微生物学报

Core mechanism	Key scientific principles	Typical carbon source combinations and application cases	Industrial value and advantages
Energy coupling & thermodynamic driving	(1) Cells couple catabolic ATP with anabolic energy carriers (GTP/UTP) via precise regulation (2) Carbon sources divide functions to meet diverse energy demands (3) Key metabolite modulation drives thermodynamically unfavorable reactions (4) Co-culture syntrophy improves overall thermodynamic favorability	(1) Heterologous transhydrogenase system was introduced into E. coli with optimized electron transport chain to realize co-regeneration of NAD(P)H and ATP The titer of D-pantothenic acid reached 86.03 g/L with a productivity of 0.80 g/(L·h)^[35] (2) Glucose and glycerol: glucose provides rapid energy supply, while glycerol sustains cell growth and generates amino acid precursors^[36]	(1) Optimizes energy allocation, boosts product synthesis while sustaining cell viability (2) Breaks single-carbon-source energy bottleneck
Redox balance & electron carrier adaptation	(1) NADH dominates catabolism; NADPH functions in anabolism and oxidative stress (2) Different carbon sources yield distinct NADH/NADPH ratios (3) Multi-carbon coupling flexibly tunes intracellular reducing power ratio (4) Modular pathways reduce strain burden and enable precise redox control	(1) CO₂-formate co-conversion: 11.24% C1 assimilation, 0.48 g/(L·h) formate consumption, 10.10 g/L FFAs^[43] (2) S. cerevisiae glucose-formate co-utilization: ethanol, FFAs, and longifolene yields up by 184%, 490%, and 100%^[44] (3) mXR-XDH xylose and PRK-Rubisco CO₂ fixation modules: 0.47 g/g ethanol yield^[45]	(1) Resolves reducing power imbalance from static regulation (2) Precisely matches product-specific reducing power demand (3) Synergizes carbon flux and reducing power supply
Molecular mechanism & relief strategies of carbon catabolite repression	(1) CCR drives preferential use of preferred carbons (e.g., glucose), represses non-preferred carbon metabolic genes, and causes diauxic growth (2) Core mechanisms: PTS phosphorylation regulation, transcriptional control by factors like CreA (3) Low glucose can activate rather than inhibit non-glucose carbon metabolism under specific conditions	(1) Knockout of key regulatory genes: pstG^[50], crr^[51], ccpA^[52], scrI^[53] (2) Adaptive evolution and engineering improve glucose/xylose co-utilization inE. coli and elevates ethanol yield^[54] (3) Low-dose glucose activates galactose catabolic pathway^[55]	(1) Eliminates diauxic growth and shortens fermentation cycle (2) Enables simultaneous efficient utilization of multiple carbon sources (3) Significantly improves overall industrial process efficiency
Carbon flux redirection & conservation mechanism	(1) Redirects carbon flux to target pathways while sustaining cell growth (2) Carbon conservation maximizes substrate-to-product carbon conversion efficiency (3) Low-oxidation carbons cut CO₂-related carbon loss (4) Synthetic consortia split pathways to lower single-strain burden	(1) Heterologous β-glucosidase and β-xylosidase genes were integrated intoS. cerevisiae to achieve co-fermentation of cellulosic sugars^[60] (2) Methylotrophs coupled with lignocellulosic sugars: methane provides NADPH while sugars supply carbon skeletons for ectoine biosynthesis^[63] (3) Dual-chamber bioreactor: CO₂ generated from ethanol fermentation is recycled for succinic acid synthesis^[65]	(1) Improves carbon economy and reduces carbon loss (2) Achieves natural complementation of reducing power and carbon metabolism (3) Supports high-value utilization of low-cost C1/C2 carbons (methane, CO₂)

Core mechanism	Key scientific principles	Typical carbon source combinations and application cases	Industrial value and advantages
Energy coupling & thermodynamic driving	(1) Cells couple catabolic ATP with anabolic energy carriers (GTP/UTP) via precise regulation (2) Carbon sources divide functions to meet diverse energy demands (3) Key metabolite modulation drives thermodynamically unfavorable reactions (4) Co-culture syntrophy improves overall thermodynamic favorability	(1) Heterologous transhydrogenase system was introduced into E. coli with optimized electron transport chain to realize co-regeneration of NAD(P)H and ATP The titer of D-pantothenic acid reached 86.03 g/L with a productivity of 0.80 g/(L·h)^[35] (2) Glucose and glycerol: glucose provides rapid energy supply, while glycerol sustains cell growth and generates amino acid precursors^[36]	(1) Optimizes energy allocation, boosts product synthesis while sustaining cell viability (2) Breaks single-carbon-source energy bottleneck
Redox balance & electron carrier adaptation	(1) NADH dominates catabolism; NADPH functions in anabolism and oxidative stress (2) Different carbon sources yield distinct NADH/NADPH ratios (3) Multi-carbon coupling flexibly tunes intracellular reducing power ratio (4) Modular pathways reduce strain burden and enable precise redox control	(1) CO₂-formate co-conversion: 11.24% C1 assimilation, 0.48 g/(L·h) formate consumption, 10.10 g/L FFAs^[43] (2) S. cerevisiae glucose-formate co-utilization: ethanol, FFAs, and longifolene yields up by 184%, 490%, and 100%^[44] (3) mXR-XDH xylose and PRK-Rubisco CO₂ fixation modules: 0.47 g/g ethanol yield^[45]	(1) Resolves reducing power imbalance from static regulation (2) Precisely matches product-specific reducing power demand (3) Synergizes carbon flux and reducing power supply
Molecular mechanism & relief strategies of carbon catabolite repression	(1) CCR drives preferential use of preferred carbons (e.g., glucose), represses non-preferred carbon metabolic genes, and causes diauxic growth (2) Core mechanisms: PTS phosphorylation regulation, transcriptional control by factors like CreA (3) Low glucose can activate rather than inhibit non-glucose carbon metabolism under specific conditions	(1) Knockout of key regulatory genes: pstG^[50], crr^[51], ccpA^[52], scrI^[53] (2) Adaptive evolution and engineering improve glucose/xylose co-utilization inE. coli and elevates ethanol yield^[54] (3) Low-dose glucose activates galactose catabolic pathway^[55]	(1) Eliminates diauxic growth and shortens fermentation cycle (2) Enables simultaneous efficient utilization of multiple carbon sources (3) Significantly improves overall industrial process efficiency
Carbon flux redirection & conservation mechanism	(1) Redirects carbon flux to target pathways while sustaining cell growth (2) Carbon conservation maximizes substrate-to-product carbon conversion efficiency (3) Low-oxidation carbons cut CO₂-related carbon loss (4) Synthetic consortia split pathways to lower single-strain burden	(1) Heterologous β-glucosidase and β-xylosidase genes were integrated intoS. cerevisiae to achieve co-fermentation of cellulosic sugars^[60] (2) Methylotrophs coupled with lignocellulosic sugars: methane provides NADPH while sugars supply carbon skeletons for ectoine biosynthesis^[63] (3) Dual-chamber bioreactor: CO₂ generated from ethanol fermentation is recycled for succinic acid synthesis^[65]	(1) Improves carbon economy and reduces carbon loss (2) Achieves natural complementation of reducing power and carbon metabolism (3) Supports high-value utilization of low-cost C1/C2 carbons (methane, CO₂)

Strain	Substrate combination	Target product	Titer/productivity	References
Saccharomyces cerevisiae	Glucose/formate/CO₂	Fatty acids	10.10 g/L	[43]
Saccharomyces cerevisiae	Xylose/formate/CO₂	Ethanol, free fatty acids, and longifolene	18.40%, 49%, and nearly 100% increase	[44]
Saccharomyces cerevisiae	Maltose/glucose/CO₂	Ethanol	Productivity of 0.47 g/g	[45]
Escherichia coli	Glucose/formate	Pyruvate	1.88 mol/mol	[47]
Escherichia coli	Glucose/formate	L-malic acid	1.65 mol/mol	[47]
Escherichia coli	Glucose/formate/H₂	MVA	57.60% increase	[66]
Escherichia coli	Glucose/formate	MVA	3.80 g/L	[67]
Yarrowia lipolytica	Glucose/formate/CO₂	Succinic acid	97.54 g/L; 0.64 g/g	[68]
Yarrowia lipolytica	Glucose/formate/CO₂	Succinic acid	20% increase	[69]
Escherichia coli	Glucose/formate/CO₂	Succinic acid	2.46 g/L; 0.43 mol/mol	[70]
Corynebacterium glutamicum	Glucose/formate	Succinic acid	1 134 mmol/L; 1.67 mol/mol	[71]
Umbelopsis isabellina	Glucose:formate=1:3.90	Lipids	70% higher lipid titer	[72]
Pseudomonas putida KT2440	Glucose/formate	Biomass/polymer precursors	25% higher biomass	[73]
Escherichia coli	Glucose/methanol	Biomass	Methanol consumption: 1.70 mmol/h	[74]
Escherichia coli	Xylose/methanol	Ethanol	1.89 g/L	[75]
Serratia marcescens HBQA7	Glucose/methanol	Geraniol	574.12 mg/L	[76]
Serratia marcescens HBQA7	Glucose/methanol	α-bisabolol	1 256.41 mg/L	[76]
In vitro enzyme biosystem	Xylose/methanol	L-lactic acid	6 g/L; carbon efficiency of 88.75%	[77]
Escherichia coli	Glucose/methanol	2,4-DHB	14.60 g/L	[78]
Escherichia coli	Xylose/methanol	(R)-1,3-butanediol	13.71 g/L	[79]
Pichia pastoris	Glucose:methanol=4:1	Biomass	20% increase in cell density	[80]
Pichia pastoris	Xylose/methanol	Biomass	7.50 g/L	[81]

Strain	Substrate combination	Target product	Titer/productivity	References
Saccharomyces cerevisiae	Glucose/formate/CO₂	Fatty acids	10.10 g/L	[43]
Saccharomyces cerevisiae	Xylose/formate/CO₂	Ethanol, free fatty acids, and longifolene	18.40%, 49%, and nearly 100% increase	[44]
Saccharomyces cerevisiae	Maltose/glucose/CO₂	Ethanol	Productivity of 0.47 g/g	[45]
Escherichia coli	Glucose/formate	Pyruvate	1.88 mol/mol	[47]
Escherichia coli	Glucose/formate	L-malic acid	1.65 mol/mol	[47]
Escherichia coli	Glucose/formate/H₂	MVA	57.60% increase	[66]
Escherichia coli	Glucose/formate	MVA	3.80 g/L	[67]
Yarrowia lipolytica	Glucose/formate/CO₂	Succinic acid	97.54 g/L; 0.64 g/g	[68]
Yarrowia lipolytica	Glucose/formate/CO₂	Succinic acid	20% increase	[69]
Escherichia coli	Glucose/formate/CO₂	Succinic acid	2.46 g/L; 0.43 mol/mol	[70]
Corynebacterium glutamicum	Glucose/formate	Succinic acid	1 134 mmol/L; 1.67 mol/mol	[71]
Umbelopsis isabellina	Glucose:formate=1:3.90	Lipids	70% higher lipid titer	[72]
Pseudomonas putida KT2440	Glucose/formate	Biomass/polymer precursors	25% higher biomass	[73]
Escherichia coli	Glucose/methanol	Biomass	Methanol consumption: 1.70 mmol/h	[74]
Escherichia coli	Xylose/methanol	Ethanol	1.89 g/L	[75]
Serratia marcescens HBQA7	Glucose/methanol	Geraniol	574.12 mg/L	[76]
Serratia marcescens HBQA7	Glucose/methanol	α-bisabolol	1 256.41 mg/L	[76]
In vitro enzyme biosystem	Xylose/methanol	L-lactic acid	6 g/L; carbon efficiency of 88.75%	[77]
Escherichia coli	Glucose/methanol	2,4-DHB	14.60 g/L	[78]
Escherichia coli	Xylose/methanol	(R)-1,3-butanediol	13.71 g/L	[79]
Pichia pastoris	Glucose:methanol=4:1	Biomass	20% increase in cell density	[80]
Pichia pastoris	Xylose/methanol	Biomass	7.50 g/L	[81]

Strain	Substrate combination	Target product	Titer/productivity	References
Saccharomyces cerevisiae	Glucose/acetate	Ethanol	95% of the theoretical value	[82]
Saccharomyces cerevisiae	Glucose/acetate	Ethanol	Titer close to theoretical maximum	[82]
Escherichia coli NZ-Gly303	Glucose/acetate	Glycolic acid	73.30 g/L; 1.04 g/(L·h)	[83]
Escherichia coli	Glucose/acetate	Pyruvate	9.61 g/L	[84]
Escherichia coli	Glucose/acetate	Pyruvate	5 g/L; 0.87 mol/mol	[85]
Escherichia coli	Glucose/acetate	Butyl butyrate	29.02 g/L; carbon yield of 43.30%	[86]
Corynebacterium glutamicum	Glucose/acetate	3-HP	12.57-fold	[87]
Saccharomyces cerevisiae	Xylose/acetate	Ethanol	84% higher product titer	[88]
Yarrowia lipolytica	Xylose/acetate	β-carotene	776.90 mg/L	[89]
Yarrowia lipolytica	Glucose/acetate	PHB	7.35 g/L	[90]
Issatchenkia orientalis	Xylose/acetate	3-HP	8.70 g/L (straw hydrolysate)	[91]
Halomonas bluephagenesis	Glucose/acetate	MVA	121 g/L (5 L bioreactor)	[92]
Haematococcus pluvialis	Glucose/acetate	Biomass	77.10% higher biomass	[93]
Neochloris oleoabundans	Glucose/xylose/acetate	Lipids	1.75 g/L; lipid content of 34.40%	[94]
Rhodotorula toruloides	Glucose (or xylose)/acetate	Lipids	56%; productivity of 0.19 g/(L·h)	[95]
Bacillus coagulans	Glucose/acetate	Lactic acid	15.30% increase in lactic acid titer	[96]
Aspergillus terreus	Cellulosic hydrolysate	Itaconic acid	39.60 g/L; 0.40 g/g	[97]

Strain	Substrate combination	Target product	Titer/productivity	References
Saccharomyces cerevisiae	Glucose/acetate	Ethanol	95% of the theoretical value	[82]
Saccharomyces cerevisiae	Glucose/acetate	Ethanol	Titer close to theoretical maximum	[82]
Escherichia coli NZ-Gly303	Glucose/acetate	Glycolic acid	73.30 g/L; 1.04 g/(L·h)	[83]
Escherichia coli	Glucose/acetate	Pyruvate	9.61 g/L	[84]
Escherichia coli	Glucose/acetate	Pyruvate	5 g/L; 0.87 mol/mol	[85]
Escherichia coli	Glucose/acetate	Butyl butyrate	29.02 g/L; carbon yield of 43.30%	[86]
Corynebacterium glutamicum	Glucose/acetate	3-HP	12.57-fold	[87]
Saccharomyces cerevisiae	Xylose/acetate	Ethanol	84% higher product titer	[88]
Yarrowia lipolytica	Xylose/acetate	β-carotene	776.90 mg/L	[89]
Yarrowia lipolytica	Glucose/acetate	PHB	7.35 g/L	[90]
Issatchenkia orientalis	Xylose/acetate	3-HP	8.70 g/L (straw hydrolysate)	[91]
Halomonas bluephagenesis	Glucose/acetate	MVA	121 g/L (5 L bioreactor)	[92]
Haematococcus pluvialis	Glucose/acetate	Biomass	77.10% higher biomass	[93]
Neochloris oleoabundans	Glucose/xylose/acetate	Lipids	1.75 g/L; lipid content of 34.40%	[94]
Rhodotorula toruloides	Glucose (or xylose)/acetate	Lipids	56%; productivity of 0.19 g/(L·h)	[95]
Bacillus coagulans	Glucose/acetate	Lactic acid	15.30% increase in lactic acid titer	[96]
Aspergillus terreus	Cellulosic hydrolysate	Itaconic acid	39.60 g/L; 0.40 g/g	[97]

Strain	Substrate combination	Target product	Titer/productivity	References
Clostridium carboxidivorans	Syngas	Ethanol	3 g/L	[100]
Clostridium carboxidivorans	Syngas	Butanol	0.35 g/L	[100]
Clostridium carboxidivorans	Syngas	Hexanol	1.90 g/L	[101]
Clostridium ljungdahlii	CO adjusted to 70%	Hexanol	0.39 g/L	[102]
Clostridium ljungdahlii	CO₂/H₂	Hexanol	2-5-fold	[103]
Clostridium ljungdahlii	Syngas	Ethanol	30.10 g/L (2 L fermenter)	[104]
Clostridium acetobutylicum ATCC 824	Syngas	3-HP	9.25 g/L	[105]
Enterobacter hormaechei RF2	Syngas	Ethanol	25.30 g/L (reactor)	[107]
Eubacterium limosum KIST612	Syngas	Acetic acid	34.40 g/L	[108]

