Enhancing microbial metabolic capacity through high-energy electron beam-induced intense structural variations

Domain: chemistry
Published: 2026-02-19
Editorial outcome: major_revision

Abstract
Small-molecule metabolites are key pharmaceutical resources embedded in complex organismal metabolomes. Scalable microbial production depends on metabolic activation capacity, which in turn requires efficient genetic variation. Structural variants (SVs), key drivers of phenotypic diversity, are pivotal for organism evolution, yet their highly efficient induction remains challenging. While DNA double-strand breaks (DSBs) facilitate SVs formation, existing mutagenesis technologies struggle to balance high DSB efficiency with cellular preservation, particularly in microbial strain improvement for metabolite production. Conventional irradiation methods suffer from low SVs induction rates, making strain enhancement a lengthy and labor-intensive process. Here, we systematically compare six irradiation technologies in Streptomyces lividans 1326 and identify high-energy pulsed electron beams (HEPE) as an approach which effectively induces strong DSBs while preserving cellular integrity. This results in extensive SVs that reshape genome sequences and 3D chromatin structure, leading to activation of secondary metabolite production. By integrating HEPE with high-throughput metabolomics (HEPE-HiTMS), we discover two secondary metabolites with unusual C-N linkage, respectively. Applied across various microorganisms, HEPE enables record-high clavulanic acid and microcin J25 production, and markedly increases lovastatin yields. With its ability to induce SVs with minimal cytotoxicity, HEPE represents a powerful tool for cryptic metabolite discovery and industrial strain development.

Introduction
Microbial metabolites have profoundly influenced human civilization-from traditional brewing and vinegar making to the discovery of antibiotics-and remain central to industries such as pharmaceuticals, energy and agriculture1. However, under natural conditions, the biosynthesis of metabolites is tightly regulated, with over 80% being produced at limited production levels or even undetectable2. These insufficiently produced metabolites, especially the fully silent secondary metabolites (SMs), are normally difficult to access through cultivation optimization or simple regulatory engineering, and require extensive and profound genetic rewiring to activate. This challenge contributes to the slow pace of drug discovery and extended timelines in industrial strain development. At the core lies a major bottleneck: the lack of efficient, scalable tools to boost metabolic output3.

Gene mutations, particularly structural variations (SVs > 50 bp)4–6, drive microbial evolution by disproportionately altering genomic architecture, transcriptional networks, and metabolic output7–9. While DNA double-strand breaks (DSBs) catalyze SV formation10,11, spontaneous DSBs in microbes under natural conditions are extremely rare. This limits natural mutation rates and hampers the efficiency of strain improvement for metabolites production12–14. Irradiation mutagenesis accelerates evolution by introducing severe DNA damage, thereby inducing intense mutation15. Traditional approaches such as UV and γ-ray irradiation, as well as newer methods like atmospheric room-temperature plasma (ARTP), have long been used in microbial mutagenesis16. However, these techniques are rarely used for activating cryptic metabolites, and they fail to achieve both robust DSB induction and high cellular integrity, which are essential for maximizing SV formation and mutant survival17.

To address this challenge, we systematically compare six different types of irradiation technologies using the model host Streptomyces lividans 1326 (S. lividans 1326): UV and microwave (MW; low-energy non-ionizing radiation)18; X-ray and γ-ray (high-energy ionizing radiation)19; ARTP20 and HEPE (particle radiation)21. We evaluate their capacity to induce DSBs, preserve cellular integrity, and enhance the production of SMs with diverse expression levels22. Among them, HEPE, previously unreported in microbial mutagenesis, emerges as superior, generating intense SVs that remodel genome sequences and 3D chromatin architecture, significantly boosting metabolic capacity. By coupling HEPE with high-throughput metabolomics (HEPE-HiTMS), we rapidly identify and characterize two structurally novel, previously undetectable compounds—griseobrucins and fradibactin with rare C-N linkage, respectively. Extending HEPE to diverse microorganisms (actinobacteria, bacteria, fungi), we achieve yield enhancements in the production of clavulanic acid23 (2×; record-level), lovastatin24 (7.3×), and antimicrobial peptide microcin J2525 (3×; highest reported). Our findings establish HEPE as a transformative mutagenesis tool that enhances metabolic output by driving SV-based genomic remodeling with minimal cytotoxicity. This approach provides a powerful tool for the discovery of bioactive compounds and applications in industrial biotechnology.

Results
DNA damage induction and cellular integrity across six irradiation technologies
To evaluate and compare the DNA double-strand break (DSB) induction efficiency of six irradiation technologies, we performed TdT-mediated dUTP nick end labeling (TUNEL) assays and plasmid linearization analyses. Given the heterogeneity in irradiation types and energy levels among the six technologies, direct comparison of their DNA-damaging effects using identical radiation doses was precluded. We thus irradiated samples with four escalating dose levels, covering the commonly used upper and lower detection limits of irradiation instruments to enable cross-platform analysis21 (see Materials and Methods).

Mycelia of S. lividans 1326 were irradiated with UV, MW, X-rays, γ-rays, ARTP, and HEPE, after which the cells were harvested and subjected to TUNEL assays to detect single- and double-strand DNA breaks (SSBs and DSBs). Results from fluorescence microscopy clearly revealed no detectable signals in UV-, MW-, or ARTP-treated samples at their respective maximum irradiation durations of 600s, 180s and 300s. Samples treated with X-ray and γ-ray exhibited faint signals at low doses but pronounced fluorescence at the highest doses of 100 Gy and 3 kGy respectively. Whereas HEPE generated strong signals even at minimal doses of 1 kGy, with intensity increasing in a dose-dependent manner (Fig. 1a). These findings indicate that X-ray, γ-ray, and HEPE possess robust penetration capacity to induce intracellular DNA damage, with HEPE causing the most severe DNA damage, followed by γ-ray and X-ray. In contrast, UV, MW, and ARTP showed limited penetration capacity for DNA breaks.

To further investigate DSB induction by each method, plasmid pIJ773 (4.3 kb) was irradiated and then analyzed by plasmid linearization assay. Supercoiled-to-linear conformational shifts, indicative of extracellular DSBs, were negligible in UV- and MW-treated samples even at their maximum doses. Conversely, HEPE, γ-ray, ARTP, and X-ray induced progressive plasmid linearization, with efficiency ranked as HEPE > γ-ray > ARTP > X-ray. Notably, HEPE almost fully linearized pIJ773 at minimal doses, indicating its most powerful DSBs induction effect (Supplementary Fig. 1). These results demonstrate that DSB induction correlates with radiation energy levels26, and HEPE, the energy of which can easily reach industrial safety limits (10 MeV)-exhibits unparalleled efficiency in DSB generation.

Next, scanning electron microscopy (SEM) of irradiated S. lividans 1326 cells revealed severe lysis and structural damage in UV-, MW-, X-ray-, and γ-ray treated samples at subminimal doses (90s, 60s, 50 Gy, 0.5 kGy), respectively. In contrast, ARTP (90s) and HEPE (6 kGy) caused observable damage at submaximal doses, with negligible effects observed at subminimal doses (Fig. 1b). This highlights the superiority of particle radiation (HEPE, ARTP) in preserving cellular integrity over ionizing and non-ionizing radiation.

Based on these results, HEPE shows the strongest DSB induction for generating SVs while causing minimal cell damage, an optimal balance for enhancing microbial metabolite production. Given its limited application in mutagenesis breeding, we selected two competitive controls for further evaluation: ARTP, which also moderately induces DSBs with minimal cell damage but has limited penetration, and UV, which is the most cost-effective and accessible.

Evaluating metabolite production in S. lividans 1326 by UV, ARTP and HEPE
The general host S. lividans 1326 contains the biosynthetic gene clusters (BGCs) of pigments ACT (actinorhodin) and RED (undecylprodigiosin). These two BGCs are lowly expressed or cryptic under certain culture conditions, and exhibits blue and red colors upon their activations22. We compared mutagenic efficiencies of HEPE, UV, and ARTP by screening pigmented mutants. S. lividans was irradiated and the resulting positive colorful mutants were screened, counted, and evaluated for phenotypic stability and the production of ACT and RED (Fig. 2a). A total of 308,672 surviving colonies (with and without blue or red color) were counted, ensuring that each irradiation dose contributed over 5000 colonies for subsequent mutagenic efficiency analysis (Fig. 2b). The blue and red pigmented colonies, identified as positive mutants with activated ACT and RED production, were used to calculate the positive mutation frequency. At optimal HEPE dose (6 kGy), the positive mutation frequency reached 10.7%, which is 8.9 and 6.7 times higher than that of UV and ARTP, respectively, despite comparable mortality rates (>97%, Fig. 2c–e).

To further assess the production stability of the mutations induced by the three technologies, totally 96 dual-colored mutants, 32 each from UV, ARTP and HEPE were randomly selected and subcultured for ten generations. The proportion of mutants that retained pigmentation in each generation was calculated (Supplementary Fig. 2). After ten rounds of subculturing, 60.9% of HEPE-induced mutants stably produced ACT/RED, exceeding the rates for UV (29.7%) and ARTP (42.2%), which confirm the superior genetic stability of the HEPE-derived mutants (Fig. 2f). Meanwhile, HEPE mutants exhibited higher average ACT/RED yields. Analysis of ACT/RED production at generations 3, 5, 7 and 10 in three stably pigments-producing mutants from each method showed that HEPE not only outperforms UV and ARTP in mutation efficiency but also in production stability and yield enhancement (Fig. 2g). This suggests that mutations induced by HEPE are less readily repaired than those of UV and ARTP.

Investigation of genomic and 3D chromatin alterations in S. lividans 1326 induced by HEPE
To investigate the mechanism underlying the superior mutagenic efficiency of HEPE irradiation, we sequenced all the genomes of 96 pigmented mutant strains previously obtained through UV, ARTP, and HEPE irradiation. Compared to the original S. lividans strain, the total number of genetic variants in the UV, ARTP, and HEPE groups was 2817, 2523, and 2545, respectively, with SNVs being the predominant variant type (Fig. 3a, Supplementary Table 1). Although HEPE irradiated mutants did not show increase in SNVs and indels compared to UV and ARTP irradiated mutants (Supplementary Table 1-2), they exhibited a marked increase in the SVs (151), surpassing those induced by UV (70) and ARTP (111). This confirmed our expectation that HEPE induces more DSBs, leading to a higher number of SVs10,27,28 (Fig. 3a). Notably, among all SV types, 56 translocations (TRAs) in HEPE mutants exceeded UV/ARTP by nearly 6-fold, indicating frequent occurrence of non-homologous end joining (NHEJ) in the genomes of HEPE mutants28–30 (Fig. 3a).

A comparison of the genomic distributions of SVs induced by UV, ARTP, and HEPE irradiation in S. lividans was performed using a dispersion analysis with genome binning (see Methods). The result revealed that HEPE group had a more uniform genomic distribution of SVs than the UV and ARTP groups, as evidenced by its lower CV value, although all groups significantly deviated from a uniform distribution (Supplementary Table 3). Circos plot visualization further illustrated the SVs distribution patterns (Fig. 3b, Supplementary Table 4). In the UV and ARTP groups, SVs were predominantly located at the arm ends, with TRA occurring between spatially proximate left and right arms. In contrast, the HEPE group exhibited SVs also abundantly in the core regions. Accordingly, TRA events in the HEPE group were observed between arms as well as between arm ends and the core region.

SVs can exert a greater impact on phenotype changes. This impact is not solely attributed to larger alterations in DNA sequence, dramatic changes in the three-dimensional (3D) chromosome conformation could also play a key role compared to SNVs and indels31. To investigate potential alterations in 3D conformation in HEPE mutant, we compared the chromosome architecture of the wild type (WT) and the mutant strain (H_D1) using high-throughput chromosome conformation capture (Hi-C). First, circus plot (Fig. 3c) of the significant interactions (see Methods) revealed a substantial increase in interaction number in mutant (8830) compared to WT (458). Furthermore, the 3D architecture (Fig. 3d) showed that WT maintain a compact chromosome organization, whereas the mutant exhibited a more relaxed conformation. This difference can be elucidated by the differential interaction and compact analysis (see Methods). As shown in the differential interaction heatmap and the compact matrix (Supplementary Fig. 3), WT is enriched in long-range interactions, maintaining its tighter spatial connectivity. In contrast, mutant showed enhanced short-range interactions, corresponding to a looser spatial organization. This result indicated that mutant’s reinforced local interactions disrupt the native compact 3D genome architecture. This structural relaxation likely facilitates the binding of RNA polymerase and other transcriptional machinery to DNA by increasing accessibility to regulatory regions and gene promoters, ultimately modulating the expression of secondary metabolite BGCs32.

Transcriptome sequencing was subsequently performed on the S. lividans 1326 WT strain and HEPE mutants exhibiting activation of the act or red gene cluster. Differential expression analysis confirmed substantial transcriptional alterations, including the significant upregulation of multiple genes within the red or act gene cluster (Supplementary Fig. 4). HEPE treatment also induced widespread transcriptional perturbations across numerous other BGCs (Supplementary Fig. 5, Supplementary Table 5). Additionally, random SVs can have possible unintended effects, such as growth defects. To assess off-target consequences of HEPE mutagenesis, three mutants were selected for phenotype and growth curve analysis. Compared to WT strain, mutants exhibited reduced sporulation and delayed growth (Supplementary Fig. 6). Transcriptome data linked this phenotype to a marked downregulation of the sporulation-associated genes apA and the regulatory factors whiB and whiD, which control cell division and spore septum formation33,34 (Supplementary Table 6).

Collectively, this dual disruption of DNA sequence and 3D chromatin architecture likely drives enhanced metabolite production35,36. These results support our hypothesis that HEPE generates more SVs, which induce more intense dual alterations of DNA sequence and 3D chromosome conformation, thereby leading to the higher production of ACT and RED, as previously reported in S. coelicolor31.

Application of HEPE for cryptic metabolites discovery
Given the demonstrated efficiency of HEPE in inducing extensive genomic alterations, we developed the HEPE-HiTMS workflow, a strategy which couples HEPE irradiation with high-throughput metabolomics to evaluate its potential in activating fully cryptic SMs, potentially uncovering novel structural metabolites. HEPE-HiTMS integrates BGC activation with metabolomic profiling as readout37 (Fig. 4a). Resulting MS data were computationally amalgamated into a 3D plot, correlating metabolite intensity and m/z of the compounds detected across mutants to enable algorithm-driven metabolomes analysis38.

We randomly selected two streptomycetes species, Streptomyces griseobrunneus CGMCC 29023 and Streptomyces fradiae CGMCC 29024. S. griseobrunneus was firstly irradiated with HEPE; approximately 120 mutagenic clones were cultured for MS acquisition using a high-throughput approach. The metabolomes were visualized in a 3D plot, revealing three significantly induced metabolites (m/z 386.1, 404.1, and 418.2) in the 300–500 m/z range, which were absent in the parental strain (Fig. 4b, c, Supplementary Fig. 7). The activated metabolome was further annotated using FUNEL, which confirmed that compound m/z 404.1 appears to be novel, with no matches in NPAtlas or COCONUT database (Supplementary Data 1)39. Scaling fermentation of high-yield mutants enabled purification and structural elucidation of these compounds, designated griseobrucins A-C (1-3).

