Team:NJTech China/Description

Description

Background

Gene expression is controlled by many factors, including promoter strength, cis and trans factors, cell growth stage, and other gene-level regulation. Compared to other regulation methods, promoter-based modification allows researchers to select inducers according to specific purposes or laboratory conditions and to achieve desired induction effect. Promoter engineering is the subject focusing on creating functional promoter libraries for precise control of gene expression for metabolic optimization or control analysis. As is shown in figure 1, A. Random mutations are introduced into a wild-type promoter through Ep-PCR to build a promoter library. B. Saturation mutagenesis of nucleotide spacer regions diversifies non‐consensus nucleotides within a promoter to enable wide ranges in promoter library strength. C. Hybrid promoter engineering respond to tandem upstream activations sequences to modulate core promoter expression to construct synthetic hybrid promoters with novel activity or regulation. D. Directed introduction, deletion, or modification of transcription factor-binding site (TFBS) rationally alters promoter strength of regulation. Addition of one to three distinct Gal4p TFBSs to a constitutive core promoter enabled tunable galactose induction over a 50-fold range. For example, Hal Alper et al. used the characterized library of promoters to assess the impact of phosphoenolpyruvate carboxylase levels on growth yield and deoxy-xylulose-P synthase levels on lycopene production¹. Andersen HW et al. constructed a series of Lactococcus lactis strains based on synthetic promoters, proving that LDH hardly controls glycolytic flux in terms of wild-type enzyme levels and lactic acid production². These studies have demonstrated that the valuable application of promoter engineering in the precise control of gene expression levels².

Fig. 1 Overview of promoter engineering³ (modified from Blazeck, J. et al. 2013)

Promoter Engineering of Pheromone-responsive Promoters in Saccharomyces cerevisiae

1) Wet Lab

Cell fusion is a fundamental biological process required for the entire development of most eukaryotic organisms, from fertilization to organogenesis⁴. The mating of Saccharomyces cerevisiae.is a model for studying cell fusion. It includes a series of processes such as mating pheromone recognition, zygote production, and diploid cell nucleus formation⁵. More than 200 genes are up-regulated after exposure of haploid yeast cells to mating pheromone⁶. Among them, Ste12 acts as the major transcription activator to activate the promoters of mating genes. The promoters of these genes display diversified expression capabilities and can be used to construct customized gene expression cassettes. Pheromone-responsive promoters generally have multiple copies of putative Ste12 binding sites, which are also called pheromone-responsive elements (PREs). PRE endows the minimal GAL1 core promoter with the ability to respond to pheromone. The sequence, copy number, and orientation of PRE may affect the basal and pheromone responsive expression level⁷.

In our project, we characterized the expression level of three natural promoters, pfus2, pprm1, and pfig1. As is shown in figure 2, pfus2 contains two copies of putative PREs, while pprm1 contains three and pfig1 contains five^8-10. Our results have shown that the induced expression level of the pprm1 is the lowest among the three promoters, in contrast to its three copies of PREs. To study the effect of PREs on the promoter activity, we constructed two distinct pprm1. In pprm1 Pro, we reversed the orientation of PRE in natural pprm1, while in pprm1 Ultra, we doubled the copy number of the PRE in natural pprm1.

Fig. 2

A. The orientation, sequence, and copy number of PREs in three pheromone-inducible promoters.

B. Two distinct pprm1 with different orientations and copy numbers, pprm1 Pro and pprm1 Ultra.

The diversity and plasticity of pheromone-responsive promoters allow it to be used in the construction of reporters in biosensors. However, the expensive pheromone limits the application of such promoters in cell factories. To broaden the application range of pheromone-responsive promoters, we reconstructed the pheromone signaling pathway to build a chassis that responds to cheaper inducers. According to the literature, we learned that the scaffold Ste5 plays an important role in the pheromone signaling pathway. The highly active mutant of Ste5 can activate the MAPK pathway in the absence of pheromone, inducing the expression of pheromone-responsive genes. We used the highly active mutant of Ste5 to enable the cheap inducer galactose to induce the expression of mating genes in engineered yeast. As a result, the engineered cells can utilize galactose instead of pheromone to activate the mating signaling pathway¹¹.

2) Modeling

We constructed a neural network algorithm to predict the expression level of pheromone-responsive promoters at different pheromone concentrations. After using the pheromone promoter expression library to test the model, the algorithm can accurately predict the expression level of pheromone responsive promoters based on the promoter sequence, therefore providing support for promoter engineering. Besides, considering the mechanism of the pheromone signaling pathway, we also constructed a molecular dynamics model to help set experimental conditions and process data in promoter characterization.

Application Scenario

1) Cell Factory

Compared to other signaling transduction processes, mating-specific signaling pathways are relatively independent of the asexual life history of yeast. By modifying mating-specific signaling pathways, cells can respond to specific induction conditions and change gene expression. During mixed fermentation, the specific reactions can be regulated by changing the induction conditions. For example, to avoid the toxic effect of the accumulated intermediates, cells can be induced at various stages. However, we are facing challenges including but not limited to the following one that galactose as the carbon source may affect cell growth.

