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Development of an alcohol-inducible gene expression system for recombinant protein expression in <Emphasis Type="Italic">Chlamydomonas reinhardtii</Emphasis>
Authors:Sujin Lee  Yong Jae Lee  Saehae Choi  Su-Bin Park  Quynh-Giao Tran  Jina Heo  Hee-Sik Kim
Institution:1.Cell Factory Research Center,Korea Research Institute of Bioscience and Biotechnology (KRIBB),Daejeon,Republic of Korea;2.Department of Environmental Biotechnology, KRIBB school of Biotechnology,Korea University of Science and Technology (UST),Daejeon,Republic of Korea;3.K-Biohealth,Chungbuk,Republic of Korea
Abstract:Microalgae have been widely considered for the production of valuable products, such as lipid-based biofuel, value-added pigments, and anti-photo aging reagents. More recently, microalgae have been considered an alternative host for recombinant protein production because of their economic benefits and ecofriendly characteristics. Additionally, many microalgal strains identified to date are generally recognized as safe (GRAS); therefore, the use of microalgae-based technology is promising. However, basic studies on the genetic engineering of microalgae are rare, despite their importance. Particularly, inducible promoter systems that can be applied for strain engineering or recombinant protein production are rarely studied; hence, a number of challenging issues remain unsolved. Therefore, in this study, we focused on the development of a convenient and compact-inducible promoter system that can be used in microalgae. Based on previous success with plant systems, we employed the alcohol-inducible AlcR-PalcA system, which originates from the filamentous fungus, Aspergillus nidulans. This system comprises only two components, a regulatory protein, AlcR, and an inducible promoter, PalcA. Therefore, construction and transformation of the gene cassettes can be easily performed. Ethanol-dependent gene expression was observed in the transformants with no significant growth retardation or inducer consumption observed in the cells cultivated under optimized conditions.
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