Strain	Substrate combination	Target product	Titer/productivity	References
Clostridium carboxidivorans	Syngas	Ethanol	3 g/L	[100]
Clostridium carboxidivorans	Syngas	Butanol	0.35 g/L	[100]
Clostridium carboxidivorans	Syngas	Hexanol	1.90 g/L	[101]
Clostridium ljungdahlii	CO adjusted to 70%	Hexanol	0.39 g/L	[102]
Clostridium ljungdahlii	CO₂/H₂	Hexanol	2-5-fold	[103]
Clostridium ljungdahlii	Syngas	Ethanol	30.10 g/L (2 L fermenter)	[104]
Clostridium acetobutylicum ATCC 824	Syngas	3-HP	9.25 g/L	[105]
Enterobacter hormaechei RF2	Syngas	Ethanol	25.30 g/L (reactor)	[107]
Eubacterium limosum KIST612	Syngas	Acetic acid	34.40 g/L	[108]

Strain	Substrate combination	Target product	Titer/productivity	References
Pichia pastoris Saccharomyces cerevisiae	Methanol/famate/CO₂	5-aminolevulinic acid Lactic acid	0.71 mg/L 0.21 g/L	[109]
Methanol/famate/CO₂	[109]
Methylorubrum extorquens AM1	Methanol/acetate	Violacein	118 mg/L	[110]
Eubacterium limosum	Famate/methanol	Butanol	2 mmol/L	[111]
Pseudomonas gessardii	Famate/acetate	Medium-chain-length polyhydroxyalkanoates (mcl-PHA)	0.40 g/L	[112]
Saccharomyces cerevisiae	Famate/acetate	Fatty acids	6.60 g/L	[113]
Yarrowia lipolytica	Famate/acetate/electrocatalysis CO₂	β-farnesene	14.80 g/L	[114]
Escherichia coli	Famate/acetate	Biomass	-	[115]
Clostridium acetobutylicum	Famate/acetate	Acetone-butanol-ethanol (ABE)	8.60 g/L	[116]
Escherichia coli	Famate/acetate	L-homoserine	15.96 g/L	[117]

Strain	Substrate combination	Target product	Titer/productivity	References
Pichia pastoris Saccharomyces cerevisiae	Methanol/famate/CO₂	5-aminolevulinic acid Lactic acid	0.71 mg/L 0.21 g/L	[109]
Methanol/famate/CO₂	[109]
Methylorubrum extorquens AM1	Methanol/acetate	Violacein	118 mg/L	[110]
Eubacterium limosum	Famate/methanol	Butanol	2 mmol/L	[111]
Pseudomonas gessardii	Famate/acetate	Medium-chain-length polyhydroxyalkanoates (mcl-PHA)	0.40 g/L	[112]
Saccharomyces cerevisiae	Famate/acetate	Fatty acids	6.60 g/L	[113]
Yarrowia lipolytica	Famate/acetate/electrocatalysis CO₂	β-farnesene	14.80 g/L	[114]
Escherichia coli	Famate/acetate	Biomass	-	[115]
Clostridium acetobutylicum	Famate/acetate	Acetone-butanol-ethanol (ABE)	8.60 g/L	[116]
Escherichia coli	Famate/acetate	L-homoserine	15.96 g/L	[117]

混合碳源协同代谢驱动高值化学品生物合成的研究进展

PDF下载

赵全禄 ¹^,²^,³ , 张维强 ¹^,²^,³ , 吴灼恒 ¹^,²^,³ , 王凯 ¹^,²^,³^,^* , 陈必强 ¹^,²^,³^,^* , 谭天伟 ¹^,²^,³

微生物学报 | 综述 2026,66(9): 4471-4495

收起

微生物学报 |综述 2026 , 66 (9) : 4471 -4495

混合碳源协同代谢驱动高值化学品生物合成的研究进展

全屏

赵全禄¹^,²^,³, 张维强¹^,²^,³, 吴灼恒¹^,²^,³, 王凯¹^,²^,³^,^*, 陈必强¹^,²^,³^,^*, 谭天伟¹^,²^,³

作者信息

^1.北京化工大学，绿色生物制造国家重点实验室，北京

^2.北京化工大学，国家能源生物炼制研发中心，北京

^3.北京化工大学，教育部生物炼制工程研究中心，北京

通讯作者:

王凯, 陈必强

作者简介:

作者贡献声明

赵全禄：文献的检索与归纳，负责撰写全文及修改；张维强：参与文献的深入分析与讨论，文章图表的绘制；吴灼恒：文献的调研与写作细节讨论，文章图表的绘制；王凯：取得基金支持，综述主题的选定，文献的检索与归纳，负责撰写全文及修改，指出写作建议；陈必强：综述主题的选定，综述文章的审阅，提出写作指导与修改建议；谭天伟：取得基金支持，综述主题的选定，综述文章的审阅。

Research progress in high-value chemical biosynthesis driven by synergistic metabolism of mixed carbon sources

Quanlu ZHAO¹^,²^,³, Weiqiang ZHANG¹^,²^,³, Zhuoheng WU¹^,²^,³, Kai WANG¹^,²^,³^,^*, Biqiang CHEN¹^,²^,³^,^*, Tianwei TAN¹^,²^,³

Affiliations

^1.State Key Laboratory of Green Biomanufacturing, Beijing University of Chemical Technology, Beijing, China

^2.National Energy Research and Development Center for Biorefinery, Beijing University of Chemical Technology, Beijing, China

^3.Biorefinery Engineering Research Center of the Ministry of Education, Beijing University of Chemical Technology, Beijing, China

Corresponding Author:

^*E-mail: WANG Kai, wangk@buct.edu.cn

CHEN Biqiang, chenbq@mail.buct.edu.cn

出版时间: 2026-09-04 doi: 10.13343/j.cnki.wsxb.20260296

文章导航

摘要

收起

在“碳中和”目标指引下，提升微生物细胞工厂的碳原子利用经济性已成为新时期绿色生物制造领域的核心科学问题与关键技术需求。传统发酵工艺虽具备成熟的工业应用基础，但受限于中心碳代谢途径中氧化脱羧反应导致的碳损失，原料碳利用效率难以实现根本性突破，制约了生物制造产业的绿色化与高质量发展。多碳源耦合发酵通过理性整合糖类有机底物与甲酸、甲醇、合成气等C1碳源及乙酸等C2碳源，构建碳架互补、能量协同、还原力平衡的混合营养代谢体系，为突破传统生物发酵的碳得率理论瓶颈提供技术路径。本文系统综述了合成生物学技术驱动下多碳源耦合发酵领域的研究进展，重点阐释有机底物与C1/C2化合物及工业尾气等典型耦合体系的代谢途径重构与调控机制，梳理了该技术在合成燃料、生物基材料和高附加值天然产物合成中的应用实例与研究现状，并讨论了其在碳捕获利用与生物制造高值化融合发展中的工业化前景，以期为面向碳中和的高效生物制造技术创新提供理论参考与思路借鉴。

关键词

多碳源耦合发酵 / 高值化学品 / 合成生物学 / 碳中和

Abstract

收起

Under the strategic goal of carbon neutrality, enhancing the carbon atom utilization efficiency of microbial cell factories has emerged as a core scientific challenge and imperative technical demand for advanced green biomanufacturing. Conventional fermentation processes possess mature industrial applicability, yet they are constrained by inevitable carbon losses via oxidative decarboxylation in central carbon metabolism. Such inherent limitation hinders fundamental improvement in substrate carbon conversion efficiency and restricts the green and high-quality advancement of the biomanufacturing industry. Multi-carbon source co-fermentation enables the rational integration of carbohydrate substrates with C1 feedstocks (formic acid, methanol, and syngas) and C2 feedstocks (acetic acid), constructing a mixotrophic metabolic system featured with carbon skeleton complementation, energy supply synergy, and intracellular redox balance. This strategy offers an innovative technological paradigm to break the theoretical carbon yield bottleneck of conventional bioprocesses. This paper systematically reviews the advances in multi-carbon source co-fermentation driven by synthetic biology. We emphatically elaborate on metabolic pathway reconstruction and regulatory mechanisms of typical co-fermentation systems consisting of organic substrates, C1/C2 compounds, and industrial waste gas. The state-of-the-art applications in synthetic fuels, bio-based materials, and high-value natural product biosynthesis are summarized. Furthermore, this paper discusses the industrial potential of this technology in integrating carbon capture and utilization and high-value biomanufacturing, aiming to provide a theoretical basis and strategic references for the innovation of efficient biomanufacturing towards carbon neutrality.

Key words

multi-carbon source co-fermentation / high-value chemical / synthetic biology / carbon neutrality

引用本文

赵全禄, 张维强, 吴灼恒, 王凯, 陈必强, 谭天伟. 混合碳源协同代谢驱动高值化学品生物合成的研究进展. 微生物学报, 2026 , 66 (9) : 4471 -4495 . DOI: 10.13343/j.cnki.wsxb.20260296

Quanlu ZHAO, Weiqiang ZHANG, Zhuoheng WU, Kai WANG, Biqiang CHEN, Tianwei TAN. Research progress in high-value chemical biosynthesis driven by synergistic metabolism of mixed carbon sources[J]. Acta Microbiologica Sinica, 2026 , 66 (9) : 4471 -4495 . DOI: 10.13343/j.cnki.wsxb.20260296

正文

收起

提升生物制造过程的碳原子经济性，是绿色生物制造领域亟待破解的核心科学问题之一，也是支撑国家“碳中和”目标落地的关键技术途径^[1-5]。现有生物制造体系仍高度依赖葡萄糖和木糖等有机底物的异养代谢模式，工艺路径成熟但碳利用效率存在固有瓶颈。在天然中心碳代谢网络中，糖酵解途径与三羧酸循环是微生物介导底物分解代谢与前体合成的核心通路，其代谢过程高度依赖氧化脱羧反应，以此驱动碳骨架重排与能量供给^[6]。该代谢模式存在显著的碳流失缺陷，约30%-50%的底物碳以CO₂形式不可逆释放，尤其在丙酮酸向乙酰辅酶A转化的关键节点，氧化脱羧反应直接造成碳原子经济性大幅下降^[2,7]。上述由氧化脱羧不可逆反应与天然能量代谢的偏好特性带来的固有缺陷，不仅导致原料碳利用率偏低，更从本质上制约了生物基化学品与传统石化路线的成本竞争力。

当定向合成萜类^[7-10]、长链脂肪醇^[11-12]及中长链二羧酸^[13-16]等高还原度与高能量密度的目标产物时，单一有机底物供给模式的固有缺陷会被显著放大，其体系局限性也进一步凸显。此类化合物的定向合成对碳骨架供给效率与还原力供应水平存在严格的化学计量匹配需求，而传统异养代谢途径存在路径相对固定与碳通量不足等缺点，难以同步满足细胞正常生长增殖与目标产物高效合成的双重物质及能量需求。高还原产物合成不仅需要持续且足量的碳骨架支撑分子链的延伸与组装，还依赖大量还原型辅酶推动关键还原反应进行，单一底物经戊糖磷酸途径等固有代谢通路产生的还原力通量有限，且受碳代谢物阻遏(carbon catabolite repression, CCR)效应调控，碳源会优先流向菌体基础代谢与生长繁殖，导致流向产物合成支路的碳流与还原力严重不足，进而引发胞内氧化还原稳态失衡以及中间代谢物积累等问题，最终造成原料转化率偏低、产物合成效率难以提升，无法满足实际发酵生产与工业化转化的应用要求。

为突破传统异养代谢存在的热力学瓶颈与动力学局限，研究人员聚焦低碳化合物与五碳及六碳糖的耦合代谢调控，并逐步发展形成一套重构微生物碳代谢网络^[17-21]的新型技术体系，如图1所示。该策略通过理性整合甲酸、甲醇及合成气等C1碳源与乙酸等C2碳源，耦合有机底物代谢通路，构建物质互补、能量协同及氧化还原平衡的混合营养利用体系^[22]。这类策略的核心逻辑是借助有机底物氧化分解释放的腺嘌呤核苷三磷酸(adenosine triphosphate, ATP)推动热力学层面难以自发进行的C1/C2固定过程，解除天然自养固碳过程中的能量约束；还可利用高还原态C1小分子碳源作为外源电子供体，调控胞内氧化还原稳态，完成碳流失节点的高效回补^[23-25]。这类协同代谢机制打破了单一底物发酵的热力学局限，为工业尾气与生物质水解液在内的低值废弃碳源的高值化转化提供了全新的研究思路。合成生物学技术的快速迭代，加上理性设计理念的深度融入，推动了多碳源耦合体系逐步从概念验证阶段进入工程化应用阶段。与传统底盘菌株的自然筛选相比，现代工程化策略将重点放在底盘细胞的系统性代谢重编程上，涉及碳代谢重排途径[如非氧化糖酵解(non-oxidative glycolysis, NOG)途径]的设计与构建工作^[26-28]、能量供给通路的精准匹配，以及碳代谢物阻遏等全局调控系统的优化^[29-31]。本文全面梳理合成生物学赋能下多碳源耦合发酵领域的研究进展，重点分析有机底物与C1/C2小分子碳源协同代谢的分子机制和关键改造路径，说明其在高碳效细胞工厂构建及生物燃料和药物中间体在内的高值化学品合成中的应用潜力。同时，本文也梳理了该技术目前在实验室研究与工程化放大过程中面临的核心科学问题与技术挑战，并对其未来发展方向进行了展望，旨在为面向国家“碳中和”目标的新一代绿色生物制造技术发展提供有价值的理论参考与研究思路。

1 混合碳源发酵的代谢机制与分析

收起

多碳源耦合发酵指微生物利用混合碳源，如五/六碳糖与非粮碳源(C1与C2底物)的生物过程，通过代谢途径的协同调控可实现碳源高效利用，这类技术在合成生物学与代谢工程领域应用较广，能够提升高价值化学品的产出率与碳原子经济性^[32]。与单一碳源工艺相比，多碳源耦合可优化能量分布，维持氧化还原稳态，依托相应调控机制降低碳损耗，可改善整体代谢效能^[18,33]。后续将从能量耦合与热力学驱动机制、氧化还原平衡与电子载体适配、碳分解代谢物阻遏的分子机制及解除策略、碳代谢通量重定向与守恒机制4个维度展开论述。

1.1 能量耦合与热力学驱动机制

在微生物代谢多种碳源的过程中，能量耦合机制是碳源高效利用的核心前提，细胞具备一套精细的能量调控系统，将分解代谢释放的能量载体(ATP)与合成代谢过程需要消耗的能量载体[合成蛋白质需要鸟苷-5′-三磷酸(guanosine-5′-triphosphate, GTP)，合成多糖需要尿苷三磷酸(uridine triphosphate, UTP)等]紧密衔接在一起^[32]。混合碳源发酵环境中微生物会选择性利用不同碳源，匹配自身不同的能量需求，葡萄糖一般是优先被利用的碳源，经糖酵解途径可快速供给ATP以及还原力^[34-35]；甘油可支撑细胞生长并提供特定氨基酸前体，使葡萄糖可更多用于目标产物合成，最终实现协同利用与高产^[36]。这种碳源利用的功能分工可优化细胞的能量分配，在保障细胞正常活性的前提下尽可能提升目标产物的合成效率。

热力学驱动机制在多碳源代谢中同样发挥关键作用。生物体内反应的实际吉布斯自由能变(ΔG)决定了反应的方向和驱动力，其数值受细胞内代谢物浓度的动态调控^[32]。通过调控关键代谢物的浓度，细胞可驱动标准条件下热力学不利的生化反应正向进行。研究表明，部分发酵反应本身热力学不利，但通过与互营微生物共培养可使原本难以自发进行的反应顺利发生，在共培养系统中一种微生物的代谢产物成为另一种微生物的底物，从而形成热力学上更有利的整体系统^[33-34]。近年来，通过代谢网络重构和能量通量优化，研究者已能够显著提升目标产物的生产效率。例如，在大肠埃希氏菌(Escherichia coli)中通过引入异源转氢酶系统并优化电子传递链实现了还原型烟酰胺腺嘌呤二核苷酸磷酸[nicotinamide adenine dinucleotide phosphate, NAD(P)H]与ATP的协同再生，使D-泛酸产量达到86.03 g/L，产率达0.80 g/(L·h)^[35]。这种能量耦合与热力学优化对于提高混合碳源利用效率至关重要。

1.2 氧化还原平衡与电子载体适配

在微生物代谢过程中氧化还原平衡是维持细胞生命活动和产物合成的关键因素^[36-37]。还原型烟酰胺腺嘌呤二核苷酸(nicotinamide adenine dinucleotide, NADH)和NAD(P)H是细胞内主要的还原力载体——NADH主要参与分解代谢，而NADPH主要用于合成代谢和氧化应激过程^[38]。多碳源耦合发酵可更精细地调控这些辅因子的生成和消耗，以实现氧化还原平衡的进一步优化^[39]。

