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, 47 (19), 10452-10463

De Novo Design of Programmable Inducible Promoters

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De Novo Design of Programmable Inducible Promoters

Xiangyang Liu et al. Nucleic Acids Res.

Abstract

Ligand-responsive allosteric transcription factors (aTF) play a vital role in genetic circuits and high-throughput screening because they transduce biochemical signals into gene expression changes. Programmable control of gene expression from aTF-regulated promoter is important because different downstream effector genes function optimally at different expression levels. However, tuning gene expression of native promoters is difficult due to complex layers of homeostatic regulation encoded within them. We engineered synthetic promoters de novo by embedding operator sites with varying affinities and radically reshaped binding preferences within a minimal, constitutive Escherichia coli promoter. Multiplexed cell-based screening of promoters for three TetR-like aTFs generated with this approach gave rich diversity of gene expression levels, dynamic ranges and ligand sensitivities and were 50- to 100-fold more active over their respective native promoters. Machine learning on our dataset revealed that relative position of the core motif and bases flanking the core motif play an important role in modulating induction response. Our generalized approach yields customizable and programmable aTF-regulated promoters for engineering cellular pathways and enables the discovery of new small molecule biosensors.

Figures

Figure 1.
Figure 1.
De novo promoter engineering scheme. Design workflow involves three steps: creating a promoter library with randomized bases between -35 and -10 sites of a constitutive E. coli promoter, enrichment of promoters that can bind to aTF by in vitro selection, and multiplexed screening for inducible promoters by high-throughput cell sorting followed by clonal testing.
Figure 2.
Figure 2.
In vitro selection of operators. (A) Highly similar operators are clustered together at 90% sequence identity threshold. Clusters ranked in descending order (left to right) by number of sequences within a cluster or cluster size (Y-axis). Cluster size shows characteristic exponential fit. Minimum number of sequences per cluster is five. Red, orange, green and blue represent operator libraries of length 16bp, 17bp, 18bp and 19bp, respectively. (B) Motifs of highly enriched sequences of all four operator libraries and native operator sites of PmeR, TtgR and NalC. Palindromic sequences representing putative half sites is underlined in native operator sites.
Figure 3.
Figure 3.
In vivo enrichment of inducible promoters by fluorescence-activated cell sorting. (A) Cells expressing GFP regulated by engineered promoter variants constitu-tively (top, aTF-/inducer-), co-expressing aTF without inducer (middle, aTF+/inducer-), and co-expressing aTF with inducer (bottom, aTF+/inducer+). Fold repression (FR) is the ratio of median fluorescence of aTF-/inducer- and aTF+/inducer- cells. FR is shown in the middle panel. Fold induction (FI) is the ratio of median fluorescence of aTF+/inducer+ and aTF+/inducer- cells. FI is shown in the bottom panel (B) Cells repressed by aTF (aTF+/inducer-) are sorted into bins according to their fluorescence and each bin is induced independently. Top panel is overall distribution of repressed cells. Colors represent cells sorted into different fluorescence bins. Lower panels show ligand-induced response of cells from each bin. Fold induction (FI) ratio of each bin is mentioned in the panel.
Figure 4.
Figure 4.
Characterization of transcriptional activity of individual promoters. (A) Normalized fluorescence of uninduced and induced states (maroon circles) reported as a percentage activity of constitutive apFab71 promoter. Activity of native promoter ported into E. coli (blue squares) and commonly used pLTetO promoter (green triangles) are shown for comparison. (B) Fold induction ratio between induced and uninduced fluorescence of engineered, native and pLTetO promoters. (C) Ligand dose response data fitted to a standard Hill equation. Color gradient represents fold induction and native promoter is shown as dashed line. (D) Plot of maximum induced reporter expression (Vmax) versus concentration of ligand required to reach half Vmax (Km). Both parameters estimated from fitted Hill equation. Blue dot represents native promoter.
Figure 5.
Figure 5.
Sequence determinants of promoter activity by machine learning. (A) Diversity of operator sequences and their corresponding fold induction shown as a speedometer plot. Central point corresponds to a reference sequence, in this case operator embedded with highest fold induction. Radial axis is Levenshtein distance between reference operator (center point) and the remaining inducible sequences. Angular axis is fold induction. (B) Scatter plots of fold induction predicted vs. experimental and the Spearman correlation coefficient. (C) Sequence motifs of inducible (top) and uninducible (bottom) operators. Y-axis represents bits. Gray boxes indicate differences in key sequence features between inducible and uninducible promoters. Logos were generated using web3logo tool

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