Mechanistic analysis of rhizosphere promoting bacteria on tobacco growth, continuous cropping soil, and root microbiota

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Plant growth-promoting rhizobacteria (PGPR) play a crucial role in enhancing plant growth, improving soil properties, and modulating the soil microbial environment. In this study, a variety of PGPGs, C1 for Bacillus paranthracis , C2 for Paenibacillus hunanensis , and C3 for Bacillus subtilis , were screened, and the mechanisms of action on the successional soils were investigated. The results demonstrated that the C1 treatment markedly enhanced the growth and development of tobacco plants while also exhibiting significant efficacy in improving soil physicochemical properties. To further investigate the impact of strains with strong growth-promoting effects, soil enzyme activities, microbial community composition, and functional diversity were analyzed in both the C1 treatment and CK control groups. The findings indicated that there were significant increases in sucrase, catalase, and urease in the C1 treatment when compared to the CK. Beneficial microflora were increased and functions such as metabolism and synthesis of amino acids and secondary metabolite synthesis in soil microorganisms were promoted by C1. In summary, C1 is capable of efficiently enhancing soil characteristics, facilitating the growth of tobacco, and laying a foundation for the advancement of microbial fertilizers aimed at mitigating the issues of continuous cropping obstacles.. Plant growth promoting rhizobacteria Tobacco Growth and development Soil improvement Rhizosphere microbial diversity Microbiome and metagenomic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction The phenomenon of continuous cropping obstacles has been attributed to prolonged, fixed-location, and high-frequency planting patterns, leading to declines in crop quality, reduced yields, and even total crop failure [1]. This issue is typically caused by abiotic or biotic factors in the soil [2], including alterations in soil physicochemical properties, excessive nutrient depletion, and fluctuations in soil enzyme activity [3]. These abiotic factors have been observed to inhibit crop development, trigger severe pest and disease outbreaks, and ultimately reduce crop yield [1]. In continuous cropping systems, root exudates have been found to accumulate in the soil, leading to the formation of autotoxin compounds that promote the excessive proliferation of pathogenic microorganisms[4]. This procedure, conversely, inhibits the activity of beneficial microorganisms and causes a reduction in the cycling of soil nutrients and the decomposition capacity of organic matter [5]. Over time, Regularly planting the same crops has been found to interfere with the soil's micro-ecosystem, leading to a more straightforward community structure, a rapid increase in harmful microorganisms, and a decline in helpful microbial populations [6], thereby posing significant challenges to sustainable agricultural development. Tobacco ranks among the world's key agricultural commodities. [7]. Long-term monoculture planting can lead to severe nutritional imbalances in tobacco plants, adversely affecting their yield and quality [8]. This is evident in the decline of tobacco field microecology, reduced tobacco growth quality, and a rise in pests and diseases. Consequently, Continuous cropping challenges have become a key problem affecting the tobacco industry's sustainable development [9, 10]. Multiple studies have shown that extended periods of continuous cropping lead to either an increase or loss of key microbial communities in the soil. This creates favorable conditions for the proliferation of harmful microbial groups within the soil microecology [11, 12], attributable to the imbalance in soil microbial composition and function. Under these adverse environmental conditions, enhancing yield presents a significant challenge for growers. Among a plethora of factors, the interplay between plants and microorganisms is considered pivotal in fostering a wide array of ecophysiological processes. This, in turn, can assist in alleviating the hurdles related to continuous cropping [13, 14]. However, Research indicates that root systems release antimicrobial substances that suppress the proliferation of harmful bacteria, which in turn promotes the involvement of rhizosphere microorganisms in the processes of nutrient cycling and energy transfer within the soil. This process controls plant growth and nutrient absorption [15]. The rhizosphere, the macrozone adjacent to plant roots, exhibits distinct physical, chemical, and biological properties compared to the surrounding soil. These characteristics are shaped by root activity and microbial interactions, while the colonization of rhizosphere microorganisms depends on this specific microenvironment [16]. Plant roots excrete beneficial chemical compounds that suppress soil-borne pathogens. Under stress conditions, plants actively modulate soil composition or recruit specific microbial communities via these secretions, thereby enhancing their resilience and buffering environmental fluctuations. As crucial microorganisms within the root microbiome, PGPR foster plant growth and development [17]. They do so by either directly or indirectly curbing plant diseases and boosting stress tolerance. The mechanisms underlying PGPR-mediated growth promotion involve a comprehensive regulatory system encompassing both direct and indirect pathways [18]. Firstly, PGPR whip up and let out plant hormones, like indole - 3 - acetic acid (IAA). They also boost nutrient accessibility by making phosphorus soluble, fixing nitrogen, getting potassium moving, and churning out siderophores. These processes enable the transformation of insoluble nutrients into bioavailable ones, directly boosting plant growth [19]. Additionally, Deaminase produced by PGPR modifies ethylene precursors, lowering ethylene levels in plant roots, which in turn promotes root elongation and growth [20]. Secondly, PGPR indirectly suppress plant diseases by producing antimicrobial compounds that trigger plant defense responses. This enhances plant resilience and strengthens their competitive advantage against pathogens in agricultural systems [21].Research shows that the interactions of plants with PGPR notably enhance crop characteristics and activate soil nutrients. For example, Serratia plymuthica modulates auxin biosynthesis by utilizing root-secreted growth hormone precursors, thereby promoting root development [22]. A beneficial bacterium isolated from the sweet potato rhizosphere was identified as Acetobacter through metabolomic and transcriptomic analyses. Under phosphorus-deficient conditions, this bacterium secretes organic acids that accelerate phosphorus solubilization, increasing phosphorus bioavailability for plants [23]. Similarly, Bacillus subtilis GB03 in Arabidopsis roots facilitates iron uptake by oxidizing Fe³⁺ to Fe²⁺, thereby enhancing the plant’s iron absorption capacity [24]. Moreover, soil - inhabiting PGPR generate antimicrobial substances. These substances inhibit pathogenic bacteria growth and are metabolized via intracellular lysis pathways [25]. Through vying with pathogens for nutrients and ideal environmental conditions, PGPR suppress harmful bacteria while colonizing favorable niches for reproduction [17]. In this research, diverse PGPR strains were effectively extracted from tobacco rhizosphere soil and utilized in continuously cropped tobacco soil. First, agronomic traits and root system development were assessed, revealing that strain C1 significantly enhanced tobacco growth and root development.Secondly, an evaluation of soil physicochemical characteristics and enzyme activities was conducted.The findings indicated that C1 notably promoted the generation of accessible soil nutrients, with a particular increase in available phosphorus. Additionally, C1 increased soil enzyme activity.Further microbiome and metabolome analysis indicated that C1 enriched beneficial microbial populations, enhanced the soil microenvironment, and accelerated soil nutrient cycling and transformation. These results lay a foundation for the development of microbial fertilizers to overcome continuous cropping obstacles. 2 Materials and methods 2.1 Experimental materials Test variety: Application of baking tobacco variety K326 Test soil: Four - year continuous - cropping soil sourced from the tobacco station in Linqu, Weifang. Its basic physicochemical properties are: organic matter 42.54g/kg, alkaline dissolved nitrogen 53.97mg/kg, quick-acting phosphorus 45.71 mg/kg, quick-acting potassium 178.5mg/kg, and pH value of 6.40 (Table 1). Experimental strains: The strains were furnished by Chengqiang Wang's laboratory at the College of Life Sciences, Shandong Agricultural University. They were isolated from top - notch, healthy field soils. These strains possess probiotic characteristics like phosphorus - solubilizing, potassium - solubilizing, indole - 3 - acetic acid (IAA) production, protein - degrading, and cellulose - degrading capabilities. The strains identified are as follows: C1 represents Bacillus parathoracic , C2 corresponds to Paenibacillus hunanensis , and C3 stands for Bacillus subtilis . The final bacterial product, CK2, utilized the liquid microbial fertilizer known as “Infusion of golden liquid,” sourced from Fujian Sanmu Biotechnology Co., Ltd, with its primary constituent being the gel-like Bacillus (see Table 1). Table 1 Soil and strain tested S.No Parameters Experimental result Soil samples Organic Matter 42.54 g/kg Alkaline Hydrolysable Nitrogen 53.97 mg/kg Available Phosphorus 45.71 mg/kg Available Potassium 178.5 mg/kg pH 6.40 strain C1 Bacillus parathoracic C2 Paenibacillus hunanensis C3 Bacillus subtilis 2.2 Experimental design The research was conducted under greenhouse circumstances at the Dai Zong Campus of Shandong Agricultural University through pot experiments. Two control groups were set up: the negative control, CK1, and the positive control, CK2. To prepare bacterial suspensions, strains were inoculated into LB (Luria - Bertani) medium. The mixture was then incubated at 37°C with a shaking speed of 180 rpm for 16 hours to yield the seed liquid.Subsequently, the optical density (OD600) of this seed solution was adjusted to around 2.5.The adjusted seed liquid was inoculated at a 2% rate into 50 ml of LB medium. For the controls, CK1 was given the same volume of water, while CK2 was administered an equal volume of a commercial bacterial product. The experimental group, consisting of three distinct bacterial agents (C1, C2, and C3), was administered a solution diluted to 200 times. Each treatment underwent six replications. 2.3 Measurement of physiological index of tobacco As per “Tobacco Agronomic Traits Survey and Measurement Methods” (YC/T142 - 2010) [26], The tobacco plants’ height, stem girth, count of functional leaves, as well as the greatest leaf length and width were recorded every five days following transplanting. The maximum leaf area was then determined using the formula: Leaf area = Leaf length × Leaf width × 0.6345. After 30 d of growth in the tobacco plant to select a representative plant for sampling, three plants per treatment, digging the whole plant, rinsed with water and then absorbent paper to absorb the water, the tobacco plant can be categorized into three distinct components: roots, stems, and leaves. Using an electronic balance, we first measure the fresh weight of each part. Following this, we dry the samples in an oven set to 105 ℃ for 30 minutes, then continue drying them at 80 ℃ until they reach a constant weight. Finally, document the dry weight of each individual section. A Microtek Phantom 9980XL scanner was used to measure total root length, total root surface area, average root diameter, the quantity of root tips, and the number of branches. At 5 - day intervals post - transplanting, representative plants were chosen to measure the chlorophyll content in their leaves. For the 3rd - 4th cotyledons, 3 plants per treatment were evenly chosen, and a SPAD - 502 portable chlorophyll meter was used to measure the relative chlorophyll content. 2.4 Examination of Soil Physicochemical Traits After a 30 - day growth phase, typical plants were picked for sampling. Moreover, three soil samples from each treatment were pooled together to evaluate the soil's pH level, the amount of alkaline hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) content. A pH meter was used to measure soil pH, and sodium bicarbonate extraction along with molybdenum - antimony colorimetry was applied to evaluate AP [27], while ammonium acetate in combination with a flame photometer was employed for AK measurement [28]. To assess AN [29], alkaline-dissolved nitrogen analysis was carried out, and the potassium dichromate volumetric method was used to measure OM [30]. 2.5 Assessment of Soil Enzyme Activity Thirty days after tobacco plants started growing, representative specimens were chosen for sampling.For each treatment group, three soil samples were collected and well - mixed.The activities of sucrase, urease, and catalase in the soil were measured using a kit from Beijing Prime Biological Co., Ltd. 2.6 Measurement of Soil Microbial Communities 2.6.1Sequencing of 16S rRNA Gene Amplicons from Inter-Root Soil Microorganisms Genomic DNA was isolated from inter-root soil samples utilizing the OMEGA E.Z.N.A™ Mag-Bind Soil DNA Kit, with universal primers 341F (CCTACGGGGNGGCWGCAG) and 805R (CCTACGGGGNGGCWGCAG) aimed at the V3-V4 segment of the 16S rRNA gene. For the purpose of PCR amplification, we used universal primers ITS1-ITS2, specifically ITS1F (CTTGGTCATTTAGAGAGGAAGTAA) and ITS2R (GACTACHVGGGGTATCTAATCC); additionally, another set of ITS1-ITS2 universal primers, ITS1F (CTTGGTCATTTAGAGGAAGTAA) and ITS2R (GCTGCGTTCTTCATCGATGC), were also utilized [31]. The size of the library was analyzed using 2% agarose gel electrophoresis, and the concentration was quantified with a Qubit 4.0 fluorescence measurement device to ensure consistent clustering results and high-quality sequencing output. Sequencing was performed on the Illumina MiSeq platform. 2.6.2 Amplicon Sequence Processing and Bioinformatics Analysis Sequencing data were analyzed using the BioSignal Cloud Platform (https://ngs.sangon.com/), where primer sequences were removed and low-quality reads were filtered out. Short read pairs from paired-end sequencing were combined into single sequences by employing Flash. The merged reads were subjected to additional processing to generate high-quality reads. Only data showing at least 97% similarity were considered, and unique sequences were grouped into operational taxonomic units (OTUs) using UPARSE (version 7.0.1090).The α - diversity indices (Chao 1, Ace, Simpson, Shannon, Shannoneven, and Sobs) were calculated using Mothur software [32]. To demonstrate changes in microbial composition, principal component analysis (PCA) using QIIME and R software (version 3.5.3) was carried out on treated samples. [33]. Furthermore, Redundancy Analysis (RDA) with Variance Inflation Factor (VIF) analysis was conducted to pinpoint environmental factors [34]. 2.6.3 Soil macro-genome sequencing Genomic DNA was extracted from inter - root soil samples using the OMEGA E.Z.N.A™ Mag - Bind Soil DNA Kit. DNA was quantitatively analyzed using a Qubit 4.0 fluorescence quantifier.DNA samples were sequenced on the Illumina MiSeq platform, generating substantial raw data.Clean reads underwent multi-sample hybrid assembly using Megahit software, while unmatched reads were assembled employing SPAdes software. Sequences shorter than 500 base pairs were not included in downstream analyses, which encompassed statistical evaluations and gene prediction. DIAMOND was used to align gene - set protein sequences with the KEGG database, enabling KO number assignment and quantifying functional - level abundance in samples. Additionally,sequences were analyzed against the KEGG database (http://www.kegg.jp/kegg/) using DIAMOND, with default parameters, to anticipate microbial metabolic functions. 2.7 Data analysis Results The experimental data were tabulated and plotted using Microsoft Excel 2021 and Origin 2023. Subsequently, significance of differences was analyzed via Duncan's test in SPSS Statistics 25.0. 3 Results 3.1 Genetic Diversity and Functional Identification of Rhizosphere Plant Growth-Promoting Bacteria According to the description in reference [35], strain C1 belongs to the same minimal clade as Bacillus paranthracis and exhibits the closest evolutionary relationship. Similarly, strain C2 clusters within the same minimal clade as Paenibacillus hunanensis , indicating a close evolutionary affinity. Simultaneously, the C3 strains exist within the same minimal clade as Bacillus subtilis, displaying the most closely related evolutionary ties. Initial identification of these strains, based on 16S rRNA gene sequence analysis alongside various physiological and biochemical traits, classified them as Bacillus paranthracis (C1), Paenibacillus hunanensis (C2), and Bacillus subtilis (C3). 3.2 Impact of diverse PGPR on the agronomic characteristics of tobacco To assess the traits of diverse strains, the application of different PGPRs exhibited a notable growth - enhancing impact on tobacco (Figure 1). The results show that microbial inoculation notably increased the height of tobacco plants, as well as the length, width, and maximum area of their leaves (Figure 1A, B, C, D, G). On the 10th day, compared with CK1, the CK2, C1, C2, and C3 treatments increased plant height by 44.36%, 22.18%, 7.72%, and 20.54%, respectively; leaf length by 21.95%, 7.02%, 17.56%; with CK2 showing no significant difference at this stage; leaf width by 1.91%, 12.31%, 5.51%, and 11.11%; and maximum leaf area by 1.9%, 37.59%, 13.19%, and 30.55%.On the 25th day after treatment, compared with CK1, the CK2, C1, C2, and C3 treatments increased plant height by 7.8%, 26.47%, 10%, and 11.53%, respectively; leaf length by 2.18%, 22.75%, 12.22%, and 7.31%; leaf width by 16.62%, 13.4%, and 7.23%, with CK2 showing no significant improvement; and maximum leaf area by 0.1%, 40.01%, 26.51%, and 15.68%. Notably, regardless of the diverse treatments, tobacco plants showed no significant variations in stem diameter or the count of effective leaves (Figure 1E, F). Meanwhile, further experiments were conducted to evaluate another key growth indicator, the SPAD value, which exhibited a significant enhancement following PGPR application. The findings demonstrated notable variations in how various PGPR strains impacted the SPAD values of tobacco leaves (Figure 1H). Five days after transplanting, the C1, C2, and C3 treatments, containing growth-promoting strains, resulted in a significant increase compared to CK1.By the 10th day, the SPAD value in the C1 treatment had increased by 8.50% compared to CK1. However, by the 15th day, SPAD values decreased across all treatments, though the chlorophyll content in the C1, C2, and C3 treatments remained higher than in CK1.After 20 days, the C1 treatment showed a 5.35% increase compared to CK1, outperforming all other treatment groups. By the 25th day, only the C1 treatment continued to show a growth-promoting effect, while the C3 treatment group maintained higher SPAD values than CK1.Thus, over the 25-day growth period, chlorophyll content analysis indicated that the C1 treatment exhibited superior performance compared to the CK2, C2, and C3 treatment groups (Figures 4 and 5). 3.3 Effect of different PGPG on root system of tobacco The application of different PGPR treatments resulted in significant differences in their ability to promote tobacco root growth (Figure 2). The results indicated that, compared to CK, the root diameter increased under the C1, C2, and C3 treatments, with the C1 treatment showing a notable increase of up to 20% (Figure 2A).In terms of total root length, the C1 and C2 treatments exhibited significant advantages, increasing by 80.52% and 87.91%, Compared to CK1, the C3 treatment was slightly lower, showing a 2.98% decrease (Figure 2B). In terms of root tip count, C1 and C2 exhibited the highest values, increasing by 29.55% and 54.10%, respectively, compared to CK1, while CK2 and C3 treatments followed, showing decreases of 19.70% and 36.71%, respectively (Figure 2C).Additionally, regarding root volume, the C1 treatment demonstrated a significant advantage over the other groups, surpassing CK1 by 96.09% (Figure 2D), while the CK2 and C3 treatments were lower than CK1, showing reductions of 12.31% and 20.02%, respectively.For root surface area, all treatments except C3 exhibited an increase compared to CK1, with C1 showing a significant 51.66% increase (Figure 2E).These findings suggest that the C1 treatment significantly promotes tobacco root development, leading to a more robust root system. 3.4 Effect of different PGPR on tobacco biomass Regarding tobacco biomass accumulation, the growth-promoting effects of different PGPR treatments differed significantly (Figure 3).For fresh weight, compared to CK1, the C1 treatment increased the fresh weight of roots, stems, and leaves by 99.07%, 91.55%, and 77.04%, respectively, all of which were significantly higher than in other treatment groups (Figures 3A–C).For dry weight, the C1 treatment resulted in increases of 105.56%, 47.50%, and 92.09% in the dry weight of roots, stems, and leaves, Individually, in contrast to CK1 (as shown in Figures 3D–F), it shows a remarkable growth - enhancing effect. Notably, regarding stem dry weight, the CK2 treatment was slightly lower than CK1 (Figure 3E), though the difference was not statistically significant. These findings suggest that PGPR application significantly enhances biomass accumulation in tobacco, with the C1, C2, and C3 treatments demonstrating particularly strong effects. 3.5 Effect of different PGPR on soil physicochemical properties and enzyme activities The elemental composition of the soil and its enzyme activity serve as vital indices for gauging soil fertility. As depicted in Figure 4, the impacts of diverse PGPR treatments on soil characteristics are presented.To evaluate these impacts, the levels of available nitrogen (AN), organic matter (OM), available phosphorus (AP), available potassium (AK), and pH within the experimental soil were determined. The results showed that, except for the C3 treatment, all treatments exhibited higher AN levels than CK1; Nevertheless, the disparities were not statistically notable (p ≥ 0.05) (Figure 4A).Regarding AP content, the C1 treatment led to a 28.98% increase in soil AP, and this value was notably higher compared to that in other treatments (Figure 4B). For AK content, PGPR application did not lead to significant differences, although all treatments had higher values than CK1 (Figure 4C).For OM content, the C1 treatment significantly increased soil OM by 21.44% relative to CK1 (Figure 4D). However, soil pH analysis revealed that PGPR application significantly lowered soil pH, with the C3 treatment showing the greatest reduction of 2.83%. Notably, the C2 treatment was the only one that resulted in a pH increase (Figure 4E). Figure 5 presents the activities of three key soil enzymes: sucrase (SU), catalase (CAT), and urease (UR). Notable variances in soil enzyme activities were detected among the treatments of tobacco inoculated with PGPR.Regarding SU activity, the C1 treatment exhibited a dramatic increase of 452.08%, significantly outperforming all other treatments (Figure 5A).For CAT activity, C1 exhibited the highest enzyme activity, with a 29.16% increase relative to CK1, whereas CK2 showed no significant difference (Figure 5B).For UR activity, both CK2 and C1 treatments exhibited higher UR activity than CK1, with C1 showing a notable increase of 123.89% relative to CK1 (Figure 5C). 3.6 Effect of PGPR on microbial diversity 3.6.1 Evaluation of high-throughput sequencing results and OUT clustering analysis Targeted sequencing of diverse genomic regions is essential for assessing microbial community diversity. The bacterial 16S V3 - V4 region and the fungal ITS1 1 - 2 region of nine soil samples were sequenced via the Illumina Miseq/Hiseq platform. This generated 544,949 raw reads for bacteria and 783,290 raw reads for fungi. The downstream sequencing produced bipartite sequences. To get valid data for each sample for the subsequent analysis, these sequences had to go through quality control and filtering. To obtain valid sample data for subsequent analysis, quality-control filtering of each sample's data is required. The sequence counts for bacteria and fungi were 55,288 and 86,900 respectively. The sequences from all samples were grouped into OTUs with 97% similarity, yielding 4035 OTUs for bacteria and 2343 OTUs for fungi. To validate the sequencing quality and depth, the dilution curve was used to evaluate the reliability of the sequencing data volume. With the rise in sequencing numbers, the dilution curve steadily flattened, approaching a value of 1 (Figure S2 A-B).This suggests that the majority of species present in the soil samples were identified, with the addition of further data likely yielding only a limited number of new operational taxonomic units (OTUs). The existing volume of sequencing data appears adequate, and the findings accurately reflect the true composition of the samples. Therefore, this data can be effectively utilized for future analyses concerning the diversity of the soil community and its species composition. The Venn diagram shows the distribution of OUTs specific to and shared by the species community, and the inter-root bacterial community had a total of 3615 OUTs in the two treatments, whereas 580 specific OUTs were specific to C1, and 796 were specific to the control CK. The fungal community had a total of 964 OUTs in the three treatments, while 380 and 1758 specific OUTs were specific to C1, C2, and the control CK, respectively (Figure S2 C-D). To investigate the variations and separations in the makeup of bacterial and fungal communities across various treatments, a PCA analysis was conducted at the OTU level for both bacterial and fungal communities. The bacterial community's PC1 and PC2 axes were separated at 12.37% and 71.058%, respectively.Compared to CK, the C1 treatment was distributed separately, suggesting variations in species composition across the treatments.Compared to CK, the C1 treatment was distributed separately, suggesting variations in species composition across the treatments.The fungal community exhibited segregation rates of 8.37% and 86.323% along the PC1 and PC2 axes, respectively. Moreover, it was fairly spread out across the treatments, with variations in the structure of the species composition (Figure S2 E - F). 3.6.2 Effect of Alpha and Beta diversity of soil microorganisms Regarding the impact of various treatments on the α-diversity of inter-root bacteria, when compared to the control (CK), the Chao1 and Ace indices in the C1 treatment declined. This suggests that the C1 treatment led to a reduction in the relative abundance of inter - root bacteria within the microbial community (see Figure 6A - B).The Sobs index was greater in the CK treatment compared to the C1 and C2 treatments, indicating that the CK treatment had the highest actual number of OUTs (Figure 6C).Compared to the CK, the rise in Simpson's index and decline in Shannon's index in the C1 treatment suggested that the community diversity of inter - root bacteria took a hit in the C1 treatment (Figure 6D - E). The Shannon evenness index was found to be greater in the CK treatment compared to the C1 treatment. This suggests that the inter-root bacterial community exhibited a higher level of uniformity in the CK group than in the C1 group (see Figure 6F). The various treatments had a notable impact on the α-diversity of fungi found between roots. Compared to the control group (CK), the Chao1 and Ace indices for the C1 treatment showed an increase, suggesting that it enhanced the quantity of operational taxonomic units (OTUs) among inter-root fungi, thus boosting the overall relative abundance of fungal species within the microbial community (see Figures 6G-H).The Sobs index for C1 exceeded that of the CK control, indicating a lower count of OUTs in the CK treatment compared to C1 (Figure 6I). In contrast to CK, C1's Simpson's index decreased while the Shannon index rose, suggesting that the C1 treatment enhanced community diversity (Figure 6J-K).The Shannon-Weaver index for the CK treatment was lower compared to that of the C1 treatment, suggesting that the C1 treatment exhibited a greater uniformity in the distribution of the inter-root fungal community (see Figure 6L). 3.6.3 Effect of PGPR on microbial composition and structure To elucidate how various treatments influence the species composition of microbial communities, we conducted a comparative analysis of the shifts in the dominant bacterial and fungal species at the phylum level (see Figure 7A-B). Additionally, we categorized species with a relative abundance of less than 0.01 under the label "Others."The dominant species at the phylum level of bacteria were Proteobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes, Actinobacteria, unclassified Bacteria, Candidatus Saccharibacteria, Verrucomicrobia, Cyanobacteria/Chloroplast, candidate_division_WPS-1 . Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes as the most dominant phylum. The predominant fungal phyla with the greatest relative abundance included unclassified_Fungi , Ascomycota , Zygomycota , Basidiomycota , Fungi_unidentified , Chytridiomycota , and Glomeromycota . Notably, unclassified_Fungi , Ascomycota , and Zygomycota emerged as the leading groups among them. To delve deeper into the variations in species composition across treatments at the gate level, several comparative analyses were conducted on bacteria and fungi. This involved comparing bacterial species with the highest 15 P values and fungal species with the top 8 P values. In contrast to CK (as depicted in Figure 7C - D), the C1 treatment led to a rise in the relative prevalence of Bacteroidetes, Proteobacteria, Actinobacteria, and Candidatus Saccharibacteria within the bacterial community. Simultaneously, it brought about a decline in candidate_division_WPS - 2 and the relative proportions of Planctomycetes. At the phylum level, the composition of the fungal community showed noticeable variation. In contrast to the CK treatment, the C1 treatment resulted in an increase in the relative abundance of unclassified Fungi, Ascomycota, and Basidiomycota, while simultaneously leading to a decrease in the proportion of Zygomycota. An analysis was conducted on the genus-level composition of bacteria and fungi across various treatments, revealing that the predominant genera within the bacterial community included Sphingomonas , unclassified_Bacteria , unclassified_Gemmatimonadaceae , Chujaibacter , Saccharibacteria_genera_incertae_sedis , and Gp3 , as depicted in Figure 8A. At the genus level within the fungal community, the predominant genera were chiefly unclassified_Fungi , Mortierella , an unidentified Sordariomycetes species ( Sordariomycetes_unidentified_1 ), unclassified Ascomycota, an unidentified Ascomycota species ( Ascomycota_unidentified_1_1 ), and Trichoderma , as depicted in Figure 8B. An examination of the variations in species composition of bacterial and fungal communities across different treatments revealed that, at the genus level for bacteria, treatment C1 notably enhanced the relative abundance of the genera Edaphobacter , Acidibacter , GPl , Acidobacterium , and an unidentified group from Alcaligenaceae (see Figure 8C). At the fungal genus level, C1 elevated the relative abundance of Ascomycota unidentified 1 1 , unclassified_Hypocreaceae , and Ascomycota_unidentified (Figure8D). To delve deeper into the variations in bacterial and fungal communities present in the inter-root soil of tobacco plants subjected to different treatments, linear discriminant analysis and the LEfSe method were employed to identify the species that most effectively highlighted the distinctions between the groups across the various treatments. Soil samples from tobacco plants were analyzed and categorized at five taxonomic levels, from phylum to genus.Fifty-five distinct levels of differential taxa were recognized in the bacterial community. In the CK treatment group, 32 distinct species showed increased abundance, whereas the C1 treatment group saw an increase of 21 species. At the genus level within the bacterial community, the CK treatment group was primarily characterized by the enrichment of Sphingobium , Gemmatimonadetes , unclassified_Bacteria , unclassified_Betaproteobacteria , norank_Parcubacteria , and additional unclassified_Bacteria . Notably, the phylum, order, and family - level classifications largely corresponded to the genus-level findings. In the C1 treatment group, the taxa enriched at the genus level included Chujaibacter , Acidibacter (mentioned twice), Bradyrhizobium , Rhizomicrobium , Parcubacteria , unclassified_Gemmatimonadaceae , Edaphobacter , Gp4, and Gp6 (as depicted in Figure 8E). Sixteen taxa with varying differentiation levels were identified in the fungal community. No taxa were enriched in CK; instead, all were enriched in the C1 treatment group.Sixteen differential species were present. At the phylum level, two taxa, Ascomycota and Glomeromycota , were enriched in C1. At the phylum level, C1 exhibited four notable enriched taxa: Ascomycota_unidentified , Eurotiomycetes , Sordariomycetes , and Microbotryomycetes . When examining the order level, five enriched taxa were identified in C1: Glomeromycetes , Eurotiales , Hypocreales , Sporidiobolales , and Glomerales . At the order level, four taxa are enriched in C1: Ascomycota_unidentified_1 , Trichocomaceae , Hypocreaceae , and Incertae_sedis_25 . At the genus level, the four enriched taxa in C1 are Talaromyces , Trichoderma , Ascomycota_unidentified_1_1 , and Incertae_sedis_25_unidentified (Figure 8F). 