The sex-biased chromatin modifier SMC1A promotes autoimmunity by shaping inflammatory pathways in patients with SLE

Kosmara, Despoina; Papanikolaou, Sofia; Stathopoulou, Chrysoula; Papamatheakis, Dionysios; Vatsellas, Giannis; Cimmarrusti, Arianna; Banos, Aggelos; Sidiropoulos, Prodromos; Lavigne, Matthieu D.; Verginis, Panayotis; Boumpas, Dimitrios; Spilianakis, Charalampos; Konstantopoulos, Dimitris; Nikolaou, Christoforos; Bertsias, George

doi:10.1038/s41467-025-65309-7

Download PDF

Article
Open access
Published: 24 November 2025

The sex-biased chromatin modifier SMC1A promotes autoimmunity by shaping inflammatory pathways in patients with SLE

Nature Communications volume 16, Article number: 10350 (2025) Cite this article

3144 Accesses
18 Altmetric
Metrics details

Subjects

Abstract

A strong female bias is characteristic of systemic lupus erythematosus (SLE), the prototypic systemic autoimmune disease. Here, through an unbiased transcriptome analysis, we report a pronounced female-biased expression of the cohesin complex subunit SMC1A, a genome architectural factor, in monocytes from SLE patients compared to those from healthy individuals or patients with ankylosing spondylitis, a non-sex-biased autoimmune disorder. Integration of SMC1A binding, chromatin activity, and accessibility in lupus-like monocytes reveals extensive SMC1A redistribution to active enhancers of immune/inflammatory genes, inducing their transcription. SLE monocyte transcriptomes demonstrate significant enrichment of female-biased immune/inflammatory genes among SMC1A targets, accompanied by increased secretion of cytokines including IL6, with enhanced SMC1A binding at their enhancers in lupus-like monocytes. Collectively, our study highlights SMC1A as a female-biased chromatin modifier that acquires a specific regulatory function during lupus, accentuating inflammatory pathways and providing mechanistic insights into the female-biased predisposition to SLE and other autoimmune diseases.

The conneXion between sex and immune responses

Article 21 February 2024

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Article Open access 07 February 2023

Systemic lupus erythematosus: updated insights on the pathogenesis, diagnosis, prevention and therapeutics

Article Open access 17 March 2025

Introduction

Biological sex is a major determinant of immune system function and responsiveness under physiological and pathological conditions. Females mount more vigorous innate and adaptive immune responses as compared to males¹. As a consequence, males are more vulnerable to infections², while females are predisposed to autoimmunity³. Sexual dimorphism pertains also to the development and progression of malignancies⁴, responsiveness to immunizations⁵ and transplantation⁶.

The molecular basis of sex disparities in immunity remains incompletely understood, yet it is partially attributed to escapee genes from X chromosome inactivation (XCI) and sex hormones⁷. Mammalian X chromosomes, for which males are hemizygous, are enriched for immune-related genes⁸. Some of these genes, such as toll-like receptor (TLR) 7, TLR8, CD40 ligand (CD40LG), interleukin 1 receptor associated kinase 1 (IRAK1), chromosome X open reading frame 21 (CXorf21) and X-linked inhibitor of apoptosis (XIAP), escape the XCI process, thus disrupting dosage compensation and resulting in higher expression levels in females^9,10,11. On the other hand, estrogens can modulate Toll-like receptor (TLR) responses and the synthesis of certain immune genes^9,12,13,14.

Biological sex and sex-chromosome dosage exert widespread effects on the transcriptome of blood immune cells^{15,16,17,18,19,20,21,22,23}, which may be partially explained by sex-specific epigenetic modifications and genome regulation^1,9,24. This has been suggested by the in-silico analysis of transcription factor binding sites and inference of regulatory networks that control sex-biased gene expression in immune cells and other tissues^15,21,22. However, there is currently limited evidence directly linking genome regulatory proteins to the shaping of cell-specific molecular programs, particularly in relation to sex-biased immune phenotypes^25,26.

Systemic Lupus Erythematosus (SLE) is an archetype sex-biased autoimmune disease, with a female-to-male ratio as high as 10:1^27,28. Augmented type I interferon (IFN) and TLR signaling drive the activation of lupus myeloid cells, contributing to the breakdown of self-tolerance through the maturation and expansion of autoreactive lymphocytes^29,30. Work by us and others has positioned monocytes centrally to SLE pathogenesis, as they display marked molecular and functional aberrations^31,32,33,34, secrete inflammatory cytokines^35,36,37 and infiltrate target tissues^38,39. Despite advances, patients with SLE face excessive morbidity and poor long-term outcomes, thus underlining the urgent need to expand the disease therapeutic armamentarium⁴⁰.

Previously, we probed the blood transcriptome in SLE and healthy individuals, aiming to explore the molecular regulation of the disease⁴¹. Through differential gene expression analysis between sexes, we observed that SMC1A exhibited the most prominent sex-biased expression in SLE compared to healthy counterparts. SMC1A encodes for the nuclear protein “Structural Maintenance of Chromosomes 1A” and resides on the Xp11.22 region that partially escapes XCI^42,43. Together with other subunits (SMC3, RAD21, STAG1/2), SMC1A forms the cohesin complex involved in chromosome segregation, homologous recombination, and genome regulation^44,45,46. Cohesin mediates chromatin loop formation to support enhancer-promoter communication and coordinate gene transcription⁵⁰. It also interacts with transcription factors and facilitates their binding to DNA^{46,47,48,49,50}, a fact suggestive of a putative direct regulatory role.

The implication of SMC1A/cohesin in human pathophysiology is highlighted by the discovery of mutations in these genes, which are associated with various malignancies and developmental disorders, collectively known as cohesinopathies⁵¹. Despite some uncertainties, these pathologies are likely driven by perturbed gene expression due to altered function or diminished expression of cohesin^52,53,54,55. Notably, cohesin mediates TLR-inducible inflammatory molecular programs in myeloid cells^56,57 and binds to the IFNG locus to regulate its expression in CD4⁺ T-cells⁵⁸. Nonetheless, it remains elusive whether SMC1A/cohesin plays a role in more generalized transcriptional and functional deregulations of immune cells involved in autoimmunity.

Here, we aim to delineate the role of SMC1A in autoimmune-related inflammation and the effect of female-biased SMC1A in SLE pathogenesis. Moreover, we seek to gain insights into the broader mechanisms that may underlie the escape of SMC1A from XCI, a process that remains poorly defined. We show that, in response to lupus-relevant stimuli, SMC1A binds preferentially to active enhancers of immune and inflammatory genes in monocytes and regulates cytokine production, including IL6 and IL1A. Silencing of SMC1A downregulates lupus-related genes, while transcriptome profiling of SLE patient monocytes reveals female-prominent activation of SMC1A targets. These data support a key role for SMC1A as a sex-biased chromatin modifier that shapes the inflammatory gene program of monocytes and contributes to susceptibility to SLE and other autoimmune disorders.

Results

Monocytes from SLE patients exhibit enhanced female-biased expression of SMC1A

In a previous unbiased blood transcriptome analysis⁴¹, we showed that SMC1A encoding for Structural Maintenance of Chromosomes 1A, displayed strong female-biased expression in SLE. To corroborate this finding, we combined our original cohort with blood RNA-sequencing results from additional SLE and healthy individuals. We reached a total of 15 males with SLE who were compared with an equal number of female SLE, healthy female and male counterparts thus forming a balanced dataset (Supplementary Table 1) (GEO accession number GSE279728). Following batch-effect correction, differential gene expression analysis revealed significant female-biased SMC1A mRNA levels in SLE (log2-fold change [log2FC] = 0.39, p = 0.0131) compared to healthy state (log2FC = 0.13, p = 0.2059) (Fig. 1A), thus prompting us to investigate the role of SMC1A in lupus.

**Fig. 1: *SMC1A* displays augmented female-biased expression in lupus monocytes.**

To explore whether sex divergence in SMC1A is driven by one or more circulating immune cell types, blood was collected from pairs of male and female SLE patients with low disease activity (Supplementary Table 2), as well as from age-matched healthy counterparts. We separated neutrophils, CD14⁺ monocytes, CD4⁺ T cells and CD19⁺ B cells, which constitute the majority of white blood cells and represent major subsets implicated in lupus, followed SMC1A mRNA quantification by quantitative RT-PCR (RT-qPCR). Mixed-effects analysis was used to account for nested data structure and inter-individual variability. Both T- and B-lymphocytes exhibited female overexpression of SMC1A, which, however, was comparable between the SLE and healthy state (Supplementary Fig. 1A). In contrast, CD14+ monocytes showed greater female-male difference in SMC1A mRNA levels in SLE compared to healthy counterparts, as indicated by a significant sex × disease state interaction term (β = 0.184, p = 0.026) (Fig. 1B, Supplementary Table 3). This exaggerated female-biased SMC1A expression in SLE compared to healthy monocytes mirrored the whole blood data (Fig. 1A). A similar pattern of enhanced female-biased SMC1A mRNA levels was also observed in blood CD14+ monocytes (GSE137143) from patients with multiple sclerosis—another autoimmune disorder displaying female susceptibility⁵⁹—compared to those from healthy individuals (Supplementary Fig. 1B). SLE, especially active disease, had an overall negative effect on SMC1A expression (Supplementary Tables 3–4, Supplementary Fig. 1C). Western immunoblotting in SLE patient-derived blood CD14⁺ monocytes confirmed the increased expression of SMC1A in females (Fig. 1C). These findings, coupled with evidence that transcriptional sexual dimorphism is particularly evident in monocytes/macrophages and increases after stimulation with interferon^20,60—a key mediator of SLE⁶¹—guided our decision to focus on monocytes.

To address the specificity of our observation, we used ankylosing spondylitis (AS) as a control autoimmune disease displaying modest male predominance^62,63. First, we analyzed publicly available blood transcriptome data from AS patients (GSE73754) and found no sex variation in SMC1A (Fig. 1D). Next, we recruited male and female AS patients and healthy individuals (Supplementary Table 5) and purified blood CD14⁺ monocytes. RT-qPCR revealed no difference in SMC1A mRNA levels depending on patient sex (Fig. 1E), therefore suggesting that female-biased expression of SMC1A is accentuated in the monocytes of the female-predominant autoimmune disease of lupus, but in AS.

Sex-biased SMC1A expression in SLE is determined by cell-intrinsic and -extrinsic factors

SMC1A partially escapes XCI resulting in elevated basal gene expression in female cells^17,20,64,65. This agrees with our RT-qPCR data showing increased SMC1A mRNA levels in healthy females than males across all white blood cell subsets except for neutrophils (Fig. 1B). To further implicate an X-chromosome effect on SMC1A, we explored blood transcriptomic data (GSE42331) from males with Klinefelter syndrome (KS; 47,XXY), an aneuploid genetic condition characterized by an additional X chromosome copy and increased tendency for SLE^66,67. SMC1A was significantly elevated in KS as compared with 46,XY males, and comparable to 46,XX female counterparts (Fig. 1F).

