中国科学院微生物研究所、中国微生物学会主办
文章信息
- 孙佳琦, 曹燕亭, 吕雪芹, 李江华, 刘龙, 堵国成, 陈坚, 刘延峰
- SUN Jiaqi, CAO Yanting, LÜ Xueqin, LI Jianghua, LIU Long, DU Guocheng, CHEN Jian, LIU Yanfeng
- 枯草芽孢杆菌中高效响应N-乙酰神经氨酸生物传感器的构建
- Development of biosensors highly responsive to N-acetylneuraminic acid in Bacillus subtilis
- 生物工程学报, 2023, 39(5): 2502-2516
- Chinese Journal of Biotechnology, 2023, 39(5): 2502-2516
- 10.13345/j.cjb.220961
-
文章历史
- Received: December 1, 2022
- Accepted: February 22, 2023
- Published: March 2, 2023
2. 江南大学未来食品科学中心, 江苏 无锡 214122
2. Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China
N-乙酰神经氨酸(N-acetylneuraminic acid, NeuAc)又称燕窝酸,是一种带负电荷的功能性单糖,存在于多种动物、植物、微生物中,也是人体中唾液酸的主要存在形式[1]。NeuAc不仅是唾液酸化人乳低聚糖的重要单体之一,也是神经节苷脂的传递递质,因此常作为食品添加剂促进婴幼儿的大脑和骨骼发育,有利于维持老年人的大脑健康,减缓老年痴呆症等神经系统疾病的发展[2-4]。此外,NeuAc也被应用于药物、疾病治疗等领域[5-6]。由于天然提取法和化学合成法的局限性,目前,N-乙酰神经氨酸更多地用生物合成法获得,主要包括酶法合成、全细胞催化法以及微生物发酵法。其中,酶法合成和全细胞催化都需要添加昂贵的前体物质,造成生产成本的增加,而微生物发酵法可以通过廉价碳源实现N-乙酰神经氨酸的从头合成,成为最具有潜力的生产策略[1]。
枯草芽孢杆菌(Bacillus subtilis)作为一种革兰氏阳性模式菌株,拥有成熟的基因操作工具,且无内毒素,被美国食品药品监督管理局(Food and Drug Administration, FDA)认证为食品级微生物[7]。在工业生产中,枯草芽孢杆菌因生长迅速、对培养条件要求较低、不易受噬菌体侵染等优势而备受关注,广泛应用于食品、药品、酶制剂等的生产[8-11],是合成N-乙酰神经氨酸的优良宿主。
生物传感器(biosensor)作为一种重要的合成生物学工具,在细胞工厂中具有多种应用。代谢工程中常用的生物传感器可分为基于RNA的核糖开关(riboswitches)和适体酶(aptazymes),以及基于转录因子的传感器[transcription factor (TF)-based biosensors] [12],其中基于转录因子的传感器应用更为广泛,在适应性进化、高活性酶筛选、高产菌株筛选、动态调控、基因型和表型异质性调控等方面都发挥了重要作用[13-17]。Pang等[18]在大肠杆菌(Escherichia coli)中构建了响应NeuAc的适体酶,基于此开发了生长偶联的筛选策略,并获得了NeuAc产量提高42%高产菌株。Peters等[19]在大肠杆菌中构建了基于转录因子的响应NeuAc的生物传感器,并用不同产量的菌株验证了生物传感器作为高通量筛选方法的适用性。目前,基于大肠杆菌来源的NanR,Zhang等[20]在枯草芽孢杆菌中构建了可响应NeuAc的生物传感器,但是其动态范围和灵敏度不高,限制了其在指导NeuAc生产方面的应用。
本研究选取短双歧杆菌(Bifidobacterium breve)来源NanR (Bbr_NanR)在枯草芽孢杆菌中构建了可高效响应NeuAc的生物传感器。首先测试和优化了来源于大肠杆菌的NeuAc转运蛋白NanT和NanC的转运能力,获得了一系列具有不同转运能力的菌株,以用于后续响应NeuAc的生物传感器的验证;随后,将Bbr_NanR特异性结合序列(NanR binding site)插入到枯草芽孢杆菌组成型启动子的不同位置,构建了一系列杂合启动子,并用这些杂合启动子调控绿色荧光蛋白(green fluorescent protein, GFP)的表达,选择了一系列有活性的杂合启动子用于后续实验;接下来,通过在具有NeuAc转运能力的枯草芽孢杆菌中表达Bbr_NanR,选择能响应NeuAc的杂合启动子,并在此基础之上进一步优化Bbr_NanR表达量,成功获得了一系列可以在枯草芽孢杆菌中高效响应NeuAc的生物传感器,它们能灵敏地响应胞内NeuAc浓度的变化,且具有更高的动态范围。本文构建的响应NeuAc的生物传感器可用于高产NeuAc的酶突变体和枯草芽孢杆菌菌株的筛选,为枯草芽孢杆菌生物合成NeuAc提供了高效、灵敏的分析和调控工具。
1 材料与方法 1.1 材料 1.1.1 菌株和质粒本研究所用初始菌株与质粒均为实验室保藏。详细信息见表 1和表 2。
Strains | Descriptions | Sources |
B. subtilis 168 | Wild-type strain | Lab store |
E. coli JM109 | Wild-type strain | Lab store |
BSXC | B. subtilis 168 derivate, overexpression of comK under the control of promoter PxylA | Lab store |
BS-nanT | Derivate, overexpression of nanT under the control of promoter P43 | This study |
BS-nanC | BSXC derivate, overexpression of nanC under the control of promoter P43 | This study |
BSP1 | BSXC derivate, overexpression of nanT under the control of promoter PxpaC | This study |
BSP2 | BSXC derivate, overexpression of nanT under the control of promoter PyceC | This study |
BSP3 | BSXC derivate, overexpression of nanT under the control of promoter PlytR | This study |
BSP4 | BSXC derivate, overexpression of nanT under the control of promoter PsdhB | This study |
