The response of rare bacterial in rhizosphere of tea plants to drought stress was higher than that of abundant bacterial
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 . After drought stress, the stomatal conductance, photosynthesis rate, and transpiration rate of tea leaves decreased . The contents of important biochemical components such as catechin, theanine and caffeine also decreased . 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 , and these physiological changes led to the recombination of rhizosphere microorganisms . Microorganisms could alleviate drought stress by reducing the degree of cell membrane damage and increasing the activity of antioxidant enzymes. Soluble protein and proline were important osmotic adjustment substances, which could improve the water retention capacity of plants and enhance the tolerance of plants to drought. Drought could inhibit the metabolism of plants and affect the activity of antioxidant enzymes. The CAT content of alfalfa planted with unsterilized soil was significantly higher than that of sterilized treatment, so microorganisms could improve the metabolism of plants and enhance resistance ; At present, many drought-tolerant bacteria had been excavated and utilized, which could produce IAA, proline and other growth-promoting substances to improve plant growth; the antioxidant activity and water status of plants were improved by AMF inoculation under drought conditions. The chlorophyll content of plants inoculated with drought-tolerant bacteria and AMF increased significantly, thereby enhancing the assimilation of CO 2 . 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 . Changes in the ecosystem occur through abundant microorganisms and rare microorganisms . It was undeniable that abundant microorganisms could dominate the function of ecosystems , but rare microorganisms played an important role in ecosystems as a potential driving force for the function of microbial communities . Therefore, people paid more attention to rare microorganisms. Rare microorganisms were sensitive to drought stress . 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 . 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 . The number and composition of root exudates changed due to the influence of abiotic factors . Root damage was alleviated by up-regulating root exudates under drought stress in plants . After drought stress, the concentration of free amino acids in Brassica napus increased to maintain cell expansion and reduce water loss . The sucrose, methionine, isoleucine, and phenylalanine contents of soybean plants only increased in the drought treatment group after drought stress . 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 . Under drought stress, maize inoculated with Bacillus cereus could activate the exudation of amino acids and proline to improve its drought resistance . 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.   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 ). 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’. 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, etc. (Table ). Through PCA analysis, it was found that the four treatments belonged to different quadrants (Fig. ), 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 ).   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 ). 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’.   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, etc. (Table ). Through PCA analysis, it was found that the four treatments belonged to different quadrants (Fig. ), 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 ).   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. A and B) and Shannon index (Fig. C and 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. E and 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 14,777 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 . 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 36,567 edges. Among them, the abundant sub-network and the rare sub-network had 190 and 810 nodes, respectively (Fig. A). In ‘FudingDabaicha’, the global network contains 1181 nodes and 50,524 edges. Among them, the abundant sub-network and the rare sub-network had 205 and 976 nodes, respectively (Fig. 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 and six genus significantly associated with drought resistance indicators in the two varieties were first screened by RDA (Fig. -S8; Table ). The selected phylums and 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 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. C and D), and the same situation occurred in ‘Fudingdabaicha’ (phylum: 0.94 vs. 0.0, genus: 0.92 vs. 0.08) (Fig. E and 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. C and F). Therefore, the contribution of rare bacteria in ‘Fudingdabaicha’ was greater than that in ‘Baiye No.1’. Association analysis of rare microorganisms and differential metabolites Procrustes analysis revealed that the rare phylums and genus screened in ‘Baiye No.1’ and ‘FudingDabaicha’ were highly correlated with differentially metabolites (Fig. A, p = 0.0028, M 2 = 0.34; Fig. B, p = 0.03, M 2 = 0.40). Subsequently, the relationships between rare phylums and genus and differentially metabolites were determined by spearman correlation analysis. Overall, the phylums and genus of the two varieties were positively correlated with amino acids and negatively correlated with lipids (Fig. ).   