Abstract

Autoimmune diseases affect 7.5% of the US population, and they are among the leading causes of death and disability. A notable feature of many autoimmune diseases is their greater prevalence in females than in males, but the underlying mechanisms of this have remained unclear. Through the use of high-resolution global transcriptome analyses, we demonstrated a female-biased molecular signature associated with susceptibility to autoimmune disease and linked this to extensive sex-dependent co-expression networks. This signature was independent of biological age and sex-hormone regulation and was regulated by the transcription factor VGLL3, which also had a strong female-biased expression. On a genome-wide level, VGLL3-regulated genes had a strong association with multiple autoimmune diseases, including lupus, scleroderma and Sjögren's syndrome, and had a prominent transcriptomic overlap with inflammatory processes in cutaneous lupus. These results identified a VGLL3-regulated network as a previously unknown inflammatory pathway that promotes female-biased autoimmunity. They demonstrate the importance of studying immunological processes in females and males separately and suggest new avenues for therapeutic development.

Main

Autoimmune diseases are characterized by immune responses to self antigens that result in tissue damage. It is estimated that autoimmune diseases affect 7.5% of the US population, compared with a frequency of 2.8% for cancer and 6.9% for heart diseases, and are among the leading causes of death and disability. Currently there is no cure, and commonly used immunosuppressant treatments can lead to devastating side effects, such as serious infections and cancer1,2,3.

Many autoimmune diseases, ranging from systemic disorders, such as systemic lupus erythematosus (SLE), to organ-specific diseases, such as Grave's disease, feature a greater prevalence in females than in males (female:male, 9:1 (SLE) and 7:1 (Grave's disease))2,3, whereas the risk of contracting infectious diseases is higher in men4. Overall, 78% of the people affected with autoimmune diseases are women2,3. Sex hormones are among the most-studied factors for contributions to this sex bias. The role of sex hormones has been best studied in mouse models of SLE, in which treatment with androgen is protective, whereas treatment with estrogen accelerates disease5. In humans, however, the relationship between sex hormones and autoimmunity seems to be more complicated. For example, when SLE occurs in men, the disease is often more severe, and many autoimmune diseases commonly have their onset before puberty or after menopause5,6,7. In addition, there is evidence that postmenopausal hormonal therapy does not increase disease activity or the risk of major flares in women with SLE5,8,9.

Skin is the biggest organ in humans; it is the front line of immune protection and is a sensitive indicator of immunological dysregulation10. Skin changes are prominently manifested in autoimmune diseases such as SLE. Of the eleven criteria for the diagnosis of SLE, four are cutaneous in nature: malar rash (butterfly-shaped rash across the cheeks and nose), discoid rash (raised red patches), photosensitivity (skin rash resulting from an unusual reaction to sunlight) and mucosal ulcers. Collectively, skin involvement is present in 72–85% of patients with SLE11. Systemic sclerosis, an autoimmune disease with a female:male prevalence of 11:1, is characterized by skin symptoms that include thickening and itching12.

To understand the cause of female-biased susceptibility to autoimmune diseases in humans, we investigated the sexual dimorphisms of human skin. We identified a female-biased molecular signature that was significantly associated with susceptibility to autoimmune diseases. Sex differences extended beyond the signature to genome-wide co-expression networks involving processes such as complement activation and phagocytosis. We further identified VGLL3 ('vestigial-like family member 3'), one of the sex-biased transcription factors uncovered in our analyses, as a critical regulator of the female-biased genes encoding inflammatory products, including TNFSF13B ('tumor necrosis factor superfamily member 13b'; encoding the B-cell-stimulatory molecule BAFF) and ITGAM (encoding the integrin αM), which are a therapeutic target13 and a genetic risk factor for SLE14, respectively. On a genome-wide level, the targets of VGLL3 had a strong association with multiple autoimmune diseases, including lupus, systemic sclerosis and Sjögren's syndrome (SS), and had a prominent transcriptomic overlap with inflammatory processes in cutaneous lupus. VGLL3 was also required for the optimal response to interferons in monocytes and salivary gland cells. Our results uncovered a sex-hormone-independent mechanism that predisposes females to autoimmune diseases, and they provided a foundation for the development of novel, targeted treatment measures.

Results

Sex differences in human skin

We analyzed 31 female and 51 male skin biopsy samples from healthy donors by whole-genome RNA-sequencing (RNA-seq) analyses. We identified 661 genes that were expressed differentially by the two sexes (false-discovery rate (FDR), ≤ 0.1) (Supplementary Table 1). 268 genes were upregulated in males (i.e., were male biased), including 26 genes on the Y chromosome and six genes on the X chromosome. 393 genes were upregulated in females (i.e., were female biased), including 55 genes on the X chromosome (Fig. 1a). As expected, known sex-biased gene expression, such as that of the long noncoding RNA XIST and ZFY (zinc-finger protein, Y-linked), was reproduced in our data sets (Fig. 1b). Of the 55 genes that escaped X-inactivation, seven have orthologs on the Y chromosome, and their expression in males potentially enables dosage compensation (Supplementary Fig. 1a–d). 48 genes did not have Y-linked orthologs (Supplementary Fig. 1e–g), in support of the idea that incomplete X-inactivation might contribute to sexually dimorphic traits.

Figure 1: Identification of sex-biased genes from human skin biopsies.
Figure 1

(a) Chromosomal locations of female- and male-biased genes. (b) Raw RNA-seq reads for XIST, ZFY and VGLL3 in skin from females (F) and males (M). (c) Sex-biased co-expression correlation for genes encoding products in the functional categories of complement activation (left), phagocytosis regulation (middle) and T cell proliferation (right). (d) Sex-specific co-expression correlation for the ITGAMATRN and PTX3SEPT2 gene pairs. Data are representative of two experiments b,c (in d, n = 51 males and n = 31 females).

