Enhancing Borrelidin Biosynthesis in an Indonesian Endophytic Streptomyces sp. via Medium Optimization for Potential Pharmaceutical Application

Abstract

Borrelidin is a pharmacologically active 18-membered macrolide with antibacterial, antifungal, anticancer, and antimalarial activities; however, its pharmaceutical development has been constrained by low fermentation yields. In this study, borrelidin production by an endophytic Streptomyces sp. isolated from the medicinal plant Zingiber zerumbet (L.) Smith was significantly enhanced through systematic medium optimization. A high-producing colony (BR7) was selected, and key nutritional variables were optimized using response surface methodology based on a Box–Behnken design (BBD). Statistical analysis revealed that borrelidin biosynthesis is governed primarily by strong quadratic and interaction effects among carbon and nitrogen sources rather than by individual nutrients. The optimized agro-based medium increased borrelidin concentration to approximately 83 mg/L, in close agreement with model predictions and representing a substantial improvement over the unoptimized process. The use of low-cost agricultural substrates supports the scalability and economic feasibility of this fermentation strategy. Overall, this work establishes a robust upstream bioprocess framework that supports the pharmaceutical development of borrelidin as a potential active pharmaceutical ingredient (API).

Keywords

Borrelidin medium optimization Box–Behnken design pharmaceutical fermentation

Introduction

Borrelidin is an 18-membered macrolide produced by several Streptomyces species and exhibits broad biological activity primarily through inhibition of threonyl-tRNA synthetase (ThrRS). It has been reported to possess antibacterial, antifungal, antiviral, anti-angiogenic, anti-Alzheimer, and notable antimalarial activities, highlighting its potential as a lead compound for aminoacyl-tRNA synthetase-targeted drug development.^1–10 Despite this pharmacological potential, borrelidin has not advanced toward pharmaceutical development, mainly due to its toxicity and low fermentation yields. Reported borrelidin yields range from 0.65 to 15.6 mg/L^11–15 under laboratory conditions, indicating the need for efficient production strategies. Previous research has focused largely on elucidating its biosynthetic gene cluster and enzymatic modification pathways, while only limited attention has been given to upstream bioprocess development.^3,16,17 However, low fermentation yields remain a major bottleneck restricting further development and downstream applications, whereas fermentation performance is a decisive factor in determining the feasibility of large-scale manufacturing of bioactive natural products.

Systematic investigations into the effects of medium composition, carbon–nitrogen balance, and nutrient availability on borrelidin biosynthesis, particularly, remain scarce. This gap is notable, as carbon–nitrogen signaling is a major regulatory mechanism controlling secondary metabolite production in Streptomyces.^18,19 From an industrial microbiology perspective, this represents a critical bottleneck, given that rational medium optimization is among the most effective and scalable approaches to enhance secondary metabolite production without genetic manipulation. Currently, response surface methodology (RSM) has been applied successfully to optimize microbial fermentation conditions to improve the production of industrially valuable metabolites.^{16,17,20–27}

Endophytic Streptomyces associated with medicinal plants is a promising yet underexplored source of industrially relevant natural products.^28,29 Adaptation to the plant endosphere imposes selective pressures that promote the evolution of specialized biosynthetic pathways, frequently linked to host defense and pharmacological activity. Medicinal plants are therefore recognized as valuable reservoirs of endophytes capable of producing diverse bioactive compounds.³⁰ Indonesia, one of the world’s most biodiverse regions, offers substantial but largely untapped potential for the discovery of such microorganisms. ‘Zingiber zerumbet (L.) Smith’ (Lempuyang), a traditional Indonesian medicinal plant widely used for its anti-inflammatory and antimicrobial properties,³¹ provides a distinctive ecological niche for metabolically specialized Streptomyces strains.

To date, borrelidin production by endophytic Streptomyces has not been reported, and no fermentation-based strategy has been described to enhance its biosynthesis from this source. Furthermore, the influence of low-cost agricultural substrates and complex nitrogen sources on borrelidin production remains poorly understood, despite their importance in industrial fermentation process design. Addressing these limitations is essential to facilitate the transition of borrelidin from a biologically interesting compound to a viable pharmaceutical precursor.

In this study, the systematic enhancement of borrelidin production by endophytic Streptomyces sp. from Indonesian ‘Zingiber zerumbet (L.) Smith’ was reported through rational medium optimization. By statistically evaluating key carbon and nitrogen sources, we establish an improved fermentation strategy that significantly enhances borrelidin concentration and process robustness. This work expands the diversity of known borrelidin-producing microorganisms and provides a practical upstream bioprocess framework to support the scalable production of this high-value pharmaceutical lead.