不同碳源在代谢过程中会产生不同比例的NADH与NADPH。其中，木糖经磷酸戊糖途径代谢可产生更多NADPH，这一特性对于木糖醇等需要大量还原力的生物合成途径尤为有利；而葡萄糖的代谢则主要通过糖酵解途径生成NADH。因此，通过葡萄糖与木糖的混合利用微生物能够根据目标产物的特定还原力需求更为灵活地调控胞内NADH与NADPH的供应比例^[40]。大量研究证实，胞内NADPH的高效再生是制约生物转化过程效率提升的主要限制因素之一^[41-42]，传统的静态调控手段通常难以精准匹配动态的还原力需求，容易导致胞内NADPH/NADP⁺氧化还原对比例失衡，进而对细胞的正常生长与目标产物的生物合成产生不利影响^[41]。Harth等^[42]以酿酒酵母(Saccharomyces cerevisiae)多碳源耦合发酵合成L-半乳糖酸为模型，通过人工设计重构代谢途径定向调控胞内还原力供给，辅以酶工程改造改变关键酶辅酶偏好特性，精准匹配多碳源代谢与产物合成的还原力需求，实现胞内氧化还原平衡及目标产物高效合成。Wang等^[43-44]构建CO₂-甲酸协同转化体系与酿酒酵母原料利用平台，补足还原力、优化碳同化效率，显著提升脂肪酸、乙醇等产物产量。另有研究耦合木糖代谢与Rubisco途径，结合木糖、麦芽糖共利用策略，改善辅酶失衡问题，有效提升木糖利用率与乙醇产率^[45]。

此外，多碳源协同发酵还可通过将完整代谢途径模块化拆分至不同微生物菌株中，有效减轻单一底盘细胞的代谢负担，进而更为精准地调控胞内整体的氧化还原平衡状态^[46]。例如，在大肠埃希氏菌共利用葡萄糖与甲酸的相关研究中通过对目标合成途径进行系统性优化，可使丙酮酸产量得到显著提升^[47]。这一研究结果充分表明，在混合碳源发酵体系中通过对代谢网络进行精细的动态调控，能够实现胞内碳通量与还原力供给的协同增效。

1.3 碳分解代谢物阻遏的分子机制与解除策略

碳分解代谢物阻遏是微生物在长期进化过程中形成的一种普遍调控机制。当环境中同时存在多种可利用碳源时，微生物通常会优先利用葡萄糖等有利生长的优势碳源。该机制主要通过转录水平的调控发挥作用，在葡萄糖存在的条件下会显著抑制木糖、乳糖、甘油等其他非优势碳源代谢相关基因的表达。这会导致微生物出现典型的二次生长现象，使得整体发酵周期延长，进而显著降低工业生产过程中的整体生产效率^[48]。

根据调控元件的不同CCR的分子机制主要可分为两大类：一类依赖于磷酸烯醇丙酮酸-糖磷酸转移酶系统(phosphoenolpyruvate-sugar phosphotransferase system, PTS)的磷酸化状态^[49]，另一类则通过CreA等转录因子介导转录水平的调控。在大肠埃希氏菌中PTS系统是介导CCR效应的核心元件。其通过精准调控EIIA蛋白的磷酸化状态，进而影响腺苷酸环化酶的催化活性与胞内cAMP的合成水平，最终通过cAMP受体蛋白(cAMP receptor protein, CRP)介导乳糖操纵子等非优势碳源代谢基因的表达调控^[49]。

为了克服CCR对多碳源耦合发酵的限制，研究人员开发了多种抑制解除策略。通过敲除CCR的关键调控基因(如pstG^[50]、crr^[51]、ccpA^[52]、scrI^[53])开发了同时利用多种碳源的菌株。通过适应性进化和基因改造，研究人员可提高大肠埃希氏菌对木糖的利用效率，并改善葡萄糖/木糖的共利用，从而提高乙醇产量^[54]。CCR的调控模式并非绝对固化，而是具备显著的灵活性与环境适应性。这一结论可通过非葡萄糖碳源的激活效应得到直接佐证：在特定培养条件下向体系中添加少量葡萄糖并不会触发经典CCR效应中对非偏好碳源的代谢抑制，反而可有效激活微生物对半乳糖等非葡萄糖碳源的分解代谢通路，最终实现对CCR阻遏效应的显著缓解^[55]。

1.4 碳通量重定向与守恒机制

碳通量重定向是代谢工程中的核心策略，旨在将碳流导向目标产物的合成途径，同时维持细胞的正常生长和活力^[56]。在多碳源耦合发酵中这种重定向尤为关键，因为它需要平衡不同碳源的利用并协调多个代谢途径。

碳守恒调控机制是保障碳原子从底物到目标产物转化过程中实现最大利用效率的核心机制。理论上，同型乳酸发酵途径的碳收率可达到100%，这是因为该途径能够将葡萄糖分子中的全部6个碳原子完整保留在乳酸产物中^[57]。然而，在复杂发酵体系中碳损失是常见挑战，例如以CO₂形式逸出^[58-59]。乙醇、甘油等高还原度非发酵性底物的引入，能够有效减少上述碳流失。这是由于这类碳源的氧化程度更低，在胞内代谢过程中产生的CO₂量显著低于葡萄糖等传统六碳糖底物。

在工业应用中有多种策略可实现碳通量的重定向与守恒。常见策略包括关键限速酶过表达与竞争性旁路敲除，通过增强限速酶活性或阻断竞争支路引导碳流流向目标产物合成途径。例如，在酿酒酵母中同步整合外源β-葡萄糖苷酶和β-木糖苷酶基因可实现纤维素来源糖类的共发酵，提升碳利用效率^[60]。辅酶NAD(P)H在碳通量维持过程中作用突出，像NADP⁺依赖的甘油醛-3-磷酸脱氢酶(glyceraldehyde-3-phosphate dehydrogenase, Gapdh)可提升3-羟基丙酸(3-hydroxypropionic acid, 3-HP)的产量，这是由于甘油作为底物可推动NADPH的再生^[61]。通过构建合成微生物群落打造细胞工厂可将复杂代谢途径拆分至不同微生物中，每种微生物承担一个特定步骤，可降低单一菌株的代谢负担，提升整体碳利用效率^[62]，譬如可构建利用甲烷与木质纤维素糖合成四氢嘧啶的甲基营养菌平台。甲烷代谢可富余供给NADPH，却存在碳骨架不足；木质纤维素糖类则能补充碳骨架并产生NADH，二者还原力与碳代谢天然互补。通过代谢途径人工重构实现多碳源耦合发酵，精准匹配胞内还原力分配，大幅提升碳源利用率与目标产物合成效率，为甲烷和生物质糖联产胞外酶等高附加值产物提供了理论支持^[63]。使用甘油这类非发酵性底物可减少传统发酵流程中CO₂的生成，提升碳原子经济性^[64]，例如在双室生物反应器系统中将乙醇发酵产生的CO₂用于琥珀酸合成，可展现出碳循环利用的潜力^[65]。

综上所述，混合碳源发酵通过能量与热力学调控、氧化还原稳态适配、解除碳代谢阻遏、碳通量优化多重机制协同作用，有效弥补单一碳源发酵缺陷，显著提升碳源利用效率与产物合成效能，为微生物发酵工业高效生产高价值产物提供了完善的理论支撑与技术思路(表1)。

2 混合碳源发酵生产高值化学品的优化策略与应用进展

收起

在“双碳”目标持续推进与石化资源替代需求日益迫切的背景下，微生物发酵驱动的生物制造已成为绿色合成高附加值化学品的核心技术路径，是推动化工产业低碳转型的重要方向。碳源是微生物菌体生长与目标产物合成的核心物质基础，其利用效率与原料成本直接决定发酵过程的经济性与工业化可行性。当前单一碳源发酵体系普遍存在技术瓶颈：粮食基葡萄糖面临原料成本高以及“与人争粮”的产业约束；木质纤维素来源的木糖等五碳糖易受碳代谢阻遏效应影响，天然利用效率不足；甲酸、甲醇、合成气以及乙酸等一碳和二碳底物虽来源广泛，低碳属性突出，却存在微生物代谢适配性差、产物合成得率低等问题。混合碳源发酵可通过多底物协同代谢实现不同碳源的优势互补(图2)，可有效突破单一碳源的代谢局限，在降本增效和实现工业副产碳资源高值化利用等方面优势显著，已成为生物制造领域的研究热点。

2.1 有机底物与一碳化合物的氧化还原协同体系

甲酸作为典型的一碳(C1)化合物，在微生物混合营养代谢中可发挥多重核心生理功能：可经甲酸脱氢酶(formate dehydrogenase, Fdh)催化氧化生成NAD(P)H，为胞内代谢提供电子与能量；可通过叶酸介导的C1代谢途径进入碳固定网络，为生物大分子合成直接提供碳骨架；还可在厌氧/微氧条件下缓解胞内氧化还原失衡，减少副产物积累，降低发酵过程的氧依赖。在甲酸与葡萄糖共利用的混合营养体系中葡萄糖主要承担碳骨架供给与基础能量供应的功能，甲酸则作为辅助碳源与电子供体实现碳代谢与能量产生的解偶联，进而显著提升碳利用效率，从而减少CO₂释放，强化目标产物的合成效率。目前，基于不同微生物底盘的甲酸-糖耦合代谢体系已得到广泛开发，为低碳生物制造提供了重要的技术支撑。

2.1.1 大肠埃希氏菌甲酸-糖耦合体系的代谢工程改造与产物合成

大肠埃希氏菌作为代谢工程改造的经典模式原核底盘，是甲酸-糖耦合发酵体系构建中应用最广泛的宿主。针对葡萄糖与甲酸协同利用效率不足的问题，Hu等^[47]通过代谢工程改造实现丙酮酸摩尔得率达1.88 mol/mol葡萄糖(糖酵解理论得率的94%)；整合亚磷酸盐脱氢酶介导的NADH再生系统后，L-苹果酸摩尔得率提升至1.65 mol/mol葡萄糖，为高值有机酸合成提供了参考。针对高还原力需求产物的胞内还原力供给瓶颈，Bertrand等^[66]基于甲酸与氢气的电子解偶联策略阻断丙酮酸脱羧分支途径，利用氢气(86.60%电子供给)与甲酸盐(98.40%电子供给)实现还原力高效补偿，驱动碳通量重定向至乙醛酸旁路，使甲羟戊酸产量提升57.60%，甲酸电子利用率接近99%，证实该体系适用于萜类与药物中间体合成。另有研究通过表达甲酸同化关键酶并筛选获得K4M突变株，甲羟戊酸(mevalonate, MVA)合成水平达3.80 g/L，证实葡萄糖在菌体生长阶段对甲酸利用的辅助作用^[67]。

2.1.2 真核微生物甲酸-糖耦合体系的代谢工程改造与产物合成

真核微生物凭借独特代谢特征在甲酸-糖耦合体系中展现出良好应用价值。解脂耶氏酵母(Yarrowia lipolytica)兼具优异耐酸能力与脂质合成优势，已成为核心真核底盘。Tao等^[68]将碳酸酐酶介导的CO₂再循环通路与甲酸代谢通路结合，构建FDH-CA工程菌株，实现琥珀酸产量97.54 g/L、得率0.64 g/g底物，为闭环碳循环搭建提供了新方向；Van等^[69]研发绿色电力驱动的闭合碳循环技术，电催化CO₂还原为甲酸后与糖共饲喂酵母，在甲酸/糖物质的量比5:1条件下，300 L中试产物得率比单一糖源体系高20%，为近零碳排放生物制造提供了可行路径。Liu等^[70]利用甲酸、CO₂基因工程改造大肠埃希氏菌，整合多功能代谢模块并建立两段式发酵；以甲酸与CO₂为主碳源、微量葡萄糖为辅，琥珀酸产量达2.46 g/L，碳得率0.43 mol/mol；木糖替代葡萄糖后，摇瓶产量2.62 g/L、发酵罐产量7.02 g/L，碳得率与CO₂固定量同步提升。Wang等^[43]搭建CO₂和甲酸协同转化通路，解决碳原子经济性不佳与木糖代谢还原力失衡问题，实现C1同化效率11.24%、甲酸消耗速率0.48 g/(L·h)，脂肪酸效价达10.10 g/L，较对照分别提高21.80倍和33.07倍。后续研究构建了酿酒酵母2G-3G原料高效利用平台，利用甲酸补充还原力和胞内CO₂池实现9.20%的一碳同化效率，使乙醇、游离脂肪酸和长叶烯产率分别提高18.40%、49.0%和近100%^[44]。针对木质纤维素生物乙醇生产的木糖转化效率低和辅酶失衡难点，整合突变型木糖还原酶-木糖醇脱氢酶(mutated xylose reductase-xylitol dehydrogenase, mXR-XDH)代谢模块与磷酸核酮糖激酶-核酮糖二磷酸羧化酶耦合固碳途径(phosphoribulokinase-ribulose-1,5-bisphosphate carboxylase, PRK-Rubisco)，采用麦芽糖/木糖共利用与原位CO₂固定偶联策略，使木糖消耗率达1.10 g/(L·h)，乙醇产率0.47 g/g，较对照分别提高104%和15%^[45]。

2.1.3 非模式微生物甲酸-糖耦合体系的应用拓展

多种非模式微生物在甲酸-糖耦合体系中展现出良好场景适配性。Litsanov等^[71]通过敲除副产物基因、共表达丙酮酸羧化酶与甲酸脱氢酶，使谷氨酸棒状杆菌(Corynebacterium glutamicum)的琥珀酸浓度达1 134 mmol/L，摩尔得率1.67 mol/mol，副产物乙酸得率仅0.10 mol/mol，该体系可显著降低发酵供氧需求，适用于厌氧/微氧生物合成。产油真菌Umbelopsis isabelline在甲酸/葡萄糖质量比3.90:1条件下生物量与脂质合成量分别提升20%与70%；¹³C标记证实甲酸可同时提供还原力、能量并参与碳骨架合成，还能调控脂肪酸组分分布^[72]。恶臭假单胞菌(Pseudomonas putida) KT2440以甲酸为能量补充碳源时，生物量提升25%，碳利用效率提高19%，适用于极端环境下的生物合成^[73]。

2.1.4 原核宿主甲醇-糖耦合代谢的构建与产物合成

除甲酸外，甲醇作为另一类优质非粮C1原料，其与糖的耦合利用策略也得到广泛研究。Witthoff等^[74]在大肠埃希氏菌中异源表达甲醇脱氢酶(methanol dehydrogenase, Mdh)与RuMP途径关键酶，阻断甲醛氧化支路，使甲醇同化率提升8%-25%；葡萄糖/甲醇共底物条件下，甲醇消耗速率达1.70 mmol/h，胞内3%-10%的碳来自甲醇。另有研究构建修饰的丝氨酸循环，实现甲醇/木糖强制共利用，乙醇产量达1.89 g/L^[75]。黏质沙雷氏菌(Serratia marcescens)天然具备萜类高耐受性，Wang等^[76]通过代谢工程优化，以甲醇/葡萄糖为共底物实现香叶醇产量574.12 mg/L，碳足迹降低40%；进一步整合双没药醇合成酶后，α-红没药醇产量达1 256.41 mg/L，为目前甲醇基萜类摇瓶发酵最高水平。Wu等^[77]构建体外木糖/甲醇耦合合成L-乳酸途径，实现100%理论碳得率，经关键酶定向进化后碳转化效率达88.75%，L-乳酸产量6 g/L。随后在大肠埃希氏菌中构建新型甲醇同化途径，实现2,4-二羟基丁酸(2,4-dihydroxybutanoic acid, 2,4-DHB)产量14.60 g/L^[78]。Sun等^[79]整合原核Mdh与真核二羟丙酮合酶(dihydroxyacetone Synthase, Das)构建杂合途径，实现甲醇向中心代谢物转化并伴随NADH再生，使(R)-1,3-丁二醇摇瓶产量达5.79 g/L，批式发酵产量13.71 g/L，碳摩尔得率0.35 mol/mol；¹³C标记证实甲醇可同时作为碳源与还原力供体。

2.1.5 毕赤酵母甲醇-糖共代谢的机制解析与优化

在真核底盘中毕赤酵母(Pichia pastoris)是甲醇代谢的模式菌株。Jordà等^[80]采用代谢组与非稳态¹³C通量分析，解析了葡萄糖/甲醇(4:1)的共代谢机制，结果显示混合碳源条件下细胞密度提升20%，磷酸戊糖途径(pentose phosphate pathway, PPP)氧化支路通量占Glc6P通量的55%，甲醇同化率达46%，系统能量效率显著提升。Li等^[81]基于转录组与代谢组分析揭示了毕赤酵母中甲醇与木糖代谢的正相关关系，通过磷酸盐供给优化实现甲醇利用率提升34%，木糖利用率提升357.50%，生物质干重达7.50 g/L。

综上所述，甲酸、甲醇等C1底物与糖基底物的耦合利用(表2)可通过协同供给还原力、补充碳骨架和调控胞内氧化还原平衡，有效突破单一碳源发酵的技术瓶颈，实现高值化学品的高效绿色合成。未来，通过关键酶的定向进化、代谢途径的动态精准调控以及发酵工艺的放大优化有望进一步提升C1-糖耦合体系的代谢效率与经济性，为绿色生物制造的工业化落地提供有力支撑。

2.2 有机底物与二碳化合物的物质-能量互补利用体系

将有机底物与以乙酸为典型代表的二碳化合物结合形成的能质互补体系是合成生物学与代谢工程领域优化微生物细胞工厂性能的核心策略之一，这套体系的核心特点是能让不同底物的功能实现精准划分：葡萄糖和木糖等有机底物的主要功能是为微生物代谢提供碳骨架与基础能量，乙酸则充当辅助碳源与还原力供体，通过补充NADH/NADPH调控胞内的氧化还原平衡，完成碳流的重新分配，从而使得碳损失控制在较低水平，并且提高菌株的鲁棒性，最终优化目标产物的产量、得率与生产效率^[82]。上述策略已在酵母与细菌等多种微生物底盘中得到普遍应用，为高值化学品的绿色生物制造提供了技术支持。

