Enhancement and balance of 1,4-butanediamine biosynthesis in Escherichia coli based on modular metabolic engineering

Abstract

1,4-Butanediamine is an important raw material for the synthesis of engineering plastics such as Polyamide 46 with excellent performance, which is widely used in automotive, electronics and machinery manufacturing industries. In this study, Escherichia coli BL21(DE3) was used as the starting strain to systematically modify the 1,4-butanediamine biosynthesis pathway using a modular strategy. 1,4-Butanediamine biosynthesis was re-divided into two modules: 1,4-butanediamine production module and α-ketoglutaric acid production module. By controlling the expression intensity of genes pykF, ppc, aceEF, gltA, icdA and gdhA, argD, argCB, argJ, ODC10, a recombinant producing 1,4-butanediamine strain PKT was obtained, achieving a yield of 862.82 mg/L of 1,4-butanediamine, which increased 8.62-fold compared to that of Escherichia coli BL21(DE3). Then, the fermentation medium of PKT strain was optimized. After 24 h of fermentation, the yield of 1,4-butanediamine reached 1843.60 mg/L, representing a 16.23-fold increase over the original strain. Furthermore, non-target metabolomics analysis was used to analyze the changes of metabolites during the efficient synthesis of 1,4-butanediamine by the recombinant strain PKT. The results showed that the total number of differential metabolites detected was 653. Differential metabolite pathway analysis showed that there were 9 differential metabolites in the 1,4-butanediamine synthesis pathway, with 5 involved in its synthesis pathway and 4 in its degradation branch. These differences in metabolites provide a theoretical basis for the modification of recombinant strains. The green production of 1,4- butanediamine by microbial cell factories through fermentation is the future direction. This research provides a theoretical guidance of building high yield 1,4-butanediamine engineering strains.

1. Introduction

1,4-Butanediamine, also known as putrescine, is one of the biogenic amines. In 1885, Ludwig Brieger, a doctor in Berlin, Germany, first discovered biogenic amines, including putrescine, spermine, spermidine, and cadaverine, in decomposing corpses. 1,4-Butanediamine, which is the precursor of the synthesis of various materials, has been widely used in agricultural, pharmaceutical, industrial, and other fields1, 2, 3, 4, 5. In particular, 1,4-butanediamine serves as an important raw material for the synthesis of engineering plastics such as Polyamide 46 (PA46), PA410, and PA4T, which are extensively used in automotive, electronic, and mechanical manufacturing industries⁶. At present, the synthesis methods for 1,4-butanediamine mainly include chemical synthesis and biosynthesis⁷. However, chemical synthesis relies on using non-renewable petrochemical raw materials, which leads to problems such as unsustainable production and high pressure on environmental protection. Therefore, green and environmentally friendly biosynthesis of 1,4-butanediamine is essential.

Currently, the biosynthesis methods for 1,4-butanediamine mainly include microbial fermentation and enzymatic transformation methods8, 9, 10. The enzymatic transformation method is a process that utilizes specific enzymes to convert substrates into target products. For example, Wang et al. achieved 128.9 g/L putrescine from L-arginine via whole-cell biotransformation¹¹. In another study, the best arginase (ARG_BT from Bos taurus) was screened and co-expressed with ornithine decarboxylase SpeC, converting L-arginine to putrescine with yields of 76 g/L at 0.5 L scale⁹. Yin et al. developed a two-step process consisting of L-arginine fermentation followed by whole-cell catalysis to produce 48.6 g/L putrescine from glucose¹². However, these enzymatic methods are economically and environmentally limited, as they rely on expensive substrates such as arginine or ornithine and depend on substrate supplementation rather than direct conversion from renewable carbon sources.

By contrast, microbial fermentation increases product yield by optimizing the metabolic pathways of engineered strains combined with fermentation techniques, and it uses low-cost substrates such as glucose or xylose13, 14. In Escherichia coli, sugars enter the cell and catabolize to phosphoenolpyruvate, which is then catalyzed by pyruvate kinase (pyk) to pyruvate, and then oxidized to acetyl-CoA by pyruvate dehydrogenase (aceEF). Alternatively, phosphoenolpyruvate can be converted to oxaloacetate by phosphoenolpyruvate carboxylase (ppc). Subsequently, oxaloacetate and acetyl-CoA are condensed by citrate synthase (gltA) to form citrate, which then enters the TCA cycle. Isocitrate produces α-ketoglutarate through isocitrate dehydrogenase (icdA). Then, glutamic acid is generated from α-ketoglutarate by glutamate dehydrogenase (gdhA), and ornithine is synthesized step by step by argABCDE. Ornithine is further converted to 1,4-butanediamine through ODC pathway15, 16. α-Ketoglutaric acid, as the precursor of ornithine, can be modulated to enhance ornithine production and thus indirectly improve 1,4-butanediamine synthesis (Fig. 1-1).

Fig. 1-1.