BSP6 | BSXC derivate, overexpression of nanT under the control of promoter PodhA | This study |
BSP7 | BSXC derivate, overexpression of nanT under the control of promoter P333 | This study |
BSP8 | BSXC derivate, overexpression of nanT under the control of promoter PyvyD | This study |
BSP9 | BSXC derivate, overexpression of nanT under the control of promoter Pveg | This study |
BSP10 | BSXC derivate, overexpression of nanT under the control of promoter P566 | This study |
Plasmids | Descriptions | Sources |
pHT01 | Expression vector | Lab store |
p7S6P43 | p7S6 containing P43 promoter | [7] |
p7S6P1 | p7S6 containing PxpaC promoter | Lab store |
p7S6P2 | p7S6 containing PyceC promoter | Lab store |
p7S6P3 | p7S6 containing PlytR promoter | Lab store |
p7S6P4 | p7S6 containing PsdhB promoter | Lab store |
p7S6P6 | p7S6 containing PodhA promoter | Lab store |
p7S6P7 | p7S6 containing P333 promoter | Lab store |
p7S6P8 | p7S6 containing PyvyD promoter | Lab store |
p7S6P9 | p7S6 containing Pveg promoter | Lab store |
p7S6P10 | p7S6 containing P566 promoter | Lab store |
pHT-veg-gfp | pHT01 with promoter Pveg linked with gfp | Lab store |
pHT-nanR | pHT01 with promoter PrpsT linked with nanR | This study |
pHT-N1-gfp | pHT01 with promoter N1 linked with gfp | This study |
pHT-N2-gfp | pHT01 with promoter N2 linked with gfp | This study |
pHT-N4-gfp | pHT01 with promoter N4 linked with gfp | This study |
pHT-N5-gfp | pHT01 with promoter N5 linked with gfp | This study |
pHT-N6-gfp | pHT01 with promoter N6 linked with gfp | This study |
pHT-N7-gfp | pHT01 with promoter N7 linked with gfp | This study |
pHT-N9-gfp | pHT01 with promoter N9 linked with gfp | This study |
pHT-N10-gfp | pHT01 with promoter N10 linked with gfp | This study |
pHT-N11-gfp | pHT01 with promoter N11 linked with gfp | This study |
pHT-N12-gfp | pHT01 with promoter N12 linked with gfp | This study |
pHT-N13-gfp | pHT01 with promoter N13 linked with gfp | This study |
pHT-N14-gfp | pHT01 with promoter N14 linked with gfp | This study |
pHT-N15-gfp | pHT01 with promoter N15 linked with gfp | This study |
pHT-N16-gfp | pHT01 with promoter N16 linked with gfp | This study |
pHT-N17-gfp | pHT01 with promoter N17 linked with gfp | This study |
pHT-N18-gfp | pHT01 with promoter N18 linked with gfp | This study |
pHT-N19-gfp | pHT01 with promoter N19 linked with gfp | This study |
pHT-N20-gfp | pHT01 with promoter N20 linked with gfp | This study |
pHT-N21-gfp | pHT01 with promoter N21 linked with gfp | This study |
PrpsT-N1 | Expression of nanR by PrpsT promoter in pHT-N1-gfp | This study |
PrpsT-N2 | Expression of nanR by PrpsT promoter in pHT-N2-gfp | This study |
PrpsT-N4 | Expression of nanR by PrpsT promoter in pHT-N4-gfp | This study |
PrpsT-N10 | Expression of nanR by PrpsT promoter in pHT-N10-gfp | This study |
PrpsT-N11 | Expression of nanR by PrpsT promoter in pHT-N11-gfp | This study |
PrpsT-N14 | Expression of nanR by PrpsT promoter in pHT-N14-gfp | This study |
PrpsT-N16 | Expression of nanR by PrpsT promoter in pHT-N16-gfp | This study |
