DNA sequence-dependent mechanics and protein-assisted bending in repressor-mediated loop formation

• Published in: Physical biology Vol. 10; no. 6; pp. 66005 - 14
• Main Authors: Boedicker, James Q, Garcia, Hernan G, Johnson, Stephanie, Phillips, Rob
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.12.2013
• Subjects: Base Sequence; DNA looping; DNA mechanics; DNA, Bacterial - chemistry; DNA, Bacterial - genetics; Escherichia coli - genetics; Escherichia coli Proteins - genetics; Gene Expression Regulation, Bacterial; Gene regulation; Lac Repressors - genetics; Models, Chemical; Models, Genetic; Models, Molecular; Nucleic Acid Conformation; Quantitative regulatory models; Single-molecule biophysics; Thermodynamics
• ISSN: 1478-3975; 1478-3967; 1478-3975
• DOI: 10.1088/1478-3975/10/6/066005

As the chief informational molecule of life, DNA is subject to extensive physical manipulations. The energy required to deform double-helical DNA depends on sequence, and this mechanical code of DNA influences gene regulation, such as through nucleosome positioning. Here we examine the sequence-dependent flexibility of DNA in bacterial transcription factor-mediated looping, a context for which the role of sequence remains poorly understood. Using a suite of synthetic constructs repressed by the Lac repressor and two well-known sequences that show large flexibility differences in vitro, we make precise statistical mechanical predictions as to how DNA sequence influences loop formation and test these predictions using in vivo transcription and in vitro single-molecule assays. Surprisingly, sequence-dependent flexibility does not affect in vivo gene regulation. By theoretically and experimentally quantifying the relative contributions of sequence and the DNA-bending protein HU to DNA mechanical properties, we reveal that bending by HU dominates DNA mechanics and masks intrinsic sequence-dependent flexibility. Such a quantitative understanding of how mechanical regulatory information is encoded in the genome will be a key step towards a predictive understanding of gene regulation at single-base pair resolution.