2.2.1 大肠埃希氏菌乙酸-糖能质互补体系的构建与高值化学品合成

大肠埃希氏菌作为经典原核模式的底盘，其能质互补体系的应用核心聚焦于碳流重定向与模块化途径构建，已在多类高值化学品合成中取得突破性进展。Yu等^[83]构建工程菌株NZ-Gly303，采用模块化组装碳保守代谢途径实现乙酸与葡萄糖的协同高效转化，分批补料发酵中乙醇酸产量达73.30 g/L，产率1.04 g/(L·h)，总碳摩尔产率达理论值的80%，为乙醇酸绿色合成提供了高效方案。Da等^[84]在丙酮酸合成研究中通过失活poxB、pflB、aceEF基因阻断竞争性途径，删除ldhA和mgsA消除乳酸副产物，工程菌株可从10.27 g/L葡萄糖与2 g/L乙酸中合成9.61 g/L丙酮酸。柠檬酸合成方面，通过敲除磷酸转移酶系统、优化乙酸同化途径及底物比例实现葡萄糖与乙酸的独立碳流定向合成，摇瓶产量达5 g/L，摩尔产率0.87 mol/mol^[85]。另有研究构建氧化还原耦合途径并过表达乙酰辅酶A合成酶，实现葡萄糖与乙酸的化学计量共利用，丁酰丁酸酯产量达29.02 g/L，碳产率43.30%，为目前该产物生物合成的最高纪录^[86]。谷氨酸棒杆菌通过丙二酰辅酶A途径改造从葡萄糖和乙酸中合成3-羟基丙酸，产量较单一碳源提升12.57倍^[87]。综上所述，乙酸作为来源广泛的廉价工业副产物，可通过平衡胞内辅因子含量、减少碳流失等机制显著提升大肠埃希氏菌的产物合成效率。

2.2.2 酿酒酵母乙酸-糖能质互补体系的还原力调控与代谢优化

酿酒酵母作为遗传背景清晰、工业应用最成熟的真核模式底盘，其能质互补体系的构建核心集中于胞内还原力平衡调控与乙酸代谢途径的系统优化。Khattab等^[82]通过多基因组合改造实现多底物协同高效发酵：敲除乙醛脱氢酶基因ald6减少乙酸无效生成，异源引入树干毕赤酵母木糖同化途径并共表达木糖激酶降低副产物积累，导入肠炎沙门氏菌乙酰化乙醛脱氢酶(Salmonella enterica acetylating acetaldehyde dehydrogenase, SeEutE)促进乙酸代谢与还原力耦合生成，同时敲除线粒体NADH脱氢酶基因阻断还原力无效消耗，过表达单羧酸转运体及突变型乙酰辅酶A合成酶强化乙酸摄取与同化；最终在微需氧条件下实现甘油、木糖、乙酸与葡萄糖的共发酵，4种底物总乙醇转化率超过理论最大值的95%，证实了多底物协同与还原力精准调控的叠加效应；同时进一步优化该体系，通过敲除gpd1弱化甘油合成、敲除ald6阻断乙醛向乙酸转化、删除nde1和nde2减少还原力流失，同时异源表达SeEutE增强乙酸代谢。在含100 g/L葡萄糖与4 g/L乙酸的培养基中，工程菌株可消耗1 g/L乙酸；经规律成簇的间隔短回文重复序列系统及相关蛋白9 (clustered regularly interspaced short palindromic repeats, CRISPR-associated 9, CRISPR-Cas9)精准整合改造后，低氧条件下发酵速率提升150%-343%，乙醇产量接近理论值，且菌株展现出乙醇诱导型絮凝特性。工程化酿酒酵母可实现低聚木糖、木糖与乙酸的协同利用，乙醇产量较单一糖源提升84%^[88]；上述研究表明，调控乙酸利用途径与胞内还原力平衡可有效避免氧化应激与碳源的无效流失，是提升酿酒酵母发酵性能的关键。

2.2.3 非常规微生物底盘乙酸-糖能质互补体系的应用拓展

非常规酵母与细菌底盘的能质互补体系，进一步拓展了该策略在木质纤维素转化等高值场景的应用潜力。解脂耶氏酵母作为典型产油酵母，Lin等^[89]通过整合异源木糖利用双路径、β-胡萝卜素合成模块、内源乙酰辅酶A合成路线及非氧化糖酵解通路构建木糖与乙酸协同代谢工程菌株，在25 g/L木糖和25 g/L乙酸钠培养基中β-胡萝卜素产量达776.90 mg/L (70.40 mg/g干细胞重)，较单一木糖底物提升4.20倍；另一研究中，工程菌株在葡萄糖/乙酸混合底物中聚羟基丁酸酯(polyhydroxybutyrate, PHB)积累量达细胞干重的10.20%，分批补料发酵产量达7.35 g/L^[90]。

东方伊萨酵母(Issatchenkia orientalis)具有天然耐酸性优势，工程菌株IoDY01H通过整合木糖利用与β-丙氨酸合成途径实现木糖与乙酸协同转化，在pH 5.50条件下以大麻秸秆水解物为原料合成3-羟基丙酸，产量达8.70 g/L^[91]。蓝色嗜盐单胞菌(Halomonas bluephagenesis)作为极端环境微生物底盘，Zhang等^[92]通过优化甲羟戊酸合成酶基因整合表达、利用葡萄糖与乙酸能质互补协调胞内还原力与能量平衡，结合成簇规律间隔短回文重复序列干扰技术(clustered regularly interspaced short palindromic repeats interference, CRISPRi)筛选关键调控靶基因并引入非氧化糖酵解途径，最优工程菌株摇瓶中甲羟戊酸产量达13.90 g/L，5 L反应器开放式流加发酵中进一步提升至121 g/L，创下该产物生物合成的最高纪录。

2.2.4 微藻与特殊功能细菌能质互补体系的普适性研究

微藻与特殊功能细菌的能质互补体系验证了该策略的普适性。雨生红球藻(Haematococcus pluvialis)在葡萄糖与乙酸钠混合营养条件下生物量较单一碳源分别增加77.10%和40.40%^[93]；新绿藻(Neochloris oleoabundans)在7.50 g/L葡萄糖/乙酸钠和藻类最小盐培养基(bold basal medium, BBM)中生物量达1.75 g/L，其中蛋白质含量占24%-31%，脂质含量34.40%，核磁共振分析显示游离脂肪酸占比11%^[94]。红冬孢酵母(Rhodosporidium toruloides)在葡萄糖与乙酸或木糖与乙酸共发酵中展现同步底物消耗特性，脂质含量达细胞干重的56%，脂肪酸组成与可可脂相似^[95]。凝结芽孢杆菌(Bacillus coagulans )发酵中补充10 g/L乙酸可显著上调三羧酸循环等途径基因表达，生物量与乳酸产量分别提升17.60%和15.30%^[96]。

2.2.5 乙酸-糖能质互补体系在木质纤维素全利用中的应用进展

乙酸-糖能质互补体系已成功拓展至木质纤维素全利用场景。在木质纤维素水解液的制备过程中半纤维素组分(以乙酰化木聚糖为主要成分)的脱乙酰化反应会释放大量乙酸；而纤维素在纤维素酶的酶解作用下则主要生成葡萄糖和木糖。由此形成的水解液体系天然存在有机底物与乙酸的混合碳源，为微生物的协同碳源利用提供了独特的物质基础。作为目前最具潜力替代粮食基原料的廉价可再生碳源，木质纤维素水解液的应用实践表明，有机底物与乙酸的能质互补利用体系已从实验室阶段的纯底物研究，成功推进至接近工业化应用的复杂天然底物体系，为绿色生物制造的规模化落地提供了切实可行的技术路径。如土曲霉(Aspergillus terreus)从纤维素水解液中合成衣康酸达39.60 g/L，质量得率为0.40 g/g，实现木质纤维素资源的全利用与高值化转化^[97]。

综上所述，有机底物与二碳化合物的能质互补体系通过精准的途径工程、基因调控与底物比例优化有效解决了微生物发酵过程中的氧化还原失衡、碳源损失和毒性抑制等关键瓶颈(表3)，推动了从实验室研究到工业应用的技术转化。未来，结合多组学分析与人工智能(artificial intelligence, AI)辅助代谢路径设计等前沿技术将为绿色生物制造的可持续发展提供更广阔的应用前景。

2.3 气-液双相底物耦合发酵体系

2.3.1 合成气发酵技术概述与应用优势

合成气发酵是一类利用厌氧微生物将富含一氧化碳(CO)、二氧化碳(CO₂)和氢气(H₂)的气态底物转化为生物燃料与高值化学品的可持续生物加工技术。其原料可来源于生物质气化气、钢铁工业尾气和电石炉气等多种工业副产物，不仅可实现废弃碳资源的高值化利用，还能有效规避传统糖基发酵对粮食资源的依赖，是助力“双碳”目标实现的重要技术路径。

可利用合成气的微生物均具备一氧化碳脱氢酶(carbon monoxide dehydrogenase, Codh)与氢化酶(hydrogenase, H₂ase) 2个核心功能酶。氢化酶催化H₂氧化生成还原当量提供电子；CODH催化水煤气变换反应，将CO与H₂O转化为CO₂并释放还原当量，为碳固定与产物合成供能。目前可利用合成气的微生物主要包括产乙酸菌、光合细菌与好氧羧基营养菌，其中产乙酸菌应用最广泛，通过还原性乙酰辅酶A (Wood-Ljungdahl, WL)途径固定碳，光合细菌与羧基营养菌则主要依赖还原型戊糖磷酸(Calvin-Benson-Bassham, CBB)循环^[98]。

2.3.2 Wood-Ljungdahl固碳途径代谢特征

WL途径是已知ATP消耗最低的厌氧碳固定途径，也是合成气转化为乙酰辅酶A的核心路径，分为甲基与羰基2个分支。甲基分支中，CO₂经甲酸脱氢酶还原为甲酸，与四氢叶酸结合生成甲酰-四氢叶酸(formyl-tetrahydrofolate, formyl-THF) (消耗1 mol/L ATP)，再经连续还原反应生成甲基-四氢叶酸(5-methyltetrahydrofolate-tetrahydrofolate, 5-methyl-THF)，最终由钴胺素铁硫蛋白转移至一氧化碳脱氢酶-乙酰辅酶A合酶(carbon monoxide dehydrogenase-acetyl-coA synthase, CODH-ACS)复合体。羰基分支中，CO₂经CODH催化还原为羰基，或直接由CO转化而来。最终在CODH-ACS复合体中羰基、甲基与辅酶A结合生成乙酰辅酶A^[99]。

2.3.3 基于乙酰辅酶A的产物定向合成与代谢改造

乙酰辅酶A是合成气发酵产物多样化的核心节点，可转化为多种高值化学品。食一氧化碳梭菌(Clostridium carboxidivorans)过表达adhE2单基因可使乙醇产量达3 g/L (较野生型提高50%)，共表达adhE2与fnr可使丁醇产量提升至0.35 g/L^[100]。调控培养条件后己醇产量可达1.90 g/L，外源补充乙醇后进一步提升至2.34 g/L。永达尔梭菌(Clostridium ljungdahlii)经多基因簇整合改造可直接以CO₂/H₂为唯一碳源合成己醇与丁醇^[101]。对永达尔梭菌进行多基因簇整合改造，成功以CO₂/H₂为唯一碳源能源合成己醇与丁醇，2 L发酵罐中己醇产量达0.39 g/L^[102]。同时将醛脱氢酶(aldehyde dehydrogenase, Aldh)同工酶精准失活改造，成功以合成气(CO/CO₂/H₂)为唯一碳源能源合成乙醇，生物反应器中乙醇产量较野生型提升2-5倍，乙酸选择性下降60%-80%^[103]。Yang等^[104]采用三步式代谢工程改造永达尔梭菌，强化乙醇合成、激活乙酸再同化、阻断2,3-丁二醇副产物。工程菌以合成气为唯一碳源，2 L罐乙醇产量达30.10 g/L。此外，丙酮丁醇梭菌通过内源途径可实现3-羟基丁酸产量9.25 g/L^[105]，为乙酰辅酶A向高值化学品转化提供了新的可行路径。

2.3.4 合成气发酵传质瓶颈与增效调控策略

合成气发酵为典型的气-液两相反应体系，CO、H₂等气态底物在水相中的溶解度极低，气液传质效率是限制底物利用率与发酵效率的核心瓶颈。目前研究者主要通过反应器结构优化、操作参数调控和培养基改良等策略提升传质效率与发酵性能。在反应器优化方面，搅拌釜反应器是目前应用最广泛的反应器体系，通过优化搅拌速率、气体流量与回流系统^[106]，可有效提升气液接触效率，例如筛选获得的霍氏肠杆菌(Enterobacter hormaechei) RF2菌株，在厌氧培养瓶中可合成21.80 g/L生物乙醇，在优化后的搅拌釜反应器中产量可达25.30 g/L，这也是首次报道该菌株在合成气发酵中试规模应用的潜力^[107]；双反应器策略将菌体生长与产物合成阶段分离，可使黏液真杆菌(Eubacterium limosum)的碳转化率提升至9.30 mmol/h，乙酸浓度达34.40 g/L^[108]；此外，合成气发酵与CO₂电解技术结合形成串联工艺，通过气体扩散电极结构工程可制备组成可调的合成气，为中链脂肪酸合成提供可控底物供给。

近年来，合成气发酵技术在代谢机制解析、工程菌株改造及发酵过程优化等关键维度取得显著进展，为工业尾气等废弃碳资源高值化转化与碳中和绿色生物制造体系构建提供了可行技术路径(表4)。然而，该技术工业化仍面临气液传质效率低、产物合成速率与浓度不足、菌株对有毒底物耐受性差导致发酵稳定性弱等核心瓶颈。未来需通过多学科交叉构建高性能工程菌株，开发新型反应器与原位分离技术，并结合电催化-生物转化耦合的人工碳循环技术，推动其规模化工业应用。

2.4 非糖基短链碳源耦合利用体系

在合成生物学与代谢工程领域，甲醇、甲酸及乙酸等非糖基短链底物正逐步替代传统糖类碳源，成为可持续生物制造领域的研究热点。此类底物可来源于工业有机副产物以及CO₂电/化学还原产物等多种非粮渠道，不仅原料成本低廉，还可有效降低生物制造过程对粮食资源的依赖，契合“双碳”目标下绿色制造的发展需求。单一短链底物的微生物转化普遍存在瓶颈，如甲醇和甲酸等C1底物存在同化效率低、中间代谢产物毒性强、胞内还原力与能量代谢失衡的问题，乙酸等C2底物则易引发pH胁迫、菌体生长与产物合成耦合度低等局限。通过不同短链底物的耦合利用，可实现底物间的代谢互补，平衡胞内碳骨架供给、还原力与能量代谢，有效缓解单一底物的毒性效应与代谢限制，提升碳利用效率与目标产物合成水平。该策略的核心在于通过同化途径重构、关键酶表达优化与发酵环境调控实现多底物的协同同化，目前已在多种微生物底盘中得到验证与应用。

2.4.1 C1-C1与C1-C2底物耦合的代谢构建与产物合成

针对C1底物协同利用，研究者已在毕赤酵母与酿酒酵母中异源构建甲醇/甲酸同化的甲醇-甲酸氧化耦合还原性甘氨酸(methanol-formate oxidation coupled reductive glycine, MFORG)途径，实现甲醇、甲酸盐与CO₂的协同转化。工程化毕赤酵母可合成0.71 mg/L 5-氨基乙酰丙酸(5-aminolevulinic acid, 5-ALA)与0.21 g/L乳酸，酿酒酵母工程菌株的ALA与乳酸产量分别达1.67 mg/L与0.07 g/L。¹³C示踪实验显示，产物中来自甲醇和CO₂的碳元素标记占比超过91%，验证了该途径对C1底物的高效定向转化能力，同时增强了宿主对C1底物的耐受性，为酵母底盘的C1底物高效利用提供了新方向^[109]。原核甲基营养菌作为底盘时，C1与C2底物耦合也展现出显著优势。甲基营养菌扭托甲基杆菌(Methylorubrum extorquens) AM1作为甲醇利用模式菌株，经改造合成紫罗兰素，仅以甲醇为唯一碳源时产量为11.70 mg/L，经随机诱变和定点突变优化关键途径后产量翻倍，进一步耦合乙酸盐作为辅助碳源后，产量大幅提升至118 mg/L；该研究证实C1/C2底物耦合可突破单一甲醇底物造成的莽草酸途径代谢瓶颈，解决菌体生长滞后问题，为甲基营养菌底盘的高值化应用提供新方法^[110]。在E. limosum中，Wood等^[111]发现甲醇与甲酸的比例可定向调控产物的合成方向，分批发酵中低甲醇-甲酸比例的底物组合以丁酸盐合成为主，底物消耗速率达220 mmol/(g DCW·d)，提高甲醇比例后代谢通量转向丁醇合成，丁醇产量达2 mmol/L；底物消耗呈明显的两阶段特征，发酵初期以甲酸利用为主，后期需平衡甲醇与甲酸的消耗比例，提示丁醇合成来自溢出代谢，而甲醇在胞内NADH供给中具有独特作用，该研究解析了多底物耦合的动态代谢规律，为厌氧发酵过程的底物调控提供了理论支撑。

