Next Article in Journal
MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism
Previous Article in Journal
Biochar Makes Soil Organic Carbon More Labile, but Its Carbon Sequestration Potential Remains Large in an Alternate Wetting and Drying Paddy Ecosystem
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 7
10.3390/agronomy15071548
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exogenous Sugar Alcohols Enhance Peach Seedling Growth via Modulation of Rhizosphere Bacterial Communities

by
Huili Yu
1,†,
Jiaqi Li
2,†,
Wei Shao
3,
Huimin Liu
1,
Ruiquan Dong
1,4,
Guoyi Xu
1,* and
Peng Si
1,2,*
1
Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China
2
Zhongyuan Research Center, Chinese Academy of Agricultural Sciences, Xinxiang 453514, China
3
Key Laboratory of Agriculture for Germplasm Resources Conservation and Utilization of Cassava, Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
4
College of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang 453003, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(7), 1548; https://doi.org/10.3390/agronomy15071548
Submission received: 3 May 2025 / Revised: 16 May 2025 / Accepted: 27 May 2025 / Published: 25 June 2025
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
Browse Figures
Versions Notes

Abstract

Excessive fertilizer input and low output are currently problems for peach production in China. Sugar alcohols such as sorbitol and mannitol represent promising eco-friendly fertilization strategies to improve fruit quality and optimize nutrient management. Our research explored the effect of sorbitol and mannitol on the rhizosphere environment and peach growth from the rhizosphere micro-ecology perspective. Potted peach seedlings were used as materials. Without adding or adding different sorbitol and mannitol concentration gradients (100, 200, 400) combined with potassium dihydrogen phosphate (KH2PO4), the physicochemical properties of rhizosphere soil, leaf nutrition, photosynthetic and growth index were determined, and the rhizosphere bacterial community was analyzed via Illumina Miseq high-throughput sequencing. Both sorbitol and mannitol altered the rhizosphere environment, effectively improved leaf photosynthesis, and promoted peach seedling growth; particularly, M100 had optimal affection. Sorbitol and mannitol altered the bacterial structure and reduced bacterial diversity, which observably correlated with soil organic matter and available potassium. For the rhizosphere bacterial composition, sorbitol and mannitol increased specific bacterial OTUs and induced changes in bacterial composition, among which chemoheterotrophic and nitrogen-transforming bacteria increased with the addition of sorbitol and mannitol. Association network analysis and a structural equation model showed that S100 and M100 mainly enriched Vicinamibacteraceae to regulate peach seedling growth. Overall, low-concentration sorbitol and mannitol showed the best effect in peach seedling growth through regulating the rhizosphere environment.

1. Introduction

The peach (Prunus persica L.) industry in China constitutes the primary global production region, contributing around one-third of global production, and it plays a crucial role in advancing agricultural development and rural revitalization [1]. However, inadequate orchard management has led to a decline in peach quality [2]. Excessive chemical pesticides and heavy reliance on nitrogen fertilizers have severely impacted soil quality, impaired root function, restricted nutrient absorption, and ultimately diminished fruit growth and quality [3,4,5]. Consequently, scientific fertilization strategies, particularly the use of fertilizer synergists (substances applied alongside fertilizers to improve efficiency and nutrient absorption), present promising opportunities to enhance fruit quality and optimize orchard management [5,6,7,8].
Sugar alcohols have potential as new eco-friendly synergistic carriers. Sugar alcohols such as sorbitol and mannitol commonly persist in rosaceae, participating in photosynthesis and carbon metabolism in peach [7,9]. We previously investigated sorbitol and mannitol to enhance soil microbial communities and improve the soil nutrient environment [8]. In the case of plants, sorbitol in apple regulated sugar transport carriers, thereby promoting the growth and development of pollen tubes [10]; sorbitol and mannitol were used as osmoregulatory substances to alter physio-biochemical and metabolic responses in Nigella sativa [11]. In addition, sorbitol and mannitol promoted nutrient absorption and improved fruit quality in peach [8]. Therefore, sorbitol and mannitol have broad application prospects in the development of orchard soil fertilizer synergists and high-quality fruit production technology. Potassium dihydrogen phosphate (KH2PO4) is a widely used and effective water-soluble compound fertilizer. KH2PO4 has a crucial role, preventing the premature aging of fruit trees, enhancing nutritional balance, boosting fruit yield, and improving fruit quality [12,13]. However, it remains uncertain whether sorbitol and mannitol exhibit synergistic effects with KH2PO4 and can function as novel fertilizer carriers for peach trees.
The rhizosphere serves as a medium for material exchange between roots and soil. The rhizosphere environment directly impacts the availability and utilization of water and fertilizers, as well as the growth and development of fruit trees [14,15]. Furthermore, the rhizosphere is characterized by a high concentration of active microorganisms, among which bacteria are particularly sensitive to environmental fluctuations [16,17]. Rhizosphere microorganisms exhibit diverse multifunctional roles [18]. They directly enhance plant growth and seed germination by secreting phytohormones, organic acids, fixing atmospheric nitrogen, and mobilizing soil nutrients [19]. Additionally, rhizosphere bacteria induce systemic resistance in plants against biotic stresses, such as pathogenic bacteria, through the release of biological regulators, including antibiotics, lysozymes, and volatile compounds like salicylic acid and jasmonic acid [20]. Moreover, these microorganisms contribute to plant tolerance of abiotic stress by stimulating physiological and biochemical signaling pathways. This is achieved via the production of extracellular polymers, ACC deaminase, antifreeze proteins, antioxidant enzymes, and other protective substances [21]. Therefore, rhizosphere bacteria serve as an indicator for assessing soil environmental quality and regulating root nutrition [17,22].
To enhance our understanding of the rhizosphere micro-ecological mechanism by which sorbitol and mannitol influence the growth of fruit tree growth, our research utilized potted peach seedlings as materials and measured rhizosphere nutrients, peach seedling growth, and rhizosphere bacterial community in sorbitol or mannitol combined with KH2PO4. Furthermore, we explored the interactions between rhizosphere bacteria and the peach seedling growth. This research presents an innovative approach to the development of eco-friendly fertilizers utilizing sugar alcohols, while also providing theoretical and technical support for optimizing the root micro-domain environment, enhancing fertilizer efficiency in fruit trees, and improving orchard soil management.

2. Materials and Methods

2.1. Materials and Growth Conditions

Peach (Prunus persica L.) rootstock was sourced from ‘Dongxuemi’ seedings selected at the Xinxiang Eden Garden Seedling Cooperative. Seedlings of approximately 10 cm in height were transplanted from seedling trays into pots (height 9 cm, bottom diameter 8 cm, mouth diameter is 10 cm, each pot with 500 g soil). The cultivation soil was collected from the experimental orchard at the Zhengzhou Fruit Tree Research Institute of the Chinese Academy of Agricultural Sciences (34°42′33″ N, 113°41′58″ E). The soil texture was sandy loam with the following basic physicochemical properties (Table 1): soil organic matter (SOM) 5.32 g/kg, nitrate nitrogen (NO3−-N) 11.36 mg/kg, ammonium nitrogen (NH4+-N) 20.26 mg/kg, available phosphorus (AP) 108.33 mg/kg, available potassium (AK) 537.67 mg/kg, pH 6.7, and electrical conductivity (EC) 320 μs/cm.

2.2. Treatments for the Pot Experiment

Then, 15 days after transplanting, peach seedlings with consistent growth (about 15 cm) were selected as the experimental materials in this research. Different concentrations of sorbitol (S) and mannitol (M) (purity ≥ 98.0%; Beijing Biotopped Science and Technology Co., Ltd., Beijing, China) were used as exogenous sugar alcohols in combination with KH2PO4 (Sinopharm Group Chemical Reagent Co., Ltd., Beijing, China), separately. Therefore, seven treatments were formed: control (KH2PO4, phosphorus content is 200 mg/kg); S100 (mixed with 100 mg/kg sorbitol and KH2PO4); S200 (mixed with 200 mg/kg sorbitol and KH2PO4); S400 (mixed with 400 mg/kg Sorbitol and KH2PO4); M100 (mixed with 100 mg/kg mannitol and KH2PO4); M200 (mixed with 200 mg/kg mannitol and KH2PO4); M400 (mixed with 400 mg/kg mannitol and KH2PO4). In addition, P 200 mg/kg refers to the phosphorus content in KH2PO4; 100, 200 and 400 mg/kg refer to the carbon content in sugar alcohols. Each treatment had 30 seedings. Samplings were carried out after 35 days (5 weeks) and replicated three times, and 10 seedings were selected for each replicate. During the experiment, the sugar alcohol and KH2PO4 required for each treatment of 30 peach seedlings were fully dissolved in 1.5 L of water, and 50 mL was measured with a graduated cylinder and poured into each pot only once.

