Rhizosphere microbial diversity and functional roles in tea cultivars: insights from high-throughput sequencing and functional isolates

ABSTRACT

Rhizosphere microorganisms play a significant role in influencing the growth and quality of tea plants (Camellia sinensis). However, the complex mechanisms underlying the interactions between rhizosphere microorganisms and tea plants require further investigation. In this study, we employed high-throughput sequencing and the isolation of functional rhizosphere microorganisms to examine variations in rhizosphere microbial diversity and functional characteristics among five distinct tea cultivars: Camellia sinensis cv. Wuniuzao, Fudingdahao, Fuyunliuhao, Jinxuan, and Fudingdabai, each recognized for its unique qualities and adaptability. Our results revealed significant differences in the community diversity of rhizosphere microorganisms among the different tea cultivars. The phylum Mucoromycota may exert a notable influence on the growth of cultivars Wuniuzao, Fudingdahao, and Fuyunliuhao through metabolic pathways such as lipid metabolism. Specifically, Serratia spp. and Enterobacter spp. which produce higher levels of IAA and were isolated from the rhizosphere soils of cultivars Wuniuzao and Fudingdahao, may play a critical role in promoting tea plant growth and development. Additionally, bacteria from the phylum Acidobacteriota may also contribute significantly to tea plant growth. These findings provide valuable insights into the roles of rhizosphere microorganisms in influencing the growth and quality of tea plants, offering a foundation for further exploration of microbial-assisted strategies to enhance tea cultivation.

Introduction

Tea (Camellia sinensis) is one of the world’s most important economic crops, recognized globally for its unique and diverse flavors.^1,2 The quality of tea is influenced by various abiotic and biotic factors, including global warming and deficiencies in water and fertilizers.³ In recent years, beneficial microorganisms have attracted significant attention in modern agriculture. Plant growth-promoting rhizobacteria are increasingly used to reduce abiotic and biotic stresses in agronomic crops, helping to address major challenges in crop production. Plants harbor complex and abundant microbial communities in the soil closely adhering to their roots, known as “rhizosphere microorganisms.^4,5 Rhizosphere microbiota promote nutrient mineralization, allocation, and availability, and they play vital roles in enhancing plant productivity and health in natural environments through symbiotic associations with plants.⁶ For instance, rhizosphere microbiota can influence the growth, health, and adaptability of tea plants while improving the quality of tea leaves.^7,8 In turn, rhizosphere microorganisms depend on the host plant for nutrients to support their growth and are affected by various plant-related factors, including root exudates, species, genotypes, and developmental stages.^9,10 Additionally, the plant rhizosphere microbiome can protect plants against biotic stresses (such as root rot and fungal wilt) and abiotic stresses (such as drought and salinity) through the production of antibiotics, hydrogen cyanide, and defense enzymes, thereby enhancing plant resistance.^11,12 Thus, functional rhizosphere microorganisms play a pivotal role in promoting tea plant growth, improving tea quality, and enhancing stress tolerance. However, the complex mechanisms underlying the interactions between rhizosphere microorganisms and tea plants require further investigation.

Microbiota in tea rhizosphere soil are rich and complex, comprising bacteria, fungi, archaea, nematodes, and protists, among which bacteria and fungi are the dominant groups.⁸ Among these, bacteria represent the most critical microbial community within the tea plant rhizosphere. Specifically, Proteobacteria, Firmicutes, and Acidobacteria together account for 67.81% of the total bacterial community in tea rhizosphere soil.^13,14 Similarly, the fungal phyla Ascomycota, Zygomycota, and Basidiomycota are common inhabitants of the rhizosphere, collectively comprising 88.71% of the fungal community.¹⁵ The diversity of rhizosphere microorganisms in tea plants has been shown to regulate tea plant metabolic activities, thereby influencing tea quality.¹⁶ For instance, a comparison between the root-associated microorganisms of the high-theanine tea variety Rougui and the low-theanine variety Maoxie revealed a specific group of microbes potentially involved in modulating nitrogen metabolism, thereby influencing theanine levels.¹⁷ Given the significant impact of rhizosphere microorganisms on tea plant metabolism and quality, understanding the variations in rhizosphere microbial diversity and functional characteristics among different tea cultivars is essential. Such insights could provide a scientific foundation for optimizing fertilizer application strategies and enhancing tea quality.

Beneficial rhizosphere microorganisms support tea plant growth by colonizing roots and enhancing nutrient uptake through siderophore production, nitrogen fixation, and improved solubilization of phosphorus and potassium. They also promote growth by producing extracellular polysaccharides, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, and indole-3-acetic acid (IAA), a key phytohormone.^18,19 For example, IAA produced by rhizosphere microbes loosens cell walls and boosts root exudates, strengthening plant-microbe interactions and supporting tea root growth.¹⁸ In short, a healthy rhizosphere microbiome can greatly enhance tea plant growth, yet the exact ways it improves growth and quality are still unclear. Further research is needed to uncover how these microbes affect tea metabolism and quality and clarifying these interactions can guide sustainable farming practices to boost tea yield and quality.

In this study, we employed high-throughput sequencing and the isolation of functional rhizosphere microorganisms to investigate variations in rhizosphere microbial diversity and functional characteristics among five distinct tea cultivars: Camellia sinensis cv. Wuniuzao, Fudingdahao, Fuyunliuhao, Jinxuan, and Fudingdabai. Each tea cultivar has distinct traits and adaptability. Wuniuzao is rich in amino acids, ideal for sweet, refreshing green tea. Fudingdahao adapts well and produces both sweet green tea and mellow white tea. Fuyunliuhao is resilient, mainly used for green tea, but also suited for black tea. Jinxuan offers high quality and a unique aroma, favored for oolong. Fudingdabai stands out for its drought and cold resistance. With over a decade of cultivation, their rhizosphere microbiomes have stabilized, making them ideal for exploring microbial diversity and function. This study investigates how specific microbes influence tea quality and identifies candidates for targeted fertilization to improve production and promote sustainable practices.

Materials and methods

Soil sampling

Rhizosphere soil samples were collected from five tea cultivars: Camellia sinensis cv. Wuniuzao (hereafter referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E). These cultivars, which have been cultivated in tea plantations for over 10 years as woody plants, were sampled at the Pollution-Free Tea Garden of Guangxi Vocational and Technical College, Nanning, Guangxi Zhuang Autonomous Region, China (22°34′56″N, 108°14′8″E). For each cultivar, five sampling points were selected using an S-shaped distribution pattern to ensure representativeness. At each point, the soil surrounding the tea plants was carefully removed to a depth of 10–20 cm to expose the roots. Rhizosphere soil was collected by gently shaking the roots to remove loosely attached soil, followed by the collection of tightly adhering soil. Root fragments of at least 15 cm in length, along with approximately 200 g of closely adhering soil, were placed in 50 mL pre-sterilized centrifuge tubes. The samples were immediately immersed in liquid nitrogen and transported to the laboratory for further analysis. This procedure was repeated to obtain three biological replicates for each cultivar. All samples were collected in October 2024.