Analysis of 1D/2D NMR revealed griseobrucin A as a methylated diketopiperazine (DKP) conjugated to histidine via an unusual C-N bond, rather than the canonical amide linkage40, while griseobrucins B and C are hydrolytic derivatives (Fig. 4d, Supplementary Fig. 8–10, Supplementary Table 7–9). X-ray diffraction resolved griseobrucin C’s absolute configuration (CCDC number 2432012), which is retained in variants A and B (Fig. 4d, Supplementary Fig. 11, Supplementary Table 10).

We next applied HEPE-HiTMS pipeline to S. fradiae. About 120 obtained clones were subjected to generate a 3D plot metabolomic profiling, revealing two strongly activated metabolites with identical molecular weight (m/z 890.5), undetectable in the parental strain within the 800–1000 m/z range (Fig. 4e–f, Supplementary Fig. 7). FUNEL analysis confirmed their novelty, with no match in public databases (Supplementary Data 1). These two compounds, named fradibactin A (4) & B, were purified from scaled fermentation for structural characterization.

1D/2D NMR analyses identified fradibactins as two enantiomers of hybrids siderophores merging hydroxamate and carboxylate groups, containing a unique C-N double bond with Z configuration, a linkage unreported in known hydroxamate-type siderophores (Fig. 4g, Supplementary Fig. 12, Supplementary Table 11). This unique structural element prompted us to investigate its BGC. AntiSMASH mining uncovered two hydroxamate-associated BGCs41 (Supplementary Fig. 13). Targeted gene inactivation localized fradibactins biosynthesis to the fdn cluster, which harbors an atypical aminobutyrate-2-oxoglutarate aminotransferase (fdnA) adjacent to conserved hydroxamate formation genes (fdnB-F), implicating fdnA in C-N double bond formation (Supplementary Fig. 14, Supplementary Table 12, 13). The successful discovery of the cryptic griseobrucins and fradibactins demonstrates HEPE’s exceptional capacity to activate metabolites, particularly those fully undetectable metabolites through targeted structural variations, positioning HEPE-HiTMS as a robust platform for discovering cryptic microbial metabolites in the future.

HEPE-induced yield increasing of clavulanic acid in industrial Streptomycete
Having demonstrated HEPE’s effectiveness in activating cryptic metabolites, we next applied it to boost the production of targeted metabolites in industrial strains. Unlike wild-type strains, these industrial strains had already undergone conventional mutagenesis and extensive screening, resulting in optimized metabolite production. Our objective was to leverage HEPE mutagenesis to achieve further yield improvement that are unattainable through conventional methods. We firstly selected Streptomyces clavuligerus 70116-an industrial clavulanic acid overproducer derived from WT S. clavuligerus 3585 through 6 years of conventional mutagenesis (non-HEPE) and strain selection (Fig. 5a). Approximately 104-105 spores of S. clavuligerus were subjected to HEPE irradiation on ISP2 agar plates following S. lividans mutagenesis protocol. A total of 110 surviving colonies were selected and fermented in an optimized medium. Comparison of clavulanic acid yields revealed that 27 out of 110 mutant clones exhibited at least a 10% increase in titer (Fig. 5b). The high mutation rate indicates that HEPE mutagenesis is not compromised by prior conventional mutations.

The highest-yielding mutant H_M32 reached a clavulanic acid titer of 5.5 g/L in flasks after 6 d fermentation, representing a 60% increase over the original strain (3.5 g/L). After confirming its productions stability over five rounds of subculturing (Supplementary Fig. 15a), H_M32 was advanced to large-scale cultures. It reached a yield of 6.3 g/L in a 5 L benchtop bioreactor, which signifies an additional 15% increase over the starting strain and is comparable to the record-high of 6.11 g/L23 (Fig. 5c).

To investigate the mechanism underlying the progressive increase in clavulanic acid production among these producers, we performed whole-genome sequencing on the WT, the starting strain 70116, and the highest-yielding mutant H_M32. Analysis of genome variations revealed that strain 70116, which underwent 5 rounds of mixed mutagenesis from WT (Fig. 5c), accumulated a large number of variations compared to the wild type, including 265 SNVs, 29 indels, and 7 SVs, collectively resulting in the 2.3 g/L increase in clavulanic acid production compared to WT strain. In contrast, the HEPE-irradiated mutant H_M32 achieves 2.8 g/L over 70116 with fewer total mutations (36 SNVs, 16 indels, 5 SVs) but a significantly higher proportion of SVs (Fig. 5c). This result underscores that SV frequency, rather than the total mutation count is the key driver of phenotypic enhancement, aligning with SVs’ powerful impact on transcriptional regulation and 3D genome architecture.

Application of HEPE for enhancing metabolites production in diverse industrial species
With the success in streptomyces, we extended the application of HEPE to diverse microbial taxa to evaluate its broad mutagenic potential across other bacteria and fungi. For comparative purpose, UV and ARTP were included alongside HEPE. Moreover, to apply HEPE to plasmid-dependent heterologous expression, which requires unstable multicopy plasmids and can compromise long-term strain performance, we selected an engineered Escherichia coli CGMCC 16349 strain designed for heterologous production of the antimicrobial peptide microcin J25 (mccJ25)42. Following UV, ARTP, and HEPE irradiation, 16.3% of HEPE-treated mutants exhibited >2-fold increase in mccJ25 yield, outperforming UV and ARTP (Fig. 6a–d). Notably, the top 3 mutants resulted in >3-fold yield enhancements, and remained stable after 10 rounds of subculturing in 100 mL flasks (Supplementary Fig. 15b). In scale-up 40 L bioreactors, the best mutant produced mccJ25 at a titer of 7.5 g/L, surpassing the previous literature yield of 4 g/L25 (Fig. 6e, f).

Next, we evaluated HEPE, UV, and ARTP irradiation in filamentous fungi, which are prolific producers of pharmaceutically relevant metabolites43. We selected the lovastatin-producing strain Monascus purpureus MY2, which was generated by 3 rounds of irradiation on a WT strain Y2, for this study24. Each irradiation treatment gave 60 surviving colonies for solid-phase fermentation and lovastatin analysis. HEPE irradiation (2 kGy) generated 13.3% mutants with over 2-fold yield increases, far exceeding UV and ARTP (Fig. 6g–j). Only 48.3% of HEPE mutant exhibited reduced production, compared to 68.3% (UV) and 75.0% (ARTP). The top-performing HEPE mutant was subcultured for five generations in 1 L solid-phase fermentation system to assess lovastatin production stability. The results indicated that lovastatin yield remained generally consistent across generations (Supplementary Fig. 15c). Furthermore, this mutant exhibited a more than 6-fold yield enhancement in 4 L solid-phase fermentation container, reaching a titer of 29 mg/g-nearing industrial benchmarks (Fig. 6k).

Collectively, the markedly yield improvements of clavulanic acid, mccJ25, and lovastatin across actinobacteria, proteobacteria, and fungi underscore the remarkable superiority of HEPE irradiation over UV and ARTP, positioning it as a broadly applicable tool for industrial metabolite enhancement.

Methods
Bacterial strains
The Streptomyces lividans 1326 was kindly provided by the laboratory of Prof. Wenjun Guan at Zhejiang University. The Streptomyces griseobrunneus CGMCC 29023 and Streptomyces fradiae CGMCC 29024 (GenBank accession numbers SAMN45944959 and SAMN45945006) were kindly provided by the laboratory of Prof. Min Wu at Zhejiang University. Streptomyces clavuligerus 3585, 70116 and H_M32 was stored in the laboratory of Prof. Weishan Wang at Institute of Microbiology, Chinese Academy of Sciences. Strain 70116 was derived from 3585 with 5 rounds mutagenesis of UV-Nitrosoguanidine (NTG)-Diethyl sulfate (DES)-UV-NTG. Escherichia coli CGMCC 16349, H_11F, H_4B and H_9B was stored in the Sinagri Yingtai Bio-peptide Co., Ltd. Monascus purpureus Y2, Monascus purpureus M_Y2 and Monascus purpureus H_MY2 was stored in the laboratory of Prof. Wei Liu at China Pharmaceutical University. Strain M_Y2 was derived from Y2 with 3 rounds mutagenesis of UV-MW-UV.

Mutagenesis for S. lividans 1326
The cryo-preserved cultures of S. lividans 1326 were plated on SFM agar (2% soybean, 2% mannitol, 2% agar, pH 7.0). From the agar plate, a single colony was picked and inoculated into YEME medium (0.3% yeast extract, 0.3% malt extract, 0.5% peptone, 1% glucose, 10.7% sucrose, pH 7.0) and incubated at 30°C for 1-2 days. The cultured cells were collected and washed twice with distilled water, after which they were resuspend in sterile water to a concentration of 50 mg/mL. Alternatively, the cultures were spread on SFM agar and incubated at 30°C for 5–7 days. After sporulation, spores were harvested and washed twice with distilled water to remove the retained aerial mycelia and cell debris. The purified spores were resuspended in sterile water, and the optical density of the spore suspension was adjusted to 0.4 at 595 nm. The resulting culture or spore suspension was subsequently used for mutagenesis experiments.

UV mutagenesis: 20 μL culture or spore suspension was spread in a petri dish, which was placed under the UV lamp (15 W, 254 nm, PHILIPS) at a distance of 30 cm. The light intensity of the ultraviolet lamp used was 48 μW/cm². Their radiation time were 30, 90, 180 and 600 s.

MW mutagenesis: 20 μL culture or spore suspension was transferred into 1.5 mL Eppendorf tube, which was placed in microwave oven. The irradiation time were 30, 60, 90 and 180 s. During the experiment, the centrifuge tube was removed from the microwave oven every 10 seconds and placed on ice to minimize thermal effects.

X-ray mutagenesis: 20 μL culture or spore suspension was put into 1.5 mL Eppendorf tube, which was placed in Biological X Ray Irradiator (RS2000pro, Rad Source Technologies). The irradiation doses were 20, 50, 80 and 100 Gy.

γ-ray mutagenesis: The γ-ray device is a wet static cobalt-60 irradiation system with a source strength of 18,600. 20 μL culture or spore suspension was put into 1.5 mL Eppendorf tube, and irradiated with 60Co γ-ray source. Their radiation doses were 0.2, 0.5, 1 and 3 KGy.

ARTP mutagenesis: 20 μL culture or spore suspension was coated on the surface of a stainless-steel slide and placed in the chamber of ARTP mutation system (ARTP-M, Tmaxtree Biotechnology Co., Ltd., Wuxi, China). Ultra-high purity helium was used as the plasma working gas and the operating parameters were set as follows: radio frequency power, 120 W; helium gas flow rate, 10 L/min; distance between the plasma torch nozzle exit and the sample plate, 2 mm. Their radiation time were 15, 45, 90 and 300 s.

HEPE mutagenesis: 20 μL culture or spore suspension was put into 1.5 mL Eppendorf tube and placed on the conveyor of the high-energy pulse electron accelerator (CIAE-DZ-10/20, China Institute of Atomic Energy). The operating parameters were as follows: electron beam energy, 10 MeV; electric current, 1.5 mA; scan frequency, 335 Hz; absorbed dose rate, 4 kGy/s. The irradiation doses were 1, 3, 6 and 20 KGy.

TdT mediated dUTP nick end labeling (TUNEL) assays
To directly observe DNA damage in mutants after irradiation, fluorescein isothiocyanate (FITC)-12-dUTP was used to label damaged DNA at 3′-OH ends exposed by DNA breakage, catalyzed by Terminal Deoxynucleotidyl Transferase (TdT)57. The culture suspension of S. lividans 1326 was irradiated with UV, MW, X-ray, γ-ray, ARTP, or HEPE, as described in the above method. Subsequently, the cells were treated with 4 mg/mL lysozyme for 1 h at 37°C, followed by incubation with 2% Triton X-100 for 10 min at room temperature. After centrifugation, cells were resuspended in equilibration buffer prior to staining. Cells were incubated with FITC-12-dUTP for 1 h at 37°C in the dark, then washed twice with sterile water to remove excess labeling mix.

Cell preparations were mounted on glass slides and imaged using a Nikon Eclipse Ti inverted microscope with a FITC fluorescence filter (excitation/emission 470/520 nm). Equivalent exposure times and image adjustments were used for all samples.

Mutagenesis for plasmid pIJ773
The genetic effects are presented at the molecular level by irradiating a loopful plasmid (pIJ773) with UV, MW, X-rays, γ-ray, ARTP or HEPE. The irradiation mutagenesis process and doses were consistent with the S. lividans 1326. The plasmid concentration was approximately 70 ng/µL, with a volume of 20 µL for each irradiation dose. After irradiation, the DNA was analyzed on 1.0% agarose gel electrophoresis in 1× TAE (Tris-Acetate-EDTA) buffer at 140 V for 30 min. An untreated plasmid sample served as the negative control, and the plasmid digested with HindIII-restriction enzyme was used as the positive control.

Scanning electron microscopy
Scanning electron microscopy (SEM) was employed to observe the membrane damage of mutants after irradiation treatment. Spore suspensions of S. lividans 1326 were treated with MW, UV, X-rays, 60Co, ARTP or HEPE respectively following the dose escalation described above. After irradiation, the suspensions were centrifuged at 2375 g for 10 min to collect spore pellets. The pellets were fixed with glutaraldehyde for 2 h at room temperature and then at 4°C overnight. Subsequently, the samples were rinsed with PBS for three times (10–15 min each time). Following this, the samples were fixed with 1% osmium solution for 1.5 h and rinsed again three times with PBS (10-15 min each time). The samples were then dehydrated through a graded series of ethanol (50, 70, 90 and 100%) and dried by Critical Point Dryer CPD300 (Leica). The dried specimens were sputter coated with Pt-palladium (8 mA, 60 s) by high vacuum ion sputtering apparatus (Quorum Q150TES plus). Finally, the samples were observed under a scanning electron microscope Nova Nano 450 (Thermo FEI) at 5 kv.

Calculation of Positive Mutation Frequency Induced by UV, ARTP and HEPE
20 μL spore suspension of S. lividans 1326 was irradiated with UV, ARTP or HEPE at varying doses, following their radiation mutagenesis process described previously. The UV irradiation time were 0, 30, 60, 90, 120, 150, 180 and 210 s. Using an ultraviolet lamp with a light intensity of 48 μW/cm², the resulting light energies at irradiation times of 30, 60, 90, 120, 150, 180, and 210 seconds are calculated to be 1.44, 2.88, 4.32, 5.76, 7.20, 8.64, and 10.08 mJ/cm², respectively. For ARTP, their radiation time were 0, 15, 30, 45, 60, 75, 90, 105, and 120 s. The HEPE irradiation doses were 0, 0.5, 1, 2, 3, 4, 5, 6, 7, and 8 KGy.

