Biology subjects

Aren, K.

Publications and source records attributed to Aren, K..

6 recordsLinked to original sources

bioRxiv2026

Circulating Immune Cells are Associated with Non-Inflammatory Pain in Rheumatoid Arthritis

Over half of patients with rheumatoid arthritis (RA) report clinically meaningful pain, despite treatment with disease-modifying antirheumatic drugs (DMARDs). While joint inflammation is a known cause of pain in patients with rheumatic diseases, emerging data indicate that many patients also suffer from centralized or nociplastic pain. There is a critical unmet need to characterize the altered cellular state that distinguishes patients with centralized pain. In the IMPACT study, 39 RA patients with minimal joint inflammation but varying levels of pain underwent quantitative sensory testing (QST) to assess nociplastic pain, completed patient-reported outcome (PRO) surveys, and provided blood samples for immune profiling. Supervised and unsupervised analysis of the multi-parameter spectral flow cytometry data identified immune cell populations correlated with nociplastic pain and patient-reported pain intensity. Moreover, analyses of single-cell RNA-seq from a subset of 22 patients revealed differences in cell type proportions and differential expression between the high and low pain groups. These studies provide novel insights into the role of circulating immune cells in altered central nervous system (CNS) pain regulation in adults with RA.

immunology↗

bioRxiv2025

Profibrotic monocyte-derived alveolar macrophages as a biomarker and therapeutic target in systemic sclerosis-associated interstitial lung disease

Interstitial lung disease (ILD) is present in over 60% of patients with systemic sclerosis (SSc) and is the leading cause of SSc-related deaths. Profibrotic monocyte-derived alveolar macrophages (MoAM) play a causal role in the pathogenesis of pulmonary fibrosis in animal models where their persistence in the niche requires signaling through Colony Stimulating Factor 1 Receptor (CSF1R). We hypothesized that the presence and proportion of MoAM in bronchoalveolar lavage (BAL) fluid from patients with SSc-ILD may be a biomarker of ILD severity. To test this hypothesis, we analyzed BAL fluid from 9 prospectively enrolled patients with SSc-ILD and 13 healthy controls using flow cytometry and single-cell RNA sequencing. Patients with SSc-ILD had more MoAM and interstitial macrophages in BAL fluid than healthy controls, and their abundance was associated with lung fibrosis severity. We identified changes in the MoAM transcriptome as a function of treatment with mycophenolate, an effective therapy for SSc-ILD. In SSc-ILD lung explants, spatial transcriptomics identified an expanded population of interstitial macrophages spilling into the alveolar space. Our findings suggest that the proportion of profibrotic MoAM and interstitial macrophages in BAL fluid may serve as a biomarker of SSc-ILD and credential them as possible targets for therapy.

immunology↗

bioRxiv2022

Three Distinct Transcriptional Profiles of Monocytes Correlate with Disease Activity in SSc Patients

Background/PurposePatients with systemic sclerosis (SSc) display a complex clinical phenotype. There are numerous studies that relate transcriptional signatures from PBMC or whole skin of SSc patients to disease activity. However, analyses of whole tissue RNA-sequencing studies are subjected to changes in cellular composition that can drive gene expression signatures and a loss of the ability to detect biologically important transcriptional changes within minority cell populations. Here, we focused on circulating monocytes, which have been shown to exist as two central populations classical (CM) and non-classical (NCM). MethodsSSc patients were recruited from four different sites that form PRESS: Northwestern University, University of Texas, University of Michigan and University of Utah. Comprehensive clinical data was collected for all patients. We isolated CM and NCM from these patients and age, sex, and race-matched healthy volunteers were used as controls. RNA-seq was performed on CM and NCM populations as well as on isolated bulk macrophages from skin. ResultsWe first performed RNA-seq on CM, which are the predominant population in circulation. In order to capture the variability across the SSc cohort, we defined 1790 differentially expressed genes in each patient. We then used these genes to cluster patients into 3 subgroups: Groups A-C. Group A exhibited the strongest interferon signature and innate immune pathways. Group B patients expressed genes in the same pathways but was also enriched for response to cAMP and corticosteroids. Both Group B and Group C exhibited upregulation of genes associated with vasculature development and blood vessel formation. Group C uniquely upregulated TGFB pathways. Next, we performed RNA-seq on NCM isolated from the same patients. When NCM were clustered based on the same 1790 genes as CM, we found that Groups A and C were recapitulated, while Group B was less cohesive. Our analysis stratified SSc patients based on their transcriptional profiles in monocytes but was agnostic to their clinical presentation. We found that Group B and C patients exhibited significantly worsened lung function at the time of monocyte isolation than Group A patients. However, there were no significant differences in skin disease. We then isolated macrophages from skin biopsies of SSc patients and showed that the transcriptional profile of Group A and C in SSc patients was conserved. We also used gene expression data from another study on monocytes which stratified patients based on disease presentation. We found that Group A accurately distinguished dcSSc and ncSSc patients from controls, but not lcSSc. ConclusionWe are the first to show that transcriptomic analysis of classical and non-classical circulating monocytes can unbiasedly stratify SSc patients and correlate with disease activity outcome measures.

