Combinatorial synthesis of synthetic promoters, as described here, permits systematic analysis of promoter architecture and rapid identification of promoters that implement specific functions. The spectrum of promoter functions observed in this library highlights several heuristic rules for promoter design:
- Limits of regulation. Gene expression can be regulated over five orders of magnitude. Regulated promoter activity is independent of unregulated activity. As a result, effective repression tends to increase with unregulated activity, whereas activation tends to decrease. Activation is limited by an absolute level of expression, at around 2.5% the level of the strongest unregulated promoter activities.
- Repressor operator location. The effectiveness of repression depends on the operator location with coreproximaldistal. Dual-repression may be symmetric or asymmetric, with the dominant repressor predicted by operator locations.
- One is enough. Full repression is possible with a single operator between −60 and +20 at high repressor concentrations. Activators function only upstream of −35 (distal), and have little positive or negative effect downstream at core or proximal.
- Repression dominates activation, producing asymmetric logic.
- Operator proximity. Independent regulators generate SLOPE-like logic. Operator proximity increases competitive interactions, making the logic more AND-like.
For both activation and repression, the activity of the promoter in the regulated (activated/repressed) state is not determined by the activity in the unregulated state (Rule 1). Intuitively, activation has higher r
when the unregulated activity is low, and repression has higher r
when the unregulated activity is high. Furthermore, as predicted by recent theoretical work (Bintu et al, 2005a
), repression is able to achieve extremely high levels of regulation (r
), whereas activated regulation is moderately strong (r
). These limits apply to both SIGs ( and ) and dual-input promoters (). AR promoters are a special case and exhibit a trade-off: increasing the unregulated activity increases the regulatory range (r
) at the expense of greater asymmetry (a
). For example, compare the first and last promoter in .
Rules 2 and 3 summarize the operator position and multiplicity effects for both activators and repressors. The repression trend (Rule 2) has been previously reported for promoters regulated by LacI (Lanzer and Bujard, 1988
; Elledge and Davis, 1989
). The authors of the first paper proposed a mechanistic model involving two competing effects: core
sites more effectively block polymerase binding, whereas core
sites bind to repressor more rapidly (are more accessible) as the polymerase initiation complex clears the −10 and −35 boxes. We confirmed the operator location trend for SIGs regulated by LacI and TetR alone and found that this heuristic also holds for RR promoters of both repressors. Of course, differences in operator affinity, repressor concentration, and repressor structure can overcome these rules.
We compared Rules 2 and 3 with the distribution of known E. coli
operators compiled from 1102 natural promoters in the database RegulonDB (Salgado et al, 2006
; ). In agreement with analysis made on earlier versions of the database (Collado-Vides et al, 1991
; Gralla and Collado-Vides, 1996
), we found that activator operators are most common in the distal
region (), whereas repressor operators cluster around all three promoter regions (). shows the operator density of the 554 promoters that are recognized by the polymerase subunit σ70
. The small regulatory effect observed for activator operators in the core
regions (Rule 3) appears consistent with the general scarcity of natural activator sites in these regions. Similarly, the density of repressor operators found in σ70
promoters is significantly enriched for core
sites over distal
locations, consistent with the repressor operator location trend (Rule 2).
Figure 6 The distribution of operator locations in natural promoters reflects functional trends of synthetic promoters. Operator locations are as annotated in RegulonDB 5.0 (Salgado et al, 2006). Distributions of repressor (A) and activator (B) operators as found (more ...)
The sufficiency of one operator for repressing promoter activity up to five orders of magnitude (Rule 3) raises the classic question of why natural promoters are so often regulated by redundant operators (Collado-Vides et al, 1991
). Our study used high concentrations of repressors in the range of 2–4 μM (Lutz and Bujard, 1997
), paired with strong operators (Supplementary Table S1
). At lower repressor concentrations and operator affinities, the presence of multiple binding sites can increase the effective repression r
through looping (Vilar and Leibler, 2003
; Becker et al, 2005
), cooperativity (Oehler et al, 1994
; Ptashne, 2004
; Rosenfeld et al, 2005
), or even without explicit TF–TF interactions (Bintu et al, 2005a
). These effects can also increase the steepness of response to repressor concentration (Ptashne, 2004
), or engender exceptions to the dominance of repression (Rule 4). Finally, the presence of multiple operators might increase the mutational plasticity of promoter functions (Mayo et al, 2006
Rule 5 provides insight for both AR and RR promoters: operators at the neighboring sites will tend to generate more AND-like logic (higher l
) than non-neighboring sites (i.e. distal
). In AR promoters, repression at core
produces more AND-like logic than at proximal
. This effect can be understood intuitively for RR promoters: if operators are closely spaced, binding of one repressor can inhibit the binding of the other. Removing one repressor has two conflicting effects: it increases expression due to its reduced occupancy, but it simultaneously decreases expression by allowing binding of the other repressor. This makes the overall logic more AND-like. In terms of the mathematical model, AND-like (l
>0.8) RR promoters correspond to strong balanced repression (c1
1) and exclusive interaction (ω≈0).
The library described here represents a starting point for systematic investigation of the functional repertoire of prokaryotic promoters. These simple promoters cannot include all the complex effects found in natural promoters, including those dependent on DNA bending or specific protein–protein interactions. Nevertheless, they provide a view of what is possible with the simplest genetic elements and interactions. Within this context, the heuristics described above allow the design of particular promoter functions controlled by arbitrary TF regulators. The assembly method allows for construction of any specific promoter. Other promoter architectures could be generated with this method to provide more diverse logic phenotypes, or to explore regulatory DNA in eukaryotic organisms (Ligr et al, 2006
). For example, the lac
promoter architecture, regulated by a distal
activator and multiple repressor operators (including upstream sites), can exhibit phenotypes not found in our library, such as asym-OR (Mayo et al, 2006
). In another case, a synthetic activator–activator (AA) promoter has been constructed, which exhibits near-symmetric SLOPE logic (Joung et al, 1994
). Tandem promoters are expected to generate additive logic functions more closely representing OR logic, and in fact, many natural promoters are found in tandem repeats (Collado-Vides et al, 1991
). If our heuristic rules apply to natural combinatorial promoters, we may begin to elucidate complicated functions by inspection of these non-coding DNA sequences. In this regard, effective parameterizations of logic, such as the one shown in , can provide a more intuitive understanding of the computations performed by promoters.