PrpsT-N17 | Expression of nanR by PrpsT promoter in pHT-N17-gfp | This study |
P535-N2 | Expression of nanR by P535 promoter in pHT-N2-gfp | This study |
Pt8-N2 | Expression of nanR by Pt8 promoter in pHT-N2-gfp | This study |
P566-N2 | Expression of nanR by Pt9 promoter in pHT-N2-gfp | This study |
P535-N17 | Expression of nanR by P535 promoter in pHT-N17-gfp | This study |
Pt8-N17 | Expression of nanR by Pt8 promoter in pHT-N17-gfp | This study |
P566-N17 | Expression of nanR by Pt9 promoter in pHT-N17-gfp | This study |
P566-N10 | Expression of nanR by Pt9 promoter in pHT-N10-gfp | This study |
LB培养基:5 g/L酵母粉,10 g/L蛋白胨,10 g/L NaCl,固体培养基添加20 g/L的琼脂粉。根据需要添加相应浓度的抗生素(氯霉素终浓度为5 mg/L,壮观霉素终浓度为100 mg/L,氨苄霉素终浓度为100 mg/L)。
1.1.3 主要试剂PrimeStar max聚合酶购于TaKaRa公司;质粒提取、感受态制备试剂盒购于生工生物工程(上海)股份有限公司;GeneJET PCR纯化试剂盒购于Thermo Scientific公司。
1.2 方法 1.2.1 基因序列与引物合成将NCBI数据库中B. breve UCC2003来源NanR的氨基酸序列送至苏州金唯智公司进行密码子优化(表达宿主:Bacillus subtilis 168)并合成,Bbr_NanR结合位点参考Egan等[21]的报道。文中所有引物的合成和测序反应均由苏州金唯智生物科技有限公司或者生工生物工程(上海)股份有限公司(以下简称“上海生工”)完成,本文部分引物信息见表 3,更换启动子所用引物根据表 4启动子序列进行设计。
Primer names | Primer sequences (5′→3′) | Used for |
ydbD-1f | ATCAAGAAATCGGCCGAAAAGGCG | Construction of BS-nanT (nanC) |
ydbD-1r | CTGTTTCCTGTGTGAAATTGTTATCCGCTCGGCTTTGATTATGCCTTCAGAACCGTCC | |
p7SP43-2f | GAGCGGATAACAATTTCACACAGGAAACAG | |
p7SP43-nanC-2r | CAGAAAGTATTTTAGCCTTTTTCATGTGTACATTCCTCTCTTACCTATAATGGTACCGC | |
p7SP43-nanT-2r | GGTTGTAGTACTCATGTGTACATTCCTCTCTTACCTATAATGGTACCGC | |
nanC-3f | GGAATGTACACATGAAAAAGGCTAAAATACTTTCTGGCGTATTATTACT | |
nanC-3r | CTACAGTTTAAATGACACACCAATGCGAT | |
nanT-3f | AGAGAGGAATGTACACATGAGTACTACAACCCAGAATATCCCGTG | |
nanT-3r | TTAACTTTTGGTTTTGACTAAATCGTTTTTGGCG | |
nanC_ydbD-4f | ATCGCATTGGTGTGTCATTTAAACTGTAGCAAAACATCATGTTTTGGGCTTGTCTCT | |
nanT_ydbD-4f | CGCCAAAAACGATTTAGTCAAAACCAAAAGTTAACAAAACATCATGTTTTGGGCTT GTC |
|
ydbD-4r | GAAACAAGAATGGATGAAGCGTCTTGAC | |
rh_ydbD-F | CGCAACAACGACTAATCAAAAGCATAAAGGC | |
rh_ydbD-R | CGAAAAGATTCATAGTTCCCATCTCGATACGC | |
Spc-P1-R | CACGGGATATTCTGGGTTGTAGTACTCATTTTTATCACCTCCTTTCTCAGTTTTAATAT TATTATCTACTACGTTC |
Optimization of nanT expression |
Spc-P2-R | CACGGGATATTCTGGGTTGTAGTACTCATTTTTATCACCTCCTTTATAGTCACATTTAT TTTTACGCTCAC |
|
N2-F | GACATAAGACATCAGATGTCGTATAATAAATGTAAAGGAGGTGATAAAAATGGGTAAGGGAG | Construction of plasmids carrying hybrid promoters |
N2-R | GACATCTGATGTCTTATGTCAAATAAAATTTAAATTATATCAACGTTAATAAAAGTTC AAGCGAAAACATACCACCTATCA |
|
pN-F | CGTCCATGGAGATCTTTGTCTGCA | Construction of plasmids carrying biosensor |
pN-R | CGGGATTTCCCGGCAGTCTGACAAGTTATTCTGCAATAGTTATTTGTATAGTTCATCC ATGCCATGTGTAATCCC |
|
nanR-F | GAATAACTTGTCAGACTGCCGGGAAATCCCGGCAGTCTTTTTTCCATTAAAACACGG CTTATTCTGTGCCGCCTTGGATTCTC |
|
nanR-R | TGCAGACAAAGATCTCCATGGACG | |
P535-F | GAATTGTAGGATTAAGCAACCCTCTATTTCGAGAGGCCGTTTTTTCGTCCATGGAGA TCTTTGTCTGCA |
Optimization of nanR expression |
P535-R | TCGAAATAGAGGGTTGCTTAATCCTACAATTCTTGATATAATTAGTTGTGCTAAAGGAGGTGAAATGTACACATGAGCA |
Promoters | Sequences (5′→3′) |
Pveg | TTATTAACGTTGATATAATTTAAATTTTATTTGACAAAAATGGGCTCGTGTTGTACAATAAATGT |
