The skin circadian clock gene F3 as a potential marker for psoriasis severity and its bidirectional relationship with IL-17 signaling in keratinocytes

The skin circadian clock gene F3 as a potential marker for psoriasis severity and its bidirectional relationship with IL-17 signaling in keratinocytes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The skin circadian clock gene F3 as a potential marker for psoriasis severity and its bidirectional relationship with IL-17 signaling in keratinocytes Xiuqing Yuan, Caixin Ou, Xinhui Li, Zhe Zhuang, Yongfeng Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3799546/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2024 Read the published version in International Immunopharmacology → Version 1 posted You are reading this latest preprint version Abstract Background Psoriasis is an immune-mediated skin disease where the IL-17 signaling pathway plays a crucial role in its development. Chronic circadian rhythm disorder in psoriasis pathogenesis is gaining more attention. The relationship between IL-17 signaling pathway and skin clock genes remains poorly understood. Methods GSE121212 with psoriatic lesion and healthy controls was used as exploration cohort for searching analysis. Datasets GSE117239, GSE51440, GSE137218 that contained effective biologics treating psoriasis overtime were applied to validation analysis. Single cell RNA sequencing (scRNA-seq) dataset GSE173706 were used to explore the F3 expression and related pathway activities in single cell levels. Through intersecting with high expression DEGs, F3 was selected as the signature skin circadian gene in psoriasis for further investigation. Functional analyses, including correlation analyses, prediction of transcription factors, protein-protein interaction, single gene GSEA to explore the potential roles of F3. ssGSEA algorithm was performed to uncover the immune related characteristics of psoriasis. We further explored F3 expression in specific cell population in scRNA-seq dataset, besides this, AUCell analysis was performed to explore the pathway activities and the results were further compared between specific cell cluster. Immunohistochemistry experiment, RT-qPCR was used to validate the location and expression of F3, small interfering RNA (siRNA) transfection experiment in HaCaT and transcriptome sequencing analysis were applied to explore the potential function of F3. Results F3 was significantly down-regulated in psoriasis and interacted with IL-17 signaling pathway. Low expression of F3 could upregulate the receptor of JAK-STAT signaling, thereby promoting keratinocytes inflammation. Conclusion Our research revealed a bidirectional link between the skin circadian gene F3 and the IL-17 signaling pathway in psoriasis, suggesting that F3 may interact with the IL-17 pathway by activating JAK-STAT within keratinocytes and inducing abnormal intracellular inflammation. psoriasis skin circadian clock gene IL-17 signaling pathway JAK-STAT signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Psoriasis is a chronic skin disease primarily characterized by hyper inflammation and proliferation, which results from a complex interplay of factors including genetics, environment, lifestyles, etc. [ 1 – 3 ]. The development of biologic therapies targeting inflammatory cytokines like IL-12/23, IL-17, and TNF-α inhibitors has led to significant progress in the treatment of psoriasis [ 4 , 5 ]. These medications have proven highly efficacious in alleviating skin hyperinflammatory status, inaugurated a new era in psoriasis treatment [ 6 , 7 ]. Despite the efficacy of biologic therapies in treating psoriasis, many patients experience recurrence after treatment. The underlying mechanism behind this phenomenon is not yet fully understood [ 8 ]. Chronic disruption of the circadian rhythm induced by poor lifestyle habits is widely acknowledged as a trigger for endogenous inflammation, resulting in numerous diseases such as autoimmune disorders, depressive illnesses, and metabolic disorders [ 9 , 10 ]. Unlike other tissues, skin is a special organ that could directly regulated by light-dark cycles, thus modulate cellular differentiation and immunity [ 11 ]. Psoriasis as a skin disorder distinguished by hyperinflammation and proliferation, has been implicated in the disruption of circadian rhythm in the skin [ 11 – 13 ]. Yu Z et al. proposed that patients with psoriasis experienced disrupted biological rhythms in lesion skin based on high-throughput sequencing analysis [ 14 ]. Similarly, Nemeth V et al. suggested that altered expression of the clock genes may result in pro-inflammatory effects in keratinocytes [ 15 ]. In short, previous research has indicated that circadian rhythm disturbances may instigate skin inflammation. Accordingly, investigating the skin's circadian clock gene and its interactions with particular inflammatory pathway may aid in the identification of therapeutic targets and the control of disease relapse. F3, also known as tissue factor, plays a significant role in blood coagulation cascades [ 16 ]. It is also expressed in mesenchymal cells found in colon and muscle tissues, where its low expression levels are closely associated with pathological immune responses [ 17 , 18 ]. However, its roles as one of the skin circadian genes in psoriasis have not been explored yet. Therefore, our study aimed to investigate the involvement of F3 in psoriasis pathogenesis. In our research, we identified F3 as the signature skin circadian gene that may contribute to psoriasis development and is closely linked to the IL-17 signaling pathway. Using datasets of psoriasis patients treated with cytokine inhibitors and a single-cell dataset, we validated F3 as a potential diagnostic marker and predictor of psoriasis severity. Additionally, through ex vivo experiments, we revealed a bidirectional relationship between F3 and IL-17 signaling in HaCaT cells. Our findings provide insights into the understanding of psoriasis pathogenesis and have implications for advancing disease management strategies. Materials and methods Data acquisition We performed data mining analyses on plaque psoriasis and healthy skin using the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ). The exploration cohort, GSE121212, consisting of RNA-seq analysis data with psoriatic lesions and healthy controls, was used for initial analyses. Validation analyses were conducted using GSE117239, GSE51440, and GSE137218, which contained longitudinal data on effective biologics used in treating psoriasis over time. To investigate the hub gene at the single-cell level, we utilized the scRNA-seq dataset GSE173706. Data processing was performed in R software 4.2.1, and visualization primarily employed the ggplot2 package (version 3.4.1). Differential expression analysis Differential gene expression in exploration cohort was calculated with DESeq2 package (version 1.38.3) [ 19 ]. Genes with adjusted P value (P-adj) 1.5 were considered differentially expressed genes (DEGs). DEGs with average base mean > 2^10 were considered high expressed DEGs. The results were visualized with MAplot. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs GO and KEGG enrichment analyses were conducted in DEGs using the R package clusterProfiler (version 4.0) [ 20 ]. The results of the GO enrichment analysis were divided into three categories: cellular components (CC), biological processes (BP), and molecular functions (MF). Pathways that exhibited a P-adj value of less than 0.05 were considered statistically significant. Identification and diagnostic value prediction of hub CirCaDB related gene Circadian clock genes of human skin were obtained from Ciradian Expression Profiles Database ( http://circadb.hogeneschlab.org/ ). By overlapping clock genes and high expressed DEGs using the ggVennDiagram package, F3 was selected. The difference of gene expression levels of F3 between groups was displayed. The receiver operating characteristic (ROC) curve analysis was performed to determine gene diagnostic effectiveness using the ROCit package. Batch correlation analysis of F3 To explore the potential relationships among F3 and the other genes in gene expression data, we utilized a for loop to conduct Spearman correlation analysis on the expression of F3 and other genes. Genes with P value < 0.05 were selected and then ranked by spearman correlation coefficients. By excluding non-protein coding genes, we identified the top four genes with both positive and negative correlations to F3 and analyzed their relationship with F3 using the GGally package. Identification of gene regulatory networks of F3 related genes To determine if the same transcription factors (TFs) regulate the most positively and negatively correlated genes of F3, we selected the top fifty positively and negatively related genes as representatives and conducted a TF-motif enrichment analysis using the RcisTarget package [ 21 ]. The first three motifs whose downstream genes including F3 were selected, and the related gene regulatory networks were further displayed and modified in Cytoscape (version 3.9.1) [ 22 ]. Potential relationships of F3 and genes involved in IL-17 signaling pathway To explore possible connections between F3 and other genes linked to the IL-17 signaling pathway, we incorporated both F3 and IL17-related genes to construct a protein-protein interaction (PPI) network using the String website (version 11.5) ( https://string-db.org/ ) [ 23 ]. The PPI network were further modified in Cytoscape (version 3.9.1). Gene set enrichment analysis (GSEA) of F3 To understand the roles of F3 more comprehensively, we applied the single gene GSEA analysis [ 24 ]. The gene list was ranked in descending order according to their correlation coefficients with F3, and KEGG gene set downloaded from Enrichr library ( https://maayanlab.cloud/Enrichr/ ) was utilized as the gene set [ 25 ]. The analyses were performed using clusterProfiler package [ 20 ]. P-adj < 0.05 was considered as statistic significant. The circadian related pathways and immune related pathways were visualized in GseaVis package. Accessing the correlation among F3 and immune cells The immune cell signatures were used as the gene set to perform the single sample GSEA (ssGSEA) analysis in exploration cohort [ 26 ]. The infiltration scores of 28 kinds of immune cells were calculated by implementing GSVA package (version 1.46.0) with “ssgsea” method [ 27 ]. Furthermore, we assessed the relationship among F3 expression levels and immune cell infiltration scores using Spearman’s correlation analysis. Additionally, the correlation between F3 and specific immune T cells was calculated in ggpubr package. GSEA analyses with different gene ranking orders To distinguish the difference among DEGs and F3 related genes, we ranked genes by logFC values or correlation coefficients with F3 in descending order. Then we used the hallmark gene set downloaded from the MsigDB database ( https://www.gsea-msigdb.org/ ) to perform the GSEA analysis using the clusterProfiler package [ 20 , 28 ]. By comparing the results, we could better understand the potential contributions of F3 to psoriasis. Validation of diagnostic value of F3 in psoriasis To validate the diagnostic value of F3 in psoriasis, we analyzed its expression levels in validation cohorts by comparing gene expression levels between lesions and non-lesions at baseline, as well as within lesions at different time points following treatment with biologics. Furthermore, if the dataset includes information on the Psoriasis Area and Severity Index (PASI), we categorized the samples based on their PASI score (10, severe) to investigate differences in F3 expression across severities, which were assessed for statistical significance using the Wilcoxon test. Exploration of F3 distribution in skin through scRNA-seq analysis scRNA-seq genomics allows for the identification of distinct cell clusters with functional diversity and can reveal the expression levels of hub genes specific to each cluster [ 29 ]. We applied the Seurat (version 4.0) and Harmony packages to perform all the necessary steps (NormalizeData, FindVariableFeatures, ScaleData, RunPCA, RunHarmony, RunUMAP, FindNeighbors, FindClusters) using default parameters, unless otherwise stated [ 30 , 31 ]. During the procedures, we used the top 30 Harmony dimensions as input for UMAP, and visualized the resulting clusters on the first two UMAP dimensions, using a clustering resolution of 0.5. By comparing known markers with the most highly expressed genes found by function FindAllMarkers in Seurat package, we identified the cellular identities of the ten clusters [ 32 , 33 ]. We employed the scRNAtoolVis package to visualize the expression levels of these signature cell markers. Lastly, to identify the specific cell clusters with differential F3 expression between psoriasis and normal skin, we visualized F3 expression levels on a UMAP plot. Pathway activities assessment analysis To identify which cell population exhibited activated circadian-related and IL17 signaling pathways, we utilized AUCell analysis to compute AUC values for KEGG gene sets via the AUCell R package (version 3.1.6) [ 21 ]. Pathway activities were then displayed separately by group. To determine whether F3 was correlated with these pathways in the KRT_4 cluster, we conducted spearman's correlation analysis using the ggpubr package. Genes from specific pathways with an absolute correlation coefficient > 0.15 and p < 0.05 were marked as strongly correlated and plotted in a correlation volcano plot. Immunohistochemistry and real-time quantitative PCR (RT-qPCR) experiments We obtained five paraffin-embedded psoriasis patient skin samples and five healthy individual skin samples for immunohistochemistry experiment. The skin tissues