2.3. Collection of Rhizosphere Soil Samples

Rhizosphere soil samples were collected using the method outlined in a previous study [23,24]. We removed about 2 cm of topsoil, gently collected roots along with soil, and shook off loose soil. Peach roots (length 6 cm, diameter 2 mm) with attached soil were cut with sterile scissors and placed in centrifuge tubes filled with sterile saline (9 g·L−1 NaCl). These tubes were shaken and rinsed at 200 rpm for 20 min twice. The roots were removed, and the two washing solutions were combined. After centrifuging at 4000× g for 20 min, the precipitated soil was rhizosphere soil, which was divided into two parts. One part was air-dried for analysis of rhizosphere soil characteristics. Another part was stored at −80 °C for determination of rhizosphere bacterial community.

2.4. Rhizosphere Soil Physicochemical Properties

Soil pH value was determined using water extraction potentiometric method with water and soil ratio of 2.5:1 (v/w). Soil EC was measured with an electrical conductivity meter. SOM was determined via the potassium dichromate external-heating method. NH4+-N and NO3−-N were determined using automatic intermittent chemical analyzer (CleverChem 380, Dechem-Tech, Hamburg, Germany). AP was determined via NaHCO3 extraction-molybdenum anti-colorimetric method. AK was determined using NH4OAc extraction-flame photometric method.

2.5. Determination of Peach Seedling Growth Indexes

Root activity (RA, μg·g−1(FW)h−1) was determined using the triphenyl tetrazolium chloride (TTC) method. Root volume (RV, cm3) was determined using drained method. The fresh root weight (FRW, g), leaf weight (FLW, g) and plant weight (PW, g) were weighed with electronic balance. Plant height (PH, cm) was measured using a tape measure. The leaf length and width were measured using vernier calipers to calculate the leaf area (LA, cm2). Peach seedling growth indexes were determined after 35 days (5 weeks after treating) and replicated three times, and 10 seedings were selected for each replicate.

2.6. Determination of Leaf Photosynthetic and Nutrient Index

The relative content of chlorophyll (SPAD) was determined using chlorophyll analyzer (SPAD-502, Minolta, Tokyo, Japan). Intercellular CO2 concentration (Ci), stomatal conductance (Gs), saturated water vapor pressure difference (VPD), net photosynthetic rate (A), and transpiration rate (E) were determined using portable photosynthesis meter (CIRAS-3, PP System, Amesbury, MA, USA). Leaf nitrogen (LN) was determined with automatic intermittent chemical analyzer (CleverChem 380, Dechem-Tech, Hamburg, Germany). Leaf phosphorus (LP) was determined using molybdenum anti-colorimetric method. Leaf potassium (LK) was calculated with flame spectrophotometry.

2.7. DNA Extraction, PCR Amplification and ILLUMINA Miseq Sequencing

Microbial community genomic DNA was extracted from rhizosphere samples using the E.Z.N.A.® soil DNA Kit (Omega, Norcross, GA, USA) according to instructions. The purity and concentration of DNA extract were detected, and the bacterial 16S rRNA gene was amplified with the primer pair of highly variable region V3–V4 (338F: 5′-ACTCCTACGGGAGGCAGCAG-3′; 806R: 5′-GGACTACHVGGGTWTCTAAT-3′). Purified amplicons were subjected to Illumina Miseq PE300 platform (Illumina, San Diego, CA, USA) at Shanghai Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The 16S-rRNA-amplicon-based sequencing data were deposited into the National Center for Biotechnology Information under accession number PRJNA1132496, which will be released on 1 August 2025.

2.8. Statistical Analysis

Statistical analysis was performed with Microsoft Excel 2010 and IBM SPSS 20.0, and one-way analysis of variance (ANOVA) and Duncan’s multiple comparison (α = 0.05) were used to analyze the significant differences between each treatment at a significance level of p ≤ 0.05. The comprehensive evaluation of different treatments was determined by principal component analysis in IBM SPSS 20.0 and fuzzy mathematical membership function method. Alpha diversity and bacterial community composition were calculated using the online Majorbio Cloud Platform (www.majorbio.com, 27 February 2024); the correlation between soil physicochemical properties and diversity index was determined using Pearson method, and the boxplot and heatmap were visualized in Origin 2021. Beta-diversity analysis was performed using non-metric multidimensional scaling (NMDS) analysis, which is measured by calculating the Bray–Curtis distance to measure the differences in species composition between the two samples, emphasizing the changes in community structure. The distance-based redundancy (db-RDA) analysis visualizes the relationship between soil physicochemical properties and bacterial structure. The bacterial differences in abundance and function were examined by STAMP v2.1.3 using Welch’s test and 95% confidence interval. Network analysis was conducted using Gephi 0.9 and Cytoscape 3.9.1.
In path analysis, structural equation model (SEM) is designed to characterize the variables in the path graph and assume causal relationships between these variables [25]. In order to analyze the direct or indirect effects of the rhizosphere environment on the rhizosphere bacterial community and the growth of peach seedlings after the addition of exogenous sugar alcohol, in this study, we assumed that exogenous sugar alcohols promoted the growth of peach seedlings due to the auxiliary regulation of rhizosphere bacteria. Rhizosphere soil organic matter content and sugar alcohol concentration were used as variables to reflect rhizosphere soil properties. Plant photosynthesis and physiological indexes were used to represent the growth factors of peach seedlings in SEM. SEM was constructed using Smart PLS 4.1.1.2, the model parameters were estimated using partial least squares (PLS), and the overall prediction effect of the model was measured by goodness of fit (GOF > 0.36). Statistical test was carried out using Smart PLS software [26].

3. Results

3.1. Rhizosphere Soil Physicochemical Properties and Peach Seedling Growth

Compared with control, pH and AP were not significantly different under S and M. EC was significantly decreased by 19.85% and 13.74% under S100 and M100, while other treatments had no significant effect on EC. SOM significantly increased under S and M, with M200 exhibiting the highest concentration (increased by 12.40%). NH4+-N content significantly increased by 14.61%, 56.72% and 19.04% under S200, M100 and M400 but significantly decreased by 23.91% under S100. NO3−-N content increased significantly by 21.69% under S400 but decreased significantly under other treatments. AK content was significantly increased by 10.73%, 20.58% and 27.77% under S400, M200 and M400, while other treatments had no significant effect on AK content. In conclusion, different concentrations of S and M altered the physicochemical properties of rhizosphere soil to different degrees (Table 2).
The results of leaf nutrition indicated that a low concentration of S (S100) reduced TP and TK content in leaves, and medium and high concentrations of M (M200 and M400) reduced TK (Table S1). The determination of leaf photosynthesis showed that exogenous sugar alcohols enhanced leaf photosynthesis, with S100 exhibiting the most significant effect (Table S1). Compared with control, S significantly increased RA, FRW, RV and PW; moreover, M significantly increased FRW, RV, PH and PW in peach seedlings (Table S2). Comprehensive analysis showed that exogenous sugar alcohols promoted the growth of peach seedlings, among which M100 had the best promotion effect, followed by S100 (Table 3).

3.2. Diversity of Rhizosphere Bacteria

The community richness index (Chao 1) significantly decreased with three different concentrations of S or M compared to control, and M100 had the lowest Chao 1 index (Figure 1A). Except M100, the community diversity index (Shannon) and evenness index (Pielou_e) significantly decreased under S and M compared to control (Figure 1B,C). With the addition of M, although the Shannon and Pielou_e indexes were significantly decreased under M200 and M400 treatments, there was no significant difference under M100 treatments (Figure 1B,C). This indicated that the addition of S decreased rhizosphere bacterial richness, diversity and evenness. However, the addition of M only decreased rhizosphere bacterial richness (p < 0.05, Figure 1A). The richness and diversity of bacterial communities play a pivotal biological role in maintaining functional redundancy and adaptive capacity within microbial systems, while ecologically, they underpin ecosystem stability by mediating nutrient cycling, energy flow, and resilience to environmental perturbations. S exerted a more pronounced influence on bacterial community richness and diversity compared to M, with the S400 application inducing a more substantial reduction in microbial α-diversity indexes (Shannon and Pielou_e).
The Pearson correlation results showed that the Shannon index was negatively correlated with the concentrations (Con) and SOM; the Chao 1 index was also negatively correlated with SOM, and Pielou_e was negatively correlated with Con, AK and SOM. Remarkably, NO3−-N was significantly positively correlated with the Chao 1 index under S and significantly positively correlated with the Shannon and Pielou_e index under M (Figure 1D). This indicated that the reduction in rhizosphere bacterial diversity by S and M was significantly related to Con, SOM, AK and NO3−-N (p < 0.05).
A non-metric multidimensional scaling (NMDS) analysis showed that the OTUs from rhizosphere samples were clearly separated between the control and S groups, except for the non-significant difference distance between S100 and M100 samples (Figure 1E). This indicated that sugar alcohol treatment significantly altered the bacterial community structure. Based on distance-based redundancy analysis (db-RDA), SOM and AK significantly affected the rhizosphere bacterial structure (p < 0.001). Remarkably, NO3−-N was significantly correlated with bacterial diversity. Nevertheless, NH4+-N significantly affected the bacterial structure (p < 0.05) (Figure 1F). Overall, SOM and AK had extremely significant effects on rhizosphere bacterial diversity, among which SOM had wider distribution in this influence (Figure 1D,F).

