Recruitment and Aggregation Capacity of Tea Trees to Rhizosphere Soil Characteristic Bacteria Affects the Quality of Tea Leaves

Abstract

There are obvious differences in quality between different varieties of the same plant, and it is not clear whether they can be effectively distinguished from each other from a bacterial point of view. In this study, 44 tea tree varieties (Camellia sinensis) were used to analyze the rhizosphere soil bacterial community using high-throughput sequencing technology, and five types of machine deep learning were used for modeling to obtain characteristic microorganisms that can effectively differentiate different varieties, and validation was performed. The relationship between characteristic microorganisms, soil nutrient transformation, and tea quality formation was further analyzed. It was found that 44 tea tree varieties were classified into two groups (group A and group B) and the characteristic bacteria that distinguished them came from 23 genera. Secondly, the content of rhizosphere soil available nutrients (available nitrogen, available phosphorus, and available potassium) and tea quality indexes (tea polyphenols, theanine, and caffeine) was significantly higher in group A than in group B. The classification result based on both was consistent with the above bacteria. This study provides a new insight and research methodology into the main reasons for the formation of quality differences among different varieties of the same plant.

Keywords: characteristic bacteria; nutrients; quality; rhizosphere soil; tea tree.

Figure 1

OTUs analysis of rhizosphere soil bacteria of 44 tea tree germplasm resources. A1~A44 indicate different varieties of tea trees. (A) Rarefaction curve analysis of rhizosphere soil bacterial OTUs; (B) Shannon–Wiener curve analysis of rhizosphere soil bacterial diversity; (C) rank-abundance curve plot of rhizosphere soil bacteria; (D) species accumulation curve plot of rhizosphere soil bacteria; (E) Simpson index analysis of rhizosphere soil bacteria; (F) Shannon index analysis of rhizosphere soil bacteria; (G) Chao1 index analysis of rhizosphere soil bacteria; and (H) PD whole tree index analysis of rhizosphere soil bacteria.

Figure 2

Rhizosphere soil bacterial abundance and classification analysis of 44 tea tree germplasm resources. A1~A44 indicate different varieties of tea trees. (A) Rhizosphere soil bacterial abundance analysis (the figure shows information on genera with a relative abundance of 1% or more); (B) K-mean cluster analysis of different tea tree germplasm resources based on rhizosphere soil bacterial abundance; (C) Bray–Curtis heat map analysis of bacterial abundance in group A and group B after K-mean clustering.

Figure 3

Screening of key bacteria after classification of 44 tea tree germplasm resources. A1~A44 indicate different varieties of tea trees. (A) OPLS-DA model test plot for key differential bacterial screening for group A and group B; (B) OPLS-DA model score plot analysis for group A and group B; (C) S-Plot analysis of the OPLS-DA model for group A and group B (Red dots indicate fungi with VIP > 1 and green dots indicate fungi with VIP < 1); (D) heatmap analysis of the abundance of key differential bacteria between group A and group B obtained from the OPLS-DA model; (E) Bray–Curtis heatmap analysis of group A and group B with the abundance of 195 key differential bacterial genera.

Figure 4

Machine deep learning simulation based on key bacterial abundance in 195 genera to validate classification accuracy and screening to obtain characteristic bacterial genera. A1~A44 indicate different varieties of tea trees. (A) Five different machine learning methods to validate the accuracy of key bacteria classification; (B) using RF to obtain the importance value of key bacterial genera in classification; (C) importance values of key bacterial genera in the classification were obtained using XGboost; (D) importance values of 23 characteristic bacterial genera ranked in the top 30 and shared by RF and XGboost; (E) Bray–Curtis heatmap analysis of the abundance of the 23 characteristic bacterial genera in group A and group B.

Figure 5

Machine deep learning based on the abundance of 23 characteristic bacterial genera to validate classification accuracy and abundance analysis. (A) Accuracy of five different machine learning methods to validate the classification of characteristic bacterial genera. (B) Difference analysis of abundance of characteristic bacterial genera in group A and group B.

Figure 6

qRT-PCR analysis of rhizosphere soil characteristic bacterial genera of tea tree and validation of the accuracy of their classification. (A) Box plots of qRT-PCR results for 18 characteristic bacterial genera in group A and group B tea tree germplasm resources; (B) Bray–Curtis heatmap of the number of characteristic bacterial genera in group A and group B based on qRT-PCR results of 18 characteristic bacterial genera; (C) five different machine learning methods to validate the classification accuracy of 18 characteristic bacterial genera.

Figure 7

Analysis of rhizosphere soil available nutrient content of 44 tea tree germplasm resources.

Figure 8

Leaf quality index content analysis of 44 tea tree germplasm resources.

Figure 9

Interaction relationship analysis between soil characteristic microorganisms of tea tree rhizosphere and soil available nutrients and tea quality indexes. (A) Redundancy analysis of soil characteristic microorganisms with soil available nutrients and tea quality indexes; (B) correlation network analysis of soil characteristic microorganisms with soil available nutrients and tea quality indexes; (C) PLS-SEM model analysis of soil characteristic microorganisms with soil available nutrients and tea quality indexes (*** indicates significance at the p < 0.001 level).

Figure 10

Photographs of 44 different varieties of tea trees selected for the experiment.

Figure 11

Basic principle diagram of five machine learning algorithms.