After irradiation, spores were resuspended in 500 μL sterile water, serially diluted, and plated on SFM agar. The plates were incubated at 30°C for 7 days. Colonies exhibiting red, blue, or mixed pigmentation were classified as positive mutants. Control samples were untreated spore suspensions (20 μL). The positive mutation frequency was calculated as the ratio of pigmented colonies to the total number of colonies in the control sample. The lethal rate was determined as 1 minus the survival rate. All experiments were conducted in triplicate, with sextuplicate samples for each condition.

The genetic stability of positive mutants
To assess genetic stability, the frequency of positive mutants was evaluated across 1-10 subgenerations. Cryopreserved mutants were streaked on SFM agar and incubated for 7 days (first subculture). Clones were then subcultured onto new plates for another 7-day incubation (second subculture), and this process was repeated up to the tenth subculture. Thirty-two positive mutants were randomly selected for each irradiation treatment, and their color phenotype was recorded for each subgeneration. Mutants showing mixed red and blue were considered genetically stable (scored as 1), while those showing predominantly red, blue, or a lighter mix were considered relatively stable (scored as 0.5). Mutants with wild-type coloration were deemed unstable (scored as 0). The genetic stability frequency was calculated by summing the scores and dividing by 32.

The production of ACT and RED for genetic stability assessment in 1-10 subgenerations was evaluated as follows: Three randomly selected positive mutants from each treatment were subcultured and fermented in MS medium (2% mannitol, 2% soybean) for 5 days. ACT production was measured by centrifuging 1 mL of fermentation broth (10 min, 13,500 g) and recording the absorbance of the blue supernatant at 600 nm. RED production was quantified by resuspending 10 mg of mycelial pellet in CH3OH–HCl (pH 1.5), vortexing until biomass disruption, and centrifuging the suspension (10 min, 13,500 g). The absorbance of the supernatant was measured at 530 nm. Each fermentation was performed in triplicate for each mutant.

Whole genome re-sequencing and variation analysis
Ninety-six positive mutants, treated with UV, ARTP, or HEPE, were selected for whole genome re-sequencing. These mutants, previously used for genetic stability assessment, were compared with the wild-type S. lividans 1326 as the reference strain. All strains were cultured in TSBY medium (3% tryptic soy broth medium, 0.5% yeast extract) for 1-2 days, and genomic DNA was extracted using the CTAB method58. Sequencing was performed on the Illumina HiSeq (NovaSeq 6000) platform at Wuhan Benagen Technology Co., Ltd. The genome was used for subsequent variation analysis.

Strains with over 60X genome coverage were used for variant detection, including single nucleotide variants (SNVs), insertion-deletions (indels), and structural variations (SVs). Paired-end raw reads were quality-controlled and filtered using fastp (v0.23.4), and the clean reads were mapped to the S. lividans 1326 reference genome with BWA (v2.2.1). Variants were called using the Genome Analysis Toolkit (GATK, v4.6.0.0). For indels detection, insertions and deletions < 50 bp were considered. SV detection was carried out using smoove (v0.2.6) to identify large indels (≥ 50 bp), tandem repeats, and translocations. Dispersion analysis utilizing genome binning was incorporated to characterize the distribution of SVs induced by UV, ARTP or HEPE. Based on the SVs data, a 10 kb sliding window approach was employed to assess the dispersion of SVs across the genome. Using the SVs count distribution obtained from the sliding-window analysis, we further calculated the coefficient of variation (CV) and performed a chi-square goodness-of-fit test to evaluate whether the SVs distribution in each group significantly deviated from a uniform pattern. The CV serves as a measure of dispersion: a lower value indicates a more uniform distribution, while a higher value reflects greater variability and clustering.

High-throughput chromosome conformation capture (Hi-C) of S. lividans 1326
Hi-C libraries were prepared as previously described59. Briefly, bacterial cells were cross-linked with 3% formaldehyde for 1 h at 4°C, followed by quenching with 0.375 M glycine for 15 min. Cells were lysed with lysozyme at 37°C for 10 min, and endogenous nuclease was inactivated with 0.5% SDS. Chromatin was digested with 100 U Sau3AI (NEB) and labeled with biotin-14-dCTP (Invitrogen), followed by ligation with 50 U T4 DNA ligase (NEB). After reversing cross-links, the ligated DNA was purified using the QIAamp DNA Mini Kit (Qiagen). DNA was sheared to 300–500 bp, blunt-end repaired, A-tailed, and adapters were added. The DNA was purified through biotin-streptavidin pull-down and PCR amplification. Hi-C libraries were quantified and sequenced on the MGI-seq platform (BGI, China).

After quality filtering, the clean Hi-C data was iteratively mapped to the S. lividans 1326 genome using the ICE software. The valid pairs after filtering were binned into 5 kb non overlapping genomic intervals to generate a two-dimensional contact matrix. The contacts between 5 kb bins of interactions of each sample were transferred to Fit-HiC software (v2.0.0) to calculate the corresponding cumulative probability P-value and Q-value. After calculation, the interactions in which both the P-value and Q-value were less than 0.01 and the contact count > 2 were identified as significant interactions. To more intuitively compare and visualize the interaction differences between the samples, the contact matrix of each sample was first converted into a Z-score matrix. The Z-score matrices of the two samples were then subtracted to obtain a differential interaction matrix, which was subsequently displayed as a heatmap. The compact analysis reflects the local chromosome packing state by calculating, for each genomic bin, the proportion of its total interactions that occur within a defined local genomic window relative to its interactions with the entire chromosome. These values are then visualized as a matrix. Pastis (v0.1)60 was used to construct the 3D chromosomal structure based on a 5 kb contact map, utilizing a multidimensional scaling (MDS) model. The origin and terminus regions were annotated using Centurion software61.

Transcriptome analysis
Total RNA was extracted from the tissue using Trizol reagent. RNA-seq transcriptome library was prepared following the NEBNext® Ultra II™ Directional RNA Library Prep Kit. Libraries that pass quality control was sequenced on the Illumina high-throughput sequencing platform using paired-end 150 bp reads. Bowtie 2 was used to filter the sequencing data to make clean data. Then the cleaned reads were mapped to the S. lividans 1326 genome. Transcript expression levels were quantified using RSEM. The significant differentially expressed genes were identified by edgeR with definition of |log2(fold change)| ≥ 1, false discovery rate (FDR) ≤ 1, and P < 0.05.

Screening of strains Streptomyces griseobrunneus CGMCC 29023 and Streptomyces fradiae CGMCC 29024
Mutagenesis of Streptomyces griseobrunneus CGMCC 29023: Streptomyces griseobrunneus CGMCC 29023 was irradiated with a 6 kGy dose as described for S. lividans 1326. After mutagenesis, 120 clones were inoculated into 96-well plates, each containing 400 μL TSBY medium (3% tryptic soy broth, 0.5% yeast extract). After 24 h of incubation at 30°C, 600 rpm, 20 μL from each well was transferred to a new 96-well plate with 600 μL GYB medium (2% glucose, 1% soluble starch, 0.3% corn extract, 1% yeast extract, 0.1% beef extract, 0.05% MgSO₄·7H₂O). The cultures were incubated for 7 days at 30°C, 600 rpm. After centrifugation, 300 μL of supernatant from each well was subjected to solid-phase extraction using a C18 column (Phenomenex, 55 μm, 50 mg/well) in a 96-well format. Elution was performed with 500 μL 50% MeCN (in water), and the eluates were dried in vacuo, then resuspended in 30 μL 50% MeCN for HR-ESI-MS analysis.

Mutagenesis of Streptomyces fradiae CGMCC 29024: The mutagenesis procedure for S. fradiae CGMCC 29024 followed the same protocol as for S. griseobrunneus CGMCC 29023.

Analysis of HR-ESI-MS data
High-resolution ESI-MS data were acquired using a Sciex ZenoTOF 7600 mass spectrometer (Sciex, Framingham, MA, USA), coupled to a Shimadzu Nexera LC-40DX3 HPLC system (Shimadzu, Kyoto, Japan). A Luna C18 column (Phenomenex, 1.6 μm, 30×2.1 mm) was used, and the mobile phases A and B consisted of water and MeCN, both with 0.1% formic acid. A 1 μL sample was injected into the mass spectrometer, with the following gradient elution program: 5% MeCN for 1 min, followed by 10–40% MeCN over 7 min, and 40–100% MeCN over 10 min, at a flow rate of 0.3 mL/min.

The mass spectrometer was operated in positive ion mode with the following parameters: spray voltage 5500 V, declustering potential 80 V, collision energy 10 V, accumulation time 0.5 s, and mass range 100-2000 Da. Source gas parameters were: ion source gas 1 and 2 at 50 psi, curtain gas at 35 psi, and gas temperature at 500°C.

For visualization, LC-MS data were converted to the mzXML format using ProteoWizard-MSConvert software (ver 3.0). The data were binned in 1 m/z units and medium signals were excluded from 3D plots. Signals above an intensity threshold were plotted in MATLAB using the bar3 function. Visual inspection of the 3D plots allowed identification of metabolites whose production was induced or enhanced by HEPE mutagenesis.

Purification and structural elucidation of griseobrucins A-C
Griseobrucin A-C were purified from Streptomyces griseobrunneus CGMCC 29023 as follows: The strain was inoculated from an SFM agar plate into 3 mL TSBY medium in a sterile 14 mL culture tube. After overnight growth, the culture was diluted to 0.5% (v/v) into 1 L flasks containing 200 mL GYB medium. A total of 10 L of fermentation culture was used for compound isolation. After 7 days of cultivation at 30°C, 210 rpm, the supernatant was repeatedly loaded onto a prepacked C18 column (YMC, 10 μm, 20 g). Step-wise elution was carried out using 150 mL H₂O, followed by 15% and 30% MeOH (in water). Fractions containing griseobrucin A, B, or C (as determined by HR-LC-MS) were combined, dried in vacuo, resuspended in 15% MeOH, and further purified by loading onto a Sephadex LH-20 column (Cytiva). Elution was performed with 15% MeOH, and the fractions were again combined and dried.

For griseobrucin A purification, the sample was resuspended in 15% MeOH and purified by HPLC (Thermo Scientific UltiMate 3000) on a BEH Phenyl column (Waters, 5 μm, 10×250 mm), using a gradient from 3% to 8% MeOH over 30 min, then increased to 100% MeOH at 31 min, maintaining at 100% until 35 min. Fractions from 31–35 min were pooled, dried, and resuspended in 15% MeCN for further purification on an Amide C16 column (Supelco, 5 μm, 4.6×250 mm) with a 3–8% MeCN gradient over 20 min. Fractions collected at 5 min, containing griseobrucin A (1), were lyophilized to yield 3 mg.

Griseobrucin B and C were purified similarly, using an Amide C16 column (Supelco, 5 μm, 10×250 mm) with an isocratic elution of 3% MeCN for 20 min. Fractions collected from 10–18 min were pooled, dried, resuspended in 15% MeCN, and purified by HPLC on a Luna C18 column (Phenomenex, 5 μm, 4.6×250 mm) with isocratic 3% MeOH for 10 min. Fractions containing griseobrucin B and C (at 5.5 min and 6.5 min, respectively) were lyophilized to dryness, yielding 12 mg of griseobrucin B (2) and 10 mg of griseobrucin C (3).

NMR spectra were recorded using a Bruker Ascend 600 MHz (Bruker, Rheinstetten, Germany) in H₂O:D₂O (9:1). Data were analyzed using MestReNova software (MestreLab Research).

X-ray diffraction
Crystal of griseobrucin C (3) was obtained by slow evaporating of its methanol solution at room temperature over four days. A suitable crystal was selected and mounted on a Bruker D8 Venture diffractometer (Bruker, Karlsruhe, Germany). The data was collected at 170.0 K. The structure was solved by Intrinsic Phasing with SheLXT and refined by Least Squares Minimization with the SheLXL within the Olex2 program package.

Purification and structural elucidation of fradibactin A & B
Fradibactin was purified from Streptomyces fradiae CGMCC 29024 following a similar protocol: A total of 20 L of fermentation culture was processed as described for griseobrucin. After 7 days of cultivation, the supernatant was loaded onto a prepacked C18 column (YMC, 10 μm, 20 g) and eluted with 150 mL H₂O, 15% and 30% MeCN. The 15% fraction containing fradibactin was dried, resuspended in 15% MeCN, and purified by Preparative HPLC on a Triart C18 column (YMC, 5 μm, 20×250 mm) at 10 mL/min using a gradient of 5%-40% MeCN over 40 min. Fractions from 20–30 min were pooled, dried, resuspended in 15% MeCN, and further purified by HPLC on a BEH Phenyl column (Waters, 5 μm, 10×250 mm) at 2 mL/min, using a gradient from 8% to 20% MeCN over 30 min. Fractions from 19–24 min were pooled, dried, resuspended in 15% MeCN, and purified on an Amide C16 column (Supelco, 5 μm, 4.6×250 mm) with a 5-13% MeCN gradient over 35 min. Fractions at 29 min and 31.5 min containing fradibactin A and fradibactin B, respectively, were collected and lyophilized, yielding 2 mg of fradibactin A (4) and 0.8 mg of fradibactin B.

NMR spectra were recorded using a Bruker Ascend 600 MHz (Bruker, Rheinstetten, Germany) in DMSO-d₆. Data were analyzed in MestReNova (MestreLab Research).

Genetic inactivation in strain Streptomyces fradiae CGMCC 29024
Construction of Knockout Plasmids: For the Δfdn insertional mutant, 2.1 kb upstream and downstream fragments of BGC fdn were amplified by PCR using primers 1/2 and 3/4. The 773 cassette, containing the apramycin resistance gene (apr) fused to oriT, was obtained by EcoRI/HindIII digestion of the vector pIJ773. Vector pJTU1289, carrying ampicillin and thiostrepton resistance markers (AmpR, TsrR), was digested with XbaI and KpnI. All fragments were gel-purified and assembled using the One Step Cloning Kit (Vazyme Biotech Co. Ltd) to create plasmid pΔfdn. The assembled plasmid was transformed into E. coli DH5α cells and verified by Sanger sequencing.

Conjugation and Mutant Selection: The pΔfdn plasmid was transformed into E. coli ET12567/pUZ8002. E. coli ET12567 carrying the plasmid was cultured in LB with Amp (100 μg/mL), Apr (50 μg/mL), Kan (50 μg/mL), and Cm (25 μg/mL) to an OD600 of 0.4–0.6. Cells were centrifuged (4000 g, 10 min), washed twice with LB, and resuspended in 500 μL LB. Fresh spores of S. fradiae CGMCC 29024 were collected from SFM agar, washed with sterile water, and resuspended in TES buffer (50 mM, pH 8.0). The spore suspension was heat-shocked at 50°C for 10 min, followed by a 1 min ice bath and incubation with 2× spore activation buffer (1% yeast extract, 1% casein hydrolysate, 5 mM CaCl₂) at 37°C/200 rpm for 2 hours. The spores were collected (1000 g, 5 min), resuspended in LB, and mixed with E. coli ET12567 suspension. The mixture was plated on SFM agar and incubated at 30°C for 18–20 h. Exconjugants were selected on SFM agar containing Apr (50 μg/mL) and trimethoprim (50 μg/mL). After 3–5 days of incubation, single colonies were picked, grown on SFM agar with Apr, and further validated. Exconjugants were cultured in TSBY medium (Apr, 50 μg/mL) for 2 days at 30°C/210 rpm, then subcultured without antibiotics for 1 day, followed by a second subculture for 1 day. Surviving cells were plated on SFM agar, and single colonies were tested for thiostrepton (Tsr) susceptibility (10 μg/mL) and apramycin resistance (50 μg/mL). Double-crossover mutants resistant to Apr and susceptible to Tsr were confirmed by PCR using primers 5/6 and 7/8.

