Statistical Optimization of Biomass and Endospore Production in Bacillus subtilis OS40 Using Agro-Derived Substrates

Statistical Optimization of Biomass and Endospore Production in Bacillus subtilis OS40 Using Agro-Derived Substrates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Statistical Optimization of Biomass and Endospore Production in Bacillus subtilis OS40 Using Agro-Derived Substrates Karthik Prakash M P, Gopikrishnan Venugopal, Radhakrishnan Manikkam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8870581/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The commercial application of Bacillus -based probiotics in aquaculture is constrained by the cost of biomass production and the requirement for long-term shelf-stable formulations. In this study, a low-cost cultivation strategy was developed for the marine probiotic Bacillus subtilis OS40 (GenBank 16S rRNA accession: PV685121), a gut-derived isolate from the marine fish Sardinella longiceps , with an emphasis on heat-resistant endospore formation. A sequential optimization framework integrating one-factor-at-a-time (OFAT), Plackett–Burman design, and central composite design–response surface methodology was applied to formulate a medium based on agro-derived substrates. Jaggery (2% w/v) and peptone (1.5% w/v) were identified as effective carbon and nitrogen sources, reducing the dependence on refined ingredients. Central composite design–response surface analysis revealed that agitation speed (RPM, p < 0.0001) was the principal determinant of biomass accumulation, with the MgSO₄ concentration having a secondary effect (p = 0.070), indicating a strong influence on oxygen transfer under the tested conditions in small-volume systems. Under optimized conditions (0.01% MgSO₄, 1% inoculum, 200 rpm), the biomass increased 3.10-fold (1.33 g/L) relative to that of the unoptimized control in the same vessel system (50 mL centrifuge tubes). Subsequent transfer of the optimized formulation to 2000 mL Erlenmeyer flasks, which provide substantially greater oxygen transfer yielded 9.86 ± 1.50 g/L, representing a 1.85-fold improvement over the flask-scale baseline, highlighting the central importance of vessel geometry in aerobic Bacillus sp. cultivation. Beyond vegetative growth, the optimized medium consistently supported sporulation, yielding 1.8 × 10⁷ CFU mL⁻¹ of heat-resistant spores after 72 h, whereas Luria–Bertani broth produced substantially lower sporulation (≤ 2 × 10⁶ CFU mL⁻¹). These findings indicate that a jaggery–peptone-based formulation was associated with increased biomass and measurable endospore formation of B. subtilis OS40, supporting further investigations of spore-based probiotic formulation strategies. Bacillus subtilis response surface methodology sporulation aquaculture probiotic aerobic fermentation Figures Figure 1 Figure 4 Introduction The application of probiotic bacteria in aquaculture has expanded rapidly as alternatives to antibiotic-dependent disease control and growth promotion. Among the available genera, Bacillus species are particularly attractive because of their ability to form heat-resistant endospores, their tolerance to environmental stress, and their ability to produce extracellular enzymes and antimicrobial metabolites that improve host nutrition and health ( 1 – 3 ). Numerous studies have demonstrated that supplementation with Bacillus subtilis enhances digestive enzyme activity, modulates the gut microbiota, and strengthens innate immune responses in fish and shrimp ( 4 – 7 ). This aligns with broader sustainable aquaculture strategies, such as aquamimicry, which rely on beneficial microbial communities to maintain water quality and host health ( 8 ). Despite their functional efficacy, the commercial deployment of Bacillus -based probiotics is constrained by the economics of large-scale biomass and spore production. Industrial formulations typically require cell densities of at least 10⁹ CFU g⁻¹ ( 9 ), and fermentation costs are strongly influenced by medium composition and process efficiency ( 10 ). The use of refined substrates substantially increases production expenses, motivating the exploration of low-cost agro-industrial alternatives. Unrefined sugar sources and agricultural byproducts have therefore been widely investigated for Bacillus cultivation, often providing both economic and metabolic advantages ( 11 – 13 ). Process optimization in aerobic fermentations typically employs empirical and statistical approaches, including one-factor-at-a-time (OFAT) screening and response surface methodology (RSM)-based designs such as Plackett–Burman (PBD) and central composite design (CCD). These strategies are effective for identifying influential nutritional and physical parameters, although their predictive accuracy is frequently limited by unaddressed bioreactor constraints, particularly oxygen transfer. With respect to Bacillus spp. which rely predominantly on respiratory metabolism, aeration and vessel geometry can have greater effects on biomass yield than can individual medium components. Several studies have applied RSM to optimize Bacillus sp., biomass production, typically using refined carbon sources such as glucose or sucrose, and have reported dry cell weight as the sole response variable. For example, ( 14 ) applied the same PBD–CCD–RSM pipeline to B. subtilis PW12, an aquaculture probiotic strain, achieving 14.29 ± 0.23 g/L DCW using glucose and soya peptone, without sporulation assessment. Using glucose–peptone RSM without sporulation data, The study ( 15 ) reported a concentration of 3.2 g/L for B. velezensis . The study ( 16 ) is a notable exception: they used Box–Behnken RSM on B. subtilis M 2063 to jointly maximize viability and sporulation and reported that the oxygen transfer rate (k L a) was the principal driver of both responses, directly paralleling the agitation-dominant pattern observed in the present study. However, their optimized yields required controlled bioreactor scale-up, their medium used refined glucose in the DSM formulation, and the strain was not of marine or aquaculture probiotic origin. Across all of these investigations, agro-derived substrates were not employed, and none simultaneously diagnosed vessel geometry as a confounding variable within the RSM framework itself. The present study was designed to address these specific gaps by simultaneously optimizing both vegetative biomass and heat-resistant endospore formation in a marine aquaculture probiont, using exclusively low-cost agro-derived substrates, while oxygen transfer was explicitly diagnosed through geometry-controlled validation. Bacillus subtilis OS40 (GenBank 16S rRNA accession: PV685121), a marine fish gut-derived isolate from Sardinella longiceps , has previously been shown to possess probiotic-relevant traits, including antagonistic activity against major aquaculture pathogens ( 17 ). Although statistical optimization has been widely applied to improve the biomass production of Bacillus species, most studies focus exclusively on vegetative cell density and rarely evaluate sporulation capacity or the influence of oxygen transfer constraints during small-scale optimization, and these studies are typically performed using refined laboratory substrates rather than low-cost agro-derived alternatives. Furthermore, metabolic behaviour specific to different strains can strongly affect oxygen demand and the developmental shift toward endospore formation ( 18 ). These strain-specific dynamics are particularly relevant for marine isolates such as OS40, where the interaction between substrate composition, aeration, and sporulation induction has not previously been characterized under a unified statistical optimization framework. Unlike previous RSM-based studies that primarily focus on biomass yield using refined substrates, the present study integrates three critical aspects: (i) the use of low-cost agro-derived substrates (jaggery–peptone) for economically viable production; (ii) simultaneous evaluation of vegetative biomass and functional endospore formation; and (iii) explicit identification of oxygen transfer limitations and vessel geometry as dominant confounding factors within small-scale RSM optimization systems. This combined approach provides both conceptual and practical insights into scalable probiotic production. Materials and methods Bacterial Strain and Maintenance Bacillus subtilis OS40 was previously isolated from the gut of the marine fish Sardinella longiceps . Species-level identification was confirmed by 16S rRNA gene sequencing using universal primers (27F/1492R), followed by BLASTn analysis against the NCBI database, which showed ≥ 99% sequence similarity to Bacillus subtilis reference strains. The 16S rRNA gene sequence has been deposited in GenBank under accession number PV685121. The strain is currently maintained in the institutional culture collection at the Centre for Drug Discovery and Development, Sathyabama Institute of Science and Technology, Chennai, India. Deposition in a public culture repository (e.g., MTCC/DSMZ) is ongoing. Basal medium composition For OFAT optimization, a minimal basal medium was formulated containing only phosphate buffers (K₂HPO₄ 1.0 g/L, KH₂PO₄ 0.5 g/L, pH 7.0), to which individual carbon, nitrogen, and mineral sources were added as test variables. This approach ensured that the observed growth differences were attributable to the supplemented nutrients rather than undefined carryover from complex media. All media were sterilized by autoclaving at 121°C for 15 min at 15 psi. Heat-sensitive components, where applicable, were filter-sterilized through 0.22 µm membranes and added aseptically after the medium had cooled to 50°C. One-factor-at-time (OFAT) optimization The influence of nutritional and physical parameters on Bacillus subtilis OS40 biomass was assessed via a one-factor-at-time (OFAT) approach ( 19 ). All the experiments were conducted