IL-33 expands plasma cells, disrupts germinal centers, and increases autoantibody production in mice

Domain: chemistry
Published: 2025-12-23
Editorial outcome: major_revision

Abstract
IL-33 is an inflammatory cytokine contributing to asthma, Chronic Obstructive Pulmonary Disease (COPD), and autoimmune diseases. Although recent studies suggest that IL-33 can induce the generation of autoantibodies, the role of IL-33 on B cell maturation and tolerance is poorly understood. Here, by inducing systemic overexpression of IL-33 in mice, we show that this cytokine induces the IL-5 and CD4 T cell-dependent accumulation of plasmablasts and plasma cells of all isotypes in the spleen, and leads to an increased antibody production. IL-33 also disrupts splenic architecture and elevates autoantibody production, indicating a break in peripheral tolerance. Consistently, elevated levels of IL-33 exacerbate autoantibody production, kidney damage, and decrease survival in a mouse model of lupus. Additionally, intranasal delivery of IL-33 in mice or exposure to house dust mite extract (HDM) increases autoantibodies in the lung. Notably, blocking IL-33 reduced the autoantibodies generated during HDM exposure, indicating that HDM-induced autoantibody production is IL-33 dependent. Thus, our findings implicate IL-33 in the break of peripheral B cell tolerance, opening new therapeutic avenues for the treatment of infection, COPD and autoimmune conditions.

Introduction
Interleukin-33 (IL-33) is an inflammatory cytokine of the IL-1 family1. It is primarily released by structural cells, including fibroblasts, epithelial cells, and endothelial cells in response to tissue damage and can activate inflammatory pathways in both the innate and adaptive systems2. IL-33 binds to its cognate receptor ST2 in multiple cell types, amplifying both type 1 (IFNγ, IL-12) and type 2 inflammation (IL-4, IL-13, IL-5)3. IL-33 affects the activation and function of various cell types, including mast cells, T cells, eosinophils, basophils and ILCs, and IL-33/ST2 signaling plays an important role in several inflammatory diseases. IL-33 upregulation in inflamed tissue further amplifies Type 1/Type 17 (T1/T17) and/or type 2 (T2) inflammation, influenced by the local cytokine milieu, and the increased pool of IL-33-responsive cells, resulting in pathologic inflammation. In addition, high and sustained levels of IL-33 remained increased, even after the triggering agent was removed4.

Early clinical data, as well as genetic data in asthma and COPD, suggest a potential role of IL-33 in respiratory diseases5,6. There is also emerging evidence that IL-33 plays a role in autoimmunity, although the importance of this cytokine in driving disease is less understood. In addition, autoantibodies have been detected in respiratory diseases, such as COPD, but their relevance and etiology remain unclear. Autoimmune diseases are characterized by an aberrant immune response, with B cells and T cells reacting to normal constituents of the host. A variety of mechanisms have been proposed to trigger inappropriate recognition of self-antigens, including environmental and genetic factors7. In addition, certain cytokines have been linked to the induction or progression of autoimmune diseases8,9. Increased levels of IL-33 and soluble ST2 (sST2) have been described in patients with different autoimmune conditions, including rheumatoid arthritis, systemic lupus erythematosus (SLE), multiple sclerosis, inflammatory bowel disease (IBD) and myasthenia gravis10. In mice, recombinant IL-33 induced an increase of splenic plasma cells, serum IgA, and IgE1 and recent evidence suggests that IL-33 could induce the production of autoantibodies11. However, the mechanisms underlying autoantibody production and the role of IL-33 on B cells or plasma cells are not understood.

In this study, we show that IL-33 induces the differentiation and accumulation of PBs/PCs in the spleen and mesenteric lymph nodes, leading to increases in circulating antibodies of all isotypes. IL-33 disrupts splenic architecture and germinal centers (GC), reduces the quality of the antibodies produced in response to immunization, leads to the generation of autoantibodies and accelerates pathology in a mouse model of SLE. Administration of intranasal IL-33 also induces autoantibodies in the lung, as does exposure to intranasal house dust mite (HDM). Importantly, the administration of an IL-33 blocking antibody decreases HDM-induced autoantibodies. Collectively, our data demonstrates that elevated IL-33 levels impact antibody responses and promote a break of peripheral B cell tolerance. Therefore, tight regulation of IL-33 maybe required to enable appropriate adaptive responses to infection and to prevent autoantibody-mediated pathologies.

Results
IL-33, but not other type 2 cytokines, induces expansion of PB/PC
To determine the impact of different cytokines on B cells in secondary lymphoid organs, we used hydrodynamic gene delivery (HDD) to overexpress selected cytokines in vivo (Fig. 1a). HDD is a non-viral method that allows in vivo systemic overexpression of proteins by hepatocytes via intracellular delivery of macromolecules (DNA, RNA, proteins) in rodents12. We confirmed that the liver was the main producer by tracking IL-33 expression after HDD from the different tissues (Supplementary Fig. 1a). Using this method, IL-4, IL-5, IL-13, IL-25, TSLP, IL-9 and IL-33 were overexpressed in mice and detected in serum at day 7 (Supplementary Fig. 1a). In addition, IL-33 overexpression also increased soluble ST2 in serum (Supplementary Fig. 1a). Since HDD primarily induces gene expression in the liver, we first evaluated the impact of IL-33-HDD on the liver. We observed an increase of tissue ILC2, eosinophils and to a lesser extent, B cells and CD4+ T cells, but not a significant increase of NKT cells in the liver of mice that received IL-33 (Supplementary Fig. 1a). These data suggest that the inflammation observed in the liver is related to the effect of IL-33 in the tissue, rather than a general effect induced by HDD. Overexpression of IL-33, but not IL-4, IL-5, IL-13, IL-9, IL-25 or TSLP led to a robust increase in PB/PCs in spleen (9x fold increase) (Fig. 1b and Supplementary Fig. 1e). All PB/PC populations were expanded in the spleen irrespective of the isotype expressed in mice receiving IL-33 by HDD (IgG1, IgE, IgM, IgG2a and IgA), with IgG1 and IgM being the most abundantly expanded isotypes (Fig. 1c). The expansion of PB/PCs in spleen was also accompanied by an increase in antibody levels (Fig. 1d and Supplementary Fig. 1f). Interestingly, TSLP also induced a minor expansion of plasma cells, some of which were IgE+, but no significant impact was observed on other PCs (Fig. 1b and Supplementary Fig. 1g, h). In addition, we confirmed that overexpression of IL-4 was not sufficient to trigger an induction of IgE and IgG1 PB/PC expansion in the spleen (Supplementary Fig. 1h). Interestingly, IL-33 HDD-induced PB/PC expansion was tissue-specific and only occurred in the spleen and mesenteric lymph nodes (Fig. 1e and Supplementary Fig. 1i).

Expression of the IL-33 receptor ST2 (IL1RL1) was very low on the surface of splenic CD19+ B cells at baseline, consistent with what has been previously reported13. In addition, ST2 expression has not been detected in RNA sequencing datasets (data assembled by the ImmGen consortium)14 and was not increased after IL-33 overexpression (Fig. 1f), but a marked increase was observed on CD4+ T cells in the spleen. These results suggest that IL-33 drives PB/PC expansion potentially through ST2+ T cells, but not by directly activating B2 cells through the ST2+ receptor15.

IL-33-induced PC expansion is reduced by CD4 T cell depletion
To further understand the role of T cells in IL-33 induced PB/PC expansion, we depleted either CD4+ or CD8+ T cells in the IL-33 overexpression model (Fig. 2a and Supplementary Fig. 2a). Depletion of CD4+ T cells led to a significant 50% reduction of IL-33 driven PB/PCs expansion, while CD8+ T cell depletion did not (Fig. 2b). Furthermore, CD4+ T cell depletion, specifically reduced IgG1 and IgE PB/PCs in the spleen (Fig. 2c, d), suggesting that IL-33 HDD induces a T-dependent expansion of splenic IgG1 and IgE PCs. In contrast, expansion of IgM PB/PCs (Fig. 2e) and IgM and IgG2a serum levels (Supplementary Fig. 2b) were not impacted by CD4+ T cell depletion, suggesting that IL-33 HDD can also drive a separate, T-independent response. Interestingly, we observed CD40L upregulation on CD4+ T cells upon overexpression of IL-33 (Fig. 2f) and found higher expression of CD40L on ST2+ than in ST2- CD4+ T cells (Fig. 2g). Of note, CD69 expression on CD4+ T cells, a marker of T cell activation16, was increased after IL-33 overexpression (Fig. 2h). In an effort to characterize the mechanism that drives IgG1 and IgE PB/PC expansion downstream of CD4+ T cells, we interrogated which cytokines are induced by IL-33 HDD and noticed that IL-4 and IL-13 were significantly upregulated after IL-33 HDD (Supplementary Fig. 3a). To determine the contribution of IL-4 and IL-13 in IL-33 HDD induced PB/PC expansion, we overexpressed IL-33 in IL-4Rα KO mice for 7 days and quantified PBs/PCs in spleen (Fig. 2i). Consistent with what we reported in WT mice after CD4+ T cell depletion, IL-4Rα KO mice had a significant reduction in IgG1 and IgE PB/PC expansion but still showed significant expansion of IgM and IgG2a PBs/PCs. Taken together, these findings demonstrate that IL-33 can directly activate ST2+ CD4+ T cells, which in turn upregulate CD40L, leading to B cell class switching and PB/PC differentiation to IgG1 and IgE. While this impact of IL-33 overexpression on IgG1 and IgE is mediated through IL-4/IL-13 signaling, the expansion of IgM and IgG2a PB/PCs occurs via an IL-4/IL-13 independent mechanism.