3.3. Rhizosphere Bacterial Composition

Among the rhizosphere samples at the phylum level, the bacterial phyla (>1%) were dominated by Actinobacteriota (36.56–39.32%), Proteobacteria (21.94–28.83%), Acidobacteria (7.09–12.26%), Chloroflexi (8.58–10.52%), Firmicutes (5.77–7.64%), Bacteroidota (3.06–4.54%), Gemmatimonadota (1.52–2.18%), and Myxococcota (1.34–1.76%) across all 21 samples (Figure S1A and Figure 2A). At the genus level, the dominant bacterial genera (>1%) of all samples were Pseudarthrobacter (11.76–19.35%), Vicinamibacterales (2.19–4.45%), Vicinamibacteraceae (1.90–3.57%), Sphingomonas (2.20–4.17%), Bacillus (2.15–3.59%), JG30-KF-CM45 (1.54–2.38%), Nocardioides (1.37–2.46%), KD4-96 (1.19–2.74%), unclassified_f_Microbacteriaceae (1.18–1.74%) (Figure S1B and Figure 2B). The proportion of peach rhizosphere bacteria species changed with different treatments while not changing the composition of dominant bacteria, and, also, no new phyla or genera appeared. In addition, a Venn diagram was applied to assess the distribution of bacterial OTUs with control and exogenous sugar alcohols (Figure S1C). Regarding the OTU level, the addition of S and M increased the amount of specific OTUs in rhizosphere bacteria, but the degree of increase was not related to the three concentrations (Figure S1C).
Although the dominant phyla and genera of rhizosphere bacteria in all soils were consistent, changes in the relative abundance of the dominant taxa were observed across different treatments (Figure 2). At the phylum level, Proteobacteria, Gemmatimonadota, Myxococcota, Nitrospirota and Bdellovibrionota were reduced; Acidobacteriota, Verrucomicrobiota, Methylomirabilota and Desulfobacterota were increased through the addition of S and M (Figure 2A). Specifically, Chloroflexi abundance was positively correlated with NH4+-N content, Patescibacteria abundance was negatively correlated with pH value, and Desulfobacterota was positively correlated with EC value (Figure 2A). In addition, at the genus level, the most abundant bacterial genus was Pseudarthrobacter (>10%), which was also increased by the addition of S and M. Vicinamibacteracea and Vicinamibacterales increased in S and M. However, Sphingomonas, unclassified_f_Microbacteriaceae, Gemmatimonadaceae, Agromyces, Gaiella were decreased by the addition of sorbitol and mannitol (Figure 2B). Specifically, norank_f_Gemmatimonadaceae and unclassified_f_Intrasporangiaceae were negatively correlated with pH value, unclassified_f_Intrasporangiaceae was also positively correlated with EC value, and Marmoricola was negatively correlated with AP content (Figure 2B). In brief, due to the addition of sugar alcohols, the soil properties varied, thereby changing the rhizosphere bacterial species.

3.4. Difference Analysis and Functional Prediction of Rhizosphere Bacteria

In addition to the common strategy in rhizosphere bacterial composition under S and M (Figure 2), specific bacterial communities were formed under S and M, respectively. Figure 3 shows the effect of exogenous sugar alcohols on the 15 most abundant bacteria at the genus level using the Wilcoxon rank-sum test. When S was compared with control, only Pseudarthrobacter and Vicinamibacteraceae were observably increased, whereas other bacterial genera were significantly decreased by adding S (Figure 3A). In addition, regarding M compared with control, only Cellulomonas was observably increased by adding mannitol (Figure 3B). And in a comparison of S and M, Bacillus was mainly increased in S, whereas other bacterial genera were increased in M (Figure 3C). Furthermore, the rhizosphere bacteria also had distinct differences due to S and M concentrations (Figure S2). In summary, specific bacteria were recruited into the rhizosphere of peach seedlings in response to treatment with exogenous sugar alcohols.
FAPROTAX annotation showed the functional groups with significant differences (Figure 4), among which chemoheterotrophy and acrobic_chemoheterotrophy were decreased by both S and M, and S decreased more than M. In addition, rhizosphere bacteria involved in nitrogen transformation were altered by both S and M, among which nitrate-reducing and ureolysis bacteria gathered under S, and bacteria involved in the ammonification of nitrite and nitrate reduced under M. Moreover, there were more bacteria involved in nitrogen reduction, ureolysis and nitrogen fixation in S than M.

3.5. Correlation Analysis of Rhizosphere Bacteria

Compared with control, S and M had no significant effect on the ratio of positive and negative links; however, they greatly reduced the number of positive and negative links and weakened the correlation between microorganisms (Figure S3A). In addition, the correlation network under S and M showed that most bacterial OTUs were uncorrelated with exogenous sugar alcohols, and some bacterial OTUs were only negatively correlated with S or M. Moreover, most of the related bacteria were also unrelated to plant growth factors (Figure S3B,C). The above results revealed that the associated microbial network promoting plant growth was unable to be constructed with an increase in the sugar alcohol concentration. Therefore, we utilized association network analysis to investigate the interaction of rhizosphere bacteria under different concentrations of S and M (S100, S200, S400, M100, M200, M400). Three complex interactions exist in the association network under each treatment, with obvious ecological interactions among bacterial species in each part, among which Proteobacteria and Actinobacteriota had a prominent position in the correlation network. Moreover, different concentrations of S and M notably increased Firmicutes and Acidobacteriota. It was interesting that different concentrations of S and M both decreased the number of positively correlated edges and increased the number of negatively correlated edges, leading to a more consistent number of positively and negatively correlated edges among rhizosphere bacteria (Figure S4), thereby increasing microbial network stability.
According to the comprehensive evaluation of S and M on peach seedling growth, M100 had the best promotion effect, followed by S100 (Table 2). Therefore, we emphatically analyzed the microbial network under S100 and M100 with the optimal growth-promoting effect. We further utilized network analysis to explore the relationship between rhizosphere bacterial communities, environmental factors, and plant growth factors, identifying highly correlated bacterial communities, and visualized them using Cytoscape (Figure 5). The rhizosphere bacterial phyla related to plant growth were mainly Proteobacteria, Actinobacteria, Firmicutes, Chloroflexi, Acidobacteriota, and Gemmatimonadota. Bacterial OTUs, which were obviously correlated with S100 and M100, were similarly correlated with plant growth factors (RV, FRW, LA, PH, PW). Interestingly, both S100 and M100 were significantly positively correlated with Vicinamibacterales in Acidobacteriota and negatively correlated with Sphingomonas and Altererythrobacter in Proteobacteria, as well as Streptomyccs and Blastococcus in Actinobacteriota.
We further used SEM to comprehensively investigate the effects of S100 and M100 on peach seedling growth by mediating Vicinamibacteraceae and Sphingomona (Figure 6). The results showed that S100 and M100 directly decreased rhizosphere bacterial diversity (R2 = 0.816), decreased the abundance of Sphingomona (R2 = 0.771) and increased the abundance of Vicinamibacteraceae (R2 = 0.979). In addition, peach seedling growth was not affected indirectly by Sphingomona (Std.all = −0.293, p > 0.05) but by Vicinamibacteraceae (Std.all = 0.723, p < 0.001). Therefore, S and M might promote peach seedling growth by increasing Vicinamibacterales mainly.

