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

Abstract

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.

Distinctive Cellular and Metabolic Reprogramming in Porcine Lung Mononuclear Phagocytes Infected With Type 1 PRRSV Strains

“…ML helps to classify transcriptomic data by pattern recognition (expression “fingerprinting”) and provides mechanistic insights when paired with other downstream procedures, such as pathway analysis ( 18 , 19 ). ML has been used in transcriptomic studies of viral infections in humans ( 20 , 21 ) and was recently applied to identify genes associated with feed efficiency in pigs ( 22 ).…”

Host Transcriptomic Response Following Administration of Rotavirus Vaccine in Infants’ Mimics Wild Type Infection

“…(C) Box and whisker plot of the model: the horizontal lines in the boxes indicate the median of each group; the lower and upper edges of boxes reflect interquartile ranges, and the whiskers are <1 times the interquartile range. Total score value from nine-transcript signature calculated as in ( 34 – 36 ) is represented in the y-axis.…”

“…Boxplots were built using the R package beeswarm (https:// cran.r-project.org/web/packages/beeswarm) to represent the total score for the transcript signature in the different groups analyzed. The total score was obtained using the same approach as the described in (34)(35)(36) for Disease Risk Score (DRS) calculation.…”

Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis

“…We found three genes, namely BATF , ISG15 and DNMT1 , which can distinguish viral from bacterial infections in a wide range of cohorts including different pathogens, ages and populations, and with potential to become clinical biomarkers for infectious diseases in a clinical setting. As occurred in previous studies [ 4 , 5 , 6 , 15 , 36 ], the role of biomarkers of infection is often unknown; this fact, however, does not diminish the importance of their capability to distinguish viral from bacterial infections. In our study, the concurrence of these biomarkers in a significant number of independent studies points to their important role in the process of infection, and this observation strongly suggests the need for further investigations.…”

“…For instance, Herberg et al [ 4 ] discovered a two-transcript signature from microarray expression data, which discriminated between viral and bacterial infections with no known function of the genes involved. Despite this, the two-transcript signature was successfully tested and validated in prospective and other retrospective cohorts, and using different gene-expression technologies [ 5 , 6 , 36 ]. In the same line, two long non-coding RNAs have been recently proposed as biomarkers associated with viral infections, showing high performance capability in separating viral from healthy phenotypes [ 36 ]; their role, however, is completely unknown.…”