Introduction to Synthetic Promoter Libraries

Modulation of gene expression of i.e. cellular enzyme activities (Solem and Jensen 2002), as well as regulation of transcription are amongst some of the areas where SPLs are currently being used. SPL provides an alternative method for gene regulation compared to older methods, namely those of gene knockouts and strong over expression. These two methods are usually based upon apparent rate limiting steps within metabolic pathways (Jensen and Hammer 1998).

When working with gene regulation, it is important to elucidate where expression levels are optimal for the given gene being worked on. Under these specifications it is essential to be able to have slight increments in expressional strength when attempting to optimize gene expression. This can be achieved by the usage of an SPL, where the variability in strengths can be achieved by either randomizing the spacer sequences, namely the 17 bases that reside between the -35 and -10 consensus regions, and/or some of the bases within the consensus regions, being the -35 and -10 regions.

The spacer sequences that surround the consensus regions contribute significantly to the strengths of promoters (Hammer et al. 2006). In our design, we decided to both randomize the spacer sequences as well as two bases in both consensus regions as seen in Figure 1 below. N stands for 25% each of A, C, G and T, while S stands for 50% each of C and G, and W stands for 50% A and T.

Figure 1: An SPL designed on the basis of randomizing both the spacer sequences surrounding the consensus regions (-35 and -10 regions) as well as randomizing two bases within each of the consensus regions is illustrated. N stands for 25% each of A, C, G and T, while S stands for 50% each of C and G, and W stands for 50% A and T.

Figure 2: The linear BioBrick plasmid backbone with SPL inserted between the EcoRI and XbaI sites of the BioBrick prefix is illustrated.

Figure 3: The primer binding sites on a BioBrick plasmid backbone as well as the final linear plasmid backbone that is generated by the PCR is illustrated.