IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis

doi:10.21203/rs.3.rs-7536100/v1

Download PDF

Research Article

IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis

https://doi.org/10.21203/rs.3.rs-7536100/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 08 Dec, 2025

Read the published version in Arthritis Research & Therapy →

You are reading this latest preprint version

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^highS100A12⁺ 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

IRF7 is enriched in a CD48^highS100A12⁺ 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.

Rheumatoid arthritis (RA) is a prevalent systemic autoimmune disease¹ 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^highS100A12⁺ 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^highS100A12⁺ 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

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^highS100A12⁺ 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 v2.2.7.1⁴³, 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.

Increased frequency and proinflammatory phenotype of CD48^highS100A12⁺ macrophages in the RA synovium

Following quality control (excluding cells with < 300 genes, > 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^highLYVE1⁺, FOLR2⁺ID2⁺, CD48^highS100A12⁺, HLA^highISG1⁺, CD48 + ^highCLEC10A^high, TREM2^low, and FOLR2⁺ICAM1⁺ (Fig. 1A). Disease-stratified analysis revealed a significant increase in the frequency of CD48^highS100A12⁺ macrophages with increasing disease severity, peaking in the RA synovium (Fig. 1B). Similarly, S100A12 expression was upregulated in RA macrophages and downregulated in OA macrophages (Fig. 1C).

Gene Ontology (GO) enrichment of the CD48^highS100A12⁺ marker genes highlighted the overrepresentation of proinflammatory biological processes, including defense response activation, cytokine production regulation, leukocyte migration, and inflammatory pathway modulation (Fig. 1D). 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. 1E–G), confirming the clinical importance of this proinflammatory subpopulation.

IRF7-driven transcriptional programs define CD48^highS100A12⁺ synovial macrophages

Triplicate pySCENIC analyses with random subsampling (Fig. 2A) identified a conserved set of transcription factors (TFs) enriched in CD48^highS100A12⁺ 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. 2B), with IRF7 exhibiting one of the highest regulon specificity scores (RSSs) via Jensen‒Shannon divergence (JSD) analysis (Fig. 2D). 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^highS100A12⁺ macrophages (Fig. 2E) and cells with elevated IRF7 regulon activity (Fig. 2F), indicating a specific transcriptional association. Immunohistochemical staining for IRF7 in OA and RA synovial slices was also conducted (Fig. 2G). 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. 2H).

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. 3A). Differential peak annotation revealed 108 high-confidence IRF7-bound genes that overlapped with SCENIC-predicted IRF7 target genes in human CD48^highS100A12⁺ synovial macrophages to generate a core regulatory gene set (Fig. 3B). 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. 3C).

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. 3D), mirrored by scRNA-seq UMAP visualization, which revealed elevated expression of these genes in the CD48^highS100A12⁺ subcluster (Fig. 3E). 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. 3F–G) significantly reduced the mRNA and protein levels of M1 polarization markers—TNF, CXCL10, IL-1β, PTGS2, and NF-κB1 (Fig. 3H). Western blotting corroborated these findings, demonstrating that decreased IRF7 protein expression alongside attenuation of downstream inflammatory signaling (Fig. 3G). 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. 4A). 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. 4B-C). Concurrently, a notable increase in the proportion of Foxp3⁺ regulatory T (Treg) cells within the CD3⁺ CD4⁺ T-cell population was observed (Fig. 4D-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. 4F). 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. 5A), with significantly thinner paws at the ankle joint (Fig. 5B). 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. 5C–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. 5E–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. 5G–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.

The CD48^highS100A12⁺ 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^highS100A12⁺ 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^highS100A12⁺ 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.

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

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.

