Single-cell RNA profiling suggests goflikicept-mediated immune modulation in idiopathic recurrent pericarditis

Single-cell RNA profiling suggests goflikicept-mediated immune modulation in idiopathic recurrent pericarditis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-cell RNA profiling suggests goflikicept-mediated immune modulation in idiopathic recurrent pericarditis Alexey Golovkin, Ekaterina Markelova, Elena Ignatieva, Olga Kalinina, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7507778/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Idiopathic recurrent pericarditis (IRP) is a rare autoinflammatory disorder characterized by NLRP3 inflammasome overactivation, resulting in excessive IL-1β and IL-1α production. Although IL-1 blockade shows promise as a therapeutic strategy, the underlying molecular mechanisms remain incompletely understood. We investigated the effect of goflikicept, a novel heterodimeric fusion protein that inhibits both IL-1β and IL-1α, on peripheral blood mononuclear cell (PBMC) transcriptomes from patients with IRP. Single-cell RNA sequencing was used to analyze PBMC subsets and identify treatment response-related transcriptomic signatures. Goflikicept induced temporal transcriptional reprogramming, with a particularly pronounced downregulation of IL-1-related inflammatory pathways in classical monocytes by day 35 of treatment. Furthermore, goflikicept modulated the adaptive immune response, suppressing naïve B cell activity, enhancing circulating plasma cell precursor activity, and significantly altering γδ T cell and mucosal-associated invariant T cell populations. In conclusion, goflikicept effectively normalized dysregulated immune responses in IRP, suggesting a novel therapeutic approach for NLRP3-mediated diseases. This study provides the first single-cell resolution insights into the molecular mechanisms of IL-1 blockade, informing the development of targeted therapies for autoinflammatory conditions. Biological sciences/Molecular biology/Transcriptomics Health sciences/Medical research/Drug development Health sciences/Diseases/Rheumatic diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Idiopathic recurrent pericarditis (IRP) is a rare and complex autoinflammatory disease with a poorly understood pathogenesis. Studies have suggested that triggers such as viruses or cellular debris may overactivate the NLRP3 inflammasome 1 . This overactivation results in the overproduction of interleukin (IL)-1 family proinflammatory cytokines, notably IL-1β, causing systemic symptoms, such as polyserositis, fever, and increased acute phase reactant levels, and IL-1α, suspected to promote chronic local inflammation within the pericardium, driving fibrosis and, ultimately, constriction 1 , 2 . IRP pathogenesis almost certainly involves the innate immune system, with inflammasome activation driving the recruitment of neutrophils and macrophages to the site of injury via cytokines 3 , 4 . Although a direct role of the adaptive immune system has not been conclusively demonstrated, indirect evidence, including the detection of anti-heart and anti-intercalated disc autoantibodies in some adult patients, suggests that adaptive immune mechanisms, typically implicated in autoimmune diseases, may contribute to IRP pathogenesis 4 , 5 , 6 . Because of the limited understanding of IRP pathophysiology, therapeutic strategies remain limited 1 , 7 . The most common approaches involve the use of nonsteroidal anti-inflammatory drugs 1 and colchicine 8 , 9 . Low-dose corticosteroids can also be effective; however, a significant proportion of patients develop corticosteroid dependence 10 , 11 . The use of IL-1 blockers has shown promise 12 , 13 , 14 . The randomized, controlled Anakinra-Treatment of Recurrent Idiopathic Pericarditis trial, which included patients with IRP resistant to colchicine and dependent on corticosteroid therapy, demonstrated the effectiveness of anakinra administered for 60 days 15 . A phase II study with a 24-week follow-up showed that rilonacept (a recombinant IL-1α/β trap) reduced chest pain and C-reactive protein levels after the first injection in 16 patients with recurrent pericarditis on full medical therapy 16 . Goflikicept (RPH-104), a novel heterodimeric fusion protein designed to bind with high affinity to both human IL-1β and IL-1α, is a promising therapeutic approach 17 , 18 . Although goflikicept interacts with Fc receptors (FcγRI, FcγRIIa, FcγRIIb, FcRn, and FcγIIIb), its overall affinity for these receptors is lower than that of human IgG1. In vitro preclinical testing revealed no antibody- or complement-dependent cytotoxicity 19 . Furthermore, a good laboratory practice-compliant 4-week toxicology study in cynomolgus monkeys revealed no other significant safety concerns 19 . In a phase I safety study (NCT02667639), goflikicept was well tolerated by healthy volunteers at doses up to 160 mg. Its favorable pharmacokinetic profile, with a half-life of 10 days, facilitates convenient subcutaneous administration every 2 weeks. Therefore, its potential is being explored in IL-1-driven diseases such as IRP, gout (NCT04067492), and familial Mediterranean fever (NCT05092776), as well as in acute ST-segment elevation myocardial infarction 20 . In a phase II/III study, goflikicept effectively prevented recurrences and maintained remission in patients with IRP, with an acceptable safety profile 18 . Although excessive IL-1 production is a hallmark of IRP, the specific molecular pathways responsible for this dysregulation remain largely unknown. Further, a detailed investigation of the mechanism of action of goflikicept would provide an understanding of how IL-1 inhibition affects the immune system. Specifically, examining the effects of goflikicept on innate and adaptive immune cells may reveal novel therapeutic targets for autoimmune and autoinflammatory conditions characterized by IL-1 overactivity. We investigated the effects of goflikicept on peripheral blood mononuclear cell (PBMC) subset transcriptomes in patients with IRP using single-cell transcriptome analysis. We thus aimed to identify potential molecular mechanisms underlying treatment response and provide novel insights into the fine-tuned regulation of cellular pathway activity in both target and off-target PBMC subsets during disease and treatment. Results Single-cell transcriptome landscape of PBMCs in IRP PBMCs isolated from blood samples of eight patients with IRP were subjected to single-cell RNA sequencing (scRNA-seq) using the 10X platform (Fig. 1a). For one patient, a sample was collected only at baseline (day 0). For three patients, samples were collected on day 7 after receipt of two doses of goflikicept. For the remaining four patients, samples were collected on day 0 and day 35 after receipt of four doses of goflikicept. After quality control, excluding cells of insufficient quality and likely doublets, 86,743 cells were retained for analysis, including 36,012 cells from day 0, 20,761 cells from day 7 of goflikicept treatment, and 29,970 cells from day 35 (Supplementary Fig. 1a,b). To comparatively investigate PBMC heterogeneity in patients with IRP and healthy donors, Harmony was used to integrate our IRP single-cell transcriptome data with publicly available single-cell data from healthy donor PBMCs on the 10X Genomics website 21 . This integrated dataset, comprising 142,375 cells, was subjected to uniform manifold approximation and projection dimensionality reduction, Leiden clustering, and manual and automatic annotation, resulting in the identification of eight major cell types at the first level of annotation: T cells, B cells, monocytes, natural killer (NK) cells, dendritic cells (DCs), neutrophils, platelets, and hematopoietic stem/progenitor cells (Fig. 1b, Supplementary Table 1). Clusters of neutrophils, platelets, and hematopoietic stem/progenitor cells were removed from subsequent analysis because they were not targeted during sample preparation. Monocytes were subclustered into classical, intermediate, and nonclassical monocytes (Fig. 1b, Supplementary Table 1). In the T cell cluster, CD4 + T (T helper [Th] cells), CD8 + T (T cytotoxic cells), CD4 + CD8 + T (double-positive T [DPT] cells), and CD4 low CD8 low T (double-negative T [DNT] cells) were identified (Fig. 1b, Supplementary Table 1). CD4 + T cells comprised four subtypes: T regulatory (Treg, naïve, central memory (CD4 + Tcm), and effector memory (CD4 + Tem), whereas CD8 + T cells comprised three subtypes: naïve, central memory (CD8 + Tcm), and effector memory (CD8 + Tem). Additionally, mucosal-associated invariant T (MAIT) cells, gamma-delta (γδ) T cells, and natural killer T (NKT) cells were identified. These T cell subtypes are consistent with findings in studies on peripheral T cells 22 . NK cells were subclustered into CD56 high CD16 – , CD56 low CD16 + , and proliferating NK cells (Fig. 1b, Supplementary Table 1). CD56 low CD16 + NK cells were subdivided into CD57 + and CD57 – subsets. B cells were subclustered into naïve B cells, intermediate B cells, memory B cells, and plasma cells (Fig.1b, Supplementary Table 1). Naïve, intermediate, and memory B cells were further subdivided into subsets with increased expression of IGKC or IGLC2. DCs were subclustered into type 1 and type 2 - classical (cDC2), plasmacytoid (pDC), AXL + SIGLEC6 + (ASDCs), monocyte-derived (moDCs) and, presumably, highly activated tolerogenic DCs (tolDCs). We next investigated differences in cell type abundance between patient samples from all time points and healthy donors (Fig. 2). No clusters exclusive to IRP samples were identified. The consistent presence of all cell clusters across all time points suggested robust data integration and reproducibility. Compared to healthy donor samples, pre-treatment samples from patients with IRP showed increased abundances of CD8 + T cytotoxic cells and MAIT cells and a decreased abundance of CD4 + T helper cells (Fig. 2a). Comparative subset analysis revealed a significant increase in the abundances of NKT cells and γδ T cells, along with a decrease in those of naïve B and DNT cells in pre-treatment samples from patients compared to healthy donor samples (Fig. 2b, upper row). After 7 days of goflikicept treatment, these differences remained largely consistent, except for DNT cells, which were increased in abundance at this time point. The populations of nonclassical monocytes, NK CD56 bright CD16 – cells, and NK CD56 dim CD16 + cells were expanded, whereas those of intermediate B cells were decreased (Fig. 2b, middle row). After 35 days of goflikicept treatment, the abundance of plasmablasts was increased. The relative abundances of naïve and intermediate B cells, NKT cells, and γδ T cells remained relatively stable compared to those in healthy donor samples (Fig. 2b, bottom row). We used Augur to assess cell type prioritization in the transcriptional response to goflikicept treatment on days 7 and 35. Transcriptional changes were more pronounced on day 35 than on day 7, with an increased number of cell types exhibiting higher Augur scores (Fig. 3). At the first level of annotation, only NK cells showed substantial perturbation on day 7, whereas monocytes, DCs, T cells, and, particularly, B cells, were perturbed on day 35 (Fig. 3a). The second level of annotation revealed that perturbations on day 35 were the most prominent in γδ T cells, MAIT cells, and all major monocyte subsets (Fig. 3b). Detailed analysis revealed perturbations on day 35 in naïve CD4 + T helper cells, naïve B cells, and intermediate and classical monocyte subsets (Fig. 3c). Non-classical monocyte subsets, NK cell subsets, and NKT cells were significantly perturbed after 7 days of goflikicept treatment (Fig. 3c). Goflikicept suppresses IL-1-mediated inflammation in classical monocytes Given the known mechanism of action of goflikicept, we initially focused on the IL-1β response. We compiled a signature of 33 genes associated with IL-1 and inflammation (Supplementary Table 2), which was altered particularly evidently in monocytes (Fig. 4a). The IL-1 signature score was the highest in monocytes from untreated patients with IRP and decreased with treatment to nearly the levels in healthy donor monocytes (Fig. 4b). The core set of downregulated differentially expressed genes (DEGs; |log2FC| ≥ 0.5, adjusted P ≤ 0.05) on day 35 of treatment was shared between classical and intermediate monocytes (Fig. 4c). On day 35 of treatment, most genes in classical monocytes were downregulated, with only one gene (i.e., MTRNR2L12 ) being upregulated (Fig. 4c, d). On day 7 of treatment, the interferon (IFN) response was significantly upregulated in classical monocytes, whereas downregulated responses were weakly expressed and exhibited a heterogeneous pattern. By day 35, the IFN response was reverted, and pathways associated with the inflammatory response, tumor necrosis factor alpha (TNF-α) signaling via nuclear factor kappa beta (NF-κB), and others were downregulated (Fig. 4e). Consistent herewith, PROGENy analysis showed that on day 7, only the Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathway was upregulated. However, by day 35, JAK/STAT pathway upregulation was reduced, concurrent with the downregulation of the NF-κB, TNF-α, and androgen pathways (Fig. 4f). CollecTRI analysis revealed that transcription factor activity was downregulated significantly more strongly on day 35 than on day 7. This substantial downregulation involved key inflammatory and IL-1-related genes, including AIRE , NFKBIB , and RELB (Fig. 4h). Functional enrichment analysis corroborated these findings, revealing a significantly more pronounced effect of goflikicept treatment on day 35 than on day 7. The decrease in activity was associated with a wide range of physiological and pathological processes, predominantly inflammatory in nature and mediated by IL-1 and TNF (Fig. 4e, f). B cell response to goflikicept treatment Among all B cell subsets, naïve B cells and circulating plasma cell precursors had the highest number of DEGs on day 35 compared to pre-treatment (Fig. 5a). The majority of DEGs in naïve B cells, including JUND , RPS29 , CXCR4 and NFKBIA , were downregulated on day 35 (Fig. 5b), whereas plasma cell precursor DEGs were predominantly upregulated (Fig. 5c). Several signaling pathways were downregulated in naïve B cells on day 35, indicating widespread transcriptional reprogramming (Fig. 5d). Specifically, pathways associated with immune regulation and cell survival, such as the B cell survival pathway, nerve growth factor-stimulated transcription, and oncostatin M signaling, were significantly suppressed. In addition, pathways involved in neuroimmune and stress responses, including the corticotropin-releasing hormone signaling pathway and angiotensin II receptor type 1 pathway, were downregulated. The response on day 7 was not prominent in naïve B cells, but functional analysis revealed that several pro-inflammatory pathways were downregulated, including TNF-α signaling via NF-κB, whereas the interferon-α response was slightly upregulated. Concurrently, PROGENy-defined pathway activity analysis indicated the downregulation of TGF-β, hypoxia, and TNF-α, and upregulation of JAK-STAT. In plasma cells precursors, the response also was not prominent on day 7, but rather concentrated on day 35 (Fig. 5a). On day 35, eight genes were significantly upregulated ( IGKC , PPIB , CLIC1 , SEC61G , SEC61B , S100A10 , SUB1 , and SCD99 ), and two were downregulated ( ARID1A and CDV3 ) (Fig. 5c). Functional analysis revealed that the top upregulated pathways were associated with active protein processing and secretion, including signal recognition particle (SRP)-dependent cotranslational targeting to the endoplasmic reticulum (ER), post-translational protein targeting to membranes, and tail-anchored protein insertion into the ER membrane (Fig. 5e). Additional enriched pathways had significance in immune