RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

Autor: Ruth Barral-Arca, Alberto Gómez-Carballa, Miriam Cebey-López, María José Currás-Tuala, Sara Pischedda, Sandra Viz-Lasheras, Xabier Bello, Federico Martinón-Torres, Antonio Salas
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: International Journal of Molecular Sciences, Vol 21, Iss 8, p 2748 (2020)
Druh dokumentu: article
ISSN: 1422-0067
1661-6596
DOI: 10.3390/ijms21082748
Popis: There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infections.
Databáze: Directory of Open Access Journals
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