1. INTRODUCTION
The growing interest in endophytic fungi as a source of bioactive compounds has driven research into their genetic and biosynthetic potential [1,2]. Endophytic fungi are fungi that reside inside the healthy plant tissues without causing apparent harm to their host. These fungi are involved in producing secondary metabolites that can benefit their host plants by promoting growth [3], providing stress tolerance [4], or offering protection against pathogens [5]. Additionally, the unique biochemical machinery of endophytic fungi has attracted attention for its potential to yield novel pharmaceuticals or other relevant compounds [6,7].
Fungi from the genus Fusarium are widely distributed in nature and are known for their capability to produce various bioactive compounds [8–10]. Fusarium proliferatum, a ubiquitous endophytic fungus, has been reported in diverse ecological niches, including association with medicinal plants such as ginger (Zingiber officinale Roscoe) [11]. Ginger is widely known for its traditional medicinal applications [12,13]. The use of ginger has shown potential in reducing nausea and vomiting due to chemotherapy in pediatric patients [14]. In addition, active compounds such as gingerol and shogaol are known to have strong antioxidant activity as well as their potential as anti-inflammatory agents [15]. Interestingly, these two compounds also show potential in addressing age-related neurological disorders [16]. In addition, ginger also serves as a reservoir for a diverse microbiome that may contribute to its bioactivity [17,18]. Among its microbial symbionts, F. proliferatum is of particular interest due to its ability to produce a variety of secondary metabolites with various bioactive potency, including antimicrobial and anticancer activities [19,20].
Our previous study has screened the bioactivity of the methanolic extract of F. proliferatum ZO-L2-4 isolated from the leaves of ginger. From the liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, we tentatively identify one of the metabolites in this extract as 8-O-methylbostrycoidin [11]. Despite its antimicrobial and cytotoxic potential found in the previous study, the biosynthetic capabilities of this fungal strain remain underexplored, prompting genomic studies to uncover its hidden metabolic potential.
Genome sequencing and analysis provide a comprehensive platform to unearth the biosynthetic gene clusters (BGCs) responsible for secondary metabolite production in microbes, including fungi [21]. Advances in next-generation sequencing technologies and bioinformatics tools have enabled the identification and annotation of BGCs, which offer insights into the genetic basis of metabolite biosynthesis [22]. Such studies not only enlighten the metabolic potential of fungi but also facilitate the discovery of novel bioactive compounds [23].
In this study, we report the draft genome sequence of the endophytic fungus F. proliferatum ZO-L2-4, isolated from ginger. This work aims to elucidate the genetic framework underlying its secondary metabolite biosynthesis, providing a foundation for future investigations into its biotechnological applications. By analysing the genome for BGCs, we seek to uncover the metabolic potential of F. proliferatum ZO-L2-4.
2. MATERIALS AND METHODS
2.1. Fungal material
The fungal isolate F. proliferatum ZO-L2-4 was obtained from the leaves of Z. officinale in our previous study [11]. The fungal isolate was grown on agar media consisting of Bacto agar, yeast extract, malt extract, glycerol, and demineralized water for 3 days at room temperature.
2.2. Procedure
2.2.1. Genome extraction
High molecular weight gDNA was extracted using Quick-DNA Magbead Plus Kit (ZymoResearch, D4082).
2.2.2. Library preparation and genome sequencing
Total gDNA was used for the input of the library preparation. It was prepared using xGen DNA Library Prep EZ UNI Kit (IDT, 10009822). The gDNA was fragmented using enzymatic methods to match the expected insert size. The fragmented DNA was ligated with Illumina-compatible Adapter with unique index for each sample (Forward adapter: CAAGCAGAAGACGGCATACGAGAT[i7]GTCTCGTGGGCTCGG; reverse adapter: AATGATACGGCGACCACCGAGATCTACAC[i5]TCGTCGGCAGCGTC). The PCR was conducted to amplify the library. Fusarium proliferatum (GCA_036288945.1) was used as the reference for genome assembly. The quality and quantity of library samples were determined using Tape Station and Qubit Fluorometer, respectively. Sequencing was conducted using the Illumina NextSeq 2000 platform with 300-cycle paired-end (PE150) short reads.
2.2.3. Genome assembly and annotation
Data filtering was conducted using fastp, and the filtered reads were processed for variant calling using the GATK pipeline. Read quality was assessed with FastQC and summarized with MultiQC. Data transformation was carried out with Samtools, and variant calling was performed using GATK4. Variants were filtered, and a consensus sequence was generated using Bcftools. Variant annotation was completed with SnpEff. The quality of the assembled sequence was determined using Quast and Qualimap. Genome completeness was evaluated using Benchmarking Universal Single-Copy Orthologs (BUSCOs). Gene prediction was performed using AUGUSTUS and GeneMark for evidence-based gene model prediction. The predictions generated by BRAKER were used as the gene models for downstream analyses. Predicted coding sequences were subsequently annotated using evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG-mapper) for orthology-based functional assignment, including Pfam domain identification, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping, and Clusters of Orthologous Genes (COG) category classification.
