A machine learning method for predicting disease-associated microRNA connections using network internal topology data

Jindal, Latika; Sharma, Aditi; Prasad, Kdv; Irshad, Azeem; Rivera, Richard; Karimovna, Abdurakhimova Dilora

doi:10.1016/j.health.2023.100215

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…However, the valuable information derived from our detailed computational analysis could be used for the identification of alternative biomarkers for diagnosis and/or prognosis. Indeed, it has recently been proposed that accurate expression profiling of disease-associated miRNAs (MDAs) combined with machine learning or deep learning strategies can serve as an innovative diagnostic tool for various physiological disorders in patients 64 , 65 , including certain types of sclerosis 66 , Lyssavirus infection 67 , and COVID-19 68 . Hence, this comprehensive study might provide, once the regulatory pathways involved in the sncRNA-mediated response are characterized, a new perspective for the development of innovative therapeutic and/or prophylactic strategies for the control and management of SARS-CoV-2 infection.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 remodels the landscape of small non-coding RNAs with infection time and symptom severity

Corell-Sierra,

Marquez-Molins,

Marqués

et al. 2024

npj Syst Biol Appl

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The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has significantly impacted global health, stressing the necessity of basic understanding of the host response to this viral infection. In this study, we investigated how SARS-CoV-2 remodels the landscape of small non-coding RNAs (sncRNA) from a large collection of nasopharyngeal swab samples taken at various time points from patients with distinct symptom severity. High-throughput RNA sequencing analysis revealed a global alteration of the sncRNA landscape, with abundance peaks related to species of 21-23 and 32-33 nucleotides. Host-derived sncRNAs, including microRNAs (miRNAs), transfer RNA-derived small RNAs (tsRNAs), and small nucleolar RNA-derived small RNAs (sdRNAs) exhibited significant differential expression in infected patients compared to controls. Importantly, miRNA expression was predominantly down-regulated in response to SARS-CoV-2 infection, especially in patients with severe symptoms. Furthermore, we identified specific tsRNAs derived from Glu- and Gly-tRNAs as major altered elements upon infection, with 5’ tRNA halves being the most abundant species and suggesting their potential as biomarkers for viral presence and disease severity prediction. Additionally, down-regulation of C/D-box sdRNAs and altered expression of tinyRNAs (tyRNAs) were observed in infected patients. These findings provide valuable insights into the host sncRNA response to SARS-CoV-2 infection and may contribute to the development of further diagnostic and therapeutic strategies in the clinic.

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