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Orthologous Transcription Factors in Bacteria Have Different Functions and Regulate Different Genes

Overview of attention for article published in PLoS Computational Biology, September 2007
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Title
Orthologous Transcription Factors in Bacteria Have Different Functions and Regulate Different Genes
Published in
PLoS Computational Biology, September 2007
DOI 10.1371/journal.pcbi.0030175
Pubmed ID
Authors

Morgan N Price, Paramvir S Dehal, Adam P Arkin

Abstract

Transcription factors (TFs) form large paralogous gene families and have complex evolutionary histories. Here, we ask whether putative orthologs of TFs, from bidirectional best BLAST hits (BBHs), are evolutionary orthologs with conserved functions. We show that BBHs of TFs from distantly related bacteria are usually not evolutionary orthologs. Furthermore, the false orthologs usually respond to different signals and regulate distinct pathways, while the few BBHs that are evolutionary orthologs do have conserved functions. To test the conservation of regulatory interactions, we analyze expression patterns. We find that regulatory relationships between TFs and their regulated genes are usually not conserved for BBHs in Escherichia coli K12 and Bacillus subtilis. Even in the much more closely related bacteria Vibrio cholerae and Shewanella oneidensis MR-1, predicting regulation from E. coli BBHs has high error rates. Using gene-regulon correlations, we identify genes whose expression pattern differs between E. coli and S. oneidensis. Using literature searches and sequence analysis, we show that these changes in expression patterns reflect changes in gene regulation, even for evolutionary orthologs. We conclude that the evolution of bacterial regulation should be analyzed with phylogenetic trees, rather than BBHs, and that bacterial regulatory networks evolve more rapidly than previously thought.

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Geographical breakdown

Country Count As %
United States 6 3%
Mexico 3 2%
Brazil 2 1%
Germany 2 1%
Australia 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Sweden 1 <1%
Denmark 1 <1%
Other 3 2%
Unknown 157 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 29%
Researcher 41 23%
Professor > Associate Professor 18 10%
Student > Master 16 9%
Student > Bachelor 10 6%
Other 27 15%
Unknown 14 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 98 55%
Biochemistry, Genetics and Molecular Biology 32 18%
Computer Science 15 8%
Engineering 6 3%
Medicine and Dentistry 4 2%
Other 12 7%
Unknown 11 6%