Next, we assessed whether sex-biased SMC1A expression may be affected by exogenous factors pertinent to lupus. First, we mimicked the disease by culturing CD14⁺ monocytes from healthy, age-matched women and men with sequential IFN-α/TNF and LPS challenge (Fig. 1G) following a previously described protocol⁶⁸ with slight modifications. This set-up reproduced the inflammatory environment of lupus as evidenced by the induction of the hallmark cytokines IL-1b, IL-6 and CXCL10 and the membrane markers CD80 and HLA-DR (Supplementary Fig. 2). Compared to unstimulated condition, lupus-inducing stimulation reduced SMC1A in both sexes (Fig. 1H), however, the female-biased expression of SMC1A persisted in the lupus-like state, thus resembling the pattern observed in SLE patient monocytes (Fig. 1B). We also examined whether SMC1A expression may be affected by hormonal factors such as estrogen, which can modulate the expression of inflammation-related genes, thus conferring risk to autoimmunity^12,13,69,70. Addition of estradiol to blood CD14⁺ monocytes from healthy donors treated with healthy or SLE patient-derived serum had no significant effect on SMC1A mRNA levels (Supplementary Fig. 3). Collectively, these findings suggest that in addition to X-chromosome gene-dosage, inflammatory cues pertinent to SLE might contribute to the female-biased SMC1A expression observed in the disease.

SMC1A dosage affects the expression of immune response genes in monocytes under basal and lupus-like conditions

Research involving genetic deletion or silencing of individual cohesin subunits has unveiled their role in a number of biological processes including regulation of inducible gene expression^56,57. To address the functional consequences of SMC1A levels in our disease context, we performed RNA silencing by nucleofection in basal (unstimulated) and lupus-like monocytes, followed by whole transcriptome profiling (Fig. 2A) (GEO accession number GSE279728). Experiments were performed on blood CD14+ monocytes from three independent healthy males, used as the reference sex since SMC1A expression was most affected in males with SLE. Silencing efficiency was assessed by RT-qPCR and western blot analysis (we detected an average of 56% reduction in SMC1A protein levels), and cell viability by Annexin-7AAD staining (Fig. 2B, Supplementary Fig. 4).

**Fig. 2: *SMC1A* dosage affects the expression of immune-related genes.**

By comparing the transcriptome profiles of SMC1A-silenced versus control monocytes under basal and lupus-like conditions, we identified 442 and 418 differentially expressed genes (DEG) (p < 0.05) respectively, with 34 shared genes between the two states (Fig. 2C, D). In these DEGs, SMC1A downregulation led to overall modest alterations in the mRNA levels (log2FC values represented on the x-axis of Fig. 2C, D plots), consistent with previous studies examining transcriptional changes following elimination of other cohesin members^57,71. The 826 non-shared DEGs displayed uncorrelated expression patterns between the two states (Fig. 2E; compare the blue- and green-colored dots), suggesting that SMC1A acquires distinct transcriptional associations during transition from basal to inflammatory condition. To gain functional insights, we performed pathway enrichment analysis of the SMC1A-related DEGs. We found a strong prevalence of immune-related pathways, including regulation of the MAPK cascade, response to type II interferon, immune response-regulating signaling pathway, cell chemotaxis/migration and cellular response to biotic stimulus (Fig. 2F).

To further explore the relationship between SMC1A and lupus, we focused on genes that are differentially expressed in lupus-like versus unstimulated monocytes (|log2FC| ≥ 1, adjusted p < 0.05) (GEO accession number GSE279728). These 2778 DEGs, which we term lupus-responsive genes (Supplementary Data 1), showed strong transcriptional concordance with DEGs identified in lupus-like monocytes from a previous study⁶⁸ (Supplementary Fig. 5) and in CD14⁺ monocytes from SLE patients compared to healthy controls (Fig. 3A). Moreover, lupus-responsive genes were enriched for similar hallmark inflammatory signatures as those observed in SLE patient-derived monocytes, further corroborating the disease relevance of our ex vivo lupus-like system (Fig. 3B). SMC1A-related DEGs overlapped significantly with lupus-responsive genes in both basal (55% overlap; odds ratio 4.4, p = 1.6e-69, Fisher’s exact test) and lupus-like (37% overlap; odds ratio 2.0, p = 4.2e-12) conditions. Furthermore, by summarizing the expression level changes of lupus-responsive genes, we observed that genes repressed by SMC1A downregulation tend to become transcriptionally activated in lupus-like monocytes, and vice versa (Fig. 3C, compare the fourth with the third column, and the second with the first column). Together, these data suggest that SMC1A may impact immune response genes in monocytes and that genes deregulated during lupus are particularly susceptible to SMC1A gene levels.

**Fig. 3: Lupus-responsive genes are sensitive to *SMC1A* levels.**

SMC1A is redistributed to active distal regulatory elements during lupus inflammation

SMC1A is part of the cohesin complex that regulates genome structure and expression in a cell- and context-specific manner⁵⁰. To obtain a reference map of SMC1A targets across the genome, we isolated blood CD14⁺ monocytes from independent healthy males (used as a reference sex) and performed chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) under basal and lupus-like conditions (Supplementary Fig. 6A–C) (GEO accession number GSE279726). Cells were assayed at 1-h post-LPS challenge to study the effect of SMC1A/cohesin binding on the immediate responsive genes. Such brief activation of TLR-4 by LPS may induce a gene transcription program but not proliferation^61,67, thus uncoupling the regulatory role of SMC1A from its function during sister chromatid cohesion.

Differential binding analysis in lupus-like versus unstimulated monocytes (|log2FC| > 1, p < 0.001), revealed significant enrichment of SMC1A binding at 6,839 genomic regions and a corresponding depletion at 1131 regions, suggestive of a disease-specific rewiring of SMC1A interactions with chromatin (Fig. 4A). Annotation of these regions showed that ~45% of the peaks with increased SMC1A signal in lupus-like monocytes resided within distal intergenic regions (>1 kb from the transcription start site [TSS]), whereas 75% of the SMC1A peaks with decreased signal were at gene promoters (<1 kb from the TSS) (Fig. 4B).

**Fig. 4: The chromatin occupancy of SMC1A is significantly altered in lupus inflammation.**

To integrate these results into the context of the monocyte transcriptome, we assessed the enrichment/depletion of SMC1A binding at the regulatory elements of an extensive list of genes (n = 11,971), for which we had gene expression measurements from our RNA-sequencing assays. By utilizing the FANTOM5⁷² and GeneHancer⁷² databases to annotate and link enhancer elements to genes, we found that over 7% of genes (n = 858; Supplementary Fig. 6D) exhibited increased SMC1A binding at their enhancers versus 2.4% (n = 285), which showed a decreased signal upon stimulation. By contrast, only 1% of the genes (n = 125) showed increased SMC1A binding and 4.9% (n = 585 genes; Supplementary Fig. 6E) displayed depleted SMC1A signal at their promoters (Fig. 4C). Thus, under lupus inflammation, there is a prominent genomic redistribution of SMC1A characterized by increased binding at enhancer elements and reciprocal depletion from gene promoters.

To further explore the regulatory dynamics associated with SMC1A binding, we profiled the genome-wide H3K27ac (histone 3 lysine 27 acetylation) marks, a well-characterized marker for active chromatin⁶⁸, by ChIP-sequencing in CD14⁺ monocytes from independent male donors cultured under basal and lupus-like conditions (Supplementary Fig. 6F) (GEO accession number GSE279726). The latter exhibited extensive changes, with increased H3K27ac signal in 502 genomic regions (log2FC > 1, p < 0.001) (Fig. 4D), consistent with previously described epigenome perturbations in lupus disease⁶⁹. These activated regions involved a total of 440 genes with functional enrichment for immune pathways such as regulation of T-cell activation and response to virus (Supplementary Fig. 6G).

Combining the SMC1A and H3K27ac ChIP-seq datasets, we identified a significant enrichment of SMC1A up-bound genes within the H3K27ac-enriched gene set (p = 1.2e-14, odds ratio = 6.16) (Fig. 4E). This result suggests a notable association between SMC1A binding and H3K27ac activity at these loci. Furthermore, the 295 genes with co-enriched SMC1A and H3K27ac signals were enriched in pathways related to cytokine signaling and response to virus (Fig. 4F). These data suggest a possible role for SMC1A in regulating distal enhancer activity linked to the immune-related gene program in monocytes during lupus inflammation.

The cohesin complex can facilitate and maintain chromatin remodeling and accessibility^57,73,74,75. Likewise, the chromatin environment might impact the binding and functional consequences of cohesin^76,77. To this end, we analyzed publicly available chromatin accessibility features assayed by transposase-accessible chromatin with sequencing (ATAC-seq) in the same experimental setup of lupus-like monocytes⁶⁸. Integrative analysis was performed to construct a comprehensive genomic map of ATAC-seq and H3K27ac peaks indicative of open and active chromatin regions, respectively (Fig. 5A). These data, combined with the differential binding events of SMC1A, confirmed the accentuated redistribution of SMC1A within distal active regulatory elements during transition from basal to lupus-like state (Fig. 5B). Specifically, more than 8% of genes with active enhancers/promoters (based on H3K27ac signal) (n = 9834) displayed enriched SMC1A binding at their enhancers (n = 851; odds ratio 29.2, p < 2.2e-16, by Fisher’s exact test) and more than 5% showed decreased SMC1A binding at their promoters (n = 559; odds ratio 4.97, p < 2.2e-16, by Fisher’s exact test). Therefore, the binding dynamics of SMC1A are strongly correlated with the activity and accessibility of the underlying regulatory element.

**Fig. 5: During lupus inflammation SMC1A is redistributed to active enhancers.**

To uncover transcription factors that could potentially act in concert with SMC1A to regulate the genome during lupus inflammation, we performed motif enrichment analysis⁷⁸ in chromatin regions with increased accessibility and SMC1A binding in lupus-like compared to basal monocytes (Supplementary Fig. 7). Among the top hits were STAT3, NF-κB, IRF3, and members of the ISGF3 (IFN-stimulated gene factor 3) complex –STAT1 and IRF9–⁷⁹ suggesting their potential role in SMC1A-mediated reprogramming of monocytes in lupus.

SMC1A binding at active regulatory elements induces the transcriptional activation of inflammatory genes in lupus-like monocytes

Having observed transcriptional changes related to SMC1A dosage and a differential binding pattern of SMC1A during lupus inflammation, we asked whether changes in gene expression correlate with the activity of cognate enhancers and promoters and the chromatin enrichment or depletion of SMC1A. We used the log2FC values of lupus-responsive genes (Supplementary Data 1) to assess the level of transcriptional activation or repression according to the presence or absence of epigenomic features, based on our integrative analysis (Fig. 5A, B). In the absence of differential SMC1A chromatin binding, neither the activity of enhancers (Fig. 6A) nor of promoters (Fig. 6B) had a significant effect on the magnitude of gene activation during lupus condition (Fig. 6A, B: brown- versus white-colored bars). By contrast, increased binding of SMC1A on active regulatory elements was linked to significantly higher induction of the associated genes (Fig. 6A, B: red- versus brown-colored bars, p = 1.9e-5 for enhancers; p = 8.3e-06 for promoters). Transcriptional induction was even stronger for genes (n = 24) whose both active promoter and active enhancer exhibited increased binding by SMC1A (Fig. 6C). A similar analysis of genes repressed during lupus revealed no significant variation in log2FC values according to SMC1A binding (Supplementary Fig. 8A, B). These results suggest that transcriptional activation of genes correlates with enhanced binding of SMC1A to active chromatin, as opposed to gene repression, which is independent of SMC1A enrichment or depletion.

