNAc-induced alterations in chromatin binding at the individual gene level (Fig. 6e). These findings point to a crucial link between O-GlcNAc and epigenetic complexes, where O-GlcNAc could function as a navigational factor for genomic targeting of SWI/SNF and NuRD.

To further investigate the regulatory functions of O-GlcNAc modification in OIS, we carried out RNA-seq analysis on cells expressing JUN and GATAD2A variants (Supplementary Data 7). The results of the transcriptomic analyses revealed that at D10, overexpression of JUN WT and GATAD2A WT—relative to their O-GlcNAc–deficient mutants—significantly decreased the expression of cell-cycle–related genes and increased the expression of SASP genes (Fig. 6f and Supplementary Fig. 18). Consistent with this finding, depletion of O-GlcNAc resulted in reductions in JUN and GATAD2A ChIP-seq signals at senescence-associated loci, including key cell cycle genes (CCND1, STAG2, CDK6, and E2F5) and SASP genes (IL6, IGFBP2, MIF, and CXCL1) (Fig. 6g and Supplementary Fig. 19a). Moreover, compared with that in LU-RAS control cells, knockdown of JUN and GATAD2A increased the expression of these cell cycle genes and decreased the expression of the aforementioned SASP genes. Pharmacologic reduction of O-GlcNAc modification with OSMI-4 similarly increased the abundance of cell cycle gene transcripts while decreasing SASP mRNA levels (Fig. 6h and Supplementary Fig. 19b), closely mirroring the effects of JUN and GATAD2A knockdown in LU-RAS cells. Collectively, these findings indicate that O-GlcNAc modification of JUN and GATAD2A is important for their chromatin occupancy at SASP- and cell cycle arrest-related loci and that the O-GlcNAc modified forms rather than the non-glycosylated proteins modulate the transcriptional regulation of senescence-associated gene expression.

Attenuation of O-GlcNAc modification on JUN and GATAD2A impairs cellular senescence progression and SASP-associated tumor growth

Considering that the O-GlcNAc modification of JUN and GATAD2A affects the expression of senescence-associated genes, we investigated whether O-GlcNAc influences cellular functionality via the aforementioned mechanism. Compared with LU-RAS/KD-WT cells, LU-RAS/KD-AA cells, which expressed similar levels of JUN S84A and GATAD2A S182A, exhibited significantly decreased SA-β-Gal activity and increased capacities for cell cycle progression and proliferation at D10 of the OIS process (Fig. 7a, b). Consistent results were observed in other OIS cell models (Supplementary Fig. 20). One of the principal effects of OIS in cells is the promotion of tumor expansion and spread. Consequently, we examined whether the lack of O-GlcNAc modification on JUN and GATAD2A in OIS-affected fibroblasts could induce an increase in cancer cell proliferation, enlargement, and mobility in a Transwell coculture system. This system physically partitions senescent cells and cancer cells but permits them to share media and secrete soluble components such as SASP components (Fig. 7c). The reduction in O-GlcNAc on JUN and GATAD2A attenuated certain senescence markers, including SASP production, in OIS-affected LU-RAS cells (Fig. 7d, e). OIS-affected LU-RAS/KD-WT cells significantly stimulated the expansion, migration and invasion of A549 lung cancer cells through the secretion of SASP components. However, LU-RAS/KD-AA cells did not considerably influence these malignant phenotypes of cancer cells compared to those of non-cocultured A549 cells (Fig. 7f, g).

We next used immunocompromised (nude) mice to determine whether subcutaneous coinjection of A549 cancer cells (carrying the GFP gene) and OIS-affected LU-RAS cells expressing JUN and GATAD2A variants influenced tumor progression in vivo. All LU-RAS and its variant cells were marked with the cytoplasmic membrane dye NIR790, allowing us to visualize both the tumor cells and OIS-affected cells using an in vivo imaging system (Fig. 8a). The NIR790 images revealed that OIS-affected cells remained at the injection site within the growing tumor throughout the period during which we monitored tumor development (Fig. 8b, c). As predicted, compared with injection of only A549 cells, coinjection of A549 and LU-RAS cells led to increased tumor volume. In contrast, coinjection of LU-RAS/KD-AA cells did not increase tumor growth. Moreover, we found a substantial increase in tumor size when mice were coinjected with OIS-affected cells expressing JUN WT and GATAD2A WT, unlike the findings after coinjection of LU-RAS/KD-AA cells, as determined by the integrated density of GFP fluorescence and visual inspection of the extracted tumors (Fig. 8d, e). Immunoblot and IHC analyses revealed that compared with tumors from mice injected with LU-RAS/KD-AA cells, tumors from mice injected with LU-RAS/KD-WT cells exhibited increased expression of senescence markers (p16INK4A and p21WAF1/Cip1) and increased SA-β-Gal activity (Fig. 8f, g). Additionally, in the subcutaneous coinjection model—in which A549 tumor cells and OIS-affected LU-RAS cells were present within the same lesions and could not be unequivocally distinguished by routine histology—we observed noticeable increases in MKI67 and LMNB1 (Fig. 8f), as well as IL6 and IGFBP2, in tissues from mice injected with LU-RAS and LU-RAS/KD-WT cells, compared with those injected with LU-RAS/KD-AA cells. Meanwhile, in these tissues, the A549 tumor cells—exposed to SASP factors secreted by the senescent LU-RAS cells—exhibited enhanced upregulation of the cell cycle–related factors CCND1 and STAG2, which constitute major components of the tumor cell population (Fig. 8g, h).

These results suggest that O-GlcNAc deficiency in these two key epigenetic factors can prevent the acquisition of the OIS phenotype, thereby inhibiting the stimulation of tumorigenesis both in vitro and in vivo. Hence, O-GlcNAc plays a specific role in controlling the detrimental behaviors of senescent cells, and targeting O-GlcNAc modified JUN and GATAD2A could constitute an alternative strategy for combating senescence-induced tumor development.

Methods
This research was conducted in accordance with all applicable ethical guidelines and regulations and was approved by the Ethics Committee of Dalian University of Technology under approval number DUTSCEOLS240228-05.

Cell culture and reagents
LU1015 human primary lung fibroblasts (#T1015) and SK0304 human primary benign skin fibroblasts (#T0304) were purchased from Applied Biological Materials Inc. (abm) (Richmond, Canada). Normal human epithelial cell lines (MCF10A and HaCaT) and HEK-293T (293T) cells and A549 lung cancer cells were obtained from the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China) and were used within 6 months of resuscitation. LU1015 and their corresponding cells were cultured in medium PriGrow VII (abm, #TM017), 10% fetal bovine serum (FBS) and 1% Penicillin/Streptomycin Solution (Gibco). SK0304 and their corresponding cells were cultured in medium PriGrow III (abm, #TM003), 10% FBS and 1% Penicillin/Streptomycin Solution (Gibco). MCF10A and their corresponding cells were cultured in Complete Medium (Procell, #CM-0525) and 100ng/mL choleratoxin (Sigma, #C8052). 293T and HaCaT cell lines were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% FBS and 1% penicillin/streptomycin (Gibco). A549 lung cancer cells were cultured in RPMI-1640, 10% FBS and 1% Penicillin/Streptomycin Solution (Gibco). All Cells were maintained at 37°C in a humidified atmosphere with 5% CO2 and were routinely tested for Mycoplasma contamination on a monthly basis using the PlasmoTestTM Mycoplasma Detection Kit (InvivoGen). OSMI-4 (#S8910) and Thiamet-G (#S7213, TMG) were purchased from Selleck, and doxycycline were purchased from Sigma-Aldrich. Other reagents were used as analytic grade or better.

Plasmids, lentiviral production, and transfection
LU1015 and SK0304 fibroblasts were transduced with a recombinant lentiviral vector carrying the hTERT gene (Applied Biological Materials Inc.) to achieve cellular immortalization. At 72h post-transduction, cells were cultured in growth medium supplemented with puromycin to select for stable hTERT-transduced clones. To generate a doxycycline-inducible Tet-ON human H-RASG12V expression system, a synthetic gene encoding the human H-RASG12V coding sequence was cloned into the lentiviral pLVX-Tet3G vector34 (Addgene #28061), which was modified to introduce a G418 resistance selection marker, resulting in the pLVX-Tet3G-RASG12V-G418 plasmid. Lentiviral particles were produced by transient co-transfection of 293T cells with pLVX-Tet3G-RASG12V-G418 and appropriate packaging plasmids (psPAX2 and pMD2.G) using the calcium phosphate transfection method. After 48–72h, viral supernatants were collected, filtered, and used for cell infection. LU-RAS, SK-RAS, MCF-RAS, and HaC-RAS cells were infected with the pLVX-Tet3G-RASG12V-G418 lentivirus. Following infection, stable monoclonal populations of infected cells were generated by limiting dilution and subsequent selection in G418-containing medium. Senescence was induced by treating cells with 2μg/mL doxycycline, and samples were collected at the specified time points for further analysis.

Lentiviral shRNA plasmids targeting GATAD2A and JUN were constructed using the pLKO.1-Hygro vector (Addgene, #24150), which was modified to enable simultaneous knockdown of these two genes. Specifically, an additional U6 promoter was introduced into the vector downstream of the GATAD2A shRNA cassette, and the JUN shRNA sequence was cloned under this promoter. This tandem shRNA expression system ensures that each shRNA is expressed independently. Two distinct shRNAs (shRNA-1, shRNA-2) were designed for both GATAD2A and JUN, and the sequence information is provided in the Supplementary Data 8. Hygromycin was used in the subsequent lentiviral transduction selection process to obtain monoclonal cells (LU-RAS/KD). Lentiviral shRNA plasmids for OGT (#sc-40780-V, Santa Cruz) were used for gene knockdown. Scrambled shRNA plasmid (#1864) was purchased from Addgene.

We constructed a lentiviral vector based on the pLenti-CMV-MCS-blank backbone (Addgene, #17448) to co-express full-length wild-type human GATAD2A, JUN, and O-GlcNAcylation site mutants (S182→A or S625→A for GATAD2A, and S84→A for JUN), with Zeocin as the selection marker. The original antibiotic resistance gene was replaced by the Zeocin resistance gene (ble). To enable the simultaneous expression of the two genes, 3×Flag-GATAD2A and HA-JUN were linked via a self-cleaving P2A peptide under the control of the CMV promoter. The 3×Flag and HA tags were placed at the C-terminus of GATAD2A and JUN, respectively. LU-RAS/KD-WT (stably tandem expression of Flag-GATAD2A WT and HA-JUN WT) and LU-RAS/KD-AA (stably tandem expression of Flag-GATAD2A S182A and HA-JUN S84A) cells were generated via lentiviral transduction, followed by Zeocin selection. The truncated mutants of Flag-GATAD2A and HA-JUN were subcloned into pCMV vector. The construction of pcDNA-OGT was reported previously59.

Immunoblot/lectin blot and co-immunoprecipitation (co-IP)
For immunoblot/lectin blot, cells were collected and lysed using Western/IP lysis buffer (Beyotime, #P0013) supplemented with protease and phosphatase inhibitor cocktails (Roche) for 20min on ice. Protein concentration was determined using the Bradford assay (Bio-Rad). Equivalent protein amounts were prepared by mixing the lysates with SDS-PAGE loading buffer, followed by boiling at 95°C for 5min. Proteins were separated by SDS-PAGE and subsequently transferred onto a polyvinylidene fluoride (PVDF) membrane (Immobilon-P, Millipore-Sigma). The membrane was then blocked with 5% non-fat milk for 1h to prevent non-specific binding. Primary antibodies were applied overnight at 4°C. After thorough washing to remove unbound antibody, the membrane was incubated with HRP-conjugated secondary antibodies at for 1h. The primary anti-bodies used were anti-O-GlcNAc CTD110.6 (CST, #9875, 1:1000), RasG12V (CST, #14412, 1:1000), anti-JUN (Abcam, #ab40766, 1:1000), anti-GATAD2A (CST, #17705, 1:1000), anti-OGT (Abcam, #ab96718, 1:1000), anti-OGA (CST, #60406, 1:1000), anti-GAPDH (CST, #5174, 1:4000), anti-Flag (CST, #14793, 1:1000), anti-HA (CST, #3724, 1:1000), anti-Histone 3 (CST, #4499, 1:4000), MBD2 (Abcam, #ab188474, 1:1000), MTA1 (Proteintech, #30545-1-AP, 1:1000), CHD4 (Proteintech, #14173-1-AP, 1:1000), SMCA4 (Proteintech, #21634-1-AP, 1:1000), SMRC1 (Proteintech, # 17722-1-AP, 1:3000), SNF5 (Abcam, #ab307985, 1:1000), p53 (Proteintech, #10442-1-AP, 1:4000), p21WAF1/Cip1 (Proteintech, #10355-1-AP, 1:1000), p16INK4A (Proteintech, #10883-1-AP, 1:1000), LMNB1 (Proteintech, #12987-1-AP, 1:5000), MKI67 (Proteintech, #84192-4-RR, 1:5000), SP1 (Proteintech, #21962-1-AP, 1:5000), SRSF2 (Proteintech, #20371-1-AP, 1:5000), β-actin (Proteintech, #20536-1-AP, 1:5000), TERT (Proteintech, #27586-1-AP, 1:500), FOXK1 (Proteintech, #29338-1-AP, 1:2000). Lectins WGA (Vector Laboratories, #B-1025S, 1:2000) was used for lectin blotting. The appropriate secondary antibody used were anti-mouse IgG-HRP (CST, #7076, 1:20,000), anti-rabbit IgG-HRP (CST, #7074, 1:20,000), anti-mouse IgM-HRP (Abcam, #ab97230, 1:20,000), Streptavidin-HRP (CST, #3999, 1:50,000), and the signals were detected by the ECL Plus kit (GE Health-care). All immunoblot experiments were conducted in three biological replicates.

For co-IP, cells were collected and lysed in Western/IP lysis buffer supplemented with protease and phosphatase inhibitor cocktails (Roche) on ice for 20min. The lysates were then centrifuged at 20,000×g for 10min at 4°C to remove cellular debris. The resulting supernatant was incubated with primary antibodies and protein A/G magnetic beads (Bimake, #B23201) overnight at 4°C under gentle rotation. After incubation, the immunoprecipitates were washed five times with ice-cold Western/IP lysis buffer to minimize non-specific interactions. The samples were then prepared for immunoblot analysis. The antibodies and beads used for IP were anti-JUN (Abcam, #ab40766, 1:50), anti-GATAD2A (CST, #17705, 1:50), anti-O-GlcNAc CTD110.6 (CST, #9875, 1:1000), anti-O-GlcNAc EPR19847 (Abcam, #ab202665, 1:50), anti-Flag-magnetic beads (Bimake, #B26102), and anti-HA-magnetic beads (Bimake, #B26202). For lectin pull down assay, each cell lysate was incubated with sWGA bound agarose beads (Vector Laboratories, #AL-1023S). All samples utilized the same volume (60μL) of magnetic beads to ensure consistency in experimental conditions, thereby guaranteeing the reliability of IP results. To avoid interference from IgG, antibodies from different species were used in the immunoblotting detection following the co-IP experiments.

To confirm the O-GlcNAc modification of OCPs and eliminate the possibility of contamination from O-GlcNAc interacting proteins in the immunoprecipitated samples, cells were lysed in 1× SDS lysis buffer (Beyotime, #P0013G) by heating at 95°C for 15min. The denatured samples were centrifuged, and the supernatant was diluted 15-fold with Western/IP lysis buffer. The diluted lysates were then subjected to immunoprecipitation using specific antibodies or anti-Flag/anti-HA-magnetic beads. Immunoprecipitates were subsequently analyzed using the anti-O-GlcNAc antibody.

Chromatin complexes capture
Cells (1×107) were washed with PBS and crosslinked with 1% formaldehyde for 15min to stabilize proteins interacting with chromatin for subsequent immunoblot, proteomic, and DNA cis-element analyses. The crosslinking reaction was quenched with 2.5M glycine. Cells were scraped from culture plates, pelleted by centrifugation, and resuspended in 1mL hypotonic buffer (10mM HEPES, pH 7.9; 10mM KCl; 1.5mM MgCl2, 0.34M sucrose; 10% glycerol (v/v); 0.4% Igepal CA-630 (v/v); 1mM DTT), supplemented with protease and phosphatase inhibitor cocktails, to disrupt the plasma membrane. Nuclei were isolated by centrifugation (use the supernatant as the cytoplasm fraction), gently washed with nuclear extraction buffer (20mM HEPES, pH 7.9; 150mM NaCl; 1.5mM MgCl2; 0.2mM EDTA; 10% glycerol; 0.4% Igepal CA-630 (v/v); protease and phosphatase inhibitors), incubated for 5min, and centrifuged at 1000×g. The intermediate salt strength (150mM NaCl) at this step is intended to increase ionic strength sufficiently to remove soluble nuclear constituents and weakly associated proteins, thereby partitioning soluble nuclear proteins and weakly bound complexes into the supernatant (nucleoplasm) while retaining chromatin-bound material in the pellet. Subsequently, nuclei were lysed in nuclear lysis buffer (50mM HEPES, pH 7.9; 150mM NaCl; 1.5mM MgCl2; 1% Igepal CA-630 (v/v); 0.1% SDS (w/v)) containing protease and phosphatase inhibitors, and the lysates were incubated on a rotating platform at 4°C for 1h. Chromatin was then fragmented by sonication (Sonics VCX130pb) until DNA was sheared to ~200–500bp, followed by centrifugation at 20,000×g at 4°C for 15min to remove nuclear membranes and scaffolding components. The crosslinked chromatin complexes were collected from the resulting supernatant and used for downstream assays. For detection of chromatin-associated proteins by immunoblotting, the isolated chromatin complexes were treated with benzonase nuclease (5µg/mL; HaiGene) and RNase A (10µg/mL; Sigma) prior to protein analysis.

Cell proliferation assay
Approximately 2000 cells per well were seeded into a 96-well plate and treated with either the vehicle control or 2μg/mL doxycycline for 2–10 days, with or without OSMI-4 (5μM) or TMG (50μM), for 48h. Cell proliferation was assessed using the Enhanced Cell Counting Kit-8 (CCK-8, Beyotime, #C0041) following the manufacturer’s protocol. Absorbance at 450nm was recorded for each well and normalized to the vehicle-treated control. All experiments were conducted in four biological replicates.

Transwell cancer growth and scratch experiments
LU-RAS and its derivative cells were grown to 70–80% confluency on Transwell permeable supports (Costar). Ten days after senescence induction, 5×104 A549 cells were seeded in 6-well plates, and Transwell supports containing no cells or LU-RAS and its derivative cells were placed above the A549 cells. After incubation, cell numbers were counted under a microscope. For Transwell in vitro scratch assays, A549 cells were plated in 6-well plates and cultured to confluency. A scratch was created using a P200 pipette tip, and cell debris was removed by washing with fresh medium. The medium was replaced, and the initial scratch area was imaged. LU-RAS and its derivative cells medium were then added to the culture plates. The scratches were re-imaged 96h later using a phase-contrast microscope.

EdU staining and senescence-associated beta galactosidase activity
For EdU staining, representative samples from the senescence time series were analyzed for EdU incorporation using the Click-iT EdU Alexa Fluor Imaging Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. Cells were seeded in 6-well plates and cultured under senescence-inducing conditions to the indicated time points (D0, D2, D4, D10). Immediately prior to fixation, cells were pulsed with EdU for 2h. After incubation, the medium was aspirated, and cells were washed twice with PBS and fixed with paraformaldehyde. Cells were then washed three times with washing buffer (PBS containing 3% BSA) and permeabilized with 0.3% Triton X-100 for 15min. Following three additional washes with washing buffer, cells were incubated with the Click Reaction Mixture. For nuclear counterstaining, Hoechst dye was added at a final concentration of 1μg/mL and incubated for 10min at 25°C in the dark. Excess dye was removed by washing with PBS prior to imaging. Cells exhibiting both green (EdU) and blue (Hoechst) signals were scored as EdU-positive. Regardless of the specific phase in which cells are arrested, once they enter cell-cycle arrest they lose proliferative capacity and no longer incorporate EdU. Thus, a lack of EdU incorporation (negative EdU staining) is indicative of senescent cells.

For SA-β-Gal activity assays, cells were washed with PBS and fixed in a solution containing 2% formaldehyde and 0.2% glutaraldehyde for 10min at 25°C. After fixation, cells were incubated with freshly prepared SA-β-Gal staining solution (40mM citric acid/sodium phosphate buffer, pH 6.0, 1mg/mL X-gal, 150mM NaCl, 2mM MgCl2, 5mM potassium ferrocyanide, and 5mM potassium ferricyanide) at 37°C without CO2 until visible blue staining was detected. Imaging was performed using a Leica confocal fluorescence microscope.

Flow cytometry
Cells were seeded in 6-well plates and cultured under conditions to induce senescence as previously described, with or without the addition of OSMI-4 (5μM) or TMG (50μM) for 48h. The cells were then fixed in 70% (v/v) ethanol overnight at −20°C. Following fixation, cells were washed with PBS and incubated with PBS containing RNase A (Sigma) for 30min at 37°C in the dark. Subsequently, cells were stained with 5μL of propidium iodide (PI, Sigma) for 30min at 25°C in the dark. Flow cytometry analysis was then performed using a BD flow cytometer and the ModFit LT (Version 4.1) was used to analyze Flow Cytometry Data. The flow cytometry gating strategy is presented in Supplementary Fig. 21. All experiments were conducted in four biological replicates.

