Optimization of IAA Production by Halotolerant Vreelandella titanicae J113 Through Fermentation Process Engineering with Response Surface Methodology

Abstract

Soil salinization is a significant environmental factor limiting agricultural production. Developing salt–alkali-tolerant microbial resources is important for the improvement of saline–alkali land. Plant growth-promoting rhizobacteria stimulate crop growth by producing the plant growth hormone indole-3-acetic acid (IAA), but their fermentation process under salt stress still needs optimization. Single-factor experiments and response surface methodology (RSM) were used to systematically optimize the fermentation conditions of the salt–alkali-tolerant Vreelandella titanicae J113. Key influencing factors were screened using the single-factor experiment design, and optimal process parameters were determined using the Box–Behnken design. IAA production and cell biomass were used as evaluation indicators to study the interactions of carbon sources, nitrogen sources, inorganic salts, temperature, cultivation time, and inoculum size. The optimal fermentation process was obtained: starch concentration 17.5 g/L, NaCl concentration 32.5 g/L, yeast extract 5 g/L, cultivation temperature 30 °C, inoculum size 3%, and cultivation time 144 h. After optimization, IAA production reached 23.02 μg/mL, an increase of 115% compared with before optimization. Salt stress experiments showed that the strain could still maintain high IAA production under 3% NaCl, demonstrating good salt tolerance. Maize seed germination experiments demonstrated that the optimized fermentation broth significantly promoted seed germination and seedling growth under salt stress conditions, with root length, fibrous root number, and fresh weight increasing by 61–86%, 137–200%, and 25–57%, respectively, compared to the control group. This study established an efficient IAA fermentation process for the salt–alkali-tolerant Vreelandella titanicae J113, providing technical support for developing microbial plant growth regulators suitable for saline–alkali land. The optimized strain exhibits excellent growth-promoting potential under salt stress conditions, offering favorable application prospects.

1. Introduction

Xinjiang is the most concentrated and prominent region for saline–alkali soil distribution in China [1]. The high-salt soil environment disrupts the dynamic balance of water and reactive oxygen metabolism in plants, and inhibits leaf photosynthesis [2] and root nutrient uptake [3], severely restricting regional agricultural development. The rhizosphere, a complex zone of interaction among soil, water, plant roots, and microorganisms, is critical for addressing these challenges. Plant growth-promoting rhizobacteria (PGPR), which naturally colonize the rhizosphere, are recognized for their ability to enhance root health, promote nutrient absorption, and improve plant resilience to abiotic stresses. They promote plant growth and enhance stress resistance through multiple mechanisms [4,5] and show great potential for application in saline–alkali soil improvement [6].

Among the diverse mechanisms employed by PGPR, the secretion of indole-3-acetic acid (IAA) stands out as a particularly crucial trait for promoting plant growth. IAA, a principal auxin, is an essential phytohormone that governs key developmental processes including cell division, differentiation, and root elongation [7]. This hormone is synthesized by many plant-associated microorganisms. Its production aids in rhizosphere colonization, improves plant nutrient and water acquisition, and ultimately enhances biomass and crop yield [8,9,10]. The biosynthesis of IAA is notably sensitive to the availability of carbon and nitrogen sources, as well as to environmental factors like temperature and salinity [11]. Emerging evidence indicates that IAA production in microorganisms such as PGPR may function as a stress-responsive trait, with synthesis often upregulated under suboptimal conditions—including extreme temperatures, pH shifts, nutrient scarcity, or osmotic stress [12]. This stress-induced auxin production can strengthen plant–microbe partnerships and, importantly, may occur independently of microbial biomass accumulation. Consequently, strategic optimization of medium composition and cultivation parameters can decouple cell growth from IAA synthesis, allowing for the design of specific conditions that maximize auxin yield [13,14]. Under saline–alkali stress, rhizosphere microorganisms capable of high-yield indole-3-acetic acid (IAA) production play a key mitigation role through dual mechanisms. In terms of effects, these microorganisms directly promote root elongation and branching in host plants by secreting IAA, thereby significantly increasing the root absorption surface area, effectively enhancing the crop’s ability to acquire water and essential nutrients under stressful conditions, and ultimately leading to improved biomass accumulation and yield. Studies have confirmed that inoculation with high-IAA-producing strains (such as certain Bacillus or Pseudomonas spp.) significantly improves the growth of crops like wheat and maize under saline–alkali conditions [15]. The mechanism of action can be divided into direct and indirect levels. The direct mechanism lies in the fact that exogenous IAA, as a plant growth-regulating signal, not only directly stimulates root cell division and elongation but also engages in cross-talk with the plant’s endogenous hormonal system (such as antagonistic interaction with abscisic acid, ABA), reprogramming the plant’s growth and developmental priorities to allocate more resources to the belowground parts in adaptation to stress. The indirect mechanism is more systemic: the IAA signal can upregulate the expression of stress-tolerance-related genes in plants, particularly those encoding antioxidant enzymes (e.g., SOD, POD) and proteins involved in ion compartmentalization (e.g., NHX, SOS1), thereby synergistically enhancing the plant’s ability to scavenge oxidative stress and maintain ion homeostasis [16]. Therefore, microbially derived IAA is not merely a simple growth stimulant but a crucial biological trigger that activates the plant’s own integrated physiological and molecular response network to saline–alkali stress.

However, despite the increasing interest in salt-tolerant PGPR as biofertilizers, research on optimizing their IAA production remains limited. In particular, the combined effects of salinity, nutrient availability, and culture conditions on IAA yield, as well as the use of low-cost and alternative media, are still poorly understood. In our previous study, we demonstrated that the Gram-negative moderately halophilic bacterium Vreelandella titanicae, isolated from saline–alkali soils in Xinjiang, exhibits several plant growth-promoting (PGP) traits, including IAA production, making it an ideal candidate for this study [17]. To date, research on IAA production by V. titanicae is scarce, and its optimal growth conditions remain largely undefined. Given its salt tolerance and previously identified PGP traits, this bacterium represents an attractive candidate for developing sustainable and low-input bioprocesses for IAA production. While most studies on PGPR have focused on maximizing IAA yield under standard growth conditions, its production under stress conditions in saline–alkali environments remains largely unexplored.

