IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis

IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis | 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 IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis Huatao Liu, Changhua Wu, Guojian Li, Xiaoshuai Peng, Zhaoqiang Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7536100/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Arthritis Research & Therapy → Version 1 posted 10 You are reading this latest preprint version Abstract Objectives Rheumatoid arthritis (RA) involves synovial inflammation driven by pathogenic macrophages, whose polarization is regulated by transcription factors (TFs). Interferon regulatory factor 7 (IRF7) is an innate immune regulator, but its role in RA macrophage-mediated inflammation and cartilage destruction remains unclear. This study aimed to define IRF7-dependent regulatory pathways in RA macrophages and evaluate their therapeutic potential. Methods Single-cell RNA sequencing (scRNA-seq) data and SCENIC analysis were used to identify TF-enriched macrophage subpopulations in the RA synovium. Chromatin immunoprecipitation sequencing (ChIP-seq) was used to map IRF7 binding sites, and bulk RNA-seq was used to analyze M1 polarization responses. Functional validation included IRF7 knockdown in human monocytes and intra-articular siRNA in a collagen-induced arthritis (CIA) mouse model to assess inflammatory genes, macrophage polarization, and joint pathology. Results A CD48 high S100A12 + proinflammatory macrophage subset was expanded in RA and enriched for IRF7 activity and downstream genes (PTGS2, CXCL10, NF-κB1, and IL-1β). IRF7 directly regulates these genes, and its knockdown reduces M1 polarization and inflammatory gene expression in vitro. In CIA mice, local IRF7 silencing attenuated joint inflammation, synovial hyperplasia, and bone erosion, which correlated with decreased proinflammatory macrophages and increased regulatory T cells. Conclusions IRF7 drives pathogenic macrophage polarization and inflammatory signaling in RA, linking its dysregulation to disease pathogenesis. Local targeting of IRF7 disrupts proinflammatory networks, suggesting a precise strategy to mitigate synovial inflammation without systemic immunosuppression. Rheumatoid arthritis Macrophage IRF7 Single-cell RNA sequencing Inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Key Message IRF7 is enriched in a CD48 high S100A12 + proinflammatory macrophage subset in the RA synovium and directly regulates inflammatory genes (PTGS2, CXCL10, NF-κB1, and IL-1β). Knockdown of IRF7 reduces M1 macrophage polarization and inflammatory gene expression in vitro. Local IRF7 silencing in a collagen-induced arthritis mouse model attenuates joint inflammation, synovial hyperplasia, and bone erosion while increasing regulatory T cells. Local targeting of IRF7 offers a precise therapeutic strategy to mitigate RA synovial inflammation without systemic immunosuppression. Introduction Rheumatoid arthritis (RA) is a prevalent systemic autoimmune disease 1 that affects nearly 1% of adults worldwide. 2,3 Beyond joint involvement, it manifests with extra-articular manifestations, including rheumatoid nodules, interstitial lung disease, vasculitis, bronchiectasis, and systemic comorbidities. Although its etiology remains incompletely understood, RA is characterized by chronic inflammation of the synovial tissue, which drives joint destruction and functional impairment. 4 Current first-line therapies for RA rely on synthetic disease-modifying antirheumatic drugs (sDMARDs), including methotrexate and glucocorticoids. However, these agents are associated with substantial adverse effects, such as bone marrow suppression, gonadal toxicity, increased infection risk, and high relapse rates upon discontinuation. 5 In patients with an inadequate response to sDMARDs, biological DMARDs (bDMARDs) target key inflammatory pathways, including cytokines (IL-1, IL-6, IL-17, and TNF-α), T-cell costimulatory molecules, B-cell activation, and JAK/STAT signaling. 6,7 Despite this progress, approximately 40% of patients exhibit primary or secondary resistance to bDMARDs 2 , underscoring the urgent need to identify novel therapeutic targets to improve clinical outcomes. 8 In the RA synovium, innate immune cells, particularly infiltrating macrophages, play pivotal roles in driving arthritis initiation and synovial hyperplasia (pannus formation), which invade the cartilage‒bone interface and promote bone erosion and cartilage degradation. 9–14 Macrophages exhibit remarkable plasticity, polarizing into proinflammatory M1 or anti-inflammatory M2 phenotypes in response to pathogen-associated molecular patterns (PAMPs), damage-associated molecular patterns (DAMPs), cytokines, and chemokines. 15 , 16 Dysregulation of the M1/M2 balance in synovial tissue is a hallmark of chronic synovitis, with M1-dominant polarization exacerbating inflammatory cascades and joint destruction. 15 , 17 Single-cell RNA sequencing has enabled the identification of distinct functional subsets of macrophages in RA. Notably, both patient-derived synovial tissues and experimental arthritis models show expansion of a CD48 high S100A12 + M1-like population, characterized by increased expression of alarmins (S100A8, S100A9, and S100A12) and IL-1β. 18,19 These molecules act as potent drivers of monocyte activation, fibroblast proliferation, and neutrophil recruitment, underscoring the pathogenic potential of targeting this subcluster. 20,21 Macrophage phenotypic diversity is governed by transcription factor (TF) networks that reshape epigenetic and chromatin accessibility landscapes during differentiation and activation. 22,23 Deciphering these regulatory circuits offers opportunities to develop therapies that inhibit proinflammatory pathways or promote anti-inflammatory polarization. 24 – 26 TFs such as NF-κB, STAT, AP-1, and interferon regulatory factors (IRFs) are critical mediators of macrophage plasticity, with IRF1, IRF3, and IRF5 previously linked to RA pathogenesis. 27 – 29 However, the specific role of IRF7, a regulator of type I interferon responses and innate immunity, in orchestrating pathogenic macrophage subsets in RA remains poorly defined, presenting an important knowledge gap for therapeutic innovation. 30–32 In this study, we leveraged multiomics datasets—including single-cell transcriptomics of synovial macrophages, chromatin immunoprecipitation (ChIP)-seq, and bulk RNA-seq—to identify key regulatory pathways in RA. 18 Integrative analysis was validated via the use of clinical specimens, in vitro cell models, and in vivo murine experiments. Our findings offer novel insights into the molecular mechanisms underlying macrophage-driven inflammation in RA, highlighting IRF7 as a promising therapeutic candidate within the CD48 high S100A12 + subcluster. By combining single-cell regulatory network inference with functional validation, this approach exemplifies how multiomics strategies can uncover actionable targets for complex autoimmune diseases such as RA. 20,33 Materials and methods Mice Female DBA/1J mice (8 weeks old, 20–24 g) were obtained from Jiangsu GemPharmatech Co. Ltd. (Nanjing, China). The mice were housed in specific-pathogen-free (SPF) facilities at the Laboratory Animal Center of Sun Yat-sen University and maintained at 25°C, 75% humidity, and a 12-hour light/dark cycle with ad libitum access to food and water. The animals had ad libitum access to standard chow and water. Prior to the experiment, all the mice were acclimatized for at least 7 days. All animal procedures complied with the Guide for the Care and Use of Laboratory Animals (8th Edition, National Academies Press, 2011, ISBN: 0309154006) and were approved by the Sun Yat-sen University Committee on Laboratory Animal Management and Ethics (No. SYSU-IACUC-2023-000203). Human synovial samples Synovial samples from the knee joint were collected from patients with osteoarthritis (OA) or rheumatoid arthritis (RA) during total knee arthroplasty (TKA) surgery. The study protocol was approved by the Ethics Committee of The Eighth Affiliated Hospital, Sun Yat-sen University (Shenzhen, China; Ethics No. 2022-035-03), and written informed consent was obtained from all participants. Cells Six healthy donors (HDs) and six rheumatoid arthritis (RA) patients were recruited for this study. The protocol was approved by the Ethics Committee of The Eighth Affiliated Hospital, Sun Yat-sen University (Ethics No. 2022-035-03), and written informed consent was obtained from all participants. Human whole blood samples were collected from HDs and RA patients. Single-cell RNA sequencing data processing and analysis Synovial macrophage single-cell transcriptome datasets and clinical metadata from 17 rheumatoid arthritis (RA) patients, 4 undifferentiated arthritis patients, 6 osteoarthritis (OA) patients, and 7 healthy control subjects were obtained from the European Bioinformatics Institute (EBI, E-MTAB-8322) 34 and the Gene Expression Omnibus (GEO, GSE216651 35 , GSE152805 36 ). The raw gene expression matrices were processed via Seurat v5.0.3 package 37 in R v4.3.3, which involves quality control, normalization, and batch correction. The cells were filtered on the basis of gene count (> 300 genes), feature complexity (< 3500 unique molecular identifiers [UMIs]), and mitochondrial gene expression (< 20% of total UMIs) to exclude dead cells and multiplets. Data from GSE216651 and GSE152805 were first integrated via the IntegrateLayers function with canonical correlation analysis (CCA), followed by clustering with FindClusters (resolution = 0.4) to identify major cell populations. Macrophages were isolated via marker genes (FCGR1A, ITGAM, CD14, LYZ) and further integrated with E-MTAB-8322 data via IntegrateLayers with reciprocal principal component analysis (RPCA), followed by reclustering at a lower resolution (0.3) to refine the subpopulations. Differential gene expression across macrophage subgroups was identified via FindAllMarkers (min.pct = 0.3, logFC threshold = 0.3), with subgroup distributions visualized via ggplot2 v3.4.2. The functional enrichment of DEGs in CD48 high S100A12 + macrophages was performed via clusterProfiler v4.8.1 38 and GO.db v3.17.0, with the results visualized in ggplot2. To infer transcription factor (TF) gene regulatory networks at single-cell resolution, we employed pySCENIC v0.12.1, a high-performance Python implementation of the SCENIC pipeline 39 , within a Python 3.7 environment. An 8,000-cell submatrix was first extracted from the integrated single-cell transcriptome to identify coexpression modules between TFs and their putative target genes. Regulon (gene-TF regulatory network) construction utilized the GRNBoost2 algorithm, which models coexpression relationships across cells to predict TF-target interactions. Direct DNA-binding targets of TFs were validated via motif enrichment analysis via cisTarget databases (resources.aertslab.org/cistarget). Regulon activity scores (RASs) were calculated for each cell to quantify the enrichment of TF-target gene coexpression, with each regulon comprising a TF and its motif-confirmed direct targets. Cell type-specific regulons were identified via the regulon specificity score (RSS), which was computed via the Jensen–Shannon divergence (JSD) algorithm, to measure regulon activity uniqueness across subpopulations. To mitigate sampling bias, the Seurat-integrated dataset was randomly subsampled three times, with SCENIC analysis repeated for each subsample. The results were intersected to derive consensus regulons, ensuring robustness. Finally, UMAP dimensionality reduction (umap-learn v0.5.5) was performed on the basis of TF regulon activity scores, with visualizations generated via ggplot2 v3.4.2 to map regulatory heterogeneity across macrophage subclusters. ChIP-seq data processing and analysis IRF7 ChIP-seq raw reads from mouse bone marrow-derived macrophages stimulated with 100 ng/mL lipopolysaccharide (LPS) for 24 hours (GSE62697 40 ) were obtained from the GEO database. The raw reads were trimmed via TrimGalore v0.6.1 with the following parameters: -q 25 --phred33 --length 36 -e 0.1 to remove low-quality sequences and adapter contaminants. Clean reads were aligned to the mouse genome (mm10) via Bowtie2 v2.4.4 41 , generating SAM files that were subsequently converted to BAM files via SAMtools v1.17 for downstream analysis. 42 Peak calling was performed via MACS2 v2.2.7.1 43 , with unstimulated (t0) samples serving as the negative control to identify differential IRF7 binding sites. The enrichment profiles of the ChIP-seq peaks were visualized via deepTools v3.5.2 44 , specifically the computeMatrix and plotHeatmap functions, to assess the binding intensity around transcription start sites (TSSs). Genomic annotation of peaks, including promoter and enhancer regions, was conducted via the Chipseeker v1.36.0 R package. 