Research Progress in the Improvement of Microbial Strain Tolerance and Efficiency of Biological Manufacturing Based on Transporter Engineering

doi:10.13560/j.cnki.biotech.bull.1985.2023-0750

Abstract

Abstract:

Microbial cell factories have been extensively used for sustainable production of biofuels, as well as high value and bulk chemicals. However, high concentration products or substrates, as well as stressful conditions during industrial production, may compromise fermentation efficiency and decreasing economics of production. In this context, microbial stress tolerance is crucial for green and sustainable production of the target products. In recent years, the use of transporters to protect microbial cells from toxic compounds for enhancing strain tolerance has received increasing worldwide attention. This review summarizes the progress of studies on microbial strain tolerance enhancement based on transporter engineering, analyses the current key points in the field of transporter research and discusses strategies to enhance strain tolerance based on transporter manipulation. Especially，the review highlights the applications of artificial intelligence in transporter annotation, structure simulation and substrate-transporter interaction prediction, aiming to promote the application of microorganisms in biological manufacturing.

Key words: green biological manufacturing, membrane transporter, stress tolerance, industrial microorganism, fermentation efficiency

LI Xin-yue, ZHOU Ming-hai, FAN Ya-chao, LIAO Sha, ZHANG Feng-li, LIU Chen-guang, SUN Yue, ZHANG Lin, ZHAO Xin-qing. Research Progress in the Improvement of Microbial Strain Tolerance and Efficiency of Biological Manufacturing Based on Transporter Engineering[J]. Biotechnology Bulletin, 2023, 39(11): 123-136.

Figures/Tables 4

References 101

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蛋白名称 Protein name	转运蛋白类型 Type of transporter	蛋白来源 Source of protein	宿主 Host	耐受类型 Tolerance type	改进策略 Strategy	结果 Result	参考文献 Reference
木质纤维素水解液胁迫Stress of lignocellulosic hydrolysate
ZMO0799, ZMO0800, ZMO1288, ZMO1856, ZMO0282, ZMO0798	ATP-binding Cassette（ABC）Superfamily	Zymomonas mobilis	Z. mobilis	酚醛 Phenolic aldehyde	过表达，敲除Overexpression, knockout	六种转运蛋白被认为与酚醛耐受性相关	[27]
Pdr5, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌体生长加快，菌株延滞期缩短	[28]
Pdr5, Yor1, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌株香兰素耐受性提升	[29]
酸胁迫Acid stress
Ady2	Acetate Uptake Transporter Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	菌株在乙酸，过氧化氢，甲酸胁迫下的生长性能提升，ADY2敲除突变体在3.6 g/L乙酸胁迫条件下的乙醇生产强度提高了14.66%	[25]
Rta1	Lipid-translocating Exporter Family	Cryptococcus humicola	S. cerevisiae	酸 Acid	异源表达 Heterologous expression	重组菌株在酸铝胁迫下延滞期比对照减少12 h	[30]
ThiT	Vitamin Uptake Transporter Family	L. lactisNZ9000	L. lactisNZ9000	酸 Acid	过表达 Overexpression	菌株在酸胁迫下最大存活率提升16.2倍	[31]
Zrt3	Zinc-Iron Permease Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	对乙酸介导的调节性细胞死亡具有高度抗性，液泡功能障碍减少	[32]
Jen1	Major Facilitator Superfamily（MFS）	S. cerevisiae	S. cerevisiae	L-乳酸 L-lactic acid	过表达 Overexpression	菌株乳酸产量从43.6 g/L提升到51.4 g/L	[33]
盐胁迫Salt stress
Nha2	Monovalent Cation: Proton Antiporter-1 Family	Y. lipolytica	S. cerevisiae	钠盐 Na⁺	异源表达 Heterologous expression	酿酒酵母中的Na⁺耐受性显著提升	[34]
BusA	ABC Superfamily	Tetragenococcus halophilus	E. coli	高浓度盐 High salinity	异源表达 Heterologous expression	大肠杆菌MKH13的盐耐受能力显著提升	[35]
BmrA, BmrB	ABC Superfamily	Bifidobacterium longumBBMN68	L. lactisNZ9000	胆汁盐 Bile salt	异源表达 Heterologous expression	菌株对牛胆汁（0.10%）的耐受性提高20.77倍	[36]
HKT2;1	K⁺Transporter（Trk）Family	Aeluropus lagopoides	A. lagopoides	钾盐 K⁺	异源表达Heterologous expression	使K⁺吸收缺陷（WΔ6）和Na⁺敏感酵母突变体（G19）能够在>1 mmol/L KCl浓度下生长	[37]
Trk1	Trk Family	Candida glabrata	S. cerevisiae	高浓度盐 High salinity	异源表达 Heterologous expression	细胞盐离子耐受性提升	[38]
产物胁迫Product stress
Abc2, Abc3	ABC Superfamily	Y. lipolytica	S. cerevisiae	烷烃 Alkane	异源表达 Heterologous expression	酿酒酵母对癸烷和十一烷的耐受性显著增加，ABC2转运蛋白将酿酒酵母对癸烷的耐受限度提高约80倍	[39]
AcrE, MdtE, MdtC, Cmr	Membrane Fusion Protei Family; MFS	E. coli	E. coli	脂肪酸 Fatty acid	过表达 Overexpression	菌株中链脂肪酸滴度增加两倍以上	[40]
Abc2, Abc3	ABC Superfamily	Grosmannia ciavigera, Y. Iipoiytica	S. cerevisiae	桧烯，青篙二烯Hinokiene, Pennyroyalene	异源表达 Heterologous expression	ABC3的表达使桧烯的产量提高34.5%，表达ABC2使青蒿二烯产量提高24.6%，外排率提高16.0%	[41]
YALI0F19492g	Cor413im1 Family	Y. lipolytica	Y. lipolytica	柠檬烯 Limonene	过表达 Overexpression	显著提高了菌株的柠檬烯耐受性	[42]
Esbp6	MFS	S. cerevisiae	S. cerevisiae	香豆酸Coumaric acid	过表达 Overexpression	菌株对芳香酸的耐受性提升，菌株的香豆酸分泌显著改善	[43]
Bpt1	ABC Superfamily	S. cerevisiae	S. cerevisiae	大麻二酚 Cannabidiol	过表达 Overexpression	大麻二酚产量达到6.92 mg/L，较出发菌株产量提高100倍	[44]
Trk1	Trk Family	S. cerevisiae	S. cerevisiae	丙酸Propionic acid	适应性实验室进化 Adaptive laboratory evolution	菌株对丙酸和多种有机酸的耐受能力提升	[45]
YcjP	ABC Superfamily	E. coli	E. coli	咖啡酸Caffeic acid	过表达 Overexpression	菌株的咖啡酸滴度达到 775.7 mg/L（对照589.9 mg/L）	[46]