Therefore, this study employed response surface methodology to systematically optimize the nutritional and environmental parameters for V. titanicae J113 cultivation. The aim was to evaluate their effects on IAA production and enhance IAA production under simulated saline–alkali stress, thereby establishing a reliable fermentation process to support the development of microbial plant growth regulators for saline–alkali soils.

2. Materials and Methods

2.1. Strains and Culture Media

The halotolerant bacterial strain Vreelandella titanicae J113 used in this study was provided by the Institute of Microbial Application, Xinjiang Academy of Agricultural Sciences. The isolation, identification, and basic plant growth-promoting traits (including IAA production) of this strain have been described in a previous patent/study [17]. The strain was routinely maintained on Nutrient Agar (NA) slants at 4 °C. For all experiments, a single colony from a fresh NA plate was inoculated into 50 mL of sterile Nutrient Broth (NB) in a 250 mL Erlenmeyer flask and cultivated as the seed culture at 30 °C with shaking at 150 rpm for 24 h. This actively growing seed culture was used as the standard inoculum for all subsequent fermentation experiments. All culture media were sterilized by autoclaving at 121 °C for 20 min prior to use.

The composition of NB medium was (g/L): peptone, 10.0; sodium chloride, 5.0; beef extract, 3.0; (pH 7.0 ± 0.2).

2.2. Optimization of Culture Medium Components

2.2.1. Single-Factor Experiment Design

A single-factor test was conducted to screen key components influencing bacterial growth and IAA production. The basal medium for optimization consisted of (g/L): NaCl, 30.0; K₂HPO₄, 1.0; MgSO₄·7H₂O, 0.5; pH 7.5. Different carbon sources (soluble starch, sucrose, glucose) were supplemented at 10 g/L, different organic nitrogen sources (yeast extract, peptone, beef extract) at 5 g/L, and NaCl concentrations were tested at 10, 30, and 50 g/L. For each test, 100 mL of medium in a 250 mL flask was inoculated with 3% (v/v) of the 24 h seed culture. After incubation, samples were taken for immediate analysis of IAA yield and optical density (OD₆₀₀). Each treatment was performed in triplicate.

2.2.2. Response Surface Optimization Design

Based on the results of the single-factor experiments, three significant factors—soluble starch concentration (A, 8–12 g/L), NaCl concentration (B, 25–35 g/L), and yeast extract concentration (C, 4–6 g/L)—were selected for further optimization using a Box–Behnken design (BBD) with three levels and three factors. The experimental run order was randomized to minimize bias. Model adequacy was evaluated by analysis of variance (ANOVA), lack-of-fit test, and diagnostic plots of residuals. The experimental design, comprising 17 runs with five center points, was generated and analyzed using Design-Expert software. A second-order polynomial model was fitted to predict the optimal levels for maximizing IAA yield.

2.3. Optimization of Culture Conditions [18]

2.3.1. Single-Factor Experiment Design

Using the optimized medium composition, the effects of key fermentation parameters on IAA yield were investigated. The tested parameters and their levels were: temperature (25, 30, 35 °C), inoculum size (1%, 3%, 5% v/v), medium volume in a 250 mL flask (50, 100, 150 mL/250 mL, representing varying oxygen transfer rates), fermentation time (72, 96, 120 h), and agitation speed (120, 150, 180 rpm). The experiments were carried out in 250 mL Erlenmeyer flasks containing 100 mL of the optimized medium unless otherwise stated for the medium volume test. All other conditions were kept constant as derived from the previous optimization step. Each condition was tested in triplicate.

2.3.2. Response Surface Optimization Design

The three most influential factors from the single-factor tests—fermentation time (A, 96–144 h), inoculum size (B, 2–4% v/v), and temperature (C, 28–32 °C)—were further optimized using a BBD. The design and analysis were performed using Design-Expert 13 software.

While medium volume and agitation speed also showed an influence in single-factor tests, they were fixed at their optimal levels (150 mL/250 mL flask and 150 rpm, respectively) to allow the Box–Behnken design to focus on elucidating the interactions between the three factors deemed most critical for process control: fermentation time, inoculum size, and temperature.

2.4. Analytical Methods

Bacterial Growth Measurement: Bacterial growth was monitored by measuring the optical density at 600 nm (OD₆₀₀) using a UV-1800 spectrophotometer (Shimadzu, Kyoto, Japan). In this study, OD₆₀₀ was employed as a rapid and reliable indicator to assess the relative changes in bacterial biomass under different fermentation conditions, which served as a supportive parameter alongside the primary optimization target, IAA yield. The fermentation broth was appropriately diluted with sterile physiological saline to keep the OD₆₀₀ reading within the linear range of 0.1–0.8.

IAA Quantification: The concentration of IAA in the culture supernatant was determined using the Salkowski colorimetric method [19]. Briefly, 1 mL of cell-free supernatant (obtained by centrifuging the fermentation broth at 10,000× g for 10 min at 4 °C and filtering through a 0.22 μm membrane) was mixed with 2 mL of Salkowski reagent (1 mL of 0.5 M FeCl₃ in 50 mL of 35% perchloric acid). The mixture was incubated in the dark at room temperature for 30 min, and the absorbance of the pink-colored complex was measured at 530 nm. A standard curve was prepared for each experiment using pure IAA (Sigma-Aldrich) dissolved in the corresponding sterile, cell-free basal fermentation medium (not water), covering a concentration range of 0 to 100 μg/mL. This was to control for any potential matrix effects from the culture medium.

$$Y=0.027X+0.034$$

In this equation, Y represents the OD₅₃₀ value, and X represents the IAA value.

2.5. Verification of Growth-Promoting Effect

Seed Material and Sterilization: Maize (Zea mays L.) seeds of a common cultivar were surface-sterilized by sequential treatment with 75% (v/v) ethanol for 1 min and 2% (v/v) sodium hypochlorite solution for 5 min, followed by five rinses with sterile distilled water.

