Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota.

Purpose: Human gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers. Method: The fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken). After that, the fecal microbes with high abundance were extracted for subsequent analysis. The extracted fecal microbes were further screened by least absolute shrinkage and selection operator (LASSO) regression, and the selected fecal microbe biomarkers were used for model training. In this study, we constructed six different machine learning (ML) models, and then selected the best model for constructing a JIA diagnostic tool by comparing the performance of the models based on a combined consideration of area under receiver operating characteristic curve (AUC), accuracy, specificity, F1 score, calibration curves and clinical decision curves. In addition, to further explain the model, Permutation Importance analysis and Shapley Additive Explanations (SHAP) were performed to understand the contribution of each biomarker in the prediction process. Result: A total of 231 individuals were included in this study, including 203 JIA patients and Non-JIA individuals. In the analysis of diversity at the genus level, the alpha diversity represented by Shannon value was not significantly different between the two groups, while the belt diversity was slightly different. After selection by LASSO regression, 10 fecal microbe biomarkers were selected for model training. By comparing six different models, the XGB model showed the best performance, which average AUC, accuracy and F1 score were 0.976, 0.914 and 0.952, respectively, thus being used to construct the final JIA diagnosis model. Conclusion: A JIA diagnosis model based on XGB algorithm was constructed with excellent performance, which may assist physicians in early detection of JIA patients and improve the prognosis of JIA patients.

Gene association analysis to determine the causal relationship between immune cells and juvenile idiopathic arthritis.

BACKGROUND: Juvenile idiopathic arthritis (JIA) is a type of chronic childhood arthritis with complex pathogenesis. Immunological studies have shown that JIA is an acquired self-inflammatory disease, involving a variety of immune cells, and it is also affected by genetic and environmental susceptibility. However, the precise causative relationship between the phenotype of immune cells and JIA remains unclear to date. The objective of our study is to approach this inquiry from a genetic perspective, employing a method of genetic association analysis to ascertain the causal relationship between immune phenotypes and the onset of JIA. METHODS: In this study, a two-sample Mendelian randomization (MR) analysis was used to select single nucleotide polymorphisms (SNPs) significantly associated with immune cells as instrumental variables to analyze the bidirectional causal relationship between 731 immune cells and JIA. There were four types of immune features (median fluorescence intensity (MFI), relative cellular (RC), absolute cellular (AC), and morphological parameters (MP)). Finally, the heterogeneity and horizontal reproducibility of the results were verified by sensitivity analysis, which ensured more robust results. RESULTS: We found that CD3 on CM CD8br was causally associated with JIA at the level of 0.05 significant difference (95% CI = 0.630 ~ 0.847, P = 3.33 × 10-5, PFDR = 0.024). At the significance level of 0.20, two immunophenotypes were causally associated with JIA, namely: HLA DR on CD14+ CD16- monocyte (95% CI = 0.633 ~ 0.884, P = 6.83 × 10-4, PFDR = 0.16) and HLA DR on CD14+ monocyte (95% CI = 0.627 ~ 0.882, P = 6.9 × 10-4, PFDR = 0.16). CONCLUSION: Our study assessed the causal effect of immune cells on JIA from a genetic perspective. These findings emphasize the complex and important role of immune cells in the pathogenesis of JIA and lay a foundation for further study of the pathogenesis of JIA.

DOCK8 Mutation in Patient with Juvenile Idiopathic Arthritis and Sjögren's Syndrome.

This study investigated the association between autoimmunity and immunodeficiency in pediatric patients, focusing on the case of a 15-year-old female diagnosed with juvenile idiopathic arthritis (JIA) and secondary Sjögren's syndrome. The patient presented with a variety of symptoms, including joint pain, bronchial asthma, leukopenia, and skin lesions. Genetic testing revealed a de novo mutation in the DOCK8 gene, associated with DOCK8 deficiency, a condition usually associated with immunodeficiencies. The clinical course, diagnostic pathway, and treatment history are detailed, highlighting the importance of molecular diagnostics in understanding the genetic basis of rheumatic diseases. This case highlights the need to consider innate immune errors in patients with multiple diseases or atypical symptoms of rheumatic diseases. Furthermore, the study highlights the importance of targeted treatment, including genetic counseling, to improve patient outcomes. The observed association between autoimmunity and immune deficiency reinforces the importance of molecular testing in elucidating the causes of previously idiopathic rheumatic diseases, contributing to improved patient care and quality of life.