in 6-well tissue culture plates, with each well containing 3 mL of medium. The wells were inoculated with a 2% (v/v) seed culture prepared as follows: a single colony was inoculated into nutrient broth and grown overnight (18 h, 37°C, 150 rpm). Prior to use, the seed culture density was adjusted to 0.5 McFarland standard (approximately 1–2 × 10⁸ CFU mL⁻¹) by comparison to a certified turbidity standard, and 2% (v/v) of this adjusted culture was added to each experimental vessel. The cultures were incubated at 37°C for 24 h. Uninoculated medium served as the blank. All the experiments were performed in triplicate. Carbon sources: Seven carbon sources: glycerol, glucose, starch, rice bran extract, coconut water, sugarcane juice, and jaggery—were tested at 0.5–2.5% (w/v). Nitrogen sources: Six nitrogen sources, namely, groundnut cake extract, fish hydrolysate, ammonium sulfate, peptone, yeast extract, and soybean meal extract, were evaluated at 1.0–3.0% (w/v) using the optimal carbon source identified above. Mineral salts: Micronutrient requirements were examined by supplementing the optimized carbon:nitrogen formulation with FeCl₂, MnCl₂, MgSO₄, CaCl₂, ZnSO₄, and CuSO₄ at 0.01–0.05% (w/v). pH optimization: The medium pH was adjusted to 6.0–8.5 prior to sterilization, and the pH was measured again after cultivation to confirm stability. NaCl and aeration: Salinity tolerance was assessed by adding 0–7% (w/v) NaCl to the optimized medium. Aeration effects were evaluated by varying the working volume between 2–3 mL per well, altering the headspace liquid ratio while maintaining all other culture conditions constant. Plackett–Burman design (PBD) On the basis of the OFAT results, eight factors were selected for screening via a Plackett–Burman design comprising 13 total runs (12 design runs and 1 center point): jaggery (Stock A, 10% w/v aqueous solution), peptone (Stock B, 15% w/v aqueous solution), MgSO₄ (Stock C, 1% w/v aqueous solution), NaCl (Stock D, 10% w/v aqueous solution), final pH, inoculum size, temperature, and RPM. Stock solutions were filter-sterilized (0.22 µm) and added aseptically to the basal medium at the volumes specified in Table S1 to achieve the desired final concentrations in a 25 mL working volume. The center-point run provided an internal reference for curvature assessment. Each factor was tested at two levels, designated high (+ 1) and low (-1), according to ( 9 ). The optimization experiments were conducted in 50 mL centrifuge tubes with a working volume of 25 mL. To maximize the volumetric oxygen transfer coefficient within this vessel geometry, two critical modifications were applied: ( 1 ) the tubes were incubated at a 45° inclination to increase the gas–liquid interface surface area, and ( 2 ) the caps were loosened by one-quarter turn to permit passive gas exchange while minimizing evaporation ( 15 ). These conditions ensured sufficient aeration for aerobic metabolism during the high-throughput screening phase. The nonsignificant model (p > 0.05) and low linearity ( R 2 = 0.45) confirmed the presence of curvature. This nonlinearity validated the necessity of the subsequent second-order CCD to resolve interactions that the linear PBD could not capture. The PBD served strictly for variable ranking rather than yield prediction. The cultures were subsequently grown in 50 mL centrifuge tubes containing 25 mL of medium for 24 h. The dry cell weight (DCW) was determined by centrifugation at 10,000×g for 10 min, after which the pellets were washed twice with distilled water and dried at 65°C until a constant weight was reached. Each run was performed in triplicate, and the mean DCW (g/L) was used as the response variable. The main effect of each factor was calculated as follows: $$\:Effect\:=\:(\varSigma\:\:Response\:at\:High\:Level\:-\:\varSigma\:\:Response\:at\:Low\:Level)\:/\:(Number\:of\:High-Level\:Runs)$$ Statistical significance was evaluated via analysis of variance (ANOVA) with α = 0.05. Central composite design (CCD) and response surface methodology The three most significant factors identified in the PBD screening (MgSO₄, inoculum size, and RPM) were selected for a rotatable central composite design (α = 1.682) with five center point replicates. The factor levels were coded as -1.682 (axial low), -1 (low), 0 (center), + 1 (high), and + 1.682 (axial high); although the global CCD model was significant (p < 0.05), replicate divergence in high-agitation runs, e.g., Run F1, was observed. Replicate variability at high agitation was attributed to oxygen transfer heterogeneity inherent to capped tube systems. However, this localized noise did not compromise the global optimization trend, as confirmed by the high consistency of the subsequent flask validation trials. A Rotatable Central Composite Design (CCD) was employed to optimize the MgSO₄ concentration, inoculum size, and agitation speed. The design comprised 19 runs: 8 factorial points, 6 axial points, and 5 center-point replicates. All the experiments were performed in triplicate, and the biomass was quantified as dry cell weight (DCW, g/L). A second-order polynomial model of the form $$\:Y\:=\:\beta\:₀\:+\:\varSigma\:\beta\:ᵢxᵢ\:+\:\varSigma\:\beta\:ᵢᵢxᵢ²\:+\:\varSigma\:\beta\:ᵢⱼxᵢxⱼ,$$ where Y represents the predicted biomass and x i represents the coded variables that were fitted to the data. Model adequacy was assessed via ANOVA, R², and adjusted R². Diagnostic plots (normal probability, residuals vs. predicted, and residuals vs. run order) were used to verify the model assumptions. Response surface and contour plots were generated to visualize interactions and identify optimal conditions. Although not significant in the ANOVA, MgSO₄ (A, p = 0.070) and inoculum (B, p = 0.964) were kept in the model because of their noteworthy effect sizes in the PBD screening (40.30% and 5.62%, respectively) and to maintain the model hierarchy, preventing misleading interpretations with interaction or quadratic terms. RPM (C, p < 0.0001) and its quadratic term (C², p = 0.009) indicate that agitation speed drives biomass yield, indicating oxygen transfer limitations in small-volume tube cultures. Model validation and geometry-dependent oxygen transfer assessment Validation in the Optimization Vessel The RSM-predicted optimal conditions (0.01% MgSO₄, 1% inoculum, 200 rpm) were validated in triplicate via 50 mL centrifuge tubes (25 mL working volume), matching the CCD conditions. The dry cell weight (DCW) closely matched the predicted value and clearly improved over that of the unoptimized control (PBD Run 12). Scale-up in flasks To evaluate scalability and aeration effects, the same conditions were tested in 2000 mL Erlenmeyer flasks with a working volume of 750 mL (37.5% fill ratio), which provides substantially greater headspace and an improved surface-to-volume ratio for oxygen transfer relative to the 50 mL centrifuge tube system (50% fill, 25 mL). Compared with the centrifuge tubes, the flask geometry offered greater aeration and altered mixing. Flask-level cultivation confirmed that improved aeration consistently increased the biomass yield under optimized conditions, supporting the interpretation that oxygen transfer rather than medium composition was the dominant limiting variable in the tube system. Enumeration of heat-resistant endospores Heat-resistant endospores of Bacillus subtilis OS40 were enumerated to assess the functional relevance of the optimized medium. Cultures grown in optimized medium and Luria–Bertani (LB) broth (Miller formulation: 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl; pH 7.0) were harvested after 48 h and 72 h, corresponding to the late stationary phase and active sporulation in Bacillus spp. Aliquots (one mL) of undiluted cultures were subjected to moist heat treatment at 80°C for 10 min in a thermostatically controlled water bath to selectively eliminate vegetative cells while preserving mature endospores. The samples were immediately cooled on ice for 5 min. Serial decimal dilutions were prepared, and a volume of 100 µL from the 10–5 dilution was spread onto nutrient agar plates and incubated at 37°C for up to 48 h to allow spore germination and colony development. Colonies that recovered after heat treatment were considered to represent heat-resistant endospores and are expressed as CFU mL⁻¹. $$\:CFU\:mL⁻¹\:=\:(N\:\times\:\:D)\:/\:V$$ where N = number of colonies counted, V = volume plated (mL), and D = dilution factor. Statistical analysis The OFAT data were analysed via descriptive statistics, with means and standard deviations calculated from triplicate measurements. The OFAT datasets were analysed using one-way ANOVA followed by Tukey’s HSD post hoc test (α = 0.05). PBD analysis was performed via half-normal probability plots and ANOVA to identify significant factors. The CCD data were analysed via Python (v.3.10) with the Pandas and NumPy libraries for data processing. The second-order polynomial model and analysis of variance (ANOVA) were generated via Statsmodels, whereas the optimal conditions were determined via SciPy optimization algorithms. The response surface and contour plots were visualized via Matplotlib. Results 3.1. One-Factor-at-a-Time (OFAT) Screening Screening of carbon sources identified Jaggery (2% w/v) as the optimal substrate, yielding 0.54 ± 0.01 g/L biomass and significantly outperforming refined sugars (p < 0.001). Peptone (1.5% w/v) was selected as the superior nitrogen source (0.52 ± 0.02 g/L) over inorganic salts. Mineral profiling confirmed MgSO₄ (0.03% w/v) as the critical micronutrient (0.50 ± 0.02 g/L), whereas CuSO₄ and ZnSO₄ exhibited toxicity at elevated levels. The strain exhibited maximum growth at pH 7.0 (0.64 ± 0.04 g/L), confirming a strict neutrophilic preference. Salinity tolerance peaked at 3% NaCl (0.58 ± 0.08 g/L), indicating moderate halotolerance. Additionally, increasing the culture headspace to 80% significantly improved biomass yield (0.50 ± 