Co-Inoculation of Bacillus subtilis and Priestia megaterium Promotes Growth and Shapes Rhizosphere Microbial Community of Rosa × Hybrida ‘Ruby’ Under Multiple Substrate Formulations

Abstract

Efficient cultivation is essential for the rose industry. Both substrate formulation and plant growth-promoting rhizobacteria (PGPR) application both critical, yet their synergistic effects remain limited. This study investigated the synergistic effects of Bacillus subtilis (Bs) and Priestia megaterium (Pm) combined with five substrate formulations on the growth physiology, photosynthetic characteristics, and soil properties of Rosa × hybrida ‘Ruby’. Two-way ANOVA revealed significant interactions between substrate and PGPR treatments for most growth and physiological indicators. Orthogonal experiments demonstrated that specific PGPR–substrate combinations significantly enhanced plant growth and photosynthetic performance of the studied cultivar, as well as soil quality. Principal component analysis and membership function analysis identified four substrate–PGPR combinations as optimal, with the T4 substrate (humus/perlite/vermiculite/coconut coir/peat/biochar = 5:1.5:1:1:1:0.5) showing the most pronounced effects. In this T4 substrate, PGPR inoculation significantly altered the rhizobacterial community structure. LEfSe analysis revealed 67 enriched microbial biomarkers—substantially more than single-strain treatments. The relative abundance of beneficial genera such as Acidibacter and Chryseotalea increased, and the combined bacterial treatment enhanced functional pathways associated with signal transduction, cell motility, and RNA processing. Compared to single-strain treatments, the combined bacterial application demonstrated superior regulatory effects on plant growth. The optimal combined treatments increased plant height by up to 42.7%, root activity by 103.0%, soluble protein content by 302.8%, and soil ammonium nitrogen by 168.8%. These findings demonstrated that tailored combinations of PGPR and cultivation substrates highlight the potential for optimizing rose cultivation and improving the rhizosphere microecological environment.

1. Introduction

Roses (Rosa spp.) are important ornamental and economic crops with significant value [1]. They thrive in deep, loose, fertile, moist, and well-drained soils and are highly sensitive to soil conditions [2]. However, in road greening applications, roses face multiple stressors, including environmental stress, poor soil quality, and pests. These factors severely restrict its growth [3]. Consequently, efficient cultivation and management strategies for roses have become a primary focus in current production practices.

Soilless culture technology has emerged as a promising approach for the efficient cultivation of roses [4,5,6]. This technology overcomes geographical and ecological constraints, thereby facilitating efficient resource utilization. Compared with hydroponic systems and traditional soil cultivation, substrate culture offers numerous advantages, including improved plant survival rates, accelerated fruit ripening, increased yields [7], enhanced plant growth [8], and reduced pest and disease risks [9]. Previous studies on substrate cultivation of roses have primarily focused on optimizing substrate composition. For instance, Wang et al. investigated the effects of different substrate types on the growth of standard roses and urban landscape applications [10]. More specifically, one study identified an optimal substrate ratio: vermicompost/vermiculite/mushroom residue = 0.5:0.5:2:1 for rose species growth [11]. Collectively, these studies suggest that mixed substrates outperform single component substrates, with organic–inorganic mixed combinations being more effective than purely organic or purely inorganic formulations [12]. However, the previous studies have primarily focused on substrate formulation, overlooking the potential role of microbial inoculants. The interactive effects between substrate formulation and microbial co-inoculation remain unexplored.

Cultivation substrates that are sterile or lack microbial diversity cannot provide plants with all the beneficial ecological services offered by healthy soils [4]. In contrast, Plant Growth-Promoting Rhizobacteria (PGPR) can promote plant growth through various direct and indirect mechanisms [13,14]. Studies have shown that PGPR foster plant growth and development by increasing nutrient supply, producing plant hormones, and protecting plants from pathogen damage via both direct and indirect pathways [15]. They can induce systemic resistance in plants, enhancing tolerance to a wide range of pathogens. PGPR also produce various antibacterial compounds, including antibiotics, volatile organic compounds (VOCs), and hydrolases, which inhibit pathogen growth [16,17]. Furthermore, PGPR compete with harmful pathogens for space and resources in the rhizosphere, thereby reducing infection risks [18]. Beyond these well-recognized mechanisms, recent advances have highlighted that microbial remediation in soil is orchestrated by a network of microbial enzymes—such as hydrolases, dioxygenases, and dehalogenases—that catalyze the transformation of pollutants, while quorum sensing (QS) acts as a key regulatory system modulating microbial cooperation, biofilm formation, and catabolic gene expression during degradation processes [19]. For instance, Priestia megaterium has been shown not only to solubilize phosphorus and remediate soils contaminated with heavy metals such as boron (B), lead (Pb), and cadmium (Cd) [20,21], but also to enhance soil nutrient bioavailability and shape a more stable and complex bacterial co-occurrence network in greenhouse systems, thereby contributing to sustainable agricultural production [22].

Existing studies have shown that combining substrate optimization with the inoculation of PGPR can improve the yield of vegetables and food crops systems. However, most of these studies have focused on the independent effects of either substrates or PGPR, while the synergistic interactions between the two in soilless systems and the underlying mechanisms regulating rhizosphere microbial communities remain unclear. Unlike previous studies that primarily emphasized single factors, this research is the first to investigate the interactive effects of substrates and PGPR in a soilless cultivation system for rose species and to analyze the responses of rhizosphere microbial communities using high-throughput sequencing.

This study proposes the following hypothesis: in soilless substrate cultivation, inoculation with Bacillus subtilis and Priestia megaterium can synergize with optimized substrate formulations to improve the rhizosphere microecological environment, thereby more effectively promoting the growth of roses. The main objectives of this study were (1) to evaluate the synergistic effects of different substrate formulations and PGPR on the growth and nutrient accumulation of Rosa × hybrida ‘Ruby’; (2) to identify the optimal substrate combination for cultivation; and (3) to elucidate the impact of PGPR on the structure of rhizosphere microbial communities. This research aims to provide a theoretical foundation and practical insights for optimizing the cultivation substrates of studied cultivar and innovating green cultivation models for horticultural plants.

2. Materials and Methods

2.1. Experimental Material

2.1.1. Cultivation Substrate Composition and Properties

The various substrate components and their physicochemical properties employed to mix the substrates used in this experiment are shown in

Table 1. Physical and chemical properties of substrate components used in this study.

Soilless Substrate	Bulk Density (g/cm3)	Porosity (%)	pH	Electrical Conductivity (mS/cm)	Maximum Water Holding Capacity (%, Gravimetric Basis)	Source
Humus	0.68	76	6.8	0.7	462.3	Guizhou Lvyuan Meijia Agricultural Technology Co., Ltd., Guiyang, China
Perlite	0.27	78	7.2	0.1	423.6	Lingshou County Haibin Mineral Products Trading Co., Ltd., Shijiazhuang, China
Vermiculite	0.23	82	7.8	0.2	378.6	Guangzhou Zhiyu Gardening Co., Ltd., Guangzhou, China
Coconut coir	0.22	86	5.6	0.8	780.2	Hangzhou Huaqihu Horticultural Technology Co., Ltd., Hangzhou, China
Peat	0.21	90	6.6	0.5	802.2	Shandong Flower Master Biotechnology Co., Ltd., Jinan, China
Biochar	0.62	51	9.4	0.9	384.2	Henan Lize Environmental Protection Technology Co., Ltd., Zhengzhou, China

Note: Maximum water holding capacity is the water-holding capacity under saturated conditions on a gravimetric basis, reflecting the maximum water absorption capacity of the substrates. In practical cultivation, the container capacity (volumetric basis) after gravitational drainage typically ranges from 50% to 80%. All the above data are provided by the material suppliers.

.

2.1.2. Plant Material

In this study, one-year-old R. hybrida ‘Ruby’ provided by Suqian Tiantianwang Landscaping and Flower Company, Suqian, China was selected as the experimental material. This cultivar features bright and stable flower color, compact plant type, and high ornamental value, making it widely applied in urban greening and horticultural cultivation and exhibits broad prospects for further cultivation and development. All experimental seedlings exhibited uniform growth status and were free from diseases and pests.

2.1.3. Source of Bacterial Isolates and Inoculum Preparation

Our preliminary experiments showed that treatment with Bacillus subtilis enhanced the activity of key stress-related enzymes in rose plants [23,24]. Accordingly, B. subtilis SHMCC D11420 and Priestia megaterium SHMCC D11102 were chosen for investigation. Both strains were obtained from the Shanghai Microbiological Culture Collection Co., Ltd., Shanghai, China. and stored at −20 °C in glycerol stock for routine use.