regulation and homeostasis, such as prostaglandin synthesis and regulation, chloride transport, SMAD protein signal transduction, tissue homeostasis, and homotypic cell–cell adhesion. The top downregulated pathways included CD22-mediated B cell receptor (BCR) regulation and antigen-mediated activation of BCRs leading to the generation of second messengers. T cell response to goflikicept treatment T cell subset responses to goflikicept treatment were observed on day 35 following treatment. Specifically, genes in T helper, T cytotoxic, and T regulatory cell subsets were strongly downregulated. Gene upregulation was less frequent and primarily observed in NKT, MAIT, CD4 + CD8 + T, and DNT cells. The most pronounced downregulation, affecting a relatively large number of genes, was detected in T regulatory cells, CM and EM T helper cells, CM T cytotoxic cells, MAIT cells, and γδ T cells (Fig. 6a). As γδ T and MAIT cells demonstrated the most pronounced changes among T cell subtypes on day 35 (Figs. 3, 6), we proceeded to investigate this relatively rare T cell subset in greater detail. Response patterns were similar between γδ T and MAIT cells but more pronounced in the latter. DEGs were found only on day 35 compared to pre-treatment, which corresponds with Augur prioritization (Fig. 6a). On day 35, MAIT cells exhibited significant downregulation of eight genes ( JUND , FOS , DUSP1 , NFKBIA , JUN , S100A9 , TSC22D3 , and JUNB ) (Fig. 6b), whereas in γδ T cells, five genes were downregulated ( JUN , DUSP1 , JUND , FOS , and NFKBIA ) (Fig. 6c). In γδ T cells, only the TNF-α pathway was downregulated on both days 7 and 35 . Hallmark gene set enrichment analysis revealed the downregulation of numerous pathways related to cell survival, cytokine production, cell interaction, and migration, including NF-κB/AP-1-induced apoptosis, B cell survival, Toll signaling, CD40 signaling, and CCR5 signaling in γδ T and MAIT cells on day 35 of therapy (Fig. 6d, e). Positioning of goflikicept among known drugs To characterize the molecular footprint of goflikicept and position it relative to existing pharmacological agents, we employed deep learning perturbation modeling framework using the Conditional Perturbation Autoencoder (CPA) — a deep generative neural network trained to learn drug-induced transcriptional responses. CPA models the effects of chemical perturbations in a disentangled latent space, allowing the simulation of unseen drug-dose combinations and comparison across compounds in a standardized embedding (Fig. 7). First, we trained a model on a diverse set of scRNA-seq (sci-Plex3, three cell lines treated with 188 small-molecule drugs) and bulk RNA-seq (LINCS1000) datasets. The trained model captured the effects of various drugs, as reflected in their gene expression profiles, within a latent space of drug perturbations. Next, we clustered this latent space to identify groups of drugs exhibiting similar effects. Strikingly, goflikicept clustered alongside tyrosine kinase inhibitors, non-steroidal anti-inflammatory drugs, glucocorticosteroids, and anticancer drugs, indicating potential similarities in their mechanisms of action or effects. Discussion To elucidate the aspects of IRP pathogenesis, we analyzed the single-cell transcriptome landscape of PBMCs from patients with IRP in comparison with those of healthy individuals using publicly available scRNA-seq data. We found no significant differences in the proportions of innate immune cells, including monocytes and their subsets. However, we observed intriguing differences in adaptive immune cell subsets. Specifically, increased levels of cells with cytotoxic activity (CD8 + cytotoxic T and NKT cells) in patients with IRP suggested their potential implication in the pathogenesis of pericarditis, which has not been previously reported to the best of our knowledge (Fig. 2). Moreover, the levels of CD4 + T helper, DNT, and naïve B cells were reduced in patient samples prior to treatment compared to those in healthy control samples. This finding challenges previous suggestions regarding the direct role of antibody production in IRP pathogenesis based on the presence of autoantibodies in patients with IRP. However, the observed decrease in circulating naïve B cells, potentially reflecting their recruitment to inflamed tissues and secondary lymphoid organs where they differentiate into effector cells, indicates that adaptive immune mechanisms may contribute to disease development. Notably, another study reported findings similar to ours, observing a reduction in naïve T cells alongside an increase in activated CD8 + T effector cells in patients with acute pericarditis, which the authors interpreted as indicative of a more active inflammatory response 23 . Finally, we noted a persistent increase in γδ T cells, both before treatment and throughout the observation period, when compared with their levels in controls. Characterized by their unique ability to bridge innate and adaptive immunity, γδ T cells (0.5%–5%) expressing γδ T cell receptor (TCR) 24 function as both immediate effector cells and regulators of B cell activation 25 . The ability of γδ T cells to initiate inflammation without specific antigen recognition may explain their role in driving autoimmune diseases, such as psoriasis, multiple sclerosis, and primary sclerosing cholangitis 26 . Goflikicept treatment significantly altered the cellular transcriptome even after 7 days. Populations of nonclassical monocytes and NK cell subsets were expanded, whereas that of intermediate B cells was decreased. Notably, circulating plasma cell precursors were significantly increased on day 35 post-treatment compared to the levels in healthy donors. Interestingly, Augur response prioritization analysis revealed a minimal influence of the treatment on cell subsets on day 7, primarily affecting cells with cytotoxic activity (NK cell subsets, NKT cells, and CD8 + T cells). However, on day 35 of treatment, cell responses were more prominent. As predicted, we observed changes in the activity of innate immune cells (monocyte and DC subsets), the main responders to IL-1 blockade. The influence of goflikicept on the activity of CD4 + T helper cell subsets, MAIT cells, and naïve B cells was intriguing. Notably, MAIT cells and γδ T cells exhibited the most pronounced responses to treatment 27 . Although MAIT cells have been implicated in autoimmune diseases, the observed response may reflect a drug-hypersensitivity reaction, as has been reported for diclofenac (a non-steroidal anti-inflammatory drug) 28 , 3-formylsalicylic acid, and 2,4-diamino-6-formylpteridine (a methotrexate derivative) 28 . To characterize the activity of goflikicept, we analyzed the expression of 33 genes related to IL-1 signaling (Supplementary Table 2). As expected, monocytes exhibited the highest expression levels of IL-1-related genes. Furthermore, their expression was higher in patients than in healthy donors, and treatment with goflikicept significantly suppressed their expression, predominantly in monocyte subsets. Classical monocytes were the main responders among all monocyte subsets, and the treatment downregulated numerous genes, particularly on day 35. The treatment response on day 7 was not as profound. In particular, although classical monocytes exhibited upregulation of IFN-related responses on day 7, all pathways were downregulated on day 35. As predicted, TNF-α signaling, inflammatory responses, and IL-6/STAT3 signaling were downregulated in classical monocytes following treatment with goflikicept. Notably, goflikicept decreased the expression of both key cytokine production-related transcription factors (NF-κB, JAK/STAT) and AIRE, which regulates autoantigen expression and negative selection of autoreactive T cells in the thymus and plays a major role in peripheral tolerance 29 . As antibody-producing adaptive immune cells, B cells were not initially predicted to be primary responders to the treatment. Nevertheless, both naïve B cells and circulating plasma cell precursors had large numbers of DEGs on day 35. Interestingly, goflikicept downregulated genes in naïve B cells, primarily those related to TNF-α signaling via NF-κB. Conversely, in plasma cell precursors, genes were predominantly upregulated. We speculate that the diminished activity of naïve B cells may reflect a reduced propensity for differentiation and subsequent antibody secretion. Conversely, the increased activity observed in plasma cell precursors following IL-1 blockade may reflect an ongoing process of antibody generation. T cell responses to goflikicept were assessed based on their cytokine production activity and their roles in immune response polarization and autoimmunity. By day 35, most of the major T cell subsets exhibited downregulated gene expression. The most prominent responders, exhibiting downregulation of five or more genes, were γδ T cells, effector memory T helper cells, central memory T cytotoxic cells, and MAIT cells. MAIT cells, which are highly abundant in humans (up to 10% of blood T cells 30 and 45% of liver T cells 31 ), have garnered intense research interest because of their potential role in immunity and as a therapeutic target 27 . Although their precise mechanisms are still being elucidated, MAIT cells are known to be activated through multiple pathways, including TCR-mediated antigen recognition and TCR-independent mechanisms involving inflammatory stimuli and cytokines 27 . Specifically, ligands for TLR2, TLR3, TLR4, TLR5, TLR8, and TLR9 can promote MAIT cell activation, and cytokines, such as IL-12, IL-18, IL-1β, and IL-7, can stimulate MAIT cells through their respective receptors 32 , 33 . Efficient cytokine-dependent activation of MAIT cells often requires a combination of at least two cytokines; for example, IL-12 and IL-18 synergistically promote strong TCR-independent production of IFN-γ, TNF, and granzyme B, whereas neither cytokine alone is sufficient 27 , 30 , 31 , 34 . Finally, antigen-activated human MAIT cells upregulate the B cell-stimulatory molecule CD40L and promote DC maturation and IL-12 production 35 . Although the downregulation of NFKBIA, JUN, JUND , and FOS in MAIT cells after goflikicept treatment suggests the possibility of enhanced apoptosis in these cells upon TCR-dependent stimulation, NF-κB activation, even when followed by NFKBIA suppression, can paradoxically lead to increased cytokine production and proliferation 36 . In their inactive state, γδ T cells have a migration profile is similar to that of innate immune cells; however, upon CCR7 expression, they migrate to lymph nodes to assist B-cell activation 37 , 25 . They can produce IFN-γ and TNF-α in response to intracellular pathogens, IL-4, IL-5, and IL-13 during parasite immune responses and IL-17 in defense against bacteria and fungi 26 , 38 . Human IL-17A-producing γδ T cells are of particular interest, as they can be activated independently of TCR ligation and contribute significantly to IL-17A production in the context of autoimmune diseases 39 . These cells accumulate in inflamed tissues and can be activated by various factors, including TCR agonists, IL-1β, IL-6, IL-23, and TGF-β, promoting a Th17 cytokine profile 40 . After treatment with goflikicept, the most pronounced γδ T cell response occurred on day 35. Their suppression may be attributed to the reduction in or absence of IL-1 signaling. Notably, the most significantly downregulated genes ( JUN, JUND, FOS , and NFKBIA ) were the same as those observed in MAIT cells, suggesting a similar functional effect in both cell types. Taken together, our findings indicate that IL-1 blockade exerts its most pronounced effects on monocyte, B cell, MAIT cell, and γδ T cell subsets. These effects were primarily assessed based on clinical outcomes and corresponding transcriptomic changes. Nevertheless, the clinical utility of goflikicept may also be governed by its pharmacological properties in relation to established drug categories. The clustering of goflikicept alongside tyrosine kinase inhibitors, non-steroidal anti-inflammatory drugs, and glucocorticosteroids suggests shared mechanisms of action and, consequently, its potential for broad application in clinical practice. The lack of an internal healthy control group limited our ability to fully characterize the baseline immune landscape in IRP and forced us to rely on publicly available data for comparisons; the inclusion of such a control group would have facilitated a more comprehensive understanding of disease pathogenesis. In conclusion, this study provided the first comprehensive single-cell analysis of PBMC dynamics in patients with IRP undergoing treatment with goflikicept, delineating its cell type-specific effects and revealing its capacity to induce sustained anti-inflammatory reprogramming. Although untreated patients with IRP exhibited a PBMC composition broadly similar to that of healthy controls, distinct differences in cell type abundance (e.g., monocytes and other populations) were observed. IL-1 blockade initially elicited a weak anti-inflammatory response and IFN-α response in certain cell types on day 7 of therapy, followed by pronounced anti-inflammatory responses on day 35, particularly in monocytes, characterized by a marked reduction in IL-1β-related signatures. In addition to the prominent effects on monocytes, we observed significant transcriptome changes in B cell subsets (with gene downregulation in naïve B cells and gene upregulation in circulating plasma cell precursors), as well as in MAIT and γδ T cells, suggesting broader effects on adaptive and innate immune responses. Notably, goflikicept clustered pharmacologically with IL-1 inhibitors and tyrosine kinase activity modulators, suggesting a dual targeting of inflammatory pathways. These findings highlight the central role of monocytes in resolving inflammation in IRP, while implicating other immune cell subsets in the therapeutic response. Methods Patient characteristics In total, eight patients with IRP were enrolled in this study (Fig. 1). The diagnosis of IRP was based on 2015 European Society of Cardiology guidelines on the management of pericardial diseases 41 . Given that IRP is a diagnosis of exclusion, we ruled out chronic infections (including tuberculosis), systemic rheumatic diseases, encompassing both autoimmune (e.g., connective tissue diseases) and autoinflammatory (e.g., adult-onset Still’s disease, Schnitzler syndrome) disorders, and monogenic autoinflammatory diseases (e.g., familial Mediterranean fever, TNFRSF1A-associated periodic syndrome, mevalonate kinase deficiency, and NLRP3-associated diseases). The median age of the eight patients was 49.5 years (interquartile range, 43.75–55.25, standard deviation = 14.1). To assess disease activity, we used a modified Pouchot score 42 to evaluate systemic symptoms associated with IRP. The main characteristics of the patients and samples included in the study are presented in Table 1. The eight patients with IRP showed a poor response to colchicine treatment and subsequently received biologic therapy. Seven patients (P2–P8) received goflikicept during the clinical trial NCT04692766 18 . We collected samples (during relapse and remission) from patients P2–P5, one sample during relapse from patient P1, and one sample during remission from patients P6–P8. The following criteria were used to define remission: (1) chest pain score ≤3 on the numeric rating scale, (2) C-reactive protein concentration ≤5 mg/L, and (3) absent or mild pericardial effusion (<10 mm) on echocardiography. Patients were considered responders on day 7 or day 35 of therapy if all three criteria were met. Patients were not selected based on any presumed likelihood of treatment response. Nevertheless, all patients who received goflikicept in this study responded to therapy (Fig. 1). This study was conducted in accordance with the Declaration of Helsinki and was approved by the local