Cis/transgene optimization: systematic discovery of some key gene expression elements integrating bioinformatics and computational biology

Abstract

In recombinant protein production, quantity and quality are the major challenges particularly for large scale and high-throughput production systems. The present study mainly focused on computational analysis and in silico systematic discovery of some key functional gene expression elements in microalgae Dunaliella salina as a case study which there is no or poor information in this regard. Among the key factors, we took a shot at matrix attachment regions (MARs), translation initiation sites (TIS), signal peptide (SP) sequences, gene optimization and transformation system. Computational analysis of MARs sequences provided enough information about the structure of these sequences and led us to design an artificial MAR sequence considering the essential motifs and underlain rules. As the consensus TIS, we revealed that A-3, G-6C-5C-4 and G+1C+2G+3 arrange the specific context in this microalgae which help in locating the ribosome at the correct reading frame. Bioinformatics studies unveiled the sequence of MASTRAPLLALLALLCAGSARA with the highest signal score as the specific SP for secretion systems. A multi-criteria optimization procedure was performed to redesign the coding sequence of the BAR selectable marker gene. The optimized version of the gene mainly covered the host codon preference, the less structured mRNA and exposure of TIS. As the intragenic factors, we selected an efficient promoter, a 5ˈ-UTR and an intron from the closely related species (Chlamydomonas Sp.) to construct the specific expression vectors. The expression cassettes containing optimized genetic elements could be delivered into the microalgae cells and conferred the resistance to the transformants for at least 90 generations. The findings indicated that the MARs flanking the expression cassette along with the optimized expression elements particularly codon adaptation could potentially improve transformation efficiency and stability. The findings can be efficiently deployed as an empirical model for systematic discovery of the key expression elements and optimization of the cis/transgenes.