2.4.2 甲酸-乙酸耦合体系在生物聚合物与生物燃料合成中的应用

甲酸与乙酸耦合利用可充分结合C1底物的还原力供给优势与C2底物的碳骨架供给优势，在生物聚合物及生物燃料合成领域展现出巨大应用潜力。在生物聚合物合成方面，从土壤中分离的格萨假单胞菌(Pseudomonas gessardii)天然具备利用乙酸盐与甲酸盐的能力，以5 g/L乙酸盐为碳源时菌体生长状态最优。经表达C1同化关键酶后，菌株对甲酸盐的同化速率显著提升，补料分批发酵中合成的中长链聚羟基脂肪酸酯(medium chain length-poly-β-hydroxyalkanoate, mcl-PHA)产量达0.40 g/L，占细胞干重的30.43%，为工业废酸等廉价底物资源化合成生物可降解材料提供了可行技术路径，拓展了短链底物耦合体系的适用场景^[112]。生物燃料合成研究中，酿酒酵母经代谢工程改造后以CO₂衍生物甲酸和乙酸充当底物，合成生物燃料前体游离脂肪酸(free fatty acids, FFAs)。Wang等^[113]解析并优化了菌株利用甲酸、乙酸的途径与FFA合成策略，最终构建的工程菌株的FFA滴度提升8倍，最高为6.60 g/L，给废弃碳源资源化与绿色燃料生产提供了新路径。同时Bi等^[114]又搭建了电催化-微生物转化耦合系统，借助固态电解质电催化体系将CO₂还原为乙酸与甲酸，再将二者作为碳源饲喂工程化解脂耶氏酵母，试验中乙酸为β-法尼烯合成提供碳骨架，甲酸为胞内代谢提供还原力，额外添加多聚磷酸盐构建ATP再生系统；最终，该体系实现了14.80 g/L β-法尼烯的高效合成，全流程光能到产物的能源转化效率达0.75%；该研究将电催化CO₂固定与微生物转化过程进行了高效耦合，为可再生电力驱动的绿色生物燃料合成提供了全新的发展模式。

2.4.3 短链底物耦合的核心代谢机制解析

为进一步提升短链底物的同化效率，研究者对底物耦合的核心代谢机制进行了深入挖掘，在大肠埃希氏菌中，Zelcbuch等^[115]通过激活丙酮酸甲酸裂解酶(pyruvate formate lyase, Pfl)的逆反应实现了乙酸盐与甲酸盐的高效同化，在乙醛酸循环分流缺陷的菌株中，Pfl逆反应可驱动乙酸与甲酸合成丙酮酸，显著提升菌株的生长速率，¹³C标记实验证实了菌株生长对Pfl逆反应途径的依赖性，该途径可有效提升胞内ATP的产出效率，为短链底物的同化提供了新的路径。

2.4.4 短链底物的细胞毒性与宿主耐受性研究

短链有机酸的细胞毒性是限制底物耦合体系效率的核心瓶颈之一，不同微生物底盘对乙酸和甲酸的耐受性存在显著差异，混合底物的协同毒性效应也受到广泛关注。产溶剂梭菌相关研究表明，丙酮丁醇梭菌(Clostridium acetobutylicum)对乙酸与甲酸的耐受性阈值较低：3.70-9.70 g/L的乙酸可小幅提升丙酮-丁醇-乙醇(acetone-butanol-ethanol, ABE)发酵产量，但浓度超过11.70 g/L时产物合成显著受抑；甲酸的细胞毒性更强，0.40 g/L的甲酸即可使ABE产量下降77%，1 g/L甲酸可使产量下降25%；当二者共存时(8.70 g/L乙酸和0.40 g/L甲酸) ABE产量从15.10 g/L降至8.60 g/L，呈显著的协同毒性效应。拜氏梭菌(Clostridium beijerinckii)在相同底物条件下可维持稳定的溶剂合成能力，证实其对短链有机酸具有更强的耐受性；该研究为含酸木质纤维素水解物等廉价底物的发酵利用提供了宿主筛选依据，也提示宿主的耐受性改造是短链底物耦合体系优化的重要方向^[116]。

2.4.5 短链底物耦合体系的规模化发酵应用验证

短链底物耦合利用的相关策略已在规模化发酵中得到了综合应用验证，以除草剂前体L-高丝氨酸的合成为例，Zhang等^[117]以大肠埃希氏菌为底盘，通过系统代谢工程改造实现了乙酸盐与甲酸盐的高效协同转化，首先通过敲除分支代谢途径、激活乙醛酸循环和强化L-高丝氨酸合成核心途径，构建了初始合成菌株，再通过自适应进化提升菌株对短链有机酸的耐受性，最后通过弱化糖异生途径优化胞内碳通量分布，在5 L发酵罐的补料分批发酵中仅以乙酸盐与甲酸盐为碳源，实现了15.96 g/L L-高丝氨酸的高效合成，该研究整合了途径重构、耐受性改造和通量优化等多种策略，验证了CO₂衍生短链底物规模化合成高值氨基酸的可行性，为短链底物耦合体系的工业应用提供了范例。

甲醇、甲酸、乙酸等非糖短链底物的耦合利用策略，通过代谢互补突破单一底物的毒性效应与代谢瓶颈，平衡胞内碳骨架、还原力与能量代谢，显著提升非粮碳源转化效率，在生物燃料、可降解材料及医药中间体等高值产物合成中展现出广阔前景(表5)。目前该领域虽已取得诸多进展，但仍面临底盘耐受性与同化效率不足、多底物协同代谢机制未完全解析、规模化工艺稳定性与经济性待优化等挑战。未来需结合多学科技术手段构建高性能工程菌株，开发电催化-生物转化耦合工艺，推动其在绿色生物制造中的应用，助力“双碳”目标实现。

3 总结与展望

收起

合成生物学驱动的多碳源耦合发酵技术在实验室层面已实现较高的碳转化效率，展现出替代传统发酵工艺、提升生物制造碳利用率的巨大潜力。然而，该技术从概念验证走向工业化放大与产业化应用，仍面临底盘适配性、代谢调控精准性、设计理性化程度及过程经济性等多重挑战，亟待从系统层面突破关键技术瓶颈。

底盘细胞在复杂工业环境下的鲁棒性不足，是制约耦合发酵工艺放大的核心问题^[118]。工业生物炼制原料(如木质纤维素水解液与工业尾气等)组分波动显著^[119]，且含有酚类、呋喃类与高浓度有机酸等代谢抑制物^[120-122]，易造成胞内代谢紊乱、生长抑制乃至细胞死亡，难以维持人工构建的多碳源耦合代谢网络稳定运行。因此，亟须推进底盘细胞全局抗逆改造与环境适应性进化研究^[123]。一方面，可借鉴全局代谢网络鲁棒性工程策略，对中心代谢、胁迫响应与能量供给系统进行模块化重构，提升菌株对复杂环境的耐受能力^[124-125]；另一方面，结合适应性实验室进化与理性设计^[126-128]，靶向改造膜转运系统、胁迫感应调控通路及解毒代谢模块，构建可耐受高浓度甲酸、乙酸及复合杂质的高性能底盘^[129-130]，维持底盘菌株的代谢稳态。

多底物代谢流的动态失衡与中间代谢物累积是限制碳转化效率进一步提升的关键障碍^[131]。不同碳源在跨膜转运速率、代谢途径动力学及辅因子需求上存在显著差异^[18,132]，易导致甲醛、乙醛等毒性中间体积累^[133]以及NAD(P)H/ATP平衡失调^[133-134]，造成碳流分配效率下降。未来研究应聚焦智能化动态调控网络构建^[135-136]，开发针对关键中间代谢物(如甲羟戊酸或丙酮酸等)、胞内辅因子及氧化还原状态的高灵敏度生物传感器^[137-139]，耦合光遗传开关与代谢拨动开关等正交基因线路^[140-141]，建立多碳源摄取与代谢流分配的实时反馈系统，实现多底物代谢的自适应协同与精准调控^[142-144]。

数据驱动的理性设计体系尚未完善，仍是制约多碳源耦合体系高效构建的主要短板^[145]。传统菌株改造依赖局部通路优化，难以实现全局代谢通量重排^[146-147]，且设计-构建-测试-学习(design-build-test-learn, DBTL)循环周期长以及试错成本高^[148-149]。随着生物信息学与人工智能技术的深度融合^[150]，研究者基于基因组尺度代谢网络模型(genome-scale metabolic model, GSMM)结合机器学习算法可实现多碳源条件下全局代谢通量的精准预测与关键靶点挖掘^[151-152]；通过深度学习辅助Rubisco和Fdh等关键限速酶的定向进化，可显著提升酶催化效率与底物适配性^[153-155]，从而缩短菌株迭代周期，提升合成生物学设计的理性程度与高通量应用水平^[156]。

基金

收起

国家重点研发计划(2023YFB4203500)
国家自然科学基金(22508012)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

Chong

, Ding

, Qiu

, Qian

, Li

, Pan

, Xu

. All-carbon-atom refinery of oleic acid into bifunctional chemicals using artificial consortia of Escherichia coli strains[J]. ACS Sustainable Chemistry & Engineering, 2022, 10(39): 13125-13132.

[2]

Guo

, Liu

, Bi

, Qi

, Xian

, Zhao

. Using a synthetic machinery to improve carbon yield with acetylphosphate as the core[J]. Nature Communications, 2023, 14: 5286.

[3]

Vásquez Castro

, Memari

, Ata

, Mattanovich

. Carbon efficient production of chemicals with yeasts[J]. Yeast, 2023, 40(12): 583-593.

[4]

Wang

, Wu

, Wang

, Zhang

, Jiang

, Liao

, Gao

, Xu

, Yao

, Wu

, Liu

, Wang

, Zhang

. Microbial carbon utilization for a sustainable future[J]. The Innovation Life, 2025, 3(4): 100159.

[5]

Zhang

, Fei

, Fu

, Lackner

, Zhou

, Tan

. Economic and sustainable revolution to facilitate one-carbon biomanufacturing[J]. Nature Communications, 2025, 16: 4896.

[6]

Usmani

, Sharma

, Awasthi

, Lukk

, Tuohy

, Gong

, Nguyen-Tri

, Goddard

, Bill

, Nayak

, Gupta

. Lignocellulosic biorefineries: the current state of challenges and strategies for efficient commercialization[J]. Renewable and Sustainable Energy Reviews, 2021, 148: 111258.

[7]

Bröker

, Müller

, Prüfer

, Schulze Gronover

. Combinatorial metabolic engineering in Saccharomyces cerevisiae for the enhanced production of the FPP-derived sesquiterpene germacrene[J]. Bioengineering, 2020, 7(4): 135.

[8]

Wang

, Sun

, Yang

. Metabolic engineering Escherichia coli for the production of lycopene[J]. Molecules, 2020, 25(14): 3136.

[9]

, Wang

, Xokat

, Wan

, Gao

, University

, Wang

, Li

, University

. Metabolic engineering of Corynebacterium glutamicum for producing different types of triterpenoids[J]. ACS Synthetic Biology, 2025, 14(3): 819-832.

[10]

Liu

, Huang

, Chen

, Lu

, Tian

, Hu

, Zhao

, Li

, Ji

, Liu

. Metabolic engineering of Yarrowia lipolytica for high-level production of squalene[J]. Bioresource Technology, 2024, 394: 130233.

[11]

, Dai

, Yuan

, Liu

, Tong

, Shi

. Dual pathway enables high-level production of very long-chain fatty alcohol in Rhodotorula toruloides [J]. ACS Synthetic Biology, 2025, 14(7): 2465-2471.

[12]

Fatma

, Hartman

, Poolman

, Fell

, Srivastava

, Shakeel

, Yazdani

. Model-assisted metabolic engineering of Escherichia coli for long chain alkane and alcohol production[J]. Metabolic Engineering, 2018, 46: 1-12.

[13]

Picataggio

, Rohrer

, Deanda

, Lanning

, Reynolds

, Mielenz

, Eirich

. Metabolic engineering of Candida tropicalis for the production of long-chain dicarboxylic acids[J]. Nature Biotechnology, 1992, 10(8): 894-898.

[14]

Park

. Metabolic engineering of Escherichia coli for the production of medium-chain-length polyhydroxyalkanoates rich in specific monomers[J]. FEMS Microbiology Letters, 2002, 214(2): 217-222.

[15]

Wang

, Sun

, Lin

, Ledesma-Amaro

, Wang

, Ji

. Engineering strategies for producing medium-long chain dicarboxylic acids in oleaginous yeasts[J]. Bioresource Technology, 2025, 430: 132593.

[16]

, Zhu

, Zhang

, Wen

. Mid-long chain dicarboxylic acid production via systems metabolic engineering: progress and prospects[J]. Journal of Agricultural and Food Chemistry, 2024, 72(11): 5555-5573.

[17]

Chai

, Tao

, Zhao

, Chen

. Hierarchical metabolic engineering for rewiring cellular metabolism[J]. FEMS Microbiology Reviews, 2025, 49: fuaf047.

[18]

, Chen

, Sheng

, Wang

, Sun

, Yan

, Shen

, Yuan

. Rewiring the microbial metabolic network for efficient utilization of mixed carbon sources[J]. Journal of Industrial Microbiology and Biotechnology, 2021, 48(9/10): kuab040.

[19]

Gleizer

, Ben-Nissan

, Bar-On

, Antonovsky

, Noor

, Zohar

, Jona

, Krieger

, Shamshoum

, Bar-Even

, Milo

. Conversion of Escherichia coli to generate all biomass carbon from CO₂ [J]. Cell, 2019, 179(6): 1255-1263.e12.

[20]

Xiao

, Zhao

, Wang

, Tan

, Wang

, Mao

, Zhang

, Lu

, Hu

, Zuo

, Liu

, Shan

. Sucrose-driven carbon redox rebalancing eliminates the Crabtree effect and boosts energy metabolism in yeast[J]. Nature Communications, 2025, 16: 5211.

[21]

Turlin

, Alván-Vargas

MVG

, Puiggené

, Donati

, Wenk

, Nikel

. Synthetic C₁ metabolism in Pseudomonas putida enables strict formatotrophy and methylotrophy via the reductive glycine pathway[J]. mBio, 2025, 16(9): e01976-25.

[22]

Zhan

, Liu

, Chen

, Zhu

, Li

, Liu

, Zhao

, Chen

, Zeng

. Advancing synthetic biology for sustainable one-carbon biomanufacturing[J]. Green Carbon, 2026, 4(1): 43-62.

[23]

Zhang

, Zhou

, Tian

, Chen

, Wang

, Liu

. Towards a green future: advances in biological carbon fixation strategies[J]. Green Carbon, 2025. DOI: 10.1016/j.greenca.2025.10.007 .

[24]

Reiter

, Bradley

, Büchel

, Keller

, Hegedis

, Gassler

, Vorholt

. A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol[J]. Nature Catalysis, 2024, 7(5): 560-573.

[25]

, Li

, Ye

, Liu

, Chen

. Engineering microorganisms for enhanced CO₂ sequestration[J]. Trends in Biotechnology, 2019, 37(5): 532-547.

[26]

Bogorad

, Lin

, Liao

. Synthetic non-oxidative glycolysis enables complete carbon conservation[J]. Nature, 2013, 502(7473): 693-697.

[27]

Zhu

, Kang

, Liu

, Li

, Wu

, Zhan

, He

, Liao

, Li

, Wang

, Cai

, Chen

. Engineering a non-oxidative glycolysis pathway in industrial Bacillus licheniformis for carbon-efficient production of acetyl-CoA derived biochemicals[J]. Chemical Engineering Journal, 2026, 534: 175024.

[28]

Wang

, Ding

, Luo

, Huang

, Su

, Bai

, Tu

, Wang

, Qin

, Zhang

, Wang

, Yao

, Zhang

, Wang

. Engineering a non-oxidative glycolysis pathway in Escherichia coli for high-level citramalate production[J]. Microbial Cell Factories, 2024, 23: 233.

[29]

Sonnleitner

. A comparative analysis: molecular mechanisms of carbon catabolite repression in bacteria[J]. Annual Review of Microbiology, 2025, 79: 241-262.

[30]

Simpson-Lavy

, Kupiec

. Carbon catabolite repression in yeast is not limited to glucose[J]. Scientific Reports, 2019, 9: 6491.

[31]

Shrestha

, Awasthi

, Chen

, Gin

, Petzold

, Adams

, Simmons

, Singer

. Simultaneous carbon catabolite repression governs sugar and aromatic co-utilization in Pseudomonas putida M2[J]. Applied and Environmental Microbiology, 2023, 89(10): e00852-23.

[32]

Chen

, Chen

, Liu

, Wu

, Gao

. Engineering microorganisms to produce bio-based monomers: progress and challenges[J]. Fermentation, 2023, 9(2): 137.

[33]

Bryant

, Wolin

, Wolfe

. Methanobacillus omelianskii, a symbiotic association of two species of bacteria[J]. Archiv für Mikrobiologie, 1967, 59(1/2/3): 20-31.

[34]

Diender

, Parera Olm

, Sousa

. Synthetic co-cultures: novel avenues for bio-based processes[J]. Current Opinion in Biotechnology, 2021, 67: 72-79.