DNA extraction and metagenomic sequencing

DNA was extracted within 24 hours of sample collection using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, USA) according to the manufacturer’s protocol for microbial DNA extraction from 0.5 g of soil. DNA quality was assessed using a NanoDrop 2000 spectrophotometer (NanoDrop, Wilmington, DE, USA), and DNA quantity was measured using a TBS-380 mini-fluorometer (Turner BioSystems, CA, USA). Three replicate DNA isolations for each sample were pooled and sheared into fragments of approximately 400 bp using an M220 Focused-ultrasonicator (Covaris Inc., Woburn, MA, USA). Paired-end libraries were then constructed using the EXTFLEX Rapid DNA-Seq Kit (Bioo Scientific, TX, USA). Sequencing was performed on the DNBSEQ-T7 platform at Sanshu Bio Co., Ltd. (Jiangsu, China) following the standard protocol. The raw sequence data generated in this study have been deposited in the Genome Sequence Archive²⁰ at the National Genomics Data Center,²¹ China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number GSA: CRA024833, and are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Sequencing reads assembly

Fastp (version 0.20.0) was used to remove 3′ and 5′ adaptors from the raw reads, followed by quality trimming with a minimum quality score of 20 and a minimum length of 50 bp to ensure data reliability.²² Clean reads were then assembled using MEGAHIT (version 1.1.2) through the De Bruijn graph approach, and contigs shorter than 500 bp were filtered out.²³

Gene prediction, taxonomy and gene function assignment

Open reading frames (ORFs) were predicted from the contigs of each sample using Prodigal (http://metagene.cb.k.u-tokyo.ac.jp/).²⁴ Predicted ORFs longer than 200 bp were translated into amino acid sequences and subsequently annotated using DIAMOND (version 0.8.35) with an E-value threshold of 1e-5.²⁵ A non-redundant gene catalog was constructed using CD-HIT (version 4.6.1) with a sequence identity threshold of 95% and coverage of 90%.²⁶ Clean sequencing reads were then aligned to the non-redundant gene catalog using Bowtie2 (https://bowtie-bio.sourceforge.net/bowtie2/index.shtml.) with a sequence identity threshold of 95%.

To obtain clusters of orthologous groups of proteins (COGs), the ORFs were aligned against the eggNOG database using DIAMOND (version 0.8.35) with an optimized E-value threshold of 1e-5. KEGG pathway annotation was performed by aligning the ORFs against the Kyoto Encyclopedia of Genes and Genomes (KEGG GENES) database using DIAMOND (version 0.8.35) with the same E-value threshold. KEGG functional assignments corresponding to the annotated genes were then obtained. The abundance of each functional category was calculated by summing the abundances of genes associated with KEGG Orthology (KO) pathways, Enzyme Commission (EC) numbers, and modules. Enriched metabolic pathways were identified across different hierarchical levels (levels 1, 2, and 3) and compared among the five tea cultivars using statistical tests, with significance defined as p < 0.05.

DNA extraction, polymerase chain reaction amplification and 16S rRNA gene analysis

Total DNA was extracted from 0.5 mL of rhizosphere bacterial culture using the TIANamp Bacteria DNA Kit (Tiangen Biotech, Beijing, China) according to the manufacturer’s instructions. DNA quality and quantity were assessed using a NanoDrop 1000 spectrophotometer (Thermo Scientific, USA). PCR amplification of the 16S rRNA gene was performed using the universal primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492 R (5′-GGTTACCTTGTTACGACTT-3′). The PCR reaction mixture contained 1.25 μL of each primer, 12.5 μL of Q5 High-Fidelity 2X Master Mix, 2 μL of template DNA, and nuclease-free water to a final volume of 25 μL. The PCR conditions were as follows: initial denaturation at 98°C for 30 s, followed by 30 cycles of denaturation at 98°C for 10 s, annealing at 58°C for 30 s, and extension at 72°C for 45 s, with a final extension at 72°C for 5 min. The amplified PCR products were purified and sequenced by Sanshu Bio Co., Ltd. (Jiangsu, China) using an ABI 3730-XL Genetic Analyzer (SeqGen, Inc., USA). The resulting sequences were confirmed for their identity using NCBI BLAST.

Sampling and analysis of tea shoots

Tea shoots with one bud and two leaves were hand-picked for the measurement of free amino acid content, total polyphenols, and caffeine. Free amino acids were extracted from 3 g of finely ground powder by steeping in 450 mL of boiling double-distilled water (ddH₂O) in a water bath for 45 minutes. The free amino acid content was determined using a 2% ninhydrin method with theanine as the reference standard.²⁷ Total polyphenols were extracted from 0.2 g of finely ground powder by steeping in 10 mL of 70% methanol at 70°C in a water bath for 20 minutes. The total polyphenol content was then determined using the Folin – Ciocalteu colorimetric method with gallic acid as the reference standard.²⁸ Caffeine was extracted from 3 g of finely ground powder by steeping in 450 mL of boiling ddH₂O in a water bath for 45 minutes. Lead acetate was added until precipitation was complete, and the measurement was conducted following the State Standard of China (GB/T 8312–2013).

Determination of IAA production ability of rhizosphere bacterial

The production of IAA was determined using both qualitative and quantitative assays as described by Gordon et al. (1951).²⁹ For the qualitative assay, 0.2 mL of activated bacterial suspension (OD₆₀₀ = 1.0) was inoculated into 25 mL of LB medium containing 100 mg·L⁻¹ tryptophan and cultured in a shaker at 30°C and 180 r·min⁻¹ for 24 hours. IAA production was assessed using the Salkowski reagent method, with three replicates for each sample. For the quantitative assay, the bacterial suspension (OD₆₀₀ = 1.0) was centrifuged at 10,000 rpm for 5 minutes. Then, 1 mL of the supernatant was mixed with 1 mL of Salkowski reagent, and absorbance was measured at 530 nm using IAA as the reference standard.

