---
title: "TNFAIP3/A20 dysfunction causes innate, sterile hyperinflammation independent of adaptive immunity"
id: "frontiers-in-immunology-2-tnfaip3-a20-dysfunction-drives-innate-and-sterile-hyperinflammation"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-2-tnfaip3-a20-dysfunction-drives-innate-and-sterile-hyperinflammation"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1856810"
published_at: "2026-07-17T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# TNFAIP3/A20 dysfunction causes innate, sterile hyperinflammation independent of adaptive immunity
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-2-tnfaip3-a20-dysfunction-drives-innate-and-sterile-hyperinflammation
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1856810)
- **Published At:** 2026-07-17T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study analyzes conserved, cell type–specific expression, regulation, and induction of **TNFAIP3/A20** across humans and mice and uses transgenic and gnotobiotic mouse models to probe disease drivers. - Conditional deletion of Tnfaip3 in CD11c (Itgax)-expressing cells (referred to as **A20CD11c-ko**) was generated to study consequences of impaired A20 function in antigen-presenting cells. - Contrary to expectations, systemic inflammation in A20CD11c-ko mice developed independently of B cells, T cells, and autoreactive antibodies; mice crossed onto Fcgrt-, Ighm- (µMt), or RAG2-deficient backgrounds still developed disease. - Germ-free (axenic) rederivation showed the **microbiome** was not required for disease manifestations in these models, indicating sterile, innate-driven pathology. - Single-cell RNA-sequencing (CITE-seq) of splenocytes and analysis of public single-cell atlases were used to map Tnfaip3 expression and to evaluate cytokine regulation and transcription factor influences on TNFAIP3 across cell types. - Flow cytometry, ELISAs (including cytokines and immunoglobulins), and genetic crosses were used to characterize cellular composition, humoral responses, and inflammatory mediators in affected mice. - The authors propose that autoantibodies observed in TNFAIP3-deficient contexts may be a downstream consequence rather than the primary driver, supporting an **autoinflammatory** rather than autoimmune disease model for many TNFAIP3-associated disorders. - These findings have therapeutic implications: targeting innate inflammatory circuits may be more relevant than strategies focused solely on adaptive immunity or microbiome modulation for TNFAIP3-associated conditions. - Data handling included GWAS catalog interrogation for TNFAIP3-associated SNPs, integrated single-cell analyses, and stringent quality control for sequencing and flow cytometry datasets. Details on some experimental specifics, sample sizes for certain assays, and full quantitative outcomes are provided in the original article and supplementary files.
## Clinical Analysis & Structured Key Points
About us All journals All articles Submit your research Search Login Frontiers in Immunology Sections Articles Research Topics Editorial board About journal Published in Frontiers in Immunology Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders 7 impact factor 11.3 citescore Editor & Reviewers Edited by Murugaiyan Gopal Reviewed by Panagiota Kolypetri Johnna Francis Varghese Outline Abstract 1 Introduction 2 Materials and methods 3 Results 4 Discussion Data availability statement Ethics statement Author contributions Funding Acknowledgments Conflict of interest Generative AI statement Publisher’s note Supplementary material References Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Figure 7 View in article ORIGINAL RESEARCH article Front. Immunol., 17 July 2026 Sec. Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1856810 TNFAIP3/A20 dysfunction drives innate and sterile hyperinflammation Karel F. A. Van Damme 1,2,3* P H Pieter Hertens 4,5 D S Dorine Sichien 1,2,6 K V Katrien Van der Borght 1,2,7 J V Justine Van Moorleghem 1,2 S D Sofie De Prijck 1,2 Alex Klarenbeek 1,2 E L Els Louagie 6 I L Inés Lammens 1,2,8 S V Stijn Vanhee 1,2,8 Christian Vanhove 9 P D Pieter De Bleser 5,10 Steven Van Laecke 11 A D Amélie Dendooven 12 Hamida Hammad 1,2 Lars Vereecke 2,13,14 Dirk Elewaut 2,3,15 Geert van Loo 4,5,14,16 +10 more Bart N. Lambrecht 1,2,17,18* 1. Laboratory of Mucosal Immunology, VIB-UGent Center for Inflammation Research, Ghent, Belgium 2. Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium See more Article metrics View details 279 Views Abstract Feedback mechanisms regulate immune activation and prevent excessive tissue damage. TNFAIP3, also known as A20, serves as a crucial brake on inflammation, and mutations or haploinsufficiency of this gene are linked to diseases characterized by inappropriate inflammation. In this study, we document highly conserved patterns of cell type-specific gene expression, regulation, and induction of TNFAIP3, and employ transgenic and gnotobiotic mouse models to investigate how adaptive immunity and the gut microbiome contribute to pathology arising from impaired A20 function. Contrary to our expectations, systemic inflammation resulting from Tnfaip3 deficiency in CD11c (Itgax)-expressing cells developed independently of autoreactive antibodies, B cells, and T cells. The microbiome also proved dispensable for disease manifestations in these models. These findings suggest that in diseases caused by insufficient TNFAIP3/A20 activity, autoantibodies may reflect a downstream consequence of disease rather than a causative driver, suggesting autoinflammatory rather than autoimmune pathology. These insights carry therapeutic implications for the treatment of TNFAIP3-associated diseases. 1 Introduction Feedback mechanisms are critical to fine-tune immune responses, ensuring that inflammation does not result in disproportionate tissue injury. A20, initially discovered as a tumour necrosis factor (TNF)-induced protein 3 (TNFAIP3) (1), plays an essential role to control the extent and duration of inflammation. Upon ligand binding to receptors such as the TNF receptor, interleukin-1 receptor, B or T-cell receptor, CD40, receptor activator of NF-κB (RANK), or pattern recognition receptors such as Toll-like receptors (TLRs) and NOD-like receptors (NLRs), an intracellular signaling cascade results in the activation of nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB) (2, 3) (Figure 1A). NF-κB induces the transcription of genes involved in cell survival and inflammation in a cell type- and tissue-specific manner. To limit excessive inflammation and prevent cell death, NF-κB induces the expression of TNFAIP3 (4). Through its ubiquitin (Ub)-binding domains, A20 interacts with ubiquitinated proteins to terminate sustained NF-κB signaling and inhibit cell death (5–9). Figure 1 TNFAIP3/A20 function (A) and transcription in humans (B) and mice (C). The importance of Tnfaip3 in maintaining immune homeostasis was first observed in full-body A20-knockout mice, which develop severe cachexia and organ-wide inflammation, resulting in premature mortality (10). Cell type and context-dependent functions of A20 have been identified using conditional genetic knockout and experimental models. For example, myeloid-specific A20-deficient mice spontaneously develop polyarthritis resembling rheumatoid arthritis (RA), driven by NLRP3 inflammasome-mediated macrophage necroptosis (9, 11, 12). Similarly, mice with dendritic cell (DC)-specific A20 knockout exhibit multi-organ inflammation with lymphosplenomegaly, myeloid expansion, and spontaneous lymphocyte activation (13), resembling systemic lupus erythematosus (SLE) (14) or inflammatory bowel disease (IBD) (15). In B cells, A20 is required for their normal differentiation and to prevent the formation of autoantibodies and glomerular immune complex formation (16–18). Furthermore, T cell and NK cell-targeting strategies established Tnfaip3 as an inherent checkpoint during lymphocyte development, activation, and survival (19–22). In line with these experimental findings, numerous studies identified polymorphisms in the TNFAIP3 locus linked to autoimmune, autoinflammatory, and allergic disorders (23, 24). TNFAIP3 variants have been identified in patients with conditions such SLE, RA, psoriasis, IBD, juvenile idiopathic arthritis, systemic sclerosis, type 1 diabetes mellitus, multiple sclerosis, Sjögren’s disease, and celiac disease (3, 25). Conversely, TNFAIP3 variants may also confer increased resistance to infections (26, 27). Many of these single nucleotide polymorphisms (SNP) are enriched in genomic regions involved in regulating TNFAIP3 transcription (25). Beyond non-coding variants, loss-of-function mutations in TNFAIP3 have been identified in patients with a Behçet-like disease, featuring oral ulcers, fever, mucosal and skin involvement, autoimmunity, and arthritis (28). This disease is inherited in an autosomal dominant fashion and has been termed A20 haploinsufficiency (HA20) (29, 