Ethics Committee of the Almazov National Medical Research Center (protocol number 28, dated 12 February 2018). Written informed consent was obtained from all study participants. PBMC isolation Blood samples were collected from one patient before treatment (day 0), from three patients on day 7 (post two-dose goflikicept), and from four patients on both days 0 and 35 (post four-dose goflikicept), for a total of 12 samples (Fig. 1a, Table 1). Peripheral vein blood samples were collected in vacuum tubes with K 3 EDTA (Vacutest, KIMA, Arzergrande, Italy). PBMCs were isolated using SepMate TM -15 tubes (StemCell Technologies, Vancouver, CA) and Ficoll-Paque PLUS density gradient medium (Cytiva, Marlborough, MA, USA) according to the manufacturer’s recommendations. In brief, 4 mL of density gradient medium was carefully transferred into a SepMate TM -15 tube. The diluted blood sample (3 mL of blood and 3 mL of PBS) was pipetted down the side of the tube. Tubes were centrifuged at 1,200 ´ g , room temperature for 10 min. The top layer containing the enriched PBMCs was collected, and the cells were washed with PBS and centrifuged at 300 ´ g for 8 min. scRNA-seq Single-cell capturing and library construction were performed using the Chromium Controller and Single Cell 3′ Reagent Kit v.3.1 (10 × Genomics) according to the manufacturer’s protocol and as previously described 43 . In brief, approximately 100,000 PBMCs per sample were loaded onto 10x Genomics Single Fluidics chips to generate gel beads in emulsion, which contained oligonucleotide-barcoded beads for single-cell indexing and unique molecular identifiers (UMIs) for transcript quantification. Uniquely barcoded RNAs were reverse transcribed followed by cDNA amplification in a Veriti™ 96-Well Thermal Cycler (Applied Biosystems). Libraries were barcoded with Chromium TM i7 Multiplex Kit dual indexes, pooled in equimolar ratios, and sequenced in-house (Almazov National Medical Research Centre) on an Illumina NextSeq 2000 system. Single-cell capturing and library construction were performed using the Chromium Single Cell 3¢ Reagent Kits v3 according to the manufacturer’s instructions (10x Genomics). Raw sequencing reads were aligned to GRCh38 (GENCODE v32/Ensembl 98), and feature-barcode matrices were generated using Cell Ranger v.6.1.2. Data processing To process the scRNA-seq data, we mostly followed the standard scRNA-seq analysis pipeline implemented in Scanpy 44 (v.1.10.1), a Python package for single-cell analysis, using Python (v.3.12). We also used scRNA-seq data of PBMCs from 16 healthy donors from the 10x Genomics official website (https://www.10xgenomics.com/). First, we removed genes expressed in fewer than two cells and filtered out low-quality cells based on the number of detected genes, mitochondrial UMI fraction, and total UMI count. We retained cells with gene counts between 400 and 3,500 and mitochondrial UMIs <15%. Subsequently, we used Scrublet in Scanpy to identify doublets among the PBMCs from the patients and healthy donors. We used the default parameters for scanpy.pp.scrublet (i.e., sim_doublet_ratio = 2, synthetic_doublet_umi_subsampling = 1, n_prin_comps = 30), set random_state to 42, and threshold to 0.25. We thus detected 3,079 doublets in the patients with IRP, and 604 in the healthy donors. Following filtering, the data were normalized to the median total count and log1p-transformed. The new IRP dataset and public healthy donor (N = 16) dataset were integrated using HarmonyPy (v.0.0.10) 45 over dataset, patient, and condition batches. Cell type annotation Using the integrated data, we performed unsupervised clustering with the Leiden clustering algorithm 46 . Highly expressed genes within each subset were identified using the Wilcoxon rank test implemented in Scanpy. Automatic cell type annotation was performed using Azimuth and CellTypist and compared with manual annotation. To annotate cell types with their type and function, we ran subclustering for each of the cell type from level 1 (the broadest levels). We subclustered monocytes, T cells, B cells, DCs, and NK cells at a resolution of 0.5, −3, −1.5, −0.3, and −0.5, respectively (Fig. 1b). The expression markers used to differentiate the cell types in the final annotation are listed in Supplementary Table 1. During annotation, we identified two small subclusters of classical monocytes that expressed the markers of classical monocytes along with platelet or T cell markers. These two clusters had a relatively high mean doublet score (>0.1) and were deemed to be clusters of doublets or aggregated cells and therefore excluded from analysis. Similarly, a subcluster of B cells that exhibited mixed expression of naïve B cell and T cells markers and had a relatively high mean doublet score (>0.2) was removed. Differential abundance analysis Differential abundance analysis was performed using scCODA algorithm available in the package pertpy (v.0.10.0) 47 . The analysis was performed by iteratively using each cell type as a reference, averaging the resulting estimates, and retaining only those cell types that met a false discovery rate (FDR) threshold of < 0.2. Response prioritization Response prioritization across cell types was conducted using Augur 48 , which prioritizes cell types by their transcriptional response to experimental perturbations in single-cell data by training a machine learning model to predict experimental conditions (e.g., treatment vs. control) within each cell type. Cell types for which labels are more accurately predicted are considered more responsive. We performed differential prioritization by applying permutation test to identify cell types with statistically significant differences in AUC between two prioritization time points (days 7 and 35 of treatment) vs. pre-treatment. Differential expression analysis and functional analysis Differential gene expression analysis of goflikicept-treated compared to untreated conditions was performed using MAST-RE 49 . We refrained from comparing expression profiles between the healthy control and IRP conditions (untreated or treated) because of the inability to exclude batch effects from different datasets while preserving authentic biological information. We did not identify any significant sex, age or weight -related differences across untreated or treated IRP patients’ samples. Gene set enrichment analysis was based on a univariate linear model using decoupler 50 , and P -values were obtained using Student’s t -test and adjusted using the Benjamini–Hochberg correction. Genes with FDR < 0.05 were deemed significant. Gene sets available from MSigDB were used, from such databases as KEGG, Reactome, BioCarta, WikiPathways, Hallmark, and Gene Onthology. Pathway activity inference was performed using PROGENy with a multivariate linear model (mlm) in decoupler 50 and Student’s t -test. Transcriptional factor scoring was performed based on the CollecTri network, using a univariate linear model in decoupler and Student’s t -test. Perturbation modeling and exploration of perturbation space To analyze drug-induced transcriptional responses, we employed the Conditional Perturbation Autoencoder (CPA) 51 , a deep generative neural network that learns the latent representation of cell state changes under chemical perturbations. CPA allows simulation of unseen drug-dose-time combinations and enables clustering of compounds based on their learned transcriptional signatures. By training the model on our data and the publicly available sci-Plex3 dataset 52 , which includes approximately 650,000 cells from three cell lines treated with 188 compounds, we generated a latent embedding space that captures complex, nonlinear relationships between compounds and cell states. To optimize model performance, hyperparameters such as learning rate, latent dimension size, and regularization weights were tuned using Optuna. The predictive accuracy of the CPA model was assessed using mean squared error (MSE) between observed and reconstructed gene expression profiles. Embeddings were visualized with UMAP and clustered using Leiden algorithm to assess drug similarity in latent space. CPA’s ability to recover known pharmacological clusters (e.g., HDAC inhibitors) validates its biological relevance and predictive utility. This deep learning framework enabled us to position goflikicept within a landscape of reference drugs, revealing mechanistic similarities to anti-inflammatory and immunomodulatory compounds. Declarations Informed consent was obtained from the participants for publication of the clinical case details and any accompanying data Data availability We used a public cRNA-seq dataset from PBMCs from 16 healthy donors downloaded from the 10X Genomics official website [https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_NGSC3_aggr] as control data. The GRCh38 human reference genome used for the sequencing data alignment is available on the 10X Genomics official website [https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest] Author Contributions GA – design, acquisition, analysis, interpretation of the data, manuscript preparation; ME - analysis, interpretation of the data, manuscript preparation; IE, KO, IO and KI - acquisition, analysis; AD, MY, MiA, SD, RV, VY – analysis of the data, interpretation, critical manuscript reading; MO, MV, MaA – clinical design, patients curation, interpretation of the data; LY, SM – design, interpretation of the data; KA, SE – design, conceptualization, study management. Competing interests The authors declare the following competing interests: Lavrovsky Y. and Samsonov M. are employees of RPharm company, manufacturing goflikicept. The remaining authors declare no competing interests. Funding This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2022-301) Correspondence and requests for materials should be addressed to GA ( [email protected] ). References Brucato, A., et al. Recurrent pericarditis: still idiopathic? The pros and cons of a well-honoured term. Intern Emerg Med 13 , 839–844 (2018). Adler, Y., et al. 2015 ESC Guidelines for the diagnosis and management of pericardial diseases: The Task Force for the Diagnosis and Management of Pericardial Diseases of the European Society of Cardiology (ESC) endorsed by: The European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J 36 , 2921–2964 (2015). Mauro, A.G., et al. The role of NLRP3 inflammasome in pericarditis: potential for therapeutic approaches. JACC Basic to Transl Sci 6 , 137–150 (2021). Weber, A., Wasiliew, P. & Kracht, M. Interleukin-1 (IL-1) pathway. Sci Signal 3 , 1–7 (2010). Buckley, L.F., Viscusi, M.M, Van Tassell, B.W. & Abbate, A. Interleukin-1 blockade for the treatment of pericarditis. Eur Hear J Cardiovasc Pharmacother 4 , 46–53 (2018). Abadie, B.Q. & Cremer, P.C. Interleukin-1 antagonists for the treatment of recurrent pericarditis. BioDrugs 36 , 459–472 (2022). Dmitrieva, A., et al. Nonclinical characterization of the novel IL-1 heterodimeric fusion protein RPH-104. Immunome Res 14 , A7 (2018). Myachikova, V.Y., et al. Treatment of idiopathic recurrent pericarditis with goflikicept. J Am Coll Cardiol 82 , 30–40 (2023). Safety, tolerability, pharmacokinetics and pharmacodynamics evaluation of RPH-104 administered at different doses to patients with acute gout attack. Case Med Res (2019). Efficacy and safety of RPH-104 for resolution and prevention of recurring attacks in adult subjects with familial mediterranean fever with resistance to or intolerance of colchicine [Internet]. Abbate, A., et al. Results of international, double-blind, randomized, placebo-controlled, phase IIa study of interleukin-1 blockade with RPH-104 (goflikicept) in patients with ST-segment-elevation myocardial infarction (STEMI). Circulation 150 , 580–582 (2024). International, double blind, randomized, placebo-controlled study to evaluate the effect on parameters of systemic inflammation and disease outcomes and safety of RPH-104 in subjects with acute ST-elevation myocardial infarction. 2020. Pouchot, J., et al. Adult Stillʼs disease: manifestations, disease course, and outcome in 62 patients. Medicine (Baltimore) 70 , 118–136 (1991). Hu, B., et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 20 , 6212–622 (2014). Ivanova, O.A., et al. LMNA R482L mutation causes impairments in C2C12 myoblasts subpopulations, alterations in metabolic reprogramming during differentiation, and oxidative stress. Sci Rep 15 , 5358 (2025). Wolf, F.A., Angerer, P. & Theis, F.J. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol 19 , 15 (2018). Korsunsky, I., et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16 , 1289–1296 (2019). Heumos, L., et al. Pertpy: an end-to-end framework for perturbation analysis. Preprint at https://www.biorxiv.org/content/10.1101/2024.08.04.606516v1.full (2024). Squair, J.W., Skinnider, M.A., Gautier, M., Foster, L.J. & Courtine, G. Prioritization of cell types responsive to biological perturbations in single-cell data with Augur. N at Protoc 16 , 3836–3673 (2021). Zimmerman, K.D., Espeland, M.A. & Langefeld, C.D. A practical solution to pseudoreplication bias in single-cell studies. Nat Commun 12 , 738 (2021). Badia-I-Mompel, P., et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Kuijjer ML, editor. Bioinform Adv 2 vbac016 (2022) Lotfollahi, M., et al. Predicting cellular responses to complex perturbations in high‐throughput screens. Mol Syst Biol 19 , e11517 (2023). Srivatsan, S.R., et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367 , 45–51 (2020). Zheng, G.X.Y., et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun 8 , 14049 (2017). Bonaventura, A. & Montecucco, F. Inflammation and pericarditis: Are neutrophils actors behind the scenes? J Cell Physiol 234 , 5390–5398 (2019). Cacoub, P. & Marques, C. Acute recurrent pericarditis: from pathophysiology towards new treatment strategy. Heart 106 , 1046–1051 (2020). Caforio, A.L.P., et al. Anti-heart and anti-intercalated disk autoantibodies: Evidence for autoimmunity in idiopathic recurrent acute pericarditis. Heart 96 , 779–784 (2010). Lopalco, G., et al. The autoinflammatory side of recurrent pericarditis: enlightening the pathogenesis for a more rational treatment. Trends Cardiovasc Med 31 , 265–274 (2021). Lazaros, G., et al. The role of the immunogenetic background in the development and recurrence of acute idiopathic pericarditis. Cardiology 118 , 55–62 (2011). Vantourout, P. & Hayday, A. Six-of-the-best: unique contributions of γδ T cells to immunology. Nat Rev Immunol 13 , 88–100 (2013). Brandes, M., et al. Flexible migration program regulates γδ T-cell involvement in humoral immunity. Blood 102 , 3693–3701 (2003). Shiromizu, C.M. & Jancic, C.C. γδ T lymphocytes: An effector cell in autoimmunity and infection. Front Immunol 9 , 1–8 (2018). Chiabrando, J.G., et al. Management of acute and recurrent pericarditis: JACC state-of-the-art review. J Am Coll Cardiol 75 , 76–92 (2020). Chhabra, L. & Spodick, D.H. Colchicine for pericarditis. Am J Heal Pharm 71 , 2012–2013 (2014). Imazio, M. Colchicine for pericarditis. Trends Cardiovasc Med 25 , 129–136 (2015). Imazio, M., et al. Corticosteroids for recurrent pericarditis: high versus low doses: a nonrandomized observation. Circulation 118 , 667–71 (2008). Farand, P. Acute and recurring pericarditis: more colchicine, less corticosteroids. World J Cardiol 2 , 403 (2010). Dagan, A., Langevitz, P., Shoenfeld, Y. & Shovman, O. Anakinra in idiopathic recurrent pericarditis refractory to immunosuppressive therapy; a preliminary experience in seven patients. Autoimmun Rev 18 , 627–631 (2019). Feldman, J.M., Frishman, W.H. & Aronow, W.S. Goflikicept and related IL-1 inhibitors in the treatment of recurrent pericarditis. Cardiol Rev 10.1097/CRD.0000000000000704 (2024). Brucato, A., et al. Effect of anakinra on recurrent pericarditis among patients with colchicine resistance and corticosteroid dependence: the AIRTRIP randomized clinical trial. JAMA 316 , 