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. 2025 Feb 20;10(2):583–592. doi: 10.1016/j.synbio.2025.02.010

CRISPRi-mediated multigene downregulating redirects the metabolic flux to spinosad biosynthesis in Saccharopolyspora spinosa

Zirong Zhu a,1, Li Cao a,1, Ziyuan Xia a, Xirong Liu b, Wangqion Chen a, Zirui Dai a, Duo Jin a, Jie Rang a, Shengbiao Hu a,⁎, Liqiu Xia a,⁎⁎
PMCID: PMC11910639  PMID: 40092162

Abstract

Microorganisms are often likened to complex production workshops. In Saccharopolyspora spinosa (S. spinosa), the biosynthesis of spinosad is a production line within its intricate workshop. Optimizing the entire production environment and reducing unnecessary metabolic flow is essential to increasing spinosad yield. Pyruvate serves as a crucial precursor for spinosad biosynthesis. Previous studies revealed that the pyc gene is highly expressed in the gluconeogenic pathway, leading to a pyruvate shunt. By downregulating pyc, we enhanced spinosad yield, although the improvement was below expectations. We speculated that most of the accumulated pyruvate following the pyc knockdown entered some synthetic pathways unrelated to spinosad. Through metabolic pathway and qRT-PCR analyses, we found that the expression levels of gltA1 and atoB3 within the pyruvate metabolic tributary, including the TCA cycle and ethylmalonyl-CoA pathway, were significantly increased in the pyc knockdown strain. The combined knockdown of these three genes optimized the spinosad production line, increasing its yield to 633.1 ± 38.6 mg/L, representing a 199.4 % increase. This study identifies three key genes for optimizing spinosad biosynthesis and offers insights into gene screening and the efficient construction of Spinosad-producing strains.

Keywords: Saccharopolyspora spinosa, Spinosad biosynthesis, Simultaneous multigene knockdown, Pyruvate carboxylase, Metabolism regulation

1. Introduction

To maintain normal growth and development, strains have generated efficient methods to balance the expression of various genes during evolution [1]. This balance is achieved by fine-tuning the expression levels of enzyme-coding genes, as their expression levels typically influence the efficiency of many essential metabolic pathways [2]. However, to optimize the production of desired compounds in bacteria, various synthetic systems are often constructed or enhanced, which can disrupt the balance of gene expression. This disruption may damage the fine-tuned regulatory networks and produce toxic intermediates, thereby inhibiting bacterial growth and development [3]. Responsible for the production of approximately 70 % of the world's natural products [4,5], actinomycetes' metabolic networks have garnered significant attention. Properly regulating the expression levels of key genes in various natural product metabolic pathways is therefore of great significance. In particular, balancing cellular metabolism through metabolic engineering is essential for increasing the productivity of target products.

To optimize the efficiency of value-added product production, synthetic biological methods and metabolic engineering strategies are widely used to regulate cell metabolism and develop highly efficient microbial cell factories [6,7]. For examples, Stegmüller et al. applied several rounds of metabolic engineering in Streptomyces albidoflavus to streamlined the key genes involved in the pentose phosphate pathway, the shikimic acid pathway, CoA esters supply, nybomycin biosynthesis, and export, while removing four nyb regulatory genes, leading to a 15-fold increase in nybomycin [8]. Similarly, Wang et al. designed a metabolic engineering strategy based on metabolomics and transcriptomics in Streptomyces albulus, which enhanced the expression of ε-Poly-l-lysine (ε-PL) synthetase and improved ATP and l-lysin supply, thereby significantly boosting the production of ε-PL [9]. Furthermore, Zhuang et al. achieved a titer of 43.5 ± 2.4 mg/L for anthraquinone-fused enediynes in Streptomyces sp. CB03234–S by inactivating seven potential precursor competitive biosynthetic gene clusters and overexpressing five essential biosynthetic genes [10].

The key to the success of synthetic biology and metabolic engineering lies in achieving an optimal distribution of metabolic flux within the microbial metabolic network [11]. Over the past two decades, traditional strategies such as knocking out key genes in precursor competition pathways and enhancing the expression levels or increasing the copy numbers of key genes in target metabolic pathways have been widely employed to maximize the titer of target products [12,13]. However, for natural products involving more complex metabolic pathways, these traditional approaches often lead to metabolic imbalances, resulting in slow cell growth, an excessive metabolic burden from intermediates, or the production of toxic intermediates or byproducts, thus limiting the yield of target metabolites [[14], [15], [16]]. In such cases, redirecting metabolic flux through the down-regulation of essential precursor competitive metabolic pathways can effectively balance the flux between cell growth and target product biosynthesis [17,18], thereby alleviating intracellular metabolic pressure and optimizing the utilization of metabolic flux.

