Florian Benitiere, Anamaria Necsulea, Laurent DuretPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p style="text-align: justify;">Most eukaryotic genes undergo alternative splicing (AS), but the overall functional significance of this process remains a controversial issue. It has been noticed that the complexity of organisms (assayed by the number of distinct cell types) correlates positively with their genome-wide AS rate. This has been interpreted as evidence that AS plays an important role in adaptive evolution by increasing the functional repertoires of genomes. However, this observation also fits with a totally opposite interpretation: given that ‘complex’ organisms tend to have small effective population sizes (<em>Ne</em>), they are expected to be more affected by genetic drift, and hence more prone to accumulate deleterious mutations that decrease splicing accuracy. Thus, according to this “drift barrier” theory, the elevated AS rate in complex organisms might simply result from a higher splicing error rate. To test this hypothesis, we analyzed 3,496 transcriptome sequencing samples to quantify AS in 53 metazoan species spanning a wide range of <em>Ne</em> values. Our results show a negative correlation between <em>Ne</em> proxies and the genome-wide AS rates among species, consistent with the drift barrier hypothesis. This pattern is dominated by low abundance isoforms, which represent the vast majority of the splice variant repertoire. We show that these low abundance isoforms are depleted in functional AS events, and most likely correspond to errors. Conversely, the AS rate of abundant isoforms, which are relatively enriched in functional AS events, tends to be lower in more complex species. All these observations are consistent with the hypothesis that variation in AS rates across metazoans reflects the limits set by drift on the capacity of selection to prevent gene expression errors.</p>
Alternative splicing, Random genetic drift, Life history traits, Effective population size, dNdS, Splice variants, Non-adaptive models, Ne
Bioinformatics & Computational Biology, Genome Evolution, Molecular Evolution, Population Genetics / Genomics
Henrik Kaessmann h.kaessmann@zmbh.uni-heidelberg.de, Michael Lynch mlynch11@asu.edu, Philipp Khaitovich khaitovich@eva.mpg.de, Philipp Khaitovich khaitovich@eva.mpg.de, Margarida Cardoso-Moreira margarida.cardosomoreira@crick.ac.uk, Michael L Tress mtress@cnio.es, Jianzhi Zhang jianzhi@umich.ed, Alfonso Valencia alfonso.valencia@bsc.es, Way Sung wsung@uncc.edu
e.g. John Doe john@doe.com
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