The Chao1 index and Shannon index can reveal changes in microbial richness and diversity. There was no significant difference in the Chao1 index (Fig. A and B) and Shannon index (Fig. C and 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. E and F). These findings indicated that the composition of the rhizosphere bacterial communities of the two varieties changed after drought stress.   The diversity of rhizosphere bacteria in different tea varieties was determined after drought stress, and a total of 14,777 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 . 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 36,567 edges. Among them, the abundant sub-network and the rare sub-network had 190 and 810 nodes, respectively (Fig. A). In ‘FudingDabaicha’, the global network contains 1181 nodes and 50,524 edges. Among them, the abundant sub-network and the rare sub-network had 205 and 976 nodes, respectively (Fig. 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.   To further explore the contribution of abundant and rare bacteria to the resistance of tea plants to drought stress, six phylums and six genus significantly associated with drought resistance indicators in the two varieties were first screened by RDA (Fig. -S8; Table ). The selected phylums and 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 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. C and D), and the same situation occurred in ‘Fudingdabaicha’ (phylum: 0.94 vs. 0.0, genus: 0.92 vs. 0.08) (Fig. E and 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. C and F). Therefore, the contribution of rare bacteria in ‘Fudingdabaicha’ was greater than that in ‘Baiye No.1’.   Procrustes analysis revealed that the rare phylums and genus screened in ‘Baiye No.1’ and ‘FudingDabaicha’ were highly correlated with differentially metabolites (Fig. A, p = 0.0028, M 2 = 0.34; Fig. B, p = 0.03, M 2 = 0.40). Subsequently, the relationships between rare phylums and genus and differentially metabolites were determined by spearman correlation analysis. Overall, the phylums and genus of the two varieties were positively correlated with amino acids and negatively correlated with lipids (Fig. ).   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 . 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 (Table ) 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 plants . Moreover, osmotic substances such as proline, soluble sugar, and soluble protein also accumulated (Table ), which helped plants maintain turgor by reducing cell osmotic pressure under moderate drought conditions to reduce cell damage . 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 , 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 ; 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 . 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. ), 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. A and 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 . 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 , 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 phylums and genus by RDA (Table ) 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. C and 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 . Abundant species might occupy a wider niche and had a certain competitiveness for resource grabbing, they are not sensitive to changes in environmental factors and show a wider response threshold, which makes it easier to obtain nutritional utilization potential, so they could exist for a long time. The distribution of rare microorganisms was uneven, and they were rejected because of their narrow niche . However, changes in environmental factors might make rare microorganisms active. They could provide various functions and unique metabolic pathways (amino acids, vitamins, etc.) to maintain the stability of the ecosystem. As a microbial seed bank, they could play an insurance role in the impact of climate change on the ecosystem. By increasing functional redundancy, they could improve the ability of soil microorganisms to resist external stress and maintain the stability of the ecosystem . 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 . 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 , and alleviate drought stress by osmotically regulating the accumulation of solutes such as amino acids without reducing the water content of root cells . 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 .   