Spinosad is a macrolide secondary metabolite produced through the aerobic fermentation of Saccharopolyspora spinosa (S. spinosa), characterized by high insecticidal activity, and it has been widely applied in food storage and crop pest control [[19], [20], [21], [22]]. The biosynthesis gene cluster of Spinosad, along with its biosynthesis pathway, has been described in detail [23]. The biosynthetic process of spinosad competes for common precursors with essential pathways, particularly those involved in central carbon metabolism [24]. However, primary metabolism often takes precedence over secondary metabolism in metabolic flux allocation [25] and these precursors are critical for cell growth, making it unfeasible to directly knock out genes in essential primary metabolic pathways. To address this, the CRISPR interference (CRISPRi) system, which employs the CRISPR/Cas system with a deactivated endonuclease Cas9 (dCas9) [26,27], was used in this study. This approach coordinated the balance between primary and secondary metabolism by down-regulating, rather than deleting, genes in precursor competing pathways, thereby redirecting metabolic flux toward spinosad biosynthesis and preventing the excessive accumulation of intermediates or toxic by-products.

According to the previous transcriptomic analysis of S. spinosa CCTCC M206084 (GenBank accession no. CP061007) [28], key rate-limiting enzymes in gluconeogenesis (a pathway competing with pyruvate), the EMC pathway and the TCA pathway (downstream acetyl-CoA competing pathways), exhibited high transcription levels during the three selected phases. Therefore, CRISPRi-mediated regulation was applied to downregulate the key nodes in these three metabolic pathways to promote spinosad biosynthesis. The spinosad titer was further improved by screening sgRNA combination libraries with different inhibitory strengths, and the underlying mechanisms promoting spinosad biosynthesis were elucidated using proteomic analysis. This study demonstrated the feasibility of balancing multiple critical pathways in S. spinosa using the CRISPRi system, offering a novel approach for the metabolic engineering of spinosad-like biosynthetic processes.

2. Materials and methods

2.1. Strains, plasmids, primers, medium and culture conditions

The wild-type strain S. spinosa used in this study was stored in our lab. All the strains used in this research are listed in Table S1. The plasmids and primers used in this study are listed in Table S2 and Table S3, respectively. All Escherichia coli (E. coli) strains were cultured in lysogeny broth (LB) at 37 °C. CSM medium [29] and SFM [30] were used for seed culture and fermentation of S. spinosa and its derived strains, respectively. All S. spinosa strains were cultured at 30 °C, 260 rpm. All media used to culture the strains containing the pSET-ermE∗p-dcas9-fd (abbreviated as pSET-dcas9) vector [31] need to add apramycin (50 μg/mL). TSB medium (tryptic soy broth medium) and R6 medium [32] were used for intergeneric conjugative transfer between E. coli S17-1 and S. spinosa.

2.2. Detection of biomass, precursors, and spinosad

The detection of biomass, pyruvate, short-chain acyl-CoA, and spinosad was conducted according to methods described in our previous studies [28,30,32,33]. The titers of spinosad, pyruvate, and short-chain acyl-CoA were calculated using their respective standard curves. The standard spinosad was purchased from MERCK. (Fig. S1).

2.3. Construction of recombinant plasmids and engineering strains

All gene downregulation CRISPRi plasmids were constructed by replacing the 20-nt specific guide sequence of sgRNA in the non-template strand of the target genes, as described on the CRISPR direct web (http://crispr.dbcls.jp/) [34]. For example, the construction process of pSET-dcas9-sg-pyc is as follows. Primer pairs F-sg-pyc/R-sgRNA and the plasmid pSET-dcas9 were used to amplify the sgRNA containing the 20-nt specific guide sequence targeting pyc through PCR (Fig. S2). The pSET-dcas9 plasmid [31] and amplified sgRNA were digested using restriction enzymes SpeI and EcoRI. Following purification, the target sgRNA was ligated into the corresponding restriction sites of pSET-dcas9 using T4 DNA ligase, resulting in the vector pSET-dcas9-sg-pyc. The recombinant plasmid pSET-dcas9-sg-pyc was introduced into E. coli S17-1 and subsequently transferred into S. spinosa via intergeneric conjugative transfer. The construction of other single-gene downregulation CRISPRi plasmids involved modifying only the 20-nt specific guide sequences based on the target genes and using upstream primers to amplify sgRNA. Combination inhibition fragments were assembled and integrated into pSET-dcas9 by GenScript Biotechnology Co., Ltd., Nanjing, China. Complete sequences and related descriptions are provided in the Supplementary Materials.