Gel filtration-based fractionation of nuclear extract
To analyze the co-migration of NuRD or TF-SWI/SNF components, nuclear extracts were prepared from LU-RAS/KD-WT and LU-RAS/KD-AA cells. A total of 0.3mg of nuclear extract was treated with Benzonase for 1h followed by centrifugation at maximum speed for 10min at 4°C. The supernatant was then loaded onto a Superose 6 10/300 column pre-equilibrated with a buffer containing 20mM Tris (pH 7.5), 300mM NaCl, 2mM MgCl2, 5% glycerol, 1mM PMSF, and complete protease inhibitors. Fractions of 0.25mL were collected and precipitated using trichloroacetic acid. The resulting protein pellets were resuspended in SDS-PAGE loading buffer, boiled, and analyzed by western blot, with every other fraction being loaded for electrophoresis. All experiments were conducted in four biological replicates.

Metabolic labeling and click reaction
OIS cells at different senescence time points in 10-cm dishes were cultured in medium supplemented with 100μM 1,6-Pr2GalNAz (generously provided by Prof. Bo Chen, Peking University) for 48h. Approximately 3×107 cells were harvested by trypsinization and washed three times with PBS. Crosslinked chromatin was prepared from metabolically labeled cells as described above. To specifically biotinylate O-GlcNAz modified proteins, the chromatin was incubated with 100μM alkyne-biotin (Click Chemistry Tools, #1266-25), 300μM of a premixed CuSO4-BTTAA (Click Chemistry Tools, #1236-500) complex (molar ratio 1:2), and 2.5mM freshly prepared sodium ascorbate at 25°C for 2h. Methanol (10mL) was added to the reaction and the mixture was stored at −80°C overnight. Precipitated proteins were pelleted by centrifugation at 10,000×g for 15min at 4°C, washed twice with ice-cold methanol, and resuspended in 1mL of recovery buffer. To ensure thorough decrosslinking of remote PPI, samples were heated at 100°C for 20min followed by 60°C for 2h, minimizing crosslink-driven co-capture and nonspecific recovery of non-O-GlcNAz proteins68. The resulting solution was diluted 15-fold with Western/IP lysis buffer, pre-cleared with 100μL of vehicle-magnetic beads (BEAVER Life Science, #70301), and incubated with 200μL of streptavidin-magnetic beads (BEAVER Life Science, #22308) at 25°C for 4h on a rotating platform to capture OCPs. To minimize nonspecific binding, the beads were stringently washed five times with low-salt buffer (20mM Tris-HCl, pH 8.1, 0.1% SDS, 2mM EDTA, 1% Triton X-100, 150mM NaCl), five times with high-salt buffer (buffer composition identical to low-salt but containing 500mM NaCl), and twice with LiCl buffer (10mM Tris-HCl, pH 8.0, 0.25M LiCl, 0.5% NP-40, 1% sodium deoxycholate, 1mM EDTA). Finally, the beads were resuspended in SDS-PAGE loading buffer for immunoblot analysis of OCPs. For LC-MS/MS analysis, the beads were resuspended in 500μL of 6M urea in PBS. Protein samples were reduced with 10mM DTT in water at 65°C for 15min and alkylated with 20mM iodoacetamide in water at 35°C in the dark for 30min. The buffer was subsequently exchanged with 200μL of 2M urea in PBS, after which 4μL of trypsin (Promega, 0.5μg/μL) and 2μL of 100mM CaCl2 were added to digest the proteins. Digestion was performed at 37°C for 16h, and the resulting peptides were used for LC-MS/MS analysis.

For omics-level mapping of O-GlcNAc sites, OIS cells at various senescence time points were metabolically labeled with 100μM 1,6-Pr2GalNAz (generously provided by Prof. Bo Chen, Peking University). After lysis, 1mL of crosslinked chromatin (2mg/mL) prepared from the labeled cells was incubated at 25°C for 2h with 300μM of a premixed CuSO4-BTTAA complex (molar ratio 1:2), 100μM alkyne-PC-biotin (Vector Laboratories, #CCT-1118), and 2.5mM freshly prepared sodium ascorbate. The protein pellets obtained by centrifugation were resuspended in 300μL of 8M urea in water. The suspensions were then diluted to 4M urea with 100mM ammonium bicarbonate (ABC) and reduced with 10mM DTT at 65°C for 15min, followed by alkylation with 20mM iodoacetamide at 35°C in the dark for 30min. The solution was further diluted to 0.8M urea using 50mM ABC and digested with trypsin at a 1:50 enzyme-to-substrate ratio (w/w) at 37°C for 16h. After digestion, the samples were diluted to 0.5M urea with 50mM ABC and incubated with 100μL of streptavidin-magnetic beads at 25°C for 4h. The beads were washed six times with PBS (pH 7.4) and six times with Milli-Q water. Subsequently, the beads were resuspended in 200μL of 0.1% formic acid (v/v) in water and irradiated with UV light (9J/cm2, 365nm) using a UV cross-linker (#CL-1000, UVP). After removing the streptavidin beads, the samples were evaporated to dryness and analyzed by LC-MS/MS.

LC-MS/MS analysis and label-free quantification
The digested peptides were analyzed using a timsTOF Pro2 mass spectrometer (Bruker Daltonics) coupled to a nanoElute HPLC system. Peptides were first loaded onto a 300μm i.d.×5mm C18 trap column (μ-Precolumn, Thermo Scientific) and subsequently separated on a 75μm i.d.×25cm analytical column packed in-house with 1.8μm C18 reverse-phase particles. Separation was performed using a 60-min gradient at a flow rate of 300nL/min, with buffer A (0.1% formic acid in water) and buffer B (0.1% formic acid in acetonitrile): 2% B to 22% B in 45min, followed by an increase to 35% B in 10min, then to 80% B in 5min, held at 80% B for another 5min. Each sample was analyzed in nine replicates for robust peptide identification. Data acquisition was performed in DIA (data-independent acquisition) mode using the dia-PASEF method. The method consisted of a single MS1 scan followed by twelve dia-PASEF scans, utilizing 28 variable mass windows of 25Da each, covering an m/z range of 452-1152. Each mass window was further split into two ion mobility windows, resulting in 56 total acquisition windows. The ion mobility range spanned 0.75–1.4Vs/cm2. The ion source voltage was set at 1700V, and ions were fragmented via CID with collision energy ranging from 20eV at 1/K0 of 0.6Vs/cm2 to 59eV at 1/K0 of 1.6Vs/cm2. DIA files were processed using Spectronaut software with the direct DIA workflow and default settings. Parameters included specifying Trypsin/P as the digestion enzyme, permitting peptide lengths between 7 and 52 amino acids with up to two missed cleavages, and incorporating fixed carbamidomethylation of cysteine along with variable modifications, such as methionine oxidation and protein N-terminal acetylation. A 1% FDR was set for both peptide-spectrum match (PSM) and protein identifications.

For the qualitative analysis of OCPs, representative proteins were determined if they were identified in at least six out of nine replicates analyzed, and only proteins annotated as nuclear were considered OCPs. For quantitative analysis based on DIA data, label-free quantification (LFQ) was performed using non-labeled peptide intensities. Protein quantification was based on the summed intensities of unique and razor peptides with a minimum of two quantified peptides per protein group. Missing values were imputed using a normal distribution with a downshift of 1.8 standard deviations and a width of 0.3 standard deviations to account for low-intensity features. Proteins identified in at least six replicates across nine samples in at least one experimental group were included for further statistical processing. Differentially abundant proteins were identified based on a fold change≥2 and a p≤0.05 (two-sided unpaired Student’s t test) after correcting for multiple hypothesis testing using the Benjamini-Hochberg false discovery rate (FDR) set at 0.05. To assess the reproducibility of biological replicates, PCA was performed. Functional annotation and GO enrichment analyses were conducted using Metascape software (v3.5)69. Protein-protein interaction networks were constructed and visualized using STRING (v11.0)70 and Cytoscape software (v3.6.1)71. Additional bioinformatic analyses were carried out using the statistical software R (v3.4.3) with several packages, including beanplot (v1.2), ggplot2 (v3.0.0), igraph (v1.2.1), and venneuler (v1.1-0).

Protein O-GlcNAc sites were acquired from an Orbitrap Fusion Lumos mass spectrometer (Thermo). The resuspended samples were separated with a 75µm×50cm EasySpray reversed-phase LC column (Thermo) packed by 100Å, 2µm, PepMap C18 particles. A Dionex Ultimate 3000 RPLC nanosystem (Thermo) was employed to deliver the following LC gradient: 1–7% B in 9min, 7-35% B in 302min, 35–44% B in 42min, 44–99% B in 3min (A is water with 0.1% formic acid and B is 80% acetonitrile with 0.1% formic acid). A Full MS scan (350–2000 Th) was obtained with a maximum injection time of 50ms, a resolution of 120,000, and RF lens at 60%. Monoisotopic precursor selection was enabled, and the multi-charged precursors with z=2–8 were selected for data-dependent MS/MS scans. HCD product-dependent electron-transfer/higher-energy collisional dissociation (HCD pd EThcD) was used for MS/MS fragmentation. If a HCD scan produced one of the following glycan oxonium ions (m/z 168.0655, 186.0761, 204.0865, 274.0921, 292.1027, 300.1302, 329.1455, 366.1395, 388.1463, 399.1992, and 405.213), a separated EThcD scan was performed on the captured precursor. The identification and quantification of O-GlcNAc glycopeptides were performed using MaxQuant software integrated with the Andromeda search engine (version 1.5.3.28). MS/MS spectra were searched against the SwissProt human database (20,379 sequences), which was concatenated with a Reverse Decoy database and a list of common contaminants. The search parameters for O-GlcNAc site identification followed previously established criteria64. Specifically, UV-HexNAc modified sites (resulting from UV cleavage of alkyne-PC-biotin fragments, m/z 299.12) with a localization probability above 0.75 on confidently identified UV-HexNAc modified peptides (FDR of 1%, Andromeda score >40, and delta score >8) were classified as unambiguous O-GlcNAc sites. Extracellular sites with UV-HexNAc modifications were excluded from the final O-GlcNAc site list. For quantification of O-GlcNAc sites, LFQ was applied to measure ion intensities of peptides containing UV-HexNAc modifications. Only unambiguous O-GlcNAc sites that were quantified in at least two out of three biological replicates were selected for downstream quantitative analyses, as described previously. The raw mass spectral data obtained in our study are available via iProX with the identifier PXD063140.

Immunofluorescence
Cells grown on coverslips were fixed with 4% paraformaldehyde for 10min at 25°C, followed by permeabilization with 0.1% Triton X-100. After washing with PBS, the cells were blocked with 5% goat serum for 30min to prevent nonspecific binding. Primary antibodies were then applied and incubated overnight at 4°C. The following antibodies were used: anti-Flag (CST, #8146, 1:500), anti-HA (CST, #2367, 1:500), anti-MBD2 (Abcam, #ab188474, 1:500), and anti-SNF5 (Abcam, #ab192864, 1:100). Then the cells were washed and incubated with fluorophore-conjugated secondary antibodies, including both anti-mouse IgG H&L (Alexa Fluor 488, CST, #4408) and goat anti-rabbit IgG H&L (Alexa Fluor 647, CST, #4414), at 25°C for 2h. Nuclear DNA was stained with 4′,6-diamidino-2-phenylindole (DAPI). Images were acquired using a Nikon Eclipse Ti confocal microscope with Nikon Eclipse C2 software (v5.21.00). Image resolution is 1024×1024 pixels. Images were captured using a 100× oil immersion objective. All experiments were conducted in four biological replicates.

COGC-seq/ChIP-seq and bioinformatics
COGC-seq assays were carried out as previously described56, using ~3×107 cells per experiment. Crosslinked chromatin complexes were isolated from 1,6-Pr2GalNAz metabolically labeled cells and biotinylated as detailed above. Biotinylated O-GlcNAz modified chromatin complexes were pre-cleared and then captured by incubating with 200μL of streptavidin magnetic beads at 25°C for 4h. Stringent washes were conducted using low-salt, high-salt, and LiCl wash buffers, following the protocol for OCP enrichment. The beads were resuspended in 100μL of TE buffer, followed by digestion with RNase (10μg/mL, Sigma) at 37°C for 30min. SDS was added to a final concentration of 0.5%, and the complexes were de-crosslinked overnight at 65°C with Proteinase K (0.2mg/mL). DNA was then purified using the MiniBEST DNA Fragment Purification Kit (Takara, #9761) and sequenced using the Illumina NovaSeq 6000 platform (Illumina, at Novogene Technology, China) and/or a Salus Progene sequencer (Shenzhen Salus BioMed Co., Ltd, China). Two biological replicates were performed for the sequencing.

ChIP-seq assays for Flag-GATAD2A WT, Flag-GATAD2A S182A, HA-JUN WT, HA-JUN S84A, H3K27ac, H3K27me3, H3K4me1, and H3K4me3 were performed as described26. In brief, crosslinked chromatin complexes were isolated from ~3×107 cells and sonicated using a Sonics Vcx130pb. After pre-clearing with vehicle-coated magnetic beads, chromatin complexes were immunoprecipitated with either anti-Flag magnetic beads, anti-HA magnetic beads, or antibodies against histone modifications, including H3K27me3 (CST, #9733, 1:50), H3K27ac (CST, #8173, 1:50), H3K4me1 (CST, #5326, 1:50), or H3K4me3 (CST, #9751, 1:50). Protein A/G magnetic beads were added, and the samples were incubated at 4°C for 4h. Chromatin-bead complexes were then washed extensively, five times each with low-salt and high-salt buffers, followed by two washes with LiCl buffer at 4°C. De-crosslinking was performed using Proteinase K (Sigma), and the resulting DNA was purified. Purified DNA was utilized for either qPCR or next-generation sequencing, which was carried out using the Illumina NovaSeq 6000 platform (Illumina) at Novogene Technology (Beijing, China). Each experiment was conducted in two biological replicates.

The raw data for COGC-seq and ChIP-seq are available in the GEO database under the accession number GSE295087. Fastp (v0.23.1) was used to trim reads with the parameters set as follows: length_required=50, n_base_limit=6, poly_x_min_len=10. Quality control of the clean reads was performed using FastQC (v0.11.8), and the quality of the filtered data was assessed. Clean sequencing datasets were aligned to the human reference genome (hg19) using Bowtie2 (v2.3.4.3) and processed with Samtools (v1.2). Picard was used to remove duplicate reads with default parameters. Next, Macs2 (v2.2.9.1) was used for peak calling with the parameters set, COGC-seq as follows: -f BAMPE -g hs -B -p 0.01. H3K27me3 and H3K4me1 ChIP-seq as follows: -f BAMPE -g hs -B -broad -broad-cutoff 0.05 -qvalue 0.05. H3K27ac and H3K4me3 ChIP-seq as follows: -f BAMPE -g hs -B -q 0.05. The resulting BAM files were converted into BigWig format using the bamCoverage tool from deepTools (v3.3.2.0.0) and normalized by the reads per kilobase per million mapped reads (RPKM) method. Genomic annotation, distribution, and comparisons of COGC-seq/ChIP-seq peaks were analyzed with ChIPseeker (v1.18.0). Then, deepTools computeMatrix was used to analyze COGC-seq peaks intensity and Histone ChIP-seq signal distribution in reference-point mode. The specific command was as follows: ComputeMatrix reference-point --beforeRegionStartLength 2000 --afterRegionStartLength 2000 –skipZeros. The output of ComputeMatrix was further visualized as heatmaps using deeptools plotHeatmap or plotProfile. Differential binding regions were identified using MAnorm (v1.2.0) based on normalized read counts, with criteria set at |M value|≥1 and p≤0.05. Visualization of sequencing tracks was performed using the Integrative Genomics Viewer (IGV, v2.5.1), while heatmaps and signal density profiles were generated using R package ClusterGVis (v0.99.1) and deepTools. ChromHMM (v1.22) was used to identify and characterize chromatin states based on five epigenetic modifications, including O-GlcNAc. Deduplicated BAM files for histone and O-GlcNAc samples, along with their corresponding input controls, were processed using the BinarizeBam function in ChromHMM with default settings: LearnModel-paired-b2008 hg19. An 8-state chromatin model was generated, with each state annotated as promoter, enhancer, repressed, O-GlcNAc promoter, O-GlcNAc enhancer, O-GlcNAc repressed, or O-GlcNAc-unmarked. The model was manually curated to achieve a biologically relevant balance between unique and overlapping combinatorial features. Chromatin state transitions were visualized using the geom_alluvium function from the R package ggalluvial (v3.6.1). Motif enrichment and discovery were carried out with Homer (v4.11) (-size 200 -nomotif -float -mask) and the MEME suite (v5.1.1) applying sequences for analysis. GO enrichment analysis was conducted through Metascape (v3.5). All bubble plot were generated using the R package ggplot2 (v3.3.3). Additional bioinformatics analyses, including statistical testing, were performed using the phyper function in the R software package.

DNA methylation
Whole-genome bisulfite sequencing (WGBS) was performed to assess DNA methylation at single-base resolution. Genomic DNA was extracted from ~1×10⁶ cells and treated with bisulfite using the EZ DNA Methylation-Gold Kit (Zymo Research), ensuring the conversion of unmethylated cytosines to uracil, while preserving methylated cytosines. After bisulfite treatment, library preparation was completed according to standard protocols, followed by sequencing on an Illumina platform. The raw data for bisulfite sequencing are available in the GEO database under the accession number GSE295084. Fastp (v0.23.1) was used to trim reads with the parameters set as follows: --cut_right_window_size=4 --cut_right_mean_quality=15 --length_required=36. Sequencing data were aligned to the reference genome (hg19) using Bismark (v0.22.3): -N 0 -L 20 --quiet --un --ambiguous. Duplicate reads were removed: deduplicate_bismark -p. DNA methylation levels were determined as the ratio of methylated reads to total reads at each CpG site: bismark_methylation_extractor --comprehensive --no_overlap -p --parallel 30 --split_by_chromosome --bedGraph --counts --CX_context --cytosine_report --report --buffer_size 90G. The genome-wide distribution of DNA methylation was analyzed using the scale-regions mode of DeepTools computeMatrix with the following parameters: ComputeMatrix scale-regions -S -R --beforeRegionStartLength 2000 --regionBodyLength 5000 --afterRegionStartLength 2000 --binSize 100. The output of ComputeMatrix was further visualized as heatmaps using deeptools plotProfile. R packages circlize (v0.4.15) and R were used for creating circular genome plots and karyotype-based visualizations, respectively.

RNA sequencing
Total RNA was extracted from 5×104 cells and subsequently fragmented before being reverse-transcribed to construct cDNA libraries. Sequencing of the libraries was performed using the Illumina NovaSeq 6000 platform (Illumina) at Novogene Technology (Beijing, China). Each RNA-seq experiment included two biological replicates. The raw sequencing data have been deposited in the GEO database under the accession number GSE295083. Fastp (v0.23.1) was used to trim reads with the parameters set as follows: -g -q 5 -u 50 -n 15 -l 150 --overlap_diff_limit 1 --overlap_diff_percent_limit 10. Clean reads were aligned to the human reference genome (hg19) using Hisat2 (v2.0.5): hisat2 --dta -x -1 -2 -S. After alignment, the results were sorted and converted to BAM files. Alignment quality was assessed using samtools flagstat, and gene expression counts were obtained using featureCounts (v1.5.0-p3). The specific commands were as follows: featureCounts -T 8 -p -B -C -a -o. DEGs were identified using R-R with thresholds set at counts≥10, fold change ≥2 and p≤0.05. GO functional enrichment analysis was performed using Metascape (v3.5). GSEA (v4.3.2) was carried out with the R package clusterProfiler (v4.6.0) and the MsigDB (v7.0) GMTs gene set collection, with gene sets considered significantly enriched at FDR <0.05 after 1000 permutations. Additional bioinformatics analyses were conducted with the phyper function in R.

Quantitative real-time PCR analysis (qPCR), ChIP-qPCR
Total RNA was extracted using the Trizol reagent (Invitrogen) following the manufacturer’s protocol. RNA (5μg) was reverse transcribed and amplified using the One-Step SYBR PrimeScript PLUS RT-PCR Kit (Takara) on a Thermal Cycler Dice system (TaKaRa), according to the kit instructions. The relative enrichment of various subunits at each site was quantified using the 2-ΔΔCt method. For ChIP-qPCR, ChIP assays were conducted as described above. DNA bound by HA-JUN, Flag-GATAD2A or IgG control was de-crosslinked and purified for subsequent qPCR analysis. Enrichment was determined by qPCR using primers targeting the promoters of the indicated genes. IgG was used as a negative control. The sequences of all primers used in this study were designed to target specific genes using SnapGene software (v5.1.7). All primers are listed in Supplementary Data 8. All experiments were conducted in four biological replicates.

Molecular simulation
For GATAD2A systems modeling, the MBD2-MTA1-HDAC1 complex system was obtained from the crystal structure (PDB ID: 7AO9) from the Protein Data Bank (PDB) and complemented gaps in the structure with MODELLER (v10.4). GATAD2A CR1 structure (residues 137–190) was built using MODELLER with crystal structure (PDB ID: 2L2L) as the template. The O-GlcNAc modified GADAT2A CR1 structure at S182 was built based on the GADAT2A CR1 model structure with the online platform GLYCAM. ZDOCK (v3.0.2) was utilized to predict the interactions between the non-glycosylated GATAD2A CR1 and O-GlcNAc modified GATAD2A CR1 structures with the MBD2-MTA1-HDAC1 complex, forming two distinct systems to investigate the impact of O-GlcNAc on the recruitment of MBD2-MTA1-HDAC1 and GATAD2A CR1. These two GATAD2A CR1-bound systems and MBD2-MTA1-HDAC1 system were subjected to run 500ns molecular dynamics (MD) simulations using Amber23 software.

