微生物学报

Gene names	Primer sequences (5′→3′)	Product length (bp)
sfGFP	F: ATGCGCAAAGGCGAAGAACT R: TTTATACAGTTCATCCATGCCGTGGG	714
asd	F: ATGTTGAAAGTCGGTTTCGTGGG R: CTGCTCGCGTAAGATCTTTAG	1 110
lysC	F: ATGGCATTATACGTACAGAAGTTCGGC R: TTCAGATTCGATATCAGACTTATCTAAACCGAAT	1 248
ectA	F: ATGAGCACGCCAATAACACCT R: CATACTGTCTGTTTGAAATGGACCTATGC	576
ectB	F: ATGCAGACCCAGACGCTTGA R: CGCTTGGATAACAGCATTGACTGA	1 266
ectC	F: ATGATCGTTCGTAATCTTGAAGAAGCAC R: TCACTCACTAGCAGGTGCAGC	399

Gene names	Primer sequences (5′→3′)	Product length (bp)
sfGFP	F: ATGCGCAAAGGCGAAGAACT R: TTTATACAGTTCATCCATGCCGTGGG	714
asd	F: ATGTTGAAAGTCGGTTTCGTGGG R: CTGCTCGCGTAAGATCTTTAG	1 110
lysC	F: ATGGCATTATACGTACAGAAGTTCGGC R: TTCAGATTCGATATCAGACTTATCTAAACCGAAT	1 248
ectA	F: ATGAGCACGCCAATAACACCT R: CATACTGTCTGTTTGAAATGGACCTATGC	576
ectB	F: ATGCAGACCCAGACGCTTGA R: CGCTTGGATAACAGCATTGACTGA	1 266
ectC	F: ATGATCGTTCGTAATCTTGAAGAAGCAC R: TCACTCACTAGCAGGTGCAGC	399

Recombinant plasmid names	Vector backbone	Carried target genes	Corresponding recombinant strain
pHX01	pBBR1MCS-5	sfGFP	XH26/pHX01
pHX02	pBBR1MCS-5	asd	XH26/pHX02
pHX03	pBBR1MCS-5	asd, lysC	XH26/pHX03
pHX04	pBBR1MCS-5	asd, lysC, ectA, ectB	XH26/pHX04
pHX05	pBBR1MCS-5	asd, lysC, ectA, ectB, ectC	XH26/pHX05

Recombinant plasmid names	Vector backbone	Carried target genes	Corresponding recombinant strain
pHX01	pBBR1MCS-5	sfGFP	XH26/pHX01
pHX02	pBBR1MCS-5	asd	XH26/pHX02
pHX03	pBBR1MCS-5	asd, lysC	XH26/pHX03
pHX04	pBBR1MCS-5	asd, lysC, ectA, ectB	XH26/pHX04
pHX05	pBBR1MCS-5	asd, lysC, ectA, ectB, ectC	XH26/pHX05

Test	Factor	Variant	Level
Plackett-Burman	NaCl (g/L)	X₁	87.75		146.25
Peptone (g/L)	X₂	10.00		20.00
	l-glutamate (g/L)	X₃	120.00		200.00
	Glucose (g/L)	X₄	10.00		20.00
	FeSO₄·7H₂O (g/L)	X₅	0.20		1.00
	MgSO₄·7H₂O (g/L)	X₆	0.40		1.20
	KH₂PO₄ (g/L)	X₇	3.00		15.00
	K₂HPO₄ (g/L)	X₈	9.00		21.00
Box-Behnken	NaCl (g/L)	A	87.75	117.00	146.25
Peptone (g/L)	B	10.00	15.00	20.00
	l-glutamate (g/L)	C	120.00	160.00	200.00
	Glucose (g/L)	D	10.00	15.00	20.00

Test	Factor	Variant	Level
Plackett-Burman	NaCl (g/L)	X₁	87.75		146.25
Peptone (g/L)	X₂	10.00		20.00
	l-glutamate (g/L)	X₃	120.00		200.00
	Glucose (g/L)	X₄	10.00		20.00
	FeSO₄·7H₂O (g/L)	X₅	0.20		1.00
	MgSO₄·7H₂O (g/L)	X₆	0.40		1.20
	KH₂PO₄ (g/L)	X₇	3.00		15.00
	K₂HPO₄ (g/L)	X₈	9.00		21.00
Box-Behnken	NaCl (g/L)	A	87.75	117.00	146.25
Peptone (g/L)	B	10.00	15.00	20.00
	l-glutamate (g/L)	C	120.00	160.00	200.00
	Glucose (g/L)	D	10.00	15.00	20.00

Experiment No.	Variant	Ectoine (g/L)
1	-1	1	1	1	1	-1	1	1	1.77
2	1	1	-1	-1	1	1	-1	1	1.64
3	1	-1	1	1	-1	1	-1	1	1.80
4	1	1	1	-1	-1	-1	-1	1	1.63
5	1	1	-1	1	-1	-1	1	-1	1.79
6	-1	-1	-1	-1	-1	-1	-1	-1	1.38
7	1	-1	1	1	1	-1	-1	-1	1.79
8	-1	1	-1	1	1	1	-1	-1	1.67
9	-1	-1	-1	1	-1	1	1	1	1.40
10	-1	-1	1	-1	1	1	1	-1	1.47
11	1	1	1	-1	-1	1	1	-1	1.73
12	1	-1	-1	-1	1	-1	1	1	1.45

Experiment No.	Variant	Ectoine (g/L)
1	-1	1	1	1	1	-1	1	1	1.77
2	1	1	-1	-1	1	1	-1	1	1.64
3	1	-1	1	1	-1	1	-1	1	1.80
4	1	1	1	-1	-1	-1	-1	1	1.63
5	1	1	-1	1	-1	-1	1	-1	1.79
6	-1	-1	-1	-1	-1	-1	-1	-1	1.38
7	1	-1	1	1	1	-1	-1	-1	1.79
8	-1	1	-1	1	1	1	-1	-1	1.67
9	-1	-1	-1	1	-1	1	1	1	1.40
10	-1	-1	1	-1	1	1	1	-1	1.47
11	1	1	1	-1	-1	1	1	-1	1.73
12	1	-1	-1	-1	1	-1	1	1	1.45

Source	Sum of squares	df	Mean square	F-value	P-value	Significance
Model	0.276 3	8	0.034 5	29.52	0.009 0	*
X₁: NaCl (g/L)	0.061 6	1	0.061 6	52.68	0.005 4	*
X₂: Peptone (g/L)	0.073 0	1	0.073 0	62.41	0.004 2	*
X₃: l-glutamate (g/L)	0.061 9	1	0.061 9	52.93	0.005 4	*
X₄: Glucose (g/L)	0.069 6	1	0.069 6	59.51	0.004 5	*
X₅: FeSO₄·7H₂O (g/L)	0.000 4	1	0.000 4	0.329	0.606 2
X₆: MgSO₄·7H₂O (g/L)	0.001 2	1	0.001 2	1.06	0.378 9
X₇: KH₂PO₄ (g/L)	0.007 1	1	0.007 1	6.07	0.090 5
X₈: K₂HPO₄ (g/L)	0.001 4	1	0.001 4	1.20	0.352 7
Pure error	0.003 5	3	0.001 2
Cor total	0.279 8	11

Source	Sum of squares	df	Mean square	F-value	P-value	Significance
Model	0.276 3	8	0.034 5	29.52	0.009 0	*
X₁: NaCl (g/L)	0.061 6	1	0.061 6	52.68	0.005 4	*
X₂: Peptone (g/L)	0.073 0	1	0.073 0	62.41	0.004 2	*
X₃: l-glutamate (g/L)	0.061 9	1	0.061 9	52.93	0.005 4	*
X₄: Glucose (g/L)	0.069 6	1	0.069 6	59.51	0.004 5	*
X₅: FeSO₄·7H₂O (g/L)	0.000 4	1	0.000 4	0.329	0.606 2
X₆: MgSO₄·7H₂O (g/L)	0.001 2	1	0.001 2	1.06	0.378 9
X₇: KH₂PO₄ (g/L)	0.007 1	1	0.007 1	6.07	0.090 5
X₈: K₂HPO₄ (g/L)	0.001 4	1	0.001 4	1.20	0.352 7
Pure error	0.003 5	3	0.001 2
Cor total	0.279 8	11

Experiment No.	NaCl (g/L)	Peptone (g/L)	l-glutamate (g/L)	Glucose (g/L)	Ectoine (g/L)	Experiment No.	NaCl (g/L)	Peptone (g/L)	l-glutamate (g/L)	Glucose (g/L)	Ectoine (g/L)
1	87.75	10.00	160.00	15.00	1.60	16	117.00	20.00	200.00	15.00	1.76
2	146.25	10.00	160.00	15.00	1.50	17	87.75	15.00	120.00	15.00	1.65
3	87.75	20.00	160.00	15.00	1.64	18	146.25	15.00	120.00	15.00	1.42
4	146.25	20.00	160.00	15.00	1.54	19	87.75	15.00	200.00	15.00	1.57
5	117.00	15.00	120.00	10.00	1.63	20	146.25	15.00	200.00	15.00	1.60
6	117.00	15.00	200.00	10.00	1.65	21	117.00	10.00	160.00	10.00	1.67
7	117.00	15.00	120.00	20.00	1.63	22	117.00	20.00	160.00	10.00	1.61
8	117.00	15.00	200.00	20.00	1.69	23	117.00	10.00	160.00	20.00	1.60
9	87.75	15.00	160.00	10.00	1.63	24	117.00	20.00	160.00	20.00	1.71
10	146.25	15.00	160.00	10.00	1.42	25	117.00	15.00	160.00	15.00	1.81
11	87.75	15.00	160.00	20.00	1.54	26	117.00	15.00	160.00	15.00	1.82
12	146.25	15.00	160.00	20.00	1.56	27	117.00	15.00	160.00	15.00	1.82
13	117.00	10.00	120.00	15.00	1.67	28	117.00	15.00	160.00	15.00	1.82
14	117.00	20.00	120.00	15.00	1.66	29	117.00	15.00	160.00	15.00	1.82
15	117.00	10.00	200.00	15.00	1.68