P43 | TCTTACATTTATTTTACATTTTTAGAAATGGGCGTGAAAAAAAGCGCGCGATTATGTAAAATATAAAGTGATAGCGGTACCATTATAG |
Pm1 | AAAAATAAAAAAAAAGTGTTGACAAAAAAATCAAAATATGGTATAATTGAAA |
P214 | CAAAATCGCAAAAAAGTGTTGACAACTTAACTCAGATCTGGTATAATAGAAA |
P566 | AAAAAACGGCCTCTCGAAATAGAGGGTTGACACTCTTTTGAGAATATGTTATATTATCAG |
P535 | AAAAAACGGCCTCTCGAAATAGAGGGTTGCTTAATCCTACAATTCTTGATATAATTAGTTGTGCT |
N1 | TTATTAACGTTGATATAATTTAAATTTTATTTGACAAAAATGGGCTCGTGTTGTACAATAAGACATCAGATGTCATAT |
N2 | TTATTAACGTTGATATAATTTAAATTTTATTTGACATAAGACATCAGATGTCGTATAATAAATGT |
N3 | TTATTAACGTTGATATAATTTAAATTTTATTTGACATAAGACATCAGATGTCGTACAATAAATGT |
N4 | TTATTAACGTTGATATAATTTAAATTATAAGACATCAGATGTCATATTTGACAAAAATGGGCTCGTGTTGTACAATAAATGT |
N5 | TCTTACATTTATTTTACATTTTTAGAAATGGGCGTGAAAAAAAGCGCGCGATTATGTAAAATTAGACATCAGACGTCTTATAAAGTGATAGCGGTACCATTATAG |
N6 | TCTTACATTTATTTTACATTTTTAGAAATGGGCGTGAAAATAAGACATCAGATGTCTAATATATAAAGTGATAGCGGTACCATTATAG |
N7 | TCTTACATTTATTTTACATTTTTAGAAATGGGCGTGAAAATAAGACATCAGATGTCTAAAATATAAAGTGATAGCGGTACCATTATAG |
N8 | TCTTACATTTATTTTACATTTTTATCAGACATCAGATGTCAAATGGGCGTGAAAAAAAGCGCGCGATTATGTAAAATATAAAGTGATAGCGGTACCATTATAG |
N9 | AAAAATAAAAAAAAAGTGTTGACAAAAAAATCAAAATATGGTATAATCAGACATCAGATGTCATATTGAAA |
N10 | AAAAATAAAAAAAAAGTGTTGACAATTAGACATCAGACGTCTTATAATTGAAA |
N11 | AAAAATAAAAAAAAAATAAGACATCAGAAGTCAAATGTGTTGACAAAAAAATCAAAATATGGTATAATTGAAA |
N12 | CAAAATCGCAAAAAAGTGTTGACAACTTAACTCAGATCTGGTATAATTAGACATCAGACGTCTGATAGAAA |
N13 | CAAAATCGCAAAAAAGTGTTGACAATTAGACATCAGACGTCGTATAATAGAAA |
N14 | CAAAATCGCAAATAAGACATCAGAAGTCAAATGTGTTGACAACTTAACTCAGATCTGGTATAATAGAAA |
N15 | AAAAAACGGCCTCTCGAAATAGAGGGTTGACACTCTTTTGAGAATATGTTATATTAGACATCAGACGTCTGATATCAG |
N16 | AAAAAACGGCCTCTCGAAATAGAGGGTTGACAATCAGACATCAGAAGTCTTATATTATCAG |
N17 | AAAAAACGGCCTCTCGAAATAATTAGACATCAGACGTCGTATGAGGGTTGACACTCTTTTGAGAATATGTTATATTATCAG |
N18 | AAAAAACGGCCTCTCGAAATAGAGGGTTGCTTAATCCTACAATTCTTGATATAATTAGACATCAGACGTCTGAT |
N19 | AAAAAACGGCCTCTCGAAATAGAGGGTTGCTTAAATCAGACATCAGAAGTATAATTAGTTGTGCT |
N20 | AAAAAACGGCCTCTCGAAATAGAGGGTTGCTTAAATCAGACATCAGAAGTCTAATTAGTTGTGCT |
N21 | AAAAAACGGCCTCTCGAAATATTAGACATCAGACGTCGGATGCTTAATCCTACAATTCTTGATATAATTAGTTGTGCT |
PrpsT | CACAGTGTCTTAAGGTTAAATCTTCTTCACAATAGAACAAATTGTATTCTATCAAACACACCTTTAGATTGCAATATAAA |
Pt8 | TTCAAACTATGAGAATATTATACAACACGAGCCCATTTTTGTCAAATAAAATTTAAATTAAAAATTCTATGATTCCTCAA |
BS-nanC、BS-nanT的构建:以B. subtilis 168基因组为模板,通过聚合酶链式反应(polymerase chain reaction, PCR)扩增ydbD位点上下游各1 000 bp的序列作为左右同源臂,以E. coli JM109基因组为模板,扩增nanC、nanT基因,以实验室保存质粒p7S6P43[7]为模板扩增壮观霉素抗性框和P43启动子片段。通过融合PCR将左右臂、抗性框和P43启动子片段、nanC (nanT)片段进行融合,获得基因nanC (nanT)的表达框,并转化枯草芽孢杆菌BSXC感受态,获得菌株BS-nanC (BS-nanT)。
BSP1–BSP10的构建:左右臂和nanT片段参考上述方法获得,以实验室保存的p7S6P1系列质粒[22]为模板扩增获得壮观霉素抗性框和启动子片段,然后按照上述BS-nanT的构建方法得到重组菌株BSP1–BSP10。
1.2.3 重组质粒的构建杂合启动子重组质粒的构建:以pHT- veg-gfp为模板,用相应的引物进行扩增获得带有杂合启动子的线性质粒片段,上下游引物包括杂合启动子序列、特异性结合序列,且存在一段20 bp左右的重叠(overlap)。利用Thermo Scientific GeneJET PCR纯化试剂盒对所获得的线性质粒片段进行纯化,然后取10 μL转化E. coli JM109感受态,并涂布抗性平板。次日将平板上的单菌落送上海生工测序,将测序正确的单菌落扩大培养,并提取重组质粒。优化nanR表达量时,也采用同样的环化PCR的方式替换nanR的启动子。
