Sex dimorphism of IL-17-secreting peripheral blood mononuclear cells in ankylosing spondylitis based on bioinformatics analysis and machine learning

Sex dimorphism of IL-17-secreting peripheral blood mononuclear cells in ankylosing spondylitis based on bioinformatics analysis and machine learning | 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 Sex dimorphism of IL-17-secreting peripheral blood mononuclear cells in ankylosing spondylitis based on bioinformatics analysis and machine learning Sifang Li, Hua Chao, Zihao Li, Siwen Chen, Jingyu Zhang, Wenjun Hao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4129727/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Ankylosing spondylitis (AS) with radiographic damage is more prevalent in men than in women. IL-17, which is mainly secreted from peripheral blood mononuclear cells (PBMCs), plays an important role in the development of AS. Its expression is different between male and female. However, it is still unclear whether sex dimorphism of IL-17 contribute to sex differences in AS. Methods GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 datasets were collected from the Gene Expression Omnibus (GEO) database. Differential expressed genes (DEGs) were analyzed with the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods. CIBERSORTx and EcoTyper algorithms were used for immune infiltration analyses. Machine learning based on the XGBoost algorithm model was used to identify the impact of DEGs. The Connectivity Map (CMAP) database was used as a drug discovery tool for exploring potential drugs based on the DEGs. Results According to immune infiltration analyses, mast cells accounted for the largest proportion of IL-17-secreting PBMCs, and KEGG analyses suggested an enhanced activation of mast cells among male AS patients, whereas the expression of TNF was higher in female AS patients. Other signaling pathways, including those involving metastasis-associated 1 family member 3 (MAT3) or proteasome, were found to be more activated in male AS patients. Regarding metabolic patterns, oxidative phosphorylation pathways and lipid oxidation were significantly upregulated in male AS patients. In XGBoost algorithm model, DEGs including METRN and TMC4 played important roles in the disease process. we integrated the CMAP database for systematic analyses of polypharmacology and drug repurposing, which indicated that atorvastatin, famciclocir, ATN-161 and taselisib may be applicable to the treatment of AS Conclusions We analyzed the sex dimorphism of IL-17-secreting PBMCs in AS. The results showed that mast cell activation was stronger in males, while the expression of TNF was higher in females. In addition, through machine learning and the CMAP database, we found that genes such as METRN and TMC4 may promote the development of AS, and drugs such as atorvastatin potentially could be used for AS treatment. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Ankylosing spondylitis (AS), a subtype of axial spondyloarthritis (SpA), is characterized by chronic back pain and the formation of syndesmophytes, which may lead to spinal fusion or ankylosis( 1 ). The prevalence of AS in an east Asian population was reported to be 0.79%( 2 ). Approximately two-thirds of actively employed individuals with AS have work-related issues, leading to substantial direct and indirect costs to society( 3 ). Clinical evidence suggests that a patient’s sex influences the manifestations of SpA. While most subtypes of SpA show a female predominance in incidence, AS is distinctive among the SpA family of diseases because it has a clear male dominance, with a male-to-female ratio of up to 3:1( 4 , 5 ). In addition, several factors associated with the disease differ significantly between male and female patients. Female patients tend to have a higher Bath Ankylosing Spondylitis Functionality Index (BASFI)( 6 – 8 ) and are less responsive to treatment with TNF-inhibitors( 9 ). Female patients also tend to have a lower modified Stoke Ankylosing Spondylitis Spine Score (mSASSS)( 10 , 11 ) and lower C-reactive protein levels( 8 ) compared with male patients, which means that female AS patients tend to have a higher degree of inflammation, while spare from imaging progression. These observations implicate a sex bias in the immunopathogenesis of AS. IL-17 was first discovered as a product of T cells ( 12 ), which are a type of peripheral blood mononuclear cell (PBMC). Later, scientists discovered that the IL-17 superfamily actually consists of six structurally related proinflammatory cytokines, namely IL-17A, IL-17B, IL-17C, IL-17D, IL-17E and IL-17F. In AS, serum concentrations of IL-17 (commonly referred to as IL-17A) from peripheral blood samples are also increased compared with those in healthy individuals( 13 ), and targeting IL-17 with the neutralizing antibodies secukinumab and ixekizumab ameliorates inflammation in AS( 14 , 15 ). The role of IL-17 in AS is currently believed to be linked to inflammation and to neutrophil activity through its induction of the production of IL-6 and IL-8( 16 ). IL-17 is also known to mediate bone destruction by increasing receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis( 17 ). IL-17 has been shown to be differentially expressed between male and female individuals in several contexts. For example, female patients have been shown to exhibit higher expression of IL-17 during urinary tract infections than do male patients( 18 ), while male mice show stronger expression of IL-17 in autoimmune encephalitis( 19 ). These findings suggest that the functions of IL-17 are influenced by sex. As IL-17 is a key cytokine in AS, it is possible that the differential expression of IL-17 in cells and immune contexts determines the difference in disease susceptibility and severity between male and female patients. Therefore, we thought that comparing the expression profiles of IL-17-producing PBMCs would provide a clearer understanding of the key factors for male susceptibility and potential therapeutic targets. Recent studies of autoimmune disorders have demonstrated that IL-17 is expressed by multiple lineages of innate immune cells, including mast cells, neutrophils, dendritic cells, γδ-T cells, macrophages and natural killer cells( 20 ), which are a part of PBMCs. Further, IL-17-producing mast cells and neutrophils are evident in the inflamed synovial tissues of AS patients( 21 ). In this study, we used functional enrichment analysis, immune infiltration analysis and machine learning to analyze the gene expression profiles of IL-17-secreting PBMCs from male and female AS patients. we observed that the activation of mast cells was stronger in male patients, while TNF signaling pathway was more activated in female AS patients Regarding PBMC metabolic patterns, we found that oxidative phosphorylation pathways and lipid oxidation were significantly upregulated in male patients. Finally, We identified multiple such DEGs, including METRN and TMC4, that may account for the higher incidence of AS in males. Methods Data collection AS datasets were obtained from the public repository NCBI GEO using "ankylosing spondylitis" as the search query. The target species was set to “human,” and the entry type was “series.” A total of 38 datasets were retrieved in this way. In order to ensure the quality of the data, we checked each of these datasets for information related to the experimental sample, experimental design and data type. Ultimately, five datasets (GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812) were identified from the GEO database. GSE221786 included data from RNA-seq experiments performed on platform GPL24676. It contained gene expression profiles from whole blood samples that included isolated PBMCs from control and AS patients that had been stimulated with CytoSim for 4 h to enrich the cells in IL-17. These samples were from 20 AS patients (11 male and 9 female) and 8 healthy controls (4 male and 4 female). GSE73754 (array, platform GPL10558) included gene expression profiles of whole blood samples from 52 AS patients and 20 healthy controls. GSE25101 (array, platform GPL6947) included gene expression profiles of whole blood samples from 16 AS patients and 16 healthy controls. GSE181364 (RNA-seq, platform GPL24676) included gene expression profiles of whole blood samples from 5 AS patients and 3 healthy controls. GSE205812 (RNA-seq, platform GPL24676) included gene expression profiles of whole blood samples from 3 AS patients and 3 healthy controls. Data from GSE221786 were used to conduct the main analysis of this article. Datasets GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 were used to train XGBoost machine learning models( 22 ). All gene expression information obtained from the database are available in supplementary file 1. Screening for differentially expressed genes A standardized matrix file of the microarray data was provided with dataset GSE221786. The “limma” package of R was used to identify DEGs between 20 AS samples and 8 healthy controls in GSE73754 with inclusion criteria of p 0. The expression levels of DEGs between AS subjects and healthy controls were visualized using the “ggplot2” and “pheatmap” R packages. Significant correlations between DEGs were visualized using the “corrplot” and “circlize” packages. Volcano plots were developed and Principal Component Analyses (PCA) were performed using the OmicShare tools ( https://www.omicshare.com/tools ) with the default settings. A heatmap was plotted using an online platform for data analysis and visualization ( https://www.bioinformatics.com.cn ). Functional enrichment analysis Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are commonly used methods to perform gene enrichment analyses. GSEA was performed to identify differential pathways between male and female subjects. Pre-ranked gene lists were generated based on the correlation of gene expression with the treatment condition using R package "clusterProfiler"( 23 ), with "c2.cp.v7.2.symbols.gmt" ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). Gene sets from the Molecular Signatures Database (MSigDB) were utilized for enrichment analyses, which were conducted using GSEA software (Broad Institute, Cambridge, MA, USA) with 200 permutations to estimate false discovery rates (FDR). The gene sets were considered significantly enriched at FDR < 0.25 and p < 0.05. The GSEA results were visualized by enrichment plots, highlighting key pathways that were significantly enriched in the condition of interest. To understand the biological functions of genes, GO terms and KEGG pathway analyses were applied for taxonomy-based analysis with the “ClusterProfiler” R package( 23 , 24 ). In addition, to illustrate the differences in genes involved in oxidative phosphorylation, we used the signal pathway map inherent in KEGG( 25 ). Immune infiltration analysis CIBERSORTx and EcoTyper( 26 ) algorithms were used for immune infiltration analyses. CIBERSORTx is an analytical tool that was introduced by Newman et al. to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population. EcoTyper, a