1906–1912 (2016). Klein, A., et al. Rilonacept in recurrent pericarditis: first efficacy and safety data from an ongoing phase 2 pilot clinical trial. J Am Coll Cardiol 73 , 1261 (2019). Jin, Z.S. & Wang, H.Y. Small molecules RSC001 use in breast cancer model. Immunome Res 14 (2018). Godfrey, D.I., Koay, H.F., McCluskey, J. & Gherardin, N.A. The biology and functional importance of MAIT cells. Nat Immunol 20 , 1110–1128 (2019). Keller, A.N., et al. Drugs and drug-like molecules can modulate the function of mucosal-associated invariant T cells. Nat Immunol 18 , 402–411 (2017). Crossland, K.L., et al. AIRE is not essential for the induction of human tolerogenic dendritic cells. Autoimmunity 49 , 211–218 (2016). Gherardin, N.A., et al. Human blood MAIT cell subsets defined using MR1 tetramers. Immunol Cell Biol 96 , 507–525 (2018). Dusseaux, M., et al. Human MAIT cells are xenobiotic-resistant, tissue-targeted, CD161 hi IL-17-secreting T cells. Blood 117 , 1250–1259 (2011). Ussher, J.E., et al. CD161++CD8+ T cells, including the MAIT cell subset, are specifically activated by IL-12+IL-18 in a TCR-independent manner. Eur J Immunol 44 , 195–203 (2014). van Wilgenburg, B., et al. MAIT cells are activated during human viral infections. Nat Commun 7 , 11653 (2016). Kurioka, A., et al. Shared and distinct phenotypes and functions of human cD161++ Vα7.2+ T cell subsets. Front Immunol 8 , 1031 (2017). Salio, M., et al. Activation of human mucosal-associated invariant T cells induces CD40L-dependent maturation of monocyte-derived and primary dendritic cells. J Immunol 199 , 2631–2638 (2017). Baumann, S., et al. An unexpected role for FosB in activation-induced cell death of T cells. Oncogene 22 , 1333–1339 (2003). van der Houwen, T.B., van Hagen, P.M. & van Laar, J.A.M. Immunopathogenesis of Behçet’s disease and treatment modalities. Semin Arthritis Rheum 52 , 1956 (2022). Harly, C., Peigné, C.M. & Scotet, E. Molecules and mechanisms implicated in the peculiar antigenic activation process of human Vγ9Vδ2 T cells. Front Immunol 6 , 657 (2015). Papotto, P.H., Reinhardt, A., Prinz, I. & Silva-Santos, B. Innately versatile: γδ17 T cells in inflammatory and autoimmune diseases. J Autoimmun 87 , 26–37 (2018). Caccamo, N., et al. Differentiation, phenotype, and function of interleukin-17-producing human vγ9vδ2 T cells. Blood 118 , 129–38 (2011). Table Table 1. Characteristics of the patients and samples analyzed in the present study. Patient ID Sample ID Sex Age, years Weight, kg Days of goflikicept therapy Total goflikicept dosage, mg Dose, mg/kg Modified Pouchot activity score C-reactive protein, mg/L Ferritin, ng/mL IL-6, ng/mL IL1RA, ng/mL P1 SC8 M 70 85 0 4 1.80 ND ND ND P2 SC5 M 37 89 0 0 0 4 10.6 93.23 1.89 832 P2 SC9 M 37 89 35 320 3.6 0 1.3 87.72 2.34 30 P3 SC3 M 74 83 0 0 0 2 3.5 96.72 4.21 966 P3 SC11 M 74 83 35 320 3.9 1 0.7 58.28 3.69 30 P4 SC10 F 56 80 0 0 0 1 1.6 75.51 2.84 554 P4 SC14 F 56 80 35 400 5.0 0 0.4 47.15 0.81 30 P5 SC12 F 55 75 0 0 0 3 2 81.23 3.97 2319 P5 SC13 F 55 75 35 320 4.3 1 1.6 52.97 2.74 306 P6 SC4 F 50 109 7 160 1.5 0 0.7 13.89 3.82 30 P7 SC7 M 49 90 7 160 1.8 0 2.3 289.60 4.4 30 P8 SC2 F 26 84 7 160 1.9 0 0.9 26.85 1.21 30 ND: no data Additional Declarations Yes there is potential Competing Interest. Lavrovsky Y. and Samsonov M. are employees of RPharm company, manufacturing goflikicept. The remaining authors declare no competing interests. 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11:21:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7507778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7507778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91960917,"identity":"9eefbc25-3991-4541-9ac8-26d006de35dd","added_by":"auto","created_at":"2025-09-23 07:49:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptome analysis of PBMCs from patients with IRP. a.\u003c/strong\u003e Study design. Blood samples were collected from eight patients with IRP at three time points (days 0, 7, and 35). \u003cstrong\u003eb.\u003c/strong\u003e Cell type annotation revealing the transcriptional landscape of integrated PBMCs from patients with IRP and public healthy control data. Processed transcription data were used, and uMAP projection was performed using Scanpy v1.10.1.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/d6f2b78441b621bf782f1b5c.png"},{"id":91960915,"identity":"b4b8239a-796f-4833-9493-301c31822d44","added_by":"auto","created_at":"2025-09-23 07:49:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance analysis of PBMCs in patients with IRP and healthy donors.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e Mean proportions of PBMC types during goflikicept treatment of IRP and in healthy donors. \u003cstrong\u003eb.\u003c/strong\u003e Comparative analysis of PBMCs of healthy donors and patients with IRP before goflikicept treatment (upper panel), patients with IRP after 7 days of goflikicept treatment (middle panel), and patients with IRP after 35 days of goflikicept treatment (bottom panel). Log fold change of averaged expression is shown.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ASDC, AXL\u003csup\u003e+ \u003c/sup\u003eSIGLEC6\u003csup\u003e+\u003c/sup\u003e dendritic cells; MAIT, mucosal-associated invariant T cells; NK, natural killer cells; DC, dendritic cells; DNT, double-negative T cells; moDC, monocyte-derived DCs; pDC, plasmacytoid DCs; tolDC, tolerogenic DCs; cDC, classical DCs\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/6952427727ab4ee4bf6e1777.png"},{"id":91960916,"identity":"023d6df1-f6f8-46de-8e8f-a935cf5bff65","added_by":"auto","created_at":"2025-09-23 07:49:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell type response prioritization analysis in patients with IRP after goflikicept treatment. \u003c/strong\u003eAugur scores on day 7 or 35 \u003cem\u003evs\u003c/em\u003e. pre-treatment are shown. \u003cstrong\u003ea.\u003c/strong\u003e Annotation level 1 (most generic cell type annotation [e.g., T cells, B cells, etc.]). \u003cstrong\u003eb.\u003c/strong\u003e Annotation level 2 (more detailed cell type and subtype annotation [e.g., CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, etc.]). \u003cstrong\u003ec.\u003c/strong\u003e Annotation level 3 (most detailed cell type annotation). Detailed information regarding the markers used for cell type and subtype annotation is provided in Supplementary Table 1. Only those cell types that passed the threshold of adjusted\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.05 are shown.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/9503d11a85bb0b07edcda958.png"},{"id":91960920,"identity":"abfda8a6-5bda-47e1-8a30-37a64878649f","added_by":"auto","created_at":"2025-09-23 07:49:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonocyte transcriptional response to goflikicept therapy.\u003c/strong\u003e \u003cstrong\u003ea. \u003c/strong\u003eUniform manifold approximation analysis of the expression of 33 genes related to IL-1 inflammation. UMAP projection was performed using Scanpy v1.10.1. The results are presented sequentially in patients before goflikicept treatment, 7 days after treatment, 35 days after treatment, and in healthy donors. \u003cstrong\u003eb. \u003c/strong\u003eComparative analysis of IL-1-related gene expression in monocytes during goflikicept therapy and in healthy donors.\u003cstrong\u003e c. \u003c/strong\u003eIntersection of DEGs detected in monocyte subsets on days 7 and 35 of treatment. The highest number of DEGs was found for classical monocytes on day 35. \u003cstrong\u003ed.\u003c/strong\u003e DEGs in classical monocytes on day 35 of treatment \u003cem\u003evs.\u003c/em\u003e pre-treatment. \u003cstrong\u003ee. \u003c/strong\u003eChanges in functional enrichment of classical monocytes on day 7 of treatment \u003cem\u003evs. \u003c/em\u003epre-treatment. \u003cstrong\u003ef.\u003c/strong\u003e Changes in functional enrichment of classical monocytes on day 35 of treatment \u003cem\u003evs\u003c/em\u003e. pre-treatment. \u003cstrong\u003eg. \u003c/strong\u003ePROGENy pathway activity analysis of classical monocytes on days 7 and 35 \u003cem\u003evs\u003c/em\u003e. pre-treatment. \u003cstrong\u003eh. \u003c/strong\u003eCollecTRI transcription factor activity analysis of classical monocytes on days 7 and 35 \u003cem\u003evs. \u003c/em\u003epre-treatment.\u003cstrong\u003e \u003c/strong\u003e**\u003cem\u003eP \u003c/em\u003e≤ 0.01, ***\u003cem\u003eP\u003c/em\u003e ≤ 0.001 Mann–Whitney test\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/09f49569a60c0cdf81b70cfd.png"},{"id":91960918,"identity":"f05313ed-ab22-4769-b366-309a0c897d96","added_by":"auto","created_at":"2025-09-23 07:49:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eB cell transcriptional response to goflikicept therapy. a. \u003c/strong\u003eIntersection of DEGs detected in B cell subsets on days 7 and 35 of goflikicept treatment. \u003cstrong\u003eb.\u003c/strong\u003e DEGs in naïve B cells on day 35 of treatment \u003cem\u003evs.\u003c/em\u003e pre-treatment. \u003cstrong\u003ec.\u003c/strong\u003eDEGs in circulating plasma cell precursors on day 35 of treatment \u003cem\u003evs. \u003c/em\u003epre-treatment. \u003cstrong\u003ed, e.\u003c/strong\u003eFunctional analysis of DEGs in naïve B cells (\u003cstrong\u003ed\u003c/strong\u003e) and circulating plasma cell precursors (\u003cstrong\u003ee\u003c/strong\u003e) on day 35 \u003cem\u003evs. \u003c/em\u003epre-treatment.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/e66aeb27b66d02d8d953917a.png"},{"id":91962664,"identity":"413ae9a4-8695-4c71-98db-f548b0eb6d35","added_by":"auto","created_at":"2025-09-23 07:57:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":478007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT cell transcriptional response to goflikicept therapy. a.\u003c/strong\u003e Intersection of DEGs detected in T cell subsets on day 35 of goflikicept treatment. \u003cstrong\u003eb, c\u003c/strong\u003e. DEGs in MAIT (b) and γδ T cells (\u003cstrong\u003ec\u003c/strong\u003e) on day 35 of treatment \u003cem\u003evs. \u003c/em\u003epre-treatment. \u003cstrong\u003ed, e\u003c/strong\u003e. Functional analysis of DEGs in MAIT (\u003cstrong\u003ed\u003c/strong\u003e) and γδ T cells (\u003cstrong\u003ee\u003c/strong\u003e) on day 35 \u003cem\u003evs.\u003c/em\u003e pre-treatment.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/4d8d0b2fe8bc2dc922a24f22.png"},{"id":91960919,"identity":"253be250-07c1-4355-9bfa-70d7a83cb852","added_by":"auto","created_at":"2025-09-23 07:49:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":276939,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePositioning of goflikicept within the drug perturbation space defined by scRNA-seq and bulk RNA-seq data. \u003c/strong\u003eProcessed transcription data were used, and uMAP projection was performed using Scanpy v1.10.1.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/cfee96d67916f5e0d6a9ffde.png"},{"id":91963400,"identity":"77c713d4-8cac-4ed1-a2de-a91daa3dbe44","added_by":"auto","created_at":"2025-09-23 08:05:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3154118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7507778/v1/6df06352-1c98-422b-a320-b1410c101d19.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nLavrovsky Y. and Samsonov M. are employees of RPharm company, manufacturing goflikicept. The remaining authors declare no competing interests.","formattedTitle":"Single-cell RNA profiling suggests goflikicept-mediated immune modulation in idiopathic recurrent pericarditis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic recurrent pericarditis (IRP) is a rare and complex autoinflammatory disease with a poorly understood pathogenesis. Studies have suggested that triggers such as viruses or cellular debris may overactivate the NLRP3 inflammasome\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This overactivation results in the overproduction of interleukin (IL)-1 family proinflammatory cytokines, notably IL-1β, causing systemic symptoms, such as polyserositis, fever, and increased acute phase reactant levels, and IL-1α, suspected to promote chronic local inflammation within the pericardium, driving fibrosis and, ultimately, constriction\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIRP pathogenesis almost certainly involves the innate immune system, with inflammasome activation driving the recruitment of neutrophils and macrophages to the site of injury via cytokines\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Although a direct role of the adaptive immune system has not been conclusively demonstrated, indirect evidence, including the detection of anti-heart and anti-intercalated disc autoantibodies in some adult patients, suggests that adaptive immune mechanisms, typically implicated in autoimmune diseases, may contribute to IRP pathogenesis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBecause of the limited understanding of IRP pathophysiology, therapeutic strategies remain limited\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The most common approaches involve the use of nonsteroidal anti-inflammatory drugs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and colchicine\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Low-dose corticosteroids can also be effective; however, a significant proportion of patients develop corticosteroid dependence\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The use of IL-1 blockers has shown promise\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The randomized, controlled Anakinra-Treatment of Recurrent Idiopathic Pericarditis trial, which included patients with IRP resistant to colchicine and dependent on corticosteroid therapy, demonstrated the effectiveness of anakinra administered for 60 days\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A phase II study with a 24-week follow-up showed that rilonacept (a recombinant IL-1α/β trap) reduced chest pain and C-reactive protein levels after the first injection in 16 patients with recurrent pericarditis on full medical therapy\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGoflikicept (RPH-104), a novel heterodimeric fusion protein designed to bind with high affinity to both human IL-1β and IL-1α, is a promising therapeutic approach\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Although goflikicept interacts with Fc receptors (FcγRI, FcγRIIa, FcγRIIb, FcRn, and FcγIIIb), its overall affinity for these receptors is lower than that of human IgG1. \u003cem\u003eIn vitro\u003c/em\u003e preclinical testing revealed no antibody- or complement-dependent cytotoxicity\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Furthermore, a good laboratory practice-compliant 4-week toxicology study in cynomolgus monkeys revealed no other significant safety concerns\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In a phase I safety study (NCT02667639), goflikicept was well tolerated by healthy volunteers at doses up to 160 mg. Its favorable pharmacokinetic profile, with a half-life of 10 days, facilitates convenient subcutaneous administration every 2 weeks. Therefore, its potential is being explored in IL-1-driven diseases such as IRP, gout (NCT04067492), and familial Mediterranean fever (NCT05092776), as well as in acute ST-segment elevation myocardial infarction\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In a phase II/III study, goflikicept effectively prevented recurrences and maintained remission in patients with IRP, with an acceptable safety profile\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough excessive IL-1 production is a hallmark of IRP, the specific molecular pathways responsible for this dysregulation remain largely unknown. Further, a detailed investigation of the mechanism of action of goflikicept would provide an understanding of how IL-1 inhibition affects the immune system. Specifically, examining the effects of goflikicept on innate and adaptive immune cells may reveal novel therapeutic targets for autoimmune and autoinflammatory conditions characterized by IL-1 overactivity. We investigated the effects of goflikicept on peripheral blood mononuclear cell (PBMC) subset transcriptomes in patients with IRP using single-cell transcriptome analysis. We thus aimed to identify potential molecular mechanisms underlying treatment response and provide novel insights into the fine-tuned regulation of cellular pathway activity in both target and off-target PBMC subsets during disease and treatment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptome landscape of PBMCs in IRP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePBMCs isolated from blood samples of eight patients with IRP were subjected to single-cell RNA sequencing (scRNA-seq) using the 10X platform (Fig. 1a). For one patient, a sample was collected only at baseline (day 0). For three patients, samples were collected on day 7 after receipt of two doses of goflikicept. For the remaining four patients, samples were collected on day 0 and day 35 after receipt of four doses of goflikicept. After quality control, excluding cells of insufficient quality and likely doublets, 86,743 cells were retained for analysis, including 36,012 cells from day 0, 20,761 cells from day 7 of goflikicept treatment, and 29,970 cells from day 35 (Supplementary Fig. 1a,b).