3.6.4 Correlation analysis of soil physicochemical and microbial diversity The introduction of various inter-root biotrophic bacteria influences nutrient cycling within the soil and modifies the activities of soil enzymes, leading to shifts in the microbial community present in that environment.Consequently, the interplay between soil bacterial and fungal communities and various environmental factors was examined. Redundancy Analysis (RDA) was employed to establish connections between physicochemical characteristics—namely pH, AN, OM, AP, AK—and soil enzyme activities such as SC, CAT, and UR in relation to the microbial communities present in the soil (see Figure 9A, C). The findings indicated that the physicochemical properties of the soil—specifically AK, AP, and OM—were predominantly influenced by the bacterial community, with OM following closely behind. Notably, the C1 treatment exhibited a strong positive correlation with AP, AK, and OM in the soil, with the relationship to AP being particularly pronounced. Although pH showed a strong negative correlation with C1 treatment. The C1 treatment showed a significant positive correlation with SC and CAT in the soil, yet had no significant correlation with UR. By examining the impact of the fungal community on soil nutrients and enzyme activities, AK, AN, OM, and pH were found to significantly influence the soil, while OM showed the weakest correlation. The C1 treatment showed a strong positive correlation with AN and AK, and a significant negative correlation with pH. Regarding soil enzyme activities, the fungal communities had the greatest impact on SC, with CAT being affected to a lesser extent. In soil, the C1 treatment had a positive correlation with SC and CAT. Among them, CAT showed the strongest correlation, being significantly negatively correlated with UR. To delve deeper into the interplay between microbial community composition and soil nutrients as well as enzyme activities, a correlation heat map analysis was conducted. This analysis focused on the predominant genera of both bacterial and fungal communities at the genus level, in relation to various soil environmental factors (Figure 9B.D). In the microbial community, key players such as Sphingomonas , Gp1 , Gp3 , Saccharibacteria , and Acidobacterium have been identified as significantly influencing soil physicochemical properties and enzymatic activities. Sphingomonas, Gp1, and Gp3 demonstrated a significant positive correlation with numerous factors such as AN, AP, AK, OM, SC, and CAT, but had an inverse relationship with pH levels. In contrast, Saccharibacteria and Acidobacterium exhibited a notable negative relationship with AN, AP, AK, OM, SC, and CAT, while showing a positive correlation with pH. The correlation between UR and the dominant genus of the bacterial community showed no significant difference. The correlation heatmap in( 9B) depicts the relationships between the dominant fungal genera at the genus level, soil nutrients, and enzyme activities. The findings indicated a significant positive correlation among Trichoderma, Talaromyces, and the fungal community members AN, AP, AK, SC, and CAT. Mortierella exhibited a strong negative correlation with AN, AP, AK, UR, and CAT. Conversely, the organic matter in the soil showed a notable positive relationship with Trichoderma , while maintaining a significant negative correlation with Mortierella . Conversely, soil pH had a negative correlation with Podosordaria and a significant positive correlation with Mortierella . No notable disparity was detected in the correlation of UR with the dominant genera within the fungal community. To understand the interactions between dominant species in the microbial community, correlation analysis of microbial interactions was conducted, and significant correlations were found between genera in the bacterial community ((Figure 9E-F). The highest number of genera were correlated with Micropepsaceae , where Micropepsaceae was positively correlated with GP1, Devosia, WPS-1, acidibacter, Unclassified_bacteria, Bacteroidetes, and Acetobacteraceae . The genera with negative correlation with GP4 were GP6, GP4. the most genera with negative correlation with GP4 included Bradyrhizobium, Micropepsaceae, Acetobacteraceae, while flavisolibacter, Gp6, Betaproteobacteria were positively correlated with Trinickia were positively correlated. In the fungal community correlations between genera were low and only a few genera were correlated and all were positively correlated. Chytridiomycetes were positively correlated with Pseudogymnoascus and Waitea . Waitea and Pseudogymnoascus were positively correlated and Incertae sedis 25 was positively correlated with Ascomycota . 3.6.5 Effects of functional properties in soil microbial communities Functional abundance of soil microorganisms at Pathway levels1 and Pathway levels2 levels, soil microbial metabolic functions occupy the largest proportion of the six KEGG metabolic functions, indicating that metabolism plays an important role in the life activities of microorganisms (Figure 10A). In the KEGG Pathway database, the global and overview maps, a distinct set of metabolic pathway maps, boasted the highest number of genes involved in metabolic functions. These functions, in descending order, were amino acid metabolism, carbohydrate metabolism, energy metabolism, cofactors and microbial metabolism, and nucleotide metabolism. In the various cellular process pathways, when it comes to cell communities, prokaryotes boast the largest number of genes. In the realm of environmental information processing, the pathways of transmembrane transport and signaling have the greatest quantity of genes. In gene information processing, the largest number of genes pertains to signaling. In disease pathways related to bacterial infectious diseases, there is also the highest count of genes. In organic systems pathways, aging, endoanalytic systems, and environmental adaptation are the functional expressions having the greatest number of genes. To delve deeper into the action mechanism of the inter - root biotrophic bacteria, we annotated the disparities in the diverse metabolic functions of microorganisms at the KEGG tertiary metabolic levels triggered by the inter - root biotrophic bacteria (Figure 10B). In comparison with the CK group, when it came to tobacco plants, the soil microorganisms in the C1 treatment with Bacillus paracord notably enhanced fatty acid degradation and metabolic functions, as well as the metabolic functions of glycine, serine, and threonine, lysine degradation functions, and tryptophan metabolic functions. The metabolic activities of arginine and proline, along with the metabolic role of benzoic acid, and the biosynthetic functions of phenylalanine, tyrosine, and tryptophan were significantly elevated in the C1 treatment compared to the control group, CK. Amino acids are nutrients that play a key role in enabling plants to grow and develop and keep soil microorganisms' life activities ticking over. The C1 Bacillus paracolor revved up the amino - acid biosynthesis function in the soil microorganisms around the tobacco plant roots. What's more, when it came to the synthesis of secondary metabolites of soil microorganisms, the C1 treatment outperformed the CK treatment. Compared with the CK treatment, C1 enhanced the metabolic function of organic selenium compounds and the biosynthesis of cofactors. Microorganisms in the soil play a crucial role in the cycling of materials and the flow of nutrients. To gain insight into the metabolic functions of the predominant genera of these soil microbes, a correlation analysis was performed linking the dominant bacterial species to KEGG functions (see Figure 10C). The correlation heat map showed that Chujaibacter , Rhizobiales , Saccharibacteria , Acidobacteria_Gp1 , and Trichoderma were linked to specific types of cancer. In the soil, amino acid metabolism, the biosynthesis of other secondary metabolites, glycan biosynthesis and metabolism, lipid metabolism, the metabolism of terpenoids and polyketides, and the digestive system had a significant positive correlation with folding, sorting and degradation, transcription, specific types of cancer, endocrine and metabolic diseases in the soil, bacterial infectious diseases, neurodegenerative diseases, energy metabolism, and aging. In the soil, there was a noteworthy positive correlation between Mortierella , GP1 , unclassified_Bacteria , and Betaproteobacteria and various processes such as folding, sorting, and degradation. Additionally, significant positive relationships were found between transcription, specific types of cancer, endocrine and metabolic diseases, bacterial infections, neurodegenerative disorders, energy metabolism, and aging, all of which were closely linked to the soil's functional characteristics. Specific types, Amino acid metabolism, Biosynthesis of other secondary metabolites, Glycan biosynthesis and metabolism, Lipid metabolism, Metabolism of terpenoids and polyketides, and Digestive system were significantly negatively correlated. Translation and Nucleotide metabolism in soil were significantly negatively correlated with Saccharibacteria , Acidobacteria_Gp1 , Trichoderma and significantly negatively correlated with Mortierella . Additionally, a significant negative correlation was found between Immune disease and Devosia , Streptophyta , Alphaproteobacteria ,and Rhizobiales present in soil. 4 Discussion A number of PGPR strains were extracted from the rhizosphere soil of tobacco fields located in Shandong. After identification and evaluation, three of these strains were chosen for further experiments aimed at promoting plant growth. These strains were identified as Bacillus paranthracis , Paenibacillus hunanensis , and Bacillus subtilis, while the commercially available rhizosphere growth-promoting strain used was Paenibacillus mucilaginosus . All four bacterial genera are among the most prevalent in PGPR. Bacillus paranthracis has been reported to facilitate phosphorus solubilization, nitrogen fixation, and the enhancement of soil microbial communities [36]. Paenibacillus hunanensis , Bacillus subtilis , and other Paenibacillus species also exhibit nitrogen fixation, phosphorus solubilization, and deaminase activation. These functional traits enhance nutrient uptake, thereby promoting crop biomass and yield [37]. Agronomic characteristics are essential for plant development and quality, with PGPR proven to significantly improve these traits, fostering growth and advancement. [33, 38]. Lobato et al. stated that Pseudomonas and Brucella notably increased the number of branches, leaves, chlorophyll levels, and height of blueberry plants. [39]. Similarly, inoculating four local PGPR strains into pepper seedlings significantly accelerated pepper growth rates [40, 41]. Experiments were conducted on essential agronomic indicators to assess the impact of PGPR on tobacco growth. The results revealed that the selected PGPR strains induced significant differences in tobacco growth and development. In particular, the three chosen bacterial treatments noticeably boosted various plant growth metrics, such as height, maximum leaf length, width, area, and SPAD value. Notably, the C1 treatment demonstrated more pronounced effects compared to the other treatments (see Figure 1B-D, G-H).However, there were no notable differences in stem circumference and leaf count among treatments (Figure 1E, F). Consistent with previous studies, Zhang et al. reported that applying FJS-3 to tobacco seedlings significantly increased plant height, root weight, and fresh weight by 25.56%, 24.77%, and 21.21%, respectively. When FJS-3 was applied as a compound microbial fertilizer for 30 days, these increases further rose to 30.15%, 37.36%, and 54.5%, respectively [42].Similarly, Shang et al. found that treatments with Bacillus cereus , Bacillus methylotrophicus , and Bacillus amyloliquefaciens enhanced tobacco plant height by 38.65%, 91.94%, and 90.66%, respectively, with Bacillus cereus exhibiting a twofold increase compared to the control group [43]. In a two-year study involving common wheat treated with PGPR, Cristian and colleagues discovered that the chlorophyll levels in the PGPR-inoculated wheat were consistently elevated compared to those in the non-inoculated controls.[44]. In this study, the newly screened functional bacterial strains exhibited a stronger growth-promoting effect on tobacco than commercial microbial agents. Many studies show that diverse environmental factors, like soil nutrient makeup and climate, affect the growth - enhancing effects of PGPR. This suggests that, as these functional bacteria were isolated from and adapted to a specific region, they may be more suited to tobacco fields in Shandong. Root system development directly influences root activity, including nitrogen uptake, utilization, and dry matter accumulation in plants [45]. PGPR primarily influence the shape and structure of roots, boosting their capacity to take up nutrients and water from the soil, which in turn fosters the development of the plant’s aerial components.This improved root architecture enhances plant uptake of water and nutrients, optimizing water and fertilizer utilization efficiency [46-49].The findings of this study indicated that the application of plant growth-promoting rhizobacteria (C1, C2, and C3) had a marked positive effect on the development of the tobacco root system and enhanced biomass production. This included notable improvements in root diameter, total root length, the number of root tips, root volume, root surface area, and the overall biomass of the tobacco plants (Figure 2).Consistent with previous findings [50], chlorophyll content is a key indicator of plant photosynthetic capacity [51]. Bacillus species are reported to enhance soybean growth and development. They do this by stabilizing chlorophyll levels, which in turn aids biomass accumulation [52]. In a PGPR experiment on barley, total chlorophyll content increased by 126% compared to the untreated control [53].Similarly, Chamkhi et al. found that PGPR inoculation in saffron increased both the number of leaves and chlorophyll content by 1.91-fold compared to the control group [54]. In this study, fresh and dry weights of roots, stems, and leaves were recorded, and C1 significantly promoted biomass accumulation in tobacco stems (Figure 3), aligning with the findings of Yolanda et al. Additionally, PGPR increased the SPAD values of tobacco leaves (Figure 1H), indicating that PGPR significantly enhances photosynthesis, contributing to tobacco biomass accumulation.Consequently, PGPR promotes the elongation growth of both aerial and subterranean plant components, boosts the absorption of soil nutrients and water, and thereby eases tobacco biomass accumulation.Thus, it is speculated that the novel functional bacteria may confer greater benefits than commercial strains by improving root system architecture and further stimulating above-ground plant growth (Figure 1). Soil enzyme activity is of utmost importance in the global circulation of crucial elements like carbon, phosphorus, and nitrogen. It acts as a vital yardstick for assessing the health and fertility levels of the soil [55]. Soil characteristics and nutrient amounts have a direct impact on plant development and yield. In this research, treatments C1, C2, and C3 notably enhanced the tobacco plants' soil physicochemical traits, such as AN, AP, AK, and OM (Figure 4A-D). This discovery is consistent with Jiang et al.'s results, which showed that PGPR can notably improve soil nutrient accessibility [56]. Soil enzyme activity is mainly propelled by the physiological and metabolic activities of various microbial communities, which mirror the existence and functionality of functional microbes. Investigating soil enzyme activity provides insights into microbial responses to environmental changes [56, 57]. In the soil, the actions of sucrase (SC), catalase (CAT), and urease (UR) act as crucial barometers of soil fertility and microbial activity. They have a say in the conversion of nitrogen compounds, organic matter, and other vital nutrients. Furthermore, these enzymes contribute to alleviating