Experiment No.	NaCl (g/L)	Peptone (g/L)	l-glutamate (g/L)	Glucose (g/L)	Ectoine (g/L)	Experiment No.	NaCl (g/L)	Peptone (g/L)	l-glutamate (g/L)	Glucose (g/L)	Ectoine (g/L)
1	87.75	10.00	160.00	15.00	1.60	16	117.00	20.00	200.00	15.00	1.76
2	146.25	10.00	160.00	15.00	1.50	17	87.75	15.00	120.00	15.00	1.65
3	87.75	20.00	160.00	15.00	1.64	18	146.25	15.00	120.00	15.00	1.42
4	146.25	20.00	160.00	15.00	1.54	19	87.75	15.00	200.00	15.00	1.57
5	117.00	15.00	120.00	10.00	1.63	20	146.25	15.00	200.00	15.00	1.60
6	117.00	15.00	200.00	10.00	1.65	21	117.00	10.00	160.00	10.00	1.67
7	117.00	15.00	120.00	20.00	1.63	22	117.00	20.00	160.00	10.00	1.61
8	117.00	15.00	200.00	20.00	1.69	23	117.00	10.00	160.00	20.00	1.60
9	87.75	15.00	160.00	10.00	1.63	24	117.00	20.00	160.00	20.00	1.71
10	146.25	15.00	160.00	10.00	1.42	25	117.00	15.00	160.00	15.00	1.81
11	87.75	15.00	160.00	20.00	1.54	26	117.00	15.00	160.00	15.00	1.82
12	146.25	15.00	160.00	20.00	1.56	27	117.00	15.00	160.00	15.00	1.82
13	117.00	10.00	120.00	15.00	1.67	28	117.00	15.00	160.00	15.00	1.82
14	117.00	20.00	120.00	15.00	1.66	29	117.00	15.00	160.00	15.00	1.82
15	117.00	10.00	200.00	15.00	1.68

Source	Sum of squares	df	Mean square	F-value	P-value	Significant
Model	0.339 3	14	0.024 2	423.52	<0.000 1	*
A	0.028 8	1	0.028 8	503.50	<0.000 1	*
B	0.003 4	1	0.003 4	59.42	<0.000 1	*
C	0.006 8	1	0.006 8	119.12	<0.000 1	*
D	0.001 2	1	0.001 2	21.68	0.000 4	*
AB	0.000 0	1	0.000 0	0.21	0.650 7
AC	0.017 6	1	0.017 6	306.80	<0.000 1	*
AD	0.013 0	1	0.013 0	227.11	<0.000 1	*
BC	0.002 0	1	0.002 0	35.39	<0.000 1	*
BD	0.007 1	1	0.007 1	124.78	<0.000 1	*
CD	0.000 5	1	0.000 5	8.85	0.010 0	*
A²	0.223 8	1	0.223 8	3 910.32	<0.000 1	*
B²	0.026 3	1	0.026 3	460.43	<0.000 1	*
C²	0.029 3	1	0.029 3	512.39	<0.000 1	*
D²	0.067 8	1	0.067 8	1 184.73	<0.000 1	*
Residual	0.000 8	14	0.000 1
Lack of fit	0.000 7	10	0.000 1	2.21	0.231 3	Not significant
Pure error	0.000 1	4	0.000 0
Cor total	0.340 1	28

Source	Sum of squares	df	Mean square	F-value	P-value	Significant
Model	0.339 3	14	0.024 2	423.52	<0.000 1	*
A	0.028 8	1	0.028 8	503.50	<0.000 1	*
B	0.003 4	1	0.003 4	59.42	<0.000 1	*
C	0.006 8	1	0.006 8	119.12	<0.000 1	*
D	0.001 2	1	0.001 2	21.68	0.000 4	*
AB	0.000 0	1	0.000 0	0.21	0.650 7
AC	0.017 6	1	0.017 6	306.80	<0.000 1	*
AD	0.013 0	1	0.013 0	227.11	<0.000 1	*
BC	0.002 0	1	0.002 0	35.39	<0.000 1	*
BD	0.007 1	1	0.007 1	124.78	<0.000 1	*
CD	0.000 5	1	0.000 5	8.85	0.010 0	*
A²	0.223 8	1	0.223 8	3 910.32	<0.000 1	*
B²	0.026 3	1	0.026 3	460.43	<0.000 1	*
C²	0.029 3	1	0.029 3	512.39	<0.000 1	*
D²	0.067 8	1	0.067 8	1 184.73	<0.000 1	*
Residual	0.000 8	14	0.000 1
Lack of fit	0.000 7	10	0.000 1	2.21	0.231 3	Not significant
Pure error	0.000 1	4	0.000 0
Cor total	0.340 1	28

代谢工程耦合响应面法优化盐单胞菌合成四氢嘧啶的关键技术

PDF下载

李昊鑫 ¹ , 何珊珊 ¹ , 张宗豪 ¹ , 李永臻 ¹ , 王嵘 ¹ , 韩睿 ²^,^* , 朱德锐 ¹

微生物学报 | 技术与方法 2026,66(3): 1447-1466

收起

微生物学报 | 技术与方法 2026, 66(3): 1447-1466

代谢工程耦合响应面法优化盐单胞菌合成四氢嘧啶的关键技术

全屏

李昊鑫¹, 何珊珊¹, 张宗豪¹, 李永臻¹, 王嵘¹, 韩睿²^,^*, 朱德锐¹

作者信息

^1.青海大学医学院，基础医学研究中心，青海西宁

^2.青海大学农林科学院，蔬菜遗传与生理重点实验室，青海西宁

Metabolic engineering coupled with response surface methodology for optimization of ectoine synthesis in Halomonas

Haoxin LI¹, Shanshan HE¹, Zonghao ZHANG¹, Yongzhen LI¹, Rong WANG¹, Rui HAN²^,^*, Derui ZHU¹

Affiliations

^1.Department of Basic Medical Sciences, School of Medicine, Qinghai University, Xining, Qinghai, China

^2.Key Laboratory of Vegetable Genetics and Physiology, Academy of Agriculture and Forestry Sciences, Qinghai University, Xining, Qinghai, China

出版时间: 2026-03-04 doi: 10.13343/j.cnki.wsxb.20250711

文章导航

摘要

收起

目的通过构建5种含P _tac 启动子的重组质粒pHX01-pHX05 (组合基因asd、lysC、ectA、ectB、ectC)，导入坎帕尼亚盐单胞菌(Halomonas campaniensis) XH26，构建高产工程菌株，并结合响应面法(response surface methodology, RSM)优化培养条件提高四氢嘧啶(ectoine)的积累量。方法重组质粒经大肠埃希氏菌(Escherichia coli)S17-1(λ-pir)接合转移至菌株XH26，利用庆大霉素(50 μg/mL)筛选阳性克隆子；以0.2 mmol/L IPTG诱导表达重组菌株，用HPLC检测四氢嘧啶的积累量；采用单因素试验、Plackett-Burman与Box-Behnken设计优化关键变量因素(NaCl、蛋白胨、l-谷氨酸钠、葡萄糖)。结果构建了5株重组菌株(XH26/pHX01-pHX05)。采用MG培养基培养重组菌株，发现菌株XH26/pHX04 (asd-lysC-ectA-ectB)的四氢嘧啶积累量最高，达(1.32±0.04) g/L；菌株XH26/pHX05和菌株XH26/pHX03次之，四氢嘧啶积累量分别为(1.19±0.07) g/L和(1.07±0.08) g/L；而XH26/pHX02积累量较低，为(1.02±0.14) g/L。利用响应面法优化培养基的关键成分，确定最优条件为NaCl 116.08 g/L、蛋白胨16.30 g/L、l-谷氨酸钠169.57 g/L、葡萄糖15.53 g/L。在此条件下，重组菌株XH26/pHX04的四氢嘧啶积累量提高至(1.81±0.02) g/L，较野生型菌株XH26显著提高了301.56%。结论以坎帕尼亚盐单胞菌为“底盘细胞”，利用强启动子组合过表达基因asd、lysC、ectA/B，辅以响应面法优化重组菌株的培养基条件，可显著提高重组菌株的四氢嘧啶积累量，为后续工业化生产提供了一定的技术参考依据。

关键词

四氢嘧啶 / 坎帕尼亚盐单胞菌 / 代谢工程 / 响应面法 / 基因过表达 / 合成生物学

Abstract

收起

Objective To construct high-yield engineering strains of Halomonas campaniensis XH26 by introducing five recombinant plasmids (pHX01-pHX05), each carrying the P _tac promoter and combinations of the genes asd, lysC, ectA, ectB, and ectC. This metabolic engineering strategy was coupled with the response surface methodology (RSM) for optimization of the culture conditions, thereby enhancing ectoine accumulation. Methods The recombinant plasmids were conjugally transferred from Escherichia coli S17-1(λ-pir) into H. campaniensis XH26, with positive transconjugants selected via gentamicin (50 μg/mL). Recombinant strains were induced with 0.2 mmol/L IPTG, and ectoine accumulation was quantified by HPLC. Critical nutritional variables—NaCl, peptone, l-glutamate, and glucose—were optimized through one-factor-at-a-time experiments, Plackett-Burman design, and Box-Behnken design. Results Five recombinant strains (XH26/pHX01-XH26/pHX05) were successfully constructed. Culture in the MG medium revealed that strain XH26/pHX04 (overexpressing asd-lysC-ectA-ectB) achieved the highest ectoine titer of (1.32±0.04) g/L. Strains XH26/pHX05 and XH26/pHX03 achieved the ectoine titer of (1.19±0.07) g/L and (1.07±0.08) g/L, respectively, while XH26/pHX02 yielded a lower titer of (1.02±0.14) g/L. The medium composition optimized by RSM was composed of 116.08 g/L NaCl, 16.30 g/L peptone, 169.57 g/L l-glutamate, and 15.53 g/L glucose. Under these optimized conditions, the titer of ectoine produced by XH26/pHX04 increased to (1.81±0.02) g/L, representing a significant increase of 301.56% compared with that of the wild-type strain XH26. Conclusion This study demonstrates that using H. campaniensis as a chassis and overexpressing a key gene combination (asd, lysC, ectA, ectB) under a strong promoter, synergized with culture medium optimization via RSM, can significantly boost the ectoine yield of recombinant strains. The findings provide a robust technical framework for the subsequent industrial production of ectoine.

