The rare bacteria in the rhizosphere enhanced the tolerance of tea plants to drought

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The interaction between rhizosphere microorganisms and tea plants could enhance the drought resistance of tea plants. However, there are few studies on the effects of abundant and rare microorganisms on tea plants. In this study, the contributions of abundant and rare bacteria in the rhizosphere microorganisms of ‘FudingDabaicha’ and ‘Baiye No.1’ to the resistance of tea plants to drought stress were studied using 16SrRNA sequencing, co-occurrence network analysis, and PLS-PM modeling analysis. The results showed that the activity of antioxidant enzymes and the content of osmotic substances increased significantly after drought stress. In the co-occurrence network of the two varieties, the average degree, clustering coefficient, and modularity index of the rare bacteria were greater than those of the abundant bacteria, and the path coefficient of the rare bacteria to drought was greater than that of the abundant bacteria. The contribution of rare microorganisms in ‘FudingDabaicha’ to drought stress was greater than that in ‘Baiye No.1’. The rare bacteria of the two varieties were positively correlated with amino acids and negatively correlated with lipids. The results of this study will provide new insights for the use of rhizosphere microorganisms in improving the drought resistance of tea plants. Camellia sinensis drought abundant bacteria rare bacteria root exudates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The tea plant ( Camellia sinensis L.) is a perennial woody plant. A warm and humid climate is conducive to the growth of tea plants. With the intensification of global warming, drought stress has gradually become one of the most important abiotic factors limiting plant growth and yield [ 1 ]. After drought stress, the stomatal conductance, photosynthesis rate, and transpiration rate of tea leaves decreased [ 2 ]. The contents of important biochemical components such as catechin, theanine and caffeine also decreased [ 3 ]. Therefore, drought poses a great threat to the growth and quality of tea. Rhizosphere microorganisms are complex communities living between plant roots and soil. Drought led to various physiological reactions in plants [ 4 ], and these physiological changes led to the recombination of rhizosphere microorganisms [ 5 – 7 ]. The beneficial drought resistant microorganisms were screened or retained among the recombinant microorganisms, and they improved the tolerance of plants to drought stress by interacting with plants [ 8 ]. Changes in the ecosystem occur through abundant microorganisms and rare microorganisms [ 9 ]. It was undeniable that abundant microorganisms could dominate the function of ecosystems [ 10 ], but rare microorganisms played an important role in ecosystems as a potential driving force for the function of microbial communities [ 11 – 15 ]. Therefore, people paid more attention to rare microorganisms. Rare microorganisms were sensitive to drought stress [ 16 ]. For crops, rare microorganisms could potentially contribute to the stability and constancy of maize yield under climate change, and could improve the resistance of soil communities to abiotic stresses [ 17 ]. Currently, the contributions of abundant and rare microorganisms in the rhizosphere of tea plants to drought stress resistance are still unclear. Root exudates were various compounds released into the environment by root metabolism during plant growth, including primary metabolites, secondary metabolites, and inorganic substances [ 18 ]. The number and composition of root exudates changed due to the influence of abiotic factors [ 19 ]. Root damage was alleviated by up-regulating root exudates under drought stress in plants [ 20 ]. After drought stress, the concentration of free amino acids in Brassica napus increased to maintain cell expansion and reduce water loss [ 21 ]. The sucrose, methionine, isoleucine, and phenylalanine contents of soybean plants only increased in the drought treatment group after drought stress [ 22 ]. In addition, there was a close relationship between root exudates and rhizosphere microorganisms. Changes in specific compounds in root exudates can affect the composition of the rhizosphere microbial community [ 23 ]. Under drought stress, maize inoculated with Bacillus could activate the exudation of amino acids and proline to improve its drought resistance [ 24 ]. In this study, the contributions of abundant and rare bacteria in the soil rhizosphere of ‘FudingDabaicha’ and ‘Baiye No.1’ to the resistance of tea plants to drought stress were studied using physiological and biochemical methods, 16SrRNA sequencing, co-occurrence network analysis, RDA and PLS-PM modeling analysis. Furthermore, the contents and compositions of the root exudates of the two varieties after drought stress were compared to identify differential metabolites and explore the relationships between rhizosphere bacteria and root exudates. This research will help to clarify the relative contribution of abundant and rare bacteria to the response of tea plants to drought stress, further understand the relationship between rhizosphere bacteria and the root exudates of tea plants, and provide new insights for the application of rhizosphere microorganisms in improving the drought resistance of tea plants. Results Physiological resistance indices of tea plants The effects of drought stress on different varieties of tea plants were quite different. The results showed that the activities of CAT, SOD, POD and the contents of MDA, proline, soluble sugar, and soluble protein under drought stress were significantly greater in ‘FudingDabaicha’ and ‘Baiye No.1’ than in their respective controls (FCK and ACK) (Table 1 ). Compared with those of the two varieties, the antioxidant enzyme activity and osmotic substance content of ‘FudingDabaicha’ were significantly greater than those of ‘Baiye No.1’ under drought stress. Therefore, the drought resistance of ‘FudingDabaicha’ was greater than that of ‘Baiye No.1’. Table 1 Antioxidant enzyme activity and osmotic substance content of tea plants under drought stress (* represents the difference between FM vs. FCK, AM vs. ACK;△ represents the difference between AM vs FM. */△, p < 0.05; **/△△, p < 0.01; ***/△△△, p < 0.001) Physiologic index FM FCK AM ACK SOD (U·g − 1 ) 29.65 ± 0.40 ***△△△ 25.98 ± 0.10 26.56 ± 0.40 *** 20.80 ± 0.29 POD (U·g − 1 ) 182.93 ± 3.70 ***△△ 155.44 ± 3.59 164.50 ± 2.80 *** 145.79 ± 1.49 CAT (U·g − 1 ) 135.91 ± 1.47 ***△△△ 91.85 ± 2.68 82.17 ± 1.00 *** 64.05 ± 1.46 MDA (nmol·g − 1 ) 65.11 ± 0.93 ***△△△ 39.40 ± 0.81 51.30 ± 1.10 *** 34.72 ± 0.39 Proline (ug·g − 1 ) 11.00 ± 0.08 ***△ 10.17 ± 0.05 10.83 ± 0.00 *** 9.19 ± 0.03 Soluble sugar (%) 4.13 ± 0.05 ***△△△ 2.29 ± 0.05 3.40 ± 0.10 *** 1.96 ± 0.05 Soluble protein (%) 1.92 ± 0.04 *** 1.23 ± 0.03 1.96 ± 0.10 *** 1.13 ± 0.04 Metabolomics analysis In this study, the root exudates were analyzed by using LC-MS technology. A total of 170 metabolites were identified by secondary mass spectrometry, including 43 lipids, 32 amino acids, 25 xenobiotics, 19 nucleotides, 18 carbohydrates, 9 cofactors and vitamins, 9 energy, and 20 unknowings (Table S1 ). Through PCA analysis, it was found that the four treatments belonged to different quadrants (Fig. 1 ), indicating that drought had a greater impact on the metabolism of ‘FudingDabaicha’ and ‘Baiye No.1’. In this study, metabolites were screened for p-value ≤ 0.05, VIP > 1, and FC ≥ 1.5. A total of 82 differentially metabolites were screened in AM vs. ACK, of which 66 metabolites were up-regulated and 16 metabolites were down-regulated. The up-regulated metabolites were mainly amino acids, and the down-regulated metabolites were mainly lipids. A total of 77 differentially metabolites were screened in FM vs. FCK, 57 of which were up-regulated and 20 of which were down-regulated. The main metabolites whose expression increased or decreased were the same as AM vs. ACK. (Table S2 ). Microbiome analysis Rhizosphere microbial α and β diversity under drought stress The Chao1 index and Shannon index can reveal changes in microbial richness and diversity. There was no significant difference in the Chao1 index (Fig. 2 A, 2 B) and Shannon index (Fig. 2 C, 2 D) between ‘FudingDabaicha’ and ‘Baiye No.1’ after drought stress. Principal coordinate analysis (PCoA) of the Bray-Curtis distance at the OTU level revealed that the two varieties in drought treatments (FM and AM) were far from the control (FCK and ACK) (Fig. 2 E, 2 F). These findings indicated that the composition of the rhizosphere bacterial communities of the two varieties changed after drought stress. Rhizosphere microbial structure composition and network topology The diversity of rhizosphere bacteria in different tea varieties was determined after drought stress, and a total of 14777 OTUs were detected (Table S3). Bacteria with a relative abundance > 0.1% were selected as the abundant bacteria, and those with a relative abundance < 0.01% were selected as