RNAseq analysis of human neutrophils from healthy controls, people with frailty and people with rheumatoid arthritis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE292338 | GEO | 2026/03/09
REPOSITORIES: GEO
Similar Datasets
Project description:We report gene expression profiles of blood neutrophils from (1) people newly-diagnosed with rheumatoid arthritis (early RA) before commencement of disease-modifying anti-rheumatic drug therapy, (2) DMARD-refractory RA (severe RA) before commencement of anti-TNF therapy and (3) healthy controls.
2025-05-14 | GSE274598 | GEO
Project description:We report gene expression profiles of blood neutrophils from (1) people with DMARD-refractory RA before commencement of anti-TNF therapy and (2) healthy controls.
2025-05-14 | GSE274996 | GEO
Project description:RNAseq analysis of human neutrophils from people with rheumatoid arthritis
| PRJNA1147260 | ENA
Project description:RNAseq analysis of human neutrophils from people with DMARD-refractory rheumatoid arthritis
| PRJNA1148920 | ENA
Project description:Synovial fluid and blood neutrophils from 7 rheumatoid arthritis patients and 5 matched healthy controls
2018-07-11 | GSE116899 | GEO
Project description:Frailty is defined as a clinical syndrome. It reflects a decrease in physiological reserve capacities that alters the mechanisms of adaptation to stress. Its clinical expression is modulated by biological, physiological, social and environmental changes. Thus, the accumulation of declines in these multiple systems leads to vulnerability to adverse outcomes (Fried et al., 2001; Nourhashémi et al., 2001). Indeed, frailty increases the risk of disability, falls, hospitalization and death. Age is currently a major determinant of frailty but does not explain this syndrome alone. It has been estimated that the prevalence of frailty is 4% in people aged 65-69 years and increases to 26% in people aged over 85 years (Clegg et al., 2013). However, this parameter is not sufficient to understand why long-lived people can preserve their physical function even at extreme ages (Ayers et al., 2017). The characterization of frailty has therefore become a priority in order to put in place actions to reduce or delay its harmful consequences. Early diagnostic methods are needed to identify those at risk. The search for biomarkers is part of this approach. As a result, the search for biomarkers of frailty has led to the identification of several types of molecules linked to the inflammatory response (e.g. IL-6, CRP, TNF-α and homocysteine), to nutritional status (e.g. vitamin D, albumin) or even endocrine. metabolism (e.g. IGF-1 and testosterone) (Picca et al., 2022; Mailliez et al., 2020; Alvarez-Sanchez et al, 2020). The development of new analytical techniques now allows multiplexed analysis rather than an individual biomarker to characterize a biological profile of this complex and dynamic frailty syndrome (Picca et al., 2022). Proteomic analysis is the technique of choice for studying the complete protein content of a biological sample. This technique has already proven itself in the discovery of new biomarkers in age-related diseases such as Parkinson's disease (Raghunathan et al., 2022) and Alzheimer's disease (Bai et al., 2021). Several studies have already described the plasma proteomic profile of frailty (Shamsi et al., 2012; Lin et al., 2017; Landino et al., 2021; Sathyan et al., 2020; Verghese et al., 2021), highlighting the importance of biological pathways associated with frailty: immune response, coagulation pathway or lipid metabolism. On the other hand, the determination of the level of frailty of the patients thanks to a proteomic analysis could also make it possible to improve the prediction of this syndrome as well as its phenotyping. In a first work on plasma samples, Sathyan et al. constructed a frailty prediction model that was more strongly correlated with chronological age than observed clinical frailty (Sathyan et al., 2020). Cognitive impairment plays a key role in determining vulnerability to adverse outcomes (Cesari et al., 2013), leading to the proposal in 2013 of a concept and operational definition of “cognitive frailty”. (Sugimoto et al., 2022). Proteomic analysis of cerebrospinal fluid (CSF) is therefore an approach of choice for understanding brain responses to aging and frailty syndrome. Its analysis could allow the identification of new biomarkers linked to the frailty syndrome. Indeed, many of the CSF proteins are released by the various cells of the central nervous system (CNS). This biofluid thus reflects the molecular content of neuronal and glial cells. For this reason, any change in the composition of the CSF reflects the pathophysiological state of the brain (inflammation, infection, neurodegeneration...). CSF proteomics is therefore a technique of choice to comprehensively understand the cerebral pathogenic pathways linked to frailty. The PROLIPHYC cohort is composed of patients suspected of normal pressure hydrocephalus (NPH). We have previously shown that there is an association in these patients between an alteration of the biomechanical properties of the CNS and the frailty index, and that homocysteine is the only plasma macromolecule associated with these two clinical and biomechanical parameters (Vallet et al., 2020; Guillotin et al., 2022). In this new study, we sought to characterize the protein composition of CSF related to frailty by quantitative proteomic analysis. The second objective was to develop a model for predicting frailty from the abundance of CSF proteins. This model was then compared to the frailty estimated by the frailty index. Together, our results enabled us to highlight a possible link between inhibition of the expression of proteins involved in neurogenesis and the frailty index, then to propose a first proteomic profile of frailty in the CSF and a model for predicting frailty based on the biological signature of the CNS.