1,4-Butanediamine synthesis pathway. The green lines mean overexpression, and the red mean deletion. PTS: Phosphotransferase system; G-6-P: Glucose-6-phosphate; AKG: 2-oxoglutarate; F-6-P: Fructose-6-phosphate; SUC-CoA: Succinate-CoA; PEP: Phosphoenolpyruvate; SUCC: Succinate; PYR: Pyruvate; FUM: Fumarate; AC-CoA: Acetyl-CoA; MAL: Malate; OAA: Oxaloacetate; Glu: Glutamate; ACTGLT: Acetyl-glutamate; PUT: 1,4-butanediamine; ACTGLT-P: Acetyl-glutamate-phosphate; ACTGLT-SA: Acetyl-glutamate-semialdehyde; CIT: Citrate; ACTORN: Acetyl-ornithine; ICIT: Isocitrate; Orn: Ornithine; gltA: encodes citrate synthase; gdh: encodes glutamate dehydrogenase; argA: encodes N-acetyl-glutamatesynthase; pykF: encodes pyruvate kinase 1; argB: encodes N-acetylglutamate kinase; ppc: encodes phosphoenolpyruvate carboxylase; argC: encodes N-Acetyl-γ-glutamylphosphate reductase; pdhR (aceEF): encodes pyruvate dehydrogenase; argD: encodes acetylornithine aminotransferase; speC、speF: encodes constitutive ornithine decarboxylase (ODC10); argE: encodes N-acetylornithine deacetylase; icdA: encodes isocitrate dehydrogenase; argJ: encodes ornithine acetyltransferase. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

To enhance the yield of 1,4-butanediamine by microbial fermentation method, various metabolic engineering strategies have been employed, including (a) strengthening the expression of key enzymes to enhance the 1,4-butanediamine metabolic pathways17, 18, 19; (b) using the methods of genetic mutations,knockout, weaken to block the metabolism branch of 1,4-butanediamine decomposition or its intracellular transport pathways20, 21, 22, 23, and (c) introducing novel substrate assimilation routes¹³. These efforts have led to substantial improvements in 1,4-butanediamine production. Notably, Ko et al. applied adaptive laboratory evolution and systems metabolic engineering to achieve 72.7 g/L putrescine from glucose²⁴. While these studies have demonstrated effective strategies for 1,4-butanediamine production, systematic coordination between upstream precursor supply and downstream product formation has not been the main focus of these approaches.

However, when individual steps are optimized without considering such coordination, flux imbalance would arise: overexpressing one enzyme may create a bottleneck downstream, while deleting a competing branch may disrupt flux distribution elsewhere. Such imbalances can cause feedback inhibition, by-product accumulation, or even impaired cell growth25, 26, 27, 28, 29. Resolving this issue is therefore essential for improving both productivity and yield³⁰.Modular metabolic engineering could tackle this imbalance by grouping related reactions into functional units, such as precursor supply and product synthesis, so that each module can be tuned independently while maintaining overall pathway coordination31, 32. This approach is successfully used to enhance the balance within and between modules, leading to the improved performance of engineered strains³³.

To address the metabolic flux imbalance in 1,4-butanediamine production, we apply the modular strategy that combines a 1,4-butanediamine production module and an α-ketoglutarate production module. By systematically optimizing the compatibility between the two modules, we aim to achieve a balanced flux distribution and improve 1,4-butanediamine yield. In addition, we also performed non-target metabolomics analysis on the modularly engineered strain. This helps us identify metabolic bottlenecks and detect flux redistribution between the two modules, providing directions for further optimization.

2. Methods

2.1. Strain, plasmid, and culture conditions

The genotype of the strain, including the engineered strain and plasmid used in this study: E. coli MG1655 and E. coli BL21(DE3) was used as the construction of chassis strain to synthesize 1,4-butanediamine, and E. coli JM109 was used as the cloned host for plasmid construction.

During plasmid and strain construction, cells were grown on LB solid plates containing appropriate antibiotics. Antibiotics were added at the following concentrations when needed: 50 mg/mL kanamycin, 80 mg/mL ampicillin, 25 mg/mL chloramphenicol, or 50 mg/mL streptomycin.

The target genes in this study were amplified by using matched primers and relevant host genomic DNA as a template. Heterologous genes were amplified by using primers from E. coli MG1655 and Corynebacterium glutamicum 13,032 genomes as templates. The used plasmids and strains in this study are listed in Supplementary Table 1, and the primers used are listed in Supplementary Table 2.

2.2. Plasmids construction

The pRSM3 vector was digested by using Nde I and Kpn I or Xho I. The digested vector had the same cleavage sites (Nde I and Kpn I or Xho I) as the target fragment, and the dsDNA was subsequently ligated with a ligase to construct the pRSM3 expression plasmid.

The pACM4, pCDM4, pRSM3, and pETM6 plasmids had four important cleavage sites, Bln I, Xba I, Nhe I, and Spe I. The Bln I, Xba I, Nhe I, and Spe I are four restriction endonuclease cleavage of the sticky end of all CTAG, so these four enzymes are called homotetrameric enzymes. In the process of gene tandem construction, using homotetramer plasmid, the gene to be overexpressed was firstly constructed into the original plasmid by Nde I and Xho I. Then one of the plasmids were selected as the vector, which were recovered as the vector after double digestion by Nhe I and Sal I. The remained plasmids of the desired tandem gene were double digested by using Bln I and Sal I, and the fragments were cut and recovered. Since Nhe I and Bln I are isocaudomeric enzyme, they can be ligated under the action of ligase, and finally, a tandem plasmid is constructed.