the rare bacteria [ 15 ]. In ‘Baiye No.1’, 190 OTUs of abundant bacteria and 2786 OTUs of rare bacteria were screened, and 207 OTUs of abundant bacteria and OTUs of rare bacteria were screened out in ‘FudingDabaicha’ (Table S4). The network topology of tea rhizosphere bacteria was constructed using R language and Gephi technology, and the node-level topology was calculated. The global network was constructed by all OTUs of the same variety in the drought treatment and control treatments, and the sub-networks were constructed by OTUs of abundant and rare bacteria, respectively. In ‘Baiye No.1’, the global network contained 1000 nodes and 36567 edges. Among them, the abundant sub-network and the rare sub-network had 190 and 810 nodes, respectively (Fig. 3 A). In ‘FudingDabaicha’, the global network contains 1181 nodes and 50524 edges. Among them, the abundant sub-network and the rare sub-network had 205 and 976 nodes, respectively (Fig. 3 B). Network topology features such as betweenness, closeness, eigenvector centrality, and degree were calculated based on the unique node level. Table S5 showed that the average degree, clustering coefficient, and modularity index of the rare bacteria in the two varieties were greater than those of the abundant bacteria. These results indicated that the rare bacteria in the rhizosphere soil of tea plants might play a stronger role in coping with drought stress than the abundant bacteria. Redundancy analysis (RDA) and partial least squares path modeling analysis (PLS-PM) To further explore the contribution of abundant and rare bacteria to the resistance of tea plants to drought stress, six phylums (genus) significantly associated with drought resistance indicators in the two varieties were first screened by RDA (Fig. S1 -S8; Table 2 ). The selected phylum (genus) and environmental factors were analyzed via PLS-PM modeling, and then the path coefficients of abundant bacteria and rare bacteria under drought stress conditions were subsequently calculated to determine their contributions to the process by which tea trees resist drought stress. Figure 3 showed that in ‘Baiye No.1’, the path coefficient of rare bacteria to drought (phylum: 0.61, genus: 0.64) was greater than that of abundant bacteria (phylum: 0.40, genus: 0.36) (Fig. 3 C, 3 D), and the same situation occurred in ‘Fudingdabaicha’ (phylum: 0.94 vs. 0.0, genus: 0.92 vs. 0.08) (Fig. 3 E, 3 F). This means that the contribution of rare bacteria was greater than that of abundant bacteria. Comparing the two varieties, the path coefficients of rare bacteria in ‘FudingDabaicha’ to drought were greater than those in ‘Baiye No.1’ (Fig. 3 C- 3 F). Therefore, the contribution of rare bacteria in ‘Fudingdabaicha’ was greater than that in ‘Baiye No.1’. Table 2 Screening of phylums/ genus based on RDA analysis. The microbial phylum (genus) was screened according to the correlation between microorganisms and environmental factors. Variety Abundent phylum Rare phylum Abundent genus Rare genus ‘Baiye No.1’ Actinobacteria Bacteroidetes Mesorhizobium Burkholderia candidate_division_WPS-1 Chlamydiae Hyphomicrobium Acidicapsa Cyanobacteria/Chloroplast Chloroflexi Acinetobacter Schlesneria Firmicutes Parcubacteria Arthrobacter Subdivision3_genera_incertae_sedis Planctomycetes Proteobacteria Parafilimonas Bacillus Proteobacteria Verrucomicrobia WPS-1_genera_incertae_sedis Legionella ‘FudingDabaicha’ Actinobacteria Armatimonadetes Burkholderia Pelolinea Bacteroidetes Bacteroidetes Asticcacaulis Planifilum candidate_division_WPS-1 candidate_division_WPS-1 Arthrobacter Microbacterium Cyanobacteria/Chloroplast Ignavibacteriae Gp2 Pirellula Planctomycetes Latescibacteria Methylophilus Armatimonadetes_gp4 Acidobacteria Proteobacteria Mucilaginibacter Armatimonadetes_gp5 Association analysis of rare microorganisms and differential metabolites Procrustes analysis revealed that the rare phylum (genus) screened in ‘Baiye No.1’ and ‘FudingDabaicha’ were highly correlated with differentially metabolites (Fig. 4 A, p = 0.0028, M 2 = 0.34; Fig. 4 B, p = 0.03, M 2 = 0.40). Subsequently, the relationships between rare phylum (genus) and differentially metabolites were determined by spearman correlation analysis. Overall, the phylum (genus) of the two varieties were positively correlated with amino acids and negatively correlated with lipids (Fig. 5 ). Discussion With global warming, the degree and frequency of soil drought are increasing, which seriously affects the growth and development, yield, and quality of tea plants. Under drought stress, the leaves of tea plants curled and wilted, and focal spots appeared. With the increase in drought stress, the leaves of tea plants fell off or even died. However, a certain degree of drought also stimulated plants to induce drought resistance responses to resist drought damage [ 25 ]. In this study, we found that the antioxidant enzyme activity and osmotic substance content of ‘Baiye No.1’ and ‘FudingDabaicha’ were significantly greater than those of the control under moderate drought conditions. The main reason might be that the activities of SOD, POD, and CAT were up-regulated in tea plants under mild drought stress, and excessive ROS were removed to ensure the integrity of the cell biofilm, thus reducing the effect of drought stress on tea plants [ 26 ]. Moreover, osmotic substances such as proline, soluble sugar, and soluble protein also accumulated, which helped tea plants maintain turgor by reducing cell osmotic pressure under moderate drought conditions to reduce cell damage [ 27 ]. Soil water content is closely related to soil microorganisms, and drought can cause changes in soil microbial composition. Drought strengthens the link between plants and underground microbial networks, allowing plants to rely on microorganisms to enhance their own stress protection against external stresses. In this study, the α diversity of rhizosphere bacteria of the two varieties did not change significantly after drought stress. The reason was that the change in community composition caused by the water shortage environment compensated for the potential change in community diversity [ 28 ], but the β diversity of rhizosphere bacteria changed. Drought stress did not directly lead to the emergence of drought-tolerant microorganisms or the attenuation and death of drought-intolerant microorganisms, but did cause the diversity of rhizosphere microorganisms to remain relatively stable [ 29 – 31 ]; however, drought stress could greatly change the composition of rhizosphere microorganisms. Changes in α diversity of rice plants were negligible after drought stress, but changes in β diversity were significant [ 23 ]. Compared with our results, there was no significant difference in α diversity between the two varieties after drought stress, but there was a significant difference in β diversity between the two varieties (Fig. 2 ), which was consistent with the above results. Most studies on the improvement of plant drought resistance by microorganisms had directly analyzed the effects of abundant microorganism on plants, and few studies had explored the role of rare microorganism in drought. In this study, co-occurrence network analysis revealed that the average degree, clustering coefficient, and modularity of the rare microorganism sub-network in the two varieties were higher than those of the abundant microorganism sub-network. The rare microorganism did play a certain role in drought stress (Fig. 3 A, 3 B). The impact of the environment on biological communities was positively correlated with community composition. Rare communities could respond better to the impact of environmental changes caused by grassland degradation. [ 32 ]. Due to the slow growth of rare microorganisms, they could continue to overcome pressure and maintain their diversity under environmental changes. They could act as a resource pool to respond quickly to environmental changes in response to the effects of abiotic stresses on plants [ 33 ], thereby enhancing the plant's tolerance to stress. To determine the contributions of rare bacteria and abundant bacteria to improving the response of tea plants to drought stress, we screened out the corresponding phylum (genus) by RDA and constructed a PLS-PM model with physiological indices of tea plants. By calculating the path coefficient, we found that the contribution of rare bacteria was indeed significantly greater than that of abundant bacteria (Fig. 3 c- 3 f). Drought might cause differences in microbial community assembly mechanisms. The assembly of rare bacteria was controlled by deterministic processes and environmental filtering, while the assembly of abundant bacterial communities was controlled by random processes [ 34 – 36 ]. Abundant species might occupy a wider niche and had a certain competitiveness for resource grabbing, so they could exist for a long time. However, the distribution of rare microorganisms was uneven, and they were rejected because of their narrow niche. However, a large number of metabolically active lineages of rare microorganisms were sources of