No abstract

Interaction of T cell with antigen-bearing dendritic cells (DC) results in T cell activation, but whether this interaction has physiological consequences on DC function is largely unexplored. Here we show that when antigen-bearing DCs contact T cells, DCs initiate anti-pathogenic programs. Signals of this interaction are transmitted from the T cell to the DC, through extracellular vesicles (EV) that contain genomic and mitochondrial DNA, to induce antiviral responses via the cGAS/STING cytosolic DNA-sensing pathway and expression of IRF3-dependent interferon regulated genes. Moreover, EV-treated DCs are more resistant to subsequent viral infections. In summary, our results show that T cells prime DCs through the transfer of exosomal DNA, supporting a specific role for antigen-dependent contacts in conferring protection to DCs against pathogen infection. The reciprocal communication between innate and adaptive immune cells thus allow efficacious responses to unknown threats.

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.