Transcriptomic signatures of classical monocytes reveal pro-inflammatory modules and heterogeneity in polyarticular juvenile idiopathic arthritis.

Introduction: Polyarticular juvenile idiopathic arthritis (pJIA) is a childhood-onset autoimmune disease. Immune cells contribute to persistent inflammation observed in pJIA. Despite the crucial role of monocytes in arthritis, the precise involvement of classical monocytes in the pathogenesis of pJIA remains uncertain. Here, we aimed to uncover the transcriptomic patterns of classical monocytes in pJIA, focusing on their involvement in disease mechanism and heterogeneity. Methods: A total of 17 healthy subjects and 18 premenopausal women with pJIA according to ILAR criteria were included. Classical monocytes were isolated, and RNA sequencing was performed. Differential expression analysis was used to compare pJIA patients and healthy control group. Differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was performed. Using unsupervised learning approach, patients were clustered in two groups based on their similarities at transcriptomic level. Subsequently, these clusters underwent a comparative analysis to reveal differences at the transcriptomic level. Results: We identified 440 DEGs in pJIA patients of which 360 were upregulated and 80 downregulated. GSEA highlighted TNF-α and IFN-γ response. Importantly, this analysis not only detected genes targeted by pJIA therapy but also identified new modulators of immuno-inflammation. PLAUR, IL1B, IL6, CDKN1A, PIM1, and ICAM1 were pointed as drivers of chronic hyperinflammation. Unsupervised learning approach revealed two clusters within pJIA, each exhibiting varying inflammation levels. Conclusion: These findings indicate the pivotal role of immuno-inflammation driven by classical monocytes in pJIA and reveals the existence of two subclusters within pJIA, regardless the positivity of rheumatoid factor and anti-CCP, paving the way to precision medicine.

Morphological and Transcriptional Changes in Human Bone Marrow During Natural Plasmodium vivax Malaria Infections.

BACKGROUND: The presence of Plasmodium vivax malaria parasites in the human bone marrow (BM) is still controversial. However, recent data from a clinical case and experimental infections in splenectomized nonhuman primates unequivocally demonstrated the presence of parasites in this tissue. METHODS: In the current study, we analyzed BM aspirates of 7 patients during the acute attack and 42 days after drug treatment. RNA extracted from CD71+ cell suspensions was used for sequencing and transcriptomic analysis. RESULTS: We demonstrated the presence of parasites in all patients during acute infections. To provide further insights, we purified CD71+ BM cells and demonstrated dyserythropoiesis and inefficient erythropoiesis in all patients. In addition, RNA sequencing from 3 patients showed that genes related to erythroid maturation were down-regulated during acute infections, whereas immune response genes were up-regulated. CONCLUSIONS: This study thus shows that during P. vivax infections, parasites are always present in the BM and that such infections induced dyserythropoiesis and ineffective erythropoiesis. Moreover, infections induce transcriptional changes associated with such altered erythropoietic response, thus highlighting the importance of this hidden niche during natural infections.

Transcriptomic analysis of elderly women with low muscle mass: association with immune system pathway.

Despite the well-established association of gene expression deregulation with low muscle mass (LMM), the associated biological mechanisms remain unclear. Transcriptomic studies are capable to identify key mediators in complex diseases. We aimed to identify relevant mediators and biological mechanisms associated with age-related LMM. LMM-associated genes were detected by logistic regression using microarray data of 20 elderly women with LMM and 20 age and race-matched controls extracted from our SPAH Study (GSE152073). We performed weighted gene co-expression analysis (WGCNA) that correlated the identified gene modules with laboratorial characteristics. Gene enrichment analysis was performed and an LMM predictive model was constructed using Support Vector Machine (SVM). Overall, 821 discriminating transcripts clusters were identified (|beta coefficient| >1; p-value <0.01). From this list, 45 predictors of LMM were detected by SVM and validated with 0.7 of accuracy. Our results revealed that the well-described association of inflammation, immunity and metabolic alterations is also relevant at transcriptomic level. WGCNA highlighted a correlation of genes modules involved in immunity pathways with vitamin D level (R = 0.63, p = 0.004) and the Agatston score (R = 0.51, p = 0.02). Our study generated a predicted regulatory network and revealed significant metabolic pathways related to aging processes, showing key mediators that warrant further investigation.