A successful Δfdn double-crossover mutant was obtained, which was unable to produce fradibactin.

Test of in vitro antimicrobial activity
Antimicrobial activity was tested against Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 19433, Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 25922, Stenotrophomonas maltophilia ATCC 13637, Candida albicans ATCC 90028, Candida krusei ATCC 6258, Candida glabrata ATCC 15126, and Candida parapsilosis ATCC 22019. Bacteria were cultured in LB medium at 37°C, while fungi were cultured in potato dextrose broth (PDB) at 30°C. Minimum inhibitory concentration (MIC) was determined using the microtiter method according to CLSI guidelines. Overnight cultures of the test strains were diluted to an initial OD600 of 0.005 in 100 μL per well in a 96-well plate. Compounds were tested at concentrations of 0, 0.5, 1, 2, 4, 8, 16, 32, and 64 μg/mL. Plates were incubated at 37°C for 16 hours, and OD600 was measured before and after incubation. MIC values were defined as the compound concentration that reduced OD600 by >95% compared to the untreated culture. Controls included a negative control (90 μL bacterial culture + 10 μL saline), positive controls (nisin and vancomycin), and a blank (100 μL MH-II broth without bacteria and drugs). All experiments were performed in triplicate.

Test of cytotoxic activity
The cytotoxic activity screening was conducted by experimental staff at Qingdao AC biotechnology co., Ltd using a commercially available Cell Counting Kit-8 (CCK-8) assay. The compounds were evaluated against a panel of twenty human cancer cell lines, including A549 (BNCC337696), MKN-45 (CL-0292), HCT116 (BNCC287750), HeLa (CL-0101), HepG2 (BNCC338070), K-562 (CL-0130), 786-O (CL-0010), TE-1 (CL-0231), 5637 (CL-0002), GBC-SD (CL-0085), L-02 (BNCC338358), MCF-7 (CL-0149), SF126 (CL-0435), DU145 (BNCC1000143), CAL-62 (CL-0618), PATU8988T (CL-0579), HOS (CL-0360), A-375 (CL-0014), A-673 (CL-0017), HEK-293T (CL-0005). All cells were obtained from BeNa Culture Collection or Wuhan Procell. All cell lines were authenticated by short tandem repeat (STR) profiling within 6 months prior to experimentation and were confirmed to be free of mycoplasma contamination.

Screening of strain Streptomyces clavuligerus 70116
Mutagenesis of Streptomyces clavuligerus 70116: Streptomyces clavuligerus 70116 was irradiated with HEPE (6 kGy) as described for S. lividans 1326.

Mutant Screening: After irradiation treatment, cells were resuspended in 200 μL sterile water, serially diluted, and spread on SFM agar plates. Plates were incubated at 28°C for 5–7 days. One hundred and ten clones were randomly selected and inoculated into 500 mL Erlenmeyer flasks containing 100 mL seed medium (3% soybean, 2% dextrin, 0.5% yeast extract, 0.1% K2HPO4; pH 7.0), cultured at 28°C, 250 rpm for 72 h. For small-scale flask cultivation, 250 μL of the cultures were transferred to 500 mL Erlenmeyer flask containing 30 mL fermentation medium (2% glycerol, 0.5% olein, 2% dextrin, 2.5% peptone, 2.1% morpholinepropyl sulfonic acid, 0.2% K2HPO4, 0.01% NaCl, 0.02% MgCl2, 0.02% CaCl2·2H2O, 0.003% FeCl3, 0.0005% CuCl2, 0.005% MnSO4, 0.0005% ZnCl2; pH 7.0), followed by 144 h incubation at 25°C, 250 rpm.

Clavulanic acid production analysis: Cells were removed by centrifugation (10,000 g, 10 min). The clarified supernatant containing clavulanic acid was filtered through 0.22 μm membranes prior to HPLC. Analytical separation employed an SB-C18 column (4.6 mm×100 mm) column under thermostatic control (33±0.5°C). The injection volume was 20 μL, and detection wavelength was set at 210 nm. The mobile phase consisted of 10 mmol/L tetrabutylammonium hydrogen sulfate and methanol (85:15, v/v), adjusted to pH 6.0.

Screening of strain Escherichia coli CGMCC 16349
Mutagenesis of Escherichia coli CGMCC 16349: Cryopreserved E. coli CGMCC 16349 cultures were plated on LB agar (1% Tryptone, 0.5% yeast extract, 0.5% sodium chloride, 2% agar) supplemented with 100 µg/mL Ampicillin and 50 µg/mL Kanamycin. A single colony was inoculated into LB broth and incubated overnight at 37°C. Subsequently, the cultures were washed twice with distilled water and resuspended in sterile water, adjusting the optical density to 0.4 at 595 nm. For irradiation mutagenesis, 20 μL of the cell suspension were subjected to three different treatments: UV (60, 120, 180, and 240 s), ARTP (30, 60, 90, and 120 s), or HEPE (1, 2, 3, and 4 kGy). The mutagenesis process was the same as described for S. lividans 1326.

Mutant Screening: After irradiation treatment, cells were resuspended in 200 μL sterile water, serially diluted, and spread on LB agar plates supplemented with 100 µg/mL Ampicillin and 50 µg/mL Kanamycin. Plates were incubated overnight at 37°C. Eighty clones per irradiation treatment were randomly selected and inoculated into 12 mL sterile tubes containing 5 mL TSBY medium (100 µg/mL Ampicillin, 50 µg/mL Kanamycin), cultured overnight at 37°C, 210 rpm. For small-scale fermentation, 100 μL of the cultures were transferred to 100 mL Erlenmeyer flask containing 20 mL LB medium, and incubated for 24 h at 37°C, 210 rpm.

Fed-batch fermentation in a 40 L bioreactor: The fed-batch fermentation of high-yield mutant was performed in a 40 L bioreactor (Sinagri Yingtai Bio-peptide Co., Ltd.). The medium was composed of 3.2% corn flour, 2% soybean meal, 1.3% peptone, 0.2% KH2PO4 and 2% (NH4)2SO4. Dissolved oxygen (DO) and pH were measured by a DO probe and a pH electrode, respectively. The temperature was maintained at a constant 37°C, and pH at 7.0. The fed-batch process was initiated after 5 h of cultivation, using a concentrated glucose stock solution to maintain a glucose concentration of 0.15 g/L in the bioreactor. Fermentation broth was collected every 4 h during the fermentation process for monitoring cell growth (OD600) and the titer of microcin J25.

Microcin J25 Production Analysis: Cells were removed by centrifugation (16,000 g, 10 min), and 1 mL of clarified supernatant from each flask was loaded onto a prepacked C18 column (YMC, 10 μm, 0.5 g). Elution was performed with 1 mL 50% MeCN in water, and the eluates were directly analyzed by high-resolution electrospray ionization mass spectrometry (HR-ESI-MS). The titer of microcin J25 in bioreactor was analyzed by HPLC. Fermentation broth was centrifuged at 13,500 g for 10 min. The resulting supernatant was filtered through a 0.22 μm membrane for subsequent HPLC detection. A serial dilution of microcin J25 standard stock solutions was prepared for quantitative analysis. An Agilent 1260 series HPLC system equipped with a ZORBAX 300SB-C18 column (5 μm, 4.6×250 mm) was used for detection. The mobile phase consisted of 0.1% trifluoroacetic acid in water-acetonitrile (950:50, v/v, phase A) and 0.85% trifluoroacetic acid in water-acetonitrile (100:900, v/v, phase B), flowing at a rate of 1 mL/min. The detection wavelength was set at 214 nm.

Screening of strain Monascus purpureus M_Y2
Mutagenesis of Monascus purpureus M_Y2: Cryopreserved M. purpureus M_Y2 cultures were plated on PDA medium (4.8% Potato leach powder, 8% Glucose, 5.6% Agar, pH 5.6 ± 0.2). After 7–10 days of incubation at 30°C, spores were harvested, transferred to a 50 mL flask with 10 mL sterile water, and filtered through a 20 mL sterile syringe. The spore concentration was adjusted to 10^6 CFU/mL. 20 μL of the spore suspension was irradiated with UV (20, 30, 40, and 50 s), ARTP (25, 50, 70, and 90 s), or HEPE (1, 2, 3, and 4 kGy) as described for S. lividans 1326.

Mutant Screening: After irradiation treatment, spores were suspended in 200 μL sterile water, serially diluted, and spread on PDA plates. Plates were incubated at 30°C for 7–10 days. Sixty clones from each treatment were randomly selected and inoculated into 250 mL Erlenmeyer flask containing 100 mL seed medium (8% glucose, 1% peptone, 0.2% NH₄H₂PO₄, 0.05% MgSO₄·7H₂O, 0.01% CaCl₂), followed by 48 h incubation at 30°C, 140 rpm. For solid-phase fermentation, the seed liquid was transferred to a 4 L glass fermenter with 400 g of indica rice at 10% (v/m) ratio and incubated at 30°C and 60% humidity for 14 days. After incubation for 48 h, the rice medium was turned twice daily.

Lovastatin production analysis: After fermentation, 0.5 g of the culture was extracted with 30 mL 70% acetonitrile, 30% phosphoric acid aqueous solution (0.01%) in a shaking bath at 50°C for 1 h. The solid residue was removed by centrifugation (13,500 g, 5 min), and the supernatant was filtered through a 0.22 μm microporous membrane. Lovastatin production in the filtered supernatant was quantified by HPLC on Shimadzu LC-2010AT system using a ZORBAX Eclipse XDB-C18 column (250 mm×4.6 mm, 5 μm). An isocratic elution of 70% MeCN-H₂O (0.01% phosphoric acid) over 15 min was used, with a flow rate of 1 mL/min. The ultraviolet detection wavelength was 238 nm.