Materials and Methods

Microorganism

The parental strain of Streptomyces sp. BioMCC-a.EP.1037 was isolated from medicinal plant ‘Zingiber zerumbet (L.) Smith’ (Lempuyang) collected from Jember, East Java and kept in the National Research and Innovation Agency (BRIN) culture collection, Indonesia. Based on ‘16S rRNA’ analysis, it was identified as Streptomyces rochei, which is deposited in the BRIN culture collection under accession BioMCC-a. EP.1037.

Colony Selection and Primary Screening

Colony selection was performed by serial dilution plating on ISP2 agar followed by incubation at 28°C for 14–21 days.³² Eight morphologically distinct colonies were obtained and evaluated for borrelidin production by submerged fermentation using a previously reported medium.³³ Seed cultures were incubated at 28°C and 220 rpm for 72 h, inoculated into production medium, and fermented for 7 days. Borrelidin concentration was determined by HPLC, and the highest-producing colony was selected for further experiments.³⁴

Evaluation of Soybean Meal on Borrelidin Production

To evaluate the effect of soybean meal, the highest-producing colony was cultured in two media: soybean meal-based medium and soybean-free medium. The soybean meal-based medium consisted of (w/v): 2.0% soybean meal (technical grade), 2.0% rice powder (technical grade), 0.5% yeast extract (Oxoid), 2.0% glucose (Merck), 0.25% sodium chloride (Himedia), 0.32 g/L calcium carbonate (technical grade), and 0.2% mineral solution containing CuSO₄•5H₂O, MnCl₂•4H₂O, and ZnSO₄•7H₂O 0.025 each (all from Merck). The pH was adjusted to 7.4. The formulation was used for both seed and production cultures. The seed medium for soybean meal-free fermentation contained (w/v): 0.3% beef extract (Himedia), 0.5% bacto peptone (Oxoid), 0.1% glucose (Merck), and 0.5% yeast extract (Oxoid). The pH was adjusted to 7.0. The production medium consisted of 0.1% peptone (Oxoid), 0.5% skim milk (Oxoid), 0.15% yeast extract (Oxoid), 4.5% dextrin (technical grade), and 0.5% glucose (Merck). The pH was adjusted to 7.0.

Preliminary Evaluation of Nutrients

The selected high-producing colony was used to evaluate the influence of major nutritional components on borrelidin biosynthesis. Four medium components, that is, soybean meal, glucose, yeast extract, and rice powder, were selected as independent variables. Each component was varied individually within the range of 0%–4.0% (w/v), except for yeast extract, which was varied from 0% to 1.0% (w/v), while all other components were maintained at their basal concentrations. Fermentations were conducted in 250 mL Erlenmeyer flasks containing 30 mL of medium, inoculated with 10% (v/v) seed culture into production medium, and incubated at 28°C and 220 rpm for 7 days. The results were used to define variable ranges for subsequent statistical optimization.³⁵

Borrelidin Analysis

The culture broth was harvested after 7 days of fermentation. Culture broth (600 µL) was mixed with an equal volume of ethyl acetate. A 150 µL aliquot of the ethyl acetate layer was evaporated to dryness, and the residue was dissolved in 150 µL of HPLC-grade methanol. Borrelidin was analyzed using a Shimadzu LC-20A HPLC system equipped with a photodiode array detector. Separation was performed on a C18 reverse-phase column (SunFire^→ ODS-C18, 250 × 4.6 mm, 5 µm particle size; Waters). The mobile phase consisted of acetonitrile and water containing 0.05% (v/v) trifluoroacetic acid, delivered at a flow rate of 1.0 mL/min. Detection was carried out at 254 nm. Quantification was based on an external calibration curve prepared using borrelidin standard from Sigma.^36–39

Medium Optimization Using Box–Behnken Design (BBD)

Medium optimization was performed using RSM with a BBD. Four independent variables were investigated: soybean meal (A), yeast extract (B), glucose (C), and rice powder (D). Each variable was evaluated at three coded levels (−1, 0, and +1), as shown in Table 1. A total of 29 experimental runs, including five center points, were generated using Design-Expert software (version 7, Stat-Ease Inc., USA). Borrelidin concentration (mg/L) was selected as the response variable. The experimental data were fitted to a second-order polynomial model:

Y = β_{0} + \sum_{i = 0}^{4} β_{i} X_{i} + \sum_{j = 0}^{4} β_{i i} X_{i}^{2} + \sum \sum_{i = 0}^{4} \sum_{j = 0}^{4} β_{i j} X_{i} X_{j}

(1)

Table 1.

