HMGA1 modulates cellular functionality by affecting both up- and down-regulated genes in OISTo characterise HMGA1-dependent gene regulation, we utilized the oncogenic RAS-induced senescence (OIS) culture model, IMR90 human fibroblasts expressing a 4-hydroxytamoxifen (4OHT)-inducible ER:HRASG12V fusion protein15. These cells were stably transduced with a well-characterised shRNA against HMGA1 (shA1) in a miR30 design or control miR30 backbone13,16,17, and ER:HRASG12V was then induced with 4OHT for 6 days to establish OIS or OIS-shA1 cells. As previously shown13, HMGA1 depletion suppressed SAHF formation with little impact on senescence arrest, probed by cell cycle markers (Fig. 1a, Supplementary Fig. 1b). 967 and 1,365 genes were significantly differentially expressed in proliferating (Grow, no 4OHT) and OIS conditions, respectively, with shA1 (Supplementary Fig. 1c). Typical SASP components, such as inflammatory cytokines (e.g., IL1B, IL8, IL6, and CXCL1-3) and matrix-degrading enzymes (e.g., MMP1 and MMP3), were up-regulated in OIS cells, but they were further up-regulated in OIS-shA1 cells (Fig. 1b, Supplementary Fig. 1d). This suggests that HMGA1 acts as a transcriptional buffer for the SASP. We confirmed that the IL-8 and IL-6 proteins were indeed more abundant in OIS-shA1 than OIS cells (Fig. 1a). We also performed immunofluorescence experiments for IL-8 and HMGA1 and confirmed the up-regulation of IL-8 in OIS-shA1 compared to OIS (Fig. 1c, IF and Supplementary Fig. 1f). Similar effects were observed in the transcriptomic profiles of proliferating IMR90 cells with shA1, with substantial up-regulation of SASP genes (Supplementary Fig. 1c, e), suggesting that HMGA1 has a modulatory effect on the expression of these genes in IMR90 fibroblasts.Fig. 1: The HMGA1 effect on transcription and genome-wide binding distribution in proliferating and OIS cells.a Protein-level changes of key senescence genes in proliferating IMR90 (Grow; day 0), OIS (day 6) with and without shHMGA1 (shA1)—three replicates per condition. Source data are provided as a Source Data file. b Gene expression log-fold changes (logFC) in OIS compared to Grow and OIS-shA1 compared to OIS of the genes differentially expressed in both comparisons, clustered by the direction of the change. c Immunofluorescence imaging of Grow, OIS and OIS-shA1 stained for DAPI (nuclear; blue), IL-8 (green), and HMGA1 (magenta) cells (n = 2 per condition, quantification—Supplementary Fig. 1f, Source data are provided as a Source Data file). d Overlap between HMGA1 consensus peaks in the IMR90 Grow and OIS and H1299 cells. e Correlation between AT% and average (log) HMGA1 signal in 200 kb bins. f Top: ChIP-seq normalised signal tracks of HMGA1, H3K9me3 and Lamin B1 in the Grow and OIS conditions (IGV browser); Bottom: close-up view of the HMGA1 peaks in a smaller genomic region. g Overlaps (genomic area covered in Mb) between HMGA1-dense regions, Lamin-associated domains (LADs), and H3K9me3 peaks in growing IMR90 cells. h Number of HMGA1 binding changes in OIS compared to Grow cells (Up—increased, Down—decreased). i Properties of the n = 16,420 peaks with increased (Up) and n = 4060 peaks with decreased (Down) HMGA1 binding from h: distance to the nearest HMGA1-dense region (left) and the nearest H3K9me3 peak (right). j ChIP-seq normalised signal tracks of HMGA1 and H3K27ac over representative genes with decreased HMGA1 on their gene body: MMP1, CSF2, IL6. k Distribution of log-fold changes in OIS compared to Grow and OIS-shA1 compared to OIS of the genes DE in each respective comparison classified as: 1) overlapping HMGA1-dense regions (n = 893 and 218 genes), 2) with any HMGA1 binding (n = 2580 and 518 including genes in HMGA1-dense regions), and 3) not bound by HMGA1 at all (n = 2756 and 445 genes), respectively. j, k Box plot centre line represents the median, the bounds correspond to the 0.25 and 0.75 quantiles, the whiskers represent the 0.1 and 0.9 quantiles. Significance testing was performed using two-sided Wilcoxon tests.While the SASP can be seen as a gain-of-function phenotype of senescence, a loss-of-function phenotype is also well described: senescent fibroblasts tend to lose fibrogenic activities1,18. Interestingly, HMGA1 also modulated the expression of genes involved in fibrogenic activities, with HMGA1 depletion leading to further down-regulation of ECM components, e.g., CTGF, FBLN2, FBN2, and BGN (Fig. 1b). In contrast, the effect of HMGA1 on cell cycle genes was modest at the cell population level (Supplementary Fig. 1g), consistent with the phenotypic observation of limited effect of HMGA1 on the cell cycle arrest.These data suggest that HMGA1 acts as a key modulator of the major functional shift associated with senescence, for both up- and down-regulated genes.HMGA1 binding is linked to gene repression and weak enhancersHMGA1 has been proposed to act as an ‘architectural transcription factor (TF)’, facilitating the binding of other transcriptional activators by changing the structure of DNA11, but the mechanism behind such activity is unclear. To address this question, we performed highly optimised HMGA1 ChIP-seq experiments in IMR90 cells (growing and OIS conditions) and generated high-resolution signal distributions. We observed almost 400,000 discrete peaks in each condition (Fig. 1d), demonstrating that HMGA1 binds the genome pervasively, compared to other structural proteins (e.g., ~40,000 CTCF peaks in IMR90 cells8). Similar results were obtained in H1299, a human non-small cell lung cancer (NSCLC) cell line that express endogenous HMGA1 (Fig. 1d). We further validated the HMGA1 antibody specificity for ChIP-seq in HMGA1-deficent H1299 cells that we generated using CRISPR/Cas9 gene editing (Supplementary Fig. 1h, i).Consistent with the high affinity of HMGA1 for AT-rich regions due to its 3 AT-hook and with previous reports correlating HMGA1 binding to AT-content19, HMGA1 peaks had high AT% (avg. 72%). Motif analysis revealed highly AT-rich motifs (Supplementary Fig. 1j), similar to motifs previously associated with HMGA1 and other proteins which preferentially bind AT-rich motifs, such as ARID3A20. HMGA1 binding (signal binned in 1 kb windows) correlated positively with AT% in the control cells (Fig. 1e), and even more in OIS (Pearson 0.42 in Grow and 0.47 in OIS). 95% of the 1 kb bins with over 70% AT content were bound by HMGA1.HMGA1 binding in both the Grow and OIS conditions in IMR90 cells showed the formation of broad binding profiles with a high density of peaks (Fig. 1f). We defined ‘HMGA1-dense’ regions by determining the inflection point of the distribution of the number of peaks in overlapping bins (100 kb rolling windows with 5 kb steps) and stitching together regions with a peak density greater than this threshold. ‘HMGA1-dense’ regions were similar in shape to H3K9me3 peaks (a marker of constitutive heterochromatin), with an average size of 450 kb (Fig. 1f), consistent with HMGA1’s localization at heterochromatin regions13,21 based on microscopic imaging (Supplementary Fig. 2a). HMGA1-dense regions showed good overlap with Lamin B1-associated domains (LADs) and H3K9me3 peaks in proliferating IMR90 cells (Fig. 1g). The HMGA1-dense H3K9me3 peaks overlapping were wider, more AT-rich and contained fewer genes than other H3K9me3 peaks (Supplementary Fig. 2b). While these HMGA1-dense H3K9me3 peaks tended to have overall less Lamin B1 binding than other H3K9me3 peaks, they showed a more pronounced Lamin B1 loss during OIS (Supplementary Fig 2c). Considering the critical role for both Lamin B1-loss (a hallmark of senescence) and HMGA1 in SAHF formation13,22. HMGA1-dense H3K9me3 regions are likely to contribute to SAHFs.We compared the HMGA1 signal to epigenomic features summarized at high- (1 kb bins) and low-resolution (200 kb bins). At low resolution, consistent with the visual inspection (Fig. 1f), HMGA1 was positively correlated with Lamin B1 and H3K9me3 signal and anti-correlated with euchromatic histone marks (Supplementary Fig. 2d, 200 kb bins). However, at high-resolution, HMGA1 binding was independent of most of these features (Supplementary Fig. 2d, 1 kb bins) and HMGA1 peaks often co-localized with a ‘valley’ in the signal of other histone marks (Supplementary Fig. 2e).Notably, despite covering 30% of the genome, HMGA1-dense regions only accounted for 36% of all HMGA1 peaks and, at high-resolution, HMGA1 displayed a discrete, well-defined peak pattern (Fig. 1f, bottom), more similar to that of euchromatic marks or TF footprints, than to the broad profile of heterochromatic histone marks. Annotation of the HMGA1 peaks showed significant overlap with intergenic regions (59% of the peaks) (Supplementary Fig. 2f), likely due to the high density of HMGA1 peaks observed in gene-poor heterochromatic regions. However, a large proportion of peaks (40%) overlapped gene introns, which prompted us to repeat the analysis using only ‘euchromatic’ HMGA1 peaks (i.e., not overlapping H3K9me3). These peaks were enriched over genic elements: exons, introns, exon-intron junctions, and 3’UTR regions.We found that very few promoters were bound by HMGA1 (Supplementary Fig. 2f, only 7% of all promoters, out of which only 2% were active), despite previous reports attributing promoter-binding roles to HMGA1-mediated gene activation11 and the genome-wide abundance of HMGA1. This tendency may reflect the low enrichment of AT at promoter regions23. Genes with HMGA1-bound promoters mainly clustered in olfactory receptor (OR) family and type-I interferon loci (e.g., IFNAs and IFNB1) (Supplementary Fig. 2g). The OR clusters are highly tissue-specific and, thus, heterochromatic in fibroblasts17. While IFNB1 is a known direct HMGA1-target gene24 and IFNA and IFNB1 were previously shown to be induced in ‘late senescent’ fibroblasts25, they were not expressed in OIS IMR90 cells at the time point we used (day 6).HMGA1 was also previously implicated in the formation of the enhanceosome26 and, in our data, 21% of all enhancers exhibited at least one HMGA1 peak in proliferating fibroblasts. We looked further at enhancers overlapping HMGA1-dense regions and found that they were similar between proliferating and OIS cells (83% match). HMGA1-dense enhancers were shorter in width and had lower levels of H3K27ac, a histone mark for active enhancers (Supplementary Fig. 3a), suggesting that HMGA1 may be generally linked to enhancers with reduced activity. The genes proximal to these enhancers showed enrichment for ‘Epithelial Mesenchymal Transition’, including the LUM, FBN1, FAP and FGF2 genes, but also some cell cycle related genes such as CCNA2 and PLK4 (Supplementary Fig. 3b). Out of the 1,046 genes proximal to HMGA1-dense enhancers, 123 were differentially expressed in OIS-shA1 compared to OIS, suggesting a potential role for HMGA1 in their regulation.Together, these data show that, although high-density HMGA1 peaks typically co-localize with classical heterochromatin and promoters are mostly depleted of HMGA1, a substantial number of peaks overlap enhancer or genic regions.We next focused on differential binding analysis between Grow and OIS, which yielded 21,398 increases and 6676 decreases in HMGA1 binding (Fig. 1h). The respective genomic locations showed distinct features: peaks with increased HMGA1 were closer to HMGA1-dense regions and H3K9me3 peaks and more AT-rich than peaks with decreased HMGA1 binding (Fig. 1i, Supplementary Fig. 3c). We found 1094 differentially expressed (DE) genes with increased HMGA1 binding