4. Discussion

4.1. Sorbitol and Mannitol Altered Rhizosphere Soil Properties and Promoted Peach Seedling Growth

Soil “water, fertilizer, gas and heat” coordination supply ensures the healthy growth of plants [3,5]. The root–soil interface establishes a rhizosphere microenvironment, which directly influences the availability and utilization of nutrients, thereby impacting the growth and development of plants [15,27].
In the rhizosphere environment of peach seedlings, the application of exogenous S and M increased rhizosphere SOM (Table 2). This was due to S and M as energy sources providing a carbon skeleton for the synthesis of lipids, proteins and nucleic acids, thus accelerating soil carbonization [28]. With the exception of S400, all treatments significantly reduced the rhizosphere NO3−-N content, which might be due to the stimulation of rhizosphere bacteria involved in nitrogen transformation, as well as the promotion of NO3−-N absorption by plants, leading to a reduction in rhizosphere NO3−-N content [29]. Similarly, due to the decrease in ammonifiers under M, more NH4+-N was retained in the rhizosphere and not absorbed by plants (Table 2, Figure 4).
In plant growth and development, S and M play pivotal roles in carbon and energy storage, cellular protection and plant metabolism. These compounds generate various sugar signals through the processes of photosynthesis and carbon metabolism, thereby participating in the regulation of plant growth and development [7,10,30]. Leaves serve as the primary organs for photosynthesis and nutrient storage in plants. However, due to the strong adsorption and fixation of phosphorus and potassium in the soil, these nutrients are often sequestered and become difficult for plants to utilize [31], leading to a reduction in TP and TK in leaves under S100, M200, and M400. In addition, S and M, as the main sources of carbon and energy, participated in photosynthesis and carbon metabolism, therefore enhancing the photosynthesis of peach seedling leaves (Table S1). Previous studies confirmed that exogenous sugar alcohols stimulated the development of apple pollen tubes, enhanced olive drought resistance, promoted peach fruit coloration and improved fruit quality [7,10,32,33]. And our research also found that S and M significantly promoted peach seedling growth, within which M100 had the best comprehensive growth promotion effect, followed by S100 (Table 3). As a compatible solute, M is the main compound and has a broader role in the rhizosphere and root [28]. A previous study showed that M had a greater optimization effect on soil nutrients [8]. Additionally, our findings reveal that M is more effective in increasing rhizosphere SOM, NH4+-N and AK and also in improving plant growth in peach seedlings (Table 2 and Table 3). Therefore, M possesses greater potential for improving soil nutrients and promoting plant growth compared to sorbitol (Table 2; [8]).

4.2. Sorbitol and Mannitol Assembled Rhizosphere Bacterial Community via Affecting the Rhizosphere Environment of Peach Seedlings

The rhizosphere functions as the primary “filter” for microorganisms entering the root and plays a crucial role in the selection of bacterial communities [34]. Our results indicated that S and M reduced rhizosphere bacterial diversity, increased distinct bacterial OTUs, and altered bacterial structure in peach seedlings (Figure 1 and Figure S1). Consequently, S and M provided a carbon source for rhizosphere microorganisms, recruiting specific bacteria [2,16]. Importantly, the rhizosphere microenvironment established a specific microbial community, which is different due to soil factors and plant characteristics [35]. The introduction of exogenous S and M contributes additional soil carbon sources and modifies rhizosphere soil factors, thereby inevitably altering the rhizosphere microenvironment and reshaping microbial composition and structure [17,28,36]. Pearson correlation and RDA analysis revealed that the reassembly of rhizosphere bacteria was the result of multiple soil factors under S and M in peach seedlings, where SOM and AK had significant effects on bacterial diversity and bacterial structure in the rhizosphere (Figure 1D,F).
Microorganisms, especially bacteria, are sensitive to environmental fluctuations [18]. Our results indicated the bacterial phyla of Actinobacteriota, Proteobacteria, Acidobacteria, Chloroflexi, Firmicutes, and the bacteria genera of Pseudarthrobacter, Vicinamibacterales, Vicinamibacteraceae, Sphingomonas, and Bacillus in the peach seedling rhizosphere (Figure 2). The rhizosphere’s dominant bacteria phyla and genera were different from those previously observed in apple and sugarcane, indicating that rhizosphere bacteria were influenced by different plant species [37,38]. The response of the rhizosphere bacterial community to exogenous sugar alcohols is closely related to plant adaptability to environmental conditions [39]. Plant adaptation to the environment alters the composition of rhizosphere bacteria [40], a finding that is supported by our results. Notably, the composition of dominant bacteria was consistent under different treatments; only the composition proportion changed in the rhizosphere environment of peach seedlings (Figure 2). In instances of variation in bacterial abundance, Proteobacteria was closely associated with soil carbon metabolism; therefore, S and M provided soil carbon sources and stimulated the relative abundance of rhizosphere Pseudarthrobacter of Proteobacteria in peach seedlings (Figure 2) [31]. Furthermore, Acidobacteriota with high metabolic diversity and sugar as a growth energy source [41,42], Verrucomicrobiota with beneficial microbial development potential and specialized consumption of complex polysaccharides [43,44], Methylmirabilota for methane oxidation via aerobic pathway [45], and Desulfobacterota were dominated by the sulfur cycle and participate in the global cycle of carbon and nitrogen nutrients [46]. Four of the above bacteria phyla were enriched under S and M (Figure 2A). In summary, S and M facilitated the recruitment of beneficial bacteria that primarily utilized glycogen as a growth energy source and contributed to nutrient cycling within the rhizosphere. These functional bacteria formed a unique bacterial community in the peach rhizosphere, providing nutrients and growth energy for peach seedlings [17,37]. Interestingly, the decreased rhizosphere bacteria were more abundant than the recruited rhizosphere bacteria, which indicated that the addition of exogenous substances had a screening effect on the rhizosphere bacterial community [35,36]. The compositional ratio of certain rhizosphere bacteria was significantly correlated with rhizosphere soil factors (Figure 2), further indicating that exogenous sugar alcohols impacted soil properties, thereby altering the species composition of rhizosphere bacteria.
In addition to the common strategies observed in the rhizosphere bacterial composition of peach seedlings, specific bacterial communities were formed under S and M, respectively (Figure 2 and Figure 3). As described in the research, Pseudoarthrobacter with sugar alcohol as the main carbon source and Vicinamibacteraceae with growing sugar or complex proteinaceous compounds were observed under S [41,42]. Moreover, Cellulomonas, an antioxidant bacterium with positive catalase and oxidase tests, was enriched under M (Figure 3A,B) [43]. Interestingly, Bacillus increased independently with S, while other bacterial genera increased under M (Figure 3C). These results not only indicated that M provided more abundant microbial resources and was an important reason for promoting peach growth but also further confirmed that rhizosphere bacteria were strongly altered by the soil environment and had specific bacterial communities in different habitats [35,36]. Regarding functional bacteria, chemoheterotrophic bacteria that utilize organic matter for carbon requirements and nitrogen-transforming bacteria were notably altered with the introduction of exogenous carbon sources (Figure 4).

4.3. Sorbitol and Mannitol Promote Peach Seedling Growth Through Affecting the Rhizosphere Bacterial Community Composition

Rhizosphere microorganisms play a crucial role in promoting plant growth and vitality [36]. In the production of fruit trees, optimizing the dynamic balance of rhizosphere microbiota can significantly enhance rhizosphere effects and improve the productivity of fruit trees [15]. However, in this research, perhaps the diversity of rhizosphere bacteria and the proportion of certain bacteria composition inconspicuously improved with the concentration of exogenous sugar alcohol. As a result, elevated concentrations of exogenous sugar alcohols were unable to construct microbial networks to promote plant growth (Figure 1, Figure 2 and Figure S3). This result indicated that the rhizosphere environment and plant growth improved with suitable exogenous sugar alcohols. Based on a comprehensive evaluation of the effects of exogenous sugar alcohols on peach seedling growth, M100 and S100 demonstrated more favorable outcomes (Table 2). Our research focused on employing association network analysis to explore the interactions among microorganisms under S100 and M100 (i.e., positive, negative, and neutral) [47]. The network indicated that S and M promoted the consistency of the number of edges with positive and negative correlations among rhizosphere bacteria, which was conducive to improving the stability of the rhizosphere network and enhancing the adaptation of plants to the environment (Figure S4) [48]. Further utilizing the correlation network between the environment and microorganisms to analyze S100 and M100 mediated microbial involvement in peach seedling growth. Acidobacteriota significantly increased in the associated network formed by S100 and M100; the Vicinamidobacteriaceae of Acidobacteriaceae played a particularly crucial role in constructing the rhizosphere bacterial network that promoted peach seedling growth, providing positive effects under sorbitol and mannitol and positive feedback on peach seedling growth indicators (Figure 2 and Figure 5). Acidobacteriaceae promoted the soil ecological balance and beneficial bacteria formation, thereby inhibiting the propagation of harmful and pathogenic microorganisms. Specifically, Vicinamibacteraceae as intracellular storage was observed for the key compounds polyphosphate and glycogen, and growth occurs on sugars or complex proteinaceous compounds [41,42]. In brief, Vicinamibacteraceae of Acidobacteriota was enhanced by the utilization of S and M as carbon source energy, which subsequently increased SOM in the rhizosphere and facilitated photosynthesis and growth in peach seedlings. When our research focused on the bacteria that responded negatively to the two sugar alcohols, interestingly, Proteobacteria and Actinobacteria accounted for the largest proportion in the microbial symbiotic network, but their abundance was reduced under two sugar alcohols (Figure 1 and Figure S4). Moreover, the Sphingomona and Altererythrobacter in Proteobacteria and the Streptomyccs and Blastococcus in Actinobacteria had negative effects under sugar alcohols, providing negative feedback on the growth indexes of peach seedlings (Figure 5). Exogenous sugar alcohols are mainly involved in metabolic pathways related to sugars and amino acids, and they stimulated carbon and nitrogen metabolism in plants [7,9,29]. Roots further selected microorganisms through metabolic processes and enabled bacteria such as Sphingomona and Altererythrobacter to participate in nitrogen metabolism. Blastococcus, with the potential for nitrogen fixation to colonize plants’ endosphere, decreased bacteria in the rhizosphere [49]. Microorganisms, especially bacteria, are sensitive to environmental changes, which has resulted in a decrease in the exploration of Streptomyces, significantly influenced by fungal interactions and associated with the production of alkaline volatile organic compounds (VOCs) [22,50]. In summary, exogenous sugar alcohol optimized rhizosphere bacterial composition and promoted peach seedling growth through increasing or decreasing the proportion of rhizosphere bacteria, and low-concentration S and M might promote peach seedling growth by increasing Vicinamibacterales mainly (Figure 6).
This study provided innovative insights into the future development of eco-friendly fertilizer synergists, utilizing sugar alcohols, and has significant importance for exploring the rhizosphere micro-ecological mechanism of soil synergists. However, the mechanism of specific rhizobacteria promotes the growth of peach seedlings, and the relationship between rhizobacteria function and peach seedling growth under exogenous sugar alcohol still needs to be further understood.