The specific procedures for conjugation transfer are as follows: (1) 1 mL of S. spinosa was activated for 48 h in 20 mL of CSM medium and subsequently transferred to TSB medium for 12 h at a 10 % inoculum volume. Meanwhile, E. coli S17-1 containing the recombinant plasmid was activated for 12 h in LB medium. (2) After 12 h of culture, S. spinosa was inoculated into 20 mL of TSB medium at a 20 % inoculum volume, and E. coli S17-1 was inoculated into LB medium at a 2 % inoculum volume. Both strains were cultured for 4 h (3) All the activated S. spinosa cells were transferred into a sterilized 50 mL centrifuge tube, centrifuged at 8000 rpm for 15 min, and the supernatant was discarded. Then, 25 mL of fresh TSB medium was added for washing and resuspension of the S. spinosa. The cells were centrifuged again at 8000 rpm for 15 min, the supernatant was discarded, and the S. spinosa cells were collected. The same procedure was followed for E. coli S17-1, and the step was repeated 2–3 times. (4) Twenty milliliters of TSB solution was added to resuspend S. spinosa and E. coli S17-1, respectively. E. coli S17-1 cells were diluted 20 × , 40 × , 80 × , 160 × , and 250 × , respectively, and then mixed with S. spinosa at a 1:1 ratio (total volume of 1 mL). The mixture was centrifuged at 8000 rpm for 15 min, leaving approximately 100 μL of suspension. (5) The 100 μL suspension was spread onto R6 agar medium plates and cultured for 14 h. Then, 1 mL of sterile water containing 0.1 mg of nalidixic acid and 0.05 mg of apramycin was evenly spread onto each plate. The plates were then incubated for 7–12 days until transformants appeared.

2.4. Total RNA extraction and RT-qPCR analysis

Total RNA from S. spinosa and its derived strains was extracted on the 2nd, 4th, 6th, and 8th days using the Total RNA Extractor kit (Sangon). The purity and concentration of the extracted RNA were measured using a NanoDrop 2000 spectrophotometer (Thermo). The total RNA was reverse-transcribed into cDNA using the Reverse Transcription Premix Kit (Accurate Biology Co., Ltd., Changsha, China). The 7500 Real-Time PCR System (Applied Biosystems, USA) was used to monitor the fluorescence intensity of the samples in real-time. The 16S rRNA gene was used as the endogenous control. Each experiment was performed in triplicate.

2.5. Protein detection and LC− MS/MS bioinformatic analysis

The S. spinosa-pSET-dcas9/sg-pyc/sg-gltA11/sg-atoB3 and S. spinosa CCTCC M206084 were harvested (8000× g, 10 min, 4 °C) after 144 h of culture, washed four times with phosphate-buffer saline (PBS), and quickly frozen in liquid nitrogen. The extraction, quality verification, and digestion of proteins were performed in Wininnovate Biotechnology Co., Ltd., Shenzhen, China. The specific procedure followed the description in our previous study [30].

3. Results

3.1. CRISPRi-mediated inhibitory circuit downregulates the gluconeogenesis

To minimize precursor competition in spinosad biosynthesis, the CRISPRi system was utilized to construct a multigene simultaneous downregulation circuit (Fig. 1A). Key nodes in three fundamental pathways—gluconeogenesis, the TCA cycle, and the EMC pathway—were downregulated using the CRISPRi multigene repression circuit. This intervention shifted carbon flux from primary metabolism to spinosad biosynthesis, thereby enhancing spinosad production (Fig. 1B). Before targeting specific genes, the potential cytotoxicity of the CRISPRi system was evaluated. The introduction of an empty vector into S. spinosa resulted in no significant changes in biomass or spinosad titer (Fig. S3), demonstrating that the CRISPRi system does not adversely affect cell viability or spinosad biosynthesis.

Fig. 1.

Fig. 1

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Schematic design of multigene downregulation CRISPRi cuicut. (A) Multigene downregulation CRISPRi cuicut. (B) Schematic diagram of some essential metabolic pathways in S. spinosa.

As the final product of glycolysis, pyruvate plays a critical role in spinosad biosynthesis [28,35,36]. However, under the catalysis of pyruvate carboxylase (EC 6.4.1.1), pyruvate can be converted into oxaloacetate via the gluconeogenesis pathway for intracellular metabolic replenishment. Our previous transcriptomic analysis revealed that the pyc gene (encoding pyruvate carboxylase) exhibited high transcription levels during three stages of S. spinosa growth: T1 (logarithmic growth phase, 48 h), T2 (early stationary phase, 96 h), and T3 (late stationary phase, 192 h) (Fig. S4). Moreover, the genome of S. spinosa CCTCC M206084 contains only one pyc gene (orf 03273-1979). Consequently, pyc was downregulated using CRISPRi to enhance pyruvate flux redirection towards spinosad biosynthesis.

