PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 15, 2011PLoS Computational Biology176 citationsOpen Access

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

MCMiklós CsürösUniversité de MontréalIRIgor B. RogozinUniversity of Ostrava
Eugene V. Koonin
Eugene V. KooninNational Center for Biotechnology Information

Key Points

Key points are not available for this paper at this time.

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Csürös et al. (2011) studied this question.

synapsesocial.com/papers/6a228d88df882a5024ddb2e6https://doi.org/10.1371/journal.pcbi.1002150
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Nonsense-mediated mRNA decay: from vacuum cleaner to Swiss army knife.2004 · 77 citations
  2. 2On the Impossibility of Reconstructing Ancestral Data and Phylogenies2003 · 78 citations
  3. 3Monte Carlo Statistical Methods1999 · 2,303 citations
  4. 4Analysis of nonuniformity in intron phase distribution1992 · 124 citations
  5. 5eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations2009 · 239 citations