B. subtilis was inoculated onto LB agar plates and incubated at 30 °C for 48 h. A single colony was transferred to nutrient broth and cultured at 30 °C with shaking at 180 r/min for 72 h. After centrifugation at 8000 r/min, the bacterial pellet was washed twice with sterile water and resuspended to a concentration of 1 × 10⁸ CFU mL⁻¹. For P. megaterium, the inoculum concentration was 1% (v/v) in 100 mL LB medium, and the bacteria were activated at 30 °C with shaking at 160 r/min. After centrifugation at 5000 r/min for 5 min, the pellet was resuspended in sterile water and the concentration adjusted to 1 × 10⁸ CFU mL⁻¹ using a UV-1200 spectrophotometer at OD600 (AOE, Shanghai, China). The LB medium composition was: tryptone 10 g/L, yeast extract 5 g/L, sodium chloride 10 g/L, pH 7.4.

2.2. Experimental Framework and Design

2.2.1. Experimental Two-Stage Framework

This experimental design comprises two consecutive stages: first, evaluating the growth, photosynthetic performance and soil physicochemical indices of roses to screen out the optimal cultivation substrate combination for studied cultivar; subsequently, using this optimal substrate, further explore the effects of different PGPR inoculation treatments on its rhizosphere microbial community.

2.2.2. Experimental Design: Stage 1

The experiment was conducted at the open-field test site of the Landscape Architecture Training Center, Nanjing Forestry University in Nanjing, Jiangsu Province, from March to September 2024, with a total test period of 184 days. The experiment employing a randomized complete block design. Five distinct substrate formulations (T1 to T5) were tested, as shown in

Table 2. Proportion and characteristics of cultivation substrates.

Cultivation Substrate	Composition of Cultivation Substrate	Volume Ratio	Characteristic
T1	humus, perlite, vermiculite, coconut coir, peat, biochar	4:2:1:1.5:1:0.5	Basic
T2	humus, perlite, vermiculite, coconut coir, peat, biochar	3:1.5:1:2:2:0.5	Water-retaining
T3	humus, perlite, vermiculite, coconut coir, peat, biochar	3.5:2.5:1.5:1:1:0.5	Excellent aeration
T4	humus, perlite, vermiculite, coconut coir, peat, biochar	5:1.5:1:1:1:0.5	Organic matter-rich
T5	humus, perlite, vermiculite, coconut coir, peat, biochar	3:2:1.5:1.5:1.5:0.5	Balanced

. Four inoculation treatments were applied to these substrates: (1) Sterile water control (CK); (2) B. subtilis (Bs) single inoculation; (3) P. megaterium (Pm) single inoculation; (4) Co-inoculation of B. subtilis and P. megaterium (Bs + Pm). All bacterial suspensions were applied at a concentration of 1 × 10⁸ CFU mL⁻¹. The full matrix of treatments (5 substrates × 4 inoculants = 20 treatments) is detailed in

Table 3. Different cultivation substrates and rhizobacteria treatment combinations used in this study.

Substrate	Code	PGPR Treatment	Sterile Water (mL)	Bs (mL)	Pm (mL)
T1	CK1	Control	20	0	0
T1	TS1	Bs	0	20	0
T1	TM1	Pm	0	0	20
T1	TSM1	Bs + Pm	0	10	10
T2	CK2	Control	20	0	0
T2	TS2	Bs	0	20	0
T2	TM2	Pm	0	0	20
T2	TSM2	Bs + Pm	0	10	10
T3	CK3	Control	20	0	0
T3	TS3	Bs	0	20	0
T3	TM3	Pm	0	0	20
T3	TSM3	Bs + Pm	0	10	10
T4	CK4	Control	20	0	0
T4	TS4	Bs	0	20	0
T4	TM4	Pm	0	0	20
T4	TSM4	Bs + Pm	0	10	10
T5	CK5	Control	20	0	0
T5	TS5	Bs	0	20	0
T5	TM5	Pm	0	0	20
T5	TSM5	Bs + Pm	0	10	10

Notes: TS = Bacillus subtilis inoculation; TM = Priestia megaterium inoculation; TSM = Co-inoculation of both bacteria. The numeric suffix corresponds to the substrate type. Control (CK1–CK5) received sterile water only (20 mL). Bs: B. subtilis inoculum at 1 × 10⁸ CFU mL⁻¹; Pm: P. megaterium inoculum at 1 × 10⁸ CFU mL⁻¹; Bs + Pm (TSM1–TSM5) indicates co-inoculation with Bs and Pm, 10 mL each at 1 × 10⁸ CFU mL⁻¹. The total application volume was kept constant at 20 mL for all treatment.

. For each treatment, three independent pots were used as biological replicates, with one seedling planted per pot (n = 3). For inoculation treatments, at transplanting, seedling roots were immersed in a bacterial suspension or sterile water for the control at a concentration of 1 × 10⁸ CFU/mL for 30 min. And 20 mL of the corresponding bacterial suspension or sterile water was applied to each plant by drenching the substrate around the rhizosphere every 20 days during the growth period. Standard irrigation and maintenance were applied uniformly.

The test site had flat terrain, good ventilation, no artificial shading or surrounding obstacles, and the plants were grown under open-field natural light conditions. The potted planting specifications were as follows: top diameter 15.7 cm, bottom diameter 12.4 cm, height 16.5 cm, and a substrate bulk volume of 2 L. During the experiment, the average temperature was 22.5 °C, with the maximum temperature ranging from 39.3 °C to 40.2 °C in July–August and the minimum temperature of 2.1 °C in March; the average relative air humidity was 68.2%; and the average daylight duration was 10.6 h [25]. At the start of the experiment, all R. hybrida ‘Ruby’ plants were in the vegetative growth stage, each with 6–8 fully expanded true leaves and no flower bud differentiation observed. Throughout the experiment, all treatment groups were subjected to the same planting and management measures, including irrigation and disease control. At the end of the experiment, the vegetative growth indicators of the plants were stable, and they had reached a developmental stage for transitioning to reproductive growth.

2.2.3. Experimental Design: Stage 2

Based on the results of Stage 1, the optimal substrate combination was selected. Rhizosphere soil samples were aseptically collected from the four inoculation treatment groups (CK, Bs, Pm, and SM) under this substrate, with three biological replicates per group. The microbial community structure and function were characterized by high-throughput sequencing of the 16S rRNA gene.

2.3. Measurement Methods

2.3.1. Morphological Traits and Biomass

Morphological traits—plant height and stem diameter—were measured at the start of the experiment on March 2024 to establish baseline values and subsequently at monthly intervals during the growing season (May, July, and September 2024). Plant height was measured as the vertical distance from the substrate surface to the apical growing point using a steel tape with an accuracy of 0.1 cm. Stem diameter was measured at 5 cm above the substrate surface using a digital vernier caliper with an accuracy of 0.01 cm, taking the average of two orthogonal measurements.

Destructive sampling was conducted at the end of the experiment (September 2024) to determine root length and biomass. Plants were carefully excavated and separated into roots and shoots. The root system was rinsed with deionized water, and the length of the primary root was measured from the tip to the base using a ruler with an accuracy of 0.1 cm. Fresh weights of shoots and roots were immediately measured using an electronic balance with an accuracy of 0.0001 g. Samples were then fixed at 105 °C for 30 min and subsequently dried in an oven at 60 °C for 72 h until constant weight was achieved to determine dry weight. Each treatment consisted of three biological replicates.

2.3.2. Physiological Indicators

During the vigorous growth period of studied cultivar (July 2024), robust plants were selected for physiological evaluations. The Coomassie Brilliant Blue G-250 staining method was used to determine the soluble protein content in the fully expanded functional leaves [26]. Root activity was determined using the Triphenyl tetrazolium Chloride Method (TTC) following the approach of Wang, specifically using a plant root vitality assay kit produced by Solarbio (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). The soluble sugar content of the fully expanded functional leaves was measured using the anthrone colorimetric method. Superoxide Dismutase (SOD) activity of the fully expanded functional leaves was determined using a SOD activity assay kit provided by Solarbio (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). Based on the study by Wang et al. the photosynthetic pigment content was determined using spectrophotometry in mature leaves [27]. According to the research by Liao et al. chlorophyll fluorescence parameters of the third functional leaf were measured using the Handy PEA plant efficiency analyzer (Hansatech, Norfolk, UK) [28].