Sex differences might extend beyond the 661 differentially expressed genes (DEGs) to their associated networks. To test this, we conducted gene–gene correlation analysis between the sex-specific DEGs and all other genes in males and females, separately. Indeed, sex-biased co-expression correlations were found from the gene-pair level to the pathway-wide and genome-wide levels, and this included genes encoding products involved in various immunological processes such as phagocytosis and complement activation (Fig. 1c,d and Supplementary Fig. 2a). In total, we identified 124,521 gene–gene pairs that showed significant results in only female samples (FDR ≤ 0.1) but not in male samples (P > 0.5); conversely, 158,303 gene–gene pairs showed significance in only male samples (FDR ≤ 0.1) but not in female samples (P > 0.5). We further compared the correlation results with those obtained from published microarray data sets of the skin15 and obtained high correlation concordance (Supplementary Fig. 2b). This finding indicated the presence of sex-biased, genome-wide networks and suggested that the biological effect of the sex bias was much greater than that anticipated from the initial list of DEGs.

Association between female-biased genes and autoimmunity

Analysis of biological functions that showed enrichment in the group of DEGs revealed that the female-biased genes showed enrichment for those encoding products involved in immunological and inflammatory processes, but the male-biased genes did not (Fig. 2a,b and Supplementary Fig. 3a). In addition, network analysis organized female-biased genes mainly into those encoding products involved in complement-activation pathways known to be dysregulated in autoimmune diseases (Fig. 2a).

Figure 2: Female-biased genes encode products associated with autoimmune processes.
Figure 2

(a) Enrichment of specific functional categories among female-biased genes. (b) Enrichment of specific functional categories among male-biased genes. (c) Correlation between enrichment for disease-associated loci and female:male disease prevalence ratio for female-biased DEGs. Data are representative of the analysis of 393 female-biased genes (a) and 268 male-biased genes (b), or are representative of eight complex traits among 171 susceptibility loci (c).

The sex-specific upregulation, in females, of genes encoding products related to immunity led us to hypothesize that the female-biased gene signature associates with high susceptibility to autoimmune diseases. We detected significant overlap between female-biased genes and common disease loci associated with SLE and systemic sclerosis, two female-dominant autoimmune diseases (P < 0.05; Fig. 2c). Among female-biased genes, the female:male prevalence ratio was significantly correlated with enrichment for disease-associated loci, as measured by P values (Spearman coefficient (ρ) = 0.83; P = 1.5 × 10−2), and with the observed-to-expected change (fold value; ρ = 0.88; P = 7.2 × 10−3; Fig. 2c). There was no association between male-biased genes and autoimmune diseases (data not shown). We also implemented a sampling approach to estimate the empirical P values for the enrichment, and the results were highly concordant with the hypergeometric enrichment analysis (Supplementary Fig. 3b,c).

We confirmed the increased expression, in female skin, of the genes encoding products related to immunity (Fig. 3a,b), including the gene encoding BAFF (TNFSF13B; called 'BAFF' here), whose expression is frequently increased in patients with SLE and that served as the first approved target for a biologic therapy for SLE13, and ITGAM, whose variants are associated with susceptibility to SLE14. Consistent with the systemic feature of SLE symptoms, the female-biased pattern of risk-gene expression was not restricted to skin but was also detected to variable degrees in monocytes, B cells and T cells (Fig. 3c,d and Supplementary Fig. 3d). We further observed higher expression of the same female-biased risk genes in skin and monocytes from patients with SLE than in that from sex-matched healthy control subjects (Fig. 3e,f), which supported the proposal of the involvement of their products in the pathogenesis of SLE. Collectively, these results suggested that the female-biased inflammatory genes were associated with high susceptibility to autoimmune processes.

Figure 3: Expression of female-biased genes encoding products related to immunity is dependent on SLE disease states but not on sex-hormone levels.
Figure 3

(a) Quantitative RT–PCR (qRT–PCR) analysis of female-biased genes encoding products related to immunity in whole skin of healthy humans (n = 5 per sex); results are presented relative to the mean of the expression levels in females. (b) Immunohistochemistry of the expression of genes as in a in the skin of healthy humans. Scale bars, 50 μm. (c) qRT–PCR analysis of genes as in a in monocytes of healthy humans (n = 9 per sex); results presented as in a. (d) qRT–PCR analysis of the products of genes as in a in B cells of healthy humans (n = 9 female; n = 8 male); results presented as in a. (e) qRT–PCR analysis of genes as in a in the skin of patients with SLE and of healthy subjects (N) (n = 5 per group); results are presented relative to the mean of the expression levels in healthy subjects. (f) qRT–PCR analysis of genes as in a in monocytes of patients with SLE and of healthy subjects (n = 3 per group); results presented as in e. (g,h) RNA-seq analysis of genes as in a in primary human keratinocytes treated with various concentrations (key) of estradiol (g) or testosterone (h); results are presented relative to the mean of the expression levels in untreated cells. (i) RNA-seq analysis of BAFF in skin biopsies obtained from humans of various age (horizontal axis), presented as fragments per kilobase of exon per million fragments mapped (FPKM). Each symbol (ac,eh) represents an individual donor; small horizontal lines indicate the mean (± s.e.m.). *P < 0.05 (two-tailed Student's t-test). Data are representative of three independent experiments (b), show results from three independent experiments (g,h) or show analysis for the indicated number of subjects (a,cf,i).