2.2.4. Core and accessory chromosome assessment
To characterize putative accessory chromosomes, each assembled scaffold was analyzed for genomic features, including Guanine-Cytosine (GC) content, repeat content, and gene content. GC content was calculated using Quast. Transposable element content was assessed by calculating the percentage of masked bases identified by RepeatMasker with both Dfam and custom RepeatModeler libraries trained on the reference genome F. proliferatum (GCA_036288945.1). The presence of conserved single-copy orthologs was assessed using BUSCO. Scaffolds with high repeat content, reduced BUSCO gene representation, and deviating GC content were considered as putative accessory chromosomes.
2.2.5. BGCs analysis
BGCs analysis was performed using Antibiotics and Secondary Metabolites Analysis Shell (AntiSMASH) Fungal Version. Visualization was done using Circos.
2.2.6. Fungal fermentation and extraction
Fungal endophyte F. proliferatum ZO-L2-4 was fermented on two Erlenmeyer flasks (1l) of sterile rice media containing 100 g of rice and 110 ml of distilled water, following the procedure described before [11]. Extraction of metabolite was done by adding 500 ml of ethyl acetate (EtOAc) into each flask at the end of rice fermentation, followed by solvent removal using a vacuum rotary evaporator, yielding 2.3 g crude EtOAc extract. The crude extract was then subjected to liquid-liquid partitioning between aqueous methanol and n-hexane to obtain the methanolic extract (795.5 mg) and n-hexane extract (1.5 g). The metanolic extract was used for high-resolution mass spectrometry (HRMS) analysis.
2.2.7. HRMS analysis
For HRMS analysis, the methanolic extract (50 mg) was dissolved in Mass Spectrometry (MS) grade methanol (1 ml). For the spectrometric analysis, a Q Exactive™ high resolution accurate mass LC-MS/MS system (Thermo Scientific™) was employed, coupled with a Thermo Scientific™ Vanquish™ Flex Ultra-High Performance Liquid Chromatography (UHPLC) setup. The Liquid Chromatography (LC) method utilized a flow rate of 0.3 ml/min and a 3 μl injection volume, with the mobile phase composed of 0.1% formic acid in H2O (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The gradient program started with 5% solvent B, ramping to 90% over 16 minutes, followed by an isocratic hold at 90% B for 4 minutes, and a final gradient returning to 5% B over 5 minutes. Chromatographic separation was achieved using a 2.6 μm Accucore™ Phenyl Hexyl 100 × 2 mm column. The mass spectrometer was operated within the m/z range of 150 to 1,800 for MS scans. Instrumental settings included a sheath gas flow of 15, an auxiliary gas flow of 5, a spray voltage of 3.6 kV, and a capillary temperature of 32 °C. The auxiliary gas heater temperature was maintained at 3 °C, and the S-lens Radio Frequency (RF) level was set to 50. The MS resolution was adjusted to 70,000 with an automatic gain control (AGC) target of 3e6 and a maximum injection time (IT) of 250 ms. For the dd-MS2 scans, the resolution was set to 17,500, the AGC target to 1e5, and the maximum IT to 60 ms. The number of loop counts was set to 5, with collision energies of 18, 35, and 53. TopN and isolation window values were set to 5 and 1.0 m/z, respectively. The minimum AGC target for data-dependent acquisition was 9e3, with an intensity threshold of 1.3e5, and charge exclusion was applied for ions with charges of 4–8 and >8. Isotope exclusion was enabled, with dynamic exclusion time set to 10 seconds. Caffeine served as the calibration standard in this study. Chromatogram and metabolomic data analysis were performed using Compound Discoverer 3.2 (Thermo Scientific™), interfacing with mzCloud (www.mzcloud.org) and ChemSpider (www.chemspider.com), along with publicly available databases from the Global Natural Products Social Molecular Networking (GNPS) (https://gnps.ucsd.edu).
