T-cell Responses to Dengue Virus in Humans

Dengue virus (DENV) is a leading cause of morbidity and mortality in most tropical and subtropical areas of the world. Dengue virus infection induces specific CD4+CD8– and CD8+CD4– T cells in humans. In primary infection, T-cell responses to DENV are serotype cross-reactive, but the highest response is to the serotype that caused the infection. The epitopes recognized by DENV-specific T cells are located in most of the structural and non-structural proteins, but NS3 is the protein that is most dominantly recognized. In patients with dengue hemorrhagic fever (DHF) caused by secondary DENV infection, T cells are highly activated in vivo. These highly activated T cells are DENV-specific and oligoclonal. Multiple kinds of lymphokines are produced by the activated T cells, and it has been hypothesized that these lymphokines are responsible for induction of plasma leakage, one of the most characteristic features of DHF. Thus, T-cells play important roles in the pathogenesis of DHF and in the recovery from DENV infection

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

CD4(+) CD25(+) Foxp3(+) T regulatory cells with limited TCR diversity in control of autoimmunity

The importance of high TCR diversity of T regulatory (Treg) cells for self-tolerance is poorly understood. To address this issue, TCR diversity was measured for Treg cells after transfer into IL-2Rbeta(-/-) mice, which develop lethal autoimmunity because of failed production of Treg cells. In this study, we show that high TCR diversity of pretransferred Treg cells led to selection of therapeutic Treg cells with lower TCR diversity that prevented autoimmunity. Pretransferred Treg cells with lower diversity led to selection of Treg cells through substantial peripheral reshaping with even more restricted TCR diversity that also suppressed autoimmune symptoms. Thus, in a setting of severe breakdown of immune tolerance because of failed production of Treg cells, control of autoimmunity is achieved by only a fraction of the Treg TCR repertoire, but the risk for disease increased. These data support a model in which high Treg TCR diversity is a mechanism to ensure establishing and maintaining self-tolerance

Additional file 1: of A new high-throughput sequencing method for determining diversity and similarity of T cell receptor (TCR) α and β repertoires and identifying potential new invariant TCR α chains

Table S1. Age, gender and chronic illness of 20 healthy individuals. Table S2. Numbers of unique reads, reads and nucleotides in TRA reads obtained from PBMCs of 20 healthy individuals. Table S3. Numbers of unique reads, reads and nucleotides in TRB reads obtained from PBMCs of 20 healthy individuals. Table S4. Percentage of mismatched nucleotides in in-frame and out-of-frame TCR sequences. Table S5. Occurrence frequency of out-of-frame unique sequence reads in TRA and TRB. Table S6. Percentage frequency of shared TRA reads between all pairs of individuals. Table S7. Percentage frequency of shared TRB reads between all pairs of individuals. Figure S1. Correlation of gene usage of TRAV, TRAJ, TRBV and TRBJ between healthy individuals. Figure S2. Concordance correlation coefficient in TRAV, TRAJ, TRBV and TRBJ. Figure S3. Comparison of TCR usages between in-frame and out-of-frame reads sequences. Figure S4. Diversity indices of in-frame and out-of-frame TRA and TRB. Figure S5. Correlation of TCR diversity with age. Figure S6. Correlation of TCR usage from a published FACS data with AL-PCR and Multiplex PCR. (DOCX 548 kb