We then turn to consider the implications for gene expression of the regulatory sequences themselves. We also show how such regulatory sequences can be designed to optimize the inducible response of LacI in the context of the allosteric simple repression motif considered above.
LIST OF TABLES
INTRODUCTION
Transcription and transcriptional regulation
During growth in nutrient-rich conditions, the core RNA polymerase enzyme (which consists of five subunits, ββ0α2ω) recognizes certain DNA binding sites by forming a complex with the primary sigma factor, RpoD (also written as σ70orσD), which prefers the consensus -35 and -10 sequence TTGACA(N)nTATAAT (where (N) is a spacer sequence, with non-optimal 17 bp) (Feklístov et al., 2014). When a gene is turned on, RNA polymerase translocates along the DNA and transcribes the coding sequence into an mRNA template.
Thermodynamic models
Thus, ∆ε represents the energy difference between specific and nonspecific binding of RNA polymerase to DNA. NN S represents the number of non-specific binding sites for both RNA polymerase and repressor.
Allostery
In the case of the toxin-antitoxin system Doc-PhD, the repressor (the antitoxin, PhD) contains an intrinsically disordered domain that prevents it from occupying both binding sites (Garcia-Pino et al., 2016). The inducer (blue circle) can bind to the repressor at concentration with dissociation constants K in the active state and K in the inactive state.
Status of regulatory knowledge in E. coli
This is indicated by the coefficient of variation (the ratio of the standard deviation to the average protein copy number) for each protein in the 22 growth conditions (see Figure 1.10). Here we do this by calculating the fold change in expression for each protein relative to its average expression across all 22 growth conditions and summarize the analysis in Figure 1.11.
Supplemental Information: Candidate genes with growth-dependent dif- ferential expression
Chemosensing in Escherichia coli: two regimes of two-state receptors.Proceedings of the National Academy of Sciences103.6, pp. Die kataboliet onderdrukkingsgeen van die Lac operon in Escherichia coli.Journal of Molecular Biology23.3, pp.
TUNING TRANSCRIPTIONAL REGULATION THROUGH SIGNALING: A PREDICTIVE THEORY OF ALLOSTERIC
INDUCTION
Introduction
Building on previous work (Garcia and Phillips, 2011; Brewster et al., 2014; Weinert et al., 2014), we present a statistical mechanical rendering of allostery in the context of induction and corepression, schematically shown in Fig. Specifically, we modeled the allosteric response of transcriptional repressors using the MWC model, which states that an allosteric protein oscillates between two different conformations—an active and an inactive state—in thermodynamic equilibrium (Monod et al., 1965).
Results
Instead, we measure the fold change in gene expression due to the presence of the repressor. Specifically, we show predictions for (F) leakage, (G) saturation, (H) dynamic range, (I) [EC50], and (J) effective Hill coefficient for the induction profiles.
Discussion
In addition to observing fold change changes as a function of effector concentration, our application of the MWC model allows us to explicitly predict the values of the key parameters of the induction curves, namely the leakage, saturation, dynamic range, [EC50] , and the effective Hill coefficient (see Figure 2.7). In conclusion, our application of the MWC model provides an accurate, predictive framework for understanding simple repression by allosteric transcription factors.
Methods
For our purposes, we assume that the fluorescence level of the population should be log-normally distributed about some mean value. All the data used in this work, as well as all relevant code, can be found at this dedicated website.
Supplemental Information: Inferring allosteric parameters from previ- ous data
The resulting best-fit functions for several values of ∆εAI all give nearly identical fold-change responses. In the presence of N = 10 identical promoters, the fold change Eq. 2.22) strongly depends on the allosteric energy difference∆εAI between the active and inactive states of the Lac repressor.
Supplemental Information: Induction of simple repression with multiple promoters or competitor sites
As in the main text, we assume that the rest of the genome contains NN S non-specific binding sites for the repressor. To account for the induction of the repressor, we substitute the total number of repressors Rtotin Eq. 2.28) with the number of active repressors in the cell, pA(c)Rtot.
Supplemental Information: Flow cytometry
The experimentally measured fold change values for the two sets of plate arrangements show that samples measured in the forward arrangement appear to be indistinguishable from those measured in the reverse order. The fold change in gene expression for equivalent simple-repression constructs has been determined using three independent methods: flow cytometry (this work), colorimetric Miller assays (Garcia and Phillips, 2011), and video microscopy (Brewster et al., 2014).
Supplemental Information: Single-cell microscopy
For comparison with the flow cytometry results, cells were cultured in the same manner as described in the main text. Using these experimentally measured fold-change values, the best-fitting parameter values of the model were derived and compared with those obtained from flow cytometry.
Supplemental Information: Fold-change sensitivity analysis
The fold change is calculated in the saturation inductor concentration limit (c→ ∞, see Eq. 2.7)) where the reliable regions in Fig. As in panel (A), but showing how the fold change sensitivity for different suppressor copy numbers.
Supplemental Information: Alternate characterizations of induction In this section we discuss a different way to describe the induction data, namely,
2.5 (which was made using the MWC model Eq. 2.5)), we emphasize that the Hill function approach is more complex than the MWC model (containing four parameters instead of three) and it obscures the relations with the physical parameters of the system. Shaded areas indicate boundaries of the 95% credible region. difference∆εAI between the active and inactive conformations of the repressor.
Supplemental Information: Global fit of all parameters
The fit values of the repressor copy numbers were all within one standard deviation of previously reported values from Garcia and Phillips (2011). Note that there is overlap between all repressor copy numbers and the net difference in the repressor DNA binding energies is less than 1 kBT.