2.3. Shake flask culture and fermentation

The constructed engineering strains were inoculated from solid medium into LB liquid medium containing corresponding resistance, and incubated at 37 °C, 220 r/min for 14–16 h as seed liquid. The seed liquid was inoculated into 500 mL shake flasks containing fermentation medium according to 1%(v/v) inoculum volume, and the corresponding resistance was added.

Fermentation medium:

• (1)TB medium: 11.8 g/L tryptone, 23.6 g/L yeast extract, 5 mL/L glycerol.
• (2)R/2 medium: 5 g/L yeast extract, 1 g/L peptone, 2 g/L (NH₄)₂HPO₄, 6.75 g/L KH₂PO₄, 0.85 g/L citric acid, 0.7 g/L MgSO₄·7H₂O, and 5 mg/L trace metal solution, which contained 5 mol/L HCl, 10 g/L FeSO₄·7H₂O. 2.25 g/L ZnSO₄·7H₂0, 1 g/L CuSO₄·5H₂O, 0.5 g/L MnSO₄·5H₂O, 1.23 g/L Na₂B₄O₇·10H₂O,2 g/L CaCl₂·2H₂O and 0.1 g/L (NH₄)₆Mo₇O₂₄. supplemented with 10 g/L glucose and 3 g/L (NH₄)₂SO_4. The pH was adjusted to 6.8.
• (3)R/2 fermentation medium: add 5 g/L yeast extract,1 g/L peptone,10 g/L glucose and 3 g/L (NH₄) ₂SO₄ into R/2 medium. pH 6.8. Appropriate antibiotics are necessary for use after sterilization.

2.4. Analytical method

The concentration of 1,4-butanediamine was detected by HPLC. The chromatographic column was Agilent Poroshell EC-C18 column (4.6 mm × 250 mm, particle size 4 μm). The conditions of the column were as follows: temperature 30 °C, UV wavelength 254 nm, sample size 10 μL, mobile phase A as ultra-pure water, mobile phase B as acetonitrile, flow rate 0.7 mL/min, gradient elution:0–5 min: 55% to 70% B; 5–10 min: 70% B; 10–15 min: 70 to 95% B; 15–20 min: 95% B; 20–25 min: 95 to 55% B.

2.5. Statistical analysis

All experiments were performed in three independent biological replicates(n = 3), and data are presented as mean ± standard deviation (SD); multiple-group comparisons were performed using one-way ANOVA; statistical analysis were conducted using GraphPad Prism, and figures were generated using Origin. The * symbol represents P < 0.05, ** represents P < 0.01, and *** represents P < 0.001 compared with their controls.

3. Results

3.1. 1,4-Butanediamine modular synthesis pathway of systemic reform

E. coli has a long metabolic pathway for the synthesis of 1,4-butanediamine, which is closely related to the production of ornithine. When systematically engineering the metabolic pathway for the synthesis of 1,4-butanediamine, it is necessary to consider the key central carbon metabolism pathways such as TCA. Altering these pathways can change the direction and velocity of intracellular metabolic fluxes, thereby affecting the steady state of the intracellular metabolic environment. To achieve rational and orderly transformation of the metabolic pathway and maintain a steady intracellular metabolic balance, this study employs modular metabolic engineering strategies. Specifically, the entire 1,4-butanediamine metabolic pathway is redefined into two distinct modules that designed to strengthen the target pathway, which named 1,4-butanediamine production module (module1) and α-ketoglutarate production module (module2). Based on the synthesis of 1,4-butanediamine in E. coli, the metabolic pathways and functions of each gene involved are illustrated in Fig. 3-1.

Fig. 3-1.

Metabolic pathway of 1,4-butanediamine in E. coli.

3.1.1. Metabolic modification of the 1,4-butanediamine production module

According to the analysis of E. coli metabolic pathways, to promote the synthesis of 1,4-butanediamine, the study focuses on expressing gdhA, argB, argC, argD, argJ*, ODC10, with argJ* which is after a codon optimization from C. glutamicum, and ODC10 heterologous gene which is screened.

3.1.1.1. Yield of 1,4-butanediamine in strains with single gene overexpression in the 1,4-butanediamine production module

pRSM3-gdhA, pRSM3-argCB, pRSM3-argD, pRSM3-argJ*, and pRSM3-ODC10 plasmids (Fig. 3-2a) were constructed and introduced into BL21(DE3) for 24-well plate culture with TB medium. SDS-PAGE analysis confirmed the successful expression of the heterologous ArgJ* and ODC10 proteins in the recombinant strains (Fig. S1). Overexpression of gdhA, argD, argCB, argJ* and ODC10 genes in the synthetic pathway could increase the yield of 1,4-butanediamine after 24 h cultivation (Fig. 3-2b), and overexpression of ODC10 increased the yield most, which was 2.23-fold higher than that of BL21(DE3). ODC10 which converts ornithine into the production of 1,4-butanediamine is the key limited step of 1,4-butanediamine synthesis pathway, our results showed that ODC10 which sources from Staphylococcus lugdunensis could efficiently enhance the production of 1,4-butanediamine.

Fig. 3-2.

Determination results of 1,4-butanediamine production by single gene overexpression strain of 1,4-butanediamine production module; The *** symbol represents P < 0.001 compared with their controls. (a) Construction of a single-gene overexpression vector for the 1,4-butanediamine production module; (b) Determination of the increase on 1,4-butanediamine production of gdhA, argD, argCB, argJ* and ODC10 single-gene overexpression strain(n = 3).

