The role of liver transaminase levels in methotrexate intolerance in juvenile idiopathic arthritis-a cross-sectional study.

Methotrexate (MTX) plays a key role when treating juvenile idiopathic arthritis (JIA), but MTX-intolerance is challenging. MTX-treatment might affect the liver, causing elevated levels of alanine aminotransferase (ALT), yet the role of ALT-levels in MTX-intolerance in JIA remains unclear. Our study aimed to investigate the association between ALT-levels during MTX-treatment and MTX-intolerance in JIA. Children (> 9 years old) diagnosed with JIA and treated with MTX (> 6 weeks) were eligible for enrollment. MTX-intolerance was assessed using the Methotrexate Intolerance Severity Score (MISS), completed by the parents, and defined as MISS ≥ 6 with at least 1 point for a behavioral/anticipatory/associative symptom. ALT-levels were determined at enrollment. A total of 118 children were enrolled (80 girls; 38 boys). MTX-intolerance was registered in 61%. ALT-levels did not differ between the MTX-intolerant group (median = 17.0 U/L [IQR: 14.0-26.0]) and the MTX-tolerant group (median = 20.5 U/L [IQR: 16.0-27.5]; p = 0.17). MTX-intolerance was prevalent in around 60% of both boys and girls. Nine out of 50 MTX-intolerant girls had elevated ALT-levels compared to 0/22 MTX-intolerant boys, however, there was no difference in median ALT levels between the two groups. Furthermore, the MTX-intolerant girls had a higher MISS (median = 14.0 [IQR: 9.3-17]) than the MTX-intolerant boys (median = 10.0 [IQR: 7.3-12]; p = 0.009). Our study did not find a difference in ALT-levels between MTX-intolerant and MTX-tolerant children. However, only MTX-intolerant girls and no MTX-intolerant boys showed elevated ALT-levels.

Methotrexate Intolerance in Juvenile Idiopathic Arthritis: Definition, Risks, and Management.

Juvenile idiopathic arthritis is the most common rheumatic disorder in childhood and adolescence posing a significant threat of short-term and long-term disability if left untreated. Methotrexate is a folic acid analog with various immunomodulatory properties. It has demonstrated significant efficacy for the treatment of juvenile idiopathic arthritis, often considered the preferred first-line disease-modifying anti-rheumatic drug given as monotherapy or in combination with biological drugs. Despite this, there is a considerable risk for treatment disruptions owing to the high prevalence of methotrexate intolerance, with symptoms such as nausea, stomach ache, vomiting, and behavioral symptoms. Many different risk factors for the intolerance have been proposed including gender, age, disease activity, treatment duration, dosing and administration, and genetic and psychological factors. As the studies have shown contradictory results, many questions are left unanswered. Therefore, a consensus regarding outcome measures and reporting is crucial. In this review, we describe the identification and assessment of methotrexate intolerance and evaluate potential risk factors, genetic associations as well as management strategies.

Coordinated immune dysregulation in juvenile dermatomyositis revealed by single-cell genomics.

Juvenile dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I IFN response and autoantibodies. Treatment options are limited due to an incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of patients with JDM at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment toward an immature naive state as a hallmark of JDM at diagnosis. Furthermore, we find that these changes in B cells are paralleled by T cell signatures suggestive of Th2-mediated inflammation that persist despite disease quiescence. We applied network analysis to reveal that hyperactivation of the type I IFN response in all immune populations is coordinated with previously masked cell states including dysfunctional protein processing in CD4+ T cells and regulation of cell death programming in NK cells, CD8+ T cells, and γδ T cells. Together, these findings unveil the coordinated immune dysregulation underpinning JDM and provide insight into strategies for restoring balance in immune function.

Single-cell RNA-Seq analysis reveals cell subsets and gene signatures associated with rheumatoid arthritis disease activity.