Supplemental Information: Applicability of theory to the Oid operator sequence
Here we use the previously measured binding energy ∆εRA= −17.0kBT (Garcia and Phillips, 2011). B) The same experimental data are plotted against the best-fit parameters using the complete O1, O2, O3 and Oid data sets to infer KA, KI, repressor copy number and binding energies of all operators (see Supplementary Section 2.11). Fold-change curves for different repressor-DNA binding energies ∆εRA are plotted as a function of repressor copy number when the IPTG concentration is c= 0.
Supplemental Information: Comparison of parameter estimation and fold-change predictions across strains
Fold change in expression is plotted as a function of IPTG concentration for all strains containing an O1 operator. Fold change in expression is plotted as a function of IPTG concentration for all strains containing an O2 operator.
Supplemental Information: Properties of induction titration curves In this section, we expand on the phenotypic properties of the induction response
Both the [EC50] and hvary significantly with suppressor copy number for sufficiently strong operator bond energies. Interestingly, for weak operator binding energies on the order of the O3 operator, the effective Hill coefficient is predicted not to vary with repressor copy number.
Supplemental Information: Applications to other regulatory architec- tures
2.36 (B) explores the predictions of fold change in gene expression by manipulating activator copy number, DNA binding energy, and polymerase–activator interaction energy. In the case of inducible activation, the binding of an effector molecule to an activator transcription factor increases the fold change in gene expression.
Supplemental Information: E. coli primer and strain list
Specifically, the first number refers to the antibiotic resistance cassette present for selection (2 = kanamycin, 3 = chloramphenicol, and 4 = spectinomycin), and the second number refers to the promoter used to drive expression of either YFP or LacI ( 1 = PLtetO−1 and 5 =lacUV5). Note that in 25x+11-yfp x refers to the LacI operator used, which is centered at +11 (or alternatively begins at the transcription start site).
Combinatorial promoter design for engineering silent gene expression. Proceedings of the National Academy of Sciences104.31, pp. Detailed mapping of a cis-regulatory entry function.
CHARACTERIZATION OF THE SEQUENCE-DEPENDENT OCCUPANCY OF LACI
Introduction
Current in vivo methods for determining the binding affinity of transcription factors, such as the bacterial one-hybrid (Christensen et al., 2011; Xu and Noyes, 2015), require restructuring the promoter so that it no longer resembles its genomic counterpart. Furthermore, while computational efforts to "read" the genome provide a promising route to understanding transcriptional regulation in its natural context, efforts to computationally identify the locations of transcription factor binding sites often result in false positives (Weirauch et al., 2013; Djordjevic et al. ., 2003).
Results
This allowed the determination of the scaling factors for binding of LacI and the energy matrix shown in absolutekBT energy units. Using our LacI energy matrix to predict∆εR, we find that we can make parameter-free predictions of fold change for each LacI binding site sequence as a function of the repressor copy number associated with each of ourE.
Discussion
Methods Sort-Seq librariesSort-Seq libraries
Fold change measurements were collected as previously described [cite Induction Paper] on a MACSquant Analyzer 10 Flow Cytometer (Miltenyi Biotec, Germany). The fold change in gene expression was calculated by taking the ratio of the average YFP expression of the cell population in the presence of LacI repressor to that in the absence of LacI repressor.
Supplemental Information: Summary of designed O1 binding site mutant results
Quantitative model for gene regulation by the lambda phage suppressor. Proceedings of the National Academy of Sciences79.4, pp. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence.
A SYSTEMATIC APPROACH FOR DISSECTING THE MOLECULAR MECHANISMS OF TRANSCRIPTIONAL
REGULATION IN BACTERIA
Introduction
First, a massively parallel reporter assay (Sort-Seq; Kinney et al., 2010) is performed on the promoter under multiple growth conditions to identify functional transcription factor binding sites. Operons with annotated TF binding sites are shown in blue, while those without regulatory annotations are shown in red (Gama-Castro et al., 2016).
Results
It contains three lac repressor (LacI) binding sites, two of which we consider here, and a cyclic AMP receptor (CRP) binding site. Two binding sites for XylR have been identified (see also Fig. 4.9 and Fig. 4.12F) along with a CRP binding site.
Discussion
Landing platform technologies for chromosomal integration (Kuhlman and Cox, 2010; Zhang et al., 2016) should allow massively parallel reporter assays to be performed in chromosomes rather than on plasmids. Techniques that combine these assays with transcription start site readouts ( Vvedenskaya et al., 2015a ; Vvedenskaya et al., 2015b ) can further deconvolve the molecular regulators of overlapping RNAP binding sites, or identify the contributions of individual RNAP binding sites, such as those observed on the thedgoR promoter, to better distinguish.
Methods
Kinney et al., 2010). The template plasmid used for backbone amplification contained the toxic genccdBin the site where the library was to be inserted. To calculate the overall protein ratio, the unnormalized ratios of protein replicates were log-transformed and then shifted so that the median protein ratio in each replicate was zero (ie, the median protein ratio was 1:1).
Supplemental Information: Characterization of library diversity and sorting sensitivity
Here we provide an additional characterization of mutagenized promoter libraries, using a library from themarRpromoter as a representative example (70 bp region containing RNAP and MarR repressor sites). To get a better understanding of how the mutation rate varies across libraries, we plot a histogram of the number of mutations per base pair for the entire set of sequences found in the marR promoter library (Fig. 4.8E).