2.3.3. Soil Properties

Soil samples were collected at the final harvest (September 2024), for analysis of physicochemical properties. The determination of soil organic matter was based on the study by Yin et al. using the high-temperature external heating potassium dichromate oxidation-volumetric method [29]. The determination of soil ammonium nitrogen followed the method of Ma et al. using the KCl extraction–indophenol blue colorimetric method [30]. Soil nitrate nitrogen content was determined using ultraviolet–visible spectroscopy. The available phosphorus content in soil was measured using the sodium bicarbonate method. And the content of available potassium in soil was determined by flame spectrophotometry [31]. Soil NR (nitrate reductase) and NiR (nitrite reductase) activities were both measured using kits produced by Solarbio (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China).

2.3.4. Analysis of the Microbiota by 16S rRNA Gene Sequencing

Rhizosphere substrate samples from select rose cultivation treatments were selected for bacterial community profiling via 16S rRNA gene sequencing using high throughput sequencing. Total genomic DNA was extracted using a magnetic bead-based protocol. DNA extraction, high-throughput sequencing work and primary bioinformatics analysis were all completed by BGI Tech Services Co., Ltd. in Shenzhen, China. Briefly, 100–200 mg of soil was mixed with Buffer ATL and PVP-10, homogenized, and incubated at 65 °C for 20 min. After centrifugation, the supernatant was treated with Buffer PCI and centrifuged again. The resulting solution was processed using the KingFisher system, and the DNA was eluted and stored for downstream applications. For library construction, DNA quality was assessed, and high-quality genomic DNA was amplified using primers targeting conserved and variable regions of the bacterial 16S rRNA gene. The V3–V4 region of the 16S rRNA gene was amplified using the universal primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′). Amplified DNA was then denatured to single-stranded form and circularized to produce single-stranded circular molecules. Linear DNA was digested, and DNA nanoballs (DNBs) were generated by rolling circle replication. Sequencing was performed on a high-density DNA nanoball chip using combinatorial probe–anchor synthesis (cPAS) technology. Raw sequencing data were then processed using the following quality control procedures.

Raw sequencing reads were processed using Cutadapt v2.6 to remove primer and adapter sequences. Quality filtering was performed using a window-based method with a window length of 30 bp; reads were trimmed when the average quality score within the window fell below 20. Reads shorter than 75% of the original length, as well as those containing ambiguous bases (N) or exhibiting low complexity, were discarded. Paired-end reads were assembled into tags using Flash v1.2.11, requiring a minimum overlap of 15 bp and allowing a mismatch rate of 0.1 in the overlapping region.

2.3.5. Bioinformatics Analysis

High-throughput sequencing of the bacterial 16S rRNA gene was employed to obtain data on rhizosphere microbial diversity and community composition, allowing for an assessment of the effects of different treatments on microbial activity and ecological function. Subsequently, Principal Coordinates Analysis (PCoA) were used to explore the correlations between the microbial community and plant growth parameters. Additionally, PICRUSt2 v2.3.0-b software was utilized to predict the functional potential of the microbial communities, thereby linking taxonomic composition to ecological functions and obtaining an overview of the community’s functional distribution.

Assembled tags obtained from the above quality control steps were used for downstream bioinformatics analysis. Sequences were clustered into operational taxonomic units (OTUs) at 97% similarity using the UPARSE algorithm implemented in USEARCH v7.0.1090, and chimeras were removed using UCHIME v4.2.40. OTU abundance tables were generated using the usearch_global, and taxonomy was assigned with the RDP Classifier v2.2 (confidence ≥ 0.6). Unannotated or invalid OTUs were excluded. Alpha diversity indices (Chao1, ACE, Shannon, Simpson) were calculated using Mothur v1.31.2, Beta diversity is visualized using principal coordinate analysis (PCoA) based on the weighted_unifrac distance matrix. Biomarkers were identified through linear discriminant analysis effect size (LEfSe) with the threshold set as p < 0.05 by the Kruskal–Wallis test and a linear discriminant analysis (LDA) score > 2.0. Functional potential of the microbial communities was predicted from the 16S rRNA gene data using PICRUSt2. The predicted functional features were summarized and compared across different treatment groups using level 2 categories of the Clusters of Orthologous Groups (COG) classification system.

2.4. Data Processing and Statistical Analysis

In this study, continuous variables including plant growth indicators, photosynthetic characteristics, physiological indices, and soil physicochemical properties were analyzed using SPSS 28.0. A two-way analysis of variance (Two-way ANOVA) was employed to evaluate the effects of substrate formulations, PGPR treatments, and their interactions. For variables with a significant interaction (p < 0.05), simple-effects comparisons were conducted to determine the differences between PGPR treatments within each specific substrate. Principal component analysis (PCA) and fuzzy membership functions were integrated to comprehensively evaluate substrate performance. For microbial community data, Alpha diversity was analyzed using Mothur v1.31.2, Beta diversity using QIIME v1.9.1, and functional prediction using PICRUSt2 v2.3.0-b. Graphs were generated with Origin 2025. All indicators were measured with three biological replicates, and data are presented as mean ± standard deviation (SD).

3. Results

3.1. Factorial ANOVA Overview

To evaluate the comprehensive effects of cultivation substrate, PGPR inoculant, and their interaction on the growth and physiological characteristics of studied cultivar, a two-way ANOVA was performed on 24 core indicators (

Table 4. Two-way ANOVA for the effects of substrate, PGPR, and their interaction on various indicators of Rosa × hybrida ‘Ruby’.

Measured Indicators	Cultivation Substrates (p-Value)	Inoculation Treatments (p-Value)	Substrate × Inoculation Treatments (p-Value)
Plant height	0.012 *	0.028 *	0.073
Stem diameter	0.210	0.011 *	0.069
Root length	0.046 *	0.283	<0.001 ***
Fresh weight of stem	0.012 *	0.318	0.038 *
Dry weight of stem	0.016 *	0.467	0.591
Fresh weight of root	0.202	0.565	0.859
Dry weight of root	0.044 *	0.672	0.565
Chlorophyll a	<0.001 ***	<0.001 ***	<0.001 ***
Chlorophyll b	<0.001 ***	<0.001 ***	<0.001 ***
Carotenoid	<0.001 ***	<0.001 ***	<0.001 ***
Soluble sugar	<0.001 ***	<0.001 ***	<0.001 ***
Soluble protein	0.036 *	<0.001 ***	0.024 *
Root activity	<0.001 ***	<0.001 ***	<0.001 ***
Soil organic matter	0.071	<0.001 ***	0.253
Nitrate nitrogen	<0.001 ***	0.100	<0.001 ***
Ammonium nitrogen	<0.001 ***	<0.001 ***	0.002 **
Available phosphorus	<0.001 ***	<0.001 ***	<0.001 ***
Fast available potassium	<0.001 ***	0.451	0.551
NR	<0.001 ***	<0.001 ***	<0.001 ***
NiR	<0.001 ***	<0.001 ***	<0.001 ***
SOD	0.009 **	0.218	0.007 **
Fo	0.285	0.098	0.003 **
Fm	0.084	0.073	0.989
Fv/Fm	0.446	0.746	0.351

Note: NR: Nitrate Reductase; NiR: Nitrite Reductase; SOD: Superoxide Dismutase; Fo: Minimal fluorescence; Fm: Maximal fluorescence; Fv/Fm: Maximal photochemical quantum yield of PSII. *, **, and *** indicate significance at p < 0.05, p < 0.01, and p < 0.001 levels, respectively, according to two-way ANOVA test.

).

The results revealed significant substrate × inoculant interactions for multiple traits (p < 0.05), indicating that the efficacy of PGPR inoculants varied across substrates. Specifically, significant interactions were observed in 15 indicators, including root length, stem fresh weight, chlorophyll a/b, soluble sugar/protein, root activity, nitrate nitrogen, ammonium nitrogen, phosphorus, NiR, NR, SOD, Fo, and carotenoids. Furthermore, both substrate and inoculant exhibited highly significant main effects on most physiological indicators and substrate nutrient contents (p < 0.001). Notably, the interaction exerted a pronounced regulatory role in physiological metabolism and nutrient activation, with highly significant interaction effects detected for root activity, key nitrogen metabolism enzymes (NR, NiR), and available phosphorus content (p < 0.001).

Accordingly, for indicators with significant interactions, simple-effects analysis was conducted to compare inoculant treatments within each substrate. For indicators without significant interactions, data from all treatment combinations are presented to maintain systematic consistency, facilitating the intuitive identification of optimal inoculant treatments under specific substrate conditions and achieving the research objective of precise screening.