45 To identify direct IRF7 target genes, we intersected ChIP-seq-derived binding genes with IRF7 regulon-targeted genes from pySCENIC analysis, defining high-confidence regulatory interactions. The functional enrichment of these candidate genes was performed via the clusterProfiler package, which focuses on Reactome pathway analysis to characterize the biological processes regulated by IRF7 in inflammatory macrophages. 46 Bulk RNA-seq data analysis Bulk transcriptome datasets (GSE154346 21 , GSE130011 23 ) from a macrophage line under unstimulated (M0) and M1-polarized conditions were downloaded from the GEO database. To validate IRF7-mediated gene regulation, we generated a heatmap of IRF7 and its predicted downstream targets (e.g., PTGS2, CXCL10, NFKB1, and IL1B) via the pheatmap v1.0.12 package. 47 Immunohistochemical staining of clinical knee synovial samples Formalin-fixed, paraffin-embedded (FFPE) 4-µm-thick knee synovial tissue sections were deparaffinized in xylene followed by rehydration through a series of graded ethanol solutions. Antigen retrieval was executed via microwave-mediated heating at 95°C for 20 minutes in 10 mM citrate buffer (pH 6.0) to expose the target epitopes. Immunohistochemical staining was performed via an SP Rabbit & Mouse HRP Kit (DAB; Catalog No. CW2069S, CWBIO, China) following the manufacturer’s standardized protocol. Endogenous peroxidase activity was quenched with 3% hydrogen peroxide in methanol for 15 minutes, followed by blocking with 5% bovine serum albumin (BSA) in Tris-buffered saline (TBS) for 1 hour at room temperature (RT). Primary antibodies against CD68 (macrophage marker; 25747-1-AP, Proteintech; 1:200 dilution) and S100A12 (inflammatory marker; 16630-1-AP, Proteintech; 1:100 dilution) were applied overnight at 4°C. After being washed with TBS-T (0.1% Tween-20 in TBS), the sections were incubated with species-matched horseradish peroxidase (HRP)-conjugated secondary antibodies (CW2069S, CWBIO) for 45 minutes at RT. Signal development was performed using 3,3'-diaminobenzidine (DAB) substrate solution (10 minutes), and the nuclei were counterstained with hematoxylin for 2 minutes. Images were acquired via a Leica microscope (Leica Microsystems, Germany) at 400× magnification. Cell culture and transfection Human CD14 + monocytes were isolated from whole blood through density gradient centrifugation via Ficoll-Paque PLUS medium (GE Healthcare Life Sciences), followed by magnetic-activated cell sorting with anti-human CD14 monoclonal antibody-conjugated magnetic beads (Miltenyi Biotech, Germany), which was performed strictly according to the manufacturers’ protocols. The isolated cells were maintained in RPMI 1640 culture medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM L-glutamine, and a sterile antibiotic mixture (100 U/mL penicillin and 100 µg/mL streptomycin). Cultures were incubated at 37°C in a humidified atmosphere containing 5% CO₂, with half-medium replacement every 3 days to maintain cell viability and proliferation. For macrophage differentiation, CD14⁺ monocytes were seeded into culture wells and incubated with 25 ng/mL macrophage colony-stimulating factor (M-CSF; Sigma‒Aldrich, USA) for 5 days. For M1 polarization, differentiated macrophages were stimulated with 50 ng/mL lipopolysaccharide (LPS) and 20 ng/mL interferon-γ (IFN-γ; Sigma‒Aldrich, USA) for 24 hours. IRF7 knockdown was achieved by transfecting M1-polarized macrophages with a siRNA targeting IRF7 (siRNA-IRF7; iGEBIO, Guangzhou, China) via Lipofectamine™ RNAiMAX (Invitrogen) according to the manufacturer’s protocol. A nontargeting siRNA served as the negative control (NC). One day before transfection, M1 macrophages were seeded in 6-well plates at 3 × 10⁵ cells/well. At 60–80% confluence, 5 µL of siRNA-IRF7 or siRNA-NC was diluted in 45 µL OPTI-MEM (Sigma‒Aldrich, USA), mixed with Lipofectamine™ RNAiMAX reagent, and incubated for 20 minutes at 25°C before being added to the cells for 24 hours. RNA isolation Total RNA was isolated from M1-polarized macrophages via TRIzol reagent (Invitrogen, USA) according to the manufacturer’s protocol. The RNA concentration and purity were quantified and assessed via spectrophotometric analysis. The integrity of the total RNA was assessed via agarose gel electrophoresis. The qualified total RNA was stored at -80°C and utilized for subsequent analysis. Real-time quantitative polymerase chain reaction (RT‒qPCR) Complementary DNA (cDNA) was synthesized from total RNA via the PrimeScript RT Kit (TaKaRa) according to the manufacturer’s protocol with a Bio-Rad T100 Thermal Cycler. RT‒qPCR was performed on an Applied Biosystems 7500 Real-Time PCR System using SYBR Premix Ex Taq (TaKaRa). The thermal cycling protocol comprised an initial denaturation step at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 5 seconds and annealing/extension at 60°C for 20 seconds. Each sample was analyzed in technical triplicate, and the mean mRNA expression levels were calculated. Melting curve analysis was conducted to validate specific target amplification, ensuring single-product formation without primer dimers. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the internal reference gene, and relative gene expression was quantified via the 2^(-ΔΔCt) method. The forward and reverse primers for the target genes are listed in Table 1 . Table 1 RT‒qPCR real-time primer sequences. Gene Amplicon Size (bp) Forward primer (5’→3’) Reverse primer (5’→3’) IRF7 84 GCTGGACGTGACCATCATGTA GGGCCGTATAGGAACGTGC NFKB1 104 AACAGAGAGGATTTCGTTTCCG TTTGACCTGAGGGTAAGACTTCT PTGS2 94 CTGGCGCTCAGCCATACAG CGCACTTATACTGGTCAAATCCC IL1B 132 ATGATGGCTTATTACAGTGGCAA GTCGGAGATTCGTAGCTGGA TNF 220 CCTCTCTCTAATCAGCCCTCTG GAGGACCTGGGAGTAGATGAG CXCL10 198 GTGGCATTCAAGGAGTACCTC TGATGGCCTTCGATTCTGGATT Western blotting The cells were lysed on ice for 30 minutes in radioimmunoprecipitation assay (RIPA) buffer (Sigma‒Aldrich, USA) supplemented with 1% protease and phosphatase inhibitor cocktail (Roche). The cell lysate was then centrifuged at 12,000 × g at 4°C for 30 minutes. The protein concentration in the supernatant was determined via a Bicinchoninic Acid (BCA) Protein Assay Kit (Sigma‒Aldrich, USA) following the manufacturer's protocol. Equal amounts of protein were mixed with sodium dodecyl sulfate (SDS) loading buffer. The samples were then separated via 10% SDS-polyacrylamide gel electrophoresis (SDS‒PAGE) and electrotransferred onto a polyvinylidene fluoride (PVDF) membrane (Merck Millipore). The membrane was blocked with Tris-buffered saline with Tween 20 (TBST) containing 5% skim milk powder at room temperature for 1 hour. The membrane was subsequently incubated overnight at 4°C with primary antibodies specific to glyceraldehyde 3-phosphate dehydrogenase (GAPDH, CST 5174), interferon regulatory factor 7 (IRF7, Proteintech Group, 22392–1 - AP), C - X - C motif chemokine ligand 10 (CXCL10, 10937-1-AP, Proteintech Group), prostaglandin-endoperoxide synthase 2 (PTGS2, 27308-1-AP, Proteintech Group), interleukin 1 beta (IL-1β, 26048-1-AP, Proteintech Group), and nuclear factor kappa B subunit 1 (NFKB1, 14220-1-AP, Proteintech Group). After incubation with primary antibodies, the membrane was washed three times with TBST. Then, the samples were incubated at room temperature for 1 hour with an appropriate horseradish peroxidase (HRP)-conjugated secondary antibody (diluted 1:2000, Santa Cruz Biotechnology). The membrane was subsequently washed three more times with TBST. The chemiluminescent signal was detected via Immobilon Western Chemiluminescence HRP Substrate (Merck Millipore). Semiquantitative analysis of protein expression was performed via ImageJ software (National Institutes of Health, USA). Collagen-induced arthritis (CIA) mouse model The mice were randomly assigned to four experimental groups (n = 6 per group, for a total of 24) via a random number generator in R v4.3.3. Allocation was stratified to ensure balanced group distribution across cage positions and housing racks. Treatments and measurements were conducted in a randomized order to avoid systematic bias. The sample size was based on previous studies showing sufficient power (80%) to detect differences in clinical arthritis scores and histological inflammation, with an effect size of 1.2 and a significance level of 0.05. The group allocations were as follows: In the si-IRF7 group, the mice received intra-articular injections of IRF7-specific siRNA (3 nmol/mouse/dose; iGEBIO, China) into the ankle joint once weekly from day 18 to day 39 after initial immunization. Si-mock group: Mice were administered mock nontargeting siRNA via the same dosing and administration schedule. CIA model group (positive control): Mice underwent collagen-induced arthritis (CIA) induction but received no therapeutic intervention. Healthy control group (negative control): Mice were neither immunized nor treated. All interventions and assessments were performed by researchers blinded to group allocation to reduce bias. CIA was induced in female DBA/1J mice (8 weeks old, 20–24 g; GemPharmatech, China) following a validated protocol. Briefly, chicken type II collagen (Chondrex, USA) was dissolved in 0.05 M acetic acid to a final concentration of 2 mg/mL and emulsified 1:1 (v/v) with complete Freund’s adjuvant (CFA; containing 4 mg/mL Mycobacterium tuberculosis ; Chondrex, USA). The mice received an initial intradermal immunization with 100 µg of collagen emulsion at the base of the tail. On day 21, a booster injection of 100 µg of chicken collagen in incomplete Freund’s adjuvant (Chondrex, USA) was administered intraperitoneally to elicit arthritis, which typically developed 7–10 days post-booster. From day 18 post-initial immunization, the mice underwent examinations every three days to evaluate the onset of arthritis. Paw thickness was measured via a dial caliper. The primary outcome was joint inflammation, quantified by paw swelling, whereas the secondary outcomes included histopathological changes and bone erosion. On day 43, all the mice were humanely euthanized via CO₂ inhalation followed by cervical dislocation. Ankle joints were harvested and fixed for microcomputed tomography (µCT) analysis via a Siemens Inveon system to evaluate synovial inflammation and bone erosion. The inclusion and exclusion criteria were established a priori. The mice were included in the study if they were healthy, age-matched (8–10 weeks old), and free of signs of illness at the start of the experiment. Animals were excluded if they exhibited preexisting health issues, failed to recover from anesthesia, or showed abnormal behavior unrelated to experimental treatment. During analysis, data points were excluded only if technical errors occurred (e.g., sample loss, poor tissue quality for histology) or if the animals were euthanized for humane reasons unrelated to the experimental outcomes. No animals were excluded from the study. All the mice completed the experimental protocol. No outliers were removed. Flow cytometric analysis of mouse ankle tissues Fresh ankle joint samples were surface disinfected with 75% ethanol and processed under sterile conditions on ice. The tissues were dissected into small fragments via a sterile scalpel, washed twice with ice-cold sterile PBS, and digested in 1.5 U/mL Dispase II solution (Sigma‒Aldrich, USA) at 37°C for 60 minutes with gentle agitation. The digested tissues were strained through a 70-µm sterile cell strainer (BD Falcon) to obtain single-cell suspensions, which were subsequently centrifuged at 300 × g for 5 minutes at 4°C. The cell pellets were subsequently resuspended in RPMI 1640 medium (Gibco) supplemented with 10% FBS. For M2 macrophage quantification, the cells were first stained with a Zombie Violet™ Fixable Viability Kit (DAPI conjugate; Biolegend, USA) to exclude dead cells, followed by surface staining with a FITC-conjugated anti-mouse F4/80 antibody (123107, Biolegend) for 30 minutes at room temperature in the dark. After being washed, the cells were fixed and permeabilized via a fixation/permeabilization solution (Invitrogen, USA) according to the manufacturer’s protocol and then incubated with an Alexa Fluor 647-conjugated anti-mouse CD206 antibody (565250, BD Biosciences) for 60 minutes at 4°C in the dark. For regulatory T (Treg) cell phenotyping, the cells were stained with the Zombie Violet™ Fixable Viability Kit (BV421 conjugate; Biolegend, USA) and the surface markers APC-conjugated anti-mouse CD3 antibody (100235, Biolegend, USA) and FITC-conjugated anti-mouse CD4 antibody (100405, Biolegend, USA) for 15 minutes at room temperature. Following fixation/permeabilization, intracellular staining for FOXP3 was performed with a PE-conjugated anti-mouse FOXP3 antibody (320007, Biolegend, USA) for 60 minutes at 4°C in the dark. All the stained samples were washed twice with PBS, resuspended in 200 µL of 1% BSA-PBS, and transferred to FACS tubes. Flow cytometry data were acquired on a BD FACS Celesta cytometer (BD Biosciences) and analyzed via FlowJo software (version 10; BD Biosciences), with gating strategies validated by isotype controls and fluorescence-minus-one (FMO) samples. Histological and immunohistochemical analysis of mouse ankles After being euthanized, the ankle joints of the mice were dissected and fixed in 10% neutral-buffered formalin (pH 7.4) for 48 hours at room temperature. The samples were decalcified in 10% ethylenediaminetetraacetic acid (EDTA) decalcifying solution (pH 7.2) for 72 hours with gentle agitation, processed into 5-µm paraffin sections and stained with hematoxylin‒eosin (H&E) for morphological evaluation. Synovial inflammation and hyperplasia were assessed by a blinded observer via a validated scoring system and evaluated on a 0–3 scale: synovial inflammation (0 = thin synovium (1–2 cell layers), no inflammatory cell infiltration; 3 = severe thickening (polypoid hyperplasia), dense inflammatory cell aggregation, with villi formation or fibrin exudation). 