Eight Selected Colonies of Streptomyces sp. BioMCC-a.EP.1037 Grown on ISP2 Medium.

Colony	Diameter (mm)	Morphology
Colony	Diameter (mm)	Color	Front	Rear
BR1	15.13	Light gray
BR2	12.37	Grayish white
BR3	11.64	Grayish white
BR4	9.68	Light ash gray
BR5	11.93	Whitish gray
BR6	11.42	Ash gray
BR7	11.51	Gray
BR8	8.41	Ash gray

where Y is the predicted borrelidin concentration, X_i and X_j are the coded independent variables, and β are the regression coefficients.⁴⁰

Validation of Optimal Conditions

To validate the RSM model, fermentation experiments were conducted in triplicate under the predicted optimal medium composition. The experimentally obtained borrelidin concentrations were compared with the model-predicted values to assess the accuracy and reliability of the optimization.⁴¹

Statistical Analysis

The significance of the regression model and individual terms was evaluated by analysis of variance (ANOVA). Model adequacy was assessed using the coefficient of determination (R²), adjusted R², predicted R², lack-of-fit test, and adequate precision. Differences between model-predicted and experimentally obtained values were evaluated using a t-test implemented in Microsoft Excel, with statistical significance defined at p < .05.⁴²

Results and Discussion

Selection of a High-producing Colony

All eight colonies (BR1–BR8) obtained from colony selection exhibited typical Streptomyces characteristics, including gray to ash gray aerial mycelia, indicating good sporulation. Colony diameters ranged from 8.41 to 15.13 mm (Table 1), reflecting moderate physiological heterogeneity within the population.

Substantial differences in borrelidin production were observed among the isolates when cultivated under identical fermentation conditions (Figure 1). An isolate encoded BR7 consistently produced the highest borrelidin concentration and outperformed the parental culture. Such variability is commonly observed in Streptomyces production systems and is generally attributed to differences in regulatory state, enzyme expression levels, and precursor flux distribution rather than genetic instability. From a bioprocess perspective, this screening step is critical as selection of a high-producing variant can significantly enhance baseline productivity without genetic or process modification. Accordingly, BR7 was selected as the production strain for all subsequent medium optimization studies.

Figure 1.

Borrelidin Production of Eight Selected Colonies Compared to the Parental Strain.

Role of Complex Nitrogen in Borrelidin Biosynthesis

The role of complex nitrogen was examined by comparing borrelidin production by strain BR7 in the presence and absence of soybean meal (Figure 2). The absence of soybean meal resulted in a considerable decrease in borrelidin concentration, indicating that borrelidin biosynthesis strongly depends on complex nitrogen sources.

Figure 2.

Borrelidin Production of Colony BR7 in Soybean Meal-based and Soybean Meal-free Media as Fermentation Medium Component.

In industrial Streptomyces fermentations, substrates such as soybean meal provide amino acids, peptides, vitamins, and trace elements that support enzyme synthesis and cofactor regeneration.⁴³ These nutrients are essential not only for biomass formation but also for maintaining the metabolic capacity required in polyketide biosynthesis. The results suggest that borrelidin production cannot be sustained in minimal or chemically defined media and instead requires nutritionally rich formulations, which is the characteristic of many high-value secondary metabolites. It is noteworthy that soybean meal is an inexpensive agricultural by-product, making it well-suited for large-scale fermentation from a process economics standpoint.

Effect of Key Medium Components on Borrelidin Production

The individual effects of soybean meal, yeast extract, glucose, and rice powder on borrelidin production were evaluated to define suitable operating ranges for statistical optimization (Figure 3). These components represent the principal nitrogen source, growth factor supplement, readily metabolizable carbon source, and complex carbon source of the production medium, respectively.

Figure 3.

The Effect of Soybean Meal, Yeast Extract, Glucose, and Rice Powder Concentration on Borrelidin Production. Point Optimum Prediction: A (Soybean Meal): 20.0 g/L, B (Yeast Extract): 2.8 g/L, C (Glucose): 26.3 g/L, and D (Rice Powder): 30.6 g/L and BC: 83.5 mg/L.