3. RESULTS AND DISCUSSION
3.1. Genome sequencing and assembly
From sequencing of F. proliferatum ZO-L2-4 (Fig. 1), we generated 35.2 million raw reads comprising 5.3 Gbp nucleotides, which were filtered, resulting in a final dataset of 34.6 million reads totaling 4.9 Gbp nucleotides (Table 1). Although this process reduced the amount of sequence data, the Q30 value improved, increasing from 93.8% to 95.1%. The filtered reads were assembled into the complete genome of F. proliferatum ZO-L2-4, yielding a total genome size of 43.63 Mbp comprised of 12 nuclear and one mitochondrial contigs, with the largest contig has size of 6.17 Mbp. It showed N50 of 4,304,280 bp, L50 of 5, and GC content of 48.14%. The results indicated that genome assembly was of high quality. Genome assembly also showed a BUSCO completeness score of 97.6% (88.45% coverage), suggesting that we obtained a highly contiguous and complete genome. The draft genome from this strain has been submitted to NCBI GenBank under BioSample accession of SAMN45994223 and BioProject ID of PRJNA1203388.
 | Figure 1. The morphological appearance of F. proliferatum ZO-L2-4 grown on media containing Bacto agar, yeast extract, malt extract, glycerol, and demineralized water, (A) top and (B) bottom of agar media. [Click here to view] |
Table 1. Assembly statistics of F. proliferatum ZO-L2-4.
| Item | Value |
|---|---|
| Before filtering | |
| Total reads | 35.221294 M |
| Total bases | 5.318415 G |
| Q20 bases | 97.357389 % |
| Q30 bases | 93.809545% |
| GC content | 48.298102% |
| After Filtering | |
| Total reads | 34.580216 M |
| Total bases | 4.954088 G |
| Q20 bases | 98.257572% |
| Q30 bases | 95.064806% |
| GC content | 48.141149% |
| Genome size | 43,628,202 |
| Coverage | 88.45 % |
| Contigs | 13 |
| Largest contig (bp) | 6,173,545 bp |
| N50 | 4,304,280 bp |
| N90 | 2,463,860 bp |
| L50 | 5 |
| L90 | 10 |
| BUSCO (C) | 97.6% |
| BUSCO (S) | 97.5% |
| BUSCO (D) | 0.1% |
| BUSCO (F) | 0.3% |
| BUSCO (M) | 2.1% |
3.2. Genome annotation
From gene prediction using AUGUSTUS, we found that 11,308 genes (8.0%) and 35,654 protein coding sequences (CDSs (25.3%), while from analysis using GeneMark, it showed 1,039 genes (1.5%), 5,472 mRNAs (8%), and 1,6791 CDSes (24.7%). As shown in Figure 2, gene annotation against KEGG database revealed that those genes were classified into metabolism (40.6%), human diseases (20.7%), genetic information processing (11.6%), organismal systems (11.1%), cellular processes (10.0%), and environmental information (6.0%). In metabolism class, most of the genes are involved in the function of global maps (1,792), followed by carbohydrate metabolism (316), and amino acid metabolism (310). The annotation data were published in Zenodo (https://doi.org/10.5281/zenodo.15833454)
 | Figure 2. Functional annotation of the genes from F. proliferatum ZO-L2-4 according to the KEGG database. [Click here to view] |
3.3. Functional annotation and orthology assignment
Gene models predicted by BRAKER were further annotated to provide orthology-based validation. Out of 16,801 predicted coding sequences, 98.5% were assigned to orthologous groups, 79.3% were assigned a functional description, and 78.1% contained at least one Pfam domain. KEGG annotation was identified for 34.9% of CDSes, while 79.3% were classified into COG functional categories. These results indicate a high proportion of protein-coding genes with conserved domains and known orthologs, supporting the completeness and reliability of the gene prediction (Table 2).
Table 2. Functional annotation summary of predicted CDS.
| Feature | *Percentage (%) |
|---|---|
| CDS assigned to orthologous groups | 98.5 |
| CDS assigned functional descriptions | 79.3 |
| CDS containing at least one Pfam domain | 78.1 |
| CDS with KEGG annotations | 34.9 |
| CDS classified into COG categories | 79.3 |
*Percentages are relative to 16,801 predicted CDS.
3.4. Core and accessory chromosome assessment
To distinguish core from putative accessory chromosomes, we examined GC content, BUSCO gene count, and interspersed repeat content across all contigs (Table 3). Most contigs showed relatively uniform GC content (approximately 49%) and high BUSCO gene counts, typical of conserved core chromosomes. In contrast, several contigs, particularly ZO-L2-4_12, ZO-L2-4_10, and ZO-L2-4_11, showed markedly reduced BUSCO counts (zero or near zero). Specifically, ZO-L2-4_12 showed significantly elevated interspersed repeat percentages (4.40%). These patterns are characteristic of accessory chromosomes, which frequently display lower conserved gene density and increased repeat accumulation relative to the core genome. Collectively, these results suggest the presence of accessory genomic regions within the assembly, particularly in the contig ZO-L2-4_12. This contig does not appear to contain BGC.