2024-08-10 | PXD043497 | Pride
Project description:Neutrophils play a key role in the pathophysiology of rheumatoid arthritis (RA) where release of ROS and proteases directly causes damage to joints and tissues. Neutrophil function is directly affected by Janus Kinase (JAK) inhibitor drugs, including tofacitinib and baricitinib, which are clinically effective treatments for RA. However clinical trials have reported increased infection rates and transient neutropenia during therapy. The subtle differences in the mode of action, efficacy and safety of JAK inhibitors has been the primary research topic of many clinical trials and systematic reviews, to provide a more precise and targeted treatment to patients. The aim of this study was to determine both the differences in the metabolome of neutrophils from healthy controls and people with RA, and the effect of different JAK inhibitors on the metabolome of healthy and RA neutrophils. Isolated neutrophils from healthy controls (HC) (n=6) and people with RA (n=7) were incubated with baricitinib, tofacitinib or a pan-JAK inhibitor (all 200ng/mL) for 2h. Metabolites were extracted and 1H nuclear magnetic resonance (NMR) was applied to study the metabolic changes. Multivariate analyses and machine learning models show a divergent metabolic pattern in RA neutrophils compared to HC at baseline (F1 score = 86.7%) driven by energy metabolites (ATP, ADP, GTP and glucose). No difference was observed in the neutrophil metabolome when treated with JAK inhibitors. However, JAK inhibitors significantly inhibited ROS production and baricitinib decreased NET production (p-value<0.05). Bacterial killing was not inhibited by JAK inhibitors, indicating their effect on neutrophils is beneficial to inhibit joint damage in RA without impairing host defence. This study highlights altered energy metabolism in RA neutrophils which may explain the cause of their dysregulation in inflammatory disease.
2023-07-11 | MTBLS4766 | MetaboLights
Project description:Frailty is an aging-associated syndrome resulting from diminished capacity to respond to stressors, disrupted homeostasis. It is a significant risk factor for disability and mortality. Although frailty is usually studied in old age, it is present in mid-life. Given the increases in mortality statistics among middle-aged Americans, understanding molecular drivers of frailty in a younger, diverse cohort may facilitate identifying pathways for early intervention. We analyzed frailty-associated, genome-wide transcriptional changes in middle-aged African Americans (AAs) and whites. Next generation RNA sequencing was completed using total RNA from peripheral blood mononuclear cells. We analyzed differential gene expression patterns and completed a parametric analysis of gene set enrichment (PAGE). Differential gene expression was validated using RT-qPCR. We identified 5,082 genes differentially expressed with frailty. Frailty altered gene expression patterns and biological pathways differently in AAs and whites, including inflammatory and immune response pathways. The validation study showed a significant two-way interaction between frailty, race, and expression of the cytokine IL1B and the transcription factor EGR1. The glucose transporter, SLC2A6, the neutrophil receptor, FCGR3B, and the accessory protein, C17orf56, were decreased with frailty. These results suggest that there may be demographic dependent, divergent biological pathways underlying frailty diagnosis in middle-aged adults.
2019-08-21 | GSE129534 | GEO
Project description:Rheumatoid arthritis (RA) is a chronic, inflammatory joint disease of unknown etiology and pronounced inter-patient heterogeneity. To characterize RA at the molecular level and to uncover key pathomechanisms, we performed whole-genome gene expression analyses. Synovial tissues from rheumatoid arthritis patients were compared to those from osteoarthritis patients and to normal donors. Keywords: disease state analysis
2004-11-04 | GSE1919 | GEO
Project description:Genome wide microRNA profiling in peripheral blood mononuclear cells from rheumatoid arthritis (RA) patients and healthy controls. The Affymetrix miRNA 4.0 chip was used to obtain RNA profiles in 28 RA patients and 18 healthy controls.
2020-03-17 | GSE124373 | GEO