IL-5 from IL-33 overexpression expands IgM and IgG2a PB/PCs
Notably, IL-33 overexpression also increased IL-5 levels in the serum (Fig. 3a and Supplementary Fig. 3a). IL-5 has previously been shown to drive differentiation of mouse B cells to plasma cells17 and plays a key role in IgM production18. We also noted that IL-5 overexpression led to a moderate increase of PB/PCs in the spleen, albeit the induction was much less robust than what we observe upon IL-33 overexpression (Fig. 1a). We further compared the increase of serum IgM and IgG2a in mice receiving IL-33 or IL-5 HDD and noted a consistent and significant increase in serum with both cytokines (Fig. 3b). In mice receiving IL-33 HDD, we measured intracellular IL-5 from ST2+ cells. The analysis shows that multiple ST2+ cell types increase IL-5 expression after IL-33 HDD, including ILC2, mast cells, eosinophils and CD44+ Sca1+ cells, potentially contributing to the increase in serum IL-5 (Supplementary Fig. 3b).

To probe if the increase of IgM and IgG2a triggered by IL-33 HDD was dependent on IL-5, we neutralized IL-5 using an anti-IL-5 antibody in mice receiving IL-33 HDD (Fig. 3c). We confirmed the efficacy of the anti-IL-5 by measuring blood eosinophils (Supplementary Fig. 3c). We observed that IgM, but not IgG1 or IgE PB/PCs in the spleen were significantly blocked by the IL-5 antibody treatment compared to isotype control (Fig. 3d). Circulating IgM and IgG2a increases (but not IgG1 nor IgE) were also blocked with the anti-IL-5 treatment (Fig. 3e). Consistent with this result, IL-5R expression was uniquely detected on the surface of IgM, but not IgG1 or IgE PCs following IL-33 HDD (Fig. 3f).

Together these data suggest that in response to overexpression of IL-33 (and in the absence of immunization), PB/PC expansion is driven through 2 separate mechanisms; a CD4+ T cell dependent mechanism that expands IgG1 and IgE PCs and a T cell-independent, IL-5 driven mechanism that expands IgM and IgG2a PB/PCs (Supplementary Fig. 3d).

We then determined if combining CD4+ T cell depletion with IL-5 blockade can prevent all PB/PC expansion, by overexpressing IL-33 and treating mice with a combination of anti-CD4, anti-IL-5 or a combination of the two antibodies (Fig. 3g). Strikingly, we found that the IL-33 induced expansion of PB/PCs of all isotypes in the spleen was only prevented when treating with the two antibodies combined, leading to a reduction in all Ig isotypes in serum (Fig. 3h).

IL-33 overexpression suppresses germinal centers in the spleen
Plasma cells can arise from different B cell subsets with different functional consequences. To understand which B cell subset contributes to the IL-33-induced PB/PC expansion, we profiled splenic B cells 7 days after overexpressing IL-33 and observed a decrease in total splenic B cells, including naïve B2 cells, B1 cells and marginal zone (MZ) B cells (Fig. 4a). This suggests that IL-33 can promote differentiation of pre-existing B cells into PB/PCs and does not induce proliferation of B cells. In addition, we observed a decrease in GC B cells in the spleen (Fig. 4b), mesenteric lymph nodes and Peyer’s patches (Supplementary Fig. 4a), suggesting that the PB/PC expansion is independent of germinal centers.

To evaluate the overall impact of IL-33 overexpression in the spleen, we performed unbiased global transcriptome analysis following IL-33 HDD and analyzed cell type enrichment. Consistent with our previous observation, IL-33 HDD led to a general depletion of genes associated with B cell populations and an increase of genes associated with plasma cells (Fig. 4c). Interestingly, cell enrichment analysis revealed that not all B cell subsets were equally impacted by IL-33 HDD. CD11c+/CD21+ and Tbet+/CD21+ ratios were increased and uniquely observed in IL-33 HDD (Fig. 4d and Supplementary Fig. 4b), suggesting an increase of age-associated B cells (ABC) and subsequently a change in the phenotype of the remaining B cells towards an alternative memory B cell lineage. Overexpression of IL-33 also impacted T cells, promoting genes associated with Th1 and Th2 cell differentiation (Fig. 4c).

Histological analysis of the spleen showed that IL-33 overexpression induced a disorganization of the spleen white pulp with poorly discernible regions (Supplementary Fig. 4c). Other cytokines, such as IL-25 did not trigger that effect (Supplementary Fig. 4d). To further examine GC organization, we used immunofluorescence to stain for B cells (CD19+), T cells (CD3+), naïve B cells (CD19+ IgD+) and GC cells (Bcl6+) (Fig. 4e–g) and observed that IL-33 overexpression decreased the percentage of follicular area (white pulp) (Fig. 4f) along with decreased total B cells and T cells in the spleen (Supplementary Fig. 4e). The remaining follicles were small (Fig. 4e, g), appear to have lost GC structures and notably had decreased Bcl6+ cells within the follicles (T cells and B cells) (Fig. 4g and Supplementary Fig. 4f). Furthermore, HDD IL-33 significantly decreased lymphotoxin alpha levels, a cytokine that has been shown to be critical for the lymph node structural development19 (Supplementary Fig. 4g). When analyzing the spatial distribution of ST2 in the follicles, we confirmed an increased expression of the ST2 receptor on CD3+ T cells (but not on CD19+ B cells) in the follicles after IL-33 overexpression (Fig. 4h).

IL-33 impairs humoral response following an NP-KLH immunization
To further probe the functionality of GC responses following systemic overexpression of IL-33, we immunized mice with NP-KLH with alum as an adjuvant, a well-established T-dependent GC mediated model20 (Model 1, Fig. 5a). As expected, control mice immunized with NP-KLH showed an increased frequency of GC B cells in the draining lymph nodes (Fig. 5b) which was blocked when IL-33 HDD was performed seven days after NP-KLH immunization (Fig. 5b). Overexpression of IL-33 did not alter NP+ memory or naïve B cells in the draining lymph node suggesting that despite the disruption in GC, the presence of antigen was sufficient to drive similar numbers of NP+ B cells relative to control mice (Fig. 5b). These data demonstrate that IL-33 can disrupt an ongoing GC reaction by decreasing GC B cells.

We then overexpressed IL-33 prior to NP-KLH immunization (Model 2, Fig. 5c) and observed that GC B cells in draining lymph nodes were generated at a lower level compared to control mice, suggesting that overexpression of IL-33 does not completely block the GC formation in the draining lymph node (Fig. 5d) or the spleen (Supplementary Fig. 5a), but rather drives splenic disorganization. As previously observed in the draining lymph node in model 1, neither NP+ memory B cells nor naïve B cells were impacted by the overexpression of IL-33 in model 2 (Fig. 5d). However, in the spleen, and similarly to what we observed in non-immunized mice (Fig. 4a), memory and MZ B cells were decreased (Supplementary Fig. 5b) suggesting that in addition to the impact on NP specific B cells, the overexpression of IL-33 could drive the differentiation of non-NP-KLH specific B cells into plasma cells and therefore reducing the existing B cell pool. Interestingly, CD5+ Cd1d+ B cells were also decreased, which have been reported to have regulatory functions21 (Supplementary Fig. 5c). We analyzed the T cell compartment in this model 2 and found that IL-33 overexpression leads to important changes in the spleen of NP-KLH vaccinated mice (Fig. 5e) by decreasing splenic CD4+ T cell, but not CD8+ T cells. In addition, the number of Tfh and Tfr cells was significantly reduced, contributing to the GC disruption.

Next, we quantified PB/PCs and compared the effect of IL-33 overexpression prior to NP-KLH vaccination (Model 2) and found that both NP+ and NP- plasma cells were increased in the draining lymph nodes and the spleen (Fig. 5f and Supplementary Fig. 5d). We analyzed the quantity and the quality of the antibodies produced after immunization. The relative affinity of the antibody pool can be evaluated by measuring the ratio of NP2/NP27 titers. Coating with BSA-NP2 (low valency capture) only captures high-affinity antibodies, while BSA-NP27 will bind both high and low-affinity antibodies15. Remarkably, we observed that overexpression of IL-33 increased both NP2 (high affinity) and NP27 (total) specific IgM titers (Fig. 5g). In contrast, NP2 (high affinity) specific IgG1 titers were decreased as well as the ratio NP2/NP27 IgG1, suggesting that the NP specific IgG1 antibodies generated in the mice that received IL-33 HDD were of lower affinity compared to control immunized mice. These results suggest that the disruption of the GC structures induced by IL-33 overexpression impacts the quality of the antibody response to immunization, increasing the abundance of antibodies produced but decreasing their relative affinity towards the antigen.

