The Mutational Robustness of Influenza A Virus.
Elisa VisherShawn E WhitefieldJohn T McCroneWilliam J FitzsimmonsAdam S LauringPublished in: PLoS pathogens (2016)
A virus' mutational robustness is described in terms of the strength and distribution of the mutational fitness effects, or MFE. The distribution of MFE is central to many questions in evolutionary theory and is a key parameter in models of molecular evolution. Here we define the mutational fitness effects in influenza A virus by generating 128 viruses, each with a single nucleotide mutation. In contrast to mutational scanning approaches, this strategy allowed us to unambiguously assign fitness values to individual mutations. The presence of each desired mutation and the absence of additional mutations were verified by next generation sequencing of each stock. A mutation was considered lethal only after we failed to rescue virus in three independent transfections. We measured the fitness of each viable mutant relative to the wild type by quantitative RT-PCR following direct competition on A549 cells. We found that 31.6% of the mutations in the genome-wide dataset were lethal and that the lethal fraction did not differ appreciably between the HA- and NA-encoding segments and the rest of the genome. Of the viable mutants, the fitness mean and standard deviation were 0.80 and 0.22 in the genome-wide dataset and best modeled as a beta distribution. The fitness impact of mutation was marginally lower in the segments coding for HA and NA (0.88 ± 0.16) than in the other 6 segments (0.78 ± 0.24), and their respective beta distributions had slightly different shape parameters. The results for influenza A virus are remarkably similar to our own analysis of CirSeq-derived fitness values from poliovirus and previously published data from other small, single stranded DNA and RNA viruses. These data suggest that genome size, and not nucleic acid type or mode of replication, is the main determinant of viral mutational fitness effects.
Keyphrases
- body composition
- physical activity
- genome wide
- nucleic acid
- wild type
- dna methylation
- electronic health record
- copy number
- sars cov
- magnetic resonance imaging
- high resolution
- machine learning
- induced apoptosis
- oxidative stress
- single molecule
- endoplasmic reticulum stress
- artificial intelligence
- computed tomography
- cell free
- mass spectrometry
- deep learning
- cell death
- genetic diversity