The soybean meal used in this study was a commercial grade originating from Brazil. Soybean meal, a common by-product from soybean oil processing, is known for its affordability and nutritional profile richness, especially its amino acid content (48.57% crude protein, 9.88% sugar, 0.8% starch, 11.54% moisture, 27.81% nitrogen-free extract, 1.58% crude fat, and 4.04% crude fiber).⁴⁴ In our experiment, the actinomycete cultures grown in media supplemented with soybean meal consistently produced higher levels of borrelidin than those cultivated without it (Figure 3). A previous study by Li et al.,⁴⁵ demonstrated that the use of soybean meal in the fermentation of Bacillus sp. TTMP20 increased the yield of tetramethyl pyrazine production by nearly 50% compared to tryptone. These indicated that fermentation medium containing soybean meal contributes positively to borrelidin biosynthesis and may lead to a key point in further medium optimization.

Borrelidin production increased with soybean meal concentration up to an intermediate level, beyond which no further improvement was observed. This trend shows that moderate nitrogen availability supports biosynthesis, whereas excessive nitrogen likely diverts metabolic flux toward biomass formation rather than secondary metabolism. Yeast extract exhibited a narrow optimal range: moderate supplementation enhanced borrelidin production, while higher concentrations suppressed it. Yeast extract may supply essential vitamins and amino acids to stimulate metabolic activity; excessive amounts introduce readily assimilable nutrients that repress secondary metabolite pathways.^18,46

Glucose displayed a classical carbon catabolite repression effect, consistent with its well-established role in suppressing antibiotic biosynthesis in Streptomyces.^19,47,48 Moderate glucose levels supported borrelidin formation, whereas higher concentrations markedly reduced production. In contrast, rice powder, a complex and slowly metabolized carbon source, positively influenced borrelidin production over a broader concentration range. Increasing rice powder levels led to higher borrelidin titers up to an optimal threshold, likely because gradual carbon release supports sustained secondary metabolism without triggering strong repression. The findings of Selvaraj et al.⁴⁹ revealed that statistical optimization of the culture medium enhances the yield of secondary metabolites from Streptomyces sp. CMSTAAHAL-3, with the optimal combination of glucose and soybean meal, significantly increases antibiotic production.

The strong and non-linear responses observed for all four components indicate that borrelidin biosynthesis is governed by interacting nutritional and metabolic factors rather than by any single variable. Because changes in one component clearly altered the response to others, one-factor-at-a-time optimization was insufficient to identify a true production optimum. Consequently, a multivariate statistical approach was required, and RSM was applied using a BBD.

Statistical Optimization Using BBD

A BBD was employed to quantify the combined effects of soybean meal (A), yeast extract (B), glucose (C), and rice powder (D) on borrelidin production by S. rochei. The design space was defined based on preliminary experiments and encompassed nutritionally relevant ranges for industrial fermentation (Table 2). A total of 29 experimental runs, including five center points, were conducted, and the resulting borrelidin concentrations are summarized in Table 3.

Table 2.

Independent Variables and Coded Levels Used in Box–Behnken Design.

Variables	Optimization Using Box–Behnken Design: RSM
	Symbol	Coded Level
	Symbol	−1	0	+1
Soybean meal (g/L)	A	10.0	15.0	20.0
Yeast extract (g/L)	B	2.5	3.75	5.0
Glucose (g/L)	C	20.0	25.0	30.0
Rice powder (g/L)	D	30.0	35.0	40.0

Table 3.

Box–Behnken Design Matrix and Result for the Optimization of Borrelidin Production.

Run	Factor A: Soybean Meal (g/L)	Factor B: Yeast Extract (g/L)	Factor C: Glucose (g/L)	Factor D: Rice Powder (g/L)	Response: Borrelidin Concentration BC (mg/L)
1	0.000	0.000	0.000	0.000	74.852
2	−1.000	0.000	0.000	1.000	82.082
3	0.000	0.000	−1.000	1.000	53.128
4	0.000	0.000	0.000	0.000	86.452
5	−1.000	−1.000	0.000	0.000	55.526
6	0.000	0.000	−1.000	−1.000	52.535
7	0.000	0.000	0.000	0.000	74.559
8	1.000	0.000	1.000	0.000	71.476
9	1.000	0.000	0.000	−1.000	71.247
10	0.000	−1.000	1.000	0.000	42.300
11	−1.000	0.000	0.000	−1.000	79.863
12	0.000	1.000	−1.000	0.000	35.137
13	1.000	0.000	−1.000	0.000	39.893
14	0.000	−1.000	−1.000	0.000	64.362
15	0.000	0.000	0.000	0.000	75.943
16	0.000	0.000	0.000	0.000	76.983
17	1.000	0.000	0.000	1.000	56.923
18	0.000	1.000	0.000	−1.000	39.436
19	1.000	−1.000	0.000	0.000	73.141
20	0.000	0.000	1.000	1.000	34.900
21	−1.000	1.000	0.000	0.000	59.411
22	0.000	1.000	0.000	1.000	57.323
23	0.000	1.000	1.000	0.000	50.845
24	0.000	0.000	1.000	−1.000	56.004
25	0.000	−1.000	0.000	1.000	44.051
26	0.000	−1.000	0.000	−1.000	70.272
27	−1.000	0.000	1.000	0.000	42.501
28	−1.000	0.000	−1.000	0.000	52.776
29	1.000	1.000	0.000	0.000	70.330

