期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 21, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/ijms21082748
关键词
biomarkers; RNA-seq; lncRNA; virus; machine learning
资金
- Instituto de Salud Carlos III: project GePEM (Instituto de Salud Carlos III(ISCIII)) [PI16/01478]
- DIAVIR (Instituto de Salud Carlos III(ISCIII)) [DTS19/00049]
- FEDER
- Resvi-Omics (Instituto de Salud Carlos III(ISCIII)) [PI19/01039]
- project BI-BACVIR (PRIS-3
- Agencia de Conocimiento en Salud (ACIS)-Servicio Gallego de Salud (SERGAS)-Xunta de Galicia
- Spain)
- project ReSVinext (Instituto de Salud Carlos III(ISCIII)) [PI16/01569]
- Enterogen (Instituto de Salud Carlos III(ISCIII)) [PI19/01090]
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.
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