5. Conclusions

The results indicated that S and M stimulated the rhizosphere environment of peach seedlings, effectively promoted peach seedling growth, and showed the potential for KH2PO4 as a synergistic agent. Among the treatments, M100 exhibited the most significant growth promotion effect, followed by S100. The rhizosphere bacterial community was markedly influenced by S and M, leading to an increase in specific bacterial OTUs, a decrease in bacterial community diversity, and alteration in the bacterial community structure. Pearson correlation analysis highlighted the critical roles of rhizosphere SOM and AK in altering bacterial diversity and reshaping the bacterial structure. Additionally, NO3−-N was significantly correlated with bacterial diversity, and NH4+-N significantly affected the bacterial structure. Chemoheterotrophic and nitrogen-transforming bacteria increased with the addition of S and M. For bacterial composition, despite the similarity of the bacterial composition, the relative abundance of different bacteria was significantly different under S and M, among which M provided more abundant microbial resources. The rhizosphere environment and peach seedling growth improved with suitable exogenous sugar alcohols, facilitating peach seedling growth by regulating the composition ratio of rhizosphere bacteria, particularly by increasing the abundance of Vicinamibacteraceae.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15071548/s1, Method S1: Collection of rhizosphere soil samples; Table S1: Effects of exogenous sugar alcohols and KH2PO4 co-application on leaf nutrition and photosynthesis of peach seedlings; Table S2: Effects of exogenous sugar alcohols and KH2PO4 co-application on growth index of peach seedlings; Figure S1: Effects of exogenous sugar alcohols and KH2PO4 co-application on rhizosphere bacterial composition of peach seedlings; Figure S2: Effects of exogenous sugar alcohols on rhizosphere bacterial difference. And unclassified bacterial genera have been removed above; Figure S3: Co-occurrence network of bacterial OTUs, and the correlation between bacterial community, rhizosphere properties and peach seedling growth; Figure S4: Single factor correlation network analysis among bacteria of top 100 OTUs.

Author Contributions

H.Y. and J.L. contributed to the conception and design of the manuscript; J.L. carried out data analyses and wrote the original draft; W.S. and H.L. carried out material preparation and data measurements; R.D. assisted in the operation of the experiment. J.L., W.S., G.X. and P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Research and Development Program in Henan Province (No. 221111111800), the Central Public-interest Scientific Institution Basal Research Fund (No. 1610192023105), Agricultural Science and Technology Innovation Program (ASTIP) of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2024-ZFRI), and Scientific and Technological Project of Henan Province (No. 242102110160).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
SSorbitol
MMannitol
S100Mixed with 100 mg/kg sorbitol and KH2PO4
S200Mixed with 200 mg/kg sorbitol and KH2PO4
S400Mixed with 400 mg/kg sorbitol and KH2PO4
M100Mixed with 100 mg/kg mannitol and KH2PO4
M200Mixed with 200 mg/kg mannitol and KH2PO4
M400Mixed with 400 mg/kg mannitol and KH2PO4
SOMSoil organic matter
NO3−-NNitrate nitrogen
NH4+-NAmmonium nitrogen
APAvailable phosphorus
AKAvailable potassium
ECElectrical conductivity
RARoot activity
TTCTriphenyl tetrazolium chloride
RVRoot volume
FRWFresh root weight
FLWLeaf weight
PWPlant weight
PHPlant height
LALeaf area
SPADThe relative content of chlorophyll
CiIntercellular CO2 concentration
GsStomatal conductance
VPDSaturated water vapor pressure difference
ANet photosynthetic rate
ETranspiration rate
LNLeaf nitrogen
LPLeaf phosphorus
LKLeaf potassium
SEMStructural equation model