Key words

ectoine / Halomonas campaniensis / metabolic engineering / response surface methodology / gene overexpression / synthetic biology

引用本文

李昊鑫, 何珊珊, 张宗豪, 李永臻, 王嵘, 韩睿, 朱德锐. 代谢工程耦合响应面法优化盐单胞菌合成四氢嘧啶的关键技术. 微生物学报, 2026 , 66 (3) : 1447 -1466 . DOI: 10.13343/j.cnki.wsxb.20250711

Haoxin LI, Shanshan HE, Zonghao ZHANG, Yongzhen LI, Rong WANG, Rui HAN, Derui ZHU. Metabolic engineering coupled with response surface methodology for optimization of ectoine synthesis in Halomonas[J]. Acta Microbiologica Sinica, 2026 , 66 (3) : 1447 -1466 . DOI: 10.13343/j.cnki.wsxb.20250711

正文

收起

相容溶质四氢嘧啶(ectoine)是嗜盐/耐盐微生物胞内合成的一类环状氨基酸衍生物。四氢嘧啶能够维持蛋白质水化层和生物膜的结构稳定性，以此抵抗高盐、高温、干燥及辐射等条件胁迫^[1-2]，广泛应用于生物医药健康、生物制剂及工业酶保护剂等领域^[3]。四氢嘧啶的合成以天冬氨酸(aspartate, Asp)为前体物质：首先由天冬氨酸激酶(aspartate kinase, LysC)催化Asp转化为天冬氨酸-β-半醛(l-aspartate-β-semialdehyde, ASA)，随后该中间产物再经二氨基丁酸转氨酶(diaminobutyrate transaminase, EctB)、二氨基丁酸乙酰基转移酶(diaminobutyrate acetyltransferase, EctA)与四氢嘧啶合成酶(ectoine synthase, EctC)依次催化，最终合成四氢嘧啶^[4]。在工业化发酵生产中，嗜盐微生物可能存在生长速率缓慢、碳代谢流分配失衡、次级代谢物积累偏低以及高盐条件诱导等生长条件限制，严重制约了四氢嘧啶的工业化实际生产^[5-6]。为解决上述的技术瓶颈，研究人员通过构建重组表达质粒(如基因簇ectABC、ask-ectABC或ask-lysC-ectABC)，转化至非嗜盐宿主大肠埃希氏菌(Escherichia coli)或谷氨酸棒杆菌(Corynebacterium glutamicum)，异源表达相关合成基因以实现高效生产四氢嘧啶。例如，Wang等^[7]通过提高宿主E. coli基因簇ectABC的拷贝数(5倍)，并过表达天冬氨酸激酶(threonine-resistant aspartokinase, ThrA)，增加合成前体的代谢流，实现四氢嘧啶的高积累量(5 L发酵罐35.33 g/L)；Jiang等^[8]将来源于假单胞菌(Pseudomonas)的ectABC操纵子整合至C. glutamicum菌株，采用“插件式阻遏蛋白库”动态调控策略，提高四氢嘧啶积累量至45.52 g/L。由此可见，强化前体供应可能是提高四氢嘧啶积累量的系统代谢工程策略之一。

尽管异源表达系统在一定程度上可以提升四氢嘧啶的积累量，但仍存在一些技术难题亟待解决，如常温宿主嗜盐酶类的活性受损、辅因子NADPH竞争导致的代谢负担，以及诱导剂依赖引起的生产成本增加等^[9-11]。基于同源嗜盐宿主菌株构建“底盘细胞”工厂，可能是一种更优的合成生物学研究策略。盐单胞菌(Halomonas)具有耐高盐、增殖传代快和易于工程改造等特点，已被成功应用于聚羟基脂肪酸酯(polyhydroxyvalkanoates, PHA)、四氢嘧啶等次级代谢的工业化生产。Zhang等^[12]采用CRISPRi技术筛选蓝色盐单胞菌(Halomonas bluephagenesis)增产的关键基因mvaS (编码3-羟基-3-甲基戊二酰辅酶A合酶)，成功将甲羟戊酸的积累量提高至121 g/L。Hu等^[13]将H. bluephagenesis基因簇ectABC的启动子替换为组成型强启动子Porin P140，同时过表达上游基因asd和lysC，提高四氢嘧啶积累量至85-93 g/L。Zhu等^[14]发现，大肠杆菌(E. coli)BL21菌株异源表达盐单胞菌(Halomonas sp.) QHL1的基因簇ectABC，仅能有限地提升重组菌株的耐盐度，可能与非嗜盐宿主E. coli的遗传背景、代谢途径兼容性以及酶催化活性有关。至此，为解决异源系统存在的代谢不兼容性等瓶颈，本研究聚焦坎帕尼亚盐单胞菌(Halomonas campaniensis)XH26这一嗜盐菌株，构建多基因(asd、lysC、ectA、ectB、ectC)强启动子组合的重组质粒，并耦合响应面法(response surface methodology, RSM)优化培养条件，以期显著提升四氢嘧啶的积累量。基于同源表达宿主的适配调控，过表达四氢嘧啶的相关合成基因，系统强化代谢流。本研究不仅为H. campaniensis XH26的代谢工程改造提供了可复用的技术框架，更可为其他嗜盐微生物的遗传表达操作、次级代谢物高效生产提供关键参考。

1 材料与方法

收起

1.1 菌株和培养基

野生型H. campaniensis XH26，CCTCC M 2019776M，分离自青海小柴旦盐湖；质粒接合供体菌为大肠杆菌(Escherichia coli) S17-1(λ-pir)，购自天津物源生物科技有限公司。

LB培养基(g/L)：胰蛋白胨10.0，酵母提取物5.0，NaCl 10.0 (标准LB)。限制性LB培养基添加20.0 g/L NaCl (即LB₂₀)或60.0 g/L NaCl (LB₆₀)，用于嗜盐菌的培养与抗性筛选。嗜盐菌基础培养基^[15](Oesterhelt-Stoeckenius medium, OSM) (g/L)：l-谷氨酸钠5.61，酶水解酪素7.50，NaCl 87.50，MgSO₄·7H₂O 24.65，Na₃C₆H₅O₇ 3.00，CaCl₂ 0.20，KCl 55.88，酵母提取物2.00。发酵培养基^[8](MG) (g/L)：l-谷氨酸钠120.0，葡萄糖15.0，蛋白胨10.0，NaCl 117.0，KH₂PO₄ 3.0，K₂HPO₄ 9.0，MgSO₄·7H₂O 0.4，FeSO₄·7H₂O 0.2，以1.0 mol/L NaOH溶液调节pH至7.85。固体培养基添加15.0 g/L琼脂粉，121 ℃灭菌20 min。菌株接种量为1%，37 ℃、120 r/min培养72 h。

1.2 主要试剂和仪器

四氢嘧啶标准对照品(HPLC级，纯度>95%)，Fluka分析公司；甲醇(HPLC级)，ThermoFisher Scientific公司；异丙基-β-d-硫代半乳糖苷，北京酷来搏科技有限公司；微孔过滤器(0.22 μm水系膜)，天津亳津科技有限公司；质粒小提试剂盒、蓝光切胶仪，天根生化科技(北京)有限公司。

蓝光切胶仪，天根生化科技(北京)有限公司；冷冻研磨仪，上海万柏生物科技有限公司；电泳仪，北京六一生物科技有限公司；高效液相色谱仪、色谱分析柱，Agilent公司；液相色谱柱，Shimadzu公司；紫外分光光度计，上海舜宇恒平科学仪器有限公司。

1.3 抗生素抗性筛选

分别使用含有氯霉素(chloramphenicol, Chl)、氨苄青霉素(ampicillin, Amp)、庆大霉素(gentamicin, Gen)及四环素(tetracycline, Tet)的OSM培养基对菌株XH26进行抗生素抑菌率测试。菌株XH26于37 ℃、180 r/min培养6 h，使用紫外分光光度计测定OD₆₀₀约0.6，取1 mL菌液收集菌体，8 000 r/min离心5 min。使用无菌NaCl溶液(60 g/L)洗涤菌体2次后，调整菌悬液至5×10⁵ CFU/mL。在无菌96孔板中，每孔依次加入100 μL上述菌悬液与100 μL含抗生素的OSM培养基(最终抗生素浓度梯度为0.5-256.0 μg/mL)。实验同时设置阴性对照(菌液+无抗生素培养基)。将96孔板37 ℃培养12 h后，测定各孔OD₆₀₀值。计算各抗生素的抑菌率，如公式(1)所示。

抑菌率=[1-(OD_实验组-OD_空白)/(OD_阴性对照-OD_空白)]×100%(1)