3.1.1.2. Yield of 1,4-butanediamine in strains with tandem gene and ODC10 co-overexpression in the 1,4-butanediamine production module

According to the above experimental results, enhancing the overexpression of argB, argC, argD, argJ* and ODC10 can promote the synthesis of 1,4-butanediamine and further increase its production. This study is about overexpressing gdhA, argB, argC, argD, argJ* and ODC10 in series, the P1, P2, P3, P4 and T1 plasmids have been built well (Fig. 3-3a). The plasmids were transformed into BL21 (DE3), meanwhile, TB medium was used for 24-well plate culture experiments. After 24 h, the yield of 1,4-butanediamine increased as more tandem genes were introduced. Specifically, the yield of the P4 strain, which overexpresses four genes, increased by 2.45 times. Meanwhile, the T1 strain, which contains two plasmids, produced 6.87 times more (Fig. 3-3b). These results indicated that the overexpression of five genes in the 1,4-butanediamine production module could efficiently convert α-ketoglutaric acid into 1,4-butanediamine, and ornithine decarboxylase is one of the key enzymes.

Fig. 3-3.

Determination of 1,4-butanediamine production of strains with tandem gene overexpression of 1,4-butanediamine production module; The *** represents P < 0.001 compared with their controls. (a) Construction of tandem gene and ODC10 co-overexpression vectors in the 1,4-butanediamine production module; (b) Results of the 1,4-butanediamine production in the strain overexpressing the tandem genes of the 1,4-butanediamine production module. (n = 3).

3.1.2. Metabolic modification of α-ketoglutaric acid production module

In E. coli, the metabolic flux flows downstream along the metabolic pathways. To enhance the biosynthesis of 1,4-butanediamine, it is necessary to strengthen the accumulation of upstream precursors in the 1,4-butanediamine synthesis pathway, thereby enhancing the downstream synthesis of 1,4-butanediamine. According to the analysis of metabolic pathways, in 1,4-butanediamine synthesis pathway, α-ketoglutaric acid is the precursor of the pathway (Fig. 3-1). So, to strengthen the α-ketoglutaric acid biosynthesis, we need to modify the metabolic pathways of α-ketoglutaric acid. In this study, pykF, ppc, aceEF, gltA, icdA were overexpressed.

3.1.2.1. Yield of 1,4-butanediamine in strains with single gene overexpression in the α-ketoglutarate production module

After constructing pRSM3-pykF, pRSM3-aceEF, pRSM3-ppc, pRSM3-gltA, pRSM3-icdA plasmids (Fig. 3-4a), we introduced them into BL21(DE3) and cultured in 24-well plates with TB medium. Within 24 h of culture, the overexpression of the above genes all improved the output of 1,4-butanediamine. Among these, the strain overexpressing aceEF showed the greatest increase in production, produced 1.28-fold higher than that of the BL21 (DE3) (Fig. 3-4b). This indicated that the overexpression of aceEF from E. coli MG1655 efficiently converts pyruvate into acetyl-CoA, thereby further directing metabolic flux towards the TCA cycle and the synthesis of α-ketoglutaric acid.

Fig. 3-4.

Results of the determination of 1,4-butanediamine production of strains overexpressing single gene of α-ketoglutaric acid production module; The ** symbol represents P < 0.01, and *** represents P < 0.001 compared with their controls. (a) Construction of single-gene overexpression vector for the α-ketoglutarate production module; (b) Effect of pykF、ppc、aceEF、gltA、icdA single overexpressing gene strains on 1,4-butanediamine production (n = 3).

3.1.2.2. Yield of 1,4-butanediamine in strains with tandem gene overexpression in the α-ketoglutarate production module

According to the experimental results, strengthening the expression of pykF, ppc, aceEF, gltA, icdA individually can promote the synthesis of 1,4-butanediamine. We assumed that the study of pykF, ppc, aceEF, gltA, icdA overexpressing in series can further improve the production of 1,4-butanediamine. Plasmids K1, K2, K3, K4 and K5 (Fig. 3-5a) were constructed and introduced into E. coli BL21(DE3). The results of 1,4-butanediamine production were shown in Fig. 3-5b. The yield of K5 strain overexpressing five genes was increased by 1.62-fold, indicating that the overexpression of five genes in the α-ketoglutaric acid production module could efficiently convert phosphoenolpyruvate into α-ketoglutaric acid, and further improve the yield of 1,4-butanediamine.

Fig. 3-5.

The yield of the tandem expression strain of α-ketoglutaric acid production module; The * symbol represents P < 0.05, ** represents P < 0.01, and *** represents P < 0.001 compared with their controls. (a) Construction of tandem expression vector of α-ketoglutaric acid production module; (b) Tandem gene overexpression strain of α-ketoglutarate acid production module increased 1,4-butanediamine production (n = 3).

3.1.3. Two kinds of different production module expression plasmid copy number

In this study, the plasmid copy number method was used to regulate the module strength. Plasmids with different copy numbers were used: pRSM3 (∼100; Ori: RSF1030), pETM6 (40; Ori: ColE1), pCDM4 (20–40; Ori: CloDF13), pCOM4 (20–40; Ori: ColA), pACM4 (10–12; Ori: P15A) to regulate the production modules³⁴.