\u003c/p\u003e\n\u003cp\u003eTo comparatively investigate PBMC heterogeneity in patients with IRP and healthy donors, Harmony was used to integrate our IRP single-cell transcriptome data with publicly available single-cell data from healthy donor PBMCs on the 10X Genomics website\u003csup\u003e21\u003c/sup\u003e. This integrated dataset, comprising 142,375 cells, was subjected to uniform manifold approximation and projection dimensionality reduction, Leiden clustering, and manual and automatic annotation, resulting in the identification of eight major cell types at the first level of annotation: T cells, B cells, monocytes, natural killer (NK) cells, dendritic cells (DCs), neutrophils, platelets, and hematopoietic stem/progenitor cells (Fig. 1b, Supplementary Table 1). Clusters of neutrophils, platelets, and hematopoietic stem/progenitor cells were removed from subsequent analysis because they were not targeted during sample preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMonocytes were subclustered into classical, intermediate, and nonclassical monocytes (Fig. 1b, Supplementary Table 1). In the T cell cluster, CD4\u003csup\u003e+\u003c/sup\u003e T (T helper\u0026nbsp;[Th]\u0026nbsp;cells), CD8\u003csup\u003e+\u003c/sup\u003e T (T cytotoxic cells), CD4\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T (double-positive T\u0026nbsp;[DPT]\u0026nbsp;cells), and CD4\u003csup\u003elow\u003c/sup\u003e CD8\u003csup\u003elow\u003c/sup\u003e T (double-negative T\u0026nbsp;[DNT]\u0026nbsp;cells) were identified (Fig. 1b, Supplementary Table 1). CD4\u003csup\u003e+\u003c/sup\u003e T cells comprised four subtypes: T regulatory (Treg, na\u0026iuml;ve, central memory (CD4\u003csup\u003e+\u003c/sup\u003e Tcm), and effector memory (CD4\u003csup\u003e+\u003c/sup\u003e Tem), whereas CD8\u003csup\u003e+\u003c/sup\u003e T cells comprised three subtypes: na\u0026iuml;ve, central memory (CD8\u003csup\u003e+\u003c/sup\u003e Tcm), and effector memory (CD8\u003csup\u003e+\u003c/sup\u003e Tem). Additionally, mucosal-associated invariant T (MAIT) cells, gamma-delta (\u0026gamma;\u0026delta;) T cells, and natural killer T (NKT) cells were identified. These T cell subtypes are consistent with findings in studies on peripheral T cells\u003csup\u003e22\u003c/sup\u003e. NK cells were subclustered into CD56\u003csup\u003ehigh\u0026nbsp;\u003c/sup\u003eCD16\u003csup\u003e\u0026ndash;\u003c/sup\u003e, CD56\u003csup\u003elow\u0026nbsp;\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e, and proliferating NK cells (Fig. 1b, Supplementary Table 1). CD56\u003csup\u003elow\u0026nbsp;\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e NK cells were subdivided into CD57\u003csup\u003e+\u003c/sup\u003e and CD57\u003csup\u003e\u0026ndash;\u003c/sup\u003e subsets. B cells were subclustered into na\u0026iuml;ve B cells, intermediate B cells, memory B cells, and plasma cells (Fig.1b, Supplementary Table 1). Na\u0026iuml;ve, intermediate, and memory B cells were further subdivided into subsets with increased expression of IGKC or IGLC2. DCs were subclustered into type 1 and type 2 - classical (cDC2), plasmacytoid (pDC), AXL\u003csup\u003e+\u003c/sup\u003e SIGLEC6\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(ASDCs), monocyte-derived (moDCs) and, presumably, highly activated tolerogenic DCs (tolDCs).\u003c/p\u003e\n\u003cp\u003eWe next investigated differences in cell type abundance between patient samples from all time points and healthy donors (Fig. 2). No clusters exclusive to IRP samples were identified. The consistent presence of all cell clusters across all time points suggested robust data integration and reproducibility. Compared to healthy donor samples, pre-treatment samples from patients with IRP showed increased abundances of CD8\u003csup\u003e+\u003c/sup\u003e T cytotoxic cells and MAIT cells and a decreased abundance of CD4\u003csup\u003e+\u003c/sup\u003e T helper cells (Fig. 2a). Comparative subset analysis revealed a significant increase in the abundances of NKT cells and \u0026gamma;\u0026delta; T cells, along with a decrease in those of na\u0026iuml;ve B and DNT cells in pre-treatment samples from patients compared to healthy donor samples (Fig. 2b, upper row). After 7 days of goflikicept treatment, these differences remained largely consistent, except for DNT cells, which were increased in abundance at this time point. The populations of nonclassical monocytes, NK CD56\u003csup\u003ebright\u0026nbsp;\u003c/sup\u003eCD16\u003csup\u003e\u0026ndash;\u003c/sup\u003e cells, and NK CD56\u003csup\u003edim\u0026nbsp;\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e cells were expanded, whereas those of intermediate B cells were decreased (Fig. 2b, middle row). After 35 days of goflikicept treatment, the abundance of plasmablasts was increased. The relative abundances of na\u0026iuml;ve and intermediate B cells, NKT cells, and \u0026gamma;\u0026delta; T cells remained relatively stable compared to those in healthy donor samples (Fig. 2b, bottom row).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used Augur to assess cell type prioritization in the transcriptional response to goflikicept treatment on days 7 and 35. Transcriptional changes were more pronounced on day 35 than on day 7, with an increased number of cell types exhibiting higher Augur scores (Fig. 3). At the first level of annotation, only NK cells showed substantial perturbation on day 7, whereas monocytes, DCs, T cells, and, particularly, B cells, were perturbed on day 35 (Fig. 3a). The second level of annotation revealed that perturbations on day 35 were the most prominent in \u0026gamma;\u0026delta; T cells, MAIT cells, and all major monocyte subsets (Fig. 3b). Detailed analysis revealed perturbations on day 35 in na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T helper cells, na\u0026iuml;ve B cells, and intermediate and classical monocyte subsets (Fig. 3c). Non-classical monocyte subsets, NK cell subsets, and NKT cells were significantly perturbed after 7 days of goflikicept treatment (Fig. 3c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGoflikicept suppresses IL-1-mediated inflammation in classical monocytes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the known mechanism of action of goflikicept, we initially focused on the IL-1\u0026beta; response. We compiled a signature of 33 genes associated with IL-1 and inflammation (Supplementary Table 2), which was altered particularly evidently in monocytes (Fig. 4a). The IL-1 signature score was the highest in monocytes from untreated patients with IRP and decreased with treatment to nearly the levels in healthy donor monocytes (Fig. 4b). The core set of downregulated differentially expressed genes (DEGs; |log2FC| \u0026ge; 0.5, adjusted \u003cem\u003eP\u003c/em\u003e \u0026le; 0.05) on day 35 of treatment was shared between classical and intermediate monocytes (Fig. 4c). On day 35 of treatment, most genes in classical monocytes were downregulated, with only one gene (i.e., \u003cem\u003eMTRNR2L12\u003c/em\u003e) being upregulated (Fig. 4c, d). On day 7 of treatment, the interferon (IFN) response was significantly upregulated in classical monocytes, whereas downregulated responses were weakly expressed and exhibited a heterogeneous pattern.\u0026nbsp;By day 35, the IFN response was reverted, and pathways associated with the inflammatory response, tumor necrosis factor alpha (TNF-\u0026alpha;) signaling via nuclear factor kappa beta (NF-\u0026kappa;B), and others were downregulated (Fig. 4e). Consistent herewith, PROGENy analysis showed that on day 7, only the Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathway was upregulated. However, by day 35, JAK/STAT pathway upregulation was reduced, concurrent with the downregulation of the NF-\u0026kappa;B, TNF-\u0026alpha;, and androgen pathways (Fig. 4f). CollecTRI analysis revealed that transcription factor activity was downregulated significantly more strongly on day 35 than on day 7. This substantial downregulation involved key inflammatory and IL-1-related genes, including \u003cem\u003eAIRE\u003c/em\u003e, \u003cem\u003eNFKBIB\u003c/em\u003e, and \u003cem\u003eRELB\u003c/em\u003e (Fig. 4h). Functional enrichment analysis corroborated these findings, revealing a significantly more pronounced effect of goflikicept treatment on day 35 than on day 7. The decrease in activity was associated with a wide range of physiological and pathological processes, predominantly inflammatory in nature and mediated by IL-1 and TNF (Fig. 4e, f).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB cell response to goflikicept treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong all B cell subsets, na\u0026iuml;ve B cells and circulating plasma cell precursors had the highest number of DEGs on day 35 compared to pre-treatment (Fig. 5a). The majority of DEGs in na\u0026iuml;ve B cells, including \u003cem\u003eJUND\u003c/em\u003e, \u003cem\u003eRPS29\u003c/em\u003e, \u003cem\u003eCXCR4\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e, were downregulated on day 35 (Fig. 5b), whereas plasma cell precursor DEGs were predominantly upregulated (Fig. 5c). Several signaling pathways were downregulated in na\u0026iuml;ve B cells on day 35, indicating widespread transcriptional reprogramming (Fig. 5d). Specifically, pathways associated with immune regulation and cell survival, such as the B cell survival pathway, nerve growth factor-stimulated transcription, and oncostatin M signaling, were significantly suppressed. In addition, pathways involved in neuroimmune and stress responses, including the corticotropin-releasing hormone signaling pathway and angiotensin II receptor type 1 pathway, were downregulated. The response on day 7 was not prominent in na\u0026iuml;ve B cells, but functional analysis revealed that several pro-inflammatory pathways were downregulated, including TNF-\u0026alpha; signaling via NF-\u0026kappa;B, whereas the interferon-\u0026alpha; response was slightly upregulated. Concurrently, PROGENy-defined pathway activity analysis indicated the downregulation of TGF-\u0026beta;, hypoxia, and TNF-\u0026alpha;, and upregulation of JAK-STAT.\u003c/p\u003e\n\u003cp\u003eIn plasma cells precursors, the response also was not prominent on day 7, but rather concentrated on day 35 (Fig. 5a). On day 35, eight genes were significantly upregulated (\u003cem\u003eIGKC\u003c/em\u003e,\u003cem\u003e\u0026nbsp;PPIB\u003c/em\u003e,\u003cem\u003e\u0026nbsp;CLIC1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;SEC61G\u003c/em\u003e,\u003cem\u003e\u0026nbsp;SEC61B\u003c/em\u003e,\u003cem\u003e\u0026nbsp;S100A10\u003c/em\u003e,\u003cem\u003e\u0026nbsp;SUB1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;SCD99\u003c/em\u003e), and two were downregulated (\u003cem\u003eARID1A\u003c/em\u003e and \u003cem\u003eCDV3\u003c/em\u003e) (Fig. 5c). Functional analysis revealed that the top upregulated pathways were associated with active protein processing and secretion, including signal recognition particle (SRP)-dependent cotranslational targeting to the endoplasmic reticulum (ER), post-translational protein targeting to membranes, and tail-anchored protein insertion into the ER membrane (Fig. 5e). Additional enriched pathways had significance in immune regulation and homeostasis, such as prostaglandin synthesis and regulation, chloride transport, SMAD protein signal transduction, tissue homeostasis, and homotypic cell\u0026ndash;cell adhesion. The top downregulated pathways included CD22-mediated B cell receptor (BCR) regulation and antigen-mediated activation of BCRs leading to the generation of second messengers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT cell response to goflikicept treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT cell subset responses to goflikicept treatment were observed on day 35 following treatment. Specifically, genes in T helper, T cytotoxic, and T regulatory cell subsets were strongly downregulated. Gene upregulation was less frequent and primarily observed in NKT, MAIT, CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T, and DNT cells. The most pronounced downregulation, affecting a relatively large number of genes, was detected in T regulatory cells, CM and EM T helper cells, CM T cytotoxic cells, MAIT cells, and \u0026gamma;\u0026delta; T cells (Fig. 6a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs \u0026gamma;\u0026delta; T and MAIT cells demonstrated the most pronounced changes among T cell subtypes on day 35 (Figs. 3, 6),\u0026nbsp;we proceeded to investigate this relatively rare T cell subset in greater detail. Response patterns were similar between \u0026gamma;\u0026delta;\u0026nbsp;T and MAIT cells but more pronounced in the latter. DEGs were found only on day 35 compared to pre-treatment, which corresponds with Augur prioritization (Fig. 6a). On day 35, MAIT cells exhibited significant downregulation of eight genes (\u003cem\u003eJUND\u003c/em\u003e,\u003cem\u003e\u0026nbsp;FOS\u003c/em\u003e,\u003cem\u003e\u0026nbsp;DUSP1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;NFKBIA\u003c/em\u003e,\u003cem\u003e\u0026nbsp;JUN\u003c/em\u003e,\u003cem\u003e\u0026nbsp;S100A9\u003c/em\u003e,\u003cem\u003e\u0026nbsp;TSC22D3\u003c/em\u003e, and \u003cem\u003eJUNB\u003c/em\u003e) (Fig. 6b), whereas in\u0026nbsp;\u0026gamma;\u0026delta;\u0026nbsp;T cells, five genes were downregulated (\u003cem\u003eJUN\u003c/em\u003e, \u003cem\u003eDUSP1\u003c/em\u003e, \u003cem\u003eJUND\u003c/em\u003e, \u003cem\u003eFOS\u003c/em\u003e, and \u003cem\u003eNFKBIA\u003c/em\u003e) (Fig. 6c). In\u0026nbsp;\u0026gamma;\u0026delta;\u0026nbsp;T cells, only the TNF-\u0026alpha;\u003cem\u003e\u0026nbsp;\u003c/em\u003epathway was downregulated on both days 7 and 35\u003cem\u003e.