References

  1. Xu, L.; Chen, C. Economic situation and development countermeasures of Chinese peach industry. J. Fruit Sci. 2023, 40, 133–143. (In Chinese) [Google Scholar] [CrossRef]
  2. Li, G.M.; Peng, F.T.; Xiao, Y.S.; Zhang, H.M.; Wang, Z.Y.; Wang, Z.T. Evaluation of soil nutrient status and N, P load risk in peach orchards atmountainous area in mid-Shandong. J. Shandong Agric. Univ. (Nat. Sci.) 2011, 42, 392–400. (In Chinese) [Google Scholar]
  3. Yan, K.F.; Peng, F.T.; Fang, L.; Li, Y.; Zhang, H.M. Effects of different irrigation and fertilizer methods on soil moisture, nutrient and plant growth in peach orchard. J. Soil Water Conserv. 2013, 27, 160–164+283. [Google Scholar] [CrossRef]
  4. Yang, G.; Wang, Y.; Wang, S.Q.; Zhao, X. Legume cover with optimal nitrogen management and nitrification inhibitor enhanced net ecosystem economic benefits of peach orchard. Sci. Total Environ. 2023, 873, 162141. [Google Scholar] [CrossRef]
  5. Li, Y.Y.; Lv, Y.; Peng, F.T.; Xiao, Y.S. Effect of glycine combined with urea on the absorption, transportation and transformation of nitrogen in wild peach seedlings (Amygdalu spersica (L.) Batsch). Sci. Hortic. 2023, 308, 111549. [Google Scholar] [CrossRef]
  6. Chen, X.S.; Guo, W.W.; Xu, J.; Cong, P.H.; Wang, L.R.; Liu, C.H.; Li, X.Y.; Wu, S.J.; Yao, Y.X.; Chen, X.L. Genetic improvement and promotion of fruit quality of main fruit trees. Sci. Agric. Sin. 2015, 48, 3524–3540. (In Chinese) [Google Scholar] [CrossRef]
  7. Liu, H.M.; Yu, H.L.; Shao, W.; Xu, B.B.; Zhang, Z.H.; Shi, Z.Y.; Zhao, X.F.; Xu, G.Y.; Yang, J.H.; Si, P. Effects of sorbitol and mannitol combined with NPK on the growth, fruit quality and nutrient absorption of peach. J. Fruit Sci. 2021, 38, 911–921. (In Chinese) [Google Scholar] [CrossRef]
  8. Yu, H.L.; Shao, W.; Xu, G.Y.; Xie, N.; Yang, X.J.; Gao, D.T.; Si, P. Soil amendment with sorbitol and mannitol changes the soil microbial community and its enzymatic activities. J. Soils Sediments 2022, 23, 1857–1876. [Google Scholar] [CrossRef]
  9. Morandi, B.; Corelli, G.L.; Rieger, M.; Lo, B.R. Carbohydrate availability affects growth and metabolism in peach fruit. Physiol. Plant. 2008, 133, 229–241. [Google Scholar] [CrossRef]
  10. Li, C.; Meng, D.; Piñeros, M.A.; Mao, Y.; Dandekar, A.M.; Cheng, L. A sugar transporter takes up both hexose and sucrose for sorbitol-modulated in vitro pollen tube growth in apple. Plant Cell 2020, 32, 449–469. [Google Scholar] [CrossRef]
  11. Arif, Y.; Singh, P.; Siddiqui, H.; Shiraz, M.; Mir, A.R.; Alam, P.; Hayat, S. Comparative study of stress generated by osmolytes on the growth, photosynthesis and metabolic responses in Nigella sativa. Biocatal. Agric. Biotechnol. 2023, 52, 102818. [Google Scholar] [CrossRef]
  12. Fan, L.; Zhao, J.G. The current status and prospect of potassium dihydrogen phosphate in China. Phosphate Compd. Fertil. 2006, 21, 34–37. (In Chinese) [Google Scholar]
  13. Li, T.; Liu, H.H.; Zhang, Y.P.; Chen, Q.Y.; Zuo, B.B. Study on regulation of KH2PO4 on senescence mechanism of the leaves in muskmelon fruiting nodes. J. Hebei Agric. Univ. 2018, 41, 61–66. (In Chinese) [Google Scholar] [CrossRef]
  14. Qu, Q.; Zhang, Z.; Peijnenburg, W.J.; Liu, W.; Lu, T.; Hu, B.; Chen, J.; Chen, J.; Lin, Z.; Qian, H. Rhizosphere Microbiome Assembly and Its Impact on Plant Growth. J. Agric. Food Chem. 2020, 68, 5024–5038. [Google Scholar] [CrossRef]
  15. Fu, X.M.; Mao, X.M.; Yang, H.X.; Long, C.R.; Liu, H.M.; Du, Y.X.; Dong, M.C.; Yue, J.Q.; Li, J.X. Review on rhizosphere microorganisms of fruit trees. China Fruits 2021, 217, 5–9. (In Chinese) [Google Scholar] [CrossRef]
  16. Jones, D.L.; Hinsinger, P. The rhizosphere: Complex by design. Plant Soil 2008, 312, 1–6. [Google Scholar] [CrossRef]
  17. Ling, N.; Wang, T.; Kuzyakov, Y. Rhizosphere bacteriome structure and functions. Nat. Commun. 2022, 13, 836. [Google Scholar] [CrossRef]
  18. Lugtenberg, B.; Kamilova, F. Plant-Growth-Promoting Rhizobacteria. Annu. Rev. Microbiol. 2009, 63, 541–556. [Google Scholar] [CrossRef]
  19. Lu, L.; Liu, N.; Fan, Z.; Liu, M.; Zhang, X.; Tian, J.; Yu, Y.; Lin, H.; Huang, Y.; Kong, Z. A novel PGPR strain, Streptomyces lasalocidi JCM 3373T, alleviates salt stress and shapes root architecture in soybean by secreting indole-3-carboxaldehyde. Plant Cell Environ. 2024, 47, 1941–1956. [Google Scholar] [CrossRef]
  20. Qiao, Y.Z.; Wang, Z.D.; Sun, H.; Guo, H.Y.; Song, Y.; Zhang, H.; Ruan, Y.; Xu, Q.C.; Huang, Q.W.; Shen, Q.R.; et al. Synthetic community derived from grafted watermelon rhizosphere provides protection for ungrafted watermelon against via microbial synergistic effects. Microbiome 2024, 12, 101. [Google Scholar] [CrossRef]
  21. Al-Turki, A.; Murali, M.; Omar, A.F.; Rehan, M.; Sayyed, R.Z. Recent advances in PGPR-mediated resilience toward interactive effects of drought and salt stress in plants. Front. Microbiol. 2023, 14, 1214845. [Google Scholar] [CrossRef] [PubMed]
  22. Hermans, S.M.; Buckley, H.L.; Case, B.S.; Curran-Cournane, F.; Taylor, M.; Lear, G. Using soil bacterial communities to predict physico-chemical variables and soil quality. Microbiome 2020, 8, 79. [Google Scholar] [CrossRef] [PubMed]
  23. Marasco, R.; Rolli, E.; Fusi, M.; Michoud, G.; Dafonchio, D. Grapevine rootstocks shape underground bacterial microbiome and networking but not potential functionality. Microbiome 2018, 6, 3. [Google Scholar] [CrossRef] [PubMed]
  24. Li, J.Q.; Xun, M.; Shi, J.Y.; Chen, B.; Cheng, Y.J.; Zhang, W.W.; Yang, H.Q. Root 1-aminocyclopropane-l-carboxylic acid (ACC) and rhizosphere ACC deaminase-producing bacteria affect apple root architecture under soil compaction stress. Plant Soil 2023, 489, 629–643. [Google Scholar] [CrossRef]
  25. Fanin, N.; Bertrand, I. Aboveground litter quality is a better predictor than belowground microbial communities when estimating carbon mineralization along a land-use gradient. Soil Biol. Biochem. 2016, 94, 48–60. [Google Scholar] [CrossRef]
  26. David, J.K. A Primer on Partial Least Squares Structural Equation Modeling. Long Range Plan. 2013, 46, 184–185. [Google Scholar] [CrossRef]
  27. Xu, H.; Yang, H.S.; Xu, Y.; Mao, Z.Q.; Su, H.R. Research progress on rhizosphere environment of fruit trees. J. Shandong Agric. Univ. (Nat. Sci.) 2004, 35, 476–480. (In Chinese) [Google Scholar]
  28. Ho, S.L.; Chao, Y.C.; Tong, W.F.; Yu, S.M. Sugar coordinately and differentially regulates growth-and stress-related gene expression via a complex signal transduction network and multiple control mechanisms. Plant Physiol. 2001, 125, 877–890. [Google Scholar] [CrossRef]
  29. Prayogo, C.; Jones, J.E.; Baeyens, J.; Bending, G.D. Impact of biocharon mineralisation of C and N from soil and willow litter and its relationship with microbial community biomass and structure. Biol. Fertil. Soils 2014, 50, 695–702. [Google Scholar] [CrossRef]
  30. Tonon, T.; Li, Y.; McQueen, M.S. Mannitol biosynthesis in algae: More widespread and diverse than previously thought. New Phytol. 2017, 213, 1573–1579. [Google Scholar] [CrossRef]
  31. Zhou, Z.; Tran, P.Q.; Kieft, K.; Anantharaman, K. Genome diversification in globally distributed novel marine Proteobacteria is linked to environmental adaptation. ISME J. 2020, 14, 2060–2077. [Google Scholar] [CrossRef] [PubMed]
  32. Mechri, B.; Tekaya, M.; Attia, F.; Hammami, M.; Chehab, H. Drought stress improved the capacity of Rhizophagus irregularis for inducing the accumulation of oleuropein and mannitol in olive (Olea europaea) roots. Plant Physiol. Biochem. 2020, 156, 178–191. [Google Scholar] [CrossRef] [PubMed]
  33. Li, Q.L.; Yang, W.J.; Gao, D.T.; Wei, Z.F.; Yu, H.L. Effects of Sorbitol and Sucrose on Soluble Sugar Content of Peach Fruits and Leaves and Fruits Quality. J. Henan Agric. Sci. 2019, 48, 110–116. (In Chinese) [Google Scholar] [CrossRef]
  34. Compant, S.; Clément, C.; Essitsch, A. Plant growth-promoting bacteria in the rhizo- and endosphere of plants: Their role, colonization, mechanisms involved and prospects for utilization. Soil Biol. Biochem. 2010, 42, 669–678. [Google Scholar] [CrossRef]
  35. Liu, L.L.; Huang, X.Q.; Zhang, J.B.; Cai, Z.C.; Jiang, K.; Chang, Y.Y. Deciphering the relative importance of soil and plant traits on the development of rhizosphere microbial communities. Soil Biol. Biochem. 2020, 148, 107909. [Google Scholar] [CrossRef]
  36. Vieira, S.; Sikorski, J.; Dietz, S.; Herz, K.; Schrumpf, M.; Bruelheide, H.; Scheel, D.; Friedrich, M.W.; Overmann, J. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 2020, 14, 463–475. [Google Scholar] [CrossRef]
  37. Cao, H.; Jia, M.F.; Xun, M.; Wang, X.S.; Chen, K.; Yang, H.Q. Nitrogen transformation and microbial community structure varied in apple rhizosphere and rhizoplane soils under biochar amendment. J. Soils Sediments 2021, 21, 853–868. [Google Scholar] [CrossRef]