3.1.3.1. Yield of 1,4-butanediamine in strains with different copy numbers of the 1,4-butanediamine and α-ketoglutarate production modules

To switch different copy numbers of the 1,4-butanediamine production module, we built P5, P6, P7 and P8 plasmids (Fig. 3-6a), and introduced them into E. coli BL21 (DE3), using TB culture medium for 48-well culture experiments. After 24 h, the yield of 1,4-butanediamine increased least in strain P6 with low copies of plasmids, and increased most in strain P8 with medium copies of plasmids (Fig. 3-6b). Compared with E. coli BL21(DE3), the yield of P8 increased by 2.54-fold. In the meantime, we built K4, K6, K8, K10 and K12 plasmids, as well as K5, K7, K9, K11, K13 plasmids to replace different copy numbers of the α-ketoglutarate production module (Fig. 3-6 cd), and transform them into BL21 (DE3). Within 24 h of 48-well cultivation, it was observed that strain K13 with low copy of plasmids had the highest production, which was 2.06-fold higher than that of BL21(DE3) (Fig. 3–6ef). These results indicated that pCOM4, which is composed of medium copy number and low promoter strength³⁴, provides the optimal expression balance for the expression of 1,4-butanediamine and α-ketoglutarate production modules.

Fig. 3-6.

Results of 1,4-butanediamine production by strains with different copy numbers of 1,4-butanediamine and α-ketoglutarate production modules; The * symbol represents P < 0.05, ** represents P < 0.01, and *** represents P < 0.001 compared with their controls. (a) Construction of a series of expression vectors of the 1,4-butanediamine production module; (b) Yield of strains with different copy numbers of the 1,4-butanediamine production module(n = 3) (c), (d) Construction of a series of expression vectors for the α-ketoglutaric acid production module; (e) 1,4-butanediamine production by strains with different copy numbers of the α-ketoglutarate acid production module(n = 3).

3.1.4. Compatibility of different modules in the biosynthesis of 1,4-Butanediamine

The study focuses on the 1,4-butanediamine synthesis pathway in E. coli, which includes the 1,4-butanediamine and the α-ketoglutaric acid production modules. We optimized these two modules, resulting in different levels of 1,4-butanediamine production. To further improve the production of 1,4-butanediamine, we optimized the assembly and expression of the two modules, as well as investigating their impact on the production of 1,4-butanediamine. The function of combinatorial expression of plasmids with different copy numbers of two production modules were verified in BL21 (DE3), with a total of 18 combinations (Table 3). The production of 1,4-butanediamine was detected by fermentation with 1 mL TB medium in 48-well plate for 24 h. After 24 h, BL21 (DE3) can produce 70.63 mg/L 1,4-butanediamine, meanwhile, the yield of combination strain PK3 can reach 539.22 mg/L, which is about 7.63-fold higher than that of BL21 (DE3) (Fig. 3-7). This suggests that the modular metabolic engineering strategies of 1,4-butanediamine in E. coli are effective. The optimal combination of the two modules is choosing top copy number of 1,4-butanediamine production module (copy number:100) and low copy number of α-ketoglutaric acid production module number (copy number:10–12). This combination is suitable for the production of 1,4-butanediamine the most. However, the ODC10 was not overexpressed in PK3 strain, and the intensity of 1,4-butanediamine production module did not reach the highest level. To obtain higher 1,4-butanediamine yield, further overexpression of ODC10 is needed.

Table 3.

Combinations of the two production modules.

		Kan	Amp	Smr	Chl	Kan
		K5	K7	K9	K11	K13
Kan	P4		PK1	PK2	PK3
Amp	P5	PK4		PK5	PK6	PK7
Smr	P6	PK8	PK9		PK10	PK11
Chl	P7	PK12	PK13	PK14		PK15
Kan	P8		PK16	PK17	PK18

Fig. 3-7.

Results of 1,4-butanediamine yield of two production modules combined strains(n = 3); The ** symbol represents P < 0.01, and *** represents P < 0.001 compared with their controls.

3.1.5. Construction of PKT strain

We had selected the optimal combination of 1,4-butanediamine production module and α-ketoglutaric acid production module, obtaining the strain-PK3. Ornithine decarboxylase (ODC10) is the key enzyme in the 1,4-butanediamine synthesis pathway. Therefore, to synthesize 1,4-butanediamine more efficiently, ODC10 was further expressed in the strain PK3, and the pET22b-ODC10 plasmid was introduced into strain PK3 to obtain strain PKT. After 24 h, the 1,4-butanediamine yield of PKT reached 862.82 mg/L, and compared to BL21 (DE3), the yield of PKT was increased by 8.62-fold (Fig. 3-8).

Fig. 3-8.

Results of 1,4-butanediamine production by PKT strain(n = 3); The *** symbol represents P < 0.001 compared with their controls.