生物传感器重组质粒的构建:分别以杂合启动子重组质粒为模板,用引物pN-F/R扩增质粒骨架及杂合启动子表达的绿色荧光蛋白基因gfp,以pHT-nanR为模板,用引物nanR-F/R扩增PrpsT启动子表达的nanR基因,利用Thermo Scientific GeneJET PCR纯化试剂盒将所得片段纯化,经Gibson组装[23]后转化E. coli JM109感受态,并涂布抗性平板。次日将平板上的单菌落送上海生工测序,将测序正确的单菌落扩大培养,并提取重组质粒。
1.2.4 感受态细胞的制备和转化大肠杆菌感受态制备与转化:超级感受态细胞制备试剂盒购于生工生物工程(上海)股份有限公司,按照说明书要求制备、转化E. coli JM109感受态细胞,用于质粒的构建。
枯草芽孢杆菌感受态制备与转化:参考Zhang等[24]的方法,在B. subtilis 168基因组上整合了一个受木糖诱导型启动子PxylA调控的comK基因,得到菌株BSXC作为本研究的出发菌株,利用木糖诱导制备感受态细胞。挑取平板活化的B. subtilis单菌落在1 mL LB培养基中于37 ℃、220 r/min的条件下培养12 h,加入LB培养基稀释至5 mL,并加入终浓度为3%的木糖,于37 ℃、220 r/min条件下诱导2 h后获得感受态。将适量的质粒或片段加入100 µL B. subtilis感受态细胞,于37 ℃、220 r/min的条件下培养1.5 h后涂布相应抗性平板,置于37 ℃恒温培养箱培养。
1.2.5 胞内NeuAc浓度检测胞内NeuAc测定样品制备:取1 mL发酵液,置于预冷为4 ℃的离心机,6 000 r/min离心5 min,弃尽上清液,将菌体重悬于1 mL预冷的无菌水中洗涤1次,6 000 r/min离心5 min,弃尽上清液,用0.2 mL无菌水重悬菌体,后加入0.4 mL乙腈和0.4 mL甲醇混匀,并置于–20 ℃过夜萃取。将萃取液于4 ℃、12 000 r/min离心10 min,上清液置于冷冻干燥机过夜干燥。干燥后的粉末重悬于一定体积的无菌水以浓缩样品,用于高效液相色谱检测。
高效液相色谱(high performance liquid chromatography, HPLC)检测:取样品通过0.22 μm的滤膜,采用Aminex HPX-87H柱(300 nm×7.8 nm)进行HPLC检测,检测参数如下:采用10 mmol/L H2SO4作为流动相,0.5 mL/min流速进样,进样量为10 μL,柱温为40 ℃,紫外检测器波长为210 nm。
1.2.6 GFP荧光强度检测方法将培养过夜的种子液以2%转接到含有0.2 mL LB培养基的96浅孔板中,37 ℃、900 r/min培养8 h,用酶标仪在490 nm的激发波长、530 nm的发射波长和60的增益值下测定GFP的荧光强度;在600 nm的波长下测定OD600。减去培养基的背景噪声后,计算荧光强度与OD600的比值,再减去无GFP表达的对照菌株的荧光强度与OD600的比值,即得到相对荧光强度。
2 结果与分析 2.1 NeuAc转运蛋白NanT和NanC的测试和表达优化在响应小分子代谢物的生物传感器构建过程中,常常采用在培养基中添加不同浓度的小分子代谢物来验证生物传感器的响应性能,而枯草芽孢杆菌不具备将胞外NeuAc转运进胞内的能力,因此,首先需要构建能将胞外NeuAc转运进胞内的枯草芽孢杆菌,便于外源添加NeuAc以验证生物传感器的性能。为了不影响内源基因的表达,选择ydbC和ydbD基因的间隔位置作为基因组整合表达nanC和nanT的位点。按照方法1.1.2,将由P43启动子调控nanC (nanT)的表达框整合到枯草芽孢杆菌BSXC的基因组,得到菌株BS-nanC (BS-nanT)。为了比较NanC和NanT转运NeuAc的能力,将菌株BS-nanC和BS-nanT分别在添加了10 g/L和80 g/L NeuAc的LB培养基中发酵12 h,随后检测其胞内NeuAc含量。
与BS-nanC相比,BS-nanT转运NeuAc的能力更强,且外源添加不同浓度的NeuAc时胞内浓度差异较大,是验证响应NeuAc的生物传感器的更优宿主(图 1A)。BS-nanT在外源添加10–80 g/L NeuAc的LB培养基中发酵12 h时,胞内NeuAc浓度也存在差异,但是胞内NeuAc浓度总体偏低,最高也只有310 μg/mL (图 1B),这不利于对响应NeuAc浓度范围广的生物传感器的验证,于是进一步对nanT进行表达优化。选取枯草芽孢杆菌中9个不同强度的内源启动子(P1、P2、P3、P4、P6、P7、P8、P9和P10)[22]替换P43启动子调控nanT的表达,得到菌株BSP1–BSP10。如图 2所示,当在添加了10 g/L NeuAc的LB培养基中发酵12 h时,菌株BSP1–BSP10的胞内NeuAc含量在230–635 μg/mL之间变化。其中,BSP1、BSP2、BSP7和BSP9之间的胞内NeuAc含量差异显著,且胞内NeuAc浓度范围更广(230–635 μg/mL),可用于后续响应NeuAc生物传感器的性能验证。
2.2 响应NeuAc生物传感器的设计与构建基于转录因子的生物传感器通常包括3个部分:响应特定代谢物的转录因子,含有转录因子结合位点的启动子以及受其调控表达的报告基因[25-26]。响应NeuAc生物传感器的原理如图 3A所示,当胞内不存在NeuAc时,Bbr_NanR作为阻遏蛋白结合在其结合位点上,抑制响应启动子调控的报告基因的表达;当胞内存在NeuAc时,胞内NeuAc与Bbr_NanR的结合会影响Bbr_NanR的阻遏活性,从而激活响应启动子的表达。
因此,首先设计了含有NanR结合位点的杂合启动子。如图 3B所示,将Bbr_NanR特异性结合位点放在了6个组成型启动子Pveg、P43、Pm1、P214、P566、P535[27-28]的–35区前、–35区和–10区中间、–10区后,设计了21个带有Bbr_NanR结合位点的杂合启动子N1–N21 (序列如表 4所示)。以pHT01质粒为载体,分别用杂合启动子表达GFP,将构建成功的质粒转化BS-nanT菌株以检测杂合启动子活性。如图 4所示,虽然大多数杂合启动子在插入NanR的结合序列后活性明显降低甚至失去活性,但是杂合启动子N1、N2、N10、N11、N14、N16、N17显示出了接近甚至超过强组成型启动子P566的活性,因此,选择这些杂合启动子用于后续响应NeuAc生物传感器的构建。
为了验证这些高活性的杂合启动子能否被Bbr_NanR抑制,能否被NeuAc激活,在杂合启动子调控GFP表达框的上游引入PrpsT启动子表达的nanR,然后将构建成功的7个生物传感器质粒转入BS-nanT,发现当不添加NeuAc时,杂合启动子N1、N2、N10、N16和N17能被一定程度地抑制;当外源添加10 g/L NeuAc时,这几个杂合启动子也能被激活(图 5A)。其中N2、N10和N17能被激活至少2倍,这有利于灵敏地响应低浓度的胞内NeuAc。因此,选择了这几个杂合启动子进行后续实验。接下来分析了外源添加不同浓度的NeuAc时杂合启动子N2、N10、N17的表达情况,如图 5B–5D所示,虽然它们都能被激活,但是当胞外添加10 g/L的NeuAc (低浓度)时它们的表达强度已接近杂合启动子被完全激活的强度,因此,推测nanR的表达量不足以用于响应更高浓度的胞内NeuAc,并利用不同强度的启动子优化nanR表达。