Results

Rhizosphere microorganisms’ community composition in different tea cultivars

After stringent quality filtering, a total of 1,053,536,506 clean reads were obtained from the rhizosphere soil samples of the five tea cultivars (Table S1). At the species level, 11 bacterial sequences with a mean relative abundance of ≥ 1% were identified, spanning eight phyla: Acidobacteriota, Actinomycetota, Candidatus Rokubacteria, Chloroflexota, Gemmatimonadota, Myxococcota, Pseudomonadota, and Verrucomicrobiota (Figure 1(b)). Notably, the most abundant bacterial species across all cultivars was an Acidobacteriota bacterium, with relative abundances of 12.33% (cultivar A), 10.94% (cultivar B), 14.70% (cultivar C), 17.96% (cultivar D), and 14.67% (cultivar E) (Figure 1(a)). In contrast, the fungal community was represented by 20 sequences with a mean relative abundance of ≥ 1%, belonging to five phyla: Ascomycota, Basidiomycota, Chytridiomycota, Mucoromycota, and Olpidiomycota (Figure 2(a)). The overall taxonomic structure of the fungal community was similar in cultivars A, B, and C. Interestingly, the phylum Mucoromycota, including Ambispora gerdemannii, Ambispora leptoticha, Cetraspora pellucida, Diversispora epigaea, Entrophospora candida, Entrophospora sp. SA101, Funneliformis geosporum, Geosiphon pyriformis, Gigaspora margarita, Gigaspora rosea, Paraglomus brasilianum, Paraglomus occultum, Racocetra persica, Rhizophagus clarus, and Rhizophagus irregularis, was predominantly detected in the rhizosphere samples of cultivars A, B, and C, accounting for 75% of the fungal community, but was notably absent in the rhizosphere samples of cultivars D and E (Figures 2(a,b)). These findings indicate that the overall taxonomic structure of the rhizosphere microbial communities in cultivars A, B, and C was similar and notably distinct from that observed in cultivars D and E.

Figure 1.

Relative abundance of classified sequences at species level. Sequences with a homolog ≥ 80% were used and only species with a mean abundance of ≥ 1% were included. (a) Distribution of bacterial species in the five tea cultivars. (b) the selective bacterium (species with a mean abundance of ≥ 1%) fraction at phylum level. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

Figure 2.

Relative abundance of classified sequences at species level. Sequences with a homolog ≥ 80% were used and only species with a mean abundance of ≥ 1% were included. (a) Distribution of fungal species in the five tea cultivars. (b) the selective fungi (species with a mean abundance of ≥ 1%) fraction at phylum level. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

Rhizosphere microorganisms’ diversity in different tea cultivars

To further assess microbial diversity among the rhizosphere soils of different tea cultivars, we employed Principal Coordinates Analysis (PCoA) based on Bray – Curtis dissimilarity. The analysis revealed that cultivars A and B exhibited similar bacterial community profiles, which were notably distinct from those of cultivars D and E (Figure 3). Specifically, the relative abundance of the Acidobacteriota bacterium was highest in cultivar D at 17.96% (Figure 4(a)), followed by Candidatus Rokubacteria bacterium, which reached 8.15% in cultivar D, a higher level compared to other cultivars (Figure 4(b)). In contrast, the relative abundance of Verrucomicrobiota bacterium was highest in cultivar A, reaching 4.39%, compared to the other cultivars (Figure 4(c)). For the fungal community, the composition in cultivars A and B was distinct from that observed in cultivar D (Figure 5). Notably, Glutinoglossum americanum was the most abundant fungal species in cultivar D, reaching 7.45% (Figure 6(a)), while Saitozyma podzolica was most prevalent in cultivar B, accounting for 1.58% (Figure 6(b)). Taken together, these results reveal significant differences in the community diversity of rhizosphere microorganisms among the different tea cultivars.

Figure 3.

PCoA ordination visualizing the bacterial community assemblages in the five tea cultivars. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

Figure 4.

Relative abundance of Acidobacteriota bacterium (a), Candidatus Rokubacteria bacterium (b), and Verrucomicrobiota bacterium (c) in the five tea cultivars. p < 0.05. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

Figure 5.

PCoA ordination visualizing the fungal community assemblages in the five tea cultivars. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

Figure 6.

Relative abundance of Glutinoglossum americanum (a), and Saitozyma podzolica (b) in the five tea cultivars. p < 0.05. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

The functional profiles of rhizosphere microorganisms in different tea cultivars

To elucidate the functional implications of differences in rhizosphere microbial communities among the five tea cultivars, we conducted KEGG enrichment analysis. At level 1, six enriched biological metabolic pathways were identified (Figure 7(a)), including cellular processes (7.73%–8.05%), environmental information processing (8.67%–8.90%), genetic information processing (8.06%–8.29%), human diseases (8.11%–8.40%), metabolism (62.44%–63.43%), and organismal systems (3.86%–3.96%). However, no significant differences were observed among the cultivars at this level. At level 2, 24 enriched pathways with a relative abundance of ≥ 1% were identified (Figure 7(b)). Among these, carbohydrate metabolism (16.21%–16.50%) was the most enriched pathway, followed by amino acid metabolism (12.63%–12.91%). Notably, nine of these 24 pathways exhibited significant differences among the cultivars. For example, pathways such as “lipid metabolism” and “metabolism of other amino acids” were more enriched in cultivars A and B, while other pathways were significantly enriched in cultivars D and E (p < 0.05; Figure S1). At level 3, 25 pathways with a relative abundance of ≥ 1% were identified (Figure 7(c)). Among these, “ABC transporters” (3.32%–3.72%) was the most enriched pathway, followed by the “two-component system” (3.43%–3.55%) and “quorum sensing” (3.07%–3.51%). Furthermore, 11 of these 25 pathways showed significant differences among the cultivars. With the exception of the “starch and sucrose metabolism” pathway, other pathways such as “ABC transporters,” “carbon fixation pathways in prokaryotes,” “citrate cycle,” and “methane metabolism” were predominantly enriched in cultivars D and E (p < 0.05; Figure S2).

Figure 7.

Relative abundance of identifiable biological metabolic pathways at level 1 (a), level 2 (b), and level 3 (c) in the five tea cultivars. Camellia sinensis cv. Wuniuzao (referred to as A), Fudingdahao (B), Fuyunliuhao (C), Jinxuan (D), and Fudingdabai (E).

To further explore the association between metabolic pathways and the rhizosphere microbial communities of tea plants, we performed Pearson correlation analysis between metabolic pathways (levels 2 and 3) and rhizosphere microorganisms. The results showed that the Acidobacteriota bacterium, the most abundant bacterial species in cultivar D (Figure 4(b)), was significantly positively correlated with the pathways “infectious disease: bacterial” and “nucleotide metabolism,” with correlation coefficients of 0.923 (p < 0.05) and 0.880 (p < 0.05), respectively (level 2; Table 1). At level 3, it was significantly positively correlated with “aminoacyl-tRNA biosynthesis” (r = 0.926, p < 0.05), “pentose phosphate pathway” (r = 0.931, p < 0.05), “pyrimidine metabolism” (r = 0.926, p < 0.05), and “quorum sensing” (r = 0.902, p < 0.05) (Table 2). These results suggest that Acidobacteriota may play an important role in the growth and development of cultivar D through pathways such as “nucleotide metabolism,” “pentose phosphate pathway,” “pyrimidine metabolism,” and “quorum sensing.” For the fungal community, the phylum Mucoromycota, which dominated the rhizosphere of cultivars A, B, and C (Figures 2(a,b)), was significantly positively correlated with the pathways “lipid metabolism” (r = 0.999, p < 0.01), “biosynthesis of other secondary metabolites” (r = 0.960, p < 0.01), “glycan biosynthesis and metabolism” (r = 0.891, p < 0.05), and “metabolism of terpenoids and polyketides” (r = 0.968, p < 0.01) (level 2; Table 3). At level 3, it was significantly positively correlated with “glycine, serine and threonine metabolism” (r = 0.994, p < 0.01) and “starch and sucrose metabolism” (r = 0.932, p < 0.05) (Table 4). These results suggest that Mucoromycota may have a significant influence on the growth of cultivars A, B, and C through pathways such as “lipid metabolism,” “biosynthesis of other secondary metabolites,” and “starch and sucrose metabolism.”

