Multiplexed Salivary miRNA Quantification for Predicting Severe COVID-19 Symptoms in Children Using Ligation-RPA

Insights

A new portable assay rapidly detects salivary microRNA (miRNA) biomarkers for predicting severe COVID-19 in children. This non-invasive method aids early risk identification and clinical decision-making, especially in resource-limited settings.

Area of Science:

  • Biochemistry
  • Molecular Biology
  • Pediatric Infectious Diseases

Background:

  • SARS-CoV-2 can cause severe complications in children, necessitating early identification of high-risk cases.
  • Salivary microRNA (miRNA) alterations are potential biomarkers for predicting COVID-19 severity.
  • Current miRNA quantification methods like sequencing are not rapid or non-invasive, limiting clinical utility.

Purpose of the Study:

  • To develop a rapid, specific, and sensitive non-invasive assay for quantifying salivary miRNA biomarkers to predict severe COVID-19 in children.
  • To establish a portable platform for miRNA quantification as an alternative to traditional sequencing methods.

Main Methods:

  • Development of a ligation-recombinase polymerase amplification (RPA) assay targeting specific salivary miRNAs (miR-1273, miR-296, miR-29).
  • Validation of portable RNA extraction against benchtop methods using clinical samples (R-square > 0.85, r > 0.92).
  • Quantification of miRNA levels in 154 clinical samples to assess diagnostic accuracy (AUC of 0.98).

Main Results:

  • The ligation-RPA assay demonstrated high sensitivity (quantifying down to 1 fM) and 100% specificity.
  • Portable extraction methods showed strong correlation with benchtop methods.
  • Significant downregulation of specific miRNAs was observed in severe COVID-19 cases compared to non-severe cases.

Conclusions:

  • The developed portable ligation-RPA assay is a highly accurate and sensitive tool for non-invasively predicting severe COVID-19 in children using salivary miRNA biomarkers.
  • This platform facilitates timely clinical decisions and resource optimization, particularly in resource-limited settings.

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