were sectioned at 3 um thickness and underwent deparaffinization, rehydration, hydrogen peroxide blockage, antigen retrieval, and incubation with F3 antibody (1:100, Abcam, MA, USA) for 30 min. As the secondary antibody, we used goat anti-rabbit IgG peroxidase conjugate (proteintech, USA) for 20 min at 37℃. Signal detection was performed using DAB staining, and slides were observed under a bright-field microscope at 20x magnification. To quantitatively analyze the staining intensity, we employed Image-Pro Plus 6 software to calculate the Average Optical Density (AOD) score. Concurrently, RT-qPCR analysis was conducted on skin samples from 20 individuals with psoriatic lesions and 18 healthy individuals without skin disorders. This investigation allowed us to quantitatively examine the differences in F3 expression levels between psoriatic and normal skin ex vivo. The research obtained ethical approval from the Research Ethics Committee of the Dermatology Hospital at Southern Medical University. Cell culture and IL-17A stimulation The spontaneously immortalized human keratinocyte cell line HaCaT (BNCC, Cat. NO. 3405CA93) was cultured in DMEM (Gbico, C11995500BT, USA) supplemented with 10% fetal bovine serum (Biological Industries,04-001-1A, IL). HaCaT cells were cultured with different concentration gradient cytokines human IL-17A (PeproTech, PP-200-17, USA) to establish a psoriatic over proliferation model. After a 48-hour incubation with cytokines, the cells were harvested for RNA extraction. RNA extraction and RT-qPCR Total RNA was extracted from HaCaT cells using TRI Reagent® (Sigma, Cat. No. T9424, USA) following the recommended protocol provided by the manufacturer. Next, SYBR® Green Pro Taq HS Premix (Accurate Biology, AG11701, CN) was used to reverse-transcribe total RNAs into cDNA. The qPCR was conducted using Evo M-MLV RT Kit with gDNA Clean for qPCR Ⅱ (Accurate Biology, AG11728, CN) following the manufacturer’s standard protocols. The expression of Human GAPDH served as a baseline to normalize all data. The primers used for F3 and GAPDH amplification were custom synthesized by Sangon Biotech (China). The primer sequences employed in this study are provided below: F3 (F) 5’-ACTTGGCACGGGTCTTCTCCTAC, F3 (R) TTGTTGGCTGTCCGAGGTTTGTC-3’; GAPDH (F) 5’-AGAAGGCTGGGGCTCATTTG, GAPDH (R) GCAGGAGGCATTGCTGATGAT-3’. SiRNA transfection and transcriptome sequencing analysis siRNAs targeting the F3 gene were synthesized by RiboBio (China) and transfected into HaCaT cells using the riboFECTTM CP Transfection Kit (RiboBio, C10511-05, CN), following the manufacturer's protocol. The siRNA sequences used were as follows: si-1: CATCCTGGCTATATCTCTA; si-2: GTGGTATTTGTGGTCATCA; si-3: GAACGGACTTTAGTCAGAA; si-N: provided by RiboBio Company. The experiment comprised two groups: si-F3 (experimental group, three samples) and si-N (negative control group, one sample). RNA extraction methodology was consistent for siRNA-interfered HaCaT cells to determine siRNA knockdown efficiency. Subsequently, RNA samples underwent transcriptome sequencing analysis on the Illumina/MGI platform. FASTQ files were generated using Illumina's bcl2fastq software (v2.20.0.422). Quality control assessment, alignment, and generation of gene count matrices were performed on the original FASTQ files. Differential gene expression analysis on gene count matrices was conducted using the DESeq2 package (version 1.38.3), considering genes with P-adj 0.6 as DEGs. Heatmaps displaying DEG expression in each sample were generated using the "pheatmap" package. Furthermore, the "clusterProfiler" package was employed to perform GO enrichment analysis, unveiling potential biological processes associated with the DEGs. Results Datasets information We utilized the RNA-seq dataset GSE121212 for exploratory analysis of skin tissue, while validation of our findings was performed using gene profiling array datasets GSE117239, GSE51440, GSE137218, and scRNA-seq dataset GSE173706. An overview of the datasets used in our computational analyses was provided in Table 1 . Table 1 Summary of five GEO datasets involving in exploration and validation cohorts. GEO Dataset Experiment type Cohort Samples GSE121212 Bulk RNA-seq Exploration 38 Ctrl 28 Pso GSE117239 Expression profiling array Validation 84 NL 240 LS GSE51440 Expression profiling array Validation 20 NL 39 LS GSE137218 Expression profiling array Validation 14 NL 70 LS GSE173706 scRNA-seq Validation 8 Nor 14 LS Identification of DEGs According to the criteria set above, a total of 3528 DEGs (1111 upregulated, 2417 downregulated) were selected, with 159 genes being considered high-expression DEGs. The upregulated DEGs were labeled in red, downregulated in green and those high-expression in green (Fig. 1 A). Enriched GO and KEGG pathways We conducted functional enrichment analysis using all DEGs to explore the potential biological pathways associated with psoriasis onset. Our analysis revealed that 880 GO pathways, including 685 BP, 67 CC, 128 MF, and 29 KEGG pathways, were significantly enriched in DEGs with P-adj < 0.05 (Table S1 ). The GO and KEGG analyses indicated that most DEGs were associated with immune or inflammatory response, with the most statistically significant pathways being regulation of cytokine production, cytokine-cytokine receptor interaction, Th17 cell differentiation, IL-17 signaling pathway, PPAR signaling pathway, and TNF signaling pathway (Figs. 1 B, C). These results were consistent with the known psoriasis etiology. Identification of F3 as the signature skin circadian gene We obtained 98 human skin-related circadian genes from the CircaDB database, and F3 was identified as the signature skin circadian gene through the intersection with the 159 high-expressed DEGs (Fig. 1 D). In comparison to healthy controls, F3 expression levels were significantly decreased in psoriasis (p < 0.0001) (Fig. 1 E). Meanwhile, the AUC value of F3 was 0.9652 (95%CI: [0.9219, 1]) (Fig. 1 F). These findings implicated that F3 had excellent sensitivity and specificity for diagnosing psoriasis from healthy controls. Batch correlation analysis of F3 We performed batch correlation analysis of F3 using a for loop in R software. Among the gene list ranked by correlation coefficient values with F3, protein-coding genes ID4, ZDHHC9, TUFT1, and ZNF12 were found to be most positively correlated with F3, with correlation coefficients of 0.946, 0.927, 0.906, and 0.877, respectively. Conversely, IL17A, MLKL, RCE1, and SLC38A7 were most negatively correlated with F3, with correlation coefficients of -0.888, -0.880, -0.823, and − 0.804, respectively (Figs. 1 G, H). Gene regulatory network analysis of F3 and its correlated genes Our gene regulatory network analysis of F3's most correlated genes suggested that homer_AAYTAGGTCA_RORgt, swissregulon_hs_RORA.p2, and transfac_pro_M08966 were potential motifs that bind to F3 and its closely related genes (Figs. 2 A-C). These motifs were associated with RAR-related orphan receptors, specifically RORC, RORA, and RORB. Surprisingly, the bit patterns of these motifs are remarkably similar (Figs. 2 D-F). In addition to potentially regulating F3 by binding to its promoter region, they may also simultaneously regulate genes such as IL17A, ADGRF1, PIPNM1, PAI14, ZNF652, KANK1, SLC8A1, NISIG2, and EXPH5 (Fig. 2 G). Notably, among these genes, IL17A, ADGRF1, and PIPNM1 were upregulated in psoriasis compared to healthy controls, while others were downregulated. Our findings illuminated that common upstream regulators may be involved in the regulation of both the most positively and negatively correlated genes with F3 in psoriasis. Potential relationships between F3 and genes involved in IL-17 signaling pathway Our data mining from the STRING website revealed a potential correlation between F3 and genes coding for TNF, IL6, IL1B, PTGS2, MMP9, IFNG, JUN, CXCL8, CCL2, and CSF3 (Fig. 2 H). These genes acted as effector genes that target specific cells in IL-17 pathway activities, namely in psoriasis, they mainly involved in neutrophil infiltration of the epidermis. Based on the findings, F3 was likely involved in the innate immune aspect of the IL-17 pathway, though its potential association with the pathway's adaptive immune response remains unclear. F3 correlated with skin circadian rhythm and T cell differentiation There was 183 KEGG pathways statistically enriched in single gene GSEA analysis (Table S2 ). Alongside the IL-17 pathway, other pathways related to circadian clock and adaptive immune cell differentiation were identified. Interestingly, our analysis showed that F3 was not only correlated with the IL-17 signaling pathway and circadian processes but was also associated with the differentiation of adaptive immune T cells, such as Th17 cells, Th1 cells, and Th2 cells (Fig. 3 A). Immune signature ssGSEA analysis result The ssGSEA analysis of immune signatures showed that psoriasis lesions were infiltrated with various immune cells compared to normal skin (Fig. 3 B). The correlation analysis between F3 and immune cells revealed a negative correlation of both innate and adaptive immune cells, particularly with their activated states (Figs. 3 C, D). Furthermore, based on our single gene GSEA analysis, F3 may be associated with the process of T helper cells developing into activated states. These results suggested that F3 may serve as a potential marker of inflammation in psoriasis. F3 negatively associated with psoriasis pathology Our GSEA analysis revealed 31 and 41 hallmark pathways were statistically enriched based on genes ranked by DEGs and correlation coefficients with F3, respectively (Table S3 ). The analysis showed that psoriasis was characterized by the enrichment of immune-related pathways such as interferon alpha/gamma response and cell cycle-related pathways such as MYC/E2F targets (Figs. 4 A, B). Interestingly, the pathways enriched in the GSEA analysis based on correlation coefficients of F3 presented almost opposing results compared to DEGs. These findings suggested that F3 may be negatively associated with hyperinflammatory and proliferation states in psoriasis. F3 as a diagnostic marker and predictor of psoriasis severity We examined F3 expression levels in validation cohorts using the above-mentioned grouping criteria to assess its diagnostic value. Our findings showed that F3 expression was significantly lower in psoriatic lesions (LS) compared to non-lesion (NL) groups (p < 0.0001) (Figs. 4 C-E). Interestingly, there was a statistically significant decrease in F3 expression levels as psoriasis severity increased (Fig. 4 C). Conversely, treatment of psoriasis with effective inflammatory cytokine inhibitors resulted in a gradual increase in F3 expression levels over time, which reached levels similar to those observed in the NL group after 12 weeks of treatment (Figs. 4 C-E). F3 expressed lower in differentiated keratinocytes With Seurat and Harmony packages, we processed the scRNA-seq dataset. We identified ten cell population based on the following gene expression signatures: KRT14, KRT5, KRT10, KRT1, SBSN, KLK7 (keratinocytes, KRT_1-KRT_4); HLA-DRA, KLA-DPB1, CD3D, CD3E (immune cells, IMM_1-IMM_2); MLANA, DCT (melanocytes, MELA); DCN, LUM (fibroblast, FIB); ACTA2, TAGLN (pericytes, PERI); CLDN5, VWF (vascular endocytes, ENDO) (Figs. 5 A, B). Based on the clustering, we found F3 mostly enriched in KRT_4 cluster and were significantly different between groups (Fig. 5 C). Pathway activities assessment in cell clusters Based on the AUCell analysis, we found circadian related pathways and IL-17 signaling pathway mainly enriched in keratinocytes, and all pathways significantly differently activated in KRT_4 cluster between group (Figs. 6 A-C). Interestingly, in KRT_4 cell population, F3 owned significant correlation with both circadian related pathways and IL-17 signaling pathway (Fig. 6 D). In correlation volcano plot, RORA was the significantly correlated gene in circadian rhythm pathway; CACNA1H, ITPR3, GNG12, GNB4, CALM3 were the genes in circadian entrainment; while S100A8, S100A9, S100A7, JUND, HSP90AB1, JUND, HSP90AA1 were the genes in IL-17 signaling pathway (Fig. 6 E). Validation of the expression and location of F3 in skin Based on scRNA-seq analysis, F3 was found to be primarily expressed in KRT_4 cell population. Immunohistochemistry experiments were conducted to validate this finding, confirming that F3 was predominantly expressed in differentiated keratinocytes and exhibited decreased expression in psoriatic skin compared to controls (Fig. 7 ), consistent with the scRNA-seq analysis. The difference in AOD between the two groups was statistically significant as shown in Fig. S1 . Additionally, RT-qPCR analysis further demonstrated significant downregulation of F3 in psoriasis compared to normal skin (p < 0.001) (Fig. 8 A). IL-17A exhibited dose-dependent inhibition of F3 expression in HaCaT cells With the aimed to investigate the effect of IL-17A on F3 expression levels, we measured the effect of different concentrations of IL-17A (0, 25, 50, 75, 100 and 150ng/ml) on F3 gene expression in HaCaT cells through experimental methods. As the concentration of IL-17A increased, F3 expression levels showed a clear stepwise decrease (Fig. 8 B). In summary, our results demonstrated that IL-17A could effectively inhibit F3 expression, and this inhibition had a certain dose-dependent effect. F3 interacted with IL17 signaling pathway in HaCaT cells To further investigate the functional role of F3 in keratinocytes, we conducted an RNA interference (RNAi) experiment. The knock-down efficiency of siRNAs was visualized in a histogram (Fig. 8 C). Within the DEGs identified (Fig. 8 D), F3 and FST exhibited downregulation, while the remaining genes showed upregulation in the si-F3 group. Through GO enrichment analysis of biological processes, we identified a total of 175 enriched GO_BP pathways. Notably, significant enrichments were observed in biological processes such as positive regulation of receptor signaling via JAK-STAT, acute inflammatory response, and activation of plasma proteins involved in the inflammatory response (Fig. 8 E). Discussion Circadian rhythm played critical roles in a range of disease pathogenesis, from physical illnesses such as cardiovascular disease, kidney disease, and malignant tumors, to mental health disorders such as depression and anxiety [ 34 – 38 ]. The disruption of circadian rhythms is involved in the pathogenesis of various diseases, through the mediation of immune and metabolic disturbances [ 39 – 41 ]. Hence, physical intervention, or molecularly targeting circadian genes could help to avoid illness and to optimize treatment outcomes [ 42 – 44 ]. Not surprisingly, circadian disruptions also play an important role in psoriasis. By leveraging the periodicity of the circadian rhythm, NS et al. found that nightly topical application of glucocorticoids was more effective than daytime use [ 45 ]. Subsequently, researchers identified perturbations in clock gene transcriptional profiles in psoriatic lesions compared to healthy skin [ 14 , 46 ]. Further mechanistic investigations in animal models revealed that mutations in the circadian gene CLOCK/PER2 could influence the development and progression of psoriasis by affecting the direct modulation of immune cell response to IL-23 [ 47 ]. Despite recent attention on core circadian genes, only limited research has been conducted on the role of peripheral skin circadian genes in psoriasis. To validate the differential expression of F3, we conducted RT-qPCR and immunohistochemistry experiments. Results confirmed downregulation of F3 in psoriatic lesions and its predominant expression in differentiating keratinocytes. Knockdown of F3 using siRNAs demonstrated significant efficiency in HaCaT cells. Transcriptome sequencing analysis of the knockdown samples identified several DEGs, including IL6R as one of the highly upregulated genes. IL6R, upon binding to IL-6 ligand, activates downstream signaling pathways such as IL-6/STAT3, leading to the production of inflammatory mediators in keratinocytes [ 48 ]. Activation of IL6R also influences cell cycle regulation, cellular differentiation, and growth of keratinocytes, potentially contributing to chronic inflammation and abnormal keratinization observed in psoriasis [ 49 ]. Previous research by AR et al. showed that IL6R acts as an upstream signal for STAT3-induced psoriasis-like dermatitis in transgenic mice, reducing dermatitis upon deletion of IL-6Rα in keratinocytes [ 50 ]. GO_BP enrichment analysis of DEGs revealed a correlation between interfering with F3 expression and positive regulation of the JAK-STAT signaling pathway receptor. Notably, the IL-17 pathway can modulate intracellular inflammatory responses through JAK-STAT activation [ 51 ]. Interestingly, both IL6R and IL6 are crucial genes in the JAK-STAT and IL17 signaling pathways, respectively. These findings suggested F3 potentially involved in the IL17 signaling pathway, regulating key genes such as IL6R, thereby activating JAK-STAT within keratinocytes and inducing aberrant intracellular inflammation. In conclusion, the bidirectional relationship between F3 and IL-17 signaling may explain why F3 is a potential marker for representing psoriasis severity. These findings offer valuable insights into the role of F3 in modulating key signaling pathways involved in psoriasis pathogenesis and progression. Despite the valuable insights provided by our study, there are some limitations. Firstly, we only conducted siRNA knockdown experiments in HaCaT cells for initial validation, and further investigations using animal models are necessary to explore F3's function in the whole organism. Additionally, we did not examine the roles of other skin circadian genes or investigate how disruption of the circadian rhythm pathway as a whole affect keratinocyte inflammation. Future studies should address these gaps to gain a more comprehensive understanding of psoriasis development. Conclusions Our study identified the skin circadian clock gene F3 as a potential marker for psoriasis and its severity. We also revealed a bidirectional relationship between F3 and IL-17 signaling in keratinocytes. In conclusion, we hope our findings will provide new insights into the pathogenesis of psoriasis and offer potential avenues for the development of novel therapeutic strategies. Abbreviations siRNA Small interfering RNA GEO Gene Expression Omnibus DEGs differentially expressed genes GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes ROC receiver operating characteristic TFs transcription factors PPI protein-protein interaction GSEA Gene set enrichment analysis ssGSEA single sample GSEA PASI Psoriasis Area and Severity Index RT-qPCR real-time quantitative PCR AOD Average Optical Density Declarations Acknowledgments We are grateful to the authors of GSE121212, GSE117239, GSE51440, GSE137218, and GSE173706 for sharing their data. We would also like to thank the members of GZDlab for providing valuable guidance and junjunlab (https://github.com/junjunlab/) for useful R packages. Additionally, we acknowledge the hard work of our researchers and the valuable advice from the reviewers. Author contributions The research was conceptualized by XQY and CXO. XQY conducted the data analysis and drafted the manuscript, while CXO performed the experimental work and revised the manuscript. XHL and ZZ were responsible for collecting tissue samples and conducting experiments. The manuscript was reviewed and revised by XQY, CXO, and XHL. YFC provided supervision throughout the research process. All authors made contributions to the article and approved the final version for submission. Funding This research received no external funding. Availability of data and materials We analyzed datasets GSE121212, and GSE117239, GSE51440, GSE137218 and GSE173706 in this research. These five public datasets were all available in the GEO ( http://www.ncbi.nlm.nih.gov/geo ) database. Ethical approval and consent to participate This study was approved by the Research Ethics Committee of Dermatology Hospital of Southern Medical University (Guangzhou, China) (IRB#2023024). Informed consent was obtained from all patients included in this study. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet 2021;397:1301–15. https://doi.org/10.1016/S0140-6736(20)32549-6 . Shen M, Xiao Y, Jing D, Zhang G, Su J, Lin S, et al. Associations of combined lifestyle and genetic risks with incident psoriasis: A prospective cohort study among UK Biobank participants of European ancestry. J Am Acad Dermatol 2022;87:343–50. https://doi.org/10.1016/j.jaad.2022.04.006 . Madden SK, Flanagan KL, Jones G. How lifestyle factors and their associated pathogenetic mechanisms impact psoriasis. Clin Nutr 2020;39:1026–40. https://doi.org/10.1016/j.clnu.2019.05.006 . Ghoreschi K, Balato A, Enerbäck C, Sabat R. Therapeutics targeting the IL-23 and IL-17 pathway in psoriasis. Lancet 2021;397:754–66. https://doi.org/10.1016/S0140-6736(21)00184-7 . Lowes MA, Suárez-Fariñas M, Krueger JG. Immunology of psoriasis. Annu Rev Immunol 2014;32:227–55. https://doi.org/10.1146/annurev-immunol-032713-120225 . Armstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. JAMA 2020;323:1945–60. https://doi.org/10.1001/jama.2020.4006 . van de Kerkhof PC. From Empirical to Pathogenesis-Based Treatments for Psoriasis. J Invest Dermatol 2022;142:1778–85. https://doi.org/10.1016/j.jid.2022.01.014 . Puig L, Costanzo A, Muñoz-Elías EJ, Jazra M, Wegner S, Paul CF, et al. The biological basis of disease recurrence in psoriasis: a historical perspective and current models. Br J Dermatol 2022;186:773–81. https://doi.org/10.1111/bjd.20963 . Huang W, Ramsey KM, Marcheva B, Bass J. Circadian rhythms, sleep, and metabolism. J Clin Invest 2011;121:2133–41. https://doi.org/10.1172/JCI46043 . Patke A, Young MW, Axelrod S. Molecular mechanisms and physiological importance of circadian rhythms. Nat Rev Mol Cell Biol 2020;21:67–84. https://doi.org/10.1038/s41580-019-0179-2 . Duan J, Greenberg EN, Karri SS, Andersen B. The circadian clock and diseases of the skin. FEBS Lett 2021;595:2413–36. https://doi.org/10.1002/1873-3468.14192 . Luengas-Martinez A, Paus R, Iqbal M, Bailey L, Ray DW, Young HS. Circadian rhythms in psoriasis and the potential of chronotherapy in psoriasis management. Exp Dermatol 2022;31:1800–9. https://doi.org/10.1111/exd.14649 . Li W-Q, Qureshi AA, Schernhammer ES, Han J. Rotating night-shift work and risk of psoriasis in US women. J Invest Dermatol 2013;133:565–7. https://doi.org/10.1038/jid.2012.285 . Yu Z, Gong Y, Cui L, Hu Y, Zhou Q, Chen Z, et al. High-throughput transcriptome and pathogenesis analysis of clinical psoriasis. J Dermatol Sci 2020;98:109–18. https://doi.org/10.1016/j.jdermsci.2020.03.006 . Németh V, Horváth S, Kinyó Á, Gyulai R, Lengyel Z. Expression Patterns of Clock Gene mRNAs and Clock Proteins in Human Psoriatic Skin Samples. Int J Mol Sci 2021;23:121. https://doi.org/10.3390/ijms23010121 . Iqbal MB, Johns M, Cao J, Liu Y, Yu S-C, Hyde GD, et al. PARP-14 combines with tristetraprolin in the selective posttranscriptional control of macrophage tissue factor expression. Blood 2014;124:3646–55. https://doi.org/10.1182/blood-2014-07-588046 . Kinchen J, Chen HH, Parikh K, Antanaviciute A, Jagielowicz M, Fawkner-Corbett D, et al. Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease. Cell 2018;175:372–386.e17. https://doi.org/10.1016/j.cell.2018.08.067 . Camps J, Breuls N, Sifrim A, Giarratana N, Corvelyn M, Danti L, et al. Interstitial Cell Remodeling Promotes Aberrant Adipogenesis in Dystrophic Muscles. Cell Rep 2020;31:107597. https://doi.org/10.1016/j.celrep.2020.107597 . Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8 . Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2021;2:100141. https://doi.org/10.1016/j.xinn.2021.100141 . Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017;14:1083–6. https://doi.org/10.1038/nmeth.4463 . Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504. https://doi.org/10.1101/gr.1239303 . Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021;49:D605–12. https://doi.org/10.1093/nar/gkaa1074 . Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545–50. https://doi.org/10.1073/pnas.0506580102 . Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016;44:W90-97. https://doi.org/10.1093/nar/gkw377 . Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017;18:248–62. https://doi.org/10.1016/j.celrep.2016.12.019 . Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013;14:7. https://doi.org/10.1186/1471-2105-14-7 . Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015;1:417–25. https://doi.org/10.1016/j.cels.2015.12.004 . Ak S, R S, X A, Rs G, Jt G, R R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 2013;498. https://doi.org/10.1038/nature12172 . Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 2019;16:1289–96. https://doi.org/10.1038/s41592-019-0619-0 . Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 2015;33:495–502. https://doi.org/10.1038/nbt.3192 . Merleev A, Ji-Xu A, Toussi A, Tsoi LC, Le ST, Luxardi G, et al. Proprotein convertase subtilisin/kexin type 9 is a psoriasis-susceptibility locus that is negatively related to IL36G. JCI Insight 2022;7:e141193. https://doi.org/10.1172/jci.insight.141193 . Solé-Boldo L, Raddatz G, Schütz S, Mallm J-P, Rippe K, Lonsdorf AS, et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol 2020;3:188. https://doi.org/10.1038/s42003-020-0922-4 . Chellappa SL, Vujovic N, Williams JS, Scheer FAJL. Impact of Circadian Disruption on Cardiovascular Function and Disease. Trends Endocrinol Metab 2019;30:767–79. https://doi.org/10.1016/j.tem.2019.07.008 . Firsov D, Bonny O. Circadian rhythms and the kidney. Nat Rev Nephrol 2018;14:626–35. https://doi.org/10.1038/s41581-018-0048-9 . Zhou L, Zhang Z, Nice E, Huang C, Zhang W, Tang Y. Circadian rhythms and cancers: the intrinsic links and therapeutic potentials. J Hematol Oncol 2022;15:21. https://doi.org/10.1186/s13045-022-01238-y . Crouse JJ, Carpenter JS, Song YJC, Hockey SJ, Naismith SL, Grunstein RR, et al. Circadian rhythm sleep-wake disturbances and depression in young people: implications for prevention and early intervention. Lancet Psychiatry 2021;8:813–23. https://doi.org/10.1016/S2215-0366(21)00034-1 . Difrancesco S, Lamers F, Riese H, Merikangas KR, Beekman ATF, van Hemert AM, et al. Sleep, circadian rhythm, and physical activity patterns in depressive and anxiety disorders: A 2-week ambulatory assessment study. Depress Anxiety 2019;36:975–86. https://doi.org/10.1002/da.22949 . Crislip GR, Johnston JG, Douma LG, Costello HM, Juffre A, Boyd K, et al. Circadian Rhythm Effects on the Molecular Regulation of Physiological Systems. Compr Physiol 2021;12:2769–98. https://doi.org/10.1002/cphy.c210011 . Curtis AM, Bellet MM, Sassone-Corsi P, O’Neill LAJ. Circadian clock proteins and immunity. Immunity 2014;40:178–86. https://doi.org/10.1016/j.immuni.2014.02.002 . Ma D, Li S, Molusky MM, Lin JD. Circadian autophagy rhythm: a link between clock and metabolism? Trends Endocrinol Metab 2012;23:319–25. https://doi.org/10.1016/j.tem.2012.03.004 . Ruan W, Yuan X, Eltzschig HK. Circadian rhythm as a therapeutic target. Nat Rev Drug Discov 2021;20:287–307. https://doi.org/10.1038/s41573-020-00109-w . Xiang K, Xu Z, Hu Y-Q, He Y-S, Wu G-C, Li T-Y, et al. Circadian clock genes as promising therapeutic targets for autoimmune diseases. Autoimmun Rev 2021;20:102866. https://doi.org/10.1016/j.autrev.2021.102866 . Smolensky MH, Portaluppi F, Manfredini R, Hermida RC, Tiseo R, Sackett-Lundeen LL, et al. Diurnal and twenty-four hour patterning of human diseases: acute and chronic common and uncommon medical conditions. Sleep Med Rev 2015;21:12–22. https://doi.org/10.1016/j.smrv.2014.06.005 . Nguyen S, Bahakeem H, Alkhalifah A, Cavalié M, Boukari F, Montaudié H, et al. Topical corticosteroids application in the evening is more effective than in the morning in psoriasis: results of a prospective comparative study. J Eur Acad Dermatol Venereol 2017;31:e263–4. https://doi.org/10.1111/jdv.14027 . Németh V, Horváth S, Kinyó Á, Gyulai R, Lengyel Z. Expression Patterns of Clock Gene mRNAs and Clock Proteins in Human Psoriatic Skin Samples. Int J Mol Sci 2021;23:121. https://doi.org/10.3390/ijms23010121 . Ando N, Nakamura Y, Aoki R, Ishimaru K, Ogawa H, Okumura K, et al. Circadian Gene Clock Regulates Psoriasis-Like Skin Inflammation in Mice. J Invest Dermatol 2015;135:3001–8. https://doi.org/10.1038/jid.2015.316 . Xu H, Liu J, Niu M, Song S, Wei L, Chen G, et al. Soluble IL-6R-mediated IL-6 trans-signaling activation contributes to the pathological development of psoriasis. J Mol Med (Berl) 2021;99:1009–20. https://doi.org/10.1007/s00109-021-02073-3 . Miao X, Xiang Y, Mao W, Chen Y, Li Q, Fan B. TRIM27 promotes IL-6-induced proliferation and inflammation factor production by activating STAT3 signaling in HaCaT cells. Am J Physiol Cell Physiol 2020;318:C272–81. https://doi.org/10.1152/ajpcell.00314.2019 . Ravipati A, Nolan S, Alphonse M, Dikeman D, Youn C, Wang Y, et al. IL-6R/Signal Transducer and Activator of Transcription 3 Signaling in Keratinocytes rather than in T Cells Induces Psoriasis-Like Dermatitis in Mice. J Invest Dermatol 2022;142:1126–1135.e4. https://doi.org/10.1016/j.jid.2021.09.012 . McInnes IB, Szekanecz Z, McGonagle D, Maksymowych WP, Pfeil A, Lippe R, et al. A review of JAK-STAT signalling in the pathogenesis of spondyloarthritis and the role of JAK inhibition. Rheumatology (Oxford) 2022;61:1783–94. https://doi.org/10.1093/rheumatology/keab740 . Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf TableS1.xlsx TableS2.xlsx TableS3.xlsx Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2024 Read the published version in International Immunopharmacology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3799546","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263760920,"identity":"d53adffb-9400-4d5d-8af0-03448afd535c","order_by":0,"name":"Xiuqing Yuan","email":"","orcid":"","institution":"Dermatology Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiuqing","middleName":"","lastName":"Yuan","suffix":""},{"id":263760921,"identity":"da136e53-a2fd-4ad3-b12e-0f1b85ec7e7b","order_by":1,"name":"Caixin Ou","email":"","orcid":"","institution":"Dermatology Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Caixin","middleName":"","lastName":"Ou","suffix":""},{"id":263760922,"identity":"42204ae5-44d0-4c9f-ad9d-d16a4b548213","order_by":2,"name":"Xinhui Li","email":"","orcid":"","institution":"Dermatology Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinhui","middleName":"","lastName":"Li","suffix":""},{"id":263760923,"identity":"827f89b2-9c36-4b5d-b5eb-fec7a4fee952","order_by":3,"name":"Zhe Zhuang","email":"","orcid":"","institution":"Dermatology Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Zhuang","suffix":""},{"id":263760924,"identity":"0aa7133d-4bc2-432e-bbcf-af02fa4f23f0","order_by":4,"name":"Yongfeng Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYFCCBAaGigoJOXnStJw5Y2Fs2ECSlrNtFYkMB4jVIO+e/Ezi4DyJBMYG5oePbhCjxfDMMzOJg9sk8tgZ2IyNc4jSMiOHTfrjNolixgYeNmmitUgcnCOR2HCAWC3yEiAtDaRoMeB5Zmxx4JiEsWEzsX6Rb09+eONATZ2cPHvzw8fE2XKAgUUCzGImRjnYlgYG5g/EKh4Fo2AUjIIRCgCgyTDGut8cogAAAABJRU5ErkJggg==","orcid":"","institution":"Dermatology Hospital of Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-12-24 08:29:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3799546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3799546/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.intimp.2024.111993","type":"published","date":"2024-05-01T00:45:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49019603,"identity":"20ade242-7b5a-4774-803b-bb66e39b5a0c","added_by":"auto","created_at":"2024-01-01 08:01:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":611603,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of skin circadian gene F3. \u003cstrong\u003eA\u003c/strong\u003e MAplot of DEGs between psoriasis and control groups (Up, upregulated DEGs, in red; NS, not significant genes, in grey; Down, downregulated DEGs, in blue; DEGs with average base mean \u0026gt; 1000, in green). \u003cstrong\u003eB, C\u003c/strong\u003e Enriched GO and KEGG pathways of DEGs (P-adj \u0026lt; 0.05). \u003cstrong\u003eD\u003c/strong\u003eIdentification of circadian clock gene F3 in psoriasis using Venn tools. \u003cstrong\u003eE\u003c/strong\u003eGene expression levels of F3 in psoriasis (PSO) and control group (CTRL). \u003cstrong\u003eF \u003c/strong\u003eROC curve analysis of F3. \u003cstrong\u003eG, H\u003c/strong\u003e Correlation analysis of F3 (* P-adj \u0026lt; 0.05, ** P-adj \u0026lt; 0.01, *** P-adj \u0026lt; 0.001, **** P-adj \u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/89e9b6854616074dd6590a5a.png"},{"id":49019724,"identity":"7db7740a-a4fd-4201-bcf8-993048a054ab","added_by":"auto","created_at":"2024-01-01 08:09:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1144731,"visible":true,"origin":"","legend":"\u003cp\u003eTranscription factors of F3 related genes predicted by RcisTarget. \u003cstrong\u003eA-C\u003c/strong\u003e Recovery curve of the gene-set on the motif ranking of the first three TFs. \u003cstrong\u003eD-F\u003c/strong\u003e Motif sequence diagram of the first three TFs. \u003cstrong\u003eG \u003c/strong\u003eRegulating networks of the first three TFs. up-regulated genes in psoriasis marked in orange; down-regulated in blue. \u003cstrong\u003eH\u003c/strong\u003ePPI network of F3 and IL17 signaling pathway related genes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/c7fc4de5908ee8d44c23b796.png"},{"id":49019606,"identity":"ca42debc-fcc0-47d4-981c-4351d2a37a36","added_by":"auto","created_at":"2024-01-01 08:01:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431665,"visible":true,"origin":"","legend":"\u003cp\u003eImmune related analysis of F3. \u003cstrong\u003eA\u003c/strong\u003e Single gene GSEA analysis of F3. \u003cstrong\u003eB\u003c/strong\u003e Immune-infiltrated difference between psoriasis and control groups. \u003cstrong\u003eC\u003c/strong\u003e Correlation analysis between F3 and immune cells. \u003cstrong\u003eD\u003c/strong\u003e Correlation analysis between F3 and specific T cells. * p \u0026lt;0.05, ** p \u0026lt;0.01, *** p \u0026lt;0.001, **** p \u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/3b5b5e7abb8984bf2c3a41bd.png"},{"id":49019608,"identity":"b9554dd4-028f-4df6-82fa-715b34989c32","added_by":"auto","created_at":"2024-01-01 08:01:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283169,"visible":true,"origin":"","legend":"\u003cp\u003eDifference of F3 expression among groups in validation cohorts. \u003cstrong\u003eA\u003c/strong\u003e GSEA hallmark analysis of DEGs. \u003cstrong\u003eB \u003c/strong\u003eF3 related single-gene GSEA hallmarks analysis. \u003cstrong\u003eC-E \u003c/strong\u003eDifferential F3 expression in various groups across different datasets related to biologic treatments, and changes in F3 expression levels at 0-, 1-, and 12-weeks post-treatment with biologics.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/1626280457f43cc524661c8f.png"},{"id":49019723,"identity":"d42d3c54-f31a-454a-994b-ea6ba7dc3ceb","added_by":"auto","created_at":"2024-01-01 08:09:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":445003,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell RNA sequencing analysis of plaque psoriasis and normal skin. \u003cstrong\u003eA\u003c/strong\u003e The UMAP plot exhibiting average expression of well-established cell type markers used to identify all cell populations. \u003cstrong\u003eB\u003c/strong\u003e Cell population annotation in UMAP plot based in signature cell type markers.\u003cstrong\u003e C\u003c/strong\u003e Average expression of F3 in cell population between PP and NS.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/0b8431295d13bdf806b0124f.png"},{"id":49019728,"identity":"09226f93-d17d-4a45-95af-c9dc7d753aec","added_by":"auto","created_at":"2024-01-01 08:09:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":635601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003eCircadian related pathways and IL-17 signaling pathway activities in all cell clusters, keratinocytes and KRT_4 cluster. \u003cstrong\u003eD\u003c/strong\u003e Correlation analysis of F3 and specific pathways in KRT_4 cluster. \u003cstrong\u003eE\u003c/strong\u003e Volcano plot depicting genes in specific pathways negatively and positively correlated to F3 with p \u0026lt; 0.05 in KRT_4 cell population.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/8e972dbee18e1170e8fa81f7.png"},{"id":49019611,"identity":"83747553-f00f-4100-bdae-34ab058b7a88","added_by":"auto","created_at":"2024-01-01 08:01:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5100236,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemistry of F3 in plaque psoriasis and healthy skin samples. \u003cstrong\u003eA-E\u003c/strong\u003e Immunohistochemistry of F3 in plaque psoriasis skin samples. \u003cstrong\u003eF-J\u003c/strong\u003e Immunohistochemistry of F3 in healthy individual skin samples.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/8e1b751c56fd6c49b6959cdd.png"},{"id":49019607,"identity":"c48cc884-5f49-4960-b3ed-3ef488bff2fd","added_by":"auto","created_at":"2024-01-01 08:01:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":330564,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of F3 expression and potential function. \u003cstrong\u003eA\u003c/strong\u003e The relative expression of F3 was higher in PP than NS. \u003cstrong\u003eB\u003c/strong\u003e The relative expression of F3 in HaCaT cell line gradually decreased with increasing concentrations of IL-17A after 48 hours of stimulation. \u003cstrong\u003eC\u003c/strong\u003e Interference with different siRNAs led to a significant decrease in F3 expression compared to the si-N group (*** p \u0026lt; 0.001). \u003cstrong\u003eD \u003c/strong\u003eDEGs of RNA-seq data between si-F3 (si-1, 2, 3) and si-N group. \u003cstrong\u003eE\u003c/strong\u003e Enriched GO_BP pathways associated with DEGs (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/a80bf2eb27caaa5c21cfdb54.png"},{"id":53895595,"identity":"17513f61-2b43-47c8-be2a-f3a891c14ec6","added_by":"auto","created_at":"2024-04-02 00:45:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4752426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/758ecf58-0733-4475-bace-d0845d202421.pdf"},{"id":49019727,"identity":"fc6db3dd-8baa-4c4f-8e58-51ede1a85399","added_by":"auto","created_at":"2024-01-01 08:09:15","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":7815,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/cbc5cb299a5e8b9ccf50bc4b.pdf"},{"id":49019726,"identity":"0500741f-c98e-4a76-a5af-d4a3576242aa","added_by":"auto","created_at":"2024-01-01 08:09:14","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":145871,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/f868aa663c3b8c49a24404c5.xlsx"},{"id":49019614,"identity":"b09f1d36-0acd-48ef-8e07-40fbc1348a36","added_by":"auto","created_at":"2024-01-01 08:01:14","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":59336,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/fe32d329eabbe48907e38e32.xlsx"},{"id":49019725,"identity":"75977921-b93f-443b-8763-d99210f3e605","added_by":"auto","created_at":"2024-01-01 08:09:14","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":36208,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3799546/v1/c6f8d5df966abfcfe56de4a4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The skin circadian clock gene F3 as a potential marker for psoriasis severity and its bidirectional relationship with IL-17 signaling in keratinocytes","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsoriasis is a chronic skin disease primarily characterized by hyper inflammation and proliferation, which results from a complex interplay of factors including genetics, environment, lifestyles, etc. [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The development of biologic therapies targeting inflammatory cytokines like IL-12/23, IL-17, and TNF-α inhibitors has led to significant progress in the treatment of psoriasis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These medications have proven highly efficacious in alleviating skin hyperinflammatory status, inaugurated a new era in psoriasis treatment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite the efficacy of biologic therapies in treating psoriasis, many patients experience recurrence after treatment. The underlying mechanism behind this phenomenon is not yet fully understood [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic disruption of the circadian rhythm induced by poor lifestyle habits is widely acknowledged as a trigger for endogenous inflammation, resulting in numerous diseases such as autoimmune disorders, depressive illnesses, and metabolic disorders [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Unlike other tissues, skin is a special organ that could directly regulated by light-dark cycles, thus modulate cellular differentiation and immunity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Psoriasis as a skin disorder distinguished by hyperinflammation and proliferation, has been implicated in the disruption of circadian rhythm in the skin [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Yu Z et al. proposed that patients with psoriasis experienced disrupted biological rhythms in lesion skin based on high-throughput sequencing analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, Nemeth V et al. suggested that altered expression of the clock genes may result in pro-inflammatory effects in keratinocytes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In short, previous research has indicated that circadian rhythm disturbances may instigate skin inflammation. Accordingly, investigating the skin's circadian clock gene and its interactions with particular inflammatory pathway may aid in the identification of therapeutic targets and the control of disease relapse.