Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs

An Elevated IL10 mRNA Combined with Lower TNFA mRNA Level in Active Rheumatoid Arthritis Peripheral Blood

We aimed to investigate the expression of pro-inflammatory cytokine genes TNFA, IL6, IL12B, IL23, IL18 and immunoregulatory genes FOXP3, TGFB1, and IL10 in the peripheral blood of patients with rheumatoid arthritis (RA) at messenger ribonucleic acid (mRNA) level. The total RNA was isolated from peripheral blood samples. Real-time quantitative PCR was used to perform TaqMan-based assays to quantify mRNAs from 8 target genes. IL23A was upregulated (1.7-fold), whereas IL6 (5-fold), FOXP3 (4-fold), and IL12B (2.56-fold) were downregulated in patients compared to controls. In addition, we found a strong positive correlation between the expression of FOXP3 and TNFA and a moderate correlation between FOXP3 and TGFB1. These data showed the imbalance of the T helper (Th) 1/Th17/ T regulatory (Treg) axis at a systemic level in RA. In cases with active disease, the IL10 gene expression was approximately 2-fold higher; in contrast, the expression of FOXP3 was significantly decreased (3.38-fold). The main part of patients with higher disease activity expressed upregulation of IL10 and downregulation of TNFA. Different disease activity cohorts could be separated based on IL10, TNFA and IL12B expression combinations. In conclusion, our results showed that active disease is associated with an elevated IL10 and lower TNFA mRNA level in peripheral blood cells of RA patients.

Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions

Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.