Comparative genomic analysis of fungal genomes reveals intron-rich ancestors View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-10

AUTHORS

Jason E Stajich, Fred S Dietrich, Scott W Roy

ABSTRACT

BACKGROUND: Eukaryotic protein-coding genes are interrupted by spliceosomal introns, which are removed from transcripts before protein translation. Many facets of spliceosomal intron evolution, including age, mechanisms of origins, the role of natural selection, and the causes of the vast differences in intron number between eukaryotic species, remain debated. Genome sequencing and comparative analysis has made possible whole genome analysis of intron evolution to address these questions. RESULTS: We analyzed intron positions in 1,161 sets of orthologous genes across 25 eukaryotic species. We find strong support for an intron-rich fungus-animal ancestor, with more than four introns per kilobase, comparable to the highest known modern intron densities. Indeed, the fungus-animal ancestor is estimated to have had more introns than any of the extant fungi in this study. Thus, subsequent fungal evolution has been characterized by widespread and recurrent intron loss occurring in all fungal clades. These results reconcile three previously proposed methods for estimation of ancestral intron number, which previously gave very different estimates of ancestral intron number for eight eukaryotic species, as well as a fourth more recent method. We do not find a clear inverse correspondence between rates of intron loss and gain, contrary to the predictions of selection-based proposals for interspecific differences in intron number. CONCLUSION: Our results underscore the high intron density of eukaryotic ancestors and the widespread importance of intron loss through eukaryotic evolution. More... »

PAGES

r223

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/gb-2007-8-10-r223

    DOI

    http://dx.doi.org/10.1186/gb-2007-8-10-r223

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1019393156

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/17949488


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