Table 1.

Pearson correlation coefficient between biological metabolic pathway (level 2) and tea plant rhizosphere bacteria.

Biological metabolic pathways (level 2)	Acidobacteriota bacterium	Actinomycetota bacterium	Alphaproteobacteria bacterium	Betaproteobacteria bacterium	Candidatus Rokubacteria bacterium	Chloroflexota bacterium
Biosynthesis of other secondary metabolites	−0.793	−0.056	−0.004	0.204	−.952*	−0.649
Cell growth and death	0.828	0.105	0.184	−0.014	.961**	0.63
Cell motility	−.947*	−0.161	−0.425	0.109	−.943*	−0.702
Cellular community-prokaryotes	0.877	−0.055	0.453	0.227	.935*	0.709
Endocrine system	−.915*	0.15	−0.452	−0.156	−.953*	−0.791
Energy metabolism	0.711	0.162	−0.163	−0.504	0.851	0.57
Folding, sorting and degradation	0.845	0.122	0.067	−0.512	.882*	0.69
Glycan biosynthesis and metabolism	−0.685	−0.182	0.17	0.392	−0.863	−0.528
Infectious disease: bacterial	.923*	−0.051	0.226	−0.153	.999**	0.792
Lipid metabolism	−.911*	−0.057	−0.259	0.104	−.988**	−0.726
Membrane transport	0.789	0.09	0.143	0.019	.945*	0.603
Metabolism of other amino acids	−.967**	0.041	−0.413	0.038	−.984**	−0.804
Metabolism of terpenoids and polyketides	−0.825	0.013	−0.066	0.146	−.972**	−0.699
Neurodegenerative disease	0.783	0.03	0.422	0.343	.879*	0.582
Nucleotide metabolism	.880*	0.135	0.18	−0.199	.969**	0.678
Replication and repair	−0.78	−0.346	−0.452	−0.235	−0.845	−0.445
Translation	0.808	0.262	0.071	−0.351	.898*	0.58
Biological metabolic pathways (level 2)	Deltaproteobacteria bacterium	Gammaproteobacteria bacterium	Gemmatimonadota bacterium	Trebonia kvetii		Verrucomicrobiota bacterium
Biosynthesis of other secondary metabolites	−.931*	.976**	−.951*	.984**		.921*
Cell growth and death	.937*	−.935*	.986**	−.971**		−0.832
Cell motility	−.894*	0.828	−0.855	0.87		0.763
Cellular community-prokaryotes	0.789	−0.841	.947*	−.902*		−0.602
Endocrine system	−0.753	0.859	−.924*	.909*		0.603
Energy metabolism	.905*	−.903*	0.788	−.881*		−.994**
Folding, sorting and degradation	0.866	−0.859	0.735	−0.847		−.923*
Glycan biosynthesis and metabolism	−.935*	.923*	−0.852	.912*		.989**
Infectious disease: bacterial	0.877	−.958*	.934*	−.978**		−0.832
Lipid metabolism	−.919*	.935*	−.947*	.965**		0.833
Membrane transport	.925*	−.935*	.995**	−.971**		−0.822
Metabolism of other amino acids	−0.836	.887*	−.905*	.926*		0.725
Metabolism of terpenoids and polyketides	−.905*	.983**	−.967**	.996**		.880*
Neurodegenerative disease	0.791	−0.796	.956*	−0.868		−0.563
Nucleotide metabolism	.950*	−.931*	.923*	−.953*		−.892*
Replication and repair	−.908*	0.73	−.908*	0.81		0.646
Translation	.962**	−.879*	0.838	−.888*		−.945*

”*” represents a significant correlation at P<0.05 level; “**” represents a significant correlation at P<0.01 level.

Table 2.

Pearson correlation coefficient between biological metabolic pathway (level 3) and tea plant rhizosphere bacteria.

Biological metabolic pathways (level 3)	Acidobacteriota bacterium	Actinomycetota bacterium	Alphaproteobacteria bacterium	Betaproteobacteria bacterium	Candidatus Rokubacteria bacterium	Chloroflexota bacterium
ABC transporters	0.798	0.135	0.077	−0.126	.949*	0.607
Alanine, aspartate and glutamate metabolism	0.719	0.079	−0.181	−0.611	0.828	0.625
Amino sugar and nucleotide sugar metabolism	−0.396	−0.188	0.501	0.481	−0.655	−0.308
Aminoacyl-tRNA biosynthesis	.926*	0.017	0.227	−0.199	.990**	0.767
Arginine and proline metabolism	0.798	0.044	−0.065	−0.415	.927*	0.682
Butanoate metabolism	0.565	0.117	−0.363	−0.7	0.711	0.497
Carbon fixation pathways in prokaryotes	0.762	0.094	−0.113	−0.546	0.867	0.644
Citrate cycle	0.528	0.289	−0.311	−0.714	0.64	0.387
Glycine, serine and threonine metabolism	−0.874	−0.096	−0.171	0.158	−.977**	−0.686
Glyoxylate and dicarboxylate metabolism	0.538	0.296	−0.324	−0.577	0.709	0.379
Methane metabolism	0.839	0.068	0.038	−0.306	.958*	0.691
Oxidative phosphorylation	0.802	0.432	0.276	−0.165	0.847	0.471
Pentose phosphate pathway	.931*	−0.326	0.687	0.128	0.811	0.877
Purine metabolism	0.83	0.232	0.138	−0.196	.937*	0.593
Pyrimidine metabolism	.926*	0.017	0.227	−0.199	.990**	0.767
Pyruvate metabolism	0.251	0.18	−0.529	−.924*	0.332	0.237
Quorum sensing	.902*	0.066	0.325	0.013	.978**	0.7
Starch and sucrose metabolism	−0.734	−0.057	0.066	0.183	−.925*	−0.601
Biological metabolic pathways (level 3)	Deltaproteobacteria bacterium	Gammaproteobacteria bacterium	Gemmatimonadota bacterium	Trebonia kvetii	Verrucomicrobiota bacterium
ABC transporters	.957*	−.950*	.966**	−.972**	−.897*
Alanine, aspartate and glutamate metabolism	0.838	−.881*	0.712	−0.845	−.978**
Amino sugar and nucleotide sugar metabolism	−0.802	0.811	−0.7	0.771	.950*
Aminoacyl-tRNA biosynthesis	.902*	−.943*	.915*	−.962**	−0.856
Arginine and proline metabolism	.903*	−.959**	0.857	−.946*	−.968**
Butanoate metabolism	0.778	−0.812	0.615	−0.759	−.974**
Carbon fixation pathways in prokaryotes	0.874	−.903*	0.762	−0.877	−.978**
Citrate cycle	0.786	−0.713	0.539	−0.668	−.948*
Glycine, serine and threonine metabolism	−.943*	.947*	−.947*	.971**	.882*
Glyoxylate and dicarboxylate metabolism	0.867	−0.798	0.676	−0.766	−.987**
Methane metabolism	.930*	−.964**	.906*	−.966**	−.940*
Oxidative phosphorylation	.968**	−0.762	0.815	−0.804	−0.832
Pentose phosphate pathway	0.477	−0.639	0.657	−0.686	−0.336
Purine metabolism	.979**	−.908*	.917*	−.933*	−.910*
Pyrimidine metabolism	.902*	−.943*	.915*	−.962**	−0.856
Pyruvate metabolism	0.468	−0.456	0.176	−0.369	−0.766
Quorum sensing	.907*	−.909*	.964**	−.953*	−0.776
Starch and sucrose metabolism	−.920*	.972**	−.953*	.978**	.918*