\u003c/p\u003e \u003cp\u003eF3, also known as tissue factor, plays a significant role in blood coagulation cascades [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is also expressed in mesenchymal cells found in colon and muscle tissues, where its low expression levels are closely associated with pathological immune responses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, its roles as one of the skin circadian genes in psoriasis have not been explored yet. Therefore, our study aimed to investigate the involvement of F3 in psoriasis pathogenesis.\u003c/p\u003e \u003cp\u003eIn our research, we identified F3 as the signature skin circadian gene that may contribute to psoriasis development and is closely linked to the IL-17 signaling pathway. Using datasets of psoriasis patients treated with cytokine inhibitors and a single-cell dataset, we validated F3 as a potential diagnostic marker and predictor of psoriasis severity. Additionally, through ex vivo experiments, we revealed a bidirectional relationship between F3 and IL-17 signaling in HaCaT cells. Our findings provide insights into the understanding of psoriasis pathogenesis and have implications for advancing disease management strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eData acquisition\u003c/p\u003e\n\u003cp\u003eWe performed data mining analyses on plaque psoriasis and healthy skin using the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e). The exploration cohort, GSE121212, consisting of RNA-seq analysis data with psoriatic lesions and healthy controls, was used for initial analyses. Validation analyses were conducted using GSE117239, GSE51440, and GSE137218, which contained longitudinal data on effective biologics used in treating psoriasis over time. To investigate the hub gene at the single-cell level, we utilized the scRNA-seq dataset GSE173706. Data processing was performed in R software 4.2.1, and visualization primarily employed the ggplot2 package (version 3.4.1).\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis\u003c/p\u003e\n\u003cp\u003eDifferential gene expression in exploration cohort was calculated with DESeq2 package (version 1.38.3) [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Genes with adjusted P value (P-adj)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log fold change (logFC) values\u0026thinsp;\u0026gt;\u0026thinsp;1.5 were considered differentially expressed genes (DEGs). DEGs with average base mean\u0026thinsp;\u0026gt;\u0026thinsp;2^10 were considered high expressed DEGs. The results were visualized with MAplot.\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses were conducted in DEGs using the R package clusterProfiler (version 4.0) [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The results of the GO enrichment analysis were divided into three categories: cellular components (CC), biological processes (BP), and molecular functions (MF). Pathways that exhibited a P-adj value of less than 0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003eIdentification and diagnostic value prediction of hub CirCaDB related gene\u003c/p\u003e\n\u003cp\u003eCircadian clock genes of human skin were obtained from Ciradian Expression Profiles Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://circadb.hogeneschlab.org/\u003c/span\u003e\u003c/span\u003e). By overlapping clock genes and high expressed DEGs using the ggVennDiagram package, F3 was selected. The difference of gene expression levels of F3 between groups was displayed. The receiver operating characteristic (ROC) curve analysis was performed to determine gene diagnostic effectiveness using the ROCit package.\u003c/p\u003e\n\u003cp\u003eBatch correlation analysis of F3\u003c/p\u003e\n\u003cp\u003eTo explore the potential relationships among F3 and the other genes in gene expression data,\u003c/p\u003e\n\u003cp\u003ewe utilized a for loop to conduct Spearman correlation analysis on the expression of F3 and other genes. Genes with P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected and then ranked by spearman correlation coefficients. By excluding non-protein coding genes, we identified the top four genes with both positive and negative correlations to F3 and analyzed their relationship with F3 using the GGally package.\u003c/p\u003e\n\u003cp\u003eIdentification of gene regulatory networks of F3 related genes\u003c/p\u003e\n\u003cp\u003eTo determine if the same transcription factors (TFs) regulate the most positively and negatively correlated genes of F3, we selected the top fifty positively and negatively related genes as representatives and conducted a TF-motif enrichment analysis using the RcisTarget package [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The first three motifs whose downstream genes including F3 were selected, and the related gene regulatory networks were further displayed and modified in Cytoscape (version 3.9.1) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003ePotential relationships of F3 and genes involved in IL-17 signaling pathway\u003c/p\u003e\n\u003cp\u003eTo explore possible connections between F3 and other genes linked to the IL-17 signaling pathway, we incorporated both F3 and IL17-related genes to construct a protein-protein interaction (PPI) network using the String website (version 11.5) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The PPI network were further modified in Cytoscape (version 3.9.1).\u003c/p\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) of F3\u003c/p\u003e\n\u003cp\u003eTo understand the roles of F3 more comprehensively, we applied the single gene GSEA analysis [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The gene list was ranked in descending order according to their correlation coefficients with F3, and KEGG gene set downloaded from Enrichr library (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003c/span\u003e) was utilized as the gene set [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The analyses were performed using clusterProfiler package [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. P-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistic significant. The circadian related pathways and immune related pathways were visualized in GseaVis package.\u003c/p\u003e\n\u003cp\u003eAccessing the correlation among F3 and immune cells\u003c/p\u003e\n\u003cp\u003eThe immune cell signatures were used as the gene set to perform the single sample GSEA (ssGSEA) analysis in exploration cohort [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. The infiltration scores of 28 kinds of immune cells were calculated by implementing GSVA package (version 1.46.0) with \u0026ldquo;ssgsea\u0026rdquo; method [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, we assessed the relationship among F3 expression levels and immune cell infiltration scores using Spearman\u0026rsquo;s correlation analysis. Additionally, the correlation between F3 and specific immune T cells was calculated in ggpubr package.\u003c/p\u003e\n\u003cp\u003eGSEA analyses with different gene ranking orders\u003c/p\u003e\n\u003cp\u003eTo distinguish the difference among DEGs and F3 related genes, we ranked genes by logFC values or correlation coefficients with F3 in descending order. Then we used the hallmark gene set downloaded from the MsigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/\u003c/span\u003e\u003c/span\u003e) to perform the GSEA analysis using the clusterProfiler package [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. By comparing the results, we could better understand the potential contributions of F3 to psoriasis.\u003c/p\u003e\n\u003cp\u003eValidation of diagnostic value of F3 in psoriasis\u003c/p\u003e\n\u003cp\u003eTo validate the diagnostic value of F3 in psoriasis, we analyzed its expression levels in validation cohorts by comparing gene expression levels between lesions and non-lesions at baseline, as well as within lesions at different time points following treatment with biologics. Furthermore, if the dataset includes information on the Psoriasis Area and Severity Index (PASI), we categorized the samples based on their PASI score (\u0026lt;\u0026thinsp;5, mild; 5\u0026ndash;10, moderate; \u0026gt;10, severe) to investigate differences in F3 expression across severities, which were assessed for statistical significance using the Wilcoxon test.\u003c/p\u003e\n\u003cp\u003eExploration of F3 distribution in skin through scRNA-seq analysis\u003c/p\u003e\n\u003cp\u003escRNA-seq genomics allows for the identification of distinct cell clusters with functional diversity and can reveal the expression levels of hub genes specific to each cluster [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. We applied the Seurat (version 4.0) and Harmony packages to perform all the necessary steps (NormalizeData, FindVariableFeatures, ScaleData, RunPCA, RunHarmony, RunUMAP, FindNeighbors, FindClusters) using default parameters, unless otherwise stated [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. During the procedures, we used the top 30 Harmony dimensions as input for UMAP, and visualized the resulting clusters on the first two UMAP dimensions, using a clustering resolution of 0.5. By comparing known markers with the most highly expressed genes found by function FindAllMarkers in Seurat package, we identified the cellular identities of the ten clusters [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. We employed the scRNAtoolVis package to visualize the expression levels of these signature cell markers. Lastly, to identify the specific cell clusters with differential F3 expression between psoriasis and normal skin, we visualized F3 expression levels on a UMAP plot.\u003c/p\u003e\n\u003cp\u003ePathway activities assessment analysis\u003c/p\u003e\n\u003cp\u003eTo identify which cell population exhibited activated circadian-related and IL17 signaling pathways, we utilized AUCell analysis to compute AUC values for KEGG gene sets via the AUCell R package (version 3.1.6) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Pathway activities were then displayed separately by group. To determine whether F3 was correlated with these pathways in the KRT_4 cluster, we conducted spearman's correlation analysis using the ggpubr package. Genes from specific pathways with an absolute correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.15 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were marked as strongly correlated and plotted in a correlation volcano plot.\u003c/p\u003e\n\u003cp\u003eImmunohistochemistry and real-time quantitative PCR (RT-qPCR) experiments\u003c/p\u003e\n\u003cp\u003eWe obtained five paraffin-embedded psoriasis patient skin samples and five healthy individual skin samples for immunohistochemistry experiment. The skin tissues were sectioned at 3 um thickness and underwent deparaffinization, rehydration, hydrogen peroxide blockage, antigen retrieval, and incubation with F3 antibody (1:100, Abcam, MA, USA) for 30 min. As the secondary antibody, we used goat anti-rabbit IgG peroxidase conjugate (proteintech, USA) for 20 min at 37℃. Signal detection was performed using DAB staining, and slides were observed under a bright-field microscope at 20x magnification. To quantitatively analyze the staining intensity, we employed Image-Pro Plus 6 software to calculate the Average Optical Density (AOD) score. Concurrently, RT-qPCR analysis was conducted on skin samples from 20 individuals with psoriatic lesions and 18 healthy individuals without skin disorders. This investigation allowed us to quantitatively examine the differences in F3 expression levels between psoriatic and normal skin ex vivo. The research obtained ethical approval from the Research Ethics Committee of the Dermatology Hospital at Southern Medical University.\u003c/p\u003e\n\u003cp\u003eCell culture and IL-17A stimulation\u003c/p\u003e\n\u003cp\u003eThe spontaneously immortalized human keratinocyte cell line HaCaT (BNCC, Cat. NO. 3405CA93) was cultured in DMEM (Gbico, C11995500BT, USA) supplemented with 10% fetal bovine serum (Biological Industries,04-001-1A, IL). HaCaT cells were cultured with different concentration gradient cytokines human IL-17A (PeproTech, PP-200-17, USA) to establish a psoriatic over proliferation model. After a 48-hour incubation with cytokines, the cells were harvested for RNA extraction.