[35]

Zou

, Zhao

, Tang

, Zhang

, Liu

, Zheng

. Improved production of D-pantothenic acid in Escherichia coli by integrated strain engineering and fermentation strategies[J]. Journal of Biotechnology, 2021, 339: 65-72.

[36]

Bekiaris

, Klamt

. Network-wide thermodynamic constraints shape NAD(P)H cofactor specificity of biochemical reactions[J]. Nature Communications, 2023, 14: 4660.

[37]

, Grant

, Lim

. NAD(H) and NADP(H) in plants and mammals[J]. Molecular Plant, 2025, 18(6): 938-959.

[38]

Zhen

, Ren

, Zhu

. The redox requirement and regulation during cell proliferation[J]. Trends in Endocrinology & Metabolism, 2024, 35(5): 385-399.

[39]

Ding

, Yuan

, Sun

, Yin

. Dynamic and static regulation of nicotinamide adenine dinucleotide phosphate: strategies, challenges, and future directions in metabolic engineering[J]. Molecules, 2024, 29(15): 3687.

[40]

, Chang

, Li

, Cao

, Zhuang

, Wu

, Yu

, Tang

. Metabolic engineering for sustainable xylitol production from diverse carbon sources in Pichia pastoris [J]. Microbial Cell Factories, 2025, 24: 59.

[41]

Lee

, Kim

, Jin

, Seo

. Engineering of NADPH regenerators in Escherichia coli for enhanced biotransformation[J]. Applied Microbiology and Biotechnology, 2013, 97(7): 2761-2772.

[42]

Harth

, Wagner

, Sens

, Choe

, Benz

, Weuster-Botz

, Oreb

. Engineering cofactor supply and NADH-dependent d-galacturonic acid reductases for redox-balanced production of l-galactonate in Saccharomyces cerevisiae [J]. Scientific Reports, 2020, 10: 19021.

[43]

Wang

, Da

, Bi

, Liu

, Chen

, Wang

, Liu

, Nielsen

, Tan

. A one-carbon chemicals conversion strategy to produce precursor of biofuels with Saccharomyces cerevisiae [J]. Renewable Energy, 2023, 208: 331-340.

[44]

Wang

, Su

, Bi

, Zhang

, Cai

, Liu

, Wang

, Chen

, Nielsen

, Liu

, Tan

. The transition from 2G to 3G-feedstocks enabled efficient production of fuels and chemicals[J]. Green Energy & Environment, 2024, 9(11): 1759-1770.

[45]

, Wang

, Chen

, Wang

, Fan

, Tan

. Engineered yeast with a CO₂-fixation pathway to improve the bio-ethanol production from xylose-mixed sugars[J]. Scientific Reports, 2017, 7: 43875.

[46]

Pan

, Yang

, Qiu

, Jiang

, Zhang

, Jiang

, Xin

, Jiang

. Construction of coculture system containing Escherichia coli with different microbial species for biochemical production[J]. ACS Synthetic Biology, 2023, 12(8): 2208-2216.

[47]

, Guo

, Gao

, Song

, Liu

, Chen

. Synergistic metabolism of glucose and formate increases the yield of short-chain organic acids in Escherichia coli [J]. ACS Synthetic Biology, 2022, 11(1): 135-143.

[48]

Lee

, Kim

, Cho

, Jung

, Bhatia

, Gurav

, Ahn

, Park

, Choi

, Yang

. Controlling catabolite repression for isobutanol production using glucose and xylose by overexpressing the xylose regulator[J]. Journal of Biotechnology, 2022, 359: 21-28.

[49]

Wang

, Wang

, Jin

, Ruan

, Jin

. Implications of carbon catabolite repression for Aspergillus-based cell factories: a review[J]. Biotechnology Journal, 2024, 19(2): 2300551.

[50]

严涛, 赵锦芳, 高文慧, 王金华, 王永泽, 赵筱, 周胜德. 大肠杆菌工程菌ptsG基因敲除及其缺陷株混合糖同型乙醇发酵[J]. 生物工程学报, 2013, 29(7): 937-945.

Yan

, Zhao

, Gao

, Wang

, Zhao

, Zhou

. Knockout of the ptsG gene in engineered Escherichia coli for homoethanol fermentation from sugar mixture[J]. Chinese Journal of Biotechnology, 2013, 29(7): 937-945 (in Chinese).

[51]

Kim

, Tremaine

, Grass

, Purdy

, Landick

, Kiley

, Reed

. Systems metabolic engineering of Escherichia coli improves coconversion of lignocellulose-derived sugars[J]. Biotechnology Journal, 2019, 14(9): 1800441.

[52]

Ludwig

. The Bacillus subtilis catabolite control protein CcpA exerts all its regulatory functions by DNA-binding[J]. FEMS Microbiology Letters, 2001, 203(1): 125-129.

[53]

Vassiliadis

, Wong

, Andrianopoulos

, Monahan

. A genome-wide analysis of carbon catabolite repression in Schizosaccharomyces pombe [J]. BMC Genomics, 2019, 20: 251.

[54]

Dev

, Jilani

, Yazdani

. Adaptation on xylose improves glucose-xylose co-utilization and ethanol production in a carbon catabolite repression (CCR) compromised ethanologenic strain[J]. Microbial Cell Factories, 2022, 21: 154.

[55]

Mwiti

, Yeo

, Jeong

, Choi

, Kim

. Activation of galactose utilization by the addition of glucose for the fermentation of agar hydrolysate using Lactobacillus brevis ATCC 14869[J]. Biotechnology Letters, 2022, 44(7): 823-830.

[56]

Montaño López

, Duran

, Avalos

. Physiological limitations and opportunities in microbial metabolic engineering[J]. Nature Reviews Microbiology, 2022, 20(1): 35-48.

[57]

Bintsis

. Lactic acid bacteria as starter cultures: an update in their metabolism and genetics[J]. AIMS Microbiology, 2018, 4(4): 665-684.

[58]

Wang

, Li

, Yang

, Xu

, Yang

, Zou

. Carbon-economic biosynthesis of poly-2-hydrobutanedioic acid driven by nonfermentable substrate ethanol[J]. Green Chemistry, 2022, 24(17): 6599-6612.

[59]

Xiong

, Ji

, Lei

, Yang

, Bai

, Zhang

, Cheng

. Synergetic energy coupled thermal catalytic systems for CO₂ reduction[J]. eScience, 2025, 5(3): 100306.

[60]

Yan

, Luan

, Yin

, Niu

, Wu

, Yang

, Li

, Zhao

, Bao

. Co-fermentation of glucose-xylose-cellobiose-XOS mixtures using a synthetic consortium of recombinant Saccharomyces cerevisiae strains[J]. Fermentation, 2023, 9(8): 775.

[61]

Batista

, Chaves

, Oliveira

, Pantaleão

, dos Santos Neves

, da Silva

. Glycerol as substrate and NADP⁺-dependent glyceraldehyde-3-phosphate dehydrogenase enable higher production of 3-hydroxypropionic acid through the β-alanine pathway in E. coli [J]. Bioresource Technology, 2024, 393: 130142.

[62]

Bai

, Lin

, Liang

, Li

, Song

, Yuan

. Engineering synthetic microbial consortium for efficient conversion of lactate from glucose and xylose to generate electricity[J]. Biochemical Engineering Journal, 2021, 172: 108052.

[63]

Ngoc Pham

, Duc Nguyen

, Hoang Anh Mai

, Yeol Lee

. Development of a novel methanotrophic platform to produce ectoine from methane and lignocellulose-derived sugars[J]. Chemical Engineering Journal, 2023, 463: 142361.

[64]

Chilakamarry

, Sakinah

AMM

, Zularisam

, Pandey

. Glycerol waste to value added products and its potential applications[J]. Systems Microbiology and Biomanufacturing, 2021, 1(4): 378-396.

[65]

Qian

, Zheng

. Fixation of CO₂ from ethanol fermentation for succinic acid production in a dual-chamber bioreactor system[J]. Biochemical Engineering Journal, 2023, 191: 108809.

[66]

Bertrand

, Panich

, Cowan

, Roberts

, Rodriguez

, Artier

, Toppari

, Baidoo

EEK

, Chen

, Petzold

, Hudson

, Shih

, Singer

, Keasling

. Feedstock-efficient conversion through hydrogen and formate-driven metabolism in Escherichia coli [J]. Metabolic Engineering, 2026, 93: 194-207.

[67]

Cowan

, Hillers

, Rainaldi

, Collas

, Choudhary

, Zakaria

, Bieberach

, Carruthers

, Grabovac

, Gin

, Cawthon

, Chen

, Turumtay

, Baidoo

EEK

, Petzold

, Feist

, Tejedor-Sanz

, Kensy

, Simmons

, Keasling

, et al. Fast growth and high-titer bioproduction from renewable formate via metal-dependent formate dehydrogenase in Escherichia coli [J]. Nature Communications, 2025, 16: 5908.

[68]

Tao

, Deng

, Hao

, Cui

, Qi

. Engineering of CO₂ recycling and formate metabolism for succinic acid production in Yarrowia lipolytica [J]. Bioresource Technology, 2025, 436: 133029.

[69]

Van Winden

, Mans

, Breestraat

, Verlinden

RAJ

, Mielgo-Gómez

, de Hulster

EAF

, de

Bruijn HMCJ

, Noorman

. Towards closed carbon loop fermentations: cofeeding of Yarrowia lipolytica with glucose and formic acid[J]. Biotechnology and Bioengineering, 2022, 119(8): 2142-2151.

[70]

Liu

, Niu

, Zhang

, Song

, Xu

, Wang

, Feng

, Chen

. Engineering Escherichia coli to use formate and CO₂ as carbon sources for the synthesis of succinic acid in a two-stage fermentation[J]. Chemical Engineering Journal, 2026, 539: 177153.

[71]

Litsanov

, Brocker

, Bott

. Toward homosuccinate fermentation: metabolic engineering of Corynebacterium glutamicum for anaerobic production of succinate from glucose and formate[J]. Applied and Environmental Microbiology, 2012, 78(9): 3325-3337.

[72]

Liu

, Oyetunde

, Hollinshead

, Hermanns

, Tang

, Liao

, Liu

. Exploring eukaryotic formate metabolisms to enhance microbial growth and lipid accumulation[J]. Biotechnology for Biofuels, 2017, 10: 22.

[73]

Zobel

, Kuepper

, Ebert

, Wierckx

, Blank

. Metabolic response of Pseudomonas putida to increased NADH regeneration rates[J]. Engineering in Life Sciences, 2017, 17(1): 47-57.

[74]

Witthoff

, Schmitz

, Niedenführ

, Nöh

, Noack

, Bott

, Marienhagen

. Metabolic engineering of Corynebacterium glutamicum for methanol metabolism[J]. Applied and Environmental Microbiology, 2015, 81(6): 2215-2225.

[75]

Meyer

, Keller

, Hartl

, Gröninger

, Kiefer

, Vorholt

. Methanol-essential growth of Escherichia coli [J]. Nature Communications, 2018, 9: 1508.

[76]

Wang

, Gou

, Liu

, Wu

, Zhou

, Yang

, Fan

, Cai

. Engineering a robust and versatile terpenoid production platform in Serratia marcescens HBQA7[J]. Chemical Engineering Journal, 2025, 524: 169601.

[77]

, Lu

, Zeng

, Li

, Xu

, Gui

, Guo

, Deng

, Bie

, Zhang

, He

, Zhu

, Fu

, Yu

. Innovative coupling pathway for second- and third-generation biomass: efficient L-lactate synthesis from xylose and C1 compounds[J]. Chemical Engineering Journal, 2025, 505: 159573.

[78]

Dong

, Sun

, Guo

, Ma

, Xian

, Zhang

. Highly efficient biosynthesis of 2,4-dihydroxybutyric acid by a methanol assimilation pathway in engineered Escherichia coli [J]. Green Chemistry, 2023, 25(19): 7662-7672.

[79]

Sun

, Liu

, Chen

. Metabolic engineering of Escherichia coli to utilize methanol as a co-substrate for the production of (R)-1,3-butanediol[J]. Biotechnology Notes, 2023, 4: 104-111.

[80]

Jordà

, Suarez

, Carnicer

, ten Pierick

, Heijnen

, van Gulik

, Ferrer

, Albiol

, Wahl

. Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary ¹³C flux analysis[J]. BMC Systems Biology, 2013, 7: 17.

[81]

, Yang

, Wang

, Zhan

, Bai

, Yang

. Enhanced methanol-xylose co-utilization strategy in Komagataella phaffii [J]. Journal of Biotechnology, 2025, 399: 117-126.

[82]

Khattab

SMR

, Katahira

, Watanabe

. Engineering Saccharomyces cerevisiae for ethanol production from glycerol, xylose, acetic acid, and glucose[J]. Bioresource Technology, 2025, 435: 132921.

[83]

, Shao

, Li

, Fan

, Xu

, Lu

, Bi

, Zhu

, Zhang

. Construction of a carbon-conserving pathway for glycolate production by synergetic utilization of acetate and glucose in Escherichia coli [J]. Metabolic Engineering, 2020, 61: 152-159.

[84]

, Liu

, Zhu

, Li

. Coutilization of glucose and acetate for the production of pyruvate by engineered Escherichia coli [J]. Biochemical Engineering Journal, 2021, 170: 107990.

[85]

Nam

, Ye

, Hwang

, Jung

. Convergent synthesis of two heterogeneous fluxes from glucose and acetate for high-yield citramalate production[J]. Journal of Agricultural and Food Chemistry, 2024, 72(11): 5797-5804.

[86]

Zhang

, Liu

, Li

, Zhang

. Construction of a redox-coupled pathway co-metabolizing glucose and acetate for high-yield production of butyl butyrate in Escherichia coli [J]. Bioresource Technology, 2024, 413: 131437.

[87]

Wang

, Hou

, Cui

, Wang

, Chen

. Engineering Corynebacterium glutamicum for the efficient production of 3-hydroxypropionic acid from glucose via the β-alanine pathway[J]. Synthetic and Systems Biotechnology, 2024, 9(4): 752-758.

[88]

Procópio

, Lee

, Shin

, Tramontina

, Ávila

, Brenelli

, Squina

, Damasio

, Rabelo

, Goldbeck

, Franco

, Leak

, Jin

, Basso

. Metabolic engineering of Saccharomyces cerevisiae for second-generation ethanol production from xylo-oligosaccharides and acetate[J]. Scientific Reports, 2023, 13: 19182.

[89]

Lin

, University

, Ma

, Zhang

, Yang

, University

, Zhao

, Zhang

. Enhanced β-carotene production in Yarrowia lipolytica via co-utilization of xylose and acetic acid[J]. Journal of Agricultural and Food Chemistry, 2025, 73(46): 29662-29673.

[90]

, Qiao

, Liu

, Stephanopoulos

. Engineering Yarrowia lipolytica for poly-3-hydroxybutyrate production[J]. Journal of Industrial Microbiology and Biotechnology, 2017, 44(4/5): 605-612.

[91]

Jeong

, Lee

, Liu

, Kim

, Jin

, Zhao

, Oh

. Acetate metabolism during xylose fermentation enhances 3-hydroxypropionic acid production in engineered acid-tolerant Issatchenkia orientalis [J]. Bioresource Technology, 2025, 437: 133113.

[92]

Zhang

, Yuan

, Wang

, Chen

. Metabolic engineering of Halomonas bluephagenesis for high-level mevalonate production from glucose and acetate mixture[J]. Metabolic Engineering, 2023, 79: 203-213.

[93]

Joun

, Sirohi

, Sim

. The effects of acetate and glucose on carbon fixation and carbon utilization in mixotrophy of Haematococcus pluvialis [J]. Bioresource Technology, 2023, 367: 128218.

[94]

Silva

, Prete

CEC

, Zambrano

, de Mello

, Tischer

, Andrade

. Combining glucose and sodium acetate improves the growth of Neochloris oleoabundans under mixotrophic conditions[J]. AMB Express, 2016, 6: 10.

[95]

Gong

, Zhou

, Shen

, Yang

, Wang

, Zuo

, Hou

, Zhao

. Co-fermentation of acetate and sugars facilitating microbial lipid production on acetate-rich biomass hydrolysates[J]. Bioresource Technology, 2016, 207: 102-108.

[96]

Gong

, Liu

, Wu

, Li

, He

. Simultaneously enhancing microbial biomass formation and lactic acid synthesis in Bacillus coagulans via acetate supplementation[J]. Journal of Environmental Management, 2025, 395: 127996.

[97]

Saha

, Kennedy

, Bowman

, Qureshi

, Nichols

. Itaconic acid production by Aspergillus terreus from glucose up to pilot scale and from corn stover and wheat straw hydrolysates using new manganese tolerant medium[J]. Biocatalysis and Agricultural Biotechnology, 2022, 43: 102418.

[98]

Sun

, Atiyeh

, Kumar

, Zhang

. Enhanced ethanol production by Clostridium ragsdalei from syngas by incorporating biochar in the fermentation medium[J]. Bioresource Technology, 2018, 247: 291-301.

[99]

Monir

, Aziz

, Khatun

, Yousuf

. Bioethanol production through syngas fermentation in a tar free bioreactor using Clostridium butyricum [J]. Renewable Energy, 2020, 157: 1116-1123.