in OIS on the gene body and 460 DE genes with decreased binding. The DE genes with increased HMGA1-binding were enriched for cell cycle signatures (Supplementary Fig. 3d), including CCNB1, CDK1 and PCNA, and tended to be down-regulated (62% of DE genes) during OIS. In contrast, while the DE genes with decreased HMGA1 binding included some SASP components, such as MMP1, CSF2 and IL6 (Fig. 1j, Supplementary Fig. 3d), reduced HMGA1 binding showed no overall direction of gene expression change.To gain a better understanding of the effect of HMGA1 binding on gene expression, we cross-examined HMGA1 occupancy and HMGA1-responsive genes. The genes bound by HMGA1 and DE in OIS, particularly within HMGA-dense regions, were mostly down-regulated and this trend was largely alleviated with HMGA1 knock-down (OIS-shA1 compared to OIS), supporting that these regions become repressive during OIS in an HMGA1-dependent manner (Fig. 1k, Supplementary Fig. 3e). As mentioned earlier, HMGA1-dense regions showed good overlap with H3K9me3, which may contribute to its repressive role. However, the HMGA1-dense H3K9me3 regions were mostly gene-poor and inactive, with only 44 out of a total of 764 genes expressed in IMR90 cells. In contrast, non-H3K9me3 HMGA1-dense regions overlapped with 2738 genes, 1595 of which were expressed in IMR90 cells. Thus, our data suggest that HMGA1-dense regions gain their gene repressive activity during OIS, even without direct deposition of the heterochromatic mark H3K9me3. In contrast, genes lacking HMGA1 binding tended to be up-regulated during OIS, and, interestingly, this was also HMGA1-dependent (Fig. 1k).Thus, our data show that HMGA1 is linked to gene repression in HMGA1-dense regions and activation in regions lacking HMGA1 during senescence.HMGA1 promotes chromatin compartmentalizationOur data so far suggest that HMGA1 has a profound impact on both gene repression and activation during OIS, which is not easily explained by current models (e.g., heterochromatic repression; activation through direct binding to regulatory elements). HMGA1 plays a key role in the chromatin re-organization of senescent cells, contributing to SAHF formation, and therefore HMGA1 may affect gene expression by 3D repositioning. To interrogate HMGA1’s role in 3D genome organization, we performed Hi-C experiments with shA1 in both Grow and OIS conditions (451 and 306 million reads, respectively, after removal of artefacts), matching our previously published Grow and OIS dataset8. The agreement between replicates was assessed using HiCRep27 (Supplementary Fig. 3f).Differential Hi-C interaction analysis at 200 kb resolution revealed that, in the OIS condition, HMGA1 depletion resulted in 18,500 changes, the majority of which involved HMGA1-bound regions (Fig. 2a, b, Supplementary Fig. 3g), indicating a direct role for HMGA1 in chromatin 3D re-organization. OIS is known to exhibit distal trans-compaction, which is thought to reflect SAHF formation3,6,7,8. Strikingly, the OIS-associated trans-compaction was largely reversed with shA1 (Fig. 2a, d), further reinforcing the essential role of HMGA1 in SAHF formation.Fig. 2: HMGA1 leads to global re-organisation of chromatin architecture in OIS.a Hi-C interaction maps (resolution 200 kb) of Grow, Grow-shA1, OIS, and OIS-shA1 conditions, with H3K9me3 peaks marked (blue) and arcs representing increased (red) and decreased (blue) interactions. b Number of significantly increased (Inc) and decreased (Dec) interactions in OIS-shA1 compared to OIS, crosschecked against the changes in OIS compared to Grow. c The number of OIS-shA1 compared to OIS interaction changes where one or both regions involved is bound by HMGA1 (minimum 5 peaks per bin). d The distance between the regions involved in differential interactions against the interaction log-fold changes in OIS compared to Grow (left) and OIS-shA1 compared to OIS (right); the vertical dashed line marks the 2 Mb distance threshold denoting distal interactions. e Log-fold changes of the differential interactions between A-compartment regions (AA), B-compartment regions (BB) and between A- and B-compartment regions (AB), categorised by local (within 2 Mb) and distal (>2 Mb) interactions, in the OIS compared to Grow (purple) and OIS-shA1 compared to OIS (orange) comparisons. f Differential interaction networks of chromosome 4 between nodes representing 200 kb bins, coloured by H3K9me3 status, and edges representing increased (red) and decreased (blue) interactions; left: OIS compared to grow and right: OIS-shA1 compared to OIS; nodes are positioned according to the Fruchterman-Reingold layout calculated based on the edge weights representing the increased interaction log-fold changes.Consistent with a previous study linking OIS to stronger chromatin A/B compartmentalization7, our OIS model also displayed stronger compartmentalization, marked by reduced A-B and increased A-A and distal B-B interactions (Fig. 2e, Supplementary Fig. 3h), despite the relative stability of A/B compartment classification during OIS in IMR90 cells8. While HMGA1 depletion had a limited impact on compartment switches (OIS-shA1 compared to OIS, corr. 0.98 between the first PCs), it largely reversed the enhanced compartmentalization during OIS, except with a further increase in A-A interactions (Fig. 2e). A weaker but similar trend was also observed in normal fibroblasts with sh-A1 (Grow-shA1) condition, with reduced B-B interactions and increased A-A interactions, suggesting a more general role of HMGA1 towards supporting chromatin compartmentalization and heterochromatin organisation (Supplementary Fig. 3i).These results indicate that the degree of local HMGA1 deposition has a global impact on high-order chromatin status, possibly through