machine learning framework for the identification of cell states and ecosystems from bulk, single-cell and spatially-resolved expression data, has been used to extend CIBERSORTx for large-scale profiling of cellular ecosystems. Correlations between DEGs and immune cell infiltration in AS were also analyzed. For this analysis, data from healthy controls were omitted, and data representing expression of DEGs was extracted. Correlation analyses were performed with the results of immune infiltration according to Pearson’s correlation coefficient. The results of these analyses were presented using the “ggplot2” package. Machine learning In the model-development phase, an XGBoost algorithm model( 22 ) was constructed to analyze the contribution of DEGs between AS patients and healthy controls. A backward stepwise analysis was performed to select variables with a threshold of p < 0.05 according to the Akaike information criterion. The prediction of potential small-molecule drugs The Connectivity Map (CMAP) database ( https://clue.io/ ) is a drug discovery tool for exploring potential biological associations among diseases, genes and drugs. Specifically, the model allows the prediction of small molecule drugs that may induce or reverse the biological processes associated with DEGs. In this study, drugs with negative scores in T cell-related cell lines (for example, Jurkat cells) and osteogenesis-related cell lines (for example, U2OS cells) were explored, and information regarding the clinical research stages of the drugs and their mechanisms of action were drawn from information provided within the database. Results Sex dimorphism of T cell and mast cell among patients In database GSE221786, we obtained RNA-seq data from cells secreting IL-17 PBMC from 11 male and 9 female AS patients. We first observed the overall gene expression differences between male and female patients and found significant differences (Fig. 1 A). Next, we used CIBERSORTx to investigate the sequencing results for information regarding immune cell infiltration, and we found that mast cells accounted for the majority of these IL-17 secreting cells, followed by T cells (Fig. 1 B). We then used a KEGG analysis to uncover differences in the activation of cellular signaling pathways. We found that among the immune-related pathways, T-cell receptor (TCR)-related genes were the most significantly enriched (Fig. 1 C). In addition, according to GSEA, the activation of the TCR signaling pathway was more prominent in female subjects than in male subjects (Fig. 1 D, E). Specifically, the upregulation of expression of CD4 suggests a stronger T cell activation in female AS patients as compared to the activation in male AS patients (Fig. 1 F). A GSEA enrichment analysis also showed that among IL17-secreting cells from male subjects, the activation of mast cells was more prominent as compared to female subjects (Fig. 1 G). We identified multiple upregulated genes associated with mast cell activation in male subjects, including IL13, PTPN6, VAMP8 and LAT2 (Fig. 1 H). A) A Principal Component Analysis (PCA) showing differences in gene expression between male and female individuals with AS. B) CIBERSORTx analysis showing sex differences in immune infiltration among individuals with AS. Bar plot shows relative composition of PBMCs. C) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) between male and female AS patients. D,E) GSEA enrichment plot (D) and diagram (E) of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS focusing on genes involved in T-cell receptor (TCR) signaling. F) Heatmap showing the DEGs related to the PID_TCR_PATHWAY dataset between male and female patients with AS. G) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS from the GOBP_MAST_CELL_ACTIVATION dataset. H) Heatmap showing the DEGs related to the GOBP_MAST_CELL_ACTIVATION dataset between male and female AS patients. Immune infiltration and enrichment analyses of immune-related signaling pathways between normal male and female subjects In a comparative analysis of the patients, it was observed that male individuals displayed a stronger activation of mast cells, accompanied by elevated expression of key genes such as LAT2. We further investigated whether this difference is generally associated with sex or represents a specific manifestation within the context of AS disease. Therefore, we compare immune infiltration and related signaling pathways between normal male and female subjects. We analyzed overall gene expression in male and female control subjects who had not been diagnosed with AS, and we found significant differences (Fig. 2 A). In particular, we found that female subjects had higher average expression levels of XIST, TSIX and other non-coding RNAs located on the X chromosome. In male subjects, we identified several specific overexpressed protein-coding genes on autosomes, including CRIP1, S100A8, MARCO, NLGN4Y and BCORP1 (Fig. 2 B). We then performed KEGG analyses of DEGs to investigate the activation of cellular signaling pathways, and we found that among immune-related pathways, genes related to the TCR signaling pathway were the most significantly enriched (Fig. 2 C). With regard to immune infiltration, a CIBERSORTx analysis demonstrated that in control subjects, as in patients diagnosed with AS, mast cells were the predominant IL-17-secreting cells (Fig. 2 D). There was no statistical difference in mast cell invasion compared to the AS group (Fig. 2 E). Similarly, we also found that there was no significant difference in the proportion of T cells between healthy individuals and subjects with AS (Fig. 2 F). We performed GSEA focused on the activation of T cells and mast cells in male and female control subjects. We found that there was no difference in the activation of T cells and mast cells between male and female control subjects, despite significant differences in the secretion of IL-17 (Fig. 2 G-J). A) A Principal Component Analysis (PCA) showing differences in gene expression between healthy control male and female individuals. B) A volcano map displaying differentially expressed genes (DEGs) between male and female control subjects. C) KEGG pathway enrichment analysis of DEGs between male and female control subjects. D) CIBERSORTx analysis showing differences in immune infiltration between male and female control subjects. E,F) Comparison of the numbers of mast cells and T cells between the ankylosing spondylitis (AS) group (n = 20) and the control group (n = 8). P-values were obtained with t-tests. G) GSEA enrichment plots of IL-17-secreting cells from male controls compared with IL-17-secreting cells from female controls with AS using the PID_TCR_PATHWAY dataset. H) Heatmap showing the DEGs related to the PID_TCR_PATHWAY dataset between male and female healthy control subjects. I) GSEA enrichment plots of IL-17-secreting cells from male control subjects compared with IL-17-secreting cells from female controls with AS for the GOBP_MAST_CELL_ACTIVATION dataset. J) Heatmap showing DEGs related to the GOBP_MAST_CELL_ACTIVATION dataset between control male and female individuals. Sex-specific pathways in IL-17-secreting cells Considering the significant impact of signal pathway activation on cellular outcomes, we analyzed the differences in the expression of genes involved in pathways leading to IL-17 secretion cell activation pathways between male and female AS patients. In the KEGG pathway enrichment analysis on the relevant DEGs, we observed that the TNF signaling pathway was the most enriched (Fig. 3 A). Interestingly, TNF signaling is one of the pathways most extensively studied in AS disease( 27 ). GSEA revealed that the expression of TNF signaling-related genes in samples associated with female subjects was significantly increased (Fig. 3 B, C). In particular, the expression level of TNF itself was found to be significantly increased in women (Fig. 3 B). Increased activity in this pathway may offer an explanation for why female AS patients do not respond as sensitively to TNF inhibitor treatments as male patients do. We explored in more depth the signaling pathways that are activated in female AS patients compared to male patients. Our findings revealed that the pathways involving nectin, FAK, SHP2 and FOXO also demonstrated increased activation in female patients at the level of gene transcription (Fig. 3 D). However, as the prevalence of AS is higher in male subjects than in female subjects, the signaling pathways activated in males may hold more significance for disease progression. Therefore, we also analyzed the signaling pathways that are more highly activated in male patients and discovered that genes involved in pathways related to MTA3, the proteasome, NKC cells and NDK/dynamin were more highly expressed in male patients compared to female patients (Fig. 3 E-J). We propose that these signaling pathways may represent potential therapeutic targets for the treatment of AS. A) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) related to signal transduction pathways between male and female control subjects. B) Heatmap showing the DEGs related to the PID_TNF_PATHWAY between male and female individuals with AS. C) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the PID_TNF_PATHWAY and PID_NECTIN_PATHWAY. D,E) GSEA enrichment plots showing the most highly enriched signaling pathways in female (D) and male (E) subjects. F-I GSEA enrichment plots and diagrams of IL-17-secreting cells from male patients with AS compared with female patients with AS for BIOCARTA_MTA3_PATHWAY (F,G) and GOCC_PROTEASOME_ACCESSORY_COMPLEX (H,I). J) Heatmap showing the DEGs related to the GOCC_PROTEASOME_ACCESSORY_COMPLEX between male and female individuals with AS. Differences in metabolic patterns of IL-17-secreting cells between male and female AS patients In addition to the signaling pathways mentioned above, based on previous studies showing that the metabolic pattern of immune cells greatly influences their status( 28 , 29 ), we analyzed the differences in metabolic patterns of IL-17-secreting cells between male and female subjects. We performed KEGG pathway enrichment analysis on the DEGs between male and female subjects with AS, with particular focus on metabolism-related pathways. We found that carbohydrate metabolism and lipid metabolism were the most enriched pathways (Fig. 4 A). Next, we performed GSEA and found that male subjects exhibited greater enrichment of genes in carbohydrate metabolism and aerobic phosphorylation pathways (Fig. 4 B, C). In addition, male AS subjects also exhibited upregulation in genes encoding proteins in oxidative phosphorylation respiratory chain complexes I through V (Fig. 4 D, E). GSEA demonstrated that male subjects exhibited an enrichment of upregulated genes in the fatty acid oxidation pathway (Fig. 4 F, G). Specific genes upregulated in male AS subjects included PCK2, BDH2, ACADS, ECHDC2, ACAA1, AKT1, ABCD4, CRAT and ECH1 (Fig. 4 G). Meanwhile, we also compared the changes in metabolic patterns between male patients with AS