Statistics and reproducibility
Data are presented as the mean ± SEM of three independent biological replicates unless otherwise stated. No statistical method was used to predetermine sample size. No data were excluded from the analyses. Comparisons among groups were performed using one-way ANOVA. Statistical analyses were performed using IBM SPSS Statistics (version 25), and statistical significance was defined as *P<0.05, **P<0.01, ***P<0.001. n and P values are described in the figures or figure legends or Source Data file. All experiments were independently repeated at least three times with similar results.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “Enhancing Microbial Metabolic Capacity through High-Energy Electron Beam-Induced Intense Structural Variations” by Feng et al., reports the set-up of different irradiation conditions to enhance the genetic diversity of S. lividans with the aim to improve its metabolic activity.\nThe organization of the manuscript allows to easily follow the relevance of the study.\nGiven that the first part of the study focuses on the evaluation of different sources of irradiations on the induction of structural variations the authors should go deeper in the analysis\nPlease find below few points that should be developed to further assess the effects of the irradiation sources\nWhat are the lengths of the INDELs in the different experiments?\nDo they differ between irradiations sources?\nWhat does it mean?\nWhere do insertions come from?\nAre the origins of insertions in the vicinity (1D or 3D) of the rearranged regions?\nThe authors should also discuss that error free/prone repair mechanisms might differ between irradiations sources in addition to the number of DSB induced. The balance between these parameters lead to the outcome of repair that is measured by the number of SV.\nIn other words, the repair pathways used by S. lividans upon irradiation might differ and lead to different outcome that either correlates or not with the DSB formation.\nI would suggest to show the Hi-C results as matrix to better highlight the putative change in 3D organization upon irradiation and repair. This would allow to show multi contacts.\nMinor points\nFigure legends should be more detailed\nThe duration of the UV irradiation is not a dose but depends on the dose rate. Thus, UV doses must be given in J/cm2 or J/m2.\nThe TUNEL assays have been performed immediately after irradiation. I am wondering whether some irradiation sources would induce breaks with different kinetics. This should be discussed or reference should be added.", "reviewer_2": "This study introduces high-energy pulsed electron beams (HEPE) as a highly effective mutagenesis tool for enhancing microbial metabolite production. Through a systematic comparison with five conventional irradiation methods (UV, MW, γ-ray, X-ray, and ARTP) in Streptomyces lividans, the authors demonstrate that HEPE induces substantially higher rates of double-strand breaks (DSBs) and structural variations (SVs), while also activating metabolic pathways more efficiently and preserving superior cell viability. By coupling HEPE with high-throughput metabolomics (HEPE-HiTMS), the researchers successfully unlocked two cryptic secondary metabolites—griseobrucins and fradibactins—and achieved increased production yields of industrially relevant compounds, including clavulanic acid, microcin J25, and lovastatin, across diverse microbial species. The methods presented in this paper essentially fall within the scope of conventional mutagenesis techniques. While they demonstrate some novelty in the discovery of unknown natural products, their potential to drive progress in novel product discovery remains limited unless the mechanistic links between mutations and phenotypic changes are elucidated. The strategy of simply inducing random genomic mutations to generate new products is, in essence, a long-established research paradigm. Thus, several assays require further validation or supplementation, and numerous inaccuracies need to be corrected. Therefore, I recommend publication only after substantial revision. Specific suggestions for revision are provided below.\nMajor Comments\n1.HEPE-induced structural variations (SVs) drive the activation of cryptic biosynthetic gene clusters (BGCs), leading to increased secondary metabolite production. However, the mechanistic link between specific SVs and BGC activation is not fully established, the causality between specific SVs and metabolite production could be explored more mechanistically, e.g., RNA-seq data (or qRT-PCR validation) to demonstrate that specific BGCs (e.g., gbn, fdn) are transcriptionally upregulated in HEPE mutants, or discuss correlations of specific SV types (e.g., translocations, deletions) with changes in the regulatory architecture of BGCs.\n2.The Hi-C analysis reveals changes in chromatin architecture, but the link between SV-induced 3D genome remodeling and metabolite activation remains speculative. Can it be identified whether any SVs disrupt known regulatory domains, such as promoters, repressors, or insulation boundaries?\n3.The comparison of six irradiation technologies lacks normalization for absorbed radiation dose. For a fair comparison, please provide quantitative values such as dose equivalents (Gy or kGy), D10 values, and LET for each treatment.\n4.The broad claim that HEPE is a “universal” tool for microbial strain improvement is premature given that only five species were tested. Please either qualify this claim or include additional examples from more phylogenetically diverse microbes.\n5.The structural characterization of griseobrucins and fradibactins is impressive, but the biological activity data are minimal. It would be better to test or discuss additional bioactivities beyond basic antimicrobial screening.\n6.The HEPE-HiTMS approach is powerful, but no integration is shown between the metabolomic and genomic datasets. Please clarify whether mutants producing new metabolites shared common SVs or genomic rearrangements near the associated BGCs.\n7.The manuscript does not sufficiently compare HEPE to modern genome engineering tools like CRISPR/Cas9 or BGC refactoring. Please discuss the advantages and limitations of HEPE relative to synthetic biology approaches, especially in precision, scalability, and strain traceability.\n8.No discussion is included on the possible unintended effects of random SVs, such as growth defects, metabolic imbalance, or genome instability. Please add phenotypic data, e.g., growth curves, morphology, or biomass yield to assess off-target consequences of HEPE mutagenesis.\n9.The functional validation of the fdn and gbn clusters (e.g., via gene knockouts or overexpression) is minimal. It would strengthen the argument if you could show that disrupting these clusters abolishes metabolite production in HEPE mutants.\nMinor Comments\n1.The term “expression of metabolome profile” (Abstract, line 38-39) is ambiguous. Consider rephrasing as “activation of secondary metabolite production” or “enhancement of metabolite biosynthesis.”\n2.Please avoid overuse of vague qualifiers such as “significantly” or “markedly” unless supported by statistical metrics.\n3.Please clarify how radiation doses for HEPE were selected and calibrated relative to the other platforms.\n4.Several phrases are awkward or non-standard (e.g., “super pure helium,” “expression of pigments stably expressed mutants,” “mutational stability”). Please revise for clarity and consistent scientific terminology.\n5.Figure legends and tables should include statistical tests and sample sizes (n), especially for yield comparisons. For instance, in Fig. 2f/g and Fig. 5c.\n6.In the discussion, it would be helpful to comment briefly on the regulatory or biosafety implications of using HEPE-induced strains for industrial-scale production, especially when compared to genome-edited strains.\n7.In the Hi-C analysis, the manuscript claims HEPE-induced SVs are “uniformly distributed”, but does not quantify the degree of randomness or clustering. Consider including an enrichment or dispersion analysis to support this observation.\n8.It is not clear if the observed metabolic enhancements are stable in long-term culture or industrial conditions (e.g., fed-batch). Please add a note on stability testing beyond the few subcultures mentioned.\n9.The term “record-high yield” is used for correlated metabolites production, but no direct comparison reference values were given. Please provide comparative data of existing production records.\n10.Please indicate whether HEPE exposure affects plasmid stability or chromosomal integration in strains with engineered constructs (e.g., E. coli expressing mccJ25).", "reviewer_3": "The manuscript by Feng et al reports on a side-by-side comparison of the use of various irradiation methods to impact the expression of secondary metabolites in Streptomyces, E. coli, and the filamentous fungus Monascus purpureus. Radiation has long been a mainstay strategy for developing improved secondary metabolite production strains on an industrial scale. Traditionally, this has been achieved with readily accessible methods such as exposure to UV or gamma radiation and chemical mutagens. The goal of these approaches is to disrupt the chromosome sufficiently to cause mutations that enhance the expression of biosynthetic genes without gravely impairing the physiology or growth characteristics of the parent strain. In this work, the authors compare UV, microwave, X-ray, gamma ray, atmospheric room temperature plasma (ARTP), and high-energy pulsed electron beam (HEPE) radiation strategies using the model organism Streptomyces lividans. This well-known model organism produces two coloured secondary metabolites, actinorhodin (blue color, ACT) and uncedylprodigiosin (red color, RED). These pigments are facile markers of secondary metabolite gene expression, making S. lividans a commonly used model organism in natural products research.\nThe authors find that HEPE is significantly superior to the other irradiation strategies in enhancing the production of ACT and RED. Furthermore, they show that HEPE has a minimal impact on the overall morphology of S. lividans mycelia as assessed by scanning electron microscopy compared to the other radiation methods. They show that the HEPE approach can enhance the production of otherwise cryptic biosynthetic gene clusters that include at least two novel metabolites and two different streptomyces species. They demonstrate that HEPE can improve the production of key metabolites in Streptomyces clavuligerus, E coli, and a Monascus species, each of which produces valuable industrially accessed compounds.\nThis is an intriguing report; it uniquely compares irradiation methods for strain improvement to produce secondary metabolites. The history of this research area is often shrouded in mystery and secrets that the pharmaceutical industry has kept hidden to enhance secondary metabolite production in industrial strains. As a result, there are few systematic reports outlining general strategies for industrial strain development. This report suggests that HDPE offers a robust and superior approach for generating candidate industrial strains. Therefore, it is a valuable contribution to the field of natural product production.\nWhile this advancement is impressive, the manuscript lacks many details, making it difficult to read and interpret. For example, the authors conclude that double-strand breaks are the ideal marker of genetic rearrangements conducive to producing secondary metabolites, but this has not been systematically studied. The authors should either conduct additional work to demonstrate that double-strand breaks are the critical markers of strain improvement or tone down this claim throughout the manuscript.\nThe general applicability of the method is also questionable. HEPE requires unique instrumentation that is not widely accessible. The authors should be aware of this limitation.\nSeveral minor issues also make reading the manuscript frustrating. For instance:\nLine 121 states that the ACT and RED biosynthetic gene clusters are cryptic, but this is not well established. After fermentation on various media, Streptomyces lividans is known to be a prolific producer of both pigments. Therefore, the authors should be cautious with such definitive statements. It might be that these pigments are produced more rapidly in mutant than in wild-type strains, but claiming that the BGCs are cryptic contradicts decades of Streptomyces research.\nAdditionally, it's unclear whether the authors have quantified the increase in pigment production beyond the simple observation of red and blue colonies. This lack of detail is a recurring issue throughout the manuscript.\nLine 129, the authors report mutation rates in percentage, but referring to such values as rates, which are defined as a measure of change over time, is confusing and perhaps misleading.\nIn Figure 1, the scale bars are reported in the legend, but if they are in the panels, they are impossible to see.\nAll figure legends lack sufficient detail, forcing readers to dig into the manuscript or materials and methods to understand what is being done, what is being measured, the number of replicates or isolates used, and so on. I recommend a complete revision and rewriting of the figure legends to make it easier for readers to grasp the key information clearly and understand the strategies employed without having to delve deeply into the main text or methods section.\nA series of perplexing sentence structures sometimes make the manuscript difficult to read. For example:\nLine 51, “which are normally inefficient to achievable secondary metabolites…” is grammatically incorrect\nLine 54 “this challenge causes the slow pace of drug discovery...” The use of the word ‘cause’ here is questionable. A word like contributes is probably better.\nLine 59 “While DNA double strand breaks (DSBs) catalyze SV formation, yet spontaneous DSBs in microbes under natural conditions are extremely rare...” This sentence is disconnected from the previous one.\nLine 62, “introducing various strong DNA damage” is grammatically incorrect.\nThe document must be carefully analyzed for grammatical errors that make following the narrative frustrating and challenging."}, "author_response": "Response to reviewer’ comments:\nReviewer 1:\nMajor comments 1: What are the lengths of the INDELs in the different experiments? Do they\ndiffer between irradiations sources? What does it mean?\nAn insertion or deletion (indel) is the gain or loss of one or more nucleotides spanning <50\nbase pairs. Analysis of sequencing data revealed that UV-induced insertions ranged from 1 to 46 bp\n(average: 9 bp), while deletions ranged from 1 to 32 bp (average: 4 bp). For ARTP, insertions ranged\nfrom 1 to 46 bp (average: 7 bp), and deletions ranged from 1 to 28 bp (average: 3 bp). For HEPE,\ninsertions ranged from 1 to 45 bp (average: 7 bp), and deletions ranged from 1 to 14 bp (average: 3\nbp). These results are summarized in the Table S2. No significant differences in the average length\nof indels were observed across different irradiation sources, indicating that despite their distinct\ntypes of radiation sources, UV, ARTP, and HEPE all induced random, small-fragment indels\nmutations in the genome of S. lividans 1326 following irradiation. This further suggests that the\nhigher activation efficiency of HEPE, compared to UV or ARTP, is not attributable to differences in\ngenomic small-fragment indels mutations. The complete data of indels generated by the three\nirradiation techniques in mutagenized strains have been provided in the supplementary Excel file\nnamed “indels”.\nTable S2. Summary of genomic indels lengths in S. lividans 1326 induced by different irradiation\nsources.\nLength Small-fragment length (< 50 bp)\nInsertion Deletion\nIrradiation type Range Average Range Average\nUV 1-46 9 1-32 4\nARTP 1-46 7 1-28 3\nHEPE 1-45 7 1-14 3\nWhere do insertions come from? Are the origins of insertions in the vicinity (1D or 3D) of the\nrearranged regions?\nInsertions are rearrangements with one or several donor sequences inserted into a non-adjacent\nchromosomal locus. Donor sequences originate from experimentally induced as well as spontaneous\ngenome instability11. While variant analysis allows us to infer the insertion site within the reference\ngenome, it remains challenging to pinpoint the exact donor locus of the inserted sequence or to\nestablish whether the insertion originated in the spatial vicinity of the rearranged region.\nMajor comments 2: The authors should also discuss that error free/prone repair mechanisms might\ndiffer between irradiations sources in addition to the number of DSB induced. The balance between\nthese parameters lead to the outcome of repair that is measured by the number of SV. In other words,\nthe repair pathways used by S. lividans upon irradiation might differ and lead to different outcome\nthat either correlates or not with the DSB formation.\nAs you mentioned, mutational outcome depends not only on the number of DSBs but also on\nthe nature of the DNA damage and the subsequent cellular repair response across different\nirradiation sources. According to your comment, we have expanded our Discussion section (now\nlines 308-322) to incorporate this context, with the specific additions as follows:\nTo cope with irradiation-induced DNA DSBs, cells kickstart the error-prone NHEJ pathway\nfor rapid repair. This survival mechanism directly contributes to the generation of SVs. The\nmutational outcome depends not only on the number of DSBs but also on the nature of the DNA\ndamage and the subsequent cellular repair response across different irradiation sources14. For\ninstance, high LET radiation, such as HEPE, produce complex and clustered DNA lesions almost\ninstantaneously that are challenging to repair and might be preferentially handled by rapid but error-\nprone mechanisms like NHEJ15. In contrast, UV radiation mainly generates cyclobutane–pyrimidine\ndimers, which are commonly repaired by photolyase-mediated photoreactivation47. Plasma\nirradiation, however, induces oxidative DNA damage or strand breaks through reactive species\nintracellularly48. Such oxidative DNA lesions are mainly repaired by two mechanisms: base excision\nrepair (BER) and nucleotide excision repair (NER)49. SSBs are typically repaired by BER, whereas\nDSBs are generally repaired by homologous recombination (HR) or NHEJ mechanisms. Differences\nin the nature of DNA damage and repair mechanisms explain why HEPE generates more SVs. A\nmore thorough understanding of the mechanisms underlying SVs formation will be advanced\nthrough continued characterization of SVs breakpoints and the development in vitro and in vivo\nmodel systems that can generate chromosomal SVs\nMajor comments 3: I would suggest to show the Hi-C results as matrix to better highlight the\nputative change in 3D organization upon irradiation and repair. This would allow to show multi\ncontacts.\nThanks for your advice.\nWe have now included genome-wide Hi-C contact matrices comparing the control and\nirradiated conditions in the revised manuscript to illustrate global changes in 3D architecture. These\nnew matrix plots has been added as Figure S3 in the revised manuscript. The description in the\nResults section (lines 166-176) has been updated to correspond with these additions, as shown below:\nTo investigate potential alterations in 3D conformation in HEPE mutant, we compared the\nchromosome architecture of the wild type (WT) and the mutant strain (H_D1) using high-throughput\nchromosome conformation capture (Hi-C). First, circus plot (Fig. 3c) of the significant interactions\n(see Materials and Methods) revealed a substantial increase in interaction number in mutant (8830)\ncompared to WT (458). Furthermore, the 3D architecture (Fig. 3d) showed that WT maintain a\ncompact chromosome organization, whereas the mutant exhibited a more relaxed conformation.\nThis difference can be elucidated by the differential interaction and compact analysis (see Materials\nand Methods). As shown in the differential interaction heatmap and the compact matrix (Fig. S3),\nWT is enriched in long-range interactions, maintaining its tighter spatial connectivity. In contrast,\nmutant showed enhanced short-range interactions, corresponding to a looser spatial organization.\nFigure S3. Interaction difference analysis between WT S. lividans 1326 and its mutant (H_D1)\nderived from HEPE mutagenesis. a, The differential interaction heatmap (mutant versus WT). Both\naxed represent genomic positions in bins. Red or blue indicates loci with stronger interaction\nintensity in the mutant or WT sample, respectively. Deeper color denotes a greater difference. b,\nGenome compaction changes between samples. The interaction strength differences at various\ngenomic distances (y-axis) for each locus (x-axis) are compared. Red or blue indicates stronger\ninteractions in WT or mutant, respectively. Deeper color denotes a greater difference.\nMinor points 1: Figure legends should be more detailed\nThanks for your suggestion, we have changed all figure legends with adding sufficient details,\nyou can find these newly added details in blue color within file Article File with tracked changes.\nMinor points 2: The duration of the UV irradiation is not a dose but depends on the dose rate. Thus,\nUV doses must be given in J/cm2 or J/m2.\nThe light energy is calculated as the product of light intensity and irradiation time. Using an\nultraviolet lamp with a light intensity of 48 μW/cm², the resulting light energies at irradiation times\nof 30, 60, 90, 120, 150, 180, and 210 seconds are calculated to be 1.44, 2.88, 4.32, 5.76, 7.20, 8.64,\nand 10.08 mJ/cm², respectively. This description has been added to the main text lines 451-453.\nMinor points 3: The TUNEL assays have been performed immediately after irradiation. I am\nwondering whether some irradiation sources would induce breaks with different kinetics. This\nshould be discussed or reference should be added\nThanks for your advice.\nTUNEL assay detects DNA strand breaks by using terminal deoxynucleotidyl transferase to\ncatalyze the attachment of modified deoxynucleotides to the DNA broken ends. In our study, the\nTUNEL assay was performed immediately after irradiation primarily to capture the direct DNA\nstrand breaks induced by the radiation source used. Our main goal was to compare the initial DNA\ndamage response across the treatment groups, prior to the initiation of any repair processes.\nHowever, your concern regarding the induction of DNA breaks with different kinetics by some\nirradiation sources is justified. This variation arises from differences in the complexity of the\nresulting DNA lesions and the repair pathways involved. Different types of damage engage distinct\ncellular repair pathways and operate at varying repair rates. This may affect the intensity of the\n\"break signals\" detected by TUNEL at a specific time point. As you suggested, we have now\nexpanded the Discussion section (now lines 308-322) to address the potential influence of radiation\nsources on repair pathways. The relevant text corresponds to the response to Major Comment 2\nabove. We have also added supporting references (15, 46-48).\nReviewer 2:\nMajor Comments 1: HEPE-induced structural variations (SVs) drive the activation of cryptic\nbiosynthetic gene clusters (BGCs), leading to increased secondary metabolite production. However,\nthe mechanistic link between specific SVs and BGC activation is not fully established, the causality\nbetween specific SVs and metabolite production could be explored more mechanistically, e.g.,\nRNA-seq data (or qRT-PCR validation) to demonstrate that specific BGCs (e.g., gbn, fdn) are\ntranscriptionally upregulated in HEPE mutants, or discuss correlations of specific SV types (e.g.,\ntranslocations, deletions) with changes in the regulatory architecture of BGCs.\nThanks for your advice.\nBased on your suggestion, we have performed additional RNA-seq experiments. We selected\nthe S. lividans 1326 wild-type strain and three HEPE mutants in which the act or red gene clusters\nwere activated for transcriptome sequencing. The results demonstrate that transcription levels of\nnumerous secondary metabolic genes were upregulated in HEPE mutants, including those\nresponsible for ACT and RED biosynthesis (new results are presented in Figure S4). These mutant\nstrains harbored various types of SVs, such as insertions, deletions, and translocations, with no\ncommon SV identified across all mutants. The results indicate an inconsistent association between\nthe SVs in different mutant strains and the activation of the target gene clusters. This is further\nsupported by the observation that in the three mutant strains, the specific genes that were upregulated\nwithin the ACT and RED gene clusters also varied. Consequently, the mechanisms underlying\ncluster activation likely vary among strains, which led us to forgo the idea of employing the\nchallenging approach by performing individual gene knockouts to validate the impact of different\nSV variants on metabolic activation.\nIn addition, transcriptomic data revealed extensive metabolic alterations in the mutants, which\nwere further reflected in the 3D genome conformation. We supplemented the analysis by comparing\nthe 3D genomic architecture of mutant strain with that of the wild-type (WT) to elucidate the local\nor global genome changes. First, circus plot (New Fig. 3c) of the significant interactions revealed a\nsubstantial increase in interaction number in mutant (8830) compared to WT (458). Specifically, the\ninteraction intensity within the ACT and RED gene cluster regions was markedly enhanced,\nindicating significant alterations in spatial architecture. Furthermore, the 3D architecture (Fig. 3d) showed that WT maintain a compact chromosome organization, whereas the mutant exhibited a\nmore relaxed conformation. This structural relaxation likely facilitates the binding of RNA\npolymerase and other transcriptional machinery to DNA by increasing physical accessibility to\nregulatory regions and gene promoters, ultimately modulating the expression of secondary\nmetabolite BGC32. This global architecture change is unlikely to be caused by small-scale SNVs or\nindels, but is primarily attributed to large-scale SVs.\nIn summary, while a direct causative link between a specific SV and transcriptional\nupregulation remains unconfirmed, the collective evidence supports a central role for SVs in driving\nthe broad transcriptomic reprogramming, metabolic remodeling, and altered 3D genome\narchitecture observed in these mutants.\nFigure S4. Volcano plot of differentially expressed genes between WT S. lividans 1326 and its\nmutants derived from HEPE mutagenesis. H_D1, H_D28, and H_D34 were three mutants obtained\nby HEPE irradiation. Each point represents a gene. The x-axis shows the log (fold change) in\n2\nexpression (Mutant/ WT). The y-axis shows the -log (p-value) from the statistical test (edgeR).\n₁₀\nGenes with |log (fold change)| ≥ 1 and p-value < 0.05 are considered significant and are\n2\nhighlighted in red (up-regulated) and blue (down-regulated). Gray points represent non-significant\ngenes. Selected genes of interest within the ACT and/or RED BGCs are explicitly labeled with gene\nsymbols. The dashed lines indicate the significance thresholds.\nFigure 3. The genomic alterations induced by HEPE irradiation mutagenesis. c, Comparison\nof the significant interactions between WT S. lividans 1326 and its mutant (H_D1) derived from\nHEPE mutagenesis. BGCs of actinorhodin and undecylprodigiosin were labeled as act and red.\nFrom outer to inner in the Circle plot: Circle 1, identical to Circle 5 in panel (b). Circle 2, gene\nintensity. The significant interactions are shown as blue line links, with the color gradient (dark to\nlight) indicating increasing p-values. d, Comparison of the 3D chromosome conformation between\nWT S. lividans 1326 and its mutant (H_D1) derived from HEPE mutagenesis.\nMajor Comments 2: The Hi-C analysis reveals changes in chromatin architecture, but the link\nbetween SV-induced 3D genome remodeling and metabolite activation remains speculative. Can it\nbe identified whether any SVs disrupt known regulatory domains, such as promoters, repressors, or\ninsulation boundaries?\nThanks for your advice.\nWe analyzed genomic variations in mutant strains of S. lividans 1326 with activated\nactinorhodin (ACT) and undecylprodigiosin (RED) gene clusters but did not identify any SVs that\ndirectly broke the pathway-specific regulatory proteins within the clusters. However, in several\nHEPE mutants, we detected SVs—including deletions and translocations—in the upstream regions\nof genes encoding TetR or SARP family regulators. The genes redD and actII-4 encode positive\nregulators for the biosynthesis of RED and ACT, respectively. The regulatory factors encoded by\nboth genes belong to the SARP family. Another ACT pathway-specific regulator exists in S. lividans:\nActII-1, a member of the TetR family. It has been reported to act as a repressor of ACT biosynthesis\n(See additional document). These variants near regulatory genes may influence their expression or\nfunction, thereby potentially driving the activation of the ACT or RED clusters.\nStructural variations (SVs), encompassing insertions, deletions, translocations, and other\nrearrangements, exert their impact not only through disrupting discrete genetic elements like\npromoters or repressors but, more profoundly, by remodeling the 3D genome architecture and\nrewiring metabolic networks. To test this, we conducted two critical experiments: transcriptome\nsequencing (RNA-seq) and comparative 3D genome conformation analysis between S.\nlividans 1326 WT and mutant strains. As detailed in our response to your Major Comment 1 above,\ntranscriptomic analysis revealed that in three distinct mutant strains, the upregulated biosynthetic\ngenes responsible for ACT and RED production varied significantly, indicating notable differences\nin their respective activation mechanism. The mutants displayed extensive transcriptional\nreprogramming and a globally altered chromosome architecture. We believe that SVs disrupt both\nthe linear DNA sequence and the higher-order 3D conformation, thereby driving the observed\nenhancement in metabolite production. In future work, we will perform more in-depth studies to\nprovide more experimental evidence linking these SVs to metabolite activation.\nMajor Comments 3: The comparison of six irradiation technologies lacks normalization for\nabsorbed radiation dose. For a fair comparison, please provide quantitative values such as dose\nequivalents (Gy or kGy), D10 values, and LET for each treatment.\nWe sincerely appreciate your comment regarding the necessity of dose normalization for fair\ncomparison of different irradiation technologies. While we recognize the importance of normalized\ndose metrics for direct comparison, the nature of six irradiation technologies presents challenges in\nobtaining complete dose equivalence data. Specifically, UV employs photons, microwaves utilize\nelectromagnetic waves, X-ray and γ-ray are high-energy electromagnetic radiation, ARTP are based\non plasma, and HEPE involves electron beams. The fundamental differences in irradiation\nmechanisms and energy level among these various types of irradiations makes it difficult to\nstandardize their doses uniformly. To address this limitation, we irradiated samples with four\nincrementally increasing doses, covering the commonly used range from the lower to the upper\ndetection limits of the irradiation instrument.\nMajor Comments 4: The broad claim that HEPE is a “universal” tool for microbial strain\nimprovement is premature given that only five species were tested. Please either qualify this claim\nor include additional examples from more phylogenetically diverse microbes.\nWe agree that describing HEPE as “universal” based on results from only five microbial strains\nis premature. In this study, HEPE was applied to five strains spanning three phylogenetically distinct\ngroups: Escherichia coli (bacterium), Streptomyces griseobrunneus, Streptomyces fradiae,\nand Streptomyces clavuligerus (actinomycetes), and Monascus purpureus (filamentous fungus).\nWhile these representatives suggest broad applicability, we acknowledge that a more comprehensive\nphylogenetic diversity would be required to support a claim of universality. Accordingly, we have\nreplaced the term “universal” with more accurate descriptors such as “broadly applicable” in the\nmain text.\nMajor Comments 5: The structural characterization of griseobrucins and fradibactins is impressive,\nbut the biological activity data are minimal. It would be better to test or discuss additional\nbioactivities beyond basic antimicrobial screening.\nThank you for the positive assessment of our structural characterization work and for this\nsuggestion regarding the biological evaluation. In our initial biological screening, compounds\ngriseobrucins and fradibactins showed no antimicrobial activity against any of the tested bacterial\nstrains in vitro. According to your comment, we have conducted additional cytotoxic activity\nscreening using the CCK-8 assay. The compounds were evaluated against a panel of twenty human\ncancer cell lines, including A549, MKN-45, HCT 116, HeLa, HepG2, K-562, 786-O, TE-1, 5637,\nGBC-SD, L-02, MCF7, SF126, DU145, CAL-62, PATU8988T, HOS, A-375, A-673, 293T.\nRegrettably, neither griseobrucins nor fradibactins demonstrated no discernible cytotoxic activity\nagainst the tested human cancer cell lines.\nThe griseobrucins belong to the diketopiperazine (DKP) class of natural products. In addition\nto their documented antibacterial and antitumor activities, recent researches have revealed that\nDKPs compounds can participate in intercellular communication through quorum sensing49, exhibit\nhypoglycemic effects50, and demonstrate antioxidant activity51. Inspired by these findings, we\npropose that future research should include a systematic biological evaluation of the griseobrucins\nto explore their potential pharmacological roles in quorum sensing regulation, oxidative stress\nmitigation, and metabolic regulation. Additionally, fradibactin represent a carboxylic acid-\nhydroxamic acid hybrid siderophore. Recent advances have shown that conjugating siderophore\nmolecules with antibiotics to utilize bacterial iron uptake systems for intracellular drug delivery\nconstitutes a promising strategy for developing novel antimicrobial agents52. Subsequent studies\ncould attempt to conjugate fradibactin with clinically relevant antibiotics and evaluate the\nantimicrobial activity of the resulting conjugates against ESKAPE pathogens and other clinically\nsignificant strains. The results and discussion regarding the bioactivity of these compounds have\nbeen integrated into the main text (lines 328–333).\nMajor Comments 6: The HEPE-HiTMS approach is powerful, but no integration is shown between\nthe metabolomic and genomic datasets. Please clarify whether mutants producing new metabolites\nshared common SVs or genomic rearrangements near the associated BGCs.\nOur approach focused on validating the HEPE methodology using S. lividans 1326, where the\nBGCs for ACT and RED are normally silent. The successful activation of these clusters confirmed\nHEPE as an effective mutagen for cryptic BGCs. We sequenced 32 HEPE-induced S.\nlividans mutants and observed a high frequency of SVs.\nBuilding on this, the HEPE-HiTMS pipeline was employed and successfully identified two\nnovel classes of natural products. However, due to constraints in time and resources, we were unable\nto conduct large-scale genome sequencing of the numerous mutant strains producing these new\nmetabolites. The high-throughput genome sequencing of S. lividans 1326 mutants provided\nsufficient clues to connect SVs with metabolites production. Analysis of the existing sequencing\ndata from the ACT/RED-activating mutants in S. lividans 1326 revealed no shared, identical SVs or\ngenomic rearrangements in the vicinity of their respective BGCs. This finding is consistent with the\nnature of HEPE as a non-targeted mutagen—the DNA damage it induces and the subsequent repair\nprocesses are stochastic, leading to diverse genomic alterations even when the phenotypic outcome\n(e.g., activation of a specific BGC) is similar. Furthermore, although no common SVs were observed\nwithin the gene clusters themselves, the newly added transcriptomic and 3D conformational data\nfrom the mutant strains-as detailed in our response to your Comment 1-reveal that both the ACT\nand RED gene clusters are located within regions of significantly enhanced chromosome\ninteractions. This supports your hypothesis that SVs likely contribute to metabolite activation by\naltering the spatial architecture surrounding the relevant BGCs.\nMajor Comments 7: The manuscript does not sufficiently compare HEPE to modern genome\nengineering tools like CRISPR/Cas9 or BGC refactoring. Please discuss the advantages and\nlimitations of HEPE relative to synthetic biology approaches, especially in precision, scalability,\nand strain traceability.\nWhile synthetic biology approaches such as CRISPR/Cas9 enable targeted genomic edits, they\nheavily rely on genetic engineering manipulation. In contrast, HEPE operates without the need for\ngenetic manipulation or the introduction of exogenous systems. This key advantage makes HEPE\nparticularly suitable for applications subject to strict regulatory constraints and for use in wild-type\nindustrial strains with limited genetic tools. Furthermore, as requested, we have now added a new\nparagraph (now lines 334-350) to the Discussion section in the revised manuscript discussing the\nadvantages and limitations of HEPE compared to CRISPR/Cas9 or BGC refactoring approaches,\nwith particular emphasis on precision, scalability, and strain traceability. The detailed contents are\nas follows:\nWhile HEPE requires specialized accelerators, its mutagenic efficiency significantly reduces\nscreening time, workload and costs, offsetting initial investments. Compared to synthetic biology\napproaches such as CRISPR/Cas9 or BGC refactoring, HEPE offers distinct advantages as well as\ncertain limitations. CRISPR/Cas9 enable targeted genomic edits but heavily relies on genetic\nengineering manipulation. In contrast, HEPE operates without the need for genetic manipulation or\nthe introduction of exogenous systems, making it particularly suitable for applications subject to\nstrict regulatory constraints and for use in wild-type industrial strains with limited genetic tools. In\nterms of precision, HEPE introduces random mutations across the genome, differing fundamentally\nfrom the single-nucleotide accuracy of CRISPR/Cas9. Although this limits its utility for specific\nedits, it allows for broad mutagenesis and discovery of unexpected functional mutations-thereby\nserving as a valuable complement to targeted approaches. Regarding scalability, HEPE is highly\nscalable for generating diverse mutant libraries in a single step, bypassing the need for complex\nDNA construct design and transformation procedures required for CRISPR-based methos or BGC\nrefactoring. This makes it particularly efficient for high-throughput mutagenesis in non-model or\ngenetically recalcitrant strains. For strain traceability, CRISPR/Cas9 leaves precise and trackable\nedits, whereas HEPE-induced mutations are stochastic and require whole-genome sequencing to\nestablish mutational profiles and track strain.\nMajor Comments 8: No discussion is included on the possible unintended effects of random SVs,\nsuch as growth defects, metabolic imbalance, or genome instability. Please add phenotypic data,\ne.g., growth curves, morphology, or biomass yield to assess off-target consequences of HEPE\nmutagenesis.\nAssessing the potential unintended effects of random SVs is necessary for evaluating the HEPE\nmutagenesis method. We have now performed phenotype and growth curves analysis for randomly\nselected HEPE-mutagenized clones alongside with the wild-type. As shown in the new Figure S5,\nmutagenized clones exhibit reduced sporulation and delayed growth compared to the wild-type\nstrain. We have incorporated these results in the main text (lines 183-188) as follows:\nAdditionally, random SVs can have possible unintended effects, such as growth defects. To\nassess off-target consequences of HEPE mutagenesis, three mutants were selected for phenotype\nand growth curve analysis. Compared to WT strain, mutants exhibited reduced sporulation and\ndelayed growth (Fig. S5). Transcriptome data linked this phenotype to a marked downregulation of\nthe sporulation-associated gene sapA and the regulatory factors whiB and whiD, which control cell\ndivision and spore septum formation33, 34 (Table S5).\nFigure S5. Phenotypic consequences of HEPE irradiation in S.lividans 1326. H_D1, H_D28, and\nH_D34 were three mutants obtained by HEPE irradiation. a, Mutants phenotypes of wild type (WT)\nand selected mutant strains on solid media. b, Growth curve of WT and selected mutant strains in liquid\nculture. Data are presented as mean ± SEM of two replicates cultures.\nTable S5. Expression of sporulation-associated genes from RNA sequencing. H_D1, H_D28, and\nH_D34 were three mutants obtained by HEPE irradiation. Compared to WT, all three mutant\nstrains exhibited a significant downregulation of gene expression (p < 0.001 in each case). Each\nsample was performed with three replicates.\nGene WT H_D1 H_D28 H_D34\nsapA 282.77 202.66 69.19 72.13\nwhiB_1 22.54 10.27 5.14 5.74\nwhiB_2 105.13 73.28 39.64 43.75\nWhi_D 12.40 0.92 4.99 3.08\nMajor Comments 9: The functional validation of the fdn and gbn clusters (e.g., via gene knockouts\nor overexpression) is minimal. It would strengthen the argument if you could show that disrupting\nthese clusters abolishes metabolite production in HEPE mutants.\nWe have knocked out the fdn gene cluster, which is responsible for fradibactins biosynthesis,\nvia the homologous recombination method. Mass spectra analysis confirmed the absence of signals\ncorresponding to the associated metabolites (Figure S11). We also knocked out the proposed gbn\ngene cluster, putatively involved in griseobrucins biosynthesis, using the same method. However,\nmass spectra analysis indicated that the related metabolites signals remained present, suggesting\nthat the proposed gbn cluster is not responsible for the biosynthesis of these compounds.\nConsidering identifying BGCs are not our main task of this study, we now removed the information\nof previous predicted gbn BGC from our manuscript and started to discover the actual gene cluster\nfor griseobrucins in future work.\nFigure S11. Gene inactivation of fdnA using the homologous recombination method. a, scheme of\nthe construction of fdnA replacement mutant. b, PCR confirmation of the fdnA inactivation by\ninsertion of an apramycin resistance-oriT cassette (aac(3)IV+oriT). d, verification of metabolite\nalteration in fdnA inactivation mutant.\nMinor Comments 1: The term “expression of metabolome profile” (Abstract, line 38-39) is\nambiguous. Consider rephrasing as “activation of secondary metabolite production” or\n“enhancement of metabolite biosynthesis.”\nWe have followed your suggestion and replaced it with \" activation of secondary metabolite\nproduction \".\nMinor Comments 2: Please avoid overuse of vague qualifiers such as “significantly” or “markedly”\nunless supported by statistical metrics.\nWe have replaced these terms with more precise, quantitative descriptions or have provided\nthe corresponding statistical evidence where applicable.\nMinor Comments 3: Please clarify how radiation doses for HEPE were selected and calibrated\nrelative to the other platforms.\nRadiation doses for HEPE were 0.5, 1, 2, 3, 4, 5, 6, 7 and 8KGy. The minimum dose of 0.5\nKGy was selected based on the lower limit of the instrument. To determine the optimal HEPE dose\nfor activating secondary metabolite production in the selected bacterial strain, a gradient of doses\nwas tested. The highest dose of 8 kGy was chosen based on preliminary experiments, which resulted\nin a 99.7% lethality rate, representing a near-lethal condition.\nWhile we recognize the importance of dose normalization, the irradiation source type of HEPE\nis completely different from that of other platforms, and its energy is several orders of magnitude\nhigher. As such, direct dose normalization is not feasible. The irradiation source characteristics of\neach platform have already been described in our response to your Major Comment 3. Consequently,\nwe established dose gradients for comparative analysis based on the operational range (from\nminimum to maximum. Additionally, in the mutagenic efficiency section of this article, we\ncompared positive mutation frequency of each platform under conditions of comparable lethality.\nThis approach, to some extent, ensures a relatively equivalent basis for comparison.\nMinor Comments 4: Several phrases are awkward or non-standard (e.g., “super pure helium,”\n“expression of pigments stably expressed mutants,” “mutational stability”). Please revise for clarity\nand consistent scientific terminology.\nThanks for your advice.\nWe have replaced “super pure helium”, “expression of pigments stably expressed mutants” and\n“mutational stability” with “Ultra-high purity helium”, “stably pigments-producing mutants” and\n“genetic stability”, respectively. Furthermore, we have performed a comprehensive line-by-line\nreview of the entire manuscript to eliminate awkward phrasing, improve clarity, and ensure the\nconsistent use of scientific terminology.\nMinor Comments 5: Figure legends and tables should include statistical tests and sample sizes (n),\nespecially for yield comparisons. For instance, in Fig. 2f/g and Fig. 5c.\nThanks for your advice.\nIn Fig. 2f, the term “genetic frequency” was used to represent the hereditary stability of positive\nclones. Thirty-two positive clones were randomly selected from each irradiation treatment for\nanalysis. This metric reflects the proportion of pigmented clones within each subculture generation\nrelative to the total number of clones. Error bars are not applicable for this representation.\nFig. 2g compared the average yields of ACT and RED produced by the mutants generated via\nUV, ARTP and HEPE irradiation. This figure legend has been revised to include statistical tests and\nsample sizes. The detailed information were as follows: Data are presented as mean ± SEM from\nthree independent samples, each performed with three replicates (n = 3). Significance was assessed\nby one-way ANOVA. *, P < 0.05; **, P < 0.01; ***, P < 0.001.\nWe have also added appropriate statistical tests and sample sizes descriptions to Fig. 5c. The\ndetailed information was as follows: Data are presented as mean ± SEM from three independent\nexperiments (n = 3). Significance was assessed by one-way ANOVA. *, P < 0.05; ***, P < 0.001.\nMinor Comments 6: In the discussion, it would be helpful to comment briefly on the regulatory or\nbiosafety implications of using HEPE-induced strains for industrial-scale production, especially\nwhen compared to genome-edited strains.\nBeyond laboratory efficacy, the industrial-scale application of HEPE-mutagenized strains\nwarrants consideration of regulatory and biosafety. Unlike genome editing, which often falls under\ngenetically modified organism regulations due to the use of foreign DNA or targeted genetic\nmodifications, HEPE is a non-transgenic method that avoids exogenous DNA integration. However,\nthe extensive and random mutations induced by HEPE demand rigorous stability and safety\nassessments to ensure consistent industrial performance. The substantial genomic alterations still\nrequire thorough evaluation to guarantee product safety and environmental safety before large-scale\nuse. We have now added some description in the Discussion section (lines 351-358) that address\nthis point.\nMinor Comments 7: In the Hi-C analysis, the manuscript claims HEPE-induced SVs are\n“uniformly distributed”, but does not quantify the degree of randomness or clustering. Consider\nincluding an enrichment or dispersion analysis to support this observation.\nThanks for your advice.\nAccording to your suggestion, we have incorporated a dispersion analysis utilizing genome\nbinning to characterize the distribution of HEPE-induced SVs. Based on the SVs data, a 10-kb\nsliding window approach was employed to assess the dispersion of SVs across the genome. Using\nthe SVs count distribution obtained from the sliding-window analysis, we further calculated the\ncoefficient of variation (CV) and performed a chi-square goodness-of-fit test to evaluate whether\nthe SVs distribution in each group significantly deviated from a uniform pattern. The CV serves as\na measure of dispersion: a lower value indicates a more uniform distribution, while a higher value\nreflects greater variability and clustering. The results demonstrated that HEPE group had a more\nuniform genomic distribution of SVs than the UV and ARTP groups, as evidenced by its lower CV\nvalue, although all groups significantly deviated from a uniform distribution (Table S3). In light of\nthis result, we have updated the text describing the distribution of SVs induced by UV, ARTP or\nHEPE in the Result section (lines 155-159).\nTable S3. Dispersion analysis of SVs in the genome among UV, ARTP and HEPE groups.\nGroup Mean (μ) Standard_Deviation (σ) Coefficient_of_Variation p-value\n(CV=σ/μ)\nUV 5.78 14.96 2.587 <0.0001\nARTP 2.31 4.96 2.145 <0.0001\nHEPE 5.3 8.13 1.534 <0.0001\nMinor Comments 8: It is not clear if the observed metabolic enhancements are stable in long-term\nculture or industrial conditions (e.g., fed-batch). Please add a note on stability testing beyond the\nfew subcultures mentioned.\nIn this study, enhanced production of target metabolites was observed in three industrial strains:\nclavulanic acid in Streptomyces clavuligerus, microcin J25 (mccJ25) in Escherichia coli and\nlovastatin in Aspergillus purpureus. Due to its rapid growth and short culture cycle (2-3 days for\nseed and fermentation cycle), E. coli was subjected to ten consecutive subcultures to assess\nhereditary stability. In contrast, the extended culture periods required for Streptomyces (7-10 days\nfor seed and fermentation cycle) and A. purpureus (16-20 days for seed culture and solid-state\nfermentation) limited the stability evaluation to five subculture generations.\nFor the HEPE-mutagenized S. clavuligerus mutants, the highest-yielding mutant was selected\nand subcultured in 250 ml flasks to monitor clavulanic acid production. The results showed that\nclavulanic acid production remained stable at approximately 5g/L across subcultured generations.\nThese results are presented in Fig. S12a and described in the manuscript at lines 248-250. For the\nHEPE-mutagenized E. coli mutants, the three highest-yielding mutants were chosen for subculturing\nin 100 ml flasks. The results indicated that mccJ25 production by the tenth generation was\ncomparable to the first generation, demonstrating stability. These findings are shown in Fig. S12b\nand described in the manuscript at lines 272-273. Similarly, the highest-yielding mutant of A.\npurpureus obtained via HEPE mutagenesis was subcultured over multiple generations in 1 L solid-\nphase fermentation container. Lovastatin production remained generally stable, and by the fifth\ngeneration, the yield was still higher-approximately fivefold greater-than that of the starting strain.\nThe corresponding data are presented in Fig. S12c and described in the manuscript at lines 282-284.\nWhile the high-yielding phenotype of these metabolites demonstrated hereditary stability in\nlaboratory-scale cultures, further studies under industrial conditions-such as scaled-up fed-batch\nfermentation-will be necessary to evaluate its performance in a production environment. Due to\ncurrent limitations in time and resources, such experiments have not been incorporated into the\npresent study. We plan to conduct these investigations in collaboration with industrial partners in\nfuture work.\nFigure S12. Production stability assessed in HEPE-mutagenized industrial strains: clavulanic acid\n(a) in S. clavuligerus, MccJ25 (b) in E. coli, and lovastatin (c) in A. purpureus.\nMinor Comments 9: The term “record-high yield” is used for correlated metabolites production,\nbut no direct comparison reference values were given. Please provide comparative data of existing\nproduction records.\nWe have now provided direct comparisons with the current production records for both\nclavulanic acid and microcin J25 (mccJ25) in the revised manuscript and cited relevant references.\nThe context and citation for clavulanic acid have been included in lines 250-251 as follows: “It\nreached a yield of 6.3 g/L in a 5 L benchtop bioreactor, which signifies an additional 15% increase\nover the starting strain and is comparable to the record-high of 6.11 g/L23”. Similarly, the context\nand citation for mccJ25 have been added in lines 273-275 as follows: “In scale-up 40 L bioreactors,\nthe best mutant achieved an unprecedented mccJ25 titers of 7.5 g/L - the highest yield reported to\ndate, surpassing the previous literature yield of 4 g/L25”.\nMinor Comments 10: Please indicate whether HEPE exposure affects plasmid stability or\nchromosomal integration in strains with engineered constructs (e.g., E. coli expressing mccJ25).\nIn our current study, we did not directly design experiments to specifically measure plasmid\nstability or chromosomal integration post-HEPE exposure. However, wo can provide the following\nobservations based on our existing data. As HEPE introduces random mutations throughout the\nentire genome, plasmids within engineered strains could potentially be damaged following HEPE\nexposure. However, in clones selected based on improved production of the target metabolite after\nHEPE treatment, the plasmids they harbored remained stable and continued to express the target\nmetabolite efficiently. The fact that we successfully obtained high-producing mutants from\nengineered E. coli strains (which contained plasmid-based expression system for mccJ25) suggests\nthat the core engineered constructs remained functional after HEPE treatment. Moreover, the yield\nof mccJ25 remained stable during the ten consecutive subcultures in the HEPE mutagenized mutants,\nindirectly indicates that the essential genetic elements were not universally disrupted by the\nmutagenesis process.\nReviewer 3:\nMajor Comments 1: While this advancement is impressive, the manuscript lacks many details,\nmaking it difficult to read and interpret. For example, the authors conclude that double-strand breaks\nare the ideal marker of genetic rearrangements conducive to producing secondary metabolites, but\nthis has not been systematically studied. The authors should either conduct additional work to\ndemonstrate that double-strand breaks are the critical markers of strain improvement or tone down\nthis claim throughout the manuscript.\nThanks for your advice.\nDNA double-strand breaks (DSBs) catalyze structure variation formation10, which drive\nmicrobial evolution by altering genomic architecture, transcriptional networks, and metabolic\noutput7-9. Based on the \"DSB–SV–phenotype\" theoretical framework, we hypothesized that a\nmutagenic method capable of inducing more DSBs would likely generate more SVs, thereby more\neffectively activating silent metabolic pathways. To test this hypothesis, we systematically\ncompared three physical mutagenesis methods-UV, ARTP, and HEPE-in terms of DSB levels,\nsecondary metabolite activation efficiency, and genomic variation profiles in Streptomyces lividans\n1326. Both TUNEL assay and plasmid linearization experiments showed that HEPE treatment\nproduced more DSBs compared to UV and ARTP (Fig.1, Fig. S1). Phenotypic results revealed that\nHEPE demonstrated higher activation efficiency for the actinorhodin (ACT) and undecylprodigiosin\n(RED) gene clusters than UV and ARTP (Fig.2). The sequencing results indicated that three\ntreatments did not differ significantly in the number of indels or SNVs. The key difference lay in\nthe fact that HEPE induced more SVs (Fig. 3a). Additionally, we supplemented two critical\nexperiments: transcriptome sequencing (RNA-seq) and comparative 3D genome conformation\nanalysis between S. lividans 1326 WT and mutant strains. The results revealed that the mutant\ndisplayed extensive transcriptional reprogramming (Figure S4) and a globally altered 3D\nchromosome architecture (Figure 3c-d). This global change is unlikely to be caused by small-scale\nSNVs or indels, but is primarily attributed to large-scale SVs.