3.2. Growth Characteristics Analysis

3.2.1. Plant Height

Measurement of plant height in studied cultivar across March, May, July, and September revealed distinct temporal characteristics. During the early growth stage (March to May), and across all five substrates, none of the inoculation treatments exhibited a statistically significant increase in plant height compared to the control (Figure 1). In contrast, significant promotive effects emerged during the later growth stage (July to September). As illustrated in Figure 1b, the combined bacterial treatment (TSM2) in T2 resulted in a significant increase in plant height by 42.72% compared to the control (CK2) in September (p ≤ 0.05). Similarly, the single-strain treatment of Bacillus subtilis (TS3) in Substrate T3 significantly enhanced plant height by 34.92% relative to its control (CK3) (Figure 1c). In Substrate T5, the single-strain treatment of B. subtilis (TS5) also led to a significant height increase of 39.88% over CK5 in September (Figure 1e). An earlier response was observed in Substrate T4, where significant growth promotion was recorded in July (Figure 1d). Both the single-strain treatment of B. subtilis (TS4) and the single-strain treatment of P. megaterium (TM4) treatments significantly increased plant height by 21.78% and 23.75%, respectively, compared to the control (CK4) (p ≤ 0.05). Conversely, as shown in Figure 1a, none of the inoculation treatments in Substrate T1 induced a significant increase in plant height at any measured time point compared to the control (CK1). Some treatments even showed a tendency toward lower plant height than the control. This indicates that the combination of the tested microbial inoculants with the T1 cultivation substrate did not exert a significant growth-promoting effect on rose plant height under the experimental conditions.

3.2.2. Stem Diameter

As shown in Figure 2, in substrate T1, the stem diameter of roses treated with the single-strain treatment of B. subtilis (TS1) increased extremely significantly by 34.76% compared to the control (CK1) in September (p ≤ 0.01). Meanwhile, the single-strain treatment of P. megaterium (TM1) resulted in a significant increase of 29.19% relative to CK1 (p ≤ 0.05). According to Figure 2c, the stem diameter of roses subjected to the single-strain treatment of B. subtilis (TS3) increased significantly by 42.58% compared to CK3 in September. In substrate T4, the single-strain treatment of P. megaterium (TM4) also significantly enhanced the stem diameter by 26.78% over CK4 in September. However, no significant differences in stem diameter were observed between the other bacterial treatments and the control in substrate T4. Notably, in substrate T5, the stem diameter of roses receiving the single-strain treatment of B. subtilis (TS5) was significantly lower than that of the control group in May (p ≤ 0.05).

3.2.3. Root Length

As shown in Figure 3a, in substrate T2, the root length of roses treated with the single-strain treatment of P. megaterium (TM2) was significantly increased by 43.85% compared to the control (CK2) (p ≤ 0.01). The combined bacterial treatment (TSM2) resulted in an even greater increase of 54.03% relative to CK2 (p ≤ 0.01), representing the most effective treatment. In contrast, root length values observed in the TSM1 and TM5 treatments of substrate T1 and T5 were significantly lower than those of their respective controls (CK1 and CK5). The substrate formulations for substrate T1 and T5 were based on basic and balanced formulations, respectively, and the observed inhibitory effects may be associated with the intrinsic properties or background nutrient levels of these substrates.

3.2.4. Aboveground and Belowground Biomass

As illustrated in Figure 3b–e, inoculation with PGPR did not lead to a statistically significant increase in stem fresh weight, stem dry weight, root fresh weight, or root dry weight across all treatment groups. Nevertheless, in the T2 cultivation substrate, all bacterial treatments (TS, TM and TSM) promoted the growth of both above-ground and below-ground biomass compared to the control (CK2). Notably, the TM2 treatment exhibited the most pronounced effect on stem fresh weight, showing an increase of 64.81% relative to CK2. In contrast, for both stem dry weight and root dry weight in the T5 substrate, the single-strain inoculants (TS, TM) and the combined bacterial inoculant (TSM) performed poorly, with values generally lower than those of CK5. These results suggest that the T5 substrate may not establish a synergistic relationship with the introduced bacterial strains to promote biomass accumulation. Instead, certain properties of the T5 substrate might have induced inhibitory effects or even provoked antagonistic interactions with the PGPR inoculants.

3.3. Analysis of Photosynthesis and Physiological Characteristic

3.3.1. Photosynthetic Pigments

As presented in

Table 5. Effects of different treatments on photosynthetic pigments.

Treatment Group	Treatment	Chlorophyll a (mg/kg)	Chlorophyll b (mg/kg)	Carotenoid (mg/kg)	Fo	Fm	Fv/Fm
T1	CK1	19.20 ± 1.93 b	11.16 ± 1.11 b	5.27 ± 0.39 b	464.00 ± 45.40 b	2365.67 ± 325.26 b	0.80 ± 0.04 a
	TS1	27.15 ± 0.79 a	17.32 ± 1.05 a	7.45 ± 0.27 a	476.00 ± 39.69 b	3265.00 ± 96.14 a	0.85 ± 0.01 a
	TM1	25.82 ± 1.08 a	15.63 ± 1.00 a	6.94 ± 0.43 a	512.67 ± 83.53 b	3180.00 ± 736.71 ab	0.83 ± 0.07 a
	TSM1	17.99 ± 2.43 b	10.11 ± 1.44 b	4.54 ± 0.57 b	660.33 ± 31.66 a	3257.67 ± 60.43 a	0.80 ± 0.01 a
T2	CK2	9.30 ± 1.00 d	5.45 ± 0.51 d	2.46 ± 0.28 c	407.00 ± 45.04 b	1997.00 ± 230.67 a	0.79 ± 0.03 a
	TS2	17.87 ± 0.83 b	9.97 ± 0.43 b	4.45 ± 0.24 b	535.67 ± 51.73 a	2696.00 ± 406.10 a	0.80 ± 0.01 a
	TM2	12.99 ± 0.77 c	7.34 ± 0.38 c	3.09 ± 0.13 c	418.33 ± 13.65 b	2469.67 ± 309.95 a	0.83 ± 0.02 a
	TSM2	23.59 ± 2.34 a	13.87 ± 1.72 a	6.02 ± 0.72 a	521.33 ± 26.39 a	2554.00 ± 887.52 a	0.78 ± 0.08 a
T3	CK3	14.83 ± 1.34 b	8.75 ± 0.7 b	3.93 ± 0.25 b	350.33 ± 42.03 b	2375.67 ± 297.02 a	0.85 ± 0.03 a
	TS3	23.84 ± 1.52 a	14.33 ± 1.21 a	6.37 ± 0.40 a	531.00 ± 112.01 a	3075.67 ± 769.25 a	0.82 ± 0.03 a
	TM3	23.2 ± 0.27 a	13.25 ± 0.19 a	6.12 ± 0.14 a	574.33 ± 53.08 a	2646.67 ± 675.8 a	0.78 ± 0.05 a
	TSM3	25.85 ± 3.23 a	14.4 ± 1.53 a	6.78 ± 1.40 a	505.00 ± 47.89 a	2884.33 ± 1248.9 a	0.80 ± 0.07 a
T4	CK4	20.44 ± 2.03 a	12.21 ± 1.32 a	4.58 ± 0.41 a	661.33 ± 157.16 a	2306.33 ± 927.07 a	0.65 ± 0.23 a
	TS4	17.46 ± 2.73 a	9.85 ± 1.67 a	4.87 ± 0.63 a	510.67 ± 35.02 a	2825.33 ± 700.05 a	0.81 ± 0.07 a
	TM4	17.57 ± 2.78 a	10.02 ± 1.60 a	4.84 ± 0.67 a	496.67 ± 171.00 a	2554.67 ± 320.66 a	0.80 ± 0.09 a
	TSM4	17.43 ± 2.58 a	9.71 ± 1.43 a	4.45 ± 0.75 a	430.00 ± 60.36 a	2621.67 ± 719.94 a	0.83 ± 0.04 a
T5	CK5	17.98 ± 2.02 ab	10.17 ± 1.16 ab	4.75 ± 0.59 ab	454.33 ± 145.71 a	2977.00 ± 534.42 a	0.85 ± 0.05 a
	TS5	14.5 ± 2.17 b	8.17 ± 1.16 b	4.04 ± 0.60 b	571.33 ± 32.04 a	2890.00 ± 241.94 a	0.80 ± 0.02 a
	TM5	21.92 ± 1.81 a	12.47 ± 1.10 a	5.66 ± 0.50 a	512.67 ± 61.33 a	2919.67 ± 964.11 a	0.81 ± 0.08 a
	TSM5	14.07 ± 3.06 b	7.94 ± 1.65 b	3.99 ± 0.90 b	599.33 ± 144.11 a	3386.00 ± 320.63 a	0.82 ± 0.04 a

Note: T1–T5 represent different cultivation substrates. TS = Bacillus subtilis inoculation; TM = Priestia megaterium inoculation; TSM = Co-inoculation of both bacteria; CK: Control. Different lowercase letters in the same column indicate significant differences among treatments (p < 0.05), according to Tukey’s (HSD) test.

