scholarly journals A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells

2004 ◽  
Vol 63 (11) ◽  
pp. 1387-1392 ◽  
Author(s):  
N Olsen
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Gene Expression Signature ◽  
Peripheral Blood Mononuclear ◽  
Recent Onset ◽  
Expression Signature ◽  
Blood Mononuclear Cells

scholarly journals Prevalence of Inflammatory Pathways Over Immuno-Tolerance in Peripheral Blood Mononuclear Cells of Recent-Onset Type 1 Diabetes

2022 ◽  
Vol 12 ◽  
Author(s):  
Aritania Sousa Santos ◽  
Edécio Cunha-Neto ◽  
Nelson Vinicius Gonfinetti ◽  
Fernanda Bernardi Bertonha ◽  
Pauline Brochet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Peripheral Blood Mononuclear Cells ◽  
Immune Tolerance ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Peripheral Blood Mononuclear ◽  
Regulatory Pathways ◽  
Recent Onset ◽  
Inflammatory Pathways ◽  
Blood Mononuclear Cells

BackgroundChanges in innate and adaptive immunity occurring in/around pancreatic islets had been observed in peripheral blood mononuclear cells (PBMC) of Caucasian T1D patients by some, but not all researchers. The aim of our study was to investigate whether gene expression patterns of PBMC of the highly admixed Brazilian population could add knowledge about T1D pathogenic mechanisms.MethodsWe assessed global gene expression in PBMC from two groups matched for age, sex and BMI: 20 patients with recent-onset T1D (≤ 6 months from diagnosis, in a time when the autoimmune process is still highly active), testing positive for one or more islet autoantibodies and 20 islet autoantibody-negative healthy controls.ResultsWe identified 474 differentially expressed genes between groups. The most expressed genes in T1D group favored host defense, inflammatory and anti-bacterial/antiviral effects (LFT, DEFA4, DEFA1, CTSG, KCNMA1) and cell cycle progression. Several of the downregulated genes in T1D target cellular repair, control of inflammation and immune tolerance. They were related to T helper 2 pathway, induction of FOXP3 expression (AREG) and immune tolerance (SMAD6). SMAD6 expression correlated negatively with islet ZnT8 antibody. The expression of PDE12, that offers resistance to viral pathogens was decreased and negatively related to ZnT8A and GADA levels. The increased expression of long non coding RNAs MALAT1 and NEAT1, related to inflammatory mediators, autoimmune diseases and innate immune response against viral infections reinforced these dataConclusionsOur analysis suggested the activation of cell development, anti-infectious and inflammatory pathways, indicating immune activation, whereas immune-regulatory pathways were downregulated in PBMC from recent-onset T1D patients with a differential genetic profile.

scholarly journals Increased Responsiveness to Toll-Like Receptor 4 Stimulation in Peripheral Blood Mononuclear Cells from Patients with Recent Onset Rheumatoid Arthritis

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
M. L. Kowalski ◽  
A. Wolska ◽  
J. Grzegorczyk ◽  
J. Hilt ◽  
M. Jarzebska ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Toll Like Receptors ◽  
Gene Expressions ◽  
Tlr2 Expression ◽  
Peripheral Blood Mononuclear ◽  
Recent Onset ◽  
Blood Mononuclear Cells

Background. Cell signaling via Toll-like receptors (TLRs) leads to synovial inflammation in rheumatoid arthritis (RA). We aimed to assess effects of TLR2 and TLR4 stimulation on proinflammatory cytokine production by peripheral blood mononuclear cells (PBMCs) from patients with recent-onset RA, osteoarthrosis (OA), and healthy control (HC).Methods. PBMCs were stimulated with LPS, biglycan and cytokine mix. Cytokines were analyzed in supernatants with ELISA. Expression of toll-like receptors mRNA in leukocytes was analyzed using real-time qPCR.Results. PBMCs from RA patients spontaneously produced less IL-6 and TNFαthan cells from OA and HC subjects. LPS increased cytokines' production in all groups. In RA patients increase was dramatic (30 to 48-fold and 17 to 31-fold, for respective cytokines) compared to moderate (2 to 8-fold) in other groups. LPS induced 15-HETE generation in PBMCs from RA (mean 251%) and OA patients (mean 43%), although only in OA group, the increase was significant. TLR2 and TLR4 gene expressions decreased in response to cytokine mix, while LPS enhanced TLR2 expression in HC and depressed TLR4 expression in OA patients.Conclusion. PBMCs from recent-onset RA patients are overresponsive to stimulation with bacterial lipopolysaccharide. TLR expression is differentially regulated in healthy and arthritic subjects.

scholarly journals Early Prediction of Clinical Response to Anti-TNF Treatment using Multi-omics and Machine Learning in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Niyaz Yoosuf ◽  
Mateusz Maciejewski ◽  
Daniel Ziemek ◽  
Scott A. Jelinsky ◽  
Lasse Folkersen ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Machine Learning ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Learning Models ◽  
Peripheral Blood Mononuclear ◽  
Blood Mononuclear Cells ◽  
Machine Learning Models

Abstract ObjectivesAdvances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for rheumatoid arthritis patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The present study was aimed to detect biomarkers and expression signatures of treatment response to TNF inhibition.MethodsPeripheral blood mononuclear cells were obtained from 39 patients collected before anti-TNF treatment initiation (day 0) and after three months. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in peripheral blood mononuclear cells, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry, and the level of proteins in plasma. Finally, using biological measurements, we run machine learning models to predict non-response. ResultsThe gene expression analysis in baseline samples revealed notably a higher expression of the gene EPPK1 in future responders. We also detected the suppression of genes and proteins following treatment, including suppression of expression of the gene, T-cell inhibitor CHI3L1, and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomics data showed high predictive utility (ROC AUC ± SEM: 0.81 ± 0.17) in classifying non-response to anti-TNF treatment in RA.ConclusionsOur integrative multi-omics analyses identified new biomarkers for prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.

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