Table 3. Summary of contig features used to identify putative accessory chromosomes.
| *Contig | GC content (%) | BUSCO gene count | Interspersed repeats (%) |
|---|---|---|---|
| ZO-L2-4_1 | 0.49 | 906 | 0.80 |
| ZO-L2-4_2 | 0.49 | 637 | 1.35 |
| ZO-L2-4_3 | 0.49 | 655 | 0.88 |
| ZO-L2-4_4 | 0.49 | 395 | 0.76 |
| ZO-L2-4_5 | 0.49 | 552 | 0.95 |
| ZO-L2-4_6 | 0.49 | 427 | 1.14 |
| ZO-L2-4_7 | 0.49 | 413 | 1.33 |
| ZO-L2-4_8 | 0.49 | 153 | 1.48 |
| ZO-L2-4_9 | 0.49 | 251 | 1.24 |
| ZO-L2-4_10 | 0.49 | 5 | 1.23 |
| ZO-L2-4_11 | 0.49 | 7 | 1.43 |
| ZO-L2-4_12 | 0.48 | 0 | 4.40 |
*Contig ZO-L2-4_13, representing the mitochondrial chromosome, was not included in this summary.
Contig ZO-L2-4_12 exhibited two notable genomic features: a high proportion of repetitive sequences and the complete absence of BUSCO genes. These characteristics are consistent with previously described accessory chromosomes, which are dispensable for viability but may contribute to adaptive traits such as host-pathogen interaction [24]. To evaluate the potential involvement of ZO-L2-4_12 in the pathogenicity of F. proliferatum, we performed a focused analysis using three complementary computational approaches: signal peptide prediction (SignalP), effector protein prediction (EffectorP), and homology search against a curated pathogenicity gene database (PHIB-BLAST). The overlap among predicted secreted proteins, effectors, and PHIB-BLAST is summarized in Figure 3.
 | Figure 3. Venn diagram showing the overlap of predicted pathogenicity-related proteins encoded in contig ZO-L2-4_12 using three independent approaches: SignalP, and BLASTP search against the PHI-base database (E-value < 0.05). The diagram illustrates the number of proteins uniquely predicted by each tool, as well as those shared between two or all three prediction strategies. [Click here to view] |
SignalP was employed to identify proteins with classical N-terminal signal peptides, which are indicative of secretion through the endoplasmic reticulum-Golgi pathway [25]. Proteins secreted via this pathway often include enzymes and effectors involved in host colonization and virulence. From contig ZO-L2-4_12, a total of eight proteins were predicted to contain signal peptides, highlighting their potential role as secreted pathogenicity factors.
EffectorP was used to identify candidate effector proteins, which are proteins capable of modulating host defense responses [26]. Effector prediction is particularly important in filamentous plant pathogens, where effectors are central to host specificity and virulence. The analysis yielded 32 candidate effector proteins located on ZO-L2-4_12, supporting the hypothesis that this contig may harbor genes relevant to pathogenicity.
To further characterize the functional potential of genes encoded on ZO-L2-4_12, we performed a PHIB-BLAST (PHI-base BLAST) search, in which predicted protein sequences were queried against PHI-base (Pathogen-Host Interaction Database), a curated repository of experimentally validated pathogenicity, virulence, and effector genes from fungal pathogens [27]. We applied E-value threshold of <0.05 to retain only high-confidence homologs. This analysis identified 45 proteins on ZO-L2-4_12 that exhibit significant similarity to known virulence-associated proteins, suggesting potential functional roles in host-pathogen interactions.
Collectively, the identification of secreted proteins, candidate effectors, and homologs of known pathogenicity genes on contig ZO-L2-4_12 suggests that this putative accessory chromosome may contribute to the virulence of F. proliferatum. Although it lacks core conserved genes, its gene content needs further investigation, particularly with respect to host specificity and adaptation.
3.5. BGCs and HRMS analysis
Secondary metabolites produced by Fusarium display a highly diverse chemical framework. They are usually biosynthesized by multi-domain core synthases with several additional enzymes within a biosynthetic pathway to create the final compound. These enzymes are encoded by a collective of genes that are co-regulated and located adjacent to each other, forming BGCs [28].