IL-33 breaks tolerance and accelerates disease in NZB/WF1 mice
Our data show that IL-33 overexpression disrupts GCs while inducing an antibody response, suggesting that this response could be predominantly extrafollicular. Extrafollicular responses are associated with autoantibody production in systemic autoimmunity, such as in patients with SLE22, and GCs are important structures to enforce self-tolerance in the periphery23,24. We thus hypothesized that elevated levels of IL-33 might contribute to loss of tolerance in mice. Interestingly, autoantibodies to double stranded DNA (dsDNA) (Fig. 6a) and αvβ6 (Supplementary Fig. 6a), autoantibodies which have been reported in patients with SLE and ulcerative colitis, respectively, were significantly increased in WT mice 7 days after IL-33 overexpression. dsDNA autoantibodies were partially blocked by anti-IL-5 treatment (Fig. 6a). Depletion of CD4+ T cells also led to a partial reduction, albeit less pronounced than IL-5 blockade, while the combination of anti-IL-5 and anti-CD4 treatment completely blocked dsDNA autoantibody production. We investigated the specificity of the response and observed that the majority of the anti-dsDNA autoantibodies were IgM with some presence of IgG1 (Fig. 6b), while αvβ6 autoantibodies were primarily IgM and IgA with no IgG1 autoantibody detected (Supplementary Fig. 6a).

We then investigated the impact of IL-33 HDD in a model of autoimmunity. NZB/W F1 mice develop spontaneous autoimmunity with features that resemble SLE, including autoantibody production, immune complex deposition in the kidney leading to proteinuria, glomerulonephritis and premature death25 (Fig. 6c). NZB/WF1 mice that received IL-33 HDD showed a lower probability of survival compared to mice that received the EV control, suggesting that IL-33 accelerates pathology in the model (Fig. 6d). Four weeks after the HDD, we also observed an increase in PB/PC numbers in the spleen of NZB/WF1 mice that received IL-33 HDD, similar to the effect in WT mice (Fig. 6e). Of note, IL-33 overexpression altered splenic B cell subsets of NZB/W F1 mice, accumulating age-associated B cells and decreasing marginal zone B cells (Fig. 5f). We also analyzed the impact of IL-33 overexpression in the antibody levels in serum and reported that NZB/WF1 mice have higher baseline levels of anti-dsDNA compared to WT (Fig. 6g), but irrespective of genetic background, IL-33 overexpression led to an increase in dsDNA levels in serum. This effect was initially driven by a significant increase of IgM (observed at week 3), followed by a trend towards increased anti-dsDNA IgG1 (Fig. 6h). Finally, we measured the albumin to creatine ratio (ACR) in the urine of these mice four weeks after HDD. NZB/WF1 mice receiving IL-33 HDD had a significant increase of ACR, with the average above 300, which indicates an ongoing severe proteinuria (Fig. 6i). Of note, the ACR was also measured in WT mice receiving HDD IL-33 or EV. Although, WT mice receiving IL-33 HDD had a trend towards an increase in the ACR ratio, the increase was not as consistent as it was observed in NZB/W mice (Supplementary Fig. 6b). Interestingly, WT mice that had the highest ACR ratio also corresponded to the WT mice with the higher anti-dsDNA levels (Supplementary Fig. 6c). Consequently, these results show that IL-33 overexpression increases splenic PB/PCs and autoantibodies, induces kidney pathology in genetically predisposed mice and decreases their probability of survival. These findings suggest that in the context of spontaneous autoimmunity, IL-33 exacerbates disease and increases autoantibody production.

Lung autoantibodies altered by IL-33 challenge and antibody treatment
IL-33 is an alarmin that is a key driver of airway inflammation and remodeling and plays a significant role in the pathophysiology of respiratory diseases such as asthma and COPD4. To determine if IL-33 induces production of autoantibody during pulmonary inflammation and under physiologically relevant settings, we dosed mice with intranasal IL-33 for 4 weeks (Fig. 7a). The levels of IL-33 in the lung at 1 and 4 weeks after the beginning of the challenge ranged from 1000 to 3000 pg/mg tissue (Fig. 7b and Supplementary Fig. 7c). Interestingly, IL-33 was not detected in the serum (Fig. 7b) suggesting that IL-33 was localized to the lung and not systemic in this model, further confirming the physiological relevance of the model.

Interestingly, PBs and PCs were expanded in the lung after challenging mice with IL-33 for 4 weeks (Fig. 7c), which included expansion of IgM, IgA and IgG1 plasma cells (Fig. 7d). Consistent with this increase in lung PBs and PCs, we also observed an increase of IgM, IgG1 and IgA antibodies in the lung lysate (Fig. 7e). Notably, lung IgG2a was not increased in this model highlighting the difference between a systemic and a mucosal IL-33 challenge. We measured autoantibodies in the lung lysate by assessing anti-dsDNA titers and confirmed that IL-33 challenge in the lung also breaks peripheral tolerance, leading to an increase in autoantibodies in the tissue. Of note, we did not observe an increase in the number of germinal center B cells in the lung after 4 weeks of intranasal IL-33 administration (Supplementary Fig. 7a), suggesting that the expansion of PB/PCs happens independently of inducible organized structures in the lung, such as iBALT.

In addition, we characterized the T cell response in this model by harvesting mice challenged with saline or IL-33 for 4 days (Supplementary Fig. 7b). We observed an increase of GATA3+ Th2 and Tbet+ Th1 cells in the lung (Supplementary Fig. 7d), consistent with the results from the spleen after systemic IL-33 expression. An increase of CD69+ T cells suggested an increase of T cell activation already after one week of IL-33 exposure (Supplementary Fig. 7e). We further analyzed ST2+ T cells to characterize which T cells were specifically responding to IL-33 and observed that 50% of ST2+ T cells were GATA3+ Th2 cells and 20% were Tbet+ Th1 cells (Supplementary Fig. 7f), suggesting a critical role of these cells in this model. Upon IL-33 challenge, ILC2 were also increased in the lung (Supplementary Fig. 7g) as well as IL-5 (Supplementary Fig. 7h).

Finally, to assess the role of IL-33 and IL-5 in antibody production in a well-established mouse model of severe chronic airway inflammation4, mice were chronically exposed to house dust mite (HDM) for up to 15 weeks (Fig. 7g). First, we examined the role of T cells in the development of the IgM and IgG response (Supplementary Fig. 7i). To block the T-B cell interaction, mice were treated prophylactically with anti-CD40L for 4 weeks. As anticipated, treatment with anti-CD40L significantly reduced IgG plasma cells, confirming the T cell dependency of IgG production in this model. In contrast, IgM production remained unaffected, indicating that IgM production occurs independently of T cells. Next, we investigated the specific role of IL-5 in antibody production. Prophylactic treatment with an anti-IL-5 blocked antigen-specific IgM response, while sparing the IgG response. These results highlight the role of IL-5 in driving the T-independent response in a relevant model of lung inflammation. Finally, we evaluated the impact of blocking IL-33 on autoantibody production in this model. Starting at week 4, mice were treated with either an IL-33 blocking antibody or an isotype control (Fig. 7g). We measured anti-nuclear autoantibodies and observed an increase after 15 weeks of HDM challenge. Interestingly, blocking IL-33 decreased the levels of autoantibodies, with values similar to those observed in saline control mice (Fig. 7i). The decrease of autoantibodies was accompanied by a decrease of ST2+ T cells (Supplementary Fig. 7j). These results confirmed the physiological relevance of our findings, demonstrating the impact of IL-33 on autoantibody production.

Methods
In vivo Study design
Hydrodynamic DNA delivery (HDD) was performed, as previously described55. Briefly, pRG977 plasmid coding for mouse cytokines or empty vector was diluted in sterile saline to a volume of approximately 10% of body weight and injected into the tail vein of female mice over 6 to 10 s. The plasmid carrier was pRG97756. Cytokine sequences were added or leave empty as a control (EV control). Different plasmids were used (IL-4, IL-5, IL-13, IL-9, TSLP, IL-33 and IL-25) at different doses ranging from 5ug to 25ug. To study the contribution of T cells and IL-5, mice were treated prophylactically, twice a week with 25 mg/kg of Rat IgG2b anti-mouse CD4 (Bioxcell, clone GK1.5), Rat IgG2b anti-mouse CD8a (Bioxcell, clone 2.43), Rat IgG1 anti-mouse IL-5 (Bioxcell, clone TRK5), isotype control rat IgG2b (Bioxcell, clone LTF-2) or isotype control rat IgG1 (Bioxcell, clone TNP6A7). To understand the effect of HDD IL-33 in immunization, mice were subcutaneously immunized with NP-KLH (Biosearch Technology) + aluminum adjuvant (Thermo Scientific) (50 ug in 200 uL total volume). For survival experiments in NZB/WF1 mice, mice were bled every two weeks to analyze antibody levels. For lung models, two models were used, either challenging with IL-33 (1) or HDM (2). (1) Mice were challenged 3 times per week during 4 weeks with either saline as a control or IL-33 (1 μg/20 μL). (2) Mice were challenged with HDM or saline as a control 3 times per week for 15 weeks. Starting at week 4, mice were therapeutically treated with an IL-33 blocking antibody or an isotype control. To identify the T cell contribution in the model, HDM-challenged mice were prophylactically treated twice a week with 25 mg/Kg of anti-mouse CD40L (Bioxcell, clone MR-1).