\u0026nbsp;\u003c/em\u003eHallmark gene set enrichment analysis revealed the downregulation of numerous pathways related to cell survival, cytokine production, cell interaction, and migration, including NF-\u0026kappa;B/AP-1-induced apoptosis, B cell survival, Toll signaling, CD40 signaling, and CCR5 signaling in \u0026gamma;\u0026delta; T and MAIT cells on day 35 of therapy (Fig. 6d, e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePositioning of goflikicept among known drugs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the molecular footprint of goflikicept and position it relative to existing pharmacological agents, we employed deep learning perturbation modeling framework using the Conditional Perturbation Autoencoder (CPA) \u0026mdash; a deep generative neural network trained to learn drug-induced transcriptional responses. CPA models the effects of chemical perturbations in a disentangled latent space, allowing the simulation of unseen drug-dose combinations and comparison across compounds in a standardized embedding (Fig. 7). First, we trained a model on a diverse set of scRNA-seq (sci-Plex3, three cell lines treated with 188 small-molecule drugs) and bulk RNA-seq (LINCS1000) datasets. The trained model captured the effects of various drugs, as reflected in their gene expression profiles, within a latent space of drug perturbations. Next, we clustered this latent space to identify groups of drugs exhibiting similar effects. Strikingly, goflikicept clustered alongside tyrosine kinase inhibitors, non-steroidal anti-inflammatory drugs, glucocorticosteroids, and anticancer drugs, indicating potential similarities in their mechanisms of action or effects.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo elucidate the aspects of IRP pathogenesis, we analyzed the single-cell transcriptome landscape of PBMCs from patients with IRP in comparison with those of healthy individuals using publicly available scRNA-seq data. We found no significant differences in the proportions of innate immune cells, including monocytes and their subsets. However, we observed intriguing differences in adaptive immune cell subsets. Specifically, increased levels of cells with cytotoxic activity (CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T and NKT cells) in patients with IRP suggested their potential implication in the pathogenesis of pericarditis, which has not been previously reported to the best of our knowledge (Fig. 2). Moreover, the levels of CD4\u003csup\u003e+\u003c/sup\u003e T helper, DNT, and na\u0026iuml;ve B cells were reduced in patient samples prior to treatment compared to those in healthy control samples. This finding challenges previous suggestions regarding the direct role of antibody production in IRP pathogenesis based on the presence of autoantibodies in patients with IRP. However, the observed decrease in circulating na\u0026iuml;ve B cells, potentially reflecting their recruitment to inflamed tissues and secondary lymphoid organs where they differentiate into effector cells, indicates that adaptive immune mechanisms may contribute to disease development. Notably, another study reported findings similar to ours, observing a reduction in na\u0026iuml;ve T cells alongside an increase in activated CD8\u003csup\u003e+\u003c/sup\u003e T effector cells in patients with acute pericarditis, which the authors interpreted as indicative of a more active inflammatory response\u003csup\u003e23\u003c/sup\u003e. Finally, we noted a persistent increase in \u0026gamma;\u0026delta; T cells, both before treatment and throughout the observation period, when compared with their levels in controls. Characterized by their unique ability to bridge innate and adaptive immunity, \u0026gamma;\u0026delta; T cells (0.5%\u0026ndash;5%) expressing \u0026gamma;\u0026delta; T cell receptor (TCR)\u003csup\u003e24\u003c/sup\u003e function as both immediate effector cells and regulators of B cell activation\u003csup\u003e25\u003c/sup\u003e. The ability of \u0026gamma;\u0026delta; T cells to initiate inflammation without specific antigen recognition may explain their role in driving autoimmune diseases, such as psoriasis, multiple sclerosis, and primary sclerosing cholangitis\u003csup\u003e26\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGoflikicept treatment significantly altered the cellular transcriptome even after 7 days. Populations of nonclassical monocytes and NK cell subsets were expanded, whereas that of intermediate B cells was decreased. Notably, circulating plasma cell precursors were significantly increased on day 35 post-treatment compared to the levels in healthy donors. Interestingly, Augur response prioritization analysis revealed a minimal influence of the treatment on cell subsets on day 7, primarily affecting cells with cytotoxic activity (NK cell subsets, NKT cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells). However, on day 35 of treatment, cell responses were more prominent. As predicted, we observed changes in the activity of innate immune cells (monocyte and DC subsets), the main responders to IL-1 blockade. The influence of goflikicept on the activity of CD4\u003csup\u003e+\u003c/sup\u003e T helper cell subsets, MAIT cells, and na\u0026iuml;ve B cells was intriguing. Notably, MAIT cells and \u0026gamma;\u0026delta; T cells exhibited the most pronounced responses to treatment\u003csup\u003e27\u003c/sup\u003e. Although MAIT cells have been implicated in autoimmune diseases, the observed response may reflect a drug-hypersensitivity reaction, as has been reported for diclofenac (a non-steroidal anti-inflammatory drug)\u003csup\u003e28\u003c/sup\u003e, 3-formylsalicylic acid, and 2,4-diamino-6-formylpteridine (a methotrexate derivative)\u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo characterize the activity of goflikicept, we analyzed the expression of 33 genes related to IL-1 signaling (Supplementary Table 2). As expected, monocytes exhibited the highest expression levels of IL-1-related genes. Furthermore, their expression was higher in patients than in healthy donors, and treatment with goflikicept significantly suppressed their expression, predominantly in monocyte subsets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClassical monocytes were the main responders among all monocyte subsets, and the treatment downregulated numerous genes, particularly on day 35. The treatment response on day 7 was not as profound. In particular, although classical monocytes exhibited upregulation of IFN-related responses on day 7, all pathways were downregulated on day 35. As predicted, TNF-\u0026alpha; signaling, inflammatory responses, and IL-6/STAT3 signaling were downregulated in classical monocytes following treatment with goflikicept. Notably, goflikicept decreased the expression of both key cytokine production-related transcription factors (NF-\u0026kappa;B, JAK/STAT) and AIRE, which regulates autoantigen expression and negative selection of autoreactive T cells in the thymus and plays a major role in peripheral tolerance\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs antibody-producing adaptive immune cells, B cells were not initially predicted to be primary responders to the treatment. Nevertheless, both na\u0026iuml;ve B cells and circulating plasma cell precursors had large numbers of DEGs on day 35. Interestingly, goflikicept downregulated genes in na\u0026iuml;ve B cells, primarily those related to TNF-\u0026alpha; signaling via NF-\u0026kappa;B. Conversely, in plasma cell precursors, genes were predominantly upregulated. We speculate that the diminished activity of na\u0026iuml;ve B cells may reflect a reduced propensity for differentiation and subsequent antibody secretion. Conversely, the increased activity observed in plasma cell precursors following IL-1 blockade may reflect an ongoing process of antibody generation.\u003c/p\u003e\n\u003cp\u003eT cell responses to goflikicept were assessed based on their cytokine production activity and their roles in immune response polarization and autoimmunity. By day 35, most of the major T cell subsets exhibited downregulated gene expression. The most prominent responders, exhibiting downregulation of five or more genes, were \u0026gamma;\u0026delta; T cells, effector memory T helper cells, central memory T cytotoxic cells, and MAIT cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAIT cells, which are highly abundant in humans (up to 10% of blood T cells\u003csup\u003e30\u003c/sup\u003e and 45% of liver T cells\u003csup\u003e31\u003c/sup\u003e), have garnered intense research interest because of their potential role in immunity and as a therapeutic target\u003csup\u003e27\u003c/sup\u003e. Although their precise mechanisms are still being elucidated, MAIT cells are known to be activated through multiple pathways, including TCR-mediated antigen recognition and TCR-independent mechanisms involving inflammatory stimuli and cytokines\u003csup\u003e27\u003c/sup\u003e. Specifically, ligands for TLR2, TLR3, TLR4, TLR5, TLR8, and TLR9 can promote MAIT cell activation, and cytokines, such as IL-12, IL-18, IL-1\u0026beta;, and IL-7, can stimulate MAIT cells through their respective receptors\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e. Efficient cytokine-dependent activation of MAIT cells often requires a combination of at least two cytokines; for example, IL-12 and IL-18 synergistically promote strong TCR-independent production of IFN-\u0026gamma;, TNF, and granzyme B, whereas neither cytokine alone is sufficient\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e. Finally, antigen-activated human MAIT cells upregulate the B cell-stimulatory molecule CD40L and promote DC maturation and IL-12 production\u003csup\u003e35\u003c/sup\u003e. Although the downregulation of \u003cem\u003eNFKBIA, JUN, JUND\u003c/em\u003e, and \u003cem\u003eFOS\u003c/em\u003e in MAIT cells after goflikicept treatment suggests the possibility of enhanced apoptosis in these cells upon TCR-dependent stimulation, NF-\u0026kappa;B activation, even when followed by \u003cem\u003eNFKBIA\u003c/em\u003e suppression, can paradoxically lead to increased cytokine production and proliferation\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn their inactive state, \u0026gamma;\u0026delta; T cells have a migration profile is similar to that of innate immune cells; however, upon CCR7 expression, they migrate to lymph nodes to assist B-cell activation\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e. They can produce IFN-\u0026gamma; and TNF-\u0026alpha; in response to intracellular pathogens, IL-4, IL-5, and IL-13 during parasite immune responses and IL-17 in defense against bacteria and fungi\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e. Human IL-17A-producing \u0026gamma;\u0026delta; T cells are of particular interest, as they can be activated independently of TCR ligation and contribute significantly to IL-17A production in the context of autoimmune diseases\u003csup\u003e39\u003c/sup\u003e. These cells accumulate in inflamed tissues and can be activated by various factors, including TCR agonists, IL-1\u0026beta;, IL-6, IL-23, and TGF-\u0026beta;, promoting a Th17 cytokine profile\u003csup\u003e40\u003c/sup\u003e. After treatment with goflikicept, the most pronounced \u0026gamma;\u0026delta; T cell response occurred on day 35. Their suppression may be attributed to the reduction in or absence of IL-1 signaling. Notably, the most significantly downregulated genes (\u003cem\u003eJUN, JUND, FOS\u003c/em\u003e, and \u003cem\u003eNFKBIA\u003c/em\u003e) were the same as those observed in MAIT cells, suggesting a similar functional effect in both cell types.\u003c/p\u003e\n\u003cp\u003eTaken together, our findings indicate that IL-1 blockade exerts its most pronounced effects on monocyte, B cell, MAIT cell, and \u0026gamma;\u0026delta; T cell subsets. These effects were primarily assessed based on clinical outcomes and corresponding transcriptomic changes. Nevertheless, the clinical utility of goflikicept may also be governed by its pharmacological properties in relation to established drug categories. The clustering of goflikicept alongside tyrosine kinase inhibitors, non-steroidal anti-inflammatory drugs, and glucocorticosteroids suggests shared mechanisms of action and, consequently, its potential for broad application in clinical practice.\u003c/p\u003e\n\u003cp\u003eThe lack of an internal healthy control group limited our ability to fully characterize the baseline immune landscape in IRP and forced us to rely on publicly available data for comparisons; the inclusion of such a control group would have facilitated a more comprehensive understanding of disease pathogenesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study provided the first comprehensive single-cell analysis of PBMC dynamics in patients with IRP undergoing treatment with goflikicept, delineating its cell type-specific effects and revealing its capacity to induce sustained anti-inflammatory reprogramming. Although untreated patients with IRP exhibited a PBMC composition broadly similar to that of healthy controls, distinct differences in cell type abundance (e.g., monocytes and other populations) were observed. IL-1 blockade initially elicited a weak anti-inflammatory response and IFN-\u0026alpha; response in certain cell types on day 7 of therapy, followed by pronounced anti-inflammatory responses on day 35, particularly in monocytes, characterized by a marked reduction in IL-1\u0026beta;-related signatures. In addition to the prominent effects on monocytes, we observed significant transcriptome changes in B cell subsets (with gene downregulation in na\u0026iuml;ve B cells and gene upregulation in circulating plasma cell precursors), as well as in MAIT and \u0026gamma;\u0026delta; T cells, suggesting broader effects on adaptive and innate immune responses. Notably, goflikicept clustered pharmacologically with IL-1 inhibitors and tyrosine kinase activity modulators, suggesting a dual targeting of inflammatory pathways. These findings highlight the central role of monocytes in resolving inflammation in IRP, while implicating other immune cell subsets in the therapeutic response.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, eight patients with IRP were enrolled in this study (Fig. 1). The diagnosis of IRP was based on 2015 European Society of Cardiology guidelines on the management of pericardial diseases\u003csup\u003e41\u003c/sup\u003e. Given that IRP is a diagnosis of exclusion, we ruled out chronic infections (including tuberculosis), systemic rheumatic diseases, encompassing both autoimmune (e.g., connective tissue diseases) and autoinflammatory (e.g., adult-onset Still\u0026rsquo;s disease, Schnitzler syndrome) disorders, and monogenic autoinflammatory diseases (e.g., familial Mediterranean fever, TNFRSF1A-associated periodic syndrome, mevalonate kinase deficiency, and NLRP3-associated diseases). The median age of the eight patients was 49.5 years (interquartile range, 43.75\u0026ndash;55.25, standard deviation = 14.1). To assess disease activity, we used a modified Pouchot score\u003csup\u003e42\u003c/sup\u003e to evaluate systemic symptoms associated with IRP. The main characteristics of the patients and samples included in the study are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe eight patients with IRP showed a poor response to colchicine treatment and subsequently received biologic therapy. Seven patients (P2\u0026ndash;P8) received goflikicept during the clinical trial NCT04692766\u003csup\u003e18\u003c/sup\u003e. We collected samples (during relapse and remission) from patients P2\u0026ndash;P5, one sample during relapse from patient P1, and one sample during remission from patients P6\u0026ndash;P8. The following criteria were used to define remission: (1) chest pain score \u0026le;3 on the numeric rating scale, (2) C-reactive protein concentration \u0026le;5 mg/L, and (3) absent or mild pericardial effusion (\u0026lt;10 mm) on echocardiography. Patients were considered responders on day 7 or day 35 of therapy if all three criteria were met. Patients were not selected based on any presumed likelihood of treatment response. Nevertheless, all patients who received goflikicept in this study responded to therapy (Fig. 1).\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the local Ethics Committee of the Almazov National Medical Research Center (protocol number 28, dated 12 February 2018). Written informed consent was obtained from all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePBMC isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected from one patient before treatment (day 0), from three patients on day 7 (post two-dose goflikicept), and from four patients on both days 0 and 35 (post four-dose goflikicept), for a total of 12 samples (Fig. 1a, Table 1). Peripheral vein blood samples were collected in vacuum tubes with K\u003csub\u003e3\u003c/sub\u003eEDTA\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(Vacutest, KIMA, Arzergrande, Italy). PBMCs were isolated using SepMate\u003csup\u003eTM\u003c/sup\u003e-15 tubes (StemCell Technologies, Vancouver, CA) and Ficoll-Paque PLUS density gradient medium (Cytiva, Marlborough, MA, USA) according to the manufacturer\u0026rsquo;s recommendations. In brief, 4 mL of density gradient medium was carefully transferred into a SepMate\u003csup\u003eTM\u003c/sup\u003e-15 tube. The diluted blood sample (3 mL of blood and 3 mL of PBS) was pipetted down the side of the tube. Tubes were centrifuged at 1,200\u0026nbsp;\u0026acute;\u0026nbsp;\u003cem\u003eg\u003c/em\u003e, room temperature for 10 min. The top layer containing the enriched PBMCs was collected, and the cells were washed with PBS and centrifuged at 300\u0026nbsp;\u0026acute;\u0026nbsp;\u003cem\u003eg\u003c/em\u003e for 8 min.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escRNA-seq\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell capturing and library construction were performed using the Chromium Controller and Single Cell 3\u0026prime; Reagent Kit v.3.1 (10\u0026thinsp;\u0026times;\u0026thinsp;Genomics) according to the manufacturer\u0026rsquo;s protocol and as previously described\u003csup\u003e43\u003c/sup\u003e. In brief, approximately 100,000 PBMCs per sample were loaded onto 10x Genomics Single Fluidics chips to generate gel beads in emulsion, which contained oligonucleotide-barcoded beads for single-cell indexing and unique molecular identifiers (UMIs) for transcript quantification. Uniquely barcoded RNAs were reverse transcribed followed by cDNA amplification in a Veriti\u0026trade; 96-Well Thermal Cycler (Applied Biosystems). Libraries were barcoded with Chromium\u003csup\u003eTM\u003c/sup\u003e i7 Multiplex Kit dual indexes, pooled in equimolar ratios, and sequenced in-house (Almazov National Medical Research Centre) on an Illumina NextSeq 2000 system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSingle-cell capturing and library construction were performed using the Chromium Single Cell 3\u0026cent;\u0026nbsp;Reagent Kits v3 according to the manufacturer\u0026rsquo;s instructions (10x\u0026thinsp;Genomics). Raw sequencing reads were aligned to GRCh38 (GENCODE v32/Ensembl 98), and feature-barcode matrices were generated using Cell Ranger v.6.1.2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo process the scRNA-seq data, we mostly followed the standard scRNA-seq analysis pipeline implemented in Scanpy\u003csup\u003e44\u003c/sup\u003e (v.1.10.1), a Python package for single-cell analysis, using Python (v.3.12). We also used scRNA-seq data of PBMCs from 16 healthy donors from the 10x Genomics official website (https://www.10xgenomics.com/). First, we removed genes expressed in fewer than two cells and filtered out low-quality cells based on the number of detected genes, mitochondrial UMI fraction, and total UMI count. We retained cells with gene counts between 400 and 3,500 and mitochondrial UMIs \u0026lt;15%. Subsequently, we used Scrublet in Scanpy to identify doublets among the PBMCs from the patients and healthy donors. We used the default parameters for scanpy.pp.scrublet (i.e., sim_doublet_ratio = 2, synthetic_doublet_umi_subsampling = 1, n_prin_comps = 30), set random_state to 42, and threshold to 0.25. We thus detected 3,079 doublets in the patients with IRP, and 604 in the healthy donors. Following filtering, the data were normalized to the median total count and log1p-transformed. The new IRP dataset and public healthy donor (N = 16) dataset were integrated using HarmonyPy (v.0.0.10)\u003csup\u003e45\u003c/sup\u003e over dataset, patient, and condition batches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCell type annotation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the integrated data, we performed unsupervised clustering with the Leiden clustering algorithm\u003csup\u003e46\u003c/sup\u003e. Highly expressed genes within each subset were identified using the Wilcoxon rank test implemented in Scanpy. Automatic cell type annotation was performed using Azimuth and CellTypist and compared with manual annotation. To annotate cell types with their type and function, we ran subclustering for each of the cell type from level 1 (the broadest levels).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe subclustered monocytes, T cells, B cells, DCs, and NK cells at a resolution of 0.5, \u0026minus;3, \u0026minus;1.5, \u0026minus;0.3, and \u0026minus;0.5, respectively (Fig. 1b). The expression markers used to differentiate the cell types in the final annotation are listed in Supplementary Table 1. During annotation, we identified two small subclusters of classical monocytes that expressed the markers of classical monocytes along with platelet or T cell markers. These two clusters had a relatively high mean doublet score (\u0026gt;0.1) and were deemed to be clusters of doublets or aggregated cells and therefore excluded from analysis. Similarly, a subcluster of B cells that exhibited mixed expression of na\u0026iuml;ve B cell and T cells markers and had a relatively high mean doublet score (\u0026gt;0.2) was removed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferential abundance analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential abundance analysis was performed using scCODA algorithm available in the package pertpy (v.0.10.0)\u003csup\u003e47\u003c/sup\u003e. The analysis was performed by iteratively using each cell type as a reference, averaging the resulting estimates, and retaining only those cell types that met a false discovery rate (FDR) threshold of \u0026lt; 0.2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eResponse prioritization\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResponse prioritization across cell types was conducted using Augur\u003csup\u003e48\u003c/sup\u003e, which prioritizes cell types by their transcriptional response to experimental perturbations in single-cell data by training a machine learning model to predict experimental conditions (e.g., treatment vs. control) within each cell type. Cell types for which labels are more accurately predicted are considered more responsive. We performed differential prioritization by applying permutation test to identify cell types with statistically significant differences in AUC between two prioritization time points (days 7 and 35 of treatment) \u003cem\u003evs.\u003c/em\u003e pre-treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferential expression analysis and functional analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis of goflikicept-treated compared to untreated conditions was performed using MAST-RE\u003csup\u003e49\u003c/sup\u003e. We refrained from comparing expression profiles between the healthy control and IRP conditions (untreated or treated) because of the inability to exclude batch effects from different datasets while preserving authentic biological information. We did not identify any significant sex, age or weight -related differences across untreated or treated IRP patients\u0026rsquo; samples. Gene set enrichment analysis was based on a univariate linear model using decoupler\u003csup\u003e50\u003c/sup\u003e, and \u003cem\u003eP\u003c/em\u003e-values were obtained using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test and adjusted using the Benjamini\u0026ndash;Hochberg correction.\u0026nbsp;Genes with FDR \u0026lt; 0.05 were deemed significant. Gene sets available from MSigDB were used, from such databases as KEGG, Reactome, BioCarta, WikiPathways, Hallmark, and Gene Onthology. Pathway activity inference was performed using PROGENy with a multivariate linear model (mlm) in decoupler\u003csup\u003e50\u003c/sup\u003e and Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. Transcriptional factor scoring was performed based on the CollecTri network, using a univariate linear model in decoupler and Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePerturbation modeling and exploration of perturbation space\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze drug-induced transcriptional responses, we employed the Conditional Perturbation Autoencoder (CPA)\u003csup\u003e51\u003c/sup\u003e, a deep generative neural network that learns the latent representation of cell state changes under chemical perturbations. CPA allows simulation of unseen drug-dose-time combinations and enables clustering of compounds based on their learned transcriptional signatures. By training the model on our data and the publicly available sci-Plex3 dataset\u003csup\u003e52\u003c/sup\u003e, which includes approximately 650,000 cells from three cell lines treated with 188 compounds, we generated a latent embedding space that captures complex, nonlinear relationships between compounds and cell states. To optimize model performance, hyperparameters such as learning rate, latent dimension size, and regularization weights were tuned using Optuna. The predictive accuracy of the CPA model was assessed using mean squared error (MSE) between observed and reconstructed gene expression profiles. Embeddings were visualized with UMAP and clustered using Leiden algorithm to assess drug similarity in latent space. CPA\u0026rsquo;s ability to recover known pharmacological clusters (e.g., HDAC inhibitors) validates its biological relevance and predictive utility. This deep learning framework enabled us to position goflikicept within a landscape of reference drugs, revealing mechanistic similarities to anti-inflammatory and immunomodulatory compounds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eInformed consent was obtained from the participants for publication of the clinical case details and any accompanying data\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a public cRNA-seq dataset from PBMCs from 16 healthy donors downloaded from the 10X Genomics official website [https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_NGSC3_aggr] as control data. The GRCh38 human reference genome used for the sequencing data alignment is available on the 10X Genomics official website [https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest]\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGA \u0026ndash; design, acquisition, analysis, interpretation of the data, manuscript preparation; ME - analysis, interpretation of the data, manuscript preparation; IE, KO, IO and KI - acquisition, analysis; AD, MY, MiA, SD, RV, VY \u0026ndash; analysis of the data, interpretation, critical manuscript reading; MO, MV, MaA \u0026ndash; clinical design, patients curation, interpretation of the data; LY, SM \u0026ndash; design, interpretation of the data; KA, SE \u0026ndash; design, conceptualization, study management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following competing interests:\u0026nbsp;Lavrovsky Y. and Samsonov M. are employees of RPharm company, manufacturing goflikicept. The remaining authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2022-301)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to GA ([email protected]).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrucato, A., et al. Recurrent pericarditis: still idiopathic? The pros and cons of a well-honoured term. \u003cem\u003eIntern Emerg Med\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 839\u0026ndash;844 (2018).\u003c/li\u003e\n\u003cli\u003eAdler, Y., et al. 2015 ESC Guidelines for the diagnosis and management of pericardial diseases: The Task Force for the Diagnosis and Management of Pericardial Diseases of the European Society of Cardiology (ESC) endorsed by: The European Association for Cardio-Thoracic Surgery (EACTS). \u003cem\u003eEur Heart J\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2921\u0026ndash;2964 (2015). \u003c/li\u003e\n\u003cli\u003eMauro, A.G., et al. The role of NLRP3 inflammasome in pericarditis: potential for therapeutic approaches. \u003cem\u003eJACC Basic to Transl Sci\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 137\u0026ndash;150 (2021). \u003c/li\u003e\n\u003cli\u003eWeber, A., Wasiliew, P. \u0026amp; Kracht, M. Interleukin-1 (IL-1) pathway. \u003cem\u003eSci Signal\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 1\u0026ndash;7 (2010). \u003c/li\u003e\n\u003cli\u003eBuckley, L.F., Viscusi, M.M, Van Tassell, B.W. \u0026amp; Abbate, A. Interleukin-1 blockade for the treatment of pericarditis. \u003cem\u003eEur Hear J Cardiovasc Pharmacother\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 46\u0026ndash;53 (2018). \u003c/li\u003e\n\u003cli\u003eAbadie, B.Q. \u0026amp; Cremer, P.C. Interleukin-1 antagonists for the treatment of recurrent pericarditis. \u003cem\u003eBioDrugs\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 459\u0026ndash;472 (2022). \u003c/li\u003e\n\u003cli\u003eDmitrieva, A., et al. Nonclinical characterization of the novel IL-1 heterodimeric fusion protein RPH-104. \u003cem\u003eImmunome Res\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, A7 (2018). \u003c/li\u003e\n\u003cli\u003eMyachikova, V.Y., et al. Treatment of idiopathic recurrent pericarditis with goflikicept. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 30\u0026ndash;40 (2023). \u003c/li\u003e\n\u003cli\u003eSafety, tolerability, pharmacokinetics and pharmacodynamics evaluation of RPH-104 administered at different doses to patients with acute gout attack. \u003cem\u003eCase Med Res\u003c/em\u003e (2019). \u003c/li\u003e\n\u003cli\u003eEfficacy and safety of RPH-104 for resolution and prevention of recurring attacks in adult subjects with familial mediterranean fever with resistance to or intolerance of colchicine [Internet]. \u003c/li\u003e\n\u003cli\u003eAbbate, A., et al. Results of international, double-blind, randomized, placebo-controlled, phase IIa study of interleukin-1 blockade with RPH-104 (goflikicept) in patients with ST-segment-elevation myocardial infarction (STEMI). \u003cem\u003eCirculation\u003c/em\u003e \u003cstrong\u003e150\u003c/strong\u003e, 580\u0026ndash;582 (2024). \u003c/li\u003e\n\u003cli\u003eInternational, double blind, randomized, placebo-controlled study to evaluate the effect on parameters of systemic inflammation and disease outcomes and safety of RPH-104 in subjects with acute ST-elevation myocardial infarction. 2020. \u003c/li\u003e\n\u003cli\u003ePouchot, J., et al. Adult Stillʼs disease: manifestations, disease course, and outcome in 62 patients. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 118\u0026ndash;136 (1991). \u003c/li\u003e\n\u003cli\u003eHu, B., et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. \u003cem\u003eClin Cancer Res\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 6212\u0026ndash;622 (2014). \u003c/li\u003e\n\u003cli\u003eIvanova, O.A., et al. LMNA R482L mutation causes impairments in C2C12 myoblasts subpopulations, alterations in metabolic reprogramming during differentiation, and oxidative stress. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 5358 (2025). \u003c/li\u003e\n\u003cli\u003eWolf, F.A., Angerer, P. \u0026amp; Theis, F.J. SCANPY: Large-scale single-cell gene expression data analysis. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 15 (2018). \u003c/li\u003e\n\u003cli\u003eKorsunsky, I., et al. Fast, sensitive and accurate integration of single-cell data with Harmony. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1289\u0026ndash;1296 (2019). \u003c/li\u003e\n\u003cli\u003eHeumos, L., et al. Pertpy: an end-to-end framework for perturbation analysis. Preprint at https://www.biorxiv.org/content/10.1101/2024.08.04.606516v1.full (2024).