Science

Screening a new promoter library for use in p

Plant scientists are actively striving to successfully introduce valuable traits into plants and use them as bioreactors for ‘molecular pharming,’ wherein pharmaceutically useful molecules are produced in living organisms serving as chemical factories. The molecules produced includes important therapeutic proteins that can be expressed in a plant by altering its synthetic switches or metabolic pathways. Genetic elements like DNA promoters and terminators are crucial for creating this platform, since they control the expression of the “gene-of-interest.” While most promoter characterization studies typically use the green fluorescent protein (GFP) reporter assay to analyze promoter strength, the ‘dual-luciferase’ assay, which uses a dual-luminescence reporter system, seems to be a better substitute because it is ratiometric, sensitive , and generates less background noise.

To re-wire metabolic networks, multiple genes have to be expressed under different promoters — DNA regions triggering the expression of specific genes. This prevents the simultaneous overexpression of all genes that can cause metabolic imbalance. As such, scientists strive for “optimal gene expression,” in which promoters are chosen based on quantitative evaluation rather than the usual empirical selection. Since modifying the genomic content of the plant is challenging, more pragmatic and rational synthetic biology approaches are needed to quantitatively assess promoters and use them.

To address these gaps in knowledge, a research team from China has used a systematic approach to screen a library of promoters and terminators, where their performance was transiently evaluated in reporter assays. The characterized promoters were then employed for engineering the betalain pathway in Nicotiana benthamiana, to tobacco related. The study conducted by the team that was led by Professor Yong Wang and Dr. Jianhua Li of the Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, was published on June 1 in BioDesign Research.

The researchers used promoter sequences from multiple plants and virus species including Arabidopsis, maize, potato, rice, and plant-infecting retroviruses. First, they created a single expression vector with the GFP reporter gene cloned under P_CsVMV promoter derived from the Cassava vein mosaic virus. Next, they constructed a dual expression vector where the Renilla luciferase (R-luc) gene was driven by the P_CsVMV promoter, and the firefly luciferase (F-luc) gene was driven by a series of 19 individual promoters. These constructs were infiltrated in N. benthamiana leaves and also transfected in tobacco BY21 cells or suspension culture. In both reporter assays that contained GFP and luciferase, P_CsVMV displayed the highest activity among all tested promoters. The results were validated using mRNA-protein correlation where R-luc / F-luc correlated better than GFP, indicating that it is more reliable and accurate. “We demonstrated that different intensities of multiple promoter sequences are necessary for modulating metabolic pathways through the precise control of gene expression,”Says Prof. Wang, highlighting the major finding of their study.

Of the 13 terminator sequences tested, the Arabidopsis terminator T_AtHsp18.2 outperformed the frequently used T_35S and T_nos terminators. To understand how their selected terminators and promoters could together affect gene expression, the researchers evaluated 105 terminator-promoter combinations and found that the expression of the best (P_CsVMV / T_AtHsp18.2) and worst performing combinations (P_AtRD29B / T_nos) could differ by a massive 326-fold! “This underscores how crucial it is to choose the right combination for the best transgenic expression, “ Prof. Wang explains.

To have their findings strongly backed, they went ahead to provide a proof-of-concept in planta using the betalain synthetic pathway. They used “betalain” as the metabolic target since it is a red pigment found in fruits like beetroots and prickly pears and can visually indicate gene expression. In congruence with their speculations, the researchers found that the highest yield of betalain was not found when the two key pathway genes, CYP76AD1 and DODA, were driven by the strongest promoter P_CsVMV. Instead, the best combination for maximum betalain production was P_CsVMV and P_AtUbq10, which worked the magic together.

The results demonstrate that quantitatively characterized promoters can be used to ensure optimal gene expression. This is possible if gene transcription can be defined in a thoughtful and controlled manner. Prof. Wang elaborates: “Design-led rational engineering is very vital for creating the best solution. Our study not only demonstrates the various intensities of multiple promoter sequences, but also adds to the arsenal of plant promoters for plant metabolic engineering. “

Plant molecular pharming — promoted!

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Reference

Authors

Chenfei Tian1.2Yixin Zhang3, Jianhua Li1 and Yong Wang1

Affiliations

1CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China

2University of Chinese Academy of Sciences, Beijing 100039, China

3College of Life Science, Jilin Agricultural University, Changchun 130118, China

About Professor Yong Wang

Prof. Yong Wang is the Principal Investigator at the Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences CAS, China. In 2004, he graduated with a PhD in Biochemical Engineering from East China University of Science and Technology (ECUST). Prof. Wang’s major research interests include synthetic biology, metabolic engineering, biochemical engineering, and bioprocess engineering. He and his team di lui focus on defining design principles for the creation of natural or synthetic substances using synthetic biology and creating more effective and affordable ways to produce biological products. He has authored over 70 papers in international peer-reviewed journals.


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