Shared genomic segments analysis identifies MHC class I and class III molecules as genetic risk factors for juvenile idiopathic arthritis.

Juvenile idiopathic arthritis (JIA) is a complex rheumatic disease encompassing several clinically defined subtypes of varying severity. The etiology of JIA remains largely unknown, but genome-wide association studies (GWASs) have identified up to 22 genes associated with JIA susceptibility, including a well-established association with HLA-DRB1. Continued investigation of heritable risk factors has been hindered by disease heterogeneity and low disease prevalence. In this study, we utilized shared genomic segments (SGS) analysis on whole-genome sequencing of 40 cases from 12 multi-generational pedigrees significantly enriched for JIA. Subsets of cases are connected by a common ancestor in large extended pedigrees, increasing the power to identify disease-associated loci. SGS analysis identifies genomic segments shared among disease cases that are likely identical by descent and anchored by a disease locus. This approach revealed statistically significant signals for major histocompatibility complex (MHC) class I and class III alleles, particularly HLA-A∗02:01, which was observed at a high frequency among cases. Furthermore, we identified an additional risk locus at 12q23.2-23.3, containing genes primarily expressed by naive B cells, natural killer cells, and monocytes. The recognition of additional risk beyond HLA-DRB1 provides a new perspective on immune cell dynamics in JIA. These findings contribute to our understanding of JIA and may guide future research and therapeutic strategies.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis.

BACKGROUND: Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. METHODS: To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk. RESULTS: We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks. CONCLUSION: Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning.

BACKGROUND: Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level. METHODS: The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier. RESULTS: We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model's performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model. CONCLUSIONS: We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.

[Monogenic variants in Laccase domain-containing 1 (LACC1) as the cause of juvenile arthritis]. / Monogene Varianten in "laccase domain-containing 1" (LACC1) als Ursache einer juvenilen Arthritis.

Monogenic mutations in laccase domain-containing 1 (LACC1) are associated with clinical pictures that mimic severe courses of polyarticular or systemic juvenile idiopathic arthritis. The diseases are characterized by an early onset during the first year of life, a familial clustering and a high inflammatory activity. The courses are mostly difficult to influence and often lead to sequelae. In this article four cases from two families are presented in which the homozygous mutation p.T276fs* in LACC1 was detected. The children initially suffered from polyarticular or systemic forms of juvenile arthritis. Of the patients two are currently being treated with tocilizumab and methotrexate and one female patient without a basis treatment is currently only receiving local repeated intra-articular steroids. A fourth female patient underwent an allogeneic bone marrow transplantation due to a relapse of an acute lymphatic leukemia. Since then, no further inflammatory symptoms have occurred. The cases presented are compared with the other 50 courses published to date. In addition, recent studies investigating the influence of LACC1 mutations, particularly on macrophage function, are summarized.

Recombinant Interleukin-1 Receptor Antagonist Is an Effective First-Line Treatment Strategy in New-Onset Systemic Juvenile Idiopathic Arthritis, Irrespective of HLA-DRB1 Background and IL1RN Variants.