For JUN systems modeling, we took the DNA-interacting domain (residues 9-106) of SNF5 and a 14 base-pair DNA helical structure from one cryo-EM structure of SWI/SNF complex (PDB ID: 7Y8R), and the AlphaFold2 modeling structure of the JUN N-terminal domain (residues 69–91) of JUN (UniProt ID: P05412), which includes the glycosylation site S84 JUN, to predict their interactions with HDOCK lite (v1.1). The O-GlcNAc site S84 JUN was specified as an interface residue, and AI-based molecular surface interaction fingerprinting (MaSIF) algorithm was used to detect the potential interface and to analyse the docking results. Several potential interfaces for JUN N-terminal domain were identified, most are located at the opposite of the DNA binding pocket of SNF5 and are formed by Q59, L54, R53, and Q20.

Supplementary Table 1 presents the simulation setup for each system. All MD simulations were performed using Amber23 with the Amber14 force field. The structure was solvated in a cubic TIP3P water box with a 1nm distance from the edge, and the system was neutralized by sodium ions. After four steps of energy minimization, the temperature of the system was gradually increased to 300K over 100ps to perform 5ns NVT equilibration followed by 5ns NPT equilibration. Finally, production MD simulations at 300K and 1 atm were carried out with the LINCS algorithm to restraint the hydrogen positions at their equilibrium distances, allowing for an integration time step of 2fs. Energies and coordinates were saved every 10ps for postproduction analysis of the MD simulations. All MD simulations were performed on a high-performance computer cluster running the Linux operating system. Cpptraj and GetContacts packages were used to analyse the MD simulation results.

Xenograft mouse model
Ethical approval for all animal experiments was granted by the Ethics Committee of Dalian University of Technology (DUTSCEOLS240228-05). The maximal tumor size or burden permitted by the committee was 1.5cm in diameter, and this limit was not exceeded in any of the experiments. GFP-stably expressing A549 lung cancer cells were established in our laboratory. To create tumor xenografts, 5-week-old nude mice with equal numbers of males and females (Changsheng Biotechnology Co., Ltd., Benxi, China) were used. All nude mice were housed in an SPF-level barrier system. Housing conditions: 12-h light/dark cycle (08:00–20:00), temperature 24°C±1°C, relative humidity 40–70%, with free access to water and food. For tumor formation, 2.5×106 A549-GFP cells were injected subcutaneously, alone or combined with 5×106 LU-RAS or its derivative cells, which were induced into senescence with doxycycline at D10. Cells were injected into the left armpit of anesthetized mice in a 100μL suspension of RPMI 1640 medium, forming a subcutaneous tumor. Each group consisted of six mice (n=6). To enable tracking of senescent LU-RAS cells, they were pre-stained with CellBriteTM NIR790 Cytoplasmic Membrane Dye (Biotium #30079; 786/820nm) prior to injection. Tumor growth and fluorescence signals from A549-GFP and senescent LU-RAS cells were monitored twice weekly using the PerkinElmer IVIS Lumina imaging system. Fluorescence signals were standardized for each image and normalized using ImageJ macros for image processing. Tumor signal intensity was further analyzed semi-manually using ImageJ macros, with data expressed as fluorescence integrated density. Tumor size was monitored with caliper every 4 days until 28 days. After the experimental endpoint, mice were euthanized, and tumors were excised, fixed, and subjected to sectioning and staining procedures, including H&E staining, SA-β-Gal staining, and immunostaining with antibodies for IL-6 (Proteintech, #21865-1-AP, 1:100), IGFBP2 (Proteintech, #11065-3-AP, 1:100), CCND1 (Proteintech, # 26939-1-AP, 1:1000), and STAG2 (Proteintech, #19837-1-AP, 1:100). Tumor volume was calculated using the formula: Tumor volume (mm3)=width2×length×0.52.

