OENRC4 Regulatory biopart collection
Collection Name
Urtecho et al（2019）
Biopart Type
promoter
Brief Description
Promoters are the key drivers of gene expression and are largely responsible for the regulation of cellular responses to time and environment. In E. coli, decades of studies have revealed most, if not all, of the sequence elements necessary to encode promoter function. Despite our knowledge of these motifs, it is still not possible to predict the strength and regulation of a promoter from primary sequence alone. Here we develop a novel multiplexed assay to study promoter function in E. coli by building a site-specific genomic recombination-mediated cassette exchange (RMCE) system that allows for the facile construction and testing of large libraries of genetic designs integrated into precise genomic locations. We build and test a library of 10,898 σ70 promoter variants consisting of all combinations of a set of eight −35 elements, eight −10 elements, three UP elements, eight spacers, and eight backgrounds. We find that the −35 and −10 sequence elements can explain approximately 74% of the variance in promoter strength within our dataset using a simple log-linear statistical model. Simple neural network models explain greater than 95% of the variance in our dataset by capturing nonlinear interactions with the spacer, background, and UP elements.
Chassis
E. coli
Reporter Gene
sfGFP in RMCE donor cassette
Inducer
random 20nt barcode in the 3’ UTR that uniquely identifies the promoter variant followed by a transcriptional terminator
Culture Condition
To measure the expression of each promoter, we grew the library to exponential phase in defined media before extracting and sequencing both RNA and DNA barcodes. To account for differences in the abundance of each barcoded promoter, we calculated expression by normalizing the number of RNA counts to the number of DNA counts for all barcodes
Qualification method
To measure the expression of each promoter, we grew the library to exponential phase in defined media before extracting and sequencing both RNA and DNA barcodes. To account for differences in the abundance of each barcoded promoter, we calculated expression by normalizing the number of RNA counts to the number of DNA counts for all barcodes mapped to a single promoter (Figure 2D). In total, we performed three biological replicates in which the promoter library was grown on three occasions in separate cultures before being processed for RNA and DNA sequencing of the barcodes. In addition, we performed technical replicates of one sample in which two RNA and DNA samples from a single culture of the library were processed in parallel for sequencing. Expression of our promoter variants spanned a 100-fold range and were highly consistent between biological replicates (R2=0.952, p < 2.2 × 10−16) (Figure 2E). In addition, we observed a predictable segregation between our negative controls and promoter variants containing consensus −10 and −35 elements. We found a large spread in promoter activity even amongst those promoters containing consensus −10 and −35 sequences, with the strongest promoter containing consensus −10 and −35 regions having 29.9-fold higher activity than the weakest (Figure 3A). In general, for all data we see strong trends of closer to consensus −10 and −35 regions generally increasing transcription strength of the promoters (Figure 3B). However, there are many exceptions and the variance between promoters with identical UP, −10, and −35 regions can vary dramatically depending upon the the spacer and background sequences.
Reference
PMID:29388765 (Urtecho G, Tripp AD, Insigne KD, Kim H, Kosuri S. Systematic Dissection of Sequence Elements Controlling σ70 Promoters Using a Genomically Encoded Multiplexed Reporter Assay in Escherichia coli. Biochemistry. 2019 Mar 19;58(11):1539-1551. doi: 10.1021/acs.biochem.7b01069. Epub 2018 Dec 21. PMID: 29388765; PMCID: PMC6389444.)
Submitter
Biocuration
Functions
Sequence