蛋白名称 Protein name	转运蛋白类型 Type of transporter	蛋白来源 Source of protein	宿主 Host	耐受类型 Tolerance type	改进策略 Strategy	结果 Result	参考文献 Reference
木质纤维素水解液胁迫Stress of lignocellulosic hydrolysate
ZMO0799, ZMO0800, ZMO1288, ZMO1856, ZMO0282, ZMO0798	ATP-binding Cassette（ABC）Superfamily	Zymomonas mobilis	Z. mobilis	酚醛 Phenolic aldehyde	过表达，敲除Overexpression, knockout	六种转运蛋白被认为与酚醛耐受性相关	[27]
Pdr5, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌体生长加快，菌株延滞期缩短	[28]
Pdr5, Yor1, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌株香兰素耐受性提升	[29]
酸胁迫Acid stress
Ady2	Acetate Uptake Transporter Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	菌株在乙酸，过氧化氢，甲酸胁迫下的生长性能提升，ADY2敲除突变体在3.6 g/L乙酸胁迫条件下的乙醇生产强度提高了14.66%	[25]
Rta1	Lipid-translocating Exporter Family	Cryptococcus humicola	S. cerevisiae	酸 Acid	异源表达 Heterologous expression	重组菌株在酸铝胁迫下延滞期比对照减少12 h	[30]
ThiT	Vitamin Uptake Transporter Family	L. lactisNZ9000	L. lactisNZ9000	酸 Acid	过表达 Overexpression	菌株在酸胁迫下最大存活率提升16.2倍	[31]
Zrt3	Zinc-Iron Permease Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	对乙酸介导的调节性细胞死亡具有高度抗性，液泡功能障碍减少	[32]
Jen1	Major Facilitator Superfamily（MFS）	S. cerevisiae	S. cerevisiae	L-乳酸 L-lactic acid	过表达 Overexpression	菌株乳酸产量从43.6 g/L提升到51.4 g/L	[33]
盐胁迫Salt stress
Nha2	Monovalent Cation: Proton Antiporter-1 Family	Y. lipolytica	S. cerevisiae	钠盐 Na⁺	异源表达 Heterologous expression	酿酒酵母中的Na⁺耐受性显著提升	[34]
BusA	ABC Superfamily	Tetragenococcus halophilus	E. coli	高浓度盐 High salinity	异源表达 Heterologous expression	大肠杆菌MKH13的盐耐受能力显著提升	[35]
BmrA, BmrB	ABC Superfamily	Bifidobacterium longumBBMN68	L. lactisNZ9000	胆汁盐 Bile salt	异源表达 Heterologous expression	菌株对牛胆汁（0.10%）的耐受性提高20.77倍	[36]
HKT2;1	K⁺Transporter（Trk）Family	Aeluropus lagopoides	A. lagopoides	钾盐 K⁺	异源表达Heterologous expression	使K⁺吸收缺陷（WΔ6）和Na⁺敏感酵母突变体（G19）能够在>1 mmol/L KCl浓度下生长	[37]
Trk1	Trk Family	Candida glabrata	S. cerevisiae	高浓度盐 High salinity	异源表达 Heterologous expression	细胞盐离子耐受性提升	[38]
产物胁迫Product stress
Abc2, Abc3	ABC Superfamily	Y. lipolytica	S. cerevisiae	烷烃 Alkane	异源表达 Heterologous expression	酿酒酵母对癸烷和十一烷的耐受性显著增加，ABC2转运蛋白将酿酒酵母对癸烷的耐受限度提高约80倍	[39]
AcrE, MdtE, MdtC, Cmr	Membrane Fusion Protei Family; MFS	E. coli	E. coli	脂肪酸 Fatty acid	过表达 Overexpression	菌株中链脂肪酸滴度增加两倍以上	[40]
Abc2, Abc3	ABC Superfamily	Grosmannia ciavigera, Y. Iipoiytica	S. cerevisiae	桧烯，青篙二烯Hinokiene, Pennyroyalene	异源表达 Heterologous expression	ABC3的表达使桧烯的产量提高34.5%，表达ABC2使青蒿二烯产量提高24.6%，外排率提高16.0%	[41]
YALI0F19492g	Cor413im1 Family	Y. lipolytica	Y. lipolytica	柠檬烯 Limonene	过表达 Overexpression	显著提高了菌株的柠檬烯耐受性	[42]
Esbp6	MFS	S. cerevisiae	S. cerevisiae	香豆酸Coumaric acid	过表达 Overexpression	菌株对芳香酸的耐受性提升，菌株的香豆酸分泌显著改善	[43]
Bpt1	ABC Superfamily	S. cerevisiae	S. cerevisiae	大麻二酚 Cannabidiol	过表达 Overexpression	大麻二酚产量达到6.92 mg/L，较出发菌株产量提高100倍	[44]
Trk1	Trk Family	S. cerevisiae	S. cerevisiae	丙酸Propionic acid	适应性实验室进化 Adaptive laboratory evolution	菌株对丙酸和多种有机酸的耐受能力提升	[45]
YcjP	ABC Superfamily	E. coli	E. coli	咖啡酸Caffeic acid	过表达 Overexpression	菌株的咖啡酸滴度达到 775.7 mg/L（对照589.9 mg/L）	[46]

模型 Model	功能 Function	AI算法 AI method	特征提取方法 Feature extraction method	数据集 Data set	年份Year	参考文献Reference
转运蛋白分类和功能注释Classification and functional annotation of transporters
ISTRF	鉴定蔗糖转运蛋白	随机森林 Random Forest	k-separated-bigrams-PSSM	382个SUT蛋白（蔗糖转运蛋白）和911个非SUT蛋白	2022	[69]
Fiamenghi’s model	鉴定木糖转运蛋白	梯度提升决策树 Gradient Boosting Decision Tree Classifier	HMM and PSSM profiles	396个蛋白质（其中25个能够转运木糖）	2022	[70]
Srinivasan’s model	鉴定托烷生物碱（TA）转运蛋白	监督分类器 Supervised Classifier Models	基因的序列信息	15个TA和15 333个非TA基因	2021	[71]
GT-Finder	葡萄糖转运蛋白家族的分类	BERT语言模型 Pre-trained BERT Language Models	使用预训练的BERT模型生成上下文相关的词嵌入	510个GLUT、225个SGLT和190个SWEET葡萄糖转运蛋白	2021	[72]
TooT-T	鉴别转运蛋白与非转运蛋白	支持向量机 Support Vector Machine	位置特异性迭代氨基酸组成、成对氨基酸组成和伪氨基酸组成	900个转运蛋白与660个非转运蛋白	2020	[73]
膜转运蛋白的结构预测Structure prediction of transporters
Bergman’s model	研究底物结合诱导的ω-3脂肪酸转运蛋白MFSD2A中的构象转变	深度神经网络分类算法 Deep Neural Network（DNN）Classification Algorithm	将每个轨迹帧转换为适合输入到DNN的视觉表示	分子动力学模拟的数据，4 000个轨迹帧（步幅160 ps）表示OFS集合和4 000帧（步幅为160 ps）表示选择的OcS状态集合	2023	[74]
Mitrovic’s model	研究糖转运蛋白超家族转运周期中的构象变化	卷积神经网络 Convolutional Neural Network	通过协同进化得分过滤实验接触图	实验确定的结构的接触图	2023	[75]
底物与转运蛋白相互作用预测Prediction of the interactions between substrates and transporters
Denger’s model	预测大肠杆菌、拟南芥、酿酒酵母以及人类膜转运蛋白的底物	支持向量机 Support Vector Machine	氨基酸组成、PSSM	具有已知底物的膜转运蛋白的数据集来自手动策划的Swiss-Prot数据库	2022	[76]
Nguyen’s model	鉴定转运蛋白的底物特异性	神经网络 Neural Network	使用词嵌入向量作为蛋白质的特征	1 197个转运蛋白，其中73个氨基酸转运蛋白、221个电子转运蛋白、88个氢离子转运蛋白、78个脂质转运蛋白、455个蛋白质/mRNA转运蛋白，84个糖转运蛋白，198种其他转运蛋白和1 050种膜蛋白	2019	[77]