0.01 g/L), highlighting the dependence on oxygen transfer efficiency. Plackett–Burman Design: Statistical Screening of Critical Factors Experimental Design and Response Plackett–Burman design (PBD) was employed as a robust screening tool to filter eight process variables and identify the most critical drivers of biomass production. Table 1. Analysis of the main effects Rank Factor Name Effect Value Contribution % Direction 1 MgSO₄ 7.77 40.30% Positive (+) 2 RPM -5.35 19.12% Negative (-) 3 Temp (°C) -4.20 11.78% Negative (-) 4 Inoculum -2.90 5.62% Negative (-) 5 NaCl 2.53 4.29% Positive (+) 6 Jaggery 1.65 1.82% Positive (+) 7 Final pH 1.33 1.19% Positive (+) 8 Peptone -1.27 1.07% Negative (-) The PBD was employed solely for variable ranking, as the model was statistically nonsignificant (p = 0.2766) and unsuitable for yield prediction. Despite the low model fit ( R 2 adj = 0.4568) caused by unmodelled interactions or curvature, the Pareto analysis successfully ranked the variables by effect magnitude (Figure 6). On the basis of this ranking, MgSO₄, agitation, and inoculum size were identified as the dominant factors and selected for subsequent optimization via CCD to resolve the nonlinear interactions. Owing to the inherent variability of biological fermentation in high-throughput screening vessels (50 mL tubes), the primary objective of this phase was variable ranking rather than precise predictive modelling. Central Composite Design and Response Surface Methodology Aeration dominated the biomass yield. At lower agitation speeds (150–175 RPM), the biomass yields were consistently low (averaging 0.32–0.48 g/L). Although temperature exhibited a measurable negative main effect during Plackett–Burman screening (Effect = −4.20), it was maintained at 37 °C during the CCD phase, as this condition represents the established physiological optimum for B. subtilis . However, increasing the agitation to 200 RPM resulted in a sharp, significant increase in biomass, with maximum yields exceeding 2.0 g/L. The experimental data were fitted to a second-order polynomial equation via multiple regression analysis. Table 2. Analysis of variance (ANOVA) for the quadratic model Source Sum of Squares df Mean Square F Value p Value Model 1723.51 9 191.5 9.53 < 0.0001 A-MgSO₄ 56.4 1 56.4 2.81 0.107 B-Inoculum % 22.15 1 22.15 1.1 0.304 C-RPM 1254.88 1 1254.88 62.45 < 0.0001 AC (Interaction) 14.5 1 14.5 0.72 0.404 BC (Interaction) 45.12 1 45.12 2.25 0.147 C² (RPM²) 166.42 1 166.42 8.28 0.008 Residual 462.15 23 20.09 Total 2185.66 32 ( R 2 = 0.7885; adjusted R 2 = 0.7058) The experimental results were analysed via multiple regression analysis. The final polynomial model, expressed in coded factors, is given by Equation (2). All variables represent coded factor levels: where A, B, and C are the coded levels of MgSO₄, inoculum size, and agitation speed, respectively. Interaction effects and response surface analysis To investigate the interactive effects of the variables on biomass yield, 3D response surface plots and 2D contour plots were generated. These visualizations highlight the critical relationships between physical parameters and nutrient concentrations. Experimental Validation of Model Predictions in the Optimization Vessel The RSM model predicted a maximum biomass of 1.36 g/L at 200 rpm, 0.01% MgSO₄, and 1% inoculum (Table 3). Validation in the same vessel system used for optimization (50 mL Centrifuge tubes, 25 mL working volume) produced 1.33 ± 0.07 g/L, showing excellent agreement with the model (2.1% deviation). Relative to the unoptimized control (0.43 ± 0.14 g/L), the optimized condition yielded a 3.10-fold improvement. Table 3. Model Validation in 50 mL Centrifuge Tubes Condition MgSO₄ (%) Inoculum (%) RPM Predicted (g/L) Experimental (g/L) Deviation Fold Increase Baseline 0.03 1% 150 – 0.43 ± 0.14 – 1.0 Optimized 0.01 1% 200 1.36 1.33 ± 0.07 2.1% 3.10× Effect of Vessel Geometry and Oxygen Transfer on Biomass Yield To assess aeration-dependent scalability, the same conditions were evaluated in 2000 mL conical flasks (750 mL working volume), which provide substantially greater oxygen transfer than Centrifuge tubes do. Biomass increased markedly under both baseline and optimized conditions due to improved aeration, with the optimized flask culture achieving a 1.85-fold improvement over the flask baseline (Table 4). Table 4. Effect of vessel geometry on biomass production Condition MgSO₄ (%) Inoculum (%) RPM Yield (g/L) Fold Increase vs. Baseline Baseline (Flask) 0.03 1% 150 5.34 ± 0.48 1.0 Optimized (Flask) 0.01 1% 200 9.86 ± 1.50 1.85× Enumeration of heat-resistant endospores Beyond biomass enhancement, the capacity to support sporulation is critical for Bacillus -based probiotic production, as heat-resistant endospores enable long-term storage, feed incorporation, and gastrointestinal survival (19). Heat treatment (80°C, 10 min) was used to selectively enumerate spores by eliminating vegetative cells. Cultures grown in the optimized jaggery-peptone medium produced recoverable heat-resistant spores at both 48 h and 72 h (Table 5). At 48 h, spore counts reached 7.5 ± 0.7 × 10⁶ CFU/mL, representing 31.3% of total viable cells (2.4 ± 0.6 × 10⁷ CFU/mL). At 72 h, absolute spore counts increased to 1.8 ± 0.7 × 10⁷ CFU/mL; however, sporulation efficiency decreased to 15.4%, which may reflect continued vegetative growth under the tested conditions (total viable cells: 1.17 ± 0.09 × 10⁸ CFU/mL). This temporal pattern is consistent with biphasic Bacillus growth dynamics, where nutrient depletion triggers sporulation initiation around 24-36 h (18), but residual carbon sources support additional vegetative growth even as sporulation proceeds. The observed sporulation efficiency demonstrates the ability of the optimized medium to support functional endospore formation (25,26). Discussion The cultivation performance of Bacillus subtilis OS40 observed in this study reflects the combined influence of medium composition and physical growth conditions. Although the OFAT–PBD–CCD strategy enabled the identification of favourable nutritional components, the statistical outcomes consistently point to agitation, and therefore oxygen availability, as the principal factor governing biomass accumulation. This trend is consistent with aerobic Bacillus sp. metabolism, even in early bioprocess studies (16,17). Jaggery proved to be a more effective carbon source than refined glucose, a result that is not unexpected given its mixed sugar content and the presence of trace minerals. Similar advantages of unrefined sugar substrates have been reported for other Bacillus systems (11,13). In the present work, peptone supported more stable biomass formation than inorganic nitrogen sources, which is consistent with reports showing that complex organic nitrogen enhances growth kinetics and metabolic activity in Bacillus spp. (27). Magnesium supplementation also contributed to improved yields, in agreement with its known role in cellular energetics and enzyme function (28,29) Despite these nutritional effects, the CCD analysis revealed that RPM was the only statistically significant main factor, indicating that the cultures were largely oxygen-limited in the small-volume tube system. The pronounced increase in biomass upon transfer to Erlenmeyer flasks further supports this interpretation and highlights the importance of vessel geometry and mixing dynamics, as discussed previously for aerobic microbial processes (16). Nevertheless, the optimized medium retained a measurable advantage over the baseline formulation under enhanced aeration, suggesting that medium composition remains relevant once the primary physical limitation is alleviated. An important outcome of this work is the demonstration that the optimized medium supports not only vegetative growth but also the formation of heat-resistant endospores. In Bacillus , sporulation is initiated when nutrient balance shifts, particularly when the carbon-to-nitrogen ratio declines, leading to activation of the Spo0A phosphorelay and commitment to developmental differentiation (18,19). The observed sporulation suggests that the formulation supports physiological conditions compatible with entry into the sporulation pathway. By contrast, Luria–Bertani broth maintains high concentrations of readily assimilable nutrients and is primarily designed to sustain vegetative growth. Under such conditions, Spo0A activation is repressed, and sporulation remains minimal even during prolonged incubation. Media that maintain respiratory metabolism while slowly imposing nutrient stress promote Spo0A phosphorylation and the sequential action of sporulation-specific sigma factors (σ^F, σ^E), which govern forespore development (18,19). The sporulation efficiencies obtained in this study fall within the range reported for B. subtilis cultivated in low-cost or minimal formulations (26), supporting the practical relevance of the medium for spore-based probiotic production. From a process development perspective, these findings indicate that nutritional optimization alone is insufficient if oxygen transfer is not adequately addressed. Aeration strategy and reactor configuration will therefore be critical determinants of productivity during further scale-up, likely exceeding the influence of individual medium components. The present formulation is compatible with such systems and may serve as a suitable basis for subsequent bioreactor-level optimization. Overall, this study shows that a jaggery–peptone-based medium can support both higher biomass formation and functional sporulation of B. subtilis OS40, while also underlining the central role of oxygen transfer in governing process performance. These observations provide a useful starting point for the development of cost-sensitive, spore-based probiotic processes for aquaculture. Conclusion This study applied established statistical optimization approaches to improve biomass and endospore production of Bacillus subtilis OS40 using agro-derived substrates. Agitation speed was strongly associated with biomass yield under small-volume cultivation conditions. The optimized formulation supported reproducible endospore formation, providing a basis for further evaluation in probiotic development studies. Declarations Funding This work was supported by the Ministry of Earth Sciences (MoES), Government of India (Project No. MoES/36/OOIS/Extra/92/2022). The authors also acknowledge institutional support from Sathyabama Institute of Science and Technology, Chennai, India. Competing Interests The authors declare that they have no known financial or non-financial competing interests that are directly or indirectly related to the work submitted for publication. Author Contributions Karthik Prakash M P: Conceptualization, Methodology, Investigation, Data analysis, Writing – original draft. Gopikrishnan Venugopal: Supervision, Funding acquisition, Project administration, Writing – review and editing. Radhakrishnan Manikkam: Validation, Formal analysis, Writing – review and editing. Data Availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Ethical Approval This article does not contain any studies with human participants or animals performed by any of the authors. 