”*” represents a significant correlation at P<0.05 level; “**” represents a significant correlation at P<0.01 level.

Table 3.

Pearson correlation coefficient between biological metabolic pathway (level 2) and tea plant rhizosphere fungus.

Biological metabolic pathways (level 2)	Ambispora gerdemannii	Ambispora leptoticha	Cetraspora pellucida	Diversispora epigaea	Entrophospora candida
Amino acid metabolism	0.385	0.04	−0.014	0.127	0.41
Biosynthesis of other secondary metabolites	.960**	.975**	.954*	.980**	0.752
Cancer: overview	0.697	0.797	0.864	0.719	0.231
Carbohydrate metabolism	0.495	0.465	0.316	0.583	.924*
Cell growth and death	−.981**	−.909*	−.883*	−.931*	−0.771
Cell motility	.975**	0.814	0.851	0.797	0.498
Cellular community-prokaryotes	−.930*	−0.76	−0.717	−0.808	−0.779
Drug resistance: antimicrobial	−0.45	−0.452	−0.339	−0.533	−0.745
Endocrine system	.928*	0.777	0.738	0.818	0.763
Energy metabolism	−0.875	−.960**	−.988**	−.917*	−0.516
Folding, sorting and degradation	−.899*	−.906*	−.966**	−0.847	−0.386
Glycan biosynthesis and metabolism	.891*	.968**	.973**	.945*	0.615
Infectious disease: bacterial	−.988**	−.931*	−.923*	−.935*	−0.705
Lipid metabolism	.999**	.912*	.910*	.916*	0.685
Membrane transport	−.963**	−.905*	−0.865	−.936*	−0.814
Metabolism of cofactors and vitamins	0.538	0.787	0.741	0.785	0.606
Metabolism of other amino acids	.978**	0.842	0.843	0.849	0.646
Metabolism of terpenoids and polyketides	.968**	.965**	.934*	.979**	0.789
Neurodegenerative disease	−.891*	−0.712	−0.652	−0.775	−0.817
Nucleotide metabolism	−.993**	−.932*	−.944*	−.922*	−0.631
Replication and repair	.917*	0.694	0.692	0.72	0.61
Translation	−.944*	−.919*	−.960**	−.882*	−0.493
Biological metabolic pathways (level 2)	Entrophospora sp.SA101	Funneliformis geosporum	Geosiphon pyriformis	Gigaspora margarita	Gigaspora rosea
Amino acid metabolism	0.1	−0.377	0.107	−0.091	0.05
Biosynthesis of other secondary metabolites	.944*	0.782	0.176	.917*	.969**
Cancer: overview	0.664	0.821	−0.325	.899*	0.841
Carbohydrate metabolism	0.636	0.192	0.802	0.218	0.352
Cell growth and death	−.885*	−0.633	−0.171	−0.831	−.914*
Cell motility	0.706	0.528	−0.21	0.823	.885*
Cellular community-prokaryotes	−0.766	−0.396	−0.182	−0.649	−0.762
Drug resistance: antimicrobial	−0.586	−0.262	−0.492	−0.282	−0.348
Endocrine system	0.778	0.434	0.149	0.677	0.777
Energy metabolism	−0.866	−.892*	0.039	−.986**	−.984**
Folding, sorting and degradation	−0.768	−0.801	0.262	−.974**	−.968**
Glycan biosynthesis and metabolism	.903*	0.866	0.084	.957*	.976**
Infectious disease: bacterial	−.885*	−0.688	−0.054	−.886*	−.945*
Lipid metabolism	0.856	0.646	0.027	0.87	.938*
Membrane transport	−.900*	−0.631	−0.247	−0.809	−.896*
Metabolism of cofactors and vitamins	0.808	0.846	0.427	0.727	0.721
Metabolism of other amino acids	0.785	0.542	−0.036	0.802	0.876
Metabolism of terpenoids and polyketides	.947*	0.746	0.204	.891*	.952*
Neurodegenerative disease	−0.741	−0.329	−0.28	−0.574	−0.703
Nucleotide metabolism	−0.856	−0.703	0.024	−.914*	−.967**
Replication and repair	0.644	0.323	0.011	0.631	0.746
Translation	−0.805	−0.756	0.142	−.948*	−.974**
Biological metabolic pathways (level 2)	Glutinoglossum americanum	Olpidium bornovanus	Paraglomus brasilianum	Paraglomus occultum	Quaeritorhiza haematococci
Amino acid metabolism	−0.479	−0.263	0.108	0.404	0.681
Biosynthesis of other secondary metabolites	−.908*	−.987**	0.832	0.393	−0.275
Cancer: overview	−0.679	−0.746	0.509	−0.104	−0.412
Carbohydrate metabolism	−0.596	−0.552	0.728	.893*	−0.058
Cell growth and death	.921*	.965**	−0.772	−0.421	0.079
Cell motility	−0.858	−.888*	0.522	0.099	0.128
Cellular community-prokaryotes	.939*	.885*	−0.684	−0.502	−0.15
Drug resistance: antimicrobial	0.754	0.563	−0.682	−0.763	0.147
Endocrine system	−.964**	−.898*	0.696	0.488	0.105
Energy metabolism	0.757	.906*	−0.709	−0.116	0.41
Folding, sorting and degradation	0.775	.884*	−0.575	0.035	0.238
Glycan biosynthesis and metabolism	−0.782	−.926*	0.767	0.23	−0.394
Infectious disease: bacterial	.964**	.985**	−0.759	−0.348	0.123
Lipid metabolism	−.938*	−.971**	0.717	0.313	−0.053
Membrane transport	.916*	.960**	−0.804	−0.483	0.108
Metabolism of cofactors and vitamins	−0.478	−0.67	0.778	0.375	−0.698
Metabolism of other amino acids	−.949*	−.932*	0.649	0.298	0.061
Metabolism of terpenoids and polyketides	−.945*	−.995**	0.846	0.446	−0.238
Neurodegenerative disease	.886*	0.841	−0.677	−0.564	−0.184
Nucleotide metabolism	.896*	.965**	−0.699	−0.233	0.108
Replication and repair	−0.774	−0.8	0.5	0.266	0.289
Translation	0.788	.912*	−0.619	−0.066	0.171
Biological metabolic pathways (level 2)	Racocetra persica	Rhizophagus clarus	Rhizophagus irregularis	Saitozyma podzolica	Saitozyma sp.JCM24511
Amino acid metabolism	−0.446	0.097	0.012	.927*	0.837
Biosynthesis of other secondary metabolites	0.705	.956*	.942*	0.468	0.409
Cancer: overview	0.867	0.865	0.823	0.036	0.047
Carbohydrate metabolism	0.03	0.311	0.436	0.591	0.397
Cell growth and death	−0.543	−.909*	−0.861	−0.642	−0.586
Cell motility	0.49	.912*	0.76	0.669	0.695
Cellular community-prokaryotes	−0.303	−0.777	−0.721	−0.839	−0.749
Drug resistance: antimicrobial	−0.196	−0.365	−0.516	−0.498	−0.248
Endocrine system	0.354	0.798	0.751	0.804	0.706
Energy metabolism	−0.859	−.970**	−.931*	−0.195	−0.206
Folding, sorting and degradation	−0.798	−.981**	−0.878	−0.287	−0.33
Glycan biosynthesis and metabolism	0.81	.954*	.932*	0.261	0.249
Infectious disease: bacterial	−0.627	−.955*	−.906*	−0.594	−0.536
Lipid metabolism	0.58	.947*	0.872	0.633	0.597
Membrane transport	−0.531	−.885*	−0.859	−0.635	−0.563
Metabolism of cofactors and vitamins	0.769	0.657	0.771	−0.116	−0.177
Metabolism of other amino acids	0.488	.900*	0.809	0.713	0.671
Metabolism of terpenoids and polyketides	0.665	.944*	.936*	0.528	0.451
Neurodegenerative disease	−0.216	−0.708	−0.66	−0.862	−0.768
Nucleotide metabolism	−0.646	−.972**	−.887*	−0.548	−0.537
Replication and repair	0.233	0.754	0.609	0.817	0.828
Translation	−0.719	−.974**	−0.868	−0.395	−0.434