coordinated effects on both HMGA1-rich and HMGA1-poor regions within chromatin networks.The k-core decomposition of the chromatin networkPrevious studies linked SAHF formation exclusively to the increased distal contacts between heterochromatic regions, in a pairwise manner. Here, we characterized instead the global changes in the chromatin network using a graph-theoretical approach to identify densely connected clusters. We reasoned that this will not only reveal the nature of chromatin compaction, but also the global impact of OIS on the interactions network, including effects on non-heterochromatic regions.We constructed networks for each chromosome using genomic bins as nodes and differential interactions between conditions as edges (Fig. 2f). In OIS, we found large clusters of increased interactions, which were disrupted in the presence of shA1, consistent with SAHF formation (Fig. 2f). The highly interacting clusters often corresponded to HMGA1-dense regions overlapping H3K9me3, but H3K9me3-independent clusters were also present (e.g., chromosome 1, Fig. 3a). HMGA1-dense regions without H3K9me3 also organized around the densely interacting clusters. Interestingly, we also observed regions which were excluded from these clusters, marked by many decreased interactions, during OIS, in an HMGA1-dependent manner.Fig. 3: Classification of genomic regions based on their differential interactions connectivity patterns.a Differential interactions network of OIS compared to Grow on chromosome 1 with nodes coloured by their overlap with H3K9me 3 and HMGA1-dense regions. b Model representation of the k-core decomposition of an example network with k-max equal to 3. c The k-core decomposition of the network of increased interactions in OIS compared to Grow on chromosome 1, with the size of each node (200 kb bins) reflecting the degree (number of interactions) of the node and the k-core value as the node colour; edge colour reflects the minimum k-core of its nodes; arrow indicates the nodes with the maximum k-core value (k-max). d Normalised ChIP-seq signal tracks of HMGA1, Lamin B1, and H3K9me3 in Grow and OIS around the regions which form the degeneracy core (k-max) of chromosome 1 shown in c. e The k-core decomposition (left) of the chromosome 3 network (200 kb bins) of increased contacts in OIS compared to Grow, with the node colour corresponding to its k-core and the node size to its degree; the full network of differential interactions highlighting the HMGA1 (middle) and H3K9me3 (right) normalised ChIP-seq signal of each node; arrow indicates the cluster of regions with low k-core and HMGA1, but high H3K9me3 ChIP-seq signal. f Heatmap of the k-core values, classification, and epigenetic properties, represented by the ChIP-seq (or ATAC-seq) scaled normalised signal of H3K9me3, HMGA1, LaminB1 in Grow and OIS, H3K27me3, ATAC-seq (accessibility), and H3K27ac, of all the bins with k-core of at least 1. g The network of (all) differential interactions of OIS compared to Grow on chromosome 2 with nodes classified as: Core (yellow), Peri (peri-core, magenta), AltCore (alternative core, cyan), ExCore (excluded from cores, navy blue) and other (grey). h The A/B compartment score and H3K9me3 ChIP-seq signal of the genomic bins in each of the classes. i The distributions of the HMGA1 signal, the AT%, H3K27ac and H3K27me3 signal of the bins in each class. j Lamin B1 signal in Grow (left) and OIS (right) of all the bins in each of the classes. h–j Compared regions: n = 2900 Core, n = 915 Peri, n = 2892 AltCore, n = 2368 ExCore, and 5235 Other. Box plot centre line represents the median, the bounds correspond to the 0.25 and 0.75 quantiles, the whiskers represent the 0.1 and 0.9 quantiles. P-values derived from two-sided Wilcoxon testing.Several measures exist for analysing the connectivity of a graph: e.g., the degree distribution or centrality measures. To capture dynamic nature of chromatin networks, we applied the k-core decomposition28 to the differential Hi-C connectivity maps (Fig. 3b, c): it identifies dense subgraphs, i.e., highly interconnected subsets of the network (relative to the number of nodes), allowing for both intuitive and quantitative assessment of network structure (see Supplementary Information).We first focused on the networks of increased interactions during OIS to determine the k-core values of the genomic bins (nodes). The k-core values correlated overall with epigenetic properties, capturing distinct chromatin units (Fig. 3f): Regions with a high k-core (>5) were enriched for HMGA1, H3K9me3, and depleted of Lamin B1 in OIS, likely reflecting SAHF formation. Regions with medium k-core (3–5) tended to retain Lamin B1 in OIS cells. These regions overlapped with H3K9me3, but not with HMGA1, possibly corresponding to residual perinuclear heterochromatin in OIS cells. Low k-core (<3) was mostly associated with euchromatic features, marked by enrichment in H3K27ac and ATAC-seq signal.We analysed the degeneracy cores of each chromosome (bins with maximal k-core in the networks of increased interactions) and found that chromosomes 1–14 and 18 had prominent cores, with k-max over 10 (Supplementary Fig. 4a), while chromosomes with high gene density, such as 17, 19 and 22, had low k-max values. Interestingly, k-max values strongly correlated with HMGA1 binding, more than with other features, including H3K9me3 (Supplementary Fig. 4a, b). The chromosomes with low k-max values indeed had lower AT% distributions and fewer HMGA1 peaks (Supplementary Fig. 4d).Chromosome 1 had the most prominent degeneracy-core, with k-max equal to 32 (Fig. 3c). This subgraph corresponded to the cluster identified earlier as being highly enriched for HMGA1 but depleted of H3K9me3 (Fig. 3a, d), highlighting the central