48 For IF analysis, the sections were blocked in 5% bovine serum albumin (BSA) in PBS for 1 hour at room temperature and then incubated overnight at 4°C with the following primary antibodies: a rat recombinant anti-CD68 antibody (ab53444, Abcam, USA; 1:200) and a rabbit polyclonal anti-S100A12 antibody (16630-1-AP, Proteintech, China; 1:100). After washing, the sections were incubated with the following fluorescently conjugated secondary antibodies: goat anti-rabbit IgG H&L (Alexa Fluor® 488; ab150077; Abcam; 1:500) and goat anti-rat IgG H&L (Alexa Fluor® 555; ab150158; Abcam; 1:500) for 1 hour at room temperature in the dark. The slides were mounted with mounting medium and antifading agent (with DAPI) (S2110, SolarBio, China) and imaged via a fluorescence microscope (Leica Microsystems, Germany). IHC was performed via a streptavidin‒biotin peroxidase kit (CW2069S, Cowin Bio, China) according to the manufacturer’s protocol. The sections were pretreated with citrate buffer (pH 6.0) for antigen retrieval and then incubated overnight at 4°C with the following primary antibodies: rabbit anti-CD68 antibody (28058-1-AP, Proteintech; 1:200), rabbit anti-S100A12 antibody (16630-1-AP, Proteintech; 1:100), rabbit anti-IRF7 antibody (22392-1-AP, Proteintech; 1:100), and rabbit anti-PTGS2 antibody (27308-1-AP, Proteintech; 1:100). Following washes, the sections were incubated with HRP-conjugated secondary antibodies and developed with 3,3’-diaminobenzidine (DAB) substrate. The slides were counterstained with hematoxylin, dehydrated, and mounted with DPX mounting medium (Sigma‒Aldrich, USA). IHC slides were imaged via a microscope (Leica Microsystems, Germany) at ×200 magnification. The average optical density (AOD) of positive staining was quantified via ImageJ software (NIH, USA) with the IHC Toolbox plugin, normalized to background levels and expressed as the mean ± SD across three independent fields per sample. Statistical analysis All in vitro experiments (RT‒qPCR and Western blotting) were performed in triplicate, and the data are presented as the means ± standard deviations (SDs). Statistical significance was determined via Student's t test for two groups and one-way analysis of variance (ANOVA) followed by a Bonferroni correction for three or more groups. Statistical analyses were conducted via R and GraphPad Prism 9. A P value < 0.05 was considered statistically significant. Results Increased frequency and proinflammatory phenotype of CD48 high S100A12 + macrophages in the RA synovium Following quality control (excluding cells with 3500 features, or > 20% mitochondrial RNA), we analyzed 49,669 synovial macrophages from 17 RA patients, 4 UA patients, 6 OA patients, and 7 healthy controls (HCs). Unsupervised clustering identified nine distinct macrophage subpopulations: TREM2 high , CD48 + SPP1 + , FOLR2 high LYVE1 + , FOLR2 + ID2 + , CD48 high S100A12 + , HLA high ISG1 + , CD48 + high CLEC10A high , TREM2 low , and FOLR2 + ICAM1 + (Fig. 1 A). Disease-stratified analysis revealed a significant increase in the frequency of CD48 high S100A12 + macrophages with increasing disease severity, peaking in the RA synovium (Fig. 1 B). Similarly, S100A12 expression was upregulated in RA macrophages and downregulated in OA macrophages (Fig. 1 C). Gene Ontology (GO) enrichment of the CD48 high S100A12 + marker genes highlighted the overrepresentation of proinflammatory biological processes, including defense response activation, cytokine production regulation, leukocyte migration, and inflammatory pathway modulation (Fig. 1 D). Immunohistochemical (IHC) validation in knee synovial tissues revealed that RA samples contained a greater density of CD68 + panmacrophage and S100A12 + inflammatory cells than OA control samples did (Fig. 1 E–G), confirming the clinical importance of this proinflammatory subpopulation. IRF7-driven transcriptional programs define CD48 high S100A12 + synovial macrophages Triplicate pySCENIC analyses with random subsampling (Fig. 2 A) identified a conserved set of transcription factors (TFs) enriched in CD48 high S100A12 + macrophages, including NFIL3, TGIF1, FOSL2, IRF7, and STAT1. Heatmap visualization of regulon activity scores (RASs) revealed preferential activation of these TFs in S100A12 + subpopulations (Fig. 2 B), with IRF7 exhibiting one of the highest regulon specificity scores (RSSs) via Jensen‒Shannon divergence (JSD) analysis (Fig. 2 D). Previously, it was reported that IRF7 is involved in various autoimmune diseases, such as systemic sclerosis and systemic lupus erythematosus (47), but the role of IRF7 in macrophage activation in rheumatoid arthritis still needs further research. UMAP dimensionality reduction based on TF activity profiles demonstrated near-complete spatial overlap between CD48 high S100A12 + macrophages (Fig. 2 E) and cells with elevated IRF7 regulon activity (Fig. 2 F), indicating a specific transcriptional association. Immunohistochemical staining for IRF7 in OA and RA synovial slices was also conducted (Fig. 2 G). We observed a noteworthy increase in the proportion of IRF7-positive cells within the synovial tissue of RA patients compared with that of OA patients (Fig. 2 H). IRF7 knockdown suppresses inflammatory gene expression in M1-polarized macrophages Analysis of IRF7 chromatin immunoprecipitation sequencing (ChIP-seq) data from LPS-stimulated mouse bone marrow-derived macrophages revealed increased IRF7-binding sites in the promoter and enhancer regions following inflammatory activation (Fig. 3 A). Differential peak annotation revealed 108 high-confidence IRF7-bound genes that overlapped with SCENIC-predicted IRF7 target genes in human CD48 high S100A12 + synovial macrophages to generate a core regulatory gene set (Fig. 3 B). Reactome pathway enrichment analysis highlighted the involvement of these genes in canonical inflammatory cascades, including TNF signaling, NF-κB activation, IL-17 signaling, and Toll-like receptor pathways, with the key effectors CXCL10, PTGS2, NF-κB1, IL-1β, and FOS (Fig. 3 C). Bulk RNA-seq data from human M1-polarized macrophages (stimulated with LPS/IFN-γ) confirmed the upregulation of IRF7 and its predicted target genes (Fig. 3 D), mirrored by scRNA-seq UMAP visualization, which revealed elevated expression of these genes in the CD48 high S100A12 + subcluster (Fig. 3 E). Compared with those in control siRNA-treated cells, functional validation in human CD14 + peripheral blood monocyte-derived macrophages revealed that siRNA-mediated IRF7 knockdown (Fig. 3 F–G) significantly reduced the mRNA and protein levels of M1 polarization markers—TNF, CXCL10, IL-1β, PTGS2, and NF-κB1 (Fig. 3 H). Western blotting corroborated these findings, demonstrating that decreased IRF7 protein expression alongside attenuation of downstream inflammatory signaling (Fig. 3 G). These results establish IRF7 as a critical driver of proinflammatory gene expression during M1 macrophage polarization. Localized knockdown of IRF7 alters the proportions of macrophages and regulatory T (Treg) cells in collagen-induced arthritis mice To explore the role of IRF7 in the pathogenesis of arthritis, a collagen-induced arthritis (CIA) mouse model was established in DBA/1J mice (Fig. 4 A). Flow cytometric analysis revealed that, compared with the si-mock group and the arthritis positive control group, local intra-articular injection of IRF7 siRNA into the ankle joint significantly increased the mean fluorescence intensity (MFI) of CD206 on F4/80 + macrophages. This increase in MFI indicated the promotion of M2 macrophage polarization (Fig. 4 B-C). Concurrently, a notable increase in the proportion of Foxp3 + regulatory T (Treg) cells within the CD3 + CD4 + T-cell population was observed (Fig. 4 D-E). Immunofluorescence staining further revealed that local injection of IRF7 siRNA led to a partial reduction in the synovial infiltration of S100A12 + inflammatory macrophages (Fig. 4 F). These findings suggest that localized IRF7 knockdown modulates the immune cell composition in arthritic joints, potentially influencing the inflammatory microenvironment and disease progression. Localized IRF7 knockdown attenuates ankle joint inflammation in collagen-induced arthritis mice Compared with the si-mock and positive control groups, the si-IRF7 group exhibited reduced ankle joint swelling (Fig. 5 A), with significantly thinner paws at the ankle joint (Fig. 5 B). Three-dimensional microCT reconstruction revealed that bone erosion and trabecular bone loss, key pathological features of CIA, were markedly mitigated in the si-IRF7 group, as reflected by increased bone volume/total volume (BV/TV) ratios (Fig. 5 C–D). Compared with positive controls, histological analysis via H&E staining revealed decreased synovial hyperplasia in si-IRF7-treated mice, characterized by reduced villous hypertrophy and inflammatory cell infiltration (Fig. 5 E–F). Immunohistochemical (IHC) staining of the ankle synovium revealed reduced expression of the proinflammatory marker S100A12 and downstream targets of IRF7—including PTGS2, NF-κB1, and IL-1β—in the si-IRF7 group, which was consistent with diminished IRF7 protein levels (Fig. 5 G–R). These findings collectively indicate that localized IRF7 knockdown suppresses synovial inflammation, attenuates bone destruction, and downregulates key inflammatory mediators in CIA mice, highlighting the therapeutic potential of targeting IRF7 in arthritic joints. Discussion The CD48 high S100A12 + macrophage subpopulation in the RA synovium is defined by its robust expression of alarmins (e.g., S100A8, S100A9, and S100A12) and chemokines (e.g., CXCL8), 49 which act as potent drivers of innate immune activation by recruiting monocytes, fibroblasts, and neutrophils to inflamed joints. 50,51 This subset also represents a primary source of IL-1β, a key proinflammatory cytokine linking innate immunity to synovial hyperplasia and bone erosion in RA. 52 The positive correlation between the frequency of CD48highS100A12 + macrophages and disease progression—from undifferentiated arthritis (UA) to established RA—and the downregulation of S100A12 in the osteoarthritis (OA) synovium underscore its specificity for RA pathogenesis. Gene ontology enrichment further implicated this subcluster in canonical inflammatory pathways, aligning with its role in sustaining chronic synovitis. Interferon regulatory factor 7 (IRF7), a master regulator of type I interferon (IFN-I) responses, has emerged here as a critical transcription driver of the CD48 high S100A12 + phenotype. While IRF7 is well characterized in systemic autoimmune diseases such as systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) 53 – 55 , its role in RA macrophage biology was previously undefined. Our multiomics analysis bridges this gap by demonstrating that IRF7 activity is uniquely enriched in pathogenic synovial macrophages, where it directly regulates downstream inflammatory effectors (e.g., PTGS2, CXCL10, NF-κB1, and IL-1β) involved in TNF, NF-κB, and Toll-like receptor signaling. Another study revealed that IRF7 knockout exacerbates joint inflammation in a K/BxN serum transfer model via impaired IFN-β production and attenuates disease in collagen-induced arthritis (CIA) by reducing M1 macrophage polarization. This discrepancy likely reflects context-dependent roles for IRF7: in K/BxN arthritis, where inflammation is driven by preformed antibodies, IRF7 may have dual effects on both pro- and anti-inflammatory macrophage subsets; in contrast, in RA and CIA—characterized by persistent innate immune activation—pathologically elevated IRF7 tips the balance toward M1 polarization and excessive cytokine production. 56–57 Notably, there was an increase in Foxp3 + regulatory T (Treg) cells following local IRF7 knockdown in CIA mice, suggesting that IRF7 may also modulate adaptive immunity, potentially via crosstalk with T-cell populations. However, systemic IRF7 inhibition carries risks of immunodeficiency and viral susceptibility, which we mitigated through intra-articular delivery of siRNA, a strategy that selectively targets synovial macrophages while sparing systemic immunity. 