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Optimization of mammalian expression vector by cis-regulatory element combinations
Lu-Yu Zhou1,2,3, Shuang Zhang1,2,3, Li-Yun Li1,2,3
1College of Veterinary Medicine, Henan Agricultural University, Zhengzhou, 450046, Henan, People's Republic of China.
Molecular Genetics and Genomics : MGG
|June 15, 2023
Summary
Optimizing cis-regulatory elements in expression vectors significantly enhances gene and recombinant protein production in mammalian cells. Strategic combinations, not just more elements, improve expression for biosynthesis and diagnostics.
Area of Science:
- Molecular Biology
- Gene Expression Regulation
- Biotechnology
Background:
- Gene expression regulation in mammalian cells using combined cis-regulatory elements is underexplored.
- Understanding these combinations is crucial for optimizing gene expression vectors.
Purpose of the Study:
- To investigate the impact of various combinations of cis-regulatory elements on gene expression.
- To optimize expression vectors for enhanced protein production, including SARS-CoV-2 RBD.
Main Methods:
- Construction of expression vectors with diverse combinations of promoters, enhancers, introns, and terminators.
- Comparison of expression levels using fluorescence microscopy, quantitative real-time PCR (qRT-PCR), and western blot.
- Expression analysis of enhanced green fluorescent protein (eGFP) and SARS-CoV-2 receptor binding domain (RBD) protein.
Main Results:
- Optimized combinations of cis-acting elements significantly regulate protein expression levels.
- A specific vector (CMV enhancer, EF-1α core promoter, TEF terminator) increased eGFP expression threefold and recombinant RBD protein 2.63-fold.
- Synergistic effects are not always observed when combining multiple regulatory elements.
Conclusions:
- Strategic selection and combination of cis-regulatory elements are key to maximizing gene expression.
- Findings offer insights for optimizing expression vectors in biosynthesis, diagnostics, and therapeutics, including COVID-19 related reagents.
- This study provides a framework for designing efficient gene expression systems.
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