30). These associations underline the critical importance of feedback by A20 in maintaining immune homeostasis. Given the clinical impact of TNFAIP3 dysregulation, we first established a data-driven framework describing its expression, regulation, and induction across species. The strong conservation between humans and mice prompted us to investigate universal drivers of TNFAIP3-associated disorders in mice. Because autoantibodies are a hallmark of many immune-mediated disorders, and they are frequently detected in patients with HA20 (30–32), we tested their contribution to systemic inflammation in mice with a CD11c Cre-driven loss of Tnfaip3. Furthermore, given prior evidence linking the microbiome to immune dysregulation (33–36), we examined whether the microbiome alters systemic inflammation in these mice. 2 Materials and methods 2.1 Mouse housing, genetics, and rederivation Experiments were performed with a mixture of male and female mice, unless explicitly mentioned otherwise. All animals were maintained at specific-pathogen-free conditions in individually ventilated cages with 12-hour day/night cycles. Food and water was provided ad libitum for the duration of the experiments, unless described differently. All in vivo experimental procedures were approved by the animal ethical committee of the VIB Center for Inflammation Research. All experiments were performed on mice of C57Bl/6 genetic background. Conditional A20/Tnfaip3 knockout mice, in which exons 4 and 5 of the Tnfaip3 gene are flanked by two LoxP sites, were generated as described before (37) and crossed to mice expressing a CD11c-driven Cre recombinase (38). These animals are hereafter referred to as A20CD11c-ko mice hereafter. To evaluate the role of IgGs, B cells and all lymphocytes, we further crossbred these mice to homozygous Fcgrt-deficient (39), Ighm-deficient (‘µMt’) (40), and a RAG2-deficient (41) backgrounds, respectively. For breeding, due to infertility of A20CD11c-ko mice, homozygous Tnfaip3-floxed Cre-negative females were cohoused with heterozygous Tnfaip3-floxed Cre-expressing males. To evaluate the targeting by the Itgax Cre, these mice were interbred with mice expressing TdTomato in the Rosa26 locus preceded by a floxed stop codon (42). Germ-free or axenic mice were generated by embryo transfer in axenic recipients at the germ-free mouse facility of the University of Ghent. Axenic mice were housed under positive-pressure flexible film isolators (North Kent Plastics). 2.2 Analysis of single-cell RNA-sequencing datasets TNFAIP3 expression was evaluated in the Tabula Sapiens (43), non-immune cells in the Tabula Muris (44), and mouse splenocytes as described previously (45). The regulatory potential calculation of transcription factors on TNFAIP3 was carried out as described previously (45). Cytokines affecting TNFAIP3 expression in human cells were retrieved from CytoSig (46). Cytokines represented by at least three independent datasets and significantly associated with altered TNFAIP3 expression were selected using a Wilcoxon signed-rank test. The top 15 cytokines were subsequently visualized as previously described (45). The in vivo effect of cytokines on Tnfaip3 expression was assessed in the Immune Dictionary (47). We extracted the annotation as provided, experimental group, and Tnfaip3 expression for each cell from an integrated Seurat object (https://singlecell.broadinstitute.org/single_cell/study/SCP2554/dictionary-of-immune-responses-to-cytokines-at-single-cell-resolution) in R v4.4.0. We calculated the average Tnfaip3 (LogNormalized) RNA counts for each cytokine and cell type (with at least 3 cells). Per cell type and cytokine, Log2 fold changes and p values (Wilcoxon test) were calculated in comparison to the PBS (control) group and filtered if p 1 population were retained and their Log2 fold changes per cell population and cytokine were plotted using ComplexHeatmap v2.20.0. 2.3 GWAS studies The GWAS catalog v1.0.2 was downloaded on October 15th, 2025 from https://www.ebi.ac.uk/gwas/docs/file-downloads (48). All SNPs associated with the gene(s) of interested, including both reported and mapped genes, were retained. Non-disease-related associations were excludes. The number of unique SNPs, identified by their RSIDs, was summarized and plotted per association. 