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. J Autoimmun Jun. 2020;110:102400. 10.1016/j.jaut.2019.102400.
McInnes IB, Schett G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet Jun. 2017;10(10086):2328–37. 10.1016/S0140-6736(17)31472-1.
Yang J, McGovern A, Martin P, et al. Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis. Nat Commun. 2020;11(1). 10.1038/s41467-020-18180-7.
Ho CH, Silva AA, Tomita B, Weng HY, Ho IC. Differential impacts of TNFalpha inhibitors on the transcriptome of Th cells. Arthritis Res Ther Jul. 2021;23(1):199. 10.1186/s13075-021-02558-z.
Yamada S, Nagafuchi Y, Wang M, et al. Immunomics analysis of rheumatoid arthritis identified precursor dendritic cells as a key cell subset of treatment resistance. Ann Rheum Dis Jun. 2023;82(6):809–19. 10.1136/ard-2022-223645.
Canavan M, Marzaioli V, McGarry T, et al. Rheumatoid arthritis synovial microenvironment induces metabolic and functional adaptations in dendritic cells. Clin Exp Immunol Nov. 2020;202(2):226–38. 10.1111/cei.13479.
Komatsu N, Takayanagi H. Mechanisms of joint destruction in rheumatoid arthritis - immune cell-fibroblast-bone interactions. Nat Rev Rheumatol Jul. 2022;18(7):415–29. 10.1038/s41584-022-00793-5.
Kuo DDJ, Cohn IS, Zhang F, Wei K, Rao DA, Rozo C, Sokhi UK, Shanaj S, Oliver DJ, Echeverria AP, DiCarlo EF, Brenner MB, Bykerk VP, Goodman SM, Raychaudhuri S, Rätsch G, Ivashkiv LB, Donlin LT. HBEGF + macrophages in rheumatoid arthritis induce fibroblast invasiveness. Sci Transl Med. 2019 May 8 2019;11(491):eaau8587.
Lewis MJ, Barnes MR, Blighe K, et al. Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes. Cell Rep Aug. 2019;27(9):2455–e24705. 10.1016/j.celrep.2019.07.091.
McHugh J. CCL21-CCR7 axis in RA: linking inflammation and bone erosion. Nat Rev Rheumatol Oct. 2019;15(10):576. 10.1038/s41584-019-0296-5.
Qin YCM, Jin HZ, Huang W, Zhu C, Bozec A, Huang J, Chen Z. Age-associated B cells contribute to the pathogenesis of rheumatoid arthritis by inducing activation of fibroblast-like synoviocytes via TNF-α-mediated ERK1/2 and JAK-STAT1 pathways. Ann Rheum Dis. 2022 Nov 2022;81(11):1504–1514. 10.1136/annrheumdis-2022-222605
Mulder K, Patel AA, Kong WT, et al. Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity Aug. 2021;10(8):1883–e19005. 10.1016/j.immuni.2021.07.007.
Zong D, Huang B, Li Y, et al. Chromatin accessibility landscapes of immune cells in rheumatoid arthritis nominate monocytes in disease pathogenesis. BMC Biol Apr. 2021;16(1):79. 10.1186/s12915-021-01011-6.
Xiong D, Wang Y, You M. A gene expression signature of TREM2(hi) macrophages and gammadelta T cells predicts immunotherapy response. Nat Commun Oct. 2020;8(1):5084. 10.1038/s41467-020-18546-x.
Zhang F, Wei K, Slowikowski K, et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat Immunol Jul. 2019;20(7):928–42. 10.1038/s41590-019-0378-1.
Cheng L, Wang Y, Wu R, et al. New Insights From Single-Cell Sequencing Data: Synovial Fibroblasts and Synovial Macrophages in Rheumatoid Arthritis. Front Immunol. 2021;12:709178. 10.3389/fimmu.2021.709178.
Boutet MA, Courties G, Nerviani A, et al. Novel insights into macrophage diversity in rheumatoid arthritis synovium. Autoimmun Rev Mar. 2021;20(3):102758. 10.1016/j.autrev.2021.102758.
He L, Jhong JH, Chen Q, et al. Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors. Cell Rep Nov. 2021;2(5):109955. 10.1016/j.celrep.2021.109955.
Richter FC, Udalova I. Macrophage commonalities across tissues and inflammation. Nat Rev Immunol Jan. 2022;22(1):2. 10.1038/s41577-021-00659-z.
Green ID, Pinello N, Song R, et al. Macrophage development and activation involve coordinated intron retention in key inflammatory regulators. Nucleic Acids Res Jul. 2020;9(12):6513–29. 10.1093/nar/gkaa435.
Yokoyama-Kokuryo W, Yamazaki H, Takeuchi T, et al. Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis. Arthritis Res Ther Mar. 2020;12(1):46. 10.1186/s13075-020-2137-y.
Culemann S, Gruneboom A, Nicolas-Avila JA, et al. Locally renewing resident synovial macrophages provide a protective barrier for the joint. Nature Aug. 2019;572(7771):670–5. 10.1038/s41586-019-1471-1.
Udalova IA, Mantovani A, Feldmann M. Macrophage heterogeneity in the context of rheumatoid arthritis. Nat Rev Rheumatol Aug. 2016;12(8):472–85. 10.1038/nrrheum.2016.91.
Kamada R, Yang W, Zhang Y, et al. Interferon stimulation creates chromatin marks and establishes transcriptional memory. Proc Natl Acad Sci U S A Sep. 2018;25(39):E9162–71. 10.1073/pnas.1720930115.
Lee EJ, Lilja S, Li X, Schäfer S, Zhang H, Benson M. Bulk and single cell transcriptomic data indicate that a dichotomy between inflammatory pathways in peripheral blood and arthritic joints complicates biomarker discovery. Cytokine. 2020;127. 10.1016/j.cyto.2019.154960.