\nBased on these results, we thought more double-strand breaks means the formation of more\nstructure variations, which disrupt both the linear DNA sequence and the higher-order 3D\nconformation, thereby driving the observed enhancement in metabolite production. Nevertheless,\nthese findings remain insufficient to establish DSB as a generalizable marker for predicting the\nmetabolic activation potential of microbial strains. In accordance with your recommendation, we\nhave tone down this claim in this manuscript. We will conduct additional experiments to\ndemonstrate the link between the DSBs and the strain improvement in our future work.\nMajor Comments 2: The general applicability of the method is also questionable. HEPE requires\nunique instrumentation that is not widely accessible. The authors should be aware of this limitation.\nAs you rightly noted, the requirement for specialized accelerators represents the limitation of\nHEPE application. However, such equipment is commonly available in research institutions.\nMoreover, its operation is highly efficient: samples are transported via a conveyor belt to the\nirradiation source, allowing for the simultaneous processing of numerous samples in a rapid manner.\nTherefore, through collaboration with relevant research facilities to access this instrumentation, the\nmethod remains feasible for practical research applications. We have explicitly acknowledged this\nconstraint in the revised manuscript (line 334) and expanded our discussion regarding both the\nadvantages and limitations of HEPE in lines 335-350.\nMinor Comments 1: Line 121 states that the ACT and RED biosynthetic gene clusters are cryptic,\nbut this is not well established. After fermentation on various media, Streptomyces lividans is known\nto be a prolific producer of both pigments. Therefore, the authors should be cautious with such\ndefinitive statements. It might be that these pigments are produced more rapidly in mutant than in\nwild-type strains, but claiming that the BGCs are cryptic contradicts decades of Streptomyces\nresearch. Additionally, it's unclear whether the authors have quantified the increase in pigment\nproduction beyond the simple observation of red and blue colonies. This lack of detail is a recurring\nissue throughout the manuscript.\nAs you stated, the ACT and RED biosynthetic gene clusters are activated after fermentation on\nvarious media. We have corrected the statement in the main text as follows: These two BGCs are\nlowly expressed or cryptic under certain culture conditions, and exhibits blue and red colors upon\ntheir activations (in lines 122-123). Additionally, we have quantified the increase in pigment\nproduction beyond the simple observation of red and blue colonies. Three mutants from each\nirradiation method were selected to evaluate the pigment production stabilities and enhancement.\nThe production of ACT and RED were determined by the absorbance at 600 nm and 530 nm,\nrespectively. The specific details were described in lines 473-480, and the results were showed in\nthe Fig.2g.\nF\nFigure 2. Comparison of mutagenic efficiency among UV, ARTP and HEPE in S. lividans 1326.\ng, Comparison of average ACT and RED yields produced by the mutants of UV, ARTP and HEPE\nirradiation. Data are presented as mean ± SEM from three independent samples, each performed\nwith three replicates (n = 3). Significance was assessed by one-way ANOVA. *, P < 0.05; **, P <\n0.01; ***, P < 0.001.\nMinor Comments 2: Line 129, the authors report mutation rates in percentage, but referring to\nsuch values as rates, which are defined as a measure of change over time, is confusing and perhaps\nmisleading.\nThe values we reported (as percentages) represent the proportion or frequency of observed\nmutations in our cell population at a specific time point, rather than a rate measured over time.\nAccordingly, we have replaced 'mutation rate' with the more accurate term 'mutation frequency'\nthroughout the text, including in the Results section and in the corresponding figure/legend (Fig.\n2c-e).\nMinor Comments 3: In Figure 1, the scale bars are reported in the legend, but if they are in the\npanels, they are impossible to see.\nWe apologize for omitting the scale bars in the panels of Figure 1. We have added them to the\npanels.\nFigure 1. Comparison of the DNA (a) and cell structure (b) damage induced by six irradiation\ntechnologies. a, TUNEL-based fluorescence micrographs showing DNA strand breaks. Scale bar is\n5 µm. b, Scanning electron micrographs revealing cellular damage. Scale bar is 1 µm. The\nirradiation doses are specified in the upper left of each image.\nMinor Comments 4: All figure legends lack sufficient detail, forcing readers to dig into the\nmanuscript or materials and methods to understand what is being done, what is being measured, the\nnumber of replicates or isolates used, and so on. I recommend a complete revision and rewriting of\nthe figure legends to make it easier for readers to grasp the key information clearly and understand\nthe strategies employed without having to delve deeply into the main text or methods section.\nThank you for your suggestion, we have corrected all figure legends with sufficient details,\nyou can find the newly added details in blue color within file Article File with tracked changes.\nMinor Comments 5: A series of perplexing sentence structures sometimes make the manuscript\ndifficult to read. For example:\nLine 51, “which are normally inefficient to achievable secondary metabolites…” is grammatically\nincorrect\nLine 54 “this challenge causes the slow pace of drug discovery...” The use of the word ‘cause’ here\nis questionable. A word like contributes is probably better.\nLine 59 “While DNA double strand breaks (DSBs) catalyze SV formation, yet spontaneous DSBs\nin microbes under natural conditions are extremely rare...” This sentence is disconnected from the\nprevious one.\nLine 62, “introducing various strong DNA damage” is grammatically incorrect.\nWe have corrected these grammatical errors you mentioned above. Moreover, we have\ncarefully reviewed the entire manuscript and corrected some perplexing sentence structures.\nFinally, we would like to once again express our sincere gratitude to the three reviewers for\ntheir constructive comments on our manuscript, as well as thank the editor for the additional time\ngranted to complete the revision and for supporting us in performing additional experiments.\nWe hope that our response satisfactorily addresses the key points. And again, we are sincerely\ngrateful to all the reviewers for their positive assessment of our work and their insightful suggestions.\nReviewer #2 (Remarks to the Author):\nThe authors show that HEPE generates thousands of structural variants per genome, yet the RNA-\nseq analysis is restricted to the focus of two best-characterized clusters (ACT and RED).\nStreptomyces typically harbours 20–30 biosynthetic gene clusters per strain, so the central question\nis: how many of these remaining BGCs are also transcriptionally perturbed, and why do only a\nminority produce detectable metabolites? A simple differential-expression count across all\nannotated BGCs should be provided, together with the percentage of clusters whose key\nbiosynthetic genes move from silent to expressed after HEPE treatment. If the majority remain silent\ndespite massive structural change, the authors should discuss the buffering mechanisms that allow\nthe cell to tolerate wholesale genomic rearrangements without global metabolic chaos—an issue\nhighly relevant to microbial robustness and genome evolution. I therefore recommend:\n1. Re-analyse the existing RNA-seq data set to quantify expression changes in every annotated BGC,\nand present the data as a new supplementary figure.\nThanks for your advice.\nIn the present study, transcriptome sequencing was performed on the S. lividans 1326 wild-\ntype strain and its three mutants generated via HEPE treatment. Bioinformatic analysis using\nAntiSMASH tool identified 27 putative BGCs within the S. lividans 1326 genome. As suggested,\nwe conducted differential expression analysis for all 27 annotated BGCs. The results are\nsummarized in a new figure and table (Figure S5，Table S5).\nFigure S5a-c indicate that HEPE treatment induced widespread transcriptional perturbations in\nmost BGCs. At the cluster level, a general spatial pattern can be observed: BGCs located near the\nboth ends of the chromosome tended to be downregulated, while those in the central region were\ngenerally upregulated. This may be related to an overall loosening of chromosomal architecture in\nthe HEPE-treated mutants. Quantitatively, approximately one-third (9 BGCs) were down-regulated\nand 41% (11 BGCs) were up-regulated.\nA more detailed gene-level analysis is provided in Figure S5d and Table S5. At this level, the\nproportions of differentially expressed genes varied among the three mutant strains, consistent with\nthe stochastic nature of HEPE mutagenesis. Among differentially expressed genes, 5-13% were up-\nregulated, with key biosynthetic genes representing only 1.6-6.5% of the total in these mutants.\nFurthermore, within a gene cluster, transcriptional changes among key genes or between key genes\nand regulators may be inconsistent, preventing the coordinated activation of the entire metabolic\npathway. This limited and stochastic up-regulation of critical pathway components appears\ninsufficient to drive the coordinated expression required for efficient metabolites biosynthesis and\naccumulation, likely explaining why only a minority of corresponding metabolites were detected.\nFigure S5. Transcriptomic landscape of annotated BGCs in S. lividans 1326 following HEPE\ntreatment. a-c, Heatmaps depicting gene expression changes in three HEPE-derived mutants:\nH_D1 (a), H_D28 (b), and H_D34 (c). Each tile corresponds to an individual gene. The x-axis\ndisplays genes arranged within BGCs. The y-axis lists all annotated BGCs across the genome. Color\nintensity reflects log (fold change) values in expression (Mutant vs WT) from three biological\n2\nreplicates, where red indicating upregulation, blue indicating downregulation. Deeper color denotes\na greater differential expression. Light gray indicates blank (no data). d, Gene counts with altered\nexpression across all annotated BGCs. Genes are categorized as significantly up-regulated (up),\nup-regulated key biosynthetic genes (up_key), significantly down-regulated (down), and no significant\nchange in expression (no change; p-value > 0.05). A darker color indicates a higher count\nTable S5 Percentage of transcriptionally altered BGC-associated genes in S. lividans 1326 after\nHEPE treatment. H_D1, H_D28, and H_D34 were three mutants obtained by HEPE irradiation.\nGenes are categorized as significantly up-regulated (Up), up-regulated key biosynthetic genes\n(Up_key), significantly down-regulated (Down), and no significant change in expression (No\nchange; p-value > 0.05).\nMutant Up (%) Up_key (%) Down (%) No change (%)\nH_D1 13.81 6.54 38.72 47.45\nA_D28 5.45 1.63 40.72 53.81\nH_D34 13.09 5.81 32.00 54.90\n2. Add a dedicated Discussion section that addresses why extensive SV load does not translate into\nwidespread metabolite diversification: possible explanations include redundancy of pathway-\nspecific regulators, dosage sensitivity of metabolic precursors, or rapid compensatory mutations that\nsilence newly formed clusters.\nThank you for suggesting possible explanations for this issue. We have added a dedicated\nDiscussion section that addresses why extensive SV load does not translate into widespread\nmetabolite diversification. The specific content is as follows and has been incorporated into the\nmanuscript at lines 348-359.\nDespite a high load of SVs constantly reshaping the genomic landscape of BGCs, the observed\nchemical diversity may represent only a fraction of their genomic potential. This discrepancy arises\nfrom multi-layered biological constraints. Many secondary metabolite BGCs are governed by\ndedicated pathway-specific regulators, yet such regulatory networks frequently exhibit functional\nredundancy or hierarchical control55. Consequently, even if genomic rearrangements activate silent\nBGCs, their expression may still be repressed by upstream global regulators or remain inactive due\nto the absence or dysfunction of required pathway-specific activators. Furthermore, secondary\nmetabolites biosynthesis depends critically on precursor molecules supplied by primary metabolism.\nNewly activated BGCs often impose a metabolic burden, leading to insufficient precursor flux and\nultimately aborted product synthesis. Under evolutionary pressure, activated gene clusters may\nundergo secondary mutations that rapidly silence these resource-consuming BGCs to restore growth\nfitness. This silencing-driven selection further explains why novel compounds frequently escape\ndetection at the final stage of analyses.\n3. Relate these findings to the broader literature on genome instability and stress resistance in\nfilamentous actinobacteria, highlighting how Streptomyces can apparently “absorb” large-scale\nchromosomal restructuring while maintaining viability.\nWe have followed your guidance to review the relevant literature and have now integrated the\nfindings of this study. A corresponding discussion has been added at lines 309-324 of the revised\nmanuscript. The specific content is presented below.\nHEPE irradiation can induce extensive SVs in the Streptomyces genome while preserving\ncellular viability. This resilience can be attributed to its inherent genomic instability and stress\nresponse strategies. The Streptomyces genome appears architecturally organized to tolerate\nrestructuring. Essential genes for primary metabolism and growth are clustered in the core region\nof the chromosome, while secondary metabolite BGCs and other adaptive features are often located\non more plastic chromosomal arms. This compartmentalization allows the arms to undergo HEPE-\ninduced SVs while buffering the essential core from fatal disruption. Furthermore, Streptomyces\npossesses linear chromosomes with terminal inverted repeats (TIRs), creating \"open ends\" that\nfacilitate end-joining recombination and chromosomal rearrangements46. Such genomic instability\ndrives Streptomyces’ rapid evolution, aiding adaptation to competitive soil microenvironments.\nUnder stresses like HEPE irradiation, genomic instability increases, triggering an SOS-like response.\nStreptomyces also harbors diverse two-component systems and mobile genetic elements that\nmediate DNA recombination and repair upon sensing stress signals46, 47. Robust DNA repair systems\nallow it to withstand radiation-induced DNA damage and even convert such damage into genetic\ndiversity. This genomic instability coupled with robust stress resistance enables Streptomyces not\nonly to survive tenaciously but also to evolve into a vast natural repository of medicinal compounds.\n4. The authors need to deposit the raw FASTQ files of the RNA-seq data and the processed count\nmatrix in a public database (e.g., NCBI GEO or ENA) and include the accession code in the Data\nAvailability section prior to acceptance.\nThe raw RNA-seq FASTQ files and the processed gene count matrix generated in this study\nhave been deposited in the NCBI GEO database under accession number GSE315268 (publicly\naccessible). This accession code has been included in the Data Availability section (lines 780-782)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript has been improved. The authors responded and clarified both minor and major points.", "reviewer_2": "The authors show that HEPE generates thousands of structural variants per genome, yet the RNA-seq analysis is restricted to the focus of two best-characterized clusters (ACT and RED). Streptomyces typically harbours 20–30 biosynthetic gene clusters per strain, so the central question is: how many of these remaining BGCs are also transcriptionally perturbed, and why do only a minority produce detectable metabolites? A simple differential-expression count across all annotated BGCs should be provided, together with the percentage of clusters whose key biosynthetic genes move from silent to expressed after HEPE treatment. If the majority remain silent despite massive structural change, the authors should discuss the buffering mechanisms that allow the cell to tolerate wholesale genomic rearrangements without global metabolic chaos—an issue highly relevant to microbial robustness and genome evolution. I therefore recommend:\n1. Re-analyse the existing RNA-seq data set to quantify expression changes in every annotated BGC, and present the data as a new supplementary figure.\n2. Add a dedicated Discussion section that addresses why extensive SV load does not translate into widespread metabolite diversification: possible explanations include redundancy of pathway-specific regulators, dosage sensitivity of metabolic precursors, or rapid compensatory mutations that silence newly formed clusters.\n3. Relate these findings to the broader literature on genome instability and stress resistance in filamentous actinobacteria, highlighting how Streptomyces can apparently “absorb” large-scale chromosomal restructuring while maintaining viability.\n4. The authors need to deposit the raw FASTQ files of the RNA-seq data and the processed count matrix in a public database (e.g., NCBI GEO or ENA) and include the accession code in the Data Availability section prior to acceptance.\nSuch additions will convert the current descriptive observation into a mechanistic insight of interest to both natural-product and evolutionary-genomics communities.", "reviewer_3": "The authors have adequately addressed my previous comments."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have addressed all my concerns."}, "author_response": ""}]

Decision letter