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. 2021 Aug 4;13(8):1540.
doi: 10.3390/v13081540.

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

Beatriz Sierra  1 , Ana Cristina Magalhães  2   3   4 , Daniel Soares  2   3 , Bruno Cavadas  2   3 , Ana B Perez  1 , Mayling Alvarez  1 , Eglis Aguirre  1 , Claudia Bracho  1 , Luisa Pereira  2   3 , Maria G Guzman  1
Affiliations

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

Beatriz Sierra et al. Viruses. .

Abstract

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.

Keywords: Dengue fever disease; common mechanisms of action; in silico evaluation of drugs; multi-tissue transcriptomics; tissue specialization.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Top 20 Gene Ontology Biological Process (GO-BP) pathways in the (A) spleen and (B) liver cells (cut-off values: nominal p-value < 0.05 and FDR < 0.25). Positive normalized enrichment score (NES; in purple) represents upregulated pathways in the infected individuals versus controls, while negative NES values (in green) represent downregulated pathways in the infected individuals versus controls.
Figure 2
Figure 2
Top 20 Gene Ontology Biological Process (GO-BP) in the two blood datasets: (A) GSE18090 and (B) GSE51808 pathways (cut-off values: nominal p-value < 0.05 and FDR < 0.25). Positive NES (in purple) represents upregulated pathways in the infected individuals versus controls, while negative NES values (in green) represent downregulated pathways in the infected individuals versus controls.
Figure 3
Figure 3
CMap-identified compounds (score equal to or below −90) that potentially impact Dengue haemorrhagic fever treatment, according to their mechanism of action in the blood, spleen and liver. (A) Bar plot of the distribution of drugs by broad pharmacological classification; (B) heatmap of drugs per mechanism identified in one or more tissues; (C) circus plot of common mechanisms of action.
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