role of HMGA1 in forming these densely interacting clusters. The striking aggregation of these HMGA1-dense regions on chromosome 1 during OIS suggests that HMGA1 alone can contribute to chromatin compaction. Some chromosomes (e.g., chromosomes 1 and 9) had a prominent dense subgraph on each arm, while the degeneracy-core of most other chromosomes spanned both chromosome arms, reflected by distal interaction increases between the two arms during OIS. We also found other dense subgraphs with relatively high k-core values: chromosome 3 had a secondary dense subgraph which was marked by H3K9me3 but lower HMGA1 than its degeneracy core (Fig. 3e), reinforcing the link between high k-core values and HMGA1. Moreover, this secondary cluster retained Lamin B1, whereas the main, HMGA1-dense cluster exhibited Lamin B1 disruption (Supplementary Fig. 4c), reflecting our previous observation regarding the correlation between Lamin B1 loss and HMGA1 binding (Supplementary Fig. 2c).We incorporated decreased connectivity in our k-core analysis to classify genomic regions involved in differential interactions (Fig. 3g). Regions with high k-core values (min. k-core 6) were defined as Core and regions with increased interactions with the Core were classified as Peri. Smaller clusters (min. 4) which were independent or detached from Core regions were classified as alternative cores, AltCore. All regions excluded from Core and AltCore (decreased interactions) were classified as ExCore.In terms of A/B compartments, Core and Peri regions mostly corresponded to the B-compartment (Fig. 3h, negative AB score), while AltCore and ExCore regions were associated with both A and B compartments, and Other regions were mostly in the A compartment (positive AB score). We also reanalysed the (epi)genetic features according to these classes (Fig. 3h–j): Core regions had the highest levels of HMGA1 binding, were highly heterochromatic (marked by H3K9me3, highest AT% and lowest gene density) and lost Lamin B1 in OIS. Peri regions had lower HMGA1 and H3K9me3 than Core. AltCore regions were enriched for H3K9me3 but had low levels of HMGA1 and tended to retain Lamin B1 in OIS. They were also more euchromatic than Core and Peri regions, marked by higher ATAC-seq and H3K27ac levels. ExCore regions were mostly euchromatic (low AT%, gene dense and high ATAC and H3K27ac signal), with low H3K9me3, but not as euchromatic as the unclassified, ‘Other’ regions (Fig. 3h–j).HMGA1 substantially affected the inter-connectivity of these classes: the increased interactions forming the Core and Peri-Core connections in OIS were cancelled in OIS-shA1 (Fig. 4a). These regions may collectively contribute to SAHFs. Notably, the exclusion of AltCore and ExCore regions from Core was also largely HMGA1-dependent (Fig. 4a), supporting the coupling of direct and indirect roles of HMGA1 to shape global chromatin environment.Fig. 4: The effects of HMGA1 loss on the chromatin interactions network and gene expression.a The log-fold changes of the differential interactions between the regions in each pair of classes in OIS compared to Grow (purple) and OIS-shA1 compared to OIS (orange). b The distributions of the gene expression log-fold changes in the OIS compared to Grow and OIS-shA1 compared to OIS comparisons of the genes within the five classes which are also DE in their respective comparisons (n = 253 and 130 genes in Core, n = 225 and 98 genes in Peri, n = 1015 and 398 genes in AltCore, n = 1088 and 418 genes in ExCore, and n = 4333 and 1411 genes in Other). c Gene expression log-fold changes in OIS compared to Grow and OIS-shA1 compared to OIS of the respective DE genes within ‘Core’ regions with (n = 9 and 6 genes) and without H3K9me3 (n = 208 and 101 genes). d The position of the Core CCNA2 gene (left) in the networks of differential interactions on chromosomes 4 in OIS compared to Grow and the ChIP-seq signal of HMGA1, H3K9me3 and H3K27ac at the CCNA2 locus (right). e The position of the ExCore gene MMP1 in the chromosome 11 network of differential interactions in OIS—Grow. f The top MSigDB Hallmarks gene sets enriched in the genes down-regulated (in OIS compared to Grow) and overlapping ‘Core’ and ‘AltCore’ regions, and the genes up-regulated (in OIS compared to Grow) and overlapping the ‘ExCore’ regions. g The increased (left) and decreased (right) de novo interactions in OIS-shA1 compared to OIS, according to the A- or B- compartment assignments of the interacting regions. h Gene set enrichment analysis against the MSigDB Hallmarks of the genes DE in OIS-shA1 compared to OIS and involved in the de novo increased interactions in the same comparison. P-values derived with EnrichR—Fischer’s exact test adjusted for multiple comparisons with Benjamini-Hochberg. i ChIP-seq normalised signal tracks of Grow and OIS Lamin B1, H3K9me3, HMGA1, and H3K27ac of the CXCL2 gene locus and the regions involved in increased (red) and decreased (blue) interactions in the OIS compared to Grow and OIS-shA1 compared to OIS, respectively. j The average contact frequencies in OIS of the enhancers within HMGA1-dense regions (n = 833 bins overlapping these enhancers) compared to other enhancers (n = 2610 bins overlapping these enhancers). k The interaction log-fold changes in OIS compared to Grow and OIS-shA1 compared to OIS of only the enhancers within HMGA1-dense regions (n = 833 bins overlapping these enhancers). b, c, j, k Box plot centre line represents the median, the bounds correspond to the 0.25 and 0.75 quantiles, the whiskers represent the 0.1 and 0.9 quantiles. P-values derived from two-sided Wilcoxon testing.Architectural role of HMGA1 in gene regulation during senescenceFinally, we characterized the genes and their expression changes (in either OIS compared