and normal male subjects, and we found that the expression levels of genes associated with oxidative phosphorylation were increased in AS patients, but the levels of genes associated with fatty acid oxidation remained unchanged (Fig. 4 H,I). These results suggest that changes in oxidative phosphorylation may contribute to the onset of the disease. A) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) related to metabolism between male and female control subjects. B,C) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the circulating carbohydrate concentration and hallmark oxidative phosphorylation pathways. D) Heatmap showing the upregulated DEGs related to the hallmark oxidative phosphorylation dataset between males and females with AS. E) In the signal pathway diagram of the electronic respiratory chain, the red box represents the genes that are elevated in male patients compared to female patients. The green box indicates the genes that are elevated in female patients compared to male patients. The black box indicates genes that are not changed between male and female patients. F,G) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the GOBP_fatty_acid_beta_oxidation dataset. H,I) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from control group of males for GOBP_OXIDATIVE_PHOSPHORYLATION and FATTY_ACID_BETA_OXIDATION datasets. Sexual dimorphism in secretory proteins of IL-17-secreting cells AS symptoms primarily manifest in localized regions of the spine, and circulating immune cells that act at these locales along with their secreted proteins play important roles in disease progression. Therefore, we initially identified genes that were more highly expressed in male and female patients compared to control groups (Fig. 5 A, B), and then we identified genes in these sets that encode secreted proteins. We discovered that a few of the secreted proteins overexpressed at the transcriptional level in male AS patients were not pro-inflammatory molecules (Fig. 5 C). However, transcripts encoding proteins promoting osteogenesis, such as FGF2, WNT and DLL1, were found to be highly expressed. In contrast, the expression of pro-inflammatory molecules, such as IL-6 and IL-10, was higher in female AS patients as compared to male patients (Fig. 5 E, F). Additionally, we analyzed the chromosomal locations of genes whose expression differed between males and females. Our findings indicate that genes overexpressed in male patients are primarily located on the Y chromosome. However, for female patients, genes with high expression levels are more prevalent on chromosome 12 than on the X chromosome (Fig. 5 F) A,B) Venn diagrams displaying the genes elevated in (A)male and (B)female ankylosing spondylitis (AS) patients compared to female patients, and the genes with elevated transcriptional levels in control subjects compared to female control subjects. C) Heatmap showing the differentially expressed genes related to secretory proteins. D,E) GSEA enrichment plots and heatmap of IL-17-secreting cells from male patients with AS compared with female patients with AS for the HALLMARK_INFLAMMATORY_RESPONSE dataset. F) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with female patients with AS according to the chromosomal location of the gene. Identification of candidate drugs for AS treatment by machine learning and CMAP analyses Next, we utilized databases GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 and employed the XGBoost algorithm for machine learning to aid in assessing the impact of the DEGs that encode secreted proteins on disease onset (Fig. 6 A). We obtained scoring results for the secreted proteins. We also used the CMAP database to search for medications that might affect the activities of these secreted proteins, leading to the identification of potential therapeutic interventions (Fig. 6 B). The information derived from the CMAP analysis was also used to identify the progress of candidate drugs through clinical trials and to determine their likely mechanisms of action, providing a reference for the further development of treatments (Fig. 6 B). A) Top 29 features selected using XGBoost and the corresponding variable importance score. Y-axis indicates the importance score, which is the relative number of a variable that is used to distribute the data; X-axis indicates the top 29 weighted variables. B) CMAP-predicted drugs that may intervene in the occurrence of AS. U-2OS, human osteosarcoma cells. Jurkat cells, human T-cell leukemia cell line. CD34, hematopoietic stem cell line. PHH, primary human hepatocytes. THP1, acute monocytic leukemia cell line. HEK293, human embryonic kidney cell line. Discussion In this study, we investigated the gene expression patterns of IL17-secreting cells from the blood of both male and female AS patients. Our observations revealed that the activation of the TCR signaling pathway was more prominent in female subjects than in male subjects, while mast cells are more highly activated in male patients. This research offers a potential explanation for the increased BASFI score in female AS patients relative to male AS patients. Moreover, our analysis extended to the differential activation of immune signaling pathways between Sexs, finding transcriptomic evidence of a notably higher expression of TNF in female patients relative to male patients, which may provide insight into the relative insensitivity of females to TNF inhibitors. Some researchers have found that MTA3 could activate Wnt signaling pathway ( 30 ). Therefore, MTA3 signaling pathways that are more highly activated in male patients may contribute to ligament osteogenesis by activating Wnt signaling pathway. Additionally, our exploration into the metabolic profiles of these cells uncovered that genes involved in oxidative phosphorylation pathways and lipid oxidation processes were predominantly upregulated in immune cells from male patients as opposed to female patients. The metabolic patterns of T cells help to determine their functionality( 31 – 33 ). Metabolism within these cells affects their proliferation, differentiation and overall immune response. Activated effector T cells tend to exhibit augmented anabolic metabolic pathways, such as aerobic glycolysis, while memory T cells tend to more strongly engage catabolic pathways, like fatty acid oxidation( 31 ). Moreover, select lipids act as metabolic regulators, intertwining environmental signals with cellular signaling pathways to influence T cell biology( 33 ). In this study, we also uncovered transcriptomic evidence that in male patients, IL-17-secreting cells likely exhibit an increase in both aerobic glycolysis and fatty acid oxidation. Although classical activation pathways appear weaker in male patients as compared to female patients, their state of high-energy metabolism suggests a higher level of cellular activation in male subjects. This indicates that there may exist undiscovered T cell activation pathways that underlie AS. This study also uncovered some previously unrecognized genes that may be related to AS. For example, transcripts coding for METRN, also known as meteorin, were found to be highly expressed in IL-17-secreting cells in male AS patients and to have an impact on disease occurrence. This protein has not previously been associated directly with AS, although it is known to serve as an important promoter of the formation of axonal networks during neurogenesis. In addition, METRNL has been reported to attenuate lipid-induced inflammation and insulin resistance via AMPK- or PPARδ-dependent pathways in skeletal muscle of mice( 34 ). Other such proteins that we uncovered include TMC4 (transmembrane channel-like protein 4), which is predicted to enable mechanosensitive ion channel activity and to be involved in ion transmembrane transport, although further research into its biological roles is required. GNLY (granulysin) is a member of the saposin-like protein family and is located in T cell cytotoxic granules, which are released upon antigen stimulation; no other studies have reported connections of its expression with AS. WNT7A promotes the osteogenic differentiation of human mesenchymal stem cells( 35 ), which is considered to be a mechanism driving ligament osteogenesis in AS. However, there are currently no reports on its mechanism of action in AS. We propose that WNT7A might warrant additional research into the mechanism of ectopic osteogenesis in AS. Our research using CMAP identified several interesting drugs that deserve further exploration in the context of AS. Alisertib, an aurora kinase inhibitor, promotes apoptosis and autophagy in human osteosarcoma U-2 OS and MG-63 cells by activating mitochondrial pathways and inhibiting the p38 MAPK/PI3K/Akt/mTOR signaling pathway( 36 ). While no studies have investigated the use of this drug in the treatment of AS, we propose that it may reduce ectopic bone formation by promoting apoptosis of local osteoblasts. Atorvastatin, an HMG-CoA reductase inhibitor, is used as a lipid-lowering medication for the treatment of myocardial infarction. It has also been shown, however, to alleviate bone loss and aortic valve atherosclerosis in LDLR mice( 37 ), suggesting that it might also reduce the occurrence of ectopic bone formation in AS. This study analyzed the sequencing data of blood samples from AS patients and inferred possible factors for disease differences between male and female AS patients. However, further analysis is required to identify mechanisms to explain these sex-based differences. In addition, further in vitro and in vivo research is required to confirm our conclusions and to more deeply investigate the associated factors. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets analysed during the present study are available in the GEO repository. The collated data can also be found in Supplementary Document 1. Competing interests The authors declare no competing interests. Funding The work was supported by National Natural Science Foundation of China (Grant no 82372370; 82172384; 81972039), Department of Science and Technology of Guangdong Province (Grant no 2021B1515020080) and KELIN New Talent Project of The First Affiliated Hospital, Sun Yat-sen University (Grant no Y12001). Authors' contributions Li Sifang completed the conception and design of the project. All authors contributed to data collection and analysis. The initial draft was written by Li Sifang and Chao Hua. Liu Hui and Chao Hua completed the final review and editing of the manuscript. Li Sifang and Chao Hua contributed equally to this paper. All authors read and approved the final manuscript. 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A five-year prospective study of spinal radiographic progression and its predictors in men and women with ankylosing spondylitis. Arthritis research & therapy. 2018;20(1):162. Hallström M, Klingberg E, Deminger A, Rehnman JB, Geijer M, Forsblad-d'Elia H. Physical function and sex differences in radiographic axial spondyloarthritis: a cross-sectional analysis on Bath Ankylosing Spondylitis Functional Index. Arthritis research & therapy. 