Sweeney SE, Kimbler TB, Firestein GS. Synoviocyte innate immune responses: II. Pivotal role of IFN regulatory factor 3. J Immunol Jun. 2010;15(12):7162–8. 10.4049/jimmunol.0903944.
Andrilenas KK, Ramlall V, Kurland J, Leung B, Harbaugh AG, Siggers T. DNA-binding landscape of IRF3, IRF5 and IRF7 dimers: implications for dimer-specific gene regulation. Nucleic Acids Res Mar. 2018;16(5):2509–20. 10.1093/nar/gky002.
Qing F, Liu Z. Interferon regulatory factor 7 in inflammation, cancer and infection. Front Immunol. 2023;14:1190841. 10.3389/fimmu.2023.1190841.
Ikushima H, Negishi H, Taniguchi T. The IRF family transcription factors at the interface of innate and adaptive immune responses. Cold Spring Harb Symp Quant Biol. 2013;78:105–16. 10.1101/sqb.2013.78.020321.
Zhou X, Huang D, Wang R, et al. Targeted therapy of rheumatoid arthritis via macrophage repolarization. Drug Deliv Dec. 2021;28(1):2447–59. 10.1080/10717544.2021.2000679.
Alivernini S, MacDonald L, Elmesmari A, et al. Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat Med Aug. 2020;26(8):1295–306. 10.1038/s41591-020-0939-8.
Tang S, Yao L, Ruan J, et al. Single-cell atlas of human infrapatellar fat pad and synovium implicates APOE signaling in osteoarthritis pathology. Sci Transl Med Jan. 2024;24(731):eadf4590. 10.1126/scitranslmed.adf4590.
Chou CH, Jain V, Gibson J, et al. Synovial cell cross-talk with cartilage plays a major role in the pathogenesis of osteoarthritis. Sci Rep Jul. 2020;2(1):10868. 10.1038/s41598-020-67730-y.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol Jun. 2018;36(5):411–20. 10.1038/nbt.4096.
Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Cambridge (Mass)) Aug. 2021;28(3):100141. 10.1016/j.xinn.2021.100141.
Van de Sande B, Flerin C, Davie K, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc Jul. 2020;15(7):2247–76. 10.1038/s41596-020-0336-2.
Cohen M, Matcovitch O, David E, et al. Chronic exposure to TGFbeta1 regulates myeloid cell inflammatory response in an IRF7-dependent manner. EMBO J Dec. 2014;17(24):2906–21. 10.15252/embj.201489293.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods Mar. 2012;4(4):357–9. 10.1038/nmeth.1923.
Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience Feb. 2021;16(2). 10.1093/gigascience/giab008.
Zhang Y, Liu T, Meyer CA, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137. 10.1186/gb-2008-9-9-r137.
Ramírez F, Dündar F, Diehl S, Grüning BA, Manke T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res Jul. 2014;42:W187–91. 10.1093/nar/gku365. (Web Server issue).
Yu G, Wang LG, He QY. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics (Oxford England) Jul. 2015;15(14):2382–3. 10.1093/bioinformatics/btv145.
Rothfels K, Milacic M, Matthews L, et al. Using the Reactome Database. Current protocols Apr. 2023;3(4):e722. 10.1002/cpz1.722.
Brand DD, Latham KA, Rosloniec EF. Collagen-induced arthritis. Nat Protoc. 2007;2(5):1269–75. 10.1038/nprot.2007.173.
Seeuws S, Jacques P, Van Praet J, et al. A multiparameter approach to monitor disease activity in collagen-induced arthritis. Arthritis Res Ther. 2010;12(4):R160. 10.1186/ar3119.
Roszkowski L, Jaszczyk B, Plebańczyk M, Ciechomska M. S100A8 and S100A12 Proteins as Biomarkers of High Disease Activity in Patients with Rheumatoid Arthritis That Can Be Regulated by Epigenetic Drugs. International J Mol sciences Dec. 2022;31(1). 10.3390/ijms24010710.
Roszkowski L, Ciechomska M. Tuning Monocytes and Macrophages for Personalized Therapy and Diagnostic Challenge in Rheumatoid Arthritis. Cells Jul. 2021;22(8). 10.3390/cells10081860.
Wang Y, Han CC, Cui D, Li Y, Ma Y, Wei W. Is macrophage polarization important in rheumatoid arthritis? Int Immunopharmacol Sep. 2017;50:345–52. 10.1016/j.intimp.2017.07.019.
Kurowska-Stolarska M, Alivernini S. Synovial tissue macrophages in joint homeostasis, rheumatoid arthritis and disease remission. Nat Rev Rheumatol Jul. 2022;18(7):384–97. 10.1038/s41584-022-00790-8.
Ning S, Pagano JS, Barber GN. IRF7: activation, regulation, modification and function. Genes Immun Sep. 2011;12(6):399–414. 10.1038/gene.2011.21.
Gu L, Casserly D, Brady G, et al. Myeloid cell nuclear differentiation antigen controls the pathogen-stimulated type I interferon cascade in human monocytes by transcriptional regulation of IRF7. Nat Commun Jan. 2022;10(1):14. 10.1038/s41467-021-27701-x.
Xu WD, Zhang YJ, Xu K, et al. IRF7, a functional factor associates with systemic lupus erythematosus. Cytokine Jun. 2012;58(3):317–20. 10.1016/j.cyto.2012.03.003.
Sweeney SE, Corr M, Kimbler TB. Role of interferon regulatory factor 7 in serum-transfer arthritis: regulation of interferon-beta production. Arthritis Rheum Apr. 2012;64(4):1046–56. 10.1002/art.33454.
Sin WX, Yeong JP, Lim TJF, Su IH, Connolly JE, Chin KC. IRF-7 Mediates Type I IFN Responses in Endotoxin-Challenged Mice. Front Immunol. 2020;11:640. 10.3389/fimmu.2020.00640.