Molecular mechanism for female-biased risk-gene expression

To search for the molecular mechanism underlying sex-biased risk-gene expression, we used RNA-seq to assess the effects of physiological or 100-fold-concentrated levels of estradiol or testosterone on gene expression in primary human keratinocytes. Treatment with the sex hormones did not alter the expressions of the female-biased genes encoding products related to immunity (Fig. 3g,h). More broadly, none of the 661 sex-specific DEGs were significantly regulated by treatment with estradiol or testosterone in the settings assessed (data not shown). To address the possibility that keratinocytes lose their responsiveness to sex hormones after ex vivo culture, we turned to our transcriptomics data of skin and reasoned that the expression of sex-biased genes would decrease with age if they were regulated by sex hormones. We observed no correlation between expression and biological age for the genes investigated (Fig. 3i and Supplementary Fig. 3e–i). Overall, we found no compelling evidence in support of the direct regulation of female-biased risk genes by sex hormones.

Another potential mechanism for the regulation of the risk genes would be via sex-biased transcription factors. We identified eight putative female-biased transcription factors, on the basis of their annotated function, from the 100 genes that were most significantly female biased (ranked by FDR; Supplementary Tables 1 and 2). From transcriptomic analyses of primary keratinocytes from three different female subjects, we found that six of the eight genes were expressed in keratinocytes (Supplementary Table 2). We were able to achieve efficient knockdown of five of the six genes by RNA-mediated interference (RNAi) (Supplementary Fig. 4a–e and Supplementary Table 2). We found that RNAi of VGLL3 decreased the abundance of ITGAM and BAFF mRNA, but RNAi of KDM6A (which encodes the lysine demethylase UTX; gene called 'UTX' here), ZFX (an X-linked gene that encodes the transcription factor ZFX), FEZ1 (which encodes the adaptor FEZ1) or FHL1 ('four and a half Lin11, Isl-1 and Mec-3 (LIM) domains') did not (Fig. 4a and Supplementary Fig. 4a–e). Knockdown of VGLL3 did not affect the expression of UTX or ZFX (Supplementary Fig. 4f), which suggested that the effect of VGLL3 on the SLE-associated genes ITGAM and BAFF was specific.

Figure 4: VGLL3 regulates genes associated with autoimmune diseases.
Figure 4

(a) qRT–PCR analysis of ITGAM, BAFF and C3 in primary human keratinocytes after RNAi with small interfering RNA (siRNA) with a scrambled sequence (Scr Ri) or siRNA targeting VGLL3, UTX, ZFX, FEZ or FHL (Ri; key); results are presented relative to the mean of the expression levels in cells transfected with Scr Ri. (b) qRT–PCR analysis of VGLL3 in skin from healthy females and males (n = 4 per group); results are presented relative to the mean of the expression levels in females. (c) qRT–PCR analysis of VGLL3 in primary human keratinocytes (n = 4 donors per group); results presented as in b. (d) Immunohistochemistry of VGLL3 in skin from healthy subjects (Normal) and patients with SLE (SLE); second row of each pair is an enlargement of the area outlined above. Scale bars, 50 μm (main images) or 4 μm (magnified images). (e) RNA-seq analysis of the ten female-biased immunological transcripts (left margin) in primary human keratinocytes after RNAi of VGLL3, presented as expression (log2 fold) and q value (right margin). (f) Literature-based network analysis of VGLL3-regulated genes encoding products related to autoimmune disease. (g) Expression (log2 fold) of VGLL3 targets in the skin of healthy subjects (N) and patients with SCLE (SCLE), as well as in keratinocytes after RNAi of VGLL3 (right). (h) Density plot of the expression (log2 fold) of genes upregulated in SCLE (SCLE genes) and genes not upregulated in SCLE (Non-SCLE genes), assessed after knockdown of VGLL3. Each symbol (b,c) represents an individual donor; small horizontal lines indicate the mean (± s.e.m.). *P < 0.05 and **P = 2.53 × 10−8 (two-tailed Student's t-test (ac) or Mann–Whitney–Wilcoxon test (h)). Data are results from three (a) or two (e) independent experiments, are results from the indicated number of subjects (b,c,g) or from the analysis of 208 genes (f), or are representative of three (d) or five (h) independent experiments.

VGLL3 is a homolog of the Drosophila gene vg ('vestigial'), which encodes a cofactor of Scalloped, the homolog of the transcription enhancer TEF-1 (ref. 16), and it has sex-dependent dominance in salmon17. The higher expression of VGLL3 (FDR = 7.2 × 10−4) in females was confirmed in skin and in keratinocytes (Fig. 4b,c). We found that, consistent with its transcriptional functions, VGLL3 had a phenotype of localization to the nucleus in healthy skin that was more distinct in females than in males (Fig. 4d). In contrast, in lesional skin from males and females with SLE, VGLL3 was concentrated in the nuclei of cells from either sex (Fig. 4d), indicative of disease-dependent regulation.

