Methotrexate Provokes Disparate Folate Metabolism Gene Expression and Alternative Splicing in Ex Vivo Monocytes and GM-CSF- and M-CSF-Polarized Macrophages.

Macrophages constitute important immune cell targets of the antifolate methotrexate (MTX) in autoimmune diseases, including rheumatoid arthritis. Regulation of folate/MTX metabolism remains poorly understood upon pro-inflammatory (M1-type/GM-CSF-polarized) and anti-inflammatory (M2-type/M-CSF-polarized) macrophages. MTX activity strictly relies on the folylpolyglutamate synthetase (FPGS) dependent intracellular conversion and hence retention to MTX-polyglutamate (MTX-PG) forms.

The association between blood MxA mRNA and long-term disease activity in early multiple sclerosis.

Background: Myxovirus resistance protein A (MxA) is a protein that is upregulated by interferon-beta. Homeostatic MxA mRNA levels are potentially correlated with inflammatory disease activity in multiple sclerosis (MS) and could have an important role in MS pathology.

Aim: To investigate the association between myxovirus resistance protein A (MxA) mRNA levels in blood and disease activity and progression in MS over a long-term follow-up period.

The Analysis of Platelet-Derived circRNA Repertoire as Potential Diagnostic Biomarker for Non-Small Cell Lung Cancer.

Tumor-educated Platelets (TEPs) have emerged as rich biosources of cancer-related RNA profiles in liquid biopsies applicable for cancer detection. Although human blood platelets have been found to be enriched in circular RNA (circRNA), no studies have investigated the potential of circRNA as platelet-derived biomarkers for cancer. In this proof-of-concept study, we examine whether the circRNA signature of blood platelets can be used as a liquid biopsy biomarker for the detection of non-small cell lung cancer (NSCLC).

The bivariate NRIP1/ZEB2 RNA marker permits non-invasive presymptomatic screening of pre-eclampsia.

Using genome-wide transcriptome analysis by RNA sequencing of first trimester plasma RNA, we tested whether the identification of pregnancies at risk of developing pre-eclampsia with or without preterm birth or growth restriction is possible between weeks 9-14, prior to the appearance of clinical symptoms. We implemented a metaheuristic approach in the self-learning SVM algorithm for differential gene expression analysis of normal pregnancies (n = 108), affected pregnancies (n = 34) and non-pregnant controls (n = 19). Presymptomatic candidate markers for affected pregnancies were validated by RT-qPCR in first trimester samples (n = 34) from an independent cohort.