1.4 过表达质粒的设计与构建

通过化学合成法分步合成5个质粒(由苏州金唯智生物科技有限公司合成)，分别命名为pHX01-pHX05 (图1A)。在质粒pBBR1MCS-5 (图1B)中插入P _tac 启动子、核糖体结合位点序列(ribosome binding site, RBS)、超折叠绿色荧光蛋白报告基因(sfGFP)以及终止子，构建重组质粒pHX01 (即pBBR-P _tac -sfGFP)。依此方法依次构建后续4个重组质粒：天冬氨酸半醛脱氢酶基因(asd)重组质粒pHX02 (pBBR-P _tac -asd)；基因asd与基因lysC重组质粒pHX03 (pBBR-P _tac -asd-lysC)；基因asd、基因lysC、l-2,4-二氨基丁酸乙酰转移酶基因(ectA)及l-2,4-二氨基丁酸转氨酶基因(ectB)重组质粒pHX04 (pBBR-P _tac -asd-lysC-ectA-ectB)；基因asd、基因lysC、基因ectA、基因ectB及四氢嘧啶合成酶基因(ectC)重组质粒pHX05 (pBBR-P _tac -asd-lysC-ectA-ectB-ectC)。

1.5 质粒接合转移与工程菌株验证

以携带重组质粒pHX01-pHX05的E. coli S17-1为供体菌，菌株XH26为受体菌，通过接合转移法构建重组菌株，分别命名为：XH26/pHX01、XH26/pHX02、XH26/pHX03、XH26/pHX04及XH26/pHX05。具体步骤如下：供体菌与受体菌分别在LB与LB₆₀培养基中37 ℃、180 r/min培养12 h后，各取1 mL菌液以8 000 r/min离心2 min收集菌体。将等体积的菌泥混合至100 μL LB₂₀培养基后，涂布LB₂₀平板，37 ℃培养8 h。刮取培养后菌苔重悬于100 μL LB₆₀培养基中，梯度稀释后涂布于含有Gen的LB₆₀筛选平板，37 ℃培养24 h。提取阳性克隆菌株质粒，并使用表1的引物进行PCR验证，1%琼脂糖凝胶电泳分析结果。本实验所有重组质粒与重组菌株见表2。

1.6 摇瓶发酵与四氢嘧啶定量分析

系列重组菌株和野生型菌株XH26均以体积分数为1%分别接种至OSM或MG培养基。当培养液OD₆₀₀达到0.6-0.8时添加0.1-1.0 mmol/L IPTG进行诱导，于37 ℃、180 r/min条件下持续培养72 h。经4 ℃、8 000 r/min离心10 min回收菌体，采用机械研磨法破碎细胞，提取胞内四氢嘧啶。在预冷的研钵中充分研磨菌体；随后加入5 mL体积分数为80%的甲醇溶液，冰浴超声萃取30 min；4 ℃、12 000 r/min离心15 min后，收集上清液，经0.22 μm微孔滤膜过滤，所得滤液采用高效液相色谱(high performance liquid chromatography, HPLC)进行定量分析^[16]。色谱分析条件：色谱柱为C18柱，流动相为20 mmol/L KH₂PO₄缓冲液与甲醇按照96:4体积比例配制，流速1.0 mL/min，柱温30 ℃，进样量5 μL。采用12.5% SDS-PAGE分析重组菌株的蛋白表达情况(20 μL蛋白样品)。

1.7 单因素试验与响应面优化

采用MG培养重组菌株XH26/pHX04 (37 ℃、180 r/min培养72 h)，并进行8因素单变量试验(n=3)和四氢嘧啶积累量分析。设置单因素条件：NaCl (58.50-175.50 g/L, Δ29.25)、蛋白胨(10.00-30.00 g/L, Δ5.00)、l-谷氨酸钠(80.00-240.00 g/L, Δ40.00)、葡萄糖(10.00-30.00 g/L, Δ5.00)、FeSO₄·7H₂O (0.2.00-1.00 g/L, Δ0.20)、MgSO₄·7H₂O (0.40-1.20 g/L, Δ0.20)、KH₂PO₄ (3.00-15.00 g/L, Δ3.00)、K₂HPO₄ (9.00-21.00 g/L, Δ3.00)。基于单因素试验结果，采用Design-Expert v13.0软件优化条件组合，采用Plackett-Burman设计分析关键因素(表3)，变量为NaCl (X₁)、蛋白胨(X₂)、l-谷氨酸钠(X₃)、葡萄糖(X₄)、FeSO₄·7H₂O (X₅)、MgSO₄·7H₂O (X₆)、KH₂PO₄ (X₇)、K₂HPO₄ (X₈)，响应值为四氢嘧啶的积累量。响应面优化实验采用Box-Behnken设计(4因素3水平，表3)，并验证响应模型。按1%接种量，将野生菌株XH26与重组菌株XH26/pHX04分别接种于OSM培养基与优化MG培养基，37 ℃、180 r/min培养72 h；每间隔8 h测定菌液OD₆₀₀和四氢嘧啶积累量，对比分析两者的生长曲线和产物积累变化。

1.8 数据处理

建立检测四氢嘧啶的标准曲线，即y=13 250.8x+17.2，R²=0.995，式中y为HPLC峰面积，x为四氢嘧啶的积累量(g/L)。使用Prism软件和Adobe Illustrator软件进行图形绘制。采用SPSS软件(v.27.0)进行统计和方差分析(analysis of variance, ANOVA)，以评估组间差异，显著性水平设定为α=0.05。

2 结果与分析

收起

2.1 筛选盐单胞菌XH26的敏感性抗生素

采用OSM培养基(含有0.5-256.0 μg/mL的Chl、Amp、Gen及Tet)培养野生型菌株XH26，筛选最佳抑菌抗生素和最低抑菌浓度。结果显示，不同浓度的Chl、Amp、Gen及Tet均能抑制菌株XH26生长，但存在显著差异。64.0 μg/mL Chl的抑菌率为60.13% (图2A)；128.0 μg/mL Amp的抑菌率为66.4% (图2B)；4.0 μg/mL Gen的抑菌率为68.62% (图2C)；8.0 μg/mL Tet的抑菌率为50.12% (图2D)。由此表明，Gen对菌株XH26生长具有良好的抑制能力，最终确定重组菌株选择性培养基的工作浓度为50.0 μg/mL Gen。

2.2 质粒元件的组合与有效表达

采用接合转移法将质粒pHX01转化至表达宿主菌株E. coli S17-1和XH26，以验证P _tac 启动子、RBS、报告基因sfGFP、终止子以及Gen抗性基因的表达情况。选择0.5 mmol/L IPTG诱导表达阳性克隆子的报告基因sfGFP，并采用蓝光切胶仪与荧光显微镜(激发波长488 nm)检测报告基因sfGFP的表达效果(图3)。蓝光激发照射显示，未诱导的菌株E. coli S17-1/pHX01 (图3A中1)和菌株XH26/pHX01 (图3B中5、6)均无绿色荧光；经50 μg/mL IPTG诱导的菌株E. coli S17-1/pHX01 (图3A中2、4)和菌株XH26/pHX01 (图3B中7、8)均呈现出明显的绿色荧光。荧光显微镜观察显示，经诱导的菌株E. coli S17-1/pHX01 (图3C)与菌株XH26/pHX01 (图3D)均检测到明显的绿色荧光信号。由此表明，重组质粒pHX01的功能表达元件均能正常工作，适用于盐单胞菌表达宿主的遗传操作。

2.3 筛选阳性重组菌株与PCR验证

筛选阳性重组菌株，并进行菌落PCR验证。结果显示，菌株XH26/pHX02扩增出基因asd (1 110 bp，图4B)；菌株XH26/pHX03同时扩增出基因asd与lysC (1 248 bp，图4C)；菌株XH26/pHX04进一步扩增出基因ectA (576 bp)以及基因ectB (1 266 bp，图4D)；菌株XH26/pHX05在菌株XH26/pHX04基础上，额外扩增出基因ectC (399 bp，图4E)。由此表明，重组质粒pHX02-pHX05均成功转入宿主菌株XH26。

2.4 重组菌株四氢嘧啶的积累量分析与目的蛋白表达验证

IPTG诱导培养野生菌株XH26和系列重组菌株(XH26/pHX02-pHX05)，并分析胞内四氢嘧啶的积累量。结果显示，重组菌株XH26/pHX04的四氢嘧啶积累量最高，为(0.66±0.04) g/L (图5A)。随后优化XH26/pHX04的诱导条件，发现在0.2 mmol/L IPTG条件下，四氢嘧啶的积累量最高(0.80±0.01) g/L (图5B)。更换MG培养基后，菌株XH26/pHX04的四氢嘧啶积累量进一步提升至(1.32±0.04) g/L (图5C)。SDS-PAGE分析显示(图5D)，各重组菌株均成功表达预期的目的蛋白，即菌株XH26/pHX02表达Asd (40.2 kDa)，菌株XH26/pHX03在此基础上新增表达LysC (44.2 kDa)，菌株XH26/pHX04进一步新增表达EctA (21.2 kDa)与EctB (46.5 kDa)，菌株XH26/pHX05则额外表达EctC (14.8 kDa)。

2.5 重组菌株XH26/pHX04四氢嘧啶积聚的最佳单因素条件

优化重组菌株XH26/pHX04的发酵条件，重点考察NaCl、蛋白胨、l-谷氨酸钠、葡萄糖、FeSO₄·7H₂O、MgSO₄·7H₂O、KH₂PO₄及K₂HPO₄共8种因素浓度对四氢嘧啶积累量的影响。结果表明，各因素的最适浓度和相应的积累量分别为：NaCl 87.75 g/L (1.41 g/L)、蛋白胨15.00 g/L (1.80 g/L)、l-谷氨酸钠160.00 g/L (1.77 g/L)、葡萄糖15.00 g/L (1.41 g/L)、FeSO₄·7H₂O 0.60 g/L (1.33 g/L)、MgSO₄·7H₂O 0.80 g/L (1.30 g/L)、KH₂PO₄ 9.00 g/L (1.32 g/L)和K₂HPO₄ 15.00 g/L (1.38 g/L) (图6A-6H)。