Fig 3. Schematic workflow for microbial factory optimization¹²

(modified from Naseri, G. et al. 2020)

2) Biosensor

Biosensor is an analysis and detection tool composed of bioreceptor (identification elements), transducers, and signals amplification devices. Its principle is based on the biological reaction between biosensors containing a variety of bioactive materials (enzymes, antibodies, antigens, nucleic acids, etc.) with the substance to be detected. The concentration signal generated in the biological reaction is converted by the transducer into a quantitatively processed signals, which are amplified and output by the secondary instrument to obtain information on the quantity and concentration of the analyte. In the aspect of environmental monitoring, the application of nano-biosensor can detect heavy metal ion, mutagen, pollutant toxicity, hormone, and other pollutants, providing a powerful tool for environmental monitoring. In the food field, it can be used for the detection of food ingredients, quality indicators, food microorganisms, food additives and so on. In the medical field, some nanomaterials are surface modified to enable them to diagnose highly sensitive molecular markers in the internal environment, such as releasing proteins or antibodies to respond to tissue damage, inflammation, and infection.

We have learned that existing sensors based on physical indicators to detect chemicals in the environment have the problems of low sensitivity and high cost. If mating specific signaling pathways are modified to make yeast respond to specific signals, such as toxic and explosive substances in the environment, the levels of trace compounds in the environment can be quantitatively monitored by reporter genes. With respect to the biosensors applications of our system, specific detection targets need to be considered. Antibiotic in the environment provides a possibility.

Fig 4. Diverse biosensors used for screening combinatorial libraries¹²

(modified from Naseri, G. et al. 2020)

3) Application in iGEM

In the project of the UCSF_UCB team in 2014, they studied the phenomenon that cell populations somehow overcome the intrinsic biological noise found in all cellular populations, and are able to respond in a concerted fashion. They designed several feedback loops to adjust the inter-cell communication. They screened several natural promoters with different characteristics. For example, the pheromone-responsive promoter paga1 was found to have the highest inducible expression level with very low basal expression. This year, our modified promoter can quickly respond to signals at a specific inducer concentration or respond to signals in a linear relationship under different inducer concentrations. This promoter may be used in the 2014 UCSF_UCB team's project to reduce population noise, increase the speed or change the threshold of quorum sensing.

In 2015, Chalmers-Gothenburg's team created an innovative system that is both time and material-efficient by automatically identifying and eliminating contamination in bioreactors. The detection system goes by the name DAS (detection, and amplification of the signal), and is based on the pheromone response pathway in Saccharomyces cerevisiae. To produce a strong output signal when a contaminant is detected, they coupled the VP64 activation domain with dCas9 to produce a strong activation transcription factor. The artificial promoter designed by our team this year has a low threshold for inducers, thus can respond more sensitively to external signals and activate the reporter pathway. The promoter designed by our team, if applied to the DAS system of the Chalmers-Gothenburg team, may be able to detect pollutants more quickly and accurately, while the enhanced expression of signal substances can facilitate the observation of experimenters or staff.

References

[1] Alper, H., Fischer, C., Nevoigt, E., and Stephanopoulos, G. (2005) Tuning genetic control through promoter engineering, Proceedings of the National Academy of Sciences of the United States of America 102,> 12678.

[2] Andersen, H. W., Pedersen, M. B., Hammer, K., and Jensen, P. R. (2001) Lactate dehydrogenase has no control on lactate production but has a strong negative control on formate production in Lactococcus lactis, European Journal of Biochemistry 268>, 6379-6389.

[3] Blazeck, J., and Alper, H. S. (2013) Promoter engineering: Recent advances in controlling transcription at the most fundamental level, Biotechnol J 8, 46-+.

[4] Grote, E. (2010) Secretion is required for late events in the cell-fusion pathway of mating yeast, J Cell Sci 123, 1902-1912.

[5] Merlini, L., Dudin, O., and Martin, S. G. (2013) Mate and fuse: how yeast cells do it, Open Biol 3, 130008.

[6] Roberts, C. J., Nelson, B., Marton, M. J., Stoughton, R., Meyer, M. R., Bennett, H. A., He, Y. D. D., Dai, H. Y., Walker, W. L., Hughes, T. R., Tyers, M., Boone, C., and Friend, S. H. (2000) Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles, Science 287, 873-880.

[7] Su, T.-C., Tamarkina, E., and Sadowski, I. (2010) Organizational constraints on Ste12 cis-elements for a pheromone response in Saccharomyces cerevisiae, The FEBS Journal 277, 3235-3248.

[8] Elion, E. A., Trueheart, J., and Fink, G. R. (1995) Fus2 localizes near the site of cell fusion and is required for both cell fusion and nuclear alignment during zygote formation, Journal of Cell Biology 130, 1283-1296.

[9] Erdman, S., Lin, L., Malczynski, M., and Snyder, M. (1998) Pheromone-regulated Genes Required for Yeast Mating Differentiation, Journal of Cell Biology 140, 461-483.

[10] Heiman, M. G., and Walter, P. (2000) Prm1p, a pheromone-regulated multispanning membrane protein, facilitates plasma membrane fusion during yeast mating, The Journal of cell biology 151, 719-730.

[11] Lamson, R. E., Takahashi, S., Winters, M. J., and Pryciak, P. M. (2006) Dual role for membrane localization in yeast MAP kinase cascade activation and its contribution to signaling fidelity, Current Biology 16, 618-623.

[12] Naseri, G., and Koffas, M. A. G. (2020) Application of combinatorial optimization strategies in synthetic biology, Nature Communications 11.