**Fig. 6: SMC1A binding at active regulatory elements drives transcription of inflammatory genes in monocytes activated with lupus-relevant stimuli.**

To corroborate these results, we also examined the effect of SMC1A downregulation on the expression levels (log2FC values) of lupus-responsive genes according to the presence or absence of the aforementioned epigenomic features in their respective regulatory regions. Genes with combined enhancer/promoter activity and enriched SMC1A binding at the cognate enhancer only (Fig. 6D: group B, n = 301) showed an overall significant trend for downregulation upon SMC1A silencing. A more pronounced decrease was observed in genes with increased SMC1A binding at both their cognate active enhancers and promoters (Fig. 6D: group C, n = 24). Notably, these two groups of genes were enriched in inflammatory pathways, including cytokine signaling and response to virus (Fig. 6E). By contrast, genes with no active regulatory elements and no enriched binding by SMC1A (Fig. 6D: group A, n = 530), which also demonstrated reduced expression upon SMC1A silencing, participated in non-immune/inflammatory pathways, suggesting these might represent indirect effects (Fig. 6F).

Focusing on group B + C genes, we identified 277 genes that exhibited active enhancer, enriched SMC1A binding at the cognate enhancer and transcriptional upregulation (log2FC ≥ 1, adj. p < 0.05) under lupus conditions (Fig. 7Α, Supplementary Data 2). Enrichment analysis showed overrepresentation of cytokine-mediated signaling, response to LPS, response to virus, T-cell activation and positive regulation of cytokine production pathways (Fig. 7Β). These genes may be considered putative direct targets of SMC1A as exemplified in the case of IL6 and GBP5 genes (Fig. 7C). Collectively, during lupus, SMC1A, potentially in collaboration with specific immune transcription factors, may be recruited at the active regulatory regions of immune-related and inflammatory genes, orchestrating their transcriptional regulation.

**Fig. 7: SMC1A-regulated genes are involved in key inflammatory and immune pathways in SLE-like monocytes.**

SMC1A-regulated inflammatory genes demonstrate female-biased expression in monocytes from SLE patients

In view of the above and considering that SMC1A exhibits female-biased expression in SLE, we sought evidence of its potential influence on sex-related transcriptional differences in patient monocytes. Blood was collected from a new cohort of female (n = 8) and male (n = 10) SLE patients, matched for age and disease characteristics (Supplementary Table 6), and gene expression profiling was performed in purified CD14⁺ monocytes by QuantSeq 3′ mRNA sequencing (GEO accession number GSE279728). We found a total of 745 genes with sexual differential expression (|log2-FC| > 1, p < 0.05) (Fig. 8A). Female-overexpressed genes in SLE monocytes (n = 596; Supplementary Data 3) showed overrepresentation of inflammatory pathways, including cytokine signaling, T-cell and immune response activation (Fig. 8B). On the other hand, genes with male-biased expression (n = 149; Supplementary Data 3) were enriched in ribosome biogenesis, cellular metabolism and mitochondrial function (Fig. 8C). Consistent with the gene expression data, female SLE monocytes secreted higher levels of the immune-response cytokines IL-6, IL-1b, TNF, and IL-10 as compared to their male counterparts (Fig. 8D). Together, our data underscore significant sexual differences in the molecular and inflammatory profile of SLE monocytes.

**Fig. 8: Female-biased expression of SMC1A-regulated Inflammatory genes and cytokine release in SLE monocytes.**

Next, we focused on the 277 genes regulated by SMC1A in lupus environment (Fig. 4G, Supplementary Data 2) and compared their expression levels in female versus male SLE monocytes (Fig. 8E). First, we aggregated the expression levels (following z-transformation) of all SMC1A target genes in each SLE individual. Increased SMC1A regulon activity was noted in female than male monocytes (p = 0.0239) (Fig. 8F). In accordance, out of the 277 SMC1A target genes, 62 (22.4%) were overexpressed in females, while only 2 (0.7%) were overexpressed in males with SLE. Likewise, female-biased genes in SLE monocytes showed significant enrichment for those regulated by SMC1A (odds ratio 11.5, p = 2.1e-06, Fisher’s exact test). Notably, genes with stronger female-biased expression in SLE patients were more sensitive to SMC1A dosage in SLE-like monocytes (Fig. 8G).

Then, we explored whether these associations are specific to SLE by interrogating publicly available RNA-sequencing profiles from a large collection of healthy female and male monocytes²⁰. Applying the same thresholds for sexually differential expression (|log2FC| > 1, adjusted p < 0.05) in both our SLE dataset and the aforementioned healthy dataset, we found that 51 out of 277 SMC1A-regulated genes were female-biased in SLE monocytes, but none in healthy monocytes. The opposite trend (SMC1A target genes with female-biased expression exclusively in the healthy state) was observed for only one gene. Collectively, these findings are consistent with SMC1A assuming a distinct regulatory role in lupus and suggest that its overexpression in female SLE monocytes may be linked to increased expression of its target genes, contributing to a more prominent inflammatory phenotype compared to male counterparts.

Female lupus-like monocytes exhibit elevated expression levels of IL6 and IL1A, along with increased SMC1A binding at their enhancers

We further investigated whether sex differences in SMC1A levels correlate with differential expression of its target genes by monitoring matched female and male CD14⁺ monocytes (n = 6 pairs) under unstimulated and stimulated conditions (sequential IFN-α/TNF and LPS, Fig. 1G). We selected two hallmark SMC1A-regulated genes, IL6 and IL1A, which are implicated in SLE autoimmunity.^80,81 In patients with SLE, higher disease activity scores correlate positively with circulating IL-1α and IL-6 levels^82,83, and serum IL-6 levels increase prior to SLE onset⁸⁴. By RT-qPCR, both genes were significantly overexpressed in female compared to male monocytes under basal state (Fig. 9A, B). Following stimulation, both IL6 and IL1A were induced in either sex; however, female lupus-like monocytes had significantly higher transcript levels compared to male counterparts.

**Fig. 9: Female lupus-like monocytes show increased expression levels of *IL6* and *IL1A* due to enhanced SMC1A occupancy at their cognate enhancers.**

We then employed mixed model analysis to investigate whether the transcriptional levels of these genes could be predicted by SMC1A abundance, and whether this relationship is influenced by donor sex and/or the underlying cell state (lupus-like versus basal). SMC1A mRNA significantly predicted the expression of both IL6 (p < 0.001) and IL1A (p < 0.001) (Fig. 9C, D). This effect was more pronounced than that of sex (i.e., higher beta coefficients of SMC1A). These data support the positive regulatory role of SMC1A on IL6 and IL1A and suggest that sex differences in the expression of these genes may be explained –at least to some extent– by variations in SMC1A levels.

To obtain more direct evidence linking sexual differences in SMC1A with the transcription of its target genes, we quantified SMC1A binding at the regulatory elements of IL6 and IL1A in female versus male lupus-like monocytes. Utilizing our ChIP-seq data from male CD14⁺ monocytes (Fig. 4), we defined the active chromatin regions –marked by H3K27Ac and ATAC-seq peaks– with increased SMC1A binding during lupus inflammation at the enhancer and promoter of IL6 and the enhancer of IL1A. Following ChIP experiments in matched female and male donors, we observed SMC1A enrichment at the IL6 and IL1A enhancers, thus confirming our ChIP-seq data. In both elements, fold enrichment of SMC1A (over a negative control genome region) was significantly higher in female compared to male lupus-like monocytes (Fig. 9E). These data suggest that female overexpression of IL6 and IL1A in the lupus environment might be facilitated by enhanced SMC1A binding at the cognate active enhancers, highlighting putative biological consequences of sex variation in SMC1A abundance. As a functional confirmation, lowering SMC1A in SLE-like monocytes decreased the mRNA levels of IL6 and IL1A (Fig. 9F) as well as the secreted levels of IL-6 (Fig. 9G).

Discussion

Although certain mechanisms explaining the predominance of autoimmunity in women have been proposed^7,27, the molecular basis for this bias remains incompletely understood. Here, we implicate the cohesin complex member SMC1A, a chromatin architectural factor that escapes XCI, in sex-divergent inflammatory responses in the context of lupus. Using an unbiased approach, we identified monocytes as a pathophysiologically relevant immune cell type exhibiting exaggerated enhanced female-biased SMC1A expression in both patients and an ex vivo model of SLE, compared to healthy individuals and those with AS. Under lupus conditions, genes with deregulated expression are particularly sensitive to SMC1A dosage and mechanistically, SMC1A assumes a specific regulatory role by repositioning to the active, accessible enhancers of a subset of immune and inflammatory genes, driving the transcriptional reprogramming of monocytes. IL6 and IL1A, two SMC1A regulated-genes with an important role in autoimmunity, are significantly elevated in female SLE-like monocytes as opposed to male ones. This increase in expression may be attributed to sexual differences in SMC1A abundance and its binding at the respective enhancers. Importantly, the female-biased transcriptome in SLE patient-derived monocytes shows significant enrichment for SMC1A-regulated genes involved in inflammatory processes, therefore highlighting the role of this sex-biased genome organizer in predisposing to autoimmunity.

A key observation in our study was the prominent female-bias of SMC1A expression in blood immune cells, particularly monocytes, of SLE compared to healthy individuals. While we did not examine for SMC1A biallelic patterns in these samples, our finding is consistent with the concept proposed by Anguera and colleagues that SLE female immune cells display abnormal maintenance of XCI due to paucity of repressive epigenetic marks on the inactive X chromosome^7,85,86,87. This enables the re-activation of escapee genes leading to increased mRNA levels as illustrated for CD40LG⁸⁷. Of note, SMC1A tended to be downregulated in SLE and after exposure to lupus milieu, possibly due to a negative feedback mechanism. Even under these conditions, females retained significantly higher expression of SMC1A than males, a pattern not observed in AS. Whether this discrepancy between the two disorders is due to differences in genetic²⁰, cytokine (type I IFN in SLE⁶¹, IL-23/IL-17 in AS⁸⁸) or epigenetic landscapes, remains to be defined. These findings support the relevance of our approach in systematically exploring sexual genomic divergences not only in the normal state but also in the context of sex-biased immune disorders.

Sex-related molecular differences manifest cell-type specificity^15,19,20 and may be particularly prominent in monocytes/macrophages^{19,20,22,23,60}. This is reiterated by our studies in SLE-like monocytes, where SMC1A exhibits sex-dependent chromatin interactions linked to gene expression. Monocytes are pivotal to SLE disease with multifaceted roles in sensing and reacting to danger signals, promoting autoreactive lymphocytes and contributing to organ injury^32,38,89. Our evidence further indicates that these cells can shape sexual inflammatory responses in lupus. In fact, through a comparative transcriptome analysis in patient-derived monocytes, we herein define the molecular fingerprints of female and male SLE, thus enhancing our understanding of the susceptibility and heterogeneity of this complex disease. Moreover, these data could provide a platform for developing personalized treatment strategies to overcome sex disparities in the effectiveness of immunotherapies⁹⁰.

Consistent with the regulatory role of the cohesin complex⁴⁶, we demonstrated that SMC1A accumulates at the active genome elements of lupus-responsive genes in monocytes. This agrees with a study in retinal pigment epithelium where cohesin (Rad21) bound at highly active chromatin enriched for H3K27ac and H3K4me3 marks⁹¹. Besides acting as an insulator protein (when colocalized with CTCF) at topologically associating domain (TAD) boundaries, cohesin may directly control transcription by multiple mechanisms such as facilitating enhancer–promoter connections, collaborating with RNA polymerase II⁹² and/or tissue-specific transcription factors^50,55. Despite the overall reduced SMC1A binding at promoter regions during transition from naïve to lupus-like state, we observed the strongest induction in genes with concerted SMC1A enrichment to both active enhancers and cognate promoters. These genes were particularly influenced by SMC1A downregulation pointing towards direct -rather than secondary- effects. Although we did not map the genome structure, our findings align with those of Robles-Rebollo et al.⁹³, who proposed that cohesin links the probabilities of enhancer and target gene bursting in stimulated murine macrophages, an observation not entirely explained by spatial proximity. Additionally, our data reiterate the importance of cohesin presence at transcription start sites for gene expression^91,94.

