Prostaglandin E(2) in a TLR3- and 7/8-agonist-based DC maturation cocktail generates mature, cytokine-producing, migratory DCs but impairs antigen cross-presentation to CD8(+) T cells

Mature dendritic cells (DCs) represent cellular adjuvants for optimal antigen presentation in cancer vaccines. Recently, a combination of prostaglandin E(2) (PGE(2)) with Toll-like receptor agonists (TLR-P) was proposed as a new standard to generate superior cytokine-producing DCs with high migratory capacity. Here, we compare TLR-P DCs with conventional DCs matured only with the proinflammatory cytokines TNFα and IL-1ß (CDCs), focussing on the interaction of resulting DCs with CD8(+) T-cells. TLR-P matured DCs showed elevated expression of activation markers such as CD80 and CD83 compared to CDCs, together with a significantly higher migration capacity. Secretion of IL-6, IL-8, IL-10, and IL-12 was highest after 16 h in TLR-P DCs, and only TLR-P DCs secreted active IL-12p70. TLR-P DCs as well as CDCs successfully primed multifunctional CD8(+) T-cells from naïve precursors specific for the peptide antigens Melan-A, NLGN4X, and PTP with comparable priming efficacy and T-cell receptor avidity. CD8(+) T-cells primed by TLR-P DCs showed significantly elevated expression of the integrin VLA-4 and a trend for higher T-cell numbers after expansion. In contrast, TLR-P DCs displayed a substantially reduced capability to cross-present CMVpp65 protein antigen to pp65-specific T cells, an effect that was dose-dependent on PGE(2) during DC maturation and reproducible with several responder T-cell lines. In conclusion, TLR-P matured DCs might be optimal presenters of antigens not requiring processing such as short peptides. However, PGE(2) seems less favorable for maturation of DCs intended to process and cross-present more complex vaccine antigens such as lysates, proteins or long peptides

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND: The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system. METHODS: In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings. RESULTS: Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host. CONCLUSIONS: Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity