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Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research. [ABSTRACT FROM AUTHOR]

Mixed-effects regression of single-cell data accounts for confounding variation and reveals an expanded CD4+ T cell population in rheumatoid arthritis. Pinpointing culprits in single-cell data: New techniques analyzing single cells are being applied to clinical samples. These techniques generate large datasets, and it can be challenging to identify rare cell types associated with disease. Fonseka et al. developed a simple statistical method to do just that while simultaneously controlling for confounding biological and technical variation. Using their method on single-cell mass and flow cytometry data revealed an expanded CD4+ T cell subset in the blood of rheumatoid arthritis patients that was later seen to contract upon treatment. The authors' method performed better than the current gold standard and could be a potentially widely applied tool for the analysis of high-dimensional single-cell data. High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4+ T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4+ T cells, identified as CD27− HLA-DR+ effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10−3). The frequency of CD27− HLA-DR+ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27− HLA-DR+ cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27− HLA-DR+ cells were associated with RA (meta-analysis P = 2.3 × 10−4). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27− HLA-DR+ cells, which comprised ~10% of synovial CD4+ T cells. CD27− HLA-DR+ cells expressed a distinctive effector memory transcriptomic program with T helper 1 (TH1)– and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data. [ABSTRACT FROM AUTHOR]

Local delivery of therapeutics for the treatment of inflammatory arthritis (IA) is limited by short intra-articular half-lives. Since IA severity often fluctuates over time, a local drug delivery method that titrates drug release to arthritis activity would represent an attractive paradigm in IA therapy. Here we report the development of a hydrogel platform that exhibits disassembly and drug release controlled by the concentration of enzymes expressed during arthritis flares. In vitro, hydrogel loaded with triamcinolone acetonide (TA) releases drug on-demand upon exposure to enzymes or synovial fluid from patients with rheumatoid arthritis. In arthritic mice, hydrogel loaded with a fluorescent dye demonstrates flare-dependent disassembly measured as loss of fluorescence. Moreover, a single dose of TA-loaded hydrogel but not the equivalent dose of locally injected free TA reduces arthritis activity in the injected paw. Together, our data suggest flare-responsive hydrogel as a promising next-generation drug delivery approach for the treatment of IA.

The article focuses on effective treatments for chronic and debilitating inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis. Topics discussed include importance of drug targeting in improving therapeutic outcomes and minimize systemic toxicity; development of hydrogel microfibers from ascorbyl palmitate, an amphiphile; and use of poly(lactic-co-glycolic acid) nanoparticles for increasing tissue permeability at the site of inflammation.

An abstract of the article "Nod/Ripk2 signaling in dendritic cells activates IL-17A-secreting innate lymphoid cells and drives colitis in T-bet~/~.Rag2~/~(TRUC) mice," by Joerg Ermann and colleagues is presented.

Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods-LDSC-SEG, SNPsea, and scDRS-that have successfully identified relevant cell types in other diseases. Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA sequencing data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. This revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two understudied loci, ENTR1 (SDCCAG3) and B3GNT2. Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Mouse and human data implicate the NOD1 and NOD2 sensors of the intestinal microbiome and the associated signal transduction via the receptor interacting protein kinase 2 (RIPK2) as a potential key signaling node for the development of inflammatory bowel disease (IBD) and an attractive target for pharmacological intervention. The TRUC mouse model of IBD was strongly indicated for evaluating RIPK2 antagonism for its effect on intestinal inflammation based on previous knockout studies with NOD1, NOD2, and RIPK2. We identified and profiled the BI 706039 molecule as a potent and specific functional inhibitor of both human and mouse RIPK2 and with favorable pharmacokinetic properties. We dosed BI 706039 in the spontaneous TRUC mouse model from age 28 to 56 days. Oral, daily administration of BI 706039 caused dose-responsive and significant improvement in colonic histopathological inflammation, colon weight, and terminal levels of protein-normalized fecal lipocalin (all P values <0.001). These observations correlated with dose responsively increasing systemic levels of the BI 706039 compound, splenic molecular target engagement of RIPK2, and modulation of inflammatory genes in the colon. This demonstrates that a relatively low oral dose of a potent and selective RIPK2 inhibitor can modulate signaling in the intestinal immune system and significantly improve disease associated intestinal inflammation. NEW & NOTEWORTHY The RIPK2 kinase at the apex of microbiome immunosensing is an attractive target for pharmacological intervention. A low oral dose of a RIPK2 inhibitor leads to significantly improved intestinal inflammation in the murine TRUC model of colitis. A selective and potent inhibitor of the RIPK2 kinase may represent a new class of therapeutics that target microbiome-driven signaling for the treatment of IBD.