, inoculation with PGPR significantly enhanced the content of photosynthetic pigments in rose leaves. The single-strain treatment of B. subtilis (TS1, TS2, TS3) increased chlorophyll a content by 41.41%, 92.15%, and 60.76%, respectively, compared to their corresponding controls (CK1, CK2, CK3) (p ≤ 0.05). Similarly, the single-strain treatment of P. megaterium (TM1, TM2, TM3) also led to significant increases in chlorophyll a content by 34.48%, 39.68%, and 56.44% over CK1, CK2, and CK3, respectively (p ≤ 0.05). Notably, the combined bacterial treatment (TSM2, TSM3) exhibited the most pronounced effects, enhancing chlorophyll a content by 153.66% and 74.31% compared to CK2 and CK3 (p ≤ 0.05). However, there was no significant difference in chlorophyll a content between the TS4 treatment and CK4 in substrate T4.

A similar promoting trend was observed in chlorophyll b content. All single-strain treatments (TS1–TS3 and TM1–TM3) significantly elevated chlorophyll b levels, with TS2 and TS3 showing the most substantial increases of 82.93% and 63.77% relative to CK2 and CK3 (p ≤ 0.05). Relative to the controls CK2 and CK3, the TSM2 and TSM3 resulted in a significant increase in chlorophyll b content, showing increases of 154.49% and 64.57%, respectively (p ≤ 0.05). However, the TSM1 and TSM5 treatments showed a trend of decreased chlorophyll b content in their corresponding substrates.

With respect to carotenoid content, as shown in Table 4, the single-strain treatments (TS1, TM1, TS2, TS3, TM3) significantly increased the carotenoid content in rose leaves compared to their respective controls (p ≤ 0.05). Among these, the TS2 and TM3 treatments exhibited the most pronounced effects, with increases of 80.89% and 55.72% relative to the control groups CK2 and CK3, respectively. The TSM2 and TSM3 also significantly raised carotenoid levels by 144.71% and 72.51% over CK2 and CK3, respectively (p ≤ 0.05).

In summary, the response of rose photosynthetic pigments to PGPR inoculation varied considerably depending on the bacterial and substrate combination. the combined bacterial treatment consistently demonstrated superior performance in promoting the accumulation of chlorophyll a, chlorophyll b, and carotenoids compared to single-strain treatment.

3.3.2. Chlorophyll Fluorescence

As shown in Table 4, the initial fluorescence (Fo) was significantly increased (p ≤ 0.05) by single-strain treatments (TS2, TS3, TM3) as well as by the combined bacterial treatments (TSM1, TSM2, TSM3). In contrast, the maximum fluorescence (Fm) was significantly enhanced (p ≤ 0.05) only in the TS1 and TSM1 treatments, with no significant changes observed in the other treatments. Notably, in treatment substrate T1, T2 and T4 both single-strain treatments (TS and Tm) increased the maximum quantum efficiency of PSII (Fv/Fm), although these increases did not reach statistical significance. This lack may be attributed to the weak synergistic effects between the bacterial (B. subtilis and P. megaterium) and the T1, T2, and T4 cultivation substrates. Specifically, the T4 substrate, which was formulated with rich organic matter, inherently possessed high nutrient levels. This high background nutrient content likely diminished the marginal benefits provided by the PGPR through mechanisms such as phosphorus solubilization and potassium release. Overall, compared to single-strain treatments, the combined bacterial treatment exhibited a relatively minor effect on enhancing the Fv/Fm value.

3.3.3. Soluble Sugar and Soluble Protein

As demonstrated in Figure 4a, inoculation with PGPR significantly enhanced the soluble protein content in rose leaves. the single-strain treatment of B. subtilis (TS3, TS4) resulted in an extremely significant increase of 183.41% and 181.09%, respectively, compared to their corresponding controls (CK3 and CK4) (p ≤ 0.01). Similarly, the single-strain treatments of P. megaterium (TM1, TM2, TM3, and TM4) also led to extremely significant elevations in soluble protein content. Among these, the TM4 treatment exhibited the most pronounced effect, showing a 202.39% increase over CK4 (p ≤ 0.01). The combined bacterial treatment (TSM3, TSM4) demonstrated the highest efficacy, elevating soluble protein content by 212.68% and 302.82% compared to CK3 and CK4, respectively (p ≤ 0.01).

As shown in Figure 4b, a significant promoting effect was also observed on soluble sugar content. the single-strain treatment of B. subtilis (TS1, TS3, TS4, TS5) significantly increased soluble sugar levels. Specifically, TS3 induced an extremely significant increase of 54.63% relative to CK3 (p ≤ 0.01), while TS1, TS4, and TS5 led to significant increases of 13.61%, 14.82%, and 20.26% over CK1, CK4, and CK5, respectively (p ≤ 0.05). The single-strain treatment of P. megaterium (TM1, TM3, TM4) also significantly enhanced soluble sugar accumulation, with TM3 showing the most substantial effect, an 83.39% increase compared to CK3 (p ≤ 0.01). Furthermore, the combined bacterial treatment (TSM) significantly promoted soluble sugar content across all five cultivation substrates. Treatments TSM1, TSM2, TSM3, and TSM5 resulted in extremely significant increases of 27.99%, 54.56%, 51.48%, and 43.83% compared to their respective controls (CK1, CK2, CK3, and CK5) (p ≤ 0.01). In summary, the application of different bacterial suspensions, particularly in the T3 and T4 cultivation substrates, significantly promoted the accumulation of both soluble proteins and soluble sugars in rose plants.

3.3.4. Antioxidant Enzyme Activity

The influence of different cultivation substrate and plant growth-promoting rhizobacteria (PGPR) combination treatments on superoxide dismutase (SOD) enzyme activity content was not pronounced, as shown in Figure 4c. Among all treatments, only the combined bacterial treatment (TSM5) reached a significant level, showing a 92.37% increase in SOD enzyme activity compared to the control CK5 (p ≤ 0.05). In contrast, the increases observed in other treatments did not reach statistical significance, and some treatments even exhibited a decreasing trend in SOD enzyme activity compared to their respective controls.

3.3.5. Root Activity

As shown in Figure 4d, all treatments significantly enhanced the root activity of roses across the various cultivation substrates (p ≤ 0.01). Among the single-strain treatments of B. subtilis (TS1, TS2, TS5) exhibited the most pronounced effects, increasing root activity by 88.14%, 103.11%, and 117.73% compared to their corresponding controls CK1, CK2, and CK5, respectively (p ≤ 0.01). The single-strain treatment of P. megaterium (TM3, TM5) also led to extremely significant increases in root activity, by 41.99% and 44.07% relative to CK3 and CK5 (p ≤ 0.01). Regarding the combined bacterial treatment, the promoting effects varied considerably among different treatments. Notably, the TSM2 and TSM5 treatments showed substantial enhancements, increasing root activity by 102.95% and 87.10% compared to CK2 and CK5, respectively (p ≤ 0.01).

3.4. Soil Physicochemical Analysis

3.4.1. Soil Organic Matter

As shown in Figure 5a, the single-strain treatment (TM2, TS4, TM4, TS5) significantly increased the soil organic matter content compared to their corresponding controls (CK2, CK4, CK5) (p ≤ 0.05). The increases were 32.79%, 13.61%, 13.11%, and 17.88%. Overall, within the T4 cultivation substrate, both the single-strain treatment of B. subtilis (TS) and P. megaterium (TM) significantly enhanced the soil organic matter content.