seed banks [ 37 ]. They could play a role in high taxonomic diversity when the ecosystem was disturbed and enhance the resistance to or restoration of soil microbial communities [ 38 ]. Therefore, it was confirmed that the rare bacteria of the two varieties in this study were more sensitive to drought than were the abundant bacteria. Under drought stress, plants not only change the total amount of root exudates input, but also may change the components of root exudate [ 34 ]. These plants used the most abundant metabolites to produce exudates under current environmental conditions. Studies had shown that amino acids accumulated in the leaves and roots of plants under drought stress, which might help maintain root development to obtain deeper water reserves [ 19 ], and alleviate drought stress by osmotically regulating the accumulation of solutes such as amino acids without reducing the water content of root cells [ 18 ]. In our study, we also found that amino acids (L-leucine, L-glutamic acid, L-lysine, L-phenylalanine, and L-arginine) were enriched in the two varieties under drought stress. Amino acids were important carbon and nitrogen sources for rhizosphere microorganisms, and were helpful for improving the decomposition activity and extracellular enzyme activity of rhizosphere microorganisms, promoting the growth and stimulation of rhizosphere microorganisms, and accelerating the accumulation and turnover rate of soil organic matter [ 38 – 40 ]. Materials and methods Sample collection and experimental design The experiment was carried out in the artificial climate chamber of Shandong Agricultural University (N-36°19′42″, E-117°11′35″) with 1-year-old ‘FudingDabaicha’ and ‘Baiye No.1’ clone tea seedlings as materials. The soil was obtained from Shandong Taishan Tea Valley Agricultural Development Co., Ltd. (N-36°13′28.592″, E-116°57′17.305). The experiment included two soil water contents: control (75% water holding capacity) and moderate drought treatment (35% water holding capacity). The two treatments were marked as ACK and AM in ‘Baiye No.1’, and FCK and FM in ‘FudingDabaicha’. Three tea plants were planted in each pot, and each treatment was repeated three times. The soil water content was controlled by the daily weighing method. The tea plants were first incubated at 60% water holding capacity for 7 days, after which the experiment was carried out. After 7 days of drought stress, the antioxidant enzyme activity and osmotic substance content of the second leaf under the top bud were determined. The rhizosphere soil was mixed and stored at -80°C for the determination of rhizosphere bacterial diversity. Root exudates were collected for metabolomics analysis. Determination of the resistance index The activity of SOD was determined using nitroblue tetrazole, the activity of POD was determined using the guaiacol method, and the activity of CAT was determined using ultraviolet spectrophotometry [ 41 ]. MDA was determined using the thiobarbituric acid method [ 5 ]. The soluble sugar content was determined by anthrone-sulfuric acid colorimetry [ 42 ], the soluble protein content was determined by the Bradford method [ 43 ], and the proline content was determined by the sulfosalicylic acid method [ 44 ]. Extraction and detection of root exudates The sample was melted at 4°C, and 400 µL of methanol was added to 100 µL of sample solution, which was then vortexed for 1 min, and centrifuged at 12000 rpm for 10 min at 4°C. The 500 µL of supernatant was vacuum concentrated and dried, and 150 µL of 80% methanol was added for re-dissolution. After mixing, the supernatant was centrifuged at 12000 rpm at 4°C for 10 min, and the supernatant was taken as the sample to be tested [ 45 ]. High performance liquid chromatography (Vanquish Core HPLC, Thermo Fisher Scientific, Shanghai, China) and an ACQUITY UPLC® HSS T3 column (1.8 µm, 2.1×150 mm, Waters Corporation, USA) were used for detection. The temperature of the automatic sampler was set to 8°C, the flow rate was 0.25 mL/min, the column temperature was 40°C, and 2 µL was injected for gradient elution. The mobile phase was positive ion 0.1% formic acid water (C), 0.1% formic acid acetonitrile (D), and negative ion 5 mM ammonium formate water (A)-acetonitrile (B). The gradient elution program was 0–1 min, 2% B/D; 1–9 min, 2%-50% B/D; 9–12 min, 50%-98% B/D; 12-13.5 min, 98% B/D; 13.5–14 min, 98%-2% B/D; 14–20 min, 2% D-positive mode (14–17 min, 2% B-negative mode). A mass spectrometer (Thermo-QE-HF-X, Thermo Fisher Scientific, Shanghai, China) was used for detection. The instrument used an electrospray ion source (ESI) and positive and negative ion ionization modes. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was 2.50 kV, the sheath gas was 30 arb, and the auxiliary gas was 10 arb. The capillary temperature was 325°C, and the full scan was performed at a resolution of 60000. The scanning range was 81-1000, and HCD was used for secondary cracking. The collision voltage was 30 eV, and unnecessary MS/MS information was dynamically excluded. DNA extraction, sequence processing and analysis Soil total DNA was extracted with an “Omega Soil DNA Kit” (Omega Bio-Tek, Norcross, Georgia, USA). The quality of the DNA was determined using agarose gel electrophoresis and Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). The V4-V5 variable region (Bolyen et al. 2019) of the 16S rRNA gene was amplified by high fidelity PCR. 16S rRNA sequencing was conducted on the Illumina MiSeq Benchtop Sequencer (Illumina, USA). The sequencing primers were 515F (5'-GTGCCAGCMGCCGCGG-3') and 907R (5'-CCGTCAATTCMTTTRAGTTT-3'). Statistical analysis The physiological data analysis was conducted using SPSS v23.0. One-way analysis of variance (ANOVA) was used to compare the averages between treatments. The Metlin ( http://metlin.scripps.edu ) and MoNA ( https://mona.fiehnlab.ucdavis.edu ) databases were used to identify the main metabolites in the roots, and the differentially metabolites were screened based on p -value ≤ 0.05, VIP > 1, and FC ≥ 1.5. Principal component analysis (PCA) was performed on the differentially metabolites using the ade4 package of the R language (v4.3.0). The vegan package of the R language was used to calculate the α and β diversity indexes. Gephi (ver. 0.9.1, https://gephi.org ) software was used to construct a visual co-occurrence network, and the network-level and node-level topological properties were further calculated to compare the topological structures of rare sub-networks and abundant sub-networks. Redundancy analysis (RDA) was performed using the vegan package of the R language to clarify the effects of physiological indicators on rare and abundant microorganisms. PLS Path Modeling with R ( http://www.gastonsanchez.com/PLS Path Modelling with R.pdf) was used for PLS-PM modeling analysis to calculate the path coefficient and determine the relative contribution of abundant microorganisms and rare microorganisms. The procrustes function and corrplot package in R were used for procrustes analysis and spearman correlation analysis to evaluate the associations between rare microorganisms and differentially metabolites. Declarations Acknowledgements Not applicable. Authors' contributions The authors confirm their contribution to the paper as follows: methodology, data curation: X.Y.; investigation, visualization: X.Z.; draft manuscript preparation: X.Y., X.Z.; manuscript review: X.H.; funding acquisition, supervision: X.H.. All authors reviewed the results and approved the final version of the manuscript. Funding This research was sponsored by the Natural Science Foundation of Shandong Province (Grant No. ZR2019BC062), and Tai'an City Science and Technology Innovation Major Special Project (Grant No. 2022NYLZ08). Data availability The data underlying this article are available in the article. Ethics approval and consent to participate For this article no studies with human participants or animals were performed by any of the authors. All the experiments were performed in accordance with relevant guidelines and regulations. Consent for publication All authors approve the manuscript and consent to the publication of the work. Competing interests The authors declare that they have no conflict of interest. References Chen Y, Yao Z, Sun Y, Wang E, Tian C, Sun Y, Liu J, Sun C, Tian L. Current studies of the effects of drought stress on root exudates and rhizosphere microbiomes of crop plant species. Int J Mol Sci. 2022;23(4):2374. Rahimi M, Kordrostami M, Mortezavi M. 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Strategies and structure feature of the aboveground and belowground microbial community respond to drought in wild rice ( Oryza longistaminata ). 2021; 14:79. Bastida F, Torres IF, Hernández T, García C. The impacts of organic amendments: do they confer stability against drought on the soil microbial community? Soil Boil Biochem. 2017;113:173–83. Tóth Z, Táncsics A, Kriszt B, Kröel-Dulay G, Ónodi G, Hornung E. Extreme effects of drought on composition of the soil bacterial community and decomposition of plant tissue. Eur J Soil Sci. 2017;68(4):504–13. Liu M, Ren Y, Zhang W. Rare bacteria can be used as ecological indicators of grassland degradation. Microorganisms. 