\u003c/li\u003e\n\u003cli\u003eSquair, J.W., Skinnider, M.A., Gautier, M., Foster, L.J. \u0026amp; Courtine, G. Prioritization of cell types responsive to biological perturbations in single-cell data with Augur. N\u003cem\u003eat Protoc\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 3836\u0026ndash;3673 (2021). \u003c/li\u003e\n\u003cli\u003eZimmerman, K.D., Espeland, M.A. \u0026amp; Langefeld, C.D. A practical solution to pseudoreplication bias in single-cell studies. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 738 (2021). \u003c/li\u003e\n\u003cli\u003eBadia-I-Mompel, P., et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Kuijjer ML, editor. \u003cem\u003eBioinform Adv\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e vbac016 (2022)\u003c/li\u003e\n\u003cli\u003eLotfollahi, M., et al. Predicting cellular responses to complex perturbations in high‐throughput screens. \u003cem\u003eMol Syst Biol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, e11517 (2023). \u003c/li\u003e\n\u003cli\u003eSrivatsan, S.R., et al. Massively multiplex chemical transcriptomics at single-cell resolution. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e367\u003c/strong\u003e, 45\u0026ndash;51 (2020). \u003c/li\u003e\n\u003cli\u003eZheng, G.X.Y., et al. Massively parallel digital transcriptional profiling of single cells. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 14049 (2017). \u003c/li\u003e\n\u003cli\u003eBonaventura, A. \u0026amp; Montecucco, F. Inflammation and pericarditis: Are neutrophils actors behind the scenes? \u003cem\u003eJ Cell Physiol\u003c/em\u003e \u003cstrong\u003e234\u003c/strong\u003e, 5390\u0026ndash;5398 (2019). \u003c/li\u003e\n\u003cli\u003eCacoub, P. \u0026amp; Marques, C. Acute recurrent pericarditis: from pathophysiology towards new treatment strategy. \u003cem\u003eHeart \u003c/em\u003e\u003cstrong\u003e106\u003c/strong\u003e, 1046\u0026ndash;1051 (2020). \u003c/li\u003e\n\u003cli\u003eCaforio, A.L.P., et al. Anti-heart and anti-intercalated disk autoantibodies: Evidence for autoimmunity in idiopathic recurrent acute pericarditis. \u003cem\u003eHeart\u003c/em\u003e \u003cstrong\u003e96\u003c/strong\u003e, 779\u0026ndash;784 (2010).\u003c/li\u003e\n\u003cli\u003eLopalco, G., et al. The autoinflammatory side of recurrent pericarditis: enlightening the pathogenesis for a more rational treatment. \u003cem\u003eTrends Cardiovasc Med\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 265\u0026ndash;274 (2021). \u003c/li\u003e\n\u003cli\u003eLazaros, G., et al. The role of the immunogenetic background in the development and recurrence of acute idiopathic pericarditis. \u003cem\u003eCardiology\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 55\u0026ndash;62 (2011). \u003c/li\u003e\n\u003cli\u003eVantourout, P. \u0026amp; Hayday, A. Six-of-the-best: unique contributions of \u0026gamma;\u0026delta; T cells to immunology. \u003cem\u003eNat Rev Immunol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 88\u0026ndash;100 (2013). \u003c/li\u003e\n\u003cli\u003eBrandes, M., et al. Flexible migration program regulates \u0026gamma;\u0026delta; T-cell involvement in humoral immunity. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 3693\u0026ndash;3701 (2003). \u003c/li\u003e\n\u003cli\u003eShiromizu, C.M. \u0026amp; Jancic, C.C. \u0026gamma;\u0026delta; T lymphocytes: An effector cell in autoimmunity and infection. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1\u0026ndash;8 (2018). \u003c/li\u003e\n\u003cli\u003eChiabrando, J.G., et al. Management of acute and recurrent pericarditis: JACC state-of-the-art review. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 76\u0026ndash;92 (2020). \u003c/li\u003e\n\u003cli\u003eChhabra, L. \u0026amp; Spodick, D.H. Colchicine for pericarditis. \u003cem\u003eAm J Heal Pharm\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 2012\u0026ndash;2013 (2014). \u003c/li\u003e\n\u003cli\u003eImazio, M. Colchicine for pericarditis. \u003cem\u003eTrends Cardiovasc Med\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 129\u0026ndash;136 (2015). \u003c/li\u003e\n\u003cli\u003eImazio, M., et al. Corticosteroids for recurrent pericarditis: high versus low doses: a nonrandomized observation. \u003cem\u003eCirculation\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 667\u0026ndash;71 (2008). \u003c/li\u003e\n\u003cli\u003eFarand, P. Acute and recurring pericarditis: more colchicine, less corticosteroids. \u003cem\u003eWorld\u003c/em\u003e \u003cem\u003eJ Cardiol\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 403 (2010). \u003c/li\u003e\n\u003cli\u003eDagan, A., Langevitz, P., Shoenfeld, Y. \u0026amp; Shovman, O. Anakinra in idiopathic recurrent pericarditis refractory to immunosuppressive therapy; a preliminary experience in seven patients. \u003cem\u003eAutoimmun Rev\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 627\u0026ndash;631 (2019). \u003c/li\u003e\n\u003cli\u003eFeldman, J.M., Frishman, W.H. \u0026amp; Aronow, W.S. Goflikicept and related IL-1 inhibitors in the treatment of recurrent pericarditis. \u003cem\u003eCardiol Rev\u003c/em\u003e 10.1097/CRD.0000000000000704 (2024). \u003c/li\u003e\n\u003cli\u003eBrucato, A., et al. Effect of anakinra on recurrent pericarditis among patients with colchicine resistance and corticosteroid dependence: the AIRTRIP randomized clinical trial. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e316\u003c/strong\u003e, 1906\u0026ndash;1912 (2016). \u003c/li\u003e\n\u003cli\u003eKlein, A., et al. Rilonacept in recurrent pericarditis: first efficacy and safety data from an ongoing phase 2 pilot clinical trial. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 1261 (2019). \u003c/li\u003e\n\u003cli\u003eJin, Z.S. \u0026amp; Wang, H.Y. Small molecules RSC001 use in breast cancer model. \u003cem\u003eImmunome Res\u003c/em\u003e 14 (2018). \u003c/li\u003e\n\u003cli\u003eGodfrey, D.I., Koay, H.F., McCluskey, J. \u0026amp; Gherardin, N.A. The biology and functional importance of MAIT cells. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1110\u0026ndash;1128 (2019). \u003c/li\u003e\n\u003cli\u003eKeller, A.N., et al. Drugs and drug-like molecules can modulate the function of mucosal-associated invariant T cells. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 402\u0026ndash;411 (2017). \u003c/li\u003e\n\u003cli\u003eCrossland, K.L., et al. AIRE is not essential for the induction of human tolerogenic dendritic cells. \u003cem\u003eAutoimmunity\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 211\u0026ndash;218 (2016). \u003c/li\u003e\n\u003cli\u003eGherardin, N.A., et al. Human blood MAIT cell subsets defined using MR1 tetramers. \u003cem\u003eImmunol Cell Biol\u003c/em\u003e \u003cstrong\u003e96\u003c/strong\u003e, 507\u0026ndash;525 (2018). \u003c/li\u003e\n\u003cli\u003eDusseaux, M., et al. Human MAIT cells are xenobiotic-resistant, tissue-targeted, CD161 hi IL-17-secreting T cells. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 1250\u0026ndash;1259 (2011). \u003c/li\u003e\n\u003cli\u003eUssher, J.E., et al. CD161++CD8+ T cells, including the MAIT cell subset, are specifically activated by IL-12+IL-18 in a TCR-independent manner. \u003cem\u003eEur J Immunol\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 195\u0026ndash;203 (2014). \u003c/li\u003e\n\u003cli\u003evan Wilgenburg, B., et al. MAIT cells are activated during human viral infections. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 11653 (2016). \u003c/li\u003e\n\u003cli\u003eKurioka, A., et al. Shared and distinct phenotypes and functions of human cD161++ V\u0026alpha;7.2+ T cell subsets. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1031 (2017). \u003c/li\u003e\n\u003cli\u003eSalio, M., et al. Activation of human mucosal-associated invariant T cells induces CD40L-dependent maturation of monocyte-derived and primary dendritic cells. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e199\u003c/strong\u003e, 2631\u0026ndash;2638 (2017). \u003c/li\u003e\n\u003cli\u003eBaumann, S., et al. An unexpected role for FosB in activation-induced cell death of T cells. \u003cem\u003eOncogene\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 1333\u0026ndash;1339 (2003). \u003c/li\u003e\n\u003cli\u003evan der Houwen, T.B., van Hagen, P.M. \u0026amp; van Laar, J.A.M. Immunopathogenesis of Beh\u0026ccedil;et\u0026rsquo;s disease and treatment modalities. \u003cem\u003eSemin Arthritis Rheum \u003c/em\u003e\u003cstrong\u003e52\u003c/strong\u003e, 1956 (2022). \u003c/li\u003e\n\u003cli\u003eHarly, C., Peign\u0026eacute;, C.M. \u0026amp; Scotet, E. Molecules and mechanisms implicated in the peculiar antigenic activation process of human V\u0026gamma;9V\u0026delta;2 T cells. \u003cem\u003eFront Immunol \u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 657 (2015). \u003c/li\u003e\n\u003cli\u003ePapotto, P.H., Reinhardt, A., Prinz, I. \u0026amp; Silva-Santos, B. Innately versatile: \u0026gamma;\u0026delta;17 T cells in inflammatory and autoimmune diseases. \u003cem\u003eJ Autoimmun\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 26\u0026ndash;37 (2018). \u003c/li\u003e\n\u003cli\u003eCaccamo, N., et al. Differentiation, phenotype, and function of interleukin-17-producing human v\u0026gamma;9v\u0026delta;2 T cells. Blood \u003cstrong\u003e118\u003c/strong\u003e, 129\u0026ndash;38 (2011). \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eCharacteristics of the patients and samples analyzed in the present study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"926\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003ePatient ID\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003eSex\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003eAge, years\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003eWeight,\u003cbr\u003ekg\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003eDays of goflikicept therapy\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003eTotal goflikicept dosage, mg\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003eDose, mg/kg\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003eModified Pouchot activity score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003eC-reactive protein, mg/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003eFerritin, ng/mL\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003eIL-6, ng/mL\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003eIL1RA, ng/mL\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e1.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003eND\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003eND\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003eND\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e10.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e93.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e1.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e832\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e320\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e3.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e1.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e87.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e2.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e3.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e96.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e4.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e966\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e320\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e3.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e0.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e58.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e3.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e75.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e2.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e554\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e400\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e5.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e0.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e47.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e0.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e81.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e3.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e2319\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e320\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e4.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e52.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e2.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e306\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e109\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e160\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e0.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e13.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e3.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e160\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e1.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e2.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e289.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e4.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003eP8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003eSC2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e160\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e1.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e0.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e26.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e1.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eND: no data\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7507778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7507778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdiopathic recurrent pericarditis (IRP) is a rare autoinflammatory disorder characterized by NLRP3 inflammasome overactivation, resulting in excessive IL-1β and IL-1α production. Although IL-1 blockade shows promise as a therapeutic strategy, the underlying molecular mechanisms remain incompletely understood. We investigated the effect of goflikicept, a novel heterodimeric fusion protein that inhibits both IL-1β and IL-1α, on peripheral blood mononuclear cell (PBMC) transcriptomes from patients with IRP. Single-cell RNA sequencing was used to analyze PBMC subsets and identify treatment response-related transcriptomic signatures. Goflikicept induced temporal transcriptional reprogramming, with a particularly pronounced downregulation of IL-1-related inflammatory pathways in classical monocytes by day 35 of treatment. Furthermore, goflikicept modulated the adaptive immune response, suppressing na\u0026iuml;ve B cell activity, enhancing circulating plasma cell precursor activity, and significantly altering γδ T cell and mucosal-associated invariant T cell populations. In conclusion, goflikicept effectively normalized dysregulated immune responses in IRP, suggesting a novel therapeutic approach for NLRP3-mediated diseases. This study provides the first single-cell resolution insights into the molecular mechanisms of IL-1 blockade, informing the development of targeted therapies for autoinflammatory conditions.\u003c/p\u003e","manuscriptTitle":"Single-cell RNA profiling suggests goflikicept-mediated immune modulation in idiopathic recurrent pericarditis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:49:29","doi":"10.21203/rs.3.rs-7507778/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f423985-3a17-4163-bbc2-c8c47e8c9093","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54008053,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":54008054,"name":"Health sciences/Medical research/Drug development"},{"id":54008055,"name":"Health sciences/Diseases/Rheumatic diseases"}],"tags":[],"updatedAt":"2026-03-18T09:34:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:49:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7507778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7507778","identity":"rs-7507778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}