2023;25(1):182. Rouvier E, Luciani MF, Mattéi MG, Denizot F, Golstein P. CTLA-8, cloned from an activated T cell, bearing AU-rich messenger RNA instability sequences, and homologous to a herpesvirus saimiri gene. J Immunol. 1993;150(12):5445-56. Mei Y, Pan F, Gao J, Ge R, Duan Z, Zeng Z, et al. Increased serum IL-17 and IL-23 in the patient with ankylosing spondylitis. Clinical rheumatology. 2011;30(2):269-73. Baeten D, Baraliakos X, Braun J, Sieper J, Emery P, van der Heijde D, et al. Anti-interleukin-17A monoclonal antibody secukinumab in treatment of ankylosing spondylitis: a randomised, double-blind, placebo-controlled trial. Lancet (London, England). 2013;382(9906):1705-13. Mease PJ, McInnes IB, Kirkham B, Kavanaugh A, Rahman P, van der Heijde D, et al. Secukinumab Inhibition of Interleukin-17A in Patients with Psoriatic Arthritis. The New England journal of medicine. 2015;373(14):1329-39. Fossiez F, Djossou O, Chomarat P, Flores-Romo L, Ait-Yahia S, Maat C, et al. T cell interleukin-17 induces stromal cells to produce proinflammatory and hematopoietic cytokines. The Journal of experimental medicine. 1996;183(6):2593-603. Adamopoulos IE, Chao CC, Geissler R, Laface D, Blumenschein W, Iwakura Y, et al. Interleukin-17A upregulates receptor activator of NF-kappaB on osteoclast precursors. Arthritis research & therapy. 2010;12(1):R29. Yu M, Pal S, Paterson CW, Li JY, Tyagi AM, Adams J, et al. Ovariectomy induces bone loss via microbial-dependent trafficking of intestinal TNF+ T cells and Th17 cells. The Journal of clinical investigation. 2021;131(4). Nacka-Aleksić M, Djikić J, Pilipović I, Stojić-Vukanić Z, Kosec D, Bufan B, et al. Male rats develop more severe experimental autoimmune encephalomyelitis than female rats: sexual dimorphism and diergism at the spinal cord level. Brain, behavior, and immunity. 2015;49:101-18. Onishi RM, Gaffen SL. Interleukin-17 and its target genes: mechanisms of interleukin-17 function in disease. Immunology. 2010;129(3):311-21. Appel H, Maier R, Wu P, Scheer R, Hempfing A, Kayser R, et al. Analysis of IL-17(+) cells in facet joints of patients with spondyloarthritis suggests that the innate immune pathway might be of greater relevance than the Th17-mediated adaptive immune response. Arthritis Res Ther. 2011;13(3):R95. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. ACM. 2016. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology. 2012;16(5):284-7. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic acids research. 2023;51(D1):D587-d92. Jin Z, Sato Y, Kawashima M, Kanehisa M. KEGG tools for classification and analysis of viral proteins. Protein science : a publication of the Protein Society. 2023;32(12):e4820. Steen CB, Liu CL, Alizadeh AA, Newman AM. Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx. Methods in molecular biology (Clifton, NJ). 2020;2117:135-57. Croft M, Siegel RM. Beyond TNF: TNF superfamily cytokines as targets for the treatment of rheumatic diseases. Nature reviews Rheumatology. 2017;13(4):217-33. Krausgruber T, Redl A, Barreca D, Doberer K, Romanovskaia D, Dobnikar L, et al. Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation. Immunity. 2023;56(2):289-306.e7. Harrington JS, Ryter SW, Plataki M, Price DR, Choi AMK. Mitochondria in health, disease, and aging. Physiological reviews. 2023;103(4):2349-422. Jiao T, Li Y, Gao T, Zhang Y, Feng M, Liu M, et al. MTA3 regulates malignant progression of colorectal cancer through Wnt signaling pathway. Tumour Biol. 2017;39(3):1010428317695027. Buck MD, O'Sullivan D, Klein Geltink RI, Curtis JD, Chang CH, Sanin DE, et al. Mitochondrial Dynamics Controls T Cell Fate through Metabolic Programming. Cell. 2016;166(1):63-76. Soriano-Baguet L, Brenner D. Metabolism and epigenetics at the heart of T cell function. Trends in immunology. 2023;44(3):231-44. Lim SA, Su W, Chapman NM, Chi H. Lipid metabolism in T cell signaling and function. Nature chemical biology. 2022;18(5):470-81. Jung TW, Lee SH, Kim HC, Bang JS, Abd El-Aty AM, Hacımüftüoğlu A, et al. METRNL attenuates lipid-induced inflammation and insulin resistance via AMPK or PPARδ-dependent pathways in skeletal muscle of mice. Experimental & molecular medicine. 2018;50(9):1-11. Yang L, Li Q, Zhang J, Li P, An P, Wang C, et al. Wnt7a promotes the osteogenic differentiation of human mesenchymal stem cells. International journal of molecular medicine. 2021;47(6). Niu NK, Wang ZL, Pan ST, Ding HQ, Au GH, He ZX, et al. Pro-apoptotic and pro-autophagic effects of the Aurora kinase A inhibitor alisertib (MLN8237) on human osteosarcoma U-2 OS and MG-63 cells through the activation of mitochondria-mediated pathway and inhibition of p38 MAPK/PI3K/Akt/mTOR signaling pathway. Drug design, development and therapy. 2015;9:1555-84. Rajamannan NM. Atorvastatin Attenuates Bone Loss and Aortic Valve Atheroma in LDLR Mice. Cardiology. 2015;132(1):11-5. Additional Declarations No competing interests reported. Supplementary Files SupplementaryDocument1.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2024 Reviews received at journal 01 May, 2024 Reviews received at journal 24 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers agreed at journal 09 Apr, 2024 Reviewers invited by journal 06 Apr, 2024 Editor assigned by journal 06 Apr, 2024 Editor invited by journal 22 Mar, 2024 Submission checks completed at journal 22 Mar, 2024 First submitted to journal 19 Mar, 2024 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. 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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-4129727","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283550014,"identity":"569ef7c8-d7cc-4404-a1d0-facf828b8cff","order_by":0,"name":"Sifang Li","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Sifang","middleName":"","lastName":"Li","suffix":""},{"id":283550015,"identity":"20b3999e-3164-4409-9116-5bc66fa9bde8","order_by":1,"name":"Hua Chao","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Chao","suffix":""},{"id":283550016,"identity":"3c844fed-084a-4a45-baaa-6a8c753fdb32","order_by":2,"name":"Zihao Li","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Li","suffix":""},{"id":283550017,"identity":"266100dd-fd76-4c31-9582-06872b780a27","order_by":3,"name":"Siwen Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Siwen","middleName":"","lastName":"Chen","suffix":""},{"id":283550018,"identity":"020ffce0-467e-47f0-a1dc-f657dee64a9d","order_by":4,"name":"Jingyu Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"Zhang","suffix":""},{"id":283550019,"identity":"ce65f1ae-321f-4bd2-86d4-4248221bd6e9","order_by":5,"name":"Wenjun Hao","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Hao","suffix":""},{"id":283550021,"identity":"61e1d47b-b86f-41cc-bfb8-063886b39bec","order_by":6,"name":"Shuai Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Zhang","suffix":""},{"id":283550024,"identity":"d9b1d8bd-73a7-482d-be28-1af8c3394ec1","order_by":7,"name":"Caijun Liu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Caijun","middleName":"","lastName":"Liu","suffix":""},{"id":283550026,"identity":"a540c671-553f-469e-8150-dcc793ccff0b","order_by":8,"name":"Hui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACPmYGNgYGg39y/EDOgQcwYR48WtggWg4YSzYAtSQQpQWMGA4kbjgApIjTws5j9pin4I6x8bXDD4G21CXOn5HA+OBtG4O8OU6H8Zgb8xg8kzO7nWYA1HI4ccONBGbDuW0MhjsbcGoxk+YxYDY2u50A0gJ0oUQCmzRvGwOQi19L4ubZ6R9gDmP/TYQWoHukc0C2MCc23EhgY8avha1Mco5BmrHE7ZyCAwkGh403nHnYLDnnnIThBhxa+PkPb5N488dGjn92+uYPHyrqZOe3Jx/88KbMRh6XLWjAgMGxgYGxAciSIEo9GNgTr3QUjIJRMApGCgAA0N1WCXrp5WEAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-03-19 11:03:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4129727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4129727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53659781,"identity":"c4931597-f517-4121-a33e-132ad6383800","added_by":"auto","created_at":"2024-03-28 16:03:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3515951,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration and enrichment analyses of immune-related signaling pathways between male and female subjects with ankylosing spondylitis (AS).\u003c/p\u003e\n\u003cp\u003eA) A Principal Component Analysis (PCA) showing differences in gene expression between male and female individuals with AS. B) CIBERSORTx analysis showing sex differences in immune infiltration among individuals with AS. Bar plot shows relative composition of PBMCs. C) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) between male and female AS patients. D,E) GSEA enrichment plot (D) and diagram (E) of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS focusing on genes involved in T-cell receptor (TCR) signaling. F) Heatmap showing the DEGs related to the PID_TCR_PATHWAY dataset between male and female patients with AS. G) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS from the GOBP_MAST_CELL_ACTIVATION dataset. H) Heatmap showing the DEGs related to the GOBP_MAST_CELL_ACTIVATION dataset between male and female AS patients.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/e49589e85a191bcf716743bd.png"},{"id":53657725,"identity":"815b6da5-b0fc-4647-9d1e-0f2799049e13","added_by":"auto","created_at":"2024-03-28 15:55:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2321446,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration and enrichment analyses of immune-related signaling pathways between male and female healthy control subjects.\u003c/p\u003e\n\u003cp\u003eA) A Principal Component Analysis (PCA) showing differences in gene expression between healthy control male and female individuals. B) A volcano map displaying differentially expressed genes (DEGs) between male and female control subjects. C) KEGG pathway enrichment analysis of DEGs between male and female control subjects. D) CIBERSORTx analysis showing differences in immune infiltration between male and female control subjects. E,F) Comparison of the numbers of mast cells and T cells between the ankylosing spondylitis (AS) group (n = 20) and the control group (n = 8). P-values were obtained with t-tests. G) GSEA enrichment plots of IL-17-secreting cells from male controls compared with IL-17-secreting cells from female controls with AS using the PID_TCR_PATHWAY dataset. H) Heatmap showing the DEGs related to the PID_TCR_PATHWAY dataset between male and female healthy control subjects. I) GSEA enrichment plots of IL-17-secreting cells from male control subjects compared with IL-17-secreting cells from female controls with AS for the GOBP_MAST_CELL_ACTIVATION dataset. J) Heatmap showing DEGs related to the GOBP_MAST_CELL_ACTIVATION dataset between control male and female individuals.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/8eea8f65d4dbd4efa6437d3f.png"},{"id":53657722,"identity":"1c7cf948-df96-4ca9-9e65-bb2396bcddf5","added_by":"auto","created_at":"2024-03-28 15:55:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2617391,"visible":true,"origin":"","legend":"\u003cp\u003eSex-specific pathways within IL-17-secreting cells.\u003c/p\u003e\n\u003cp\u003eA) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) related to signal transduction pathways between male and female control subjects. B) Heatmap showing the DEGs related to the PID_TNF_PATHWAY between male and female individuals with AS. C) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the PID_TNF_PATHWAY and PID_NECTIN_PATHWAY. D,E) GSEA enrichment plots showing the most highly enriched signaling pathways in female (D) and male (E) subjects. F-I GSEA enrichment plots and diagrams of IL-17-secreting cells from male patients with AS compared with female patients with AS for BIOCARTA_MTA3_PATHWAY (F,G) and GOCC_PROTEASOME_ACCESSORY_COMPLEX (H,I). J) Heatmap showing the DEGs related to the GOCC_PROTEASOME_ACCESSORY_COMPLEX between male and female individuals with AS.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/6e786ac9dd107ac1bbe07a28.png"},{"id":53657724,"identity":"fef28d45-c91d-4350-bf27-a0ca545dfed3","added_by":"auto","created_at":"2024-03-28 15:55:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3004965,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in metabolic patterns of IL-17-secreting cells between male and female ankylosing spondylitis (AS) patients.\u003c/p\u003e\n\u003cp\u003eA) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) related to metabolism between male and female control subjects. B,C) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the circulating carbohydrate concentration and hallmark oxidative phosphorylation pathways. D) Heatmap showing the upregulated DEGs related to the hallmark oxidative phosphorylation dataset between males and females with AS. E) In the signal pathway diagram of the electronic respiratory chain, the red box represents the genes that are elevated in male patients compared to female patients. The green box indicates the genes that are elevated in female patients compared to male patients. The black box indicates genes that are not changed between male and female patients. F,G) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the GOBP_fatty_acid_beta_oxidation dataset. H,I) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from control group of males for GOBP_OXIDATIVE_PHOSPHORYLATION and FATTY_ACID_BETA_OXIDATION datasets.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/eba20b8e3220c5917e8d6756.png"},{"id":53657726,"identity":"d249864a-9ba6-4580-b23d-1313a1152887","added_by":"auto","created_at":"2024-03-28 15:55:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3817949,"visible":true,"origin":"","legend":"\u003cp\u003eSexual dimorphism in secretory proteins in IL-17-secreting cells.\u003c/p\u003e\n\u003cp\u003eA,B) Venn diagrams displaying the genes elevated in (A)male and (B)female ankylosing spondylitis (AS) patients compared to female patients, and the genes with elevated transcriptional levels in control subjects compared to female control subjects. C) Heatmap showing the differentially expressed genes related to secretory proteins. D,E) GSEA enrichment plots and heatmap of IL-17-secreting cells from male patients with AS compared with female patients with AS for the HALLMARK_INFLAMMATORY_RESPONSE dataset. F) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with female patients with AS according to the chromosomal location of the gene.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/29c911fc840c7bf14b820adf.png"},{"id":53657728,"identity":"5c856bbb-5580-434d-aa0d-dc3b98c98176","added_by":"auto","created_at":"2024-03-28 15:55:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":389449,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of candidate drugs for ankylosing spondylitis (AS) treatment by machine learning and CMAP analyses.\u003c/p\u003e\n\u003cp\u003eA) Top 29 features selected using XGBoost and the corresponding variable importance score. Y-axis indicates the importance score, which is the relative number of a variable that is used to distribute the data; X-axis indicates the top 29 weighted variables. B) CMAP-predicted drugs that may intervene in the occurrence of AS. U-2OS, human osteosarcoma cells. Jurkat cells, human T-cell leukemia cell line. CD34, hematopoietic stem cell line. PHH, primary human hepatocytes. THP1, acute monocytic leukemia cell line. HEK293, human embryonic kidney cell line.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/d426f168ea2a0f00026ba563.png"},{"id":53661118,"identity":"e8575d6a-6873-4933-88ea-61ae936a0919","added_by":"auto","created_at":"2024-03-28 16:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4683750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/4a5dc70f-a07c-4f72-a056-aa29f45537b6.pdf"},{"id":53657729,"identity":"f026867c-5b64-4e3d-ad01-8392e18a88b9","added_by":"auto","created_at":"2024-03-28 15:55:13","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29448177,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDocument1.zip","url":"https://assets-eu.researchsquare.com/files/rs-4129727/v1/30dbb0f3ba63be367bca392a.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex dimorphism of IL-17-secreting peripheral blood mononuclear cells in ankylosing spondylitis based on bioinformatics analysis and machine learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnkylosing spondylitis (AS), a subtype of axial spondyloarthritis (SpA), is characterized by chronic back pain and the formation of syndesmophytes, which may lead to spinal fusion or ankylosis(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The prevalence of AS in an east Asian population was reported to be 0.79%(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Approximately two-thirds of actively employed individuals with AS have work-related issues, leading to substantial direct and indirect costs to society(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClinical evidence suggests that a patient\u0026rsquo;s sex influences the manifestations of SpA. While most subtypes of SpA show a female predominance in incidence, AS is distinctive among the SpA family of diseases because it has a clear male dominance, with a male-to-female ratio of up to 3:1(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition, several factors associated with the disease differ significantly between male and female patients. Female patients tend to have a higher Bath Ankylosing Spondylitis Functionality Index (BASFI)(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and are less responsive to treatment with TNF-inhibitors(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Female patients also tend to have a lower modified Stoke Ankylosing Spondylitis Spine Score (mSASSS)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and lower C-reactive protein levels(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) compared with male patients, which means that female AS patients tend to have a higher degree of inflammation, while spare from imaging progression. These observations implicate a sex bias in the immunopathogenesis of AS.\u003c/p\u003e \u003cp\u003eIL-17 was first discovered as a product of T cells (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), which are a type of peripheral blood mononuclear cell (PBMC). Later, scientists discovered that the IL-17 superfamily actually consists of six structurally related proinflammatory cytokines, namely IL-17A, IL-17B, IL-17C, IL-17D, IL-17E and IL-17F. In AS, serum concentrations of IL-17 (commonly referred to as IL-17A) from peripheral blood samples are also increased compared with those in healthy individuals(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and targeting IL-17 with the neutralizing antibodies secukinumab and ixekizumab ameliorates inflammation in AS(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The role of IL-17 in AS is currently believed to be linked to inflammation and to neutrophil activity through its induction of the production of IL-6 and IL-8(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). IL-17 is also known to mediate bone destruction by increasing receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIL-17 has been shown to be differentially expressed between male and female individuals in several contexts. For example, female patients have been shown to exhibit higher expression of IL-17 during urinary tract infections than do male patients(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), while male mice show stronger expression of IL-17 in autoimmune encephalitis(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These findings suggest that the functions of IL-17 are influenced by sex. As IL-17 is a key cytokine in AS, it is possible that the differential expression of IL-17 in cells and immune contexts determines the difference in disease susceptibility and severity between male and female patients. Therefore, we thought that comparing the expression profiles of IL-17-producing PBMCs would provide a clearer understanding of the key factors for male susceptibility and potential therapeutic targets. Recent studies of autoimmune disorders have demonstrated that IL-17 is expressed by multiple lineages of innate immune cells, including mast cells, neutrophils, dendritic cells, γδ-T cells, macrophages and natural killer cells(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), which are a part of PBMCs. Further, IL-17-producing mast cells and neutrophils are evident in the inflamed synovial tissues of AS patients(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we used functional enrichment analysis, immune infiltration analysis and machine learning to analyze the gene expression profiles of IL-17-secreting PBMCs from male and female AS patients. we observed that the activation of mast cells was stronger in male patients, while TNF signaling pathway was more activated in female AS patients Regarding PBMC metabolic patterns, we found that oxidative phosphorylation pathways and lipid oxidation were significantly upregulated in male patients. Finally, We identified multiple such DEGs, including METRN and TMC4, that may account for the higher incidence of AS in males.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eAS datasets were obtained from the public repository NCBI GEO using \"ankylosing spondylitis\" as the search query. The target species was set to \u0026ldquo;human,\u0026rdquo; and the entry type was \u0026ldquo;series.\u0026rdquo; A total of 38 datasets were retrieved in this way. In order to ensure the quality of the data, we checked each of these datasets for information related to the experimental sample, experimental design and data type. Ultimately, five datasets (GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812) were identified from the GEO database.