The AntiSMASH analysis revealed that F. proliferatum ZO-L2-4 has 43 BGCs spread across 11 contigs, as shown in Figure 4 and Table 4. These include seven terpene biosynthetic genes, seven nonribosomal peptide synthase (NRPS)-like, six polyketide synthases (PKS) with five type I PKS (T1PKS) and one type III PKS (T3PKS), five NRPS, four ribosomally synthesized and post-translationally modified peptides, fungal-like subtype (fungal-RiPP-like), four hybrid NRPS + T1PKS, three indole biosynthetic genes, two hybrid NRPS-like + T1PKS, one arylpolyene, one betalactone, one isocyanide, one hybrid isocyanide-NRP + NRPS, and one hybrid NRP-metallophore + NRPS. These findings were confirmed by a previous study, which revealed that a Fusarium species could contain BGC numbers ranging from 39 to 57 clusters, dominated by terpene synthases, NRPS, and PKS [29,30]. Among these, only 13 BGCs showed homology with known clusters based on MIBig comparison. Only six BGCs displayed high similarity (>70%) from these, such as oxyjavanicin, choline, bikaverin, Alternaria citri toxin (ACT)-toxin II, koraiol, and gibepyrone-A (Table 4)
 | Figure 4. Circos map visualization of BGC analysis from F. proliferatum ZO-L2-4 genome. NRP = nonribosomal peptide. [Click here to view] |
Table 4. Identified BGCs from the F. proliferatum ZO-L2-4.
| BGC | Type | From | To | Known clusters with the highest similarity (%) |
|---|---|---|---|---|
| Contig ZO-L2-4_1 | ||||
| Region 1.1 | NRPS | 237,707 | 285,728 | Unknown |
| Region 1.2 | Fungal-RiPP-like | 549,575 | 610,696 | Unknown |
| Region 1.3 | NRPS-like | 5,570,792 | 5,614,120 | Unknown |
| Region 1.4 | NRPS-like,T1PKS | 6,005,753 | 6,072,904 | Unknown |
| Contig ZO-L2-4_2 | ||||
| Region 2.1 | NRP-metallophore, NRPS | 2,138,052 | 2,201,906 | Unknown |
| Region 2.2 | Terpene | 3,390,235 | 3,412,099 | Aqualestatin S1 (40%) |
| Region 2.3 | Terpene | 3,868,191 | 3,891,488 | Unknown |
| Region 2.4 | T1PKS | 4,066,290 | 4,113,604 | Oxyjavanicin (100%) |
| Region 2.5 | Fungal-RiPP-like | 4,340,993 | 4,401,533 | Unknown |
| Contig ZO-L2-4_3 | ||||
| Region 3.1 | NRPS-like | 270,809 | 315,956 | Unknown |
| Region 3.2 | NRPS-like | 525,045 | 568,899 | Choline (100%) |
| Region 3.3 | Betalactone | 932,321 | 964,947 | Unknown |
| Region 3.4 | Arylpolyene | 1,334,114 | 1,376,455 | Unknown |
| Region 3.5 | NRPS-like | 2,408,304 | 2,452,221 | Unknown |
| Region 3.6 | NRPS, T1PKS | 4,533,256 | 4,585,528 | Equisetin (54%) |
| Region 3.7 | NRPS-like, T1PKS | 4,779,645 | 4,848,963 | Fusaric acid (65%) |
| Contig ZO-L2-4_4 | ||||
| Region 4.1 | Indole | 746,977 | 768,188 | Unknown |
| Region 4.2 | Terpene | 859,873 | 880,598 | Unknown |
| Region 4.3 | NRPS, T1PKS | 1,170,666 | 1,225,786 | Unknown |
| Region 4.4 | Indole | 746,977 | 768,188 | Unknown |
| Contig ZO-L2-4_5 | ||||
| Region 5.1 | NRPS-like | 32,210 | 75,260 | Fusaridione A (12%) |
| Region 5.2 | T1PKS | 90,716 | 139,305 | Bikaverin (71%) |
| Region 5.3 | NRPS | 530,476 | 578,108 | Unknown |
| Region 5.4 | Terpene | 1,801,366 | 1,822,532 | Unknown |
| Region 5.5 | Isocyanide | 4,348,739 | 4,390,918 | Unknown |
| Contig ZO-L2-4_6 | ||||
| Region 6.1 | NRPS | 53,182 | 97,207 | Acetylaranotin (30%) |
| Region 6.2 | T3PKS | 1,563,878 | 1,605,388 | Unknown |
| Region 6.3 | T1PKS | 2,892,833 | 2,940,319 | Unknown |
| Contig ZO-L2-4_7 | ||||
| Region 7.1 | Fungal-RiPP-like | 9,404 | 70,894 | Unknown |
| Region 7.2 | NRPS | 2,989,892 | 3,052,311 | Unknown |
| Contig ZO-L2-4_8 | ||||
| Region 8.1 | Fungal-RiPP-like | 485,275 | 545,807 | Unknown |
| Region 8.2 | NRPS, T1PKS | 549,091 | 600,955 | ACT-toxin II (100%) |
| Region 8.3 | Terpene | 895,270 | 916,162 | Koraiol (100%) |
| Region 8.4 | T1PKS | 2,730,974 | 2,780,024 | Unknown |
| Contig ZO-L2-4_9 | ||||
| Region 9.1 | NRPS-like | 446,951 | 490,121 | Unknown |
| Region 9.2 | NRPS | 2,657,370 | 2,706,780 | Beauvericin (20%) |
| Region 9.3 | Indole | 2,950,626 | 2,972,870 | Unknown |
| Contig ZO-L2-4_10 | ||||
| Region 10.1 | Isocyanide-NRP, NRPS | 1,265,414 | 1,355,558 | Unknown |
| Region 10.2 | NRPS-like | 1,720,590 | 1,763,937 | Unknown |
| Region 10.3 | T1PKS | 2,034,473 | 2,082,727 | Trichoxide (41%) |
| Contig ZO-L2-4_11 | ||||
| Region 11.1 | Terpene | 1,426,788 | 1,448,108 | Unknown |