2023;11(3):754. Sun Y, Deng X, Tao C, Liu H, Shen Z, Liu Y, Li R, Shen Q. Temporal dynamics of rare and abundant soil bacterial taxa from different fertilization regimes under various environmental disturbances. Msystems. 2022;7(5):e00559–22. Chen W, Ren K, Isabwe A, Chen H, Liu M, Yang J. Correction to: Stochastic processes shape microeukaryotic community assembly in a subtropical river across wet and dry seasons. Microbiome. 2019;7:138. Chase JM, Myers JA. Disentangling the importance of ecological niches from stochastic processes across scales. Philos Trans R Soc Lond B Biol Sci. 2011;366(1576):2351–63. Yang Y, Cheng K, Li K, Jin Y, He X. Deciphering the diversity patterns and community assembly of rare and abundant bacterial communities in a wetland system. Sci Total Environ. 2022;838:156334. Jiao S, Wang J, Wei G, Chen W, Lu Y. Dominant role of abundant rather than rare bacterial taxa in maintaining agro-soil microbiomes under environmental disturbances. Chemosphere. 2019;235:248–59. Jiao S, Chen W, Wei G. Biogeography and ecological diversity patterns of rare and abundant bacteria in oil-contaminated soils. Mol Ecol. 2017;26(19):5305–17. Zhalnina K, Louie KB, Hao Z, Mansoori N, Da Rocha UN, Shi S, Cho H, Karaoz U, Loqué D, Bowen BP, Firestone MK. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3(4):470–80. Drake JE, Gallet-Budynek A, Hofmockel KS, Bernhardt ES, Billings SA, Jackson RB, Johnsen KS, Lichter J, McCarthy HR, McCormack ML, Moore DJ. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long-term enhancement of forest productivity under elevated CO 2 . Ecol Lett. 2011;14(4):349–57. Chari NR, Taylor BN. Soil organic matter formation and loss are mediated by root exudates in a temperate forest. Nat Geosci. 2022;15:1011–6. Zhang J, Kirkham MB. Lipid peroxidation in sorghum and sunflower seedlings as affected by ascorbic acid, benzoic acid, and propyl gallate. J Plant Physiol. 1996;149(5):489–93. Heath RL, Packer L. Photoperoxidation in isolated chloroplasts: I. Kinetics and stoichiometry of fatty acid peroxidation. Arch Biochem Biophys. 1968;125(1):189–98. Riazi A, Matsuda K, Arslan A. Water-stress induced changes in concentrations of proline and other solutes in growing regions of young barley leaves. J Exp Bot. 1985;36(11):1716–25. Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976;72(1–2):248–54. Xie H, Chen Z, Feng X, Wang M, Luo Y, Wang Y, Xu P. L-theanine exuded from Camellia sinensis roots regulates element cycling in soil by shaping the rhizosphere microbiome assembly. Sci Total Environ. 2022;837:155801. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfile2.docx Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Editor assigned by journal 30 Aug, 2024 Submission checks completed at journal 30 Aug, 2024 First submitted to journal 21 Aug, 2024 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-4950519","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347353445,"identity":"edac5e26-ce2d-4b49-918d-5a6ff9c2c3c5","order_by":0,"name":"Xinhan You","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xinhan","middleName":"","lastName":"You","suffix":""},{"id":347353446,"identity":"09495da8-0c1f-4f90-a9b1-99ee32d3a45d","order_by":1,"name":"Xiaoxia Zhao","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Zhao","suffix":""},{"id":347353447,"identity":"43e9ccef-ea8c-40ae-a379-687b88e4875f","order_by":2,"name":"Xiaoyang Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACAzBZwcDYAKJ5iNdyhmQtjG2kaDFnP3tMmndenWz/jATGB2/bGOTNCWmx7MlLNubddth4xo0EZsO5bQyGOxsIOexAjuFj3m0HEhtuJLBJ87YxJBgcIKTl/BuDw7xz6hLn30hg/02clhsgWxqYEzcAbWEmUssbY8M5xw4bbzzzsFlyzjkJww2EHZZjJvGmpk523vHkgx/elNnIE7QFBJgg0QGOGgki1IPU/iBO3SgYBaNgFIxUAAClH0JHYWdy5wAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-08-21 09:43:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4950519/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4950519/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05860-5","type":"published","date":"2024-11-29T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65716769,"identity":"f2070e80-a95f-425b-a45f-52ea36944521","added_by":"auto","created_at":"2024-10-01 15:52:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56616,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of tea root exudates under different treatments.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/42249e06518ec8d8cf54c25e.png"},{"id":65716771,"identity":"c2da2def-764a-4119-ac78-432c7ac14aa1","added_by":"auto","created_at":"2024-10-01 15:52:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213867,"visible":true,"origin":"","legend":"\u003cp\u003eThe α and β diversity of rhizosphere bacteria of tea plants under different treatments. \u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eE\u003c/strong\u003e were the Chao1 index, Shannon index, and PCoA of ‘Baiye No.1’, respectively. \u003cstrong\u003eB\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e, \u003cstrong\u003eF\u003c/strong\u003e were the Chao1 index, Shannon index, and PCoA of ‘FudingDabaicha’, respectively.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/4abc450217a37ed86131b99c.png"},{"id":65716774,"identity":"0756b373-fee9-4936-ae43-8b36e61c08ee","added_by":"auto","created_at":"2024-10-01 15:52:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":544383,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network analysis of rhizosphere microorganisms and its structural equation model with stress resistance factors. \u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e: ‘Baiye No.1’; \u003cstrong\u003eB\u003c/strong\u003e, \u003cstrong\u003eE\u003c/strong\u003e, \u003cstrong\u003eF\u003c/strong\u003e: ‘FudingDabaicha’. AMRP, AMAP were rare phylum and abundant phylum of ‘Baiye No.1’, respectively; AMRG, AMAG were rare genus and abundant genus of ‘Baiye No.1’, respectively; FMRP, FMAP were rare phylum and abundant phylum of ‘FudingDabaicha’, respectively; FMRG, FMAG were rare genus and abundant genus of ‘FudingDabaicha’, respectively; DRI, physiological indexes of drought resistance.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/e26b9ab7f58ac654092cf8bf.png"},{"id":65716770,"identity":"6a8f5127-6c4f-4e87-9563-1b069043eef7","added_by":"auto","created_at":"2024-10-01 15:52:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137882,"visible":true,"origin":"","legend":"\u003cp\u003eProcrustes analysis between rare phylums (genus) and root exudates. \u003cstrong\u003eA\u003c/strong\u003e, ‘Baiye No.1’; \u003cstrong\u003eB\u003c/strong\u003e, ‘FudingDabaicha’. Solid circle and hollow circle represent bacteria and root exudates, respectively.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/c6c1a5a11dcb7eddd2fa2179.png"},{"id":65717328,"identity":"c0228775-5697-489d-b85a-8bea224b814a","added_by":"auto","created_at":"2024-10-01 16:00:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":779777,"visible":true,"origin":"","legend":"\u003cp\u003eThe spearman correlation analysis between rare phylums (genus) and root exudates. *, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001. \u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eB\u003c/strong\u003e represent the association analysis of rare phylum, rare genus, and differential metabolites in the rhizosphere of ‘Baiye No.1’, respectively; \u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD \u003c/strong\u003erepresent the association analysis of rare phylum, rare genus, and differential metabolites in the rhizosphere of ‘FudingDabaicha’, respectively.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/8e8450f35ffe82d07d880a61.png"},{"id":70391467,"identity":"2231d23b-48c0-4b44-8442-a0c6acdaa4d4","added_by":"auto","created_at":"2024-12-02 17:30:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2270402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/61837e2a-3150-4ebe-a2dd-1cae514e6191.pdf"},{"id":65718239,"identity":"6595e8f1-cf02-4ffd-807f-c30f1fcf17bb","added_by":"auto","created_at":"2024-10-01 16:08:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":800866,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/d6460234c734aac6e2d70d38.xlsx"},{"id":65717330,"identity":"32778e8b-f013-43d7-8dc6-9bee27ff8994","added_by":"auto","created_at":"2024-10-01 16:00:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2643665,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4950519/v1/e88e50d1b7fdd57f6e2e2180.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The rare bacteria in the rhizosphere enhanced the tolerance of tea plants to drought","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe tea plant (\u003cem\u003eCamellia sinensis\u003c/em\u003e L.) is a perennial woody plant. A warm and humid climate is conducive to the growth of tea plants. With the intensification of global warming, drought stress has gradually become one of the most important abiotic factors limiting plant growth and yield [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. After drought stress, the stomatal conductance, photosynthesis rate, and transpiration rate of tea leaves decreased [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The contents of important biochemical components such as catechin, theanine and caffeine also decreased [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, drought poses a great threat to the growth and quality of tea.