57 This localized approach highlights the translational potential of IRF7 as a therapeutic target, addressing unmet needs in RA patients with inadequate responses to current biologics. Several limitations warrant future investigations. While our data establish a causal link between IRF7 and macrophage polarization in vitro and in vivo, the precise mechanisms by which IRF7 coordinates with other transcription factors (e.g., NFIL3 and STAT1) in CD48 high S100A12 + cells remain unclear. Additionally, the long-term effects of local IRF7 inhibition on joint homeostasis and the risk of infection require evaluation in chronic models. Moving forward, generating macrophage-specific IRF7-deficient mice will help dissect the cell autonomous vs. nonautonomous effects of IRF7 in CIA, and spatial transcriptomics could reveal regional heterogeneity in IRF7 activity within the synovial microenvironment. In conclusion, our study identified IRF7 as a potential regulator of pathogenic synovial macrophages in RA, integrating multiomics data with functional validation to support its candidacy as a macrophage-directed therapeutic target. By selectively disrupting IRF7-mediated inflammation in the joint, this approach offers a promising strategy to alleviate synovitis and joint destruction while minimizing systemic immune suppression. Abbreviations IRF7 interferon regulatory factor 7 RA rheumatoid arthritis OA osteoarthritis Disease-modifying antirheumatic drugs (DMARDs) CIA collagen-induced arthritis siRNA small interfering RNA scRNA-seq single-cell RNA sequencing Chip-seq chromatin immunoprecipitation sequencing micro-CT microcomputed tomography Hematoxylin and eosin (H&E) staining IHC immunohistochemistry PTGS2 prostaglandin-endoperoxide synthase 2 TNFα: tumor necrosis factor alpha NF-κB1 nuclear factor kappa-light-chain-enhancer of activated B cells 1 IL-1β interleukin-1β CXCL10 C-X-C motif chemokine ligand 10 Declarations Competing interests The authors declare that they have no conflict of interest. Generative AI statement The author(s) declare that no generative AI was used in the creation of this manuscript. Funding This work was supported by National Natural Science Foundation of China (82172349, 82102529), Shenzhen Science and Technology Program (RCBS20221008093103013), and Guangdong Natural Science Foundation (2022A1515011531, 2023A1515010226). Author Contribution HL, CW, and GL: Writing – original draft, Writing – review & editing, Data curation, Formal Analysis, Investigation, Validation, Visualization. XP, and ZZ: Data curation, Investigation, Validation, Writing – original draft, Writing – review & editing. ZS, JL, and XW: Data curation, Formal Analysis, Investigation, Writing – original draft, Writing –review & editing. YW, and HS: Investigation, Writing – original draft, Writing – review & editing, Funding acquisition, Supervision, Project administration. WL, PW, and GZ: Investigation, Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing. Acknowledgement This work was supported by National Natural Science Foundation of China, Shenzhen Science and Technology Program, and Guangdong Natural Science Foundation, which are gratefully acknowledged. Data Availability All bioinformatics data analyzed in this study, including GSE216651, GSE152805, GSE62697, GSE154346, and GSE130011, were sourced from publicly available datasets in the GEO database. E-MTAB-8322 is sourced from publicly available datasets in the EMBL-EBI database. References Sparks JA. Rheumatoid Arthritis. Ann Intern Med . Jan 1. 2019;170(1):ITC1-ITC16. 10.7326/AITC201901010 Aletaha D, Smolen JS. Diagnosis and Management of Rheumatoid Arthritis: A Review. JAMA . Oct 2. 2018;320(13):1360–1372. 10.1001/jama.2018.13103 Finckh A, Gilbert B, Hodkinson B, et al. Global epidemiology of rheumatoid arthritis. Nat Rev Rheumatol Oct. 2022;18(10):591–602. 10.1038/s41584-022-00827-y . Scherer HU, Haupl T, Burmester GR. The etiology of rheumatoid arthritis. 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10:38:18","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173229,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/ea39e3988c87a666948d23a7.html"},{"id":92074663,"identity":"e3f76cad-3ee2-4805-a131-9d707601371e","added_by":"auto","created_at":"2025-09-24 10:38:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22653553,"visible":true,"origin":"","legend":"\u003cp\u003eIncreased frequency and proinflammatory signature of CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages in rheumatoid arthritis synovium. (A) UMAP visualization of synovial macrophages clustered into nine subpopulations on the basis of scRNA-seq data. (B) Disease-stratified analysis showing an increased proportion of CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages in the RA synovium compared with those in the UA, OA, and HC groups. (C) UMAP plots depicting S100A12 expression intensity across macrophage subclusters in different disease states. (D) Gene Ontology (GO) enrichment of biological processes in CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e marker genes, highlighting enrichment for defense response activation, cytokine production, and leukocyte migration. (E) Representative images of knee synovial tissues from RA and OA patients. (F–G) Immunohistochemical (IHC) staining for the panmacrophage marker CD68 and the inflammatory marker S100A12, with semiquantitative analysis (n=5). Scale bar: 100 μm. The data are shown as the means ± SDs. Statistical significance: ** P \u0026lt; 0.01, **** P \u0026lt; 0.0001 (Student’s t test).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/e7f9aedbdad4f55b0f12fb3f.png"},{"id":92074673,"identity":"74f8d453-7702-4675-9fad-807c8649d01e","added_by":"auto","created_at":"2025-09-24 10:38:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14080890,"visible":true,"origin":"","legend":"\u003cp\u003eIRF7 is a specific transcriptional regulator of CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages. (A) Venn diagram showing overlapping transcription factors (TFs) identified by triplicate SCENIC analyses, with the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e subcluster enriched for NFIL3, TGIF1, FOSL2, IRF7, and STAT1. (B) Heatmap of regulon activity scores (RASs) for TFs across macrophage subclusters. (C) UMAP dimensionality reduction of TF activity profiles across subclusters. (D) Ranking of TFs in CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages by the regulon specificity score (RSS, calculated via Jensen‒Shannon divergence). (E–F) UMAP plots highlighting spatial overlap between the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e subcluster (E) and cells with elevated IRF7 regulon activity (F). (G) Representative IHC images of IRF7 expression in RA and OA synovial tissues. (H) Semiquantitative analysis of IRF7\u003csup\u003e+\u003c/sup\u003e cells (n=5). Scale bar: 100 μm. Statistical significance: **** P \u0026lt; 0.0001 (Student’s t test).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/6073d4931c7b82794c0d2982.png"},{"id":92074678,"identity":"722fb74d-4e7a-4d49-800b-e800f64da28d","added_by":"auto","created_at":"2025-09-24 10:38:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14561600,"visible":true,"origin":"","legend":"\u003cp\u003eIRF7 directly regulates downstream inflammatory genes in M1 macrophages. (A) ChIP-seq peak heatmaps showing increased IRF7 binding to promoter/enhancer regions in LPS-stimulated M1 macrophages. (B) Venn diagram of 108 overlapping genes from the IRF7 ChIP-seq data and the SCENIC-predicted target genes. (C) Reactome pathway enrichment of IRF7-regulated genes, highlighting the involvement of NF-κB, TNF, and Toll-like receptor signaling (key genes: IL-1β, FOS, NF-κB1, PTGS2, and CXCL10). (D) Bulk RNA-seq heatmap showing the upregulation of IRF7 and target genes in M1-polarized macrophages (GSE130011, GSE154346). (E) UMAP plots of NFKB1, PTGS2, IL1B, and CXCL10 expression in the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e subcluster. (F) RT‒qPCR analysis of IRF7 and M1 marker genes in siRNA-treated macrophages (n=3). (G–H) Western blot validation of IRF7 and downstream protein expression following IRF7 knockdown in M1-polarized macrophages (n=3). Statistical significance: * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, **** P \u0026lt; 0.0001 (one-way ANOVA with the Bonferroni post hoc correction).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/f832eed293c6dd0e33837ab9.png"},{"id":92074706,"identity":"3e58fe96-cfb9-4327-9961-d8e65d6f1b17","added_by":"auto","created_at":"2025-09-24 10:38:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11322825,"visible":true,"origin":"","legend":"\u003cp\u003eLocal IRF7 knockdown alters the immune cell composition in CIA mice. (A) Schematic of intra-articular IRF7 siRNA treatment in collagen-induced arthritis (CIA) model mice. (B–C) Flow cytometry analysis of the CD206 mean fluorescence intensity (MFI) in F4/80+ macrophages from ankle joints (n=6). NC: 2225 ± 225.9, si-IRF7: 3395 ± 369.4, Positive: 978.2 ± 147.9, si-mock: 2022 ± 170.6. (D–E) Frequencies of Foxp3\u003csup\u003e+\u003c/sup\u003e Tregs among CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cells (n=6). NC: 1.66% ± 0.15%, si-IRF7: 3.27% ± 0.28%, Positive: 0.47% ± 0.17%, si-mock: 1.10% ± 0.22%. (F) Immunofluorescence staining for S100A12\u003csup\u003e+\u003c/sup\u003e inflammatory macrophages in the ankle synovium of different groups. Data are presented as mean ± SD. Statistical significance: **** P \u0026lt; 0.0001 (one-way ANOVA with Bonferroni post hoc correction).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/80b96cd62add281c63024dfe.png"},{"id":92074666,"identity":"178d14cf-2853-4ba9-afcc-930f456f0097","added_by":"auto","created_at":"2025-09-24 10:38:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42195369,"visible":true,"origin":"","legend":"\u003cp\u003eLocal IRF7 inhibition attenuates joint inflammation and bone erosion in CIA mice. (A) Representative ankle joint images on day 42 postimmunization. (B) Quantification of paw thickness at the ankle joint (n=6 per group). 42 Days after the first immunization: NC: 8.33 ± 0.02, si-IRF7: 10.39 ± 0.54, positive: 12.08 ± 0.80, si-mock: 12.65 ± 0.57, P\u0026lt;0.001. (C–D) Micro-CT analysis of bone erosion, expressed as the trabecular bone volume/total volume (BV/TV) ratio (n=6). NC: 31.53 ± 1.30, si-IRF7: 26.24 ± 1.19, positive: 18.54 ± 1.11, si-mock: 17.03 ± 0.86. (E–F) H\u0026amp;E staining and histological scoring of synovial hyperplasia and inflammation (n=6). NC: 0.00 (0.00 - 0.00), si-IRF7: 1.50 (1.00 - 2.25), positive: 2.50 (1.75 - 3.00), and si-mock: 3.00 (2.75 - 3.00). Data are shown as medians with 25% - 75% percentiles. Statistical significance: * P \u0026lt; 0.05 (Kruskal‒Wallis test, followed by post hoc Dunn’s test with Bonferroni correction for multiple comparisons). (G–R) IHC staining for CD68, S100A12, IRF7, NF-κB1, PTGS2, and IL-1β in the ankle synovium, with semiquantitative analysis by ImageJ (n=6). Scale bar: 100 μm. Statistical significance: * P \u0026lt; 0.05, **** P \u0026lt; 0.0001 (one-way ANOVA with Bonferroni post hoc correction).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/7117494c6c4ad939e777dfe7.png"},{"id":92074689,"identity":"04e999d3-609b-411b-ba7b-84ec2b284c15","added_by":"auto","created_at":"2025-09-24 10:38:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":850106,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental workflow diagram. Flow chart depicting multiomics analysis (scRNA-seq, ChIP-seq, and RNA-seq) and functional validation (in vitro siRNA knockdown, in vivo CIA model) to identify IRF7 as a therapeutic target in RA.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/0dac349b8ac4442583c9d528.png"},{"id":98245120,"identity":"c1e51a87-1704-4c62-a6bf-a2ffa1e246e1","added_by":"auto","created_at":"2025-12-15 16:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":95222996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/483f776c-22e4-4b7a-99ff-f754e8a9ee32.pdf"},{"id":92074695,"identity":"24ae5ac5-9943-4b79-9ad8-8155217a2189","added_by":"auto","created_at":"2025-09-24 10:38:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":236508,"visible":true,"origin":"","legend":"","description":"","filename":"ARRIVEChecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/d39a651fbec3a075d56cedd6.pdf"},{"id":92075485,"identity":"26df7c95-0e6d-4bb8-bf8d-21bfdf550f83","added_by":"auto","created_at":"2025-09-24 10:46:16","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3928572,"visible":true,"origin":"","legend":"","description":"","filename":"WesternBlot.zip","url":"https://assets-eu.researchsquare.com/files/rs-7536100/v1/e11ef2b551719fded67de850.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis","fulltext":[{"header":"Key Message","content":"\u003cp\u003eIRF7 is enriched in a CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e proinflammatory macrophage subset in the RA synovium and directly regulates inflammatory genes (PTGS2, CXCL10, NF-κB1,\u0026nbsp;and\u0026nbsp;IL-1β).\u003c/p\u003e\n\u003cp\u003eKnockdown of IRF7 reduces M1 macrophage polarization and inflammatory gene expression in vitro.\u003c/p\u003e\n\u003cp\u003eLocal IRF7 silencing in a collagen-induced arthritis mouse model attenuates joint inflammation, synovial hyperplasia, and bone erosion while increasing regulatory T cells.\u003c/p\u003e\n\u003cp\u003eLocal targeting of IRF7 offers a precise therapeutic strategy to mitigate RA synovial inflammation without systemic immunosuppression.