No competing interests reported.

Download PDF

Journal Publication

published 08 Dec, 2025

Read the published version in Arthritis Research & Therapy →

Editorial decision: Revision requested
13 Oct, 2025
Reviews received at journal
10 Oct, 2025
Reviews received at journal
10 Oct, 2025
Reviewers agreed at journal
22 Sep, 2025
Reviewers agreed at journal
21 Sep, 2025
Reviewers agreed at journal
21 Sep, 2025
Reviewers invited by journal
15 Sep, 2025
Editor assigned by journal
15 Sep, 2025
Submission checks completed at journal
15 Sep, 2025
First submitted to journal
04 Sep, 2025

You are reading this latest preprint version

IRF7 orchestrates proinflammatory macrophage polarization and joint destruction in rheumatoid arthritis

Status:

Journal Publication

Version 1

Abstract

Figures

Key Message

Introduction

Materials and methods

Mice

Human synovial samples

Cells

Single-cell RNA sequencing data processing and analysis

ChIP-seq data processing and analysis

Bulk RNA-seq data analysis

Immunohistochemical staining of clinical knee synovial samples

Cell culture and transfection

RNA isolation

Real-time quantitative polymerase chain reaction (RT‒qPCR)

Western blotting

Collagen-induced arthritis (CIA) mouse model

Flow cytometric analysis of mouse ankle tissues

Histological and immunohistochemical analysis of mouse ankles

Statistical analysis

Results

Increased frequency and proinflammatory phenotype of CD48highS100A12+ macrophages in the RA synovium

IRF7-driven transcriptional programs define CD48highS100A12+ synovial macrophages

IRF7 knockdown suppresses inflammatory gene expression in M1-polarized macrophages

Localized IRF7 knockdown attenuates ankle joint inflammation in collagen-induced arthritis mice

Discussion

Abbreviations

Declarations

Competing interests

Generative AI statement

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

Increased frequency and proinflammatory phenotype of CD48^highS100A12⁺ macrophages in the RA synovium

IRF7-driven transcriptional programs define CD48^highS100A12⁺ synovial macrophages