Corn (Zea mays L.) was selected as the model plant due to its significance as a major global cereal crop and its high sensitivity to saline–alkali stress. This choice allows for the evaluation of the strain’s potential in promoting the growth of economically important crops under adverse soil conditions.

Treatment Preparation: The bioactivity of the bacterial fermentation product was evaluated under saline and saline–alkaline stress. All treatment solutions were prepared using the optimized basal fermentation medium (as described in Section 2.1 and Section 2.3) as the solvent. Four treatments were established (each with a total volume of 40 mL per Petri dish): (T1) 137 mM NaCl solution (salt stress control); (T2) 137 mM NaCl solution supplemented with 10% (v/v) filter-sterilized (0.22 μm) fermentation supernatant of V. titanicae J113 from the optimized culture; (T3) 2 mM NaHCO₃ + 137 mM NaCl solution (saline–alkaline stress control); (T4) 2 mM NaHCO₃ + 137 mM NaCl solution supplemented with 10% (v/v) filter-sterilized fermentation supernatant at a final concentration of 10% (v/v). For treatments T2 and T4, 4 mL of sterile fermentation supernatant was mixed with 36 mL of the corresponding salt (or salt–alkali) solution prepared in the optimized basal medium. The pH of all treatment solutions was adjusted to 7.8 ± 0.1.

Germination Test: For each treatment, 20 surface-sterilized seeds were placed on a double layer of sterile filter paper in a 9 cm Petri dish, and 10 mL of the corresponding treatment solution was added. Each treatment was replicated three times (a total of 60 seeds per treatment). The Petri dishes were sealed with parafilm and incubated in a growth chamber at 25 °C in the dark. Germination (radicle emergence > 2 mm) was recorded daily for 7 days. On the 7th day, the root length and shoot height of 10 randomly selected seedlings from each replicate were measured using a ruler. The fresh weight of these seedlings was measured after carefully blotting off surface moisture [20].

2.6. Data Processing Methods

All experimental data were expressed as the mean ± standard deviation. One-way analysis of variance (p < 0.05) was performed using SPSS 22.0 software. Response surface analysis was completed using Design-Expert 13 software.

3. Results

3.1. Optimization Results of Culture Medium Components

3.1.1. Results of Single-Factor Experiments

The results of the single-factor experiments (

Table 1. Results of single-factor experiments of culture medium components.

Treated Group	OD600 (nm)	IAA (μg/mL)
CK	1.29 ± 0.08	5.13 ± 0.91
1% NaCl	1.24 ± 0.11	5.38 ± 1.64
3% NaCl	1.345 ± 0.23	9.5 ± 0.09 ***
5% NaCl	1.278 ± 0.02	4.86 ± 0.52
Starch	1.117 ± 0.04 **	10.71 ± 0.27 ***
Sucrose	1.187 ± 0.09	5.48 ± 1.08
Glucose	1.258 ± 0.08	6.05 ± 0.17
Yeast Extract	0.933 ± 0.10 **	7.48 ± 0.64 *
Tryptone	1.296 ± 0.21	4.59 ± 0.3
Beef Extract	1.377 ± 0.03	5.2 ± 0.84

Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

) show that different culture medium components have significant effects on the growth of the J113 strain and the yield of IAA. Among them, the 3% NaCl treatment group had an IAA yield of 9.5 ± 0.09 μg/mL, which was significantly higher than that of other concentration treatments (p < 0.001). When starch was used as the carbon source, the IAA yield was the highest (10.71 ± 0.27 μg/mL), and when yeast extract powder was used as the nitrogen source, the IAA yield was 7.48 ± 0.64 μg/mL, both of which were significantly better than those of other treatment groups.

Based on the results of the above single-factor experiments, in order to optimize the composition of the culture medium and explore the interaction effects among various factors, response surface analysis was conducted on the three key factors that had the most significant impact on IAA production: starch (carbon source), NaCl (inorganic salt), and yeast extract powder (nitrogen source). Starch showed good results within the range of 10–20 g/L, so the central level was set at 15 g/L; NaCl had the highest yield at 3% (30 g/L), so it was set as the central level; and yeast extract powder showed a significant promoting effect within the range of 5–10 g/L, so the central level was set at 7.5 g/L. Based on this, using the Box–Behnken design principle, a response surface experimental scheme with three factors and three levels was established. The factor and level designs are shown in

Table 2. Response surface optimization factors levels for culture medium components.

Level	Factor
	A (Starch)/g/L	B (NaCl)/g/L	C (Yeast Extract Powder)/g/L
−1	10	27.5	5
0	15	30	7.5
1	20	32.5	10

.

3.1.2. Optimization Results of Response Surface

(1) Regression Model and Analysis of Variance.

This study evaluated the main influencing factors of IAA production by V. titanicae. J113 and the interactions among different variables. Therefore, the effects of three independent variables (starch, NaCl, and yeast extract powder) at three levels on IAA production (dependent variable) were investigated. The experiment was conducted according to the Box–Behnken design. The actual IAA production is shown in

Table 3. Box–Behnken experimental design scheme and response values on medium composition optimization.

Run Numbers	A: (Starch)/g/L	B: (NaCl)/g/L	C: (Yeast Extract Powder)/g/L	IAA μg/mL
1	12.50	27.50	5.00	16.53
2	17.50	27.50	5.00	12.86
3	12.50	32.50	5.00	11.83
4	17.50	32.50	5.00	19.42
5	12.50	30.00	2.50	13.05
6	17.50	30.00	2.50	13.23
7	12.50	30.00	7.50	11.42
8	17.50	30.00	7.50	16.60
9	15.00	27.50	2.50	15.05
10	15.00	32.50	2.50	14.23
11	15.00	27.50	7.50	12.71
12	15.00	32.50	7.50	16.38
13	15.00	30.00	5.00	17.34
14	15.00	30.00	5.00	19.83
15	15.00	30.00	5.00	14.49
16	15.00	30.00	5.00	16.42
17	15.00	30.00	5.00	17.68

.