  38. Wang, M.; Qi, X.; Shi, Y.; Zhao, J.; Ahmad, S.; Akhtar, K.; Chen, B.; Lian, T.; He, B.; Wen, R. Sugarcane straw returning is an approaching technique for the improvement of rhizosphere soil functionality, microbial community, and yield of different sugarcane cultivars. Front. Microbiol. 2023, 14, 1133973. [Google Scholar] [CrossRef]
  39. Gao, J.L.; Luo, Y.; Wei, Y.L.; Huang, Y.L.; Zhang, H.; He, W.L.; Sheng, H.M.; An, L.Z. Effect of aridity and dune type on rhizosphere soil bacterial communities of Caragana microphylla in desert regions of northern China. PLoS ONE 2019, 14, e0224195. [Google Scholar] [CrossRef]
  40. Kalinowski, T.; Halden, R.U. Can stress enhance phytoremediation of polychlorinated biphenyls? Environ. Eng. Sci. 2012, 29, 1047–1052. [Google Scholar] [CrossRef]
  41. Kristensen, J.M.; Singleton, C.; Clegg, L.A.; Petriglieri, F.; Nielsen, P.H. High Diversity and Functional Potential of Undescribed “Acidobacteriota” in Danish Wastewater Treatment Plants. Front. Microbiol. 2021, 12, 643950. [Google Scholar] [CrossRef] [PubMed]
  42. Huber, K.J.; Overmann, J. Vicinamibacteraceae fam. nov., the first described family within the subdivision 6 Acidobacteria. Int. J. Syst. Evol. Microbiol. 2018, 68, 2331–2334. [Google Scholar] [CrossRef] [PubMed]
  43. Cani, P.D.; Depommier, C.; Derrien, M.; Everard, A.; de Vos, W.M. Akkermansia muciniphila: Paradigm for next-generation beneficial microorganisms. Nature reviews. Gastroenterol. Hepatol. 2022, 19, 625–637. [Google Scholar] [CrossRef]
  44. Orellana, L.H.; Francis, T.B.; Ferraro, M.; Hehemann, J.H.; Fuchs, B.M.; Amann, R.I. Verrucomicrobiota are specialist consumers of sulfated methyl pentoses during diatom blooms. ISME J. 2022, 16, 630–641. [Google Scholar] [CrossRef]
  45. Zhu, B.L.; Karwautz, C.; Andrei, S.; Klingl, A.; Pernthaler, J.; Lueders, T. A novel Methylomirabilota methanotroph potentially couples methane oxidation to iodate reduction. mLife 2022, 1, 323–328. [Google Scholar] [CrossRef]
  46. Magnuson, E.; Altshuler, I.; Freyria, N.J.; Leveille, R.J.; Whyte, L.G. Sulfur-cycling chemolithoautotrophic microbial community dominates a cold, anoxic, hypersaline Arctic Spring. Microbiome 2023, 11, 203. [Google Scholar] [CrossRef]
  47. Kim, S.K.; Kook, M.; Yan, Z.F.; Trinh, H.; Zheng, S.D.; Yang, J.E.; Park, S.Y.; Yi, T.H. Cellulomonas aurantiaca sp. nov., isolated from a soil sample from a tangerine field. Antonie Leeuwenhoek 2019, 112, 1623–1632. [Google Scholar] [CrossRef]
  48. Fan, K.; Weisenhorn, P.; Gilbert, J.A.; Chu, H. Wheat rhizosphere harbors a less complex and more stable microbial co-occurrence pattern than bulk soil. Soil Biol. Biochem. 2018, 125, 251–260. [Google Scholar] [CrossRef]
  49. Zhang, H.; Huang, M.; Zhang, W.; Gardea-Torresdey, J.L.; White, J.C.; Ji, R.; Zhao, L. Silver Nanoparticles Alter Soil Microbial Community Compositions and Metabolite Profiles in Unplanted and Cucumber-Planted Soils. Environ. Sci. Technol. 2020, 54, 3334–3342. [Google Scholar] [CrossRef]
  50. Jones, S.E.; Elliot, M.A. Streptomyces Exploration: Competition, Volatile Communication and New Bacterial Behaviours. Trends Microbiol. 2017, 25, 522–531. [Google Scholar] [CrossRef]
Agronomy 15 01548 g001
Figure 1. Effects of exogenous sugar alcohols and KH2PO4 co-application on rhizosphere bacterial diversity of peach seedlings. (A–C): The Chao 1 (A), the Shannon (B), and the Pielou_e index (C) are calculated to reflect the Alpha diversity of bacterial communities. Control: untreated soil, S: sorbitol treated soil, M: mannitol treated soil. Significant differences among treatments are marked with different lower-case letters (Duncan’s New Multiple Range Method, p < 0.05). (D): Analysis of between rhizosphere bacterial diversity and soil physicochemical properties using Pearson correlation. ‘Con’ represents different concentrations of sorbitol or mannitol. Red represents positive correlation, blue represents negative correlation. (E): Non-metric multidimensional scaling (NMDS) is based on Bray–Curtis distance at the OTU level. (F): Distance-based redundancy analysis (db-RDA) illustrates the association between samples and soil physicochemical properties. *** Significant correlation is found at the 0.001 level (bilateral). ** Significant correlation is found at the 0.01 level (bilateral). * Significant correlation is found at the 0.05 level (bilateral).
Figure 1. Effects of exogenous sugar alcohols and KH2PO4 co-application on rhizosphere bacterial diversity of peach seedlings. (A–C): The Chao 1 (A), the Shannon (B), and the Pielou_e index (C) are calculated to reflect the Alpha diversity of bacterial communities. Control: untreated soil, S: sorbitol treated soil, M: mannitol treated soil. Significant differences among treatments are marked with different lower-case letters (Duncan’s New Multiple Range Method, p < 0.05). (D): Analysis of between rhizosphere bacterial diversity and soil physicochemical properties using Pearson correlation. ‘Con’ represents different concentrations of sorbitol or mannitol. Red represents positive correlation, blue represents negative correlation. (E): Non-metric multidimensional scaling (NMDS) is based on Bray–Curtis distance at the OTU level. (F): Distance-based redundancy analysis (db-RDA) illustrates the association between samples and soil physicochemical properties. *** Significant correlation is found at the 0.001 level (bilateral). ** Significant correlation is found at the 0.01 level (bilateral). * Significant correlation is found at the 0.05 level (bilateral).
Agronomy 15 01548 g001
Agronomy 15 01548 g002
Figure 2. Top 15 bacteria in the rhizosphere of peach seedlings and their correlation with soil properties. (A): Heatmap of the top 15 rhizosphere bacterial abundance and Pearson correlation analysis between bacteria and soil physicochemical properties at the phylum level. (B): Heatmap of the top 15 rhizosphere bacterial abundance and Pearson correlation analysis between bacteria and soil physicochemical properties at the genus level. Red represents positive correlation; blue represents negative correlation. ** Significant correlation is found at the 0.01 level (bilateral). * Significant correlation is found at the 0.05 level (bilateral).
Figure 2. Top 15 bacteria in the rhizosphere of peach seedlings and their correlation with soil properties. (A): Heatmap of the top 15 rhizosphere bacterial abundance and Pearson correlation analysis between bacteria and soil physicochemical properties at the phylum level. (B): Heatmap of the top 15 rhizosphere bacterial abundance and Pearson correlation analysis between bacteria and soil physicochemical properties at the genus level. Red represents positive correlation; blue represents negative correlation. ** Significant correlation is found at the 0.01 level (bilateral). * Significant correlation is found at the 0.05 level (bilateral).
Agronomy 15 01548 g002
Agronomy 15 01548 g003
Figure 3. Effects of exogenous sugar alcohols on rhizosphere bacterial difference. And unclassified bacterial genera have been removed above. (A): Difference between control and sorbitol treatment (control vs. S). (B): Difference between control and mannitol treatment (control vs. M). (C): Difference between sorbitol and mannitol treatment (S vs. M). Control: untreated soil, S: sorbitol-treated soil, M: mannitol-treated soil. The differences between two groups were calculated using Wilcoxon rank-sum test. When the distribution shift of the differences between groups was 0.28, it could be inferred that the treatment group significantly changed the relative abundance of microorganisms. ** Significant correlation is found at the 0.01 level (bilateral). * Significant correlation is found at the 0.05 level. Red/blue frames mean S or M treatment significantly increased the bacterial genera.
Figure 3. Effects of exogenous sugar alcohols on rhizosphere bacterial difference. And unclassified bacterial genera have been removed above. (A): Difference between control and sorbitol treatment (control vs. S). (B): Difference between control and mannitol treatment (control vs. M). (C): Difference between sorbitol and mannitol treatment (S vs. M). Control: untreated soil, S: sorbitol-treated soil, M: mannitol-treated soil. The differences between two groups were calculated using Wilcoxon rank-sum test. When the distribution shift of the differences between groups was 0.28, it could be inferred that the treatment group significantly changed the relative abundance of microorganisms. ** Significant correlation is found at the 0.01 level (bilateral). * Significant correlation is found at the 0.05 level. Red/blue frames mean S or M treatment significantly increased the bacterial genera.