在2.1中,研究发现当外源添加10 g/L的NeuAc时,菌株BSP1、BSP2、BSP9、BSP7的胞内NeuAc浓度在230–635 μg/mL范围内存在显著差异。为了能获得响应NeuAc浓度范围广且灵敏的生物传感器,后续选择这4株菌去验证生物传感器对不同浓度NeuAc的响应效果。分别选取N2、N10和N17启动子表达GFP,在此基础上选取3个不同强度的启动子P535、Pt8、P566优化nanR的表达。将构建成功的重组质粒P535-N2、Pt8-N2、P566-N2、P566-N10、P535-N17、Pt8-N17、P566-N17分别转入菌株BSP1、BSP2、BSP7、BSP9中。当外源添加10 g/L的NeuAc时,发现P566-N10虽然能够响应NeuAc,但是激活后的杂合启动子活性远远小于无nanR存在时杂合启动子的活性,对此,推测nanR的表达量太高,P566-N10可能需要更高浓度的胞内NeuAc才能被更高强度地激活。除P566-N10外的6个生物传感器都可以灵敏地响应NeuAc,且激活强度可达到18–122倍(图 6)。其中P535-N2响应NeuAc的动态范围最广,为(180–20 245) AU/OD;P566-N2响应NeuAc的激活强度最大,为122倍,为已报道的枯草芽孢杆菌中响应NeuAc的生物传感器[20]的2倍。此外,本文只是在胞内NeuAc浓度为0–635 μg/mL的范围内对几个生物传感器进行了验证,635 μg/mL的胞内NeuAc浓度并未使P535-N2、Pt8-N2和P566-N2达到饱和状态,因此,这3个生物传感器响应的NeuAc浓度范围可能比所使用的0–635 μg/mL范围更广,可以用于分析和检测更高浓度的胞内NeuAc。
3 讨论与结论N-乙酰神经氨酸由于在食品和医药等方面的广泛应用及潜在价值而备受关注,枯草芽孢杆菌作为食品安全级模式微生物,是生产NeuAc的理想宿主。目前,Zhang等[22]以枯草芽孢杆菌作为宿主菌株生产NeuAc,使用葡萄糖作为唯一碳源,在5 L发酵罐中分批补料发酵,NeuAc产量达到30.10 g/L。然而,枯草芽孢杆菌中NeuAc合成途径的改造存在细胞生长与产物生产不平衡、中间产物过度积累等一系列问题,限制了NeuAc的高效合成。响应目标产物的生物传感器常结合流式细胞术实现其在高通量筛选中的应用,也常用于基因回路的设计以构建智能细胞工厂,提高生物合成效率。虽然本研究构建的响应NeuAc的生物传感器响应的是胞内NeuAc浓度变化,但是目前已有越来越多的研究结合这种生物传感器和流式细胞仪来进行高通量筛选,并获得了胞外目标产物产量增加的菌株,证实了这类生物传感器在高产菌株筛选中的应用潜力[16, 18, 29-30]。另外,本文发现当在枯草芽孢杆菌中适量表达大肠杆菌来源的NanT时,不同的胞外NeuAc浓度会导致不同的胞内NeuAc浓度。因此,结合基于液滴微流控的共培养,构建的响应胞内NeuAc的生物传感器也可用于胞外NeuAc产量增加的菌株的高通量筛选。构建用于枯草芽孢杆菌的高效响应NeuAc的生物传感器,对于指导NeuAc的合成具有重要意义。
本研究首先引入大肠杆菌来源的NeuAc转运蛋白NanT,并选取10个不同强度的启动子对其进行表达优化,获得4株具有不同NeuAc转运能力的枯草芽孢杆菌作为响应NeuAc生物传感器的验证宿主。然后选取6个组成型启动子,分别在其不同区域插入Bbr_NanR结合位点并验证活性,获得了7个有活性的杂合启动子,在此基础上进一步引入和优化Bbr_NanR的表达,得到了6个能够高效响应NeuAc的生物传感器。其中P535-N2具有最广的动态范围,为(180–20 245) AU/OD;P566-N2的激活倍数最高,可达122倍,为枯草芽孢杆菌中已报道的响应NeuAc生物传感器[20]的2倍。
本研究侧重对响应NeuAc生物传感器的构建及响应性能的验证,在后续的研究中,可以通过增加胞内NeuAc浓度进一步探究生物传感器的动态范围。此外,本研究构建的生物传感器可结合流式细胞仪应用于枯草芽孢杆菌中NeuAc合成途径的高活性酶突变体及高产菌的筛选、群体质量控制等,为枯草芽孢杆菌进一步高效生产NeuAc奠定了基础。
[1] |
YANG HQ, LU LP, CHEN XZ. An overview and future prospects of sialic acids. Biotechnology Advances, 2021, 46: 107678. DOI:10.1016/j.biotechadv.2020.107678
|
[2] |
RÖHRIG CH, CHOI SSH, BALDWIN N. The nutritional role of free sialic acid, a human milk monosaccharide, and its application as a functional food ingredient. Critical Reviews in Food Science and Nutrition, 2017, 57(5): 1017-1038. DOI:10.1080/10408398.2015.1040113
|
[3] |
van KARNEBEEK CDM, BONAFÉ L, WEN XY, TARAILO-GRAOVAC M, BALZANO S, ROYER- BERTRAND B, ASHIKOV A, GARAVELLI L, MAMMI I, TUROLLA L, BREEN C, DONNAI D, CORMIER-DAIRE V, HERON D, NISHIMURA G, UCHIKAWA S, CAMPOS-XAVIER B, ROSSI A, HENNET T, BRAND-ARZAMENDI K, et al. NANS-mediated synthesis of sialic acid is required for brain and skeletal development. Nature Genetics, 2016, 48(7): 777-784. DOI:10.1038/ng.3578
|
[4] |
WANG B. Molecular mechanism underlying sialic acid as an essential nutrient for brain development and cognition. Advances in Nutrition, 2012, 3(3): 465S-472S. DOI:10.3945/an.112.001875
|
[5] |