3.2. PKT strains fermentation optimization

In the study, we adopted the R/2 medium employed by the Sang Yup Lee team²¹ on the shake flask fermentation of PKT strains (Fig. 3-9a). We found that R/2 medium containing 10 g/L glucose and 3 g/L ammonium sulfate were more suitable for producing 1,4-butanediamine. This may because the R/2 medium contained inorganic salt ions and trace metal ions associated with the growth of E. coli, and adding ammonium sulfate can provide ammonium ions, which is helpful for the synthesis of 1,4-butanediamine. Glucose has important effect on fermentation product yield³⁵, we doubled glucose and ammonium sulfate content to see if it could further improve the production of 1,4-butanediamine, but consequently, this step did not increase the yield as expected. PKT strain exhibits a higher glucose utilization rate at 10 g/L compared to 20 g/L after 24 h (Fig. 3-9b). To solve this problem, we could optimize the glucose utilization way of PKT strains and obtain higher output of 1,4-butanediamine in the future. To enrich the medium in this study, we added 10 g/L glucose and 3 g/L ammonium sulfate on the basis of R/2 medium, as well as adding 5 g/L yeast extract and 1 g/L peptone. After shaking flask fermentation experiments of PKT strains, it could be found that the production of 1,4-butanediamine had been improved to 1, 288.01 mg/L successfully.

Fig. 3-9.

PKT strain fermentation medium replacement(n = 3) (a) Yield of fermentation by PKT strain in different media; (b) Glucose utilization rate of PKT strain is higher at R/2 medium containing 10 g/L glucose compared to 20 g/L glucose (c)The results of PKT strain in 2 L shake flask fermentation.

The fermentation of E. coli is sensitive to the dissolved oxygen partial pressure and its changes in the medium. Dissolved oxygen not only affects the fermentation kinetic parameters, but also the physiological behavior of E. coli, consequently influencing yield. During the fermentation process, the dissolved oxygen can be increased by enhancing the culture volume. Refer to the above 250 mL flask fermentation conditions, we added the PKT strains into the 2 L shake flask fermentation, and the yield of 1,4-butanediamine reached 1843.60 mg/L after 24 h fermentation in the 2 L shaking flask (Fig. 3-9c).

3.3. Results of metabonomic analysis

3.3.1. Difference analysis of metabolites

Using non-targeted metabolomics technology, the intracellular metabolites of BL21(DE3) and PKT were detected after 24 h fermentation in R/2 fermentation medium. The key metabolites and metabolic regulatory network during the synthesis of 1,4-butanediamine in PKT strain were analyzed. Among them, the samples of BL21(DE3) and PKT were in group A and group B respectively (Fig. 3-10). PCA is relatively aggregated in group A and group B, indicating that the samples in group A have little difference and the data is reliable, and PCA is obviously separated between groups, indicating that the samples in group B have great difference. The results of PLS-DA analysis and OPLS-DA analysis of the two groups of metabolites showed that R2X exceeded 0.5, R2Y and Q2 values were close to 1, indicating that the model had high stability and reliability, and the prediction ability of the model was good. In addition, 200 permutation tests were performed, and the Q2 scores were lower than the R2 scores of the original model, indicating that the model was very reliable.

Fig. 3-10.

PCA model, PLS-DA model and OPLS-DA model results of A and B (a) PCA score plot of comparison group A.vs.B; (b) PLS-DA score plot of comparison group A.vs.B; (c) PLS-DA replacement of comparison group A.vs.B Test plot; (d) OPLS-DA score plot comparing group A.vs.B; (e) OPLS-DA replacement of comparison group A.vs.B.

3.3.2. Screening of differential metabolites

By testing the metabolites of group A and group B, the total number of different metabolites detected for 653, including 428 raised metabolites and 225 metabolites. The volcano plot (Fig. 3-11) based on the results of the multiple of variation Analysis (FC Analysis), the t-test, and the synthesis of the results of the two methods shows the differential levels of metabolites.

Fig. 3-11.

Comparison group A.vs.B volcano chart Metabolites were classified using HMDB Superclass and represented by different colors and shapes on the graph; Green indicates down-regulation and red indicates up-regulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

VIP value in the study of metabolomics has important application significance, which can provide valuable reference information for researchers. Obtaining VIP values using the OPLS-DA model is an effective method to help researchers measure significant differences between metabolites. By calculating the VIP value, we can evaluate the importance and differences of metabolites between groups, thereby gaining a deeper understanding of the key information in metabolomics data (Fig. 3-12).

Fig. 3-12.

VIP top30 difference multiple and VIP chart of comparison group A.vs.B The size of the point corresponds to the VIP value of OPLS-DA, the larger the point, the greater the VIP value.

In untargeted metabolomics studies, hierarchical clustering analysis can more accurately screen marker metabolites and further study the changes of related metabolic processes. In addition, it is possible to examine the stability of the expression of the target metabolite in the samples within the group. These metabolites have similar expression patterns within the same cluster, which may imply that they are in relatively close reaction steps in the metabolic process. Through these analysis methods, we can fully understand and explain the relationship of different metabolites in biological body and the mechanism of action.

Among the top-ranked differential metabolites, 1,5-naphthalenediamine, histamine, indole-3-acrylate, methylpyrazine, 2-naphthylamine, 1,2-dihydroisoquinoline, indole, indole-3-lactate, tryptophan, and D-tryptophan are the top 10 metabolites, and all these metabolites are significantly upregulated. Therefore, metabolic flux in PKT strains may flow to the synthesis of these metabolites, thereby limiting the synthesis of 1,4-butanediamine (Fig. 3-13).

Fig. 3-13.

Hierarchical clustering results of differential metabolites in comparison group A.vs.B(Only the top 50 VIP values are drawn).