模型 Model	功能 Function	AI算法 AI method	特征提取方法 Feature extraction method	数据集 Data set	年份Year	参考文献Reference
转运蛋白分类和功能注释Classification and functional annotation of transporters
ISTRF	鉴定蔗糖转运蛋白	随机森林 Random Forest	k-separated-bigrams-PSSM	382个SUT蛋白（蔗糖转运蛋白）和911个非SUT蛋白	2022	[69]
Fiamenghi’s model	鉴定木糖转运蛋白	梯度提升决策树 Gradient Boosting Decision Tree Classifier	HMM and PSSM profiles	396个蛋白质（其中25个能够转运木糖）	2022	[70]
Srinivasan’s model	鉴定托烷生物碱（TA）转运蛋白	监督分类器 Supervised Classifier Models	基因的序列信息	15个TA和15 333个非TA基因	2021	[71]
GT-Finder	葡萄糖转运蛋白家族的分类	BERT语言模型 Pre-trained BERT Language Models	使用预训练的BERT模型生成上下文相关的词嵌入	510个GLUT、225个SGLT和190个SWEET葡萄糖转运蛋白	2021	[72]
TooT-T	鉴别转运蛋白与非转运蛋白	支持向量机 Support Vector Machine	位置特异性迭代氨基酸组成、成对氨基酸组成和伪氨基酸组成	900个转运蛋白与660个非转运蛋白	2020	[73]
膜转运蛋白的结构预测Structure prediction of transporters
Bergman’s model	研究底物结合诱导的ω-3脂肪酸转运蛋白MFSD2A中的构象转变	深度神经网络分类算法 Deep Neural Network（DNN）Classification Algorithm	将每个轨迹帧转换为适合输入到DNN的视觉表示	分子动力学模拟的数据，4 000个轨迹帧（步幅160 ps）表示OFS集合和4 000帧（步幅为160 ps）表示选择的OcS状态集合	2023	[74]
Mitrovic’s model	研究糖转运蛋白超家族转运周期中的构象变化	卷积神经网络 Convolutional Neural Network	通过协同进化得分过滤实验接触图	实验确定的结构的接触图	2023	[75]
底物与转运蛋白相互作用预测Prediction of the interactions between substrates and transporters
Denger’s model	预测大肠杆菌、拟南芥、酿酒酵母以及人类膜转运蛋白的底物	支持向量机 Support Vector Machine	氨基酸组成、PSSM	具有已知底物的膜转运蛋白的数据集来自手动策划的Swiss-Prot数据库	2022	[76]
Nguyen’s model	鉴定转运蛋白的底物特异性	神经网络 Neural Network	使用词嵌入向量作为蛋白质的特征	1 197个转运蛋白，其中73个氨基酸转运蛋白、221个电子转运蛋白、88个氢离子转运蛋白、78个脂质转运蛋白、455个蛋白质/mRNA转运蛋白，84个糖转运蛋白，198种其他转运蛋白和1 050种膜蛋白	2019	[77]