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Springer, Cham, pp 89–99. https://doi.org/10.1007/978-3-030-85465-2_4 Ungureanu N, Vlăduț V, Biriș SȘ (2022) Sustainable valorization of waste and by-products from sugarcane processing. Sustainability 14:11089. https://doi.org/10.3390/su141711089 Valle Vargas MF, Villamil Diaz LM, Ruiz Pardo RY et al (2024) Design of an agro-industrial by-products-based media for probiotic bacteria production. Sci Rep 14:17955. https://doi.org/10.1038/s41598-024-68783-z Behera SS, Ray RC (2016) Solid state fermentation for production of microbial cellulases: recent advances and improvement strategies. Int J Biol Macromol 86:656–669. https://doi.org/10.1016/j.ijbiomac.2015.10.090 Singh V, Haque S, Niwas R et al (2017) Strategies for fermentation medium optimization: an in-depth review. Front Microbiol 7:2036. https://doi.org/10.3389/fmicb.2016.02087 Garcia-Ochoa F, Gomez E (2009) Bioreactor scale-up and oxygen transfer rate in microbial processes: an overview. Biotechnol Adv 27:153–176. https://doi.org/10.1016/j.biotechadv.2008.10.006 Hixson AW, Gaden EL (1950) Oxygen transfer in submerged fermentation. Ind Eng Chem 42:1792–1801. https://doi.org/10.1021/ie50489a031 Piggot PJ, Hilbert DW (2004) Sporulation of Bacillus subtilis. Curr Opin Microbiol 7:579–586. https://doi.org/10.1016/j.mib.2004.10.001 Setlow P (2014) Spore resistance properties. Microbiol Spectr 2:TBS-0003-2012. https://doi.org/10.1128/microbiolspec.TBS-0003-2012 Prakash MPK, Padmanaban D, Loganthan C et al (2024) Investigating antagonistic activity of fish gut bacterial isolates against aquaculture pathogens. Int J Agric Technol 20:1197–1200 Moonsamy G, Singh S, Roets-Dlamini Y et al (2025) Development of a high-cell-density production process for a biotherapeutic yeast Saccharomyces cerevisiae var boulardii. Fermentation 11:186. https://doi.org/10.3390/fermentation11040186 Shi Y, Niu X, Yang H et al (2024) Optimization of fermentation media and growth conditions of Bacillus velezensis using Plackett–Burman design and response surface methodology. Front Microbiol 15:1355369. https://doi.org/10.3389/fmicb.2024.1355369 Myers RH (2016) Response surface methodology: process and product optimization using designed experiments. Wiley, New York Nicholson WL, Munakata N, Horneck G et al (2000) Resistance of Bacillus endospores to extreme terrestrial and extraterrestrial environments. Microbiol Mol Biol Rev 64:548–572. https://doi.org/10.1128/MMBR.64.3.548-572.2000 Duré LMM, Mascarin GM, Bettiol W (2025) Optimization of endospore production by solid and liquid fermentation for Bacillus velezensis formulations. Braz J Microbiol 56:1253–1261. https://doi.org/10.1007/s42770-025-01626-9 Mazhar H, Ullah I, Ali U et al (2023) Optimization of low-cost solid-state fermentation media for thermostable lipase production. Biocatal Agric Biotechnol 47:102559. https://doi.org/10.1016/j.bcab.2022.102559 Lu Z, He S, Kashif M et al (2023) Effect of ammonium stress on phosphorus solubilization of Bacillus aryabhattai. BMC Genomics 24:550. https://doi.org/10.1186/s12864-023-09559-z Sachla AJ, Soni V, Piñeros M et al (2024) The Bacillus subtilis yqgC-sodA operon protects magnesium-dependent enzymes by supporting manganese efflux. J Bacteriol 206:e00052-24. https://doi.org/10.1128/jb.00052-24 Schaeffer P, Millet J, Aubert JP (1965) Catabolic repression of bacterial sporulation. Proc Natl Acad Sci USA 54:704–711. https://doi.org/10.1073/pnas.54.3.704 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 14 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8870581","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595858465,"identity":"0ef8c806-f3ca-4aed-b38e-0f4d6ea1d572","order_by":0,"name":"Karthik Prakash M P","email":"","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Karthik","middleName":"Prakash M","lastName":"P","suffix":""},{"id":595858467,"identity":"c07bb081-7701-4fbc-b6f2-39cef423b646","order_by":1,"name":"Gopikrishnan Venugopal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJCCA0BowA9iJRQQr8XAQLIBpMWAeHsMDAwOgBjEaJF37z14mOfMH2Pj86sTPzwwYJDnFzuAX4vhmXMJh3luGJiZ3Xi7WQLoMMOZsxMIaJmRY3BwxgcDG7MbZzeAtCQY3CZWi/GMs5t/EKVFXiLH4MAHoMMM+Hu3EWeLAc+5hAMfzhgbS9zg3WaRYCBB2C/y7b2HPyQckzPs7z+7+eaPCht5fmlCthzggbIkwCol8CsH29IA08J/gLDqUTAKRsEoGJkAANglSxOf4+50AAAAAElFTkSuQmCC","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Gopikrishnan","middleName":"","lastName":"Venugopal","suffix":""},{"id":595858470,"identity":"cc9fe0f2-90c5-46e5-91a8-c65e52314ad0","order_by":2,"name":"Radhakrishnan Manikkam","email":"","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Radhakrishnan","middleName":"","lastName":"Manikkam","suffix":""}],"badges":[],"createdAt":"2026-02-13 10:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8870581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8870581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109405753,"identity":"28a8c097-0067-45fe-a9d2-42c3194aaa5a","added_by":"auto","created_at":"2026-05-17 13:20:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131848,"visible":true,"origin":"","legend":"\u003cp\u003eOne-Factor-at-a-Time (OFAT) optimization of nutritional and physical parameters for \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 biomass production. (A) Effect of carbon sources (0.5–2.5% w/v), identifying Jaggery (2%) as the most effective substrate (p \u0026lt; 0.001). (B) Effect of nitrogen sources (1.0–3.0% w/v), where Peptone (1.5%) supported significantly higher growth compared to inorganic sources (p \u0026lt; 0.001). (C) Screening of mineral salts, with MgSO₄ (0.03%) resulting in the highest biomass accumulation (p \u0026lt; 0.001). (D) Effect of initial pH, indicating a specific neutrophilic peak at pH 7.0 (p \u0026lt; 0.001). (E) Influence of NaCl concentration, showing optimal production at 3% NaCl (p \u0026lt; 0.01). (F) Effect of headspace volume (aeration), where 80% headspace significantly enhanced biomass yield (p \u0026lt; 0.01). Data represent mean ± standard deviation of independent triplicates (n=3). Asterisks indicate statistical significance (* p \u0026lt; 0.01; *** p \u0026lt; 0.001) determined via one-way ANOVA with post-hoc analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8870581/v1/2ce06667699b02389198ed8e.png"},{"id":109305603,"identity":"67397629-ed6c-42d7-b52c-c19f6c3c5d49","added_by":"auto","created_at":"2026-05-15 10:05:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72342,"visible":true,"origin":"","legend":"\u003cp\u003eGrowth and sporulation kinetics of \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 in the optimized Jaggery-Peptone medium. The culture was monitored at 24, 48, and 72 h. N.D\u003cstrong\u003e.\u003c/strong\u003eindicates Not Detected, signifying that heat-resistant spores were below the assay detection limit at 24 h. Data represent mean ± SD of independent triplicates (n=3).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8870581/v1/1fd1ad5b5201cfbfc4629489.png"},{"id":109405356,"identity":"ad227540-7d5b-45f6-ae9d-0d73ccaba7eb","added_by":"auto","created_at":"2026-05-17 13:17:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":342137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8870581/v1/f55aa700-644e-445c-acda-261dd03dec9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Statistical Optimization of Biomass and Endospore Production in Bacillus subtilis OS40 Using Agro-Derived Substrates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe application of probiotic bacteria in aquaculture has expanded rapidly as alternatives to antibiotic-dependent disease control and growth promotion. Among the available genera, \u003cem\u003eBacillus\u003c/em\u003e species are particularly attractive because of their ability to form heat-resistant endospores, their tolerance to environmental stress, and their ability to produce extracellular enzymes and antimicrobial metabolites that improve host nutrition and health (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Numerous studies have demonstrated that supplementation with \u003cem\u003eBacillus subtilis\u003c/em\u003e enhances digestive enzyme activity, modulates the gut microbiota, and strengthens innate immune responses in fish and shrimp (\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This aligns with broader sustainable aquaculture strategies, such as aquamimicry, which rely on beneficial microbial communities to maintain water quality and host health (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their functional efficacy, the commercial deployment of \u003cem\u003eBacillus\u003c/em\u003e-based probiotics is constrained by the economics of large-scale biomass and spore production. Industrial formulations typically require cell densities of at least 10⁹ CFU g⁻\u0026sup1; (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and fermentation costs are strongly influenced by medium composition and process efficiency (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The use of refined substrates substantially increases production expenses, motivating the exploration of low-cost agro-industrial alternatives. Unrefined sugar sources and agricultural byproducts have therefore been widely investigated for \u003cem\u003eBacillus\u003c/em\u003e cultivation, often providing both economic and metabolic advantages (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProcess optimization in aerobic fermentations typically employs empirical and statistical approaches, including one-factor-at-a-time (OFAT) screening and response surface methodology (RSM)-based designs such as Plackett\u0026ndash;Burman (PBD) and central composite design (CCD). These strategies are effective for identifying influential nutritional and physical parameters, although their predictive accuracy is frequently limited by unaddressed bioreactor constraints, particularly oxygen transfer. With respect to \u003cem\u003eBacillus\u003c/em\u003e spp. which rely predominantly on respiratory metabolism, aeration and vessel geometry can have greater effects on biomass yield than can individual medium components.