Cohesins are linked to cell- and context-specific genome effects^46,91 and we herein establish a critical role for SMC1A in the inflammatory gene program of human monocytes under steady-state and lupus conditions. This agrees with observations that acute myeloid leukemia cells bearing cohesin mutations exhibit impaired abundance of immune/inflammation-related genes^56,57. Likewise, macrophages and hematopoietic stem cells (HSC) from Rad21-null or heterozygous mice exhibit a blunted inducible inflammatory response, attributed to disturbed accessibility and activation of cognate enhancers enriched with IFN-stimulated response elements and IRF-PU.1 composite motifs⁵⁷ and uncoupled burst frequencies of inducible enhancers and promoters⁹³. Studies in murine HSCs have also highlighted that Rad21 mediates NF-κB signaling by inducing the enhancer activity of co-transcription factors such as ETS, AP1 and Runt family members⁵⁶. To this end, our in-silico analyses suggest that the specificity of SMC1A-mediated inflammatory reprogramming in SLE-like monocytes is possibly facilitated by the engagement of key immune-related transcription factors, including STAT3, NF-κB, STAT1, IRF3 and IRF9. These findings are consistent with work in LPS-stimulated macrophages revealing that cohesin-dependent inducible enhancers show enriched binding of STAT2 and IRF3⁹³. Moreover, STAT1 and IRF9 form a complex with STAT2 to create the ISGF3 complex⁷⁹ which regulates the type I IFN transcriptional response. Notably, the aforementioned factors play significant roles in the immune function including their involvement in autoimmune disorders like SLE^95,96.

Previous research has provided a mechanistic basis for how certain X-linked genes with incomplete dosage compensation may account for female susceptibility to SLE. One example is TLR7, which is overexpressed in SLE and whose biallelic expression correlates with augmented type I IFN synthesis by plasmacytoid dendritic cells^97,98 and the maturation of memory B-cells¹¹. A second is CXorf21¹⁰ encoding for the SLC15A4-associated adapter that mediates TLR7/9-induced IRF5 activation⁹⁹. Our work implicates SMC1A as a master genome regulator that shapes the expression of multiple immune and inflammation-related genes in monocytes. Female-biased expression of SMC1A was linked to heightened inflammatory responses, exemplified by the SMC1A-regulated genes IL6, IL1A, and GBP5, which showed female overexpression in SLE patients or in monocytes cultured under lupus-like conditions. Of note, IL6 and GBP5 have been previously classified as female-biased genes in healthy monocytes²⁰ and in the whole blood upon influenza vaccination¹⁰⁰. We demonstrate that the higher expression of IL6 and IL1A in female than male SLE-like monocytes may be driven –at least to some extent– by augmented SMC1A binding at the corresponding enhancers in the former group.

To this end, we propose a mechanism by which failure of female SLE monocytes to sufficiently downregulate SMC1A in response to SLE-like inflammation may contribute to exaggerated inflammatory gene expression (Fig. 10). This concept is further supported by observations that aged hematopoietic stem and progenitor cells exhibit impaired downregulation of Rad21/cohesin, which hinders their ability to constrain NF-κB-mediated inflammation⁵⁶. Nonetheless, we cannot rule out the possibility that the female propensity to SLE is driven also by sex differences in SMC1A expression in other immune and non-immune²⁶ cell types.

**Fig. 10: Proposed mechanism of female-biased *SMC1A* expression in shaping the inflammatory molecular program of monocytes and predisposition to lupus autoimmunity.**

In conclusion, our work highlights the role of SMC1A/cohesin as an X-linked chromatin modifier that shapes a genome environment permissive to inflammatory gene expression, shedding light on the molecular basis of sexual differences in immune responses and providing a rationale for relevant chromosome conformation studies. Increased abundance and interaction of SMC1A with active enhancers culminate in augmented levels of inflammatory genes and soluble mediators by female monocytes exposed to lupus-inducing stimuli, therefore rendering them prone to autoimmunity. SMC1A and its target genes represent potential biomarkers for risk stratification and/or druggable targets for the prevention and treatment of SLE and related female-biased immune pathologies.

Methods

Patients, healthy donors, and ethics committee approval

Patients with SLE were recruited from the Rheumatology Clinics of the University Hospital of Heraklion and the “Attikon” University Hospital, meeting the 2019 EULAR/ACR classification criteria¹⁰¹ (Supplementary Tables 1, 2, 4, and 6). SLE disease activity was assessed using the validated SLEDAI-2000 (SLEDAI- 2K)¹⁰², with low activity defined according to the Lupus Low Disease Activity State (LLDAS)¹⁰³. Patients with axial spondyloarthritis, classified per ASAS criteria¹⁰⁴, were also recruited from the University Hospital of Heraklion, and their disease activity was measured using the ASDAS-CRP index¹⁰⁵. All patients abstained from medications for at least 12 h prior to blood collection. Healthy blood samples were obtained from the Blood Transfusion Unit of the University Hospital of Heraklion. The study was approved by the Ethics Committees of the University Hospital of Heraklion (protocol no. 5944/14-6-2017) and the Attikon University Hospital (protocol no. 299/3-6-2020). All participants provided written informed consent.

Peripheral blood immune cell subtypes isolation

Peripheral blood mononuclear cells were isolated from heparinized blood by density-gradient centrifugation using Lymphocyte Separation Media (Biowest, #L0560). Erythrocytes were removed by hypotonic lysis (1 ml ddH2O for 25 s followed by 1 ml 1.8% NaCl). Cell viability was consistently >95%, as measured by trypan blue dye (Gibco, #15250-061) exclusion in all experiments. CD14⁺ monocytes and CD4⁺ T-lymphocytes were magnetically isolated by positive selection, while CD19 + B-lymphocytes were isolated by negative selection (MACS, Miltenyi Biotec, #130-050-201, #130-045-101, #130-091-151). The purity of all cell types was confirmed by flow cytometry (CD14+, CD4+, CD19+ cells/total live cells), and only preparations with >94% purity were used. Peripheral blood neutrophils were isolated from heparinized human blood using a histopaque double-gradient density centrifugation (Sigma, #11191).

Monocyte culture

Magnetically isolated CD14⁺ monocytes were cultured in RPMI-1640, GlutaMAX Supplement (Gibco, #61870-010), including 10% heat-inactivated FBS (Gibco, #10437028), 100 IU/mL penicillin and 100 μg/mL streptomycin (Gibco, #15070063). To simulate SLE, CD14⁺ monocytes isolated from healthy donors were stimulated with 10 ng/ml human macrophage colony-stimulating factor (M-CSF; Peprotech, #300-25), 10 ng/ml recombinant human TNF (Peprotech, #300-01Α), 100 IU/ml IFN-α (PBL Science, #PBL11200-2) overnight at 37 °C/5% CO₂, followed by 10 ng/ml LPS (Sigma, #L4391) challenge for 1–4 h⁶⁸. In other experiments, monocytes obtained from healthy donors were cultured in presence of serum derived from SLE patients or healthy donors (20% v/v) with or without 17-beta-estradiol (10⁻⁹M; kindly provided by Prof. E. Castanas, University of Crete) for 18 h at 37 °C/5% CO₂.

Flow cytometry

Cells were stained for membrane markers for 20 min at 4 °C in PBS (Biosera, #LM-S2041) and 5% FBS. Conjugated antibodies against CD14, HLA-DR and CD80 were from BioLegend (#325604, #307628 and #305220, respectively). For apoptosis detection, cultured monocytes were stained with annexin V (BioLegend, #640922) and 7-AAD (Biolegend, #420404) according to the manufacturer’s recommendations. Samples were acquired on a BDFACSCanto II (BD Biosciences) and analyzed using the FlowJo software (Tree Star).

Enzyme-linked immunosorbent assay

Human IL-6 (R&D, #DY206-05), IL-1B (Invitrogen, #88-7261-88), and CXCL10 (Invitrogen, #BMS284INST) levels in culture supernatants, harvested at the indicated time points, were measured using sandwich ELISA following the manufacturer’s instructions. Light absorbance was recorded with an ELx800 microplate reader (Biotek). All samples were analyzed in duplicates (technical replicates).

TaqMan gene expression assay

Total RNA was isolated from cells using the NucleoSpin RNA extraction kit (Macherey-Nagel, #740955) following the manufacturer’s instructions. First-strand complementary DNA (cDNA) was synthesized using the PrimeScript RT-PCR Kit (Takara, #RR037A). DNA samples (50 ng/reaction) were combined with TaqMan Universal Master Mix II, no UNG (Applied Biosystems, #4440040), and reactions were performed using the CFX Connect Real-Time System (BioRad). Each multiplex RT-PCR reaction had a total volume of 20 μl. The SMC1A primers and probe (Hs00196849_m1) and GAPDH primers and probe (Hs02786624_g1) were part of TaqMan gene expression assays from Applied Biosystems.

Quantitative polymerase chain reaction

Total RNA from monocytes was collected using the TRIzol reagent (Invitrogen, #15596026) and TURBO DNA-free Kit (Invitrogen, #AM1907,) according to the manufacturer’s instructions. First-strand complementary DNA synthesis was performed using the PrimeScript RT-PCR Kit (Takara, #RR037A). qPCR was carried out using the KAPA SYBR Fast Universal Kit (Kapa Biosystems, #KK4602) at a CFX Connect, Real-Time System (Bio-Rad) or a Corbett Research Rotor-Gene 6000 Real-Time PCR Detection System (Qiagen). Expression was normalized to 18S and GAPDH and calculated by the change-in-threshold method (2^–ΔΔCT). The primer sequences used were the following: SMC1A forward, 5′- CATCAAAGCTCGTAACTTCCTCG −3′; SMC1A reverse, 5′- CCCCAGAACGACTAATCTCTTCA −3′; 18S forward, 5′- CTCAGCGTGTGCCTACCCTA −3′; 18S reverse, 5′- GCAAGCTTATGACCCGCACT − 3′; GAPDH forward, 5′- CATGTTCCAATATGATTCCACC −3′; GAPDH reverse, 5′- GATGGGATTTCCATTGATGAC −3′; IL6 forward, 5′- CAGATGAGTACAAAAGTCCTGA −3′; IL6 reverse, 5′- CTACATTTGCCGAAGAGCCC −3′; IL1A forward, 5′- CGCCAATGACTCAGAGGAAGA −3′, IL1A reverse, 5′- AGGGCGTCATTCAGGATGAA −3′; GBP5 forward, 5′- CACGAAACTGGTCCCTGATG −3′; GBP5 reverse, 5′- TACCCCTTAGCCTGTAAGCC −3′; IFNB forward, 5′- AGCTCCAAGAAAGGACGAACA −3′, IFNB reverse, 5′-GCCCTGTAGGTGAGGTTGAT −3′; CXCL10 forward, 5′- GTGGCATTCAAGGAGTACCTC −3′; CXCL10 reverse, 5′- TGATGGCCTTCGATTCTGGATT −3′; SLPI forward, 5′- CCCAAACCCAACAAGGAGGA −3′; SLPI reverse, 5′- GCAACACTTCAAGTCACGCT −3′.