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a prevalent systemic autoimmune disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e that affects nearly 1% of adults worldwide. \u003csup\u003e2,3\u003c/sup\u003e Beyond joint involvement, it manifests with extra-articular manifestations, including rheumatoid nodules, interstitial lung disease, vasculitis, bronchiectasis, and systemic comorbidities. Although its etiology remains incompletely understood, RA is characterized by chronic inflammation of the synovial tissue, which drives joint destruction and functional impairment. \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCurrent first-line therapies for RA rely on synthetic disease-modifying antirheumatic drugs (sDMARDs), including methotrexate and glucocorticoids. However, these agents are associated with substantial adverse effects, such as bone marrow suppression, gonadal toxicity, increased infection risk, and high relapse rates upon discontinuation. \u003csup\u003e5\u003c/sup\u003e In patients with an inadequate response to sDMARDs, biological DMARDs (bDMARDs) target key inflammatory pathways, including cytokines (IL-1, IL-6, IL-17, and TNF-α), T-cell costimulatory molecules, B-cell activation, and JAK/STAT signaling. \u003csup\u003e6,7\u003c/sup\u003e Despite this progress, approximately 40% of patients exhibit primary or secondary resistance to bDMARDs \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, underscoring the urgent need to identify novel therapeutic targets to improve clinical outcomes.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn the RA synovium, innate immune cells, particularly infiltrating macrophages, play pivotal roles in driving arthritis initiation and synovial hyperplasia (pannus formation), which invade the cartilage‒bone interface and promote bone erosion and cartilage degradation. \u003csup\u003e9\u0026ndash;14\u003c/sup\u003e Macrophages exhibit remarkable plasticity, polarizing into proinflammatory M1 or anti-inflammatory M2 phenotypes in response to pathogen-associated molecular patterns (PAMPs), damage-associated molecular patterns (DAMPs), cytokines, and chemokines.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Dysregulation of the M1/M2 balance in synovial tissue is a hallmark of chronic synovitis, with M1-dominant polarization exacerbating inflammatory cascades and joint destruction.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSingle-cell RNA sequencing has enabled the identification of distinct functional subsets of macrophages in RA. Notably, both patient-derived synovial tissues and experimental arthritis models show expansion of a CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e M1-like population, characterized by increased expression of alarmins (S100A8, S100A9, and S100A12) and IL-1β. \u003csup\u003e18,19\u003c/sup\u003e These molecules act as potent drivers of monocyte activation, fibroblast proliferation, and neutrophil recruitment, underscoring the pathogenic potential of targeting this subcluster. \u003csup\u003e20,21\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMacrophage phenotypic diversity is governed by transcription factor (TF) networks that reshape epigenetic and chromatin accessibility landscapes during differentiation and activation. \u003csup\u003e22,23\u003c/sup\u003e Deciphering these regulatory circuits offers opportunities to develop therapies that inhibit proinflammatory pathways or promote anti-inflammatory polarization.\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e TFs such as NF-κB, STAT, AP-1, and interferon regulatory factors (IRFs) are critical mediators of macrophage plasticity, with IRF1, IRF3, and IRF5 previously linked to RA pathogenesis.\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e However, the specific role of IRF7, a regulator of type I interferon responses and innate immunity, in orchestrating pathogenic macrophage subsets in RA remains poorly defined, presenting an important knowledge gap for therapeutic innovation. \u003csup\u003e30\u0026ndash;32\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn this study, we leveraged multiomics datasets\u0026mdash;including single-cell transcriptomics of synovial macrophages, chromatin immunoprecipitation (ChIP)-seq, and bulk RNA-seq\u0026mdash;to identify key regulatory pathways in RA. \u003csup\u003e18\u003c/sup\u003e Integrative analysis was validated via the use of clinical specimens, in vitro cell models, and in vivo murine experiments. Our findings offer novel insights into the molecular mechanisms underlying macrophage-driven inflammation in RA, highlighting IRF7 as a promising therapeutic candidate within the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e subcluster. By combining single-cell regulatory network inference with functional validation, this approach exemplifies how multiomics strategies can uncover actionable targets for complex autoimmune diseases such as RA. \u003csup\u003e20,33\u003c/sup\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMice\u003c/h2\u003e\u003cp\u003eFemale DBA/1J mice (8 weeks old, 20\u0026ndash;24 g) were obtained from Jiangsu GemPharmatech Co. Ltd. (Nanjing, China). The mice were housed in specific-pathogen-free (SPF) facilities at the Laboratory Animal Center of Sun Yat-sen University and maintained at 25\u0026deg;C, 75% humidity, and a 12-hour light/dark cycle with ad libitum access to food and water. The animals had ad libitum access to standard chow and water. Prior to the experiment, all the mice were acclimatized for at least 7 days. All animal procedures complied with the \u003cem\u003eGuide for the Care and Use of Laboratory Animals\u003c/em\u003e (8th Edition, National Academies Press, 2011, ISBN: 0309154006) and were approved by the Sun Yat-sen University Committee on Laboratory Animal Management and Ethics (No. SYSU-IACUC-2023-000203).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHuman synovial samples\u003c/h3\u003e\n\u003cp\u003eSynovial samples from the knee joint were collected from patients with osteoarthritis (OA) or rheumatoid arthritis (RA) during total knee arthroplasty (TKA) surgery. The study protocol was approved by the Ethics Committee of The Eighth Affiliated Hospital, Sun Yat-sen University (Shenzhen, China; Ethics No. 2022-035-03), and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch3\u003eCells\u003c/h3\u003e\n\u003cp\u003eSix healthy donors (HDs) and six rheumatoid arthritis (RA) patients were recruited for this study. The protocol was approved by the Ethics Committee of The Eighth Affiliated Hospital, Sun Yat-sen University (Ethics No. 2022-035-03), and written informed consent was obtained from all participants. Human whole blood samples were collected from HDs and RA patients.\u003c/p\u003e\n\u003ch3\u003eSingle-cell RNA sequencing data processing and analysis\u003c/h3\u003e\n\u003cp\u003eSynovial macrophage single-cell transcriptome datasets and clinical metadata from 17 rheumatoid arthritis (RA) patients, 4 undifferentiated arthritis patients, 6 osteoarthritis (OA) patients, and 7 healthy control subjects were obtained from the European Bioinformatics Institute (EBI, E-MTAB-8322) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and the Gene Expression Omnibus (GEO, GSE216651 \u003csup\u003e35\u003c/sup\u003e, GSE152805 \u003csup\u003e36\u003c/sup\u003e). The raw gene expression matrices were processed via Seurat v5.0.3 package \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e in R v4.3.3, which involves quality control, normalization, and batch correction. The cells were filtered on the basis of gene count (\u0026gt;\u0026thinsp;300 genes), feature complexity (\u0026lt;\u0026thinsp;3500 unique molecular identifiers [UMIs]), and mitochondrial gene expression (\u0026lt;\u0026thinsp;20% of total UMIs) to exclude dead cells and multiplets.\u003c/p\u003e\u003cp\u003eData from GSE216651 and GSE152805 were first integrated via the IntegrateLayers function with canonical correlation analysis (CCA), followed by clustering with FindClusters (resolution\u0026thinsp;=\u0026thinsp;0.4) to identify major cell populations. Macrophages were isolated via marker genes (FCGR1A, ITGAM, CD14, LYZ) and further integrated with E-MTAB-8322 data via IntegrateLayers with reciprocal principal component analysis (RPCA), followed by reclustering at a lower resolution (0.3) to refine the subpopulations. Differential gene expression across macrophage subgroups was identified via FindAllMarkers (min.pct\u0026thinsp;=\u0026thinsp;0.3, logFC threshold\u0026thinsp;=\u0026thinsp;0.3), with subgroup distributions visualized via ggplot2 v3.4.2. The functional enrichment of DEGs in CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages was performed via clusterProfiler v4.8.1 \u003csup\u003e38\u003c/sup\u003e and GO.db v3.17.0, with the results visualized in ggplot2. To infer transcription factor (TF) gene regulatory networks at single-cell resolution, we employed pySCENIC v0.12.1, a high-performance Python implementation of the SCENIC pipeline \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, within a Python 3.7 environment. An 8,000-cell submatrix was first extracted from the integrated single-cell transcriptome to identify coexpression modules between TFs and their putative target genes.\u003c/p\u003e\u003cp\u003eRegulon (gene-TF regulatory network) construction utilized the GRNBoost2 algorithm, which models coexpression relationships across cells to predict TF-target interactions. Direct DNA-binding targets of TFs were validated via motif enrichment analysis via cisTarget databases (resources.aertslab.org/cistarget).\u003c/p\u003e\u003cp\u003eRegulon activity scores (RASs) were calculated for each cell to quantify the enrichment of TF-target gene coexpression, with each regulon comprising a TF and its motif-confirmed direct targets. Cell type-specific regulons were identified via the regulon specificity score (RSS), which was computed via the Jensen\u0026ndash;Shannon divergence (JSD) algorithm, to measure regulon activity uniqueness across subpopulations.\u003c/p\u003e\u003cp\u003eTo mitigate sampling bias, the Seurat-integrated dataset was randomly subsampled three times, with SCENIC analysis repeated for each subsample. The results were intersected to derive consensus regulons, ensuring robustness. Finally, UMAP dimensionality reduction (umap-learn v0.5.5) was performed on the basis of TF regulon activity scores, with visualizations generated via ggplot2 v3.4.2 to map regulatory heterogeneity across macrophage subclusters.\u003c/p\u003e\n\u003ch3\u003eChIP-seq data processing and analysis\u003c/h3\u003e\n\u003cp\u003eIRF7 ChIP-seq raw reads from mouse bone marrow-derived macrophages stimulated with 100 ng/mL lipopolysaccharide (LPS) for 24 hours (GSE62697 \u003csup\u003e40\u003c/sup\u003e) were obtained from the GEO database. The raw reads were trimmed via TrimGalore v0.6.1 with the following parameters: -q 25 --phred33 --length 36 -e 0.1 to remove low-quality sequences and adapter contaminants. Clean reads were aligned to the mouse genome (mm10) via Bowtie2 v2.4.4 \u003csup\u003e41\u003c/sup\u003e, generating SAM files that were subsequently converted to BAM files via SAMtools v1.17 for downstream analysis. \u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePeak calling was performed via MACS2 v2.2.7.1\u003csup\u003e43\u003c/sup\u003e, with unstimulated (t0) samples serving as the negative control to identify differential IRF7 binding sites. The enrichment profiles of the ChIP-seq peaks were visualized via deepTools v3.5.2 \u003csup\u003e44\u003c/sup\u003e, specifically the computeMatrix and plotHeatmap functions, to assess the binding intensity around transcription start sites (TSSs). Genomic annotation of peaks, including promoter and enhancer regions, was conducted via the Chipseeker v1.36.0 R package. \u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo identify direct IRF7 target genes, we intersected ChIP-seq-derived binding genes with IRF7 regulon-targeted genes from pySCENIC analysis, defining high-confidence regulatory interactions. The functional enrichment of these candidate genes was performed via the clusterProfiler package, which focuses on Reactome pathway analysis to characterize the biological processes regulated by IRF7 in inflammatory macrophages. \u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBulk RNA-seq data analysis\u003c/h2\u003e\u003cp\u003eBulk transcriptome datasets (GSE154346 \u003csup\u003e21\u003c/sup\u003e, GSE130011 \u003csup\u003e23\u003c/sup\u003e) from a macrophage line under unstimulated (M0) and M1-polarized conditions were downloaded from the GEO database. To validate IRF7-mediated gene regulation, we generated a heatmap of IRF7 and its predicted downstream targets (e.g., PTGS2, CXCL10, NFKB1, and IL1B) via the pheatmap v1.0.12 package.