Statistics analysis and reproducibility
Statistical analyses were conducted using a two-sided unpaired Student’s t test for single comparisons with GraphPad Prism 9.0 and Microsoft Excel 2019. A p<0.05 was considered statistically significant. Data are presented as means±SD. To ensure reproducibility, blots were repeated in three biological replicates, as detailed in the Methods and figure legends. RNA-seq, ChIP-seq, WGBS, and COGC-seq experiments were performed with two biological replicates, whereas micrographs and other biochemical experiments were conducted independently with four biological replicates per condition. All experimental results were successfully reproduced. Sample sizes were chosen based on previous experiments, and no statistical method was used to predetermine sample size for all experiments. For box plots, the centerline indicates the median, the box represents the 25th to 75th percentiles, and whiskers denote the minimum and maximum values. Correlations were assessed using Pearson’s correlation coefficient (r). The specific n values for each dataset are provided in the corresponding figure legends. Raw sequencing data have been deposited in the Gene Expression Omnibus database (accession number GSE295083, GSE295084, GSE295087), while raw mass spectrometry data are accessible via the iProX repository under the identifier PXD063140.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the manuscript by Zhang et al. the authors provide valuable insight into the role of the chromatin O-GlcNAc modification upon establishment of oncogene-induced senescence (OIS). The authors implement a thorough strategy to demonstrate that O-GlcNAc chromatin-associated proteins (OCPs) display distinct regulation patterns during OIS induction and identify JUN and GATAD2A as such OCPs significantly contributing to OIS. The study is interesting, as it reveals a potentially important molecular switch of senescence in the OIS context, at the epigenetic level. However, there are a number of concerns that must be addressed to validate the potentially interesting findings:\n1. In several parts of the manuscript where senescence has been assessed (eg Fig 1a, d, Fig 7a, Fig 8e and Extended Data Fig 1) this has been done only via SA-β-Gal staining alone, which however, in light of recent advancements in the field, is no longer sufficient to conclusively demonstrate senescence, as it is frequently accompanied by false positives-negatives. The authors should instead follow the revised recommended guidelines for senescence detection (Gorgoulis et al., 2019; Ogrodnik et al., 2024). According to the guidelines, initial screening for senescence using SA-β-Gal and/or lipofuscin staining is expected, followed by verification of senescence via absence of key proliferation markers, preferably MKI67, and the expression (p16INK4A, p21WAF1/Cip1) or loss (LMNB1) of hallmark senescence markers, as also published by Kohli et al., 2021.\n2. A very serious concern that pertains to most of the displayed results in the manuscript is the lack of robust statistical validation, as most of the shown experiments are representative of “at least two” or just two biological replicates. This implies that the presented results may be largely inconclusive. The authors should increase the number of biological replicates (and this should be reflected in their statistical analyses which should be displayed for all major observations).\n3. Quantification of differences in key experiments is missing – but it is required, especially since the differences are not always clearly visible. For example, in Fig 1d-e the panels are difficult to evaluate without proper quantification. In Fig 5i, quantification of the IF stainings over multiple experiments is required, especially for the GATAD2A part where DAPI staining does not appear to be of the same intensity, as it should, between WT and S128A conditions. The same applies for Fig 7d-e where a quantification and statistics are necessary for the interpretation of the shown results.\n4. In lines 241-243 the authors mention that the chromatin states occupied by O-GlcNac increased in OIS by 20%, but this is not clear in any of the figure panels. Please explain.\n5. In lines 416-418 it is mentioned that the glycosylation restraint of JUN and GATAD2A extinguished the chromatin accumulation of SNF5 and MBD2 at D10 when OIS was fully established, but that is not accurate. A reduction of SNF5 and MBD2 levels (also requiring quantification) was observed both on D0 and D10.\n6. In lines 442-444 the authors mention that compared to a “deficiency of O-GlcNAc”, cells in D10 with JUNwt and GATAD2Awt overexpression showed a significant decrease in cell cycle-related gene expression, however this is not clear. Is JUNwt and GATAD2Awt overexpression sufficient to decrease the expression of cell cycle genes regardless of O-GlcNAc levels? The authors need to address that.\n7. In lines 463-464 it is mentioned that SASP was restricted after reduction of O-GlcNAc on JUN and GATAD2A, but this is unfortunately not experimentally tested. No SASP marker alterations are shown via an appropriate assay. Some SASP factors are examined in Fig. 8e, but that is in a different setting.\n8. In Extended Data Fig 2a, the presented data does not appear to be in line with Fig 1e where TMG treatment decreased EdU+ cells. Please explain.\n9. Regarding the pull-down assays in Fig 4d and Extended Data Fig 11, proper IgG controls should be included there. Also, the rationale behind using sWGA for the pull-down assay should be presented in the main text.\n10. In Fig 8e, some markers are not entirely convincing (CCND1 and especially STAG1) as their expression level changes do not seem to fully support the claim about senescence-induced tumour development. The authors need to provide better images and quantifications thereof and/or an explanation about the not so obvious differences among the displayed conditions.\n11. In Fig 1d-e no information is provided about some of the treatments (ie OSMI-4, TMG). The role of OSMI-4 is highlighted for the first time in lines 330-331.\n12. The Discussion part is too extended – the authors should try to make it more concise and elaborate. The manuscript should be also checked carefully on appropriate use of English, as certain text parts come across as confusing.", "reviewer_2": "The authors aim to identify the role of epigenetic modifications in age-related diseases that govern cell senescence. The focus is specifically on identifying the role of O-GlcNAc modifications in the senescence process induced by oncogenes. Overall, it provides insights into the dynamics of epigenetic factors that drive the senescence process. The authors have combined both experimental and computational techniques to address their hypotheses. The study addresses the regulatory mechanisms of cellular senescence in tumor progression and provides insights into the potential targets for the treatment of age-related tumors.\n1) The methods section of molecular dynamics (MD) simulations is good; however, please create a table that shows the setup of each system, including the total number of molecules, box dimensions, water/ions, etc.\n2) In general, please write “molecular dynamics simulations” instead of “molecular simulation techniques” throughout the manuscript, just to be consistent. For example, in line no. 399, it says “Using molecular simulation techniques.” Please change to “Using molecular dynamics simulations.” It appears that all-atom simulations are employed, so it may also be written as “Using all-atom molecular dynamics simulations.” In addition, please update the simulations to molecular dynamics simulations in the figure captions of Fig. 5 as well.\n3) Please note the plots that are provided for simulations (Fig 5 k, l) , such as RMSD plots for 500 ns, these can be written as “time evolution plots” instead of “curves” in the captions. The curves are when there is a density plot vs. RMSD or distance, which can be referred to as the peak of the curve. In addition, to see the clear shift between the two systems, Non-glycosylated and O-GlcNAc systems, it's best to show as a probability density plot that can show a clear shift in the distance.\n4) Based on the results and methods sections, it appears that there is a single replica of the MD simulations for the systems. I recommend that the authors include at least three replicas of simulation to strengthen its robustness. Replicas would be more statistically meaningful, and each independent replica ideally can have different initial configurations.\n5) It’s beneficial to calculate the contact area as shown in the extended figure. 15 and 16, and display snapshots of some of the contacts. To display the residue-wise contact, or the number of contacts, it is best to calculate residue-wise contacts throughout the MD trajectory and show them in the contact maps. Please ensure that you identify the cutoff distance for contact calculations. Also, when showing the interactions in the snapshot, please ensure to write the distance of that as well.\n6) Data availability for codes used for molecular dynamics simulations, data analysis on an open platform like GitHub or any other that provides a link, as well as trajectory availability on Zenodo or any other source that works.\nOverall, the work offers novel insights into the field, providing both experimental and computational analysis. The findings have significant implications in the fields of chromatin and epigenetics. The study also cites known literature effectively and offers original insights. The conclusions are supported by the data; however, minor revisions based on the suggestions above can improve clarity, thereby enhancing reproducibility.", "reviewer_3": "This manuscript untitled “ Time-resolved muti-omics profiling uncovers a promotive role of chromatin O-GlcNAc modification in shaping the senescence-associated transcriptional programme“ by Zhang N. and co-authors explores the epigenetic dynamics underlying oncogene-induced senescence, with a particular focus on the O-GlcNAcylation of chromatin-associated proteins. Using a time-resolved multi-omics approach, the authors integrate proteomics, O-GlcNAc site mapping, ChIP-seq, and transcriptomics to identify key regulatory nodes responsive to O-GlcNAcylation. The study highlights two functional sites of Jun-S84 and GATAD2A-S182 as modulators of the senescence program, and assesses their physiological relevance through in vivo tumorigenicity assays in nude mice.\nThe topic is of great interest to the field and addresses an understudied layer of chromatin regulation during senescence. The manuscript presents an interesting hypothesis and a broad dataset, but the current version suffers from significant experimental and methodological weaknesses that limit the current impact and reliability of the conclusions. The issues, raised below, particularly regarding fractionation protocols, experimental controls, and data reproducibility, need to be addressed thoroughly to substantiate the authors' conclusions. Major revisions are needed before the manuscript can be considered for publication.\nMAJOR POINTS\n1 - Chromatin-associated protein isolation strategy is insufficiently described and potentially misleading. The methodology used to isolate chromatin-associated proteins lacks critical detail. As presented, it appears the authors may have isolated crosslinked nuclear proteins along with chromatin-associated complexes, rather than tightly bound chromatin fractions. The protocol does not convincingly support the claim that the proteomics, O-GlcNAc site mapping, and western-blot data are exclusive to chromatin-bound proteins. Clarification and proper experimental controls (e.g., nuclear markers exclusively bond and unbound to chromatin) are needed to validate the enrichment strategy.\n2 - Lack of IgG or background controls in immunoprecipitation and enrichment-based assays. The manuscript consistently omits negative controls for both ChIP-seq and [co]-immunoprecipitation experiments. These controls are essential to assess non-specific binding and to validate the specificity of enrichment (SWGA and streptavidin pull-down). Their absence weakens the confidence in all IP-derived data and conclusions. Moreover, internal controls such as IgG heavy/light chains should be shown on immunoblots, as their inclusion would significantly strengthen the confidence in the specificity and reproducibility of the IP/co-IP data. The authors must include or discuss control experiments to substantiate their findings.\n3 - Inadequate detail on computational analysis pipelines for omics datasets. The bioinformatics methods section is underdeveloped. While the tools used are standard, key parameters for alignment, peak calling, normalization, motif analysis, duplicate removal are not disclosed. Notably, it is unclear whether PCR duplicates were removed from the ChIP-seq datasets, a crucial step to avoid artifacts. The authors should comprehensively describe all computational workflows to ensure reproducibility and to allow evaluation of their validity.\n4 - Biological replicates are insufficient across key experiments. Also it is still acceptable for some experiments, including ChIP-seq and proteomics, most of the functional assays appear to have been performed with only n = 2 biological replicates. This is below the standard for biochemical experiments, where n = 3 is the bare minimum for statistical robustness. The authors should include a statistical analysis of their datasets to address whether observed differences are significant or potentially stochastic.\nMoreover, the figures display error bars representing the standard error of the mean (SEM). However, the standard practice in the field, particularly for datasets with limited biological replicates, is to use standard deviation (SD), as it more accurately reflects the underlying biological variability. Given the small number of replicates in many experiments (n = 2 in several cases), the use of SEM may artificially reduce the apparent variability and could mislead readers about the robustness of the findings. The authors must replot their data using SD or, at minimum, clearly justify the use of SEM.\n5 - Inconsistency between O-GlcNAc coverage and enrichment signal at genomic features. On page 9, line 242, the authors report O-GlcNAc modification ranging from 64–84% of the genome. This high prevalence is not reflected in Figures 2i and 3c, which display signal intensities around peak centers (±2 kb). The apparent discrepancy between global prevalence and localized enrichment patterns should be addressed, and data interpretation refined accordingly.\n6 - Inaccuracies in site novelty claims and citation of existing data. O-GlcNAc sites on FOXK1, RFX5, and C2H1 (line 318-319) claimed as novel are in fact reported in publicly available O-GlcNAc databases, often with multiple references. Similarly, the evolutionary conservation of FOXK1 and ARID2 O-GlcNAc sites (page 13, lines 352–353) is mentioned in the text but not shown in the main or supplemental figures. In addition, the manuscript should indicate that several O-GlcNAc sites on GATAD2A and JUN have already been reported in the literature, with some located in close proximity to S182 on GATAD2A. The mass spectra shown in Fig. 5c are also too small to be interpreted properly, and should be presented at higher resolution. The authors should also clarify whether other previously described sites in the vicinity of S182—such as T175, T180, S182, S185, and T188—were detected in their analysis, and if not, whether this is due to technical limitations or biological variability. Moreover, results obtained from domain deletion mutants (Fig. 5j) should be interpreted with caution, as at least eight additional O-GlcNAc sites have been reported in the deleted region of GATAD2A (Δ1) (Fig. 5j). The manuscript must be carefully revised to reflect existing knowledge and avoid overstatement of novelty.\n7 - The term “peak” is used ambiguously throughout the figures versus the manuscript. It is unclear whether the authors refer to signal enrichment peaks, transcription start sites (TSS), or promoter regions. Terminology should be clarified and used consistently between the figures and the text.\n8 - The statement on page 6 that “enhancing O-GlcNAc modification moderately accelerated the formation of the senescence phenotype” seems underestimated, as the β-galactosidase assay shows nearly a two-fold increase (although this is not obvious in the representative images in Fig. 1d), and there are fewer EdU+ cells. SA-β-gal and EdU images require statistical analysis to validate these observations.\n9 - Cell cycle Analysis. The authors should explain why cell cycle arrest occurs in S phase for MCF-Ras and HaC-Ras (Extended Fig. 1g), while G2/M arrest is observed for SK-Ras and LU-Ras cells (Fig. 1b). A reference should be provided If known in the literature. In addition, EdU labeling does not correlate with FACS analysis for MCF-RAS and HaC-RAS cells (Extended Fig. 1g/1i): fewer EdU+ cells are detected at day 10, yet FACS analysis indicates a block in S phase.\n10- OGT expression and efficacy of the OGT inhibitor. The authors report that the OGT gene is downregulated at the transcriptomic level during OIS progression (approximately two-fold), while OGT protein levels appear elevated on Western blot (Fig. 1c), suggesting a possible post-transcriptional regulation. The current statement in the discussion (lines 538–539) remains vague and insufficiently refered to the data presented. Moreover, it is striking that no decrease in OGT protein levels is observed upon treatment with 50 µM Thiamet-G (which is a very high concentration compared to what is generally used in other cell lines), and conversely, no increase in OGT is seen with OSMI-4 treatment (Fig. 1f), despite the literature describing compensatory mechanisms regulating O-GlcNAc enzyme expression. The authors should address this point.\n- In Figure 4d, no evidence is provided for the efficacy of the OGT inhibitor. Each experiment should validate the extent of the inhibition by a pan-O-GlcNAc Western blot.\n11- Page 10, line 284: there is a discrepancy between Fig. 3g and the text, where “cellular stress response” is indicated as downregulated in OIS, while it is shown among the upregulated overlapping genes.\n12 - The rationale for using 1,6-Pr₂GalNAz (Fig. 2) and GalNAz (Fig. 5) for proteomic analyses should be justified.\n13 - Several figures suffer from low resolution and extremely small font sizes. Figure 5, in particular, is unreadable even upon digital zoom. All figures must be revised for clarity and publication-quality resolution.\n14 – Figure 5. Co-immunoprecipitation results (e.g., Fig. 5f) lack the reverse IP validation, which is considered best practice when technically feasible. If reverse IP is not possible, the authors should justify this and provide alternative supporting evidence.\nMoreover, conclusions drawn from co-IP results in Fig. 5f should be verified (Line 373). The authors state that “GATAD2A S182A marginally associates with only MTA1 and HDAC1, but not with the DNA methylation reader MBD2”. However, MTA1 co-IP seems to follow its input level, and MBD2 and HDAC1 remain detectable by co-IP. Without quantification and statistical analysis, these conclusions remain subjective. Quantification of colocalized proteins should also be provided for IF experiments (Fig. 5i).\n15 - The rationale behind co-downregulation and co-expression of both JUN and GATAD2A WT or mutants in the same cells is unclear. If the goal is to dissect their individual contributions, co-expression confounds interpretation. The authors should revise the experimental design to test mutants independently.\n16 - The chromatin binding of JUN and GATAD2A mutants (Fig. 6a) is not convincingly demonstrated. Given the concerns about fractionation purity, it is unclear whether reduced chromatin association reflects impaired nuclear localization or altered chromatin binding per se. Fractionation experiments should be expanded to include cytoplasmic, nuclear soluble, and chromatin-bound fractions. Inclusion of input controls in Fig. 6a is also recommended.\n17- Unless missed, there are no results showing protein levels of overexpressed 3X-FLAG-GATAD2A and HA-JUN, compared to endogenous levels. These controls should be included to appreciate their relative expression.\n18- Insufficient clarity in molecular simulation results. The molecular dynamics simulations presented in the main figures (Fig. 5k–l) and in the extended data (Extended Data Figs. 15–16) should be clarified. In particular, the amplitude of the differences between WT and mutant proteins appears inconsistent between the main and extended figures, which raises concerns about the selection of representative simulations. Furthermore, the parameters referred to as \"Distance I\" and \"Distance II\" must be clearly defined. In addition, the interaction data surrounding the S182 residue of GATAD2A (Extended Fig. 15) deserve a more explicit interpretation to support the conclusions drawn. The significance of red arrows (Extended Fig. 16) used in molecular simulations should be clarified.\n19 - In vivo experiments. Differences in NIR769 fluorescence between vehicle LU-RAS and LU-RAS KD-WT (Fig. 8a) appear contradictory, as KD-WT cells have a lower proliferation rate than vehicle-treated LU-RAS in in vitro experiments (Extended Fig. 20).\n20 - Extended Fig. 20: Statistical analyses should be provided for panels a and b. In panel a, Western blot showing expression levels of WT and mutant proteins should also be included.\n21- Conclusions drawn from IHC on tumor tissues (Fig. 8e) suffer from discrepancies between the text and the images. No clear accumulation of CCND1 and STAG2 can be observed in AA cells compared to WT cells, in contrast to what is stated in the manuscript (lines 491–493). In the absence of quantification and statistical analysis, it is difficult to draw reliable conclusions from these IHC results.\nMINOR COMMENTS\n22- Fig. 7e: There is a discrepancy between the figure and the legend regarding the scratch assay time points (48h/96h). Please verify.\n23- Line 486: it seems that RFP fluorescence is used to refer to GFP fluorescence. Please confirm and correct as needed.\n24- Line 573: Reference #18 cited in the discussion does not seem related to senescence. Please check its relevance.\n25- Lines 622–623: The statement that “specific pharmacological inhibition of glycosylation of these key factors, like converging on the OGT that activates JUN, might be a feasible approach for manipulating senescence phenotypes” appears overestimated. Given the broad range of OGT targets and the enzyme’s essential roles, such a therapeutic strategy would likely be toxic and unrealistic.\nTYPO AND FIGURE LABELING ERRORS\n• Fig. 1c: “chromtain” should be corrected to “chromatin”\n• Fig. 1b legend: “indicted” should read “indicated”\n• Figs. 1d–g and Extended Figs. 2A, 2B: “OSIM-4” should be corrected to “OSMI-4”\n• Fig. 3g: “quantitavely”, “overlaped”, and “nagative” should be corrected to “quantitatively”, “overlapped”, and “negative”\n• Extended Fig. 9: “mentylation” should be corrected to “methylation”"}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed some of my initial concerns, however there are a few that unfortunately remain:\n1. In Fig 5i, the IF panels showing only one cell at a time are still not convincing, as it is impossible to observe actual differences across the conditions. It is surprising that Pearson’s coefficient and the corresponding plots so strongly indicate the opposite to what the images display.\n2. The blots in Fig 5 unfortunately remain problematic. Although the authors in their responses mention the incorporation of IgG controls (they actually show them in the IPs of Fig 4), these controls are still missing from Fig 5 and Extended Data Figs.\n3. I still disagree with the authors that the data presented in Fig 1e and Ext Data Fig 2a are in accordance with one another. In Fig 1e, EU staining labels proliferating cells (in S phase). That proportion of cells evidently decreases upon TMG treatment. On the contrary, Ext Data Fig 2a shows an elevated S phase in MCF-RAS cells. The authors should provide a reasonable explanation. Most of the experiments were performed in LU-RAS cells, and differences such as this one may raise concerns regarding cell line bias and data reproducibility.", "reviewer_2": "Please note that the authors have addressed all my comments and concerns from the previous review. I have no further comments, and I recommend the manuscript for publication.", "reviewer_3": "All the revisions and comments raised in the previous round have been thoroughly addressed by the authors. The additional controls, experiments and statistical analyses provided are appropriate and provide convincing support for the main conclusions of the study. The manuscript has been significantly improved in both clarity and scientific rigour. I have no further concerns, and I consider the revised version to be acceptable for publication in its current form.\nHowever, a few minor typographical and punctuation errors remain and should be corrected carefully during the final proofreading stage, as listed below.\n- Figure 1 legend (d–g): “OSIM-4” should read “OSMI-4.”\nExtended Figure legends:\n-Figure 1 (c): punctuation error : a full stop is missing at the end of the sentence \"and MKI67. The immunoblots ...\"\n-Figure 2(a): “indicted” should read “indicated”\n- Extended Figure 9 (images a, b, c): “mentylation” should read “methylation”", "reviewer_4": "The authors have thoroughly and thoughtfully addressed my previous comments. Their revisions have improved the overall robustness of the data and clarified the main conclusions, rendering the manuscript suitable for publication pending final editorial checks."}, "author_response": "Dear editor,\nWe are grateful for the opportunity to submit a revised version of our manuscript,\n“Time-resolved multiomics profiling reveals the role of chromatin O-GlcNAc\nmodification in shaping the senescence-associated transcriptional program”\n(NCOMMS-25-41520). We thank you, the three reviewers, and the co-reviewer for the\nthoughtful and constructive comments that substantially improved our study.\nIn this revision, we performed extensive new experiments, added missing controls,\nclarified methods and analyses in depth, corrected textual inaccuracies, improved figure\nquality, and strengthened statistics across datasets. We believe the revised manuscript\naddresses all major and minor concerns and now presents a much stronger and more\nreproducible body of work. Below, we summarize the main changes and how they\naddress the reviewers’ points. A point-by-point response is included in the Response to\nReviewers.\nSummary of Substantive Revisions\n Enhanced senescence validation across experiments\n Increased biological replicates and strengthened statistics\n Added missing controls and clarified fractionation\n Clarified computational pipelines and interpretations\n Molecular dynamics simulations — robustness and clarity\n Corrected novelty statements and improved MS evidence\n In vivo tumor studies — quantification and interpretation\n Textual and figure quality improvements\nWe sincerely thank you and the reviewers for the constructive guidance. We hope\nthat the breadth of new data, strengthened controls, methodological clarity, and rigorous\nstatistics now meet Nature Communications’ standards. We would be grateful for\nfurther consideration of our revised manuscript.