Autophagy Is a Tolerance-Avoidance Mechanism that Modulates TCR-Mediated Signaling and Cell Metabolism to Prevent Induction of T Cell Anergy.

In response to activation, CD4+ T cells upregulate autophagy. However, the functional consequences of that upregulation have not been fully elucidated. In this study, we identify autophagy as a tolerance-avoidance mechanism. Our data show that inhibition of autophagy during CD4+ T cell activation induces a long-lasting state of hypo-responsiveness that is accompanied by the expression of an anergic gene signature. Cells unable to induce autophagy after T cell receptor (TCR) engagement show inefficient mitochondrial respiration and decreased turnover of the protein tyrosine phosphatase PTPN1, which translates into defective TCR-mediated signaling. In vivo, inhibition of autophagy during antigen priming induces T cell anergy and decreases the severity of disease in an experimental autoimmune encephalomyelitis mouse model. Interestingly, CD4+ T cells isolated from the synovial fluid of juvenile idiopathic arthritis patients, while resistant to suboptimal stimulation-induced anergy, can be tolerized with autophagy inhibitors. We propose that autophagy constitutes a tolerance-avoidance mechanism, which determines CD4+ T cell fate.

Autophagy and T cell metabolism.

Autophagy, a highly conserved catabolic process that involves the degradation and recycling of intracellular components in the lysosome, has emerged as a key process in the maintenance of T cell homeostasis and the regulation of T cell differentiation and function. In this review, we provide an overview of the mechanisms that mediate the regulation of autophagy in T cells and discuss different cellular processes that are under the control of autophagy in CD4+ and CD8+ T cells. A special emphasis is placed on the role that autophagy plays in the modulation of T cell metabolism and the consequences of this regulation on functional states and programs of differentiation in specific T cell populations.

Breaking free from chemical spreadsheets.

Drug discovery scientists often consider compounds and data in terms of groups, such as chemical series, and relationships, representing similarity or structural transformations, to aid compound optimisation. This is often supported by chemoinformatics algorithms, for example clustering and matched molecular pair analysis. However, chemistry software packages commonly present these data as spreadsheets or form views that make it hard to find relevant patterns or compare related compounds conveniently. Here, we review common data visualisation and analysis methods used to extract information from chemistry data. We introduce a new framework that enables scientists to work flexibly with drug discovery data to reflect their thought processes and interact with the output of algorithms to identify key structure-activity relationships and guide further optimisation intuitively.