Barral Arca, RuthGómez Carballa, AlbertoCebey López, MiriamCurrás Tuala, María JoséPischedda, SaraViz Lasheras, SandraBello Paderne, XabierMartinón Torres, FedericoSalas Ellacuriaga, Antonio2020-11-272020-11-272020Barral-Arca, R.; Gómez-Carballa, A.; Cebey-López, M.; Currás-Tuala, M.J.; Pischedda, S.; Viz-Lasheras, S.; Bello, X.; Martinón-Torres, F.; Salas, A. RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans. Int. J. Mol. Sci. 2020, 21, 2748http://hdl.handle.net/10347/23849There 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 infectionseng© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/BiomarkersRNA-seqlncRNAVirusMachine learningjournal article10.3390/ijms210827481422-0067open access