\u003c/p\u003e \u003cp\u003eSeveral studies have applied RSM to optimize \u003cem\u003eBacillus\u003c/em\u003e sp., biomass production, typically using refined carbon sources such as glucose or sucrose, and have reported dry cell weight as the sole response variable. For example, (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) applied the same PBD\u0026ndash;CCD\u0026ndash;RSM pipeline to \u003cem\u003eB. subtilis\u003c/em\u003e PW12, an aquaculture probiotic strain, achieving 14.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 g/L DCW using glucose and soya peptone, without sporulation assessment. Using glucose\u0026ndash;peptone RSM without sporulation data, The study (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) reported a concentration of 3.2 g/L for \u003cem\u003eB. velezensis\u003c/em\u003e. The study (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) is a notable exception: they used Box\u0026ndash;Behnken RSM on \u003cem\u003eB.\u003c/em\u003e subtilis M 2063 to jointly maximize viability and sporulation and reported that the oxygen transfer rate (k\u003csub\u003eL\u003c/sub\u003ea) was the principal driver of both responses, directly paralleling the agitation-dominant pattern observed in the present study. However, their optimized yields required controlled bioreactor scale-up, their medium used refined glucose in the DSM formulation, and the strain was not of marine or aquaculture probiotic origin. Across all of these investigations, agro-derived substrates were not employed, and none simultaneously diagnosed vessel geometry as a confounding variable within the RSM framework itself. The present study was designed to address these specific gaps by simultaneously optimizing both vegetative biomass and heat-resistant endospore formation in a marine aquaculture probiont, using exclusively low-cost agro-derived substrates, while oxygen transfer was explicitly diagnosed through geometry-controlled validation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 (GenBank 16S rRNA accession: PV685121), a marine fish gut-derived isolate from \u003cem\u003eSardinella longiceps\u003c/em\u003e, has previously been shown to possess probiotic-relevant traits, including antagonistic activity against major aquaculture pathogens (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Although statistical optimization has been widely applied to improve the biomass production of \u003cem\u003eBacillus\u003c/em\u003e species, most studies focus exclusively on vegetative cell density and rarely evaluate sporulation capacity or the influence of oxygen transfer constraints during small-scale optimization, and these studies are typically performed using refined laboratory substrates rather than low-cost agro-derived alternatives. Furthermore, metabolic behaviour specific to different strains can strongly affect oxygen demand and the developmental shift toward endospore formation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These strain-specific dynamics are particularly relevant for marine isolates such as OS40, where the interaction between substrate composition, aeration, and sporulation induction has not previously been characterized under a unified statistical optimization framework.\u003c/p\u003e \u003cp\u003eUnlike previous RSM-based studies that primarily focus on biomass yield using refined substrates, the present study integrates three critical aspects: (i) the use of low-cost agro-derived substrates (jaggery\u0026ndash;peptone) for economically viable production; (ii) simultaneous evaluation of vegetative biomass and functional endospore formation; and (iii) explicit identification of oxygen transfer limitations and vessel geometry as dominant confounding factors within small-scale RSM optimization systems. This combined approach provides both conceptual and practical insights into scalable probiotic production.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBacterial Strain and Maintenance\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 was previously isolated from the gut of the marine fish \u003cem\u003eSardinella longiceps\u003c/em\u003e. Species-level identification was confirmed by 16S rRNA gene sequencing using universal primers (27F/1492R), followed by BLASTn analysis against the NCBI database, which showed\u0026thinsp;\u0026ge;\u0026thinsp;99% sequence similarity to Bacillus subtilis reference strains. The 16S rRNA gene sequence has been deposited in GenBank under accession number PV685121. The strain is currently maintained in the institutional culture collection at the Centre for Drug Discovery and Development, Sathyabama Institute of Science and Technology, Chennai, India. Deposition in a public culture repository (e.g., MTCC/DSMZ) is ongoing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBasal medium composition\u003c/h3\u003e\n\u003cp\u003eFor OFAT optimization, a minimal basal medium was formulated containing only phosphate buffers (K₂HPO₄ 1.0 g/L, KH₂PO₄ 0.5 g/L, pH 7.0), to which individual carbon, nitrogen, and mineral sources were added as test variables. This approach ensured that the observed growth differences were attributable to the supplemented nutrients rather than undefined carryover from complex media. All media were sterilized by autoclaving at 121\u0026deg;C for 15 min at 15 psi. Heat-sensitive components, where applicable, were filter-sterilized through 0.22 \u0026micro;m membranes and added aseptically after the medium had cooled to 50\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eOne-factor-at-time (OFAT) optimization\u003c/h3\u003e\n\u003cp\u003eThe influence of nutritional and physical parameters on \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 biomass was assessed via a one-factor-at-time (OFAT) approach (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). All the experiments were conducted in 6-well tissue culture plates, with each well containing 3 mL of medium. The wells were inoculated with a 2% (v/v) seed culture prepared as follows: a single colony was inoculated into nutrient broth and grown overnight (18 h, 37\u0026deg;C, 150 rpm). Prior to use, the seed culture density was adjusted to 0.5 McFarland standard (approximately 1\u0026ndash;2 \u0026times; 10⁸ CFU mL⁻\u0026sup1;) by comparison to a certified turbidity standard, and 2% (v/v) of this adjusted culture was added to each experimental vessel. The cultures were incubated at 37\u0026deg;C for 24 h. Uninoculated medium served as the blank. All the experiments were performed in triplicate. Carbon sources: Seven carbon sources: glycerol, glucose, starch, rice bran extract, coconut water, sugarcane juice, and jaggery\u0026mdash;were tested at 0.5\u0026ndash;2.5% (w/v). Nitrogen sources: Six nitrogen sources, namely, groundnut cake extract, fish hydrolysate, ammonium sulfate, peptone, yeast extract, and soybean meal extract, were evaluated at 1.0\u0026ndash;3.0% (w/v) using the optimal carbon source identified above. Mineral salts: Micronutrient requirements were examined by supplementing the optimized carbon:nitrogen formulation with FeCl₂, MnCl₂, MgSO₄, CaCl₂, ZnSO₄, and CuSO₄ at 0.01\u0026ndash;0.05% (w/v). pH optimization: The medium pH was adjusted to 6.0\u0026ndash;8.5 prior to sterilization, and the pH was measured again after cultivation to confirm stability. NaCl and aeration: Salinity tolerance was assessed by adding 0\u0026ndash;7% (w/v) NaCl to the optimized medium. Aeration effects were evaluated by varying the working volume between 2\u0026ndash;3 mL per well, altering the headspace liquid ratio while maintaining all other culture conditions constant.