| Region 11.2 | Terpene | 1,518,355 | 1,540,343 | Unknown |
| Region 11.3 | NRPS, T1PKS | 2,022,581 | 2,114,649 | Gibepyrone-A (80%) |
NRP = non-ribosomal peptide.
Terpene synthases are responsible for the biosynthesis of a wide array of natural terpenoid structures. NRPS encodes multi-modular enzymes that synthesize oligopeptides from amino acid monomers. A complete NRPS enzyme comprises a domain for adenylation, peptide acyl-carrier, and condensation. Each module may contain epimerization or N-methylation domains, enhancing the structural chemical diversity of non-ribosomal peptides. Meanwhile, a PKS encodes enzymes with several domains that at least consist of beta-keto synthase, acyl-transferase, and acyl-carrier protein domains, which together synthesize a polyketide chain [31]. Terpenoids, non-ribosomal peptides, and polyketides, which these BGCs generate, are the most prolific class of compounds produced by the Fusarium species [10,32].
The BGCs identification revealed that F. proliferatum ZO-L2-4 has BGCs that are responsible for the production of pigments and mycotoxins (Fig. 5). Region 2.4 had homology with T1PKS of oxyjavanicin from Fusarium fujikuroi (MIBiG accession BGC0001242) [33]. Region 5.2 was found to share high similarity with the bikaverin BGC of F. fujikuroi (BGC0000030) [34]. Oxyjavanicin and bikaverin are polyketide pigments that have been reported to show antibacterial activities [35]. Region 3.2 showed the closest similarity to choline BGC from Aspergillus nidulans (BGC0002276) [36]. Choline is essential for the biosynthesis of phosphatidylcholine, a phospholipid in fungal cell membranes [37]. Region 8.2 showed similarity to ACT-toxin II BGC of Alternaria alternata (BGC0001254) [38], and Region 8.3 showed high similarity to BGC of koraiol from F. fujikuroi (BGC0001642) [39]. ACT-toxin is a compound that is highly toxic to susceptible citrus cultivars [40]. Koraiol belongs to a product of a terpene synthase/cyclase, which has been identified in various Fusarium accessory chromosome sequences. However, the functional role of these secondary metabolites as virulence factors of Alternaria and Fusarium hosts remains unclear [24]. AntiSMASH analysis showed that Region 11.3 has a close similarity to the BGC of gibepyrone-A from F. fujikuroi (BGC0001606) [41]. This compound is a fungal toxin isolated from the rice pathogen F. fujikuroi [42].
 | Figure 5. Comparison of the BGC components in F. proliferatum ZO-L2-4 with the identified BGCs responsible for the biosynthesis of oxyjavanicin, choline, bikaverin, ACT-toxin II, koraiol, and gibepyrone-A. [Click here to view] |
Among the identified BGCs (Table 4), only beauvericin was detected in the methanolic extract of F. proliferatum through putative metabolite identification employing HRMS (Figs. 6 and 7, Table 5) [43–50]. The result showed that beauvericin seems to be the predominant metabolite detected in this analysis. Beauvericin is a cyclohexadepsipeptide that consists of an alternating sequence of three D-hydroxyisovaleric acids (D-Hiv) and three N-methyl-L-phenylalanine (NMe-Phe). It is synthesized through an NRPS [51]. In our findings, it is comprised of two modules, M1 and M2. M1 consists of the condensation domain, the adenylation domain, and the peptidyl carrier protein, which together load and incorporate D-Hiv to the next module, M2. M2 consists of a similar domain as M1, with the addition of an N-methyltransferase domain that methylates the amino group of phenylalanine, turning it into NMe-Phe. M2 incorporates NMe-Phe to the D-Hiv. Collectively, M1 and M2 form one D-Hiv–NMe-Phe dipeptide unit in one cycle. The cycle is iterated for two more times using the generated peptides to form the six-membered-ring structure of beauvericin [51]. Despite the 20% sequence similarity with known beauvericin clusters, Region 9.2 shows domain architecture and metabolite output consistent with beauvericin biosynthesis. This suggests a divergent or strain-specific variant, possibly shaped by gene relocation or other mechanisms [52]. The compounds of the remaining BGCs were not detected in the extract. This might be because the BGCs remain silent under the fermentation conditions used in our laboratory [53].
 | Figure 6. Total ion chromatogram of untargeted LC-HRMS from the methanolic extract of F. proliferatum ZO-L2-4. [Click here to view] |
 | Figure 7. Putative beauvericin analogues identified via GNPS molecular networking. GNPS-based molecular networking reveals a cluster of nodes corresponding to beauvericin and structurally related analogues. The proximity and high spectral similarity of features with [M + H]+ m/z 555.305, 541.290, 523.279, and 829.471 suggest the presence of unknown or uncharacterized compounds within the beauvericin molecular family. [Click here to view] |
Table 5. Putative metabolites identified in a F. proliferatum extract using untargeted HRMS analysis.
| Compound Class | Putative name | Formula | Annot. DeltaMass [ppm] | Calc. MW | RT [minutes] | Area (Max.) | Ref |
|---|---|---|---|---|---|---|---|
| Alkaloids/Indole Derivatives | Tryptophol | C9H9N O | –0.48 | 161.08399 | 6.597 | 27,646,505.79 | [43] |
| Terpenoids | Terpestacin | C9H9O4 | –2.12 | 402.27616 | 10.82 | 2,448,498,950 | [44] |
| Steroids | Ergosterol peroxide | C9H9O3 | –2.39 | 428.32802 | 14.497 | 38,143,383.23 | [45] |
| Alkaloids/Indole Derivatives | Indole | C9H9N | –1.45 | 117.05768 | 15.358 | 103,237,263.8 | [46] |
| Nonribosomal peptide | Beauvericin | C9H9 N9O9 | –2.82 | 783.40727 | 15.459 | 1,099,713,3422 | [47, 48] |
| Alkaloids/Indole Derivatives | Terpendole E | C9H9N O3 | –2.18 | 437.29204 | 16.159 | 18,581,919.99 | [49, 50] |
Metabolite profiling was conducted using HRMS with Compound Discoverer 3.2, and annotations were performed via GNPS molecular networking. Compound identifications are putative, based on spectral matching and database comparisons. Based on HRMS analysis, beauvericin seems to be the predominant metabolite detected in the F. proliferatum ZO-L2-4 methanolic extract.
Besides beauvericin, HRMS analysis also showed the presence of tryptophol, terpestacin, ergosterol peroxide, indole, and terpendole E (Table 5). Indole, terpendole E, and terpestacin biosynthetic genes were not detected in the BGC analysis, but they might be produced by the unidentified BGCs in this study. Indole BGC of Region 9.3 might be responsible for the production of indole that was detected in the extract. Terpendole E is an indole diterpene that is biosynthesized by the hydroxylation of paspaline by TerQ, a P450 monooxygenase protein. In Chaunopycnis alba, the TerQ gene is clustered with six other genes, including two P450 monooxygenases (TerP and TerK), an Flavin Adenine Dinucleotide (FAD)-dependent monooxygenase (TerM), a terpene cyclase (TerB), and two prenyltransferases (TerC and TerF) [54]. TerF and TerP from C. alba (BGC0001260.3) exhibited homology with two sequences in the indole BGC of Region 4.1 in this study, and the detected terpendole E in this study might be produced via this BGC. Meanwhile, terpestacin is a sesterterpene that is synthesized via terpene synthase BGC. In F. proliferatum NRRL62905, this BGC comprises four genes, including tpcB, tpcA, tpcD, and tpcC, which respectively encode P450-1, terpene cyclase, flavin-dependent oxidase, and P450-2 [55]. AntiSMASH analysis showed that Region 2.3 and 11.1 consist of P450 and terpene synthase genes, which might be involved in the terpestacin biosynthesis. As we employed short-read sequencing in this study, the undetected BGCs for indole, terpendole E, and terpestacin might be fragmented and separated by gaps in the assembly, resulting in the detection of incomplete BGCs [56,57].