2.6 影响重组菌株XH26/pHX04四氢嘧啶积聚的关键因素

甄选各因素的最适浓度，采用Plackett-Burman实验筛选关键变量。实验结果(表4)与统计学分析结果显示(表5)，NaCl、蛋白胨、l L l-谷氨酸钠和葡萄糖对菌株XH26/pHX04胞内四氢嘧啶积累具有极显著正向影响(P<0.01)，而其余因素无显著作用(P>0.05)。所建模型显著有效(F=29.52, P<0.05)，且拟合良好(R²=0.987 5)。因此，选定上述4种成分为关键因素进行后续响应面优化，其余组分浓度保持不变。

2.7 多因素交互分析重组菌株XH26/pHX04的四氢嘧啶积累量

采用Design-Expert v13.0软件设计了4因素组合试验(表6)，并以菌株XH26/pHX04的四氢嘧啶积累量(Y)为响应变量，构建了二次多项式回归模型：Y=1.82-0.049 0A+0.016 8B+0.023 8C+0.010 2D-0.001 7AB+0.066 2AC+0.057 0AD+0.022 5BC+0.042 2BD+0.011 2CD-0.185 7A²-0.063 7B²-0.067 2C²-0.102 2D²。方差分析结果显示(表7)，该模型整体高度显著(P<0.05)，且拟合优度优异(R²=0.997 6)；此外，失拟项不显著(P=0.231 3>0.05)，表明残差主要源于随机误差，模型具备良好的预测可靠性。响应面分析结果表明(图7)，NaCl分别与蛋白胨、l-谷氨酸钠和葡萄糖的交互作用显著，且均在NaCl 117.00 g/L附近达到峰值。蛋白胨与葡萄糖间也存在显著交互作用，最优浓度均为15.00 g/L。基于响应面模型预测的最佳发酵条件(NaCl 116.08 g/L、蛋白胨16.30 g/L、l-谷氨酸钠169.57 g/L、葡萄糖15.53 g/L)，进行3批次独立摇瓶重复实验以验证模型的实际预测效能。结果显示，重组菌株XH26/pHX04的四氢嘧啶积累量达到(1.81±0.02) g/L，与模型预测值(1.83 g/L)高度一致，相对误差仅为0.88% (<5.00%)。由此表明，所构建的响应面回归模型准确可靠。

2.8 比较分析野生菌株和重组菌株XH26/pHX04的生长特性

菌株生长特性分析显示(图8)，野生菌株XH26和重组菌株XH26/pHX04均在8 h左右进入对数生长期，野生菌株XH26在16 h左右结束对数生长期，生长速度逐渐变得缓慢，48 h完全进入平台期，此时胞内四氢嘧啶的积累量可达0.454 g/L；重组菌株XH26/pHX04同样在16 h左右结束对数生长期，但平台期启动更晚，48-72 h为主要平台期，随着培养时间的延长，四氢嘧啶积累量仍然能持续累积，72 h达到平均值峰值1.798 g/L。由此表明，重组菌株XH26/pHX04进入平台期的时间更晚且持续时间更长，且四氢嘧啶的持续合成能力显著优于原始菌株XH26。

3 讨论

收起

3.1 盐单胞菌“底盘细胞”与四氢嘧啶合成相关基因的增强型表达

盐单胞菌凭借独特的耐盐碱生理特性已成为合成生物学中一类极具应用潜力的新型“底盘细胞”工厂^[1]。首先，与传统表达宿主相比，该类菌株能够在高盐/碱性条件下实现开放式培养，这不仅显著降低了菌株的污染风险，也为特殊次级代谢物的盐碱条件发酵提供了可能的解决方案^[17-18]。在实际应用方面，盐单胞菌已展现出独特的生产潜能，如H. bluephagenesis菌株已被成功用于开放式生产PHA^[19]；菌株Halomonas sp. AAD6和KM-1用于高效合成结冷胶、丙酮酸等^[20-21]。其次，涉及适配盐单胞菌表达宿主的分子工具开发也取得了重要进展，包括pSEVA系列载体、孔蛋白启动子文库和类T7诱导系统在内的多种遗传操作元件^[22-24]。尤其值得注意的是，CRISPR/Cas9系统已成功应用于H. bluephagenesis的基因组编辑和染色体整合，大幅提升了可编程性遗传操作水平^[25-26]。基于此，本研究以H. campaniensis XH26为“底盘细胞”，通过接合转移强化四氢嘧啶合成基因簇的表达，在高盐培养基中实现了该化合物积累量的显著提升，进一步证明了盐单胞菌作为耐盐细胞工厂在合成生物学中的工程价值。

3.2 基因导入策略与多基因表达的负效应

在合成生物学领域，关联基因或基因簇的协同表达和定向强化关键中间体的代谢流可能是提升次级代谢物合成能力的有效策略之一^[27]。Wang等^[28]利用E. coli BL21(DE3)菌株异源表达基因簇ectABC，同时过表达相关前体合成基因lysC与asd，通过连续补料发酵四氢嘧啶，其积累量高达60.7 g/L。Eun等^[29]通过删除C. glutamicum的基因crtEb和crtYe/f，成功构建α-胡萝卜素合成途径，经54 h补料发酵后叶黄素的积累量为1.78 g/L。Zhong等^[30]利用C. glutamicum共表达基因xylA、xylB与EcxylE，采用5 L发酵罐以木糖生产l-高丝氨酸，其积累量可达93.1 g/L。然而，外源基因的导入并非简单叠加，过量表达易引发“代谢负荷” (metabolic burden)^[31-32]。该效应主要表现为能量、辅因子、氨基酸及转录翻译资源的过度消耗。

本研究中，菌株XH26/pHX05在菌株XH26/pHX04 (含asd、lysC、ectA、ectB)基础上额外导入基因ectC后，四氢嘧啶积累量从(1.32±0.04) g/L降至(1.19±0.07) g/L，该现象可从3方面机制解释。(1) 基因ectC过表达破坏了四氢嘧啶合成途径的酶平衡。Zhang等^[33]发现，当基因ectA:ectB:ectC拷贝数比例为1:2:1时，四氢嘧啶积累量达12.9 g/L，而仅将基因ectC拷贝数增至3倍，产物积累量便骤降至1.04 g/L。Gießelmann等^[34]在谷氨酸棒状杆菌异源合成四氢嘧啶的研究中发现，EctB作为限速酶直接决定合成途径通量。过度表达下游EctC会导致上游中间产物供应不足，最终降低产物积累量。(2) 基因ectC过表达会引起胞内资源竞争。González-Colell等^[35]通过构建多基因竞争模型发现，过表达外源基因会竞争RNA聚合酶与核糖体等有限资源，本体系中高表达基因ectC，势必削弱asd、lysC等前体基因的转录与翻译能力，进而影响前体供应，该过程也符合Sabi与Tuller^[36]所揭示的tRNA池竞争导致翻译失衡的机制。此外，基因过表达ectC也可能干扰四氢嘧啶合成与降解的动态平衡。Schwibbert等^[37]在盐单胞菌中证实四氢嘧啶积累可诱导四氢嘧啶水解酶编码基因(doeA)表达，本研究中菌株XH26/pHX05过表达基因ectC所引起的代谢扰动激活了四氢嘧啶降解途径，从而降低其积累量。综上所述，基因ectC的单独导入与过表达并未有效提升产物合成，反而因破坏代谢平衡、引发资源竞争并激活降解途径，最终导致胞内四氢嘧啶积累量降低。

3.3 碳氮源的协同调控对四氢嘧啶合成的驱动作用

碳、氮源可通过调整自身种类与配比，调控胞内碳氮代谢流、前体供应及胁迫响应过程，进而对四氢嘧啶的合成效率产生显著影响^[38]。本研究采用响应面法优化得到最佳培养基配比(NaCl 116.08 g/L，蛋白胨16.30 g/L，l-谷氨酸钠169.57 g/L，葡萄糖15.53 g/L)印证了上述理论，揭示了碳氮源协同调控的具体模式：高浓度l-谷氨酸钠作为氨基供体促进EctB催化生成前体l-2,4-二氨基丁酸^[39]；适量葡萄糖提供能量(ATP)、还原力(NADPH)及草酰乙酸等碳骨架^[40]。该响应面模型进一步揭示，碳、氮源浓度过高或过低会引发代谢流分配失衡与代谢溢流效应，最终降低四氢嘧啶积累量^[41]；同时，16.30 g/L蛋白胨可提供氨基酸、小肽及生长因子，多途径促进菌体生长^[42]。此外，116.08 g/L NaCl构建的高盐环境能激活基因簇ectABC，驱动碳氮代谢流向四氢嘧啶合成途径汇聚^[43]。此次优化确定的多因素浓度组合，实现了前体供应、能量代谢与胁迫响应的高效平衡：其中碳氮比(C/N)的精确调控对四氢嘧啶合成通量起决定性作用^[44]，渗透压与营养素的协同调控策略，为适配嗜盐菌株代谢特性、提升四氢嘧啶合成效率提供了可行方向^[45]；同时，这种多因素协同优化方法与近期微生物细胞工厂代谢工程的系统优化理念高度契合^[46-48]，为同源底盘细胞的工业化应用奠定了坚实基础。

4 结论

收起

本研究围绕H. campaniensis XH26的同源代谢改造展开核心探索，通过P _tac 强启动子驱动关键基因(asd、lysC、ectA、ectB)过表达，其中asd与lysC强化前体天冬氨酸-β-半醛的供应，ectA与ectB加速中间产物向四氢嘧啶的转化，再耦合响应面法精准优化培养基组分，最终使重组菌株的四氢嘧啶积累量从野生型的0.454 g/L提升至(1.81±0.02) g/L，增幅达301.56%。这一成果不仅验证了“前体强化+关键步骤调控”策略在该同源底盘中的有效性，更首次建立了针对XH26的“基因克隆-接合转移-培养优化”完整技术体系，填补了该菌株代谢工程改造的研究空白，为后续嗜盐微生物次级代谢物的同源合成提供了可复用的技术范式。

需客观说明的是，当前(1.81±0.02) g/L的产量为摇瓶基础研究水平，与文献中“基因组编辑菌株+发酵罐补料”的工业化高产数据存在差距，此差异源于研究阶段的定位不同，而非改造策略的局限。同时，当前使用的诱导性P _tac 启动子虽证实了菌株XH26的代谢强化潜力，但也暴露了关键基因表达比例难以动态调控的问题，为后续提升菌株生产能力指明了方向。基于本研究建立的基础框架，后续将从两方面推进菌株生产能力的突破：一是结合代谢组学数据筛选菌株XH26基因组内不同强度的内源性启动子，精准调控基因ectA、ectB、ectC的表达比例，消除因酶活失衡导致的代谢瓶颈；二是开发CRISPR/Cas9介导的基因组整合技术，将优化后的表达模块定点插入染色体，构建无质粒、无抗性标记的稳定工程菌株，为推动研究向产业化转化奠定关键基础。

基金

收起

国家自然科学基金(32260019)
青海中央引导地方科技发展资金(2024ZY015)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

, CHEN

. Halomonas as a chassis[J]. Essays in Biochemistry, 2021, 65(2): 393-403.