RNA-seq analysis of female primary human keratinocytes showed that, in addition to downregulating BAFF and ITGAM, knockdown of VGLL3 downregulated seven of ten female-biased genes encoding products related to immunity that were expressed in proliferating or post-confluent keratinocytes (Fig. 4e). At a threshold of q < 0.05 and |log2 fold| > 0.5, there were a total of 208 genes whose expression was decreased by VGLL3-specific RNAi in keratinocytes (Supplementary Table 3). To investigate whether genetic variants that affect the function or expression of VGLL3 would also affect the expression of the targets of VGLL3, we conducted expression quantitative trait loci (eQTL) analysis surrounding the region within 1 Mb (upstream or downstream) of VGLL3. We observed the strongest cis-eQTL (i.e., those that are within 1 Mb of the gene's boundaries) signal at chromosome 3, position 87902673 (P = 4 × 10−5; Supplementary Fig. 5a). Furthermore, we identified nine targets of VGLL3 that were substantially associated with the VGLL3-associated cis-eQTLs (Supplementary Fig. 5b), indicative of a trans-eQTL effect. We observed considerable enrichment for VGLL3 targets among the female-biased genes (P = 7.7 × 10−7) but not among the male-biased genes. VGLL3 targets showed enrichment for ten pathways related to immunity (Supplementary Fig. 5c), and autoimmune diseases were among the top three disease states that showed enrichment for VGLL3 targets (97 genes (47% of VGLL3 targets); P = 3.63 × 10−12). Network analysis of the 97 genes revealed additional nodes of autoimmune pathogenesis (Fig. 4f). Levels of the matrix metallopeptidase MMP-9 are greater in patients with SLE, systemic sclerosis, multiple sclerosis (MS), SS, polymyositis or rheumatoid arthritis (RA) than in healthy people18. Genome-wide association studies have shown that variants of ETS1, which encodes a transcription factor, confer susceptibility to SLE, RA and ankylosing spondylitis19. Excess interleukin 7 (IL-7) is present in patients with SS, MS or RA, and IL-7 promotes autoimmunity in lupus mice and can be used to predictsclinical response to interferon-β (IFN-β) in patients with MS20,21,22. Expression of the adhesion molecule ICAM-1 is upregulated in the brains of patients with MS and in mice with lupus nephritis and arthritis, and its use has been advocated for controlling autoimmune diseases23,24,25. Collectively, these lines of evidence support the proposal of a role for VGLL3 in promoting the expression of multiple genes related to autoimmune disease.

VGLL3 targets in autoimmune diseases

If VGLL3 contributes to higher susceptibility to autoimmune diseases in females, then one prediction would be that its targets are associated more tightly with such female-biased diseases than with diseases that do not exhibit significant sex differences. To investigate this, we performed transcriptomic analysis of skin biopsies from patients with subacute cutaneous lupus erythematosus (SCLE), morphea or systemic sclerosis. SCLE is a female-biased, lupus-specific eruption that features prominent skin involvement. We first attempted to identify the subset of VGLL3 targets whose expression was upregulated in SCLE, using less-stringent criteria at the identification stage. Analysis of the VGLL3 targets (genes decreased by RNAi of VGLL3 at a threshold of q < 0.05 and a change in expression of <0.8-fold) and the gene sets dysregulated in SCLE (genes upregulated in SCLE at a threshold of q < 0.05 and a change in expression of ≥1.5-fold) revealed a significant overlap in these genes (P = 2.9 × 10−5); we found 51 genes whose expression was increased in SCLE that were positively regulated by VGLL3 (Fig. 4g and Supplementary Table 4). The overlap included the true type I interferon (IFN-α and IFN-β)–response genes LY6E, OAS1, MX1 and IFI44 (refs. 26,27) (Fig. 4g and Supplementary Table 4), consistent with the proposal of a central role for type I interferons in the pathogenesis of SLE28,29. Similarly, we found that after knockdown of VGLL3, genes that were normally upregulated in SCLE (Supplementary Fig. 6a and Supplementary Table 5) were significantly more downregulated genome wide than 'non-SCLE' genes (genes not upregulated in SCLE) (P = 2.53 × 10−8) (Fig. 4h). In contrast, genes whose expression was increased in plaque psoriasis (Supplementary Table 5), a chronic inflammatory skin condition that has no sex bias30, showed minimal correlation with regulation by VGLL3 (Supplementary Fig. 6b,c). Likewise, genes whose expression was increased in SCLE showed minimal correlation with regulation by FEZ1 (Supplementary Fig. 6d), as knockdown of its expression did not reduce the expression of female-biased genes encoding products related to autoimmunity (Fig. 4a). Genes whose expression was increased in SCLE also showed minimal correlation with those regulated by FYN, which is not known to exhibit transcription factor activities (Supplementary Fig. 6e) (Supplementary Fig. 6d,e); this suggested that the regulation of genes whose expression was increased in SCLE was specific to VGLL3. Of note, sex differences in the expression of VGLL3 and the targets of VGLL3, including BAFF and ITGAM, were less apparent in comparisons of male and female patients with SCLE (Supplementary Fig. 6f), consistent with VGLL3's being a sex-biased risk factor before disease manifestation and a general regulator that is brought to comparable functional levels in the two sexes as autoimmune conditions arise. Consistent with that scenario was the similar patterns with which VGLL3 localized to the nucleus in skin lesions from males and females with SCLE (Fig. 4d). Similarly, VGLL3-regulated genes had significantly higher expression in skin lesions from patients with morphea (female:male, 4:1 (ref. 31); P = 3 × 10−4) and limited scleroderma, a subtype of systemic sclerosis (female:male, 4:1 (ref. 32); P = 1.4 × 10−2), than in skin from healthy subjects (Fig. 5a,b and Supplementary Fig. 6g). In a gene-by-gene analysis of the highest-ranked targets of VGLL3 that are expressed in patients with scleroderma and morphea33, we found that expression of a majority of the targets was higher in skin from patients with these diseases than in skin from healthy subjects (Fig. 5c–h).

Figure 5: VGLL3 targets encode products involved in multiple autoimmune conditions.
Figure 5

(a,b) Density plot of the null distribution for the P value (mean signed log10) of the mean expression of VGLL3 targets (arrows) in limited scleroderma (a) and morphea (b). (c,d) Expression of VGLL3 targets (horizontal axes; ranked by expression (low (left) to high (right)) in samples form healthy donors) in limited scleroderma (lSSc) (c) and morphea (d). (eh) Expression of VGLL3 targets (e,f) and genes that are not targets of VGLL3 (Non-target) (g,h) in skin from donors with lSSc (e,g) or morphea (f,h) or from healthy donors (N). P < 0.001 (a,b), P = 0.0412 (e), P = 0.0101 (f), P = 0.8686 (g) and P = 0.6449 (h) (Mann–Whitney U-test). Data are representative of the analysis of 16 patients with lSSc and 15 healthy subjects (a) or of five patients with morphea and 15 healthy subjects (b) (2,000 simulation trials were used to generate the null distributions shown in a,b) or are representative of five independent analyses (ch).