3.4.2. Soil Ammonium and Nitrate Nitrogen Content

As shown in Figure 5b, the combined bacterial treatment (TSM2, TSM3 and TSM4) affected soil nitrate nitrogen content to varying degrees. Among them, TSM3 and TSM4 significantly increased nitrate nitrogen content by 76.21%and 56.86%, respectively, compared to their controls in the T3 and T4 substrates (p ≤ 0.05). In contrast, the TSM2 treatment led to a significant decrease in nitrate nitrogen content relative to CK2 (p ≤ 0.05), suggesting that the combination of PGPR and the T2 substrate may have exerted a negative effect on nitrate accumulation. No significant changes were observed between other treatments and their respective controls. Overall, the combined bacterial treatment (TSM) demonstrated a more pronounced effect on enhancing soil nitrate nitrogen content compared to the single-strain treatment (TS, TM).

According to Figure 5c, the single-strain treatment of P. megaterium (TM1, TM2) along with the single-strain treatment of B. subtilis (TS3, TS4) significantly increased ammonium nitrogen content by 67.66%, 96.68%, 151.39%, and 109.94%, respectively, compared to their controls CK1, CK2, CK3, and CK4 (p ≤ 0.05). The combined bacterial treatment (TSM1, TSM3, TSM4) also significantly enhanced ammonium nitrogen accumulation by 79.79%, 168.84% and 86.22% relative to CK1, CK3, and CK4, respectively (p ≤ 0.05). In summary, the combinations of PGPR with the T3 and T4 substrates yielded the most effective promotion of soil ammonium nitrogen content. However, no significant increase in ammonium nitrogen content was observed for any bacterial treatment combined with the T5 substrate.

3.4.3. Soil Available Phosphorus and Available Potassium Content

As shown in Figure 5d, the application of PGPR significantly influenced the content of soil available phosphorus, with the effects varying considerably depending on the specific bacterial strain and cultivation substrate combination. The single-strain treatment of P. megaterium (TM2, TM4, TM5) increased the available phosphorus content by 10.98%, 60.23%, and 18.22%, respectively, compared to their corresponding controls (CK2, CK4, CK5) (p ≤ 0.05). Notably, the single-strain treatment of B. subtilis (TS4) also led to a significant increase of 45.26% over CK4 (p ≤ 0.05). In contrast, the TS2 treatment resulted in a significant decrease in available phosphorus. This reduction might be attributed to the promoted plant growth and consequent enhanced phosphorus uptake by the roses, effectively depleting this nutrient from the soil pool. The combined bacterial treatment (TSM4 and TSM5) also effectively enhanced the phosphorus availability, showing significant increases of 19.40% and 35.40% compared to the controls in the T4 and T5 substrates, respectively (p ≤ 0.05). These results collectively indicate that the combinations of bacterial strains with the T4 and T5 substrates exhibited a synergistic effect on improving phosphorus availability.

As shown in Figure 5e, all substrate and PGPR treatments increased soil fast available potassium content, but not significantly. the single-strain treatment of B. subtilis increased potassium content across all substrates, showing better synergy with the substrates than either the single-strain treatment of P. megaterium and the combined bacterial treatment. This was likely because the substrates’ inherently high available potassium background level diminished the marginal gain from PGPR solubilization, making statistical significance difficult to achieve.

3.4.4. Soil NR

As shown in Figure 6a, treatment TS1 increased NR activity by 56.5% over CK1 (p ≤ 0.05), while TS2, TS3, TS4, and TS5 led to highly significant increases of 78.1%, 48.3%, 261.7%, and 164.5% relative to CK2, CK3, CK4, CK5, respectively (p ≤ 0.01). Among these, TS4 and TS5 exhibited the most pronounced enhancement. In contrast, the single-strain treatment of P. megaterium did not result in a statistically significant increase in NR activity. The combined bacterial treatment also effectively promoted NR activity. Treatments TSM2, TSM3, TSM4, and TSM5 increased NR activity by 86.9%, 48.6%, 166.2%, and 122.7%, respectively, compared to their corresponding controls (CK2, CK3, CK4, CK5) (p ≤ 0.01). Overall, the T4 substrate demonstrated the strongest synergistic effect with the bacterial treatments. Notably, the single-strain treatment of B. subtilis outperformed both the single-strain treatment of P. megaterium and the combined bacterial treatment in enhancing soil NR activity across the majority of substrate conditions.

3.4.5. Soil NiR

As shown in Figure 6b, the TS1 and TS2 treatments resulted in an extremely significant increase in NiR content by 146.5% and 120.9%, respectively, compared to their corresponding controls CK1 and CK2 (p ≤ 0.01). The TS4 treatment also led to a significant increase of 47.49% relative to CK4 (p ≤ 0.05). In contrast, the combined bacterial treatment TSM1 and TM3 caused an extremely significant decrease in NiR content compared to CK1 and CK3 (p ≤ 0.01). In summary, these results demonstrate that B. subtilis significantly enhanced NiR activity, whereas P. megaterium showed poor adaptability across different substrates, even exhibiting inhibitory effects in certain substrate conditions. Although the combined bacterial treatment displayed moderate stability, its overall effectiveness in promoting NiR content was inferior to that of the single-strain treatment of Bacillus subtilis.

3.5. Comprehensive Evaluation Analysis

3.5.1. Correlation Analysis

This study revealed the close relationship between the growth, physiology, photosynthesis, and soil physicochemical indicators of roses through Pearson correlation analysis, as shown in Figure 7. The results showed significant correlations among multiple key indicators: plant height was significantly positively correlated with stem dry weight, soluble protein content, and soil nitrate reductase activity; stem diameter was significantly positively correlated with Fm and soluble protein content. Extremely significant positive correlations were observed among biomass components, indicating synergistic growth across different plant parts. Photosynthetic pigments (chlorophyll a, chlorophyll b, and carotenoids) were highly positively correlated, reflecting stable coordinated variation within the photosynthetic system. Notably, soluble protein content was positively correlated with soil organic matter and ammonium nitrogen content, while root activity was positively correlated with soil nitrate reductase activity and chlorophyll fluorescence parameters. These correlations demonstrate a coordinated variation trend among plant phenotype, physiological function, soil nutrient status, and microbial activity.

3.5.2. Principal Component Analysis

To comprehensively evaluate the effects of different treatments on the multidimensional indicators of roses and to reduce dimensionality in identifying the main variation patterns among treatments, principal component analysis (PCA) was performed on all 24 indicators. The eigenvalues, variance contribution rates, and cumulative contribution rates of each principal component are presented in Table 5 and Supplementary Materials Table S1. Based on the commonly used criteria of eigenvalues greater than 1 and a cumulative variance contribution rate exceeding 80%, the first seven principal components were extracted as key integrated dimensions for evaluation. These components collectively accounted for 85.94% of the total variance, effectively capturing the majority of the variation in the original dataset with minimal loss of information.

A trait is considered significantly correlated with a principal component only when the absolute value of its factor loading exceeds 0.4. A negative loading coefficient indicates a negative correlation between the trait and the corresponding principal component. The trait with the largest absolute correlation coefficient greater than 0.4 corresponds to the respective principal component. As shown in

Table 6. Eigenvalues and contribution rates of principal components.

Principal Component	Principal Component 1	Principal Component 2	Principal Component 3	Principal Component 4	Principal Component 5	Principal Component 6	Principal Component 7
Eigenvalue	5.98	4.48	3.08	2.59	1.84	1.68	1.34
Contribution Rate	25.93%	17.94%	12.31%	10.37%	7.34%	6.71%	5.34%
Cumulative Contribution Rate	25.93%	41.87%	54.17%	64.54%	71.88%	78.60%	85.94%

, the eigenvalue of the first principal component (PC1) was λ1 = 5.98, with a contribution rate of 25.93%. It mainly included chlorophyll a, chlorophyll b, and carotenoids, reflecting the plant’s photosynthetic response characteristics. The eigenvalue of the second principal component (PC2) was λ2 = 4.48, with a contribution rate of 17.94%. It mainly included fresh root weight, dry root weight, fresh stem weight, and dry stem weight, reflecting plant morphological and biomass characteristics. The eigenvalue of the third principal component (PC3) was λ3 = 3.08, with a contribution rate of 12.31%. It primarily included soil ammonium nitrogen, available potassium, and root activity, reflecting the physicochemical properties of the cultivation substrates under different treatments. The eigenvalue of the fourth principal component (PC4) was λ4 = 2.60, with a contribution rate of 10.37%. It mainly included root length, nitrate nitrogen, and available phosphorus, reflecting the characteristics of nutrient uptake by plants. The eigenvalue of the fifth principal component (PC5) was λ5 = 1.84, with a contribution rate of 7.34%. It primarily included stem diameter and plant height, soluble sugar and soluble protein, and soil organic matter, reflecting the overall physiological growth status of the plant. The eigenvalue of the sixth principal component (PC6) was λ6 = 1.68, with a contribution rate of 6.71%. It mainly included Fo and Fv/Fm, reflecting the status of chlorophyll fluorescence in plants. The eigenvalue of the seventh principal component (PC7) was λ7 = 1.34, with a contribution rate of 5.34%. It mainly included nitrate reductase (NR) activity and root vitality, reflecting the enzyme activity status of the cultivation substrate.

3.5.3. Membership Function Analysis

To achieve a comprehensive ranking among different treatment combinations, the normalized scores of the seven principal components were evaluated using the membership function method. For each treatment, the membership degree (μ) and comprehensive score (D value) were calculated to assess the overall advantage in promoting rose growth. Based on the variance contribution rates of the first seven principal components, the corresponding weight coefficients were assigned as follows: 0.285, 0.214, 0.147, 0.124, 0.087, 0.080, and 0.064. The comprehensive analysis showed that TM1, TSM2, TS3, TS4, TSM3, and TSM5 were the top six treatment combinations in overall performance (details in Supplementary Materials Table S2), indicating their significant advantages in promoting plant growth, enhancing environmental adaptability, or optimizing the rhizosphere. Furthermore, the combinations of the optimized substrate T4 with specific PGPR, represented by treatments TS4 and the TSM series, performed excellently across multiple experimental groups. These results confirm the significant synergistic effect of T4 substrate improvement combined with specific microbial inoculation in promoting rose growth and optimizing the rhizosphere environment, establishing this as the optimal substrate formulation.

3.6. Rhizosphere Microbial Community Structure Analysis

Based on our previous findings, the combined application of the T4 group cultivation substrate with bacterial treatment demonstrated the most significant promoting effect on rose growth. To further investigate the impact of PGPR on the microbial community in the rose rhizosphere and to elucidate how changes in this community regulate root growth, soil sampling was conducted from the treatment group.

3.6.1. Dimensionality Reduction Analysis

To assess the impact of B. subtilis on the rhizosphere microbial community of roses, high-throughput 16S rRNA sequencing was conducted, yielding 573,925 high-quality reads. After quality control, sequences with ≥97% similarity were retained for analysis. Principal coordinate analysis (PCoA) at the genus level (Figure 8a) revealed distinct separation among treatment groups, indicating that B. subtilis and P. megaterium altered the composition of the rhizosphere bacterial community under substrate cultivation. Further analysis revealed that the first principal component (PC1) and second principal component (PC2) accounted for 22.71% and 18.28% of the total variation, respectively, with the cumulative contribution of both principal components reaching 40.99%. Notably, the SM group clustered separately from the Bs and Pm groups, indicating that the SM exerted a more distinct effect on community structure compared to single-strain inoculants.

As shown in Figure 8b, at the OTU level, the four treatment groups contained 4490, 4646, 4522, and 4557 OTUs, respectively. Among all treatments, the four groups shared 2644 OTUs, the number of unique OTUs in each group was 283 for CK, with 250 unique OTUs in the Bs group, 307 unique OTUs in the Pm group, and 417 unique OTUs in the SM group. The higher number of unique OTUs in the SM group suggests that the composite inoculant was associated with a trend of increased community richness and harbored a larger set of treatment-specific microbial taxa. This observation aligns with the distinct clustering of the SM group in the PCoA plot, indicating that the composite inoculant drove greater structural differentiation in the rhizosphere bacterial community compared to the single-strain inoculants.

3.6.2. Rhizosphere Microbial Diversity

Bacterial alpha diversity (Shannon index) and richness (Chao index) were calculated to evaluate the effects of Bacillus treatments on rhizosphere microbial diversity under substrate cultivation (Figure 9). Compared to CK, Bs, Pm, and SM treatments showed no significant changes in microbial diversity (p > 0.05). However, the SM led to a relative decrease of 2.17% (p = 0.058) in the Shannon index and 5.37% (p = 0.118) in the Chao index, compared to the CK. In contrast, no statistically significant difference in the Chao index was observed between the Bs and CK groups, despite a numerical increase of 5.88% in the Bs group (p = 0.091). The Pm treatment did not result in significant differences in alpha diversity indices compared to the CK group. Regarding beta diversity, the Bs treatment did not significantly differ from the CK group. However, the Pm and SM treatments significantly increased beta diversity, exerting stronger regulatory effects on the structure of the rhizosphere microbial community. To further explore whether the absence of significant differences in alpha diversity masked functional variations, we performed functional prediction using PICRUSt2.

3.6.3. Root Microbial Community Structure Characteristics

Rhizosphere growth-promoting bacteria significantly regulated the soil microbial community structure in rose rhizosphere under optimal substrate cultivation. Species interaction network analysis (Figure 10a–c) revealed that the topological structure of interaction networks among key species Acidibacter ferrireducens and Chryseolinea serpens underwent directional changes under different treatments, reconstructing microbial synergistic and competitive relationships and enhancing community stability. Family-level species composition (Figure 10d) showed that Reyranellaceae served as the niversally present and treatment-sensitive key bacterial family across all treatments, while Bs, Pm, SM directionally adjusted the abundance of functional taxa including Rhodospirillaceae and Burkholderiaceae, thereby optimizing community structure. Analysis of key species abundance differences (Figure 10e) demonstrated that the regulatory effects of different growth-promoting bacteria treatments varied: Fulvivirgaceae exhibited the highest abundance in the Pm group, and Reyranellaceae abundance was significantly elevated in the SM group. The SM group exerted more specific regulation on key species, consistent with the PCoA result that the SM group’s community structure was distinct from single-strain inoculant groups. In conclusion, rhizosphere growth-promoting bacteria significantly altered the rose rhizosphere microbial community structure by reconstructing interaction networks, reshaping community composition, and regulating key species abundance. The regulatory effect of the SM group differed from Bs and Pm, providing a foundation for rhizosphere microecological regulation.

3.6.4. LEfSe Analysis and PICRUSt2 Functional Prediction

LEfSe analysis (LDA score threshold ≥ 2.0, log₁₀) identified a total of 195 differential microbial taxa across the CK, Bs, Pm, and SM (details in Supplementary Materials Figure S1). The SM group displayed the most marked alterations in microbial taxa, with 67 enriched biomarkers dominated by taxa affiliated with Bacteroidota (Betaproteobacteria, Flavobacteriia), Comamonadaceae, Reyranellaceae, as well as the genera Devosia and Reyranella. The Pm group harbored 50 biomarkers, with core taxa mainly assigned to Deltaproteobacteria (Desulfuromonadales, Geobacter) and Cytophagaceae. Only 32 biomarkers were detected in the Bs group, primarily belonging to Chloroflexota (Anaerolineaceae). The CK group featured 46 biomarkers dominated by Chloroflexota (Anaerolineae) and Vicinamibacteria.

Consistent with these shifts in microbial community structure, PICRUSt2 functional prediction results (Figure 11) revealed that, relative to the CK group, the Bs group enriched the abundance of poorly characterized functional categories; the Pm group exhibited a slight increasing trend in functions related to cellular processes and signaling; and the SM group likewise exhibited a slight increase in poorly characterized functional categories, while core metabolic functions remained stable across all treatments. The coordinated variations between community structure and functional potential indicate that the regulatory effects of inoculated growth-promoting bacteria on rhizosphere microbial functions may be ascribed to the differential enrichment of specific microbial taxa, and the structural and functional shifts induced by the SM and Pm treatments were both more pronounced than those induced by the Bs treatment.

4. Discussion

4.1. Combined Effects of PGPR and Substrate Formulations on Rose Growth and Soil Properties

Bacillus species, as important plant growth-promoting rhizobacteria (PGPR), can enhance plant growth and photosynthesis through multiple mechanisms, as demonstrated in crops such as tomato [32,33,34]. However, the combined effects of PGPR with different cultivation substrate formulations remain unclear. In this study, Bacillus subtilis and Priestia megaterium were applied in combination with various substrate formulations to roses. The results revealed significant differences in growth and physiological responses: certain treatments markedly improved plant height, biomass, chlorophyll content, photochemical efficiency, and soil fertility, consistent with previous findings. Notably, some treatments exhibited negative effects, potentially related to microbial transformation processes. For instance, the TSM2 treatment led to a marked decrease in soil nitrate nitrogen content. The decrease in nitrate nitrogen content under the TSM2 treatment may be attributed to enhanced microbial immobilization of inorganic nitrogen induced by specific substrate–strain interactions. This is consistent with previous findings that the addition of organic materials can significantly promote microbial nitrate immobilization in agricultural soils, thereby reducing soil nitrate accumulation [35]. These findings indicate complex interactions between substrates and bacterial strains, where specific combinations may lead to either synergistic or antagonistic effects.

The physicochemical properties of the cultivation substrate directly regulate plant nutrient uptake and root development [36]. Previous studies have demonstrated that PGPR inoculation can significantly improve soil properties, such as increasing organic matter and available phosphorus content, as well as enhancing soil enzyme activities [37,38,39]. This study found that the combined application of specifically formulated substrates with PGPR, including B. subtilis and P. megaterium, effectively optimized the physicochemical properties of the substrates. This was evidenced by a significant increase in the content of key nutrients—such as organic matter, ammonium nitrogen, nitrate nitrogen, available phosphorus, and available potassium—along with enhanced soil enzyme activities. These results align with existing research, further confirming the potential of PGPR in improving soil fertility.

4.2. Effects of Rhizosphere Growth-Promoting Bacteria on the Soil Microbial Community Structure

High-throughput 16S rRNA sequencing revealed that Bacillus strains (Bs, Pm, and SM) significantly reshaped the rose rhizosphere microbiome. PCoA showed clear separation between treatments, indicating distinct community structures, with the composite SM treatment exerting the strongest effect—consistent with reports of PGPR-mediated microbiome modulation in other crops [40]. While α-diversity indices (Shannon, Chao1) did not change significantly, a slight decline under SM suggested a shift toward dominance by specific taxa.

At the genus level, beneficial groups such as Acidibacter, Chryseotalea, Saccharibacteria, Reyranellaceae, and Flavobacterium increased across all treatments. Their known functions in nitrogen fixation, organic matter decomposition, and nutrient cycling likely contributed to plant growth promotion and stress resilience [41,42,43]. Notably, Saccharibacteria and Undibacter were particularly enriched under SM, indicating synergistic support for potentially beneficial bacteria. Conversely, taxa like Aggregatilinea, Gp4, and Gp6 declined, possibly due to competitive exclusion or antagonism from the inoculated Bacillus strains. Together, these results demonstrate the targeted remodeling of the rhizosphere micro-ecosystem by introduced Bacillus consortia.

Although alpha diversity of the rhizosphere bacterial community did not differ significantly among treatments, previous studies have shown that similar microbial diversity levels do not necessarily imply functional equivalence. For instance, Abis et al. [44] demonstrated that reduced microbial diversity increased VOC emissions but decreased VOC diversity, indicating that functional changes can occur without shifts in diversity metrics. Similarly, Young et al. [45] reported that peat and bark-based substrates supported comparable microbial diversity but exhibited distinct volatile organic compound profiles.

Finally, PICRUSt2-based functional prediction analysis revealed that composite treatments, particularly Pm and SM, enriched pathways related to cell motility and signal transduction mechanisms. These pathways suggested the genetic potential for quorum sensing (QS) communication system within the rhizosphere microbiomes [19]. The predicted enhancement in signal transduction may imply the capacity for synthesis and perception of QS signaling molecules [46], thereby coordinating collective behaviors such as catabolic enzyme expression. Concurrently, increased cell motility facilitates microbial chemotaxis and the formation of QS-regulated biofilms, establishing a physical foundation for cooperation. Furthermore, cooperative signaling alongside potential quorum-quenching activities may jointly optimize the structure of the rhizosphere community, suppressing pathogens while promoting beneficial interactions [47]. Consequently, the superior plant growth-promotion effects observed with Pm and SM treatments may partly originate from their role in shaping a more collaborative and resilient root-associated microbial community potentially through QS-mediated mechanisms. Therefore, the effects of B. subtilis and P. megaterium may be partly attributed on the rose rhizosphere microbiome may be partly attributed to QS signaling molecules [48]. These signals may suppress certain indigenous microorganisms while simultaneously recruiting and cooperating with beneficial taxa, such as Acidibacter, Chryseotalea and Reyranellaceae, potentially forming a cooperative alliance. The superior effect of the combined bacterial consortium (SM) stems from the synergistic interaction between the signaling networks of the two strains, thereby enabling the more effective construction of a stable and efficient rhizosphere micro-ecology. However, it should be noted that these interpretations are based on functional gene predictions from 16S rRNA sequencing data, and direct evidence of QS signaling activity is needed to confirm these hypotheses.

4.3. Rhizosphere Regulation and Agronomic Implications Under Substrate-Microbe Synergy

This study indicates that the combination of specific cultivation substrates with inoculated Bacillus strains may synergistically influence plant growth, soil properties, and the microbial community in the rose rhizosphere. This supports the view that the substrate-microbe system can function as an integrated entity exhibiting ecological multifunctionality, potentially coordinating multi-dimensional interactions among plants, soil, and microbes through the combination of physicochemical frameworks and bioactive factors [49].

Specifically, the observed growth-promotion effects may result from the combined action of substrate improvement and microbial activity. On one hand, the substrate provides root support and basic nutrients; on the other hand, the inoculated Bacillus strains may contribute to plant growth through both direct and indirect pathways. Concurrently, the enrichment of beneficial bacterial groups and the predicted upregulation of quorum sensing (QS)-related functions suggest that a micro-ecology conducive to biocontrol may develop within a suitable substrate environment. The underlying mechanisms could involve the production of antagonistic substances, niche competition, and the regulatory effect of QS signaling on the native microbiota [50].

Within the context of sustainable agriculture, the positive effects demonstrated by the combined application of the SM group and specific substrate formulations indicate that comprehensive management strategies emphasizing the compatibility between substrate and bacterial strains could help enhance the stability and functionality of the rhizosphere microecology [51]. Such approaches focus on harnessing and guiding the synergistic interplay between the substrate environment and microbial processes to promote plant health and reduce reliance on chemical inputs [52,53].

4.4. Research Limitations and Future Prospects

Although this study preliminarily revealed that the combination of T4 cultivation substrate with B. subtilis and P. megaterium effectively promotes rose growth by increasing plant height, stem diameter, root activity, photosynthetic pigment content, and antioxidant capacity and significantly improves soil fertility by enhancing nutrient content and enzyme activity, its limitations must be noted. The main constraint lies in the sample size (n = 3). Given the inherent variability in plant growth, although such a sample size is common in controlled experiments, its statistical power may be insufficient to detect subtle yet biologically important differences, thereby limiting the generalizability of the conclusions to some extent. While the functional predictions based on PICRUSt2 provide valuable insights into the microbial community’s potential functions, these results should be treated with caution. As PICRUSt2 is based on the inference from 16S rRNA gene sequences, the predicted functions do not represent experimentally validated data. Future studies should aim to experimentally validate the predicted functions to enhance the accuracy of functional inferences.

Based on this, future research could proceed in the following directions: increasing biological replicates to validate and enhance the reliability of the statistical findings; verifying the broad applicability of this combined technology under different environmental conditions, across more plant varieties, or in field trials; and conducting more in-depth molecular or ecological mechanism studies to fully elucidate the underlying mechanisms of growth promotion and soil improvement.

5. Conclusions

The combined application of plant growth-promoting rhizobacteria with different cultivation substrate formulations not only enhanced rose plant height, stem diameter, root activity, photosynthetic pigment content, and antioxidant capacity but also significantly improved soil nutrient content and enzyme activities. Among all treatments, the T4 cultivation substrate (composed of humus, perlite, vermiculite, coconut coir, peat, and biochar in a ratio of 5:1.5:1:1:1:0.5 combined with different formulations of Bacillus subtilis and Priestia megaterium exhibited the most significant promoting effects on R. hybrida ‘Ruby’ growth physiology and optimization of soil physicochemical properties. High-throughput 16S rRNA gene sequencing analysis of the T4 substrate combined with B. subtilis and P. megaterium treatments revealed that these two PGPR strains altered the rhizobacterial community structure, increased the relative abundance of beneficial bacterial groups, and optimized metabolic pathways related to signal transduction and cellular motility, thereby synergistically improving rose growth physiology and the rhizosphere microecological environment. This integrated PGPR–substrate strategy offers a green, sustainable solution for ornamental horticulture, reducing chemical fertilizer and pesticide reliance while promoting organic substrate utilization, in line with low-carbon eco-friendly cultivation goals. The optimized combination also holds promise for large-scale rose production and provides a reference for tailored microbial–substrate synergistic systems in other ornamental crops.