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmunohistochemical staining of clinical knee synovial samples\u003c/h3\u003e\n\u003cp\u003eFormalin-fixed, paraffin-embedded (FFPE) 4-\u0026micro;m-thick knee synovial tissue sections were deparaffinized in xylene followed by rehydration through a series of graded ethanol solutions. Antigen retrieval was executed via microwave-mediated heating at 95\u0026deg;C for 20 minutes in 10 mM citrate buffer (pH 6.0) to expose the target epitopes. Immunohistochemical staining was performed via an SP Rabbit \u0026amp; Mouse HRP Kit (DAB; Catalog No. CW2069S, CWBIO, China) following the manufacturer\u0026rsquo;s standardized protocol. Endogenous peroxidase activity was quenched with 3% hydrogen peroxide in methanol for 15 minutes, followed by blocking with 5% bovine serum albumin (BSA) in Tris-buffered saline (TBS) for 1 hour at room temperature (RT). Primary antibodies against CD68 (macrophage marker; 25747-1-AP, Proteintech; 1:200 dilution) and S100A12 (inflammatory marker; 16630-1-AP, Proteintech; 1:100 dilution) were applied overnight at 4\u0026deg;C. After being washed with TBS-T (0.1% Tween-20 in TBS), the sections were incubated with species-matched horseradish peroxidase (HRP)-conjugated secondary antibodies (CW2069S, CWBIO) for 45 minutes at RT. Signal development was performed using 3,3'-diaminobenzidine (DAB) substrate solution (10 minutes), and the nuclei were counterstained with hematoxylin for 2 minutes. Images were acquired via a Leica microscope (Leica Microsystems, Germany) at 400\u0026times; magnification.\u003c/p\u003e\n\u003ch3\u003eCell culture and transfection\u003c/h3\u003e\n\u003cp\u003eHuman CD14\u0026thinsp;+\u0026thinsp;monocytes were isolated from whole blood through density gradient centrifugation via Ficoll-Paque PLUS medium (GE Healthcare Life Sciences), followed by magnetic-activated cell sorting with anti-human CD14 monoclonal antibody-conjugated magnetic beads (Miltenyi Biotech, Germany), which was performed strictly according to the manufacturers\u0026rsquo; protocols. The isolated cells were maintained in RPMI 1640 culture medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM L-glutamine, and a sterile antibiotic mixture (100 U/mL penicillin and 100 \u0026micro;g/mL streptomycin). Cultures were incubated at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂, with half-medium replacement every 3 days to maintain cell viability and proliferation. For macrophage differentiation, CD14⁺ monocytes were seeded into culture wells and incubated with 25 ng/mL macrophage colony-stimulating factor (M-CSF; Sigma‒Aldrich, USA) for 5 days.\u003c/p\u003e\u003cp\u003eFor M1 polarization, differentiated macrophages were stimulated with 50 ng/mL lipopolysaccharide (LPS) and 20 ng/mL interferon-γ (IFN-γ; Sigma‒Aldrich, USA) for 24 hours. IRF7 knockdown was achieved by transfecting M1-polarized macrophages with a siRNA targeting IRF7 (siRNA-IRF7; iGEBIO, Guangzhou, China) via Lipofectamine\u0026trade; RNAiMAX (Invitrogen) according to the manufacturer\u0026rsquo;s protocol. A nontargeting siRNA served as the negative control (NC). One day before transfection, M1 macrophages were seeded in 6-well plates at 3 \u0026times; 10⁵ cells/well. At 60\u0026ndash;80% confluence, 5 \u0026micro;L of siRNA-IRF7 or siRNA-NC was diluted in 45 \u0026micro;L OPTI-MEM (Sigma‒Aldrich, USA), mixed with Lipofectamine\u0026trade; RNAiMAX reagent, and incubated for 20 minutes at 25\u0026deg;C before being added to the cells for 24 hours.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRNA isolation\u003c/h2\u003e\u003cp\u003eTotal RNA was isolated from M1-polarized macrophages via TRIzol reagent (Invitrogen, USA) according to the manufacturer\u0026rsquo;s protocol. The RNA concentration and purity were quantified and assessed via spectrophotometric analysis. The integrity of the total RNA was assessed via agarose gel electrophoresis. The qualified total RNA was stored at -80\u0026deg;C and utilized for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eReal-time quantitative polymerase chain reaction (RT‒qPCR)\u003c/h2\u003e\u003cp\u003eComplementary DNA (cDNA) was synthesized from total RNA via the PrimeScript RT Kit (TaKaRa) according to the manufacturer\u0026rsquo;s protocol with a Bio-Rad T100 Thermal Cycler. RT‒qPCR was performed on an Applied Biosystems 7500 Real-Time PCR System using SYBR Premix Ex Taq (TaKaRa). The thermal cycling protocol comprised an initial denaturation step at 95\u0026deg;C for 30 seconds, followed by 40 cycles of denaturation at 95\u0026deg;C for 5 seconds and annealing/extension at 60\u0026deg;C for 20 seconds. Each sample was analyzed in technical triplicate, and the mean mRNA expression levels were calculated.\u003c/p\u003e\u003cp\u003eMelting curve analysis was conducted to validate specific target amplification, ensuring single-product formation without primer dimers. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the internal reference gene, and relative gene expression was quantified via the 2^(-ΔΔCt) method. The forward and reverse primers for the target genes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRT‒qPCR real-time primer sequences.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmplicon\u003c/p\u003e\u003cp\u003eSize (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForward primer\u003c/p\u003e\u003cp\u003e(5\u0026rsquo;\u0026rarr;3\u0026rsquo;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReverse primer\u003c/p\u003e\u003cp\u003e(5\u0026rsquo;\u0026rarr;3\u0026rsquo;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRF7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCTGGACGTGACCATCATGTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGGGCCGTATAGGAACGTGC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNFKB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAACAGAGAGGATTTCGTTTCCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTTTGACCTGAGGGTAAGACTTCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTGS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTGGCGCTCAGCCATACAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCGCACTTATACTGGTCAAATCCC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL1B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATGATGGCTTATTACAGTGGCAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGTCGGAGATTCGTAGCTGGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCTCTCTCTAATCAGCCCTCTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAGGACCTGGGAGTAGATGAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCXCL10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGTGGCATTCAAGGAGTACCTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTGATGGCCTTCGATTCTGGATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eWestern blotting\u003c/h2\u003e\u003cp\u003eThe cells were lysed on ice for 30 minutes in radioimmunoprecipitation assay (RIPA) buffer (Sigma‒Aldrich, USA) supplemented with 1% protease and phosphatase inhibitor cocktail (Roche). The cell lysate was then centrifuged at 12,000 \u0026times; g at 4\u0026deg;C for 30 minutes. The protein concentration in the supernatant was determined via a Bicinchoninic Acid (BCA) Protein Assay Kit (Sigma‒Aldrich, USA) following the manufacturer's protocol.\u003c/p\u003e\u003cp\u003eEqual amounts of protein were mixed with sodium dodecyl sulfate (SDS) loading buffer. The samples were then separated via 10% SDS-polyacrylamide gel electrophoresis (SDS‒PAGE) and electrotransferred onto a polyvinylidene fluoride (PVDF) membrane (Merck Millipore).\u003c/p\u003e\u003cp\u003eThe membrane was blocked with Tris-buffered saline with Tween 20 (TBST) containing 5% skim milk powder at room temperature for 1 hour. The membrane was subsequently incubated overnight at 4\u0026deg;C with primary antibodies specific to glyceraldehyde 3-phosphate dehydrogenase (GAPDH, CST 5174), interferon regulatory factor 7 (IRF7, Proteintech Group, 22392\u0026ndash;1 - AP), C - X - C motif chemokine ligand 10 (CXCL10, 10937-1-AP, Proteintech Group), prostaglandin-endoperoxide synthase 2 (PTGS2, 27308-1-AP, Proteintech Group), interleukin 1 beta (IL-1β, 26048-1-AP, Proteintech Group), and nuclear factor kappa B subunit 1 (NFKB1, 14220-1-AP, Proteintech Group).\u003c/p\u003e\u003cp\u003eAfter incubation with primary antibodies, the membrane was washed three times with TBST. Then, the samples were incubated at room temperature for 1 hour with an appropriate horseradish peroxidase (HRP)-conjugated secondary antibody (diluted 1:2000, Santa Cruz Biotechnology). The membrane was subsequently washed three more times with TBST. The chemiluminescent signal was detected via Immobilon Western Chemiluminescence HRP Substrate (Merck Millipore). Semiquantitative analysis of protein expression was performed via ImageJ software (National Institutes of Health, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCollagen-induced arthritis (CIA) mouse model\u003c/h2\u003e\u003cp\u003eThe mice were randomly assigned to four experimental groups (n\u0026thinsp;=\u0026thinsp;6 per group, for a total of 24) via a random number generator in R v4.3.3. Allocation was stratified to ensure balanced group distribution across cage positions and housing racks. Treatments and measurements were conducted in a randomized order to avoid systematic bias. The sample size was based on previous studies showing sufficient power (80%) to detect differences in clinical arthritis scores and histological inflammation, with an effect size of 1.2 and a significance level of 0.05. The group allocations were as follows:\u003c/p\u003e\u003cp\u003eIn the si-IRF7 group, the mice received intra-articular injections of IRF7-specific siRNA (3 nmol/mouse/dose; iGEBIO, China) into the ankle joint once weekly from day 18 to day 39 after initial immunization.\u003c/p\u003e\u003cp\u003eSi-mock group: Mice were administered mock nontargeting siRNA via the same dosing and administration schedule.\u003c/p\u003e\u003cp\u003eCIA model group (positive control): Mice underwent collagen-induced arthritis (CIA) induction but received no therapeutic intervention.\u003c/p\u003e\u003cp\u003eHealthy control group (negative control): Mice were neither immunized nor treated.\u003c/p\u003e\u003cp\u003eAll interventions and assessments were performed by researchers blinded to group allocation to reduce bias.\u003c/p\u003e\u003cp\u003eCIA was induced in female DBA/1J mice (8 weeks old, 20\u0026ndash;24 g; GemPharmatech, China) following a validated protocol. Briefly, chicken type II collagen (Chondrex, USA) was dissolved in 0.05 M acetic acid to a final concentration of 2 mg/mL and emulsified 1:1 (v/v) with complete Freund\u0026rsquo;s adjuvant (CFA; containing 4 mg/mL \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e; Chondrex, USA). The mice received an initial intradermal immunization with 100 \u0026micro;g of collagen emulsion at the base of the tail. On day 21, a booster injection of 100 \u0026micro;g of chicken collagen in incomplete Freund\u0026rsquo;s adjuvant (Chondrex, USA) was administered intraperitoneally to elicit arthritis, which typically developed 7\u0026ndash;10 days post-booster.\u003c/p\u003e\u003cp\u003eFrom day 18 post-initial immunization, the mice underwent examinations every three days to evaluate the onset of arthritis. Paw thickness was measured via a dial caliper. The primary outcome was joint inflammation, quantified by paw swelling, whereas the secondary outcomes included histopathological changes and bone erosion.\u003c/p\u003e\u003cp\u003e On day 43, all the mice were humanely euthanized via CO₂ inhalation followed by cervical dislocation. Ankle joints were harvested and fixed for microcomputed tomography (\u0026micro;CT) analysis via a Siemens Inveon system to evaluate synovial inflammation and bone erosion.\u003c/p\u003e\u003cp\u003eThe inclusion and exclusion criteria were established a priori. The mice were included in the study if they were healthy, age-matched (8\u0026ndash;10 weeks old), and free of signs of illness at the start of the experiment. Animals were excluded if they exhibited preexisting health issues, failed to recover from anesthesia, or showed abnormal behavior unrelated to experimental treatment. During analysis, data points were excluded only if technical errors occurred (e.g., sample loss, poor tissue quality for histology) or if the animals were euthanized for humane reasons unrelated to the experimental outcomes. No animals were excluded from the study. All the mice completed the experimental protocol. No outliers were removed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometric analysis of mouse ankle tissues\u003c/h2\u003e\u003cp\u003eFresh ankle joint samples were surface disinfected with 75% ethanol and processed under sterile conditions on ice. The tissues were dissected into small fragments via a sterile scalpel, washed twice with ice-cold sterile PBS, and digested in 1.5 U/mL Dispase II solution (Sigma‒Aldrich, USA) at 37\u0026deg;C for 60 minutes with gentle agitation. The digested tissues were strained through a 70-\u0026micro;m sterile cell strainer (BD Falcon) to obtain single-cell suspensions, which were subsequently centrifuged at 300 \u0026times; g for 5 minutes at 4\u0026deg;C. The cell pellets were subsequently resuspended in RPMI 1640 medium (Gibco) supplemented with 10% FBS.