\u003c/p\u003e \u003cp\u003eRhizosphere microorganisms are complex communities living between plant roots and soil. Drought led to various physiological reactions in plants [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and these physiological changes led to the recombination of rhizosphere microorganisms [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The beneficial drought resistant microorganisms were screened or retained among the recombinant microorganisms, and they improved the tolerance of plants to drought stress by interacting with plants [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Changes in the ecosystem occur through abundant microorganisms and rare microorganisms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It was undeniable that abundant microorganisms could dominate the function of ecosystems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], but rare microorganisms played an important role in ecosystems as a potential driving force for the function of microbial communities [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, people paid more attention to rare microorganisms. Rare microorganisms were sensitive to drought stress [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For crops, rare microorganisms could potentially contribute to the stability and constancy of maize yield under climate change, and could improve the resistance of soil communities to abiotic stresses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Currently, the contributions of abundant and rare microorganisms in the rhizosphere of tea plants to drought stress resistance are still unclear.\u003c/p\u003e \u003cp\u003eRoot exudates were various compounds released into the environment by root metabolism during plant growth, including primary metabolites, secondary metabolites, and inorganic substances [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The number and composition of root exudates changed due to the influence of abiotic factors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Root damage was alleviated by up-regulating root exudates under drought stress in plants [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. After drought stress, the concentration of free amino acids in \u003cem\u003eBrassica napus\u003c/em\u003e increased to maintain cell expansion and reduce water loss [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The sucrose, methionine, isoleucine, and phenylalanine contents of soybean plants only increased in the drought treatment group after drought stress [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, there was a close relationship between root exudates and rhizosphere microorganisms. Changes in specific compounds in root exudates can affect the composition of the rhizosphere microbial community [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Under drought stress, maize inoculated with Bacillus could activate the exudation of amino acids and proline to improve its drought resistance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the contributions of abundant and rare bacteria in the soil rhizosphere of \u0026lsquo;FudingDabaicha\u0026rsquo; and \u0026lsquo;Baiye No.1\u0026rsquo; to the resistance of tea plants to drought stress were studied using physiological and biochemical methods, 16SrRNA sequencing, co-occurrence network analysis, RDA and PLS-PM modeling analysis. Furthermore, the contents and compositions of the root exudates of the two varieties after drought stress were compared to identify differential metabolites and explore the relationships between rhizosphere bacteria and root exudates. This research will help to clarify the relative contribution of abundant and rare bacteria to the response of tea plants to drought stress, further understand the relationship between rhizosphere bacteria and the root exudates of tea plants, and provide new insights for the application of rhizosphere microorganisms in improving the drought resistance of tea plants.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological resistance indices of tea plants\u003c/h2\u003e \u003cp\u003eThe effects of drought stress on different varieties of tea plants were quite different. The results showed that the activities of CAT, SOD, POD and the contents of MDA, proline, soluble sugar, and soluble protein under drought stress were significantly greater in \u0026lsquo;FudingDabaicha\u0026rsquo; and \u0026lsquo;Baiye No.1\u0026rsquo; than in their respective controls (FCK and ACK) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with those of the two varieties, the antioxidant enzyme activity and osmotic substance content of \u0026lsquo;FudingDabaicha\u0026rsquo; were significantly greater than those of \u0026lsquo;Baiye No.1\u0026rsquo; under drought stress. Therefore, the drought resistance of \u0026lsquo;FudingDabaicha\u0026rsquo; was greater than that of \u0026lsquo;Baiye No.1\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntioxidant enzyme activity and osmotic substance content of tea plants under drought stress (* represents the difference between FM \u003cem\u003evs.\u003c/em\u003e FCK, AM \u003cem\u003evs.\u003c/em\u003e ACK;△ represents the difference between AM \u003cem\u003evs\u003c/em\u003e FM. */△, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **/△△, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***/△△△, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysiologic index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFCK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACK\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOD (U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003csup\u003e***△△△\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e26.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOD (U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e182.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003csup\u003e***△△\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e155.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e164.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e145.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAT (U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e135.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003csup\u003e***△△△\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e91.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e82.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e64.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDA (nmol\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003csup\u003e***△△△\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e39.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e51.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline (ug\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003e***△\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble sugar (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003e***△△△\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble protein (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics analysis\u003c/h2\u003e \u003cp\u003eIn this study, the root exudates were analyzed by using LC-MS technology. A total of 170 metabolites were identified by secondary mass spectrometry, including 43 lipids, 32 amino acids, 25 xenobiotics, 19 nucleotides, 18 carbohydrates, 9 cofactors and vitamins, 9 energy, and 20 unknowings (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Through PCA analysis, it was found that the four treatments belonged to different quadrants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating that drought had a greater impact on the metabolism of \u0026lsquo;FudingDabaicha\u0026rsquo; and \u0026lsquo;Baiye No.1\u0026rsquo;. In this study, metabolites were screened for p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, and FC\u0026thinsp;\u0026ge;\u0026thinsp;1.5. A total of 82 differentially metabolites were screened in AM \u003cem\u003evs.\u003c/em\u003e ACK, of which 66 metabolites were up-regulated and 16 metabolites were down-regulated. The up-regulated metabolites were mainly amino acids, and the down-regulated metabolites were mainly lipids. A total of 77 differentially metabolites were screened in FM \u003cem\u003evs.\u003c/em\u003e FCK, 57 of which were up-regulated and 20 of which were down-regulated. The main metabolites whose expression increased or decreased were the same as AM \u003cem\u003evs.