To address the role of VGLL3 in a sex-biased autoimmune condition not located mainly in the skin, we extended our analyses to SS, an autoimmune condition characterized by inflammation of salivary and lacrimal glands with a reported female:male ratio as high as 20:1 (ref. 34). Our examination of a published data set35 showed that the expression of VGLL3 mRNA was higher in the parotid tissue from patients with primary SS than in that from healthy control subjects (Fig. 6a). Concurrently, expression of the VGLL3 'node' targets MMP9, ETS1, IL7 and IL7R (IL-7 receptor) was also higher in patients with SS (Fig. 6b). Notably, the IL-7 axis has been shown to be pivotal in the pathogenesis of SS, with both IL-7 and its receptor being overexpressed in this condition36,37. IL-7 enhances the T helper type 1 response and T-cell-dependent activation of monocytes and B cells, and it promotes lymphocyte infiltration of target organs mediated by IFN-γ and the ligand of the chemokine receptor CXCR3 (refs. 37,38). Furthermore, IL-7 has been shown to be a successful therapeutic target in this syndrome38. In a gene-by-gene view, we found that VGLL3 target genes had higher expression in inflamed parotid tissue than in normal tissue (Fig. 6c), a trend not observed for non-target genes (Fig. 6d). Collectively, the expression of VGLL3 target gene was higher than that of non-target genes in patients with SS (Supplementary Fig. 6h), and the expression of genes upregulated in SS decreased significantly after knockdown of VGLL3 (Fig. 6e). Consistent with our observations of tissue from patients with SCLE, VGLL3 was mainly localized in the cell nucleus in affected tissue from patients with SS (data not shown). Collectively, we observed higher expression of VGLL3 target genes in tissue from patients with the four autoimmune diseases assessed than in non-diseased tissue, and this increase was not observed for non-target genes. Therefore, the VGLL3-regulated genes were linked to multiple female-biased autoimmune diseases.

Figure 6: VGLL3 regulation of genes altered in SS.
Figure 6

(a,b) Expression of VGLL3 mRNA (a) and of MMP9, ETS1, IL7 and IL7R mRNA (b) in parotid tissue from patients with SS (n = 24) and control subjects (n = 25); results are presented relative to those of control subjects, set as 1. (c,d) Expression of VGLL3 targets (c) and randomly selected genes that are not targets of VGLL3 (d) in patients with SS (genes (horizontal axis) ranked as in Fig. 5c,d). (e) Density plot of the expression (log2 fold) of 'SS genes' (expression increased in SS at a threshold of a change in expression of 1.5-fold and q < 0.05) and 'non-SS genes' (expression not increased as described above), assessed after knockdown of VGLL3. P < 2.2 × 10−6 (Mann–Whitney–Wilcoxon test). (f) qRT–PCR analysis of BAFF, ITGAM and FCER1G in monocytes treated by RNAi with siRNA with a scrambled sequence (Scr Ri-1 and Scr Ri-2) or siRNA targeting VGLL3 (VGLL3 Ri-1 and VGLL3 Ri-2). (g) qRT–PCR analysis of LY6E, OAS1, MX1 and IFI44 in monocytes treated with siRNA as in f in the presence (IFN-α + IFN-β) or absence (UT) of treatment with IFN-α and IFN-β; results are presented relative to the mean of the expression levels in untreated or Scr-Ri-treated cells. (h) Expression of BAFF, IL7 and MMP9 mRNA in cultured salivary gland cells treated with siRNA as in f and not treated with interferons (UT) or treated with IFN-α alone or together with IFN-γ (key); results presented as in g. Each symbol (fh) represents an individual donor; small horizontal lines indicate the mean (± s.e.m.). *q < 0.05 (a,b) or *P < 0.05 (Student's t-test; fh). Data are representative of analysis of 24 patients with SS and 25 healthy subjects (a,b; mean + s.e.m.), or of five independent analyses (ce) or are results from three independent experiments (fh).

To investigate whether VGLL3 regulates genes encoding products that promote autoimmunity in cell types other than keratinocytes, we studied the response of three female-biased genes induced in SLE, BAFF, ITGAM and FCER1G (which encodes the Fc fragment of receptor for immunoglobulin E), to disruption of VGLL3 expression in monocytes. Alterations in monocytes are hallmarks of SLE, including increased production of BAFF, which is involved in B cell differentiation and T cell activation28. We observed that VGLL3 was required for optimal expression of BAFF and FCER1G in monocytes from female subjects (Fig. 6f), which indicated that VGLL3 participated in promoting the expression of female-biased genes encoding products related to autoimmunity in monocytes. We further examined a potential role for VGLL3 in regulating type I interferon responses in both monocytes and cultured salivary gland cells, given the central role of type I interferons in the pathogenesis of SLE28 and SS39 and our observation of interferon-response genes among the VGLL3 target gene set. By focusing on the expression of the true type-I-interferon-response genes LY6E, OAS1, MX1 and IFI44 in peripheral blood mononuclear cells26,27, we confirmed their induction by IFN-α and IFN-β in monocytes and found that their maximal induction required VGLL3 expression (Fig. 6g). Similar to what we observed in monocytes, VGLL3 was required for the induction of pro-inflammatory-product-encoding genes BAFF, IL7 and MMP9 by IFN-α, with or without co-stimulation by IFN-γ, in salivary gland cells (Fig. 6h), consistent with published findings showing that cytokine induction can be dependent on both type I interferons and IFN-γ in these cells40. This observation indicated that VGLL3 might promote inflammation events via supporting type I interferon responses.

