The inflammatory and IFN pathways of innate immunity play a key role in the resistance and pathogenesis of coronavirus disease 2019 (COVID-19). Innate sensors and SARS-CoV-2–associated molecular patterns (SAMPs) remain to be completely defined. Here, we identified single-stranded RNA (ssRNA) fragments from the SARS-CoV-2 genome as direct activators of endosomal TLR7/8 and MyD88 pathway. The same sequences induced human DC activation in terms of phenotype and function, such as IFN and cytokine production and Th1 polarization. A bioinformatic scan of the viral genome identified several hundreds of fragments potentially activating TLR7/8, suggesting that products of virus endosomal processing potently activate the IFN and inflammatory responses downstream of these receptors. In vivo, SAMPs induced MyD88-dependent lung inflammation characterized by accumulation of proinflammatory and cytotoxic mediators and immune cell infiltration, as well as splenic DC phenotypical maturation. These results identified TLR7/8 as a crucial cellular sensor of ssRNAs encoded by SARS-CoV-2 involved in host resistance and the disease pathogenesis of COVID-19.

Dendritic cells (DCs) exhibit a specialized antigen-presenting function and play crucial roles in both innate and adaptive immune responses. Due to their ability to cross-present tumor cell-associated antigens to naïve T cells, DCs are instrumental in the generation of specific T-cell-mediated antitumor effector responses in the control of tumor growth and tumor cell dissemination. Within an immunosuppressive tumor microenvironment, DC antitumor functions can, however, be severely impaired. In this review, we focus on the mechanisms of DC capture and activation by tumor cell antigens and the role of the tumor microenvironment in shaping DC functions, taking advantage of recent studies showing the phenotype acquisition, transcriptional state and functional programs revealed by scRNA-seq analysis. The therapeutic potential of DC-mediated tumor antigen sensing in priming antitumor immunity is also discussed.

Dendritic cells (DCs) constitute a complex network of cell subsets with common functions but also with many divergent aspects. All dendritic cell subsets share the ability to prime T cell response and to undergo a complex trafficking program related to their stage of maturation and function. For these reasons, dendritic cells are implicated in a large variety of both protective and detrimental immune responses, including a crucial role in promoting anti-tumor responses. Although cDC1s are the most potent subset in tumor antigen cross-presentation, they are not sufficient to induce full-strength anti-tumor cytotoxic T cell response and need close interaction and cooperativity with the other dendritic cell subsets, namely cDC2s and pDCs. This review will take into consideration different aspects of DC biology, including the functional role of dendritic cell subsets in both fostering and suppressing tumor growth, the mechanisms underlying their recruitment into the tumor microenvironment, as well as the prognostic value and the potentiality of dendritic cell therapeutic targeting. Understanding the specificity of dendritic cell subsets will allow to gain insights on role of these cells in pathological conditions and to design new selective promising therapeutic approaches.

The growth of publicly available data informing upon genetic variations, mechanisms of disease, and disease subphenotypes offers great potential for personalized medicine. Computational approaches are likely required to assess a large number of novel genetic variants. However, the integration of genetic, structural, and pathophysiological data still represents a challenge for computational predictions and their clinical use. We addressed these issues for alpha-1-antitrypsin deficiency, a disease mediated by mutations in the SERPINA1 gene encoding alpha-1-antitrypsin. We compiled a comprehensive database of SERPINA1 coding mutations and assigned them apparent pathological relevance based upon available data. "Benign" and "pathogenic" variations were used to assess performance of 31 pathogenicity predictors. Well-performing algorithms clustered the subset of variants known to be severely pathogenic with high scores. Eight new mutations identified in the ExAC database and achieving high scores were selected for characterization in cell models and showed secretory deficiency and polymer formation, supporting the predictive power of our computational approach. The behavior of the pathogenic new variants and consistent outliers were rationalized by considering the protein structural context and residue conservation. These findings highlight the potential of computational methods to provide meaningful predictions of the pathogenic significance of novel mutations and identify areas for further investigation.