Mice
All procedures were conducted in compliance with protocols approved by the Institutional Animal Care and Use Committee of Regeneron Pharmaceuticals. Female C57BL/6 WT mice were produced and maintained in a specific pathogen-free facility at Regeneron Pharmaceuticals. NZB/WF1 mice were purchased from Charles River (mice were redirected from the Jackson Laboratory, strain Code 100008). IL-4R KO and IL-33 humanized mice were generated on a hybrid 129S6/C57BL/6 background. Mice were 15–20 weeks at the beginning of the experiment, except experiments in Fig. 7c–h, where mice were 6 months old. Experimental and control animals used in the experiments were co-housed. At the end of the experiments, mice were euthanized by CO2 followed by cervical dislocation.

Tissue processing and flow cytometry analysis
Spleens and lymph nodes were collected and mashed on 12-well, 70μm filter plates (Corning Costar) in complete RPMI 1640 media (cRPMI) to generate single-cell suspensions. Peyer’s patches (PP) were dissociated by incubating in a 12-well, 70μm filter plate (Corning Costar) in dissociation buffer (HBSS, 10% FBS, 5mM EDTA, 15mM HEPES) during 15 min at 37°C. PP were transfer to cRPMI and mashed. Right inferior lung lobe was harvested in liberase TH 0.7 U/mL, DNase 20ug/mL and HBSS with Ca and Mg. Lung was chop into small pieces and incubated at 37°C for 20 min. Tissue was transferred into gentleMACS C tubes (Miltenyi), and tubes were attached upside down on a gentleMACS dissociator (Miltenyi). RBC lysis was performed on single-cell suspensions for spleen and lung. Colon was harvested, washed, and cleaned. Tissue was incubated in dissociation solution containing HBSS, FBS, HEPES and EDTA during 40 min at 37°C to remove intestinal epithelial cells. After, tissue was transferred into the enzymatic digestion solution containing collagenase D, DNAse I and Dispase and incubated during 45 min at 37°C. Remaining tissue was then mashed and filtered through a 70μm cell strainer to recover cells from the lamina propia. Cells were labeled with LIVE/DEAD Blue solution (Thermo Fisher Scientific) for 10 min in the dark at room temperature (RT). Cells were then blocked using Fc Block (Tongo Biosciences) for 10 min at 4°C, followed by incubation with extracellular antibody mix for 30 min at 4°C in Brilliant Stain Buffer (BD Biosciences). The antibodies used for staining are included in Supplementary Table 1. For intracellular staining, samples were fixed and permeabilized (BD Cytofix/Cytoperm and BD Perm/Wash buffer) and resuspended with the antibody mix. Samples were acquired on BD Symphony A5 (BD Bioscience). Data was analyzed using OMIQ software (Dotmatics).

RNA-seq data generation and analysis
The protocols for RNA extraction and sequencing library preparation were similar to those described earlier57. Strand-specific RNA-seq libraries were prepared from 500ng RNA using the KAPA stranded mRNA-Seq Kit (KAPA Biosystems). The libraries were amplified by twelve-cycle PCR. Sequencing was performed on Illumina HiSeq® 2000 (Illumina) by a multiplexed single read run with 33 cycles. The resulting FASTQ files were analyzed via FastQC to ensure sufficient data quality. After RNA extraction and sequencing library preparation, reads were mapped to the human genome (National Center for Biotechnology Information GRCh37) using Array Studio software (Omicsoft, Cary, NC). Enrichment of cell type signatures was assessed using the xCell package in R.

Histology analysis
Spleens were fixed in 4% paraformaldehyde (PFA) for 24 h at RT and preserved in 70% ethanol. Longitudinal sections were paraffin-embedded and stained with hematoxylin and eosin (H&E) for assessment of the red and white pulp structure. Multiplex immunofluorescence staining was performed by the Preclinical Histology Core in Regeneron on 4μm sections of formalin-fixed paraffin-embedded spleens. Antibodies used were BCL6 (0.8μg/mL, Cell Signaling Technologies 5650S), IgD (2μg/mL, Bio-Rad MCA4693), CD19 (0.05μg/mL, Cell Signaling Technologies 90176), and CD3 (0.6μg.mL Dako A0452). ST2 RNA probes (Molecular Instruments) were used to detect ST2 RNA by Hybridization Chain Reaction (HCR, Molecular Instruments). All IHC/ISH staining was performed on Leica Bond Rx auto-stainers (Leica Biosystems) using the Bond Refine polymer-HRP detection system with TSA-amplification. TSA-conjugated 543 (Biotium), 594 (Biotium), 647 (Invitrogen) fluorophores, and Biotin-TSA with Streptavidin-750 (Invitrogen) fluorophores were used for visualization by fluorescence microscopy, and slides were counterstained with DAPI.

Total mouse isotyping
Concentrations mouse IgG1, IgG2a, IgG3, IgA was determined by using mouse isotyping MSD kit from Mesoscale. Briefly, plates are coated with SULFO-TAG labeled detection antibodies (anti-IgA, IgG1, IgG2a, IgG2b, IgG3, IgM). 25 μL of calibrator or sample are added to each well. Serum samples were diluted at 1:20,000, and protein lysates were diluted at 1:100 in 1% BSA/PBS. After incubation of 25 μl of a mix of detection antibody, plates are washed and 150 μL of Read Buffer T is added. Plates are analyzed using the QuickPlex SQ120 plate reader (Meso Scale Discovery, Rockville, Md). Total mouse IgE was quantified using the OptEIA kit (BD Biosciences) according to the manufacturer’s instructions. Briefly, plates were coated with an anti-IgE coating antibody. After blocking for 3 h, calibrator and samples were added and incubates O/N at 4°C. Serum samples were diluted at 1:50, and lung samples were diluted at 1:2 in BD assay diluent (PBS 10% FBS). After washing, a mix of detection antibody and Streptavidin-HRP agent was added. Before revealing the ELISA, plates were washed 7 times to avoid a high background. Plates were read using a SpectraMax M5 spectrophotometer.

ELISA anti NP2 and NP27 antibody levels
Anti-NP2 and NP27 specific IgM and IgG1 were measured by ELISA. Plates were coated overnight at 4°C with 4μg/mL of NP2-BSA or NP27-BSA (Bioresearch Technologies). After blocking with 1% BSA during 2h at RT, serum samples were added, and a 3-fold serial dilution was performed starting at 1:10,000. After 2h incubation at RT, bound antibodies were detected either with an anti-mouse IgG1-HRP (Invitrogen A10551) at 1/5000 or with an anti-mouse IgM-HRP (Invitrogen 04-06820) at 1/10,000. Plates were revealed using TMB substrate, and the reaction was stopped with 1M H2SO4. To measure the relative affinity, the ratio of the titer NP2/NP8 was calculated.

ELISA anti-dsDNA and derp1 antibody levels
Total anti-dsDNA Ig were measured using the kit from Alpha Diagnostic (# 5110), following the manufacturer instructions. Anti-dsDNA or derp1 specific IgM and IgG1 were measured by ELISA. Plates were coated overnight at 4C with 4 microg/mL of dsDNA or 2 microg/mL of derp1. After blocking with 1% BSA during 2h at RT, serum samples were added, and a 3-fold serial dilution was performed starting at 1:50. After 2h incubation at RT, bound antibodies were detected either with an anti-mouse IgG1-HRP (Invitrogen A10551) at 1/5000 or with an anti-mouse IgM-HRP (Invitrogen 04-06820) at 1/10000. Plates were revealed using TMB substrate, and the reaction was stopped with 1M H2SO4.

ELISA Anti-nuclear autoantibody
Total anti-mouse anti-nuclear antibodies (ANA) were quantified using the Mouse ANA ELISA kit (antibodies.com, A87504) according to the manufacturer’s instructions. Briefly, lung samples were added at 1:40 in the sample dilution buffer. Standards and samples were added to pre-coated wells and incubated for 90 min at 37°C. After washing, biotin label antigen was added and incubated at 37°C for 60 min, followed by an HRP streptavidin conjugate incubation. Plates were revealed and read using a SpectraMax M5 spectrophotometer.

ELISA Mouse soluble ST2/IL-33R
Soluble ST2 was quantified using Mouse ST2/IL-33R Elisa Kit-Quantikine (Biotechne, MST200), according to the manufacturer’s instructions. Briefly, standards and samples were added to pre-coated wells and incubated. Serum samples were diluted at 1:10 in calibrator diluent. After washing, the anti-mouse ST2 antibody was incubated, followed by substrate development. The reaction was stopped, and absorbance was read using a SpectraMax M5 spectrophotometer.

Cytokine analysis
Cytokine levels were measured with a customized mouse Procarta plex panel (Thermo Fisher) using Luminex xMAP technology. The experiment was conducted following the manufacturer’s instructions. Briefly, magnetic beads were added to the plate. After washing the beads, 25 μl of universal assay buffer was added, followed by standards and samples. Serum samples were diluted 1:4, and lung samples were added neat. After overnight incubation at 4C, plates were washed followed by detection antibody mixture incubation during 45 min and streptavidin-PE incubation during 30 min. Plates were washed, and 120 μL of reading buffer was added. After 5 min incubation, the plates were read using the Luminex FlexMap 3D reader.

Albumin to creatine ratio in urine
Metabolic cages were set up at 4 weeks after HDD to collect urine overnight. Albumin was measured using Albuwell M (Mouse Albumin ELISA Kit, Ethos Biosciences) and following the manufacturer's instructions. Creatinine levels are measured in urine using the ADVIA® Chemistry XPT blood chemistry analyzer (Siemens).