oxidative stress in plants [58, 59]. In this research, the application of PGPR showed a marked improvement in the activities of sucrase (S-SC), catalase (S-CAT), and urease (S-UR) when compared to the CK treatment, which was linked to higher levels of organic matter (OM) and available nitrogen (AN) in the rhizosphere soil. These findings align with earlier studies [60, 61]. Overall, PGPR greatly enhanced the nutrient levels and physicochemical attributes of tobacco plants, closely linked to changes in the soil bacterial community (Figures 6-9). PGPR application modifies the community structure of native soil microorganisms to a certain degree.Go head - to - head with native microorganisms for the scarce soil nutrients and spatial layout. Alternatively, spur plant roots to excrete distinct secretions that attract and prompt the beneficial microorganisms in the soil to convert soil nutrients [62]. In this study, the application of C1 brought about a decline in the number of bacterial and fungal Operational Taxonomic Units (OUTs) in the soil. Regarding bacterial diversity, C1 put a damper on species diversity and richness; however, it beefed up the relative abundance of the dominant species. C1 - Parachromobacterium augmented the relative abundance of dominant phyla like Bacteroidetes , Proteobacteria , Actinobacteria , and Candidatus Saccharibacteria . The phylum Proteobacteria has complex physiological metabolic types that are important for the carbon cycle [63]. The phylum Bacteroidetes possesses the ability to metabolize sugars and can participate in the production of methane as well as the conversion process of dissolved organic carbon [64]. Actinobacteria play a role in nitrogen and phosphorus metabolism in soil and aid in the breakdown of readily available phosphorus and nitrogen [65]Moreover, C1 enhanced the relative prevalence of Ascomycota and Basidiomycota within the fungal community. As the primary fungal decomposers in the soil, the fungal genera within the Ascomycota phylum were predominantly saprophytes. These saprophytes had the ability to kick - start the conversion of organic matter in the soil. Moreover, the stammers were a big player in breaking down the lignocellulose present in the soil [66]. The microbial genus level was analyzed and C1 increased the relative abundance of dominant genera in the bacterial community of Acidobacterium , Alcaligenaceae , Pandoraea , and Thiomonas . Compared with the CK, Acidobacterium are eosinophilic and oligotrophic chemo-organotrophic bacteria that regulate pH in the soil. It was found that Acidobacterium were able to cause a significant increase in the mortality of Colorado potato beetle larvae [67]. Alcaligenaceae also had a positive effect on pH stabilization in the soil. Pandoraea alleviates drought stress and enhances growth characteristics in soybeans [68]. Certain reports have discovered that the existence of genes associated with the urea degradation process within Thiomonas strains can boost the rate at which urea is broken down in the soil, thereby enhancing soil fertility. Additionally, it has been noted that the breakdown of urea facilitates the precipitation of toxic metals like iron, aluminum, and arsenic [69]. C1 raised the relative prevalence of unclassified_Hypocreaceae , unidentified_Ascomycota , and Trichoderma within the fungal community. Researchers discovered that Trichoderma , a genus within the Hypocreaceae family, offers both biocontrol benefits and promotes plant growth. These advantages encompass antibacterial properties, antioxidant capabilities, insect - repelling features, and functions that stimulate plant development. [70]. Soil microorganisms are crucial for the cycling and transformation of substances like soil nutrients, root secretions, and apoplastic materials [71]. The functions of soil microorganisms are intricately linked to elements like organic matter, mineral nutrients, and their own life processes within the soil, and they maintain a state of dynamic equilibrium. Consequently, researching the structure and function of the soil microbial community holds immense significance for understanding the transformation mechanisms of soil nutrients and soil fertility [72, 73]. KEGG functional annotation analysis of the structural functions within soil microbial communities indicated that the metabolic functions of soil microorganisms are of great significance in life activities. Among the six KEGG metabolic functions, these metabolic functions of soil microorganisms boast the largest number of genes. Application of C1 enhanced the metabolic functions of tobacco inter-root microorganisms, promoting fatty acid degradation as well as glycine, serine, and threonine metabolism. Overall, the introduction of inter-root biotrophic bacteria enhanced the metabolic functions of inter-root microorganisms, as well as boosted microbial cycling and energy conversion in soil substances [74, 75]. 5 Conclusion This study clarified the impacts of different PGPR on tobacco growth, development, and soil physicochemical properties, and selected the optimal PGPR, T1. The results indicated that PGPR application markedly advanced tobacco growth and development and held prominent benefits in enhancing soil physicochemical properties. A comparative analysis of soil enzyme activity, microbiome, and metagenome was also carried out between the top - performing C1 treatment group and the CK control group. The results showed that the activities of enzymes including sucrase, catalase, and urease were notably higher in the C1 group than in CK. Furthermore, the T1 strain facilitated the growth of beneficial microbes and boosted metabolic functions associated with fatty acid breakdown as well as the metabolism of glycine, serine, and threonine. This lays the groundwork for the advancement of high - efficiency microbial fertilizers and the exploration of high - efficiency PGPR. Declarations Author contributions Ran Wang and Chengguang Zhu: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft. Lei Tian: Data Curation, Writing - Original Draft. Lili Wang, Hao Zong, Mingfeng Yang, Fuyu Peng and Mingming Sun: Resources, Supervision. Zongpeng Zhao, Yuhai Du and Zengbo Fan: Software, Validation, Writing - Original Draft. Li Zhang and Qiang Zhang: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing. Consent for publication Written informed consent for publication of this paper was obtained from all authors. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Funding This work was supported by This work was supported by the Foundation of Research and application of efficient cultivation technology for integrated tobacco and wheat production(2024371300260411), Analysis of the characteristic styles and mellowing characteristics of American functional Roubaix tobaccos (202302004), Construction and Application of Quality Management Control Model for Tobacco Production and Acquisition in Shandong Province(202301001), Natural Science Foundation of Shandong Province (ZR202211230214), Science and Technology Program of Shandong weifang Tobacco Limited Company (2024-34) and Major Science and Technology Projects of China National Tobacco Corporation, Shandong Provincial Company (202404). Data availability The datasets generated during and/or analyzed during the current study were available from the corresponding author on reasonable request. The sequenced raw reads generated in this study have been submitted to the National Center for Biotechnology Information (NCBI) with BioProject ID: C1:PQ895541, C2:PQ895542, C3:PQ895543. Declaration of Competing Interest Here, we confirm that although this study received funding, the design, implementation, data analysis, and interpretation of results have maintained independence and objectivity. The authors declare no potential conflicts of interest that could compromise the impartiality of the research with the funding company. All research processes adhere to the principles of academic integrity, ensuring the transparency and credibility of the findings. References Ma Z, Guan Z, Liu Q, Hu Y, Liu L, Wang B, Huang L, Li H, Yang Y, Han M: Obstacles in continuous cropping: mechanisms and control measures . Advances in agronomy 2023, 179 :205-256. 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Das PP, Singh KR, Nagpure G, Mansoori A, Singh RP, Ghazi IA, Kumar A, Singh J: Plant-soil-microbes: A tripartite interaction for nutrient acquisition and better plant growth for sustainable agricultural practices . Environmental Research 2022, 214 :113821. Fazeli-Nasab B, Piri R, Rahmani AF: Assessment of the role of rhizosphere in soil and its relationship with microorganisms and element absorption . Plant Protection: From Chemicals to Biologicals 2022, 225 . Bhattacharyya SS, Ros GH, Furtak K, Iqbal HM, Parra-Saldívar R: Soil carbon sequestration–An interplay between soil microbial community and soil organic matter dynamics . Science of The Total Environment 2022, 815 :152928. Khan N, Ali S, Zandi P, Mehmood A, Ullah S, Ikram M, Ismail I, Babar M: Role of sugars, amino acids and organic acids in improving plant abiotic stress tolerance . Pak J Bot 2020, 52 (2):355-363. Yoo JY, Ko KS, Vu BN, Lee YE, Yoon SH, Pham TT, Kim J-Y, Lim J-M, Kang YJ, Hong JC: N-acetylglucosaminyltransferase II is involved in plant growth and development under stress conditions . Frontiers in Plant Science 2021, 12 :761064. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6220721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442177795,"identity":"3e1c1d9b-2a4b-47f9-85c7-ec35a50f3519","order_by":0,"name":"Ran Wang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Wang","suffix":""},{"id":442177796,"identity":"1268ea70-3d34-4c84-a423-447e30a43873","order_by":1,"name":"Chengguang Zhu","email":"","orcid":"","institution":"Technology Center of Shandong China Tobacco Industrial Co., 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13:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6220721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6220721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80524781,"identity":"3b71fbac-da48-4066-bb95-9c083db3224f","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":323767,"visible":true,"origin":"","legend":"\u003cp\u003eAgronomic traits of tobacco. (A) Phenotype, (B) Plant height, (C) Maximum leaf length, (D) Maximum leaf width, (E)Stem circumference, (F)Stem number of leaves, (G) Maximum leaf area, (H) SPAD.Different letters in front of the same compound and in bar graphs denote significant differences (one-way ANOVA test; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/2b42cba449f01c39417bda74.png"},{"id":80524784,"identity":"aae48fd2-34bc-4b8d-97fe-7f2e7e1ead0f","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":710944,"visible":true,"origin":"","legend":"\u003cp\u003eRoot system. (A) root diameter, (B) Total root length, (C) Number of apex, (D) Root volume, (E) Root surface area. Different letters in front of the same compound and in bar graphs denote significant differences (one-way ANOVA test; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/f68608117bb518c554004d86.jpg"},{"id":80524786,"identity":"f2d10c74-ac4e-4cbe-9011-9e61072edf58","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1810876,"visible":true,"origin":"","legend":"\u003cp\u003eSubstance accumulation. (A) Fresh weight of root, (B) Fresh weight of stem, (C) Fresh weight of leaf, (D) Dry weight of root, (E) Dry weight of Stem, (F) Dry weight of leaf. Different letters in front of the same compound and in bar graphs denote significant differences (one-way ANOVA test; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/be96c4ba6416a9f8c9d42496.jpg"},{"id":80525429,"identity":"a095a121-5b84-4f04-9fb1-8f7901beef9e","added_by":"auto","created_at":"2025-04-14 09:48:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2692293,"visible":true,"origin":"","legend":"\u003cp\u003esoil physicochemical properties. (A) AN, (B) AP, (c) AK, (D) OM, (E) pH. Different letters in front of the same compound and in bar graphs denote significant differences (one-way ANOVA test; \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/1e2de2eb797d6ddee9973b22.jpg"},{"id":80524785,"identity":"75fd653e-1f77-498b-b6ea-7d98b737ec40","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1513393,"visible":true,"origin":"","legend":"\u003cp\u003enzyme activities. (A) Sucrase, (B) Catalase, (C) Urease. Different letters in front of the same compound and in bar graphs denote significant differences (one-way ANOVA test; \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/87c026e157f790ba6ec2550f.jpg"},{"id":80524782,"identity":"c3dbacbc-e659-44cd-a301-8942dea03607","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":105001,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha-diversity index of rhizosphere bacteria (A) Chao1, (B) Ace, (C) Sobs, (D) Simpson, (E) Shannon, (F) Shannoneven. Alpha-diversity index of rhizosphere fungi (G) Chao1, (H) Ace, (I) Sobs, (J) Simpson, (K) Shannon, (L) Shannoneven.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/bf5f6c7594c660a7b8e5cc09.png"},{"id":80525430,"identity":"c4cd42fc-6470-4221-9d20-0b50930b11cc","added_by":"auto","created_at":"2025-04-14 09:48:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":22353206,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of microbial phylum level (A) Composition and relative abundance of bacteria, (B) fungal, (C) Comparative analysis of phylum level bacterial community, (D) fungal\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/6887ff09cdbb7d4d9232541e.jpg"},{"id":80524787,"identity":"08f6a964-8f00-4541-b857-712e9d15559a","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4157395,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of microbial genus level (A) Species composition analysis of dominant bacteria, (B) fungal, (C) Comparative analysis of genus level bacterial community, (D) fungal, (E) LEfSe analysis results of bacterial (F) fungal\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/26a17edb8f4402afcbb3e59f.jpg"},{"id":80524789,"identity":"7b0ecb96-13d0-4e7d-a5ca-ee8070670d91","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4646516,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis (A) RDA analysis of nutrient and enzyme activities in rhizosphere bacterial, (B) fungal, (C) Correlation of soil nutrient and enzyme activity with dominant genera of bacteria, (D) fungal. Species correlation analyses based on bacterial (E) and fungal (F) communities at the genus level.\u003c/p\u003e","description":"","filename":"figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/e01d3077713aaf72c28162ad.jpg"},{"id":80524790,"identity":"37e88257-3b0b-4e28-aa0d-6eb752d065a0","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":7184094,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 9 Analysis of metagenome (A) Histogram of the number of genes at KEGG Pathway1 and Pathway2 levels, (B) Influence of rhizosphere growth-promoting bacteria on the metabolic level of soil microorganisms KEGG Pathway3, (C)Heat map analysis of correlation between dominant genera and KEGG function in soil\u003c/p\u003e","description":"","filename":"figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/ee321f70eeca01c29ab583e4.jpg"},{"id":83522509,"identity":"6bd16af7-2249-4580-b8c8-d393fbe79882","added_by":"auto","created_at":"2025-05-28 00:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27672780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/e0f0d9ca-db82-4bca-a798-2bf3d869ef09.pdf"},{"id":80524788,"identity":"d6997b3b-2400-4a31-a602-8dd374a54d73","added_by":"auto","created_at":"2025-04-14 09:40:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":327412,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6220721/v1/ba3f959655363f40ded7b5f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanistic analysis of rhizosphere promoting bacteria on tobacco growth, continuous cropping soil, and root microbiota","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe phenomenon of continuous cropping obstacles has been attributed to prolonged, fixed-location, and high-frequency planting patterns, leading to declines in crop quality, reduced yields, and even total crop failure [1]. This issue is typically caused by abiotic or biotic factors in the soil\u0026nbsp;[2], including alterations in soil physicochemical properties, excessive nutrient depletion, and fluctuations in soil enzyme activity\u0026nbsp;[3]. These abiotic factors have been observed to inhibit crop development, trigger severe pest and disease outbreaks, and ultimately reduce crop yield\u0026nbsp;[1]. In continuous cropping systems, root exudates have been found to accumulate in the soil, leading to the formation of autotoxin compounds that promote the excessive proliferation of pathogenic microorganisms[4]. This procedure, conversely, inhibits the activity of beneficial microorganisms and causes a reduction in the cycling of soil nutrients and the decomposition capacity of organic matter\u0026nbsp;[5]. Over time, Regularly planting the same crops has been found to interfere with the soil\u0026apos;s micro-ecosystem, leading to a more straightforward community structure, a rapid increase in harmful microorganisms, and a decline in helpful microbial populations\u0026nbsp;[6], thereby posing significant challenges to sustainable agricultural development.