Multiomics approaches and genetic engineering of metabolism for improved biorefinery and wastewater treatment in microalgae

被引:17
作者
Kuo, Eva YuHua [1 ,2 ]
Yang, Ru-Yin [1 ]
Chin, Yuan Yu [1 ]
Chien, Yi-Lin [1 ,2 ]
Chen, Yu Chu [1 ]
Wei, Cheng-Yu [1 ]
Kao, Li-Jung [1 ]
Chang, Yi-Hua [1 ]
Li, Yu-Jia [1 ]
Chen, Te-Yuan [3 ]
Lee, Tse-Min [1 ,2 ,3 ]
机构
[1] Natl Sun Yat Sen Univ, Dept Marine Biotechnol & Resources, Kaohsiung 804, Taiwan
[2] Natl Sun Yat Sen Univ, Frontier Ctr Ocean Sci & Technol, Kaohsiung, Taiwan
[3] Natl Sun Yat Sen Univ, Doctoral Degree Program Marine Biotechnol, Kaohsiung, Taiwan
关键词
biorefinery; genetic engineering; microalgae; multiomics; wastewater treatment; STABLE NUCLEAR TRANSFORMATION; DIATOM PHAEODACTYLUM-TRICORNUTUM; CHLAMYDOMONAS-REINHARDTII; DUNALIELLA-SALINA; GREEN-ALGA; BIOFUEL PRODUCTION; CHLORELLA SP; HOMOLOGOUS RECOMBINATION; MEDIATED TRANSFORMATION; TRANSIENT EXPRESSION;
D O I
10.1002/biot.202100603
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Microalgae, a group of photosynthetic microorganisms rich in diverse and novel bioactive metabolites, have been explored for the production of biofuels, high value-added compounds as food and feeds, and pharmaceutical chemicals as agents with therapeutic benefits. This article reviews the development of omics resources and genetic engineering techniques including gene transformation methodologies, mutagenesis, and genome-editing tools in microalgae biorefinery and wastewater treatment (WWT). The introduction of these enlisted techniques has simplified the understanding of complex metabolic pathways undergoing microalgal cells. The multiomics approach of the integrated omics datasets, big data analysis, and machine learning for the discovery of objective traits and genes responsible for metabolic pathways was reviewed. Recent advances and limitations of multiomics analysis and genetic bioengineering technology to facilitate the improvement of microalgae as the dual role of WWT and biorefinery feedstock production are discussed.
引用
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页数:17
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