The sgRNA for pyc (sg-pyc) was designed targeting nt 793–812 downstream of the start codon, and the recombinant plasmid (pSET-dcas9/sg-pyc) was introduced into S. spinosa CCTCC M206084. The spinosad production in S. spinosa-pSET-dcas9/sg-pyc (the pyc downregulated strain) reached 418.8 ± 26.7 mg/L, representing a 31.9 % increase compared to the control strain, with no significant change in biomass (Fig. 2A and B). Meanwhile, RT-qPCR results indicated that the relative expression levels of the pyc gene in all four selected growth phases were reduced to 21.8 %–31.1 % of their original levels (Fig. 2C). These findings provided initial evidence that downregulation of the pyruvate competition pathway in S. spinosa can enhance spinosad biosynthesis while maintaining normal strain growth and development.

Fig. 2.

Fig. 2

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Effects of CRISPRi-mediated downregulation of pyc in S. spinosa. (A) Biomass accumulation curve of S. spinosa and S. spinosa-pSET-dcas9/sg-pyc. (B) Spinosad accumulation of S. spinosa and S. spinosa-pSET-dcas9/sg-pyc. (C) Relative expression level of pyc gene in S. spinosa-pSET-dcas9/sg-pyc at 2nd, 4th, 6th, 8th day. Error bars are calculated from three independent determinations in each sample.

3.2. Identification of acetyl-CoA competition pathways in S. spinosa-pSET-cas9/sg-pyc

Considering that the biomass of pyc downregulated strain did not decrease, the observed increase in spinosad production was below expectations. Therefore, the underlying reasons for this limited increase were investigated. Quantitative analyses of pyruvate and acetyl-CoA were conducted in S. spinosa-pSET-dcas9/sg-pyc. The results showed significant pyruvate accumulation in S. spinosa-pSET-dcas9/sg-pyc (Fig. 3A), while acetyl-CoA contents remained largely unchanged on days 2 and 4, showing only a slight increase on day 8 (Fig. 3B). This is likely due to competition from pyruvate downstream metabolic pathways for acyl-CoA precursors.

Fig. 3.

Fig. 3

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Effects of pyc downregulation on precursors accumulation and key genes transcriptional levels involved in TCA cycle and ECM pathway. (A) Pyruvate accumulation of S. spinosa and S. spinosa-pSET-dcas9/sg-pyc. (B) Acetyl-CoA accumulation of S. spinosa and S. spinosa-pSET-dcas9/sg-pyc. (C) Transcriptional level of key genes involved in TCA cycle and ECM pathway of S. spinosa and S. spinosa-pSET-dcas9/sg-pyc. Error bars are calculated from three independent determinations in each sample. Univariate variance, nsP > 0.05, ∗P < 0.05, ∗∗P < 0.005, ∗∗∗P < 0.001.

Based on previous transcriptomic analyses, RT-qPCR was used to verify the expression of key genes in the downstream pyruvate metabolic pathway. The results showed that four key genes involved in the acetyl-CoA competitive metabolic pathways (TCA cycle and EMC pathway) in S. spinosa-pSET-dcas9/sg-pyc exhibited high transcription levels during the analyzed period (Figure S4, Fig. 3C). Therefore, these two pathways were chosen for downregulation to test the hypothesis.

3.3. CRISPRi-mediated inhibitory circuit downregulates key nodes in TCA cycle

Malonyl-CoA and methyl-malonyl-CoA, the two direct precursors of spinosad biosynthesis, are primarily derived from acetyl-CoA. The TCA cycle, the terminal metabolic pathway for sugars, lipids, and amino acids, plays a crucial role in sustaining cell growth and development by utilizing acetyl-CoA. Acetyl-CoA is consumed in the first step of the TCA cycle, where citrate synthase (EC 2.3.3.1) catalyzes its reaction with oxaloacetate to produce citrate. To redirect acetyl-CoA toward malonyl-CoA and methyl-malonyl-CoA biosynthesis and enhance spinosad yield, we attempted to downregulate the TCA cycle by suppressing gltA (citrate synthase encoding gene) expression.