\u003c/p\u003e\n\u003cp\u003eTobacco ranks among the world\u0026apos;s key agricultural commodities. [7]. Long-term monoculture planting can lead to severe nutritional imbalances in tobacco plants, adversely affecting their yield and quality [8]. This is evident in the decline of tobacco field microecology, reduced tobacco growth quality, and a rise in pests and diseases. Consequently, Continuous cropping challenges have become a key problem affecting the tobacco industry\u0026apos;s sustainable development [9, 10]. Multiple studies have shown that extended periods of continuous cropping lead to either an increase or loss of key microbial communities in the soil. This creates favorable conditions for the proliferation of harmful microbial groups within the soil microecology [11, 12], attributable to the imbalance in soil microbial composition and function. Under these adverse environmental conditions, enhancing yield presents a significant challenge for growers. Among a plethora of factors, the interplay between plants and microorganisms is considered pivotal in fostering a wide array of ecophysiological processes. This, in turn, can assist in alleviating the hurdles related to continuous cropping [13, 14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, Research indicates that root systems release antimicrobial substances that suppress the proliferation of harmful bacteria, which in turn promotes the involvement of rhizosphere microorganisms in the processes of nutrient cycling and energy transfer within the soil. This process controls plant growth and nutrient absorption [15]. The rhizosphere, the macrozone adjacent to plant roots, exhibits distinct physical, chemical, and biological properties compared to the surrounding soil. These characteristics are shaped by root activity and microbial interactions, while the colonization of rhizosphere microorganisms depends on this specific microenvironment [16]. Plant roots excrete beneficial chemical compounds that suppress soil-borne pathogens. Under stress conditions, plants actively modulate soil composition or recruit specific microbial communities via these secretions, thereby enhancing their resilience and buffering environmental fluctuations.\u003c/p\u003e\n\u003cp\u003eAs crucial microorganisms within the root microbiome, PGPR foster plant growth and development [17]. They do so by either directly or indirectly curbing plant diseases and boosting stress tolerance. The mechanisms underlying PGPR-mediated growth promotion involve a comprehensive regulatory system encompassing both direct and indirect pathways [18]. Firstly, PGPR whip up and let out plant hormones, like indole - 3 - acetic acid (IAA). They also boost nutrient accessibility by making phosphorus soluble, fixing nitrogen, getting potassium moving, and churning out siderophores. These processes enable the transformation of insoluble nutrients into bioavailable ones, directly boosting plant growth [19]. Additionally, Deaminase produced by PGPR modifies ethylene precursors, lowering ethylene levels in plant roots, which in turn promotes root elongation and growth [20]. Secondly, PGPR indirectly suppress plant diseases by producing antimicrobial compounds that trigger plant defense responses. This enhances plant resilience and strengthens their competitive advantage against pathogens in agricultural systems [21].Research shows that the interactions of plants with PGPR notably enhance crop characteristics and activate soil nutrients. For example, \u003cem\u003eSerratia plymuthica\u003c/em\u003e modulates auxin biosynthesis by utilizing root-secreted growth hormone precursors, thereby promoting root development [22]. A beneficial bacterium isolated from the sweet potato rhizosphere was identified as Acetobacter through metabolomic and transcriptomic analyses. Under phosphorus-deficient conditions, this bacterium secretes organic acids that accelerate phosphorus solubilization, increasing phosphorus bioavailability for plants [23]. Similarly, \u003cem\u003eBacillus subtilis\u003c/em\u003e GB03 in Arabidopsis roots facilitates iron uptake by oxidizing Fe\u0026sup3;⁺ to Fe\u0026sup2;⁺, thereby enhancing the plant\u0026rsquo;s iron absorption capacity [24]. Moreover, soil - inhabiting PGPR generate antimicrobial substances. These substances inhibit pathogenic bacteria growth and are metabolized via intracellular lysis pathways [25]. Through vying with pathogens for nutrients and ideal environmental conditions, PGPR suppress harmful bacteria while colonizing favorable niches for reproduction [17].\u003c/p\u003e\n\u003cp\u003eIn this research, diverse PGPR strains were effectively extracted from tobacco rhizosphere soil and utilized in continuously cropped tobacco soil. First, agronomic traits and root system development were assessed, revealing that strain C1 significantly enhanced tobacco growth and root development.Secondly, an evaluation of soil physicochemical characteristics and enzyme activities was conducted.The findings indicated that C1 notably promoted the generation of accessible soil nutrients, with a particular increase in available phosphorus. Additionally, C1 increased soil enzyme activity.Further microbiome and metabolome analysis indicated that C1 enriched beneficial microbial populations, enhanced the soil microenvironment, and accelerated soil nutrient cycling and transformation. These results lay a foundation for the development of microbial fertilizers to overcome continuous cropping obstacles.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Experimental materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTest variety: Application of baking tobacco variety K326\u003c/p\u003e\n\u003cp\u003eTest soil: Four - year continuous - cropping soil sourced from the tobacco station in Linqu, Weifang. Its basic physicochemical properties are: organic matter 42.54g/kg, alkaline dissolved nitrogen 53.97mg/kg, quick-acting phosphorus 45.71 mg/kg, quick-acting potassium 178.5mg/kg, and pH value of 6.40 (Table 1).\u003c/p\u003e\n\u003cp\u003eExperimental strains: The strains were furnished by Chengqiang Wang\u0026apos;s laboratory at the College of Life Sciences, Shandong Agricultural University. They were isolated from top - notch, healthy field soils. These strains possess probiotic characteristics like phosphorus - solubilizing, potassium - solubilizing, indole - 3 - acetic acid (IAA) production, protein - degrading, and cellulose - degrading capabilities. The strains identified are as follows: C1 represents\u0026nbsp;\u003cem\u003eBacillus parathoracic\u003c/em\u003e, C2 corresponds to\u0026nbsp;\u003cem\u003ePaenibacillus hunanensis\u003c/em\u003e, and C3 stands for\u0026nbsp;\u003cem\u003eBacillus subtilis\u003c/em\u003e. The final bacterial product, CK2, utilized the liquid microbial fertilizer known as\u0026nbsp;\u0026ldquo;Infusion of golden liquid,\u0026rdquo;\u0026nbsp;sourced from Fujian Sanmu Biotechnology Co., Ltd, with its primary constituent being the gel-like Bacillus (see Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Soil and strain tested\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eS.No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eExperimental result\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSoil samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eOrganic Matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e42.54 g/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eAlkaline Hydrolysable Nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e53.97 mg/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eAvailable Phosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e45.71 mg/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eAvailable Potassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e178.5 mg/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003estrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus parathoracic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003ePaenibacillus hunanensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Experimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted under greenhouse circumstances at the Dai Zong Campus of Shandong Agricultural University through pot experiments. Two control groups were set up: the negative control, CK1, and the positive control, CK2. To prepare bacterial suspensions, strains were inoculated into LB (Luria - Bertani) medium. The mixture was then incubated at 37\u0026deg;C with a shaking speed of 180 rpm for 16 hours to yield the seed liquid.Subsequently, the optical density (OD600) of this seed solution was adjusted to around 2.5.The adjusted seed liquid was inoculated at a 2% rate into 50 ml of LB medium. For the controls, CK1 was given the same volume of water, while CK2 was administered an equal volume of a commercial bacterial product. The experimental group, consisting of three distinct bacterial agents (C1, C2, and C3), was administered a solution diluted to 200 times. Each treatment underwent six replications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Measurement of physiological index of tobacco\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs per\u0026nbsp;\u0026ldquo;Tobacco Agronomic Traits Survey and Measurement Methods\u0026rdquo;\u0026nbsp;(YC/T142 - 2010) [26], The tobacco plants\u0026rsquo;\u0026nbsp;height, stem girth, count of functional leaves, as well as the greatest leaf length and width were recorded every five days following transplanting. The maximum leaf area was then determined using the formula: Leaf area = Leaf length\u0026nbsp;\u0026times;\u0026nbsp;Leaf width\u0026nbsp;\u0026times;\u0026nbsp;0.6345.\u003c/p\u003e\n\u003cp\u003eAfter 30 d of growth in the tobacco plant to select a representative plant for sampling, three plants per treatment, digging the whole plant, rinsed with water and then absorbent paper to absorb the water, the tobacco plant can be categorized into three distinct components: roots, stems, and leaves. Using an electronic balance, we first measure the fresh weight of each part. Following this, we dry the samples in an oven set to 105\u0026nbsp;℃\u0026nbsp;for 30 minutes, then continue drying them at 80\u0026nbsp;℃\u0026nbsp;until they reach a constant weight. Finally, document the dry weight of each individual section. A Microtek Phantom 9980XL scanner was used to measure total root length, total root surface area, average root diameter, the quantity of root tips, and the number of branches.\u003c/p\u003e\n\u003cp\u003eAt 5 - day intervals post - transplanting, representative plants were chosen to measure the chlorophyll content in their leaves. For the 3rd - 4th cotyledons, 3 plants per treatment were evenly chosen, and a SPAD - 502 portable chlorophyll meter was used to measure the relative chlorophyll content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Examination of Soil Physicochemical Traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter a 30 - day growth phase, typical plants were picked for sampling. Moreover, three soil samples from each treatment were pooled together to evaluate the soil\u0026apos;s pH level, the amount of alkaline hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) content. A pH meter was used to measure soil pH, and sodium bicarbonate extraction along with molybdenum - antimony colorimetry was applied to evaluate AP [27], while ammonium acetate in combination with a flame photometer was employed for AK measurement [28]. To assess AN [29], alkaline-dissolved nitrogen analysis was carried out, and the potassium dichromate volumetric method was used to measure OM [30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Assessment of Soil Enzyme Activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThirty days after tobacco plants started growing, representative specimens were chosen for sampling.For each treatment group, three soil samples were collected and well - mixed.The activities of sucrase, urease, and catalase in the soil were measured using a kit from Beijing Prime Biological Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Measurement of Soil Microbial Communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1Sequencing of 16S rRNA Gene Amplicons from Inter-Root Soil Microorganisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was isolated from inter-root soil samples utilizing the OMEGA E.Z.N.A\u0026trade;\u0026nbsp;Mag-Bind Soil DNA Kit, with universal primers 341F (CCTACGGGGNGGCWGCAG) and 805R (CCTACGGGGNGGCWGCAG) aimed at the V3-V4 segment of the 16S rRNA gene. For the purpose of PCR amplification, we used universal primers ITS1-ITS2, specifically ITS1F (CTTGGTCATTTAGAGAGGAAGTAA) and ITS2R (GACTACHVGGGGTATCTAATCC); additionally, another set of ITS1-ITS2 universal primers, ITS1F (CTTGGTCATTTAGAGGAAGTAA) and ITS2R (GCTGCGTTCTTCATCGATGC), were also utilized [31]. The size of the library was analyzed using 2% agarose gel electrophoresis, and the concentration was quantified with a Qubit 4.0 fluorescence measurement device to ensure consistent clustering results and high-quality sequencing output. Sequencing was performed on the Illumina MiSeq platform.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.6.2 Amplicon Sequence Processing and Bioinformatics Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSequencing data were analyzed using the BioSignal Cloud Platform (https://ngs.sangon.com/), where primer sequences were removed and low-quality reads were filtered out. Short read pairs from paired-end sequencing were combined into single sequences by employing Flash. The merged reads were subjected to additional processing to generate high-quality reads. Only data showing at least 97% similarity were considered, and unique sequences were grouped into operational taxonomic units (OTUs) using UPARSE (version 7.0.1090).The \u0026alpha; - diversity indices (Chao 1, Ace, Simpson, Shannon, Shannoneven, and Sobs) were calculated using Mothur software [32]. To demonstrate changes in microbial composition, principal component analysis (PCA) using QIIME and R software (version 3.5.3) was carried out on treated samples. [33]. Furthermore, Redundancy Analysis (RDA) with Variance Inflation Factor (VIF) analysis was conducted to pinpoint environmental factors [34].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.3 Soil macro-genome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from inter - root soil samples using the OMEGA E.Z.N.A\u0026trade; Mag - Bind Soil DNA Kit. DNA was quantitatively analyzed using a Qubit 4.0 fluorescence quantifier.DNA samples were sequenced on the Illumina MiSeq platform, generating substantial raw data.Clean reads underwent multi-sample hybrid assembly using Megahit software, while unmatched reads were assembled employing SPAdes software. Sequences shorter than 500 base pairs were not included in downstream analyses, which encompassed statistical evaluations and gene prediction. DIAMOND was used to align gene - set protein sequences with the KEGG database, enabling KO number assignment and quantifying functional - level abundance in samples. Additionally,sequences were analyzed against the KEGG database\u003c/p\u003e\n\u003cp\u003e(http://www.kegg.jp/kegg/) using DIAMOND, with default parameters, to anticipate microbial metabolic functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Data analysis Results \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental data were tabulated and plotted using Microsoft Excel 2021 and Origin 2023. Subsequently, significance of differences was analyzed via Duncan\u0026apos;s test in SPSS Statistics 25.0.