3.3.3. KEGG pathway enrichment analysis of differential metabolites

Taking the 653 differential metabolites obtained through screening as the research objects, the analysis via the KEGG (Kyoto Encyclopedia of Genes and Genomes) database provides an important approach to understanding the advanced functions and uses of cells, organisms, and ecosystems at the molecular level. Genome sequencing and high-throughput experimental technology of large-scale data set contains lots of important information, help to reveal the complex metabolic network within the organism. Through KEGG pathway enrichment analysis, we can clearly see the reform has brought PKT the metabolic disorder, especially has the greatest impact on metabolic pathway and purine metabolism. This further highlights the genetic modification's influence on the function and metabolism of the organism, suggesting that these metabolic pathways of E. coli have great significance on the study of synthesis of 1,4-butanediamine (Fig. 3-14).

Fig. 3-14.

(a) KEGG pathway enrichment diagram of comparison group A.vs.B(Top30 at most). Level 1 pathway categories: Metabolism (M), Genetic Information Processing (G), Environmental Information Processing (E), Cellular Processes (C), Organismal Systems (O), Human Diseases (H), Drug Development (D). Bubble chart of KEGG pathway enrichment for Group A vs. Group B. The size of the circles reflects the number of differential metabolites present, while the colour of the circles represents the degree of significance, increasing from blue to red. (b) Bar plot of KEGG pathway enrichment for Group A vs. Group B.

By performing KEGG ID Mapping on the differential metabolites between comparison groups and combining the results onto the 1,4-butanediamine biosynthetic pathway, we obtained a pathway map of the annotated differential metabolites (Fig. 3-15). A total of 9 differential metabolites were identified, all of which were up-regulated. Among these, 5 metabolites were associated with 1,4-butanediamine synthesis, while the remaining 4 were linked to its degradation branches. The non-target metabolomics analysis revealed accumulation of several intermediates in the 1,4-butanediamine synthesis pathway of the PKT strain, including 2-phosphoglycerate, acetyl-CoA, citrate, agmatine and N-acetylornithine. These accumulations suggest that metabolic flux through these nodes is constrained. In the future study, it is necessary to release the metabolic flow through these intermediate metabolites to make the carbon flux flow smoothly and efficiently to 1,4-butanediamine. In addition, accumulation of metabolites in degradation branches closely related to 1,4-butanediamine—namely glutamine, proline, N-acetyl-aminobutanoate, and 4-amino-butanoate—indicates that carbon flux is being diverted into these metabolites. In subsequent studies, the production of 1,4-butanediamine can be further improved by knockout or weakening the branch pathways of these metabolites.

Fig. 3-15.

Up-regulated differential metabolites pathway annotation diagram Yellow and green highlighting indicate up-regulated differential metabolites in the 1,4-butanediamine degradation branches and synthesis pathway, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

Although previous studies have engineered 1,4-butanediamine biosynthetic pathways in E. coli or C. glutamium, little effort has been made to optimize 1,4-butanediamine biosynthetic pathways using modular metabolic engineering strategies.

In this study, the 1,4-butanediamine synthesis pathway was divided into two modules: 1,4-butanediamine production module and α-ketoglutarate production module. Each module can be individually designed and tuned to achieve 1,4-butanediamine biosynthesis. In addition, when two modules are assembled, the expression level of each module can be adjusted to avoid metabolic imbalance and improve product yield. With the precursor synthesis design of downstream pathways, we built the 1,4-butanediamine production module by expressing five genes36, 37, 38 from α-ketoglutarate to the production of ornithine, and the higher yield 1,4-butanediamine strain-P4 could be obtain. Compared to BL21 (DE3), the P4 strain achieved a 2.54-fold increase in 1,4-butanediamine production. Furthermore, by overexpressing the ODC10 in the P4 strain, we obtained the T1 with a production level 6.87 times higher than that of BL21 (DE3). The results proved the effectiveness of overexpressing six genes in the 1,4-butanediamine production module. And ornithine decarboxylase has a key role in raising the output of 1,4-butanediamine³⁹. At the same time, through the design of the upstream pathway of precursor synthesis, we constructed the α-ketoglutarate production module by overexpressing the five genes starting from phosphoenolpyruvate to the production of α-ketoglutarate33, 40, 41, 42, and the high-yield 1,4-butanediamine strain K13 could be obtained. Compared to E. coli BL21 (DE3), K13 yields 2.06-fold more 1,4-butanediamine. Although the modification of 1,4-butanediamine production module and α-ketoglutaric acid production module have effectively improved the production of 1,4-butanediamine, the assembly and balance problems in the two modules still need to be solved. By using the modular engineering strategy, the two modules were assembled and quickly balanced and optimized. Finally, the strain PKT was obtained. The production of 1,4-butanediamine reached 1843.60 mg/L. This result is basically consistent with other studies in shake-flask fermentation level (Table 4), demonstrating that our modular assembly strategy is effective for improving 1,4-butanediamine production. But compared with enzymatic transformation methods and fed-batch fermentation, our current titer is relatively modest (Table 4). This gap is expected, as these methods use expensive and unsustainable substrates or optimized bioreactor conditions, whereas our work relies on glucose in shake flasks.

Table 4.

Comparison table of 1,4-butanediamine production.