In general, we found that rare bacteria in the rhizosphere played a stronger role in the response of tea plants to drought stress than abundant bacteria by studying the response of rhizosphere soil microorganisms to drought stress in two different tea plants. Rare microorganisms were an important resource pool for genetic diversity and functional diversity, and played a role in ensuring the stability of ecosystems. These insights will provide new ideas for studying tea tree-microbe interactions under drought stress, and also provide a new perspective for studying the spatial pattern of tea tree microorganisms in arid ecosystems.   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″). Camellia sinensis cultivar ‘FudingDabaicha’ and ‘Baiye No.1’, one-year-old seedlings, were collected from Nanjing Yarun Tea Industry Co., Lt, China (36°28’N, 116°82’E). The soil was obtained from Shandong Taishan Tea Valley Agricultural Development Co., Ltd. (N-36°13′28.592″, E-116°57′17.305). The collected soil was sieved through a 200-mesh sieve and dried for use. The basic physical and chemical properties of the soil were pH 5.7, alkali-hydrolyzable nitrogen 175.40 mg·kg − 1 , available phosphorus 170.10 mg·kg − 1 , and available potassium 121 mg·kg − 1 . 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 (10 cm*10 cm*10 cm), 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. Remove the plant with soil, shake off the bulk of soil without root system, and brush the soil close to the surface of the root system with a sterile brush. The three repeated soil pond samples of the same treatment were fully mixed and stored at -80 °C for DNA extraction, sequence processing and analysis. The remaining tea seedlings were removed from the soil and the roots of the tea seedlings were carefully washed with sterile water to avoid damage to the roots. The cleaned roots were transferred to 500mL sterile water, shaken for 3 h, and the roots were taken out. The liquid was the root exudates. Three replicates were set for the same treatment, and the root exudates were evaporated to 10 mL at -80 °C for root exudates detection. Determination of enzyme activity SOD was determined by tetrazolium reduction method. An SOD unit is defined as the amount of enzyme required to inhibit the photoreduction of 50% NBT. The three test tubes were marked as measuring tube, light control tube and dark control tube. 550 mmol·L − 1 potassium phosphate buffer (pH 7.8), 130 mmol·L − 1 methionine solution, 750 µmol·L − 1 NBT solution, 20 µmol·L − 1 riboflavin solution, 100 µmol·L − 1 EDTA-2Na and distilled water were added to the three test tubes, respectively. The enzyme solution was added to the measuring tube, and the other test tubes were added with the same amount of distilled water as the enzyme solution. The test tube was placed in 1000 lx fluorescence color reaction for 15 min, and the black cloth was covered to terminate the reaction. The absorbance at 560 nm was measured with a dark control tube as a blank control . POD was determined by guaiacol oxidation method. The two test tubes were marked as the measurement tube and the blank tube. Enzyme solution, 0.1% guaiacol, distilled water were added respectively, and then 0.18% H 2 O 2 was added to the measurement tube. The reaction was carried out at 25 °C for 10 min, and 5% metaphosphoric acid was added to terminate the reaction. The absorbance at 470 nm was measured with the blank tube as the control . CAT was determined by ultraviolet absorption method. The enzyme activity unit (U) was reduced by 0.1 under A 240 within 1 min. Two test tubes were added with TrisHCl buffer (pH 7.0) and distilled water. One test tube was added with live enzyme, and the other test tube was added with dead enzyme. The test tube was placed in a water bath at 25 °C for 3 min, and 200 mmol·L − 1 H 2 O 2 was added. The absorbance was measured immediately at A 240 (distilled water zero) . Membrane lipid peroxide MDA Thiobarbituric acid test was used for determination. The sample homogenate (0.2 mL) was mixed with 4 mL extraction medium containing 0.5% (w·v − 1 ) thiobarbituric acid and 5% (w·v − 1 ) trichloroacetic acid. The reaction was terminated by water cooling after incubation at 95 °C for 40 min. The sample was centrifuged at 2500 g for 10 min, and the absorbance at 532 nm was measured . Osmotic substances Determination of proline content: The leaves were ground into homogenate in 96% ethanol and washed with 50% ethanol. The extract was centrifuged at 3500 g for 10 min, and the supernatant was added with reactive ninhydrin acid reagent (ninhydrin, 6 M phosphoric acid, 60% glacial acetic acid) and 99% glacial acetic acid. After 45 min of water bath at 100 °C, the test tube was cooled and 5 ml benzene was added to measure the absorbance of the organic phase . Soluble sugar was extracted by anthrone method. The sample was ground with quartz sand and transferred into a test tube. After boiling water extraction for 30 min, the extract was filtered into a volumetric flask. Add 1.5mL water to 0.5mL extract, 0.5mL anthrone ethyl acetate, 5mL concentrated sulfuric acid, mix well, take out in boiling water bath for 1 min, cool naturally, and measure at 630 nm wavelength . Soluble protein was determined by Coomassie brilliant blue method. Proteins were extracted with 50 mM potassium phosphate buffer (pH 7.0) and evaluated using bovine serum albumin as a standard. Centrifuge 5000 g at 4 °C for 10 min, add the supernatant (1 mL) to 5 mL of Coomassie Brilliant Blue G-250 and mix well. After ten min of reaction, the absorbance of the sample (2 mL) was measured by fluorescence spectrophotometry at 595 nm. Each treatment was repeated three times . 