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Ruffing AM, Davis RW, Lane TW. Advances in engineering algae for biofuel production. Curr Opin Biotechnol 2022; 78:102830. [PMID: 36332347 DOI: 10.1016/j.copbio.2022.102830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 12/14/2022]
Abstract
While algae demonstrate potential as a sustainable fuel source, low productivities limit the economic realization of algal biofuels. High-throughput strain engineering, omics-informed genome-scale modeling, and microbiome engineering are key technologies for enabling algal biofuels. High-throughput strain engineering efforts generate improved traits, including high biomass productivity and lipid content, in diverse algal species. Genome-scale models, constructed with the aid of omics data, provide insight into metabolic limitations and guide rational algal strain engineering efforts. As outdoor cultivation systems introduce exogenous organisms, microbiome engineering seeks to eliminate harmful organisms and introduce beneficial species. Optimizing algal biomass production and lipid content using these technologies may overcome the productivity barrier for the commercialization of algal biofuels.
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Affiliation(s)
- Anne M Ruffing
- Sandia National Laboratories, Molecular and Microbiology, P.O. Box 5800, MS 1413, Albuquerque, NM 87185, USA.
| | - Ryan W Davis
- Sandia National Laboratories, Bioresource and Environmental Security, P.O. Box 969, MS 9292, Livermore, CA 94551, USA
| | - Todd W Lane
- Sandia National Laboratories, Bioresource and Environmental Security, P.O. Box 969, MS 9292, Livermore, CA 94551, USA
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Chen J, Huang Y, Shu Y, Hu X, Wu D, Jiang H, Wang K, Liu W, Fu W. Recent Progress on Systems and Synthetic Biology of Diatoms for Improving Algal Productivity. Front Bioeng Biotechnol 2022; 10:908804. [PMID: 35646842 PMCID: PMC9136054 DOI: 10.3389/fbioe.2022.908804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Microalgae have drawn much attention for their potential applications as a sustainable source for developing bioactive compounds, functional foods, feeds, and biofuels. Diatoms, as one major group of microalgae with high yields and strong adaptability to the environment, have shown advantages in developing photosynthetic cell factories to produce value-added compounds, including heterologous bioactive products. However, the commercialization of diatoms has encountered several obstacles that limit the potential mass production, such as the limitation of algal productivity and low photosynthetic efficiency. In recent years, systems and synthetic biology have dramatically improved the efficiency of diatom cell factories. In this review, we discussed first the genome sequencing and genome-scale metabolic models (GEMs) of diatoms. Then, approaches to optimizing photosynthetic efficiency are introduced with a focus on the enhancement of biomass productivity in diatoms. We also reviewed genome engineering technologies, including CRISPR (clustered regularly interspaced short palindromic repeats) gene-editing to produce bioactive compounds in diatoms. Finally, we summarized the recent progress on the diatom cell factory for producing heterologous compounds through genome engineering to introduce foreign genes into host diatoms. This review also pinpointed the bottlenecks in algal engineering development and provided critical insights into the future direction of algal production.
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Affiliation(s)
- Jiwei Chen
- Department of Marine Science, Ocean College, Zhejiang University, Hangzhou, China
| | - Yifan Huang
- Department of Marine Science, Ocean College, Zhejiang University, Hangzhou, China
| | - Yuexuan Shu
- Department of Marine Science, Ocean College, Zhejiang University, Hangzhou, China
| | - Xiaoyue Hu
- Center for Data Science, Zhejiang University, Hangzhou, China
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Di Wu
- Department of Marine Science, Ocean College, Zhejiang University, Hangzhou, China
| | - Hangjin Jiang
- Center for Data Science, Zhejiang University, Hangzhou, China
| | - Kui Wang
- Department of Marine Science, Ocean College, Zhejiang University, Hangzhou, China
| | - Weihua Liu
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Weiqi Fu
- Department of Marine Science, Ocean College, Zhejiang University, Hangzhou, China
- Center for Systems Biology and Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
- *Correspondence: Weiqi Fu,
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