Discussion

There is a critical need to understand the biological differences between men and women, including how they influence the manifestation of different diseases and the response to therapy41,42. Autoimmune diseases represent one of the most prominent examples of sexually dimorphic human diseases, with a notable predominance in females. Our data demonstrated that even in healthy people, there were widespread sex-dependent differences in the activity of multiple immunological pathways. The sex-biased genes identified here overlapped genetic risk variants that have been previously identified for autoimmune diseases, including SLE and systemic sclerosis, and their expression was increased in sites of involvement. This finding suggested that these sex-biased genes contributed to not only increased disease susceptibility but possibly also heightened disease activity. In this context, we note that being female is the strongest risk factor for the development of autoimmunity, and it dwarfs the identified autoimmune genetic risk variants. Thus, these results provided novel insights into how sex contributes to autoimmune disease etiology. Furthermore, they suggest possibilities for the identification of high-risk populations using biomarkers based on these risk-associated genes.

In contrast to the enrichment for genes encoding pro-autoimmune factors among female-biased genes, male-biased genes were specifically associated with those encoding transcription factors, some of which have been linked to anti-inflammatory processes. For instance, skin grafts engineered to produce the transcription factor HOXA3 confer diminished inflammatory responses43. HOXA5 is induced by the anti-inflammatory agent colchicine44, and the transcription factor SIX2 is repressed in chronic inflammation45. Diminished abundance of the transcription factor FOXF1 is associated with pulmonary inflammation, and loss FOXF1 enhances production of the chemokine CXCL12 and inflammation46. The transcription factor HES1 suppresses CXCL1 expression induced by Toll-like-receptors47. Further studies will be needed to investigate the potential roles of the factors encoded by these genes, as well as other male-biased genes, in protection from autoimmunity.

Notably, our results suggest that sex differences in immunological regulation extend beyond the DEGs identified in this study to extensive genome-wide co-expression networks. For example, in females, expression of the SLE risk factor ITGAM is positively correlated with that of ARTN (artemin), the monocyte counterpart of the T cell, B cell and natural killer cell dipeptidyl peptidase IV (CD26), whose inhibitors are promising drugs for various autoimmune diseases48, whereas this is not seen in males. Similarly, in females there is a positive correlation between expression of PTX3, which regulates clearance of apoptotic cells49, and that of SEPT2, a GTP-binding, cytoskeleton-interacting protein and a putative autoantigen in systemic sclerosis50, but this is not observed in males. Therefore, ITGAM-ARTN and PTX3-SEPT2 might be regulated and their products might function in a common pathway in females but not in males. The presence of such DEG sets on a genome-wide level indicates additional layers of sexually dimorphic immune regulation beyond mRNA levels.

VGLL3 is a putative transcription factor16, and it had a prominent female-biased expression pattern in our data. We identified VGLL3 as a previously unrecognized inflammatory pathway in autoimmunity and a critical regulator of female-biased inflammatory processes. Of note, it has been shown that in salmon, VGLL3 exhibits sex-dependent dominance, promoting later maturation in females17. The findings that VGLL3 promotes the expression of several existing autoimmune-disease drug targets and genes encoding inflammatory molecules, including BAFF (SLE), MMP9 (SLE, systemic sclerosis, MS, SS and polymyositis), IL-7 (SLE, SS, MS and RA) and ICAM-1 (SLE, MS and RA), and that it influences type I interferon responses in immune and non-immune cell populations positions it at the intersection of multiple autoimmune pathways for potential therapeutic targeting. Notably, we demonstrated that in males affected by cutaneous lupus, expression of VGLL3 was similar to that seen in females and that this was associated with translocation of VGLL3 to the nucleus in actively inflamed tissue, consistent with VGLL3's role as a transcription factor; this indicated that in affected males, the VGLL3-regulated pathway became activated. This would make VGLL3 an attractive therapeutic target because it is present in diseased tissue of both females and males and a reduction in functional VGLL3 to levels observed in healthy males would be considered to be unlikely to cause serious side effects. Further studies are needed to address the regulation of VGLL3 and the mechanisms involved in its activation with disease onset in males.

In summary, our results have identified transcriptomic differences between females and males that were associated with extensive genome-wide co-expression gene networks that influenced various immunological processes, including various autoimmune processes. Furthermore, these results identified a VGLL3-regulated gene network as a previously unrecognized inflammatory pathway that promoted female-biased autoimmunity, and they demonstrated the importance of studying immunological processes in females and males separately. Because many of these diseases are inadequately controlled with existing treatments, identifying a unifying molecular basis underlying multiple autoimmune diseases might have far-reaching implications for the development of novel therapeutics.

Methods

Skin biopsies from normal tissue and SCLE lesions.

All subjects provided informed consent for biopsies from normal skin and SCLE skin lesions. Cases of SCLE biopsies were identified from the University of Michigan Pathology Database under Institutional Review Board (IRB) #HUM72843. Patients who met both clinical and histologic criteria for SCLE were included. Fresh skin samples were acquired according to IRB #HUM66116. All patient recruitment and samples were treated according to the Declaration of Helsinki.

Identification of sex-biased genes.

RNA-seq data of 82 normal skin samples (GSE63980) were used to identify genes that are differentially expressed between the two sexes in the skin. The gender effect for expression of each gene was modeled by linear regression, with the age of the patient at biopsy as a covariate. Specifically, we used the RNA-seq data of normal skin samples from our large cohort that studied the transcriptomes of psoriasis (GSE63980). We obtained the sex and age of biopsy for 82 patients in the cohort, and we used the pipeline and gene model described previously for RNA-seq analysis, including read mapping, assembly and quantification of gene expression51. We performed analysis for genes only expressed in at least 20% of the normal samples. The algorithm DESeq was used for expression normalization. Rank-based inverse normalization was then applied on each gene's normalized expression values. Linear regression was used to model the gender effect for expression each gene (i.e., differential expression analysis), and P values were computed using the Wald test. We used the age of the patient at the time of biopsy as a covariate. False discovery rate (FDR) was used to control the multiple testing.