\u003c/p\u003e\n\u003cp\u003eRNA extraction and RT-qPCR\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from HaCaT cells using TRI Reagent\u0026reg; (Sigma, Cat. No. T9424, USA) following the recommended protocol provided by the manufacturer. Next, SYBR\u0026reg; Green Pro Taq HS Premix (Accurate Biology, AG11701, CN) was used to reverse-transcribe total RNAs into cDNA. The qPCR was conducted using Evo M-MLV RT Kit with gDNA Clean for qPCR Ⅱ (Accurate Biology, AG11728, CN) following the manufacturer\u0026rsquo;s standard protocols. The expression of Human GAPDH served as a baseline to normalize all data. The primers used for F3 and GAPDH amplification were custom synthesized by Sangon Biotech (China). The primer sequences employed in this study are provided below: F3 (F) 5\u0026rsquo;-ACTTGGCACGGGTCTTCTCCTAC, F3 (R) TTGTTGGCTGTCCGAGGTTTGTC-3\u0026rsquo;; GAPDH (F) 5\u0026rsquo;-AGAAGGCTGGGGCTCATTTG, GAPDH (R) GCAGGAGGCATTGCTGATGAT-3\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eSiRNA transfection and transcriptome sequencing analysis\u003c/p\u003e\n\u003cp\u003esiRNAs targeting the F3 gene were synthesized by RiboBio (China) and transfected into HaCaT cells using the riboFECTTM CP Transfection Kit (RiboBio, C10511-05, CN), following the manufacturer's protocol. The siRNA sequences used were as follows: si-1: CATCCTGGCTATATCTCTA; si-2: GTGGTATTTGTGGTCATCA; si-3: GAACGGACTTTAGTCAGAA; si-N: provided by RiboBio Company. The experiment comprised two groups: si-F3 (experimental group, three samples) and si-N (negative control group, one sample). RNA extraction methodology was consistent for siRNA-interfered HaCaT cells to determine siRNA knockdown efficiency. Subsequently, RNA samples underwent transcriptome sequencing analysis on the Illumina/MGI platform. FASTQ files were generated using Illumina's bcl2fastq software (v2.20.0.422). Quality control assessment, alignment, and generation of gene count matrices were performed on the original FASTQ files. Differential gene expression analysis on gene count matrices was conducted using the DESeq2 package (version 1.38.3), considering genes with P-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and logFC values\u0026thinsp;\u0026gt;\u0026thinsp;0.6 as DEGs. Heatmaps displaying DEG expression in each sample were generated using the \"pheatmap\" package. Furthermore, the \"clusterProfiler\" package was employed to perform GO enrichment analysis, unveiling potential biological processes associated with the DEGs.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDatasets information\u003c/p\u003e\n\u003cp\u003eWe utilized the RNA-seq dataset GSE121212 for exploratory analysis of skin tissue, while validation of our findings was performed using gene profiling array datasets GSE117239, GSE51440, GSE137218, and scRNA-seq dataset GSE173706. An overview of the datasets used in our computational analyses was provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of five GEO datasets involving in exploration and validation cohorts.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGEO Dataset\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExperiment type\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSamples\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE121212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBulk RNA-seq\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExploration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 Ctrl 28 Pso\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE117239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExpression profiling array\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84 NL 240 LS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE51440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExpression profiling array\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 NL 39 LS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE137218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExpression profiling array\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 NL 70 LS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE173706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003escRNA-seq\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 Nor 14 LS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIdentification of DEGs\u003c/p\u003e\n\u003cp\u003eAccording to the criteria set above, a total of 3528 DEGs (1111 upregulated, 2417 downregulated) were selected, with 159 genes being considered high-expression DEGs. The upregulated DEGs were labeled in red, downregulated in green and those high-expression in green (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnriched GO and KEGG pathways\u003c/p\u003e\n\u003cp\u003eWe conducted functional enrichment analysis using all DEGs to explore the potential biological pathways associated with psoriasis onset. Our analysis revealed that 880 GO pathways, including 685 BP, 67 CC, 128 MF, and 29 KEGG pathways, were significantly enriched in DEGs with P-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The GO and KEGG analyses indicated that most DEGs were associated with immune or inflammatory response, with the most statistically significant pathways being regulation of cytokine production, cytokine-cytokine receptor interaction, Th17 cell differentiation, IL-17 signaling pathway, PPAR signaling pathway, and TNF signaling pathway (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB, C). These results were consistent with the known psoriasis etiology.\u003c/p\u003e\n\u003cp\u003eIdentification of F3 as the signature skin circadian gene\u003c/p\u003e\n\u003cp\u003eWe obtained 98 human skin-related circadian genes from the CircaDB database, and F3 was identified as the signature skin circadian gene through the intersection with the 159 high-expressed DEGs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). In comparison to healthy controls, F3 expression levels were significantly decreased in psoriasis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Meanwhile, the AUC value of F3 was 0.9652 (95%CI: [0.9219, 1]) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). These findings implicated that F3 had excellent sensitivity and specificity for diagnosing psoriasis from healthy controls.\u003c/p\u003e\n\u003cp\u003eBatch correlation analysis of F3\u003c/p\u003e\n\u003cp\u003eWe performed batch correlation analysis of F3 using a for loop in R software. Among the gene list ranked by correlation coefficient values with F3, protein-coding genes ID4, ZDHHC9, TUFT1, and ZNF12 were found to be most positively correlated with F3, with correlation coefficients of 0.946, 0.927, 0.906, and 0.877, respectively. Conversely, IL17A, MLKL, RCE1, and SLC38A7 were most negatively correlated with F3, with correlation coefficients of -0.888, -0.880, -0.823, and \u0026minus;\u0026thinsp;0.804, respectively (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG, H).\u003c/p\u003e\n\u003cp\u003eGene regulatory network analysis of F3 and its correlated genes\u003c/p\u003e\n\u003cp\u003eOur gene regulatory network analysis of F3's most correlated genes suggested that homer_AAYTAGGTCA_RORgt, swissregulon_hs_RORA.p2, and transfac_pro_M08966 were potential motifs that bind to F3 and its closely related genes (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). These motifs were associated with RAR-related orphan receptors, specifically RORC, RORA, and RORB. Surprisingly, the bit patterns of these motifs are remarkably similar (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD-F). In addition to potentially regulating F3 by binding to its promoter region, they may also simultaneously regulate genes such as IL17A, ADGRF1, PIPNM1, PAI14, ZNF652, KANK1, SLC8A1, NISIG2, and EXPH5 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). Notably, among these genes, IL17A, ADGRF1, and PIPNM1 were upregulated in psoriasis compared to healthy controls, while others were downregulated. Our findings illuminated that common upstream regulators may be involved in the regulation of both the most positively and negatively correlated genes with F3 in psoriasis.\u003c/p\u003e\n\u003cp\u003ePotential relationships between F3 and genes involved in IL-17 signaling pathway\u003c/p\u003e\n\u003cp\u003eOur data mining from the STRING website revealed a potential correlation between F3 and genes coding for TNF, IL6, IL1B, PTGS2, MMP9, IFNG, JUN, CXCL8, CCL2, and CSF3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH). These genes acted as effector genes that target specific cells in IL-17 pathway activities, namely in psoriasis, they mainly involved in neutrophil infiltration of the epidermis. Based on the findings, F3 was likely involved in the innate immune aspect of the IL-17 pathway, though its potential association with the pathway's adaptive immune response remains unclear.\u003c/p\u003e\n\u003cp\u003eF3 correlated with skin circadian rhythm and T cell differentiation\u003c/p\u003e\n\u003cp\u003eThere was 183 KEGG pathways statistically enriched in single gene GSEA analysis (Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). Alongside the IL-17 pathway, other pathways related to circadian clock and adaptive immune cell differentiation were identified. Interestingly, our analysis showed that F3 was not only correlated with the IL-17 signaling pathway and circadian processes but was also associated with the differentiation of adaptive immune T cells, such as Th17 cells, Th1 cells, and Th2 cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eImmune signature ssGSEA analysis result\u003c/p\u003e\n\u003cp\u003eThe ssGSEA analysis of immune signatures showed that psoriasis lesions were infiltrated with various immune cells compared to normal skin (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The correlation analysis between F3 and immune cells revealed a negative correlation of both innate and adaptive immune cells, particularly with their activated states (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). Furthermore, based on our single gene GSEA analysis, F3 may be associated with the process of T helper cells developing into activated states. These results suggested that F3 may serve as a potential marker of inflammation in psoriasis.\u003c/p\u003e\n\u003cp\u003eF3 negatively associated with psoriasis pathology\u003c/p\u003e\n\u003cp\u003eOur GSEA analysis revealed 31 and 41 hallmark pathways were statistically enriched based on genes ranked by DEGs and correlation coefficients with F3, respectively (Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). The analysis showed that psoriasis was characterized by the enrichment of immune-related pathways such as interferon alpha/gamma response and cell cycle-related pathways such as MYC/E2F targets (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Interestingly, the pathways enriched in the GSEA analysis based on correlation coefficients of F3 presented almost opposing results compared to DEGs. These findings suggested that F3 may be negatively associated with hyperinflammatory and proliferation states in psoriasis.\u003c/p\u003e\n\u003cp\u003eF3 as a diagnostic marker and predictor of psoriasis severity\u003c/p\u003e\n\u003cp\u003eWe examined F3 expression levels in validation cohorts using the above-mentioned grouping criteria to assess its diagnostic value. Our findings showed that F3 expression was significantly lower in psoriatic lesions (LS) compared to non-lesion (NL) groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). Interestingly, there was a statistically significant decrease in F3 expression levels as psoriasis severity increased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Conversely, treatment of psoriasis with effective inflammatory cytokine inhibitors resulted in a gradual increase in F3 expression levels over time, which reached levels similar to those observed in the NL group after 12 weeks of treatment (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-E).\u003c/p\u003e\n\u003cp\u003eF3 expressed lower in differentiated keratinocytes\u003c/p\u003e\n\u003cp\u003eWith Seurat and Harmony packages, we processed the scRNA-seq dataset. We identified ten cell population based on the following gene expression signatures: KRT14, KRT5, KRT10, KRT1, SBSN, KLK7 (keratinocytes, KRT_1-KRT_4); HLA-DRA, KLA-DPB1, CD3D, CD3E (immune cells, IMM_1-IMM_2); MLANA, DCT (melanocytes, MELA); DCN, LUM (fibroblast, FIB); ACTA2, TAGLN (pericytes, PERI); CLDN5, VWF (vascular endocytes, ENDO) (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Based on the clustering, we found F3 mostly enriched in KRT_4 cluster and were significantly different between groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003ePathway activities assessment in cell clusters\u003c/p\u003e\n\u003cp\u003eBased on the AUCell analysis, we found circadian related pathways and IL-17 signaling pathway mainly enriched in keratinocytes, and all pathways significantly differently activated in KRT_4 cluster between group (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). Interestingly, in KRT_4 cell population, F3 owned significant correlation with both circadian related pathways and IL-17 signaling pathway (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). In correlation volcano plot, RORA was the significantly correlated gene in circadian rhythm pathway; CACNA1H, ITPR3, GNG12, GNB4, CALM3 were the genes in circadian entrainment; while S100A8, S100A9, S100A7, JUND, HSP90AB1, JUND, HSP90AA1 were the genes in IL-17 signaling pathway (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eValidation of the expression and location of F3 in skin\u003c/p\u003e\n\u003cp\u003eBased on scRNA-seq analysis, F3 was found to be primarily expressed in KRT_4 cell population. Immunohistochemistry experiments were conducted to validate this finding, confirming that F3 was predominantly expressed in differentiated keratinocytes and exhibited decreased expression in psoriatic skin compared to controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e), consistent with the scRNA-seq analysis. The difference in AOD between the two groups was statistically significant as shown in Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. Additionally, RT-qPCR analysis further demonstrated significant downregulation of F3 in psoriasis compared to normal skin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eIL-17A exhibited dose-dependent inhibition of F3 expression in HaCaT cells\u003c/p\u003e\n\u003cp\u003eWith the aimed to investigate the effect of IL-17A on F3 expression levels, we measured the effect of different concentrations of IL-17A (0, 25, 50, 75, 100 and 150ng/ml) on F3 gene expression in HaCaT cells through experimental methods. As the concentration of IL-17A increased, F3 expression levels showed a clear stepwise decrease (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). In summary, our results demonstrated that IL-17A could effectively inhibit F3 expression, and this inhibition had a certain dose-dependent effect.