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

Identifieur interne : 000981 ( Pmc/Curation ); précédent : 000980; suivant : 000982

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

Auteurs : Miklos Csuros [Canada] ; Igor B. Rogozin [États-Unis] ; Eugene V. Koonin [États-Unis]

Source :

RBID : PMC:3174169

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.


Url:
DOI: 10.1371/journal.pcbi.1002150
PubMed: 21935348
PubMed Central: 3174169

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PMC:3174169

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<name sortKey="Kulozik, Ae" uniqKey="Kulozik A">AE Kulozik</name>
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<name sortKey="Lejeune, F" uniqKey="Lejeune F">F Lejeune</name>
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<name sortKey="Muller, J" uniqKey="Muller J">J Muller</name>
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<name sortKey="Szklarczyk, D" uniqKey="Szklarczyk D">D Szklarczyk</name>
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<name sortKey="Csuros, M" uniqKey="Csuros M">M Csuros</name>
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<name sortKey="Felsenstein, J" uniqKey="Felsenstein J">J Felsenstein</name>
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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS Comput. Biol</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">ploscomp</journal-id>
<journal-title-group>
<journal-title>PLoS Computational Biology</journal-title>
</journal-title-group>
<issn pub-type="ppub">1553-734X</issn>
<issn pub-type="epub">1553-7358</issn>
<publisher>
<publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">21935348</article-id>
<article-id pub-id-type="pmc">3174169</article-id>
<article-id pub-id-type="publisher-id">PCOMPBIOL-D-11-00005</article-id>
<article-id pub-id-type="doi">10.1371/journal.pcbi.1002150</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2">
<subject>Biology</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2">
<subject>Computer Science</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes</article-title>
<alt-title alt-title-type="running-head">Intron-rich Ancestors of Eukaryotes</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Csuros</surname>
<given-names>Miklos</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1">
<sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Rogozin</surname>
<given-names>Igor B.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Koonin</surname>
<given-names>Eugene V.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="cor1">
<sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<label>1</label>
<addr-line>Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America</addr-line>
</aff>
<contrib-group>
<contrib contrib-type="editor">
<name>
<surname>Ponting</surname>
<given-names>Chris P.</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1">University of Oxford, United Kingdom</aff>
<author-notes>
<corresp id="cor1">* E-mail:
<email>csuros@iro.umontreal.ca</email>
(MC);
<email>koonin@ncbi.nlm.nih.gov</email>
(EVK)</corresp>
<fn fn-type="con">
<p>Conceived and designed the experiments: MC IBR EVK. Performed the experiments: MC. Analyzed the data: MC IBR EVK. Contributed reagents/materials/analysis tools: MC. Wrote the paper: MC EVK.</p>
</fn>
</author-notes>
<pub-date pub-type="collection">
<month>9</month>
<year>2011</year>
</pub-date>
<pub-date pub-type="epub">
<day>15</day>
<month>9</month>
<year>2011</year>
</pub-date>
<volume>7</volume>
<issue>9</issue>
<elocation-id>e1002150</elocation-id>
<history>
<date date-type="received">
<day>30</day>
<month>12</month>
<year>2010</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>6</month>
<year>2011</year>
</date>
</history>
<permissions>
<copyright-statement>This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.</copyright-statement>
<copyright-year>2011</copyright-year>
<license xlink:href="https://creativecommons.org/publicdomain/zero/1.0/">
<license-p>This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.</license-p>
</license>
</permissions>
<abstract>
<p>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.</p>
</abstract>
<abstract abstract-type="summary">
<title>Author Summary</title>
<p>In eukaryotes, protein-coding genes are interrupted by non-coding introns. The intron densities widely differ, from 6–7 introns per kilobase of coding sequence in vertebrates, some invertebrates and plants, to only a few introns across the entire genome in many unicellular forms. We applied a robust statistical methodology, Markov Chain Monte Carlo, to reconstruct the history of intron gain and loss throughout the evolution of eukaryotes using a set of 245 homologous genes from 99 genomes that represent the diversity of eukaryotes. Intron-rich ancestors were confidently inferred for each major eukaryotic group including 53% to 74% of the human intron density for the last eukaryotic common ancestor, and 120% to 130% of the human value for the last common ancestor of animals. Evolution of eukaryotic genes involved primarily intron loss, with substantial gain only at the bases of several major branches including plants and animals. Thus, the common ancestor of all extant eukaryotes was a complex organism with a gene architecture resembling those in multicellular organisms. The line of descent from the last common ancestor to mammals was an uninterrupted intron-rich state that, given the error-prone splicing in intron-rich organisms, was conducive to the elaboration of functional alternative splicing.</p>
</abstract>
<counts>
<page-count count="9"></page-count>
</counts>
</article-meta>
</front>
</pmc>
</record>

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