Gene–gene co-expression analysis and functional characterization.

We conducted gene–gene correlation analysis between sex-specific DEGs versus all genes in males and females, separately. Gene–gene co-expression was measured by Spearman rank correlation coefficient (ρ); P values were computed using algorithm AS89, and we computed FDR for multiple-testing comparison. In total, there were 434,900 gene pairs with significant correlation (FDR ≤ 0.1) in both male and female correlation analyses; we also identified 124,521 gene–gene pairs that show significant correlation in only female samples (FDR ≤ 0.1) but not in male samples (P > 0.5), and 158,303 gene–gene pairs show only significant correlation in only male samples (FDR ≤ 0.1) but not in female samples (P > 0.5). To assess whether the difference in gene–gene correlation between sexes is significant, we devised a permutation approach. Specifically, we first permuted the sex labels and calculated the difference in gene–gene correlations between the pseudo-male and pseudo-female samples. The permutation was performed 10,000 times to construct the null distribution for the difference in correlation patterns. The significance was then estimated by comparing the observed gene–gene gender correlation difference to the null distribution for the same gene–gene pair. Functional enrichment analysis was performed using hypergeometric distribution, with biological annotations retrieved from the Gene Ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) and Biocarta databases. Enriched functions were identified using FDR ≤ 0.1. Spearman correlation (ρ) was used to study the difference (ρdiff = ρmaleρfemale) in co-expression networks between the two sexes and to study and compare the co-expression networks of the most notable enriched biological functions/pathways between the two sexes.

Disease-association screening.

We retrieved disease-associated genetic signals available from the NHGRI catalog52 and Immunobase (https://www.immunobase.org/), and we processed the data to consolidate the disease names and maintain only signals that achieve genome-wide association (P ≤ 5 × 10−8). Combining results from the two sources retained 9,599 variant-to-trait associations. We further merged genetic variants from same locus by using a ±500-kb interval as a criterion to select the most significant marker in each locus and focused on complex skin traits with at least five associated loci. Enrichment of sex-biased genes was assessed by the hypergeometric test, using all skin-expressing genes from the RNA-seq cohort as background. The female-to-male prevalence ratios for skin-associated traits were retrieved from previous literature data12,30,53,54.

In addition to the hypergeometric enrichment approach to compute the significance between female-biased genes versus genes from complex skin disease loci, we also devised a sampling strategy. For each of the complex skin disease we investigated, we randomly selected the same number of loci from the NHGRI disease catalog and enumerated the randomly selected loci which comprised of female-biased genes. We repeated the process 10,000 times and constructed the null distribution for the expected number of overlap. We then estimated the significance by comparing the observed overlap against the null distribution. This robust approach presented empirical P values for the eight diseases which were highly concordant with what we observed using the enrichment approach, replicating the findings that loci associated with SLE, systemic sclerosis and atopic dermatitis are enriched with female-biased genes (Supplementary Fig. 5a). Supplementary Figure 5b shows the null distribution for the expected overlap for random loci, and the red lines illustrate the observed overlap results from SLE/SS (top) and AD (bottom), respectively.

Expressed quantitative trait loci (eQTL) analysis.

To examine whether the variants that affect the functions or expression of VGLL3 would also affect the expression of the VGLL3 targets, we first identified the nonsynonymous or splice site variants for VGLL3 using the phase 3 1000 Genomes. Among the nine nonsynonymous and one splice variants identified from 1000 Genomes project, all of them are rare variants or variants with low minor-allele frequencies, thus we were not able to conduct eQTL analysis on these variants as they are not well-imputed in our genetic cohorts. We then turned to markers that can influence the expression of VGLL3 by conducting a cis-eQTL analysis surrounding the ±1-Mb region of VGLL3. The results are shown in Supplementary Figure 5 and illustrated the strongest cis-eQTL signal at chr3:87902673 (P = 4 × 10−5). We then investigated whether this VGLL3 cis-eQTL would influence the expression of VGLL3 target genes (208 genes decreased by VGLL3 knockdown in keratinocytes at the q < 0.05, |log2FC| > 0.5 threshold; as in Supplementary Table 3), and notably, we identified nine VGLL3 targets that are also significantly associated with the cis-eQTLs, indicating a trans-eQTL effect (Supplementary Fig. 5).

Cell culture, stimulation, RNAi and gene expression analyses.

Normal human keratinocytes (NHKs) were established from healthy adults as previously described55 and grown in medium 154 CF (ThermoFisher Scientific). Informed consent was obtained from all subjects. NHKs were used at passage 1 or 2. For sex hormone stimulation, estradiol (Sigma E2578) or testosterone (Sigma T1500) was applied to passage 1 cells for 24 h. Monocytes were isolated as indicated below and maintained in RPMI (ATCC) with 10% FBS (ThermoFisher Scientific). A253 salivary gland cells were obtained from ATCC and cultured in McCoy's 5a medium (ATCC) with 10% FBS (ThermoFisher Scientific). siRNA was introduced by electroporation using Lonza 4D-nucleofector following the manufacturer's instructions. For IFN stimulation, IFN-α (R&D systems) was used at 1,000 U/ml, IFN-β (R&D systems) at 1,000 U/ml and IFN-γ (R&D systems) at 2,000 U/ml. Interferons were applied to cells that had been electroporated with the indicated siRNA for 12 h, and RNA was collected by the Qiagen RNeasy plus kit. qRT–PCR was performed on a 7900HT Fast Real-time PCR system (Applied Biosystems) with TaqMan Universal PCR Master Mix (ThermoFisher Scientific). RNA-seq libraries were prepared using the Illumina Truseq RNA library prep kits and sequenced on the Illumina HiSeq platform. Differential expression analyses were performed using EdgeR, and functional enrichment and literature-based network analyses were performed with the Genomatix software. To study whether genes upregulated in SCLE are regulated by VGLL3 on a genome-wide level, we first defined genes whose expression was increased in SCLE as those upregulated in SCLE relative to their expression in normal tissue at a threshold of a change in expression of ≥2-fold and q < 0.01 (Supplementary Table 5) and defined 'non-SCLE genes' as the rest of genes. We then performed density plots of the 'log2 fold' change in expression after VGLL3 knockdown for SCLE and non-SCLE genes, respectively. Mean expression changes for the two groups of genes were calculated, and the Mann–Whitney–Wilcoxon test was used for significance.