\nSincerely,\nYubo Liu, PhD\non behalf of all authors\nResponse to Reviewers\nReviewer 1#\n1. In several parts of the manuscript where senescence has been assessed (eg Fig 1a,\nd, Fig 7a, Fig 8e and Extended Data Fig 1) this has been done only via SA-β-Gal staining\nalone, which however, in light of recent advancements in the field, is no longer\nsufficient to conclusively demonstrate senescence, as it is frequently accompanied by\nfalse positives-negatives. The authors should instead follow the revised recommended\nguidelines for senescence detection (Gorgoulis et al., 2019; Ogrodnik et al., 2024).\nAccording to the guidelines, initial screening for senescence using SA-β-Gal and/or\nlipofuscin staining is expected, followed by verification of senescence via absence of\nkey proliferation markers, preferably MKI67, and the expression (p16INK4A,\np21WAF1/Cip1) or loss (LMNB1) of hallmark senescence markers, as also published\nby Kohli et al., 2021.\nReply：\nWe appreciate the reviewer’s focus on whether the senescence markers employed\nare sufficient to substantiate the occurrence of cellular senescence. As correctly noted,\nmolecular hallmarks of senescence have been refined in recent years. We have carefully\nconsidered the reviewer’s suggestions, consulted the cited literature, and incorporated\nthe recommended markers to strengthen our evidence.\na) We originally used SA-β-Gal staining as an initial screening assay for senescence.\nb) In the revised manuscript, we have added MKI67 as a proliferation marker in Fig.\n1f, Fig. 1g, Extended Data Fig. 1c, Extended Data Fig. 1h, Extended Data Fig. 2c,\nFig. 7d. As shown, MKI67 is absent in senescent cells.\nc) We had previously examined changes in p16INK4A, p21WAF1/Cip1, and p53 during the\nsenescence process. In the revision, we now include these markers systematically\nacross all senescence experiments (Fig. 1f, Fig. 1g, Extended Data Fig. 1c,\nExtended Data Fig. 1h, Fig. 7d, Fig. 8e).\nd) We additionally incorporated LMNB1, a recognized hallmark of senescence.\nConsistent with established literature, LMNB1 expression is reduced during the\nsenescence progression observed in our models (Fig. 1f, Fig. 1g, Extended Data\nFig. 1c, Extended Data Fig. 1h, Extended Data Fig. 2c, Fig. 7d). In Fig. 8e, because\nthe mouse model involved co-injecting A549 cancer cells together with OIS\nLU‑RAS cells expressing JUN and GATAD2A variants, the immunoblotting of\ntumor samples reflects a mixture of A549 and LU‑RAS cells. Consequently, SASP\nfactors secreted by the OIS cells enhanced the proliferation of A549 cells (which\nconstitute the majority of the tumor mass), resulting in higher MKI67 and LMNB1\nexpression in the A549+LU‑RAS/Vehicle and A549+LU‑RAS/KD‑WT groups\ncompared to A549 and A549+LU‑RAS/KD‑AA groups.\ne) Furthermore, we assessed cell-cycle alterations associated with senescence by flow\ncytometry (Fig. 1b, Extended Data Fig. 1g, Extended Data Fig. 2a, Fig. 7b), and\nwe evaluated proliferative capacity using CCK-8 assays (Extended Data Fig. 1b,\nExtended Data Fig. 1e, Extended Data Fig. 2d, Extended Data Fig. 20c) and EdU\nincorporation (Fig. 1e, Extended Data Fig. 1d, Extended Data Fig. 1i, Extended\nData Fig. 2b, Extended Data Fig. 20d).\nCollectively, these results provide convergent and robust evidence that the\nsenescent phenotypes observed in our study are genuine. Importantly, modulation of\ncellular O-GlcNAcylation—particularly on the key regulators JUN and GATAD2A—\nexerts a measurable impact on senescence phenotypes.\n2. A very serious concern that pertains to most of the displayed results in the manuscript\nis the lack of robust statistical validation, as most of the shown experiments are\nrepresentative of “at least two” or just two biological replicates. This implies that the\npresented results may be largely inconclusive. The authors should increase the number\nof biological replicates (and this should be reflected in their statistical analyses which\nshould be displayed for all major observations).\nReply：\nWe thank the reviewer for highlighting the number of biological replicates and the\nstatistical robustness of the reported results. We accept this recommendation and\nprovide the following clarifications and updates.\na) Clarification and augmentation of replicates in biochemical and molecular assays\nIn the original manuscript, figure legends stated “at least two” replicates,\nprimarily because a small subset of Western blot (WB) experiments had n = 2. In fact,\nthe vast majority of our WB, biochemical and molecular assays—such as SA-β-Gal\nand EdU staining, flow cytometry, CCK-8 assays, quantitative PCR, cell migration,\nand Transwell assays—were conducted with n = 3, and yielded statistically significant\nresults.\nTo further enhance statistical rigor in the revised manuscript, we have standardized\nWB experiments to n = 3, increased other biochemical and molecular assays to n = 4,\nand expanded the corresponding statistical analyses throughout the manuscript (e.g. Fig.\n1a-b, d-e; Extended Data Fig. 1b, d-g, i; Extended Data Fig. 2a, b, d; Fig 6g-h; Extended\nData Fig. 19; Extended Data Fig. 20c-d;Fig. 7a-c, f-g; Fig 8h). The results demonstrate\nthat increasing biological replicates did not alter the conclusions; the findings remain\nconsistent with those in the original submission. We have also revised the figure legends\nto explicitly report the exact number of biological replicates for each experiment.\nb) Replicates and statistical justification in omics experiments\nFor proteomics: the OCPs quantitative proteomics in Fig. 2 includes n = 9\nbiological replicates, and the site-specific profiling of O-GlcNAc modifications on\nOCPs in Fig. 5 includes n = 3, meeting accepted statistical standards for proteomic\nanalyses.\nFor next-generation sequencing: COGC-seq, RNA-seq, and DNA methylation\nsequencing were performed with n = 2 biological replicates. We note that ENCODE\nstandards for ChIP-seq explicitly establish at least two independent biological replicates;\nsimilarly, the ENCODE Whole-Genome Bisulfite Sequencing (WGBS) standards\nspecify at least two biological replicates per condition. Methodological studies further\nindicate that, given appropriate sequencing depth and data quality, two biological\nreplicates generally provide robust performance for ChIP-seq peak identification and\nreliable power for conventional RNA-seq differential expression analyses, while\nacknowledging that additional replicates can improve sensitivity for small effects\n(Nucleic Acids Research, 39(1), 190–201; Bioinformatics, 30(3), 301–304; Nature Cell\nBiology, 22(7), 842–855, 2020). In our study, each assay included two independent\nbiological replicates and met commonly recommended depths: RNA-seq ≥ 20 million\nmapped reads per sample, ChIP-seq ≥ 30 million uniquely mapped reads per sample,\nand WGBS ≥ 200 million mapped reads per sample. All datasets passed standard quality\nmetrics (e.g., alignment rate, library complexity, duplication) with cross-replicate\nconsistency assessments. Analyses employed appropriate frameworks for high-\ndimensional count data, using negative binomial–based models within standard\npipelines. Hence, our experimental design, sequencing depth, and statistical procedures\nmeet established standards and requirements.\nc) Replicates in Molecular Dynamics Simulations\nIn the revised manuscript, we increased the number of independent molecular\ndynamics (MD) simulation replicates by extending the trajectories to n = 3. The results\ndemonstrate good reproducibility across replicates, indicating that the MD findings are\nrobust.\nd) Replicates in animal studies\nIn the original study, animal experiments were conducted with n = 6 biological\nreplicates. In the revised manuscript, we increased the number of biological replicates\nfor IHC staining to n = 6 and performed statistical analyses accordingly (Fig. 8f).\nIn summary, with the additional experiments and expanded reporting, the number\nof biological replicates across our study now adheres to statistical requirements. The\nresults are statistically significant and consistently support our conclusions.\n3. Quantification of differences in key experiments is missing – but it is required,\nespecially since the differences are not always clearly visible. For example, in Fig 1d-\ne the panels are difficult to evaluate without proper quantification. In Fig 5i,\nquantification of the IF stainings over multiple experiments is required, especially for\nthe GATAD2A part where DAPI staining does not appear to be of the same intensity,\nas it should, between WT and S128A conditions. The same applies for Fig 7d-e where\na quantification and statistics are necessary for the interpretation of the shown results.\nReply：\nWe appreciate the reviewer’s observation that key experiments lacked quantitative\nreadouts. We accept this recommendation. Building on the increased number of\nbiological replicates, we have now provided quantitative analyses and statistical testing\nfor all relevant assays, including: SA-β-Gal staining (Fig. 1a, Fig. 1d, Extended Data\nFig. 1f, Fig. 7a); Flow cytometric analysis (Fig. 1b, Extended Data Fig. 1f, Extended\nData Fig. 2a, Fig. 7b); EdU staining (Fig. 1e, Extended Data Fig. 1d, Extended Data\nFig. 1i, Fig. 2b, Extended Data Fig. 20d); transwell co-culture assays (Fig. 7f); co-\nculture wound-healing/scratch assays (Fig. 7g); IHC assays (Fig. 8h). The quantitative\nresults are consistent with, and further substantiate, the conclusions of this study.\nRegarding the IF stainings in Fig. 5i, we agree with the reviewer’s concern.\nBecause the DAPI staining did not appear to have uniform intensity, we repeated the\nexperiment in the revised manuscript with n = 4. The new data show that DAPI intensity\nis consistent across samples. Additionally, we quantified the colocalization in the IF\nimages using Pearson’s correlation coefficient. The quantitative analysis confirms that\nO-GlcNAcylation modulates nuclear colocalization between JUN and SNF5, as well as\nbetween GATAD2A and MBD2.\n4. In lines 241-243 the authors mention that the chromatin states occupied by O-\nGlcNAc increased in OIS by 20%, but this is not clear in any of the figure panels.\nPlease explain.\nReply：\nWe appreciate the reviewer’s attention to the statement in Fig. 3a regarding a “20%\nincrease” in chromatin states occupied by O-GlcNAc during OIS. In the revised\nmanuscript, we have clarified this description and annotated the figure to facilitate\ninterpretation.\nSpecifically, this result derives from an integrative analysis combining multiple\nchromatin epigenetic marks with COGC-seq data to assess the genome-wide co-\noccurrence of O-GlcNAc with or without distinct chromatin-state markers, using\nChromHMM. In Fig. 3a, the left panel summarizes changes in the genomic occupancy\nof different chromatin states. The right panel provides a quantitative analysis, showing\nthat the summed fraction of O-GlcNAc-related chromatin states—defined as O-\nGlcNAc promoter, O-GlcNAc enhancer, O-GlcNAc-repressed, and O-GlcNAc-\nunmarked—increases as OIS progresses. Concretely, this aggregate fraction rose from\napproximately 64.1% at D2 (O-GlcNAc promoter, 2.35%; O-GlcNAc enhancer, 0%;\nO-GlcNAc-repressed, 0.51%; O-GlcNAc-unmarked, 61.25%) to approximately 83.5%\nat D10 (O-GlcNAc promoter, 3.71%; O-GlcNAc enhancer, 0%; O-GlcNAc-repressed,\n16.01%; O-GlcNAc-unmarked, 63.79%).\nAccordingly, we have revised the text in the manuscript to read: “The fraction of\nO-GlcNAc-related chromatin states can be categorized into four functional classes: O-\nGlcNAc promoter, O-GlcNAc enhancer, O-GlcNAc occupied transcription-repressed\nchromatin (O-GlcNAc-repressed), and those lacking any of the profiled histone\nmodifications (O-GlcNAc-unmarked). Consistent with the genome-wide elevation of\nOCP binding, the cumulative fraction of chromatin states occupied by O-GlcNAc (i.e.,\nthe sum of O-GlcNAc promoter, O-GlcNAc-repressed, and O-GlcNAc-unmarked)\nincreased in OIS cells, from a minimum of 64.1% at D2 to a maximum of 83.5% at D10\nof the genome”.\n5. In lines 416-418 it is mentioned that the glycosylation restraint of JUN and\nGATAD2A extinguished the chromatin accumulation of SNF5 and MBD2 at D10 when\nOIS was fully established, but that is not accurate. A reduction of SNF5 and MBD2\nlevels (also requiring quantification) was observed both on D0 and D10.\nReply：\nWe appreciate the reviewer’s comment and agree that our original description of\nhow the chromatin binding of SNF5 and MBD2 is influenced by the glycosylation of\nJUN and GATAD2A was not sufficiently precise. As shown in Fig. 6a, at both D0 and\nD10, restraining the glycosylation of JUN and GATAD2A reduces their own chromatin\noccupancy and concomitantly diminishes the chromatin association of their respective\ninteracting partners, SNF5 and MBD2. This observation directly supports the\nconclusion that O-GlcNAcylation modulates the interactions between JUN–SNF5 and\nGATAD2A–MBD2, which in turn accounts for the chromatin engagement of SNF5 and\nMBD2. Given that our study focuses on the role of JUN and GATAD2A O-\nGlcNAcylation during OIS progression, the ChIP-seq data at the D10 time point for\nMBD2 and SNF5 in LU-RAS/KD-WT and LU-RAS/KD-AA cells (Fig. 6d) further\ncorroborate this mechanism.\nIn the revised manuscript, we have refined the sentence as follows: “Moreover,\nrestraining the glycosylation of JUN and GATAD2A diminishes the chromatin\naccumulation of SNF5 and MBD2 at both D0 and D10, indicating a role for O-\nGlcNAcylation in mediating the interactions and colocalization among these factors”.\n6. In lines 442-444 the authors mention that compared to a “deficiency of O-GlcNAc”,\ncells in D10 with JUNwt and GATAD2Awt overexpression showed a significant\ndecrease in cell cycle-related gene expression, however this is not clear. Is JUNwt and\nGATAD2Awt overexpression sufficient to decrease the expression of cell cycle genes\nregardless of O-GlcNAc levels? The authors need to address that.\nReply：\nWe thank the reviewer for raising the question of whether overexpression of\nJUNWT and GATAD2AWT is sufficient to drive changes in cell-cycle and SASP-related\ngene expression. In Fig. 6f, our RNA-seq analysis in LU-RAS/KD cells at the D10 time\npoint—where JUNWT and GATAD2AWT exhibit expression levels comparable to their\nglycosylation-deficient mutants (Fig. 5d, f, h; Extended Data Fig. 14i)—shows that\ndiffering glycosylation states of JUN and GATAD2A induce differential expression\nacross multiple cell‑cycle and SASP-associated genes. These findings indicate that O-\nGlcNAcylation plays an important role in how JUN and GATAD2A regulate these\ntranscriptional programs.\nTo further distinguish the effects of O-GlcNAcylated JUN and GATAD2A from\nthose of the proteins per se, we extended the analyses in Fig. 6g by: (i) adding D10 LU-\nRAS control groups to benchmark the impact of JUN and GATAD2A expression on\ndownstream target genes; and (ii) pharmacologically reducing O-GlcNAcylation with\nOSMI-4 to assess the glycosylation dependence of the observed transcriptional effects.\nAt D10, relative to LU-RAS controls, knockdown of JUN and GATAD2A increased the\nexpression of cell-cycle genes (CCND1, STAG2, CDK6, E2F5) and decreased the\nexpression of SASP genes (IL6, IGFBP2, MIF, CXCL1). This finding is consistent with\nprior reports that JUN and other AP-1 family transcription factors regulate SASP gene\nexpression and the progression of cellular senescence (Nature cell biology, 2020, 22(7):\n842-855). Notably, OSMI-4–mediated reduction of O-GlcNAcylation significantly\nincreased the transcript levels of these cell-cycle genes while concomitantly decreasing\nthe mRNA levels of SASP genes (Fig. 6h and Extended Data Fig. 19b), closely\nmirroring the effects observed upon JUN and GATAD2A knockdown in LU-RAS cells.\nCollectively, these results support the conclusion that the O-GlcNAcylated forms of\nJUN and GATAD2A—rather than the unglycosylated proteins alone—govern the\ntranscriptional regulation of cell-cycle and SASP gene expression. We have made\ncorresponding revisions at the relevant locations in the manuscript as well.\n7. In lines 463-464 it is mentioned that SASP was restricted after reduction of O-\nGlcNAc on JUN and GATAD2A, but this is unfortunately not experimentally tested.\nNo SASP marker alterations are shown via an appropriate assay. Some SASP factors\nare examined in Fig. 8e, but that is in a different setting.\nReply：\nWe appreciate the reviewer’s observation that, in Fig. 7a–b, we did not directly\nassess reductions in SASP following the reduction of O-GlcNAc on JUN and\nGATAD2A in the senescence phenotype assays. We acknowledge that, in these\nfunctional experiments, we did not measure the impact of altered O-GlcNAcylation of\nJUN and GATAD2A on secreted SASP factors. Our inference regarding SASP\nmodulation was originally based on transcriptomic evidence under the same\nexperimental conditions (Fig. 6g), where multiple SASP genes showed altered\nexpression.\nTo strengthen the evidentiary basis for this conclusion, in the revised manuscript\nwe have added ELISA assays to quantify secreted SASP factors in conditioned media\nfrom LU-RAS, LU-RAS/KD-WT, and LU-RAS/KD-AA cells, specifically measuring\nIL6, IL1B, IGFBP2, MIF, TNFA, CXCL1, CXCL10, and CCL5. As shown in Fig. 7c,\nreduction of O-GlcNAc on JUN and GATAD2A resulted in a significant decrease in the\nsecretion of multiple SASP factors, thereby corroborating our original interpretation\nand aligning with the overall conclusions of the study. We have made corresponding\nrevisions at the relevant locations in the manuscript as well.\n8. In Extended Data Fig 2a, the presented data does not appear to be in line with Fig 1e\nwhere TMG treatment decreased EdU+ cells. Please explain.\nReply：\nWe thank the reviewer for raising the point regarding the interpretation of our EdU\nstaining results. EdU (5-ethynyl-2′-deoxyuridine) is a thymidine analog that is\nincorporated into newly synthesized DNA. In this study, EdU was not present\ncontinuously during senescence induction. Instead, for each assay we added EdU only\nduring a 2-hour pulse immediately prior to fixation at the indicated time points along\nthe senescence time course (D0–D10). Consequently, as senescence progresses and\nproliferation diminishes, DNA replication is increasingly suppressed, leading to a lower\nfraction of EdU-positive cells. In Extended Data Fig. 2a, TMG treatment induced\ncell-cycle arrest, which is consistent with the reduced proportion of EdU-positive cells\nobserved in Fig. 1e.\nTo preclude misinterpretation, we have revised the Methods to more explicitly\ndescribe the EdU staining protocol, as follows.\nFor EdU staining, representative samples from the senescence time series were\nanalyzed for EdU incorporation using the Click-iT EdU Alexa Fluor Imaging Kit\n(Thermo Fisher Scientific) according to the manufacturer’s instructions. Cells were\nseeded in 6-well plates and cultured under senescence-inducing conditions to the\nindicated time points (D0, D2, D4, D10). Immediately prior to fixation, cells were\npulsed with EdU for 2 h. After incubation, the medium was aspirated, and cells were\nwashed twice with PBS and fixed with paraformaldehyde. Cells were then washed three\ntimes with washing buffer (PBS containing 3% BSA) and permeabilized with 0.3%\nTriton X-100 for 15 min. Following three additional washes with washing buffer, cells\nwere incubated with the Click Reaction Mixture. For nuclear counterstaining, Hoechst\ndye was added at a final concentration of 1 μg/mL and incubated for 10 min at 25 °C in\nthe dark. Excess dye was removed by washing with PBS prior to imaging. Cells\nexhibiting both green (EdU) and blue (Hoechst) signals were scored as EdU-positive.\n9. Regarding the pull-down assays in Fig 4d and Extended Data Fig 11, proper IgG\ncontrols should be included there. Also, the rationale behind using sWGA for the pull-\ndown assay should be presented in the main text.\nReply：\nWe appreciate the reviewer’s comment regarding the absence of appropriate\ncontrols in our immunoprecipitation (IP) and pull-down experiments. We have\nincorporated the suggested controls in the revised manuscript. Specifically, we added\nIgG controls or IgG-conjugated magnetic bead controls to the IP assays presented in\nFig. 4d, 4e, 5d, 5e, 5f, 5h, and 5j. In addition, we introduced a negative control in\nExtended Data Fig. 11a by using agarose beads lacking succinylated wheat germ\nagglutinin (sWGA) lectin to rule out nonspecific binding to the matrix.\nOur study employs complementary orthogonal approaches to demonstrate\nO-GlcNAc modification across multiple members of the NuRD and SWI/SNF\ncomplexes: i) Metabolically labeled proteomics to detect O-GlcNAc at the proteome\nlevel. ii) Classical biochemical IP using O-GlcNAc–specific antibodies. iii)\nLectin-based pull-down employing sWGA, which binds O-GlcNAc and has been\nreported to offer improved specificity for O-GlcNAc–modified proteins relative to\nWGA (RSC advances, 2023, 13(1): 264-280; Clinical proteomics, 2014, 11(1): 8). The\nconvergence of these methods provides robust, cross-validated evidence that several\nsubunits of the NuRD and SWI/SNF complexes are O-GlcNAcylated.\nTo further strengthen the evidentiary basis for the sWGA pull-down results, we\noptimized the workflow in the revised manuscript by adapting procedures described in\nthe literature (Nature Communications, 2025, 16(1): 5959). In brief, we first performed\nIP of GATAD2A and JUN in OIS LU-RAS cells, eluted the bound proteins, and then\nsubjected the eluates to sWGA-agarose pull-down. The pull-down products were\nsubsequently analyzed to assess the O-GlcNAc status of GATAD2A and JUN. As\nshown in Extended Data Fig. 11b, both GATAD2A and JUN display clear O-GlcNAc\nglycosylation in LU-RAS cells at D10, corroborating our central conclusions. We have\nmade corresponding revisions at the relevant locations in the main text as follows:\n“Pull‑down assays using succinylated wheat germ agglutinin (sWGA), a lectin that\nspecifically recognizes the O‑GlcNAc moiety, also corroborated these findings\n(Extended Data Fig. 11)”.\n10. In Fig 8e, some markers are not entirely convincing (CCND1 and especially\nSTAG1) as their expression level changes do not seem to fully support the claim about\nsenescence-induced tumour development. The authors need to provide better images\nand quantifications there of and/or an explanation about the not so obvious differences\namong the displayed conditions.\nReply：\nWe appreciate the reviewer’s careful assessment of Fig. 8g and the concern that\nCCND1—and particularly STAG1—do not appear entirely consistent with\nsenescence-induced tumor development. Below, we clarify the experimental context\nand the resulting IHC patterns.\nIn our in vivo model, we performed subcutaneous co-injection of A549 cancer\ncells together with OIS LU-RAS cells expressing JUN and GATAD2A variants. Within\nthe resulting tumor tissues, it is not feasible to unambiguously distinguish A549 tumor\ncells from senescent LU-RAS cells by histology alone. Senescent LU-RAS cells are\ngrowth-arrested and comprise a minor fraction of the subcutaneous tumors. As\ndemonstrated earlier in the manuscript, cell-cycle–related genes such as CCND1 and\nSTAG1 are downregulated in senescent LU-RAS cells. In contrast, neighboring A549\ntumor cells are exposed to SASP factors secreted by the senescent LU-RAS cells, which\nenhance A549 proliferation. Consequently, A549 cells constitute the majority of the\ntumor mass and exhibit elevated expression of CCND1 and STAG1 within the tumor\ncontext. In the CCND1 IHC images, the A549+Vehicle and A549+KD-WT groups\n(both displaying an OIS phenotype in the LU-RAS compartment) show regions with\nweak CCND1 DAB staining corresponding to senescent LU-RAS cells. Adjacent tumor\nregions, predominantly composed of A549 cells, exhibit stronger CCND1 DAB\nstaining compared with the A549 and A549+KD-AA groups. This spatial pattern—\nreduced CCND1 in senescent LU-RAS cells alongside increased CCND1 in\nneighboring A549 cells—is consistent with senescence-induced tumor development\nmediated by SASP. In the revised manuscript, we have clarified and discussed this issue\nin the corresponding sections of the main text.\nWe acknowledge that the originally provided STAG1 IHC results did not fully\nsupport the claim regarding senescence-induced tumor development. In the revised\nmanuscript, we repeated the STAG1 IHC experiment with improved imaging and\ncontrols and now provide more compelling results that align with the proposed\nmechanism. To strengthen interpretability and address potential ambiguity, we\nadditionally performed OD-based quantification of DAB areas signals for all IHC\nmarkers, including CCND1 and STAG1(Fig 8f). These quantitative analyses support\nour conclusions and are included in the revised figures.\nThe revised text explicitly explains the mixed-cell nature of the tumors, the\ndownregulation of CCND1/STAG1 in senescent LU-RAS cells, and the SASP-driven\nupregulation of these markers in proliferating A549 cells. We trust that these\nclarifications, additional experiments, and quantitative analyses address the reviewer’s\nconcerns and provide an interpretation of the IHC observations in Fig. 8g.\n11. In Fig 1d-e no information is provided about some of the treatments (ie OSMI-4,\nTMG). The role of OSMI-4 is highlighted for the first time in lines 330-331.\nReply：\nWe appreciate the reviewer’s suggestion. In the revised manuscript, we have\nmoved the descriptions of OSMI-4 and TMG as inhibitors of OGT and OGA,\nrespectively, to the main text corresponding to Fig. 1d–e. In addition, we have explicitly\nclarified in the Results text that we manipulated cellular O-GlcNAc levels by modifying\nOGT expression and by employing OGT/OGA inhibitors, as follows: “Further, we\nmodulated cellular O-GlcNAc levels by altering OGT expression using\nshRNA-mediated knockdown and an OGT overexpression construct (pcDNA-OGT),\nand by pharmacologically inhibiting OGT or OGA with the potent inhibitors OSMI-4\nand Thiamet-G (TMG)”.\n12. The Discussion part is too extended – the authors should try to make it more concise\nand elaborate. The manuscript should be also checked carefully on appropriate use of\nEnglish, as certain text parts come across as confusing.\nReply：\nWe appreciate the reviewer’s recommendation. In the revised manuscript, we have\nstreamlined the Discussion to enhance focus and concision. In addition, the entire\nmanuscript has undergone professional English language editing by American Journal\nExperts (AJE) to ensure appropriate usage and improve clarity and readability.\nReviewer 2#\n1. The methods section of molecular dynamics (MD) simulations is good; however,\nplease create a table that shows the setup of each system, including the total number of\nmolecules, box dimensions, water/ions, etc.\nReply\nWe thank the reviewer's valuable suggestion. We have added Supplementary Data\nTable 9 to show the simulation details for each system, including the total number of\nmolecules, box dimensions, water/ions, etc. We believe this addition provides a clearer\nand more comprehensive overview of our simulation setups, thereby enhancing the\nreproducibility and transparency of our work.