Statistical analysis
All statistical analyses were performed with GraphPad Prism software. Normality of data was evaluated by using the Shapiro-Wilk test. For multiple comparisons, normal data with non-statistically different SDs (assessed by using the Brown-Forsythe test) were interpreted by using 1-way ANOVA, followed by the Tukey post hoc test for multiple comparisons. Non-normal data or data with significantly different SDs were interpreted by using the Kruskal-Wallis test, followed by the Dunn post hoc test for multiple comparisons. When only comparing two groups, the Mann-Whitney U test (for not normally distributed samples) or the Unpaired Student’s t test (for normally distributed samples) were conducted. Differences were considered statistically significant at a P value of less than 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Prior studies have shown that IL-33 can increase splenic plasma cells, serum IgA and IgE, and may induce production of autoantibodies. In this study, Conde et al. used multiple models of murine inflammation to show that IL-33 expression leads to aberrant differentiation of B cells into plasma cells with increased production of antibodies, including IgG1 and IgM. Using systemic hydrodynamic delivery of DNA plasmids encoding IL-33, they showed that IL-33 expression increased disease in a mouse model of SLE. Additionally, exogenous intranasal administration of human IL-33 induced autoantibody production, an effect similarly seen with HDM challenge. IL-33 blocking antibodies reduced the pathology seen in HDM challenge. Additionally, the authors provide mechanistic evidence for aspects of the autoantibody production that are ST2+ T cell-dependent (IgG1 and IgE via IL-13) and T cell-independent (IgM and IgG2a via IL-5). The authors conclude that these preclinical studies suggest therapeutic utility of IL-33 blocking antibodies in diseases associated with autoantibodies. These results build upon the work of other groups that have reported that IL-33 promotes autoimmunity in mouse models. Also, a recent publication in Nature by Amisaki and colleagues revealed that IL-33 induced tertiary lymphoid structures (Nature. 2025 Feb;638(8052):1076-1084). This study is especially pertinent to the submitted manuscript and its relationship to this project should be added to the discussion. Overall, the work is impactful, conceptually novel, and supported by orthogonal approaches, representing an significant addition to the field. However, there are aspects of the study that would benefit from revisions:\nMajor points\nLine 82 - The authors sought to determine the impact of cytokines involved in Th2 responses on B cell function by using hydrodynamic gene delivery as a method of overexpression of these cytokines. They chose Il-4, IL-5, IL-13, IL-33, and IL-25. It is not clear why they did not choose to examine TSLP, an alarmin released by the airway epithelium in a similar fashion to IL-33 and IL-25. TSLP has been previously reported to induce polyclonal B-cell activation and autoimmunity, so it would seem to be an important cytokine to investigate in this system. (Iseki M et al, Thymic stromal lymphopoietin (TSLP)-induced polyclonal B-cell activation and autoimmunity are mediated by CD4+ T cells and IL-4, International Immunology, March 2012, 24(3):183–195). Therefore, TSLP would be a logical choice as a target in the system that the authors used. Similarly, the Th2 cytokine IL-9 also has important effects on B lymphocyte functon(Gokhale S et al, Upregulated Expression of the IL-9 Receptor on TRAF3-Deficient B Lymphocytes Confers Ig Isotype Switching Responsiveness to IL-9 in the Presence of Antigen Receptor Engagement and IL-4. J Immunol. April 2023, 210(8):1059-1073.) Therefore, the authors could consider TSLP and IL-9 in their studies because they are either Th2 cytokines or drive Th2 responses.\nLine 111 - The authors observed that IL-33 overexpression drove an increase in CD4+ T cell expression of CD40L. What was the effect of IL-33 overexpression on CD40 expression on B lymphocytes?\nLine 138 - IL-5 has been reported to cause isotype switching to IgA and IL-5 was increased in the submitted study as a result of IL-33 overexpression; however, serum IgA levels were unaffected by IL-33 overexpression in the submitted report. Can the authors speculate why this might be the case?\nLine 173 - What is the effect of IL-33 overexpression on lymphotoxin alpha, a cytokine that was reported by Matsumoto and colleagues to be critical for lymph node structural development? (Affinity maturation without germinal centres in lymphotoxin-alpha-deficient mice. Nature. 1996 Aug 1;382(6590):462-6). B-cell memory and affinity maturation are not absolutely dependent on the presence of germinal centers. This group showed that B-cell memory and affinity maturation were increased in lymphotoxin alpha knockout mice despite the absence of well-formed germinal centers. The authors should consider whether IL-33 may downregulate lymphotoxin alpha expression, thus leading to germinal center dysregulation. IL-33 has also been reported to regulate lymphotoxin beta expression, so this should be considered.\nLine 310 - The authors report that IL-33 drove a \"break in tolerance.\" Given this result, a major question is what is the effect of IL-33 on T follicular regulatory cells and B regulatory cells? It seems plausible that IL-33 diminishes the function of either or both of these regulatory cell populations. This should be investigated since IL-33 seems to change the Treg phenotype in the lung from a predominantly Foxp3 expressing population to one that expresses GATA-3.\nAdditional major points\na. The DNA plasmids were injected systemically via the tail vein, but it was noted that the liver as first pass was the major site of HDD driven cytokine production. Robust serum levels of IL-33 are shown. Can the authors speculate or have evidence for the site of IL-33 production liver hepatocyte vs endothelial cells? Does the spleen reveal any increase in IL-33 production in the HDD model. The site of IL-33 would be important to decipher the events leading to germinal center loss.\nb. Is there increased sST2 in the blood or spleen of the IL-33 HDD mice?\nc. The production of IL-13 from St2+ T cells to drive IgG1 and IgE in response to IL-33 is plausible and tested by the authors. How were ILC2s defined in Fig S3B? Was there any increase in IL-13 from these cells with IL-33 HDD? Presumably, CD69 expression is increased following antigenic TCR activation, how does IL-33 lead to increased T cell activation and subsequent IL-13 expression in the HDD model (Fig 2)?\nd. In the NP-KLH model, IL-33 HDD increased IgM titers, but induced lower affinity antibodies in IgG1 subset. Could the IgM production be a direct effect on ST2 on B1 cells outside of the germinal center? Is the decrease in high affinity antibodies due B cell specific effects or loss of the germinal center?\ne. Showing IL-33 blockade in the IL-33 HDD overexpressing model of SLE (NZB/W F1) would help support the authors claim of potential therapy of IL-33 inhibition in autoimmune conditions. Based on the evidence provided, I would expect IL-33 antagonism to significantly decrease disease in these mice, both at baseline and in IL-33 HDD models.\nf. Given changes in conical germinal center structure increased PCs recovered from lung in the exogenous administration of IL-33 (Fig. 7) or HDM with IL-33 blockade model, was there evidence of changes in iBALT within the lung? This would support the authors hypothesis of IL-33 modifying the extrafollicular response.\nMinor points\na. Information regarding the cytokine plasmids used for HDD should be provided, source or manufacturer.\nb. Methods for NP2/NP28 assay differ in figure and legends. Figure 5G refers to NP27 as the high and low affinity antigen (also referred to in text), but methods describe both an NP8 and NP28 as the high and low affinity antibodies.\nc. In multiple figure legends and within the text state mice were injected by HDD with the indicated cytokines, this should be written to indicate mice were injected by HDD with the DNA plasmids encoding the indicated cytokines.\nd. Figure 1D legend states the n=6, however the PVs/spleen graph has a n of 7 for the IL-33 HDD group, while the PCs/mesenteric LN graph has a n=6.\ne. Similarly, there exist differences in the n for similarly performed experiments. In fig 2C, the IL-33+Iso group has an n of 11, while in panel D the n=7. Why the difference in n, were these different cohorts?\nf. Across multiple figures, the statistical comparisons provided among groups is not consistent. In Fig 2C no comparison is provided between the EV and IL-33 group (but is in fig. E). Conversely, fig 2E does not display the comparison between IL-33+Iso and IL-33+anti CD4 (whereas Fig 2C contains this comparison). The relevant comparisons would be EV vs IL-33, and IL-33+Iso vs IL-33 + anti CD4/CD8.\ng. It is unclear what group the data in Fig 2G are derived. I think the groups should be EV and IL-33 (similar to Fig 2H), but are currently listed as ST2- and ST2+. Or is this only within the IL-33 HDD group?’\nh. Fig 3A, the addition of the IL-5 HDD group would serve as a good control and be helpful to evaluate the subsequent comparisons in Fig 3B-C.\ni. Fig 3B-C should show statistical comparisons for IL-33 vs EV.\nj. Was serum IgE measured in the anti IL-5/IL-33 HDD model (Figure 3)? This seems important, also the title of the figure “Anti-IL-5 administration blocks the expansion of IgE and IgG2a PB/PCs” seems incorrect, as the presented data suggest anti-IL-5 blocks IgM PCs and IgM production, and IgG2a production.\nk. As noted above regarding comparisons, It would appear that the relevant comparisons for Fig 3D-E would be EV vs IL-33, and IL-33+Iso vs IL-33 + anti il-5. Comparisons of these groups should be represented across all graphs.\nl. Fig 3 legend, line 794 syntax: “Three days later, mice were HDD either…” Should be: “Three days later, mice underwent HDD either…”\nm. Fig 3 legend notes n=5 for Fig 3B, but the IL-33 group appears to have n=4.\nn. Fig 6A should include comparisons of antibody treated groups to Iso.\no. Fig 7A schematic is confusing, recommend similar structure as Fig 2A, 3C, 5A/C, 6C.\np. Figure 7E refers to T cells as “GATA+”, however based on text, these should be identified as GATA3+.