bioRxivBioinformaticsPreprint
AURTHO: autoregulation as facilitator of cis-acting element discovery of orthologous transcription factors
Sinaeda AndersseniD, Aymeric Naômé, Cédric JadotiD, Alain BransiD, Pierre TocquiniD, Sébastien RigaliiD
Abstract
Transcriptional regulation is key in bacteria for providing an adequate response in time and space to changing environmental conditions. However, despite decades of research, the binding sites and therefore the target genes and the function of most transcription factors (TFs) remain unknown. Filling this gap in knowledge through conventional methods represents a colossal task which we demonstrate here can be significantly facilitated by a widespread feature in transcriptional control: the autoregulation of TFs implying that the yet unknown transcription factor binding site (TFBS) is neighbouring the TF itself. In this work, we describe the "AURTHO" methodology (AUtoregulation of oRTHOlogous transcription factors), consisting of analyzing upstream regions of orthologous TFs in order to uncover their associated TFBSs. AURTHO enabled the de novo identification of novel TFBSs with an unprecedented improvement in terms of quantity and reliability. DNA-protein interaction studies on a selection of candidate cis-acting elements yielded an >90% success rate, demonstrating the efficacy of AURTHO at highlighting true TF-TFBS couples and confirming the identification in a near future of a plethora of TFBSs across all bacterial species. Key pointsO_LITranscription factor (TF) autoregulation implies that their binding site (TFBS) is in their close vicinity C_LIO_LIWe developed and assessed the AURTHO methodology (AUtoregulation of oRTHOlogous TFs) for TFBS discovery C_LIO_LIOur results shows that AURTHO greatly facilitates the identification of highly reliable novel TFBSs C_LI
- organism: bacteria (other)
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 0
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