\n2. In general, please write “molecular dynamics simulations” instead of “molecular\nsimulation techniques” throughout the manuscript, just to be consistent. For example,\nin line no. 399, it says “Using molecular simulation techniques.” Please change to\n“Using molecular dynamics simulations.” It appears that all-atom simulations are\nemployed, so it may also be written as “Using all-atom molecular dynamics\nsimulations.” In addition, please update the simulations to molecular dynamics\nsimulations in the figure captions of Fig. 5 as well.\nReply\nWe appreciate the reviewer’s suggestion. As you suggested, we have modified the\ndescription of the molecular dynamics simulations in the manuscript.\n3. Please note the plots that are provided for simulations (Fig 5 k, l), such as RMSD\nplots for 500 ns, these can be written as “time evolution plots” instead of “curves” in\nthe captions. The curves are when there is a density plot vs. RMSD or distance,\nwhich can be referred to as the peak of the curve. In addition, to see the clear shift between\nthe two systems, Non-glycosylated and O-GlcNAc systems, it's best to show as a\nprobability density plot that can show a clear shift in the distance.\nReply\nWe are grateful for this insightful suggestion. The relevant text concerning the\ntime evolution plots has been updated in the manuscript. Following your advice, we\nhave added the probability density plots for the distance plots in Figure 5k, l (see\nExtended Data Fig. 15l, Fig. 16k) and, as per Comment 4, included the plots from\ndifferent replicas. The clear difference between the systems is consistently observed\nacross all replicas, confirming that the structural changes are reproducible and not\nstochastic events but are consistently induced by glycosylation.\n4. Based on the results and methods sections, it appears that there is a single replica of\nthe MD simulations for the systems. I recommend that the authors include at least three\nreplicas of simulation to strengthen its robustness. Replicas would be more statistically\nmeaningful, and each independent replica ideally can have different initial\nconfigurations.\nReply\nWe are grateful to the reviewer for this excellent suggestion. In direct response,\nwe have conducted three additional independent simulation replicas for each system,\neach with distinct initial configurations, as recommended. The new data\noverwhelmingly supports our original findings. As now shown in Figure 5k, l, Extended\nData Fig. 15m and Fig. 16l (the time evolution plots and probability density plots of the\nkey distances), the stark contrast between the glycosylated and non-glycosylated\nsystems is consistently observed in every single independent replica. This\nreproducibility provides compelling evidence that the structural changes induced by\nglycosylation are deterministic and not attributable to chance or the specifics of a single\nsimulation setup.\nWe thank the reviewer for the comment, which has allowed us to present a much\nmore statistically sound and convincing result.\n5. It’s beneficial to calculate the contact area as shown in the extended figure. 15 and\n16, and display snapshots of some of the contacts. To display the residue-wise contact,\nor the number of contacts, it is best to calculate residue-wise contacts throughout the\nMD trajectory and show them in the contact maps. Please ensure that you identify the\ncutoff distance for contact calculations. Also, when showing the interactions in the\nsnapshot, please ensure to write the distance of that as well.\nWe sincerely thank the reviewer for this valuable suggestion. We agree that a\nquantitative, residue-level analysis of contacts across the entire simulation significantly\nstrengthens the structural analysis of the protein-protein interaction. As recommended,\nwe have now performed a comprehensive residue-wise contact analysis for the\nGATAD2A and Jun systems over the full MD trajectories using GetContacts package.\nThis tool provides a more sophisticated analysis than simple distance-based\ncalculations, as it specifically identifies and categorizes key physicochemical\ninteraction types, including hydrogen bonds, ionic interactions, hydrophobic contacts,\nand π interactions, in addition to calculating spatial distances.\nWe calculated the interaction frequencies between different components\nthroughout the trajectories of different systems. A contact was defined using a cut-off\ndistance of 4.5 Å between heavy atoms for spatial proximity, while the software's\ninternal algorithms were used to classify the specific nature of each interaction. The\nresulting contact maps are presented in Extended Data Fig. 15j. From the resulting\ncontact maps, it is easy to see that tighter interactions occurred between GlcNAcylated-\nGATAD2A and MTA1 and between GlcNAcylated-GATAD2A and MBD2 (Extended\nData Fig. 15i).\nIn the Jun systems, the non-glycosylated system lost all stable interaction with\nBAF47 during the final 100 ns (Figure 5l). This instability was accompanied by an\nincrease in the number of BAF47 residues (Leu43, Lys45, Ser49, Trp51, Arg53, Glu95)\ncontacting Jun in the non-glycosylated system compared to the O-GlcNAcylated state\n(Trp51, Arg53, Ala55, Val57), as evidenced by the contact map (Extended Data Fig.\n16i). This suggests that the non-glycosylated Jun underwent more extensive\nconformational sampling to establish binding.\nSpecification of Cut-off Distance and Methods: The cut-off distance (4.5 Å) and\nthe use of GetContacts for detailed interaction typing are explicitly stated in the\nMethods section of the revised manuscript.\nAnnotation of Distances and Interaction Types in Snapshots: For the representative\nsnapshot(s) in Extended Data Fig. 16b, we have now clearly labeled key intermolecular\ninteractions with the distances between S84JUN–E59SNF5.\n6. Data availability for codes used for molecular dynamics simulations, data analysis\non an open platform like GitHub or any other that provides a link, as well as trajectory\navailability on Zenodo or any other source that works.\nReply\nWe thank the reviewer for highlighting the importance of data transparency and\nreproducibility. In response to this comment, we have now publicly shared the\nsimulation input files, analysis scripts, and a subset of the trajectory data.\nCode and Input Files: The complete set of molecular dynamics simulation input\nparameters, along with the scripts used for data analysis, have been uploaded to our\nGitHub repository: https://github.com/ATOMS-xjtlu/NuRD.\nTrajectory Data: Due to the large size of the full trajectory files, we have uploaded\na representative subset of the trajectories (e.g., centered and stripped of water/ions) to\nthe Zenodo open-access repository. This dataset is permanently archived and available\nunder the following DOI: 10.5281/zenodo.17062795.\nWe have also updated the Data Availability Statement in the manuscript to include\nthese links. We believe this will greatly facilitate the replication of our results and\nfurther analysis by the community.\nReviewer 3#\n1. Chromatin-associated protein isolation strategy is insufficiently described and\npotentially misleading. The methodology used to isolate chromatin-associated proteins\nlacks critical detail. As presented, it appears the authors may have isolated crosslinked\nnuclear proteins along with chromatin-associated complexes, rather than tightly bound\nchromatin fractions. The protocol does not convincingly support the claim that the\nproteomics, O-GlcNAc site mapping, and western-blot data are exclusive to chromatin-\nbound proteins. Clarification and proper experimental controls (e.g., nuclear markers\nexclusively bond and unbound to chromatin) are needed to validate the enrichment\nstrategy.\nReply：\nWe appreciate the reviewer’s concern regarding the clarity of our chromatin-\nassociated protein isolation strategy. In this study, to enrich chromatin complexes for\nboth O‑GlcNAz–based proteomic analyses and next-generation sequencing of the DNA\ncis-elements bound by OCPs, our isolation approach for crosslinked chromatin\ncomplexes was adapted primarily from established ChIP workflows for chromatin\ncomplex preparation (Methods, 2009, 48(3): 240-248; Nucleic acids research, 2016,\n44(7): e67-e67), and was further optimized by integrating an O‑GlcNAc metabolic\nlabeling procedure (ACS Central Science, 2019, 5(4): 663-670). This overall\nframework was previously detailed and validated in our earlier O‑GlcNAc proteomics\nstudy in tumor cells (Nature Communications, 2020, 11, 5898).\nIn the revised manuscript, we have expanded the Methods to delineate each critical\nstep. Briefly, cells were crosslinked with formaldehyde to stabilize proteins interacting\nwith chromatin in preparation for proteomic and DNA cis‑element analyses. We then\nperformed cytoplasm–nucleus fractionation using a hypotonic buffer in which the\nprincipal lytic component was an optimized 0.4% Igepal CA‑630 (v/v). Nuclear pellets\nwere gently washed with a nuclear extraction buffer (20 mM HEPES, pH 7.9; 150 mM\nNaCl; 1.5 mM MgCl ; 0.2 mM EDTA; 10% glycerol; 0.4% Igepal CA‑630 (v/v);\n2\nsupplemented with protease and phosphatase inhibitors), incubated for 5 min, and\ncentrifuged at 1,000 × g. The intermediate salt strength (150 mM NaCl) at this step is\nintended to increase ionic strength sufficiently to remove soluble nuclear constituents\nand weakly associated proteins, thereby partitioning “soluble nuclear proteins” and\nweakly bound complexes into the supernatant while retaining chromatin‑bound\nmaterial in the pellet (our optimized formaldehyde crosslinking conditions could not\nfix soluble nuclear proteins to chromatin). Subsequently, nuclei were lysed in nuclear\nlysis buffer (50 mM HEPES, pH 7.9; 150 mM NaCl; 1.5 mM MgCl ; 1% Igepal CA‑630\n2\n(v/v); 0.1% SDS (w/v)). Chromatin was fragmented by sonication and centrifuged at\n20,000 × g to remove nuclear membranes and scaffolding components. The resulting\nsupernatant contained crosslinked chromatin complex fragments, which were used for\ndownstream metabolic‑labeling proteomics, COGC-seq, immunoprecipitation, and\nlectin pulldown assays. These procedural details are now provided in the revised\nMethods.\nWe also recognize that formaldehyde crosslinking can capture protein–protein\ninteractions (PPIs) between OCPs and secondary or distal interactors. To minimize\npotential nonspecific carryover, as described in the original Methods (Metabolic\nlabeling and click reaction section), we implemented a decrosslinking step prior to the\nenrichment of biotinylated O-GlcNAz–modified proteins, following a widely used\nprotocol (Fowler, Carol B., et al., Lab Invest. 2007, 87, 836) demonstrated to effectively\nreverse formaldehyde crosslinks. Specifically, O-GlcNAz PPI complexes were\nincubated in 1 mL recovery buffer (20 mM Tris-HCl, pH 4; 2% SDS; 0.2 M glycine) at\n100°C for 20 min and then at 60°C for 2 h. OCPs were subsequently captured using\nstreptavidin beads and stringently washed with standard low-salt and LiCl buffers\ncommonly used in ChIP, further removing nonspecifically bound proteins. The\nresulting proteins were subjected to immunoblotting, sWGA lectin, or\nLC-MS/MS-based proteomics. The effectiveness of this strategy is shown in Extended\nData Fig. 3a. Moreover, only differentially quantified OCPs with nuclear localization\nwere considered as regulatory candidates, further reducing any residual impact of\nnonspecific contamination on downstream analyses. We have incorporated assays for\nnuclear markers that are exclusively bound to chromatin (Histone H3) and those that\nare unbound (β-actin for cytoplasm, SRSF2 for nucleoplasm, and SP1 for partially\nchromatin-bound), as additional controls in Fig. 1c, Fig. 2b, Fig. 4b, and Fig. 6a.\n2. Lack of IgG or background controls in immunoprecipitation and enrichment-based\nassays. The manuscript consistently omits negative controls for both ChIP-seq and [co]-\nimmunoprecipitation experiments. These controls are essential to assess non-specific\nbinding and to validate the specificity of enrichment (SWGA and streptavidin pull-\ndown). Their absence weakens the confidence in all IP-derived data and conclusions.\nMoreover, internal controls such as IgG heavy/light chains should be shown on\nimmunoblots, as their inclusion would significantly strengthen the confidence in the\nspecificity and reproducibility of the IP/co-IP data. The authors must include or discuss\ncontrol experiments to substantiate their findings.\nReply：\nWe appreciate the reviewer’s concern regarding the absence of IgG and\nbackground controls in our immunoprecipitation (IP) and enrichment-based assays. We\nagree that these controls are essential for assessing non-specific binding and validating\nenrichment specificity, and we have implemented the recommended controls in the\nrevised manuscript.\na) In the updated figures (Fig. 4d, 4e, 5d, 5e, 5f, 5h, 5j and Extended Data Fig. 14),\nwe incorporated either IgG controls or empty vector controls for all IP experiments.\nThe IgG heavy and light chains are explicitly shown in the revised co-IP panels to\ndocument the control signals.\nb) To exclude matrix-derived background in lectin-based enrichment, we included a\nnegative control using agarose beads lacking succinylated wheat germ agglutinin\n(sWGA) in Extended Data Fig. 11a, thereby ruling out non-specific interactions\nwith the resin.\nc) To further reinforce the sWGA pull-down evidence, we optimized our workflow\nby adapting procedures described in the literature (Nature Communications, 2025,\n16(1): 5959). Briefly, we first immunoprecipitated GATAD2A and JUN from OIS\nLU-RAS cells, eluted the bound proteins, and then subjected the eluates to\nsWGA-agarose pull-down. The captured material was analyzed to determine the\nO-GlcNAc status of GATAD2A and JUN. As shown in Extended Data Fig. 11b,\nboth GATAD2A and JUN exhibit clear O-GlcNAc glycosylation in LU-RAS cells\nat D10, which corroborates our central conclusions.\nd) Regarding ChIP-seq, we analyzed input controls for all samples in the original\nsubmission and retained them in the revision to enable subtraction of genomic\nbackground during bioinformatic processing. For ChIP‑qPCR, IgG ChIP controls\nwere included as negative controls in the original submission.\n3. Inadequate detail on computational analysis pipelines for omics datasets. The\nbioinformatics methods section is underdeveloped. While the tools used are standard,\nkey parameters for alignment, peak calling, normalization, motif analysis, duplicate\nremoval are not disclosed. Notably, it is unclear whether PCR duplicates were removed\nfrom the ChIP-seq datasets, a crucial step to avoid artifacts. The authors should\ncomprehensively describe all computational workflows to ensure reproducibility and to\nallow evaluation of their validity.\nReply：\nWe thank the reviewer for recommending that we provide detailed computational\nanalysis pipelines for the omics datasets. In the revised Methods, we have added a\nconcise yet complete description of the software, versions, and parameters used,\nadhering to the principle that—unless explicitly stated—analyses were performed with\ndefault settings. For all second-generation sequencing datasets, PCR duplicates were\nremoved using standard procedures to minimize amplification-derived artifacts.\nSpecifically, for COGC-seq and ChIP-seq, Fastp (v0.23.1) was used to trim reads\nwith the parameters set as follows: length_required = 50, n_base_limit = 6,\npoly_x_min_len = 10. Quality control of the clean reads was performed using FastQC,\nand the quality of the filtered data was assessed. Clean sequencing datasets were aligned\nto the human reference genome (hg19) using Bowtie2 (v2.3.4.3) and processed with\nSamtools (v1.2). Picard was used to remove duplicate reads with default parameters.\nNext, Macs2 (v2.2.9.1) was used for peak calling with the parameters set, COGC-seq\nas follows: -f BAMPE -g hs -B -p 0.01. H3K27me3 and H3K4me1 ChIP-seq as follows:\n-f BAMPE -g hs -B -broad -broad-cutoff 0.05 -qvalue 0.05. H3K27ac and H3K4me3\nChIP-seq as follows: -f BAMPE -g hs -B -q 0.05. The resulting BAM files were\nconverted into BigWig format using the bamCoverage tool from deepTools (v3.3.2.0.0)\nand normalized by the reads per kilobase per million mapped reads (RPKM) method.\nGenomic annotation, distribution, and comparisons of COGC-seq/ChIP-seq peaks were\nanalyzed with ChIPseeker (v1.18.0). Then, deepTools computeMatrix was used to\nanalyze COGC-seq peaks intensity and Histone ChIP-seq signal distribution in\nreference-point mode. The specific command was as follows: ComputeMatrix\nreference-point --beforeRegionStartLength 2000 --afterRegionStartLength 2000 –\nskipZeros. The output of ComputeMatrix was further visualized as heatmaps using\ndeeptools plotHeatmap or plotProfile. Differential binding regions were identified\nusing MAnorm (v1.2.0) based on normalized read counts, with criteria set at |M value|\n≥ 1 and p value ≤ 0.05. Visualization of sequencing tracks was performed using the\nIntegrative Genomics Viewer (IGV, v2.5.1), while heatmaps and signal density profiles\nwere generated using R package ClusterGVis (v0.99.1) and deepTools. ChromHMM\n(v1.22) was used to identify and characterize chromatin states based on five epigenetic\nmodifications, including O-GlcNAc. Deduplicated BAM files for histone and O-\nGlcNAc samples, along with their corresponding input controls, were processed using\nthe BinarizeBam function in ChromHMM with default settings: LearnModel -paired -\nb 200 8 hg19. An 8-state chromatin model was generated, with each state annotated as\npromoter, enhancer, repressed, O-GlcNAc promoter, O-GlcNAc enhancer, O-GlcNAc\nrepressed, or O-GlcNAc-unmarked. The model was manually curated to achieve a\nbiologically relevant balance between unique and overlapping combinatorial features.\nChromatin state transitions were visualized using the geom_alluvium function from the\nR package ggalluvial (v3.6.1). Motif enrichment and discovery were carried out with\nHomer (v4.11) (-size 200 -nomotif -float -mask) and the MEME suite (v5.1.1) applying\nsequences for analysis. Gene ontology (GO) enrichment analysis was conducted\nthrough Metascape (v3.5). All bubble plot were generated using the R package ggplot2\n(v3.3.3). Additional bioinformatics analyses, including statistical testing, were\nperformed using the phyper function in the R software package.For whole-genome\nbisulfite sequencing (WGBS), the raw data have been deposited in GEO under\naccession GSE295084. Fastp (version 0.23.1) was used to trim reads with the\nparameters set as follows: --cut_right_window_size=4 --cut_right_mean_quality =15 -\n-length_required=36. Sequencing data were aligned to the reference genome (hg19)\nusing Bismark (v0.22.3): -N 0 -L 20 --quiet --un --ambiguous. Duplicate reads were\nremoved: deduplicate_bismark -p. DNA methylation levels were determined as the\nratio of methylated reads to total reads at each CpG site:\nbismark_methylation_extractor --comprehensive --no_overlap -p --parallel 30 --\nsplit_by_chromosome --bedGraph --counts --CX_context --cytosine_report --report --\nbuffer_size 90G. The genome-wide distribution of DNA methylation was analyzed\nusing the scale-regions mode of DeepTools computeMatrix with the following\nparameters: ComputeMatrix scale-regions -S -R --beforeRegionStartLength 2000 --\nregionBodyLength 5000 --afterRegionStartLength 2000 --binSize 100. The output of\nComputeMatrix was further visualized as heatmaps using deeptools plotProfile. R\npackages circlize (v0.4.15) and R were used for creating circular genome plots and\nkaryotype-based visualizations, respectively.\nFor RNA-seq, Fastp (version 0.23.1) was used to trim reads with the parameters\nset as follows: -g -q 5 -u 50 -n 15 -l 150 --overlap_diff_limit 1 --\noverlap_diff_percent_limit 10. Clean reads were aligned to the human reference\ngenome (hg19) using Hisat2 (v2.0.5): hisat2 --dta -x -1 -2 -S. After alignment, the\nresults were sorted and converted to BAM files. Alignment quality was assessed using\nsamtools flagstat, and gene expression counts were obtained using featureCounts\n(v1.5.0-p3). The specific commands were as follows: featureCounts -T 8 -p -B -C -a -\no. Differentially expressed genes (DEGs) were identified using R-R- with thresholds\nset at counts ≥ 10, fold change ≥ 2 and p value ≤ 0.05. GO functional enrichment\nanalysis was performed using Metascape (v3.5). Gene set enrichment analysis (GSEA)\n(v4.3.2) was carried out with the R package clusterProfiler (v4.6.0) and the MsigDB\n(v7.0) GMTs gene set collection, with gene sets considered significantly enriched at\nFDR < 0.05 after 1000 permutations. Additional bioinformatics analyses were\nconducted with the phyper function in R.\n4. Biological replicates are insufficient across key experiments. Also it is still\nacceptable for some experiments, including ChIP-seq and proteomics, most of the\nfunctional assays appear to have been performed with only n = 2 biological replicates.\nThis is below the standard for biochemical experiments, where n = 3 is the bare\nminimum for statistical robustness. The authors should include a statistical analysis of\ntheir datasets to address whether observed differences are significant or potentially\nstochastic.\nMoreover, the figures display error bars representing the standard error of the\nmean (SEM). However, the standard practice in the field, particularly for datasets with\nlimited biological replicates, is to use standard deviation (SD), as it more accurately\nreflects the underlying biological variability. Given the small number of replicates in\nmany experiments (n = 2 in several cases), the use of SEM may artificially reduce the\napparent variability and could mislead readers about the robustness of the findings. The\nauthors must replot their data using SD or, at minimum, clearly justify the use of SEM.\nReply：\nWe thank the reviewer for drawing attention to the number of biological replicates\nand the statistical robustness of our results. Regarding the issue that certain figures\ndisplayed error bars representing the SEM, we agree with the reviewer’s\nrecommendation. We accept these recommendations and provide the following\nclarifications and updates.\na) Clarification and augmentation of replicates in biochemical and molecular assays\nIn the original manuscript, several figure legends stated “at least two” replicates,\nprimarily because a small subset of Western blot (WB) experiments had n = 2. In fact,\nthe vast majority of our WB, biochemical, and molecular assays—including SA-β-Gal\nstaining, EdU staining, flow cytometry, CCK-8 assays, quantitative PCR, cell migration,\nand Transwell assays—were conducted with n = 3 and yielded statistically significant\nresults. To further enhance statistical rigor in the revised manuscript, we performed\nadditional experiments, standardized WB experiments to n = 3, increased other\nbiochemical and molecular assays to n = 4, and expanded the corresponding statistical\nanalyses throughout the manuscript (e.g. Fig. 1a-b, d-e; Extended Data Fig. 1b, d-g, i;\nExtended Data Fig. 2a, b, d; Fig 6g-h; Extended Data Fig. 19; Extended Data Fig. 20c-\nd; Fig. 7a-c, f-g; Fig 8h). The results demonstrate that increasing biological replicates\ndid not alter the conclusions; the findings remain consistent with those reported in the\noriginal submission. We have also revised the figure legends to explicitly report the\nexact number of biological replicates for each experiment.\nb) Replicates and statistical justification in omics experiments\nFor proteomics, the OCPs quantitative proteomics in Fig. 2 includes n = 9\nbiological replicates, and the site-specific profiling of O-GlcNAc modifications on\nOCPs in Fig. 5 includes n = 3, meeting accepted statistical standards for proteomic\nanalyses. For next-generation sequencing, COGC-seq, RNA-seq, and DNA methylation\nsequencing (WGBS) were performed with n = 2 biological replicates. We note that\nENCODE standards for ChIP-seq explicitly establish at least two independent biological\nreplicates; similarly, the ENCODE Whole-Genome Bisulfite Sequencing (WGBS)\nstandards specify at least two biological replicates per condition.\nMethodological studies further indicate that, given appropriate sequencing depth and\ndata quality, two biological replicates generally provide robust performance for ChIP-\nseq peak identification and reliable power for conventional RNA-seq differential\nexpression analyses, while acknowledging that additional replicates can improve\nsensitivity for small effects (Nucleic Acids Research, 39(1), 190–201; Bioinformatics,\n30(3), 301–304; Nature Cell Biology, 22(7), 842–855, 2020). In our study, each assay\nincluded two independent biological replicates and met commonly recommended\ndepths: RNA-seq ≥ 20 million mapped reads per sample, ChIP-seq ≥ 30 million\nuniquely mapped reads per sample, and WGBS ≥ 200 million mapped reads per sample.\nAll datasets passed standard quality metrics (e.g., alignment rate, library complexity,\nduplication) with cross-replicate consistency assessments. Analyses employed\nappropriate frameworks for high-dimensional count data, using negative binomial–\nbased models within standard pipelines. Hence, our experimental design, sequencing\ndepth, and statistical procedures meet established standards and requirements.\nc) Replicates in molecular dynamics simulations\nIn the revised manuscript, we increased the number of independent molecular\ndynamics (MD) simulation replicates by extending the trajectories to n = 3. The results\nexhibit strong reproducibility across replicates, indicating that the MD findings are\nrobust.\nd) Replicates in animal studies\nIn the original study, animal experiments were conducted with n = 6 biological\nreplicates. In the revised manuscript, we retained n = 6 biological replicates for IHC\nstaining and performed statistical analyses accordingly (Fig. 8g).\ne) Statistical analyses\nGiven the small number of replicates in several biochemical experiments, we\nreplotted the data using SD to represent variability, ensuring consistency with the\nrevised statistical reporting.\nIn summary, with the additional experiments and expanded reporting, the number\nof biological replicates across our study now adheres to statistical requirements. The\nresults remain statistically significant and consistently support our conclusions.\n5. Inconsistency between O-GlcNAc coverage and enrichment signal at genomic\nfeatures. On page 9, line 242, the authors report O-GlcNAc modification ranging from\n64–84% of the genome. This high prevalence is not reflected in Figures 2i and 3c, which\ndisplay signal intensities around peak centers (±2 kb). The apparent discrepancy\nbetween global prevalence and localized enrichment patterns should be addressed, and\ndata interpretation refined accordingly.\nReply：\nWe appreciate the opportunity to clarify the analyses underlying Fig. 2i and to\nrefine the wording on page 9, line 242, regarding the genomic extent of O-GlcNAc–\nassociated chromatin states. Fig. 3c presents the dynamics of histone ChIP-seq and\nDNA methylation signals at COGC-seq peak loci, rather than “O-GlcNAc coverage and\nenrichment at genomic features.” Here are our clarifications and corrections.