\u003c/em\u003e ACK. (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eRhizosphere microbial α and β diversity under drought stress\u003c/h2\u003e \u003cp\u003eThe Chao1 index and Shannon index can reveal changes in microbial richness and diversity. There was no significant difference in the Chao1 index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and Shannon index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) between \u0026lsquo;FudingDabaicha\u0026rsquo; and \u0026lsquo;Baiye No.1\u0026rsquo; after drought stress. Principal coordinate analysis (PCoA) of the Bray-Curtis distance at the OTU level revealed that the two varieties in drought treatments (FM and AM) were far from the control (FCK and ACK) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These findings indicated that the composition of the rhizosphere bacterial communities of the two varieties changed after drought stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere microbial structure composition and network topology\u003c/h2\u003e \u003cp\u003eThe diversity of rhizosphere bacteria in different tea varieties was determined after drought stress, and a total of 14777 OTUs were detected (Table S3). Bacteria with a relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.1% were selected as the abundant bacteria, and those with a relative abundance\u0026thinsp;\u0026lt;\u0026thinsp;0.01% were selected as the rare bacteria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In \u0026lsquo;Baiye No.1\u0026rsquo;, 190 OTUs of abundant bacteria and 2786 OTUs of rare bacteria were screened, and 207 OTUs of abundant bacteria and OTUs of rare bacteria were screened out in \u0026lsquo;FudingDabaicha\u0026rsquo; (Table S4).\u003c/p\u003e \u003cp\u003eThe network topology of tea rhizosphere bacteria was constructed using R language and Gephi technology, and the node-level topology was calculated. The global network was constructed by all OTUs of the same variety in the drought treatment and control treatments, and the sub-networks were constructed by OTUs of abundant and rare bacteria, respectively. In \u0026lsquo;Baiye No.1\u0026rsquo;, the global network contained 1000 nodes and 36567 edges. Among them, the abundant sub-network and the rare sub-network had 190 and 810 nodes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In \u0026lsquo;FudingDabaicha\u0026rsquo;, the global network contains 1181 nodes and 50524 edges. Among them, the abundant sub-network and the rare sub-network had 205 and 976 nodes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Network topology features such as betweenness, closeness, eigenvector centrality, and degree were calculated based on the unique node level. Table S5 showed that the average degree, clustering coefficient, and modularity index of the rare bacteria in the two varieties were greater than those of the abundant bacteria. These results indicated that the rare bacteria in the rhizosphere soil of tea plants might play a stronger role in coping with drought stress than the abundant bacteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRedundancy analysis (RDA) and partial least squares path modeling analysis (PLS-PM)\u003c/h2\u003e \u003cp\u003eTo further explore the contribution of abundant and rare bacteria to the resistance of tea plants to drought stress, six phylums (genus) significantly associated with drought resistance indicators in the two varieties were first screened by RDA (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S8; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe selected phylum (genus) and environmental factors were analyzed via PLS-PM modeling, and then the path coefficients of abundant bacteria and rare bacteria under drought stress conditions were subsequently calculated to determine their contributions to the process by which tea trees resist drought stress. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that in \u0026lsquo;Baiye No.1\u0026rsquo;, the path coefficient of rare bacteria to drought (phylum: 0.61, genus: 0.64) was greater than that of abundant bacteria (phylum: 0.40, genus: 0.36) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), and the same situation occurred in \u0026lsquo;Fudingdabaicha\u0026rsquo; (phylum: 0.94 \u003cem\u003evs.\u003c/em\u003e 0.0, genus: 0.92 \u003cem\u003evs.\u003c/em\u003e 0.08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). This means that the contribution of rare bacteria was greater than that of abundant bacteria. Comparing the two varieties, the path coefficients of rare bacteria in \u0026lsquo;FudingDabaicha\u0026rsquo; to drought were greater than those in \u0026lsquo;Baiye No.1\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Therefore, the contribution of rare bacteria in \u0026lsquo;Fudingdabaicha\u0026rsquo; was greater than that in \u0026lsquo;Baiye No.1\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScreening of phylums/ genus based on RDA analysis. The microbial phylum (genus) was screened according to the correlation between microorganisms and environmental factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbundent phylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRare phylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbundent genus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRare genus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lsquo;Baiye No.1\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacteroidetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMesorhizobium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecandidate_division_WPS-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChlamydiae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHyphomicrobium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAcidicapsa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyanobacteria/Chloroplast\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChloroflexi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSchlesneria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFirmicutes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eParcubacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eArthrobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSubdivision3_genera_incertae_sedis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlanctomycetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eParafilimonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVerrucomicrobia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWPS-1_genera_incertae_sedis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLegionella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lsquo;FudingDabaicha\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArmatimonadetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePelolinea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBacteroidetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacteroidetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAsticcacaulis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePlanifilum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecandidate_division_WPS-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ecandidate_division_WPS-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eArthrobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMicrobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyanobacteria/Chloroplast\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIgnavibacteriae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGp2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePirellula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlanctomycetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLatescibacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMethylophilus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eArmatimonadetes_gp4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAcidobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMucilaginibacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eArmatimonadetes_gp5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation analysis of rare microorganisms and differential metabolites\u003c/h2\u003e \u003cp\u003eProcrustes analysis revealed that the rare phylum (genus) screened in \u0026lsquo;Baiye No.1\u0026rsquo; and \u0026lsquo;FudingDabaicha\u0026rsquo; were highly correlated with differentially metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.0028, M\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, p\u0026thinsp;=\u0026thinsp;0.03, M\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.40). Subsequently, the relationships between rare phylum (genus) and differentially metabolites were determined by spearman correlation analysis. Overall, the phylum (genus) of the two varieties were positively correlated with amino acids and negatively correlated with lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith global warming, the degree and frequency of soil drought are increasing, which seriously affects the growth and development, yield, and quality of tea plants. Under drought stress, the leaves of tea plants curled and wilted, and focal spots appeared. With the increase in drought stress, the leaves of tea plants fell off or even died. However, a certain degree of drought also stimulated plants to induce drought resistance responses to