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Genetic Diversity and Functional Identification of Rhizosphere Plant Growth-Promoting Bacteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the description in reference [35], strain C1 belongs to the same minimal clade as \u003cem\u003eBacillus paranthracis\u003c/em\u003e and exhibits the closest evolutionary relationship. Similarly, strain C2 clusters within the same minimal clade as \u003cem\u003ePaenibacillus\u003c/em\u003e \u003cem\u003ehunanensis\u003c/em\u003e, indicating a close evolutionary affinity. Simultaneously, the C3 strains exist within the same minimal clade as Bacillus subtilis, displaying the most closely related evolutionary ties. Initial identification of these strains, based on 16S rRNA gene sequence analysis alongside various physiological and biochemical traits, classified them as Bacillus paranthracis (C1), Paenibacillus hunanensis (C2), and Bacillus subtilis (C3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Impact of diverse PGPR on the agronomic characteristics of tobacco\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the traits of diverse strains, the application of different PGPRs exhibited a notable growth - enhancing impact on tobacco (Figure 1). The results show that microbial inoculation notably increased the height of tobacco plants, as well as the length, width, and maximum area of their leaves (Figure 1A, B, C, D, G). On the 10th day, compared with CK1, the CK2, C1, C2, and C3 treatments increased plant height by 44.36%, 22.18%, 7.72%, and 20.54%, respectively; leaf length by 21.95%, 7.02%, 17.56%; with CK2 showing no significant difference at this stage; leaf width by 1.91%, 12.31%, 5.51%, and 11.11%; and maximum leaf area by 1.9%, 37.59%, 13.19%, and 30.55%.On the 25th day after treatment, compared with CK1, the CK2, C1, C2, and C3 treatments increased plant height by 7.8%, 26.47%, 10%, and 11.53%, respectively; leaf length by 2.18%, 22.75%, 12.22%, and 7.31%; leaf width by 16.62%, 13.4%, and 7.23%, with CK2 showing no significant improvement; and maximum leaf area by 0.1%, 40.01%, 26.51%, and 15.68%. Notably, regardless of the diverse treatments, tobacco plants showed no significant variations in stem diameter or the count of effective leaves (Figure 1E, F).\u003c/p\u003e\n\u003cp\u003eMeanwhile, further experiments were conducted to evaluate another key growth indicator, the SPAD value, which exhibited a significant enhancement following PGPR application. The findings demonstrated notable variations in how various PGPR strains impacted the SPAD values of tobacco leaves (Figure 1H). Five days after transplanting, the C1, C2, and C3 treatments, containing growth-promoting strains, resulted in a significant increase compared to CK1.By the 10th day, the SPAD value in the C1 treatment had increased by 8.50% compared to CK1. However, by the 15th day, SPAD values decreased across all treatments, though the chlorophyll content in the C1, C2, and C3 treatments remained higher than in CK1.After 20 days, the C1 treatment showed a 5.35% increase compared to CK1, outperforming all other treatment groups. By the 25th day, only the C1 treatment continued to show a growth-promoting effect, while the C3 treatment group maintained higher SPAD values than CK1.Thus, over the 25-day growth period, chlorophyll content analysis indicated that the C1 treatment exhibited superior performance compared to the CK2, C2, and C3 treatment groups (Figures 4 and 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Effect of different PGPG on root system of tobacco\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe application of different PGPR treatments resulted in significant differences in their ability to promote tobacco root growth (Figure 2). The results indicated that, compared to CK, the root diameter increased under the C1, C2, and C3 treatments, with the C1 treatment showing a notable increase of up to 20% (Figure 2A).In terms of total root length, the C1 and C2 treatments exhibited significant advantages, increasing by 80.52% and 87.91%, Compared to CK1, the C3 treatment was slightly lower, showing a 2.98% decrease (Figure 2B). In terms of root tip count, C1 and C2 exhibited the highest values, increasing by 29.55% and 54.10%, respectively, compared to CK1, while CK2 and C3 treatments followed, showing decreases of 19.70% and 36.71%, respectively (Figure 2C).Additionally, regarding root volume, the C1 treatment demonstrated a significant advantage over the other groups, surpassing CK1 by 96.09% (Figure 2D), while the CK2 and C3 treatments were lower than CK1, showing reductions of 12.31% and 20.02%, respectively.For root surface area, all treatments except C3 exhibited an increase compared to CK1, with C1 showing a significant 51.66% increase (Figure 2E).These findings suggest that the C1 treatment significantly promotes tobacco root development, leading to a more robust root system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Effect of different PGPR on tobacco biomass\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding tobacco biomass accumulation, the growth-promoting effects of different PGPR treatments differed significantly (Figure 3).For fresh weight, compared to CK1, the C1 treatment increased the fresh weight of roots, stems, and leaves by 99.07%, 91.55%, and 77.04%, respectively, all of which were significantly higher than in other treatment groups (Figures 3A\u0026ndash;C).For dry weight, the C1 treatment resulted in increases of 105.56%, 47.50%, and 92.09% in the dry weight of roots, stems, and leaves, Individually, in contrast to CK1 (as shown in Figures 3D\u0026ndash;F), it shows a remarkable growth - enhancing effect. Notably, regarding stem dry weight, the CK2 treatment was slightly lower than CK1 (Figure 3E), though the difference was not statistically significant. These findings suggest that PGPR application significantly enhances biomass accumulation in tobacco, with the C1, C2, and C3 treatments demonstrating particularly strong effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Effect of different PGPR on soil physicochemical properties and enzyme activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe elemental composition of the soil and its enzyme activity serve as vital indices for gauging soil fertility. As depicted in Figure 4, the impacts of diverse PGPR treatments on soil characteristics are presented.To evaluate these impacts, the levels of available nitrogen (AN), organic matter (OM), available phosphorus (AP), available potassium (AK), and pH within the experimental soil were determined. The results showed that, except for the C3 treatment, all treatments exhibited higher AN levels than CK1; Nevertheless, the disparities were not statistically notable (p \u0026ge; 0.05) (Figure 4A).Regarding AP content, the C1 treatment led to a 28.98% increase in soil AP, and this value was notably higher compared to that in other treatments (Figure 4B). For AK content, PGPR application did not lead to significant differences, although all treatments had higher values than CK1 (Figure 4C).For OM content, the C1 treatment significantly increased soil OM by 21.44% relative to CK1 (Figure 4D). However, soil pH analysis revealed that PGPR application significantly lowered soil pH, with the C3 treatment showing the greatest reduction of 2.83%. Notably, the C2 treatment was the only one that resulted in a pH increase (Figure 4E).\u003c/p\u003e\n\u003cp\u003eFigure 5 presents the activities of three key soil enzymes: sucrase (SU), catalase (CAT), and urease (UR). Notable variances in soil enzyme activities were detected among the treatments of tobacco inoculated with PGPR.Regarding SU activity, the C1 treatment exhibited a dramatic increase of 452.08%, significantly outperforming all other treatments (Figure 5A).For CAT activity, C1 exhibited the highest enzyme activity, with a 29.16% increase relative to CK1, whereas CK2 showed no significant difference (Figure 5B).For UR activity, both CK2 and C1 treatments exhibited higher UR activity than CK1, with C1 showing a notable increase of 123.89% relative to CK1 (Figure 5C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Effect of PGPR on microbial diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.1 Evaluation of high-throughput sequencing results and OUT clustering analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTargeted sequencing of diverse genomic regions is essential for assessing microbial community diversity. The bacterial 16S V3 - V4 region and the fungal ITS1 1 - 2 region of nine soil samples were sequenced via the Illumina Miseq/Hiseq platform. This generated 544,949 raw reads for bacteria and 783,290 raw reads for fungi. The downstream sequencing produced bipartite sequences. To get valid data for each sample for the subsequent analysis, these sequences had to go through quality control and filtering. To obtain valid sample data for subsequent analysis, quality-control filtering of each sample\u0026apos;s data is required. The sequence counts for bacteria and fungi were 55,288 and 86,900 respectively. The sequences from all samples were grouped into OTUs with 97% similarity, yielding 4035 OTUs for bacteria and 2343 OTUs for fungi.\u003c/p\u003e\n\u003cp\u003eTo validate the sequencing quality and depth, the dilution curve was used to evaluate the reliability of the sequencing data volume. With the rise in sequencing numbers, the dilution curve steadily flattened, approaching a value of 1 (Figure S2 A-B).This suggests that the majority of species present in the soil samples were identified, with the addition of further data likely yielding only a limited number of new operational taxonomic units (OTUs). The existing volume of sequencing data appears adequate, and the findings accurately reflect the true composition of the samples. Therefore, this data can be effectively utilized for future analyses concerning the diversity of the soil community and its species composition.\u003c/p\u003e\n\u003cp\u003eThe Venn diagram shows the distribution of OUTs specific to and shared by the species community, and the inter-root bacterial community had a total of 3615 OUTs in the two treatments, whereas 580 specific OUTs were specific to C1, and 796 were specific to the control CK. The fungal community had a total of 964 OUTs in the three treatments, while 380 and 1758 specific OUTs were specific to C1, C2, and the control CK, respectively (Figure S2 C-D).\u003c/p\u003e\n\u003cp\u003eTo investigate the variations and separations in the makeup of bacterial and fungal communities across various treatments, a PCA analysis was conducted at the OTU level for both bacterial and fungal communities. The bacterial community\u0026apos;s PC1 and PC2 axes were separated at 12.37% and 71.058%, respectively.Compared to CK, the C1 treatment was distributed separately, suggesting variations in species composition across the treatments.Compared to CK, the C1 treatment was distributed separately, suggesting variations in species composition across the treatments.The fungal community exhibited segregation rates of 8.37% and 86.323% along the PC1 and PC2 axes, respectively. Moreover, it was fairly spread out across the treatments, with variations in the structure of the species composition (Figure S2 E - F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.2 Effect of Alpha and Beta diversity of soil microorganisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the impact of various treatments on the\u0026nbsp;\u0026alpha;-diversity of inter-root bacteria, when compared to the control (CK), the Chao1 and Ace indices in the C1 treatment declined. This suggests that the C1 treatment led to a reduction in the relative abundance of inter - root bacteria within the microbial community (see Figure 6A - B).The Sobs index was greater in the CK treatment compared to the C1 and C2 treatments, indicating that the CK treatment had the highest actual number of OUTs (Figure 6C).Compared to the CK, the rise in Simpson\u0026apos;s index and decline in Shannon\u0026apos;s index in the C1 treatment suggested that the community diversity of inter - root bacteria took a hit in the C1 treatment (Figure 6D - E). The Shannon evenness index was found to be greater in the CK treatment compared to the C1 treatment. This suggests that the inter-root bacterial community exhibited a higher level of uniformity in the CK group than in the C1 group (see Figure 6F).\u003c/p\u003e\n\u003cp\u003eThe various treatments had a notable impact on the\u0026nbsp;\u0026alpha;-diversity of fungi found between roots. Compared to the control group (CK), the Chao1 and Ace indices for the C1 treatment showed an increase, suggesting that it enhanced the quantity of operational taxonomic units (OTUs) among inter-root fungi, thus boosting the overall relative abundance of fungal species within the microbial community (see Figures 6G-H).The Sobs index for C1 exceeded that of the CK control, indicating a lower count of OUTs in the CK treatment compared to C1 (Figure 6I). In contrast to CK, C1\u0026apos;s Simpson\u0026apos;s index decreased while the Shannon index rose, suggesting that the C1 treatment enhanced community diversity (Figure 6J-K).The Shannon-Weaver index for the CK treatment was lower compared to that of the C1 treatment, suggesting that the C1 treatment exhibited a greater uniformity in the distribution of the inter-root fungal community (see Figure 6L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.3 Effect of PGPR on microbial composition and structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate how various treatments influence the species composition of microbial communities, we conducted a comparative analysis of the shifts in the dominant bacterial and fungal species at the phylum level (see Figure 7A-B). Additionally, we categorized species with a relative abundance of less than 0.01 under the label \u0026quot;Others.\u0026quot;The dominant species at the phylum level of bacteria were \u003cem\u003eProteobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes, Actinobacteria, unclassified Bacteria, Candidatus Saccharibacteria, Verrucomicrobia, Cyanobacteria/Chloroplast, candidate_division_WPS-1\u003c/em\u003e. \u003cem\u003eProteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes\u003c/em\u003e as the most dominant phylum. The predominant fungal phyla with the greatest relative abundance included \u003cem\u003eunclassified_Fungi\u003c/em\u003e, \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eZygomycota\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e, \u003cem\u003eFungi_unidentified\u003c/em\u003e, \u003cem\u003eChytridiomycota\u003c/em\u003e, and \u003cem\u003eGlomeromycota\u003c/em\u003e. Notably, \u003cem\u003eunclassified_Fungi\u003c/em\u003e, \u003cem\u003eAscomycota\u003c/em\u003e, and \u003cem\u003eZygomycota\u003c/em\u003e emerged as the leading groups among them.\u003c/p\u003e\n\u003cp\u003eTo delve deeper into the variations in species composition across treatments at the gate level, several comparative analyses were conducted on bacteria and fungi. This involved comparing bacterial species with the highest 15 P values and fungal species with the top 8 P values. In contrast to CK (as depicted in Figure 7C - D), the C1 treatment led to a rise in the relative prevalence of Bacteroidetes, Proteobacteria, Actinobacteria, and Candidatus Saccharibacteria within the bacterial community. Simultaneously, it brought about a decline in candidate_division_WPS - 2 and the relative proportions of Planctomycetes. At the phylum level, the composition of the fungal community showed noticeable variation. In contrast to the CK treatment, the C1 treatment resulted in an increase in the relative abundance of unclassified Fungi, Ascomycota, and Basidiomycota, while simultaneously leading to a decrease in the proportion of Zygomycota.\u003c/p\u003e\n\u003cp\u003eAn analysis was conducted on the genus-level composition of bacteria and fungi across various treatments, revealing that the predominant genera within the bacterial community included \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eunclassified_Bacteria\u003c/em\u003e, \u003cem\u003eunclassified_Gemmatimonadaceae\u003c/em\u003e, \u003cem\u003eChujaibacter\u003c/em\u003e, \u003cem\u003eSaccharibacteria_genera_incertae_sedis\u003c/em\u003e, and \u003cem\u003eGp3\u003c/em\u003e, as depicted in Figure 8A. At the genus level within the fungal community, the predominant genera were chiefly \u003cem\u003eunclassified_Fungi\u003c/em\u003e, \u003cem\u003eMortierella\u003c/em\u003e, an unidentified Sordariomycetes species (\u003cem\u003eSordariomycetes_unidentified_1\u003c/em\u003e), unclassified Ascomycota, an unidentified Ascomycota species (\u003cem\u003eAscomycota_unidentified_1_1\u003c/em\u003e), and \u003cem\u003eTrichoderma\u003c/em\u003e, as depicted in Figure 8B.\u003cem\u003e\u0026nbsp;\u003c/em\u003eAn examination of the variations in species composition of bacterial and fungal communities across different treatments revealed that, at the genus level for bacteria, treatment C1 notably enhanced the relative abundance of the \u003cem\u003egenera Edaphobacter\u003c/em\u003e, \u003cem\u003eAcidibacter\u003c/em\u003e, \u003cem\u003eGPl\u003c/em\u003e, \u003cem\u003eAcidobacterium\u003c/em\u003e, and an unidentified group from \u003cem\u003eAlcaligenaceae\u0026nbsp;\u003c/em\u003e(see Figure 8C). At the fungal genus level, C1 elevated the relative abundance of \u003cem\u003eAscomycota unidentified 1 1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;unclassified_Hypocreaceae\u003c/em\u003e, and \u003cem\u003eAscomycota_unidentified\u003c/em\u003e (Figure8D).