Agronomy 15 01548 g003
Agronomy 15 01548 g004
Figure 4. Effects of exogenous sugar alcohols on the function differences in rhizobacteria. (A): Function difference between control and sorbitol treatment (control vs. S). (B): Function difference between control and mannitol treatment (control vs. M). (C): Function difference between sorbitol and mannitol treatment (S vs. M). Control: untreated soil, S: sorbitol-treated soil, M: mannitol-treated soil. The differences between two groups were calculated using Wilcoxon rank-sum test. When the distribution shift of the differences between groups was 0.28, it could be inferred that the treatment group significantly changed the relative abundance of microorganisms. Functional differences with highly significant correlations. *** Significant correlation was found at the 0.001 level (bilateral), ** Significant correlation is found at the 0.01 level (bilateral), * Significant correlation is found at the 0.05 level.
Figure 4. Effects of exogenous sugar alcohols on the function differences in rhizobacteria. (A): Function difference between control and sorbitol treatment (control vs. S). (B): Function difference between control and mannitol treatment (control vs. M). (C): Function difference between sorbitol and mannitol treatment (S vs. M). Control: untreated soil, S: sorbitol-treated soil, M: mannitol-treated soil. The differences between two groups were calculated using Wilcoxon rank-sum test. When the distribution shift of the differences between groups was 0.28, it could be inferred that the treatment group significantly changed the relative abundance of microorganisms. Functional differences with highly significant correlations. *** Significant correlation was found at the 0.001 level (bilateral), ** Significant correlation is found at the 0.01 level (bilateral), * Significant correlation is found at the 0.05 level.
Agronomy 15 01548 g004
Agronomy 15 01548 g005
Figure 5. Correlation network between rhizosphere soil properties, peach seedling growth and bacterial community under S100 and M100. (A,B): The top 100 with OTU level total abundance and corresponding environmental factors were selected for Spearman correlation analysis, and the network diagram shows nodes that are significantly related to environmental factors and plant growth factor (p < 0.05, absolute value of Spearman correlation coefficient > 0.5). The node name is displayed at the bacterial genus level. Different colors represent different species and environmental factors. The red line indicates positive correlation; blue line indicates negative correlation. Thickness of the line indicates the magnitude of the correlation coefficient. (C): Both S100 and M100 increased Vicinamibacteraceae and reduced Sphingomona. Two bacterial abundances increased with red depth and decreased with blue depth.
Figure 5. Correlation network between rhizosphere soil properties, peach seedling growth and bacterial community under S100 and M100. (A,B): The top 100 with OTU level total abundance and corresponding environmental factors were selected for Spearman correlation analysis, and the network diagram shows nodes that are significantly related to environmental factors and plant growth factor (p < 0.05, absolute value of Spearman correlation coefficient > 0.5). The node name is displayed at the bacterial genus level. Different colors represent different species and environmental factors. The red line indicates positive correlation; blue line indicates negative correlation. Thickness of the line indicates the magnitude of the correlation coefficient. (C): Both S100 and M100 increased Vicinamibacteraceae and reduced Sphingomona. Two bacterial abundances increased with red depth and decreased with blue depth.
Agronomy 15 01548 g005
Agronomy 15 01548 g006
Figure 6. Structural equation models (SEMs) describe the effects of rhizosphere soil properties and bacteria on peach growth under S100 and M100. The red and blue lines indicate positive and negative correlation, respectively. The solid and dashed lines represent significant and non-significant relationships in the model, respectively (*** p < 0.001; p > 0.05). The model reliability and validity: Factor loading > 0.7; Cronbach‘s Alpha > 0.7; Cr > 0.7; AVE > 0.5; and the model fit degree GOF > 0.36, it indicates that this model has good reliability, validity, and goodness of fit.
Figure 6. Structural equation models (SEMs) describe the effects of rhizosphere soil properties and bacteria on peach growth under S100 and M100. The red and blue lines indicate positive and negative correlation, respectively. The solid and dashed lines represent significant and non-significant relationships in the model, respectively (*** p < 0.001; p > 0.05). The model reliability and validity: Factor loading > 0.7; Cronbach‘s Alpha > 0.7; Cr > 0.7; AVE > 0.5; and the model fit degree GOF > 0.36, it indicates that this model has good reliability, validity, and goodness of fit.
Agronomy 15 01548 g006
Table 1. The basic physicochemical properties of cultivation soil.
Table 1. The basic physicochemical properties of cultivation soil.
Soil ParametersSOM (g/kg)NO3−-N (mg/kg)NH4+-N (mg/kg)AP (mg/kg)AK (mg/kg)PHEC (μg/cm)
5.3211.3620.26108.33537.676.7320
Table 2. Effects of exogenous sugar alcohols and KH2PO4 co-application on rhizosphere soil characteristics of peach seedlings.
Table 2. Effects of exogenous sugar alcohols and KH2PO4 co-application on rhizosphere soil characteristics of peach seedlings.
TreatmentpHEC (μg/cm)SOM (%)NH4+-N (mg/kg)NO3−-N (mg/kg)AP (mg/kg)AK (mg/kg)
Control6.77 ± 0.06 a436.67 ± 40.41 a1.29 ± 0.06 c12.87 ± 0.72 d3.32 ± 0.30 b163.38 ± 10.22 ab290.32 ± 24.79 c
S1006.83 ± 0.06 a350.00 ± 20.00 b1.37 ± 0.05 b9.79 ± 0.71 e2.33 ± 0.38 c172.88 ± 14.32 a289.43 ± 3.15 c
S2006.77 ± 0.06 a466.67 ± 30.55 a1.35 ± 0.05 bc14.85 ± 0.43 bc2.20 ± 0.52 c170.95 ± 7.03 ab308.50 ± 3.28 bc
S4006.73 ± 0.06 a480.00 ± 26.46 a1.36 ± 0.05 b13.30 ± 0.48 cd4.04 ± 0.33 a160.16 ± 14.75 ab321.47 ± 14.71 b
M1006.83 ± 0.11 a376.67 ± 23.09 b1.39 ± 0.04 ab20.17 ± 2.18 a2.86 ± 0.43 bc155.34 ± 7.73 ab287.22 ± 18.11 c
M2006.77 ± 0.06 a443.33 ± 20.82 a1.45 ± 0.05 a12.34 ± 0.98 d2.37 ± 0.24 c174.81 ± 17.44 a350.06 ± 15.98 a
M4006.73 ± 0.06 a450.00 ± 26.46 a1.41 ± 0.03 ab15.32 ± 0.71 b2.55 ± 0.05 c149.54 ± 6.30 b370.93 ± 16.91 a
Control: KH2PO4, P 200 mg/kg; S100: mixed with 100 mg/kg sorbitol and KH2PO4; S200: mixed with 200 mg/kg sorbitol and KH2PO4; S400: mixed with 400 mg/kg Sorbitol and KH2PO4; M100: mixed with 100 mg/kg mannitol and KH2PO4; M200: mixed with 200 mg/kg mannitol and KH2PO4; M400: mixed with 200 mg/kg mannitol and KH2PO4; pH: power of hydrogen; EC: electric conductivity; SOM: soil organic matter; AP: available phosphorus; AK: available potassium. All values are mean ± standard deviation (n = 3), different letters in the same row meant significant difference at 0.05 level (p < 0.05) (the same below).
Table 3. Comprehensive principal component analysis on growth of peach seedlings.
Table 3. Comprehensive principal component analysis on growth of peach seedlings.
TreatmentPrincipal Component
Score 1		Principal Component
Score 2		Principal Component
Score 3		Comprehensive ScoreRank
Control−0.320214330.574044271−0.0833758120.5816335196
S1000.5204981990.031939305−0.2823408181.04226882
S2000.452137836−0.095817916−0.4983599670.5397630137
S4000.4995103680.2175325660.0521098830.7134966785
M1000.134921768−0.5153450080.4869905381.3642806971
M2000.3406025080.1812771380.6025797320.7354173464
M4000.1954866510.5610959040.2482325310.8687490893
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yu, H.; Li, J.; Shao, W.; Liu, H.; Dong, R.; Xu, G.; Si, P. Exogenous Sugar Alcohols Enhance Peach Seedling Growth via Modulation of Rhizosphere Bacterial Communities. Agronomy 2025, 15, 1548. https://doi.org/10.3390/agronomy15071548

AMA Style

Yu H, Li J, Shao W, Liu H, Dong R, Xu G, Si P. Exogenous Sugar Alcohols Enhance Peach Seedling Growth via Modulation of Rhizosphere Bacterial Communities. Agronomy. 2025; 15(7):1548. https://doi.org/10.3390/agronomy15071548

Chicago/Turabian Style

Yu, Huili, Jiaqi Li, Wei Shao, Huimin Liu, Ruiquan Dong, Guoyi Xu, and Peng Si. 2025. "Exogenous Sugar Alcohols Enhance Peach Seedling Growth via Modulation of Rhizosphere Bacterial Communities" Agronomy 15, no. 7: 1548. https://doi.org/10.3390/agronomy15071548

APA Style

Yu, H., Li, J., Shao, W., Liu, H., Dong, R., Xu, G., & Si, P. (2025). Exogenous Sugar Alcohols Enhance Peach Seedling Growth via Modulation of Rhizosphere Bacterial Communities. Agronomy, 15(7), 1548. https://doi.org/10.3390/agronomy15071548

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop