Differential expression analysis and profiling of hepatic miRNA and isomiRNA in dengue hemorrhagic fever

Oliveira, Layanna Freitas de; Andrade, Amanda Araújo Serrão de; Pagliari, Carla; Carvalho, Leda Viegas de; Silveira, Taiana S; Cardoso, Jedson Ferreira; Silva, André Luíz Teles e; Vasconcelos, Janaína Mota de; Moreira-Nunes, Caroline Aquino; Burbano, Rommel Rodrı́guez; Nunes, Márcio Roberto Teixeira; Santos, Eduardo José Melo dos; Júnior, João Lídio da Silva Gonçalves Vianez

doi:10.1038/s41598-020-72892-w

Cited by 18 publications

(17 citation statements)

References 24 publications

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“…Exosome miR-146a can act on different immune cells, making recipient cells more susceptible to many viral infections[ 60 ]. Surprisingly, some studies assessed that miR-146a-5p is associated with induced autophagy, which is a process in cell degradation and recycling for DENV replication[ 61 – 63 ]. Thus, miR-146a-5p has the potential to serve as a circulating biomarker for dengue pathogenesis.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Xie

Yin

et al. 2021

PLoS Negl Trop Dis

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Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.

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Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Xie

Yin

et al. 2021

PLoS Negl Trop Dis

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“…Immune response pathways were related to NF-kB, CC and CX families, IL, and TLR. This is the first description of the human microRNA and isomicroRNA profile in liver tissues from DHF cases and the results demonstrated the association of miR-126-5p, miR-122-5p, and miR-146a-5p with DHF liver pathogenesis, involving endothelial repair and vascular permeability regulation, control of homeostasis, and expression of inflammatory cytokines ( de Oliveira et al, 2021 ). However, in clinical treatment, tissue samples are not readily available, and it is desirable to study the relationship between miRNA and the pathogenesis of DENV infection and DHF in serum or blood samples.…”

Section: The Role Of Mirna In Regulating Denv and Related Signaling Pathwaysmentioning

confidence: 85%

“…Although growing evidence has demonstrated the association between cellular miRNAs and viral infection and some miRNAs involved in inflammatory response has been described, these studies have mainly focused on cell models and little is known about the relationship between miRNAs and the pathogenesis of DHF. A recent study sequenced miRNomes from six deaths and compared them with five controls to characterize microRNAs expression profiles in human liver tissue during DHF ( de Oliveira et al, 2021 ). Eight microRNAs were found to be differentially expressed, including endothelial cell regulatory molecule miR-126-5p, liver specific homeostasis regulator miR-122-5p, and interferon regulator miR-146a-5p.…”

Section: The Role Of Mirna In Regulating Denv and Related Signaling Pathwaysmentioning

confidence: 99%

“…Eight microRNAs were found to be differentially expressed, including endothelial cell regulatory molecule miR-126-5p, liver specific homeostasis regulator miR-122-5p, and interferon regulator miR-146a-5p. Enrichment analysis with predicted target genes of microRNAs revealed regulatory pathways of apoptosis, involving MAPK, RAS, CDK, and FAS ( de Oliveira et al, 2021 ). Immune response pathways were related to NF-kB, CC and CX families, IL, and TLR.…”

Section: The Role Of Mirna In Regulating Denv and Related Signaling Pathwaysmentioning

confidence: 99%

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microRNAs, the Link Between Dengue Virus and the Host Genome

Lin

et al. 2021

Front. Microbiol.

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Dengue virus (DENV) is a small envelope virus of Flaviviridae that is mainly transmitted by Aedes aegypti and Aedes albopictus. It can cause dengue fever with mild clinical symptoms or even life-threatening dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). At present, there are no specific drugs or mature vaccine products to treat DENV. microRNAs (miRNAs) are a class of important non-coding small molecular RNAs that regulate gene expression at the post-transcriptional level. It is involved in and regulates a series of important life processes, such as growth and development, cell differentiation, cell apoptosis, anti-virus, and anti-tumor. miRNAs also play important roles in interactions between host and viral genome transcriptomes. Host miRNAs can directly target the genome of the virus or regulate host factors to promote or inhibit virus replication. Understanding the expression and function of miRNAs during infection with DENV and the related signal molecules of the miRNA-mediated regulatory network will provide new insights for the development of miRNA-based therapies.

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“…While the other seven genes have not been specifically studied in dengue, they are known to be involved in pathways that have been implicated in DENV or other viral infections. For instance, TGFβ signaling has been associated with dengue severity [ 70 ], and the RAS pathway may be a target of miRNAs expressed in DHF [ 71 ]. Interestingly, six of the eight genes were differentially expressed in SD progressors early in the disease course (days 2–6), indicating that they are biomarkers of a defective early host response rather than signs of ongoing SD pathogenesis.…”

Section: Discussionmentioning

confidence: 99%

An 8-gene machine learning model improves clinical prediction of severe dengue progression

et al. 2022

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Background Each year 3–6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed. Methods We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were “locked” prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD. Results We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2–100) sensitivity and 79.7% (95% CI 75.5–83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7–25.6) and 99.0% (95% CI 97.7–100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3–94.1) sensitivity and 39.7% (95% CI 34.7–44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression. Conclusions The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

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Differential expression analysis and profiling of hepatic miRNA and isomiRNA in dengue hemorrhagic fever

Cited by 18 publications

References 24 publications

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

microRNAs, the Link Between Dengue Virus and the Host Genome

An 8-gene machine learning model improves clinical prediction of severe dengue progression

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