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 1000 g 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 1000 g at 4 °C for 10 min, and the supernatant was taken as the sample to be tested . 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 60,000. 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 16 S rRNA gene was amplified by high fidelity PCR. 16 S rRNA sequencing was conducted on the Illumina MiSeq Benchtop Sequencer (Illumina, USA). The sequencing primers were 515 F (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  and MoNA  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 software (v4.3.0). The vegan package of the R software was used to calculate the α and β diversity indexes. Gephi 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 software to clarify the effects of physiological indicators on rare and abundant microorganisms. PLS Path Modeling was used for PLS-PM modeling analysis to calculate the path coefficient and determine the relative contribution of abundant microorganisms and rare microorganisms. These analyses for were based on R software (v.4.1.0) using the “plspm” package . The procrustes function and corrplot package in R software were used for procrustes analysis and spearman correlation analysis to evaluate the associations between rare microorganisms and differentially metabolites.   The experiment was carried out in the artificial climate chamber of Shandong Agricultural University (N-36°19′42″, E-117°11′35″). Camellia sinensis cultivar ‘FudingDabaicha’ and ‘Baiye No.1’, one-year-old seedlings, were collected from Nanjing Yarun Tea Industry Co., Lt, China (36°28’N, 116°82’E). The soil was obtained from Shandong Taishan Tea Valley Agricultural Development Co., Ltd. (N-36°13′28.592″, E-116°57′17.305). The collected soil was sieved through a 200-mesh sieve and dried for use. The basic physical and chemical properties of the soil were pH 5.7, alkali-hydrolyzable nitrogen 175.40 mg·kg − 1 , available phosphorus 170.10 mg·kg − 1 , and available potassium 121 mg·kg − 1 . 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 (10 cm*10 cm*10 cm), 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. Remove the plant with soil, shake off the bulk of soil without root system, and brush the soil close to the surface of the root system with a sterile brush. The three repeated soil pond samples of the same treatment were fully mixed and stored at -80 °C for DNA extraction, sequence processing and analysis. The remaining tea seedlings were removed from the soil and the roots of the tea seedlings were carefully washed with sterile water to avoid damage to the roots. The cleaned roots were transferred to 500mL sterile water, shaken for 3 h, and the roots were taken out. The liquid was the root exudates. Three replicates were set for the same treatment, and the root exudates were evaporated to 10 mL at -80 °C for root exudates detection.   SOD was determined by tetrazolium reduction method. An SOD unit is defined as the amount of enzyme required to inhibit the photoreduction of 50% NBT. The three test tubes were marked as measuring tube, light control tube and dark control tube. 550 mmol·L − 1 potassium phosphate buffer (pH 7.8), 130 mmol·L − 1 methionine solution, 750 µmol·L − 1 NBT solution, 20 µmol·L − 1 riboflavin solution, 100 µmol·L − 1 EDTA-2Na and distilled water were added to the three test tubes, respectively. The enzyme solution was added to the measuring tube, and the other test tubes were added with the same amount of distilled water as the enzyme solution. The test tube was placed in 1000 lx fluorescence color reaction for 15 min, and the black cloth was covered to terminate the reaction. The absorbance at 560 nm was measured with a dark control tube as a blank control . POD was determined by guaiacol oxidation method. The two test tubes were marked as the measurement tube and the blank tube. Enzyme solution, 0.1% guaiacol, distilled water were added respectively, and then 0.18% H 2 O 2 was added to the measurement tube. The reaction was carried out at 25 °C for 10 min, and 5% metaphosphoric acid was added to terminate the reaction. The absorbance at 470 nm was measured with the blank tube as the control . CAT was determined by ultraviolet absorption method. The enzyme activity unit (U) was reduced by 0.1 under A 240 within 1 min. Two test tubes were added with TrisHCl buffer (pH 7.0) and distilled water. One test tube was added with live enzyme, and the other test tube was added with dead enzyme. The test tube was placed in a water bath at 25 °C for 3 min, and 200 mmol·L − 1 H 2 O 2 was added. The absorbance was measured immediately at A 240 (distilled water zero) .   