OBJECTIVE: Human leukocyte antigen (HLA)-DRB1*15:01 has been recently associated with interstitial lung disease (LD), eosinophilia, and drug reactions in systemic juvenile idiopathic arthritis (sJIA). Additionally, genetic variants in IL1RN have been linked to poor response to anakinra. We sought to reproduce these findings in a prospective cohort study of patients with new-onset sJIA treated with anakinra as first-line therapy. METHODS: HLA and IL1RN risk alleles were identified via whole-genome sequencing. Treatment responses and complications were compared between carriers versus noncarriers. RESULTS: Seventeen of 65 patients (26%) carried HLA-DRB1*15:01, comparable with the general population, and there was enrichment for HLA-DRB1*11:01, a known risk locus for sJIA. The rates of clinical inactive disease (CID) at 6 months, 1 year, and 2 years were generally high, irrespective of HLA-DRB1 or IL1RN variants, but significantly lower in carriers of an HLA-DRB1*11:01 allele. One patient, an HLA-DRB1*15:01 carrier, developed sJIA-LD. Of the three patients with severe drug reactions to biologics, one carried HLA-DRB1*15:01. The prevalence of eosinophilia did not significantly differ between HLA-DRB1*15:01 carriers and noncarriers at disease onset (6.2% vs 14.9%, P = 0.67) nor after the start of anakinra (35.3% vs 37.5% in the first 2 years of disease). CONCLUSION: We observed high rates of CID using anakinra as first-line treatment irrespective of HLA-DRB1 or IL1RN variants. Only one of the 17 HLA-DRB1*15:01 carriers developed sJIA-LD, and of the three patients with drug reactions to biologics, only one carried HLA-DRB1*15:01. Although thorough monitoring for the development of drug hypersensitivity and refractory disease courses in sJIA, including sJIA-LD, remains important, our data support the early start of biologic therapy in patients with new-onset sJIA irrespective of HLA-DRB1 background or IL1RN variants.

Identification of key biomarkers in RF-negative polyarticular and oligoarticular juvenile idiopathic arthritis by bioinformatic analysis.

OBJECTIVE: Juvenile idiopathic arthritis (JIA) is a broad term used to describe arthritis of unknown origin. JIA commonly persists into adulthood, often causing substantial morbidity such as restricted joint function, which can lead to challenges in employment and independence. This study aims to identify diagnostic biomarkers and investigate the role of immune cells in the pathogenesis of rheumatoid factor-negative polyarticular juvenile idiopathic arthritis (RF-negative pJIA) and oligoarticular juvenile idiopathic arthritis (oJIA). METHODS: We retrieved a JIA dataset from the GEO database and conducted an analysis of differentially expressed genes (DEGs). Subsequently, functional enrichment analysis was performed on the DEGs. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key modules. Additionally, we constructed a protein‒protein interaction network to identify hub genes that serve as signature genes. Furthermore, we employed CIBERSORT to classify immune cell infiltration. RESULTS: From the GSE20307 dataset, we identified a total of 1438 DEGs in RF-negative pJIA and 688 DEGs in oJIA. WGCNA clustered the data into 6 modules in pJIA and 4 modules in oJIA. Notably, the ME5 and ME2 modules exhibited significant associations with pJIA and oJIA, respectively. In both pJIA and oJIA, we identified six hub genes, four of which demonstrated high diagnostic sensitivity and specificity in pJIA, while five showed high diagnostic sensitivity and specificity in oJIA. CIBERSORT analysis suggested the potential involvement of these signature genes in immune cell infiltration. CONCLUSION: In this study, we identified JUN, CXCL8, SOCS3, and KRAS as biomarkers for RF-negative pJIA and JUN, CXCL8, SOCS3, PTGS2, and NFKBIA as biomarkers for oJIA. Furthermore, our findings suggest that Tfh cells may play a role in the early onset of both RF-negative pJIA and oJIA.

Population-level single-cell genomics reveals conserved gene programs in systemic juvenile idiopathic arthritis.