[100]

Cheng

, Li

, Lin

, Yang

. Metabolic engineering of Clostridium carboxidivorans for enhanced ethanol and butanol production from syngas and glucose[J]. Bioresource Technology, 2019, 284: 415-423.

[101]

, Ko

, Gong

, Lee

, Um

. Production of hexanol as the main product through syngas fermentation by Clostridium carboxidivorans P7[J]. Frontiers in Bioengineering and Biotechnology, 2022, 10: 850370.

[102]

Lauer

, Philipps

, Jennewein

. Metabolic engineering of Clostridium ljungdahlii for the production of hexanol and butanol from CO₂ and H₂ [J]. Microbial Cell Factories, 2022, 21: 85.

[103]

Liu

, Zhang

, Shi

, Fan

, Zha

, Li

, Zhang

, Yi

, Wang

, Jia

, Gu

, Bengelsdorf

, Li

. Aldehyde dehydrogenase inactivation triggers metabolic reprogramming in Clostridium ljungdahlii [J]. Green Carbon, 2026. DOI: 10.1016/j.greenca.2026.03.002 .

[104]

Yang

, Li

, Ye

, Hu

, Lu

, Jiang

, Gu

. Stepwise engineering of Clostridium ljungdahlii for efficient ethanol production from single-carbon gases[J]. Green Carbon, 2026, 4(1): 89-95.

[105]

, Humphreys

, Magnusson

, Wachter

, Urban

, Hebdon

, Xiong

, Chou

, Ching Maness

. Acetogenic production of 3-Hydroxybutyrate using a native 3-hydroxybutyryl-CoA dehydrogenase[J]. Frontiers in Microbiology, 2022, 13: 948369.

[106]

Devarapalli

, Atiyeh

, Phillips

, Lewis

, Huhnke

. Ethanol production during semi-continuous syngas fermentation in a trickle bed reactor using Clostridium ragsdalei [J]. Bioresource Technology, 2016, 209: 56-65.

[107]

Pati

, Mohanty

, Mohapatra

, Samantaray

. Bioethanol production by Enterobacter hormaechei through carbon monoxide-rich syngas fermentation[J]. International Journal of Energy Research, 2022, 46(14): 20096-20106.

[108]

Lee

, Kim

, Ji

, Jourdin

, Straathof

AJJ

, Chang

. Microbial cell viability-driven operational strategy for enhanced acetate production in syngas fermentation[J]. Journal of Environmental Chemical Engineering, 2025, 13(3): 116531.

[109]

Guo

, Zhang

, Wang

, Qin

, Feng

, Chen

, Wang

. Engineering yeasts to co-utilize methanol or formate coupled with CO₂ fixation[J]. Metabolic Engineering, 2024, 84: 1-12.

[110]

Quynh Le

, Anh Mai

, Na

, Lee

. Development of Methylorubrum extorquens AM1 as a promising platform strain for enhanced violacein production from co-utilization of methanol and acetate[J]. Metabolic Engineering, 2022, 72: 150-160.

[111]

Wood

, Marcellin

, Plan

, Virdis

. High methanol-to-formate ratios induce butanol production in Eubacterium limosum [J]. Microbial Biotechnology, 2022, 15(5): 1542-1549.

[112]

Kim

, Kim

, Seo

, Yang

, Lee

, Hur

, Lee

, Kim

, Oh

. Medium chain length polyhydroxyalkanoate production by engineered Pseudomonas gessardii using acetate-formate as carbon sources[J]. Journal of Microbiology, 2024, 62(7): 569-579.

[113]

Wang

, Wu

, Du

, Liu

, Zhu

, Feng

, Bi

, Zhang

, Liu

, Chen

, Wang

, Tan

. Metabolic engineering of Saccharomyces cerevisiae for conversion of formate and acetate into free fatty acids[J]. Fermentation, 2023, 9(11): 984.

[114]

, Wang

, Xu

, Wang

, Chen

, Fang

, Tan

, Zeng

, Tan

. Biofuel synthesis from carbon dioxide via a bio-electrocatalysis system[J]. Chem Catalysis, 2023, 3(3): 100557.

[115]

Zelcbuch

, Lindner

, Zegman

, Vainberg Slutskin

, Antonovsky

, Gleizer

, Milo

, Bar-Even

. Pyruvate formate-lyase enables efficient growth of Escherichia coli on acetate and formate[J]. Biochemistry, 2016, 55(17): 2423-2426.

[116]

Cho

, Shin

, Kim

. Effects of acetic and formic acid on ABE production by Clostridium acetobutylicum and Clostridium beijerinckii [J]. Biotechnology and Bioprocess Engineering, 2012, 17(2): 270-275.

[117]

Zhang

, Liu

, Wang

, University

, Yu

. Sustainable production of L-homoserinesolely from CO₂-derived acetate and formate by engineered E. coli strain[J]. ACS Sustainable Chemistry & Engineering, 2024, 12(52): 18704-18711.

[118]

Olsson

, Rugbjerg

, Torello Pianale

, Trivellin

. Robustness: linking strain design to viable bioprocesses[J]. Trends in Biotechnology, 2022, 40(8): 918-931.

[119]

Trivellin

, Ekman

, Persson

, Gupta

, Olsson

, Desai

. Impact of fluctuating environments on the fitness and robustness of evolving laboratory and industrial Saccharomyces cerevisiae strains[J]. bioRxiv, 2025: 2025.2012.2005.692621.

[120]

Tran

, Zhao

. Engineering robust microorganisms for organic acid production[J]. Journal of Industrial Microbiology and Biotechnology, 2022, 49(2): kuab067.

[121]

, Liu

, Zhao

. Response mechanisms of Saccharomyces cerevisiae to the stress factors present in lignocellulose hydrolysate and strategies for constructing robust strains[J]. Biotechnology for Biofuels and Bioproducts, 2022, 15: 28.

[122]

Liu

, Zhang

, Yuan

, Jiang

, Zhao

, Huang

, Liu

. Omics-based analyses revealed metabolic responses of Clostridium acetobutylicum to lignocellulose-derived inhibitors furfural, formic acid and phenol stress for butanol fermentation[J]. Biotechnology for Biofuels, 2019, 12: 101.

[123]

Mao

, Zhang

, Chen

, Wei

, Liu

, Nielsen

, Chen

, Xu

. Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms[J]. Biotechnology Advances, 2024, 74: 108401.

[124]

徐美娟, 上官春雨, 陈鑫, 张显, 杨套伟, 饶志明. 谷氨酸棒杆菌耐受胁迫机制及工业鲁棒性合成生物学研究进展[J]. 生物工程学报, 2021, 37(3): 831-845.

, Shangguan

, Chen

, Zhang

, Yang

, Rao

. Advances in stress tolerance mechanisms and synthetic biology for the industrial robustness of Corynebacterium glutamicum [J]. Chinese Journal of Biotechnology, 2021, 37(3): 831-845 (in Chinese).

[125]

Zhang

, Qian

, Si

, Xu

, Han

, Ni

. Significantly improved solvent tolerance of Escherichia coli by global transcription machinery engineering[J]. Microbial Cell Factories, 2015, 14: 175.

[126]

Peng

, Zhang

, Qi

, Liang

. Advances in adaptive laboratory evolution applications for Escherichia coli [J]. Synthetic and Systems Biotechnology, 2025, 10(4): 1306-1321.

[127]

López-deÁvila

, Monsalve-Fonnegra

, Rodríguez-Cabal

. Adaptive laboratory evolution and transcriptomic profiling reveal carbon-nitrogen metabolic reprogramming enabling aerobic co-fermentation of glucose and xylose in Saccharomyces cerevisiae [J]. PLoS One, 2026, 21(1): e0341927.

[128]

Driessen

JLSP

, Johnsen

, Pogrebnyakov

, Mohamed

ETT

, Mussatto

, Feist

, Jensen

, Nielsen

. Adaptive laboratory evolution of Bacillus subtilis to overcome toxicity of lignocellulosic hydrolysate derived from Distiller’s dried grains with solubles (DDGS)[J]. Metabolic Engineering Communications, 2023, 16: e00223.

[129]

Wang

, Fan

, Tuyishime

, Liu

, Zhang

, Gao

, Zhang

, Ni

, Feng

, Yuan

, Ma

, Zheng

, Sun

, Ma

. Adaptive laboratory evolution enhances methanol tolerance and conversion in engineered Corynebacterium glutamicum [J]. Communications Biology, 2020, 3: 217.

[130]

, Xu

, Zhan

, Qin

, Fan

, Zhang

. Metabolic engineering of the acid-tolerant yeast Pichia kudriavzevii for efficient L-malic acid production at low pH[J]. Metabolic Engineering, 2023, 75: 170-180.

[131]

王倩, 高教琪, 周雍进. 葡萄糖和木糖高效共利用代谢工程研究进展[J]. 生物工程学报, 2024, 40(8): 2710-2730.

Wang

, Gao

, Zhou

. Metabolic engineering for the efficient co-utilization of glucose and xylose[J]. Chinese Journal of Biotechnology, 2024, 40(8): 2710-2730 (in Chinese).

[132]

Sauer

. Metabolic networks in motion: ¹³C-based flux analysis[J]. Molecular Systems Biology, 2006, 2: MSB4100109.

[133]

Klein

, Irla

, Gil

López M

, Brautaset

, Fernandes Brito

. Unravelling formaldehyde metabolism in bacteria: road towards synthetic methylotrophy[J]. Microorganisms, 2022, 10(2): 220.

[134]

, Liu

, Yang

, Fu

, Wang

. Engineering microbial metabolic homeostasis for chemicals production[J]. Critical Reviews in Biotechnology, 2025, 45(2): 373-392.

[135]

Brockman

, Prather

KLJ

. Dynamic metabolic engineering: new strategies for developing responsive cell factories[J]. Biotechnology Journal, 2015, 10(9): 1360-1369.

[136]

Tan

, Prather

. Dynamic pathway regulation: recent advances and methods of construction[J]. Current Opinion in Chemical Biology, 2017, 41: 28-35.

[137]

Rogers

, Taylor

, Church

. Biosensor-based engineering of biosynthetic pathways[J]. Current Opinion in Biotechnology, 2016, 42: 84-91.

[138]

Upadhyay

LSB

, Verma

. Recent advances in phosphate biosensors[J]. Biotechnology Letters, 2015, 37(7): 1335-1345.

[139]

Black

, Kussat

, Lee

CWJ

, Qu

, Hu

, Caza

, Kronstad

. Genetically encoded sensors for monitoring intracellular redox health of the pathogenic fungus Cryptococcus neoformans [J]. ACS Sensors, 2025, 10(12): 9347-9358.

[140]

Bezold

, Scheffer

, Wendering

, Razaghi-Moghadam

, Trauth

, Pook

, Nußhär

, Hasenjäger

, Nikoloski

, Essen

, Taxis

. Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast[J]. Metabolic Engineering, 2023, 79: 97-107.

[141]

Gao

, Guo

, Hu

, Liu

, Chen

, Xia

, Liu

. Engineering a CRISPRi circuit for autonomous control of metabolic flux in Escherichia coli [J]. ACS Synthetic Biology, 2021, 10(10): 2661-2671.

[142]

Doong

, Gupta

, Prather

KLJ

. Layered dynamic regulation for improving metabolic pathway productivity in Escherichia coli [J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(12): 2964-2969.

[143]

Wang

, Han

, Zhang

, Ma

, Lin

, Wang

, Li

, Zheng

, Wu

, Ye

, Chen

. Reversible thermal regulation for bifunctional dynamic control of gene expression in Escherichia coli [J]. Nature Communications, 2021, 12: 1411.

[144]

Ream

, Prather

KLJ

. Engineered autonomous dynamic regulation of metabolic flux[J]. Nature Reviews Bioengineering, 2024, 2(3): 233-243.

[145]

Yan

, Cao

, Ding

, Yuan

. Design and construction of microbial cell factories based on systems biology[J]. Synthetic and Systems Biotechnology, 2023, 8(1): 176-185.

[146]

Stephanopoulos

, Alper

, Moxley

. Exploiting biological complexity for strain improvement through systems biology[J]. Nature Biotechnology, 2004, 22(10): 1261-1267.

[147]

Carbonell

, Jervis

, Robinson

, Yan

, Dunstan

, Swainston

, Vinaixa

, Hollywood

, Currin

, Rattray

NJW

, Taylor

, Spiess

, Sung

, Williams

, Fellows

, Stanford

, Mulherin

, Le Feuvre

, Barran

, Goodacre

, et al. An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals[J]. Communications Biology, 2018, 1: 66.

[148]

Gurdo

, Volke

, McCloskey

, Nikel

. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories[J]. New Biotechnology, 2023, 74: 1-15.

[149]

Ching

, Himmelstein

, Beaulieu-Jones

, Kalinin

, Do

, Way

, Ferrero

, Agapow

, Zietz

, Hoffman

, Xie

, Rosen

, Lengerich

, Israeli

, Lanchantin

, Woloszynek

, Carpenter

, Shrikumar

, Xu

, Cofer

, et al. Opportunities and obstacles for deep learning in biology and medicine[J]. Journal of the Royal Society Interface, 2018, 15(141): 20170387.

[150]

Kundu

, Beura

, Mondal

, Das

, Ghosh

. Machine learning for the advancement of genome-scale metabolic modeling[J]. Biotechnology Advances, 2024, 74: 108400.

[151]

Gong

, Chen

, Jiao

, Gong

, Pan

, Liu

, Zhang

, Tan

. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: current advances and future prospects[J]. Biotechnology Advances, 2024, 72: 108319.

[152]

Yang

, Lal

, Bowden

, Astudillo

, Hameedi

, Kaur

, Hill

, Yue

, Arnold

. Active learning-assisted directed evolution[J]. Nature Communications, 2025, 16: 714.

[153]

Zhao

, Cai

, Li

, Zhang

. Engineering rubisco to enhance CO₂ utilization[J]. Synthetic and Systems Biotechnology, 2024, 9(1): 55-68.

[154]

Tang

, Zhang

, Shi

, Xie

, Yang

, Lu

, Zhang

, Bai

, Kan

, Yao

. Directed evolution of formate dehydrogenase and its application in the biosynthesis of L-phenylglycine from phenylglyoxylic acid[J]. Molecular Catalysis, 2021, 513: 111666.

[155]

Yang

, Wu

, Arnold

. Machine-learning-guided directed evolution for protein engineering[J]. Nature Methods, 2019, 16(8): 687-694.

[156]

Zhang

, Zheng

, Chan

, Chang

, Poh

. Emerging biosensor and assay-enabled high-throughput screening solutions for enzyme and strain engineering[J]. Current Opinion in Biotechnology, 2026, 98: 103439.

2026年第66卷第9期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.13343/j.cnki.wsxb.20260296

接收时间：2026-04-12
首发时间：2026-09-09
出版时间：2026-09-04

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2026-04-12
录用日期：2026-05-26

基金

the National Key Research and Development Program of China(2023YFB4203500)

国家重点研发计划(2023YFB4203500)

the National Natural Science Foundation of China(22508012)

国家自然科学基金(22508012)

作者信息

^1.北京化工大学，绿色生物制造国家重点实验室，北京

^2.北京化工大学，国家能源生物炼制研发中心，北京

^3.北京化工大学，教育部生物炼制工程研究中心，北京

通讯作者:

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/wswxb/CN/10.13343/j.cnki.wsxb.20260296

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT

Core mechanism	Key scientific principles	Typical carbon source combinations and application cases	Industrial value and advantages
Energy coupling & thermodynamic driving	(1) Cells couple catabolic ATP with anabolic energy carriers (GTP/UTP) via precise regulation (2) Carbon sources divide functions to meet diverse energy demands (3) Key metabolite modulation drives thermodynamically unfavorable reactions (4) Co-culture syntrophy improves overall thermodynamic favorability	(1) Heterologous transhydrogenase system was introduced into E. coli with optimized electron transport chain to realize co-regeneration of NAD(P)H and ATP The titer of D-pantothenic acid reached 86.03 g/L with a productivity of 0.80 g/(L·h)^[35] (2) Glucose and glycerol: glucose provides rapid energy supply, while glycerol sustains cell growth and generates amino acid precursors^[36]	(1) Optimizes energy allocation, boosts product synthesis while sustaining cell viability (2) Breaks single-carbon-source energy bottleneck
Redox balance & electron carrier adaptation	(1) NADH dominates catabolism; NADPH functions in anabolism and oxidative stress (2) Different carbon sources yield distinct NADH/NADPH ratios (3) Multi-carbon coupling flexibly tunes intracellular reducing power ratio (4) Modular pathways reduce strain burden and enable precise redox control	(1) CO₂-formate co-conversion: 11.24% C1 assimilation, 0.48 g/(L·h) formate consumption, 10.10 g/L FFAs^[43] (2) S. cerevisiae glucose-formate co-utilization: ethanol, FFAs, and longifolene yields up by 184%, 490%, and 100%^[44] (3) mXR-XDH xylose and PRK-Rubisco CO₂ fixation modules: 0.47 g/g ethanol yield^[45]	(1) Resolves reducing power imbalance from static regulation (2) Precisely matches product-specific reducing power demand (3) Synergizes carbon flux and reducing power supply
Molecular mechanism & relief strategies of carbon catabolite repression	(1) CCR drives preferential use of preferred carbons (e.g., glucose), represses non-preferred carbon metabolic genes, and causes diauxic growth (2) Core mechanisms: PTS phosphorylation regulation, transcriptional control by factors like CreA (3) Low glucose can activate rather than inhibit non-glucose carbon metabolism under specific conditions	(1) Knockout of key regulatory genes: pstG^[50], crr^[51], ccpA^[52], scrI^[53] (2) Adaptive evolution and engineering improve glucose/xylose co-utilization inE. coli and elevates ethanol yield^[54] (3) Low-dose glucose activates galactose catabolic pathway^[55]	(1) Eliminates diauxic growth and shortens fermentation cycle (2) Enables simultaneous efficient utilization of multiple carbon sources (3) Significantly improves overall industrial process efficiency
Carbon flux redirection & conservation mechanism	(1) Redirects carbon flux to target pathways while sustaining cell growth (2) Carbon conservation maximizes substrate-to-product carbon conversion efficiency (3) Low-oxidation carbons cut CO₂-related carbon loss (4) Synthetic consortia split pathways to lower single-strain burden	(1) Heterologous β-glucosidase and β-xylosidase genes were integrated intoS. cerevisiae to achieve co-fermentation of cellulosic sugars^[60] (2) Methylotrophs coupled with lignocellulosic sugars: methane provides NADPH while sugars supply carbon skeletons for ectoine biosynthesis^[63] (3) Dual-chamber bioreactor: CO₂ generated from ethanol fermentation is recycled for succinic acid synthesis^[65]	(1) Improves carbon economy and reduces carbon loss (2) Achieves natural complementation of reducing power and carbon metabolism (3) Supports high-value utilization of low-cost C1/C2 carbons (methane, CO₂)

