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Reconstruction of the core and extended regulons of global transcription factors.

Dufour YS, Kiley PJ, Donohue TJ - PLoS Genet. (2010)

Bottom Line: Our results show that this approach correctly predicted many regulon members, provided new insights into the biological functions of the respective regulons for these regulators, and suggested models for the evolution of the corresponding transcriptional networks.In addition, the members of the so-called extended regulons for the FNR-type regulators vary even among closely related species, possibly reflecting species-specific adaptation to environmental and other factors.The comparative genomics approach we developed is readily applicable to other regulatory networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

ABSTRACT
The processes underlying the evolution of regulatory networks are unclear. To address this question, we used a comparative genomics approach that takes advantage of the large number of sequenced bacterial genomes to predict conserved and variable members of transcriptional regulatory networks across phylogenetically related organisms. Specifically, we developed a computational method to predict the conserved regulons of transcription factors across alpha-proteobacteria. We focused on the CRP/FNR super-family of transcription factors because it contains several well-characterized members, such as FNR, FixK, and DNR. While FNR, FixK, and DNR are each proposed to regulate different aspects of anaerobic metabolism, they are predicted to recognize very similar DNA target sequences, and they occur in various combinations among individual alpha-proteobacterial species. In this study, the composition of the respective FNR, FixK, or DNR conserved regulons across 87 alpha-proteobacterial species was predicted by comparing the phylogenetic profiles of the regulators with the profiles of putative target genes. The utility of our predictions was evaluated by experimentally characterizing the FnrL regulon (a FNR-type regulator) in the alpha-proteobacterium Rhodobacter sphaeroides. Our results show that this approach correctly predicted many regulon members, provided new insights into the biological functions of the respective regulons for these regulators, and suggested models for the evolution of the corresponding transcriptional networks. Our findings also predict that, at least for the FNR-type regulators, there is a core set of target genes conserved across many species. In addition, the members of the so-called extended regulons for the FNR-type regulators vary even among closely related species, possibly reflecting species-specific adaptation to environmental and other factors. The comparative genomics approach we developed is readily applicable to other regulatory networks.

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Major sub-families of the CRP/FNR-type transcription factors in 87 representative α-proteobacteria.The hierarchical tree representation of the amino acid sequence similarities was constructed by partitioning protein groups using increasing clustering stringency (inflation value, see Materials and Methods). The bold numbers within each box represent the number of individual proteins within each cluster and the number below represents the number of species possessing at least one of these proteins. The bottom of the tree shows names for the major 8 sub-families using nomenclature described previously [3]. Minor sub-families could not be classified definitively, so these sub-families are designated by roman numerals.
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pgen-1001027-g001: Major sub-families of the CRP/FNR-type transcription factors in 87 representative α-proteobacteria.The hierarchical tree representation of the amino acid sequence similarities was constructed by partitioning protein groups using increasing clustering stringency (inflation value, see Materials and Methods). The bold numbers within each box represent the number of individual proteins within each cluster and the number below represents the number of species possessing at least one of these proteins. The bottom of the tree shows names for the major 8 sub-families using nomenclature described previously [3]. Minor sub-families could not be classified definitively, so these sub-families are designated by roman numerals.

Mentions: After searching all sequenced α-proteobacterial genomes in the Integrated Microbial Genomes database (img.jgi.doe.gov) in January 2009 (∼150 genome sequences) for proteins of the CRP/FNR super-family, we first found that α-proteobacteria from the genera Rickettsia, Ehrlichia, Wolbachia, and Bartonella do not possess proteins in the CRP/FNR super-family. Accordingly, these genera were not studied further. Among the remaining genera, we selected 87 representative α-proteobacterial species that altogether contained 697 proteins in the CRP/FNR super-family (Table S1). To assemble these 697 proteins into functionally related sets, we took a clustering approach derived from the ORTHOMCL algorithm [17], which identifies connected sets of proteins in networks constructed from protein sequence similarities. When we applied this clustering approach multiple times with increasing stringency, we uncovered a hierarchical relationship between proteins of the different families (Figure 1). Ultimately, 607 of the 697 proteins were clustered into 7 major sub-families that could not be further sub-divided solely by more stringent clustering, suggesting that the proteins within each of these 7 major sub-families are very closely related. The 7 α-proteobacterial protein families and their relationships are also consistent with the phylogenic tree obtained by neighbor joining of the 2002 dataset [3], supporting the conclusion that both approaches are capturing the same functional groups.