Systemic autoimmune and autoinflammatory diseases are characterized by genetic and cellular heterogeneity. While current single-cell genomics methods provide insights into known disease subtypes, these analysis methods do not readily reveal novel cell-type perturbation programs shared among distinct patient subsets. Here, we performed single-cell RNA-Seq of PBMCs of patients with systemic juvenile idiopathic arthritis (SJIA) with diverse clinical manifestations, including macrophage activation syndrome (MAS) and lung disease (LD). We introduced two new computational frameworks called UDON and SATAY-UDON, which define patient subtypes based on their underlying disrupted cellular programs as well as associated biomarkers or clinical features. Among twelve independently identified subtypes, this analysis uncovered what we believe to be a novel complement and interferon activation program identified in SJIA-LD monocytes. Extending these analyses to adult and pediatric lupus patients found new but also shared disease programs with SJIA, including interferon and complement activation. Finally, supervised comparison of these programs in a compiled single-cell pan-immune atlas of over 1,000 healthy donors found a handful of normal healthy donors with evidence of early inflammatory activation in subsets of monocytes and platelets, nominating possible biomarkers for early disease detection. Thus, integrative pan-immune single-cell analysis resolved what we believe to be new conserved gene programs underlying inflammatory disease pathogenesis and associated complications.

IL12B and IL17 genes polymorphisms associated with differential susceptibility to juvenile idiopathic arthritis and juvenile-onset systemic lupus erythematosus in Chinese children.

Genetic factors play a crucial role in the immune response of juvenile idiopathic arthritis (JIA) and juvenile-onset systemic lupus erythematosus (JSLE). This study aimed to investigate the association of IL12B (rs3212227, rs6887695) and IL17 (rs2275913, rs763780) gene polymorphisms with the susceptibility of JIA and JSLE in Chinese children. A total of 303 healthy controls and 304 patients including 160 JIA and 144 patients were analyzed, and the genetic polymorphisms were genotyped by using a Sequenom MassArray system. There was a significant association between the IL12B rs3212227 genotype and the increased risk of JSLE (P = .01). For rs6887695, the minor allele C was significantly associated with the increased risk of JIA (odds ratio = 1.48, 95% confidence interval [CI] = 1.12-1.95, P = .005). Moreover, rs6887695 genotype was significantly associated with both JIA and JSLE susceptibility (P < .05). Besides, IL12B haplotype GC significantly associated with the increased risk of JIA (P = .016). However, no significant difference was found between the IL17 (rs2275913, rs763780) gene polymorphisms and JIA or JSLE susceptibility (P > .05). And similar genotype distributions of IL12B and IL17 polymorphisms were found between the patients with nephritis and without nephritis in JSLE (P > .05). Our results indicated that IL12B polymorphisms was associated with an increased risk for the development of JIA and JSLE in Chinese children, highlighting the involvement of inflammation in the pathogenesis of JIA and JSLE. Moreover, there was a risk haplotype in IL12B which could increase the risk of JIA.

MicroRNA-27a-3p enhances the inflammatory phenotype of Juvenile Idiopathic Arthritis fibroblast-like synoviocytes.

BACKGROUND: Juvenile Idiopathic Arthritis (JIA) is the most prevalent chronic pediatric rheumatic disorder. In joints of JIA patients, aggressive phenotypic changes in fibroblast-like synoviocytes (FLS) of the synovial lining play a key role in inflammation. MicroRNAs are dysregulated in rheumatoid arthritis and JIA, including miR-27a-3p. However, it is not understood if miR-27a-3p, enriched in JIA synovial fluid (SF) and leukocytes, alters FLS function. METHODS: Primary JIA FLS cells were transfected with a miR-27a-3p mimic or a negative control microRNA (miR-NC) and stimulated with pooled JIA SF or inflammatory cytokines. Viability and apoptosis were analyzed by flow cytometry. Proliferation was evaluated using a 3H-thymidine incorporation assay. Cytokine production was assessed by qPCR and ELISA. Expression of TGF-ß pathway genes was determined using a qPCR array. RESULTS: MiR-27a-3p was constitutively expressed in FLS. Overexpression of miR-27a-3p caused increased interleukin-8 secretion in resting FLS, and interleukin-6 was elevated in SF-activated FLS compared to miR-NC. Furthermore, stimulation with pro-inflammatory cytokines augmented FLS proliferation in miR-27a-3p-transfected FLS relative to miR-NC. Expression of multiple TGF-ß pathway genes was modulated by overexpression of miR-27a-3p. CONCLUSIONS: MiR-27a-3p significantly contributes to FLS proliferation and cytokine production, making it a potential candidate for epigenetic therapy that targets FLS in arthritis.

Bone mineral density in egyptian children with juvenile idiopathic arthritis: possible correlation to serum RANKL / osteoprotegerin (OPG) ratio and OPG gene polymorphisms.

BACKGROUND: Children with juvenile idiopathic arthritis (JIA) are at higher risk of decreased bone mineral density (BMD) compared with healthy children due to genetic, disease and medication-related causes. This study aims to investigate the possible effects of osteoprotegerin (OPG) gene polymorphisms and serum levels of osteoprotegerin (OPG) and receptor activator of nuclear factor κB-ligand (RANKL) and RANKL/OPG ratio on BMD in children with JIA. METHODS: OPG gene rs2073617, rs3134069, serum RANKL, OPG and RANKL/OPG ratio were evaluated in 60 JIA children and 100 matched healthy controls. BMD was evaluated by lumbar dual energy X-ray absorptiometry (DEXA) according to which patients were classified in 2 groups (DEXA z-score above and below - 2). Composite disease activity was measured using the Juvenile Arthritis Disease Activity Score (JADAS) 27-joints. Articular damage was scored using the juvenile arthritis damage index (JADI). RESULTS: Patients aged 12.05 ± 3.2 years, included 38 females and 31% had BMD z-score below-2. Systemic-onset JIA was the most frequent phenotype (38%). Genotypes and alleles frequencies of the 2 studied polymorphisms did not differ between patients and controls (p > 0.05 for all) while serum RANKL and RANKL/OPG ratio were significantly higher in patients compared to controls (p = < 0.001 and 0.03 respectively). Patients with BMD < -2 had significantly greater frequencies of rs2073617 TT genotype and T allele (p < 0.001), higher serum RANKL, RANKL/OPG ratio (p = 0.01, 0.002), female predominance (p = 0.02), higher articular and extra-articular damage index (p = 0.008,0.009) and more frequent steroid usage (p = 0.02) compared to patients with BMD z-score >-2. Multivariate analysis showed rs2073617 TT genotype, RANKL/OPG ratio, long disease duration (above 36 months) and use of steroid to be associated with decreased BMD (p = 0.03,0.04,0.01,0.01 respectively) in JIA children. CONCLUSIONS: Egyptian children with JIA have decreased BMD. rs2073617 TT genotype and T allele, RANKL/OPG ratio are possible determinants of reduced BMD in JIA. Our results underline the importance of frequent monitoring of BMD in JIA children and trying to control disease activity to preserve long term bone health.

PADI4 and IL-33 gene polymorphisms with susceptibility to systemic lupus erythematosus and juvenile idiopathic arthritis, a systematic review and meta-analysis.

BACKGROUND: This study evaluated the association between peptidyl arginine deiminase type IV (PADI4) and interleukin 33 (IL-33) with systemic lupus erythematosus (SLE) and juvenile idiopathic arthritis (JIA). METHOD: We searched the PubMed, Web of Science, Embase and Cochrane Library databases to retrieve articles published up to January 20, 2023. Stata/SE 17.0 (College Station, TX) software was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). The cohort study, case-control study focusing on the PADI4, IL-33 polymorphism, and SLE, JIA were retrieved. The data included basic information of each study and the genotypes and allele frequencies. RESULTS: Studies in PADI4 rs2240340 = 2 and 3 IL-33(rs1891385 = 3, rs10975498 = 2, rs1929992 = 4) were found in 6 articles. Overall, only the IL-33 rs1891385 show significant association between SLE in all 5 models. The results were OR (95% CI) = 1.528 (1.312, 1.778), P = .000 in Allele model (C vs A), OR (95% CI) =1.473 (1.092, 1.988), P = .000 in Dominant model (CC + CA vs AA), 2.302 (1.583, 3.349), P = .000 in Recessive model (CC vs CA + AA), 2.711 (1.845, 3.983), P = .000 in Homozygote model (CC vs AA), 5.568 (3.943, 7.863), P = .000 in Heterozygote model (CA vs AA). PADI4 rs2240340, IL-33 rs10975498, IL-33 rs1929992 were not found to be association with the risk of SLE and JIA. In gene model, statistically significant association was found between IL-33 rs1891385 and SLE in sensitivity analysis. Egger's publication bias plot showed there was no publication bias (P = .165). Only in recessive model the heterogeneity test was significant (I2 = 57.9%, P ≤ .093) of IL-33 rs1891385. CONCLUSION: The current study suggests that in all 5 model, IL-33 rs1891385 polymorphism may be associated with genetic susceptibility to SLE. There was unclear association found between PADI4 rs2240340, IL-33 rs10975498, and IL-33 rs1929992 polymorphisms and SLE and JIA. Due to the limitations of included studies and the risk of heterogeneity, additional research is required to confirm our findings. PROSPERO REGISTRATION NUMBER: CRD42023391268.

Experience of using genetically engineered biological drugs in children with a systemic form of JIA in the Karaganda region.

Despite all the achievements of science and medicine, juvenile idiopathic arthritis today remains one of the main childhood diseases that lead to severe irreversible consequences. This, in turn, makes it urgent to search for effective drugs for the treatment of juvenile idiopathic arthritis, of which interleukin 1 (anakinra) and interleukin 6 (tocilizumab) inhibitors are becoming increasingly popular. AIM: to analyse the efficacy of genetically engineered biological drugs, namely anakinra and tocilizumab in children with systemic juvenile idiopathic arthritis among patients of the Karaganda region. The study involved 176 patients aged 4-17 years with a diagnosis of systemic juvenile idiopathic arthritis and with resistance to methotrexate for 3 months. Among all patients, 64 children received injections of anakinra, and 63 received tocilizumab in standard doses. The control group consisted of 50 patients of the same age category. Assessment of the efficacy of treatment was conducted at 2, 4, 8, 16, 24, and 48 weeks using ACR Pediatric criteria. The clinical effect of both drugs was detected as early as the second week after the start of therapy. At week 12 of the study in the tocilizumab group, the efficacy of treatment for ACR Pediatric 30, 50, and 70 reached 82 %, 71 %, and 69 %, and in the anakinra group - 89 %, 81 %, and 80 % respectively, while in the control group ACR Pediatric 30 after 12 weeks of treatment was achieved in 21 % of patients, ACR Pediatric 50 - in 12 %, and ACR Pediatric 70 - in 9 % (p Keywords: systemic arthritis, polyarthritis, tocilizumab, anakinra, genetically engineered biological drugs.

Clinical characteristics and genotype analysis of a Chinese patient with juvenile arthritis due to novel LACC1 frameshift mutation and literature review.

BACKGROUND: Some juvenile idiopathic arthritis (JIA) patients have a familial aggregation of the disease, and a few have been found to have a juvenile arthritis (JA) phenotype caused by a genetic mutation. JA due to LACC1 defects is a rare condition and it was never reported in China. METHODS: The clinical and molecular characteristics of a child with LACC1 gene mutation-related juvenile arthritis, diagnosed by high-throughput sequencing in Wuhan children's Hospital in 2021 were analyzed retrospectively; The literature and database were reviewed to summarize the clinical data and genotype characteristics of patients with JA caused by LACC1 gene mutation. RESULTS: Here, we report a 19-month-old Chinese male patient who presented with bilateral limb edema without a history of fever. Laboratory tests showed had moderate anemia and signs of inflammation: hemoglobin of 76 g/L, white blood cell count of 20.53 × 109 , and platelet count of 1194 × 109 ; MRI showed the patient had synovitis and tenosynovitis in bilateral hands and wrists. Whole-exome sequencing (WES) detected compound heterozygous variants, novel c.446_449dupTAAA and c.889T>C, in the LACC1 gene. Of the 52 patients reported in the literature (including this case), 38.9% had clinical symptoms of systemic juvenile idiopathic arthritis (sJIA), which tended to be caused by loss-of-function (LOF) mutation. Findings in this study expanded the spectrum of pathogenic variants and reveal the phenotypic heterogeneity of LACC1-JA. CONCLUSIONS: Our study reported a rare case of juvenile arthritis, which is due to the compound heterozygous mutation of LACC1, including a new novel frameshift mutation c.446_449dupTAAA, and LACC1 C297R variant causes disease by potentially modifying the local conformation of proteins. The clinical and genetic findings in our study show that LACC1-JA is highly heterogeneous, and gene testing is required for juvenile arthritis patients with a high inflammatory response at a young onset age.

Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types.

Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.

Study of the shared gene signatures of polyarticular juvenile idiopathic arthritis and autoimmune uveitis.

Objective: To explore the shared gene signatures and potential molecular mechanisms of polyarticular juvenile idiopathic arthritis (pJIA) and autoimmune uveitis (AU). Method: The microarray data of pJIA and AU from the Gene Expression Omnibus (GEO) database were downloaded and analyzed. The GEO2R tool was used to identify the shared differentially expressed genes (DEGs) and genes of extracellular proteins were identified among them. Then, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (IRGs) related to pJIA and AU. Moreover, the shared transcription factors (TFs) and microRNAs (miRNAs) in pJIA and AU were acquired by comparing data from HumanTFDB, hTFtarget, GTRD, HMDD, and miRTarBase. Finally, Metascape and g: Profiler were used to carry out function enrichment analyses of previously identified gene sets. Results: We found 40 up-regulated and 15 down-regulated shared DEGs via GEO2R. Then 24 shared IRGs in positivity-related modules, and 18 shared IRGs in negatively-related modules were found after WGCNA. After that, 3 shared TFs (ARID1A, SMARCC2, SON) were screened. And the constructed TFs-shared DEGs network indicates a central role of ARID1A. Furthermore, hsa-miR-146 was found important in both diseases. The gene sets enrichment analyses suggested up-regulated shared DEGs, TFs targeted shared DEGs, and IRGs positivity-correlated with both diseases mainly enriched in neutrophil degranulation process, IL-4, IL-13, and cytokine signaling pathways. The IRGs negatively correlated with pJIA and AU mainly influence functions of the natural killer cell, cytotoxicity, and glomerular mesangial cell proliferation. The down-regulated shared DEGs and TFs targeted shared DEGs did not show particular functional enrichment. Conclusion: Our study fully demonstrated the flexibility and complexity of the immune system disorders involved in pJIA and AU. Neutrophil degranulation may be considered the shared pathogenic mechanism, and the roles of ARID1A and MiR-146a are worthy of further in-depth study. Other than that, the importance of periodic inspection of kidney function is also noteworthy.

Genetic Background and Molecular Mechanisms of Juvenile Idiopathic Arthritis.

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in the paediatric population. JIA comprises a heterogeneous group of disorders with different onset patterns and clinical presentations with the only element in common being chronic joint inflammation. This review sought to evaluate the most relevant and up-to-date evidence on current knowledge regarding the pathogenesis of JIA subtypes to provide a better understanding of these disorders. Despite significant improvements over the past decade, the aetiology and molecular mechanisms of JIA remain unclear. It has been suggested that the immunopathogenesis is characterised by complex interactions between genetic background and environmental factors that may differ between JIA subtypes. Human leukocyte antigen (HLA) haplotypes and non-HLA genes play a crucial role in the abnormal activation of both innate and adaptive immune cells that cooperate in causing the inflammatory process. This results in the involvement of proinflammatory cytokines, including tumour necrosis factor (TNF)α, interleukin (IL)-1, IL-6, IL-10, IL-17, IL-21, IL-23, and others. These mediators, interacting with the surrounding tissue, cause cartilage stress and bone damage, including irreversible erosions. The purpose of this review is to provide a comprehensive overview of the genetic background and molecular mechanisms of JIA.