”*” represents a significant correlation at P<0.05 level; “**” represents a significant correlation at P<0.01 level.

Table 4.

Pearson correlation coefficient between biological metabolic pathway (level 3) and tea plant rhizosphere fungus.

Biological metabolic pathways (level 3)	Ambispora gerdemannii	Ambispora leptoticha	Cetraspora pellucida	Diversispora epigaea	Entrophospora candida
ABC transporters	−.973**	−.945*	−.927*	−.954*	−0.741
Alanine, aspartate and glutamate metabolism	−0.834	−.945*	−.985**	−.888*	−0.441
Amino sugar and nucleotide sugar metabolism	0.681	.885*	0.869	0.862	0.575
Aminoacyl-tRNA biosynthesis	−.992**	−.930*	−.938*	−.922*	−0.645
Arginine and proline metabolism	−.929*	−.987**	−.997**	−.961**	−0.616
Butanoate metabolism	−0.722	−.901*	−.941*	−0.836	−0.378
Carbon fixation pathways in prokaryotes	−0.878	−.957*	−.993**	−.907*	−0.479
Citrate cycle	−0.684	−0.823	−.894*	−0.74	−0.224
Fatty acid degradation	0.698	0.402	0.389	0.446	0.475
Glycine, serine and threonine metabolism	.994**	.939*	.937*	.938*	0.687
Glycolysis	−0.721	−0.821	−.903*	−0.733	−0.195
Glyoxylate and dicarboxylate metabolism	−0.757	−.889*	−.926*	−0.833	−0.405
Methane metabolism	−.967**	−.976**	−.981**	−.961**	−0.653
Oxidative phosphorylation	−.930*	−0.792	−0.847	−0.759	−0.406
Pentose phosphate pathway	−0.754	−0.54	−0.539	−0.56	−0.477
Purine metabolism	−.980**	−.922*	−.937*	−.908*	−0.611
Pyrimidine metabolism	−.992**	−.930*	−.938*	−.922*	−0.645
Pyruvate metabolism	−0.351	−0.598	−0.688	−0.488	0.062
Quorum sensing	−.993**	−0.871	−0.858	−.889*	−0.718
Starch and sucrose metabolism	.932*	.971**	.936*	.984**	0.791
Valine, leucine and isoleucine degradation	0.443	0.136	0.085	0.215	0.448
Biological metabolic pathways (level 3)	Entrophospora sp.SA101	Funneliformis geosporum	Geosiphon pyriformis	Gigaspora margarita	Gigaspora rosea
ABC transporters	−.909*	−0.716	−0.152	−.885*	−.950*
Alanine, aspartate and glutamate metabolism	−0.836	−.921*	0.112	−.997**	−.973**
Amino sugar and nucleotide sugar metabolism	0.856	.908*	0.248	0.862	0.853
Aminoacyl-tRNA biosynthesis	−0.861	−0.696	0.024	−.907*	−.959**
Arginine and proline metabolism	−.915*	−0.869	−0.025	−.984**	−.999**
Butanoate metabolism	−0.797	−.955*	0.075	−.962**	−.917*
Carbon fixation pathways in prokaryotes	−0.851	−.893*	0.104	−.998**	−.988**
Citrate cycle	−0.678	−.879*	0.236	−.925*	−0.874
Fatty acid degradation	0.388	0.009	−0.078	0.332	0.442
Glycine, serine and threonine metabolism	.881*	0.7	0.046	.901*	.961**
Glycolysis	−0.665	−0.853	0.362	−.941*	−.882*
Glyoxylate and dicarboxylate metabolism	−0.785	−.891*	0.052	−.936*	−.915*
Methane metabolism	−.910*	−0.804	−0.034	−.958*	−.993**
Oxidative phosphorylation	−0.659	−0.549	0.247	−0.827	−.880*
Pentose phosphate pathway	−0.504	−0.219	0.128	−0.5	−0.575
Purine metabolism	−0.84	−0.699	0.025	−.907*	−.961**
Pyrimidine metabolism	−0.861	−0.696	0.024	−.907*	−.959**
Pyruvate metabolism	−0.447	−0.83	0.319	−0.754	−0.641
Quorum sensing	−0.831	−0.565	−0.071	−0.808	−.893*
Starch and sucrose metabolism	.959**	0.79	0.26	.896*	.950*
Valine, leucine and isoleucine degradation	0.192	−0.256	0.093	0.014	0.14
Biological metabolic pathways (level 3)	Glutinoglossum americanum	Olpidium bornovanus	Paraglomus brasilianum	Paraglomus occultum	Quaeritorhiza haematococci
ABC transporters	0.895*	0.972**	−0.787	−0.374	0.177
Alanine, aspartate and glutamate metabolism	0.735	0.879*	−0.677	−0.048	0.461
Amino sugar and nucleotide sugar metabolism	−0.587	−0.78	0.772	0.265	−0.628
Aminoacyl-tRNA biosynthesis	.936*	.975**	−0.715	−0.265	0.109
Arginine and proline metabolism	0.859	.965**	−0.776	−0.23	0.366
Butanoate metabolism	0.613	0.794	−0.653	−0.01	0.586
Carbon fixation pathways in prokaryotes	0.775	.908*	−0.688	−0.077	0.399
Biological metabolic pathways (level 3)	Glutinoglossum americanum	Olpidium bornovanus	Paraglomus brasilianum	Paraglomus occultum	Quaeritorhiza haematococci
Citrate cycle	0.503	0.711	−0.496	0.17	0.477
Fatty acid degradation	−0.752	−0.59	0.312	0.3	0.49
Glycine, serine and threonine metabolism	−.918*	−.978**	0.741	0.305	−0.122