\u003c/p\u003e \u003cp\u003eGSE221786 included data from RNA-seq experiments performed on platform GPL24676. It contained gene expression profiles from whole blood samples that included isolated PBMCs from control and AS patients that had been stimulated with CytoSim for 4 h to enrich the cells in IL-17. These samples were from 20 AS patients (11 male and 9 female) and 8 healthy controls (4 male and 4 female). GSE73754 (array, platform GPL10558) included gene expression profiles of whole blood samples from 52 AS patients and 20 healthy controls. GSE25101 (array, platform GPL6947) included gene expression profiles of whole blood samples from 16 AS patients and 16 healthy controls. GSE181364 (RNA-seq, platform GPL24676) included gene expression profiles of whole blood samples from 5 AS patients and 3 healthy controls. GSE205812 (RNA-seq, platform GPL24676) included gene expression profiles of whole blood samples from 3 AS patients and 3 healthy controls. Data from GSE221786 were used to conduct the main analysis of this article. Datasets GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 were used to train XGBoost machine learning models(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). All gene expression information obtained from the database are available in supplementary file 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScreening for differentially expressed genes\u003c/h2\u003e \u003cp\u003eA standardized matrix file of the microarray data was provided with dataset GSE221786. The \u0026ldquo;limma\u0026rdquo; package of R was used to identify DEGs between 20 AS samples and 8 healthy controls in GSE73754 with inclusion criteria of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt; 0. The expression levels of DEGs between AS subjects and healthy controls were visualized using the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; R packages. Significant correlations between DEGs were visualized using the \u0026ldquo;corrplot\u0026rdquo; and \u0026ldquo;circlize\u0026rdquo; packages. Volcano plots were developed and Principal Component Analyses (PCA) were performed using the OmicShare tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicshare.com/tools\u003c/span\u003e\u003cspan address=\"https://www.omicshare.com/tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the default settings. A heatmap was plotted using an online platform for data analysis and visualization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are commonly used methods to perform gene enrichment analyses. GSEA was performed to identify differential pathways between male and female subjects. Pre-ranked gene lists were generated based on the correlation of gene expression with the treatment condition using R package \"clusterProfiler\"(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), with \"c2.cp.v7.2.symbols.gmt\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene sets from the Molecular Signatures Database (MSigDB) were utilized for enrichment analyses, which were conducted using GSEA software (Broad Institute, Cambridge, MA, USA) with 200 permutations to estimate false discovery rates (FDR). The gene sets were considered significantly enriched at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The GSEA results were visualized by enrichment plots, highlighting key pathways that were significantly enriched in the condition of interest. To understand the biological functions of genes, GO terms and KEGG pathway analyses were applied for taxonomy-based analysis with the \u0026ldquo;ClusterProfiler\u0026rdquo; R package(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In addition, to illustrate the differences in genes involved in oxidative phosphorylation, we used the signal pathway map inherent in KEGG(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eCIBERSORTx and EcoTyper(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) algorithms were used for immune infiltration analyses. CIBERSORTx is an analytical tool that was introduced by Newman et al. to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population. EcoTyper, a machine learning framework for the identification of cell states and ecosystems from bulk, single-cell and spatially-resolved expression data, has been used to extend CIBERSORTx for large-scale profiling of cellular ecosystems.\u003c/p\u003e \u003cp\u003eCorrelations between DEGs and immune cell infiltration in AS were also analyzed. For this analysis, data from healthy controls were omitted, and data representing expression of DEGs was extracted. Correlation analyses were performed with the results of immune infiltration according to Pearson\u0026rsquo;s correlation coefficient. The results of these analyses were presented using the \u0026ldquo;ggplot2\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMachine learning\u003c/h2\u003e \u003cp\u003eIn the model-development phase, an XGBoost algorithm model(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) was constructed to analyze the contribution of DEGs between AS patients and healthy controls. A backward stepwise analysis was performed to select variables with a threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 according to the Akaike information criterion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe prediction of potential small-molecule drugs\u003c/h2\u003e \u003cp\u003eThe Connectivity Map (CMAP) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a drug discovery tool for exploring potential biological associations among diseases, genes and drugs. Specifically, the model allows the prediction of small molecule drugs that may induce or reverse the biological processes associated with DEGs. In this study, drugs with negative scores in T cell-related cell lines (for example, Jurkat cells) and osteogenesis-related cell lines (for example, U2OS cells) were explored, and information regarding the clinical research stages of the drugs and their mechanisms of action were drawn from information provided within the database.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSex dimorphism of T cell and mast cell among patients\u003c/h2\u003e \u003cp\u003eIn database GSE221786, we obtained RNA-seq data from cells secreting IL-17 PBMC from 11 male and 9 female AS patients. We first observed the overall gene expression differences between male and female patients and found significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Next, we used CIBERSORTx to investigate the sequencing results for information regarding immune cell infiltration, and we found that mast cells accounted for the majority of these IL-17 secreting cells, followed by T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eWe then used a KEGG analysis to uncover differences in the activation of cellular signaling pathways. We found that among the immune-related pathways, T-cell receptor (TCR)-related genes were the most significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In addition, according to GSEA, the activation of the TCR signaling pathway was more prominent in female subjects than in male subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, E). Specifically, the upregulation of expression of CD4 suggests a stronger T cell activation in female AS patients as compared to the activation in male AS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). A GSEA enrichment analysis also showed that among IL17-secreting cells from male subjects, the activation of mast cells was more prominent as compared to female subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). We identified multiple upregulated genes associated with mast cell activation in male subjects, including IL13, PTPN6, VAMP8 and LAT2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA) A Principal Component Analysis (PCA) showing differences in gene expression between male and female individuals with AS. B) CIBERSORTx analysis showing sex differences in immune infiltration among individuals with AS. Bar plot shows relative composition of PBMCs. C) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) between male and female AS patients. D,E) GSEA enrichment plot (D) and diagram (E) of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS focusing on genes involved in T-cell receptor (TCR) signaling. F) Heatmap showing the DEGs related to the PID_TCR_PATHWAY dataset between male and female patients with AS. G) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS from the GOBP_MAST_CELL_ACTIVATION dataset. H) Heatmap showing the DEGs related to the GOBP_MAST_CELL_ACTIVATION dataset between male and female AS patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration and enrichment analyses of immune-related signaling pathways between normal male and female subjects\u003c/h2\u003e \u003cp\u003eIn a comparative analysis of the patients, it was observed that male individuals displayed a stronger activation of mast cells, accompanied by elevated expression of key genes such as LAT2. We further investigated whether this difference is generally associated with sex or represents a specific manifestation within the context of AS disease. Therefore, we compare immune infiltration and related signaling pathways between normal male and female subjects.\u003c/p\u003e \u003cp\u003eWe analyzed overall gene expression in male and female control subjects who had not been diagnosed with AS, and we found significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In particular, we found that female subjects had higher average expression levels of XIST, TSIX and other non-coding RNAs located on the X chromosome. In male subjects, we identified several specific overexpressed protein-coding genes on autosomes, including CRIP1, S100A8, MARCO, NLGN4Y and BCORP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We then performed KEGG analyses of DEGs to investigate the activation of cellular signaling pathways, and we found that among immune-related pathways, genes related to the TCR signaling pathway were the most significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eWith regard to immune infiltration, a CIBERSORTx analysis demonstrated that in control subjects, as in patients diagnosed with AS, mast cells were the predominant IL-17-secreting cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). There was no statistical difference in mast cell invasion compared to the AS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Similarly, we also found that there was no significant difference in the proportion of T cells between healthy individuals and subjects with AS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eWe performed GSEA focused on the activation of T cells and mast cells in male and female control subjects. We found that there was no difference in the activation of T cells and mast cells between male and female control subjects, despite significant differences in the secretion of IL-17 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA) A Principal Component Analysis (PCA) showing differences in gene expression between healthy control male and female individuals. B) A volcano map displaying differentially expressed genes (DEGs) between male and female control subjects. C) KEGG pathway enrichment analysis of DEGs between male and female control subjects. D) CIBERSORTx analysis showing differences in immune infiltration between male and female control subjects. E,F) Comparison of the numbers of mast cells and T cells between the ankylosing spondylitis (AS) group (n\u0026thinsp;=\u0026thinsp;20) and