[2]

ZHANG

, LIU

, KONG

, YE

, WANG

. Multiple functions of compatible solute ectoine and strategies for constructing overproducers for biobased production[J]. Molecular Biotechnology, 2024, 66(8): 1772-1785.

[3]

LIU

, GORISH

BMT

, QARIA

, HUSSAIN

, ABDELMULA

WIY

, ZHU

. Unlocking ectoine’s postbiotic therapeutic promise: mechanisms, applications, and future directions[J]. Probiotics and Antimicrobial Proteins, 2025: 1-26.

[4]

张培霞, 陶宇杰, 乔丽娟, 王嵘, 韩睿, 朱德锐, 沈国平. 转录组学分析Fe₃O₄纳米颗粒胁迫下盐单胞菌XH26与四氢嘧啶代谢相关的差异表达基因[J]. 微生物学报, 2025, 65(1): 239-255.

ZHANG

, TAO

, QIAO

, WANG

, HAN

, ZHU

, SHEN

. Transcriptomics reveals differentially expressed genes related to ectoine metabolism in Halomonas campaniensis XH26 under Fe₃O₄nanoparticle stress[J]. Acta Microbiologica Sinica, 2025, 65(1): 239-255 (in Chinese).

[5]

, YE

, LIN

, YI

, WANG

, HUANG

, WU

, LIU

, CHEN

. Flux optimization using multiple promoters in Halomonas bluephagenesis as a model chassis of the next generation industrial biotechnology[J]. Metabolic Engineering, 2024, 81: 249-261.

[6]

HOBMEIER

, CANTONE

, NGUYEN

, PFLÜGER-GRAU

, KREMLING

, KUNTE

, PFEIFFER

, MARIN-SANGUINO

. Adaptation to varying salinity in Halomonas elongata: much more than ectoine accumulation[J]. Frontiers in Microbiology, 2022, 13: 846677.

[7]

WANG

, SONG

, CUI

, WANG

, LUO

. Metabolic engineering of Escherichia coli for efficient production of ectoine[J]. Journal of Agricultural and Food Chemistry, 2025, 73(1): 646-654.

[8]

JIANG

, SONG

, YOU

, ZHANG

, XU

, RAO

. High-yield ectoine production in engineered Corynebacterium glutamicum by fine metabolic regulation via plug-in repressor library[J]. Bioresource Technology, 2022, 362: 127802.

[9]

WANG

, ZHANG

, ZHU

, WANG

, BAI

, QIAN

, ZHOU

, YIN

, ZHANG

. Expression of a deep-sea bacterial laccase from Halomonas alkaliantartica and its application in dyes decolorization[J]. Annals of Microbiology, 2023, 73(1): 19.

[10]

LEI

, CHEN

, YUAN

, WU

, YAO

. Metabolic control for high-efficiency ectoine synthesis in engineered Escherichia coli [J]. ACS Synthetic Biology, 2025, 14(8): 3177-3185.

[11]

SZÉLIOVÁ

, KRAHULEC

, ŠAFRÁNEK

, LIŠKOVÁ

, TURŇA

. Modulation of heterologous expression from PBAD promoter in Escherichia coli production strains[J]. Journal of Biotechnology, 2016, 236: 1-9.

[12]

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.

[13]

, SUN

, ZHANG

, LIU

, YI

, HE

, SCRUTTON

, CHEN

. Ectoine hyperproduction by engineered Halomonas bluephagenesis [J]. Metabolic Engineering, 2024, 82: 238-249.

[14]

ZHU

, LIU

, HAN

, SHEN

, LONG

, WEI

, LIU

. Identification and characterization of ectoine biosynthesis genes and heterologous expression of the ectABC gene cluster from Halomonas sp. QHL1 a moderately halophilic bacterium isolated from Qinghai Lake[J]. Journal of Microbiology, 2014, 52(2): 139-147.

[15]

ZHANG

, CUI

, CAO

, LI

, ZHU

, XING

. Whole genome sequencing of the halophilic Halomonas qaidamensis XH36, a novel species strain with high ectoine production[J]. Antonie Van Leeuwenhoek, 2022, 115(4): 545-559.

[16]

逯心玥, 李昊鑫, 张培霞, 师博涵, 李永臻, 王嵘, 朱德锐, 韩睿. 靶向代谢组学分析添加天冬氨酸培养盐单胞菌时四氢嘧啶的代谢通路变化[J]. 微生物学报, 2025, 65(10): 4621-4636.

, LI

, ZHANG

, SHI

, LI

, WANG

, ZHU

, HAN

. Targeted metabolomics reveals changes in metabolic pathways related to ectoine in Halomonas cultured with aspartate[J]. Acta Microbiologica Sinica, 2025, 65(10): 4621-4636 (in Chinese).

[17]

KIVISTÖ

, SANTALA

, KARP

. Non-sterile process for biohydrogen and 1,3-propanediol production from raw glycerol[J]. International Journal of Hydrogen Energy, 2013, 38(27): 11749-11755.

[18]

OREN

. Industrial and environmental applications of halophilic microorganisms[J]. Environmental Technology, 2010, 31(8/9): 825-834.

[19]

YUE

, LING

, YANG

, CHEN

, DENG

, WU

, CHEN

. A seawater-based open and continuous process for polyhydroxyalkanoates production by recombinant Halomonas campaniensis LS21 grown in mixed substrates[J]. Biotechnology for Biofuels, 2014, 7(1): 108.

[20]

KAZAK SARILMISER

, ATES

, OZDEMIR

, ARGA

, TOKSOY ONER

. Effective stimulating factors for microbial levan production by Halomonas smyrnensis AAD6T[J]. Journal of Bioscience and Bioengineering, 2015, 119(4): 455-463.

[21]

JIN

, SHI

, KAWATA

. Metabolomics-based component profiling of Halomonas sp. KM-1 during different growth phases in poly(3-hydroxybutyrate) production[J]. Bioresource Technology, 2013, 140: 73-79.

[22]

ZHAO

, ZHANG

, CHEN

, LI

, WU

, OUYANG

, CHEN

. Novel T7-like expression systems used for Halomonas [J]. Metabolic Engineering, 2017, 39: 128-140.

[23]

SHEN

, YIN

, YE

, XIANG

, NING

, HUANG

, CHEN

. Promoter engineering for enhanced P(3HB-co-4HB) production by Halomonas bluephagenesis [J]. ACS Synthetic Biology, 2018, 7(8): 1897-1906.

[24]

MARTÍNEZ-GARCÍA

, GOÑI-MORENO

, BARTLEY

, McLAUGHLIN

, SÁNCHEZ-SAMPEDRO

, PASCUAL del POZO

, PRIETO HERNÁNDEZ

, MARLETTA

, de LUCREZIA

, SÁNCHEZ-FERNÁNDEZ

, FRAILE

, de LORENZO

. SEVA 3.0: an update of the standard European vector architecture for enabling portability of genetic constructs among diverse bacterial hosts[J]. Nucleic Acids Research, 2020, 48(D1): D1164-D1170.

[25]

, HU

, YIN

, HUANG

, XIANG

, ZHANG

, WANG

, HAN

, CHEN

. Stimulus response-based fine-tuning of polyhydroxyalkanoate pathway in Halomonas [J]. Metabolic Engineering, 2020, 57: 85-95.

[26]

JIANG

, YAO

, CHEN

. Controlling cell volume for efficient PHB production by Halomonas [J]. Metabolic Engineering, 2017, 44: 30-37.

[27]

PARK

, SELLÉS VIDAL

, BELL

, ZABRET

, SOLDAT

, KAVŠČEK

, LEDESMA-AMARO

. Efficient synthesis of limonene production in Yarrowia lipolytica by combinatorial engineering strategies[J]. Biotechnology for Biofuels and Bioproducts, 2024, 17(1): 94.

[28]

WANG

, CHEN

, WANG

, DU

, KANG

. Engineering Escherichia coli for high-yield production of ectoine[J]. Green Chemical Engineering, 2023, 4(2): 217-223.

[29]

EUN H, PRABOWO

CPS

, LEE

. Gram-per-litre-scale production of lutein by engineered Corynebacterium [J]. Nature Synthesis, 2025, 4(10): 1200-1211.

[30]

ZHONG

, MA

, CAO

, ZHANG

, QI

, WEI

, JIANG

, XU

, LIU

. Metabolic engineering of Corynebacterium glutamicum for efficient l-homoserine production from lignocellulose-derived sugars[J]. ACS Sustainable Chemistry & Engineering, 2025, 13(9): 3441-3451.