Immunoblotting

Whole-cell extracts were lysed using RIPA Lysis Buffer (Millipore, #20-188,) supplemented with protease and phosphatase inhibitors (Roche, #04693124001, #04906837001) by vortexing on ice (every 5 min for 20 min with 1-min breaks). The lysates were centrifuged at maximum speed for 10 min at 4 °C, and the supernatant (protein lysate) was collected. Protein concentrations were measured using the Pierce BCA protein assay kit (Thermo Scientific, #23227). β-Mercaptoethanol (6×) was added to the samples followed by heating for 5 min at 95 °C. Equal amounts of protein (20 μg per well) were loaded onto 10% SDS-PAGE gels and transferred to Immobilon-PSQ PVDF membranes (Millipore, #ISEQ00010,) using a transfer apparatus (BioRad). Membranes were blocked with 5% BSA (BioFroxx, #9048-46-8) in tris-buffered saline with 0.1% Tween 20 (TBS-T) for 1 h, followed by overnight incubation at 4 °C with primary antibodies and a 1-h incubation with HRP-conjugated anti-mouse or anti-rabbit secondary antibodies. Blots were developed using enhanced chemiluminescence (Millipore, #WBLUF0100) and detected with an ImageBlot system (BioRad). Band intensities were quantified using Image Lab software, with total protein levels normalized to housekeeping protein expression. The primary antibodies used were: anti-SMC1A (Bethyl Laboratories, #A300-055A), anti-GAPDH (Invitrogen, #39-8600), and anti-ACTIN (Millipore, #MAB1501). Secondary antibodies used were anti-mouse HRP (Millipore #12-349) and anti-rabbit HRP (Invitrogen, #31460). Information on the dilutions used can be found in Supplementary Table 7.

Electroporation of primary monocytes with siRNAs

Blood CD14⁺ monocytes isolated from healthy donors were incubated overnight in RPMI-1640, GlutaMAX Supplement including 10% heat-inactivated FBS, 100 IU/mL penicillin, 100 μg/mL streptomycin and 10 ng/ml MCSF, in a humidified 37 °C/5% CO₂ incubator. 10⁶ cells/reaction were electroporated with 100 nM ON-TARGETplus SMC1A siRNA (Dharmacon, #L-006833-00-0005; SMC1A si monocytes) and ON-TARGETplus Non-targeting siRNA (Dharmacon, #D-001810-01-05; ctrl si monocytes) using Amaxa Human Monocyte Nucleofector Kit (Lonza Bioscience, #VPA-1007) and program Y-010. Cells were incubated with transfection mix in growth medium for 4 h and then were washed and incubated for 1–2 days with IFNα, TNF and LPS (see above). Real-time RT-qPCR and Western immunoblotting were used to evaluate silencing efficiency.

Chromatin immunoprecipitation

Blood CD14⁺ monocytes (30–50 × 10⁶), isolated from healthy donors, were crosslinked at room temperature for 10 min by adding 1% formaldehyde (ThermoScientific, #28906) to the growth medium. The crosslinking solution also contained 50 mM HEPES (pH 7.5), 100 mM NaCl, 1 mM EDTA (pH 8.0), and 0.5 mM EGTA (pH 8.0). After 10 min, crosslinking was quenched by the addition of 125 mM glycine for 10 min. Cells were pelleted at 4 °C and washed with ice-cold PBS. The crosslinked cells were lysed on ice for 10 min in lysis buffer (50 mM HEPES-KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40, and 0.25% Triton X-100) supplemented with 1X protease/phosphatase inhibitors. The lysates were centrifuged at 2000 × g for 5 min at 4 °C. The pellet was resuspended and sonicated in shearing buffer (50 mM Tris-HCl pH 8.0, 10 mM EDTA, 0.1% sodium deoxycholate, and 1X protease/phosphatase inhibitors) using a Bioruptor (Diagenode) with 30 s ON and 30 s OFF at high power output for 20 cycles. After sonication, samples were centrifuged at 15,000 × g for 10 min at 4 °C, and a small aliquot of the sonicated extract was saved as input. The remaining extract was incubated with Dynabeads Protein A and G (Invitrogen, #10001D/#10003D) for 30 min at 4 °C. Precleared extracts were then incubated with 90 μL Dynabeads (45 μL each of Dynabeads A and G) along with 2 μg or 10 μg of the appropriate antibody (specified below) overnight at 4 °C. ChIP-grade antibodies used were anti-H3K27ac (Abcam, #ab4729) and anti-SMC1A (Bethyl Laboratories, #A300-055A). Following overnight incubation, antibody-bound Dynabeads were washed once with low-salt wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.1, 150 mM NaCl), once with high-salt wash buffer (containing 500 mM NaCl), once with LiCl wash buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA, 250 mM LiCl, and 1% NP-40), and twice with TE buffer. Cross-links were reversed by overnight incubation at 65 °C. RNA and proteins were digested using RNase A and Proteinase K (Thermo Scientific, #EN0531, #EO0491), respectively, and DNA was purified using the MinElute PCR Purification Kit (Qiagen, #28004).

Chromatin immunoprecipitation coupled with polymerase chain reaction

Immunoprecipitated DNA was analyzed by real-time RT-qPCR and results were calculated using the percent input method (Thermo Scientific) and normalized to a negative control region (gene desert). The primer sequences used for ChIP-PCR were purchased by Eurofins Genomics and include: IL6 TSS forward, 5′- TCCAGCCCAGCATTAACAAG −3′; IL6 TSS reverse, 5′- CCAAAGCCCAACAGATGCTA −3′; IL6 enhancer forward, 5′- GGTGGATTGACGGGATCTGA −3′; IL6 enhancer reverse, 5′- GGGCTGAGGTGGGATAATCA −3′; IL1A enhancer forward, 5′- CAGTTCCATGCAAACCTGCC −3′; IL1A enhancer reverse, 5′- TCTTTGCCACCTCATTCCCC −3′; gene desert forward, 5′- ATGTCAGGCCCATGAACGAT −3′; gene desert reverse, 5′- GCATTCATGGAGTCCAGGCTTT −3′

ChIP-sequencing library preparation

Immunoprecipitated and input DNA samples were quantified with the qubit fluorometric method (Thermo Scientific) and ChIP-seq NGS libraries were prepared at the Greek Genome Center of the Biomedical Research Foundation Academy of Athens (BRFAA)¹⁰⁶. Quantitation and quality control of the libraries was performed with Qubit HS DNA kit (Invitrogen, #Q32854) and Agilent Bioanalyzer DNA1000 kit (#5067-1504) respectively. Approximately 25 M, 100 base pair (bp) long, single-end reads were produced per sample, in the Illumina NovaSeq 6000 sequencer.

ChIP-sequencing pre-processing

ChIP-seq raw fastQ files were pre-processed by first assessing quality control (QC) with FASTQC. Low-quality 3′ read ends (minimum two-color chemistry score of 20), N tails, and adapter remains were trimmed/clipped using cutadapt¹⁰⁷, allowing reads of at least 25 nucleotide length. Filtered reads were then mapped against the UCSC hg19 human reference genome using bwa-mem aligner¹⁰⁸ with default parameters. Mapped reads were further filtered using samtools¹⁰⁹, in order to discard chimeric alignments, alignments with more than 2 mismatches, and alignments with MAPQ quality lower than 10. A deduplicated version of alignments was also created using samtools markdup command (version 1.10), in order to eliminate the effect of PCR over-amplification during visualization. Alignment QC was further accessed using deeptools¹¹⁰. Specifically, biological replicates and conditions were examined regarding their similarity/dissimilarity with Pearson’s R correlation analysis (pairwise Pearson’s R metrics) and Principal Component Analysis (PCA), using genomic bin counts of 1 kb and 3 kb windows. Visual inspection of ChIP-seq signal was also applied by generating UCSC genome browser tracks¹¹¹ (generation of bigWib files with deeptools bamCoverage¹¹⁰).

Assay for transposase-accessible chromatin-sequencing pre-processing and analysis

ATAC-seq raw FASTQ files were downloaded from the NCBI GEO database (accession code GSE100383) using the SRA toolkit (version 2.9.6; https://hpc.nih.gov/apps/sratoolkit.html), specifically the prefetch command. The FASTQ files were concatenated by biosample ID and processed for quality control (QC), adapter clipping, quality trimming, mapping, and alignment filtering, as described above. Peak files for each biological replicate (n = 5 unstimulated monocytes; n = 3 lupus-like monocytes) were also downloaded from the GEO repository. Replicates of both alignments (BAM files) and peaks (BED files) were merged for visualization purposes using samtools¹⁰⁹ and bedtools¹¹² (version 2.30), respectively. Merged peaks were further processed with bedtools merge to eliminate overlapping peak regions.

ChIP-sequencing peak calling

For each SMC1A ChIP-seq IP-INPUT pair, peaks were called by macs2¹¹³ with the following custom parameters enabled: “--nomodel”, “--shift 100”, and “--extsize 200”. H3K27ac ChIP-seq peak calling was performed by epic2¹¹⁴, an ultra-performant reimplementation of the SICER peak caller¹¹⁵, keeping duplicates (-kd parameter), using a bin of 400 base pairs (bps), allowing 1 gap, and a fragment size of 150 bps. Peaks spanning ENCODE blacklisted regions¹¹⁶ or including less than 10 mapped reads were excluded.

Differential peak analysis

Differential binding analysis for SMC1A ChIP-seq and differential accessibility analysis for ATAC-seq were conducted with the diffBind R package (version 2.10)¹¹⁷ using the default peak merging strategy of the tool as a reference, disabling summit calling, enabling TMM normalization, and using DESeq2¹¹⁸ for statistical testing. Analysis of differential H3K27ac enrichment was performed with a later version of diffBind (version 3.10). Differentially enriched regions were detected (DESeq2), and filtered by applying log2-fold change (log2FC) ≥ 1 and p ≤ 0.001 thresholds respectively for SMC1A and H3K27ac ChIP-seq assays, and log2FC ≥ 3 and p ≤ 0.001 thresholds for ATAC-seq. Alternatively enriched regions were summarized with custom-generated volcano plots using R. Peaks were then annotated regarding their genomic position, using ChiPSeeker¹¹⁹. Specifically, the genome locations of the peaks were assigned to different transcript-related features (such as promoter, 5′ UTR, 3′ UTR, exon, intron, distal intergenic region) and the nearest gene, obtained from TxDb.Hsapiens.UCSC.hg19.knownGene. The euler diagram showing gene sets with differential binding of SMC1A, differential enrichment of H3K27ac and shared genes within each set was generated with the R package eulerr (version 7.0.2). For this analysis, only the nearest peak region to the transcription start site was retained for each gene, to avoid mixing up- and down-bound peaks for the same gene.

RNA sequencing

RNA was isolated from unstimulated (basal-state) and stimulated (SLE-like) monocytes (ctrl si or SMC1A si), using the NucleoSpin RNA extraction kit (Macherey-Nagel, #740955) according to manufacturer’s instructions. RNA was quantitated with the Nanodrop photometric method and quality control was performed with the Agilent Bioanalyzer RNA nano kit (#5067-1511). Libraries were prepared with Illumina TruSeq RNA library prep kit (#RS-122-2001) at the Greek Genome Center of BRFAA. Approximately 25 M, 100 base pair (bp) long, single-end reads were produced per sample, in the Illumina NovaSeq 6000 sequencer.