SALCEDO J, BARBERA R, MATENCIO E, ALEGRÍA A, LAGARDA MJ. Gangliosides and sialic acid effects upon newborn pathogenic bacteria adhesion: an in vitro study. Food Chemistry, 2013, 136(2): 726-734. DOI:10.1016/j.foodchem.2012.08.078
|
[6] |
CHEESEMAN J, KUHNLE G, SPENCER DIR, OSBORN HMI. Assays for the identification and quantification of sialic acids: challenges, opportunities and future perspectives. Bioorganic & Medicinal Chemistry, 2021, 30: 115882.
|
[7] |
ZHANG XL, LIU YF, LIU L, WANG M, LI JH, DU GC, CHEN J. Modular pathway engineering of key carbon-precursor supply-pathways for improved N-acetylneuraminic acid production in Bacillus subtilis. Biotechnology and Bioengineering, 2018, 115(9): 2217-2231. DOI:10.1002/bit.26743
|
[8] |
LIU YF, LIU L, LI JH, DU GC, CHEN J. Synthetic biology toolbox and chassis development in Bacillus subtilis. Trends in Biotechnology, 2019, 37(5): 548-562. DOI:10.1016/j.tibtech.2018.10.005
|
[9] |
YANG S, DU GC, CHEN J, KANG Z. Characterization and application of endogenous phase-dependent promoters in Bacillus subtilis. Applied Microbiology and Biotechnology, 2017, 101(10): 4151-4161. DOI:10.1007/s00253-017-8142-7
|
[10] |
NIU TF, LV XQ, LIU ZM, LI JH, DU GC, LIU L. Synergetic engineering of central carbon and nitrogen metabolism for the production of N-acetylglucosamine in Bacillus subtilis. Biotechnology and Applied Biochemistry, 2020, 67(1): 123-132. DOI:10.1002/bab.1845
|
[11] |
YANG H, LIU YF, LI JH, LIU L, DU GC, CHEN J. Systems metabolic engineering of Bacillus subtilis for efficient biosynthesis of 5-methyltetrahydrofolate. Biotechnology and Bioengineering, 2020, 117(7): 2116-2130. DOI:10.1002/bit.27332
|
[12] |
CARPENTER AC, PAULSEN IT, WILLIAMS TC. Blueprints for biosensors: design, limitations, and applications. Genes (Basel), 2018, 9(8): 375. DOI:10.3390/genes9080375
|
[13] |
CAO YT, TIAN RZ, LV XQ, LI JH, LIU L, DU GC, CHEN J, LIU YF. Inducible population quality control of engineered Bacillus subtilis for improved N-acetylneuraminic acid biosynthesis. ACS Synthetic Biology, 2021, 10(9): 2197-2209. DOI:10.1021/acssynbio.1c00086
|
[14] |
钱蕾, 刘延峰, 李江华, 刘龙, 堵国成. 适应性进化和改造质粒稳定性促进枯草芽孢杆菌合成N-乙酰神经氨酸. 食品与发酵工业, 2021, 47(5): 1-6. QIAN L, LIU YF, LI JH, LIU L, DU GC. Regulating the synthesis of N-acetylneuraminic acid based on adaptive evolution and plasmid stability modification in Bacillus subtilis. Food and Fermentation Industries, 2021, 47(5): 1-6 (in Chinese). |
[15] |
DABIRIAN Y, GONÇALVES TEIXEIRA P, NIELSEN J, SIEWERS V, DAVID F. FadR-based biosensor- assisted screening for genes enhancing fatty acyl-CoA pools in Saccharomyces cerevisiae. ACS Synthetic Biology, 2019, 8(8): 1788-1800. DOI:10.1021/acssynbio.9b00118
|
[16] |
LIU YF, ZHUANG YY, DING DQ, XU YR, SUN JB, ZHANG DW. Biosensor-based evolution and elucidation of a biosynthetic pathway in Escherichia coli. ACS Synthetic Biology, 2017, 6(5): 837-848. DOI:10.1021/acssynbio.6b00328
|
[17] |
YEOM SJ, KIM M, KWON KK, FU YY, RHA E, PARK SH, LEE H, KIM H, LEE DH, KIM DM, LEE SG. A synthetic microbial biosensor for high- throughput screening of lactam biocatalysts. Nature Communications, 2018, 9: 5053. DOI:10.1038/s41467-018-07488-0
