Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine...

Area of Science:

• Virology
• Genomics
• Immunology

Background:

• Gene expression analysis is crucial for understanding Ebola virus disease pathogenesis.
• RNA sequencing (RNA-Seq) and NanoString are common platforms for gene expression profiling.
• Evaluating platform concordance is essential for reliable research findings.

Purpose of the Study:

• To assess the concordance between RNA-Seq and NanoString for gene expression analysis in Ebola virus-infected non-human primates (NHPs).
• To identify robust gene signatures for Ebola virus infection detection.
• To compare the capabilities of RNA-Seq and NanoString in identifying key immune response genes.

Main Methods:

• Comparative analysis of gene expression data from RNA-Seq and NanoString platforms.
• Spearman correlation and Bland-Altman analysis to evaluate platform concordance.
• Machine learning (Supervised Magnitude-Altitude Scoring) to identify gene signatures.
• Logistic regression modeling and independent validation in human cell lines.

Main Results:

• High concordance observed between RNA-Seq and NanoString (Spearman coefficients 0.78-0.88).
• OAS1 identified as a potent gene signature, achieving 100% accuracy in predicting Ebola virus infection in NHPs and human cells.
• Twelve common immune-related genes identified, with RNA-Seq uniquely detecting additional immune regulatory genes.

Conclusions:

• RNA-Seq and NanoString are highly concordant for Ebola virus gene expression studies in NHPs.
• OAS1 serves as a reliable and broadly applicable biomarker for Ebola virus infection.
• RNA-Seq offers broader gene detection capabilities, complementing NanoString's targeted approach.