The quadratic model was selected as the best fitting model. Based on the R² (0.8464) and adjusted R² (0.7540) values, it can be concluded that the experimental results are in good agreement with the model’s predicted data. A regression analysis was performed on the data in Table 3, and the following quadratic polynomial equation in terms of coded factors was obtained:

Y = 17.15 − 1.160A + 0.589B + 0.194C + 2.81AB + 1.25AC + 1.12BC − 1.50A² − 0.487B² − 2.07C²

The normality of residuals was evaluated using the normality graph of residuals, and the residuals were normally distributed with no significant deviations. Additionally, the curves of actual levels and predicted levels indicated a high degree of similarity between the predicted values and the experimental values (

Table 4. Analysis of variance (ANOVA) for the fitted quadratic model on medium composition optimization.

Source	Sum of Squares	df	Mean Square	F-Value	p-Value
Model	87.90	9	9.77	4.28	0.0341
A	10.76	1	10.76	4.72	0.0663
B	2.77	1	2.77	1.22	0.3065
C	0.3003	1	0.3003	0.1318	0.7273
AB	31.70	1	31.70	13.91	0.0074
AC	6.25	1	6.25	2.74	0.1417
BC	5.04	1	5.04	2.21	0.1806
A2	9.53	1	9.53	4.18	0.0801
B2	0.9996	1	0.9996	0.4386	0.5290
C2	18.08	1	18.08	7.93	0.0259
Residual	15.96	7	2.28
Lack of Fit	0.8473	3	0.2824	0.0748	0.9704
Pure Error	15.11	4	3.78
Cor Total	103.85	16

). Variance analysis was conducted using Design-Expert software. The F values and the analysis of variance for each factor showed that the influence on IAA production was in the order: starch concentration (A) > NaCl concentration (B) > yeast extract powder concentration (C).

(2) Response surface analysis of medium component optimization.

Through model evaluation and regression equation determination, a response surface graph was created using Design-Expert software. Figure 1 shows the effects of three factors (starch concentration A), NaCl concentration B, and yeast extract powder concentration C on IAA production. The starch concentration has a significant impact on IAA production. When the NaCl concentration is 30 g/L and the yeast extract powder concentration is 5 g/L, the starch concentration increases from 10 g/L to 20 g/L, and the IAA production increases from 13.05 μg/mL to 13.23 μg/mL. The NaCl concentration has a positive effect within the range of 27.5–32.5 g/L. When the starch concentration is 15 g/L and the yeast extract powder concentration is 5 g/L, the NaCl concentration increases from 27.5 g/L to 32.5 g/L, and the IAA production changes from 15.05 μg/mL to 14.23 μg/mL.

The IAA production shows a trend of increasing first and then decreasing with the yeast extract powder concentration within the experimental range of 2.5–7.5 g/L. When the starch concentration is 15 g/L and the NaCl concentration is 30 g/L, the yeast extract powder concentration increases from 2.5 g/L to 5 g/L, and the IAA production increases from 14.23 μg/mL to 17.34 μg/mL (predicted value), and then decreases to 16.38 μg/mL when it increases to 7.5 g/L.

The response surface graphs of the effects of the interaction of each factor on IAA production are shown in Figure 1a–d. Figure 1a,b respectively display the three-dimensional surface plots and contour plots of the combined effects of starch concentration and NaCl concentration. When the yeast extract powder concentration is fixed at 5 g/L, the IAA production shows a trend of increasing first and then decreasing within the range of 10–20 g/L for starch concentration. The IAA production reaches the maximum value within the range of 15–17.5 g/L for starch concentration. When the starch concentration exceeds 17.5 g/L, the IAA production begins to decrease. The response surface slope analysis shows that the influence of starch concentration is more significant than that of NaCl concentration. According to the 3D surface and contour plots of the combined effects of the starch and yeast extract powder combinations (Figure 1c,d), under the condition of a fixed NaCl concentration of 30 g/L, the influence of starch concentration is more significant than that of yeast extract powder. The yeast extract powder concentration within the range of 2.5–5 g/L shows an increase in IAA production with the concentration increase, but it begins to decrease when it exceeds 5 g/L, indicating that an excessively high concentration of yeast extract powder may have an inhibitory effect on the production of IAA by the strain.

(3) Optimization and verification of the IAA-producing medium for the strain.

To verify the predicted results experimentally, based on the known regression equation, only the optimal scheme needs to be analyzed. The results show that the optimal medium ratio for the J113 strain to produce IAA is: starch 17.5 g/L, NaCl 32.5 g/L, and yeast extract powder 5 g/L. Under these conditions, the model predicts that the IAA yield is 19.801 μg/mL. To verify the reliability of the optimization results, three repeated verification experiments were conducted. The actual measured IAA average yield was 20.57 μg/mL, which was close to the model prediction value, indicating that the optimized culture components are effective. The IAA yield after optimization increased by 92.1% compared to the basic medium before optimization (10.71 μg/mL), approaching a doubling growth. The optimization effect was significant.

3.2. Optimization Results of Culture Conditions

3.2.1. Results of Single-Factor Experiments

The results of single-factor experiments (

Table 5. Results of single-factor experiments on culture conditions.

Treated Group	OD600 (nm)	IAA (μg/mL)
25 °C	1.105 ± 0.08	10.20 ± 0.92
30 °C	1.65 ± 0.12 **	18.34 ± 1.65 **
35 °C	1.375 ± 0.10 *	11.31 ± 1.02
1% Inoculum size	1.119 ± 0.09	13.53 ± 1.22
3% Inoculum size	1.195 ± 0.11 **	20.57 ± 1.85 **
5% Inoculum size	1.175 ± 0.10	12.05 ± 1.08
50 mL Liquid volume	0.995 ± 0.07	14.64 ± 1.32
100 mL Liquid volume	1.147 ± 0.09	13.90 ± 1.25
150 mL Liquid volume	1.179 ± 0.10 **	21.31 ± 1.92 **
72 h Culture	1.112 ± 0.08	1.123 ± 0.09
96 h Culture	1.123 ± 0.09	10.20 ± 0.92
120 h Culture	1.165 ± 0.10 **	21.31 ± 1.92 **
120 rpm	1.344 ± 0.12	13.46 ± 1.21
150 rpm	1.246 ± 0.11 **	21.38 ± 1.92 **
180 rpm	1.375 ± 0.12 *	16.23 ± 1.46

Note: * p < 0.05, ** p < 0.001.

) show that different culture conditions have a significant impact on the growth of the J113 strain and the IAA yield. Among them, the IAA yield reached 21.31 μg/mL at 120 h of cultivation time, which was significantly better than other treatment times; the IAA yield of the 3% inoculation volume treatment group was 20.57 μg/mL, which was significantly higher than other inoculation volumes; the IAA yield at 30 °C cultivation temperature was 18.34 μg/mL, which was significantly better than other temperature conditions.

Based on the results of the above single-factor experiments, in order to optimize the cultivation conditions of the system and explore the interaction effects among various factors, the three key factors that had the most significant impact on IAA production—cultivation time, inoculation amount, and cultivation temperature—were selected for response surface analysis. The cultivation time ranged from 72 to 120 h. IAA production significantly increased with the extension of cultivation time, reaching a peak at 120 h. Therefore, the central level was set at 120 h, and the optimization space within the range of 96 to 144 h was investigated; the inoculation amount ranged from 1% to 5%. The IAA production was the highest at a 3% inoculation amount and the bacterial growth was good, so 3% was set as the central level and the optimization range of 2% to 4% was investigated; the cultivation temperature ranged from 25 to 35 °C. The IAA production was significantly highest at 30 °C, so 30 °C was set as the central level and the optimization range of 28 to 32 °C was investigated. Based on this, using the Box–Behnken design principle, a response surface experimental scheme with three factors and three levels was established. The factor and level designs are shown in

Table 6. Response surface optimization factor levels for cultivation conditions.

Level	Factor
	A (Culture Time)/h	B (Inoculum Size)/%	C (Temperature)/°C
−1	96	2	28
0	120	3	30
1	144	4	32

.

3.2.2. Optimization of Cultivation Conditions Based on Response Surface Analysis

(1) Regression model and variance analysis.

This study evaluated the main influencing factors of IAA production by V.titanicae. J113 and the interactions among different variables. Therefore, the effects of three independent variables (culture time, inoculation amount, and temperature) at three levels on IAA production (dependent variable) were investigated. The experiment was conducted according to the Box–Behnken design. The actual IAA production is shown in Table 6.

A quadratic model was selected as the best fitting model. Based on the values of R² (0.9488) and adjusted R² (0.8830), it can be concluded that the experimental results are in good agreement with the data predicted by the model. A regression analysis was performed on the data in

Table 7. Box–Behnken experimental design scheme and response values on culture condition optimization.

Run Numbers	A: (Culture Time)/h	B: (Inoculum Size)/%	C: (Temperature)/°C	IAA μg/mL
1	96	2	30	17.60
2	144	2	30	21.31
3	96	4	30	18.34
4	144	4	30	23.16
5	96	3	28	17.60
6	144	3	28	20.20
7	96	3	32	18.71
8	144	3	32	22.05
9	120	2	28	19.09
10	120	4	28	20.20
11	120	2	32	19.83
12	120	4	32	21.31
13	120	3	30	22.05
14	120	3	30	21.68
15	120	3	30	21.31
16	120	3	30	20.57
17	120	3	30	21.31

, and the following quadratic polynomial equation in terms of coded factors was obtained:

Y = 21.38 − 1.81A + 0.648B + 0.601C + 0.277AB + 0.185AC + 0.092BC − 0.874A² − 0.407B² − 0.869C²

The normality of residuals was evaluated using the normality graph of residuals, and the residuals were normally distributed without significant deviations. Additionally, the curves of actual levels and predicted levels indicated a high degree of similarity between the predicted values and experimental values (

Table 8. Analysis of variance (ANOVA) for the fitted quadratic model on culture condition optimization.

Source	Sum of Squares	df	Mean Square	F-Value	p-Value
Model	40.73	9	4.53	14.42	0.0010
A	26.17	1	26.17	83.38	<0.0001
B	3.35	1	3.35	10.69	0.0137
C	2.89	1	2.89	9.21	0.0190
AB	0.3080	1	0.3080	0.9813	0.3549
AC	0.1369	1	0.1369	0.4361	0.5301
BC	0.0342	1	0.0342	0.1090	0.7509
A2	3.22	1	3.22	10.26	0.0150
B2	0.6975	1	0.6975	2.22	0.1797
C2	3.18	1	3.18	10.14	0.0154
Residual	2.20	7	0.3139
Lack of Fit	0.9925	3	0.3308	1.10	0.4467
Pure Error	1.20	4	0.3012
Cor Total	42.93	16

). Through variance analysis using Design-Expert software, the F values of each factor were compared, and the significance of each influencing factor was ranked. The results were: A (culture time) > B (inoculation amount) > C (culture temperature).

(2) Analysis of response surface diagrams for optimization of cultivation conditions.

The effects of cultivation time (A), inoculum size (B), and temperature (C) on IAA production, as derived from the regression model and data in Table 6, are summarized as follows. Cultivation time had the most significant impact: holding inoculum size and temperature at their center points (3% and 30 °C), increasing the time from 96 h to 144 h (as represented by Runs 1 and 2 in Table 7) raised the predicted IAA yield from 17.60 to 23.16 μg/mL. Inoculum size showed a positive but lesser effect within the tested range. Temperature exhibited a quadratic effect, with an optimum near 30 °C.

