Sarath Chandra Janga1,*and Bruno Contreras-Moreira2,3,4,*
1MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, United Kingdom.
2Estación Experimental de Aula Dei /CSIC, Universidad de Zaragoza, Av.Montañana 1.005, 50059 Zaragoza, España.
3Fundación ARAID, Paseo María Agustín 36, Zaragoza, España.
4Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, Spain
Sarath Chandra Janga: email@example.com Bruno Contreras-Moreira: firstname.lastname@example.org
Bruno Contreras-Moreira: email@example.com
In 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, iEscherichia coli, on to gene expression data for a number of non-redundant experimental conditions and show that TFs are generally expressed at a lower level than other functional classes. We also demonstrate that different conditions harbor varying number of active TFs with an average of about 15% of the total repertoire being significantly expressed across conditions, with certain stress and drug induced conditions exhibiting as high as one-third of the total collection of TFs. Our results also show that activators are more abundant than repressors in the set of significantly expressed TFs across conditions, 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) starting from a literature curated static set of TF-TF regulatory interactions. This analysis 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. 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.
To identify active TFs in each condition we first plotted the expression level of each TF across conditions. These transcriptional patterns can be browsed and downloaded here. Since different TFs are expressed to varying levels it is not possible to identify active TFs based on a single expression level threshold. Therefore we defined Significant Expression Threshold (SET) as described in Materials and Methods for each TF, enabling us to detect active TFs. Note that threshold values are marked with arrows in the diagrams.
Condition-specific TF-TF subnetworks can be browsed from this link. These networks are the basis for the identification of markers for each condition as described in the manuscript. An animated GIF which shows the observed network re-wiring is available here.