The experimental data were fitted to a second-order polynomial model. ANOVA confirmed that the model was statistically significant (F = 4.50, p = .0040), indicating that the quadratic equation adequately described the response surface (Table 4). The coefficient of determination (R² = 0.818) indicated that more than 80% of the total variation in borrelidin production was explained by the model. The adjusted R² (0.636) remained acceptable after accounting for model complexity, and the lack-of-fit was not significant (p = .0835), demonstrating that model deviation was primarily due to experimental error. An adequate precision value of 6.99 further confirmed a sufficient signal-to-noise ratio for navigating the design space.

Table 4.

Analysis of Variance (ANOVA) for the Quadratic Model Applied to Optimize Borrelidin Production.

Source	Sum of Squares	df	Mean Square	F Value	p Value Prob > F
Model	5284.94	14	377.50	4.50	.0040
A: Soybean meal	9.81	1	9.81	0.12	.7374
B: Yeast extract	115.13	1	115.13	1.37	.2610
C: Glucose	3.169E-003	1	3.169E-003	3.777E-005	.9952
D: Rice powder	139.74	1	139.74	1.67	.2178
AB	11.21	1	11.21	0.13	.7202
AC	438.01	1	438.01	5.22	.0384
AD	68.42	1	68.42	0.82	.3818
BC	356.64	1	356.64	4.25	.0583
BD	486.38	1	486.38	5.80	.0304
CD	117.68	1	117.68	1.40	.2560
A²	5.98	1	5.98	0.071	.7933
B²	1028.12	1	1028.12	12.25	.0035
C²	2826.40	1	2826.40	33.69	<.0001
D²	429.36	1	429.36	5.12	.0401
Residual	1174.68	14	83.91
Lack-of-fit	1076.52	10	107.65	4.39	.0835
Pure error	98.16	4	24.54
Cor total	6459.62	28
Std. Dev.	9.16	R ²	0.8182	Std. Dev.
Mean	60.15	Adj R²	0.6363	Mean
C.V.%	15.23	Pred R²	0.0163	C.V.%
Press	6354.14	Adeq precision	6.986	PRESS

Note: Final equation in terms of coded factors: Borrelidin concentration (mg/L) = 77.8 + 0.9 × A − 3.1 × B + 0.01 × C − 3.4 × D − 1.7 × A × B + 10.5 × A × C − 4.1 × A × D + 9.4 × B × C + 11.0 × B × D − 5.4 × C × D − 1.0 × A² − 12.6 × B² − 20.9 × C² − 8.1 × D²

None of the four nutrients exhibited a statistically significant linear effect (p > .2), indicating that borrelidin production was not controlled by any single component alone. Instead, biosynthesis was dominated by interaction and quadratic effects. Two interaction terms were statistically significant: soybean meal × glucose (A×C, p = .038) and yeast extract × rice powder (B × D, p = .030), while yeast extract × glucose (B × C) approached significance (p = .058). These interactions highlight the tight coupling between carbon utilization and nitrogen metabolism in regulating borrelidin biosynthesis.

Significant quadratic effects were observed for yeast extract (B², p = .0035), glucose (C², p < .0001), and rice powder (D², p = .040), confirming the presence of well-defined optima for these variables. The strong quadratic effect of glucose indicates that borrelidin biosynthesis is highly sensitive to carbon flux, with both deficiency and excess impairing production. The three-dimensional response surface plots (Figure 4) further illustrate that maximal borrelidin production occurs only within a narrow window of balanced nutrient supply, reinforcing the importance of carbon–nitrogen balance in secondary metabolism.

