A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity.

Coronavirus Disease-19 (COVID-19) symptoms range from mild to severe illness; the cause for this differential response to infection remains unknown. Unravelling the immune mechanisms acting at different levels of the colonization process might be key to understand these differences. We carried out a multi-tissue (nasal, buccal and blood; n = 156) gene expression analysis of immune-related genes from patients affected by different COVID-19 severities, and healthy controls through the nCounter technology. Mild and asymptomatic cases showed a powerful innate antiviral response in nasal epithelium, characterized by activation of interferon (IFN) pathway and downstream cascades, successfully controlling the infection at local level. In contrast, weak macrophage/monocyte driven innate antiviral response and lack of IFN signalling activity were present in severe cases. Consequently, oral mucosa from severe patients showed signals of viral activity, cell arresting and viral dissemination to the lower respiratory tract, which ultimately could explain the exacerbated innate immune response and impaired adaptative immune responses observed at systemic level. Results from saliva transcriptome suggest that the buccal cavity might play a key role in SARS-CoV-2 infection and dissemination in patients with worse prognosis. Co-expression network analysis adds further support to these findings, by detecting modules specifically correlated with severity involved in the abovementioned biological routes; this analysis also provides new candidate genes that might be tested as biomarkers in future studies. We also found tissue specific severity-related signatures mainly represented by genes involved in the innate immune system and cytokine/chemokine signalling. Local immune response could be key to determine the course of the systemic response and thus COVID-19 severity. Our findings provide a framework to investigate severity host gene biomarkers and pathways that might be relevant to diagnosis, prognosis, and therapy.

Multi-Tissue Transcriptomic-Informed In Silico Investigation of Drugs for the Treatment of Dengue Fever Disease.

Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico-informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, "Adrenergic receptor antagonist", "ATPase inhibitor", "NF-kB pathway inhibitor" and "Serotonin receptor antagonist", were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.