\u003c/p\u003e\n\u003ch3\u003ePlackett–Burman design (PBD)\u003c/h3\u003e\n\u003cp\u003eOn the basis of the OFAT results, eight factors were selected for screening via a Plackett\u0026ndash;Burman design comprising 13 total runs (12 design runs and 1 center point): jaggery (Stock A, 10% w/v aqueous solution), peptone (Stock B, 15% w/v aqueous solution), MgSO₄ (Stock C, 1% w/v aqueous solution), NaCl (Stock D, 10% w/v aqueous solution), final pH, inoculum size, temperature, and RPM. Stock solutions were filter-sterilized (0.22 \u0026micro;m) and added aseptically to the basal medium at the volumes specified in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e to achieve the desired final concentrations in a 25 mL working volume. The center-point run provided an internal reference for curvature assessment. Each factor was tested at two levels, designated high (+\u0026thinsp;1) and low (-1), according to (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The optimization experiments were conducted in 50 mL centrifuge tubes with a working volume of 25 mL. To maximize the volumetric oxygen transfer coefficient within this vessel geometry, two critical modifications were applied: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the tubes were incubated at a 45\u0026deg; inclination to increase the gas\u0026ndash;liquid interface surface area, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the caps were loosened by one-quarter turn to permit passive gas exchange while minimizing evaporation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These conditions ensured sufficient aeration for aerobic metabolism during the high-throughput screening phase. The nonsignificant model (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and low linearity (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.45) confirmed the presence of curvature. This nonlinearity validated the necessity of the subsequent second-order CCD to resolve interactions that the linear PBD could not capture. The PBD served strictly for variable ranking rather than yield prediction.\u003c/p\u003e \u003cp\u003eThe cultures were subsequently grown in 50 mL centrifuge tubes containing 25 mL of medium for 24 h. The dry cell weight (DCW) was determined by centrifugation at 10,000\u0026times;g for 10 min, after which the pellets were washed twice with distilled water and dried at 65\u0026deg;C until a constant weight was reached. Each run was performed in triplicate, and the mean DCW (g/L) was used as the response variable.\u003c/p\u003e \u003cp\u003eThe main effect of each factor was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Effect\\:=\\:(\\varSigma\\:\\:Response\\:at\\:High\\:Level\\:-\\:\\varSigma\\:\\:Response\\:at\\:Low\\:Level)\\:/\\:(Number\\:of\\:High-Level\\:Runs)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStatistical significance was evaluated via analysis of variance (ANOVA) with \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eCentral composite design (CCD) and response surface methodology\u003c/h3\u003e\n\u003cp\u003eThe three most significant factors identified in the PBD screening (MgSO₄, inoculum size, and RPM) were selected for a rotatable central composite design (α\u0026thinsp;=\u0026thinsp;1.682) with five center point replicates. The factor levels were coded as -1.682 (axial low), -1 (low), 0 (center), +\u0026thinsp;1 (high), and +\u0026thinsp;1.682 (axial high); although the global CCD model was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), replicate divergence in high-agitation runs, e.g., Run F1, was observed. Replicate variability at high agitation was attributed to oxygen transfer heterogeneity inherent to capped tube systems. However, this localized noise did not compromise the global optimization trend, as confirmed by the high consistency of the subsequent flask validation trials.\u003c/p\u003e \u003cp\u003eA Rotatable Central Composite Design (CCD) was employed to optimize the MgSO₄ concentration, inoculum size, and agitation speed. The design comprised 19 runs: 8 factorial points, 6 axial points, and 5 center-point replicates. All the experiments were performed in triplicate, and the biomass was quantified as dry cell weight (DCW, g/L). A second-order polynomial model of the form\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Y\\:=\\:\\beta\\:₀\\:+\\:\\varSigma\\:\\beta\\:ᵢxᵢ\\:+\\:\\varSigma\\:\\beta\\:ᵢᵢxᵢ\u0026sup2;\\:+\\:\\varSigma\\:\\beta\\:ᵢⱼxᵢxⱼ,$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eY\u003c/em\u003e represents the predicted biomass and \u003cem\u003ex\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e represents the coded variables that were fitted to the data. Model adequacy was assessed via ANOVA, R\u0026sup2;, and adjusted R\u0026sup2;. Diagnostic plots (normal probability, residuals vs. predicted, and residuals vs. run order) were used to verify the model assumptions. Response surface and contour plots were generated to visualize interactions and identify optimal conditions.\u003c/p\u003e \u003cp\u003eAlthough not significant in the ANOVA, MgSO₄ (A, p\u0026thinsp;=\u0026thinsp;0.070) and inoculum (B, p\u0026thinsp;=\u0026thinsp;0.964) were kept in the model because of their noteworthy effect sizes in the PBD screening (40.30% and 5.62%, respectively) and to maintain the model hierarchy, preventing misleading interpretations with interaction or quadratic terms. RPM (C, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and its quadratic term (C\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.009) indicate that agitation speed drives biomass yield, indicating oxygen transfer limitations in small-volume tube cultures.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel validation and geometry-dependent oxygen transfer assessment\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eValidation in the Optimization Vessel\u003c/h2\u003e \u003cp\u003eThe RSM-predicted optimal conditions (0.01% MgSO₄, 1% inoculum, 200 rpm) were validated in triplicate via 50 mL centrifuge tubes (25 mL working volume), matching the CCD conditions. The dry cell weight (DCW) closely matched the predicted value and clearly improved over that of the unoptimized control (PBD Run 12).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eScale-up in flasks\u003c/h3\u003e\n\u003cp\u003eTo evaluate scalability and aeration effects, the same conditions were tested in 2000 mL Erlenmeyer flasks with a working volume of 750 mL (37.5% fill ratio), which provides substantially greater headspace and an improved surface-to-volume ratio for oxygen transfer relative to the 50 mL centrifuge tube system (50% fill, 25 mL). Compared with the centrifuge tubes, the flask geometry offered greater aeration and altered mixing. Flask-level cultivation confirmed that improved aeration consistently increased the biomass yield under optimized conditions, supporting the interpretation that oxygen transfer rather than medium composition was the dominant limiting variable in the tube system.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnumeration of heat-resistant endospores\u003c/h2\u003e \u003cp\u003eHeat-resistant endospores of \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 were enumerated to assess the functional relevance of the optimized medium. Cultures grown in optimized medium and Luria\u0026ndash;Bertani (LB) broth (Miller formulation: 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl; pH 7.0) were harvested after 48 h and 72 h, corresponding to the late stationary phase and active sporulation in \u003cem\u003eBacillus\u003c/em\u003e spp. Aliquots (one mL) of undiluted cultures were subjected to moist heat treatment at 80\u0026deg;C for 10 min in a thermostatically controlled water bath to selectively eliminate vegetative cells while preserving mature endospores. The samples were immediately cooled on ice for 5 min. Serial decimal dilutions were prepared, and a volume of 100 \u0026micro;L from the \u003csup\u003e10\u0026ndash;5\u003c/sup\u003e dilution was spread onto nutrient agar plates and incubated at 37\u0026deg;C for up to 48 h to allow spore germination and colony development. Colonies that recovered after heat treatment were considered to represent heat-resistant endospores and are expressed as CFU mL⁻\u0026sup1;.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:CFU\\:mL⁻\u0026sup1;\\:=\\:(N\\:\\times\\:\\:D)\\:/\\:V$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;number of colonies counted, \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;volume plated (mL), and \u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;dilution factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe OFAT data were analysed via descriptive statistics, with means and standard deviations calculated from triplicate measurements. The OFAT datasets were analysed using one-way ANOVA followed by Tukey\u0026rsquo;s HSD post hoc test (α\u0026thinsp;=\u0026thinsp;0.05). PBD analysis was performed via half-normal probability plots and ANOVA to identify significant factors. The CCD data were analysed via Python (v.3.10) with the Pandas and NumPy libraries for data processing. The second-order polynomial model and analysis of variance (ANOVA) were generated via Statsmodels, whereas the optimal conditions were determined via SciPy optimization algorithms. The response surface and contour plots were visualized via Matplotlib.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. One-Factor-at-a-Time (OFAT) Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScreening of carbon sources identified Jaggery (2% w/v) as the optimal substrate, yielding 0.54 \u0026plusmn; 0.01 g/L biomass and significantly outperforming refined sugars (p \u0026lt; 0.001). Peptone (1.5% w/v) was selected as the superior nitrogen source (0.52 \u0026plusmn; 0.02 g/L) over inorganic salts. Mineral profiling confirmed MgSO₄ (0.03% w/v) as the critical micronutrient (0.50 \u0026plusmn; 0.02 g/L), whereas CuSO₄ and ZnSO₄ exhibited toxicity at elevated levels.\u003c/p\u003e\n\u003cp\u003eThe strain exhibited maximum growth at pH 7.0 (0.64 \u0026plusmn; 0.04 g/L), confirming a strict neutrophilic preference. Salinity tolerance peaked at 3% NaCl (0.58 \u0026plusmn; 0.08 g/L), indicating moderate halotolerance. Additionally, increasing the culture headspace to 80% significantly improved biomass yield (0.50 \u0026plusmn; 0.01 g/L), highlighting the dependence on oxygen transfer efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlackett\u0026ndash;Burman Design: Statistical Screening of Critical Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Design and Response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlackett\u0026ndash;Burman design (PBD) was employed as a robust screening tool to filter eight process variables and identify the most critical drivers of biomass production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis of the main effects\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMgSO₄\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.30%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRPM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.12%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.78%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInoculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNaCl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJaggery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinal pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeptone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe PBD was employed solely for variable ranking, as the model was statistically nonsignificant (p = 0.2766) and unsuitable for yield prediction. Despite the low model fit (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e = 0.4568) caused by unmodelled interactions or curvature, the Pareto analysis successfully ranked the variables by effect magnitude (Figure 6). On the basis of this ranking, MgSO₄, agitation, and inoculum size were identified as the dominant factors and selected for subsequent optimization via CCD to resolve the nonlinear interactions. Owing to the inherent variability of biological fermentation in high-throughput screening vessels (50 mL tubes), the primary objective of this phase was variable ranking rather than precise predictive modelling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCentral Composite Design and Response Surface Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAeration dominated the biomass yield. At lower agitation speeds (150\u0026ndash;175 RPM), the biomass yields were consistently low (averaging 0.32\u0026ndash;0.48 g/L). Although temperature exhibited a measurable negative main effect during Plackett\u0026ndash;Burman screening (Effect = \u0026minus;4.20), it was maintained at 37 \u0026deg;C during the CCD phase, as this condition represents the established physiological optimum for \u003cem\u003eB. subtilis\u003c/em\u003e. However, increasing the agitation to 200 RPM resulted in a sharp, significant increase in biomass, with maximum yields exceeding 2.0 g/L. The experimental data were fitted to a second-order polynomial equation via multiple regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Analysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evariance\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(ANOVA) for the quadratic model\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1723.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e191.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA-MgSO₄\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-Inoculum %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-RPM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1254.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1254.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e62.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC (Interaction)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBC (Interaction)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u0026sup2; (RPM\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e166.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e166.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e462.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2185.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.7885; adjusted \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.7058)\u003c/p\u003e\n\u003cp\u003eThe experimental results were analysed via multiple regression analysis. The final polynomial model, expressed in coded factors, is given by Equation (2). All variables represent coded factor levels:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere A, B, and C are the coded levels of MgSO₄, inoculum size, and agitation speed, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eeffects\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eresponse surface analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the interactive effects of the variables on biomass yield, 3D response surface plots and 2D contour plots were generated. These visualizations highlight the critical relationships between physical parameters and nutrient concentrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Experimental Validation of Model Predictions in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOptimization Vessel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RSM model predicted a maximum biomass of 1.36 g/L at 200 rpm, 0.01% MgSO₄, and 1% inoculum (Table 3). Validation in the same vessel system used for optimization (50 mL Centrifuge tubes, 25 mL working volume) produced 1.33 \u0026plusmn; 0.07 g/L, showing excellent agreement with the model (2.1% deviation). Relative to the unoptimized control (0.43 \u0026plusmn; 0.14 g/L), the optimized condition yielded a 3.10-fold improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Model Validation in 50 mL Centrifuge Tubes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMgSO₄ (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInoculum (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRPM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFold Increase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e150\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.43 \u0026plusmn; 0.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33 \u0026plusmn; 0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.10\u0026times;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of Vessel Geometry and Oxygen Transfer on Biomass Yield\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess aeration-dependent scalability, the same conditions were evaluated in 2000 mL conical flasks (750 mL working volume), which provide substantially greater oxygen transfer than Centrifuge tubes do. Biomass increased markedly under both baseline and optimized conditions due to improved aeration, with the optimized flask culture achieving a 1.85-fold improvement over the flask baseline (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Effect of vessel geometry on biomass production\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMgSO₄ (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInoculum (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRPM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFold Increase vs. Baseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaseline (Flask)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.34 \u0026plusmn; 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOptimized (Flask)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.86 \u0026plusmn; 1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.85\u0026times;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEnumeration of heat-resistant endospores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond biomass enhancement, the capacity to support sporulation is critical for \u003cem\u003eBacillus\u003c/em\u003e-based probiotic production, as heat-resistant endospores enable long-term storage, feed incorporation, and gastrointestinal survival (19). Heat treatment (80\u0026deg;C, 10 min) was used to selectively enumerate spores by eliminating vegetative cells.\u003c/p\u003e\n\u003cp\u003eCultures grown in the optimized jaggery-peptone medium produced recoverable heat-resistant spores at both 48 h and 72 h (Table 5). At 48 h, spore counts reached 7.5 \u0026plusmn; 0.7 \u0026times; 10⁶ CFU/mL, representing 31.3% of total viable cells (2.4 \u0026plusmn; 0.6 \u0026times; 10⁷ CFU/mL). At 72 h, absolute spore counts increased to 1.8 \u0026plusmn; 0.7 \u0026times; 10⁷ CFU/mL; however, sporulation efficiency decreased to 15.4%, which may reflect continued vegetative growth under the tested conditions (total viable cells: 1.17 \u0026plusmn; 0.09 \u0026times; 10⁸ CFU/mL). This temporal pattern is consistent with biphasic \u003cem\u003eBacillus\u003c/em\u003e growth dynamics, where nutrient depletion triggers sporulation initiation around 24-36 h (18), but residual carbon sources support additional vegetative growth even as sporulation proceeds. The observed sporulation efficiency demonstrates the ability of the optimized medium to support functional endospore formation (25,26).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe cultivation performance of \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 observed in this study reflects the combined influence of medium composition and physical growth conditions. Although the OFAT\u0026ndash;PBD\u0026ndash;CCD strategy enabled the identification of favourable nutritional components, the statistical outcomes consistently point to agitation, and therefore oxygen availability, as the principal factor governing biomass accumulation. This trend is consistent with aerobic \u003cem\u003eBacillus\u003c/em\u003e sp. metabolism, even in early bioprocess studies (16,17).\u003c/p\u003e\n\u003cp\u003eJaggery proved to be a more effective carbon source than refined glucose, a result that is not unexpected given its mixed sugar content and the presence of trace minerals. Similar advantages of unrefined sugar substrates have been reported for other \u003cem\u003eBacillus\u003c/em\u003e systems (11,13). In the present work, peptone supported more stable biomass formation than inorganic nitrogen sources, which is consistent with reports showing that complex organic nitrogen enhances growth kinetics and metabolic activity in \u003cem\u003eBacillus\u003c/em\u003e spp. (27). Magnesium supplementation also contributed to improved yields, in agreement with its known role in cellular energetics and enzyme function \u0026nbsp;(28,29)\u003c/p\u003e\n\u003cp\u003eDespite these nutritional effects, the CCD analysis revealed that RPM was the only statistically significant main factor, indicating that the cultures were largely oxygen-limited in the small-volume tube system. The pronounced increase in biomass upon transfer to Erlenmeyer flasks further supports this interpretation and highlights the importance of vessel geometry and mixing dynamics, as discussed previously for aerobic microbial processes (16). Nevertheless, the optimized medium retained a measurable advantage over the baseline formulation under enhanced aeration, suggesting that medium composition remains relevant once the primary physical limitation is alleviated.