to Grow or OIS-shA1 compared to OIS) in each of the classes. Overall, the genes within Core, Peri, and AltCore regions in OIS cells had a down-regulation tendency, which was reversed by HMGA1 depletion, while genes excluded from cores (ExCore) were more likely to be up-regulated during OIS and further up-regulated with shA1 (Fig. 4b). This suggests that incorporating genes in heterochromatic cores may contribute to their down-regulation in an HMGA1-dependent manner and excluding them from cores may contribute to their up-regulation in OIS.As expected, due to their highly heterochromatic features, genes within Core regions consisted of many gene families known to be repressed in most tissues, such as olfactory receptors and the type-I interferon genes (Supplementary Fig. 4e). While promoters of these genes were often HMGA1-bound (Supplementary Fig. 2g), the involvement in Cores might explain why we did not observe IFNB1 induction, which has been implicated in senescence25, in our experimental model, despite the abundant evidence for HMGA–mediated activation of IFNB111. Interestingly, we found that Core regions without H3K9me3 (HMGA1-dense only) were mostly responsible for the trend of HMGA1-dependent down-regulation during OIS (Fig. 4c), as the Core regions with H3K9me3 overlapped very few expressed genes.Core down-regulated genes reflected cell-cycle signatures, including genes such as CCNA2 and CDK1 (Fig. 4d, f). This is reminiscent of the incorporation of cell cycle genes into SAHFs, as has long been postulated9,29. They were also enriched for epithelial-mesenchymal transition (EMT), which included many commonly used fibroblast markers, such as COL1A2, DCN, LUM, VCAN and FAP30. It has been shown that senescent fibroblasts tend to lose fibrogenic activities and our data provide a mechanistic insight into the senescence-associated shift of cell identity1. Notably, these genes were not necessarily marked by H3K9me3, but rather they were within a high HMGA1 binding environment, which led to their incorporation into the dense H3K9me3 Cores (Fig. 4d). Other down-regulated genes, including another cell cycle gene, PCNA, were included in Peri regions with increased interactions with Core regions. Genes down-regulated within AltCore regions were also enriched for cell cycle regulators (e.g., RB1 and BUB1), but also for TGFβ signalling (e.g., TGFB1 and TGFBR1) (Fig. 4d). On the other hand, up-regulated genes excluded (ExCore) from the Cores and AltCores included CDKN2A (p16) and CDKN2B (p15), as well as key SASP factors, such as IL1, MMP1,3 and CXCL1,2,3 (Fig. 4e, f).So far, we assessed the impact of HMGA1 (via shA1) on the chromatin re-organization of OIS (relative to Grow), which consists of mostly ‘cancelling’ the connectivity changes occurring in OIS—Grow, with very few changes in the same direction, e.g., increased in OIS and in OIS-shA1. However, we also found substantial interaction changes in OIS-shA1 from OIS which did not change during OIS (Fig. 2b). These de novo changes reflected the higher A-A and A-B distal contacts in OIS-shA1 relative to OIS (Fig. 2e). Indeed, de novo decreased interactions in OIS-shA1 consisted mostly of B-B pairs (Fig. 4g), suggesting the enhanced disconnection of heterochromatic regions with shA1. In contrast, the de novo gained interactions in OIS-shA1 mostly consisted of A-A interactions, demonstrating an increased connectivity between euchromatic regions (Fig. 4g). 71% of these increases were potentially regulatory, corresponding to contacts between gene promoters and enhancers, involving a substantial proportion of the DE genes in OIS-shA1 relative to OIS (632 genes). These genes were enriched for NF-kB signalling and inflammatory response, including the MMP and IL1 genes (Fig. 4h), providing a potential mechanism for the further up-regulation of the inflammatory SASP in OIS-shA1. One notable example was the contact between the CXCL genes (including CXCL2 and CXCL8/IL8) and a distal enhancer situated 5 Mb away (Fig. 4i), which was bound by HMGA1 in OIS, potentially hindering the interaction with the CXCL locus in the presence of HMGA1. HMGA1-dense enhancers, which appeared to be less active (Supplementary Fig. 3a), indeed showed lower values of average connectivity than other enhancers in OIS cells (Fig. 4j), and HMGA1 depletion promoted their connectivity in the OIS context, potentially contributing to its buffer activities in gene regulation (Fig. 4k).Together, this evidence suggests that HMGA1 plays an important role in chromatin organization by promoting compartmentalization and modulating the potency of enhancers. The impact of HMGA1 on global chromatin configuration, rather than local binding of individual gene promoters, may represent an additional layer of gene regulation.HMGA1-driven transcriptional programmes at single-cell levelOur data suggest a profound impact of HMGA1 on chromatin architecture, and consequently, on the senescent transcriptome. However, these conclusions are based on bulk experiments that represent the average changes over millions of cells and do not take into account the reported heterogeneity of the senescent phenotype2. To better understand how the gene expression is modulated by HMGA1, we analysed the senescence transcriptome (with and without shA1) at single-cell level in IMR90 cells (Fig. 5a). After filtering low quality cells, the final dataset consisted of around 11,000 cells. Principal component analysis showed good agreement between replicates and as expected, HMGA1 expression levels increased in OIS and decreased with shA1 (Supplementary Fig. 5a, b).Fig. 5: Senescence transcriptional programmes at single-cell level.a Single-cell UMAP projection of Grow, OIS and OIS-shA1 cells (coloured by condition but each