Glycolysis	0.624	0.749	−0.481	0.186	0.41
Glyoxylate and dicarboxylate metabolism	0.592	0.794	−0.628	−0.017	0.481
Methane metabolism	.894*	.980**	−0.768	−0.265	0.263
Oxidative phosphorylation	0.715	0.825	−0.45	0.025	−0.096
Pentose phosphate pathway	0.833	0.691	−0.414	−0.264	−0.285
Purine metabolism	0.85	.944*	−0.677	−0.204	0.107
Pyrimidine metabolism	.936*	.975**	−0.715	−0.265	0.109
Pyruvate metabolism	0.203	0.425	−0.299	0.355	0.644
Quorum sensing	.938*	.951*	−0.704	−0.365	−0.028
Starch and sucrose metabolism	−.889*	−.975**	0.865	0.45	−0.322
Valine, leucine and isoleucine degradation	−0.577	−0.351	0.197	0.425	0.573
Biological metabolic pathways (level 3)	Racocetra persica	Rhizophagus clarus	Rhizophagus irregularis	Saitozyma podzolica	Saitozyma sp.JCM24511
ABC transporters	−0.633	−.940*	−.899*	−0.539	−0.495
Alanine, aspartate and glutamate metabolism	−.910*	−.966**	−.931*	−0.112	−0.126
Amino sugar and nucleotide sugar metabolism	0.848	0.805	0.862	−0.027	−0.056
Aminoacyl-tRNA biosynthesis	−0.645	−.971**	−.898*	−0.569	−0.538
Arginine and proline metabolism	−0.826	−.992**	−.965**	−0.315	−0.288
Butanoate metabolism	−.949*	−.897*	−.891*	0.067	0.046
Carbon fixation pathways in prokaryotes	−0.875	−.983**	−.936*	−0.195	−0.207
Citrate cycle	−.889*	−0.859	−0.795	0.087	0.004
Fatty acid degradation	−0.037	0.496	0.378	.909*	0.846
Glycine, serine and threonine metabolism	0.634	.963**	.897*	0.569	0.538
Glycolysis	−.893*	−.896*	−0.824	−0.002	−0.057
Glyoxylate and dicarboxylate metabolism	−0.865	−.888*	−0.853	−0.024	−0.067
Methane metabolism	−0.75	−.989**	−.945*	−0.429	−0.401
Oxidative phosphorylation	−0.509	−.890*	−0.713	−0.548	−0.631
Pentose phosphate pathway	−0.196	−0.631	−0.539	−0.774	−0.703
Purine metabolism	−0.638	−.959**	−0.866	−0.523	−0.53
Pyrimidine metabolism	−0.645	−.971**	−.898*	−0.569	−0.538
Pyruvate metabolism	−.889*	−0.624	−0.604	0.469	0.37
Quorum sensing	−0.488	−.903*	−0.825	−0.713	−0.668
Starch and sucrose metabolism	0.703	.929*	.939*	0.438	0.365
Valine, leucine and isoleucine degradation	−0.313	0.192	0.127	.893*	0.783

”*” represents a significant correlation at P<0.05 level; “**” represents a significant correlation at P<0.01 level.

Isolation and preliminary screening of functional rhizosphere microorganisms

As reported previously, IAA produced by specific groups of microbes can facilitate cell wall loosening and enhance root exudate production, thereby fostering stronger plant – microbe interactions and stimulating tea root development.¹⁸ To further investigate the specific mechanisms by which rhizosphere microorganism isolates influence tea quality, IAA production by different isolates was measured, and the top five isolates were further analyzed using 16S rRNA sequencing. We found that isolates A17 and A22 from cultivar A produced the highest IAA levels, reaching 25.5 μg/mL and 23.0 μg/mL, respectively. In comparison, isolates B2, B6, and B8 from cultivar B produced 21.5 μg/mL, 21.5 μg/mL, and 21.0 μg/mL of IAA, respectively (Table 5). Sequencing analysis indicated that isolates A17 and A22 were identified as Serratia nematodiphila and S. nematodiphila DZ0503SBS1, respectively, while isolates B2, B6, and B8 were identified as Enterobacter quasihormaechei (Table 5). Both Serratia spp. and Enterobacter spp. are known for their potential as plant growth-promoting bacteria; however, their specific mechanisms in influencing tea plant development and quality require further investigation.^30–35

Table 5.

IAA production from the top 5 isolates and results of the NCBI comparison.

Isolate	Sample description (cultivars)	IAA production (μg/ml)	DNA identification results	Identities (%)
A17	Wuniuzao	25.5 ± 2.774	Serratia nematodiphila	99.03
A22	Wuniuzao	23.0 ± 5.272	Serratia nematodiphila DZ0503SBS1	98.28
B2	Fudingdahao	21.5 ± 0.584	Enterobacter quasihormaechei	99.22
B6	Fudingdahao	21.5 ± 0.616	Enterobacter quasihormaechei	98.94
B8	Fudingdahao	21.0 ± 1.824	Enterobacter quasihormaechei	99.22

± standard deviation.