The interaction effects are visualized in the response surface plots (Figure 2a–d). Figure 2a illustrates the interaction between cultivation time and inoculum size at a fixed temperature of 30 °C. The surface and its corresponding contour plot (Figure 2b) indicate that IAA production increased substantially with longer cultivation times and moderately with larger inoculum sizes. The elliptical contours confirm a significant interaction between these two factors. Similarly, Figure 2c depicts the interaction between cultivation time and temperature at a fixed inoculum size of 3%. The optimal region for IAA production is identified within the range of 130–144 h and 30–32 °C, as seen in the contour plot (Figure 2d). Consistent with the ANOVA results, cultivation time (Factor A) had the steepest slope on the response surface, confirming it as the most influential factor.

(3) Optimization and verification of the culture conditions for IAA production by the strain.

To verify the predicted results experimentally, based on the known regression equation, only the optimal scheme needs to be analyzed. The results show that the optimal culture conditions for IAA production by strain J113 are as follows: the inoculation amount should be precise to 4%, the culture temperature should be controlled at 30 °C, and the IAA production reaches its peak at 144 h, with a production level of 22.836 μg/mL. To verify the reliability of the optimized results, three repeated verification experiments were conducted. The actual measured average IAA production was 23.02 μg/mL, which is close to the predicted value of the model, indicating that the optimized culture conditions by the response surface method are effective.

3.3. Verification of Growth-Promoting Effect

Following the optimization of medium composition and fermentation conditions, this study further validated the growth-promoting effects of the J113 strain fermentation broth through maize seed germination experiments under salt stress. As shown in Figure 3 and

Table 9. Quantitative analysis of growth parameters of maize seedlings under different salt stress treatments.

Treated Group	Shoot Length		Root Length		Main Root		Fibrous Roots		Fresh Weight
	Value/cm	Increase/%	Value/cm	Increase /%	Number	Increase /%	Number	Increase /%	Value/g	Increase /%
NaCl	3.1 ± 0.1		3.5 ± 0.1		7		16		0.5 ± 0.04
NaCl + J113	5.3 ± 0.62	71%	6.53 ± 1.5	86%	11	57%	38	137%	0.75 ± 0.05	50%
NaHCO + NaCl	3.07 ± 0.35		3.8 ± 0.4		8		15		0.56 ± 0.1
NaHCO + NaCl + J113	3.23 ± 0.35	5%	6.1 ± 0.2	61%	10	25%	45	200%	0.68 ± 0.08	21%

, salt stress treatment (NaCl) significantly inhibited maize seed germination, with shoot length, root length, number of primary roots, number of fibrous roots, and fresh weight measuring 3.1 cm, 3.5 cm, 7, 16, and 0.5 g, respectively. Inoculation with the J113 fermentation broth (NaCl + J113 treatment group) significantly improved all growth parameters of maize seedlings. Specifically, root length increased from 3.5 cm to 6.53 cm, an increase of 86%; the number of fibrous roots increased from 16 to 38, an increase of 137%; fresh weight increased from 0.5 g to 0.75 g, an increase of 50%; shoot length and the number of primary roots also increased by 71% and 57%, respectively, indicating that the J113 strain effectively alleviated the inhibitory effects of NaCl stress on maize germination and seedling growth. Under combined salt stress conditions (NaHCO₃ + NaCl treatment group), the growth of maize seedlings was more severely inhibited, with all parameters lower than those under single NaCl stress. However, inoculation with the J113 fermentation broth (NaHCO + NaCl + J113 treatment group) still significantly promoted seedling growth, with a particularly notable increase in the number of fibrous roots, which increased by 200% compared to the control. Root length and fresh weight also increased by 61% and 21%, respectively. It is worth noting that the promoting effect of the J113 strain on shoot length under combined salt stress was relatively weak (5%), which may be related to the higher pH environment caused by NaHCO₃. These results demonstrate that the optimized J113 strain fermentation broth effectively promotes maize seed germination and seedling growth under salt stress, with the growth-promoting effects primarily manifesting in the promotion of root development (increasing root length and number) and biomass accumulation (increasing fresh weight). These findings confirm that the J113 strain, optimized using response surface methodology, not only exhibits high IAA synthesis capability but also holds significant application potential in saline–alkali environments, providing an experimental basis for the subsequent development of microbial inoculants specifically for saline–alkali soil remediation.

4. Discussion

As a one of major distribution area of saline–alkali land in China, Xinjiang faces serious soil salinization issues that severely restrict agricultural development, and using plant growth-promoting rhizobacteria (PGPR) to improve saline–alkali land has become a current research hotspot. Most PGPR can produce IAA, thereby stimulating the development of lateral roots. In addition, by increasing the uptake of nutrients and water, IAA mitigates some abiotic stress effects such as salinity and drought [21]. Therefore, optimizing IAA production by salt-tolerant bacteria may be a rapid, effective and sustainable way to address this issue. In this study, the fermentation process for indole-3-acetic acid (IAA) production by the halotolerant strain V. titanicae J113 was systematically optimized using single-factor experiments and response surface methodology (RSM). The results demonstrated a significant enhancement in IAA yield. Furthermore, the effectiveness of the optimized fermentation broth in promoting maize seed growth under salt stress was validated.

Carbon and nitrogen sources serve as fundamental substrates for microbial energy metabolism and secondary metabolite biosynthesis, and their types and concentrations exert a decisive influence on the biosynthesis of indole-3-acetic acid (IAA). The results of this study indicate that for the strain V. titanicae J113, starch is the optimal carbon source for IAA synthesis, yielding (10.71 ± 0.27 μg/mL), which is significantly superior to glucose and sucrose. This finding differs from some existing studies; for example, Oliva et al. [22] reported that in the strain V. titanicae QH24, glucose supported the highest IAA production, whereas starch performed relatively poorly. Such interspecies variability highlights the complexity of IAA biosynthetic pathway regulation, indicating that even within the same genus, carbon source preference may be governed by unique enzymatic systems and metabolic networks. Our results align with the research of Ahmad et al. [23] on Micrococcus aloeverae DCB-20, which also emphasized the potential of complex carbon sources in supporting high-level IAA production. This may be attributed to the fact that glucose promotes bacterial biomass production, whereas under conditions of higher salt content, complex carbon sources are more conducive to balancing the accumulation of both biomass and metabolites.

Regarding nitrogen sources, yeast extract as an organic nitrogen source significantly promoted IAA synthesis in J113 (7.48 ± 0.64 μg/mL), outperforming peptone and beef extract. Yeast extract is rich in amino acids, peptides, vitamins, and growth factors, which likely serve as cofactors for key enzymes (such as nitrilase and amine oxidase) in the IAA biosynthetic pathway or as direct precursors (e.g., tryptophan), thereby stimulating IAA biosynthesis [24,25]. Ahmad et al. similarly noted in their study that the addition of organic nitrogen sources (e.g., casein hydrolysate) significantly enhances IAA yield. Nitrogen sources can be classified as either inorganic or organic. While high concentrations of inorganic nitrogen (e.g., NH₄NO₃) have been shown to inhibit IAA production in certain PGPR strains, likely by suppressing tryptophan synthesis or associated enzyme activities [26], studies have demonstrated that organic nitrogen (e.g., yeast extract) significantly enhances IAA accumulation. Their research reported a 2.5-fold increase in auxin yield when yeast extract was used, a finding consistent with our own experimental results, further supporting the premise that organic nitrogen sources are generally more conducive to IAA biosynthesis in saline–alkaline environments.

Through systematic optimization of fermentation conditions via response surface methodology (RSM), the IAA yield of V. titanicae J113 was enhanced by 115%, demonstrating the crucial role of methodical fermentation optimization in promoting microbial metabolite accumulation. This approach resonates with the strategy reported by Melini et al. [27], who achieved significant auxin yield improvement in Pantoea agglomerans C1 through RSM-guided optimization, underscoring the broad applicability of systematic parameter refinement in maximizing the biosynthetic potential of plant growth-promoting microorganisms.

Environmental factors including salinity, cultivation time, temperature, and inoculum size play critical regulatory roles in both bacterial growth and indole-3-acetic acid (IAA) synthesis. Salinity exerts a direct influence on the physio-chemical and biological properties of soil, ultimately impairing plant growth and productivity. The detrimental effects of salinity on plants primarily stem from osmotic stress, specific ion toxicity, nutrient imbalances, or a combination thereof. In the present study, IAA production reached 9.5 ± 0.09 μg/mL under 3% NaCl conditions. Response surface methodology (RSM) optimization further pinpointed the optimal NaCl concentration at approximately 32.5 g/L. This suggests that moderate salt stress not only failed to inhibit IAA synthesis in strain J113 but potentially activated its biosynthetic machinery. This observation aligns with reports indicating that certain salt-tolerant rhizobia maintain substantial IAA yields even under 4% NaCl stress [28], yet contrasts with studies demonstrating IAA suppression under high salinity [29]. This specific response may originate from the upregulation of the IAA biosynthetic pathway in J113 under osmotic stress, potentially representing a symbiotic strategy to enhance host plant resilience against salt stress [30]. Cultivation time emerged as the most significant factor affecting IAA yield. Production increased progressively with incubation time, peaking at 23.02 μg/mL after 144 h. This pattern is consistent with the characteristic accumulation of IAA as a secondary metabolite, typically occurring during the stationary phase of bacterial growth. It also corroborates findings from numerous PGPR studies, where extended fermentation periods (120–168 h) are required to achieve maximum IAA production [31]. Temperature significantly influenced metabolic activity. The highest IAA yield (18.34 ± 1.65 μg/mL) was observed at 30 °C, with deviations above or below this optimum leading to reduced production. The selection of this temperature reflects the requirement of the enzyme systems involved in metabolite production for their operational environment. Inoculum size directly impacted final IAA yield by regulating initial biomass. An inoculum size of 3% yielded the optimal IAA production (20.57 ± 1.85 μg/mL). Suboptimal inoculation led to prolonged lag phases, while excessive inoculation triggered premature nutrient competition, both scenarios being detrimental to efficient IAA accumulation. This finding is consistent with established fermentation optimization principles for various bacterial species.

The optimal temperature of 30 °C for IAA production aligns with the presumed thermal optimum of the key enzymatic systems involved. Inoculum size directly influenced the final IAA yield, with 3% (v/v) being optimal. A lower inoculum may extend the lag phase, delaying production, while a higher inoculum (e.g., 5%) could lead to rapid initial consumption of dissolved oxygen and key nutrients, creating a suboptimal environment for the sustained secondary metabolism required for high IAA accumulation—a common phenomenon in bacterial fermentations. Regarding the metabolism and synthesis of IAA in Vreelandella, genomic evidence suggests the operation of a tryptophan-dependent pathway. Our finding that yeast extract (a rich tryptophan source) was the optimal nitrogen source strongly supports this route for strain J113. Furthermore, IAA synthesis in this genus appears to be stress-responsive. The upregulation of production under moderate saline stress observed in this study suggests that the biosynthetic machinery may be activated as an adaptive strategy, potentially enhancing plant–microbe interactions under challenging environmental conditions.

A more extensive root system architecture enables plants to absorb water from the soil more efficiently. This morphological adaptation allows plants to cope with unfavorable environments [32]. Inoculating crops with plant growth-promoting rhizobacteria (PGPR) can significantly alter the RSA. These changes are primarily manifested through the stimulation of root elongation and increased density, thereby enhancing the capacity for water and nutrient uptake [33,34,35]. Research by Sapre et al. [36] demonstrated that inoculation with the salt-tolerant strain IG3 promoted plant growth under both non-stress and salt stress conditions. The shoot and root lengths of uninoculated oat seedlings were significantly shorter, whereas inoculation with the PGPR strain IG3 resulted in a marked increase in both parameters. The maize seed germination experiment in this study further confirms the significant growth-promoting potential of the optimized J113 fermentation broth. Under saline–alkali stress, application of the fermentation broth significantly increased the root length, number of fibrous roots, and fresh weight of maize seedlings by 86%, 137%, and 50%, respectively. One of the core physiological functions of IAA is to promote cell division and elongation, playing a key role particularly in root development [37]. Therefore, by secreting IAA, J113 directly stimulates the morphogenesis of the maize root system under salt stress, thereby enhancing the plant’s ability to absorb water and nutrients, ultimately resulting in biomass accumulation. This finding highlights the particular efficacy of J113 in mitigating salt damage, specifically its expertise in promoting root development, which is crucial for plants to establish a stable root configuration in saline–alkali adversity. The observed growth promotion is primarily attributed to bacterial metabolites, notably IAA, rather than residual nutrients. Our data (Table 4) shows that bacterial growth (OD600) peaked at 72 h and remained constant thereafter until 120 h, the harvest time for the plant bioassay. This plateau indicates that the readily available carbon and nitrogen sources in the fermentation medium were largely depleted by the late stationary phase. Furthermore, the fermentation broth underwent centrifugation and 100-fold dilution during the preparation of the final treatment solution. The combination of nutrient depletion at the physiological level and subsequent physical removal/dilution during processing ensures that the concentration of free, plant-available nutrients in the applied supernatant is biologically negligible. Therefore, the significant physiological benefits correlate strongly with the strain’s high-IAA-producing capability under stress, as demonstrated in this study.

A comparative summary of the IAA production capacity of V. titanicae J113 alongside other reported halotolerant or plant growth-promoting bacteria is provided in

Table 10. Comparison of indole-3-acetic acid (IAA) production levels among different microbial strains.

Strain	Optimal Conditions/Key Features for IAA Production	Maximum IAA Yield Reported (μg/mL)	Reference/Note
Vreelandella titanicae J113 (This study)	Optimized via RSM: starch 17.5 g/L, yeast extract 5 g/L, NaCl 32.5 g/L, 30 °C, 144 h. Halotolerant.	23.02	(Current study, under 3.25% NaCl)
Pantoea agglomerans C1	Optimized via RSM and metabolomics; fed-batch process.	102.2	[14,27]
Vreelandella titanicae (Halotolerant strain)	Cultured in seawater-based medium. Halotolerant.	2.1	[22] (Strain from cited study)
Burkholderia phytofirmans PsJN	L-Tryptophan-dependent biosynthesis; promotes maize growth.	15.0	[11] (Value estimated from plant growth promotion data)
Micrococcus aloeverae DCB-20	High IAA producer; utilizes tryptophan-independent pathway.	154.3	[23]
Bacillus spp. (Zinc-solubilizing isolates)	Plant growth-promoting rhizobacteria (PGPR).	~8.5 (Range: 7.2–10.1)	[28]

. The data in the table indicate that the optimized yield achieved by strain J113 under saline stress is competitive among related microbes. Notably, it substantially exceeds the yield reported for another V. titanicaestrain, demonstrating the significant impact of our systematic fermentation optimization. The comparison also reveals the potential for further yield enhancement through more advanced cultivation strategies. Overall, the results position V. titanicae J113 as a robust IAA producer with particular relevance for applications in saline–alkaline environments.

In this study, the fermentation process for the halotolerant bacterium V. titanicae J113 was successfully optimized using response surface methodology (RSM), yielding reliable parameters for its potential application. While these findings provide a strong foundation for the practical use of J113, the authors recognize that the current study is limited to physiological and phenotypic observations. Therefore, future research should focus on: (i) molecular mechanism analysis, including elucidation of the IAA synthesis pathway and key genes involved in plant–microbe interactions [38]; (ii) field validation of bacterial survival to assess colonization and promoting effects in real saline–alkali environments [39]; and (iii) formulation and application technology development to enhance strain viability and establish scalable processes [40]. Addressing these aspects will be essential to advance the application of J113 in saline–alkali land improvement and sustainable agriculture.

5. Limitations and Future Perspectives

This study has several methodological and practical limitations that define the scope of the current conclusions and outline directions for future research. First, regarding methodological specificity, the germination assay lacked controls with only basal medium or only the fermentation supernatant. Consequently, the observed growth promotion is most conservatively interpreted as the supernatant’s effect in mitigating salt stress rather than an inherent growth-promoting effect. Moreover, the sole use of the Salkowski colorimetric assay, while suitable for high-throughput comparative optimization, lacks the specificity of chromatographic methods (e.g., HPLC-MS). Future work should employ such techniques to validate absolute IAA concentrations. Second, concerning experimental design and scalability, the use of yeast extract, a complex nitrogen source, introduces variability; future studies employing defined media would enhance reproducibility and mechanistic insight. Critical translational aspects—such as bioreactor operation (e.g., kLa, feeding strategies), metabolite stability in formulation, and productization pathways—remain to be addressed. Finally, at the mechanistic level, the specific IAA biosynthetic pathway in V. titanicae J113 remains uncharacterized. Genomic analysis and targeted precursor feeding experiments are needed to elucidate the predominant route.

6. Conclusions

In this study, response surface methodology (RSM) was employed to optimize the fermentation process for indole-3-acetic acid (IAA) production by the halotolerant plant growth-promoting rhizobacterium V. titanicae J113. The optimal medium composition was determined as follows: starch 17.5 g/L, NaCl 32.5 g/L, and yeast extract 5 g/L. The optimal culture conditions were identified as: temperature 30 °C, inoculum size 3%, and cultivation time 144 h. Under these optimized conditions, the IAA yield reached 23.02 μg/mL, representing a 115% increase compared to the pre-optimization level. The strain demonstrated efficient IAA synthesis even under 3% NaCl stress, indicating excellent salt tolerance. Maize seed germination experiments confirmed that the optimized fermentation broth alleviated the inhibitory effects of salt stress on maize seedling growth, particularly promoting root development and biomass accumulation. In conclusion, V. titanicae J113 is a promising strain with both high IAA production capacity and significant plant growth-promoting potential in saline–alkali soils. This study provides solid fermentation process data and a theoretical foundation for development as microbial plant growth regulators suitable for saline–alkali soil improvement.