Rheumatoid arthritis (RA) management leans toward achieving remission or low disease activity. In this study, we conducted single-cell RNA sequencing (scRNA-Seq) of peripheral blood mononuclear cells (PBMCs) from 36 individuals (18 patients with RA and 18 matched controls, accounting for age, sex, race, and ethnicity), to identify disease-relevant cell subsets and cell type-specific signatures associated with disease activity. Our analysis revealed 18 distinct PBMC subsets, including an IFN-induced transmembrane 3-overexpressing (IFITM3-overexpressing) IFN-activated monocyte subset. We observed an increase in CD4+ T effector memory cells in patients with moderate-high disease activity (DAS28-CRP ≥ 3.2) and a decrease in nonclassical monocytes in patients with low disease activity or remission (DAS28-CRP < 3.2). Pseudobulk analysis by cell type identified 168 differentially expressed genes between RA and matched controls, with a downregulation of proinflammatory genes in the γδ T cell subset, alteration of genes associated with RA predisposition in the IFN-activated subset, and nonclassical monocytes. Additionally, we identified a gene signature associated with moderate-high disease activity, characterized by upregulation of proinflammatory genes such as TNF, JUN, EGR1, IFIT2, MAFB, and G0S2 and downregulation of genes including HLA-DQB1, HLA-DRB5, and TNFSF13B. Notably, cell-cell communication analysis revealed an upregulation of signaling pathways, including VISTA, in both moderate-high and remission-low disease activity contexts. Our findings provide valuable insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Coordinated immune dysregulation in Juvenile Dermatomyositis revealed by single-cell genomics.

Juvenile Dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I interferon response and autoantibodies. Treatment options are limited due to incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of JDM patients at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment towards an immature naive state as a hallmark of JDM. Furthermore, we find that these changes in B cells are paralleled by signatures of Th2-mediated inflammation. Additionally, our work identified SIGLEC-1 expression in monocytes as a composite measure of heterogeneous type I interferon activity in disease. We applied network analysis to reveal that hyperactivation of the type I interferon response in all immune populations is coordinated with dysfunctional protein processing and regulation of cell death programming. This analysis separated the ubiquitously expressed type I interferon response into a central hub and revealed previously masked cell states. Together, these findings reveal the coordinated immune dysregulation underpinning JDM and provide novel insight into strategies for restoring balance in immune function.

B cell MHC haplotype affects follicular inclusion, germinal center participation and plasma cell differentiation in a mouse model of lupus.

Introduction: MHC class II molecules are essential for appropriate immune responses against pathogens but are also implicated in pathological responses in autoimmune diseases and transplant rejection. Previous studies have shed light on the systemic contributions of MHC haplotypes to the development and severity of autoimmune diseases. In this study, we addressed the B cell intrinsic MHC haplotype impact on follicular inclusion, germinal center (GC) participation and plasma cell (PC) differentiation in the context of systemic lupus erythematosus (SLE). Methods: We leveraged the 564Igi mouse model which harbors a B cell receptor knock-in from an autoreactive B cell clone recognizing ribonuclear components, including double-stranded DNA (dsDNA). This model recapitulates the central hallmarks of the early stages of SLE. We compared 564Igi heterozygous offspring on either H2b/b, H2b/d, or H2d/d background. Results: This revealed significantly higher germinal center (GC) B cell levels in the spleens of H2b/b and H2b/d as compared to H2d/d (p<0.0001) mice. In agreement with this, anti-dsDNA-antibody levels were higher in H2b/b and H2b/d than in H2d/d (p<0.0001), with H2b/b also being higher compared to H2b/d (p<0.01). Specifically, these differences held true both for autoantibodies derived from the knock-in clone and from wild-type (WT) derived clones. In mixed chimeras where 564Igi H2b/b, H2b/d and H2d/d cells competed head-to-head in the same environment, we observed a significantly higher inclusion of H2b/b cells in GC and PC compartments relative to their representation in the B cell repertoire, compared to H2b/d and H2d/d cells. Furthermore, in mixed chimeras in which WT H2b/b and WT H2d/d cells competed for inclusion in GCs associated with an epitope spreading process, H2b/b cells participated to a greater extent and contributed more robustly to the PC compartment. Finally, immature WT H2b/b cells had a higher baseline of BCRs with an autoreactive idiotype and were subject to more stringent negative selection at the transitional stage. Discussion: Taken together, our findings demonstrate that B cell intrinsic MHC haplotype governs their capacity for participation in the autoreactive response at multiple levels: follicular inclusion, GC participation, and PC output. These findings pinpoint B cells as central contributors to precipitation of autoimmunity.

VMAP: Vaginal Microbiome Atlas During Pregnancy.

The vaginal microbiome has been shown to be associated with pregnancy outcomes including preterm birth (PTB) risk. Here we present VMAP: Vaginal Microbiome Atlas during Pregnancy (http://vmapapp.org), an application to visualize features of 3,909 vaginal microbiome samples of 1,416 pregnant individuals from 11 studies, aggregated from raw public and newly generated sequences via an open-source tool, MaLiAmPi. Our visualization tool (http://vmapapp.org) includes microbial features such as various measures of diversity, VALENCIA community state types (CST), and composition (via phylotypes and taxonomy). This work serves as a resource for the research community to further analyze and visualize vaginal microbiome data in order to better understand both healthy term pregnancies and those associated with adverse outcomes.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.