[31]

, YAN

, JONES

, TANG

, FONG

, KOFFAS

MAG

. Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications[J]. Trends in Biotechnology, 2016, 34(8): 652-664.

[32]

KASTBERG

, ARD R, JENSEN

, WORKMAN

. Burden imposed by heterologous protein production in two major industrial yeast cell factories: identifying sources and mitigation strategies[J]. Frontiers in Fungal Biology, 2022, 3: 827704.

[33]

ZHANG

, LIANG

, ZHAO

, MA

, LUO

, LI

, XU

. Metabolic engineering of Escherichia coli for ectoine production with a fermentation strategy of supplementing the amino donor[J]. Frontiers in Bioengineering and Biotechnology, 2022, 10: 824859.

[34]

GIEßELMANN

, DIETRICH

, JUNGMANN

, KOHLSTEDT

, JEON

, YIM SS, SOMMER

, ZIMMER

, MÜHLHAUS

, SCHRODA

, JEONG

, BECKER

, WITTMANN

. Metabolic engineering of Corynebacterium glutamicum for high-level ectoine production: design, combinatorial assembly, and implementation of a transcriptionally balanced heterologous ectoine pathway[J]. Biotechnology Journal, 2019, 14(9): 1800417.

[35]

GONZÁLEZ-COLELL

, MACÍA

. General analyses of gene expression dependencies on genetic burden[J]. Frontiers in Bioengineering and Biotechnology, 2020, 8: 1017.

[36]

SABI

, TULLER

. Modelling and measuring intracellular competition for finite resources during gene expression[J]. Journal of the Royal Society, Interface, 2019, 16(154): 20180887.

[37]

SCHWIBBERT

, MARIN-SANGUINO

, BAGYAN

, HEIDRICH

, LENTZEN

, SEITZ

, RAMPP

, SCHUSTER

, KLENK

, PFEIFFER

, OESTERHELT

, KUNTE

. A blueprint of ectoine metabolism from the genome of the industrial producer Halomonas elongata DSM 2581T[J]. Environmental Microbiology, 2011, 13(8): 1973-1994.

[38]

CHEN

, LIU

, MENG

, JIANG

, XIONG

, WANG

, YANG

, LIU

. Elucidating the salt-tolerant mechanism of Halomonas cupida J9 and unsterile ectoine production from lignocellulosic biomass[J]. Microbial Cell Factories, 2024, 23(1): 237.

[39]

PÉREZ-GARCÍA

, ZIERT

, RISSE

, WENDISCH

. Improved fermentative production of the compatible solute ectoine by Corynebacterium glutamicum from glucose and alternative carbon sources[J]. Journal of Biotechnology, 2017, 258: 59-68.

[40]

KHALEQUE

, NAZEM-BOKAEE

, GUMULYA

, CARLSON

, KAKSONEN

. Simulating compatible solute biosynthesis using a metabolic flux model of the biomining acidophile, Acidithiobacillus ferrooxidans ATCC 23270[J]. Research in Microbiology, 2024, 175(1/2): 104115.

[41]

NAKAYAMA

, KAWAMOTO

, MIYOSHI

. Ectoine production from putrefactive non-volatile amines in the moderate halophile Halomonas elongata [J]. Earth and Environmental Science, 2020, 439(1): 012001.

[42]

CANTERA

, TAMARIT

, STRONG

, SÁNCHEZ-ANDREA

, ETTEMA

TJG

, SOUSA

. Prospective CO₂ and CO bioconversion into ectoines using novel microbial platforms[J]. Reviews in Environmental Science and Bio/Technology, 2022, 21(3): 571-581.

[43]

SALAR-GARCÍA

, BERNAL

, PASTOR

, SALVADOR

, ARGANDOÑA

, NIETO

, VARGAS

, CÁNOVAS

. Understanding the interplay of carbon and nitrogen supply for ectoines production and metabolic overflow in high density cultures of Chromohalobacter salexigens [J]. Microbial Cell Factories, 2017, 16(1): 23.

[44]

ZOU

, KAOTHIEN-NAKAYAMA

, NAKAYAMA

. Enhanced accumulation of γ-aminobutyric acid by deletion of aminotransferase genes involved in γ-aminobutyric acid catabolism in engineered Halomonas elongata [J]. Applied and Environmental Microbiology, 2024, 90(9): e0073424.

[45]

LIM SE, CHO

, CHOI

, NA

, LEE

. High production of ectoine from methane in genetically engineered Methylomicrobium alcaliphilum 20Z by preventing ectoine degradation[J]. Microbial Cell Factories, 2024, 23(1): 127.

[46]

HUANG

, ZHANG

, REN

, JI

, SUN

. The synthesis of ectoine enhance the assimilation of ammonia nitrogen in hypersaline wastewater by the salt-tolerant assimilation bacteria sludge[J]. Science of The Total Environment, 2024, 913: 169694.

[47]

ASIRI

, CHEN

, HWANGBO

, SHAO

, CHU

. From organic wastes to bioplastics: feasibility of nonsterile poly(3-hydroxybutyrate) production by Zobellella denitrificans ZD1[J]. ACS Omega, 2020, 5(38): 24158-24168.

[48]

WANG

, LI

, ZHANG

, YU

, ZHU

. Simultaneous heterotrophic nitrification and aerobic denitrification at high concentrations of NaCl and ammonia nitrogen by Halomonas bacteria[J]. Water Science and Technology, 2017, 76(2): 386-395.

2026年第66卷第3期

PDF下载

143

引用本文

BibTeX

文章信息

doi: 10.13343/j.cnki.wsxb.20250711

接收时间：2025-09-17
首发时间：2026-03-12
出版时间：2026-03-04

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2025-09-17
录用日期：2025-11-27

基金

National Natural Science Foundation of China(32260019)

国家自然科学基金(32260019)

Qinghai Central Government Guide Local Science and Technology Development Fund(2024ZY015)

青海中央引导地方科技发展资金(2024ZY015)

作者信息

^1.青海大学医学院，基础医学研究中心，青海西宁

^2.青海大学农林科学院，蔬菜遗传与生理重点实验室，青海西宁

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.20250711

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

Gene names	Primer sequences (5′→3′)	Product length (bp)
sfGFP	F: ATGCGCAAAGGCGAAGAACT R: TTTATACAGTTCATCCATGCCGTGGG	714
asd	F: ATGTTGAAAGTCGGTTTCGTGGG R: CTGCTCGCGTAAGATCTTTAG	1 110
lysC	F: ATGGCATTATACGTACAGAAGTTCGGC R: TTCAGATTCGATATCAGACTTATCTAAACCGAAT	1 248
ectA	F: ATGAGCACGCCAATAACACCT R: CATACTGTCTGTTTGAAATGGACCTATGC	576
ectB	F: ATGCAGACCCAGACGCTTGA R: CGCTTGGATAACAGCATTGACTGA	1 266
ectC	F: ATGATCGTTCGTAATCTTGAAGAAGCAC R: TCACTCACTAGCAGGTGCAGC	399

Gene names

Primer sequences (5′→3′)

Product length (bp)

sfGFP

F: ATGCGCAAAGGCGAAGAACT

R: TTTATACAGTTCATCCATGCCGTGGG

714

asd

F: ATGTTGAAAGTCGGTTTCGTGGG

R: CTGCTCGCGTAAGATCTTTAG

1 110

lysC

F: ATGGCATTATACGTACAGAAGTTCGGC

R: TTCAGATTCGATATCAGACTTATCTAAACCGAAT

1 248

ectA

F: ATGAGCACGCCAATAACACCT

R: CATACTGTCTGTTTGAAATGGACCTATGC

576

ectB

F: ATGCAGACCCAGACGCTTGA

R: CGCTTGGATAACAGCATTGACTGA

1 266

ectC

F: ATGATCGTTCGTAATCTTGAAGAAGCAC

R: TCACTCACTAGCAGGTGCAGC

399

Recombinant plasmid names	Vector backbone	Carried target genes	Corresponding recombinant strain
pHX01	pBBR1MCS-5	sfGFP	XH26/pHX01
pHX02	pBBR1MCS-5	asd	XH26/pHX02
pHX03	pBBR1MCS-5	asd, lysC	XH26/pHX03
pHX04	pBBR1MCS-5	asd, lysC, ectA, ectB	XH26/pHX04
pHX05	pBBR1MCS-5	asd, lysC, ectA, ectB, ectC	XH26/pHX05

Recombinant plasmid names

Vector backbone

Carried target genes

Corresponding recombinant strain

pHX01

pBBR1MCS-5

sfGFP

XH26/pHX01

pHX02

pBBR1MCS-5

asd

XH26/pHX02

pHX03

pBBR1MCS-5

asd, lysC

XH26/pHX03

pHX04

pBBR1MCS-5

asd, lysC, ectA, ectB

XH26/pHX04

pHX05

pBBR1MCS-5

asd, lysC, ectA, ectB, ectC

XH26/pHX05

Test	Factor	Variant	Level
Plackett-Burman	NaCl (g/L)	X₁	87.75		146.25
Peptone (g/L)	X₂	10.00		20.00
	l-glutamate (g/L)	X₃	120.00		200.00
	Glucose (g/L)	X₄	10.00		20.00
	FeSO₄·7H₂O (g/L)	X₅	0.20		1.00
	MgSO₄·7H₂O (g/L)	X₆	0.40		1.20
	KH₂PO₄ (g/L)	X₇	3.00		15.00
	K₂HPO₄ (g/L)	X₈	9.00		21.00
Box-Behnken	NaCl (g/L)	A	87.75	117.00	146.25
Peptone (g/L)	B	10.00	15.00	20.00
	l-glutamate (g/L)	C	120.00	160.00	200.00
	Glucose (g/L)	D	10.00	15.00	20.00