\ni) Basis and interpretation of Fig. 2i (COGC-seq peak analysis)\nThe results shown in Fig. 2i are derived from peak calls based on our COGC-seq\ndata. Peaks represent genomic loci with enriched occupancy of OCPs. Enrichment was\nassessed relative to input controls and called using MACS2 under standard criteria,\nthereby ensuring appropriate background subtraction and peak fidelity. Fig. 2i reports\nthe proportion of peaks annotated to genomic features within each experimental group\n(e.g., the fraction of peaks located in promoters relative to all peaks in that group), and\nthe distribution of signal intensity for each peak within its local genomic context (±2\nkb around the peak center). These findings indicate that, as OIS progresses, OCP\nbinding intensifies across chromatin, and the distribution of OCP-bound cis-elements,\nincluding promoters, shifts accordingly.\nii) Clarification of the “64–84% of the genome” statement and ChromHMM\nresults (Fig. 3a)\nThe prior wording “O-GlcNAc modification ranging from 64–84% of the genome”\nwas imprecise. Specifically, this result arises from an integrative ChromHMM analysis\nthat combines multiple chromatin marks with COGC-seq to quantify the co-occurrence\nof O-GlcNAc with distinct chromatin states. In Fig. 3a, the left panel summarizes\nchanges in genomic occupancy across chromatin states. The right panel provides a\nquantitative analysis, showing that the summed fraction of O‑GlcNAc–related\nchromatin states (calculated within the specified regions rather than genome‑wide)—\ndefined as O-GlcNAc promoter, O-GlcNAc enhancer, O-GlcNAc-repressed, and O-\nGlcNAc-unmarked—increases as OIS progresses. Concretely, this aggregate fraction\nrose from approximately 64.1% at D2 (O-GlcNAc promoter, 2.35%; O-GlcNAc\nenhancer, 0%; O-GlcNAc-repressed, 0.51%; O-GlcNAc-unmarked, 61.25%) to\napproximately 83.5% at D10 (O-GlcNAc promoter, 3.71%; O-GlcNAc enhancer, 0%;\nO-GlcNAc-repressed, 16.01%; O-GlcNAc-unmarked, 63.79%). We emphasize that\nthese percentages reflect the composition within the specified ChromHMM-segmented\nregions considered in the analysis, rather than a literal proportion of the entire genome.\nThese dynamics indicate that O-GlcNAc-associated occupancy patterns shift as OIS\nadvances, accompanied by altered crosstalk with other epigenetic marks. The\nincreasing share of epigenetically defined chromatin states involving O-GlcNAc is\nconsistent with O-GlcNAc’s participation in epigenetic regulatory programs governing\ngene expression during OIS.\nTo more precisely describe these results, we have revised the text in the main\nmanuscript to read as follows:\nFor Fig.2i: “COGC-seq peak calling analysis revealed a progressive increase in\nboth peak-centered signal intensity and the proportion of peaks mapping to promoters\nfor OCPs during the OIS process, accompanied by distinct peak-associated genes and\nunique characteristics of OCP loci across the genome.”\nFor Fig.3a: “The fraction of O-GlcNAc-related chromatin states can be\ncategorized into four functional classes: O-GlcNAc promoter, O-GlcNAc enhancer, O-\nGlcNAc occupied transcription-repressed chromatin (O-GlcNAc-repressed), and those\nlacking any of the profiled histone modifications (O-GlcNAc-unmarked). Consistent\nwith the genome-wide elevation of OCP binding, the cumulative fraction of chromatin\nstates occupied by O-GlcNAc (i.e., the sum of O-GlcNAc promoter, O-GlcNAc-\nrepressed, and O-GlcNAc-unmarked) increased in OIS cells, from a minimum of 64.1%\nat D2 to a maximum of 83.5% at D10 of the genome.”\n6. Inaccuracies in site novelty claims and citation of existing data. O-GlcNAc sites on\nFOXK1, RFX5, and C2H1 (line 318-319) claimed as novel are in fact reported in\npublicly available O-GlcNAc databases, often with multiple references. Similarly, the\nevolutionary conservation of FOXK1 and ARID2 O-GlcNAc sites (page 13, lines 352–\n353) is mentioned in the text but not shown in the main or supplemental figures. In\naddition, the manuscript should indicate that several O-GlcNAc sites on GATAD2A and\nJUN have already been reported in the literature, with some located in close proximity\nto S182 on GATAD2A. The mass spectra shown in Fig. 5c are also too small to be\ninterpreted properly, and should be presented at higher resolution. The authors should\nalso clarify whether other previously described sites in the vicinity of S182—such as\nT175, T180, S182, S185, and T188—were detected in their analysis, and if not, whether\nthis is due to technical limitations or biological variability. Moreover, results obtained\nfrom domain deletion mutants (Fig. 5j) should be interpreted with caution, as at least\neight additional O-GlcNAc sites have been reported in the deleted region of GATAD2A\n(Δ1) (Fig. 5j). The manuscript must be carefully revised to reflect existing knowledge\nand avoid overstatement of novelty.\nReply：\nWe appreciate the careful evaluation of the novelty of the reported O-GlcNAc sites\nand the request for comparison with existing datasets. The manuscript has been revised\nto provide more precise descriptions and to avoid any overstatement of novelty. Specific\nclarifications and corrections are outlined below.\nIt is acknowledged that O-GlcNAc sites on FOXK1, RFX5, and C2H1 have been\ncataloged in established O-GlcNAc databases (The O-GlcNAc Database v2.0) and\ncorresponding citations have been added in the revision. Accordingly, wording\nimplying that these sites were newly discovered has been removed, with appropriate\ncitations provided. In alignment with the comment, the revised text explicitly states that\nseveral O-GlcNAc sites on GATAD2A and JUN have been reported previously,\nincluding sites in close proximity to GATAD2A S182, with citations added.\nA comparative conservation analysis indicates that FOXK1 T679 and ARID2\nS1494 are not conserved across species. A tempered statement is retained to reflect the\nmixed conservation landscape: “a subset of these sites is well conserved across species\n(e.g., S182/S625 of GATAD2A and S84 of JUN).”\nHCD-pd-EThcD tandem mass spectrometry was employed to enhance depth and\nsite specificity for O-GlcNAc proteomics (ACS Chem Biol, 2021, 16(10), 1917–1923).\nIn our dataset, multiple sites on JUN and GATAD2A were identified, including JUN\nS83 and S84, and GATAD2A S182, S321, S326, S327, S329, S330, S333, S337, S340,\nS343, S586, S589, S590, S625, and S629. Among these, JUN S84 and GATAD2A S182,\nS327, S330, S586, S589, S625 were identified as high-confidence sites (Localization\nprob > 0.75); these are listed in the revised Supplementary data table (Supplementary\nData Table 6). To ensure analytical rigor, sites with suboptimal MS/MS spectra were\nexcluded from downstream analyses (with a MaxQuant O-GlcNAc site localization\nprobability above 0.75). Regarding the note on sites near GATAD2A S182 (T175, T180,\nS185, and T188), these neighboring sites were not detected in the present MS dataset.\nThis outcome is plausibly attributable to biological variability. Many database-reported\nO-GlcNAc sites derive from tumor cells or tissues, where O-GlcNAcylation is\nfrequently elevated. By contrast, the present study utilized normal human embryonic\nlung fibroblasts, in which lower modification abundance likely reduces detectability,\neven with high-sensitivity MS. We have made corresponding revisions at the relevant\nlocations in the manuscript as well. On the other hand, we noted that the reported data\non GATAD2A O-GlcNAc sites were obtained using GalT1 (Y289L) chemoenzymatic\nlabeling (ACS chemical biology, 17(3), 513–520; Angewandte Chemie (International\ned. in English), 62(17), e202300500; ACS chemical biology,13(8), 1983–1989;\nAnalytical chemistry, 92(14), 9807–9814). Differences in labeling methods may affect\nthe results of site identification. We have made corresponding revisions at the relevant\nlocations in the manuscript as well. On the other hand, we noted that the reported data\non GATAD2A O-GlcNAc sites were obtained using GalT1 (Y289L) chemoenzymatic\nlabeling (ACS chemical biology, 17(3), 513–520; Angewandte Chemie (International\ned. in English), 62(17), e202300500; ACS chemical biology,13(8), 1983–1989;\nAnalytical chemistry, 92(14), 9807–9814). Differences in labeling methods may affect\nthe results of site identification. We have made corresponding revisions at the relevant\nlocations in the manuscript as well.\nAs noted, ΔGATAD2A-1 (aa 1–240) encompasses multiple previously reported\npotential O-GlcNAc sites, including those near S182. In Fig. 5j, truncation mutants\nwere used to delineate the GATAD2A segment mediating interaction with the NuRD\ncomplex. Substitution of S182 with alanine markedly reduced the interaction of\nΔGATAD2A-1 with MTA1 and MBD2 and concomitantly decreased O-GlcNAcylation\nof this fragment. Residual O-GlcNAc signal in the S182A mutant indicates the likely\npresence of additional sites within this region. Nevertheless, convergent results in Fig.\n5d and Extended Data Fig. 14 support the interpretation that S182 is the predominantly\nglycosylation site on GATAD2A under the present experimental conditions; this has\nbeen clarified in the revision.\nThe low apparent resolution and small display size of the MS spectra were caused\nby automatic compression by the submission system. The original files were uploaded\nat 300 dpi. Larger, high-resolution images have now replaced the affected panels to\nensure clear visualization of spectral features.\nStatements regarding novelty have been systematically re-evaluated against\nexisting knowledge and database entries. The manuscript has been revised to avoid\noverstatement. We thank the Reviewer for constructive comments that have improved\nthe clarity and rigor of the manuscript.\n7. The term “peak” is used ambiguously throughout the figures versus the manuscript.\nIt is unclear whether the authors refer to signal enrichment peaks, transcription start\nsites (TSS), or promoter regions. Terminology should be clarified and used consistently\nbetween the figures and the text.\nReply：\nWe appreciate the Reviewer’s concern that “peak” was used ambiguously across\nthe figures and the main text. In the revised manuscript, “peak” uniformly refers to\nsignal-enrichment peaks from COGC-seq or ChIP-seq identified by MACS2 (callpeak).\nIn some passages, “promoter peaks” or “intergenic/enhancer peaks” denote the subset\nof COGC-seq peaks annotated as promoter or enhancer regions, respectively, within the\ncorresponding experimental condition. We have added an explicit definition of “peak”\nin both the main text and figure legends and ensured consistent usage throughout the\nmanuscript and all figures. In addition, we have replaced the OCP-binding sites with\nCOGC‑seq peaks.\n8. The statement on page 6 that “enhancing O-GlcNAc modification moderately\naccelerated the formation of the senescence phenotype” seems underestimated, as the\nβ-galactosidase assay shows nearly a two-fold increase (although this is not obvious in\nthe representative images in Fig. 1d), and there are fewer EdU+ cells. SA-β-gal and\nEdU images require statistical analysis to validate these observations.\nReply：\nWe appreciate the reviewer’s suggestion. In the revised manuscript, we have\nperformed comprehensive quantification of all SA-β-gal staining and EdU-positive\ncells across this study. Using ImageJ for image-based analysis, we examined LU-RAS\ncells at D4 of OIS (Fig. 1d) under conditions that enhance O-GlcNAc modification,\nTMG treatment or overexpression of OGT. Both interventions approximately doubled\nthe proportion of SA-β-gal–positive cells and reduced the proportion of EdU-positive\ncells by roughly twofold, with statistically significant differences. Accordingly, we have\nrevised the text to state that “enhancing O‑GlcNAc modification approximately\ndoubled SA‑β‑gal–positive cells, reduced EdU‑positive cells by about twofold, and\neffectively accelerated the senescence phenotype.”\n9. Cell cycle Analysis. The authors should explain why cell cycle arrest occurs in S\nphase for MCF-Ras and HaC-Ras (Extended Fig. 1g), while G2/M arrest is observed\nfor SK-Ras and LU-Ras cells (Fig. 1b). A reference should be provided If known in the\nliterature. In addition, EdU labeling does not correlate with FACS analysis for MCF-\nRAS and HaC-RAS cells (Extended Fig. 1g/1i): fewer EdU+ cells are detected at day\n10, yet FACS analysis indicates a block in S phase.\nReply：\nWe appreciate the reviewer’s observation that different cell types in our study\nexhibit distinct cell-cycle arrest states after OIS. While senescence-associated arrest\ntypically occurs in G1 or G2/M, prior studies have also reported S-phase arrest,\nlikely reflecting cell-type specificity and the particular modality by which senescence is\ninduced. For example, in MCF10A breast epithelial cells—the same line used in our\nstudy—aphidicolin-induced senescence has been reported to associate with S-phase\narrest, accompanied by canonical senescence markers such as elevated SA-β-gal\nactivity, nuclear enlargement, and increased p21 (Cell death & disease, 2014, 5(7):\ne1351-e1351). In the revised manuscript, we clarify these context-specific differences\nand provide appropriate citations. “LU-RAS and SK-RAS fibroblasts undergoing OIS\nexhibit G2/M arrest, consistent with many established features of cellular senescence.\nBy contrast, MCF-RAS epithelial cells arrest in S phase, in agreement with prior reports\n(Cell death & disease, 2014, 5(7): e1351-e1351), and HaC-RAS epithelial cells show\na similar S-phase arrest. These differences may reflect cell-type–specific programs and\nthe distinct modalities by which senescence is induced.”\nWe also appreciate the reviewer’s comment regarding the lack of correlation\nbetween EdU labeling and FACS analysis for MCF-RAS and HaC-RAS cells. EdU\n(5-ethynyl-2′-deoxyuridine) is a thymidine analog incorporated into newly synthesized\nDNA and thus reports active DNA replication during the labeling interval. In our study,\nEdU was not present continuously throughout senescence induction. Instead, for each\nassay we administered a 2-hour EdU pulse immediately prior to fixation at the indicated\ntime points along the senescence time course (D0-D10). As senescence advances and\nproliferative capacity declines, DNA replication becomes increasingly suppressed,\nyielding a reduced fraction of EdU-positive cells during the short pulse window. In\nExtended Data Fig. 1i, both MCF-RAS and HaC-RAS epithelial cells are arrested in S\nphase at D10. At this stage, despite S-phase DNA content detected by FACS, ongoing\nDNA synthesis during the brief EdU pulse is minimal, resulting in few EdU-positive\ncells. This mechanistic distinction—DNA content versus active DNA synthesis—\naccounts for the observed discrepancy between FACS profiles and EdU incorporation\nat late senescence time points.\nTo preclude misinterpretation, we have revised the Methods to more explicitly\ndescribe the EdU staining protocol, as follows:\nFor EdU staining, representative samples from the senescence time series were\nanalyzed for EdU incorporation using the Click-iT EdU Alexa Fluor Imaging Kit\n(Thermo Fisher Scientific) according to the manufacturer’s instructions. Cells were\nseeded in 6-well plates and cultured under senescence-inducing conditions to the\nindicated time points (D0, D2, D4, D10). Immediately prior to fixation, cells were\npulsed with EdU for 2 h. After incubation, the medium was aspirated, and cells were\nwashed twice with PBS and fixed with paraformaldehyde. Cells were then washed three\ntimes with washing buffer (PBS containing 3% BSA) and permeabilized with 0.3%\nTriton X-100 for 15 min. Following three additional washes with washing buffer, cells\nwere incubated with the Click Reaction Mixture. For nuclear counterstaining, Hoechst\ndye was added at a final concentration of 1 μg/mL and incubated for 10 min at 25 °C in\nthe dark. Excess dye was removed by washing with PBS prior to imaging. Cells\nexhibiting both green (EdU) and blue (Hoechst) signals were scored as EdU-positive.\n10. OGT expression and efficacy of the OGT inhibitor. The authors report that the OGT\ngene is downregulated at the transcriptomic level during OIS progression\n(approximately two-fold), while OGT protein levels appear elevated on Western blot\n(Fig. 1c), suggesting a possible post-transcriptional regulation. The current statement\nin the discussion (lines 538–539) remains vague and insufficiently refered to the data\npresented. Moreover, it is striking that no decrease in OGT protein levels is observed\nupon treatment with 50 µM Thiamet-G (which is a very high concentration compared\nto what is generally used in other cell lines), and conversely, no increase in OGT is seen\nwith OSMI-4 treatment (Fig. 1f), despite the literature describing compensatory\nmechanisms regulating O-GlcNAc enzyme expression. The authors should address this\npoint.\nIn Figure 4d, no evidence is provided for the efficacy of the OGT inhibitor. Each\nexperiment should validate the extent of the inhibition by a pan-O-GlcNAc Western\nblot.\nReply：\nWe appreciate the reviewer’s insightful comment regarding the discrepancy\nbetween OGT transcript and protein levels during OIS. In LU-RAS cells, we observed\nan approximately twofold reduction in OGT mRNA across the OIS time course,\nwhereas OGT protein levels were increased by Western blot (Fig. 1c). As the reviewer\nsuggested, this divergence likely reflects post-transcriptional regulation, including\naltered protein stability.\nTo directly address this, we have added new experiments in the revised manuscript\n(Extended Data Fig. 4c) assessing OGT protein half-life during OIS. Using\ncycloheximide chase, we compared OGT half-life at D0 versus D10 and further\nevaluated the role of proteolytic degradation with MG132. We found that OGT half-life\nis prolonged during OIS, and MG132 treatment led to a further increase in OGT protein\nabundance. These results support a model in which enhanced OGT stability contributes\nto the observed increase in OGT protein despite reduced transcript levels. In parallel,\nour data in Fig. 1c and Fig. 1d show that pharmacological modulation of global\nO-GlcNAcylation does not appreciably alter OGT protein abundance. Although OGT\nitself has been reported to be O-GlcNAc-modified (Journal of Biological Chemistry,\n2000, 275(15): 10983-10988; Biochemical and biophysical research communications,\n2018, 499(2): 354-360), our findings indicate that the OIS-associated changes in OGT\nstability are largely independent of O-GlcNAc levels. We have incorporated this\ninterpretation and the new stability data into the revised Discussion.\nWe appreciate the reviewer’s comment regarding the apparent insensitivity of OGT\nprotein abundance to pharmacological modulation with Thiamet-G and OSMI-4. While\nprior studies have reported that changing homeostatic O-GlcNAc levels can influence\nOGT protein levels (Annual review of biochemistry, 2011, 80(1): 825-858), in our\nsystem we did not observe compensatory changes in OGT protein following treatment\nwith either compound. We propose that, although the relatively high concentrations of\nThiamet-G and OSMI-4 used here effectively modulate OGA and OGT activity,\ncompensatory regulation is highly dependent on cell type and stress context and may\nmanifest at the level of UDP-GlcNAc substrate flux rather than as changes in OGT\nabundance. For example, in our previous work, chemotherapeutic stimulation of MCF-7\nbreast cancer cells markedly increased flux through the hexosamine biosynthetic\npathway (HBP, Cell death & disease, 2018, 9(5): 485), elevating the glycosyl donor\nUDP-GlcNAc and thereby enhancing global protein O-GlcNAcylation, despite no\ndetectable changes in OGT or OGA protein expression. Although OGT itself can be\nO-GlcNAc–modified, in the present study Thiamet-G and OSMI-4 robustly altered\ncellular O-GlcNAcylation without measurably changing OGT protein abundance. These\nfindings suggest that the OIS-associated changes in OGT protein abundance are largely\nindependent of O-GlcNAc levels. We have expanded the Discussion in the revised\nmanuscript to address potential compensatory mechanisms underlying this issue.\nWe appreciate the reviewer’s comment regarding Fig. 4d. In the revised\nmanuscript, we have added pan-O-GlcNAc Western blots for all experiments involving\nOGT inhibitors, including Fig. 4d and Extended Data Fig. 11a. The pan-O-GlcNAc\nWestern blots for Fig. 4e have been provided in the revised Fig. 4d.\n11. Page 10, line 284: there is a discrepancy between Fig. 3g and the text, where\n“cellular stress response” is indicated as downregulated in OIS, while it is shown among\nthe upregulated overlapping genes.\nReply：\nWe appreciate the reviewer’s suggestion. There was indeed a writing error in our\n in our\noriginal text. In fact, genes related to the cellular stress response and SASP belong to\nthe upregulated overlapping gene set, whereas cell-cycle and proliferative-arrest–\nrelated genes are downregulated in OIS. We have corrected this in the revised\nmanuscript.\n12. The rationale for using 1,6-Pr₂GalNAz (Fig. 2) and GalNAz (Fig. 5) for proteomic\nanalyses should be justified.\nReply：\nWe acknowledge a writing error in the original manuscript. Specifically, in Fig. 5a\nand the Methods section, we incorrectly referred to 1,6-Pr₂GalNAz as GalNAz. In fact,\nthroughout all O-GlcNAc proteomics experiments in this study—including O-GlcNAc\nsites mass spectrometry—we employed 1,6-Pr₂GalNAz as the metabolic labeling probe.\nThis error has been corrected in the revised manuscript. We regret any confusion this\nmay have caused and have conducted a careful review of the entire text to identify and\nrectify similar issues elsewhere.\n13. Several figures suffer from low resolution and extremely small font sizes. Figure 5,\nin particular, is unreadable even upon digital zoom. All figures must be revised for\nclarity and publication-quality resolution.\nReply：\nWe appreciate the reviewer’s observation regarding figure quality. During\nsubmission, our original high‑resolution images (300 DPI) were automatically\ndown‑sampled by the submission system, which resulted in suboptimal on‑screen\nresolution for the reviewers. We also acknowledge the reviewer’s helpful note that\nseveral panels, including Fig. 5, were presented at insufficient dimensions. In the\nrevised manuscript, we have reformatted and resized each figure to ensure appropriate\npage layout and panel dimensions, and we have re‑embedded all figures at publication\nstandards.\n14. Figure 5. Co-immunoprecipitation results (e.g., Fig. 5f) lack the reverse IP\nvalidation, which is considered best practice when technically feasible. If reverse IP is\nnot possible, the authors should justify this and provide alternative supporting evidence.\nMoreover, conclusions drawn from co-IP results in Fig. 5f should be verified (Line\n373). The authors state that “GATAD2A S182A marginally associates with only MTA1\nand HDAC1, but not with the DNA methylation reader MBD2”. However, MTA1 co-\nIP seems to follow its input level, and MBD2 and HDAC1 remain detectable by co-IP.\nWithout quantification and statistical analysis, these conclusions remain subjective.\nQuantification of colocalized proteins should also be provided for IF experiments (Fig.\n5i).\nReply：\nWe appreciate the reviewer’s suggestion. In the revised manuscript, we have\nperformed additional reverse IP validations for the co-immunoprecipitation\nexperiments shown in Fig. 4e, Fig. 5f, and Fig. 5h. The results are provided in Extended\nData Fig. 14d–f. These reverse IP results are consistent with, and further support, the\nconclusions presented in the original manuscript.\nWe thank also the reviewer for the helpful comment regarding the co-IP results in\nFig. 5f. Upon careful re-examination of the raw data, we identified an error in the\noriginal manuscript: the MTA1 co-IP immunoblot was mistakenly presented as the\ninput MTA1 control. In the revised manuscript, we have provided the correct input\nMTA1 blot, which confirms that MTA1 expression does not differ appreciably across\nexperimental groups. Concurrently, we observe a clear reduction in the interaction\nbetween MTA1 and GATAD2A S182A. Accordingly, we have refined the description\nof Fig. 5f in the revised text to read: “The glycosylation-site mutation in GATAD2A\nmoderately reduces its interaction with MTA1 and HDAC1, while markedly\ndiminishing its interaction with the DNA-methylation reader MBD2 and the\ntranscriptional repressor CHD4.”\nIn addition, we have quantitatively analyzed the colocalization results in Fig. 5i in\nthe revised manuscript, and the findings are consistent with the conclusions reported in\nthe original version.\n15. The rationale behind co-downregulation and co-expression of both JUN and\nGATAD2A WT or mutants in the same cells is unclear. If the goal is to dissect their\nindividual contributions, co-expression confounds interpretation. The authors should\nrevise the experimental design to test mutants independently.\nReply：\nThe reviewer inquired about the co-expression of JUN and GATAD2A (WT or\nmutants) in our cellular models. We provide the following clarifications:\ni) Through multi-omics analyses, we identified JUN and GATAD2A as\nrepresentative chromatin-associated glycoproteins that play important roles in\nsenescence-associated transcriptional reprogramming and the establishment of\nsenescence phenotypes during OIS. Previously, in LU-RAS cells, we individually\nknocked down JUN or GATAD2A, and also performed dual knockdown of JUN and\nGATAD2A. We observed that suppression of either JUN or GATAD2A alone modestly\nincreased cell proliferation, whereas simultaneous knockdown led to a pronounced\nincrease, indicating that JUN and GATAD2A contribute concomitantly to the OIS\nprocess. From a technical standpoint, generating stable cell lines with separate\nknockouts of two genes would require multiple selection markers, which is not\nconducive to subsequent rescue-expression experiments. Therefore, we conducted\ndownstream experiments using LU-RAS/KD cells with combined JUN and GATAD2A\nknockdown as the experimental backbone. These results are presented in revised\nExtended Data Fig. 14g.\nii) With respect to the individual functions of glycosylated JUN and GATAD2A,\nwe previously assessed their separate effects on cell proliferation. We now include these\nresults in Extended Data Fig. 14h and i. In LU-RAS/KD cells, we performed rescue\nexpression of either JUN or GATAD2A using WT or glycosylation-deficient mutants.\nWe found that neither the JUN nor the GATAD2A mutant suppressed cell proliferation,\nwhereas JUNWT or GATAD2AWT each partially suppressed proliferation, albeit to a\nsignificantly lesser extent than that observed in LU‑RAS cells. These findings further\nsupport a cooperative role of JUN and GATAD2A in OIS.\nIn addition, our multi-omics data indicate that JUN and GATAD2A contribute to\ndistinct transcriptional regulatory complexes and exert non-overlapping functions\nduring OIS transcriptional reprogramming; co-expression of JUN and GATAD2A does\nnot produce cross-interference in the regulation of downstream gene expression, and\nthus does not confound the interpretation of each factor’s regulatory role. Consistently,\nsubsequent ChIP-seq and protein–protein interaction assays for transcriptional\nregulatory complexes were performed separately for each factor and interpreted\nindependently. Moreover, investigating the functions of JUN and GATAD2A by\nrescuing their expression separately within the same LU-RAS/KD cells would require\nadditional selection markers, which poses technical challenges. For these reasons, we\nemployed co-expression of both JUN and GATAD2A (WT or mutants) in the relevant\nexperiments in this study.