Guanine Nucleotide Exchange Factor αPIX Leads to Activation of the Rac 1 GTPase/Glycogen Phosphorylase Pathway in Interleukin (IL)-2-stimulated T Cells

Recently, we have reported that the active form of Rac 1 GTPase binds to the glycogen phosphorylase muscle isoform (PYGM) and modulates its enzymatic activity leading to T cell proliferation. In the lymphoid system, Rac 1 and in general other small GTPases of the Rho family participate in the signaling cascades that are activated after engagement of the T cell antigen receptor. However, little is known about the IL-2-dependent Rac 1 activator molecules. For the first time, a signaling pathway leading to the activation of Rac 1/PYGM in response to IL-2-stimulated T cell proliferation is described. More specifically, αPIX, a known guanine nucleotide exchange factor for the small GTPases of the Rho family, preferentially Rac 1, mediates PYGM activation in Kit 225 T cells stimulated with IL-2. Using directed mutagenesis, phosphorylation of αPIX Rho-GEF serines 225 and 488 is required for activation of the Rac 1/PYGM pathway. IL-2-stimulated serine phosphorylation was corroborated in Kit 225 T cells cultures. A parallel pharmacological and genetic approach identified PKCθ as the serine/threonine kinase responsible for αPIX serine phosphorylation. The phosphorylated state of αPIX was required to activate first Rac 1 and subsequently PYGM. These results demonstrate that the IL-2 receptor activation, among other early events, leads to activation of PKCθ. To activate Rac 1 and consequently PYGM, PKCθ phosphorylates αPIX in T cells. The biological significance of this PKCθ/αPIX/Rac 1 GTPase/PYGM signaling pathway seems to be the control of different cellular responses such as migration and proliferation.     

Predictors of major infections in systemic lupus erythematosus

Introduction  

Infections commonly complicate the course of systemic lupus erythematosus (SLE). Our aim is to investigate the clinical predictors of major infections in patients with SLE.     

TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure–Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H₂O₂. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection = 23.4% in assay (1) and protection = 15.2% in assay (2); and for compound 7a with protection = 46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.