The content of major secondary metabolites in buds of different tea cultivars

Among the numerous chemical constituents in tea plants, amino acids and caffeine are regarded as key quality indicators.^36–38 Several free amino acids, particularly theanine, impart umami and sweetness to tea,^38,39 while caffeine contributes to its bitterness.⁴⁰ Thus, the complex balance of these constituents plays a critical role in determining the overall flavor profile of tea. In this study, higher concentrations of free amino acids and caffeine were found in the buds of cultivars A and B compared to the other cultivars (p < 0.05; Figure 8a,b). In contrast, polyphenol content did not differ significantly among the cultivars (p > 0.05; Figure 8(c)).

Figure 8.

The concentrations of free amino acids (a), caffeine (b), and catechins (c) in the buds of different tea cultivars. The means of free amino acid content (μg/g) ± SD values of the three replicates are shown. Asterisks in each cultivar represent significant differences among all tea cultivars (LSD test; *p < 0.05).

Discussion

Rhizosphere microorganisms form diverse communities that significantly influence the metabolic activities of tea plants.^{8,17,18,41,42} Differences in metabolite content among tea cultivars are particularly important in determining tea quality and flavor,¹⁶ and the composition of rhizosphere microbial communities directly affects the quality and flavor profiles of different tea varieties.⁴³ In this study, we observed that certain bacteria, such as Acidobacteriota and Candidatus Rokubacteria, as well as fungi, such as Glutinoglossum americanum, were more abundant in cultivar D (Figures 1 and 2) In contrast, the phylum Mucoromycota was predominantly detected in the rhizosphere of cultivars A, B, and C (Figure 2(a,b)). These results reveal differences in the diversity of rhizosphere microbial communities among tea cultivars, providing a necessary basis for screening microorganisms that may influence tea quality and flavor characteristics.

The diversity of rhizosphere microorganisms in tea plants can regulate plant metabolic activities, thereby influencing tea quality. In this study, we found that Mucoromycota was predominantly detected in the rhizosphere of cultivars A, B, and C, accounting for 75% of the fungal community (Figure 2a,b). Mucoromycota is a phylum of early-diverging fungal lineages primarily associated with terrestrial plants,⁴⁴ known for its ability to produce polyunsaturated fatty acids with chain lengths of up to 18 carbon atoms and to efficiently convert nutrients into lipids, although some Mucoromycota species obtain lipids from host plants and are unable to produce them independently.⁴⁵ We found that Mucoromycota was significantly positively correlated with the “lipid metabolism” pathway, which was enriched in cultivars A and B (Figure S1, Table 3). Additionally, the contents of free amino acids and caffeine in the buds of cultivars A and B were higher than those in other cultivars (Figure 8a,b). Collectively, these findings suggest that Mucoromycota may play an important role in the growth and quality of cultivars A, B, and C through metabolic pathways such as “lipid metabolism.”

Serratia spp. is a genus of Gram-negative, facultatively anaerobic bacteria belonging to the Enterobacteriaceae family. Previous studies have reported that phytohormone production, acyl-homoserine lactone (AHL) signaling, ACC deaminase activity, nitrogen fixation, phosphorus and zinc solubilization, and biofilm formation in Serratia spp. play crucial roles in stimulating plant growth under normal conditions.^46–48 Notably, S. nematodiphila NII-0928 has been reported to promote plant growth primarily through IAA production,⁴⁹ while Enterobacter sp. DBA51 has been shown to exhibit phosphate-solubilizing activity and IAA production.⁵⁰ However, the effects of these strains on tea plants have rarely been reported. In this study, we found that isolates A17, A22, B2, B6, and B8, obtained from cultivars A and B, produced higher levels of IAA compared to other isolates and were identified as S. nematodiphila, S. nematodiphila DZ0503SBS1, and E. quasihormaechei, respectively (Table 5). These findings highlight the potential growth-promoting effects of Serratia spp. and Enterobacter spp. on tea plant growth under normal conditions.

Acidobacteriota is a highly prevalent bacterial phylum in soils, consistent with our finding that Acidobacteriota bacterium was the most abundant bacterial taxon in the rhizosphere of all tea cultivars examined (Figure 1a). Acidobacteriota was first described in 1991 following the identification of Acidobacterium capsulatum from an acidic mineral habitat.⁵¹ This phylum plays a significant role in biogeochemical cycling, particularly in phosphate and nitrogen cycling (Figure 9), by incorporating essential nutrients into the soil and making them available to plants.^52–54 Additionally, Acidobacteriota can transport inorganic phosphate via the high-affinity phosphate transport system (pstSCAB) located on the outer cell membrane.^55,56 In this study, we observed that Acidobacteriota bacterium was most abundant in cultivar D compared to the other cultivars (Figure 4a). Although metabolic pathways such as “ABC transporters,” “Carbon fixation pathways in prokaryotes,” “Citrate cycle,” and “Methane metabolism” were predominantly enriched in cultivars D and E (p < 0.05; Figure S2), the specific mechanisms by which these pathways may contribute to tea plant growth promotion require further investigation.

Figure 9.

Biofertilization function and mechanism of tea plant rhizosphere microorganisms.

In conclusion, our results reveal significant differences in the community diversity of rhizosphere microorganisms among different tea cultivars. The phylum Mucoromycota may play an important role in the growth of cultivars A, B, and C through metabolic pathways such as “lipid metabolism.” Specifically, Serratia spp. and Enterobacter spp., which were isolated from the rhizosphere soils of cultivars A and B and produced higher levels of IAA, may contribute critically to tea plant growth and development due to their plant growth-promoting properties. Additionally, Acidobacteriota bacterium may also play a key role in supporting tea plant growth. Future research should focus on elucidating the specific regulatory mechanisms underlying these interactions to advance our understanding of rhizosphere microorganism – tea plant relationships and to facilitate the development of microbial strategies for enhancing tea production and quality.

Funding Statement

This work was supported by the Guangxi Natural Science Foundation under Grant [number 2024GXNSFBA010358]; the Guangxi Natural Science Foundation under Grant [number 2022GXNSFAA035613]; the Middle-aged and Young Teachers’ Basic Ability Promotion Project of Guangxi under Grant [number 2023KY1029]; the National Natural Science Foundation of China under Grant [number 42467008]; the Guangxi Vocational and Technical College Project [2022] [No. 133-221101Y].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in the Genome Sequence Archive²⁰ in National Genomics Data Center,²¹ China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA024833) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Supplementary Information

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15592324.2025.2543448