Heterogeneity of Genetic Sequence within Quasi-species of Influenza Virus Revealed by Single-Molecule Sequencing

Influenza viruses are characterized by high mutation rates and genetic diversity, which have the potential to compromise the efficacy of vaccines and to facilitate the emergence of pandemic strains. These mutations are primarily introduced by the error-prone RNA-dependent RNA polymerase during viral replication. It has been demonstrated that conventional RNA sequencing methodologies frequently result in the masking of low-frequency mutations, a phenomenon that can be attributed to technical inaccuracies. This limitation impedes the acquisition of comprehensive knowledge regarding the early-stage evolution of viruses.

A major challenge in mutation analysis is distinguishing true biological variants from sequencing artifacts, especially when assessing rare mutations within viral populations. To address this, we applied a single-UMI (sUMI) sequencing method to influenza virus populations derived from single particles, achieving an error rate of ∼10 ⁻⁵ per base.

The findings of this study demonstrate that the efficacy of the sUMI method in facilitating precise detection of low-frequency mutations. These results indicate that the observed mutations are not merely a result of technical noise but rather are indicative of biological variation. Information-theoretic analyses, including Shannon entropy and Jensen–Shannon divergence, showed non-random mutation distributions and enhanced sequence diversity in viral samples relative to in vitro controls. These findings imply that selective pressures act during replication, influencing mutation retention and propagation.

This study proposes a high-precision framework for the quantification of viral genome diversity and the identification of early adaptive mutations. This finding also substantiates the approach’s viability in the context of mutation prediction through the utilization of logistic models.

Collectively, these results contribute to a deeper understanding of viral evolution and provide a foundation for real-time surveillance of emerging variants, resistance mutations, and vaccine escape potential. The approach has broad implications for forecasting influenza evolution and improving preparedness for future outbreaks.