QuantSeq 3’ mRNA sequencing

Βlood-derived CD14⁺ monocytes of 10 male and 8 female SLE patients cultured for 18 h with 10 ng/ml M-CSF (Peprotech, #300-25). Supernatants were collected for measurement of cytokines and cell pellets for RNA isolation using the NucleoSpin RNA extraction kit (Macherey-Nagel, #740955). RNA samples underwent quality control, and NGS libraries were generated using the QuantSeq 3′ mRNA-Seq Library Prep Kit FWD (Lexogen, #191.96), according to the manufacturer’s protocol, at the Genomics Facility of IMBB (FORTH, Heraklion). High-throughput sequencing (75 bp, single-end) was performed on an Illumina platform (NextSeq 500), following the manufacturer’s recommended protocol.

RNA-sequencing and Quant-sequencing data analysis

Raw fastQ files were preprocessed with the same quality trimming and adapter clipping strategy as described above for ChIP-seq preprocessing. Filtered reads were then mapped against the UCSC hg19 human reference transcriptome using STAR aligner¹²⁰ by setting the “--outFilterMultimapNmax” parameter to 5. As with ChIP-seq data, mapped reads were filtered in order to discard chimeric alignments, alignments with more than 2 mismatches, and keep only uniquely aligned reads (MAPQ = 255). Counting on Ensembl genes and statistical analysis (differential expression analysis) was performed with metaseqR¹²¹ using DESeq2¹¹⁸ for normalization and differential testing, and applying default settings for each assay (exon count summarization for RNA-seq and 3’ UTR summarization for Quant-seq). For paired-samples comparisons, “donor id” was included in the DESeq2 model.

Integrative analysis of ChIP-sequencing, ATAC-sequencing and RNA-sequencing data

Differentially SMC1A-bound regions were intersected with Ensembl Transcription Start Sites (TSSs; a total of 57,735 records from the July 2020 version) and FANTOM5⁷² enhancer elements (enhancer TSSs [eTSSs]; 65,423 human enhancers from phases 1 and 2 of the project, derived from http://fantom.gsc.riken.jp/5/datafiles/phase2.2/extra/Enhancers/human_permissive_enhancers_phase_1_and_2.bed.gz), each extended by 500 base pairs, upstream and downstream. Only promoters from transcribed genes, including protein-coding genes, lincRNAs, miRNAs, and lncRNAs were kept in the dataset (pseudogenes and other dubious ORFs were excluded). Enhancers were linked to their putative target genes using the GeneHancer⁷² database, which provides enhancer-gene associations. To define actively transcribed regulatory elements, H3K27ac and ATAC-seq peaks under lupus-like condition were intersected (using bedtools¹¹²) with the promoters and enhancers as described above.

Visualization

SMC1A ChIP-seq H3K27ac ChIP-seq and ATAC-seq signals were summarized (bin-counts) and visualized across active promoter regions (2 kb around active TSSs) and active enhancer regions (2 kb region eTSSs) as heatmaps and average profiles, using seqMINER¹²² and custom R scripts. Replicates for each condition were first downsampled to the same alignment depth, and then merged accordingly. Cross-condition normalization was also performed with respect to the alignment depth.

Generation of UCSC genome browser tracks

To generate genome-wide views of NGS signal, for all assays described in this study, deeptools bamCoverage¹¹⁰ was utilized, with a bin size of 20, CPM normalization, exactScaling, and the default effectiveGenomeSize for hg19 reference. This procedure generated bigWig signal files that were used to create tracks comparable with the UCSC genome browser.

Gene Ontology and motif enrichment analysis

To identify enriched biological processes, molecular functions, and cellular components associated with our gene sets, we conducted Gene Ontology (GO) enrichment analysis using the enrichGO function from the Clusterprofiler¹²³ package. The analysis was performed with the following parameters: genes were annotated using the “SYMBOL” key type and mapped to the human gene annotation database org.Hs.eg.db. The Benjamini-Hochberg method was applied to adjust p-values for multiple comparisons and enrichments. Motif enrichment analysis was performed using the i-cisTarget⁷⁸, screening for chromatin regions with increased accessibility and SMC1A binding in lupus-like compared to normal monocytes.

Measurement of secreted cytokines

Secreted cytokines from the supernatants of SLE monocytes, cultured for 18 h with 10 ng/ml M-CSF (Peprotech, #300-25), were measured with the Human Anti-Virus Response Panel (13-plex) from BioLegend (#740349) according to the manufacturer’s instructions. Data acquisition was done on BDFACSCanto II (BD Biosciences) and analyzed using the LEGENDplex Data Analysis Software (https://legendplex.qognit.com/).

Statistical analysis

Data were analyzed using STATA (version 18.5, StataCorp, Texas, USA) or GraphPad Prism 9 software. Results are expressed as median or mean ± standard error (SE). Statistical analysis was conducted using paired or independent-samples t-tests, as appropriate. For assays involving clusters of female and male samples (patients, healthy donors, lupus-like monocytes), linear mixed models (GLMM) were employed to account for the nested data structure and inter-individual variability. In these models, sex (female versus male) was treated as a within-cluster predictor alongside other covariates. Depending on data skewness, either a Gamma or Gaussian distribution was applied, with a log- or identity-link function, respectively. Post-hoc comparisons were performed using Tukey’s Least Significant Difference (LSD) test. The relationship between SLE disease activity (SLEDAI-2K) and SMC1A expression levels in patient-derived CD14⁺ monocytes was assessed by linear regression. Two-tailed p-values are reported.

Data availability

The high-throughput data generated for this study have been deposited in GEO under accession numbers GSE279726 and GSE279728. Accession codes used in the study include also GSE137143, GSE73754, GSE42331 and GSE100383. Flow cytometry data have been deposited to Zenodo, under the accession link https://doi.org/10.5281/zenodo.17106197. All data are included in the Supplementary Information or available from the authors, as are unique reagents used in this Article. The raw numbers for charts and graphs are available in the Source Data file. Source data are provided with this paper.

Code availability

Scripts related to specific analysis in the paper can be found on GitHub at: https://github.com/christoforos-nikolaou/SMC1A-in-Inflammation, https://github.com/spapanikolaou/SMC1A, https://doi.org/10.5281/zenodo.17086703.