representing two replicates), highlighting key senescence genes: MKI67, IL8, CXCL1, IL1B, MMP3, and CDKN1A; expression values (log-transformed) were scaled to be between 0 and 10 for visualisation, with grey representing no expression detected. b Distribution of UCell single-cell scores for selected MSigDB Hallmarks gene sets in n = 6165 Grow, n = 2828 OIS, and n = 2073 OIS-shA1 cells; P-values derived from two-sided Wilcoxon testing. Box plot centre line represents the median, the bounds correspond to the 0.25 and 0.75 quantiles, the whiskers represent the 0.1 and 0.9 quantiles. c UMAP projection of the OIS and OIS-shA1 conditions only for Milo testing of cell neighbourhoods and clustering based on the log-fold changes between OIS and OIS-shA1. d Representative markers of the four Milo clusters of differential expression between OIS and OIS-shA1 cells at single-cell level, with expression values scaled between 0 and 1 and averaged over the cell neighbourhoods in each cluster from c. e Representative markers for clusters 1–4 coloured by scaled expression values on the UMAP projection of OIS and OIS-shA1 cells. f Schematic representation of the features of the four clusters of senescent cells identified using overrepresentation analysis, highlighting the clusters over-represented in OIS (2 and 4) and in OIS-shA1 (1 and 3), respectively. Although inflammatory SASP (iSASP) and p16 have been collectively considered senescence hallmarks, they represent distinct types of senescence at the single-cell level. Clusters 3 and 4 express cell-cycle genes and Cluster 4 resembles the previously described NOTCH-related ‘early phase senescence’ with augmented fibroblastic features33,34. g Dimensionality reduction (PCA) of the log-fold changes of OIS cells (bulk RNA-seq) in response to shHMGA1, shCEBPB, shp53 and double knock-down of p53 and CEBPB. h UMAP projection of the Grow and OIS cells, coloured by UCell scoring of the gene signatures of the genes activated in OIS and up-regulated by shA1 (repressed by HMGA1, top) and down-regulated by the double knock-down of p53 and CEBPB (activated by p53 + CEBPB, bottom).We confirmed the robustness of the senescent transcriptome by checking the down-regulation of cell-cycle markers (e.g., E2F1, CCNA2, MKI67) and the up-regulation of key SASP factors (e.g., IL1B and IL8). Notably, these senescence markers followed interesting patterns across the cells, with substantial heterogeneity (Fig. 5a). We also computed an enrichment score for the Hallmarks Signatures in The Molecular Signatures Database31 (MSigDB) (Fig. 5b) and observed that E2F targets were largely repressed in OIS compared to Growing cells, but less so in OIS-shA1 cells. This is consistent with our previous data, showing OIS-shA1 cells are more susceptible to senescence escape than OIS cells with simultaneous knock-down of p1613. The enrichment of NF-kB signalling in OIS was further enhanced with shA1, consistent with the bulk-RNA-seq results (Fig. 1b, Supplementary Fig. 1c, d). Other notable signatures included p53 Pathway and Notch signalling genes, which were more enriched in OIS and less so in OIS-shA1 cells (Fig. 5b).We used the Milo32 statistical testing method to characterise the shift in OIS-shA1 cells and partial overlap with the HRASG12V-expressing cells, consistent with their maintained senescence-like status13. We determined which senescence sub-populations are under- or over-represented upon HMGA1 depletion and found 4 clusters: two over- (Clusters 1 and 3) and two under-represented (Clusters 2 and 4) in OIS (Fig. 5c).Both Clusters 1 and 2 reflected typical senescence features, but to different degrees. While both lacked expression of cell cycle genes, Cluster 1 (low-HMGA1) exhibited more prominently the inflammatory SASP (e.g., IL6, IL1B, MMP1, MMP3), and was strongly depleted of fibrogenic features (Fig. 5d, e, Supplementary Fig. 5c). Thus, Cluster 1 most likely represents the senescence-associated cell identity and functionality shift1, which has been described based on whole-population assays (Fig. 1a, b). While Cluster 2 expressed the highest levels of HMGA1 and CDKN2A/p16, the inflammatory SASP was modest but instead expressed other secretory/cell surface components (Fig. 5d, e, Supplementary Fig. 5d). Therefore, HMGA1 may contribute to fine-tuning between these senescence states.Clusters 3 and 4 retained fibroblastic features (represented by EMT signature), but more prominently in Cluster 4 (e.g., FN1, SPARC and MYLK), with little expression of the inflammatory SASP components (Fig. 5d, Supplementary Fig. 5e, f). This phenotype is highly reminiscent of NOTCH-mediated secondary senescence33,34. We previously showed that NOTCH-induced senescence (NIS) represents an early phase of senescence, characterised by enhanced fibrogenic and reduced inflammatory features33. Our current results suggest that HMGA1 plays a role in the regulation of NIS-like dynamics during senescence, reinforcing that NIS represents a distinct feature with a unique molecular signature as an integral part of senescence. In contrast, Cluster 3 (low-HMGA1) substantially expressed cell cycle genes (Fig. 5d), suggesting that these cells are the major source of the unstable senescence feature observed in OIS-shA1 cells at population-level13.Our data show that HMGA1 contributes to the dynamic heterogeneity of senescence, by regulating genes involved in key signatures, i.e., cell proliferation, inflammatory SASP, and cell identity (fibroblast/EMT) markers (model—Fig. 5f).The interplay between HMGA1 and key senescence TFs mediates the senescence spectrumThe enrichment of the genes affected by HMGA1 for NF-kB signalling (de novo interactions—Fig. 4h and ExCore—Fig. 4f) suggests that HMGA1 may refine transcription factor activity