Core mechanism

Key scientific principles

Typical carbon source combinations and application cases

Industrial value and advantages

Energy coupling & thermodynamic driving

(1) Cells couple catabolic ATP with anabolic energy carriers (GTP/UTP) via precise regulation

(2) Carbon sources divide functions to meet diverse energy demands

(3) Key metabolite modulation drives thermodynamically unfavorable reactions

(4) Co-culture syntrophy improves overall thermodynamic favorability

(1) Heterologous transhydrogenase system was introduced into E. coli with optimized electron transport chain to realize co-regeneration of NAD(P)H and ATP The titer of D-pantothenic acid reached 86.03 g/L with a productivity of 0.80 g/(L·h)^[35]

(2) Glucose and glycerol: glucose provides rapid energy supply, while glycerol sustains cell growth and generates amino acid precursors^[36]

(1) Optimizes energy allocation, boosts product synthesis while sustaining cell viability

(2) Breaks single-carbon-source energy bottleneck

Redox balance & electron carrier adaptation

(1) NADH dominates catabolism; NADPH functions in anabolism and oxidative stress

(2) Different carbon sources yield distinct NADH/NADPH ratios

(3) Multi-carbon coupling flexibly tunes intracellular reducing power ratio

(4) Modular pathways reduce strain burden and enable precise redox control

(1) CO₂-formate co-conversion: 11.24% C1 assimilation, 0.48 g/(L·h) formate consumption, 10.10 g/L FFAs^[43]

(2) S. cerevisiae glucose-formate co-utilization: ethanol, FFAs, and longifolene yields up by 184%, 490%, and 100%^[44]

(3) mXR-XDH xylose and PRK-Rubisco CO₂ fixation modules: 0.47 g/g ethanol yield^[45]

(1) Resolves reducing power imbalance from static regulation

(2) Precisely matches product-specific reducing power demand

(3) Synergizes carbon flux and reducing power supply

Molecular mechanism & relief strategies of carbon catabolite repression

(1) CCR drives preferential use of preferred carbons (e.g., glucose), represses non-preferred carbon metabolic genes, and causes diauxic growth

(2) Core mechanisms: PTS phosphorylation regulation, transcriptional control by factors like CreA

(3) Low glucose can activate rather than inhibit non-glucose carbon metabolism under specific conditions

(1) Knockout of key regulatory genes: pstG^[50], crr^[51], ccpA^[52], scrI^[53]

(2) Adaptive evolution and engineering improve glucose/xylose co-utilization inE. coli and elevates ethanol yield^[54]

(3) Low-dose glucose activates galactose catabolic pathway^[55]

(1) Eliminates diauxic growth and shortens fermentation cycle

(2) Enables simultaneous efficient utilization of multiple carbon sources

(3) Significantly improves overall industrial process efficiency

Carbon flux redirection & conservation mechanism

(1) Redirects carbon flux to target pathways while sustaining cell growth

(2) Carbon conservation maximizes substrate-to-product carbon conversion efficiency

(3) Low-oxidation carbons cut CO₂-related carbon loss

(4) Synthetic consortia split pathways to lower single-strain burden

(1) Heterologous β-glucosidase and β-xylosidase genes were integrated intoS. cerevisiae to achieve co-fermentation of cellulosic sugars^[60]

(2) Methylotrophs coupled with lignocellulosic sugars: methane provides NADPH while sugars supply carbon skeletons for ectoine biosynthesis^[63]

(3) Dual-chamber bioreactor: CO₂ generated from ethanol fermentation is recycled for succinic acid synthesis^[65]

(1) Improves carbon economy and reduces carbon loss

(2) Achieves natural complementation of reducing power and carbon metabolism

(3) Supports high-value utilization of low-cost C1/C2 carbons (methane, CO₂)

Strain	Substrate combination	Target product	Titer/productivity	References
Saccharomyces cerevisiae	Glucose/formate/CO₂	Fatty acids	10.10 g/L	[43]
Saccharomyces cerevisiae	Xylose/formate/CO₂	Ethanol, free fatty acids, and longifolene	18.40%, 49%, and nearly 100% increase	[44]
Saccharomyces cerevisiae	Maltose/glucose/CO₂	Ethanol	Productivity of 0.47 g/g	[45]
Escherichia coli	Glucose/formate	Pyruvate	1.88 mol/mol	[47]
Escherichia coli	Glucose/formate	L-malic acid	1.65 mol/mol	[47]
Escherichia coli	Glucose/formate/H₂	MVA	57.60% increase	[66]
Escherichia coli	Glucose/formate	MVA	3.80 g/L	[67]
Yarrowia lipolytica	Glucose/formate/CO₂	Succinic acid	97.54 g/L; 0.64 g/g	[68]
Yarrowia lipolytica	Glucose/formate/CO₂	Succinic acid	20% increase	[69]
Escherichia coli	Glucose/formate/CO₂	Succinic acid	2.46 g/L; 0.43 mol/mol	[70]
Corynebacterium glutamicum	Glucose/formate	Succinic acid	1 134 mmol/L; 1.67 mol/mol	[71]
Umbelopsis isabellina	Glucose:formate=1:3.90	Lipids	70% higher lipid titer	[72]
Pseudomonas putida KT2440	Glucose/formate	Biomass/polymer precursors	25% higher biomass	[73]
Escherichia coli	Glucose/methanol	Biomass	Methanol consumption: 1.70 mmol/h	[74]
Escherichia coli	Xylose/methanol	Ethanol	1.89 g/L	[75]
Serratia marcescens HBQA7	Glucose/methanol	Geraniol	574.12 mg/L	[76]
Serratia marcescens HBQA7	Glucose/methanol	α-bisabolol	1 256.41 mg/L	[76]
In vitro enzyme biosystem	Xylose/methanol	L-lactic acid	6 g/L; carbon efficiency of 88.75%	[77]
Escherichia coli	Glucose/methanol	2,4-DHB	14.60 g/L	[78]
Escherichia coli	Xylose/methanol	(R)-1,3-butanediol	13.71 g/L	[79]
Pichia pastoris	Glucose:methanol=4:1	Biomass	20% increase in cell density	[80]
Pichia pastoris	Xylose/methanol	Biomass	7.50 g/L	[81]

Strain

Substrate combination

Target product

Titer/productivity

References

Saccharomyces cerevisiae

Glucose/formate/CO₂

Fatty acids

10.10 g/L

[43]

Saccharomyces cerevisiae

Xylose/formate/CO₂

Ethanol, free fatty acids, and longifolene

18.40%, 49%, and nearly 100% increase

[44]

Saccharomyces cerevisiae

Maltose/glucose/CO₂

Ethanol

Productivity of 0.47 g/g

[45]

Escherichia coli

Glucose/formate

Pyruvate

1.88 mol/mol

[47]

Escherichia coli

Glucose/formate

L-malic acid

1.65 mol/mol

[47]

Escherichia coli

Glucose/formate/H₂

MVA

57.60% increase

[66]

Escherichia coli

Glucose/formate

MVA

3.80 g/L

[67]

Yarrowia lipolytica

Glucose/formate/CO₂

Succinic acid

97.54 g/L; 0.64 g/g

[68]

Yarrowia lipolytica

Glucose/formate/CO₂

Succinic acid

20% increase

[69]

Escherichia coli

Glucose/formate/CO₂

Succinic acid

2.46 g/L; 0.43 mol/mol

[70]

Corynebacterium glutamicum

Glucose/formate

Succinic acid

1 134 mmol/L; 1.67 mol/mol

[71]

Umbelopsis isabellina

Glucose:formate=1:3.90

Lipids

70% higher lipid titer

[72]

Pseudomonas putida KT2440

Glucose/formate

Biomass/polymer precursors

25% higher biomass

[73]

Escherichia coli

Glucose/methanol

Biomass

Methanol consumption: 1.70 mmol/h

[74]

Escherichia coli

Xylose/methanol

Ethanol

1.89 g/L

[75]

Serratia marcescens HBQA7

Glucose/methanol

Geraniol

574.12 mg/L

[76]

Serratia marcescens HBQA7

Glucose/methanol

α-bisabolol

1 256.41 mg/L

[76]

In vitro enzyme biosystem

Xylose/methanol

L-lactic acid

6 g/L; carbon efficiency of 88.75%

[77]

Escherichia coli

Glucose/methanol

2,4-DHB

14.60 g/L

[78]

Escherichia coli

Xylose/methanol

(R)-1,3-butanediol

13.71 g/L

[79]

Pichia pastoris

Glucose:methanol=4:1

Biomass

20% increase in cell density

[80]

Pichia pastoris

Xylose/methanol

Biomass

7.50 g/L

[81]

Strain	Substrate combination	Target product	Titer/productivity	References
Saccharomyces cerevisiae	Glucose/acetate	Ethanol	95% of the theoretical value	[82]
Saccharomyces cerevisiae	Glucose/acetate	Ethanol	Titer close to theoretical maximum	[82]
Escherichia coli NZ-Gly303	Glucose/acetate	Glycolic acid	73.30 g/L; 1.04 g/(L·h)	[83]
Escherichia coli	Glucose/acetate	Pyruvate	9.61 g/L	[84]
Escherichia coli	Glucose/acetate	Pyruvate	5 g/L; 0.87 mol/mol	[85]
Escherichia coli	Glucose/acetate	Butyl butyrate	29.02 g/L; carbon yield of 43.30%	[86]
Corynebacterium glutamicum	Glucose/acetate	3-HP	12.57-fold	[87]
Saccharomyces cerevisiae	Xylose/acetate	Ethanol	84% higher product titer	[88]
Yarrowia lipolytica	Xylose/acetate	β-carotene	776.90 mg/L	[89]
Yarrowia lipolytica	Glucose/acetate	PHB	7.35 g/L	[90]
Issatchenkia orientalis	Xylose/acetate	3-HP	8.70 g/L (straw hydrolysate)	[91]
Halomonas bluephagenesis	Glucose/acetate	MVA	121 g/L (5 L bioreactor)	[92]
Haematococcus pluvialis	Glucose/acetate	Biomass	77.10% higher biomass	[93]
Neochloris oleoabundans	Glucose/xylose/acetate	Lipids	1.75 g/L; lipid content of 34.40%	[94]
Rhodotorula toruloides	Glucose (or xylose)/acetate	Lipids	56%; productivity of 0.19 g/(L·h)	[95]
Bacillus coagulans	Glucose/acetate	Lactic acid	15.30% increase in lactic acid titer	[96]
Aspergillus terreus	Cellulosic hydrolysate	Itaconic acid	39.60 g/L; 0.40 g/g	[97]

Strain

Substrate combination

Target product

Titer/productivity

References

Saccharomyces cerevisiae

Glucose/acetate

Ethanol

95% of the theoretical value

[82]

Saccharomyces cerevisiae

Glucose/acetate

Ethanol

Titer close to theoretical maximum

[82]

Escherichia coli NZ-Gly303

Glucose/acetate

Glycolic acid

73.30 g/L; 1.04 g/(L·h)

[83]

Escherichia coli

Glucose/acetate

Pyruvate

9.61 g/L

[84]

Escherichia coli

Glucose/acetate

Pyruvate

5 g/L; 0.87 mol/mol

[85]

Escherichia coli

Glucose/acetate

Butyl butyrate

29.02 g/L; carbon yield of 43.30%

[86]

Corynebacterium glutamicum

Glucose/acetate

3-HP

12.57-fold

[87]

Saccharomyces cerevisiae

Xylose/acetate

Ethanol

84% higher product titer

[88]

Yarrowia lipolytica

Xylose/acetate

β-carotene

776.90 mg/L

[89]

Yarrowia lipolytica

Glucose/acetate

PHB

7.35 g/L

[90]

Issatchenkia orientalis

Xylose/acetate

3-HP

8.70 g/L (straw hydrolysate)

[91]

Halomonas bluephagenesis

Glucose/acetate

MVA

121 g/L (5 L bioreactor)

[92]

Haematococcus pluvialis

Glucose/acetate

Biomass

77.10% higher biomass

[93]

Neochloris oleoabundans

Glucose/xylose/acetate

Lipids

1.75 g/L; lipid content of 34.40%

[94]

Rhodotorula toruloides

Glucose (or xylose)/acetate

Lipids

56%; productivity of 0.19 g/(L·h)

[95]

Bacillus coagulans

Glucose/acetate

Lactic acid

15.30% increase in lactic acid titer

[96]

Aspergillus terreus

Cellulosic hydrolysate

Itaconic acid

39.60 g/L; 0.40 g/g

[97]

Strain	Substrate combination	Target product	Titer/productivity	References
Clostridium carboxidivorans	Syngas	Ethanol	3 g/L	[100]
Clostridium carboxidivorans	Syngas	Butanol	0.35 g/L	[100]
Clostridium carboxidivorans	Syngas	Hexanol	1.90 g/L	[101]
Clostridium ljungdahlii	CO adjusted to 70%	Hexanol	0.39 g/L	[102]
Clostridium ljungdahlii	CO₂/H₂	Hexanol	2-5-fold	[103]
Clostridium ljungdahlii	Syngas	Ethanol	30.10 g/L (2 L fermenter)	[104]
Clostridium acetobutylicum ATCC 824	Syngas	3-HP	9.25 g/L	[105]
Enterobacter hormaechei RF2	Syngas	Ethanol	25.30 g/L (reactor)	[107]
Eubacterium limosum KIST612	Syngas	Acetic acid	34.40 g/L	[108]

Strain

Substrate combination

Target product

Titer/productivity

References

Clostridium carboxidivorans

Syngas

Ethanol

3 g/L

[100]

Clostridium carboxidivorans

Syngas

Butanol

0.35 g/L

[100]

Clostridium carboxidivorans

Syngas

Hexanol

1.90 g/L

[101]

Clostridium ljungdahlii

CO adjusted to 70%

Hexanol

0.39 g/L

[102]

Clostridium ljungdahlii

CO₂/H₂

Hexanol

2-5-fold

[103]

Clostridium ljungdahlii

Syngas

Ethanol

30.10 g/L (2 L fermenter)

[104]

Clostridium acetobutylicum ATCC 824

Syngas

3-HP

9.25 g/L

[105]

Enterobacter hormaechei RF2

Syngas

Ethanol

25.30 g/L (reactor)

[107]

Eubacterium limosum KIST612

Syngas

Acetic acid

34.40 g/L

[108]

Strain	Substrate combination	Target product	Titer/productivity	References
Pichia pastoris Saccharomyces cerevisiae	Methanol/famate/CO₂	5-aminolevulinic acid Lactic acid	0.71 mg/L 0.21 g/L	[109]
Methanol/famate/CO₂	[109]
Methylorubrum extorquens AM1	Methanol/acetate	Violacein	118 mg/L	[110]
Eubacterium limosum	Famate/methanol	Butanol	2 mmol/L	[111]
Pseudomonas gessardii	Famate/acetate	Medium-chain-length polyhydroxyalkanoates (mcl-PHA)	0.40 g/L	[112]
Saccharomyces cerevisiae	Famate/acetate	Fatty acids	6.60 g/L	[113]
Yarrowia lipolytica	Famate/acetate/electrocatalysis CO₂	β-farnesene	14.80 g/L	[114]
Escherichia coli	Famate/acetate	Biomass	-	[115]
Clostridium acetobutylicum	Famate/acetate	Acetone-butanol-ethanol (ABE)	8.60 g/L	[116]
Escherichia coli	Famate/acetate	L-homoserine	15.96 g/L	[117]

Strain

Substrate combination

Target product

Titer/productivity

References

Pichia pastoris

Saccharomyces cerevisiae

Methanol/famate/CO₂

5-aminolevulinic acid

Lactic acid

0.71 mg/L

0.21 g/L