\nq. Fig 7H comparison should include HDM 15W Iso to HDM 15W anti-IL-33.\nr. The supplemental figures are very blurry, particularly Fig S3. I appreciate figure S3D and wonder if it should be included in the test as a graphical summary.\ns. Line 349 - \"expenses\" should be \"expense\"", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "IL-33 is an alarmin that under TH2 inflammatory conditions is mainly expressed in barrier epithelium, endothelial cells, and fibroblast reticular cells. Some inflammatory cells may also express IL-33. The cytokine is known to play an important role in allergic reactions, helminth infections, cancer, tissue fibrosis, chronic inflammation, organ transplantation, and rheumatic immune diseases. It activates various immune cells like mast cells, ILC2, Tregs, NK cells, macrophages, and also T helper 2 cells.\nIn the present paper, Conde et al. initially studied the effect of systemic overexpression of various cytokines, including IL-5 and IL-33 by hydrodynamic gene delivery (HDD) on B cell responses. Systemic overexpression of IL-33 resulted in the expansion of antibody secreting plasmablasts and plasma cells in spleen and mesenteric lymph nodes together with an increase in serum antibody levels of all isotypes. Plasma cells were not expanded in lung, lamina propria, colon and Peyer’s patch. Plasma cell frequencies in bone marrow were not studied. As expected, IL-33 overexpression led to a boost of T-dependent antibody responses, probably via promotion of Th2 cell activation. Additionally, the paper demonstrated that systemic IL-33 overexpression also boosts T-independent antibody responses and that this effect is blocked by injection of anti-IL-5 antibodies. These results are interesting because they indicate that IL-33 can stimulate T-independent antibody responses through IL-5.\nTo investigate the potential role of IL-33 in more physiologically and pathophysiologically relevant models, antibody responses after intranasal exposure to IL-33 and after IL-33 blockade in a house dust mite allergy model were examined. These results indicate that IL-33 has an impact on antibody responses also under these pathophysiologically relevant conditions. Given the importance of IL-33 in allergic reactions and its impact on Th2 helper cells results are not unexpected.\nMajor concerns\nThe authors demonstrate that overexpressing IL-33 systemically enhances both T-dependent and T-independent antibody responses, and that the latter is mediated by IL-5 induction. However, these data are derived from a model that uses unphysiological systemic cytokine overexpression. More relevant experiments using intranasal IL-33 and blocking it in house dust mite allergy suggest IL-33 influences antibody production physiologically. Yet, the exact mechanisms of IL-33's effects on plasma cell responses remain unclear. For publication in a high-ranking journal, research on the role of the IL-33/IL-5 axis in boosting T-independent antibody responses under physiological and/or pathophysiological conditions is needed.\nMinor concerns:\n- Please check the quality of the supplemental figures, which are not sufficient, at least in the files I have downloaded. Particularly Supplemental Fig 1 A and Supplemental Fig 3 are of bad quality. The other Supplemental Figures are better but still not of high quality (maybe this is a problem of my copy ?).\n- Figure legend of Supplemental Fig 1. D: …(% of lived cells) should read (% of live)\n- - The writing style is partly inadequate and confusing. Figure 1 shows serum antibodies in IL-33-expressing mice and plasma cell frequencies in mice overexpressing various cytokines, including IL-5 and IL-33. Figure 3 again shows antibodies in IL-33-expressing mice, this time compared to antibodies in IL-5-overexpressing mice. In this context Figure 1A is cited again (repeatedly) mentioned in the main body of the text. The Results section also contains speculations that are later disproven by the data. For example, “IL-33 drives PB/PC expansion, potentially through ST2+ T cells, but not by directly impacting B cells”.", "reviewer_4": "This study provides significant insights into the dual mechanisms by which IL-33 drives plasmablast/plasma cell expansion and disrupts germinal center organization. The results are supported by multiple models (HDD, depletion/blockade, SLE-prone mice, and HDM lung model), and the conclusions are well justified. However, to strengthen the translational impact and mechanistic clarity, I suggest the following revisions:1.Although IL-33 impairs Bcl6+ cells and lowers NP2/NP27 ratios, the causal relationship remains inferential. A temporal or cell lineage tracking analysis could strengthen this mechanistic connection.2.The increase in dsDNA and αvβ6 autoantibodies is intriguing, but their functional relevance (e.g., tissue deposition or organ damage) is not evaluated. Antibody deposition assays or correlation with proteinuria and inflammation would be highly informative.It would be beneficial if the authors could address this issue in the Discussion section."}, "author_response": "We thank the reviewer for the comment.\nMajor points\nLine 82 - The authors sought to determine the impact of cytokines involved in Th2 responses on B cell function by using hydrodynamic gene delivery as a method of overexpression of these cytokines. They chose Il-4, IL-5, IL-13, IL-33, and IL-25. It is not clear why they did not choose to examine TSLP, an alarmin released by the airway epithelium in a similar fashion to IL-33 and IL-25. TSLP has been previously reported to induce polyclonal B-cell activation and autoimmunity, so it would seem to be an important cytokine to investigate in this system. (Iseki M et al, Thymic stromal lymphopoietin (TSLP)-induced polyclonal B-cell activation and autoimmunity are mediated by CD4+ T cells and IL-4, International Immunology, March 2012, 24(3):183–195). Therefore, TSLP would be a logical choice as a target in the system that the authors used. Similarly, the Th2 cytokine IL-9 also has important effects on B lymphocyte functon(Gokhale S et al, Upregulated Expression of the IL-9 Receptor on TRAF3-Deficient B Lymphocytes Confers Ig Isotype Switching Responsiveness to IL-9 in the Presence of Antigen Receptor Engagement and IL-4. J Immunol. April 2023, 210(8):1059-1073.) Therefore, the authors could consider TSLP and IL-9 in their studies because they are either Th2 cytokines or drive Th2 responses.\nWe agree with the reviewer that addition of TSLP and IL-9 HDD would be valuable for this manuscript. As such, we have now compared the effects of IL-33 HDD with TSLP and IL-9, along with other type 2 cytokines that were previously included. While we did not see any impact of IL-9 on plasma cells, TSLP increased a subset of plasma cells (mainly IgE+ PCs). These findings are now included in Figure 1 and in the main text:\n“Interestingly, TSLP also induced a minor expansion of plasma cells, some of which were IgE+, but no significant impact was observed on other PCs (Fig. 1B, S1G, S1H)”\nLine 111 - The authors observed that IL-33 overexpression drove an increase in CD4+ T cell expression of CD40L. What was the effect of IL-33 overexpression on CD40 expression on B lymphocytes?\nWe have not been able to reliably stain CD40 on the surface of B cells with commercially available antibodies, including in germinal center B cells after immunization, which in theory should be a positive control.\nTherefore, addressing this point has been technically challenging.\nLine 138 - IL-5 has been reported to cause isotype switching to IgA and IL-5 was increased in the submitted study as a result of IL-33 overexpression; however, serum IgA levels were unaffected by IL-33 overexpression in the submitted report. Can the authors speculate why this might be the case?\nIgA is an important mucosal antibody, and significant increases of IgA were observed in the lung after IL-33 HDD, rather than serum. This data correlates with the increase of IgA that was reported in Figure 7. We have included this information in Supplemental Figure 1F.\nLine 173 - What is the effect of IL-33 overexpression on lymphotoxin alpha, a cytokine that was reported by Matsumoto and colleagues to be critical for lymph node structural development? (Affinity maturation without germinal centres in lymphotoxin-alpha-deficient mice. Nature. 1996 Aug 1;382(6590):462-6). B-cell memory and affinity maturation are not absolutely dependent on the presence of germinal centers. This group showed that B-cell memory and affinity maturation were increased in lymphotoxin alpha knockout mice despite the absence of well-formed germinal centers. The authors should consider whether IL-33 may downregulate lymphotoxin alpha expression, thus leading to germinal center dysregulation. IL-33 has also been reported to regulate lymphotoxin beta expression, so this should be considered.\nWe thank the reviewer for this interesting suggestion. We have now looked at LT alpha expression in the spleen of mice that received IL-33 HDD compared to EV. IL-33 significantly decreased the expression of LT alpha, suggesting this may be a potential mechanism by which IL-33 induces germinal center dysregulation. We have added the data in Fig. S4G and in the main text:\n“Furthermore, HDD IL-33 significantly decreased lymphotoxin alpha levels, a cytokine that has been shown to be critical for the lymph node structural development (Fig. S4G)”\nLine 310 - The authors report that IL-33 drove a \"break in tolerance.\" Given this result, a major question is what is the effect of IL-33 on T follicular regulatory cells and B regulatory cells? It seems plausible that IL-33 diminishes the function of either or both of these regulatory cell populations. This should be investigated since IL-33 seems to change the Treg phenotype in the lung from a predominantly Foxp3 expressing population to one that expresses GATA-3.\nWe have now analyzed T follicular regulatory and B regulatory cells in the spleen in the NP-KLH model. B regs were analyzed by gating on CD43- CD1d+ CD5+ cells, as defined in Yanaba et al. 2008.\nSimilar to the reported decrease of Tfh cells, we also observed a decreased in Tfr cells and Bregs, probably contributing to the break in tolerance. We have included this data in figure 5E, supplemental figure 5C and the information in the text:\n“Interestingly, CD5+Cd1d+ B cells were also decreased, which have been reported to have regulatory functions20(Fig. S5C).”