[Increased suppressor activity of transformed ex vivo regulatory T-cells in comparison with unstimulated cells of the same donor]. / Povyshennaia supressornaia aktivnost' transformirovannykh ex vivo reguliatornykh T-kletok v sravnenii s nestimulirovannymi kletkami togo zhe donora.

Regulatory T-cells CD4⁺CD25⁺FoxP3⁺CD127low (Tregs) play a key role in the maintenance of tolerance to auto antigens, inhibit function of effector T and B lymphocytes, and provide a balance between effector and regulatory arms of immunity. Patients with autoimmune diseases have decreased Treg numbers and impaired suppressive activity. Transformed ex vivo autologous Tregs could restore destroyed balance of the immune system. We developed a method for Treg precursor cell cultivation. Following the method, we were able to grown up 300-400 million of Tregs cells from 50 ml of peripheral blood during a week. Transformed ex vivo Tregs are 90-95% CD4⁺CD25⁺FoxP3⁺CD127low and have increased expression of transcription genes FoxP3 and Helios. Transformed ex vivo Tregs have increased demethylation of FoxP3 promoter and activated genes of proliferation markers Cycline B1, Ki67 and LGALS 1. Transformed ex vivo Tregs have increased suppressive activity and up to 80-90% these cells secrete cytokines TNFα и IFNγ. Our data suggest transformed ex vivo autologous Tregs have genetic, immunophenotypic and functional characteristics for regulatory T-cells and further can be used for adoptive immunotherapy autoimmune diseases and inhibition of transplantation immunity.

Toxicology analysis by means of the JSM-method.

MOTIVATION: A model for learning potential causes of toxicity from positive and negative examples and predicting toxicity for the dataset used in the Predictive Toxicology Challenge (PTC) is presented. The learning model assumes that the causes of toxicity can be given as substructures common to positive examples that are not substructures of negative examples. This assumption results in the choice of a learning model, called the JSM-method, and a language for representing chemical compounds, called the Fragmentary Code of Substructure Superposition (FCSS). By means of the latter, chemical compounds are represented as sets of substructures which are 'biologically meaningful' from the expert point of view. RESULTS: The chosen learning model and representation language show comparatively good performance for the PTC dataset: for three sex/species groups the predictions were ROC optimal, for one group the prediction was nearly optimal. The predictions tend to be conservative (few predictions and almost no errors), which can be explained by the specific features of the learning model. AVAILABILITY: by request to finn@viniti.ru; serge@viniti.ru, http://ki-www2.intellektik.informatik.tu-darmstadt.de/~jsm/QDA.