\u003c/p\u003e\u003cp\u003eFor M2 macrophage quantification, the cells were first stained with a Zombie Violet\u0026trade; Fixable Viability Kit (DAPI conjugate; Biolegend, USA) to exclude dead cells, followed by surface staining with a FITC-conjugated anti-mouse F4/80 antibody (123107, Biolegend) for 30 minutes at room temperature in the dark. After being washed, the cells were fixed and permeabilized via a fixation/permeabilization solution (Invitrogen, USA) according to the manufacturer\u0026rsquo;s protocol and then incubated with an Alexa Fluor 647-conjugated anti-mouse CD206 antibody (565250, BD Biosciences) for 60 minutes at 4\u0026deg;C in the dark.\u003c/p\u003e\u003cp\u003eFor regulatory T (Treg) cell phenotyping, the cells were stained with the Zombie Violet\u0026trade; Fixable Viability Kit (BV421 conjugate; Biolegend, USA) and the surface markers APC-conjugated anti-mouse CD3 antibody (100235, Biolegend, USA) and FITC-conjugated anti-mouse CD4 antibody (100405, Biolegend, USA) for 15 minutes at room temperature. Following fixation/permeabilization, intracellular staining for FOXP3 was performed with a PE-conjugated anti-mouse FOXP3 antibody (320007, Biolegend, USA) for 60 minutes at 4\u0026deg;C in the dark.\u003c/p\u003e\u003cp\u003eAll the stained samples were washed twice with PBS, resuspended in 200 \u0026micro;L of 1% BSA-PBS, and transferred to FACS tubes. Flow cytometry data were acquired on a BD FACS Celesta cytometer (BD Biosciences) and analyzed via FlowJo software (version 10; BD Biosciences), with gating strategies validated by isotype controls and fluorescence-minus-one (FMO) samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eHistological and immunohistochemical analysis of mouse ankles\u003c/h2\u003e\u003cp\u003eAfter being euthanized, the ankle joints of the mice were dissected and fixed in 10% neutral-buffered formalin (pH 7.4) for 48 hours at room temperature. The samples were decalcified in 10% ethylenediaminetetraacetic acid (EDTA) decalcifying solution (pH 7.2) for 72 hours with gentle agitation, processed into 5-\u0026micro;m paraffin sections and stained with hematoxylin‒eosin (H\u0026amp;E) for morphological evaluation. Synovial inflammation and hyperplasia were assessed by a blinded observer via a validated scoring system and evaluated on a 0\u0026ndash;3 scale: synovial inflammation (0\u0026thinsp;=\u0026thinsp;thin synovium (1\u0026ndash;2 cell layers), no inflammatory cell infiltration; 3\u0026thinsp;=\u0026thinsp;severe thickening (polypoid hyperplasia), dense inflammatory cell aggregation, with villi formation or fibrin exudation). \u003csup\u003e48\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eFor IF analysis, the sections were blocked in 5% bovine serum albumin (BSA) in PBS for 1 hour at room temperature and then incubated overnight at 4\u0026deg;C with the following primary antibodies: a rat recombinant anti-CD68 antibody (ab53444, Abcam, USA; 1:200) and a rabbit polyclonal anti-S100A12 antibody (16630-1-AP, Proteintech, China; 1:100). After washing, the sections were incubated with the following fluorescently conjugated secondary antibodies: goat anti-rabbit IgG H\u0026amp;L (Alexa Fluor\u0026reg; 488; ab150077; Abcam; 1:500) and goat anti-rat IgG H\u0026amp;L (Alexa Fluor\u0026reg; 555; ab150158; Abcam; 1:500) for 1 hour at room temperature in the dark. The slides were mounted with mounting medium and antifading agent (with DAPI) (S2110, SolarBio, China) and imaged via a fluorescence microscope (Leica Microsystems, Germany).\u003c/p\u003e\u003cp\u003eIHC was performed via a streptavidin‒biotin peroxidase kit (CW2069S, Cowin Bio, China) according to the manufacturer\u0026rsquo;s protocol. The sections were pretreated with citrate buffer (pH 6.0) for antigen retrieval and then incubated overnight at 4\u0026deg;C with the following primary antibodies: rabbit anti-CD68 antibody (28058-1-AP, Proteintech; 1:200), rabbit anti-S100A12 antibody (16630-1-AP, Proteintech; 1:100), rabbit anti-IRF7 antibody (22392-1-AP, Proteintech; 1:100), and rabbit anti-PTGS2 antibody (27308-1-AP, Proteintech; 1:100). Following washes, the sections were incubated with HRP-conjugated secondary antibodies and developed with 3,3\u0026rsquo;-diaminobenzidine (DAB) substrate. The slides were counterstained with hematoxylin, dehydrated, and mounted with DPX mounting medium (Sigma‒Aldrich, USA).\u003c/p\u003e\u003cp\u003eIHC slides were imaged via a microscope (Leica Microsystems, Germany) at \u0026times;200 magnification. The average optical density (AOD) of positive staining was quantified via ImageJ software (NIH, USA) with the IHC Toolbox plugin, normalized to background levels and expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD across three independent fields per sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll in vitro experiments (RT‒qPCR and Western blotting) were performed in triplicate, and the data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs). Statistical significance was determined via Student's t test for two groups and one-way analysis of variance (ANOVA) followed by a Bonferroni correction for three or more groups. Statistical analyses were conducted via R and GraphPad Prism 9. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eIncreased frequency and proinflammatory phenotype of CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages in the RA synovium\u003c/h2\u003e\u003cp\u003eFollowing quality control (excluding cells with \u0026lt;\u0026thinsp;300 genes, \u0026gt;\u0026thinsp;3500 features, or \u0026gt;\u0026thinsp;20% mitochondrial RNA), we analyzed 49,669 synovial macrophages from 17 RA patients, 4 UA patients, 6 OA patients, and 7 healthy controls (HCs). Unsupervised clustering identified nine distinct macrophage subpopulations: TREM2\u003csup\u003ehigh\u003c/sup\u003e, CD48\u003csup\u003e+\u003c/sup\u003eSPP1\u003csup\u003e+\u003c/sup\u003e, FOLR2\u003csup\u003ehigh\u003c/sup\u003eLYVE1\u003csup\u003e+\u003c/sup\u003e, FOLR2\u003csup\u003e+\u003c/sup\u003eID2\u003csup\u003e+\u003c/sup\u003e, CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e, HLA\u003csup\u003ehigh\u003c/sup\u003eISG1\u003csup\u003e+\u003c/sup\u003e, CD48\u0026thinsp;+\u0026thinsp;\u003csup\u003ehigh\u003c/sup\u003eCLEC10A\u003csup\u003ehigh\u003c/sup\u003e, TREM2\u003csup\u003elow\u003c/sup\u003e, and FOLR2\u003csup\u003e+\u003c/sup\u003eICAM1\u003csup\u003e+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Disease-stratified analysis revealed a significant increase in the frequency of CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages with increasing disease severity, peaking in the RA synovium (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Similarly, S100A12 expression was upregulated in RA macrophages and downregulated in OA macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene Ontology (GO) enrichment of the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e marker genes highlighted the overrepresentation of proinflammatory biological processes, including defense response activation, cytokine production regulation, leukocyte migration, and inflammatory pathway modulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Immunohistochemical (IHC) validation in knee synovial tissues revealed that RA samples contained a greater density of CD68\u003csup\u003e+\u003c/sup\u003e panmacrophage and S100A12\u003csup\u003e+\u003c/sup\u003e inflammatory cells than OA control samples did (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u0026ndash;G), confirming the clinical importance of this proinflammatory subpopulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eIRF7-driven transcriptional programs define CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e synovial macrophages\u003c/h2\u003e\u003cp\u003eTriplicate pySCENIC analyses with random subsampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) identified a conserved set of transcription factors (TFs) enriched in CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages, including NFIL3, TGIF1, FOSL2, IRF7, and STAT1. Heatmap visualization of regulon activity scores (RASs) revealed preferential activation of these TFs in S100A12\u003csup\u003e+\u003c/sup\u003e subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), with IRF7 exhibiting one of the highest regulon specificity scores (RSSs) via Jensen‒Shannon divergence (JSD) analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Previously, it was reported that IRF7 is involved in various autoimmune diseases, such as systemic sclerosis and systemic lupus erythematosus (47), but the role of IRF7 in macrophage activation in rheumatoid arthritis still needs further research. UMAP dimensionality reduction based on TF activity profiles demonstrated near-complete spatial overlap between CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and cells with elevated IRF7 regulon activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), indicating a specific transcriptional association. Immunohistochemical staining for IRF7 in OA and RA synovial slices was also conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). We observed a noteworthy increase in the proportion of IRF7-positive cells within the synovial tissue of RA patients compared with that of OA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eIRF7 knockdown suppresses inflammatory gene expression in M1-polarized macrophages\u003c/h2\u003e\u003cp\u003eAnalysis of IRF7 chromatin immunoprecipitation sequencing (ChIP-seq) data from LPS-stimulated mouse bone marrow-derived macrophages revealed increased IRF7-binding sites in the promoter and enhancer regions following inflammatory activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Differential peak annotation revealed 108 high-confidence IRF7-bound genes that overlapped with SCENIC-predicted IRF7 target genes in human CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e synovial macrophages to generate a core regulatory gene set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Reactome pathway enrichment analysis highlighted the involvement of these genes in canonical inflammatory cascades, including TNF signaling, NF-κB activation, IL-17 signaling, and Toll-like receptor pathways, with the key effectors CXCL10, PTGS2, NF-κB1, IL-1β, and FOS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBulk RNA-seq data from human M1-polarized macrophages (stimulated with LPS/IFN-γ) confirmed the upregulation of IRF7 and its predicted target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), mirrored by scRNA-seq UMAP visualization, which revealed elevated expression of these genes in the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e subcluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Compared with those in control siRNA-treated cells, functional validation in human CD14\u0026thinsp;+\u0026thinsp;peripheral blood monocyte-derived macrophages revealed that siRNA-mediated IRF7 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF\u0026ndash;G) significantly reduced the mRNA and protein levels of M1 polarization markers\u0026mdash;TNF, CXCL10, IL-1β, PTGS2, and NF-κB1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Western blotting corroborated these findings, demonstrating that decreased IRF7 protein expression alongside attenuation of downstream inflammatory signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These results establish IRF7 as a critical driver of proinflammatory gene expression during M1 macrophage polarization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLocalized knockdown of IRF7 alters the proportions of macrophages and regulatory T (Treg) cells in collagen-induced arthritis mice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the role of IRF7 in the pathogenesis of arthritis, a collagen-induced arthritis (CIA) mouse model was established in DBA/1J mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Flow cytometric analysis revealed that, compared with the si-mock group and the arthritis positive control group, local intra-articular injection of IRF7 siRNA into the ankle joint significantly increased the mean fluorescence intensity (MFI) of CD206 on F4/80\u003csup\u003e+\u003c/sup\u003e macrophages. This increase in MFI indicated the promotion of M2 macrophage polarization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). Concurrently, a notable increase in the proportion of Foxp3\u003csup\u003e+\u003c/sup\u003e regulatory T (Treg) cells within the CD3\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T-cell population was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E). Immunofluorescence staining further revealed that local injection of IRF7 siRNA led to a partial reduction in the synovial infiltration of S100A12\u003csup\u003e+\u003c/sup\u003e inflammatory macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). These findings suggest that localized IRF7 knockdown modulates the immune cell composition in arthritic joints, potentially influencing the inflammatory microenvironment and disease progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eLocalized IRF7 knockdown attenuates ankle joint inflammation in collagen-induced arthritis mice\u003c/h2\u003e\u003cp\u003eCompared with the si-mock and positive control groups, the si-IRF7 group exhibited reduced ankle joint swelling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), with significantly thinner paws at the ankle joint (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Three-dimensional microCT reconstruction revealed that bone erosion and trabecular bone loss, key pathological features of CIA, were markedly mitigated in the si-IRF7 group, as reflected by increased bone volume/total volume (BV/TV) ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;D). Compared with positive controls, histological analysis via H\u0026amp;E staining revealed decreased synovial hyperplasia in si-IRF7-treated mice, characterized by reduced villous hypertrophy and inflammatory cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE\u0026ndash;F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eImmunohistochemical (IHC) staining of the ankle synovium revealed reduced expression of the proinflammatory marker S100A12 and downstream targets of IRF7\u0026mdash;including PTGS2, NF-κB1, and IL-1β\u0026mdash;in the si-IRF7 group, which was consistent with diminished IRF7 protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG\u0026ndash;R). These findings collectively indicate that localized IRF7 knockdown suppresses synovial inflammation, attenuates bone destruction, and downregulates key inflammatory mediators in CIA mice, highlighting the therapeutic potential of targeting IRF7 in arthritic joints.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e macrophage subpopulation in the RA synovium is defined by its robust expression of alarmins (e.g., S100A8, S100A9, and S100A12) and chemokines (e.g., CXCL8), \u003csup\u003e49\u003c/sup\u003e which act as potent drivers of innate immune activation by recruiting monocytes, fibroblasts, and neutrophils to inflamed joints. \u003csup\u003e50,51\u003c/sup\u003e This subset also represents a primary source of IL-1β, a key proinflammatory cytokine linking innate immunity to synovial hyperplasia and bone erosion in RA.\u003csup\u003e52\u003c/sup\u003e The positive correlation between the frequency of CD48highS100A12\u0026thinsp;+\u0026thinsp;macrophages and disease progression\u0026mdash;from undifferentiated arthritis (UA) to established RA\u0026mdash;and the downregulation of S100A12 in the osteoarthritis (OA) synovium underscore its specificity for RA pathogenesis. Gene ontology enrichment further implicated this subcluster in canonical inflammatory pathways, aligning with its role in sustaining chronic synovitis.\u003c/p\u003e\u003cp\u003eInterferon regulatory factor 7 (IRF7), a master regulator of type I interferon (IFN-I) responses, has emerged here as a critical transcription driver of the CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e phenotype. While IRF7 is well characterized in systemic autoimmune diseases such as systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) \u003csup\u003e\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, its role in RA macrophage biology was previously undefined. Our multiomics analysis bridges this gap by demonstrating that IRF7 activity is uniquely enriched in pathogenic synovial macrophages, where it directly regulates downstream inflammatory effectors (e.g., PTGS2, CXCL10, NF-κB1, and IL-1β) involved in TNF, NF-κB, and Toll-like receptor signaling. Another study revealed that IRF7 knockout exacerbates joint inflammation in a K/BxN serum transfer model via impaired IFN-β production and attenuates disease in collagen-induced arthritis (CIA) by reducing M1 macrophage polarization. This discrepancy likely reflects context-dependent roles for IRF7: in K/BxN arthritis, where inflammation is driven by preformed antibodies, IRF7 may have dual effects on both pro- and anti-inflammatory macrophage subsets; in contrast, in RA and CIA\u0026mdash;characterized by persistent innate immune activation\u0026mdash;pathologically elevated IRF7 tips the balance toward M1 polarization and excessive cytokine production. \u003csup\u003e56\u0026ndash;57\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eNotably, there was an increase in Foxp3\u0026thinsp;+\u0026thinsp;regulatory T (Treg) cells following local IRF7 knockdown in CIA mice, suggesting that IRF7 may also modulate adaptive immunity, potentially via crosstalk with T-cell populations. However, systemic IRF7 inhibition carries risks of immunodeficiency and viral susceptibility, which we mitigated through intra-articular delivery of siRNA, a strategy that selectively targets synovial macrophages while sparing systemic immunity.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e This localized approach highlights the translational potential of IRF7 as a therapeutic target, addressing unmet needs in RA patients with inadequate responses to current biologics.\u003c/p\u003e\u003cp\u003eSeveral limitations warrant future investigations. While our data establish a causal link between IRF7 and macrophage polarization in vitro and in vivo, the precise mechanisms by which IRF7 coordinates with other transcription factors (e.g., NFIL3 and STAT1) in CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e cells remain unclear. Additionally, the long-term effects of local IRF7 inhibition on joint homeostasis and the risk of infection require evaluation in chronic models. Moving forward, generating macrophage-specific IRF7-deficient mice will help dissect the cell autonomous vs. nonautonomous effects of IRF7 in CIA, and spatial transcriptomics could reveal regional heterogeneity in IRF7 activity within the synovial microenvironment.\u003c/p\u003e\u003cp\u003eIn conclusion, our study identified IRF7 as a potential regulator of pathogenic synovial macrophages in RA, integrating multiomics data with functional validation to support its candidacy as a macrophage-directed therapeutic target. By selectively disrupting IRF7-mediated inflammation in the joint, this approach offers a promising strategy to alleviate synovitis and joint destruction while minimizing systemic immune suppression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIRF7 interferon regulatory factor 7\u003c/p\u003e\u003cp\u003eRA rheumatoid arthritis\u003c/p\u003e\u003cp\u003eOA osteoarthritis\u003c/p\u003e\u003cp\u003eDisease-modifying antirheumatic drugs (DMARDs)\u003c/p\u003e\u003cp\u003eCIA collagen-induced arthritis\u003c/p\u003e\u003cp\u003esiRNA small interfering RNA\u003c/p\u003e\u003cp\u003escRNA-seq single-cell RNA sequencing\u003c/p\u003e\u003cp\u003eChip-seq chromatin immunoprecipitation sequencing\u003c/p\u003e\u003cp\u003emicro-CT microcomputed tomography\u003c/p\u003e\u003cp\u003eHematoxylin and eosin (H\u0026amp;E) staining\u003c/p\u003e\u003cp\u003eIHC immunohistochemistry\u003c/p\u003e\u003cp\u003ePTGS2 prostaglandin-endoperoxide synthase 2\u003c/p\u003e\u003cp\u003eTNFα: tumor necrosis factor alpha\u003c/p\u003e\u003cp\u003eNF-κB1 nuclear factor kappa-light-chain-enhancer of activated B cells 1\u003c/p\u003e\u003cp\u003eIL-1β interleukin-1β\u003c/p\u003e\u003cp\u003eCXCL10 C-X-C motif chemokine ligand 10\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eGenerative AI statement\u003c/h2\u003e\u003cp\u003eThe author(s) declare that no generative AI was used in the creation of this manuscript.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by National Natural Science Foundation of China (82172349, 82102529), Shenzhen Science and Technology Program (RCBS20221008093103013), and Guangdong Natural Science Foundation (2022A1515011531, 2023A1515010226).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHL, CW, and GL: Writing – original draft, Writing – review \u0026amp; editing, Data curation, Formal Analysis, Investigation, Validation, Visualization. XP, and ZZ: Data curation, Investigation, Validation, Writing – original draft, Writing – review \u0026amp; editing. ZS, JL, and XW: Data curation, Formal Analysis, Investigation, Writing – original draft, Writing –review \u0026amp; editing. YW, and HS: Investigation, Writing – original draft, Writing – review \u0026amp; editing, Funding acquisition, Supervision, Project administration. WL, PW, and GZ: Investigation, Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by National Natural Science Foundation of China, Shenzhen Science and Technology Program, and Guangdong Natural Science Foundation, which are gratefully acknowledged.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll bioinformatics data analyzed in this study, including GSE216651, GSE152805, GSE62697, GSE154346, and GSE130011, were sourced from publicly available datasets in the GEO database. E-MTAB-8322 is sourced from publicly available datasets in the EMBL-EBI database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSparks JA. Rheumatoid Arthritis. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 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Front Immunol. 2020;11:640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.00640\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.00640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, Macrophage, IRF7, Single-cell RNA sequencing, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-7536100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7536100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives \u003c/strong\u003eRheumatoid arthritis (RA) involves synovial inflammation driven by pathogenic macrophages, whose polarization is regulated by transcription factors (TFs). Interferon regulatory factor 7 (IRF7) is an innate immune regulator, but its role in RA macrophage-mediated inflammation and cartilage destruction remains unclear. This study aimed to define IRF7-dependent regulatory pathways in RA macrophages and evaluate their therapeutic potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Single-cell RNA sequencing (scRNA-seq) data and SCENIC analysis were used to identify TF-enriched macrophage subpopulations in the RA synovium. Chromatin immunoprecipitation sequencing (ChIP-seq) was used to map IRF7 binding sites, and bulk RNA-seq was used to analyze M1 polarization responses. Functional validation included IRF7 knockdown in human monocytes and intra-articular siRNA in a collagen-induced arthritis (CIA) mouse model to assess inflammatory genes, macrophage polarization, and joint pathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA CD48\u003csup\u003ehigh\u003c/sup\u003eS100A12\u003csup\u003e+\u003c/sup\u003e proinflammatory macrophage subset was expanded in RA and enriched for IRF7 activity and downstream genes (PTGS2, CXCL10, NF-κB1, and IL-1β). IRF7 directly regulates these genes, and its knockdown reduces M1 polarization and inflammatory gene expression in vitro. In CIA mice, local IRF7 silencing attenuated joint inflammation, synovial hyperplasia, and bone erosion, which correlated with decreased proinflammatory macrophages and increased regulatory T cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eIRF7 drives pathogenic macrophage polarization and inflammatory signaling in RA, linking its dysregulation to disease pathogenesis. Local targeting of IRF7 disrupts proinflammatory networks, suggesting a precise strategy to mitigate synovial inflammation without systemic immunosuppression.\u003c/p\u003e","manuscriptTitle":"IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 10:38:10","doi":"10.21203/rs.3.rs-7536100/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T04:16:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T15:52:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T12:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79152587572693981845368392719090807566","date":"2025-09-22T15:39:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233009072750704192737466067994735497947","date":"2025-09-21T09:11:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101622172426162045850346995668913541705","date":"2025-09-21T05:17:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T03:51:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T08:13:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T07:53:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Arthritis Research \u0026 Therapy","date":"2025-09-04T12:11:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"199d1231-59f2-400e-9dd3-3ba34ee7e74f","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:10:44+00:00","versionOfRecord":{"articleIdentity":"rs-7536100","link":"https://doi.org/10.1186/s13075-025-03708-3","journal":{"identity":"arthritis-research-and-therapy","isVorOnly":false,"title":"Arthritis Research \u0026 Therapy"},"publishedOn":"2025-12-08 15:58:14","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-09-24 10:38:10","video":"","vorDoi":"10.1186/s13075-025-03708-3","vorDoiUrl":"https://doi.org/10.1186/s13075-025-03708-3","workflowStages":[]},"version":"v1","identity":"rs-7536100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7536100","identity":"rs-7536100","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}