\n“In addition, the number of Tfh and Tfr cells was significantly reduced, contributing to the GC disruption.”\nAdditional major points\na. The DNA plasmids were injected systemically via the tail vein, but it was noted that the liver as first pass was the major site of HDD driven cytokine production. Robust serum levels of IL-33 are shown. Can the authors speculate or have evidence for the site of IL-33 production liver hepatocyte vs endothelial cells? Does the spleen reveal any increase in IL-33 production in the HDD model. The site of IL-33 would be important to decipher the events leading to germinal center loss.\nLiu et al 1999 were the first group to describe the HDD technique. They reported that hepatocytes are the main cells that are transduced upon HDD. In addition, we have confirmed this data by tracking the expression of IL-33 in different tissues after HDD. Hepatocytes start producing the cytokines transfected and systemic increases are measured in the serum from mice. These data are now incorporated as Fig S1A and in the main text:\n“We confirmed that the liver was the main producer by tracking IL-33 expression after HDD from the different tissues (Fig. S1A).”\nb. Is there increased sST2 in the blood or spleen of the IL-33 HDD mice?\nSoluble ST2 is increased in serum of mice that received IL-33 HDD compared to empty vector control. We have included these data in Supplemental figure 1 and added the information in the manuscript:\n“In addition, IL-33 overexpression also increased soluble ST2 in serum (Fig. S1C).”\nc. The production of IL-13 from St2+ T cells to drive IgG1 and IgE in response to IL-33 is plausible and tested by the authors. How were ILC2s defined in Fig S3B?\nILC2 were gated as TCRb-, B220-, CD127+, ST2+, Sca1+ and indicated in the figure legend.\nWas there any increase in IL-13 from these cells with IL-33 HDD?\nWe have shown that IL-13 is increased downstream of IL-33 HDD (Fig S3A). ILC2 have been described to be a source of IL-13 (Spits et al, 2011), suggesting that ILC2 could be contributing to the increase of IL-13, in addition to other cell types including Th2 cells.\nPresumably, CD69 expression is increased following antigenic TCR activation, how does IL-33 lead to increased T cell activation and subsequent IL-13 expression in the HDD model (Fig 2)?\nCD69 has been shown to be upregulated on T cells upon different stimuli (Craston et al, 1997). ST2 expression is induced when IL-33 is increased. IL-33 can directly activate T cells through the ST2 receptor and that promotes the increase of CD69 expression.\nd. In the NP-KLH model, IL-33 HDD increased IgM titers, but induced lower affinity antibodies in IgG1 subset. Could the IgM production be a direct effect on ST2 on B1 cells outside of the germinal center?\nWe find NP+ B1 cells in the spleen and we agree with the reviewer that these cells could be contributing to the increase of IgM in the model. B1 cells are known to express ST2 receptor, and they are activated by IL-33. However, we think that the main driver of IgM in this model is IL-5 that is induced by IL-33. Our data shows that blocking IL-5 significantly reduces IgM PCs as well as IgM titers (Figure 3 and Fig 7)\nIs the decrease in high affinity antibodies due B cell specific effects or loss of the germinal center?\nGerminal center structures are important structures where somatic hypermutation and production of high affinity antibodies can occur. We think that the loss of germinal center structures contributes to the decrease in affinity. The loss in the splenic architecture is accompanied by the decrease in GC B cells (bcl6+) that participate in the process of somatic hypermutation.\ne. Showing IL-33 blockade in the IL-33 HDD overexpressing model of SLE (NZB/W F1) would help support the authors claim of potential therapy of IL-33 inhibition in autoimmune conditions. Based on the evidence provided, I would expect IL-33 antagonism to significantly decrease disease in these mice, both at baseline and in IL-33 HDD models.\nWe thank the reviewer for the question. However, there are a few limitations with the proposed experiment:\n1. In NZB/W F1 mice, we have not detected increases in IL-33 or soluble ST2 (IL-33 receptor) over time, suggesting that IL-33 is not a significant driver of baseline pathology in these mice. There are no available tools to block mouse IL-33 and delivery of NZB/W F1 from Jackson Labs has been significant delayed for months, which makes it challenging to evaluate this further (experiments in Figure 7 were done with humanized IL-33 mice).\n2. Our data show that exogenous administration of IL-33 can exacerbate disease pathology. Adding IL-33 and blocking it seems self-evident.\nf. Given changes in conical germinal center structure increased PCs recovered from lung in the exogenous administration of IL-33 (Fig. 7) or HDM with IL-33 blockade model, was there evidence of changes in iBALT within the lung? This would support the authors hypothesis of IL-33 modifying the extrafollicular response.\niBALT are inducible structures and the intranasal IL-33 administration does not induce GC B cells in the lung after 4 weeks. This data suggests that in this model iBALT might not be induced and that the increased of plasma cells and antibodies in the lung occurs independently of organized structures. We have added the data in Fig 7SA and in the text:\n“Of note, we did not observe an increase in the number of germinal center B cells in the lung after 4 weeks of intranasal IL-33 administration (Fig S7A), suggesting that the expansion of PB/PCs happens independently of inducible organized structures in the lung, such as iBALT”\nHDM induces the increase of lymphoid structures. Therapeutic treatment with anti Il-33 does not block the formation of lymphoid aggregates, suggesting that lymphoid aggregates are independent of IL-33.\nEp: epithelium, V: vasculature, Alv: alveoli, LA: Lymphoid aggregates\nMinor points\na. Information regarding the cytokine plasmids used for HDD should be provided, source or manufacturer.\nThe plasmid carrier was pRG977. Cytokine sequences were added or leave empty as control (EV control). We have provided this information in the method section.\nb. Methods for NP2/NP28 assay differ in figure and legends. Figure 5G refers to NP27 as the high and low affinity antigen (also referred to in text), but methods describe both an NP8 and NP28 as the high and low affinity antibodies.\nWe thank the reviewer for the comment. We have modified the method section (NP28à NP27)\nc. In multiple figure legends and within the text state mice were injected by HDD with the indicated cytokines, this should be written to indicate mice were injected by HDD with the DNA plasmids encoding the indicated cytokines.\nWe thank the reviewer for the comment, and we have revised the text accordingly.\nd. Figure 1D legend states the n=6, however the PVs/spleen graph has a n of 7 for the IL-33 HDD group, while the PCs/mesenteric LN graph has a n=6.\nWe have modified the figure legend accordingly.\ne. Similarly, there exist differences in the n for similarly performed experiments. In fig 2C, the IL-33+Iso group has an n of 11, while in panel D the n=7. Why the difference in n, were these different cohorts?\nWe thank the reviewer for alerting us about this. This resulted from a copy/paste error from Excel to GraphPad prism. This error has been corrected and the figure modified accordingly, and we confirmed that the isotype control group has n=7.\nf. Across multiple figures, the statistical comparisons provided among groups is not consistent. In Fig 2C no comparison is provided between the EV and IL-33 group (but is in fig. E). Conversely, fig 2E does not display the comparison between IL-33+Iso and IL-33+anti CD4 (whereas Fig 2C contains this comparison). The relevant comparisons would be EV vs IL-33, and IL-33+Iso vs IL-33 + anti CD4/CD8.\nFor simplicity, if multiple groups in the figure, only significant differences were graphed.\nWe agree that the comparison between IL-33+Iso and IL-33+anti CD4 are relevant. We modified the figure, added “ns” in the relevant comparisons and clarified in the figure legends which statistical comparisons were provided in the figure:\n“*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001, ns = non significative. For simplicity, only significant differences and relevant non-significant differences were graphed.”\ng. It is unclear what group the data in Fig 2G are derived. I think the groups should be EV and IL-33 (similar to Fig 2H), but are currently listed as ST2- and ST2+. Or is this only within the IL-33 HDD group?’\nAs mentioned by reviewer, this comparison was performed only within IL-33 HDD mice, comparing ST2- vs ST2+ cells. In EV, there were not enough cells to be able to compare these two groups. This information is now clarified in the figure and figure legend.\nh. Fig 3A, the addition of the IL-5 HDD group would serve as a good control and be helpful to evaluate the subsequent comparisons in Fig 3B-C.\nWe agree that IL-5 HDD is a good control. This data can be found in Supplemental Figure 1A. Concentration of serum IL-5 after IL-5 HDD is 25 ng/ml (mean). Because this concentration is very high, plotting it in the same figure as IL-33 HDD could be misleading.\ni. Fig 3B-C should show statistical comparisons for IL-33 vs EV.\nThe relevant statistical comparisons have been added in Figure 3.\nj. Was serum IgE measured in the anti IL-5/IL-33 HDD model (Figure 3)? This seems important, also the title of the figure “Anti-IL-5 administration blocks the expansion of IgE and IgG2a PB/PCs” seems incorrect, as the presented data suggest anti-IL-5 blocks IgM PCs and IgM production, and IgG2a production.\nWe appreciate the reviewer for pointing this out. We have corrected accordingly:\n“Fig 3. Anti- IL-5 administration blocks the expansion of IgM and IgG2a PB/PCs”\nIn addition, we have added now the ELISA results measuring serum IgE levels (Figure 3E):\n“Circulating IgM and IgG2a increases (but not IgG1 nor IgE) were also blocked with the anti-IL-5 treatment (Fig. 3E)”\nk. As noted above regarding comparisons, It would appear that the relevant comparisons for Fig 3D-E would be EV vs IL-33, and IL-33+Iso vs IL-33 + anti il-5. Comparisons of these groups should be represented across all graphs.