\u003c/p\u003e\n\u003cp\u003eTo delve deeper into the variations in bacterial and fungal communities present in the inter-root soil of tobacco plants subjected to different treatments, linear discriminant analysis and the LEfSe method were employed to identify the species that most effectively highlighted the distinctions between the groups across the various treatments. Soil samples from tobacco plants were analyzed and categorized at five taxonomic levels, from phylum to genus.Fifty-five distinct levels of differential taxa were recognized in the bacterial community. In the CK treatment group, 32 distinct species showed increased abundance, whereas the C1 treatment group saw an increase of 21 species. At the genus level within the bacterial community, the CK treatment group was primarily characterized by the enrichment of \u003cem\u003eSphingobium\u003c/em\u003e, \u003cem\u003eGemmatimonadetes\u003c/em\u003e, \u003cem\u003eunclassified_Bacteria\u003c/em\u003e, \u003cem\u003eunclassified_Betaproteobacteria\u003c/em\u003e, \u003cem\u003enorank_Parcubacteria\u003c/em\u003e, and additional \u003cem\u003eunclassified_Bacteria\u003c/em\u003e. Notably, the phylum, order, and family - level classifications largely corresponded to the genus-level findings. In the C1 treatment group, the taxa enriched at the genus level included \u003cem\u003eChujaibacter\u003c/em\u003e, \u003cem\u003eAcidibacter\u003c/em\u003e (mentioned twice), \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eRhizomicrobium\u003c/em\u003e, \u003cem\u003eParcubacteria\u003c/em\u003e, \u003cem\u003eunclassified_Gemmatimonadaceae\u003c/em\u003e, \u003cem\u003eEdaphobacter\u003c/em\u003e, Gp4, and Gp6 (as depicted in Figure 8E).\u003c/p\u003e\n\u003cp\u003eSixteen taxa with varying differentiation levels were identified in the fungal community. No taxa were enriched in CK; instead, all were enriched in the C1 treatment group.Sixteen differential species were present. At the phylum level, two taxa, \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eGlomeromycota\u003c/em\u003e, were enriched in C1. At the phylum level, C1 exhibited four notable enriched taxa: \u003cem\u003eAscomycota_unidentified\u003c/em\u003e, \u003cem\u003eEurotiomycetes\u003c/em\u003e, \u003cem\u003eSordariomycetes\u003c/em\u003e, and \u003cem\u003eMicrobotryomycetes\u003c/em\u003e. When examining the order level, five enriched taxa were identified in C1: \u003cem\u003eGlomeromycetes\u003c/em\u003e, \u003cem\u003eEurotiales\u003c/em\u003e, \u003cem\u003eHypocreales\u003c/em\u003e, \u003cem\u003eSporidiobolales\u003c/em\u003e, and \u003cem\u003eGlomerales\u003c/em\u003e. At the order level, four taxa are enriched in C1: \u003cem\u003eAscomycota_unidentified_1\u003c/em\u003e, \u003cem\u003eTrichocomaceae\u003c/em\u003e, \u003cem\u003eHypocreaceae\u003c/em\u003e, and \u003cem\u003eIncertae_sedis_25\u003c/em\u003e. At the genus level, the four enriched taxa in C1 are \u003cem\u003eTalaromyces\u003c/em\u003e, \u003cem\u003eTrichoderma\u003c/em\u003e, \u003cem\u003eAscomycota_unidentified_1_1\u003c/em\u003e, and Incertae_sedis_25_unidentified (Figure 8F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.4 Correlation analysis of soil physicochemical and microbial diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe introduction of various inter-root biotrophic bacteria influences nutrient cycling within the soil and modifies the activities of soil enzymes, leading to shifts in the microbial community present in that environment.Consequently, the interplay between soil bacterial and fungal communities and various environmental factors was examined. Redundancy Analysis (RDA) was employed to establish connections between physicochemical characteristics\u0026mdash;namely pH, AN, OM, AP, AK\u0026mdash;and soil enzyme activities such as SC, CAT, and UR in relation to the microbial communities present in the soil (see Figure 9A, C). The findings indicated that the physicochemical properties of the soil\u0026mdash;specifically AK, AP, and OM\u0026mdash;were predominantly influenced by the bacterial community, with OM following closely behind. Notably, the C1 treatment exhibited a strong positive correlation with AP, AK, and OM in the soil, with the relationship to AP being particularly pronounced. Although pH showed a strong negative correlation with C1 treatment. The C1 treatment showed a significant positive correlation with SC and CAT in the soil, yet had no significant correlation with UR. By examining the impact of the fungal community on soil nutrients and enzyme activities, AK, AN, OM, and pH were found to significantly influence the soil, while OM showed the weakest correlation. The C1 treatment showed a strong positive correlation with AN and AK, and a significant negative correlation with pH. Regarding soil enzyme activities, the fungal communities had the greatest impact on SC, with CAT being affected to a lesser extent. In soil, the C1 treatment had a positive correlation with SC and CAT. Among them, CAT showed the strongest correlation, being significantly negatively correlated with UR. To delve deeper into the interplay between microbial community composition and soil nutrients as well as enzyme activities, a correlation heat map analysis was conducted. This analysis focused on the predominant genera of both bacterial and fungal communities at the genus level, in relation to various soil environmental factors (Figure 9B.D). In the microbial community, key players such as \u003cem\u003eSphingomonas\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Gp1\u003c/em\u003e, \u003cem\u003eGp3\u003c/em\u003e, \u003cem\u003eSaccharibacteria\u003c/em\u003e, and \u003cem\u003eAcidobacterium\u003c/em\u003e have been identified as significantly influencing soil physicochemical properties and enzymatic activities. Sphingomonas, Gp1, and Gp3 demonstrated a significant positive correlation with numerous factors such as AN, AP, AK, OM, SC, and CAT, but had an inverse relationship with pH levels. In contrast, Saccharibacteria and Acidobacterium exhibited a notable negative relationship with AN, AP, AK, OM, SC, and CAT, while showing a positive correlation with pH. The correlation between UR and the dominant genus of the bacterial community showed no significant difference.\u003c/p\u003e\n\u003cp\u003eThe correlation heatmap in( 9B) depicts the relationships between the dominant fungal genera at the genus level, soil nutrients, and enzyme activities. The findings indicated a significant positive correlation among Trichoderma, Talaromyces, and the fungal community members AN, AP, AK, SC, and CAT. \u003cem\u003eMortierella\u003c/em\u003e exhibited a strong negative correlation with AN, AP, AK, UR, and CAT. Conversely, the organic matter in the soil showed a notable positive relationship with \u003cem\u003eTrichoderma\u003c/em\u003e, while maintaining a significant negative correlation with \u003cem\u003eMortierella\u003c/em\u003e. Conversely, soil pH had a negative correlation with \u003cem\u003ePodosordaria\u003c/em\u003e and a significant positive correlation with \u003cem\u003eMortierella\u003c/em\u003e. No notable disparity was detected in the correlation of UR with the dominant genera within the fungal community.\u003c/p\u003e\n\u003cp\u003eTo understand the interactions between dominant species in the microbial community, correlation analysis of microbial interactions was conducted, and significant correlations were found between genera in the bacterial community ((Figure 9E-F). The highest number of genera were correlated with \u003cem\u003eMicropepsaceae\u003c/em\u003e, where \u003cem\u003eMicropepsaceae\u0026nbsp;\u003c/em\u003ewas positively correlated with \u003cem\u003eGP1, Devosia, WPS-1, acidibacter, Unclassified_bacteria, Bacteroidetes, and Acetobacteraceae\u003c/em\u003e. The genera with negative correlation with GP4 were GP6, GP4. the most genera with negative correlation with GP4 included Bradyrhizobium, Micropepsaceae, Acetobacteraceae, while flavisolibacter, Gp6, Betaproteobacteria were positively correlated with Trinickia were positively correlated. In the fungal community correlations between genera were low and only a few genera were correlated and all were positively correlated. \u003cem\u003eChytridiomycetes\u0026nbsp;\u003c/em\u003ewere positively correlated with \u003cem\u003ePseudogymnoascus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eWaitea\u003c/em\u003e. \u003cem\u003eWaitea\u003c/em\u003e and \u003cem\u003ePseudogymnoascus\u0026nbsp;\u003c/em\u003ewere positively correlated and \u003cem\u003eIncertae sedis 25\u003c/em\u003e was positively correlated with \u003cem\u003eAscomycota\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.5 Effects of functional properties in soil microbial communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional abundance of soil microorganisms at Pathway levels1 and Pathway levels2 levels, soil microbial metabolic functions occupy the largest proportion of the six KEGG metabolic functions, indicating that metabolism plays an important role in the life activities of microorganisms (Figure 10A). In the KEGG Pathway database, the global and overview maps, a distinct set of metabolic pathway maps, boasted the highest number of genes involved in metabolic functions. These functions, in descending order, were amino acid metabolism, carbohydrate metabolism, energy metabolism, cofactors and microbial metabolism, and nucleotide metabolism. In the various cellular process pathways, when it comes to cell communities, prokaryotes boast the largest number of genes. In the realm of environmental information processing, the pathways of transmembrane transport and signaling have the greatest quantity of genes. In gene information processing, the largest number of genes pertains to signaling. In disease pathways related to bacterial infectious diseases, there is also the highest count of genes. In organic systems pathways, aging, endoanalytic systems, and environmental adaptation are the functional expressions having the greatest number of genes.\u003c/p\u003e\n\u003cp\u003eTo delve deeper into the action mechanism of the inter - root biotrophic bacteria, we annotated the disparities in the diverse metabolic functions of microorganisms at the KEGG tertiary metabolic levels triggered by the inter - root biotrophic bacteria (Figure 10B). In comparison with the CK group, when it came to tobacco plants, the soil microorganisms in the C1 treatment with Bacillus paracord notably enhanced fatty acid degradation and metabolic functions, as well as the metabolic functions of glycine, serine, and threonine, lysine degradation functions, and tryptophan metabolic functions. The metabolic activities of arginine and proline, along with the metabolic role of benzoic acid, and the biosynthetic functions of phenylalanine, tyrosine, and tryptophan were significantly elevated in the C1 treatment compared to the control group, CK. Amino acids are nutrients that play a key role in enabling plants to grow and develop and keep soil microorganisms\u0026apos; life activities ticking over. The C1 Bacillus paracolor revved up the amino - acid biosynthesis function in the soil microorganisms around the tobacco plant roots. What\u0026apos;s more, when it came to the synthesis of secondary metabolites of soil microorganisms, the C1 treatment outperformed the CK treatment. Compared with the CK treatment, C1 enhanced the metabolic function of organic selenium compounds and the biosynthesis of cofactors.\u003c/p\u003e\n\u003cp\u003eMicroorganisms in the soil play a crucial role in the cycling of materials and the flow of nutrients. To gain insight into the metabolic functions of the predominant genera of these soil microbes, a correlation analysis was performed linking the dominant bacterial species to KEGG functions (see Figure 10C). The correlation heat map showed that \u003cem\u003eChujaibacter\u003c/em\u003e, \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eSaccharibacteria\u003c/em\u003e, \u003cem\u003eAcidobacteria_Gp1\u003c/em\u003e, and \u003cem\u003eTrichoderma\u003c/em\u003e were linked to specific types of cancer. In the soil, amino acid metabolism, the biosynthesis of other secondary metabolites, glycan biosynthesis and metabolism, lipid metabolism, the metabolism of terpenoids and polyketides, and the digestive system had a significant positive correlation with folding, sorting and degradation, transcription, specific types of cancer, endocrine and metabolic diseases in the soil, bacterial infectious diseases, neurodegenerative diseases, energy metabolism, and aging. In the soil, there was a noteworthy positive correlation between \u003cem\u003eMortierella\u003c/em\u003e,\u003cem\u003e\u0026nbsp;GP1\u003c/em\u003e, \u003cem\u003eunclassified_Bacteria\u003c/em\u003e, and \u003cem\u003eBetaproteobacteria\u003c/em\u003e and various processes such as folding, sorting, and degradation. Additionally, significant positive relationships were found between transcription, specific types of cancer, endocrine and metabolic diseases, bacterial infections, neurodegenerative disorders, energy metabolism, and aging, all of which were closely linked to the soil\u0026apos;s functional characteristics. Specific types, Amino acid metabolism, Biosynthesis of other secondary metabolites, Glycan biosynthesis and metabolism, Lipid metabolism, Metabolism of terpenoids and polyketides, and Digestive system were significantly negatively correlated. Translation and Nucleotide metabolism in soil were significantly negatively correlated with \u003cem\u003eSaccharibacteria\u003c/em\u003e, \u003cem\u003eAcidobacteria_Gp1\u003c/em\u003e, \u003cem\u003eTrichoderma\u003c/em\u003e and significantly negatively correlated with \u003cem\u003eMortierella\u003c/em\u003e. Additionally, a significant negative correlation was found between Immune disease and \u003cem\u003eDevosia\u003c/em\u003e, \u003cem\u003eStreptophyta\u003c/em\u003e, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e,and \u003cem\u003eRhizobiales\u003c/em\u003e present in soil.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eA number of PGPR strains were extracted from the rhizosphere soil of tobacco fields located in Shandong. After identification and evaluation, three of these strains were chosen for further experiments aimed at promoting plant growth. These strains were identified as \u003cem\u003eBacillus paranthracis\u003c/em\u003e, \u003cem\u003ePaenibacillus\u003c/em\u003e \u003cem\u003ehunanensis\u003c/em\u003e, and Bacillus subtilis, while the commercially available rhizosphere growth-promoting strain used was \u003cem\u003ePaenibacillus\u003c/em\u003e \u003cem\u003emucilaginosus\u003c/em\u003e. All four bacterial genera are among the most prevalent in PGPR. \u003cem\u003eBacillus\u003c/em\u003e \u003cem\u003eparanthracis\u003c/em\u003e has been reported to facilitate phosphorus solubilization, nitrogen fixation, and the enhancement of soil microbial communities [36]. \u003cem\u003ePaenibacillus\u003c/em\u003e \u003cem\u003ehunanensis\u003c/em\u003e, \u003cem\u003eBacillus subtilis\u003c/em\u003e, and other \u003cem\u003ePaenibacillus\u003c/em\u003e species also exhibit nitrogen fixation, phosphorus solubilization, and deaminase activation. These functional traits enhance nutrient uptake, thereby promoting crop biomass and yield [37].\u003c/p\u003e\n\u003cp\u003eAgronomic characteristics are essential for plant development and quality, with PGPR proven to significantly improve these traits, fostering growth and advancement. [33, 38]. Lobato et al. stated that Pseudomonas and Brucella notably increased the number of branches, leaves, chlorophyll levels, and height of blueberry plants. [39]. Similarly, inoculating four local PGPR strains into pepper seedlings significantly accelerated pepper growth rates [40, 41]. Experiments were conducted on essential agronomic indicators to assess the impact of PGPR on tobacco growth. The results revealed that the selected PGPR strains induced significant differences in tobacco growth and development. In particular, the three chosen bacterial treatments noticeably boosted various plant growth metrics, such as height, maximum leaf length, width, area, and SPAD value. Notably, the C1 treatment demonstrated more pronounced effects compared to the other treatments (see Figure 1B-D, G-H).However, there were no notable differences in stem circumference and leaf count among treatments (Figure 1E, F). Consistent with previous studies, Zhang et al. reported that applying FJS-3 to tobacco seedlings significantly increased plant height, root weight, and fresh weight by 25.56%, 24.77%, and 21.21%, respectively. When FJS-3 was applied as a compound microbial fertilizer for 30 days, these increases further rose to 30.15%, 37.36%, and 54.5%, respectively [42].Similarly, Shang et al. found that treatments with \u003cem\u003eBacillus\u003c/em\u003e \u003cem\u003ecereus\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e \u003cem\u003emethylotrophicus\u003c/em\u003e, and \u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e enhanced tobacco plant height by 38.65%, 91.94%, and 90.66%, respectively, with Bacillus cereus exhibiting a twofold increase compared to the control group [43]. In a two-year study involving common wheat treated with PGPR, Cristian and colleagues discovered that the chlorophyll levels in the PGPR-inoculated wheat were consistently elevated compared to those in the non-inoculated controls.[44]. In this study, the newly screened functional bacterial strains exhibited a stronger growth-promoting effect on tobacco than commercial microbial agents. Many studies show that diverse environmental factors, like soil nutrient makeup and climate, affect the growth - enhancing effects of PGPR. This suggests that, as these functional bacteria were isolated from and adapted to a specific region, they may be more suited to tobacco fields in Shandong.\u003c/p\u003e\n\u003cp\u003eRoot system development directly influences root activity, including nitrogen uptake, utilization, and dry matter accumulation in plants [45]. PGPR primarily influence the shape and structure of roots, boosting their capacity to take up nutrients and water from the soil, which in turn fosters the development of the plant\u0026rsquo;s aerial components.This improved root architecture enhances plant uptake of water and nutrients, optimizing water and fertilizer utilization efficiency [46-49].The findings of this study indicated that the application of plant growth-promoting rhizobacteria (C1, C2, and C3) had a marked positive effect on the development of the tobacco root system and enhanced biomass production. This included notable improvements in root diameter, total root length, the number of root tips, root volume, root surface area, and the overall biomass of the tobacco plants (Figure 2).Consistent with previous findings [50], chlorophyll content is a key indicator of plant photosynthetic capacity [51]. Bacillus species are reported to enhance soybean growth and development. They do this by stabilizing chlorophyll levels, which in turn aids biomass accumulation [52]. In a PGPR experiment on barley, total chlorophyll content increased by 126% compared to the untreated control [53].Similarly, Chamkhi et al. found that PGPR inoculation in saffron increased both the number of leaves and chlorophyll content by 1.91-fold compared to the control group [54]. In this study, fresh and dry weights of roots, stems, and leaves were recorded, and C1 significantly promoted biomass accumulation in tobacco stems (Figure 3), aligning with the findings of Yolanda et al. Additionally, PGPR increased the SPAD values of tobacco leaves (Figure 1H), indicating that PGPR significantly enhances photosynthesis, contributing to tobacco biomass accumulation.Consequently, PGPR promotes the elongation growth of both aerial and subterranean plant components, boosts the absorption of soil nutrients and water, and thereby eases tobacco biomass accumulation.Thus, it is speculated that the novel functional bacteria may confer greater benefits than commercial strains by improving root system architecture and further stimulating above-ground plant growth (Figure 1).\u003c/p\u003e\n\u003cp\u003eSoil enzyme activity is of utmost importance in the global circulation of crucial elements like carbon, phosphorus, and nitrogen. It acts as a vital yardstick for assessing the health and fertility levels of the soil [55]. Soil characteristics and nutrient amounts have a direct impact on plant development and yield. In this research, treatments C1, C2, and C3 notably enhanced the tobacco plants\u0026apos; soil physicochemical traits, such as AN, AP, AK, and OM (Figure 4A-D). This discovery is consistent with Jiang et al.\u0026apos;s results, which showed that PGPR can notably improve soil nutrient accessibility [56]. Soil enzyme activity is mainly propelled by the physiological and metabolic activities of various microbial communities, which mirror the existence and functionality of functional microbes. Investigating soil enzyme activity provides insights into microbial responses to environmental changes [56, 57]. In the soil, the actions of sucrase (SC), catalase (CAT), and urease (UR) act as crucial barometers of soil fertility and microbial activity. They have a say in the conversion of nitrogen compounds, organic matter, and other vital nutrients. Furthermore, these enzymes contribute to alleviating oxidative stress in plants [58, 59]. In this research, the application of PGPR showed a marked improvement in the activities of sucrase (S-SC), catalase (S-CAT), and urease (S-UR) when compared to the CK treatment, which was linked to higher levels of organic matter (OM) and available nitrogen (AN) in the rhizosphere soil. These findings align with earlier studies [60, 61]. Overall, PGPR greatly enhanced the nutrient levels and physicochemical attributes of tobacco plants, closely linked to changes in the soil bacterial community (Figures 6-9).\u003c/p\u003e\n\u003cp\u003ePGPR application modifies the community structure of native soil microorganisms to a certain degree.Go head - to - head with native microorganisms for the scarce soil nutrients and spatial layout. Alternatively, spur plant roots to excrete distinct secretions that attract and prompt the beneficial microorganisms in the soil to convert soil nutrients [62]. In this study, the application of C1 brought about a decline in the number of bacterial and fungal Operational Taxonomic Units (OUTs) in the soil. Regarding bacterial diversity, C1 put a damper on species diversity and richness; however, it beefed up the relative abundance of the dominant species. C1 -\u0026nbsp;\u003cem\u003eParachromobacterium\u003c/em\u003e augmented the relative abundance of dominant phyla like\u0026nbsp;\u003cem\u003eBacteroidetes\u003c/em\u003e,\u0026nbsp;\u003cem\u003eProteobacteria\u003c/em\u003e,\u0026nbsp;\u003cem\u003eActinobacteria\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e. The phylum \u003cem\u003eProteobacteria\u003c/em\u003e has complex physiological metabolic types that are important for the carbon cycle\u0026nbsp;[63]. The phylum \u003cem\u003eBacteroidetes\u0026nbsp;\u003c/em\u003epossesses the ability to metabolize sugars and can participate in the production of methane as well as the conversion process of dissolved organic carbon\u0026nbsp;[64]. Actinobacteria play a role in nitrogen and phosphorus metabolism in soil and aid in the breakdown of readily available phosphorus and nitrogen\u0026nbsp;[65]Moreover, C1 enhanced the relative prevalence of Ascomycota and Basidiomycota within the fungal community. As the primary fungal decomposers in the soil, the fungal genera within the\u0026nbsp;\u003cem\u003eAscomycota\u003c/em\u003e phylum were predominantly saprophytes. These saprophytes had the ability to kick - start the conversion of organic matter in the soil. Moreover, the stammers were a big player in breaking down the lignocellulose present in the soil\u0026nbsp;[66]. The microbial genus level was analyzed and C1 increased the relative abundance of dominant genera in the bacterial community of \u003cem\u003eAcidobacterium\u003c/em\u003e\u003cem\u003e, Alcaligenaceae\u003c/em\u003e, \u003cem\u003ePandoraea\u003c/em\u003e, and \u003cem\u003eThiomonas\u003c/em\u003e. Compared with the CK, \u003cem\u003eAcidobacterium\u003c/em\u003e are eosinophilic and oligotrophic chemo-organotrophic bacteria that regulate pH in the soil. It was found that \u003cem\u003eAcidobacterium\u0026nbsp;\u003c/em\u003ewere able to cause a significant increase in the mortality of Colorado potato beetle larvae\u0026nbsp;[67]. \u003cem\u003eAlcaligenaceae\u003c/em\u003e also had a positive effect on pH stabilization in the soil. \u003cem\u003ePandoraea\u0026nbsp;\u003c/em\u003ealleviates drought stress and enhances growth characteristics in soybeans\u0026nbsp;[68]. Certain reports have discovered that the existence of genes associated with the urea degradation process within Thiomonas strains can boost the rate at which urea is broken down in the soil, thereby enhancing soil fertility. Additionally, it has been noted that the breakdown of urea facilitates the precipitation of toxic metals like iron, aluminum, and arsenic\u0026nbsp;[69]. C1 raised the relative prevalence of\u0026nbsp;\u003cem\u003eunclassified_Hypocreaceae\u003c/em\u003e,\u0026nbsp;\u003cem\u003eunidentified_Ascomycota\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eTrichoderma\u003c/em\u003e within the fungal community. Researchers discovered that\u0026nbsp;\u003cem\u003eTrichoderma\u003c/em\u003e, a genus within the\u0026nbsp;\u003cem\u003eHypocreaceae\u0026nbsp;\u003c/em\u003efamily, offers both biocontrol benefits and promotes plant growth. These advantages encompass antibacterial properties, antioxidant capabilities, insect - repelling features, and functions that stimulate plant development.\u0026nbsp;[70].\u003c/p\u003e\n\u003cp\u003eSoil microorganisms are crucial for the cycling and transformation of substances like soil nutrients, root secretions, and apoplastic materials [71]. The functions of soil microorganisms are intricately linked to elements like organic matter, mineral nutrients, and their own life processes within the soil, and they maintain a state of dynamic equilibrium. Consequently, researching the structure and function of the soil microbial community holds immense significance for understanding the transformation mechanisms of soil nutrients and soil fertility [72, 73]. KEGG functional annotation analysis of the structural functions within soil microbial communities indicated that the metabolic functions of soil microorganisms are of great significance in life activities. Among the six KEGG metabolic functions, these metabolic functions of soil microorganisms boast the largest number of genes. Application of C1 enhanced the metabolic functions of tobacco inter-root microorganisms, promoting fatty acid degradation as well as glycine, serine, and threonine metabolism. Overall, the introduction of inter-root biotrophic bacteria enhanced the metabolic functions of inter-root microorganisms, as well as boosted microbial cycling and energy conversion in soil substances [74, 75].\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study clarified the impacts of different PGPR on tobacco growth, development, and soil physicochemical properties, and selected the optimal PGPR, T1. The results indicated that PGPR application markedly advanced tobacco growth and development and held prominent benefits in enhancing soil physicochemical properties. A comparative analysis of soil enzyme activity, microbiome, and metagenome was also carried out between the top - performing C1 treatment group and the CK control group. The results showed that the activities of enzymes including sucrase, catalase, and urease were notably higher in the C1 group than in CK. Furthermore, the T1 strain facilitated the growth of beneficial microbes and boosted metabolic functions associated with fatty acid breakdown as well as the metabolism of glycine, serine, and threonine. This lays the groundwork for the advancement of high - efficiency microbial fertilizers and the exploration of high - efficiency PGPR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRan Wang and Chengguang Zhu: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft. Lei Tian: Data Curation, Writing - Original Draft. Lili Wang, Hao Zong, Mingfeng Yang, Fuyu Peng and Mingming Sun: Resources, Supervision. Zongpeng Zhao, Yuhai Du and Zengbo Fan: Software, Validation, Writing - Original Draft. Li Zhang and Qiang Zhang: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication of this paper was obtained from all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by This work was supported by the Foundation of Research and application of efficient cultivation technology for integrated tobacco and wheat production(2024371300260411), Analysis of the characteristic styles and mellowing characteristics of American functional Roubaix tobaccos (202302004), Construction and Application of Quality Management Control Model for Tobacco Production and Acquisition in Shandong Province(202301001),\u0026nbsp;Natural Science Foundation of Shandong Province (ZR202211230214), Science and Technology Program of Shandong weifang Tobacco Limited Company (2024-34) and Major Science and Technology Projects of China National Tobacco Corporation, Shandong Provincial Company (202404).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study were available from the corresponding author on reasonable request.\u0026nbsp;The sequenced raw reads generated in this study have been submitted to the National Center for Biotechnology Information (NCBI) with BioProject ID:\u0026nbsp;C1:PQ895541, C2:PQ895542, C3:PQ895543.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, we confirm that although this study received funding, the design, implementation, data analysis, and interpretation of results have maintained independence and objectivity. The authors declare no potential conflicts of interest that could compromise the impartiality of the research with the funding company. All research processes adhere to the principles of academic integrity, ensuring the transparency and credibility of the findings.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa Z, Guan Z, Liu Q, Hu Y, Liu L, Wang B, Huang L, Li H, Yang Y, Han M: \u003cstrong\u003eObstacles in continuous cropping: mechanisms and control measures\u003c/strong\u003e. \u003cem\u003eAdvances in agronomy \u003c/em\u003e2023, \u003cstrong\u003e179\u003c/strong\u003e:205-256.\u003c/li\u003e\n\u003cli\u003eTan G, Liu Y, Peng S, Yin H, Meng D, Tao J, Gu Y, Li J, Yang S, Xiao N: \u003cstrong\u003eSoil potentials to resist continuous cropping obstacle: Three field cases\u003c/strong\u003e. \u003cem\u003eEnvironmental research \u003c/em\u003e2021, \u003cstrong\u003e200\u003c/strong\u003e:111319.\u003c/li\u003e\n\u003cli\u003eChen Y, Yang L, Zhang L, Li J, Zheng Y, Yang W, Deng L, Gao Q, Mi Q, Li X: \u003cstrong\u003eAutotoxins in continuous tobacco cropping soils and their 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Plant growth-promoting rhizobacteria (PGPR) play a crucial role in enhancing plant growth, improving soil properties, and modulating the soil microbial environment. In this study, a variety of PGPGs, C1 for \u003cem\u003eBacillus paranthracis\u003c/em\u003e, C2 for \u003cem\u003ePaenibacillus hunanensis\u003c/em\u003e, and C3 for \u003cem\u003eBacillus subtilis\u003c/em\u003e, were screened, and the mechanisms of action on the successional soils were investigated. The results demonstrated that the C1 treatment markedly enhanced the growth and development of tobacco plants while also exhibiting significant efficacy in improving soil physicochemical properties. To further investigate the impact of strains with strong growth-promoting effects, soil enzyme activities, microbial community composition, and functional diversity were analyzed in both the C1 treatment and CK control groups. The findings indicated that there were significant increases in sucrase, catalase, and urease in the C1 treatment when compared to the CK. Beneficial microflora were increased and functions such as metabolism and synthesis of amino acids and secondary metabolite synthesis in soil microorganisms were promoted by C1. In summary, C1 is capable of efficiently enhancing soil characteristics, facilitating the growth of tobacco, and laying a foundation for the advancement of microbial fertilizers aimed at mitigating the issues of continuous cropping obstacles..\u003c/p\u003e","manuscriptTitle":"Mechanistic analysis of rhizosphere promoting bacteria on tobacco growth, continuous cropping soil, and root microbiota","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 09:40:28","doi":"10.21203/rs.3.rs-6220721/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3aaadea6-c81a-4814-84c2-c5b757de48cd","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-28T00:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 09:40:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6220721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6220721","identity":"rs-6220721","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}