Quantifying the regulatory role of individual transcription factors in Escherichia coli

Abstract

SUMMARY Gene regulation often results from the action of multiple transcription factors (TFs) acting at a promoter, obscuring the individual regulatory effect of each TF on RNA polymerase (RNAP). Here we measure the fundamental regulatory interactions of TFs in E. coli by designing synthetic target genes that isolate individual TFs’ regulatory effects. Using a thermodynamic model, each TF’s regulatory interactions are decoupled from TF occupancy and interpreted as acting through (de)stabilization of RNAP and (de)acceleration of transcription initiation. We find that the contribution of each mechanism depends on TF identity and binding location; regulation immediately downstream of the promoter is insensitive to TF identity, but the same TFs regulate by distinct mechanisms upstream of the promoter. These two mechanisms are uncoupled and can act coherently, to reinforce the observed regulatory role (activation/repression), or incoherently, wherein the TF regulates two distinct steps with opposing effects.

Tunable transcription factor library for robust quantification of regulatory properties in Escherichia coli

“…The library is particularly effective when combined with other genetic resources that allow for systematic control of promoter architectures. The vignettes of these sections make use of two reporter libraries, the Zaslaver's transcriptional reporter library (Zaslaver et al , 2004 ) and a “TF‐binding position library” from our lab which enables controlled movement of a TF‐binding site on a synthetic promoter (preprint: Guharajan et al , 2021 ).…”

“…We measured ZntR regulatory function at two locations immediately downstream of the promoter (centered at +16.5 and +21.5). From previous work on a handful of TFs, we anticipate that TFs regulate at these locations through pure steric hindrance where the fold change will be repressive and a reflection only of a single parameter, the occupancy of the TF (Ptashne et al , 1980; Ackers et al , 1982; Brewster et al , 2014; Forcier et al , 2018; preprint: Guharajan et al , 2021). In Fig 4E, we show the fold change for these two binding locations as a function of TF copy number both without added zinc (solid line) and with 100 µM zinc (dashed line).…”

“…Next, we infer the regulatory effect of GalR/GalS at other binding locations on the promoter. For this, we use a general model of gene regulation that quantifies the regulatory role of the TF (preprint: Guharajan et al , 2021),where FC max represents the maximum fold change at saturating TF concentration, is the binding affinity measured as in the previous plots and is the effective TF concentration, i.e., TF copy number normalized by the binding site affinity. Importantly, the steric model fit to that data are recovered by setting FC max = 0.…”

“…Our library is designed to be a complementary tool aimed at aiding studies seeking to determine a TFs regulatory function once bound (Fig 1A ). The titratable TF library is versatile and can be combined with other single‐gene mutant library collections such as the transcriptional reporter library (Zaslaver et al , 2006 ), the keio single‐gene deletion library (Baba et al , 2006 ), or the isolated regulatory position sweep library developed in our lab previously (preprint: Guharajan et al , 2021 ) in order to make controlled measurements of TF regulation. The ability to isolate and control the expression of individual TFs allows for characterization of the role of individual TFs without entanglement from their physiological function or role in higher‐order network effects such as autoregulation (Potvin‐Trottier et al , 2016 ; Ali et al , 2020 ).…”

The unreasonable effectiveness of equilibrium gene regulation through the cell cycle

“…As such, equilibrium approaches, although do not consider explicitly many essential processes, have performed exceedingly well in both qualitative and even quantitatively understanding the major features of gene expression. They have been successfully widespread in modeling gene regulation since early pioneering work (Ackers et al, 1982;von Hippel et al, 1974) and have been applied to both prokaryotes (Guharajan et al, 2021;Kinney et al, 2010;Kuhlman et al, 2007;Lagator et al, 2022;Vilar and Saiz, 2005) and eukaryotes (Bashor et al, 2019;Carey et al, 1990;Gertz et al, 2009;Ptashne and Gann, 2002;Vilar and Saiz, 2011). The underlying assumption is that the equilibrium binding patterns, such as those observed in biochemical in vitro experiments, can be extrapolated to a living growing and multiplying cell.…”