Speaker
Description
Defective viral genomes (DVGs) interfere with infectious standard virus (STV) replication and are considered promising antiviral agents. In longitudinal influenza A virus (IAV) infections, DVG accumulation drives oscillatory virus dynamics (von-Magnus effect \cite{1}). Experimental evaluation of individual DVGs is resource-intensive, motivating computational prioritization.
We aim to identify DVGs influencing STV titers using Granger-causality analysis of sequencing data from a longitudinal IAV infection \cite{2}.
For 1,968 DVGs, two ordinary least squares models were compared: a restricted model trained on STV titers, and a full model including DVG trajectories. Upon evaluation of goodness of predictions, DVGs were classified as Granger-causing (DVG predicts STV titers), Granger-caused (STV titers predict DVG), Granger-bi-directional (mutual predictive influence), or non-related \cite{3,4}.
We found that 109 DVGs significantly influenced STV titers. A previously validated DVG that reduced STV titers by five orders of magnitude in experiments was classified as Granger-bi-directional, whereas a candidate reducing STV titers by only three orders of magnitude was categorized as non-related. These results indicate that Granger causality-based classification reflects antiviral efficacy and enables prioritization of potent DVGs.
This proof-of-concept shows that Granger-causality allows systematic identification of DVGs with antiviral potential, reducing experimental effort.
Bibliography
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urldate = {2026-05-10},
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}
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