Reconstruction of the core and extended regulons of global transcription factors.

Dufour YS, Kiley PJ, Donohue TJ - PLoS Genet. (2010)

Major sub-families of the CRP/FNR-type transcription factors in 87 representative α-proteobacteria.The hierarchical tree representation of the amino acid sequence similarities was constructed by partitioning protein groups using increasing clustering stringency (inflation value, see Materials and Methods). The bold numbers within each box represent the number of individual proteins within each cluster and the number below represents the number of species possessing at least one of these proteins. The bottom of the tree shows names for the major 8 sub-families using nomenclature described previously [3]. Minor sub-families could not be classified definitively, so these sub-families are designated by roman numerals.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2908626&req=5

pgen-1001027-g001: Major sub-families of the CRP/FNR-type transcription factors in 87 representative α-proteobacteria.The hierarchical tree representation of the amino acid sequence similarities was constructed by partitioning protein groups using increasing clustering stringency (inflation value, see Materials and Methods). The bold numbers within each box represent the number of individual proteins within each cluster and the number below represents the number of species possessing at least one of these proteins. The bottom of the tree shows names for the major 8 sub-families using nomenclature described previously [3]. Minor sub-families could not be classified definitively, so these sub-families are designated by roman numerals.
Mentions: After searching all sequenced α-proteobacterial genomes in the Integrated Microbial Genomes database (img.jgi.doe.gov) in January 2009 (∼150 genome sequences) for proteins of the CRP/FNR super-family, we first found that α-proteobacteria from the genera Rickettsia, Ehrlichia, Wolbachia, and Bartonella do not possess proteins in the CRP/FNR super-family. Accordingly, these genera were not studied further. Among the remaining genera, we selected 87 representative α-proteobacterial species that altogether contained 697 proteins in the CRP/FNR super-family (Table S1). To assemble these 697 proteins into functionally related sets, we took a clustering approach derived from the ORTHOMCL algorithm [17], which identifies connected sets of proteins in networks constructed from protein sequence similarities. When we applied this clustering approach multiple times with increasing stringency, we uncovered a hierarchical relationship between proteins of the different families (Figure 1). Ultimately, 607 of the 697 proteins were clustered into 7 major sub-families that could not be further sub-divided solely by more stringent clustering, suggesting that the proteins within each of these 7 major sub-families are very closely related. The 7 α-proteobacterial protein families and their relationships are also consistent with the phylogenic tree obtained by neighbor joining of the 2002 dataset [3], supporting the conclusion that both approaches are capturing the same functional groups.

Bottom Line: Our results show that this approach correctly predicted many regulon members, provided new insights into the biological functions of the respective regulons for these regulators, and suggested models for the evolution of the corresponding transcriptional networks.In addition, the members of the so-called extended regulons for the FNR-type regulators vary even among closely related species, possibly reflecting species-specific adaptation to environmental and other factors.The comparative genomics approach we developed is readily applicable to other regulatory networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

ABSTRACT
The processes underlying the evolution of regulatory networks are unclear. To address this question, we used a comparative genomics approach that takes advantage of the large number of sequenced bacterial genomes to predict conserved and variable members of transcriptional regulatory networks across phylogenetically related organisms. Specifically, we developed a computational method to predict the conserved regulons of transcription factors across alpha-proteobacteria. We focused on the CRP/FNR super-family of transcription factors because it contains several well-characterized members, such as FNR, FixK, and DNR. While FNR, FixK, and DNR are each proposed to regulate different aspects of anaerobic metabolism, they are predicted to recognize very similar DNA target sequences, and they occur in various combinations among individual alpha-proteobacterial species. In this study, the composition of the respective FNR, FixK, or DNR conserved regulons across 87 alpha-proteobacterial species was predicted by comparing the phylogenetic profiles of the regulators with the profiles of putative target genes. The utility of our predictions was evaluated by experimentally characterizing the FnrL regulon (a FNR-type regulator) in the alpha-proteobacterium Rhodobacter sphaeroides. Our results show that this approach correctly predicted many regulon members, provided new insights into the biological functions of the respective regulons for these regulators, and suggested models for the evolution of the corresponding transcriptional networks. Our findings also predict that, at least for the FNR-type regulators, there is a core set of target genes conserved across many species. In addition, the members of the so-called extended regulons for the FNR-type regulators vary even among closely related species, possibly reflecting species-specific adaptation to environmental and other factors. The comparative genomics approach we developed is readily applicable to other regulatory networks.

Show MeSH
Related in: MedlinePlus