2.4 Flow cytometry All organs were isolated following terminal bleeding and kept on ice until processing. Spleens were cut into small pieces and digested in RPMI containing 2% fetal bovine serum (FBS), 20 µg/mL Liberase (Roche; 05 401 119 001) and 10 U/mL DNAse I (Roche; DN2) at 37 °C for 30’. Following centrifugation, cells were passed through a 70 µm filter (Corning; 431751) and red blood cell lysis was carried out on ice for 3 minutes. To generate single-cell suspensions of the bone marrow, the tibial bone was flushed out with RPMI through a 70 µm filter, followed by an osmotic lysis step for 30 seconds on ice. The Peyer’s patches were passed through a nylon mesh with 70 µm pores to generate a single cell suspension, which was followed by a washing step. For the isolation of immune cells from the intestinal lamina propria, the distal 5.4 cm of the colon was opened, cleaned roughly in cold PBS with curved tweezers, cut into small segments of 0.5 x 0.5 cm, and stored in 2% FBS/RPMI on ice until further processing. Tissues were washed twice with pre-warmed RPMI and then incubated twice for 20 minutes in 2 mM EDTA/RPMI in a shaking water bath at 37 °C, with a washing step in between. Next, cells were enzymatically digested using collagenase VIII (Sigma; 2139) 1 mg/mL and DNase at 1/2000 for approximately 30 minutes at 37 °C in a shaking warm water bath. After digestion, cells were passed through a 70 µm cell strainer, suspended in 2% FBS/RPMI on ice until staining. 104 counting beads (eBioscience cat. 01-1234-42) were added to each well and the cells were stained with fluorescently labeled antibodies and Fc block (kind gift from Louis Boon (JJP Biologics), clone 2.4G2) during 30 min at 4 °C. Where applicable, intracellular staining was carried out in a second phase after fixation and permeabilization (Invitrogen; 00-5523-00). Samples were measured on a BD LSRFortessa or BD FACSymphony A5. Downstream analysis was performed in Flowjo v10.9.0 (BD). Gating was carried out as displayed in Supplementary Figure 4. The populations not shown were identified as follows: cDC1s as XCR1+ cDCs; cDC2s as CD172a+ cDCs; eosinophils as SiglecFhi SSC-Ahi; γδ T cells as CD3e+ CD90+ CD4- CD8a- γδTCR+; ILCs as CD90hi CD3eneg; pDCs as Siglec H+ BST2+; germinal center B cells as GL7+ CD95+ B cells; plasma cells as CD138+ CD43+ or CD138+ IgD-. 2.5 CITE-sequencing on wild-type and A20CD11c-ko splenocytes 3 A20CD11c-ko mice and 3 littermate Tnfaip3fl/flItgax-Cre- mice were included for single cell sequencing, all female and 12 weeks old. Mice were injected IV with pentobarbital containing fluorescently labeled anti-CD45 antibodies to exclude circulating cells. To facilitate cell sorting, we carried out magnetic-activated cell sorting (MACS) on enzymatically digested splenocytes to remove neutrophils, B and T cells from a portion of the splenocytes. Briefly, 80x106 splenocytes were stained with FITC-labeled CD3, CD19 and Ly-6G antibodies and Fc block for 30’. Following washing, 160 µL anti-FITC beads (Miltenyi Biotec; 130-048-701) were added for 15’ at 4 °C. The resuspended cells were then passed through a LS column (Miltenyi Biotec; 130-042-401) in a magnetic field and the effluent was collected. All samples were manually counted and 4x106 cells were isolated and spun down. The cell pellet was resuspended and incubated for 30’ on ice with 50 µL of staining mix in PBS containing 0.04% BSA, fluorescently labeled antibodies, Fc block, TruStain FcX Block (BioLegend; 101320), the mouse cell surface protein antibody panel containing 160 oligo-conjugated antibodies and 9 TotalSeq-A isotype controls (TotalSeq-A, BioLegend) and TotalSeq-A cell hashing antibodies (BioLegend) diluted 1:1000. 35000 viable cells (a fixed number of CD45-, CD19+, CD3+, Ly6G+, CD11c or 120G8+ and residual CD45+ cells) were sorted per organ in BSA-coated tubes on FACSAria II and III (BD Biosciences). All antibody details are included in Supplementary File 5. Sorted single-cell suspensions were resuspended at an estimated final concentration of 1000 cells/µl and loaded on a Chromium GemCode Single Cell Instrument (10x Genomics) to generate single-cell gel beads-in-emulsion (GEM). Biological replicates were multiplexed per lane. The scRNA-Seq libraries were prepared using the GemCode Single Cell 3’ Gel Bead and Library kit, version 3 (10x Genomics) according to the manufacturer’s instructions with the addition of amplification primers (3nM each, 5’CCTTGGCACCCGAGAATT*C*C - 5’GTGACTGGAGTTCAGACGTGTGC*T*C) during cDNA amplification to enrich the TotalSeq-A cell surface and hashing protein oligos respectively. Size selection with SPRIselect Reagent Kit (Beckman