by modulating the chromatin environment. Consistently, HMGA1-responsive genes showed enrichment for gene sets representing direct targets of TFs active in OIS (e.g., NF-kB, C/EBPβ, p53, Supplementary Fig. 5g).To experimentally validate whether HMGA1 acted in conjunction with other known TFs during OIS, we compared its transcriptional effect to those of C/EBPβ (a key TF responsible for inflammatory SASP35) and p5336. For this, we stably expressed shCEBPB or shp53 in ER:HRASG12V IMR90 cells, then oncogenic RAS was induced by 4OHT as in the OIS-shA1 setting. We also included the transcriptome of OIS with a double knock-down of C/EBPβ and p53 due to their complex interplay and potential synergistic effect36.As expected33,35,37, the C/EBPβ depletion blunted the inflammatory SASP after oncogenic RAS induction, but it also restored some fibroblast (EMT) markers, mirroring HMGA1 depletion in the OIS condition (Supplementary Fig. 6a, b). Interestingly, the effects of both C/EBPβ and p53 depletion were mostly potentiated by the double knock-down (Supplementary Fig. 6c–e), reinforcing their cooperative activity during OIS.We applied dimensionality reduction (principal component analysis) to the log-fold changes in response to all the knock-down experiments of HRASG12V-expressing cells and found that HMGA1 acted in the opposite direction of the C/EBPβ and p53 activity (Fig. 5g). Furthermore, at the single-cell level, we used the signature of the genes activated by the combined effect of these TFs to calculate an enrichment score (C/EBPβ + p53) for the OIS cells and found a striking overlap with the HMGA1-repressed gene signature during OIS (HMGA1-buffered genes), suggesting that HMGA1 depletion in the OIS context enhances the activity of these TFs (Fig. 5h). Indeed, based on our previous ChIP-seq datasets, the C/EBPβ + p53 signature was enriched for direct targets of these TFs17,36. These data support that HMGA1 can modulate TF activities through global chromatin architectural alteration.Buffering effect of HMGA1 on the pro-inflammatory signature in cancerHMGA1 up-regulation is frequently observed in many cancer types, including lung cancer, and linked to poor survival38. We tested the transcriptional effect and the chromatin binding of HMGA1 in the H1299 NSCLC cell line. The transcriptional changes in H1299 cells stably expressing shA1 revealed a similar alteration as in IMR90 fibroblasts, consisting of the up-regulation of pro-inflammatory genes (Fig. 6a) and the down-regulation of Notch signalling genes, such as NOTCH1, JAG2 and MGP. Moreover, the HMGA1 binding profile was conserved between the H1299 and IMR90 cells (Fig. 1d), with 232,290 common peaks and while peak calling identified more IMR90-specific peaks, only ~10,000 regions were completely devoid of HMGA1 binding in H1299, compared to IMR90 cells.Fig. 6: The effect of HMGA1 on the transcriptome of lung adenocarcinoma.a Expression changes in H1299 cells in response to shHMGA1, highlighting differentially expressed genes (top) and gene enrichment analysis against the MSigDB Hallmarks of the genes up-regulated by shHMGA1 in H1299 cells (bottom). Expression P-values derived from edgeR differential expression testing and adjusted for multiple testing using Benjamini-Hochberg correction. b Gene enrichment analysis of the markers of H1299 cells with high and low HMGA1 expression from a single-cell expression dataset of H1299 cells (see Methods). c Distribution of the expression at single-cell level of the representative markers of the same H1299 cells from b with low (n = 131) and high (n = 187) expression of HMGA1, respectively. d Cell populations of normal lung, early and advanced lung adenocarcinoma and the percentage of cells expressing HMGA1. e Top enrichment results (MSigDB Hallmarks) for the gene markers of epithelial cells in the advanced tumour with high (top) and low (bottom) HMGA1 expression. f Expression distribution of representative genes for the same cells from e, with high (n = 861) and low (n = 3084) HMGA1 expression. a, b, e Gene enrichment P values calculated with the EnrichR software using Fischer’s exact test and adjusted for multiple testing with Benjamini-Hochberg. c, f P values derived from two-sided Wilcoxon testing. Box plot centre line represents the median, the bounds correspond to the 0.25 and 0.75 quantiles, the whiskers represent the 0.1 and 0.9 quantiles.The similar gene expression changes and protein binding profile of HMGA1 observed in H1299 cells open exciting avenues for investigating the impact of HMGA1 in cancer. As HMGA1 binding correlated with AT-content, the binding of specific enhancers and genes may reflect their sequence features and may lead to the same genes being affected across different biological contexts. We took advantage of the recent advances in single-cell transcriptomics and compared the profiles of cells with high and low HMGA1 levels (as a proxy for protein level) in publicly available single-cell datasets. First, we analysed the single-cell profiles of H1299 cells39 and found that cells with low HMGA1 were more likely to express genes involved in pro-inflammatory signalling, such as CXCL1, CXCL2, and IL6, compared to cells with high HMGA1 (Fig. 6b, c). We also checked the transcriptomic associations of HMGA1 in a human lung cancer dataset40, which analysed multiple stages of lung cancer. We found that HMGA1 is expressed in a higher proportion of epithelial cells in the advanced stages, compared to normal lung samples (Fig. 6d). Moreover, cells with high and low HMGA1 also showed a polarised expression of pro-inflammatory genes, which were more likely to be expressed in the low HMGA1 cells (Fig. 6e, f), suggesting that HMGA1 may buffer the pro-inflammatory phenotype in human cancers.
