AURTHO: autoregulation as facilitator of cis-acting element discovery of orthologous transcription factors

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.

An Overview of Bioinformatics and its Application: A Ray of Hope towards Microbial Biotechnology

“…Studying protein-DNA interactions can be done in four main ways: micro-array analysis of gene expressions in response to various cell stressors; (ii) statistical analysis of the promoter regions of orthologous genes (functionally equivalent genes in different organisms identified as best homologs); (iii) worldwide analysis of dimer frequency patterns in the intergenic region of a genome, which is the promoter region between adjacent protein coding regions; and (iv) atomic bond level biochemical modeling to comprehend how a protein will bind to nucleotides. Of the four ways, only the microarray analysis methodology is based on experimental data; the other two rely on sequence analysis and mathematical modeling (128,129).…”