1. Characterization of natural pheromone-responsive promoters
There exists hundreds of pheromone-responsive genes in haploid Saccharomyces cerevisiae1.The promoters of these genes constitute a complex promoter library. This year, our team focuses on pheromone-responsive promoters. Ste12 acts as a major transcriptional activator to activate promoters of mating genes. Multiple copies of putative Ste12 binding sites are prevalent in pheromone-responsive promoters, which are also known as pheromone response elements (PREs). In the absence of the mating pheromone, two inhibitors, Dig1 and Dig2, binding to Ste12, independently repress Ste12 through different mechanisms. Upon pheromone stimulation, the activation of the MAPK pathway leads to phosphorylation of Dig1 and Dig2, resulting in their disassociation from Ste12 and promoting gene transcription2.
Fig. 1 Ste12 induces pheromone-responsive gene expression under the regulation of signal pathway
PRE can confer the ability to respond to pheromones in the minimal GAL1 core promoter. Additionally, the sequence, copy number, and arrangement of PRE also affect the basal and pheromone responsive expression level 3. Inspired by some literature on pheromone-responsive genes, we decided to study three pheromone-inducible promoters: pfus2, pprm1 , and pfig1In these three promoters, the copy number, the arrangement, and orientation of the PREs vary considerably. We used GFP as the reporter protein. After pheromone treatment, the intensity of these inducible promoters is reflected by the expression level of GFP which is quantified through flow cytometry. Then, we will analyze the relationship between pheromone concentration and treatment time for each promoter based on the data, demonstrating the diverse expression capability of pheromone induced promoters.
Fig. 2 PREs in pfig1, pprm1 and pfus2
(The PRE consensus sequence is TGAAACA. Numbers between any two PREs indicate the spacing in nucleotides.)
Fig.3 Experimental procedure for promoter characterization
Mathematical models are another important tool for characterizing promoters. In our project, neural network algorithms can predict the expression level of pheromone-inducible promoters. The molecular dynamics model provides guidance for the design of experimental conditions and is the basis for obtaining a relatively accurate pheromone concentration profile of the promoter through as few experiments as possible.
2. Modification of natural promoters
Based on the characterization results, we found that although pprm1 possesses three PREs, it has a low level of induced expression which indicates a good potential for transformation. Based on the literature, we learned the general effect of PRE arrangement on promoter strength. For example, an increase in the copy number of PRE can enhance the induced expression level of the promoter. And a PRE which is aligned in the direction of the promoter may be more effective than a PRE which is in the opposite direction of the promoter4. In the first transformation scheme, we copied and pasted the 3 × PRE sequence from natural pprm1 The modified pprm1 is referred to as pprm1 Pro which contains three PREs oriented in the same direction as the promoter. As a consequence, the two modified promoters exhibit characteristics remarkably different from the wild type.
Fig. 4 Improvement of pprm1
3. Mating signal pathway modification
The diversity and plasticity of pheromone-responsive promoters allow them to be used to construct biosensor reporter gene expression cassettes. But expensive inducers limit the utility of such promoters in the construction of cell factories. By modifying the signaling pathway, we expect to enable alternative inducers to trigger the activation of pheromone responsive promoters.
The pheromone signaling pathway in yeast consists of a serial of heterotrimeric GTP-binding proteins (G protein) and MAPK cascade. Ste2 and Ste3 are G protein-coupled receptors involved in pheromone recognition and activation of the Gα subunit. Upon pheromone stimulation, the Gβγ subunit is separated from the activated Gα subunit and recruits the scaffold protein Ste5. Ste5 binds to the MAPK components, including Ste11, Ste7, and Fus3, which are localized to the plasma membrane together with Ste5. The plasma membrane localized Ste20 activates Ste11 that is recruited to the cell cortex, triggering a cascade of signaling. The activated MAPK components alter the activity of related proteins through covalent modification, thus modulating gene expression levels. Ultimately, the cell cycle is arrested and cells enter into mating state. In particular, Ste5 plays an important role in the signaling pathway by associating G proteins with MAPK components.
Fig. 5 Mating signaling pathway
Through a review of the literature, we identified a Ste5 mutant, termed Ste5ΔN-CTM. The 214 amino acids on the N terminal of Ste5 contain a membrane-binding site and mediate the membrane localization of Ste5. By deleting these N-terminal sequences, the activation of Ste5 is no longer dependent on G protein. Meanwhile, with the transmembrane sequence CTM added to the C terminus, Ste5 can be recruited to the plasma membrane constitutively5. The Ste5ΔN-CTM protein can activate the mating-specific MAPK pathway in the absence of pheromones and trigger the expression of pheromone-responsive genes. We constructed the ste5Δ strain and introduced the pgal1-Ste5ΔN-CTM gene. The engineered yeast can enter into mating state under galactose treatment and regulate the expression of pheromone responsive promoters.
Fig. 6 Structure and function of Ste5ΔN-CTM
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