Source:http://linkedlifedata.com/resource/pubmed/id/20333180
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| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2010-3-24
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| pubmed:abstractText |
A major mode of gene expression evolution is based on changes in cis-regulatory elements (CREs) whose function critically depends on the presence of transcription factor-binding sites (TFBS). Because CREs experience extensive TFBS turnover even with conserved function, alignment-based studies of CRE sequence evolution are limited to very closely related species. Here, we propose an alternative approach based on a stochastic model of TFBS turnover. We implemented a maximum likelihood model that permits variable turnover rates in different parts of the species tree. This model can be used to detect changes in turnover rate as a proxy for differences in the selective pressures acting on TFBS in different clades. We applied this method to five TFBS in the fungi methionine biosynthesis pathway and three TFBS in the HoxA clusters of vertebrates. We find that the estimated turnover rate is generally high, with half-life ranging between approximately 5 and 150 My and a mode around tens of millions of years. This rate is consistent with the finding that even functionally conserved enhancers can show very low sequence similarity. We also detect statistically significant differences in the equilibrium densities of estrogen- and progesterone-response elements in the HoxA clusters between mammal and nonmammal vertebrates. Even more extreme clade-specific differences were found in the fungal data. We conclude that stochastic models of TFBS turnover enable the detection of shifts in the selective pressures acting on CREs in different organisms. The analysis tool, called CRETO (Cis-Regulatory Element Turn-Over) can be downloaded from http://www.bioinf.uni-leipzig.de/Software/creto/.
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| pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-10598584,
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http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-17694055,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-17805289,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-18303948,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-18501470,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-18809929,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-19043578,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20333180-9892669
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:issn |
1759-6653
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| pubmed:author | |
| pubmed:issnType |
Electronic
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| pubmed:volume |
1
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
85-98
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| pubmed:dateRevised |
2010-9-28
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| pubmed:year |
2009
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| pubmed:articleTitle |
Measuring transcription factor-binding site turnover: a maximum likelihood approach using phylogenies.
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| pubmed:affiliation |
Lehrstuhl für Bioinformatik, Institut für Informatik, Universität Leipzig, Leipzig, Germany.
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| pubmed:publicationType |
Journal Article
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