Two gltA genes (orf 06851-4114, orf 06867-4125) were identified in the S. spinosa CCTCC M206084 genome and designated as gltA1 and gltA2, respectively. The sgRNAs of gltA1 and gltA2 were designed to target nt 240–259 and nt 34–53 with respect to the start codon, respectively. The plasmids pSET-dcas9/sg-gltA1 and pSET-dcas9/sg-gltA2 were constructed and introduced into S. spinosa. The results indicated that spinosad production in S. spinosa-pSET-dcas9/sg-gltA1 increased by 47.4 % (468 ± 29.5 mg/L), with a slight reduction in biomass, whereas spinosad production, biomass, and strain activity were significantly reduced in S. spinosa-pSET-dcas9/sg-gltA2 (Fig. 4A and B). Transcription levels of gltA1 and gltA2 were measured in S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2, respectively. Compared with S. spinosa-pSET-dcas9/sg-gltA1, the transcription level of S. spinosa-pSET-dcas9/sg-gltA2 was severely inhibited in all selected periods (Fig. 4C), which may account for the observed decline in spinosad titer, biomass, and strain activity in S. spinosa-pSET-dcas9/sg-gltA2.

Fig. 4.

Fig. 4

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Effects of CRISPRi-mediated downregulation of key nodes in TCA cycl. (A) Biomass accumulation curve of S. spinosa, S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2. (B) Spinosad accumulation curve of S. spinosa, S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2. (C) Relative expression level of gltA1 and gltA2 genes in S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2, respectively. (D) Concentrations of acetyl-CoA in S. spinosa, S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2. (E) Concentrations of malonyl-CoA in S. spinosa, S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2. (F) Concentrations of methyl-malonyl-CoA in S. spinosa, S. spinosa-pSET-dcas9/sg-gltA1 and S. spinosa-pSET-dcas9/sg-gltA2. Error bars are calculated from three independent determinations in each sample. Univariate variance, nsP > 0.05, ∗P < 0.05, ∗∗P < 0.005, ∗∗∗P < 0.001.

The detection of precursors also indicated that the contents of three short-chain acyl-CoA (acetyl-CoA, malonyl-CoA and methyl-malonyl-CoA) in S. spinosa-pSET-dcas9/sg-gltA1 were increased in different degrees (Fig. 4D, E, F). However, only elevated levels of acetyl-CoA and methyl-malonyl-CoA were observed in S. spinosa-pSET-dcas9/sg-gltA2, indicating that although inhibition of gltA2 promoted the accumulation of acetyl-CoA to some degree, the conversion of acetyl-CoA to malonyl-CoA was also inhibited, which may be caused by the growth retardation of S. spinosa-pSET-dcas9/sg-gltA2.

3.4. CRISPRi-mediated inhibitory circuit downregulates key nodes in EMC pathway

The EMC pathway plays a critical role in modulating the flux of acetyl-CoA to adjust metabolic balance under different carbon sources or metabolic stress conditions. The first step of the EMC pathway involves the assimilation of acetyl-CoA, where acetyl-CoA is catalyzed by acetyl-CoA C-acetyltransferase (EC 2.3.1.9) to form acetoacetyl-CoA (https://www.kegg.jp/). As mentioned earlier, acetyl-CoA serves as a critical precursor for spinosad biosynthesis. Therefore, the acetyl-CoA C-acetyltransferase encoding genes were targeted for downregulation to attenuate the EMC pathway and mitigate acetyl-CoA competition.

The S. spinosa CCTCC M206084 genome contains 12 genes predicted to encode acetyl-CoA C-acetyltransferase. Based on previous transcriptomic analysis and RT-qPCR results (Figure S4, Fig. 3C), two atoB genes (orf 07451-4479 and orf 14507-8842) with high transcription levels were selected and renamed atoB2 and atoB3, respectively. sgRNAs of atoB2 and atoB3 were designed to target nt 90–109 and nt 106–125 with respect to the start codon, respectively. Two plasmids (pSET-dcas9/sg-atoB2 and pSET-dcas9/sg-atoB3) were constructed and introduced into S. spinosa. Analysis of spinosad titer revealed that respective repression of atoB2 and atoB3 resulted in an increased spinosad titer with a slight decline in biomass (Fig. 5A and B). Among these, S. spinosa-pSET-dcas9/sg-atoB3 achieved a higher spinosad yield of 551.2 ± 27.2 mg/L, representing a 73.6 % increase compared to the original strain. RT-qPCR analysis showed that the relative transcriptional levels of atoB2 and atoB3 in S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3 were reduced to 0.25–0.29 and 0.27–0.41, respectively (Fig. 5C).

Fig. 5.