\nThe relevant statistical comparisons have been added in Figure 3.\nl. Fig 3 legend, line 794 syntax: “Three days later, mice were HDD either…” Should be: “Three days later, mice underwent HDD either…”\nWe thank the reviewer for the comment. The figure legend has been modified accordingly.\nm. Fig 3 legend notes n=5 for Fig 3B, but the IL-33 group appears to have n=4.\nWe thank the reviewer for the comment. The figure legend has been modified accordingly\nn. Fig 6A should include comparisons of antibody treated groups to Iso.\nThe relevant statistical comparisons have been added.\no. Fig 7A schematic is confusing, recommend similar structure as Fig 2A, 3C, 5A/C, 6C.\nWe thank the reviewer for this suggestion; we have changed the figure accordingly.\np. Figure 7E refers to T cells as “GATA+”, however based on text, these should be identified as GATA3+.\nWe thank the reviewer for the comment. The figure has been modified accordingly\nq. Figure 7H comparison should include HDM 15W Iso to HDM 15W anti-IL-33.\nWe thank the reviewer for the comment. The figure has been modified accordingly.\nr. The supplemental figures are very blurry, particularly Fig S3. I appreciate figure S3D and wonder if it should be included in the test as a graphical summary.\nWe thank the reviewer for this great suggestion.\ns. Line 349 - \"expenses\" should be \"expense\"\nText has been modified.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nIL-33 is an alarmin that under TH2 inflammatory conditions is mainly expressed in barrier epithelium, endothelial cells, and fibroblast reticular cells. Some inflammatory cells may also express IL-33. The cytokine is known to play an important role in allergic reactions, helminth infections, cancer, tissue fibrosis, chronic inflammation, organ transplantation, and rheumatic immune diseases. It activates various immune cells like mast cells, ILC2, Tregs, NK cells, macrophages, and also T helper 2 cells.\nIn the present paper, Conde et al. initially studied the effect of systemic overexpression of various cytokines, including IL-5 and IL-33 by hydrodynamic gene delivery (HDD) on B cell responses. Systemic overexpression of IL-33 resulted in the expansion of antibody secreting plasmablasts and plasma cells in spleen and mesenteric lymph nodes together with an increase in serum antibody levels of all isotypes. Plasma cells were not expanded in lung, lamina propria, colon and Peyer’s patch. Plasma cell frequencies in bone marrow were not studied. As expected, IL-33 overexpression led to a boost of T-dependent antibody responses, probably via promotion of Th2 cell activation. Additionally, the paper demonstrated that systemic IL-33 overexpression also boosts T-independent antibody responses and that this effect is blocked by injection of anti-IL-5 antibodies. These results are interesting because they indicate that IL-33 can stimulate T-independent antibody responses through IL-5.\nTo investigate the potential role of IL-33 in more physiologically and pathophysiologically relevant models, antibody responses after intranasal exposure to IL-33 and after IL-33 blockade in a house dust mite allergy model were examined. These results indicate that IL-33 has an impact on antibody responses also under these pathophysiologically relevant conditions. Given the importance of IL-33 in allergic reactions and its impact on Th2 helper cells results are not unexpected.\nMajor concerns\nThe authors demonstrate that overexpressing IL-33 systemically enhances both T-dependent and T-independent antibody responses, and that the latter is mediated by IL-5 induction. However, these data are derived from a model that uses unphysiological systemic cytokine overexpression. More relevant experiments using intranasal IL-33 and blocking it in house dust mite allergy suggest IL-33 influences antibody production physiologically. Yet, the exact mechanisms of IL-33's effects on plasma cell responses remain unclear. For publication in a high-ranking journal, research on the role of the IL-33/IL-5 axis in boosting T-independent antibody responses under physiological and/or pathophysiological conditions is needed.\nWe have now studied the IgM response in a more pathophysiological mouse model using the HDM lung inflammation model. We characterized the IgM response in the lung of HDM challenged mice. We first confirmed that the response was T independent while the IgG1 response is T dependent. This model can be used as a dual model to address the T independent and T dependent component. Furthermore, we followed the IgM and IgG response in mice prophylactically treated with an anti-IL-5 antibodies and observed a decreased of IgM but not IgG antibodies. We have summarized this new information in the text.\n“First, we examined the role of T cells in the development of the IgM and IgG response (Fig S7I). To block the T-B cell interaction, mice were treated prophylactically with anti-CD40L for 4 weeks. As anticipated, treatment with anti-CD40L significantly reduced IgG plasma cells, confirming the T cell dependency of IgG production in this model. In contrast, IgM production remained unaffected, indicating that IgM production occurs independently of T cells. Next, we investigated the specific role of IL-5 in antibody production. Prophylactic treatment with an anti-IL-5 blocked antigen specific IgM response, while sparing the IgG response. These results highlight the role of IL-5 in driving the T-independent response in a relevant model of lung inflammation.”\nMinor concerns:\n- Please check the quality of the supplemental figures, which are not sufficient, at least in the files I have downloaded. Particularly Supplemental Fig 1 A and Supplemental Fig 3 are of bad quality. The other Supplemental Figures are better but still not of high quality (maybe this is a problem of my copy ?).\nWe thank the reviewer for that. The quality has been checked and improved.\n- Figure legend of Supplemental Fig 1. D: …(% of lived cells) should read (% of live)\nWe thank the reviewer for that. The figure has been changed.\n- - The writing style is partly inadequate and confusing.\nFigure 1 shows serum antibodies in IL-33-expressing mice and plasma cell frequencies in mice overexpressing various cytokines, including IL-5 and IL-33. Figure 3 again shows antibodies in IL-33-expressing mice, this time compared to antibodies in IL-5-overexpressing mice. In this context Figure 1A is cited again (repeatedly) mentioned in the main body of the text. The Results section also contains speculations that are later disproven by the data. For example, “IL-33 drives PB/PC expansion, potentially through ST2+ T cells, but not by directly impacting B cells”.\nWe have modified the text to specify that B2 cells (rather than total B cells) are not directly impacted by IL-33 (because of the lack of St2 receptor).\nIn addition, we have worked on the text that describes the results from Figure 3 to improve the clarity.\nReviewer #4 (Remarks to the Author):\nThis study provides significant insights into the dual mechanisms by which IL-33 drives plasmablast/plasma cell expansion and disrupts germinal center organization. The results are supported by multiple models (HDD, depletion/blockade, SLE-prone mice, and HDM lung model), and the conclusions are well justified. However, to strengthen the translational impact and mechanistic clarity, I suggest the following revisions:\n1.Although IL-33 impairs Bcl6+ cells and lowers NP2/NP27 ratios, the causal relationship remains inferential. A temporal or cell lineage tracking analysis could strengthen this mechanistic connection.\nYes, it is inferential, but the structure of the spleen is lost, and Tfh cells are lost. As a result, the site where affinity maturation occurs no longer exists. We believe that it is not the loss of Bcl6+ cells alone but rather the loss of the structure itself that lowers the NP2/NP27 ratios.\n2.The increase in dsDNA and αvβ6 autoantibodies is intriguing, but their functional relevance (e.g., tissue deposition or organ damage) is not evaluated. Antibody deposition assays or correlation with proteinuria and inflammation would be highly informative.It would be beneficial if the authors could address this issue in the Discussion section.\nWe measured the ACR ratio in WT mice and did not observe a consistent increase in mice receiving HDD IL-33, suggesting that IL-33 induces pathology in genetically predisposed mice. We have added that information in the result section as well as the discussion:\n“Of note, ACR urine ratio was also measured in compared between WT mice receiving HDD IL-33 or EV. Although, WT mice receiving IL-33 HDD had a trend towards an increase in the ACR ratio, the increase was not as consistent as it was observed in NZB/W mice (Fig. S6B). Consequently, these results show that IL-33 overexpression increases splenic PB/PCs and autoantibodies, induces kidney pathology in genetically predisposed mice and decreases their probability of survival.”\n“Interestingly, kidney pathology triggered by high and sustained levels of IL-33 was more consistent in NZB/W F1 than in WT, highlighting the role of IL-33 accelerating autoimmunity.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made a good faith effort to respond to all the reviewers and their additional experiments have made the manuscript stronger. They have addressed the critiques appropriately.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The revision significantly improved the quality of the paper. I no longer have any major concerns and support publication.\nHowever, one little minor error was still not corrected:\n- Figure legend of Supplemental Fig 1. D: …(% of lived cells) should read (% of live)\nI recommend correcting this prior to publication.", "reviewer_4": "No additional experiments are requested. Please implement the text clarifications and, if readily available, add the simple correlation outputs to the supplement. Upon these minor changes, I would support acceptance."}, "author_response": "We thank the reviewer for this comment.\nWe thank the reviewer for this comment.\nWe thank the reviewer for this comment. We have modified the figure legend accordingly.\nWe thank the reviewer for this comment. We have implemented the text clarifications following the editor suggestions."}]

Decision letter