Figure 4.

Surface Plots Illustrating the Combined Effects of: The top left to right (A) Glucose and Rice Powder, (B) Soybean Meal and Glucose, and (C) Soybean Meal and Rice Powder. The bottom left to right shows (D) Yeast Extract and Glucose, (E) Yeast Extract and Rice Powder, and (F) Glucose and Rice Powder on Borrelidin production.

Based on the final borrelidin concentration (82.82 mg/L) and 7 days of fermentation, the volumetric productivity was calculated to be 0.492 mg/L/h, highlighting the efficiency of the optimized fermentation process and its relevance for future scale-up consideration.

Validation of the Optimized Medium

Numerical optimization predicted an optimal medium containing 20.0 g/L soybean meal, 2.8 g/L yeast extract, 26.3 g/L glucose, and 30.6 g/L rice powder, corresponding to a predicted borrelidin concentration of 83.5 mg/L (Table 5). Validation experiments conducted in triplicate under these conditions yielded an average experimental borrelidin concentration of 82.82 mg/L, which was not significantly different from the predicted value at a 99.5% confidence level.

Table 5.

Predicted and Actual Values Based on Triplicate Experiments of Borrelidin Production Under Optimum Conditions.

	Soybean Meal (g/L)	Yeast Extract (g/L)	Glucose (g/L)	Rice Powder (g/L)	Borrelidin Production (mg/L)
Optimum conditions	20.0	2.8	26.3	30.6	83.5 (predicted)
Validation result					82.82 ± 5.03^a (actual)

Note: ^aMean ± standard deviation (n = 3).

Although the predicted R² value was relatively low, the close agreement between the predicted and experimentally obtained borrelidin concentrations confirms that the model successfully identified the optimal production condition within the design space and identified a true high-production region. This optimization resulted in a substantial increase in borrelidin concentration compared with the unoptimized medium, demonstrating that medium engineering alone can markedly enhance borrelidin biosynthesis. These results further indicate that the biosynthetic potential of endophytic S. rochei can be effectively unlocked through rational nutrient design, providing a viable route for translating plant-associated actinomycetes into industrial bioprocesses.

Conclusion

This study demonstrates that borrelidin production by an endophytic S. rochei isolated from ‘Zingiber zerumbet (L.) Smith’ can be substantially enhanced through rational medium optimization. Systematic screening and statistical modeling identified soybean meal, yeast extract, glucose, and rice powder as the key nutritional determinants of borrelidin biosynthesis. BBD analysis revealed that production is governed primarily by strong quadratic and interaction effects, reflecting tight metabolic regulation by carbon–nitrogen balance rather than by any single nutrient.

Model-guided optimization increased borrelidin concentration to approximately 83 mg/L, which represents a fourfold increase compared to the parental strain, representing a marked improvement over the unoptimized medium and placing borrelidin production within a range relevant for early-stage process development of macrolide antibiotics. Importantly, this enhancement was achieved using low-cost agricultural substrates, supporting the technical and economic feasibility of scale-up. Collectively, these findings establish a practical upstream bioprocess framework for borrelidin production and demonstrate that endophytic Streptomyces strains can be converted into industrially relevant producers through medium optimization alone. This work provides a solid foundation for future strain improvement, bioreactor optimization, and pharmaceutical development of borrelidin.

Footnotes

Acknowledgements

The authors thank Dr. Catur Sriherwanto, BSc, MSi, for their support.

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 author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Data Availability Statement

All the data is available with the authors and shall be provided upon request.

Declaration of Conflicting Interests

The authors declare no conflicts of interest related to the research, authorship, or publication of this article.

Ethical Approval

This study does not involve experiments on animals or human subjects.

Funding

The authors acknowledge financial support for this research and its publication. This work was partially funded by the Science and Technology Research Partnership for Sustainable Development of the Japan International Cooperation Agency (Grant No. JP19jm0110009), and further supported by a priority grant from the Research Organization for Life Sciences and Environment, National Research and Innovation Agency (DIPA No. 3/III.5/HK/2025).

Informed Consent

Not applicable.

Use of Artificial Intelligence-assisted Tools:

We acknowledge the use of the AI-based application Microsoft Copilot solely for language editing. The manuscript text was not generated using AI. The tool was used only to refine language clarity and stylistic alignment. All scientific content, data analysis, interpretation, and conclusions were independently developed and fully verified by the authors, who take full responsibility for the final version of the manuscript.

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