Fig. 5

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Effects of CRISPRi-mediated downregulation of key nodes in EMC pathway. (A) Biomass accumulation curve of S. spinosa, S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3. (B) Spinosad accumulation curve of S. spinosa, S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3. (C) Relative expression level of atoB2 and atoB3 genes in S. spinosa, S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3, respectively. (D) Concentrations of acetyl-CoA in S. spinosa, S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3. (E) Concentrations of malonyl-CoA in S. spinosa, S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3. (F) Concentrations of methyl-malonyl-CoA in S. spinosa, S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3. Error bars are calculated from three independent determinations in each sample. Univariate variance, nsP > 0.05, ∗P < 0.05, ∗∗P < 0.005, ∗∗∗P < 0.001.

The short-chain acyl-CoA content in S. spinosa-pSET-dcas9/sg-atoB2 and S. spinosa-pSET-dcas9/sg-atoB3 was measured, revealing that acetyl-CoA, malonyl-CoA, and methyl-malonyl-CoA all accumulated to varying degrees, with higher levels observed in S. spinosa-pSET-dcas9/sg-atoB3 compared to S. spinosa-pSET-dcas9/sg-atoB2 (Fig. 5D, E, F). These results demonstrated that the short-chain acyl-CoA content exhibited a positive correlation with spinosad yield within a specific range.

3.5. Combinatorial optimization of metabolic pathways to further increase spinosad production

As mentioned above, the downregulation of key nodes in the three core carbon metabolism pathways promoted the biosynthesis of spinosad individually, with gltA1 and atoB3 inhibition exhibiting greater effects compared to other genes in their respective pathways. Therefore, the three sgRNAs cassettes (promotor-sg-pyc-terminator, promotor-sg-gltA1-terminator, and promotor-sg-atoB3-terminator) were combined to construct a multigene CRISPRi downregulation circuit, and the plasmid pSET-dcas9/sg-pyc/sg-gltA1/sg-atoB3 was introduced into S. spinosa and verified (Fig. S5A). However, the pyc-gltA1-atoB3 downregulated strain PCA1B3 exhibited marked growth retardation and significantly reduced spinosad production (Fig. 6A and B). This suggests that simultaneous downregulation of these three target genes at the current inhibition level may cause metabolism imbalance, resulting in an insufficient supply of metabolites for primary metabolism, and, consequently, growth retardation and reduced spinosad titer.

Fig. 6.

Fig. 6

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Optimization of sgRNA and effects of CRISPRi-mediated multigene downregulation. (A, D) Biomass accumulation curve of S. spinosa, PCA1B3, PCA11B3, PCA1B31 and PCA11B31. (B, E) Spinosad accumulation of S. spinosa, PCA1B3, PCA11B3, PCA1B31 and PCA11B31. (C) Schematic design of sgRNAs. Error bars are calculated from three independent determinations in each sample.

It is reported that the farther the sgRNA is positioned from the start codon of the target gene, the weaker the inhibitory effect of CRISPRi [13,27]. Therefore, we adjusted the downregulation strength by altering the position of sgRNA on the target gene. Since there was no biomass decrease in pyc downregulated strain, optimizations of sgRNA were conducted in gltA1 and atoB3. sgRNAs positioned farther from the start codon of gltA1 and atoB3 were redesigned to target nt 492–511 (sg-gltA11) and nt 416–435 (sg-atoB31) with respect to the start codon, respectively. Subsequently, three permutations of sg-gltA1, sg-gltA11, sg-atoB3, and sg-atoB31 were connected with sg-pyc (all sequences contain a promoter and a terminator, respectively), and the corresponding multigene downregulation plasmids were transferred into S. spinosa (Fig. 6C).

The results indicated that after sgRNA optimization, both biomass and spinosad production significantly increased compared to PCA1B3, with PCA11B (the strain carrying pSET-dcas9/sg-pyc/sg-gltA11/sg-atoB3) showing the highest spinosad titer, reaching 633.1 ± 38.6 mg/L (Fig. 6D and E). It was confirmed that the CRISPRi repression strength can be adjusted by modifying the sgRNAs targeting position. It is worth mentioning that the target location of sg-gltA11 and sg-atoB31 were farer away from the start codon than sg-gltA1 and sg-atoB3. As a result, the inhibition of gltA1 and atoB3 in multigene downregulation strains was weakened while the relative expression fold of pyc showed no significant change (Figs. S5B, C, D). The results of pyruvate and shore-chain acyl-CoA determination also consistent with the previous results in single-gene repression strains (Fig. S6). Additionally, PCA11B3 was confirmed to exhibit stable spinosad production after five subcultures (Fig. S7).

