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A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes

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Title
A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002150
Pubmed ID
Authors

Miklos Csuros, Igor B. Rogozin, Eugene V. Koonin

Abstract

Protein-coding genes in eukaryotes are interrupted by introns, but intron densities widely differ between eukaryotic lineages. Vertebrates, some invertebrates and green plants have intron-rich genes, with 6-7 introns per kilobase of coding sequence, whereas most of the other eukaryotes have intron-poor genes. We reconstructed the history of intron gain and loss using a probabilistic Markov model (Markov Chain Monte Carlo, MCMC) on 245 orthologous genes from 99 genomes representing the three of the five supergroups of eukaryotes for which multiple genome sequences are available. Intron-rich ancestors are confidently reconstructed for each major group, with 53 to 74% of the human intron density inferred with 95% confidence for the Last Eukaryotic Common Ancestor (LECA). The results of the MCMC reconstruction are compared with the reconstructions obtained using Maximum Likelihood (ML) and Dollo parsimony methods. An excellent agreement between the MCMC and ML inferences is demonstrated whereas Dollo parsimony introduces a noticeable bias in the estimations, typically yielding lower ancestral intron densities than MCMC and ML. Evolution of eukaryotic genes was dominated by intron loss, with substantial gain only at the bases of several major branches including plants and animals. The highest intron density, 120 to 130% of the human value, is inferred for the last common ancestor of animals. The reconstruction shows that the entire line of descent from LECA to mammals was intron-rich, a state conducive to the evolution of alternative splicing.

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Geographical breakdown

Country Count As %
Germany 4 2%
Spain 4 2%
United States 3 2%
United Kingdom 3 2%
Australia 2 1%
Netherlands 1 <1%
Colombia 1 <1%
Brazil 1 <1%
Ukraine 1 <1%
Other 5 3%
Unknown 153 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 28%
Researcher 42 24%
Student > Master 19 11%
Student > Bachelor 9 5%
Professor > Associate Professor 9 5%
Other 34 19%
Unknown 16 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 104 58%
Biochemistry, Genetics and Molecular Biology 44 25%
Environmental Science 2 1%
Computer Science 2 1%
Earth and Planetary Sciences 2 1%
Other 6 3%
Unknown 18 10%