环境信号驱动的真核微藻代谢调控与绿色应用

杜娅茹; 袁红莉; 杨金水

doi:10.12211/2096-8280.2026-035

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环境信号驱动的真核微藻代谢调控与绿色应用

特约评述 | 更新时间：2026-07-08

- 环境信号驱动的真核微藻代谢调控与绿色应用
- Environmental signal-driven regulation of eukaryotic microalgal metabolism and its green applications
- 合成生物学 2026年页码：1-17
- 作者机构：
  
  中国农业大学生物学院，畜禽生物育种全国重点实验室，北京，100193
- 作者简介：
  
  杜娅茹（2001—），女，博士研究生。研究方向为微藻遗传改造。
  杨金水（1975—），男，副教授，博士，博士生导师。研究方向为环境微生物学，涵盖微藻生物能源、生物质微生物转化、重金属污染的微生物修复等。成果收录于PNAS、New Phytologist、Bioresource Technology等。
- 基金信息：
- DOI：10.12211/2096-8280.2026-035
  中图分类号： Q812
- 收稿：2026-04-30，
  
  修回：2026-06-27，
  
  网络首发：2026-07-08，
- 稿件说明：
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杜娅茹，袁红莉，杨金水. 环境信号驱动的真核微藻代谢调控与绿色应用［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-035

DU Yaru， YUAN Hongli， YANG Jinshui. Environmental signal-driven regulation of eukaryotic microalgal metabolism and its green applications［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-035
杜娅茹，袁红莉，杨金水. 环境信号驱动的真核微藻代谢调控与绿色应用［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-035 DOI：

DU Yaru， YUAN Hongli， YANG Jinshui. Environmental signal-driven regulation of eukaryotic microalgal metabolism and its green applications［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-035 DOI：

摘要

真核微藻在绿色制造与生态修复中应用潜力巨大。然而，其产物积累受环境信号影响显著及代谢调控精度不足制约了其产业化应用。本文系统梳理了微藻感知光照、营养、盐度等环境信号的机制，阐述了ROS、Ca²⁺及MAPK级联信号传导网络如何驱动微藻脂质、类胡萝卜素和胞外多糖代谢重编程的研究进展。在此基础上，进一步阐述了CRISPR/Cas定点编辑及CRISPRa/i表达调控等遗传工具在代谢工程中的应用和基于环境信号调控的微藻在废水处理中的潜在应用。现有研究表明，结合人工智能辅助模型，在完善遗传工具的同时利用合生生物学技术将环境响应元件整合入代谢工程设计，构建动态可控的代谢调控网络，实现碳流与能量分配的动态调节，并在规模化条件下保证其可行性已成为突破微藻产业化瓶颈的关键路径和微藻生物制造未来的重要发展方向。

Abstract

Eukaryotic microalgae have attracted increasing attention as promising chassis organisms for green biomanufacturing and ecological remediation because of their capacity for efficient photosynthetic carbon fixation and biosynthesis of diverse high-value compounds

including lipids

carotenoids

polyunsaturated fatty acids

extracellular polysaccharides

and bioactive metabolites. However

their industrial application remains constrained by unstable product accumulation

limited precision in metabolic regulation

and the strong dependence on environmental fluctuations. Light intensity and quality

nutrient availability

salinity

temperature

and other environmental factors can substantially reshape microalgal growth

stress responses

and carbon allocation

thereby affecting both biomass productivity and target product yield. Therefore

understanding how microalgae perceive and transduce environmental signals is essential for developing controllable and scalable microalgal biomanufacturing systems. This review systematically summarizes the mechanisms by which microalgae sense major environmental signals

including light

nutrient limitation

and osmotic or salt stress. Particular attention is given to the roles of reactive oxygen species

calcium signaling

and mitogen-activated protein kinase cascades in mediating stress perception and intracellular signal transduction. We further discuss how these signaling networks drive metabolic reprogramming associated with lipid accumulation

carotenoid biosynthesis

and extracellular polysaccharide production. Building on this foundation

recent advances in genetic engineering tools for microalgae are reviewed

with emphasis on CRISPR/Cas-mediated genome editing and CRISPR activation or interference systems for transcriptional regulation. These tools provide important technical support for targeted pathway modification

regulatory element characterization

and dynamic control of metabolic flux. In addition

this review highlights the potential applications of environmentally responsive microalgal systems in wastewater treatment

resource recovery

and sustainable biomanufacturing. Current studies indicate that integrating environmental response elements into synthetic biology-based metabolic engineering strategies may enable the construction of dynamic and tunable regulatory networks. With the assistance of artificial intelligence-driven modeling

such systems could improve the prediction of environmental responses

optimize genetic circuit design

and coordinate carbon flux and energy distribution under changing cultivation conditions. Nevertheless

major challenges remain

including the nonlinear coupling of environmental signaling pathways

insufficient standardization and transferability of regulatory elements across microalgal species

and the lack of closed-loop control systems linking environmental inputs with genetic regulation. Future development should focus on temporal metabolic programming

environment-responsive synthetic regulatory circuits

and AI-assisted design-build-test-learn platforms to improve the robustness

controllability

and scalability of microalgal biomanufacturing.

关键词

Keywords

references

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