Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methods, interrogating total RNA-sequencing data sets from peripheral whole blood of treatment-naïve patients with RRMS and NMO and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between patients with NMO and RRMS, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors identified biological associations and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Mitoxantrone and vorinostat-two identified small molecules with previously reported use in patients with NMO and experimental autoimmune encephalomyelitis-reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA-sequencing data further enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding further enhances detection of disease-specific signatures and possible therapeutic targets.

Longitudinal assessment and stability of long non-coding RNA gene expression profiles measured in human peripheral whole blood collected into PAXgene blood RNA tubes.

OBJECTIVE: Long non-coding RNAs (lncRNAs) are emerging as novel biomarkers for a variety of chronic conditions including autoimmune disease. PAXgene Blood RNA tubes are routinely used in clinical research and molecular diagnostic development to capture RNA profiles in peripheral whole blood. While the stability of mRNA expression profiles captured using PAXgene tubes has been documented previously, no previous work has determined the stability of lncRNA expression profiles observed in PAXgene tubes stored at - 80 °C. Here we sought to determine the effects on lncRNA expression profiles following - 80 °C storage of total RNA templates, cDNA synthesized using fresh or frozen total RNA template, and the impact of freeze-thaw cycles on both total RNA and cDNA obtained from PAXgene tubes. RESULTS: We find that storage of whole blood in PAXgene tubes, total RNA and cDNA for up to 1 year at - 80 °C or up to ten total RNA or cDNA freeze-thaw cycles do not significantly alter lncRNA expression profiles compared to baseline. As monthly expression profiles were determined, some month to month lncRNA expression variability was observed. However, all monthly observations fell within the 95% confidence interval calculated at baseline.

PD-1 up-regulation on CD4+ T cells promotes pulmonary fibrosis through STAT3-mediated IL-17A and TGF-ß1 production.

Pulmonary fibrosis is a progressive inflammatory disease with high mortality and limited therapeutic options. Previous genetic and immunologic investigations suggest common intersections between idiopathic pulmonary fibrosis (IPF), sarcoidosis, and murine models of pulmonary fibrosis. To identify immune responses that precede collagen deposition, we conducted molecular, immunohistochemical, and flow cytometric analysis of human and murine specimens. Immunohistochemistry revealed programmed cell death-1 (PD-1) up-regulation on IPF lymphocytes. PD-1+CD4+ T cells with reduced proliferative capacity and increased transforming growth factor-ß (TGF-ß)/interleukin-17A (IL-17A) expression were detected in IPF, sarcoidosis, and bleomycin CD4+ T cells. PD-1+ T helper 17 cells are the predominant CD4+ T cell subset expressing TGF-ß. Coculture of PD-1+CD4+ T cells with human lung fibroblasts induced collagen-1 production. Strikingly, ex vivo PD-1 pathway blockade resulted in reductions in TGF-ß and IL-17A expression from CD4+ T cells, with concomitant declines in collagen-1 production from fibroblasts. Molecular analysis demonstrated PD-1 regulation of the transcription factor STAT3 (signal transducer and activator of transcription 3). Chemical blockade of STAT3, using the inhibitor STATTIC, inhibited collagen-1 production. Both bleomycin administration to PD-1 null mice or use of antibody against programmed cell death ligand 1 (PD-L1) demonstrated significantly reduced fibrosis compared to controls. This work identifies a critical, previously unrecognized role for PD-1+CD4+ T cells in pulmonary fibrosis, supporting the use of readily available therapeutics that directly address interstitial lung disease pathophysiology.