Thiobarbituric acid test was used for determination. The sample homogenate (0.2 mL) was mixed with 4 mL extraction medium containing 0.5% (w·v − 1 ) thiobarbituric acid and 5% (w·v − 1 ) trichloroacetic acid. The reaction was terminated by water cooling after incubation at 95 °C for 40 min. The sample was centrifuged at 2500 g for 10 min, and the absorbance at 532 nm was measured .   Determination of proline content: The leaves were ground into homogenate in 96% ethanol and washed with 50% ethanol. The extract was centrifuged at 3500 g for 10 min, and the supernatant was added with reactive ninhydrin acid reagent (ninhydrin, 6 M phosphoric acid, 60% glacial acetic acid) and 99% glacial acetic acid. After 45 min of water bath at 100 °C, the test tube was cooled and 5 ml benzene was added to measure the absorbance of the organic phase . Soluble sugar was extracted by anthrone method. The sample was ground with quartz sand and transferred into a test tube. After boiling water extraction for 30 min, the extract was filtered into a volumetric flask. Add 1.5mL water to 0.5mL extract, 0.5mL anthrone ethyl acetate, 5mL concentrated sulfuric acid, mix well, take out in boiling water bath for 1 min, cool naturally, and measure at 630 nm wavelength . Soluble protein was determined by Coomassie brilliant blue method. Proteins were extracted with 50 mM potassium phosphate buffer (pH 7.0) and evaluated using bovine serum albumin as a standard. Centrifuge 5000 g at 4 °C for 10 min, add the supernatant (1 mL) to 5 mL of Coomassie Brilliant Blue G-250 and mix well. After ten min of reaction, the absorbance of the sample (2 mL) was measured by fluorescence spectrophotometry at 595 nm. Each treatment was repeated three times .   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 1000 g 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 1000 g at 4 °C for 10 min, and the supernatant was taken as the sample to be tested . 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 60,000. 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.   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 16 S rRNA gene was amplified by high fidelity PCR. 16 S rRNA sequencing was conducted on the Illumina MiSeq Benchtop Sequencer (Illumina, USA). The sequencing primers were 515 F (5’-GTGCCAGCMGCCGCGG-3’) and 907R (5’-CCGTCAATTCMTTTRAGTTT-3’).   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  and MoNA  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 software (v4.3.0). The vegan package of the R software was used to calculate the α and β diversity indexes. Gephi 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 software to clarify the effects of physiological indicators on rare and abundant microorganisms. PLS Path Modeling was used for PLS-PM modeling analysis to calculate the path coefficient and determine the relative contribution of abundant microorganisms and rare microorganisms. These analyses for were based on R software (v.4.1.0) using the “plspm” package . The procrustes function and corrplot package in R software were used for procrustes analysis and spearman correlation analysis to evaluate the associations between rare microorganisms and differentially metabolites.   Below is the link to the electronic supplementary material. Supplementary Material 1: Table S1: Metabolites of ‘FudingDabaicha’ and ‘Baiye No.1’. Table S2: Differential metabolites of ‘FudingDabaicha’ and ‘Baiye No.1’ under drought stress. Table S3: Rhizosphere microbial community composition of ‘Baiye No.1’ and ‘Fudingdabaicha’ under drought stress. Table S4: Abundant bacteria and rare bacteria of ‘FudingDabaicha’ and ‘Baiye No.1’. Table S5: Topological parameters of co-occurrence networks in ‘FudingDabaicha’ and ‘Baiye No.1’. Supplementary Material 2: figure S1: Redundant analysis of relationships between abundant phylum and drought resistance indicators in ‘Baiye No.1’. Figure S2: Redundant analysis of relationships between abundant genus and drought resistance indicators in ‘Baiye No.1’. Figure S3: Redundant analysis of relationships between rare phylum and drought resistance indicators in ‘Baiye No.1’. Figure S4: Redundant analysis of relationships between rare genus and drought resistance indicators in ‘Baiye No.1’. Figure S5: Redundant analysis of relationships between abundant phylum and drought resistance indicators in ‘FudingDabaicha’. Figure S6: Redundant analysis of relationships between abundant genus and drought resistance indicators in ‘FudingDabaicha’. Figure S7: Redundant analysis of relationships between rare phylum and drought resistance indicators in ‘FudingDabaicha’. Figure S8: Redundant analysis of relationships between rare genus and drought resistance indicators in ‘FudingDabaicha’.   