Tryptophol and ergosterol peroxide were not detected in the BGC analysis because their biosynthesis does not involve a canonical BGC. Instead, they are formed through primary metabolic pathways or simple enzymatic conversions outside canonical BGC. Typtophol is derived from L-tryptophan via the tryptophan-dependent enzymatic [43]. Meanwhile, ergosterol peroxide is a steroid that is biosynthesized by oxidative modification of ergosterol [58].
In our previous study [11], a pyridine-containing polyketide, 8-O-methylbostrycoidin was tentatively identified by LC-MS/MS analysis from the methanolic extract of F. proliferatum ZO-L2-4, however, it was not detected in the current HRMS analysis. We assumed that the production of this compound may be suppressed following repeated fungal fermentation. Therefore, further study on isolation and structure elucidation of secondary metabolites produced by this fungus would be needed to verify the result from BGCs analysis in combination with the metabolite profile from HRMS analysis afforded in the present study.
Our previous study also reported that the methanolic extract of F. proliferatum ZO-L2-4 showed promising antibacterial and cytotoxicity [11]. The detected compounds in the extract might contribute to the reported bioactivities. For instance, beauvericin was previously reported to have antimicrobial activity against Candida albicans, Escherichia coli, and Staphylococcus aureus, with the strongest effect observed against S. aureus (the Minimum Inhibition Concentration/MIC = 3.91 μM), which is comparable to the activity of amoxicillin as a positive control [59]. Indole alkaloids isolated from the endophytic fungus Robillarda sessilis demonstrated moderate antibacterial activity against Methicillin-Resistant Staphylococcus aureus (MRSA) (MIC = 12.5?μg/ml) [60], while ethyl 3-indoleacetate from F. proliferatum T2-10 exhibited anticancer potential as a horseradish peroxidase-activated prodrug [61,62]. Terpestacin was shown to possess antibacterial activities against Mycobacterium marinum ATCCBAA-535 with an IC50 of 84 μM [63]. Ergosterol peroxide was found to have antibacterial activity by disrupting the electron transport chain and oxidative phosphorylation in the bacterial membrane cells [64]. Terpendole E was reported to have potential as a specific inhibitor of Eg5 myosin protein in the development of antitumor drugs [65].
Unlike previously sequenced F. proliferatum strains, our isolate originated from ginger rhizomes. Endophytic origin of fungi could offer a more diverse BGCs compared to their counterparts that are isolated from a non-endophytic source [66]. Thus, the high proportion of the unknown BGCs in our study indicates an untapped biosynthetic potential of this strain, making it a valuable source for further genome mining and activation studies for drug discovery.
4. CONCLUSION
The Anti-SMASH analysis revealed that F. proliferatum ZO-L2-4 has 43 BGCs for secondary metabolites, comprising seven terpene biosynthetic genes, seven NRPS-like, six PKS (five T1PKS and one T3PKS), five NRPS, four fungal-RiPP-like, four hybrid NRPS + T1PKS, three indole biosynthetic genes, two hybrid NRPS-like + T1PKS, one arylpolyene, one betalactone, one isocyanide, one hybrid isocyanide-NRP + NRPS, and one hybrid NRP-metallophore + NRPS. Among these, only 13 BGCs showed the highest similarity with known clusters based on MIBig comparison. Out of these, only six BGCs displayed high similarity. This was confirmed by HRMS analysis, which detected beauvericin and other metabolites in the methanolic extract of the fungus. However, the remaining BGCs are considered unknown by far. These findings create opportunities for targeted genome mining techniques, including gene knockout or heterologous expression to activate silent BGCs and produce novel bioactive secondary metabolites for drug discovery and development.
5. ACKNOWLEDGMENT
Financial support from The Directorate General of Higher Education, Ministry of Education, Culture, Research and Technology, Republic of Indonesia, through Fundamental Research Program 2024, grant number: B/519-39/UN.14.4.A/PT.01.03/2024, is gratefully acknowledged. The authors also acknowledge the facilities, scientific and technical support from National Research and Innovation Agency (BRIN), Gunungkidul, through E-Layanan Sains (ELSA) BRIN.
6. AUTHORS CONTRIBUTION
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be an author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.
7. CONFLICTS OF INTEREST
The authors report no financial or any other conflicts of interest in this work.
8. ETHICAL APPROVALS
This study does not involve experiments on animals or human subjects.
9. DATA AVAILABILITY
All the data is available with the authors and shall be provided upon request.
10. PUBLISHER’S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
11. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY
The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.
12. SUPPLEMENTARY MATERIAL
The supplementary material can be accessed at the Link here: https://japsonline.com/admin/php/uploadss/4766_pdf.pdf
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