3.6. Proteomic and transcriptional level analysis of spinosad biosynthesis in PCA11B3

The spinosad gene cluster mainly contains 23 genes, of which 19 spn genes (spnA–S) are related to forosamine biosynthesis, polyketide briding, tri-O-methylrhamnose biosynthesis and polyketide biosynthesis. The expression level of spn genes is directly related to the biosynthesis of spinosad. Therefore, 12 spn genes were selected according to different transcription directions for RT-qPCR to evaluate their expression levels (Fig. 7A). As we expected, most of the selected spn genes in PCA11B3 were upregulated, which directly caused the increase in spinosad titer.

Fig. 7.

Fig. 7

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Effects of multigene downregulation on Spn family genes and metabolism of PCA11B3. (A) Relative expression level of representative spn genes in strain PCA11B3. (B) Bubble plot of the most significant pathways with KEGG enrichment of DEPs. (C) Mainly metabolic pathways with DEPs related with spinosad biosynthesis in PCA11B3. Error bars are calculated from three independent determinations in each sample.

To further analyze how spinosad biosynthesis is promoted in PCA11B3, non-labeled data-independent acquisition (DIA) quantitative proteome technology was conducted to evaluate the protein expression level and corresponding biological function in PCA11B3. Differential expression proteins (DEPs) were identified by screening for fold change (FC) greater than 5 times or less than 0.2 times. A total of 784 DEPs were founded in PCA11B3, of which 661 were upregulated and 123 downregulated. Enrichment analysis of KEGG secondary classification suggesting that the DEPs mainly involved in pathways of glycolysis/gluconeogenesis (20), pyruvate metabolism (19), ribosome (16), citrate cycle (14), glyoxylate and dicarboxylate metabolism (13), and Biosynthesis of amino acids (32), etc (Fig. 7B). The above results indicated that multigene repression of pyc, gltA1, and atoB3 affected the overall metabolic network of S. spinosa. A major metabolic network map was constructed based on the data identified in the comparative proteomic data to illustrate the promotion of spinosad biosynthesis in PCA11B3, including the main pathways involved in pyruvate and acetyl-coA metabolism, such as glycolysis/gluconeogenesis, TCA cycle, glyoxylate metabolism, and amino acid metabolism. (Fig. 7C). The inhibition of pyc reduced the metabolic flux of pyruvate in gluconeogenesis, which is then converted to acetyl-CoA. The repression of gltA1 and atoB3 not only weaken the TCA cycle but also reduce glyoxylate production. In addition, the metabolic pathways of multiple amino acids are enhanced, thereby providing a large number of precursors for the spinosad biosynthesis. These results demonstrated the feasibility of CRISPRi-based static multigene regulation in spinosad synthesis networks.

4. Discussion

In this study, we employed the CRISPRi technology to regulate the core carbon metabolic pathways in S. spinosa, exploring the possibility of optimizing spinosad biosynthesis through multigene downregulation. By simultaneously downregulating the pyc, gltA1, and atoB3 genes, we successfully altered the metabolic flux and significantly increased spinosad production. This study highlights the potential of CRISPRi technology in metabolic engineering and provides valuable insights for future metabolic optimization strategies.

We firstly repressed pyc gene and resulted an increase in the concentration of pyruvate and spinosad. This result suggests that the downregulation of pyc caused a significant shift in the metabolic flux of pyruvate, reducing the carbon flow through gluconeogenesis and directing more carbon resources towards spinosad biosynthesis. This finding supports our hypothesis that reducing the overutilization of pyruvate can enhance the production of spinosad. Previous studies have also reported that pyruvate accumulation is commonly observed in high-yield spinosyn-producing engineered strains [28,37], which aligns with the phenomena observed in our experiment. Yang et al. When we downregulated gltA1 and atoB3 genes, a significant accumulation of acetyl-CoA, malonyl-CoA, and methylmalonyl-CoA were observed. These short-chain acyl-CoA are key precursors in spinosad biosynthesis, and increasing their accumulation within the cell can directly promote spinosad production [38,39], indicating that inhibition of the TCA cycle and the EMC pathway facilitated the more efficient utilization of short-chain acyl-CoA for spinosad synthesis.

However, when pyc, gltA1, and atoB3 were repressed simultaneously, the strain occurred growth retardation and metabolic imbalance, suggesting that excessive inhibition of these metabolic pathways led to a shortage of essential metabolites, disrupting normal cell growth and metabolism. By further optimizing the CRISPRi system and adjusting the targeting positions of the sgRNAs, we found that reducing the repression strength of gltA1 and atoB3 helped avoid metabolic imbalance and further promoted spinosad synthesis. Ultimately, the PCA11B3 strain exhibited the highest spinosad titer.

Interestingly, the simultaneous regulation of pyc, gltA1, and atoB3 (in PCA11B3) led to changes in the transcription levels of many spn genes. The metabolic pathways in which these genes are located directly affect the supply of carbon sources and energy within the cell, thereby indirectly regulating the precursor and metabolic environment required for spinosad synthesis (the target product of spn genes). In PCA11B3, the concentration of key intracellular metabolites such as acetyl-CoA is significantly increased, and the accumulation of acetyl-CoA can up-regulate the synthesis of secondary metabolites by activating transcription factors or specific regulatory mechanisms [40,41]. In addition, the downregulation of pyc, gltA1 and atoB3 not only directly affects the accumulation of short-chain acyl-CoA, but also indirectly affects the transcription of spn genes by regulating the TCA cycle, glycolysis, amino acid metabolism and other pathways (Fig. 7).

The results of this study demonstrate that downregulation of pyc, gltA1, and atoB3 can effectively reconfigure the metabolic flux within the cell, optimizing spinosad biosynthesis. Specifically, downregulation of pyc reduced the carbon flux through gluconeogenesis and lowered the consumption of pyruvate, allowing more carbon to flow into the spinosad biosynthetic pathway. Simultaneously, the downregulation of gltA1 and atoB3 slowed the activity of the TCA cycle and the EMC pathway, leading to the accumulation of acetyl-CoA and providing more precursors for spinosad synthesis. The accumulation of acetyl-CoA not only promoted spinosad biosynthesis but also enhanced the levels of other short-chain acyl-CoA intermediates (e.g., malonyl-CoA and methylmalonyl-CoA), further boosting spinosad production. However, excessive inhibition of these key metabolic pathways could lead to metabolic imbalance, resulting in growth suppression and a decline in production. Thus, balancing the repression strength of these pathways and avoiding excessive suppression are crucial for optimizing spinosad yield.

At present, there have been a lot of studies focused on the positive related enzymes in the spinosad synthesis pathway, but few studies on the metabolic flow competition in the precursor branched-chain metabolic pathway or essential metabolic pathway [30,[39], [40], [41], [42]]. This study utilizes a CRISPRi multigene downregulation strategy, achieving significant progress in enhancing spinosad biosynthesis. Compared to previous studies focusing on single-gene regulation or specific pathway modifications, our approach demonstrated stronger potential for metabolic engineering. Prior research has utilized genetic engineering to regulate spinosad biosynthetic pathways, but most studies have concentrated on single gene regulation or targeting specific pathways [[43], [44], [45]]. In contrast, by simultaneously downregulating multiple key nodes using CRISPRi technology, we successfully enhanced spinosad production and optimized metabolic flux. This strategy offers a novel approach for metabolic engineering. Although we made significant strides in optimizing the spinosad biosynthetic pathway, growth retardation and metabolic imbalance remain notable challenges when metabolic pathways are excessively downregulated. Future research can focus on fine-tuning CRISPRi repression strength to avoid metabolic flux imbalance while further improving the yield.

In conclusion, this study successfully optimized the metabolic pathways in S. spinosa through multigene downregulation using CRISPRi technology, significantly increasing spinosad production and providing new insights into metabolic engineering. Our findings demonstrate that fine-tuning metabolic flux, especially through multigene downregulation strategies, can effectively enhance the synthesis of target products. While challenges related to metabolic imbalance remain, with further optimization of the CRISPRi system, this approach holds great promise for the production of other natural products in the future.

CRediT authorship contribution statement

Zirong Zhu: Writing – original draft, Methodology, Conceptualization. Li Cao: Writing – review & editing. Ziyuan Xia: Visualization, Software, Investigation. Xirong Liu: Visualization, Software, Investigation. Wangqion Chen: Visualization, Software, Investigation. Zirui Dai: Writing – review & editing. Duo Jin: Writing – review & editing. Jie Rang: Funding acquisition. Shengbiao Hu: Supervision. Liqiu Xia: Supervision, Methodology, Funding acquisition.

Funding information

This work was supported by funding from the National Natural Science Foundation of China (31770106, 32200062) and the Natural Science Foundation of Hunan Province (2024JJ5258).

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: XirongLiu is currently employed by Hunan Norchem Pharmaceutical Co., Ltd.

Footnotes

Peer review under the responsibility of Editorial Board of Synthetic and Systems Biotechnology.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.synbio.2025.02.010.

Contributor Information

Shengbiao Hu, Email: shengbiaohu@hunnu.edu.cn.

Liqiu Xia, Email: xialq@hunnu.edu.cn.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
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