Strain	Main strategy	Yield (g/L)	Authors
E.coli	microbial fermentation:	1.843 (Shake flask)	Sun et al. (This article)
E.coli	microbial fermentation	1.68 (Shake flask) 24.2 (Fed-batch fermentation)	Qian et al.19
E.coli	microbial fermentation	3.1 (Shake flask) 34.77 (Fed-batch fermentation)	Han et al.13
C.glutamium	microbial fermentation	6 (Shake flask)	Schneider et al.43
C.glutamium	microbial fermentation	19 (Fed-batch fermentation)	Schneider et al.23
E.coli	microbial fermentation	72.7 (Fed-batch fermentation)	Ko et al.24
E.coli	microbial fermentation and enzymatic transformation	48.6	Yin et al.12
E.coli	enzymatic transformation	76	Wang et al.9
E.coli	enzymatic transformation	128.9	Wang et al.11

To further investigate the PKT strain, non-target metabolomics analysis was conducted. A total of 653 differential metabolites were screened by non-targeted metabolomics analysis, including 428 up-regulated metabolites and 225 down-regulated metabolites. The top 20 metabolites with significant differences were all up-regulated compared with the control group, and were closely related to tryptophan, histidine, ornithine, and arginine. The second ranked histamine and its precursor histidine, the third ranked indole-3-allylic acid, indole, indole-3-lactic acid and its precursor tryptophan were all showed accumulation in PKT strain. In the metabolic pathway of E. coli, ornithine and glutamic acid are the precursor amino acids required for the production of 1,4-butanediamine. However, the accumulation of tryptophan and histidine in E. coli indicates that some metabolic flows are directed to the synthesis of tryptophan and histidine, which affect the synthesis efficiency of 1,4-butanediamine. Studies have shown that the introduction of heterologous proteins can promote the production of D-type amino acids in organisms⁴⁴. In the PKT strain, a heterologous plasmid was introduced, so an accumulation of D-tryptophan was observed, which suggests that it is necessary to consider weakening the synthesis of tryptophan and histidine.

By mapping the results of non-targeted metabolomics analysis to the biosynthetic pathway of 1,4-butanediamine, it was found that in 1,4-butanediamine synthesis pathway of intermediate metabolites, 2-phosphoglycerate, acetyl-CoA, citric acid, N-acetylornithine, and agmatine have accumulated, suggesting that node bottlenecks exist in these metabolic flows. In subsequent studies, overexpressing the genes for enolase (eno), aconitase (acnA), acetylornithine deacetylase (argE), and agmatine deiminase (speB) can release the metabolic flux through these intermediate metabolites, allowing the carbon flux to flow towards 1,4-butanediamine. In the degradation branches closely related to 1,4-butanediamine, the accumulation of glutamine, proline, N-acetyl-L-glutamate, and 4-aminobutyrate indicates that carbon flux is being diverted to these byproducts. In subsequent studies, knocking out or weakening these byproduct pathways can further increase the yield of 1,4-butanediamine. For example, knocking out the gene glnA for glutamine synthetase can increase the accumulation of glutamate⁴⁵, or knocking out speG can prevent the degradation of 1,4-butanediamine³⁹, additionally, the expression of glutamate kinase (proB) can be weakened by replacing the RBS and the start codon, or inserting a terminator⁴⁶.

In summary, our study demonstrates that modular metabolic engineering is an effective strategy for improving 1,4-butanediamine production. The metabolomics data point to specific targets for further engineering. Beyond these pathway-level interventions, three broader strategies will also be pursued: enhancing NADPH supply, switching from the current multi-expression-cassette system to polycistronic constructs or genome integration for better genetic stability, and adopting fed-batch fermentation with controlled pH and dissolved oxygen to enable higher cell density and product accumulation.

5. Conclusion

This study presented a modular approach to systematically modify the 1,4-butanediamine biosynthesis pathway in the initial strain-E. coli BL21(DE3). The pathway was divided into two modules: 1,4-butanediamine production and α-ketoglutaric acid production modules, resulting in the creation of the recombinant 1,4-butanediamine-producing strain PKT. Through regulation of gene expression levels for pykF, ppc, aceEF, gltA, icdA, gdhA, argD, argCB, argJ*, and ODC10, the 1,4-butanediamine yield reached 862.82 mg/L, an 8.62-fold increase compared to the initial strain. Optimization of the fermentation medium for the PKT strain led to a 1,4-butanediamine yield of 1843.60 mg/L after 24 h, representing a 16.23-fold improvement over the original strain. Non-target metabolomic analysis identified 653 differential metabolites, with 428 up-regulated and 225 down-regulated metabolites. Pathway analysis revealed 9 differential metabolites in the 1,4-butanediamine synthesis pathway, with 4 linked to 1,4-butanediamine synthesis and 5 to its degradation branch. These metabolic variances offer a foundation for strain modification, guiding the development of high-yield 1,4-butanediamine engineering strains. This research provides a theoretical guidance of building high yield 1,4-butanediamine engineering strains.

CRediT authorship contribution statement

Xiangxiang Sun: Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Zongdao Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Data curation, Conceptualization. Yanling Sun: Writing – review & editing, Visualization, Validation, Data curation, Conceptualization. Dandan Li: Validation, Investigation. Kexin Zhao: Investigation. Di Ma: Visualization. Yuhan Jiao: Investigation. Fuping Lu: Supervision, Resources. Ming Li: Supervision, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ming Li reports financial support was provided by National Key Research and Development Program of China. Ming Li reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary material

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.