resist drought damage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, we found that the antioxidant enzyme activity and osmotic substance content of \u0026lsquo;Baiye No.1\u0026rsquo; and \u0026lsquo;FudingDabaicha\u0026rsquo; were significantly greater than those of the control under moderate drought conditions. The main reason might be that the activities of SOD, POD, and CAT were up-regulated in tea plants under mild drought stress, and excessive ROS were removed to ensure the integrity of the cell biofilm, thus reducing the effect of drought stress on tea plants [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, osmotic substances such as proline, soluble sugar, and soluble protein also accumulated, which helped tea plants maintain turgor by reducing cell osmotic pressure under moderate drought conditions to reduce cell damage [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSoil water content is closely related to soil microorganisms, and drought can cause changes in soil microbial composition. Drought strengthens the link between plants and underground microbial networks, allowing plants to rely on microorganisms to enhance their own stress protection against external stresses. In this study, the α diversity of rhizosphere bacteria of the two varieties did not change significantly after drought stress. The reason was that the change in community composition caused by the water shortage environment compensated for the potential change in community diversity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but the β diversity of rhizosphere bacteria changed. Drought stress did not directly lead to the emergence of drought-tolerant microorganisms or the attenuation and death of drought-intolerant microorganisms, but did cause the diversity of rhizosphere microorganisms to remain relatively stable [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; however, drought stress could greatly change the composition of rhizosphere microorganisms. Changes in α diversity of rice plants were negligible after drought stress, but changes in β diversity were significant [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Compared with our results, there was no significant difference in α diversity between the two varieties after drought stress, but there was a significant difference in β diversity between the two varieties (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which was consistent with the above results.\u003c/p\u003e \u003cp\u003eMost studies on the improvement of plant drought resistance by microorganisms had directly analyzed the effects of abundant microorganism on plants, and few studies had explored the role of rare microorganism in drought. In this study, co-occurrence network analysis revealed that the average degree, clustering coefficient, and modularity of the rare microorganism sub-network in the two varieties were higher than those of the abundant microorganism sub-network. The rare microorganism did play a certain role in drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The impact of the environment on biological communities was positively correlated with community composition. Rare communities could respond better to the impact of environmental changes caused by grassland degradation. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Due to the slow growth of rare microorganisms, they could continue to overcome pressure and maintain their diversity under environmental changes. They could act as a resource pool to respond quickly to environmental changes in response to the effects of abiotic stresses on plants [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], thereby enhancing the plant's tolerance to stress.\u003c/p\u003e \u003cp\u003eTo determine the contributions of rare bacteria and abundant bacteria to improving the response of tea plants to drought stress, we screened out the corresponding phylum (genus) by RDA and constructed a PLS-PM model with physiological indices of tea plants. By calculating the path coefficient, we found that the contribution of rare bacteria was indeed significantly greater than that of abundant bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Drought might cause differences in microbial community assembly mechanisms. The assembly of rare bacteria was controlled by deterministic processes and environmental filtering, while the assembly of abundant bacterial communities was controlled by random processes [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Abundant species might occupy a wider niche and had a certain competitiveness for resource grabbing, so they could exist for a long time. However, the distribution of rare microorganisms was uneven, and they were rejected because of their narrow niche. However, a large number of metabolically active lineages of rare microorganisms were sources of seed banks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. They could play a role in high taxonomic diversity when the ecosystem was disturbed and enhance the resistance to or restoration of soil microbial communities [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, it was confirmed that the rare bacteria of the two varieties in this study were more sensitive to drought than were the abundant bacteria.\u003c/p\u003e \u003cp\u003eUnder drought stress, plants not only change the total amount of root exudates input, but also may change the components of root exudate [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These plants used the most abundant metabolites to produce exudates under current environmental conditions. Studies had shown that amino acids accumulated in the leaves and roots of plants under drought stress, which might help maintain root development to obtain deeper water reserves [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and alleviate drought stress by osmotically regulating the accumulation of solutes such as amino acids without reducing the water content of root cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our study, we also found that amino acids (L-leucine, L-glutamic acid, L-lysine, L-phenylalanine, and L-arginine) were enriched in the two varieties under drought stress. Amino acids were important carbon and nitrogen sources for rhizosphere microorganisms, and were helpful for improving the decomposition activity and extracellular enzyme activity of rhizosphere microorganisms, promoting the growth and stimulation of rhizosphere microorganisms, and accelerating the accumulation and turnover rate of soil organic matter [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and experimental design\u003c/h2\u003e \u003cp\u003eThe experiment was carried out in the artificial climate chamber of Shandong Agricultural University (N-36\u0026deg;19\u0026prime;42\u0026Prime;, E-117\u0026deg;11\u0026prime;35\u0026Prime;) with 1-year-old \u0026lsquo;FudingDabaicha\u0026rsquo; and \u0026lsquo;Baiye No.1\u0026rsquo; clone tea seedlings as materials. The soil was obtained from Shandong Taishan Tea Valley Agricultural Development Co., Ltd. (N-36\u0026deg;13\u0026prime;28.592\u0026Prime;, E-116\u0026deg;57\u0026prime;17.305). The experiment included two soil water contents: control (75% water holding capacity) and moderate drought treatment (35% water holding capacity). The two treatments were marked as ACK and AM in \u0026lsquo;Baiye No.1\u0026rsquo;, and FCK and FM in \u0026lsquo;FudingDabaicha\u0026rsquo;. Three tea plants were planted in each pot, and each treatment was repeated three times. The soil water content was controlled by the daily weighing method.\u003c/p\u003e \u003cp\u003eThe tea plants were first incubated at 60% water holding capacity for 7 days, after which the experiment was carried out. After 7 days of drought stress, the antioxidant enzyme activity and osmotic substance content of the second leaf under the top bud were determined. The rhizosphere soil was mixed and stored at -80\u0026deg;C for the determination of rhizosphere bacterial diversity. Root exudates were collected for metabolomics analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of the resistance index\u003c/h2\u003e \u003cp\u003eThe activity of SOD was determined using nitroblue tetrazole, the activity of POD was determined using the guaiacol method, and the activity of CAT was determined using ultraviolet spectrophotometry [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. MDA was determined using the thiobarbituric acid method [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The soluble sugar content was determined by anthrone-sulfuric acid colorimetry [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], the soluble protein content was determined by the Bradford method [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and the proline content was determined by the sulfosalicylic acid method [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExtraction and detection of root exudates\u003c/h2\u003e \u003cp\u003eThe sample was melted at 4\u0026deg;C, and 400 \u0026micro;L of methanol was added to 100 \u0026micro;L of sample solution, which was then vortexed for 1 min, and centrifuged at 12000 rpm for 10 min at 4\u0026deg;C. The 500 \u0026micro;L of supernatant was vacuum concentrated and dried, and 150 \u0026micro;L of 80% methanol was added for re-dissolution. After mixing, the supernatant was centrifuged at 12000 rpm at 4\u0026deg;C for 10 min, and the supernatant was taken as the sample to be tested [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHigh performance liquid chromatography (Vanquish Core HPLC, Thermo Fisher Scientific, Shanghai, China) and an ACQUITY UPLC\u0026reg; HSS T3 column (1.8 \u0026micro;m, 2.1\u0026times;150 mm, Waters Corporation, USA) were used for detection. The temperature of the automatic sampler was set to 8\u0026deg;C, the flow rate was 0.25 mL/min, the column temperature was 40\u0026deg;C, and 2 \u0026micro;L was injected for gradient elution. The mobile phase was positive ion 0.1% formic acid water (C), 0.1% formic acid acetonitrile (D), and negative ion 5 mM ammonium formate water (A)-acetonitrile (B). The gradient elution program was 0\u0026ndash;1 min, 2% B/D; 1\u0026ndash;9 min, 2%-50% B/D; 9\u0026ndash;12 min, 50%-98% B/D; 12-13.5 min, 98% B/D; 13.5\u0026ndash;14 min, 98%-2% B/D; 14\u0026ndash;20 min, 2% D-positive mode (14\u0026ndash;17 min, 2% B-negative mode).