Coulter; B23318) was used to separate amplified cDNA molecules for 3’ gene expression and cell surface protein construction. TotalSeq-A protein library construction including sample index PCR using Illumina’s TruSeq Small RNA primer sets and SPRIselect size selection was performed according to the manufacturer’s instructions. The cDNA content of pre-fragmentation and post-sample index PCR samples was analyzed using the 2100 BioAnalyzer (Agilent). Sequencing libraries were loaded on an Illumina NovaSeq flow cell at VIB Nucleomics core with sequencing settings according to the recommendations of 10x Genomics, pooled in a 70:20:10 ratio for the combined 3’ gene expression, cell surface and hashing protein samples, respectively. The Cell Ranger pipeline (10x Genomics, version 6.1.2) was used to perform sample demultiplexing and to generate FASTQ files for read 1, read 2 and the i7 sample index for the gene expression, cell surface and hashing protein libraries. Read 2 of the gene expression libraries was mapped to the mouse reference genome (GRCm38.99). Subsequent barcode processing, unique molecular identifiers filtering and gene counting was performed using the Cell Ranger suite. CITE-seq reads were quantified using the feature-barcoding functionality. The average of the mean reads per cell across all gene expression libraries was 22500, with an average sequencing saturation of 44%, as calculated by Cell Ranger. For each sample individually, the Cell Ranger count matrices were transformed into SeuratObjects with Seurat. The HTO, ADT and RNA data were integrated. Doublets were removed with the DoubletFinder v2.0.3 package and merged in Seurtt. Cells with extreme parameters were removed (≥ 50000 RNA reads, ≤ 200 or ≥ 6000 genes, ≤ 5 or ≥ 140 ADTs detected, ≤ 10 or ≥ 6000 ADT reads, or ≥ 10% mitochondrial reads). The HTO and ADT counts were normalized via centered log transformation and scaled. The RNA counts were log normalized. The PCAs were calculated for both RNA and ADT data using the general Seurat workflow. A weighted nearest neighbor graph was created in Seurat based on the first 40 PCAs of RNA and first 20 PCAs of ADT data (49). We clustered at a resolution of 1 (smart local moving algorithm), which resulted in a good separation between the major cell populations. We determined differentially expressed genes and proteins and annotated clusters manually. Further subsetting was carried out on T and NK cells, and on conventional dendritic cells to enable a more fine-grained annotation. Data wrangling and visualization was carried out using tidyverse v2.0.0, viridis v0.6.5, ggpubr v0.6.0, scales v1.3.0. Identification of biological replicates was based on the highest number of hashing antibodies. Expression levels of Tnfaip3 were min-max scaled for visualization purposes. Differentially expressed genes were calculated using the default FindMarkers function in Seurat. Genes expressed in at least 10% of the population, with an absolute log2 fold-change of > 0.5, and an adjusted p value< 0.05 (Wilcoxon Rank Sum test) were used as input for pathfindR v2.4.1 to evaluate enrichment of Mus musculus KEGG pathways. Additional pathway analyses were performed using gene sets retrieved from msigdbr v7.5.1. For each pathway, per-cell module scores were calculated using the default Seurat AddModuleScore function. Statistical differences in pathway activity between conditions were assessed using a Wilcoxon rank-sum test. 2.6 ELISAs Serum immunoglobulins were measured by enzyme-linked immunosorbent assay (ELISA) in serum in half area microplates (Greiner; 675061). Wells were incubated with 50 µL of coating antibodies overnight at 4 °C and blocked with 150 µL 1% casein (Merck; C7078) in PBS for two hours. Samples were diluted in 0.1% casein buffer (IgA: 1/200; IgM: 1/3200; IgG: 1/30000). 50 µL of samples or standards were incubated for 2 hours at RT on an orbital shaker. Afterwards, detection antibodies were added for 1 hour. Mouse IL-6 (Invitrogen; 88-7064) and TNF (Invitrogen;. 88-7324) were measured by ELISA, according to the manufacturer’s instructions. B-cell activating factor (BAFF) was quantified on ELISA (R&D; DY2106-05) according the manufacturer’s instructions. Mouse anti-dsDNA IgG or IgA were quantified on ELISA (Alpha Diagnostic; 5120) according to the manufacturers’ instructions. For the measurement of anti-cardiolipin antibodies, microplates (Greiner; 675061) were coated with 50
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