|
[18] |
PANG QX, HAN H, LIU XQ, WANG ZG, LIANG QF, HOU J, QI QS, WANG Q. In vivo evolutionary engineering of riboswitch with high-threshold for N-acetylneuraminic acid production. Metabolic Engineering, 2020, 59: 36-43. DOI:10.1016/j.ymben.2020.01.002
|
[19] |
PETERS G, de PAEPE B, de WANNEMAEKER L, DUCHI D, MAERTENS J, LAMMERTYN J, de MEY M. Development of N-acetylneuraminic acid responsive biosensors based on the transcriptional regulator NanR. Biotechnology and Bioengineering, 2018, 115(7): 1855-1865. DOI:10.1002/bit.26586
|
[20] |
ZHANG XL, CAO YT, LIU YF, LIU L, LI JH, DU GC, CHEN J. Development and optimization of N-acetylneuraminic acid biosensors in Bacillus subtilis. Biotechnology and Applied Biochemistry, 2020, 67(4): 693-705. DOI:10.1002/bab.1942
|
[21] |
EGAN M, O'CONNELL MOTHERWAY M, van SINDEREN D. A GntR-type transcriptional repressor controls sialic acid utilization in Bifidobacterium breve UCC2003. FEMS Microbiology Letters, 2015, 362(4): 1-9.
|
[22] |
ZHANG XL, WANG CY, LV XQ, LIU L, LI JH, DU GC, WANG M, LIU YF. Engineering of synthetic multiplexed pathways for high-level N-acetylneuraminic acid bioproduction. Journal of Agricultural and Food Chemistry, 2021, 69(49): 14868-14877. DOI:10.1021/acs.jafc.1c06017
|
[23] |
GIBSONDG, SMITH HO, HUTCHISON CA Ⅲ, VENTER JC, MERRYMAN C. Chemical synthesis of the mouse mitochondrial genome. Nature Methods, 2010, 7(11): 901-903. DOI:10.1038/nmeth.1515
|
[24] |
ZHANG XZ, ZHANG YH P. Simple, fast and high-efficiency transformation system for directed evolution of cellulase in Bacillus subtilis. Microbial Biotechnology, 2011, 4(1): 98-105. DOI:10.1111/j.1751-7915.2010.00230.x
|
[25] |
JEON Y, LEE YJ, KIM K, JANG G, YOON Y. Transcription factor-based biosensors for detecting pathogens. Biosensors, 2022, 12(7): 470. DOI:10.3390/bios12070470
|
[26] |
UMENO D, KIMURA Y, KAWAI-NOMA S. Transcription factors as evolvable biosensors. Analytical Sciences, 2021, 37(5): 699-703. DOI:10.2116/analsci.20SCR12
|
[27] |
LU ZH, YANG SH, YUAN X, SHI YY, OUYANG L, JIANG SJ, YI L, ZHANG GM. CRISPR-assisted multi-dimensional regulation for fine-tuning gene expression in Bacillus subtilis. Nucleic Acids Research, 2019, 47(7): e40. DOI:10.1093/nar/gkz072
|
[28] |
LIU DY, MAO ZT, GUO JX, WEI LY, MA HW, TANG YJ, CHEN T, WANG ZW, ZHAO XM. Construction, model-based analysis, and characterization of a promoter library for fine-tuned gene expression in Bacillus subtilis. ACS Synthetic Biology, 2018, 7(7): 1785-1797. DOI:10.1021/acssynbio.8b00115
|
[29] |
YANG P, WANG J, PANG QX, ZHANG FY, WANG JS, WANG Q, QI QS. Pathway optimization and key enzyme evolution of N-acetylneuraminate biosynthesis using an in vivo aptazyme-based biosensor. Metabolic Engineering, 2017, 43: 21-28. DOI:10.1016/j.ymben.2017.08.001
|
[30] |
RAMAN S, ROGERS JK, TAYLOR ND, CHURCH GM. Evolution-guided optimization of biosynthetic pathways. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(50): 17803-17808. DOI:10.1073/pnas.1409523111
|