Transduction of Human T Cells with a Novel T-Cell Receptor Confers Anti-HCV Reactivity

Hepatitis C Virus (HCV) is a major public health concern, with no effective vaccines currently available and 3% of the world's population being infected. Despite the existence of both B- and T-cell immunity in HCV-infected patients, chronic viral infection and HCV-related malignancies progress. Here we report the identification of a novel HCV TCR from an HLA-A2-restricted, HCV NS3:1073–1081-reactive CTL clone isolated from a patient with chronic HCV infection. We characterized this HCV TCR by expressing it in human T cells and analyzed the function of the resulting HCV TCR-transduced cells. Our results indicate that both the HCV TCR-transduced CD4+ and CD8+ T cells recognized the HCV NS3:1073–1081 peptide-loaded targets and HCV+ hepatocellular carcinoma cells (HCC) in a polyfunctional manner with cytokine (IFN-γ, IL-2, and TNF-α) production as well as cytotoxicity. Tumor cell recognition by HCV TCR transduced CD8− Jurkat cells and CD4+ PBL-derived T cells indicated this TCR was CD8-independent, a property consistent with other high affinity TCRs. HCV TCR-transduced T cells may be promising for the treatment of patients with chronic HCV infections

Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model

We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative (‘non-physical’) approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host