\u003c/p\u003e \u003cp\u003eA mass spectrometer (Thermo-QE-HF-X, Thermo Fisher Scientific, Shanghai, China) was used for detection. The instrument used an electrospray ion source (ESI) and positive and negative ion ionization modes. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was 2.50 kV, the sheath gas was 30 arb, and the auxiliary gas was 10 arb. The capillary temperature was 325\u0026deg;C, and the full scan was performed at a resolution of 60000. The scanning range was 81-1000, and HCD was used for secondary cracking. The collision voltage was 30 eV, and unnecessary MS/MS information was dynamically excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, sequence processing and analysis\u003c/h2\u003e \u003cp\u003eSoil total DNA was extracted with an \u0026ldquo;Omega Soil DNA Kit\u0026rdquo; (Omega Bio-Tek, Norcross, Georgia, USA). The quality of the DNA was determined using agarose gel electrophoresis and Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). The V4-V5 variable region (Bolyen et al. 2019) of the 16S rRNA gene was amplified by high fidelity PCR. 16S rRNA sequencing was conducted on the Illumina MiSeq Benchtop Sequencer (Illumina, USA). The sequencing primers were 515F (5'-GTGCCAGCMGCCGCGG-3') and 907R (5'-CCGTCAATTCMTTTRAGTTT-3').\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe physiological data analysis was conducted using SPSS v23.0. One-way analysis of variance (ANOVA) was used to compare the averages between treatments. The Metlin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metlin.scripps.edu\u003c/span\u003e\u003cspan address=\"http://metlin.scripps.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MoNA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mona.fiehnlab.ucdavis.edu\u003c/span\u003e\u003cspan address=\"https://mona.fiehnlab.ucdavis.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were used to identify the main metabolites in the roots, and the differentially metabolites were screened based on \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, and FC\u0026thinsp;\u0026ge;\u0026thinsp;1.5. Principal component analysis (PCA) was performed on the differentially metabolites using the ade4 package of the R language (v4.3.0).\u003c/p\u003e \u003cp\u003eThe vegan package of the R language was used to calculate the α and β diversity indexes. Gephi (ver. 0.9.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gephi.org\u003c/span\u003e\u003cspan address=\"https://gephi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software was used to construct a visual co-occurrence network, and the network-level and node-level topological properties were further calculated to compare the topological structures of rare sub-networks and abundant sub-networks. Redundancy analysis (RDA) was performed using the vegan package of the R language to clarify the effects of physiological indicators on rare and abundant microorganisms. PLS Path Modeling with R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gastonsanchez.com/PLS\u003c/span\u003e\u003cspan address=\"http://www.gastonsanchez.com/PLS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Path Modelling with R.pdf) was used for PLS-PM modeling analysis to calculate the path coefficient and determine the relative contribution of abundant microorganisms and rare microorganisms. The procrustes function and corrplot package in R were used for procrustes analysis and spearman correlation analysis to evaluate the associations between rare microorganisms and differentially metabolites.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm their contribution to the paper as follows: methodology, data curation: X.Y.; investigation, visualization: X.Z.; draft manuscript preparation: X.Y., X.Z.; manuscript review: X.H.; funding acquisition, supervision: X.H.. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was sponsored by the Natural Science Foundation of Shandong Province (Grant No. ZR2019BC062), and Tai\u0026apos;an City Science and Technology Innovation Major Special Project (Grant No. 2022NYLZ08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article are available in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this article no studies with human participants or animals were performed by any of the authors. All the experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approve the manuscript and consent to the publication of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen Y, Yao Z, Sun Y, Wang E, Tian C, Sun Y, Liu J, Sun C, Tian L. Current studies of the effects of drought stress on root exudates and rhizosphere microbiomes of crop plant species. Int J Mol Sci. 2022;23(4):2374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahimi M, Kordrostami M, Mortezavi M. 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Sci Total Environ. 2022;837:155801.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Camellia sinensis, drought, abundant bacteria, rare bacteria, root exudates","lastPublishedDoi":"10.21203/rs.3.rs-4950519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4950519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought can seriously affect the yield and quality of tea. The interaction between rhizosphere microorganisms and tea plants could enhance the drought resistance of tea plants. However, there are few studies on the effects of abundant and rare microorganisms on tea plants. In this study, the contributions of abundant and rare bacteria in the rhizosphere microorganisms of \u0026lsquo;FudingDabaicha\u0026rsquo; and \u0026lsquo;Baiye No.1\u0026rsquo; to the resistance of tea plants to drought stress were studied using 16SrRNA sequencing, co-occurrence network analysis, and PLS-PM modeling analysis. The results showed that the activity of antioxidant enzymes and the content of osmotic substances increased significantly after drought stress. In the co-occurrence network of the two varieties, the average degree, clustering coefficient, and modularity index of the rare bacteria were greater than those of the abundant bacteria, and the path coefficient of the rare bacteria to drought was greater than that of the abundant bacteria. The contribution of rare microorganisms in \u0026lsquo;FudingDabaicha\u0026rsquo; to drought stress was greater than that in \u0026lsquo;Baiye No.1\u0026rsquo;. The rare bacteria of the two varieties were positively correlated with amino acids and negatively correlated with lipids. The results of this study will provide new insights for the use of rhizosphere microorganisms in improving the drought resistance of tea plants.\u003c/p\u003e","manuscriptTitle":"The rare bacteria in the rhizosphere enhanced the tolerance of tea plants to drought","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-01 15:52:50","doi":"10.21203/rs.3.rs-4950519/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T12:57:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-30T12:24:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-30T05:12:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-08-21T09:42:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"182a5497-5db8-47bb-96c3-2ca7998b4100","owner":[],"postedDate":"October 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:29:40+00:00","versionOfRecord":{"articleIdentity":"rs-4950519","link":"https://doi.org/10.1186/s12870-024-05860-5","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-11-29 15:58:10","publishedOnDateReadable":"November 29th, 2024"},"versionCreatedAt":"2024-10-01 15:52:50","video":"","vorDoi":"10.1186/s12870-024-05860-5","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05860-5","workflowStages":[]},"version":"v1","identity":"rs-4950519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4950519","identity":"rs-4950519","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}