Cytoplasmic retention sites in p190RhoGEF confer anti-apoptotic activity to an EGFP-tagged protein.

p190RhoGEF is a large multi-functional protein with guanine nucleotide exchange (GEF) activity. The C-terminal region of p190RhoGEF is a highly interactive domain that binds multiple factors, including proteins with anti-apoptotic activities. We now report that transfection of EGFP-tagged p190RhoGEF protects Neuro 2a cells from stress-induced apoptosis and that anti-apoptotic activity is localized to cytoplasmic retention sequences (CRS-1 and CRS-2) in the C-terminal region of p190RhoGEF. Cytoplasmic retention is conferred to an EGFP fluorescent marker when fused to either CRS-1 or CRS-2. Both cytoplasmic retention and anti-apoptotic activity are lost by deleting CRS-1 and CRS-2 in the p190RhoGEF sequence and can be recovered by restoring either CRS-1 or CRS-2 to the EGFP-tagged sequence. Since the CRS-1 and CRS-2 contain the JIP-1 and 14-3-3 binding sites, we propose that anti-apoptotic activity may be conferred by the binding of p190RhoGEF to JIP-1 or 14-3-3, possibly by altering their interactive properties or nucleocytoplasmic movements. Taken together, our findings support a model whereby multiple interactions of p190RhoGEF confer homeostatic properties to differentiated neurons and may link neuronal homeostasis to the regulation of NF-L expression

Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis : predictive model based on machine learning

Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. In the present study, we used predictive models based on machine learning to detect variables associated with achieving MDA in patients with recent-onset PsA. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest-type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. We used a confusion matrix to visualize the performance of the model. The sample comprised 158 patients. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. In our model, the variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index). The percentage of hits in the confusion matrix was 85.94%. A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA