Metabolic Model-Driven Optimization of Streptomyces Metabolic Network for High-Yield Production of Gougerotin

Metabolic Model-Driven Optimization of Streptomyces Metabolic Network for High-Yield Production of Gougerotin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolic Model-Driven Optimization of Streptomyces Metabolic Network for High-Yield Production of Gougerotin Qianying Zhou, Hua Yu, Yutong Liao, Xi Wang, Beibei Ge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6962713/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, a genome-scale metabolic model (GEM) of Streptomyces noursei strain CK-15 was constructed, integrating the biosynthetic pathway of gougerotin. Based on UPA algorithm analysis, three extracluster regulatory targets- AHCi , PUNP1 ( PNP ), and metK -were identified and validated by gene overexpression experiments. All engineered strains showed accelerated sporulation and enhanced antimicrobial activity in fermentation broth. Notably, overexpression of AHCi , PUNP1 , and metK increased gougerotin yields by 43.08%, 238%, and 32.87%, respectively, reaching titers of 1.08 g/L, 2.56 g/L, and 1.01 g/L. Proteomic analysis revealed that AHCi regulated pathways involved in membrane transport, environmental information processing, amino acid metabolism, and tRNA biosynthesis; PUNP1 enhanced cofactor, nucleotide metabolism, and energy supply; while metK elevated intracellular SAM levels to promote methylation and peptide precursor biosynthesis, concurrently maintaining stable central carbon metabolism and efficient product efflux. This study elucidates the synergistic roles of these regulatory targets in gougerotin biosynthesis and provides a novel strategy for enhancing microbial secondary metabolite production via metabolic model-based optimization. Streptomyces biosynthetic gene cluster proteomics metabolic model Figures Figure 1 Figure 2 Figure 3 Introduction Streptomyces species are prolific producers of secondary metabolites, accounting for nearly 50% of known antibiotics[ 1 , 2 ], and producing various pharmaceuticals including antifungal, anticancer, immunosuppressive, and antiparasitic agents [ 3 ]. In response to rising antimicrobial resistance, discovering novel and efficient antibiotics and natural products has become increasingly urgent. Advances in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have enabled new strategies for strain improvement and secondary metabolite overproduction [ 4 , 5 ]. Gougerotin, a water-soluble, basic peptide-nucleoside antibiotic[ 6 ], was first isolated from Streptomyces gougerotii [ 7 ] and other species including S. toyocaensis [ 8 ], S. S-514 [ 9 ] and S. noursei CK-15. It exhibits broad-spectrum bioactivities including antitumor, antiviral, antifungal, and acaricidal effects[ 10 , 11 ]. Its structure, consisting of a nucleoside and peptide moiety, was first proposed by Fox and Watanabe[ 12 ], and confirmed by 13 C NMR analysis [ 13 ]. Total synthesis was later achieved by [ 14 ]. In China, Streptomyces noursei CK-15 was identified as a gougerotin-producing strain with strong agricultural application potential [ 15 ]. The structural similarity between the 4-amino-glucosamine nucleoside moiety of gougerotin and the CGA intermediate in kasugamycin S biosynthesis prompted the identification and cloning of its biosynthetic gene cluster. A 28.7 kb fragment was cloned into pSET152 and integrated into the chromosome of Streptomyces coelicolor M1146, yielding the heterologous strain M1146-D6-4H [ 10 ]. The minimal cluster contains 15 genes: 13 biosynthetic enzymes, 1 pathway-specific regulator (GouR), a putative secretion protein, and an MFS transporter gene ( gouM ). The pathway initiates with the coupling of cytosine and UDP-glucuronic acid to form CGA, which is oxidized by GouA, aminated by GouH to produce 4-amino-CGA, with gouF , gouA , and gouH acting cooperatively. The peptide moiety, composed of D-serine and sarcosine, involves gouG , gouI , and gouL for D-serine biosynthesis [ 16 , 17 ]. GouK activates amino acids into acyl-CoAs, transferred to the nucleoside by GouJ. Unlike other nucleoside antibiotics, sarcosine is synthesized via glycine activation by GouK followed by methylation by GouN [ 10 ]. Disruption of gouC and gouD abolishes gougerotin production and alters sarcosine structure, indicating their roles in deacetylation and methyl transfer, respectively[ 11 ]. To enhance production, Tan Huarong’s group engineered S. graminearus strains harboring wild-type (pGOU) or engineered (pGOUe) clusters. Yields increased 1.3- and 2.1-fold, respectively. Supplementation with precursors like cytosine, serine, or glycine further stimulated production, reaching 2.5-fold higher yields upon glycine addition [ 18 ]. Replacing native promoters of gouA , gouM , and gouN with the constitutive hrdB promoter also significantly boosted production [ 19 ]. Given gougerotin’s intracellular toxicity, its regulation and secretion are critical. GouR represses the gouL - gouB operon and activates gouM to enable efficient export [ 20 ]. Secondary metabolism depends not only on gene cluster activation but also on precursor availability and energy balance within the primary metabolism. Metabolites like acetyl-CoA, pyruvate, and TCA intermediates supply energy and building blocks for secondary biosynthesis. Strategies including overexpression of key enzymes, metabolic rerouting, and precursor supplementation have proven effective. For instance, boosting glucose metabolism in S. coelicolor enhances actinorhodin production [ 21 , 22 ]. Similarly, erythromycin production in S. erythraea increased 50% following GEM-guided amino acid feeding [ 23 ], and spinosad production in S. spinosa improved by 86.5% [ 24 ]. GEMs offer a systems-level approach to simulate metabolic fluxes and guide genetic modifications [ 25 , 26 ]. Integration of omics datasets further refines GEM accuracy. In S. coelicolor A3(2) , transcriptome-proteome integration revealed that translation efficiency of secondary metabolite genes declines post-exponential growth [ 27 ]. Metabolomics has linked upregulated amino acid and central carbon intermediates to balhimycin biosynthesis in Amycolatopsis balhimycina [ 28 ], and desferrioxamine derivatives in S. chartreusis [ 29 ]. These approaches also guided gene knockouts and overexpression strategies, enhancing precursor supply and reducing toxicity [ 30 ]. COBRA-based GEM frameworks continue to play pivotal roles in optimizing metabolic engineering of high-value natural products. Discussion In this study, a genome-scale metabolic model (GEM) for Streptomyces noursei strain CK-15 was reconstructed by embedding 12 key reactions involved in gougerotin biosynthesis, as previously reported in the literature [ 10 ], into the scaffold of the Sco-GEM model for Streptomyces coelicolor M145. This integrated model ensured stoichiometric consistency among metabolic reactions and served as a quantitative platform for subsequent flux analysis. Such GEM-based approaches enable comprehensive insight into metabolic crosstalk during secondary metabolite biosynthesis and facilitate the rational identification of regulatory targets. Based on model-guided analysis using the Unbiased Pathway Analysis (UPA) algorithm, three key out-of-cluster overexpression candidates were identified: AHCi , PUNP1 , and metK . S-adenosylhomocysteine hydrolase ( AHCi ) plays a central role in methylation metabolism. It catalyzes the hydrolysis of S-adenosylhomocysteine (SAH) into adenosine and homocysteine, maintaining the intracellular balance between S-adenosylmethionine (SAM) and SAH and preventing SAH-mediated feedback inhibition of methyltransferases [ 31 ]. AHCi contains a unique dual Rossmann-fold domain architecture, enabling its catalytic function through co-binding of substrates and NAD⁺ cofactors. Overexpression of AHCi enhances methylation efficiency by facilitating SAH clearance and promoting smooth SAM cycling. In Burkholderia glumae , quorum sensing activates both SAHH and methionine synthase to optimize cellular methylation flux [ 32 ]. Purine-nucleoside phosphorylase ( PNP or PUNP1 ) is a pivotal enzyme in purine salvage pathways, catalyzing the phosphorolysis of purine nucleosides to generate free bases and ribose-1-phosphate, thereby alleviating dependence on de novo purine synthesis. In Streptomyces calvus , the biosynthesis of nucleocidin involves the sequential action of PNP (nucPNP) and a downstream enzyme NucV, which catalyze S-methyladenosine transformation and AMP generation [ 33 ]. Beyond its metabolic role, PNP also participates in post-transcriptional regulation. In S. coelicolor , the rpsO-pnp operon is regulated by temporally active and stress-responsive promoters, modulating RNA stability and nucleotide homeostasis[ 34 ]. Moreover, bldA -dependent UUA-containing tRNAs are required for nucleocidin biosynthesis, further implicating PNP in the regulation of secondary metabolism [ 35 ]. Intracellular (p)ppGpp can inhibit PNPase activity, thereby stabilizing mRNA under stress conditions [ 36 ]. Overexpression of pnp in Streptomyces antibioticus alters mRNA poly(A) tail length and stability, revealing novel regulatory mechanisms of RNA processing [ 37 ].Overall, PNP and related enzymes in Streptomyces not only play a crucial role in regulating purine metabolism and nucleotide salvage, but also exhibit diverse biological functions by modulating RNA stability and natural product biosynthesis[ 33 – 38 ]. MetK encodes SAM synthetase, which catalyzes the ATP-dependent conversion of methionine into SAM-the universal methyl donor involved in numerous methylation reactions. MetK is considered a metabolic bottleneck in many organisms, and its enhanced expression has been shown to significantly boost antibiotic yields [ 39 ]. For instance, overexpression of metKcs in Streptomyces xinghaiensis TL01 increased tylosin production by 12%, and co-expression with other genes elevated production by 23% [ 40 ]. In S. avermitilis , the expression of metK correlates with aveR and aveA1 transcription and avermectin yields [ 41 ]. Co-overexpression of lmbW and metK in S. lincolnensis improved lincomycin A yield while suppressing undesired lincomycin B [ 42 ]. Moreover, metK has been implicated in enhancing the biosynthesis of actinorhodin, pikromycin, erythromycin A, and nosiheptide in various Streptomyces species [ 43 – 46 ].Studies on the cloning and expression of the metK gene in Streptomyces lividans TK24 and Actinoplanes teichomyceticus (the producer of teicoplanin) further confirmed its role in enhancing antibiotic production[ 47 ]. Despite the enhanced gougerotin production observed in the engineered strains, a decrease in dry cell weight during the stationary phase was noted, suggesting that excessive secondary metabolite accumulation might compromise cellular homeostasis. Therefore, future efforts should aim at implementing inducible promoters or dynamic flux control strategies to fine-tune the balance between growth and production[ 48 – 50 ].Overall, this study successfully reconstructed a genome-scale metabolic model for S. noursei strain CK-15 and identified and validated three key regulatory nodes- AHCi , PUNP1 , and metK -for improving gougerotin biosynthesis. Our findings emphasize the essential roles of precursor supply, energy metabolism, and methylation cycles in regulating secondary metabolism. Rational engineering of these nodes provides a robust strategy for the enhanced production of gougerotin and potentially other nucleoside antibiotics, while also offering insights into the systemic regulation of Streptomyces metabolism. Conclusions In this study, a genome-scale metabolic model of Streptomyces noursei strain CK-15 was reconstructed, incorporating the complete gougerotin biosynthetic pathway. Using the UPA algorithm, we identified three extracluster regulatory targets- AHCi , PUNP1 , and metK . Overexpression of these genes led to accelerated sporulation, enhanced mycelial growth, and significantly increased gougerotin production by 43.08%, 238%, and 32.87%, respectively, with final titers reaching 1.08 g/L, 2.56 g/L, and 1.01 g/L. Antifungal activity of the fermentation broth was also improved. Proteomic analysis revealed that AHCi modulates membrane transport, environmental signaling, and amino acid and tRNA metabolism; PUNP1 optimizes cofactor and nucleotide metabolism along with energy supply; and metK enhances intracellular SAM levels, promoting methylation reactions and peptide precursor biosynthesis while maintaining central carbon metabolism and facilitating effective product efflux. Collectively, this study demonstrates that systematic modeling and multi-target metabolic regulation can be effectively leveraged to improve the yield of nucleoside antibiotics such as gougerotin. The CK-15 GEM provides a foundational tool for systems metabolic engineering of Streptomyces , paving the way for industrial-scale biosynthesis of valuable secondary metabolites. Methods 1. Bacterial Strains, Plasmids, and Culture Media The Streptomyces noursei strain CK-15 was cultured and subjected to conjugation transfer on SFM solid medium. Seed cultures were grown in liquid seed medium at 30°C. Spores stored in glycerol tubes were streaked onto SFM plates and incubated at 30°C for 3–5 days. Wild-type CK-15 and overexpression strains were cultured on SFM plates; spores were harvested and stored in 20% glycerol at -80°C. For each overexpression strain, three independent mutants were preserved. Spore suspensions of wild-type and overexpression strains were adjusted to the same optical density (OD600) measured by a microplate reader. For each gene, spore suspensions of three mutant clones were mixed, and 50 µL of this mixture was evenly spread on SFM plates. Plates were incubated at 30°C for 2 days before inoculation into seed medium and cultured at 30°C, 220 rpm for 24 h. Subsequently, 10% inoculum was transferred to 50 mL fresh gougerotin fermentation medium for fermentation at 30°C, 220 rpm, for 72 h. Mature spores were collected from SFM plates when gray-black spores formed, using sterile glycerol tubes and cotton swabs under a laminar flow hood, and stored in 20% glycerol at -80°C. Strains and plasmids used in this study are listed in Table S1. 2. Preparation of Competent Cells for Calcium Transformation The day before conjugation, E. coli ET12567 carrying target plasmids was inoculated into LB medium and cultured at 37°C until OD600 reached 0.4–0.6. CK-15 spores were evenly spread on SFM plates and incubated at 30°C for 3–5 days until gray-black spores covered the surface. Spores were collected with cotton swabs and suspended in TES buffer for immediate use or stored in 20% glycerol at -80°C. For each EP tube, 1 mL LB was added and centrifuged at 12,000 rpm for 1 min; this was repeated three times to pellet cells, which were resuspended in 100 µL LB. Spores were washed twice by resuspension in 1 mL TES and centrifugation at 12,000 rpm for 1 min. Finally, spores were resuspended in 500 µL TES. Spores were heat shocked in a 50°C water bath for 10 min, then mixed with 2× spore germination buffer and 20 µL 0.5 M CaCl₂. Heat-treated spores were mixed with E. coli ET12567 cells at a 10:1 ratio and spread on SFM agar supplemented with 10 mM Mg²⁺. Plates were air-dried in a laminar flow hood and incubated at 30°C for 16 h. After incubation, 1.5 mL overlay solution containing 40 µL apramycin and 40 µL nalidixic acid in ddH₂O was added evenly to each plate. Once dried, plates were further incubated at 30°C for 3–5 days to allow exconjugant formation. Exconjugants were streaked onto SFM plates containing 0.1% apramycin and 0.1% nalidixic acid and incubated for 2–3 days. After visible mycelial growth, strains were transferred to antibiotic-free seed medium and cultured at 30°C for 2–3 days. PCR verification was performed to confirm correct exconjugants. Correct exconjugants were cultured on antibiotic-free SFM plates to produce spores, which were harvested and stored in 20% glycerol at − 80°C. 3. Construction and Validation of Overexpression Strains Based on known gougerotin biosynthetic gene clusters from Streptomyces hygroscopicus available on NCBI, homologous genes were identified in the CK-15 genome using local BLAST. Primers were designed to amplify target genes from CK-15 genomic DNA. Genomic DNA extraction was performed using Tiangen Bacteria Genomic DNA Kit (spin column), and PCR was carried out with Phanta Max Super-Fidelity DNA Polymerase (Nanjing Novozan Biotech). PCR reaction (50 µL total) contained 13 µL ddH₂O, 25 µL 2× Phanta MAX Buffer, 1 µL dNTP mix, 2 µL forward primer, 2 µL reverse primer (Table S2), 1 µL polymerase, 1 µL template DNA, and 5 µL 50% DMSO. PCR conditions: initial denaturation at 95°C for 10 min; 31 cycles of 95°C for 30 s, 65°C for 30 s, and 72°C for 30–60 s/kb; final extension at 72°C for 10 min. Plasmid extraction was performed using Tiangen Plasmid Mini Kit. Double digestion was carried out with FastDigest EcoR I and Nde I (Thermo Scientific). Reaction system (20 µL): 1 µg DNA, 2 µL 10× FastDigest Green Buffer, 1 µL EcoRI, 1 µL NdeI, and nuclease-free water to 20 µL. Digestion at 37°C for 40 min. Digested products were confirmed by agarose gel electrophoresis and purified by gel extraction (Omega Gel Extraction Kit). Gibson assembly was performed on ice with a molar ratio of 1:3 (vector:insert). The reaction mix contained 2 µL vector, 1 µL insert, 10 µL 2× Gibson Assembly Master Mix, and nuclease-free water to 20 µL. The mixture was incubated at 50°C for 30 min. Five microliters of assembled plasmid were transformed into competent E. coli DH5α or ET12567 (pUZ8002) cells on ice for 20–30 min, followed by heat shock at 42°C for 90 s, then placed on ice for 2 min. After addition of 1 mL LB, cells were incubated at 37°C, 220 rpm for 30 min, and plated on LB agar containing 50 mg/mL apramycin overnight at 37°C. Single colonies were picked and cultured in LB with apramycin. Colony PCR was performed using YEASEN 2× Hieff PCR Master Mix (with dye), 5 µL water, 2 µL 50% DMSO, 1 µL each primer (Table S2), and 1–2 µL template DNA. PCR conditions were as above. Correct clones were confirmed by sequencing and stored in 20% glycerol. 4. Intergeneric Conjugation between Streptomyces and E. coli Conjugation conditions were optimized for CK-15 as described above. Briefly, E. coli ET12567 harboring the target plasmid was grown in LB to OD600 0.4–0.6. CK-15 spores were plated on SFM and incubated at 30°C for 3–5 days. Spores were harvested and washed with TES buffer, then heat shocked at 50°C for 10 min, mixed with 2× spore germination buffer and CaCl₂, and combined with E. coli cells at a 10:1 ratio. Mixture was plated on SFM agar with 10 mM Mg²⁺ and incubated at 30°C for 16 h. Plates were overlaid with selective antibiotic solution containing apramycin and nalidixic acid, then incubated for 3–5 days for exconjugant growth. Exconjugants were purified on selective plates, verified by PCR, and stored as described. 5. Proteomic Sequencing and Annotation of Overexpression Strains Proteomic sequencing of CK-15 and overexpression strains ( AHCi , PUNP1 , metK ) was performed by Senris Biotechnology (Shenzhen). Functional annotation of CK-15 genes was conducted using the eggNOG-mapper online tool ( https://eggnog-mapper.embl.de/ ), including Gene Ontology (GO), KEGG pathway, and PFAM domain annotations. TBtools software was used to organize annotation results for differential expression and pathway enrichment analyses. Metabolic pathways of CK-15 were analyzed via the KEGG Organisms database. 6. Extraction and Quantification of Gougerotin One milliliter of fermentation broth was centrifuged at 4000 rpm, 4°C for 10 min. The supernatant was diluted 100-fold with ddH₂O and filtered through a 0.22 µm aqueous membrane. LC-MS analysis was performed using an Agilent Ultivo Triple Quadrupole LC/TQ system equipped with a Waters ACQUITY UPLC BEH Amide Column (130 Å, 1.7 µm, 2.1 × 100 mm) and a VanGuard Pre-column (130 Å, 1.7 µm, 2.1 × 5 mm). Mobile phase A was 5 mM ammonium acetate in water; phase B was acetonitrile; flow rate 0.3 mL/min; injection volume 1.00 µL; run time 17 min. Details of detection methods, column wash, and MRM parameters are provided in Tables S3 - S5. 7. Measurement of Growth Curves for Overexpression Strains Mycelial dry weight was used to assess growth. Initially, empty 1.5 mL centrifuge tubes were weighed. One milliliter of fermentation broth was added and centrifuged at 12,000 rpm for 2 min; supernatant was carefully discarded. Tubes were dried at 65°C for 5 days and reweighed. The difference in weight before and after drying represented mycelial dry weight. 8. Determination of Antimicrobial Activity Using Rhodotorula The inhibition activity of CK-15 wild-type and OE strains was assessed by the Oxford cup method using Rhodotorula as an indicator. Spores stored in 20% glycerol were streaked on SFM plates and incubated at 30°C for 2 days. A 1 cm² agar block was inoculated into 30 mL seed medium and cultured at 30°C, 220 rpm for 24 h. Then, 10% inoculum was transferred to 50 mL fresh fermentation medium for 72 h under the same conditions. One milliliter fermentation broth was centrifuged at 12,000 rpm for 1 min; supernatant was filtered through a 0.22 µm membrane. PDA medium was supplemented with 1 mL Rhodotorula suspension (OD = 1.2), mixed, and poured into plates (20 mL per plate). Sterile Oxford cups were placed on plates, and 200 µL filtered fermentation supernatant was added into each cup. Each treatment had four replicates. Plates were incubated at 30°C for 48 h, and inhibition zone diameters were measured. Results 1. Reconstruction of a Genome-Scale Metabolic Model for Gougerotin Biosynthesis in Streptomyces Based on previously reported gougerotin biosynthetic pathways, we first constructed gene–protein–reaction (GPR) associations for the gougerotin biosynthesis process (Table 1 , Table S12), assigning specific names to each reaction. For certain biosynthetic steps where enzymatic mechanisms remain unclear-such as reaction GOUI-we proposed reasonable biochemical reactions based on the chemical structures of substrates and products. All reactions were examined to ensure mass balance, thereby completing the preliminary reconstruction of a genome-scale metabolic pathway for gougerotin biosynthesis. Given the high genomic similarity between Streptomyces coelic olor M145 and Streptomyces noursei strain CK-15, and the availability of a well-curated genome-scale metabolic model (GEM) for S. coelicolor (Sco-GEM), we used Sco-GEM as the scaffold to build the CK-15 GEM. We integrated the 12-step gougerotin biosynthetic pathway (Table 1 ) into Sco-GEM using the COBRA toolbox. After successful model debugging and validation, the pathway was effectively incorporated, thus establishing a genome-scale model of gougerotin biosynthesis in S. noursei CK-15. 2. Identification of Genetic Targets for Enhanced Gougerotin Production Using the reconstructed GEM of S. noursei CK-15, we performed metabolic simulations with the COBRA toolbox, including prediction of gene overexpression targets. The UPA algorithm implemented in the UP Finder tool was used to identify reactions strongly associated with increased gougerotin production. This analysis identified 31 relevant reactions. Based on the ratio metric in UPA, the top five reactions were prioritized (Table 2 ). Among these, AHCi and PUNP1 ( PNP ) were selected as primary candidates due to their direct relevance to gougerotin biosynthesis. The secondary candidate metK was also selected based on its strong involvement in methylation metabolism. These three genes ( AHCi , PUNP1 , and metK ) were subsequently chosen for genetic overexpression in CK-15. 3. Construction and Validation of Overexpression Strains The candidate genes AHCi , PUNP1 , and metK were amplified by PCR. Agarose gel electrophoresis confirmed the correct amplicon sizes: AHCi – 1458 bp, PUNP1–816 bp, and metK – 1209 bp (Figure S1B-C). After ligation with linearized plasmids (Figure S1A) and sequence verification, overexpression strains were constructed. PCR from these strains yielded products of expected sizes: OE- AHCi – 1637 bp, OE- PUNP1–995 bp, and OE- metK – 1388 bp (Figure S1D-F), confirming successful integration of the target genes. 4. Phenotypic Characterization of Overexpression Strains The wild-type CK-15 and OE strains ( AHCi , PUNP1 , metK ) were cultured on SFM agar for 4 days. Compared with the wild-type, OE strains showed accelerated sporulation, with OE- metK being the fastest, followed by OE- PUNP1 (Fig. 1 A). Biomass accumulation was assessed during fermentation. All OE strains exhibited faster growth during the exponential phase (0–12 h), with OE- PUNP1 showing the highest rate, followed by OE- AHCi and OE- metK . Biomass peaked at 12 h and declined thereafter in OE strains, likely due to excessive resource allocation toward secondary metabolite production (Fig. 1 B). 5. Quantification of Gougerotin Production and Antibacterial Activity LC-MS quantification showed that gougerotin titers in OE strains were significantly higher than the wild-type. Titers reached 1.01 g/L ( metK ), 1.08 g/L ( AHCi ), and 2.56 g/L ( PUNP1 ), representing increases of 32.87%, 43.08%, and 238%, respectively (Fig. 2 A-B). Antimicrobial assays using Rhodotorula as the indicator strain showed that OE strains produced larger inhibition zones than the wild-type (Fig. 2 C-E), confirming enhanced gougerotin production. 6. Proteomic Insights into High-Yield Mechanisms in OE Strains In the OE-AHCi strain, 161 differentially expressed proteins (DEPs) were identified, mainly enriched in membrane transport (B09131), environmental information processing (A09130), amino acid metabolism (01007), and tRNA biosynthesis (03016) (Fig. 3 A). Sixteen proteins were significantly upregulated, related to energy metabolism, transport, stress response, and signaling (Table S6), while nucleoside metabolism and transcriptional regulation proteins such as QueC and WP_016578370.1 were downregulated to 0.45- and 0.41-fold, respectively (Table S7). Key carbon metabolism pathways (e.g., glycolysis and TCA) remained stable(Table 3 ). SAH removal by AHCi relieved feedback inhibition on methyltransferase GouN, enhancing peptide assembly. PKS and NRPS cluster expression shifted, with Cluster 33 (nystatin A1) and Cluster 18 (NAPAA) slightly upregulated (~ 3%), while Cluster 25 (fengycin) was downregulated (Fig. 3 D). Table 3 Metabolic pathway analysis of AHCi 、 PUNP1 、 metK overexpression strain Metabolic Pathway ID Metabolic Pathway Name Ratio Protein ID Functional Description OE AHCi Map 00010 Glycolysis/Gluconeogenesis 0.982 WP_016578696.1 Pyruvate kinase Map 00020 Tricarboxylic acid cycle/TCA cycle 0.989 WP_016574684.1 Succinate dehydrogenase, cytochrome b556 subunit Map 02010 ABC transport system 0.987 WP_044375029.1 Extracellular solute-binding protein OE PUNP1 Map 00230 Purine metabolism 1.067 WP_016575475.1 Purine nucleoside phosphorylase, PNP 1.011 WP_016573195.1 S-methyl-5'-thioadenosine phosphorylase 0.964 WP_038521406.1 Adenosine deaminase Map 00010 Glycolysis/Gluconeogenesis 0.987 WP_016578696.1 Pyruvate kinase Map 00020 TCA cycle 0.988 WP_016574684.1 Succinate dehydrogenase cytochrome b556 subunit Map 00190 Oxidative phosphorylation 1.033 WP_016578292.1 NADH-ubiquinone oxidoreductase subunit D Map 02010 ABC transporters 1.000 WP_016574517.1 ABC transporter Map 00290 Valine, leucine and isoleucine biosynthesis 1.010 WP_016575311.1 Glycolate oxidase small subunit OE metK Map 00010 Glycolysis/Gluconeogenesis 1.017 WP_016577205.1 ROK family glucokinase Map 00030 Pentose phosphate pathway 1.059 WP_016570654.1 RibuLose-phosphate 3-epimerase Map 00051 Fructose and mannose metabolism 1.020 WP_020929314.1 Dihydroxyacetone kinase subunit DhaK Map 00071 Fatty acid degradation 0.967 WP_044379372.1 Acyl-CoA dehydrogenase family protein 0.966 WP_016574814.1 Acyl-CoA dehydrogenase family protein 1.013 WP_016574473.1 Acyl-CoA dehydrogenase family protein Map 00190 Oxidative phosphorylation 1.034 WP_016578292.1 Acetyl-CoA C-acetyltransferase Map 02010 ABC transport system 1.006 WP_044375029.1 Methionyl-tRNA ligase Map 00450 Selenium compound metabolism/Aminoacyl-tRNA biosynthesis 1.004 WP_016571776.1 Methionyl-tRNA ligase In the OE-PUNP1 strain, DEPs were mainly enriched in cofactor/vitamin metabolism (B09108), nucleotide metabolism (A09104), global metabolism (A09100), oxidative phosphorylation (00190), and glycolysis/gluconeogenesis (00010) (Fig. 3 B). Twenty-two proteins were significantly upregulated (Table S8), while others involved in lipid metabolism and transcription were downregulated (Table S9). PUNP1 upregulation (Ratio = 1.067) enhanced purine salvage and CGA intermediate formation. Central carbon metabolism remained stable. WP_016578292.1 (oxidative phosphorylation) was slightly upregulated, while WP_038521406.1 (adenosine deaminase) was downregulated (Table 3 ), balancing purine-pyrimidine metabolism. Cluster 34 (kaidamycin) and Cluster 36 (type I PKS) proteins were slightly upregulated, while ABC transporters in Cluster 13 were slightly downregulated (Fig. 3 E). In the OE-metK strain, DEPs were enriched in prokaryotic community pathways (B09145), membrane transport (B09131), amino acid metabolism (B09105), and cell processes (A09140) (Fig. 3 C). Upregulated proteins included hydrolases and those related to nanostructure assembly (Table S10), while fatty acid metabolism and P450 proteins were suppressed (Table S11). Glycolysis, pentose phosphate, and fructose/mannose pathways (e.g., WP_016577205.1) showed slight upregulation, ensuring precursor supply. ATP synthase WP_016578292.1 was mildly upregulated; ABC transporter WP_044375029.1 remained stable, aiding efficient gougerotin secretion (Table 3 ). Elevated SAM levels enhanced methyltransferase activity (e.g., GouN), while WP_016571776.1 (methionyl-tRNA synthetase) remained largely unchanged, indicating adaptive methylation regulation (Table 3 ). Secondary metabolite clusters such as Cluster 33 (nystatin A1), Cluster 20 (chlortetracycline), and Cluster 28 (colimycin P1) were slightly upregulated, while MbtH protein in Cluster 10 (salinomycin) was significantly downregulated (Fig. 3 F). Declarations Ethical Approval Not applicable. This study did not involve any human or animal subjects. Funding This study was supported by grants from the State Key Laboratory for Biology of Plant Diseases and Insect Pests (SKLOF202305). 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J Bacteriol 2011; 193: 7021–7022. doi:10.1128/JB.06262-11 Li Y, Liang S, Wang J, et al. Enhancing the production of tacrolimus by engineering target genes identified in important primary and secondary metabolic pathways and feeding exogenous precursors. Bioprocess Biosyst Eng 2019; 42: 1081–1098. doi:10.1007/s00449-019-02106-9 Brzezinski K. S-adenosyl-l-homocysteine Hydrolase: A Structural Perspective on the Enzyme with Two Rossmann-Fold Domains. Biomolecules 2020; 10: 1682. doi:10.3390/biom10121682 Kang Y, Kim H, Goo E, et al. Unraveling the role of quorum sensing-dependent metabolic homeostasis of the activated methyl cycle in a cooperative population of Burkholderia glumae. Sci Rep 2019; 9: 11038. doi:10.1038/s41598-019-47460-6 Ngivprom U, Kluaiphanngam S, Ji W, et al. Characterization of NucPNP and NucV involved in the early steps of nucleocidin biosynthesis in streptomyces calvus. RSC Adv 2021; 11: 3510–3515. doi:10.1039/d0ra10878b Bralley P, Gatewood ML, Jones GH. Transcription of the rpsO-pnp operon of Streptomyces coelicolor involves four temporally regulated, stress responsive promoters. Gene 2014; 536: 177–185. doi:10.1016/j.gene.2013.10.055 Zhu XM, Hackl S, Thaker MN, et al. Biosynthesis of the fluorinated natural product nucleocidin in streptomyces calvus is dependent on the bldA-specified leu-tRNA(UUA) molecule. Chembiochem 2015; 16: 2498–2506. doi:10.1002/cbic.201500402 Gatewood ML, Jones GH. (p)ppGpp inhibits polynucleotide phosphorylase from streptomyces but not from Escherichia coli and increases the stability of bulk mRNA in Streptomyces coelicolor. J Bacteriol 2010; 192: 4275–4280. doi:10.1128/JB.00367-10 Bralley P, Jones GH. Overexpression of the polynucleotide phosphorylase gene (pnp) of Streptomyces antibioticus affects mRNA stability and poly(A) tail length but not ppGpp levels. Microbiology (Reading) 2003; 149: 2173–2182. doi:10.1099/mic.0.26334-0 Filić Ž, Bielen A, Šarić E, et al. Evaluation of the Structure-Function Relationship of SGNH Lipase from Streptomyces rimosus by Site-Directed Mutagenesis and Computational Approach. Int J Mol Sci 2024; 25: 595. doi:10.3390/ijms25010595 Watanabe T, Kimura Y, Umeno D. MetJ-Based Mutually Interfering SAM-ON/SAM-OFF Biosensors. ACS Synth Biol 2024; 13: 624–633. doi:10.1021/acssynbio.3c00621 Dai P, Qin Y, Li L, et al. Enhancing tylosin production by combinatorial overexpression of efflux, SAM biosynthesis, and regulatory genes in hyperproducing Streptomyces xinghaiensis strain. Synth Syst Biotechnol 2023; 8: 486–497. doi:10.1016/j.synbio.2023.07.002 Yoon G-S, Ko K-H, Kang H-W, et al. Characterization of S-adenosylmethionine synthetase from streptomyces avermitilis NRRL8165 and its effect on antibiotic production. Enzyme and Microbial Technology 2006; 39: 466–473. doi:10.1016/j.enzmictec.2005.11.049 Pang A-P, Du L, Lin C-Y, et al. Co-overexpression of lmbW and metK led to increased lincomycin A production and decreased byproduct lincomycin B content in an industrial strain of Streptomyces lincolnensis . J Appl Microbiol 2015; 119: 1064–1074. doi:10.1111/jam.12919 Kim D-J, Huh J-H, Yang Y-Y, et al. Accumulation of S-adenosyl-l-methionine enhances production of actinorhodin but inhibits sporulation in streptomyces lividans TK23. J Bacteriol 2003; 185: 592–600. doi:10.1128/JB.185.2.592-600.2003 Maharjan S, Oh T-J, Lee HC, et al. Heterologous expression of metK1-sp and afsR-sp in streptomyces venezuelae for the production of pikromycin. Biotechnol Lett 2008; 30: 1621–1626. doi:10.1007/s10529-008-9735-0 Wang Y, Wang Y, Chu J, et al. Improved production of erythromycin a by expression of a heterologous gene encoding S-adenosylmethionine synthetase. Appl Microbiol Biotechnol 2007; 75: 837–842. doi:10.1007/s00253-007-0894-z Zhang X, Fen M, Shi X, et al. Overexpression of yeast S-adenosylmethionine synthetase metK in streptomyces actuosus leads to increased production of nosiheptide. Appl Microbiol Biotechnol 2008; 78: 991–995. doi:10.1007/s00253-008-1394-5 Kim D-Y, Hwang Y-I, Choi S-U. Cloning of metK from Actinoplanes teichomyceticus ATCC31121 and effect of its high expression on antibiotic production. J Microbiol Biotechnol 2011; 21: 1294–1298. doi:10.4014/jmb.1101.01018 Bompadre O, Andrey G. Chromatin topology in development and disease. Curr Opin Genet Dev 2019; 55: 32–38. doi:10.1016/j.gde.2019.04.007 Li L, Liu X, Jiang W, et al. Recent advances in synthetic biology approaches to optimize production of bioactive natural products in actinobacteria. Front Microbiol 2019; 10: 2467. doi:10.3389/fmicb.2019.02467 Xiao C, Pan Y, Huang M. Advances in the dynamic control of metabolic pathways in saccharomyces cerevisiae . Eng Microbiol 2023; 3: 100103. doi:10.1016/j.engmic.2023.100103 Table 1 and 2 Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Table1and2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6962713","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482928700,"identity":"35bdfca5-a6ef-4433-843d-d98ce3d7975a","order_by":0,"name":"Qianying Zhou","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qianying","middleName":"","lastName":"Zhou","suffix":""},{"id":482928701,"identity":"d84198af-3b6c-40e2-829d-88cd38c0a418","order_by":1,"name":"Hua Yu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Yu","suffix":""},{"id":482928702,"identity":"4d64a9d6-447c-4f01-b977-a5df876d13aa","order_by":2,"name":"Yutong Liao","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Liao","suffix":""},{"id":482928703,"identity":"dcbafd25-e8a3-40f9-9868-997d9d950c02","order_by":3,"name":"Xi Wang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Wang","suffix":""},{"id":482928704,"identity":"09cd51a4-390f-4b25-80b4-3bde7528374f","order_by":4,"name":"Beibei Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYFACHgaGxAYGHn7StUg2kKSFEajc4ACxGuTdew8+eLjDRsb4+OGHH38w2OUR1GJ45lyyQeKZNB6zM2nG0jwMycWEtczIMZNIbDvMY3aDwUCageEA0F+EtMx/Y/4jse0/j/EM9s8/fxCjRV6Cx4whse0AjwGQIcFDjBYDnrxkicQzyTwSZ3LKrHkMkomwpf3swY8/d9jZ87cf33zzR4UdEbYcQOUSUg+yhaCho2AUjIJRMAoA71c8E+7X9jsAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Beibei","middleName":"","lastName":"Ge","suffix":""}],"badges":[],"createdAt":"2025-06-24 07:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6962713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6962713/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86446487,"identity":"e008a7bf-8ab8-4142-b235-3b08be0aa1f4","added_by":"auto","created_at":"2025-07-10 17:56:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153395,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic characterization of overexpression strains. A. Growth phenotypes of \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003emetK\u003c/em\u003e, and \u003cem\u003ePUNP1 \u003c/em\u003eoverexpression strains. B. Mycelial dry weight changes over time for \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003emetK\u003c/em\u003e, and \u003cem\u003ePUNP1 \u003c/em\u003eoverexpression strains.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6962713/v1/283dd5a4d6408ae964ee250b.png"},{"id":86446486,"identity":"8a5f881c-ba8f-4111-a65c-b847a3616046","added_by":"auto","created_at":"2025-07-10 17:56:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":217901,"visible":true,"origin":"","legend":"\u003cp\u003eGougerotin production and antimicrobial activity of overexpression strains A. Gougerotin yields of AHCi, metK, and PUNP1 overexpression strains. B. LC-MS chromatograms of gougerotin from AHCi, metK, and PUNP1 overexpression strains. C. Antimicrobial activity of fermentation broths from AHCi, metK, and PUNP1 overexpression strains against Rhodotorula spp.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6962713/v1/296c821f0402bbbc8b182948.png"},{"id":86446990,"identity":"e76b15dd-fd40-4e64-a4b7-26a22f6e09f8","added_by":"auto","created_at":"2025-07-10 18:04:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":491124,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis and changes in protein abundance within secondary metabolite biosynthetic gene clusters of overexpression strains A - C. KEGG pathway enrichment analysis of the \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK \u003c/em\u003eoverexpression strains. D - F. Changes in protein abundance within other secondary metabolite biosynthetic gene clusters in \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK \u003c/em\u003eoverexpression strains. Note:Cluster 4: Gougerotin (Other); Cluster 7: Himastatin (NRP); Cluster 8: Salinamide A/B/C/D/E/F, Desmethylsalinamide C/E (NRP: Cyclic depsipeptide + Polyketide: Modular type I polyketide); Cluster 9: Glycinocin A (NRP); Cluster 10: Salinosporamide A (NRP + Polyketide); Cluster 14: Legonoxamine A / Desferrioxamine B / Legonoxamine B (Other); Cluster 17: Geosmin (Terpene); Cluster 20: Kinamycin (Polyketide); Cluster 21: Leucomycin (Polyketide); Cluster 28: Coelimycin P1 (Polyketide: Modular type I polyketide); Cluster 33: Nystatin A1 (Polyketide: Modular type I polyketide + Saccharide: Hybrid/tailoring saccharide); Cluster 36: Type I polyketide synthase (T1PKS).Cluster IDs are named according to antiSMASH predictions based on the corresponding genome sequences. Error bars represent the standard error of the mean (SEM, n = 3, three biologically independent samples).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6962713/v1/201b7d474ae036bb7cf36f3b.png"},{"id":89843940,"identity":"f8e029b3-6e94-4788-9970-6fc9a29f0a57","added_by":"auto","created_at":"2025-08-25 15:47:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820124,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6962713/v1/2acbe16c-a549-44a9-a05b-6f492525278d.pdf"},{"id":86447865,"identity":"fa1d5e3d-64a4-4391-8e53-0676573d233f","added_by":"auto","created_at":"2025-07-10 18:12:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":321116,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6962713/v1/537a8c35be93c16bf4e88361.docx"},{"id":86446485,"identity":"a6df6dd1-90bf-47bb-8425-6488649ba8ed","added_by":"auto","created_at":"2025-07-10 17:56:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17360,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6962713/v1/b73a3b600bddcf0ca22dca1a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMetabolic Model-Driven Optimization of \u003cem\u003eStreptomyces \u003c/em\u003eMetabolic Network for High-Yield Production of Gougerotin\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eStreptomyces\u003c/em\u003e species are prolific producers of secondary metabolites, accounting for nearly 50% of known antibiotics[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and producing various pharmaceuticals including antifungal, anticancer, immunosuppressive, and antiparasitic agents [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In response to rising antimicrobial resistance, discovering novel and efficient antibiotics and natural products has become increasingly urgent. Advances in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have enabled new strategies for strain improvement and secondary metabolite overproduction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGougerotin, a water-soluble, basic peptide-nucleoside antibiotic[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], was first isolated from \u003cem\u003eStreptomyces gougerotii\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and other species including \u003cem\u003eS. toyocaensis\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], \u003cem\u003eS. S-514\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and \u003cem\u003eS. noursei\u003c/em\u003e CK-15. It exhibits broad-spectrum bioactivities including antitumor, antiviral, antifungal, and acaricidal effects[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Its structure, consisting of a nucleoside and peptide moiety, was first proposed by Fox and Watanabe[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and confirmed by \u003csup\u003e13\u003c/sup\u003eC NMR analysis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Total synthesis was later achieved by [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In China, \u003cem\u003eStreptomyces noursei\u003c/em\u003e CK-15 was identified as a gougerotin-producing strain with strong agricultural application potential [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe structural similarity between the 4-amino-glucosamine nucleoside moiety of gougerotin and the CGA intermediate in kasugamycin S biosynthesis prompted the identification and cloning of its biosynthetic gene cluster. A 28.7 kb fragment was cloned into pSET152 and integrated into the chromosome of \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e M1146, yielding the heterologous strain M1146-D6-4H [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The minimal cluster contains 15 genes: 13 biosynthetic enzymes, 1 pathway-specific regulator (GouR), a putative secretion protein, and an MFS transporter gene (\u003cem\u003egouM\u003c/em\u003e). The pathway initiates with the coupling of cytosine and UDP-glucuronic acid to form CGA, which is oxidized by GouA, aminated by GouH to produce 4-amino-CGA, with \u003cem\u003egouF\u003c/em\u003e, \u003cem\u003egouA\u003c/em\u003e, and \u003cem\u003egouH\u003c/em\u003e acting cooperatively. The peptide moiety, composed of D-serine and sarcosine, involves \u003cem\u003egouG\u003c/em\u003e, \u003cem\u003egouI\u003c/em\u003e, and gouL for D-serine biosynthesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. GouK activates amino acids into acyl-CoAs, transferred to the nucleoside by GouJ. Unlike other nucleoside antibiotics, sarcosine is synthesized via glycine activation by GouK followed by methylation by GouN [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Disruption of \u003cem\u003egouC\u003c/em\u003e and \u003cem\u003egouD\u003c/em\u003e abolishes gougerotin production and alters sarcosine structure, indicating their roles in deacetylation and methyl transfer, respectively[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo enhance production, Tan Huarong\u0026rsquo;s group engineered \u003cem\u003eS. graminearus\u003c/em\u003e strains harboring wild-type (pGOU) or engineered (pGOUe) clusters. Yields increased 1.3- and 2.1-fold, respectively. Supplementation with precursors like cytosine, serine, or glycine further stimulated production, reaching 2.5-fold higher yields upon glycine addition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Replacing native promoters of \u003cem\u003egouA\u003c/em\u003e, \u003cem\u003egouM\u003c/em\u003e, and \u003cem\u003egouN\u003c/em\u003e with the constitutive \u003cem\u003ehrdB\u003c/em\u003e promoter also significantly boosted production [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Given gougerotin\u0026rsquo;s intracellular toxicity, its regulation and secretion are critical. GouR represses the \u003cem\u003egouL\u003c/em\u003e-\u003cem\u003egouB\u003c/em\u003e operon and activates \u003cem\u003egouM\u003c/em\u003e to enable efficient export [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecondary metabolism depends not only on gene cluster activation but also on precursor availability and energy balance within the primary metabolism. Metabolites like acetyl-CoA, pyruvate, and TCA intermediates supply energy and building blocks for secondary biosynthesis. Strategies including overexpression of key enzymes, metabolic rerouting, and precursor supplementation have proven effective. For instance, boosting glucose metabolism in \u003cem\u003eS. coelicolor\u003c/em\u003e enhances actinorhodin production [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, erythromycin production in \u003cem\u003eS. erythraea\u003c/em\u003e increased 50% following GEM-guided amino acid feeding [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and spinosad production in \u003cem\u003eS. spinosa\u003c/em\u003e improved by 86.5% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. GEMs offer a systems-level approach to simulate metabolic fluxes and guide genetic modifications [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIntegration of omics datasets further refines GEM accuracy. In \u003cem\u003eS. coelicolor A3(2)\u003c/em\u003e, transcriptome-proteome integration revealed that translation efficiency of secondary metabolite genes declines post-exponential growth [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Metabolomics has linked upregulated amino acid and central carbon intermediates to balhimycin biosynthesis in \u003cem\u003eAmycolatopsis balhimycina\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and desferrioxamine derivatives in \u003cem\u003eS. chartreusis\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These approaches also guided gene knockouts and overexpression strategies, enhancing precursor supply and reducing toxicity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. COBRA-based GEM frameworks continue to play pivotal roles in optimizing metabolic engineering of high-value natural products.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, a genome-scale metabolic model (GEM) for \u003cem\u003eStreptomyces noursei\u003c/em\u003e strain CK-15 was reconstructed by embedding 12 key reactions involved in gougerotin biosynthesis, as previously reported in the literature [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], into the scaffold of the Sco-GEM model for \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e M145. This integrated model ensured stoichiometric consistency among metabolic reactions and served as a quantitative platform for subsequent flux analysis. Such GEM-based approaches enable comprehensive insight into metabolic crosstalk during secondary metabolite biosynthesis and facilitate the rational identification of regulatory targets. Based on model-guided analysis using the Unbiased Pathway Analysis (UPA) algorithm, three key out-of-cluster overexpression candidates were identified: \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eS-adenosylhomocysteine hydrolase (\u003cem\u003eAHCi\u003c/em\u003e) plays a central role in methylation metabolism. It catalyzes the hydrolysis of S-adenosylhomocysteine (SAH) into adenosine and homocysteine, maintaining the intracellular balance between S-adenosylmethionine (SAM) and SAH and preventing SAH-mediated feedback inhibition of methyltransferases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. \u003cem\u003eAHCi\u003c/em\u003e contains a unique dual Rossmann-fold domain architecture, enabling its catalytic function through co-binding of substrates and NAD⁺ cofactors. Overexpression of \u003cem\u003eAHCi\u003c/em\u003e enhances methylation efficiency by facilitating SAH clearance and promoting smooth SAM cycling. In \u003cem\u003eBurkholderia glumae\u003c/em\u003e, quorum sensing activates both SAHH and methionine synthase to optimize cellular methylation flux [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePurine-nucleoside phosphorylase (\u003cem\u003ePNP\u003c/em\u003e or \u003cem\u003ePUNP1\u003c/em\u003e) is a pivotal enzyme in purine salvage pathways, catalyzing the phosphorolysis of purine nucleosides to generate free bases and ribose-1-phosphate, thereby alleviating dependence on de novo purine synthesis. In \u003cem\u003eStreptomyces calvus\u003c/em\u003e, the biosynthesis of nucleocidin involves the sequential action of \u003cem\u003ePNP\u003c/em\u003e (nucPNP) and a downstream enzyme NucV, which catalyze S-methyladenosine transformation and AMP generation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Beyond its metabolic role, PNP also participates in post-transcriptional regulation. In \u003cem\u003eS. coelicolor\u003c/em\u003e, the rpsO-pnp operon is regulated by temporally active and stress-responsive promoters, modulating RNA stability and nucleotide homeostasis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, \u003cem\u003ebldA\u003c/em\u003e-dependent UUA-containing tRNAs are required for nucleocidin biosynthesis, further implicating \u003cem\u003ePNP\u003c/em\u003e in the regulation of secondary metabolism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Intracellular (p)ppGpp can inhibit PNPase activity, thereby stabilizing mRNA under stress conditions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Overexpression of pnp in \u003cem\u003eStreptomyces antibioticus\u003c/em\u003e alters mRNA poly(A) tail length and stability, revealing novel regulatory mechanisms of RNA processing [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].Overall, \u003cem\u003ePNP\u003c/em\u003e and related enzymes in \u003cem\u003eStreptomyces\u003c/em\u003e not only play a crucial role in regulating purine metabolism and nucleotide salvage, but also exhibit diverse biological functions by modulating RNA stability and natural product biosynthesis[\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMetK encodes SAM synthetase, which catalyzes the ATP-dependent conversion of methionine into SAM-the universal methyl donor involved in numerous methylation reactions. MetK is considered a metabolic bottleneck in many organisms, and its enhanced expression has been shown to significantly boost antibiotic yields [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For instance, overexpression of metKcs in \u003cem\u003eStreptomyces xinghaiensis\u003c/em\u003e TL01 increased tylosin production by 12%, and co-expression with other genes elevated production by 23% [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In \u003cem\u003eS. avermitilis\u003c/em\u003e, the expression of metK correlates with aveR and aveA1 transcription and avermectin yields [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Co-overexpression of lmbW and metK in \u003cem\u003eS. lincolnensis\u003c/em\u003e improved lincomycin A yield while suppressing undesired lincomycin B [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, metK has been implicated in enhancing the biosynthesis of actinorhodin, pikromycin, erythromycin A, and nosiheptide in various \u003cem\u003eStreptomyces\u003c/em\u003e species [\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].Studies on the cloning and expression of the \u003cem\u003emetK\u003c/em\u003e gene in \u003cem\u003eStreptomyces lividans\u003c/em\u003e TK24 and Actinoplanes teichomyceticus (the producer of teicoplanin) further confirmed its role in enhancing antibiotic production[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the enhanced gougerotin production observed in the engineered strains, a decrease in dry cell weight during the stationary phase was noted, suggesting that excessive secondary metabolite accumulation might compromise cellular homeostasis. Therefore, future efforts should aim at implementing inducible promoters or dynamic flux control strategies to fine-tune the balance between growth and production[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].Overall, this study successfully reconstructed a genome-scale metabolic model for \u003cem\u003eS. noursei\u003c/em\u003e strain CK-15 and identified and validated three key regulatory nodes-\u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK\u003c/em\u003e-for improving gougerotin biosynthesis. Our findings emphasize the essential roles of precursor supply, energy metabolism, and methylation cycles in regulating secondary metabolism. Rational engineering of these nodes provides a robust strategy for the enhanced production of gougerotin and potentially other nucleoside antibiotics, while also offering insights into the systemic regulation of \u003cem\u003eStreptomyces\u003c/em\u003e metabolism.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, a genome-scale metabolic model of \u003cem\u003eStreptomyces noursei\u003c/em\u003e strain CK-15 was reconstructed, incorporating the complete gougerotin biosynthetic pathway. Using the UPA algorithm, we identified three extracluster regulatory targets-\u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK\u003c/em\u003e. Overexpression of these genes led to accelerated sporulation, enhanced mycelial growth, and significantly increased gougerotin production by 43.08%, 238%, and 32.87%, respectively, with final titers reaching 1.08 g/L, 2.56 g/L, and 1.01 g/L. Antifungal activity of the fermentation broth was also improved.\u003c/p\u003e\u003cp\u003eProteomic analysis revealed that \u003cem\u003eAHCi\u003c/em\u003e modulates membrane transport, environmental signaling, and amino acid and tRNA metabolism; \u003cem\u003ePUNP1\u003c/em\u003e optimizes cofactor and nucleotide metabolism along with energy supply; and \u003cem\u003emetK\u003c/em\u003e enhances intracellular SAM levels, promoting methylation reactions and peptide precursor biosynthesis while maintaining central carbon metabolism and facilitating effective product efflux.\u003c/p\u003e\u003cp\u003eCollectively, this study demonstrates that systematic modeling and multi-target metabolic regulation can be effectively leveraged to improve the yield of nucleoside antibiotics such as gougerotin. The CK-15 GEM provides a foundational tool for systems metabolic engineering of \u003cem\u003eStreptomyces\u003c/em\u003e, paving the way for industrial-scale biosynthesis of valuable secondary metabolites.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1. Bacterial Strains, Plasmids, and Culture Media\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eStreptomyces noursei strain\u003c/em\u003e CK-15 was cultured and subjected to conjugation transfer on SFM solid medium. Seed cultures were grown in liquid seed medium at 30\u0026deg;C. Spores stored in glycerol tubes were streaked onto SFM plates and incubated at 30\u0026deg;C for 3\u0026ndash;5 days. Wild-type CK-15 and overexpression strains were cultured on SFM plates; spores were harvested and stored in 20% glycerol at -80\u0026deg;C. For each overexpression strain, three independent mutants were preserved. Spore suspensions of wild-type and overexpression strains were adjusted to the same optical density (OD600) measured by a microplate reader. For each gene, spore suspensions of three mutant clones were mixed, and 50 \u0026micro;L of this mixture was evenly spread on SFM plates. Plates were incubated at 30\u0026deg;C for 2 days before inoculation into seed medium and cultured at 30\u0026deg;C, 220 rpm for 24 h. Subsequently, 10% inoculum was transferred to 50 mL fresh gougerotin fermentation medium for fermentation at 30\u0026deg;C, 220 rpm, for 72 h. Mature spores were collected from SFM plates when gray-black spores formed, using sterile glycerol tubes and cotton swabs under a laminar flow hood, and stored in 20% glycerol at -80\u0026deg;C. Strains and plasmids used in this study are listed in Table S1.\u003c/p\u003e\n\u003ch3\u003e2. Preparation of Competent Cells for Calcium Transformation\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eThe day before conjugation, \u003cem\u003eE. coli\u003c/em\u003e ET12567 carrying target plasmids was inoculated into LB medium and cultured at 37\u0026deg;C until OD600 reached 0.4\u0026ndash;0.6. CK-15 spores were evenly spread on SFM plates and incubated at 30\u0026deg;C for 3\u0026ndash;5 days until gray-black spores covered the surface. Spores were collected with cotton swabs and suspended in TES buffer for immediate use or stored in 20% glycerol at -80\u0026deg;C. For each EP tube, 1 mL LB was added and centrifuged at 12,000 rpm for 1 min; this was repeated three times to pellet cells, which were resuspended in 100 \u0026micro;L LB. Spores were washed twice by resuspension in 1 mL TES and centrifugation at 12,000 rpm for 1 min. Finally, spores were resuspended in 500 \u0026micro;L TES. Spores were heat shocked in a 50\u0026deg;C water bath for 10 min, then mixed with 2\u0026times; spore germination buffer and 20 \u0026micro;L 0.5 M CaCl₂. Heat-treated spores were mixed with \u003cem\u003eE. coli\u003c/em\u003e ET12567 cells at a 10:1 ratio and spread on SFM agar supplemented with 10 mM Mg\u0026sup2;⁺. Plates were air-dried in a laminar flow hood and incubated at 30\u0026deg;C for 16 h. After incubation, 1.5 mL overlay solution containing 40 \u0026micro;L apramycin and 40 \u0026micro;L nalidixic acid in ddH₂O was added evenly to each plate. Once dried, plates were further incubated at 30\u0026deg;C for 3\u0026ndash;5 days to allow exconjugant formation. Exconjugants were streaked onto SFM plates containing 0.1% apramycin and 0.1% nalidixic acid and incubated for 2\u0026ndash;3 days. After visible mycelial growth, strains were transferred to antibiotic-free seed medium and cultured at 30\u0026deg;C for 2\u0026ndash;3 days. PCR verification was performed to confirm correct exconjugants. Correct exconjugants were cultured on antibiotic-free SFM plates to produce spores, which were harvested and stored in 20% glycerol at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/div\u003e\n\u003ch3\u003e3. Construction and Validation of Overexpression Strains\u003c/h3\u003e\n\u003cp\u003eBased on known gougerotin biosynthetic gene clusters from \u003cem\u003eStreptomyces hygroscopicus\u003c/em\u003e available on NCBI, homologous genes were identified in the CK-15 genome using local BLAST. Primers were designed to amplify target genes from CK-15 genomic DNA. Genomic DNA extraction was performed using Tiangen Bacteria Genomic DNA Kit (spin column), and PCR was carried out with Phanta Max Super-Fidelity DNA Polymerase (Nanjing Novozan Biotech). PCR reaction (50 \u0026micro;L total) contained 13 \u0026micro;L ddH₂O, 25 \u0026micro;L 2\u0026times; Phanta MAX Buffer, 1 \u0026micro;L dNTP mix, 2 \u0026micro;L forward primer, 2 \u0026micro;L reverse primer (Table S2), 1 \u0026micro;L polymerase, 1 \u0026micro;L template DNA, and 5 \u0026micro;L 50% DMSO. PCR conditions: initial denaturation at 95\u0026deg;C for 10 min; 31 cycles of 95\u0026deg;C for 30 s, 65\u0026deg;C for 30 s, and 72\u0026deg;C for 30\u0026ndash;60 s/kb; final extension at 72\u0026deg;C for 10 min.\u003c/p\u003e\n\u003cp\u003ePlasmid extraction was performed using Tiangen Plasmid Mini Kit. Double digestion was carried out with FastDigest \u003cem\u003eEcoR\u003c/em\u003eI and \u003cem\u003eNde\u003c/em\u003eI (Thermo Scientific). Reaction system (20 \u0026micro;L): 1 \u0026micro;g DNA, 2 \u0026micro;L 10\u0026times; FastDigest Green Buffer, 1 \u0026micro;L EcoRI, 1 \u0026micro;L NdeI, and nuclease-free water to 20 \u0026micro;L. Digestion at 37\u0026deg;C for 40 min. Digested products were confirmed by agarose gel electrophoresis and purified by gel extraction (Omega Gel Extraction Kit).\u003c/p\u003e\n\u003cp\u003eGibson assembly was performed on ice with a molar ratio of 1:3 (vector:insert). The reaction mix contained 2 \u0026micro;L vector, 1 \u0026micro;L insert, 10 \u0026micro;L 2\u0026times; Gibson Assembly Master Mix, and nuclease-free water to 20 \u0026micro;L. The mixture was incubated at 50\u0026deg;C for 30 min. Five microliters of assembled plasmid were transformed into competent \u003cem\u003eE. coli\u003c/em\u003e DH5\u0026alpha; or ET12567 (pUZ8002) cells on ice for 20\u0026ndash;30 min, followed by heat shock at 42\u0026deg;C for 90 s, then placed on ice for 2 min. After addition of 1 mL LB, cells were incubated at 37\u0026deg;C, 220 rpm for 30 min, and plated on LB agar containing 50 mg/mL apramycin overnight at 37\u0026deg;C. Single colonies were picked and cultured in LB with apramycin. Colony PCR was performed using YEASEN 2\u0026times; Hieff PCR Master Mix (with dye), 5 \u0026micro;L water, 2 \u0026micro;L 50% DMSO, 1 \u0026micro;L each primer (Table S2), and 1\u0026ndash;2 \u0026micro;L template DNA. PCR conditions were as above. Correct clones were confirmed by sequencing and stored in 20% glycerol.\u003c/p\u003e\n\u003ch3\u003e4. Intergeneric Conjugation between \u003cem\u003eStreptomyces\u003c/em\u003e\u0026nbsp;and\u0026nbsp;\u003cem\u003eE. coli\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eConjugation conditions were optimized for CK-15 as described above. Briefly, \u003cem\u003eE. coli\u003c/em\u003e ET12567 harboring the target plasmid was grown in LB to OD600 0.4\u0026ndash;0.6. CK-15 spores were plated on SFM and incubated at 30\u0026deg;C for 3\u0026ndash;5 days. Spores were harvested and washed with TES buffer, then heat shocked at 50\u0026deg;C for 10 min, mixed with 2\u0026times; spore germination buffer and CaCl₂, and combined with \u003cem\u003eE. coli\u003c/em\u003e cells at a 10:1 ratio. Mixture was plated on SFM agar with 10 mM Mg\u0026sup2;⁺ and incubated at 30\u0026deg;C for 16 h. Plates were overlaid with selective antibiotic solution containing apramycin and nalidixic acid, then incubated for 3\u0026ndash;5 days for exconjugant growth. Exconjugants were purified on selective plates, verified by PCR, and stored as described.\u003c/p\u003e\n\u003ch3\u003e5. Proteomic Sequencing and Annotation of Overexpression Strains\u003c/h3\u003e\n\u003cp\u003eProteomic sequencing of CK-15 and overexpression strains (\u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, \u003cem\u003emetK\u003c/em\u003e) was performed by Senris Biotechnology (Shenzhen). Functional annotation of CK-15 genes was conducted using the eggNOG-mapper online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eggnog-mapper.embl.de/\u003c/span\u003e\u003c/span\u003e), including Gene Ontology (GO), KEGG pathway, and PFAM domain annotations. TBtools software was used to organize annotation results for differential expression and pathway enrichment analyses. Metabolic pathways of CK-15 were analyzed via the KEGG Organisms database.\u003c/p\u003e\n\u003ch3\u003e6. Extraction and Quantification of Gougerotin\u003c/h3\u003e\n\u003cp\u003eOne milliliter of fermentation broth was centrifuged at 4000 rpm, 4\u0026deg;C for 10 min. The supernatant was diluted 100-fold with ddH₂O and filtered through a 0.22 \u0026micro;m aqueous membrane. LC-MS analysis was performed using an Agilent Ultivo Triple Quadrupole LC/TQ system equipped with a Waters ACQUITY UPLC BEH Amide Column (130 \u0026Aring;, 1.7 \u0026micro;m, 2.1 \u0026times; 100 mm) and a VanGuard Pre-column (130 \u0026Aring;, 1.7 \u0026micro;m, 2.1 \u0026times; 5 mm). Mobile phase A was 5 mM ammonium acetate in water; phase B was acetonitrile; flow rate 0.3 mL/min; injection volume 1.00 \u0026micro;L; run time 17 min. Details of detection methods, column wash, and MRM parameters are provided in Tables S3 - S5.\u003c/p\u003e\n\u003ch3\u003e7. Measurement of Growth Curves for Overexpression Strains\u003c/h3\u003e\n\u003cp\u003eMycelial dry weight was used to assess growth. Initially, empty 1.5 mL centrifuge tubes were weighed. One milliliter of fermentation broth was added and centrifuged at 12,000 rpm for 2 min; supernatant was carefully discarded. Tubes were dried at 65\u0026deg;C for 5 days and reweighed. The difference in weight before and after drying represented mycelial dry weight.\u003c/p\u003e\n\u003ch3\u003e8. Determination of Antimicrobial Activity Using\u0026nbsp;\u003cem\u003eRhodotorula\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe inhibition activity of CK-15 wild-type and OE strains was assessed by the Oxford cup method using \u003cem\u003eRhodotorula\u003c/em\u003e as an indicator. Spores stored in 20% glycerol were streaked on SFM plates and incubated at 30\u0026deg;C for 2 days. A 1 cm\u0026sup2; agar block was inoculated into 30 mL seed medium and cultured at 30\u0026deg;C, 220 rpm for 24 h. Then, 10% inoculum was transferred to 50 mL fresh fermentation medium for 72 h under the same conditions. One milliliter fermentation broth was centrifuged at 12,000 rpm for 1 min; supernatant was filtered through a 0.22 \u0026micro;m membrane. PDA medium was supplemented with 1 mL \u003cem\u003eRhodotorula\u003c/em\u003e suspension (OD\u0026thinsp;=\u0026thinsp;1.2), mixed, and poured into plates (20 mL per plate). Sterile Oxford cups were placed on plates, and 200 \u0026micro;L filtered fermentation supernatant was added into each cup. Each treatment had four replicates. Plates were incubated at 30\u0026deg;C for 48 h, and inhibition zone diameters were measured.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. Reconstruction of a Genome-Scale Metabolic Model for Gougerotin Biosynthesis in Streptomyces\u003c/h3\u003e\n\u003cp\u003eBased on previously reported gougerotin biosynthetic pathways, we first constructed gene\u0026ndash;protein\u0026ndash;reaction (GPR) associations for the gougerotin biosynthesis process (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S12), assigning specific names to each reaction. For certain biosynthetic steps where enzymatic mechanisms remain unclear-such as reaction GOUI-we proposed reasonable biochemical reactions based on the chemical structures of substrates and products. All reactions were examined to ensure mass balance, thereby completing the preliminary reconstruction of a genome-scale metabolic pathway for gougerotin biosynthesis.\u003c/p\u003e\n\u003cp\u003eGiven the high genomic similarity between \u003cem\u003eStreptomyces coelic\u003c/em\u003e\u003cem\u003eolor\u003c/em\u003e M145 and \u003cem\u003eStreptomyces noursei\u003c/em\u003e strain CK-15, and the availability of a well-curated genome-scale metabolic model (GEM) for \u003cem\u003eS. coelicolor\u003c/em\u003e (Sco-GEM), we used Sco-GEM as the scaffold to build the CK-15 GEM. We integrated the 12-step gougerotin biosynthetic pathway (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) into Sco-GEM using the COBRA toolbox. After successful model debugging and validation, the pathway was effectively incorporated, thus establishing a genome-scale model of gougerotin biosynthesis in \u003cem\u003eS. noursei\u003c/em\u003e CK-15.\u003c/p\u003e\n\u003ch3\u003e2. Identification of Genetic Targets for Enhanced Gougerotin Production\u003c/h3\u003e\n\u003cp\u003eUsing the reconstructed GEM of \u003cem\u003eS. noursei\u003c/em\u003e CK-15, we performed metabolic simulations with the COBRA toolbox, including prediction of gene overexpression targets. The UPA algorithm implemented in the UP Finder tool was used to identify reactions strongly associated with increased gougerotin production. This analysis identified 31 relevant reactions. Based on the ratio metric in UPA, the top five reactions were prioritized (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, \u003cem\u003eAHCi\u003c/em\u003e and \u003cem\u003ePUNP1\u003c/em\u003e (\u003cem\u003ePNP\u003c/em\u003e) were selected as primary candidates due to their direct relevance to gougerotin biosynthesis. The secondary candidate \u003cem\u003emetK\u003c/em\u003e was also selected based on its strong involvement in methylation metabolism. These three genes (\u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK\u003c/em\u003e) were subsequently chosen for genetic overexpression in CK-15.\u003c/p\u003e\n\u003ch3\u003e3. Construction and Validation of Overexpression Strains\u003c/h3\u003e\n\u003cp\u003eThe candidate genes \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK\u003c/em\u003e were amplified by PCR. Agarose gel electrophoresis confirmed the correct amplicon sizes: \u003cem\u003eAHCi\u003c/em\u003e \u0026ndash; 1458 bp, \u003cem\u003ePUNP1\u0026ndash;816\u003c/em\u003e bp, and \u003cem\u003emetK\u003c/em\u003e \u0026ndash; 1209 bp (Figure S1B-C). After ligation with linearized plasmids (Figure S1A) and sequence verification, overexpression strains were constructed. PCR from these strains yielded products of expected sizes: OE-\u003cem\u003eAHCi\u003c/em\u003e \u0026ndash; 1637 bp, OE-\u003cem\u003ePUNP1\u0026ndash;995\u003c/em\u003e bp, and OE-\u003cem\u003emetK\u003c/em\u003e \u0026ndash; 1388 bp (Figure S1D-F), confirming successful integration of the target genes.\u003c/p\u003e\n\u003ch3\u003e4. Phenotypic Characterization of Overexpression Strains\u003c/h3\u003e\n\u003cp\u003eThe wild-type CK-15 and OE strains (\u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, \u003cem\u003emetK\u003c/em\u003e) were cultured on SFM agar for 4 days. Compared with the wild-type, OE strains showed accelerated sporulation, with OE-\u003cem\u003emetK\u003c/em\u003e being the fastest, followed by OE-\u003cem\u003ePUNP1\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Biomass accumulation was assessed during fermentation. All OE strains exhibited faster growth during the exponential phase (0\u0026ndash;12 h), with OE-\u003cem\u003ePUNP1\u003c/em\u003e showing the highest rate, followed by OE-\u003cem\u003eAHCi\u003c/em\u003e and OE-\u003cem\u003emetK\u003c/em\u003e. Biomass peaked at 12 h and declined thereafter in OE strains, likely due to excessive resource allocation toward secondary metabolite production (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003e5. Quantification of Gougerotin Production and Antibacterial Activity\u003c/h3\u003e\n\u003cp\u003eLC-MS quantification showed that gougerotin titers in OE strains were significantly higher than the wild-type. Titers reached 1.01 g/L (\u003cem\u003emetK\u003c/em\u003e), 1.08 g/L (\u003cem\u003eAHCi\u003c/em\u003e), and 2.56 g/L (\u003cem\u003ePUNP1\u003c/em\u003e), representing increases of 32.87%, 43.08%, and 238%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Antimicrobial assays using \u003cem\u003eRhodotorula\u003c/em\u003e as the indicator strain showed that OE strains produced larger inhibition zones than the wild-type (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC-E), confirming enhanced gougerotin production.\u003c/p\u003e\n\u003ch3\u003e6. Proteomic Insights into High-Yield Mechanisms in OE Strains\u003c/h3\u003e\n\u003cp\u003eIn the OE-AHCi strain, 161 differentially expressed proteins (DEPs) were identified, mainly enriched in membrane transport (B09131), environmental information processing (A09130), amino acid metabolism (01007), and tRNA biosynthesis (03016) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Sixteen proteins were significantly upregulated, related to energy metabolism, transport, stress response, and signaling (Table S6), while nucleoside metabolism and transcriptional regulation proteins such as QueC and WP_016578370.1 were downregulated to 0.45- and 0.41-fold, respectively (Table S7). Key carbon metabolism pathways (e.g., glycolysis and TCA) remained stable(Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). SAH removal by AHCi relieved feedback inhibition on methyltransferase GouN, enhancing peptide assembly. PKS and NRPS cluster expression shifted, with Cluster 33 (nystatin A1) and Cluster 18 (NAPAA) slightly upregulated (~\u0026thinsp;3%), while Cluster 25 (fengycin) was downregulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetabolic pathway analysis of \u003cem\u003eAHCi\u003c/em\u003e、\u003cem\u003ePUNP1\u003c/em\u003e、\u003cem\u003emetK\u003c/em\u003e overexpression strain\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolic Pathway ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolic Pathway Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRatio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFunctional Description\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eOE AHCi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolysis/Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016578696.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePyruvate kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTricarboxylic acid cycle/TCA cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016574684.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuccinate dehydrogenase, cytochrome b556 subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 02010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABC transport system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_044375029.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracellular solute-binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eOE PUNP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMap 00230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePurine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016575475.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePurine nucleoside phosphorylase, PNP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016573195.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS-methyl-5\u0026apos;-thioadenosine phosphorylase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_038521406.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenosine deaminase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolysis/Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016578696.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePyruvate kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCA cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016574684.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuccinate dehydrogenase cytochrome b556 subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxidative phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016578292.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNADH-ubiquinone oxidoreductase subunit D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 02010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABC transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016574517.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABC transporter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValine, leucine and isoleucine biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016575311.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolate oxidase small subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003e\u003cem\u003eOE metK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolysis/Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016577205.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROK family glucokinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePentose phosphate pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016570654.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRibuLose-phosphate 3-epimerase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFructose and mannose metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_020929314.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDihydroxyacetone kinase subunit DhaK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMap 00071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eFatty acid degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_044379372.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcyl-CoA dehydrogenase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016574814.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcyl-CoA dehydrogenase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016574473.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcyl-CoA dehydrogenase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxidative phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016578292.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcetyl-CoA C-acetyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 02010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABC transport system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_044375029.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethionyl-tRNA ligase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap 00450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelenium compound metabolism/Aminoacyl-tRNA biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP_016571776.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethionyl-tRNA ligase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the OE-PUNP1 strain, DEPs were mainly enriched in cofactor/vitamin metabolism (B09108), nucleotide metabolism (A09104), global metabolism (A09100), oxidative phosphorylation (00190), and glycolysis/gluconeogenesis (00010) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Twenty-two proteins were significantly upregulated (Table S8), while others involved in lipid metabolism and transcription were downregulated (Table S9). PUNP1 upregulation (Ratio\u0026thinsp;=\u0026thinsp;1.067) enhanced purine salvage and CGA intermediate formation. Central carbon metabolism remained stable. WP_016578292.1 (oxidative phosphorylation) was slightly upregulated, while WP_038521406.1 (adenosine deaminase) was downregulated (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), balancing purine-pyrimidine metabolism. Cluster 34 (kaidamycin) and Cluster 36 (type I PKS) proteins were slightly upregulated, while ABC transporters in Cluster 13 were slightly downregulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eIn the OE-metK strain, DEPs were enriched in prokaryotic community pathways (B09145), membrane transport (B09131), amino acid metabolism (B09105), and cell processes (A09140) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Upregulated proteins included hydrolases and those related to nanostructure assembly (Table S10), while fatty acid metabolism and P450 proteins were suppressed (Table S11). Glycolysis, pentose phosphate, and fructose/mannose pathways (e.g., WP_016577205.1) showed slight upregulation, ensuring precursor supply. ATP synthase WP_016578292.1 was mildly upregulated; ABC transporter WP_044375029.1 remained stable, aiding efficient gougerotin secretion (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Elevated SAM levels enhanced methyltransferase activity (e.g., GouN), while WP_016571776.1 (methionyl-tRNA synthetase) remained largely unchanged, indicating adaptive methylation regulation (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Secondary metabolite clusters such as Cluster 33 (nystatin A1), Cluster 20 (chlortetracycline), and Cluster 28 (colimycin P1) were slightly upregulated, while MbtH protein in Cluster 10 (salinomycin) was significantly downregulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve any human or animal subjects.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the State Key Laboratory for Biology of Plant Diseases and Insect Pests (SKLOF202305).\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. Additional data are available from the corresponding author upon reasonable request\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eButler MS, Blaskovich MA, Cooper MA. Antibiotics in the clinical pipeline at the end of 2015. J Antibiot (Tokyo) 2017; 70: 3\u0026ndash;24. doi:10.1038/ja.2016.72\u003c/li\u003e\n\u003cli\u003ePalazzotto E, Tong Y, Lee SY, et al. Synthetic biology and metabolic engineering of actinomycetes for natural product discovery. 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Overexpression of yeast S-adenosylmethionine synthetase \u003cem\u003emetK \u003c/em\u003ein \u003cem\u003estreptomyces actuosus\u003c/em\u003e leads to increased production of nosiheptide. Appl Microbiol Biotechnol 2008; 78: 991\u0026ndash;995. doi:10.1007/s00253-008-1394-5\u003c/li\u003e\n\u003cli\u003eKim D-Y, Hwang Y-I, Choi S-U. Cloning of \u003cem\u003emetK \u003c/em\u003efrom Actinoplanes teichomyceticus ATCC31121 and effect of its high expression on antibiotic production. J Microbiol Biotechnol 2011; 21: 1294\u0026ndash;1298. doi:10.4014/jmb.1101.01018\u003c/li\u003e\n\u003cli\u003eBompadre O, Andrey G. Chromatin topology in development and disease. Curr Opin Genet Dev 2019; 55: 32\u0026ndash;38. doi:10.1016/j.gde.2019.04.007\u003c/li\u003e\n\u003cli\u003eLi L, Liu X, Jiang W, et al. Recent advances in synthetic biology approaches to optimize production of bioactive natural products in actinobacteria. Front Microbiol 2019; 10: 2467. doi:10.3389/fmicb.2019.02467\u003c/li\u003e\n\u003cli\u003eXiao C, Pan Y, Huang M. Advances in the dynamic control of metabolic pathways in \u003cem\u003esaccharomyces cerevisiae\u003c/em\u003e. Eng Microbiol 2023; 3: 100103. doi:10.1016/j.engmic.2023.100103\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1 and 2","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Streptomyces, biosynthetic gene cluster, proteomics, metabolic model","lastPublishedDoi":"10.21203/rs.3.rs-6962713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6962713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, a genome-scale metabolic model (GEM) of \u003cem\u003eStreptomyces noursei\u003c/em\u003e strain CK-15 was constructed, integrating the biosynthetic pathway of gougerotin. Based on UPA algorithm analysis, three extracluster regulatory targets-\u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e (\u003cem\u003ePNP\u003c/em\u003e), and \u003cem\u003emetK\u003c/em\u003e-were identified and validated by gene overexpression experiments. All engineered strains showed accelerated sporulation and enhanced antimicrobial activity in fermentation broth. Notably, overexpression of \u003cem\u003eAHCi\u003c/em\u003e, \u003cem\u003ePUNP1\u003c/em\u003e, and \u003cem\u003emetK\u003c/em\u003e increased gougerotin yields by 43.08%, 238%, and 32.87%, respectively, reaching titers of 1.08 g/L, 2.56 g/L, and 1.01 g/L. Proteomic analysis revealed that \u003cem\u003eAHCi\u003c/em\u003e regulated pathways involved in membrane transport, environmental information processing, amino acid metabolism, and tRNA biosynthesis; \u003cem\u003ePUNP1\u003c/em\u003e enhanced cofactor, nucleotide metabolism, and energy supply; while \u003cem\u003emetK\u003c/em\u003e elevated intracellular SAM levels to promote methylation and peptide precursor biosynthesis, concurrently maintaining stable central carbon metabolism and efficient product efflux. This study elucidates the synergistic roles of these regulatory targets in gougerotin biosynthesis and provides a novel strategy for enhancing microbial secondary metabolite production via metabolic model-based optimization.\u003c/p\u003e","manuscriptTitle":"Metabolic Model-Driven Optimization of Streptomyces Metabolic Network for High-Yield Production of Gougerotin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 17:56:33","doi":"10.21203/rs.3.rs-6962713/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4247eb88-0e49-4278-9218-00d9069a58f2","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T15:39:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 17:56:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6962713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6962713","identity":"rs-6962713","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}