Test

Factor

Variant

Level

-1

Plackett-Burman

NaCl (g/L)

X₁

87.75

146.25

Peptone (g/L)

X₂

10.00

20.00

l-glutamate (g/L)

X₃

120.00

200.00

Glucose (g/L)

X₄

10.00

20.00

FeSO₄·7H₂O (g/L)

X₅

0.20

1.00

MgSO₄·7H₂O (g/L)

X₆

0.40

1.20

KH₂PO₄ (g/L)

X₇

3.00

15.00

K₂HPO₄ (g/L)

X₈

9.00

21.00

Box-Behnken

NaCl (g/L)

87.75

117.00

146.25

Peptone (g/L)

10.00

15.00

20.00

l-glutamate (g/L)

120.00

160.00

200.00

Glucose (g/L)

10.00

15.00

20.00

Experiment No.	Variant	Ectoine (g/L)
1	-1	1	1	1	1	-1	1	1	1.77
2	1	1	-1	-1	1	1	-1	1	1.64
3	1	-1	1	1	-1	1	-1	1	1.80
4	1	1	1	-1	-1	-1	-1	1	1.63
5	1	1	-1	1	-1	-1	1	-1	1.79
6	-1	-1	-1	-1	-1	-1	-1	-1	1.38
7	1	-1	1	1	1	-1	-1	-1	1.79
8	-1	1	-1	1	1	1	-1	-1	1.67
9	-1	-1	-1	1	-1	1	1	1	1.40
10	-1	-1	1	-1	1	1	1	-1	1.47
11	1	1	1	-1	-1	1	1	-1	1.73
12	1	-1	-1	-1	1	-1	1	1	1.45

Experiment

No.

Variant

Ectoine (g/L)

X₁

X₂

X₃

X₄

X₅

X₆

X₇

X₈

-1

1.77

-1

1.64

-1

1.80

-1

1.63

-1

1.79

-1

1.38

-1

1.79

-1

1.67

-1

1.40

-1

1.47

-1

1.73

-1

1.45

Source	Sum of squares	df	Mean square	F-value	P-value	Significance
Model	0.276 3	8	0.034 5	29.52	0.009 0	*
X₁: NaCl (g/L)	0.061 6	1	0.061 6	52.68	0.005 4	*
X₂: Peptone (g/L)	0.073 0	1	0.073 0	62.41	0.004 2	*
X₃: l-glutamate (g/L)	0.061 9	1	0.061 9	52.93	0.005 4	*
X₄: Glucose (g/L)	0.069 6	1	0.069 6	59.51	0.004 5	*
X₅: FeSO₄·7H₂O (g/L)	0.000 4	1	0.000 4	0.329	0.606 2
X₆: MgSO₄·7H₂O (g/L)	0.001 2	1	0.001 2	1.06	0.378 9
X₇: KH₂PO₄ (g/L)	0.007 1	1	0.007 1	6.07	0.090 5
X₈: K₂HPO₄ (g/L)	0.001 4	1	0.001 4	1.20	0.352 7
Pure error	0.003 5	3	0.001 2
Cor total	0.279 8	11

Source

Sum of squares

Mean square

F-value

P-value

Significance

Model

0.276 3

0.034 5

29.52

0.009 0

X₁: NaCl (g/L)

0.061 6

52.68

0.005 4

X₂: Peptone (g/L)

0.073 0

62.41

0.004 2

X₃: l-glutamate (g/L)

0.061 9

52.93

0.005 4

X₄: Glucose (g/L)

0.069 6

59.51

0.004 5

X₅: FeSO₄·7H₂O (g/L)

0.000 4

0.329

0.606 2

X₆: MgSO₄·7H₂O (g/L)

0.001 2

1.06

0.378 9

X₇: KH₂PO₄ (g/L)

0.007 1

6.07

0.090 5

X₈: K₂HPO₄ (g/L)

0.001 4

1.20

0.352 7

Pure error

0.003 5

0.001 2

Cor total

0.279 8

Experiment No.	NaCl (g/L)	Peptone (g/L)	l-glutamate (g/L)	Glucose (g/L)	Ectoine (g/L)	Experiment No.	NaCl (g/L)	Peptone (g/L)	l-glutamate (g/L)	Glucose (g/L)	Ectoine (g/L)
1	87.75	10.00	160.00	15.00	1.60	16	117.00	20.00	200.00	15.00	1.76
2	146.25	10.00	160.00	15.00	1.50	17	87.75	15.00	120.00	15.00	1.65
3	87.75	20.00	160.00	15.00	1.64	18	146.25	15.00	120.00	15.00	1.42
4	146.25	20.00	160.00	15.00	1.54	19	87.75	15.00	200.00	15.00	1.57
5	117.00	15.00	120.00	10.00	1.63	20	146.25	15.00	200.00	15.00	1.60
6	117.00	15.00	200.00	10.00	1.65	21	117.00	10.00	160.00	10.00	1.67
7	117.00	15.00	120.00	20.00	1.63	22	117.00	20.00	160.00	10.00	1.61
8	117.00	15.00	200.00	20.00	1.69	23	117.00	10.00	160.00	20.00	1.60
9	87.75	15.00	160.00	10.00	1.63	24	117.00	20.00	160.00	20.00	1.71
10	146.25	15.00	160.00	10.00	1.42	25	117.00	15.00	160.00	15.00	1.81
11	87.75	15.00	160.00	20.00	1.54	26	117.00	15.00	160.00	15.00	1.82
12	146.25	15.00	160.00	20.00	1.56	27	117.00	15.00	160.00	15.00	1.82
13	117.00	10.00	120.00	15.00	1.67	28	117.00	15.00	160.00	15.00	1.82
14	117.00	20.00	120.00	15.00	1.66	29	117.00	15.00	160.00	15.00	1.82
15	117.00	10.00	200.00	15.00	1.68

Experiment No.

NaCl (g/L)

Peptone (g/L)

l-glutamate (g/L)

Glucose (g/L)

Ectoine (g/L)

Experiment No.

NaCl (g/L)

Peptone (g/L)

l-glutamate (g/L)

Glucose (g/L)

Ectoine (g/L)

87.75

10.00

160.00

15.00

1.60

117.00

20.00

200.00

15.00

1.76

146.25

10.00

160.00

15.00

1.50

87.75

15.00

120.00

15.00

1.65

87.75

20.00

160.00

15.00

1.64

146.25

15.00

120.00

15.00

1.42

146.25

20.00

160.00

15.00

1.54

87.75

15.00

200.00

15.00

1.57

117.00

15.00

120.00

10.00

1.63

146.25

15.00

200.00

15.00

1.60

117.00

15.00

200.00

10.00

1.65

117.00

10.00

160.00

10.00

1.67

117.00

15.00

120.00

20.00

1.63

117.00

20.00

160.00

10.00

1.61

117.00

15.00

200.00

20.00

1.69

117.00

10.00

160.00

20.00

1.60

87.75

15.00

160.00

10.00

1.63

117.00

20.00

160.00

20.00

1.71

146.25

15.00

160.00

10.00

1.42

117.00

15.00

160.00

15.00

1.81

87.75

15.00

160.00

20.00

1.54

117.00

15.00

160.00

15.00

1.82

146.25

15.00

160.00

20.00

1.56

117.00

15.00

160.00

15.00

1.82

117.00

10.00

120.00

15.00

1.67

117.00

15.00

160.00

15.00

1.82

117.00

20.00

120.00

15.00

1.66

117.00

15.00

160.00

15.00

1.82

117.00

10.00

200.00

15.00

1.68

Source	Sum of squares	df	Mean square	F-value	P-value	Significant
Model	0.339 3	14	0.024 2	423.52	<0.000 1	*
A	0.028 8	1	0.028 8	503.50	<0.000 1	*
B	0.003 4	1	0.003 4	59.42	<0.000 1	*
C	0.006 8	1	0.006 8	119.12	<0.000 1	*
D	0.001 2	1	0.001 2	21.68	0.000 4	*
AB	0.000 0	1	0.000 0	0.21	0.650 7
AC	0.017 6	1	0.017 6	306.80	<0.000 1	*
AD	0.013 0	1	0.013 0	227.11	<0.000 1	*
BC	0.002 0	1	0.002 0	35.39	<0.000 1	*
BD	0.007 1	1	0.007 1	124.78	<0.000 1	*
CD	0.000 5	1	0.000 5	8.85	0.010 0	*
A²	0.223 8	1	0.223 8	3 910.32	<0.000 1	*
B²	0.026 3	1	0.026 3	460.43	<0.000 1	*
C²	0.029 3	1	0.029 3	512.39	<0.000 1	*
D²	0.067 8	1	0.067 8	1 184.73	<0.000 1	*
Residual	0.000 8	14	0.000 1
Lack of fit	0.000 7	10	0.000 1	2.21	0.231 3	Not significant
Pure error	0.000 1	4	0.000 0
Cor total	0.340 1	28

Source

Sum of squares

Mean square

F-value

P-value

Significant

Model

0.339 3

0.024 2

423.52

<0.000 1

0.028 8

503.50

<0.000 1

0.003 4

59.42

<0.000 1

0.006 8

119.12

<0.000 1

0.001 2

21.68

0.000 4

0.000 0

0.21

0.650 7

0.017 6

306.80

<0.000 1

0.013 0

227.11

<0.000 1

0.002 0

35.39

<0.000 1

0.007 1

124.78

<0.000 1

0.000 5

8.85

0.010 0

A²

0.223 8

3 910.32

<0.000 1

B²

0.026 3

460.43

<0.000 1

C²

0.029 3

512.39

<0.000 1

D²

0.067 8

1 184.73

<0.000 1

Residual

0.000 8

0.000 1

Lack of fit

0.000 7

0.000 1

2.21

0.231 3

Not significant

Pure error

0.000 1

0.000 0

Cor total

0.340 1