Peripheral blood mononuclear cell (PBMC) isolation.

PBMCs from healthy individuals and patients with SLE were obtained as part of this study, and informed consent was obtained from all subjects. PBMCs were isolated from whole blood using the Ficoll method. Monocyte, T cells and B cells were isolated from PBMCs using MACS negative selection kits.

Immunohistochemistry.

Formalin-fixed, paraffin-embedded specimens on slides were heated for 30 min at 55 °C, rehydrated and epitope-retrieved with Tris-EDTA, pH 9. Slides were blocked, incubated with primary antibody (anti-C3, Sigma HPA020432, 1:500 dilution; anti-VGLL3, Sigma HPA054953, 1:250 dilution; anti-DOCK2, Sigma HPA036488, 1:300 dilution) overnight at 4 °C, washed, incubated with secondary antibody (anti-rabbit-IgG, Vector Laboratories, BA-1000, 1:50 dilution), developed with DAB (3,3′ diaminobenzidine, BD Biosciences 550850) and counterstained using hematoxylin. Images presented are representative of three experiments.

Statistical analysis.

Statistical tests used are described as in the individual Online Methods subsections and in the figure legends. Student's t-tests are two-tailed. The exact P values, when not specified in the figures, are as follows:

Figure 3a (left to right): 0.026, 0.020, 0.036, 0.012, 0.024, 0.033. Figure 3c (left to right): 0.020, 0.014, 0.043. Figure 3d (left to right): 0.027, 0.025, 0.026, 0.025. Figure 3e (left to right): 0.024, 0.044, 0.036, 0.011. Figure 3f (left to right): 0.021, 0.037, 0.043. Figure 3g: 0.041. Figure 3h: 0.043. Figure 4a (left to right): 0.011, 0.001. Figure 4b: 0.001. Figure 4c: 0.010. Figure 6a: 0.049. Figure 6b (left to right): 0.011, 0.0003, 0.0085, 0.004. Figure 6f (left to right): 0.032, 0.020, 0.045, 0.048. Figure 6g (left to right): 0.00003, 0.004, 0.0004, 0.010, 0.0065, 0.007, 0.007, 0.004, 0.021, 0.015, 0.016, 0.014. Figure 6h (left to right): 0.022, 0.0004, 0.002, 0.022, 0.0003, 0.0005, 0.013.

Data availability.

The data that support the findings of this study are available from the corresponding author upon request. RNA-seq data are available in the GEO database with the accession code GSE63980.

Accessions

Primary accessions

Gene Expression Omnibus

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Acknowledgements

We thank A.A. Dlugosz for critical discussions and reading of the manuscript; S. Stoll, Y. Xu, T. Quan, Y. Li, L. Wolterink and L. Reingold for technical help; and A. Libs for help with biopsy samples and files. Supported by the US National Institutes of Health (K08-AR060802 and R01-AR069071to J.E.G.; and R03-AR066337 and K08-AR063668 to J.M.K.), an A. Alfred Taubman Medical Research Institute Kenneth and Frances Eisenberg Emerging Scholar Award (J.E.G.), the Doris Duke Charitable Foundation (2013106 to J.E.G.) and a Pfizer Aspire Award (J.E.G.).

Author information

Affiliations

  1. Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA.

    • Yun Liang
    • , Lam C Tsoi
    • , Xianying Xing
    • , Maria A Beamer
    • , William R Swindell
    • , Mrinal K Sarkar
    • , Philip E Stuart
    • , Paul W Harms
    • , Rajan P Nair
    • , James T Elder
    • , John J Voorhees
    •  & Johann E Gudjonsson
  2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

    • Lam C Tsoi
  3. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

    • Lam C Tsoi
  4. Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.

    • Celine C Berthier
  5. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.

    • Paul W Harms
  6. Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, USA.

    • James T Elder
  7. Department of Internal Medicine, Division of Rheumatology, University of Michigan, Ann Arbor, Michigan, USA.

    • J Michelle Kahlenberg

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Contributions

Y.L., J.E.G., J.T.E., J.M.K. and J.J.V. designed the study and wrote the manuscript; Y.L., X.X., M.A.B., P.W.H., P.E.S., M.K.S., R.P.N. and C.C.B. collected and analyzed data; and L.C.T. and W.R.S. analyzed data. All authors reviewed and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Johann E Gudjonsson.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–6

Excel files

  1. 1.

    Supplementary Table 1

    Lists of gender biased genes

  2. 2.

    Supplementary Table 2

    Details for TF screening for regulation of gender biased genes.

  3. 3.

    Supplementary Table 3

    VGLL3-regulated genes in keratinocytes

  4. 4.

    Supplementary Table 4

    Overlap between VGLL3-regulated genes and lupus-upregulated genes

  5. 5.

    Supplementary Table 5

    Lists of SCLE- and psoriasis-altered genes

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DOI

https://doi.org/10.1038/ni.3643

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