\n16. The chromatin binding of JUN and GATAD2A mutants (Fig. 6a) is not convincingly\ndemonstrated. Given the concerns about fractionation purity, it is unclear whether\nreduced chromatin association reflects impaired nuclear localization or altered\nchromatin binding per se. Fractionation experiments should be expanded to include\ncytoplasmic, nuclear soluble, and chromatin-bound fractions. Inclusion of input\ncontrols in Fig. 6a is also recommended.\nReply：\nWe agree with the reviewer’s suggestion. In the revised Fig. 6a, we have included\nthe cytoplasmic, nuclear-soluble, and chromatin-bound fractions, along with the\ncorresponding input controls. The results show that, upon mutation of the glycosylation\nsites, JUN and GATAD2A are redistributed, with increased presence in the nuclear-\nsoluble fraction and the cytoplasm, respectively. We have described these findings in\nthe main text.\n17. Unless missed, there are no results showing protein levels of overexpressed 3X-\nFLAG-GATAD2A and HA-JUN, compared to endogenous levels. These controls\nshould be included to appreciate their relative expression.\nReply：\nWe accept the reviewer’s suggestion. In this study, overexpression of\nFlag-GATAD2A and HA-JUN was performed in LU-RAS/KD cells with knockdown\nof the corresponding genes. In the revised manuscript, we have added to Extended Data\nFig. 14i a comparison between the overexpressed Flag-GATAD2A/HA-JUN and the\nresidual levels of GATAD2A/JUN in LU-RAS/KD cells. We also included comparisons\nin the LU-RAS group between overexpressed and endogenous GATAD2A and JUN.\nThe results show that the expression levels of overexpressed Flag-GATAD2A and\nHA-JUN are substantially higher than the residual GATAD2A and JUN in LU-RAS/KD\ncells, and slightly higher than their endogenous levels.\n18. Insufficient clarity in molecular simulation results. The molecular dynamics\nsimulations presented in the main figures (Fig. 5k–l) and in the extended data (Extended\nData Figs. 15–16) should be clarified. In particular, the amplitude of the differences\nbetween WT and mutant proteins appears inconsistent between the main and extended\nfigures, which raises concerns about the selection of representative simulations.\nFurthermore, the parameters referred to as \"Distance I\" and \"Distance II\" must be\nclearly defined. In addition, the interaction data surrounding the S182 residue of\nGATAD2A (Extended Fig. 15) deserve a more explicit interpretation to support the\nconclusions drawn. The significance of red arrows (Extended Fig. 16) used in molecular\nsimulations should be clarified.\nReply：\nThank you for the reviewer’s thorough review and valuable comments regarding\nthe clarity of our molecular dynamics simulations. We have carefully revised the\nmanuscript and supplementary materials to address your concerns. Below, we provide\na point-by-point response to the issues raised.\ni) Insufficient clarity in molecular simulation results; inconsistencies in amplitude\nof differences between WT and mutant proteins between main and extended figures:\nWe thank the reviewer for highlighting this issue. We have now reorganized the\nextended data figures and included additional simulation replicates to ensure\nconsistency and representativeness. The main text (Section 2.5) has been revised to\nprovide a clearer description of the simulation results. Furthermore, we have added\nmore representative trajectories and statistical analyses to better support the observed\ndifferences between WT and mutant proteins. The updated figures (now labeled as\nExtended Data Figs. 15-16) provide a more consistent and comprehensive presentation\nof the results.\nii) Definition of \"Distance I\" and \"Distance II\":\nWe apologize for the lack of clarity in defining these parameters. We have now\nexplicitly defined \"Distance I\" and \"Distance II\" in the main text (Section 2.5) and in\nthe figure legends of the relevant extended data figures. Specifically:\nDistance I refers to the distance between MBD2 and HDAC1 (S85HDAC1-\nP157MBD2).\nDistance II represents the distance between MBD2 and MTA1 (K302MTA1-\nE162MBD2).\nThese definitions have been added to improve clarity and reproducibility.\niii) Interpretation of interaction data surrounding S182 residue of GATAD2A\n(Extended Fig. 15):\nWe have expanded the legend of Extended Data Fig. 15 to include a detailed\nexplanation of the interactions near S182. Specifically, we now describe how O-\nGlcNAc S182 forms an interaction network with adjacent residues on MTA1 (E301\nand K302) and MBD2 (S168, R166, I165, and V164). This provides stronger support\nfor our conclusion that O-GlcNAc-S182 plays a critical role in stabilizing the protein\ncomplex.\niv) Clarification of the significance of red arrows in Extended Data Fig. 16:\nWe have updated the legend of Extended Data Fig. 16 to explicitly state that the\nred arrows indicate the key elements in the plot, the O-GlcNAc group at different times.\n19. In vivo experiments. Differences in NIR769 fluorescence between vehicle LU-RAS\nand LU-RAS KD-WT (Fig. 8a) appear contradictory, as KD-WT cells have a lower\nproliferation rate than vehicle-treated LU-RAS in in vitro experiments (Extended Fig.\n20).\nReply：\nThe reviewer noted an apparent discrepancy between the fluorescence intensities\nobserved in the in vivo experiments for LU-RAS vehicle and LU-RAS/KD-WT and the\nproliferation outcomes in the in vitro experiments. We address this as follows:\nIn the in vivo experiments, we co-injected equal numbers of NIR790-labeled\nLU-RAS (or its derivatives) and A549 cells subcutaneously into mice. The LU-RAS\ncells used were at D10 of OIS induction, as stated in the Methods and in the Fig. 8\nlegend. At D10, LU-RAS vehicle and LU-RAS/KD-WT cells are senescent and possess\nonly minimal proliferative capacity. After subcutaneous injection and continued\nmaintenance of the mice, these D10 OIS cells may further lose proliferative potential.\nAdditionally, immune cells in vivo may phagocytose a fraction of the senescent cells.\nThis will lead to fluctuations in the number of the OIS cells.\nConsistent with this, the NIR790 fluorescence associated with the OIS cells\nshowed no marked differences among groups in Fig. 8b. To make this clearer, we\nquantified fluorescence intensities across all mice and found no significant differences\nin NIR790 signal either between experimental groups or across time points within\ngroups. In the revised manuscript, we have included more representative fluorescence\nimages. Moreover, at the reviewer’s request, we increased the number of in vitro\nbiological replicates for the CCK-8 and related assays to n = 4. Statistical analysis in\nExtended Data Fig. 20 shows that the proliferation of LU‑RAS/KD‑WT is indeed lower\nthan that of LU‑RAS vehicle cells over D0–D10, with statistical significance. We infer\nthat this effect results from the higher expression of recombinant GATAD2A and JUN\nin LU‑RAS/KD‑WT cells compared with LU‑RAS vehicle cells (Extended Data Fig.\n14i). The relatively elevated levels of wild‑type GATAD2A and JUN are expected to\nproduce greater amounts of O‑glycosylated GATAD2A and JUN proteins, thereby\npromoting proliferative arrest during senescence. This is further supported by the\nobservation that GATAD2A and JUN bearing glycosylation‑site mutations—despite\nexpression levels comparable to the wild type—exhibit a reduced contribution to the\nsenescence phenotype, underscoring the role of O‑GlcNAc‑modified GATAD2A and\nJUN in cellular senescence.\n20. Extended Fig. 20: Statistical analyses should be provided for panels a and b. In\npanel a, Western blot showing expression levels of WT and mutant proteins should also\nbe included.\nReply：\nWe agree with the reviewer’s suggestion. In the revised manuscript, we have\nadded to Extended Data Fig. 20a the expression levels of WT and mutant JUN and\nGATAD2A in SK-RAS derivatives. We performed statistical analyses for Extended Data\nFig. 20b and 20c, and increased the number of biological replicates for these experiments\nto n = 4. Using immunoprecipitation, we also demonstrated that S182 in GATAD2A and\nS84 in JUN are predominant glycosylation sites during the OIS process in SK‑RAS cells\nExtended Data Fig. 20d.\n21. Conclusions drawn from IHC on tumor tissues (Fig. 8e) suffer from discrepancies\nbetween the text and the images. No clear accumulation of CCND1 and STAG2 can be\nobserved in AA cells compared to WT cells, in contrast to what is stated in the\nmanuscript (lines 491–493). In the absence of quantification and statistical analysis, it\nis difficult to draw reliable conclusions from these IHC results.\nReply：\nWe appreciate the reviewer’s careful evaluation of Fig. 8e and the concern that\nthere is no clear accumulation of CCND1 and STAG2 in AA cells relative to WT cells.\nBelow, we clarify the experimental context and the resulting IHC patterns.\nIn our in vivo system, A549 cancer cells were co-injected subcutaneously with\nOIS LU-RAS cells expressing JUN and GATAD2A variants. Within the ensuing tumor\ntissues, histology alone does not allow unambiguous discrimination between A549\ntumor cells and senescent LU-RAS cells. The senescent LU-RAS population is growth-\narrested and represents only a minor fraction of the subcutaneous tumors.\nAs shown in the manuscript, cell cycle–associated genes such as CCND1 and\nSTAG1 are downregulated in senescent LU-RAS cells. By contrast, adjacent A549\ntumor cells are exposed to SASP factors released by senescent LU-RAS cells, which\nenhance A549 proliferation. Consequently, A549 cells form the bulk of the tumor mass\nand, in this context, display elevated expression of CCND1 and STAG1. In the CCND1\nIHC images, the A549+Vehicle and A549+KD-WT groups—both featuring an OIS\nphenotype in the LU-RAS compartment—contain areas with weak CCND1 DAB\nstaining that correspond to senescent LU-RAS cells. Neighboring regions, composed\npredominantly of A549 cells, show stronger CCND1 DAB staining than the A549 and\nA549+KD-AA groups. This spatial distribution—reduced CCND1 in senescent LU-\nRAS cells alongside increased CCND1 in adjacent A549 cells—is consistent with\nSASP-mediated, senescence-induced tumor development. In the revised manuscript,\nwe have clarified and discussed this issue in the corresponding sections of the main text.\nWe acknowledge that the initially provided STAG1 IHC data did not fully\nsubstantiate the claim regarding senescence-driven tumor development. In the revised\nmanuscript, we repeated the STAG1 IHC with improved imaging and controls and now\npresent more convincing evidence aligned with the proposed mechanism. To enhance\ninterpretability and reduce ambiguity, we additionally conducted OD-based\nquantification of DAB areas signals for all IHC markers, including CCND1 and STAG1.\nThese quantitative results support our conclusions and are included in the revised\nfigures (Fig 8h).\nThe revised text explicitly details the mixed-cell composition of the tumors, the\ndownregulation of CCND1/STAG1 in senescent LU-RAS cells, and the SASP-driven\nupregulation of these markers in proliferating A549 cells. We believe that these\nclarifications, new experiments, and quantitative analyses address the reviewer’s\nconcerns and provide a coherent interpretation of the IHC findings in Fig. 8g.\n22. Fig. 7e: There is a discrepancy between the figure and the legend regarding the\nscratch assay time points (48h/96h). Please verify.\nReply：\nHere, we mistakenly described the time point in the original figure legend. As\nshown in the figure, the scratch assay was imaged at 96 h to assess wound closure. We\nhave corrected this in the revised manuscript.\n23. Line 486: it seems that RFP fluorescence is used to refer to GFP fluorescence.\nPlease confirm and correct as needed.\nReply：\nWe sincerely apologize for the writing error. The correct description should be\nGFP fluorescence. We have corrected this in the revised manuscript.\n24. Line 573: Reference #18 cited in the discussion does not seem related to senescence.\nPlease check its relevance.\nReply：\nWe agree with the reviewer that Reference #18 was cited in error. We have\nremoved this citation at the indicated location in the revised manuscript and have cross-\nchecked all other references for accuracy.\n25. Lines 622–623: The statement that “specific pharmacological inhibition of\nglycosylation of these key factors, like converging on the OGT that activates JUN,\nmight be a feasible approach for manipulating senescence phenotypes” appears\noverestimated. Given the broad range of OGT targets and the enzyme’s essential roles,\nsuch a therapeutic strategy would likely be toxic and unrealistic.\nReply：\nWe concur with the reviewer that our original description was insufficiently\nprecise. We have revised the text to read: “Accordingly, glycosylated factors such as\nJUN and GATAD2A represent promising anti-senescence targets; selectively inhibiting\nthe O-GlcNAc modification of these factors—by developing substrate-specific O-\nGlcNAc inhibition technologies—may offer a feasible strategy to modulate senescence\nphenotypes.”\nTYPO AND FIGURE LABELING ERRORS\n• Fig. 1c: “chromtain” should be corrected to “chromatin”\n• Fig. 1b legend: “indicted” should read “indicated”\n• Figs. 1d–g and Extended Figs. 2A, 2B: “OSIM-4” should be corrected to “OSMI-4”\n• Fig. 3g: “quantitavely”, “overlaped”, and “nagative” should be corrected to\n“quantitatively”, “overlapped”, and “negative”\n• Extended Fig. 9: “mentylation” should be corrected to “methylation”\nReply：\nWe thank the reviewer for the meticulous examination. These writing and labeling\nerrors have been corrected in the revised manuscript.\nResponse to Reviewers\nReviewer 1#\n1. In Fig 5i, the IF panels showing only one cell at a time are still not convincing, as it\nis impossible to observe actual differences across the conditions. It is surprising that\nPearson’s coefficient and the corresponding plots so strongly indicate the opposite to\nwhat the images display.\nReply：\nWe thank the reviewer for this comment. We would like to clarify the rationale and\nrobustness of the colocalization analysis presented in Fig. 5i:\n(1) In the previously revised manuscript, we presented immunofluorescence images of\nindividual cells captured using high magnification confocal microscopy. This\napproach ensures high-resolution visualization of colocalization between O-\nGlcNAc modified proteins in the nucleus. For quantitative analysis, we followed\nestablished methods using Scatter J plugin in Image J software. The reference for\ndeveloping this plugin is Journal of Open Research Software, 2016, 4(1): e5-e5.\nThe software defines that a set of datapoints is formed out of the grey values of\npixels at the same position in the different input images. Therefore, a datapoint\ncontains the results of all measurements taken at the corresponding position. Each\ndatapoint corresponds to a pixel position in the spatial domain. Specifically, the\ngreen signal intensity of MBD2 or SNF5 was plotted on the Y-axis, and the red\nsignal intensity of Flag-GATAD2A or HA-JUN on the X-axis. Despite showing\nonly a single cell, each nucleus contains numerous spots, providing statistically\nmeaningful data. Notably, this single-cell, spot-based colocalization analysis is\nwidely applied in the literature and has been shown to be statistically robust\n(Autophagy. 2022;18(5):990-1004; Cancer research. 2025;85(17):3313-3334). We\nhave updated the Fig. 5i legend to clarify the meaning of the quantitative analysis.\n(2) In our previous study, we also conducted experiments using lower-magnification\nconfocal microscopy to capture multiple cells within the same field of view, and\nthis result has been presented in the response letter below. These images confirmed\nthat O-GlcNAc modification significantly affects colocalization of Flag-\nGATAD2A with MBD2, and HA-JUN with SNF5. However, due to limited\nresolution, it was difficult to accurately distinguish the fine nuclear colocalization\nregions necessary for precise quantitative analysis. By contrast, the high-\nmagnification single-cell images in the previously revised figure provide more\ndetailed and reliable visualization of nuclear protein complex localization, suitable\nfor quantitative statistics.\n(3) In addition to the immunofluorescence data in Fig. 5i, other experiments, including\nco-IP and pulldown assays (Fig. 4e, Fig. 5f–h) and ChIP-seq analyses (Fig. 6b–d),\nconsistently demonstrate interactions between GATAD2A and MBD2, and\nbetween JUN and SNF5. Together, these independent lines of evidence support the\nreliability of the colocalization results presented in Fig. 5i.\n(4) The reviewer pointed out that “the Pearson’s coefficient and the corresponding\nplots strongly indicate the opposite of what the images display.” In response, we\nsuggest that the reviewer refer to the high-resolution colocalization images we have\nprovided for further evaluation. After mutation of the O-GlcNAc sites, the\ninteractions between GATAD2A and MBD2, as well as between JUN and SNF5,\nwere markedly reduced; however, these proteins remained localized within the\nnucleus. Colocalization is indicated when green and red fluorescence signals (spots)\noverlap at the same position within a given region, whereas a lack of complete\noverlap between the two signals at the same pixel suggests the absence of\ncolocalization. By analyzing the overlap of all nuclear pixels, the Pearson’s\ncorrelation coefficient was calculated, where an R value of 1 represents perfect\ncolocalization and an R value of 0 indicates no colocalization. Our results show\nthat O-GlcNAc modified GATAD2A and JUN exhibit a high degree of\ncolocalization with their respective complex partners, MBD2 and SNF5. In\ncontrast, mutation of the O-GlcNAc glycosylation sites in GATAD2A and JUN\nsignificantly diminished their colocalization with these interacting partners,\nsuggesting that O-GlcNAc modification plays an essential regulatory role in\nmaintaining these protein protein interactions.\n2. The blots in Fig 5 unfortunately remain problematic. Although the authors in their\nresponses mention the incorporation of IgG controls (they actually show them in the\nIPs of Fig 4), these controls are still missing from Fig 5 and Extended Data Figs.\nReply：\nWe would like to thank the reviewer for raising the question regarding the IgG\ncontrol in Fig. 5 and the corresponding extended figures. During the revision of our\nprevious manuscript, we had already considered this issue raised by the reviewer and\nincluded additional experiments in the revised version to provide the necessary IgG\ncontrol groups. We offer the following explanations for the IgG control in Fig. 5 and\nthe corresponding extended figures.\n(1) In the co-IP experiments in Fig. 5 d–f, h, j and Extended Data Fig. 14 b–c, where\nwe expressed Flag-tagged GATAD2A or HA-tagged JUN, we used commercially\navailable Flag or HA tag antibody-conjugated magnetic beads. These beads were\ncoupled with high-quality mouse monoclonal IgG antibodies via covalent bonding.\nDue to the covalent fixation of the antibodies to the bead surface, they cannot be\neasily eluted, which means that almost no IgG will detach from the beads during\nthe experiment. As a result, IgG heavy or light chains are not detected in\nsubsequent immunoblotting experiments. This characteristic has been supported\nby several published studies, including our previous work (Nature communications,\n2020, 11, 5898，Nature Communications, 2025, 16, 4792; Nature Communications,\n2025, 16, 5959).\n(2) Additionally, even if a small amount of coupled IgG is eluted from the beads, to\navoid interference from IgG, we used antibodies from different species in the\nsubsequent immunoblotting detection following the co-IP experiments in Fig. 5 d–\nf, h, j and Extended Data Fig. 14 b–c. These antibodies included anti-Flag, anti-\nHA, anti-SNF5, anti-MBD2, anti-MTA1, anti-CHD4, and anti-SMRC1, all of\nwhich are rabbit-derived antibodies. This has been noted in the figure legend of the\ncurrent revision. Previous studies have shown that using antibodies from different\nspecies can effectively prevent IgG interference (Nature Communications, 2025,\n16, 227; Nature Communications, 2024, 15, 7398, Oncogene. 2023;42(30):2329-\n2346). To meet the requirements for control groups, we included empty vector\ngroups (vehicle) as negative controls in all IP-Flag and IP-HA experiments. No\ncorresponding bands were detected in the vehicle group using immunomagnetic\nbeads, confirming the reliability of the positive bands obtained in the experiment.\nAlthough no IgG control was included in these experiments, the same amount of\nimmunomagnetic beads (60 μL) was used consistently across all experimental\ngroups to ensure consistency in experimental conditions and the reliability of\nresults. This description has been added to the corresponding figure legends and\n\"Methods\" section of the revised manuscript.\n(3) For the immunoblotting results using Flag or HA tag antibody conjugated magnetic\nbeads, we have included the raw data (full membrane with an adequate molecular\nweight range at both the upper and lower ends) in the source data. Below, we\npresent some of the results, showing that using immunomagnetic beads and\nsubsequent immunoblotting with antibodies from different species indeed does not\ndetect IgG heavy or light chains.\nIn summary, based on the results and evidence provided, we believe that the co-IP\nexperimental results in Fig. 5 and the corresponding extended figures are reliable.\n3. I still disagree with the authors that the data presented in Fig 1e and Ext Data Fig 2a\nare in accordance with one another. In Fig 1e, EU staining labels proliferating cells (in\nS phase). That proportion of cells evidently decreases upon TMG treatment. On the\ncontrary, Ext Data Fig 2a shows an elevated S phase in MCF-RAS cells. The authors\nshould provide a reasonable explanation. Most of the experiments were performed in\nLU-RAS cells, and differences such as this one may raise concerns regarding cell line\nbias and data reproducibility.\nReply：\nThe reviewer has raised concerns regarding the EdU staining and cell cycle\nexperimental results in OIS cells, as well as the regulation mechanisms of different\ntypes of cells undergoing distinct cell cycle arrest. We offer the following further\nexplanations:\n(1) As the reviewer correctly pointed out, EdU staining is a classic method used to\nlabel cells in the DNA synthesis phase (S phase), and it is often employed to reflect\ncellular proliferative activity. In this study, the EdU staining protocol followed a\nstandard procedure. The reference for developing this method is Cytometry Part A,\n2009, 75(6): 535–546. Following this method, we add EdU to the cell culture dish\n2 hours before detection. This method is based on established protocols (Cell, 2016,\n173(6): 1370–1384.e16; Cell, 2016, 167(5): 1264–1280.e18) and has been\npreviously described in our earlier manuscript.\n(2) In this study, all EdU staining experiments were conducted on cells that had already\nundergone senescence induction. These OIS cells exhibited senescence phenotypes\nand had mostly undergone cell cycle arrest, halting proliferation before EdU was\nadded. We acknowledge that different types of senescence may involve arrest at\ndistinct phases of the cell cycle, as confirmed by the flow cytometry results in this\nstudy. However, it is important to note that these senescent cells had lost their DNA\nreplication capability prior to the EdU staining, and thus no DNA synthesis occurs\nin the S phase. As a result, they were unable to incorporate EdU and exhibited EdU\nnegative staining. Therefore, the EdU staining experiment in this study cannot be\ndirectly correlated with the flow cytometry cell cycle arrest results but serves to\nconfirm whether the cells have ceased proliferating, acting as one of the markers\nof cellular senescence.\n(3) In Fig. 1b, e, and Extended Data Fig. 2a, we observed that LU-RAS cells primarily\narrest in the G2 phase following senescence, while MCF-RAS cells arrest in the S\nphase. TMG treatment increased cell cycle arrest in both LU-RAS and MCF-RAS\ncells at the D4 time point, indicating that O-GlcNAc modification plays a\nregulatory role in the senescence phenotype. It is essential to clarify that before\nperforming the EdU staining, these cells had already entered a state of cell cycle\narrest. Regardless of whether the cells arrested in the S phase or G2 phase, the\narrested cells (which represent different proportions in the experimental groups)\nlost their proliferative capacity and were unable to incorporate EdU, resulting in\nEdU negative staining. These results reflect that the EdU negative status\ncorresponds to the loss of proliferative activity, rather than reflecting differences\nin specific cell cycle arrest phases.\n(4) Moreover, although we observed that different cell types undergo different cell\ncycle arrest states following OIS induction, the primary focus of this study is not\nto investigate the differences in cell cycle arrest at different phases, but to explore\nthe overall regulatory function and mechanisms of O-GlcNAc modification in\ncellular senescence. Most of the mechanistic studies were conducted using LU-\nRAS cells. Overall, our multi-omics analysis results consistently suggest that O-\nGlcNAc modification promotes cell cycle arrest and senescence by suppressing the\ntranscription of several cell cycle related genes, such as CCND1, STAG2, E2F5,\nand CDK6. While the O-GlcNAc regulated cell cycle-related genes in LU-RAS\ncells may differ from those in MCF-RAS cells, which could explain why different\ncell types arrest in distinct cell cycle phases, the core molecular mechanism by\nwhich O-GlcNAc modification regulates cell cycle factor transcription to alter the\nsenescence phenotype remains consistent. Thus, even though different cell types\nmay exhibit distinct phases of cycle arrest during senescence, the underlying\nmechanism is attributed to the transcriptional repression of cell cycle factors\nmediated by O-GlcNAc modification. Therefore, we are confident that despite\ndifferences in cell cycle arrest mechanisms across cell lines, the regulatory role of\nO-GlcNAc modification on cell cycle factors during senescence is consistent. This\ndiscussion has been further elaborated upon in the revised manuscript.\nReviewer 3#\nAll minor typographical and punctuation errors have been corrected during the final\nproofreading stage.\n- Figure 1 legend (d–g): “OSIM-4” should read “OSMI-4.”\nExtended Figure legends:\n-Figure 1 (c): punctuation error: a full stop is missing at the end of the sentence \"and\nMKI67. The immunoblots ...\"\n-Figure 2(a): “indicted” should read “indicated”\n- Extended Figure 9 (images a, b, c): “mentylation” should read “methylation”\nReply：\nThank you for your thorough review and for pointing out the minor typographical\nand punctuation errors. We have carefully corrected all the issues mentioned."}]

Decision letter