the control group (n\u0026thinsp;=\u0026thinsp;8). P-values were obtained with t-tests. G) GSEA enrichment plots of IL-17-secreting cells from male controls compared with IL-17-secreting cells from female controls with AS using the PID_TCR_PATHWAY dataset. H) Heatmap showing the DEGs related to the PID_TCR_PATHWAY dataset between male and female healthy control subjects. I) GSEA enrichment plots of IL-17-secreting cells from male control subjects compared with IL-17-secreting cells from female controls with AS for the GOBP_MAST_CELL_ACTIVATION dataset. J) Heatmap showing DEGs related to the GOBP_MAST_CELL_ACTIVATION dataset between control male and female individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSex-specific pathways in IL-17-secreting cells\u003c/h2\u003e \u003cp\u003eConsidering the significant impact of signal pathway activation on cellular outcomes, we analyzed the differences in the expression of genes involved in pathways leading to IL-17 secretion cell activation pathways between male and female AS patients. In the KEGG pathway enrichment analysis on the relevant DEGs, we observed that the TNF signaling pathway was the most enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Interestingly, TNF signaling is one of the pathways most extensively studied in AS disease(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). GSEA revealed that the expression of TNF signaling-related genes in samples associated with female subjects was significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). In particular, the expression level of TNF itself was found to be significantly increased in women (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Increased activity in this pathway may offer an explanation for why female AS patients do not respond as sensitively to TNF inhibitor treatments as male patients do.\u003c/p\u003e \u003cp\u003eWe explored in more depth the signaling pathways that are activated in female AS patients compared to male patients. Our findings revealed that the pathways involving nectin, FAK, SHP2 and FOXO also demonstrated increased activation in female patients at the level of gene transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). However, as the prevalence of AS is higher in male subjects than in female subjects, the signaling pathways activated in males may hold more significance for disease progression. Therefore, we also analyzed the signaling pathways that are more highly activated in male patients and discovered that genes involved in pathways related to MTA3, the proteasome, NKC cells and NDK/dynamin were more highly expressed in male patients compared to female patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-J). We propose that these signaling pathways may represent potential therapeutic targets for the treatment of AS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) related to signal transduction pathways between male and female control subjects. B) Heatmap showing the DEGs related to the PID_TNF_PATHWAY between male and female individuals with AS. C) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the PID_TNF_PATHWAY and PID_NECTIN_PATHWAY. D,E) GSEA enrichment plots showing the most highly enriched signaling pathways in female (D) and male (E) subjects. F-I GSEA enrichment plots and diagrams of IL-17-secreting cells from male patients with AS compared with female patients with AS for BIOCARTA_MTA3_PATHWAY (F,G) and GOCC_PROTEASOME_ACCESSORY_COMPLEX (H,I). J) Heatmap showing the DEGs related to the GOCC_PROTEASOME_ACCESSORY_COMPLEX between male and female individuals with AS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in metabolic patterns of IL-17-secreting cells between male and female AS patients\u003c/h2\u003e \u003cp\u003eIn addition to the signaling pathways mentioned above, based on previous studies showing that the metabolic pattern of immune cells greatly influences their status(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), we analyzed the differences in metabolic patterns of IL-17-secreting cells between male and female subjects. We performed KEGG pathway enrichment analysis on the DEGs between male and female subjects with AS, with particular focus on metabolism-related pathways. We found that carbohydrate metabolism and lipid metabolism were the most enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eNext, we performed GSEA and found that male subjects exhibited greater enrichment of genes in carbohydrate metabolism and aerobic phosphorylation pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). In addition, male AS subjects also exhibited upregulation in genes encoding proteins in oxidative phosphorylation respiratory chain complexes I through V (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E). GSEA demonstrated that male subjects exhibited an enrichment of upregulated genes in the fatty acid oxidation pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, G). Specific genes upregulated in male AS subjects included PCK2, BDH2, ACADS, ECHDC2, ACAA1, AKT1, ABCD4, CRAT and ECH1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Meanwhile, we also compared the changes in metabolic patterns between male patients with AS and normal male subjects, and we found that the expression levels of genes associated with oxidative phosphorylation were increased in AS patients, but the levels of genes associated with fatty acid oxidation remained unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH,I). These results suggest that changes in oxidative phosphorylation may contribute to the onset of the disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA) KEGG pathway enrichment analysis of differentially expressed genes (DEGs) related to metabolism between male and female control subjects. B,C) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the circulating carbohydrate concentration and hallmark oxidative phosphorylation pathways. D) Heatmap showing the upregulated DEGs related to the hallmark oxidative phosphorylation dataset between males and females with AS. E) In the signal pathway diagram of the electronic respiratory chain, the red box represents the genes that are elevated in male patients compared to female patients. The green box indicates the genes that are elevated in female patients compared to male patients. The black box indicates genes that are not changed between male and female patients. F,G) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from female patients with AS for the GOBP_fatty_acid_beta_oxidation dataset. H,I) GSEA enrichment plots of IL-17-secreting cells from male patients with AS compared with IL-17-secreting cells from control group of males for GOBP_OXIDATIVE_PHOSPHORYLATION and FATTY_ACID_BETA_OXIDATION datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSexual dimorphism in secretory proteins of IL-17-secreting cells\u003c/h2\u003e \u003cp\u003eAS symptoms primarily manifest in localized regions of the spine, and circulating immune cells that act at these locales along with their secreted proteins play important roles in disease progression. Therefore, we initially identified genes that were more highly expressed in male and female patients compared to control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B), and then we identified genes in these sets that encode secreted proteins. We discovered that a few of the secreted proteins overexpressed at the transcriptional level in male AS patients were not pro-inflammatory molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). However, transcripts encoding proteins promoting osteogenesis, such as FGF2, WNT and DLL1, were found to be highly expressed.\u003c/p\u003e \u003cp\u003eIn contrast, the expression of pro-inflammatory molecules, such as IL-6 and IL-10, was higher in female AS patients as compared to male patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F). Additionally, we analyzed the chromosomal locations of genes whose expression differed between males and females. Our findings indicate that genes overexpressed in male patients are primarily located on the Y chromosome. However, for female patients, genes with high expression levels are more prevalent on chromosome 12 than on the X chromosome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA,B) Venn diagrams displaying the genes elevated in (A)male and (B)female ankylosing spondylitis (AS) patients compared to female patients, and the genes with elevated transcriptional levels in control subjects compared to female control subjects. C) Heatmap showing the differentially expressed genes related to secretory proteins. D,E) GSEA enrichment plots and heatmap of IL-17-secreting cells from male patients with AS compared with female patients with AS for the HALLMARK_INFLAMMATORY_RESPONSE dataset. F) GSEA enrichment plots and diagram of IL-17-secreting cells from male patients with AS compared with female patients with AS according to the chromosomal location of the gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of candidate drugs for AS treatment by machine learning and CMAP analyses\u003c/h2\u003e \u003cp\u003eNext, we utilized databases GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 and employed the XGBoost algorithm for machine learning to aid in assessing the impact of the DEGs that encode secreted proteins on disease onset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We obtained scoring results for the secreted proteins. We also used the CMAP database to search for medications that might affect the activities of these secreted proteins, leading to the identification of potential therapeutic interventions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The information derived from the CMAP analysis was also used to identify the progress of candidate drugs through clinical trials and to determine their likely mechanisms of action, providing a reference for the further development of treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA) Top 29 features selected using XGBoost and the corresponding variable importance score. Y-axis indicates the importance score, which is the relative number of a variable that is used to distribute the data; X-axis indicates the top 29 weighted variables. B) CMAP-predicted drugs that may intervene in the occurrence of AS. U-2OS, human osteosarcoma cells. Jurkat cells, human T-cell leukemia cell line. CD34, hematopoietic stem cell line. PHH, primary human hepatocytes. THP1, acute monocytic leukemia cell line. HEK293, human embryonic kidney cell line.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the gene expression patterns of IL17-secreting cells from the blood of both male and female AS patients. Our observations revealed that the activation of the TCR signaling pathway was more prominent in female subjects than in male subjects, while mast cells are more highly activated in male patients. This research offers a potential explanation for the increased BASFI score in female AS patients relative to male AS patients.