References

Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626–638 (2016).
Article CAS PubMed Google Scholar
Scully, E. P., Haverfield, J., Ursin, R. L., Tannenbaum, C. & Klein, S. L. Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat. Rev. Immunol. 20, 442–447 (2020).
Article CAS PubMed PubMed Central Google Scholar
Whitacre, C. C. Sex differences in autoimmune disease. Nat. Immunol. 2, 777–780 (2001).
Article CAS PubMed Google Scholar
Haupt, S., Caramia, F., Klein, S. L., Rubin, J. B. & Haupt, Y. Sex disparities matter in cancer development and therapy. Nat. Rev. Cancer 21, 393–407 (2021).
Article CAS PubMed PubMed Central Google Scholar
Flanagan, K. L., Fink, A. L., Plebanski, M. & Klein, S. L. Sex and gender differences in the outcomes of vaccination over the life course. Annu. Rev. Cell Dev. Biol. 33, 577–599 (2017).
Article CAS PubMed Google Scholar
Xiao, Y. et al. Sex as a biological variable: mechanistic insights and clinical relevance in solid organ transplantation. Am. J. Transplant. 23, 1661–1672 (2023).
Forsyth, K. S., Jiwrajka, N., Lovell, C. D., Toothacre, N. E. & Anguera, M. C. The conneXion between sex and immune responses. Nat. Rev. Immunol. 24, 487–502 (2024).
Hattori, M. et al. The DNA sequence of human chromosome 21. Nature 405, 311–319 (2000).
Article ADS CAS PubMed Google Scholar
Jiwrajka, N. & Anguera, M. C. The X in seX-biased immunity and autoimmune rheumatic disease. J. Exp. Med. 219, e20211487 (2022).
Odhams, C. A. et al. Interferon inducible X-linked gene CXorf21 may contribute to sexual dimorphism in Systemic Lupus Erythematosus. Nat. Commun. 10, 2164–2164 (2019).
Article ADS PubMed PubMed Central Google Scholar
Souyris, M. et al. TLR7 escapes X chromosome inactivation in immune cells. Sci. Immunol. 3, eaap8855 (2018).
Seillet, C. et al. The TLR-mediated response of plasmacytoid dendritic cells is positively regulated by estradiol in vivo through cell-intrinsic estrogen receptor alpha signaling. Blood 119, 454–464 (2012).
Article CAS PubMed Google Scholar
Dragin, N. et al. Estrogen-mediated downregulation of AIRE influences sexual dimorphism in autoimmune diseases. J. Clin. Invest 126, 1525–1537 (2016).
Article PubMed PubMed Central Google Scholar
Anesi, N., Miquel, C. H., Laffont, S. & Guery, J. C. The influence of sex hormones and X chromosome in immune responses. Curr. Top. Microbiol. Immunol. 441, 21–59 (2023).
CAS PubMed Google Scholar
Oliva, M. et al. The impact of sex on gene expression across human tissues. Science 369, eaba3066 (2020).
Jansen, R. et al. Sex differences in the human peripheral blood transcriptome. BMC Genom. 15, 33 (2014).
Article Google Scholar
Guo, S. et al. Identification and analysis of the human sex-biased genes. Brief. Bioinform. 19, 188–198 (2018).
CAS PubMed Google Scholar
Whitney, A. R. et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl. Acad. Sci. USA 100, 1896–1901 (2003).
Article ADS CAS PubMed PubMed Central Google Scholar
Eady, J. J. et al. Variation in gene expression profiles of peripheral blood mononuclear cells from healthy volunteers. Physiol. Genom. 22, 402–411 (2005).
Article CAS Google Scholar
Schmiedel, B. J. et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701–1715.e1716 (2018).
Article CAS PubMed PubMed Central Google Scholar
Raznahan, A. et al. Sex-chromosome dosage effects on gene expression in humans. Proc. Natl. Acad. Sci. USA 115, 7398–7403 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Bongen, E. et al. Sex differences in the blood transcriptome identify robust changes in immune cell proportions with aging and influenza infection. Cell Rep. 29, 1961–1973 e1964 (2019).
Article CAS PubMed PubMed Central Google Scholar
Piasecka, B. et al. Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc. Natl. Acad. Sci. USA 115, E488–E497 (2018).
Article CAS PubMed Google Scholar
Chlamydas, S., Markouli, M., Strepkos, D. & Piperi, C. Epigenetic mechanisms regulate sex-specific bias in disease manifestations. J. Mol. Med. 100, 1111–1123 (2022).
Article CAS PubMed Google Scholar
Cheng, M. I. et al. The X-linked epigenetic regulator UTX controls NK cell-intrinsic sex differences. Nat. Immunol. 24, 780–791 (2023).
Article CAS PubMed Google Scholar
Liang, Y. et al. A gene network regulated by the transcription factor VGLL3 as a promoter of sex-biased autoimmune diseases. Nat. Immunol. 18, 152–160 (2017).
Article CAS PubMed Google Scholar
Christou, E. A. A., Banos, A., Kosmara, D., Bertsias, G. K. & Boumpas, D. T. Sexual dimorphism in SLE: above and beyond sex hormones. Lupus 28, 3–10 (2019).
Article CAS PubMed Google Scholar
Gergianaki, I. et al. Epidemiology and burden of systemic lupus erythematosus in a Southern European population: data from the community-based lupus registry of Crete, Greece. Ann. Rheum. Dis. 76, 1992–2000 (2017).
Article PubMed Google Scholar
Caielli, S., Wan, Z. & Pascual, V. Systemic lupus erythematosus pathogenesis: interferon and beyond. Annu. Rev. Immunol. 41, 533–560 (2023).
Article CAS PubMed Google Scholar
Crow, M. K. Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets. Ann. Rheum. Dis. 82, 999–1014 (2023).
Article CAS PubMed Google Scholar
Nikolakis, D. et al. Restoration of aberrant gene expression of monocytes in systemic lupus erythematosus via a combined transcriptome-reversal and network-based drug repurposing strategy. BMC Genom. 24, 207 (2023).
Article CAS Google Scholar
Gkirtzimanaki, K. et al. IFNalpha impairs autophagic degradation of mtDNA promoting autoreactivity of SLE monocytes in a STING-dependent fashion. Cell Rep. 25, 921–933.e925 (2018).
Article CAS PubMed PubMed Central Google Scholar
Hirose, S., Lin, Q., Ohtsuji, M., Nishimura, H. & Verbeek, J. S. Monocyte subsets involved in the development of systemic lupus erythematosus and rheumatoid arthritis. Int. Immunol. 31, 687–696 (2019).
Article CAS PubMed PubMed Central Google Scholar
Jin, Z. et al. Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy. Lupus Sci. Med. 4, e000202 (2017).
Article ADS PubMed PubMed Central Google Scholar
Jing, C. et al. Macrophage metabolic reprogramming presents a therapeutic target in lupus nephritis. Proc. Natl. Acad. Sci. USA 117, 15160–15171 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Steinbach, F. et al. Monocytes from systemic lupus erythematous patients are severely altered in phenotype and lineage flexibility. Ann. Rheum. Dis. 59, 283–288 (2000).
Article CAS PubMed PubMed Central Google Scholar
Stergioti, E. M. et al. Transcriptomic and proteomic profiling reveals distinct pathogenic features of peripheral non-classical monocytes in systemic lupus erythematosus. Clin. Immunol. 255, 109765 (2023).
Article CAS PubMed Google Scholar
Kuriakose, J. et al. Patrolling monocytes promote the pathogenesis of early lupus-like glomerulonephritis. J. Clin. Investig. 129, 2251–2265 (2019).
Article PubMed PubMed Central Google Scholar
Richoz, N. et al. Distinct pathogenic roles for resident and monocyte-derived macrophages in lupus nephritis. JCI Insight 7, e159751 (2022).
Fanouriakis, A., Tziolos, N., Bertsias, G. & Boumpas, D. T. Update omicronn the diagnosis and management of systemic lupus erythematosus. Ann. Rheum. Dis. 80, 14–25 (2021).
Article PubMed Google Scholar
Panousis, N. I. et al. Combined genetic and transcriptome analysis of patients with SLE: distinct, targetable signatures for susceptibility and severity. Ann. Rheum. Dis. 78, 1079–1089 (2019).
Article CAS PubMed Google Scholar
Carrel, L. & Willard, H. F. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 434, 400–404 (2005).
Article ADS CAS PubMed Google Scholar
Tukiainen, T. et al. Landscape of X chromosome inactivation across human tissues. Nature 550, 244–248 (2017).
Article ADS PubMed PubMed Central Google Scholar
Busslinger, G. A. et al. Cohesin is positioned in mammalian genomes by transcription, CTCF and Wapl. Nature 544, 503–507 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Davidson, I. F. & Peters, J.-M. Genome folding through loop extrusion by SMC complexes. Nat. Rev. Mol. Cell Biol. 22, 445–464 (2021).
Article CAS PubMed Google Scholar
Horsfield, J. A. Full circle: a brief history of cohesin and the regulation of gene expression. FEBS J. 290, 1670–1687 (2023).
Article CAS PubMed Google Scholar
Parelho, V. et al. Cohesins functionally associate with CTCF on mammalian chromosome arms. Cell 132, 422–433 (2008).
Article CAS PubMed Google Scholar
Yan, J. et al. Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell 154, 801–813 (2013).
Article CAS PubMed Google Scholar
Prenzel, T. et al. Cohesin is required for expression of the estrogen receptor-alpha (ESR1) gene. Epigenetics Chromatin 5, 13 (2012).
Article CAS PubMed PubMed Central Google Scholar
Schmidt, D. et al. A CTCF-independent role for cohesin in tissue-specific transcription. Genome Res. 20, 578–588 (2010).
Article CAS PubMed PubMed Central Google Scholar
Banerji, R., Skibbens, R. V. & Iovine, M. K. How many roads lead to cohesinopathies?. Dev. Dyn. 246, 881–888 (2017).
Article CAS PubMed Google Scholar
Liu, J. et al. Transcriptional dysregulation in NIPBL and cohesin mutant human cells. PLoS Biol. 7, e1000119–e1000119 (2009).
Article PubMed PubMed Central Google Scholar
Mannini, L. et al. Mutant cohesin affects RNA polymerase II regulation in Cornelia de Lange syndrome. Sci. Rep. 5, 16803–16803 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Boudaoud, I. et al. Connected gene communities underlie transcriptional changes in Cornelia de Lange syndrome. Genetics 207, 139–151 (2017).
Article CAS PubMed PubMed Central Google Scholar
Sasca, D. et al. Cohesin-dependent regulation of gene expression during differentiation is lost in cohesin-mutated myeloid malignancies. Blood 134, 2195–2208 (2019).
Article PubMed PubMed Central Google Scholar
Chen, Z. et al. Cohesin-mediated NF-kappaB signaling limits hematopoietic stem cell self-renewal in aging and inflammation. J. Exp. Med. 216, 152–175 (2019).
Article CAS PubMed PubMed Central Google Scholar
Cuartero, S. et al. Control of inducible gene expression links cohesin to hematopoietic progenitor self-renewal and differentiation. Nat. Immunol. 19, 932–941 (2018).
Article CAS PubMed PubMed Central Google Scholar
Hadjur, S. et al. Cohesins form chromosomal cis-interactions at the developmentally regulated IFNG locus. Nature 460, 410–413 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
Catala-Senent, J. F. et al. A deep transcriptome meta-analysis reveals sex differences in multiple sclerosis. Neurobiol. Dis. 181, 106113 (2023).
Article CAS PubMed Google Scholar
Gal-Oz, S. T. et al. ImmGen report: sexual dimorphism in the immune system transcriptome. Nat. Commun. 10, 4295 (2019).
Article ADS PubMed PubMed Central Google Scholar
Ronnblom, L. & Leonard, D. Interferon pathway in SLE: one key to unlocking the mystery of the disease. Lupus Sci. Med 6, e000270 (2019).
Article PubMed PubMed Central Google Scholar
Fairweather, D., Beetler, D. J., McCabe, E. J. & Lieberman, S. M. Mechanisms underlying sex differences in autoimmunity. J. Clin. Investig. 134, e180076 (2024).
Nelson, D. A., Kaplan, R. M., Kurina, L. M. & Weisman, M. H. Incidence of ankylosing spondylitis among male and female United States Army personnel. Arthritis Care Res. 75, 332–339 (2023).
Article Google Scholar
Craig, I. W., Mill, J., Craig, G. M., Loat, C. & Schalkwyk, L. C. Application of microarrays to the analysis of the inactivation status of human X-linked genes expressed in lymphocytes. Eur. J. Hum. Genet. 12, 639–646 (2004).
Article CAS PubMed Google Scholar
Parenti, I. et al. Overall and allele-specific expression of the SMC1A gene in female Cornelia de Lange syndrome patients and healthy controls. Epigenetics 9, 973–979 (2014).
Article PubMed PubMed Central Google Scholar
Scofield, R. H. et al. Klinefelter’s syndrome (47,XXY) in male systemic lupus erythematosus patients: support for the notion of a gene-dose effect from the X chromosome. Arthritis Rheum. 58, 2511–2517 (2008).
Article PubMed PubMed Central Google Scholar
Seminog, O. O., Seminog, A. B., Yeates, D. & Goldacre, M. J. Associations between Klinefelter’s syndrome and autoimmune diseases: English national record linkage studies. Autoimmunity 48, 125–128 (2015).
Article CAS PubMed Google Scholar
Park, S. H. et al. Type I interferons and the cytokine TNF cooperatively reprogram the macrophage epigenome to promote inflammatory activation. Nat. Immunol. 18, 1104–1116 (2017).
Article CAS PubMed PubMed Central Google Scholar
Fernandez Lahore, G. et al. Polymorphic estrogen receptor binding site causes Cd2-dependent sex bias in the susceptibility to autoimmune diseases. Nat. Commun. 12, 5565 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Kovats, S. Estrogen receptors regulate innate immune cells and signaling pathways. Cell. Immunol. 294, 63–69 (2015).
Article CAS PubMed PubMed Central Google Scholar
Hsieh, T. S. et al. Enhancer-promoter interactions and transcription are largely maintained upon acute loss of CTCF, cohesin, WAPL or YY1. Nat. Genet. 54, 1919–1932 (2022).
Article CAS PubMed PubMed Central Google Scholar
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Faure, A. J. et al. Cohesin regulates tissue-specific expression by stabilizing highly occupied cis-regulatory modules. Genome Res. 22, 2163–2175 (2012).
Article CAS PubMed PubMed Central Google Scholar
Hakimi, M. A. et al. A chromatin remodelling complex that loads cohesin onto human chromosomes. Nature 418, 994–998 (2002).
Article ADS CAS PubMed Google Scholar
Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).
Article ADS PubMed PubMed Central Google Scholar
Maya-Miles, D. et al. Crosstalk between chromatin structure, cohesin activity and transcription. Epigenetics Chromatin 12, 47 (2019).
Article PubMed PubMed Central Google Scholar
Munoz, S., Minamino, M., Casas-Delucchi, C. S., Patel, H. & Uhlmann, F. A role for chromatin remodeling in cohesin loading onto chromosomes. Mol. Cell 74, 664–673.e665 (2019).
Article CAS PubMed PubMed Central Google Scholar
Herrmann, C., Van de Sande, B., Potier, D. & Aerts, S. i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules. Nucleic Acids Res. 40, e114 (2012).
Article CAS PubMed PubMed Central Google Scholar
Platanitis, E. et al. A molecular switch from STAT2-IRF9 to ISGF3 underlies interferon-induced gene transcription. Nat. Commun. 10, 2921 (2019).
Article ADS PubMed PubMed Central Google Scholar
Cavalli, G. et al. Interleukin 1alpha: a comprehensive review on the role of IL-1alpha in the pathogenesis and treatment of autoimmune and inflammatory diseases. Autoimmun. Rev. 20, 102763 (2021).
Article CAS PubMed Google Scholar
Nepal, D. & Gazeley, D. Role of IL-6 and IL-6 targeted therapy in systemic lupus erythematosus. Rheumatology 62, 3804–3810 (2023).
Article CAS PubMed Google Scholar
Dorner, T. et al. Mechanism of action of baricitinib and identification of biomarkers and key immune pathways in patients with active systemic lupus erythematosus. Ann. Rheum. Dis. 81, 1267–1272 (2022).
Article PubMed Google Scholar
Guthridge, J. M. et al. Adults with systemic lupus exhibit distinct molecular phenotypes in a cross-sectional study. EClinicalMedicine 20, 100291 (2020).
Article PubMed PubMed Central Google Scholar
Lu, R. et al. Dysregulation of innate and adaptive serum mediators precedes systemic lupus erythematosus classification and improves prognostic accuracy of autoantibodies. J. Autoimmun. 74, 182–193 (2016).
Article CAS PubMed PubMed Central Google Scholar
Pyfrom, S. et al. The dynamic epigenetic regulation of the inactive X chromosome in healthy human B cells is dysregulated in lupus patients. Proc. Natl. Acad. Sci. USA 118, e2024624118 (2021).
Syrett, C. M. & Anguera, M. C. When the balance is broken: X-linked gene dosage from two X chromosomes and female-biased autoimmunity. J. Leukoc. Biol. 106, 919–932 (2019).
Article CAS PubMed Google Scholar
Wang, J. et al. Unusual maintenance of X chromosome inactivation predisposes female lymphocytes for increased expression from the inactive X. Proc. Natl. Acad. Sci. USA 113, E2029–E2038 (2016).
CAS PubMed PubMed Central Google Scholar
Taams, L. S., Steel, K. J. A., Srenathan, U., Burns, L. A. & Kirkham, B. W. IL-17 in the immunopathogenesis of spondyloarthritis. Nat. Rev. Rheumatol. 14, 453–466 (2018).
Article CAS PubMed Google Scholar
Congy-Jolivet, N. et al. Monocytes are the main source of STING-mediated IFN-alpha production. EBioMedicine 80, 104047 (2022).
Article CAS PubMed PubMed Central Google Scholar
Klein, S. L. & Morgan, R. The impact of sex and gender on immunotherapy outcomes. Biol. Sex. Differ. 11, 24 (2020).
Article PubMed PubMed Central Google Scholar
Nakato, R. et al. Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors. Nat. Commun. 14, 5647 (2023).
Article ADS CAS PubMed PubMed Central Google Scholar
Schaaf, C. A. et al. Genome-wide control of RNA polymerase II activity by cohesin. PLoS Genet. 9, e1003382 (2013).
Article CAS PubMed PubMed Central Google Scholar
Robles-Rebollo, I. et al. Cohesin couples transcriptional bursting probabilities of inducible enhancers and promoters. Nat. Commun. 13, 4342 (2022).
Article ADS CAS PubMed PubMed Central Google Scholar
Watrin, E., Kaiser, F. J. & Wendt, K. S. Gene regulation and chromatin organization: relevance of cohesin mutations to human disease. Curr. Opin. Genet. Dev. 37, 59–66 (2016).
Article CAS PubMed Google Scholar
Thibault, D. L. et al. IRF9 and STAT1 are required for IgG autoantibody production and B cell expression of TLR7 in mice. J. Clin. Investig 118, 1417–1426 (2008).
Article CAS PubMed PubMed Central Google Scholar
Zhao, M. et al. IL-6/STAT3 pathway induced deficiency of RFX1 contributes to Th17-dependent autoimmune diseases via epigenetic regulation. Nat. Commun. 9, 583 (2018).
Article ADS PubMed PubMed Central Google Scholar
Hagen, S. H. et al. Heterogeneous escape from X chromosome inactivation results in sex differences in type I IFN responses at the single human pDC level. Cell Rep. 33, 108485 (2020).
Article CAS PubMed PubMed Central Google Scholar
Laffont, S. et al. X-Chromosome complement and estrogen receptor signaling independently contribute to the enhanced TLR7-mediated IFN-alpha production of plasmacytoid dendritic cells from women. J. Immunol. 193, 5444–5452 (2014).
Article CAS PubMed Google Scholar
Heinz, L. X. et al. TASL is the SLC15A4-associated adaptor for IRF5 activation by TLR7-9. Nature 581, 316–322 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Wen, F., Guo, J., Li, Z. & Huang, S. Sex-specific patterns of gene expression following influenza vaccination. Sci. Rep. 8, 13517 (2018).
Article ADS PubMed PubMed Central Google Scholar
Aringer, M. et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann. Rheum. Dis. 78, 1151–1159 (2019).
Article PubMed Google Scholar
Gladman, D. D., Ibanez, D. & Urowitz, M. B. Systemic lupus erythematosus disease activity index 2000. J. Rheumatol. 29, 288–291 (2002).
PubMed Google Scholar
Franklyn, K. et al. Definition and initial validation of a Lupus Low Disease Activity State (LLDAS). Ann. Rheum. Dis. 75, 1615–1621 (2016).
Article CAS PubMed Google Scholar
Rudwaleit, M. et al. The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann. Rheum. Dis. 68, 777–783 (2009).
Article CAS PubMed Google Scholar
Machado, P. M., Landewe, R., Heijde, D. V. & Assessment of SpondyloArthritis international, S. Ankylosing Spondylitis Disease Activity Score (ASDAS): 2018 update of the nomenclature for disease activity states. Ann. Rheum. Dis. 77, 1539–1540 (2018).
Article PubMed Google Scholar
Ford, E., Nikopoulou, C., Kokkalis, A. & Thanos, D. A method for generating highly multiplexed ChIP-seq libraries. BMC Res Notes 7, 312 (2014).
Article PubMed PubMed Central Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 3 (2011).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).
Article PubMed PubMed Central Google Scholar
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Article PubMed PubMed Central Google Scholar
Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Article CAS PubMed PubMed Central Google Scholar
Nassar, L. R. et al. The UCSC genome browser database: 2023 update. Nucleic Acids Res. 51, D1188–D1195 (2023).
Article CAS PubMed Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Article CAS PubMed PubMed Central Google Scholar
Liu, T. Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells. Methods Mol. Biol. 1150, 81–95 (2014).
Article ADS CAS PubMed Google Scholar
Stovner, E. B. & Saetrom, P. epic2 efficiently finds diffuse domains in ChIP-seq data. Bioinformatics 35, 4392–4393 (2019).
Article CAS PubMed Google Scholar
Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952–1958 (2009).
Article CAS PubMed PubMed Central Google Scholar
Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Article ADS Google Scholar
Stark, R. & Brown, G. D. DiffBind: Differential binding analysis of ChIP-Seq peak data. http://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf (2011).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central Google Scholar
Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
Article CAS PubMed Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Article CAS PubMed Google Scholar
Moulos, P. & Hatzis, P. Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns. Nucleic Acids Res. 43, e25 (2015).
Article PubMed Google Scholar
Ye, T. et al. seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 39, e35 (2011).
Article CAS PubMed Google Scholar
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Article CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank the physicians and nursing staff of the Rheumatology Clinic for their care of patients with SLE, as well as the Blood Transfusion Unit at the University Hospital of Iraklio. We are grateful to Myrto Nikoloudaki for retrieving data from the patients’ medical charts, Giorgos Panagopoulos for his assistance with the ChIPseq library preparation, Spiros Georgakis for his guidance with the silencing experiments, and Dimitra Nikoleri and Panagiota Goutakoli for their support with Flow cytometry and the LegendPlex assay. The graphical illustrations shown in Figs. 1G, 2A, 5A, and 10 were created with BioRender.com under the license agreements numbers PG28QK3AZT, VH28QK3H1F, IQ28QK3LU2 and JO28QK2REN, respectively. This research was funded by the Hellenic Foundation for Research and Innovation (HFRI) (project number HFRI-FM17-3648) to George Bertsias and also in part, by the Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKY) to D.K. Also, this work was supported by a grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (agreement no.742390) to D.T.B.