\u003c/p\u003e\n\u003cp\u003eAn important outcome of this work is the demonstration that the optimized medium supports not only vegetative growth but also the formation of heat-resistant endospores. In \u003cem\u003eBacillus\u003c/em\u003e, sporulation is initiated when nutrient balance shifts, particularly when the carbon-to-nitrogen ratio declines, leading to activation of the Spo0A phosphorelay and commitment to developmental differentiation (18,19). The observed sporulation suggests that the formulation supports physiological conditions compatible with entry into the sporulation pathway. By contrast, Luria\u0026ndash;Bertani broth maintains high concentrations of readily assimilable nutrients and is primarily designed to sustain vegetative growth. Under such conditions, Spo0A activation is repressed, and sporulation remains minimal even during prolonged incubation.\u003c/p\u003e\n\u003cp\u003eMedia that maintain respiratory metabolism while slowly imposing nutrient stress promote Spo0A phosphorylation and the sequential action of sporulation-specific sigma factors (\u0026sigma;^F, \u0026sigma;^E), which govern forespore development (18,19). The sporulation efficiencies obtained in this study fall within the range reported for \u003cem\u003eB. subtilis\u003c/em\u003e cultivated in low-cost or minimal formulations (26), supporting the practical relevance of the medium for spore-based probiotic production.\u003c/p\u003e\n\u003cp\u003eFrom a process development perspective, these findings indicate that nutritional optimization alone is insufficient if oxygen transfer is not adequately addressed. Aeration strategy and reactor configuration will therefore be critical determinants of productivity during further scale-up, likely exceeding the influence of individual medium components. The present formulation is compatible with such systems and may serve as a suitable basis for subsequent bioreactor-level optimization.\u003c/p\u003e\n\u003cp\u003eOverall, this study shows that a jaggery\u0026ndash;peptone-based medium can support both higher biomass formation and functional sporulation of \u003cem\u003eB. subtilis\u003c/em\u003e OS40, while also underlining the central role of oxygen transfer in governing process performance. These observations provide a useful starting point for the development of cost-sensitive, spore-based probiotic processes for aquaculture.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study applied established statistical optimization approaches to improve biomass and endospore production of \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 using agro-derived substrates. Agitation speed was strongly associated with biomass yield under small-volume cultivation conditions. The optimized formulation supported reproducible endospore formation, providing a basis for further evaluation in probiotic development studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Earth Sciences (MoES), Government of India (Project No. MoES/36/OOIS/Extra/92/2022). The authors also acknowledge institutional support from Sathyabama Institute of Science and Technology, Chennai, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known financial or non-financial competing interests that are directly or indirectly related to the work submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKarthik Prakash M P: Conceptualization, Methodology, Investigation, Data analysis, Writing – original draft.\u003c/p\u003e\n\u003cp\u003eGopikrishnan Venugopal: Supervision, Funding acquisition, Project administration, Writing – review and editing.\u003c/p\u003e\n\u003cp\u003eRadhakrishnan Manikkam: Validation, Formal analysis, Writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAly SM, Ahmed YAG, Ghareeb AAA, Mohamed MF (2008) Studies on Bacillus subtilis and Lactobacillus acidophilus as potential probiotics on the immune response and resistance of Tilapia nilotica (Oreochromis niloticus) to challenge infections. 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Microbiol Mol Biol Rev 64:548\u0026ndash;572. https://doi.org/10.1128/MMBR.64.3.548-572.2000\u003c/li\u003e\n \u003cli\u003eDur\u0026eacute; LMM, Mascarin GM, Bettiol W (2025) Optimization of endospore production by solid and liquid fermentation for Bacillus velezensis formulations. Braz J Microbiol 56:1253\u0026ndash;1261. https://doi.org/10.1007/s42770-025-01626-9\u003c/li\u003e\n \u003cli\u003eMazhar H, Ullah I, Ali U et al (2023) Optimization of low-cost solid-state fermentation media for thermostable lipase production. Biocatal Agric Biotechnol 47:102559. https://doi.org/10.1016/j.bcab.2022.102559\u003c/li\u003e\n \u003cli\u003eLu Z, He S, Kashif M et al (2023) Effect of ammonium stress on phosphorus solubilization of Bacillus aryabhattai. BMC Genomics 24:550. https://doi.org/10.1186/s12864-023-09559-z\u003c/li\u003e\n \u003cli\u003eSachla AJ, Soni V, Pi\u0026ntilde;eros M et al (2024) The Bacillus subtilis yqgC-sodA operon protects magnesium-dependent enzymes by supporting manganese efflux. J Bacteriol 206:e00052-24. https://doi.org/10.1128/jb.00052-24\u003c/li\u003e\n \u003cli\u003eSchaeffer P, Millet J, Aubert JP (1965) Catabolic repression of bacterial sporulation. Proc Natl Acad Sci USA 54:704\u0026ndash;711. https://doi.org/10.1073/pnas.54.3.704\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"current-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Current Microbiology","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Bacillus subtilis, response surface methodology, sporulation, aquaculture probiotic, aerobic fermentation","lastPublishedDoi":"10.21203/rs.3.rs-8870581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8870581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe commercial application of \u003cem\u003eBacillus\u003c/em\u003e-based probiotics in aquaculture is constrained by the cost of biomass production and the requirement for long-term shelf-stable formulations. In this study, a low-cost cultivation strategy was developed for the marine probiotic \u003cem\u003eBacillus subtilis\u003c/em\u003e OS40 (GenBank 16S rRNA accession: PV685121), a gut-derived isolate from the marine fish \u003cem\u003eSardinella longiceps\u003c/em\u003e, with an emphasis on heat-resistant endospore formation. A sequential optimization framework integrating one-factor-at-a-time (OFAT), Plackett\u0026ndash;Burman design, and central composite design\u0026ndash;response surface methodology was applied to formulate a medium based on agro-derived substrates. Jaggery (2% w/v) and peptone (1.5% w/v) were identified as effective carbon and nitrogen sources, reducing the dependence on refined ingredients.\u003c/p\u003e \u003cp\u003eCentral composite design\u0026ndash;response surface analysis revealed that agitation speed (RPM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was the principal determinant of biomass accumulation, with the MgSO₄ concentration having a secondary effect (p\u0026thinsp;=\u0026thinsp;0.070), indicating a strong influence on oxygen transfer under the tested conditions in small-volume systems. Under optimized conditions (0.01% MgSO₄, 1% inoculum, 200 rpm), the biomass increased 3.10-fold (1.33 g/L) relative to that of the unoptimized control in the same vessel system (50 mL centrifuge tubes). Subsequent transfer of the optimized formulation to 2000 mL Erlenmeyer flasks, which provide substantially greater oxygen transfer yielded 9.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50 g/L, representing a 1.85-fold improvement over the flask-scale baseline, highlighting the central importance of vessel geometry in aerobic \u003cem\u003eBacillus\u003c/em\u003e sp. cultivation.\u003c/p\u003e \u003cp\u003eBeyond vegetative growth, the optimized medium consistently supported sporulation, yielding 1.8 \u0026times; 10⁷ CFU mL⁻\u0026sup1; of heat-resistant spores after 72 h, whereas Luria\u0026ndash;Bertani broth produced substantially lower sporulation (\u0026le;\u0026thinsp;2 \u0026times; 10⁶ CFU mL⁻\u0026sup1;). These findings indicate that a jaggery\u0026ndash;peptone-based formulation was associated with increased biomass and measurable endospore formation of \u003cem\u003eB. subtilis\u003c/em\u003e OS40, supporting further investigations of spore-based probiotic formulation strategies.\u003c/p\u003e","manuscriptTitle":"Statistical Optimization of Biomass and Endospore Production in Bacillus subtilis OS40 Using Agro-Derived Substrates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 10:05:06","doi":"10.21203/rs.3.rs-8870581/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T19:19:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T20:52:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T13:34:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Current Microbiology","date":"2026-02-13T10:14:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"current-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Current Microbiology","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3f233fbd-c6e1-43e5-bbcf-fe00c8409439","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T10:05:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 10:05:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8870581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8870581","identity":"rs-8870581","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}