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Vizcaino, A.

Publications and source records attributed to Vizcaino, A..

2 recordsLinked to original sources

Prevalent phenotypic mutation impairs binding of broadly neutralizing antibodies to influenza hemagglutinin

Mistakes during protein synthesis, such as transcription errors, occur often and lead to non-inheritable amino acid replacements generally known as phenotypic mutations. We recently used a consensus approach in high-throughput sequencing to determine the error landscape for influenza-hemagglutinin mRNA. We found single-site errors to occur with widely different frequencies. Here we show that the most prevalent transcription error encodes a phenotypic mutation that impairs binding of broadly neutralizing antibodies. The error occurs in 0.2-0.5% of mRNA molecules and, consequently, many virions will expose hemagglutinin variants bearing the encoded amino acid replacement. Our results point to a mechanism of antibody evasion, akin to programmed recoding, in which evading mutations are encoded by transcription errors promoted by inheritable RNA sequence/structure patterns.

microbiology↗

Influenza evolution/adaptation samples a highly non-random error landscape for hemagglutinin-encoding RNA

Natural selection acts on diversity generated by errors in the biosynthesis of the genetic material. Previous work has shown, however, that such errors are not necessarily fully random. We have used a model influenza strain and a unique-molecular-identifier-based high-throughput sequencing approach to assess the error landscape for hemagglutinin-encoding RNA. Single-site errors occur at highly variable frequencies, with differences that span several orders of magnitude, plausibly reflecting specific RNA sequence/structure patterns. Remarkably, influenza evolution/adaptation preferentially selects mutations encoded by the higher frequency errors, as shown by analyses of mutations fixed in natural strains over many decades and by analyses of antibody-escape mutations found in laboratory experiments on strains of the 2009 pandemics. Our results support that RNA error landscapes may provide information useful for predicting influenza evolution and point to high-frequency errors encoding antibody-evading mutations as potential contributors to the rapid evolution of influenza viruses.

microbiology↗