\u003c/p\u003e\n\u003cp\u003eF3 interacted with IL17 signaling pathway in HaCaT cells\u003c/p\u003e\n\u003cp\u003eTo further investigate the functional role of F3 in keratinocytes, we conducted an RNA interference (RNAi) experiment. The knock-down efficiency of siRNAs was visualized in a histogram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC). Within the DEGs identified (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD), F3 and FST exhibited downregulation, while the remaining genes showed upregulation in the si-F3 group. Through GO enrichment analysis of biological processes, we identified a total of 175 enriched GO_BP pathways. Notably, significant enrichments were observed in biological processes such as positive regulation of receptor signaling via JAK-STAT, acute inflammatory response, and activation of plasma proteins involved in the inflammatory response (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCircadian rhythm played critical roles in a range of disease pathogenesis, from physical illnesses such as cardiovascular disease, kidney disease, and malignant tumors, to mental health disorders such as depression and anxiety [\u003cspan additionalcitationids=\"CR35 CR36 CR37\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The disruption of circadian rhythms is involved in the pathogenesis of various diseases, through the mediation of immune and metabolic disturbances [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Hence, physical intervention, or molecularly targeting circadian genes could help to avoid illness and to optimize treatment outcomes [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Not surprisingly, circadian disruptions also play an important role in psoriasis. By leveraging the periodicity of the circadian rhythm, NS et al. found that nightly topical application of glucocorticoids was more effective than daytime use [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Subsequently, researchers identified perturbations in clock gene transcriptional profiles in psoriatic lesions compared to healthy skin [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Further mechanistic investigations in animal models revealed that mutations in the circadian gene CLOCK/PER2 could influence the development and progression of psoriasis by affecting the direct modulation of immune cell response to IL-23 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Despite recent attention on core circadian genes, only limited research has been conducted on the role of peripheral skin circadian genes in psoriasis.\u003c/p\u003e \u003cp\u003eTo validate the differential expression of F3, we conducted RT-qPCR and immunohistochemistry experiments. Results confirmed downregulation of F3 in psoriatic lesions and its predominant expression in differentiating keratinocytes. Knockdown of F3 using siRNAs demonstrated significant efficiency in HaCaT cells. Transcriptome sequencing analysis of the knockdown samples identified several DEGs, including IL6R as one of the highly upregulated genes.\u003c/p\u003e \u003cp\u003eIL6R, upon binding to IL-6 ligand, activates downstream signaling pathways such as IL-6/STAT3, leading to the production of inflammatory mediators in keratinocytes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Activation of IL6R also influences cell cycle regulation, cellular differentiation, and growth of keratinocytes, potentially contributing to chronic inflammation and abnormal keratinization observed in psoriasis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Previous research by AR et al. showed that IL6R acts as an upstream signal for STAT3-induced psoriasis-like dermatitis in transgenic mice, reducing dermatitis upon deletion of IL-6Rα in keratinocytes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGO_BP enrichment analysis of DEGs revealed a correlation between interfering with F3 expression and positive regulation of the JAK-STAT signaling pathway receptor. Notably, the IL-17 pathway can modulate intracellular inflammatory responses through JAK-STAT activation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Interestingly, both IL6R and IL6 are crucial genes in the JAK-STAT and IL17 signaling pathways, respectively. These findings suggested F3 potentially involved in the IL17 signaling pathway, regulating key genes such as IL6R, thereby activating JAK-STAT within keratinocytes and inducing aberrant intracellular inflammation.\u003c/p\u003e \u003cp\u003eIn conclusion, the bidirectional relationship between F3 and IL-17 signaling may explain why F3 is a potential marker for representing psoriasis severity. These findings offer valuable insights into the role of F3 in modulating key signaling pathways involved in psoriasis pathogenesis and progression.\u003c/p\u003e \u003cp\u003eDespite the valuable insights provided by our study, there are some limitations. Firstly, we only conducted siRNA knockdown experiments in HaCaT cells for initial validation, and further investigations using animal models are necessary to explore F3's function in the whole organism. Additionally, we did not examine the roles of other skin circadian genes or investigate how disruption of the circadian rhythm pathway as a whole affect keratinocyte inflammation. Future studies should address these gaps to gain a more comprehensive understanding of psoriasis development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study identified the skin circadian clock gene F3 as a potential marker for psoriasis and its severity. We also revealed a bidirectional relationship between F3 and IL-17 signaling in keratinocytes. In conclusion, we hope our findings will provide new insights into the pathogenesis of psoriasis and offer potential avenues for the development of novel therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003esiRNA\u0026nbsp; \u0026nbsp;Small interfering RNA\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp; \u0026nbsp; \u0026nbsp;differentially expressed genes\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG\u0026nbsp; \u0026nbsp;\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eTFs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;transcription factors\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;protein-protein interaction\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp;Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003essGSEA \u0026nbsp;single sample GSEA\u003c/p\u003e\n\u003cp\u003ePASI \u0026nbsp; \u0026nbsp; \u0026nbsp;Psoriasis Area and Severity Index\u003c/p\u003e\n\u003cp\u003eRT-qPCR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;real-time quantitative PCR\u003c/p\u003e\n\u003cp\u003eAOD \u0026nbsp; \u0026nbsp; \u0026nbsp;Average Optical Density\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe are grateful to the authors of GSE121212, GSE117239, GSE51440, GSE137218, and GSE173706 for sharing their data. We would also like to thank the members of GZDlab for providing valuable guidance and junjunlab (https://github.com/junjunlab/) for useful R packages. Additionally, we acknowledge the hard work of our researchers and the valuable advice from the reviewers.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe research was conceptualized by XQY and CXO. XQY conducted the data analysis and drafted the manuscript, while CXO performed the experimental work and revised the manuscript. XHL and ZZ were responsible for collecting tissue samples and conducting experiments. The manuscript was reviewed and revised by XQY, CXO, and XHL. YFC provided supervision throughout the research process. All authors made contributions to the article and approved the final version for submission.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eWe analyzed datasets GSE121212, and GSE117239, GSE51440, GSE137218 and GSE173706 in this research. These five public datasets were all available in the GEO (\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/a\u003e) database.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of Dermatology Hospital of Southern Medical University (Guangzhou, China) (IRB#2023024). Informed consent was obtained from all patients included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGriffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet 2021;397:1301\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(20)32549-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)32549-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen M, Xiao Y, Jing D, Zhang G, Su J, Lin S, et al. Associations of combined lifestyle and genetic risks with incident psoriasis: A prospective cohort study among UK Biobank participants of European ancestry. J Am Acad Dermatol 2022;87:343\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jaad.2022.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jaad.2022.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadden SK, Flanagan KL, Jones G. How lifestyle factors and their associated pathogenetic mechanisms impact psoriasis. Clin Nutr 2020;39:1026\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clnu.2019.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2019.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhoreschi K, Balato A, Enerb\u0026auml;ck C, Sabat R. Therapeutics targeting the IL-23 and IL-17 pathway in psoriasis. Lancet 2021;397:754\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(21)00184-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)00184-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLowes MA, Su\u0026aacute;rez-Fari\u0026ntilde;as M, Krueger JG. Immunology of psoriasis. Annu Rev Immunol 2014;32:227\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-immunol-032713-120225\u003c/span\u003e\u003cspan address=\"10.1146/annurev-immunol-032713-120225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. JAMA 2020;323:1945\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2020.4006\u003c/span\u003e\u003cspan address=\"10.1001/jama.2020.4006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan de Kerkhof PC. From Empirical to Pathogenesis-Based Treatments for Psoriasis. J Invest Dermatol 2022;142:1778\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jid.2022.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jid.2022.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuig L, Costanzo A, Mu\u0026ntilde;oz-El\u0026iacute;as EJ, Jazra M, Wegner S, Paul CF, et al. The biological basis of disease recurrence in psoriasis: a historical perspective and current models. Br J Dermatol 2022;186:773\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bjd.20963\u003c/span\u003e\u003cspan address=\"10.1111/bjd.20963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang W, Ramsey KM, Marcheva B, Bass J. Circadian rhythms, sleep, and metabolism. J Clin Invest 2011;121:2133\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1172/JCI46043\u003c/span\u003e\u003cspan address=\"10.1172/JCI46043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatke A, Young MW, Axelrod S. Molecular mechanisms and physiological importance of circadian rhythms. Nat Rev Mol Cell Biol 2020;21:67\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41580-019-0179-2\u003c/span\u003e\u003cspan address=\"10.1038/s41580-019-0179-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan J, Greenberg EN, Karri SS, Andersen B. The circadian clock and diseases of the skin. FEBS Lett 2021;595:2413\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/1873-3468.14192\u003c/span\u003e\u003cspan address=\"10.1002/1873-3468.14192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuengas-Martinez A, Paus R, Iqbal M, Bailey L, Ray DW, Young HS. Circadian rhythms in psoriasis and the potential of chronotherapy in psoriasis management. Exp Dermatol 2022;31:1800\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/exd.14649\u003c/span\u003e\u003cspan address=\"10.1111/exd.14649\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W-Q, Qureshi AA, Schernhammer ES, Han J. Rotating night-shift work and risk of psoriasis in US women. J Invest Dermatol 2013;133:565\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/jid.2012.285\u003c/span\u003e\u003cspan address=\"10.1038/jid.2012.285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Gong Y, Cui L, Hu Y, Zhou Q, Chen Z, et al. High-throughput transcriptome and pathogenesis analysis of clinical psoriasis. J Dermatol Sci 2020;98:109\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jdermsci.2020.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jdermsci.2020.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026eacute;meth V, Horv\u0026aacute;th S, Kiny\u0026oacute; \u0026Aacute;, Gyulai R, Lengyel Z. Expression Patterns of Clock Gene mRNAs and Clock Proteins in Human Psoriatic Skin Samples. Int J Mol Sci 2021;23:121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23010121\u003c/span\u003e\u003cspan address=\"10.3390/ijms23010121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIqbal MB, Johns M, Cao J, Liu Y, Yu S-C, Hyde GD, et al. PARP-14 combines with tristetraprolin in the selective posttranscriptional control of macrophage tissue factor expression. Blood 2014;124:3646\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1182/blood-2014-07-588046\u003c/span\u003e\u003cspan address=\"10.1182/blood-2014-07-588046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinchen J, Chen HH, Parikh K, Antanaviciute A, Jagielowicz M, Fawkner-Corbett D, et al. Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease. Cell 2018;175:372\u0026ndash;386.e17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2018.08.067\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2018.08.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamps J, Breuls N, Sifrim A, Giarratana N, Corvelyn M, Danti L, et al. Interstitial Cell Remodeling Promotes Aberrant Adipogenesis in Dystrophic Muscles. Cell Rep 2020;31:107597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.celrep.2020.107597\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2020.107597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-014-0550-8\u003c/span\u003e\u003cspan address=\"10.1186/s13059-014-0550-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2021;2:100141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.xinn.2021.100141\u003c/span\u003e\u003cspan address=\"10.1016/j.xinn.2021.100141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAibar S, Gonz\u0026aacute;lez-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017;14:1083\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nmeth.4463\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.4463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.1239303\u003c/span\u003e\u003cspan address=\"10.1101/gr.1239303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021;49:D605\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkaa1074\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016;44:W90-97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkw377\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkw377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017;18:248\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.celrep.2016.12.019\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2016.12.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013;14:7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-14-7\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-14-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A, Birger C, Thorvaldsd\u0026oacute;ttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015;1:417\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cels.2015.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.cels.2015.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAk S, R S, X A, Rs G, Jt G, R R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 2013;498. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature12172\u003c/span\u003e\u003cspan address=\"10.1038/nature12172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 2019;16:1289\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41592-019-0619-0\u003c/span\u003e\u003cspan address=\"10.1038/s41592-019-0619-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 2015;33:495\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nbt.3192\u003c/span\u003e\u003cspan address=\"10.1038/nbt.3192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerleev A, Ji-Xu A, Toussi A, Tsoi LC, Le ST, Luxardi G, et al. Proprotein convertase subtilisin/kexin type 9 is a psoriasis-susceptibility locus that is negatively related to IL36G. JCI Insight 2022;7:e141193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1172/jci.insight.141193\u003c/span\u003e\u003cspan address=\"10.1172/jci.insight.141193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSol\u0026eacute;-Boldo L, Raddatz G, Sch\u0026uuml;tz S, Mallm J-P, Rippe K, Lonsdorf AS, et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol 2020;3:188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42003-020-0922-4\u003c/span\u003e\u003cspan address=\"10.1038/s42003-020-0922-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChellappa SL, Vujovic N, Williams JS, Scheer FAJL. Impact of Circadian Disruption on Cardiovascular Function and Disease. Trends Endocrinol Metab 2019;30:767\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tem.2019.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.tem.2019.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirsov D, Bonny O. Circadian rhythms and the kidney. Nat Rev Nephrol 2018;14:626\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41581-018-0048-9\u003c/span\u003e\u003cspan address=\"10.1038/s41581-018-0048-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L, Zhang Z, Nice E, Huang C, Zhang W, Tang Y. Circadian rhythms and cancers: the intrinsic links and therapeutic potentials. J Hematol Oncol 2022;15:21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13045-022-01238-y\u003c/span\u003e\u003cspan address=\"10.1186/s13045-022-01238-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrouse JJ, Carpenter JS, Song YJC, Hockey SJ, Naismith SL, Grunstein RR, et al. Circadian rhythm sleep-wake disturbances and depression in young people: implications for prevention and early intervention. Lancet Psychiatry 2021;8:813\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(21)00034-1\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(21)00034-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDifrancesco S, Lamers F, Riese H, Merikangas KR, Beekman ATF, van Hemert AM, et al. Sleep, circadian rhythm, and physical activity patterns in depressive and anxiety disorders: A 2-week ambulatory assessment study. Depress Anxiety 2019;36:975\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/da.22949\u003c/span\u003e\u003cspan address=\"10.1002/da.22949\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrislip GR, Johnston JG, Douma LG, Costello HM, Juffre A, Boyd K, et al. Circadian Rhythm Effects on the Molecular Regulation of Physiological Systems. Compr Physiol 2021;12:2769\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cphy.c210011\u003c/span\u003e\u003cspan address=\"10.1002/cphy.c210011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtis AM, Bellet MM, Sassone-Corsi P, O\u0026rsquo;Neill LAJ. Circadian clock proteins and immunity. Immunity 2014;40:178\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.immuni.2014.02.002\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2014.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa D, Li S, Molusky MM, Lin JD. Circadian autophagy rhythm: a link between clock and metabolism? Trends Endocrinol Metab 2012;23:319\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tem.2012.03.004\u003c/span\u003e\u003cspan address=\"10.1016/j.tem.2012.03.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan W, Yuan X, Eltzschig HK. Circadian rhythm as a therapeutic target. Nat Rev Drug Discov 2021;20:287\u0026ndash;307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41573-020-00109-w\u003c/span\u003e\u003cspan address=\"10.1038/s41573-020-00109-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang K, Xu Z, Hu Y-Q, He Y-S, Wu G-C, Li T-Y, et al. Circadian clock genes as promising therapeutic targets for autoimmune diseases. Autoimmun Rev 2021;20:102866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.autrev.2021.102866\u003c/span\u003e\u003cspan address=\"10.1016/j.autrev.2021.102866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolensky MH, Portaluppi F, Manfredini R, Hermida RC, Tiseo R, Sackett-Lundeen LL, et al. Diurnal and twenty-four hour patterning of human diseases: acute and chronic common and uncommon medical conditions. Sleep Med Rev 2015;21:12\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.smrv.2014.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.smrv.2014.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen S, Bahakeem H, Alkhalifah A, Cavali\u0026eacute; M, Boukari F, Montaudi\u0026eacute; H, et al. Topical corticosteroids application in the evening is more effective than in the morning in psoriasis: results of a prospective comparative study. J Eur Acad Dermatol Venereol 2017;31:e263\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jdv.14027\u003c/span\u003e\u003cspan address=\"10.1111/jdv.14027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026eacute;meth V, Horv\u0026aacute;th S, Kiny\u0026oacute; \u0026Aacute;, Gyulai R, Lengyel Z. Expression Patterns of Clock Gene mRNAs and Clock Proteins in Human Psoriatic Skin Samples. Int J Mol Sci 2021;23:121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23010121\u003c/span\u003e\u003cspan address=\"10.3390/ijms23010121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndo N, Nakamura Y, Aoki R, Ishimaru K, Ogawa H, Okumura K, et al. Circadian Gene Clock Regulates Psoriasis-Like Skin Inflammation in Mice. J Invest Dermatol 2015;135:3001\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/jid.2015.316\u003c/span\u003e\u003cspan address=\"10.1038/jid.2015.316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Liu J, Niu M, Song S, Wei L, Chen G, et al. Soluble IL-6R-mediated IL-6 trans-signaling activation contributes to the pathological development of psoriasis. J Mol Med (Berl) 2021;99:1009\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00109-021-02073-3\u003c/span\u003e\u003cspan address=\"10.1007/s00109-021-02073-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao X, Xiang Y, Mao W, Chen Y, Li Q, Fan B. TRIM27 promotes IL-6-induced proliferation and inflammation factor production by activating STAT3 signaling in HaCaT cells. Am J Physiol Cell Physiol 2020;318:C272\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/ajpcell.00314.2019\u003c/span\u003e\u003cspan address=\"10.1152/ajpcell.00314.2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavipati A, Nolan S, Alphonse M, Dikeman D, Youn C, Wang Y, et al. IL-6R/Signal Transducer and Activator of Transcription 3 Signaling in Keratinocytes rather than in T Cells Induces Psoriasis-Like Dermatitis in Mice. J Invest Dermatol 2022;142:1126\u0026ndash;1135.e4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jid.2021.09.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jid.2021.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcInnes IB, Szekanecz Z, McGonagle D, Maksymowych WP, Pfeil A, Lippe R, et al. A review of JAK-STAT signalling in the pathogenesis of spondyloarthritis and the role of JAK inhibition. Rheumatology (Oxford) 2022;61:1783\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/rheumatology/keab740\u003c/span\u003e\u003cspan address=\"10.1093/rheumatology/keab740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"psoriasis, skin circadian clock gene, IL-17 signaling pathway, JAK-STAT signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-3799546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3799546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePsoriasis is an immune-mediated skin disease where the IL-17 signaling pathway plays a crucial role in its development. Chronic circadian rhythm disorder in psoriasis pathogenesis is gaining more attention. The relationship between IL-17 signaling pathway and skin clock genes remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGSE121212 with psoriatic lesion and healthy controls was used as exploration cohort for searching analysis. Datasets GSE117239, GSE51440, GSE137218 that contained effective biologics treating psoriasis overtime were applied to validation analysis. Single cell RNA sequencing (scRNA-seq) dataset GSE173706 were used to explore the F3 expression and related pathway activities in single cell levels. Through intersecting with high expression DEGs, F3 was selected as the signature skin circadian gene in psoriasis for further investigation. Functional analyses, including correlation analyses, prediction of transcription factors, protein-protein interaction, single gene GSEA to explore the potential roles of F3. ssGSEA algorithm was performed to uncover the immune related characteristics of psoriasis. We further explored F3 expression in specific cell population in scRNA-seq dataset, besides this, AUCell analysis was performed to explore the pathway activities and the results were further compared between specific cell cluster. Immunohistochemistry experiment, RT-qPCR was used to validate the location and expression of F3, small interfering RNA (siRNA) transfection experiment in HaCaT and transcriptome sequencing analysis were applied to explore the potential function of F3.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eF3 was significantly down-regulated in psoriasis and interacted with IL-17 signaling pathway. Low expression of F3 could upregulate the receptor of JAK-STAT signaling, thereby promoting keratinocytes inflammation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur research revealed a bidirectional link between the skin circadian gene F3 and the IL-17 signaling pathway in psoriasis, suggesting that F3 may interact with the IL-17 pathway by activating JAK-STAT within keratinocytes and inducing abnormal intracellular inflammation.\u003c/p\u003e","manuscriptTitle":"The skin circadian clock gene F3 as a potential marker for psoriasis severity and its bidirectional relationship with IL-17 signaling in keratinocytes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-01 08:01:09","doi":"10.21203/rs.3.rs-3799546/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9376ad5-c6ff-49fd-80ef-c74f72a4a5f6","owner":[],"postedDate":"January 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-02T00:45:39+00:00","versionOfRecord":{"articleIdentity":"rs-3799546","link":"https://doi.org/10.1016/j.intimp.2024.111993","journal":{"identity":"international-immunopharmacology","isVorOnly":true,"title":"International Immunopharmacology"},"publishedOn":"2024-05-01 00:45:39","publishedOnDateReadable":"May 1st, 2024"},"versionCreatedAt":"2024-01-01 08:01:09","video":"","vorDoi":"10.1016/j.intimp.2024.111993","vorDoiUrl":"https://doi.org/10.1016/j.intimp.2024.111993","workflowStages":[]},"version":"v1","identity":"rs-3799546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3799546","identity":"rs-3799546","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}