genomics↗

bioRxiv2021

RNA-Seq analysis identifies novel roles for the primary cilia gene SPAG17 and the SOX9 locus non-coding RNAs in systemic sclerosis

Systemic sclerosis (SSc) is characterized by immune activation, vasculopathy, and unresolving fibrosis in the skin, lungs, and other organs. We performed RNA-Seq analysis on skin biopsies and peripheral blood mononuclear cells (PBMCs) from SSc patients and controls to better understand SSc pathogenesis. We analyzed these data to 1) test for case-control differences, and 2) identify genes whose expression levels correlate with SSc severity as measured by local skin score, modified Rodnan skin score (MRSS), forced vital capacity (FVC), or diffusion capacity for carbon monoxide (DLCO). We found that PBMCs from SSc patients showed a strong type 1 interferon signature. This signal replicated in the skin, with additional signals for increased extracellular matrix (ECM) genes, classical complement pathway activation, and the presence of B cells. Notably, we observed a marked decrease in the expression of SPAG17, a cilia component, in SSc skin. We identified genes that correlated with MRSS, DLCO, and FVC in SSc PBMCs and skin using weighted gene co-expression analysis (WGCNA). These genes were largely distinct from the case/control differentially expressed genes. In PBMCs, type 1 interferon signatures negatively correlated with DLCO. In SSc skin, ECM gene expression positively correlated with MRSS. Network analysis of SSc skin genes correlated with clinical features identified the non-coding RNAs SOX9-AS1 and ROCR, both near the SOX9 locus, as highly connected, "hub-like" genes in the network. These results identify non-coding RNAs and SPAG17 as novel factors potentially implicated in SSc pathogenesis.

genomics↗

bioRxiv2021

Distinct T cell chromatin landscapes in scleroderma subtypes

ObjectivesSystemic sclerosis (SSc; scleroderma) disproportionately affects biological females, and results of multiple studies implicate lymphocyte derangements in disease pathogenesis supporting use of mycophenolate mofetil (MMF) treatment. Here, we surveyed chromatin accessibility of circulating CD4+ T lymphocytes from SSc patients commencing MMF to gain molecular insights into systemic immune activation. MethodsPeripheral blood samples were collected longitudinally. We used the Assay for Transposase-Accessible Chromatin by sequencing (ATAC-seq) to interrogate genome-wide chromatin accessibility profiles of peripheral CD4+ T cells compared with publicly available healthy control (HC) data. ResultsATAC-seq libraries were generated for 18 SSc patients [78% with diffuse cutaneous (dc), 78% female, and 17% + anticentromere autoantibodies (ACA)]. Disease status (SSc vs. HC), biological sex, and serum autoantibody type were significantly associated with CD4+ T cell epigenomic profile variability, while MMF treatment had no significant effect. Present serum ACAs associated with elevated T helper 2 (Th2) cell proportions. +ACA patients consistently displayed distinct epigenetic profiles of increased open chromatin at gene loci encoding fibrosis-driving Th2 cytokines IL-4, IL-13, and the IL-4 receptor. ConclusionsOur results demonstrate the utility of interrogating chromatin accessibility profiles of patient CD4+ cells to stratify and understand better clinical SSc heterogeneity. They highlight a potential mechanism underlying the female sex preponderance for SSc as females had more open chromatin. Our findings nominate Th2 cell activation as a novel mechanistic hallmark and therapeutic opportunity to address SSc, especially in those with +ACA.

immunology↗

bioRxiv2017

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

BackgroundCardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups.\n\nMethodsWe used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters.\n\nResultsFour SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5-33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1-52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters.\n\nConclusionsMachine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.

genomics↗