Author information

These authors contributed equally: Despoina Kosmara, Sofia Papanikolaou.

Authors and Affiliations

Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
Despoina Kosmara, Sofia Papanikolaou, Chrysoula Stathopoulou, Dionysios Papamatheakis, Arianna Cimmarrusti, Prodromos Sidiropoulos, Matthieu D. Lavigne, Panayotis Verginis, Charalampos Spilianakis & George Bertsias
Rheumatology and Clinical Immunology, University of Crete Medical School, Heraklion, Greece
Despoina Kosmara, Sofia Papanikolaou, Chrysoula Stathopoulou, Arianna Cimmarrusti, Prodromos Sidiropoulos & George Bertsias
Institute of Bioinnovation, Biomedical Sciences Research Center “Alexander Fleming”, Athens, Greece
Sofia Papanikolaou, Dimitris Konstantopoulos & Christoforos Nikolaou
Department of Biology, University of Crete, Heraklion, Crete, Greece
Dionysios Papamatheakis & Charalampos Spilianakis
Greek Genome Center, Biomedical Research Foundation Academy of Athens, Athens, Greece
Giannis Vatsellas
Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation Academy of Athens, Athens, Greece
Aggelos Banos & Dimitrios Boumpas
Laboratory of Immune Regulation and Tolerance, Division of Basic Sciences, Medical School, University of Crete, Heraklion, Greece
Panayotis Verginis

Authors

Despoina Kosmara
View author publications
Search author on:PubMed Google Scholar
Sofia Papanikolaou
View author publications
Search author on:PubMed Google Scholar
Chrysoula Stathopoulou
View author publications
Search author on:PubMed Google Scholar
Dionysios Papamatheakis
View author publications
Search author on:PubMed Google Scholar
Giannis Vatsellas
View author publications
Search author on:PubMed Google Scholar
Arianna Cimmarrusti
View author publications
Search author on:PubMed Google Scholar
Aggelos Banos
View author publications
Search author on:PubMed Google Scholar
Prodromos Sidiropoulos
View author publications
Search author on:PubMed Google Scholar
Matthieu D. Lavigne
View author publications
Search author on:PubMed Google Scholar
Panayotis Verginis
View author publications
Search author on:PubMed Google Scholar
Dimitrios Boumpas
View author publications
Search author on:PubMed Google Scholar
Charalampos Spilianakis
View author publications
Search author on:PubMed Google Scholar
Dimitris Konstantopoulos
View author publications
Search author on:PubMed Google Scholar
Christoforos Nikolaou
View author publications
Search author on:PubMed Google Scholar
George Bertsias
View author publications
Search author on:PubMed Google Scholar

Contributions

De.K. performed most of the experiments and contributed to manuscript writing. S.P. analyzed all genomics data and generated figures. C.St. assisted with RNA-seq and ChIP-seq assays. D.P. performed RNA/DNA FISH experiments. G.V. supervised RNA/DNA sequencing. A.C. purified monocytes from patients and controls and performed RNA silencing. A.B. interpreted results. PS recruited patients. M.D.L. supervised Quant-seq experiments and edited the manuscript. P.V. and D.B. interpreted results, reviewed, and edited the manuscript. C.Sp. contributed to methodology design, data interpretation, and manuscript review. Di.K. performed integrated bioinformatic analyses. C.N. contributed to study design and methodology, conceived analytical approaches, interpreted results, and wrote parts of the manuscript. G.B. conceived the study, designed the methodology, recruited patients, analyzed data, supervised the research, and wrote the manuscript.

Corresponding author

Correspondence to George Bertsias.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information file

Transparent Peer Review file

Supplementary Dataset 1

Supplementary Dataset 2

Supplementary Dataset 3

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Kosmara, D., Papanikolaou, S., Stathopoulou, C. et al. The sex-biased chromatin modifier SMC1A promotes autoimmunity by shaping inflammatory pathways in patients with SLE. Nat Commun 16, 10350 (2025). https://doi.org/10.1038/s41467-025-65309-7

Download citation

Received: 19 December 2024
Accepted: 08 October 2025
Published: 24 November 2025
Version of record: 24 November 2025
DOI: https://doi.org/10.1038/s41467-025-65309-7