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Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach

AuthorsJanga, Sarath Chandra; Contreras-Moreira, Bruno CSIC ORCID
KeywordsGene regulation
Transcription factors
Network dynamics
Escherichia coli
Issue Date23-Aug-2010
CitationJanga C, Contreras-Moreira B. Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach. Nucleic Acids Research 38 (20): 6841-6856 (2010)
AbstractIn prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called Transcription Factors (TFs). In this study, we map the complete repertoire of ~ 300 TFs of the bacterial model, Escherichia coli, onto gene expression data for a number of non-redundant experimental conditions and show that TFs are generally expressed at a lower level than other gene classes. We also demonstrate that different conditions harbor varying number of active TFs, with an average of about 15% of the total repertoire, with certain stress and drug induced conditions exhibiting as high as one-third of the collection of TFs. Our results also show that activators are more frequently expressed than repressors, indicating that activation of promoters might be a more common phenomenon than repression in bacteria. Finally, to understand the association of TFs with different conditions and to elucidate their dynamic interplay with other TFs, we develop a network-based framework to identify TFs which act as markers, those which are responsible for condition-specific transcriptional rewiring. This approach allowed us to pinpoint several marker TFs as being central in various specialized conditions like drug-induction or growth condition variations, which we discuss in light of previously reported experimental findings. Further analysis showed that a majority of identified markers effectively control the expression of their regulons and in general transcriptional programs of most conditions can be effectively rewired by a very small number of TFs. It was also found that closeness is a key centrality measure which can aid in the successful identification of marker TFs in regulatory networks. Our results suggest the utility of the network-based approaches developed in this study to be applicable for understanding other interactomic datasets.
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