\u003c/p\u003e \u003cp\u003eMoreover, our analysis extended to the differential activation of immune signaling pathways between Sexs, finding transcriptomic evidence of a notably higher expression of TNF in female patients relative to male patients, which may provide insight into the relative insensitivity of females to TNF inhibitors. Some researchers have found that MTA3 could activate Wnt signaling pathway (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Therefore, MTA3 signaling pathways that are more highly activated in male patients may contribute to ligament osteogenesis by activating Wnt signaling pathway.\u003c/p\u003e \u003cp\u003eAdditionally, our exploration into the metabolic profiles of these cells uncovered that genes involved in oxidative phosphorylation pathways and lipid oxidation processes were predominantly upregulated in immune cells from male patients as opposed to female patients. The metabolic patterns of T cells help to determine their functionality(\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Metabolism within these cells affects their proliferation, differentiation and overall immune response. Activated effector T cells tend to exhibit augmented anabolic metabolic pathways, such as aerobic glycolysis, while memory T cells tend to more strongly engage catabolic pathways, like fatty acid oxidation(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Moreover, select lipids act as metabolic regulators, intertwining environmental signals with cellular signaling pathways to influence T cell biology(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In this study, we also uncovered transcriptomic evidence that in male patients, IL-17-secreting cells likely exhibit an increase in both aerobic glycolysis and fatty acid oxidation. Although classical activation pathways appear weaker in male patients as compared to female patients, their state of high-energy metabolism suggests a higher level of cellular activation in male subjects. This indicates that there may exist undiscovered T cell activation pathways that underlie AS.\u003c/p\u003e \u003cp\u003eThis study also uncovered some previously unrecognized genes that may be related to AS. For example, transcripts coding for METRN, also known as meteorin, were found to be highly expressed in IL-17-secreting cells in male AS patients and to have an impact on disease occurrence. This protein has not previously been associated directly with AS, although it is known to serve as an important promoter of the formation of axonal networks during neurogenesis. In addition, METRNL has been reported to attenuate lipid-induced inflammation and insulin resistance via AMPK- or PPARδ-dependent pathways in skeletal muscle of mice(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Other such proteins that we uncovered include TMC4 (transmembrane channel-like protein 4), which is predicted to enable mechanosensitive ion channel activity and to be involved in ion transmembrane transport, although further research into its biological roles is required. GNLY (granulysin) is a member of the saposin-like protein family and is located in T cell cytotoxic granules, which are released upon antigen stimulation; no other studies have reported connections of its expression with AS. WNT7A promotes the osteogenic differentiation of human mesenchymal stem cells(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), which is considered to be a mechanism driving ligament osteogenesis in AS. However, there are currently no reports on its mechanism of action in AS. We propose that WNT7A might warrant additional research into the mechanism of ectopic osteogenesis in AS.\u003c/p\u003e \u003cp\u003eOur research using CMAP identified several interesting drugs that deserve further exploration in the context of AS. Alisertib, an aurora kinase inhibitor, promotes apoptosis and autophagy in human osteosarcoma U-2 OS and MG-63 cells by activating mitochondrial pathways and inhibiting the p38 MAPK/PI3K/Akt/mTOR signaling pathway(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). While no studies have investigated the use of this drug in the treatment of AS, we propose that it may reduce ectopic bone formation by promoting apoptosis of local osteoblasts. Atorvastatin, an HMG-CoA reductase inhibitor, is used as a lipid-lowering medication for the treatment of myocardial infarction. It has also been shown, however, to alleviate bone loss and aortic valve atherosclerosis in LDLR mice(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), suggesting that it might also reduce the occurrence of ectopic bone formation in AS.\u003c/p\u003e \u003cp\u003eThis study analyzed the sequencing data of blood samples from AS patients and inferred possible factors for disease differences between male and female AS patients. However, further analysis is required to identify mechanisms to explain these sex-based differences. In addition, further in vitro and in vivo research is required to confirm our conclusions and to more deeply investigate the associated factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the present study are available in the GEO repository. The collated data can also be found in Supplementary Document 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by National Natural Science Foundation of China (Grant no 82372370; 82172384; 81972039), Department of Science and Technology of Guangdong Province (Grant no 2021B1515020080) and KELIN New Talent Project of The First Affiliated Hospital, Sun Yat-sen University (Grant no Y12001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLi Sifang completed the conception and design of the project. All authors contributed to data collection and analysis. The initial draft was written by Li Sifang and Chao Hua. Liu Hui and Chao Hua completed the final review and editing of the manuscript. Li Sifang and Chao Hua contributed equally to this paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSieper J, Poddubnyy D. Axial spondyloarthritis. Lancet. 2017;390(10089):73-84.\u003c/li\u003e\n\u003cli\u003eStolwijk C, van Onna M, Boonen A, van Tubergen A. Global Prevalence of Spondyloarthritis: A Systematic Review and Meta-Regression Analysis. Arthritis Care Res (Hoboken). 2016;68(9):1320-31.\u003c/li\u003e\n\u003cli\u003eNavarro-Comp\u0026aacute;n V, Sepriano A, El-Zorkany B, van der Heijde D. Axial spondyloarthritis. Ann Rheum Dis. 2021;80(12):1511-21.\u003c/li\u003e\n\u003cli\u003evan der Horst-Bruinsma IE, Zack DJ, Szumski A, Koenig AS. 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Wnt7a promotes the osteogenic differentiation of human mesenchymal stem cells. International journal of molecular medicine. 2021;47(6).\u003c/li\u003e\n\u003cli\u003eNiu NK, Wang ZL, Pan ST, Ding HQ, Au GH, He ZX, et al. Pro-apoptotic and pro-autophagic effects of the Aurora kinase A inhibitor alisertib (MLN8237) on human osteosarcoma U-2 OS and MG-63 cells through the activation of mitochondria-mediated pathway and inhibition of p38 MAPK/PI3K/Akt/mTOR signaling pathway. Drug design, development and therapy. 2015;9:1555-84.\u003c/li\u003e\n\u003cli\u003eRajamannan NM. Atorvastatin Attenuates Bone Loss and Aortic Valve Atheroma in LDLR Mice. Cardiology. 2015;132(1):11-5.\u003c/li\u003e\n\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":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4129727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4129727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnkylosing spondylitis (AS) with radiographic damage is more prevalent in men than in women. IL-17, which is mainly secreted from peripheral blood mononuclear cells (PBMCs), plays an important role in the development of AS. Its expression is different between male and female. However, it is still unclear whether sex dimorphism of IL-17 contribute to sex differences in AS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 datasets were collected from the Gene Expression Omnibus (GEO) database. Differential expressed genes (DEGs) were analyzed with the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods. CIBERSORTx and EcoTyper algorithms were used for immune infiltration analyses. Machine learning based on the XGBoost algorithm model was used to identify the impact of DEGs. The Connectivity Map (CMAP) database was used as a drug discovery tool for exploring potential drugs based on the DEGs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to immune infiltration analyses, mast cells accounted for the largest proportion of IL-17-secreting PBMCs, and KEGG analyses suggested an enhanced activation of mast cells among male AS patients, whereas the expression of TNF was higher in female AS patients. Other signaling pathways, including those involving metastasis-associated 1 family member 3 (MAT3) or proteasome, were found to be more activated in male AS patients. Regarding metabolic patterns, oxidative phosphorylation pathways and lipid oxidation were significantly upregulated in male AS patients. In XGBoost algorithm model, DEGs including METRN and TMC4 played important roles in the disease process. we integrated the CMAP database for systematic analyses of polypharmacology and drug repurposing, which indicated that atorvastatin, famciclocir, ATN-161 and taselisib may be applicable to the treatment of AS\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe analyzed the sex dimorphism of IL-17-secreting PBMCs in AS. The results showed that mast cell activation was stronger in males, while the expression of TNF was higher in females. In addition, through machine learning and the CMAP database, we found that genes such as METRN and TMC4 may promote the development of AS, and drugs such as atorvastatin potentially could be used for AS treatment.\u003c/p\u003e","manuscriptTitle":"Sex dimorphism of IL-17-secreting peripheral blood mononuclear cells in ankylosing spondylitis based on bioinformatics analysis and machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 15:55:07","doi":"10.21203/rs.3.rs-4129727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-04T07:40:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-02T01:12:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T13:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e7a5206e-b63d-45b6-93c9-479485abc704","date":"2024-04-10T07:20:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"365611c6-9aa3-4a5e-b826-6a12a4ecba0f","date":"2024-04-09T07:54:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-07T03:28:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-07T03:10:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-22T10:50:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-22T10:23:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2024-03-19T11:01:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0021b88-87e5-4b09-a480-560bd73581f1","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-12T05:26:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 15:55:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4129727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4129727","identity":"rs-4129727","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}