Mapping DNA sequence to transcription factor binding energy in vivo

Abstract

Despite the central importance of transcriptional regulation in systems biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to analyze a promoter sequence and identify the locations, regulatory roles, and energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for interpreting transcriptional regulatory sequences using in vivo methods (i.e. the massively parallel reporter assay Sort-Seq) to formulate quantitative models that map a transcription factor binding site's DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 k B T of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor's sequence specificity.

Cell-free gene-regulatory network engineering with synthetic transcription factors

“…The results are in line with previous findings that promoter tuning in S. cerevisiae can be accomplished by relatively subtle affinity changes in a single binding site created by mutations in flanking or single-core site mutations of high entropy (54). They also correspond to recent results obtained in E. coli (55).…”

“…Although the development of new technologies is steadily enabling progress in this area, our understanding of GRNs remains limited, as exemplified by our inability to predict in vivo gene-expression levels in essentially any organism and the difficulty associated with de novo engineering of GRNs. Although methods exist for high-throughput in vitro characterization of transcription-factor-binding specificities (34,(56)(57)(58), and medium-to high-throughput approaches are used to understand gene regulation in vivo (33,54,55,59), both approaches have limitations. Both an advantage and disadvantage of in vitro methods is that they generally include only the smallest number of components necessary, i.e., a transcription factor, dsDNA target, and a defined buffer solution.…”

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

“…Energy matrices are either given in A.U. (arbitrary units) or, for several cases where the gene has a simple repression or activation architecture with a single RNA polymerase (RNAP) site, are assigned k B T energy units following the procedure in Kinney et al, 2010 and validated on repression by lac repressor in Barnes et al, 2019 . The details of how and when absolute units are determined can be found in Appendix 3 Section 'Inference of scaling factors for energy matrices'.…”

“…In both of these cases, we now hypothesize that these newly discovered binding site-transcription factor pairs exert their control through repression. The ability to extract the quantitative features of regulatory control through energy matrices means that we can take a nearly unstudied gene such as ykgE , which is regulated by an understudied transcription factor YieP, and quickly get to the point at which we can do quantitative modeling in the style that we and many others have performed on the lac operon ( Vilar and Leibler, 2003 ; Vilar et al, 2003 ; Bintu et al, 2005 ; Kinney et al, 2010 ; Garcia and Phillips, 2011 ; Vilar and Saiz, 2013 ; Barnes et al, 2019 ; Phillips et al, 2019 ).…”

“…This allows us to employ the tools of statistical physics to describe the input-output properties of each of these promoters which can be explored much further with in-depth experimental dissection like those done by Razo-Mejia et al, 2018 and Chure et al, 2019 and summarized in Phillips et al, 2019 . In this sense, the Sort-Seq approach can provide a quantitative framework to not only discover and quantitatively dissect regulatory interactions at the promoter level, but also provides an interpretable scheme to design genetic circuits with a desired expression output ( Barnes et al, 2019 ).…”